Search results for: deep seated gravitational slope deformation

Authors: Abhishek Dixit, Sandip S. Sathe, Chandan Mahanta

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The Siang river originates in Angsi glacier in southern Tibet (there known as the Yarlung Tsangpo). After traveling through Indus-Tsangpo suture zone and deep gorges near Namcha Barwa peak, it takes a south-ward turn and enters India, where it is known as Siang river and becomes a major tributary of the Brahmaputra in Assam plains. In this study, we have analyzed the bed sediment of the Siang river at two locations (one at extreme upstream near the India-China border and one downstream before Siang Brahmaputra confluence). We have also sampled bed sediment at the remote location of Yammeng river, an eastern tributary of Siang. The magnetic hysteresis properties show the combination of paramagnetic and weak ferromagnetic behavior with a multidomain state. Moreover, curie temperature analysis shows titanomagnetite solid solution series, which is causing the weak ferromagnetic signature. Given that the magnetic mineral was in a multidomain state, the presence of Ti, Fe carrying heave mineral, may be inferred. The Chemical index of alteration shows less weathered sediment. However, the Yammeng river sample being close to source shows fresh grains subjected to physical weathering and least chemically alteration. Enriched Ca and K and depleted Na and Mg with respect to upper continental crust concentration also points toward the less intense chemical weathering along with the dominance of calcite weathering.

Keywords: bed sediment, magnetic properties, Siang, weathering

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Authors: S. Benessam, T. H. Debieche, A. Drouiche, S. Mahdid, F. Zahi

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The marsh is the impermeable depression. It is not very deep and presents the stagnant water. Their water level varies according to the contributions of water (rain, groundwater, stream etc.), when this last reaches the maximum level of the marsh, it flows towards the downstream through the discharge system. The marsh accumulates all the liquid and solid contributions of upstream part. In the North-East Algerian, the Redjla marsh is located on the course of the Tassift river. Its contributions of water come from the upstream part of the river, often characterized by the presence of several pollutants in water related to the urban effluents, and its discharge system supply the downstream part of the river. In order to determine the effect of the marsh on the water quality of the river this study was conducted. A two-monthly monitoring of the physicochemical parameters and water chemistry of the river were carried out, before and after the marsh, during the period from November 2013 to January 2015. The results show that the marsh plays the role of a natural purifier of water of Tassift river, present by drops of conductivity and concentration of the pollutants (ammonium, phosphate, iron, chlorides and bicarbonates) between the upstream part and downstream of the marsh. That indicates that these pollutants are transformed with other chemical forms (case of ammonium towards nitrate), precipitated in complex forms or/and adsorbed by the sediments of the marsh. This storage of the pollutants in the ground of the marsh will be later on a source of pollution for the plants and river water.

Keywords: marsh, natural purification, urban pollution, nitrogen

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Authors: Farid Eltom A. E., Mustafa Abdul-Halim, Abdalla Markaz, Sami Atta, Mohamed Azhari, Ahmed Rashed

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Smart environment sensors integrated with ‘Internet of Things’ (IoT) technology can provide a new concept in tracking, sensing, and monitoring objects in the environment. The aim of the study is to evaluate the farm environmental monitoring system based on (IoT) and to realize the automated management of agriculture and the implementation of precision production. Until now, irrigation monitoring operations in Sudan have been carried out using traditional methods, which is a very costly and unreliable mechanism. However, by utilizing soil moisture sensors, irrigation can be conducted only when needed without fear of plant water stress. The result showed that software application allows farmers to display current and historical data on soil moisture and nutrients in the form of line charts. Design measurements of the soil factors: moisture, electrical, humidity, conductivity, temperature, pH, phosphorus, and potassium; these factors, together with a timestamp, are sent to the data server using the Lora WAN interface. It is considered scientifically agreed upon in the modern era that artificial intelligence works to arrange the necessary procedures to take care of the terrain, predict the quality and quantity of production through deep analysis of the various operations in agricultural fields, and also support monitoring of weather conditions.

Keywords: smart environment, monitoring systems, IoT, LoRa Gateway, center pivot

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Authors: Parsa Mahdavi, M. Amin Hariri-Ardebili

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The development of accurate machine learning and deep learning models traditionally demands hands-on expertise and a solid background to fine-tune hyperparameters. With the continuous expansion of datasets in various scientific and engineering domains, researchers increasingly turn to machine learning methods to unveil hidden insights that may elude classic regression techniques. This surge in adoption raises concerns about the adequacy of the resultant meta-models and, consequently, the interpretation of the findings. In response to these challenges, automated machine learning (AutoML) emerges as a promising solution, aiming to construct machine learning models with minimal intervention or guidance from human experts. AutoML encompasses crucial stages such as data preparation, feature engineering, hyperparameter optimization, and neural architecture search. This paper provides a comprehensive overview of the principles underpinning AutoML, surveying several widely-used AutoML platforms. Additionally, the paper offers a glimpse into the application of AutoML on various engineering datasets. By comparing these results with those obtained through classical machine learning methods, the paper quantifies the uncertainties inherent in the application of a single ML model versus the holistic approach provided by AutoML. These examples showcase the efficacy of AutoML in extracting meaningful patterns and insights, emphasizing its potential to revolutionize the way we approach and analyze complex datasets.

