Block Codes Research Articles

Prediction of Thermal Cracking During Construction of Massive Monolithic Structures

The problem of early crack formation caused by temperature stresses in hardening concrete is very relevant for massive monolithic reinforced concrete structures. The aim of the work is to develop a method for thermal cracking risk prediction during the construction of massive monolithic reinforced concrete structures. The innovation of the research consists in taking into account the dependence of the concrete elastic modulus and strength on the time and temperature of hardening. The significance of the study lies in analysis of methods for reducing the risk of early cracking using the example of a real structure. The object of the study is a fragment of a massive monolithic dock wall. The analysis is performed by the finite element method using a program developed by the authors in the MATLAB environment. Verification of the developed software was performed by comparison with the solution in ANSYS using a linear elastic model without time dependence of the elastic modulus. Next, various options were used to set the dependence of the mechanical characteristics of concrete on the time and temperature of hardening. An analysis was conducted of the possibility of reducing the risk of early cracking by reducing the length of the concrete block, correcting the heat exchange conditions of the surfaces, and reducing the heat generation of concrete.

The Rosencrantz Coin: Predictability and Structure in Non-Ergodic Dynamics—From Recurrence Times to Temporal Horizons

We examine the Rosencrantz coin that can “stick” in states for extended periods. Non-ergodic dynamics is highlighted by logarithmically growing block lengths in sequences. Traditional entropy decomposition into predictable and unpredictable components fails due to the absence of stationary distributions. Instead, sequence structure is characterized by block probabilities and Stirling numbers of the second kind, peaking at block size n/logn. For large n, combinatorial growth dominates probability decay, creating a deterministic-like structure. This approach shifts the focus from predicting states to predicting temporal horizons, providing insights into systems beyond traditional equilibrium frameworks.

Smart Report design using SET Hierarchies and Bex queries in SAP S/4 HANA

Statutory Reporting for the National Association of Insurance Commissioners (NAIC) is a mandatory activity for businesses and they have to produce results every quarter. Insurance clients in particular using SAP for their Financial Accounting may have to define multiple code blocks for their reporting like Product code for their policies, Reinsurance Code, Reinsurance Company, Category of product required for Statutory reporting to name a few. They use these code blocks to produce exhibits for the STAT reporting which has each and every line and column of the report defined based on the combination of these code blocks. This paper discusses a smart report design Architecture for SAP reporting which helps easily maintain such exhibit reports. Key words: SAP S/4 HANA, Embedded Analytics, Statutory (STAT) Reporting, ABAP CDS Views, SET Hierarchies, HRRP.

Development and effectiveness evaluation of an interactive e-learning environment to enhance digital health literacy in cancer patients: study protocol for a randomized controlled trial

BackgroundThe Internet allows cancer patients to access information about their disease at any time. However, the quality of online information varies widely and is often inaccurate or does not provide all the details patients need to make informed decisions. Additionally, patients’ often limited ability to find and evaluate cancer-related online information can lead to misinformation.ObjectiveAn interactive e-learning environment to promote digital health literacy will be developed and evaluated for effectiveness.Primary hypothesisCancer patients who use the e-learning environment (IG1.1–IG1.3) or the content of the environment as a non-interactive PDF file (IG2) will show greater improvement in their digital health literacy from baseline to 8 weeks after baseline compared to patients who receive no such intervention, but are referred to a standard information brochure.MethodsThe hypothesis will be tested in a stratified randomized controlled superiority trial with five parallel groups and the primary endpoint of digital health literacy. In an e-learning environment, patients will learn strategies to use when searching for reliable cancer-related online information. During development, a prototype will be refined through focus groups and tested for usability by experts and patients. 660 cancer patients will be recruited using convenience sampling and randomly assigned in a 3:1:1 ratio to IG1.1-IG1.3 (three variants of the environment), IG2, or the control group. Two thirds of the 660 participants will be recruited through the German Cancer Information Service (CIS) and one third through non-CIS routes. Allocation will follow stratified randomization, accounting for recruitment route (CIS vs. non-CIS) and cancer type (breast cancer vs. other cancers), with variable block length. The primary outcome, digital health literacy, will be measured at baseline, 2 weeks, and 8 weeks after baseline.ConclusionIf the results support the primary hypothesis, then the e-learning environment could empower patients to retrieve more reliable information about their disease. Concerns about the generalizability of the results, since a disproportionate number of inquiries to the CIS come from breast cancer patients, are addressed by a proportionally stratified randomization strategy and diversified recruitment routes.

