Search results for: multiple equations

Authors: Andrew John Zeller, Artjoms Formulevics

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Banking and financial services are rapidly transitioning from being monolithic structures focusing merely on their own financial offerings to becoming integrated players in multiple customer journeys and supply chains. Banks themselves are refocusing on being liquidity providers and underwriters in these networks, while the general concept of ‘embeddedness’ builds on the market readily available API (Application Programming Interface) architectures to flexibly deliver services to various requestors, i.e., online retailers who need finance and insurance products to better serve their customers, respectively. With this new flexibility come new requirements for enhanced cybersecurity. API structures are more decentralized and inherently prone to change. Unfortunately, this has not been comprehensively addressed in the literature. This paper tries to fill this gap by looking at security approaches and technologies relevant to API architectures found in embedded finance. After presenting the research methodology applied and introducing the major bodies of knowledge involved, the paper will discuss six dominating technology trends shaping high-level financial services architectures. Subsequently, embedded finance and the respective usage of API strategies will be described. Building on this, security considerations for APIs in financial and insurance services will be elaborated on before concluding with some ideas for possible further research.

Keywords: embedded finance, embedded banking strategy, cybersecurity, API management, data security, cybersecurity, IT management

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Authors: Antonio Breda d’Azevedo

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Regularity has often been present in the form of regular polyhedra or tessellations; classical examples are the nine regular polyhedra consisting of the five Platonic solids (regular convex polyhedra) and the four Kleper-Poinsot polyhedra. These polytopes can be seen as regular maps. Maps are cellular embeddings of graphs (with possibly multiple edges, loops or dangling edges) on compact connected (closed) surfaces with or without boundary. The n-dimensional abstract polytopes, particularly the regular ones, have gained popularity over recent years. The main focus of research has been their symmetries and regularity. Planification of polyhedra helps its spatial construction, yet it destroys its symmetries. To our knowledge there is no “planification” for n-dimensional polytopes. However we show that it is possible to make a “surfacification” of the n-dimensional polytope, that is, it is possible to construct a restrictedly-marked map representation of the abstract polytope on some surface that describes its combinatorial structures as well as all of its symmetries. We also show that there are infinitely many ways to do this; yet there is one that is more natural that describes reflections on the sides ((n−1)-faces) of n-simplices with reflections on the sides of n-polygons. We illustrate this construction with the 4-tetrahedron (a regular 4-polytope with automorphism group of size 120) and the 4-cube (a regular 4-polytope with automorphism group of size 384).

Keywords: abstract polytope, automorphism group, N-simplicies, symmetry

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Authors: Niladri Sekhar Barma, Satish Dhandole

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Damage identification in the context of safety, nowadays, has become a fundamental research interest area in the field of mechanical, civil, and aerospace engineering structures. The following research is aimed to identify damage in a mechanical beam structure and quantify the severity or extent of damage in terms of loss of stiffness, and obtain an updated analytical Finite Element (FE) model. An FE model is used for analysis, and the location of damage for single and multiple damage cases is identified numerically using the modal strain energy method and mode shape curvature method. Experimental data has been acquired with the help of an accelerometer. Fast Fourier Transform (FFT) algorithm is applied to the measured signal, and subsequently, post-processing is done in MEscopeVes software. The two sets of data, the numerical FE model and experimental results, are compared to locate the damage accurately. The extent of the damage is identified via modal frequencies using a mixed numerical-experimental technique. Mode shape comparison is performed by Modal Assurance Criteria (MAC). The analytical FE model is adjusted by the direct method of model updating. The same study has been extended to some real-life structures such as plate and GARTEUR structures.

Keywords: damage identification, damage quantification, damage detection using modal analysis, structural damage identification

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Authors: Natarajan Tirupattur Srinivasan

