Investigating the strategic relationship between information quality and e-government benefits

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThis thesis focuses on investigating the relationship between improvements in information quality and the benefits and performance of e-Government organisations. As information quality is a multidimensional measure, it is very crucial to determine what aspects of it are critical to organisations to help them to devise effective information quality improvement strategies. These strategies are potentially capable of changing government organisational structures and business processes. To develop effective information quality improvement strategies, it is important to explore the relationships between information quality (‘cause’) and organisational benefits and performance (‘effect’). The limited research on information quality and organisations performance focuses on private sectors and pays little attention to governments and public organisations. To the best of the author’s knowledge, there is no single study which covers the relationships between information quality and organisations performance in Kuwait. E-Government success literature has rarely investigated information quality as a contributor to the success of e-Government initiatives. This thesis makes a step forward and contributes to the body of knowledge by examining the nature, direction and strength of the connections between information quality and the success of e-Government initiatives as it proposes and discusses a conceptual model (Figure 3.6) and contextual framework by means of which organisations performance and information quality research can be viewed. This thesis adopts a hypothetic-deductive and inductive approach with mixed methods, to conduct the present study. Quantitative and qualitative methods were then utilised to empirically validate the conceptual framework. The author claims that the relationships between information quality and strategic benefits along with institutional value were in upright agreement. Similarly, both qualitative and quantitative analyses highlighted that improvement in different aspects of information quality can lead to a better organisational image. Usability and usefulness attributes of information quality came on the top of the key influencers on both strategic benefits and institutional value. Furthermore, analyses highlighted some differences among information sharing participants’ views regarding the relationship between constructs investigated in this research. Figure 6.5 presents a revised research model including the new constructs, such as, cost savings, improved decision-making, and increased citizen satisfaction, which have been found to be affected by information quality and affect organisational performance.Public Authority of Applied Education

Environmental Impact of Polymer Fiber Manufacture

This review focuses on the effects on the environment due to the production of polymer-solvent solutions and the manufacture of polymeric fibers of thicknesses from a nanometer up to a millimeter using these solutions. The most common polymeric fiber manufacture methods are reviewed based on their effects on the environment, particularly from the use of hazardous materials and energy consumption. Published literature is utilized to analyze and quantify energy consumption of the manufacturing methods electrospinning, phase separation, self-assembly, template synthesis, drawing and pressurized gyration. The results show that during the manufacturing stage of the lifecycle of polymeric fibers, pressurized gyration is more environmentally efficient primarily due to its mass-producing features and fast processing of polymeric solutions into fibers, it also works best with water-based solutions. Further green alternatives are described such as the use of sustainable polymers and solvents to enhance the environmental benefit. Overall, it is shown that the most effective method of curbing the environmental impact of manufacturing polymeric fibers is the use of nontoxic, water-soluble polymers along with the evasion of toxic solvents

Microbiological analysis of root canal infections using high throughput sequencing on the Illumina MiSeq platform

