Browse Articles

Complementary metal-oxide-semiconductor (CMOS) time of evaporation measurement system for binary chemical monitoring

Abstract Accurate, real-time analysis of binary liquid mixtures is essential in chemical sensing, especially for miniaturized, low-cost applications. We present a complementary metal-oxide-semiconductor (CMOS)-based platform—ITEMS (Integrated Time-of-Evaporation Measurement System)—designed to monitor binary mixtures via high-resolution capacitive sensing of evaporation dynamics. ITEMS employs an integrated capacitive sensor to detect time-resolved dielectric changes during droplet evaporation under controlled temperatures. By extracting features such as intermediate evaporation time (Δt₂), total evaporation time (ToE), and capacitance variation (ΔCap), ITEMS provides multidimensional insights into solvent composition. We validated the system across ethanol–water, methanol–water, and methanol–ethanol mixtures, with concentrations from 0 to 100% and temperatures between 25 °C and 60 °C. Our analysis reveals that evaporation time and dielectric response exhibit nonlinear dependencies on solvent concentration, particularly at elevated temperatures. Comparative modeling using linear regression and LOESS confirms LOESS’s superiority in capturing these trends, yielding lower Root Mean Square Error (RMSE) values across all datasets. The CMOS integration enables compact packaging, low sample volume requirements (< 1 μL), and direct digital interfacing via a microcontroller and graphical user interface (GUI). These results establish ITEMS as a robust, scalable platform for high-sensitivity, real-time chemical composition monitoring in environmental, biomedical, and industrial applications.

Editorial Note: Can digital transformation promote green innovation in enterprises? The moderating effect of heterogeneous environmental regulations

RoboA reinforces planarian stem cell fate through FoxA and Anosmin1a

Altered abundance in cancer patients gut of diadenylate cyclase-encoding bacteria

How self-governance willingness and participation efficacy shape residents’ satisfaction with urban public services: Evidence from neighborhood renewal in Hangzhou, China

Using an original survey of 2,202 households across 36 renewed neighborhoods in Hangzhou, China, this study examines how resident agency—operationalized as self-governance willingness and participation efficacy—shapes satisfaction with urban public services within a legally structured participatory governance context. To address endogeneity, we estimate two-stage least squares models with district and street fixed effects, instrumenting self-governance willingness with distance to the provincial government offices and participation efficacy with policy awareness and community involvement; instrument diagnostics indicate strong and valid instruments. In the second stage, both constructs are positively associated with satisfaction. Mediation analyses show that participation efficacy is associated with lower perceived disparities between renewed and newly built neighborhoods, and perceived disparities, in turn, are negatively related to satisfaction, consistent with partial mediation in which effective participatory channels translate engagement into experienced fairness. By contrast, self-governance willingness is positively related to disparity sensitivity, yielding a suppression pattern absent adequate institutional responsiveness. Results are robust to additive-index specifications and the original five-point outcome, and with significantly larger effects among low-income, urban-hukou, small-unit groups. The findings underscore the value of institutionalized, responsive participatory arrangements that align with resident preferences, clarify responsibility allocation, and sustain feedback loops to enhance procedural fairness and, in turn, satisfaction within legally structured neighborhood renewal.

Two axes of white matter development

Hybrid deep learning model for air quality prediction and its impact on healthcare

Creative arts therapies for stroke patients: A systematic review and meta-analysis protocol

