Block Structure Research Articles

Shufflenetv2UNet: An improved neural network model for grassland sample coverage extraction

AbstractAccurate extraction of grassland sample coverage is crucial for regional ecological environment monitoring. Due to the strong feature learning capability, high flexibility, and scalability of deep learning methods, they have great potential in grassland sample extraction modelling. However, we still lack a model that can achieve both lightweight structure and effective performance for small object segmentation to considering the small target characteristics of grassland vegetation and the requirements for model deployment in later stages. Here, we combined the UNet model, which performs well in small target segmentation, with the lightweight network Shufflenetv2 model, proposing an improved UNet neural network, Shufflenetv2UNet, for grassland sample coverage extraction. The core of Shufflenetv2UNet is the removal of maximum pooling and double‐layer convolution modules from downsampling in the UNet neural network. In addition, the Inverted Residual Block structure module from Shufflenetv2 was added to achieve a lightweight model and improved extraction accuracy. The Shufflenetv2UNet achieves an accuracy of 98.23%, with a parameter size of 50.74 M, and a model inference speed of 0.004 s. Compared to existing extraction methods, this model has advantages in prediction accuracy, parameter size, and model inference speed. Moreover, Shufflenetv2UNet achieved different types of grassland sample coverage extractions, with good robustness, generalization, and universality, enabling investigators to quickly and accurately obtain grassland sample coverage. This allows more dynamic and accurate ground measurement data for regional grassland environmental monitoring.

Intelligent fault diagnosis in power distribution networks using LSTM-DenseNet network

This paper introduces a novel fault diagnosis method that combines DenseNet and Long Short-Term Memory (LSTM) networks. The DenseNet utilizes its unique dense block structure to detect subtle variations in three-phase voltage and zero-sequence current signals. In addition, the Squeeze-and-Excitation (SE) module is introduced in DenseNet. The SE module enhances DenseNet's feature representation by adapting the importance of each channel in the feature map. Furthermore, integrating the LSTM model enables capturing time-domain features of fault signals, enhancing the analysis of waveform changes and trends. These extracted features are subsequently fused in a cascaded manner, leveraging the strengths of both approaches to obtain a more comprehensive information representation. To better explain the capability of feature extraction in each part of the model, t-distributed Stochastic Neighbor Embedding (t-SNE) method is used for visual analysis. The proposed method is evaluated using two distribution network models, namely the 10 kV and IEEE34 networks, in simulation. The verification results indicate that the proposed method achieves exceptionally high accuracy in fault identification for both tested distribution network models, with rates of 99.87 % and 99.82 %, respectively, while also demonstrating robust performance in noisy environments. This performance surpasses that of other related methods, underscoring the enhanced effectiveness of our approach.

Building Extraction from Unmanned Aerial Vehicle (UAV) Data in a Landslide-Affected Scattered Mountainous Area Based on Res-Unet

Building extraction in landslide-affected scattered mountainous areas is essential for sustainable development, as it improves disaster risk management, fosters sustainable land use, safeguards the environment, and bolsters socio-economic advancement; however, this process entails considerable challenges. This study proposes a Res-Unet-based model to extract landslide-affected buildings from unmanned aerial vehicle (UAV) data in scattered mountain regions, leveraging the feature extraction capabilities of ResNet and the precise localization abilities of U-Net. A landslide-affected, scattered mountainous region within the Three Gorges Reservoir area was selected as a case study to validate the model’s performance. Experimental results indicate that Res-Unet displays high accuracy and robustness in building recognition, attaining accuracy (ACC), intersection-over-union (IOU), and F1-score values of 0.9849, 0.9785, and 0.9892, respectively. This enhancement can be attributed to the combined model, which amalgamates the skip connections, the symmetric architecture of U-Net, and the residual blocks of ResNet. This integration preserves low-level detail during recovery at higher levels, facilitating the extraction of multi-scale features while also mitigating the vanishing gradient problem prevalent in deep network training through the residual block structure, thus enabling the extraction of more complex features. The proposed Res-Unet approach shows significant potential for the accurate recognition and extraction of buildings in complex terrains through the efficient processing of remote sensing images.

