Nitrogen-enriched porous organic polymers for high-performance CO2/N2 separation in mixed-matrix membranes

The development of high-performance mixed matrix membranes (MMMs) for CO2/N2 separation requires fillers that simultaneously enhance gas permeability and selectivity without introducing interfacial defects. In this study, we report the synthesis of nitrogen-containing porous organic polymers (POPs) via a one-step Friedel-Crafts polymerization, designed to introduce CO2-philic functional groups directly into the polymer backbone while preserving intrinsic porosity. Structural and gas sorption analyses confirmed that the synthesized POPs exhibit high microporosity, surface area, and nitrogen content, which together enhance CO2 adsorption affinity. When incorporated into Matrimid-based MMMs, the POPs significantly improved CO2 permeability and CO2/N2 selectivity, with the pp-tpta filler (containing the highest nitrogen content) delivering the most pronounced performance enhancement. Further optimization using a high-permeability 6FDA-DAM polyimide matrix yielded a membrane with a CO2 permeability of 1967 Barrer and a selectivity of 33.4 at 20 wt% pp-tpta loading, surpassing the 2008 Robeson Upper Bound. Solubility-diffusivity analyses, supported by both experimental measurements and molecular dynamics simulations, revealed that the pp-tpta filler enhances CO2/N2 separation primarily by increasing CO2 solubility and diffusivity within the membrane matrix. These results underscore the potential of rationally designed, nitrogen-rich POP fillers as effective, scalable materials for advanced gas separation membranes.

Exploring the relationship between air quality and happiness in South Korea using artificial neural networks

This study investigates the relationship between air quality and subjective happiness across South Korean districts using artificial neural network (ANN)-based modeling. By aggregating the Korean National Assembly Futures Institute's happiness survey (2020-2021) data with the Korean Ministry of Environment's air quality data, among others, six major air pollutants were examined for their potential associations with the happiness ladder at the minuscule city level throughout South Korea. Complex non-linear patterns were observed. Among the pollutants, PM2.5 exhibited the most consistent negative association with the happiness ladder. The robust modeling and training strategies provide insights into the intricate relationships between air quality factors and the individual happiness ladder. The analysis effectively captures subtle relationships under fixed socioeconomic and happiness-related conditions, highlighting varying confidence intervals across multiple scenarios. These findings underscore the potential of ANN-based modeling in assessing the environmental factors of subjective happiness. Despite limitations related to the spatiotemporal scale of the annual happiness survey, this study contributes to the methods by applying deep learning techniques to infer the relationship between air quality and happiness, providing evidence that may inform environmental policymaking and urban sustainability strategies.

Surfactant-stabilized cyclopentane hydrate emulsions for removing tetramethylammonium hydroxide from semiconductor wastewater

This study presents an innovative hydrate-based wastewater treatment approach specifically designed for semiconductor wastewater containing tetramethylammonium hydroxide (TMAH), utilizing cyclopentane (CP)in-water emulsions stabilized by nonionic surfactants Span 80 and Vitamin E-TS. The stabilized emulsions significantly accelerated hydrate formation kinetics, reducing the induction time from 313.33 min to approximately 12.67 min (a 96 % decrease), thus greatly enhancing the process's energy efficiency. Structural analyses using powder X-ray diffraction (PXRD) and Raman spectroscopy confirmed that TMAH molecules at concentrations of 515 mg/L were effectively excluded from the hydrate structure, demonstrating no interference with hydrate crystalline structures or cage occupancy. Furthermore, incorporating Vitamin E-TS as an antiagglomerant promoted the formation of porous hydrate structures, increasing TMAH removal efficiency substantially from 45.51 % to 62.96 %, though reducing water recovery from 69.74 % to 61.64 %. Subsequent post-washing procedures further increased TMAH removal efficiency to 74.88 % but decreased water recovery to 49.24 % due to partial hydrate melting. These findings underscore the importance of balancing water recovery and contaminant removal efficiency, providing essential insights for optimizing hydrate-based wastewater treatment processes, and highlighting its potential as an energy-efficient and sustainable solution for semiconductor wastewater and other industrial wastewater containing persistent organic contaminants.

