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An Integrated Review of Transitional Care for Families of Pre-Term Infants

This study was conducted to identify the key elements of transitional care for families with pre-term infants in South Korea. We used an integrative review methodology proposed by Whittemore and Knafle. During the problem identification step, the review question was constructed via the population, intervention, outcome, and timeframe (PIOT) format. During the literature search step, integrative reviews of the published literature from nine electronic databases were undertaken and a total of 14 studies were reviewed that met our inclusion criteria. During the data evaluation step, the quality of the literature was assessed using the Mixed Methods Appraisal Tool (MMAT) developed by Hong et al. We identified three domains and 10 key attributes of transitional care for families of pre-term infants. The parenting empowerment domain included growth and development, developmental promotion, nutrition, safe environment, general parenting, and parent–infant interaction. The emotional support domain included counseling, advocacy, and community network. The social support domain included professional collaboration. Efforts should continue to further develop programs and policies to enhance transitional care for families of pre-term infants that reflect South Korean nursing practices.

A medical disease assisted diagnosis method based on lightweight fuzzy SZGWO-ELM neural network model.

The application of neural network model in intelligent diagnosis usually encounters challenges such as continuous adjustment of network parameters and significant cost in training the network facing numerous complex physiological data. To address this challenge, this paper introduces a fuzzy SZGWO-ELM neural network model for medical disease aid diagnosis with fuzzy membership function and ELM network to refine the improved Gray Wolf optimization algorithm. Firstly, the Z-type membership function is introduced as the inertia weight to get a balance for the grey wolf in seeking the optimal solution globally and locally and ensuring fast convergence. Secondly, the S-type membership function is utilized as the adaptive weight to flexibly adjust the grey wolf search step size to facilitate a quick approximation of the optimal solution. Finally, the improved Gray Wolf optimization algorithm is used to optimize the parameters of the ELM neural network model, termed as SZGWO-ELM. This method can eliminate the need for extensive network parameter adjustments and quickly locate the optimal solution to the problem using a lightweight neural network. The performance of the SZGWO is assessed by using metrics like convergence, mean, and standard deviation. Multiple experiments reveal that this method shows superior performance. Furthermore, five publicly accessible medical disease datasets from UCI were conducted to evaluate the performance of SZGWO-ELM network model comparing with different classify model, and the results in terms of precision, sensitivity, specificity and accuracy can achieve 99.52%, 94.14%, 99.26% and 96.08%, respectively, which illustrate that the proposed SZGWO-ELM neural network significantly enhance the model's accuracy, providing better support for doctors in disease diagnosis.

Multi‐Objective Optimization and Experimental Research of Ship Form Based on Improved Bare‐Bones Multi‐Objective Particle Swarm Optimization Algorithm

ABSTRACTShip form optimization poses a complex and high‐dimensional engineering challenge. Therefore, when conducting multi‐objective optimization research of ship forms, traditional intelligent optimization algorithms are prone to falling into local optima solution and difficult to converge. In order to effectively improve the diversity and convergence performance of the algorithm, this paper improves the bare‐bones multi‐objective particle swarm optimization (BBMOPSO) algorithm by dynamically adjusting the local and global search step sizes, and verifies the algorithm's reliability through standard function testing. Then, a multi‐objective optimization design framework with high efficiency and high integration is constructed. Taking DTMB 5512 as the research case, Free Form Deformation (FFD) method is used for hull deformation, and the proposed algorithm is used for multi‐objective optimization of resistance performance and motion response. Ship model tests were conducted on the DTMB 5512's original hull. And the numerical simulations were compared with the ship model tests. Finally, under the constructed multi‐objective optimization design framework, satisfactory solutions were obtained through the improved algorithm, which confirms the effectiveness and practicality of the improved algorithm. The results show that the algorithm improved in this paper can provide some theoretical basis and technical support for green ship design and low‐carbon shipping.

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. The present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimization problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimization problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency.