Introduction Stroke is a leading cause of long-term disability and mortality worldwide. Survivors can experience a range of physical and emotional challenges, often leading to depression, anxiety, and a poorer quality of life. Creative arts therapies (CATs), an umbrella term encompassing music, art, dance/movement, drama, and creative writing therapies, have increasingly been explored in stroke survivor populations as interventions to improve psychological outcomes. Qualitative analysis suggests these therapies can be helpful, but the exact efficacy of CATs in stroke rehabilitation, as well as the optimal intervention types and treatment protocols, has yet to be established. This systematic review and meta-analysis plans to evaluate the effect of CATs on depression, anxiety, and quality of life among adults recovering from stroke. Methods This protocol has been prospectively registered with PROSPERO (CRD420251237926). Eligible studies will include primary quantitative research involving creative arts interventions. Searches will be conducted in Medline, Embase, and PsycInfo from inception to December 2025. Two reviewers will independently screen records, extract data, and assess study quality and the certainty of the evidence using the RoB 2, ROBINS-I, and GRADE tools. Restricted maximum likelihood random-effects meta-analyses of Cohen’s d effect sizes and risk ratios will be performed to calculate pooled effect sizes for each outcome. Subgroup analyses will explore moderators such as the effect of study design, intervention type, session frequency, and patient setting. Dissemination of results Results will be disseminated through a peer-reviewed publication, conference presentations, and clinical networks to inform evidence-based guidelines on the use of CATs in multidisciplinary stroke care.

Spatial patterning of contractility by a self-organized mechanogen activity gradient underlies Drosophila gastrulation

Correction: Involvement of propionate, citrulline, homoserine, and succinate in oral microbiome metabolite-driven periodontal disease progression

Woa-wtconv-kanformer for long term time series forecasting

Multivariate time series analysis and prediction are of great significance in traffic management, weather forecasting and other practical applications. However, most of the existing research focuses on using the traditional transformer model as the framework to predict short series or predict with time domain features, and the effect of removing noise interference with irregular frequency is not good. At the same time, because the model based on the transformer framework needs to adjust too many hyperparameters, improper parameter design in practical use will lead to model performance degradation, so we propose the WOA-WTConv-KANformer model. The model is optimized based on the itransformer time series prediction model. Before embedding the time nodes of each series into the variable token and input into the encoder layer, the WTConv2d model is used to process the data with wavelet frequency to extract the frequency domain and time domain features. So that the model can solve the non-stationarity problem of time series data caused by the frequency domain problem. In order to realize the effective training of the model, we also use the whale optimization algorithm to make the model reasonably adjust the hyperparameters before formal training. At the same time, the KAN module is used as the linear layer instead of MLP during the training and use of the model, so that the model can improve the performance of different prediction lengths. The number of training parameters is also reduced. Through five public prediction datasets, it is shown that our model can achieve performance improvement on different prediction lengths, and the training efficiency is also improved, which proves the potential of the model in the field of real-world time series prediction.

Extreme rainfall over land exacerbated by marine heatwaves

Building new hydrography and virtual watersheds to conserve freshwater fisheries

Social inclusion among young adults with mental illness and complex needs in Flexible Assertive Community Treatment: A qualitative study on team staff’s understanding and experiences

Background Social inclusion is increasingly highlighted through policies, practices, research, and broader societal awareness. Young adults with mental illness and complex needs are among the most marginalized in society and face significant challenges. Public health and welfare services play a critical role in either facilitating or hindering social inclusion through adequate or inadequate service provision. The services are often critiqued for treating individuals in isolation from their social contexts. Methods In this qualitative study semi-structural individual interviews and thematic analysis were used to explore: How do service providers in FACT teams understand and promote social inclusion among young adults with mental illness and complex needs?. Results The results showed that staff employed efforts to promote social inclusion and acknowledged this as important. However, they had limited conceptual understanding and revealed a lack of systematic promotion in the teams. Several barriers for social inclusion were evident in the material, from individual and structural to societal. Conclusions There is a need to develop and operationalize the concept to enable systematic and holistic approaches to promote social inclusion, moving beyond employing single interventions. Policies must be developed, and training and education provided to create context sensitive mental health services.