Spatiotemporal-dependent reliability analysis with adaptive sampling physics-informed neural networks

Balancing accuracy and computational efficiency remains a challenge for simulation-based time-dependent reliability analysis (TRA) under uncertainty. Traditional TRA methods often fall short in focusing solely on fixed hotspot(s) of complex engineering systems, neglecting the dynamic nature of potential failure regions, such as moving hotspots. To address these limitations, this paper proposes a spatiotemporal-dependent reliability analysis (STDRA) framework for engineering systems governed by partial differential equations (PDEs). Key contributions include: (1) resolving incompleteness by employing a physics-informed neural network (PINN) to calculate global performance across all spatiotemporal "spots" in the investigated PDE system; (2) enabling STDRA by deriving results through Monte Carlo simulations of the PINN-based framework, without the need for design of experiment (DoE) samples; and (3) enhancing accuracy and efficiency through a novel adaptive spatiotemporal sampling (ASTS) strategy, which optimally trains the PINN by focusing on critical spatial and temporal domains. The proposed ASTS-PINN-based STDRA framework is validated using a 2D isotropic elastic plate and a complex laser cladding process, showcasing its superiority over existing state-of-the-art TRA methods.

Predicting renewable energy stock volatility: A GARCH-CNN approach with indicator analysis

The renewable energy (RE) stock market is experiencing rapid growth, driven by environmentally conscious investors seeking to support a greener future while pursuing profitable opportunities. This study aims to forecast RE stock volatility, which is critical for managing risks in the RE stock market. We employ a Convolutional Neural Network (CNN) model combined with Generalized Autoregressive Conditional Heteroscedasticity (GARCH) forecasts to predict RE stock volatility. Three groups of indicators-internal stock, financial market, and policy uncertainty-are incorporated as additional inputs. The results demonstrate that integrating internal stock and financial market indicators significantly reduces prediction errors compared to the traditional GARCH model. Conversely, models incorporating the policy uncertainty indicator produce higher errors, suggesting that these indicators may introduce noise. SHapely Additive exPlanations (SHAP) analysis identifies the internal stock indicator, particularly the squared log returns of RE stocks, as a dominant factor, with the financial market indicator serving as a complementary factor. By integrating deep learning with econometric models, this study enhances the prediction of RE stock volatility and underscores the importance of selecting appropriate indicators. The findings provide valuable insights for investors and policymakers seeking to better understand and manage RE investment risks, highlighting the key drivers of RE stock volatility.

On the real zeros of depth 1 quasimodular forms

We discuss the critical points of modular forms, or more generally the zeros of quasimodular forms of depth 1 for PSL2(Z). In particular, we consider the derivatives of the unique weight k modular forms fk with the maximal number of consecutive zero Fourier coefficients following the constant 1. Our main results state that (1) every zero of a depth 1 quasimodular form near the derivative of the Eisenstein series in the standard fundamental domain lies on the geodesic segment {z is an element of H : R(z) = 1/2}, and (2) more than quarter of zeros of fk in the standard fundamental domain lie on the geodesic segment {z is an element of H:R(z) = 1/2} for large enough k with k equivalent to 0 (mod 12). (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Prediction of Amyloid Positivity in Lewy Body Disease Using Early-Phase 18F-FP-CIT PET Images

Purpose: To explore whether alterations in regional cerebral perfusion observed on early-phase F-18-FP-CIT PET imaging could predict beta-amyloid positivity in patients with Lewy body disease (LBD). Methods: We enrolled 132 patients with LBD (78 dementia with Lewy bodies and 54 Parkinson disease) who underwent dual-phase F-18-FP-CIT PET and 18F-FBB PET scans at initial assessment. Patients were divided into the amyloid-positive (n=69) and amyloid-negative (n=63) groups. We compared regional uptake on early-phase F-18-FP-CIT PET images between the 2 groups, whereas a linear discriminant analysis (LDA) was performed to predict beta-amyloid positivity based on the standard uptake value ratios (SUVRs) of each region of interest. Mediation analyses were performed to evaluate whether regional cerebral perfusion mediated the association between beta-amyloid load and longitudinal changes in the Mini-Mental State Examination (MMSE) scores. Results: There were no significant differences in age, sex, educational attainment, MMSE scores, motor deficits, or striatal dopamine depletion between the amyloid-positive and amyloid-negative groups. The amyloid-positive group exhibited decreased uptake in the parietal, precuneus, middle/inferior temporal, and isthmus cingulate cortices, as well as increased uptake in the caudate, compared with the amyloid-negative group on early-phase F-18-FP-CIT PET images. LDA prediction model demonstrated that SUVRs of the inferior parietal cortex and caudate optimally distinguished the 2 groups. Greater beta-amyloid burden was associated with a more rapid decline in MMSE scores, which was partially mediated by inferior parietal hypoperfusion. Conclusions: Alterations in regional cerebral perfusion on early-phase F-18-FP-CIT PET imaging may serve as a useful biomarker for predicting beta-amyloid deposition in LBD.

