Block Codes Research Articles

The temperature block length for the wet facing masonry under freezing

Introduction. The outer layer of exterior walls is the facing masonry, experiencing a complex stress-strain state when taking its own weight, climatic loads, and exposures (wind pressure, humidity, and air temperature). An analysis of available research results, as well as the requirements of regulatory documents showed that no consideration is given to the temperature deformations of wet masonry when freezing. In this regard, the studies were conducted to assess the impact of this effect on the stress-strain state of the facing masonry.Aim. To obtain a universal dependence of the temperature block length of the facing masonry, including its freezing.Materials and methods. The study is applied to the facing masonry (facing layer) of multilayer exterior walls of buildings, modeled in the LIRA-CAD software package, which implements the finite element method in the form of displacement method. The research results of the work of M.A. Mury "Temperature deformations of wet brickwork" (2008) are used here.Results. An empirical dependence of the temperature block length of the facing masonry for exterior multilayer walls is obtained based on the analysis of the thermal stress state of the masonry resulting from numerical calculation. A logically justified structural solution of the facing masonry unit with antifriction interface with the floor slab is proposed.Conclusions. According to the results, the length of the masonry temperature block is not only affected by the temperature difference and the stiffness of the supporting structure, but also by the humidity conditions of its operation. When the humidity of the masonry increases from 6 to 12 %, the design length of the temperature block significantly decreases. The lengths of temperature blocks, set according to existing methods (including methods of design standards), have an overestimated value, and thus the durability of facing masonry is essentially reduced, or even destructed in a number of cases.

Diblock Copolymers of Poly(ethylene oxide)-b-poly(propylene oxide) Stabilize a Blood-Brain Barrier Model under Oxidative Stress.

The blood-brain barrier (BBB) is a highly restrictive barrier at the interface between the brain and the vascular system. Even under BBB dysfunction, it is extremely difficult to deliver therapies across the barrier, limiting the options for treatment of neurological injuries and disorders. To circumvent these challenges, there is interest in developing therapies that directly engage with the damaged BBB to restore its function. Previous studies revealed that poloxamer 188 (P188), a water-soluble triblock copolymer of poly(ethylene oxide) (PEO) and poly(propylene oxide) (PPO), partially mitigated BBB dysfunction in vivo. In the context of stabilization of the damaged BBB, the mechanism of action of PEO-PPO block copolymers is unknown, and there has been minimal exploration of polymers beyond P188. In this study, a human-based in vitro BBB model under oxidative stress was used to investigate polymer-BBB interactions since oxidative stress is closely linked with BBB dysfunction in many neurological injuries and disorders. PEO-PPO block copolymers of varied numbers of chemically distinct blocks, PEO block length, and functionality of the end group of the PPO block were assessed for their efficacy in improving key physiological readouts associated with BBB dysfunction. While treatment with P188 did not mitigate damage in the in vitro BBB model, treatment with three diblock copolymers improved barrier integrity under oxidative stress to a similar extent. Of the considered variations in the block copolymer design, the reduction in the number of chemically distinct blocks had the strongest influence on therapeutic function. The demonstrated efficacy of three alternative PEO-PPO diblock copolymers in this work reveals the potential of these polymers as a class of therapeutics that directly treat the damaged BBB, expanding the options for treatment of neurological injuries and disorders.

Crabtree: Rust API Test Synthesis Guided by Coverage and Type

Rust type system constrains pointer operations, preventing bugs such as use-after-free. However, these constraints may be too strict for programming tasks such as implementing cyclic data structures. For such tasks, programmers can temporarily suspend checks using the unsafe keyword. Rust libraries wrap unsafe code blocks and expose higher-level APIs. They need to be extensively tested to uncover memory-safety bugs that can only be triggered by unexpected API call sequences or inputs. While prior works have attempted to automatically test Rust library APIs, they fail to test APIs with common Rust features, such as polymorphism, traits, and higher-order functions, or they have scalability issues and can only generate tests for a small number of combined APIs. We propose Crabtree, a testing tool for Rust library APIs that can automatically synthesize test cases with native support for Rust traits and higher-order functions. Our tool improves upon the test synthesis algorithms of prior works by combining synthesis and fuzzing through a coverage- and type-guided search algorithm that intelligently grows test programs and input corpus towards testing more code. To the best of our knowledge, our tool is the first to generate well-typed tests for libraries that make use of higher-order trait functions. Evaluation of Crabtree on 30 libraries found four previously unreported memory-safety bugs, all of which were accepted by the respective authors.