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Quantum mechanics( QM) and (special) relativity (SR) have indeed revolutionized the very thinking of physicists, and the spectacular successes achieved over a century due to these two theories are mind-boggling. However, there is still a strong disquiet among some physicists. While the mathematical structure of these two theories has been established beyond any doubt, their physical interpretations are still being contested by many. Even after a hundred years of their existence, we cannot answer a very simple question, “What is an electron”? Physicists are struggling even now to come to grips with the different interpretations of quantum mechanics with all their ramifications. However, it is indeed strange that the (special) relativity theory of Einstein enjoys many orders of magnitude of “acceptance”, though both theories have their own stocks of weirdness in the results, like time dilation, mass increase with velocity, the collapse of the wave function, quantum jump, tunnelling, etc. Here, in this paper, it would be shown that by postulating an intrinsic internal motion to these enigmatic electrons, one can build a fairly consistent picture of reality, revealing a very simple picture of nature. This is also evidenced by Schrodinger’s ‘Zitterbewegung’ motion, about which so much has been written. This leads to a helical trajectory of electrons when they move in a laboratory frame. It will be shown that the helix is a three-dimensional wave having all the characteristics of our familiar 2D wave. Again, the helix, being a geodesic on an imaginary cylinder, supports ‘quantization’, and its representation is just the complex exponentials matching with the wave function of quantum mechanics. By postulating the instantaneous velocity of the electrons to be always ‘c’, the velocity of light, the entire relativity comes alive, and we can interpret the ‘time dilation’, ‘mass increase with velocity’, etc., in a very simple way. Thus, this model unifies both QM and SR without the need for a counterintuitive postulate of Einstein about the constancy of the velocity of light for all inertial observers. After all, if the motion of an inertial frame cannot affect the velocity of light, the converse that this constant also cannot affect the events in the frame must be true. But entire relativity is about how ‘c’ affects time, length, mass, etc., in different frames.

Keywords: quantum reconstruction, special theory of relativity, quantum mechanics, zitterbewegung, complex wave function, helix, geodesic, Schrodinger’s wave equations

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: E. Mendoza, D. Jackson-Maldonado, G. Avecilla-Ramírez, A. Mondaca

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Assessing language proficiency is a major issue in psycholinguistic research. There are multiple tools that measure language dominance and language proficiency in hearing bilinguals, however, this is not the case for Deaf bilinguals. Specifically, there are few, if not none, assessment tools useful in the description of the multilingual abilities of Mexican Deaf signers. Because of this, the linguistic characteristics of Mexican Deaf population have been poorly described. This paper attempts to explain the necessary changes done in order to adapt the Bilingual Language Profile (BLP) to Mexican Sign Language (LSM) and written/oral Spanish. BLP is a Self-Evaluation tool that has been adapted and translated to several oral languages, but not to sign languages. Lexical, syntactic, cultural, and structural changes were applied to the BLP. 35 Mexican Deaf signers participated in a pilot study. All of them were enrolled in Higher Education programs. BLP was presented online in written Spanish via Google Forms. No additional information in LSM was provided. Results show great heterogeneity as it is expected of Deaf populations and BLP seems to be a useful tool to create a bilingual profile of the Mexican Deaf population. This is a first attempt to adapt a widely tested tool in bilingualism research to sign language. Further modifications need to be done.

Keywords: deaf bilinguals, assessment tools, bilingual language profile, mexican sign language

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Authors: Hyun-E Yeom

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Patients’ views about the cause of hypertension may influence their present and proactive behaviors to regulate high blood pressure. This study aimed to examine the internal structure underlying the causal beliefs about hypertension and the influence of causal beliefs on self-care intention and medical compliance in Korean patients with hypertension. The causal beliefs of 145 patients (M age = 57.7) were assessed using the Illness Perception Questionnaire-Revised. An exploratory factor analysis was used to identify the factor structure of the causal beliefs, and the factors’ influence on self-care intention and medication compliance was analyzed using multiple and logistic regression analyses. The four-factor structure including psychological, fate-related, risk and habitual factors was identified and the psychological factor was the most representative component of causal beliefs. The risk and fate-related factors were significant factors affecting lower intention to engage in self-care and poor compliance with medication regimens, respectively. The findings support the critical role of causal beliefs about hypertension in driving patients’ current and future self-care behaviors. This study highlights the importance of educational interventions corresponding to patients’ awareness of hypertension for improving their adherence to a healthy lifestyle and medication regimens.

Keywords: hypertension, self-care, beliefs, medication compliance

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Authors: Jingsen Lian, Steffen Nijhuis, Gregory Bracken, Kai Lan

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This study explores the digital reconstruction of the Chengde Mountain Resort (CMR), a UNESCO World Heritage Site recognized for its cultural landscape significance. Using mixed methods, the research combines spatial, textual, and graphical data to reconstruct the historical evolution of CMR's landscape across four phases from 1704 to the present. Data acquisition includes 3D point clouds, historical maps, traditional paintings, poetry, land-use records, academic papers, engineering drawings, and old photographs. Interdisciplinary techniques such as georectification, 3D modeling, and textual analysis were employed to integrate these diverse datasets into a cohesive Web-GIS platform. The reconstructed data illustrates dynamic landscape changes, reflecting shifting cultural and ecological priorities. The Web-GIS platform facilitates data visualization, querying, and customization, serving multiple stakeholders, including researchers, government planners, and local communities. This study underscores the value of digital tools in cultural heritage preservation, offering a model for adaptive and participatory management of historical sites while promoting open access and stakeholder engagement.

