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Privacy-preserving Facial Emotion Classification with Visual Micro-Doppler Signatures for Hearing Aid Applications
No full textFacial expressions are a crucial aspect of non-verbal communication and often reflect underlying emotional states. Researchers often use facial emotion detection as a tool to gain insights into cognitive processes, emotional states and cognitive load. The conventional camera-based methods to sense human emotions are privacy intrusive, lack adaptability, and are sensitive to variability. These technologies have limited generalization and may not adapt well to variations in ambient lighting, facial landmark localization, facial occlusions and emotion intensity. Radio Frequency (RF) sensing offers promising avenues for improvement with contactless, non-invasive, privacy-preserving and reliable radar-based measurements. The proposed framework utilizes deep-learning techniques to classify facial micro-doppler signatures, generated from an ultra-wideband (UWB) radar. The method relies on continuous multi-level feature learning from radar time-frequency Doppler measurements. The spatiotemporal facial features are extracted from the radar data to train deep learning models. The proposed system achieves a high multiclass classification accuracy of 77% on the continuous streamed data covering basic emotions of anger, disgust, fear, happy, neutral and sadness. The system can transform next-generation multi-modal hearing aids with emotion-aware listening effort and cognitive load detection. This can be particularly useful in translating the emotion-assisted cognitive effort for real-time speech enhancement and personalized auditory experience
Virtual Error-Based Data-Driven P-Type Adaptive Predictive Control and Its Applications
No full textIn this paper, a P-type adaptive predictive control (PAPC) method is presented for a category of unknown multi-input multi-output (MIMO) discrete-time systems with nonaffine nonlinear dynamics. First, the unknown nonlinear model is altered to a linear form containing an unknown pseudo-partial derivative (PPD) matrix utilizing the partial-form dynamic linearization (PFDL). A predictive model is then established by employing the modified projection algorithm, an auto-regressive model, and an output estimation technique. Based on the predictive model, an adaptive learning law that incorporates estimated tracking error information is used to generate the virtual error. Then, a data-driven PFDL-PAPC algorithm is constructed by replacing the actual tracking error in the P-type controller with the virtual one. The bounded convergence properties of the output estimation and tracking error dynamics are theoretically analyzed using the contraction mapping principle. The effectiveness of the PFDL-PAPC method is demonstrated through coupled tanks and actual data-based blast furnace ironmaking experiments
Evaluation of Knowledge, Attitudes, and Skills in Evidence-Based Nursing Practice Among Master’s Degree Nursing Students
Get PDFBackground: Evidence-Based Nursing Practice (EBNP) plays a crucial role in ensuring high-quality patient care. This study evaluates master’s degree nursing students’ knowledge, attitudes, and skills related to EBNP, identifying strengths and key gaps that require curriculum improvements to enhance their competencies in evidence-based practice. Methods: A cross-sectional study was conducted among 103 master’s degree nursing students at Wrocław Medical University. Data were collected using a demographic questionnaire and the standardized Polish version of the Evidence-Based Practice Profile Questionnaire (EBP2Q). Results: The findings indicate that students demonstrated generally positive attitudes toward EBNP (mean score: 53.43 ± 10.05 out of 70). However, knowledge of research terminology was moderate (44.66 ± 18.01 out of 85), and the frequency of EBNP utilization in practice was relatively low (22.15 ± 8.74 out of 45). Significant differences were observed based on study mode and academic progression, with part-time students scoring higher in attitudes toward competency development (p = 0.02). A weak but positive correlation was found between professional experience and the frequency of EBNP utilization (r = 0.182, p = 0.068), while knowledge of research terminology showed a non-significant association with age (r = 0.167, p = 0.092). Conclusions: These findings highlight the need for targeted curriculum enhancements, particularly in research literacy, practical application opportunities, and the integration of mentorship and educational resources. Strengthening EBNP education will better equip nursing students to implement evidence-based practices in clinical settings, ultimately improving patient care quality
Microneedle arrays for brain drug delivery: the potential of additive manufacturing
