Scale Features Research Articles

Panopticon: A novel deep learning model to detect single transit events with no prior data filtering in PLATO light curves

To prepare for the analyses of the future PLATO light curves, we develop a deep learning model to detect transits in high precision photometric light curves. Since PLATO's main objective is the detection of temperate Earth-sized planets around solar-type stars, the code is designed to detect individual transit events. The filtering step, required by conventional detection methods, can affect the transit, which could be an issue for long and shallow transits. To protect the transit shape and depth, the code is also designed to work on unfiltered light curves. The model is based on the Unet family architecture, but it is able to more efficiently extract and combine features of various length scale, leading to a more robust detection scheme. We trained the model on a set of simulated PLATO light curves in which we injected, at the pixel level, planetary, eclipsing binary, or background eclipsing binary signals. We also included a variety of noises in our data, such as granulation, stellar spots, and cosmic rays. We then assessed the capacity of to detect transits in a separate dataset. The approach is able to recover 90% of our test population, including more than 25% in the Earth-analog regime, directly in unfiltered light curves. We report that the model also recovers transits irrespective of the orbital period, and it is therefore able to reliably retrieve transits on a single event basis. These figures were obtained when accepting a false alarm rate of 1%. When keeping the false alarm rate low ($<0.01$%) is still able to recover more than 85% of the transit signals. Any transit deeper than ∼180ppm is essentially guaranteed to be recovered. This method is able to recover transits on a single event basis, and it does so with a low false alarm rate. Due to the nature of machine learning, the inference time is minimal, around 0.2,s per light curve of 126,720 points. Thanks to light curves being one dimensional, the model training is also fast, on the order of a few hours per model. This speed in training and inference, coupled with the recovery effectiveness and precision of the model, make this approach an ideal tool to complement or be used ahead of classical approaches.

UniLF: A novel short-term load forecasting model uniformly considering various features from multivariate load data.

Accurate short-term load forecasting (STLF) provides important support for the economic and stable operation of the power system. Although various deep learning methods have achieved good results in STLF, they usually model load features only from a limited perspective, i.e., they do not uniformly utilize the three features of multivariate load data: the influence of covariates, multiscale features and local-global variations. The insufficient mining of these three features limits the improvement of prediction accuracy. To address the above problems, we design a novel STLF model called UniLF based on Transformer framework, which contains the proposed convolutional enhancement-fusion embedding method to capture the correlations between load and covariates for embedding, the proposed feature reconstruction-decomposition block to distill multiscale features as well as more detailed local-global variations from 2D space and the core mask-guided multiscale interactive self-attention mechanism to further realize the enhanced interactions of scale features and temporal features. Experiments conducted on three load datasets from Australia, Panama and Austria show that UniLF achieves superior forecasting accuracy with competitive practical efficiency under different prediction lengths, providing a new solution for STLF.

Differences in hair cortisol to cortisone ratio between depressed and non-depressed adolescent women

Research on stress has demonstrated that the hypothalamic-pituitary-adrenal (HPA) axis contributes to major depressive disorder in youth. Hair glucocorticoids are key biological markers of chronic stress. We assessed group differences in hair cortisol and cortisone concentrations, and the cortisol/cortisone ratio between depressed adolescent women and a non-depressed comparison group. Further, within the depression group, we explored the contribution of symptom severity and clinical correlates of depression in relation to glucocorticoid concentrations. Hair samples of three centimeters for 74 adolescent women (41 in the depression group and 33 in the comparison group), aged between 12 and 19 years old, were analyzed. Depressive and anxiety symptoms were measured using the Beck Youth Inventory II and clinical correlates of depression were measured using the Childhood Trauma Questionnaire–Short Form and the Borderline Personality Features Scale for Children. No significant differences emerged between the depression group and the comparison group on hair cortisol or hair cortisone concentrations. However, groups differed significantly on the cortisol/cortisone ratio, a proposed proxy of 11-beta-hydroxysteroid dehydrogenase activity, with a higher ratio for the depression group. Within the depression group, neither symptom severity nor clinical correlates were associated with glucocorticoid concentrations. Although cross-sectional, our findings highlight the importance of future studies to test whether the group difference found in cortisol/cortisone ratio is the result of alterations in 11-beta-hydroxysteroid dehydrogenase enzymes (type 1 or 2) activity. Further research is thus needed to clarify the role of these enzymes in major depressive disorder in youth and to develop more targeted intervention strategies.

