Dynamic Expansion Research Articles

Cosmic entanglement sudden birth: expansion-induced entanglement in hydrogen atoms

Hydrogen is the most dominant atom in the universe and is considered the main component of baryonic matter. Thus far, the quantum features of the unbounded hydrogen atoms in the background of the universe and the possibility of emerging unique quantum effects, such as entanglement on the cosmological scale, have not been considered. In this work, we demonstrate that the dynamical expansion of the universe leads to the emergence of natural entanglement in the hyperfine structure of atomic hydrogen. Our findings reveal that there exists a critical age for the universe where hydrogen atoms naturally build up entanglement, resulting from the expansion of the universe. More precisely, when the universe reaches the age of about 2.5 × 1018 seconds (about 80 billion years old), the hyperfine structure entanglement in hydrogen atoms naturally takes off, demonstrating a peculiar quantum phenomenon known as entanglement sudden birth. This expansion-induced entanglement becomes maximum at about 3.6 × 1018 seconds (about 115 billion years), after the Big Bang. By analyzing the fate of seed atoms formed in the early universe, this study underscores the significance of unique quantum mechanical features, such as entanglement, on cosmological scales.

Optimizing Advanced Encryption Standard (AES) for Enhanced File Security in University Networks Using Dynamic Key Expansion

Optimizing Advanced Encryption Standard (AES) for Enhanced File Security in University Networks Using Dynamic Key Expansion

Adaptive generic prototype network with geodesic distance for cross-domain few-shot fault diagnosis

Since the fault samples of equipment are limited and the working condition is often variable, it is valuable for researching the cross-domain few-shot fault diagnosis method to assure the safe operation of various machines. As the well-known few-shot classification approaches, the traditional prototype networks are difficult to process the complex sample distributions caused by variable operating condition data and the substantial distributional discrepancies between different machines, which seriously affects the accuracy of cross-domain fault diagnosis. To address these issues, this study proposes a new adaptive geodesic prototype network (AGPN), which can extract the category prototypes with enhanced adaptability and generalization capabilities. Firstly, a geodesic distance-driven learning strategy is developed to better measure the distance between complex samples in the embedding space. Secondly, an adaptive area prototype with a dynamic expansion coefficient is proposed, which allows for more flexible representation of different data categories. Furthermore, an adaptive momentum prototype method is put forward via a model-agnostic adaptive momentum factor, which can reduce the prototype oscillation during training and maximize the learning ability of model. The proposed AGPN is successfully applied to fault diagnosis across bearings with different operating conditions. Compared with the existing few-shot diagnosis methods, the proposed method possesses higher diagnostic accuracy and training stability, thus it is more suitable for cross-domain few-shot fault diagnosis.

Dynamic Expansion and Merging of the Equatorial Ionization Anomaly During the 10–11 May 2024 Super Geomagnetic Storm

This study investigates the responses of the equatorial and low-latitude ionosphere in the American–Atlantic longitude sector during the super geomagnetic storm that occurred on 10–11 May 2024. The investigation utilizes multi-instrument datasets, including ground-based observations (GNSS TEC, ionosonde, and Fabry–Perot interferometer) as well as space-borne satellite measurements (GOLD, Swarm, DMSP, and TIMED). Our findings reveal significant day-to-day variations in the storm-time equatorial ionization anomaly (EIA), summarized as follows: (1) During the main phase of the storm, the low- and mid-latitude ionosphere experienced a positive storm, with TEC drastically enhanced by 50–100% within a few hours. The EIA crests exhibited a substantial poleward expansion, reaching as high as ±35° MLAT. This expansion was caused by the enhanced fountain effect driven by penetration electric fields, along with increased ambipolar diffusion due to transient meridional wind surges. (2) During the recovery phase of the storm, the global ionosphere was characterized by a substantial negative storm with a 50–80% depletion in TEC. The EIA crests were notably suppressed and merged into a single equatorial band, which can be attributed to the composition change effect and the influence of disturbance dynamo electric fields. These results illustrate the complex processes of magnetosphere–ionosphere–thermosphere coupling during a superstorm, highlighting the significant impacts of space weather on the global ionosphere.

