Multiple Sets Research Articles

The WeFEEL Approach: Examining an Emotional Intelligence Curriculum’s Impact on Young Children’s Development and Social Interactions With Their Families

ABSTRACT This study is driven by a collective call from stakeholders and by the occurrence of violent events within society to address the importance of emotional intelligence and emotional awareness in early childhood development. Recognizing the significance of these skills in fostering well-being and success, there is a growing demand for targeted supplemental programs aimed at teaching emotional intelligence from a young age. The WeFEEL Approach is a comprehensive approach based on emotional intelligence facets. It aims to foster self-regulation and empathy with particular emphasis on co-regulation in young children and their at-risk families. The present mixed-method study, examining the impact of the WeFEEL Emotional Intelligence Approach, was informed by ethnographic methods for data collection and grounded theory analysis. Through teacher/family focus groups and video analysis of classroom implementation, the findings suggest a positive impact on students in terms of emotional knowledge growth and application of the WeFEEL framework with generalizability to multiple settings. Further, teachers and families expressed a positive shift in classroom and home emotional climate and an increase in empathy-driven interactions. Implications for the field of early childhood education are discussed.

An Environment Recognition Algorithm for Staircase Climbing Robots

For deformed wheel-based staircase-climbing robots, the accuracy of staircase step geometry perception and scene mapping are critical factors in determining whether the robot can successfully ascend the stairs and continue its task. Currently, while there are LiDAR-based algorithms that focus either on step geometry detection or scene mapping, few comprehensive algorithms exist that address both step geometry perception and scene mapping for staircases. Moreover, significant errors in step geometry estimation and low mapping accuracy can hinder the ability of deformed wheel-based mobile robots to climb stairs, negatively impacting the efficiency and success rate of task execution. To solve the above problems, we propose an effective LiDAR-Inertial-based point cloud detection method for staircases. Firstly, we preprocess the staircase point cloud, mainly using the Statistical Outlier Removal algorithm to effectively remove the outliers in the staircase scene and combine the vertical angular resolution and spatial geometric relationship of LiDAR to realize the ground segmentation in the staircase scene. Then, we perform post-processing based on the point cloud map obtained from LiDAR SLAM, extract the staircase point cloud and project and fit the staircase point cloud by Ceres optimizer, and solve the dimensional information such as depth and height of the staircase by combining with the mean filtering method. Finally, we fully validate the effectiveness of the method proposed in this paper by conducting multiple sets of SLAM and size detection experiments in real different staircase scenarios.

A Noise Reduction Method for Signal Reconstruction and Error Compensation of a Maglev Gyroscope Under Persistent External Interference

To eliminate the noise interference caused by continuous external environmental disturbances on the rotor signals of a maglev gyroscope, this study proposes a noise reduction method that integrates an adaptive particle swarm optimization variational modal decomposition algorithm with a strategy for error compensation of the trend term in reconstructed signals, significantly improving the azimuth measurement accuracy of the gyroscope torque sensor. The optimal parameters for the variational modal decomposition algorithm were determined using the adaptive particle swarm optimization algorithm, allowing for the accurate decomposition of noisy rotor signals. Additionally, using multi-scale permutation entropy as a criterion for discriminant, the signal components were filtered and summed to obtain the denoised reconstructed signal. Furthermore, an empirical mode decomposition algorithm was employed to extract the trend term of the reconstructed signal, which was then used to compensate for the errors in the reconstructed signal, achieving significant noise reduction. On-site experiments were conducted on the high-precision GNSS baseline of the Xianyang Yuan Tunnel in the second phase of the project to divert water from the Han River to the Wei River, where this method was applied to process and analyze multiple sets of rotor signals. The experimental results show that this method effectively suppresses continuous external environmental interference, reducing the average standard deviation of the compensated signals by 46.10% and the average measurement error of the north azimuth by 45.63%. Its noise reduction performance surpasses that of the other four algorithms.

Value sets and the problem of redundancy in value set repositories.

