Aim: To evaluate the use of patient-controlled sedation with propofol in outpatients with anxiety and fear of gynaecological procedures, considering patient satisfaction, feasibility and costs.

Design: This quantitative study used a descriptive retrospective design.

Review Methods: This study evaluated patient satisfaction, procedure feasibility and direct costs for PCS with propofol for outpatients with anxiety and fear of gynaecological outpatient procedures. Data including patient age and evaluation of the procedure, as well as procedure-related information describing the type of procedure, duration of the procedure, drugs used, vital signs, interventions and evaluations by staff was extracted from medical records.

Data Sources: Data was collected from the medical records of all outpatients who completed a gynaecological procedure using PCS with propofol during the period February 2021 until July 2023 at the GC, Vrinnevi Hospital (Sweden).

Results: A total of 128 gynaecological procedures with patient-controlled sedation were performed successfully. Few transient cardiopulmonary events occurred (n = 13), and only one needed use of a jaw thrust to resolve desaturation. The feasibility of the procedure and sedation were assessed to ‘Easy’ (84%, 96%) and no sedation-related cancellations of the procedure were reported. Patients' overall satisfaction was high and they were reported to be ‘Very satisfied’ in 86% of the procedures. The mean cost per patient was 85% lower when the procedure was completed at a gynaecological outpatient clinic instead of a daycare surgical unit.

Conclusion: Patient-controlled sedation outpatients with anxiety and fear for gynaecological procedures has been shown to be a well-tolerated method of sedation that gives high patient satisfaction, allows procedures to be completed in a high-quality way and has the potential reduce direct costs.

Implications for the Profession and/or Patient Care: Patient-controlled sedation with propofol in outpatients with anxiety and fear of gynaecological procedures may improve patient satisfaction and procedure with the potential to reduce direct costs.

Impact: The findings of this study contribute to the growing body of knowledge on patient-controlled sedation (PCS) in gynaecological procedures. By demonstrating that PCS with propofol is feasible, safe and associated with high patient satisfaction, this study highlights its potential to reduce patient anxiety and improve the sense of control during procedures. In addition, PCS may decrease the need for premedication with oral benzodiazepines and reduce referrals to day-surgery units, thereby saving healthcare resources and increasing accessibility for patients. Taken together, the results suggest that PCS can be an important tool in enhancing both the quality and efficiency of gynaecological care.

Reporting Method: Reporting followed the STROBE checklist for cross-sectional studies.

Patient or Public Contribution: No patient or public contribution. 

This research investigates the needs and preferences of low-income angioplasty patients and their caregivers in India during post-angioplasty recovery. Through in-depth interviews and contextual inquiries, the study uncovers critical informational, physical, and emotional needs. Patients often lack access to reliable health information, leading to misconceptions about care and medication adherence. Pain management and emotional support are significant concerns for both patients and caregivers. The study proposes the integration of digital health solutions to address these challenges, providing a platform for reliable information, communication, and support. This research emphasizes the need for context-sensitive interventions to improve patient outcomes and enhance the quality of life for vulnerable populations in developing countries.

Background: Safe Environment for Every Kid (SEEK) is one of few evidence-based approaches to identify psychosocial problems and facilitate support to families within pediatric primary care. The Swedish version of SEEK, called BarnSäkert (“Child Safe”), is being evaluated as a complex intervention in the Swedish child health services (CHS) for children aged 0–6 years.

Objective: Assessment of parents’ views of the acceptability and efficacy of the BarnSäkert/SEEK model within the CHS in Sweden. Participants and setting: Mothers and fathers (n = 353) whose children were enrolled in the CHS.

Methods: An anonymous web-based survey posed questions regarding how parents perceived BarnSäkert/SEEK and whether services had been offered, accessed or planned as a result. Efficacy was measured as parents’ reports of an improved life situation or having been helped by the model.

Results: Among parents who discussed their situation with the nurse, 80 % reported that it had helped, 24 % had received help that they otherwise would not have and 20 % that their situation had improved. Appropriateness of the model was scored at 91/100 by mothers and 86/100 by fathers. Logistic regression showed significantly higher odds ratios for efficacy measures and service uptake for parents who were younger, born outside of Sweden or had lower levels of education.