Continual compression model for online continual learning

Task-Free Continual Learning (TFCL) presents a notably demanding but realistic ongoing learning concept, aiming to address catastrophic forgetting in sequential learning systems. In this paper, we tackle catastrophic forgetting by introducing an innovative dynamic expansion framework designed to adaptively enhance the model’s capacity for novel data learning while also remembering the information learnt in the past, by using a minimal-size processing architecture. Our proposed framework incorporates three key mechanisms to mitigate model’ forgetting: (1) by employing a Maximum Mean Discrepancy (MMD)-based expansion mechanism that assesses the disparity between previously acquired knowledge and that from the new training data, serving as a signal for the model’s architecture expansion; (2) a component discarding mechanism that eliminates components characterized by redundant information, thereby optimizing the model size while fostering knowledge diversity; (3) a novel training sample selection strategy that leads to the diversity of the training data for each task. We conduct a series of TFCL experiments that demonstrate the superiority of the proposed framework over all baselines while utilizing fewer components than alternative dynamic expansion models. The results on the Split Mini ImageNet dataset, after splitting the original dataset into multiple tasks, are improved by more than 2% when compared to the closest baseline.

Development of an innovative THM fully coupled three-dimensional finite element program and its applications

A thermal-hydraulic-mechanical (THM) field coupling three-dimensional (3D) finite element (FE) program is developed for complex THM coupled problems in engineering practice. This 3D program incorporates a thermo-mechanical coupled constitutive model known as Tsinghua-Thermo-Soil. The program solves the hydraulic and mechanical fields together and the thermal field separately (i.e., the T-HM scheme). Validation is done against the analytical solutions of one-dimensional (1D) steady-state forced convection-conduction and 1D thermo-elastic consolidation processes. Additionally, effects of the dynamic viscosity coefficient and thermal expansion coefficient of water are analyzed for 1D thermo-elastic consolidation coupled problem. It is revealed that for soils in long-term consolidation and under high loading levels, convective effect is significant and the temperature distribution differs from that obtained by considering only heat conduction. A coupled THM problem of foundations involving an actual engineering energy raft is analyzed. The response of a linear elastic foundation under the combined effect of assumed long-term cyclic thermal loading and mechanical loading process is studied. The results demonstrate that heating leads to the locally accumulation of excess pore pressure and reduces settlement and differential settlement, while cooling has the opposite effects. Due to the heat injected into the foundation exceeding the heat extracted, the ground temperature within several meters of burial depth gradually increases over time, meanwhile the average differential settlement decreases.

Coordinated Flaw Disclosure for AI: Beyond Security Vulnerabilities

Harm reporting in Artificial Intelligence (AI) currently lacks a structured process for disclosing and addressing algorithmic flaws, relying largely on an ad-hoc approach. This contrasts sharply with the well-established Coordinated Vulnerability Disclosure (CVD) ecosystem in software security. While global efforts to establish frameworks for AI transparency and collaboration are underway, the unique challenges presented by machine learning (ML) models demand a specialized approach. To address this gap, we propose implementing a Coordinated Flaw Disclosure (CFD) framework tailored to the complexities of ML and AI issues. This paper reviews the evolution of ML disclosure practices, from ad hoc reporting to emerging participatory auditing methods, and compares them with cybersecurity norms. Our framework introduces innovations such as extended model cards, dynamic scope expansion, an independent adjudication panel, and an automated verification process. We also outline a forthcoming real-world pilot of CFD. We argue that CFD could significantly enhance public trust in AI systems. By balancing organizational and community interests, CFD aims to improve AI accountability in a rapidly evolving technological landscape.

A single-cell 3D dynamic volume control system for chondrocytes.

