Search results for: field regulated reluctance machine

Authors: Nolvi Francisco Baggio Filho, Roniele Belusso

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Many industrial processes require a precise linear motion. Usually, this movement is achieved with the use of rotary motors combined with electrical control systems and mechanical systems such as gears, pulleys and bearings. Other types of devices are based on linear motors, where the linear motion is obtained directly. The Linear Stepper Motor (MLP) is an excellent solution for industrial applications that require precise positioning and high speed. This study presents an MLP formed by a linear structure and static ferromagnetic material, and a mover structure in which three coils are mounted. Mechanical suspension systems allow a linear movement between static and mover parts, maintaining a constant air gap. The operating principle is based on the tendency of alignment of magnetic flux through the path of least reluctance. The force proportional to the intensity of the electric current and the speed proportional to the frequency of the excitation coils. The study of this device is still based on the use of a numerical and experimental analysis to verify the relationship among electric current applied and planar force developed. In addition, the magnetic field in the air gap region is also monitored.

Keywords: linear stepper motor, planar traction force, reluctance magnetic, industry applications

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Authors: Dora Triki, Irena Vida, Claude Obadia

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Social networks such as e-Forums or dating sites often face the reluctance of key members to participate. Relying on the conation theory, this study investigates this phenomenon and proposes solutions to mitigate the issue. We show that highly experienced e-Forum members refuse to share business information in a peer to peer information exchange forums. However, forums managers can mitigate this behavior by developing a sentiment of belongingness to the network. Furthermore, by selecting only elite forum participants with ample experience, they can reduce the reluctance of key information providers to engage in information exchange. Our hypotheses are tested with PLS structural equations modeling using survey data from members of a French e-Forum dedicated to the exchange of business information about exporting.

Keywords: conation, e-Forum, information exchange, members participation

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Authors: Joelle Penniston, E. B. Gueguim-Kana

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Biohydrogen has been identified as a promising alternative to the use of non-renewable fossil reserves, owing to its sustainability and non-polluting nature. pH is considered as a key parameter in fermentative biohydrogen production processes, due to its effect on the hydrogenase activity, metabolic activity as well as substrate hydrolysis. The present study assesses the influence of regulating pH on dark fermentative biohydrogen production. Four experimental hydrogen production schemes were evaluated. Two were implemented using suspended cells under regulated pH growth conditions (Sus_R) and suspended and non-regulated pH (Sus_N). The two others regimes consisted of alginate immobilized cells under pH regulated growth conditions (Imm_R) and immobilized and non-pH regulated conditions (Imm_N). All experiments were carried out at 37.5°C with glucose as sole source of carbon. Sus_R showed a lag time of 5 hours and a peak hydrogen fraction of 36% and a glucose degradation of 37%, compared to Sus_N which showed a peak hydrogen fraction of 44% and complete glucose degradation. Both suspended culture systems showed a higher peak biohydrogen fraction compared to the immobilized cell system. Imm_R experiments showed a lag phase of 8 hours, a peak biohydrogen fraction of 35%, while Imm_N showed a lag phase of 5 hours, a peak biohydrogen fraction of 22%. 100% glucose degradation was observed in both pH regulated and non-regulated processes. This study showed that biohydrogen production in batch mode with suspended cells in a non-regulated pH environment results in a partial degradation of substrate, with lower yield. This scheme has been the culture mode of choice for most reported studies in biohydrogen research. The relatively lower slope in pH trend of the non-regulated pH experiment with immobilized cells (Imm_N) compared to Sus_N revealed that that immobilized systems have a better buffering capacity compared to suspended systems, which allows for the extended production of biohydrogen even under non-regulated pH conditions. However, alginate immobilized cultures in flask systems showed some drawbacks associated to high rate of gas production that leads to increased buoyancy of the immobilization beads. This ultimately impedes the release of gas out of the flask.

