Search Results (10,649)

Hydrogels are widely known for their ability to increase soil water retention and for their potential slow nutrient release mechanism. They have been constantly improved to meet the growing demand for sustainability in agriculture. Research focused on the development of biodegradable hydrogels, produced from industrial cellulose waste, are an ecological and efficient alternative soil ameliorant for the improvement of agricultural land. The objective of this study was to evaluate the impacts of two types of hydrogel (processed in a glass reactor versus a twin-screw extruder) on soils with different textures (clay and sandy loam), testing their water retention capacity, nitrogen leaching, and effects on seed germination. The methodology included the evaluation of water retention capacity at different pressures with different hydrogel addition rates in the soil, leaching tests in columns filled with soil and hydrogel layers, and germination tests of sorghum and corn. The results indicated that the addition of hydrogel significantly improved water retention, especially in sandy loam soils. The hydrogels also reduced nitrogen leaching, acting as nitrification inhibitors and limiting the conversion of ammonium to nitrate, with greater effectiveness in clayey soils. In the tested formulations, it was observed that the hydrogel doses applied to the columns favored nitrogen retention in the region close to the roots, directly influencing the initial stages of germination. This behavior highlights the potential of hydrogels as tools for directing nutrients in the soil profile, indicating that adjustments to the C:N ratio, nutrient release rate, and applied doses can optimize their application for different crops. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

Refractory full-thickness macular holes (rFTMHs) present a significant challenge in vitreoretinal surgery, with reported incidence rates of 4.2–11.2% following standard vitrectomy with internal limiting membrane (ILM) peeling and gas tamponade. Risk factors include large hole size (>400 µm), chronicity (>6 months), high myopia, incomplete ILM peeling, and post-operative noncompliance. Multiple surgical techniques exist, though comparative evidence remains limited. Current options include the inverted ILM flap technique, autologous ILM transplantation (free flap or plug), lens capsular flap transplantation (autologous or allogenic), preserved human amniotic membrane transplantation, macular subretinal fluid injection, macular fibrin plug with autologous platelet concentrates, and autologous retinal transplantation. Closure rates range from 57.1% to 100%, with selection depending on hole size, residual ILM, patient posturing ability, etc. For non-posturing patients, fibrin plugs are preferred. Residual ILM cases may benefit from extended peeling or flap techniques, while large holes often require scaffold-based (lens capsule, amniotic membrane) or fibrin plug approaches. Pseudophakic patients should avoid posterior capsular flaps due to lower success rates. Despite promising outcomes, the lack of randomized trials necessitates further research to establish evidence-based guidelines. Personalized surgical planning, considering anatomical and functional goals, remains crucial in optimizing visual recovery in rFTMHs. Full article

Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. Full article

Background/Objectives: The present study aimed to evaluate the oral health and patient satisfaction of flexible and non-metal clasp dentures (NMCD) compared to removable partial dentures (RPD) using a systematic review. Methods: The PICOS framework of this review was as follows: Do rehabilitations involving flexible dentures or NMCD have a similar success rate to those using RPD? Thus, the PICOS approach involves the following topics: (P) Population/Problem: partial edentulous adult patients; (I) Intervention: patients rehabilitated with flexible dentures or NMCD; (C) Comparison: patients rehabilitated with standard RPD; (O) Outcome: clinical parameters such as oral health, masticatory function, and patient satisfaction; and (S) Study Type: clinical trials and observational studies (cohort, case–control, and cross-sectional). No language restrictions were applied to the studies. The search strategy consisted of the following keywords in different databases: ((flexible) OR (nonmetal) OR (non-metal) OR (thermoplastic)) AND (denture). Only clinical trials and observational studies (cohort, case–control, and cross-sectional studies) from the last 15 years were included, and no language restrictions were applied. Studies that did not describe the denture material were excluded. Results: Of the 2197 potentially relevant records, 14 studies were included in the present review. Two studies reported retrospective results, while twelve reported a prospective evaluation. Considering the thermoplastic materials, five studies evaluated polyester, five polyamides, three polyacetals, and only one study evaluated polyetheretherketone (PEEK). Flexible dentures and NMCD demonstrated similar periodontal status and bone levels on abutment teeth to RPD after up to 12 months. Flexible dentures exhibited a higher degree of redness of the mucosa after 12 months. One study showed a lower maximum bite force for flexible dentures compared to RPD. No study has performed a clinical evaluation of mastication and chewing ability. Conclusions: Despite increased short-term patient satisfaction for flexible dentures and NMCD, there is weak evidence to support a similar clinical performance of flexible dentures and NMCD to RPD. Full article

