Search results for: deep eutectic solvent

Authors: Prama Mukhopadhyay

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Starting from transport to food, today’s world platform economy and digital markets have taken over almost every sphere of consumers’ lives. Sellers and buyers are getting connected through platforms, which is acting as an intermediary. It has made consumer’s life easier in terms of time, price, choice and other factors. Having said that, there are several concerns regarding platforms. There are competition law concerns like unfair pricing, deep discounting by the platforms which affect the consumer welfare. Apart from that, the biggest problem is lack of transparency with respect to the business models, how it operates, price calculation, etc. In most of the cases, consumers are unaware of how their personal data are being used. In most of the cases, they are unaware of how algorithm uses their personal data to determine the price of the product or even to show the relevant products using their previous searches. Using personal or non-personal data without consumer’s consent is a huge legal concern. In addition to this, another major issue lies with the question of liability. If a dispute arises, who will be responsible? The seller or the platform? For example, if someone ordered food through a food delivery app and the food was bad, in this situation who will be liable: the restaurant or the food delivery platform? In this paper, the researcher tries to examine the legal concern related to platform economy from the consumer protection and consumer welfare perspectives. The paper analyses the cases from different jurisdictions and approach taken by the judiciaries. The author compares the existing legislation of EU, US and other Asian Countries and tries to highlight the best practices.

Keywords: competition, consumer, data, platform

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Authors: Shan Jia, Jinbao Chen, Jinhua Zhou, Jiacheng Qian

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Lunar exploration is a necessary foundation for deep-space exploration. For the functional limitations of the fixed landers which are widely used currently and are to expand the detection range by the use of wheeled rovers with unavoidable path-repeatability, a movable lunar soft-landing device based on cantilever type buffer mechanism and leg-foot type walking mechanism is presented. Firstly, a 20 DoFs quadruped configuration based on pushrod is proposed. The configuration is of the bionic characteristics such as hip, knee and ankle joints, and can make the kinematics of the whole mechanism unchanged before and after buffering. Secondly, the multi-function main/auxiliary buffers based on crumple-energy absorption and screw-nut mechanism, as well as the telescopic device which could be used to protect the plantar force sensors during the buffer process are designed. Finally, the kinematic model of the whole mechanism is established, and the configuration optimization of the whole mechanism is completed based on the performance requirements of slope adaptation and obstacle crossing. This research can provide a technical solution integrating soft-landing, large-scale inspection and material-transfer for future lunar exploration and even mars exploration, and can also serve as the technical basis for developing the reusable landers.

Keywords: configuration design, lunar soft-landing device, movable, optimization

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Authors: Qian Qiao, Tongjin Xiao, Hongtu He, Jiaxin Yu

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This study is focused on the synergetic effects of water and sulfur dioxide treatments (SO₂ treatments) on the mechanochemical wear of SLS glass. It is found that the wear behavior of SLS glass in humid air is very sensitive to the water and SO₂ treatment environments based on the wear test using a ball-on-flat reciprocation tribometer. When SLS glass is treated with SO₂-without, the presence of water, the wear resistance of SLS glass in humid air becomes significantly higher compared to the pristine glass. However, when SLS glass is treated with SO₂ with the presence of water, the wear resistance of SLS glass decreases remarkably with increasing in the relative humidity (RH) from 0% to 90%. Further analyses indicate that when sodium ions are leached out of SLS glass surface via the water and SO₂ treatments, the mechanochemical properties of SLS glass surface become different depending on the RH. At lower humidity, the nano hardness of the Na⁺-leached surface is higher, and it can contribute to the enhanced wear resistance of SLS glass. In contrast, at higher humidity conditions, the SLS glass surface is more hydrophilic, and substantial wear debris can be found inside the wear track of SLS glass. Those phenomena suggest that adhesive wear and abrasive wear dominate the wear mechanism of SLS glass in humid air, causing the decreased wear resistance of SLS glass with increasing the RH. These results may not only provide a deep understanding of the wear mechanism of SLS glass but also helpful for operation process of functional and engineering glasses.

