Search results for: slice thickness accuracy

Authors: Amira Masood, Zainah Alshouri, Noor Bantan, Samira Kutbi

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Artificial intelligence is continuously becoming more advanced and more widespread and is present in many of our day-to-day lives as a means of assistance in numerous different fields. A growing number of people, companies, and corporations are utilizing Canva and its AI tools as a method of quick and easy media production. Hence, in order to test the integrity of the rapid growth of AI, this paper will explore the usefulness of Canva's advanced design features as well as their accuracy by determining user satisfaction through a survey-based research approach and by investigating whether or not AI is successful enough that it eliminates the need for human alterations.

Keywords: artificial intelligence, canva, features, users, satisfaction

Procedia PDF Downloads 111

Authors: Zilong Peng, Jun Fan

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With the improvement of the living standards, the requirement on the acoustic comfort of the vehicle interior environment is becoming higher. The rain-the-roof producing interior noise is a common phenomenon for the vehicle, which usually discourages the conversation, especially for the heavy rain. This paper presents some numerical results about the rain-the-roof noise. The impact of each water drop is modeled as a short pulse, and the excitation locations on the roof are generated randomly. The vehicle body is simplified to a box closed with some certain-thickness shells. According to the main frequency components of the rain excitation, the analyzing frequency range is divided as low, high and middle frequency domains, which makes the vehicle body are modeled using finite element method (FEM), statistical energy analysis (SEA) and hybrid FE-SEA method, respectively. Furthermore, the effect of spatial distribution density and size of the rain on the sound pressure level are also discussed. These results may provide a guide for designing a more silent vehicle in the special weather.

Keywords: rain-the-roof noise, vehicle, finite element method, statistical energy analysis

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Authors: Elham Rouhollahi, Soheil Zorofchian Moghadamtousi, Salma Baig, Mahmood Ameen Abdulla, Zahurin Mohamed

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This study was designed to assess cutaneous wound healing potential of hexane extract of Curcuma purpurascens rhizomes (HECP). Twenty-four rats were divided into 4 groups: 1. Negative, 2. Low dose, 3. High dose and 4. Treatment, with 6 rats in each group. Full-thickness incisions with a diameter of 2 cm were made on the back of each rat. Rats were topically treated two times a day for 15 days. Group 1-4 were treated with sterile distilled water, 5% and 10% of extract and intrasite gel, respectively. Masson's trichrome and hematoxylin staining techniques are employed for histological analysis revealed strong wound healing potential closer to that of conventional drug intrasite gel. HECP significantly decreased wound area and an increase in hydroxyproline, cellular proliferation, the number of blood vessels and the level of collagen synthesis was observed. Thus, it could be concluded that HECP possesses strong wound healing potential.

Keywords: Curcuma purpurascens, wound healing, histopathology, hematoxylin staining

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Authors: T. N. Tran Thi, J. Morse, C. Detlefs, P. K. Cook, C. Yıldırım, A. C. Jakobsen, T. Zhou, J. Hartwig, V. Zurbig, D. Caliste, B. Fernandez, D. Eon, O. Loto, M. L. Hicks, A. Pakpour-Tabrizi, J. Baruchel

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The ability to grow boron-doped diamond epilayers of high crystalline quality is a prerequisite for the fabrication of diamond power electronic devices, in particular high voltage diodes and metal-oxide-semiconductor (MOS) transistors. Boron and intrinsic diamond layers are homoepitaxially overgrown by microwave assisted chemical vapour deposition (MWCVD) on single crystal high pressure, high temperature (HPHT) grown bulk diamond substrates. Various epilayer thicknesses were grown, with dopant concentrations ranging from 1021 atom/cm³ at nanometer thickness in the case of 'delta doping', up 1016 atom/cm³ and 50µm thickness or high electric field drift regions. The crystalline quality of these overgrown layers as regards defects, strain, distortion… is critical for the device performance through its relation to the final electrical properties (Hall mobility, breakdown voltage...). In addition to the optimization of the epilayer growth conditions in the MWCVD reactor, other important questions related to the crystalline quality of the overgrown layer(s) are: 1) what is the dependence on the bulk quality and surface preparation methods of the HPHT diamond substrate? 2) how do defects already present in the substrate crystal propagate into the overgrown layer; 3) what types of new defects are created during overgrowth, what are their growth mechanisms, and how can these defects be avoided? 4) how can we relate in a quantitative manner parameters related to the measured crystalline quality of the boron doped layer to the electronic properties of final processed devices? We describe synchrotron-based techniques developed to address these questions. These techniques allow the visualization of local defects and crystal distortion which complements the data obtained by other well-established analysis methods such as AFM, SIMS, Hall conductivity…. We have used Grazing Incidence X-ray Diffraction (GIXRD) at the ID01 beamline of the ESRF to study lattice parameters and damage (strain, tilt and mosaic spread) both in diamond substrate near surface layers and in thick (10–50 µm) overgrown boron doped diamond epi-layers. Micro- and nano-section topography have been carried out at both the BM05 and ID06-ESRF) beamlines using rocking curve imaging techniques to study defects which have propagated from the substrate into the overgrown layer(s) and their influence on final electronic device performance. These studies were performed using various commercially sourced HPHT grown diamond substrates, with the MWCVD overgrowth carried out at the Fraunhofer IAF-Germany. The synchrotron results are in good agreement with low-temperature (5°K) cathodoluminescence spectroscopy carried out on the grown samples using an Inspect F5O FESEM fitted with an IHR spectrometer.