Get PDFFor a long time, the treatment of brain diseases has been a significant challenge. Drug delivery to the brain has recently become one of the most challenging problems for patients with severe forms of central nervous system diseases. The blood–brain barrier (BBB) poses a significant challenge for drug delivery to the brain. While extensive efforts focus on finding materials to overcome the BBB for brain tumor treatment, it limits the penetration of chemotherapeutic drugs for the broader treatment of brain diseases. The oral method of drug administration has several drawbacks, such as the loss of drugs because of metabolism and gastrointestinal environmental issues. Besides, using the intravenous route to administer medicines has several disadvantages, including discomfort at the injection site, infection, bleeding, anxiety, and incompetence toward patients. Fabrication and development of microneedles to overcome the drawbacks mentioned above of traditional drug delivery methods may be a viable alternative. Drug delivery using microneedle arrays (MNAs) has recently been shown to be an effective method for delivering drugs to the brain. Different fabricating methods like three-dimensional printing could be used for the fabrication of personalized drug delivery systems, like MNAs, with precise control over spatiotemporal drug distribution. This article presents a review of using MNAs for drug delivery to the brain
The potential contribution of flood management ponds to pondscape biodiversity: Evidence from dragonflies
Get PDFAfter decades of river channelisation, restoration projects are flourishing, many with the aim of reducing flood risk by re‐connecting rivers to their floodplains and making space for water. Pond creation is often included to enable temporary storage of surface water as a natural flood management (NFM) measure in catchment‐scale restoration programmes. However, evidence on the potential benefits of these newly created NFM ponds for biodiversity is still scarce, including for flagship wetland groups such as dragonflies (Odonata). To examine this, inventories of adult Odonata were undertaken in the Eddleston Water catchment (Scottish Borders). Twenty ponds were surveyed: ten newly created NFM ponds and 10 pre‐existing ponds (used as reference sites). Pond creation for NFM has strengthened the regional Odonata populations and increased habitat availability, with all recorded species of dragonflies now found at more sites. NFM ponds display slightly higher Odonata alpha species richness than reference ponds. Community composition of NFM ponds was similar to that of reference ponds, as were their environmental conditions. Richness in Odonata was positively correlated with macrophyte richness and the percentage of pond shoreline covered in emergent vegetation, along with pond density. Practical implication. Our investigation shows that ponds created primarily as NFM measures can host Odonata communities as diverse as established ponds, provided they are well located, well designed, and managed in ways that allow the development of aquatic vegetation in the pond and on its shoreline. As such, the flood management ponds of the Eddleston Water catchment are a good example of effective nature‐based solution implementation, tackling the twin crises of climate change and biodiversity loss
Trade Credit Financing, Social Trust, and Financial Distress: Evidence from Chinese Listed Companies
No full textThis research investigates the relationship between trade credit financing and firms’ financial distress using a sample of Chinese listed companies from 2000 to 2020. Our results reveal a significant U-shaped relationship between firms’ trade credit financing and the likelihood of financial distress and that social trust moderates this curvilinear relationship. Our evidence suggests that firm liquidity and financial constraints are two underlying channels through which trade credit financing produces this U-shaped impact on firm bankruptcy risk. Our results are robust to alternative measures of key variables and tests for endogeneity (reverse causality and omitted variables)
Evaluating the Energy Costs of SHA-256 and SHA-3 (KangarooTwelve) in Resource-Constrained IoT Devices
Get PDFThe rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility and post-quantum resilience for lightweight resource-constrained devices. This paper presents a comparative evaluation of the energy costs associated with SHA-256 and SHA-3 hashing in Contiki 3.0, using three generationally distinct IoT platforms: Sky Mote, Z1 Mote, and Wismote. Unlike previous studies that rely on hardware acceleration or limited scope, our work conducts a uniform, software-only analysis across all motes, employing consistent radio duty cycling, ContikiMAC (a low-power Medium Access Control protocol) and isolating the cryptographic workload from network overhead. The empirical results from the Cooja simulator reveal that while SHA-3 provides advanced security features, it incurs significantly higher CPU and, in some cases, radio energy costs particularly on legacy hardware. However, modern platforms like Wismote demonstrate a more balanced trade-off, making SHA-3 viable in higher-capability deployments. These findings offer actionable guidance for designers of secure IoT systems, highlighting the practical implications of cryptographic selection in energy-sensitive environments
Family members’ experiences with intensive care unit diaries