Research on ACMD-ICYCBD method for rolling bearing fault feature extraction

Aiming at the difficulty in obtaining the eigenfrequency of the vibration component of rolling bearing faults in a strong background noise environment and the problem of extraction efficiency, the adaptive chirp mode decomposition (ACMD) combined with Improved maximum second-order cyclostationary blind deconvolution (ICYCBD) fault feature extraction algorithm is proposed. Firstly, to improve the signal-to-noise ratio, the original signal is adaptively decomposed using the ACMD method, and the optimal components are selected based on the principle of maximizing the correlation gini coefficient index. Secondly, to improve the accuracy of parameter setting and extraction efficiency, an improved CYCBD method is proposed to estimate the cyclic frequency set of CYCBD using the proposed enhanced energy harmonic product spectrum (EEHPS) method for the optimal components, the envelope spectrum peak factor index is improved by proposing the envelope spectral period pulse factor (EPPF) index, and the filtering length of the CYCBD is selected adaptively using the step search to obtain the optimized filtered signal. Finally, the envelope spectrum analysis is carried out to extract the fault information accurately. The simulation signals and experimental data show that the method can quickly and accurately extract the fault characteristics of rolling bearings under strong background noise, and the comparison with other methods shows the effectiveness and superiority of the proposed method.

Clutaxis: an information-driven source search method balancing exploration and exploitation in turbulent environments

Locating unknown emission sources in turbulent environments is a challenging yet crucial task, particularly in emergency response scenarios. Existing studies have developed information-theoretic approaches to fuse intermittent information collected by mobile sensors regarding the sources. This fused information is then used to support source-term estimation (STE) in various search algorithms. Among these, the cognitive strategy—a promising information-driven search algorithm—leverages a reward-based action selection mechanism to balance exploration and exploitation during each search step. However, this mechanism is hampered by a high computational load and rigid search trajectories, limiting its application in real-world systems. To address these issues, this paper proposes a novel information-driven search method called Clutaxis, based on a global exploration and exploitation tradeoff principle. Specifically, a particle filter is leveraged to maintain the STE. After projecting the particle filter samples onto a 2D search scene, the density-based spatial clustering of applications with noise (DBSCAN) algorithm is used to extract the density information of the samples, which is then used to construct a belief source area (BSA). By leveraging the uncertainty of the BSA, Clutaxis adopts explorative or exploitative actions with no restrictions on motion direction. Through dedicated simulations, the experimental results demonstrate the robustness of Clutaxis to key parameters and its advantages in computational complexity and search performance compared to two state-of-the-art algorithms (Infotaxis and Entrotaxis) and two Clutaxis variants (Clutaxis_ER and Clutaxis_EI).

Anytime algorithm based on adaptive variable-step-size mechanism for path planning of UAVs

For autonomous Unmanned Aerial Vehicles (UAVs) flying in real-world scenarios, time for path planning is always limited, which is a challenge known as the anytime problem. Anytime planners address this by finding a collision-free path quickly and then improving it until time runs out, making UAVs more adaptable to different mission scenarios. However, current anytime algorithms based on A* have insufficient control over the suboptimality bounds of paths and tend to lose their anytime properties in environments with large concave obstacles. This paper proposes a novel anytime path planning algorithm, Anytime Radiation A* (ARaA*), which can generate a series of suboptimal paths with improved bounds through decreasing search step sizes and can generate the optimal path when time is sufficient. The ARaA* features two main innovations: an adaptive variable-step-size mechanism and elliptic constraints based on waypoints. The former helps achieve fast path searching in various environments. The latter allows ARaA* to control the suboptimality bounds of paths and further enhance search efficiency. Simulation experiments show that the ARaA* outperforms Anytime Repairing A* (ARA*) and Anytime D* (AD*) in controlling suboptimality bounds and planning time, especially in environments with large concave obstacles. Final flight experiments demonstrate that the paths planned by ARaA* can ensure the safe flight of quadrotors.

An efficient weapon detection system using NSGCU-DCNN classifier in surveillance

Weapon detection systems play a significant role in ensuring public safety, specifically in surveillance scenarios. But, poor performance has been shown by the prevailing research methodologies towards occluded and customized weapons. Thus, to resolve this issue, a Non-linear and Scalar Growing Cosine Unit based Deep Convolutional Neural Network (NSGCU-DCNN)-based efficient weapon detection system in the surveillance video is proposed. Primarily, the input video is converted into frames. The pre-processing process is applied for each frame. By utilizing Gaussian Kernelized Double Plateau Histogram Equalization (GKDPHE), contrast enhancement is performed grounded on the frame’s brightness. After that, for each frame, the motions are estimated by using the Frobenius Norm-based Three Step Search (FN-TSS) algorithm. Thereafter, by utilizing Weibull Distributed Viola Jones (WDVJ), the objects are detected, and the detected objects are cropped for further analysis. Next, the edge information of each object is determined by the Canny Edge Detection (CED) algorithm. Then, the frame is further converted into a 3x3 block. Afterward, the silhouette coefficient is calculated between the frames and the mask images. Further, the features are extracted and selected from the silhouette estimated frame. The NSGCU-DCNN classifies the weapons grounded on the selected features. The experimental outcomes exhibited that when contrasted with the prevailing techniques, the proposed mechanism withstands high-accuracy rates.

