The Greater Amanzule Peatland (GAP) in Ghana represents an important ecosystem facing dynamic land cover changes as a result of both natural and anthropogenic factors. This study integrates multispectral and radar remote sensing data from Landsat-7 and -8, L-band SAR, and SRTM to conduct a machine learning-based analysis of these changes from 2010 to 2020 and to predict future scenarios up to 2040. Findings reveal a 12% expansion in peatland cover, equivalent to approximately 6,570 ± 308.59 hectares, despite declines in specific peatland types. Concurrently, anthropogenic land uses have increased, evidenced by an 85% rise in rubber plantations (from 30,530 ± 110.96 hectares to 56,617 ± 220.90 hectares) and a 6% reduction in natural forest cover (5,965 ± 353.72 hectares). Sparse vegetation, including smallholder farms, decreased by 35% from 45,064 ± 163.79 hectares to 29,424 ± 114.81 hectares. Projections for 2030 and 2040 indicate minimal changes based on current trends; however, these do not consider potential impacts from climate change, large-scale development projects, and demographic shifts, necessitating cautious interpretation. The results highlight areas of stability and vulnerability within the GAP and offer critical insights for the development of targeted conservation strategies. Additionally, the methodological framework presented provides a robust approach for accurate and detailed landscape-scale monitoring of tropical peatlands, applicable to other regions facing similar environmental challenges.

This paper presents a novel approach of welding thick steel plates that offers time and energy savings compared with conventional techniques. The combination of gas metal arc welding (GMAW) and hot-wire technology simplifies the joint configuration and enhances the process tolerance. In this study, a square butt joint was prepared with as-cut edges and a thickness of 15 mm. The relationship between the welding current and the deposition rate of solo GMAW showed limitations and low process tolerance. Increasing the welding current led to a larger deposited volume with unnecessary weld penetration. An independent deposition volume due to hot-wire insertion was used to improve process tolerance. This approach provided an additional volume without increasing the welding current and reduced unnecessary penetration. With optimized parameters, full-penetration single-pass welding was achieved. Compared with the formation of a typical single-v butt joint at a similar welding speed of 30 cm/min, the proposed process reduced the minimum arc time and power consumption by approximately 83% and 62%, respectively. Moreover, a single pass at a travel speed of 60 cm/min was achieved with approximately 91% and 81% less arc time and power consumption, respectively. In summary, the combined process simplifies the joint configuration, enables full-penetration single-pass welding, and reduces time and energy requirements.

Leucine is an essential amino acid that cannot be produced endogenously in the human body and therefore needs to be obtained from dietary sources. Leucine plays a pivotal role in stimulating muscle protein synthesis along with isoleucine and valine as the group of branched-chain amino acids making them as one of the most popular dietary supplements for athletes and gym-goers. Individual effects of leucine, however, are not fully clarified, as most of the studies focused on the grouped effects of branched-chain amino acids. In recent years, leucine and its metabolites have been shown to stimulate muscle protein synthesis mainly via mammalian target of rapamycin complex 1 signaling pathway, thereby improving muscle atrophy in cancer cachexia. Interestingly, cancer research suggests that leucine may have either anti-cancer or carcinogenic effects. In the current manuscript, we aim to review leucine's roles in muscle protein synthesis, tumor suppression, and tumor progression, specifically summarizing the molecular mechanisms of leucine's action. The role of leucine is controversial in hepatocellular carcinoma, whereas its carcinogenic effects have been demonstrated in breast and pancreatic cancers. In summary, leucine being used as nutritional supplement for athletes needs more attention, as its oncogenic effects may have been identified by recent studies. Anti-cancer or carcinogenic effects of leucine in various cancers should be further investigated to have clear conclusions.

Mitral valve disease is a common heart disease in dogs. The aim of this study was to describe the cases of dogs who underwent mitral valve repair using a v-clamp device and to provide their long-term follow-up data on cardiac function after mitral valve repair. Four dogs with mitral valve regurgitation who experienced coughing and dyspnea underwent surgical mitral valve repair between December 2023 and June 2024. The patients were evaluated by transthoracic and transesophageal echocardiography. Echocardiography revealed mitral valve leaflet regurgitation. Mitral valve repair was performed under general anesthesia using a v-clamp device introduced through an introducer wire guide. Echocardiography was conducted at baseline and the 6-month follow-up. Blood analysis results after surgical repair were normal. Follow-up echocardiography revealed no complications related to the procedure, with all dogs demonstrating improved respiratory signs and quality of life after repair. No adverse reactions were reported after surgery. A v-clamp device was used to repair mitral valve regurgitation in four dogs. Mitral valve repair using a v-clamp device is another treatment option for mitral valve disease in dogs.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

This paper presents a comprehensive comparative analysis of four different computational vision algorithms for implementing an unpalleting system in a factory environment, under typical edge computing constraints. The primary objective is to automate the process of detecting and locating specific variable-shaped objects on a pallet, enabling a robotic system to accurately unstack them. The four algorithms evaluated are Pattern Matching, Scale-Invariant Feature Transform, Oriented FAST and Rotated BRIEF, and Haar cascade classifiers. Each technique is described in detail in this paper, and their implementations are outlined. Experimental results are thoroughly analyzed, assessing the algorithms’ performance based on accuracy, robustness to variability, computational speed, detection sensitivity, and resource consumption. The findings reveal the strengths and limitations of each algorithm, providing valuable insights for selecting the most appropriate technique based on the specific requirements of the unpalleting system. This research contributes to the advancement of automated object detection and localization in industrial environments, paving the way for more efficient and reliable unpalleting systems.

The continuous casting process of helical fiber reinforced metal matrix composites was proposed, and the experimental equipment was designed and manufactured. The relationship model be-tween the process parameters and the helical shape of the fibers in the metal matrix is analyzed by differential geometry. Helical carbon fiber reinforced lead matrix composites were prepared under different conditions. When the melting temperature and cooling intensity were increased, the helical radius of helical carbon fiber in lead matrix was increased. When the fiber rotation speed was increased, the helical radius was decreased, and the helical pitch was de-creased proportionally. The experimental results are consistent with the process model. Carbon fiber rein-forced aluminum composites were prepared. Scanning electron microscopy showed that the infiltration between the aluminum matrix and the carbon fiber was not complete. As the volume fraction of carbon fiber which was added in composites is too small, the mechanical properties of the composites have not been significantly improved.

Adult neurogenesis involves generation of functional neurons from neural progenitor cells, which have the potential to complement and restore damaged neurons and neural circuits. Therefore, the development of drugs that stimulate neurogenesis represents a promising strategy in stem cell therapy and neural regeneration, greatly facilitating the reconstruction of neural circuits in cases of neurodegeneration and brain injury. Our study reveals that compound A5, previously designed and synthesized by our team, exhibits remarkable neuritogenic activities, effectively inducing neurogenesis in neural stem/progenitor cells (NSPCs). Subsequently, transcriptome analysis using high-throughput Illumina RNA-seq technology was performed to further elucidate the underlying molecular mechanisms by which compound A5 promotes neurogenesis. Notably, comparative transcriptome analysis showed that the up-regulated genes were mainly associated with neurogenesis and the down-regulated genes were mainly concerned with cell cycle progression. Furthermore, we confirmed that compound A5 significantly affected the expression of transcription factors related to neurogenesis and cell cycle regulatory proteins. Collectively, these findings identify a new compound with neurogenic activity and may provide insights into drug discovery for neural repair and regeneration.

The study compares three machine learning and deep learning models—Support Vector Regression (SVR), Recurrent Neural Networks (RNN), and Long Short-Term Memory (LSTM)—for predicting market prices using the NGX All-Share Index dataset. The models were evaluated using multiple error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), and R-squared. RNN and LSTM were tested with both 30 and 60-day windows, with performance compared to SVR. LSTM delivered better R-squared values, with a 60-day LSTM achieving the best accuracy (R-squared = 0.993) when using a combination of endogenous market data and technical indicators. SVR showed reliable results in certain scenarios but struggled in fold 2 with sudden spike that shows high probability of not capturing the entire underlying NGX pattern in the dataset correctly as witnessed with the high validation loss during the period. Additionally, RNN faced the vanishing gradient problem that limits its long-term performance. Despite challenges, LSTM's ability to handle temporal dependencies, especially with the inclusion of On-Balance Volume, led to significant improvements in prediction accuracy. The use of the Optuna optimization framework further enhanced model training and hyperparameter tuning, contributing to the performance of the LSTM model.

Background: The last-mile delivery phase, the final stage where goods move from a distribution center to customers, is pivotal but faces significant inefficiencies and high costs due to its complexity. Recent advancements in Edge AI or Edge Intelligence (EI) offer promising solutions to these challenges. Methods: This study explores how AI-driven technologies and real-time data processing, combined with EI, can enhance last-mile delivery operations. A thorough literature review was conducted to assess technological advancements, and a Delphi method was used to systematically and empirically assess the impact of EI solutions on both operational efficiency and customer satisfaction. Results: Although EI technologies offer substantial benefits, EU companies are hesitant to adopt these innovations due to high implementation costs. However, firms that have embraced these technologies report significant improvements, including better route optimization, reduced delivery times, and enhanced service reliability. These findings highlight the need for a culture of innovation and the recruitment of experts with advanced qualifications to drive technological advancement in last-mile logistics. Conclusions: The integration of EI represents a significant step towards more efficient, cost-effective, and customer-focused last-mile delivery solutions. Future research should aim to refine these technologies and explore their long-term impacts on the logistics industry.

The intelligent morphing wing structure has the characteristics of flexibility, large deformation and adaptive change, which are different from those of the conventional wing. The motion law of the variable-camber leading edge is analysed by the numerical simulation method based on kinematics theory to solve the problem of verifying the motion function and structural strength of the variable camber wing. The driving deformation and aerodynamic load simulation of the variable camber leading edge are realised by designing a follow-up load test apparatus. Based on the numerical simulation of the mechanical boundary, the motion trajectory of the upper and lower wing loading points was predicted, and a multi-point cooperative control system was developed. Multi-sensor iteration was used to ensure the loading accuracy of the test control. The test results show that the average error of the deflection angle is 4.59% in the variable camber leading edge motion test. In the variable camber leading edge strength test, the average error of applied load magnitude and direction is 0.54% and 0.24%, respectively.

Network Function Virtualization (NFV) allows for the dynamic provisioning of Virtualized Network Functions, adapting services to the complex and real-time network environment for enhanced network performance. Despite the benefits of NFV, virtual network functions (VNFs) migration and the energy consumption pose significant challenges due to the dynamic nature of the physical network, especially in the edge-cloud continuum network. Currently, people tackle this challenge by predicting the network traffic and then proactively migrating the VNFs using the predicted values. There are two challenges here. First, when SFCs belong to different network service providers, the historical network traffic data is held by the service providers, and they are reluctant to share the historical data due to privacy concerns. Without the historical network traffic data of each SFC, the resource provider that owns the underlying network cannot effectively predict the network traffic; in addition, without the feedback of the migration results of the underlying physical network, the network traffic prediction will be difficult to match the needs of the physical network. In this manuscript, we solve the problem of network traffic prediction in the NFV-based edge-cloud continuum network for multiple network service providers. We aim to improve migration performance and reduce energy consumption while ensuring resource and latency requirements. To protect the privacy of each network service provider, we apply an FL algorithm. After that, to improve the migration performance, we tailor the loss function. In the loss function, factors such as the number of migrations, the number of migration failures, and the energy consumption of the NFV-based edge-cloud continuum network are added. Additionally, to obtain the results of the SFC migration, we develop a mathematical model for the problem of multiple network service providers in the NFV-based network. The effectiveness of our algorithm is evaluated through extensive simulations, and the results show a significant reduction in the number of migrated nodes and energy consumption while improving the SFC acceptance ratio compared to state-of-the-art schemes which only use the difference between predicted and actual flows to define the loss function.

Breast cancer is the most prevalent type of disease among women. It has become one of the foremost causes of death among women globally. Early detection plays a significant role in administering personalized treatment and improving patient outcomes. Mammography procedures are often used to detect early-stage cancer cells. This traditional method of mammography while valuable has limitations of potential for false positives and negatives, patient discomfort, and radiation exposure. Therefore, there is a probe for more accurate techniques required in detecting breast cancer, leading to exploring the potential of machine learning in the classification of diagnostic images due to its efficiency and accuracy. This study conducted a comparative analysis of pre-trained CNNs (ResNet50 and VGG16) and Vision Transformers (ViT-Base and SWIN Transformer) with the inclusion of ViT-Base trained from scratch model architectures to effectively classify breast cancer mammographic images into benign and malignant cases. The Swin transformer exhibits superior performance with 99.8% accuracy and a precision of 99.8%. These findings demonstrate the efficiency of deep learning to accurately classify breast cancer mammographic images for the diagnosis of breast cancer, leading to improvement in patient outcomes.

