Past States Research Articles

Multi-agent reinforcement learning with synchronized and decomposed reward automaton synthesized from reactive temporal logic

Multi-agent systems (MAS) consist of multiple autonomous agents interacting to achieve collective objectives. Multi-agent reinforcement learning (MARL) enhances these systems by enabling agents to learn optimal behaviors through interaction, thus improving their coordination in dynamic environments. However, MARL faces significant challenges in adapting to complex dependencies on past states and actions, which are not adequately represented by the current state alone in reactive systems. This paper addresses these challenges by considering MAS operating under task specifications formulated as Generalized Reactivity of rank 1 (GR(1)). These synthesized strategies are used as a priori knowledge to guide the learning. To tackle the difficulties of handling non-Markovian tasks in reactive systems, we propose a novel synchronized decentralized training paradigm that guides agents to learn within the MARL framework using a reward structure constructed from decomposed synthesized strategies of GR(1). We initially formalize the synthesis of GR(1) strategies as a reachability problem of winning states of the system. Subsequently, we develop a decomposition mechanism that constructs individual reward structures for decentralized MARL, incorporating potential values calculated through value iteration. Theoretical proofs are provided to verify that the safety and liveness properties are preserved. We evaluate our approach against other state-of-the-art methods under various GR(1) specifications and scenario maps, demonstrating superior learning efficacy and optimal rewards per episode. Additionally, we show that the decentralized training paradigm outperforms the centralized training paradigm. The value iteration strategy used to calculate potential values for the reward structure is compared against two other strategies, showcasing its advantages.

Linking deep-time subduction history to modern day expressions of dynamic topography

Dynamic topography refers to vertical deflections of Earth’s surface from viscous flow within the mantle. Here we investigate how past subduction history affects present dynamic topography. We assimilate two plate reconstructions into TERRA forward mantle convection models to calculate past mantle states and predict Earth’s present dynamic topography; a comparison is made with a database of observed oceanic residual topography. The two assimilated plate reconstructions ‘Earthbyte’ and ‘Tomopac’ show divergent subduction histories across an extensive deep-time interval within Pacific-Panthalassa. We find that introducing an alternative subduction history perturbs our modelled present-day dynamic topography on the same order as the choice of radial viscosity. Additional circum-Pacific intra-oceanic subduction in Tomopac consistently produces higher correlations to the geoid (more than 20% improvement). At spherical harmonic degrees 1–40, dynamic topography models with intra-oceanic subduction produce universally higher correlations with observations and improve fit by up to 37%. In northeast Asia, Tomopac models show higher correlations (0.46 versus 0.18) to observed residual topography and more accurately predict approximately 1 km of dynamic subsidence within the Philippine Sea plate. We demonstrate that regional deep-time changes in subduction history have widespread impacts on the spatial distribution and magnitude of present-day dynamic topography. Specifically, we find that local changes to plate motion histories can induce dynamic topography changes in faraway regions located thousands of kilometres away. Our results affirm that present-day residual topography observations provide a powerful, additional constraint for reconstructing ancient subduction histories.

An optimal control for non-autonomous second-order stochastic differential equations with delayed arguments

Abstract Optimal control of non-autonomous second-order stochastic differential equations with delayed arguments is indispensable for managing systems exposed to uncertainty, time-dependent dynamics, and historical influences. These equations underpin a wide range of applications, including finance, engineering, and biology, where it’s imperative to make informed decisions that mitigate risks or maximize returns while considering the inherent randomness, evolving conditions, and the impact of past states. By employing optimal control techniques, we can devise strategies that are resilient to uncertainty, adaptable to changing circumstances, and capable of accounting for the memory effects of previous events. This empowers us to optimize system performance, bolster stability, and attain desired objectives in intricate and dynamic environments. So, the goal of this article is to introduce a novel model of second-order perturbed stochastic differential equations incorporating non-local finite delay and deviated arguments in the setting of Hilbert spaces. Moreover, essential criteria are presented to examine the existence of a mild solution and evaluate the potential for approximate and optimal control of the proposed system. These results have been obtained by using evolution operators, fixed point techniques, random analytic methods, and compact semigroup theory. Further, to support the theoretical results, the optimal controllability of our model was studied by considering the Lagrange problem. Finally, the results were applied to discuss the approximate controllability of a partial differential equation. These models have the potential to advance the understanding and application of optimal control techniques for a wider range of complex systems.

