Saved Queries

by Saihu Lu, Yuhan Liu, Guangqiang He, Zhongrui Bai, Zhenfeng Li, Pang Wu, Xianxiang Chen, Lidong Du, Peng Wang and Zhen Fang

Bioengineering 2025, 12(10), 1112; https://doi.org/10.3390/bioengineering12101112 (registering DOI) - 15 Oct 2025

Abstract

Cough is a key symptom reflecting respiratory health, with its frequency and pattern providing valuable insights into disease progression and clinical management. Objective and reliable cough detection systems are therefore of broad significance for healthcare and remote monitoring. However, existing algorithms often struggle to jointly model spatial and temporal information, limiting their robustness in real-world applications. To address this issue, we propose a cough recognition framework based on frequency-modulated continuous-wave (FMCW) radar, integrating a deep convolutional neural network (CNN) with a Self-Attention mechanism. The CNN extracts spatial features from range-Doppler maps, while Self-Attention captures temporal dependencies, and effective data augmentation strategies enhance generalization by simulating position variations and masking local dependencies. To rigorously evaluate practicality, we collected a large-scale radar dataset covering diverse positions, orientations, and activities. Experimental results demonstrate that, under subject-independent five-fold cross-validation, the proposed model achieved a mean F1-score of

0.974 \pm 0.016

and an accuracy of

99.05 \pm 0.55

%, further supported by high precision of

98.77 \pm 1.05

%, recall of

96.07 \pm 2.16

%, and specificity of

99.73 \pm 0.23

%. These results confirm that our method is not only robust in realistic scenarios but also provides a practical pathway toward continuous, non-invasive, and privacy-preserving respiratory health monitoring in both clinical and telehealth applications. Full article

(This article belongs to the Special Issue Artificial Intelligence for Better Healthcare and Precision Medicine, 2nd Edition)

►▼ Show Figures

Graphical abstract

24 pages, 710 KB

Open AccessArticle

On Fintech and Financial Inclusion: Evidence from Qatar

by Ashwaq Al-Sharshani, Fatma Al-Sharshani and Ali Malik

J. Risk Financial Manag. 2025, 18(10), 586; https://doi.org/10.3390/jrfm18100586 (registering DOI) - 15 Oct 2025

Abstract

This study examines the role of fintech adoption in enhancing financial inclusion in Qatar, with a particular focus on the mediating influence of access barriers. A structured questionnaire was administered to 220 respondents, of which 200 valid responses were retained for analysis after screening for completeness and outliers. The constructs of fintech adoption (FA), financial inclusion (FI), and access barriers (AB) were measured using validated multi-item scales adapted from prior literature. Measurement reliability and validity were confirmed through Cronbach’s alpha, composite reliability, and average variance extracted (AVE), alongside confirmatory factor analysis (CFA) for construct validity. A structural equation modeling (SEM) approach was employed to test the hypothesized relationships, using maximum likelihood estimation with bootstrap standard errors and confidence intervals. Model fit indices indicated excellent fit (χ² = 48.983, df = 51, p = 0.554; CFI = 1.000; TLI = 1.003; RMSEA = 0.000; SRMR = 0.036). Factor loadings were all significant (p < 0.001), supporting convergent validity. However, the structural paths from FA to FI (β = −0.020, p = 0.827), AB to FI (β = −0.077, p = 0.394), and FA to AB (β = 0.054, p = 0.527) were not significant. The indirect mediation effect of AB was also statistically insignificant (β = −0.004, p = 0.700). Full article

(This article belongs to the Special Issue Behavioral Finance and Sustainable Green Investing)

►▼ Show Figures

Figure A1

18 pages, 2668 KB

Open AccessArticle

Design Analysis of Migration Nozzles Using CFD

by Makhsuda Juraeva and Dong-Jin Kang

Polymers 2025, 17(20), 2766; https://doi.org/10.3390/polym17202766 (registering DOI) - 15 Oct 2025

