Hash Table Research Articles

Document retrieval using clustering-based Aquila hash-Q optimization with query expansion based on pseudo relevance feedback

A document retrieval system helps users to retrieve the relevant documents corresponding to their query quickly and easily. In the real world, document retrieval is a difficult task due to high volumes of data, unstructured data, and different formats of data. Even though many research techniques are introduced, major problems like vocabulary mismatch and non-linear matching still need to be solved. In this work, the Aquila hash-q optimizer is the proposed matching technique with the clustering technique to retrieve the document in a time-efficient manner for the user query without collision. First, preprocessing is done by eliminating the stop words from the document, stemming, and grouping documents in a cluster into a single document using Hierarchical Density-based Sampling Spatial Cluster of Applications with Noise (HDBSSCAN) clustering. This clustering algorithm is powerful, robust to noise, and scalable and identifies clusters of documents that are related to each other. Additionally, the sampling technique used in this clustering algorithm increases the clustering speed by reducing the size of the document which improves the performance of document retrieval systems. Secondly, the queries are searched using the Aquila hash-q optimizer matching technique by which the relevant documents are retrieved. The Aquila hash-q optimization works by pre-computing a hash table of the terms in a document collection and then using this hash table to quickly identify the relevant documents from the given query. This can significantly improve the speed of document retrieval, especially for large document collections. Aquila hash-q optimization can improve the accuracy, efficiency, and scalability of document retrieval systems. The effectiveness of the Hierarchical Density-Based Clustering Aquila Optimization approach is determined by various analyses through NPL, LISA, and CACM data in terms of precision @ 5 (0.497), precision @ 10 (0.425), Mean Average Precision (MAP) (0.462) by comparing our approach with various methods. As a result, the Aquila hash-q optimizer is the proposed matching technique to retrieve the document in a time-efficient manner for the user query without collision.

ZZPolyCalc: An open-source code with fragment caching for determination of Zhang-Zhang polynomials of carbon nanostructures

Determination of topological invariants of graphene flakes, nanotubes, and fullerenes constitutes a challenging task due to its time-intensive nature and exponential scaling. The invariants can be organized in a form of a combinatorial polynomial commonly known as the Zhang-Zhang (ZZ) polynomial or the Clar covering polynomial. We report here a computer program, ZZPolyCalc, specifically designed to compute ZZ polynomials of large carbon nanostructures. The curse of the exponential scaling is avoided for a broad class of nanostructures by employing a sophisticated bookkeeping algorithm, in which each fragment appearing in the recursive decomposition is stored in the cache repository of molecular fragments indexed by a hash of the corresponding adjacency matrix. Although exponential scaling persists for the remaining nanostructures, the computational time is reduced by a few orders of magnitude owing to efficient use of hash-based fragment bookkeeping. The provided benchmark timings show that ZZPolyCalc allows for treating much larger carbon nanostructures than previously envisioned. Program summaryProgram Title: ZZPolyCalcCPC Library link to program files:https://doi.org/10.17632/vmnr5xfb6t.1Developer's repository link:https://github.com/quantumint/zzpolycalcLicensing provisions: GPLv3Programming language: Fortran 2008, CNature of problem: Calculations of Zhang-Zhang (ZZ) polynomials for carbon nanostructures have traditionally been performed by the application of a recursive algorithm, which scales exponentially with the size of the system. For large nanostructures, the problem often becomes intractable due to prohibitive computational cost. Consequently, ZZ polynomials were reported so far only for small or model systems, for which analytical formulas could be determined; larger systems, such as graphene flakes, fullerenes, or nanotubes, have been intractable with the existing implementations.Solution method: The process of determination of ZZ polynomials, based on recursive decomposition of a given carbon nanostructure, produces many identical fragments. In ZZPolyCalc, we avoid redundant calculations for such fragments by storing all the intermediate data in a hash table and reusing it efficiently whenever necessary. This new algorithm is particularly effective for elongated graphene flakes or carbon nanotubes, where the scaling becomes polynomial, but it also greatly reduces the computational time for other, less regular nanostructures.