Keywords: synchrotron X-ray diffaction, crystalline quality, defects, diamond overgrowth, rocking curve imaging

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Justas Dilys

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ThispaperaddressestheimplementationandoptimizationofanUnscentedKalmanFilter(UKF) for the Permanent Magnet Synchronous Motor (PMSM) sensorless control using an ARM Cortex- M3 microcontroller. A various optimization levels based on arithmetic calculation reduction was implemented in ARM Cortex-M3 microcontroller. The execution time of UKF estimator was up to 90µs without loss of accuracy. Moreover, simulation studies on the Unscented Kalman filters are carried out using Matlab to explore the usability of the UKF in a sensorless PMSMdrive.

Keywords: unscented kalman filter, ARM, PMSM, implementation

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Authors: Z. Kadivar, M. Vahidinia, A. Mousavinia

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Kopet-Dagh Mountain Range is located in the north and northeast of Iran. Mozduran Formation in the east of Kopet-Dagh is mainly composed of limestone, dolomite, with shale and sandstone interbedded. Mozduran Formation is reservoir rock of the Khangiran gas field. The location of the study was east Kopet-Dagh basin (Northeast Iran) where the deliberate thickness of formation is 418 meters. In the present study, a total of 57 samples were gathered. Moreover, 100 thin sections were made out of 52 samples. According to the findings of the thin section study, 18 genera and nine species of foraminifera and algae were identified. Based on the index fossils, the age of the Mozduran Formation was identified as Upper Jurassic (Kimmerdgian-Tithonian) in the east of Kopet-Dagh basin. According to the magnetite data (total intensity and RTP map), there is a disconformity (low intensity) between the Kashaf-Rood Formation and Mozduran Formation. At the top, where among Mozduran Formation and Shurijeh Formation, is high intensity and a widespread disconformity (high intensity).

Keywords: upper jurassic, magnetometre, mozduran formation, stratigraphy

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Authors: Asnakew Mulualem Tegegne, Tarun Kumar Lohani, Abunu Atlabachew Eshete

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Groundwater potential delineation is essential for efficient water resource utilization and long-term development. The scarcity of potable and irrigation water has become a critical issue due to natural and anthropogenic activities in meeting the demands of human survival and productivity. With these constraints, groundwater resources are now being used extensively in Ethiopia. Therefore, an innovative convolutional neural network (CNN) is successfully applied in the Gunabay watershed to delineate groundwater potential based on the selected major influencing factors. Groundwater recharge, lithology, drainage density, lineament density, transmissivity, and geomorphology were selected as major influencing factors during the groundwater potential of the study area. For dataset training, 70% of samples were selected and 30% were used for serving out of the total 128 samples. The spatial distribution of groundwater potential has been classified into five groups: very low (10.72%), low (25.67%), moderate (31.62%), high (19.93%), and very high (12.06%). The area obtains high rainfall but has a very low amount of recharge due to a lack of proper soil and water conservation structures. The major outcome of the study showed that moderate and low potential is dominant. Geodetoctor results revealed that the magnitude influences on groundwater potential have been ranked as transmissivity (0.48), recharge (0.26), lineament density (0.26), lithology (0.13), drainage density (0.12), and geomorphology (0.06). The model results showed that using a convolutional neural network (CNN), groundwater potentiality can be delineated with higher predictive capability and accuracy. CNN-based AUC validation platform showed that 81.58% and 86.84% were accrued from the accuracy of training and testing values, respectively. Based on the findings, the local government can receive technical assistance for groundwater exploration and sustainable water resource development in the Gunabay watershed. Finally, the use of a detector-based deep learning algorithm can provide a new platform for industrial sectors, groundwater experts, scholars, and decision-makers.