Lignin is insoluble in water, limiting its use in the synthesis of adhesives. Therefore, in order to increase the amount of lignin raw materials used in the synthesis of adhesives, a kind of aminated lignin compounds was prepared through lignin amination reaction, and conducting structural analysis in this paper. The particle size of the amino lignin is about 100 nm, with the weight average molecular weight of 57,627 g/mol and a water solubility of 0.45g/100ml. The reaction mechanisms were proposed that the phenolic hydroxyl groups of lignin react with ammonia molecules, and thereby successfully introducing amino groups to generate the aminated lignin compounds. Therefore, this article enriches the scientific theory of lignin reactions and provides reference for the widespread application of lignin raw materials in the field of adhesives.

Postbiotic is a preparation of inanimate microorganisms and/or their components that confers a health benefit on the host. Different bacterial strain and preparation method result in different functions. Herein, a postbiotic preparation named Postbiotic-P by intercepting 1-5 KD of the cell-free supernatants of L. caseiparacasei P6 fermentation broth was prepared, and its multifunctional activities were evaluated. The results showed that Postbiotic-P exerted good antihemolytic (10%-50% Postbiotic-P resulted in 4.9%-48.1% antihemolytic rate), anti-inflammatory (8% to 40% Postbiotic-P is comparable to 259.1-645.4 μg/mL of diclofenac sodium equivalents), antioxidant [reducing power (1.25%-12.5% Postbiotic-P resulted in the increase of OD700 from 0.279-2.322), DPPH free radical scavenging activity (2.5%-50% Postbiotic-P resulted in 38.9%-92.4% free radical scavenging rate), and hydroxyl radical scavenging activity (0.5%-4% Postbiotic-P resulted in 4.66%-10.38% hydroxyl radical scavenging rate)], and antimicrobial activities (against 2 Gram-positive, 8 Gram-negative and 1 fungus). Furthermore, it was proved that Postbiotic-P could significantly inhibit the increase of the peroxide value and the malondialdehyde content in cookies, indicating the application potential of Postbiotic-P in food preservation. Taken together, Postbiotic-P has good application potential for the development of health care products and functional foods. However, further studies are needed to determine their precise mechanism of action, to clarify the active components responsible for the function, and to validate the biological activities in animal models or clinical trials.

Objective: Mechanical bowel preparation is essential for the visualisation of the colon during colonoscopy, and previous studies have identified changes in gut microbiota composition after bowel preparation and colonoscopy. Considering the gut microbiota is increasingly implicated in mood, we aimed to explore the potential impact of this intervention on mood and the microbiota-gut-brain axis. Methods: We conducted a pre-post intervention study, with timepoints of one week before and one month after bowel preparation and colonoscopy, in community-dwelling adults referred for colonoscopy. Our primary outcome was change in average Hospital Anxiety and Depression Scale depression sub-scores using generalised estimating equations. We also examined changes in average anxiety, stress, and quality of life scores, and faecal microbiota composition and potential function using 16S rRNA sequencing. We further explored associations between changes in depressive symptoms and the gut microbiota and conducted post-hoc analyses to explore potential effect modifiers. Key Results: 59 participants were included in the analyses. Depressive symptoms decreased one-month post-procedure compared to baseline (adjusted β=−0.64; 95%CI: −1.18, −0.11). Irritable bowel syndrome (IBS) appeared to moderate this relationship (1.78; 0.292, 3.26); depressive symptoms increased in those with, and decreased in those without, IBS. Changes in gut microbiome composition included reduced alpha diversity, modest effects on beta-diversity, and increases in genera associated with potential health benefits, one-month post-procedure compared to baseline. Increases in the CLR-transformed abundance of Ruminococcaceae UCG-009 were associated with improvements in depressive symptoms. Conclusions: There is preliminary evidence of a potential mental health effect of bowel preparation and colonoscopy, which may be associated with changes to the gut microbiota. There may also be a differential mental health impact of this procedure for those with and without IBS. Considering the limitations of our study, further research is required to confirm these findings and their clinical relevance.

Nanocomposites of TiO₂ Degussa P25 nanoparticles/activated carbon (TiO₂/AC ) were prepared at various mass ratios of (4:1), (3:2), (2:3), and (1:4) by a facile process. The effects of TiO₂/AC mass ratios on the structural, morphological, and photocatalytic properties were systematically studied in comparison with bare TiO₂ and bare AC. TiO₂ nanoparticles exhibited dominant anatase and minor rutile phases, and a crystallite size of approximately 21 nm; while AC had XRD peaks of graphite and carbon, and a crystallite size of 49 nm. The composites exhibited tight decoration of TiO₂ nanoparticles on micron-/submicron AC particles, and uniform TiO₂/AC composites were obtained as evidenced by the uniform distribution of Ti, O, and C in an EDS mapping. Moreover, Raman spectra show the typical vibration modes of anatase TiO₂ (e.g., E_1g⁽¹⁾, B_1g⁽¹⁾, E_g⁽³⁾) and carbon materials with D and G bands. The TiO₂/AC with (4:1), (3:2), and (2:3) possessed higher reaction rate constants (k) in photocatalytic degradation of methylene blue (MB) than that of either TiO₂ or AC. Among the investigated materials, TiO₂/AC = 4:1 achieved the highest photocatalytic activity with a high k of 55.2×10^-3 min^-1 and an MB removal efficiency of 96.6% after 30 min treatment under UV-Vis irradiation (120 mW/cm²). The enhanced photocatalytic activity for TiO₂/AC is due to the synergistic effect of the high adsorption capability of AC and the high photocatalytic activity of TiO₂. Furthermore, TiO₂/AC promotes the separation of photoexcited electron/hole (e^-/h⁺) pairs to reduce their recombination rate and thus enhance photocatalytic activity. The optimal TiO₂/AC composite in this study can be used for treating industrial or household wastewater with organic pollutants.

Of the 100,000 railroad bridges in the United States, 50% are over 100 years old. Many of these bridges do not meet the minimum vertical clearance standards, making them susceptible to impacts from over-height vehicles. Impacts can cause structural damage and unwanted disruption to railroad bridge services; rapid notification of the railroad authorities is crucial to ensure that the bridges are safe for continued use and to affect timely repairs. Therefore, researchers have developed approaches to identify these impacts to railroad bridges. Some recent approaches use machine learning to more effectively identify impacts from the sensor data. Typically, the collected sensor data is transmitted to a central location for processing. However, the challenge with this centralized approach is that the transfer of data to a central location can take considerable time, which is undesirable for time-sensitive events like impact detection that require rapid assessment and response to potential damage. To address the challenges posed by the centralized approach, this study develops a framework for edge implementation of machine learning predictions on wireless smart sensors. Wireless sensors are used because of their ease of installation and lower costs compared to their wired counterparts. The framework is implemented on the Xnode wireless smart sensor platform, thus bringing artificial intelligence models directly to the sensor nodes and eliminating the need to transfer data to a central location for processing. This framework is demonstrated using data obtained from events on a railroad bridge near Chicago; results illustrate the efficacy of the proposed edge computing framework for such time-sensitive structural health monitoring applications.

Fabrication of durable materials with excellent oil adsorption capacity and separation performance for the treatment of oily wastewater is meaningful based on smart responsiveness. Herein, a solvent-responsive melamine sponge (MS) was developed via silanization followed by the in-situ growth of zeolitic imidazolate framework-8 (ZIF-8). Detailed characterization of the resultant composite MS was conducted using scanning electron microscopy (SEM), energy dispersive spectrometer (EDS), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The multiscale hierarchical MS substrate exhibited highly hydrophobic property in the pH range of 1-11, along with a satisfactory adsorption capacity in the range of 65.4-134.2 g/g for different oils. The modified surface transformed from superhydrophobicity/superlipophilicity to superhydrophilicity/underwater superoleophobicity upon ethanol wetting, reverting to its original superhydrophobic state upon drying. The separation flux of the MS substrate was above 1.5×104L/m2h for both oil and water removal, along with the separation efficiency more than 98.7%. No obvious changes of the separation performance after 50 successive immiscible oil/water separation indicated the excellent durability and robustness of the anchored ZIF-8 nanoparticles on the surface of modified MS substrate. More importantly, oil-in-water emulsion separation was successfully carried out via the ZIF-8 MS composite with a high separation efficiency over 99.1%. The developed smart sponge with high oil absorption capacity, excellent chemical stability and fire resistance exhibited a wide range of potential practical applications in the convenient treatment of oily wastewater.

Air pollution has created a lot of problems in the developed and developing countries. To avoid or reduce these problems, constant monitoring of the air should be ensured. The conventional techniques is costly because it requires a lot of money and time consuming. Biomonitoring has been the alternative method. Moss, lichens and plants are biomonitors available to entrap air pollutants. The aim of this paper is to discuss one of the ways of monitoring air pollution – Moss bag technique. To do this, types, choice, preparation, handling of bags after preparation of moss were discussed. From the literatures consulted, it was discovered that there were differences in the techniques used by the researchers. In all, the use of mosses with emphasis on the employment of moss bag have proved to be a powerful tool in airborne particulate and toxic elements. To conclude, developing countries should focus more on this technique because it will reduce cost of air monitoring.

Predicting credit default risk is important to financial institutions, as accurately predicting the likelihood of a borrower defaulting on their loans will help to reduce financial losses, thereby maintaining profitability and stability. Although machine learning models have been used in assessing large applications with complex attributes for these predictions, there is still a need to identify the most effective techniques and to also address the issue of data imbalance. In this research, we conducted a comparative analysis of random forest, decision tree, SVM (Support Vector Machine), XGBoost (eXtreme Gradient Boosting), ADABoost (ADAptive Boosting) and multilayered perceptron with three-hidden layers, to predict credit default using loan data from LendingClub. Additionally, we also combined the model predictions using voting and stacking ensemble methods to enhance the models' performance. Furthermore, various sampling techniques was explored to handle the issue of class imbalance observed in the dataset, with the result showing that the balanced data performs better than the imbalanced data. Our proposed model achieved an accuracy of 93.7%, a precision of 95.6% and a recall of 95.5%, which shows the potential of ensemble methods in improving credit default predictions and can provide lending platforms with the tool to reduce default rates and financial losses. In conclusion, the findings from this study have broader implications for financial institutions, offering a robust approach to risk assessment beyond the Lend-ingClub dataset.

An easy to implement and cost-effective Fiber Bragg Grating (FBG) sensor interrogation technique based on a ring Erbium-Doped Fiber Laser (EDFL) topology is proposed and experimentally assessed. The FBG sensor is part of the EDFL cavity and must have a central wavelength located within the linear region of the EDF’s amplified spontaneous emission (ASE) spectrum which occurs between 1530 and 1540nm. In this manner, the wavelength-encoded response of the FBG under strain is converted to a linear variation of the laser output power, removing the need for spectrum analysis as well as any limitations from the use of external edge-filtering components. In addition, the laser linewidth is significantly reduced with respect to the FBG bandwidth thus improving the resolution of the system, whereas its sensitivity can be controlled through the pumping power. The performance of the system has been characterized by modeling and experiments for EDF's with different lengths, doping concentrations and pumping power levels. The influence of mode-hopping in the laser cavity on the resolution and accuracy of the system has also been investigated.

The COVID-19 pandemic has highlighted the need for a robust medical infrastructure and crisis management strategy as part of smart-city applications, with technology playing a crucial role. The Internet of Things (IoT) has emerged as a promising solution, leveraging sensor arrays, wireless communication networks, and artificial intelligence (AI)-driven decision-making. Advancements in Edge Computing (EC), Deep Learning (DL), and Deep Transfer Learning (DTL) have made IoT more effective in healthcare and pandemic-resilient infrastructure. When combined with medically oriented IoT setups, DL-architectures are suitable for integrating into pandemic-compliant medical infrastructure. The development of intelligent pandemic-compliant infrastructure requires combining IoT, edge and cloud computing, image processing, and AI tools to monitor adherence to social distancing norms, mask-wearing protocols, and contact tracing. The proliferation of 5G wireless communication has enabled ultra-wide broadband wireless communication, with high reliability and low latency, thereby enabling seamless medical support as part of smart-city applications. Such set-ups are designed to be ever-ready to deal with virus-triggered pandemic-like medical emergencies. This study presents the design of a pandemic-compliant mechanism leveraging IoT optimized for healthcare applications, edge- and cloud-computing frameworks, and a suite of DL-tools. The framework uses a composite attention-driven framework incorporating various DL-pre-trained models (DPTM) for protocol adherence and contact tracing. When connected to public networks, it can detect specific cyber-attacks. The results confirm the effectiveness of the proposed methodologies.

Background: Ice formation on power transmission lines presents significant risks, including structural damage and power outages. Traditional methods for detecting ice on these lines have limitations, particularly in challenging environmental conditions. This study aims to enhance ice detection accuracy by introducing a novel image segmentation and edge detection methodology using improved multi-scale Retinex and advanced morphological operations. Method: The proposed method integrates multi-stage image processing techniques, including image enhancement, grayscale conversion, thresholding, segmentation, object isolation, and edge detection. High-resolution binocular cameras were used to capture images of ice formations on transmission lines. The effectiveness of the method was assessed through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methodologies. Results: The proposed method demonstrated superior performance in both ice detection and thickness measurement. The method effectively identified and isolated ice formations under various conditions with an average accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03%. Additionally, the analysis of ice thickness measurements further highlighted the method’s accuracy, with a notably low root mean squared error (RMSE) of 1.20 mm and mean absolute error (MAE) of 1.10 mm, along with a strong R-squared value of 0.95. These statistical metrics show that the proposed method not only outperforms previous methods in consistency and alignment with manual measurements but also stands out as the most reliable option for tasks requiring precise measurements, offering significant advancements in accuracy and reliability over existing methodologies. Conclusions: The enhanced image segmentation and edge detection approach provides a more accurate and reliable method for monitoring ice formations on power transmission lines.

