Search results for: dimensional accuracy

Authors: Lesley Karen Penera

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Despite the countless that has been drawn to investigate Philippine English for a myriad of reasons, none was known to have ventured on a probe of its grammatical features as used in a technology-driven linguistic landscape by two generations in the digital age. Propelled by the assumption of an emerging Philippine English variety, this paper determined the grammatical features that characterize the digital native-immigrants’ Philippine English. It also ascertained whether mistake or deviation instigated the use of the features, and established this variety’s level of comprehensibility. This exploratory mixed-methods inquiry employed some qualitative and quantitative data drawn from a social networking site, the digital native-immigrant group, and the comprehensibility-raters who were selected through non-random purposive sampling. The study yields 8 grammatical features, mostly deemed results of deviation, yet the texts characterized by such features were mostly rated with excellent comprehensibility. This substantiates some of the grammatical features identified in earlier studies, provides evidentiary proof that the digital groups’ Philippine English is not bound by the standard of syntactic accuracy and corroborates the assertion on language’s manipulability as an instrument fashioned to satisfy the users’ need for successful communication in actual instances for use of English past the walls of any university where the variety is cultivated. The same could also be rationalized by some respondents’ position on grammar and accuracy to be less vital than one’s facility to communicate effectively.

Keywords: comprehensibility, deviation, digital immigrants, digital natives, mistake, Philippine English variety

Procedia PDF Downloads 164

Authors: Jane E. A. Lewis, Paul Williams, Jane H. Davies

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Introduction: There is, at present, a clear and recognized need to optimize the diagnosis of peripheral arterial disease (PAD), particularly in non-specialist settings such as primary care, and this arises from several key facts. Firstly, PAD is a highly prevalent condition. In 2010, it was estimated that globally, PAD affected more than 202 million people and furthermore, this prevalence is predicted to further escalate. The disease itself, although frequently asymptomatic, can cause considerable patient suffering with symptoms such as lower limb pain, ulceration, and gangrene which, in worse case scenarios, can necessitate limb amputation. A further and perhaps the most eminent consequence of PAD arises from the fact that it is a manifestation of systemic atherosclerosis and therefore is a powerful predictor of coronary heart disease and cerebrovascular disease. Objective: This cross sectional study aimed to individually and cumulatively compare sensitivity and specificity of the (i) ankle brachial index (ABI) and (ii) pulse volume waveform (PVW) recorded by the same automated device, with the presence or absence of peripheral arterial disease (PAD) being verified by an Ultrasound Duplex Scan (UDS). Methods: Patients (n = 205) referred for lower limb arterial assessment underwent an ABI and PVW measurement using volume plethysmography followed by a UDS. Presence of PAD was recorded for ABI if < 0.9 (noted if > 1.30) if PVW was graded as 2, 3 or 4 or a hemodynamically significant stenosis > 50% with UDS. Outcome measure was agreement between measured ABI and interpretation of the PVW for PAD diagnosis, using UDS as the reference standard. Results: Sensitivity of ABI was 80%, specificity 91%, and overall accuracy 88%. Cohen’s kappa revealed good agreement between ABI and UDS (k = 0.7, p < .001). PVW sensitivity 97%, specificity 81%, overall accuracy 84%, with a good level of agreement between PVW and UDS (k = 0.67, p < .001). The combined sensitivity of ABI and PVW was 100%, specificity 76%, and overall accuracy 85% (k = 0.67, p < .001). Conclusions: Combing these two diagnostic modalities within one device provided a highly accurate method of ruling out PAD. Such a device could be utilized within the primary care environment to reduce the number of unnecessary referrals to secondary care with concomitant cost savings, reduced patient inconvenience, and prioritization of urgent PAD cases.

Keywords: ankle brachial index, peripheral arterial disease, pulse volume waveform, ultrasound duplex scan

Procedia PDF Downloads 167

Authors: Noe Brice Nkoumbou Kaptchouang, Pierre-Guy Vincent, Yann Monerie

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The present work addresses the modelling and the simulation of crack initiation and propagation in ductile materials which failed by void nucleation, growth, and coalescence. One of the current research frameworks on crack propagation is the use of cohesive-volumetric approach where the crack growth is modelled as a decohesion of two surfaces in a continuum material. In this framework, the material behavior is characterized by two constitutive relations, the volumetric constitutive law relating stress and strain, and a traction-separation law across a two-dimensional surface embedded in the three-dimensional continuum. Several cohesive models have been proposed for the simulation of crack growth in brittle materials. On the other hand, the application of cohesive models in modelling crack growth in ductile material is still a relatively open field. One idea developed in the literature is to identify the traction separation for ductile material based on the behavior of a continuously-deforming unit cell failing by void growth and coalescence. Following this method, the present study proposed a semi-analytical cohesive model for ductile material based on a micromechanical approach. The strain localization band prior to ductile failure is modelled as a cohesive band, and the Gurson-Tvergaard-Needleman plasticity model (GTN) is used to model the behavior of the cohesive band and derived a corresponding traction separation law. The numerical implementation of the model is realized using the non-smooth contact method (NSCD) where cohesive models are introduced as mixed boundary conditions between each volumetric finite element. The present approach is applied to the simulation of crack growth in nuclear ferritic steel. The model provides an alternative way to simulate crack propagation using the numerical efficiency of cohesive model with a traction separation law directly derived from porous continuous model.

Keywords: ductile failure, cohesive model, GTN model, numerical simulation

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Authors: Lorena Caires Moreira, Marcos Kauffman

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This paper outlines the results of experimental research on deploying an emerging augmented reality (AR) system for real-time task assistance of highly customized and high-risk manual operations. The focus is on operators’ training capabilities and the aim is to test if such technologies can support achieving higher levels of knowledge retention and accuracy of task execution to improve health and safety (H and S) levels. The proposed solution is tested and validated using a real-world case study of electric vehicles’ battery module assembly. The experimental results revealed that the proposed AR method improved the training practices by increasing the knowledge retention levels from 40% to 84% and improved the accuracy of task execution from 20% to 71%, compared to the traditional paper-based method. The results of this research can be used as a demonstration of how emerging technologies are advancing the choice of manual, hybrid, or fully automated processes by promoting the connected worker (Industry 5.0) and supporting manufacturing in becoming more resilient in times of constant market changes.

