Search results for: single machine total weighted tardiness

Authors: Saci Abdelhakim Ferkous, Khedoudja Soudani, Smail Haddadi

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This paper presents the results of a laboratory experimental study that explores the skid resistance of asphalt concrete mixtures made of local low-performance aggregates by partially replacing sand with olive mill waste (OMW). OMW was mixed with aggregates using a dry process by replacing sand with contents of 5%, 7%, 10% and 15%. The mechanical performances of the mixtures were evaluated using the Marshall and Duriez tests. A modified accelerated polishing machine was used as polishing equipment, and a British pendulum tester (BPT) was used to test the skid resistance of the samples. Finally, texture parameter analysis was performed using scanning electron microscopy (SEM) and Mountains Map software to assess the effect of OMW on the friction coefficient evolution. Using a distinct road wheel for a modified version of an accelerated polishing machine, which is normally used to determine the polished stone value of aggregates, the results showed that the addition of OMW up to 10% conferred a better skid resistance in comparison to normal asphalt concrete. The presence of olive mill waste in the mixture until 15% guarantees a gain of 22%-29% in skid resistance after polishing compared with the reference mix. Indeed, from texture parameter analysis, it was observed that there was differential wear of the lightweight aggregates (OMW) compared to the other aggregates during the polishing process, which created a new surface microtexture that had new peaks and led to a good level of friction compared to the mixtures without OMW. In general, it was found that OMW is a promising modifier for asphalt mixtures with both engineering and economic merits.

Keywords: skid resistance, olive mill waste, polishing resistance, accelerated polishing machine, local materials, sustainable development.

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Authors: Sriraman Kothuri, Komal Teja Mattupalli

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In this paper we proposed a novel algorithm for recognizing hulls in a hand written digits. This is an extension to the work on “Digit Recognition Using Freeman Chain code”. In order to find out the hulls in a user given digit it is necessary to follow three steps. Those are pre-processing, Boundary Extraction and at last apply the Hull Detection system in a way to attain the better results. The detection of Hull Regions is mainly intended to increase the machine learning capability in detection of characters or digits. This can also extend this in order to get the hull regions and their intensities in Black Holes in Space Exploration.

Keywords: chain code, machine learning, hull regions, hull recognition system, SASK algorithm

Procedia PDF Downloads 389

Authors: T. D. Tran, H. Schlegel, R. Neugebauer

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Ball screws are an important component in machine tools. In mechatronic systems and machine tools, a ball screw has to work usually at a high speed. Otherwise the axial compliance of the ball screw, in combination with the inertia of the slide, the motor, the coupling and the screw, will cause an oscillation resonance, which limits the systems bandwidth and consequently influences performance of the motion controller. In this paper, the modal analysis method by measuring and analysing the vibrating parameters of the ball screw system to determine the dynamic characteristic of existing structures is used. On the one hand, the results of this study were obtained by the theoretical analysis and the modal testing of a ball screw system test station with the help of an impact hammer, respectively using excitation by motor. The experimental study showed oscillating forms of the ball screw for each frequency and obtained eigenfrequencies of the ball screw system. On the other hand, in this research a simulation with the help of the numerical modal analysis in order to analyse the oscillation and to find the eigenfrequencies of the ball screw system is used. Furthermore, the model order reduction by modal reduction and also according to Guyan is carried out. On the basis of these results a secure and also rapid commissioning of the control loops with regard to operating in their optimal function is targeted.

Keywords: modal analysis, ball screw, controller system, machine tools

Procedia PDF Downloads 445

Authors: Aliasghar Baziar, Hassan Masoumi, Alireza Ale Saadi

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The control of the response of electric motors plays a significant role in the damping of transient responses. In this regard, this paper presents a static VAR compensator (SVC) based on a fuzzy logic which is applied to an industrial power network consisting of three phase synchronous, asynchronous and DC motor loads. The speed and acceleration variations of a specific machine are the inputs of the proposed fuzzy logic controller (FLC). In order to verify the effectiveness and proficiency of the proposed Fuzzy Logic based SVC (FLSVC), several non-linear time-domain digital simulation tests are performed. The proposed fuzzy model can properly control the response of electric motors. The results show that the FLSVC is successful to improve the voltage profile significantly over a wide range of operating conditions and disturbances thus improving the overall dynamic performance of the network.

