Search Results (654)

The widespread adoption of heartbeat monitoring sensors has increased the demand for secure and trustworthy multimodal cardiac monitoring systems capable of accurate heartbeat pattern recognition. While existing systems offer convenience, they often suffer from critical limitations, such as variability in the number of available modalities and missing or noisy data during multimodal fusion, which may compromise both performance and data security. To address these challenges, we propose MultiHeart, which is a robust and secure multimodal interactive cardiac monitoring system designed to provide reliable heartbeat pattern recognition through the integration of diverse and trustworthy cardiac signals. MultiHeart features a novel multi-task learning architecture that includes a reconstruction module to handle missing or noisy modalities and a classification module dedicated to heartbeat pattern recognition. At its core, the system employs a multimodal autoencoder for feature extraction with shared latent representations used by lightweight decoders in the reconstruction module and by a classifier in the classification module. This design enables resilient multimodal fusion while supporting both data reconstruction and heartbeat pattern classification tasks. We implement MultiHeart and conduct comprehensive experiments to evaluate its performance. The system achieves 99.80% accuracy in heartbeat recognition, surpassing single-modal methods by 10% and outperforming existing multimodal approaches by 4%. Even under conditions of partial data input, MultiHeart maintains 94.64% accuracy, demonstrating strong robustness, high reliability, and its effectiveness as a secure solution for next-generation health-monitoring applications. Full article

Hospital in the Home (HITH) programs are emerging as a key pillar of smart city healthcare infrastructure, leveraging technology to extend care beyond traditional hospital walls. The global healthcare sector has been conceptualizing the notion of a care without walls hospital, also called HITH, where virtual care takes precedence to address the multifaceted needs of an increasingly aging population grappling with a substantial burden of chronic disease. HITH programs have the potential to significantly reduce hospital bed occupancy, enabling hospitals to better manage the ever-increasing demand for inpatient care. Although many health providers and hospitals have established their own HITH programs, there is a lack of research that provides healthcare executives and HITH program managers with management models and frameworks for such initiatives. There is also a lack of research that provides strategies for improving HITH management in the health sector. To fill this gap, the current study ran a systematic literature review to explore state-of-the-art with regard to this topic. Out of 2631 articles in the pool of this systematic review, 20 articles were deemed to meet the eligibility criteria for the study. After analyzing these studies, nine management models were extracted, which were then categorized into three categories, namely, governance models, general models, and virtual models. Moreover, this study found 23 strategies and categorized them into five groups, namely, referral support, external support, care model support, technical support, and clinical team support. Finally, implications of findings for practitioners are carefully provided. These findings provide healthcare executives and HITH managers with practical frameworks for selecting appropriate management models and implementing evidence-based strategies to optimize program effectiveness, reduce costs, and improve patient outcomes while addressing the growing demand for home-based care. Full article

This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is thoroughly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality hyperspectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations. Full article

Background: Down syndrome (DS) is a genetic disorder characterized by an extra copy of chromosome 21, often leading to intellectual disabilities, developmental delays, and an increased risk of various comorbidities, including thyroid dysfunction and mental health disorders. The relationship between thyroid dysfunction and mood disorders, particularly depression in DS populations, requires further investigation. Objective: This study aims to investigate the presence of a correlative relationship between hypothyroidism and depression in 178,840 individuals with DS, utilizing data from the National Inpatient Sample (NIS) to determine if those with comorbid hypothyroidism exhibit higher rates of depression compared to their counterparts without hypothyroidism. Methods: A retrospective analysis of the 2016–2019 NIS dataset was conducted, focusing on patients with DS, hypothyroidism, and depression diagnoses. The diagnoses were determined and labeled based on ICD-10 codes associated with NIS datapoints. Survey-weighted linear regression analyses were employed to assess the association between hypothyroidism and depression within the DS cohort, adjusting for demographic factors such as age, gender, and race. Results: This study found that individuals with DS exhibit a significantly higher prevalence of hypothyroidism (29.88%) compared to the general population (10.28%). Additionally, individuals with DS and comorbid hypothyroidism demonstrated a higher prevalence of depression (8.67%) compared to those without hypothyroidism (3.00%). These findings suggest a significant association between hypothyroidism and increased depression risk among individuals with DS. However, the overall prevalence of depression in DS (4.69%) remains substantially lower than in the general population (12.27%). Conclusions: This study highlights the importance of considering hypothyroidism as a potential contributor to depression in individuals with DS. Further research is needed to explore the underlying mechanisms of this association and potential screening and management strategies to address thyroid dysfunction and its potential psychiatric implications in DS. Full article

