Showing papers by "Jordan University of Science and Technology published in 2019"

Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016

Abstract: Objectives To use the estimates from the Global Burden of Disease Study 2016 to describe patterns of suicide mortality globally, regionally, and for 195 countries and territories by age, sex, and Socio-demographic index, and to describe temporal trends between 1990 and 2016. Design Systematic analysis. Main outcome measures Crude and age standardised rates from suicide mortality and years of life lost were compared across regions and countries, and by age, sex, and Socio-demographic index (a composite measure of fertility, income, and education). Results The total number of deaths from suicide increased by 6.7% (95% uncertainty interval 0.4% to 15.6%) globally over the 27 year study period to 817 000 (762 000 to 884 000) deaths in 2016. However, the age standardised mortality rate for suicide decreased by 32.7% (27.2% to 36.6%) worldwide between 1990 and 2016, similar to the decline in the global age standardised mortality rate of 30.6%. Suicide was the leading cause of age standardised years of life lost in the Global Burden of Disease region of high income Asia Pacific and was among the top 10 leading causes in eastern Europe, central Europe, western Europe, central Asia, Australasia, southern Latin America, and high income North America. Rates for men were higher than for women across regions, countries, and age groups, except for the 15 to 19 age group. There was variation in the female to male ratio, with higher ratios at lower levels of Socio-demographic index. Women experienced greater decreases in mortality rates (49.0%, 95% uncertainty interval 42.6% to 54.6%) than men (23.8%, 15.6% to 32.7%). Conclusions Age standardised mortality rates for suicide have greatly reduced since 1990, but suicide remains an important contributor to mortality worldwide. Suicide mortality was variable across locations, between sexes, and between age groups. Suicide prevention strategies can be targeted towards vulnerable populations if they are informed by variations in mortality rates.

An intrusion detection system for connected vehicles in smart cities

Abstract: In the very near future, transportation will go through a transitional period that will shape the industry beyond recognition. Smart vehicles have played a significant role in the advancement of intelligent and connected transportation systems. Continuous vehicular cloud service availability in smart cities is becoming a crucial subscriber necessity which requires improvement in the vehicular service management architecture. Moreover, as smart cities continue to deploy diversified technologies to achieve assorted and high-performance cloud services, security issues with regards to communicating entities which share personal requester information still prevails. To mitigate these concerns, we introduce an automated secure continuous cloud service availability framework for smart connected vehicles that enables an intrusion detection mechanism against security attacks and provides services that meet users’ quality of service (QoS) and quality of experience (QoE) requirements. Continuous service availability is achieved by clustering smart vehicles into service-specific clusters. Cluster heads are selected for communication purposes with trusted third-party entities (TTPs) acting as mediators between service requesters and providers. The most optimal services are then delivered from the selected service providers to the requesters. Furthermore, intrusion detection is accomplished through a three-phase data traffic analysis, reduction, and classification technique used to identify positive trusted service requests against false requests that may occur during intrusion attacks. The solution adopts deep belief and decision tree machine learning mechanisms used for data reduction and classification purposes, respectively. The framework is validated through simulations to demonstrate the effectiveness of the solution in terms of intrusion attack detection. The proposed solution achieved an overall accuracy of 99.43% with 99.92% detection rate and 0.96% false positive and false negative rate of 1.53%.

Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS)

Abstract: Recommender systems have been based on context and content, and now the technological challenge of making personalized recommendations based on the user emotional state arises through physiological signals that are obtained from devices or sensors. This paper applies the deep learning approach using a deep convolutional neural network on a dataset of physiological signals (electrocardiogram and galvanic skin response), in this case, the AMIGOS dataset. The detection of emotions is done by correlating these physiological signals with the data of arousal and valence of this dataset, to classify the affective state of a person. In addition, an application for emotion recognition based on classic machine learning algorithms is proposed to extract the features of physiological signals in the domain of time, frequency, and non-linear. This application uses a convolutional neural network for the automatic feature extraction of the physiological signals, and through fully connected network layers, the emotion prediction is made. The experimental results on the AMIGOS dataset show that the method proposed in this paper achieves a better precision of the classification of the emotional states, in comparison with the originally obtained by the authors of this dataset.