Keywords: biohydrogen, sustainability, suspended, immobilized

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: Krishang Surapaneni

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The goal of this project is to determine how to predict important aspects of options, including the ask price. We want to compare different machine learning models to learn the best model and the best hyperparameters for that model for this purpose and data set. Option pricing is a relatively new field, and it can be very complicated and intimidating, especially to inexperienced people, so we want to create a machine learning model that can predict important aspects of an option stock, which can aid in future research. We tested multiple different models and experimented with hyperparameter tuning, trying to find some of the best parameters for a machine-learning model. We tested three different models: a Random Forest Regressor, a linear regressor, and an MLP (multi-layer perceptron) regressor. The most important feature in this experiment is the ask price; this is what we were trying to predict. In the field of stock pricing prediction, there is a large potential for error, so we are unable to determine the accuracy of the models based on if they predict the pricing perfectly. Due to this factor, we determined the accuracy of the model by finding the average percentage difference between the predicted and actual values. We tested the accuracy of the machine learning models by comparing the actual results in the testing data and the predictions made by the models. The linear regression model performed worst, with an average percentage error of 17.46%. The MLP regressor had an average percentage error of 11.45%, and the random forest regressor had an average percentage error of 7.42%

Keywords: finance, linear regression model, machine learning model, neural network, stock price

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Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

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Authors: S. Ben Brahim, T. H. Vuong, J. David, R. Bouallegue, M. Pietrzak-David

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Electrical machine monitoring is important to protect motor from unexpected problems. Today, using wireless communication for electrical machines is interesting for both real time monitoring and diagnostic purposes. In this paper, we propose a system based on wireless communication IEEE 802.11 to control electrical machine. IEEE 802.11 standard is recommended for this type of applications because it provides a faster connection, better range from the base station, and better security. Therefore, our contribution is to study a new technique to control and monitor the rotating electrical machines (motors, generators) using wireless communication. The reliability of radio channel inside rotating electrical machine is also discussed. Then, the communication protocol, software and hardware design used for the proposed system are presented in detail and the experimental results of our system are illustrated.

Keywords: control, DFIM machine, electromagnetic field, EMC, IEEE 802.11, monitoring, rotating electrical machines, wireless communication

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Authors: Ali Asmari, Alex Symington, Htaik Than, Austin Caradonna, John Senft

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This paper presents the collaborative effort between ULC Technologies and Con Edison in developing a groundbreaking robotic cable splicing machine. The focus is on the machine's design, which integrates advanced robotics and automation to enhance safety and efficiency in power network maintenance. The paper details the operational steps of the machine, including cable grounding, cutting, and removal of different insulation layers, and discusses its novel technological approach. The significant benefits over traditional methods, such as improved worker safety and reduced outage times, are highlighted based on the field data collected during the validation phase of the project. The paper also explores the future potential and scalability of this technology, emphasizing its role in transforming the landscape of power network maintenance.

Keywords: cable splicing machine, power network maintenance, electric distribution, electric transmission, medium voltage cable

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Authors: Muogbo Chukwudi Peter, Gbabo Agidi

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Influence of different turmeric rhizome sizes and machine operational speed on the field capacity and efficiency of a developed prototype tractor-drawn turmeric planter was studied. This was done with a view to ascertaining how the field capacity and field efficiency were affected by the turmeric rhizome lengths and tractor operational speed. The turmeric rhizome planter consists of trapezoidal hopper, grooved cylindrical metering devise, rectangular frame, ground wheels made of mild steel, furrow opener, chain/sprocket drive system, three linkage point seed delivery tube and press wheel. The experiment was randomized in a factorial design of three levels of rhizome lengths (30, 45 and 60 mm) and operational speeds of 8, 10, and 12 kmh-1. About 3 kg cleaned turmeric rhizomes were introduced into each hopper of the planter and were planted 30 m2 of experimental plot. During the field evaluation of the planter, the effective field capacity, field efficiency, missing index, multiple index and percentage rhizome bruise were evaluated. 30.08% was recorded for maximum percentage bruise on the rhizome. The mean effective field capacity ranged between 0.63 – 0.96hah-1 at operational speeds of 8 and 12kmh-1 respectively and 45 mm rhizome length. The result also shows that the mean efficiency was obtained to be 65.8%. The percentage rhizome bruise decreases with increase in operational speed. The highest and lowest percentage turmeric rhizome miss index of 35% were recorded for turmeric rhizome length of 30 mm at a speed of 10 kmhr-1 and 8 kmhr-1, respectively. The potential implications of the experimental result is to determine the optimal machine process conditions for higher field capacity and gross reduction in mechanical injury (bruise) of planted turmeric rhizomes.

