Search results for: equivalent transformation algorithms

Authors: Suchitra Sivakumar, Hajime Shingyouchi, Toshinori Okajima, Kyohei Yamaguchi, Jin Kusaka

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The progressing need for powertrain electrification calls for more accurate and reliable simulation models. A battery pack serves as the most vital component for energy storage in an electrified powertrain. Hybrid electric vehicles (HEV) do not behave the same way as they age, and there are several environmental factors that account for the degradation of the battery on a system level. Therefore, in this work, a battery model was proposed to study the state of charge (SOC) variation and the internal dynamic changes that contribute to aging and performance degradation in HEV batteries. An equivalent circuit battery model (ECM) is built using MATLAB Simulink to investigate the output characteristics of the lithium-ion battery. The ECM comprises of circuit elements like a voltage source, a series resistor and a parallel RC network connected in series. A parameter estimation study is conducted on the ECM to study the dependencies of the circuit elements with the state of charge (SOC) and the terminal voltage of the battery. The battery model is extended to simulate the temperature dependence of the individual battery cell and the battery pack with the environment. The temperature dependence model accounts for the heat loss due to internal resistance build up in the battery pack during charging, discharging, and due to atmospheric temperature. The model was validated for a lithium-ion battery pack with an independent drive cycle showing a voltage accuracy of 4% and SOC accuracy of about 2%.

Keywords: battery model, hybrid electric vehicle, lithium-ion battery, thermal model

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Authors: Xingmao Ma, Wenjie Sun

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Rice paddies are frequently co-contaminated by arsenic (As) and cadmium (Cd), both of which demonstrate a high propensity for accumulation in rice grains and cause global food safety and public health concern. Even though different agricultural management strategies have been explored for their simultaneous control in rice grains, a viable solution is yet to be developed. Interestingly, several nanoagrichemicals, such as the zinc nanofertilizer and copper nanopesticide have displayed strong potential to reduce As or Cd accumulation in rice tissues. In order to determine whether these nanoagrichemicals can lower the accumulation of both As and Cd in rice, a series of bench studies were performed. Our results show that zinc oxide nanoparticles at 100 mg/Kg significantly lowered both As, and Cd in rice roots and shoots in flood irrigated rice seedlings, while equivalent amount of zinc ions only reduced As concentration in rice shoots. Zinc ions significantly increased Cd concentration in rice shoots by almost 30%. The results demonstrate a unique 'nano-effect' of zinc oxide nanoparticles, which is ascribed to the slow releasing of zinc ions from nanoparticles and the formation of different transformation products in these two treatments. We also evaluated the effect of nanoscale soil amendment, silicon oxide nanoparticles (SiO₂NPs) on the simultaneous reduction in both flooding and alternate wet and dry irrigation scheme. The effect of SiO₂NPs on As and Cd accumulation in rice tissues was strongly affected by the irrigation scheme. While 2000 mg/kg of SiO₂NPs significantly reduced As in rice roots and insignificantly reduced As in rice shoots in flooded rice, it increased As concentration in rice shoots in alternate wet and dry irrigation. In both irrigation scenarios, SiO₂NPs significantly reduced Cd concentration in rice roots, but only reduced Cd concentration in rice shoots in alternate wet and dry irrigation. Our results demonstrate a marked effect of nanoagrichemicals on the accumulation of As and Cd in rice and can be a potential solution to simultaneously control both in certain conditions.

Keywords: arsenic, cadmium, rice, nanoagrichemicals

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Authors: Theophilus Adedokun, Sylvia Zulu, Felix Awung, Sam Usadolo

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The proliferation of technology into teaching process has brought about transformation into the field of education. Language teaching is not left behind from this tremendous transformation which has drastically altered the teaching of language. It is, however, appalling that some language instructors seem to possess negative attitudes toward the use of technology in language teaching, which in this study is referred to as Computer Assisted Language Teaching (CALT). The purpose of this study, therefore, is to explore sustainable lesson that can be learnt from the attitudes of language instructors towards language teaching in some public universities. The knowledge gained from this study could inform and advance the use of Computer Assisted Language Teaching. This study considers the historical progression of CALT and recommends that a fundamental approach is required for institutions to develop and advance the use of CALT for teaching. A review of sustainable lessons learnt from the attitudes of language instructors towards CALT are provided, and the CALT experience of 3 institutions are described. Drawing from this succinct description, this study makes recommendations on how operative CALT could be executed on a personal and institutional basis.

