Ranking Of Components Research Articles

Trauma and social pathways to psychosis: Examining the role of attachment, social rank and dissociation in a clinical sample.

The trauma and social pathways model of psychosis proposes interactions between trauma, attachment, social rank and dissociation in pathways to psychosis, though this model has yet to be empirically investigated. The primary aim of this study was to examine the overall predictive value of the trauma and social pathways model using regression analysis. A secondary aim was to delineate hypothesized pathways between trauma and positive symptoms of psychosis using serial mediation analysis. This was a cross-sectional study of people attending mental health services for a psychosis-related diagnosis (N = 71). Measures of trauma, positive symptoms of psychosis, attachment, social comparison and dissociation were completed. A model of recurrent trauma, insecure attachment, social rank and dissociation predicted 23.2% of the variance in positive symptoms of psychosis. Recurrent trauma, attachment and dissociation contributed significantly to the model, while social rank did not. Further, serial mediation analysis indicated that the sequence of disorganized attachment and dissociation fully mediated the relationship between recurrent trauma and positive symptoms. Results provide preliminary support for the trauma and social pathways model of psychosis, specifically as it relates to recurrent trauma, insecure attachment and dissociation. Results did not support the social rank component of this model, however. These findings provide clear targets for the development of next-wave psychological interventions that focus on trauma-related variables in psychosis. Future studies should replicate these findings with a larger clinical sample, and consider a measure of shame to further elucidate social processes in pathways to psychosis.

Clinician care priorities and practices in the fourth trimester: perspective from a California survey

BackgroundProfessional societies such as the American College of Obstetricians and Gynecologists (ACOG) promote the idea that postpartum care is an ongoing process where there is adequate opportunity to provide services and support. Nonetheless, in practice, the guidelines ask clinicians to perform more clinical responsibilities than they might be able to do with limited time and resources.MethodsWe conducted an online survey among practicing obstetric clinicians (obstetrician/gynecologists (OB/GYNs), midwives, and family medicine doctors) in California about their priorities and care practices for the first postpartum visit and explored how they prioritize multiple clinical responsibilities within existing time and resources. Between September 2023 and February 2024, 174 out of 229 eligible participants completed the survey, a 76% response rate. From a list of care components, we used descriptive statistics to identify those that were highly prioritized by most clinicians and those that were considered a priority by very few and examined the alignment between prioritized components and recommended care practices.ResultsClinicians were highly invested in the care components that they rated as most important, indicating that they always check these components or assess them when they perceive patient need. Depression and anxiety, breast health/breast feeding issues, vaginal birth complications and family planning counseling were highly ranked components by all clinicians. In contrast, clinicians more often did not assess those care components that infrequently ranked highly among the priority listing, consisting mainly of social drivers of health such as screening and counseling for intimate partner violence, working conditions and food/housing insecurity. In both instances, we found little discordance between priorities and care practices. However, OB/GYNs and midwives differed in some care components that they prioritized highly.ConclusionsWhile there is growing understanding of how important professional society recommendations are for maternal-infant health, clinicians face barriers completing all recommendations, especially those components related to social drivers of health. However, what the clinicians do prioritize highly, they are likely to perform. Now that Medi-Cal (Medicaid) insurance is available in California for up to 12 months postpartum, there is a need to understand what care clinicians provide and what gaps remain.

Experience of Paleotectodynamic Analysis of Rank Components of Mesozoic-Cenozoic Movements and Deformations Using the Example of the Central Part of Bukharo-Khiva Region

