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The purpose is to design a system in which drones can control traffic most effectively using a deep learning algorithm.

Drones have now started entry into each facet of life. The entry of drones has made them a subject of great relevance in the present technological era. The span of drones is, however, very broad due to various kinds of usages leading to different types of drones. Out of the many usages, one usage which is presently being widely researched is traffic monitoring as traffic monitoring can hover over a particular area. This paper specifically brings out the basic algorithm You Look Only Once (YOLO) which may be used for identifying the vehicles. Consequently, using deep learning YOLO algorithm, identification of vehicles will, therefore, help in easy regulation of traffic in streetlights, avoiding accidents, finding out the culprit drivers due to which traffic jam would have taken place and recognition of a pattern of traffic at various timings of the day, thereby announcing the same through radio (namely, Frequency Modulation (FM)) channels, so that people can take the route which is the least jammed.

The study found that the object(s) detected by the deep learning algorithm is almost the same as if seen from a naked eye from the top view. This led to the conclusion that the drones may be used for traffic monitoring, in the days to come, which was not the case earlier.

The main research content and key algorithm have been introduced. The research is original. None of the parts of this research paper has been published anywhere.

The purpose of this paper is to share the process, findings and conclusions from one library’s iterative usability study of its website design to inform other libraries as they perform their own assessments.

A task-completion usability study was conducted with eight undergraduate students across two iterations: the first gauged the usability of a redesigned library website and the second gauged the effectiveness of the first iteration’s findings.

We found that users performed better when the site provided multiple access points to the same information, displayed a prominent chat feature, limited the amount of text on a given page and avoided library jargon. Not only was the second round of testing important for confirming that first-round recommendations were effective but also it proved useful in catching a problem with the site that was unintentionally created during the time between tests.

No demographic data were collected during the study, thus hindering our ability to analyze our users through these data points.

This study demonstrates the value of iterative usability testing, especially when untested changes made between site versions may produce usability issues.

This article delineates the pilot implementation of the Rohingya Archive (R-Archive). The R-Archive seeks to both confront and exploit the roles of documentation and recordkeeping in forced displacement of Rohingya people through targeted physical and bureaucratic violence in Myanmar. This grassroots activist intervention is located at the intersection of technology, rights, records, jurisdictions and economics. Using Arweave's blockweave, the R-Archive secures copies of records, such as identity documentation, land deeds and personal papers, carried into diaspora by Rohingya refugees against unauthorised alteration, deletion and loss, providing a trust infrastructure for accumulating available evidence in support of rights claims and cultural preservation.

Iterative development of functional requirements, data collection processes and identification of a technological solution for the community-based, post-custodial, blockchain-inspired R-Archive; design and testing of the R-Archive pilot; and analysis of trust and economic concerns arising.

A complex set of interconnecting considerations is raised by this use of emerging technologies in service to a vulnerable and diasporic community. Hostile governments and volatile cryptocurrencies are both threats to the distributed post-custodial R-Archive. However, the strength of the community bonds that form the archive and articulated in its records speak to the possibility of perdurance for a global Rohingya archive, and working through the challenges surfaced by its development offers the possibility to serve as a model that might be adaptable for other grassroots archival activist projects initiated by oppressed, marginalised and diasporic communities.

Personal and community safety and accessibility concerns, especially in refugee camps and under Covid-19 restrictions, presented particular challenges to carrying out the research and development that are addressed in the research design and future research plans.

The goal of this pilot was to collect and store examples of a range of documents that demonstrate different aspects of Rohingya culture and links to the homeland as well as those that record formal evidentiary relationships between members of the Rohingya community now in diaspora and the Burmese state (e.g. acknowledgements of citizenship). The pilot was intended to demonstrate the viability of using a blockchain-inspired decentralised archival system combined with a community-driven approach to data collection and then to evaluate the results for potential to scale.