Evaluation of the Compatibility Between Formation and Injection Water in Ultra-Low Permeability Reservoirs

This study focuses on the reservoir scaling and the under-injection issues of the water injection well during the water injection development of an ultra-low permeability reservoir in Xinjiang due to the complex composition of injected water. Microfluidic experiments were applied to visualize the flow channel changes during water flooding, indoor core flooding experiments were employed to analyze the permeability and ion concentration, and nuclear magnetic resonance (NMR) was used to evaluate the pore structure damage. Together, these experiments were used to clarify the scaling and precipitation characteristics as the injected water met the formation water in porous media and the effects on reservoir damage. The research results showed that the poor compatibility of the injected water with the formation water could easily produce calcium carbonate scaling. The scaling products exhibited a unique network structure of blocks and a radial distribution, mainly composed of calcium carbonate and aluminosilicate. The scaling in the porous media exhibited the characteristics of unstable crystal precipitation, migration, and repeated scaling following water mixing, while the scale crystal growth occurred in the pores and the throats. According to the scaling characteristics, the damage to the reservoir permeability by scaling can be divided into the induction, damage, and stabilization stages. The filling and clogging of the scale crystals enhanced the pore structure heterogeneity, with the median pore radius reduced by 21.61% and the permeability reduced by 50%.

Numerical investigation of the labyrinth leakage flow on the performance of a compressor cascade

This paper examines the effect of labyrinth seal structure on the evolution of vortex systems and the performance of the cascade. It is found that the labyrinth seal leakage leads to the circumferential development of the separation vortex, and the axial influence region of the concentrated shedding vortex also expands, which increases the flow loss and reduces the diffuser capacity. Among all the cases, the deteriorating effect of the cavity without a labyrinth seal is the most significant. This results in an 83% increase in total pressure loss compared to the prototype cascade. The straight tooth labyrinth and the straight tooth labyrinth with block structures can effectively reduce the leakage from the sealing cavity and weaken the deterioration caused by labyrinth seal leakage. Among these schemes, the straight tooth with block scheme has the most considerable effect, resulting in a 19% reduction in total pressure loss. As the incidence angle increases, the recirculation zone on the end wall of each scheme gradually shrinks toward the leading edge. Meanwhile, the static pressure rises, and the velocity decreases near the leading edge. Compared with the straight tooth scheme, the straight tooth with block scheme can reduce the high-loss zone by 14% at most. In addition, as the sealing clearance gradually increases, the inhibitory effect of the straight tooth with block scheme on the deterioration of cascade performance caused by the labyrinth sealing cavity is gradually enhanced compared to the straight tooth structure. The improvement effect is optimal under 0.5 mm sealing clearance.

Force analysis and improvement of main transformer bushing terminal structure

Abstract After investigation, it was found that due to natural environmental conditions such as rain and snow temperature, equipment such as down conductor above the terminals, and natural swings of wires, the terminal blocks of the down conductor bushing on the high voltage side of the main transformer of a 500kV substation will deteriorate and deform. Following the force analysis and experiment on the down conductor bushing terminal block on the high voltage side of the main transformer, it is found that the weak structure of the bushing terminal block lies in the vertical orientation of the terminal and the connection point of the transverse structure, and its horizontal external force limit value is 1.91KN. In this paper, it was finally decided to improve the terminal structure by erecting diagonal supports between the vertical and transverse structures of the terminals. The improved terminal block can be subjected to an external force of 5KN horizontally, and the terminal shape is within the safe range. It can ensure the safety and stability of the main transformer bushing terminal block.

Development of a Two-Step EOF Statistical Postprocessing Algorithm to Identify Patterns of Systematic Error and Variance within GEFSv12 Reforecasts

Abstract Analysis of seasonally varying signals of model error biases, as well as the diagnosis of space–time patterns of synoptic error, here is accomplished through the development of a statistical postprocessing algorithm based on time-extended empirical orthogonal functions (EEOFs) built on patterns of variance. This work analyzes GEFSv12 200-hPa geopotential height (Z200) model error against ERA5 reanalysis data. The mean-square error variance between GEFSv12 reforecast and ERA5 grows rapidly after day 7 of a forecast and continues to increase through the end of the 16-day forecast period. At lead time 16, the largest variance occurs in mid- to high-latitude oceanic storm tracks. Variance is highest during hemispheric winter when baroclinic energy is abundant. The seasonal cycle of error shows largest anomalies in the high latitudes during hemispheric winter. After running the titular two-step, space–time EEOF algorithm, a standardized spatial eigenspectrum shows that eigenvalues increase at longer lead times after decreasing in the medium range, demonstrating that the algorithm extracts large-scale signals of systematic error in the long range. Leading wintertime space–time eigenmode pairs include high-latitude blocking structures and Rossby wave trains. Results suggest that the large-scale systematic errors in GEFSv12 Z200 initiate in part from inaccurate representations of phase speeds of atmospheric waves in the polar jet.

