Search Results (2,783)

Rapid global urbanization during the late 20th and early 21st centuries has induced substantial land cover changes, posing significant threats to the United Nations Educational, Scientific and Cultural Organization’s (UNESCO) World Heritage Sites. In this study, we investigated the spatio-temporal change in urban development in response to land use transformations in the world cultural heritage site (CHS) of Taxila, Pakistan, to check the possible threats faced by the site. Land transfer matrices were used to assess the land cover change (LCC) between 1990 and 2024. Support vector machine and Getis–Ord Gi techniques were employed for LCC classification and spatial pattern interpretation, respectively, which were later evaluated by the high spatial resolution imagery of KH-9 (Keyhole-9), Google Earth Pro and Gaofen-2. The results indicate a significant increase in built-up area from 23.68 km² to 78.5 km², accompanied by a substantial rise in bare land from 8.56 km² to 26.5 km² between 1990 and 2024, which is quite irregular. LCC transformations were notable, with 13.1 km² of cropland and 44.8 km² vegetation being converted into 4.4 km² of built-up area and 14.5 km² into bare land during the 1990 to 2024 period. Getis–Ord Gi analysis observed a high Z-score value and showed low to high clustering patterns in the proximity of the Sarakhola and Bhir Mound sites from 1990 to 2024. Furthermore, high spatial resolution imagery indicates the loss of the core zone of the Sarakhola site from 0.0168 to 0.0032 km² from 2004 to 2024, which was the major threat to its outstanding universal venue (OUV) status. The findings of the current study indicate that the CHS under study is facing an alarming situation for conservation due to rapid urban development and encroachment. Therefore, local government should strictly implement the heritage law and revisit their policies to promote conservation efforts to maintain the authenticity and integrity of this world CHS. Full article

Background: Transforming one-dimensional (1D) biomedical signals into two-dimensional (2D) images enables the application of convolutional neural networks (CNNs) for classification tasks. In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. Methods: We select five transformation methods: Continuous Wavelet Transform (CWT), Fast Fourier Transform (FFT), Short-Time Fourier Transform (STFT), Signal Reshaping (SR), and Recurrence Plots (RPs). We used the MIT-BIH Arrhythmia Database for ECG signals and the Epilepsy EEG Dataset from the University of Bonn for EEG signals. After converting the signals from 1D to 2D, using the aforementioned methods, we employed two types of 2D CNNs: a minimal CNN and the LeNet-5 model. Our results indicate that RPs, CWT, and STFT are the methods to achieve the highest accuracy across both CNN architectures. Results: These top-performing methods achieved accuracies of 99%, 98%, and 95%, respectively, on the minimal 2D CNN and accuracies of 99%, 99%, and 99%, respectively, on the LeNet-5 model for the ECG signals. For the EEG signals, all three methods achieved accuracies of 100% on the minimal 2D CNN and accuracies of 100%, 99%, and 99% on the LeNet-5 2D CNN model, respectively. Conclusions: This superior performance is most likely related to the methods’ capacity to capture time–frequency information and nonlinear dynamics inherent in time-dependent signals such as ECGs and EEGs. These findings underline the significance of using appropriate transformation methods, suggesting that the incorporation of time–frequency analysis and nonlinear feature extraction in the transformation process improves the effectiveness of CNN-based classification for biological data. Full article