Keywords: augmented reality, extended reality, connected worker, XR-assisted operator, manual assembly, industry 5.0, smart training, battery assembly

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Authors: Suresh Hettiarachchi, Conrad Wasko, Ashish Sharma

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The ever changing and expanding urban landscape increases the stress on urban systems to support and maintain safe and functional living spaces. Flooding presents one of the more serious threats to this safety, putting a larger number of people in harm’s way in congested urban settings. Climate change is adding to this stress by creating a dichotomy in the urban flood response. On the one hand, climate change is causing storms to intensify, resulting in more destructive, rarer floods, while on the other hand, longer dry periods are decreasing the severity of more frequent, less intense floods. This variability is creating a need to be more agile and innovative in how we design for and manage urban flooding. Here, we argue that to cope with this challenge climate change brings, we need to move towards urban flood management through resilience rather than flood prevention. We also argue that dealing with the larger variation in flood response to climate change means that we need to look at flooding from all aspects rather than the single-dimensional focus of flood depths and extents. In essence, we need to rethink how we manage flooding in the urban space. This change in our thought process and approach to flood management requires a practical way to assess and quantify resilience that is built into the urban landscape so that informed decision-making can support the required changes in planning and infrastructure design. Towards that end, we propose a Simple Urban Flood Resilience Index (SUFRI) based on a robust definition of resilience as a tool to assess flood resilience. The application of a simple resilience index such as the SUFRI can provide a practical tool that considers urban flood management in a multi-dimensional way and can present solutions that were not previously considered. When such an index is grounded on a clear and relevant definition of resilience, it can be a reliable and defensible way to assess and assist the process of adapting to the increasing challenges in urban flood management with climate change.

Keywords: urban flood resilience, climate change, flood management, flood modelling

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Authors: N. Messai, A. Boumali

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In this work, we aim to solve the two dimensional Kemmer equation including Dirac oscillator interaction term, in the background space-time generated by a cosmic string which is submitted to an uniform magnetic field. Eigenfunctions and eigenvalues of our problem have been found and the influence of the cosmic string space-time on the energy spectrum has been analyzed.

Keywords: Kemmer oscillator, cosmic string, Dirac oscillator, eigenfunctions

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Authors: Dewi Juliah Ratnaningsih, Imas Sukaesih Sitanggang

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Classification is one of data mining techniques that aims to discover a model from training data that distinguishes records into the appropriate category or class. Data mining classification methods can be applied in education, for example, to determine the classification of non-active students in Indonesia Open University. This paper presents a comparison of three methods of classification: Naïve Bayes, Bagging, and C.45. The criteria used to evaluate the performance of three methods of classification are stratified cross-validation, confusion matrix, the value of the area under the ROC Curve (AUC), Recall, Precision, and F-measure. The data used for this paper are from the non-active Indonesia Open University students in registration period of 2004.1 to 2012.2. Target analysis requires that non-active students were divided into 3 groups: C1, C2, and C3. Data analyzed are as many as 4173 students. Results of the study show: (1) Bagging method gave a high degree of classification accuracy than Naïve Bayes and C.45, (2) the Bagging classification accuracy rate is 82.99 %, while the Naïve Bayes and C.45 are 80.04 % and 82.74 % respectively, (3) the result of Bagging classification tree method has a large number of nodes, so it is quite difficult in decision making, (4) classification of non-active Indonesia Open University student characteristics uses algorithms C.45, (5) based on the algorithm C.45, there are 5 interesting rules which can describe the characteristics of non-active Indonesia Open University students.

Keywords: comparative analysis, data mining, clasiffication, Bagging, Naïve Bayes, C.45, non-active students, Indonesia Open University

Procedia PDF Downloads 319

Authors: Bi Zhao

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Neural network technology has greatly improved the output of machine translation in terms of both fluency and accuracy, which greatly increases its appeal for young users. The present exploratory study aims to find out how the Chinese undergraduates perceive and use machine translation in their daily life. A survey is conducted to collect data from 100 undergraduate students from multiple Chinese universities and with varied academic backgrounds, including arts, business, science, engineering, and medicine. The survey questions inquire about their use (including frequency, scenarios, purposes, and preferences) of and attitudes (including trust, quality assessment, justifications, and ethics) toward machine translation. Interviews and tasks of evaluating machine translation output are also employed in combination with the survey on a sample of selected respondents. The results indicate that Chinese undergraduate students use machine translation on a daily basis for a wide range of purposes in academic, communicative, and entertainment scenarios. Most of them have preferred machine translation tools, but the availability of machine translation tools within a certain scenario, such as the embedded machine translation tool on the webpage, is also the determining factor in their choice. The results also reveal that despite the reportedly limited trust in the accuracy of machine translation output, most students lack the ability to critically analyze and evaluate such output. Furthermore, the evidence is revealed of the inadequate awareness of ethical responsibility as machine translation users among Chinese undergraduate students.

