Module Structure Research Articles

Study of the Dynamics and Strength of the Detachable Module for Long Cargoes under Asymmetric Loading Diagrams

This article highlights the structural features of the detachable module for the transportation of long cargoes. The choice of profiles for the detachable module was based on the resistance moments of its components. The detachable module was considered a rod structure on four supports. To determine the longitudinal loads acting on the detachable module, mathematical modeling of its longitudinal dynamics was carried out, provided they were placed on a flat car during a shunting impact. The accelerations obtained were used for the calculations of the detachable module. This article presents the results of the strength calculation of the detachable module under asymmetric loading diagrams, i.e., the action of longitudinal and lateral forces on the detachable module structure. The results of the calculations show that the maximum stresses in the structure of the detachable module when it receives longitudinal loads are 7.7% lower than the permissible ones, and when it receives lateral loads, they are 5.8% lower. Thus, the strength of the detachable module is maintained under the loading diagrams considered. This study also included a modal analysis of the detachable module structure. The first natural frequency of oscillations is found to be 20 Hz. Thus, the safety of the detachable module movement in terms of frequency analysis is ensured. This research will help to create recommendations for the design of modern modular vehicles and improve the efficiency of the transport industry.

Mixed Hodge Structures on Alexander Modules

Motivated by the limit mixed Hodge structure on the Milnor fiber of a hypersurface singularity germ, we construct a natural mixed Hodge structure on the torsion part of the Alexander modules of a smooth connected complex algebraic variety. More precisely, let U U be a smooth connected complex algebraic variety and let f : U → C ∗ f\colon U\to \mathbb {C}^* be an algebraic map inducing an epimorphism in fundamental groups. The pullback of the universal cover of C ∗ \mathbb {C}^* by f f gives rise to an infinite cyclic cover U f U^f of U U . The action of the deck group Z \mathbb {Z} on U f U^f induces a Q [ t ± 1 ] \mathbb {Q}[t^{\pm 1}] -module structure on H ∗ ( U f ; Q ) H_*(U^f;\mathbb {Q}) . We show that the torsion parts A ∗ ( U f ; Q ) A_*(U^f;\mathbb {Q}) of the Alexander modules H ∗ ( U f ; Q ) H_*(U^f;\mathbb {Q}) carry canonical Q \mathbb {Q} -mixed Hodge structures. We also prove that the covering map U f → U U^f \to U induces a mixed Hodge structure morphism on the torsion parts of the Alexander modules. As applications, we investigate the semisimplicity of A ∗ ( U f ; Q ) A_*(U^f;\mathbb {Q}) , as well as possible weights of the constructed mixed Hodge structures. Finally, in the case when f : U → C ∗ f\colon U\to \mathbb {C}^* is proper, we prove the semisimplicity and purity of A ∗ ( U f ; Q ) A_*(U^f;\mathbb {Q}) , and we compare our mixed Hodge structure on A ∗ ( U f ; Q ) A_*(U^f;\mathbb {Q}) with the limit mixed Hodge structure on the generic fiber of f f .

Real-World Video Super-Resolution with a Degradation-Adaptive Model.

Video super-resolution (VSR) remains challenging for real-world applications due to complex and unknown degradations. Existing methods lack the flexibility to handle video sequences with different degradation levels, thus failing to reflect real-world scenarios. To address this problem, we propose a degradation-adaptive video super-resolution network (DAVSR) based on a bidirectional propagation network. Specifically, we adaptively employ three distinct degradation levels to process input video sequences, aiming to obtain training pairs that reflect a variety of real-world corrupted images. We also equip the network with a pre-cleaning module to reduce noise and artifacts in the low-quality video sequences prior to information propagation. Additionally, compared to previous flow-based methods, we employ an unsupervised optical flow estimator to acquire a more precise optical flow to guide inter-frame alignment. Meanwhile, while maintaining network performance, we streamline the propagation network branches and the structure of the reconstruction module of the baseline network. Experiments are conducted on datasets with diverse degradation types to validate the effectiveness of DAVSR. Our method exhibits an average improvement of 0.18 dB over a recent SOTA approach (DBVSR) in terms of the PSNR metric. Extensive experiments demonstrate the effectiveness of our network in handling real-world video sequences with different degradation levels.

A cellulosomal double-dockerin module from Clostridium thermocellum shows distinct structural and cohesin-binding features.

