Search results for: laryngeal feature variation

Authors: Venuka Kuruwita Arachchige Don, Mohamed Shaheen, Chris Goodier

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Aerated autoclaved concrete (AAC) is known for its lightweight, easy handling, high thermal insulation, and extremely porous structure. Investigation of pore behavior in AAC is crucial for characterizing the material, standardizing design and production techniques, enhancing the mechanical, durability, and thermal performance, studying the effectiveness of protective measures, and analyzing the effects of weather conditions. The significant details of pores are complicated to observe with acknowledged accuracy. The High-resolution Field Emission Scanning Electron Microscope (FESEM) image analysis is a promising technique for investigating the pore behavior and density of AAC, which is adopted in this study. Mercury intrusion porosimeter and gas pycnometer were employed to characterize porosity distribution and density parameters. The analysis considered three different densities of AAC blocks and three layers in the altitude direction within each block. A set of understandings was presented to extract and analyze the details of pore shape, pore size, pore connectivity, and pore percentages from FESEM images of AAC. Average pore behavior outcomes per unit area were presented. Comparison of porosity distribution and density parameters revealed significant variations. FESEM imaging offered unparalleled insights into porosity behavior, surpassing the capabilities of other techniques. The analysis conducted from a multi-staged approach provides porosity percentage occupied by various pore categories, total porosity, variation of pore distribution compared to AAC densities and layers, number of two-dimensional and three-dimensional pores, variation of apparent and matrix densities concerning pore behaviors, variation of pore behavior with respect to aluminum content, and relationship among shape, diameter, connectivity, and percentage in each pore classification.

Keywords: autoclaved aerated concrete, density, imaging technique, microstructure, porosity behavior

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Authors: David Armany, Matthew Allaway, Preet Gosal, Senarath Edirimanne

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A potentially devastating complication of routine laparoscopic cholecystectomy includes iatrogenic bile duct injuries, which represent a stable incidence rate of 0.3% over the past three decades. Whilst related to several relative risks such as surgeon experience and patient factors (older age, male sex), misinterpretation of biliary tree anatomy remains the most common cause, accounting for 80% of iatrogenic Common Bile Duct injuries. Whilst extremely rare, a duplicate common bile duct anomaly remains a potential variation to encounter during biliary surgery, with 30 recognised cases in the worldwide literature, of which type Vb accounts for 4. We report the case of a rare type Vb variation encountered during intra-operative laparoscopic cholecystectomy and confirmed on cholangiogram. To our knowledge, this is the first documented Type Vb case encountered in an Australian population. Given these anomalies are asymptomatic and can perpetuate iatrogenic common bile duct injuries, awareness of all subtypes is crucial. Irrevocably, preoperative Magnetic Resonance Cholangiopancreatography can help recognise these anomalies before the operating theatre; however, their widespread adoption is limited by expensive and availability.

Keywords: duplicated common bile duct, type Vb, cholecystitis, MRCP, cholangiogram, iatrogenic CBD

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Authors: Habtamu Abera Goshu, Ping Yan

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Copy number variation (CNV) is a significant marker of the genetic and phenotypic diversity among individuals that accounts for complex quantitative traits of phenotype and diseases via modulating gene dosage, position effects, alteration of downstream pathways, modification of chromosome structure, and position within the nucleus and disrupting coding regions in the genome. Associating copy number variations (CNVs) with growth and gene expression are a powerful approach for identifying genomic characteristics that contribute to phenotypic and genotypic variation. A previous study using next-generation sequencing illustrated that the choline kinase beta (CHKB), Krüpple-like factor 6 (KLF6), glypican 1(GPC1), and cholinergic receptor muscarinic 3 (CHRM3) genes reside within copy number variable regions (CNVRs) of yak populations that overlap with quantitative trait loci (QTLs) of meat quality and growth. As a result, this research aimed to determine the association of CNVs of the KLF6, CHKB, GPC1, and CHRM3 genes with growth traits in the Datong yak breed. The association between the CNV types of the KLF6, CHKB, GPC1, and CHRM3 genes and the growth traits in the Datong yak breed was determined by one-way analysis of variance (ANOVA) using SPSS software. The CNV types were classified as a loss (a copy number of 0 or 1), gain (a copy number >2), and normal (a copy number of 2) relative to the reference gene, BTF3 in the 387 individuals of Datong yak. These results indicated that the normal CNV types of the CHKB and GPC1 genes were significantly (P<0.05) associated with high body length, height and weight, and chest girth in six-month-old and five-year-old Datong yaks. On the other hand, the loss CNV types of the KLF6 gene is significantly (P<0.05) associated with body weight and length and chest girth at six-month-old and five-year-old Datong yaks. In the contrary, the gain CNV type of the CHRM3 gene is highly (P<0.05) associated with body weight, length, height, and chest girth in six-month-old and five-year-old. This work provides the first observation of the biological role of CNVs of the CHKB, KLF6, GPC1, and CHRM3 genes in the Datong yak breed and might, therefore, provide a novel opportunity to utilize data on CNVs in designing molecular markers for the selection of animal breeding programs for larger populations of various yak breeds. Therefore, we hypothesized that this study provided inclusive information on the application of CNVs of the CHKB, KLF6, GPC1, and CHRM3 genes in growth traits in Datong yaks and its possible function in bovine species.

