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Air pollution, especially elevated particulate matter concentrations, presents a substantial risk to public health and environmental sustainability in urban regions. By employing machine learning and hybrid ensemble models, this study develops a robust frame work for predicting the Air Quality Index (AQI). A multi-step imputation method was used to preprocess the dataset containing metrological variables and air contaminants in order to handle missing values. AQI was selected as the target variable. To assess the possibility of target dependency, two feature configurations were taken in to consideration: a full-featured set and a reduced set that excluded PM2.5 and PM10. Multiple models were used, including Linear Regression, Decision Tree, Random Forest, Gradient Boosting, KNN, MLP and LSTM as well as ensemble methods like Voting and Stacking regressor. Baseline models, namely persistence and SMA were incorporated for comparative analysis. To assess the performance RMSE, MAE, MAPE, RMSLE and R2 with a temporal train test split were used. The Voting regressor achieves the lowest RMSE (10.938) and highest R2 (0.974), while the Stacking regressor offers the lowest MAE and MAPE demonstrating the superior performance of ensemble models. The LSTM model captures temporal patterns but performs below ensemble models. Models with fewer features perform noticeably worse, underscoring the significance of particulate matter. SHAP analysis shows PM2.5 and PM10 as the most influential features while robustness analysis supports stable performance.

This study presents a comprehensive finite element investigation into the design optimization of an ultra-high temperature ceramic matrix composite thruster for green bipropellant systems. Focusing on ZrB2-SiC-Cfiber composites, it explores their thermal and mechanical response under realistic transient combustion conditions. Two geometries, a simplified and a complex full-featured model, were evaluated to assess the impact of geometric fidelity on stress prediction. The complex thruster model (CTM) offered improved resolution of temperature gradients and stress concentrations, especially near flange and convergent regions, and was adopted for optimization. A parametric study with nine wall thickness profiles identified a 2 mm tapered configuration in both convergent and divergent sections that minimized mass while maintaining structural integrity. This optimized profile reduced peak thermal stress and overall mass without compromising safety margins. Transient thermal and strain analyses showed that thermal stress dominates initially (≤3 s), while thermal strain becomes critical later due to stiffness degradation. Damage risk was evaluated using temperature-dependent stress margins at four critical locations. Time-dependent failure maps revealed throat degradation for short burns and flange cracking for longer durations. All analyses were conducted under hot-fire conditions without cooling. The validated methodology supports durable, lightweight nozzle designs for future green propulsion missions.

Gait speed is a critical biomarker for assessing mobility, rehabilitation, and neuromuscular function. While deep learning (DL) models have shown promise in predicting gait speed using electromyography (EMG) data, including extensive feature sets can increase computational complexity without necessarily improving accuracy. With 17 discrete gait speeds and 35 participants' gait data, this study explores the impact of feature pruning in a deep neural network (DNN) trained only on frequency-domain muscle activation features, excluding domain-specific knowledge in the feature selection process. A group-wise sequential feature selection method was applied to reduce the original 540 features to 60. The test results demonstrate that feature pruning significantly reduced training time (by 79%) and computational cost (81.4% fewer parameters) while improving root mean square error (RMSE) from 0.793 km/h to 0.610 km/h. Despite excluding domain-specific guidance, the pruned model performed better than the full-featured DNN at all gait speeds. These findings suggest that systematic feature reduction can enhance model performance and generalizability, even without expert-driven feature selection. Future studies should examine the approach across diverse populations and gait conditions to validate its applicability.Clinical relevance- Accurate gait speed prediction is crucial for diagnosing and monitoring neuromuscular disorders, assessing fall risk, and evaluating rehabilitation progress. This study demonstrates that deep learning can be used in gait speed predictions even without expert-driven feature selection, making AI-driven gait analysis more accessible in real-world clinical settings. Feature pruning will facilitate the deployment of predictive models on portable and wearable devices for continuous gait monitoring and early detection of mobility impairments.

Cellular senescence is a critical stress response program implicated in embryonic development, wound healing, aging, and immunity, and it backs up apoptosis as an ultimate cell-cycle exit mechanism. In analogy to replicative exhaustion of telomere-eroded cells, premature types of senescence-referring to oncogene-, therapy-, or virus-induced senescence-are widely considered irreversible growth arrest states as well. We discuss here that entry into full-featured senescence is not necessarily a permanent endpoint, but dependent on essential maintenance components, potentially transient. Unlike a binary state switch, we view senescence with its extensive epigenomic reorganization, profound cytomorphological remodeling, and distinctive metabolic rewiring rather as a journey toward a full-featured arrest condition of variable strength and depth. Senescence-underlying maintenance-essential molecular mechanisms may allow cell-cycle reentry if not continuously provided. Importantly, senescent cells that resumed proliferation fundamentally differ from those that never entered senescence, and hence would not reflect a reversion but a dynamic progression to a post-senescent state that comes with distinct functional and clinically relevant ramifications.