Keywords: CNN, geodetector, groundwater influencing factors, Groundwater potential, Gunabay watershed

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Authors: Ta-Huang Sun, Ming-Hao Hsieh, Min-Chuan Wang, Der-Jun Jan

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Electrochromism refers to the persistent and reversible change of optical properties by an applied voltage pulse. There are many transition metal oxides exhibiting electrochromism, e.g. oxides of W, Ni, Ir, V, Ti, Co and Mo. Organic materials especially some conducting polymers such as poly(aniline), poly(3, 4-propylene- dioxythiophene) also received much attention for electrochromic (EC) applications. Electrochromic materials attract considerable interest because of their potential applications, such as information displays, smart windows, variable reflectance mirrors, and variable-emittance thermal radiators. In this study, the EC characteristics are investigated on an all-solid-state EC device composed of a-WO₃ and Ni(OH)₂ with a Ta₂O₅ protective layer which is prepared by magnetron sputtering. It is found that the transmittance modulation increases with decreasing the film thickness of Ta₂O₅. On the other hand, the transmittance modulation is 57% as the Ni(OH)₂/ITO is prepared by the linear-sweep potential cycling of the sputter-deposited Ta₂O₅/NiO/ITO in a 0.5 M LiClO₄+H₂O electrolyte. However, when Ni(OH)₂/ITO is prepared by a 0.01 M HCl electrolyte, the transmittance modulation of EC device can be improved to 61%.

Keywords: electrochromic device, tungsten oxide, nickel, Ta₂O₅

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 172

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Deli Mona, Rafika Husni

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The aim of this research is to evaluate the effect of lighting distance on surface hardness of light composite resin. We held laboratory experimental research with post-test only group design. The samples used are 30 disc-like hybrid composite resins with the diameter is 6 mm and the thickness is 2 mm, lighted by an LED for 20 seconds. They were divided into 3 groups, and every group was consisted by 10 samples, which were 0 mm, 2 mm, and 5 mm lighting distance group. Every samples group was treated with hardness test, Vicker Hardness Test, then analyzed with one-way ANOVA test to evaluate the effect of lighting distance differences on surface hardness of light composite resin. Statistic test result shown hardness mean change of composite renin between 0 mm and 2 mm lighting distance with 0.00 significance (p<0.05), between 0 mm and 5 mm lighting distance with 0.00 significance (p<0.05), and 2 mm and 5 mm lighting distance with 0.05 significance (p<0.05). According to the result of this research, we concluded that the further lighting distance, the more surface hardness decline of hybrid composite resin.

Keywords: composite resin hybrid, tip distance, microhardness, light curing LED

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Authors: Ratnadip Natwarbhai Bhoi

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The objective of this research paper is to study waste tire crumb rubber granules as a partial concrete replacement by the different percentages of facing layer thickness and without facing layer in the production of rubber cement composite paver block. The physical properties of RCCRP compressive strength, flexural strength, abrasion strength density, and water absorption testing by the IS 15658:2006 method. All these physical properties depend upon the ratio of crumb rubber uses. The result showed that the with facing layer at 15 mm, 25 mm, totally rubberized and without facing layer had little effect on compressive strength, flexural strength and abrasion resistance properties. Water absorption is also important for the service life of the product. The crumb rubber paver block also performed quite well in both compressive strength and abrasion resistance. The rubber cement composite rubber paver block is suitable for nonstructural purposes, such as being lightweight and easy installation for the walkway, sidewalks, and playing area applications.

