Search results for: top load washing machine

Authors: Marco Correa

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The increasing reduction of the volumes of surface water sources supplying most municipalities, as well as the rising demand for treated water, combined with the disposal of effluents from washing of decanters and filters of water treatment plants generates a continuous search for correct environmentally solutions to these problems. The effluents generated by the water treatment industry need to be suitably processed for return to the environment or re-use. This article shows alternatives for sludge dehydration from the water treatment plants (WTP) and eventual disposal of sludge drained. Using the simple design methodology, it is presented a case study for drainage in tanks geotextile, full-scale, which involve five sledge drainage tanks from WTP of the city of Rio Verde. Aiming to the reutilization of drained water from the sledge and enabling its reuse both at the beginning of the treatment process at the WTP and in less noble services as for watering the gardens of the local town hall. The sludge will be used to in the production of building materials.

Keywords: dehydration, effluent discharges, re-use, sludge, WTP sludge

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Authors: Haftay Abraha Tadesse

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Background: Staphylococcus is a genus of worldwide distributed bacteria correlated to several infectious of different sites in humans and animals. They are among the most important causes of infection that are associated with the consumption of contaminated food. Objective: The objective of this study was to determine the isolates, antimicrobial susceptibility patterns and Public Health Significance of Staphylococcus aureus in raw meat from butchery and abattoir houses of Mekelle, Northern Ethiopia. Methodology: A cross-sectional study was conducted from April to October 2019. Socio-demographic data and Public Health Significance were collected using a predesigned questionnaire. The raw meat samples were collected aseptically in the butchery and abattoir houses and transported using an ice box to Mekelle University, College of Veterinary Sciences, for isolating and identification of Staphylococcus aureus. Antimicrobial susceptibility tests were determined by the disc diffusion method. Data obtained were cleaned and entered into STATA 22.0 and a logistic regression model with odds ratio was calculated to assess the association of risk factors with bacterial contamination. A P-value < 0.05 was considered statistically significant. Results: In the present study, 88 out of 250 (35.2%) were found to be contaminated with Staphylococcus aureus. Among the raw meat specimens, the positivity rate of Staphylococcus aureus was 37.6% (n=47) and (32.8% (n=41), butchery and abattoir houses, respectively. Among the associated risks, factories not using gloves reduces risk was found to (AOR=0.222; 95% CI: 0.104-0.473), Strict Separation b/n clean & dirty (AOR= 1.37; 95% CI: 0.66-2.86) and poor habit of hand washing (AOR=1.08; 95%CI: 0.35 3.35) was found to be statistically significant and have associated with Staphylococcus aureus contamination. All isolates of thirty-seven of Staphylococcus aureus were checked and displayed (100%) sensitive to doxycycline, trimethoprim, gentamicin, sulphamethoxazole, amikacin, CN, Co trimoxazole and nitrofurantoi. Whereas the showed resistance to cefotaxime (100%), ampicillin (87.5%), Penicillin (75%), B (75%), and nalidixic acid (50%) from butchery houses. On the other hand, all isolates of Staphylococcus aureus isolate 100% (n= 10) showed sensitive chloramphenicol, gentamicin and nitrofurantoin, whereas they showed 100% resistance of Penicillin, B, AMX, ceftriaxone, ampicillin and cefotaxime from abattoirs houses. The overall multi-drug resistance pattern for Staphylococcus aureus was 90% and 100% of butchery and abattoir houses, respectively. Conclusion: 35.3% Staphylococcus aureus isolated were recovered from the raw meat samples collected from the butchery and abattoirs houses. More has to be done in the development of hand washing behavior and availability of safe water in the butchery houses to reduce the burden of bacterial contamination. The results of the present finding highlight the need to implement protective measures against the levels of food contamination and alternative drug options. The development of antimicrobial resistance is nearly always a result of repeated therapeutic and/or indiscriminate use of them. Regular antimicrobial sensitivity testing helps to select effective antibiotics and to reduce the problems of drug resistance development towards commonly used antibiotics.

Keywords: abattoir house, AMR, butchery house, S. aureus

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Authors: Parthiban S. R.