Keywords: landscape mapping, cultural landscape, heritage, case study, mixed methods

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Authors: Dina H. Gabr, Mona A. El Bannan

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With growing worldwide concern for global warming, green finance has become the fuel that pushes the world to act in combating and mitigating climate change. Coupled with adopting the Paris Agreement and the United Nations Sustainable Development Goals, Green finance became a vital tool in creating a pathway to sustainable development, as it connects the financial world with environmental and societal benefits. This paper provides a comprehensive review of the concepts and definitions of green finance and the importance of 'green' impact investments today. The core challenge in combating climate change is reducing and controlling Greenhouse gas emissions; therefore, this study explores the solutions green finance provides putting emphasis on the use of renewable energy, which is necessary for enhancing the transition to the green economy. With increasing attention to the concept of green finance, multiple forms of green investments and financial tools have come to fruition; the most prominent are green bonds. The rise of green bonds, a debt market to finance climate solutions, provide a promising mechanism for sustainable finance. Following the review, this paper compiles a comprehensive green bond dataset, presenting a statistical study of the evolution of the green bonds market from its first appearance in 2006 until 2021.

Keywords: climate change, GHG emissions, green bonds, green finance, sustainable finance

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Authors: M. Mohemmed Sha Mohamed Kunju, Abdalla A. Al-Ameen Abdurahman, T. Manesh Thankappan, A. Mohamed Mustaq Ahmed Hameed

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Web service composition is an effective approach to complete the web based tasks with desired quality. A single web service with limited functionality is inadequate to execute a specific task with series of action. So, it is very much required to combine multiple web services with different functionalities to reach the target. Also, it will become more and more challenging, when these services are from different providers with identical functionalities and varying QoS, so while composing the web services, the overall QoS is considered to be the major factor. Also, it is not true that the expected QoS is always attained when the task is completed. A single web service in the composed chain may affect the overall performance of the task. So care should be taken in different aspects such as functionality of the service, while composition. Dynamic and automatic service composition is one of the main option available. But to achieve the actual functionality of the task, quality of the individual web services are also important. Normally the QoS of the individual service can be evaluated by using the non-functional parameters such as response time, throughput, reliability, availability, etc. At the same time, the QoS is not needed to be at the same level for all the composed services. So this paper proposes a framework that allows composing the services in terms of QoS by setting the appropriate weight to the non-functional parameters of each individual web service involved in the task. Experimental results show that the importance given to the non-functional parameter while composition will definitely improve the performance of the web services.

Keywords: composition, non-functional parameters, quality of service, web service

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Authors: Baladitya Swaika, Rahul Khatry

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Computer Adaptive Tests (CATs) is one of the most efficient ways for testing the cognitive abilities of students. CATs are based on Item Response Theory (IRT) which is based on item selection and ability estimation using statistical methods of maximum information selection/selection from posterior and maximum-likelihood (ML)/maximum a posteriori (MAP) estimators respectively. This study aims at combining both classical and Bayesian approaches to IRT to create a dataset which is then fed to a neural network which automates the process of ability estimation and then comparing it to traditional CAT models designed using IRT. This study uses python as the base coding language, pymc for statistical modelling of the IRT and scikit-learn for neural network implementations. On creation of the model and on comparison, it is found that the Neural Network based model performs 7-10% worse than the IRT model for score estimations. Although performing poorly, compared to the IRT model, the neural network model can be beneficially used in back-ends for reducing time complexity as the IRT model would have to re-calculate the ability every-time it gets a request whereas the prediction from a neural network could be done in a single step for an existing trained Regressor. This study also proposes a new kind of framework whereby the neural network model could be used to incorporate feature sets, other than the normal IRT feature set and use a neural network’s capacity of learning unknown functions to give rise to better CAT models. Categorical features like test type, etc. could be learnt and incorporated in IRT functions with the help of techniques like logistic regression and can be used to learn functions and expressed as models which may not be trivial to be expressed via equations. This kind of a framework, when implemented would be highly advantageous in psychometrics and cognitive assessments. This study gives a brief overview as to how neural networks can be used in adaptive testing, not only by reducing time-complexity but also by being able to incorporate newer and better datasets which would eventually lead to higher quality testing.

