Search Results (1,057)

A facile and versatile strategy to obtain NPs@ZnAlO nanocomposite materials, comprising controlled-size nanoparticles (NPs) within a ZnAlO matrix is reported. The mono-(Au, Ni) and bimetallic (Ni-Au) NPs serving as an active phase were prepared by the polyol-alkaline method, while the ZnAlO support was obtained via the thermal decomposition of its corresponding layered double hydroxide (LDH) precursors. X-ray diffraction (XRD) patterns confirmed the successful fabrication of the nanocomposites, including the synthesis of the metallic NPs, the formation of LDH-like structure, and the subsequent transformation to ZnO phase upon LDH calcination. The obtained nanostructures confirmed the nanoplate-like morphology inherited from the original LDH precursors, which tended to aggregate after the addition of gold NPs. According to the UV-Vis spectroscopy, loading NPs onto the ZnAlO support enhanced the light absorption and reduced the band gap energy. ATR-DRIFT spectroscopy, H₂-TPR measurements, and XPS analysis provided information about the functional groups, surface composition, and reducibility of the materials. The catalytic performance of the developed nanostructures was evaluated by the photodegradation of bisphenol A (BPA), under simulated solar irradiation. The conversion of BPA over the bimetallic Ni-Au@ZnAlO reached up to 95% after 180 min of irradiation, exceeding the monometallic Ni@ZnAlO and Au@ZnAlO catalysts. Its enhanced activity was correlated with good dispersion of the bimetals, narrower band gap, and efficient charge carrier separation of the photo-induced e⁻/h⁺ pairs. Full article

This study investigates the effects of samarium (Sm) promotion on the catalytic activity of 5 weight percent Ni catalysts for partial oxidation of methane (POM)-based hydrogen production supported on a Si-Al mixed oxide (10SiO₂+90Al₂O₃) system. Several 5% Ni-based catalysts supported on silica–alumina was used to test the POM at 600 °C. Sm additions ranged from 0 to 2 wt.%. Impregnation was used to create these catalysts, which were then calcined at 500 °C and examined using BET, H₂-TPR, XRD, FTIR, TEM, Raman spectroscopy, and TGA methods. Methane conversion (57.85%) and hydrogen yield (56.89%) were greatly increased with an ideal Sm loading of 1 wt.%, indicating increased catalytic activity and stability. According to catalytic tests, 1 wt.% Sm produced high CH₄ conversion and H₂ production, as well as enhanced stability and resistance to carbon deposition. Nitrogen physisorption demonstrated a progressive decrease in pore volume and surface area with the addition of Sm, while maintaining mesoporosity. At moderate Sm loadings, H₂-TPR and XRD analyses showed changes in crystallinity and increased NiO reducibility. Sm incorporation into the support and its impact on the ordering of carbon species were indicated by FTIR and Raman spectra. The optimal conditions to maximize H₂ yield were successfully identified through optimization of the best catalyst, and there was good agreement between the theoretical predictions (87.563%) and actual results (88.39%). This displays how successfully the optimization approach achieves the intended outcome. Overall, this study demonstrates that the performance and durability of Ni-based catalysts for generating syngas through POM are greatly enhanced by the addition of a moderate amount of Sm, particularly 1 wt.%. Full article

