Online Real-Time Credit Card Processing Models

Although a variety of payment mechanisms have been developed over the years for online businesses, payment by credit cards remain the leading mechanism for online payments. For real-time online credit card processing, a merchant needs to install a third-party proprietary software in the merchant e-commerce server. However, many issues need to be resolved before integrating a third-party payment solution to a merchant e-commerce system. In this paper, we attempt to address the current state of the online real-time credit card processing models. We also discuss several factors such as cost, complexity and security issues related to implementing such a system

Security Measures in Mobile Commerce: Problems and Solutions

Due to the advent of the Internet, electronic business transactions have exploded around the globe. Along with the Internet, wireless technology has exponentially developed as well. Today, new technologies that allow mobile (cellular) phones and other handheld devices to access the Internet have made wireless business transactions possible. This phenomenon is known as mobile commerce or M-Commerce. It has been predicted that the number of mobile phones connected to the mobile Internet will exceed the number of Internet-connected PCs before 2007. The mobile phone will therefore become the most prevalent device for accessing the Internet. Several industry analysts predict that Mcommerce will constitute a multibillion dollar business by 2005. However, M-Commerce brings new challenges in providing information security as information travels through multiple networks often across wireless links. What must be done to secure financial transactions via mobile commerce? Generally speaking, M-Commerce creates more security concerns than traditional E-Commerce. In this paper, security measures in M-Commerce, wireless security, and the application of cryptography for key generation, authentication, digital signature and digital certificate are discussed

Keyphrase distance analysis technique from news articles as a feature for keyphrase extraction: An unsupervised approach

Due to the rapid expansion of information and online sources, automatic keyphrase extraction remains an important and challenging problem in the field of current study. The use of keyphrases is extremely beneficial for many tasks, including information retrieval (IR) systems and natural language processing (NLP). It is essential to extract the features of those keyphrases for extracting the most significant keyphrases as well as summarizing the texts to the highest standard. In order to analyze the distance between keyphrases in news articles as a feature of keyphrases, this research proposed a region-based unsupervised keyphrase distance analysis (KDA) technique. The proposed method is broken down into eight steps: gathering data, data preprocessing, data processing, searching keyphrases, distance calculation, averaging distance, curve plotting, and lastly, the curve fitting technique. The proposed approach begins by gathering two distinct datasets containing the news items, which are then used in the data preprocessing step, which makes use of a few preprocessing techniques. This preprocessed data is then employed in the data processing phase, where it is routed to the keyphrase searching, distance computation, and distance averaging phases. Finally, the curve fitting method is used after applying a curve plotting analysis. These two benchmark datasets are then used to evaluate and test the performance of the proposed approach. The proposed approach is then contrasted with different approaches to show how effective, advantageous, and significant it is. The results of the evaluation also proved that the proposed technique considerably improved the efficiency of keyphrase extraction techniques. It produces an F1-score value of 96.91% whereas its present keyphrases are 94.55%

A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11

Keyphrases Frequency Analysis from Research Articles: A Region-Based Unsupervised Novel Approach

Due to the advancement of technology and the exponential proliferation of digital sources and textual data, the extraction of high-quality keyphrases and the summarizing of content at a high standard has become increasingly difficult in current research. Extracting high-quality keyphrases and summing texts at a high level demands the use of keyphrase frequency as a feature for keyword extraction, which is becoming more popular. This article proposed a novel unsupervised keyphrase frequency analysis (KFA) technique for feature extraction of keyphrases that is corpus-independent, domain-independent, language-agnostic, and length-free documents, and can be used by supervised and unsupervised algorithms. This proposed technique has five essential phases: data acquisition; data pre-processing; statistical methodologies; curve plotting analysis; and curve fitting technique. First, the technique begins by collecting five different datasets from various sources and then feeding those datasets into the data pre-processing phase using text pre-processing techniques. The preprocessed data is then transmitted to the region-based statistical process, followed by the curve plotting phase, and finally, the curve fitting approach. Afterward, the proposed technique is tested and assessed using five (5) standard datasets. Then, the proposed technique is compared with our recommended systems to prove its efficacy, benefits, and significance. Finally, the experimental findings indicate that the proposed technique effectively analyses the keyphrase frequency from articles and delivers the keyphrase frequency of 70.63% in 1st region and 10.74% in 2nd region of the total present keyphrase frequency

Effect of the Dual Implantation of Gallium on the Electrical Activity of Sulphur in GAAS