Keywords: Copy number variation, growth traits, yak, genes

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Authors: Thi Thi Zin, Pyke Tin, Ikuo Kobayashi, Yoichiro Horii

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Today dairy farm experts and farmers have well recognized the importance of dairy cow Body Condition Score (BCS) since these scores can be used to optimize milk production, managing feeding system and as an indicator for abnormality in health even can be utilized to manage for having healthy calving times and process. In tradition, BCS measures are done by animal experts or trained technicians based on visual observations focusing on pin bones, pin, thurl and hook area, tail heads shapes, hook angles and short and long ribs. Since the traditional technique is very manual and subjective, the results can lead to different scores as well as not cost effective. Thus this paper proposes an algebraic geometric imaging approach for an automatic dairy cow BCS system. The proposed system consists of three functional modules. In the first module, significant landmarks or anatomical points from the cow image region are automatically extracted by using image processing techniques. To be specific, there are 23 anatomical points in the regions of ribs, hook bones, pin bone, thurl and tail head. These points are extracted by using block region based vertical and horizontal histogram methods. According to animal experts, the body condition scores depend mainly on the shape structure these regions. Therefore the second module will investigate some algebraic and geometric properties of the extracted anatomical points. Specifically, the second order polynomial regression is employed to a subset of anatomical points to produce the regression coefficients which are to be utilized as a part of feature vector in scoring process. In addition, the angles at thurl, pin, tail head and hook bone area are computed to extend the feature vector. Finally, in the third module, the extracted feature vectors are trained by using Markov Classification process to assign BCS for individual cows. Then the assigned BCS are revised by using multiple regression method to produce the final BCS score for dairy cows. In order to confirm the validity of proposed method, a monitoring video camera is set up at the milk rotary parlor to take top view images of cows. The proposed method extracts the key anatomical points and the corresponding feature vectors for each individual cows. Then the multiple regression calculator and Markov Chain Classification process are utilized to produce the estimated body condition score for each cow. The experimental results tested on 100 dairy cows from self-collected dataset and public bench mark dataset show very promising with accuracy of 98%.

Keywords: algebraic geometric imaging approach, body condition score, Markov classification, polynomial regression

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Authors: Gaetano Zazzaro, Angelo Martone, Roberto V. Montaquila, Luigi Pavone

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Seizure is the main factor that affects the quality of life of epileptic patients. The diagnosis of epilepsy, and hence the identification of epileptogenic zone, is commonly made by using continuous Electroencephalogram (EEG) signal monitoring. Seizure identification on EEG signals is made manually by epileptologists and this process is usually very long and error prone. The aim of this paper is to describe an automated method able to detect seizures in EEG signals, using knowledge discovery in database process and data mining methods and algorithms, which can support physicians during the seizure detection process. Our detection method is based on Artificial Neural Network classifier, trained by applying the multilayer perceptron algorithm, and by using a software application, called Training Builder that has been developed for the massive extraction of features from EEG signals. This tool is able to cover all the data preparation steps ranging from signal processing to data analysis techniques, including the sliding window paradigm, the dimensionality reduction algorithms, information theory, and feature selection measures. The final model shows excellent performances, reaching an accuracy of over 99% during tests on data of a single patient retrieved from a publicly available EEG dataset.

Keywords: artificial neural network, data mining, electroencephalogram, epilepsy, feature extraction, seizure detection, signal processing

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Authors: Rasha Elhawari

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Heritage language learners (HLL) are part of the linguistic reality in Foreign Language Learning (FLL). These learners present several characteristics that are different from non-heritage language learners. They have a personal connection with the language and their motivation to learn the language is partly because of this personal connection. In Canada there is a large diversity in the foreign language learning classroom; the Arabic language classroom is no exception. The Arabic HLL is unique for more than one reason. First, is the fact that the Arabic language is spoken across twenty-two Arab countries across the Arab World. Across the Arab World there is a standard variation and a local dialect that co-exist side by side, i.e. diaglossia exists in a strong and unique way as a feature of Arabic. Second, Arabic is the language that all Muslims across the Muslim World use for their prayers. This raises a number of points when we consider Arabic as a Heritage Language; namely the role of diaglossia, culture and religion. The fact that there is a group of leaners that can be regarded as HLL who are not of Arabic speaking background but are Muslims and use the language for religious purposes is unique, thus course developers and language instructors need take this into consideration. The paper takes a closer look at this distinction and establishes sub-groups the Arabic HLLs in a language and/or culture specific way related mainly to the Arabic HLL. It looks at the learners at the beginners’ Arabic class at the undergraduate university level over a period of three years in order to define this learner. Learners belong to different groups and backgrounds but they all share common characteristics. The paper presents a detailed look at the learner types present at this class in order to help prepare and develop material for this specific learner group. The paper shows that separate HLL and non-HLL courses, especially at the introductory and intermediate level, is successful in resolving some of the pedagogical problems that occur in the Arabic as a Foreign Language classroom. In conclusion, the paper recommends the development of HLL courses at the early levels of language learning. It calls for a change in the pedagogical practices to overcome some of the challenges learner in the introductory Arabic class can face.

