Search Results (14,065)

The product form serves as a crucial information carrier for expressing design concepts and encompasses significant valuable references. During the product iteration process, changes in design subjects, such as designers and decision-makers, result in substantial variability and uncertainty in the direction of product form evolution. To address these issues, an evolutionary design method for product forms based on the gray Markov model and an evolutionary algorithm is proposed in this study. Firstly, quadratic curvature entropy is utilized to quantify historical form features of product evolution. Subsequently, the original data on product form feature evolution are fitted and predicted using the gray Markov model, thereby obtaining the predicted value of the latest generation of product form features, which is determined to be 0.14586. Finally, this study uses this predicted value to construct a fitness function in the framework of an evolutionary algorithm, which in turn identifies next-generation product forms that can stimulate designers’ creative thinking. The method’s application is illustrated using the side outer contour of the Audi A4 automobile as an example. The research findings demonstrate that combining the gray Markov model with an evolutionary algorithm can effectively simulate designers’ understanding of previous generations’ design concepts and achieve stable inheritance of these design concepts during product iteration. This approach mitigates the risk of abrupt changes in design concepts caused by designers and decision-makers due to personal cognitive biases, thereby enhancing product development efficiency. Full article

We provide a short review of the recent developments in entropic cosmology based on two thermodynamic laws of the apparent horizon, namely the first and the second laws of thermodynamics. The first law essentially provides the change in entropy of the apparent horizon during the cosmic evolution of the universe; in particular, it is expressed by TdS=−d(ρV)+WdV (where W is the work density and other quantities have their usual meanings). In this way, the first law actually links various theories of gravity with the entropy of the apparent horizon. This leads to a natural question—“What is the form of the horizon entropy corresponding to a general modified theory of gravity?”. The second law of horizon thermodynamics states that the change in total entropy (the sum of horizon entropy + matter fields’ entropy) with respect to cosmic time must be positive, where the matter fields behave like an open system characterised by a non-zero chemical potential. The second law of horizon thermodynamics importantly provides model-independent constraints on entropic parameters. Finally, we discuss the standpoint of entropic cosmology on inflation (or bounce), reheating and primordial gravitational waves from the perspective of a generalised entropy function. Full article

This document introduces methods for describing long texts as groups of repeating symbols or patterns. The process converts a series of real-number values into texts. Developed tailored algorithms for identifying repeated sequences in the text are applied to decompose the text into nested tree-like structures of repeating symbols and is called the Nested Repeated Sequence Decomposition Model (NRSDM). The NRSDM is especially valuable for extracting repetitive behaviors in oscillatory but non-periodic and chaotic processes where the classical Fourier transform has limited application. The NRSDM along with the two graphical representations proposed here form a promising tool for characterizing long texts configured to represent the behavior of unidimensional processes. Full article

Building segmentation has extensive research value and application prospects in high-resolution remote sensing image (HRSI) processing. However, complex architectural contexts, varied building morphologies, and non-building occlusions make building segmentation challenging. Compared with traditional methods, deep learning-based methods present certain advantages in terms of accuracy and intelligence. At present, the most popular option is to first apply a single neural network to encode an HRSI, then perform a decoding process through up-sampling or using a transposed convolution operation, and then finally obtain the segmented building image with the help of a loss function. Although effective, this approach not only tends to lead to a loss of detail information, but also fails to fully utilize the contextual features. As an alternative, we propose a novel network called NPSFF-Net. First, using an improved pseudo-Siamese network composed of ResNet-34 and ResNet-50, two sets of deep semantic features of buildings are extracted with the support of transfer learning, and four encoded features at different scales are obtained after fusion. Then, information from the deepest encoded feature is enriched using a feature enhancement module, and the resolutions are recovered via the operations of skip connections and transposed convolutions. Finally, the discriminative features of buildings are obtained using the designed feature fusion algorithm, and the optimal segmentation model is obtained by fitting a cross-entropy loss function. Our method obtained intersection-over-union values of 89.45% for the Aerial Imagery Dataset, 71.88% for the Massachusetts Buildings Dataset, and 68.72% for the Satellite Dataset I. Full article