Cellulosomes are intricate cellulose-degrading multi-enzymatic complexes produced by anaerobic bacteria, which are valuable for bioenergy development and biotechnology. Cellulosome assembly relies on the selective interaction between cohesin modules in structural scaffolding proteins (scaffoldins) and dockerin modules in enzymes. Although the number of tandem cohesins in the scaffoldins is believed to determine the complexity of the cellulosomes, tandem dockerins also exist, albeit very rare, in some cellulosomal components whose assembly and functional roles are currently unclear. In this study, we characterized the structure and mode of assembly of a tandem bimodular double-dockerin, which is connected to a putative S8 protease in the cellulosome-producing bacterium, Clostridium thermocellum. Crystal and NMR structures of the double-dockerin revealed two typical type I dockerin folds with significant interactions between them. Interaction analysis by isothermal titration calorimetry and NMR titration experiments revealed that the double-dockerin displays a preference for binding to the cell-wall anchoring scaffoldin ScaD through the first dockerin with a canonical dual-binding mode, while the second dockerin module was unable to bind to any of the tested cohesins. Surprisingly, the double-dockerin showed a much higher affinity to a cohesin from the CipC scaffoldin of Clostridium cellulolyticum than to the resident cohesins from C. thermocellum. These results contribute valuable insights into the structure and assembly of the double-dockerin module, and provide the basis for further functional studies on multiple-dockerin modules and cellulosomal proteases, thus highlighting the complexity and diversity of cellulosomal components.

GEA-MSNet: A Novel Model for Segmenting Remote Sensing Images of Lakes Based on the Global Efficient Attention Module and Multi-Scale Feature Extraction

The decrease in lake area has garnered significant attention within the global ecological community, prompting extensive research in remote sensing and computer vision to accurately segment lake areas from satellite images. However, existing image segmentation models suffer from poor generalization performance, the imprecise depiction of water body edges, and the inadequate inclusion of water body segmentation information. To address these limitations and improve the accuracy of water body segmentation in remote sensing images, we propose a novel GEA-MSNet segmentation model. Our model incorporates a global efficient attention module (GEA) and multi-scale feature fusion to enhance the precision of water body delineation. By emphasizing global semantic information, our GEA-MSNet effectively learns image features from remote sensing data, enabling the accurate detection and segmentation of water bodies. This study makes three key contributions: firstly, we introduce the GEA module within the encode framework to aggregate shallow feature semantics for the improved classification accuracy of lake pixels; secondly, we employ a multi-scale feature fusion structure during decoding to expand the acceptance domain for feature extraction while prioritizing water body features in images; thirdly, extensive experiments are conducted on both scene classification datasets and Tibetan Plateau lake datasets with ablation experiments validating the effectiveness of our proposed GEA module and multi-scale feature fusion structure. Ultimately, our GEA-MSNet model demonstrates exceptional performance across multiple datasets with an average intersection ratio union (mIoU) improved to 75.49%, recall enhanced to 83.79%, pixel accuracy (PA) reaching 90.21%, and the f1-score significantly elevated to 83.25%.

Graph-theoretical prediction of biological modules in quaternary structures of large protein complexes.

The functional complexity of biochemical processes is strongly related to the interplay of proteins and their assembly into protein complexes. In recent years, the discovery and characterization of protein complexes have substantially progressed through advances in cryo-electron microscopy, proteomics, and computational structure prediction. This development results in a strong need for computational approaches to analyse the data of large protein complexes for structural and functional characterization. Here, we aim to provide a suitable approach, which processes the growing number of large protein complexes, to obtain biologically meaningful information on the hierarchical organization of the structures of protein complexes. We modelled the quaternary structure of protein complexes as undirected, labelled graphs called complex graphs. In complex graphs, the vertices represent protein chains and the edges spatial chain-chain contacts. We hypothesized that clusters based on the complex graph correspond to functional biological modules. To compute the clusters, we applied the Leiden clustering algorithm. To evaluate our approach, we chose the human respiratory complex I, which has been extensively investigated and exhibits a known biological module structure experimentally validated. Additionally, we characterized a eukaryotic group II chaperonin TRiC/CCT and the head of the bacteriophage Φ29. The analysis of the protein complexes correlated with experimental findings and indicated known functional, biological modules. Using our approach enables not only to predict functional biological modules in large protein complexes with characteristic features but also to investigate the flexibility of specific regions and coformational changes. The predicted modules can aid in the planning and analysis of experiments. Jupyter notebooks to reproduce the examples are available on our public GitHub repository: https://github.com/MolBIFFM/PTGLtools/tree/main/PTGLmodulePrediction.

Oscillations of photocurrent in the structure of a space-time electro-optical modulator in the presence of monopolar injection from illuminated contact

The effect of injection current on transient photocurrent is studied in the case of arbitrary light absorption in space-time electro-optical modulator in the presence of charge carrier trapping. Photocurrent oscillations caused by the generation of alternating space-charge regions in the form of domains that move from the collector to the emitter, are found. It is shown that the injection current stabilizes an electric field on the emitter.