CI-YOLO: A lightweight foreign object detection model for inspecting transmission line

Timely detection and elimination of foreign objects on transmission lines are of significance for the secure operation of power grids. With the widespread utilization of unmanned aerial vehicles (UAVs) for inspecting transmission lines, how to effectively balance the resource consumption and accuracy of detection model presents a challenge. To tackle this problem, this study proposes a foreign object detection model based on an improved YOLOv8n, realizing both lightweight and enhanced detection performance. Firstly, a SC Block structure is devised to substitute Bottleneck in C2f module of Backbone, minimizing the spatial and channel redundancy among features, enhancing the network performance, and reducing the consumption of computational resources. Subsequently, a novel dual attention mechanism, IEMA, is added after C2f module of Backbone, strengthening the crucial spatial and channel feature expression capability and significantly increasing the detection accuracy. Lastly, a new feature fusion network, CCFF_IEMA, is designed to replace Neck network, effectively integrating detailed features and reducing the parameter count and FLOPs. Experimental results indicate that in comparison to standard YOLOv8n, the parameters of our proposed CI-YOLO model are decreased by 40 %, FLOPs are reduced by 19.8 %, while achieving improvements of 0.7 % in Precision, 3.2 % in Recall, and 3.1 % in mAP0.5, respectively. The CI-YOLO model is beneficial to deployment on UAVs and provides a novel approach for detecting the foreign objects.

Bigger versus smaller: Children's understanding of size comparison words becomes more precise with age.

The ability to compare plays a key role in how humans learn, but words that describe relations between objects, like comparisons, are difficult to learn. We examined how children learn size comparison words, and how their interpretations of these change across development. One-hundred-and-forty children in England (36-107 months; 68 girls; majority White) were asked to build block structures that were bigger, longer, smaller, shorter, or taller than an experimenter's. Children were most successful with words that refer to size increases. Younger children were less accurate with smaller and shorter, often building bigger structures. The dimensional aspect of taller emerged gradually. These findings suggest that children's interpretation of the meaning of size comparison words changes and becomes more precise across development.

Native gold from alluvial deposits of the Kyvvozhsky district and its probable primary sources (Volsko-Vymskoe rise, Middle Timan)

Based on the study of typomorphic features of gold from alluvial deposits of the Kyvvozh region of Middle Timan, including industrial placers, the most probable genetic types of primary sources were determined. The size of gold particles varies widely, reaching the size of small nuggets; along with rounded ones, there are weakly rounded and unrounded particles. Many of them have undergone repeated deformations in the form of envelope-shaped bends, dents and tears. Most gold coins have high-fineness rims. Gold always contains Ag, sometimes Cu, Pd and Cu. Gold particles with a block structure with high-silver vein zones are often found. In intergrowths with gold and in the form of inclusions are noted in it pyrite, galena, and occasionally minerals of the cobaltine-gersdorfite series, ankerite, galenobismutite, native bismuth, aurostibite, and also sudovikovite (PtSe2), which was first identified in the region. Three types of gold have been identified: 1 – homogeneous silver-containing, 2 – block with highly silvery vein-like zones, 3 – rare silver-containing with impurities of Cu, Pd. The morphology, composition and structure of placer gold indicate its entry into placers from various sources, including nearby sources. Of greatest interest are the zones of development in Riphean rocks of hydrothermal veinlet-disseminated sulfide mineralization, oriented in the northwest direction, partially exposed during the development of placers. Gold with Cu and Pd impurities is most likely associated with derivatives of mafic magmatism. The Volsko-Vymskoe, as well as the Tsilemskoe and Chetlasskoe uplifts of Middle Timan can be considered promising in terms of primary gold content and deserve further study and prospecting.