In order to improve grid security while pursuing a grid operation economy and new energy consumption rates, this paper proposes a short-term optimal scheduling method based on security quantification for the grid containing a pumped-storage power plant. The method first establishes a grid security evaluation model to evaluate grid security from the perspective of grid resilience. Then, a short-term optimal dispatch model of the grid based on security quantification is constructed with the new energy consumption rate and grid loss as the objectives. In addition, an efficient intelligent optimization algorithm, Dung Beetle Optimization, is introduced to solve the scheduling model, dynamically updating the evaluation intervals during the iterative solution process and evaluating the grid security level and selecting the best result after the iterative solution. Finally, the improvement in the term IEEE 30-bus grid connected to a pumped-storage power plant is used as an example to verify the effectiveness of the proposed method and model. Full article

Water distribution network design must consider cost and reliability, with reliability being complex to assess, involving mechanical, hydraulic, and water quality aspects. Current metrics like flow entropy and resilience index have known flaws. This paper presents a new index, addressing the known weaknesses of the original resilience index and including critical network features in reliability assessment. The new proposed index introduces a novel pressure surplus threshold, setting more realistic pressure limits in operational management. Full article

Abstract: The refractory complex concentrated alloy (RCCA) 5Al–5Cr–5Ge–1Hf–6Mo–33Nb–19Si–20Ti–5Sn–1W (at.%) was studied in the as-cast and heat-treated conditions. The partitioning of solutes in the as-cast and heat-treated microstructures and relationships between solutes, between solutes and the parameters VEC and Δχ, and between these parameters, most of which are reported for the first time for metallic UHTMs, were shown to be important for the properties of the stable phases A15–Nb₃X and the D8_m βNb₅Si₃. The nano-hardness and Young’s modulus of the A15–Nb₃X and the D8_m βNb₅Si₃ of the heat-treated alloy were measured using nanoindentation and changes in these properties per solute addition were discussed. The aforementioned relationships, the VEC versus Δχ maps and the VEC, Δχ, time, or VEC, Δχ, Young’s modulus or VEC, Δχ, nano-hardness diagrams of the phases in the as-cast and heat-treated alloy, and the properties of the two phases demonstrated the importance of synergy and entanglement of solutes, parameters and phases in the microstructure and properties of the RCCA. The significance of the new data and the synergy and entanglement of solutes and phases for the design of metallic ultra-high temperature materials were discussed. Full article

Borehole hydraulic fracturing in coal seams can prevent dynamic coal mine disasters and promote the sustainability of the mining industry, and microseismic signal recognition is a prerequisite and foundation for microseismic monitoring technology that evaluates the effectiveness of hydraulic fracturing. This study constructed ultra-lightweight CNN models specifically designed to identify microseismic waveforms induced by borehole hydraulic fracturing in coal seams, namely Ul-Inception28, Ul-ResNet12, Ul-MobileNet17, and Ul-TripleConv8. The three best-performing models were selected to create both a probability averaging ensemble CNN model and a voting ensemble CNN model. Additionally, an automatic threshold adjustment strategy for CNN identification was introduced. The relationships between feature map entropy, training data volume, and model performance were also analyzed. The results indicated that our in-house models surpassed the performance of the InceptionV3, ResNet50, and MobileNetV3 models from the TensorFlow Keras library. Notably, the voting ensemble CNN model achieved an improvement of at least 0.0452 in the F1 score compared to individual models. The automatic threshold adjustment strategy enhanced the identification threshold’s precision to 26 decimal places. However, a continuous zero-entropy value in the feature maps of various channels was found to detract from the model’s generalization performance. Moreover, the expanded training dataset, derived from thousands of waveforms, proved more compatible with CNN models comprising hundreds of thousands of parameters. The findings of this research significantly contribute to the prevention of dynamic coal mine disasters, potentially reducing casualties, economic losses, and promoting the sustainable progress of the coal mining industry. Full article