Hyperspectral imaging, a rapidly evolving technique, furnishes exhaustive information on Earth's surface reflectance over an extensive spectrum of wavelengths. We have developed a more precise method for analyzing spectral data by meticulously selecting plant leaf pixels and employing a relative reference approach. This enhanced technique correlates camera pixel values with light intensity, bypassing the need for complex equations and thereby providing clearer insights into vegetation vitality. The findings of this study not only enhance the precision of hyperspectral imaging but also bolster our capability for ecological monitoring and understanding environmental health. A novel algorithm based on the red-edge slope was established to classify images, exploiting the spectral reflectance data gleaned from the hyperspectral Liquid Crystal Tunable Filter (LCTF) camera. Compared with conventional SVM and K-means algorithms, our novel algorithm demonstrated commendable performance, achieving an overall accuracy of 90% with a Kappa Coefficient of 0.86. This comparison highlights the novel algorithm's relative strength and potential for large-scale satellite and drone imagery analysis applications. The advancements facilitated by this research are instrumental in ecological assessments, conservation efforts, and resource management strategies, signaling a leap forward in the era of environmental monitoring and analysis.

Microparticles derived from biomaterials are becoming increasingly popular for various applications, particularly in drug delivery systems. In this study, the water-in-oil (W/O) emulsification-diffusion method was used to create cellulose (C), silk fibroin (SF), and C/SF composite microparticles. We then observed the morphology of all obtained microparticles using scanning electron microscopy (SEM), evaluated their functional groups using attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR), and conducted thermogravimetric analysis using a thermogravimetric analyzer (TGA). SEM micrographs indicated that the native SF microparticles have the highest spherical shape with smooth surfaces. With blue dextran, the C microparticle was smaller compared to the native microparticle, while the drug-loaded SF microparticles were bigger than the native microparticle. The morphological surfaces of the C/SF composite microparticles were varied in shape and surface depending on the C/SF ratio used. The spherical shape of the C/SF composite microparticle was increased as SF content increased. Furthermore, the size of drug-loaded C/SF composite microparticles was gradually increased by the SF content. The significant functional groups in the C and SF structures were identified based on the ATR-FTIR data, and a suggestion was made regarding the interaction between the functional groups of each polymer. When compared to both native polymers, the C/SF composite microparticles exhibit improved thermal stability. The C/SF composite microparticle at a 1:3 ratio had the lowest drug release content, whereas the hydrophilicity of the C microparticle affected the highest drug release content. As a result, one crucial factor affecting the medication released from the microparticle is its thermal stability. According to the obtained results, C, SF, and C/SF composite microparticles show promise as delivery systems for drugs with controlled release.

An integrated approach to active flow control is proposed by finding both the drooping leading edge and the morphing trailing edge for flow management. This strategy aims to manage flow separation control by utilizing the synergistic effects of both control mechanisms, which we call the Combined Morphing Leading Edge and Trailing Edge (CoMpLETE) technique. This design is inspired by a bionic porpoise nose and the flap movements of the cetacean species. The motion of this mechanism achieves a continuous, wave-like, variable airfoil camber. The dynamic motion of the airfoil's upper and lower surface coordinates in response to unsteady conditions is achieved by combining the thickness-to-chord (t/c) distribution with the time-dependent camber line equation. A parameterization model was constructed to mimic the motion around the morphing airfoil at various deflection amplitudes at stall angle of attack and morphing actuation start times. The mean properties and qualitative trends of the flow phenomena are captured by the transition-SST (Shear Stress Transport) model. The effectiveness of the dynamically morphing airfoil as a flow control approach is evaluated by obtaining the flow field data, such as velocity streamlines, vorticity contours, and aerodynamic forces. Different cases are investigated for the CoMpLETE morphing airfoil, which evaluates the airfoil’s parameters, such as its morphing location, deflection amplitude, and morphing starting time. The morphing airfoil’s performance is analyzed to provide further insights into the dynamic lift and drag force variations at pre-defined deflection frequencies of 0.5 Hz, 1 Hz, and 2 Hz. The findings demonstrate that adjusting the airfoil camber reduces streamwise adverse pressure gradients, thus preventing significant flow separation. Although the trailing edge deflection and its location along the chord influence the generation and separation of the Leading-Edge Vortex (LEV), these results show that the combined effect of the morphing leading edge and trailing edge has the potential to mitigate flow separation. The morphing airfoil successfully contributes to the flow reattachment and significantly increases the maximum lift coefficient (c_(l,max))). This work also broadens its focus to investigate the aerodynamic effects of a dynamically morphing leading and trailing edge, which seamlessly transitions along the side edges. The aerodynamic performance analysis is investigated across varying morphing frequencies, amplitudes, and actuation times.

The recyclable paper based on photochromic materials not only reduces the pollution in the paper manufacture process, but also reduces the pollution caused by the use of ink, which receives wide attention. In this paper, a serial of PMoA-PWA/ZnO/PVP hybrid films which had different ratio of PMoA/PWA was prepared by the ultrasonic composite method. The results indicated that the hybrid film prepared when the ratio of PMoA to PWA was 3 had the best photochromic performance. In this system, ZnO was the photosensitizer while PMoA/PWA was the chromophore. The photochromic mechanism of PMoA-PWA/ZnO/PVP hybrid film was based on the photogenerated electron transfer mechanism. ZnO generated photoelectron under the excitation of visible light, then PMoA and PWA obtained the photoelectron and produced photoreduction reaction to generate heteropolyblue. The visible light photochromic paper was prepared by loaded PMoA-PWA/ZnO/PVP hybrid film (A3) on A4 paper. Application test showed that the prepared paper had extremely stable, excellent and reversible visible light photochromic properties, whether it was printing patterns or words and could replace the ordinary paper to realize the reuse of paper.

In this research, catalytic steam reforming acetic acid derived from the aqueous portion of bio-oil for hydrogen production was investigated by using different Ni/ATC (Attapulgite Clay) catalysts prepared by precipitation, impregnation and mechanical blending methods. The fresh and reduced catalysts were characterized by XRD, N₂ adsorption-desorption, TEM and H₂-TPR. The comprehensive results demonstrated that the interaction between active metallic Ni and ATC carrier was significantly improved in Ni/ATC catalyst prepared by precipitation method, and in which the mean Ni particle size was the smallest (~13 nm) resulted in the highest metal dispersion (7.5%). The catalytic performance of the three catalysts was evaluated through the process of steam reforming of acetic acid in a fixed-bed reactor under atmospheric pressure at two different temperatures, such as 550 ℃ and 650 ℃. Results showed that the Ni/ATC (PM-N/ATC) prepared by precipitation method, achieved the highest H₂ yield of ~82% and little lower acetic acid conversion efficiency of ~85% than that (~95%) of Ni/ATC (IM-NATC) prepared by impregnation method. In addition, the deactivation catalysts after reaction for 4 h were analyzed by XRD, TGA-DTG and TEM, which demonstrated that the catalyst deactivation was not caused by the amount of carbon deposition, but owed to the significant agglomeration and sintering of Ni particles in the carrier.

A novel multicomponent glasses with nominal compositions of (50-x)MoO3:xWO3:25La2O3:25B2O3, x = 0, 10, 20, 30, 40, 50 mol% doped with 3 mol % Eu2O3 were prepared using a conventional melt quenching method. Their structure, thermal behavior and luminescent properties were investigated by Raman spectroscopy, differential thermal analysis and photoluminescence spectroscopy. Physical parameters as density, molar volume, oxygen molar volume and oxygen packing density were determined. Glasses are characterized with high glass transition temperature. Raman analysis revealed that the glass structure is built up mainly from tetrahedral (MoO4)2- and (WO4)2- units providing Raman bands of around 317 cm-1, 341-352 cm-1, 832-820 cm-1 and 928-935 cm-1. At the same time with the replacement of MoO3 with WO3 some fraction of WO6 octahedra are produced, which number increases with the increasing WO3 content. A strong red emission from the 5D0 level of Eu3+ ions was registered under near UV (395 nm) excitation using the 7F0 →5L6 transition of Eu3+. PL emission gradually increases with increasing WO3 content evidencing that WO3 is more appropriate component than MoO3. The integrated fluorescence intensity ratio R (5D0 →7F2/5D0 →7F1) was calculated to estimate the degree of asymmetry around the active ion, suggesting a location of Eu3+ in non-centrosymmetric sites.

Gracilaria lamaneiformis, a red seaweed, is an abundant source of bioactive polysaccharides with significant health-promoting properties. Nevertheless, the broad application of G. lamaneiformis in the nutraceutical and pharmaceutical sectors remains constrained due to the absence of comprehensive data. This review provides a detailed examination of the preparation methods, structural characteristics, and biological activities of G. lamaneiformis polysaccharides (GLP). We explore both conventional and advanced extraction techniques, highlighting the efficiency and yield improvements achieved through methods such as microwave-, ultrasonic-, and enzyme-assisted extraction. The structural elucidation of GLP using modern analytical techniques, including high-performance liquid chromatography, gas chromatography, and nuclear magnetic resonance spectroscopy, is discussed, providing comprehensive insights into their molecular composition and configuration. Furthermore, we critically evaluate the diverse biological activities of GLP, including their antioxidant, anti-inflammatory, antitumor, and gut microbiota modulation properties. This review underscores the therapeutic potential of GLP and suggests future research directions to fully harness their health benefits.

A novel domination invariant defined by Boutrig and Chellali in the recent: total vertex-edge domination. In this paper we obtain an improved upper bound of total vertex edge-domination number of a tree. If is a connected tree with order , then with and we characterize the trees attaining this upper bound. Furthermore we provide a characterization of trees with

This article explores the cutting-edge advancement of gypsum and cement building boards infused with shape-stabilized n-octadecane, an organic phase change material (PCM). The primary focus is on improving energy efficiency and providing electromagnetic interference (EMI) shielding capabilities for contemporary buildings. The research investigates the integration of these materials into construction materials, using red-mud Carbon Foam as a stabilizer for n-octadecane (OD@CCF). Various analyses, including microstructural examination, porosity assessment, and additive dispersion, were conducted using X-ray microtomography and density measurements. Thermal conductivity measurements demonstrated the enhancement of composite boards as the OD@CCF content increased, while mechanical tests indicated an optimal additive content of up to 20%. The thermal-regulated capabilities of these advanced panels were evaluated in a custom-designed room model equipped with a homemade environmental chamber, illustrating a consistent temperature environment during heating and cooling cycles. The incorporation of OD@CCF into cement boards exhibited improved thermal energy storage properties. Moreover, the examined composite boards displayed efficient electromagnetic shielding performance within the frequency range of 3.2–7.0 GHz, achieving EMI values of approximately 18 and 19.5 dB for gypsum and cement boards, respectively, meeting the entry-level value required for commercial applications.

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) nanosheets have shown extensive applications due to their excellent physical and chemical properties. However, the low light absorption efficiency limits their application in optoelectronics. By rolling up 2D TMDCs nanosheets, the one-dimensional (1D) TMDCs nanoscrolls are formed with spiral tubular structure, tunable interlayer spacing and opening ends. Due to their increased thickness of scroll structure, the light absorption is enhanced. Meanwhile, the rapid electron transportation is confined along the 1D structure. Therefore, the TMDCs nanoscrolls show improved optoelectronic performance compared to 2D nanosheets. In addition, the high specific surface area and active edge site from bending strain of basal plane make them promising materials for catalytic reaction. Thus, the TMDCs nanoscrolls have attracted intensive attention in recent years. In this review, the structure of TMDCs nanoscrolls is firstly demonstrated and followed by various preparation methods of the TMDCs nanoscrolls. Afterwards, the applications of TMDCs nanoscrolls in the fields of photodetection, hydrogen evolution reaction, and gas sensing are discussed.

A co-curing resin system consisting of 9368 epoxy resin for prepreg and 6808 epoxy resin for resin transfer molding (RTM) was developed. A corresponding preparation method of novel polymer composite bolted T-joint with internal skeleton and external skin was proposed based on prepreg-RTM co-curing process, and novel T-joints were fabricated. A series of conventional configuration T-joints based on RTM process and T-joints made of 2A12 aluminum alloy were prepared simultaneously. Bending performances were studied on these T-joints experimentally. The results indicate that 9368 epoxy resin and 6808 epoxy resin exhibit good compatibility in rheological and thermophysical properties. The novel T-joints prepared with prepreg-RTM co-curing process show no obvious fiber local winding or resin-rich regions inside, and the interface quality between internal skeleton and external skin is excellent. The main failure modes of the novel T-joint under bending load include separation of skin and skeleton and fracture along the thickness on the base panel; the skeleton carries the main bending load, but there is still load transfer between external skin and internal skeleton through their interface. The internal damages of the novel T-joint are highly consistent with surface damages observed visually, facilitating the detection and timely discovery of damages. The initial stiffness, damage initiation load, and ultimate load of the novel T-joint are 1.65 times, 5.89 times, and 3.45 times that of conventional T-joint, respectively. When considering the influence of density, the relative initial stiffness and relative ultimate load of the novel T-joint are 1.44 times and 2.07 times that of aluminum alloy T-joint, respectively.