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Spiral heat exchangers are known as excellent heat exchanger because of far compact and high heat transfer efficiency. An innovative spiral heat exchanger based on polymer materials is designed for waste heat recovery process. Such a design based on polymer film technology provides better corrosion and chemical resistance compared to conventional metal heat exchangers. Due to the smooth surface of polymer film fouling is reduced. A new arrangement for flow of hot flue gas and cold fluid is employed for design, flue gas flows in axial path while the cold fluid flows in a spiral path. Heat load recovery achieved with the presented heat exchanger is in the range of 1.5 kW thermic but potential heat recovery about 3.5 kW might be achievable. To measure the performance of the spiral tube heat exchanger, its model is suitably designed and fabricated so as to perform experimental tests. The paper gives analysis of spiral tube heat exchanger.

Keywords: spiral heat exchanger, polymer based materials, fouling factor, heat load

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Authors: Brian Tung, Yam Wing Siu, Tsun Se Cheong

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Purpose: This research aims to explore how personal and environmental factors, especially the socio-economic changes and behavioral changes fostered by the COVID-19 outbreak pandemic, affect the financial vulnerability of a specific segment of people in financial distress. Innovative research methodology of machine learning will be applied to data collected from over 300 local individuals in Hong Kong seeking counseling or similar services in recent years. Results: First, machine learning has found that too much exposure to digital services and information on digitized services may lead to adverse effects on respondents’ financial vulnerability. Second, the improvement in financial literacy level provides benefits to the financially vulnerable group, especially those respondents who have started with a lower level. Third, serious addiction to digital technology can lead to worsened debt servicing ability. Machine learning also has found a strong correlation between debt servicing situations and income-seeking behavior as well as spending behavior. In addition, if the vulnerable groups are able to make appropriate investments, they can reduce the probability of incurring financial distress. Finally, being too active in borrowing and repayment can result in a higher likelihood of over-indebtedness. Conclusion: Findings can be employed in formulating a better counseling strategy for professionals. Debt counseling services can be more preventive in nature. For example, according to the findings, with a low level of financial literacy, the respondents are prone to overspending and unable to react properly to the e-marketing promotion messages pop-up from digital services or even falling into financial/investment scams. In addition, people with low levels of financial knowledge will benefit from financial education. Therefore, financial education programs could include tech-savvy matters as special features.

Keywords: personal finance, digitization of the economy, COVID-19 pandemic, addiction to digital technology, financial vulnerability

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Authors: Ehsan Mehryaar

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The rate of penetration is (RoP) one of the vital factors in the cost and time of tunnel boring projects; therefore, predicting it can lead to a substantial increase in the efficiency of the project. RoP is heavily dependent geological properties of the project site and TBM properties. In this study, 151-point data from Queen’s water tunnel is collected, which includes unconfined compression strength, peak slope index, angle with weak planes, and distance between planes of weaknesses. Since the size of the data is small, it was observed that it is imbalanced. To solve that problem synthetic minority oversampling technique is utilized. The model based on the model tree is proposed, where each leaf consists of a support vector machine model. Proposed model performance is then compared to existing empirical equations in the literature.

Keywords: Model tree, SMOTE, rate of penetration, TBM(tunnel boring machine), SVM

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Authors: J. BalaRaju, M. Govinda Raj, C. S. N. Murthy

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Every organization in the competitive world tends to improve its economy by increasing their production and productivity rates. Unequivocally, the production in Indian underground mines over the years is not satisfactory, due to a variety of reasons. There are manifold of avenues for the betterment of production, and one such approach is through enhanced utilization of mechanized equipment such as Load Haul Dumper (LHD). This is used as loading and hauling purpose in underground mines. In view of the aforementioned facts, this paper delves into identification of the key influencing factors such as LHDs maintenance effectiveness, vehicle condition, operator skill and utilization of the machines on performance of LHDs. An attempt has been made for improvement of performance of the equipment through evaluation of Overall Equipment Effectiveness (OEE). Two different approaches for evaluation of OEE have been adopted and compared under various operating conditions. The use of OEE calculation in terms of percentage availability, performance and quality and the hitherto existing situation of the underground mine production is evaluated. Necessary recommendations are suggested to mining industry on the basis of OEE.

Keywords: utilization, maintenance, availability, performance and quality

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Authors: Soonwon Kang, Jinwoong Choi, Sungnam Hong, Seung-Kyung Kye, Sun-Kyu Park

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The modular bridge is to be constructed by assembling standardized precast segments. This bridge is classified as a slab type and T-girder type. The T-girder bridge has transverse joint. However, it did not perform the verification on the transverse joint, but the slab type was done on the analytic study on the joint. Therefore, it is necessary for precast modular T-girder bridge that has a transverse joint to propose an appropriated model. In this study, specimens and analysis models compared integrated type with segmented type. Results of the integrated and segmented specimens, each of the deflection was 98.40mm and 74.66mm when the maximum load was 269.71kN and 248.29kN, in case of the modeling the specimens, each of the deflection was 84.04mm, 69.39mm when the maximum load was 269.71kN, 248.29kN, therefore, the precast T-girder modular bridges form the analytic model proposed appropriate.