Keywords: rhizome sizes, operational speed, field capacity. field efficiency, turmeric rhizome, planter

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Authors: Yizhen Zhao, Adam S. Z. Belloum, Goncalo Maia Da Costa, Zhiming Zhao

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Machine learning has evolved from an area of academic research to a real-word applied field. This change comes with challenges, gaps and differences exist between common practices in academic environments and the ones in production environments. Following continuous integration, development and delivery practices in software engineering, similar trends have happened in machine learning (ML) systems, called MLOps. In this paper we propose a framework that helps to streamline and introduce best practices that facilitate the ML lifecycle in an industrial setting. This framework can be used as a template that can be customized to implement various machine learning experiment. The proposed framework is modular and can be recomposed to be adapted to various use cases (e.g. data versioning, remote training on cloud). The framework inherits practices from DevOps and introduces other practices that are unique to the machine learning system (e.g.data versioning). Our MLOps practices automate the entire machine learning lifecycle, bridge the gap between development and operation.

Keywords: cloud computing, continuous development, data versioning, DevOps, industrial setting, MLOps

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Authors: Kittipong Tripetch, Nobuhiko Nakano

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This paper proposes one stage and two stage CMOS Complementary Regulated Cascode Distributed Amplifier (CRCDA) design based on Inductive and Transformer coupling techniques. Usually, Distributed amplifier is based on inductor coupling between gate and gate of MOSFET and between drain and drain of MOSFET. But this paper propose some new idea, by coupling with differential primary windings of transformer between gate and gate of MOSFET first stage and second stage of regulated cascade amplifier and by coupling with differential secondary windings transformer of MOSFET between drain and drain of MOSFET first stage and second stage of regulated cascade amplifier. This paper also proposes polynomial modeling of Silicon Transformer passive equivalent circuit from Nanyang Technological University which is used to extract frequency response of transformer. Cadence simulation results are used to verify validity of transformer polynomial modeling which can be used to design distributed amplifier without Cadence. 4 parameters of scattering matrix of 2 port of the propose circuit is derived as a function of 4 parameters of impedance matrix.

Keywords: CMOS regulated cascode distributed amplifier, silicon transformer modeling with polynomial, low power consumption, distribute amplification technique

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Authors: Abdul Amir Hazbavi

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The present study is an attempt to provide a relatively comprehensive preview of the Iranian English translators’ perception on Machine Translation. Furthermore, the study tries to shed light on the status of implementation of Machine Translation among the Iranian English Translators. To reach the aforementioned objectives, the Localization Industry Standards Association’s questioner for measuring perceptions with regard to the adoption of a technology innovation was adapted and used to investigate three parameter among the participants of the study, namely familiarity with Machine Translation, general perception on Machine Translation and implementation of Machine Translation systems in translation tasks. The participants of the study were 224 last-year undergraduate Iranian students of English translation at 10 universities across the country. The study revealed a very low level of adoption and a very high level of willingness to get familiar with and learn about Machine Translation, as well as a positive perception of and attitude toward Machine Translation by the Iranian English translators.