Keywords: Arabic, Heritage Language, langauge learner, teaching

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Authors: Shohei Maruyama, Yasuo Matsuyama, Sachiyo Aburatani

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The development of the method to annotate unknown gene functions is an important task in bioinformatics. One of the approaches for the annotation is The identification of the metabolic pathway that genes are involved in. Gene expression data have been utilized for the identification, since gene expression data reflect various intracellular phenomena. However, it has been difficult to estimate the gene function with high accuracy. It is considered that the low accuracy of the estimation is caused by the difficulty of accurately measuring a gene expression. Even though they are measured under the same condition, the gene expressions will vary usually. In this study, we proposed a feature extraction method focusing on the variability of gene expressions to estimate the genes' metabolic pathway accurately. First, we estimated the distribution of each gene expression from replicate data. Next, we calculated the similarity between all gene pairs by KL divergence, which is a method for calculating the similarity between distributions. Finally, we utilized the similarity vectors as feature vectors and trained the multiclass SVM for identifying the genes' metabolic pathway. To evaluate our developed method, we applied the method to budding yeast and trained the multiclass SVM for identifying the seven metabolic pathways. As a result, the accuracy that calculated by our developed method was higher than the one that calculated from the raw gene expression data. Thus, our developed method combined with KL divergence is useful for identifying the genes' metabolic pathway.

Keywords: metabolic pathways, gene expression data, microarray, Kullback–Leibler divergence, KL divergence, support vector machines, SVM, machine learning

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Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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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: Hilal Keklicek, Fatih Erbahceci, Elif Kirdi, Ali Yalcin, Semra Topuz, Ozlem Ulger, Gul Sener

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Objectives and Goals: The stride-to-stride fluctuations in gait is a determinant of qualified locomotion as known as gait variability. Gait variability is an important predictive factor of fall risk and useful for monitoring the effects of therapeutic interventions and rehabilitation. Comparison of gait variability in individuals with trans-tibial lower limb loss and trans femoral lower limb loss was the aim of the study. Methods: Ten individuals with traumatic unilateral trans femoral limb loss(TF), 12 individuals with traumatic transtibial lower limb loss(TT) and 12 healthy individuals(HI) were the participants of the study. All participants were evaluated with treadmill. Gait characteristics including mean step length, step length variability, ambulation index, time on each foot of participants were evaluated with treadmill. Participants were walked at their preferred speed for six minutes. Data from 4th minutes to 6th minutes were selected for statistical analyses to eliminate learning effect. Results: There were differences between the groups in intact limb step length variation, time on each foot, ambulation index and mean age (p < .05) according to the Kruskal Wallis Test. Pairwise analyses showed that there were differences between the TT and TF in residual limb variation (p=.041), time on intact foot (p=.024), time on prosthetic foot(p=.024), ambulation index(p = .003) in favor of TT group. There were differences between the TT and HI group in intact limb variation (p = .002), time on intact foot (p<.001), time on prosthetic foot (p < .001), ambulation index result (p < .001) in favor of HI group. There were differences between the TF and HI group in intact limb variation (p = .001), time on intact foot (p=.01) ambulation index result (p < .001) in favor of HI group. There was difference between the groups in mean age result from HI group were younger (p < .05).There were similarity between the groups in step lengths (p>.05) and time of prosthesis using in individuals with lower limb loss (p > .05). Conclusions: The pilot study provided basic data about gait stability in individuals with traumatic lower limb loss. Results of the study showed that to evaluate the gait differences between in different amputation level, long-range gait analyses methods may be useful to get more valuable information. On the other hand, similarity in step length may be resulted from effective prosthetic using or effective gait rehabilitation, in conclusion, all participants with lower limb loss were already trained. The differences between the TT and HI; TF and HI may be resulted from the age related features, therefore, age matched population in HI were recommended future studies. Increasing the number of participants and comparison of age-matched groups also recommended to generalize these result.

Keywords: lower limb loss, amputee, gait variability, gait analyses

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Authors: Pallavi Gajjar, Vinayak Malhotra

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Rockets have been known to have played a predominant role in spacecraft propulsion. The quintessential aspect of combustion-related requirements of a rocket engine is the minimization of the surrounding risks/hazards. Over time, it has become imperative to understand the combustion rate variation in presence of external energy source(s). Rocket propulsion represents a special domain of chemical propulsion assisted by high speed flows in presence of acoustics and thermal source(s). Jet noise leads to a significant loss of resources and every year a huge amount of financial aid is spent to prevent it. External heat source(s) induce high possibility of fire risk/hazards which can sufficiently endanger the operation of a space vehicle. Appreciable work had been done with justifiable simplification and emphasis on the linear variation of external energy source(s), which yields good physical insight but does not cater to accurate predictions. Present work experimentally attempts to understand the correlation between inter-energy conversions with the non-linear placement of external energy source(s). The work is motivated by the need to have better fire safety and enhanced combustion. The specific objectives of the work are a) To interpret the related energy transfer for combustion in presence of alternate external energy source(s) viz., thermal and acoustic, b) To fundamentally understand the role of key controlling parameters viz., separation distance, the number of the source(s), selected configurations and their non-linear variation to resemble real-life cases. An experimental setup was prepared using incense sticks as potential fuel and paraffin wax candles as the external energy source(s). The acoustics was generated using frequency generator, and source(s) were placed at selected locations. Non-equidistant parametric experimentation was carried out, and the effects were noted on regression rate changes. The results are expected to be very helpful in offering a new perspective into futuristic rocket designs and safety.