Keywords: automated machine learning, uncertainty, engineering dataset, regression

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Authors: Ahmed Soliman Nagiub, Mahmoud Fayez, Heba Khaled, Said Ghoniemy

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Accurate perception is a critical component in enabling autonomous vehicles to understand their driving environment. The acquisition of 3D information about objects, including their location and pose, is essential for achieving this understanding. This survey paper presents a comprehensive review of 3D object detection techniques specifically tailored for autonomous vehicles. The survey begins with an introduction to 3D object detection, elucidating the significance of the third dimension in perceiving the driving environment. It explores the types of sensors utilized in this context and the corresponding data extracted from these sensors. Additionally, the survey investigates the different types of datasets employed, including their formats, sizes, and provides a comparative analysis. Furthermore, the paper categorizes and thoroughly examines the perception methods employed for 3D object detection based on the diverse range of sensors utilized. Each method is evaluated based on its effectiveness in accurately detecting objects in a three-dimensional space. Additionally, the evaluation metrics used to assess the performance of these methods are discussed. By offering a comprehensive overview of 3D object detection techniques for autonomous vehicles, this survey aims to advance the field of perception systems. It serves as a valuable resource for researchers and practitioners, providing insights into the techniques, sensors, and evaluation metrics employed in 3D object detection for autonomous vehicles.

Keywords: computer vision, 3D object detection, autonomous vehicles, deep learning

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Authors: Si-Jie Ji, Santhanamoorthi Nachimuthu, Jyh-Chiang Jiang

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With the rapid development of industrialization and urbanization, air pollution poses significant global environmental challenges, contributing to acid rain, global warming, and adverse health effects. Therefore, it is necessary to monitor the concentration of toxic gases in the atmospheric environment in real-time and to deploy cost-effective gas sensors capable of detecting their emissions. In this study, we systematically investigated the sensing capabilities of the two-dimensional MoSe₂ for seven key environmental gases (NO, NO₂, CO, CO₂, SO₂, SO₃, and O₂) using density functional theory (DFT) and non-equilibrium Green’s function (NEGF) calculations. We also investigated the impact of H₂O as an interfering gas. Our results indicate that the MoSe₂ monolayer is thermodynamically stable and exhibits strong gas-sensing capabilities. The calculated adsorption energies indicate that these gases can stably adsorb on MoSe₂, with SO₃ exhibiting the strongest adsorption energy (-0.63 eV). Electronic structure analysis, including projected density of states (PDOS) and Bader charge analysis, demonstrates significant changes in the electronic properties of MoSe₂ upon gas adsorption, affecting its conductivity and sensing performance. We find that oxygen (O₂) adsorption notably influenced the deformation of MoSe₂. To comprehensively understand the potential of MoSe₂ as a gas sensor, we used the NEGF method to assess the electronic transport properties of MoSe₂ under gas adsorption, evaluating current-voltage (I-V), resistance-voltage (R-V) characteristics, and transmission spectra to determine sensitivity, selectivity, and recovery time compared to pristine MoSe₂. Sensitivity, selectivity, and recovery time are analyzed at a bias voltage of 1.7V, showing excellent performance of MoSe₂ in detecting SO₃, among other gases. The pronounced changes in electronic transport behavior induced by SO₃ adsorption confirm MoSe₂’s strong potential as a high-performance gas-sensing material. Overall, this theoretical study provides new insights into the development of high-performance gas sensors, demonstrating the potential of MoSe₂ as a gas-sensing material, particularly for gases like SO₃.

Keywords: density functional theory, gas sensing, MoSe₂, non-equilibrium Green’s function, SO

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Authors: Mamata Mohanty, J. T. Shahu

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The in-situ deep mixing is an effective ground improvement technique which involves columnar inclusion into soft ground to increase its bearing capacity and reduce settlement. The first part of the study presents the results of unconfined compression on cement-admixed clay prepared at different cement content and subjected to varying curing periods. It is found that cement content is a prime factor controlling the strength of the cement-admixed clay. Besides cement content, curing period is important parameter that adds to the strength of cement-admixed clay. Increase in cement content leads to significant increase in Unconfined Compressive Strength (UCS) values especially at cement contents greater than 8%. The second part of the study investigated the bearing capacity of the clay ground improved by a group of end-bearing column using model tests under plain-strain condition. This study mainly focus to examine the effect of cement contents on the ultimate bearing capacity and failure stress of the improved clay ground. The study shows that the bearing capacity of the improved ground increases significantly with increase in cement contents of the soil-cement columns. A considerable increase in the stiffness of the model ground and failure stress was observed with increase in cement contents.