Keywords: translation technology, machine translation, perception, implementation

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Authors: Leidy M. Aldana, Jorge E. Camargo

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Adversarial Machine Learning has gained importance in recent years as Cybersecurity has gained too, especially malware, it has affected different entities and people in recent years. This paper shows a literature review about defense methods created to prevent adversarial machine learning attacks, firstable it shows an introduction about the context and the description of some terms, in the results section some of the attacks are described, focusing on detecting adversarial examples before coming to the machine learning algorithm and showing other categories that exist in defense. A method with five steps is proposed in the method section in order to define a way to make the literature review; in addition, this paper summarizes the contributions in this research field in the last seven years to identify research directions in this area. About the findings, the category with least quantity of challenges in defense is the Detection of adversarial examples being this one a viable research route with the adaptive approach in attack and defense.

Keywords: Malware, adversarial, machine learning, defense, attack

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Authors: Devatha Kalyan Kumar, Aravindraj D., Sadathulla A.

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Big data are now rapidly expanding in all engineering and science and many other domains. The potential of large or massive data is undoubtedly significant, make sense to require new ways of thinking and learning techniques to address the various big data challenges. Machine learning is continuously unleashing its power in a wide range of applications. In this paper, the latest advances and advancements in the researches on machine learning for big data processing. First, the machine learning techniques methods in recent studies, such as deep learning, representation learning, transfer learning, active learning and distributed and parallel learning. Then focus on the challenges and possible solutions of machine learning for big data.

Keywords: active learning, big data, deep learning, machine learning

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Authors: Ghada A. Alfattni

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Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates. 

Keywords: imbalanced datasets, SMOTE, machine learning, logistic regression, support vector machine, nearest neighbour

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Authors: P. Srinivas, P. V. N. Prasad

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Since torque ripple is the main cause of noise and vibrations, the performance of Switched Reluctance Motor (SRM) can be improved by minimizing its torque ripple using a novel control technique called Direct Torque Control (DTC). In DTC technique, torque is controlled directly through control of magnitude of the flux and change in speed of the stator flux vector. The flux and torque are maintained within set hysteresis bands. The DTC of SRM is analysed by two methods. In one of the methods, the actual torque is computed by conducting Finite Element Analysis (FEA) on the design specifications of the motor. In the other method, the torque is computed by Simplified Torque Equation. The variation of peak current, average current, torque ripple and speed settling time with Simplified Torque Equation model is compared with FEA based model.

Keywords: direct toque control, simplified torque equation, finite element analysis, torque ripple

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Authors: Pieter Conradie, M. Marina Moller

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Web 2.0 Internet-based technologies have intruded all aspects of human life. Presently, this phenomenon is especially evident in the educational context, with increased disruptive Web 2.0 technology infusions dramatically changing educational practice. The most prominent of these Web 2.0 intrusions can be identified as Massive Open Online Courses (Coursera, EdX), video and photo sharing sites (Youtube, Flickr, Instagram), and Web 2.0 online tools utilize to create Personal Learning Environments (PLEs) (Symbaloo (aggregator), Delicious (social bookmarking), PBWorks (collaboration), Google+ (social networks), Wordspress (blogs), Wikispaces (wiki)). These Web 2.0 technologies have supported the realignment from a teacher-based pedagogy (didactic presentation) to a learner-based pedagogy (problem-based learning, project-based learning, blended learning), allowing greater learner autonomy. No longer is the educator the source of knowledge. Instead the educator has become the facilitator and mediator of the learner, involved in developing learner competencies to support life-long learning (continuous learning) in the 21st century. In this study, the self-regulated learning skills of thirty first-year university learners were explored by utilizing the Online Self-regulated Learning Questionnaire. Implementing an action research method, an intervention was affected towards improving the self-regulation skill set of the participants. Statistical significant results were obtained with increased self-regulated learning proficiency, positively impacting learner performance. Goal setting, time management, environment structuring, help seeking, task (learning) strategies and self-evaluation skills were confirmed as determinants of improved learner success.