A New Class of Braided Block Codes Constructed by Convolutional Interleavers

Parallel Concatenated Block (PCB) codes are conventionally represented as high-rate codes with low error correcting capability. To form a reliable and outstanding code, this paper presents a modification on the structure of PCB codes, which is accomplished by encoding some parity bits of one of their component codes. For the newly proposed code, named as the braided code, non-stuff bit-based convolutional interleavers are applied, aiming to minimize the design complexity while ensuring the proper permutations of the original message and selected parity bits. To precisely determine the error correcting capability, a tight bound for the minimum weight of braided code is presented. Additionally, further analyses are provided, which verify iterative decoding performance and the complexity of the constructed code. It is concluded that an outstanding braided code is formed by utilizing a reasonable number of iterations applied at its decoding processes, while maintaining its design complexity at a level similar to other well-known codes. The significant performance of short and long-length-based braided codes is evident in both waterfall and error floor regions.

Improved PEG-Based Construction of Analog Fountain Codes.

This paper proposes an improved progressive edge-growth (PEG) construction of analog fountain codes (AFCs). During edge selection, it simultaneously allocates weight coefficients in descending order. Analysis shows that our proposed construction reduces the probability of large weight coefficients involved in harmful short cycles. Simulation results indicate that it has good block error rate (BLER) in short block length regime.

Development and benchmarking of multilingual code clone detector

The diversity of programming languages is growing, making the language extensibility of code clone detectors crucial. However, this is challenging for most existing clone detection detectors because the source code handler needs modifications, which requires specialist-level knowledge of the targeted language and is time-consuming. Multilingual code clone detectors make it easier to add new language support by providing syntax information of the target language only. To address the shortcomings of existing multilingual detectors for language scalability and detection performance, we propose a multilingual code block extraction method based on ANTLR parser generation, and implement a multilingual code clone detector (MSCCD), which supports the most significant number of languages currently available and has the ability to detect Type-3 code clones. We follow the methodology of previous studies to evaluate the detection performance of the Java language. Compared to ten state-of-the-art detectors, MSCCD performs at an average level while it also supports a significantly larger number of languages. Furthermore, we propose the first multilingual syntactic code clone evaluation benchmark based on the CodeNet database. Our results reveal that even when applying the same detection approach, performance can vary markedly depending on the language of the source code under investigation. Overall, MSCCD is the most balanced one among the evaluated tools when considering detection performance and language extensibility.

Efficient and low-complexity variable-to-variable length coding for DNA storage

BackgroundEfficient DNA-based storage systems offer substantial capacity and longevity at reduced costs, addressing anticipated data growth. However, encoding data into DNA sequences is limited by two key constraints: 1) a maximum of h consecutive identical bases (homopolymer constraint h), and 2) a GC ratio between [0.5-cGC,0.5+cGC]\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ [0.5 - c_{{GC}}, 0.5 + c_{{GC}} ] $$\\end{document} (GC content constraint cGC\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c_{GC}$$\\end{document}). Sequencing or synthesis errors tend to increase when these constraints are violated.ResultsIn this research, we address a pure source coding problem in the context of DNA storage, considering both homopolymer and GC content constraints. We introduce a novel coding technique that adheres to these constraints while maintaining linear complexity for increased block lengths and achieving near-optimal rates. We demonstrate the effectiveness of the proposed method through experiments on both randomly generated data and existing files. For example, when h=4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$h = 4$$\\end{document} and cGC=0.05\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$c_{GC} = 0.05$$\\end{document}, the rate reached 1.988, close to the theoretical limit of 1.990. The associated code can be accessed at GitHub.ConclusionWe propose a variable-to-variable-length encoding method that does not rely on concatenating short predefined sequences, which achieves near-optimal rates.