Keywords: computer adaptive tests, item response theory, machine learning, neural networks

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Authors: Ahmed Alutaibi, Ganti Sudhakar

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Providing better Quality-of-Service (QoS) to end users has been a challenging problem for researchers and service providers. Building applications relying on best effort network protocols hindered the adoption of guaranteed service parameters and, ultimately, Quality of Service. The introduction of Software Defined Networking (SDN) opened the door for a new paradigm shift towards a more controlled programmable configurable behavior. Openflow has been and still is the main implementation of the SDN vision. To facilitate better QoS for applications, the network must calculate and measure certain parameters. One of those parameters is the delay between the two ends of the connection. Using the power of SDN and the knowledge of application and network behavior, SDN networks can adjust to different conditions and specifications. In this paper, we use the capabilities of SDN to implement multiple algorithms to measure delay end-to-end not only inside the SDN network. The results of applying the algorithms on an emulated environment show that we can get measurements close to the emulated delay. The results also show that depending on the algorithm, load on the network and controller can differ. In addition, the transport layer handshake algorithm performs best among the tested algorithms. Out of the results and implementation, we show the limitations of Openflow and develop suggestions to solve them.

Keywords: software defined networking, quality of service, delay measurement, openflow, mininet

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Authors: Mehrnoosh Ghadimi, Golnoush Ghadimi

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Precise monitoring of glacier velocity is critical in determining glacier-related hazards. Zardkooh Mountain was studied in terms of glacial activity rate in Zagros Mountainous region in Iran. In this study, we assessed the ability of optical and radar imagery to derive glacier-surface velocities in mountainous terrain. We processed Landsat 8 for optical data and Sentinel-1a for radar data. We used methods that are commonly used to measure glacier surface movements, such as cross correlation of optical and radar satellite images, SAR tracking techniques, and multiple aperture InSAR (MAI). We also assessed time series glacier surface displacement using our modified method, Enhanced Small Baseline Subset (ESBAS). The ESBAS has been implemented in StaMPS software, with several aspects of the processing chain modified, including filtering prior to phase unwrapping, topographic correction within three-dimensional phase unwrapping, reducing atmospheric noise, and removing the ramp caused by ionosphere turbulence and/or orbit errors. Our findings indicate an average surface velocity rate of 32 mm/yr in the Zardkooh mountainous areas.

Keywords: active rock glaciers, landsat 8, sentinel-1a, zagros mountainous region

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Authors: Y. Long, L. Liu, K. V. Branin

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One challenge faced by procurement decision-maker during the acquisition process is how to compare similar products from different suppliers and allocate orders among different products or services. This work focuses on allocating orders among multiple suppliers considering rebate. The objective function is to minimize the total acquisition cost including purchasing cost and rebate benefit. Rebate benefit is complex and difficult to estimate at the ordering step. Rebate rules vary for different suppliers and usually change over time. In this work, we developed a system to collect the rebate policies, standardized the rebate policies and developed two-stage optimization models for ordering allocation. Rebate policy with multi-tiers is considered in modeling. The discontinuous cost function of rebate benefit is formulated for different scenarios. A piecewise linear function is used to approximate the discontinuous cost function of rebate benefit. And a Mixed Integer Programing (MIP) model is built for order allocation problem with multi-tier rebate. A case study is presented and it shows that our optimization model can reduce the total acquisition cost by considering rebate rules.

Keywords: discontinuous cost function, mixed integer programming, optimization, procurement, rebate

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Authors: Sabria Barka, Imen Gdara, Zouhour Ouanès, Samia Mouelhi, Monia El Bour, Amel Hamza-Chaffai

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Because dams are large semi-closed reservoirs of pollutants originating from numerous anthropogenic activities, they represent a threat to aquatic life and they should be monitored. The present work aims to use freshwater zooplankton (Copepods and Cladocerans) in order to evaluate the environmental health status of Bir M'cherga dam in Tunisia. Animals were collected in situ monthly between October and August. Genotoxicity (micronucleus test), neurotoxicity (acetylcholinesterase, AChE) and oxidative stress (catalase, CAT and malondialdehyde, MDA) biomarkers were analyzed in zooplankton. High frequencies of micronucleus were observed in zooplankton cells during summer. AChE activities were inhibited during early winter and summer. CAT and MDA biomarker levels showed high seasonal variability, suggesting that animals are permanently exposed to multiple oxidative stress. The results of this study suggest that the Bir Mcherga dam is subject to continuous multi-origin stress, probably amplified by abiotic parameters. It is then recommended to urgently monitor freshwater environments in Tunisia, especially those used for irrigation and consumption.