This work represents an initial investigation into the electrical properties of Gallium arsenide (GaAs), consecutively doped by ion implantation with two ion species, sulphur and gallium ions, at ion energies of 63 keV and 120 keV respectively. Theoretically at these energies maximum concentrations of sulphur and gallium occur at the same depth into the surface of the GaAs samples. The principal objective of this study is to determine whether the use of dual implantation of gallium and sulphur (Ga+S) can enhance the electrical activity of the resulting active layers when compared with the implantation of sulphur alone. The secondary objective is the comparison of two different substarte materials, semi-insulating chromium-doped substrate and epitaxial substrate, with regard to resulting dopant activation and carrier mobility. Room temperature ion implantation was performed at the Avionics Laboratory, Wright Patterson Air Force Base (WPAFB), Dayton, Ohio, on both type of GaAs samples in doses ranging from 5x1012ions/cm2 to 1X1015ions/cm2• Silicon nitride (Si 3N4 ) encapsulant was used during thermal annealing which was also performed at the Avionics Laboratory at 900 for 30 minutes. The electrical characterization of these samples was made at the Solid State Laboratory, Marquette University Physics Department, by using the van der Pauw technique. Sheet carrier concentration, sheet resistance, Hall sheet coefficient, Hall mobility and activation efficiency were calculated for each sample. The experimental results show that samples with single sulphur-implant generally yield higher activation in comparison to the dual implanted samples. Contrary to expectations, the dual implantation of Ga+S did not enhance the n-type activity of the S-dopant. It might happen that excess substitutional Ga-cations produced p-type activity which compensated the desired n-type activity due to S-implant. Also, a modification of damage centers might occur during implantation and high temperature annealing raising the level of electrical compensation

Reduced-Order Model of the Russian Service Module via Loewner Framework

Loewner framework is a technique that uses frequency response data to construct a reduced order model of a given system. In the past, it has been employed in many different synthetic problems and applications like beams. In this work, we exploit the tool on data pertaining to the structural vibrations in the Russian Service module. As per our analysis, the Loewner model performs as good as the original system.Comment: 6 page

Bioinformatics and system biology approaches for identifying potential therapeutic targets for prostate cancer

Prostate cancer (PCa) is the primary cause of cancer-related deaths among men, and its incidence increases with age. Despite the availability of treatments, the high costs and complications associated with current cancer therapies pose challenges. Therefore, there is an urgent need to explore globally accessible and effective alternative medications to support prostate cancer treatment. Bioinformatics and system biology approaches were utilized to investigate repurposed medicines for prostate cancer treatment guided by host genomic biomarkers. The investigation identified 120 common differentially expressed genes (DEGs) and 24 hub genes (HubGs) as potential therapeutic targets by analyzing gene expression patterns and protein-protein interaction (PPI) networks. The top 10 hub genes CDK1, CCNA2, RRM2, ASPM, MELK, HJURP, DLGAP5, NCAPG, TTK, and HMMR are associated with the development and progression of prostate cancer. The DEGs enrichment analysis revealed significant axon guidance, cell-cell adhesion, extracellular space, and calcium signaling pathways associated with prostate cancer. Gene regulatory network analysis revealed transcription factors (FOXC1, GATA2 and E2F1 etc.) and micro-RNAs (hsa-mir-16–5p, hsa-mir-24–3p and hsa-mir-34a-5p etc.) as regulators of hub genes. The potential drug candidates, includingindirubin-3′-Monoxime (−7.7 kcal/mol), benzene sulfonamide (−8.8 kcal/mol), and cladribine (−6.6 kcal/mol) have been identified as the top 3 candidate drugs against CDK1, CCNA2 and RRM2, respectively. Besides, these drugs exhibit strong binding affinity with the target proteins and suggest their potential for repurposing in the treatment of prostate cancer. Collectively, the findings present valuable insights for advancing prostate cancer research, potentially leading to a deeper understanding and novel treatment strategies for relevant diseases

SARS-CoV-2 vaccination modelling for safe surgery to save lives: data from an international prospective cohort study

Background Preoperative SARS-CoV-2 vaccination could support safer elective surgery. Vaccine numbers are limited so this study aimed to inform their prioritization by modelling. Methods The primary outcome was the number needed to vaccinate (NNV) to prevent one COVID-19-related death in 1 year. NNVs were based on postoperative SARS-CoV-2 rates and mortality in an international cohort study (surgical patients), and community SARS-CoV-2 incidence and case fatality data (general population). NNV estimates were stratified by age (18-49, 50-69, 70 or more years) and type of surgery. Best- and worst-case scenarios were used to describe uncertainty. Results NNVs were more favourable in surgical patients than the general population. The most favourable NNVs were in patients aged 70 years or more needing cancer surgery (351; best case 196, worst case 816) or non-cancer surgery (733; best case 407, worst case 1664). Both exceeded the NNV in the general population (1840; best case 1196, worst case 3066). NNVs for surgical patients remained favourable at a range of SARS-CoV-2 incidence rates in sensitivity analysis modelling. Globally, prioritizing preoperative vaccination of patients needing elective surgery ahead of the general population could prevent an additional 58 687 (best case 115 007, worst case 20 177) COVID-19-related deaths in 1 year. Conclusion As global roll out of SARS-CoV-2 vaccination proceeds, patients needing elective surgery should be prioritized ahead of the general population.The aim of this study was to inform vaccination prioritization by modelling the impact of vaccination on elective inpatient surgery. The study found that patients aged at least 70 years needing elective surgery should be prioritized alongside other high-risk groups during early vaccination programmes. Once vaccines are rolled out to younger populations, prioritizing surgical patients is advantageous