Performance of an Absorption Refrigerator Using a Solar Thermal Collector

In the present paper, we investigate the feasibility of a thermal solar driven cold room in Gabes, southern region of Tunisia. The cold room of 109 m3 is refrigerated using an ammonia absorption machine. It is destined to preserve dates during the hot months of the year. A detailed study of the cold room leads previously to the estimation of the cooling load of the proposed storage room in the operating conditions of the region. The next step consists of the estimation of the required heat in the generator of the absorption machine to ensure the desired cold temperature. A thermodynamic analysis was accomplished and complete description of the system is determined. We propose, here, to provide the needed heat thermally from the sun by using vacuum tube collectors. We found that at least 21m² of solar collectors are necessary to accomplish the work of the solar cold room.

Modernism’s Influence on Architect-Client Relationship: Comparative Case Studies of Schroder and Farnsworth Houses

The Modernist Movement initially flourished in France, Holland, Germany and the Soviet Union. Many architects and designers were inspired and followed its principles. Two of its most important architects (Gerrit Rietveld and Ludwig Mies van de Rohe) were introduced in this paper. Each did not follow the other’s principles and had their own particular rules; however, they shared the same features of the Modernist International Style, such as Anti-historicism, Abstraction, Technology, Function and Internationalism/ Universality. Key Modernist principles translated into high expectations, which sometimes did not meet the inhabitants’ aspirations of living comfortably; consequently, leading to a conflict and misunderstanding between the designer and their clients’ needs. Therefore, historical case studies (the Schroder and the Farnsworth houses) involving two Modernist pioneer architects have been chosen. This paper is an attempt to explore some of the influential factors affecting buildings design such as: needs, gender, and question concerning commonalities between both designers and their clients. The three aspects and two designers explored here have been chosen because they have been influenced the researchers to understand the impact of those factors on the design process, building’s performance, and the dweller’s satisfaction. This is a descriptive/ analytical research based on two historical comparative case studies that involve several steps such as: key evaluation questions (KEQs), observations, document analysis, etc. The methodology is based on data collation and finding validations. The research aims to state a manifest to regulate the relation between architects and their clients to reach the optimum building performance and functional interior design that suits their clients’ needs, reflects the architects’ character, and the school they belong to. At the end, through the investigation in this paper, the different needs between both the designers and the clients have been seen not only in the building itself but also it could convert the inhabitant’s life in various ways. Moreover, a successful relationship between the architect and their clients could play a significant role in the success of projects. In contrast, not every good design or celebrated building could end up with a successful relationship between the designer and their client or full-fill the inhabitant’s aspirations.

MITOS-RCNN: Mitotic Figure Detection in Breast Cancer Histopathology Images Using Region Based Convolutional Neural Networks

Studies estimate that there will be 266,120 new cases of invasive breast cancer and 40,920 breast cancer induced deaths in the year of 2018 alone. Despite the pervasiveness of this affliction, the current process to obtain an accurate breast cancer prognosis is tedious and time consuming. It usually requires a trained pathologist to manually examine histopathological images and identify the features that characterize various cancer severity levels. We propose MITOS-RCNN: a region based convolutional neural network (RCNN) geared for small object detection to accurately grade one of the three factors that characterize tumor belligerence described by the Nottingham Grading System: mitotic count. Other computational approaches to mitotic figure counting and detection do not demonstrate ample recall or precision to be clinically viable. Our models outperformed all previous participants in the ICPR 2012 challenge, the AMIDA 2013 challenge and the MITOS-ATYPIA-14 challenge along with recently published works. Our model achieved an F- measure score of 0.955, a 6.11% improvement in accuracy from the most accurate of the previously proposed models.

Fatigue Strength of S275 Mild Steel under Cyclic Loading

This study examines the fatigue life of S275 mild steel at room temperature. Mechanical components can fail under cyclic loading during period of time, known as the fatigue phenomenon. In order to prevent fatigue induced failures, material behavior should be investigated to determine the endurance limit of the material for safe design and infinite life, thus leading to reducing the economic cost and loss in human lives. The fatigue behavior of S275 mild steel was studied and investigated. Specimens were prepared in accordance with ASTM E3-11, and fatigue tests of the specimen were conducted in accordance with ASTM E466-07 on a smooth plate, with a continuous radius between ends (hourglass-shaped plate). The method of fatigue testing was applied with constant load amplitude and constant frequency of 4 Hz with load ratio (Fully Reversal R= -1). Surface fractures of specimens were investigated using Scanning Electron Microscope (SEM). The experimental results were compared with the results of a Finite Element Analysis (FEA), using simulation software. The experiment results indicated that the endurance fatigue limit of S275 mild steel was 195.47 MPa.