Crafting high-quality value sets is time-consuming and requires a range of clinical, terminological, and informatics expertise. Despite widespread agreement on the importance of reusing value sets, value set repositories suffer from clutter and redundancy, greatly complicating efforts at reuse. When users encounter multiple value sets with the same name or ostensibly representing the same clinical condition, it can be difficult to choose amongst them or determine if any differences among them are due to error or intentional decision. This paper offers a view of value set development and reuse based on a field study of researchers and informaticists. The results emerge from an analysis of relevant literature, reflective practice, and the field research data. Qualitative analysis of our study data, the relevant literature, and our own professional experience led us to three dichotomous concepts that frame an understanding of diverse practices and perspectives surrounding value set development: Permissible values versus analytic value sets;Prescriptive versus descriptive approaches to controlled medical vocabulary use; andSemantic and empirical types of value set development and evaluation practices and the data they rely on.This three-fold framework opens up the redundancy problem, explaining why multiple value sets may or may not be needed and advancing academic understanding of value set development. In order for value set repositories to become more rather than less useful over time, software must channel user efforts into either improving existing value sets or making new ones only when absolutely necessary. This would require major, innovative changes to value set repository platforms. We believe the most direct path to giving value set developers the ability to leverage prior work is by encouraging them to compare existing value sets using advanced interfaces like VS-Hub, and by collecting and using metadata about code inclusion and exclusion decisions during the authoring process.

Challenges of Faith Community Nursing in a Midwestern Healthcare System.

Faith community nurses are positioned to play a vital role in addressing whole person health in multiple settings. A steady decline of nurses completing faith community nurse (FCN) training in a midwestern healthcare system sparked the system's hospital leadership to re-evaluate the FCN training and outreach program. To inform future education programs, an online needs assessment survey was deployed to FCNs. Focus groups were used to understand the FCNs' experiences in their role. The findings highlight a need for comprehensive educational programs, support networks, and funding mechanisms to support faith community nursing practice.

Modeling the impacts of governmental and human responses on COVID-19 spread using statistical machine learning

ABSTRACT Understanding the impacts of governmental and human responses on the pandemic control is imperative for forecasting pandemic spread under various responsive scenarios and guiding localized interventions before pharmaceutical interventions are available. This study analyzed multiple data sets, including social media, mobility, policy evaluations, and COVID-19 infection reports, to delineate the interactions between governmental and human responses and COVID-19 spread in the United States in 2020 when vaccinations were unavailable. The contributions are (1) uncovering the spatiotemporal variations in governmental and human responses during COVID-19; (2) developing a statistical machine learning algorithm that incorporates spatiotemporal dependencies and temporal lag effects to model the relationships between governmental and human responses and the pandemic spread; (3) dissecting the impacts of human responses on the pandemic across space and time. Results reveal that the determinants of COVID-19 health impacts transitioned from human mobility during the initial outbreak phase to both human mobility and stay-at-home policies during the rapid spread phase, and ultimately to the compound of human mobility, stay-at-home policies and the public awareness in the full-blown phase. These findings furnish guidance for policymakers in implementing adaptive and phased strategies.

Cost-Aware Calibration of Classifiers

Most classification techniques in machine learning are able to produce probability predictions in addition to class predictions. However, these predicted probabilities are often not well calibrated in that they deviate from the actual outcome rates (i.e., the proportion of data instances that actually belong to a certain class). A lack of calibration can jeopardize downstream decision tasks that rely on accurate probability predictions. Although several post hoc calibration methods have been proposed, they generally do not consider the potentially asymmetric costs associated with overprediction versus underprediction. In this research, we formally define the problem of cost-aware calibration and propose a metric to quantify the cost of miscalibration for a given classifier. Next, we propose three approaches to achieve cost-aware calibration, two of which are cost-aware adaptations of existing calibration algorithms; the third one (named MetaCal) is a Bayes optimal learning algorithm inspired by prior work on cost-aware classification. We carry out systematic empirical evaluations on multiple public data sets to demonstrate the effectiveness of the proposed approaches in reducing the cost of miscalibration. Finally, we generalize the definition and metric as well as solution algorithms of cost-aware calibration to account for nonlinear cost structures that may arise in real-world decision tasks. Data Ethics & Reproducibility Note: There are no data ethics considerations. The code capsule is available on Code Ocean at https://doi.org/10.24433/CO.8552538.v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2024.0038 ).

