Delicate f(R) gravity models with disappearing cosmological constant and observational constraints on the model parameters

We study the $f(R)$ theory of gravity using metric approach. In particular we investigate the recently proposed model by Hu-Sawicki, Appleby $-$ Battye and Starobinsky. In this model, the cosmological constant is zero in flat space time. The model passes both the Solar system and the laboratory tests. But the model parameters need to be fine tuned to avoid the finite time singularity recently pointed in the literature. We check the concordance of this model with the $H(z)$ and baryon acoustic oscillation data. We find that the model resembles the $\Lambda$CDM at high redshift. However, for some parameter values there are variations in the expansion history of the universe at low redshift.Comment: 16 pages and 9 figures, typos corrected, few references and minor clarifications added, revised version to appera in PR

セリアスラリーによる酸化膜の平坦化CMPに関する基礎的検討

Ceria(CeO2) slurry has a strong merit in CMF polishing to give a high removal rate. However, it has an unfavorable reputation of problems linked to its quick sedimentation, agglomeration of particles, purity, difficult cleaning, and high cost. In the present study, focusing on the particular issue of the slurry dispersibility, we examined the effect ofultra-sonic (US) treatment ofslurry on its stability and its CMF polishing characteristics in order to make ceria slurry stably applicable to the planarizationCMF. As a result, the US treatment has proved remarkably effective in the elimination of agglomerates, improvement of slurry stability, increase of removal rate and better roughness, offering a breakthrough for the realization ofhigh performance ceria slurry for CMP planarization Comparing to silica slurry, we thus obtained ceria slurry that produces same polished roughness but six times higher removal rate when polished at the pressure of 500g/cm^2.textapplication/pdfdepartmental bulletin pape

Delicate f(R) gravity models with a disappearing cosmological constant and observational constraints on the model parameters

We study the f(R) theory of gravity using the metric approach. In particular we investigate the recently proposed model by Hu and Sawicki; Appleby and Battye; and Starobinsky. In this model, the cosmologicalconstant is zero in flat space time. The model passes both the solar system and the laboratory tests. But the model parameters need to be fine-tuned to avoid the finite time singularity recently pointed to in the literature. We check the concordance of this model with the H(z) and baryon acoustic oscillation data. We find that the model resembles the ΛCDM at high redshift. However, for some parameter values there are variations in the expansion history of the universe at low redshift.journal articl

Search for D0-D̅0 Mixing in D0→K+π- Decays and Measurement of the Doubly-Cabibbo-Suppressed Decay Rate

journal articl

Diverging deep learning cognitive computing techniques into cyber forensics

More than ever before, the world is nowadays experiencing increased cyber-attacks in all areas of our daily lives. This situation has made combating cybercrimes a daily struggle for both individuals and organisations. Furthermore, this struggle has been aggravated by the fact that today's cybercriminals have gone a step ahead and are able to employ complicated cyber-attack techniques. Some of those techniques are minuscule and inconspicuous in nature and often camouflage in the facade of authentic requests and commands. In order to combat this menace, especially after a security incident has happened, cyber security professionals as well as digital forensic investigators are always forced to sift through large and complex pools of data also known as Big Data in an effort to unveil Potential Digital Evidence (PDE) that can be used to support litigations. Gathered PDE can then be used to help investigators arrive at particular conclusions and/or decisions. In the case of cyber forensics, what makes the process even tough for investigators is the fact that Big Data often comes from multiple sources and has different file formats. Forensic investigators often have less time and budget to handle the increased demands when it comes to the analysis of these large amounts of complex data for forensic purposes. It is for this reason that the authors in this paper have realised that Deep Learning (DL), which is a subset of Artificial Intelligence (AI), has very distinct use-cases in the domain of cyber forensics, and even if many people might argue that it's not an unrivalled solution, it can help enhance the fight against cybercrime. This paper therefore proposes a generic framework for diverging DL cognitive computing techniques into Cyber Forensics (CF) hereafter referred to as the DLCF Framework. DL uses some machine learning techniques to solve problems through the use of neural networks that simulate human decision-making. Based on these grounds, DL holds the potential to dramatically change the domain of CF in a variety of ways as well as provide solutions to forensic investigators. Such solutions can range from, reducing bias in forensic investigations to challenging what evidence is considered admissible in a court of law or any civil hearing and many more

Experts reviews of a cloud forensic readiness framework for organizations

Cloud computing has drastically altered the ways in which it is possible to deliver information technologies (ITs) to consumers as a service. In addition, the concept has given rise to multiple benefits for consumers and organizations. However, such a fast surge in the adoption of cloud computing has led to the emergence of the cloud as a new cybercrime environment, thus giving rise to fresh legal, technical and organizational challenges. In addition to the vast number of attacks that have had an impact on cloud computing and the fact that cloud-based data processing is carried out in a decentralized manner, many other concerns have been noted. Among these concerns are how to conduct a thorough digital investigation in cloud environments and how to be prepared to gather data ahead of time before the occurrence of an incident; indeed, this kind of preparation would reduce the amount of money, time and effort that is expended. As a number of cloud forensics challenges have not received enough attention, this study is motivated by a particular gap in research on the technical, legal and organizational factors that facilitate forensic readiness in organizations that utilize an Infrastructure as a Service (IaaS) model. This paper presents a framework with which to investigate the factors that facilitate the forensic readiness of organizations. This framework was identified by critically reviewing previous studies in the literature and by performing an in-depth examination of the relevant industrial standards. The factors were comprehensively studied and extracted from the literature; then, the factors were analysed, duplicates were removed, and the factors were categorized and synthesized to produce the framework. To obtain reliable results, the research method involved two steps: a literature review, followed by expert reviews. These techniques help us paint a comprehensive picture of the research topic and validate and confirm the results.Northern Border Universit

Holistic digital forensic readiness framework for IoT-enabled organizations

Internet of Things (IoT) are becoming commonplace in homes, buildings, cities, and nations, and IoT networks are also getting more complex and interconnected. The complexity, interconnectivity, and heterogeneity of IoT systems, however, complicate digital (forensic) investigations. The challenge is compounded due to the lack of holistic and standardized approaches. Hence, building on the ISO/IEC 27043 international standard, we present a holistic digital forensic readiness (DFR) framework. We also qualitatively evaluate the utility of the proposed DFR framework

薩摩半島北部植生調査報告

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