Keywords: attitudes, language instructors, sustainable lessons, computer assisted language teaching

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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Authors: Yang Zheng, Wei Sun

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This paper describes a new approach which can be used to interpret the experimental creep deformation data obtained from miniaturized thin plate bending specimen test to the corresponding uniaxial data based on an inversed application of the reference stress method. The geometry of the thin plate is fully defined by the span of the support, l, the width, b, and the thickness, d. Firstly, analytical solutions for the steady-state, load-line creep deformation rate of the thin plates for a Norton’s power law under plane stress (b → 0) and plane strain (b → ∞) conditions were obtained, from which it can be seen that the load-line deformation rate of the thin plate under plane-stress conditions is much higher than that under the plane-strain conditions. Since analytical solution is not available for the plates with random b-values, finite element (FE) analyses are used to obtain the solutions. Based on the FE results obtained for various b/l ratios and creep exponent, n, as well as the analytical solutions under plane stress and plane strain conditions, an approximate, numerical solutions for the deformation rate are obtained by curve fitting. Using these solutions, a reference stress method is utilised to establish the conversion relationships between the applied load and the equivalent uniaxial stress and between the creep deformations of thin plate and the equivalent uniaxial creep strains. Finally, the accuracy of the empirical solution was assessed by using a set of “theoretical” experimental data.

Keywords: bending, creep, thin plate, materials engineering

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Authors: Junqi Zou

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As one of the world’s most tech-ready countries, Singapore has initiated the Smart Nation plan to harness the full power and potential of digital technologies to transform the way people live and work, through the more efficient government and business processes, to make the economy more productive. The key evolutions of digital technology transformation in healthcare and the increasing deployment of Internet of Things (IoTs), Big Data, AI/cognitive, Robotic Process Automation (RPA), Electronic Health Record Systems (EHR), Electronic Medical Record Systems (EMR), Warehouse Management System (WMS in the most recent decade have significantly stepped up the move towards an information-driven healthcare ecosystem. The advances in information technology not only bring benefits to patients but also act as a key force in changing management accounting in healthcare sector. The aim of this study is to investigate the impacts of digital technology transformation on Singapore’s healthcare sector from a management accounting perspective. Adopting a Balanced Scorecard (BSC) analysis approach, this paper conducted an exploratory case study of a newly launched Singapore public hospital, which has been recognized as amongst the most digitally advanced healthcare facilities in Asia-Pacific region. Specifically, this study gains insights on how the new technology is changing healthcare organizations’ management accounting from four perspectives under the Balanced Scorecard approach, 1) Financial Perspective, 2) Customer (Patient) Perspective, 3) Internal Processes Perspective, and 4) Learning and Growth Perspective. Based on a thorough review of archival records from the government and public, and the interview reports with the hospital’s CIO, this study finds the improvements from all the four perspectives under the Balanced Scorecard framework as follows: 1) Learning and Growth Perspective: The Government (Ministry of Health) works with the hospital to open up multiple training pathways to health professionals that upgrade and develops new IT skills among the healthcare workforce to support the transformation of healthcare services. 2) Internal Process Perspective: The hospital achieved digital transformation through Project OneCare to integrate clinical, operational, and administrative information systems (e.g., EHR, EMR, WMS, EPIB, RTLS) that enable the seamless flow of data and the implementation of JIT system to help the hospital operate more effectively and efficiently. 3) Customer Perspective: The fully integrated EMR suite enhances the patient’s experiences by achieving the 5 Rights (Right Patient, Right Data, Right Device, Right Entry and Right Time). 4) Financial Perspective: Cost savings are achieved from improved inventory management and effective supply chain management. The use of process automation also results in a reduction of manpower costs and logistics cost. To summarize, these improvements identified under the Balanced Scorecard framework confirm the success of utilizing the integration of advanced ICT to enhance healthcare organization’s customer service, productivity efficiency, and cost savings. Moreover, the Big Data generated from this integrated EMR system can be particularly useful in aiding management control system to optimize decision making and strategic planning. To conclude, the new digital technology transformation has moved the usefulness of management accounting to both financial and non-financial dimensions with new heights in the area of healthcare management.

Keywords: balanced scorecard, digital technology transformation, healthcare ecosystem, integrated information system