Study of Air Ordering Procedure for Information Broadcasting

Wireless data broadcasting is a new way to share public information with many mobile users. To measure how well these systems work, we look at two main things: how long it takes to access the information (access latency) and how long the device has to stay active (tuning time). Indexing technology helps reduce tuning time by letting devices find information quickly and then switch to a low-power mode while waiting. Different indexing methods work better or worse, so comparing them can be hard. In this paper, I look at and compare some popular indexing methods for wireless data broadcasting, such as distributed index, exponential index, hash table, and Huffman tree index. I use probability theory to analyze how each method performs. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6393

High-Performance Computing Web Search System Based on Computer Big Data

File sharing, streaming media, collaborative computing, and other P2P systems are all unicast to establish the corresponding overlapping network. The superimposed network is generally carried out based on the existing primary network. In this way, the access of each node is random. At the same time, this will cause the topological structure of the upper and lower layers to be inconsistent. This will increase the communication delay between nodes and cause an excellent bandwidth burden to the underlying network. The existing topology matching methods still face problems, such as poor scalability and long node aggregation time. This paper aims to design a topological distributed node aggregation method based on network coordination and distributed hash table (DHT) algorithm. This paper established a two-dimensional mesh model of nodes based on equal-distance concentric circles and divided into two equal areas. The parts of multiple namespaces correspond one by one according to their location. Because nodes are kept close, neighbours can be aggregated through DHT's primary "publish" and "search" primitives. Experimental results show that the TANRA method can match the network's topology under a slight delay and a large number of nodes. The TANRA method can effectively reduce the path delay in structured networks.

Giant Modulation of Refractive Index from Picoscale Atomic Displacements.

It is shown that structural disorder-in the form of anisotropic, picoscale atomic displacements-modulates the refractive index tensor and results in the giant optical anisotropy observed in BaTiS3, a quasi-1D hexagonal chalcogenide. Single-crystal X-ray diffraction studies reveal the presence of antipolar displacements of Ti atoms within adjacent TiS6 chains along the c-axis, and threefold degenerate Ti displacements in the a-b plane. 47/49Ti solid-state NMR provides additional evidence for those Ti displacements in the form of a three-horned NMR lineshape resulting from a low symmetry local environment around Ti atoms. Scanning transmission electron microscopy is used to directly observe the globally disordered Ti a-b plane displacements and find them to be ordered locally over a few unit cells. First-principles calculations show that the Ti a-b plane displacements selectively reduce the refractive index along the ab-plane, while having minimal impact on the refractive index along the chain direction, thus resulting in a giant enhancement in the optical anisotropy. By showing a strong connection between structural disorder with picoscale displacements and the optical response in BaTiS3, this study opens a pathway for designing optical materials with high refractive index and functionalities such as large optical anisotropy and nonlinearity.

Space-efficient computation of k-mer dictionaries for large values of k

Computing k-mer frequencies in a collection of reads is a common procedure in many genomic applications. Several state-of-the-art k-mer counters rely on hash tables to carry out this task but they are often optimised for small k as a hash table keeping keys explicitly (i.e., k-mer sequences) takes O(Nkw)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(N\\frac{k}{w})$$\\end{document} computer words, N being the number of distinct k-mers and w the computer word size, which is impractical for long values of k. This space usage is an important limitation as analysis of long and accurate HiFi sequencing reads can require larger values of k. We propose Kaarme, a space-efficient hash table for k-mers using O(N+ukw)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(N+u\\frac{k}{w})$$\\end{document} words of space, where u is the number of reads. Our framework exploits the fact that consecutive k-mers overlap by k-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k-1$$\\end{document} symbols. Thus, we only store the last symbol of a k-mer and a pointer within the hash table to a previous one, which we can use to recover the remaining k-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$k-1$$\\end{document} symbols. We adapt Kaarme to compute canonical k-mers as well. This variant also uses pointers within the hash table to save space but requires more work to decode the k-mers. Specifically, it takes O(σk)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$O(\\sigma ^{k})$$\\end{document} time in the worst case, σ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma$$\\end{document} being the DNA alphabet, but our experiments show this is hardly ever the case. The canonical variant does not improve our theoretical results but greatly reduces space usage in practice while keeping a competitive performance to get the k-mers and their frequencies. We compare canonical Kaarme to a regular hash table storing canonical k-mers explicitly as keys and show that our method uses up to five times less space while being less than 1.5 times slower. We also show that canonical Kaarme uses significantly less memory than state-of-the-art k-mer counters when they do not resort to disk to keep intermediate results.