Numerical Simulation of the Dynamic Behavior of Low Permeability Reservoirs Under Fracturing-Flooding Based on a Dual-Porous and Dual-Permeable Media Model

In recent years, fracturing-flooding technology has achieved a series of successful practices in the development of low-permeability oil reservoirs. However, research on the dynamic behavior of fracturing-flooding remains limited. In this paper, a dual medium model considering anisotropic characteristics is established for the target blocks. Multiple sets of conventional water injection transitions and multi-cycle fracturing-flooding operations are designed for simulation to explore the subsequent optimal operational schemes. Simulations are conducted on the optimal transitions between conventional water injection and multi-cycle fracturing-flooding schemes for different reservoir models with varying physical properties to study the dynamic behavior of fracturing-flooding in oil reservoirs with different properties. The results indicate that, for conventional water injection schemes, the optimal transition time for both the target well group and other reservoirs with different properties corresponds to a formation pressure coefficient between 1.2 and 1.3, with the optimal injection–production ratio being 1:1. From the perspective of water cut, the accumulated oil production of multi-cycle fracturing-flooding is higher than that of conventional water injection. The optimal multi-cycle fracturing-flooding schemes for both the target well group and other reservoirs with different properties are to start fracturing-flooding when the formation pressure coefficient is around 0.8 and to begin production when it reaches 1.4.

The effects of dynamic stretching performed before and between the sets of exercises on vertical jump performance

The purpose of this study was to investigate the effects of dynamic stretching (DS) performed before and between the sets of exercises on vertical jump performance. Twelve healthy adult males performed DS on their lower limb muscles, with 10 repetitions on each side before and between four sets of three repetitions of vertical jumps. Vertical jump height was measured. Additionally, the percent change in jump height for each set was determined based on the baseline score from set 1. Heart rate was also measured at rest, before exercise, and before each set of vertical jumps. Vertical jump height was significantly higher in the latter half of the sets (p < .05) compared to the condition in which DS was only performed before exercise and to the control condition in which the participants were refrained from performing DS throughout. In addition, when DS was only performed before exercise, vertical jump height was significantly lower in set 4 compared to set 1 (p = .001). These findings suggest that DS performed between sets, in addition to performing before the exercise, produces a higher power output in vertical jump performance in the latter half of multiple sets.

Receipt of Medications for Alcohol and Opioid Use Disorders: The Importance of Service Utilization Patterns.

Medications for alcohol use disorder (MAUD) and opioid use disorder (MOUD) are underutilized evidence-based treatments. While patients often receive treatment in multiple medical treatment settings, it is unknown whether certain treatment settings or combinations of settings are associated with increased receipt of MAUD or MOUD. Data from the cross-sectional 2019 National Survey on Drug Use and Health were used to identify treatment settings where adult respondents received any past year alcohol treatment (n = 476, 33% female) or, separately, opioid treatment (n = 337, 38% female). Three-stage latent class analysis assessed the patterns of treatment settings utilization and their associations with receipt of MAUD or MOUD, adjusting for potential confounders. Only 10.9% of those in alcohol treatment received MAUD while 40.9% of those in opioid treatment received MOUD. Analyzing different treatment settings visited by respondents, a four-class model best fit the MAUD sample while a three-class model best fit the MOUD sample. There were significant differences in receipt of MAUD by treatment setting classes in unadjusted models (p < 0.05). There were no significant differences in receipt of MOUD across treatment utilization classes, but MOUD receipt was associated with severity of opioid use in all treatment setting classes. People in treatment for alcohol or opioid use were more likely to receive medication if their use was more severe, and were more likely to receive MAUD if they accessed care in many settings. As MOUD and especially MAUD are underutilized, these findings highlight opportunities to increase access to evidence-based care for alcohol and opioid use disorders.

SCRMshaw: Supervised cis-regulatory module prediction for insect genomes.