In articular cartilage, zone-specific cellular morphology is a typical characteristic of cartilage tissue, which is related with chondrocyte function, inflammation and osteoarthritis (OA). Chondrocyte hypertrophic phenotype is a criticle physiological process which indicates a hallmark of chondrocyte terminal differentiation and bone formation. Thus, developing a in vitro cell culture system for dynamic regulation of single chondrocyte volume at a three-dimensional (3D) level is particularly necessary for understanding how physical cues of matrix microenvironment regulate chondrocyte fate and the degeneration of articular cartilage. Here, based on the soft lithography techniques, we have constructed well-defined single-cell 3D dynamic volume control system to recapitulate the physiological matrix microenvironment of single chondrocyte niche. The results of finite element analysis indicated that the stress and strain distribution in the cell culture region is homogeneous during the stretching process. Additionally, 3D dynamic volume expansion and compression of single cells in physiological or hyperphysiological can be realized in this cell culture system. Our device for single-cell 3D dynamic culture provides a microphysiological culture system for chondrocytes to explore the mechanisms of cartilage hypertrophy, as well as develops a new paradigm for functional cartilage tissue engineering and regenerative medicine.

Urban Street Landscape Design System Driven by Optimized Genetic Algorithm

With the acceleration of urbanization in China, urban and rural construction land continues to expand. In order to avoid “urban street view disease” in the process of urbanization, dynamic simulation of urban street view land is of great significance. The dynamic model of urban street view land can simulate the dynamic expansion process of street view concretely and effectively, and predict the distribution and form of urban street view land in the future more accurately. How to improve the accuracy of urban street view dynamic model based on geographic cellular automata has always been the direction of unremitting exploration in academic circles. In view of the limitations of the traditional genetic algorithm model, this study constructs a genetic algorithm-logistic regression model, and takes Chengdu-Chongqing Economic Zone as a case, and optimizes the best regression coefficient of the logistic regression model through genetic algorithm fitting, so as to realize the accurate simulation of the dynamic change of urban street view land.

Emergence of IGH::CCND1 rearrangement and mutations in TP53, BTK, and BCL2 associated with therapy resistance in chronic lymphocytic leukemia

AbstractChronic lymphocytic leukemia (CLL) is an indolent low‐grade B‐cell neoplasm that generally responds well to treatment. Rarely, CLL cases exhibit an IGH::CCND1 rearrangement, presenting a diagnostic challenge in distinguishing between clonal evolution within CLL and an independent mantle cell lymphoma. We report a case of CLL with the emergence of an IGH::CCND1 rearrangement and mutations associated with treatment resistance, including TP53, BTK, and BCL2, during disease progression. Immunophenotypic, cytogenetic, and molecular analyses support that these alterations are secondary events within the CLL clone. This case demonstrates dynamic clonal selection and expansion in response to treatments, which, in turn, contributes to treatment resistance.

Knowledge Mapping and Global Trends in the Field of the Objective Structured Clinical Examination: Bibliometric and Visual Analysis (2004-2023).

The Objective Structured Clinical Examination (OSCE) is a pivotal tool for assessing health care professionals and plays an integral role in medical education. This study aims to map the bibliometric landscape of OSCE research, highlighting trends and key influencers. A comprehensive literature search was conducted for materials related to OSCE from January 2004 to December 2023, using the Web of Science Core Collection database. Bibliometric analysis and visualization were performed with VOSviewer and CiteSpace software tools. Our analysis indicates a consistent increase in OSCE-related publications over the study period, with a notable surge after 2019, culminating in a peak of activity in 2021. The United States emerged as a significant contributor, responsible for 30.86% (1626/5268) of total publications and amassing 44,051 citations. Coauthorship network analysis highlighted robust collaborations, particularly between the United States and the United Kingdom. Leading journals in this domain-BMC Medical Education, Medical Education, Academic Medicine, and Medical Teacher-featured the highest volume of papers, while The Lancet garnered substantial citations, reflecting its high impact factor (to be verified for accuracy). Prominent authors in the field include Sondra Zabar, Debra Pugh, Timothy J Wood, and Susan Humphrey-Murto, with Ronaldo M Harden, Brian D Hodges, and George E Miller being the most cited. The analysis of key research terms revealed a focus on "education," "performance," "competence," and "skills," indicating these are central themes in OSCE research. The study underscores a dynamic expansion in OSCE research and international collaboration, spotlighting influential countries, institutions, authors, and journals. These elements are instrumental in steering the evolution of medical education assessment practices and suggest a trajectory for future research endeavors. Future work should consider the implications of these findings for medical education and the potential areas for further investigation, particularly in underrepresented regions or emerging competencies in health care training.