Keywords: soda lime silicate glass, wear, water, SO₂

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Authors: Shan Jia, Jinbao Chen, Jinhua Zhou, Jiacheng Qian

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In response to the functional limitations of the fixed landers, those are to expand the detection range by the use of wheeled rovers with unavoidable path-repeatability in deep space exploration currently, a movable lander based on the leg-foot walking mechanism is presented. Firstly, a quadruped landing mechanism based on pushrod-damping is proposed. The configuration is of the bionic characteristics such as hip, knee and ankle joints, and the multi-function main/auxiliary buffers based on the crumple-energy absorption and screw-nut mechanism. Secondly, the workspace of the end of the leg-foot mechanism is solved by Monte Carlo method, and the key points on the desired trajectory of the end of the leg-foot mechanism are fitted by cubic spline curve. Finally, an optimal time-jerk trajectory based on weight coefficient is planned and analyzed by an adaptive genetic algorithm (AGA). The simulation results prove the rationality and stability of walking motion of the movable leg-foot lander in the star catalogue. In addition, this research can also provide a technical solution integrating of soft-landing, large-scale inspection and material transfer for future star catalogue exploration, and can even serve as the technical basis for developing the reusable landers.

Keywords: motion performance, trajectory planning, movable, leg-foot lander

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Authors: Bin Wang, Yong Xu, Honggang Qu, Rongmei Liu, Zhenji Gao

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Based on the geodynamic environment, basic geological characteristics of minerals and so on, gold deposits in China are divided into 11 categories, of which tectonic fracture altered rock, mid-intrudes and contact zone, micro-fine disseminated and continental volcanic types are the main prospecting kinds. The metallogenic age of gold deposits in China is dominated by the Mesozoic and Cenozoic. According to the geotectonic units, geological evolution, geological conditions, spatial distribution, gold deposits types, metallogenic factors etc., 42 gold concentration areas are initially determined and have a concentrated distribution feature. On the basis of the gold exploration density, gold concentration areas are divided into high, medium and low level areas. High ones are mainly distributed in the central and eastern regions. 93.04% of the gold exploration drillings are within 500 meters, but there are some problems, such as less and shallower of drilling verification etc.. The paper discusses the resource potentials of gold deposits and proposes the future prospecting directions and suggestions. The deep and periphery of old mines in the central and eastern regions and western area, especially in Xinjiang and Qinghai, will be the future key prospecting one and have huge potential gold reserves. If the exploration depth is extended to 2,000 meters shallow, the gold resources will double.

Keywords: gold deposits, gold deposits types, gold concentration areas, prospecting, resource potentiality

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Authors: Yik Sin Chan, Nam Weng Sit, Fook Yee Chye, van Ofwegen Leen, de Voogd Nicole, Kong Soo Khoo

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Chikungunya fever is an arboviral disease transmitted by the Aedes mosquitoes. It has resulted in epidemics of the disease in tropical countries in the Indian Ocean and South East Asian regions. The recent spread of this disease to the temperate countries such as France and Italy, coupled with the absence of vaccines and effective antiviral drugs make chikungunya fever a worldwide health threat. This study aims to investigate the anti-chikungunya virus activity of selected marine organism samples collected from Malaysian coastal areas, including seaweeds (Caulerpa racemosa, Caulerpa sertularioides and Kappaphycus alvarezii), a soft coral (Lobophytum microlobulatum) and a sponge (Spheciospongia vagabunda). Following lyophilization (oven drying at 40C for K. alvarezii) and grinding to powder form, each sample was subjected to sequential solvent extraction using hexane, chloroform, ethyl acetate, ethanol, methanol and distilled water in order to extract bioactive compounds. The antiviral activity was evaluated using monkey kidney epithelial (Vero) cells infected with the virus (multiplicity of infection=1). The cell viability was determined by Neutral Red uptake assay. 70% of the 30 extracts showed weak inhibitory activity with cell viability ≤30%. Seven of the extracts exhibited moderate inhibitory activity (cell viability: 31%-69%). These were the chloroform, ethyl acetate, ethanol and methanol extracts of C. racemosa; chloroform and ethyl acetate extracts of L. microlobulatum; and the chloroform extract of C. sertularioides. Only the hexane and ethanol extracts of L. microlobulatum showed strong inhibitory activity against the virus, resulting in cell viabilities (mean±SD; n=3) of 73.3±2.6% and 79.2±0.9%, respectively. The corresponding mean 50% effective concentrations (EC50) for the extracts were 14.2±0.2 and 115.3±1.2 µg/mL, respectively. The ethanol extract of the soft coral L. microlobulatum appears to hold the most promise for further characterization of active principles as it possessed greater selectivity index (SI>5.6) compared to the hexane extract (SI=2.1).