Chronic high-fat diet (HFD) feeding in male rats causes significant metabolic as well as inflammatory disturbances, including obesity, insulin resistance, dyslipidemia, liver and kidney dysfunction, oxidative stress, and hypothalamic dysregulation. This study assessed the therapeutic effects of chlorogenic acid (CGA), a natural polyphenol, administered at 10 mg and 100 mg/kg/day for the last 4 weeks of a 12-week HFD protocol. Both CGA doses reduced body weight gain, abdominal circumference, and visceral fat accumulation, with the higher dose showing greater efficacy. CGA improved metabolic parameters by lowering fasting glucose and insulin and enhancing lipid profiles. CGA suppressed orexigenic genes (Agrp, NPY) and upregulated anorexigenic genes (POMC, CARTPT), suggesting appetite regulation in the hypothalamus. In abdominal white adipose tissue (WAT), CGA boosted antioxidant defenses (SOD, CAT, GPx, HO-1), reduced lipid peroxidation (MDA), and suppressed pro-inflammatory cytokines including TNF-α, IFN-γ, and IL-1β, while increasing the anti-inflammatory cytokine IL-10. CGA modulated inflammatory signaling via upregulation of miR-146a and inhibition of IRAK1, TRAF6, and NF-κB. It also reduced apoptosis by downregulating p53, Bax, and Caspase-3, and restoring Bcl-2. These findings demonstrate that short-term CGA administration effectively reverses multiple HFD-induced impairments, highlighting its potential as an effective therapeutic for obesity-related metabolic disorders. Full article

Beach Sand Mycobiome is currently among the most important health challenges for viticulture in the world. Remarkably, the study of fungal communities in coastal beach sand and recreational waters remains underexplored despite their potential implications for human health. This research aimed to assess the prevalence of fungal species and the antifungal susceptibility profiles of fungi recovered from the beaches of the Persian Gulf and the Sea of Oman. Sand and seawater samples from 39 stations distributed within 13 beaches along the coastline were collected between May and July 2023. The grown isolates were identified at the species level based on morphological characteristics and DNA sequencing. Antifungal susceptibility testing was performed according to the Clinical Laboratory Standards Institute guidelines. Of 222 recovered isolates, 206 (92.8%) filamentous fungi and 16 (7.2%) yeast strains were identified. Sand-recovered fungi comprised 82.9%, while water-originated fungi accounted for 17.1%. The DNA sequencing technique categorized 191 isolates into 13 genera and 26 species. The most recovered genus was Aspergillus (68.9%), and Aspergillus terreus sensu stricto was the commonly identified species (26.14%). Voriconazole was the most effective antifungal drug against Aspergillus species. Research on fungal contamination levels at these locations could provide a foundation for establishing regulatory frameworks to diminish fungal risks, thereby enhancing public health protection. The ecological significance of fungal communities in sandy beaches to human infections remains to be explored, and earlier reports in the literature may motivate researchers to focus on detecting this mycobiome in natural environments where further investigation is warranted. Ultimately, our discovery serves as a reminder that much remains to be learned about pathogenic fungi and underscores the need for vigilance in areas where emerging pathogens have not yet been identified. Full article

Instead of being waste product of metabolism, lactate, has become a key metabolic and signaling molecule in both exercise physiology and tumor biology. Carcinogenic cells produce huge amounts of lactate through the Warburg effect, which is a hallmark of aggressive tumors, increasing acidity in the environment that can stimulates angiogenesis, immune evasion, and metastasis. Conversely, while exercise acutely elevates blood lactate concentration but it consider helpful for cancer patients. This paradox raises the following question: is exercise-induced lactate a friend or foe in cancer? This study reviews current evidence on the mechanistic, metabolic, immunological, and clinical impacts of exercise-induced lactate in cancer patients, highlighting the context-dependent effects that render lactate either beneficial or detrimental. Tumor-derived lactate seems to be pro-tumorigenic, driving immune suppression and disease progression, whereas short bursts of lactate from exercise can enhance anti-tumor immunity and metabolic reprogramming under the right conditions. Therefore, lactate’s impact on cancer is “all about the context”. Full article