Abstract

This paper presents a design analysis approach for migration nozzles used in the spinning process of synthetic fibers. A migration nozzle system consists of a yarn channel, air orifices, and a yarn loading slit. The entire system was analyzed in detail using computational fluid dynamics (CFD). The design parameters considered include the cross-sectional shape of the yarn channel, as well as the diameter and number of air orifices. Two different cross-sectional shapes, square and circle, were examined. The diameter of the air orifice varied from 0.6 mm to 2.0 mm, and both single and double orifice configurations were studied. A square cross-section resulted in the formation of a secondary vortex above the main vortex, making the circular cross-section preferable. The diameter of the air orifice significantly affects the vortex flow within the yarn channel. Vortex flow characteristics were quantified in two ways: the vorticity averaged across the cross-section in the direction of the yarn channel and the vorticity at the centerline. The highest vorticity at the centerline was observed at a diameter of 1.3 mm for single air orifice and 0.9 mm for double air orifices. These CFD results were validated through comparison with corresponding experimental data. A statistical analysis confirms that the centerline vorticity, particularly in the area of the air orifice, is a key and reliable parameter for evaluating the design of migration nozzles. Full article

(This article belongs to the Special Issue Advanced Study on Polymer-Based Textiles)

20 pages, 3590 KB

Open AccessArticle

Comparative Analysis and Validation of LSTM and GRU Models for Predicting Annual Mean Sea Level in the East Sea: A Case Study of Ulleungdo Island

by Tae-Yun Kim, Hong-Sik Yun, Hyung-Mi Yoon and Seung-Jun Lee

Appl. Sci. 2025, 15(20), 11067; https://doi.org/10.3390/app152011067 (registering DOI) - 15 Oct 2025

Abstract

This study presents a deep learning-based model for predicting annual mean sea level (MSL) in the East Sea, with a focus on the Ulleungdo Island region, which maintains an independent vertical datum. To account for long-term tidal variability, the model enables continuous estimation of hourly and annual MSL values. Two recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—were constructed and compared. Observational tide gauge data from 1 January 2000 to 3 August 2018 (covering 18.6 years and a full tidal nodal cycle) were preprocessed through missing-value and outlier treatment, followed by min–max normalization, and then structured for sequential learning. Comparative analysis demonstrated that the GRU model slightly outperformed the LSTM model in predictive accuracy and training stability. As a result, the GRU model was selected to produce annual MSL forecasts for the period 2018–2021. The GRU achieved a mean RMSE of approximately 0.44 cm during this prediction period, indicating robust performance in forecasting hourly sea level variations. The findings highlight the potential of deep learning methods to support vertical datum determination in island regions and to provide reliable sea level estimates for integration into coastal and oceanographic modeling. The proposed approach offers a scalable framework for long-term sea level prediction under evolving geodetic conditions. Full article

15 pages, 1032 KB

Open AccessArticle

Comparing the Long-Term Stability and Measurement Performance of a Self-Made Integrated Three-in-One Microsensor and Commercial Sensors for Heating, Ventilation, and Air Conditioning (HVAC) Applications

by Chi-Yuan Lee, Jiann-Shing Shieh, Guan-Quan Huang, Chen-Kai Liu, Najsm Cox and Chia-Hao Chou

Processes 2025, 13(10), 3306; https://doi.org/10.3390/pr13103306 (registering DOI) - 15 Oct 2025

Abstract

Building on our previous 310-h test of a larger MEMS sensor, this study develops and validates a miniaturized, lift-off-fabricated, and FPC-integrated three-in-one microsensor. In addition to extending the operation to 744 h, we introduce a wireless MQTT/Node-RED architecture to enable real-time IoT-level monitoring in factory HVAC ducts. The microsensor was fabricated using Micro-electro-mechanical systems (MEMS) technology and integrated with a flexible printed circuit (FPC) for improved mechanical compliance and ease of installation. To assess its durability and reliability, a 744-h long-term test was conducted in an industrial HVAC environment, where the performance of the microsensor was compared with that of two commercially available velocity sensors. The integrated sensor exhibited stable operation throughout the test and demonstrated effective measurement capabilities in the ranges of 10–40 °C for temperature, 60–90% RH for humidity, and 1.5–5.0 m/s for airflow velocity, with an overall accuracy of approximately ±3%. The results highlight the sensor’s potential for real-time environmental monitoring in factory HVAC systems, offering advantages in integration, adaptability, and cost-effectiveness compared to traditional single-function commercial sensors. Full article