The R-Archive is a community-centred and driven effort to identify and preserve, under as secure and trusted conditions as possible, digital copies of documents that are of juridical, cultural and personal value to the Rohingya people and also of significance as primary documentary evidence that might be used by international legal institutions in investigating genocide taking place in Burma and by academic researchers studying the history of Burma.

The R-Archive is novel in terms of its technological application (Arweave), the economic concerns of a vulnerable stateless population it is trying to address, and its functional complexity, in that its goal is simultaneously to serve both legal evidentiary and community archive functions. The R-Archive is also an important addition to other notable efforts in the diasporic Rohingya community that have attempted to employ the tools of technology for cultural preservation.

Many companies must choose a marketing strategy to promote new products. This includes publishing digital video advertising with interactive features on the Internet. Guided by the attribute-framing theory, this study investigates the impact of the number of positive attribute framings (NPAF) in advertising messages on consumer responses. This study aims to check whether new products are incrementally new products (INPs) or really new products (RNPs).

Content analysis was conducted to identify the forms and components of attribute framing used in advertisements and consumer responses (i.e. review valence). Furthermore, this study followed an expert scoring procedure to identify product newness (INP vs. RNP). Hierarchical polynomial regression analysis was used to investigate the relationship between the NPAF, customer response and product newness.

This study has three main findings: (1) regardless of whether the new product is INP or RNP, there is an inverted U-shaped relationship between NPAF and consumer responses; (2) the inflection point of NPAF (INP) is greater than the inflection point of NPAF (RNP) and (3) the maximum value of consumer response for INP is greater than that for RNP.

This study sheds light on the effectiveness of attribute framing in new product advertising. Compared to previous research on attribute framing of advertising, this study is based on a realistic scenario in which multiple attributes are framed within a given advertisement copy. Furthermore, secondary data were used to test the impact of NPAF on consumer responses and further explore the effects of product newness.

The purpose of this paper is to provide a shorter time cost, high-accuracy fault diagnosis method for water pumps. Water pumps are widely used in industrial equipment and their fault diagnosis is gaining increasing attention. Considering the time-consuming empirical mode decomposition (EMD) method and the more efficient classification provided by the convolutional neural network (CNN) method, a novel classification method based on incomplete empirical mode decomposition (IEMD) and dual-input dual-channel convolutional neural network (DDCNN) composite data is proposed and applied to the fault diagnosis of water pumps.

This paper proposes a data preprocessing method using IEMD combined with mel-frequency cepstrum coefficient (MFCC) and a neural network model of DDCNN. First, the sound signal is decomposed by IEMD to get numerous intrinsic mode functions (IMFs) and a residual (RES). Several IMFs and one RES are then extracted by MFCC features. Ultimately, the obtained features are split into two channels (IMFs one channel; RES one channel) and input into DDCNN.

The Sound Dataset for Malfunctioning Industrial Machine Investigation and Inspection (MIMII dataset) is used to verify the practicability of the method. Experimental results show that decomposition into an IMF is optimal when taking into account the real-time and accuracy of the diagnosis. Compared with EMD, 51.52% of data preprocessing time, 67.25% of network training time and 63.7% of test time are saved and also improve accuracy.

This method can achieve higher accuracy in fault diagnosis with a shorter time cost. Therefore, the fault diagnosis of equipment based on the sound signal in the factory has certain feasibility and research importance.

This method provides a feasible method for mechanical fault diagnosis based on sound signals in industrial applications.

At the outset of the COVID-19 pandemic the United Kingdom Government implemented a policy to rapid discharge hospital patients into care homes. This study aimed to examine how the media in the United Kingdom portrayed hospital discharge to care homes during the COVID-19 pandemic.

This study was a qualitative document analysis. Four sources (Daily Mail, The Independent, The Guardian and BBC News) were selected to represent political orientations encompassing right-wing, centrist and left-wing perspectives, and were searched for mention of hospital discharge, care homes and Covid-19 pandemic between 1st January 2020 and 24th February 2022. Article text was copied verbatim into Microsoft Word documents prior to analysis. Data were thematically analysed, followed by coding the sentiment in the included articles as well as coding the sentiment of themes and sub-themes.