The article describes the methodological basis and some first experience results of applying paleotectodynamic analysis of tectonic movements rank components and deformations to study the features of geological development of a certain territory of the Bukharo-Khiva region central part in the Mesozoic – Cenozoic. The relevance of the work is determined by the need to revise the traditional methods of historical and structural analysis, based on which the conditions of formation and age of oil and gas structural traps are determined to improve the reliability of prognostic works. Paleotectodynamic analysis, as a new direction of studying the geologic evolution history, provides an assessment of the duration of manifestation of tectodynamic systems (paleotectonic stresses, tectonic movements and deformations) of different ranks in geologic history, study of the paleotectonic development of rank components of tectonic movements and deformations in the intervals of tectodynamic system action, determination of the relative age of formation of structural elements, their inheritance, as well as determination of the relative age of formation of structural elements, their inheritance, role or contribution to the geological evolution.The main indicator, on the basis of which the possibility to identify an independent (integral) tectodynamic system is determined, is the stress field type and deformation regime. In the Mesozoic – Cenozoic of the western part of the Tien Shan, five phases of alternating change of tectonic stress fields of the first (for this region) rank and associated deformation regimes were identified. In the first approximation, they correspond to: Early – Middle Jurassic, Late Jurassic, Cretaceous, Paleogene, and Neogene-Quaternary time. On this basis, the paper substantiates the necessity to revise and modify the classical methods of historical-structural (paleotectonic) analysis, which should be performed for each rank component separately, taking into account the interaction of both peer-to-peer and multi-rank elements. In this approach, the most important indicator is the feedback sign between different rank elements of tectodynamic systems. On the example of the central part of the Bukharo-Khiva oil-and-gas-bearing region, paleotectonic maps-schemes of three ranks for the above-mentioned time intervals were obtained. The previously unknown features of the structural plans of different horizons and their evolution in the Mesozoic – Cenozoic history were established. They served as a basis for further traditional historical-structural analysis with the construction of isopachic triangles along the selected intervals for each rank component in order to assess the tectonic conditions and time of formation of anticlinal oil and gas traps.

A tensorial weighted Schatten-[formula omitted] norm model with neighbor regularization for traffic data completion and traffic system correlation exploration

Traffic data exhibit the spatiotemporal heterogeneity. Revealing traffic system correlations (TSC) from traffic data, which refers to discovering inter-station correlations incurred from movements of passengers, is a meaningful task for releasing traffic congestion and sustainable urban operations. Since traffic data involve a great loss or corruption due to equipment and transmission failures, traffic data completion (TDC) serves as a necessary task for TSC exploration. We observe that: for the TDC task, traffic data exhibit anisotropic periodicity and repetition along different temporal dimensions, which cannot be captured by traditional low-rank TDC methods; for the TSC exploration task, a traffic network obeys graphical structure and exhibits a strong spatial correlation between stations or road segments. Based on these observations, we propose a Tensorial Weighted Schatten-pNorm model with Neighbor Regularization (TWSN2R) for accomplishing the two tasks simultaneously. TWSN2R consists of a tensorial weighted Schatten-p norm term and a neighbor regularization term. The first term mines the anisotropic periodicity with a tensor form of weighted Schatten-p norm which preserves suitable rank components of traffic data by allocating different weights in both tensorial dimension and decomposed mode dimension. The second term mines a spatial correlation of a traffic network by enforcing a neighbor contribution of passenger flows incurred from the network, and extrinsically reveals an inter-station (or inter-road-segment) TSC of the network. Experiments on the subway flow dataset MetroBJ and the road network vehicle speed dataset RoadBJ demonstrate that, TWSN2R not only outperforms several state-of-the-art TDC methods, but also reveals two pieces of regulations of TSC. (a) For subway passenger flows, positive correlated stations and negative correlated stations contribute to passenger flows differently according to each station’s attributes. (b) For road networks, urban loops, expressways and transportation hubs contribute to the vehicle flows at a majority, and serve as the critical roads for mitigating the congestion of whole road networks.

Factors influencing Building Information Modelling adoption for residential property development

Appalled by the low uptake of building information modelling (BIM) in the housing sector, this study investigates the factors influencing the adoption of BIM for residential property development in Nigeria, an emerging market. The primary data utilized were elicited with the aid of a questionnaire served on the officials of real estate development firms in Nigeria. The obtained data were analyzed using statistical tools such as mean ranking, frequency distribution and principal component analysis (PCA). Based on mean ranking analysis, high cost of implementing BIM (MS = 4.38), cost of hardware installation (MS = 4.24) were found as the most influential inhibiting factors. On further analysis, the results of the PCA revealed that the factors influencing BIM adoption can be grouped into operating environment and regulatory factors, cost of acquisition/usage factors, firm management factors, usage capability factors and client demand factors, with variances of 26.834%, 18.792%, 13.439%, 6.275% and 2.909% respectively. The study concluded that issues surrounding operating environment constraints and cost of acquisition and deployment significantly underpin the uptake of BIM technology in emerging economies like Nigeria. Hence, it is imperative that real estate development organizations embrace industry collaboration and efforts made to reduce BIM acquisition and deployment cost.