The growing threat of antibiotic pollution in water systems demands innovative and sustainable remediation strategies. Nanocellulose-based magnetic metal-organic frameworks (MOFs) represent a cutting-edge solution for the adsorption and removal of antibiotics, leveraging their high porosity, magnetic properties, and environmentally friendly nature. This paper provides an in-depth review of recent advancements in nanocellulose-based MOFs, focusing on their synthesis, adsorption mechanisms, and applications in environmental remediation between 2020 and 2024. By summarizing key studies, we highlight their performance in targeting widely used antibiotics such as tetracyclines and fluoroquinolones. The integration of nanocellulose with MOFs not only enhances adsorption capacity but also offers easy recovery, recyclability, and environmental compatibility. This review explores their adsorption mechanisms, practical applications, and future research directions, contributing to the advancement of scalable and sustainable water treatment technologies.

With the rapid growth of the global mask market, consumers have put forward higher requirements for the functionality and ingredients of mask products. Conventional facial masks generally use ordinary non-woven fabrics as the mask base fabric, are frequently used with essence and contain preservatives. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, which will cause the mask to deteriorate and skin allergic reactions. In addition, conventional facial masks exhibit poor skin fit, limited breathability, inadequate absorption of nutrient solutions, and easy to drip. The nanofiber mask prepared by electrospinning technology has a high specific surface area and high porosity, which enabling improved nutrient absorption by the skin, superior skin fit, and exceptional breathability. Nanofiber facial mask products can effectively solve consumer pain points and promote the high-end development of facial mask products. Therefore, this research combines needleless electrospinning technology to develop a novel solid-state, preservative-free, and quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this research deeply explores the process parameters and influencing mechanisms of quick-dissolving nanofiber facial masks, ultimately achieving the consistent production of nanofiber facial masks with optimal morphology. Comprehensive and evaluation of the structure and performance of nanofiber facial masks from micro and macro perspectives. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30kV, a collection distance of 19cm, and a liquid supply speed of 130ml/h.

Background. Transcatheter edge-to-edge repair (TEER) is a safe and effective treatment in symptomatic patients with mitral regurgitation (MR). We aimed to investigate the short-term impact of mitral TEER on left ventricular (LV) performance and identify echocardiographic prognostic indicators. Methods. Seventy-eight patients with severe MR (organic and functional etiologies) were included retrospectively. All patients had a baseline and short-term echocardi-ography after TEER and were followed up for the primary composite endpoint (heart failure hos-pitalization and cardiovascular death) over a median period of 13 months. Results. A significant decrease in left ventricular ejection fraction (LVEF), all myocardial work indices (except global wasted work), and left atrial reservoir were observed after TEER. Additionally, there was an im-provement in the pulmonary artery systolic pressure and tricuspid annular plane systolic excur-sion/pulmonary artery systolic pressure (TAPSE/PASP) ratio. 34% of all patients presented the primary composite endpoint. A TAPSE/PASP ratio post-TEER < 0.47 (HR: 4.76, p-value = 0.039), and left atrial reservoir post-TEER < 9.0% (HR: 2.77, p-value = 0.047) were independently associ-ated with the primary endpoint. Conclusions. Echocardiography post-TEER reflects impairment in ventricular performance due to preload reduction and right ventricular to pulmonary artery coupling improvement. Short-term echocardiography after TEER identifies high-risk patients who could benefit from a close clinical follow-up.

Preventing or treating infections at implantation sites where the risk of bacterial contamination is high requires the development of intelligent drug delivery systems. In this work, honeycomb structures as a potential drug carrier were obtained for the first time in a fast, one-step anodization process of titanium grade 4, lasting only 60 seconds. Fourth generation oxide nanotube layers were obtained from an aqueous solution of 0.5% AgNO3 in the voltage range from 50 to 80 at 15(1) °C. The FE-SEM method showed the length of the oxide nanotubes from 1.02(2) µm at 50 V to 230(8) nm at 80 V. The EDS analysis revealed that during anodizing, silver particles are deposited on the Ti G4 up to 2.2(5) wt.%. Electrochemical measurements by open circuit potential and anodic polarization curves methods proved the improvement of in vitro corrosion resistance as a result of Ti G4 anodizing. Assessment of the anodized Ti G4 as gentamicin sulfate carrier carried out using ATR-FTIR and UV-VIS absorption spectroscopy revealed that the obtained oxide nanotube layers can be used for application of drugs directly to the tissue around the implant. This method of application allows full use of the therapeutic dose of the antibiotic used.

Cruciferous vegetables represent a valuable source of bioactive compounds. However, there is currently a paucity of information regarding the extent to which these compounds remain bioavailable to the body following thermal treatment and digestion processes within the digestive tract. Accordingly, the aim of this study was to evaluate the impact of heat treatment and in vitro digestion on the level of selected bioactive compounds in Brussels sprouts. The Brussels sprouts samples were subjected to a range of thermal processing techniques, which were then followed by a simulated in vitro digestion. The investigated compounds were analysed using UV-Vis spectrometry and liquid chromatography coupled to mass spectrometry (LC-MS). The findings revealed that the sous-vide method of cooking Brussels sprouts resulted in significantly higher losses of glucosinolates (GLS) in comparison to conventional cooking methods. No significant differences were observed with regard to isothiocyanates and indoles. The analysis of GLS following digestion revealed that the process was more effective after sous vide and traditional cooking, and slightly less effective after steam cooking. With regard to individual compounds, glucoraphanin (GRA), glucoraphenin (GIV) and gluconasturtiin (GNS) were found to be completely degraded, whereas methoxyglucobrassicin (metGBS) was the most resistant to digestion in both the sous vide and steamed Brussels sprouts. The results indicated that the process of simulating digestion had no significant effect on isothiocyanates and indoles. This suggests that, if present in the heat-treated samples, these compounds remained stable during the in vitro digestion procedure.

The world has changed. New technologies, new trends, new protocols, new efficient algorithms, and systems have been used widely by civilized countries to have better and more convenient living. In this research, there have been introduced many new terms, technologies, and mechanisms. Moreover, threats, attacks, and vulnerabilities have been studied and a novel scenario has been proposed in order to overcome all these violations that have been presented in this paper. The scenario is based on a security model that has been proposed for critical sectors, such as hospitals, industries, etc., which need efficient solutions to go one step further and improve the secure and efficient living and treatment of people. The aim of this research has been to understand through research the IoT security issues and propose the appropriate solutions based on the most suitable security algorithms. The results, which have been based on the complexity, the throughput, the power consumption, the strength, and the memory usage of the algorithms studied, have been hopeful and helpful for future research and experimentation.

Lung cancer remains a formidable global health challenge that necessitates inventive strategies to improve therapeutic outcomes. Conventional treatments, including surgery, chemotherapy, and radiation, have demonstrated limitations in achieving sustained responses. Therefore, the explora-tion of novel approaches encompasses a range of interventions that show promise in enhancing outcomes for patients with lung cancer, particularly those facing advanced or refractory cases. These groundbreaking interventions hold the potential to overcome cancer resistance and offer personalized solutions. Despite the rapid evolution of emerging lung cancer therapies, persistent challenges such as resistance, toxicity, and patient selection underscore the need for continued development. Consequently, the landscape of lung cancer therapy is transforming with the in-troduction of precision medicine, immunotherapy, and innovative therapeutic modalities. Addi-tionally, a multifaceted approach involving combination therapies through the integration of tar-geted agents, immunotherapies, or traditional cytotoxic treatments addresses the heterogeneity of lung cancer while minimizing adverse effects. This review provides a brief overview of the latest emerging therapies that are reshaping the landscape of lung cancer treatment. As these novel treatments progress through clinical trials and integrate into standard care, the potential for more effective, targeted, and personalized lung cancer therapies comes into focus, instilling renewed hope for patients facing challenging diagnoses.

High pressure crystallization technique has been successfully used to prepare an elusive form II of a non-steroidal anti-inflammatory drug, Mefenamic acid. Single crystal of form II was grown at 0.3 GPa from an 4:1 methanol/ ethanol mixture as a solvent using Diamond Anvil Cell. Comparison of crystal structures show that the efficient packing of MA molecules in Form II results from the structural flexibility of MA acid molecules. Compression studies performed on the single crystal of form I resulted in 14 % decrease of unit cell volume up to 2.5 GPa. No phase transition was observed up to this pressure. A reconstructive phase transition is required to induce conformational changes in the structure, which is confirmed by crystallization at high pressure results.

Due to its application as an anti-cancer drug, pazopanib (1) has attracted the interest of many researchers and several studies on pazopanib synthesis have been reported over the years. This paper provides a novel route for synthesizing N,2,3-trimethyl-2H-indazol-6-amine (5) which is a crucial building block in the synthesis of pazopanib from 3-methyl-6-nitro-1H-indazole (6). By alternating between the reduction and two methylation steps compound 5 was obtained in comparable yield (55.0%) to what has been reported (54%). Noteworthy, the last step of N2-methylation also yielded N,N,2,3-tetramethyl-2H-indazol-6-amine (5’) as a new compound ever reported. Furthermore, the data presented in this paper can serve as instrumental for future research aimed at further refining the synthesis process of pazopanib.

The unprecedented progress in Artificial Intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI computing on the edge devices. This review studied commercially available edge processors, and the processors that are still in industrial research stages. We categorized state-of-the-art edge processors based on the underlying architecture, such as dataflow, neuromorphic, and Processing in-Memory (PIM) architecture. The processors are analyzed based on their performance, chip area, energy efficiency, and application domains. The supported programming frameworks, model compression, data precision, and the CMOS fabrication process technology are discussed. Currently, most of the commercial edge processors utilize dataflow architectures. However, emerging non-von Neumann computing architectures have attracted the industry in recent years. Neuromorphic processors are highly efficient for performing computation with fewer synaptic operations, and several neuromorphic processors offer online training for secured and personalized AI applications. This review found that the PIM processors show significant energy efficiency and consume less power compared to dataflow and neuromorphic processors. The future direction of the industry would be to implement state-of-the-art deep learning algorithms in emerging non-Von Neumann computing paradigms for low power computing on the edge devices.

Herbal Coffee is getting more popular due to its unique flavor and higher concentration of bioactive compounds, mainly polyphenols. Rosemary is a unique herb with potent antioxidant properties. A green technique called ultrasound aids in releasing bioactive substances from the plant cell wall. In this context, ultrasound-assisted herbal coffee prepared and their anti-diabetic, anti-Alzheimer, and antioxidant activities were measured while the LC/MS was used to determine the bioactive components from the herbal coffee infusion. A total of 95 secondary metabolites were putatively reported, including alkaloids, phenolic acids, and flavonoids, which we identified in the herbal coffee infusion. The total phenolic content (61.5 ± 5.1 mg GAE/g), total flavonoid content (19.3 ± 0.9 mg QE/g), and total condensed tannin (7.6 ± 0.7 mg CE/g) of herbal coffee infusion were observed to be highest of the two remaining samples. The herbal coffee extracts were used to determine all samples' antioxidant, anti-diabetic, and anti-Alzheimer activities. Using LC-MS/MS, we semi-quantified 19 phenolic metabolites. The results show numerous bioactive components in herbal coffee infusion with more potent antioxidant, antidiabetic, and anti-Alzheimer potential. Moreover, more studies must be conducted to confirm how the metabolomic profile of herbal coffee affects its flavor profile.

This study re-introduces a protein-free rapid test method for nucleic acids on paper based lateral flow assays utilizing special multichannel nitrocellulose membranes and DNA-Gold conjugates, achieving significantly enhanced sensitivity, easier protocols, reduced time of detection, reduced costs of pro-duction and advanced multiplexing possibilities. It is shown for the first time a protein-free nucleic acid based lateral flow assay (NALFA) with a limit of detection of 1 pmol of DNA. The total production duration of such an assay was successfully reduced from the currently known several days to just a few hours. The simplification and acceleration of the protocol make the method more accessible and practical for various applications. The developed method supports multiplexing, enabling the sim-ultaneous detection of up to six DNA targets. This multiplexing capability is a significant improve-ment over traditional line tests and offers more comprehensive diagnostic potential in a single assay. The approach significantly reduces the run time compared to traditional line tests, which enhances the efficiency of diagnostic procedures. The protein-free aspect of this assay minimizes the prevalent complications of cross-reactivity in immunoassays especially in cases of multiplexing. It is also demonstrated that the NALFA developed in this study is amplification-free and hence does not rely on specialized technicians, nor does it involve labour-intensive steps like DNA extraction and PCR processes. Overall, this study presents a robust, efficient, and highly sensitive platform for DNA or RNA detection, addressing several limitations of current methods documented in the literature. The advancements in sensitivity, cost reduction, production time, and multiplexing capabilities mark a substantial improvement, holding great potential for various applications in diagnostics, forensics, and molecular biology.

