An Embedded Model Estimator for Non-Stationary Random Functions Using Multiple Secondary Variables

An algorithm for non-stationary spatial modelling using multiple secondary variables is developed herein, which combines geostatistics with quantile random forests to provide a new interpolation and stochastic simulation. This paper introduces the method and shows that its results are consistent and similar in nature to those applying to geostatistical modelling and to quantile random forests. The method allows for embedding of simpler interpolation techniques, such as kriging, to further condition the model. The algorithm works by estimating a conditional distribution for the target variable at each target location. The family of such distributions is called the envelope of the target variable. From this, it is possible to obtain spatial estimates, quantiles and uncertainty. An algorithm is also developed to produce conditional simulations from the envelope. As they sample from the envelope, realizations are therefore locally influenced by relative changes of importance of secondary variables, trends and variability.

Gender, talent management and firm performance: MNCs' female-focused talent management practices in Russia

PurposeThere is a substantial void in the understanding of the effect of talent management (TM) practices specifically targeted at females on firm performance. This paper investigates the relationship between female-focused TM and firm performance with the aim of demonstrating the importance of gender diversity in firms.Design/methodology/approachThe authors developed and empirically tested a contextually embedded model using data from 103 multinational corporations in Russia to examine the effect of female-focused TM on firm performance.FindingsThe authors found an overall positive relationship between female-focused TM and firm performance. The authors’ analysis also revealed significant positive effects of female-focused talent development and talent retention, but not talent attraction, on firm performance.Originality/valueThis paper contributes to the vibrant TM scholarship by focusing on female-focused talent attraction, development and retention practices.

Conclusions: Towards a Bayesian Modelling Process

In the concluding chapter we summarise the theoretical, methodological and practical outcomes of the model-based process of scientific enquiry presented in the book, against the wider background of recent developments in demography and population studies. We offer a critical self-reflection on further potential and on limitations of Bayesian model-based approaches, alongside the lessons learned from the modelling exercise discussed throughout this book. As concluding thoughts, we suggest potential ways forward for statistically-embedded model-based computational social studies, including an assessment of the future viability of the wider model-based research programme, and its possible contributions to policy and decision making.

Geostatistics assisted by machine learning for reservoir property modeling: A case study in presalt carbonates of Buzios Field, Brazil

An embedded model estimator (EMBER) petrophysical modeling algorithm has been applied to obtain effective porosity and permeability within the presalt carbonate reservoirs of the Barra Velha Formation in Buzios Field, Santos Basin. This advanced methodology was used due to the heterogeneity and complexity of the reservoirs, which makes modeling by conventional geostatistical methodologies difficult. For effective porosity modeling, we chose one facies model, one stratigraphic seismic attribute (acoustic impedance), and one structural seismic attribute (local flatness) as secondary variables. Permeability was modeled by using the best effective porosity simulation result as a secondary variable. Our results demonstrate that average effective porosity and permeability were 0.10 v/v and 440 md, respectively, indicating good reservoir quality throughout the studied area. A vertical trend of high effective porosities and permeabilities for the basal and uppermost reservoir sections was identified in our results, as well as a trend with lower values for these reservoir properties for the intermediate reservoir section. The lower section of the formation presented more continuity, and we infer it to be the best reservoir interval. We observed two horizontal trends for these reservoir properties at the formation top: one of higher values aligned to the north–south direction at the structural highs and another of lower reservoir properties related to isolated structural lows within structural highs. Correlation between modeled results and the blind test ANP-1 well upscaled properties was high, and upscaled well-log property distributions were preserved in the EMBER simulations, proving the predictive capacity of the algorithm. Finally, conditional distributions analysis indicated that the basal section of the Barra Velha Formation presents higher uncertainty for the estimation of effective porosity. Even though this interval is considered to have the best reservoir characteristics, decision making should be done with caution for this section.

Real-time detection of particleboard surface defects based on improved YOLOV5 target detection

Particleboard surface defect detection technology is of great significance to the automation of particleboard detection, but the current detection technology has disadvantages such as low accuracy and poor real-time performance. Therefore, this paper proposes an improved lightweight detection method of You Only Live Once v5 (YOLOv5), namely PB-YOLOv5 (Particle Board-YOLOv5). Firstly, the gamma-ray transform method and the image difference method are combined to deal with the uneven illumination of the acquired images, so that the uneven illumination is well corrected. Secondly, Ghost Bottleneck lightweight deep convolution module is added to Backbone module and Neck module of YOLOv5 detection algorithm to reduce model volume. Thirdly, the SELayer module of attention mechanism is added into Backbone module. Finally, replace Conv in Neck module with depthwise convolution (DWConv) to compress network parameters. The experimental results show that the PB-YOLOv5 model proposed in this paper can accurately identify five types of defects on the particleboard surface: Bigshavings, SandLeakage, GlueSpot, Soft and OliPollution, and meet the real-time requirements. Specifically, recall, F1 score, [email protected], [email protected]:.95 values of pB-Yolov5s model were 91.22%, 94.5%, 92.1%, 92.8% and 67.8%, respectively. The results of Soft defects were 92.8%, 97.9%, 95.3%, 99.0% and 81.7%, respectively. The detection of single image time of the model is only 0.031 s, and the weight size of the model is only 5.4 MB. Compared with the original YOLOv5s, YOLOv4, YOLOv3 and Faster RCNN, the PB-Yolov5s model has the fastest Detection of single image time. The Detection of single image time was accelerated by 34.0%, 55.1%, 64.4% and 87.9%, and the weight size of the model is compressed by 62.5%, 97.7%, 97.8% and 98.9%, respectively. The mAP value increased by 2.3%, 4.69%, 7.98% and 13.05%, respectively. The results show that the PB-YOLOV5 model proposed in this paper can realize the rapid and accurate detection of particleboard surface defects, and fully meet the requirements of lightweight embedded model.