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Authors: Andre Bechuke

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The years of 2015-2017 witnessed a huge campaign, and in some instances, violent protests in South Africa by students and some groups of academics advocating the decolonisation of the curriculum of universities. These protests have forced through high expectations for universities to teach a curriculum relevant to the country, and the continent as well as enabled South Africa to participate in the globalised world. To realise this purpose, most universities are currently undertaking steps to transform and decolonise their curriculum. However, the transformation process is challenged and delayed by lack of a collective understanding of the concepts ‘decolonisation’ and ‘africanisation’ that should guide its application. Even more challenging is lack of a contextual understanding of these concepts across different university disciplines. Against this background, and underpinned in a qualitative research paradigm, the perspectives of these concepts as applied by different university disciplines were examined in order to understand and establish their implementation in the curriculum transformation agenda. Data were collected by reviewing the teaching and learning plans of 8 faculties of an institution of higher learning in South Africa and analysed through content and textual analysis. The findings revealed varied understanding and use of these concepts in the transformation of the curriculum across faculties. Decolonisation, according to the faculties of Law and Humanities, is perceived as the eradication of the Eurocentric positioning in curriculum content and the constitutive rules and norms that control thinking. This is not done by ignoring other knowledge traditions but does call for an affirmation and validation of African views of the world and systems of thought, mixing it with current knowledge. For the Faculty of Natural and Agricultural Sciences, decolonisation is seen as making the content of the curriculum relevant to students, fulfilling the needs of industry and equipping students for job opportunities. This means the use of teaching strategies and methods that are inclusive of students from diverse cultures, and to structure the learning experience in ways that are not alien to the cultures of the students. For the Health Sciences, decolonisation of the curriculum refers to the need for a shift in Western thinking towards being more sensitive to all cultural beliefs and thoughts. Collectively, decolonisation of education thus entails that a nation must become independent with regard to the acquisition of knowledge, skills, values, beliefs, and habits. Based on the findings, for universities to successfully transform their curriculum and integrate the concepts of decolonisation and Africanisation, there is a need to contextually determine the meaning of the concepts generally and narrow them down to what they should mean to specific disciplines. Universities should refrain from considering an umbrella approach to these concepts. Decolonisation should be seen as a means and not an end. A decolonised curriculum should equally be developed based on the finest knowledge skills, values, beliefs and habits around the world and not limited to one country or continent.

Keywords: Africanisation, curriculum, transformation, decolonisation, multidisciplinary perspectives, South Africa’s higher education

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Authors: Vinay Agrawal, Suyash Garg, Ravindra Nagar, Vinay Chandwani

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Open ground storey with floating columns is a typical feature in the modern multistory constructions in urban India. Such features are very much undesirable in buildings built in seismically active areas. The present study proposes a feasible solution to mitigate the effects caused due to non-uniformity of stiffness and discontinuity in load path and to simultaneously hold the functional use of the open storey particularly under the floating column, through a combination of various lateral strengthening systems. An investigation is performed on an example building with nine different analytical models to bring out the importance of recognising the presence of open ground storey and floating columns. Two separate analyses on various models of the building namely, the equivalent static analysis and the response spectrum analysis as per IS: 1893-2002 were performed. Various measures such as incorporation of Chevron bracings and shear walls, strengthening the columns in the open ground storey, and their different combinations were examined. The analysis shows that, in comparison to two short ones separated by interconnecting beams, the structural walls are most effective when placed at the periphery of the buildings and used as one long structural wall. Further, it can be shown that the force transfer from floating columns becomes less horizontal when the Chevron Bracings are placed just below them, thereby reducing the shear forces in the beams on which the floating column rests.

Keywords: equivalent static analysis, floating column, open ground storey, response spectrum analysis, shear wall, stiffness irregularity

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Authors: Charles Kwaku Oppong

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The research linked urban expansion resulting from urbanization with changing morphology processes happening in peri-urban communities. Two villages of Kumasi City peri-urban were used as a case study. Appropriate analytical framework and methodology (literature review and empirical evidence) were employed to ensure that all pertinent issues of peri-urban interface are brought to light. It was discovered from the study that since peri-urban livelihood is linked with assets base; it has been found that stock of asset, as well as transformation processes, were major factors in the shaping of livelihoods strategies. For that reason, success or failure of household livelihoods was seen to relate to the kind of livelihood strategy employed. With efforts to mitigate for livelihoods failure due to peri-urban development, households' recourse to remittances, land disposal, and other means as an alternative livelihood approach. The study calls for local government policy interventions in regulating peri-urban transformation process and providing safety nets for the vulnerable.

Keywords: urban expansion, peri-urban interface, livelihoods, asset

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Authors: Mahsa Namvari, Kari Ullakko

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It is well-known that the Co-doping of ferromagnetic shape memory alloys (FSMAs) is a crucial tool to control their multifunctional properties. The present work investigates the use of small quantities of Co to fine-tune the transformation, structure, microstructure, mechanical and magnetic properties of the polycrystalline Ni₄₉.₈Mn₂₈.₅Ga₂₁.₇ (at.%) alloy, At Co concentrations of 1-1.5 at.%, a microstructure with an average grain size of about 2.00 mm was formed with a twin structure, enabling the experimental observation of magnetic-field-induced twin variant rearrangement. At higher levels of Co-doping, the grain size was essentially reduced, and the crystal structure of the martensitic phase became 2M martensite. The decreasing grain size and changing crystal structure are attributed to the progress of γ-phase precipitates. Alongside the academic aspect, the results of the present work point to the commercial advantage of fabricating 10M Co-doped Ni-Mn-Ga actuating elements made from large grains of polycrystalline ingots obtained by a standard melting facility instead of grown single crystals.

