Search Results (1,342)

The spacecraft microgravity simulation air-bearing platform is a crucial component of the spacecraft ground testing system. Special disturbances, such as the flatness and roughness of the contact surface between the air bearings and the granite platform, increasingly affect the control accuracy of the simulation experiment as the number of air bearings increases. To address this issue, this paper develops a novel compensation control system based on Active Disturbance Rejection Control (ADRC), which estimates and compensates for the disturbing forces and moments caused by the roughness and levelness of the contact surface, thereby improving the control precision of the spacecraft ground simulation system. A dynamic model of the multi-air-bearing platform under disturbance is established. A cascade ADRC algorithm based on the Linear Extended State Observer (LESO) is designed. The Gauss–Newton iteration method is used to identify the parameters of the sliding friction coefficient and the tilt angle of the air-bearing platform. A full-physics simulation experimental platform for spacecraft with rotor-based propulsion is constructed, and the proposed algorithm is validated. The experimental results show that on a marble surface with a flatness of grade 00, an overall tilt angle of 0–1 degrees, and a surface friction coefficient of 0–0.01, the position control accuracy for the simulated spacecraft can reach 1.5 cm, and the attitude control accuracy can reach 1°. Under ideal conditions, the identification accuracy for the contact surface friction coefficient is 2 × 10⁻⁴, and the recognition accuracy for the overall levelness of the marble surface can reach 1 × 10⁻³, laying the foundation for high-precision ground simulation experiments of spacecraft in multi-air-bearing scenarios. Full article

Significance: We describe a novel, specimen-free diagnostic platform that can immediately detect both a metabolite (glucose) or an infection (COVID-19) by non-invasively using Raman spectroscopy and machine learning. Aim: Current diagnostic testing for infections and glucose monitoring requires specimens, disease-specific reagents and processing, and it increases environmental waste. We propose a new hardware–software paradigm by designing and constructing a finger-scanning hardware device to acquire Raman spectroscopy readouts which, by varying the machine learning algorithm to interpret the data, allows for diverse diagnoses. Approach: A total of 455 patients were enrolled prospectively in the COVID-19 study; 148 tested positive and 307 tested negative through nasal PCR testing conducted concurrently with testing using our viral detector. The tests were performed on both outpatients (N = 382) and inpatients (N = 73) at Holy Name Medical Center in Teaneck, NJ, between June 2021 and August 2022. Patients’ fingers were scanned using an 830 nm Raman System and then, using machine learning, processed to provide an immediate result. In a separate study between April 2023 and August 2023, measurements using the same device and scanning a finger were used to detect blood glucose levels. Using a Dexcom sensor and an Accu-Chek device as references, a cross-validation-based regression of 205 observations of blood glucose was performed with a machine learning algorithm. Results: In a five-fold cross-validation analysis (including asymptomatic patients), a machine learning classifier using the Raman spectra as input achieved a specificity for COVID-19 of 0.837 at a sensitivity of 0.80 and an area under receiver operating curve (AUROC) of 0.896. However, when the data were split by time, with training data consisting of observations before 1 July 2022 and test data consisting of observations after it, the model achieved an AUROC of 0.67, with 0.863 sensitivity at a specificity of 0.517. This decrease in AUROC may be due to substantial domain shift as the virus evolves. A similar five-fold cross-validation analysis of Raman glucose detection produces an area under precision–recall curve (AUPR) of 0.58. Conclusions: The combination of Raman spectroscopy, AI/ML, and our patient interface admitting only a patient’s finger and using no specimen offers unprecedented flexibility in introducing new diagnostic tests or adapting existing ones. As the ML algorithm can be iteratively re-trained with new data and the software deployed to field devices remotely, it promises to be a valuable tool for detecting rapidly emerging infectious outbreaks and disease-specific biomarkers, such as glucose. Full article