Conclusions: Parents reported that the model was highly acceptable and efficacious in meeting their psychosocial needs. The findings lend support for application of the BarnSäkert/SEEK model in the Swedish CHS as an equitable approach to address psychosocial problems in families with young children. 

Autonomous vehicles (AVs) depend critically on vision-based perception systems, with traffic sign classification (TSC) playing a crucial role in interpreting regulatory and warning signs for safe navigation. However, these systems are highly vulnerable to adversarial attacks, subtle input perturbations that deceive deep learning models while appearing benign to human drivers. While detection has been the primary focus of defense, recovery of adversarial perturbed signs remains significantly underexplored, despite its importance for maintaining real-time decision-making and operational safety. To bridge this gap, we present the first comprehensive benchmarking of state-of-the-art image classification recovery methods adapted to the traffic sign domain. We address three domain-specific challenges for autonomous driving: (1) robustness to real-world conditions (e.g., weather, occlusion), (2) latency compatible with real-time pipelines (100 ms), and (3) preservation of geometric/structural integrity. Our adaptations combine weather-resilient preprocessing, shape-preserving restoration, and latency-aware implementation. Under unified white-box attacks, we evaluate across TSRD, BTSC, and GTSRB using recovery rate (RR), structural similarity (SSIM), and recovery time (RT). To connect latency to function, we introduce the recovery-induced distance (RID), which maps recovery time (RT) to added travel distance. PuVAE, VAE, c-GAN, and CD-GAN achieve subfewmillisecond RT with (Formula presented.) of the nominal braking distance; DIR remains within (Formula presented.) 0.3% at (Formula presented.), CSC is (Formula presented.) 1.9%–3.3%, and DiffPure incurs 150–165 ms latency, yielding (Formula presented.) 8%–10% at (Formula presented.) (multi-meter delay), thus violating real-time constraints ((Formula presented.))). Cross-dataset transfer on shared classes shows that VAE-based method generalizes better than GAN-based while maintaining timing safety. Overall, PuVAE offers the best accuracy–latency trade-off. These findings provide practical guidance for deploying recovery as a real-time, safety-aligned complement to detection in AV perception. 

[Context] In Brazil, 41% of companies use machine learning (ML) to some extent. However, several challenges have been reported when engineering ML-enabled systems, including unrealistic customer expectations and vagueness in ML problem specifications. Literature suggests that Requirements Engineering (RE) practices and tools may help to alleviate these issues, yet there is insufficient understanding of RE’s practical application and its perception among practitioners.

[Goal] This study aims to investigate the application of RE in developing ML-enabled systems in Brazil, creating an overview of current practices, perceptions, and problems in the Brazilian industry.

[Method] To this end, we extracted and analyzed data from an international survey focused on ML-enabled systems, concentrating specifically on responses from practitioners based in Brazil. We analyzed the cluster of RE-related answers gathered from 72 practitioners involved in data-driven projects. We conducted quantitative statistical analyses on contemporary practices using bootstrapping with confidence intervals and qualitative studies on the reported problems involving open and axial coding procedures.

[Results] Our findings highlight distinct RE implementation aspects in Brazil’s ML projects. For instance, (i) RE-related tasks are predominantly conducted by data scientists; (ii) the most common techniques for eliciting requirements are interviews and workshop meetings;(iii) there is a prevalence of interactive notebooks in requirements documentation; (iv) practitioners report problems that include a poor understanding of the problem to solve and the business domain, low customer engagement,and difficulties managing stakeholders expectations. Our analysis suggests that development methodology plays a role in these challenges. Agile methods appear to facilitate the management of customer expectations compared to traditional approaches; however, they also appear to introduce greater difficulties in problem understanding and customer involvement.

[Conclusion]These results provide an understanding of RE-related practices and challenges in the Brazilian ML industry, helping to guide research and initiatives toward improving the maturity of RE for ML-enabled system projects.