Keywords: Chinese undergraduates, machine translation, trust, usage

Procedia PDF Downloads 143

Authors: Soyon Kim, Edward Kim

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In order to retrieve information from the massive stream of songs in the music industry, music search by title, lyrics, artist, mood, and genre has become more important. Despite the subjectivity and controversy over the definition of music genres across different nations and cultures, automatic genre classification systems that facilitate the process of music categorization have been developed. Manual genre selection by music producers is being provided as statistical data for designing automatic genre classification systems. In this paper, an automatic music genre classification system utilizing non-negative matrix factorization (NMF) is proposed. Short-term characteristics of the music signal can be captured based on the timbre features such as mel-frequency cepstral coefficient (MFCC), decorrelated filter bank (DFB), octave-based spectral contrast (OSC), and octave band sum (OBS). Long-term time-varying characteristics of the music signal can be summarized with (1) the statistical features such as mean, variance, minimum, and maximum of the timbre features and (2) the modulation spectrum features such as spectral flatness measure, spectral crest measure, spectral peak, spectral valley, and spectral contrast of the timbre features. Not only these conventional basic long-term feature vectors, but also NMF based feature vectors are proposed to be used together for genre classification. In the training stage, NMF basis vectors were extracted for each genre class. The NMF features were calculated in the log spectral magnitude domain (NMF-LSM) as well as in the basic feature vector domain (NMF-BFV). For NMF-LSM, an entire full band spectrum was used. However, for NMF-BFV, only low band spectrum was used since high frequency modulation spectrum of the basic feature vectors did not contain important information for genre classification. In the test stage, using the set of pre-trained NMF basis vectors, the genre classification system extracted the NMF weighting values of each genre as the NMF feature vectors. A support vector machine (SVM) was used as a classifier. The GTZAN multi-genre music database was used for training and testing. It is composed of 10 genres and 100 songs for each genre. To increase the reliability of the experiments, 10-fold cross validation was used. For a given input song, an extracted NMF-LSM feature vector was composed of 10 weighting values that corresponded to the classification probabilities for 10 genres. An NMF-BFV feature vector also had a dimensionality of 10. Combined with the basic long-term features such as statistical features and modulation spectrum features, the NMF features provided the increased accuracy with a slight increase in feature dimensionality. The conventional basic features by themselves yielded 84.0% accuracy, but the basic features with NMF-LSM and NMF-BFV provided 85.1% and 84.2% accuracy, respectively. The basic features required dimensionality of 460, but NMF-LSM and NMF-BFV required dimensionalities of 10 and 10, respectively. Combining the basic features, NMF-LSM and NMF-BFV together with the SVM with a radial basis function (RBF) kernel produced the significantly higher classification accuracy of 88.3% with a feature dimensionality of 480.

Keywords: mel-frequency cepstral coefficient (MFCC), music genre classification, non-negative matrix factorization (NMF), support vector machine (SVM)

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Authors: Melody Yin

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Concussions impact a large amount of adolescents; they make up as much as half of the diagnosed concussions in America. This research proposes a hybrid machine learning model based on the combination of human/knowledge-based domains and computer-generated feature rankings to improve the accuracy of diagnosing sports related concussion (SRC). Using a data set of symptoms collected on the sideline post-SRC events, the symptom selection criteria method has been developed by using Google AutoML's important score function to identify the top 10 symptom features. In addition, symptom domains have been introduced as another parameter, categorizing the symptoms into physical, cognitive, sleep, and emotional domains. The hybrid machine learning model has been trained with a combination of the top 10 symptoms and 4 domains. From the results, the hybrid model was the best performer for symptom resolution time prediction in 2 and 4-week thresholds. This research is a proof of concept study in the use of domains along with machine learning in order to improve concussion prediction accuracy. It is also possible that the use of domains can make the model more efficient due to reduced training time. This research examines the use of a hybrid method in predicting sports-related concussion. This achievement is based on data preprocessing, using a hybrid method to select criteria to achieve high performance.

Keywords: hybrid model, machine learning, sports related concussion, symptom resolution time

Procedia PDF Downloads 171

Authors: Arunai Nambi Raj N., Kaviarasu Karunakaran, Krishnamurthy K.

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The purpose of this study was to create American Association of Physicist in Medicine (AAPM) Task Group 119 (TG 119) benchmark plans for flattening filter free beam (FFF) deliveries of intensity modulated radiation therapy (IMRT) and volumetric arc therapy (VMAT) in the Eclipse treatment planning system. The planning data were compared with the flattening filter (FF) IMRT & VMAT plan data to verify the dosimetric commissioning accuracy of FFF deliveries. AAPM TG 119 proposed a set of test cases called multi-target, mock prostate, mock head and neck, and C-shape to ascertain the overall accuracy of IMRT planning, measurement, and analysis. We used these test cases to investigate the performance of the Eclipse Treatment planning system for the flattening filter free beam deliveries. For these test cases, we generated two sets of treatment plans, the first plan using 7–9 IMRT fields and a second plan utilizing two arc VMAT technique for both the beam deliveries (6 MV FF, 6MV FFF, 10 MV FF and 10 MV FFF). The planning objectives and dose were set as described in TG 119. The dose prescriptions for multi-target, mock prostate, mock head and neck, and C-shape were taken as 50, 75.6, 50 and 50 Gy, respectively. The point dose (mean dose to the contoured chamber volume) at the specified positions/locations was measured using compact (CC‑13) ion chamber. The composite planar dose and per-field gamma analysis were measured with IMatriXX Evaluation 2D array with OmniPro IMRT Software (version 1.7b). FFF beam deliveries of IMRT and VMAT plans were comparable to flattening filter beam deliveries. Our planning and quality assurance results matched with TG 119 data. AAPM TG 119 test cases are useful to generate FFF benchmark plans. From the obtained data in this study, we conclude that the commissioning of FFF IMRT and FFF VMAT delivery were found within the limits of TG-119 and the performance of the Eclipse treatment planning system for FFF plans were found satisfactorily.

Keywords: flattening filter free beams, intensity modulated radiation therapy, task group 119, volumetric modulated arc therapy

Procedia PDF Downloads 148

Authors: Ahmet Calik

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In present study, the effect of adherend geometry on the tensile strength of adhesively single lap aluminum structures joint, bonded was numerically studied using by three dimensional finite element model. Six joint model were investigated. Analyses were performed in ANSYS commercial software. The results shows that the adherends shape has the highest effect on peel and shear stresses.