As the number of sequenced insect genomes continues to grow, there is a pressing need for rapid and accurate annotation of their regulatory component. SCRMshaw is a computational tool designed to predict cis-regulatory modules ("enhancers") in the genomes of various insect species. A key advantage of SCRMshaw is its accessibility. It requires minimal resources-just a genome sequence and training data from known Drosophila regulatory sequences, which are readily available for download. Even users with modest computational skills can run SCRMshaw on a desktop computer for basic applications, although a high-performance computing cluster is recommended for optimal results. SCRMshaw can be tailored to specific needs: users can employ a single set of training data to predict enhancers associated with a particular gene expression pattern, or utilize multiple sets to provide a first-pass regulatory annotation for a newly-sequenced genome. This protocol provides an extensive update to the previously published SCRMshaw protocol and aligns with the methods used in a recent annotation of over 30 insect regulatory genomes. It includes the most recent modifications to the SCRMshaw protocol and details an end-to-end pipeline that begins with a sequenced genome and ends with a fully-annotated regulatory genome. Relevant scripts are available via GitHub, and a living protocol that will be updated as necessary is linked to this article at protocols.io.

Clustering the Diurnal Cycle of Precipitation Using Global Satellite Data

AbstractThe diurnal cycle of precipitation (DCP) is a fundamental component of the precipitation cycles across various temporal scales. However, the absence of a comprehensive and intuitive clustering description of the DCP, including precipitation amount, frequency, intensity, and seasonality on the global scale, has impeded understanding of climate impacts on society. This study investigates the diurnal cycle and its seasonal differences in global precipitation using hierarchical clustering and high‐resolution satellite precipitation data. Results indicate that regions with coastal and topographic complexity exhibit intricate clustering patterns globally. Significant inter‐cluster differences suggest that the precipitation amount and frequency have stronger diurnal cycles than that of the precipitation intensity. Seasonal cluster transitions indicate that the global DCP exhibits significant seasonal differences. This study provides multiple sets of information‐rich and user‐friendly maps of the DCP, enhancing research on sub‐daily precipitation mechanisms and their climatic impacts.

Accuracy, Reproducibility, and Responsiveness to Treatment of Home Spirometry in Cystic Fibrosis: Multicenter, Retrospective, Observational Study.

Portable spirometers are increasingly used to measure lung function at home, but doubts about the accuracy of these devices persist. These doubts stand in the way of the digital transition of chronic respiratory disease care, hence there is a need to address the accuracy of home spirometry in routine care across multiple settings and ages. This study aimed to assess the accuracy, reproducibility, and responsiveness to the treatment of home spirometry in long-term pediatric and adult cystic fibrosis care. This retrospective observational study was carried out in 5 Dutch cystic fibrosis centers. Home spirometry outcomes (forced expiratory volume in one second [FEV1], and forced vital capacity [FVC]) for 601 anonymized users were collected during 3 years. For 81 users, data on clinic spirometry and elexacaftor/tezacaftor/ivacaftor (ETI) use were available. Accuracy was assessed using Bland-Altman plots for paired clinic-home measurements on the same day and within 7 days of each other (nearest neighbor). Intratest reproducibility was assessed using the American Thoracic Society/European Respiratory Society repeatability criteria, the coefficient of variation, and spirometry quality grades. Responsiveness was measured by the percentage change in home spirometry outcomes after the start of ETI. Bland-Altman analysis was performed for 86 same-day clinic-home spirometry pairs and for 263 nearest neighbor clinic-home spirometry pairs (n=81). For both sets and for both FEV1 and FVC, no heteroscedasticity was present and hence the mean bias was expressed as an absolute value. Overall, home spirometry was significantly lower than clinic spirometry (mean ΔFEV1clinic-home 0.13 L, 95% CI 0.10 to 0.19; mean ΔFVCclinic-home 0.20 L, 95% CI 0.14 to 0.25) and remained lower than clinic spirometry independent of age and experience. One-way ANOVA with post hoc comparisons showed significantly lower differences in clinic-home spirometry in adults than in children (Δmean 0.11, 95% CI -0.20 to -0.01) and teenagers (Δmean 0.14, 95% CI -0.25 to -0.02). For reproducibility analyses, 2669 unique measurement days of 311 individuals were included. Overall, 87.3% (2331/2669) of FEV1 measurements and 74.3% (1985/2669) of FVC measurements met reproducibility criteria. Kruskal-Wallis with pairwise comparison demonstrated that for both FVC and FEV1, coefficient of variation was significantly lower in adults than in children and teenagers. A total of 5104 unique home measurements were graded. Grade E was given to 2435 tests as only one home measurement was performed. Of the remaining 2669 tests, 43.8% (1168/2669) and 43.6% (1163/2669) received grade A and B, respectively. The median percentage change in FEV1 from baseline after initiation of ETI was 19.2% after 7-14 days and remained stable thereafter (n=33). Home spirometry is feasible but not equal to clinic spirometry. Home spirometry can confirm whether lung functions remain stable, but the context of measurement and personal trends are more relevant than absolute outcomes.

