Search results for: mobility features

Authors: Ramoshweu Melvin Sethobya, Emmanuel Chirenje, Mihlali Hobo, Simon Sebothoma

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The recent surge in residential infrastructure development around the metropolitan areas of South Africa has necessitated conditions for thorough geotechnical assessments to be conducted prior to site developments to ensure human and infrastructure safety. This paper appraises the success in the application of multi-method geophysical techniques for the delineation of sinkhole vulnerability in a residential landscape. Geophysical techniques ERT, MASW, VES, Magnetics and gravity surveys were conducted to assist in mapping sinkhole vulnerability, using an existing sinkhole as a constraint at Venterspost town, West of Johannesburg city. A combination of different geophysical techniques and results integration from those proved to be useful in the delineation of the lithologic succession around sinkhole locality, and determining the geotechnical characteristics of each layer for its contribution to the development of sinkholes, subsidence and cavities at the vicinity of the site. Study results have also assisted in the determination of the possible depth extension of the currently existing sinkhole and the location of sites where other similar karstic features and sinkholes could form. Results of the ERT, VES and MASW surveys have uncovered dolomitic bedrock at varying depths around the sites, which exhibits high resistivity values in the range 2500-8000ohm.m and corresponding high velocities in the range 1000-2400 m/s. The dolomite layer was found to be overlain by a weathered chert-poor dolomite layer, which has resistivities between the range 250-2400ohm.m, and velocities ranging from 500-600m/s, from which the large sinkhole has been found to collapse/ cave in. A compiled 2.5D high resolution Shear Wave Velocity (Vs) map of the study area was created using 2D profiles of MASW data, offering insights into the prevailing lithological setup conducive for formation various types of karstic features around the site. 3D magnetic models of the site highlighted the regions of possible subsurface interconnections between the currently existing large sinkhole and the other subsidence feature at the site. A number of depth slices were used to detail the conditions near the sinkhole as depth increases. Gravity surveys results mapped the possible formational pathways for development of new karstic features around the site. Combination and correlation of different geophysical techniques proved useful in delineation of the site geotechnical characteristics and mapping the possible depth extend of the currently existing sinkhole.

Keywords: resistivity, magnetics, sinkhole, gravity, karst, delineation, VES

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Authors: A. A. Abid

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Wave-particle interactions are considered to be the paramount in the transmission of energy in collisionless space plasmas, where electromagnetic fields confined the charged particles movement. One of the distinct features of energy transfer in collisionless plasma is wave-particle interaction which is ubiquitous in space plasmas. The three essential populations of the inner magnetosphere are cold plasmaspheric plasmas, ring-currents, and radiation belts high energy particles. The transition region amid such populations initiates wave-particle interactions among distinct plasmas and the wave mode perceived in the magnetosphere is the electromagnetic ion cyclotron (EMIC) wave. These waves can interact with numerous particle species resonantly, accompanied by plasma particle heating is still in debate. In this work we paid particular attention to how EMIC waves impact plasma species, specifically how they affect the heating of electrons and ions during storm and substorm in the Magnetosphere. Using Magnetospheric Multiscale (MMS) mission and electromagnetic hybrid simulation, this project will investigate the energy transfer mechanism (e.g., Landau interactions, bounce resonance interaction, cyclotron resonance interaction, etc.) between EMIC waves and cold-warm plasma populations. Other features such as the production of EMIC waves and the importance of cold plasma particles in EMIC wave-particle interactions will also be worth exploring. Wave particle interactions, electromagnetic hybrid simulation, electromagnetic ion cyclotron (EMIC) waves, Magnetospheric Multiscale (MMS) mission, space plasmas, inner magnetosphere

Keywords: MMS, magnetosphere, wave particle interraction, non-maxwellian distribution

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Bernard Igoche Igoche, Olumuyiwa Matthew, Peter Bednar, Alexander Gegov

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This study presents an ontology-driven approach for knowledge discovery and validation from admission databases in Nigerian polytechnic institutions. The research aims to address the challenges of extracting meaningful insights from vast amounts of admission data and utilizing them for decision-making and process improvement. The proposed methodology combines the knowledge discovery in databases (KDD) process with a structural causal model (SCM) ontological framework. The admission database of Benue State Polytechnic Ugbokolo (Benpoly) is used as a case study. The KDD process is employed to mine and distill knowledge from the database, while the SCM ontology is designed to identify and validate the important features of the admission process. The SCM validation is performed using the conditional independence test (CIT) criteria, and an algorithm is developed to implement the validation process. The identified features are then used for machine learning (ML) modeling and prediction of admission status. The results demonstrate the adequacy of the SCM ontological framework in representing the admission process and the high predictive accuracies achieved by the ML models, with k-nearest neighbors (KNN) and support vector machine (SVM) achieving 92% accuracy. The study concludes that the proposed ontology-driven approach contributes to the advancement of educational data mining and provides a foundation for future research in this domain.

Keywords: admission databases, educational data mining, machine learning, ontology-driven knowledge discovery, polytechnic education, structural causal model

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Authors: Mobarok Hossain Bhuyain

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Video surveillance processing has attracted various security fields transforming it into one of the leading research fields. Today's demand for detection and tracking of human mobility for security is very useful for human security, such as in crowded areas. Accordingly, video surveillance technology has seen a rapid advancement in recent years, with algorithms analyzing the behavior of people under surveillance automatically. The main motivation of this research focuses on the detection and tracking of the elderly and socially vulnerable people in crowded areas. Degenerate people are a major health concern, especially for elderly people and socially vulnerable people. One major disadvantage of video surveillance is the need for continuous monitoring, especially in crowded areas. To assist the security monitoring live surveillance video, image processing, and artificial intelligence methods can be used to automatically send warning signals to the monitoring officers about elderly people and socially vulnerable people.

