Search results for: axial flux induction machine

Authors: Ava Dodd, Jonathan Adams

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The purpose of this study is to explore the characteristics of developing a machine learning application using synthetic data. The study is structured to develop the application for the purpose of deploying the computer vision model. The findings discuss the realities of attempting to develop a computer vision model for practical purpose, and detail the processes, tools, and techniques that were used to meet accuracy requirements. The research reveals that synthetic data represents another variable that can be adjusted to improve the performance of a computer vision model. Further, a suite of tools and tuning recommendations are provided.

Keywords: computer vision, machine learning, synthetic data, YOLOv4

Procedia PDF Downloads 226

Authors: Surupa Shaw, Debjyoti Banerjee

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Enhancing heat transfer in compact volumes is a challenge when constrained by cost issues, especially those associated with requirements for minimizing pumping power consumption. This is particularly acute for electronic chip cooling applications. Technological advancements in microelectronics have led to development of chip architectures that involve increased power consumption. As a consequence packaging, technologies are saddled with needs for higher rates of power dissipation in smaller form factors. The increasing circuit density, higher heat flux values for dissipation and the significant decrease in the size of the electronic devices are posing thermal management challenges that need to be addressed with a better design of the cooling system. Maximizing surface area for heat exchanging surfaces (e.g., extended surfaces or “fins”) can enable dissipation of higher levels of heat flux. Fractal structures have been shown to maximize surface area in compact volumes. Self-replicating structures at multiple length scales are called “Fractals” (i.e., objects with fractional dimensions; unlike regular geometric objects, such as spheres or cubes whose volumes and surface area values scale as integer values of the length scale dimensions). Fractal structures are expected to provide an appropriate technology solution to meet these challenges for enhanced heat transfer in the microelectronic devices by maximizing surface area available for heat exchanging fluids within compact volumes. In this study, the effect of different fractal micro-channel architectures and flow structures on the enhancement of transport phenomena in heat exchangers is explored by parametric variation of fractal dimension. This study proposes a model that would enable cost-effective solutions for thermal-fluid transport for energy applications. The objective of this study is to ascertain the sensitivity of various parameters (such as heat flux and pressure gradient as well as pumping power) to variation in fractal dimension. The role of the fractal parameters will be instrumental in establishing the most effective design for the optimum cooling of microelectronic devices. This can help establish the requirement of minimal pumping power for enhancement of heat transfer during cooling. Results obtained in this study show that the proposed models for fractal architectures of microchannels significantly enhanced heat transfer due to augmentation of surface area in the branching networks of varying length-scales.

Keywords: fractals, microelectronics, constructal theory, heat transfer enhancement, pumping power enhancement

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Authors: Shaan Khosla, Jon Krohn

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In a recruiting context, machine learning models are valuable for recommendations: to predict the best candidates for a vacancy, to match the best vacancies for a candidate, and compile a set of similar candidates for any given candidate. While useful to create these models, validating their accuracy in a recommendation context is difficult due to a sparsity of data. In this report, we use network graph data to generate useful representations for candidates and vacancies. We use candidates and vacancies as network nodes and designate a bi-directional link between them based on the candidate interviewing for the vacancy. After using node2vec, the embeddings are used to construct a validation dataset with a ranked order, which will help validate new recommender systems.

Keywords: AI, machine learning, NLP, recruiting

Procedia PDF Downloads 88

Authors: Igor P. Maximkin, Arkady A. Yukhimchuk, Victor V. Baluev, Igor L. Malkov, Rafael K. Musyaev, Damir T. Sitdikov, Alexey V. Buchirin, Vasily V. Tikhonov

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Minimization of tritium diffusion leakage when developing devices handling tritium-containing media is key problems whose solution will at least allow essential enhancement of radiation safety and minimization of diffusion losses of expensive tritium. One of the ways to solve this problem is to use Al₂O₃ high-strength non-porous ceramics as a structural material of the bed body. This alumina ceramics offers high strength characteristics, but its main advantages are low hydrogen permeability (as against the used structural material) and high dielectric properties. The latter enables direct induction heating of an hydride-forming metal without essential heating of the pressure and containment vessel. The use of alumina ceramics and induction heating allows: - essential reduction of tritium extraction time; - several orders reduction of tritium diffusion leakage; - more complete extraction of tritium from metal hydrides due to its higher heating up to melting in the event of final disposal of the device. The paper presents computational and experimental results for the tritium bed designed to absorb 6 liters of tritium. Titanium was used as hydrogen isotope sorbent. Results of hydrogen realize kinetic from hydride-forming metal, strength and cyclic service life tests are reported. Recommendations are also provided for the practical use of the given bed type.