Analysis of genetic sandbodies and coal seams with coal-bearing of Middle-Lower Jurassic in the Junggar Basin

IntroductionThe sediment genetic mechanisms have always been an important branch of the “source-sink” system, and it is also a research hotspot.MethodsConglomerate, sandstone, and coal are developed of Middle-Lower Jurassic in the Junggar Basin, and the genetic mechanism is not clear, which restricts further exploration of oilfield. Based on the core carefully, test data, and outcrop sections, Badaowan and Xishanyao formations are analyzed.ResultsFive types of conglomerate lithofacies and sandstone lithofacies, two kinds of mudstone lithofacies, and one type of coal are identified. Based on the structural-genetic mechanism, the debris flow and traction current with conglomerate are developed, and a method of the semi-quantitative identifying index is proposed based on the sedimentary sequence of lithological characteristics, MPS/BTh, and grain size characteristics as the identification criteria. Depending on fluvio-delta, the genetic bodies of the distribution channel, subaqueous distribution channel, mouth bar, braided channel, and overbank sandstone are divided.DiscussionBy integrating structural, paleoclimatic, and sedimentary environment, it was observed that coal in the Badaowan Formation has the characteristics of continuous and stable distribution of thick layers, whereas multiple sets of discontinuous and instable distribution are observed in the in Xishanyao Formation. Finally, the filling model is established, and the superposition and distribution of sandbodies are clarified.

GACT-PPIS: Prediction of protein-protein interaction sites based on graph structure and transformer network

The prediction of protein-protein interaction sites (PPIS) is currently crucial for regulating many biological activities in cells and developing drugs for various diseases. Deep learning-based methods have been proposed for predicting PPIS, significantly reducing the manpower and time costs associated with traditional experimental methods such as yeast two-hybrid, mass spectrometry, and affinity purification. However, the predictive accuracy of these deep learning methods has not yet reached the expected level. Therefore, we introduce a model called GACT-PPIS.The design of the GACT-PPIS algorithm aims to utilize combined information from protein sequences and structures as input to predict protein-protein interaction sites. The core of GACT-PPIS utilizes an Enhanced Graph Attention Network (EGAT) with initial residual and identity mappings, along with a deep Transformer network as the basic units, supplemented by Graph Convolutional Networks (GCN), effectively aggregating information from neighboring nodes for each node. After multiple network layers, the information of the entire protein is also fused into the nodes, and the Transformer network further enhances the model's performance.Experimental results show that GACT-PPIS outperforms the most representative models in terms of Recall, F1-measure, MCC, AUROC, and AUPRC on the benchmark test set (Test-60). Additionally, on other independent test sets (UBTest-31-6), GACT-PPIS leads in terms of Accuracy, Precision, Recall, F1-measure, MCC, AUROC, and AUPRC compared to the most representative models. It is worth noting that GACT-PPIS demonstrates excellent generalization and versatility across different test sets, showcasing good performance on multiple test sets for the same trained GACT-PPIS model.