Microplastic (MP) pollution is an emerging concern in ruminant production, as animals are exposed to MPs through air, water, and feeds. Ruminants play a key role in MP transmission to humans via animal products and contribute to MP return to agricultural soil through excreta. Identifying effective strategies to mitigate MP pollution in the ruminant sector is crucial. A promising yet understudied approach involves the potential ability of rumen microbiota to degrade MPs. This study investigated the in vitro ruminal degradation of three widely distributed MPs—low-density polyethylene (LDPE), polyethylene terephthalate (PET), and polyamide (PA)—over 24, 48, and 72 h. PET MP exhibited the highest degradation rates (24 h: 0.50 ± 0.070%; 48 h: 0.73 ± 0.057%; and 72 h: 0.96 ± 0.082%), followed by LDPE MP (24 h: 0.03 ± 0.020%; 48 h: 0.25 ± 0.053%; and 72 h: 0.56 ± 0.066%) and PA MP (24 h: 0.10 ± 0.045%; 48 h: 0.02 ± 0.015%; and 72 h: 0.14 ± 0.067%). These findings suggest that the ruminal environment could serve as a promising tool for LDPE, PET, and PA MPs degradation. Further research is needed to elucidate the mechanisms involved, potentially enhancing ruminants’ natural capacity to degrade MPs. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Coronavirus disease 2019 (COVID-19) causes cardiovascular complications, which contributes to the high mortality rate of the disease. Emerging evidence indicates that aberrant vascular smooth muscle cell (SMC) function is a key driver of vascular disease in COVID-19. While antivirals alleviate the symptoms of COVID-19, it is not known whether these drugs directly affect SMCs. Accordingly, the present study investigated the ability of three approved COVID-19 antiviral drugs to influence SMC function. Treatment of SMCs with remdesivir (RDV), but not molnupiravir or nirmatrelvir, inhibited cell proliferation, DNA synthesis, and migration without affecting cell viability. RDV also stimulated an increase in heme oxygenase-1 (HO-1) expression that was not observed with molnupiravir or nirmatrelvir. The induction of HO-1 by RDV was abolished by mutating the antioxidant responsive element of the promoter, overexpressing dominant-negative NF-E2-related factor-2 (Nrf2), or treating cells with an antioxidant. Finally, silencing HO-1 partly rescued the proliferative and migratory response of RDV-treated SMCs, and this was reversed by carbon monoxide and bilirubin. In conclusion, the induction of HO-1 via the oxidant-sensitive Nrf2 signaling pathway contributes to the antiproliferative and antimigratory actions of RDV by generating carbon monoxide and bilirubin. These pleiotropic actions of RDV may prevent occlusive vascular disease in COVID-19. Full article

Ozone is a powerful destructive agent in the oxidative process of polymer composites. The destructive ability of ozone depends primarily on its concentration, duration of exposure, the type of polymer, and its matrix structure. In this work, nonwoven PLA/NR fibers with natural rubber contents of 5, 10, and 15 wt.% were obtained, which were then subjected to ozone oxidation for 800 min. The effect of ozone treatment was estimated using various methods of physicochemical analysis. The visual effect was manifested in the form of a change in the color of PLA/NR fibers. The method of differential scanning calorimetry revealed a change in the thermophysical characteristics. The glass transition and cold crystallization temperatures of polylactide shifted toward lower temperatures, and the degree of crystallinity increased. It was found that in PLA/NR fiber samples, the degradation process predominates over the crosslinking process, as an increase in the melt flow rate by 1.5–1.6 times and a decrease in the correlation time determined by the electron paramagnetic resonance method were observed. The IR Fourier method recorded a change in the chemical structure during ozone oxidation. The intensity of the ether bond bands changed, and new bands appeared at 1640 and 1537 cm⁻¹, which corresponded to the formation of –C=C– bonds. Full article