‘Update Bias’: Manipulating past information based on the existing circumstances

Many panel surveys elicit information about past events multiple times. It is, however, unclear whether respondents manipulate their past information and update it according to their current circumstances in the later rounds of the panel. We term such a systematic bias in reporting past information as “update bias” in this study. We systematically test the presence of update bias in panel data by comparing teenage religiosity obtained from adults first in 2019 and later in 2022 in the Netherlands. Respondents who become more (less) religious in 2022 than 2019 are likelier to report a higher (lower) teenage religiosity in 2022. Even when we use data with a narrower gap (2019 and 2020 survey waves), we still obtain similar results. Overall, the analysis provides strong evidence for update bias. We suggest that the theory of cognitive dissonance best explains our findings; individuals manipulate their teenage religiosity to minimize dissonance between the past and current religious state and to obtain a higher satisfaction. Unlike predominant existing literature that argues people modify their current beliefs according to previous anchors, we provide contrary evidence where people manipulate their past beliefs following their current circumstances.

Formulation of Impulsive Differential Equations with Time-Dependent Continuous Delay

Research in impulsive delay differential equations has been undergoing some exciting growth in recent times. This to a large extent can be attributed to the quest by mathematicians in particular and the science community as a whole to unveil nature the way it truly is. The realization that differential equations, in general, and indeed impulsive delay differential equations are very important models for describing the true state of several real-life processes/phenomena may have been the tunic. One can attest that in most human processes or natural phenomena, the present state is most often affected significantly by their past state and those that were thought of as continuous may indeed undergo abrupt change at several points or even be stochastic. In this study, a special strictly ascending continuous delay is constructed for a class of system of impulsive differential equations. It is demonstrated that even though the dynamics of the system and the delay have ideal continuity properties, the right side may not even have limits at some points due to the impact of past impulses in the present. The integral equivalence of the formulated system of equations is also obtained via a scheme similar to that of Perron by making use of certain assumptions.

A Synergistic Perspective on Multivariate Computation and Causality in Complex Systems.

What does it mean for a complex system to "compute" or perform "computations"? Intuitively, we can understand complex "computation" as occurring when a system's state is a function of multiple inputs (potentially including its own past state). Here, we discuss how computational processes in complex systems can be generally studied using the concept of statistical synergy, which is information about an output that can only be learned when the joint state of all inputs is known. Building on prior work, we show that this approach naturally leads to a link between multivariate information theory and topics in causal inference, specifically, the phenomenon of causal colliders. We begin by showing how Berkson's paradox implies a higher-order, synergistic interaction between multidimensional inputs and outputs. We then discuss how causal structure learning can refine and orient analyses of synergies in empirical data, and when empirical synergies meaningfully reflect computation versus when they may be spurious. We end by proposing that this conceptual link between synergy, causal colliders, and computation can serve as a foundation on which to build a mathematically rich general theory of computation in complex systems.

Preserving quantum information in f(Q) non-metric gravity cosmology

The effects of cosmological expansion on quantum bosonic states are investigated, using quantum information theory. In particular, a generic Bogoliubov transformation of bosonic field modes is considered and the state change on a single mode is regarded as the effect of a quantum channel. Properties and capacities of this channel are thus explored in the framework of f(Q) non-metric gravity. The reason is that non-metric gravity can be considered under the standard of gauge theories with all the advantages of such a formulation. As immediate result, we obtain that the information on a single-mode state appears better preserved, whenever the number of particles produced by the cosmological expansion is small. Specifically, we investigate a power law f(Q) model, leaving unaltered the effective gravitational coupling, and minimise the corresponding particle production. We thus show how to optimise the preservation of classical and quantum information, stored in bosonic mode states in the remote past. Finally, we compare our findings with those obtained in General Relativity.