Keywords: aluminum oxide ceramic, hydrogen pressure, hydrogen isotope storage, titanium hydride

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Authors: Shreyas Gambhirrao, Aditya Vichare, Aniket Tembhurne, Shahuraj Bhosale

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In today's rapidly evolving environment, stress has emerged as a significant health concern across different age groups. Stress that isn't controlled, whether it comes from job responsibilities, health issues, or the never-ending news cycle, can have a negative effect on our well-being. The problem is further aggravated by the ongoing connection to technology. In this high-tech age, identifying and controlling stress is vital. In order to solve this health issue, the study focuses on three key metrics for stress detection: body temperature, heart rate, and galvanic skin response (GSR). These parameters along with the Support Vector Machine classifier assist the system to categorize stress into three groups: 1) Stressed, 2) Not stressed, and 3) Moderate stress. Proposed training model, a NodeMCU combined with particular sensors collects data in real-time and rapidly categorizes individuals based on their stress levels. Real-time stress detection is made possible by this creative combination of hardware and software.

Keywords: real time stress detection, NodeMCU, sensors, heart-rate, body temperature, galvanic skin response (GSR), support vector machine

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Chen Wang, Chun Liang

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Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite (MS) length due to a deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for tumor prognostic. Conventional clinical diagnosis of MSI examines PCR products of a panel of MS markers using electrophoresis (MSI-PCR) which is laborious, time consuming, and less reliable. MSIpred, a python 2 package for automatic classification of MSI was released by this study. It computes important somatic mutation features from files in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these to predict tumor MSI status with a support vector machine (SVM) classifier trained by MAF files of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent 358-tumor test set achieved overall accuracy of over 98% and area under receiver operating characteristic (ROC) curve of 0.967. These results indicated that MSIpred is a robust pan-cancer MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.

Keywords: microsatellite instability, pan-cancer classification, somatic mutation, support vector machine

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Authors: Yan Zhang

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Predictive maintenance is a technique to predict when an in-service machine will fail so that maintenance can be planned in advance. Analytics-driven predictive maintenance is gaining increasing attention in many industries such as manufacturing, utilities, aerospace, etc., along with the emerging demand of Internet of Things (IoT) applications and the maturity of technologies that support Big Data storage and processing. This study aims to build an end-to-end analytics solution that includes both real-time machine condition monitoring and machine learning based predictive analytics capabilities. The goal is to showcase a general predictive maintenance solution architecture, which suggests how the data generated from field machines can be collected, transmitted, stored, and analyzed. We use a publicly available aircraft engine run-to-failure dataset to illustrate the streaming analytics component and the batch failure prediction component. We outline the contributions of this study from four aspects. First, we compare the predictive maintenance problems from the view of the traditional reliability centered maintenance field, and from the view of the IoT applications. When evolving to the IoT era, predictive maintenance has shifted its focus from ensuring reliable machine operations to improve production/maintenance efficiency via any maintenance related tasks. It covers a variety of topics, including but not limited to: failure prediction, fault forecasting, failure detection and diagnosis, and recommendation of maintenance actions after failure. Second, we review the state-of-art technologies that enable a machine/device to transmit data all the way through the Cloud for storage and advanced analytics. These technologies vary drastically mainly based on the power source and functionality of the devices. For example, a consumer machine such as an elevator uses completely different data transmission protocols comparing to the sensor units in an environmental sensor network. The former may transfer data into the Cloud via WiFi directly. The latter usually uses radio communication inherent the network, and the data is stored in a staging data node before it can be transmitted into the Cloud when necessary. Third, we illustrate show to formulate a machine learning problem to predict machine fault/failures. By showing a step-by-step process of data labeling, feature engineering, model construction and evaluation, we share following experiences: (1) what are the specific data quality issues that have crucial impact on predictive maintenance use cases; (2) how to train and evaluate a model when training data contains inter-dependent records. Four, we review the tools available to build such a data pipeline that digests the data and produce insights. We show the tools we use including data injection, streaming data processing, machine learning model training, and the tool that coordinates/schedules different jobs. In addition, we show the visualization tool that creates rich data visualizations for both real-time insights and prediction results. To conclude, there are two key takeaways from this study. (1) It summarizes the landscape and challenges of predictive maintenance applications. (2) It takes an example in aerospace with publicly available data to illustrate each component in the proposed data pipeline and showcases how the solution can be deployed as a live demo.