Finite Block Length NOMA MU Pairing UAV-Enable System: Performance Analysis and Optimization

Finite Block Length NOMA MU Pairing UAV-Enable System: Performance Analysis and Optimization

A Deep Learning-based Approach for Channel Estimation in Multi-access Multi-antenna Systems

This paper studies estimating the channel state information at the end of receiver (CSIR) for multiple transmitters communicating with only one receiver so that the latter can decode the incoming signal more efficiently. The transmitters and the receiver are all equipped with multi-antennas and using orthogonal space-time block codes (OSTBC). An algorithm is developed based on deep learning for estimating multi-user multiple-input multiple-output (MU-MIMO) channels. The algorithm could estimate the CSIR using a single pilot block. The proposed convolutional neural network (CNN) architecture designed for this task begins with an input layer that accepts grayscale images, followed by six convolutional blocks for feature extraction and processing. The network concludes with a fully connected layer to output the estimated channel information. It is trained using a regression loss function to map input images to accurate channel information accurately. The performance of the proposed method is compared with classical methods like least square and subspace-based methods, including Capon and rank revealing QR (RRQR) methods. CNN achieved better performance in comparison with the reference. Computer simulations are included to validate the proposed method.

Performance Enhancement for B5G/6G Networks Based on Space Time Coding Schemes Assisted by Intelligent Reflecting Surfaces with Higher Modulation Orders.

Intelligent Reflecting Surfaces (IRS) and Multiple-Input Single-Output (MISO) technologies are essential in the fifth generation (5G) networks and beyond. IRS optimizes the signal propagation and the coverage and is a viable approach to address the issues caused by fading channels that limits the spectral efficiency, while MIMO enhances data rates, reliability, and spectral efficiency by using multiple antennas at both transmitter and receiver ends. This paper proposes an IRS-assisted MISO system using the Orthogonal Space-Time Block Code (OSTBC) scheme to enhance the channel reliability and reduce the Bit Error Rate (BER) in wireless communication systems. The proposed system exploits the benefits from the transmit diversity gain of the OSTBC scheme as well as from the bit energy to noise power spectral density (Eb/No) improvement of the IRS technology. The presented work explores these combined technologies across different modulation schemes. The obtained results outperform the similar previously published works by considering higher-order modulation schemes as well as the deployment of rate ¾ OSTBC-assisted IRS. Moreover, the obtained results demonstrate that the integration of OSTBC with IRS can yield significant performance improvements in terms of Eb/No by 7 dB and 13 dB when using 16 reflecting elements and 64 reflecting elements, respectively.

Oppositely charged polymer-surfactant nanoparticles stabilized by triblock copolymers for enhanced oil loading

When oppositely charged polymers and surfactants combine, they can form a concentrated phase rich in micelles interconnected by polymer chains. Dispersing this concentrated phase as nanoparticles in an aqueous media can be a way to load and deliver hydrophobic ingredients. This study employed triblock copolymers of poly(ethylene oxide)-poly(propylene oxide)-poly(ethylene oxide), EOxPOyEOx, also known as poloxamers or pluronics, to disperse the concentrated phase, forming nanoparticles of aggregated micelles connected by the polyion chains. We showed that nanoparticles formed by hexadecyltrimethylammonium bromide (CTAB), poly(acrylic acid) (PAA), and different EOxPOyEOx copolymers can enhance the loading of oily ingredients compared to pure surfactant micelles. Dispersions with 1.6 wt% of nanoparticles exhibited water-like viscosity and loaded up to 0.48 wt% of oil. Characterization techniques, including dynamic light scattering (DLS), electrophoretic mobility, small-angle X-ray scattering (SAXS), and cryogenic transmission electronic microscopy (cryo-TEM), revealed positively charged spherical 50–180 nm nanoparticles with a core formed by concentrated micelles, that were stable for at least 4 months. Variations in the EO and PO block lengths did not impact morphology, showing that different EOxPOyEOx copolymers were suitable for dispersing the nanoparticles. Increasing EO block length size decreased the diameter of nanoparticles and enhanced stability, providing a means to control their properties. In conclusion, nanoparticles formed by oppositely charged polymer-surfactant mixtures and stabilized by triblock copolymers showed potential for applications, such as sprays, due to their effective oil loading, high stability, and facile preparation.