Keywords: adhesive, adherend, single lap joints, finite element

Procedia PDF Downloads 296

Authors: Rammah Abdlbagi, Egmen Tazcan, Kiriti Tripathi, Vinayagam Sudhakar, Thomas Swallow, Aakash Pai

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Introduction and Objectives: MRI has an increasing role in the diagnosis and staging of prostate cancer. Multiparametric MRI includes multiple sequences, including T2 weighting, diffusion weighting, and dynamic contrast enhancement (DCE). Administration of DCE is expensive, time-consuming, and requires medical supervision due to the risk of anaphylaxis. Biparametric MRI (bpMRI), without DCE, overcomes many of these issues; however, there is conflicting data on its accuracy. Furthermore, data on the concordance between bpMRI lesion and pathology specimen, as well as the rates of cancer stage upgrading after surgery, is limited within the available literature. This study aims to examine the diagnostic test accuracy of bpMRI in the diagnosis of prostate cancer and radiological assessment of prostate cancer staging. Specifically, we aimed to evaluate the ability of bpMRI to accurately localise malignant lesions to better understand its accuracy and application in MRI-targeted biopsies. Materials and Methods: One hundred and forty patients who underwent bpMRI prior to radical prostatectomy (RP) were retrospectively reviewed from a single institution. Histological grade from the prostate biopsy was compared with surgical specimens from RP. Clinically significant prostate cancer (csPCa) was defined as Gleason grade group ≥2. bpMRI staging was compared with RP histology. Results: Overall sensitivity of bpMRI in diagnosing csPCa independent of location and staging was 98.87%. Of the 140 patients, 29 (20.71%) had their prostate biopsy histology upgraded at RP. 61 (43.57%) patients had csPca noted on RP specimens in areas that were not identified on the bpMRI. 55 (39.29%) had upstaging after RP from the original staging with bpMRI. Conclusions: Whilst the overall sensitivity of bpMRI in predicting any clinically significant cancer was good, there was notably poor concordance in the location of the tumour between bpMRI and eventual RP specimen. The results suggest that caution should be exercised when using bpMRI for targeted prostate biopsies and validates the continued role of systemic biopsies. Furthermore, a significant number of patients were upstaged at RP from their original staging with bpMRI. Based on these findings, bpMRI results should be interpreted with caution and can underestimate TNM stage, requiring careful consideration of treatment strategy.

Keywords: biparametric MRI, Ca prostate, staging, post prostatectomy histology

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Authors: Tzu-Hsuan Lei, Shu-Chien Wu, Kuang-Che Lo, Shu-Chi Liu, Yu-Cheng Liu

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The focus of this study was on the effects of air propagation associated with vented holes on acoustic resistance properties. A cylindrical hole with diameter and depth of 0.7 mm and 1.0 mm, respectively, was the research target. By constructing a finite element analytical model of its sound field properties, the acoustic-specific airflow resistance relationships were obtained for the differences in sound pressure and flow velocity at the two ends of this vented hole. In addition, the acoustic properties of this vented hole were included in the in-ear earphone simulation model to complete the sound pressure curve simulation analysis of the in-ear earphone system with a vented hole of corresponding size. Then, the simulation results were compared with actual measurements obtained from the standard system. Based on the results, when the in-ear earphone vented hole simulation model considered the simulated specific airflow resistance values of this cylindrical hole, the overall simulated sound pressure performance was highly consistent with that of measured values. The difference in the first peak values of sound pressure at mid-to-low frequencies was reduced from 5.64% when the simulation model did not consider the specific airflow resistance of the cylindrical hole to 1.18%, and the accuracy of the overall simulation was around 70%. This indicates the importance of the acoustic resistance properties of vented holes. Moreover, as specific airflow resistance values were able to be further quantified, the accuracy of the entire in-ear earphone simulation was ultimately and effectively elevated.

Keywords: specific airflow resistance, vented holes, in-ear earphone, finite element method

Procedia PDF Downloads 50

Authors: Eashwar V. Somasundaram, Raoul R. Wadhwa, Jacob G. Scott

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The use of radiomics in measuring geometric properties of tumor images such as size, surface area, and volume has been invaluable in assessing cancer diagnosis, treatment, and prognosis. In addition to analyzing geometric properties, radiomics would benefit from measuring topological properties using persistent homology. Intuitively, features uncovered by persistent homology may correlate to tumor structural features. One example is necrotic cavities (corresponding to 2D topological features), which are markers of very aggressive tumors. We develop a data pipeline in R that clusters tumors images based on persistent homology is used to identify meaningful clinical distinctions between tumors and possibly new relationships not captured by established clinical categorizations. A preliminary analysis was performed on 16 Magnetic Resonance Imaging (MRI) breast tissue segments downloaded from the 'Investigation of Serial Studies to Predict Your Therapeutic Response with Imaging and Molecular Analysis' (I-SPY TRIAL or ISPY1) collection in The Cancer Imaging Archive. Each segment represents a patient’s breast tumor prior to treatment. The ISPY1 dataset also provided the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) status data. A persistent homology matrix up to 2-dimensional features was calculated for each of the MRI segmentation. Wasserstein distances were then calculated between all pairwise tumor image persistent homology matrices to create a distance matrix for each feature dimension. Since Wasserstein distances were calculated for 0, 1, and 2-dimensional features, three hierarchal clusters were constructed. The adjusted Rand Index was used to see how well the clusters corresponded to the ER/PR/HER2 status of the tumors. Triple-negative cancers (negative status for all three receptors) significantly clustered together in the 2-dimensional features dendrogram (Adjusted Rand Index of .35, p = .031). It is known that having a triple-negative breast tumor is associated with aggressive tumor growth and poor prognosis when compared to non-triple negative breast tumors. The aggressive tumor growth associated with triple-negative tumors may have a unique structure in an MRI segmentation, which persistent homology is able to identify. This preliminary analysis shows promising results in the use of persistent homology on tumor imaging to assess the severity of breast tumors. The next step is to apply this pipeline to other tumor segment images from The Cancer Imaging Archive at different sites such as the lung, kidney, and brain. In addition, whether other clinical parameters, such as overall survival, tumor stage, and tumor genotype data are captured well in persistent homology clusters will be assessed. If analyzing tumor MRI segments using persistent homology consistently identifies clinical relationships, this could enable clinicians to use persistent homology data as a noninvasive way to inform clinical decision making in oncology.