Keywords: fuzzy logic controller, VAR compensator, single cage asynchronous motor, DC motor

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Authors: M. Haruna, O. O. Onafowokan, G. Holt, K. Anderson, R. G. Middleton

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Background: Requirements for satisfactory outcomes following total hip arthroplasty (THA) include restoration of femoral offset, version, and leg length. Various techniques have been described for restoring these biomechanical parameters, with leg length restoration being the most predominantly described. We describe a “quartered head technique” (QHT) which uses a stepwise series of femoral head osteotomies to identify and preserve the centre of rotation of the femoral head during THA in order to ensure reconstruction of leg length, offset and stem version, such that hip biomechanics are restored as near to normal as possible. This study aims to identify whether using the QHT during hip arthroplasty effectively restores leg length and femoral offset to within acceptable parameters. Methods: A retrospective review of 206 hips was carried out, leaving 124 hips in the final analysis. Power analysis indicated a minimum of 37 patients required. All operations were performed using an anterolateral approach by a single surgeon. All femoral implants were cemented, collarless, polished double taper CPT® stems (Zimmer, Swindon, UK). Both cemented, and uncemented acetabular components were used (Zimmer, Swindon, UK). Leg length, version, and offset were assessed intra-operatively and reproduced using the QHT. Post-operative leg length and femoral offset were determined and compared with the contralateral native hip, and the difference was then calculated. For the determination of leg length discrepancy (LLD), we used the method described by Williamson & Reckling, which has been shown to be reproducible with a measurement error of ±1mm. As a reference, the inferior margin of the acetabular teardrop and the most prominent point of the lesser trochanter were used. A discrepancy of less than 6mm LLD was chosen as acceptable. All peri-operative radiographs were assessed by two independent observers. Results: The mean absolute post-operative difference in leg length from the contralateral leg was +3.58mm. 84% of patients (104/124) had LLD within ±6mm of the contralateral limb. The mean absolute post-operative difference in offset from contralateral leg was +3.88mm (range -15 to +9mm, median 3mm). 90% of patients (112/124) were within ±6mm offset of the contralateral limb. There was no statistical difference noted between observer measurements. Conclusion: The QHT provides a simple, inexpensive yet effective method of maintaining femoral leg length and offset during total hip arthroplasty. Combining this technique with pre-operative templating or other techniques described may enable surgeons to reduce even further the discrepancies between pre-operative state and post-operative outcome.

Keywords: leg length discrepancy, technical tip, total hip arthroplasty, operative technique

Procedia PDF Downloads 66

Authors: Jung–Min Yang

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In this paper, fault recovery for parallel interconnected asynchronous sequential machines is studied. An adversarial input can infiltrate into one of two submachines comprising parallel composition of the considered asynchronous sequential machine, causing an unauthorized state transition. The control objective is to elucidate the condition for the existence of a corrective controller that makes the closed-loop system immune against any occurrence of adversarial inputs. In particular, an efficient existence condition is presented that does not need the complete modeling of the interconnected asynchronous sequential machine.

Keywords: asynchronous sequential machines, parallel composi-tion, corrective control, fault tolerance

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Authors: Iftikhar U. Sikder

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This paper illustrates an application of granular computing approach, namely rough set theory in data mining. The paper outlines the formalism of granular computing and elucidates the mathematical underpinning of rough set theory, which has been widely used by the data mining and the machine learning community. A real-world application is illustrated, and the classification performance is compared with other contending machine learning algorithms. The predictive performance of the rough set rule induction model shows comparative success with respect to other contending algorithms.

Keywords: concept approximation, granular computing, reducts, rough set theory, rule induction

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Authors: Daniel M. Wolff, Patricia Gray, Rafael de la Parra Venegas

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An unsupervised machine listening system is constructed and applied to a dataset of 17,195 30-second marine hydrophone recordings. The system is then heuristically supplemented with anecdotal listening, contextual recording information, and supervised learning techniques to reduce the number of false positives. Features for classification are assembled by extracting the following data from each of the audio files: the spectral centroid, root-mean-squared values for each frequency band of a 10-octave filter bank, and mel-frequency cepstral coefficients in 5-second frames. In this way both time- and frequency-domain information are contained in the features to be passed to a clustering algorithm. Classification is performed using the k-means algorithm and then a k-nearest neighbors search. Different values of k are experimented with, in addition to different combinations of the available feature sets. Hypothesized class labels are 'primarily anthrophony' and 'primarily biophony', where the best class result conforming to the former label has 104 members after heuristic pruning. This demonstrates how a large audio dataset has been made more tractable with machine learning techniques, forming the foundation of a framework designed to acoustically monitor and gauge biological and anthropogenic activity in a marine environment.