Keywords: antiviral, seaweed, sponge, soft coral, vero cell

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Authors: Kemka C. Ihemelandu, Chukwuemeka U. Ihemelandu

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

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

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Authors: Nima Dastanboo, Xiao-Qing Li, Hamed Gharibdoost

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The purpose of this study is to investigate about the geological properties ahead of a tunnel face with using Electrical Resistivity Tomography ERT and Tunnel Seismic Prediction TSP303 methods. In deep tunnels with hydro-geological conditions, it is important to study the geological structures of the region before excavating tunnels. Otherwise, it would lead to unexpected accidents that impose serious damage to the project. For constructing Nosoud tunnel in west of Iran, the ERT and TSP303 methods are employed to predict the geological conditions dynamically during the excavation. In this paper, based on the engineering background of Nosoud tunnel, the important results of applying these methods are discussed. This work demonstrates seismic method and electrical tomography as two geophysical techniques that are able to detect a tunnel. The results of these two methods were being in agreement with each other but the results of TSP303 are more accurate and quality. In this case, the TSP 303 method was a useful tool for predicting unstable geological structures ahead of the tunnel face during excavation. Thus, using another geophysical method together with TSP303 could be helpful as a decision support in excavating, especially in complicated geological conditions.

Keywords: tunnel seismic prediction (TSP303), electrical resistivity tomography (ERT), seismic wave, velocity analysis, low-velocity zones

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Authors: Ranjana Dani

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Brand communication narratives in India has evolved today to reflect the vibrant and intriguing tone of voice inspired by a rich cultural heritage while addressing the culturally alert attitude of the contemporary global Indian. Brands are strongly associated with the organization's values, vision, and mission and portray this through specific ‘look and feel’ and ‘tone of voice’. It is within the brand’s visual language that COLOUR has evolved to become a most powerful weapon in the designer’s arsenal. Color is big business in Brand Design! A brand is a ‘collection of perceptions’, meaningful brand connect is about striving to occupy head and heart space in consumers. The persona of the young Indian reflects a deep attachment to cultural roots as seen through the characteristic of ‘Indie Pride,’ blended with the ambitious, aspirational traits of a modern ‘global citizen’.Studies on ‘Color Perceptions’ indicate a trend that amplifies this, and hence brands reflect a GLOCAL palette, a Global and Local Blend. This paper establishes this through case studies that expand the inspirations, selection processes, and use of innovative color palettes crafted by some dynamic brand designers. This throws light on the role of color as it generates visual impact and recall for successful brands.

Keywords: colour palettes, brand design and business, cultural context, colour perceptions, glocal, contemporaneity

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Authors: Ime R. Udotong, Justina I. R. Udotong, Ofonime U. M. John

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Ibeno, Nigeria hosts the operational base of Mobil Producing Nigeria Unlimited (MPNU), a subsidiary of ExxonMobil and the current highest oil and condensate producer in Nigeria. Besides MPNU, other oil companies operate onshore, on the continental shelf and deep offshore of the Atlantic Ocean in Ibeno, Nigeria. This study was designed to delineate oil polluted sites in Ibeno, Nigeria using geophysical methods of electrical resistivity (ER) and ground penetrating radar (GPR). Results obtained revealed that there have been hydrocarbon contaminations of this environment by past crude oil spills as observed from high resistivity values and GPR profiles which clearly show the distribution, thickness and lateral extent of hydrocarbon contamination as represented on the radargram reflector tones. Contaminations were of varying degrees, ranging from slight to high, indicating levels of substantial attenuation of crude oil contamination over time. Moreover, the display of relatively lower resistivities of locations outside the impacted areas compared to resistivity values within the impacted areas and the 3-D Cartesian images of oil contaminant plume depicted by red, light brown and magenta for high, low and very low oil impacted areas, respectively confirmed significant recent pollution of the study area with crude oil.

Keywords: electrical resistivity, geophysical investigations, ground penetrating radar, oil-polluted sites

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Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics-informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on a strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary conditions to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of the Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful in studying various optical phenomena.

Keywords: deep learning, optical soliton, physics informed neural network, partial differential equation

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Authors: S. Sharan Sundar, Nithish B. Moudhgalya, Nidhi Bhandari, Vineeth Vijayaraghavan

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Automatic Short Answer Grading is one of the prime applications of artificial intelligence in education. Several approaches involving the utilization of selective handcrafted features, graphical matching techniques, concept identification and mapping, complex deep frameworks, sentence embeddings, etc. have been explored over the years. However, keeping in mind the real-world application of the task, these solutions present a slight overhead in terms of computations and resources in achieving high performances. In this work, a simple and effective solution making use of elemental features based on statistical, linguistic properties, and word-based similarity measures in conjunction with tree-based classifiers and regressors is proposed. The results for classification tasks show improvements ranging from 1%-30%, while the regression task shows a stark improvement of 35%. The authors attribute these improvements to the addition of multiple similarity scores to provide ensemble of scoring criteria to the models. The authors also believe the work could reinstate that classical natural language processing techniques and simple machine learning models can be used to achieve high results for short answer grading.