Photonic reservoir computing for parallel task processing based on a feedback-free spin-polarized VCSEL

Time delayed reservoir computing (RC) is a novel artificial neural network that is easy to implement in hardware due to its extremely simple structure. Because of its time-division multiplexed information processing, laser-based photonic time-delayed RCs usually realize parallel processing with polarization/wavelength multiplexing. However, the performance of two different tasks is difficult to regulate separately and simultaneously in the time delayed RC system, especially for the chip-scale configuration. Here, we propose a feedback-free RC system based on a spin-polarized vertical-cavity surface-emitting semiconductor laser (VCSEL), which simplifies the whole system structure and can process time series prediction and waveform recognition tasks in parallel, with employing the input and output coding to provide the effect from past states. By separately setting the number of past states introduced by the coding for the two tasks, the performance of the two tasks can be adjusted respectively. Furthermore, by appropriately tuning the pump polarization ellipticity which is the unique feature for the spin-polarized VCSEL, the computational ability of the proposed RC can be focused on one of the two parallel tasks. Therefore, the proposed RC system is capable of dealing with different tasks with high performance, and also expected to provide a viable solution for integrated neuromorphic computing systems due to its compact, feedback-free structure.

Image encryption algorithm based on matrix projective combination-combination synchronization of an 11-dimensional time delayed hyperchaotic system

Abstract Secure image transmission is critical to protect sensitive data from unauthorized access, especially in an era of increasing digital threats. Chaotic systems with their inherent complexity and unpredictability, provide a promising solution for enhancing encryption security. To contribute to this field, we investigate a new 11-dimensional hyperchaotic system by taking advantage of its complex dynamical properties to strengthen security. The high dimensional of the system intensifies chaotic behaviors such as stability, attractors and sensitive to initial conditions, making it particularly suitable for encrypted transmission.
 Time delay is an important factor to be considered affecting the control and synchronization in nonlinear system. Additionally, time delays include the effects of past states, further increasing the unpredictability of the system. To explore these dynamics, we analyze the Lyapunov exponents, stability of equilibrium points, symmetry and dissipation. A matrix projective combination-combination synchronization scheme is proposed to synchronize four identical 11-dimensional hyperchaotic systems with time delay. Nonlinear active controllers designed based on Lyapunov stability theory are used to achieve this synchronization. This work advances an important idea for encryption and decryption algorithms, which is the secure transmission of images using affine encryption. In the affine encryption algorithm, the key is based on the solution of synchronized chaotic delayed systems and the private message of the sender and receiver. This proposed encryption and decryption algorithms have been applied on plain images. Numerical simulations and security analysis including key space, histogram, information entropy and correlation analysis are conducted to validate the theoretical results and encryption algorithm. Experimental analysis and comparisons with existing literature confirm the effectiveness and security of the proposed approach for cryptographic purposes.

Modes of Securitization and Desecuritization of Transnational Kinship Ties: Overseas Chinese in Southeast Asia amidst Rising Chinese Power

Abstract This paper explores under what context the transnational ethnic kinship ties between the overseas Chinese in Southeast Asia and China have not led to their securitization and in what context they have. As the rising great power with exponential economic growth for the past few decades, China has transformed itself from a dirt-poor backwater state to the second largest economy in the world and is also seemingly on the way to reclaim the historical dominant status in East and Southeast Asia. During this process, overseas Chinese in various home states in Southeast Asia have not experienced a uniform securitization of their kinship ties. This paper investigates some of the dynamic patterns about overseas Chinese in Southeast Asia’s relationship between the changing kin state and their home states in the recent past. Focusing on how states in Southeast Asia maintain different types of relations with China in the context of the latter’s rising, this paper contends that the ways in which overseas Chinese communities in the region manage their political space and negotiate their belonging are a function of a combination of international structural changes and domestic political factors. Empirically, this paper compares three states of Southeast Asia—Thailand, Myanmar, and Indonesia—in the different ways of their treatment of the ethnic Chinese, as well as the responding strategies utilized by the ethnic Chinese in navigating their political space and belonging.

