Browse Articles

Hybrid interferometric near infrared spectroscopy (hybrid iNIRS) enables time-of-flight–resolved non-invasive blood flow monitoring in humans in vivo

Abstract Interferometric near-infrared spectroscopy (iNIRS) uniquely offers time-of-flight (TOF) resolution for depth-resolved optical property and blood-flow assessment in tissue, but single-mode collection constrains photon throughput and single-channel implementations impose stringent analogue bandwidth and digitization rates to resolve both TOF and speckle dynamics. Conversely, continuous-wave parallel interferometric NIRS (CW-πNIRS) boosts photon detection via spatial multiplexing on camera sensors, yet sacrifices TOF information and remains limited by camera readout. Here we reconcile these trade-offs with a hybrid swept-source, hybrid iNIRS platform that combines TOF encoding with multi-speckle, heterodyne detection. Liquid-phantom experiments map the trade-space among sweep speed and speckle decorrelation, indicating that sweep rates ≥ 10 kHz are sufficient to outpace decorrelation while preserving TOF contrast under controlled conditions. In contrast, in vivo measurements require higher sweep rates (typically 50–100 kHz) to accommodate faster physiological dynamics. In vivo forearm and forehead measurements demonstrate depth-resolved blood flow measurements during cuff occlusion. By simultaneously overcoming photon-starvation and electronic-bandwidth ceilings, this approach establishes a new operating regime for diffuse optical monitoring and provides a scalable hardware foundation for haemodynamic sensing in vivo.

An explainable artificial intelligence framework for clinical decision support in stroke discharge planning

Background Stroke, a leading cause of global mortality and disability, requires accurate prediction of discharge outcomes to support early care planning. We developed an explainable artificial intelligence (AI) framework to predict four discharge categories (home, specialized care, home with help, expired) and identify key predictors. Methods This single-center retrospective study included 1,731 patients with ischemic stroke, hemorrhagic stroke, or transient ischemic attack (TIA). Twenty routinely available electronic health record variables were used. Ten classifiers were compared using stratified 5-fold cross-validation, and the final model was calibrated with training-set out-of-fold predictions and interpreted using SHapley Additive exPlanations (SHAP). Results The multilayer perceptron (MLP) achieved the highest mean cross-validated macro-F1 score and was selected as the best-performing model. On the independent hold-out test set, the MLP achieved an accuracy of 0.646, macro-specificity of 0.873, macro-precision of 0.559, macro-sensitivity of 0.557, and macro-F1 of 0.548. Class-wise area under the receiver operating characteristic curve (AUC) values were 0.901 for home, 0.874 for specialized care, 0.674 for home with help, and 0.889 for expired. SHAP analysis identified admission National Institutes of Health Stroke Scale (NIHSS), length of stay, age, and primary diagnosis as shared predictors across all discharge categories. The SHAP age-threshold analysis identified 72.0 years as a clinically relevant threshold associated with a lower likelihood of home discharge and higher likelihoods of specialized care, home with help, and expired discharge status. The model also highlighted clinically actionable or addressable domains, including blood glucose, depression, insurance type, last known well time, anticoagulant use, and treatment-related variables. Conclusion This interpretable AI-based framework identified clinically relevant predictors of stroke discharge disposition within this single-center retrospective dataset. These findings may inform future decision-support development; however, clinical implementation, resource optimization, and health-system impact require prospective multicenter validation.

Structure distortion-driven optical properties and temperature thermometry in one-dimensional hybrid lead halides

From environmental concerns to nationalist waves: Mapping the evolving Fukushima wastewater discourse on Weibo

This study employed Structural Topic Modeling (STM) on 56,526 Weibo posts to examine the evolving discourses surrounding the Fukushima wastewater release. While rational discourse focusing on technical and environmental concerns initially coexisted and competed with nationalist narratives, the discourse rapidly transformed into predominantly nationalist rhetoric driven by grassroots users. Nationalism discourse was framed through two antagonisms: 1) direct, volatile anti-Japanese sentiment rooted in historical grievances and 2) broader, consistent anti-Western skepticism anchored in geopolitical rivalries. Moving beyond the binary paradigm of Chinese cyber-nationalism as either purely top-down mobilization or bottom-up explosion, this study reveals a nuanced interplay between actor types: grassroots users predominantly led nationalistic discourses, especially anti-Japanese discourse, while Key Opinion Leaders (KOLs) focused on technical discussions but occasionally amplified anti-Western sentiment. By identifying a latent nationalist sensitivity and a victimhood-centric view of international affairs, the study demonstrates how affective public sentiment can transform environmental issues into perceived insults to national dignity, intensified by platform-mediated grassroots agency.