Keywords: artificial intelligence, intelligent systems, natural language processing, text mining

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Authors: Moshe Mello, Hilary Rutto, Tumisang Seodigeng

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The nature of tires makes them extremely challenging to recycle due to the available chemically cross-linked polymer and, therefore, they are neither fusible nor soluble and, consequently, cannot be remolded into other shapes without serious degradation. Open dumping of tires pollutes the soil, contaminates underground water and provides ideal breeding grounds for disease carrying vermins. The thermal decomposition of tires by pyrolysis produce char, gases and oil. The composition of oils derived from waste tires has common properties to commercial diesel fuel. The problem associated with the light oil derived from pyrolysis of waste tires is that it has a high sulfur content (> 1.0 wt.%) and therefore emits harmful sulfur oxide (SOx) gases to the atmosphere when combusted in diesel engines. Desulphurization of TPO is necessary due to the increasing stringent environmental regulations worldwide. Hydrodesulphurization (HDS) is the commonly practiced technique for the removal of sulfur species in liquid hydrocarbons. However, the HDS technique fails in the presence of complex sulfur species such as Dibenzothiopene (DBT) present in TPO. This study aims to investigate the viability of photodegradation (Photocatalytic oxidative desulphurization) and adsorptive desulphurization technologies for efficient removal of complex and non-complex sulfur species in TPO. This study focuses on optimizing the cleaning (removal of impurities and asphaltenes) process by varying process parameters; temperature, stirring speed, acid/oil ratio and time. The treated TPO will then be sent for vacuum distillation to attain the desired diesel like fuel. The effect of temperature, pressure and time will be determined for vacuum distillation of both raw TPO and the acid treated oil for comparison purposes. Polycyclic sulfides present in the distilled (diesel like) light oil will be oxidized dominantly to the corresponding sulfoxides and sulfone via a photo-catalyzed system using TiO2 as a catalyst and hydrogen peroxide as an oxidizing agent and finally acetonitrile will be used as an extraction solvent. Adsorptive desulphurization will be used to adsorb traces of sulfurous compounds which remained during photocatalytic desulphurization step. This desulphurization convoy is expected to give high desulphurization efficiency with reasonable oil recovery.

Keywords: adsorption, asphaltenes, photocatalytic oxidation, pyrolysis

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Authors: Anukriti Kumar, Tanmay Singh, Dinesh Kumar Vishwakarma

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Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.

Keywords: multiclass classification, convolution neural network, OpenCV

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Authors: Wesam Rababa, Jamal Al-Qawasmi

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The global climate is negatively impacted by CO2 emissions, which are mostly produced by buildings. Several green building rating systems (GBRS) have been proposed to impose low-carbon criteria in order to address this problem. The Green Globes certification is one such system that evaluates a building's sustainability level by assessing different categories of environmental impact and emerging concepts aimed at reducing environmental harm. Therefore, assessment tools at the national level are crucial in the developing world, where specific local conditions require a more precise evaluation. This study analyzed eight sustainable building assessment systems from different regions of the world, comparing a comprehensive list of CO2-related indicators with a various assessment system for conducting coverage analysis. The results show that GBRS includes both direct and indirect indicators in this regard. It reveals deep variation between examined practices, and a lack of consensus not only on the type and the optimal number of indicators used in a system, but also on the depth and breadth of coverage of various sustainable building SB attributes. Generally, the results show that most of the examined systems reflect a low comprehensive coverage, the highest of which is found in materials category. On the other hand, the most of the examined systems reveal a very low representative coverage.

Keywords: Assessment tools, CO2-related indicators, Comparative study, Green Building Rating Systems

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Authors: Saleem Abu Jaber, Khaled Igbaria

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For readers, space/place is one of the most significant references to reveal deep significances and indications in modern Arabic poetic texts. Generally, when poets evoke places and/or spaces, they do not mean to refer readers to detailed geographic or physical spaces, but to the symbolic significances and dimensions that those spaces have and through which poets encourage spacial awareness in their readers. Recently, as a result, there has been a great deal of interest in research addressing spacial poetic texts and dimensions in modern Arabic poetry in general and in Palestinian poetry in particular. Samih al-Qasim is one of the most recent prominent Palestinian revolutionary poets. Al-Qasim has published six series of poems that are well known in the Arab world. Although several researchers have studied al-Qasim's poetry, to our knowledge, yet no one has studied the aspect of spacial poetic text in his poetry. Therefore, this paper seeks to fill a gap in the scholarship that has not been addressed up to now. This article aims, not only to demonstrate the presence of spacial poetic text and dimensions throughout al-Qasim's poetry, but also to investigate the purpose for which the poet uses spacial poetic text. Our theory is that the poet, consciously and significantly, uses spacial poetic texts to magnify the Palestinian identity of the Palestinian readers.  Methodologically, we applied a descriptive analytic method, referencing al-Qasim's poetry, addressing spacial poetic texts practically but not theoretically or statistically.