Investigating the Usability of a University Website from the Users’ Perspective: An Empirical Study of Benue State University Website

Websites are becoming a major component of an organization’s success in our ever globalizing competitive world. The website symbolizes an organization, interacting or projecting an organization’s principles, culture, values, vision, and perspectives. It is an interface connecting organizations and their clients. The university, as an academic institution, makes use of a website to communicate and offer computing services to its stakeholders (students, staff, host community, university management etc). Unfortunately, website designers often give more consideration to the technology, organizational structure and business objectives of the university than to the usability of the site. Website designers end up designing university websites which do not meet the needs of the primary users. This empirical study investigated the Benue State University website from the point view of students. This research was realized by using a standardized website usability questionnaire based on the five factors of usability defined by WAMMI (Website Analysis and Measurement Inventory): attractiveness, controllability, efficiency, learnability and helpfulness. The result of the investigation showed that the university website (https://portal.bsum.edu.ng/) has neutral usability level because of the usability issues associated with the website. The research recommended feasible solutions to improve the usability of the website from the users’ perspective and also provided a modified usability model that will be used for better evaluation of the Benue State University website.

Factors Determining Intention to Pursue Genetic Testing for People in Taiwan

The Ottawa Charter for Health Promotion proposed that the role of health services should shift the focus from cure to prevention. Nowadays, besides having physical examinations, people could also conduct genetic tests to provide important information for diagnosing, treating, and/or preventing illnesses. However, because of the incompletion of the Chinese Genetic Database, people in Taiwan were still unfamiliar with genetic testing. The purposes of the present study were to: (1) Figure out people’s attitudes towards genetic testing. (2) Examine factors that influence people’s intention to pursue genetic testing by means of the Health Belief Model (HBM). A pilot study was conducted on 249 Taiwanese in 2017 to test the feasibility of the self-developed instrument. The reliability and construct validity of scores on the self-developed questionnaire revealed that this HBM-based questionnaire with 40 items was a well-developed instrument. A total of 542 participants were recruited and the valid participants were 535 (99%) between the ages of 20 and 86. Descriptive statistics, one-way ANOVA, two-way contingency table analysis, Pearson’s correlation, and stepwise multiple regression analysis were used in this study. The main results were that only 32 participants (6%) had already undergone genetic testing; moreover, their attitude towards genetic testing was more positive than those who did not have the experience. Compared with people who never underwent genetic tests, those who had gone for genetic testing had higher self-efficacy, greater intention to pursue genetic testing, had academic majors in health-related fields, had chronic and genetic diseases, possessed Catastrophic Illness Cards, and all of them had heard about genetic testing. The variables that best predicted people’s intention to pursue genetic testing were cues to action, self-efficacy, and perceived benefits (the three variables all correlated with one another positively at high magnitudes). To sum up, the HBM could be effective in designing and identifying the needs and priorities of the target population to pursue genetic testing.

Reviewing the Relation of Language and Minorities' Rights

Language is considered as a powerful and outstanding feature of ethnicity. However, humiliating and prohibiting using human language is one the most heinous and brutal acts in the form of racism. In other words, racism can be a product of physiological humiliations and discrimination, such as skin color, and can also be resulted from ethnic humiliation and discrimination such as language, customs and so on. Ethnic and racial discrimination is one of the main problems of the world that minorities and occasionally the majority have suffered from. Nowadays, few states can be found in which all individuals and its citizens are of the same race and ethnicity, culture and language. In these countries, referred to as the multinational states, (eg, Iran, Switzerland, India, etc.), there are the communities and groups which have their own linguistic, cultural and historical characteristics. Characteristics of human rights issues, diversity of issues and plurality of meanings indicate that they appear in various aspects. The states are obliged to respect, as per national and international obligations, the rights of all citizens from different angles, especially different groups that require special attention in order of the particular aspects such as ethnicity, religious and political minorities, children, women, workers, unions and in case the states are in breach of any of these items, they are faced with challenges in local, regional or international fields.