Adults' perception of the act of cooking in the domestic food environment: a scoping review protocol

The weakening of the generational transmission of culinary skills has led to less confidence and autonomy among individuals in preparing their meals, and to a pattern of food consumption, consisting of ultra-processed foods, which do not require time or the use of these skills. This scoping review aim to identify and to map the perception of adults regarding the act of cooking in the domestic food environment, over the last five years, a time frame that includes the experience of the period of social distancing due to the Covid-19 Pandemic. Scope review protocol based on the methodology proposed by the Joanna Briggs Institute. The search will be carried out in the following databases: Scopus, Medline, SciElo, EMBASE, WOS, JSTOR and Lilacs. Sources of unpublished studies/gray literature will be searched on Google Scholar. The search, selection and data extraction steps will be carried out by four reviewers, and if there are disagreements, a fifth reviewer will be consulted. The process steps will be presented through the PRISMA-ScR flowchart. The extracted data will be organized to enable evaluation and descriptive analysis. The results will be presented in tables, tables and descriptively.

Extent, range, and nature of studies examining sleep in nurses: a scoping review protocol.

This scoping review aims to comprehensively map the extent, range, and nature of studies that examine the various dimensions of nurses' sleep across all health care settings or countries. Over the past 2 decades, the importance of sleep for nurses has gained increasing attention from health care administrators, researchers, and policymakers. Despite growing research in the area, it remains unclear as to how research on sleep in nurses aligns with emerging concepts in sleep medicine more broadly, particularly in terms of how outcomes and predictors are associated with different dimensions of sleep. This review will include primary studies that examine nurses' sleep. All dimensions of sleep (eg, duration, timing, variability, quality, and common disorders) and all nurses, irrespective of career stage, will be considered for inclusion. This review will utilize the JBI methodology for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRIMSA-ScR). Databases to be searched include MEDLINE (Ovid), Emcare, Embase, PsycINFO, and Scopus. A 3-step search strategy will be undertaken to identify primary studies published in English with no date limit. The data extracted will include specific details about the nursing population, sleep dimension, outcome measures, methodology, and key findings. Figurative, tabular, and accompanying narrative synthesis will be used to present the results in line with the review questions. Open Science Framework https://osf.io/rzc4m.

High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles

High Probability Complexity Bounds for Adaptive Step Search Based on Stochastic Oracles

Colorectal cancer outcomes among Hispanic/Latino patients in the United States: a scoping review protocol.

This scoping review will identify the patterns of survival, treatment, and recurrence among Hispanic and/or Latino/a/x (H/L) patients with colorectal cancer (CRC) living in the United States (US) and Puerto Rico. Additionally, population- and individual-level determinants of cancer outcomes among H/L CRC patients will be mapped to highlight under-reported/under-investigated research areas. CRC is the third most common cancer excluding skin cancers in the US. Unlike non-Hispanic White populations, cancer is the number one cause of death in H/L populations and currently represents 21% of total deaths. Despite this, a lack of consensus exists on CRC outcomes for H/L patients. Most research on H/L individuals has examined incidence and screening of CRC, with fewer studies focusing on cancer outcomes. All epidemiological study designs and systematic reviews will be considered. The review will only include peer-reviewed studies that report on survival, treatment, and/or recurrence patterns for H/L patients with CRC residing in the US and Puerto Rico. A 3-step search with a 2-stage study selection process will be followed, as recommended by JBI and Arksey and O'Malley. Databases to be searched will include MEDLINE (PubMed), Embase (Ovid), and Scopus. A data extraction tool will be designed based on JBI recommendations. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRSIMA-ScR) will be used, with the results presented in a PRISMA diagram. Publications in English from database inception to the present will be considered. Open Science Framework https://osf.io/y6qf5.