Keywords: Ni-Mn-Ga, ferromagnetic shape memory, martensitic phase transformation, grain growth

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Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

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Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

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Authors: Mohammad Ghavami, Reza S. Dilmaghani

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This paper presents an adaptive framework for modelling financial markets using equity risk premiums, risk free rates and volatilities. The recorded economic factors are initially used to train four adaptive filters for a certain limited period of time in the past. Once the systems are trained, the adjusted coefficients are used for modelling and prediction of an important financial market index. Two different approaches based on least mean squares (LMS) and recursive least squares (RLS) algorithms are investigated. Performance analysis of each method in terms of the mean squared error (MSE) is presented and the results are discussed. Computer simulations carried out using recorded data show MSEs of 4% and 3.4% for the next month prediction using LMS and RLS adaptive algorithms, respectively. In terms of twelve months prediction, RLS method shows a better tendency estimation compared to the LMS algorithm.

Keywords: adaptive methods, LSE, MSE, prediction of financial Markets

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Authors: Rohit Dey, Sailendra Karra

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This paper aims at solving the problem of multi-target searching in a Global Positioning System (GPS) denied environment using swarm robots with limited sensing and communication abilities. Typically, existing swarm-based search algorithms rely on the presence of a global coordinate system (vis-à-vis, GPS) that is shared by the entire swarm which, in turn, limits its application in a real-world scenario. This can be attributed to the fact that robots in a swarm need to share information among themselves regarding their location and signal from targets to decide their future course of action but this information is only meaningful when they all share the same coordinate frame. The paper addresses this very issue by eliminating any dependency of a search algorithm on the need of a predetermined global coordinate frame by the unification of the relative coordinate of individual robots when within the communication range, therefore, making the system more robust in real scenarios. Our algorithm assumes that all the robots in the swarm are equipped with range and bearing sensors and have limited sensing range and communication abilities. Initially, every robot maintains their relative coordinate frame and follow Levy walk random exploration until they come in range with other robots. When two or more robots are within communication range, they share sensor information and their location w.r.t. their coordinate frames based on which we unify their coordinate frames. Now they can share information about the areas that were already explored, information about the surroundings, and target signal from their location to make decisions about their future movement based on the search algorithm. During the process of exploration, there can be several small groups of robots having their own coordinate systems but eventually, it is expected for all the robots to be under one global coordinate frame where they can communicate information on the exploration area following swarm search techniques. Using the proposed method, swarm-based search algorithms can work in a real-world scenario without GPS and any initial information about the size and shape of the environment. Initial simulation results show that running our modified-Particle Swarm Optimization (PSO) without global information we can still achieve the desired results that are comparable to basic PSO working with GPS. In the full paper, we plan on doing the comparison study between different strategies to unify the coordinate system and to implement them on other bio-inspired algorithms, to work in GPS denied environment.

Keywords: bio-inspired search algorithms, decentralized control, GPS denied environment, swarm robotics, target searching, unifying coordinate systems

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Authors: Bliss Singhal

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Epilepsy is a prevalent neurological disorder affecting approximately 50 million individuals worldwide and 1.2 million Americans. There exist millions of pediatric patients with intractable epilepsy, a condition in which seizures fail to come under control. The occurrence of seizures can result in physical injury, disorientation, unconsciousness, and additional symptoms that could impede children's ability to participate in everyday tasks. Predicting seizures can help parents and healthcare providers take precautions, prevent risky situations, and mentally prepare children to minimize anxiety and nervousness associated with the uncertainty of a seizure. This research proposes a comprehensive framework to predict seizures in pediatric patients by evaluating machine learning algorithms on unimodal neuroimaging data consisting of electroencephalogram signals. The bandpass filtering and independent component analysis proved to be effective in reducing the noise and artifacts from the dataset. Various machine learning algorithms’ performance is evaluated on important metrics such as accuracy, precision, specificity, sensitivity, F1 score and MCC. The results show that the deep learning algorithms are more successful in predicting seizures than logistic Regression, and k nearest neighbors. The recurrent neural network (RNN) gave the highest precision and F1 Score, long short-term memory (LSTM) outperformed RNN in accuracy and convolutional neural network (CNN) resulted in the highest Specificity. This research has significant implications for healthcare providers in proactively managing seizure occurrence in pediatric patients, potentially transforming clinical practices, and improving pediatric care.

