Effect of partial replacement of geopolymer binder materials on the fresh and mechanical properties: A review

The growth of demand for concrete raises concerns about the consumption of natural resources and ordinary Portland cement. Geopolymer composites show promise as a sustainable alternative for conventional cement concrete. Considering the wide range of potential geopolymer composites applications (including suitability for transportation infrastructure, underwater applications, repair and rehabilitation of structures as well as recent developments in 3D printing), the desired fresh and mechanical properties of the geopolymer composite may vary between applications: for example, rapid setting can be a merit for certain applications and a demerit for others. Therefore, the desired fresh and mechanical properties (e.g., workability, setting time, compressive strength, etc.) can be controlled for a given geopolymer source material through its partial substitution by natural or by-product materials. Recognizing the critical role of various replacement materials in enhancing the potential applications of geopolymer composites, the present review was undertaken to quantify and understand the effect of partial replacement by fly ash, metakaolin, kaolin, red mud, slag, ordinary Portland cement, and silica fume on the setting time, workability, compressive strength and flexural strength of various source materials addressed in the literature. The review also provides insights into research gaps in the field to promote future research.     

Cyclostationary crosstalk suppression by decision feedbackequalization on digital subscriber loops

Interference from digital signals in multipair cables has been shown to be cyclostationary under some conditions. This work evaluates the performance of a decision feedback equalizer (DFE) in the presence of cyclostationary interference (CI), intersymbol interference (ISI), and additive white noise (AWN). A comparison between a DFE with CI and one with stationary interference (SI) shows the ability of the DFE to substantially suppress CI. Fractionally spaced and symbol-rate DFE equalizers are also compared and the former is found to yield better performance, especially in the presence of CI. The use of a symbol-rate DFE using an adaptive timing technique that finds the receiver's best sampling phase is proposed for when the fractionally spaced DFE cannot be used because of its complexity. The results also demonstrate the potential benefits of synchronizing central office transmitter clocks, if a fractionally spaced DFE is used at the receiver     

Anomaly detection via a combination model in time series data

Since the time series data have the characteristics of a large amount of data and non-stationarity, we usually cannot obtain a satisfactory result by a single-model-based method to detect anomalies in time series data. To overcome this problem, in this paper, a combination-model-based approach is proposed by combining a similarity-measurement-based method and a model-based method for anomaly detection. First, the process of data representation is performed to generate a new data form to arrive at the purpose of reducing data volume. Furthermore, due to the anomalies being generally caused by changes in amplitude and shape, we take both the original time series data and their amplitude change data into consideration of the process of data representation to capture the shape and morphological features. Then, the results of data representation are employed to establish a model for anomaly detection. Compared with the state-of-the-art methods, experimental studies on a large number of datasets show that the proposed method can significantly improve the performance of anomaly detection with higher data anomaly resolution.

     

A Framework of Deep Optimal Features Selection for Apple Leaf Diseases Recognition

Identifying fruit disease manually is time-consuming, expert-required, and expensive; thus, a computer-based automated system is widely required. Fruit diseases affect not only the quality but also the quantity. As a result, it is possible to detect the disease early on and cure the fruits using computer-based techniques. However, computer-based methods face several challenges, including low contrast, a lack of dataset for training a model, and inappropriate feature extraction for final classification. In this paper, we proposed an automated framework for detecting apple fruit leaf diseases using CNN and a hybrid optimization algorithm. Data augmentation is performed initially to balance the selected apple dataset. After that, two pre-trained deep models are fine-tuning and trained using transfer learning. Then, a fusion technique is proposed named Parallel Correlation Threshold (PCT). The fused feature vector is optimized in the next step using a hybrid optimization algorithm. The selected features are finally classified using machine learning algorithms. Four different experiments have been carried out on the augmented Plant Village dataset and yielded the best accuracy of 99.8%. The accuracy of the proposed framework is also compared to that of several neural nets, and it outperforms them all.     

Predicting surface quality of γ-TiAl produced by additive manufacturing process using response surface method

Electron beam melting (EBM) has been found to be a promising technology for producing complex shaped parts from gamma titanium aluminide alloys (γ-TiAl). The parts produced by this process are projected to have dimensions very close to the desired final shapes. However, the surface roughness of the parts produced by EBM is excessively rough. In many applications, it is necessary to improve the quality of manufactured parts using a convenient post process. This paper determines process parameters of end milling when it is used as a post process for the parts produced by EBM. Design of experiments has been used to study the effect of the selected input parameters of end milling (spindle speed, feed rate, depth of cut and coolant type) on the surface roughness of γ-TiAl parts. Response surface methodology is used to develop a predictive model for surface roughness. Effects of the selected milling process are investigated. This paper also optimizes the selected process parameters to minimize the value of the obtained surface roughness.     

Piezoelectric properties of zinc oxide/iron oxide filled polyvinylidene fluoride nanocomposite fibers

Piezoelectric nanogenerators (PENG) with flexible and simple design have pronounced significance in fabricating sustainable devices for self-powering electronics. This study demonstrates the fabrication of electrospun nanocomposite fibers from polyvinylidene fluoride (PVDF) filled zinc oxide (ZnO)/iron oxide (FeO) nanomaterials. The nanocomposite fiber based flexible PENG shows piezoelectric output voltage of 5.9 V when 3 wt% of ZnO/FeO hybrid nanomaterial is introduced, which is 29.5 times higher than the neat PVDF. No apparent decline in output voltage is observed for almost 2000 s attributed to the outstanding durability. This higher piezoelectric output performance is correlated with the β-phase transformation studies from the Fourier transformation infrared spectroscopy and the crystallinity studies from the differential scanning calorimetry. Both these studies show respective enhancement of 3.79 and 2.16% in the β-phase crystallinity values of PVDF-ZnO/FeO 3 wt% composite. Higher dielectric constant value obtained for the same composite (three times higher than the neat PVDF) confirms the increased energy storage efficiency as well. Thus the proposed soft and flexible PENG is a promising mechanical energy harvester, and its good dielectric properties reveals the ability to use this material as good power sources for wearable and flexible electronic devices.

     

An exploratory study for investigating the critical success factors for cloud migration in the Saudi Arabian higher education context

Saudi universities have at their disposal a huge number of low cost IT resources to aid in teaching, research and learning. By migrating to cloud services, Saudi universities will be moving data and programs from local servers to the internet, thereby providing users with the ability to access and share information at any time from multiple devices. The migration to cloud-based IT resources is not yet widespread in Saudi universities due to several challenges including security, legal policies and implementation. At present, there is lack of research and guidance for Saudi universities on how to overcome these challenges and how contextual factors can influence the successful migration to the educational clouds.This research presents a framework for the successful migration to cloud technology in the Saudi Arabian universities. In this research, a set of key critical success factors (CSFs) were identified by synthesizing components from studies concerned with the migration of cloud for higher education and factors identified from the successful implementation of WBL (Web Based Learning) and ERP (Enterprise Resource Planning) on higher education in Saudi Arabia. Based on this knowledge, the proposed framework was evaluated via expert review and a survey by IT specialists from the Saudi universities. The initial CSFs were updated based on the expert reviews and the results were analysed. Based on the findings at this stage, additional CSFs were added to the framework as suggested by the experts. Subsequently, in order to confirm the reviewed CSFs, additional investigation via a structured online questionnaire was conducted and the outcome was analysed via one-sample t-test with the data integrity analysed via Cronbach’s alpha. The outcome indicated the majority of CSFs to be statistically significant except the Physical Location CSF. Potential future study and contributions are discussed.