Of 722 identified articles, 133 were eligible for inclusion as the final corpus. Data represented a moralistic narrative consisting of four themes: (1) Government as villain, (2) care homes as antiheroes, (3) patients as ideal victims and (4) moral outcomes. Most of the corpus had a negative sentiment (78.1%). One theme, moral outcomes, had considerably more positive sentiment (32.4%) than others (range 15.1%–21.9%).

A moralistic argument for improving cross-boundary interactions between health and social care services is provided, and the media can play a role pushing cross-boundary working higher up the policy agenda. Future work should examine how direct stakeholders, including those working in healthcare and care home settings, perceived the discharge policy.

This study aims to gain insights into how students perceive online examination practices and evaluation, as well as identify the key factors that impact their intentions toward online exams.

This empirical study conducted in India utilized an online survey method between May 24 and June 14, 2022. The data were collected from 712 undergraduate and postgraduate students through a Google Docs questionnaire. Descriptive analysis, exploratory factor analysis (EFA), regression and reliability analysis were conducted to analyze the data.

There are many studies related to the adoption of online learning. However, there are limited studies that explore the adoption and acceptance of online examinations and evaluations. This paper intends to fill this gap and tries to highlight important factors influencing online examination usage by the students. Five important factors emerged in the study include “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention,” The study concludes that taking tests online is preferred for several reasons, including the perceived increased dependability of grades and the decreased cost, time and effort associated with taking exams.

This research explores students' perceptions of learning management systems (LMS)-based online exams and provides insights for students to better prepare and adapt. It assists policymakers in developing fair assessment policies and improving LMS platforms. The study contributes to the theoretical understanding of e-learning and calls for further research to enhance the effectiveness of online exams. It identifies gaps in existing literature and recommends larger sample sizes, broader representation, longitudinal data collection and conversational methods for future research.

This study shows increasing research on e-learning and online examinations, mostly using quantitative methods. The important variables for understanding students' perceptions of online exams are “pedagogy, validity, reliability,” “practicality,” “security,” “facilitating condition” and “intention.” Concerns raised include validity, fairness and security. Electronic exams are more suitable for formative evaluation, and validity, reliability, security and adaptability are key pillars for successful online testing.

This study aims to, firstly, develop a red flag checklist for cryptocurrency Ponzi schemes and, secondly, to test this red flag checklist against publicly available marketing material for Mirror Trading International (MTI). The red flag checklist test seeks to establish if MTI’s marketing material posted on YouTube^® (in the form of a live video presentation) exhibits any of the red flags from the checklist.

The study uses a structured literature review and qualitative analysis of red flags for Ponzi and cryptocurrency Ponzi schemes.

A research lacuna was discovered with regard to cryptocurrency Ponzi scheme red flags. By means of a structured literature review, journal papers were identified that listed and discussed Ponzi scheme red flags. The red flags from the identified journal papers were subsequently used in a qualitative analysis. The analyses and syntheses resulted in the development of a red flag checklist for cryptocurrency Ponzi schemes, with five red flag categories, containing 18 associated red flags. The red flag checklist was then tested against MTI’s marketing material (a transcription of a live YouTube presentation). The test resulted in MTI’s marketing material exhibiting 88% of the red flags contained within the checklist.

The inherent limitations in the design of using a structured literature review and the lack of research regarding the cryptocurrency Ponzi scheme red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes. The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The red flag checklist can be applied to a cryptocurrency investment scheme’s marketing material to establish if it exhibits any of these red flags.

The study provides a red flag checklist for cryptocurrency Ponzi schemes.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.

This study intends to improve the quality of venous thromboembolism (VTE) prophylaxis practices including proper VTE risk assessment and the appropriate prophylaxis measures for surgical urology patients.