Keywords: bearing capacity, cement content, curing time, unconfined compressive strength, undrained shear strength

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Authors: Ashish Jaiswal, Ashwin Ramesh Babu, Mohammad Zaki Zadeh, Fillia Makedon, Glenn Wylie

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Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that records neural activations in the brain by capturing the blood oxygen level in different regions based on the task performed by a subject. Given fMRI data, the problem of predicting the state of cognitive fatigue in a person has not been investigated to its full extent. This paper proposes tackling this issue as a multi-class classification problem by dividing the state of cognitive fatigue into six different levels, ranging from no-fatigue to extreme fatigue conditions. We built a spatio-temporal model that uses convolutional neural networks (CNN) for spatial feature extraction and a long short-term memory (LSTM) network for temporal modeling of 4D fMRI scans. We also applied a self-supervised method called MoCo (Momentum Contrast) to pre-train our model on a public dataset BOLD5000 and fine-tuned it on our labeled dataset to predict cognitive fatigue. Our novel dataset contains fMRI scans from Traumatic Brain Injury (TBI) patients and healthy controls (HCs) while performing a series of N-back cognitive tasks. This method establishes a state-of-the-art technique to analyze cognitive fatigue from fMRI data and beats previous approaches to solve this problem.

Keywords: fMRI, brain imaging, deep learning, self-supervised learning, contrastive learning, cognitive fatigue

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Authors: Zulkifli Yusop

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The severity of December 2014 flood on the east coast of Peninsular Malaysia has raised concern over the adequacy of existing land use practices and policies. This article assesses flood responses to selective logging, plantation establishment (oil palm and rubber) and their subsequent management regimes. The hydrological impacts were evaluated on two levels: on-site (mostly in the upstream) and off-site to reflect the cumulative impact at downstream. Results of experimental catchment studies suggest that on-site impact of flood could be kept to a minimum when selecting logging strictly adhere to the existing guidelines. However, increases in flood potential and sedimentation rate were observed with logging intensity and slope steepness. Forest conversion to plantation show the highest impacts. Except on the heavily compacted surfaces, the ground revegetation is usually rapid within two years upon the cessation of the logging operation. The hydrological impacts of plantation opening and replanting could be significantly reduced once the cover crop has fully established which normally takes between three to six months after sowing. However, as oil palms become taller and the canopy gets closer, the cover crop tends to die off due to light competition, and its protecting function gradually diminishes. The exposed soil is further compacted by harvesting machinery which subsequently leads to greater overland flow and erosion rates. As such, the hydrological properties of matured oil palm plantations are generally poorer than in young plantation. In hilly area, the undergrowth in rubber plantation is usually denser compared to under oil palm. The soil under rubber trees is also less compacted as latex collection is done manually. By considering the cumulative effects of land-use over space and time, selective logging seems to pose the least impact on flood potential, followed by planting rubber for latex, oil palm and Latex Timber Clone (LTC). The cumulative hydrological impact of LTC plantation is the most severe because of its shortest replanting rotation (12 to 15 years) compared to oil palm (25 years) and rubber for latex (35 years). Furthermore, the areas gazetted for LTC are mostly located on steeper slopes which are more susceptible to landslide and erosion. Forest has limited capability to store excess rainfall and is only effective in attenuating regular floods. Once the hydrologic storage is exceeded, the excess rainfall will appear as flood water. Therefore, for big floods, rainfall regime has a much bigger influence than land use.

Keywords: selective logging, plantation, extreme rainfall, debris flow

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Authors: M. Dhana Lakshmi, S. Sakthivel Murugan

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As light passes from source to observer in the water medium, it is scattered by the suspended particulate matter. This scattering effect will plague the captured images with non-uniform illumination, blurring details, halo artefacts, weak edges, etc. To overcome this, pixel normalization with an Amended Unsharp Mask (AUM) filter is proposed to enhance the degraded image. To validate the robustness of the proposed technique irrespective of atmospheric light, the considered datasets are collected on dual locations. For those images, the maxima and minima pixel intensity value is computed and normalized; then the AUM filter is applied to strengthen the blurred edges. Finally, the enhanced image is obtained with good illumination and contrast. Thus, the proposed technique removes the effect of scattering called de-hazing and restores the perceptual information with enhanced edge detail. Both qualitative and quantitative analyses are done on considering the standard non-reference metric called underwater image sharpness measure (UISM), and underwater image quality measure (UIQM) is used to measure color, sharpness, and contrast for both of the location images. It is observed that the proposed technique has shown overwhelming performance compared to other deep-based enhancement networks and traditional techniques in an adaptive manner.

Keywords: underwater drone imagery, pixel normalization, thresholding, masking, unsharp mask filter

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Authors: Fatiha Besahraoui, M'hamed Guezzoul, Kheira Chebbah, M'hamed Bouslama

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SnO₂ thin film is characterized by Atomic Force Microscopy (AFM) and Photoluminescence Spectroscopies. AFM images show a dense surface of columnar grains with a roughness of 78.69 nm. The PL measurements at 7 K reveal the presence of PL peaks centered in IR and visible regions. They are attributed to radiative transitions via oxygen vacancies, Sn interstitials, and dangling bonds. A bands diagram model is presented with the approximate positions of intrinsic point defect levels in SnO₂ thin films. The integrated PL measurements demonstrate the good thermal stability of our sample, which makes it very useful in optoelectronic devices functioning at room temperature. The unusual behavior of the evolution of PL peaks and their full width at half maximum as a function of temperature indicates the thermal sensitivity of the point defects present in the band gap. The shallower energy levels due to dangling bonds and/or oxygen vacancies are more sensitive to the temperature. However, volume defects like Sn interstitials are thermally stable and constitute deep and stable energy levels for excited electrons. Small redshifting of PL peaks is observed with increasing temperature. This behavior is attributed to the reduction of oxygen vacancies.