The article thoroughly investigates the relationships between terrain features and tree measurements derived from aerial laser scanning (ALS) data and field surveys in a 1067-hectare forested area. A digital elevation model (DEM) was generated from ALS data, which was then used to derive additional layers such as slope, aspect, topographic position index (TPI), and landforms. The authors developed a mathematical procedure to determine the radii for the topographic position index. The canopy height model was created, and individual trees were segmented using a novel voxel aggregation method, allowing for the calculation of tree height and crown size. Accuracy assessments were conducted between ALS-derived data and field-collected data. Terrain variability within each forest unit was evaluated using characteristics such as standard deviation, entropy, and frequency. The relationships between tree height and the derived topographic features within forest subcompartments, as well as the correlation between the height yield map for the entire area and the TPI layer, were analysed. The authors found strong correlation between the topographic position index and tree heights in both cases. The presented remote-sensing-based methodology and the results can be effectively used in digital forest site mapping, complemented by field sampling and laboratory soil analyses, and, as final goal, in carbon stock assessment. Full article

High-entropy alloys (HEAs) have emerged as a novel class of materials with exceptional mechanical and corrosion properties, offering promising applications in various engineering fields. However, optimizing their performance through advanced manufacturing techniques, like laser cladding, remains an area of active research. This study investigated the effects of laser energy density on the mechanical and electrochemical properties of CoCrFeMnNi HEA coatings applied to Q235 substrates. Utilizing X-ray diffraction (XRD), this study confirmed the formation of a single-phase face-centered cubic (FCC) structure in all coatings. The hardness of the coatings peaked at 210 HV with a laser energy density of 50 J/mm². Friction and wear tests highlighted that a coating applied at 60 J/mm² exhibited the lowest wear rate, primarily due to adhesive and oxidative wear mechanisms, while the 55 J/mm² coating showed increased hardness but higher abrasive wear. Electrochemical testing revealed superior corrosion resistance for the 60 J/mm² coating, with a slow corrosion rate and minimal passivation tendency in contrast to the 55 J/mm² coating. The comprehensive evaluation indicates that the HEA coating with an energy density of 60 J/mm² exhibits exceptional wear and corrosion resistance. Full article

High-temperature oxidation resistance is the major influence on the high-temperature service stability of refractory high entropy alloys. The oxidation behavior of lightweight Al_0.2CrNbTiV refractory high entropy alloy coatings with different dilution ratios at 650 °C and 800 °C deposited by high-speed laser cladding was analyzed in this paper. The oxidation kinetic was analyzed, the oxidation resistance mechanism of the Al_0.2CrNbTiV coating was clarified with the analysis of the formation and evolution of the oxidation layer, and the effect of the dilution rate on high-temperature performances was revealed. The results showed that the oxide layer was mainly composed of rutile oxides (Ti, Cr, Nb)O₂ after isothermal oxidation at 650 °C and 800 °C for 50 h. The Al_0.2CrNbTiV coating in low dilution exhibited better oxidation performance at 650 °C, due to the dense oxide layer formed with the synergistic growth of fine AlVO₃ particles and (Ti, Cr, Nb)O₂, and higher percentage of Cr, Nb in (Ti, Cr, Nb)O₂ strengthened the lattice distortion effect to inhibit the penetration of oxygen. The oxide layer formed at 800 °C for the Al_0.2CrNbTiV coating was relatively loose, but the oxidation performance of the coating in high dilution improved due to the precipitation of Cr₂Nb-type Laves phases along grain boundaries, which inhibits the diffusion of oxygen. Full article