Keywords: anthrophony, hydrophone, k-means, machine learning

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Authors: Bernd Engel, Sara Salman Hassan Al-Maeeni

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A substantial amount of natural resources are nowadays consumed at a growing rate, as humans all over the world used materials obtained from the Earth. Machinery manufacturing industry is one of the major resource consumers on a global scale. Even though the incessant finding out of the new material, metals, and resources, it is urgent for the industry to develop methods to use the Earth's resources intelligently and more sustainable than before. Re-engineering of machine tools regarding design and failure analysis is an approach whereby out-of-date machines are upgraded and returned to useful life. To ensure the reliable future performance of the used machine components, it is essential to investigate the machine component failure through the material, design, and surface examinations. This paper presents an experimental approach aimed at inspecting the shaft of the rotary draw bending machine as a case to study. The testing methodology, which is based on the principle of the three-point bending test, allows assessing the shaft elastic behavior under loading. Furthermore, the shaft elastic characteristics include the maximum linear deflection, and maximum bending stress was determined by using an analytical approach and finite element (FE) analysis approach. In the end, the results were compared with the ones obtained by the experimental approach. In conclusion, it is seen that the measured bending deflection and bending stress were well close to the permissible design value. Therefore, the shaft can work in the second life cycle. However, based on previous surface tests conducted, the shaft needs surface treatments include re-carburizing and refining processes to ensure the reliable surface performance.

Keywords: deflection, FE analysis, shaft, stress, three-point bending

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Authors: Jie Liu, Xudong Luo, Pingping Lin, Yifan Fan

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Facebook, Twitter, Weibo, and other social media and significant e-commerce sites generate a massive amount of online texts, which can be used to analyse people’s opinions or sentiments for better decision-making. So, sentiment analysis, especially fine-grained sentiment analysis, is a very active research topic. In this paper, we survey various methods for fine-grained sentiment analysis, including traditional sentiment lexicon-based methods, machine learning-based methods, and deep learning-based methods in aspect/target/attribute-based sentiment analysis tasks. Besides, we discuss their advantages and problems worthy of careful studies in the future.

Keywords: sentiment analysis, fine-grained, machine learning, deep learning

Procedia PDF Downloads 236

Authors: Shirin Shafaei, James R. Bolton, Mohamed Gamal El Din

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Ultraviolet (UV) disinfection causes damage to the DNA or RNA of microorganisms, but many microorganisms can repair this damage after exposure to near-UV or visible wavelengths (310–480 nm) by a mechanism called photoreactivation. Photoreactivation is gaining more attention because it can reduce the efficiency of UV disinfection of wastewater several hours after treatment. The focus of many photoreactivation research activities on the single species has caused a considerable lack in knowledge about complex natural communities of microorganisms and their response to UV treatment. In this research, photoreactivation experiments were carried out on the influent of the UV disinfection unit at a municipal wastewater treatment plant (WWTP) in Edmonton, Alberta after exposure to a Medium-Pressure (MP) UV lamp system to evaluate the effect of environmental factors on photoreactivation of microorganisms in the actual municipal wastewater. The effect of reactivation fluence, temperature, and river water on photoreactivation of total coliforms was examined under indoor conditions. The results showed that higher effective reactivation fluence values (up to 20 J/cm²) and higher temperatures (up to 25 °C) increased the photoreactivation of total coliforms. However, increasing the percentage of river in the mixtures of the effluent and river water decreased the photoreactivation of the mixtures. The results of this research can help the municipal wastewater treatment industry to examine the environmental effects of discharging their effluents into receiving waters.

Keywords: photoreactivation, reactivation fluence, river water, temperature, ultraviolet disinfection, wastewater effluent

Procedia PDF Downloads 294

Authors: Fidelia A. Orji, Julita Vassileva

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This research aims to develop machine learning models for students' academic performance and study strategy prediction, which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) used in building the models are chosen based on prior studies, which revealed that the attributes are essential in students’ learning process. Previous studies revealed the individual effects of each of these attributes on students’ learning progress. However, few studies have investigated the combined effect of the attributes in predicting student study strategy and academic performance to reduce the dropout rate. To bridge this gap, we used Scikit-learn in python to build five machine learning models (Decision Tree, K-Nearest Neighbour, Random Forest, Linear/Logistic Regression, and Support Vector Machine) for both regression and classification tasks to perform our analysis. The models were trained, evaluated, and tested for accuracy using 924 university dentistry students' data collected by Chilean authors through quantitative research design. A comparative analysis of the models revealed that the tree-based models such as the random forest (with prediction accuracy of 94.9%) and decision tree show the best results compared to the linear, support vector, and k-nearest neighbours. The models built in this research can be used in predicting student performance and study strategy so that appropriate interventions could be implemented to improve student learning progress. Thus, incorporating strategies that could improve diverse student learning attributes in the design of online educational systems may increase the likelihood of students continuing with their learning tasks as required. Moreover, the results show that the attributes could be modelled together and used to adapt/personalize the learning process.