Keywords: transparent conducting oxide, photoluminescence, intrinsic point defects, semiconductors, oxygen vacancies

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Authors: Di Wei, Xing Hu, Yangjun Chen, Baofeng Li, Hong Chen

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To achieve the research purpose of guiding the spatial morphology design of blocks through the indicators to obtain a good wind environment, it is necessary to find the most suitable type and value range of each urban spatial morphology indicator. At present, most of the relevant researches is based on the numerical simulation of the ideal block shape and rarely proposes the results based on the complex actual block types. Therefore, this paper firstly attempted to make theoretical speculation on the main factors influencing indicators' effectiveness by analyzing the physical significance and formulating the principle of each indicator. Then it was verified by the field wind environment measurement and statistical analysis, indicating that Porosity(P₀) can be used as an important indicator to guide the design of block wind environment in the case of deep street canyons, while Frontal Area Density (λF) can be used as a supplement in the case of shallow street canyons with no height difference. Finally, computational fluid dynamics (CFD) was used to quantify the impact of block height difference and street canyons depth on λF and P₀, finding the suitable type and value range of λF and P₀. This paper would provide a feasible wind environment index system for urban designers.

Keywords: urban spatial morphology indicator, urban microclimate, computational fluid dynamics, block ventilation, correlation analysis

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Authors: B. Lokuketagoda, M. Weerakoon, U. Madushan, A. N. Senaratne, K. Y. Abeywardena

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Ransomware has become a common threat in past few years and the recent threat reports show an increase of growth in Ransomware infections. Researchers have identified different variants of Ransomware families since 2015. Lack of knowledge of the user about the threat is a major concern. Ransomware detection methodologies are still growing through the industry. Email is the easiest method to send Ransomware to its victims. Uninformed users tend to click on links and attachments without much consideration assuming the emails are genuine. As a solution to this in this paper R-Killer Ransomware detection tool is introduced. Tool can be integrated with existing email services. The core detection Engine (CDE) discussed in the paper focuses on separating suspicious samples from emails and handling them until a decision is made regarding the suspicious mail. It has the capability of preventing execution of identified ransomware processes. On the other hand, Sandboxing and URL analyzing system has the capability of communication with public threat intelligence services to gather known threat intelligence. The R-Killer has its own mechanism developed in its Proactive Monitoring System (PMS) which can monitor the processes created by downloaded email attachments and identify potential Ransomware activities. R-killer is capable of gathering threat intelligence without exposing the user’s data to public threat intelligence services, hence protecting the confidentiality of user data.

Keywords: ransomware, deep learning, recurrent neural networks, email, core detection engine

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Authors: Irfan Anjum Manarvi, Zahid Ali kaimkhani

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Predicting location of the fracture in human bones has been a keen area of research for the past few decades. A variety of tests for hardness, deformation, and strain field measurement have been conducted in the past; but considered insufficient due to various limitations. Researchers, therefore, have proposed further studies due to inaccuracies in measurement methods, testing machines, and experimental errors. Advancement and availability of hardware, measuring instrumentation, and testing machines can now provide remedies to these limitations. The human foot is a critical part of the body exposed to various forces throughout its life. A number of products are developed for using it for protection and care, which many times do not provide sufficient protection and may itself become a source of stress due to non-consideration of the delicacy of bones in the feet. A continuous strain or overloading on feet may occur resulting to discomfort and even fracture. Mechanical properties of Tarsals, Metatarsals, and phalanges are, therefore, the primary area of consideration for all such design applications. Hardness is one of the mechanical properties which are considered very important to establish the mechanical resistance behavior of a material against applied loads. Past researchers have worked in the areas of investigating mechanical properties of these bones. However, their results were based on a limited number of experiments and taking average values of hardness due to either limitation of samples or testing instruments. Therefore, they proposed further studies in this area. The present research has been carried out to develop a hardness map of the human foot by measuring micro hardness at various locations of these bones. Results are compiled in the form of distance from a reference point on a bone and the hardness values for each surface. The number of test results is far more than previous studies and are spread over a typical bone to give a complete hardness map of these bones. These results could also be used to establish other properties such as stress and strain distribution in the bones. Also, industrial engineers could use it for design and development of various accessories for human feet health care and comfort and further research in the same areas.

Keywords: tarsals, metatarsals, phalanges, hardness testing, biomechanics of human foot

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

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This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Rohit Semwal, Shivam Arora, Saurav, Sangita Roy

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This work proposes a supervised framework with deep convolutional neural networks (CNNs) for vision-based sign language recognition of static gestures. Our approach addresses the acquisition and segmentation of correct inputs for the CNN-based classifier. Microsoft Kinect™ sensor, despite complex environmental conditions, can track hands efficiently. Skin Colour based segmentation is applied on cropped images of hands in different poses, used to depict different sign language gestures. The segmented hand images are used as an input for our classifier. The CNN classifier proposed in the paper is able to classify the input images with a high degree of accuracy. The system was trained and tested on 39 static sign language gestures, including 26 letters of the alphabet and 13 commonly used words. This paper includes a problem definition for building the proposed system, which acts as a sign language translator between deaf/mute and the rest of the society. It is then followed by a focus on reviewing existing knowledge in the area and work done by other researchers. It also describes the working principles behind different components of CNNs in brief. The architecture and system design specifications of the proposed system are discussed in the subsequent sections of the paper to give the reader a clear picture of the system in terms of the capability required. The design then gives the top-level details of how the proposed system meets the requirements.