The authors applied the Six-Sigma define, measure, analyze, improve and control (DMAIC) improvement methodology in a pre–post interventional study that involved all adult patients above 18 years old indicated and scheduled for urology surgical interventions including endoscopic urological surgeries in a urology specialized 60-bed hospital. The pre-intervention sample included all patients meeting the inclusion criteria over a period of six months. Post-intervention sample included all patients meeting the inclusion criteria over a period of six months. The improvement areas included both the VTE risk assessment as well as the VTE prophylaxis prescription.

DMAIC methodology has achieved a substantial sustained improvement in surgical urology VTE prophylaxis practices with an average of 70% on both levels; VTE risk assessment practices and VTE prophylaxis prescribing practices were statistically significant. The post-intervention results also showed a statistically controlled process with no special cause variations. Based on the study results, the Six-Sigma DMAIC methodology can be considered of high value when applied in healthcare clinical practice improvement projects.

The project study includes some pitfalls that can be addressed as follows: 1. The lack of VTE rate incidence tracking. This limitation can be partly refuted when the authors conduct a literature review and explore that the VTE prophylaxis effectiveness had been proven with sufficient evidence to an extent that pushed several scientific societies to develop their own guidelines to support VTE prophylaxis. (Algattas et al., 2018). 2. Another limitation of this study can be that it handled only surgical patients and more specifically surgical urology patients. Of course, VTE prophylaxis is a crucial life-threatening problem not only for the surgical admitted patients but also for all the medical admitted patients either in hospital wards or ICUs. However, the prediction that surgical patients especially surgical urology patients are more prone to VTE development risk as they have -in several cases-two or three main additive risk factors which are age, procedure duration and malignancy in elderly men. (Tikkinen et al., 2014). So, the authors consider the study project to be a prototype that hopefully can be utilized for future study projects that will manage both other surgical specialty patients and medical patients on the national level and can track accurately and effectively report the VTE incidence rates.

Several recommendations can be extracted from the research project that is summarized in the following points: Paying focused attention to continuous healthcare quality improvement initiatives and projects as a main approach for healthcare improvement especially for the public health-related problems. This might be achieved through periodic region-specific or specialty-specific focus groups from which public health problems could be addressed and prioritized to be considered as a part of country healthcare campaigns regarding cost-utility and feasibility studies. The adoption of a system thinking approach in dealing with the improvement strategies; all efforts and resources are to be employed to achieve a common objective. This includes the generation of a national-wide electronic health information system that can aid in healthcare resource allocation and direct the healthcare efforts towards the most important, high-priority public health problems. Electronic national-wide health record is really an effort, and resources consuming activity, but actually, it's worth exerting efforts, and its valuable outcomes may be seen several years later. 3. Development of unified national specialized VTE prophylaxis pathways to standardize the patient-specific VTE prophylaxis plans. Standardization of healthcare pathways enables healthcare professionals to follow an evidence-based practice which will be reflected on the improvement of healthcare quality level, cost-effectiveness enhancement, and timely patient care on all levels especially in high critical areas like ER and ICU. 4. Incorporation of VTE prophylaxis costs in the universal health insurance diagnosis-related group (DRG) insurance packages and service pricing. Universal health insurance is a nationwide strategy that is aiming to cover all Egypt residents by the year 2030. Universal health insurance is being following the DRG reimbursement policy that is thought to control all the healthcare-associated costs so, the VTE prophylaxis costs shall be added as the main cost item to encourage all healthcare facilities to follow an evidence-based VTE prophylaxis pathway taking into consideration the high-risk patient categories who will definitely represent a high-cost burden on the long run if they suffer a VTE event.

DMAIC improvement methodology applications in healthcare are still relatively limited, especially on the clinical level. The study can be considered one of a kind in Egypt dealing with a comprehensive DMAIC methodology application on the clinical level.