Free-standing copper oxide (Cu₂O)-copper hydroxide (Cu(OH)₂) nanocomposites with enhanced catalytic and antibacterial functionalities were synthesized on copper mesh using a green method based on spinach leaf extract and glycerol. EDX, SEM, and TEM analyses confirmed the chemical composition and morphology. The resulting Cu2O-Cu(OH)₂@Cu mesh exhibited notable hydrophobicity, achieving a contact angle of 137.5° ± 0.6, and demonstrated the ability to separate thick oils, such as HD-40 engine oil, from water with a 90% separation efficiency. Concurrently, its photocatalytic performance was evaluated by the degradation of methylene blue (MB) under a weak light intensity of 5 mW/cm², achieving 85.5% degradation within 30 min. Although its application as a functional membrane in water treatment may raise safety concerns, the mesh showed significant antibacterial activity against both Gram-positive (Staphylococcus aureus) and Gram-negative (Escherichia coli) bacteria under both dark and light conditions. Using the disk diffusion method, strong bacterial inhibition was observed after 24 h of exposure in the dark. Upon visible light irradiation, bactericidal efficiency was further enhanced—by 17% for S. aureus and 2% for E. coli. These findings highlight the potential of the Cu₂O-Cu(OH)₂@Cu microfibers as a multifunctional membrane for industrial wastewater treatment, capable of simultaneously removing oil, degrading organic dyes, and inactivating pathogenic bacteria through photo-assisted processes. Full article

Understanding the displacement mechanism of cement slurry in drilling fluid is crucial for enhancing the safety of oil well cementing and mitigating geotechnical risks. This study investigated the oil well cementing process by simulating the displacement of drilling fluid by cement slurry in the annular space between the well casing and the surrounding formations using computational fluid dynamics (CFD). The volume-of-fluid (VOF) method in ANSYS-Fluent was employed to track the interfaces between drilling fluid, spacer fluid, and cement slurry. The study simulated fluid motion during drilling operations in the oil and gas industry, considering both smooth and irregular annular geometries around wells. The results show that the efficiency of cement slurry in displacing drilling fluid is higher in Case-2 (irregular outer walls) than in Case-1 (smooth outer walls). Under various inlet velocity conditions in Case-2, an optimal filling rate was achieved at an inlet velocity of 0.5 m/s. When the inlet velocity of the cement slurry was 0.2 m/s, a higher cement content was observed compared to 0.05 m/s, although some recirculation regions were more likely to form at this velocity. Full article

Accurate prediction of reservoir temperature is critical for optimizing geothermal energy systems, yet the complexity of geothermal data poses significant challenges for traditional modeling approaches. This study conducts a comprehensive comparative analysis of advanced machine learning models, including support vector regression (SVR), random forest (RF), Gaussian process regression (GP), deep neural networks (DNN), and graph neural networks (GNN), to evaluate their predictive performance for reservoir temperature estimation. Enhanced feature engineering techniques, including accumulated local effects (ALE) and SHAP value analysis, are employed to improve model interpretability and identify key hydrogeochemical predictors. Results demonstrate that RF outperforms other models, achieving the lowest mean squared error (MSE = 66.16) and highest R² score (0.977), which is attributed to its ensemble learning approach and robust handling of nonlinear relationships. SVR and GP exhibit moderate performance, while DNN and GNN show limitations due to overfitting and sensitivity to hyperparameter tuning. Feature importance analysis reveals that SiO₂ concentration as the most influential predictor, aligning with domain knowledge. The study highlights the interplay between model complexity, dataset size, and predictive accuracy, offering actionable insights for optimizing geothermal energy systems. By integrating advanced machine learning with enhanced feature engineering, this research provides a robust framework for improving reservoir temperature prediction, contributing to the sustainable development of geothermal energy in alignment with sustainable energy development. Full article

