Local Transformations Research Articles

Assimilation of ground-based GNSS data using a local ensemble Kalman filter

Tropical cyclones become increasingly nonlinear and dynamically unstable in high-resolution models. The initial conditions are typically sub-optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. Simultaneously, the lack of real ground-based GNSS observations over the ocean poses significant challenges when evaluating the assimilation results in oceanic regions. In this study, an Observation System Simulation Experiment is carried out based on a tropical cyclone case. Assimilation experiments using the WRF-PDAF framework are conducted. Conventional and GNSS observation operators are implemented. A diverse array of synthetic observations, encompassing temperature (T), wind components (U and V), precipitable water (PW), and zenith total delay (ZTD), are assimilated utilizing the Local Error-Subspace Transform Kalman filter (LESTKF). The findings highlight the improvement in forecast accuracy achieved through the assimilation process over the ocean. Multiple observation types further improve the forecast accuracy. The study underscores the crucial role of GNSS data assimilation techniques. The assimilation of GNSS data presents potential for advancing weather forecasting capabilities. Thus, the construction of ground-based GNSS observation stations over the ocean is promising.

EMPOWERING WARD CITIZEN FORUMS FOR LOCAL SOCIAL TRANSFORMATION IN NEPAL

The Local Governance and Community Development Program (LGCD) has been found to be adopted a transformational approach to social mobilization, aiming to address the absence of elected political representatives at the community level. Central to this approach has been identified as the Community Ward Citizen Forum (WCF) and the Awareness Centre (CAC), which play crucial roles in enhancing local governance through community engagement.The WCF has been explored to serve as a collective of community representatives’ tasks with bridging the gap between the community and local decision-making processes, thereby improving local governance. Complementing this, it has been focused that the CAC operates on a REFLECT process basis, bringing together marginalized and vulnerable community members weekly to discuss pertinent issues affecting their lives. Through facilitated discussions, participants have been found to identify key issues, devise actionable plans to address these concerns, and establish links with local service providers and Village Development Committees (VDCs) to mobilize necessary resources.Numerous studies and evaluations have been found to have highlighted the effectiveness of WCFs and CACs in initiating community transformation. The research has aimed to underscore the significance of community-based organizations in driving social transformation. It has been known that it provides an analytical overview of the initial successes achieved by WCFs and CACs in fostering social change, explores strategies to expand these successes across broader contexts, and identifies essential prerequisites for the sustained success of the transformational approach to social mobilization. It has been identified that Empowering Ward Citizen Forums (WCFs) for local social transformation involves various approaches aimed at enhancing community participation, decision-making, and overall impact. They have been presented pointwise as the findings of the research. the different approaches have been explored like capacity building, inclusive participation, access to information, collaborative partnerships, supportive policy environment, monitoring and evaluation, sustainable community projects, civic education and awareness, conflict resolution mechanisms, and celebrating successes and learning from challenges have been explored as the best approaches for the empowering the ward citizen forums.It has also been found that Empowering Ward Citizen Forums (WCFs) for local social transformation involves a strategic and inclusive approach that engages community members in meaningful ways. The strategies like establishing clear objectives, inclusive participation, capacity building, access to information, supportive environment, partnerships and collaboration, community-driven initiatives, policy advocacy, monitoring and evaluation, communication and outreach and celebrating successes have been found to be employed. By implementing these strategies, WCFs can effectively empower local communities, drive social transformation, and create positive changes that enhance quality of life and well-being for residents. Each step contributes to building a resilient and engaged community that works together towards common goals and shared aspirations.

Mobilizing alternative urbanisms in the semiperipheral smart city agenda

ABSTRACT Within critical urban studies, this inquiry examines how mobile policies arriving in a global semiperiphery contribute to developing socially just smart city paradigms. By provincializing smart city research and expanding the North–South dichotomy by analyzing a “South of the West,” the inquiry focuses on Lisbon as a semiperipheral city. It adopts a policy mobility approach and an analytical framework of smart city paradigms, combining thematic document analysis and in-depth interviews to examine the case study. The inquiry engages with local historical transformations and temporally analyzes Lisbon’s smart city agenda representing its development phases and exploring the mobility of ideas and paradigm shifts. Due to historical challenges reflected by the peripheralization of Southern Europe, local arrangements have led to political-economic and social transformations that, as the study argues, are potentialized by policy mobilities and entail smart city paradigm shifts towards social justice. Therefore, hybridizing new and old, emerging and established urban agendas provides alternatives to replicating neoliberal policies.