Parent Perspectives on Defining Neonatal Serious Illness.

Objectives: We hoped to understand (1) how parents of seriously ill neonatal intensive care unit patients conceptualize neonatal serious illness, and (2) how parent and physician perspectives on neonatal serious illness might differ. Design: This was a prospective survey study. Setting/Subjects: Parent members of the Courageous Parents Network. Measurements: We circulated a modified version of a previously developed survey. Participants were presented with a list of potential definition components and asked to rank components by importance and suggest modifications to the definition as needed. Thematic analysis of free-text responses was conducted to identify key themes in parent responses Results: Eighty-eight percent of parent participants agreed or strongly agreed with our working definition of neonatal serious illness. Parents agreed with the content of the definition but suggested using different language (especially less jargon) when using the definition to inform conversations with parents. Conclusions: The majority of parents surveyed for this study supported our definition of neonatal serious illness, which suggests that it may be useful for clinical and research applications. At the same time, parent responses revealed important differences in perceptions about serious illness between parents and physicians. In addition, parents will likely use any definition of neonatal serious illness differently than clinicians. We therefore propose that our definition be used to identify neonates with serious illness for the purposes of research and clinical care, but should not be used verbatim for communication with parents.

Defining Neonatal Serious Illness: Perspectives of Nurses and Social Workers.

Objectives: To explore (1) how neonatal nurses (NN) and social workers (SW) define serious illness and (2) how physician, nurse, and SW perceptions of serious illness differ. Design: Prospective survey study. Setting/Subjects: Members of the National Association of Neonatal Nurses or the National Association of Perinatal Social Workers. Measurements: We circulated a modified version of a previously developed survey. Participants were given a list of definition components and asked to rank components by importance and to suggest modifications. Results: Eighty-eight percent of participants agreed with our definition of neonatal serious illness. NN and SW differ in important ways in their views of neonatal serious illness when compared with physicians and parents. Conclusions: Our definition of neonatal serious illness has broad acceptability and may be useful for clinical care and research. Future work should prospectively identify patients with neonatal serious illness and establish the usefulness of our definition in real time.

A new multi-criteria importance measure and its applications to risk reduction and safety enhancement

A wide variety of importance measures have been proposed to rank components for different purposes and most of them are based on a single criterion like reliability, maintenance cost, or risk. In many practical situations, however, people need to evaluate alternatives based on multiple criteria. Thus, the single-criterion importance measure is no longer sufficient for such evaluation. Fewer research has been reported on importance measures applicable to multi-criteria engineering systems, which however fail to consider the correlation among different criteria leading to inaccurate and even misleading importance results. In this paper, a new multi-criteria importance measure (MCIM) is proposed to identify the weakest components in complex engineering systems and it objectively considers the correlation among different criteria. The proposed MCIM is then extended to two common types of complex engineering systems, series hierarchical systems and modular systems. At last, the applications and effectiveness of the proposed MCIM are illustrated by two case studies, including reducing risks of a hierarchical supply chain system and improving the safety of a train bogie modular system.