Get PDFBackground: The admission of a family member to intensive care represents an emotionally complex experience, often characterised by anxiety, stress and uncertainty. ICU diaries, compiled by nurses and family members, have been proposed as a useful tool to support caregivers’ psychological well-being, improve communication and humanise the care environment. The aim of the study was to describe the content of ICU diaries filled out by family members to explore the experiences and meanings attributed to the diaries. Methods: The study used a qualitative approach based on thematic analysis of diaries completed by 16 family members of patients admitted to intensive care units. The data were coded and analysed to identify recurrent themes and to understand the emotional and psychological experience of the family members. Results: Three main themes emerged from the analysis: time, the family context (including maintaining contact with the patient, the relatives‘emotions, fear of suffering, spirituality, the person at the centre of the relatives’ lives and connection with the outside world) and the usefulness of the diary in understanding the care process. The diaries facilitated the continuity of the affective bond with the patient, offered a space to express emotions and improved communication with healthcare professionals. Conclusion: The results highlight the value of ICU diaries in supporting family members during the patient’s admission, reducing stress and strengthening the relationship with the healthcare team. The practical implications suggest the importance of promoting the structured use of diaries to foster more empathetic and family-centred care. Clinical trial number: Not applicable
LEAGAN: A Decentralized Version-Control Framework for Upgradeable Smart Contracts
Get PDFSmart contracts are integral to decentralized systems like blockchains and enable the automation of processes through programmable conditions. However, their immutability, once deployed, poses challenges when addressing errors or bugs. Existing solutions, such as proxy contracts, facilitate upgrades while preserving application integrity. Yet, proxy contracts bring issues such as storage constraints and proxy selector clashes - along with complex inheritance management. This paper introduces a novel upgradeable smart contract framework with version control, named ”decentraLized vErsion control and updAte manaGement in upgrAdeable smart coNtracts (LEAGAN).” LEAGAN is the first decentralized updatable smart contract framework that employs data separation with Incremental Hash (IH) and Revision Control System (RCS). It updates multiple contract versions without starting anew for each update, and reduces time complexity, and where RCS optimizes space utilization through differentiated version control. LEAGAN also introduces the first status contract in upgradeable smart contracts, and which reduces overhead while maintaining immutability. In Ethereum Virtual Machine (EVM) experiments, LEAGAN shows 40% better space utilization, 30% improved time complexity, and 25% lower gas consumption compared to state-of-the-art models. It thus stands as a promising solution for enhancing blockchain system efficiency
Is virtual simulation as effective as clinical simulation: a mixed methods study comparing knowledge acquisition, self-confidence, anxiety, and cost effectiveness
Get PDFIntroductionDesktop Virtual Reality Simulation (dVRS) is a growing trend in healthcare education. The evidence base supporting this initiative is expanding yet there is limited evidence on how dVRS compares to clinical simulation (CS). The objectives of this study were to compare dVRS to CS with knowledge acquisition, self-confidence, anxiety as primary outcomes and cost effectiveness and students’ perception of dVRS as secondary outcomes.MethodsA two-stage sequential mixed methods approach was conducted to meet the objectives. In Stage 1, a two-armed randomized controlled trial was conducted with 67 nursing students. The experimental group (n = 34) were assigned to dVRS and control group (n = 33) to CS. In Stage 2, qualitative interviews with Stage 1 participants (n = 17) explored their perceptions of dVRS.ResultsIn Stage 1, mean pre and post knowledge acquisition scores were high (>80 %) across both groups but significantly higher in the control group (Mean difference (MD) = −1.6, 95 % CI (−2.5, −0.6), p = 0.02. Anxiety decreased and self-confidence increased in both groups but statistically significant differences in confidence and anxiety were observed only in the control group (MD = −0.88, 95 % CI (−1.1, −0.6), p < 0.01) and (MD = 0.55, 95 % CI (0.3, 0.7), p < 0.01) respectively. Analysis of secondary outcomes estimated difference in cost when the experimental and control groups were compared (£893 vs £2036/participant, respectively). Thematic analysis of Stage 2 qualitative data generated three themes: decision making, alignment to real-world learning, and improving the dVRS experience. Additionally, participants perceived improvements in knowledge and confidence, reported the value of the immersive aspects of dVRS, and suggested areas for improvement regarding pre-brief and debrief.ConclusionsAcross all primary outcome measures (knowledge acquisition, self-confidence and anxiety) CS was more effective, but less cost-effective, than dVRS. Moreover, dVRS was perceived to be useful and applicable as an adjunct to CS to enhance confidence, knowledge, and decision-making skills
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