To standardize metabolomics data analysis and facilitate future computational developments, it is essential to have a set of well-defined templates for common data structures. Here we describe a collection of data structures involved in metabolomics data processing and illustrate how they are utilized in a full-featured Python-centric pipeline. We demonstrate the performance of the pipeline, and the details in annotation and quality control using large-scale LC-MS metabolomics and lipidomics data and LC-MS/MS data. Multiple previously published datasets are also reanalyzed to showcase its utility in biological data analysis. This pipeline allows users to streamline data processing, quality control, annotation, and standardization in an efficient and transparent manner. This work fills a major gap in the Python ecosystem for computational metabolomics.

MicroRNAs (miRNAs) are small noncoding RNAs of 18-25 bases. miRNAs are also important new biomarkers that can be used for disease diagnosis in the future. Studies have shown that miR-124 levels are significantly elevated during acute myocardial infarction (AMI) and play a key role in the cardiovascular system. A variety of methods have been established to detect myocardial infarction-related miRNAs. However, most require complex miRNA extraction and isolation, and these methods are virtually undetectable when RNA levels are low in the sample. It may lead to biased results. Thus, it is necessary to develop a technique that can detect miRNA without extracting it, which means that intracellular detection is of great significance. Here, we improved the traditional silicon spheres and obtained a biosensor that could effectively capture and detect specific noncoding nucleic acids through the layer-by-layer assembly method. The sensor is protected by hyaluronic acid so it can successfully escape the lysosome into the cell and achieve detection. With the help of a full-featured microplate reader, we determined that the detection limit of the biosensor could reach 1 fM, meeting the needs of intracellular detection. At the same time, we prepared an oxidative stress cardiomyocyte infarction model and successfully captured the overexpressed miR-124 in the infarcted cells to achieve in situ detection. This study could provide a new potential tool to develop miRNAs for sensitive diagnosis in AMI, and the proposed strategy implies its potential for biomedical research.

To explore women's and health professionals' views on the development of a midwifery-led mHealth app intervention in antenatal care and their demands for app functionality. Descriptive qualitative research was utilized. In total, 15 pregnant or postpartum women were interviewed via in-depth interviews and 10 health professionals including obstetricians, midwives and obstetric nurses were invited to participate in a focus group discussion (FGD). All interviews and the FGD were analysed using qualitative content analysis. Four key themes emerged from the data, including (1) limitations of current maternity care services; (2) potential benefits for mHealth app-based midwifery care; (3) possible challenges for providing midwifery care through mHealth apps and (4) suggestions and needs for developing a midwifery-led mHealth app. Participants agreed on the potential need of developing a midwifery-led mHealth app in antenatal care to increase access to midwifery care services and to meet women's diverse needs. Participants preferred to develop professional, reliable, full-featured and interactive mobile applications. The main functions of midwifery-led mHealth apps included personalized assessment and health education, self-monitoring and feedback, data sharing and interactive functions. Women mentioned that online communication and consultation with midwives could help them receive continuous support outside facilities. Health professionals expressed it would be of great convenience and timeliness to send personalized messages to women and to inform them of healthy lifestyles during pregnancy. The challenges included a shortage of human resources, medico-legal risks associated with mHealth and data security risks. This study explores the individual views and functional needs of target users and healthcare providers for developing a midwifery-led mHealth app in antenatal care, which will serve as a reference for future application development. Our study has important and practical implications for guiding the development of future midwifery-led mHealth app interventions. No patient or public contribution.

The agricultural sector faces several difficulties today in ensuring the safety of food supply, including water scarcity. This study presents the design and development of a low-cost and full-featured fog-IoT/AI system targeted towards smallholder farmer communities (SFCs). However, the smallholder community is hesitant to adopt technology-based solutions. There are many overwhelming reasons for this, but the high cost, implementation complexity, and malfunctioning sensors cause inappropriate decisions. The PRIMA INTEL-IRRIS project aims to make digital and innovative agricultural technologies more appealing and available to these communities by advancing the intelligent irrigation "in-the-box" concept. Considered a vital resource, collected data are used to detect anomalies or abnormal behavior, providing information about an occurrence or a node failure. To prevent agro-field data leakage, this paper presents an innovative, smart, and sustainable low-cost irrigation system that employs artificial intelligence (AI) techniques to analyze anomalies and problems in water usage. The sensor anomaly can be detected using an autoencoder (AE) and a generative adversarial network (GAN). We will feed the autoencoders' anomaly detection models with time series records from the datasets and replace detected anomalies with the reconstructed outputs. When integrated with an IoT platform, this methodology is a tool for easing the labeling of sensor anomalies and can help create supervised datasets for future research. In addition, anomalies can be corrected by prediction models based on deep learning approaches, applying CNN/BiLSTM architecture. The results show that AEs outperform the GANs, achieving an accuracy of 90%, 95%, and 97% for soil moisture, air temperature, and air humidity, respectively. The proposed system is designed to ensure that the data are of high quality and reliable enough to make sound decisions compared to the existing platforms.