Keywords: cancer biology, oncology, persistent homology, radiomics, topological data analysis, tumor imaging

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Authors: Rodolfo Lorbieski, Silvia Modesto Nassar

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Many supervised machine learning tasks require decision making across numerous different classes. Multi-class classification has several applications, such as face recognition, text recognition and medical diagnostics. The objective of this article is to analyze an adapted method of Stacking in multi-class problems, which combines ensembles within the ensemble itself. For this purpose, a training similar to Stacking was used, but with three levels, where the final decision-maker (level 2) performs its training by combining outputs from the tree-based pair of meta-classifiers (level 1) from Bayesian families. These are in turn trained by pairs of base classifiers (level 0) of the same family. This strategy seeks to promote diversity among the ensembles forming the meta-classifier level 2. Three performance measures were used: (1) accuracy, (2) area under the ROC curve, and (3) time for three factors: (a) datasets, (b) experiments and (c) levels. To compare the factors, ANOVA three-way test was executed for each performance measure, considering 5 datasets by 25 experiments by 3 levels. A triple interaction between factors was observed only in time. The accuracy and area under the ROC curve presented similar results, showing a double interaction between level and experiment, as well as for the dataset factor. It was concluded that level 2 had an average performance above the other levels and that the proposed method is especially efficient for multi-class problems when compared to binary problems.

Keywords: stacking, multi-layers, ensemble, multi-class

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Authors: Farzaneh Shayeganfar, Ali Ramazani

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Exciton (strong electronic interaction of electron-hole) and hot carriers created by surface plasmon polaritons has been demonstrated in nanoscale optoelectronic devices, enhancing the photoresponse of the system. Herein, we employ a quantum framework to consider coupled exciton- hot carriers effects on photovoltaiv energy distribution, scattering process, polarizability and light emission of 2D-semicnductor. We use density functional theory (DFT) to design computationally a semi-functionalized 2D honeycomb silicon boride (SiB) monolayer with H atoms, suitable for photovoltaics. The dynamical stability, electronic and optical properties of SiB and semi-hydrogenated SiB structures were investigated utilizing the Tran-Blaha modified Becke-Johnson (TB-mBJ) potential. The calculated phonon dispersion shows that while an unhydrogenated SiB monolayer is dynamically unstable, surface semi-hydrogenation improves the stability of the structure and leads to a transition from metallic to semiconducting conductivity with a direct band gap of about 1.57 eV, appropriate for photovoltaic applications. The optical conductivity of this H-SiB structure, determined using the random phase approximation (RPA), shows that light adsorption should begin at the boundary of the visible range of light. Additionally, due to hydrogenation, the reflectivity spectrum declines sharply with respect to the unhydrogenated reflectivity spectrum in the IR and visible ranges of light. The energy band gap remains direct, increasing from 0.9 to 1.8 eV, upon increasing the strain from -6% (compressive) to +6% (tensile). Additionally, compressive and tensile strains lead, respectively, to red and blue shifts of optical the conductivity threshold around the visible range of light. Overall, this study suggests that H-SiB monolayers are suitable as two-dimensional solar cell materials.

Keywords: surface plasmon, hot carrier, strain engineering, valley polariton

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Authors: Ahmed M. AlHasem

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The Kuwaiti society has long been aware of climatic changes and their annual dates and trying to link them to astronomy in an attempt to forecast the future weather conditions. The reason for this concern is that many of the economic, social and living activities of the society depend deeply on the nature of the weather conditions directly and indirectly. In other words, Kuwaiti society, like the case of many human societies, has in the past tried to predict climatic conditions by linking them to astronomy or popular statements to indicate the timing of climate changes. Accordingly, this study was devoted to scientific investigation based on the statistical analysis of climatic data to show the accuracy and compatibility of some of the most important elements of the cultural heritage in relation to climate change and to relate it scientifically to precise climatic measurements for decades. The research has been divided into 10 topics, each topic has been focused on one legacy, whether by linking climate changes to the appearance/disappearance of star or a popular statement inherited through generations, through explain the nature and timing and thereby statistical analysis to indicate the proportion of accuracy based on official climatic data since 1962. The study's conclusion is that the relationship is weak and, in some cases, non-existent between the popular heritage and the actual climatic data. Therefore, it does not have a dependable relationship and a reliable scientific prediction between both the popular heritage and the forecast of weather conditions.

Keywords: astronomy, cultural heritage, statistical analysis, weather prediction

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Authors: Keltoum Bouherine, Olivier Leroy

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The present work is dedicated to study a three–dimensional (3D) self-consistent fluid simulation of microwave discharges of argon plasma in PECVD reactor. The model solves the Maxwell’s equations, continuity equations for charged species and the electron energy balance equation, coupled with Poisson’s equation, and Navier-Stokes equations by finite element method, using COMSOL Multiphysics software. In this study, the simulations yield the profiles of plasma components as well as the charge densities and electron temperature, the electric field, the gas velocity, and gas temperature. The results show that the microwave plasma reactor is outside of local thermodynamic equilibrium.The present work is dedicated to study a three–dimensional (3D) self-consistent fluid simulation of microwave discharges of argon plasma in PECVD reactor. The model solves the Maxwell’s equations, continuity equations for charged species and the electron energy balance equation, coupled with Poisson’s equation, and Navier-Stokes equations by finite element method, using COMSOL Multiphysics software. In this study, the simulations yield the profiles of plasma components as well as the charge densities and electron temperature, the electric field, the gas velocity, and gas temperature. The results show that the microwave plasma reactor is outside of local thermodynamic equilibrium.