Aim: To investigate the microbial diversity of primary and secondary root canal infections using high throughput sequencing on the illumina MiSeq and culture methods. Methods: 19 subjects were recruited for the study; ten primary infections and nine secondary infections. Samples were collected before chemo-mechanical preparation (S1) and prior to obturation (S2), respectively. Microbiological culture aliquots were serially diluted and inoculated onto various non selective and selective media for total anaerobic and total aerobic counts. For high throughput sequencing, DNA was extracted and the V3/V4 region of the 16SrRNA gene was amplified using the 347F/803R primers, sequenced using the Illumina MiSeq instrument. Raw data were analysed using an open-source bioinformatics pipeline called quantitative insights into microbial ecology (QIIME). Results: Culture: Total anaerobic counts from primary infections ranged from 1.7 X10^1- 7.9 X10^6 colony forming units (cfu)/ml (mean log10 cfu/ml ± SD: 3.08 ± 1.51), whilst total aerobic counts ranged from 3 X10^3- 4.17 X10^5 cfu/ml ( mean log10 cfu/ml ± SD:3.09 ± 1.72). The quantity of microorganisms recovered from secondary infections ranged from 3 X10^2- 4.9 X10^3 cfu/ml (mean log10 cfu/ml ± SD: 2.81 ± 0.78) and from 2.7 X10^2- 8 X10^5 (mean log10 cfu/ml ± SD: 2.60 ± 1.48) with regard to total anaerobic and total aerobic viable counts, respectively. Sequencing analysis yielded partial 16S rRNA gene sequences that were taxonomically classified into 10 phyla and 143 genera. The most represented phyla in the total sample were Firmicutes, Proteobacteria, Actinobacteria, Bacteroidetes, Synergistetes and Fusobacteria. The most dominant genera in primary S1 samples were Streptococcus, Bacillaceae and Eubacterium while Alkalibacterium, Bacillaceae and TG5 dominated the secondary infections. The majority of genera occurred at low levels. The mean number (± SD) of species-level phylotypes per canal was 63 (±14.9; range 34– 80), and 69.9 (± 12.0; range 50 – 87) in primary and secondary infections (S1) samples, respectively. A great inter-individual variation in the composition of the root canal microbiota was observed. Conclusions: The study demonstrated the extensive diversity of the bacterial communities present in root canal infections although the majority of the taxa detected were in low abundance. The study indicates that secondary infections seem more diverse than previously anticipated

A deep learning-based model for plant lesion segmentation, subtype identification, and survival probability estimation

Plants are the primary source of food for world’s population. Diseases in plants can cause yield loss, which can be mitigated by continual monitoring. Monitoring plant diseases manually is difficult and prone to errors. Using computer vision and artificial intelligence (AI) for the early identification of plant illnesses can prevent the negative consequences of diseases at the very beginning and overcome the limitations of continuous manual monitoring. The research focuses on the development of an automatic system capable of performing the segmentation of leaf lesions and the detection of disease without requiring human intervention. To get lesion region segmentation, we propose a context-aware 3D Convolutional Neural Network (CNN) model based on CANet architecture that considers the ambiguity of plant lesion placement in the plant leaf image subregions. A Deep CNN is employed to recognize the subtype of leaf lesion using the segmented lesion area. Finally, the plant’s survival is predicted using a hybrid method combining CNN and Linear Regression. To evaluate the efficacy and effectiveness of our proposed plant disease detection scheme and survival prediction, we utilized the Plant Village Benchmark Dataset, which is composed of several photos of plant leaves affected by a certain disease. Using the DICE and IoU matrices, the segmentation model performance for plant leaf lesion segmentation is evaluated. The proposed lesion segmentation model achieved an average accuracy of 92% with an IoU of 90%. In comparison, the lesion subtype recognition model achieves accuracies of 91.11%, 93.01 and 99.04 for pepper, potato and tomato plants. The higher accuracy of the proposed model indicates that it can be utilized for real-time disease detection in unmanned aerial vehicles and offline to offer crop health updates and reduce the risk of low yield

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

An effective deep learning approach for the classification of Bacteriosis in peach leave

Bacteriosis is one of the most prevalent and deadly infections that affect peach crops globally. Timely detection of Bacteriosis disease is essential for lowering pesticide use and preventing crop loss. It takes time and effort to distinguish and detect Bacteriosis or a short hole in a peach leaf. In this paper, we proposed a novel LightWeight (WLNet) Convolutional Neural Network (CNN) model based on Visual Geometry Group (VGG-19) for detecting and classifying images into Bacteriosis and healthy images. Profound knowledge of the proposed model is utilized to detect Bacteriosis in peach leaf images. First, a dataset is developed which consists of 10000 images: 4500 are Bacteriosis and 5500 are healthy images. Second, images are preprocessed using different steps to prepare them for the identification of Bacteriosis and healthy leaves. These preprocessing steps include image resizing, noise removal, image enhancement, background removal, and augmentation techniques, which enhance the performance of leaves classification and help to achieve a decent result. Finally, the proposed LWNet model is trained for leaf classification. The proposed model is compared with four different CNN models: LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The proposed model obtains an accuracy of 99%, which is higher than LeNet, Alexnet, VGG-16, and the simple VGG-19 model. The achieved results indicate that the proposed model is more effective for the detection of Bacteriosis in peach leaf images, in comparison with the existing models

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

Erratum to: 36th International Symposium on Intensive Care and Emergency Medicine

[This corrects the article DOI: 10.1186/s13054-016-1208-6.]