General framework for unsteady aerodynamic prediction of airfoils based on deep transfer learning

Analyzing the unsteady aerodynamic performance of airfoils under dynamic stall using computational fluid dynamics (CFD) is computationally intensive. Although deep learning models can quickly predict aerodynamic parameters, their generalization capability on a small-scale dataset is often poor. This paper presents a novel deep transfer learning (TL) framework that combines model-based and instance-based transfer methods, termed synergistic instance-model TL. This framework facilitates rapid predictions of unsteady aerodynamic performance for various airfoils and pitch oscillations from the small-scale dataset. The framework integrates the accelerated training speed of model-based methods with the dynamic dataset expansion benefits of instance-based approaches. Initially, a pre-trained Wasserstein-deep convolutional generative adversarial network (W-DCGAN) is developed, combining a convolutional neural network with a generative adversarial network to predict aerodynamic hysteresis loops for the SC1095 airfoil in the source domain. The framework then fine-tunes the pre-trained model and incorporates weighted source domain dataset into the small-scale target domain dataset, producing the transferred model W-DCGAN-TL. This approach significantly reduces prediction inaccuracies compared to model-based and non-TL methods when applied to the small-scale dataset. The framework's flexibility allows the use of pre-trained models and datasets from related aerodynamic problems to address issues with insufficient data. Consequently, it is expected to reduce the dependency on extensive datasets, enhance design efficiency, and minimize resource requirements.

A Progressive Planning Scheme for Dynamic Expansion of Electric Vehicle Charging Resources

A Progressive Planning Scheme for Dynamic Expansion of Electric Vehicle Charging Resources

Simulation Study on Rock Crack Expansion in CO2 Directional Fracturing

In underground construction projects, traversing hard rock layers demands concentrated CO2 fracturing energy and precise directional crack expansion. Due to the discontinuity of the rock mass at the tip of prefabricated directional fractures in CO2 fracturing, traditional simulations assuming continuous media are limited. It is challenging to set boundary conditions for high strain rate and large deformation processes. The dynamic expansion mechanism of the 3D fracture network in CO2 directional fracturing is not yet fully understood. By treating CO2 fracturing stress waves as hemispherical resonance waves and using a particle expansion loading method along with dynamic boundary condition processing, a 3D numerical model of CO2 fracturing is constructed. This model analyzes the dynamic propagation mechanism of 3D spatial fractures network in CO2 directional fracturing rock materials. The results show that in undirected fracturing, the fracture network relies on the weak structures near the rock borehole, whereas in directional fracturing, the crack propagation is guided, extending the fracture’s range. Additionally, the tip of the directional crack is vital for the re-expansion of the rock mass by high-pressure CO2 gas, leading to the formation of a symmetrical, umbrella-shaped structure with evenly developed fractures. The findings also demonstrate that the discrete element method (DEM) effectively reproduces the dynamic fracture network expansion at each stage of fracturing, providing a basis for studying the CO2 directional rock cracking mechanism.