Lightweight Ship Object Detection Algorithm for Remote Sensing Images Based on Multi-scale Perception and Feature Enhancement

As global trade and maritime traffic develop, exploring ship detection in remote sensing images has become a research hotspot. However, ships in remote sensing images are so small that it leads to a high detection leakage rate and excessive model parameters, making them difficult to apply on remote sensing equipment with limited resources. To address the challenge, we propose a light-weight ship object detection algorithm, adaptive layered multi-scale You Only Look Once version 8 (ALM-YOLOv8), based on multi-scale perception and feature enhancement. To enhance the model's perception of contextual information in complex backgrounds, a multi-scale channel fusion module is constructed to extract features of various scales. To enhance the extracted features, a small-object detection layer and a dynamic channel attention convolution that assigns dynamic weights are proposed. Additionally, this study embeds the large separable kernel attention mechanism into the original network, which lightens the model. Experiments on the HRSC2016 dataset demonstrate the effectiveness of ALM-YOLOv8.

LES Validation on Near-Field Wingtip Vortex Evolution with Wind Tunnel Characterization at Low Reynolds Number

Wind tunnel tests are commonly used to visualize the trailing vortices, but tests involving advanced optical measurement techniques can be costly. Camera-based experiments are more affordable, but the low visibility caused by smoke particles and limitations of the upright view make vortex characterization and quantification challenging. In this paper, the proposed perspective upright correction allows adjustment of the slanted plane during post-processing. This technique has reduced the complexity of wind tunnel set-up, and yet yielding a more accurate result than the native upright view. Large Eddy Simulation (LES) is then utilized with proper mesh design within the vortex core volume to effectively capture the smaller scales features of the wingtip vortex and validate against the perspective upright corrected characterization from wind tunnel experiment. The vortex core defined by its maximum tangential velocity will be analyzed against the physical wake evolution captured in wind tunnel, providing insights into the relationship between these two methodologies. The demonstrated accuracy and credibility of the CFD model can ensure that the future simulations in the extended field can be accurately predicted and analyzed.

First psychometric evaluation of the Mexican Spanish version of the 11-item BPFSC-11 and convergence with maladaptive self and interpersonal functioning.

The Borderline Personality Features Scale for Children (BPFSC-11) is a well-used, short, and easy-to-administer measure of borderline traits in young people. The aim of the current study was to evaluate the psychometric properties of the Mexican Spanish version of the BPFSC-11 in a large community-based sample of Mexican adolescents. In addition, we evaluated the convergence between the borderline construct as measured by the BPFSC-11 and general personality functioning consistent with more contemporary personality disorder formulations to assess the nomological net of the BPFSC-11 total score. A sample of 1212 adolescents (52.6% female, mean age = 15.11, SD = 1.74) were recruited from the community through schools in Mexico City and Michoacán area. Reliability of the BPFSC-11 was good (α = 0.805; ω = 0.806). A confirmatory factor analysis (CFA) model testing the unidimensional factor structure of the 11 items of the BPFSC-11 provided good fit (X2[44] = 137.96, p < 0.001; root mean square error of approximation (RMSEA) = 0.04 [90% CI: 0.03, 0.05]; comparative fit index (CFI) = 0.96; standardized root mean square residual (SRMR) = 0.03). Both configural and metric invariance for gender and age were demonstrated; however, only partial scalar invariance could be demonstrated with some items showing "bias" for gender and age. The Mexican Spanish BPFSC-11 also showed strong convergence with measures assessing contemporary conceptualization of general personality functioning. The Mexican Spanish BPFSC-11 appears to show similarly strong psychometric properties of other versions of the BPFSC-11; and the borderline construct as assessed with the BPFSC-11 shows good conversion with general personality functioning in adolescents.

Spherical harmonics texture extraction for versatile analysis of biological objects.

The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present Spherical Texture extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The Spherical Texture extraction characterizes biologically defined gene expression patterns in Drosophila melanogaster embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in Caenorhabditis elegans germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their Spherical Texture outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract polarization direction, quantifying the alignment of fluorescent markers to the migration direction. We implemented the Spherical Texture method as a plugin in ilastik to provide a parameter-free and data-agnostic application to any segmented 3D or 2D dataset. Additionally, this technique can also be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.