Keywords: combustion, acoustic energy, external energy sources, regression rate

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Authors: Vinci Mojamdar, Govind S. Gupta

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Moving beds in the presence of gas flow are widely used in metallurgical and chemical industries like blast furnaces, catalyst reforming, drying, etc. Pressure drop studies in co- and counter – current conditions have been done by a few researchers. However, to the best of authours knowledge, proper pressure drop study with lateral gas injection lacks especially in the presence of cavity and nozzle protrusion inside the packed bed. The latter study is more useful for metallurgical industries for the processes such as blast furnaces, shaft reduction and, COREX. In this experimental work, a two dimensional cold model with slot type nozzle for lateral gas injection along with the plastic beads as packing material and dry air as gas have been used. The variation of pressure drop is recorded at various horizontal and vertical directions in the presence of cavity and nozzle protrusion. The study has been performed in both moving and stationary beds. Also, the experiments have been carried out in both increasing as well as decreasing gas flow conditions. Experiments have been performed at various gas flow rates and packed bed heights. Some interesting results have been reported such as there is no pressure variation in the moving bed for both the increasing and decreasing gas flow condition that is different from the stationary bed. Pressure hysteresis loop has been observed in a stationary bed.

Keywords: lateral gas injection, moving bed, pressure drop, pressure hysteresis, stationary bed

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Authors: Krishna Mohan Bathula, Fatou Bintou Loucoubar, FNU Kaleemunnisa, Christelle Scharff, Mark Anthony De Castro

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Voice recognition algorithms such as automatic speech recognition and text-to-speech systems with African languages can play an important role in bridging the digital divide of Artificial Intelligence in Africa, contributing to the establishment of a fully inclusive information society. This paper proposes a Deep Learning model that can classify the user responses as inputs for an interactive voice response system. A dataset with Wolof language words ‘yes’ and ‘no’ is collected as audio recordings. A two stage Data Augmentation approach is adopted for enhancing the dataset size required by the deep neural network. Data preprocessing and feature engineering with Mel-Frequency Cepstral Coefficients are implemented. Convolutional Neural Networks (CNNs) have proven to be very powerful in image classification and are promising for audio processing when sounds are transformed into spectra. For performing voice response classification, the recordings are transformed into sound frequency feature spectra and then applied image classification methodology using a deep CNN model. The inference model of this trained and reusable Wolof voice response recognition system can be integrated with many applications associated with both web and mobile platforms.

Keywords: automatic speech recognition, interactive voice response, voice response recognition, wolof word classification

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Authors: Mahima Shrivastava, Soumya Kar, B. Swetha Malika, Lalu Saheb, M. Muthu Kumar, P. V. Ponambala Moorthi

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Natural hazards and manmade destruction causes both economic and societal losses. Generalized pre-mitigation strategies introduced and adopted for prevention of disaster all over the world are capable of augmenting the resiliency and optimizing the life-cycle cost of facilities. In countries like India where varied topographical feature exists requires location specific mitigation measures and strategies to be followed for better enhancement by event-driven and code-driven approaches. Present state of vindication measures followed and adopted, lags dominance in accomplishing the required development. In addition, serious concern and debate over climate change plays a vital role in enhancing the need and requirement for the development of time bound adaptive mitigation measures. For the development of long-term sustainable policies incorporation of future climatic variation is inevitable. This will further assist in assessing the impact brought about by the climate change on life-cycle cost of facilities. This paper develops more definite region specific and time bound pre-mitigation measures, by reviewing the present state of mitigation measures in India and all over the world for improving life-cycle cost of facilities. For the development of region specific adoptive measures, Indian regions were divided based on multiple-calamity prone regions and geo-referencing tools were used to incorporate the effect of climate changes on life-cycle cost assessment. This study puts forward significant effort in establishing sustainable policies and helps decision makers in planning for pre-mitigation measures for different regions. It will further contribute towards evaluating the life cycle cost of facilities by adopting the developed measures.

Keywords: climate change, geo-referencing tools, life-cycle cost, multiple-calamity prone regions, pre-mitigation strategies, sustainable policies

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Authors: Nassima Noufail, Sara Bouhali

