Evaluation of social personalized adaptive E-Learning environments : end-user point of view

The use of adaptations, along with the social affordances of collaboration and networking, carries a great potential for improving e-learning experiences. However, the review of the previous work indicates current e-learning systems have only marginally explored the integration of social features and adaptation techniques. The overall aim of this research, therefore, is to address this gap by evaluating a system developed to foster social personalized adaptive e-learning experiences. We have developed our first prototype system, Topolor, based on the concepts of Adaptive Educational Hypermedia and Social E-Learning. We have also conducted an experimental case study for the evaluation of the prototype system from different perspectives. The results show a considerably high satisfaction of the end users. This paper reports the evaluation results from end user point of view, and generalizes our method to a component-based evaluation framework

Dimension Reduction using Dual-Featured Auto-encoder for the Histological Classification of Human Lungs Tissues

Histopathology images are visual representations of tissue samples that have been processed and examined under a microscope in order to establish diagnoses for various disorders. These images are categorized by deep transfer learning due to the absence of big annotated datasets. There are some classifiers such as softmax and Support Vector Machine (SVM) used to perform multiple and binary classification respectively. Feature reduction for high dimensional images, is an emerging technique which can meet two basic criteria’s of classification i.e. it deals with over-fitting issue and it can also incredibly increase the classification accuracy. As disease diagnosis requires accurate histopathological image classification, so the proposed Dual Featured Auto-encoder (DFAE) based transfer learning is introduced with Triple Layered Convolutional Architecture. The Histological CIMA dataset is used after pre-processing by PHAT, a mathematical and computational framework to get spatial features as well as spectral features. In order to achieve the two objectives, the proposed integrated methodology uses reduced informative features from DFAE and fed them to Triple Layered Convolutional Architecture (TLCA). The conventional Convolutional Neural Network (CNN), ResNet50, Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) are also tested against reduced dimensional image data but we found moderate or even low accuracies i.e. 25% for DFAE-ResNet50, 66% for DFAE-LSTM, 33% for DFAE-GRU and 67% for DFAE-CNN. While the accuracy of our proposed architecture Dual Featured Auto-encoder with TLCA (DFAE-TLCA) is better i.e. 96.07%. The proposed methodology has the potential to revolutionize the medical research

Java on Networks of Workstations (JavaNOW): A Parallel Computing Framework Inspired by Linda and the Message Passing Interface (MPI)

Networks of workstations are a dominant force in the distributed computing arena, due primarily to the excellent price/performance ratio of such systems when compared to traditionally massively parallel architectures. It is therefore critical to develop programming languages and environments that can help harness the raw computational power available on these systems. In this article, we present JavaNOW (Java on Networks of Workstations), a Java‐based framework for parallel programming on networks of workstations. It creates a virtual parallel machine similar to the MPI (Message Passing Interface) model, and provides distributed associative shared memory similar to the Linda memory model but with a richer set of primitive operations. JavaNOW provides a simple yet powerful framework for performing computation on networks of workstations. In addition to the Linda memory model, it provides for shared objects, implicit multithreading, implicit synchronization, object dataflow, and collective communications similar to those defined in MPI. JavaNOW is also a component of the Computational Neighborhood, a Java‐enabled suite of services for desktop computational sharing. The intent of JavaNOW is to present an environment for parallel computing that is both expressive and reliable and ultimately can deliver good to excellent performance. As JavaNOW is a work in progress, this article emphasizes the expressive potential of the JavaNOW environment and presents preliminary performance results only

Effect of sorbitol in callus induction and plant regeneration in wheat

Six wheat genotypes were evaluated for their response to callus induction and regeneration on MS medium modified with different concentrations of sorbitol, that is, 0, 10, 20, 30 gL-1 along with optimum (3 mgL-1) concentration of 2,4-D. Variability was observed among different genotypes for callus induction. Highest callus induction frequency was shown by Wafaq- 2001, which was about 85.62% followed by Inqalab-91 which showed 71.94% callus induction. While minimum callus induction frequency was shown by Saleem-2000 which was about 51.21%. Regarding sorbitol concentration highest average callus induction frequency (79.20%) was obtained at 20 gL-1 and lowest average callus induction frequency (59.20%) was observed at 30 gL-1. In Wafaq-2001 and Inqalab-91 plant regeneration increased gradually by increasing the sorbitol concentration from 0 to 20 gL-1 but then it decreased. Similarly Auqab-2002 had no regeneration al all on non-sorbitol medium but showed regeneration on addition of sorbitol. Similarly time duration required for plant regeneration also decreased by increasing the concentration of sorbitol. It was also observed that sorbitol has given more strength to regenerated plant

Client Satisfaction Towards Quality of Health Services: an Assessment at Primary Healthcare of District Gujranwala

This survey designed to evaluate the satisfaction level and the factors that affect the patient satisfaction regarding health care delivery services with the aim to improve the services in the primary health care settings of Gujranwala. A Cross Sectional Study done on randomly selected patients attending the basic health units of Gujranwala, with more than18 years of age. Pretested structured "Liker scale questionnaire" was used for data collection. Out of total respondents, 62 (41.3%) clients were satisfied with the services provided by the basic health units of Gujranwala. The factors identified to determine patient satisfaction were accessibility of services, behavior of staff, health education, level of cleanliness, drug availability and miscellaneous services. Not a single ranked area of satisfaction noticed. Client\u27s occupation and income had significant relationship with the patient satisfaction level. Gender, age, and education of clients were not contributing factors; they not affect the client satisfaction level.Less than half clients were satisfied with the services provided by the basic health units. Management of health facilities needs to improve the services