High-pressure and high-temperature modulation of one-dimensional infinite chain in SeO2

The structural evolution of lone-pair compounds under high-pressure and high-temperature conditions has been a subject of fundamental interest in revealing modulated polymorphs. As one of the archetypal lone-pair compounds, selenium dioxide (SeO2) has attracted much attention due to the pressure modulation of its one-dimensional infinite W-shaped chain arrangement. Here, through swarm intelligence algorithm in conjunction with the first-principles simulation, we propose the existence of an orthorhombic Pnma-SeO2 structure, characterized by V-shaped chains interconnected via vertex-sharing SeO3 pyramids. These V-shaped chains demonstrate reduced compressibility along their chain direction compared to the W-shaped chains. Calculations indicate that Pnma-SeO2 is a semiconductor with a large indirect bandgap of 2.39 eV. Remarkably, we synthesized the predicted Pnma-SeO2 in a laser-heated diamond anvil cell at a pressure of 48.5 or 87 GPa as identified by in situ synchrotron x-ray diffraction data. Our findings lead to a significant extension of the phase diagram and transition path of SeO2 and provide key insights into understanding the pressure modulation in lone-pair compounds.

Yet Another Lock-Free Atom Table Design for Scalable Symbol Management in Prolog

Prolog systems rely on an atom table for symbol management, which is usually implemented as a dynamically resizeable hash table. This is ideal for single threaded execution, but can become a bottleneck in a multi-threaded scenario. In this work, we replace the original atom table implementation in the YAP Prolog system with a lock-free hash-based data structure, named Lock-free Hash Tries (LFHT), in order to provide efficient and scalable symbol management. Being lock-free, the new implementation also provides better guarantees, namely, immunity to priority inversion, to deadlocks and to livelocks. Performance results show that the new lock-free LFHT implementation has better results in single threaded execution and much better scalability than the original lock based dynamically resizing hash table.

M3YOLOv5: Feature enhanced YOLOv5 model for mandibular fracture detection

BackgroundIt is very important to detect mandibular fracture region. However, the size of mandibular fracture region is different due to different anatomical positions, different sites and different degrees of force. It is difficult to locate and recognize fracture region accurately. MethodsTo solve these problems, M3YOLOv5 model is proposed in this paper. Three feature enhancement strategies are designed, which improve the ability of model to locate and recognize mandibular fracture region. Firstly, Global-Local Feature Extraction Module (GLFEM) is designed. By effectively combining Convolutional Neural Network (CNN) and Transformer, the problem of insufficient global information extraction ability of CNN is complemented, and the positioning ability of the model to the fracture region is improved. Secondly, in order to improve the interaction ability of context information, Deep-Shallow Feature Interaction Module (DSFIM) is designed. In this module, the spatial information in the shallow feature layer is embedded to the deep feature layer by the spatial attention mechanism, and the semantic information in the deep feature layer is embedded to the shallow feature layer by the channel attention mechanism. The fracture region recognition ability of the model is improved. Finally, Multi-scale Multi receptive-field Feature Mixing Module (MMFMM) is designed. Deep separate convolution chains are used in this modal, which is composed by multiple layers of different scales and different dilation coefficients. This method provides richer receptive field for the model, and the ability to detect fracture region of different scales is improved. ResultsThe precision rate, mAP value, recall rate and F1 value of M3YOLOv5 model on mandibular fracture CT data set are 97.18%, 96.86%, 94.42% and 95.58% respectively. The experimental results show that there is better performance about M3YOLOv5 model than the mainstream detection models. ConclusionThe M3YOLOv5 model can effectively recognize and locate the mandibular fracture region, which is of great significance for doctors' clinical diagnosis.

Intelligent Extraction of Multi-style and Multi-template Title Block Information Based on Fuzzy Matching

ABSTRACT Title blocks are crucial primary data on drawings necessary for the operation of modern manufacturing enterprises. However, their uniform export poses a challenge due to the heterogeneity across multiple sources and the variety of export templates. Therefore, this paper proposes an intelligent extraction method for title block information (TBI) based on fuzzy matching. This method evaluates the geometrical composition of the title block, the logical relationship between information cells, and the diversification of the title block. It establishes a weighted intersection point matrix for the title block as the topological structure model. The fuzzy matching, based on the intersection matrix points, disregards the variations in the size and position of the title block and focuses only on the style that impacts the extraction of information. The matched title blocks are categorized, and the base table of each type determines the information distribution of the title block. A hash table is then created between the description cell and the value cell of information to accumulate the TBI. The method is integrated into the AutoCAD software and presented with an interactive program interface. Finally, an example is presented to further demonstrate the stability, reliability, and efficiency of the method and the system.

Unraveling High Thermal Conductivity with In-Plane Anisotropy Observed in Suspended SiP2.