Keywords: human detection, target tracking, neural network, particle filter

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Authors: Aamir Mubashar, Ibrahim Fiaz

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This research presents a three-dimensional finite element modelling strategy to simulate damage in a quasi-static three-point bending analysis of woven twill 2/2 type carbon fibre reinforced plastic (CFRP) composite on a micro-meso level using cohesive zone modelling technique. A meso scale finite element model comprised of a number of plies was developed in the commercial finite element code Abaqus/explicit. The interfaces between the plies were explicitly modelled using cohesive zone elements to allow for debonding by crack initiation and propagation. Load-deflection response of the CRFP within the quasi-static range was obtained and compared with the data existing in the literature. This provided validation of the model at the global scale. The outputs resulting from the global model were then used to develop a simulation model capturing the micro-meso scale material features. The sub-model consisted of a refined mesh representative volume element (RVE) modelled in texgen software, which was later embedded with cohesive elements in the finite element software environment. The results obtained from the developed strategy were successful in predicting the overall load-deflection response and the damage in global and sub-model at the flexure limit of the specimen. Detailed analysis of the effects of the micro-scale features was carried out.

Keywords: woven composites, multi-scale modelling, cohesive zone, finite element model

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Authors: Mohamed Hassan, Rahul Gajbhiye

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The high mobility and gravity override of CO₂ gas can be minimized by generating the CO₂ foam with the aid of surfactant. However, CO₂ is unable to generate the foam/stable foam above its supercritical point (1100 psi, 31°C). These difficulties with CO₂ foam is overcome by adding N₂ in small fraction to enhance the foam generation of CO₂ at supercritical conditions. This study shows how the addition of small quantity of N₂ helps in generating the CO₂ foam and performance of the CO₂/N₂ mixture foam in enhanced oil recovery. To investigate the performance of CO₂/N₂ foam, core-flooding experiments were conducted at elevated pressure and temperature condition (higher than supercritical CO₂ - 50°C and 1500 psi) in sandstone cores. Fluorosurfactant (FS-51) was used as a foaming agent, and n-decane was used as model oil in all the experiments. The selection of foam quality and N₂ fraction was optimized based on foam generation and stability tests. Every gas or foam flooding was preceded by seawater injection to simulate the behavior in the reservoir. The results from the core-flood experiments showed that the CO₂ and CO₂/N₂ foam flooding recovered an additional 34-40% of Original Initial Oil in Place (OIIP) indicating that foam flooding succeeded in producing more oil than pure CO₂ gas injection processes. Additionally, the performance CO₂/N₂ foam injection was better than CO₂ foam injection.

Keywords: CO₂/N₂ foam, enhanced oil recovery (EOR), supercritical CO₂, sweep efficiency

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Authors: Tibor Ajtai, Noémi Utry, Máté Pintér, Gábor Szabó, Zoltán Bozóki

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Beyond its climate and health related issues ambient light absorbing carbonaceous particulate matter (LAC) has also become a great scientific interest in terms of its regulations recently. It has been experimentally demonstrated in recent studies, that LAC is dominantly composed of traffic and wood burning aerosol particularly under wintertime urban conditions, when the photochemical and biological activities are negligible. Several methods have been introduced to quantitatively apportion aerosol fractions emitted by wood burning and traffic but most of them require costly and time consuming off-line chemical analysis. As opposed to chemical features, the microphysical properties of airborne particles such as optical absorption and size distribution can be easily measured on-line, with high accuracy and sensitivity, especially under highly polluted urban conditions. Recently a new method has been proposed for the apportionment of wood burning and traffic aerosols based on the spectral dependence of their absorption quantified by the Aerosol Angström Exponent (AAE). In this approach the absorption coefficient is deduced from transmission measurement on a filter accumulated aerosol sample and the conversion factor between the measured optical absorption and the corresponding mass concentration (the specific absorption cross section) are determined by on-site chemical analysis. The recently developed multi-wavelength photoacoustic instruments provide novel, in-situ approach towards the reliable and quantitative characterization of carbonaceous particulate matter. Therefore, it also opens up novel possibilities on the source apportionment through the measurement of light absorption. In this study, we demonstrate an in-situ spectral characterization method of the ambient carbon fraction based on light absorption and size distribution measurements using our state-of-the-art multi-wavelength photoacoustic instrument (4λ-PAS) and Single Mobility Particle Sizer (SMPS) The carbonaceous particulate selective source apportionment study was performed for ambient particulate matter in the city center of Szeged, Hungary where the dominance of traffic and wood burning aerosol has been experimentally demonstrated earlier. The proposed model is based on the parallel, in-situ measurement of optical absorption and size distribution. AAEff and AAEwb were deduced from the measured data using the defined correlation between the AOC(1064nm)/AOC(266nm) and N100/N20 ratios. σff(λ) and σwb(λ) were determined with the help of the independently measured temporal mass concentrations in the PM1 mode. Furthermore, the proposed optical source apportionment is based on the assumption that the light absorbing fraction of PM is exclusively related to traffic and wood burning. This assumption is indirectly confirmed here by the fact that the measured size distribution is composed of two unimodal size distributions identified to correspond to traffic and wood burning aerosols. The method offers the possibility of replacing laborious chemical analysis with simple in-situ measurement of aerosol size distribution data. The results by the proposed novel optical absorption based source apportionment method prove its applicability whenever measurements are performed at an urban site where traffic and wood burning are the dominant carbonaceous sources of emission.