Fingerprint Image Encryption Using a 2D Chaotic Map and Elliptic Curve Cryptography

Fingerprints are suitable as long-term markers of human identity since they provide detailed and unique individual features which are difficult to alter and durable over life time. In this paper, we propose an algorithm to encrypt and decrypt fingerprint images by using a specially designed Elliptic Curve Cryptography (ECC) procedure based on block ciphers. In addition, to increase the confusing effect of fingerprint encryption, we also utilize a chaotic-behaved method called Arnold Cat Map (ACM) for a 2D scrambling of pixel locations in our method. Experimental results are carried out with various types of efficiency and security analyses. As a result, we demonstrate that the proposed fingerprint encryption/decryption algorithm is advantageous in several different aspects including efficiency, security and flexibility. In particular, using this algorithm, we achieve a margin of about 0.1% in the test of Number of Pixel Changing Rate (NPCR) values comparing to the-state-of-the-art performances.

Production Process for Diesel Fuel Components Polyoxymethylene Dimethyl Ethers from Methanol and Formaldehyde Solution

Polyoxymethylene dimethyl ethers (PODEn) as clean diesel additive can improve the combustion efficiency and quality of diesel fuel and alleviate the problem of atmospheric pollution. Considering synthetic routes, PODE production from methanol and formaldehyde is regarded as the most economical and promising synthetic route. However, methanol used for synthesizing PODE can produce water, which causes the loss of active center of catalyst and hydrolysis of PODEn in the production process. Macroporous strong acidic cation exchange resin catalyst was prepared, which has comparative advantages over other common solid acid catalysts in terms of stability and catalytic efficiency for synthesizing PODE. Catalytic reactions were carried out under 353 K, 1 MPa and 3mL·gcat-1·h-1 in a fixed bed reactor. Methanol conversion and PODE3-6 selectivity reached 49.91% and 23.43%, respectively. Catalyst lifetime evaluation showed that resin catalyst retained its catalytic activity for 20 days without significant changes and catalytic activity of completely deactivated resin catalyst can basically return to previous level by simple acid regeneration. The acid exchange capacities of original and deactivated catalyst were 2.5191 and 0.0979 mmol·g-1, respectively, while regenerated catalyst reached 2.0430 mmol·g-1, indicating that the main reason for resin catalyst deactivation is that Brønsted acid sites of original resin catalyst were temporarily replaced by non-hydrogen ion cations. A separation process consisting of extraction and distillation for PODE3-6 product was designed for separation of water and unreacted formaldehyde from reactive mixture and purification of PODE3-6, respectively. The concentration of PODE3-6 in final product can reach up to 97%. These results indicate that the scale-up production of PODE3-6 from methanol and formaldehyde solution is feasible.

Unstructured-Data Content Search Based on Optimized EEG Signal Processing and Multi-Objective Feature Extraction

Over the last few years, the amount of data available on the globe has been increased rapidly. This came up with the emergence of recent concepts, such as the big data and the Internet of Things, which have furnished a suitable solution for the availability of data all over the world. However, managing this massive amount of data remains a challenge due to their large verity of types and distribution. Therefore, locating the required file particularly from the first trial turned to be a not easy task, due to the large similarities of names for different files distributed on the web. Consequently, the accuracy and speed of search have been negatively affected. This work presents a method using Electroencephalography signals to locate the files based on their contents. Giving the concept of natural mind waves processing, this work analyses the mind wave signals of different people, analyzing them and extracting their most appropriate features using multi-objective metaheuristic algorithm, and then classifying them using artificial neural network to distinguish among files with similar names. The aim of this work is to provide the ability to find the files based on their contents using human thoughts only. Implementing this approach and testing it on real people proved its ability to find the desired files accurately within noticeably shorter time and retrieve them as a first choice for the user.