Keywords: electron density, electric field, microwave plasma reactor, gas velocity, non-equilibrium plasma

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Authors: Muhammad Ali

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Cyber-attacks and anomaly detection on the Internet of Things (IoT) infrastructure is emerging concern in the domain of data-driven intrusion. Rapidly increasing IoT risk is now making headlines around the world. denial of service, malicious control, data type probing, malicious operation, DDos, scan, spying, and wrong setup are attacks and anomalies that can affect an IoT system failure. Everyone talks about cyber security, connectivity, smart devices, and real-time data extraction. IoT devices expose a wide variety of new cyber security attack vectors in network traffic. For further than IoT development, and mainly for smart and IoT applications, there is a necessity for intelligent processing and analysis of data. So, our approach is too secure. We train several machine learning models that have been compared to accurately predicting attacks and anomalies on IoT systems, considering IoT applications, with ANOVA-based feature selection with fewer prediction models to evaluate network traffic to help prevent IoT devices. The machine learning (ML) algorithms that have been used here are KNN, SVM, NB, D.T., and R.F., with the most satisfactory test accuracy with fast detection. The evaluation of ML metrics includes precision, recall, F1 score, FPR, NPV, G.M., MCC, and AUC & ROC. The Random Forest algorithm achieved the best results with less prediction time, with an accuracy of 99.98%.

Keywords: machine learning, analysis of variance, Internet of Thing, network security, intrusion detection

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Authors: Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo

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Rice leaf diseases significantly impact yield production in rice-dependent countries, affecting their agricultural sectors. As part of precision agriculture, early and accurate detection of these diseases is crucial for effective mitigation practices and minimizing crop losses. Hence, this study proposes an enhancement to the Convolutional Neural Network (CNN), a widely-used method for Rice Leaf Disease Image Classification, by incorporating MobileViTV2—a recently advanced architecture that combines CNN and Vision Transformer models while maintaining fewer parameters, making it suitable for broader deployment on edge devices. Our methodology utilizes a publicly available rice disease image dataset from Kaggle, which was validated by a university structural biologist following the guidelines provided by the Philippine Rice Institute (PhilRice). Modifications to the dataset include renaming certain disease categories and augmenting the rice leaf image data through rotation, scaling, and flipping. The enhanced dataset was then used to train the MobileViTV2 model using the Timm library. The results of our approach are as follows: the model achieved notable performance, with 98% accuracy in both training and validation, 6% training and validation loss, and a Receiver Operating Characteristic (ROC) curve ranging from 95% to 100% for each label. Additionally, the F1 score was 97%. These metrics demonstrate a significant improvement compared to a conventional CNN-based approach, which, in a previous 2022 study, achieved only 78% accuracy after using 5 convolutional layers and 2 dense layers. Thus, it can be concluded that MobileViTV2, with its fewer parameters, outperforms traditional CNN models, particularly when applied to Rice Leaf Disease Image Identification. For future work, we recommend extending this model to include datasets validated by international rice experts and broadening the scope to accommodate biotic factors such as rice pest classification, as well as abiotic stressors such as climate, soil quality, and geographic information, which could improve the accuracy of disease prediction.

Keywords: convolutional neural network, MobileViTV2, rice leaf disease, precision agriculture, image classification, vision transformer

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Authors: Mst Shapna Akter, Hossain Shahriar

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Neural network approaches are machine learning methods used in many domains, such as healthcare and cyber security. Neural networks are mostly known for dealing with image datasets. While training with the images, several fundamental mathematical operations are carried out in the Neural Network. The operation includes a number of algebraic and mathematical functions, including derivative, convolution, and matrix inversion and transposition. Such operations require higher processing power than is typically needed for computer usage. Central Processing Unit (CPU) is not appropriate for a large image size of the dataset as it is built with serial processing. While Graphics Processing Unit (GPU) has parallel processing capabilities and, therefore, has higher speed. This paper uses advanced Neural Network techniques such as VGG16, Resnet50, Densenet, Inceptionv3, Xception, Mobilenet, XGBOOST-VGG16, and our proposed models to compare CPU and GPU resources. A system for classifying autism disease using face images of an autistic and non-autistic child was used to compare performance during testing. We used evaluation matrices such as Accuracy, F1 score, Precision, Recall, and Execution time. It has been observed that GPU runs faster than the CPU in all tests performed. Moreover, the performance of the Neural Network models in terms of accuracy increases on GPU compared to CPU.

Keywords: autism disease, neural network, CPU, GPU, transfer learning

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Authors: Mohand Kessal, Rachid Boucetta, Mourad Tikobaini, Mohammed Zamoum

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Leakage problem of two-component fluids flow is modeled for a transient one-dimensional homogeneous bubbly flow and developed by taking into account the effect of a leak located at the middle point of the pipeline. The corresponding three conservation equations are numerically resolved by an improved characteristic method. The obtained results are explained and commented in terms of physical impact on the flow parameters.

Keywords: fluid transients, pipelines leaks, method of characteristics, leakage problem

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Authors: Stewart A. Keir, Faik A. Hamad

Abstract:

This study aims to use both analytical and experimental methods of analysis to examine the accuracy of Computational Fluid Dynamics (CFD) models that can then be used for more complex analyses, accurately representing more elaborate flow phenomena such as internal shockwaves and boundary layers. The geometry used in the analytical study and CFD model is taken from the experimental rig. The analytical study is undertaken using isentropic and adiabatic relationships and the output of the analytical study, the 'shockwave location tool', is created. The results from the analytical study are then used to optimize the redesign an experimental rig for more favorable placement of pressure taps and gain a much better representation of the shockwaves occurring in the divergent section of the nozzle. The CFD model is then optimized through the selection of different parameters, e.g. turbulence models (Spalart-Almaras, Realizable k-epsilon & Standard k-omega) in order to develop an accurate, robust model. The results from the CFD model can then be directly compared to experimental and analytical results in order to gauge the accuracy of each method of analysis. The CFD model will be used to visualize the variation of various parameters such as velocity/Mach number, pressure and turbulence across the shock. The CFD results will be used to investigate the interaction between the shock wave and the boundary layer. The validated model can then be used to modify the nozzle designs which may offer better performance and ease of manufacture and may present feasible improvements to existing high-speed flow applications.