Improved Detection of Multi-Class Bad Traffic Signs Using Ensemble and Test Time Augmentation Based on Yolov5 Models

Various factors such as natural disasters, vandalism, weather, and environmental conditions can affect the physical state of traffic signs. The proposed model aims to improve detection of traffic signs affected by partial occlusion as a result of overgrown vegetation, displaced signs (those knocked down, bent), perforated signs (those damaged with holes), faded signs (color degradation), rusted signs (corroded surface), and de-faced signs (placing graffiti, etc., by vandals). This research aims to improve the detection of bad traffic signs using three approaches. In the first approach, Spiral Pooling Pyramid-Fast (SPPF) and C3TR modules are introduced to the architecture of Yolov5 models. SPPF helps provide a multi-scale representation of the input feature map by pooling at different scales, which is useful in improving the quality of feature maps and detecting bad traffic signs of various sizes and perspectives. The C3TR module uses convolutional layers to enhance local feature extraction and transformers to boost understanding of the global context. Secondly, we use predictions of Yolov5 as base models to implement a mean ensemble to improve performance. Thirdly, test time augmentation (TTA) is applied at test time by using scaling and flipping to improve accuracy. Some signs are generated using stable diffusion techniques to augment certain classes. We test the proposed models on the CCTSDB2021, TT100K, GTSDB, and GTSRD datasets to ensure generalization and use k-fold cross-validation to further evaluate the performance of the models. The proposed models outperform other state-of-the-art models in comparison.

Conformal symmetry in quantum gravity

We study the problem of how to derive conformal symmetry in the framework of quantum gravity. We start with a generic gravitational theory which is invariant under both the general coordinate transformation (GCT) and Weyl transformation (or equivalently, local scale transformation), and then construct its BRST formalism by fixing the gauge symmetries by the extended de Donder gauge and scalar gauge conditions. These gauge-fixing conditions are invariant under global GL(4) and global scale transformations. The gauge-fixed and BRST invariant quantum action possesses a huge Poincaré-like IOSp(10|10) global symmetry, from which we can construct an extended conformal symmetry in a flat Minkowski background in the sense that the Lorentz symmetry is replaced with the GL(4) symmetry. Moreover, we construct the conventional conformal symmetry out of this extended symmetry. With a flat Minkowski background ⟨gμν⟩=ημν\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\langle g_{\\mu \ u } \\rangle = \\eta _{\\mu \ u }$$\\end{document} and a non-zero scalar field ⟨ϕ⟩≠0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\langle \\phi \\rangle \ e 0$$\\end{document}, the GL(4) and global scale symmetries are spontaneously broken to the Lorentz symmetry, thereby proving that the graviton and the dilaton are respectively the corresponding Nambu–Goldstone bosons, and therefore they must be exactly massless at nonperturbative level. One of remarkable aspects in our findings is that in quantum gravity, a derivation of conformal symmetry does not depend on a classical action, and its generators are built from only the gauge-fixing and the FP ghost actions. Finally, we address a generalized Zumino theorem in quantum gravity.

Stückelberg-modified massive Abelian 3-form theory: Constraint analysis, conserved charges and BRST algebra

For the Stückelberg-modified massive Abelian 3-form theory in any arbitrary D-dimension of spacetime, we show that its classical gauge symmetry transformations are generated by the first-class constraints. We establish that the Noether conserved charge (corresponding to the local gauge symmetry transformations) is same as the standard form of the generator for the underlying local gauge symmetry transformations (expressed in terms of the first-class constraints). We promote these classical local, continuous and infinitesimal gauge symmetry transformations to their quantum counterparts Becchi–Rouet–Stora–Tyutin (BRST) and anti-BRST symmetry transformations which are respected by the coupled (but equivalent) Lagrangian densities. We derive the conserved (anti-)BRST charges by exploiting the theoretical potential of Noether’s theorem. However, these charges turn out to be non-nilpotent. Some of the highlights of our present investigation are (i) the derivation of the off-shell nilpotent versions of the (anti-)BRST charges from the standard non-nilpotent Noether conserved (anti-)BRST charges, (ii) the appearance of the operator forms of the first-class constraints at the quantum level through the physicality criteria with respect to the nilpotent versions of the (anti-)BRST charges, and (iii) the deduction of the Curci–Ferrari-type restrictions from the straightforward equality of the coupled (anti-)BRST invariant Lagrangian densities as well as from the requirement of the absolute anticommutativity of the off-shell nilpotent versions of the conserved (anti-)BRST charges.