Using long short-term memory deep neural networks for aspect-based sentiment analysis of Arabic reviews

Abstract: This paper proposes a state-of-the-art research for aspect-based sentiment analysis of Arabic Hotels’ reviews using two implementations of long short-term memory (LSTM) neural networks. The first one is (a) a character-level bidirectional LSTM along with conditional random field classifier (Bi-LSTM-CRF) for aspect opinion target expressions (OTEs) extraction, and the second one is (b) an aspect-based LSTM for aspect sentiment polarity classification in which the aspect-OTEs are considered as attention expressions to support the sentiment polarity identification. Proposed approaches are evaluated using a reference dataset of Arabic Hotels’ reviews. Results show that our approaches outperform baseline research on both tasks with an enhancement of 39% for the task of aspect-OTEs extraction and 6% for the aspect sentiment polarity classification task.

Diseases, Injuries, and Risk Factors in Child and Adolescent Health, 1990 to 2017: Findings From the Global Burden of Diseases, Injuries, and Risk Factors 2017 Study

Abstract: Importance: Understanding causes and correlates of health loss among children and adolescents can identify areas of success, stagnation, and emerging threats and thereby facilitate effective improvement strategies. Objective: To estimate mortality and morbidity in children and adolescents from 1990 to 2017 by age and sex in 195 countries and territories. Design, Setting, and Participants: This study examined levels, trends, and spatiotemporal patterns of cause-specific mortality and nonfatal health outcomes using standardized approaches to data processing and statistical analysis. It also describes epidemiologic transitions by evaluating historical associations between disease indicators and the Socio-Demographic Index (SDI), a composite indicator of income, educational attainment, and fertility. Data collected from 1990 to 2017 on children and adolescents from birth through 19 years of age in 195 countries and territories were assessed. Data analysis occurred from January 2018 to August 2018. Exposures: Being under the age of 20 years between 1990 and 2017. Main Outcomes and Measures: Death and disability. All-cause and cause-specific deaths, disability-adjusted life years, years of life lost, and years of life lived with disability. Results: Child and adolescent deaths decreased 51.7% from 13.77 million (95% uncertainty interval [UI], 13.60-13.93 million) in 1990 to 6.64 million (95% UI, 6.44-6.87 million) in 2017, but in 2017, aggregate disability increased 4.7% to a total of 145 million (95% UI, 107-190 million) years lived with disability globally. Progress was uneven, and inequity increased, with low-SDI and low-middle-SDI locations experiencing 82.2% (95% UI, 81.6%-82.9%) of deaths, up from 70.9% (95% UI, 70.4%-71.4%) in 1990. The leading disaggregated causes of disability-adjusted life years in 2017 in the low-SDI quintile were neonatal disorders, lower respiratory infections, diarrhea, malaria, and congenital birth defects, whereas neonatal disorders, congenital birth defects, headache, dermatitis, and anxiety were highest-ranked in the high-SDI quintile. Conclusions and Relevance: Mortality reductions over this 27-year period mean that children are more likely than ever to reach their 20th birthdays. The concomitant expansion of nonfatal health loss and epidemiological transition in children and adolescents, especially in low-SDI and middle-SDI countries, has the potential to increase already overburdened health systems, will affect the human capital potential of societies, and may influence the trajectory of socioeconomic development. Continued monitoring of child and adolescent health loss is crucial to sustain the progress of the past 27 years.

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Abstract: Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations.