Keywords: classification models, learning strategy, predictive modeling, regression models, student academic performance, student motivation, supervised machine learning

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Authors: Yassir AbdelRazig, Amine Ghanem

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Construction projects are information intensive in nature and involve many activities that are related to each other. Wireless technologies can be used to improve the accuracy and timeliness of data collected from construction sites and shares it with appropriate parties. Nonetheless, the construction industry tends to be conservative and shows hesitation to adopt new technologies. A main concern for owners, contractors or any person in charge on a job site is the cost of the technology in question. Wireless technologies are not cheap. There are a lot of expenses to be taken into consideration, and a study should be completed to make sure that the importance and savings resulting from the usage of this technology is worth the expenses. This research attempts to assess the effectiveness of using the appropriate wireless technologies based on criteria such as performance, reliability, and risk. The assessment is based on a utility function model that breaks down the selection issue into alternatives attribute. Then the attributes are assigned weights and single attributes are measured. Finally, single attribute are combined to develop one single aggregate utility index for each alternative.

Keywords: analytic hierarchy process, decision theory, utility function, wireless technologies

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Authors: Samia Dardouri, Ridha Bouallegue

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Scheduling is the process of dynamically allocating physical resources to User Equipment (UE) based on scheduling algorithms implemented at the LTE base station. Various algorithms have been proposed by network researchers as the implementation of scheduling algorithm which represents an open issue in Long Term Evolution (LTE) standard. This paper makes an attempt to study and compare the performance of PF, MLWDF and EXP/PF scheduling algorithms. The evaluation is considered for a single cell with interference scenario for different flows such as Best effort, Video and VoIP in a pedestrian and vehicular environment using the LTE-Sim network simulator. The comparative study is conducted in terms of system throughput, fairness index, delay, packet loss ratio (PLR) and total cell spectral efficiency.

Keywords: LTE, multimedia flows, scheduling algorithms, mobile computing

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Authors: Kemal Efe Eseller, Göktuğ Yazici

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Laser-induced breakdown spectroscopy (LIBS) is a rapid optical atomic emission spectroscopy which is used for material identification and analysis with the advantages of in-situ analysis, elimination of intensive sample preparation, and micro-destructive properties for the material to be tested. LIBS delivers short pulses of laser beams onto the material in order to create plasma by excitation of the material to a certain threshold. The plasma characteristics, which consist of wavelength value and intensity amplitude, depends on the material and the experiment’s environment. In the present work, medicine samples’ spectrum profiles were obtained via LIBS. Medicine samples’ datasets include two different concentrations for both paracetamol based medicines, namely Aferin and Parafon. The spectrum data of the samples were preprocessed via filling outliers based on quartiles, smoothing spectra to eliminate noise and normalizing both wavelength and intensity axis. Statistical information was obtained and principal component analysis (PCA) was incorporated to both the preprocessed and raw datasets. The machine learning models were set based on two different train-test splits, which were 70% training – 30% test and 80% training – 20% test. Cross-validation was preferred to protect the models against overfitting; thus the sample amount is small. The machine learning results of preprocessed and raw datasets were subjected to comparison for both splits. This is the first time that all supervised machine learning classification algorithms; consisting of Decision Trees, Discriminant, naïve Bayes, Support Vector Machines (SVM), k-NN(k-Nearest Neighbor) Ensemble Learning and Neural Network algorithms; were incorporated to LIBS data of paracetamol based pharmaceutical samples, and their different concentrations on preprocessed and raw dataset in order to observe the effect of preprocessing.

Keywords: machine learning, laser-induced breakdown spectroscopy, medicines, principal component analysis, preprocessing

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Authors: Mohamed Elhosieny Aly Ismail

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The paper illustrates the total voltage and current harmonic distortion impact caused by fast-charging an electric bus and maintaining standard limit compliance. Measuring the current harmonic level in the range of 2 kHz-9 kHz. Also, the impact of the total demand distortions current caused by fast charger electric bus on the utility by measuring at the point of common coupling and comparing the measurement with IEEE519 -2014 standard. The results show that the total harmonic current distortion for the charger is within the limits of IEC 61000-3-12 and the fifth harmonic current was the most dominant frequency then the seventh harmonic current. The harmonic current in the range of 2 kHz- 9 kHz shows the frequency 5.1kHz is the most dominant frequency.

