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    "Doing" is never enough, if "being" is neglected: exploring midwives' perspectives on the influence of an emotional intelligence education programme: a qualitative study. [Article]

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    The role of the midwife is emotionally demanding, with many midwives experiencing high levels of stress and burnout, and a great number considering leaving the profession. This has serious implications for the delivery of high-quality, safe maternity care. One of the major factors leading to job dissatisfaction is the conflict between midwives' aspiration of truly 'being' with the woman and the institutional expectations of the role, which focuses on the 'doing' aspects of the job. 'Being' present to a woman's psychological needs, whilst meeting the institutional demands, requires high levels of emotional intelligence (EI) in the midwife. Therefore, enhancing midwives' EI could be beneficial. An EI programme was made available to midwives with the intention to promote their emotional intelligence and enable them to utilise relaxation techniques for those in their care. The aim of this study was to explore midwives' perspectives on the influence of the EI education programme on their emotional wellbeing and experiences of practice. The study took a descriptive qualitative approach. Thirteen midwives participated in focus group interviews. The data were analysed using thematic analysis. The overarching theme of 'The Ripple Effect' included three themes of 'Me and my relationships', 'A different approach to practice', and 'Confidence and empowerment'. The programme was seen to create a positive ripple effect, influencing midwives personally, their approach to practice, and feelings of confidence in their role. Attendance at an EI education programme equipped the midwives in this study with stress management skills which enhanced their emotional wellbeing and experiences of practice

    Comparative effect size distributions in strength and conditioning and implications for future research: a meta-analysis.

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    Controlled experimental designs are frequently used in strength and conditioning (S&C) to determine which interventions are most effective. The purpose of this large meta-analysis was to quantify the distribution of comparative effect sizes in S&C to determine likely magnitudes and inform future research regarding sample sizes and inference methods. Baseline and follow-up data were extracted from a large database of studies comparing at least two active S&C interventions. Pairwise comparative standardised mean difference effect sizes were calculated and categorised according to the outcome domain measured. Hierarchical Bayesian meta-analyses and meta-regressions were used to model overall comparative effect size distributions and correlations, respectively. The direction of comparative effect sizes within a study were assigned arbitrarily (e.g. A vs. B, or B vs. A), with bootstrapping performed to ensure effect size distributions were symmetric and centred on zero. The middle 25, 50, and 75% of distributions were used to define small, medium, and large thresholds, respectively. A total of 3874 pairwise effect sizes were obtained from 417 studies comprising 958 active interventions. Threshold values were estimated as: small = 0.14 [95%CrI: 0.12 to 0.15]; medium: = 0.29 [95%CrI: 0.28 to 0.30]; and large = 0.51 [95%CrI: 0.50 to 0.53]. No differences were identified in the threshold values across different outcome domains. Correlations ranged widely (0.06 ≤ r ≤0.36), but were larger when outcomes within the same outcome domain were considered. The finding that comparative effect sizes in S&C are typically below 0.30 and can be moderately correlated has important implications for future research. Sample sizes should be substantively increased to appropriately power controlled trials with pre-post intervention data. Alpha adjustment approaches used to control for multiple testing should account for correlations between outcomes and not assume independence

    Monitoring carbon emissions using deep learning and statistical process control: a strategy for impact assessment of governments' carbon reduction policies.

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    Across the globe, governments are developing policies and strategies to reduce carbon emissions to address climate change. Monitoring the impact of governments' carbon reduction policies can significantly enhance our ability to combat climate change and meet emissions reduction targets. One promising area in this regard is the role of artificial intelligence (AI) in carbon reduction policy and strategy monitoring. While researchers have explored applications of AI on data from various sources, including sensors, satellites, and social media, to identify areas for carbon emissions reduction, AI applications in tracking the effect of governments' carbon reduction plans have been limited. This study presents an AI framework based on long short-term memory (LSTM) and statistical process control (SPC) for the monitoring of variations in carbon emissions, using UK annual CO2 emission (per capita) data, covering a period between 1750 and 2021. This paper used LSTM to develop a surrogate model for the UK's carbon emissions characteristics and behaviours. As observed in our experiments, LSTM has better predictive abilities than ARIMA, Exponential Smoothing and feedforward artificial neural networks (ANN) in predicting CO2 emissions on a yearly prediction horizon. Using the deviation of the recorded emission data from the surrogate process, the variations and trends in these behaviours are then analysed using SPC, specifically Shewhart individual/moving range control charts. The result shows several assignable variations between the mid-1990s and 2021, which correlate with some notable UK government commitments to lower carbon emissions within this period. The framework presented in this paper can help identify periods of significant deviations from a country's normal CO2 emissions, which can potentially result from the government's carbon reduction policies or activities that can alter the amount of CO2 emissions

    Walking through the abstract(ed) city and co-creating urban space.

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    This paper explores how co-designing urban walkability can be augmented by an innovative hybrid approach, whereby virtual records and visualisations of the walking experience can enhance the awareness, perceptions and immersion of the participant in both real and virtual spaces. From one side of that model, the research explores how people might be intrigued enough to discover the real context, based on their experience informed and enriched by parallel images of the city. On the other side, the study aimed to develop a critical understanding of urban walking through the lens of 3D high-definition LIDAR scanning technology, where visualisation techniques were used to support studies to explore how the rich experience of walking could be captured and represented. The paper presents a theoretical framework to propose how walking could be promoted, and positively influenced by the urban environment, by regarding the city from the abstract perspective of the virtual point cloud. The research has investigated how and whether a place – real and abstracted - could act as a trigger to produce novel ideas and unfold thoughts in a participatory way. The interlinkages between motion and (visual) perception of the environment as an aesthetic experience were critical to informing how digital technology can be utilised as a virtual space within which the richness of real interactions and experiences with urban space can be represented, refined, interacted with and used within a rich(er) process of co-design

    State of health prediction of lithium-ion batteries using combined machine learning model based on nonlinear constraint optimization.