DEFORMATION REGIMES AND LOW-RANK PALEOTECTONIC STRESSES OF THE WESTERN TIAN SHAN IN THE MESOZOIC – CENOZOIC

There is a description of the results of studies of deformation regimes and reconstruction of stress fields of certain time intervals of the Mesozoic – Cenozoic history of the western part of the Tien Shan based on the geological and structural data and earthquake focal mechanisms. The relevance of research is determined by the existing difficulties in the age reference of paleostress fields, which is associated with the retrospective nature of the initial data and the features of geological evolution, as well as the existence of controversial, unresolved issues regarding the development of the western periclinal part of the Tien Shan in the Mesozoic – Cenozoic. The aim of the research is to reconstruct the rank components of paleostresses in the earth’s crust in the western part of the Tien Shan at separate stages of the Mesozoic – Cenozoic development. The time intervals for which the stress field diagrams were obtained represent cyclically manifested phases of the first-rank rhythms, which are characterized by alternation of different deformation modes. The method for reconstructing paleostresses was based on the concept of the formation of superimposed systems of cracks of various generations, which were sequentially manifested at various stages of the development of deformation processes. Analysis of the data showed that with increasing age of rocks the number of different fracture systems actually increases – the earlier systems are superimposed by the later. At the same time, the characteristic elements in Quaternary rocks manifest themselves in different ways in the older rocks. This allows them to be used for the reconstruction of the Late Cenozoic stress field based on mass measurements of fracturing in rocks of earlier Mesozoic – Cenozoic ages. Based on the step-by-step identification of the heterochronous cracks system and determination of a pair of maxima of the fracture density with asymmetric scattering, region-based diagrams of the first-rank stress fields were obtained for different stages of manifestation of Mesozoic – Cenozoic deformation regimes: 1 – from the Permian-Triassic to the Middle Jurassic; 2 – Middle-Late Jurassic; 3 – Late Jurassic – Early Cretaceous; 4 – Late Cretaceous – Paleocene – Eocene; 5 – Oligocene – Quaternary. The results obtained indicate the alternation of compression and extension phases, first-rank for the region, and give an idea of the main trends in its geodynamic evolution. They can be used in the development of a tectodynamic model of the region.

Infrared object detection via patch‐tensor model and image denoising based on weighted truncated Schatten‐p norm minimization

Infrared object detection via patch‐tensor model and image denoising based on weighted truncated Schatten‐<i>p</i> norm minimization

Multi-passage extraction-based machine reading comprehension based on verification sorting

For traditional single-passage machine reading comprehension, the text data of a single passage does not well reflect the complexity of practical application scenarios. Many researchers have shifted their research goals to study multi-passage machine reading comprehension. To solve the problem of multi-passage machine reading comprehension, this paper proposes a unified multi-module end-to-end reading comprehension model for passage retrieval, answer extraction, and multi-answer verification ranking using a pre-trained model. In this paper, the passage retrieval module selects the passage fragments with the highest probability of survival, and the answer extraction component extracts the possible candidate answers in each passage. The multi-answer verification ranking component uses an attention mechanism to fuse multiple candidate answer feature representations, obtains the score of each candidate answer and selects the candidate answer with the highest score as the final answer. Finally, through experimental validation, the proposed model achieves scores of 49.59 and 46.28 on the evaluation metrics BLUE-4 and ROUGH-L on the DuReader dataset, and scores of 44.78 and 46.45 on the metrics BLUE-1 and ROUGH-L on the MS-MARCO dataset, respectively.

Clutter Removal Method for GPR Based on Low-Rank and Sparse Decomposition With Total Variation Regularization

The performance of ground penetrating radar (GPR) target detection is seriously affected by the clutter. In this letter, an effective GPR clutter removal method is proposed based on low-rank and sparse decomposition with total variation regularization (LRSD-TVR). In the proposed method, a total variation (TV) regularization of sparse matrix is introduced to further remove the remaining clutter and to obtain a clearer target image. An iterative approach based on alternating direction method of multipliers (ADMM) is developed to solve the optimization problem of LRSD-TVR. In each iteration, the low rank component which corresponds to the clutter is computed by singular value decomposition (SVD) thresholding. Besides, the sparse component corresponding to the target is obtained by solving the sub-optimization problem reformulated in terms of TV component. The effectiveness of proposed method is verified by both numerical simulations and field experiments.