Keywords: electric vehicle, total harmonic distortion, IEEE519-2014, IEC 61000-3-12, super harmonic distortion

Procedia PDF Downloads 89

Authors: Kandi Sridhar, Charles Albert Linton

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Grapes are excellent sources of bioactive compounds, which have been suggested to be responsible for lowering the risk of chronic diseases. Fresh and freeze-dried extracts of Kyoho and Jubilee, commercial grape varieties available in Taiwan and attractive for their quality berries, were investigated for their total phenolics and total flavonoids contents and related dose-dependent antioxidants properties using various in vitro assays. The efficiency of the extraction yield ranged from 7.10 % to 25.53 % (w/w), depending on solvent used. Fresh samples of Kyoho and Jubilee exhibited total polyphenolic contents (351.56 ± 23.08 and 328.67 ± 16.54 µg GAE/mL, respectively), whereas Kyoho freeze-dried methanol: water extracts contains the good levels of total flavonoids (4767.82 ± 22.20 µg QE/mL). Kyoho and Jubilee freeze-dried extracts exhibited the highest total flavonoid contents. There was a weak correlation between total phenolic and flavonoid assays (r= -0.05, R2 = 0.02, p > 0.05). Kyoho fresh and freeze-dried samples showed the DPPH (11.51 – 77.82 %), superoxide scavenging activity (33.61 – 81.95 %), and total antioxidant inhibition (92.01 – 99.28 %), respectively. Total flavonoids were statistically correlated with EC50 DPPH scavenging radicals (r =0.91, p < 0.01), EC50 nitric oxide (r = 0.25, p > 0.05), and EC50 lipid peroxidation radicals (r = 0.38, p > 0.05). These results suggested that the two commercial grape cultivars in Taiwan could be used as a good source of natural antioxidants. Thus, consumption of grapes as a source antioxidant might lower the risk of chronic diseases. Moreover, future studies will investigate and develop phenolic acid profile for the cultivars in Taiwan.

Keywords: antioxidants, EC50 radical scavenging activity, grape cultivars, total phenolics

Procedia PDF Downloads 167

Authors: Khin Mar Yee, Mu Mu Than, Kyi Lint, Aye Aye Oo, Chan Mya Hmway, Khin Zar Chi Winn

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The dynamic changes of Land Use and Land Cover (LULC) changes in Yangon have generally resulted the improvement of human welfare and economic development since the last twenty years. Making map of LULC is crucially important for the sustainable development of the environment. However, the exactly data on how environmental factors influence the LULC situation at the various scales because the nature of the natural environment is naturally composed of non-homogeneous surface features, so the features in the satellite data also have the mixed pixels. The main objective of this study is to the calculation of accuracy based on change detection of LULC changes by Support Vector Machines (SVMs). For this research work, the main data was satellite images of 1996, 2006 and 2015. Computing change detection statistics use change detection statistics to compile a detailed tabulation of changes between two classification images and Support Vector Machines (SVMs) process was applied with a soft approach at allocation as well as at a testing stage and to higher accuracy. The results of this paper showed that vegetation and cultivated area were decreased (average total 29 % from 1996 to 2015) because of conversion to the replacing over double of the built up area (average total 30 % from 1996 to 2015). The error matrix and confidence limits led to the validation of the result for LULC mapping.

Keywords: land use and land cover change, change detection, image processing, support vector machines

Procedia PDF Downloads 114

Authors: Mustapha Mahmoud Dif, Fouzia Benali-Toumi, Mohamed Benyahia, Sofiane Bouazza, Abbes Dellal, Slimane Baha

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L. multifida is applied to treat rheumatism and cold and has hypoglycemic and anti-inflammatory properties. The present study is to optimize the extraction of phenolic compounds in Algerian Lavandula multifida. The influences of parameters including temperature (decoction and maceration) and extraction time (15min to 45 min) on the flavonoids concentration are studied. The optimal conditions are determined and the quadratic response surfaces draw from the mathematical models. Total phenols were evaluated using Folin sicaltieu methods, total flavonoids were estimated using the Tri chloral aluminum method. The maximum concentration extracted, for total flavonoids, equal to 0.043 mg/g was achieved with decoction and extraction time of 41.55 min. However, for total phenol compounds highest concentration of 0.218 mg/g, is obtained with 45 min at 49.99°C.