Keywords: intractable epilepsy, seizure, deep learning, prediction, electroencephalogram channels

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Authors: Ali Almadan, Anoop Krishnan, Ajita Rattani

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Facial recognition is a widely adopted technology in surveillance, border control, healthcare, banking services, and lately, in mobile user authentication with Apple introducing “Face ID” moniker with iPhone X. A lot of research has been conducted in the area of face recognition on datasets captured by surveillance cameras, DSLR, and mobile devices. Recently, face recognition technology has also been deployed on body-worn cameras to keep officers safe, enabling situational awareness and providing evidence for trial. However, limited academic research has been conducted on this topic so far, without the availability of any publicly available datasets with a sufficient sample size. This paper aims to advance research in the area of face recognition using body-worn cameras. To this aim, the contribution of this work is two-fold: (1) collection of a dataset consisting of a total of 136,939 facial images of 102 subjects captured using body-worn cameras in in-door and daylight conditions and (2) evaluation of various deep-learning architectures for face identification on the collected dataset. Experimental results suggest a maximum True Positive Rate(TPR) of 99.86% at False Positive Rate(FPR) of 0.000 obtained by SphereFace based deep learning architecture in daylight condition. The collected dataset and the baseline algorithms will promote further research and development. A downloadable link of the dataset and the algorithms is available by contacting the authors.

Keywords: face recognition, body-worn cameras, deep learning, person identification

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Authors: Victor Bodell, Lukas Ekstrom, Somayeh Aghanavesi

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Fuel consumption (FC) is one of the key factors in determining expenses of operating a heavy-duty vehicle. A customer may therefore request an estimate of the FC of a desired vehicle. The modular design of heavy-duty vehicles allows their construction by specifying the building blocks, such as gear box, engine and chassis type. If the combination of building blocks is unprecedented, it is unfeasible to measure the FC, since this would first r equire the construction of the vehicle. This paper proposes a machine learning approach to predict FC. This study uses around 40,000 vehicles specific and o perational e nvironmental c onditions i nformation, such as road slopes and driver profiles. A ll v ehicles h ave d iesel engines and a mileage of more than 20,000 km. The data is used to investigate the accuracy of machine learning algorithms Linear regression (LR), K-nearest neighbor (KNN) and Artificial n eural n etworks (ANN) in predicting fuel consumption for heavy-duty vehicles. Performance of the algorithms is evaluated by reporting the prediction error on both simulated data and operational measurements. The performance of the algorithms is compared using nested cross-validation and statistical hypothesis testing. The statistical evaluation procedure finds that ANNs have the lowest prediction error compared to LR and KNN in estimating fuel consumption on both simulated and operational data. The models have a mean relative prediction error of 0.3% on simulated data, and 4.2% on operational data.

Keywords: artificial neural networks, fuel consumption, friedman test, machine learning, statistical hypothesis testing

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Authors: James Jr. Mashiyane, Risuna Nkolele, Stephanie J. Müller, Gciniwe S. Dlamini, Rebone L. Meraba, Darlington S. Mapiye

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When performing language tasks in natural language processing (NLP), the dimensionality of word embeddings is chosen either ad-hoc or is calculated by optimizing the Pairwise Inner Product (PIP) loss. The PIP loss is a metric that measures the dissimilarity between word embeddings, and it is obtained through matrix perturbation theory by utilizing the unitary invariance of word embeddings. Unlike in natural language, in genomics, especially in genome sequence processing, unlike in natural language processing, there is no notion of a “word,” but rather, there are sequence substrings of length k called k-mers. K-mers sizes matter, and they vary depending on the goal of the task at hand. The dimensionality of word embeddings in NLP has been studied using the matrix perturbation theory and the PIP loss. In this paper, the sufficiency and reliability of applying word-embedding algorithms to various genomic sequence datasets are investigated to understand the relationship between the k-mer size and their embedding dimension. This is completed by studying the scaling capability of three embedding algorithms, namely Latent Semantic analysis (LSA), Word2Vec, and Global Vectors (GloVe), with respect to the k-mer size. Utilising the PIP loss as a metric to train embeddings on different datasets, we also show that Word2Vec outperforms LSA and GloVe in accurate computing embeddings as both the k-mer size and vocabulary increase. Finally, the shortcomings of natural language processing embedding algorithms in performing genomic tasks are discussed.