The influence of calcium and magnesium treatment under different molten steel conditions, as well as that of the alloy proportion and addition sequence of calcium and magnesium in composite treatment, on the evolution of inclusions in AH36 liquid steel was analyzed systematically based on thermodynamic calculations. The results show that the inclusions in molten steel are mainly Al₂O₃, which gradually transform into a liquid phase after calcium treatment with a wide range of calcium contents, indicating that calcium treatment has a significant effect on inclusion modification. Magnesium treatment mainly converts Al₂O₃ into MgO·Al₂O₃ inclusions in molten steel; however, it is not suitable to modify inclusions with magnesium treatment alone since it does not produce a significant liquid phase. The effect of calcium and magnesium composite treatment varies with the alloy content composition and the order of alloy addition. The liquid phase range of inclusions follows the order of 80%Ca + 20%Mg composite treatment > calcium treatment > 50%Ca + 50%Mg composite treatment > 20%Ca + 80%Mg composite treatment. Combining the thermodynamic and experimental analysis results, it can be concluded that the composite treatment of magnesium followed by calcium is the best. Specifically, a small amount of magnesium should be added first as the nucleating particle to promote the fine dispersion of the inclusions, thus reducing their impact on steel performance. Then, calcium should be added to modify the surface of the inclusions into a liquid phase, which can effectively reduce nozzle clogging. Full article

The generation of synthetic textile waste is a growing global concern, with an unsustainable rate of expansion. This study addresses the growing issue of synthetic textile waste by converting polyester–polyurethane (PET-PU) post-industrial scraps into microporous carbon materials, which can be utilized for wastewater treatment. Using a straightforward pyrolysis process, we achieved a high specific surface area (632 m²/g) and narrow porosity range (2–10 Å) without requiring chemical activation. The produced carbon materials effectively adsorbed methylene blue and orange II dyes, with maximum adsorption capacities of 169.49 mg/g and 147.56 mg/g, respectively. Kinetic studies demonstrated that adsorption followed a pseudo-second-order model, indicating strong interactions between the adsorbent and dyes. Regeneration tests showed that the C-PET-PU could be reused for multiple cycles with over 85% retention of its original adsorption capacity. Preliminary life cycle assessment (LCA) and life cycle cost (LCC) analysis highlighted the environmental and economic advantages of this upcycling approach, showing a reduced global warming potential and a production cost of approximately 1.65 EUR/kg. These findings suggest that transforming PET-PU waste into valuable adsorbents provides a sustainable solution for the circular economy and highlights the potential for broader applications in environmental remediation. Full article

Hydraulic linear drive systems with conventional proportional valves result in poor energy efficiency due to resistance control. In systems with multiple actuators connected to one common pressure supply, a load-sensing strategy is often used to reduce these throttling losses. However, like conventional cylinder actuators, common load-sensing systems are also not able to recuperate the energy, which is actually released when a dead load is lowered. In order to overcome these drawbacks, in this paper, new concepts of a digital hydraulic smart actuator and a load-sensitive pressure supply unit are presented, which are qualified to reduce throttling losses and, furthermore, to harvest energy from the load. According to previous research, the basic concepts used in this contribution promise energy savings in the range of 30% for certain applications, which is one of the main motivations for this study. The operating principles are based on a parallel arrangement of multiple hydraulic switching converters, representing so-called digital hydraulic transformers. Furthermore, the storage module of the presented load-sensitive pressure supply unit is able to boost the hydraulic power in the common pressure rail beyond the maximum power of the primary motor. For exemplary operating cycles of the smart actuator and the pressure supply unit, a significant reduction in the energy consumption could be shown by simulation experiments, which offers a new perspective for energy-efficient motion control. Full article

As an important part of industrial production, the optimization of circulating water systems is of great significance for improving energy efficiency and reducing operating costs. However, traditional optimization methods lack real-time and dynamic adjustment capabilities and often cannot fully cope with the complex and changeable industrial environment and energy demands. Advances in computer technology can enable people to use machine learning models to process information and data and ultimately help simplify simulation and optimization. In this paper, the circulating water system of a Fluid Catalytic Cracking (FCC) unit is optimized and evaluated based on process simulation and machine learning, adopting 284 sets of industrial operating data. The cooler network of the system is modified from a parallel structure to a series mode, and the effect is clarified using the ASPEN HYSYS software V12. Meanwhile, the fan power of the cooling tower is predicted by employing an optimized Gradient Boosting Regression (GBR) model, and the influence of the parallel-to-series transformation on the fan power is discussed. It is shown that the computer modeling results are in coincidence with the industrial data. Converting the parallel design to a series arrangement of the cooler network can significantly decrease the water consumption, with a reduction of 11%. The fan power of the cooling tower is also reduced by 8% after the optimization. Considering the changes in both water consumption and fan power, the saved total economic cost is 8.65%, and the decreased gas emission is 2142.06 kg/h. By building the optimization prediction system, the real-time sequencing and monitoring of equipment parameters are realized, which saves costs and improves process safety. Full article