Keywords: absorption, size distribution, source apportionment, wood burning, traffic aerosol

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Authors: Zouleykha Belabbes

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Learning foreign languages is sine qua non in the era of globalization, mobility, and communications, which grants access and connectedness to the world. This urgent need is highlighted in monolingual settings, however, in multilingual contexts the case is, to some extent, complicated. In effect, research on bilingualism and multilingualism lead to the issue of Cross Linguistic Influence (CLI) which seeks to explain how and under which conditions prior linguistic knowledge of first language (L1) and / or second language (L2) influences the production, comprehension and development of a third language (L3) or additional language (Ln). Moreover, the issue of age is also one of the persistent topics in the field of language acquisition. This paper aims to scrutinize the effect of age and two previously known languages: Arabic (L1) and French (L2) in acquiring English (L3) phonology in Algerian context. The study consisted of 20 participants of different age range who were presented with recorded samples of English (L3). The findings confirm the results of some previous studies on the issue of Critical Period Hypothesis (CPH) and demonstrate a tendency for the L2 phonological transfer in L3 production at the initial stages of acquisition within young and later learners that for some circumstances diminished as L3 proficiency develop.

Keywords: acquisition, age effect, cross linguistic influence, L3 phonology

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Authors: Qianxi Lv, Junying Liang

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Among the various modes of interpreting, simultaneous interpreting (SI) is regarded as a ‘complex’ and ‘extreme condition’ of cognitive tasks while consecutive interpreters (CI) do not have to share processing capacity between tasks. Given that SI exerts great cognitive demand, it makes sense to posit that the output of SI may be more compromised than that of CI in the linguistic features. The bulk of the research has stressed the varying cognitive demand and processes involved in different modes of interpreting; however, related empirical research is sparse. In keeping with our interest in investigating the quantitative linguistic factors discriminating between SI and CI, the current study seeks to examine the potential lexical simplification, syntactic complexity and sequential organization mechanism with a self-made inter-model corpus of transcribed simultaneous and consecutive interpretation, translated speech and original speech texts with a total running word of 321960. The lexical features are extracted in terms of the lexical density, list head coverage, hapax legomena, and type-token ratio, as well as core vocabulary percentage. Dependency distance, an index for syntactic complexity and reflective of processing demand is employed. Frequency motif is a non-grammatically-bound sequential unit and is also used to visualize the local function distribution of interpreting the output. While SI is generally regarded as multitasking with high cognitive load, our findings evidently show that CI may impose heavier or taxing cognitive resource differently and hence yields more lexically and syntactically simplified output. In addition, the sequential features manifest that SI and CI organize the sequences from the source text in different ways into the output, to minimize the cognitive load respectively. We reasoned the results in the framework that cognitive demand is exerted both on maintaining and coordinating component of Working Memory. On the one hand, the information maintained in CI is inherently larger in volume compared to SI. On the other hand, time constraints directly influence the sentence reformulation process. The temporal pressure from the input in SI makes the interpreters only keep a small chunk of information in the focus of attention. Thus, SI interpreters usually produce the output by largely retaining the source structure so as to relieve the information from the working memory immediately after formulated in the target language. Conversely, CI interpreters receive at least a few sentences before reformulation, when they are more self-paced. CI interpreters may thus tend to retain and generate the information in a way to lessen the demand. In other words, interpreters cope with the high demand in the reformulation phase of CI by generating output with densely distributed function words, more content words of higher frequency values and fewer variations, simpler structures and more frequently used language sequences. We consequently propose a revised effort model based on the result for a better illustration of cognitive demand during both interpreting types.

Keywords: cognitive demand, corpus-based, dependency distance, frequency motif, interpreting types, lexical simplification, sequential units distribution, syntactic complexity

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Authors: Murat Çolak, Elifnaz Tanyıldızı

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Today, human mobility with globalization has increased the interaction between countries significantly; although this contact has advanced societies in terms of civilization, it has also increased the likelihood of pandemics. The coronavirus (COVID-19) pandemic, which caused the most loss of life among them, turned into a global epidemic by covering the whole world in a short time. While there was an explosion in demand in some businesses around the world, some businesses temporarily stopped or had to stop their activities. The businesses affected by the crisis had to adapt to the new legal regulations but had to make changes in matters such as their working styles, human resources practices, and policies. One of the measures taken into account is the reduction of the workforce. The current COVID-19 crisis has posed serious challenges for many organizations and has generated an unprecedented wave of termination notices. This study examined examples of companies affected by the pandemic process and changed their working policies after the pandemic. This study aims to reveal the impact of the global COVID-19 pandemic on human resources policies and employees and how these situations will affect businesses in the future.