Keywords: CFD, nozzle, fluent, gas dynamics, shock-wave

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Authors: David P. Tantua, Abdulkader Helwan

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Biometrics has been recently used for the human identification systems using the biological traits such as the fingerprints and iris scanning. Identification systems based biometrics show great efficiency and accuracy in such human identification applications. However, these types of systems are so far based on some image processing techniques only, which may decrease the efficiency of such applications. Thus, this paper aims to develop a human palmprint identification system using multi-layer perceptron neural network which has the capability to learn using a backpropagation learning algorithms. The developed system uses images obtained from a public database available on the internet (CASIA). The processing system is as follows: image filtering using median filter, image adjustment, image skeletonizing, edge detection using canny operator to extract features, clear unwanted components of the image. The second phase is to feed those processed images into a neural network classifier which will adaptively learn and create a class for each different image. 100 different images are used for training the system. Since this is an identification system, it should be tested with the same images. Therefore, the same 100 images are used for testing it, and any image out of the training set should be unrecognized. The experimental results shows that this developed system has a great accuracy 100% and it can be implemented in real life applications.

Keywords: biometrics, biological traits, multi-layer perceptron neural network, image skeletonizing, edge detection using canny operator

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Authors: Seungho Shin, Keunwoo Choi, Ali Akbar, Sukkee Um

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In this study, the nanoscale transport properties and catalyst utilization of vertically aligned carbon nanotube (VACNT) catalyst layers are computationally predicted by the three-dimensional lattice Boltzmann simulation based on the quasi-random nanostructural model in pursuance of fuel cell catalyst performance improvement. A series of catalyst layers are randomly generated with statistical significance at the 95% confidence level to reflect the heterogeneity of the catalyst layer nanostructures. The nanoscale gas transport phenomena inside the catalyst layers are simulated by the D3Q19 (i.e., three-dimensional, 19 velocities) lattice Boltzmann method, and the corresponding mass transport characteristics are mathematically modeled in terms of structural properties. Considering the nanoscale reactant transport phenomena, a transport-based effective catalyst utilization factor is defined and statistically analyzed to determine the structure-transport influence on catalyst utilization. The tortuosity of the reactant mass transport path of VACNT catalyst layers is directly calculated from the streaklines. Subsequently, the corresponding effective mass diffusion coefficient is statistically predicted by applying the pre-estimated tortuosity factors to the Knudsen diffusion coefficient in the VACNT catalyst layers. The statistical estimation results clearly indicate that the morphological structures of VACNT catalyst layers reduce the tortuosity of reactant mass transport path when compared to conventional catalyst layer and significantly improve consequential effective mass diffusion coefficient of VACNT catalyst layer. Furthermore, catalyst utilization of the VACNT catalyst layer is substantially improved by enhanced mass diffusion and electric current paths despite the relatively poor interconnections of the ion transport paths.

Keywords: Lattice Boltzmann method, nano transport phenomena, polymer electrolyte fuel cells, vertically aligned carbon nanotube

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Authors: Gokcen Ogruk-Maz, Sinan Yildirim

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Introduction: Exchange rate forecasting is pivotal for informed financial decision-making, encompassing risk management, investment strategies, and international trade planning. However, traditional forecasting models often fail to capture the complexity and volatility of currency markets. This study explores the potential of ensemble learning techniques such as Random Forest, Gradient Boosting, and AdaBoost to enhance the accuracy and robustness of exchange rate predictions. Research Objectives The primary objective is to evaluate the performance of ensemble methods in comparison to traditional econometric models such as Uncovered Interest Rate Parity, Purchasing Power Parity, and Monetary Models. By integrating advanced machine learning techniques with fundamental macroeconomic indicators, this research seeks to identify optimal approaches for predicting exchange rate movements across major currency pairs. Methodology: Using historical exchange rate data and economic indicators such as interest rates, inflation, money supply, and GDP, the study develops forecasting models leveraging ensemble techniques. Comparative analysis is performed against traditional models and hybrid approaches incorporating Facebook Prophet, Artificial Neural Networks, and XGBoost. The models are evaluated using statistical metrics like Mean Squared Error, Theil Ratio, and Diebold-Mariano tests across five currency pairs (JPY to USD, AUD to USD, CAD to USD, GBP to USD, and NZD to USD). Preliminary Results: Results indicate that ensemble learning models consistently outperform traditional methods in predictive accuracy. XGBoost shows the strongest performance among the techniques evaluated, achieving significant improvements in forecast precision with consistently low p-values and Theil Ratios. Hybrid models integrating macroeconomic fundamentals into machine learning frameworks further enhance predictive accuracy. Discussion: The findings show the potential of ensemble methods to address the limitations of traditional models by capturing non-linear relationships and complex dynamics in exchange rate movements. While Random Forest and Gradient Boosting are effective, the superior performance of XGBoost suggests that its capacity for handling sparse and irregular data offers a distinct advantage in financial forecasting. Conclusion and Implications: This research demonstrates that ensemble learning techniques, particularly when combined with traditional macroeconomic fundamentals, provide a robust framework for improving exchange rate forecasting. The study offers actionable insights for financial practitioners and policymakers, emphasizing the value of integrating machine learning approaches into predictive modeling for monetary economics.

