Microneedles have been using nowadays in many biomedical trends and therefore gained popularity form the last few years. In this paper, the simulation techniques for solid microneedle made up of gold has been given. Usually, a patch of many microneedles arranged in the form of an array has been used for drug deliveries and other biomedical applications but we have considered here a single microneedle to understand the effect of pressure on the tip of microneedle that how it gets deformed and distorted. Here we have performed a simulation and considered the transient structure of gold microneedle in which the effect of pressure on the tip with respect to time has been calculated. The pressure of 1000 MPa has been applied with respect to variable time.
The main goal of this paper is to present a method that allows the automatic detection of communication patterns of patients with a severe state of ASD (Autism Spectrum Disorder), through audio and video processing. Patients with ASD are have social and communicative disorders that make it difficult to analyze their expression, since do not manage natural language properly. The problem could range from a slight difficulty of expression to the total impossibility of speaking. The processing of the routines to be able to extract video and sound patterns is based on the work "ASD: ML perspective for Individual Performance Evaluation". Taking the basis that stereotyped responses can be detected in a systematic way, similar to those that specialized therapists usually describe: gaze fixation, nodding, leg movements, clapping, among others; In this work, a model for the parameterization of said movements and sounds is proposed, through routines specially oriented to detect automatic patterns by audio and video processing. In this work there are the development and first results obtained from audio analysis of a use case, and main findings in relation on how the patient trend to interact with the environment. They allows to suppose that autistic verbal behavior might be interpreted by its physically manifested sounds.
Ergonomic risk assessment is traditionally human-assisted and performed filling forms or through on-site inspection by ergonomists. This frequently leads to inaccuracies due to the subjective bias. Also, it is inefficient and costly given the time and technical knowledge required. Computer-based alternatives are slowly emerging, but so far there is few consensus or uniformity between the underlying practices and technologies. A standardization of data collection in video takes through computer vision offers the opportunity to obtain considerable replication levels, which would increase the reliability of the results and the quality of the data available to ergonomists. In this work we propose a workflow that employs two open-source neural networks: STAF for workers’ body joints detection and tracking, and VIBE for 3D movement estimation. Finally, the ergonomic risk is calculated based on REBA, which is one of the most widespread standard in industrial settings. As data collection may be a bottleneck (as usual in deep learning) we propose the use of virtual scenarios generated in Unity3D. This allows to evaluate and quantify several problems associated with actual video takes, including self-occlusions, camera positioning, illumination, noisy backgrounds, and many others. The results are positively conclusive about both the use of this workflow for actual risk assessment, and the feasibility of virtual environments for controlled experimentation.
On a daily basis, a human being relies on the ability to perceive, recognize, interpret and reproduce a stream of speech. The language is biologically encoded in our brain since the day we are born. Described briefly as the exchange of thoughts, ideas and knowledge, language was difficult to be precisely localized within the brain for many years. The most important areas of the brain involved in the speech and language systems in the brain have been researched and discussed starting with patients whose brain was damaged and the main effects were speech problems in talking or in comprehension of the speech. Definitions of language, descriptions of functionality and as accurate as can be spoken organization of the main systems within the human brain have been tried to be let in the research field. Looking back in the literature, we can see that the most important role in this field is taken by the latest neuroimaging methods developed over the years and which helped researchers in exploring and mapping the brain.
Due to the immense increase in the applications of the Internet of Things, our lives have immensely changed technologically. Smart health care is related to IoT-based smart devices, machines, patients, doctors, and sensors on the internet. Health care has become an important social-economic concern for health care, health needs and availability, and private care for the particularly elderly class of society. Various smart health services are working on these applications are working as individual services, and the need of the hour is to provide a management platform where all these services work as a single unit providing all these facilities to the users. This research focuses on Smart health care systems and proposes a model for a smart health care insurance policy model using Ontology engineering, a knowledge-based system developed for a health insurance policy.
Searching contents from a query performed using Natural Language is a hard task when there is a limited time to response and no other information but a reduced set of documents. This paper presents a novel approach to expand queries in Natural Language. The working hypothesis, a short introduction to some concepts of Morphosyntactic Linguistic Wavelets that are of interest, and a use case of the implemented model are within the scope of this paper. Preliminary results indicate the self-expansion proposal is fast, and produces a good number of correct re-phrasings that can be used to recall more documents with proper answers to the original query.
Technology is being increasingly used in domains such as education, where a large part of the teacher’s or mentor’s time is wasted in drafting up informal tests for classroom use. In this paper, we will present a genetic algorithm-based program which can automatically generate optimized exam tests from a data bank of categorized questions. The user can select the number of questions included in a test and the categories relevant to the test. In the final part of the paper we show relevant performance tests results obtained with a Java implementation of this algorithm.