Keywords: Biomonitoring, Bir Mcherga Dam, cladocerans, copepods, freshwater zooplankton, genotoxicity, neurotoxicity, oxidative stress, Tunisia

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Authors: Amal A. Hassan, Faten H. Fahmy, Abd El-Shafy A. Nafeh, Hosam K. M. Youssef

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Due to the growing penetration of wind energy into the power grid, it is very important to study its interactions with the power system and to provide good control technique in order to deliver high quality power. In this paper, an intelligent control methodology is proposed for optimizing the controllers’ parameters of doubly fed induction generator (DFIG) based wind turbine generation system (WTGS). The genetic algorithm (GA) and particle swarm optimization (PSO) are employed and compared for the parameters adaptive tuning of the proposed proportional integral (PI) multiple controllers of the back to back converters of the DFIG based WTGS. For this purpose, the dynamic model of WTGS with DFIG and its associated controllers is presented. Furthermore, the simulation of the system is performed using MATLAB/SIMULINK and SIMPOWERSYSTEM toolbox to illustrate the performance of the optimized controllers. Finally, this work is validated to 33-bus test radial system to show the interaction between wind distributed generation (DG) systems and the distribution network.

Keywords: DFIG wind turine, intelligent control, distributed generation, particle swarm optimization, genetic algorithm

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Authors: Moyosola Jude Akomolafe

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Career commitment among employees is crucial to the success of any organization. However, career commitment has been reported to be very low among teachers in the public secondary schools in Nigeria. This study, therefore, examined the contributions of emotional intelligence and general self-efficacy to career commitment of among secondary school teachers in Nigeria. Descriptive research design of correlational type was adopted for the study. It made use of stratified random sampling technique was used in selecting two hundred and fifty (250) secondary schools teachers for the study. Three standardized instruments namely: The Big Five Inventory (BFI), Emotional Intelligence Scale (EIS), General Self-Efficacy Scale (GSES) and Career Commitment Scale (CCS) were adopted for the study. Three hypotheses were tested at 0.05 level of significance. Data collected were analyzed through Multiple Regression Analysis to investigate the predicting capacity of emotional intelligence and general self-efficacy on career commitment of secondary school teachers. The results showed that the variables when taken as a whole significantly predicted career commitment among secondary school teachers. The relative contribution of each variable revealed that emotional intelligence and general self-efficacy significantly predicted career commitment among secondary school teachers in Nigeria. The researcher recommended that secondary school teachers should be exposed to emotional intelligence and self-efficacy training to enhance their career commitment.

Keywords: career commitment, emotional intelligence, general self-efficacy, secondary school teachers

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Authors: Ese Urhie, Bosede Olopade, Muyiwa Oladosun, Henry Okodua

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In the past three and half decades, aside from the fact that the contribution of the industrial sector to gross domestic product in Nigeria has nose-dived, its performance has also been highly unstable. Investment funds needed to develop the industrial sector usually come from both internal and external sources. The internal sources include surplus generated within the industrial sector and surplus diverted from other sectors of the economy. It has been observed that due to the small size of the industrial sector in developing countries, very limited funds could be raised for further investment. External sources of funds which many currently industrialized and some ‘newly industrializing countries’ have benefited from including direct and indirect investment by foreign capitalists; foreign aid and loans; and investments by nationals living abroad. Foreign direct investment inflow in Nigeria has been declining since 2009 in both absolute and relative terms. High level of human capital has been identified as one of the crucial factors that explain the miraculous growth of the ‘Asian Tigers’. Its low level has also been identified as the major cause for the low level of FDI flow to Nigeria in particular and Africa in general. There has been positive, but slow improvement in human capital indicators in Nigeria in the past three decades. In spite of this, foreign direct investment inflow has not only been low; it has declined drastically in recent years. i) Why has the improvement in human capital in Nigeria failed to attract more FDI inflow? ii) To what extent does the level of human capital influence FDI inflow in Nigeria? iii) Is there a threshold of human capital stock that guarantees sustained inflow of FDI? iv) Does the quality of human capital matter? v) Does the influence of other (negative) factors outweigh the benefits of human capital? Using time series secondary data, a system of equations is employed to evaluate the effect of human capital on FDI inflow in Nigeria on one hand and the effect of FDI on the level of industrialization on the other. A weak relationship between human capital and FDI is expected, while a strong relationship between FDI and industrial growth is expected from the result.

