Nurse Manager's Responsibilities in Creating Supportive Working Conditions Post Implementation of Everyday Coping: A Hermeneutic Research Study of District Nurses' Experiences.

Aim: To gain knowledge about how district nurses experience changes in working conditions and discuss nursing manager's responsibility in facilitating working conditions for district nurses following the implementation of everyday coping.

Background: To overcome the challenges related to the sustainability of the healthcare sector, everyday coping was implemented in district nursing. The implementation was set by the government and implemented by the municipality.

Caring for a home-dwelling older parent with mental health problems: a narrative analysis of family caregivers' experiences.

Purpose: To gain knowledge regarding family caregivers of home-dwelling older adults with mental health problems and the meaning-making of the caregiver's role.

Design: An explorative qualitative study involving narrative analysis.

Methods: Semi-structured individual interviews were conducted among eight adult children.

Product Lifecycle Information Flow in E-waste Handling: a Means to Increase Circularity?

Electronic waste (e-waste) is a growing waste stream. In Europe, e-waste is regulated by the Waste Electrical and Electronic Equipment (WEEE) Directive. Each manufacturer or importer is responsible for the end-of-life (EoL) treatment of the equipment it handles, although this task is usually outsourced to producer responsibility organizations (PROs) that collect and treat the e-waste.

A systematic review of the purposes of Blockchain and fog computing integration: classification and open issues.

The fog computing concept was proposed to help cloud computing for the data processing of Internet of Things (IoT) applications. However, fog computing faces several challenges such as security, privacy, and storage. One way to address these challenges is to integrate blockchain with fog computing.

Deep Learning-Based Computer-Aided Diagnosis (CAD): Applications for Medical Image Datasets.

Computer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets.

Detecting Malware by Analyzing App Permissions on Android Platform: A Systematic Literature Review.

Smartphone adaptation in society has been progressing at a very high speed. Having the ability to run on a vast variety of devices, much of the user base possesses an Android phone. Its popularity and flexibility have played a major role in making it a target of different attacks via malware, causing loss to users, both financially and from a privacy perspective.

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study.

In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment.

Deep Learning-Based Defect Prediction for Mobile Applications.

Smartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release.

Stress Detection Using Experience Sampling: A Systematic Mapping Study.

Stress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically.

Sentimental Analysis of Twitter Users from Turkish Content with Natural Language Processing.

Artificial Intelligence has guided technological progress in recent years; it has shown significant development with increased academic studies on Machine Learning and the high demand for this field in the sector. In addition to the advancement of technology day by day, the pandemic, which has become a part of our lives since early 2020, has led to social media occupying a larger place in the lives of individuals. Therefore, social media posts have become an excellent data source for the field of sentiment analysis.

Machine Learning-Based Software Defect Prediction for Mobile Applications: A Systematic Literature Review.

Software defect prediction studies aim to predict defect-prone components before the testing stage of the software development process. The main benefit of these prediction models is that more testing resources can be allocated to fault-prone modules effectively. While a few software defect prediction models have been developed for mobile applications, a systematic overview of these studies is still missing.

A Novel Hybrid Machine Learning Based System to Classify Shoulder Implant Manufacturers.

It is necessary to know the manufacturer and model of a previously implanted shoulder prosthesis before performing Total Shoulder Arthroplasty operations, which may need to be performed repeatedly in accordance with the need for repair or replacement. In cases where the patient’s previous records cannot be found, where the records are not clear, or the surgery was conducted abroad, the specialist should identify the implant manufacturer and model during preoperative X-ray controls. In this study, an auxiliary expert system is proposed for classifying manufacturers of shoulder implants on the basis of X-ray images that is automated, objective, and based on hybrid machine learning models.

Hybrid Blockchain Platforms for the Internet of Things (IoT): A Systematic Literature Review.

In recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data.

Cybersecurity Enterprises Policies: A Comparative Study.

Cybersecurity is a critical issue that must be prioritized not just by enterprises of all kinds, but also by national security. To safeguard an organization's cyberenvironments, information, and communication technologies, many enterprises are investing substantially in cybersecurity these days. One part of the cyberdefense mechanism is building an enterprises' security policies library, for consistent implementation of security controls.