The nuclear factor-kappa B (NF-κB) signaling pathway plays a crucial role in regulating immune responses in epithelial cells. In this study, we established a porcine epithelial NF-κB reporter cell line (PK15-KBR) as an in vitro platform to screen plant-based extracts for their potential use as vaccine adjuvants against porcine epidemic diarrhea virus (PEDV). The NF-κB inducers were further tested for toxicity assessments, either using CCK-8 assays or intramuscular injection in mice, finally followed by vaccination studies to evaluate their adjuvancy. Initial experiments confirmed that TNF-α effectively activated NF-κB signaling in PK15-KBR cells in a dose-dependent manner, validating the platform’s reliability at Z’ value of 0.68. Of the 224 testers, 3 candidates, including chamomile, mulberry and Boerhaavia diffusa, showed induction activity; however, only chamomile induced a dose-dependent response in PK15-KBR cells. As a proof of concept, chamomile, used as an adjuvant in oral vaccination, demonstrated significantly higher IgG levels at an early stage (day 14, p < 0.05) and enhanced IgA titers. These findings highlight the use of the PK15-KBR cell line in identifying mucosal adjuvants and position chamomile extract as a promising candidate for enhancing vaccine-induced immunity. Full article

Background/Objectives: Over the past 25 years, numerous biological molecules, like recombinant lysosomal enzymes, neurotrophins, receptors, and therapeutic antibodies, have been tested in clinical trials for neurological diseases. However, achieving significant success in clinical applications has remained elusive. A primary challenge has been the inability of these molecules to traverse the blood–brain barrier (BBB). Recognizing this hurdle, our study aimed to utilize niosomes as delivery vehicles, leveraging the “molecular Trojan horse” technology, to enhance the transport of molecules across the BBB. Methods: Previously synthesized memantine derivatives (MP1–4) were encapsulated into niosomes for improved BBB permeability, hypothesizing that this approach could minimize peripheral drug toxicity while ensuring targeted brain delivery. Using the human neuroblastoma (SH-SY5Y) cell line differentiated into neuron-like structures with retinoic acid and then exposed to amyloid beta 1–42 peptide, we established an in vitro Alzheimer’s disease (AD) model. In this model, the potential usability of MP1–4 was assessed through viability tests (MTT) and toxicological response analysis. The niosomes’ particle size and morphological structures were characterized using scanning electron microscopy (SEM), with their loading and release capacities determined via UV spectroscopy. Crucially, the ability of the niosomes to cross the BBB and their potential anti-Alzheimer efficacy were analyzed in an in vitro transwell system with endothelial cells. Results: The niosomal formulations demonstrated effective drug encapsulation (encapsulation efficiency: 85.3% ± 2.7%), controlled release (72 h release: 38.5% ± 1.2%), and stable morphology (PDI: 0.22 ± 0.03, zeta potential: −31.4 ± 1.5 mV). Among the derivatives, MP1, MP2, and MP4 exhibited significant neuroprotective effects, enhancing cell viability by approximately 40% (p < 0.05) in the presence of Aβ1-42 at a concentration of 47 µg/mL. The niosomal delivery system improved BBB permeability by 2.5-fold compared to free drug derivatives, as confirmed using an in vitro bEnd.3 cell model. Conclusions: Memantine-loaded niosomes provide a promising platform for overcoming BBB limitations and enhancing the therapeutic efficacy of Alzheimer’s disease treatments. This study highlights the potential of nanotechnology-based delivery systems in developing targeted therapies for neurodegenerative diseases. Further in vivo studies are warranted to validate these findings and explore clinical applications. Full article