Keywords: human capital, foreign direct investment, industrialization, gross domestic product

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Authors: Apoorva Vinod, Archana Mathur, Snehanshu Saha

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Though there exists a plethora of Activation Functions (AFs) used in single and multiple hidden layer Neural Networks (NN), their behavior always raised curiosity, whether used in combination or singly. The popular AFs –Sigmoid, ReLU, and Tanh–have performed prominently well for shallow and deep architectures. Most of the time, AFs are used singly in multi-layered NN, and, to the best of our knowledge, their performance is never studied and analyzed deeply when used in combination. In this manuscript, we experiment with multi-layered NN architecture (both on shallow and deep architectures; Convolutional NN and VGG16) and investigate how well the network responds to using two different AFs (Sigmoid-Tanh, Tanh-ReLU, ReLU-Sigmoid) used alternately against a traditional, single (Sigmoid-Sigmoid, Tanh-Tanh, ReLUReLU) combination. Our results show that using two different AFs, the network achieves better accuracy, substantially lower loss, and faster convergence on 4 computer vision (CV) and 15 Non-CV (NCV) datasets. When using different AFs, not only was the accuracy greater by 6-7%, but we also accomplished convergence twice as fast. We present a case study to investigate the probability of networks suffering vanishing and exploding gradients when using two different AFs. Additionally, we theoretically showed that a composition of two or more AFs satisfies Universal Approximation Theorem (UAT).

Keywords: activation function, universal approximation function, neural networks, convergence

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Authors: Ramin Vafadary, Maryam Khanbaghi

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Forecasting electricity load is important for various purposes like planning, operation, and control. Forecasts can save operating and maintenance costs, increase the reliability of power supply and delivery systems, and correct decisions for future development. This paper compares various time series methods to forecast 24 hours ahead of electricity load. The methods considered are the Holt-Winters smoothing, SARIMA Modeling, LSTM Network, Fbprophet, and Tensorflow probability. The performance of each method is evaluated by using the forecasting accuracy criteria, namely, the mean absolute error and root mean square error. The National Renewable Energy Laboratory (NREL) residential energy consumption data is used to train the models. The results of this study show that the SARIMA model is superior to the others for 24 hours ahead forecasts. Furthermore, a Bagging technique is used to make the predictions more robust. The obtained results show that by Bagging multiple time-series forecasts, we can improve the robustness of the models for 24 hours ahead of electricity load forecasting.

Keywords: bagging, Fbprophet, Holt-Winters, LSTM, load forecast, SARIMA, TensorFlow probability, time series

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Authors: Wang Yanli, Cheng Yun, Yang Jiarui

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Due to the separation of teachers and students, online teaching during the COVID-19 epidemic was faced with many problems, such as low enthusiasm of students, distraction, low learning atmosphere, and insufficient interaction between teachers and students. The essay designed the elaborate online learning activities of the course 'Research Methods of Educational Science' based on the APT model from three aspects of multiple assessment methods, a variety of teaching methods, and online learning environment and technology. Student's online learning experience was examined from the perception of online course, the perception of the online learning environment, and satisfaction after the course’s implementation. The research results showed that students have a positive overall evaluation of online courses, a high degree of engagement in learning, positive acceptance of online learning, and high satisfaction with it, but students hold a relatively neutral attitude toward online learning. And some dimensions in online learning experience were found to have positive influence on students' satisfaction with online learning. We suggest making the good design of online courses, selecting proper learning platforms, and conducting blended learning to improve students’ learning experience. This study has both theoretical and practical significance for the design, implementation, effect feedback, and sustainable development of online teaching in the post-epidemic era.

Keywords: APT model, online learning, online learning activities, learning experience

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Authors: Arne Hitzmann, Philipp Wentscher, Alexander Gabel, Reinhard Gerndt

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Autonomous mobile robots can be found in a wide field of applications. Their types range from household robots over workshop robots to autonomous cars and many more. All of them undergo a number of testing steps during development, production and maintenance. This paper describes an approach to improve testing of robot behavior. It was inspired by the RoboCup @work competition that itself reflects a robotics benchmark for industrial robotics. There, scaled down versions of mobile industrial robots have to navigate through a workshop-like environment or operation area and have to perform tasks of manipulating and transporting work pieces. This paper will introduce an approach of automated vision-based testing of the behavior of the so called youBot robot, which is the most widely used robot platform in the RoboCup @work competition. The proposed system allows automated testing of multiple tries of the robot to perform a specific missions and it allows for the flexibility of the robot, e.g. selecting different paths between two tasks within a mission. The approach is based on a multi-camera setup using, off the shelf cameras and optical markers. It has been applied for test-driven development (TDD) and maintenance-like verification of the robot behavior and performance.