TransC-ac4C: Identification of N4-Acetylcytidine (ac4C) Sites in mRNA Using Deep Learning.

N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA that is critical in mRNA translation in terms of stability and regulation. In the past few years, numerous approaches employing convolutional neural networks (CNN) and Transformer have been proposed for the identification of ac4C sites, with each variety of approaches processing distinct characteristics. CNN-based methods excel at extracting local features and positional information, whereas Transformer-based ones stands out in establishing long-range dependencies and generating global representations. Given the importance of both local and global features in mRNA ac4C sites identification, we propose a novel method termed TransC-ac4C which combines CNN and Transformer together for enhancing the feature extraction capability and improving the identification accuracy. Five different feature encoding strategies (One-hot, NCP, ND, EIIP, and K-mer) are employed to generate the mRNA sequence representations, in which way the sequence attributes and physical and chemical properties of the sequences can be embedded. To strengthen the relevance of features, we construct a novel feature fusion method. Firstly, the CNN is employed to process five single features, stitch them together and feed them to the Transformer layer. Then, our approach employs CNN to extract local features and Transformer subsequently to establish global long-range dependencies among extracted features. We use 5-fold cross-validation to evaluate the model, and the evaluation indicators are significantly improved. The prediction accuracy of the two datasets is as high as 81.42% and 80.69%, respectively. It demonstrates the stronger competitiveness and generalization performance of our model.

Optimizing ZX-diagrams with deep reinforcement learning

Abstract ZX-diagrams are a powerful graphical language for the description of quantum processes with applications in fundamental quantum mechanics, quantum circuit optimization, tensor network simulation, and many more. The utility of ZX-diagrams relies on a set of local transformation rules that can be applied to them without changing the underlying quantum process they describe. These rules can be exploited to optimize the structure of ZX-diagrams for a range of applications. However, finding an optimal sequence of transformation rules is generally an open problem. In this work, we bring together ZX-diagrams with reinforcement learning, a machine learning technique designed to discover an optimal sequence of actions in a decision-making problem and show that a trained reinforcement learning agent can significantly outperform other optimization techniques like a greedy strategy, simulated annealing, and state-of-the-art hand-crafted algorithms. The use of graph neural networks to encode the policy of the agent enables generalization to diagrams much bigger than seen during the training phase.

Graph Artificial Intelligence in Medicine.

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters and with minimal to no retraining. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pretraining, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way toward clinically meaningful predictions.

Graph Transformer for 3D point clouds classification and semantic segmentation

Recently, graph-based and Transformer-based deep learning have demonstrated excellent performances on various point cloud tasks. Most of the existing graph-based methods rely on static graph, which take a fixed input to establish graph relations. Moreover, many graph-based methods apply maximizing and averaging to aggregate neighboring features, so that only a single neighboring point affects the feature of centroid or different neighboring points own the same influence on the centroid’s feature, which ignoring the correlation and difference between points. Most Transformer-based approaches extract point cloud features based on global attention and lack the feature learning on local neighbors. To solve the above issues of graph-based and Transformer-based models, we propose a new feature extraction block named Graph Transformer and construct a 3D point cloud learning network called GTNet to learn features of point clouds on local and global patterns. Graph Transformer integrates the advantages of graph-based and Transformer-based methods, and consists of Local Transformer that use intra-domain cross-attention and Global Transformer that use global self-attention. Finally, we use GTNet for shape classification, part segmentation and semantic segmentation tasks in this paper. The experimental results show that our model achieves good learning and prediction ability on most tasks. The source code and pre-trained model of GTNet will be released on https://github.com/NWUzhouwei/GTNet.