Research on the lightweight detection method of rail internal damage based on improved YOLOv8

To address the challenges of high computational costs, large storage demands, and low detection accuracy in internal rail damage identification, we propose a lightweight detection model, GhostMicroNet-YOLOv8n, as an enhancement of YOLOv8n. This model offers efficient and reliable technical support for detecting and classifying internal rail damage in rail flaw detection tasks. The proposed model introduces four key innovations. Firstly, the GhostHGNetV2 network is adopted as the feature extraction backbone, which reduces computational costs through structural optimization. Secondly, the Triplet Attention (TA) mechanism is incorporated to enhance the model’s ability to distinguish internal rail damage from complex backgrounds, thereby improving detection accuracy. Thirdly, the network structure is refined to better capture the scale characteristics of damage in B-scan data, improving small-target detection. Finally, the model is compressed using the Layer-Adaptive Sparsity for Magnitude-Based Pruning (LAMP) technique, significantly reducing model complexity while maintaining performance. Experimental results validate the effectiveness of the proposed model, achieving a 2.67% improvement in detection accuracy, a 91.67% reduction in parameters, a 64.20% decrease in computational cost, and an 84.87% reduction in model size compared to YOLOv8n. The model achieves an optimal balance between detection accuracy and computational efficiency, addressing the railroad industry’s demand for real-time defect detection and ensuring accurate flaw identification in resource-constrained Rail Fault Detection (RFD) vehicles.

Item-level measurement properties of the pediatric awareness and sensory motor assessment in children with medical complexity

IntroductionChildren with medical complexity (CMC) are medically fragile with severe brain damage and chronic conditions, necessitating daily care. Their neurological impairments often limit participation in childhood activities, affecting quality of life. Current assessment tools fail to detect subtle abilities in CMC, hindering development of effective rehabilitation goals and interventions. The Pediatric Awareness and Sensory Motor Assessment (PASMA) was created to fill this gap, providing sensitive measurement of sensory awareness and motor response across five domains (i.e., olfactory, visual, auditory, gustatory, and tactile).MethodsIn this retrospective study, a Rasch analysis was conducted on PASMA data for CMC. The PASMA was administered five times over ten weekdays to each child, reflecting its intended clinical use to gain a reliable sense of each child's awareness.ResultsAnalysis of data from 36 CMC revealed that the PASMA is sufficiently unidimensional, effectively measuring sensory awareness and motor response as a single construct. Its rating scale structure was validated without modifications, and the item hierarchy matched clinical expectations. High item reliability (0.97) was observed, with one item (V2 blink in response to light) slightly misfitting, but without affecting overall measures. Adequate person reliability was observed (0.81), with 15% person misfit. Person misfit did not degrade item measures or model statistics. Differential item functioning (DIF) was noted for the three easiest items on specific days. The PASMA successfully stratified participants into three distinct awareness levels (low, medium, and high awareness), without floor or ceiling effects.DiscussionThe PASMA is a valid unidimensional measure of sensory awareness and motor response in CMC. Rating scale characteristics, item hierarchy, and person separation measures all support the PASMA's measurement properties within this heterogeneous sample of CMC. DIF findings support a potential reduction in the recommended number of PASMA administrations per individual. Future research will focus on establishing rater reliability and external validity. Additional efforts will support health professionals to utilize the PASMA for baseline assessments, guiding personalized interventions, and tracking progress.ConclusionClinical use of the PASMA could provide new opportunities to detect subtle abilities, preferences, and changes in CMC, to promote meaningful participation and improve quality of life.

"No Matter the Age or Medical Complexity, People Benefit From That Intensity of Exercise": A Mixed-Methods Study Describing Rehabilitation Provider Perceptions of High-Intensity Resistance Training Among Veterans Receiving Post-Acute Care.