Optimized Deep Multi-Scale Three-Dimensional Convolutional Neural Network-Dependent Channel Estimation in 5G MIMO-OFDM Systems

Multi-Input Multi-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) is the main air interface for fifth-generation wireless communication. Channel estimation (CE) is a key factor for attaining efficient system performance of 5G wireless networks. The Deep Learning (DL) approaches employed in MIMO-OFDM systems can minimize the computational complexity of 5G networks while increasing the system performance and reliability. In this research work, an Optimized Deep Multi-Scale Three-Dimensional CNN-dependent Channel Estimation in a 5G MIMO-OFDM (DMTDCNN-CE-5G-MIMO-OFDM) System is proposed. It provides a comprehensive analysis of DMTDCNN methods, 5G channel methods, DMTDCNN-based channel estimation, and error performance for MIMO-OFDM receivers. Here, channel estimation plays a main role in the effectual system performance on the 5G wireless networks. Furthermore, DMTDCNN optimized with the Modified Social Group Optimization Algorithm (MSGOA) can develop the system performance with reliability by decreasing the computational complexity of 5G communication systems. It combines DMDTCN-MSGOA with convolutional and polar codes, utilizing hard and soft decoders, improving the quality of channel estimation by reducing PER and PAPR attenuation. In this case, 1/2-rate polar coding with block lengths of 128, 256, and 1024 bits is used, and the outcome is 1, 2, and 8 MIMO-OFDM signals that rely on 4-QAM. The proposed DMTDCNN-CE-5G-MIMO-OFDM method attains 20.89%, 33.45%, 25.67% high throughput and 15.67%, 22.57%, 38.98% low mean square compared with the existing models, such as DL-based CE in 5G MIMO-OFDM schemes (DL-CE-MIMO-OFDM), low complexity learning-dependent channel estimation OFDM by online training (LCL-CE-OFDM), dual CNN-based CE for MIMO-OFDM schemes (DCNN-CE-MIMO-OFDM) methods, respectively.

Spectral efficiency and secrecy enhancing scheme for IRS-aided SWIPT systems

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology for enabling efficient and scalable wireless communication within large-scale Internet of Things (IoT) networks. Despite its substantial potential, challenges related to spectral efficiency and overall system performance remain, underscoring the need for further advancements and innovations. Furthermore, the secure transmission of information is a crucial aspect that cannot be overlooked, as it guarantees the integrity and confidentiality of data exchanged within IoT networks. In this paper, we introduce a novel generalized spatial modulation (GSM) scheme specifically designed for intelligent reflecting surface (IRS)-Aided SWIPT systems. Moreover, the proposed scheme incorporates space-time block coding (STBC) to enhance performance. To strengthen the secrecy, a novel artificial noise (AN) generation strategy that effectively disrupts eavesdroppers while minimizing the impact on legitimate receivers is proposed. Additionally, the designed AN can be utilized by the energy-harvesting receiver to enhance its energy collection capabilities. We derived the average bit error probability (ABEP) of the system using the moment-generating function (MGF) approach and validated our analytical findings through rigorous simulations. The results obtained demonstrate convincingly that the proposed GSM scheme for IRS-Aided SWIPT systems significantly outperforms traditional methods, offering remarkable improvements in performance and efficiency.

Optimization of LDPC decoding algorithm in semi-conductor storage based on artificial neural networks

ABSTRACT Low-density parity check digit is a linear block code, which is applied to semiconductor storage to achieve effective information storage. However, in semiconductor storage, the existing ways have weak decoding performance and high bit error rate. Therefore, the decoding algorithm in the existing low-density parity check digit is optimised. Then, a semiconductor storage decoding optimisation algorithm based on artificial neural networks is designed to improve decoding performance. In addition, a multi-layer perceptron neural network is introduced to construct an optimised decoding method. The experimental results showed that when the threshold was 5, the throughput of the low-density parity check decoding algorithm based on the multi-layer perceptron decreased slowly with the increase in the signal-to-noise ratio. The minimum throughput rate was 1.18. The proposed semiconductor storage decoding optimisation algorithm based on artificial neural networks has better decoding performance. In summary, the proposed decoding optimisation method for semiconductor storage has good performance, which can effectively achieve information decoding in semiconductor storage.

Reversible data hiding in encrypted images based on histogram shifting and prediction error block coding

Reversible data hiding in encrypted images based on histogram shifting and prediction error block coding

On Optimal Finite-Length Block Codes of Size Four for Binary Symmetric Channels

On Optimal Finite-Length Block Codes of Size Four for Binary Symmetric Channels

Reversible Data Hiding in Encrypted Images Based on Histogram Shifting and Prediction Error Block Coding

Reversible Data Hiding in Encrypted Images Based on Histogram Shifting and Prediction Error Block Coding

Size-controlled antimicrobial peptide drug delivery vehicles through complex coacervation.