A Method Based on Deep Learning for Severe Convective Weather Forecast: CNN-BiLSTM-AM (Version 1.0)

In this study, we propose a model called CNN-BiLSTM-AM that utilizes deep learning techniques to forecast severe convective weather based on ERA5 hourly data and observations. The model integrates a CNN with a Bidirectional Long Short-Term Memory (BiLSTM) system and an Attention Mechanism (AM). The CNN is tasked with extracting features from the input data, while the BiLSTM effectively captures temporal dependencies. The AM enhances the results by considering the impact of past feature states on severe weather phenomena. Additionally, we assess the performance of our model in comparison to traditional network architectures, including ConvLSTM, Predrnn++, CNN, FC-LSTM, and LSTM. Our results indicate that the CNN-BiLSTM-AM model exhibits superior accuracy in precipitation forecasting. Especially with the extension of the forecast time, the model performs well across multiple evaluation metrics. Furthermore, an interpretability analysis of the convective weather mechanisms utilizing machine learning highlights the critical role of total precipitable water (PWAT) in short-term heavy precipitation forecasts. It also emphasizes the significant impact of regional variables on convective weather patterns and the role of convective available potential energy (CAPE) in fostering conditions conducive to convection. These findings not only confirm the effectiveness of deep learning in the automatic identification of severe weather features but also validate the suitability of the sample dataset employed. Given its remarkable performance and robustness, we advocate for the adoption of this model to enhance the forecast of severe convective weather across various business applications.

Driven-dissipative phases and dynamics in non-Markovian nonlinear photonics

Interactions between photons (nonlinearities) enable a powerful form of control over the state of light. This control has enabled technologies such as light sources at new wavelengths, ultra-short optical pulses, frequency-comb metrology systems, even quantum light sources. Common to a wide variety of nonlinear optical technologies is an equilibrium between an energy source, such as an external laser, and dissipation, such as radiation loss or absorption. In the vast majority of these systems, the coupling between the system and the outside world (which leads to loss) is well described as “Markovian,” meaning that the outside world has no memory of its past state. In this work, we introduce a class of driven-dissipative systems in which a nonlinear cavity experiences non-Markovian coupling to the outside world. In the classical regime, we show that these non-Markovian cavities can have extremely low thresholds for nonlinear effects, as well as self-pulsing instabilities at THz rates, and rich phase diagrams with alternating regions of stability and instability. In the quantum regime, we show how these systems, when implemented on state-of-the-art platforms, can enable generation of strongly squeezed cavity states with intensity fluctuations that can be more than 15 dB below the classical limit, in contrast to the Markovian driven-dissipative cavity, in which the limit is 3 dB. In the regime of few-photon nonlinearity, such non-Markovian cavities can enable a deterministic protocol to generate Fock states of high order, which are long-desired, but still elusive at optical frequencies. We expect that exploiting non-Markovian couplings in nonlinear optics should in the future lead to even richer possibilities than those discussed here for both classical and quantum light manipulations.