Keywords: rubber cement, crumb rubber, composite, layer

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Authors: Jeanne Gallee, Sofia Vallila-Rohter

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Technology-based applications are increasingly being utilized in aphasia rehabilitation as a means of increasing intensity of treatment and improving accessibility to treatment. These interactive therapies, often available on tablets, lead individuals to complete language and cognitive rehabilitation tasks that draw upon skills such as the ability to name items, recognize semantic features, count syllables, rhyme, and categorize objects. Tasks involve visual and auditory stimulus cues and provide feedback about the accuracy of a person’s response. Research has begun to examine the efficacy of tablet-based therapies for aphasia, yet much remains unknown about how individuals interact with these therapy applications. Thus, the current study aims to examine the efficacy of a tablet-based therapy program for anomia, further examining how strategy training might influence the way that individuals with aphasia engage with and benefit from therapy. Individuals with aphasia are enrolled in one of two treatment paradigms: traditional therapy or strategy therapy. For ten weeks, all participants receive 2 hours of weekly in-house therapy using Constant Therapy, a tablet-based therapy application. Participants are provided with iPads and are additionally encouraged to work on therapy tasks for one hour a day at home (home logins). For those enrolled in traditional therapy, in-house sessions involve completing therapy tasks while a clinician researcher is present. For those enrolled in the strategy training group, in-house sessions focus on limiting cue use in order to maximize lexical retrieval attempts and naming opportunities. The strategy paradigm is based on the principle that retrieval attempts may foster long-term naming gains. Data have been collected from 7 participants with aphasia (3 in the traditional therapy group, 4 in the strategy training group). We examine cue use, latency of responses and accuracy through the course of therapy, comparing results across group and setting (in-house sessions vs. home logins).

Keywords: aphasia, speech-language pathology, traumatic brain injury, language

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Authors: Recep Gundogan, Turgay Dindaroglu, Hikmet Gunal, Mustafa Ulukavak, Ron Bingner

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A majority of total soil losses in agricultural areas could be attributed to ephemeral gullies caused by heavy rains in conventionally tilled fields; however, ephemeral gully erosion is often ignored in conventional soil erosion assessments. Ephemeral gullies are often easily filled from normal soil tillage operations, which makes capturing the existing ephemeral gullies in croplands difficult. This study was carried out to determine topographic features, including slope and aspect composite topographic index (CTI) and initiation points of gully channels, using images obtained from unmanned aerial vehicle (UAV) images. The study area was located in Topcu stream watershed in the eastern Mediterranean Region, where intense rainfall events occur over very short time periods. The slope varied between 0.7 and 99.5%, and the average slope was 24.7%. The UAV (multi-propeller hexacopter) was used as the carrier platform, and images were obtained with the RGB camera mounted on the UAV. The digital terrain models (DTM) of Topçu stream micro catchment produced using UAV images and manual field Global Positioning System (GPS) measurements were compared to assess the accuracy of UAV based measurements. Eighty-one gully channels were detected in the study area. The mean slope and CTI values in the micro-catchment obtained from DTMs generated using UAV images were 19.2% and 3.64, respectively, and both slope and CTI values were lower than those obtained using GPS measurements. The total length and volume of the gully channels were 868.2 m and 5.52 m³, respectively. Topographic characteristics and information on ephemeral gully channels (location of initial point, volume, and length) were estimated with high accuracy using the UAV images. The results reveal that UAV-based measuring techniques can be used in lieu of existing GPS and total station techniques by using images obtained with high-resolution UAVs.

Keywords: aspect, compound topographic index, digital terrain model, initial gully point, slope, unmanned aerial vehicle

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Authors: Mohamed Hussein

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This paper studies the effect of boundary retaining walls properties on the behavior of the raft foundation. Commercial software program Sap2000 was used in this study. The soil was presented as continuous media (follows the Winkler assumption). Shell elements were employed to model the raft plate. A parametric study has been carried out to examine the effect of boundary retaining walls properties on the behavior of raft plate. These parameters namely, height of the boundary retaining walls, thickness of the boundary retaining walls, flexural rigidity of raft plate, bearing capacity of supporting soil and the earth pressure of boundary soil. The main results which were obtained from this study are positive, negative bending moment, shear stress and deflection in raft plate, where these parameters are considered the main parameters used in design of raft foundation. It was concluded that the boundary retaining walls have a significant effect on the straining actions in raft plate.

Keywords: Sap2000, boundary retaining walls, raft foundations, Winkler model, flexural rigidity

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Authors: Adam Abdalla Elbashir Adam, Xiaomin Cheng, Xiang Shui Miao

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In this work, the effect of the magnetron sputtering system parameters on the optical properties of the Mn doped GeTe were investigated. The optical properties of the Ge_1-xMn_xTe thin films with different thicknesses are determined by analyzing the transmittance and reflectance data. The energy band gaps of the amorphous Mn-doped GeTe thin films with different thicknesses were calculated. The obtained results demonstrated that the energy band gap values of the amorphous films are quite different and they are dependent on the films thicknesses. The extinction coefficients of amorphous Mn-doped GeTe thin films as function of wavelength for different thicknesses were measured. The results showed that the extinction coefficients of all films are varying inversely with their optical transmission. Moreover, the results emphasis that, not only the microstructure, electrical and magnetic properties of Mn doped GeTe thin films vary with the films thicknesses but also the optical properties differ with the film thickness.