Keywords: Internet of Things, machine learning, predictive maintenance, streaming data

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Authors: Sunthorn Sittisakuljaroen

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The edge waviness in hot rolled steel is a common defect. Variables that effect for such defect include as raw material and machine. These variables are necessary to consider. This research studied the defect of edge waviness for SS 400 of metal sheet manufacture. Defect of metal sheets divided into two groups. The specimens were investigated on chemical composition and mechanical properties to find the difference. The results of investigate showed that not different to a standard significantly. Therefore the roll milled machine for sample need to adjustable rollers for press on metal sheet which was more appropriate to adjustable at both ends.

Keywords: edge waviness, hot rolling steel, metal sheet defect, SS 400, roll leveller

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Authors: James Ladzekpo

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Background: The urgent need to identify new pharmacological targets for diabetes treatment and prevention has been amplified by the disease's extensive impact on individuals and healthcare systems. A deeper insight into the biological underpinnings of diabetes is crucial for the creation of therapeutic strategies aimed at these biological processes. Current predictive models based on genetic variations fall short of accurately forecasting diabetes. Objectives: Our study aims to pinpoint key epigenetic factors that predispose individuals to diabetes. These factors will inform the development of an advanced predictive model that estimates diabetes risk from genetic profiles, utilizing state-of-the-art statistical and data mining methods. Methodology: We have implemented a recursive feature elimination with cross-validation using the support vector machine (SVM) approach for refined feature selection. Building on this, we developed six machine learning models, including logistic regression, k-Nearest Neighbors (k-NN), Naive Bayes, Random Forest, Gradient Boosting, and Multilayer Perceptron Neural Network, to evaluate their performance. Findings: The Gradient Boosting Classifier excelled, achieving a median recall of 92.17% and outstanding metrics such as area under the receiver operating characteristics curve (AUC) with a median of 68%, alongside median accuracy and precision scores of 76%. Through our machine learning analysis, we identified 31 genes significantly associated with diabetes traits, highlighting their potential as biomarkers and targets for diabetes management strategies. Conclusion: Particularly noteworthy were the Gradient Boosting Classifier and Multilayer Perceptron Neural Network, which demonstrated potential in diabetes outcome prediction. We recommend future investigations to incorporate larger cohorts and a wider array of predictive variables to enhance the models' predictive capabilities.

Keywords: diabetes, machine learning, prediction, biomarkers

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Authors: Parisi L., Hamili D., Azlan N.

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The aim of this study was to design and simulate a particular type of Asynchronous State Machine (ASM), namely a ‘traffic light controller’ (TLC), operated at a frequency of 0.5 Hz. The design task involved two main stages: firstly, designing a 4-bit binary counter using J-K flip flops as the timing signal and subsequently, attaining the digital logic by deploying ASM design process. The TLC was designed such that it showed a sequence of three different colours, i.e. red, yellow and green, corresponding to set thresholds by deploying the least number of AND, OR and NOT gates possible. The software Multisim was deployed to design such circuit and simulate it for circuit troubleshooting in order for it to display the output sequence of the three different colours on the traffic light in the correct order. A clock signal, an asynchronous 4-bit binary counter that was designed through the use of J-K flip flops along with an ASM were used to complete this sequence, which was programmed to be repeated indefinitely. Eventually, the circuit was debugged and optimized, thus displaying the correct waveforms of the three outputs through the logic analyzer. However, hazards occurred when the frequency was increased to 10 MHz. This was attributed to delays in the feedback being too high.

Keywords: asynchronous state machine, traffic light controller, circuit design, digital electronics

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Authors: Kuei-Ling Sun, Emily Chia-Yu Su

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Allergy is a hypersensitive overreaction of the immune system to environmental stimuli, and a major health problem. These overreactions include rashes, sneezing, fever, food allergies, anaphylaxis, asthmatic, shock, or other abnormal conditions. Allergies can be caused by food, insect stings, pollen, animal wool, and other allergens. Their development of allergies is due to both genetic and environmental factors. Allergies involve immunoglobulin E antibodies, a part of the body’s immune system. Immunoglobulin E antibodies will bind to an allergen and then transfer to a receptor on mast cells or basophils triggering the release of inflammatory chemicals such as histamine. Based on the increasingly serious problem of environmental change, changes in lifestyle, air pollution problem, and other factors, in this study, we both collect allergens and non-allergens from several databases and use several machine learning methods for classification, including logistic regression (LR), stepwise regression, decision tree (DT) and neural networks (NN) to do the model comparison and determine the permutations of allergen amino acid’s sequence.