Keywords: andragogy, online self-regulated learning questionnaire, self-regulated learning, web 2.0

Procedia PDF Downloads 383

Authors: Haroon Jan Khan, Xian-Feng Xu, Syed Nasir Shah, Anooshay Niazi

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CNC machines are not only used on a large scale but also now become a prominent necessity among households and smaller businesses. Printed Circuit Boards manufactured by the chemical process are not only risky and unsafe but also expensive and time-consuming. A 3-axis precise CNC machine has been developed, which not only fabricates PCB but has also been used for multi-tasks just by changing the materials used and tools, making it versatile. The advanced CNC machine takes data from CAM software. The TB-6560 controller is used in the CNC machine to adjust variation in the X, Y, and Z axes. The advanced machine is efficient in automatic drilling, engraving, and cutting.

Keywords: CNC, G-code, CAD, CAM, Proteus, FLATCAM, Easel

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Authors: A. Hassannia, S. Ramezani

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The technology of superconductivity has developed in many power system devices such as transmission cable, transformer, current limiter, motor and generator. Superconducting wires can carry high density current without loss, which is the capability that is used to design the compact, lightweight and more efficient electrical machines. Superconducting motors have found applications in marine and air propulsion systems as well as superconducting generators are considered in low power hydraulic and wind generators. This paper presents a rim-driven superconducting synchronous generator for hydraulic power plant. The rim-driven concept improves the performance of hydro turbine. Furthermore, high magnetic field that is produced by superconducting windings allows replacing the rotor core. As a consequent, the volume and weight of the machine is decreased significantly. In this paper, a 1 MW coreless rim-driven superconducting synchronous generator is designed. Main performance characteristics of the proposed machine are then evaluated using finite elements method and compared to an ordinary similar size synchronous generator.

Keywords: coreless machine, electrical machine design, hydraulic generator, rim-driven machine, superconducting generator

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Authors: Ogwueleka Francisca Nonyelum, Jiya Muhammad

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Poor communication between the victims of the burglaries, road and fire accidents and the agencies, and lack of quick emergency response by the agencies is solved through Machine-to-Machine (M2M) communication. A distress caller is expected to make a call through a network to the respective agency for emergency response but due to some challenges, this often becomes arduous and futile. This research puts forth an Internet-based architecture for Machine-to-Machine (M2M) communication to enhance information dissemination in National Public Security Communication System (NPSCS) network. M2M enables the flow of data between machines and machines and ultimately machines and people with information flowing from a machine over a network, and then through a gateway to a system where it is reviewed and acted on. The research findings showed that Internet-based architecture for M2M communication is most suitable for deployment of a public security network which will allow machines to use Internet to talk to each other.

Keywords: machine-to-machine (M2M), internet-based architecture, network, gateway

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Authors: Nasser Al-Rawahi

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The study aims at determining self-regulated learning strategies used by Omani physical education candidates to learn sport skills. The data were collected by a self-regulated learning theory questionnaire. The sample of the study comprised of 145 undergraduate physical education students enrolled in the department of physical education at the College of Education, Sultan Qaboos University. The findings of the study revealed that the most commonly used strategies for learning sport skills by Omani physical education candidate are ‘the effort learning strategies, planning learning strategies and evaluation learning strategies’. However, the reflection learning strategies, self-monitoring and self-efficacy learning strategies were revealed as the least used strategies by the PE candidates in learning and acquiring sport skills. Based on these findings, suggestions and recommendations for future research were provided.

Keywords: learning strategies, physical education candidates, self-regulated learning theory, Oman

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Authors: Soumia Hebbri

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English language has become a major medium for communication across borders. Nowadays, it is seen as a communicative medium not only for business but also for academic purposes. Some scientists describe English language as a way to enjoy an admired position in many countries. It is neither a national nor an official language in North Africa; it is considered as the most widely taught foreign language at the educational system. In order to achieve mastery of a foreign language, learners must develop the four principal language skills: Reading, writing, listening and speaking. However, being able to interact orally with others, using effectively the target language, is nowadays very important. People who cannot speak a foreign language cannot be considered effective language users, even if they can read and understand it. The teachers’ role in promoting foreign language acquisition is very important, as they are responsible for providing students appropriate contexts to foster communicative situations that allow students to express themselves and interact in the target language. So, we should understand the student’s reasons of their reluctance in oral participation when dealing with oral communicative tasks, in order to get insights about the possible motivating factors that may improve their involvement and participation in the classroom.