Although Unmanned Aircraft Systems (UASs) offer valuable services, they also introduce certain risks—particularly to individuals on the ground—referred to as third-party risk (TPR). In general, ground-level TPR tends to rise alongside the density of people who might use these services, leading current regulations to heavily restrict UAS operations in populated regions. These operational constraints hinder the ability to gather safety insights through the conventional method of learning from real-world incidents. To address this, a promising alternative is to use dynamic simulations that model UAS collisions with humans, providing critical data to inform safer UAS design. In the automotive industry, the modelling and simulation of car crashes has been well developed. For small UAS, this dynamical modelling and simulation approach has focused on the effect of the varying weight and kinetic energy of the UAS, as well as the geometry and location of the impact on a human body. The objective of this research is to quantify the effects of UAS material and shape on-ground TPR through dynamical modelling and simulation. To accomplish this objective, five camera–drone types are selected that have similar weights, although they differ in terms of airframe structure and materials. For each of these camera–drones, a dynamical model is developed to simulate impact, with a biomechanical human body model validated for impact. The injury levels and probability of fatality (PoF) results, obtained through conducting simulations with these integrated dynamical models, are significantly different for the camera–drone types. For the uncontrolled vertical impact of a 1.2 kg UAS at 18 m/s on a model of a human head, differences in UAS designs even yield an order in magnitude difference in PoF values. Moreover, the highest PoF value is a factor of 2 lower than the parametric PoF models used in standing regulation. In the same scenario for UAS types with a weight of 0.4 kg, differences in UAS designs even considered yield an order when regarding the magnitude difference in PoF values. These findings confirm that the material and shape design of a UAS plays an important role in reducing ground TPR, and that these effects can be addressed by using dynamical modelling and simulation during UAS design. Full article

Introduction: Limited access to pulmonary rehabilitation (PR) has contributed to the rise of telerehabilitation (TPR) for COPD patients. Positive comparable effects are observed in exercise tolerance, quality of life (QoL), and dyspnoea with TPR. However, patient adherence to TPR is an outcome that has not been sufficiently analysed. Objective: To analyse adherence, satisfaction, and quality-of-life improvements in COPD patients following the TPR program to determine whether telerehabilitation is comparable to conventional therapy or usual care. Methods: A systematic search was conducted using four electronic databases, retrieving 392 articles. Two independent researchers selected and evaluated these articles based on predefined eligibility criteria. A third researcher was consulted in the event of disagreements. Results: Primary outcomes: Adherence to PR and/or usual care showed a minimum reported value of 62% and a maximum reported value of 91%, while TPR adherence had the lowest reported value of 21% and the highest reported value of 93.5%. Five articles compared TPR to PR and/or usual care, showing that TPR adherence is higher or similar to other interventions, whereas only one article found lower TPR adherence compared to PR. Secondary outcomes: A higher number of dropouts were reported for PR and usual care compared to TPR. Three publications analysed satisfaction and demonstrated that patients are satisfied across groups. Tertiary outcomes: Comparable improvements in QoL were found for TPR and PR, both being superior to usual care. Conclusions: This systematic review reveals heterogeneity in classifying adherence for pulmonary rehabilitation and telerehabilitation. Adherence classification may be standardised in future studies for consistent analysis. Full article

The automated detection and classification of immature macauba (Acrocomia aculeata) fruits is critical for improving post-harvest processing and quality control. In this study, we present a comparative evaluation of two state-of-the-art YOLO architectures, YOLOv11x and YOLOv12x, trained on the newly constructed VIC01 dataset comprising 1600 annotated images captured under both background-free and natural background conditions. Both models were implemented in PyTorch and trained until the convergence of box regression, classification, and distribution-focal losses. Under an IoU (intersection over union) threshold of 0.50, YOLOv11x and YOLOv12x achieved an identical mean average precision (mAP₅₀) of 0.995 with perfect precision and recall or TPR (true positive rate). Averaged over IoU thresholds from 0.50 to 0.95, YOLOv11x demonstrated superior spatial localization performance (mAP_50–95 = 0.973), while YOLOv12x exhibited robust performance in complex background scenarios, achieving a competitive mAP_50–95. Inference throughput averaged 3.9 ms per image for YOLOv11x and 6.7 ms for YOLOv12x, highlighting a trade-off between speed and architectural complexity. Fused model representations revealed optimized layer fusion and reduced computational overhead (GFLOPs), facilitating efficient deployment. Confusion-matrix analyses confirmed YOLOv11x’s ability to reject background clutter more effectively than YOLOv12x, whereas precision–recall and F1-score curves indicated both models maintain near-perfect detection balance across thresholds. The public release of the VIC01 dataset and trained weights ensures reproducibility and supports future research. Our results underscore the importance of selecting architectures based on application-specific requirements, balancing detection accuracy, background discrimination, and computational constraints. Future work will extend this framework to additional maturation stages, sensor fusion modalities, and lightweight edge-deployment variants. By facilitating precise immature fruit identification, this work contributes to sustainable production and value addition in macauba processing. Full article