Comparison of E-Waste Management in Switzerland and in Australia: A Qualitative Content Analysis

E-waste/Waste electrical and electronic equipment (WEEE) is one of the fastest growing waste streams across the globe. This paper aims to compare the e-waste management system in Switzerland and Australia in terms of four features - legislative initiatives, disposal practice, collection and financial mechanisms. The qualitative content analysis is employed as a research method in the study. Data were collected from various published academic research papers, industry reports, and web sources. In addition, a questionnaire survey is conducted in Australia to understand the public awareness and opinions on the features. The results of the study provide valuable insights to policymakers in Australia developing better e-waste management system in conjunction with the public consensus, and the state-of-the-art operational strategies currently being practiced in Switzerland.

Optimized Brain Computer Interface System for Unspoken Speech Recognition: Role of Wernicke Area

In this paper, we propose an optimized brain computer interface (BCI) system for unspoken speech recognition, based on the fact that the constructions of unspoken words rely strongly on the Wernicke area, situated in the temporal lobe. Our BCI system has four modules: (i) the EEG Acquisition module based on a non-invasive headset with 14 electrodes; (ii) the Preprocessing module to remove noise and artifacts, using the Common Average Reference method; (iii) the Features Extraction module, using Wavelet Packet Transform (WPT); (iv) the Classification module based on a one-hidden layer artificial neural network. The present study consists of comparing the recognition accuracy of 5 Arabic words, when using all the headset electrodes or only the 4 electrodes situated near the Wernicke area, as well as the selection effect of the subbands produced by the WPT module. After applying the articial neural network on the produced database, we obtain, on the test dataset, an accuracy of 83.4% with all the electrodes and all the subbands of 8 levels of the WPT decomposition. However, by using only the 4 electrodes near Wernicke Area and the 6 middle subbands of the WPT, we obtain a high reduction of the dataset size, equal to approximately 19% of the total dataset, with 67.5% of accuracy rate. This reduction appears particularly important to improve the design of a low cost and simple to use BCI, trained for several words.

The Low-Cost Design and 3D Printing of Structural Knee Orthotics for Athletic Knee Injury Patients

Knee orthotics play an important role in aiding in the recovery of those with knee injuries, especially athletes. However, structural knee orthotics is often very expensive, ranging between $300 and $800. The primary reason for this project was to answer the question: can 3D printed orthotics represent a viable and cost-effective alternative to present structural knee orthotics? The primary objective for this research project was to design a knee orthotic for athletes with knee injuries for a low-cost under $100 and evaluate its effectiveness. The initial design for the orthotic was done in SolidWorks, a computer-aided design (CAD) software available at Loyola Marymount University. After this design was completed, finite element analysis (FEA) was utilized to understand how normal stresses placed upon the knee affected the orthotic. The knee orthotic was then adjusted and redesigned to meet a specified factor-of-safety of 3.25 based on the data gathered during FEA and literature sources. Once the FEA was completed and the orthotic was redesigned based from the data gathered, the next step was to move on to 3D-printing the first design of the knee brace. Subsequently, physical therapy movement trials were used to evaluate physical performance. Using the data from these movement trials, the CAD design of the brace was refined to accommodate the design requirements. The final goal of this research means to explore the possibility of replacing high-cost, outsourced knee orthotics with a readily available low-cost alternative.

Classification of Computer Generated Images from Photographic Images Using Convolutional Neural Networks

This paper presents a deep-learning mechanism for classifying computer generated images and photographic images. The proposed method accounts for a convolutional layer capable of automatically learning correlation between neighbouring pixels. In the current form, Convolutional Neural Network (CNN) will learn features based on an image's content instead of the structural features of the image. The layer is particularly designed to subdue an image's content and robustly learn the sensor pattern noise features (usually inherited from image processing in a camera) as well as the statistical properties of images. The paper was assessed on latest natural and computer generated images, and it was concluded that it performs better than the current state of the art methods.