Keywords: sign language, CNN, HCI, segmentation

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Authors: Mohammad Zaid, Md. Rehan Sadique

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Underground structures are an integral part of urban infrastructures. Tunnels are being used for the transportation of humans and goods from distance to distance. Terrorist attacks on underground structures such as tunnels have resulted in the improvement of design methodologies of tunnels. The design of underground tunnels must include anti-terror design parameters. The study has been carried out to analyse the rock tunnel when subjected to internal blast loading. The finite element analysis has been carried out for 30m by 30m of the cross-section of the tunnel and 35m length of extrusion of the rock tunnel model. The effect of tunnel diameter and overburden depth of tunnel has been studied under internal blast loading. Four different diameters of tunnel considered are 5m, 6m, 7m, and 8m, and four different overburden depth of tunnel considered are 5m, 7.5m, 10m, and 12.5m. The mohr-coulomb constitutive material model has been considered for the Quartzite rock. A concrete damage plasticity model has been adopted for concrete tunnel lining. For the trinitrotoluene (TNT) Jones-Wilkens-Lee (JWL) material model has been considered. Coupled-Eulerian-Lagrangian (CEL) approach for blast analysis has been considered in the present study. The present study concludes that a shallow tunnel having smaller diameter needs more attention in comparison to blast resistant design of deep tunnel having a larger diameter. Further, in the case of shallow tunnels, more bulging has been observed, and a more substantial zone of rock has been affected by internal blast loading.

Keywords: finite element method, blast, rock, tunnel, CEL, JWL

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Authors: Malgorzata J. Rybak-Smith, Benedicte Thiebaut, Simon Johnson, Peter Bishop, Helen E. Townley

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Gadolinium doped titania (TiO2:Gd) nanoparticles (NPs) can be activated by X-ray radiation to generate Reactive Oxygen Species (ROS), which can be effective in killing cancer cells. As such, treatment with these NPs can be used to enhance the efficacy of conventional radiotherapy. Incorporation of the NPs in to tumour tissue will permit the extension of radiotherapy to currently untreatable tumours deep within the body, and also reduce damage to neighbouring healthy cells. In an attempt to find a fast and scalable method for the synthesis of the TiO2:Gd NPs, the use of Flame Spray Pyrolysis (FSP) was investigated. A series of TiO2 NPs were generated with 1, 2, 5 and 7 mol% gadolinium dopant. Post-synthesis, the TiO2:Gd NPs were silica-coated to improve their biocompatibility. Physico-chemical characterisation was used to determine the size and stability in aqueous suspensions of the NPs. All analysed TiO2:Gd NPs were shown to have relatively high photocatalytic activity. Furthermore, the FSP synthesized silica-coated TiO2:Gd NPs generated enhanced ROS in chemico. Studies on rhabdomyosarcoma (RMS) cell lines (RD & RH30) demonstrated that in the absence of irradiation all TiO2:Gd NPs were inert. However, application of TiO2:Gd NPs to RMS cells, followed by irradiation, showed a significant decrease in cell proliferation. Consequently, our studies showed that the X-ray-activatable TiO2:Gd NPs can be prepared by a high-throughput scalable technique to provide a novel and affordable anticancer therapy.

Keywords: cancer, gadolinium, ROS, titania nanoparticles, X-ray

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Authors: Gökhan Altay, Cafer Kayadelen

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Rock fall is a movement of huge rock blocks from dip slopes due to physical effects. It generally occurs where loose tuffs lying under basalt flow or stringcourse is being constituted by limestone layers which stand on clay. By corrosion of some parts, big cracks occur on layers and these cracks continue to grow with the effect of freezing-thawing. In this way, the breaking rocks fall down from these dip slopes. Earthquakes which can induce lots of rock movements is another reason for rock fall events. In Turkey, we have a large number of regions prone to the earthquake as in the World so this increases the possibility of rock fall events. A great number of rock fall events take place in Turkey as in the World every year. The rock fall events occurring in urban areas cause serious damages in houses, roads and workplaces. Sometimes it also hinders transportation and furthermore it maybe kills people. In Turkey, rock fall events happen mostly in Spring and Winter because of freezing- thawing of water in rock cracks frequently. In mountain and inclined areas, rock fall is risky for engineering construction and environment. Some countries can invest significant money for these risky areas. For instance, in Switzerland, approximately 6.7 million dollars is spent annually for a distance of 4 km, to the systems to prevent rock fall events. In Turkey, we have lots of urban areas and engineering structure that have the rock fall risk. The embankments are preferable for rock fall events because of its low maintenance and repair costs. Also, embankments are able to absorb much more energy according to other protection systems. The current design method of embankments is only depended on field tests results so there are inadequate studies about this design method. In this paper, the field test modeled in three dimensions and analysis are carried out with the help of ANSYS programme. By the help of field test from literature the numerical model validated. After the validity of numerical models additional parametric studies performed. Changes in deformation of embankments are investigated by the changes in, geometry, velocity and impact height of falling rocks.