An assessment of air passenger confidence a year into the COVID-19 crisis: A segmentation analysis of passengers in Norway.

The COVID-19 crisis has become the most intense and long-lasting in the history of aviation. There is already a significant literature on the immediate impact of the outbreak, as well as on speculation on the future evolution of the industry. This paper seeks to contribute to this discussion by assessing a year into the crisis the demand related aspects and passenger behavioural impacts of the pandemic.

A Conceptual Design of Smart Management System for Flooding Disaster.

Disasters pose a real threat to the lives and property of citizens; therefore, it is necessary to reduce their impact to the minimum possible. In order to achieve this goal, a framework for enhancing the current disaster management system was proposed, called the smart disaster management system. The smart aspect of this system is due to the application of the principles of information and communication technology, especially the Internet of Things.

Proton Therapy for Mandibula Plate Phantom.

Purpose: In this study, the required dose rates for optimal treatment of tumoral tissues when using proton therapy in the treatment of defective tumours seen in mandibles has been calculated. We aimed to protect the surrounding soft and hard tissues from unnecessary radiation as well as to prevent complications of radiation. Bragg curves of therapeutic energized protons for two different mandible (molar and premolar) plate phantoms were computed and compared with similar calculations in the literature.

Managing Strong Emotions: Nurses' Recognition and Responses to Personal Emotions When Approaching Parents With Cancer and Their Dependent Children.

Care for sick parents and their dependent children (<18 years) has become a pertinent issue in health care. Nurses play an important role in caring for parents with cancer as well as their children. By utilizing a constructivist grounded theory approach, the aim of this study was to expand the research-based knowledge of nurses' experiences of personal emotions in this research field and to develop a subsequent explanatory theory extending the existing concept of emotional labor.

A Novel Framework Using Deep Auto-Encoders Based Linear Model for Data Classification.

This paper proposes a novel data classification framework, combining sparse auto-encoders (SAEs) and a post-processing system consisting of a linear system model relying on Particle Swarm Optimization (PSO) algorithm. All the sensitive and high-level features are extracted by using the first auto-encoder which is wired to the second auto-encoder, followed by a Softmax function layer to classify the extracted features obtained from the second layer. The two auto-encoders and the Softmax classifier are stacked in order to be trained in a supervised approach using the well-known backpropagation algorithm to enhance the performance of the neural network.

Ready for digital transformation? The effect of organisational readiness, innovation, airport size and ownership on digital change at airports.

This study investigates the effect of organisational readiness, innovation and airport size and ownership on digital change at airports. Data is collected from a survey of managers at 94 airports worldwide and analysed using partial least squares structural equation modelling. Organisational readiness is found to have a direct effect on digital change.

Parents' and nurses' ideal collaboration in treatment-centered and home-like care of hospitalized preschool children - a qualitative study.

Background: The hospitalization of children requires collaboration between parents and nurses in partnerships. This study examines parents' and nurses' experiences of ideal collaboration in treatment-centered and home-like care of hospitalized preschool children.

Methods: This qualitative study is part of a larger study of 12 parents and 17 nurses who were responsible for 11 hospitalized children.

Failing to Fail nursing students among mentors: A confirmatory factor analysis of the Failing to Fail scale.

Aim: The aim was to explore the psychometric properties with respect to the internal consistency reliability of the subject-specific questionnaire "Failing to Fail."

Design: Cross-sectional study.

Methods: Exploratory factor analysis with varimax rotation.

Women's childbearing location preferences in South Ethiopia: a qualitative study.

Maternal mortality is unacceptably high in developing countries, and maternal health care service utilization is associated with improved maternal outcomes. We shed light on conditions that influence women's preferences for childbirth location. Based on a qualitative descriptive design, 25 interviews were conducted with women of childbearing age in Southern Ethiopia in 2015.

Recognition of Insufficient Competence-Nurses' Experiences in Direct Involvement With Adolescent Children of Cancer Patients.

Background: A parent's cancer may have disruptive impact on his/her adolescent children. Currently, nurses have been regarded as central actors in direct involvement with patients' minor children. Development of an extended nursing role has become a pertinent issue.