Experiences of informal caregivers of people with dementia in low- and middle-income countries: a systematic review protocol.

This review aims to synthesize the experiences of informal caregivers of people with dementia in low- and middle-income countries (LMICs). Globally, the burden of dementia is increasing disproportionately in LMICs. Informal caregivers play a vital role and face multiple challenges in LMICs, even though they also often lack the awareness and skills to provide adequate care for people living with dementia. Many LMICs have limited resources and caregivers lack support services. Understanding their experiences and perceptions may improve interventions, helping caregivers to better support those living with dementia. Studies from all settings in LMICs will be considered. Qualitative data from qualitative or mixed methods studies that explore caregivers' experiences and perceptions will be included in this review. Participants are informal caregivers of people diagnosed with dementia at any stage, regardless of age, gender, or their relationship with people living with dementia. This review will follow the JBI methodology for a qualitative systematic reviews, with meta-aggregation as the synthesis method. The review will be reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A 3-step search strategy will be used to locate published and unpublished studies in MEDLINE, CINAHL, Embase, PsycINFO, AgeLine, LILACS, African Index Medicus, ProQuest Dissertations and Theses, Open Dissertations, Web of Science Core Collection, and Google Scholar. There will be no date or language limitations. All studies will be screened against the inclusion criteria and data will be extracted and critically appraised for methodological quality by 2 independent reviewers using JBI tools. Confidence in the final synthesized findings will be assessed using the ConQual approach. PROSPERO CRD42023453814.

CMRLCCOA: Multi-Strategy Enhanced Coati Optimization Algorithm for Engineering Designs and Hypersonic Vehicle Path Planning.

The recently introduced coati optimization algorithm suffers from drawbacks such as slow search velocity and weak optimization precision. An enhanced coati optimization algorithm called CMRLCCOA is proposed. Firstly, the Sine chaotic mapping function is used to initialize the CMRLCCOA as a way to obtain better-quality coati populations and increase the diversity of the population. Secondly, the generated candidate solutions are updated again using the convex lens imaging reverse learning strategy to expand the search range. Thirdly, the Lévy flight strategy increases the search step size, expands the search range, and avoids the phenomenon of convergence too early. Finally, utilizing the crossover strategy can effectively reduce the search blind spots, making the search particles constantly close to the global optimum solution. The four strategies work together to enhance the efficiency of COA and to boost the precision and steadiness. The performance of CMRLCCOA is evaluated on CEC2017 and CEC2019. The superiority of CMRLCCOA is comprehensively demonstrated by comparing the output of CMRLCCOA with the previously submitted algorithms. Besides the results of iterative convergence curves, boxplots and a nonparametric statistical analysis illustrate that the CMRLCCOA is competitive, significantly improves the convergence accuracy, and well avoids local optimal solutions. Finally, the performance and usefulness of CMRLCCOA are proven through three engineering application problems. A mathematical model of the hypersonic vehicle cruise trajectory optimization problem is developed. The result of CMRLCCOA is less than other comparative algorithms and the shortest path length for this problem is obtained.

Boosting optimal symbolic planning: Operator-potential heuristics

Heuristic search guides the exploration of states via heuristic functions h estimating remaining cost. Symbolic search instead replaces the exploration of individual states with that of state sets, compactly represented using binary decision diagrams (BDDs). In cost-optimal planning, heuristic explicit search performs best overall, but symbolic search performs best in many individual domains, so both approaches together constitute the state of the art. Yet combinations of the two have so far not been an unqualified success, because (i) h must be applicable to sets of states rather than individual ones, and (ii) the different state partitioning induced by h may be detrimental for BDD size. Many competitive heuristic functions in planning do not qualify for (i), and it has been shown that even extremely informed heuristics can deteriorate search performance due to (ii).Here we show how to achieve (i) for a state-of-the-art family of heuristic functions, namely potential heuristics. These assign a fixed potential value to each state-variable/value pair, ensuring by LP constraints that the sum over these values, for any state, yields an admissible and consistent heuristic function. Our key observation is that we can express potential heuristics through fixed potential values for operators instead, capturing the change of heuristic value induced by each operator. These reformulated heuristics satisfy (i) because we can express the heuristic value change as part of the BDD transition relation in symbolic search steps. We run exhaustive experiments on IPC benchmarks, evaluating several different instantiations of potential heuristics in forward, backward, and bi-directional symbolic search. Our operator-potential heuristics turn out to be highly beneficial, in particular they hardly ever suffer from (ii). Our best configurations soundly beat previous optimal symbolic planning algorithms, bringing them on par with the state of the art in optimal heuristic explicit search planning in overall performance.