Keywords: phase change magnetic materials, transmittance, absorbance, extinction coefficients

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Authors: Aslı Yörük, Mevla Yahya, Banu Tavat

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It is well established that bilinguals outperform monolinguals on executive function tasks. However, the effects of bilingualism on prospective memory (PM), which also requires executive functions, have not been investigated vastly. This study aimed to compare bi and monolingual participants' PM performance in focal and non-focal PM tasks. Considering that bilinguals have greater executive function abilities than monolinguals, we predict that bilinguals’ PM performance would be higher than monolinguals on the non-focal PM task, which requires controlled monitoring processes. To investigate these predictions, we administered the focal and non-focal PM task and measured the PM and ongoing task performance. Forty-eight Turkish-English bilinguals residing in North Macedonia and forty-eight Turkish monolinguals living in Turkey between the ages of 18-30 participated in the study. They were instructed to remember responding to rarely appearing PM cues while engaged in an ongoing task, i.e., spatial working memory task. The focality of the task was manipulated by giving different instructions for PM cues. In the focal PM task, participants were asked to remember to press an enter key whenever a particular target stimulus appeared in the working memory task; in the non-focal PM task, instead of responding to a specific target shape, participants were asked to remember to press the enter key whenever the background color of the working memory trials changes to a specific color (yellow). To analyze data, we performed a 2 × 2 mixed factorial ANOVA with the task (focal versus non-focal) as a within-subject variable and language group (bilinguals versus monolinguals) as a between-subject variable. The results showed no direct evidence for a bilingual advantage in PM. That is, the group’s performance did not differ in PM accuracy and ongoing task accuracy. However, bilinguals were overall faster in the ongoing task, yet this was not specific to PM cue’s focality. Moreover, the results showed a reversed effect of PM cue's focality on the ongoing task performance. That is, both bi and monolinguals showed enhanced performance in the non-focal PM cue task. These findings raise skepticism about the literature's prevalent findings and theoretical explanations. Future studies should investigate possible alternative explanations.

Keywords: bilingualism, executive functions, focality, prospective memory

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Authors: Luo Jiaoyang, Yu Hongyang

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In this paper, we explore a method that can be used in the working scene of intelligent industrial robots to confirm the identity information of operators to ensure that the robot executes instructions in a sufficiently safe environment. This approach uses three information modalities, namely visible light, depth, and sound. We explored a variety of fusion modes for the three modalities and finally used the joint feature learning method to improve the performance of the model in the case of noise compared with the single-modal case, making the maximum noise in the experiment. It can also maintain an accuracy rate of more than 90%.

Keywords: multimodal, kinect, machine learning, distance image

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Authors: Dwarakanadha Reddy Peram, D. Swarnalatha, C. Gopinath

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The main aim of this research work was to design and development characterization of Pioglitazone transdermal drug delivery system by using various polymers such as Olibanum with different concentration by solvent evaporation technique. The prepared formulations were evaluated for different physicochemical characteristics like thickness, folding endurance, drug content, percentage moisture absorption, percentage moisture loss, percentage elongation break test and weight uniformity. The diffusion studies were performed by using modified Franz diffusion cells. The result of dissolution studies shows that formulation, F3 (Olibanum with 50 mg) showed maximum release of 99.95 % in 12hrs, whereas F1 (Olibanum and EC backing membrane) showed minimum release of 93.65% in 12 hr. Based on the drug release and physicochemical values obtained the formulation F3 is considered as an optimized formulation which shows higher percentage of drug release of 99.95 % in 12 hr. The developed transdermal patches increase the therapeutic efficacy and reduced toxic effect of pioglitazone.