Keywords: word embeddings, k-mer embedding, dimensionality reduction

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Authors: Tatiana A. Dolenko, Sergey A. Burikov, Alexander O. Efitorov, Sergey A. Dolenko

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In this study, a comparative analysis of the approaches associated with the use of neural network algorithms for effective solution of a complex inverse problem – the problem of identifying and determining the individual concentrations of inorganic salts in multicomponent aqueous solutions by the spectra of Raman scattering of light – is performed. It is shown that application of artificial neural networks provides the average accuracy of determination of concentration of each salt no worse than 0.025 M. The results of comparative analysis of input data compression methods are presented. It is demonstrated that use of uniform aggregation of input features allows decreasing the error of determination of individual concentrations of components by 16-18% on the average.

Keywords: inverse problems, multi-component solutions, neural networks, Raman spectroscopy

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Authors: Taher El-Bitar, Maha El-Meligy, Eman El-Shenawy, Almosilhy Almosilhy, Nader Dawood

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The steel contains 0.3% C and 0.004% B, beside Mn, Cr, Mo, and Ni. The alloy was processed by using 20-ton capacity electric arc furnace (EAF), and then refined by ladle furnace (LF). Liquid steel was cast as rectangular ingots. Dilatation test showed the critical transformation temperatures Ac₁, Ac₃, M_s and M_f as 716, 835, 356, and 218 °C. The ingots were austenitized and soaked and then rough rolled to thin slabs with 80 mm thickness. The thin slabs were then reheated and soaked for finish rolling to 6.0 mm thickness plates. During the rough rolling, the roll force increases as a result of rolling at temperatures less than recrystallization temperature. However, during finish rolling, the steel reflects initially continuous static recrystallization after which it shows strain hardening due to fall of temperature. It was concluded that, the steel plates were successfully heat treated by quenching-tempering at 250 ºC for 20 min.

Keywords: armor steel, austenitizing, critical transformation temperatures (CTTs), dilatation curve, martensite, quenching, rough and finish rolling processes, soaking, tempering, thermo-mechanical processing

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Authors: Amr Mansour Mohsen, Hesham Ahmed Hassan, Amira M. Idrees

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Emotions classification of text documents is applied to reveal if the document expresses a determined emotion from its writer. As different supervised methods are previously used for emotion documents’ classification, in this research we present a novel model that supports the classification algorithms for more accurate results by the support of TF-IDF measure. Different experiments have been applied to reveal the applicability of the proposed model, the model succeeds in raising the accuracy percentage according to the determined metrics (precision, recall, and f-measure) based on applying the refinement of the lexicon, integration of lexicons using different perspectives, and applying the TF-IDF weighting measure over the classifying features. The proposed model has also been compared with other research to prove its competence in raising the results’ accuracy.

Keywords: emotion detection, TF-IDF, WEKA tool, classification algorithms

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Authors: Sicheng Liu

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Based on the key issue of the current fourth world studies, the article aims to analyze the features of character-settings in Chinese ethnic minority films. As a generalizable transformation, this feature progresses from a microcosmic representation. It argues that, as the mediation, films note down the current state of people and their surroundings, while the ‘fourth world’ theorization (or the fourth cinema) provides a new perspective to ethnic minority topics in China. Like the ‘fourth cinema’ focusing on the depiction of indigeneity groups, the ethnic minority films portrait the non-Han nationalities in China. Both types possess the motif of returning history-writing to the minority members’ own hand. In this article, the discussion entirely involves three types of cinematic role-settings in Chinese minority themed films, which illustrates that, similar to the creative principle of the fourth film, the themes and narratives of these films are becoming more individualized, with more concern to minority grassroots.

Keywords: 'fourth world', Chinese ethnic minority films, ethnicity and culture reflection, 'mother tongue' (muyu), highlighting to individual spiritual

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Authors: Subhamita Chakraborty, Shubhabrata Datta, Sujay Kumar Mukherjea, Partha Protim Chattopadhyay

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Manufacturing of multiphase high strength steel in hot strip mill have drawn significant attention due to the possibility of forming low temperature transformation product of austenite under continuous cooling condition. In such endeavor, reliable prediction of temperature profile of hot strip coil is essential in order to accesses the evolution of microstructure at different location of hot strip coil, on the basis of corresponding Continuous Cooling Transformation (CCT) diagram. Temperature distribution profile of the hot strip coil has been determined by using finite volume method (FVM) vis-à-vis finite difference method (FDM). It has been demonstrated that FVM offer greater computational reliability in estimation of contact pressure distribution and hence the temperature distribution for curved and irregular profiles, owing to the flexibility in selection of grid geometry and discrete point position, Moreover, use of finite volume concept allows enforcing the conservation of mass, momentum and energy, leading to enhanced accuracy of prediction.