(This article belongs to the Special Issue Micro/Nano Manufacturing Processes: Theories and Optimization Techniques (2nd Edition))

17 pages, 669 KB

Open AccessReview

Polyglycerol Systems in Additive Manufacturing: Structure, Properties, and Processing

by Julie Pearl M. Andal, Roxanne R. Navarro and Reymark D. Maalihan

Macromol 2025, 5(4), 48; https://doi.org/10.3390/macromol5040048 (registering DOI) - 15 Oct 2025

Abstract

Additive manufacturing (AM) demands materials that combine precise printability with reliable thermal and mechanical performance. Polyglycerol (PG)-based macromolecular systems offer exceptional tunability through controlled architecture and chemical modification, enabling their use across both light-based and extrusion AM platforms. Strategic enhancements such as chemical functionalization, network formation, and hybrid reinforcement have expanded their capabilities from biomedical to structural applications, delivering improved stability, strength, and functionality. Despite these advances, performance-processing trade-offs and dispersion challenges remain barriers to widespread adoption. This review synthesizes current knowledge on PG-based materials in AM, mapping key structure-property-processing relationships and identifying strategies to advance their development as versatile and sustainable options for next-generation manufacturing. Full article

►▼ Show Figures

Figure 1

30 pages, 1971 KB

Open AccessArticle

Integrating VMD and Adversarial MLP for Robust Acoustic Detection of Bolt Loosening in Transmission Towers

by Yong Qin, Yu Zhou, Cen Cao, Jun Hu and Liang Yuan

Electronics 2025, 14(20), 4062; https://doi.org/10.3390/electronics14204062 (registering DOI) - 15 Oct 2025

Abstract

The structural integrity of transmission towers, as the backbone of power grids, is critical to overall grid safety, relying heavily on the reliability of bolted connections. Dynamic loads such as wind-induced vibrations can cause bolt loosening, potentially leading to structural deformation, cascading failures, and large-scale blackouts. Traditional manual inspection methods are inefficient, subjective, and hazardous. Existing automated approaches are often limited by environmental noise sensitivity, high computational complexity, sensor placement dependency, or the need for extensive labeled data. To address these challenges, this paper proposes a portable acoustic detection system based on Variational Mode Decomposition (VMD) and an Adversarial Multilayer Perceptual Network (AT-MLP). The VMD method effectively processes non-stationary and nonlinear acoustic signals to suppress noise and extract robust time–frequency features. The AT-MLP model then performs state identification, incorporating adversarial training to mitigate distribution discrepancies between training and testing data, thereby significantly improving generalization and noise robustness. Comparison results and analysis demonstrate that the proposed VMD and AT-MLP framework effectively mitigates structural variability and environmental interference, providing a reliable solution for bolt loosening detection. The proposed method bridges structural mechanics, acoustic signal processing, and lightweight intelligence, offering a scalable solution for condition assessment and risk-aware maintenance of transmission towers. Full article

(This article belongs to the Special Issue AI-Driven Innovations for Smart Energy Systems: Prediction, Detection, and Optimization)

22 pages, 2666 KB

Open AccessArticle

Examination of Age-Depth Models Through Loess-Paleosol Sections in the Carpathian Basin

by László Makó, Péter Cseh and Júlia Hupuczi

Quaternary 2025, 8(4), 55; https://doi.org/10.3390/quat8040055 (registering DOI) - 15 Oct 2025

Abstract

The Carpathian Basin holds exceptional significance for Quaternary research, particularly in loess studies. In this study, we attempted to create age-depth models based on age data from scientific journals to investigate accumulation rates. We examined eleven open profile sections for loess and paleosol, including seven in Hungary, two in Croatia, and two in Serbia. We demonstrated that radiocarbon age data are much more useful and reliable than OSL/IRSL data for this type of investigation. The results indicate that the Pécel, Dunaszekcső, Madaras and Katymár sections exhibit accumulation rates an order of magnitude higher than the other sections, exceeding one millimeter per year. These findings suggest that, owing to the basin’s geographic position, these areas were consistently exposed to dust deposition, irrespective of changes in climate or wind direction. A secondary accumulation maximum was also detected along the Katymár–Surduk axis, indicating an additional phase of intensified sediment deposition within this transect. The absence of a young sediment maximum in the Máza section is interpreted as resulting from a shift in prevailing wind direction, which caused the incoming dust to be intercepted by the Mecsek Mountains. Full article