Hard disk failure prediction is an important proactive maintenance method for storage systems. Recent years have seen significant progress in hard disk failure prediction using high-quality SMART datasets. However, in industrial applications, data loss often occurs during SMART data collection, transmission, and storage. Existing machine learning-based hard disk failure prediction models perform poorly on low-quality datasets. Therefore, this paper proposes a hard disk fault prediction technique based on low-quality datasets. Firstly, based on the original Backblaze dataset, we construct a low-quality dataset, Backblaze-, by simulating sector damage in actual scenarios and deleting 10% to 99% of the data. Time series features like the Absolute Sum of First Difference (ASFD) were introduced to amplify the differences between positive and negative samples and reduce the sensitivity of the model to SMART data loss. Considering the impact of different quality datasets on time window selection, we propose a time window selection formula that selects different time windows based on the proportion of data loss. It is found that the poorer the dataset quality, the longer the time window selection should be. The proposed model achieves a True Positive Rate (TPR) of 99.46%, AUC of 0.9971, and F1 score of 0.9871, with a False Positive Rate (FPR) under 0.04%, even with 80% data loss, maintaining performance close to that on the original dataset. Full article

The conversion of CO₂ into CH₄ through the Sabatier reaction is one of the key processes that can reduce CO₂ emissions into the atmosphere. This work aims to develop Ni-Al aerogel-based thermo-photocatalysts with large specific surface areas prepared using a sol–gel method and subsequent supercritical drying in CO₂. Different Al/Ni molar ratios were selected for the development of the catalysts, characterized using ICP-OES, N₂ adsorption–desorption isotherms, XRD, H₂-TPR, TEM, UV-Vis DRS, and XPS techniques. Thermo-photocatalytic activity tests were performed in a photoreactor with two different light sources (λ = 365 nm, λ = 470 nm) at a temperature range from 300 °C to 450 °C and a pressure of 10 bar. The catalyst with the highest Ni loading (AG 1/3) produced the best catalytic results, reaching CO₂ conversion and CH₄ selectivity levels of 82% and 100%, respectively, under visible light at 450 °C. In contrast, the catalysts with the lowest nickel loading produced the lowest results, most likely due to their low amounts of active Ni. These results suggest that supercritical drying is an efficient method for developing active thermo-photocatalysts with high Ni dispersion, suitable for Sabatier reactions under mild reaction conditions. Full article

The regulation of the active sites of a catalyst is important for its application. Herein, a series of Ag₁Cu_x/SBA-15 catalysts with different molar ratios of Ag to Cu were synthesized via the impregnation method, and the active sites of Ag₁Cu_x were regulated via various pretreatment conditions. These as-prepared Ag₁Cu_x/SBA-15 catalysts were characterized by many technologies, and their catalytic performance was estimated through CO catalytic oxidation. Among these catalysts, Ag₁Cu_0.025/SBA-15, with a Ag/Cu molar ratio of 1:0.025 and pretreated under the condition of 500 °C O₂/Ar for 2 h, followed by 300 °C H₂ for another 2 h, presented optimal CO degradation performance, which could realize the oxidation of 98% CO at 34 °C (T₉₈ = 34 °C). Meanwhile, Ag₁Cu_0.025/SBA-15 also displayed great reusability. Characterization results, such as X-ray diffraction (XRD), ultraviolet–visible diffuse reflectance spectra (UV-vis DRS), temperature-programmed H₂ reduction (H₂-TPR), and physical adsorption, suggested that the optimal catalytic performance of Ag₁Cu_0.025/SBA-15 was ascribed to its high interspersion of Ag nanoparticles, better low-temperature reduction ability, the interaction between Ag and Cu, and its high surface area and large pore volume. This study provides guidance for the regulation of active sites for low-temperature catalytic degradation. Full article