Keywords: simulation, modeling, thermal analysis, coil cooling, contact pressure, finite volume method

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Authors: Carlos Roncero-Parra, Alfonso Parreño-Torres, Jorge Mateo Sotos, Alejandro L. Borja

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In this research, different methods based on machine/deep learning algorithms are presented for the classification and diagnosis of patients with mental disorders such as alzheimer. For this purpose, the signals obtained from 32 unipolar electrodes identified by non-invasive EEG were examined, and their basic properties were obtained. More specifically, different well-known machine learning based classifiers have been used, i.e., support vector machine (SVM), Bayesian linear discriminant analysis (BLDA), decision tree (DT), Gaussian Naïve Bayes (GNB), K-nearest neighbor (KNN) and Convolutional Neural Network (CNN). A total of 668 patients from five different hospitals have been studied in the period from 2011 to 2021. The best accuracy is obtained was around 93 % in both ADM and ADA classifications. It can be concluded that such a classification will enable the training of algorithms that can be used to identify and classify different mental disorders with high accuracy.

Keywords: alzheimer, machine learning, deep learning, EEG

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Authors: Dong Liu

Abstract:

By sending a high-frequency probe wave and a low-frequency pump wave to a specimen, the vibroacoustic method evaluates the defect’s severity according to the modulation index of the received signal. Many studies experimentally proved the significant sensitivity of the modulation index to the tiny contact type defect. However, it has also been found that the modulation index was highly affected by the frequency of probe or pump waves. Therefore, the chirp signal has been introduced to the VAM method since it can assess multiple frequencies in a relatively short time duration, so the robustness of the VAM method could be enhanced. Consequently, the signal processing method needs to be modified accordingly. Various studies utilized different algorithms or combinations of algorithms for processing the VAM signal method by chirp excitation. These signal process methods were compared and used for processing a VAM signal acquired from the steel samples.

Keywords: vibroacoustic modulation, nonlinear acoustic modulation, nonlinear acoustic NDT&E, signal processing, structural health monitoring

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Authors: Xin Shi, Wei Tan, Guorui Zhu

Abstract:

The contraction-expansion array (CEA) microfluidic device is widely used for particle focusing and particle separation. Without the introduction of external fields, it can manipulate particles using hydrodynamic forces, including inertial lift forces and Dean drag forces. The focusing pattern of the particles in a CEA channel can be affected by the structural parameter, block ratio, and flow streamlines. Here, two typical focusing patterns with five different structural parameters were investigated, and the force mechanism was analyzed. We present nine CEA channels with different aspect ratios based on the process of changing the particle equilibrium positions. The results show that 10-15 μm particles have the potential to generate a side focusing line as the structural parameter (¬R𝓌) increases. For a determined channel structure and target particles, when the Reynolds number (Rₑ) exceeds the critical value, the focusing pattern will transform from a single pattern to a double pattern. The parameter α/R𝓌 can be used to calculate the critical Reynolds number for the focusing pattern transformation. The results can provide guidance for microchannel design and biomedical analysis.

Keywords: microfluidic, inertial focusing, particle separation, Dean flow

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Authors: Yanpei Zhen

Abstract:

The nonlinear Schrödinger (NLS) equation models wave dynamics in many physical problems related to fluids, plasmas, and optics. The standing periodic waves are known to be modulationally unstable, and rogue waves (localized perturbations in space and time) have been observed on their backgrounds in numerical experiments. The exact solutions for rogue waves arising on the periodic standing waves have been obtained analytically. It is natural to ask if the rogue waves persist on the standing periodic waves in the integrable discretizations of the integrable NLS equation. We study the standing periodic waves in the semidiscrete integrable system modeled by the high-order Ablowitz-Ladik (AL) equation. The standing periodic wave of the high-order AL equation is expressed by the Jacobi cnoidal elliptic function. The exact solutions are obtained by using the separation of variables and one-fold Darboux transformation. Since the cnoidal wave is modulationally unstable, the rogue waves are generated on the periodic background.

Keywords: Darboux transformation, periodic wave, Rogue wave, separating the variables

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Authors: Cristian Pauna

Abstract:

With few steps away from the 2020, investments in financial markets is a common activity nowadays. In the electronic trading environment, the automated investment software has become a major part in the business intelligence system of any modern financial company. The investment decisions are assisted and/or made automatically by computers using mathematical algorithms today. The complexity of these algorithms requires computer assistance in the investment process. This paper will present several investment strategies that can be automated with algorithmic trading for Deutscher Aktienindex DAX30. It was found that, based on several price action mathematical models used for high-frequency trading some investment strategies can be optimized and improved for automated investments with good results. This paper will present the way to automate these investment decisions. Automated signals will be built using all of these strategies. Three major types of investment strategies were found in this study. The types are separated by the target length and by the exit strategy used. The exit decisions will be also automated and the paper will present the specificity for each investment type. A comparative study will be also included in this paper in order to reveal the differences between strategies. Based on these results, the profit and the capital exposure will be compared and analyzed in order to qualify the investment methodologies presented and to compare them with any other investment system. As conclusion, some major investment strategies will be revealed and compared in order to be considered for inclusion in any automated investment system.