Flag-transitive 2-designs with prime-square or prime-cube block length

Flag-transitive 2-designs with prime-square or prime-cube block length

Tailoring copolymer architectures and macromolecular interactions for enhanced nanotherapeutic delivery: A design-by-architecture approach

Amphiphilic copolymers with precise macromolecular topologies are crucial in nanomedicine for enhancing drug delivery by improving solubilization, stabilization, and therapeutic efficacy on target tissues. This study employs a design-by-architecture approach to identify factors influencing interactions between synthesized macromolecules and biological systems for optimal drug nanodelivery. Copolymers combining poly(ε-caprolactone) blocks and poly(polyethylene glycol methacrylate) or poly(glycerol methacrylate), were synthesized to investigate how architectural adjustments impact self-assembly, nanoparticle size, drug loading, and biomolecule interactions. Linear and brush block copolymers with varied block lengths were studied for their self-assembly behavior in aqueous environments. Both copolymer types formed smaller nanoparticles with extended hydrophilic blocks, with brush block copolymers showing superior performance due to their peculiar macromolecular architecture. Incorporating ‘clickable’ glycidyl units within the hydrophilic block minimally affected particle size. Controlled functionalization with thiol groups resulted in stable nanoparticles with enhanced size reduction and mucoadhesive properties, potentially improving targeted drug delivery and therapeutic effects.

Constacyclic codes over F q S T and their applications

Let q = p r , where p is an odd prime and r is a positive integer. Consider a ring F q S T , where S = F q + u F q + v F q + uv F q with u 2 = 1 , v 2 = 1 , uv = vu and T = F q + u F q + v F q + w F q + uv F q + uw F q + vw F q + uvw F q with u 2 = 1 , v 2 = 1 , w 2 = 1 , uv = vu , uw = wu , vw = wv . In this paper, we examine the algebraic structure of constacyclic codes over the ring F q S T of block length ( α , β , γ ) . Further, a construction of quantum error-correcting codes (QECCs) from constacyclic codes over F q S T is also given. Moreover, we derive a number of new QECCs.

Block encodings of discrete subgroups on a quantum computer

We introduce a block encoding method for mapping discrete subgroups to qubits on a quantum computer. This method is applicable to general discrete groups, including crystal-like subgroups such as BI of SU(2) and V of SU(3). We detail the construction of primitive gates—the inversion gate, the group multiplication gate, the trace gate, and the group Fourier gate—utilizing this encoding method for BT and for the first time BI group. We also provide resource estimations to extract the gluon viscosity. The inversion gates for BT and BI are benchmarked on the quantum computer with estimated fidelities of 40−4+5% and 4−3+5%, respectively. Published by the American Physical Society 2024

Factorizers for distributed sparse block codes

Distributed sparse block codes (SBCs) exhibit compact representations for encoding and manipulating symbolic data structures using fixed-width vectors. One major challenge however is to disentangle, or factorize, the distributed representation of data structures into their constituent elements without having to search through all possible combinations. This factorization becomes more challenging when SBCs vectors are noisy due to perceptual uncertainty and approximations made by modern neural networks to generate the query SBCs vectors. To address these challenges, we first propose a fast and highly accurate method for factorizing a more flexible and hence generalized form of SBCs, dubbed GSBCs. Our iterative factorizer introduces a threshold-based nonlinear activation, conditional random sampling, and an ℓ ∞ -based similarity metric. Its random sampling mechanism, in combination with the search in superposition, allows us to analytically determine the expected number of decoding iterations, which matches the empirical observations up to the GSBC’s bundling capacity. Secondly, the proposed factorizer maintains a high accuracy when queried by noisy product vectors generated using deep convolutional neural networks (CNNs). This facilitates its application in replacing the large fully connected layer (FCL) in CNNs, whereby C trainable class vectors, or attribute combinations, can be implicitly represented by our factorizer having F-factor codebooks, each with C F fixed codevectors. We provide a methodology to flexibly integrate our factorizer in the classification layer of CNNs with a novel loss function. With this integration, the convolutional layers can generate a noisy product vector that our factorizer can still decode, whereby the decoded factors can have different interpretations based on downstream tasks. We demonstrate the feasibility of our method on four deep CNN architectures over CIFAR-100, ImageNet-1K, and RAVEN datasets. In all use cases, the number of parameters and operations are notably reduced compared to the FCL.