GROWTH PERFORMANCE AND FEED CONVERSION RATIO (FCR) OF HYBRID FINGERLINGS (CATLA CATLA X LABEO ROHITA) FED ON COTTONSEED MEAL, SUNFLOWER MEAL AND BONE MEAL

An experiment was conducted in six glass aquaria to study the growth performance and feed conversion ratio (FCR) of hybrid fingerlings (Catla catla x Labeo rohita) fed on sunflower meal, cottonseed meal and bone meal. Two replicates for each ingredient were followed. The feed was supplied at the rate of 4% of wet body weight of fingerlings twice a day. The hybrid (Catla catla x Labeo rohita) fingerlings gained highest body weight (1.62 ± 0.0 g) on sunflower meal, followed by cottonseed meal (1.61 ± 0.01 g) and bone meal (1.52 ± 0.0 g). The total length obtained by hybrid fish was 6.35 ± 0.05 cm on sunflower meal, 6.12 ± 0.05 cm on cottonseed meal and 5.85 ± 0.05 cm on bone meal. The overall mean values of FCR were lower (better) on sunflower meal (1.78 ± 0.05), followed by cottonseed meal (2.17 ± 0.01) and bone meal (2.46 ± 0.01). Thus, The sunflower meal and cottonseed meal, on the basis of growth performance and better FCR, can be included in the feed formulation for hybrid fingerlings

Application of soft computing models with input vectors of snow cover area in addition to hydro-climatic data to predict the sediment loads

The accurate estimate of sediment load is important for management of the river ecosystem, designing of water infrastructures, and planning of reservoir operations. The direct measurement of sediment is the most credible method to estimate the sediments. However, this requires a lot of time and resources. Because of these two constraints, most often, it is not possible to continuously measure the daily sediments for most of the gauging sites. Nowadays, data-based sediment prediction models are famous for bridging the data gaps in the estimation of sediment loads. In data-driven sediment predictions models, the selection of input vectors is critical in determining the best structure of models for the accurate estimation of sediment yields. In this study, time series inputs of snow cover area, basin effective rainfall, mean basin average temperature, and mean basin evapotranspiration in addition to the flows were assessed for the prediction of sediment loads. The input vectors were assessed with artificial neural network (ANN), adaptive neuro-fuzzy logic inference system with grid partition (ANFIS-GP), adaptive neuro-fuzzy logic inference system with subtractive clustering (ANFIS-SC), adaptive neuro-fuzzy logic inference system with fuzzy c-means clustering (ANFIS-FCM), multiple adaptive regression splines (MARS), and sediment rating curve (SRC) models for the Gilgit River, the tributary of the Indus River in Pakistan. The comparison of different input vectors showed improvements in the prediction of sediments by using the snow cover area in addition to flows, effective rainfall, temperature, and evapotranspiration. Overall, the ANN model performed better than all other models. However, as regards sediment load peak time series, the sediment loads predicted using the ANN, ANFIS-FCM, and MARS models were found to be closer to the measured sediment loads. The ANFIS-FCM performed better in the estimation of peak sediment yields with a relative accuracy of 81.31% in comparison to the ANN and MARS models with 80.17% and 80.16% of relative accuracies, respectively. The developed multiple linear regression equation of all models show an R$^{2}$ value of 0.85 and 0.74 during the training and testing period, respectively

A limited survey of aflatoxins and zearalenone in feed and feed ingredients from Pakistan

This work presents current information on the presence of aflatoxins (AFs) and zearalenone (ZEN) in feed and feed ingredients from Punjab, Pakistan. The 105 samples tested were concentrated feed, i.e., cotton seed meal (18 samples) and soybean meal (14), and feed ingredients, i.e., crushed corn (17), crushed wheat (15), barley (17). and poultry feed (24). Samples were analyzed using high-performance liquid chromatography equipped with a fluorescence detector. Analysis revealed that 69 of 105 samples were contaminated with AFs, and the highest mean concentrations of AFB1 (6.20 μg/kg) and total AFs (9.30 μg/kg) were found in poultry feed samples. The mean total AF concentrations ranged from the limit of quantification to 165.5 μg/kg. However, 75 of the 105 samples were positive for ZEN. The highest mean concentration (19.45 μg/kg) was found in poultry feed samples. The mean ZEN concentrations were 0.15 to 145.30 μg/kg. The prevalence of AFs and ZEN was high in feed and feed ingredients and needs urgent attention

Low-velocity impact characterization of fiber-reinforced composites with hygrothermal effect

In this article, low-velocity impact characteristics of UHN125C carbon fiber/epoxy composite, including unidirectional (0°), cross-directional (0°/90°), and quasi-isotropic layups, were experimentally measured. The effect of the fiber orientation angle and stacking sequences on impact force and induced strain were measured via an instrumented drop-weight apparatus with special concern for the moisture absorption effect. Dried specimens were immersed in distilled water for a certain period of time to absorb water for intermediate and saturated moisture content. It was observed that the impulse was reduced with the increase in moisture content; on the other hand, strain increased with moisture, as measured by DBU-120A strain-indicating software (MADSER Corp., El Paso, TX). Impact damage is widely recognized as one of the most detrimental damage forms in composite laminates because it dissipates the incident energy by a combination of matrix damage, fiber fracture, and fiber-matrix debonding. Therefore, it is extremely important to know the impact strength of a structure, especially for applications in industries such as aerospace, ship design, and some other commercial applications. The use of composite materials in engineering applications is increasing rapidly because they have higher strength-to-weight ratios than metals. The strength, stiffness, and, eventually, the life of composite materials are affected more than conventional materials by the presence of moisture and temperature. Thus, it is necessary to analyze the response of composites in a hydrothermal environment