Impact of digital fingerprint image quality on the fingerprint recognition accuracy

Abstract: Despite the large body of work on fingerprint identification systems, most of it focused on using specialized devices. Due to the high price of such devices, some researchers directed their attention to digital cameras as an alternative source for fingerprints images. However, such sources introduce new challenges related to image quality. Specifically, most digital cameras compress captured images before storing them leading to potential losses of information. This study comes to address the need to determine the optimum ratio of the fingerprint image compression to ensure the fingerprint identification system’s high accuracy. This study is conducted using a large in-house dataset of raw images. Therefore, all fingerprint information is stored in order to determine the compression ratio accurately. The results proved that the used software functioned perfectly until a compression ratio of (30–40%) of the raw images; any higher ratio would negatively affect the accuracy of the used system.

A comprehensive survey of arabic sentiment analysis

Abstract: Sentiment analysis (SA) is a continuing field of research that lies at the intersection of many fields such as data mining, natural language processing and machine learning It is concerned with the automatic extraction of opinions conveyed in a certain text Due to its vast applications, many studies have been conducted in the area of SA especially on English texts, while other languages such as Arabic received less attention This survey presents a comprehensive overview of the works done so far on Arabic SA (ASA) The survey groups published papers based on the SA-related problems they address and tries to identify the gaps in the current literature laying foundation for future studies in this field

Positron Emission Tomography-Guided Treatment in Early-Stage Favorable Hodgkin Lymphoma: Final Results of the International, Randomized Phase III HD16 Trial by the German Hodgkin Study Group.

Abstract: PURPOSECombined-modality treatment (CMT) with 2× ABVD (doxorubicin, bleomycin, vinblastine, and dacarbazine) and small-field radiotherapy is standard of care for patients with early-stage favorable...

K -Means clustering and neural network for object detecting and identifying abnormality of brain tumor

Abstract: Brain tumor diagnosis is a challenging and difficult process in view of the assortment of conceivable shapes, regions, and image intensities. The pathological detection and identification of brain tumor and comparison among normal and abnormal tissues need grouped scientific techniques for features extraction, displaying, and measurement of the disease images. Our study shows an improved automated brain tumor segmentation and identification approach using ANN from MR images without human mediation by applying the best attributes toward preparatory brain tumor case revelation. To obtain the exact district region of brain tumor from MR images, we propose a brain tumor segmentation technique that has three noteworthy improvement focuses. To begin with, K-means clustering will be utilized as a part of the principal organization in the process of improving the MR image to be marked in the districts regions in light of their gray scale. Second, ANN is utilized to choose the correct object in view of training phase. Third, texture feature of brain tumor area will be extracted to the division stage. With respect to the brain tumor identification, the grayscale features are utilized to analyze and diagnose the brain tumor to differentiate the benign and malignant cases. According to the study results demonstrated that: (1) enhancement adaptive strategy was utilized as post-processing in brain tumor identification; (2) identify and build an assessment foundation of automated segmentation and identification for brain tumor cases; (3) highlight the methods based on region growing method and K-means clustering technique to select the best region; and (4) evaluate the proficiency of the foreseen outcomes by comparing ANN and SVM segmentation outcomes, and brain tumor cases classification. The ANN approach classifier recorded accuracy of 94.07% with line assumption (brain tumor cases classification) and sensitivity of 90.09% and specificity of 96.78%.

An efficient employment of internet of multimedia things in smart and future agriculture

Abstract: Efficiently managing the irrigation process has become necessary to utilize water stocks due to the lack of water resources worldwide. Parched plant leads into hard breathing process, which would result in yellowing leaves and sprinkles in the soil. In this work, yellowing leaves and sprinkles in the soil have been observed using multimedia sensors to detect the level of plant thirstiness in smart farming. We modified the IoT concepts to draw an inspiration towards the perspective vision of ’Internet of Multimedia Things’ (IoMT). This research focuses on the smart employment of internet of Multimedia sensors in smart farming to optimize the irrigation process. The concepts of image processing work with IOT sensors and machine learning methods to make the irrigation decision. sensors reading have been used as training data set indicating the thirstiness of the plants, and machine learning techniques including the state-of-the-art deep learning were used in the next phase to find the optimal decision. The conducted experiments in this research are promising and could be considered in any smart irrigation system. The experimental results showed that the use of deep learning proves to be superior in the Internet of Multimedia Things environment.