Background: Wiedemann–Steiner syndrome (WSS), a rare autosomal-dominant disorder caused by haploinsufficiency of the KMT2A gene product, is part of a group of disorders called chromatinopathies. Chromatinopathies are neurodevelopmental disorders caused by mutations affecting the proteins responsible for chromatin remodeling and transcriptional regulation. The resulting gene expression dysregulation mediates the onset of a series of clinical features such as developmental delay, intellectual disability, facial dysmorphism, and behavioral disorders. Aim of the Study: The aim of this study was to investigate a 10-year-old girl who presented with clinical features suggestive of WSS. Methods: Clinical and genetic investigations were performed. Whole exome sequencing (WES) was used for genetic testing, performed using Illumina technology. The bidirectional capillary Sanger resequencing technique was used in accordance with standard methodology to validate a mutation discovered by WES in all family members who were available. Utilizing computational protein modeling for structural and functional studies as well as in silico pathogenicity prediction models, the effect of the mutation was examined. Results: WES identified a de novo heterozygous missense variant in the KMT2A gene KMT2A(NM_001197104.2): c.3451C>G, p.(Arg1151Gly), absent in the gnomAD database. The variant was classified as Likely Pathogenetic (LP) according to the ACMG criteria and was predicted to affect the CXXC-type zinc finger domain functionality of the protein. Modeling of the resulting protein structure suggested that this variant changes the protein flexibility due to a variation in the Gibbs free energy and in the vibrational entropy energy difference between the wild-type and mutated domain, resulting in an alteration of the DNA binding affinity. Conclusions: A novel and de novo mutation discovered by the NGS approach, enhancing the mutation spectrum in the KMT2A gene, was characterized and associated with WSS. This novel KMT2A gene variant is suggested to modify the CXXC-type zinc finger domain functionality by affecting protein flexibility and DNA binding. Full article

Machine learning (ML) and artificial intelligence (AI) have emerged as the most advanced technologies today for solving issues as well as assessing and forecasting occurrences. The use of AI and ML in various organizations seeks to capitalize on the benefits of vast amounts of data based on scientific approaches, notably machine learning, which may identify patterns of decision-making and minimize the need for human intervention. The purpose of this research work is to develop a suitable neural network model, which is a component of AI and ML, to assess and forecast crack propagation in a bearing with a seeded crack. The bearing was continually run for many hours, and data were retrieved at time intervals that might be utilized to forecast crack growth. The variables root mean square (RMS), crest factor, signal-to-noise ratio (SNR), skewness, kurtosis, and Shannon entropy were collected from the continuously running bearing and utilized as input parameters, with the total crack area and crack width regarded as output parameters. Finally, utilizing several methodologies of the Neural Network tool in MATLAB, a realistic ANN model was trained to predict the crack area and crack width. It was observed that the ANN model performed admirably in predicting data with a better degree of accuracy. Through analysis, it was observed that the SNR was the most relevant parameter in anticipating data in bearing crack propagation, with an accuracy rate of 99.2% when evaluated as a single parameter, whereas in multiple parameter analysis, a combination of kurtosis and Shannon entropy gave a 99.39% accuracy rate. Full article

This work focused on the solubility of ethane in three promising ionic liquids {1-Hexyl-3-methylimidazolium bis(trifluormethylsulfonyl) imide [HMIM][Tf2N], 1-Butyl-3-methyl-imidazolium dimethyl-phosphate [BMIM][DMP], and 1-Propyl-3-methylimidazolium bis(trifluoromethyl-sulfonyl)-imide [PMIM][Tf2N]}. The solubilities were measured at 303.15 K to 343.15 K and pressures up to 1.4 MPa using a gravimetric microbalance. The overall ranking of ethane solubility in the ionic liquids from highest to lowest is the following: [HMIM][Tf2N] > [PMIM][Tf2N] > [BMIM][DMP]. The Peng–Robinson equation of state was used to model the experimental data using three different mixing rules: van der Waals one, van der Waals two, and Wong–Sandler mixing rules combined with the Non-Random Two-Liquid model. The average absolute deviations for the three mixing rules for the ionic liquids at the three temperatures were 4.39, 2.45, and 2.45%, respectively. Henry’s Law constants for ethane in [BMIM] [DMP] were the highest (lowest solubility) amongst other ionic liquids studied in this work. The solubility ranking for the 3 ILs was confirmed by calculating their overall polarity parameter (N) using COSMO-RS. The selectivity of CO₂ over C₂H₆ was estimated at three temperatures, and the overall ranking of the selectivity was in the following order: [PMIM][Tf2N] > [BMIM][DMP] > [HMIM][Tf2N] > Selexol. Selexol is an efficient and widely used physical solvent in gas sweetening. It has lower selectivity than the three ionic liquids studied. [PMIM][Tf2N], a promising solvent, has the highest selectivity among the three ILs studied and would, therefore, be the best choice if, in addition to carbon dioxide capture, ethane co-absorption was to be avoided. The enthalpy and entropy of solvation at infinite dilution were also estimated. Full article