New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

In this paper, we propose two algorithms to enhance the interpretability of the hyperbola in B-scans obtained with a Ground Penetrating Radar (GPR). These hyperbolas are the responses of buried objects or cavities. To correctly detect and classify them, a denoising is typically necessary for GPR images as the signal-to-noise ratio is low, and the various interfaces naturally present in the earth have a strong response. Both algorithms are based on a sparse convolutional coding model plus a low rank component. It is solved through an Alternating Direction Method of Multipliers (ADMM) framework. In order to take into account the presence of outliers and the artifacts caused by the acquisition, the second algorithm is based on the Huber norm instead of the classic <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm. These algorithms are tested on a real dataset labeled by geophysicists. The results show the denoising efficiency of this approach, and in particular the robustness of the second algorithm.

Personalizing Hybrid-Based Dialogue Agents

In this paper, we present a continuation of our work on the personification of dialogue agents. We expand upon the previously demonstrated models—the ranking and generative models—and propose new hybrid models. Because there is no single definitive way to build a hybrid model, we explore various architectures where the components adopt different roles, sequentially and in parallel. Applying the perplexity and BLEU performance metrics, we discover that the Retrieve and Refine and KG model—a modification of the Retrieve and Refine model where the ranking and generative components work in parallel and compete based on the proximity of the candidate found by the ranking model with a knowledge-grounded generation block—achieves the best performance, with values of 1.64 for perplexity and 0.231 for BLEU scores.

Fully Inert Subgroups of Completely Decomposable Groups with Homogeneous Components of Finite Rank

Fully Inert Subgroups of Completely Decomposable Groups with Homogeneous Components of Finite Rank

Designing a Mobile Application for Working Memory Training through Understanding the Psychological and Physiological Characteristics of Older Adults

Cognitive function declines with age, and when cognitive deterioration reaches a critical value and pathological changes occur, the brain neurons are irreversible. The aging of working memory even has profound adverse effects on older adults. This study aims to understand the psychological and physiological characteristics of older adults and to achieve mobile application design solutions that train working memory. According to the user study, the factors influencing the design of mobile applications for working memory training for older adults were mainly focused on six dimensions: training content, motivation, emotion, interaction, current state, and experience. Design opportunities were transformed, and seven new design strategies were obtained. Nine product functions with the highest priority were selected: daily practice, challenge mode, level-by-level difficulty selection, novice teaching, practice mode, sharing function, two-player mode, ranking, and desktop components. Finally, an interactive prototype was designed for usability testing, and the product solution was iterated based on expert evaluation and user feedback. The results indicate that the interface design provides a good user experience when applied daily. The process and results will be applied to make more solutions for training cognitive functions to be used in different situations.

15 Ranking and rating severity: stakeholder perspectives of neurodevelopmental impairment after preterm birth

Abstract Background Children born very preterm are at increased risk of neurodevelopmental impairment (NDI). NDI rates are used to make critical healthcare decisions. The Canadian Neonatal Follow-Up Network (CNFUN) defines severe NDI at age 18–24 months as Bayley-III Cognitive, Motor or Language composite scores &amp;lt;70, need for hearing aids or cochlear implants, bilateral visual impairment or non-ambulatory cerebral palsy (Gross Motor Classification System level 3 or higher). This definition was developed using the perspectives of clinicians and scientists. Stakeholders including parents/caregivers, persons born preterm and community partners have never been asked how they categorize disabilities and rank different impairments. Objectives To evaluate stakeholder perspective of severity of disability and ranking of different health conditions all defined as severe by CNFUN using clinical vignettes. Design/Methods Ten clinical vignettes describing 18 month old children with combinations of single and multiple impairments based on the CNFUN definition of severe NDI and one clinical vignette describing a child without NDI (control) were co-developed by clinicians and parents. The survey of 11 vignettes was distributed to: (1) British Columbian parents of very preterm children during follow-up clinic visits; and (2) Canadian and international parents, families, individuals with lived experience with prematurity and/or NDI and their social networks using a snowball sampling technique. Participants rated the severity of each vignette on a scale from 0 (worst possible health) to 10 (best possible health) and answered if they perceived the vignette to represent a severe disability. Results were analyzed descriptively and mean differences from the control were compared using a linear mixed effects model. Results 827 participants and 4553 vignettes were completed with no significant difference by population sampled. Participants classified the clinical vignettes as describing a severe condition ranging from 5% for the cognitive delay vignette to 55% for the cerebral palsy and language delay vignette. Mean difference in rank compared to control is shown in Figure 1. Only the ranking of the latter vignette was significantly different from the control. Conclusion Most key stakeholders do not perceive clinical vignettes describing CNFUN definitions of severe NDI as truly severe. Components of severe NDI rank differently but only the cerebral palsy and language delay vignette varies significantly. Current CNFUN definitions of severe NDI need to be revised.