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In this work, we develop a semi-supervised solution for the purpose of action detection in videos and propose an efficient algorithm for video segmentation. The approach is divided into video segmentation, feature extraction, and classification. In the first part, a video is segmented into clips, and we used the K-means algorithm for this segmentation; our goal is to find groups based on similarity in the video. The application of k-means clustering into all the frames is time-consuming; therefore, we started by the identification of transition frames where the scene in the video changes significantly, and then we applied K-means clustering into these transition frames. We used two image filters, the gaussian filter and the Laplacian of Gaussian. Each filter extracts a set of features from the frames. The Gaussian filter blurs the image and omits the higher frequencies, and the Laplacian of gaussian detects regions of rapid intensity changes; we then used this vector of filter responses as an input to our k-means algorithm. The output is a set of cluster centers. Each video frame pixel is then mapped to the nearest cluster center and painted with a corresponding color to form a visual map. The resulting visual map had similar pixels grouped. We then computed a cluster score indicating how clusters are near each other and plotted a signal representing frame number vs. clustering score. Our hypothesis was that the evolution of the signal would not change if semantically related events were happening in the scene. We marked the breakpoints at which the root mean square level of the signal changes significantly, and each breakpoint is an indication of the beginning of a new video segment. In the second part, for each segment from part 1, we randomly selected a 16-frame clip, then we extracted spatiotemporal features using convolutional 3D network C3D for every 16 frames using a pre-trained model. The C3D final output is a 512-feature vector dimension; hence we used principal component analysis (PCA) for dimensionality reduction. The final part is the classification. The C3D feature vectors are used as input to a multi-class linear support vector machine (SVM) for the training model, and we used a multi-classifier to detect the action. We evaluated our experiment on the UCF101 dataset, which consists of 101 human action categories, and we achieved an accuracy that outperforms the state of art by 1.2%.

Keywords: video segmentation, action detection, classification, Kmeans, C3D

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Authors: Aminu Musa Muhammad, Muhammad Kabiru Abubakar

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Monthly changes in Phytoplankton periodicity, nutrient levels, temperature, pH, suspended solids, dissolved solids, conductivity, dissolved oxygen and biochemical oxygen demand of Kazaure Dam, Jigawa State, Nigeria were studied for a period of six months (July-Dec.-2011). Physico-chemical result showed that temperature and pH ranged between17-25˚C and 5.5-7.5, while dissolved solids and suspended solids ranged between 95-155 mg/L and 0.13-112 mg/L respectively. Dissolved oxygen (DO), Biochemical oxygen demand (BOD), Chemical oxygen demand (COD), conductivity, nitrate, phosphate and sulphate ion concentrations were within the ranges of 3.5-3.6 mg/L, 4.8-7.2 mg/L, 8.10-12.30 mg/L, 21-58µΩ/cm, 0.2-8.1 mg/L, 2.4-18.1 mg/L, and 1.22-15.60 mg/L respectively. A total of 4514 Org/L phytoplankton were recorded, of which four classes of algae were identified. These comprised of Chlorophyta (44.1%), Cyanophyta(30.62%), Bacillariophyta(3.2%), Euglenophyta (32.1%). Descriptive statistics of the result showed that phytoplankton count varied with variation of physico-chemical parameters at 5% level during the study period. The abundance and distribution of the algae varied with the variation in the physico-chemical parameters. Pearson correlation showed that temperature and nutrients were significantly correlated with phytoplankton, while DO, sulphate and pH were insignificantly correlated, while there was no significant correlation with COD and phytoplankton.

Keywords: correlation, phytoplankton, physico chemical, kazaure dam

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Authors: Olteanu Constanta, Olteanu Lucian

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Researchers note that algebraic reasoning and sense making is essential for building conceptual knowledge in school mathematics. Consequently, pre-service teachers’ own reasoning and sense making are useful in fostering and developing students’ algebraic reasoning and sense making. This article explores the forms of reasoning and sense making that pre-service mathematics teachers exhibit and use in the process of analysing problem-posing tasks with a focus on first-degree equations. Our research question concerns the characteristics of the problem-posing tasks used for reasoning and sense making of first-degree equations as well as the characteristics of pre-service teachers’ reasoning and sense making in problem-posing tasks. The analyses are grounded in a post-structuralist philosophical perspective and variation theory. Sixty-six pre-service primary teachers participated in the study. The results show that the characteristics of reasoning in problem-posing tasks and of pre-service teachers are selecting, exploring, reconfiguring, encoding, abstracting and connecting. The characteristics of sense making in problem-posing tasks and of pre-service teachers are recognition, relationships, profiling, comparing, laddering and verifying. Beside this, the connection between reasoning and sense making is rich in line of flight in problem-posing tasks, while the connection is rich in line of rupture for pre-service teachers.

Keywords: first-degree equations, problem posing, reasoning, rhizomatic assemblage, sense-making, variation theory

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Authors: Peter James C. Icalia, Agapita J. Salces, Loida Valenzuela, Kangseok Seo, Geronima Ludan

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This study evaluated polymorphism of 11 microsatellite markers in four local genetic groups of cattle. Batanes cattle which has never been studied using microsatellites is evaluated for its genetic distance from the Ilocos cattle while Brahman and Holstein-Sahiwal are also included as there were insemination programs by the government using these two breeds. PCR products that were genotyped for each marker were analyzed using POPGENEv32. Results showed that 55% (Fst=0.5501) of the genetic variation is due to the differences between populations while the remaining 45% is due to individual variation. The Fst value also indicates that there were very great differences from population to population using the range proposed by Sewall and Wright. The constructed phylogenetic tree based on Nei’s genetic distance using the modified neighboor joining procedure of PHYLIPv3.5 showed the admixture of Brahman and Holstein-Sahiwal having them grouped in the same clade. Batanes and Ilocos cattle were grouped in a different cluster showing that they have descended from a single parental population. This would presumably address the claim that Batanes and Ilocos cattle are genetically distant from other groups and still exist despite the artificial insemination program of the government using Brahman and other imported breeds. The knowledge about the genetic structure of this population supports the development of conservation programs for the smallholder farmers.