Interdependence and coordination challenges: EV charging infrastructure and carbon emissions in the Yangtze River Delta

The ambition towards net-zero emission fuels the significance of electric vehicle charging infrastructure (EVCI) as a strategic asset. Yet, a conspicuous gap remains in the comprehensive quantitative analysis of its impact on carbon emissions stemming from fossil fuel combustion, referred to as ODIAC-CE. This study embarks on a longitudinal comparison of EVCI and ODIAC-CE data in 2018 and 2020, further classifying cities by scale to analyze the association between expansion of EVCI and ODIAC-CE change. Utilizing a battery of analytical tools, including correlation analysis, spatial autocorrelation, and coupling coordination analysis, the study dissects the evolving relationship between EVCI and ODIAC-CE within Yangtze River Delta in China. The results underscore a growing interdependence between EVCI expansion and ODIAC-CE change, yet pronounced heterogeneities in coupling coordination are evident across urban scales. Megacity and supercity exhibit quality coordination between rapid expansion of EVCI and ODIAC-CE dynamics. However, in most large, medium-sized, and small cities, the impact of EVCI growth on ODIAC-CE change has proven to be inconsistent or mismatched, affected by various factors such as location and infrastructure, industrial and technological patterns, and social practice and awareness. The study provides systematic insights into potential solutions for decarbonization through EVCI deployment at regional and city levels.

Research on the Sustainable Evolution Mechanism of Dual-Dimensional Convergence Innovation in Digital Products

The complex characteristics of cross-disciplinarity, dynamic expansion, ambiguity, diversity, and the rapid changes in demand significantly amplify the uncertainties in digital product innovation. The existing innovation theories, such as “stage-gate”, open innovation, agile development, and data-driven decision making, are insufficient for fully and effectively addressing these uncertainties. Based on a case study of a fintech app, we reveal that digital product innovation is similar to biological evolution, exhibiting dual life-like features of “inheritance” and “mutation” within “dual-dimensional convergence”. However, unlike natural evolution, the evolutionary process of digital product innovation can augment its use of the digital ecosystem and capabilities, establish a data-driven rapid proactive selection mechanism for the main three stages, and quickly enhance product competitiveness. The complexity of knowledge in the innovation process can be partially solved through the use of a micro-knowledge integration learning mechanism formed by the interactions of social and cognitive translation. This study also discovers that market competition and policy regulation are two unique innovation-driven characteristics in digital product innovation. This mechanism can achieve the earlier clarification of product evolution’s direction, reduce the three major uncertainties of innovation, and improve efficiency in the utilization of innovation resources to achieve sustainable development.

Dynamic Expansion and Contraction of Multiple Sclerosis T2-weighted Hyperintense Lesions are Present Below the Threshold of Visual Perception.

The study of T2-weighted hyperintense lesions resulting from autoimmune inflammatory injury and associated volumes within the CNS remains fundamental to the diagnosis and disease surveillance of multiple sclerosis (MS). We investigated the dynamic changes of individual T2-weighted hyperintense MS lesions on MRI and hypothesized that variations may be present below the threshold of visual perception when evaluating longitudinal data. A retrospective study was performed of people with MS, incorporating data from three consecutive MRI time points acquired within a single academic center. All included MRI studies lacked formal imaging interpretations of newly enlarging or contracting T2-weighted hyperintensities. Well defined, non-coalescing, individual T2-weighted hyperintense lesions were targeted. A total of 8-12 lesions were randomly selected in a blinded fashion at MRI time point 1 and 3-dimensional lesion volumes followed over MRI time points 2 and 3. The impact of treatment on lesion expansion and relationship to brain MRI advancement, patient-reported progression of disease, and physician-identified progression was also studied. The study cohort was comprised of 115 people (81 (70.4%) female; mean disease duration of 6.62 years(y) (standard deviation: 6.68y)) who were primarily White (79.1%). A total of 1,426 focal T2-weighted hyperintense MS lesions were identified on MRI time point 1 and longitudinally followed over MRI time points 2 and 3. In the evaluation of raw changes in individual T2-weighted hyperintense lesion volumes from MRI time point 1 to MRI time point 2, a similar number of individuals were observed with predominantly expanding (49/115; 42.6%) or contracting (51/115; 44.3%) lesions. However, the majority of lesions expanded in volume (48/115; 41.7%) versus those that contracted (45/115; 39.1%) when evaluating MRI time point 3 to time point 1. Those individuals not on active treatment had a 67.15% reduction in the odds of more individual lesions predominantly contracting in volume relative to those on low-efficacy disease modifying therapy treatment (95% CI=[-83.89, -33.01], p=0.0008) and 74.02% reduction for those on high-efficacy treatment (95% CI=[-87.37%,-46.56%], p<0.0001). Dynamic changes in T2-weighted hyperintense lesions are abundant, occurring below the threshold of visual perception and are present more frequently in untreated individuals. MS = multiple sclerosis; DMT = disease modifying therapy; 2D = 2-dimensional; 3D = 3-dimensional; LMS = Lambda, Mu, and Sigma.