Keywords: spatial poetic text, Samih al-Qasim, space and identity, Palestinian poetry

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Authors: Zohreh Ghorbani Madavani, Masoumeh Mikaeili

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Social media, as one of the main tools in today’s world, imposes deep impacts on human life, particularly in various cultural and literary areas. By providing platforms for direct communication between writers and audiences, these spaces have made great changes in the styles of literary works, writing, and publishing. The impacts of digital communication are very visible not only in the content of literary works but also in narrative structures, writing styles, and interaction of writers with audiences. Applying an analytical- descriptive approach, the present study investigates the impacts of internet communications and social media on the literary works of the Arab world and describes some instances of such impacts on the works of one of the most reputed contemporary Arab novelists, Ahlam Mosteghanemi. In this study, we specifically emphasize the changes in themes, narrative techniques, and writing styles of Mosteghanemi and investigate how she leverages digital environment facilities and potentials in creating works suited to her audiences’ needs and expectations. This study indicates that social media has significantly helped the democratization of authorship and diversity in contemporary Arabic literature and has enabled writers to have more direct and interactive relationships.

Keywords: social media, digital impacts, narrative changes, writing style, contemporary literature, Ahlam Mosteghanemi

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Authors: Olanrewaju B. Lasisi

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The position of Ijebu-Ode, the historical capital of the Ijebu Kingdom, at the center of gravity of Ijebu land is enclosed by the 180-km-long earthwork and suggests a centrally controlled project. This paper reflects on the first stratigraphic drawing of the banks and ditches of this earthwork, and place its construction mechanism in a chronological framework. Nine radiocarbon dates obtained at the site suggest that the earthwork was built in the late 14th or early 15th century. This suggests a relationship with the Ijebu Kingdom, which pre-existed the opening of the Atlantic trade but first became visible only in the Portuguese records in the 1480s. In June 2017, more earthworks were found but within the core of Ijebu Land. This most recent finding points to an extension of territory from the center to the outlying villages. One central question about this discovery of monumental architectures that was functional around the 14th century or before is in its mode of construction. Apparently, iron tools must have been used in the construction of ‘a 20m deep ditch that runs 180km in circumference.’ Thus, the discovery of iron-working sites around the vicinity of the earthwork is a pointer to this building process that is up till now shrouded in mystery. By comparing the chronology of Ijebu earthworks with the evidence of Iron working in south western Nigeria around the first half of the first millennium AD, it can be thought that the rise in polity triggered the knowledge of metallurgy in the region.

Keywords: archaeology, earthworks, Ijebu, metallurgy

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Authors: Praveen Huded, Suresh Dash

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Pile foundations are one of the most preferred deep foundation system for high rise or heavily loaded structures. In many instances, the failure of the pile founded structures in liquefiable soils had been observed even in many recent earthquakes. Recent centrifuge and shake table experiments on two layered soil system have credibly shown that failure of pile foundation can occur because of buckling, as the pile behaves as an unsupported slender structural element once the surrounding soil liquefies. However the buckling capacity depends on largely on the depth of soil liquefied and its residual strength. Hence it is essential to check the pile against the possible buckling failure. Beam on non-linear Winkler Foundation is one of the efficient method to model the pile-soil behavior in liquefiable soil. The pile-soil interaction is modelled through p-y springs, different author have proposed different types of p-y curves for the liquefiable soil. In the present paper the influence two such p-y curves on the buckling capacity of pile foundation is studied considering initial geometric and non-linear behavior of pile foundation. The proposed method is validated against experimental results. Significant difference in the buckling capacity is observed for the two p-y curves used in the analysis. A parametric study is conducted to understand the influence of pile diameter, pile flexural rigidity, different initial geometric imperfections, and different soil relative densities on buckling capacity of pile foundation.