Context: Software engineering (SE) artifacts and documents, such as requirements specifications, user stories, test cases, and concepts of operations (ConOps), are typically written in natural language, making their manipulation challenging. Natural Language Processing (NLP) is a viable solution for managing these tasks.

Objective: To conduct a systematic literature review to explore the current use of NLP in SE artifacts and tasks, supplementedby a tertiary study focusing on the emerging role of Large Language Models (LLMs) in software engineering re-search.

Method: We searched digital libraries for relevant papers and applied inclusion and exclusion criteria to filter the primary studies. We then analyzed NLP techniques applied to SE documents and examined their usage in this context. Our research methodology followed Kitchenham and Charters’ guidelines. Additionally, we conducted a tertiary study to synthesize findings from existing systematic literature reviews and surveys specifically addressing LLMs in software engineering.

Results: We selected 60 primary studies to identify the most common methods for NLP pipelines, feature extraction, language models, and machine learning algorithms used in SE. We also assessed the purposes of these methods, their benefits for SE, their difficulty, and their contribution to SE advancement. The tertiary study revealed a rapid proliferation of LLM-focused research, with comprehensive reviews documenting exponential growth in publications and widespread adoption across diverse SE tasks.

Conclusion: Requirements are the most frequently addressed artifacts using NLP techniques, with preprocessing and part-of-speech (POS) tagging being widely used. There is a notable increase in the use of large language models for various SE tasks, such as requirements elicitation, source code generation, bug fixing, and software testing. The tertiary study confirms that LLMs represent a pivotal shift in the research landscape, warranting dedicated investigation to understand their transformative impact on NLP applications in software engineering.

Context: Consistent requirements and system specifications are essential for the compliance of software systems towards the General Data Protection Regulation (GDPR). Both artefacts need to be "grounded" in the original text and conjointly assure the achievement of privacy by design (PbD). Objectives: There is little understanding of the perspectives of practitioners on specification objectives and goals to address PbD. Existing approaches to GDPR and PbD do not account for the complex intersection between problem and solution space expressed in GDPR. In this study we explore the demand for conjoint requirements and system specification for PbD and suggest an initial version of an approach to address this demand. Methods: We reviewed existing secondary and related primary studies on GDPR compliance and conducted interviews with practitioners to (1) investigate the state-of-practice in requirements and system specifications for GDPR compliance and (2) understand the underlying specification objectives and goals (e.g., traceability). We developed and evaluated an initial version of an approach for requirements and systems specification for PbD, and evaluated it against the specification objectives. Results: The relationship between problem and solution space, as expressed in GDPR, is instrumental in supporting PbD. We demonstrate how our approach, based on the modeling GDPR content with original legal concepts, contributes to specification objectives of capturing legal knowledge, supporting specification transparency for roles involved, and traceability. Conclusion: In addition to assuring traceability, GDPR demands need to be addressed throughout different levels of abstraction in the engineering lifecycle to achieve PbD. Legal knowledge specified in the GDPR text should be captured in specifications to address the demands of different stakeholders and ensure compliance. While our results confirm the suitability of our approach to address practical needs, we also revealed specific needs for the future effective operationalization of our suggested approach.

Automotive functions such as advanced driver assistance (ADAS) and autonomous driving (AD) lead to the requirement of deploying radars in vehicles. These automotive radars interfere with each other when vehicles are in traffic, called mutual interference. Interference causes a decrease in target detection probability and an increase in false alarm rate simultaneously. This can make automotive functions dependent on automotive radars unreliable. Mitigating interference is therefore important for efficient deployment of automotive radars. In this paper, we introduce a novel method for interference mitigation based on principal component analysis (PCA) using the Alternating Least Square (ALS) algorithm. The method is tested successfully with a set of signals obtained from the radar measurement in the server interference environment. 