Data and Service Management in Densely Crowded Environments: Challenges, Opportunities, and Recent Developments

Abstract: Densely crowded environments such as stadiums and metro stations have shown shortcomings when users request data and services simultaneously. This is due to the excessive amount of requested and generated traffic from the user side. Based on the wide availability of user smart-mobile devices, and noting their technological advancements, devices are not being categorized only as data/service requesters anymore, but are readily being transformed to data/ service providing network-side tools. In essence, to offload some of the workload burden from the cloud, data can be either fully or partially replicated to edge and mobile devices for faster and more efficient data access in such dense environments. Moreover, densely crowded environments provide an opportunity to deliver, in a timely manner, through node collaboration, enriched user-specific services using the replicated data and device-specific capabilities. In this article, we first highlight the challenges that arise in densely crowded environments in terms of data/service management and delivery. Then we show how data replication and service composition are considered promising solutions for data and service management in densely crowded environments. Specifically, we describe how to replicate data from the cloud to the edge, and then to mobile devices to provide faster data access for users. We also discuss how services can be composed in crowded environments using service-specific overlays. We conclude the article with most of the open research areas that remain to be investigated.

Identification of Escherichia coli from broiler chickens in Jordan, their antimicrobial resistance, gene characterization and the associated risk factors.

Abstract: Avian pathogenic Escherichia coli (APEC) is the principle cause of colibacillosis affecting poultry. The main challenge to the poultry industry is antimicrobial resistance and the emergence of multidrug resistant bacteria that threaten the safety of the food chain. Risk factors associated with emergence of antimicrobial resistance among avian pathogenic E. coli were correlated with the inappropriate use of antimicrobials along with inadequate hygienic practices, which encourages the selection pressure of antimicrobial resistant APEC. The aim of this study was to isolate, identify, serogroup and genotype APEC from broilers, assess their antibiotic resistance profile, expressed genes and the associated risk factors. APEC was isolated from the visceral organs of sick chickens with a prevalence of 53.4%. The most prevalent serotypes were O1, O2, O25 and O78, in percentage of 14.8, 12.6, 4.4 and 23.7%, respectively. Virulence Associated Genes; SitA, iss, iucD, iucC, astA, tsh cvi and irp2 were detected in rate of 97.4, 93.3, 75, 74, 71, 46.5, 39 and 34%, respectively and 186 (69.2%) isolates possess > 5–10 genes. The highest resistance was found against sulphamethoxazole-trimethoprim, florfenicol, amoxicillin, doxycycline and spectinomycin in percentage; 95.5, 93.7, 93.3, 92.2 and 92.2%, respectively. Sixty-eight percent of APEC isolates were found to have at least 5 out of 8 antimicrobial resistant genes. The most predominant genes were Int1 97%, tetA 78.4%, bla TEM 72.9%, Sul1 72.4%, Sul2 70.2%. Two risk factors were found to be associated with the presence of multi-drug resistant APEC in broiler chickens, with a P value ≤0.05; the use of ground water as source of drinking water and farms located in proximity to other farms. This study characterized the VAGs of avian pathogenic E. coli and establish their antimicrobial resistance patterns. The widespread of antimicrobial resistance of APEC isolates and detection of ARGs highlighted the need to monitor the spread of ARGs in poultry farms and the environment in Jordan. Use of ground water and closely located farms were significant risk factors associated with the presence of MDR APEC in broiler chickens in Jordan.

