Search Results (435)

This study investigates the valorisation of sour orange (Citrus aurantium L.) flowers using supercritical antisolvent fractionation (SAF) with CO₂ as an antisolvent. SAF was applied to selectively recover bioactive compounds from ethanolic extracts, using supercritical CO₂ to induce precipitation. Response Surface Methodology (RSM) was employed to optimize operational conditions across a pressure range of 8.7–15 MPa and CO₂ flow rates of 0.6–1.8 kg/h, at a constant temperature of 40 °C. Pressure showed a statistically significant positive effect on precipitate yield, while higher CO₂ flow rates led to reduced recovery. High-performance liquid chromatography (HPLC) analysis identified naringin (33.7%), neohesperidin (21.6%), and synephrine (9.0%) as the main components of the enriched fractions. SAF enabled the selective concentration of these compounds, supporting its application as a green separation technique. As a complementary evaluation, preliminary in silico predictions of ADMET properties and skin permeability were performed. The results indicated favourable absorption, low predicted toxicity, and limited dermal permeation for the major flavonoids. These findings are consistent with available experimental and regulatory safety data. Overall, the study demonstrates the potential of SAF as an effective green technology for the selective extraction and enrichment of high-value bioactive compounds derived from Citrus aurantium flowers, with promising applications in cosmetic, nutraceutical, and pharmaceutical formulations. Full article

Decarbonizing air transport is a major challenge in the global energy transition since electrification is not yet feasible. Sustainable aviation fuel (SAF) is a promising solution because it can reduce CO₂ emissions without major infrastructure changes. This study proposes a decentralized model for producing SAF in Spain through the gasification of residual lignocellulosic biomass followed by a refinement process using Fischer–Tropsch (FT) synthesis. The model uses underexploited agricultural residues such as cereal straw, vine pruning, and olive pruning, converting them into syngas in medium-scale facilities situated near biomass sources. The syngas is then transported to a central upgrading unit to produce SAF compliant with ASTM D7566 standards. The following two configurations were evaluated: one with a single gasification plant and upgrading unit and another with three gasification plants supplying one central FT facility. Energy yields, capital and operational expenditures (CAPEX and OPEX), logistic costs, and the levelized cost of fuel (LCOF) were assessed. Under a conservative scenario using one-third of the available certain types of biomass from three regions of Spain, annual SAF production could reach 517.6 million liters, with unit costs ranging from 1.63 to 1.24 EUR/L and up to 47,060 tonnes of CO₂ emissions avoided per year. The findings support the model’s technical and economic viability and its alignment with circular economy principles and climate policy goals. This approach offers a scalable and replicable pathway for decarbonizing the aviation sector using local renewable resources. Full article

To address the issues of false detections and missed detections in object detection for intelligent driving scenarios, this study focuses on optimizing the YOLOv10 algorithm to reduce model complexity while enhancing detection accuracy. The method involves three key improvements. First, it involves the design of multi-scale flexible convolution (MSFC), which can capture multi-scale information simultaneously, thereby reducing network stacking and computational load. Second, it reconstructs the neck network structure by incorporating Shallow Auxiliary Fusion (SAF) and Advanced Auxiliary Fusion (AAF), enabling better capture of multi-scale features of objects. Third, it improves the detection head through the combination of multi-scale convolution and channel adaptive attention mechanism, enhancing the diversity and accuracy of feature extraction. Results show that the improved YOLOv10 model has a size of 13.4 MB, meaning a reduction of 11.8%, and that the detection accuracy mAP@0.5 reaches 93.0%, outperforming mainstream models in comprehensive performance. This work provides a detection framework for intelligent driving scenarios, balancing accuracy and model size. Full article