Localized phase transformation strengthening in CoNi-based superalloys

Segregation processes during high-temperature deformation can be either detrimental or beneficial in γ′-strengthened superalloys depending on which elements segregate. In intermediate CoNi-based superalloys only softening due to extensive microtwinning was observed so far. By analyzing the creep behavior of a Ta-free and a Ta-containing CoNi-based superalloy at 750 °C and 620 MPa, we show that strengthening segregation behaviors along various planar defects, including microtwinning, can be achieved in this alloy class. While both alloys deformed under planar fault formation, the Ta-containing alloy outperformed the Ta-free alloy. Atomic-scale energy dispersive spectroscopy investigations revealed strengthening effects by local phase transformations along superlattice intrinsic and extrinsic stacking faults in both alloys. However, microtwinning is enhanced in the Ta-free alloy, while it is impeded in the Ta-containing alloy. Given the faster diffusivity of Ta compared to W, kinetic effects on localized phase transformation strengthening and their subsequent influence on the creep behavior are discussed.

GTC: GNN-Transformer co-contrastive learning for self-supervised heterogeneous graph representation

Graph Neural Networks (GNNs) have emerged as the most powerful weapon for various graph tasks due to the message-passing mechanism’s great local information aggregation ability. However, over-smoothing has always hindered GNNs from going deeper and capturing multi-hop neighbors. Meanwhile, most methods follow a semi-supervised learning manner, the label scarcity would limit their applicability in real-world systems. Unlike GNNs, Transformers can model global information and multi-hop interactions via multi-head self-attention and a proper Transformer structure can show more immunity to over-smoothing. So, can we propose a novel framework to combine GNN and Transformer, integrating both GNN’s local information aggregation and Transformer’s global information modeling ability to eliminate the over-smoothing problem and achieve self-supervised graph representation? To realize this, this paper proposes a collaborative learning scheme for GNN-Transformer and constructs GTC architecture. GTC leverages the GNN and Transformer branch to encode node information from different views respectively, and establishes contrastive learning tasks based on the encoded cross-view information to realize self-supervised heterogeneous graph representation. For the Transformer branch, we propose Metapath-aware Hop2Token and CG-Hetphormer, which can cooperate with GNNs to attentively encode neighborhood information from different levels. As far as we know, this is the first attempt in the field of graph representation learning to utilize both GNNs and Transformer to collaboratively capture different view information and conduct cross-view contrastive learning. The experiments on real datasets show that GTC exhibits superior performance compared with state-of-the-art methods. Codes can be available at https://github.com/PHD-lanyu/GTC.

Unleashing the potential of digitalization: the nexus of psychological needs, enjoyment, volition, engagement and training and development for local government workplace transformation

Unleashing the potential of digitalization: the nexus of psychological needs, enjoyment, volition, engagement and training and development for local government workplace transformation

Formation temperature of microcrystalline dolomite in oil-rich black shale: Implications for hydrocarbon accumulation in a volcanic-lacustrine rift basin

Lacustrine microcrystalline dolostones in the ancient records have been commonly interpreted as of an evaporitic origin, on the basis of their numerous occurrences in modern evaporative shallow lakes. This interpretation obscures the forming mechanisms of dolostones formed in a deep lacustrine setting, where dolostones interbedded with shale become a focus of hydrocarbon source rocks in recent years. In this study, an alternative non-evaporitic origin of dolomite is interpreted for a Permian lacustrine laminated dolostone. The dolostones are thinly intercalated with tuffaceous shales and composed mainly of volumetrically abundant, low-ordered, subhedral to euhedral, microcrystalline dolomites with a variable amount of pyroclastics. Evidence for evaporation (e.g., the presence of halite, gypsum and shrinkage cracks) and typical methanogenic microfossils is absent. Instead, our dolostones are unevenly and locally distributed as thin beds or lenses and intercalated with tuffaceous shale and have a formation temperature ranging from 31.4 to 73.8 °C, as estimated from dolomite clumped oxygen? isotope thermometry. In addition, the dolostones have a87Sr/86Sr ratio similar to that of the mantle (0.705154 on average) and extremely negative δ18O (−2.9‰ to −24‰) and positive δ13C (5.1‰–10.8‰) values. A lateral trend of increasing formation temperature of the dolostone toward the volcanic center correlates with the trends of: 1) an increasing amount of pyroclastics, euhedral dolomite crystals, Fe, and TOC, and an increasingly positive δEu value, and 2) decreasing values of 87Sr/86Sr and δ18O. The correlation suggests that a local temperature increase and frequent elemental (Fe and Mg) cycling between the conditions of dolomite supersaturation and undersaturation, which are controlled by episodic volcanic-hydrothermal activities are the causes for dolomite nucleation and growth, resulting in the formation of massive dolostone during deposition and shallow burial. The process also affected organic matter accumulation and local transformation to immature or early mature hydrocarbon during deposition and early burial. The results establish a volcanism-controlled temperature range for primary dolomite nucleation and growth, as well as hydrocarbon generation in a deep lake environment during the period of deposition and early burial, and provide insights on shale oil exploration in petroliferous rift basins.