An overview of colistin resistance, mobilized colistin resistance genes dissemination, global responses, and the alternatives to colistin: A review

Abstract: Colistin, also known as polymyxin E, is an antimicrobial agent that is effective against a variety of Gram-negative bacilli, especially the Enterobacteriaceae family. Recently, the wide dissemination of colistin-resistance has brought strong attention to the scientific society because of its importance as the last resort for the treatment of carbapenem-resistant Enterobacteriaceae infections and its possible horizontal transmission. The mobilized colistin resistance (mcr) gene was identified as the gene responsible for unique colistin resistance. Indeed, despite many studies that have revealed a pan variation in the existence of this gene, not only for the mcr genes main group but also for its many subgroups, the problem is growing and worsening day after day. In this regard, this review paper is set to review the updated data that has been published up to the end of 2019 third quarter, especially when related to colistin resistance by the mcr genes. It will include the present status of colistin resistance worldwide, the mcr gene dissemination in different sectors, the discovery of the mcr variants, and the global plan to deal with the threat of antimicrobial resistance. In line with global awareness, and to stop antibiotic misuse and overuse, especially in agricultural animals, the study will further discuss in detail the latest alternatives to colistin use in animals, which may contribute to the elimination of inappropriate antibiotic use and to the help in preventing infections. This review will advance our understanding of colistin resistance, while supporting the efforts toward better stewardship, for the proper usage of antimicrobial drugs in humans, animals, and in the environment.

Energy-Efficient Fuzzy-Logic-Based Clustering Technique for Hierarchical Routing Protocols in Wireless Sensor Networks.

Abstract: In wireless sensor networks, the energy source is limited to the capacity of the sensor node's battery. Clustering in WSN can help with reducing energy consumption because transmission energy is related to the distance between sender and receiver. In this paper, we propose a fuzzy logic model for cluster head election. The proposed model uses five descriptors to determine the opportunity for each node to become a CH. These descriptors are: residual energy, location suitability, density, compacting, and distance from the base station. We use this fuzzy logic model in proposing the Fuzzy Logic-based Energy-Efficient Clustering for WSN based on minimum separation Distance enforcement between CHs (FL-EEC/D). Furthermore, we adopt the Gini index to measure the clustering algorithms' energy efficiency in terms of their ability to balance the distribution of energy through WSN sensor nodes. We compare the proposed technique FL-EEC/D with a fuzzy logic-based CH election approach, a k-means based clustering technique, and LEACH. Simulation results show enhancements in energy efficiency in terms of network lifetime and energy consumption balancing between sensor nodes for different network sizes and topologies. Results show an average improvement in terms of first node dead and half nodes dead.

A Comparison of the Surface Properties of CAD/CAM and Conventional Polymethylmethacrylate (PMMA).

Abstract: Purpose To compare surface properties of 2 brands of pre‐polymerized resin blocks for complete dentures (CAD/CAM PMMA) to conventional heat‐polymerized PMMA. Materials and Methods A total of 45 rectangular specimens (25 × 25 × 3 mm) were fabricated from 3 brands of PMMA (n = 15/group): AvaDent CAD/CAM PMMA, Tizian‐Schutz CAD/CAM PMMA, Meliodent conventional PMMA. Specimens were examined for wettability using the sessile drop method, surface roughness using a digital contact profilometer, and microhardness using Vickers hardness number. Statistical analysis was performed using one‐way ANOVA and Tukey pairwise multiple comparisons. p‐Values of ≤0.05 were considered significant. Results AvaDent specimens demonstrated the highest mean contact angle (72.87 ± 48°) and the highest mean Vickers hardness number (20.62 ± 0.33). The conventional heat‐polymerized specimens showed the highest mean surface roughness (0.22 ± 0.071 μm). Tizian‐Schutz specimens showed the lowest mean surface roughness (0.12 ± 0.02 μm). Conclusions As CAD/CAM PMMA groups exhibited significantly more favorable surface properties in comparison to the conventional heat‐polymerized groups, CAD/CAM dentures are expected to be more durable. Different brands of CAD/CAM PMMA may have inherent variations in surface properties.