The Whale Optimization Algorithm and Its Implementation in MATLAB

Optimization is an important tool in making decisions and in analysing physical systems. In mathematical terms, an optimization problem is the problem of finding the best solution from among the set of all feasible solutions. The paper discusses the Whale Optimization Algorithm (WOA), and its applications in different fields. The algorithm is tested using MATLAB because of its unique and powerful features. The benchmark functions used in WOA algorithm are grouped as: unimodal (F1-F7), multimodal (F8-F13), and fixed-dimension multimodal (F14-F23). Out of these benchmark functions, we show the experimental results for F7, F11, and F19 for different number of iterations. The search space and objective space for the selected function are drawn, and finally, the best solution as well as the best optimal value of the objective function found by WOA is presented. The algorithmic results demonstrate that the WOA performs better than the state-of-the-art meta-heuristic and conventional algorithms.

Relevant LMA Features for Human Motion Recognition

Motion recognition from videos is actually a very complex task due to the high variability of motions. This paper describes the challenges of human motion recognition, especially motion representation step with relevant features. Our descriptor vector is inspired from Laban Movement Analysis method. We propose discriminative features using the Random Forest algorithm in order to remove redundant features and make learning algorithms operate faster and more effectively. We validate our method on MSRC-12 and UTKinect datasets.

Fully Printed Strain Gauges: A Comparison of Aerosoljet-Printing and Micropipette-Dispensing

Strain sensors based on a change in resistance are well established for the measurement of forces, stresses, or material fatigue. Within the scope of this paper, fully additive manufactured strain sensors were produced using an ink of silver nanoparticles. Their behavior was evaluated by periodic tensile tests. Printed strain sensors exhibit two advantages: Their measuring grid is adaptable to the use case and they do not need a carrier-foil, as the measuring structure can be printed directly onto a thin sprayed varnish layer on the aluminum specimen. In order to compare quality characteristics, the sensors have been manufactured using two different technologies, namely aerosoljet-printing and micropipette-dispensing. Both processes produce structures which exhibit continuous features (in contrast to what can be achieved with droplets during inkjet printing). Briefly summarized the results show that aerosoljet-printing is the preferable technology for specimen with non-planar surfaces whereas both technologies are suitable for flat specimen.

Suitability of Class F Flyash for Construction Industry: An Indian Scenario

The present study evaluates the properties of class F fly ash as a replacement of natural materials in civil engineering construction industry. The low-lime flash similar to class F is the prime variety generated in India, although it has significantly smaller volumes of high-lime fly ash as compared to class C. The chemical and physical characterization of the sample is carried out with the number of experimental approaches in order to investigate all relevant features present in the samples. For chemical analysis, elementary quantitative results from point analysis and scanning electron microscopy (SEM)/dispersive spectroscopy (EDS) techniques were used to identify the element images of different fractions. The physical properties found very close to the range of common soils. Furthermore, the fly ash-based bricks were prepared by the same sample of class F fly ash and the results of compressive strength similar to that of Standard Clay Brick Grade 1 available in the local market of India.

Consolidating Service Engineering Ontologies Building Service Ontology from SOA Modeling Language (SoaML)

As a term for characterizing a process of devising a service system, the term ‘service engineering’ is still regarded as an ‘open’ research challenge due to unspecified details and conflicting perspectives. This paper presents consolidated service engineering ontologies in collecting, specifying and defining relationship between components pertinent within the context of service engineering. The ontologies are built by way of literature surveys from the collected conceptual works by collating various concepts into an integrated ontology. Two ontologies are produced: general service ontology and software service ontology. The software-service ontology is drawn from the informatics domain, while the generalized ontology of a service system is built from both a business management and the information system perspective. The produced ontologies are verified by exercising conceptual operationalizations of the ontologies in adopting several service orientation features and service system patterns. The proposed ontologies are demonstrated to be sufficient to serve as a basis for a service engineering framework.

Analysis of Cooperative Learning Behavior Based on the Data of Students' Movement

The purpose of this paper is to analyze the cooperative learning behavior pattern based on the data of students' movement. The study firstly reviewed the cooperative learning theory and its research status, and briefly introduced the k-means clustering algorithm. Then, it used clustering algorithm and mathematical statistics theory to analyze the activity rhythm of individual student and groups in different functional areas, according to the movement data provided by 10 first-year graduate students. It also focused on the analysis of students' behavior in the learning area and explored the law of cooperative learning behavior. The research result showed that the cooperative learning behavior analysis method based on movement data proposed in this paper is feasible. From the results of data analysis, the characteristics of behavior of students and their cooperative learning behavior patterns could be found.