Sustainable aviation fuel (SAF) has demonstrated significant potential to reduce carbon emissions in the aviation industry. Multiple national and international initiatives have been launched to accelerate SAF adoption, yet large-scale commercialization continues to face technological, operational, and regulatory barriers. Industry 4.0 provides a suite of advanced technologies that can address these challenges and improve SAF operations across the supply chain. This study conducts an integrative literature review to identify and synthesize research on the application of Industry 4.0 technologies in the production and distribution of SAF. The findings highlight that technologies such as artificial intelligence (AI), Internet of Things (IoT), blockchain, digital twins, and 3D printing can enhance feedstock logistics, optimize conversion pathways, improve certification and compliance processes, and strengthen overall supply chain transparency and resilience. By mapping these applications to the six key workstreams of the SAF Grand Challenge, this study presents a practical framework linking technological innovation to both strategic and operational aspects of SAF commercialization. Integrating Industry 4.0 solutions into SAF production and supply chains contributes to reducing life cycle greenhouse gas (GHG) emissions, strengthens low-carbon energy systems, and supports the United Nations Sustainable Development Goal 13 (SDG 13). The findings from this research offer practical guidance to policymakers, industry practitioners, investors, and technology developers seeking to accelerate the global shift toward carbon neutrality in aviation. Full article

This article presents a novel method for detecting series arc faults (SAFs) in residential applications using time-domain imaging (TDI) and Long Short-Term Memory (LSTM) networks. The proposed method transforms current signals into grayscale images by filtering out the fundamental frequency, allowing key arc fault characteristics—such as high-frequency noise and waveform distortions—to become visually apparent. The use of Ensemble Empirical Mode Decomposition (EEMD) helped isolate meaningful signal components, although it was computationally intensive. To address real-time requirements, a simpler yet effective TDI method was developed for generating 2D images from current data. These images were then used as inputs to an LSTM network, which captures temporal dependencies and classifies both arc faults and appliance types. The proposed TDI-LSTM model was trained and tested on 7000 labeled datasets across five common household appliances. The experimental results show an average detection accuracy of 98.1%, with reduced accuracy for loads using thyristors (e.g., dimmers). The method is robust across different appliance types and conditions; comparisons with prior methods indicate that the proposed TDI-LSTM approach offers superior accuracy and broader applicability. Trade-offs in sampling rates and hardware implementation were discussed to balance accuracy and system cost. Overall, the TDI-LSTM approach offers a highly accurate, efficient, and scalable solution for series arc fault detection in smart home systems. Full article

Non-invasive ultrasound diagnosis, combined with deep learning, is frequently used for detecting thyroid diseases. However, real-time detection on portable devices faces limitations due to constrained computational resources, and existing models often lack sufficient capability for small object detection of thyroid nodules. To address this, this paper proposes an improved lightweight small object detection network framework called LISA-YOLO, which enhances the lightweight multi-scale collaborative fusion algorithm. The proposed framework exploits the inherent symmetrical characteristics of ultrasound images and the symmetrical architecture of the detection network to better capture and represent features of thyroid nodules. Specifically, an improved depthwise separable convolution algorithm replaces traditional convolution to construct a lightweight network (DG-FNet). Through symmetrical cross-scale fusion operations via FPN, detection accuracy is maintained while reducing computational overhead. Additionally, an improved bidirectional feature network (IMS F-NET) fully integrates the semantic and detailed information of high- and low-level features symmetrically, enhancing the representation capability for multi-scale features and improving the accuracy of small object detection. Finally, a collaborative attention mechanism (SAF-NET) uses a dual-channel and spatial attention mechanism to adaptively calibrate channel and spatial weights in a symmetric manner, effectively suppressing background noise and enabling the model to focus on small target areas in thyroid ultrasound images. Extensive experiments on two image datasets demonstrate that the proposed method achieves improvements of 2.3% in F1 score, 4.5% in mAP, and 9.0% in FPS, while maintaining only 2.6 M parameters and reducing GFLOPs from 6.1 to 5.8. The proposed framework provides significant advancements in lightweight real-time detection and demonstrates the important role of symmetry in enhancing the performance of ultrasound-based thyroid diagnosis. Full article