The effects of hookah/waterpipe smoking on general health and the cardiovascular system

Abstract: Hookah or waterpipe smoking or use is an emerging trend in the US population, especially among the youth. The misperception of hookah being less harmful than cigarettes and the availability of different but “appealing” flavors are considered among the main reasons for this trend. Hookah users however are exposed to many of the same toxic compounds/by-products as cigarette users, but at dramatically higher levels, which might lead to more severe negative health effects. In fact, hookah users are at risks of infections, cancers, lung disease, and other medical conditions. Moreover, because of the overlapping toxicant/chemical profile to conventional cigarettes, hookah smoke effects on the cardiovascular system are thought to be comparable to those of conventional cigarettes. A major source of tobacco addiction is nicotine, whose levels in hookah are extremely variable as they depend on the type of tobacco used. Taken together, in this review of literature, we will provide insights on the negative health effects of hookah in general, with a focus on what is known regarding its impact on the cardiovascular system.

Gold nanoparticles and angiogenesis: molecular mechanisms and biomedical applications

Abstract: Angiogenesis is the formation of new blood vessels from pre-existing vessels. It is a highly regulated process as determined by the interplay between pro-angiogenic and anti-angiogenic factors. Under certain conditions the balance between angiogenesis stimulators and inhibitors is altered, which results in a shift from physiological to pathological angiogenesis. Therefore, the goal of therapeutic targeting of angiogenic process is to normalize vasculature in target tissues by enhancing angiogenesis in disease conditions of reduced vascularity and blood flow, such as tissue ischemia, or alternatively to inhibit excessive and abnormal angiogenesis in disorders like cancer. Gold nanoparticles (AuNPs) are special particles that are generated by nanotechnology and composed of an inorganic core containing gold which is encircled by an organic monolayer. The ability of AuNPs to alter vasculature has captured recent attention in medical literature as potential therapeutic agents for the management of pathologic angiogenesis. This review provides an overview of the effects of AuNPs on angiogenesis and the molecular mechanisms and biomedical applications associated with their effects. In addition, the main synthesis methods, physical properties, uptake mechanisms, and toxicity of AuNPs are briefly summarized.

An enhanced J48 classification algorithm for the anomaly intrusion detection systems

Abstract: In this paper, we have developed an enhanced J48 algorithm, which uses the J48 algorithm for improving the detection accuracy and the performance of the novel IDS technique. This enhanced J48 algorithm is seen to help in an effective detection of probable attacks which could jeopardise the network confidentiality. For this purpose, the researchers used many datasets by integrating different approaches like the J48, Naive Bayes, Random Tree and the NB-Tree. An NSL KDD intrusion dataset was applied while carrying out all experiments. This dataset was divided into 2 datasets, i.e., training and testing, which was based on the data processing. Thereafter, a feature selection method based on the WEKA application was used for evaluating the efficacy of all the features. The results obtained suggest that this algorithm showed a better, accurate and more efficient performance without using the above-mentioned features when compared to the feature selection procedure. An implementation of this algorithm guaranteed the dataset classification based on a detection accuracy of 99.88% for all the features when using the 10-fold cross-validation test, a 90.01% accuracy for the supplied test set after using the complete test datasets along with all the features and a 76.23% accuracy for supplying the test set after using the test-21 dataset along with all features.

Immunotherapy for HER2-positive breast cancer: recent advances and combination therapeutic approaches.

Abstract: Cancer immunotherapy has evolved dramatically with improved understanding of immune microenvironment and immunosurveillance. The immunogenicity of breast cancer is rather heterogeneous. Specific subtypes of breast cancer such as estrogen receptor (ER)-negative, human EGF receptor 2 (HER2)-positive, and triple-negative breast cancer (TNBC) have shown evidence of immunogenicity based on tumor-immune interactions. Several preclinical and clinical studies have explored the potential for immunotherapy to improve the clinical outcomes for different subtypes of breast cancer. This review describes the immune microenvironment of HER2-positive breast cancer and summarizes recent clinical advances of immunotherapeutic treatments in this breast cancer subtype. The review provides rationale and ongoing clinical evidence to the use of immune checkpoint inhibitors, therapeutic vaccines, and adoptive T cell immunotherapy in breast cancer. In addition, the present paper describes the most relevant clinical progress of strategies for the combination of immunotherapy with standard treatment modalities in HER2-positive breast cancer including chemotherapy, targeted therapy, and radiotherapy.