Keywords: human resource management, crisis management, COVID-19, business function

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Authors: Maria Christofi, George Plastiras, Rafaella Elia, Vaggelis Tsiourtis, Theocharis Theocharides, Miltiadis Katsaros

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The rapidly expanding urban areas of the world constitute a challenge of how we need to make the transition to "the next urbanization", which will be defined by new analytical tools and new sources of data. This paper is about the production of a spatial application, the ‘FUMapp’, where space and its initiative will be available literally, in meters, but also abstractly, at a sensed level. While existing spatial applications typically focus on illustrations of the urban infrastructure, the suggested application goes beyond the existing: It investigates how our environment's perception adapts to the alterations of the built environment through a dataset construction of biophysical measurements (eye-tracking, heart beating), and physical metrics (spatial characteristics, size of stimuli, rhythm of mobility). It explores the intersections between architecture, cognition, and computing where future design can be improved and identifies the flexibility and livability of the ‘available space’ of specific examined urban paths.

Keywords: biophysical data, flexibility of urban, livability, next urbanization, spatial application

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Authors: Yun-Pang Flötteröd, Jakob Erdmann

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The human behaviors during evacuations are quite complex. One of the critical behaviors which affect the efficiency of evacuation is route choice. Therefore, the respective simulation modeling work needs to function properly. In this paper, Simulation of Urban Mobility’s (SUMO) current dynamic route modeling during evacuation, i.e. the rerouting functions, is examined with a real case study. The result consistency of the simulation and the reality is checked as well. Four influence factors (1) time to get information, (2) probability to cancel a trip, (3) probability to use navigation equipment, and (4) rerouting and information updating period are considered to analyze possible traffic impacts during the evacuation and to examine the rerouting functions in SUMO. Furthermore, some behavioral characters of the case study are analyzed with use of the corresponding detector data and applied in the simulation. The experiment results show that the dynamic route modeling in SUMO can deal with the proposed scenarios properly. Some issues and function needs related to route choice are discussed and further improvements are suggested.

Keywords: evacuation, microscopic traffic simulation, rerouting, SUMO

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Authors: Juliet Udoudom

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Inappropriate complement selection constitutes one of the major features of non-standard complementation in the Nigerian users of English output of sentence construction. This paper investigates complement clause choice in Written Educated Nigerian English (ENE) and offers some results. It aims at determining preferred and dispreferred patterns of complement clause selection in respect of verb heads in English by selected Nigerian users of English. The complementation data analyzed in this investigation were obtained from experimental tasks designed to elicit complement categories of Verb – Noun -, Adjective – and Prepositional – heads in English. Insights from the Government – Binding relations were employed in analyzing data, which comprised responses obtained from one hundred subjects to a picture elicitation exercise, a grammaticality judgement test, and a free composition task. The findings indicate a general tendency for clausal complements (CPs) introduced by the complementizer that to be preferred by the subjects studied. Of the 235 tokens of clausal complements which occurred in our corpus, 128 of them representing 54.46% were CPs headed by that, while whether – and if-clauses recorded 31.07% and 8.94%, respectively. The complement clause-type which recorded the lowest incidence of choice was the CP headed by the Complementiser, for with a 5.53% incident of occurrence. Further findings from the study indicate that semantic features of relevant embedding verb heads were not taken into consideration in the choice of complementisers which introduce the respective complement clauses, hence the that-clause was chosen to complement verbs like prefer. In addition, the dispreferred choice of the for-clause is explicable in terms of the fact that the respondents studied regard ‘for’ as a preposition, and not a complementiser.

Keywords: complement, complement clause complement selection, complementisers, government-binding

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Authors: Milena França Marques

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Due to the climate crisis, several countries such as Brazil began to look for energy alternatives, finding green hydrogen as a possible solution. In addition to not emitting polluting gasses, it also has a large energy capacity, being an excellent alternative for the transport sector, the third sector that emits the most Greenhouse Gases (GHG) in Brazil. Therefore, this work aims to suggest a route for using green hydrogen, through the analysis of plans implemented in other countries, the Brazilian situation, and its difficulties in the development of hydrogen and electromobility, aiming to understand how its value chain works, as well as how to make the Brazilian fleet more efficient and decarbonize. As a result, 68 structuring measures were suggested for the first 5 axes of the National Hydrogen Program (PNH2) using the Three-Year Plan as a basis. Categorizations of measures were also made, definitions of those responsible for their development and implementation, as well as deadlines for them to be met. It is concluded that the study has the potential to promote national energy-environmental mobility transition planning realistically, capable of developing hydrogen and electromobility in Brazil, in addition to contributing to achieving the goals established by its Nationally Determined Contribution (NDC).

Keywords: climate change, electromobility, hydrogen, roadmap

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Authors: Esra Yaldız, Tugba Bulbul Bahtiyar, Dicle Aydın

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Humans meet their need for shelter via housing which they structure in line with habits and necessities. In housing culture, traditional dwelling has an important role as a social and cultural transmitter. It provides concrete data by being planned in parallel with users’ life style and habits, having their own dynamics and components as well as their designs in harmony with nature, environment and the context they exist. Textures of traditional dwelling create a healthy and cozy living environment by means of adaptation to natural conditions, topography, climate, and context; utilization of construction materials found nearby and usage of traditional techniques and forms; and natural isolation of construction materials used. One of the examples of traditional settlements in Anatolia is Kilistra (Gökyurt) settlement of Konya province. Being among the important centers of Christianity in the past, besides having distinctive architecture, culture, natural features, and geographical differences (climate, geological structure, material), Kilistra can also be identified as a traditional settlement consisting of family, religious and economic structures as well as cultural interaction. The foundation of this study is the traditional residential texture of Kilistra with its unique features. The objective of this study is to assess the conformity of traditional residential texture of Kilistra with present topography, climatic data, and geographical values within the context of human scale construction, usage of green space, indigenous construction materials, construction form, building envelope, and space organization in housing.