The production of synthetic natural gas in the context of power-to-gas is a promising technology for the utilization of CO₂. Ni-based catalysts supported on carbon nanotubes (CNTs) were prepared through incipient wetness impregnation and characterized using N₂ adsorption, X-ray diffraction (XRD), transmission electron microscopy (TEM), thermogravimetric analysis (TGA), and temperature-programmed reduction (TPR). The catalysts were tested for CO₂ methanation in the 200–400 °C temperature range and at atmospheric pressure. The results demonstrated that the catalytic activity increased with the addition of the CNTs and Ni loading. The selectivity towards CH₄ was close to 100% for the Ni/ZrO₂/CNT catalysts. Reduction of the calcined catalyst at 500 °C using H₂ modified the surface chemistry of the catalyst, leading to an increase in the Ni particles. The CO₂ conversion was dependent on the Ni loading and the temperature reduction in the NiO species. The 10Ni/ZrO₂/CNT catalyst was highly stable in CO₂ methanation at 350 °C for 24 h. Thus, CNTs combined with Ni and ZrO₂ were considered promising for use as catalysts in CO₂ methanation at low temperatures. Full article

Background/Objectives: The physiological activation of cytotoxic CD8^pos T cells (CTLs) relies on the engagement of the TCR/CD3 complex with cognate peptide-HLA class I (pHLA-I) on target cells, triggering cell lysis with appropriate cytokine release and minimized off-target toxicity. In contrast, current immunotherapies for CD19-expressing hematological malignancies, such as chimeric antigen receptor (CAR) T cells and bispecific T cell engagers (BiTEs), bypass TCR/pHLA interactions, resulting in CTL hyperactivation and excessive cytokine release, which frequently cause severe immune-related adverse events (irAEs). Thus, there is a pressing need for T cell-based therapies that preserve physiological activation while maintaining antitumor efficacy. Methods: To address this, we developed CD19-ReTARG^TPR, a novel fusion protein consisting of the immunodominant cytomegalovirus (CMV) pp65-derived peptide TPRVTGGAM (TPR) covalently presented by a soluble HLA-B*07:02/β2-microglobulin complex fused to a high-affinity CD19-targeting Fab antibody fragment. The treatment of CD19-expressing cancer cells with CD19-ReTARG^TPR makes them recognizable for pre-existing anti-CMV_pp65 CTLs via physiological TCR-pHLA engagement. Results: Our preclinical data demonstrate that CD19-ReTARG^TPR efficiently redirects anti-CMV CTLs to eliminate CD19-expressing cancer cells, including both established cell lines and primary chronic lymphocytic leukemia (CLL) cells. Unlike CD19-directed CAR T cells or the CD19/CD3 BiTE blinatumomab, CD19-ReTARG^TPR mediated robust cytotoxic activity without triggering supraphysiological cytokine release. Importantly, this approach retained efficacy even against cancer cells with low CD19 expression. Conclusions: In summary, we provide a robust proof-of-concept study and show that CD19-ReTARG^TPR offers a promising alternative strategy for T cell redirection, enabling the selective and effective killing of CD19-expressing malignancies while minimizing cytokine-driven toxicities through physiological CTL activation pathways. Full article

The aim of this study was to improve the water–gas shift efficiency of Co/CeO₂ catalyst by incorporating praseodymium and rhenium. The catalysts were synthesized via combustion method and characterized using X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, Scanning Electron Microscope (SEM), H₂-temperature programmed reduction (H₂-TPR), NH₃-temperature programmed desorption (NH₃-TPD), Raman spectroscopy, and X-ray photoelectron spectroscopy (XPS). These characterization techniques evaluate the increase of the surface acidity and oxygen vacancies in Co-based catalysts, which leads to an increase in water–gas shift performance because CO molecules prefer to react with surface oxygen, then followed by the production of CO₂ and oxygen vacancies which act as active sites for H₂O dissociation. The 1%Re4%Co/Ce-5%Pr-O catalyst exhibited a maximum CO conversion of 86% at 450 °C, substantially outperforming the 5%Co/Ce-5%Pr-O catalyst, which showed only 62% CO conversion at 600 °C. In addition, 1%Re4%Co/Ce-5%Pr-O catalyst is more resistant towards deactivation than 5%Co/Ce-5%Pr-O. The result presented that the catalytic activity of 1%Re4%Co/Ce-5%Pr-O catalyst was kept constant for the whole period of 50 h, while a 6% decrease in water–gas shift activity was found for the 5%Co/Ce-5%Pr-O catalyst. Moreover, the addition of rhenium into the Co/Ce-Pr-O catalyst reveals that the enhancement of oxygen vacancy concentration, oxygen mobility, and surface acidity, thereby enhances CO conversion efficiency. Full article