Keywords: exchange rate forecasting, ensemble learning, financial modeling, machine learning, monetary economics, XGBoost

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Authors: Jigg Pelayo, Ricardo Villar, Einstine Opiso

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The rampant and unintended spread of urban areas resulted in increasing artificial component features in the land cover types of the countryside and bringing forth the urban heat island (UHI). This paved the way to wide range of negative influences on the human health and environment which commonly relates to air pollution, drought, higher energy demand, and water shortage. Land cover type also plays a relevant role in the process of understanding the interaction between ground surfaces with the local temperature. At the moment, the depiction of the land surface temperature (LST) at city/municipality scale particularly in certain areas of Misamis Oriental, Philippines is inadequate as support to efficient mitigations and adaptations of the surface urban heat island (SUHI). Thus, this study purposely attempts to provide application on the Landsat 8 satellite data and low density Light Detection and Ranging (LiDAR) products in mapping out quality automated LST model and crop-level land cover classification in a local scale, through theoretical and algorithm based approach utilizing the principle of data analysis subjected to multi-dimensional image object model. The paper also aims to explore the relationship between the derived LST and land cover classification. The results of the presented model showed the ability of comprehensive data analysis and GIS functionalities with the integration of object-based image analysis (OBIA) approach on automating complex maps production processes with considerable efficiency and high accuracy. The findings may potentially lead to expanded investigation of temporal dynamics of land surface UHI. It is worthwhile to note that the environmental significance of these interactions through combined application of remote sensing, geographic information tools, mathematical morphology and data analysis can provide microclimate perception, awareness and improved decision-making for land use planning and characterization at local and neighborhood scale. As a result, it can aid in facilitating problem identification, support mitigations and adaptations more efficiently.

Keywords: LiDAR, OBIA, remote sensing, local scale

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Authors: Jagadish Rao Padubidri, Mehak Bhandary, Sowmya J. Rao

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Introduction: The significance of the human foot and its measurements in identifying an individual has been proved a lot of times by different studies in different geographical areas and its association to the stature and gender of the individual has been justified by many researches. In our study we have used different foot measurements including the length, width, malleol height and navicular height for establishing its association to stature and gender and to find out its accuracy. The purpose of this study is to show the relation of foot measurements with stature and gender, and to derive Multiple and Logistic regression equations for stature and gender estimation in South Indian population. Materials and Methods: The subjects for this study were 200 South Indian students out of which 100 were females and 100 were males, aged between 18 to 24 years. The data for the present study included the stature, foot length, foot breath, foot malleol height, foot navicular height of both right and left foot. Descriptive statistics, T-test and Pearson correlation coefficients were derived between stature, gender and foot measurements. The stature was estimated from right and left foot measurements for both male and female South Indian population using multiple regression analysis and logistic regression analysis for gender estimation. Results: The means, standard deviation, stature, right and left foot measurements and T-test in male population were higher than in females. LFL (Left foot length) is more than RFL (Right Foot length) in male groups, but in female groups the length of both foot are almost equal [RFL=226.6, LFL=227.1]. There is not much of difference in means of RFW (Right foot width) and LFW (Left foot width) in both the genders. Significant difference were seen in mean values of malleol and navicular height of right and left feet in male gender. No such difference was seen in female subjects. Conclusions: The study has successfully demonstrated the correlation of foot length in stature estimation in all the three study groups in both right and left foot. Next in parameters are Foot width and malleol height in estimating stature among male and female groups. Navicular height of both right and left foot showed poor relationship with stature estimation in both male and female groups. Multiple regression equations for both right and left foot measurements to estimate stature were derived with standard error ranging from 11-12 cm in males and 10-11 cm in females. The SEE was 5.8 when both male and female groups were pooled together. The logistic regression model which was derived to determine gender showed 85% accuracy and 92.5% accuracy using right and left foot measurements respectively. We believe that stature and gender can be estimated with foot measurements in South Indian population.

Keywords: foot length, gender, stature, South Indian

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Authors: M. A. C. S. Sampath Fernando, James M. Curran, Renate Meyer

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Forensic DNA analysis has received much attention over the last three decades, due to its incredible usefulness in human identification. The statistical interpretation of DNA evidence is recognised as one of the most mature fields in forensic science. Peak heights in an Electropherogram (EPG) are approximately proportional to the amount of template DNA in the original sample being tested. A stutter is a minor peak in an EPG, which is not masking as an allele of a potential contributor, and considered as an artefact that is presumed to be arisen due to miscopying or slippage during the PCR. Stutter peaks are mostly analysed in terms of stutter ratio that is calculated relative to the corresponding parent allele height. Analysis of mixture profiles has always been problematic in evidence interpretation, especially with the presence of PCR artefacts like stutters. Unlike binary and semi-continuous models; continuous models assign a probability (as a continuous weight) for each possible genotype combination, and significantly enhances the use of continuous peak height information resulting in more efficient reliable interpretations. Therefore, the presence of a sound methodology to distinguish between stutters and real alleles is essential for the accuracy of the interpretation. Sensibly, any such method has to be able to focus on modelling stutter peaks. Bayesian nonparametric methods provide increased flexibility in applied statistical modelling. Mixture models are frequently employed as fundamental data analysis tools in clustering and classification of data and assume unidentified heterogeneous sources for data. In model-based clustering, each unknown source is reflected by a cluster, and the clusters are modelled using parametric models. Specifying the number of components in finite mixture models, however, is practically difficult even though the calculations are relatively simple. Infinite mixture models, in contrast, do not require the user to specify the number of components. Instead, a Dirichlet process, which is an infinite-dimensional generalization of the Dirichlet distribution, is used to deal with the problem of a number of components. Chinese restaurant process (CRP), Stick-breaking process and Pólya urn scheme are frequently used as Dirichlet priors in Bayesian mixture models. In this study, we illustrate an infinite mixture of simple linear regression models for modelling stutter ratio and introduce some modifications to overcome weaknesses associated with CRP.

Keywords: Chinese restaurant process, Dirichlet prior, infinite mixture model, PCR stutter

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