Background/Objectives: Variations in image clarity across different OCT devices, along with the inconsistent delineation of RNFL boundaries, pose a challenge to achieving consistent diagnoses for glaucoma. Recently, deep learning methods such as GANs for image transformation have been gaining attention. This paper introduces deep learning methods to transform low-clarity images from one OCT device into high-clarity images from another, concurrently estimating the retinal nerve fiber layer (RNFL) segmentation lines in the enhanced images. Methods: We applied two deep learning methods, pix2pix and cycle-GAN, and provided a comparison of their performance by evaluating the similarity between the generated and actual images, as well as comparing the generated RNFL boundary delineation with the actual boundaries. Results: The image conversion performance was compared based on two criteria: Fréchet Inception Distance (FID) and curve dissimilarity. In the comparison of FID values, the cycle-GAN method showed significantly lower values than the pix2pix method (p-value < 0.001). In terms of curve similarity, the cycle-GAN method also demonstrated higher similarity to the actual curves compared to both manually annotated curves and the pix2pix method (p-value < 0.001). Conclusions: We demonstrated that the cycle-GAN method produces more consistent and precise outcomes in the converted images compared to the pix2pix method. The resulting segmented lines showed a high degree of similarity to those manually annotated by clinical experts in high-clarity images, surpassing the boundary accuracy observed in the original low-clarity scans. Full article

Background/Objectives: Transforming Growth Factor-beta (TGFβ1) plays a core role in the process of pulmonary fibrosis (PF). The progression of pulmonary fibrosis can be alleviated by siRNA-based inhibiting TGF-β1. However, the limitations of naked siRNA lead to the failure of achieving therapeutic effect. This study aimed to design lipid nanoparticles (LNPs) that can deliver siTGF-β1 to the lungs for therapeutic purposes. Methods: The cytotoxicity and transfection assay in vitro were used to screen ionizable lipids (ILs). Design of Experiments (DOE) was used to obtain novel LNPs that can enhance resistance to atomization shear forces. Meanwhile, the impact of LNPs encapsulating siTGF-β1 (siTGFβ1-LNPs) on PF was investigated. Results: When DLin-DMA-MC3 (MC3) was used as the ILs, the lipid phase ratio was MC3:DSPC:DMG-PEG2000:cholesterol = 50:10:3:37, and N/P = 3.25; the siTGFβ1-LNPs could be stably delivered to the lungs via converting the siTGFβ1-LNPs solution into an aerosol (atomization). In vitro experiments have confirmed that siTGFβ1-LNPs have high safety, high encapsulation, and can promote cellular uptake and endosomal escape. In addition, siTGFβ1-LNPs significantly reduced inflammatory infiltration and attenuated deposition of extracellular matrix (ECM) and protected the lung tissue from the toxicity of bleomycin (BLM) without causing systemic toxicity. Conclusions: The siTGFβ1-LNPs can be effectively delivered to the lungs, resulting in the silencing of TGF-β1 mRNA and the inhibition of the epithelial–mesenchymal transition pathway, thereby delaying the process of PF, which provides a new method for the treatment and intervention of PF. Full article