Keywords: precast, T-girder modular bridge, finite element analysis, joint

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Authors: Baofeng Pan, Heng Liu

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Because of the high strength and large carrying capacity, the semi-rigid base is widely used in modern road engineering. However, hydrodynamic pressure, which is one of the main factors to cause early damage of semi-rigid base, cannot be avoided in the nature environment when pavement is subjected to some loadings such as the passing vehicles. In order to investigating how moisture-induced-damage of semi-rigid base influenced by hydrodynamic pressure, a new and effective experimental research method is provided in this paper. The results show that: (a) The washing action of high hydrodynamic pressure is the direct cause of strength reducing of road semi-rigid base. (b) The damage of high hydrodynamic pressure mainly occurs at the beginning of the scoring test and with the increasing of testing time the influence reduces. (c) Under the same hydrodynamic pressure, the longer the specimen health age, the stronger ability to resist moisture induced damage.

Keywords: semi-rigid base, hydrodynamic pressure, moisture-induced-damage, experimental research

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Authors: Ruoyang Tang, Jianguo Nie

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Lateral-torsional bracing members are critical to the stability of girder systems during the construction phase of steel-concrete composite bridges, and the interaction effect of multiple girders plays an essential role in the determination of buckling load. In this paper, an investigation is conducted on the lateral-torsional buckling behavior of the steel girder system which is composed of three or four I-shaped girders and braced by solid web crossbeams. The buckling load for such girder system is comprehensively analyzed and an analytical solution is developed for uniform pressure loading conditions. Furthermore, post-buckling analysis including initial geometric imperfections is performed and parametric studies in terms of bracing density, stiffness ratio as well as the number and spacing of girders are presented in order to find the optimal bracing plans for an arbitrary girder layout. The theoretical solution of critical load on account of local buckling mode shows good agreement with the numerical results in eigenvalue analysis. In addition, parametric analysis results show that both bracing density and stiffness ratio have a significant impact on the initial stiffness, global stability and failure mode of such girder system. Taking into consideration the effect of initial geometric imperfections, an increase in bracing density between adjacent girders can effectively improve the bearing capacity of the structure, and higher beam-girder stiffness ratio can result in a more ductile failure mode.

Keywords: bracing member, construction stage, lateral-torsional buckling, steel girder system

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Utkan Mutman, Omer Ayhan

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In highway work in a field that is selected in Kocaeli/TURKEY district to the ground improvement and piling is done. In this study, the degree of improvement was observed on the ground after the columns made of stone and bored piles in the field and compared. In this context, improving the ground before and after analysis and solution analysis made with values obtained by the finite element method, which was made Plaxis program. On the improved ground, in order to control of manufactured bored piles, continuity of bored piles and pile load tests were carried out. In addition, the test of load capacity specified in the project is made of stone columns. Test results of the soil improvement were observed to be successful, the results obtained in the field and the results obtained from Plaxis program were compared.

Keywords: bored piling, stone columns, plaxis, soil improvement

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Authors: Y. J. Chen, T. M. Yue, Z. N. Guo

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This paper reports the effects of laser energy on the characteristics of bubbles generated in the weld zone and the formation of new chemical bonds at the Polyethylene Terephthalate (PET)/Ti joint interface in laser joining of PET to Ti. The samples were produced by using different laser energies ranging from 1.5 J – 6 J in steps of 1.5 J, while all other joining parameters remained unchanged. The types of chemical bonding at the joint interface were analysed by the x-ray photoelectron spectroscopy (XPS) depth-profiling method. The results show that the characteristics of the bubbles and the thickness of the chemically bonded interface, which contains the laser generated bonds of Ti–C and Ti–O, increase markedly with increasing laser energy input. The tensile failure load of the joint depends on the combined effect of the amount and distribution of the bubbles formed and the chemical bonding intensity of the joint interface.