Keywords: microsatellites, cattle, Philippines, populations, genetic structure

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Authors: Leila Ben Haj Said, Sihem Bellagha, Karim Allaf

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The present study deals with the dehydrofreezing assisted by instant controlled pressure drop (DIC) treatment of apple fruits. Samples previously dehydrated until different water contents (200, 100, and 30% dry basis (db)) and DIC treated were frozen at two different freezing velocities (V+ and V-), depending on the thermal resistance established between the freezing airflow and the sample surface. The effects of sample water content (W) and freezing velocity (V) on freezing curves and characteristics, exudate water (EW) and texture variation were examined. Lower sample water content implied higher freezing rates, lower initial freezing points (IFP), lower practical freezing time (PFT), and lower specific freezing time (SFT). EW (expressed in g exudate water/100 g water in the product) of 200% and 100% db apple samples was approximately 3%, at low freezing velocity (V-). Whereas, it was lower than 0.5% for apple samples with 30% db water content. Moreover, the impact of freezing velocity on EW was significant and very important only for high water content samples. For samples whose water content was lower than 100% db, firmness (maximum puncture force) was as higher as the water content was lower, without any insignificant impact of freezing velocity.

Keywords: dehydrofreezing, instant controlled pressure drop DIC, freezing time, texture

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Authors: Mohamed Bagues, Ikbel Souli, Feiza Boussora, Kamel Nagaz

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In southern Tunisia, two local barley accessions CV. Ardhaoui; 'Bengardeni' and 'Karkeni' were cultivated in the field under deficit drip irrigation with saline water. Three treatments were used: control or full irrigation T0 (100%ETc) and stressed T1 (75%ETc), T2 (50%ETc). Proline and soluble sugars contents increase significantly under drought between accessions compared to control and varies between growth stages. Moreover, the increasing of Ca2+ concentration enhances the absorption of Na+ ion, consequently K+/Na+ decrease significantly between accessions, these results suggest that a high tolerance of Bengardeni accession to drought stress. Therefore, drought tolerance indices (STI, SSI, MP, GMP, YSI and TOL) were used to identify high yielding and drought tolerant between accessions. MP explained the variation of GYi. GMP and STI explained the variation of GYs. The high values of MP, STI and GMP were associated with higher yielding accession. Higher TOL value is associated with significant grain yield reduction in stressed environment suggesting higher stress responses of accessions. Significant positive correlations between MP, STI and GMP and negative between YSI and SSI. MP, STI, GMP and YSI, TOL, SSI are not correlated with each other.

Keywords: drought, proline, soluble sugars, minerals, yield, drought tolerance indices, barley

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Authors: Nashaat A. Deiab, Aida Radwan, Mohamed Elnagdy, Mohamed S. Yahiya, Rasha Moustafa

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This study is to evaluate and investigate the dosimetric performance of our institution's 3D treatment planning system, Elekta PrecisePLAN, for open 6MV fields including square, rectangular, variation in SSD, centrally blocked, missing tissue, square MLC and MLC shaped fields guided by the recommended QA tests prescribed in AAPM TG53, NCS report 15 test packages, IAEA TRS 430 and ESTRO booklet no.7. The study was performed for Elekta Precise linear accelerator designed for clinical range of 4, 6 and 15 MV photon beams with asymmetric jaws and fully integrated multileaf collimator that enables high conformance to target with sharp field edges. Seven different tests were done applied on solid water equivalent phantom along with 2D array dose detection system, the calculated doses using 3D treatment planning system PrecisePLAN, compared with measured doses to make sure that the dose calculations are accurate for open fields including square, rectangular, variation in SSD, centrally blocked, missing tissue, square MLC and MLC shaped fields. The QA results showed dosimetric accuracy of the TPS for open fields within the specified tolerance limits. However large square (25cm x 25cm) and rectangular fields (20cm x 5cm) some points were out of tolerance in penumbra region (11.38 % and 10.9 %, respectively). For the test of SSD variation, the large field resulted from SSD 125 cm for 10cm x 10cm filed the results recorded an error of 0.2% at the central axis and 1.01% in penumbra. The results yielded differences within the accepted tolerance level as recommended. Large fields showed variations in penumbra. These differences between dose values predicted by the TPS and the measured values at the same point may result from limitations of the dose calculation, uncertainties in the measurement procedure, or fluctuations in the output of the accelerator.

Keywords: quality assurance, dose calculation, 3D treatment planning system, photon beam

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Authors: Lotfi Farah, Nadir Farah, Zaiem Kamar

Abstract:

This paper achieves a maximum power point tracking (MPPT) controller using a high-efficiency reduced rules neuro-fuzzy inference system (HE2RNF) for a 100 kW stand-alone photovoltaic (PV) system connected to the grid. The suggested HE2RNF based MPPT seeks the optimal duty cycle for the boost DC-DC converter, making the designed PV system working at the maximum power point (MPP), then transferring this power to the grid via a three levels voltage source converter (VSC). PV current variation and voltage variation are chosen as HE2RNF-based MPPT controller inputs. By using these inputs with the duty cycle as the only single output, a six rules ANFIS is generated. The high performance of the proposed HE2RNF numerically in the MATLAB/Simulink environment is shown. The 0.006% steady-state error, 0.006s of tracking time, and 0.088s of starting time prove the robustness of this six reduced rules against the widely used twenty-five ones.