Nonequilibrium pulsed heating freezes sintering of supported metal nanocatalysts

Abstract Supported metal nanoparticles always experience thermal-induced sintering that severely depresses their catalytic performance and durability. Recently, ultrafast heating technology has been widely adopted in synthesizing diverse metal nanoparticles or even single atoms on supporting materials, offering enhanced anti-sintering capabilities compared to conventional heating protocols. However, the mechanisms and kinetics underlying this anti-sintering behavior during ultrafast heating processes remain poorly understood. Here, using in situ scanning transmission electron microscopy and theoretical analysis, we microscopically reveal a metastable state for supported metal nanoparticles induced by ultrafast heating pulses, which significantly mitigates the thermal-induced sintering while effectively improves the crystallization degree of metal nanoparticles together with the metal/support interfaces. Our results show that Pt nanoparticles supported on graphene flakes are thermodynamically unstable yet kinetically stable across the ultrafast heating pulses; as a consequence, the Pt/graphene interface is gradually optimized in a lattice scale, leading to remarkable sintering resistance for Pt nanoparticles. These atomic-scale insights provide thorough fundamental understandings for ultrafast heating in stabilizing metal nanoparticles and may further guide the high-throughput production of robust supported metal nanocatalysts.

Structural characterization of metal binding in human tyrosylprotein sulfotransferase 2, TPST2

Contaminants fingerprinting in environmental matrices of Radon concentration in groundwater: A baseline study in Alappuzha (Kerala) and the associated health effects

Accumulation of Radon in groundwater from aquifer lithologies can pose significant health risks. This study investigates its concentration in coastal Alappuzha from the Kerala state in India to assess health risks from ingestion and inhalation in groundwater samples of both the pre-monsoon and post-monsoon seasons. The activity(RAD7detector)in post-monsoon samples remained relatively higher (2.59–66.46 Bq/L; average:20.25 Bq/L) compared to the pre-monsoon (1.63–46.38 Bq/L; average: 13.78 Bq/L), reflecting the effect of precipitation on enhanced Radon contamination. However, the samples presently do not exceed the World Health Organization recommended maximum value (100 Bq/L). Relatively higher average radiation doses for stomach and lungs (6.12–6.20 Svy -1 ) in post-monsoon samples compared to pre-monsoon (4.12–4.22 Svy -1 ) show more exposures in the post-monsoon season. Our estimations of the total effective dose from two different pathways provide valuable baseline data on radon exposure in a coastal region of India, where health risks could increase due to higher precipitation and more frequent heavy rainfall events in the near future. Such conditions may enhance groundwater recharge and promote the downward migration of radon-rich soil gases into aquifers, potentially elevating radon concentrations, particularly in uranium-bearing lithological settings.

Inferring the internal structure of groups through the integration of statistical learning and causal reasoning

Abstract Human social life unfolds within richly structured networks of overlapping relationships, including friendships, hierarchies, and collaborations. Yet the observable interactions that reveal these networks are often sparse and noisy, making it unclear how people could infer the latent structure of their social environments from such limited evidence. We propose that humans integrate domain-general statistical learning with domain-specific models of social structures to rapidly construct causal representations that support explanation, prediction, and planning. Across three behavioral experiments, we show that participants can infer underlying social structures (Experiment 1), predict social behavior (Experiment 2), and reason about the spread of social influence (Experiment 3), based on brief, abstract videos of social interactions. These judgments were closely captured by a computational model grounded in our account and could not be explained by simpler cue-based accounts. Statistical learning and causal reasoning operate in concert to support rapid, flexible understanding of social structures.

Evaluating the efficiency of formaldehyde degradation from aqueous environment by sonozone technique

Assessing protected areas as climate refugia for threatened plant species in Britain

Climate change is causing the loss or movement of suitable habitats, forcing species to undergo range shifts. However, many may be unable to move to suitable locations, resulting in increased extinctions. Climate refugia, areas maintaining suitable conditions for species now and in the future, offer protection from climate change. These refugia are categorised as in situ, where species are currently present, and ex situ, where they are not. We aimed to identify climate refugia for rare vascular plant species in Britain and examined whether refugia with the highest number of suitable species share any common features. Using predicted 2080 distributions, we identified protected areas in Britain that act as in situ or ex situ refugia for 12 of Plantlife’s focal plant species. This study revealed that many current habitats will become unsuitable by 2080, highlighting the urgent need to protect refugia. The limited availability of in situ refugia underscores the importance of ex situ refugia as potential habitats. Our findings indicated that protected areas with higher elevations, larger elevational ranges, higher latitude and longitude and larger area will provide climate refugia for a greater number of species. This information can be used to guide the selection and management of protected areas and identify receptor sites for species introductions.