Keywords: laser direct joining, Ti/PET interface, laser energy, XPS depth profiling, chemical bond, tensile failure load

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Authors: Zhaoan Wang

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Research in predictive maintenance modeling has improved in the recent years to predict failures and needed maintenance with high accuracy, saving cost and improving manufacturing efficiency. However, classic prediction models provide little valuable insight towards the most important features contributing to the failure. By analyzing and quantifying feature importance in predictive maintenance models, cost saving can be optimized based on business goals. First, multiple classifiers are evaluated with cross-validation to predict the multi-class of failures. Second, predictive performance with features provided by different feature selection algorithms are further analyzed. Third, features selected by different algorithms are ranked and combined based on their predictive power. Finally, linear explainer SHAP (SHapley Additive exPlanations) is applied to interpret classifier behavior and provide further insight towards the specific roles of features in both local predictions and global model behavior. The results of the experiments suggest that certain features play dominant roles in predictive models while others have significantly less impact on the overall performance. Moreover, for multi-class prediction of machine failures, the most important features vary with type of machine failures. The results may lead to improved productivity and cost saving by prioritizing sensor deployment, data collection, and data processing of more important features over less importance features.

Keywords: automated supply chain, intelligent manufacturing, predictive maintenance machine learning, feature engineering, model interpretation

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Authors: Mostefa Mimoune

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Test results on concrete-filled steel tubular stub columns under axial compression are presented. The study was mainly focused on square hollow section SHS columns; 27 columns were tested. The main experimental parameters considered were the thickness of the tube, columns length and cross section sizes. Existing design codes and theoretical model were used to predict load-carrying capacities of composite section to compare the accuracy of the predictions by using the recommendations of DTR-BC (Algerian code), CSA (Canadian standard), AIJ, EC4, DBJ, AISC, BS and EC4. Experimental results indicate that the studied parameters have significant influence on both the compressive load capacity and the column failure mode. All codes used in the comparison, provide higher resistance compared to those of tests. Equation method has been suggested to evaluate the axial capacity of the composite section seem to be in agreement with tests.

Keywords: axial loading, composite section, concrete-filled steel tubes, square hollow section

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Authors: R. K. Chaurasiya, N. D. Londhe, S. Ghosh

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The study of the electrical signals produced by neural activities of human brain is called Electroencephalography. In this paper, we propose an automatic and efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. In the proposed approach, we start with extracting the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the support-vectors using Support Vector Machine (SVM). The experimental are performed on real and standard dataset. A very high level of classification accuracy is obtained in the result of classification.

Keywords: discrete wavelet transform, electroencephalogram, pattern recognition, principal component analysis, support vector machine

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Authors: Azita Ramezani, Ghazal Mashhadiagha, Bahareh Sanabakhsh

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This study researches the combination of Random. Forest classifiers with large language models (LLMs) and natural language processing (NLP) to improve diagnostic accuracy in chest X-ray analysis using the Indiana University dataset. Utilizing advanced NLP techniques, the research preprocesses textual data from radiological reports to extract key features, which are then merged with image-derived data. This improved dataset is analyzed with Random Forest classifiers to predict specific clinical results, focusing on the identification of health issues and the estimation of case urgency. The findings reveal that the combination of NLP, LLMs, and machine learning not only increases diagnostic precision but also reliability, especially in quickly identifying critical conditions. Achieving an accuracy of 99.35%, the model shows significant advancements over conventional diagnostic techniques. The results emphasize the large potential of machine learning in medical imaging, suggesting that these technologies could greatly enhance clinician judgment and patient outcomes by offering quicker and more precise diagnostic approximations.

Keywords: natural language processing (NLP), large language models (LLMs), random forest classifier, chest x-ray analysis, medical imaging, diagnostic accuracy, indiana university dataset, machine learning in healthcare, predictive modeling, clinical decision support systems

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Authors: Kasim Görenekli, Ali Gülbağ

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This study presents a comprehensive analysis of water consumption patterns in Kocaeli province, Turkey, utilizing various machine learning approaches. We analyzed data from 5,000 water subscribers across residential, commercial, and official categories over an 80-month period from January 2016 to August 2022, resulting in a total of 400,000 records. The dataset encompasses water consumption records, weather information, weekends and holidays, previous months' consumption, and the influence of the COVID-19 pandemic.We implemented and compared several machine learning models, including Linear Regression, Random Forest, Support Vector Regression (SVR), XGBoost, Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Gated Recurrent Units (GRU). Particle Swarm Optimization (PSO) was applied to optimize hyperparameters for all models.Our results demonstrate varying performance across subscriber types and models. For official subscribers, Random Forest achieved the highest R² of 0.699 with PSO optimization. For commercial subscribers, Linear Regression performed best with an R² of 0.730 with PSO. Residential water usage proved more challenging to predict, with XGBoost achieving the highest R² of 0.572 with PSO.The study identified key factors influencing water consumption, with previous months' consumption, meter diameter, and weather conditions being among the most significant predictors. The impact of the COVID-19 pandemic on consumption patterns was also observed, particularly in residential usage.This research provides valuable insights for effective water resource management in Kocaeli and similar regions, considering Turkey's high water loss rate and below-average per capita water supply. The comparative analysis of different machine learning approaches offers a comprehensive framework for selecting appropriate models for water consumption prediction in urban settings.