Revisiting the Modularity-Disease transmission Link: Uncovering the importance of intra-modular structure

Studies have shown that the internal structure of modules is hardly important for the spread of epidemics. However, most of these studies have assumed that intra-module connectivity and inter-module connectivity do not affect each other. In reality, changes in the internal structure of modules may affect inter-module links and thus change the modularity of the entire network. Therefore, we have developed a theoretical network model with adjustable modularity to investigate the impact of this situation on disease transmission. Our findings indicate that the intra-module structure plays a crucial role in disease outbreaks. Changes in intra-module structure lead to significant numerical changes in peak prevalence and duration of disease. This implies that the potential impact of changes in exposure patterns within modules should also be considered when investigating the exact impact of modular social networks on disease burden.

Promoting sustainability in 3D printed sand casting through adaptive sand mold structures

The use of 3D printed sand molds offers a high degree of flexibility in structural design and satisfying customer requirements. The optimization and adjustment of the structure of sand mold module with the required functionality helps to maximize the application potential of binder jet 3D printing technology. In this study, an adaptive sand mold structure design method was proposed to meet the sustainable requirements of 3D printed sand casting. Firstly, the functional modular and module clustering of the sand mold is carried out and then each module is adaptive structure design, to reduce the carbon emission and cost of the 3D printed casting process. Therefore, taking the small casting furnace cover and medium-sized cast engine block as examples, the life cycle framework of the 3D printed sand casting process is established, and the sustainable advantages of the adaptive structure method are explored through the calculation and analysis of carbon emission and cost models. The results show that the adaptive structure method reduces the carbon emissions of the two castings by 9.2% and 11.9%, respectively, and the cost of two castings by 6.7% and 11.1%, respectively. This approach provides more sustainable design ideas for 3D printed sand casting and 3D printed sand mass production.

RDPGNet: A road extraction network with dual-view information perception based on GCN

Automatic road extraction from satellite images is a popular research hotspot topic in the field of remote sensing. However, the complexity of road features and structures hinders their precise extraction. Road features have different saliency due to the occlusion of other objects and diverse road types. Road structures are scattered across various locations and affected by geographical conditions, population distribution, and transportation demands. These issues render current methods based on convolutional neural networks prone to fragmented and incomplete road extraction results. To address these issues, a Road Extraction Network with Dual-View Information Perception Based on GCN (RDPGNet) is proposed, where the GCN-based dual-view perceptron (GDVP) uses the superior information interaction capability of the GCN to explore road information under a dual view. In the GDVP, we first design the road feature saliency graph reasoning module (RFSG) and road structure homogeneous space graph reasoning module (RSHS) based on the features and structures. The RFSG obtains the similarity of road feature information in different regions as a similarity matrix to participate in the graph reasoning process, ensuring that equal treatment is given to regions with varying saliency levels. The RSHS projects the road structure onto a corresponding homogeneous space while aggregating and interacting with information through graph convolution, thereby enhancing the network’s perception of roads in diverse locations. Second, because multi-view information usually shares a potential common representation, a multi-view information fusion and alignment strategy (MVFA) is designed to comprehensively model road information. Experimental results obtained from two public datasets indicate that RDPGNet outperforms other state-of-the-art networks.

Mudslide susceptibility assessment based on a two-channel residual network

In response to the challenges posed by rugged terrain in Yunnan, hindering large-scale mudslide screening efforts, this article introduces a dual-channel Convolutional Neural Network (CNN) constructed using elevation data from historical mudslide-prone valleys (Digital Elevation Model, DEM) and remote sensing imagery. The network is designed to facilitate the comprehensive assessment of potential mudslide hazards in gullies, serving as a crucial tool for early mudslide disaster warning. The model initially employs an enhanced residual structure to extract fundamental features from both types of data. Subsequently, it leverages the SE module and deep separable structure to emphasize the importance of relevant features and expedite model convergence. Finally, the model classifies the gullies under evaluation based on their similarity to gullies where mudslides have previously occurred. Experimental results demonstrate the model’s robust performance in assessing mudflow-prone gullies, achieving an impressive precision rate of up to 81.10% and a recall rate of 82.76%. When applied to evaluate the potential hazard of mudslide gullies across the entirety of Nujiang Prefecture, the model predicts that 87.80% of the mudslide locations are at an extremely high risk. These findings underscore the viability of utilizing image-based gully feature analysis for assessing the hazard levels of mudslide-prone gullies.