Keywords: Algorithmic trading, automated investment systems, limit conditions, trading principles, trading strategies

Procedia PDF Downloads 190

Authors: Balamurugan L.

Abstract:

Artificial intelligence (AI) and its applications in Intellectual Property Rights (IPR) has been significantly growing in recent years. In last couple of years, AI tools for Patent Research and Patent Analytics have been well-stabilized in terms of accuracy of references and representation of identified patent insights. However, AI tools for Patent Prosecution and Patent Litigation are still in the nascent stage and there may be a significant potential if such market is explored further. Our research is primarily focused on identifying potential whitespaces and schematic algorithms to automate the Patent Prosecution and Patent Litigation Process of the Intellectual Property. The schematic algorithms may assist leading AI tool developers, to explore such opportunities in the field of Intellectual Property. Our research is also focused on identification of pitfalls of the AI. For example, Information Security and its impact in IPR, and Potential remediations to sustain the IPR in the digital environment.

Keywords: artificial intelligence, patent analytics, patent drafting, patent litigation, patent prosecution, patent research

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Authors: T. Topór, A. Derkowski, P. Ziemiański

Abstract:

Formation of organic matter (OM)-hosted nanopores upon thermal maturation are one of the key factor controlling methane storage potential in unconventional shale-gas reservoirs. In this study, the subcritical CO₂ and N₂ gas adsorption measurements combined with scanning electron microscopy and supercritical methane adsorption have been used to characterize pore system and methane storage potential in black shales from the Baltic Basin (Poland). The samples were collected from a virtually equivalent Llandovery strata across the basin and represent a complete digenetic sequence, from thermally immature to overmature. The results demonstrate that the thermal maturation is a dominant mechanism controlling the formation of OM micro- and mesopores in the Baltic Basin shales. The formation of micro- and mesopores occurs in the oil window (vitrinite reflectance; leavedVR; ~0.5-0.9%) as a result of oil expulsion from kerogenleft OM highly porous. The generated hydrocarbons then turn into solid bitumen causing pore blocking and substantial decrease in micro- and mesopore volume in late-mature shales (VR ~0.9-1.2%). Both micro- and mesopores are regenerated in a middle of the catagenesis range (VR 1.4-1.9%) due to secondary cracking of OM and gas formation. The micropore volume in investigated shales is almost exclusively controlled by the OM content. The contribution of clay minerals to micropore volume is insignificant and masked by a strong contribution from OM. Methane adsorption capacity in the Baltic Basin shales is predominantly controlled by microporous OM with pores < 1.5 nm. The mesopore volume (2-50 nm) and mesopore surface area have no effect on methane sorption behavior. The adsorbed methane density equivalent, calculated as absolute methane adsorption divided by micropore volume, reviled a decrease of the methane loading potential in micropores with increasing maturity. The highest methane loading potential in micropores is observed for OM before metagenesis (VR < 2%), where the adsorbed methane density equivalent is greater than the density of liquid methane. This implies that, in addition to physical adsorption, absorption of methane in OM may occur before metagenesis. After OM content reduction using NaOCl solution methane adoption capacity substantially decreases, suggesting significantly greater adsorption potential for OM microstructure than for the clay minerals matrix.

Keywords: maturation, methane sorption, organic matter, porosity, shales

Procedia PDF Downloads 232

Authors: Abdelrahman A. Ramzy, Bassel S. Abdallah, Mohamed E. Bahgat, Sarah M. Abdelkader, Sherif H. ElGohary

Abstract:

Attention-Deficit/Hyperactivity Disorder (ADHD), epilepsy, and autism affect millions of children worldwide, many of which are undiagnosed despite the fact that all of these disorders are detectable in early childhood. Late diagnosis can cause severe problems due to the late treatment and to the misconceptions and lack of awareness as a whole towards these disorders. Moreover, electroencephalography (EEG) has played a vital role in the assessment of neural function in children. Therefore, quantitative EEG measurement will be utilized as a tool for use in the evaluation of patients who may have ADHD, epilepsy, and autism. We propose a screening tool that uses EEG signals and machine learning algorithms to detect these disorders at an early age in an automated manner. The proposed classifiers used with epilepsy as a step taken for the work done so far, provided an accuracy of approximately 97% using SVM, Naïve Bayes and Decision tree, while 98% using KNN, which gives hope for the work yet to be conducted.

Keywords: ADHD, autism, epilepsy, EEG, SVM

Procedia PDF Downloads 185