Keywords: pioglitazone, olibanum, transdermal drug delivery system, drug release percantage

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Authors: Monica Gonzalez Machorro

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Dementia is hard to diagnose because of the lack of early physical symptoms. Early dementia recognition is key to improving the living condition of patients. Speech technology is considered a valuable biomarker for this challenge. Recent works have utilized conventional acoustic features and machine learning methods to detect dementia in speech. BERT-like classifiers have reported the most promising performance. One constraint, nonetheless, is that these studies are either based on human transcripts or on transcripts produced by automatic speech recognition (ASR) systems. This research contribution is to explore a method that does not require transcriptions to detect early Alzheimer’s disease (AD) and mild cognitive impairment (MCI). This is achieved by fine-tuning a pre-trained ASR model for the downstream early AD and MCI tasks. To do so, a subset of the thoroughly studied Pitt Corpus is customized. The subset is balanced for class, age, and gender. Data processing also involves cropping the samples into 10-second segments. For comparison purposes, a baseline model is defined by training and testing a Random Forest with 20 extracted acoustic features using the librosa library implemented in Python. These are: zero-crossing rate, MFCCs, spectral bandwidth, spectral centroid, root mean square, and short-time Fourier transform. The baseline model achieved a 58% accuracy. To fine-tune HuBERT as a classifier, an average pooling strategy is employed to merge the 3D representations from audio into 2D representations, and a linear layer is added. The pre-trained model used is ‘hubert-large-ls960-ft’. Empirically, the number of epochs selected is 5, and the batch size defined is 1. Experiments show that our proposed method reaches a 69% balanced accuracy. This suggests that the linguistic and speech information encoded in the self-supervised ASR-based model is able to learn acoustic cues of AD and MCI.

Keywords: automatic speech recognition, early Alzheimer’s recognition, mild cognitive impairment, speech impairment

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Authors: DoHyeung Kim, DaeHee Seo, ByungDoo Kim, ByungGil Lee

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As many recent researches being implemented in aviation and maritime aspects, strong doubts have been raised concerning the reliability of the estimation of collision risk. It is shown that using position and velocity of objects can lead to imprecise results. In this paper, therefore, a new approach to the estimation of collision risks using pose and shape of objects is proposed. Simulation results are presented validating the accuracy of the new criterion to adapt to collision risk algorithm based on fuzzy logic.

Keywords: collision risk, pose, shape, fuzzy logic

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Authors: Tridipa Biswas, Kamal Pandey

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A Digital Elevation Model (DEM) is a simple representation of a surface in 3 dimensional space with elevation as the third dimension along with X (horizontal coordinates) and Y (vertical coordinates) in rectangular coordinates. DEM has wide applications in various fields like disaster management, hydrology and watershed management, geomorphology, urban development, map creation and resource management etc. Cartosat-1 or IRS P5 (Indian Remote Sensing Satellite) is a state-of-the-art remote sensing satellite built by ISRO (May 5, 2005) which is mainly intended for cartographic applications.Cartosat-1 is equipped with two panchromatic cameras capable of simultaneous acquiring images of 2.5 meters spatial resolution. One camera is looking at +26 degrees forward while another looks at –5 degrees backward to acquire stereoscopic imagery with base to height ratio of 0.62. The time difference between acquiring of the stereopair images is approximately 52 seconds. The high resolution stereo data have great potential to produce high-quality DEM. The high-resolution Cartosat-1 stereo image data is expected to have significant impact in topographic mapping and watershed applications. The objective of the present study is to generate high-resolution DEM, quality evaluation in different elevation strata, generation of ortho-rectified image and associated accuracy assessment from CARTOSAT-1 data based Ground Control Points (GCPs) for Aglar watershed (Tehri-Garhwal and Dehradun district, Uttarakhand, India). The present study reveals that generated DEMs (10m and 30m) derived from the CARTOSAT-1 stereo pair is much better and accurate when compared with existing DEMs (ASTER and CARTO DEM) also for different terrain parameters like slope, aspect, drainage, watershed boundaries etc., which are derived from the generated DEMs, have better accuracy and results when compared with the other two (ASTER and CARTO) DEMs derived terrain parameters.

Keywords: ASTER-DEM, CARTO-DEM, CARTOSAT-1, digital elevation model (DEM), ortho-rectified image, photogrammetry, RPC, stereo pair, terrain parameters

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Authors: Asmat Nawaz, Ceylan Zafer, Ali K. Erdinc, Kaiying Wang, M. Nadeem Akram