Keywords: allergy, classification, decision tree, logistic regression, machine learning

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Authors: Elyes Zarrouk, Yassine Benayed, Faiez Gargouri

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This paper presents a new hybrid model for isolated Arabic words recognition. To do this, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities within the Dynamic Bayesian networks (DBN). This paper deals a comparative study between DBN and SVM/DBN systems for multi-dialect isolated Arabic words. Performance using SVM/DBN is found to exceed that of DBNs trained on an identical task, giving higher recognition accuracy for four different Arabic dialects. In fact, the average of recognition rates for the four dialects with SVM/DBN was 87.67% while 83.01% with DBN.

Keywords: dynamic Bayesian networks, hybrid models, supports vectors machine, Arabic isolated words

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Authors: Mirna Guevara, Edgar Santoyo, Daniel Perez-Zarate, Erika Almirudis

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The Acoculco caldera located in Puebla (Mexico) has been preliminary identified as a blind hot-dry rock geothermal system. Two drilled wells suggest the existence of high temperatures >300°C and non-conventional tools are been applied to study this system. A comprehensive survey of soil-gas (CO₂) flux measurements (1,500 sites) was carried out during the dry seasons over almost two years (2015 and 2016). Isotopic analyses of δ¹³CCO₂ were performed to discriminate the origin source of the CO2 fluxes. The soil CO2 flux measurements were made in situ by the accumulation chamber method, whereas gas samples for δ13CCO2 were selectively collected from the accumulation chamber with evacuated gas vials via a septum. Two anomalous geothermal zones were identified as a result of these campaigns: Los Azufres (19°55'29.4'' N; 98°08'39.9'' W; 2,839 masl) and Alcaparrosa (19°55'20.6'' N; 98°08'38.3'' W; 2,845 masl). To elucidate the origin of the C in soil CO₂ fluxes, the isotopic signature of δ¹³C was used. Graphical Statistical Analysis (GSA) and a three end-member mixing diagram were used to corroborate the presence of distinctive statistical samples, and trends for the diffuse gas fluxes. Spatial and temporal distributions of the CO₂ fluxes were studied. High CO₂ emission rates up to 38,217 g/m2/d and 33,706 g/m2/d were measured for the Los Azufres and Alcaparrosa respectively; whereas the δ¹³C signatures showed values ranging from -3.4 to -5.5 o/oo for both zones, confirming their magmatic origin. This study has provided a valuable framework to set the direction of further exploration campaigns in the Acoculco caldera. Acknowledgements: The authors acknowledge the funding received from CeMIE-Geo P09 project (SENER-CONACyT).

Keywords: accumulation chamber method, carbon dioxide, diffusive degassing, geothermal exploration

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Authors: Yuanxiang Miao

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Couchsurfing is a free homestay and social networking service accessible via the website and mobile app. Couchsurfers can directly request free accommodations from others and receive offers from each other. However, it is typically difficult for people to make a decision that accepts or declines a request when they receive it from Couchsurfers because they do not know each other at all. People are expected to meet up with some Couchsurfers who are kind, generous, and interesting while it is unavoidable to meet up with someone unfriendly. This paper utilized classification algorithms of Machine Learning to help people to find out the Good Couchsurfers and Not Good Couchsurfers on the Couchsurfing website. By knowing the prior experience, like Couchsurfer’s profiles, the latest references, and other factors, it became possible to recognize what kind of the Couchsurfers, and furthermore, it helps people to make a decision that whether to host the Couchsurfers or not. The value of this research lies in a case study in Kyoto, Japan in where the author has hosted 54 Couchsurfers, and the author collected relevant data from the 54 Couchsurfers, finally build a model based on classification algorithms for people to predict Couchsurfers. Lastly, the author offered some feasible suggestions for future research.

Keywords: Couchsurfing, Couchsurfers prediction, classification algorithm, hospitality tourism platform, hospitality sciences, machine learning

Procedia PDF Downloads 133

Authors: Derssie Mebratu

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As a large variety of micro services, web services, social graphic applications, and media applications are continuously developed, it is substantially vital to design and build a reliable, efficient, and faster memory tiering system. Despite limited design, implementation, and deployment in the last few years, several techniques are currently developed to improve a memory tiering system in a cloud. Some of these techniques are to develop an optimal scanning frequency; improve and track pages movement; identify pages that recently accessed; store pages across each tiering, and then identify pages as a hot, warm, and cold so that hot pages can store in the first tiering Dynamic Random Access Memory (DRAM) and warm pages store in the second tiering Compute Express Link(CXL) and cold pages store in the third tiering Non-Volatile Memory (NVM). Apart from the current proposal and implementation, we also develop a new technique based on a machine learning algorithm in that the throughput produced 25% improved performance compared to the performance produced by the baseline as well as the latency produced 95% improved performance compared to the performance produced by the baseline.