Identifying nonlinear dynamical systems using subset regression

Abstract The data-driven discovery of governing equations for dynamical systems has emerged as a transformative paradigm, enabling the extraction of interpretable and generalizable models from observational data. While modern techniques have advanced this field, traditional subset regression remains a foundational yet underutilized tool due to its reliance on uncorrelated residuals, a requirement often violated by time-series data. In this work, we revisit subset regression to identify dynamical systems governed by ordinary differential equations (ODEs), partial differential equations (PDEs), and differential algebraic equations (DAEs). We propose subset regression with known number of active features (sub-KNAFE), a user-determined sparsity mechanism that flexibly adapts to various complex nonlinear systems, while retaining the computational efficiency and inherent interpretability of traditional subset regression. We integrate sub-KNAFE with the SINDy framework, overcoming the limitation of subset regression in dynamical system identification. Numerical tests across a range of signal-to-noise ratios and dataset sizes demonstrate sub-KNAFE’s superior noise robustness and data efficiency. Practical utility for sub-KNAFE is validated on two real-world datasets: the classic Lynx-Hare ecological population data and the ISO New England power system dataset, demonstrating its strong potential for practical deployment in scientific discovery and engineering applications.

Mechanistic Atomic Hydrogen Chemistry for Ruthenium Deposition: From Ligand Elimination to Area-Selective Patterning

Construction and empirical analysis of a quantitative model on the relationship between budget control and financial performance in management accounting—Evidence from Russian enterprises

Currently, many enterprises face issues in budget management, such as superficial budgeting, lax execution, and insufficient feedback, leaving the mechanism linking budgetary control and financial performance unclear. Drawing on management accounting and financial management theories, this study constructs a research model encompassing direct, mediating, and moderating effects and conducts a quantitative empirical analysis using Russian enterprises as the research sample. Empirical analyses are conducted using both publicly available data from the Russian Financial Statements Database (RFSD) ( https://github.com/irlcode/RFSD ) and the collected survey data. In terms of variable measurement, budget control is evaluated across three dimensions—budget formulation, execution, and feedback—with mean scores ranging from 3.7 to 3.9, indicating that most enterprises place considerable emphasis on the establishment of budgeting systems. The mean financial performance score ranges from 3.5 to 3.6, suggesting a moderately favorable performance level. Empirical results reveal a significant positive correlation between budget control and financial performance (r > 0.5). In the regression model, the coefficient of budget control is significantly positive, and the explanatory power of the model increases from 0.183 to 0.361 after incorporating budget control, confirming its direct contribution to performance improvement. Further mediation analysis indicates that resource allocation efficiency and internal management processes serve as significant intermediaries between budget control and financial performance, with indirect effects accounting for 29.4% of the total effect. This suggests that budget control primarily enhances performance indirectly by improving internal management mechanisms. The moderation analysis shows that, within the Russian institutional environment, firm size and governance structure strengthen the positive impact of budget control, whereas external environmental uncertainty weakens it. These findings provide empirical evidence for understanding the performance effects of budget control under specific institutional and economic contexts and offer practical guidance for optimizing budget management in Russian enterprises and other transitional economies.