The anisotropic thermal transport properties of low-symmetry two-dimensional materials play an important role in understanding heat dissipation and optimizing thermal management in integrated devices. Examples of efficient energy dissipation and enhanced power sustainability have been demonstrated in nanodevices based on materials with anisotropic thermal transport properties. However, the exploration of materials with high thermal conductivity and strong in-plane anisotropy remains challenging. Herein, we demonstrate the observation of anisotropic in-plane thermal conductivities of few-layer SiP2 based on the micro-Raman thermometry method. For suspended SiP2 nanoflake, the thermal conductivity parallel to P-P chain direction (κ∥b) can reach 131 W m-1 K-1 and perpendicular to P-P chain direction (κ⊥b) is 89 W m-1 K-1 at room temperature, resulting in a significant anisotropic ratio (κ∥b/κ⊥b) of 1.47. Note that such a large anisotropic ratio mainly results from the higher phonon group velocity along the P-P chain direction. We also found that the thermal conductivity can be effectively modulated by increasing the SiP2 thickness, reaching a value as high as 202 W m-1 K-1 (120 W m-1 K-1) for κ∥b (κ⊥b) at 111 nm thickness, which is the highest among layered anisotropic phosphide materials. Notably, the anisotropic ratio always remains at a high level between 1.47 and 1.68, regardless of the variation of SiP2 thickness. Our observation provides a new platform to verify the fundamental theory of thermal transport and a crucial guidance for designing efficient thermal management schemes of anisotropic electronic devices.

Role of microfibril angle in molecular deformation of cellulose fibrils in Pinus massoniana compression wood and opposite wood studied by in-situ WAXS

Upon tensile stress, the spiral cellulose fibrils in wood cell walls rotate like springs with decreasing microfibril angle (MFA), and the cellulose molecules elongate in the chain direction. Compression wood with high MFA and opposite wood with low MFA were comparatively studied by in-situ tensile tests combined with synchrotron radiation WAXS in the present study. FTIR spectroscopy revealed that compression wood had a higher lignin content and fewer acetyl groups. For both types of wood, the lattice spacing d004 increased and the MFA decreased gradually with the increase of tensile stress. At stresses beyond the yield point, cellulose lattice strain depended linearly on macroscopic stress, while the MFA depended linearly on macroscopic strain. The deformation mechanisms of compression wood and opposite wood are not essentially different but differ in their deformation behavior. Specifically, the contribution ratio of lattice strain and cellulose fibril reorientation to macroscopic strain was 0.25 and 0.53 for compression wood, and 0.40 and 0.33 for opposite wood, respectively. Due to the geometric effects of MFA, a greater contribution of cellulose fibril reorientation to the macroscopic deformation was detected in compression wood than in opposite wood.

Efficient Vertical Structure Correlation and Power Line Inference.

High-resolution three-dimensional data from sensors such as LiDAR are sufficient to find power line towers and poles but do not reliably map relatively thin power lines. In addition, repeated detections of the same object can lead to confusion while data gaps ignore known obstacles. The slow or failed detection of low-salience vertical obstacles and associated wires is one of today's leading causes of fatal helicopter accidents. This article presents a method to efficiently correlate vertical structure observations with existing databases and infer the presence of power lines. The method uses a spatial hash key which compares an observed tower location to potential existing tower locations using nested hash tables. When an observed tower is in the vicinity of an existing entry, the method correlates or distinguishes objects based on height and position. When applied to Delaware's Digital Obstacle File, the average horizontal uncertainty decreased from 206 to 56 ft. The power line presence is inferred by automatically comparing the proportional spacing, height, and angle of tower sets based on the more accurate database. Over 87% of electrical transmission towers were correctly identified with no false negatives.

Sequence Sensitivity in Membrane Remodeling by Polyampholyte Condensates.