Keywords: traditional residential architecture, Kilistra, Anatolia, Konya

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Authors: Delphine Ribes Lemay, Valentine Bernasconi, André Andrade, Lara DéFayes, Mathieu Salzmann, FréDéRic Kaplan, Nicolas Henchoz

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Digitization and preservation of large heritage induce high maintenance costs to keep up with the technical standards and ensure sustainable access. Creating impactful usage is instrumental to justify the resources for long-term preservation. The Museum für Gestaltung of Zurich holds one of the biggest poster collections of the world from which 52’000 were digitised. In the process of building a digital installation to valorize the collection, one objective was to develop an algorithm capable of predicting the next poster to show according to the ones already displayed. The work presented here describes the steps to build an algorithm able to automatically create sequences of posters reflecting associations performed by curator and professional designers. The exposed challenge finds similarities with the domain of song playlist algorithms. Recently, artificial intelligence techniques and more specifically, deep-learning algorithms have been used to facilitate their generations. Promising results were found thanks to Recurrent Neural Networks (RNN) trained on manually generated playlist and paired with clusters of extracted features from songs. We used the same principles to create the proposed algorithm but applied to a challenging medium, posters. First, a convolutional autoencoder was trained to extract features of the posters. The 52’000 digital posters were used as a training set. Poster features were then clustered. Next, an RNN learned to predict the next cluster according to the previous ones. RNN training set was composed of poster sequences extracted from a collection of books from the Gestaltung Museum of Zurich dedicated to displaying posters. Finally, within the predicted cluster, the poster with the best proximity compared to the previous poster is selected. The mean square distance between features of posters was used to compute the proximity. To validate the predictive model, we compared sequences of 15 posters produced by our model to randomly and manually generated sequences. Manual sequences were created by a professional graphic designer. We asked 21 participants working as professional graphic designers to sort the sequences from the one with the strongest graphic line to the one with the weakest and to motivate their answer with a short description. The sequences produced by the designer were ranked first 60%, second 25% and third 15% of the time. The sequences produced by our predictive model were ranked first 25%, second 45% and third 30% of the time. The sequences produced randomly were ranked first 15%, second 29%, and third 55% of the time. Compared to designer sequences, and as reported by participants, model and random sequences lacked thematic continuity. According to the results, the proposed model is able to generate better poster sequencing compared to random sampling. Eventually, our algorithm is sometimes able to outperform a professional designer. As a next step, the proposed algorithm should include a possibility to create sequences according to a selected theme. To conclude, this work shows the potentiality of artificial intelligence techniques to learn from existing content and provide a tool to curate large sets of data, with a permanent renewal of the presented content.

Keywords: Artificial Intelligence, Digital Humanities, serendipity, design research

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Authors: Dechrit Maneetham

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The aim of this paper was to design a void and head controlled electric power wheelchair (EPW). A novel activate the control system for quadriplegics with voice, head and neck mobility. Head movement has been used as a control interface for people with motor impairments in a range of applications. Acquiring measurements from the module is simplified through a synchronous a motor. Axis measures the two directions namely x and y. At the same time, patients can control the motorized wheelchair using voice signals (forward, backward, turn left, turn right, and stop) given by it self. The model of a dc motor is considered as a speed control by selection of a PID parameters using genetic algorithm. An experimental set-up constructed, which consists of micro controller as controller, a DC motor driven EPW and feedback elements. This paper is tuning methods of parameter for a pulse width modulation (PWM) control system. A speed controller has been designed successfully for closed loop of the dc motor so that the motor runs very closed to the reference speed and angle. Intelligent wheelchair can be used to ensure the person’s voice and head are attending the direction of travel asserted by a conventional, direction and speed control.

Keywords: wheelchair, quadriplegia, rehabilitation , medical devices, speed control

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Authors: Muhammet Baldan, Emel Timuçin

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Molecular solubility prediction plays a crucial role in various fields, such as drug discovery, environmental science, and material science. In this study, we compare the performance of five machine learning algorithms—linear regression, support vector machines (SVM), random forests, gradient boosting machines (GBM), and neural networks—for predicting molecular solubility using the AqSolDB dataset. The dataset consists of 9981 data points with their corresponding solubility values. MACCS keys (166 bits), RDKit properties (20 properties), and structural properties(3) features are extracted for every smile representation in the dataset. A total of 189 features were used for training and testing for every molecule. Each algorithm is trained on a subset of the dataset and evaluated using metrics accuracy scores. Additionally, computational time for training and testing is recorded to assess the efficiency of each algorithm. Our results demonstrate that random forest model outperformed other algorithms in terms of predictive accuracy, achieving an 0.93 accuracy score. Gradient boosting machines and neural networks also exhibit strong performance, closely followed by support vector machines. Linear regression, while simpler in nature, demonstrates competitive performance but with slightly higher errors compared to ensemble methods. Overall, this study provides valuable insights into the performance of machine learning algorithms for molecular solubility prediction, highlighting the importance of algorithm selection in achieving accurate and efficient predictions in practical applications.