This paper presents a validated, cost-effective technique for the wideband characterization of low-height substrate materials in RF circuits. The method utilizes traditional resonant structures to accurately determine essential parameters—relative permittivity and loss tangent—and delivers reliable results even in the presence of unavoidable fabrication imperfections, thereby enhancing its reliability for RF designers. While it covers a broad frequency range (8–16 GHz), the proposed technique can be adapted for other ranges by modifying the resonant structure dimensions. By combining reflection coefficient and input impedance measurements of a multimode patch antenna, substrate properties are accurately extracted using an iterative numerical fitting process, i.e., secant algorithm. This approach provides RF designers with the precise material data necessary to enhance circuit performance and is especially useful for low-height substrates. The technique’s validity is demonstrated through excellent agreement between simulations and measurements, establishing that the technique provides a practical, solution for industrial and research applications. Full article

This study systematically investigates the utilization of marble industry waste—waste marble powder (WMP) as partial cement replacement and waste marble aggregates (WMA) as partial fine aggregate replacement—in self-compacting concrete (SCC). A detailed experimental program evaluated the effects of various replacement levels (5%, 10%, and 20% for WMP; 20%, 30%, and 40% for WMA) on compressive strength and durability, particularly resistance to aggressive sulfuric acid environments. Results indicated that a 5% WMP replacement increased compressive strength by 4.9%, attributed primarily to the filler effect, whereas higher levels (10–20%) led to strength reductions due to limited pozzolanic activity and cement dilution. In contrast, WMA replacement consistently enhanced strength (maximum increase of 11.5% at 30% substitution) due to improved particle packing and aggregate-paste interface densification. Durability tests revealed significantly reduced compressive strength losses and mass loss in marble-containing mixtures compared to control samples, with optimal acid resistance observed at 20% WMP and 40% WMA replacements. A comprehensive life cycle assessment demonstrated notable reductions in environmental impacts, including up to 20% decreases in Global Warming Potential (GWP) at 20% WMP replacement. A desirability-based eco-cost-mechanical optimization—simultaneously integrating mechanical strength, environmental indicators, and production cost—identified the 10% WMP substitution mix as the most sustainable option, achieving optimal balance among key performance criteria. These findings underscore the significant potential for marble waste reuse in SCC, promoting environmental sustainability, resource efficiency, and improved concrete durability in chemically aggressive environments. Full article

Blood pressure is a vital indicator of cardiovascular health and plays a crucial role in the early detection and management of heart-related diseases. However, current practices for recording blood pressure readings are still largely manual, leading to inefficiencies and data inconsistencies. To address this challenge, we propose a deep learning-based method for automated digit recognition and measurement-type classification (systolic, diastolic, and pulse) from images of blood pressure monitors. A total of 2147 images were collected and expanded to 3649 images using data augmentation techniques. We developed and trained three YOLOv8 variants (small, medium, and large). Post-training quantization (PTQ) was employed to optimize the models for edge deployment in a mobile health (mHealth) application. The quantized INT8 YOLOv8-small (YOLOv8s) model emerged as the optimal model based on the trade-off between accuracy, inference time, and model size. The proposed model outperformed existing approaches, including the RT-DETR (Real-Time DEtection TRansformer) model, achieving 99.28% accuracy, 96.48% F1-score, 641.40 ms inference time, and a compact model size of 11 MB. The model was successfully integrated into the mHealth application, enabling accurate, fast, and automated blood pressure tracking. This end-to-end solution provides a scalable and practical approach for enhancing blood pressure monitoring via an accessible digital platform. Full article

This study examines the impact of key printing parameters on the dimensional accuracy of 3D printing, specifically Fused Deposition Modeling (FDM) using PLA, utilizing two widely adopted printers: the LulzBot TAZ Pro and the Prusa MK4. A simplified parallel-line model was used to systematically evaluate the effects of print speed, nozzle temperature, bed temperature, and layer height on accuracy along the X, Y, and Z axes. The results showed that the Prusa MK4 generally provided better dimensional accuracy at lower print speeds (20–40 mm/s), higher nozzle temperatures (230 °C), and smaller layer heights (0.05 mm). In contrast, the LulzBot TAZ Pro performed better at higher print speeds (40–60 mm/s) and with thicker layers (0.2 mm). Scanning electron microscopy analysis further revealed distinct surface morphologies depending on the printer and parameter settings. These findings offer practical guidance for selecting suitable print settings across various application areas. Full article