One of the biggest problems of the World Wide Web and pub has probably, several times, been experiencing different, but almost identical, or nearly identical content results in a simple Google search. In the case of publishers, duplicate content represents plagiarism or copyright infringement, therefore large financial losses. Google is seriously dealing with content duplication and may penalize duplicate sites or pages as a result. There are many software packages and services that identify and/or solve this problem, but they are not free. The current paper describes how to remove duplicate, or nearly identical, content from a database, using utilities that implement specific algorithms. One of these utilities is Natural Language Toolkit (NLTK) written in Python. NLTK is a platform for creating programs in Python, to work with data in human language. The way to detect duplicate content is described for the applications running on open source, Linux and Windows operating systems.
In this paper we will elaborate on specific architectural elements for designing a decision support system for cyber risks management in critical infrastructures. The development of any information system follows standard steps, from the design to the market ready product. The design phase is critical as it ensures that the final system is adjusted to the needs and requirements of the customer. The paper starts with the identification of architecture elements of a decision support system. Throughout the paper we will focus on the input and output elements of a decision support system. We will propose categories of data that are required managing cyber risks in the critical infrastructure domain. We will identify the critical design aspects these two elements. In conclusion we will elaborate on the implications of context and human element on the architecture for the DSS, as well as future propose future research activities in this area.
Cloud computing has always been a boon to the end-users by providing access for the storage and retrieval of data on demand rather than using their own devices. However, as count of users using cloud is increasing, and the resources count is finite, the challenges and issues are also getting more. One of the main challenges is balancing the load in the data centre. The dynamically changing requirements of the users need to be considered and should be executed on heterogeneous nodes rather than homogeneous nodes to minimize the response time and maximize resource utilization. Often there arises a need to cater to the load when the number of users multiplies exponentially. Thus, load balancing plays a great role in improving performance by maximizing resource utilization. As task scheduling which is a part of load balancing is an NP-hard problem, Swarm Intelligence techniques are best in designing efficient and effective load balancing algorithms. These algorithms are designed keeping in mind the collective behavior of different insects and how they search for food. Algorithms based on this approach have significantly shown much improvement. This manuscript has presented a comparison of various load balancing algorithms based on the different performance metrics in cloud computing.
The aim of this paper is to show and explain the implementation of this custom design pattern in developing iOS mobile applications, in regards to adopting the SOLID [14, 15] principles. We also do not rely on any interface builder tool, we code the user interface elements. One of the main purposes of this research was to find and assess the best combination of the existing software design patterns based on the ease of understanding, granularity, maintainability, modularity and scalability. The existing design patterns used in this custom implementation are Singleton, Factory, Builder, MVC, MVVM and Coordinator, all wrapped around the Rx-Swift reactive framework [1]. The study results in the performance analysis of this custom design pattern using the Swift programming language [17]. The mobile application used for case study is UBOOKER [16] which domain is business and modelling, representing the usage of algorithms, multimedia content (photos) and ample data.
The study is motivated to explore the collaboration between the authors, the patterns which are being used by the authors and the areas on which the authors are working. The end product of the research is a set of generic guidelines for the field of business process management. Business process management conference, which was one of the most famous conference is been used to develop the dataset for the study. Business process management conference started since 2003 so the papers since 2003 till 2016 were collected to create the dataset. The collaboration of authors is been studied by applying certain filters over the raw data of business process management conference. The research pattern of different authors was been studied individually from each paper and later they were all combined by identifying the common or similar aspects of the processes to form a set of frameworks. The study has created a generic set of five frameworks. Each for a specific domain of business process management i.e. business process similarity, business process abstraction, business process model redesign, business process monitoring, and business process mining. The study also provides some key facts about the most collaborative authors and the domains on which the authors or the research community is mainly focusing.
The purpose of this research project was to carry out an overview of Specification. On what land office is in position to facilitate the Sector level transaction by land sale service, which is now semi-automated through the Irembo Portal. The study was carried out using both quantitative and qualitative research methodology approach. Various users, agencies and stakeholders were interviewed. In order to analyze data, Statistical Package for the Social Science (IBM SPSS) and visual paradigm software were used to simulate the current workflow and designed workflow. As a result, we developed a suggested workflow that demonstrates the use of fingerprint the Public Key Infrastructure (PKI) as an ICT-enabled solution to automate the entire process. The interconnection of different database systems would help the proposed solution: National Identification Agency (NIDA), Land Administration Information System (LAIS), the Electronic Mortgage Registration System (eMRS); Rwanda Revenue Authority (RRA) and the Banks.
Schema transformation is the process of generating dimensional models from conceptual models. Transformation methodologies use entity relationship diagrams as input and provides guidelines to produce resultant dimensional models. Previous studies reveal that only ERD models are used for the transformation. Many techniques have been introduced for the conversion of basic ERD model into different data warehouse schemas i.e. star schema, snowflake schema etc. After applying and concluding that existing transformation techniques of ER model are not capable enough to generate star model when EER is provided as an input, this study is conducted to design data warehouse schema using Enhanced ER model as it possesses some additional features that provide more detailed information about the database system. A semi-automated technique is developed to produce star models from EERs. The study provides the solution for the complex associations i.e. disjoint and overlapping entities by translating them into hierarchies and then, after applying some rules, produce dimensions of the star model. It is thus declared by the results that proposed technique helps the novice and intermediate designer to develop data warehouse models more precisely.