Keywords: machine learning, bayesian optimization, memory tiering, CXL, DRAM

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Authors: Himanshu Rohela

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BACKGROUND: Ewing sarcoma can arise in either bone or soft tissue. Extraskeletal Ewing sarcoma (EES) is an uncommon primary tumor of the soft tissues, accounting for 20 30% of all reported cases of ES. AIM: Was to investigate demographic distribution, survival analysis and factors affecting the survival and recurrence in patients of EES. METHODS: Retrospective study of 19 biopsy-proven EES was performed. Overall survival (OS) using log-rank test and factors affecting OS and local recurrence (LR) were evaluated for the entire cohort. RESULTS: Patients with EES had a mean age of 19.5 and it was more commonly seen in males (63%). Axial location (58%) and solitary presentation (84%) were more common. The average size was 11 cm, 3 of 19 were metastatic at presentation, with the lung beings the most common site for metastasis. 17 received NACT, 16 with VAC-IE regimen and 1 underwent a second line with GEM/DOCE regimen. Unplanned surgery was done in 2 of 19. 3 patients received definitive RT and 13 underwent surgical-wide local excision. 2 of 13 showed good response to NACT. 10 patients required readmission out of which 6 patients had chemotherapy-related complications, 2 had surgical site complications and one patient developed secondary AML post-completion of treatment. A total of 4 patients had a recurrence. One had local recurrence alone, one had distant recurrence alone and 2 patients had a distant and local recurrence both. Tumor size >10 cm, axial location, and previous unplanned surgery was associated with higher LR rate. The mean overall survival was 32 months (2.66 years), with higher rates seen in non-metastatic and non-recurrent settings. CONCLUSIONS: Early and accurate diagnosis is the key to the management of EES, with promising results seen via NACT and RO resection regimens. But further studies with larger study groups are needed to standardize the treatment protocol and evaluate its efficacy.

Keywords: Ewings, sarcoma, extraskeletal, chemotherapy

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Authors: Ioannis P. Panapakidis, Marios N. Moschakis

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With the continuous increment of smart meter installations across the globe, the need for processing of the load data is evident. Clustering-based load profiling is built upon the utilization of unsupervised machine learning tools for the purpose of formulating the typical load curves or load profiles. The most commonly used algorithm in the load profiling literature is the K-means. While the algorithm has been successfully tested in a variety of applications, its drawback is the strong dependence in the initialization phase. This paper proposes a novel modified form of the K-means that addresses the aforementioned problem. Simulation results indicate the superiority of the proposed algorithm compared to the K-means.

Keywords: clustering, load profiling, load modeling, machine learning, energy efficiency and quality

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Authors: Tawfik Thelaidjia, Salah Chenikher

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Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features. In this paper, feature extraction from faulty bearing vibration signals is performed by a combination of the signal’s Kurtosis and features obtained through the preprocessing of the vibration signal samples using Db2 discrete wavelet transform at the fifth level of decomposition. In this way, a 7-dimensional vector of the vibration signal feature is obtained. After feature extraction from vibration signal, the support vector machine (SVM) was applied to automate the fault diagnosis procedure. To improve the classification accuracy for bearing fault prediction, particle swarm optimization (PSO) is employed to simultaneously optimize the SVM kernel function parameter and the penalty parameter. The results have shown feasibility and effectiveness of the proposed approach

Keywords: condition monitoring, discrete wavelet transform, fault diagnosis, kurtosis, machine learning, particle swarm optimization, roller bearing, rotating machines, support vector machine, vibration measurement

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Authors: Pedro Martinez-Santos, Víctor Gómez-Escalonilla, Silvia Díaz-Alcaide, Esperanza Montero, Miguel Martín-Loeches

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Groundwater monitoring networks are critical in evaluating the vulnerability of groundwater resources to depletion and contamination, both in space and time. Groundwater monitoring networks typically grow over decades, often in organic fashion, with relatively little overall planning. The groundwater monitoring networks in the Madrid area, Spain, were reviewed for the purpose of identifying gaps and opportunities for improvement. Spatial analysis reveals the presence of various monitoring networks belonging to different institutions, with several hundred observation wells in an area of approximately 4000 km2. This represents several thousand individual data entries, some going back to the early 1970s. Major issues included overlap between the networks, unknown screen depth/vertical distribution for many observation boreholes, uneven time series, uneven monitored species, and potentially suboptimal locations. Results also reveal there is sufficient information to carry out a spatial and temporal analysis of groundwater vulnerability based on machine learning applications. These can contribute to improve the overall planning of monitoring networks’ expansion into the future.