Keywords: random forest, machine learning, comparison, feature extraction

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Authors: Faezah Shekh Abdullah, Mohd. Azizuddin Mohd. Yussof, Komathy Thiagarajan, Hasnoorina Husin, Noor Azreena Abd Aziz

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Polycystic ovary syndrome has been associated with multiple disorders such as endocrine disorder, metabolic syndrome, infertility, and endometrial cancer. Women with polycystic ovary syndrome (PCOS) are anticipated to develop three times more chances for endometrial cancer than women without PCOS. This study, therefore, was conducted to determine the association between polycystic ovary syndrome and endometrial cancer and to determine the cervical cytology features of PCOS. Patients attending the Subfertility Clinic of the National Population and Family Development Board were recruited and examined physically by medical practitioners. They were categorized into two groups; i) the PCOS group if they met Rotterdam Criteria 2004 and ii) the control group if they did not meet Rotterdam Criteria 2004. Cervical sampling was done on all patients via the Liquid-Based Cytology (LBC) method in the pre-and post-subfertility treatment. A total of 167 patients participated in the study, of which 79 belonged to the PCOS group and 88 to the control group. The findings showed no cervical and endometrial cancer cases in both groups. The Liquid-Based Cytology results in the PCOS group displayed more cases with cellular changes, i.e., benign inflammation, atrophic smear and Candida sp. infection. To conclude, no association was found between polycystic ovary syndrome and endometrial cancer. A more holistic study with a higher number of participants can further determine the association between endometrial cancer and PCOS. Furthermore, a longer duration between LBC pre- and post-subfertility treatment should be implied to observe changes in the cervical cells.

Keywords: endometrial cancer, liquid-based cytology, PCOS, polycystic ovary syndrome

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Authors: Panagiota Katsikouli, William Hamilton Byrne, Thomas Gammeltoft-Hansen, Tijs Slaats

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An individual who demonstrates a well-founded fear of persecution or faces real risk of being subjected to torture is eligible for asylum. In Danish law, the exact legal thresholds reflect those established by international conventions, notably the 1951 Refugee Convention and the 1950 European Convention for Human Rights. These international treaties, however, remain largely silent when it comes to how states should assess asylum claims. As a result, national authorities are typically left to determine an individual’s legal eligibility on a narrow basis consisting of an oral testimony, which may itself be hampered by several factors, including imprecise language interpretation, insecurity or lacking trust towards the authorities among applicants. The leaky ground, on which authorities must assess their subjective perceptions of asylum applicants' credibility, questions whether, in all cases, adjudicators make the correct decision. Moreover, the subjective element in these assessments raises questions on whether individual asylum cases could be afflicted by implicit biases or stereotyping amongst adjudicators. In fact, recent studies have uncovered significant correlations between decision outcomes and the experience and gender of the assigned judge, as well as correlations between asylum outcomes and entirely external events such as weather and political elections. In this study, we analyze a publicly available dataset containing approximately 8,000 summaries of asylum cases, initially rejected, and re-tried by the Refugee Appeals Board (RAB) in Denmark. First, we look for variations in the recognition rates, with regards to a number of applicants’ features: their country of origin/nationality, their identified gender, their identified religion, their ethnicity, whether torture was mentioned in their case and if so, whether it was supported or not, and the year the applicant entered Denmark. In order to extract those features from the text summaries, as well as the final decision of the RAB, we applied natural language processing and regular expressions, adjusting for the Danish language. We observed interesting variations in recognition rates related to the applicants’ country of origin, ethnicity, year of entry and the support or not of torture claims, whenever those were made in the case. The appearance (or not) of significant variations in the recognition rates, does not necessarily imply (or not) bias in the decision-making progress. None of the considered features, with the exception maybe of the torture claims, should be decisive factors for an asylum seeker’s fate. We therefore investigate whether the decision can be predicted on the basis of these features, and consequently, whether biases are likely to exist in the decisionmaking progress. We employed a number of machine learning classifiers, and found that when using the applicant’s country of origin, religion, ethnicity and year of entry with a random forest classifier, or a decision tree, the prediction accuracy is as high as 82% and 85% respectively. tentially predictive properties with regards to the outcome of an asylum case. Our analysis and findings call for further investigation on the predictability of the outcome, on a larger dataset of 17,000 cases, which is undergoing.

Keywords: asylum adjudications, automated decision-making, machine learning, text mining

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Authors: Muhsin Kizhisseri, Jorg Schluter, Saleh Gharie

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Atherosclerosis is the main reason of stroke, which is one of the deadliest diseases in the world. The carotid artery in the brain is the prominent location for atherosclerotic progression, which hinders the blood flow into the brain. The inclusion of computational fluid dynamics (CFD) into the diagnosis cycle to understand the hemodynamics of the patient-specific carotid artery can give insights into stroke prediction. Realistic outlet boundary conditions are an inevitable part of the numerical simulations, which is one of the major factors in determining the accuracy of the CFD results. The Windkessel model-based outlet boundary conditions can give more realistic characteristics of the distal vascular branches of the carotid artery, such as the resistance to the blood flow and compliance of the distal arterial walls. This study aims to find the most influential distal branches of the carotid artery by using the Windkessel model parameters in the outlet boundary conditions. The parametric study approach to Windkessel model parameters can include the geometrical features of the distal branches, such as radius and length. The incorporation of the variations of the geometrical features of the major distal branches such as the middle cerebral artery, anterior cerebral artery, and ophthalmic artery through the Windkessel model can aid in identifying the most influential distal branch in the carotid artery. The results from this study can help physicians and stroke neurologists to have a more detailed and accurate judgment of the patient's condition.