Abstract A015: Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022

Abstract Background: Mammography screening is associated with at least a 20% reduction in breast cancer mortality. However, there are racial/ethnic differences in breast cancer mortality. For example, non-Hispanic Black (NHB) women are significantly more likely to die of breast cancer than non-Hispanic White (NHW) women. Few studies have reported small-area estimates for mammography screening among the entire US population; differences by race were either not examined or resulted in mixed findings. The American Cancer Society (ACS) and the United States Preventive Services Taskforce (USPSTF) recommend regular mammography screening from age 40 through 74 years. The current study aims to identify areas of up-to-date mammography screening and to elucidate whether mammography screening varied by geographical location, race, or ethnicity. Methods: Mammography screening within the past 12 months was retrieved from the 2015-2022 Behavioral Risk Factor Surveillance System data. The study population consisted of adult women aged 40 and older residing within the United States. 2020 US census tract population estimates were used to generate gender (all women), race/ethnicity (NHB, NHW, non-Hispanic Asian, and Hispanic/Latina), income, and age group weights for each US census tract. Synthetic estimates of mammography screening uptake at the census tract were conducted using our novel PARIS model (Probability of mammography screening within the past year, given age, race/ethnicity, income, and state) using a multilevel logistic regression model. Regional differences in mammography screening by US Census Bureau regions and divisions were examined. Results: Up-to-date mammography among all women in the sample was 55.98% (SD = 2.07; F (3, 72429) = 2886.81, p < .0001). Mammography screening examined by race indicated the greatest screening rates among non-Hispanic Black women (M = 60.78, SD = 3.11) and least among Asian women (M = 48.68, SD = 3.15). Among four US Census Bureau regions, the West region had the lowest rates of mammography screening among women (M = 54.74, SD = 1.85) while the South region had the highest rates (M = 56.55, SD = 2.18; F(3, 72429) = 2886.81, p < .0001). After further parsing geographical units, the West Pacific region, among the 9 US Census Bureau divisions, had the lowest mammography screening (M = 54.53, SD = 1.81) while the East South Central division had the highest rates of mammography screening (M = 56.94, SD = 2.10; F(8, 72429) = 1360.60, p < .0001).Conclusion: Using novel geospatial methods, the current study elucidated significant differences in mammography screening by race and place. These results suggest that Black women and women living in the South have higher up-to-date mammography screening compared to other women. Thus, the existing inequities in breast cancer outcomes may be explained by differences in the cancer care continuum (delays in follow-up care, treatment, and breast cancer survival). Further examination of the interplay between screening uptake, late-stage diagnosis, and death is needed. Citation Format: Sydney P. Howard, Justin X. Moore, Elinita Pollard, Ryan Johnson, Lovoria Williams, Adebola Adegboyega, Jie Chen, Jorge Cortes, Aimee James. Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022 [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A015.

A variant-informed decision support system for tackling COVID-19: a transfer learning and multi-attribute decision-making approach.

The global impact of the COVID-19 pandemic, characterized by its extensive societal, economic, and environmental challenges, escalated with the emergence of variants of concern (VOCs) in 2020. Governments, grappling with the unpredictable evolution of VOCs, faced the need for agile decision support systems to safeguard nations effectively. This article introduces the Variant-Informed Decision Support System (VIDSS), designed to dynamically adapt to each variant of concern's unique characteristics. Utilizing multi-attribute decision-making (MADM) techniques, VIDSS assesses a country's performance by considering improvements relative to its past state and comparing it with others. The study incorporates transfer learning, leveraging insights from forecast models of previous VOCs to enhance predictions for future variants. This proactive approach harnesses historical data, contributing to more accurate forecasting amid evolving COVID-19 challenges. Results reveal that the VIDSS framework, through rigorous K-fold cross-validation, achieves robust predictive accuracy, with neural network models significantly benefiting from transfer learning. The proposed hybrid MADM approach integrated approaches yield insightful scores for each country, highlighting positive and negative criteria influencing COVID-19 spread. Additionally, feature importance, illustrated through SHAP plots, varies across variants, underscoring the evolving nature of the pandemic. Notably, vaccination rates, intensive care unit (ICU) patient numbers, and weekly hospital admissions consistently emerge as critical features, guiding effective pandemic responses. These findings demonstrate that leveraging past VOC data significantly improves future variant predictions, offering valuable insights for policymakers to optimize strategies and allocate resources effectively. VIDSS thus stands as a pivotal tool in navigating the complexities of COVID-19, providing dynamic, data-driven decision support in a continually evolving landscape.