Physics learning module on google sites-assisted case-based learning measure-ment material

This study introduced the development of a physics learning module on the topic of measurements based on case-based learning, supported by Google Sites. In the research background, the importance of innovative approaches in physics education for enhancing student understanding had been recognized. Therefore, the primary aim of this research was to develop an effective instructional module that would improve students' comprehension of measurement concepts. The research adopted a research and development approach using the 4D development model (Define, Design, Develop, Disseminate). During the "Define" phase, the need for the module and the scope of the teaching material were identified. Subsequently, in the "Design" phase, the module structure was designed, and case-based learning strategies were planned. The "Develop" phase involved creating the module as per the specified design, including the integration of case-based scenarios and the utilization of the Google Sites platform. In the final "Disseminate" phase, an assessment was conducted on the usability and effectiveness of the developed module. The result of this study yielded a comprehensive physics learning module centered around measurement concepts and utilizing case-based learning techniques. The module was successfully validated as an effective teaching tool capable of enhancing students' understanding of measurement concepts.

ОЦЕНКА ТЕХНИЧЕСКОГО СОСТОЯНИЯ СТАЛЬНЫХ КОНСТРУКЦИЙ КОМПЛЕКСА «ЯМАЛ СПГ».

The article presents the results of scientific and technical support for the construction of a complex for the extraction, preparation, liquefaction of gas, shipment of it and its condensate, located at the Yuzhno-Tambeyskoye field. During the work, the following were performed: visual and instrumental inspection of steel structures of technological modules and related engineering structures; study and analysis of project documentation; verification calculations of frames. These works were carried out in order to assess the stressstrain and technical condition of the steel building structures of the complex and make recommendations for further development of construction and safe operation of structures at the field.

Ascent and descent of Artinian module structures under flat base changes

Let φ : R → S be a flat local homomorphism between commutative Noetherian local rings. In this paper, the ascent and descent of Artinian module structures between R and S are investigated. For an Artinian R-module A, the structure of A ⊗ R S is described. As an application, the Artinianess of certain local cohomology modules is clarified.

Strawberry Maturity Recognition Based on Improved YOLOv5

Strawberry maturity detection plays an essential role in modern strawberry yield estimation and robot-assisted picking and sorting. Due to the small size and complex growth environment of strawberries, there are still problems with existing recognition systems’ accuracy and maturity classifications. This article proposes a strawberry maturity recognition algorithm based on an improved YOLOv5s model named YOLOv5s-BiCE. This algorithm model is a replacement of the upsampling algorithm with a CARAFE module structure. It is an improvement on the previous model in terms of its content-aware processing; it also widens the field of vision and maintains a high level of efficiency, resulting in improved object detection capabilities. This article also introduces a double attention mechanism named Biformed for small-target detection, optimizing computing allocation, and enhancing content perception flexibility. Via multi-scale feature fusion, we utilized double attention mechanisms to reduce the number of redundant computations. Additionally, the Focal_EIOU optimization method was introduced to improve its accuracy and address issues related to uneven sample classification in the loss function. The YOLOv5s-BiCE algorithm was better at recognizing strawberry maturity compared to the original YOLOv5s model. It achieved a 2.8% increase in the mean average precision and a 7.4% increase in accuracy for the strawberry maturity dataset. The improved algorithm outperformed other networks, like YOLOv4-tiny, YOLOv4-lite-e, YOLOv4-lite-s, YOLOv7, and Fast RCNN, with recognition accuracy improvements of 3.3%, 4.7%, 4.2%, 1.5%, and 2.2%, respectively. In addition, we developed a corresponding detection app and combined the algorithm with DeepSort to apply it to patrol robots. It was found that the detection algorithm exhibits a fast real-time detection speed, can support intelligent estimations of strawberry yield, and can assist picking robots.

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Ensuring tire quality is crucial in the manufacturing industry, particularly for race cars, where defective tires present a significant safety risk. Visual inspection for defects in tires is crucial; however, identifying defects in complex, textured tires has been proven to be a challenging task. This paper tackles this challenge by introducing XAFCNN, an Explainable Attention-based Fused Convolutional Neural Network for tire defect detection. XAFCNN’s novel architecture, including a Special Attention Module (SAM) and custom CNN structure, coupled with Grad-CAM visualization, prevents overfitting, enhances local feature mapping, enables detection of small defects, and proffers valuable insights into the model’s reasoning, enabling confident interpretation of its predictions. The model was trained on a dataset from a leading global tire manufacturer, including 38,710 x-ray images of defective tires and 83,985 defect-free tire images, covering 15 defect types and 50 design patterns. The results demonstrate the model’s exceptional performance compared to literature, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. This research, with its substantial dataset and high-performing model, advances automated tire defect detection, satisfying the industry’s need for accurate and reliable inspections, ultimately enhancing human safety.