Keywords: ANSYS, embankment, impact height, numerical analysis, rock fall

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Authors: Umm-E-Hani, Faiza Babar, Hanif Durad

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Malware-detecting approaches suffer many limitations, due to which all anti-malware solutions have failed to be reliable enough for detecting zero-day malware. Signature-based solutions depend upon the signatures that can be generated only when malware surfaces at least once in the cyber world. Another approach that works by detecting the anomalies caused in the environment can easily be defeated by diligently and intelligently written malware. Solutions that have been trained to observe the behavior for detecting malicious files have failed to cater to the malware capable of detecting the sandboxed or protected environment. Machine learning and deep learning-based approaches greatly suffer in training their models with either an imbalanced dataset or an inadequate number of samples. AI-based anti-malware solutions that have been trained with enough samples targeted a selected feature vector, thus ignoring the input of leftover features in the maliciousness of malware just to cope with the lack of underlying hardware processing power. Our research focuses on producing an anti-malware solution for detecting malicious PE files by circumventing the earlier-mentioned shortcomings. Our proposed framework, which is based on automated feature engineering through autoencoders, trains the model over a fairly large dataset. It focuses on the visual patterns of malware samples to automatically extract the meaningful part of the visual pattern. Our experiment has successfully produced a state-of-the-art accuracy of 99.54 % over test data.

Keywords: malware, auto encoders, automated feature engineering, classification

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Authors: Geeta Kumari, Prabu Vairakannu

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Coal is the largest fossil fuel. In India, around 37 % of coal resources found at a depth of more than 300 meters. In India, more than 70% of electricity production depends on coal. Coal on combustion produces greenhouse and pollutant gases such as CO₂, SOₓ, NOₓ, and H₂S etc. Underground coal gasification (UCG) technology is an efficient and an economic in-situ clean coal technology, which converts these unmineable coals into valuable calorific gases. The UCG syngas (mainly H₂, CO, CH₄ and some lighter hydrocarbons) which can utilized for the production of electricity and manufacturing of various useful chemical feedstock. It is an inherent clean coal technology as it avoids ash disposal, mining, transportation and storage problems. Gasification of underground coal using steam as a gasifying medium is not an easy process because sending superheated steam to deep underground coal leads to major transportation difficulties and cost effective. Therefore, for reducing this problem, we have used CO₂ as a gasifying medium, which is a major greenhouse gas. This paper focus laboratory scale underground coal gasification experiment on a coal block by using CO₂ as a gasifying medium. In the present experiment, first, we inject oxygen for combustion for 1 hour and when the temperature of the zones reached to more than 1000 ºC, and then we started supplying of CO₂ as a gasifying medium. The gasification experiment was performed at an atmospheric pressure of CO₂, and it was found that the amount of CO produced due to Boudouard reaction (C+CO₂  2CO) is around 35%. The experiment conducted to almost 5 hours. The maximum gas composition observed, 35% CO, 22 % H₂, and 11% CH4 with LHV 248.1 kJ/mol at CO₂/O₂ ratio 0.4 by volume.

Keywords: underground coal gasification, clean coal technology, calorific value, syngas

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Authors: Bolanle Deborah Motilewa, E.K. Rowland Worlu, Gbenga Mayowa Agboola, Marvellous Aghogho Chidinma Gberevbie

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Businesses operating in the modern business world are faced with varying challenges; amongst which is the need to ensure that they are performing their societal function of being responsible in the society in which they operate. This responsibility to society is generally termed as corporate social responsibility. For many years, the practice of corporate social responsibility (CSR) was solely philanthropic, where organizations gave ‘charity’ or ‘alms’ to society, without any link to the organization’s mission and objectives. However, there has arisen a shift in the application of CSR from an act of philanthropy to a strategy with a business model engaged in by organizations to create a win-win situation of performing their societal obligation, whilst simultaneously performing their economic obligation. In more recent times, the term has moved from CSR to creating shared value, which is simply corporate policies and practices that enhance the competitiveness of a business organization while simultaneously advancing social and economic conditions in the communities in which the company operates. Creating shared value has in more recent light found more meaning in underdeveloped countries, faced with deep societal challenges that businesses can solve whilst creating economic value. This study thus reviews literature on CSR, conceptualizing the shift to creating shared value and finally viewing its potential significance in Africa’s development.