►▼ Show Figures

Figure 1

19 pages, 24197 KB

Open AccessArticle

Amplitude Normalization for Speed-Induced Modulation in Rotating Machinery Measurements

by Zhiwen Fang, Qing Zhang and Xinfa Shi

Sensors 2025, 25(20), 6374; https://doi.org/10.3390/s25206374 (registering DOI) - 15 Oct 2025

Abstract

Rotating machinery under variable-speed conditions suffers from amplitude modulation (AM) effects induced by speed fluctuations, complicating accurate fault detection. To address this issue, an amplitude normalization method based on support vector regression (SVR) is proposed to estimate and remove the AM effects. The method employs a correlation-based feature selection strategy to construct feature vectors strongly associated with rotational speed, thereby enabling the accurate quantification of speed-induced AM effects. The robust nonlinear fitting capability of SVR is then utilized to model and remove these effects, enhancing fault signal clarity. The proposed method is validated through two case studies and compared with advanced amplitude normalization techniques, demonstrating its superior accuracy, robustness, and reliability. Experimental results demonstrate that the proposed method accurately estimates and eliminates speed-induced AM, significantly improving fault diagnosis accuracy by up to 34.7%. Full article

(This article belongs to the Special Issue Smart Sensors for Machine Condition Monitoring and Fault Diagnosis)

►▼ Show Figures

Figure 1

23 pages, 3051 KB

Open AccessArticle

Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory Variants

by Gaetano Manzo, Kathryn Borkowski and Ivan Ovcharenko

Genes 2025, 16(10), 1223; https://doi.org/10.3390/genes16101223 (registering DOI) - 15 Oct 2025

Abstract

Background/Objective: Genome-wide association studies (GWAS) have linked many noncoding variants to complex traits and diseases, but distinguishing as-sociation from causation remains difficult. Deep learning models—particularly CNN- and Transformer-based architectures—are widely used for this task, yet comparisons are hindered by inconsistent benchmarks and evaluation practices. We aimed to establish a standardized assessment of leading models for predicting variant effects in enhancers and for prioritizing putative causal SNPs. Methods: We evaluated state-of-the-art deep learning models under consistent training and evaluation conditions on nine datasets derived from MPRA, raQTL, and eQTL ex-periments. These datasets profile the regulatory impact of 54,859 single-nucleotide polymorphisms (SNPs) across four human cell lines. Performance was compared for two related tasks: predicting the direction and magnitude of regulatory impact in enhancers and identifying likely causal SNPs within linkage disequilibrium (LD) blocks. We addi-tionally assessed the effect of fine-tuning on Transformer-based models and the impact of certainty in experimental results. Results: CNN models such as TREDNet and SEI performed best for predicting the reg-ulatory impact of SNPs in enhancers. Hybrid CNN–Transformer models (e.g., Borzoi) performed best for causal variant prioritization within LD blocks. Fine-tuning benefits Transformers but remains insufficient to close the performance gap. Conclusions: Under a unified benchmark, CNN architectures are most reliable for esti-mating enhancer regulatory effects of SNPs, while hybrid CNN–Transformer models are superior for causal SNP identification within LD. These comparisons help guide model selection for variant-effect prediction in noncoding regions. Full article

(This article belongs to the Section Bioinformatics)

►▼ Show Figures

Figure 1

17 pages, 3043 KB

Open AccessArticle

3D Effects on the Stability of Upstream-Raised Tailings Dams in Narrow Valleys

by Raul Conceição, Gonçalo Ferreira, Henrique Lopes and João Camões Lourenço

Infrastructures 2025, 10(10), 277; https://doi.org/10.3390/infrastructures10100277 (registering DOI) - 15 Oct 2025