Keywords: groundwater monitoring, observation networks, machine learning, madrid

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

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This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

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Authors: Davoud Jafari, Paolo Di Marco, Alessandro Franco, Sauro Filippeschi

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This paper summarizes the results of an investigation into the heat pipe heat transfer for solar collector applications. The study aims to show the feasibility of a concentrating solar collector, which is coupled with a heat pipe. Particular emphasis is placed on the capillary and boiling limits in capillary porous structures, with different mesh numbers and wick thicknesses. A mathematical model of a cylindrical heat pipe is applied to study its behaviour when it is exposed to higher heat input at the evaporator. The steady state analytical model includes two-dimensional heat conduction in the HP’s wall, the liquid flow in the wick and vapor hydrodynamics. A sensitivity analysis was conducted by considering different design criteria and working conditions. Different wicks (mesh 50, 100, 150, 200, 250, and, 300), different porosities (0.5, 0.6, 0.7, 0.8, and 0.9) with different wick thicknesses (0.25, 0.5, 1, 1.5, and 2 mm) are analyzed with water as a working fluid. Results show that it is possible to improve heat transfer capability (HTC) of a HP by selecting the appropriate wick thickness, the effective pore radius, and lengths for a given HP configuration, and there exist optimal design criteria (optimal thick, evaporator adiabatic and condenser sections). It is shown that the boiling and wicking limits are connected and occurs in dependence on each other. As different parts of the HP external surface collect different fractions of the total incoming insolation, the analysis of non-uniform heat flux distribution indicates that peak heat flux is not affecting parameter. The parametric investigations are aimed to determine working limits and thermal performance of HP for medium temperature SC application.

Keywords: screened heat pipes, analytical model, boiling and capillary limits, concentrating collector

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Authors: Sudhir Kumar, V. R. Sinha

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Background: The topical and systemic toxicity associated with present nonmelanoma skin cancer (NMSC) treatment therapy using 5-Fluorouracil (5-FU) make it necessary to develop a novel delivery system having lesser toxicity and better control over drug release. Solid lipid nanoparticles offer many advantages like: controlled and localized release of entrapped actives, nontoxicity, and better tolerance. Aim:-To investigate safety and efficacy of 5-FU loaded solid lipid nanoparticles as a topical delivery system for the treatment of nonmelanoma skin cancer. Method: Topical solid lipid nanoparticles of 5-FU were prepared using Compritol 888 ATO (Glyceryl behenate) as lipid component and pluronic F68 (Poloxamer 188), Tween 80 (Polysorbate 80), Tyloxapol (4-(1,1,3,3-Tetramethylbutyl) phenol polymer with formaldehyde and oxirane) as surfactants. The SLNs were prepared with emulsification method. Different formulation parameters viz. type and ratio of surfactant, ratio of lipid and ratio of surfactant:lipid were investigated on particle size and drug entrapment efficiency. Results: Characterization of SLNs like–Transmission Electron Microscopy (TEM), Differential Scannig calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), Particle size determination, Polydispersity index, Entrapment efficiency, Drug loading, ex vivo skin permeation and skin retention studies, skin irritation and histopathology studies were performed. TEM results showed that shape of SLNs was spherical with size range 200-500nm. Higher encapsulation efficiency was obtained for batches having higher concentration of surfactant and lipid. It was found maximum 64.3% for SLN-6 batch with size of 400.1±9.22 nm and PDI 0.221±0.031. Optimized SLN batches and marketed 5-FU cream were compared for flux across rat skin and skin drug retention. The lesser flux and higher skin retention was obtained for SLN formulation in comparison to topical 5-FU cream, which ensures less systemic toxicity and better control of drug release across skin. Chronic skin irritation studies lacks serious erythema or inflammation and histopathology studies showed no significant change in physiology of epidermal layers of rat skin. So, these studies suggest that the optimized SLN formulation is efficient then marketed cream and safer for long term NMSC treatment regimens. Conclusion: Topical and systemic toxicity associated with long-term use of 5-FU, in the treatment of NMSC, can be minimized with its controlled release with significant drug retention with minimal flux across skin. The study may provide a better alternate for effective NMSC treatment.