Enhanced deep learning model for anomaly object detection and tracking from surveillance videos

Multicenter evaluation of BACT-Info. and an infection algorithm using Urine Flow Cytometry among clinically diagnosed UTI patients in Indonesia

Urinary tract infections (UTIs) are the most common infections in both outpatient and inpatient settings, contributing significantly to morbidity, reduced quality of life, and antimicrobial overuse. Although urine culture remains the diagnostic gold standard, it poses practical limitations in clinical workflows. Rapid diagnostic methods such as urine flow cytometry (UF) offer potential for timely, reliable UTI detection. We conducted a multicenter diagnostic study to evaluate the performance of BACT-Info. and UTI-Info. flags on the UF-5000/4000 system in detecting UTIs, using presumptive Gram staining and urine culture (≥10⁵ CFU/mL) as references. A total of 763 patients with suspected UTI were enrolled, and 721 patients—with uropathogenic bacteria and complete data—were included in the final analysis (384 with culture-confirmed UTI and 337 without UTI). The diagnostic value of nitrituria was highly specific, while leukocyte esterase was sensitive. For BACT-count and WBC-count of UF-5000/4000, the AUCs were 0.85 and 0.69, respectively. Using cutoffs of WBC > 82.05/µL or bacteria >975.4/µL, the UTI-Info flag demonstrated 89% sensitivity, 54% specificity, 69% positive predictive value, and 82% negative predictive value. Positive and negative likelihood ratios were 1.93 and 0.20, respectively. The agreement between the BACT-Info. flag and Gram typing showed Kappa values of 0.716 for Gram-negative and 0.216 for Gram-positive bacteria when compared to culture, and 0.721 and 0.401, respectively, when compared to presumptive Gram staining. The UF-5000/4000 UTI-Info. and BACT-Info. flags show promise as a rapid UTI screening tool. The combination of these flags with urinalysis parameters, nitrite testing and leukocyte has potential to be developed into a diagnostic algorithm for early and sensitive UTI prediction. Such an approach may reduce unnecessary urine cultures and support timely, appropriate empiric antibiotic therapy. Establishing optimal cutoffs tailored to specific clinical settings is essential to enhance diagnostic accuracy and improve clinical utility.

Data-efficient machine learning approach for predicting asthma attack risk

Mimicking Extradiol Dioxygenase Reactivity on Iridium

Machine learning-enhanced 3GPP channel modeling for 5G networks: A vendor-calibrated framework with cross-scenario validation

Accurate channel characterization across diverse propagation environments is foundational to 5G network planning, yet existing machine learning approaches rarely integrate standardized 3GPP frameworks with vendor-specific equipment parameters. This study presents a regression-based framework combining 3GPP TR 38.901 channel models with five supervised learning algorithms—linear regression, polynomial regression (degree 2), support vector regression (SVR), decision tree, and artificial neural network (ANN)—trained on 10,000 deterministic samples spanning Urban Macro (UMa), Urban Micro (UMi), Rural Macro (RMa), and Indoor Hotspot (InH) scenarios at five carrier frequencies (0.7–60 GHz). Vendor-calibrated parameterization using authenticated Nokia AirScale 64T64R, Huawei AAU5940, and ZTE AAU 5G specifications grounds the simulated link budgets in commercial equipment characteristics, providing deployment-aligned (though formula-derived rather than field-measured) performance estimates (see Limitations). All five regression architectures are evaluated identically across all five carrier frequencies and all scenario types, enabling direct comparison under controlled conditions. For throughput prediction, the ANN and decision tree achieve the highest accuracy ( R 2  = 0.998, RMSE ≤ 24 Mbps averaged across five independent random splits; 95% CI: R 2 ∈ [ 0.997 , 0.999 ] , RMSE ∈ [ 19.1 , 22.4 ]  Mbps), while linear and polynomial regressors show substantial error ( R 2 ≤ 0.56 ), reflecting the strongly nonlinear throughput surface. For path loss estimation under Urban Micro NLOS conditions, all models attain near-perfect fit ( R 2 ≈ 1.0 , MSE < 0.02 dB 2 ), confirming that simple regressors suffice for log-distance targets. Vendor link budgets quantify the Nokia–Huawei throughput gap (1.88× at 100 m) and the ZTE 28 GHz peak capacity (1688.6 Mbps at 100 m), establishing a breakeven inter-site distance of approximately 150 m below which FR2 outperforms FR1. Cross-scenario generalization experiments reveal a critical failure mode: models trained on LOS-urban data yield strongly negative R 2 on Rural Macro scenarios ( < − 3 ), while mixed-scenario training recovers generalization to R 2  > 0.75 across all environments. Permutation-based feature importance identifies distance as the dominant predictor (importance 0.65–0.85), with frequency importance rising to ≈0.40 at millimeter-wave bands. Sensitivity analysis confirms robustness ( R 2  > 0.90) under realistic parameter perturbations (±10% distance, ±5% frequency, ±2 dB EIRP). These results provide evidence-based guidelines for model selection, training data composition, and deployment in 5G/6G network planning.