Improved Quantum Rebound Attacks on Double Block Length Hashing with Round-Reduced AES-256 and ARIA-256

At EUROCRYPT 2020, Hosoyamada and Sasaki proposed the first dedicated quantum collision attacks on hash functions. Their proposal presented a quantum adaptation of the rebound attack and revealed that differential trails, which have too low probability for use in classical settings, might be exploitable in quantum settings. After their work, subsequent research has actively delved into analyzing the security of hash functions in the quantum setting.In this paper, we revisit the quantum rebound attacks on the double block hash function Hirose instantiated with 10-round AES-256 (HCF-AES-256) and 7-round ARIA-256 (HCF-ARIA-256) proposed by Chauhan et al. and Baek et al., respectively. Initially, we identify the flaws in their work and reevaluate the complexity of the attacks. We reveal that the flaws stem from not considering the issue that the S-box differential equation has one solution on average. Earlier research addressed this problem by adding auxiliary bits to the search space. If this method is used to correct the flaws, the resulting time complexities are 217.36 and 220.94 times higher than their proposals. Consequently, in some settings, their attacks become less efficient than generic attacks.Subsequently, we propose improved quantum rebound attacks using nested quantum amplitude amplification and quantum state preparation. Our improved attack efficiently pre-filters the search space, leading to a reduction in overall time complexity. We first classically reduce the search space and employ quantum state preparation to generate a superposition state over the pre-filtered search space. We then use nested quantum amplitude amplification to further reduce the search space quantumly. As a result, we achieve a reduction in the time complexity of the quantum rebound attacks on HCF-AES-256 and HCF-ARIA-256 by factors of 211.2 and 219.5, respectively, making the attacks more efficient than generic attacks again.

DNA Hierarchical Superstructures from Micellar Units: Stiff Hydrogels and Anisotropic Nanofibers.

Supramolecular materials have been assembled using a wide range of interactions, including the hydrophobic effect, DNA base-pairing, and hydrogen bonding. Specifically, DNA amphiphiles with a hydrophobic building block self-assemble into diverse morphologies depending on the length and composition of both blocks. Herein, we take advantage of the orthogonality of different supramolecular interactions - the hydrophobic effect, Watson-Crick-Franklin base pairing and RNA kissing loops - to create hierarchical self-assemblies with controlled morphologies on both the nanometer and the micrometer scales. Assembly through base-pairing leads to the formation of hybrid, multi-phasic hydrogels with high stiffness and self-healing properties. Assembly via hydrophobic core interactions gives anisotropic, discrete assemblies, where DNA fibers with one sequence are terminated with DNA spheres bearing different sequences. This work opens new avenues for the bottom-up construction of DNA-based materials, with promising applications in drug delivery, tissue engineering, and the creation of complex DNA structures from a minimum array of components.

PIV investigation of the effects of simulated ice blocks on the turbulent flow dynamics around a partially submerged horizontal circular cylinder

This paper presents a novel particle image velocimetry study of the effects of stationary upstream ice blocks on the turbulent flow dynamics around a single, fixed ice boom pontoon. The pontoon was modelled using a half-submerged horizontal circular cylinder. Different lengths, thicknesses, and undersurfaces of the ice blocks were examined. The Reynolds number was maintained at 25,500. The results show that the cylinder encounters an approach flow with lower momentum but higher turbulence intensity when placed behind a long ice block compared to a shorter one, and the presence of undersurface roughness reduces the momentum but enhances the turbulence intensity of the approach flow. The recirculation length, mean velocities, and turbulence levels around the cylinder reduce with increasing ice block length and subsequent roughening of its undersurface. However, a larger recirculation bubble forms from the ice block, and the mean velocities and turbulence levels increase when the ice block thickness increases.

A path splitting and pruning strategy on list decoder for PAC codes

AbstractRecently, Arıkan proposed a polarization‐adjusted convolutional (PAC) codes, demonstrating their superior error correction performance over polar codes at short block lengths. It was confirmed that PAC codes approached the optimal performance achievable with limited code length. This paper proposes a novel low‐complexity list decoding algorithm for PAC codes, incorporating path splitting and pruning strategies based on a set of highly reliable information bits. Simulation results reveal that the proposed algorithm significantly reduces sorting complexity and average list size, all while incurring negligible performance loss. Unlike previous pruning algorithms designed for polar codes, the proposed strategy eliminates the need to individually assess the reliability of decoding paths in each decoding process. Instead, the algorithm minimizes redundant decoding paths through a high‐reliability information bit set, constructed using Monte Carlo experiments.