Keywords: stroke, carotid artery, computational fluid dynamics, patient-specific, Windkessel model, distal vascular branches

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Authors: Abhimanyu Thakur, Youngjin Lee

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Glioma is a lethal brain cancer whose early diagnosis and prognosis are limited due to the dearth of a suitable technique for its early detection. Current approaches, including magnetic resonance imaging (MRI), computed tomography (CT), and invasive biopsy for the diagnosis of this lethal disease, hold several limitations, demanding an alternative method. Recently, extracellular vesicles (EVs) have been used in numerous biomarker studies, majorly exosomes and microvesicles (MVs), which are found in most of the cells and biofluids, including blood, cerebrospinal fluid (CSF), and urine. Remarkably, glioma cells (GMs) release a high number of EVs, which are found to cross the blood-brain-barrier (BBB) and impersonate the constituents of parent GMs including protein, and lncRNA; however, biophysical properties of EVs have not been explored yet as a biomarker for glioma. We isolated EVs from cell culture conditioned medium of GMs and regular primary culture, blood, and urine of wild-type (WT)- and glioma mouse models, and characterized by nano tracking analyzer, transmission electron microscopy, immunogold-EM, and differential light scanning. Next, we measured the biophysical parameters of GMs-EVs by using atomic force microscopy. Further, the functional constituents of EVs were examined by FTIR and Raman spectroscopy. Exosomes and MVs-derived from GMs, blood, and urine showed distinction biophysical parameters (roughness, adhesion force, and stiffness) and different from that of regular primary glial cells, WT-blood, and -urine, which can be attributed to the characteristic functional constituents. Therefore, biophysical features can be potential diagnostic biomarkers for glioma.

Keywords: glioma, extracellular vesicles, exosomes, microvesicles, biophysical properties

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Authors: Alisha Gupta

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Osteoarthritis (OA) is the most common form of arthritis which is a degenerative joint disease causing joints to begin to break down and underlying bones to change. This “wear and tear” most frequently affects hands, hips, and knees. This is important because OA pain is considered to be a leading cause of mobility impairment in older adults, with hip and knee OA ranked 11th highest contributors to global disability. Bone tissue engineering utilizing polymer scaffolds and hydrogels is an emerging field for treating osteoarthritis. Polymer scaffolds provide a three-dimensional structure for tissue growth, and hydrogels can be used to deliver drugs and growth factors. The combination of the two materials creates a hybrid structure that can better withstand physiological and mechanical demands while also providing a more controlled environment for drug and nutrient delivery. I think using bone tissue engineering for making scaffold-hydrogel composites that are self-regenerating and vascularizing might be useful in solving this problem. Successful implementation can reconstruct healthy, simulated bone tissue on deficient applicants.

Keywords: tissue engineering, regenerative medicine, scaffold-hydrogel composites, osteoarthritis

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Authors: Fathiah Mohamed Zuki, Robert George Edyvean

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Electrostatic interaction energy (∆EEDL) is a part of the Extended Derjaguin-Landau-Verwey-Overbeek (XDLVO) theory, which, together with van der Waals (∆EVDW) and acid base (∆EAB) interaction energies, has been extensively used to investigate the initial adhesion of bacteria to surfaces. Electrostatic or electrical double layer interaction energy is considerably affected by surface potential, however it cannot be determined experimentally and is usually replaced by zeta (ζ) potential via electrophoretic mobility. This paper focuses on the effect of ionic concentration as a function of pH and the effect of mineral grain size on ζ potential. It was found that both ionic strength and mineral grain size play a major role in determining the value of ζ potential for the adhesion of P. putida to hematite and quartz surfaces. Higher ζ potential values lead to higher electrostatic interaction energies and eventually to higher total XDLVO interaction energy resulting in bacterial repulsion.

Keywords: XDLVO, electrostatic interaction energy, zeta potential, P. putida, mineral

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Authors: Iram Gul, Maria Manzoor, Muhammad Arshad

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The enhanced phytoextraction techniques have been proposed for the remediation of heavy metals contaminated soil. Chelating agents enhance the availability of Cd, which is the main factor in the phytoremediation. This study was conducted to assessed the potential of two Pelargonium species (Pelargonium zonale, Pelargonium hortorum) in EDTA enhanced phytoextraction of Cd using pot experiment. Different doses of EDTA (0, 1, 2, 3, 4, 5 mmol kg-1) was used, and results showed that there was significant increase (approximately 2.1 folds) in the mobility of Cd at EDTA 5 mg kg-1 as compared to control. Both plants have TF and BCF more than 1 and have potential for the phytoextraction of Cd. However, the Pelargonium hortorum showed higher biomass and Cd uptake as compared to Pleragonium zonale. The maximum Cd accumulation in shoot and root of Pelargonium zonale was 484.4 and 264.41 mg kg-1 respectively at 2 mmol kg-1. However, the Pelargonium hortorum accumulate 996.9 and 350 mg kg-1 of Cd in shoot and root respectively at 4 mmol kg-1. Pelargonium hortorum uptake approximately 10.7 folds higher Cd concentration as compared to the Pelargonium zonale. Results revealed that P. hortorum performed better than P. zonal even at higher Cd and EDTA doses however toxicity and leaching potential of increased Cd and EDTA concentrations needs to be explored before field application.