This paper presented a technique for classifying multiple object scenes in video as rigid and non-rigid objects. This paper presented the application of affine motion and clustering methods. Using this technique, we are going to convert the motion into different layers. We are using the “person pushing a cart” sequence. We are able to convert it into three layers. Then present the whole sequence with an image for every layer. Further, we call these images as reference image. This reference image when compared with subsequent image gives some deviation between the similarities of these images with respect to appearance of the image. We use the techniques of others to evaluate the periodicity of these images. Our assumption is that the change of appearances of rigid objects (vehicle) is slower than the non-rigid (human) objects. The presented framework is also applicable to other sequences with multiple motions.
Blockchain is an ever-growing ledger that keeps a permanent record of all transactions that took place on the network in a secure and chronological order and cannot be changed or tampered with. Many researchers from library and information science community have undergone to study and explain the ways for utilizing this new technology at their workplaces. Resource sharing amongst multiple learning ecosystems comes with several challenges as multiple access, transactional records, authorization, trust issues and security of learning environment. This paper is an attempt to explain the methods to share learning resources using blockchain technology. It includes the study of current functioning models in resource sharing and their setbacks. Current access and authorization systems are designed for centralized server and distributed databases. A tentative design is proposed to develop a library resource circulation system in which blockchain is used as a platform to manage access control on resources as well.
This article describes an original software package conceived for an optimal design and for predictive maintenance of high voltage commutation equipment (switches). Initial data was measured in the industrial environment and, by consequent, it was used for establishing a digital testing and design procedure for an optimal diagnosis and assessment of the physical state of internal High Voltage contacts. All solutions used for the monitoring and design improvement application, described here, can be partially used for other high voltage devices (transformers, surge arresters etc.). In this paper, we will present a dedicated fuzzy-logic algorithm, applied in order to establish the main issues concerning its predictive maintenance, conclusions also applied for the future design of such pieces of equipment. This fuzzy algorithm could be an example for the integration of advanced algorithms and software solutions mainly for the necessary refurbishment of any high voltage equipment in service.
Tea (Camellia Sinensis) is among the most popular crops worldwide. The crop has been cultivated for more than 300 years and is a source of various categories of tea which include: green, black, yellow, white Oolong, ilex, among others. Among these categories of tea, black tea is the most popular with an estimated consumption of 78
The need for the advancement of technology is to lead human life comfortable. The use of Internet of Things (IoT) is changing the face of development in all sectors of human life. Due to the presence and an increase in smart home technology, it has led to increase of the need of intelligent learning of the inhabitant’s and detecting their activities such as security, energy efficient automation, resource management etc. However, uses of IoT have not been effectively utilized in many developing countries in Africa where energy is not properly used for the domestic and other purposes. Power consumption has become a great problem due to inefficient use of appliances and unreliable occupancy detection. Therefore, this paper provides the specified IoT solution to the problem where an energy efficient system is proposed for efficient energy management. In this paper, motion sensors and smart appliances are installed in a house to automate the house and to detect motion and occupational of inhabitant in the room/house for energy efficient monitoring. The energy usage is recorded and support vector machine (SVM) algorithm is used to predict the future needed energy. The evaluation results of the system show that the proposed systems provide a smart home with energy efficient compared to the non-smart house energy system and thus the recommendations for smart house for household in developing countries in Africa.
The online mode of learning is gaining popularity in the education field due to various reasons. The rapid expansion in the practice of web-based technologies have made educators to take advantage of ICT based learning in Higher Education Institutes worldwide in emergency situations. The occurrence of novel COVID-19 pandemic shifts the ongoing physical education system from face-to-face learning to virtual learning worldwide. Virtual learning is not sufficient for the fulfillment of undergraduate students’ learning requirments as they require practical knowledge as well. Therefore, higher education institutes need the integration of blended learning methods for the improvement of students’ learning outcomes in their respective field of studies. Blended learning is the latest trend of implementing online learning strategy along with other e-learning tools. This research paper focuses on the development of a blended virtual model using a probabilistic model i.e. Bayesian network classifier for the prediction of students’ academic performance. The blended enriched virtual model is adapted that includes online and offline learning. Online learning includes online lectures, chat collaborations and online courses. Whereas offline face to face learning includes physical classroom lectures and lab sessions for practical work. The proposed BN model is applied to undergraduate computing students for analysis of learning outcomes of Data Structures and Algorithms subject. According to the findings of proposed BN model that if students properly attend the classroom lectures followed by their lab practical in Face-to-face learning and the proper online learning activities like lectures, chats and online courses, the learning outcomes of the students may be improved and the proposed BN model also reports the accuracy of about 85%.