Scalable all‐perovskite double‐ and triple‐junction solar modules: Modeling for configuration optimization

AbstractWe modeled the photovoltaic conversion of all‐perovskite (PVK) double‐ and triple‐junction solar modules to clarify the configurations suitable for the monolithically series‐interconnected structure, which offers high scalability by fully exploiting the advantages of the thin‐film modules over wafer‐based crystalline‐silicon modules. We first formulated the photovoltaic processes of single cells and modules by reference to previously reported data, next optimized the module structure parameters including the bandgaps of PVKs, cell widths, and transparent‐electrode thicknesses, and then evaluated the annually averaged conversion efficiencies (ηannual) defined by the ratio of the annual energy yield to the annual insolation in outdoor environments using a meteorological database. The double‐junction four‐terminal (2J‐4T) module overcomes the shortcomings involved in the two‐terminal module consisting of series‐connected top and bottom cells, providing higher ηannual and more options of the top‐cell bandgap; the latter allows us to select a more durable PVK composition. However, the dual output (four terminals) is practically a serious drawback. The double‐junction voltage‐matched (2J‐VM) configuration eliminates this drawback, that is, realizes the single output (two terminals) with taking over the advantages of 2J‐4T, and hence, 2J‐VM would be the most promising candidate. However, when the VM configuration is applied to the triple‐junction modules, the ohmic loss and optical loss in the transparent electrodes used for the three submodules are more detrimental. To mitigate this shortcoming, we proposed a new configuration of the triple‐junction series/parallel‐connecting voltage‐matched (3J‐SPVM) module. This uses only two substrates with securing high ηannual and other advantages of the VM configuration, which contributes to cost reduction. Consequently, 3J‐SPVM is potentially the most promising configuration for widespread use.

Modular catalytic activity of nonribosomal peptide synthetases depends on the dynamic interaction between adenylation and condensation domains

Nonribosomal peptide synthetases (NRPSs) are large multidomain enzymes for the synthesis of a variety of bioactive peptides in a modular and pipelined fashion. Here, we investigated how the condensation (C)domain and the adenylation (A)domain cooperate with each other for the efficient catalytic activity in microcystin NRPS modules. We solved two crystal structures of the microcystin NRPS modules, representing two different conformations in the NRPS catalytic cycle. Our data reveal that the dynamic interaction between the C and the A domains in these modules is mediated by the conserved "RXGR" motif, and this interaction is important for the adenylation activity. Furthermore, the "RXGR" motif-mediated dynamic interaction and its functional regulation are prevalent in different NRPSs modules possessing both the A and the C domains. This study provides new insights into the catalytic mechanism of NRPSs and their engineering strategy for synthetic peptides with different structures and properties.

Significance of genetic code module structure in gene expression and GC content enhancement in RNA sequences

The existent algebraic models of the genetic code contribute to the understanding of the physio-chemical characteristics of the amino acids. However, the process of translating a gene into a phenotype is highly complex. Moreover, the intricacy of gene expression gets further multiplied due to the biases in the codon usage. This paper explores an algebraic structure called module on the set of codons as well as on that of RNA sequences. We study the potential implications of these structures on gene expression and the GC content of an RNA sequence. The base order {C,U,G,A} appears to possess greater biological significance than many of the orders previously studied. We have developed a novel algorithm to generate RNA sequences with high GC content, aiming to enhance the thermostability of biomolecules. The insights gained from this investigation may have applications in biomolecular modeling and docking, protein engineering, drug development, and related fields.

Performance analysis of a three-stage quadrature RC generator with operational amplifiers

This paper presents the special features of RC harmonic oscillation generators and their widespread use and in particular the quadrature generators which provide two output signals dephased at 90º or 270º. Quadrature generators can be classified as those with an aperiodic frequency-determining circuit or with a phase inverter group which are used to generate oscillations of one or more fixed frequencies. Studies of a three-stage quadrature RC generator circuit with operational amplifiers have been performed. The results obtained from the simulation and experimental studies performed are presented for the selected circuit. It can be assumed that the experimental and simulation results completely coincide to an accuracy of up to 20% for the amplitude of the generated signals and to the total accuracy for the generated frequency. Quadrature generators are very widely used in communication technology and, most importantly, in the structure of digital frequency, phase and quadrature-amplitude modulators and demodulators, in vector RLC meters and many other electronic circuits and devices in practice.