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    Accurate State of Health (SOH) estimation of battery systems is critical to vehicle operation safety. However, it's difficult to guarantee the performance of a single model due to the unstable quality of raw data obtained from lithium-ion battery aging and the complexity of operating conditions in actual vehicle operation. Therefore, this paper combines a long short-term memory (LSTM) network with strong temporality, and support vector regression (SVR) with nonlinear mapping and small sample learning. A novel LSTM-SVR combined model with strong input features, less computational burden and multiple advantage combinations is proposed for accurate and robust SOH estimation. The nonlinear constraint optimization is used to assign weights to individual models in terms of minimizing the sum of squared errors of the combined models, which can combine strengths while compensating for weaknesses. Furthermore, voltage, current and temperature change curves during the battery charging were analyzed, and indirect health features (IHFs) with a strong correlation with capacity decline were extracted as model inputs using correlation analysis and principal component analysis (PCA). The NASA dataset was used for validation, and the results show that the LSTM-SVR combined model has good SOH estimation performance, with MAE and RMSE all less than 0.75% and 0.97%

    The extradition of Mike Lynch: should the forum bar be amended?

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    Discusses the extradition of Mike Lynch to the US to face charges of wire fraud, conspiracy to commit wire fraud and securities fraud, for overinflating the value of a software company, and the arguments that the extradition should have been barred on the ground of forum. Considers the operation of the forum bar

    Proceedings of the 10th International congress on architectural technology (ICAT 2024): architectural technology transformation.

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    The profession of architectural technology is influential in the transformation of the built environment regionally, nationally, and internationally. The congress provides a platform for industry, educators, researchers, and the next generation of built environment students and professionals to showcase where their influence is transforming the built environment through novel ideas, businesses, leadership, innovation, digital transformation, research and development, and sustainable forward-thinking technological and construction assembly design

    A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction.

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    Battery life prediction is of great significance to the safe operation, and the maintenance costs are reduced. This paper proposed a hybrid framework considering feature extraction to solve the problem of data backward, large sample data and uneven distribution of high-dimensional feature space, then to achieve a more accurate and stable prediction performance. By feature extraction, the measured data can be directly fed into the life prediction model. The hybrid framework combines variational mode decomposition, the multi-kernel support vector regression model and the improved sparrow search algorithm. Better parameters of the estimation model are obtained by introducing elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm. The comparison is conducted by dataset from National Aeronautics and Space Administration, which shows that the proposed framework has a more accurate and stable prediction performance

    Transport of nanoparticles in porous media and associated environmental impact: a review.

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    The release of nanoparticles into the environment occurs at different stages during their life cycle, with significant harmful effects on the human (e.g., lung inflammation and heart problems) and the ecosystem (e.g., soil and groundwater contamination). While colloids (particles >1 micrometre) behaviour in porous media is influenced by filtration, nanoparticles (<100 nanometres) behaviour is driven by Brownian motion and quantum effects. Recognising these disparities is essential for applications like groundwater remediation and drug delivery, enabling precise strategies based on the differing transport dynamics of colloids and nanoparticles. The extent of the impact of nanoparticle release on the environment is strongly influenced by their type, size, concentration, and interaction with porous media. The main factor preventing the use of nanoparticles for environmental remediation and other related processes is the toxicity arising from their uncontrolled distribution beyond the application points. Finding a suitable dosing strategy for applying nanoparticles in porous media, necessary for the correct placement and deposition in target zones, is one of the significant challenges researchers and engineers face in advancing the use of nanoparticles for subsurface application. Thus, further studies are necessary to create a model-based strategy to prevent nanoparticle dispersion in a porous media. In general, this review explores the transport of nanoparticles in porous media concerning its application for environmental remediation. The aim of this study is captured under the following: a) Identifying the properties of nanoparticles and porous media to develop an innovative remediation approach to reclaim contaminated aquifers effectively. b) Identify critical parameters for modelling an effective strategy for nanoparticle-controlled deposition in porous media. This would require a general understanding of the onset and mapping of the different nanoparticle depositional mechanisms in porous media. c) Identify existing or closely related studies using model-based strategies for controlling particulate transport and dispersion in porous media, focusing on their shortcomings

    Stagnation-point Brinkman flow of nanofluid on a stretchable plate with thermal radiation.

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    The study is an analytical exploration of hybrid nanofluid flow at a stagnation-point with Brinkman effect on a stretchable plate with thermal radiation. All of the aforementioned factors were taken into account when developing the mathematical model based on the Navier–Stokes equations for nanofluids, leading to a system of partial differential equations. Using suitable scaling, these equations are reduced to system of ordinary differential equations. The outcome of the system of ordinary differential equations are solved analytically and closed-form solutions are obtained in terms of incomplete error function. The results are analysed for the many significant flow characteristics with the profiles of velocity and temperature explored graphically. The amount of the heat transfer is increased due to the interaction between nanoparticles and the wall, and the wall surface is cooled when wall suction is present


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