While personal thermal comfort is critical for well-being and productivity, it is often overlooked by traditional building management systems that rely on uniform settings. Modern data-driven approaches often fail to capture the complex interactions between various data streams. This pilot study introduces a high-accuracy, interpretable framework for thermal comfort classification, designed to identify the most significant predictors from a comprehensive suite of environmental, physiological, and anthropometric data in a controlled group of young adults. Initially, an XGBoost model using the full 24-feature dataset achieved the best performance at 91% accuracy. However, after using SHAP analysis to identify and select the most influential features, the performance of our ensemble models improved significantly; notably, a Random Forest model’s accuracy rose from 90% to 94%. Our analysis confirmed that for this homogeneous cohort, environmental parameters—specifically temperature, humidity, and CO₂—were the dominant predictors of thermal comfort. The primary strength of this methodology lies in its ability to create a transparent pipeline that objectively identifies the most critical comfort drivers for a given population, forming a crucial evidence base for model design. The analysis also revealed that the predictive value of heart rate variability (HRV) diminished when richer physiological data, such as diastolic blood pressure, were included. For final validation, the optimized Random Forest model, using only the top 10 features, was tested on a hold-out set of 100 samples, achieving a final accuracy of 95% and an F1-score of 0.939, with all misclassifications occurring only between adjacent comfort levels. These findings establish a validated methodology for creating effective, context-aware comfort models that can be embedded into intelligent building management systems. Such adaptive systems enable a shift from static climate control to dynamic, user-centric environments, laying the critical groundwork for future personalized systems while enhancing occupant well-being and offering significant energy savings. Full article

More recently, artificial intelligence (AI)-generated content (AIGC) is fundamentally transforming multiple sectors, including materials discovery, healthcare, education, scientific research, and industrial manufacturing. As for the complexities and challenges of intelligent robot design, AIGC has the potential to offer a new paradigm, assisting in conceptual and technical design, functional module design, and the training of the perception ability to accelerate prototyping. Taking the design of an apple-harvesting robot, for example, we demonstrate a basic framework of the AIGC-assisted robot design methodology, leveraging the generation capabilities of available multimodal large language models, as well as the human intervention to alleviate AI hallucination and hidden risks. Second, we study the enhancement effect on the robot perception system using the generated apple images based on the large vision-language models to expand the actual apple images dataset. Further, an apple-harvesting robot prototype based on an AIGC-aided design is demonstrated and a pick-up experiment in a simulated scene indicates that it achieves a harvesting success rate of 92.2% and good terrain traversability with a maximum climbing angle of 32°. According to the tentative research, although not an autonomous design agent, the AIGC-driven design workflow can alleviate the significant complexities and challenges of intelligent robot design, especially for beginners or young engineers. Full article

An efficient integrated energy system (IES) can enhance the potential of building energy conservation and carbon mitigation. However, imbalances between user-side demand and supply side output present formidable challenges to the operational dispatch of building energy systems. To mitigate heat rejection and improve dispatch optimization, an integrated building energy system incorporating waste heat recovery via an absorption heat pump based on the flow temperature model is adopted. A comprehensive analysis was conducted to investigate the correlation among heat pump operational strategies, thermal comfort, and the dynamic thermal storage capacity of piping network systems. The optimization calculations and comparative analyses were conducted across five cases on typical season days via the CPLEX solver with MATLAB R2018a. The simulation results indicate that the operational modes of absorption heat pump reduced the costs by 4.4–8.5%, while the absorption rate of waste heat increased from 37.02% to 51.46%. Additionally, the utilization ratio of battery and thermal storage units decreased by up to 69.82% at most after considering the pipeline thermal inertia and thermal comfort, thus increasing the system’s energy-saving ability and reducing the pressure of energy storage equipment, ultimately increasing the scheduling flexibility of the integrated building energy system. Full article