Adaptive cognitive driven cross modal network for few shot fine grained recognition

Abstract Current few-shot learning (FSL) methods struggle with fine-grained texture loss, inefficient cross-modal knowledge integration, and catastrophic forgetting. To resolve these bottlenecks, we propose the Adaptive cognitive driven cross modal network (ACD-Net) for few-shot fine-grained recognition. Inspired by human cognition, ACD-Net introduces three systematic innovations. First, the Adaptive Dual-Domain Cognitive Attention (ADCA) module employs Two-Dimensional Discrete Wavelet Transform and Gated Recurrent Units to decouple high-frequency textures from noise and dynamically localize discriminative regions. Second, the Graph-Guided Semantic-to-Visual Distillation (GSD) strategy utilizes Graph Convolutional Networks and a bilinear attention mechanism to seamlessly embed structured semantic priors into the visual space, generating exceptionally robust category prototypes. Finally, the Dynamic Balanced Anti-forgetting (DBAF) loss function mitigates catastrophic forgetting during fine-tuning by adaptively adjusting regularization weights based on gradient orthogonality between old and novel tasks. Extensive evaluations on the miniImageNet, CUB-200-2011, and Medical-44 datasets demonstrate that ACD-Net achieves state-of-the-art results, elevating average accuracy by 1.75% and 1.36% in 1-shot and 5-shot scenarios, respectively. Ultimately, ACD-Net establishes an innovative paradigm for FSL, offering pragmatic solutions for complex real-world deployments including industrial defect inspection and intelligent clinical diagnosis.

Exploring cost trajectories of patients admitted to short-term residential care in the Netherlands

Objective Short-term residential care (STRC) is a Dutch form of post-acute care intended to return older adults home to live independently, yet fewer than 55% of patients are discharged home. Because post-acute care costs are unevenly distributed, average cost trajectories may obscure clinically meaningful variation. This study examines variation in STRC cost trajectories and identifies patient characteristics associated with high-cost group membership. Methods We conducted a retrospective longitudinal observational study using national health claims data from Statistics Netherlands for patients admitted to STRC between 1 February and 31 July 2022. Reimbursed costs across seven categories (STRC, inpatient and outpatient hospital care, district care, long-term care at home, nursing home admission, and geriatric rehabilitation) were measured from one month before to five months after admission. We defined a palliative care group a priori and applied group-based trajectory modelling to the remaining cohort. Two logistic regressions assessed patient-level predictors of high-cost membership. Results Among 16,278 patients, mean six-month costs were €29,859 (SD = €21,088). We identified an a priori palliative care group (n = 3,277; €23,200), a latent high-cost (n = 3,205; €58,478) and a latent low-cost (n = 9,796; €22,723) group. The high-cost group accounted for 39% of total costs, with the largest shares attributable to hospital care, nursing home admission, and longer STRC stays. These patients were more often readmitted to hospital within two weeks of discharge (16.9% versus 3.2%) and discharged to a nursing home (29.8% versus 10.7%). Dementia, institutional living, and several diagnosis groups (including stroke, oncology, organ failure, and cardiovascular disease) were associated with high-cost membership, but overall explanatory power was low (McFadden pseudo R² ≤ 0.05). Conclusions STRC cost trajectories were highly skewed and poorly predicted by routinely available patient characteristics, suggesting cost variation reflects differences in care delivery more than patient case-mix. These findings point to three priorities: strengthening transitions from STRC back to home, critically evaluating STRC placement for patients likely to require nursing home admission, and scrutinizing hospital use during STRC episodes. Cost trajectories offer a promising outcome measure for evaluating intermediate and integrated care.