Undersea communication cables (UCCs) are critical for global connectivity but remain vulnerable to physical and cyber threats. This paper presents insights from the NATO Science for Peace and Security Project: Hybrid Space and Submarine Architecture to Ensure Information Security of Telecommunications (HEIST), a system designed to enhance the redundancy and resilience of UCCs. The proposed architecture aims to efficiently reroute data from submarine cables to satellite networks upon detection of threats via enhanced subsea sensing and monitoring. We discuss the components, including secure communication links, real-time situational awareness, and smart contracting for automated routing of compromised UCC communications. We demonstrate the operational capabilities of HEIST in mitigating disruptions through a simulation environment. The architecture indicates improved robustness to accidental or malicious attacks against submarine cables, reinforcing the strategic importance of hybrid multi-domain networks for resilient telecommunications. As such, the HEIST provides a blueprint for a future robust and resilient backbone for the internet. 

Climate change is one of the most important challenges for society to solve. However, it cannot be fixed solely through greenhouse gas reductions; it requires proactive efforts. This paper investigates how Digital Twins (DT) can serve as a support for Climate Change Adaptation (CCA), which is required to achieve climate resilience. Data was collected from a workshop targeting the role of DT in CCA, which consisted of 92 participants from the public sector in various European countries. The data was analyzed and disseminated to establish guiding principles on how DT can be deployed and designed to best fit the characteristics of CCA work. Firstly, use cases were identified based on cross-examination between CCA activities and stakeholders. Secondly, a list of needs for developing DTs to serve as CCA support was developed. Together, these aspects provide a solid base for more effective DT design in supporting the transition to a more resilient society.

Purpose This study explores women's experiences of a novel prehabilitation intervention, integrating early vaginal dilator therapy before and during pelvic radiotherapy. Methods This qualitative study included sixteen women scheduled for pelvic radiotherapy. All participants received a pre-treatment intervention with individualized information and support to start vaginal dilator therapy prior to radiotherapy. Semi-structured interviews were conducted with all sixteen women before or during treatment. Of these, ten also participated in follow-up interviews after radiotherapy. Data were analyzed using reflexive thematic analysis. Results Four themes were constructed reflecting women's experiences of the new prehabilitation intervention: (1) Regaining control while facing the unknown, (2) Navigating identity, (3) Conceptualizing vaginal and sexual health, and (4) Building trust. Women found that vaginal dilator therapy during prehabilitation enabled a sense of control and readiness for upcoming sexual and vaginal health concerns. The early support was perceived as timely, acceptable, and empowering, regardless of women's initial motivation to engage. None of the women preferred to only receive the information after treatment, reflecting the value of proactive care . Conclusion Integrating vaginal dilator therapy into prehabilitation may enhance preparedness, promote self-care, and reduce distress during pelvic radiotherapy. Further research is needed to confirm benefits and tailor support to individual needs.

The complex dynamics in telecommunication networks that vary over time is due to network reconfiguration, hardware reinstallation, and other factors related to user behavior. This requires machine learning (ML) models that are robust to dynamic and time-varying data distributions in order to assist reliable network operations. This motivates us to focus on building a generalized ML model that is robust to temporal changes in data distributions by continuously training on various data distributions, that is, concepts, at different times. In a federated learning (FL) setting, when there is a concept drift at one client, iterative aggregation of client model weights causes model entanglement. This may slow down both the forgetting of the data concept learned at previous rounds and also the learning the new data concept in the current round. Traditionally, models are trained using both old and new datasets to prevent performance degradation due to catastrophic forgetting and to simultaneously adapt to new data. This requires the storage of old and new datasets. Continuous increase in the training set size with changing concepts leads to increased arithmetic operations on the data, which subsequently causes an increase in computation requirements. In this article, we propose and evaluate various sample selection methods to sustain the overall performance of ML models while reducing the number of training samples. The proposed methods reduced the training set size, addressed high computation requirement, reduced the impact of catastrophic forgetting, and helped to adapt to new data concepts better. 