Improved building MEP systems semantic segmentation in point clouds using a novel multi-class dataset and local–global vector transformer network

Point cloud semantic segmentation for mechanical, electrical, and plumbing (MEP) systems is crucial for establishing MEP systems digital twins. Deep learning has shown promise in automatically deriving point-wise semantic labels from point clouds. However, the utilization of 3D deep learning methods for directly segmenting complex, occluded, and densely distributed multi-class MEP systems in common building point clouds remains unexplored due to the lack of specialized public datasets. To address this, our study introduces a public, richly annotated, and multi-class point cloud dataset for MEP systems in common buildings, consisting of 92.10 million points across four buildings, 56 areas, and nine component categories. Subsequently, we propose Trans2Net, a novel and customized 3D deep learning method for the automatic and precise segmentation of MEP components, featuring local vector transformer module, global-aid vector transformer module, and learnable feature fusion operator that enhance the model’s sensitivity to local features, capture global context with sparse points, and dynamically fuse weighted local and global features. Experimental results demonstrate that Trans2Net significantly outperforms seven other state-of-the-art methods on the MEP dataset, achieving an overall accuracy of 97.39%, a mean accuracy of 95.45%, and a mean intersection over union (mIoU) of 90.76%. Notably, it surpasses the second-ranked method, PointMeta, by a margin of 9.91% in mIoU. This study provides a novel paradigm for automatically understanding MEP systems in point clouds and offers robust support for digital twin management of MEP systems, contributing to the prolonged lifespans of MEP systems.

Modeling local deformation, damage distribution, and phase transformation in zirconia particle-reinforced TRIP steel composites

This study investigates tensile deformation, damage analysis, and the effect of microtexture on a 10 % vol. zirconia particle-reinforced TRIP steel composite. In situ tensile tests and simulations were conducted, with sequential SEM images captured during tensile loading tests and electron backscatter diffraction (EBSD) providing initial crystal orientation data. SEM images were utilized in digital image correlation (DIC) analysis to calculate the local plastic strain distribution using VEDDAC software. Initial micrographs and EBSD data were transformed into geometry files for simulations, employing crystal plasticity with a dislocation density-based model and a newly proposed phase field approach-based ductile-brittle damage criterion for both austenite and ceramic particles. MATLAB's image processing functions were used to compare and validate experimentally observed damage instances with simulation predictions. The effect of microtexture combined with various physical quantities was explored using MTEX. The results show that the simulations accurately predict strain concentration around ceramic particles, with a higher strain observed in the middle region compared to the DIC observations. The simulated strain in the austenitic matrix is found to be 1.4 times higher than the DIC measurements. Quantitative analysis of damage pixels reveals that the proposed critical plastic strain value aligns closely with experimental results, showing an error of - 0.25 % compared to 1.121 % for a smaller strain case. This comprehensive analysis provides insight into the effect of microstructural attributes on material performance and behavior under tensile deformation, leading to a validated microstructural-informed damage-inclusive crystal plasticity model.

Large deviations in the symmetric simple exclusion process with slow boundaries: A hydrodynamic perspective

We revisit the one-dimensional model of the symmetric simple exclusion process slowly coupled with two unequal reservoirs at the boundaries. In its non-equilibrium stationary state, the large deviations functions of density and current have been recently derived using exact microscopic analysis by Derrida, Hirschberg and Sadhu in [J. Stat. Phys. 182, 15 (2021)]. We present an independent derivation using the hydrodynamic approach of the macroscopic fluctuation theory (MFT). The slow coupling introduces additional boundary terms in the MFT-Action, which modifies the spatial boundary conditions for the associated variational problem. For the density large deviations, we explicitly solve the corresponding Euler-Lagrange equations using a simple local transformation of the optimal fields. For the current large deviations, our solution is obtained using the additivity principle. In addition to recovering the expression of the large deviations functions, our solution describes the most probable path for these rare fluctuations.