Abstract

Tailings dams are unique structures due to the materials they store and the methods applied in their construction, often resulting in complex three-dimensional (3D) problems. Most current slope-stability analyses neglect the 3D effects without significant consequences. However, certain conditions, such as the valley shape, the spatial variability of the tailings’ resistance, and the presence of internal dikes, may render the 2D simplification inadequate. For translational slides, the sliding-mass width-to-height ratio (W/H) is a reliable estimator of the 3D effects. However, it is unclear whether this geometric ratio is the most suitable for rotational slides, where the width of the sliding mass varies along its height. This paper presents a parametric study of the 3D effects of the dam’s height (H_M) and the valley shape, namely the abutments’ slope angle with the horizontal (β) and the thalweg width (L_M), on the overall stability of a tailings dam raised by the upstream method, by means of 2D and 3D Limit Equilibrium (LE) analyses. The study evaluates the dam stability using a straightforward and practical methodology, specifically the FS_3D to FS_2D ratio (R_3D/2D), to compare the results of the 3D and 2D analyses, adapting current state-of-the-art techniques originally for translational slides, focused on pre-defined, closed-form slip-surface geometry, to rotational ones where the main focus is the geometry of the whole structure as a physical constraint for the sliding mass. The results show that the model average width-to-height ratio (W_M,avr/H_M), developed in this study, may be a better estimator of the 3D effects for rotational slides than the W/H ratio. Full article

(This article belongs to the Special Issue Preserving Life Through Dams)

►▼ Show Figures

Figure 1

39 pages, 5033 KB

Open AccessArticle

Territorial Functional Pattern Reconstruction Integrating Set-Theoretic and Functional Mappings with Game-Theoretic Analysis to Reconcile Development and Conservation in China

by Dinghua Ou, Xiaofan Cheng, Zijia Yan, Kun Ruan, Qingyan Huang, Zhi Zhao, Ziheng Yang, Jing Qin and Jianguo Xia

Land 2025, 14(10), 2060; https://doi.org/10.3390/land14102060 (registering DOI) - 15 Oct 2025

Abstract

The contradiction between economic development and ecological protection has become a common challenge for territorial governance in developing countries around the world. However, extant studies have neglected the coupling and symbiotic relationship between humans and nature, resulting in significant functional conflicts, insufficient stability, and imbalances in ecological and economic benefits in the reconstruction of territorial spatial functional pattern (TSFP), making it difficult to achieve synergies between development and protection. The question that arises is how the TSFP can be reconstructed in order to achieve harmonious coexistence between humans and nature. This remains a challenging problem in the context of the synergizing development and protection of the TSFP. This study innovatively integrates set-theoretic principles and functional mappings with game-theoretic analysis to develop Territorial Spatial Functional Pattern Reconstruction (TSFPR) model designed to foster harmonious human–nature coexistence, and validates the model using geospatial data from Qionglai City, China. Empirical evidence demonstrates that, in comparison with conventional methods, TSFPR model significantly mitigates the territorial spatial functional conflicts (TSFCs), enhances stability and ecological and economic benefits, and achieves the expected harmonious coexistence between humans and nature. The analysis confirms that the territorial spatial functional conflict (TSFC) coordination index established in this study provides a reliable criterion for identifying superior territorial spatial functions (TSFs). The proposed TSFPR model is an expansion of the theory of spatial optimization modelling, and it provides a tool for reconstructing the TSFP for the harmonious coexistence between humans and nature. In summary, the utilization of the TSFPR model to reconstruct the TSFP for harmonious coexistence between humans and nature provides a novel solution for coordinating the development and protection of territorial space governance. Full article

20 pages, 580 KB

Open AccessReview

Vascular Access Devices for Stem Cell Transplantation: A Review of Catheter Types—A Crucial Step Towards the Enhancement of Patient Care

by Sławomir Milczarek, Piotr Kulig, Oliwia Piotrowska, Alina Zuchmańska, Martyna Brzosko and Bogusław Machaliński

Cancers 2025, 17(20), 3325; https://doi.org/10.3390/cancers17203325 (registering DOI) - 15 Oct 2025

Abstract

Central venous access devices (CVADs) play a pivotal role in managing stem cell recipients, providing reliable access for the administration of chemotherapy, blood products, progenitor infusion, parenteral nutrition, and other crucial treatments. This review critically evaluates the various types of CVADs commonly employed in transplant settings, examining their indications, complications, and best practices to enhance patient outcomes. Moreover, it emphasizes the significance of broadening the selection algorithm for vascular devices and incorporating patient expectations and comfort into routine clinical practice. Full article

(This article belongs to the Section Transplant Oncology)