In nursing education, simulation-based learning (SBL) is often used to bridge theoretical knowledge and practical application, supporting nursing students in developing their critical thinking (CT) skills. Despite the benefits of using SBL in nursing education, research gaps remain in understanding student learning outcomes. Furthermore, there is a lack of studies describing the specific use of technology in its application. The objective of this study was to explore strategies for learning and the use of technology to enhance nursing students’ CT within the SBL context. This research was conducted as a qualitative pilot study, using a semistructured interview technique to gather insights from teachers at 2 universities in the south of Sweden. The obtained data were analysed in accordance with the phenomenographic analysis introduced by Sjöström and Dahlgren. The results revealed participants’ perceptions of useful strategies for student learning and different ways of using technology. In particular, the results are reflected in 5 descriptions of categories: motivating environment, facilitating preparations, active participation, student-centeredness and reflective observations. While the findings may not be directly applicable to clinical practice, the study’s findings offer examples of effective strategies for student learning and technology use, thus providing valuable guidance for teachers implementing SBL in nursing education. To gain a more comprehensive understanding of using SBL as a teaching method, future research should aim to investigate nursing students’ experiences of how CT is promoted via simulations. 

Multi-user extended reality (XR) is an advanced technology that immerses users simultaneously into the meta-verse, aiming to provide a real-time interaction experience in different scenarios. However, different users' network conditions cause data transmission to be asynchronous. It forces users' behavior in the metaverse to go out of sync, seriously impacting the user experience. This paper presents a framework for human-centred synchronization of networks and metaverse applications, often denoted as Intelligent Realities, using Digital Twin (DT) technologies for the networks (network DTs) as well as for the individual users' XR parts (XR DTs). Our framework uses users' attention as a key input to dynamically allocate network resources and align content, thereby keeping widely distributed users in sync. To motivate this framework, we surveyed recent DT-based XR and network systems and found a significant gap: few studies work on synchronizing systems between network DTs and XR DTs. This highlights the need to explore integrated solutions for networked real-time multi-user interaction in the metaverse. 

Radar interference has become a serious and multifaceted issue for the automotive industry due to the rapid growth of Frequency Modulated Continuous Wave (FMCW) sensors in modern vehicles. The dense deployment of radar sensors led to mutual jamming effects and, therefore, can be considered as a form of radio pollution. Fortunately, FMCW radar, by modulating information over both time and frequency domains simultaneously, benefits from multiple degrees of freedom for the separation of multiple transceived signals. However, in the absence of a common standard for sensor manufacturers, the interference effect is still a challenge. This research addresses the interference issue in FMCW radar by developing extensions for the existing solutions and technology. We focus our research on time domain processing using a Two Dimensional (2D) Autoregressive (AR) model, which is employed for estimation and reconstruction of affected parts of the signal. The 2D sampling technique was able to model the signal in a smaller regression window size with the same accuracy compared to the One Dimensional (1D) counterpart model. Further, the developed AR was equipped with dynamic sampling and sample substitution techniques to be able to act as an adaptive mitigator for various interference patterns in radar data. The designed mitigator was applied to several experimental signals collected from practical vehicle maneuvers to evaluate its mitigation performance. Results validated the capability of the Two Dimensional Autoregressive (2DAR) mitigator for recovering the detected signal towards its original form. In the second part of this work, the challenging problem of synchronization of multiple radars is studied for generating a controllable interfered signal and also developing a multi-radar measurement platform. However, a fully synchronized scenario demands an advanced and customized design, but with a simple frame-level synchronization, many aspects and capabilities of current radar sensors could be identified in this study. The framelevel synchronization led to the generation of a semi-controllable spread ghost target, which was later focused by the proposed compensation technique. Eventually, the ghost generation and compression processes are introduced as a possible solution for interference in limited environments.

Artificial Intelligence (AI) has become the latest technology disrupter that has the potential to dramatically change the nature of economy and sustainability in the decades to come. It is still in the nascent stage where wide spread adoption of AI methods including Machine Learning (ML), federated learning, unsupervised automation is still under the scrutiny for public sector adoption. As a foresight exercise, this chapter discusses the role of AI in cities. It provides an outlook on the use of AI in cities for city intelligence and their impact on Sustainable Development Goals (SDGs) as determined by the United Nations (UN). Use-cases, frameworks, and best practices for AI use in cities proposed by nations and international bodies are presented along with technical components that can enable AI adoption. Additionally, a detailed description of AI based techniques is provided to give a technical understanding of AI algorithms from academic and industry perspectives. The focus on data and federated components for discovery and interoperability enabling AI is presented to enable readers to build a vision for future connected urban AI ecosystems. Finally, concerns with the adoption of AI in the public sector are presented along with the conclusions of the chapter. 