Background: Among abiotic stresses to agricultural crops, drought stress is the most prolific and has worldwide detrimental impacts. The soybean (Glycine max) is one of the most important sources of nutrition to both livestock and humans. Different plant introductions (PI) of soybeans have been identified to have different drought tolerance levels. Objectives: Here, two soybean lines, Pana (drought sensitive) and PI 567731 (drought tolerant) were selected to identify chemical compounds and pathways which could be targets for metabolomic analysis induced by abiotic stress. Methods: Extracts from the two lines are analyzed by direct infusion electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. The high mass resolution and accuracy of the method allows for identification of ions from hundreds of different compounds in each cultivar. The exact m/z of these species were filtered through SoyCyc and the Human Metabolome Database to identify possible molecular formulas of the ions. Next, the exact m/z values were converted into Kendrick masses and their Kendrick mass defects (KMD) computed, which were then sorted from high to low KMD. This latter process assists in identifying many additional molecular formulas, and is noted to be particularly useful in identifying formulas whose mass difference corresponds to two hydrogen atoms. Results: In this study, more than 460 ionic formulas were identified in Pana, and more than 340 ionic formulas were identified in PI 567731, with many of these formulas reported from soybean for the first time. Conclusions: Using the SoyCyc matches, the metabolic pathways from each cultivar were compared, providing lists of molecular targets available to profile effects of abiotic stress on these soybean cultivars. Key metabolites include chlorophylls, pheophytins, mono- and diacylglycerols, cycloeucalenone, squalene, and plastoquinones and involve pathways which include the anabolism and catabolism of chlorophyll, glycolipid desaturation, and biosynthesis of phytosterols, plant sterols, and carotenoids. Full article

The escalating challenges of municipal solid waste (MSW) management, exacerbated by the classification of MSW as hazardous waste due to the presence of heavy metals (HMs) and toxic compounds, necessitate innovative treatment strategies. Plasma pyrolysis has emerged as a promising technology for converting MSW into valuable energy byproducts, such as syngas, bio-oil, and slag, while significantly reducing waste volume. However, maintaining optimal operational parameters during the plasma pyrolysis process remains a complex challenge that can adversely affect both the efficiency and the quality and quantity of outputs. To address this issue, the integration of the Internet of Things (IoT) presents a transformative approach. By leveraging IoT technologies, real-time monitoring and advanced data analytics can be employed to optimize the operational conditions of plasma pyrolysis systems, ensuring consistent performance and maximizing resource recovery. This review explores the synergistic integration of plasma pyrolysis and IoT as a novel strategy for MSW management. The slag from plasma treatment can be efficiently channeled into anaerobic digestion (AD) systems, promoting resource recovery through biogas production and the generation of nutrient-rich digestate. This synergy not only mitigates the environmental impacts associated with traditional MSW disposal methods but also paves the way for sustainable energy recovery and resource management. Ultimately, this review presents a comprehensive framework for exploiting plasma pyrolysis and IoT in addressing the pressing issues of hazardous MSW, thereby fostering a circular economy through innovative waste-to-energy solutions. Full article

Aquafeed production is a fast-growing industry, seeking novel, cost-efficient raw materials to diversify traditional ingredients like fish meal and oil. Insects, particularly BSF larvae, convert by-products and waste into value-added biomass. In this study, by-products and co-products from two major fish-transforming industries in the Iberian Peninsula, i.e., tuna heads (THs) and codfish frames (CFs), hydrolysates of THs and CFs, and TH oils, were supplied to BSF larvae to improve their profile in n-3 fatty acids (FAs), namely EPA and DHA, and their protein/amino acid content. By testing the replacement levels of a control diet with by-products and co-products, we evaluated the amount of n-3 FA that could be added to BSF larval tissues. The results showed that high levels of a hydrolysed diet negatively impacted larval survival. In addition, parameters such as the moisture, protein content, and viscosity of the substrate affected bioconversion rates. Nevertheless, BSF fed with these diets contained high levels of lysine (5.8–8.4%, dry weight (DW)), methionine (1.5–2.4%, DW), and n-3 FA (14.4% DW: EPA 6.7% and DHA 7.1%). These findings suggest that BSF can effectively convert fish by-products into a nutrient-rich biomass for aquafeeds, supporting the diversification of raw material sources and promoting a circular bioeconomy. Full article