Keywords: L multifidi, phenolic content, optimization, time, temperature

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Authors: Hong Zhang

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The main purpose of this study is to show that machine translation (MT) post-editing (PE) training can help our Chinese students learn Spanish as a second language. Our hypothesis is that they might make better use of it by learning PE skills specific for foreign language learning. We have developed PE training materials based on the data collected in a previous study. Training material included the special error types of the output of MT and the error types that our Chinese students studying Spanish could not detect in the experiment last year. This year we performed a pilot study in order to evaluate the PE training materials effectiveness and to what extent PE training helps Chinese students who study the Spanish language. We used screen recording to record these moments and made note of every action done by the students. Participants were speakers of Chinese with intermediate knowledge of Spanish. They were divided into two groups: Group A performed PE training and Group B did not. We prepared a Chinese text for both groups, and participants translated it by themselves (human translation), and then used Google Translate to translate the text and asked them to post-edit the raw MT output. Comparing the results of PE test, Group A could identify and correct the errors faster than Group B students, Group A did especially better in omission, word order, part of speech, terminology, mistranslation, official names, and formal register. From the results of this study, we can see that PE training can help Chinese students learn Spanish as a second language. In the future, we could focus on the students’ struggles during their Spanish studies and complete the PE training materials to teach Chinese students learning Spanish with machine translation.

Keywords: machine translation, post-editing, post-editing training, Chinese, Spanish, foreign language learning

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Authors: Priscila A. Bernardes, Marcos E. Buzanskas, Luciana C. A. Regitano, Ricardo V. Ventura, Danisio P. Munari

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The best linear unbiased predictor (BLUP) is a method commonly used in genetic evaluations of breeding programs. However, this approach can lead to higher inbreeding coefficients in the population due to the intensive use of few bulls with higher genetic potential, usually presenting some degree of relatedness. High levels of inbreeding are associated to low genetic viability, fertility, and performance for some economically important traits and therefore, should be constantly monitored. Unreliable pedigree data can also lead to misleading results. Genomic information (i.e., single nucleotide polymorphism – SNP) is a useful tool to estimate the inbreeding coefficient. Runs of homozygosity have been used to evaluate homozygous segments inherited due to direct or collateral inbreeding and allows inferring population selection history. This study aimed to evaluate runs of homozygosity (ROH) and inbreeding in a population of Nelore beef cattle. A total of 814 animals were genotyped with the Illumina BovineHD BeadChip and the quality control was carried out excluding SNPs located in non-autosomal regions, with unknown position, with a p-value in the Hardy-Weinberg equilibrium lower than 10⁻⁵, call rate lower than 0.98 and samples with the call rate lower than 0.90. After the quality control, 809 animals and 509,107 SNPs remained for analyses. For the ROH analysis, PLINK software was used considering segments with at least 50 SNPs with a minimum length of 1Mb in each animal. The inbreeding coefficient was calculated using the ratio between the sum of all ROH sizes and the size of the whole genome (2,548,724kb). A total of 25.711 ROH were observed, presenting mean, median, minimum, and maximum length of 3.34Mb, 2Mb, 1Mb, and 80.8Mb, respectively. The number of SNPs present in ROH segments varied from 50 to 14.954. The longest ROH length was observed in one animal, which presented a length of 634Mb (24.88% of the genome). Four bulls were among the 10 animals with the longest extension of ROH, presenting 11% of ROH with length higher than 10Mb. Segments longer than 10Mb indicate recent inbreeding. Therefore, the results indicate an intensive use of few sires in the studied data. The distribution of ROH along the chromosomes showed that chromosomes 5 and 6 presented a large number of segments when compared to other chromosomes. The mean, median, minimum, and maximum inbreeding coefficients were 5.84%, 5.40%, 0.00%, and 24.88%, respectively. Although the mean inbreeding was considered low, the ROH indicates a recent and intensive use of few sires, which should be avoided for the genetic progress of breed.

Keywords: autozygosity, Bos taurus indicus, genomic information, single nucleotide polymorphism

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Authors: Adrienne Kline, Jaydip Desai

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Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

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Authors: C. Bay, A. Mahr, H. Groneberg, F. Döpper

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Additive Manufacturing processes are becoming increasingly established in the industry for the economic production of complex prototypes and functional components. Laser beam melting (LBM), the most frequently used Additive Manufacturing technology for metal parts, has been gaining in industrial importance for several years. The LBM process chain – from material storage to machine set-up and component post-processing – requires many manual operations. These steps often depend on the manufactured component and are therefore not standardized. These operations are often not performed in a standardized manner, but depend on the experience of the machine operator, e.g., levelling of the build plate and adjusting the first powder layer in the LBM machine. This lack of standardization limits the reproducibility of the component quality. When processing metal powders with inhalable and alveolar particle fractions, the machine operator is at high risk due to the high reactivity and the toxic (e.g., carcinogenic) effect of the various metal powders. Faulty execution of the operation or unintentional omission of safety-relevant steps can impair the health of the machine operator. In this paper, all the steps of the LBM process chain are first analysed in terms of their influence on the two aforementioned challenges: reproducibility and work safety. Standardization to avoid errors increases the reproducibility of component quality as well as the adherence to and correct execution of safety-relevant operations. The corresponding lean method 5S will therefore be applied, in order to develop approaches in the form of recommended actions that standardize the work processes. These approaches will then be evaluated in terms of ease of implementation and their potential for improving reproducibility and work safety. The analysis and evaluation showed that sorting tools and spare parts as well as standardizing the workflow are likely to increase reproducibility. Organizing the operational steps and production environment decreases the hazards of material handling and consequently improves work safety.