A dynamic-ranking-assisted co-evolutionary algorithm for constrained multimodal multi-objective optimization

Constrained multimodal multi-objective optimization problems (CMMOPs) are characterized by multiple constrained Pareto sets (CPSs) sharing the same constrained Pareto front (CPF). The challenge lies in efficiently identifying equivalent CPSs while maintaining a balance among convergence, diversity, and constraints. Addressing this challenge, we propose a dynamic-ranking-based constraint handling technique implemented in a co-evolutionary algorithm, named DRCEA, specifically designed for solving CMMOPs. To search for equivalent CPSs, we introduce a co-evolutionary framework involving two populations: a convergence-first population and a constraint-first population. The co-evolutionary framework facilitates knowledge transfer and sustains diverse solutions. Subsequently, a dynamic ranking strategy is employed with dynamic weight parameters that consider both dominance and constraint relationships among individuals. Within the convergence-first population, the weight parameter for convergence gradually decreases, while the constraint parameter increases. Conversely, in the constraint-first population, the weight parameter for constraints gradually decreases, while the convergence parameter increases. This approach ensures a well-balanced consideration of convergence and constraints within the two distinct populations. Experimental results on the CMMOP test suite and the real-world CMMOP test scenario validate the effectiveness of the proposed dynamic-ranking-based constraint handling technique, demonstrating the superiority of DRCEA over seven state-of-the-art algorithms.

Seismic and outcrop-based 3D characterization of fault damage zones in sandstones, Rio do Peixe Basin, Brazil

Fault damage zones, composed of sub-seismic deformation structures, are difficult to detect using seismic data. Still, they can be related to fault throw, which is widely measured in the subsurface. This research employs a multiscale approach that integrates outcrop studies with seismic reflection data to investigate the attributes of fault damage zones affecting porous sandstones. We provide an in-depth understanding of the 3D fault zone volume, investigating correlations between geometric attributes of faults (width of damage zone, frequency of subsidiaries structures, and fault termination) in the outcrop and the fault throw in the subsurface, with insights from deformation mechanisms within the fault zone. The methods encompass 1D scanlines to constrain the damage zone width on the outcrop. At the same time, the subsurface analysis uses 3D seismic data, seismic attributes, and deep learning neural network (DNN) fault volumes to interpret fault geometries and quantify fault throw at different depths. Results show that the area presents a complex fault zone with multiple fault sets, deformation bands, and fracture corridors trending mainly NE-SW, NW-SE, and E-W. The integration of surface and subsurface fault data enabled the identification of two portions in each fault set outcropping at the fault tip for E-W/ESE-WNW-striking faults and the central part of the fault for NE-SW-striking faults. Faults with the greatest length do not outcrop with the largest damage zone width since they are outcropping the tip of the fault. The parallel faults overlap their damage zones, increasing the deformation zones in the affected sandstones. Fault throw and damage zone width present a positive correlation. This relation is affected by fault segments and subsidiary faults.

Flow prediction and dimensionless feature analysis in orifice microchannels under cavitation conditions based on similarity criteria

A novel set of dimensionless numbers for predicting cavitation flow in gas-liquid flow through a throttling orifice is proposed. Multiple sets of cavitation data are obtained from the literature, and eight dimensionless groups are extracted using dimensional analysis. Subsequently, self-similarity among these groups is established, leading to the proposal of new dimensionless correlations (CfW and CfWd). Investigation results indicate that the established correlations between dimensionless groups can predict cavitation flow in orifices under a wide range of hydrodynamic and geometric conditions. The predictive model is validated using different sets of experimental data, improving accuracy (average relative error decreased by more than 57%) and covering more physical conditions compared to previous correlation predictions. Furthermore, dimensionless parameters with significant influence on the cavitation discharge coefficient are identified by the explanatory machine learning technique SHapley Additive exPlanations (SHAP) and sensitivity analysis. Based on these findings, this investigation contributes to a reduction in the number of variables involved in cavitation experiments or simulations.

CFD simulation and Grid study of Flying Wing Standard Model

Abstract In order to study the influence of mesh size variation on the numerical simulation of typical flying wing model, based on the verification and validation of NNW-FlowStar software, the numerical simulation of flying wing model with multiple sets of unstructured grids with succession variation in Ma=0.9 is carried out, and the lift coefficient, drag coefficient and pitching moment coefficient under corresponding conditions are obtained. Results show that as the height of the first layer decreases, lift coefficient is gradually approaching converged. When the height of the first layer reaches 0.04, the simulation error is already within 1%.

Prevenance and determinants of violence against healthcare workers in multiple settings: a cross sectional study

Prevenance and determinants of violence against healthcare workers in multiple settings: a cross sectional study