Keywords: africapitalism, corporate social responsibility, development, shared value

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Authors: Oyindamola Kayode, Sarah George, Roberto Borrageiro, Mike Shirran

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A phenomenon identified by our industrial partner has experienced sag on AA3104 can body stock (CBS) transfer bar during transportation of the slab from the breakdown mill to the finishing mill. Excessive sag results in bottom scuffing of the slab onto the roller table, resulting in surface defects on the final product. It has been found that increasing the strain rate on the breakdown mill final pass results in a slab resistant to sag. The creep response for materials hot deformed at different Zener–Holloman parameter values needs to be evaluated experimentally to gain better understanding of the operating mechanism. This study investigates this identified phenomenon through laboratory simulation of the breakdown mill conditions for various strain rates by utilizing the Gleeble at UCT Centre for Materials Engineering. The experiment will determine the creep response for a range of conditions as well as quantifying the associated material microstructure (sub-grain size, grain structure etc). The experimental matrices were determined based on experimental conditions approximate to industrial hot breakdown rolling and carried out on the Gleeble 3800 at the Centre for Materials Engineering, University of Cape Town. Plane strain compression samples were used for this series of tests at an applied load that allow for better contact and exaggerated creep displacement. A tantalum barrier layer was used for increased conductivity and decreased risk of anvil welding. One set of tests with no in-situ hold time was performed, where the samples were quenched after deformation. The samples were retained for microstructure analysis of the micrographs from the light microscopy (LM), quantitative data and images from scanning electron microscopy (SEM) and energy dispersive X-ray (EDX), sub-grain size and grain structure from electron back scattered diffraction (EBSD).

Keywords: aluminium alloy, can-body stock, hot rolling, creep response, Zener-Hollomon parameter

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Authors: Kun Xu, Yuan Xu, Jia Qiao

Abstract:

The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: A. Y. Purnama, N. Yasufuku

Abstract:

The bridge structure is a building that has to be maintained, especially for the elastomeric bearing. The girder of the bridge needs to be lifted upward to maintain this elastomeric bearing, that needs high cost. Nowadays, integral abutment bridges are becoming popular. The integral abutment bridge is less costly because the elastomeric bearings are eliminated, which reduces the construction cost and maintenance costs. However, when this elastomeric bearing removed, the girder movement due to environmental thermal forces directly support by pile foundation, and it needs to be considered in the design. In case of pile foundation in a stiff soil, in the top area of the pile cannot move freely due to the fixed condition by soil stiffness. Pre-bored pile system can be used to increase the flexibility of pile foundation using a pre-bored hole that filled with elastic materials, but the behavior of soil-pile interaction and soil response due to this system is still rarely explained. In this paper, an experimental study using small-scale laboratory model test conducted in a half size model. Single flexible pile model embedded in sandy soil with the pre-bored ring, which filled with the filler material. The testing box made from an acrylic glass panel as observation area of the pile shaft to monitor the displacement of the pile during the lateral loading. The failure behavior of the soil inside the pre-bored ring and around the pile shaft was investigated to determine the point of pile rotation and the movement of this point due to the pre-bored ring system along the pile shaft. Digital images were used to capture the deformations of the soil and pile foundation during the loading from the acrylic glass on the side of the testing box. The results were presented in the form of lateral load resistance charts against the pile shaft displacement. The failure pattern result also established due to the cyclic lateral loading. The movement of the rotational point was measured due to the pre-bored system filled with appropriate filler material. Based on the findings, design considerations for pre-bored pile system due to cyclic lateral loading can be introduced.

Keywords: failure behavior, pre-bored pile system, cyclic lateral loading, sandy soil

Procedia PDF Downloads 233

Authors: Paolo Sassi, Jorge Freiria, Gabriel Usera

Abstract:

In this work, a finite volume fluid flow solver is coupled with a discrete element method module for the simulation of the dynamics of free and elastic bodies in interaction with the fluid and between themselves. The open source fluid flow solver, caffa3d.MBRi, includes the capability to work with nested overlapping grids in order to easily refine the grid in the region where the bodies are moving. To do so, it is necessary to implement a recognition function able to identify the specific mesh block in which the device is moving in. The set of overlapping finer grids might be displaced along with the set of bodies being simulated. The interaction between the bodies and the fluid is computed through a two-way coupling. The velocity field of the fluid is first interpolated to determine the drag force on each object. After solving the objects displacements, subject to the elastic bonding among them, the force is applied back onto the fluid through a Gaussian smoothing considering the cells near the position of each object. The fishnet is represented as lumped masses connected by elastic lines. The internal forces are derived from the elasticity of these lines, and the external forces are due to drag, gravity, buoyancy and the load acting on each element of the system. When solving the ordinary differential equations system, that represents the motion of the elastic and flexible bodies, it was found that the Runge Kutta solver of fourth order is the best tool in terms of performance, but requires a finer grid than the fluid solver to make the system converge, which demands greater computing power. The coupled solver is demonstrated by simulating the interaction between the fluid, an elastic fishnet and a set of free bodies being captured by the net as they are dragged by the fluid. The deformation of the net, as well as the wake produced in the fluid stream are well captured by the method, without requiring the fluid solver mesh to adapt for the evolving geometry. Application of the same strategy to the simulation of elastic structures subject to the action of wind is also possible with the method presented, and one such application is currently under development.