►▼ Show Figures

Figure 1

15 pages, 2937 KB

Open AccessArticle

Denoising Degraded PCOS Ultrasound Images Using an Enhanced Denoising Diffusion Probabilistic Model

by Jincheng Peng, Zhenyu Guo, Xing Chen and Ming Zhou

Electronics 2025, 14(20), 4061; https://doi.org/10.3390/electronics14204061 (registering DOI) - 15 Oct 2025

Abstract

Currently, for polycystic ovary syndrome (PCOS), diagnostic methods are mainly divided into hormonal indicators and ultrasound imaging. However, ultrasound images are often affected by noise and artifacts during the imaging process. This significantly degrades image quality and increases the difficulty of diagnosis. This paper proposes a PCOS ultrasound image denoising method based on an improved DDPM. During the forward diffusion process of the original model, Gaussian noise is progressively added using a cosine-based scheduling strategy. In the reverse diffusion process, a conditional noise predictor is introduced and combined with the original ultrasound image information to iteratively denoise and recover a clear image. Additionally, we fine-tuned and optimized the model to better suit the requirements of PCOS ultrasound image denoising. Experimental results show that our model outperforms state-of-the-art methods in both noise suppression and structural fidelity. It delivers a fully automated PCOS-ultrasound denoising pipeline whose diffusion-based restoration preserves clinically salient anatomy, improving the reliability of downstream assessments. Full article

(This article belongs to the Special Issue Edge AI for Biomedical Applications: Innovations in Sensing, Computing and Security)

►▼ Show Figures

Figure 1

16 pages, 2167 KB

Open AccessArticle

Continuous Circulation of Hepatitis E and A Viruses During COVID-19 Pandemic Lockdowns in Munich, Germany—Experience from Three Years of Wastewater Surveillance

by Jasmin Javanmardi, Mathias Schemmerer, Karina Wallrafen-Sam, Jessica Neusser, Raquel Rubio-Acero, Michael Hoelscher, Thomas Kletke, Bernhard Boehm, Michael Schneider, Elisabeth Waldeck, Martin Hoch, Merle M. Böhmer, Christof Geldmacher, Jan Hasenauer, Jürgen J. Wenzel and Andreas Wieser

Microorganisms 2025, 13(10), 2379; https://doi.org/10.3390/microorganisms13102379 - 15 Oct 2025

Abstract

The COVID-19 pandemic has increased interest in wastewater-based epidemiology (WBE) as a reliable and cost-effective framework for monitoring the spread of microbes. However, WBE frameworks have rarely been applied to the study of fecal–oral transmissible diseases, except for poliomyelitis. Here, we investigated the presence of hepatitis A virus (HAV) and hepatitis E virus (HEV) in wastewater in Munich. We collected wastewater samples between July 2020 and November 2023. A total of 186 samples were processed using centrifugation and analyzed for HAV- and HEV-RNA using RT-qPCR. As a reference, we used notification data from clinically or laboratory-diagnosed hepatitis A and E cases. Lockdown stringency levels were derived from official documentation. Our results show that 87.6% of wastewater samples were positive for HEV at concentrations of 9.0 × 10¹ to 2.5 × 10⁵ copies/L, while HAV was only detectable in 7.5% of the samples at viral loads of 4.6 × 10¹ to 2.4 × 10³ copies/L. We also detected differences in HEV concentrations but not in case numbers when comparing lockdown and no-lockdown periods. This study covers all but the first lockdowns in Bavaria. We present a unique real-world dataset evaluating the impact of lockdown interventions on hepatitis A and E case numbers, as well as on the concentrations of HAV and HEV in wastewater. Person-to-person spread and eating out appear to have contributed to the transmission of HEV. In addition, the consistently high HEV concentrations in sewage support the findings of serological studies, indicating a substantial burden of undetected subclinical infections. Full article

(This article belongs to the Special Issue Surveillance of Health-Relevant Pathogens Employing Wastewater)

►▼ Show Figures

Figure 1

Show export options Show export options

Select all

Export citation of selected articles as:

Error

Oops... you haven't selected anything for export.

Displaying article 1-50 on page 1 of 1579.

Go to page 1 2 3 4 5

Search Results (78,949)

Further Information

Guidelines

MDPI Initiatives

Follow MDPI