Effect on silicon nitride thin films properties at various powers of RF magnetron sputtering

Silicon nitride thin films have numerous applications in microelectronics and optoelectronics fields due to their unique properties. In this work, silicon nitride thin films were produced using radio frequency (R.F.) magnetron sputtering technique at various sputtering powers. The prepared thin films were characterized with XRD, FE-SEM, FTIR, surface profiler, AFM and spectral reflectance techniques for structure, surface morphology, chemical bonding information, growth rate, surface roughness and optical properties. The results showed that silicon nitride thin films were amorphous in nature. The films were smooth and densely packed with no voids or cracks at the surface. FTIR characterization informed about Si-N bonding existence which confirmed the formation of silicon nitride films. The sputtering power showed the impetus effect on growth rate, surface roughness and optical properties of produced films

Perspective Chapter: Effect of Gold Seed Layer Annealing on the Surface Roughness and Nanostructure Growth

ZnO has gain a great attention in many applications due to its wide band gap. Orientation and alignment of ZnO nanorods are the key objectives of fundamental applied research. They may be produced by both physical and chemical methods, however the chemical method has the advantages of low temperature and pressure conditions. The electronic properties of ZnO nanorods are more superior then the thin films. Most of the applications of ZnO nanorods depends on the morphology, orientation and interspacing among them. Seed layer on the substrate has a key role in the morphology of ZnO nanorods. In this chapter the, orientation, alignment and a clear mechanism of ZnO nanorods production in hydrothermal method is presented. The experimental results deduced that the ZnO nanorods are produced in the precursor solution and move down to the substrate through 001 face stab between the successive grains generated through annealing of gold seed layer, and as a result an oriented and aligned array of the nanorods are formed on the substrate

Effect of orientation and configuration of ZnO nanorods on electrical conductivity prepared through hydrothermal method on suspended substrate

Hexagonal oriented and dumbbell shaped ZnO nanorods were prepared through hydrothermal method on gold coated glass substrate suspended in growth solution at various precursors concentrations. Tower and dumbbell shaped ZnO nanorods were observed on the substrates. The morphology, crystalline structure, and electrical conductivity of the synthesized ZnO nanorods were observed through Field emission scanning electron microscopy (FESEM), X-rays diffraction (XRD) and four point probe. Each sample consists of ZnO nanorods having various direction of orientation. The FESEM, XRD and Four point probe studies reveals that the orientation and configuration of ZnO nanorods has a significant effect on the electrical conductivity. ZnO nanorods with well hexagonal shape and orientation has the better electrical conductivity (10688 seimens/meter, 7022.2 seimens/meter and 8238 seimens/meter) then the dispersed and congested nanorods

Adopting the Appropriate Performance Measures for Soft Computing-based Estimation by Analogy

Soft Computing based estimation by analogy is a lucrative research domain for the software engineering research community. There are a considerable number of models proposed in this research area. Therefore, researchers are of interest to compare the models to identify the best one for software development effort estimation. This research showed that most of the studies used mean magnitude of relative error (MMRE) and percentage of prediction (PRED) for the comparison of their estimation models. Still, it was also found in this study that there are quite a number of criticisms done on accuracy statistics like MMRE and PRED by renowned authors. It was found that MMRE is an unbalanced, biased, and inappropriate performance measure for identifying the best among competing estimation models. The accuracy statistics, e.g., MMRE and PRED, are still adopted in the evaluation criteria by the domain researchers, stating the reason for “widely used,” which is not a valid reason. This research study identified that, since there is no practical solution provided so far, which could replace MMRE and PRED, the researchers are adopting these measures. The approach of partitioning the large dataset into subsamples was tried in this paper using estimation by analogy (EBA) model. One small and one large dataset were considered for it, such as Desharnais and ISBSG release 11. The ISBSG dataset is a large dataset concerning Desharnais. The ISBSG dataset was partitioned into subsamples. The results suggested that when the large datasets are partitioned, the MMRE produces the same or nearly the same results, which it produces for the small dataset. It is observed that the MMRE can be trusted as a performance metric if the large datasets are partitioned into subsamples

Efficacy Of Tranexamic Acid in Reducing Blood Loss in Primary Total Knee Replacement

Objective: To determine the efficacy of tranexamic acid in reducing blood loss in primary total knee replacement. Material and Methods: A total of 96 patients having a diagnosis of primary knee osteoarthritis made up the population sample. The Total Knee Replacement patients were separated into two groups. Patients in Group B used Intra venous tranexamic acid, but those in Group A did not use tranexamic acid during the course of the operation or afterwards. Results: Mean age of the patients recorded in group A 63.79±6.60 (years) and in group B 62.96±7.89 (years). The majority of the patients in both groups were females. After surgery, Group B patients who received tranexamic acid reported less blood loss and less haemoglobin reduction as compared to the control group. Conclusion: From our study, we conclude that Tranexamic acid used intravenously during total knee arthroplasty considerably lowers postoperative blood loss