In skilled nursing facilities (SNFs), i-STRONGER is a novel, high-intensity resistance training approach that incorporates progressive resistance training to promote greater improvements in patient function compared to usual care. To inform large-scale expansion of i-STRONGER as standard-of-care in SNFs, this mixed-methods study assessed rehabilitation providers' perceptions of i-STRONGER and purported needs for its adoption. Forty-three rehabilitation providers participated in an 18-week, interactive i-STRONGER training program. Post-training, the validated Perceived Characteristics of Intervention Scale was used to evaluate i-STRONGER relative advantage, compatibility with practice, complexity, potential for reinvention (ie, adaptability), trialability, and risk. Providers reported the top 3 needs for successful i-STRONGER adoption and described i-STRONGER perceptions in training modules and focus groups. A convergent mixed-methods design was used to evaluate and describe clinician perceptions of i-STRONGER. i-STRONGER program conceptually aligned with providers' practice. The patterns and was endorsed as feasible and adaptable as part of patient care; however, initial hesitancies surrounding patients' physical abilities and motivation were noted as barriers and appeared to reflect negative age-related beliefs and attitudes within SNF cultures. Providers feared patients would be unwilling or unable to engage with i-STRONGER activities due to their age, deconditioning, medical complexity, or expectation that rehabilitation should feel "easier." As providers began implementing i-STRONGER in real-time, concerns surrounding i-STRONGER's potential risks lessened as providers observed improvements in patient function and motivation, and patients were described as "embracing" i-STRONGER principles. Patient acceptance and satisfaction, equipment, time, and clear communication among providers were cited as critical needs for i-STRONGER adoption. With i-STRONGER, a safe and effective approach for delivering resistance training to older adults in SNF settings, "seeing is believing." Real-time implementation of i-STRONGER facilitated an evolution of perspective among providers, fueled by observable, positive changes in patient function and affect as well as unexpected patient participation. Findings support i-STRONGER use in SNF settings, provided that specific training strategies showcase older adults with medical complexity participating in progressive resistance training.

Post-processing methods for delay embedding and feature scaling of reservoir computers

Reservoir computing is a machine learning method that is well-suited for complex time series prediction tasks. Both delay embedding and the projection of input data into a higher-dimensional space play important roles in enabling accurate predictions. We establish simple post-processing methods that train on past node states at uniformly or randomly-delayed timeshifts. These methods improve reservoir computer prediction performance through increased feature dimension and/or better delay embedding. Here we introduce the multi-random-timeshifting method that randomly recalls previous states of reservoir nodes. The use of multi-random-timeshifting allows for smaller reservoirs while maintaining large feature dimensions, is computationally cheap to optimise, and is our preferred post-processing method. For experimentalists, all our post-processing methods can be translated to readout data sampled from physical reservoirs, which we demonstrate using readout data from an experimentally-realised laser reservoir system.

Classification of Hydrometeors During a Stratiform Precipitation Event in the Rainy Season of Liupanshan

This study conducted a classification analysis of hydrometeor types during a typical stratiform mixed cloud precipitation event in the rainy season using data from the Liupan Mountains micro rain radar power spectra. The primary research findings are as follows: (1) Utilizing the RaProM method synthesizes the information of particle falling velocity, equivalent radar reflection coefficient, particle scale characteristics at different stages, and the location of the bright zone in the zero-degree layer to classify hydrometeors during this precipitation process, and the results show that drizzle and raindrop distribution time periods do not match with the raindrop spectra and rain intensities observed by the DSG5 ground-based precipitation gauge. (2) Sensitivity experiments conducted on the RaProM method revealed that after modifying the discrimination thresholds for drizzle and raindrops, the distributions of drizzle and raindrops were more aligned with ground-based raindrop spectrum observations. Furthermore, these adjustments also showed better consistency with the radar reflectivity factor, Doppler velocity, and velocity spectrum width thresholds used by existing millimeter-wave cloud radars to discriminate between drizzle and raindrops. (3) Various kinds of hydrometeors show different vertical distribution characteristics in three precipitation stages: weak, strong, and weak. In the two weak precipitation stages, hydrometeors mainly existed in the form of snowflakes at altitudes above the zero-degree layer and in the form of drizzle at altitudes below the zero-degree layer. The vertical distribution disparity of hydrometeors between the mountain peak and base sites demonstrates that terrain significantly influences hydrometeors during the precipitation process.

Mechanics of Bio-Inspired Protective Scales

Natural armors found in animals like fish and armadillos offer inspiration for designing protective systems that balance puncture resistance and flexibility. Although segmented armors have been used historically, modern applications are hindered by a limited understanding of their mechanics. This study addresses these challenges by presenting two novel bio-inspired scale structures with overlapping and staggered configurations, modeled after the elasmoid designs found in fish. Their shapes differ significantly from other artificial scales commonly described in the literature, which are typically flat. Instead, these scales feature a support that extends vertically from the substrate, transitioning into an inclined surface that serves as the protective component. Finite element method tests evaluated their performance in puncture resistance and flexibility. The results showed that one type of scale provided better puncture resistance, while the other type offered greater flexibility. These findings highlight how small geometric variations can significantly influence the balance between protection and flexibility. The results offer new insights into the mechanisms of natural armor and propose innovative designs for personal protective equipment, such as bulletproof vests, protective gloves, and fireproof systems. The finite element simulations employed to test the protective systems can also serve as valuable tools for the scientific community to assess and refine designs.