Keywords: additive manufacturing, lean production, reproducibility, work safety

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Authors: Mahsa Oroojeni Mohammad Javad

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Globalization of economic activity and rapid growth of information technology has resulted in shorter product lifecycles, reduced transport capacity, dynamic and changing customer behaviors, and an increased focus on supply chain design in recent years. The design of the supply chain network is one of the most important supply chain management decisions. These decisions will have a long-term impact on the efficacy and efficiency of the supply chain. In this paper, a two-objective mixed-integer linear programming (MILP) model is developed for designing and optimizing a closed-loop green supply chain network that, to the greatest extent possible, includes all real-world assumptions such as multi-level supply chain, the multiplicity of production technologies, and multiple modes of transportation, with the goals of minimizing the total cost of the chain (first objective) and minimizing total emissions of emissions (second objective). The ε-constraint and CPLEX Solver have been used to solve the problem as a single-objective problem and validate the problem. Finally, the sensitivity analysis is applied to study the effect of the real-world parameters’ changes on the objective function. The optimal management suggestions and policies are presented.

Keywords: closed-loop supply chain, multi-level green supply chain, mixed-integer programming, transportation modes

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Authors: Adekunle Taiwo Adenike, I. K. Ogundoyin

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Lassa fever is a neglected tropical virus that has become a significant public health issue in Nigeria, with the country having the greatest burden in Africa. This paper presents a Geo-Computational Model for Analysis and Prediction of Lassa Fever Dynamics and Outbreaks in Nigeria. The model investigates the dynamics of the virus with respect to environmental factors and human populations. It confirms the role of the rodent host in virus transmission and identifies how climate and human population are affected. The proposed methodology is carried out on a Linux operating system using the OSGeoLive virtual machine for geographical computing, which serves as a base for spatial ecology computing. The model design uses Unified Modeling Language (UML), and the performance evaluation uses machine learning algorithms such as random forest, fuzzy logic, and neural networks. The study aims to contribute to the control of Lassa fever, which is achievable through the combined efforts of public health professionals and geocomputational and machine learning tools. The research findings will potentially be more readily accepted and utilized by decision-makers for the attainment of Lassa fever elimination.

Keywords: geo-computational model, lassa fever dynamics, lassa fever, outbreak prediction, nigeria

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Authors: K. R. Sultana, K. Pope, Y. S. Muzychka

Abstract:

In recent years, the research of heat transfer or phase change phenomena of liquid water droplets experiences a growing interest in aircraft icing, power transmission line icing, marine icing and wind turbine icing applications. This growing interest speeding up the research from single to multiple droplet phenomena. Impingements of multiple droplets and the resulting solidification phenomena after impact on a very cold surface is computationally studied in this paper. The model used in the current study solves the flow equation, composed of energy balance and the volume fraction equations. The main aim of the study is to investigate the effects of several thermo-physical properties (density, thermal conductivity and specific heat) on droplets freezing. The outcome is examined by various important factors, for instance, liquid fraction, total freezing time, droplet temperature and total heat transfer rate in the interface region. The liquid fraction helps to understand the complete phase change phenomena during solidification. Temperature distribution and heat transfer rate help to demonstrate the overall thermal exchange behaviors between the droplets and substrate surface. Findings of this research provide an important technical achievement for ice modeling and prediction studies.

Keywords: droplets, CFD, thermos-physical properties, solidification

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Authors: Ahmed Fawzy, Khaled Ayad, Gh. M. Koura, W. Reda

Abstract:

The excessive frontal plane motion of the lower extremity during sports activities is thought to be a contributing factor to many traumatic and overuse injuries of the knee joint, little is known about the biomechanical factors that contribute to this loading pattern. Objectives: The purpose of this study was to investigate if there is a relationship between hip external rotators isometric strength and the value of frontal plane projection angle (FPPA) during single leg landing tasks in normal male subjects. Methods: One hundred (male) subjects free from lower extremity injuries for at least six months ago participated in this study. Their mean age was (23.25 ± 2.88) years, mean weight was (74.76 ± 13.54) (Kg), mean height was (174.23 ± 6.56) (Cm). The knee frontal plane projection angle was measured by digital video camera using single leg landing task. Hip external rotators isometric strength were assessed by portable hand held dynamometer. Muscle strength had been normalized to the body weight to obtain more accurate measurements. Results: The results demonstrated that there was no significant relationship between hip external rotators isometric strength and the value of FPPA during single leg landing tasks in normal male subjects. Conclusion: It can be concluded that there is no relationship between hip external rotators isometric strength and the value of FPPA during functional activities in normal male subjects.