Crop Water Status Analysis from Complex Agricultural Data Using UMAP-Based Local Biplot

To optimize growth and management, precision agriculture relies on a deep understanding of agricultural dynamics, particularly crop water status analysis. Leveraging unmanned aerial vehicles, we can efficiently acquire high-resolution spatiotemporal samples by utilizing remote sensors. However, non-linear relationships among data features, localized within specific subgroups, frequently emerge in agricultural data. Interpreting these complex patterns requires sophisticated analysis due to the presence of noise, high variability, and non-stationarity behavior in the collected samples. Here, we introduce Local Biplot, a methodological framework tailored for discerning meaningful data patterns in non-stationary contexts for precision agriculture. Local Biplot relies on the well-known uniform manifold approximation and projection method, such as UMAP, and local affine transformations to codify non-stationary and non-linear data patterns while maintaining interpretability. This lets us find important clusters for transformation and projection within a single global axis pair. Hence, our framework encompasses variable and observational contributions within individual clusters. At the same time, we provide a relevance analysis strategy to help explain why those clusters exist, facilitating the understanding of data dynamics while favoring interpretability. We demonstrated our method’s capabilities through experiments on both synthetic and real-world datasets, covering scenarios involving grass and rice crops. Moreover, we use random forest and linear regression models to predict water status variables from our Local Biplot-based feature ranking and clusters. Our findings revealed enhanced clustering and prediction capability while emphasizing the importance of input features in precision agriculture. As a result, Local Biplot is a useful tool to visualize, analyze, and compare the intricate underlying patterns and internal structures of complex agricultural datasets.

Extracting actigraphy-based walking features with structured functional principal components

Objective. We extract walking features from raw accelerometry data while accounting for varying cadence and commonality of features among subjects. Walking is the most performed type of physical activity. Thus, we explore if an individual’s physical health is related to these walking features. Approach. We use data collected using ActiGraph GT3X+ devices (sampling rate = 80 Hz) as part of the developmental epidemiologic cohort study, I = 48, age = 78.7±5.7 years, 45.8% women. We apply structured functional principal component analysis (SFPCA) to extract features from walking signals on both, the subject-specific and the subject-spectrum-specific level of a fast-paced 400 m walk, an indicator of aerobic fitness in older adults. We also use the subject-specific level feature scores to study their associations with age and physical performance measures. Specifically, we transform the raw data into the frequency domain by applying local Fast Fourier Transform to obtain the walking spectra. SFPCA decomposes these spectra into easily interpretable walking features expressed as cadence and acceleration, which can be related to physical performance. Main results. We found that five subject-specific and 19 subject-spectrum-specific level features explained more than 85% of their respective level variation, thus significantly reducing the complexity of the data. Our results show that 54% of the total data variation arises at the subject-specific and 46% at the subject-spectrum-specific level. Moreover, we found that higher acceleration magnitude at the cadence was associated with younger age, lower BMI, faster average cadence and higher short physical performance battery scores. Lower acceleration magnitude at the cadence and higher acceleration magnitude at cadence multiples 2.5 and 3.5 are related to older age and higher blood pressure. Significance. SFPCA extracted subject-specific level empirical walking features which were meaningfully associated with several health indicators and younger age. Thus, an individual’s walking pattern could shed light on subclinical stages of somatic diseases.

Hormone receptor expression in low-grade adenosquamous carcinoma of the breast progressing to high-grade metaplastic carcinoma: A case report.

Breast low-grade adenosquamous carcinoma is an uncommon cancer that has been neglected in genetic and pathophysiological research. Consequently, medical practitioners face challenges in the effective diagnosis and treatment of this condition. We present the case of a 57-year-old Asian female patient who presented with bilateral breast masses on physical examination. Ultrasound and an MRI revealed a highly suspicious malignant mass in her right breast that was completely removed surgically. After pathological analysis, the diagnosis was low-grade adenosquamous carcinoma with local high-grade transformation, and some of the tumor components were estrogen receptor positive. The patient underwent appropriate postoperative chemotherapy and achieved a favorable outcome. During the follow-up period after surgical resection, the patient did not experience any local recurrence or distant metastasis. Owing to the rare combination of estrogen receptor positivity and high-grade progression, this patient also required adjuvant chemotherapy. This enhances the essential foundation for diagnosing and treating this rare disease, and facilitates the implementation of treatment plans.