Keywords: Pile foundation , Liquefaction, Buckling load, non-linear py curve, Opensees

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Authors: Maider Gómez Palmero, Ana Carrasco Pérez, Paula de la Sen de la Cruz, Francisco Javier Royo Herrer, Sonia Ascaso Malo

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The production of bio-based products for different purposes is one of the strategies that has grown the most at European and even global levels, seeking to contribute to mitigating the impacts associated with climate change and to achieve the ambitious objectives set in this regard. However, the substitution of fossil-based products for bio-based products requires a challenging and deep transformation and adaptation of the secondary and primary sectors and, more specifically, in the latter, the agro-industries. The first step to developing a bio-based value chain focuses on the availability of a resource with the right characteristics for the substitution sought. This, in turn, requires a significant reshaping of the forestry/agricultural sector but also of the agro-industry, which has a relevant potential to be deployed as a supplier and develop a robust logistical supply chain and to market a biobased raw material at a competitive price. However, this transformation may involve a profound restructuring of its traditional business model to incorporate biorefinery concepts. In this sense, agro-industries that generate by-products in their processes that are currently not valorized, such as potato processing rejects or the starch found in washing water, constitute a potential raw material that can be used for different bio-applications. This article aims to explore this potential to evaluate the most suitable bio applications to target and identify opportunities and challenges.

Keywords: starch valorisation, biorefinery, bio-based raw materials, bio-applications

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Authors: Bowei Yuan, Shi Li, Liuyang Song, Huaqing Wang, Lingli Cui

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Convolutional neural networks (CNN) have received extensive attention in the field of fault diagnosis. Many fault diagnosis methods use CNN for fault type identification. However, when the amount of raw data collected by sensors is massive, the neural network needs to perform a time-consuming classification task. In this paper, a mechanical fault diagnosis method based on vibration signal down-sampling and the improved one-dimensional convolutional neural network is proposed. Through the robust principal component analysis, the low-rank feature matrix of a large amount of raw data can be separated, and then down-sampling is realized to reduce the subsequent calculation amount. In the improved one-dimensional CNN, a smaller convolution kernel is used to reduce the number of parameters and computational complexity, and regularization is introduced before the fully connected layer to prevent overfitting. In addition, the multi-connected layers can better generalize classification results without cumbersome parameter adjustments. The effectiveness of the method is verified by monitoring the signal of the centrifugal pump test bench, and the average test accuracy is above 98%. When compared with the traditional deep belief network (DBN) and support vector machine (SVM) methods, this method has better performance.

Keywords: fault diagnosis, vibration signal down-sampling, 1D-CNN

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Authors: Bharti Jain, Rajeev Jain, Abuzar Kabir, Torki Zughaibi, Shweta Sharma

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Drug-facilitated crimes (DFCs) entail the use of a single drug or a mixture of drugs to render a victim unable. Traditionally, biological samples have been gathered from victims and conducted analysis to establish evidence of drug administration. Nevertheless, the rapid metabolism of various drugs and delays in analysis can impede the identification of such substances. For this, the present article describes a rapid, sustainable, highly efficient and miniaturized protocol for the identification and quantification of three sedative-hypnotic drugs, namely diazepam, chlordiazepoxide and ketamine in alcoholic beverages and complex food samples (cream of biscuit, flavored milk, juice, cake, tea, sweets and chocolate). The methodology involves utilizing fabric phase sorptive extraction (FPSE) to extract diazepam (DZ), chlordiazepoxide (CDP), and ketamine (KET). Subsequently, the extracted samples are subjected to analysis using gas chromatography-mass spectrometry (GC-MS). Several parameters, including the type of membrane, pH, agitation time and speed, ionic strength, sample volume, elution volume and time, and type of elution solvent, were screened and thoroughly optimized. Sol-gel Carbowax 20M (CW-20M) has demonstrated the most effective extraction efficiency for the target analytes among all evaluated membranes. Under optimal conditions, the method displayed linearity within the range of 0.3–10 µg mL–¹ (or µg g–¹), exhibiting a coefficient of determination (R2) ranging from 0.996–0.999. The limits of detection (LODs) and limits of quantification (LOQs) for liquid samples range between 0.020-0.069 µg mL-¹ and 0.066-0.22 µg mL-¹, respectively. Correspondingly, the LODs for solid samples ranged from 0.056-0.090 µg g-¹, while the LOQs ranged from 0.18-0.29 µg g-¹. Notably, the method showcased better precision, with repeatability and reproducibility both below 5% and 10%, respectively. Furthermore, the FPSE-GC-MS method proved effective in determining diazepam (DZ) in forensic food samples connected to drug-facilitated crimes (DFCs). Additionally, the proposed method underwent evaluation for its whiteness using the RGB12 algorithm.