Keywords: 2-dimensional motion analysis, hip strength, kinematics, knee injuries

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Authors: Soheila Sadeghi

Abstract:

Accurate forecasting of project performance metrics is crucial for successfully managing and delivering urban road reconstruction projects. Traditional methods often rely on static baseline plans and fail to consider the dynamic nature of project progress and external factors. This research proposes a machine learning-based approach to forecast project performance metrics, such as cost variance and earned value, for each Work Breakdown Structure (WBS) category in an urban road reconstruction project. The proposed model utilizes time series forecasting techniques, including Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) networks, to predict future performance based on historical data and project progress. The model also incorporates external factors, such as weather patterns and resource availability, as features to enhance the accuracy of forecasts. By applying the predictive power of machine learning, the performance forecasting model enables proactive identification of potential deviations from the baseline plan, which allows project managers to take timely corrective actions. The research aims to validate the effectiveness of the proposed approach using a case study of an urban road reconstruction project, comparing the model's forecasts with actual project performance data. The findings of this research contribute to the advancement of project management practices in the construction industry, offering a data-driven solution for improving project performance monitoring and control.

Keywords: project performance forecasting, machine learning, time series forecasting, cost variance, earned value management

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Authors: R. Akbari, M. A. Ehyaei, R. Shahi Shavvon

Abstract:

Nowadays, solar energy is used for energy purposes such as the use of thermal energy for domestic, industrial and power applications, as well as the conversion of the sunlight into electricity by photovoltaic cells. In this study, the thermodynamic simulation of the solar Rankin cycle with phase change material (paraffin) was first studied. Then energy and exergy analyses were performed. For optimization, a single and multi-objective genetic optimization algorithm to maximize thermal and exergy efficiency was used. The parameters discussed in this paper included the effects of input pressure on turbines, input mass flow to turbines, the surface of converters and collector angles on thermal and exergy efficiency. In the organic Rankin cycle, where solar energy is used as input energy, the fluid selection is considered as a necessary factor to achieve reliable and efficient operation. Therefore, silicon oil is selected for a high-temperature cycle and water for a low-temperature cycle as an operating fluid. The results showed that increasing the mass flow to turbines 1 and 2 would increase thermal efficiency, while it reduces and increases the exergy efficiency in turbines 1 and 2, respectively. Increasing the inlet pressure to the turbine 1 decreases the thermal and exergy efficiency, and increasing the inlet pressure to the turbine 2 increases the thermal efficiency and exergy efficiency. Also, increasing the angle of the collector increased thermal efficiency and exergy. The thermal efficiency of the system was 22.3% which improves to 33.2 and 27.2% in single-objective and multi-objective optimization, respectively. Also, the exergy efficiency of the system was 1.33% which has been improved to 1.719 and 1.529% in single-objective and multi-objective optimization, respectively. These results showed that the thermal and exergy efficiency in a single-objective optimization is greater than the multi-objective optimization.

Keywords: exergy analysis, genetic algorithm, rankine cycle, single and multi-objective function

Procedia PDF Downloads 132

Authors: Nicholas V. Scott, Jack McCarthy

Abstract:

Military, law enforcement, and counter terrorism organizations are often tasked with target detection and image characterization of scenes containing explosive materials in various types of environments where light scattering intensity is high. Mitigation of this photonic noise using classical digital filtration and signal processing can be difficult. This is partially due to the lack of robust image processing methods for photonic noise removal, which strongly influence high resolution target detection and machine learning-based pattern recognition. Such analysis is crucial to the delivery of reliable intelligence. Polarization filters are a possible method for ambient glare reduction by allowing only certain modes of the electromagnetic field to be captured, providing strong scene contrast. An experiment was carried out utilizing a polarization lens attached to a hyperspectral imagery camera for the purpose of exploring the degree to which an imaged polarized scene of potassium, phosphorus, and nitrogen mixture allows for improved target detection and image segmentation. Preliminary imagery results based on the application of machine learning algorithms, including competitive leaky learning and distance metric analysis, to polarized hyperspectral imagery, suggest that polarization filters provide a slight advantage in image segmentation. The results of this work have implications for understanding the presence of explosive material in dry, desert areas where reflective glare is a significant impediment to scene characterization.

Keywords: explosive material, hyperspectral imagery, image segmentation, machine learning, polarization

Procedia PDF Downloads 124