აჭარა-გურიის გეოლოგიური აგებულება მიღებული მაღლივი-კოსმოსური მასალების დეშიფრირებით

The geological structure of the Adjara-Guria region is defined by the fault zones of the orthgonal-diagonal system, which are the constituent parts (segments) of the transregional lineaments, and are well interpreted on high-space images.The block structure of the regionis determined by fault zones. Nine tectonic blocks of different configurations and geological structures are separated. There are deciphered 11 and 12 km wide tectonic zones with meridian direction, compiled,replaced by a large number of faults and related dykes. The gold occurencesin the region are associated with meridian structures, that’s why the identified tectonic zones are promising spaces for gold.

Deep Learning Method of Precious Wood Image Classification Based on Microscopic Computed Tomography

Correctly identifying precious wood species is crucial for import and export trade and furniture material identification. This study utilizes nondestructive testing (Microscopic Computed Tomography, Micro-CT) to capture microscopic images of the transverse, radial, and tangential sections of 24 precious wood species, creating a comprehensive dataset. The SLConNet deep learning model is developed, enhancing recognition accuracy through multi-scale convolution and an improved residual block structure. The experiment results show that the classification accuracy of the transverse, radial and tangential sections is 98.72, 96.75 and 95.36 % respectively when the gain value is 0.8. The model outperforms traditional models like Alexnet, ResNet50, Inception-V3, and Xception. This research highlights the efficiency of nondestructive testing in obtaining a large number of microscopic wood images, compared to traditional anatomical methods. The SLConNet model showcases high accuracy in precision, recall, and specificity, suggesting its potential for widespread applications in wood classification.

Jane Jacobs's urban vitality focusing on three-facet criteria and its confluence with urban physical complexity

Urban vitality is crucial for sustainability, contributing to social cohesion, information exchange, and overall quality of life. Understanding and quantifying urban vitality is essential for effective planning and policymaking, yet it remains a complex task. Previous studies have primarily focused on single-dimensional measures of urban vitality, overlooking the multifaceted nature emphasized by Jane Jacobs, including spatiotemporal dimensions. This study revisits Jacobs's urban vitality, focusing on three key indicators of intensity, diversity, and consistency of urban vitality. Additionally, we explore the relationships between urban complexity and urban vitality. We employ variety, uniformity, and connectivity metrics to assess urban complexity, which examines the physical structures of buildings, streets, and blocks. Linear regression and Maximal Information-based Nonparametric Exploration statistics for non-linearity analysis are used to determine the associations between urban complexity and urban vitality. Our findings reveal nuanced details of the relationships. Notably, variety and connectivity variables have more significant influences on urban vitality than those related to uniformity. Furthermore, the analysis highlights the differential impacts of complexity variables on different types of vitality. These insights provide valuable guidance for planners and policymakers, facilitating the design of sustainable and vibrant cities by considering optimal complexity measures of built environment that enhance urban vitality.

Lightweight Detection of Train Underframe Bolts Based on SFCA-YOLOv8s

Improving the accuracy and detection speed of bolt recognition under the complex background of the train underframe is crucial for the safety of train operation. To achieve efficient detection, a lightweight detection method based on SFCA-YOLOv8s is proposed. The underframe bolt images are captured by a self-designed track-based inspection robot, and a dataset is constructed by mixing simulated platform images with real train underframe bolt images. By combining the C2f module with ScConv lightweight convolution and replacing the Bottleneck structure with the Faster_Block structure, the SFC2f module is designed for feature extraction to improve detection accuracy and speed. It is compared with FasterNet, GhostNet, and MobileNetV3. Additionally, the CA attention mechanism is introduced, and MPDIoU is used as the loss function of YOLOv8s. LAMP scores are used to rank the model weight parameters, and unimportant weight parameters are pruned to achieve model compression. The compressed SFCA-YOLOv8s model is compared with models such as YOLOv5s, YOLOv7, and YOLOX-s in comparative experiments. The results indicate that the final model achieves an average detection accuracy of 93.3% on the mixed dataset, with a detection speed of 261 FPS. Compared with other classical deep learning models, the improved model demonstrates superior performance in detection effectiveness, robustness, and generalization. Even in the absence of sufficient real underframe bolt images, the algorithm enables the trained network to better adapt to real environments, improving bolt recognition accuracy and detection speed, thus providing technical references and theoretical support for subsequent related research.