This review is focused on current state-of-the-art research on electroactive-based materials and their synthesis, as well as their physicochemical and biological properties. Special attention is paid to pristine intrinsically conducting polymers (ICPs) and their composites with other organic and inorganic components, well-defined micro- and nanostructures, and enhanced surface areas compared with those of conventionally prepared ICPs. Hydrogels, due to their defined porous structures and being filled with aqueous solution, offer the ability to increase the amount of immobilized chemical, biological or biochemical molecules. When other components are incorporated into ICPs, the materials form composites; in this particular case, they form conductive composites. The design and synthesis of conductive composites result in the inheritance of the advantages of each component and offer new features because of the synergistic effects between the components. The resulting structures of ICPs, conducting polymer hydrogels and their composites, as well as the unusual physicochemical properties, biocompatibility and multi-functionality of these materials, facilitate their bioapplications. The synergistic effects between constituents have made these materials particularly attractive as sensing elements for biological agents, and they also enable the immobilization of bioreceptors such as enzymes, antigen–antibodies, and nucleic acids onto their surfaces for the detection of an array of biological agents. Currently, these materials have unlimited applicability in biomedicine. In this review, we have limited discussion to three areas in which it seems that the use of ICPs and materials, including their different forms, are particularly interesting, namely, biosensors, delivery of drugs and tissue engineering.

To obtain a cost-effective and high-performance composite material of polybutylene adipate-terephthalate (PBAT), for this study we selected microcrystalline cellulose, which is inexpensive and easily available, as the reinforcing medium. Hexadecyl trimethoxysilane (HDTMS) containing a long carbon chain was chosen to silanize the microcrystalline cellulose (MCC) to obtain silanized cellulose (SG). Three types of SGs with different degrees of silanization were obtained by controlling the reaction ratio. Characterization of these three types of SGs was conducted using FTIR, TEM, and water absorption analysis. Subsequently, PBAT/SG composites were prepared through a sol-gel method, and the effects of these three types of SGs on the thermal stability, compatibility, mechanical properties, and dynamic thermomechanical properties of PBAT were evaluated. Furthermore, the mechanism behind enhancing the mechanical properties of the composite was analyzed. The results demonstrated successful synthesis of SG. As the reaction ratio between HDTMS and MCC increased, the nanoparticle size increased, while the water absorption decreased significantly. After SG was added to the PBAT composites, the yield stress increased while maintaining good thermal stability. Both the SEM and DMA results indicated good compatibility of the PBAT/SG composites. Analysis of the mechanical properties revealed that the tensile strength initially increased and then decreased with increasing blending ratio for all three composites tested; among them, the PBAT/SG2 composites exhibit superior performance, with a maximum tensile strength reaching 22 MPa at an 85/15 blending ratio—nearly 30% higher than that of pure PBAT alone. The addition of SG significantly improved the strength of the PBAT, and we speculated as to the underlying mechanism involved. This study provides a new idea for the industrial-scale development of degradable polyesters with low cost and good mechanical properties.

Micro/Nano Robot is an intelligent and efficient microrobot that can perform specific tasks under the influence of external stimuli. Depending on the application scenarios, the microrobot can adaptively transform into appropriate functional forms under different external stimuli, thus perfectly matching the needs. To date, microbots have been widely used in targeted therapy, drug delivery, tissue engineering, environmental remediation, and other fields. Although the applications of microrobots are promising, there are only a few reviews that can focus on the preparation methods and driving mechanisms. Therefore, it is necessary to outline the current status of the development of these microrobots in order to provide some new insights for the further development of the field. Therefore, this paper reviews the research progress of microrobots in terms of preparation methods, stimulus response mechanisms and applications, and highlights the applicability of different preparation methods and stimulus types. Finally, the current challenges faced by microrobots are highlighted and possible solutions are proposed to facilitate the practical application of microrobots.

Poly-ether-ether ketone (PEEK) is a great potential thermoplastic in industry and medical treatment and health. In this work, the PEEK/GO and PEEK/MoS2 composites were prepared by a novel hot isostatic pressing method. The addition of GO alters the tribological behaviours mechanism, fatigue wear mechanism is predominant to PEEK/GO composites. However, the combination of abrasive and adhesive wear mechanisms is observed for PEEK/MoS2 composites and PEEK. The reason is the hardness and tensile strength of composites are increased with the appropriate addition of GO. The response time to stable friction state of PEEK/GO and PEEK/MoS2 composites reduces in compared with PEEK, which conduce to shorten running-in time, reduce energy consumption and improve the tribological performances of composites. The addition of GO and MoS2 can effectively decrease the friction coefficient and wear rate, and the optimal content of GO and MoS2 was 0.7 wt.% and 15 wt.%, respectively. The results indicate that PEEK/GO and PEEK/MoS2 are impressive composites that possess super tribological properties.

Graphene-oxide (G) was prepared by the Hummers’ method. A G-COOH layer was synthesised using chloroacetic acid and G. To fabricate carboxylated graphene-RuO2 (G-COORu) nano¬¬-composites, RuO2 nano particles were grown on graphene layers using a one-step thermal method, -COOH(G-COOH), and RuCl3. All materials were characterised using X-ray diffraction, transmission electron microscopy, scanning electron microscopy, 13C-nuclear magnetic resonance as well as X-ray photoelectron, Fourier-transform infrared spectroscopy, and Raman. The electrochemical characteristics of the G-COORu supercapacitors were analysed using electrochemical impedance spectroscopy, cyclic voltammetry, constant current charge–discharge tests, and Nyquist impedance plots. The supercapacitors exhibit a specific capacitance of ~125 F g-1 at 100 mA cm-2 within the potential range of 0–1.0 V. The method used here provides a simple approach for the deposition of RuO2 nano particles on graphene layers and can be widened to the fabrication of other classes of hybrids based on G layers for specific technical applications.

Edge computing helps radio-supported mobile systems to process computing tasks on the side of servers. The proximity to an edge computing node avoids excessive latencies, which benefits the calculation of real-time algorithms. Special computing needs arise because mobile systems are often so limited in weight and energy that they cannot carry powerful on-board computers. We propose the use of FPGA-based co-processors (FPGA: Field Programmable Gate Array) to handle computations in an edge node. The calculation of the Fast Fourier Transformation (FFT) will be presented as an example of a co-processor. The use of FPGA-based co-processors poses a particular challenge when a mobile system leaves its radio cell, and the computing context must be transferred to another edge node. The article first addresses specific edge computing requirements, such as case-by-case reconfiguration of computing hardware and the handover mechanism from one edge node to another. Using the example of an FPGA-based FFT co-processor, we describe its development, which was carried out under the condition that mobile clients can request different co-processors and that they can also change the edge node when changing the radio cell. The latter requires passing the co-processor context. For the FPGA, this means that the co-processor is part of a partially reconfigurable environment and must support the handover mechanisms in hardware. At the end we indicate the need for FPGA resources and compare this with alternative solutions.

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge-fog-cloud computing, and machine learning (ML)/Artificial Intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), Blockchain, and Digital Twins (DT). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR Metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces Microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike an all-encompassing Metaverses, each Microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each Microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge-fog-cloud computing framework. The Microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the Metaverse.

Edge detection is one of the key issues in the field of computer vision and remote sensing image analysis. Although many different edge-detection methods have been proposed for gray-scale, color, and multispectral images, they still face difficulties when extracting edge features from hyperspectral images (HSIs) that contain a large number of bands with very narrow gap in the spectral domain. Inspired by the clustering characteristic of the gravitation, a novel edge-detection algorithm for HSIs is presented in this paper. In the proposed method, we first construct a joint feature space by combining the spatial and spectral features. Each pixel of HSI is assumed to be a celestial object in the joint feature space, which exerts gravitational force to each of its neighboring pixel. Accordingly, each object travels in the joint feature space until it reaches a stable equilibrium. At the equilibrium, the image is smoothed and the edges are enhanced, where the edge pixels can be easily distinguished by calculating the gravitational potential energy. The proposed edge-detection method is tested on several benchmark HSIs and the obtained results were compared with those of three state-of-the-art approaches. The experimental results confirm the efficacy of the proposed method

Endometrial preparation is crucial in in vitro fertilization (IVF) protocols to create optimal conditions for embryo implantation and pregnancy. Successful embryo nidation requires a receptive endometrium, which is influenced by factors such as endometrial thickness, hormonal environment, autocrine and paracrine growth factors, and the presence of resident and non-resident stem cells within the endometrium basalis. Synchronization between endometrial and embryonic development is also essential. Endometrial preparation, whether achieved through spontaneous cycles, natural cycles with minimal support, ovulation induction, or hormone replacement therapy (HRT), results from a combination of biochemical and biophysical conditions, each independent of the others. These conditions are difficult to pinpoint within a fixed period (Window of Implantation, WOI) using any specific examination due to the variability in estrogen and progesterone production rates, their metabolic clearance rates (MCR) when administered exogenously, and their metabolism when given systemically, topically, or orally. These variations also occur in different phenotypes and over different durations. Moreover, the synchronization of endometrial preparation with incomplete decidualization and the uncertain duration of the implantation window pose challenges. It is also unknown if there are compliance mechanisms similar to diapause seen in other species. This review aims to outline a clinical approach that, while not an absolute clinical recommendation due to the lack of definitive certainties, assists in understanding complementary phenomena and provides a useful orientation for clinical practice.

Colonoscopy, essential for diagnosing and treating colorectal cancer, relies on effective bowel preparation to inspect the colonic mucosa fully. Traditional education methods include printed instructions and verbal explanations, but these may not ensure clear patient understanding. Poor bowel preparation can obscure mucosal visibility, delaying cancer diagnosis and treatment. Using the outpatient’s Android mobile device, the mobile health care model is provided as a nursing guide to assist in bowel preparation for colonoscopy. This single-blinded, randomized trial en-rolled patients for colonoscopy at a Northern Taiwan gastroenterology center. Eligible patients were physically and mentally healthy and could communicate in Chinese. The study tested the ColonClean app during bowel preparation. After the procedure, endoscopists rated the prepara-tion quality using the Aronchick Scale. Data analysis with SPSS software (version 25.0) evaluated if the app significantly improved bowel preparation compared to those who did not use it. Forty patients were recruited for both the control (conventional method) and experimental (conven-tional method plus ColonClean app) groups. In the experimental group, endoscopists assessed bowel preparation quality; all achieved at least "Fair," with 75% receiving "Excellent" or "Good" ratings. The ColonClean App enhanced bowel preparation quality more effectively than tradi-tional nursing guidance.

During routine bridge maintenance, edge detection allows the partial condition of the bridge to be viewed. However, many edge detection methods often have unsatisfactory performances when dealing with images with complex backgrounds. Moreover, the processing often involves the manual selection of thresholds, which can result in repeated testing and comparisons. To address these problems in this paper, the wavelet transform modulus maxima method is used to detect the target image, and then the threshold value of the image can be determined automatically according to the OTSU method to remove the pseudo-edges. Thus, the real image edges can be detected. The results show that the information entropy and SSIM of the detection results are the highest when compared with the commonly used Canny and Laplace algorithms, which means that the detection quality is optimal. To more fully illustrate the advantages of the algorithms, images with more complex backgrounds were detected and the processing results of the algorithms in this paper are still optimal. In addition, the automatic selection of thresholds saves the operator’s effort and improves the detection efficiency. Thanks to the combined use of the above two methods, detection quality and efficiency are significantly improved, which has a good application in engineering practice.

For public safety and physical security, currently more than a billion closed-circuit television (CCTV) cameras are deployed around the world. Proliferation of artificial intelligence (AI) and machine learning (ML) technologies has gained significant applications including crowd surveillance. The state-of-the-art distance and area estimation algorithms either need multiple cameras or a reference scale as a ground truth. It is an open question to obtain an estimation using a single camera without a scale reference. In this paper, we propose a novel solution called E-SEC, which estimates interpersonal distance between a pair of dynamic human objects, area occupied by a dynamic crowd, and density using a single edge camera. The E-SEC framework comprises edge CCTV cameras responsible for capture a crowd on video frames leveraging a customized YOLOv3 model for human detection. E-SEC contributes an interpersonal distance estimation algorithm vital for monitoring the social distancing of a crowd, and an area estimation algorithm for dynamically determining an area occupied by a crowd with changing size and position. A unified output module generates the crowd size, interpersonal distances, social distancing violations, area, and density per every frame. Experimental results validate the accuracy and efficiency of E-SEC with a range of different video datasets.

This study investigates the necessity of particle asymmetry for self-propulsion in nanomotors. While conventional wisdom posits that asymmetric designs are crucial for generating phoretic forces or localized bubble propulsion, recent research suggests that symmetrical particles may also exhibit motility. To address this debate, we developed a robust workflow for synthesizing gold grafted silica nanoparticles with precise control over size and shape, enabling the direct comparison of their motile behavior by dynamic light scattering and particle tracking velocimetry. Our results indicate that the inherent asymmetry generated during isotropic gold nanoparticle deposition onto silica surfaces may enable particle motility.