An Innovative Method Based on Wavelet Analysis for Chipless RFID Tag Detection

Chipless RFID tags have attractive low-cost advantages. However, traditional RFID anti-collision algorithms cannot be applied due to a lack of computing and processing capabilities. Problems with multitag detection must be solved to commercialize chipless RFID tags. In this paper, an innovative method for frequency-domain chipless RFID tag detection is proposed. The tags’ scattered signals are processed via wavelet analysis, and a time–frequency plot that can read the code is obtained. When the distance between tags is too close to distinguish in the time–frequency plot, independent component analysis is used to separate individual scattered signals from mixed echo signals; then, the code is read by means of wavelet analysis. To validate the proposed method, C-shaped frequency-domain chipless RFID tag models and a multitag detection simulation scenario were constructed in selected software. The short-time matrix pencil method (STMPM), short-time Fourier transform (STFT), and the proposed method were compared. When the tag spacing is 0.05 m, the code can be read successfully. Compared with the STMPM, the proposed method greatly reduces the computational quantity and shortens the reading time. Furthermore, adjustment of the window width and search step parameters is avoided.

Assessing Outcome Measurements and Impact of Simulation in Neurocritical Care Training: A Systematic Review.

AIM: The use of simulation training in neurocritical care is increasing. Yet, the pooled impact on patient and trainee outcomes remains unclear. This systematic review aims to determine the outcome measurements used after simulation training in neurocritical care and to synthesize the current evidence about the impact of simulation training on these outcomes. METHODS: A 3-step search was conducted in CINAHL, Cochrane, MEDLINE, PsychINFO, and Scopus. The inclusion criteria were composed of studies exploring simulation training in neurocritical care, published in English between 2000 and 2023. Two reviewers independently conducted screening, critical appraisal, and data extraction, using standardized Joanna Briggs Institute tools. Meta-analysis was precluded because of clinical, methodological, and statistical heterogeneity. RESULTS: Nine relevant studies were found: 1 quality improvement project and 8 quasi-experimental studies. The overall quality of the relevant studies was moderate to high (61.1%-77.8%). Three types of outcome measurements for simulation in neurocritical care were identified: knowledge and clinical performance; confidence and comfort; and teamwork, communication, and leadership skills. Simulation training was associated with a significant improvement in knowledge and clinical performance, and confidence and comfort, but not in communication and leadership skills. CONCLUSION: Significant improvement in trainees' outcomes was observed. The current literature includes significant heterogeneity in the methods of evaluating simulation outcomes, although no patient outcomes were observed. Investigating the effect of simulation in neurocritical care training on patient outcomes in future studies is warranted.

In Silico Approaches to Polyherbal Synergy: Protocol for a Scoping Review.

According to the World Health Organization, more than 80% of the world's population relies on traditional medicine. Traditional medicine is typically based on the use of single herbal drugs or polyherbal formulations (PHFs) to manage diseases. However, the probable mode of action of these formulations is not well studied or documented. Over the past few decades, computational methods have been used to study the molecular mechanism of phytochemicals in single herbal drugs. However, the in silico methods applied to study PHFs remain unclear. The aim of this protocol is to develop a search strategy for a scoping review to map the in silico approaches applied in understanding the activity of PHFs used as traditional medicines worldwide. The scoping review will be conducted based on the methodology developed by Arksey and O'Malley and the recommendations of the Joanna Briggs Institute (JBI). A set of predetermined keywords will be used to identify the relevant studies from five databases: PubMed, Embase, Science Direct, Web of Science, and Google Scholar. Two independent reviewers will conduct the search to yield a list of relevant studies based on the inclusion and exclusion criteria. Mendeley version 1.19.8 will be used to remove duplicate citations, and title and abstract screening will be performed with Rayyan software. The JBI System for the Unified Management, Assessment, and Review of Information tool will be used for data extraction. The scoping review will be reported based on the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. Based on the core areas of the scoping review, a 3-step search strategy was developed. The initial search produced 3865 studies. After applying filters, 875 studies were short-listed for further review. Keywords were further refined to yield more relevant studies on the topic. The findings are expected to determine the extent of the knowledge gap in the applications of computational methods in PHFs for any traditional medicine across the world. The study can provide answers to open research questions related to the phytochemical identification of PHFs, criteria for target identification, strategies applied for in silico studies, software used, and challenges in adopting in silico methods for understanding the mechanisms of action of PHFs. This study can thus provide a better understanding of the application and types of in silico methods for investigating PHFs. PRR1-10.2196/56646.