Keywords: Cadmium, EDTA, Pelargonium, phytoextraction

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Authors: Davi Marinho de Araujo Falcão, Ronaldo Moreira Salles, Paulo Henrique Maranhão

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Disruption Tolerant Networks (DTN) are an evolution of Mobile Adhoc Networks (MANET) and work good in scenarioswhere nodes are sparsely distributed, with low density, intermittent connections and an end-to-end infrastructure is not possible to guarantee. Therefore, DTNs are recommended for high latency applications that can last from hours to days. The maritime scenario has mobility characteristics that contribute to a DTN network approach, but the concern with data security is also a relevant aspect in such scenarios. Continuing the previous work, which evaluated the performance of some DTN protocols (Epidemic, Spray and Wait, and Direct Delivery) in three warship scenarios and proposed the application of discriminant analysis, as a classification technique for secure connections, in the Epidemic protocol, thus, the current article proposes a new analysis of the directional discriminant function with opening angles smaller than 90 degrees, demonstrating that the increase in directivity influences the selection of a greater number of secure connections by the directional discriminant Epidemic protocol.

Keywords: DTN, discriminant function, epidemic protocol, security, tactical messages, warship scenario

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Authors: Mantas Sakalas, Paulius Sakalas

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This paper presents an analysis and design of a ultrawideband 1.8GHz to 43GHz Low Noise Amplifier (LNA) in 0.1 μm Galium Arsenide (GaAs) pseudomorphic High Electron Mobility Transistor (pHEMT) technology. The feedback based bandwidth extension techniques is analyzed and based on the outcome, a two stage LNA is designed. The impedance fine tuning is implemented by using Transmission Line (TL) structures. The measured performance shows a good agreement with simulation results and an outstanding wideband noise matching. The measured small signal gain was 12 dB, whereas a 3 dB gain flatness in range from 1.8 - 43 GHz was reached. The noise figure was below 4 dB almost all over the entire frequency band of 1.8GHz to 43GHz, the output power at 1 dB compression point was 6 dBm and the DC power consumption was 95 mW. To the best knowledge of the authors the designed LNA outperforms the State of the Art (SotA) reported LNA designs in terms of combined parameters of noise figure within the addressed ultra-wide 3 dB bandwidth, linearity and DC power consumption.

Keywords: feedback amplifiers, GaAs pHEMT, monolithic microwave integrated circuit, LNA, noise matching

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Authors: Yemane Kahsay Berhe

Abstract:

The multipurpose cactus pear plant with great potential as a source of food and livestock feed faced a threat from Dactylopius spp in different countries. Specifically, D. coccus is an important pest damaging significant areas in Tigray-Ethiopia. Using pest-resistant cultivars is an important element of an integrated pest management strategy, and studying the mechanisms of resistance is vital. It can be chemical or physical, such as oxalate crystals and other cladode characteristics. Cladode features of six cultivars (three O. ficus-indica, two O. cochenillifera, and one O. robusta) were examined for resistance to D. coccus in a completely randomized design (CRD) with three replications. ‘Rojo Pelón’ (O. ficus-indica), ‘Robusta’ (O. robusta), and ‘Bioplástico’ (O. cochinillifera) are resistant cultivars; and ‘Atlixco’ and ‘Chicomostoc’ (O. ficus-indica) and ‘Nopalea’ (O. cochinillifera) are susceptible. Cultivars showed a significant difference in cladode weight in g, cladode length, cladode width, and cladode thickness in cm, where cladode thickness was higher in ‘Rojo Pelón’ followed by ‘Robusta’. Calcium oxalates number per mm was higher in ‘Bioplástico’ (20.7+2.08) followed by ‘Robusta’ (18.9+2.31) and ‘Rojo Pelón’ (15.9+0.34); and similarly, epidermis thickness found higher in ‘Bioplástico’ (0.21+0.032) and ‘Robusta’ (0.19+0.014), but similar with ‘Rojo Pelón’ (0.18+0.026). However, cuticle thickness didn’t show a difference among cultivars. Cladode thickness, calcium oxalates number, and epidermis thickness had positive correlations with resistance. These results demonstrate that calcium oxalates number and epidermis thickness might positively affect D. coccus resistance in O. ficus-indica. This feeding-barring role and the insect-plant interaction need to be studied.

Keywords: cactus pear, resiatnce, druses, epidermis thickness

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Authors: Jefunnie Matahum, Yu-Chi Kuo, Chao-Ming Su, Tzong-Rong Ger

Abstract:

Magnetic cell labeling is of great importance in various applications in biomedical fields such as cell separation and cell sorting. Since analytical methods for quantification of cell uptake of magnetic nanoparticles (MNPs) are already well established, image analysis on single cell level still needs more characterization. This study reports an alternative non-destructive quantification methods of single-cell uptake of positively charged MNPs. Magnetophoresis experiments were performed to calculate the number of MNPs in a single cell. Mobility of magnetic cells and the area of intracellular MNP stained by Prussian blue were quantified by image processing software. ICP-MS experiments were also performed to confirm the internalization of MNPs to cells. Initial results showed that the magnetic cells incubated at 100 µg and 50 µg MNPs/mL concentration move at 18.3 and 16.7 µm/sec, respectively. There is also an increasing trend in the number and area of intracellular MNP with increasing concentration. These results could be useful in assessing the nanoparticle uptake in a single cell level.

Keywords: magnetic nanoparticles, single cell, magnetophoresis, image analysis

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