With the rapid development of new energy vehicles and power electronics industry, the stability and durability of automotive wiring harness connections have become key technical issues. In this paper, a thermal shock test is designed for aging phenomenon caused by extreme temperature conditions of 25 square copper wire harness-terminal connectors in actual use of vehicles. The results show that after 72 thermal shock cycles, the grain organization of copper wire inside connector is significantly refined and the percentage of large-angle grain boundaries is increased. In addition, the distribution of cubic texture orientation {100} <001> weakens and transforms towards the shear texture {111} <111> parallel to the Z direction of common grain organization. In tensile test, the average tensile strength of joints after thermal shock increased by 6.08 %. The resistance of aged joints increased by 14.75 %, but still met the electrical safety standards. The temperature rise of joint resistance increased by 3.81 degrees C after being connected to a constant current of 100A and lasting for 2.5 h. In terms of corrosion resistance, the improvement of joints after thermal shock test is related to microstructural changes in joint material. Grain refinement introduced a high fraction of grain boundaries and more grain boundaries were able to act as barriers to prevent the penetration and diffusion of corrosive media.

This paper examines the transition to electric mobility (e-mobility) in Kisumu, Kenya’s third-largest city, focusing on the enablers, progress, barriers, and impacts of e-mobility initiatives in a secondary African city. In alignment with Kenya’s commitment to a green economy, Kisumu has emerged as a key site for experimenting and implementing e-mobility solutions aimed at lowering greenhouse gas emissions while addressing critical transportation and energy challenges. These interventions are essential in the city’s transition towards sustainable urban mobility. The study evaluates key projects which have introduced electric motorcycles and off-grid solar-powered charging hubs in urban and peri-urban regions. The overall goal of these initiatives is to mitigate the adverse environmental footprints of fossil-based vehicles while providing socioeconomic benefits to local operators such as cost reductions and job creation. Using a mixed-method approach of systematic literature review, data collection, and case study evaluations, the paper outlines the progress of e-mobility initiatives in Kisumu highlighting successes, challenges and impacts. It reveals that e-mobility has made some contribution to emissions reductions and financial gains for boda operators while significant hurdles include inadequate infrastructure, high upfront costs, and regulatory shortfalls. The paper concludes with recommendations on how to enable the scale-up of e-mobility initiatives in Kisumu, offering important lessons for secondary cities across sub-Saharan Africa that aspire to integrate e-mobility in their sustainable urban development efforts.

Background: Breathlessness is a common and distressing symptom across a wide range of medical conditions. Different aspects (dimensions) of breathlessness can be assessed using the Multidimensional Dyspnoea Profile (MDP) and Dyspnoea-12 (D12) questionnaires. We aimed to examine whether the order of completing MDP and D12 affects the breathlessness responses in people with cardiorespiratory disease.

Methods: This was a randomised controlled trial embedded within a longitudinal clinical study. Outpatients with cardiorespiratory disease were randomly assigned to either first complete the MDP or the D12. Primary outcome was mean difference in D12 total score between groups, secondary outcome was mean difference in D12 and MDP subdomain scores. Both outcomes were compared to the minimal clinically important difference (MCID) for each scale.

Results: All 182 participants from the longitudinal study were included. 93 were randomized to complete MDP first and 89 to D12 first. Characteristics such as age, sex, main cause of breathlessness and smoking status were similar between groups. The mean difference for D12 total score (MCID = 2.8) was −1.5 (−4.2 to 1.3, p = 0.26) between groups. Mean differences between groups in subdomain scores were also below the corresponding MCID.

Conclusion: The order of completion of MDP and D12 did not impact the scores significantly, but the study lacked power to find smaller yet clinically significant differences. The study supports that the most practical order of completing the instruments can be used in future research and in clinical settings.