Midwives' and registered nurses' role and scope of practice in acute early pregnancy care services: a scoping review.

The objective of this scoping review was to investigate and describe what is reported on the role and scope of practice of midwives and registered nurses providing care for women with pregnancy complications under 20 weeks' gestation in acute clinical settings in Australia. In many high-income countries, women experiencing unexpected complications in early pregnancy attend an acute care service, such as an emergency department, rather than a maternity or obstetric unit. This service structure can impact the care women receive, and determine who provides it. Women and their partners, who are often experiencing emotional distress, have reported difficult experiences when accessing acute services, particularly emergency departments, which are not traditionally staffed by midwives. The role and scope of practice of both midwives and registered nurses providing acute early pregnancy care in most high-income countries, including Australia, is poorly reported. Documenting this area of practice is an important first step in facilitating ongoing research in this important aspect of pregnancy care. Published and gray literature that described the role and scope of practice of midwives and/or registered nurses providing care in acute early pregnancy settings in Australia were considered for this review. A scoping review of the literature was conducted following JBI methodological guidance and reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) checklist. A 3-step search strategy was conducted to explore evidence from databases and search engines, gray literature sources, and selected reference lists. After screening, data were extracted from records selected for the final review, mapped, and analyzed using content analysis. The evidence reviewed (n=23) included primary research studies, conference abstracts, and gray literature, such as clinical guidance documents, academic theses, and websites from January 2008 to October 2023. The most common setting for care provision was the emergency department. Midwives' and registered nurses' roles and scope of practice in acute early pregnancy care in Australia can be summarized into 4 areas: physical care, psychosocial support, care co-ordination, and communication. Women's access to midwifery care at this time in pregnancy appears to be limited. Registered nurses, usually employed in emergency departments, have the most prominent role and scope in the provision of care for women with acute early pregnancy complications. Descriptions of midwives' practice focuses more on psychosocial support and follow-up care, particularly in early pregnancy assessment service models. This review highlights the inconsistency in midwives' and registered nurses' roles and scope in acute early pregnancy in Australia, a finding which is relevant to other international settings. Both professions could further fulfill role and scope capacity in the provision of supportive, individualized, and timely care for women and families accessing a range of acute early pregnancy services. Emergency departments are the usual practice domain of registered nurses who may be limited in terms of the scope of care they can provide to women with early pregnancy complications. Leaders of the midwifery profession should conduct further research into innovative service models that embed a role for midwives in all settings that provide care for pregnant women, regardless of gestation. Open Science Framework osf.io/7zchu.

Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions

Carbon emissions play a pivotal role in driving global warming, with commercial and residential sectors ranking as the third-largest source after industrial and transportation sectors. Consequently, establishing a framework for spatial characterizing and predicting carbon emissions from these sectors is vital for effective carbon reduction planning. This study proposes an optimized Backpropagation Neural Network, incorporating a Spatial Weight Matrix, to model the relationship between land use and carbon emissions, taking spatial effects into account. Additionally, an optimized Random Forest model with variable search step lengths is developed for large-scale and multi-class land use prediction. Beijing, China serves as the study site, yielding the following findings: (1) The optimized Backpropagation Neural Network outperforms traditional models, achieving goodness of fit of 0.999 and 0.997 in training and testing datasets, respectively. (2) The optimized Random Forest model, while reducing prediction precision for ecological areas (Root Mean Square Error increases by 4.12 %), enhances overall model performance by 73.95 % in a single iteration. (3) By 2025, commercial and residential carbon emissions in Beijing are projected to reach 8.91 million tons. (4) The study characterizes the spatial patterns of commercial and residential carbon emissions at a 1 km resolution for the next 15 years, revealing statistical features. (5) Expansion of mixed commercial and residential land relies on service-oriented development, positively impacting regional carbon emission intensity control.