Lignocellulosic biomass from water hyacinth, a readily available waste material, holds potential for producing commercial products such as xylose, which can be further converted into value-added products like xylitol. However, the complex structure of lignocellulosic biomass necessitates energy-intensive processes to release fermentable sugars. Chemical pre-treatment methods, such as alkali pre-treatment, offer a viable approach to degrade lignocellulose biomass. In this study, water hyacinth biomass (WHB) was treated with 3% potassium hydroxide and subjected to autoclaving to hydrolyse the sample. The total xylose released during the process was quantified using a UV-Vis spectrophotometer and was found to 0.253 g/g of water hyacinth biomass when the sample was treated for 20 min at 2% biomass concentration. The morphological changes in the treated biomass compared to the untreated sample were analysed using Field Emission Scanning Electron Microscopy (FE-SEM). Crystallinity alterations were evaluated through X-Ray Diffraction (XRD), while Fourier-Transform Infrared Spectroscopy (FTIR) was employed to study the changes in chemical states of the biomass. The primary objective of this study was to identify a reliable pre-treatment method for processing water hyacinth biomass, facilitating the efficient release of fermentable sugars for downstream applications. Full article

Geometric building models are essential in BIM technology. The reconstruction results using current methods are usually represented using mesh, which is limited to visualization purposes and hard to directly import into BIM or modeling software for further application. In this paper, we propose a building model reconstruction method based on a transformer network (DeepBuilding). Instead of reconstructing the polyhedron model of buildings, we strive to recover the CAD modeling operation of constructing the building models from the building point cloud. By representing the building model with its modeling sequence, the reconstruction results can be imported into BIM software for further application. We first translate the procedure of constructing a building model into a command sequence that can be vectorized and processed by the transformer network. Then, we propose a transformer-based network that can convert input point clouds into the vectorized representation of the modeling sequences by decoding the geometry information encoded in the point features. A tool is developed to convert the vectorized modeling sequence into a 3D shape representation (such as mesh) or file format that other BIM software supports. Comprehensive experiments are conducted, and the evaluation results demonstrate that our method can produce competitive reconstruction results with high geometric fidelity while preserving more details of the building reconstruction. Full article

This study was inspired by an emerging trend in contemporary cities: the transformation of trams into mobile spaces for recreation, education, and work. Despite the growing popularity of this concept, which is linked to the search for more sustainable transport options, there is a marked lack of guidelines, methodological frameworks, and reference case studies necessary to support these projects. This study fills this gap by illustrating the design guidelines developed for a project in Gwangmyeong, a new Korean town. These guidelines provide a structured framework for converting existing trams into mobile venues such as restaurants, classrooms, and work and conference spaces. Employing the design thinking approach, the guidelines comprise three primary design phases—Understand, Define, and Materialize—each consisting of two sub-phases, and specify the technical tools, roles, and outputs needed. The proposed guidelines are illustrated using material from the Gwangmyeong project. As the first of their kind, these guidelines provide a valuable case study and reference materials for designers, offering possible benchmarks for the technical and financial evaluation of such projects. This study hopes to stimulate discussions on the development and refinement of similar methodologies, addressing the growing interest in design discourse. Full article

The effective capacitance of the common mode port serves as a critical metric for assessing the common mode noise suppression capability of transformers in power converters. Conventionally, the evaluation of transformers in single-ended topologies, such as flyback converters, using a network analyzer necessitates a reference static point and a dynamic point at the transformer port. However, a full-bridge transformer without a center tap lacks a reference static point in both the primary and secondary stages. Consequently, this paper proposes an innovative measurement technique to evaluate the common mode noise suppression capability of full-bridge transformers. This method accounts for the intrinsic parameters of the transformer and refines the high-frequency equivalent circuit model for accurate measurement. Ultimately, the validity of the proposed model is confirmed through experiments conducted on a CLLC converter prototype, offering the industry a straightforward and efficient approach to assessing and testing the common mode noise suppression performance of transformers without a center tap. Full article