Tensor Rank Regularization with Bias Compensation for Millimeter Wave Channel Estimation

This paper presents a novel method of tensor rank regularization with bias compensation for channel estimation in a hybrid millimeter wave MIMO-OFDM system. Channel estimation is challenging due to the unknown number of multipath components that determines the channel rank. In general, finding the intrinsic rank of a tensor is a non-deterministic polynomial-time (NP) hard problem. However, by leveraging the sparse characteristics of millimeter wave channels, we propose a modified CANDECOMP/PARAFAC (CP) decomposition-based method that jointly estimates the tensor rank and channel component matrices. Our approach differs from most existing works that assume the number of channel paths is known and the proposed method is able to estimate channel parameters accurately without the prior knowledge of number of multipaths. The objective of this work is to estimate the tensor rank by a novel sparsity-promoting prior that is incorporated into a standard alternating least squares (ALS) function. We introduce a weighting parameter to control the impact of the previous estimate and the tensor rank estimation bias compensation in the regularized ALS. The channel information is then extracted from the estimated component matrices. Simulation results show that the proposed scheme outperforms the baseline l1 strategy in terms of accuracy and robustness. It also shows that this method significantly improves rank estimation success at the expense of slightly more iterations.

Imbalanced low-rank tensor completion via latent matrix factorization

Tensor completion has been widely used in computer vision and machine learning. Most existing tensor completion methods empirically assume the intrinsic tensor is simultaneous low-rank in all over modes. However, tensor data recorded from real-world applications may conflict with these assumptions, e.g., face images taken from different subjects often lie in a union of low-rank subspaces, which may result in a quite high rank or even full rank structure in its sample mode. To this aim, in this paper, we propose an imbalanced low-rank tensor completion method, which can flexibly estimate the low-rank incomplete tensor via decomposing it into a mixture of multiple latent tensor ring (TR) rank components. Specifically, each latent component is approximated using low-rank matrix factorization based on TR unfolding matrix. In addition, an effective proximal alternating minimization algorithm is developed and theoretically proved to maintain the global convergence property, that is, the whole sequence of iterates is convergent and converges to a critical point. Extensive experiments on both synthetic and real-world tensor data demonstrate that the proposed method achieves more favorable completion results with less computational cost when compared to the state-of-the-art tensor completion methods.

Is handling unbalanced datasets for machine learning uplifts system performance?: A case of diabetic prediction

Background and aimsHealthcare is a sensitive sector, and addressing the class imbalance in the healthcare domain is a time-consuming task for machine learning-based systems due to the vast amount of data. This study looks into the impact of socioeconomic disparities on the healthcare data of diabetic patients to make accurate disease predictions. MethodsThis study proposed a systematic approach of Closest Distance Ranking and Principal Component Analysis to deal with the unbalanced dataset. A typical machine learning technique was used to analyze the proposed approach. The data set of pregnant diabetic women is analysed for accurate detection. ResultsThe results of the case are analysed using sensitivity, which demonstrates that the minority class's lack of information makes it impossible to forecast the results. On the other hand, the unbalanced dataset was treated using the proposed technique and evaluated with the machine learning algorithm which significantly increased the performance of the system. ConclusionThe performance of the machine learning-based system was significantly enhanced by the unbalanced dataset which was processed with the proposed technique and evaluated with the machine learning algorithm. For the first time, an unbalanced dataset was treated with a combination of Closest Distance Ranking and Principal Component Analysis.