To address the challenges of insufficient model generalization, high false alarm rates due to the scarcity of leakage data, and frequent minor leakage alarms in traditional weak leakage (the leakage amount is less than 1%) detection methods for gas transmission pipelines, this paper proposes a real-time weak leakage detection framework for natural gas pipelines based on the combination of the generalized likelihood ratio (GLR) and ensemble learning. Compared to traditional methods, the core innovations of this study include the following: (1) For the first time, GLR statistics are integrated with an ensemble learning strategy to construct a dynamic detection model for pipeline operating states through multi-sensor collaboration, significantly enhancing the model’s robustness in noisy environments by fusing pressure data from the pipeline inlet and outlet, as well as outlet flow data. (2) An adaptive threshold selection mechanism that dynamically optimizes alarm thresholds using the distribution characteristics of GLR statistics is designed, overcoming the sensitivity limitations of traditional fixed thresholds in complex operating conditions. (3) An ensemble decision module is developed based on a voting strategy, effectively reducing the high false alarm rates associated with single models. The model’s leakage detection capability under normal and noisy pipeline conditions was validated using a self-built gas pipeline leakage test platform. The results show that the proposed method can achieve the precise detection of pipeline leakage rates as small as 0.5% under normal and low-noise conditions while reducing the false alarm rate to zero. It can also detect leakage rates of 1.5% under strong noise interference. These findings validate its practical value in complex industrial scenarios. This study provides a high-sensitivity, low-false-alarm, intelligent solution for pipeline safety monitoring, which is particularly suitable for early warning of weak leaks in long-distance pipelines. Full article

Accurately predicting the impacts of climate change on hydrological fluxes in ungauged basins continues to be a complex task. In this study, we investigated the transferability of the model parameters SCS-SMA-LR, available in the ATHYS platform, to simulate hydrological behavior within catchments of a large South Mediterranean transboundary basin, i.e., the Medjerda bordering Tunisia and Algeria, characterized by contrasting climatic and physiographic conditions. A robustness analysis was set up for donor and receptor catchments situated in the Medjerda catchment in Tunisia. The model was initially calibrated for two donor catchments, for the 127 km² catchment of the Lakhmess watershed situated on the right bank and for the 362 km² catchment of the Raghay watershed situated on the left bank of the Medjerda basin in Tunisia, using input data from 1990 to 1994. The model performance was evaluated through multiple accuracy criteria based on the Best Linear Unbiased Estimator (BLUE) for the automatic calibration to quantify the model simulation, proving its good performance. The temporal transferability was assessed by evaluating model performance, transferring the calibrated parameters for the two catchments as validation on data for 3-year periods outside the calibration domain to test the robustness of the model through a diachronic analysis from different decades, i.e., for the periods 1994–1997, 2001–2004, and 2014–2017, respectively. The spatial transferability was assessed by transferring the parameters calibrated on the donor catchments to be applied to the receptor catchments based on similarity and data availability. The model was upgraded to a greater catchment for data from 1994 to 2016 for the right bank, the Siliana Upstream catchment, and to the nearest catchment with a similar area for the data from 2008 to 2017 for the left bank of the Medjerda basin, the Bouheurtma catchment. The capacity of the soil reservoir and the flow velocity parameters proved to have an important impact on the modeling implementations at, respectively, 123.03 mm and 1 m/s for Raghay, and 95.05 mm and 2.5 m/s for Lakhmes. The results show that the space–time transfer process of model parameters produces an acceptable simulation of flow volumes and timing. The proposed methodology proved to be a successful way to monitor ungauged catchments and strengthens the robustness of the SCS-SMA-LR model for hydrological modeling and impact studies in ungauged basins of the Southern Mediterranean region. Full article

Technological advancements in low-cost devices, the Internet of Things (IoT), numerical models, big data infrastructures, and high-performance computing are revolutionizing urban management, particularly air quality governance. This study examines the application of smart technologies to address urban air quality challenges using integrated sensor networks and predictive models. The decision support system (DSS), AERQ, incorporates the AERMOD modeling tool, achieving a 10 m spatial and 1 h temporal resolution for air quality predictions. It processes hourly climate and traffic data via a high-performance computing (HPC) platform, significantly enhancing prediction accuracy and decision-making efficiency. AERMOD has been calibrated and validated for NO₂, showing a good performance against observations. Tested in Cagliari, Sardinia, Italy, AERQ demonstrated a 99% reduction in computation time compared to modern desktop systems, delivering detailed 5-year scenarios in under 15 h. AERQ equips stakeholders with air quality indices, scenario analyses, and mitigation strategies, combining advanced visualization tools with actionable insights. By enabling data-driven decisions, the system empowers policymakers, urban planners, and citizens to improve air quality and public health. This study underscores the transformative potential of integrating advanced technologies into urban management, providing a scalable model for efficient, informed, and responsive air quality governance. Full article