Intrinsically disordered peptides (IDPs) have been found to undergo liquid-liquid phase separation (LLPS) and produce complex coacervates that play numerous regulatory roles in the cell. Recent experimental studies have discovered that LLPS at or near the membrane surface helps in the biomolecular organization during signaling events and can significantly alter the membrane morphology. However, the molecular mechanism and microscopic details of such processes still remain unclear. Here we study the effect of polyampholyte and polyelectrolyte condensation on two different anionic membranes, as they represent a majority of naturally occurring IDPs. The polyampholytes are fifty-residue polymers, made of glutamate(E) and lysine(K) with different charge patterns. The polyelectrolytes are separate chains of E25 and K25. We first calibrate the MARTINI v3.0 force field and then perform long-time-scale coarse-grained molecular dynamics simulations. We find that condensates formed by all the polyampholytes get adsorbed on the membrane. However, the strong polyampholytes (i.e., blocky sequences) can remodel the membranes more prominently than the weaker ones (i.e., scrambled sequences). Condensates formed by the blocky sequences induce a significant negative curvature (∼0.1 nm-1) and local demixing of lipids, whereas those by the scrambled sequences tend to wet the membrane to a greater extent without generating significant curvature or demixing. We perform several microscopic analyses to characterize the nature of the interaction between membranes and these condensates. Our analyses of interaction energetics reveal that membrane remodeling and/or wetting are favored by enhanced interactions between polyampholytes with lipids and the counterions.

Square impossible differential attack and security of AES in known plaintext scenario

In this work, we examine the security of the 8-round AES, under the known plaintext attack scenario, a type of cryptographic attack in which an attacker has access to the plaintext and corresponding ciphertext pairs. We present an innovative impossible differential (ID) attack technique, which utilizes a specific ID characteristic, to perform the first known plaintext attack on the 8-round AES with a 256-bit key. Additionally, we propose a new attack methodology, known as the Square Impossible Differential (SID) attack, to enhance the effectiveness of the ID attacks on AES in chosen ciphertext or plaintext scenarios. The SID attack is a combination of a square attack and an ID attack. Our methodology introduces various new approaches, including the key indicator vectors, eliminating the key candidate through the Meet-in-The-Middle technique and mounting the guess and determine attack through the hash tables for the two-round decryption of one column of AES while determining the subkeys constituting the impossible differential characteristic for a given plaintext/ciphertext difference pair. Our approach demonstrates lower computational complexity compared to previous methods, and our analysis shows that the complexities of our known plaintext attack and SID attack are estimated to be 2220 and 2209, respectively.

T2FM: A novel hashtable based type-2 fuzzy frequent itemsets mining

Association rule mining (ARM) is an important research issue in the field of data mining that aims to find relations among different items in binary databases. The conventional ARM algorithms consider the frequency of the items in binary databases, which is not sufficient for real time applications. In this paper, a novel hash table based Type-2 fuzzy mining algorithm (T2FM) with an efficient pruning strategy is presented for discovering multiple fuzzy frequent itemsets from quantitative databases. The algorithm employs a hash table based structure for efficient storage and retrieval of item/itemset which reduces the search efficiency to O(1) or constant time. Previously, type-2 based Apriori and FP-growth based fuzzy frequent itemsets mining were proposed, which required large amounts of computation and a greater number of candidate generation and processing. Meanwhile, the proposed approach reduces a huge amount of computation by finding the common keys before the actual intersection operation takes place. An efficient pruning strategy is proposed to avoid unpromising candidates in order to speed up the computations. Several experiments are carried out to verify the efficiency of the approach in terms of runtime and memory for different minimum support threshold and the results show that the designed approach provides better performance compared to the state-of-the-art algorithms.

RT-CBCH: Real-Time VPN Traffic Service Identification Based on Sampled Data in High-Speed Networks

Virtual Private Network (VPN) technology can bypass censorship and access geographically locked services. Some harmful information may be hidden in VPN traffic and circumvent the surveillance systems, bringing a significant challenge to network security. Considering the increasing richness of service types in VPN traffic, identifying traffic service facilitates further targeting harmful VPN traffic. Therefore, VPN traffic service identification is critical in network management. The existing identification methods use complete traffic for analysis. However, massive data analysis in high-speed networks consumes enormous resources, limiting the real-time processing of traffic identification. This paper proposes a real-time VPN traffic service identification method named RT-CBCH. We construct features that are still available after sampling and design a fast traffic processing structure based on Counting Bloom Filter and Chained Hash Table (CBCH). Experimental results validate the real-time capability, stability and accuracy of our method. At the sampling ratio of 1/256, it takes only 23.63 seconds to process the mixed traffic of 900-second traffic generated on a 10 Gbps link and our collected V2Ray traffic, which is increasingly common in VPN traffic. Under different sampling ratios, the identification results remain respectable, with an overall accuracy of about 90% for application service and over 99% for V2Ray proxy service. Furthermore, comparisons with similar work illustrate the high accuracy and low resource consumption of RT-CBCH. Experimental results show that our method can stably implement real-time VPN traffic service identification from sampled data in high-speed networks.