Hydrotreated ester and fatty acids to jet (HEFA-tJ) is presently the most developed and economically attractive pathway to produce sustainable aviation fuel (SAF). An ongoing systematic study of the critical variables of different pathways to SAF has revealed significantly lower greenhouse gas (GHG) reduction potential for the HEFA-tJ pathway compared to competing markets using the same resources for road diesel production. Moderate yield variations between air and road pathways lead to several hundred thousand tons less GHG reduction per project, which is generally not evaluated thoroughly in standard environmental assessments. This work demonstrates that, although the HEFA-tJ market seems to have more attractive features than biodiesel/renewable diesel, considerable viability risks might manifest as HEFA-tJ fuel market integration rises. The need for more transparent data and effort in this regard, before envisaging making decisions regarding the volume of HEFA-tJ production, is emphasized. Overall, reducing the carbon intensity of road diesel appears to be less capital-intensive, less risky, and several times more efficient in reducing GHG emissions. Full article

Skin autofluorescence (SAF) detection technology represents a noninvasive, convenient, and cost-effective optical detection approach. It can be employed for the differentiation of various diseases, including metabolic diseases and dermatitis, as well as for monitoring the treatment efficacy. Distinct from diffuse reflection signals, the autofluorescence signals of biological tissues are relatively weak, making them challenging to be captured by photoelectric sensors. Moreover, the absorption and scattering properties of biological tissues lead to a substantial attenuation of the autofluorescence of biological tissues, thereby worsening the signal-to-noise ratio. This has also imposed limitations on the development and application of compact-sized autofluorescence detection systems. In this study, a compact LED light source and a CMOS sensor were utilized as the excitation and detection devices for skin tissue autofluorescence, respectively, to construct a mobile and wireless skin tissue autofluorescence detection system. This system can achieve the detection of skin tissue autofluorescence with a high signal-to-noise ratio under the drive of a simple power supply and a single-chip microcontroller. The detection time is less than 0.1 s. To enhance the stability of the system, a pressure sensor was incorporated. This pressure sensor can monitor the pressure exerted by the skin on the detection system during the testing process, thereby improving the accuracy of the detection signal. The developed system features a compact structure, user-friendliness, and a favorable signal-to-noise ratio of the detection signal, holding significant application potential in future assessments of skin aging and the risk of diabetic complications. Full article

This study proposes a non-semantic multimodal approach to predict segment access frequency (SAF) in lecture archives. Such archives, widely used as supplementary resources in modern education, often consist of long, unedited recordings that are difficult to navigate and review efficiently. The predicted SAF, an indicator of student viewing behavior, serves as a practical proxy for student engagement. The increasing volume of recorded material renders manual editing and annotation impractical, making the automatic identification of high-SAF segments crucial for improving accessibility and supporting targeted content review. The approach focuses on lecture archives from a real-world blended learning context, characterized by resource constraints such as no specialized hardware and limited student numbers. The model integrates multimodal features from instructor’s actions (via OpenPose and optical flow), audio spectrograms, and slide page progression—a selection of features that makes the approach applicable regardless of lecture language. The model was evaluated on 665 labeled one-minute segments from one such course. Experiments show that the best-performing model achieves a Pearson correlation of 0.5143 in 7-fold cross-validation and 61.05% average accuracy in a downstream three-class classification task. These results demonstrate the system’s capacity to enhance lecture archives by automatically identifying key segments, which aids students in efficient, targeted review and provides instructors with valuable data for pedagogical feedback. Full article