The shear-based fracturing of deep fractured rocks under compression-shear loading is one of the most prevalent failure modes due to the existence of in situ stress. In order to study the shear fracture mechanical properties of fractured rocks, a double-edge cracked flattened Brazilian disc (DCFBD) sample was developed by introducing two platforms into a double-edge cracked Brazilian disc (DCBD). Extensive finite element analyses were conducted on DCFBD samples to determine the stress intensity factors (SIFs) and T-stress. A comprehensive dataset of SIFs and T-stress was obtained, which provided accurate descriptions of the compression-shear fracture tests performed on this specimen. Furthermore, the effects of the load distribution angle γ, dimensionless crack length α, and crack inclination angle θ on dimensionless SIFs Y_I, Y_II, and T-stress T* were discussed. It showed that the effect of load distribution angle γ on the dimensionless SIFs Y_I and Y_II can be disregarded when the dimensionless crack length α ≥ 0.60 and load distribution angle γ ≤ 20°. However, it should be considered for the T-stress for larger crack inclination angles. Moreover, it was experimentally validated that the DCFBD samples with appropriate crack lengths and load distribution angles can achieve shear (true mode II) fracture, as demonstrated through a series of fracture tests conducted on these specimens. The results will advance the development of rock shear fracture testing technology. Full article

The combination of chemotherapeutic agents with immune checkpoint inhibitors (ICIs) has revolutionized cancer treatment. However, its success is often limited by insufficient immune priming in certain tumors, including pediatric malignancies. In this report, we explore clinical trials currently investigating the use of immunogenic cell death (ICD)-inducing chemotherapies in combination with ICIs for both adult and pediatric cancers. Given the limited clinical data available for pediatric tumors, we focused on recent preclinical studies evaluating the efficacy of these combinations in neuroblastoma (NB). Finally, to address this gap, we propose an innovative strategy to assess the impact of ICD-inducing chemotherapies on antitumor immune responses in NB. Using tumor spheroids derived from a transgenic NB mouse model, we validated our previous in vivo findings concerning how anthracyclines, specifically mitoxantrone and doxorubicin, significantly enhance MHC class I surface expression, stimulate IFNγ and granzyme B production by CD8⁺ T cells and NK cells, and promote immune cell recruitment. Importantly, these anthracyclines also upregulated PD-L1 expression on NB spheroids. This screening platform yielded results similar to in vivo findings, demonstrating that mitoxantrone and doxorubicin are the most potent immunomodulatory agents for NB. These data suggest that the creation of libraries of ICD inducers to be tested on tumor spheroids could reduce the number of combinations to be tested in vivo, in line with the principles of the 3Rs. Furthermore, these results highlight the potential of chemo-immunotherapy regimens to counteract the immunosuppressive tumor microenvironment in NB, paving the way for improved therapeutic strategies in pediatric cancers. They provide compelling evidence to support further clinical investigations of these combinations to enhance outcomes for children with malignancies. Full article

The existing methods for transformer core-loosening fault diagnosis primarily focus on fundamental frequency analysis, neglecting higher-frequency components, which limits early detection accuracy. This study proposes a comprehensive approach integrating full-band vibration analysis, including high-order harmonics, to enhance diagnostic precision. A theoretical model coupling magnetostriction and thermodynamics was developed, combined with empirical mode decomposition (EMD) and Pearson’s correlation coefficient for fault characterization. A 10 kV transformer core vibration test platform was constructed, capturing signals under normal, partially loose, and completely loose states. The simulation results aligned with the experimental data, showing vibration accelerations of 0.01 m/s² (Phase A) and 0.023 m/s² (Phase B). A multi-physics coupling model incorporating Young’s modulus variations simulated core loosening, revealing increased high-frequency components (up to 1000 Hz) and vibration amplitudes (0.2757 m/s² for complete loosening). The joint EMD–Pearson method quantified fault severity, yielding correlation values of 0.0007 (normal), 0.0044 (partial loosening), and 0.0116 (complete loosening), demonstrating a clear positive correlation with fault progression. Experimental validation confirmed the model’s reliability, with the simulations matching the test results. This approach addresses the limitations of traditional methods by incorporating high-frequency analysis and multi-physics modeling, significantly improving early fault detection accuracy and providing a quantifiable diagnostic framework for transformer core health monitoring. Full article