Keywords: mMachine learning, water consumption prediction, particle swarm optimization, COVID-19, water resource management

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Authors: Houxiang Zhu, Chun Liang

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The CRISPR-Cpf1 system has been successfully applied in genome editing. However, target efficiency of the CRISPR-Cpf1 system varies among different gRNA sequences. The published CRISPR-Cpf1 gRNA data was reanalyzed. Many sequences and structural features of gRNAs (e.g., the position-specific nucleotide composition, position-nonspecific nucleotide composition, GC content, minimum free energy, and melting temperature) correlated with target efficiency were found. Using machine learning technology, a support vector machine (SVM) model was created to predict target efficiency for any given gRNAs. The first web service application, CRISPR-DT (CRISPR DNA Targeting), has been developed to help users design optimal gRNAs for the CRISPR-Cpf1 system by considering both target efficiency and specificity. CRISPR-DT will empower researchers in genome editing.

Keywords: CRISPR-Cpf1, genome editing, target efficiency, target specificity

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Authors: Tigist Girma Kidane, Yalew Dessalegn Asfaw

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Abstract: The current investigation aims to measure the longitudinal and transversal three-point bending tests of bamboo fibres polypropylene composites (BFPPCs) for the application of the automobile industry. Research has not been done on the properties of Ethiopian bamboo fibres for the utilization of composite development. The samples of bamboo plants have been harvested in 3–groups of age, 2–harvesting seasons, and 3–regions of bamboo species. Roll milling machine used for the extraction of bamboo fibres which has been developed by the authors. Chemical constituents measured using gravimetric methods. Unidirectional bamboo fibres prepreg has been produced using PP and hot press machine, then BFPPCs were produced using 6 layers of prepregs at automatic hot press machine. Age, harvesting month, and bamboo species have a statistically significant effect on the longitudinal and transverse flexural strength (FS), modulus of elasticity (MOE), and failure strain at α = 0.05 as evaluated by one-way ANOVA. 2–yrs old of BFPPCs have the highest FS and MOE, whereas November has the highest value of flexural properties. The highest to the lowest FS and MOE of BFPPCs has measured in Injibara, Mekaneselam, and Kombolcha, respectively. The transverse 3-point bending test has a lower FS and MOE compared to the longitudinal direction. The chemical constituents of Injibara, Mekaneselam, and Kombolcha have the highest to the lowest, respectively. 2-years old of bamboo fibres has the highest chemical constituent. The chemical constituents improved the flexural properties. Bamboo fibres in Ethiopia can be relevant for composite development, which has been applied in the area of requiring higher flexural properties.

Keywords: age, bamboo species, flexural properties, harvesting season, polypropylene

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Authors: Asal Pournaghshband, Mohammed A. Zaki

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This paper presents a detailed numerical investigation into the structural performance of splay anchor configurations for strengthening concrete beams with Carbon Fiber Reinforced Polymer (CFRP) sheets. CFRP is widely used in retrofitting concrete structures to improve flexural strength and extend service life. However, premature debonding limits the tensile capacity of CFRP sheets, reducing the effectiveness of these applications. This study addresses this limitation by exploring the potential of splay anchors as an emerging anchorage technique that mitigates debonding issues through improved load transfer mechanisms. Building on existing experimental studies, the research uses ABAQUS software to validate different splay anchor configurations and simulate real-world performance. The parametric study examines key anchor parameters, including diameter, spacing, and embedment depth, to evaluate their effects on bond strength, load distribution, and the flexural capacity of strengthened beams. Systematic analysis of these parameters allows for identifying configurations that enhance debonding resistance and increase the load-carrying capacity of CFRP-strengthened beams. Improved debonding resistance contributes to greater structural durability, reduced maintenance costs, and extended service life for retrofitted structures, particularly relevant for aging infrastructure like bridges and buildings. This approach not only advances sustainable retrofitting practices but also provides practical solutions tailored to infrastructure demands.