The von Neumann architecture is no longer sufficient for handling large-scale data. In-memory computing has emerged as the potent method for breaking through the memory bottleneck. A new 10T SRAM bitcell with row and column control lines called RC-SRAM is proposed in this article. The architecture based on RC-SRAM can achieve bi-directional and operand-controllable logic-in-memory and search operations through different signal configurations, which can comprehensively respond to various occasions and needs. Moreover, we propose threshold-controlled logic gates for sensing, which effectively reduces the circuit area and improves accuracy. We validate the RC-SRAM with a 28 nm CMOS technology, and the results show that the circuits are not only full featured and flexible for customization but also have a significant increase in the working frequency. At VDD = 0.9 V and T = 25 °C, the bi-directional search frequency is up to 775 MHz and 567 MHz, and the speeds for row and column Boolean logic reach 759 MHz and 683 MHz.

Older individuals often face disabilities or diseases that lower their quality of life (QoL). While inpatient rehabilitation can initially enhance QoL, there is often a lack of continuation at home. Virtual coaches (VCs) as specific embodied conversational agents promise appropriate support for home rehabilitation. They emerge as complementary digital aids to ensure care continuity. This paper presents the results of implementing a full-featured VC for older patients' home rehabilitation in a multi-stage study, summarizing the main results regarding QoL outcomes and user experience tests. The study confirms the intervention as an engaging means for rehabilitation (mostly above user experience thresholds) and improvements of QoL (>10% between experimental and control groups).

Based on the effective Schrödinger-Poisson model a new physical mechanism for resonant hot-electron generation at irradiated half-space metal-vacuum interface of electron gas with arbitrary degree of degeneracy is proposed. The energy dispersion of undamped plasmons in the coupled Hermitian Schrödinger-Poisson system reveals an exceptional point coinciding the minimum energy of plasmon conduction band. Existence of such exceptional behavior is a well-know character of damped oscillation which in this case refers to resonant wave-particle interactions analogous to the collisionless Landau damping effect. The damped Schrödinger-Poisson system is used to model the collective electron tunneling into the vacuum. The damped plasmon energy dispersion is shown to have a full-featured exceptional point structure with variety of interesting technological applications. In the band gap of the damped collective excitation,depending on the tunneling parameter value, there is a resonant energy orbital for which the wave-like growing of collective excitations cancels the damping of the single electron tunneling wavefunction. This important feature is solely due to dual-tone wave-particle oscillations, characteristics of the collective excitations in the quantum electron system leading to a resonant photo-plasmonic effect, as a collective analog of the well-known photo-electric effect. The few nanometer wavelengths high-energy collective photo-electrons emanating from the metallic surfaces can lead to a much higher efficiency of plasmonic solar cell devices, as compared to their semiconductor counterpart of electron-hole excitations at the Fermi energy level. The photo-plasmonic effect may also be used to study the quantum electron tunneling and electron spill-out at metallic surfaces. Current findings may help to design more efficient spasers by using the feature-rich plasmonic exceptional point structure.

Spinal cord injury (SCI) is a destructive condition that results in lasting neurological damage resulting in disruption of the connection between the central nervous system and the rest of the body. Currently, there are several approaches in the treatment of a damaged spinal cord; however, none of the methods allow the patient to return to the original full-featured state of life before the injury. Cell transplantation therapies show great potential in the treatment of damaged spinal cords. The most examined type of cells used in SCI research are mesenchymal stromal cells (MSCs). These cells are at the center of interest of scientists because of their unique properties. MSCs regenerate the injured tissue in two ways: (i) they are able to differentiate into some types of cells and so can replace the cells of injured tissue and (ii) they regenerate tissue through their powerful known paracrine effect. This review presents information about SCI and the treatments usually used, aiming at cell therapy using MSCs and their products, among which active biomolecules and extracellular vesicles predominate.

Globally, most individuals who are susceptible to depression do not receive adequate or timely treatment. Unguided computerized cognitive behavioral therapy (cCBT) has the potential to bridge this treatment gap. However, the real-world effectiveness of unguided cCBT interventions, particularly in low- and middle-income countries (LMICs), remains inconclusive. In this study, we aimed to report the design and development of a new unguided cCBT-based multicomponent intervention, TreadWill, and its pragmatic evaluation. TreadWill was designed to be fully automated, engaging, easy to use, and accessible to LMICs. To evaluate the effectiveness of TreadWill and the engagement level, we performed a double-blind, fully remote, and randomized controlled trial with 598 participants in India and analyzed the data using a completer's analysis. The users who completed at least half of the modules in TreadWill showed significant reduction in depression-related (P=.04) and anxiety-related (P=.02) symptoms compared with the waitlist control. Compared with a plain-text version with the same therapeutic content, the full-featured version of TreadWill showed significantly higher engagement (P=.01). Our study provides a new resource and evidence for the use of unguided cCBT as a scalable intervention in LMICs. ClinicalTrials.gov NCT03445598; https://clinicaltrials.gov/ct2/show/NCT03445598.