Structural distortion-driven design of cobalt-free high-entropy perovskite electrodes for high-performance solid oxide cells

High-entropy oxides (HEOs) offer a promising platform for advanced air electrodes in solid oxide electrochemical cells (SOCs), yet the fundamental mechanisms underpinning their enhanced catalytic performance remain elusive. Here, we systematically engineer cobalt-free HEOs of the form (Pr0.2Bi0.2Sr0.2La0.2X0.2)MnO3-delta (X = Ba, Ca, Nd, Gd) by modulating the Goldschmidt tolerance factor to control structural distortion. Fourier electron density analysis reveals distinct octahedral tilting and lattice asymmetry across the series. We uncover a strong correlation between lattice asymmetry, oxygen-ion diffusion characteristics, defect formation, and electrochemical kinetics. Among the compositions, the Nd-substituted variant (PBSLNM) achieves an optimal distortion profile and exhibits outstanding performance, delivering a peak power density of 1.59 W/cm2 in fuel cell mode and a current density of 0.73 A/cm2 at 1.3 V in electrolysis mode at 700 degrees C, with excellent durability over 500 h. Density functional theory calculations reveal that structural distortion lowers the oxygen vacancy formation energy, elevates the O 2p band center, and induces heterogeneous electronic distributions that promote both oxygen reduction and evolution reactions. Our findings establish structural distortion as a critical descriptor for HEO performance and provide a rational design strategy for high-performance SOC air electrodes.

Causal unsupervised semantic segmentation

Unsupervised semantic segmentation aims to achieve high-quality semantic grouping without human annotations. With the advent of self-supervised learning, various frameworks utilize pre-trained features for the unsupervised prediction. However, a significant challenge in this unsupervised setup is determining the appropriate level of granularity required for segmenting concepts. To address it, we propose a novel framework, CAusal Unsupervised Semantic sEgmentation (CAUSE), which leverages insights from causal inference. Specifically, we bridge an intervention-oriented approach to define two-step unsupervised prediction: (i) constructing a discretized concept clusterbook as a mediator, representing concept prototypes, (ii) concept-wise self-supervised learning for pixel-level grouping using an explicit link from the mediator. Through extensive experiments, we corroborate the effectiveness of CAUSE and achieve state-of-the-art in unsupervised semantic segmentation.

Near-field thermophotovoltaic heat exchanger for harvesting extremely high-density thermal energy

Thermophotovoltaic systems are solid-state heat engines that convert heat from various sources, such as solar radiation and combustion gases, into electricity. Through spectral control of thermal radiation, efficiencies up to 44% have been demonstrated from a single-junction thermophotovoltaic cell. Thermophotovoltaic systems benefit from harnessing near-field radiation through photon tunneling across nanogaps, enabling compact utilization of high-density thermal energy. To further enhance output from a fixed footprint, higher thermal energy density is required, necessitating extension of the power generation area beyond the heat input area. However, existing system geometries for such extension enlarge the footprint and intensify electrical resistive loss, particularly in near-field thermophotovoltaic systems. Here, a near-field thermophotovoltaic heat exchanger is proposed, which achieves efficient area extension by compactly stacking thermophotovoltaic units, enabling a power generation area tens of times larger than the footprint while minimizing electrical resistive loss. Through system optimization, the near-field thermophotovoltaic heat exchanger generates 228.5 W of electrical power from a 1 cm2 footprint with a 1600 K heat source, i.e., an order of magnitude higher output than conventional near-field thermophotovoltaic systems, with an efficiency of 36.8%. A far-field thermophotovoltaic heat exchanger designed without nanogaps also achieves threefold higher power output. The near-field thermophotovoltaic heat exchanger achieves a mass-specific power of up to 140.3 kW/kg, significantly surpassing competing systems, while being scalable by increasing the number of thermophotovoltaic units. This strategy offers a compact and scalable pathway to advance thermophotovoltaic technology and can be extended to solid-state energy converters such as thermophotonic, thermoradiative, and thermoelectric engines.