As artificial intelligence (AI) becomes increasingly utilized to perform tasks requiring human intelligence, patients who are pregnant may turn to AI for advice on over-the-counter (OTC) medications. However, medications used in pregnancy may pose profound safety concerns limited by data availability. This study focuses on a chatbot’s ability to accurately provide information regarding OTC medications as it relates to patients that are pregnant. A prospective, descriptive design was used to compare the responses generated by the Chat Generative Pre-Trained Transformer 3.5 (ChatGPT-3.5) to the information provided by UpToDate^®. Eighty-seven of the top pharmacist-recommended OTC drugs in the United States (U.S.) as identified by Pharmacy Times were assessed for safe use in pregnancy using ChatGPT-3.5. A piloted, standard prompt was input into ChatGPT-3.5, and the responses were recorded. Two groups independently rated the responses compared to UpToDate on their correctness, completeness, and safety using a 5-point Likert scale. After independent evaluations, the groups discussed the findings to reach a consensus, with a third independent investigator giving final ratings. For correctness, the median score was 5 (interquartile range [IQR]: 5–5). For completeness, the median score was 4 (IQR: 4–5). For safety, the median score was 5 (IQR: 5–5). Despite high overall scores, the safety errors in 9% of the evaluations (n = 8), including omissions that pose a risk of serious complications, currently renders the chatbot an unsafe standalone resource for this purpose. Full article

Composite laminates are widely used in aerospace, automotive, construction, and luxury industries, owing to their superior mechanical properties and design flexibility. However, detecting manufacturing defects and in-service damage remains a vital challenge for structural safety. While traditional unsupervised machine learning methods have been used in structural health monitoring (SHM), their high false positive rates limit their reliability in real-world applications. This issue is mostly inherited from their limited ability to capture small temporal variations in Lamb wave signals and their dependence on shallow architectures that suffer with complex signal distributions, causing the misclassification of damaged signals as healthy data. To address this, we suggested an unsupervised anomaly detection framework that integrates a self-attention autoencoder with a Gaussian mixture model (SAE-GMM). The model is solely trained on healthy Lamb wave signals, including high-quality synthetic data generated via a generative adversarial network (GAN). Damages are detected through reconstruction errors and probabilistic clustering in the latent space. The self-attention mechanism enhances feature representation by capturing subtle temporal dependencies, while the GMM enables a solid separation among signals. Experimental results demonstrated that the proposed model (SAE-GMM) achieves high detection accuracy, a low false positive rate, and strong generalization under varying noise conditions, outperforming traditional and deep learning baselines. Full article

Machine learning techniques have revolutionized physical layer security (PLS) and provided opportunities for optimizing the performance and security of modern communication systems. In this study, we propose the first machine learning-assisted random communication system (ML-RCS). It comprises a pretrained decision tree (DT)-based receiver that extracts binary information from the transmitted random noise carrier signals. The ML-RCS employs skewed alpha-stable ($\alpha$-stable) noise as a random carrier to encode the incoming binary bits securely. The DT model is pretrained on an extensively developed dataset encompassing all the selected parameter combinations to generate and detect the $\alpha$-stable noise signals. The legitimate receiver leverages the pretrained DT and a predetermined key, specifically the pulse length of a single binary information bit, to securely decode the hidden binary bits. The performance evaluations included the single-bit transmission, confusion matrices, and a bit error rate (BER) analysis via Monte Carlo simulations. The fact that the BER reached 10⁻³ confirms the ability of the proposed system to establish successful secure communication between a transmitter and legitimate receiver. Additionally, the ML-RCS provides an increased data rate compared to previous random communication systems. From the perspective of security, the confusion matrices and computed false negative rate of 50.2% demonstrate the failure of an eavesdropper to decode the binary bits without access to the predetermined key and the private dataset. These findings highlight the potential ability of unconventional ML-RCSs to promote the development of secure next-generation communication devices with built-in PLSs. Full article