Novel Chiral Self-Assembled Nano-Fluorescence Materials with AIE Characteristics for Specific Enantioselective Recognition of L-Lysine.

In this paper, two aggregation-induced emission (AIE) chiral fluorescent materials, S-1 and S-2, were synthesized. The two materials are based on BINOL and H8-BINOL backbones, respectively, and large electron-absorbing groups are attached to the chiral backbones through the Knoevenagel reaction. At the same time, the CD signals of these two chiral fluorescent materials are gradually weakened (fw gradually increases) as they continue to aggregate. However, S-2 underwent a flip-flop from a negative to positive chiral CD signal at fw ≥ 90. And both materials also showed significant enantioselective recognition of lysine, demonstrating their potential as novel chiral fluorescent probes. Among them, the enantioselective fluorescence enhancement ratios (ef) of S-1 and S-2 for lysine were 10.0 and 10.3, respectively, while different degrees of blue shifts were produced by the ICT mechanism during the recognition process. In addition, the self-assembled morphology of the two nanomaterials is different; S-1 comprises hollow-core vesicles that are more likely to aggregate to form larger self-assembled vesicles, whereas S-2 is a solid block structure. When L/D-lysine was added alone, the morphology of S-1 was more distinctly different compared to S-2. With the addition of L-lysine, S-1 was dispersed and regularly spherical, whereas with the addition of D-lysine, S-1 itself remained in the form of aggregated large vesicles. This suggests that both S-1 and S-2 are important in the fields of chiral optics, chiral recognition, and nanoscale self-assembly.

Biomimetic supported polyoxometalate structures designed and its applications for the ultra-deep oxidative desulfurization of fuels

Inspired by the fact that human nose cilia can effectively contact with particles to prevent their entry into our body, this kind novel biomimetic structures are designed and synthesized to enable the loading efficiency between the polyoxometalate catalytic species (POMs) and the carrier, thereby improving their loading rate and promoting their catalytic performance. The mullite whiskers-uniformed cordierite (CMW) is used as a carrier to support POMs, in which mullite whiskers regarding as the cilia in our nose, this kind catalyst could not only overcome the shortcomings of simple POMs with low specific surface and easy to self-agglomerate, but also could greatly increase their contact probability with the carrier. At the same time, the advantage of the cordierite block structure also greatly conducive to their actual application value on the reusing operation. A series of Keggin-type POM-loaded bulk materials, POM@CMW, [VimAm]Br@POM@CMW, [DVim]Br@POM@CMW, have been developed with various ionic liquid-based POMs (ionic liquid = ILs, chosen these two kinds of [VimAm]Br and [DVim]Br) to a comparative study. The oxidative desulfurization reaction (ODS) for fuels was performed using the obtained materials as catalysts and H2O2 as oxidant, without the additional extractants for the existing of ILs in catalyst. Through experimental evaluations, [DVim]Br@POM@CMW was found the excellent efficiency on the Sulfur removal, with an DBT removal of 96.34 % at 70 °C for 120 min. According to the FT-IR and SEM results after the reaction, the catalyst could retain their original structure and the dibenzothiophene (DBT) removal could still be maintained above 95 % after 7 reuses. Furthermore, the oxidation reactivity of different substrates was in the following order: DBT > 4,6-dimethyldibenzothiophene (4,6-DMDBT) > thiophene (BT) and the high activity in actual diesel oil could be also exhibited. The enhanced reaction activity, excellent structural stability, and recyclability can be attributed to the abundance of active sites and outstanding mechanical properties provided by cordierite/mullite whiskers as a carrier, indicating their potential in the novel biomimetic-structured POMs-loaded catalysts.