Keywords: supervisory control, testing, markers, mono vision, automation

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Authors: Osama Amer, Yaser Abdel-Aty, Mohamed Abd El Hady

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The present paper illustrates an experimental approach to provide understanding of the mechanical behavior and failure mechanisms of different typologies of unreinforced three-leaf masonry walls of historical Islamic architectural heritage in Egypt. The main objective of this study is to investigate the propagation of possible cracking, ultimate load, deformations and failure mechanisms. Experimental data on interface-shear and compression tests on large scale three-leaf masonry wallets are provided. The wallets were built basically of Egyptian limestone and modified lime mortar. External wallets were built of stone blocks while the inner leaf was built of rubble limestone. Different loading conditions and dimensions of core layer for two types of collar joints (with and without shear keys) are considered in the tests. Mechanical properties of the constituent materials of masonry were tested and a database of characteristic properties was created. The results of the experiments will highlight the properties, force-displacement curves, stress distribution of multiple-leaf masonry walls contributing to the derivation of rational design rules and validation of numerical models.

Keywords: masonry, three-leaf walls, mechanical behavior, testing, architectural heritage

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Authors: Abbas Nesayan

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Background: In recent years, autism has been under consideration in public and research area. Autistic children have dysfunction in communication, socialization, repetitive and stereotyped behaviors. In addition, they clinically suffer from difficulty in attention, challenge with familiar behaviors and sensory processing problems. Several variables are linked to sensory processing problems in autism, one of these variables is working memory. Working memory is part of the executive function which provides the necessary ability to completing multiple stages tasks. Method: This study has categorized in correlational research methods. After determining of entry criteria, according to purposive sampling method, 50 children were selected. Dunn’s sensory profile school companion was used for assessment of sensory processing patterns; behavioral rating inventory of executive functions was used (BRIEF) for assessment of working memory. Pearson correlation coefficient and linear regression were used for data analyzing. Results: The results showed the significant relationship between sensory processing patterns (low registration, sensory seeking, sensory sensitivity and sensory avoiding) with working memory in autistic children. Conclusion: According to the findings, there is the significant relationship between the patterns of sensory processing and working memory. So, in order to improve the working memory could be used some interventions based on the sensory processing.

Keywords: sensory processing patterns, working memory, autism, autistic children

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Authors: Fatema Ali Lanewala, Akhtar Jamal Khan

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The ocular complications in renal transplant recipients at the biggest transplant center in Pakistan were seen to be diverse, multiple, and sight-threatening. These complications could mainly be due to the primary disease causing renal failure, the process of transplantation, and/or the medications used pre and post-transplantation. A retrospective case series recently published in the Journal of Pakistan Medical Association highlights the common ocular pathologies encountered in renal transplant population. Majority of the patients suffered from cataract, which is a known side-effect of long-term steroids routinely used for graft survival. There was a unique finding in Pakistani population, never reported before from any other transplant centre world over; a large number of recipients was reported to be suffering from night blindness, which significantly improved on vitamin A supplementation. There were a variety of other ocular complications seen which emphasizes the necessity of ocular care and routine examination of transplant recipient’s eyes by an ophthalmologist in order to avoid visual compromise and improve the quality of life of the transplant recipient.

Keywords: cataract, night blindness, ocular complications, renal transplantation

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Authors: Chellammal Surianarayanan

Abstract:

Healthcare industry is generating large amounts of data driven by various needs such as record keeping, physician’s prescription, medical imaging, sensor data, Electronic Patient Record(EPR), laboratory, pharmacy, etc. Healthcare data is so big and complex that they cannot be managed by conventional hardware and software. The complexity of healthcare big data arises from large volume of data, the velocity with which the data is accumulated and different varieties such as structured, semi-structured and unstructured nature of data. Despite the complexity of big data, if the trends and patterns that exist within the big data are uncovered and analyzed, higher quality healthcare at lower cost can be provided. Hadoop is an open source software framework for distributed processing of large data sets across clusters of commodity hardware using a simple programming model. The core components of Hadoop include Hadoop Distributed File System which offers way to store large amount of data across multiple machines and MapReduce which offers way to process large data sets with a parallel, distributed algorithm on a cluster. Hadoop ecosystem also includes various other tools such as Hive (a SQL-like query language), Pig (a higher level query language for MapReduce), Hbase(a columnar data store), etc. In this paper an analysis has been done as how healthcare big data can be processed and analyzed using Hadoop ecosystem.