Keywords: EL, EFL, ET, TEFL, communication

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Authors: Lu Xi, Li Pan, Wen Mengmeng

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The stiffness of each component has different influences on the stiffness of the machine tool. Taking the five-axis gantry machining center as an example, we made the modal analysis of the machine tool, followed by raising and lowering the stiffness of the pillar, slide plate, beam, ram and saddle so as to study the stiffness matching among these components on the standard of whether the stiffness of the modified machine tool changes more than 50% relative to the stiffness of the original machine tool. The structural optimization of the machine tool can be realized by changing the stiffness of the components whose stiffness is mismatched. For example, the stiffness of the beam is mismatching. The natural frequencies of the first six orders of the beam increased by 7.70%, 0.38%, 6.82%, 7.96%, 18.72% and 23.13%, with the weight increased by 28Kg, leading to the natural frequencies of several orders which had a great influence on the dynamic performance of the whole machine increased by 1.44%, 0.43%, 0.065%, which verified the correctness of the optimization method based on stiffness matching proposed in this paper.

Keywords: machine tool, optimization, modal analysis, stiffness matching

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Authors: Ahmad FAROOQ, Xinfeng Zhang, Fahad Sabah, Raheem Sarwar

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In Traditional Chinese Medicine, tongue diagnosis is a vital inspection tool (TCM). In this study, we explore the potential of machine learning in tongue diagnosis. It begins with the cataloguing of the various classifications and characteristics of the human tongue. We infer 24 kinds of tongues from the material and coating of the tongue, and we identify 21 attributes of the tongue. The next step is to apply machine learning methods to the tongue dataset. We use the Weka machine learning platform to conduct the experiment for performance analysis. The 457 instances of the tongue dataset are used to test the performance of five different machine learning methods, including SVM, Random Forests, Decision Trees, and Naive Bayes. Based on accuracy and Area under the ROC Curve, the Support Vector Machine algorithm was shown to be the most effective for tongue diagnosis (AUC).

Keywords: medical imaging, image retrieval, machine learning, tongue

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Authors: Joseph C. Chen

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Machine vision system provides automatic inspection to reduce manufacturing costs considerably. However, only a few principles have been found to optimize machine vision system and help it function more accurately in industrial practice. Mostly, there were complicated and impractical design techniques to improve the accuracy of machine vision system. This paper discusses implementing the Six Sigma Define, Measure, Analyze, Improve, and Control (DMAIC) approach to optimize the setup parameters of machine vision system when it is used as a direct measurement technique. This research follows a case study showing how Six Sigma DMAIC methodology has been put into use.

Keywords: DMAIC, machine vision system, process capability, Taguchi Parameter Design

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Authors: Guru Prakash, Revanth Dugalam

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A road’s condition often deteriorates due to harsh loading such as overload due to trucks, and severe environmental conditions such as heavy rain, snow load, and cyclic loading. In absence of proper maintenance planning, this results in potholes, wide cracks, bumps, and increased roughness of roads. In this paper, a data-driven model will be developed to detect these damages using vibration and image signals. The key idea of the proposed methodology is that the road anomaly manifests in these signals, which can be detected by training a machine learning algorithm. The use of various machine learning techniques such as the support vector machine and Radom Forest method will be investigated. The proposed model will first be trained and tested with artificially simulated data, and the model architecture will be finalized by comparing the accuracies of various models. Once a model is fixed, the field study will be performed, and data will be collected. The field data will be used to validate the proposed model and to predict the future road’s health condition. The proposed will help to automate the road condition monitoring process, repair cost estimation, and maintenance planning process.