The application of virtual reality technology in architectural pedagogy for building constructions

Abstract: The recent development in information technology has huge opportunities to improve the architectural education in terms of methodologies, strategies and tools. Building construction courses taught in the College of Architecture and Design at Jordan University of Science and Technology mainly depend on the traditional_Teacher–centered_method of teaching. This research suggests a virtual environment technology as a tool to develop new educational approach for these courses. This study developed computer software for this purpose to deal with building construction using virtual reality technology (BC\VR software). This software is designed by the authors for research purpose and presents 4D model (3D model and time dimension) for certain building construction phases using VR technology to do immersive and non-immersive virtual reality experience for the users. This research aims at evaluating the (BC\VR Software) in architectural education of building construction courses as a case study at Jordan University of Science and Technology (JUST) in terms of three axes: providing students with the building construction information, achieving enjoyment, and the integrating with other courses. The study sample was selected from the population of building construction students at Jordan University of Science and Technology (JUST). A structured questionnaire was designed and distributed to the students of the abovementioned classes. The results show that the VR software has the ability to achieve the three axes better than those of the traditional teaching method. As a conclusion, using the BC\VR software as a tool in building construction courses is very useful and effective for the students. The VR technology is also applicable on other architectural courses.

Experimental study and analysis of solar still desalination using phase change materials

Abstract: A solar unit to investigate the effect of PCM on the temperature change of the water during the entire day (day time and night time) was designed. The unit is also used to test the effect of PCM on the amount of fresh water produced. Candle wax (tricosane) was used as PCM and its amount was varied to achieve the ratio of mass of PCM to mass of water of 0, 0.17, 0.35 and 0.51 respectively. The amount of water used was fixed throughout all the experiment at 3 kg tap water. The wax was placed in copper tubes immersed in water. The results showed that presence of PCM causes the appearance of two zones in which the temperature is strongly affected by the PCM. The first zone appears due to the melting of the PCM (during day time) and the second zone appears due to solidification (during night time) of the PCM. The effect of the PCM is prominent in the second zone where the temperature remains constant at the PCM melting point. The length of the solidification zone is proportional to the amount of PCM. Fresh water production is strongly affected by the presence of the PCM. During the day time fresh water production is inversely proportion to the value of R. However, during the night time fresh water production is directly proportional to the value of R.

ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function

Abstract: Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naive, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.

Laser beam machining of carbon fiber reinforced composites: a review

Abstract: Carbon fiber reinforced polymer (CFRP) composites gained wide acceptance in aerospace, automotive and marine industries due to their superior properties. It became the major structural material that substitutes metals in many weight-critical components such as the new A350 and the B787 aircrafts, with composite content to exceed the 50%. Although CFRP structures are manufactured to near-net-shape, edge trimming, drilling, sawing, milling, and grinding operations are unavoidable. Being anisotropic, inhomogeneous and highly abrasive, their conventional machining is normally associated with delamination, fibers pull-out, inadequate surface quality, and tool wear. Other nontraditional processes which include abrasive water jet machining (AWJM), ultrasonic machining (USM), and electrodischarge machining (EDM) offer substitute to the conventional methods. Laser beam machining (LBM) is an emerging technology offering an excellent alternative for machining CFRP composites. This paper reviews the research work carried out in the area of LBM of CFRP materials. It reports the experimental and theoretical studies covering the process accuracy in terms kerf width, kerf depth and edge quality, and the thermal characteristics in terms of heat-affected zone (HAZ). Minimizing the kerf taper, increasing kerf depth, and eliminating the HAZ in the polymer matrix are considered the major obstacles of CFRP industrial applications. Methods of improving the machining productivity by reducing the machining time and increasing the material removal rate (MRR) and kerf depth are reviewed. Several mathematical and statistical modeling and optimization techniques have been critically examined. The concept of specific energy and its impact on HAZ and kerf width is introduced. The relationship between laser type and HAZ is discussed. The current work furthermore outlines the possible trends for future research.