AI on spaceborne platforms optimizes operations and increases automation, crucial for satellites with limited downlink capacity. It can ensure that only valuable information is transmitted, minimizing resources spent on unnecessary data, which is especially important in hyperspectral Earth Observation missions, producing large data volumes. Our previous work showed that the 1D-CNN, 1D-Justo-LiuNet, outperformed 2D-CNNs and Vision Transformers for hyperspectral segmentation with an accuracy of 0.93 and 4563 parameters, making our model the best choice for in-orbit deployment. While the state of the art has deployed 1D-CNNs on low-power platforms, such as Unmanned Aerial Vehicles, they have still not been deployed in space before. In this work, we mark the first deployment and testing of a 1D-CNN in a satellite. We implement a C version of the 1D-Justo-LiuNet and, after ground validation, we deploy it on board the HYPSO-1 satellite. We demonstrate in-flight segmentation of hyperspectral images via the 1D-CNN to classify pixels into sea, land, and cloud categories. We show how in-orbit segmentation improves satellite operations, increases automation, and optimizes downlink. We give examples of how in-orbit segmentation addresses mission challenges in HYPSO-1, such as incomplete data reception, incorrect satellite pointing, and cloud cover, helping to decide whether to transmit or discard data on board. An additional CNN autonomously interprets the segmented images, enabling on-board decisions on data downlink. Full article

Arrayed ultraviolet (UV) LED light sources have been widely applied in various semiconductor processes, ranging from photopolymerization to lithography. In practical cases, based on data provided by manufacturers, calibration of individual UV LEDs is often needed before their real usage in high-precision applications. In this paper, we present a high-precision, automated light source measurement platform, which can be applied to the performance evaluation of various types of light sources. In order to minimize errors introduced by the automated measurement system, the platform employs a sub-pixel measurement technique, along with a twist-angle method, to perform multiple measurements and analyses of the spatial intensity distribution of the light source on a given plane. Through noise analysis of repeated measurements, the platform’s effectiveness and reliability are validated within a certain tolerance range. The high-precision automated light source measurement platform demonstrates excellent performance in the precise control and data acquisition of complex light sources. The light source dataset derived from the test results can provide guidance for the optimization of light sources in fields such as lighting, imaging, and lithography. Full article

Generative adversarial networks (GANs) have revolutionised various fields by creating highly realistic images, videos, and audio, thus enhancing applications such as video game development and data augmentation. However, this technology has also given rise to deepfakes, which pose serious challenges due to their potential to create deceptive content. Thousands of media reports have informed us of such occurrences, highlighting the urgent need for reliable detection methods. This study addresses the issue by developing a deep learning (DL) model capable of distinguishing between real and fake face images generated by StyleGAN. Using a subset of the 140K real and fake face dataset, we explored five different models: a custom CNN, ResNet50, DenseNet121, MobileNet, and InceptionV3. We leveraged the pre-trained models to utilise their robust feature extraction and computational efficiency, which are essential for distinguishing between real and fake features. Through extensive experimentation with various dataset sizes, preprocessing techniques, and split ratios, we identified the optimal ones. The 20k_gan_8_1_1 dataset produced the best results, with MobileNet achieving a test accuracy of 98.5%, followed by InceptionV3 at 98.0%, DenseNet121 at 97.3%, ResNet50 at 96.1%, and the custom CNN at 86.2%. All of these models were trained on only 16,000 images and validated and tested on 2000 images each. The custom CNN model was built with a simpler architecture of two convolutional layers and, hence, lagged in accuracy due to its limited feature extraction capabilities compared with deeper networks. This research work also included the development of a user-friendly web interface that allows deepfake detection by uploading images. The web interface backend was developed using Flask, enabling real-time deepfake detection, allowing users to upload images for analysis and demonstrating a practical use for platforms in need of quick, user-friendly verification. This application demonstrates significant potential for practical applications, such as on social media platforms, where the model can help prevent the spread of fake content by flagging suspicious images for review. This study makes important contributions by comparing different deep learning models, including a custom CNN, to understand the balance between model complexity and accuracy in deepfake detection. It also identifies the best dataset setup that improves detection while keeping computational costs low. Additionally, it introduces a user-friendly web tool that allows real-time deepfake detection, making the research useful for social media moderation, security, and content verification. Nevertheless, identifying specific features of GAN-generated deepfakes remains challenging due to their high realism. Future works will aim to expand the dataset by using all 140,000 images, refine the custom CNN model to increase its accuracy, and incorporate more advanced techniques, such as Vision Transformers and diffusion models. The outcomes of this study contribute to the ongoing efforts to counteract the negative impacts of GAN-generated images. Full article