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Hybrid halide Perovskites with the general formula ABX₃, where X = Cl, Br or I, are considered as an ideal candidates for the preparation of photovoltaic devices. The most commonly and successfully used hybrid halide perovskite for photovoltaic applications is CH₃NH₃PbI₃ and its analogue prepared from lead chloride, commonly symbolized as CH₃NH₃PbI₃_ₓClₓ. Some researcher groups are using lead free (Sn replaces Pb) and mixed halide perovskites for the fabrication of the devices. Both mesoporous and planar structures have been developed. By Comparing mesoporous structure in which the perovskite materials infiltrate into mesoporous metal oxide scaffold, the planar architecture is much simpler and easy for device fabrication. In a typical perovskite solar cell, a perovskite absorber layer is sandwiched between the hole and electron transport. Upon the irradiation, carriers are created in the absorber layer that can travel through hole and electron transport layers and the interface in between. We fabricated inverted planar heterojunction structure ITO/PEDOT/ Perovskite/PCBM/Al, based solar cell via two-step spin coating method. This is also called Sequential deposition method. A small amount of cheap additive H₂O was added into PbI₂/DMF to make a homogeneous solution. We prepared four different solution such as (W/O H₂O, 1% H₂O, 2% H₂O, 3% H₂O). After preparing, the whole night stirring at 60℃ is essential for the homogenous precursor solutions. We observed that the solution with 1% H₂O was much more homogenous at room temperature as compared to others. The solution with 3% H₂O was precipitated at once at room temperature. The four different films of PbI₂ were formed on PEDOT substrates by spin coating and after that immediately (before drying the PbI₂) the substrates were immersed in the methyl ammonium iodide solution (prepared in isopropanol) for the completion of the desired perovskite film. After getting desired films, rinse the substrates with isopropanol to remove the excess amount of methyl ammonium iodide and finally dried it on hot plate only for 1-2 minutes. In this study, we added H₂O in the PbI₂/DMF precursor solution. The concept of additive is widely used in the bulk- heterojunction solar cells to manipulate the surface morphology, leading to the enhancement of the photovoltaic performance. There are two most important parameters for the selection of additives. (a) Higher boiling point w.r.t host material (b) good interaction with the precursor materials. We observed that the morphology of the films was improved and we achieved a denser, uniform with less cavities and almost full surface coverage films but only using precursor solution having 1% H₂O. Therefore, we fabricated the complete perovskite solar cell by sequential deposition technique with precursor solution having 1% H₂O. We concluded that with the addition of additives in the precursor solutions one can easily be manipulate the morphology of the perovskite film. In the sequential deposition method, thickness of perovskite film is in µm and the charge diffusion length of PbI₂ is in nm. Therefore, by controlling the thickness using other deposition methods for the fabrication of solar cells, we can achieve the better efficiency.

Keywords: methylammonium lead iodide, perovskite solar cell, precursor composition, sequential deposition

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Authors: Swathi Ganesan, Nalinda Somasiri, Rebecca Jeyavadhanam, Gayathri Karthick

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The COVID-19 crisis presents a substantial and critical hazard to worldwide health. Since the occurrence of the disease in late January 2020 in the UK, the number of infected people confirmed to acquire the illness has increased tremendously across the country, and the number of individuals affected is undoubtedly considerably high. The purpose of this research is to figure out a predictive machine learning archetypal that could forecast COVID-19 cases within the UK. This study concentrates on the statistical data collected from 31st January 2020 to 31st March 2021 in the United Kingdom. Information on total COVID cases registered, new cases encountered on a daily basis, total death registered, and patients’ death per day due to Coronavirus is collected from World Health Organisation (WHO). Data preprocessing is carried out to identify any missing values, outliers, or anomalies in the dataset. The data is split into 8:2 ratio for training and testing purposes to forecast future new COVID cases. Support Vector Machines (SVM), Random Forests, and linear regression algorithms are chosen to study the model performance in the prediction of new COVID-19 cases. From the evaluation metrics such as r-squared value and mean squared error, the statistical performance of the model in predicting the new COVID cases is evaluated. Random Forest outperformed the other two Machine Learning algorithms with a training accuracy of 99.47% and testing accuracy of 98.26% when n=30. The mean square error obtained for Random Forest is 4.05e11, which is lesser compared to the other predictive models used for this study. From the experimental analysis Random Forest algorithm can perform more effectively and efficiently in predicting the new COVID cases, which could help the health sector to take relevant control measures for the spread of the virus.