Keywords: 5-FU, topical formulation, solid lipid nanoparticles, non melanoma skin cancer

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Seyed Ali Mahdipour, Maryam Shafeei Sarvestani

Abstract:

Radiation pressure acceleration (RPA) and target normal sheath acceleration (TNSA) are the most important methods of Laser-accelerated proton beams (LAP) planning systems.LAP has inspired novel applications that can benefit from proton bunch properties different from conventionally accelerated proton beams. The secondary neutron and photon produced in the collision of protons with beamline components are of the important concern in proton therapy. Various published Monte Carlo researches evaluated the beamline and shielding considerations for TNSA method, but there is no studies directly address secondary neutron and photon production from RPA method in LAP. The purpose of this study is to calculate the flux distribution of neutron and photon secondary radiations on the first area ofLAP and to determine the optimize thickness and radius of the energyselector in a LAP planning system based on RPA method. Also, we present the Monte Carlo calculations to determine the appropriate beam pipe for shielding a LAP planning system. The GEANT4 Monte Carlo toolkit has been used to simulate a secondary radiation production in LAP. A section of new multifunctional LAP beamlinehas been proposed, based on the pulsed power solenoid scheme as a GEANT4 toolkit. The results show that the energy selector is the most important source of neutron and photon secondary particles in LAP beamline. According to the calculations, the pure Tungsten energy selector not be the proper case, and using of Tungsten+Polyethylene or Tungsten+Graphitecomposite selectors will reduce the production of neutron and photon intensities by approximately ~10% and ~25%, respectively. Also the optimal radiuses of energy selectors were found to be ~4 cm and ~6 cm for a 3 degree and 5 degree proton deviation angles, respectively.

Keywords: neutron, photon, flux distribution, energy selector, GEANT4 toolkit

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Authors: Manal Suleiman, George Abu-Aqil, Uraib Sharaha, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman, Mahmoud Huleihel

Abstract:

Klebsiella pneumoniae is one of the most aggressive multidrug-resistant bacteria associated with human infections resulting in high mortality and morbidity. Thus, for an effective treatment, it is important to diagnose both the species of infecting bacteria and their susceptibility to antibiotics. Current used methods for diagnosing the bacterial susceptibility to antibiotics are time-consuming (about 24h following the first culture). Thus, there is a clear need for rapid methods to determine the bacterial susceptibility to antibiotics. Infrared spectroscopy is a well-known method that is known as sensitive and simple which is able to detect minor biomolecular changes in biological samples associated with developing abnormalities. The main goal of this study is to evaluate the potential of infrared spectroscopy in tandem with Random Forest and XGBoost machine learning algorithms to diagnose the susceptibility of Klebsiella pneumoniae to antibiotics within approximately 20 minutes following the first culture. In this study, 1190 Klebsiella pneumoniae isolates were obtained from different patients with urinary tract infections. The isolates were measured by the infrared spectrometer, and the spectra were analyzed by machine learning algorithms Random Forest and XGBoost to determine their susceptibility regarding nine specific antibiotics. Our results confirm that it was possible to classify the isolates into sensitive and resistant to specific antibiotics with a success rate range of 80%-85% for the different tested antibiotics. These results prove the promising potential of infrared spectroscopy as a powerful diagnostic method for determining the Klebsiella pneumoniae susceptibility to antibiotics.

Keywords: urinary tract infection (UTI), Klebsiella pneumoniae, bacterial susceptibility, infrared spectroscopy, machine learning

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Authors: Shreya Goyal

Abstract:

The study aims to build user-friendly software to understand patient arrival patterns and compute the number of potential patients who will visit a particular health facility for a given period by using a machine learning algorithm. The underlying machine learning algorithm used in this study is the Support Vector Machine (SVM). Accurate prediction of patient arrival allows hospitals to operate more effectively, providing timely and efficient care while optimizing resources and improving patient experience. It allows for better allocation of staff, equipment, and other resources. If there's a projected surge in patients, additional staff or resources can be allocated to handle the influx, preventing bottlenecks or delays in care. Understanding patient arrival patterns can also help streamline processes to minimize waiting times for patients and ensure timely access to care for patients in need. Another big advantage of using this software is adhering to strict data protection regulations such as the Health Insurance Portability and Accountability Act (HIPAA) in the United States as the hospital will not have to share the data with any third party or upload it to the cloud because the software can read data locally from the machine. The data needs to be arranged in. a particular format and the software will be able to read the data and provide meaningful output. Using software that operates locally can facilitate compliance with these regulations by minimizing data exposure. Keeping patient data within the hospital's local systems reduces the risk of unauthorized access or breaches associated with transmitting data over networks or storing it in external servers. This can help maintain the confidentiality and integrity of sensitive patient information. Historical patient data is used in this study. The input variables used to train the model include patient age, time of day, day of the week, seasonal variations, and local events. The algorithm uses a Supervised learning method to optimize the objective function and find the global minima. The algorithm stores the values of the local minima after each iteration and at the end compares all the local minima to find the global minima. The strength of this study is the transfer function used to calculate the number of patients. The model has an output accuracy of >95%. The method proposed in this study could be used for better management planning of personnel and medical resources.