Keywords: PV, MPPT, ANFIS, HE2RNF-based MPPT controller, VSC, grid connection

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Authors: Chetan R. Patel, G. J. Joshi

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The present study is carried out on six lane divided urban arterial road in Patna and Pune city of India. Both the road having distinct differences in terms of the vehicle composition and the road side parking. Arterial road in Patan city has 33% of non-motorized mode, whereas Pune arterial road dominated by 65% of Two wheeler. Also road side parking is observed in Patna city. The field studies using vidiographic techniques are carried out for traffic data collection. Data are extracted for one minute duration for vehicle composition, speed variation and flow rate on selected arterial road of the two cities. Speed flow relationship is developed and capacity is determine. Equivalency factor in terms of dynamic car unit is determine to represent the vehicle is single unit. The variation in the capacity due to side friction, presence of non motorized traffic and effective utilization of lane width is compared at concluding remarks.

Keywords: arterial road, capacity, dynamic equivalency factor, effect of non motorized mode, side friction

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Authors: Sandeep Das

Abstract:

Terminal post is one of the most important features of a Battery. The design and manufacturing of post are very much critical especially when threaded inserts (Bolt-on type) are used since all the collected energy is delivered from the lead part to the threaded insert (Cu or Cu alloy). Any imperfection at the interface may cause Voltage drop, high resistance, high heat generation, etc. This may be because of sudden change of material properties from lead to Cu alloys. To avoid this problem, a scheme of material gradation is proposed for achieving continuous variation of material properties for the Post used in commercially available lead acid battery. The Functionally graded (FG) material for the post is considered to be composed of different layers of homogeneous material. The volume fraction of the materials used corresponding to each layer is calculated by considering its variation along the direction of current flow (z) according to a power law. Accordingly, the effective properties of the homogeneous layers are estimated and the Post composed of this FG material is modeled using the commercially available ANSYS software. The solid 186 layered structural solid element has been used for discretization of the model of the FG Post. A thermal electric analysis is performed on the layered FG model. The model developed has been validated by comparing the results of the existing Post model& experimental analysis

Keywords: ANSYS, functionally graded material, lead-acid battery, terminal post

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Authors: T. M. Samuel, O. O. Aremu, I. O. Ismaila, L. I. Onu, B. O. Adetifa, S. E. Adegbite, O. O. Olokoshe

Abstract:

The imperative of anthropometry in designing to fit cannot be overemphasized. Of essence is the variability of measurements among population for which data is collected. In this paper anthropometric data were collected for the design of gari-frying facility such that work system would be designed to fit the gari-frying population in the Southwestern states of Nigeria comprising Lagos, Ogun, Oyo, Osun, Ondo, and Ekiti. Twenty-seven body dimensions were measured among 120 gari-frying processors. Statistical analysis was performed using SPSS package to determine the mean, standard deviation, minimum value, maximum value and percentiles (2nd, 5th, 25th, 50th, 75th, 95th, and 98th) of the different anthropometric parameters. One sample t-test was conducted to determine the variation within the population. The 50th percentiles of some of the anthropometric parameters were compared with those from other populations in literature. The correlation between the worker’s age and the body anthropometry was also investigated.The mean weight, height, shoulder height (sitting), eye height (standing) and eye height (sitting) are 63.37 kg, 1.57 m, 0.55 m, 1.45 m, and 0.67 m respectively.Result also shows a high correlation with other populations and a statistically significant difference in variability of data within the population in all the body dimensions measured. With a mean age of 42.36 years, results shows that age will be a wrong indicator for estimating the anthropometry for the population.

Keywords: anthropometry, cassava processing, design to fit, gari-frying, workstation design

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Authors: Sudha T., Naveen C.

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Dengue is a serious public health issue that causes significant annual economic and welfare burdens on nations. However, enhanced optimization techniques and quantitative modeling approaches can predict the incidence of dengue. By advocating for a data-driven approach, public health officials can make informed decisions, thereby improving the overall effectiveness of sudden disease outbreak control efforts. The National Oceanic and Atmospheric Administration and the Centers for Disease Control and Prevention are two of the U.S. Federal Government agencies from which this study uses environmental data. Based on environmental data that describe changes in temperature, precipitation, vegetation, and other factors known to affect dengue incidence, many predictive models are constructed that use different machine learning methods to estimate weekly dengue cases. The first step involves preparing the data, which includes handling outliers and missing values to make sure the data is prepared for subsequent processing and the creation of an accurate forecasting model. In the second phase, multiple feature selection procedures are applied using various machine learning models and optimization techniques. During the third phase of the research, machine learning models like the Huber Regressor, Support Vector Machine, Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR) are compared with several optimization techniques for feature selection, such as Harmony Search and Genetic Algorithm. In the fourth stage, the model's performance is evaluated using Mean Square Error (MSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) as assistance. Selecting an optimization strategy with the least number of errors, lowest price, biggest productivity, or maximum potential results is the goal. In a variety of industries, including engineering, science, management, mathematics, finance, and medicine, optimization is widely employed. An effective optimization method based on harmony search and an integrated genetic algorithm is introduced for input feature selection, and it shows an important improvement in the model's predictive accuracy. The predictive models with Huber Regressor as the foundation perform the best for optimization and also prediction.