Keywords: COVID-19, machine learning, supervised learning, unsupervised learning, linear regression, support vector machine, random forest

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Authors: A. Sopharak, B. Uyyanonvara, S. Barman

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Diabetic retinopathy is characterized by the development of retinal microaneurysms. The damage can be prevented if disease is treated in its early stages. In this paper, we are comparing Support Vector Machine (SVM) and Naïve Bayes (NB) classifiers for automatic microaneurysm detection in images acquired through non-dilated pupils. The Nearest Neighbor classifier is used as a baseline for comparison. Detected microaneurysms are validated with expert ophthalmologists’ hand-drawn ground-truths. The sensitivity, specificity, precision and accuracy of each method are also compared.

Keywords: diabetic retinopathy, microaneurysm, naive Bayes classifier, SVM classifier

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Authors: L. Capitanu, V. Floresci, L. L. Badita

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Starting from the idea that the greatest pressure and velocity of composite melted is in the die nozzle, was an experimental nozzle with wear samples of sizes and weights which can be measured with precision as good. For a larger accuracy of measurements, we used a method for radiometric measuring, extremely accurate. Different nitriding steels have been studied as nitriding treatments, as well as some special steels and alloyed steels. Besides these, there have been preliminary attempts made to describe and checking corrosive action of thermoplastics on metals.

Keywords: plastics, composites with short glass fibres, moulding, wear, experimental modelling, glass fibres content influence

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Authors: Huseyi̇n Das, Bülent Tarim, Sunay Demi̇r, Nurçi̇n Küçükkent, Sevi̇l Cengi̇z, Engi̇n Tülek, Veci̇hi̇ Aksakal

Abstract:

Atak-S laying hens are a high-performance strain obtained by crossing of the Rhode Island Red (RIR) X the Barred Plymouth Rock (BR) and are being produced in the Ankara Poultry Research Institute since 1997. Phenotypic and genetic improving studies are continued for this strain. In this study, 2 from IGF and 1 from IGFBP2, totally 3 different SNP polymorphisms were examined in 200 Atak-S chickens. Genotypes of SNPs were compared using ANOVA to body weight and egg number thorough 32 weeks of age, body weight at sexual maturity, age at sexual maturity and also egg quality traits such as egg shell breaking strength, shell thickness, Haugh unit, albumen index, yolk index, shape index. Only IGF(a) locus was in agreement with Hardy-Weinberg equilibrium, while, the other loci were not. As a result of the performance comparisons to the 3 SNP loci, it was determined that there has a significant association (P<0.05) between only TC genotypes of the IGF(b) locus and body weight at 32 weeks of age, but there was not any association to the other traits.

Keywords: Atak-S, Igf, Igfbp2, single nucleotide polymorphism

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Authors: Olga Tkacheva, Pavel Arkhipov, Alexey Rudenko, Yurii Zaikov

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The aluminum electrolysis process in the conventional cryolite-alumina electrolyte with cryolite ratio of 2.7 was carried out at an initial temperature of 970 °C and the anode current density of 0.5 A/cm² in a 15A lab-scale cell in order to study the formation of the side ledge during electrolysis and the alumina distribution between electrolyte and side ledge. The alumina contained 35.97% α-phase and 64.03% γ-phase with the particles size in the range of 10-120 μm. The cryolite ratio and the alumina concentration were determined in molten electrolyte during electrolysis and in frozen bath after electrolysis. The side ledge in the electrolysis cell was formed only by the 13^th hour of electrolysis. With a slight temperature decrease a significant increase in the side ledge thickness was observed. The basic components of the side ledge obtained by the XRD phase analysis were Na₃AlF₆, Na₅Al₃F₁₄, Al₂O₃, and NaF^.5CaF₂^.AlF₃. As in the industrial cell, the increased alumina concentration in the side ledge formed on the cell walls and at the ledge-electrolyte-aluminum three-phase boundary during aluminum electrolysis in the lab cell was found (FTP No 05.604.21.0239, IN RFMEFI60419X0239).

Keywords: alumina distribution, aluminum electrolyzer, cryolie-alumina electrolyte, side ledge

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Authors: Hang Zhou, Limin Zhu

Abstract:

This paper investigates the fabrication of microstructures on Si surfaces by using electrochemical anodic oxidation with agarose stamps. The fabricating process is based on a selective anodic oxidation reaction that occurs in the contact area between a stamp and a Si substrate. The stamp which is soaked in electrolyte previously acts as a current flow channel. After forming the oxide patterns as an etching mask, a KOH aqueous is used for the wet etching of Si. A complicated microstructure array of 1 cm2 was fabricated by the method with high accuracy.

Keywords: microstructures, anodic oxidation, silicon, agarose stamps

Procedia PDF Downloads 310