Due to the escalating threat of the pathogens' capability of quick adaptation to antibiotics, finding new alternatives is crucial. Although antimicrobial peptides (AMPs) are highly potent and effective, their therapeutic use is limited' as they are prone to enzymatic degradation, are cytotoxic and have low retention. To overcome these challenges, we investigate the complexation of the cationic AMP colistin with diblock copolymers poly(ethylene oxide)-b-poly(methacrylic acid) (PEO-b-PMAA) forming colistin-complex coacervate core micelles (colistin-C3Ms). We present long-term stable kinetically controlled colistin-C3Ms that can be prepared from several block lengths of PEO-b-PMAA polymers, where the polymerisation degree governs the overall micellar size. To achieve precise control over size and polydispersity, which are crucial for drug delivery applications, we investigate the hybridisation of PEO-b-PMAA polymers with varying chain lengths or PMAA homopolymers in ternary complex coacervation systems with colistin. This results in size-tunable colistin-C3Ms, ranging, depending on the mixing ratios, from micellar sizes of 26 nm to 100 nm. With size tunability at rather narrow size distributions and high stability, ternary colistin-C3Ms offer potential advancements in C3M drug delivery, paving the way for more effective and targeted treatments for bacterial infections in precision medicine.

POLYMERIC ION-CONDUCTING MEMBRANES BASED ON PEO-CONTAINING INTRAMOLECULAR POLYCOMPLEXES

In the present study, two series of solid solvent-free polymeric membranes based on diblock copolymers MePEO-b-PAAm (DBC) and triblock copolymers PAAm-b-PEO-b-PAAm (TBC), comprising chemically complementary poly(ethylene oxide) and poly(acryl amide) have been prepared by using casting technique. The dielectric properties of the prepared membranes in the frequency range of 102–105 Hz at room temperature as a function of water content and the length of the PEO block, as well as the peculiarities of water absorption processes under variable humidity, were investigated. It was found out that increasing the length of the PEO block led to an increase in the ionic conductivity of the obtained membranes. Such results confirmed the significant contribution of oxyethylene chains in ensuring the conductivity of lithium and solar cell batteries. The increase of relative humidity from 33% to 98% resulted in the increase of conductivity of DBC and TBC membranes up to 2.77·10-6–8.48·10-4 S·cm-1. According to the obtained results, block copolymers containing the interacting polymer components can be considered as potential solid polymer matrices for proton exchange membranes and they open the way for their application in fuel cells.

Design of Extruded Nanostructured Composites via Decoupling of the Cellulose Nanofibril/Poly(butylene adipate-co-terephthalate) Interface.

The full exploitation of the outstanding mechanical properties of cellulose nanofibrils (CNFs) as potential reinforcements in nanocomposite materials is limited by the poor interactions at the CNF-polymer matrix interface. Within this work, tailor-made copolymers were designed to mediate the interface between CNFs and biodegradable poly(butylene adipate-co-terephthalate) (PBAT), and their effect on extruded nanocomposite performance was tested. For this purpose, two well-defined amphiphilic anchor-tail diblock copolymer structures were compared, with a fixed anchor block length and a large difference in the hydrophobic tail block length. The aim was to evaluate the impact of the copolymers' chain length on the nanocomposite interface. The presence of amphiphilic diblock copolymers significantly improved the mechanical properties compared to those of PBAT nanocomposites containing unmodified CNFs. In particular, the copolymer with a longer tail was more effective for CNF-PBAT dispersion interactions, leading to a 65% increase of Young's modulus of neat PBAT, while retaining high deformability (670%). The results provide insights into the effectiveness of a waterborne third component at the CNF-matrix interface and its structure-property relationship.

Robot-Assisted Synthesis of Structure-Controlled Star-Cluster Hydrogels with Targeted Mechanophysical Properties for Biomedical Applications.

Advancements in polymer chemistry have enabled the design of macromolecular structures with tailored properties for diverse applications. Reversible addition-fragmentation chain-transfer (RAFT) polymerization is a controlled technique for precise polymer design. Automation tools further enhance polymer synthesis by enabling the rapid, reproducible preparation of polymer libraries. This study utilizes an automated platform and a biologically friendly bio-Fenton RAFT synthesis method to create hydrogels with embedded star polymers derived from complex block copolymers with controlled block lengths and sequences. Automation improves the efficiency compared to manual methods, while the choice of prepolymer and polymerization techniques ensures biocompatibility. Hydrogels formed by cross-linking linear block copolymers exhibit tunable physical, chemical, and mechanical properties. By systematically altering the prepolymer block sequences, promising hydrogel candidates for enhanced cell biocompatibility and proliferation are identified. These synthetic hydrogels mimic cellular microenvironments and offer a robust platform for biomedical applications, paving the way for an efficient hydrogel design and synthesis.