Keywords: CFRP strengthening, splay anchors, concrete beams, structural retrofitting, numerical analysis

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Authors: H. A. Alguhi, W. A. Elsaigh

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This paper provides a tensile stress-strain (σ-ε) relationship for High-Strength Steel Fiber Reinforced Concrete (HSFRC). Load-deflection (P-δ) behavior of HSFRC beams tested under four-point flexural load were used with inverse analysis to calculate the tensile σ-ε relationship for various tested concrete grades (70 and 90MPa) containing 60 kg/m3 (0.76 %) of hook-end steel fibers. A first estimate of the tensile (σ-ε) relationship is obtained using RILEM TC 162-TDF and other methods available in literature, frequently used for determining tensile σ-ε relationship of Normal-Strength Concrete (NSC) Non-Linear Finite Element Analysis (NLFEA) package ABAQUS® is used to model the beam’s P-δ behavior. The results have shown that an element-size dependent tensile σ-ε relationship for HSFRC can be successfully generated and adopted for further analyzes involving HSFRC structures.

Keywords: tensile stress-strain, flexural response, high strength concrete, steel fibers, non-linear finite element analysis

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Authors: Pavan K. Emani, Shashank Kothari, V. S. Phanikanth

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The ultimate load analysis of RC pile groups has assumed a lot of significance under liquefying soil conditions, especially due to post-earthquake studies of 1964 Niigata, 1995 Kobe and 2001 Bhuj earthquakes. The present study reports the results of numerical simulations on pile groups subjected to monotonically increasing lateral loads under design amounts of pile axial loading. The soil liquefaction has been considered through the non-linear p-y relationship of the soil springs, which can vary along the depth/length of the pile. This variation again is related to the liquefaction potential of the site and the magnitude of the seismic shaking. As the piles in the group can reach their extreme deflections and rotations during increased amounts of lateral loading, a precise modeling of the inelastic behavior of the pile cross-section is done, considering the complete stress-strain behavior of concrete, with and without confinement, and reinforcing steel, including the strain-hardening portion. The possibility of the inelastic buckling of the individual piles is considered in the overall collapse modes. The model is analysed using Riks analysis in finite element software to check the post buckling behavior and plastic collapse of piles. The results confirm the kinds of failure modes predicted by centrifuge test results reported by researchers on pile group, although the pile material used is significantly different from that of the simulation model. The extension of the present work promises an important contribution to the design codes for pile groups in liquefying soils.

Keywords: collapse load analysis, inelastic buckling, liquefaction, pile group

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Authors: David Rafetseder, Walter Bauer, Florian Poltschak, Wolfgang Amrhein

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A flat double-stator linear induction machine (DSLIM) with a short squirrel-cage mover is designed for high thrust force at moderate speed < 5m/s. The performance and motor parameters are determined on the basis of a 2D time-transient simulation with the finite element (FE) software Maxwell 2015. Design guidelines and transformation rules for space vector theory of the LIM are presented. Resulting thrust calculated by flux and current vectors is compared with the FE results showing good coherence and reduced noise. The parameters of the equivalent circuit model are obtained.

Keywords: equivalent circuit model, finite element model, linear induction motor, space vector theory

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Authors: Khin Fai Chen, Jee-Hou Ho, Eng Hwa Yap

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An acoustic micro-energy harvester (AMEH) is developed to convert wasted acoustical energy into useful electrical energy. AMEH is mathematically modeled using lumped element modelling (LEM) and Euler-Bernoulli beam (EBB) modelling. An experiment is designed to validate the mathematical model and assess the feasibility of AMEH. Comparison of theoretical and experimental data on critical parameter value such as Mm, Cms, dm and Ceb showed the variances are within 1% to 6%, which is reasonably acceptable. Hence, AMEH mathematical model is validated. Then, AMEH undergoes bandwidth tuning for performance optimization for further experimental work. The AMEH successfully produces 0.9 V⁄(m⁄s^2) and 1.79 μW⁄(m^2⁄s^4) at 60Hz and 400kΩ resistive load which only show variances about 7% compared to theoretical data. By integrating a capacitive load of 200µF, the discharge cycle time of AMEH is 1.8s and the usable energy bandwidth is available as low as 0.25g. At 1g and 60Hz resonance frequency, the averaged power output is about 2.2mW which fulfilled a range of wireless sensors and communication peripherals power requirements. Finally, the design for AMEH is assessed, validated and deemed as a feasible design.