The use of CH₄ and CO₂ as fuels in direct internal reforming solid oxide fuel cells (DIR-SOFCs) is a promising strategy for efficient power generation with reduced greenhouse gas emissions. In this study, Ni catalysts supported on Ce–Pr mixed oxides with varying Pr contents (0–80 mol%) were synthesized, calcined at 1200 °C, and tested for dry reforming of methane (DRM), aiming at their application as catalytic layers in SOFC anodes. Physicochemical characterization (XRD, TPR, TEM) showed that increasing Pr loading enhances catalyst reducibility and promotes the formation of the Pr₂NiO₄ phase, which contributes to the generation of smaller Ni⁰ particles after reduction. Catalytic tests revealed that all samples exhibited low-carbon deposition, attributed to the large Ni crystallites. The catalyst with 80 mol% Pr showed the best performance, achieving the highest CH₄ conversion (72%), a H₂/CO molar ratio of 0.89, and improved stability. These findings suggest that Ni/Ce_0.2Pr_0.8 could be a promising candidate for use as a catalyst layer of anodes in DIR-SOFC anodes. Although electrochemical data are not yet available, future work will evaluate the catalyst’s performance and durability under SOFC-relevant conditions. Full article

This paper studies a supply chain comprising a supplier, a third-party remanufacturer (TPR), and a retailer. The retailer sells both genuine and remanufactured products (i.e., Model O). Leveraging information advantages, the retailer may engage in brand deception by mislabeling remanufactured products as genuine to obtain extra profits (i.e., Model BD). AI-powered anti-counterfeiting technologies (AIT) (i.e., Model BA) and revenue-sharing contracts (i.e., Model C) are considered countermeasures. The findings reveal that (1) brand deception reduces (increases) sales of genuine (remanufactured) products, prompting the supplier (TPR) to lower (raise) wholesale prices. The asymmetric profit erosion effect highlights the gradual erosion of profits for the supplier, retailer, and TPR under brand deception. (2) The bi-interval adaptation effect indicates that AIT is particularly effective in industries with low adaptation resistance. When both the relabeling rate and industry adaptation resistance are low (high), Model BA (Model O) achieves a triple win. (3) Sequentially, when the industry adaptation resistance is low, AIT can significantly improve total profits, consumer surplus (CS), and social welfare (SW). Compared to Model BD, revenue-sharing offers slight advantages in CS but notable disadvantages in SW. Full article

Background: Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) significantly enhance the median survival of patients with lung adenocarcinoma (ADC) that harbor EGFR-sensitive mutations. However, most patients inevitably experience tumor relapse owing to drug resistance. We aimed to identify potential serum biomarkers for predicting post-EGFR-TKI treatment relapse in patients with advanced-stage lung ADC. Methods: Among 27 patients, including 6 and 21 with early and late relapse, respectively, differentially expressed proteins between patients with early and late relapses were identified using liquid chromatography and tandem mass spectrometry and subsequently validated using Western blotting. Predictive ability was assessed using the receiver operating characteristic curve and area under the curve (AUC) analysis. The association between the clinical variables and treatment response was evaluated using the chi-square test. Results: The serum expression levels of the translocated promoter region (TPR), junction plakoglobin (JUP), and fibrinogen alpha chain (FGA) were significantly higher in patients with late rather than early relapse. The findings indicated that TPR and FGA exhibited good diagnostic performance, with AUCs of 0.946 (p = 0.002; 95% confidence interval [CI], 0.84–1.05) and 0.809 (p = 0.034; 95% CI, 0.65–0.97), respectively. Conclusions: Our results suggest that the TPR and FGA levels are potential predictors of post-EGFR-TKI treatment relapse. Full article