Keywords: SVM, data-driven, road health monitoring, pot-hole

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Authors: Sulila Anar

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Institutions of any kind are regulated by laws which could be formal or informal, visible or invisible that influences the very structure of the institutions itself. Here in this paper the attempt will be to see how institutions of higher education are regulated by the regulatory institutions by taking the case of India, the third largest education system in the world. The attempt is to try to see how regulation of higher education creates a space for contestation among regulatory institutions based on secondary resources and how this affects the governance of university to achieve the goals and visions.

Keywords: higher education, regulation, autonomy, space

Procedia PDF Downloads 367

Authors: Dalia. G. Aseel, E. E. Hafez, S. M. Hammad

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Viral RNAs of both potato leaf roll virus (PLRV) and potato virus Y (PVY) were extracted from infected potato leaves collected from different Egyptian regions. Differential Display Polymerase Chain Reaction (DD-PCR) using (Endogluconase, β-1,3-glucanases, Chitinase, Peroxidase and Polyphenol oxidase) primers (forward strand) for was performed. The obtained data revealed different banding patterns depending on the viral type and the region of infection. Regarding PLRV, a 58 up regulated and 19 down regulated genes were detected, while, 31 up regulated and 14 down regulated genes were observed in case of PVY. Based on the nucleotide sequencing, variable phylogenetic relationships were reported for the three sequenced genes coding for: Induced stolen tip protein, Disease resistance RPP-like protein and non-specific lipid-transfer protein. In a complementary approach, using the quantitative Real-time PCR, the expressions of PRs genes understudy were estimated in the infected leaves by PLRV and PVY of three potato cultivars (Spunta, Diamont and Cara). The infection with both viruses inhibited the expressions of the five PRs genes. On the contrary, infected leaves by PLRV or PVY elevated the expression of some defense genes. This interaction also may be enhanced and/or inhibited the expression of some genes responsible for the plant defense mechanisms.

Keywords: PLRV, PVY, PR genes, DD-PCR, qRT-PCR, sequencing

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Authors: Xiaobo Xi, Ruihong Zhang

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At present, the orchard ditching and fertilizing technology has a series of problems, such as easy tree roots damage, high energy consumption and uneven fertilizing. In this paper, a gas explosion subsoiling and fertilizer injection machine was designed, which used high pressure gas to shock soil body and then injected fertilizer. The drill pipe mechanism with pneumatic chipping hammer excitation and hydraulic assistance was designed to drill the soil. The operation of gas and liquid fertilizer supply was controlled by PLC system. The 3D model of the whole machine was established by using SolidWorks software. The machine prototype was produced, and field experiments were carried out. The results showed that soil fractures were created and diffused by gas explosion, and the subsoiling effect radius reached 40 cm under the condition of 0.8 MPa gas pressure and 30 cm drilling depth. What’s more, the work efficiency is 0.048 hm²/h at least. This machine could meet the agronomic requirements of orchard, garden and city greening fertilization, and the tree roots were not easily damaged and the fertilizer evenly distributed, which was conducive to nutrient absorption of root growth.

Keywords: gas explosion subsoiling, fertigation, pneumatic chipping hammer exciting, soil compaction

Procedia PDF Downloads 177

Authors: Vareesha Masood, Ammara Gul, Nabeeha Areej, Muhammad Asif Masood, Hamna Imran

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The universal use of PDF files has prompted hackers to use them for malicious intent by hiding malicious codes in their victim’s PDF machines. Machine learning has proven to be the most efficient in identifying benign files and detecting files with PDF malware. This paper has proposed an approach using a decision tree classifier with parameters. A modern, inclusive dataset CIC-Evasive-PDFMal2022, produced by Lockheed Martin’s Cyber Security wing is used. It is one of the most reliable datasets to use in this field. We designed a PDF malware detection system that achieved 99.2%. Comparing the suggested model to other cutting-edge models in the same study field, it has a great performance in detecting PDF malware. Accordingly, we provide the fastest, most reliable, and most efficient PDF Malware detection approach in this paper.

Keywords: PDF, PDF malware, decision tree classifier, random forest classifier

Procedia PDF Downloads 55