Keywords: computational fluid dynamics, discrete element method, fishnets, nested overlapping grids

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Authors: Yake Zhang, Peng Xu

Abstract:

The development of building energy models (BEM) required for architectural design and analysis is a time-consuming and complex process, demanding a deep understanding and proficient use of simulation software. To streamline the generation of complex building energy models, this study proposes an automated method for generating building energy models using a large language model and the BEM library aimed at improving the efficiency of model generation. This method leverages a large language model to parse user-specified requirements for target building models, extracting key features such as building location, window-to-wall ratio, and thermal performance of the building envelope. The BEM library is utilized to retrieve energy models that match the target building’s characteristics, serving as reference information for the large language model to enhance the accuracy and relevance of the generated model, allowing for the creation of a building energy model that adapts to the user’s modeling requirements. This study enables the automatic creation of building energy models based on natural language inputs, reducing the professional expertise required for model development while significantly decreasing the time and complexity of manual configuration. In summary, this study provides an efficient and intelligent solution for building energy analysis and simulation, demonstrating the potential of a large language model in the field of building simulation and performance modeling.

Keywords: artificial intelligence, building energy modelling, building simulation, large language model

Procedia PDF Downloads 26

Authors: Yongjun Ahn, Sung Gyu Choi, Seung-Yeop Kwak

Abstract:

Selective removal of free fatty acid (FFA) and chlorophyll in extra virgin olive oil (EVOO) is necessary to enhance the thermal stability in the condition of the deep frying. In this work, we demonstrated improving the thermal stability of EVOO by selective removal of free fatty acid and chlorophyll using (3-Aminopropyl)trimethoxysilane (APTMS) functionalized mesoporous silica with controlled pore size. The adsorption kinetics of free fatty acid and chlorophyll into the mesoporous silica were quantitatively analyzed by Freundlich and Langmuir model. The highest chlorophyll adsorption efficiency was shown in the pore size at 5 nm, suggesting that the interaction between the silica and the chlorophyll could be optimized at this point. The amino-functionalized mesoporous silica showed drastically improved removal efficiency of FFA than the bare silica. Moreover, beneficial compounds like tocopherol and phenolic compounds maintained even after adsorptive removal. Extra virgin olive oil treated by aminopropyl-functionalized silica had a smoke point high enough to be used as commercial frying oil. Based on these results, it is expected to attract the considerable amount of interest toward facile adsorptive refining process of EVOO using pore size controlled and amino-functionalized mesoporous silica.

Keywords: mesoporous silica, extra virgin olive oil, selective adsorption, thermal stability

Procedia PDF Downloads 241

Authors: S. M. Jogdand, D. D. Jagtap, N. R. Dalal

Abstract:

Sweet orange (Citrus sinensis Osbeck) is one of the most important commercially cultivated fruit crop in India. It stands on second position amongst citrus group after mandarin. Irrigation and fertigation are vital importance of sweet orange orchard and considered to be the most critical cultural operations. The soil acts as the reservoir of water and applied nutrients, the interaction between irrigation and fertigation leads to the ultimate quality and production of fruits. The increasing cost of fertilizers and scarcity of irrigation water forced the farmers for optimum use of irrigation and nutrients. The experiment was conducted with object to find out irrigation and nutrient interaction in sweet orange to optimize the use of both the factors. The experiment was conducted in medium to deep soil. The irrigation level I3,drip irrigation at 90% ER (effective rainfall) and fertigation level F3 80% RDF (recommended dose of fertilizer) recorded significantly maximum plant height, plant spread, canopy volume, number of fruits, weight of fruit, fruit yield kg/plant and t/ha followed by F2 , fertigation with 70% RDF. The interaction effect of irrigation and fertigation on growth was also significant and the maximum plant height, E-W spread, N-S spread, canopy volume, highest number of fruits, weight of fruit and yield kg/plant and t/ha was recorded in T9 i.e. I3F3 drip irrigation at 90% ER and fertigation with 80% of RDF followed by I3F2 drip irrigation at 90% ER and fertigation with 70% of RDF.

Keywords: sweet orange, fertigation, irrigation, interactions

Procedia PDF Downloads 178

Authors: Samina Yasmin, Anzar Khaliq, Zareen Tabassum

Abstract:

Acute power shortage in Pakistan requires an urgent attention to take preliminary steps to spread energy awareness at all levels. One such initiative is taken at Habib University (HU), Pakistan, through renewable energy course, one of the core offerings, where students are trained to investigate various aspects of renewable energy concepts. The course is offered to all freshmen enrolled at HU regardless of their academic backgrounds and degree programs. A four-credit modular course includes both theory and laboratory elements. Hands-on laboratories play an important role in science classes, particularly to enhance the motivation and deep understanding of energy science. A set of selected hands-on activities included in course introduced students to explore the latest developments in the field of renewable energy such as dye-sensitized solar cells, gas chromatography, global warming, climate change, fuel cell energy and power of biomass etc. These projects not only helped HU freshmen to build on energy fundamentals but also provided them greater confidence in investigating, questioning and experimenting with renewable energy related conceptions. A feedback survey arranged during and end of term revealed the effectiveness of the hands-on laboratory to enhance the common understanding of real world problems related to energy such as awareness of energy saving, the level of concern about global climate change, environmental pollution and science of energy behind the energy usage.

Keywords: biochemical approaches, energy curriculum, hands-on laboratory, renewable energy

Procedia PDF Downloads 256