Keywords: machine learning, SVM, HIPAA, data

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Authors: Ebru Dural, M. Zulfu Asık

Abstract:

Laminated glass is a kind of safety glass, which is made by 'sandwiching' two glass sheets and a polyvinyl butyral (PVB) interlayer in between them. When the glass is broken, the interlayer in between the glass sheets can stick them together. Because of this property, the hazards of sharp projectiles during natural and man-made disasters reduces. They can be widely applied in building, architecture, automotive, transport industries. Laminated glass can easily undergo large displacements even under their own weight. In order to explain their true behavior, they should be analyzed by using large deflection theory to represent nonlinear behavior. In this study, a nonlinear mathematical model is developed for the analysis of laminated glass cylindrical shell which is free in radial directions and restrained in axial directions. The results will be verified by using the results of the experiment, carried out on laminated glass cylindrical shells. The behavior of laminated composite cylindrical shell can be represented by five partial differential equations. Four of the five equations are used to represent axial displacements and radial displacements and the fifth one for the transverse deflection of the unit. Governing partial differential equations are derived by employing variational principles and minimum potential energy concept. Finite difference method is employed to solve the coupled differential equations. First, they are converted into a system of matrix equations and then iterative procedure is employed. Iterative procedure is necessary since equations are coupled. Problems occurred in getting convergent sequence generated by the employed procedure are overcome by employing variable underrelaxation factor. The procedure developed to solve the differential equations provides not only less storage but also less calculation time, which is a substantial advantage in computational mechanics problems.

Keywords: laminated glass, mathematical model, nonlinear behavior, PVB

Procedia PDF Downloads 320

Authors: Aysegul Sarac

Abstract:

Washer-dryers are the machines which can either wash the laundries or can dry them. In other words, we can define a washer-dryer as a washing machine and a dryer in one machine. Washing machines are characterized by the loading capacity, cabinet depth and spin speed. Dryers are characterized by the drying technology. On the other hand, energy efficiency, water consumption, and noise levels are main characteristics that influence customer decisions to buy washers. Water condensation technology is the most common drying technology existing in the washer-dryer market. Water condensation technology uses water to dry the laundry inside the machine. Thus, in this type of the drying technology water consumption is at high levels comparing other technologies. Water condensation technology sprays cold water in the drum to condense the humidity of hot weather in order to dry the laundry inside. Thus, water consumption influences the drying performance. The scope of this study is to optimize water consumption during drying process under a constraint of energy consumption and drying performance. We are using 6-Sigma methodology to find the optimum water consumption by comparing drying performances of different drying algorithms.

Keywords: optimization, 6-Sigma methodology, washer-dryers, water condensation technology

Procedia PDF Downloads 362

Authors: Harun Aydilek, Asiye Aydilek, Ali Allahverdi

Abstract:

Our objective is to minimize the total tardiness in an m-machine two-stage assembly flowshop scheduling problem. The objective is an important performance measure because of the fact that the fulfillment of due dates of customers has to be taken into account while making scheduling decisions. In the literature, the problem is considered with zero setup times which may not be realistic and appropriate for some scheduling environments. Considering separate setup times from processing times increases machine utilization by decreasing the idle time and reduces total tardiness. We propose two new algorithms and adapt four existing algorithms in the literature which are different versions of simulated annealing and genetic algorithms. Moreover, a dominance relation is developed based on the mathematical formulation of the problem. The developed dominance relation is incorporated in our proposed algorithms. Computational experiments are conducted to investigate the performance of the newly proposed algorithms. We find that one of the proposed algorithms performs significantly better than the others, i.e., the error of the best algorithm is less than those of the other algorithms by minimum 50%. The newly proposed algorithm is also efficient for the case of zero setup times and performs better than the best existing algorithm in the literature.

Keywords: algorithm, assembly flowshop, scheduling, simulation, total tardiness

Procedia PDF Downloads 333