Keywords: piezoelectric, acoustic, energy harvester

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Authors: Umut Çınar

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The fact that ozone gas has color bleaching properties has made the use of ozone gas widespread in the textile sector as well as in many other sectors. Ozone gas, which is a strong oxidative agent on the fabric, causes the paint on the fabric to wear off and lighten its color with an aged appearance. Within the scope of this thesis, parameters affecting the bleaching properties of ozone gas on reactive dyed knitted fabric, which is rare in the literature, were investigated. Ozone concentration, time, and pH values were analyzed with the Box Behnken experimental design method, and optimum conditions were determined. After the experiments, wear and opacity values were measured with the help of a spectrophotometer. With the help of the Design Expert program, the graphics related to the data were prepared and interpreted with Box Behnken and ANOVA. These experiments on reactive dyed knitted fabric were tested on these parameters, and the spectrophotometric values of the fabric and optimum parameters in abrasion and opacity were revealed.

Keywords: ozone, reactive dye, bleaching, textile, garment wash, sustainability, washing, Box–Behnken, experimental design

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Rishabh Ambavanekar

Abstract:

Expressive Aphasia (EA) is an oral disability, common among stroke victims, in which the Broca’s area of the brain is damaged, interfering with verbal communication abilities. EA currently has no technological solutions and its only current viable solutions are inefficient or only available to the affluent. This prompts the need for an affordable, innovative solution to facilitate recovery and assist in speech generation. This project proposes a novel concept: using a wearable low-cost electroencephalography (EEG) device-based brain-computer interface (BCI) to translate a user’s inner dialogue into words. A low-cost EEG device was developed and found to be 10 to 100 times less expensive than any current EEG device on the market. As part of the BCI, a machine learning (ML) model was developed and trained using the EEG data. Two stages of testing were conducted to analyze the effectiveness of the device: a proof-of-concept and a final solution test. The proof-of-concept test demonstrated an average accuracy of above 90% and the final solution test demonstrated an average accuracy of above 75%. These two successful tests were used as a basis to demonstrate the viability of BCI research in developing lower-cost verbal communication devices. Additionally, the device proved to not only enable users to verbally communicate but has the potential to also assist in accelerated recovery from the disorder.

Keywords: neurotechnology, brain-computer interface, neuroscience, human-machine interface, BCI, HMI, aphasia, verbal disability, stroke, low-cost, machine learning, ML, image recognition, EEG, signal analysis

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Authors: Soumya Sajjan

Abstract:

Liver cancer is the most common cancer disease worldwide in men and women, and is one of the few cancers still on the rise. Liver disease is the 4th leading cause of death. According to new NHS (National Health Service) figures, deaths from liver diseases have reached record levels, rising by 25% in less than a decade; heavy drinking, obesity, and hepatitis are believed to be behind the rise. In this study, we focus on Development of Diagnostic Classifier for Ultrasound liver lesion. Ultrasound (US) Sonography is an easy-to-use and widely popular imaging modality because of its ability to visualize many human soft tissues/organs without any harmful effect. This paper will provide an overview of underlying concepts, along with algorithms for processing of liver ultrasound images Naturaly, Ultrasound liver lesion images are having more spackle noise. Developing classifier for ultrasound liver lesion image is a challenging task. We approach fully automatic machine learning system for developing this classifier. First, we segment the liver image by calculating the textural features from co-occurrence matrix and run length method. For classification, Support Vector Machine is used based on the risk bounds of statistical learning theory. The textural features for different features methods are given as input to the SVM individually. Performance analysis train and test datasets carried out separately using SVM Model. Whenever an ultrasonic liver lesion image is given to the SVM classifier system, the features are calculated, classified, as normal and diseased liver lesion. We hope the result will be helpful to the physician to identify the liver cancer in non-invasive method.

Keywords: segmentation, Support Vector Machine, ultrasound liver lesion, co-occurance Matrix

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Authors: Hamid Oumer Seid

Abstract:

Piled raft foundation (PRF) is a union of pile and raft working together through the interaction of soil-pile, pile-raft, soil-raft and pile-pile to provide adequate bearing capacity and controlled settlement. A uniform pile positioning is used in PRF; however, there is a wide room for optimization through parametric study under vertical load to result in a safer and economical foundation. Addis Ababa is found in seismic zone 3 with a peak ground acceleration (PGA) above the threshold of damage, which makes investigating the performance of PRF under seismic load considering the dynamic kinematic soil structure interaction (SSI) vital. The study area is located in Addis Ababa around Mexico (commercial bank) and Kirkos (Nib, Zemen and United Bank) in which input parameters (pile length, pile diameter, pile spacing, raft area, raft thickness and load) are taken. A finite difference-based numerical software, FLAC3D V6, was used for the analysis. The Kobe (1995) and Northridge (1994) earthquakes were selected, and deconvolution analysis was done. A close load sharing between pile and raft was achieved at a spacing of 7D with different pile lengths and diameters. The maximum settlement reduction achieved is 9% for a pile of 2m diameter by increasing length from 10m to 20m, which shows pile length is not effective in reducing settlement. The installation of piles results in an increase in the negative bending moment of the raft compared with an unpiled raft. Hence, the optimized design depends on pile spacing and the raft edge length, while pile length and diameter are not significant parameters. An optimized piled raft configuration (𝐴𝐺/𝐴𝑅 = 0.25 at the center and piles provided around the edge) has reduced pile number by 40% and differential settlement by 95%. The dynamic analysis shows acceleration plot at the top of the piled raft has PGA of 0.25𝑚2/𝑠𝑒𝑐 and 0.63𝑚2/𝑠𝑒𝑐 for Northridge (1994) and Kobe (1995) earthquakes, respectively, due to attenuation of seismic waves. Pile head displacement (maximum is 2mm, and it is under the allowable limit) is affected by the PGA rather than the duration of an earthquake. End bearing and friction PRF performed similarly under two different earthquakes except for their vertical settlement considering SSI. Hence, PRF has shown adequate resistance to seismic loads.

Keywords: FLAC3D V6, earthquake, optimized piled raft foundation, pile head department

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Authors: Emily Wood, James K. Westphal, Itamar Lerner

Abstract:

Background: Polysomnography (PSG) recordings are regularly used in research and clinical settings to study sleep and sleep-related disorders. Typical PSG studies are conducted in professional laboratories and performed by qualified researchers. However, the number of sleep labs worldwide is disproportionate to the increasing number of individuals with sleep disorders like sleep apnea and insomnia. Consequently, there is a growing need to supply cheaper yet reliable means to measure sleep, preferably autonomously by subjects in their own home. Over the last decade, a variety of devices for self-monitoring of sleep became available in the market; however, very few have been directly validated against PSG to demonstrate their ability to perform reliable automatic sleep scoring. Two popular mobile EEG-based systems that have published validation results, the DREEM 3 headband and the Z-Machine, have never been directly compared one to the other by independent researchers. The current study aimed to compare the performance of DREEM 3 and the Z-Machine to help investigators and clinicians decide which of these devices may be more suitable for their studies. Methods: 26 participants have completed the study for credit or monetary compensation. Exclusion criteria included any history of sleep, neurological or psychiatric disorders. Eligible participants arrived at the lab in the afternoon and received the two devices. They then spent two consecutive nights monitoring their sleep at home. Participants were also asked to keep a sleep log, indicating the time they fell asleep, woke up, and the number of awakenings occurring during the night. Data from both devices, including detailed sleep hypnograms in 30-second epochs (differentiating Wake, combined N1/N2, N3; and Rapid Eye Movement sleep), were extracted and aligned upon retrieval. For analysis, the number of awakenings each night was defined as four or more consecutive wake epochs between sleep onset and termination. Total sleep time (TST) and the number of awakenings were compared to subjects’ sleep logs to measure consistency with the subjective reports. In addition, the sleep scores from each device were compared epoch-by-epoch to calculate the agreement between the two devices using Cohen’s Kappa. All analysis was performed using Matlab 2021b and SPSS 27. Results/Conclusion: Subjects consistently reported longer times spent asleep than the time reported by each device (M= 448 minutes for sleep logs compared to M= 406 and M= 345 minutes for the DREEM and Z-Machine, respectively; both ps<0.05). Linear correlations between the sleep log and each device were higher for the DREEM than the Z-Machine for both TST and the number of awakenings, and, likewise, the mean absolute bias between the sleep logs and each device was higher for the Z-Machine for both TST (p<0.001) and awakenings (p<0.04). There was some indication that these effects were stronger for the second night compared to the first night. Epoch-by-epoch comparisons showed that the main discrepancies between the devices were for detecting N2 and REM sleep, while N3 had a high agreement. Overall, the DREEM headband seems superior for reliably scoring sleep at home.

Keywords: DREEM, EEG, seep monitoring, Z-machine

Procedia PDF Downloads 106