3D Reconstruction of Ancient Buildings Using UAV Images and Neural Radiation Field with Depth Supervision

The 3D reconstruction of ancient buildings through inclined photogrammetry finds a wide range of applications in surveying, visualization and heritage conservation. Unlike indoor objects, reconstructing ancient buildings presents unique challenges, including the slow speed of 3D reconstruction using traditional methods, the complex textures of ancient structures and geometric issues caused by repeated textures. Additionally, there is a hash conflict problem when rendering outdoor scenes using neural radiation fields. To address these challenges, this paper proposes a 3D reconstruction method based on depth-supervised neural radiation fields. To enhance the representation of the geometric neural network, the addition of a truncated signed distance function (TSDF) supplements the existing signed distance function (SDF). Furthermore, the neural network’s training is supervised using depth information, leading to improved geometric accuracy in the reconstruction model through depth data obtained from sparse point clouds. This study also introduces a progressive training strategy to mitigate hash conflicts, allowing the hash table to express important details more effectively while reducing feature overlap. The experimental results demonstrate that our method, under the same number of iterations, produces images with clearer structural details, resulting in an average 15% increase in the Peak Signal-to-Noise Ratio (PSNR) value and a 10% increase in the Structural Similarity Index Measure (SSIM) value. Moreover, our reconstruction model produces higher-quality surface models, enabling the fast and highly geometrically accurate 3D reconstruction of ancient buildings.

Arching of force chains in excavated granular material

The physical arch and the arch effect are related to many geotechnical problems, such as the stability of mine openings, a tunnel’s shape, and disaster prevention. However, observing the arch in geo-material is challenging due to Earth’s invisible force network distribution. The arch shape is a critical parameter related to the arching effect, which is still an open question. Specifically, the force network evolution after local excavation in granular material is revealed using the photo-elastic technique, and the system pressure variation is calculated qualitatively in particle scale according to the G2 algorithm. Besides, the force chain arch is formed by strong force chains, which are important in redistributing the stress field. The principal direction of the spatial correlation colormap represents the direction of the strong force chains in the network. Based on this preferred angle between the strong force chain and the horizontal direction, a simplified force chain arch trajectory is iteratively rebuilt. Then, the total force chain distribution is studied considering both G2 and the total length of strong force chains, with the corresponding results demonstrating a relationship between the changes in these characteristics and the force chain arch formation.

Artificial intelligence-driven design of the assembled major cat allergen Fel d 1 to improve its spatial folding and IgE-reactivity

BackgroundCat-derived allergens are considered as one of the most common causes of allergic diseases worldwide. Fel d 1 is a major cat allergen and plays an important role in immunoglobulin E (IgE)-reaction diagnosis. However, the two separate chains of Fel d 1 exhibited lower IgE-reactivity than its complete molecule of an assembled form, which makes it difficult to efficiently prepare and limits the application of Fel d 1 in molecular diagnosis of cat allergy. MethodsWe first applied artificial intelligence (AI) based tool AlphaFold2 to build the 3-dimensional structures of Fel d 1 with different connection modes between two chains, which were evaluated by ERRAT program and were expressed in Escherichia coli. We then calculated the expression ratios of soluble form/inclusion bodies form of optimized Fel d 1. The Circular Dichroism (CD), High Performance Liquid Chromatography-Size Exclusion Chromatography (HPLC-SEC) and reducing/non-reducing SDS-PAGE were performed to characterize the folding status and dimerization of the optimized fusion Fel d 1. The improvement of specific-IgE reactivity to optimized fusion Fel d 1 was investigated by enzyme linked immunosorbent assay (ELISA). ResultsAmong several linkers, 2 × GGGGS got the highest scores, with an overall quality factor of 100. The error value of the residues around the junction of 2 × GGGGS was lower than others. It exhibited highest proportion of soluble protein than other Fel d 1 constructs with ERRAT (GGGGS, KK as well as direct fusion Fel d 1). The results of CD and HPLC-SEC showed the consistent folding and dimerization of two fused subunits between the optimized fusion Fel d 1 and previously well-defined direct fusion Fel d 1. The overall IgE-binding absorbance of optimized fusion Fel d 1 tested by ELISA was improved compared with that of the direct fusion Fel d 1. ConclusionWe firstly provided an AI-design strategy to optimize the Fel d 1, which could spontaneously fold into its native-like structure without additional refolding process or eukaryotic folding factors. The improved IgE-binding activity and simplified preparation method could greatly facilitate it to be a robust allergen material for molecular diagnosis of cat allergy.