Keywords: drug facilitated crime, fabric phase sorptive extraction, food forensics, white analytical chemistry

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Authors: H. K. Ramaraju

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World is in deep trouble and deeper denial. Worse, the denial is now entirely on the side of action. It is well accepted that climate change is a reality. Scientists say we need to cap temperature increases at 2°C to avoid catastrophe, which means capping emissions at 450 ppm .We know global average temperatures have already increased by 0.8°C and there is enough green house gas in the atmosphere to lead to another 0.8°C increase. There is still a window of opportunity, a tiny one, to tackle the crisis. But where is the action? In the 1990’s, when the world did even not understand, let alone accept, the crises, it was more willing to move to tackle climate change. Today we are in reverse in gear. The rich world has realized it is easy to talk big, but tough to take steps to actually reduce emissions. The agreement was that these countries would reduce so that the developing World could increase. Instead, between 1990 and 2006, their carbon dioxide emissions increased by a whopping 14.5 percent, even green countries of Europe are unable to match words with action. Stop deforestation and take a 20 percent advantage in our carbon balance sheet, with out doing anything at home called REDD (reducing emissions from deforestation and forest degradation) and push for carbon capture and storage (CCS) technologies. There are warning signs elsewhere and they need to be read correctly and acted up on , if not the cases like flood –act of nature or manmade disaster. The full length paper orient in proper understanding of the issues and identifying the most appropriate course of action.

Keywords: catastrophe, deforestation, emissions, waste water

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Authors: Eiman Kattan

Abstract:

This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Mohammadjavad Yaghoubi, Arul Arulrajah, Mahdi M. Disfani, Suksun Horpibulsuk, Myint W. Bo, Stephen P. Darmawan

Abstract:

This paper investigates the changes in the strength development of a high water content soft soil stabilised with alkaline activation of fly ash (FA) to use in deep soil mixing (DSM) technology. The content of FA was 20% by dry mass of soil, and the alkaline activator was sodium silicate (Na2SiO3). Samples were cured for 3, 7, 14, 28 and 56 days to evaluate the effect of curing time on strength development. To study the effect of adding slag (S) to the mixture on the strength development, 5% S was replaced with FA. In addition, the effect of the initial unit weight of samples on strength development was studied by preparing specimens with two different static compaction stresses. This was to replicate the field conditions where during implementing the DSM technique, the pressure on the soil while being mixed, increases with depth. Unconfined compression strength (UCS), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) tests were conducted on the specimens. The results show that adding S to the FA based geopolymer activated by Na2SiO3 decreases the strength. Furthermore, samples prepared at a higher unit weight demonstrate greater strengths. Moreover, samples prepared at lower unit weight reached their final strength at about 14 days of curing, whereas the strength development continues to 56 days for specimens prepared at a higher unit weight.

Keywords: alkaline activation, curing time, fly ash, geopolymer, slag

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Authors: Arunima Shukla, Vikas Almadi, Devesh Jaiswal, Sunil Saini, Bhusan S. Patil

Abstract:

Lightning protection consists of three main parts; mainly air termination system, down conductor, and earth termination system. Earth termination system is the most important part as earth is the sink and source of charges. Therefore, even when the charges are captured and delivered to the ground, and an easy path is not provided to the charges, earth termination system would lead to problems. Soil has significantly different resistivities ranging from 10 Ωm for wet organic soil to 10000 Ωm for bedrock. Different methods have been discussed and used conventionally such as deep-ground-well method and altering the length of the rod. Those methods are not considered economical. Therefore, it was a general practice to use charcoal along with salt to reduce the soil resistivity. Bentonite is worldwide acceptable material, that had led our interest towards study of bentonite at first. It was concluded that bentonite is a clay which is non-corrosive, environment friendly. Whereas bentonite is suitable only when there is moisture present in the soil, as in the absence of moisture, cracks will appear on the surface which will provide an open passage to the air, resulting into increase in the resistivity. Furthermore, bentonite without moisture does not have enough bonding property, moisture retention, conductivity, and non-leachability. Therefore, bentonite was used along with the other backfill material to overcome the dependency of bentonite on moisture. Different experiments were performed to get the best ratio of bentonite and carbon backfill. It was concluded that properties will highly depend on the quantity of bentonite and carbon-based backfill material.