Sparse large-scale high-order fuzzy cognitive maps guided by spearman correlation coefficient

Time series prediction is one of the most important applications of Fuzzy Cognitive Maps (FCMs). In general, the state of FCMs in forecasting depends only on the state of the previous moment, but in fact it is also affected by the past state. Hence Higher-Order Fuzzy Cognitive Maps (HFCMs) are proposed based on FCMs considering historical information and have been widely used for time series forecasting. However, using HFCMs to deal with sparse and large-scale multivariate time series are still a challenge, while large-scale data makes it difficult to determine the causal relationship between nodes because of the increased number of nodes, so it is necessary to explore the relationship between nodes to guide large-scale HFCMs learning. Therefore, a sparse large-scale HFCMs learning algorithm guided by Spearman correlation coefficient, called SG-HFCM, is proposed in the paper. The SG-HFCM model is specified as follows: First, the solving of HFCMs model is transform into a regression model and an adaptive loss function is utilized to enhance the robustness of the model. Second, l1-norm is used to improve the sparsity of the weight matrix. Third, in order to more accurately characterize the correlation relationship between variables, the Spearman correlation coefficients is added as a regular term to guide the learning of weight matrices. When calculating the Spearman correlation coefficient, through splitting domain interval method, we can better understand the characteristics of the data, and get better correlation in different small intervals, and more accurately characterize the relationship between the variables in order to guide the weight matrix. In addition, the Alternating Direction Multiplication Method and quadratic programming method are used to solve the algorithms to get better solutions for the SG-HFCM, where the quadratic programming can well ensure that the range of the weights and obtaining the optimal solution. Finally, by comparing with five algorithms, the SG-HFCM model showed an average improvement of 11.93% in prediction accuracy for GRNs, indicating that our proposed model has good predictive performance.

The Amhara of Ethiopia: Embracing and Using Imposed Identity to Resist Injustice

Ethnic identities often solidify in response to perceived or actual injustices endured by groups. Historically, Amharic-speaking people in Ethiopia have resisted ethnic identification, aligning instead with broader Ethiopian nationalism. However, the rise of extreme ethnonationalist forces in the country has subjected the group to negative narratives, violence, and marginalisation, associating them with past state domination. In response, the Amhara have increasingly embraced ethnic identity as a form of self-defence. This study employs thematic analysis to explore the experiences of the Amhara people and the subsequent emergence of their collective identity, including the rise of resistance movements. Despite this new alignment, Amhara elites and activists paradoxically maintain a strong commitment to Ethiopian unity, reflecting a complex duality in their socio-political stance. This balancing act illustrates their struggle to survive while remaining loyal to national unity. The article argues that sustained violence and marginalisation have catalysed the rise of Amhara group consciousness, transforming Ethiopia’s political landscape. This study offers broader insights into how group mentality can emerge as a response to systematic and sustained injustice and the implications this has for redefining power politics in Ethiopia and beyond, providing insights for policymaking and future research.

Manifestations Reported by Inflammatory Bowel Disease Patients in University Hospital.

Inflammatory bowel disease (IBD) may affect organs outside the intestines, it is called extraintestinal manifestations of IBD. Data on the prevalence of mu-cocutaneous manifestations in IBD patients are very limited, therefore, the aim of this study was to assess the prevalence of skin and mucosal lesions and to determine the relationship with demographic factors, clinical features, and systemic treatment. Prospective study included 162 out-patients with IBD who were managed in the tertiary care center. Ulcerative colitis (UC) was diagnosed in 117 patients, Crohn's disease (CD) in 45. Patients completed the questionnaire containing demographic and IBD data, questions about mucocutaneous lesions (in past or present state). Overall mucocutaneous lesions were reported by 48.1% of IBD patients. Skin lesions were reported by 40.7% of patients, oral mucosal lesions were reported by 16.7%, without significant differences between sexes or IBD types. In 47 (29%) of patients, skin lesions appeared together with IBD or during the course of the disease. The most common skin lesions were psoriasis (8.0%), erythema nodosum (5.6%), pyoderma gangrenosum and acne (3.7% each). UC patients mostly reported about psoriasis (9.4%), while CD patients about erythema nodosum (11.1%). There were more frequent skin lesions in patients with more extensive UC type (p = 0.01), while no difference was noticed between different types of CD. The average duration of IBD in patients with skin lesions was similar to those without lesions (9.3±6.7 vs. 9.4±6.7 years). Mucocutaneous lesions were reported by 48.1% of inflammatory bowel disease patients. The frequency of mucocutaneous lesions does not differ significantly between UC and CD, and a longer duration of illness is not a predictive factor for the appearance of lesions. More extensive UC is related to higher frequency of skin lesions.