In this study, various diamine monomers were used to synthesize aramid polymer films via a low-temperature solution condensation reaction with diacid chloride. For diamines with relatively high basicity, the reaction system became opaque because amine salt formation inhibited polymer synthesis. Meanwhile, low-basicity diamines with strong electron-withdrawing groups, such as CF3 and sulfone, were smoothly polymerized without amine salt formation to provide highly viscous solutions. The acid byproduct HCl generated during polymerization was removed by adding propylene oxide to the reaction vessel and converting the acid into highly volatile inert substances. The resulting solutions were used as varnishes without any additional purification, and polymer films with an excellent appearance were easily obtained through a conventional casting and convection drying process. The films neither tore nor broke when pulled or bent by hand; furthermore, even when heated up to 400°C, they did not decompose or melt. Moreover, polymers prepared from 2,2-bis (trifluoromethyl) benzidine (TFMB) and bis (4-aminophenyl) sulfone (pAPS) did not exhibit glass transition until decomposition. The prepared polymer films showed a high elastic modulus of more than 4.1 GPa and a high tensile strength of more than 52 MPa. In particular, TFMB-, pAPS-, and 2,2-bis (4-aminophenyl) hexafluoropropane-based polymer films were colorless and transparent, with very high light transmittances of 95%, 96%, and 91%, respectively, at 420 nm and low yellow indexes of 2.4, 1.9, and 4.3, respectively.

Bimetallic Ni/Cu core-shell and Cu-Ni heterostructural micro- and nanoparticles were prepared using two simple successive reduction procedures. Monometallic Ni and Cu particles were synthesized for comparison. The phase constitutions and morphological features of the particles were studied by means of X-ray diffraction, energy-dispersive X-ray spectroscopy and scanning electron microscopy methods. All the obtained mono- and bimetallic particles were applied as electrocatalysts in the electrohydrogenation of p-nitrophenol (p-NPh). An additional electrochemical reduction of copper cations during the hydrogen saturation of Cu-containing particles was established. Some of the prepared particles exhibited high catalytic activity with an increase in the hydrogenation rate of p-NPh by more than 3 times compared to its electrochemical reduction on the non-activated cathode, and p-NPh conversion yielded the maximum values. The main product of p-NPh electrocatalytic hydrogenation over Ni-Cu particles is p-aminophenol, which is an intermediate in the synthesis of many drugs. p-Aminophenol formation is confirmed by UV-Vis spectra.

During the past few decades, hybrid nanoparticles (HNPs) based on a magnetic material and gold have attracted interest for applications in catalysis, diagnostics and nanomedicine. In this paper, magnetic CoFe2O4/Au HNPs with an average particle size of 10–20 nm decorated with 2-nm gold clusters were prepared using methionine as a reducer and an anchor between CoFe2O4 and gold. The obtained nanoparticles were studied by X-Ray diffraction (XRD), transmission electron microscopy (TEM), Fourier-transform infrared (FT-IR) spectroscopy and X-Ray photoelectron spectroscopy (XPS) and UV-Vis spectroscopy techniques. The TEM images of the HNPs obtained after one, six and ten gold deposition cycles showed that the surface of cobalt ferrite was covered with gold nanoclusters, which slightly increased with an increase in the number of gold deposition cycles (from 2.12 ± 0.15 nm after one cycle to 2.46 ± 0.13 nm after ten cycles) without any change in surface density. The magnetic measurements showed that the obtained HNPs possessed typical ferrimagnetic behaviour, which corresponds to that of CoFe2O4 nanoparticles. The toxicity evaluation of the synthesised HNPs on Chlorella vulgaris indicated that they can be applied to biomedical applications such as magnetic hyperthermia, photothermal therapy, drug delivery, bioimaging and biosensing.

The dolomite dust-emulsified asphalt composite with excellent mechanical properties was successfully prepared using alkali activation. The effects of different alkali concentrations and emulsified asphalt contents on the mechanical properties of the materials were studied. The mechanical properties and microstructure of the composites were analyzed using compressive strength tests, bending strength tests, and SEM characterization. The experimental results show that the specimens have excellent mechanical properties: the 7-day compressive strength can reach 76.67 MPa, and the bending strength is about twice that of silicate-based geopolymer-emulsified asphalt composite. With an increase in emulsified asphalt content, the compressive strength of the samples decreases, while the bending strength increases first and then decreases. When the emulsified asphalt content is 1%, the bending strength of the sample is up to 28.81 MPa, which is 25% higher than that of the sample without emulsified asphalt. This indicates that an appropriate emulsified asphalt content can play a toughening role in the system, providing a new idea for designing high-toughness alkali-activated materials.

Tryptanthrin has not been widely applied in clinical practice due to its poor solubility and low bioavailability in spite of possessing several biological and pharmacological activities. Here, to improve the solubility of tryptanthrin, two types of novel tryptanthrin-loaded micelles were prepared. One was tryptanthrin physically encapsulated by distearoyl phosphatidylethanolamine polyethylene glycol (DSPE-PEG) and the other was pegylated tryptanthrin synthesized by acid-sensitive hydrazone bond and further prepared as micelles. Molecular imprinting technology was used to separate pegylated tryptanthrin and free mPEG-COOH in the preparation of PEGylated tryptanthrin micelles with considerably high separation efficiency. The solubility of tryptanthrin-loaded DSPE-PEG micelles (TDMs) and PEGylated tryptanthrin micelles (PTMs) was increased by 300 and 1493 fold compared with that of tryptanthrin, respectively. The PTMs increased the solubility of tryptanthrin more effectively and 95% of tryptanthrin was released from PTMs at pH 5.5 in 12 h. The cytotoxicity of PTMs decreased under physiological conditions compared with that of tryptanthrin, whereas at pH 5.5, the PTMs showed comparable cytotoxicity with that of tryptanthrin, indicating successful drug release from the carrier in response to tumor cell pH. Overall, we elucidated an efficient method to improve water solubility of tryptanthrin and indicated pegylated tryptanthrin is a promising prodrug.

Nickel molybdate, NiMoO₄, nanoparticles were synthesized via calcination of an oxalate complex in static air at 500 °C. The oxalate complex was analyzed by thermal gravimetric analysis (TGA) and Fourier transform infrared spectroscopy (FTIR). The as-synthesized nickel molybdate was characterized by Brunauer–Emmett–Teller technique (BET), X-ray diffraction (XRD), and transmission electron microscopy (TEM) and its catalytic efficiency was tested in the reduction reaction of the three-nitrophenol isomers. The nickel molybdate displays a very high activity in the catalytic reduction of the nitro functional group to an amino. The reduction progress was controlled using UV-Vis absorption.

The prevalence of cardiac amyloidosis (CA), especially as a cause of heart failure, has significantly increased in recent years. Early detection and accurate assessment of the disease burden are crucial for initiating timely treatment and ensuring precise prognosis. CA primarily results from the infiltration of the myocardium by either immunoglobulin light chain fibrils (AL) or transthyretin fibrils (ATTR), leading to restrictive cardiomyopathy and eventual death if untreated. Over the past decade, advancements in diagnostic imaging and heightened clinical awareness have revealed a substantial presence of CA, particularly ATTR, among the elderly. These diagnostic improvements encompass echocardiography, cardiac computerized tomography scan, magnetic resonance imaging, and radionuclide scintigraphy with bone-avid tracers. Concurrently, significant progress has been made in therapeutic options, with new disease-modifying treatments now available that can dramatically alter the disease trajectory and improve survival rates when administered early. However, despite these advancements, there remains an urgent need for the early and accurate detection of CA to ensure that patients can fully benefit from these emerging therapies.

In different fields in our life, like physics, coding theory and computer science, graph labeling dramas an vital role and appears in many applications. A labeling of a graph is a map which assign each element in with a positive integer number. An edge irregular total -labeling is a function such that where and are weights for any two distinct edges. In this case, has total edge irregularity strength (TEIS) if is minimum. In our paper, we defined a new type of graphs called a triple star snake graph and star snake graph . Also, we investigated TEIS for a triple star snake graph . After that, we generalized the results for star snake graph .

Recently, there has been a growing interest in distributed edge computing resource scheduling. For example, application scenarios such as intelligent traffic systems and Internet of Things(IoT) intelligent monitoring require the scheduling and migration of distributed resources. Distributed resource scheduling needs to consider the cost of resource scheduling, with the primary goal of finding the optimal strategy among various feasible solutions. There are different definitions for optimization objectives in different application scenarios, such as cost, transmission delay, energy consumption, etc. Current research mainly considers the optimization problem of local resource scheduling but needs more consideration of global resource scheduling. This paper defines a global resource scheduling problem for distributed edge computing and proves that the problem is NP-Hard. A heuristic solution strategy based on the Ant Colony Algorithm(ACO) was proposed, and Particle Swarm Optimization(PSO) was used to optimize the parameters of the ACO. Finally, an experimental comparative analysis was conducted to demonstrate that the algorithm proposed in this paper has good accuracy and iteration cost performance.

The study of photonic crystals has emerged as an attractive area of research in nanoscience in the last years. In this work we study the properties of a two-dimensional photonic crystal composed of dielectric rods. The unit cell of the system is composed of six rods organized on the sites of a C6 triangular lattice. We induce a topological phase by introducing an angular perturbation ϕ in the pristine system. The topology of the system is then determined by using the so-called k.p perturbed model. Our results show that the system presents a topological and a trivial phases, depending on the sign of the angular perturbation ϕ. The topological character of the system is probed by evaluating the electromagnetic energy density and analyzing its distribution in the real space, in particular on the maximal Wyckoff points. We also find two edge modes at the interface between the trivial and topological photonic crystals, which present a pseudospin topological behavior. By applying the Bulk-Edge correspondence, we study the pseudospin edge modes and conclude that they are robust against defects, disorder and reflection. Moreover, the localization of the edge modes leads to the confinement of light and the interface behaves as a waveguide for the propagation of electromagnetic waves. Finally, we show that the two edge modes present energy flux propagating in opposite directions, which is the photonic analogue of the quantum spin Hall effect.

Deep learning neural networks require an immense amount of computation, especially in the training phase of the network when networks with multiple layers of intermediate neurons need to be built. In this paper, we will focus on the PSO algorithm with the aim of significantly accelerating the DLNN training phase by taking advantage of the GPGPU architecture and the Apache Spark analytics engine for large-scale data processing tasks. PSO is a bio-inspired stochastic optimization method whose goal is to iteratively improve the solution to a (usually complex) problem by attempting to approximate a given objective. However, parallelizing an efficient PSO is not a straightforward process due to the complexity of the computations performed on the swarm of particles and the iterative execution of the algorithm until a solution close to the objective with minimal error is achieved. In the present work, two parallelizations of the PSO algorithm have been implemented , both designed for a distributed execution environment. The synchronous parallel PSO implementation ensures consistency at the cost of potential idle time due to global synchronization, while the asynchronous parallel PSO approach improves execution time by reducing the need for global synchronization, making it more suitable for large datasets and distributed environments such as Apache Spark. Both variants of PSO have been implemented to distribute the computational load supported by this algorithm –due to the costly fitness evaluation and updating of particle positions– across the different Spark cluster executor nodes to effectively achieve coarse-grained parallelism, resulting in a significant performance increase over current sequential variants of PSO.

This study demonstrates the successful synthesis of Ni/Y2O3 nanocomposite particles through the application of ultrasound-assisted precipitation using the Ultrasonic Spray Pyrolysis technique. The particle size was predicted accurately using a modified equation, and they were collected in a water suspension with Polyvinylpyrrolidone (PVP) as the stabiliser. The presence of the Y2O3 core and Ni shell was confirmed with Transmission Electron Microscopy (TEM) and with electron diffraction. The TEM observations revealed the formation of round particles with an average diameter of 466 nm, while the lattice parameter on the Ni particle’s surface was measured to be 0.343 nm. The Ni/Y2O3 nanocomposite particle suspensions were lyophilized, to obtain a dried material that was suitable for embedding into a Polylactic acid (PLA) matrix. The resulting PLA/Ni/Y2O3 composite material was successfully extruded successfully, so that a 3D printed technology Fused Filament Fabrication could be used further for the production of tensile test tubes. The tensile tests showed that the addition of lyophilised Ni/Y2O3 into the PLA matrix decreased the tensile strength by 21.8 %, while the tensile modulus was not influenced. Dynamic Mechanical Analysis (DMA) showed that the addition of the Ni/Y2O3 particles increased the glass transition temperature by 5 °C, and increased the storage modulus in the glass transition range significantly. These findings demonstrate the potential for utilising Ni/Y2O3 nanocomposite particles in 3D printing applications, and warrant further exploration of their mechanical properties and potential applications in various fields.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

With the rapid development of the Internet of Things, massive amounts of data will be generated at the network edge, resulting in increased latency of the traditional cloud computing model. Cloud service providers deploy edge servers near the users to improve the quality of service. Due to the large scale of networks, the inefficiency of edge server deployment and task scheduling will result in excessive latency and severe workload imbalance among edge servers. In this paper, we propose a Server Deployment and Task Scheduling (SDTS) framework in multi-edge collaborative computing, which can effectively reduce latency and ensure workload balancing. First, a metropolitan area network (MAN) is divided into dense candidate regions (DCRs) based on the density characteristics of mobile end devices, and then servers are deployed independently in each sub-region. Then, a two-step clustering algorithm is designed to deploy servers as close as possible to the mobile devices. Finally, a task scheduling algorithm is presented based on an edge scheduling layer in DCRs, which can leverage collaboration between edge servers to improve the performance of the entire edge computing system. The experimental results based on the Cellular Base Station (CBS) dataset of Shanghai Telecom indicate that the proposed approach outperforms several representative strategies in terms of latency and workload balancing.