Background and Clinical Significance: Serratus Anterior Muscle Pain Syndrome (SAMPS) is an underdiagnosed cause of anterior chest wall pain, often attributed to myofascial trigger points of the serratus anterior muscle (SAM) or dysfunction of the Long Thoracic Nerve (LTN), leading to significant disability and affecting ipsilateral upper limb movement and quality of life. Current diagnosis relies on exclusion and physical examination, with limited treatment options beyond conservative approaches. This case report presents a novel approach to chronic SAMPS, successfully diagnosed using Sonoguided Digital Palpation (SDP) and treated with ultrasound-guided hydrodissection of the LTN using 5% dextrose in water (D5W) without local anesthetic (LA), in a patient where conventional treatments had failed. Case Presentation: A 72-year-old male presented with a three-year history of persistent left chest pain radiating to the upper back, exacerbated by activity and mimicking cardiac pain. His medical history included two percutaneous coronary interventions. Physical examination revealed tenderness along the anterior axillary line and a positive hyperirritable spot at the mid axillary line at the 5th rib level. SDP was used to visualize the serratus anterior fascia (SAF) and LTN, and to reproduce the patient’s concordant pain by palpating the LTN. Ultrasound-guided hydrodissection of the LTN was then performed using 20–30cc of D5W without LA to separate the nerve from the surrounding tissues, employing a “fascial unzipping” technique. The patient reported immediate pain relief post-procedure, with the pain reducing from 9/10 to 1/10 on the Numeric Rating Scale (NRS), and sustained relief and functional improvement at the 12-month follow-up. Conclusions: Sonoguided Digital Palpation (SDP) of the LTN can serve as a valuable diagnostic adjunct for visualizing and diagnosing SAMPS. Ultrasound-guided hydrodissection of the LTN with D5W without LA may provide a promising and safe treatment option for patients with chronic SAMPS refractory to conservative management, resulting in rapid and sustained pain relief. Further research, including controlled trials, is warranted to evaluate the long-term efficacy and generalizability of these findings and to compare D5W to other injectates. Full article

Accurate short-term rainfall forecasting, an essential component of the broader framework of nowcasting, is crucial for managing extreme weather events. Traditional forecasting approaches, whether radar-based or satellite-based, often struggle with limited spatial coverage or temporal accuracy, reducing their effectiveness. This study tackles these challenges by implementing the Local Analysis and Prediction System (LAPS) enhanced with a forward advection nowcasting module, integrating multiple remote sensing rainfall datasets. Specifically, we combine weather radar data with three different satellite-derived rainfall products (H-SAF, GPM, and TRMM) to assess their impact on nowcasting performance for a rainfall event in Attica, Greece (29–30 September 2018). The results demonstrate that combining high-resolution radar data with the broader coverage and high temporal frequency of satellite retrievals, particularly H-SAF, leads to more accurate predictions with lower uncertainty. The assimilation of H-SAF with radar rainfall retrievals (HX experiment) substantially improved forecast skill, reducing the unbiased Root Mean Square Error by almost 60% compared to the control experiment for the 60 min rainfall nowcast and 55% for the 90 min rainfall nowcast. This work validates the effectiveness of the specific LAPS/advection configuration and underscores the importance of multi-source data assimilation for weather prediction. Full article

Sustainable aviation fuels (SAFs) are vitally important for aviation decarbonization. The laminar burning velocity (LBV), a key parameter reflecting the combustion behavior of fuel/oxidizer mixtures, serves as a fundamental metric for evaluating SAF performance. This paper systematically reviews and evaluates the LBV experiment method and the performance of traditional aviation fuel, SAFs produced via different pathways, and individual components (n-alkanes, iso-alkanes, cycloalkanes, and aromatic hydrocarbons, as well as the impacts of isomers and homologues) in aviation fuels. It is found that LBV values of different SAFs exhibit significant fluctuations, approaching or slightly deviating from those of conventional aviation fuels. Carbon number, branching degree, substituent types, and testing methods in the components all affect LBV performance. Specifically, increased branching in iso-alkanes reduces LBV, cyclohexane and benzene show higher LBV than their methylated counterparts (methylcyclohexane and toluene), and n-alkylcyclohexanes/benzenes with short (C1–C3) side chains demonstrate minimal LBV variation. Spherical flame methods yield more consistent (and generally lower) LBV values than stagnation flame techniques. These findings provide insights for optimizing SAF–conventional fuel blends and enhancing drop-in compatibility while ensuring operational safety and usability. Full article

Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease with a heterogeneous organ involvement, for which reliable biomarkers are still being studied. The implication of advanced glycation end products (AGEs), resulting from oxidative stress, and their interaction with the receptor for AGEs (RAGE) has been studied in pathologies with chronic proinflammatory status, offering potential relevance in SLE. This systematic review aimed to evaluate the utility of skin autofluorescence (SAF)—a non-invasive proxy for AGE accumulation—as a biomarker for disease severity, activity, and impact in SLE patients. Following PRISMA guidelines, six studies assessing SAF and/or circulating AGEs and soluble RAGE (sRAGE) in SLE were analyzed. Findings consistently showed higher AGE levels in SLE patients compared to healthy controls, with several correlations between SAF/AGEs and disease features such as SLEDAI scores, organ involvement, inflammatory markers, and damage indices. Decreased sRAGE levels were also observed, possibly due to consumption by AGEs. Some studies further reported predictive associations between specific AGEs or their ratios with sRAGE and particular clinical phenotypes. Although heterogeneity among studies limits definitive conclusions, the AGEs–sRAGE axis—and especially SAF—emerges as a promising candidate for future biomarker development in SLE. Further large-scale longitudinal studies are needed to confirm its clinical utility. Full article

The continuous development of both numerical weather model outputs and remote sensing-derived products has enabled a wide range of applications across various fields, such as agricultural water management, where the need for robust gridded weather data and recurring Earth Observations (EO) is fundamental for estimating crop water requirements (CWR) and yield. This study used the latest reanalysis dataset, AgERA5, combined with the up-to-date CM SAF SARAH-3 Satellite-Based Radiation Data as meteorological inputs of the SAFY dynamic crop growth model and a one-step evapotranspiration formula for CWR and yield estimates at the farm scale of tomato crops. The Sentinel-2 (S2) estimates of Leaf Area Index (LAI) were used to force the SAFY model as soon as they became available during the growing stage, according to the satellite passages over the area of interest. The SAFY model was calibrated with ground-based weather observations and S2 LAI data on tomato crops that were collected in several farms in Campania Region (Southern Italy) during the irrigation season, which spans from April to August. To validate the method, the model estimates were compared with field observations of irrigation volumes and harvested yield from a monitored farm in the same region for the year 2021. Results demonstrated that integrating AgERA5 and CM SAF weather datasets with S2 imagery for assimilation into the SAFY model enables accurate estimates of both CWR and yield. Full article

Sustainable aviation fuels (SAFs) are currently considered a key element in the decarbonization of the aviation sector, offering a feasible solution to reduce life cycle greenhouse gas emissions without requiring fundamental changes in aircraft or infrastructure. This article provides a comprehensive overview of the current state of SAFs, including their classification, production technologies, economic aspects, and environmental performance. The analysis covers both currently certified SAF pathways, such as HEFA and FT-SPK, and emerging technologies like alcohol-to-jet and power-to-liquid, assessing their technological maturity, feedstock availability, and scalability. Economic challenges related to high production costs, investment risks, and policy dependencies are discussed, alongside potential mechanisms to support market deployment. Furthermore, the article reviews SAFs’ emission performance, including CO₂ and non-CO₂ effects, based on existing life cycle assessment (LCA) studies, with an emphasis on variability caused by feedstock type and production method. The findings highlight that, while SAFs can significantly reduce aviation-related emissions compared to fossil jet fuels, the magnitude of benefits depends strongly on supply chain design and sustainability criteria. There are various certified pathways for SAF production, as well as new technologies that can further contribute to the development of the industry. Properly selected biomass sources and production technologies can reduce greenhouse gas emissions by more than 70% compared to conventional fuels. The implementation of SAFs faces obstacles related to cost, infrastructure, and regulations, which hinder its widespread adoption. The study concludes that although SAFs represent a promising pathway for aviation climate mitigation, substantial scaling efforts, regulatory support, and continued technological innovation are essential to achieve their full potential. Full article