Tuning polymer micelle size and dynamics with solvophobic block structure

Hypothesis: Micelles formed by copolymers with mixed solvophobic blocks have attracted much attention lately. It is expected that changing the mixed blocks sequence can be used as a tool to influence the micellization behavior in a way that is not equivalent to simply varying the incompatibility parameter for pure diblock-copolymers.Simulations: By using coarse-grained simulations, the micellization behavior of copolymers with twelve types of solvophobic blocks, which differed in the fraction f as well as the sequence of solvophilic units in the solvophobic blocks, was studied. To enable quantitative comparison of systems with different f, an “effective” incompatibility parameter was introduced.Findings: The micelles size distributions and dynamics were found to be significantly dependent on both f and the fine details of the sequence. When the solvophobic block sequences were obtained statistically using a model of copolymerization, a strong influence of the reactivity ratios on the resulting micelles was found. Even a moderate tendency to form gradient-like sequences resulted in a noticeable increase in the micelles size. The dynamic properties of the micelles were studied by investigating the rate at which the chains escape from them. The addition of soluble units into the solvophobic block was shown to dramatically decrease the tendency to form kinetically frozen states; gradient-like sequences demonstrated more sluggish dynamics. The physical reasons for the observed peculiarities are discussed in detail.

Cold-sintered self-assembly of layer-ordered hydroxyapatite nanowire arrays for efficient oil/water separation

Modified membranes and block materials with abundant porous structures have been extensively studied for oil/water separation due to their unique wettability and high specific surface area, crucial factors for high separation efficiency. This study used oleic acid to induce the hierarchical self-assembly of hydrophobic hydroxyapatite nanowires during the cold sintering process, producing a dense, layer-wise stretchable block structure. This cold sintering process sample exhibits interlayer expansion when immersed in water, spatially transitioning from a dense block to a loose, two-dimensional hydrophobic film. The small-volume, dense block contains a hydrophobic layer structure, enriching the specific surface area and integrating the advantages of hydrophobic membranes and block materials with a high specific surface area, offering a novel approach to creating dense and efficient oil/water separation materials. It grants an economical and environmentally friendly solution for large-scale oil/water separation in practical applications.

Exfoliation of Ti3 C2 MXene using (NH4 )2TiF6 Etchant and Simultaneous Synthesis of Ti3 C2 MXene/NH4 TiOF3 Mesocrystals Hybrids

Abstract A new single-step method is suggested to fabricate block structures consisting of NH4TiOF3 wrapped around Ti3C2 MXene (Ti3C2) by etching Ti3AlC2 MAX phase with (NH4)2TiF6. The optimal reaction conditions for Ti3AlC2 MAX phase and (NH4)2TiF6 are systematically investigated. (NH4)2TiF6 selectively etches the Al in the Ti3AlC2 MAX phase and promotes promoting the mild hydrolysis reaction of (NH4)2TiF6 to form NH4TiOF3 crystals. The exterior shapes of Ti3C2/NH4TiOF3 hybrids can be tuned by adjusting the reagent concentration, reaction temperature. The phase composition, morphology and photophysical properties of the Ti3C2/NH4TiOF3 hybrids are investigated using various characterization techniques. Ti3AlC2 MAX phase reacts with (NH4)2TiF6 at 60 °C for 24 h to form hybrids which are surrounded by NH4TiOF3 crystals simultaneously with the formation of Ti3C2 nanosheet. The resulting Ti3C2/NH4TiOF3 are porous and finely structured, showing potential for various applications in biotechnology, sensing, and environmental fields. This method provides important insights into future applications of Ti3C2/NH4TiOF3 hybrids.

Elephant in the Room: How to Assess the Vote-weighting System in the Council of the EU

The article emphasises the complementarity of research based on voting power theory with the analysis of the structure of blocking. Combining both methods enables a more comprehensive assessment of the vote weighting system in the Council of the EU, in particular the impact of changes in the voting system on the position of individual Member States and the power balance in this institution. I make two contributions to the existing literature. Firstly, the study develops a non-random voting model, dedicated to QMV in the Council, which abandons the assumption that each coalition of players is equally likely and takes into account the role of the European Commission as an agenda-setter. This not only allows us to exclude many coalitions that are only theoretically possible, but also to analyse the blocking structure from the perspective of different states or coalitions in the Council. Secondly, it shows that the application of blocking structure analysis provides novel insights into the Council’s voting system. Unlike the a priori voting power approach, such analysis does not focus on distribution of winning power in the Council, but on the Member States’ ability to build minimal blocking coalitions. Hence, it complements the application of mathematical indices of voting power in the assessment of the voting system in this institution.