Keywords: backfill material, bentonite, grounding material, low resistivity

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Authors: F. Pires, V. Geraldo, O. N. Oliveira Jr., M. Raposo

Abstract:

Catechins are powerful antioxidants which have attractive properties useful for tumor therapy. Considering their antioxidant activity, these molecules can act as a scavenger of the reactive oxygen species (ROS), alleviating the damage of cell membrane induced by oxidative stress. The complexity and dynamic nature of the cell membrane compromise the analysis of the biophysical interactions between drug and cell membrane and restricts the transport or uptake of the drug by intracellular targets. To avoid the cell membrane complexity, we used biomimetic systems as liposomes and Langmuir monolayers to study the interaction between catechin and membranes at the molecular level. Liposomes were formed after the dispersion of anionic 1,2-dipalmitoyl-sn-glycero-3-[phospho-rac-(1-glycerol)(sodium salt) (DPPG) phospholipids in an aqueous solution, which mimic the arrangement of lipids in natural cell membranes and allows the entrapment of catechins. Langmuir monolayers were formed after dropping amphiphilic molecules, DPPG phospholipids, dissolved in an organic solvent onto the water surface. In this work, we mixed epigallocatechin-3-gallate (EGCG) with DPPG liposomes and exposed them to ultra-violet radiation in order to evaluate the antioxidant potential of these molecules against oxidative stress induced by radiation. The presence of EGCG in the mixture decreased the rate of lipid peroxidation, proving that EGCG protects membranes through the quenching of the reactive oxygen species. Considering the high amount of hydroxyl groups (OH groups) on structure of EGCG, a possible mechanism to these molecules interact with membrane is through hydrogen bonding. We also investigated the effect of EGCG at various concentrations on DPPG Langmuir monolayers. The surface pressure isotherms and infrared reflection-absorption spectroscopy (PM-IRRAS) results corroborate with absorbance results preformed on liposome-model, showing that EGCG interacts with polar heads of the monolayers. This study elucidates the physiological action of EGCG which can be incorporated in lipid membrane. These results are also relevant for the improvement of the current protocols used to incorporate catechins in drug delivery systems.

Keywords: catechins, lipid membrane, anticancer agent, molecular interactions

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

Abstract:

Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: M. N. Islam, B. Boswell, Y. R. Ginting

Abstract:

The influence of canned cycles and cutting parameters on hole quality in cryogenic drilling has been investigated experimentally and analytically. A three-level, three-parameter experiment was conducted by using the design-of-experiment methodology. The three levels of independent input parameters were the following: for canned cycles—a chip-breaking canned cycle (G73), a spot drilling canned cycle (G81), and a deep hole canned cycle (G83); for feed rates—0.2, 0.3, and 0.4 mm/rev; and for cutting speeds—60, 75, and 100 m/min. The selected work and tool materials were aluminum 6061-6T and high-speed steel (HSS), respectively. For cryogenic cooling, liquid nitrogen (LN2) was used and was applied externally. The measured output parameters were the three widely used quality characteristics of drilled holes—diameter error, circularity, and surface roughness. Pareto ANOVA was applied for analyzing the results. The findings revealed that the canned cycle has a significant effect on diameter error (contribution ratio 44.09%) and small effects on circularity and surface finish (contribution ratio 7.25% and 6.60%, respectively). The best results for the dimensional accuracy and surface roughness were achieved by G81. G73 produced the best circularity results; however, for dimensional accuracy, it was the worst level.

Keywords: circularity, diameter error, drilling canned cycle, pareto ANOVA, surface roughness

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Authors: Weiming Hu, Xu Li, Xiaonan Li, Zhong Xu, Li Yuan, Xuan Dong

Abstract:

Maneuver decision-making plays a critical role in high-performance intelligent driving. This paper proposes a risk assessment-based decision-making network (RADMN) to address the problem of driving strategy for the commercial vehicle. RADMN integrates two networks, aiming at identifying the risk degree of collision and rollover and providing decisions to ensure the effectiveness and reliability of driving strategy. In the risk assessment module, risk degrees of the backward collision, forward collision and rollover are quantified for hazard recognition. In the decision module, a deep reinforcement learning based on multi-objective optimization (DRL-MOO) algorithm is designed, which comprehensively considers the risk degree and motion states of each traffic participant. To evaluate the performance of the proposed framework, Prescan/Simulink joint simulation was conducted in highway scenarios. Experimental results validate the effectiveness and reliability of the proposed RADMN. The output driving strategy can guarantee the safety and provide key technical support for the realization of autonomous driving of commercial vehicles.

Keywords: decision-making strategy, risk assessment, multi-objective optimization, commercial vehicle

Procedia PDF Downloads 129