Can feedback from students to teachers improve different dimensions of teaching quality in primary and secondary education? A hierarchical meta-analysis

Abstract This meta-analysis summarizes the available evidence on the effectiveness of student feedback interventions in primary and secondary schools on different aspects of teaching quality. It aims to examine indications of success conditions for an effective use of student feedback in practice. The analysis included 23 studies with 314 effect sizes. We estimated an overall weighted mean effect size of d = 0.27 based on a three-level random-effects model. For effectiveness with respect to various dimensions of teaching quality, only minor and mostly nonsignificant differences were found. Further moderator analyses showed larger effect sizes for studies in which teachers received support for interpreting student feedback and implementing changes in their teaching. Moreover, studies in which teachers were encouraged to discuss the feedback with their students have larger effect sizes than studies in which teachers were not encouraged to do so. Item and answering scale characteristics also showed significant effects on the effectiveness of student feedback. Implications for further research and the use of student feedback in schools are discussed.

Advanced machine learning models for robust prediction of water quality index and classification

ABSTRACT This study presents an in-depth analysis of machine learning (ML) techniques for predicting water quality index and water quality classification using a dataset containing water quality metrics such as temperature, specific conductance, salinity, dissolved oxygen, depth, pH, and turbidity from multiple monitoring stations. Data preprocessing included imputation for missing values, feature scaling, and categorical encoding, ensuring balanced input features. This research evaluated artificial neural networks, decision trees, support vector machines, random forests, XGBoost, and long short-term memory (LSTM) networks. Results demonstrate that XGBoost and LSTM significantly outperformed other models, with XGBoost achieving an accuracy range of 99.07–99.99% and LSTM attaining an R2 of 0.9999. Compared with prior studies, our approach enhances predictive accuracy and robustness, showcasing advanced generalization capabilities. The proposed models exhibit significant improvements over traditional methods in handling complex, multivariate water quality data, positioning them as promising tools for water quality prediction and environmental management. These findings underscore the potential of ML for developing reliable, scalable water quality monitoring solutions, providing valuable insights for policymakers and environmental managers dedicated to sustainable water resource management.

The divide expand consolidate scheme for unrestricted second order Møller-Plesset perturbation theory ground state energies.

The linear scaling divide-expand-consolidate (DEC) framework is expanded to include unrestricted Hartree-Fock references. By partitioning the orbital space and employing local molecular orbitals, the full molecular calculation can be performed as independent calculations on individual fragments, making the method well-suited for massively parallel implementations. This approach also incorporates error control through the fragment optimization threshold (FOT), which maintains precision and consistency throughout the calculations. A benchmark was conducted for correlation energies of open-shell systems and the relative energies of both open- and closed-shell molecules at the MP2 level of theory. The full calculation result is achieved as the FOT approaches zero. For correlation energies, an FOT of 10-3 is sufficient to recover over 98% of the full result in all cases. However, for relative energies and the electronic energy component of oxidation potentials, a tighter FOT of 10-4 is required to keep the DEC error within 10% for both open- and closed-shell molecules. This is likely due to a lack of systematic error cancellation for the molecules with vastly different chemical natures. Therefore, for accurate relative energies, the FOT should be an order of magnitude lower, and additional caution is needed, particularly for large systems. The DEC method extension to unrestricted references maintains favorable features of linear scaling and can be implemented in a massively parallel algorithm to calculate correlation energies for large open-shell systems.

Spatiotemporal Evolution of the Water System's Structure and Its Relationship with Urban System Based on Fractal Dimension: A Case Study of the Huaihe River Basin, China.

The formation and development of cities are inseparable from a certain scale of water resources. The information contained in the morphological structures of cities and water systems is often overlooked. Exploring the spatiotemporal evolution of water system structures (WSS) and urban system structures (USS) can reveal the "urban-water" relationship from a new perspective. The Huaihe River Basin (HRB) was selected as the case area, based on the theory of fractal dimensions, grid dimension and multifractal spectrum methods were used to depict the structural evolutionary characteristics of water systems and urban systems from different dimensions. Then, through a comparative analysis of fractal parameters and spectral lines, the characteristics and changing patterns of the "urban-water" relationship in the HRB from 1980 to 2019 were revealed. The results indicate the following: (1) The water system structure in the HRB is complex and exhibits distinct scale characteristics, showing improvement overall and at larger scales while continuously degrading at smaller scales. (2) Both the water system and urban system exhibit increasingly complex spatial development characteristics; however, the USS continues to optimize over time, while the WSS experiences degradation. (3) The development patterns of the water system and urban system are significant differences in the HRB. Urban development mainly relies on outward expansion, whereas the water system is primarily characterized by intensive enhancement. (4) Because of the rapid development of urban areas, water scarcity may occur in densely populated urban areas or larger cities in the future. The research results can serve as a scientific reference for urban planning and water resource management in the HRB.