Keywords: deep learning model, dengue fever, prediction, optimization

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Authors: Rabiaa Gammoudi, Najet Rebei, Othman Hasnaoui

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Photovoltaic systems use different techniques for tracking the Maximum Power Point (MPPT) to provide the highest possible power to the load regardless of the climatic conditions variation. In this paper, the proposed method is the Open Circuit (OC) method with sudden and random variations of insolation. The simulation results of the water pumping system controlled by OC method are validated by an experimental experience in real-time using a test bench composed by a centrifugal pump powered by a PVG via a boost chopper for the adaptation between the source and the load. The output of the DC/DC converter supplies the motor pump LOWARA type, assembly by means of a DC/AC inverter. The control part is provided by a computer incorporating a card DS1104 running environment Matlab/Simulink for visualization and data acquisition. These results show clearly the effectiveness of our control with a very good performance. The results obtained show the usefulness of the developed algorithm in solving the problem of degradation of PVG performance depending on the variation of climatic factors with a very good yield.

Keywords: PVWPS (PV Water Pumping System), maximum power point tracking (MPPT), open circuit method (OC), boost converter, DC/AC inverter

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Authors: Sun Shi, Sun Cheng

Abstract:

The pedestrian corridor is the most crowded area in the public space. The crowded severity has been focused on the field of evacuation strategies of the entrance in large public spaces. The aim of this paper is to analyze the walking efficiency in different spaces of pedestrian corridor with the variation of spatial parameters. The congestion condition caused by the variation of walking efficiency is modeled as well. This study established the space model of the walking corridor by setting the width, slope, turning form and turning angle of the pedestrian corridor. The pedestrian preference of walking mode varied with the difference of the crowded severity, walking speed, field of vision, sight direction and the expected destination, which is influenced by the characters of physical space form. Swarm software is applied to build Agent model. According to the output of the Agent model, the relationship between the pedestrian corridor width, ground slope, turning forms, turning angle and the walking efficiency, crowded severity is acquired. The results of the simulation can be applied to pedestrian corridor design in order to reduce the crowded severity and the potential safety risks caused by crowded people.

Keywords: crowded severity, multi-agent, pedestrian preference, urban space design

Procedia PDF Downloads 211

Authors: J. A. Louw Coetzee, Josua P. Meyer

Abstract:

The thermodynamic properties of different refrigerants in combination with the variation in geometrical properties (hydraulic diameter, aspect ratio, and inclination angle) of a rectangular microchannel determine the two-phase frictional pressure gradient. The effect of aspect ratio on frictional pressure drop had not been investigated enough during adiabatic two-phase flow and condensation in rectangular microchannels. This experimental study was concerned with measurement of the frictional pressure gradient in a rectangular microchannel, with hydraulic diameter of 900 μm. The aspect ratio of this microchannel was varied over a range that stretched from 0.3 to 3 in order to capture the effect of aspect ratio variation. A commonly used refrigerant, R134a, was used in the tests that spanned over a mass flux range of 100 to 1000 kg m-2 s-1 as well as the whole vapour quality range. This study formed part of a refrigerant condensation experiment and was therefore conducted at a saturation temperature of 40 °C. The study found that there was little influence of the aspect ratio on the frictional pressure drop at the test conditions. The data was compared to some of the well known micro- and macro-channel two-phase pressure drop correlations. Most of the separated flow correlations predicted the pressure drop data well at mass fluxes larger than 400 kg m-2 s-1 and vapour qualities above 0.2.

Keywords: aspect ratio, microchannel, two-phase, pressure gradient

Procedia PDF Downloads 364

Authors: Shengbiao Wu, Weihua Cao, Min Wu, Can Liu

Abstract:

This paper presents an intelligent tuning method of microwave filter based on complex neural network and improved space mapping. The tuning process consists of two stages: the initial tuning and the fine tuning. At the beginning of the tuning, the return loss of the filter is transferred to the passband via the error of phase. During the fine tuning, the phase shift caused by the transmission line and the higher order mode is removed by the curve fitting. Then, an Cauchy method based on the admittance parameter (Y-parameter) is used to extract the coupling matrix. The influence of the resonant cavity loss is eliminated during the parameter extraction process. By using processed data pairs (the amount of screw variation and the variation of the coupling matrix), a tuning model is established by the complex neural network. In view of the improved space mapping algorithm, the mapping relationship between the actual model and the ideal model is established, and the amplitude and direction of the tuning is constantly updated. Finally, the tuning experiment of the eight order coaxial cavity filter shows that the proposed method has a good effect in tuning time and tuning precision.

Keywords: microwave filter, scattering parameter, coupling matrix, intelligent tuning

Procedia PDF Downloads 293