Manufacturing Multi-layer Core-sheath Polymeric Fibres Using Novel Gyrospinning

Polymeric fibres are used for essential and sophisticated practical applications such as nanosensors, tissue engineering scaffolds, and fibre-reinforced composites. This thesis presents a substantial move forward in comprehending the physics underpinning their formation. Polymeric fibres can be generated through centrifugal spinning and the pressurised gyration processes of a polymer solution within a reservoir. The core difference between these processes is that pressure is employed as a control parameter with the rotation speed in the latter. For both processes, experimental and numerical investigations were conducted into the behaviour exhibited by the polymeric solution inside a transparent reservoir. Fibre generation took less than one second. Polyethylene oxide was used as the model polymer and distilled water as the solvent. For centrifugal spinning, the experiments were conducted at three rotational speeds (7000, 8500, and 10000 rpm). The investigation was conducted at three nitrogen gas pressures (0.1, 0.2, and 0.3 MPa) at a set rotational speed (10000 rpm) for pressurised gyration. High-speed camera images were used to depict the behaviour of the fluid within the reservoir. Increasing the gas pressure to 0.3 MPa significantly enhanced the homogeneity of the fibre distribution, morphology, and production yield. Forming of polymeric core-sheath nanofibres where the sheath contains functional additives is gaining prominence due to their numerous potential applications, most notably in functional scenarios such as antiviral filtration, attracting significant attention due to the Covid pandemic. This research pursued core-sheath fibre production. A novel vessel was successfully purpose-designed and constructed to generate core-sheath polymeric fibres. Investigations were conducted into numerous water-insoluble and water-soluble polymer solutions. As core materials, polyethylene oxide (PEO) and polyvinyl alcohol (PVA) were used. Poly (lactic acid) (PLA) and poly(caprolactone) (PCL) were used as sheath materials, whereas PLA and PCL were used as core and sheath materials, respectively. The fluid behaviour of the core-sheath within the vessel was studied with and without applied pressure using computational fluid dynamics to simulate the core-sheath flow within the chamber. A high-speed camera was used to observe the behaviour of jetted solutions at core-sheath generation vessel openings, and the best scenario was achieved using 6000 rpm spinning speed with 0.2 MPa (twice atmospheric) applied pressure. The surface morphology of core-sheath fibres was studied using a scanning electron microscope, and focused ion beam milling assisted scanning electron microscopy was used to investigate the cross-sectional features of the produced fibres. Laser confocal scanning microscopy was also used to verify the core-sheath structure of the fibres, which were further characterised by Fourier transform infrared spectroscopy and differential scanning calorimetry. The results obtained confirmed the presence of core-sheath fibre materials in examined samples. Thus, using a variety of polymer solutions, both theoretically and experimentally, how core-sheath fibres evolve in a vessel that can serve as scalable manufacturing pressurised gyration production process was elucidated. As the final instalment to this thesis, a gyrospinning device capable of producing multi-layer (≥3) core-sheath polymeric fibres using a novel vessel consisting of a tri-chamber and a novel rotary union capable of continuously infusing three fluids was successfully designed and constructed. Furthermore, it precisely controls crucial process parameters such as spinning speed, gas pressure, polymer solution flow rates, temperatures, humidity, and fibre collectors. Moreover, the manufacturing process is controlled by an industrial automation system for continuous operation to meet the lack of mass production and reduce waste