Instance Segmentation and 3D Pose Estimation of Tea Bud Leaves for Autonomous Harvesting Robots

In unstructured tea garden environments, accurate recognition and pose estimation of tea bud leaves are critical for autonomous harvesting robots. Due to variations in imaging distance, tea bud leaves exhibit diverse scale and pose characteristics in camera views, which significantly complicates the recognition and pose estimation process. This study proposes a method using an RGB-D camera for precise recognition and pose estimation of tea bud leaves. The approach first constructs an for tea bud leaves, followed by a dynamic weight estimation strategy to achieve adaptive pose estimation. Quantitative experiments demonstrate that the instance segmentation model achieves an mAP@50 of 92.0% for box detection and 91.9% for mask detection, improving by 3.2% and 3.4%, respectively, compared to the YOLOv8s-seg instance segmentation model. The pose estimation results indicate a maximum angular error of 7.76°, a mean angular error of 3.41°, a median angular error of 3.69°, and a median absolute deviation of 1.42°. The corresponding distance errors are 8.60 mm, 2.83 mm, 2.57 mm, and 0.81 mm, further confirming the accuracy and robustness of the proposed method. These results indicate that the proposed method can be applied in unstructured tea garden environments for non-destructive and precise harvesting with autonomous tea bud-leave harvesting robots.

Interconnectedness of borderline personality pathology and affective temperaments in patients with mood disorders: a network analysis

Purpose Mood disorders frequently coexist with borderline personality pathology (BPP), presenting considerable clinical challenges. Affective temperaments (AT) play a role in modulating mood disorders and influence the manifestation of illness. BPP and AT share common characteristics, such as emotional instability, impulsivity, and difficulties in interpersonal relationships. However, research on the relationship between BPP and AT remains limited. This study employed network analysis to explore the network structure between BPP and AT in a cohort of patients with mood disorders. Materials and methods A total of 720 patients with mood disorders (major depressive disorder, n = 267; bipolar affective disorder, n = 453) were included in the analysis. The Personality Assessment Inventory-Borderline Features Scale (PAI-BOR) and the Temperament Evaluation of Memphis, Pisa, Paris, and San Diego-Auto questionnaire short version (TEMPS-A) were utilized. Network analysis was conducted to identify central symptoms and network structures across the five AT dimensions and 24 BPP items. Results The BPP and TEMPS-A items were intricately interwoven, with no distinct groupings. The most significant symptoms identified were “Mood shifts” and “Cyclothymic temperament.” Additionally, irritable temperament was strongly linked to "Little control over anger," while anxious temperament was closely associated with "Worry about people leaving.” Conclusion The overlap of BPP and AT in the network, without a clear separation, suggests a significant interconnection between these clinical concepts. The centrality of “Mood shifts” and “Cyclothymic temperament” in this network underscores the importance of targeting these symptoms in the treatment of patients with prominent BPP and AT.

SWOT Data Assimilation with Correlated Error Reduction: Fitting Model and Error Together

Abstract The Surface Water Ocean Topography (SWOT) satellite mission provides high-resolution two-dimensional sea surface height (SSH) data with swath coverage. However, spatially correlated errors affect these SSH measurements, particularly in the cross-track direction. The scales of errors can be similar to the scales of ocean features. Conventionally, instrumental errors and ocean signals have been solved for independently in two stages. Here, we have developed a one-stage procedure that solves for the correlated error at the same time that data are assimilated into a dynamical ocean model. This uses the ocean dynamics to distinguish ocean signals from observation errors. We test its performance relative to the two-stage method using simplified dynamics and a data set consisting of westward propagating Rossby waves, along with correlated instrumental errors of varying magnitudes. In a series of tests, we found that the one-stage approach consistently outperforms the two-stage approach when estimating SSH signal and correlated errors. The one-stage approach can recover over 95% of the SSH signal, while skill for the two-stage approach drops significantly as error increases. Our findings suggest that solving for the correlated errors within the assimilation framework can provide an effective analysis approach, reducing the risks of confounding signal and instrument noise.