Measurements and physical-layer modelling of transmission loss for gas turbine engine sensor networks

The aim of this study is to extract a physical-layer wireless channel model from a set of channel measurements, in support of the wider, collaborative, WIDAGATE project to assess the potential of wireless sensor networks for the condition monitoring of gas turbine engines. The collaborative partners in WIDAGATE are Rolls-Royce, Selex and University College London. The resulting model is being incorporated into a complete system protocol stack as part of the wider project. The physical layer channel model incorporates interference [1] and noise in addition to signal transmission characteristics

The effect of government of Pakistan’s Common Facility Centre (CFC) program on small and medium enterprise (SME) competitiveness: the role of SMEs’ dynamic capabilities

The small and medium enterprise (SME) sector is considered the backbone of a country's economic development process. SMEs in developing countries face the challenges of resource and capability shortages that hinder their productivity, innovation, and competitiveness in domestic and international markets. To safeguard their SMEs from the negative effects of resource shortages, governments develop industrial clusters and design support programs for clustered firms. One such program, called the 'Common Facility Centre' (CFC) program, was designed by the Government of Pakistan (GOP) to preserve its manufacturing sector's SME competitiveness through the provision of advanced production technologies and technological knowledge and skills. This study is designed to investigate the effect of this CFC program on the competitiveness of recipient SMEs in Pakistan. The study also intends to explore the role dynamic capabilities play for SMEs harnessing greater competitive benefits from this support program. Extant research on the effectiveness of governments' support programs has produced mixed results. Previous studies have also rarely considered how internal capabilities of firms impact the competitiveness effects of these support program. The theoretical framework for this study is based on resource-based theory (RBT) and dynamic capabilities theory (DCT). Using a multidimensional competitiveness measure, this research hypothesises that the use of the CFC program enhances production, innovation, internationalisation and market competitiveness among SMEs. It is also hypothesises that SME absorptive capacity (ACAP) and networking capability (NCAP) moderate the effect of the CFC program on their competitiveness. By using a cross-sectional survey and a self-administered structured questionnaire, data is collected from 224 users (SMEs) of the CFC program all over Pakistan. Findings reveal that use of the CFC program has a positive significant effect on production, innovation, and market-based competitiveness of user SMEs, but no significant effect on the internalisation competitiveness dimension. The ACAP of user firms does not moderate the effect of CFC program use on any of the competitiveness dimensions. NCAP of user firms moderates the effect of CFC program use, but only on the internationalisation competitiveness of user SMEs. Both significant and non-significant findings offer useful insights for research and practice

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

A review on removal of pharmaceuticals from water by adsorption

Pharmaceuticals and personal care products are recognized as emerging pollutants in water resources. Various treatment options have been investigated for the removal of pharmaceuticals that include both conventional (e.g., biodegradation, adsorption, activated sludge) and advanced (e.g., membrane, microfiltration, ozonation) processes. This article reviews literature for adsorptive removal of pharmaceuticals from water sources. Adsorbents from various origins were reviewed for their capacity to remove pharmaceuticals from water. These adsorbents include carbonaceous materials, clay minerals, siliceous adsorbents, and polymeric materials. The adsorption capacity of adsorbents to adsorb pharmaceuticals from water is discussed in this study. The review discusses the mechanism for adsorption of pharmaceuticals onto adsorbents as well. Finally, effectiveness of processing parameters during adsorption processes is presented

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

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

Credit Rating Agencies, Financial Regulations and the Capital Markets

This thesis studies the role of credit rating agencies (CRAs) in capital markets, and the effects of two important regulatory decisions that are taken to improve the quality of information available to the capital markets. In particular, this thesis examines a) the importance of credit ratings to the debt markets and the level of trust investors place on CRAs b) whether the adoption of International Financial Reporting Standards (IFRS) improves the quality of accounting information in European Union, and c) whether implementation of Market Abuse Directive (MAD) has been successful in deterring the market manipulation activities, improving the quality and flow of information to the capital markets, and reducing selective disclosure of private information. Chapter 2 of this thesis shows that the extent of investors’ reliance on the credit ratings depends on whether or not these ratings correspond to the ratings that are expected based on publically available information. Chapter 3 demonstrates that the reporting under IFRS is associated with higher credit ratings and a lower probability and level of rating disagreements between CRAs. The results in Chapter 4 reveal a decrease in the level of market manipulation activities and the provision of selective disclosures subsequent to the implementation of MAD. Chapter 4 also provides evidence of more timely and accurate information flowing to the security markets after implementation of MAD. Overall the findings in this thesis show that the participants in the capital markets prefer credit ratings that have strong association with the publically available information and that financial regulations introduced during the last decade enhanced the quantity and quality of information available to the capital markets