A total $m$-labelling $\alpha$ on graph $\Gamma$ is called an edge irregular total $m$-labelling if for any two different edges of $\Gamma$, their weights respect to $\alpha$ are distinct, where the weight of any edge is defined as the sum of its label and the labels of its end vertices. We determine the minimum $m$ such that $\Gamma$ can be labelled by an edge irregular total $m$-labelling whenever $\Gamma$ is an odd staircase graph or an even staircase graph.

Air pollution is related to the development of people's livelihood. As one of the main pollutants in the air, formaldehyde affects people's physical and mental health. In order to remove formaldehyde and better protect the health of residents, it is very necessary to develop efficient adsorption materials. In this study, a cellulose composite aerogel Microcrystalline Cellulose/ N-(2- aminoethyl) -3- aminopropyl methyl dimethoxysilane (MCC/APMDS) modified by APMDS was explored. The pore volume and specific surface area of the modified aerogel decreased, the average pore diameter increased, and the adsorption capacity of formaldehyde was stronger. The adsorption experiment shows that the sample prepared under the conditions of APMDS dosage of 6 wt%, reaction time of 4 h and reaction temperature of 90℃ has the largest average pore size (14.56 nm) and the largest formaldehyde adsorption capacity (9.52 mg/g). The excellent adsorption of the composite aerogel is due to high porosity and rich active amino groups on the surface. This study provides valuable information for the preparation of adsorption materials with high formaldehyde adsorption capacity for air purification.

Starch is a biopolymer with wide potential for the generation of new biodegradable packages due to its high availability and low price. However, due to its weak functional properties, it is necessary to limit the interaction of some hydroxyl, and to evaluate blends with other polymers to improve their performance. Glycerol plasticized acetylated corn starch films were developed by the casting method, and the impact of incorporating chitosan (TPS:CH) at various proportions (75:25, 50:50, and 25:75 v/v) was studied. The effect of the chitosan ratios on the films' physical, mechanical, water vapor barrier, and thermal properties was evaluated. Chitosan protonated amino groups promote the formation of intermolecular bonds, improving the tensile strength, the thermal stability, the water adsorption capacity, and the gas barrier of starch films. Where the film composed of TPS25-CH75 was the one that presented the best barrier to water vapor. These composite films are a good option for development of biodegradable packaging.

Polyvinyl guanidineacetic (PVG) was prepared by the chemical grafting between poly(vinyl alcohol) (PVA) and guanidineacetic acid. The results showed that the guanidineacetic groups were successfully introduced into the polyvinyl alcohol by fourier transform infrared (FTIR) and thermogravimetry (TG). The effects of the amount of catalyst and reaction time on the PVG grafting rate were investigated. The PVG/NWF composite membrane as a heterogeneous catalyst for biodiesel production was successfully prepared by the solvent phase inversion. The effects of mass ratio of methanol/soybean oil and reaction temperature on the conversions using the composite membrane for transesterification were studied. And the reusability of the composite membrane and the kinetics of the reaction catalyzed by the composite membrane were also investigated. The conversions obtained from the model are in good agreement with the experimental data.

The analysis of particulate matter (PM) in dilute solutions is an important target for environmental, geochemical and biochemical researches. Here we show how the microdrop technology may allow to control, through the evaporation of small droplets, the deposition of insoluble materials dispersed in a solution on a well-defined area with a specific spatial pattern. Using this technology the superficial density of the deposited solute can be accurately controlled. In particular, it becomes possible to deposit an extremely reduced amount of insoluble material -in the order of few μg- on a confined area, thus allowing a relatively high superficial density to be reached within a limited time. In this work we quantitatively compare the microdrop technique for the preparation of particulate matter samples with the classical filtering technique. After having been optimized, the microdrop technique allows to obtain a more homogeneous deposition and limit sample consumption of a factor ~25. This method is potentially suitable for many novel applications in different scientific fields.

The global polyvinyldene flouride market is estimated to reach $937,278.5 thousand by 2019, therefore to develop new membranes and gain pioneering ideas, which could create innovative business opportunities, a fundamental knowledge about membrane properties fabricated from recent commercially available PVDF polymers is highly mandatory. In this study, we successfully prepared nine non-woven supported PVDF membranes using a phase inversion precipitation method starting from a 15 wt% PVDF solution in N-methyl-2-pyrrolidone. Various membrane morphologies were obtained by using (1) PVDF polymers with diverse molecular weight in a range from 300.000 Da to 700.000 Da and (2) different temperatures of the coagulation bath (20, 40, and 60 ±2°C) used for the films precipitation. Environmental Scanning Electron Microscope (ESEM) was used for surface and cross-section morphologies characterization. Atomic Force Microscope (AFM) was employed to investigate surface roughness, while Contact Angle (CA) instrument was used for membranes wettability studies. Fourier Transform Infrared Spectroscopy (FTIR) results show that the fabricated membranes are formed by a mixture of TGTG’ chains in α phase crystalline domains and all-TTTT trans planar zigzag chains characteristic to β phase. Moreover, generated results indicate that the phases content and membrane morphologies depend on the polymer molecular weight and conditions used for the membranes preparation. The diversity of fabricated membranes could be applied by the End User Industries for different applications.

"Industry 5.0” is the latest industrial revolution. A variety of cutting-edge technologies, including artificial intelligence, the Internet of Things (IoT), and others, come together to form it. This new era will bring about significant changes in the way businesses operate, allowing them to become more cost-effective, more efficient, and produce higher-quality goods and services. Because sen-sors are getting better, 5G networks are being put in place, and more industrial equipment and machinery are becoming available, the manufacturing sector is going through a significant period of transition right now. These newly scalable opportunities make it possible to use and spread blockchain architectures on the shop floor, which is made possible by the ever-decreasing costs associated with implementing blockchain technology. Even though modern production models make use of the cloud (both internal and external services), networks and systems can take ad-vantage of the cloud's relatively low cost, scalability, increased computational power, real-time communication, and data transfer capabilities to create much smarter and more autonomous systems. This paper presents the results of an investigation into how blockchain services for large-scale industry networks could benefit from increased levels of security, transparency, and efficiency. We discuss the ways in which decentralized networks that make use of protocols and meshes might make things better with these technologies, which are not going away anytime soon. We emphasize the significance of new design in regards to cybersecurity, data integrity, and storage by using straightforward examples that have the potential to lead to the excellence of distributed systems.

Changes in the amount and the distribution of mean and turbulent quantities in the free shear layer wake of a 2D NACA 0012 airfoil and AR 4 NACA 0012 wing with passive segmented rigid trailing edge (TE) extensions was investigated at the University of Dayton Low Speed Wind Tunnel (UD-LSWT). The TE extensions were intentionally placed at zero degrees with respect to the chord line to study the effects of segmented extensions without changing the effective angle of attack. Force based experiments was used to determine the total lift coefficient variation of hte wing with seven segmented trailing edge extensions distributed across the span. The segmented trailing edge extensions had negligible effect of lift coefficient but showed measurable decrement in sectional and total drag coefficient. Investigation of turbulent quantities (obtained through Particle Image Velocimetry (PIV)) such as Reynolds stress, streamwise and transverse RMS in the wake, reveal a significant decrease in magnitude when compared to the baseline. The decrease in the magnitude of turbulent parameters was supported by the changes in coherent structures obtained through two-point correlations. Apart from the reduction in drag, the lower turbulent wake generated by the extensions has implications in reducing structural vibrations and acoustic tones.

Sailing sports are experiencing a period of radical innovations. Traditional displacing monohulls have always been the reference for yachting in the past, but the continuous quest for performances has generated many variations and many convergences with surprising results. On the aerodynamic side conventional soft sails have been sometime replaced by rigid wings, but the biggest innovation has been the use of traction kites as a propulsion device. On the hydrodynamic side we have seen the rapid growth of hydrofoils, an old concept that is having a rebirth. Probably the most remarkable expression of innovation and integration between concepts having different roots is the hydrofoil kite-board, point of encounter of the traditional wave surfing, the traction kite innovation and the hydrofoil technology. Hydrofoil kite-board can reach speeds up to 3 times the wind speed, are at least one order of magnitude cheaper than any boat with comparable performances, easy to manage and races are spectacular. Hydrofoil are object of investigation trying to further improve performances as well as to increase the stability and the “sailability". In the following we will present a concept that extends the range of efficiency of the foil through a completely passive morphing of the wing.

The growing importance of edge and fog computing in the modern IT infrastructure is driven by the rise of decentralized applications. However, resource allocation within these frameworks is challenging due to varying device capabilities and dynamic network conditions. Conventional approaches often result in poor resource use and slowed advancements. This study presents a novel strategy for enhancing resource allocation in edge and fog computing by integrating machine learning with blockchain for reliable trust management. Our proposed framework, called CyberGuard, leverages blockchain’s inherent immutability and decentralization to establish a trustworthy and transparent network for monitoring and verifying edge and fog computing transactions. CyberGuard combines the Trust2Vec model with conventional machine learning models like SVM, KNN, and Random Forests, creating a robust mechanism for assessing trust and security risks. Through detailed optimization and case studies, CyberGuard demonstrates significant improvements in resource allocation efficiency and overall system performance in real-world scenarios. Our results highlight CyberGuard’s effectiveness, evidenced by a remarkable accuracy, precision, recall, and F1-Score of 98.18%, showcasing the transformative potential of our comprehensive approach in edge and fog computing environments.

Objectives: The study aimed to develop a deep learning-based edge AI model deployed on electrocardiograph (ECG) devices for real-time detection of atrial fibrillation (AF)-risk during sinus rhythm (SR) using standard 10-second 12-lead electrocardiograms (ECGs). Methods: A novel approach was used to convert standard 12-lead ECGs into binary images for model input, and a lightweight convolutional neural network (CNN)-based model was trained using data collected by the Japan Agency for Medical and Research Development (AMED) between 2019 and 2022. Patients over 40 years old with digital, SR ECGs were retrospectively enrolled and divided into AF and non-AF groups. Data labeling was supervised by cardiologists. The dataset was randomly allocated into training, validation, and internal testing datasets. External testing was conducted on data collected from other hospitals.　Results: The best-trained model achieved an AUC of 0.82 and 0.80, sensitivity of 79.5% and 72.3%, specificity of 77.8% and 77.7%, precision of 78.2% and 76.4%, and overall accuracy of 78.6% and 75.0% in the internal and external testing datasets, respectively. The deployed model and app package utilized 2.5MB and 40MB of the available ROM and RAM capacity on the edge ECG device, correspondingly. Processing time for AF-risk detection was approximately 2 seconds.　Conclusion: The model maintains comparable performance and improves its suitability for deployment on resource-constrained ECG devices, thereby expanding its potential impact to a wide range of healthcare settings. Its successful deployment enables real-time AF-risk detection during SR, allowing for timely intervention to prevent AF-related serious consequences like stroke and premature death.

Solid porous materials, like zeolites, have been widely used in a variety of fields such as size and shape-selective absorption/separation and catalysis because of their porosity. But there are few liquid materials exhibit permanent porosity. Porous liquids are a novel material that combine the properties of fluidity and permanent porosity. It has potential applications in many fields such as gas separation, storage and transport. Herein, we report a novel Type 1 porous liquid prepared based on Silicalite-1. The pore size of this porous liquid was determined by positron annihilation lifetime spectroscopy (PALS), and the CO2 capacities was determined by the intelligent gravimetric analyser (IGA). The unique properties of this porous liquid can promote its application in many fields such as gas storage and transport.

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scale. However, this increase in resolution creates challenges with sample characteristics such as grain size, surface roughness and porosity that can influence the spectral signature. This research explores the effect of rock sample surface preparation on the TIR spectral signatures. We applied three surface preparation methods (split, saw and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

The application of solvent method in the preparation of polypropylene powders was investigated, including the influence of various technological conditions, such as the raw material melt index of polypropylene, processing temperature, dosage of the solvent and the packing pressure. The experimental data show that using solvent method can effectively produce polypropylene powder, and the optimal technological conditions are: melt index of polypropylene being about 17.8 g/10min, processing temperature being about 166.0 ℃, dosage of the solvent being about 0.067g/ml and the packing pressure being about 0.7MPa.Under this technological conditions, polypropylene powder with excellent sphericity could be prepared, the apparent density of powder can reach 0.40 g/cm3 and the prepared powder displayed a narrow size distribution with the mean size of about 42.7 µm.

