Multimedia Communication in e-Government Interface: A Usability and User Trust Investigation

In the past few years, e-government has been a topic of much interest among those excited about the advent of Web technologies. Due to the growing demand for effective communication to facilitate real-time interaction between users and e-government applications, many governments are considering installing new tools by e-government portals to mitigate the problems associated with user – interface communication. Therefore, this study is to indicate the use of multimodal metaphors such as audio-visual avatars in e-government interfaces; to increase the user performance of communications and to reduce information overload and lack of trust that is common with many e-government interfaces. However, only a minority of empirical studies has been focused on assessing the role of audio-visual metaphors in e-government. Therefore, the subject of this thesis’ investigation was the use of novel combinations of multimodal metaphors in the presentation of messaging content to produce an evaluation of these combinations’ effects on the users’ communication performance as well as the usability of e-government interfaces and perception of trust. The thesis outlines research comprising three experimental phases. An initial experiment was to explore and compare the usability of text in the presentation of the messaging content versus recorded speech and text with graphic metaphors. The second experimental was to investigate two different styles of incorporating initial avatars versus the auditory channel. The third experiment examined a novel approach around the use of speaking avatars with human-like facial expressions, obverse speaking avatars full body gestures during the presentation of the messaging content to compare the usability and communication performance as well as the perception of trust. The achieved results demonstrated the usefulness of the tested metaphors to enhance e-government usability, improve the performance of communication and increase users’ trust. A set of empirically derived ground-breaking guidelines for the design and use of these metaphors to generate more usable e-government interfaces was the overall provision of the results.Saudi Arabia Embass

Men’s health in industries: Plastic plant pollution and prevalence of pre-diabetes and type 2 diabetes mellitus

Plastic production is prominently increasing and its pollution is an emerging environmental global health concern. This study aimed to investigate the occurrence of pre-diabetes and type 2 diabetes mellitus (T2DM) among nonsmoking plastic industry workers. Three hundred and forty volunteers male plastic industry workers were interviewed after medical history and examination; finally, 278 nonsmoking plastic industry workers were selected. The mean age for the participants was 38.03 ± 10.86 years and body mass index was 25.52 ± 3.15 (kg/m)2. The plastic industry workers had been exposed to plastic plant pollution for 8 hr daily, 6 days in a week. Subjects with glycated hemoglobin (HbA1c) less than 5.7% were considered non-diabetics; HbA1c 5.7%-6.4% were pre-diabetics; and subjects with HbA1c greater than 6.4% were considered diabetics. In plastic industry workers, the prevalence of pre-diabetes was 176 (63.30%) and T2DM was 66 (23.74%); however, 36 (12.95%) plastic plant workers were normal. The prevalence of pre-diabetes and T2DM among plastic industry workers was significantly increased with duration of working exposure in plastic industry ( p = .0001). Exposure to plastic plant pollution is associated with the prevalence of pre-diabetes and T2DM among plastic industry workers. The prevalence was associated with the duration of working exposure in plastic industry. The occupational and environmental health executives must take priority steps to minimize the plastic plant pollution from plastic industries to reduce the occurrence of pre-diabetes and T2DM among the plastic industrial workers and save the men\u27s health in industries

Recent advances in passive UHF-RFID tag antenna design for improved read range in product packaging applications: a comprehensive review

Radio frequency identification (RFID) is a rapidly developing technology, and RFID sensors have become important components in many common technology applications. The passive ultra-high frequency (UHF) tags used in RFID sensors have a higher data transfer rate and longer read range and usually come in unique small and portable application designs. However, these tags suffer from significant frequency interference when mounted on metallic materials or placed near liquid surfaces. This paper presents the recent advancements made in passive UHF-RFID tag designs proposed to resolve the interference problems. We focus on those designs that are intended to improve antenna read range as well as scalability designs for miniaturized application

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

A nonlinear convergence consensus: extreme doubly stochastic quadratic operators for multi-agent systems