Celebrity Endorsement vs. Opposition of a Celebrity: A Study of Endorsement Effects in Politics Using a Balance Theory Approach

A study to examine celebrity endorsement effects in the political sector. The focus of this study is on the likability and expertise of celebrities to see their effectiveness in situations where the celebrity either endorses a political candidate or decides to speak against a candidate. Balance Theory is used in the study to provide theoretical support for the importance of likability and expertise. Celebrity endorsement has been studied countless times from the product/services perspective and from the political advertisement perspective. This research makes an important contribution to the political realm by using Balance Theory to understand the importance of likability and expertise of a celebrity in cases of endorsement or opposition of a candidate by the celebrity. Another important contribution of this study is its focus on the celebrity opposition of a political candidate which has not been previously studied before

Collaborative detection of black hole and gray hole attacks for secure data communication in VANETs

Vehicle ad hoc networks (VANETs) are vital towards the success and comfort of self-driving as well as semi-automobile vehicles. Such vehicles rely heavily on data management and the exchange of Cooperative Awareness Messages (CAMs) for external communication with the environment. VANETs are vulnerable to a variety of attacks, including Black Hole, Gray Hole, wormhole, and rush attacks. These attacks are aimed at disrupting traffic between cars and on the roadside. The discovery of Black Hole attack has become an increasingly critical problem due to widespread adoption of autonomous and connected vehicles (ACVs). Due to the critical nature of ACVs, delay or failure of even a single packet can have disastrous effects, leading to accidents. In this work, we present a neural network-based technique for detection and prevention of rushed Black and Gray Hole attacks in vehicular networks. The work also studies novel systematic reactions protecting the vehicle against dangerous behavior. Experimental results show a superior detection rate of the proposed system in comparison with state-of-the-art techniques

Adopting the appropriate performance measures for soft computing based estimation by analogy

Soft Computing based estimation by analogy is a lucrative research domain for the software engineering research community. There are a considerable number of models proposed in this research area. Therefore, researchers are of interest to compare the models to identify the best one for software development effort estimation. This research showed that most of the studies used mean magnitude of relative error (MMRE) and percentage of prediction (PRED) for the comparison of their estimation models. Still, it was also found in this study that there are quite a number of criticisms done on accuracy statistics like MMRE and PRED by renowned authors. It was found that MMRE is an unbalanced, biased, and inappropriate performance measure for identifying the best among competing estimation models. The accuracy statistics, e.g., MMRE and PRED, are still adopted in the evaluation criteria by the domain researchers, stating the reason for "widely used, " which is not a valid reason. This research study identified that, since there is no practical solution provided so far, which could replace MMRE and PRED, the researchers are adopting these measures. The approach of partitioning the large dataset into subsamples was tried in this paper using estimation by analogy (EBA) model. One small and one large dataset were considered for it, such as Desharnais and ISBSG release 11. The ISBSG dataset is a large dataset concerning Desharnais. The ISBSG dataset was partitioned into subsamples. The results suggested that when the large datasets are partitioned, the MMRE produces the same or nearly the same results, which it produces for the small dataset. It is observed that the MMRE can be trusted as a performance metric if the large datasets are partitioned into subsamples

Integration of heterogeneous requirements using ontologies

Ontology-driven approaches are used to sustain the requirement engineering process. Ontologies can be used to define information and knowledge semantics during the requirements engineering phases, such as analysis, specification, validation and management of requirements. However, requirement analysts face difficulties in using ontologies for requirement engineering. In this study, a framework has been proposed to integrate heterogeneous requirements by using local and global ontologies

MILK PRODUCTION POTENTIAL OF PURE BRED HOLSTEIN FRIESIAN AND JERSEY COWS IN SUBTROPICAL ENVIRONMENT OF PAKISTAN

The data on 575 records of 270 Holstein Friesian and 818 records of 326 Jersey cows maintained in Punjab, Pakistan were analyzed. The cows were grouped into imported Holstein Friesian, imported Jersey, Farm born Holstein Friesian and farm born Jersey cows. Lactation milk yield of farm born Holstein Friesian and Jersey cows was significantly (P<0.05) lower than that of imported Holstein Friesian and Jersey cows. Breed group, season of calving and lactation number had significant (P<0.05) effect on lactation milk yield. The highest lactation milk yield was observed in imported and farm born Holstein Friesian cows calved during autumn, while in imported Jersey cows maximum lactation milk yield was observed in cows calved during spring season. The maximum lactation milk yield was observed in the third lactation in imported Holstein Friesian, imported Jersey and farm born Holstein Friesian cows, while in farm born Jersey cows maximum lactation milk yield was observed in the fifth lactation. The milk yield in all breed groups increased with increase in lactation length and service period