Dissecting reversible and irreversible single cell state transitions from gene regulatory networks.

Understanding cell state transitions and their governing regulatory mechanisms remains one of the fundamental questions in biology. We develop a computational method, state transition inference using cross-cell correlations (STICCC), for predicting reversible and irreversible cell state transitions at single-cell resolution by using gene expression data and a set of gene regulatory interactions. The method is inspired by the fact that the gene expression time delays between regulators and targets can be exploited to infer past and future gene expression states. From applications to both simulated and experimental single-cell gene expression data, we show that STICCC-inferred vector fields capture basins of attraction and irreversible fluxes. By connecting regulatory information with systems' dynamical behaviors, STICCC reveals how network interactions influence reversible and irreversible state transitions. Compared to existing methods that infer pseudotime and RNA velocity, STICCC provides complementary insights into the gene regulation of cell state transitions.

The Synergistic Effect of Water Reducer and Water-Repellent Admixture on the Properties of Cement-Based Material

Water reducer and water-repellent admixture are very important in improving the workability and durability of cement-based materials. However, the synergistic effect of the two types of admixtures has not been well investigated. In this study, polycarboxylate ether-based superplasticizer (PCE) and octyltriethoxysilane (OTS) were adopted as water reducer and water-repellent admixture, respectively. Their synergistic effect on the fluidity, compressive strength, and water absorption rate of cement-based materials was investigated. Particularly, the pore structure and hydration state of cement paste were analyzed using 1H Low-Field Nuclear Magnetic Resonance (1H LF NMR). The result showed that the fluidity of cement paste containing different dosages of PCE was reduced by 5–10 mm by incorporating 1% OTS, and the compressive strength at the early age of 3 d of mortar containing high PCE dosage of 0.25% decreased up to 15% by using 1% OTS. In contrast, the compressive strength of mortar containing 0.20% PCE was slightly enhanced by the addition of 1% OTS. 1H LF NMR analysis revealed that the combination of PCE and OTS would increase the pore size and total pore volume of cement paste, and more bleeding water would be generated at high PCE dosage. The intensity-weighted T2 values of the main peak (T2¯) implied that both PCE and OTS produced a retardation effect on cement hydration. However, the water absorption rate decreased by 46.6% despite the increase in pore size and total pore volume. The conflict phenomenon powerfully revealed that the internal hydrophobic treatment by OTS has been successfully achieved. Overall, the combination of 0.20% PCE and 1% OTS exerted a positive synergistic effect in improving the compressive strength and water-repelling ability of cement-based materials, which is meaningful for improving their durability and service life.

StaVia: spatially and temporally aware cartography with higher-order random walks for cell atlases

Single-cell atlases pose daunting computational challenges pertaining to the integration of spatial and temporal information and the visualization of trajectories across large atlases. We introduce StaVia, a computational framework that synergizes multi-faceted single-cell data with higher-order random walks that leverage the memory of cells’ past states, fused with a cartographic Atlas View that offers intuitive graph visualization. This spatially aware cartography captures relationships between cell populations based on their spatial location as well as their gene expression and developmental stage. We demonstrate this using zebrafish gastrulation data, underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.