We investigate a novel nonlinear consensus from the extreme points of doubly stochastic quadratic operators (EDSQO), based on majorization theory and Markov chains for time-varying multi-agent distributed systems. We describe a dynamic system that has a local interaction network among agents. EDSQO has been applied for distributed agent systems, on a finite dimensional stochastic matrix. We prove that multi-agent systems converge at a center (common value) via the extreme waited value of doubly stochastic quadratic operators (DSQO), which are only 1 or 0 or 1/2 1 2 if the exchanges of each agent member has no selfish communication. Applying this rule means that the consensus is nonlinear and low-complexity computational for fast time convergence. The investigated nonlinear model of EDSQO follows the structure of the DeGroot linear (DGL) consensus model. However, EDSQO is nonlinear and faster convergent than the DGL model and is of lower complexity than DSQO and cubic stochastic quadratic operators (CSQO). The simulation result and theoretical proof are illustrated

EDSQ operator on 2DS and limit behavior

This paper evaluates the limit behavior for symmetry interactions networks of set points for nonlinear mathematical models. Nonlinear mathematical models are being increasingly applied to most software and engineering machines. That is because the nonlinear mathematical models have proven to be more efficient in processing and producing results. The greatest challenge facing researchers is to build a new nonlinear model that can be applied to different applications. Quadratic stochastic operators (QSO) constitute such a model that has become the focus of interest and is expected to be applicable in many biological and technical applications. In fact, several QSO classes have been investigated based on certain conditions that can also be applied in other applications such as the Extreme Doubly Stochastic Quadratic Operator (EDSQO). This paper studies the behavior limitations of the existing 222 EDSQ operators on two-dimensional simplex (2DS). The created simulation graph shows the limit behavior for each operator. This limit behavior on 2DS can be classified into convergent, periodic, and fixed

The Perceptions of Nurses and Nursing Students Regarding Family Involvement in the Care of Hospitalized Adult Patients

Over the past few decades, there have been concerns regarding the humanization of healthcare and the involvement of family members in patients’ hospital care. The attitudes of hospitals toward welcoming families in this respect have improved. In Arab culture, the main core of society is considered to be the family, not the individual. The objective behind involving family in patient care is to meet patients’ support needs. Consequently, this involvement affects nurses and their attitudes toward the importance of family involvement in patient care. Objectives: To describe nurses’ and nursing students’ perceptions of family involvement in the care of hospitalized adult patients in Saudi Arabia. Design: This study used a quantitative descriptive cross-sectional design. The data were collected using a convenience sampling survey via social media. Results: A total of 270 participants (staff and students) took part in this study, including 232 (85.9%) females and 38 (14.1%) males. Moreover, a high percentage of participants (78.8%) acknowledged that family presence strongly affected the improvement of the patient’s condition. However, 69.3% of participants thought that involving family members during special care processes or cardiopulmonary resuscitation (CPR) would be traumatizing for these individuals. Moreover, there was a significant diffidence between the attitudes of the nurses and nursing students toward family involvement and the number of years of employment (F = 3.60, p < 0.05). On the contrary, there were insignificant differences between the attitudes of the nurses and nursing students toward family involvement and their gender, nationality, age, education level, and years of work experience in Saudi Arabia (p > 0.05). Furthermore, the regression analysis showed a significant negative correlation between nurses’ years of employment and their support of family involvement in patient care (ß = −0.20, SE = 0.08, t = −2.70, p = 0.01). Conclusions: Nurses with more experience showed no support for family involvement in patient care. We have to consider the clinical barriers that affect nurses’ support for family involvement in patient-centered care, such as hospital polices, guidelines, and the model used for family-centered care integration in the hospital system to facilitate the interaction between healthcare providers and family members

Liver Tumor Segmentation in CT Scans Using Modified SegNet

The main cause of death related to cancer worldwide is from hepatic cancer. Detection of hepatic cancer early using computed tomography (CT) could prevent millions of patients’ death every year. However, reading hundreds or even tens of those CT scans is an enormous burden for radiologists. Therefore, there is an immediate need is to read, detect, and evaluate CT scans automatically, quickly, and accurately. However, liver segmentation and extraction from the CT scans is a bottleneck for any system, and is still a challenging problem. In this work, a deep learning-based technique that was proposed for semantic pixel-wise classification of road scenes is adopted and modified to fit liver CT segmentation and classification. The architecture of the deep convolutional encoder–decoder is named SegNet, and consists of a hierarchical correspondence of encode–decoder layers. The proposed architecture was tested on a standard dataset for liver CT scans and achieved tumor accuracy of up to 99.9% in the training phase