Design and mechanical performance of gradient lattice structures based on triply periodic minimal surface

Total Synthesis of Conjugation-Ready Sulfated Red Algae Carrageenan Oligosaccharides for Sensing Applications

Likweli: A remarkable new species of Colobus monkey from the Lomami National Park, Democratic Republic of Congo

We describe and name a new species of African monkey, Colobus congoensis sp. nov. (Primates, Cercopithecidae), from the interfluve region of the Lomami and Congo (Lualaba) Rivers in east-central Democratic Republic of Congo (DRC). Colobus congoensis is a rare and cryptic monkey, poorly known even by local communities bordering its range, some of whom use the vernacular name Likweli for the species. Between 2018 and 2022, 114 field observations were made over an estimated range of 1,700 km 2 . Colobus congoensis is largely restricted to high, closed canopy forest on deep clay pediments and islands of terra firme forest, where it co-occurs with two other colobine species ( Piliocolobus parmentieri and Colobus angolensis ). Colobus congoensis was most frequently observed in small groups (mean = 6.2 individuals), often in mixed-species associations. Mitochondrial and morphological data confirm the attribution of C. congoensis to the genus Colobus and reveal that it is the sister to Colobus satanas , from which it is geographically separated by more than 1,200 km. Comparative analysis of C. congoensis vocalizations also reveals structural similarities with C. satanas to the exclusion of other Colobus species. Among other features, C. congoensis is distinguished from C. satanas and other Colobus species by its small size, a striking orange cream patch surrounding the mouth, philtrum, and portions of the inferior nasal alae on an otherwise black face, and a white perianal patch that is covered with fine white hairs in males and is glabrous in females. We propose a preliminary IUCN Red List classification of Endangered (EN) for C. congoensis based on its small range area and population size, coupled with the projected impact of increased hunting pressure and habitat conversion. Protection of Lomami National Park, within which most of the C. congoensis range occurs, and engagement of local communities in not hunting the species are the most important actions needed to ensure the conservation of C. congoensis .

A two-step tilt compensation method for off-axis holographic displays

Abstract Accurate diffraction between non-parallel planes is essential for holographic displays employing tilted spatial light modulators (SLMs). However, applying the standard angular spectrum method (ASM) directly to a tilted plane leads to geometric distortion and spectral aliasing. We present a two-step propagation method and its validation by experiments. The proposed method combines the standard ASM with a spatial-domain rotational transformation. The target image is first back-propagated to a parallel intermediate plane, then geometrically remapped onto the tilted SLM with phase correction. Implemented on an off-axis holographic display with $$30^\circ$$ and $$45^\circ$$ tilted SLM which leads to a large steering angle between the incident and reflected beam, the method is evaluated with multi-depth and multi-angle reconstructions. Results show improved image fidelity, stable depth performance, and robust compensation of off-axis holographic displays.

Expression of Concern: Alcohol Dehydrogenase Protects against Endoplasmic Reticulum Stress-Induced Myocardial Contractile Dysfunction via Attenuation of Oxidative Stress and Autophagy: Role of PTEN-Akt-mTOR Signaling

Efficient removal of methylene blue using Azolla/biochar composite: adsorption behavior and post-use valorization for methanol oxidation

Abstract The use of green and sustainable chemistry offers an efficient approach for synthesized innovative materials to address wastewater treatment challenges with declined energy consumption. This study addressed the significant global issues of water pollution from industrialization and population growth by designing cost effective, high-capacity materials utilizing for the removal of Methylene blue dye (MB). A novel bio-composite material (AZ/BC) was synthesized by amalgamating Azolla pinnata extract and biochar by ultrasonication technique. The resultant biomaterials were evaluated using techniques such as XRD, FTIR, and SEM to examine its surface appearance and crystalline structure. Batch adsorption experiments were conducted determine optimal conditions for MB removal, while Monte Carlo simulations were validated the molecules interaction between MB and the AZ/BC surface. The synthesized AZ/BC biomaterial exhibited a successful structural transition to uniformly stacked layers with high porosity, attaining a maximum methylene blue removal effectiveness of 96.21% within 120 min. Adsorption kinetics followed a pseudo-second-order model, and the Langmuir isothermal best described the monolayer adsorption process. Additionally, the used adsorbent was effectively repurposed as an efficient electrocatalyst for methanol oxidation in fuel cells, demonstrating excellent stability and highlighting a sustainable approach for energy production from wastewater treatment material.