The emergence of generative artificial intelligence (GAI) has revolutionized numerous aspects of our lives and presents significant opportunities in education. However, specific digital competencies are essential to effectively leverage this technology’s potential. Notably, prompt engineering proficiency presents a significant barrier to achieving optimal outcomes. In response, various solutions are being developed, including custom GPTs available through OpenAI’s ChatGPT platform. This study validates ‘GamifIcA Edu’, a specialized GPT-based assistant for gamification and serious games, designed to enable educators to implement these pedagogical approaches without requiring advanced prompt engineering expertise. This is achieved through the utilization of pre-designed instructional frameworks. The assistant’s effectiveness was evaluated using a comprehensive rubric across five distinct use-case scenarios. Each scenario underwent four different tests, representing varied learning contexts across multiple academic disciplines. The validation methodology involved a systematic assessment of the assistant’s performance in diverse educational settings. The findings demonstrate the successful implementation of this custom-designed GPT, which generated contextually appropriate responses through natural language interactions, thus eliminating the need for complex prompt structures. This research highlights the potential of instruction-based design in the development of AI assistants that empower users with limited prompt engineering knowledge to achieve expert-level results. These findings have significant implications for the democratization of AI-enhanced educational tools. Full article

The family of the β₂-integrin receptors is critically involved in host defense and homeostasis, by mediating immune cell adhesion, migration, and phagocytosis. Due to their key roles in immune surveillance and inflammation, their modulation has been recognized as an attractive drug target. However, the development of therapeutics has been limited, partly due to the high promiscuity of endogenous ligands, their functional responses, and gaps in our understanding of their disease-related molecular mechanisms. The delineation of the molecular role of β₂ integrins and their ligands has been hampered by a shortage of validated assay systems. To facilitate molecular and functional studies on the β₂-integrin family, and to enable screening of modulators, this study provides a uniform and validated assay platform. For this purpose, the major ligand-binding domains (αI) of all four β₂ integrins were recombinantly expressed in both low- and high-affinity states. By optimizing the expression parameters and selecting appropriate purification tags, all αI-domain variants could be produced with high yield and purity. Direct binding studies using surface plasmon resonance (SPR) confirmed the expected activity and selectivity profiles of the recombinant αI domains towards their reported ligands, validating our approach. In addition, the SPR studies provided additional insights into ligand binding, especially for the scarcely described family member CD11d. Alongside characterizing endogenous ligands, the platform can be employed to test pharmacologically active compounds, such as the reported β₂-integrin antagonist simvastatin. In addition, we established a bead-based adhesion assay using the recombinant αI domains, and a cell-based adhesion assay underlining most findings generated with the isolated αI domains. Interestingly, the binding of ligands to the recombinant α_DI is not dependent on divalent cation, in contrast to the full integrin CD11d/CD18, suggesting a binding mode distinct of the metal ion-dependent adhesion site (MIDAS). The setup highlights the applicability of recombinant αI domains for first screenings and direct or competitive interaction studies, while the full integrin is needed to validate those findings. Full article