A Knowledge Graph-Enhanced Attention Aggregation Network for Making Recommendations

In recent years, many researchers have devoted time to designing algorithms used to introduce external information from knowledge graphs, to solve the problems of data sparseness and the cold start, and thus improve the performance of recommendation systems. Inspired by these studies, we proposed KANR, a knowledge graph-enhanced attention aggregation network for making recommendations. This is an end-to-end deep learning model using knowledge graph embedding to enhance the attention aggregation network for making recommendations. It consists of three main parts. The first is the attention aggregation network, which collect the user’s interaction history and captures the user’s preference for each item. The second is the knowledge graph-embedded model, which aims to integrate the knowledge. The semantic information of the nodes and edges in the graph is mapped to the low-dimensional vector space. The final part is the information interaction unit, which is used for fusing the features of two vectors. Experiments showed that our model achieved a stable improvement compared to the baseline model in making recommendations for movies, books, and music.

Interdisciplinary clinician perspectives on an embedded palliative oncology model in pediatric cancer care.

9 Background: Integration of palliative care into cancer care is recognized as best practice by international oncology and pediatrics organizations. However, optimal strategies for integration of pediatric palliative care (PPC) within cancer care remain understudied. While the majority of PPC provision for cancer patients occurs through subspeciality consultation, growing evidence suggests that models embedding PPC within cancer care have the potential to improve quality of care. Embedded models rely on partnerships with multidisciplinary clinicians, whose perspectives regarding this model are not well known. Methods: We conducted 25 focus groups with 175 clinicians in an academic pediatric cancer center, with groups stratified by discipline (physicians, advance practice providers (APPs), nurses, psychosocial providers) and by care team (hematologic malignancy, bone marrow transplant, solid tumor, brain tumor). Focus groups were led by trained research facilitators and audio-recorded for subsequent targeted content analysis to identify clinician perspectives regarding embedded PPC models. Results: Across 4 physician, 5 APP, 12 nurse, and 4 psychosocial focus groups, 25 physicians, 30 APPs, 71 nurses, and 49 psychosocial providers participated, respectively. When asked to describe features of an “ideal” PPC model, physicians, APPs, and nurses predominantly discussed care delivery and identified early integration of PPC into cancer care as a core feature of an ideal model. Physicians, nurses, and psychosocial providers also emphasized the importance of having a formalized, structured care model. The need for collaboration was the dominant theme for psychosocial providers. Clinicians in all disciplines identified potential benefits from an embedded model, including access to PPC across care settings, normalization of PPC integration, and emphasis on collaboration, teamwork, communication, and earlier PPC involvement. Physicians, APPs, and nurses anticipated similar challenges with an embedded model, including possible reticence of oncology providers and inadequate PPC staffing. Nurses, APPs, and psychosocial providers also voiced concern about potential lack of clarity in delegation of roles and responsibilities between PPC and oncology providers. Conclusions: Pediatric oncology multidisciplinary providers recognize the potential value of an embedded model for integration of PPC in the care of children with cancer. Though providers at times identified similar themes with respect to ideal PPC provision and the benefits and challenges to an embedded model, some identified priorities varied by discipline. These findings highlight the importance of integrating varied interdisciplinary perspectives when developing an embedded care model to align with priorities of diverse pediatric cancer stakeholders.

The Antecedents of Thriving at Work: A Meta-Analytic Review

In this study, a systematic and comprehensive meta-analysis of the relationship between thriving at work and its antecedents is conducted. The antecedents in terms of the characteristics of unit contextual features, the resources produced at work, agentic work behaviors, and personality traits are illustrated according to the socially embedded model of thriving described by Spreitzer and research. Additionally, we examine possible cultural influence on the relationship between thriving and its antecedents at different levels of individualistic culture. According to 67 independent samples (N = 28,097), our findings reveal the correlations between thriving at work and the antecedents such as those in the form of unit contextual features, the resources produced at work, agentic work behaviors, and personality traits. Furthermore, we find that individualism moderate the relationships between certain antecedents and thriving at work. Finally, we discuss the theoretical and practical implications of this study as well as the directions for future research.