Hydrogels have gained significant attention as vehicles for transdermal delivery systems. These polymeric-structured substances have been implemented in pharmaceutical and cosmetic applications. Many factors are employed in their preparation. Light, pH, temperature and specific molecules ligands are commonly used in the production of hydrogels. The Transdermal delivery system (TDD) is a widely used route, used to deliver actives/drugs via the skin. Hydrogels as a TDD have been shown to be efficient in enhancing the efficacy in delivering the actives and drugs. The aim of this review is to discuss the hydrogels application in both dermatological therapeutic and cosmetic applications, covering the types, and properties of hydrogel polymer networks. Furthermore, this review presents one of the most extensively used polymers (Hyaluronic acid HA) in this field and discusses the properties, preparation methods and applications of HA hydrogel patches as a delivery system, including methods of loading the actives and the releasing of these actives from them.

CRISPR/Cas9 has emerged as the predominant method for genome editing due to its cost-effectiveness and broad applicability, playing a crucial role in advancing sustainable practices across various sectors. This systematic review leverages the PRISMA methodology to explore CRISPR/Cas9's impact on environmental protection, thereby supporting the Sustainable Development Goals (SDGs). Analyzing data from the Web of Science, the review found significant growth in related publications, with a 30% increase since 2014, predominantly from the US, China, Germany, and the UK. The study categorizes the scientific developments into three trends: advancements in agriculture, gene editing techniques, and biofuel production. Key discussions include the use of CRISPR/Cas9 in developing fourth-generation biofuels and environmental biosensors, as well as its applications in enhancing genetic resilience and controlling invasive species. These innovations highlight CRISPR/Cas9's potential in promoting sustainable resource management and energy generation, marking a pivotal contribution to ecological conservation and sustainability efforts.

Delve into the dynamic realms of neuroscience, neurology, and psychiatry with this groundbreaking editorial, unveiling the intricate mechanisms of brain function and dysfunction. Explore the latest advancements in molecular biology, genetics, epigenetics, pharmacology, and neuroimaging that are revolutionizing our understanding of neurological and psychiatric disorders. Uncover the challenges and gaps in translating cutting-edge research into practical clinical applications, aiming to enhance the lives of patients and their families. Discover the pivotal role of neuroplasticity in reshaping the brain's adaptability throughout life, alongside the promising potential of non-invasive brain stimulation techniques like transcranial magnetic stimulation and transcranial direct current stimulation. Embark on a journey of innovation and collaboration as researchers and clinicians strive to bridge the gap between bench and bedside, offering hope for improved diagnosis, prevention, and treatment of complex neurological and psychiatric conditions. Join us in this exploration of transformative ideas and novel interventions that hold the key to a brighter future for individuals and society.

In recent years, Edge Intelligence (EI) has emerged, combining edge computing with AI, specifically deep learning, to run AI algorithms directly on edge devices. In practical applications, EI faces challenges related to computational power, power consumption, size, and cost, with the primary challenge being the trade-off between computational power and power consumption. This has rendered traditional computing platforms unsustainable, making heterogeneous parallel computing platforms a crucial pathway for implementing EI. In our research, we leveraged the Xilinx Zynq 7000 heterogeneous computing platform, employed High-Level Synthesis (HLS) for design, and implemented two different accelerators for LeNet-5 using loop unrolling and pipelining optimization techniques. The experimental results show that when running at a clock speed of 100 MHz, the PIPELINE accelerator, compared to the UNROLL accelerator, experiences an 8.09% increase in power consumption but speeds up by 14.972 times, making the PIPELINE accelerator superior in performance. Compared to the CPU, the PIPELINE accelerator reduces power consumption by 91.37% and speeds up by 70.387 times, while compared to the GPU, it reduces power consumption by 93.35%.This study provides two different optimization schemes for edge intelligence applications through design and experimentation, and demonstrates the impact of different quantization methods on FPGA resource consumption. These experimental results can provide a reference for practical applications, thereby providing a reference hardware acceleration scheme for edge intelligence applications.

The falling cost of cameras, the advancement of AI based computer vision algorithms, and powerful hardware accelerators for deep learning have enabled wide-spread deployment of surveillance cameras with the ability to automatically analyze streaming video feeds to detect events of interest. While streaming video analytics is currently largely done in the cloud, edge computing has emerged as a pivotal component due to its advantages of low latency, reduced bandwidth, and enhanced privacy. However, a distinct gap persists between the state-of-the-art computer vision algorithms, and successful practical implementation of edge-based streaming video analytics systems. This paper presents a comprehensive review of more than 30 research papers published over the last 6 years on edge video analytics systems. The papers are analyzed across 17 distinct dimensions. Unlike prior reviews, we examine each system holistically, identifying their strengths and weaknesses in diverse implementations. Our findings suggest that certain critical topics necessary for the practical realization of edge video analytics systems are not sufficiently addressed in current research. Based on these observations, we propose research trajectories across short, medium, and long term horizons. Additionally, we explore trending topics in other computing areas that can significantly impact the field of edge video analytics.

Emerging evidence suggests that the human vermiform appendix is not a vestigial organ, but rather an immunological organ of biological relevance. It is hypothesised that the appendix acts as a bacterial ‘safe house’ for commensal gut bacteria and facilitates re-inoculation of the colon after disruption through the release of biofilms. To date, no studies have attempted to explore this potential mechanistic function of the appendix. We conducted a pre-post intervention study in adults (n=59) exploring reestablishment of the gut microbiota in those with and without an appendix after colonic disruption via bowel preparation and colonoscopy. Gut microbiota composition was measured one week before and one month after bowel preparation and colonoscopy using 16S rRNA sequencing. We observed between group differences in gut microbiota composition between those with (n=45) and without (n=13) an appendix at baseline. These differences were no longer evident one-month post-procedure, suggesting that this procedure may have ‘reset’ any potential appendix-related differences between groups. Both groups experienced reductions in gut microbiota richness and shifts in beta diversity post-procedure, with greater changes in those without an appendix, and there were five bacterial genera whose re-establishment post-procedure appeared to be moderated by appendicectomy status. This small experimental study provides preliminary evidence of a potential differential reestablishment of the gut microbiota after disruption in those with and without an appendix, warranting further investigation into the potential role of the appendix as a microbial safe house.

Two-dimensional MXene synthesized from MAX phase ceramic has the good electrical conductivity, promising to be used as electrodes. In this study, Nb4C3Tx MXene and low-entropy (Nb0.8Ti0.05V0.05Zr0.05Ta0.05)4C3Tx MXene were prepared by etching Nb4AlC3 and (Nb0.8Ti0.05V0.05Zr0.05Ta0.05)4AlC3 ceramics in the HF acid at 60 oC. By investigating the electrochemical properties of lithium batteries, it was found that the Nb4C3Tx and (Nb0.8Ti0.05V0.05Zr0.05Ta0.05)4C3Tx could provide the specific capacities of 163.7 mAh·g-1 and 130 mAh·g-1 after 50 cycles at a current density of 0.1 A·g-1, respectively, and maintain the coulombic efficiency close to 100%, good for the utilization of electrodes in the lithium batteries.

The doctoral defense is an important step towards obtaining the doctoral degree. As such, preparing for the event is necessary. Anecdotal evidence highlights that there is a wide variety of ways in which doctoral candidates prepare for the defense. In this work, I want to explore if there is a relation between the way in which a doctoral candidate prepares for the defense and two important aspects of the defense: the outcome of the defense, and the student perception during and after the defense. For this purpose, I first reviewed the literature on the topic of the preparation for the doctoral defense. Then, I carried out an international survey on the doctoral defense and analyzed the data of the 204 completed surveys with respect to the preparation for the defense using quantitative and qualitative methods. The methods I used included the statistical tests of the correlation between on one hand the preparation and on the other hand the defense outcome and student perception. I used inductive thematic analysis of the open-ended survey questions to gain deeper insight in the way candidates prepared for their defense. I found that candidates most often prepare by making their presentation, reading their thesis, and practicing. The most effective measure is the mock defense, followed by a preparatory course. Reading blogs, books, and chapters is a less effective preparation measure. The conclusion of this work is that doctoral candidates need to understand the format of their defense in order to be able to prepare properly, and that universities should explore either individual pathways to the defense or pilots using a mock defense and/or preparatory course to give their doctoral candidates the necessary tools to prepare for their doctoral defense.

This article utilized electrodeposition technology to fabricate Zn and Zn-Y₂O₃-Al₂O₃ composite coatings on Q235 steel plates. The composite coatings were obtained by adding Y₂O₃/Al₂O₃ nanoparticles (0, 5, 10, and 15 g/L) to acidic chloride solutions. This study then examined the impact of these nanoparticles on the microstructure, morphology, microhardness, hydrophobicity, and corrosion resistance of the composite coatings. The results indicate that the addition of the nanoparticles increased the nucleation sites on the surface of the coating, thereby refining the grain size and reducing the roughness of the coating surface. When 10g/L of Y₂O₃ and 10g/L of Al₂O₃ nanoparticles were added, the hardness of the composite coating (369 Hv) was 2.49 times higher than that of the pure Zn coating (148 Hv), which was attributed to the dispersion strengthening and grain refinement effects of the nanoparticles. The corrosion resistance of the Zn and Zn-Y₂O₃-Al₂O₃ composite coatings was studied using linear sweep voltammetry and AC impedance spectroscopy. The addition of the nanoparticles improved the corrosion resistance of the composite coating, and the composite coating prepared in an electrolyte containing 10 g/L of Y₂O₃ and 10 g/L of Al₂O₃ nanoparticles exhibited the best corrosion resistance. Meanwhile, the composite coating exhibited good hydrophobicity, with a contact angle of 120.9°, 1.94 times that of the pure Zn coating (62.2°). The Zn-Y₂O₃-Al₂O₃ nanocomposite coating material has significant potential advantages in the field of steel corrosion.

Solid particle cationic polyacrylamide (CPAM) is one of the most commonly used organic polymer flocculants in oilfield wastewater treatment, but there are some problems, such as slow dissolution rate, and easy to form a "fish-eye" in the process of diluting into aqueous solution. However, the current liquid CPAM products also have some problems, such as low effective content, poor storage stability, degradation in a short time and high preparation cost. In this paper, a secondary processing technology of solid particle CPAM was studied, and a CPAM suspension was successfully prepared. This suspension has an effective content of 50.00%, No separation in 7 days of storage at room temperature, no separation in 30 min of centrifugation at a speed of 2000 r/min, and diluted to a 0.40 % weak gel solution in just 16.00 min. For 1000 NTU of diatomite simulated wastewater, the optimal turbidity removal rate of the suspension was 99.50%, which was higher than the optimal turbidity removal rate of 98.40% for the inorganic flocculant polymeric aluminum chloride (PAC). For oil and gas field oily wastewater, the optimal turbidity removal rate of CPAM suspension was 35.60%, which was higher than the optimal turbidity removal rate of 28.40% for solid particle CPAM.

The exploitation of bio-based foams implies an increase in the use of renewable biological resources to reduce the rapid consumption of petroleum-derived resources. Both tannins and furfuryl alcohol are derived from forestry resources and are therefore considered as attractive precursors for the preparation of tannin-furanic foams. In addition, toughening modification of tannin-furanic foams using polyvinyl alcohol (PVOH) resulted in a more flexible network-like structure, which imparts excellent flexibility to the foams with acquirement of relative properties that are even close to those of polyurethane foams, which are the most used polymers for fabrication of insoles for athletes. In addition, the addition of PVOH did not affect the thermal insulation of the foams, resilience and elongation at break, while reducing the brittleness of the samples and improving the mechanical properties. Also the observation of the morphology of the foam indicated that the compatibility between PVOH and tannin-furanic resin is good, and the cured foam does not show fragmentation or collapse, while the bubble pore structure is uniform. The developed flexible foam derived from biomass resources endowed the foam good thermal insulation properties and high mechanical properties, and the samples exhibited suitable physical parameters to be used as flexible insoles for athletes.

EOR is a process that helps maximize recoverable oil reserves, extend field life and increase recovery rates, and is an important tool for companies to maintain production and increase return on investment. In this article, the performance of nanoclay on aqueous phase viscosity, wettability, and recycled oil in core fluidization in polyacrylamide nanofluid has been investigated in different experiments and EOR. We have used polymers due to the properties of polymers in increasing rheology and effective mobility and controlling stability and variability to resume growth oil in nanofluids. Adding 5% by weight of salt to polymer-clay nanofluid has also been investigated in samples comparable to pure nano clay without salt. The viscosity of this suspension increases by about 50% by adding a certain amount of salt. If the same amount of salt is added to the polymer diluent, the viscosity decreases drastically. The results of the wettability test also show that clay has a better potential to change the wettability in the tendency to salt. In core fluidization tests, the highest efficiency of 30% was obtained in the third recycling in the combination of clay nanoparticles and sulfone copolymer. The highest efficiency was obtained in the third scenario in core fluidization tests, that is; A combination of resin nanoparticles and sulfone copolymer. Their detailed behavior in the characteristics of wettability, rheology, and core fluidization is analyzed in this article.