Keywords: big data analytics, Hadoop, healthcare data, towards quality healthcare

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Authors: Y. Ruchi, A. Prerna, S. Deepshikha

Abstract:

Amyotrophic lateral sclerosis (ALS), also known as “Lou Gehrig’s disease”. It is a neurodegenerative disease associated with degeneration of motor neurons in the cerebral cortex, brain stem, and spinal cord characterized by distal muscle weakness, atrophy, normal sensation, pyramidal signs and progressive muscular paralysis reflecting. ALS2 is a juvenile autosomal recessive disorder, slowly progressive, that maps to chromosome 2q33 and is associated with mutations in the alsin gene, a putative GTPase regulator. In this paper we have done homology modeling of alsin2 protein using multiple templates (3KCI_A, 4LIM_A, 402W_A, 4D9S_A, and 4DNV_A) designed using the Prime program in Schrödinger software. Further modeled structure is used to identify effective binding sites on the basis of structural and physical properties using sitemap program in Schrödinger software, structural and function analysis is done by using Prosite and ExPASy server that gives insight into conserved domains and motifs that can be used for protein classification. This paper summarizes the structural, functional and binding site property of alsin2 protein. These binding sites can be potential drug target sites and can be used for docking studies.

Keywords: ALS, binding site, homology modeling, neuronal degeneration

Procedia PDF Downloads 390

Authors: Shemroy Roberts

Abstract:

Despite minor theoretical contributions in slavery heritage tourism research that have commented on the demand-side perspective, visitor behavior and engagement with slavery heritage attractions remain unexplored. Thus, there is a need for empirical studies and theoretical knowledge to understand visitor engagement with slavery heritage attractions, particularly U.K. slavery heritage museums. The purpose of this paper is to critically evaluate the factors that influence visitor engagement with U.K. slavery heritage museums. This qualitative research utilizes a passive symbolic ethnographic methodology. Seven U.K. slavery heritage museums will be used to collect data through unobtrusive internet-mediated observations of TripAdvisor reviews and online semi-structured interviews with managers and curators. Preliminary findings indicate that social media, prior knowledge, multiple motivations, cultural capital, and the design and layout of exhibits influence visitor engagement with slavery heritage museums. This research contributes to an understanding of visitor engagement with U.K. slavery heritage museums. The findings of this paper will provide insights into the factors that influence visitor engagement with U.K. slavery heritage museums to managers, curators, and decision-makers responsible for designing and managing those attractions. Therefore, the results of this paper will enable museum professionals to better manage visitor engagement with slavery heritage museums.

Keywords: museums, netnography, slavery, visitor engagement

Procedia PDF Downloads 325

Authors: Elham Jelodari Mamaghani, Christian Prins, Haoxun Chen

Abstract:

In this paper, there is concentration on collaborative transportation planning (CTP) among multiple carriers with pickup and delivery requests and time windows. This problem is a vehicle routing problem with constraints from standard vehicle routing problems and new constraints from a real-world application. In the problem, each carrier has a finite number of vehicles, and each request is a pickup and delivery request with time window. Moreover, each carrier has reserved requests, which must be served by itself, whereas its exchangeable requests can be outsourced to and served by other carriers. This collaboration among carriers can help them to reduce total transportation costs. A mixed integer programming model is proposed to the problem. To solve the model, a hybrid algorithm that combines Genetic Algorithm and Simulated Annealing (GASA) is proposed. This algorithm takes advantages of GASA at the same time. After tuning the parameters of the algorithm with the Taguchi method, the experiments are conducted and experimental results are provided for the hybrid algorithm. The results are compared with those obtained by a commercial solver. The comparison indicates that the GASA significantly outperforms the commercial solver.

Keywords: centralized collaborative transportation, collaborative transportation with pickup and delivery, collaborative transportation with time windows, hybrid algorithm of GA and SA

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Authors: Rajpal Kaur, Pooja Choudhary

Abstract:

Biometrics, which refers to identifying an individual based on his or her physiological or behavioral characteristics, has the capabilities to the reliably distinguish between an authorized person and an imposter. The Signature recognition systems can categorized as offline (static) and online (dynamic). This paper presents Surf Feature based recognition of offline signatures system that is trained with low-resolution scanned signature images. The signature of a person is an important biometric attribute of a human being which can be used to authenticate human identity. However the signatures of human can be handled as an image and recognized using computer vision and HMM techniques. With modern computers, there is need to develop fast algorithms for signature recognition. There are multiple techniques are defined to signature recognition with a lot of scope of research. In this paper, (static signature) off-line signature recognition & verification using surf feature with HMM is proposed, where the signature is captured and presented to the user in an image format. Signatures are verified depended on parameters extracted from the signature using various image processing techniques. The Off-line Signature Verification and Recognition is implemented using Mat lab platform. This work has been analyzed or tested and found suitable for its purpose or result. The proposed method performs better than the other recently proposed methods.

Keywords: offline signature verification, offline signature recognition, signatures, SURF features, HMM

Procedia PDF Downloads 387