Sustainable Fashion Choices: Exploring European Consumer Motivations behind Second-Hand Clothing Purchases

The second-hand clothing (SHC) market has seen dynamic expansion, driven by shifts in consumer attitudes. However, motivations underlying customer engagement in this market remain largely unexplored. Accordingly, this study investigates factors driving consumer sentiment towards buying used clothing. Data were collected in 2023 from a sample of 254 European consumers. The results show that positive attitudes towards SHC are associated with high levels of economic, hedonic (treasure hunting) and ethical motivations, while strong fashion interest and materialism seem to coincide with more negative evaluations of SHC. Interestingly, treasure hunting motivation emerged as the strongest driver of SHC attitudes, surpassing both economic and ethical motivations, which were similar in strength. However, mediation analysis revealed complex interaction patterns among the attitudinal variables, with the potential of reversing the direction of average regression weights for individuals as well as subgroups of customers. This study offers a more nuanced understanding of consumer behavior in the SHC market. It provides practical recommendations for the marketing strategies of SHC vendors, and lays the groundwork for future research in this area.

An Examination of CXL Memory Use Cases for In-Memory Database Management Systems Using SAP HANA

CXL-based disaggregated memory systems offer options to expand the memory beyond the limits of a single server via cache-coherent memory expansion cards or memory pools. Especially, In-Memory Database Management Systems (IMDBMSs) can benefit from alleviating two critical constraints: (1) limited memory capacity in a server and (2) long restart time during failover to reload data to memory. However, the usage and effectiveness of CXL memory in enterprise-scale IMDBMSs has yet to be validated. In this work---for the first time---we investigate dynamic memory expansion employing commercial CXL memory devices for IMDBMSs. Our detailed performance analysis reveals that the performance impact of higher latency and lower memory bandwidth impact depends on the memory access patterns of data structures (cf. (1)). Additionally, we present the feasibility of CXL shared memory between servers to improve restart times during failover (cf. (2)). Our evaluation shows the effectiveness of CXL memory integrated into the SAP HANA Cloud IMDBMS. OLTP workloads have a negligible performance degradation while OLAP workloads have a wide range of performance degradation. CXL shared memory shows a 40% reduction of the restart time for TPC-H SF10 and 84% potential reduction for TPC-H SF100.

In What Way Is Gen Zs Purchase Behavior Affected by Social Media

With the growth of the online marketplace, social media has received increasing attention as an advertising resource in recent years. An introduction to the social psychology of effective social media advertising has been studied to some extent at this stage. This paper delves into the dynamic expansion of social media and its far-reaching impact on marketing opportunities, particularly on Generation Z - a population known for its avid use of social platforms. Using a variety of search engines and specific keyword combinations, this study provides a comprehensive review of empirical studies that elucidate the relationship between purchasing behavior and Gen Z's use of social media. The study systematically integrates and evaluates various studies that have explored the impact of social media on the buying habits of Generation Z. Key aspects such as the phenomenon of followership, the quest for individuality, and the sway of social media influencers are summarized and evaluated. The paper emphasizes the critical role of social media marketing and its far-reaching impact on Generation Z's purchasing decisions.