A personalized multi-platform assessment of somatic mosaicism in the human frontal cortex.

Somatic mutations in individual cells lead to genomic mosaicism, contributing to the intricate regulatory landscape of genetic disorders and cancers. To evaluate and refine the detection of somatic mosaicism across different technologies with personalized donor-specific assembly (DSA), we obtained tissue from the dorsolateral prefrontal cortex (DLPFC) of a post-mortem neurotypical 31-year-old individual. We sequenced bulk DLPFC tissue using Oxford Nanopore Technologies (~60X), NovaSeq (~30X), and linked-read sequencing (~28X). Additionally, we applied Cas9 capture methodology coupled with long-read sequencing (TEnCATS), targeting active transposable elements. We also isolated and amplified DNA from flow-sorted single DLPFC neurons using MALBAC, sequencing 115 of these MALBAC libraries on Nanopore and 94 on NovaSeq. We constructed a haplotype-resolved assembly with a total length of 5.77 Gb and a phase block length of 2.67 Mb (N50) to facilitate cross-platform analysis of somatic genetic variations. We observed an increase in the phasing rate from 11.6% to 38.0% between short-read and long-read technologies. By generating a catalog of phased germline SNVs, CNVs, and TEs from the assembled genome, we applied standard approaches to recall these variants across sequencing technologies. We achieved aggregated recall rates from 97.3% to 99.4% based on long-read bulk tissue data, setting an upper bound for detection limits. Moreover, utilizing haplotype-based analysis from DSA, we achieved a remarkable reduction in false positive somatic calls in bulk tissue, ranging from 14.9% to 72.4%. We developed pipelines leveraging DSA information to enhance somatic large genetic variant calling in long-read single cells. By examining somatic variation using long-reads in 115 individual neurons, we identified 468 candidate somatic heterozygous large deletions (1.5Mb - 20Mb), 137 of which intersected with short-read single-cell data. Additionally, we identified 61 putative somatic TEs (60 Alus, one LINE-1) in the single-cell data. Collectively, our analysis spans personalized assembly to single-cell somatic variant calling, providing a comprehensive ab initio ad finem approach and resource in real human tissue.

Synthesis and Characterization of Gold-Nanoparticle-Loaded Block Copolymer Vectors for Biomedical Applications: A Multivariate Analysis.

Gold nanoparticles (GNPs) encapsulated in amphiphilic block copolymers are a promising system for numerous biomedical applications, although critical information on the effects of various preparation variables on the structure and properties of this unique type of nanomaterial is currently missing from the literature. In this research, we synthesized GNPs functionalized with thiol-terminated polycaprolactone (PCL-GNPs) before encapsulating them into poly(ε-caprolactone)-b-poly(ethylene glycol) (PCL-b-PEG) micellar nanoparticles via nanoprecipitation to yield GNP-loaded polymeric nanoparticles (GNP-PNPs). We explored the role of different manufacturing variables (water volume, PCL-b-PEG to PCL-GNP ratio, and PEG block length) on the sizes, morphologies, GNP occupancies, colloidal gold concentrations, and time stability of GNP-PNPs. Despite our motivation to increase colloidal gold concentrations for K-edge CT imaging applications, there was only moderate variation in the concentration of colloidal gold (cAu = ∼100-150 μg/mL) over the range of investigated experimental variables; however, postformulation exposure to compressed air flow provided samples with increased gold concentrations and CT contrast above the visual threshold in imaging phantoms. The range of formulation variables also had only a weak effect on mean effective hydrodynamic diameters (dh,eff = ∼150 nm). Statistical analysis of TEM images revealed that the mean number of GNPs within GNP-PNP vectors smaller than 50 nm, Zave,d<50nm, is generally higher for preparations involving PCL-b-PEG with the shorter of the two different PEG block lengths. Preparations with the shorter PEG block length copolymer were also found to produce GNP-PNP colloids with greater time stability in dh,eff and cAu. Consistent with our previous study using MB-MDA-231 cells, we found increased gold uptake in MCF-7 cells with increasing Zave,d<50nm. This study provides a roadmap for optimizing important figures of merit for existing biomedical applications, including CT imaging and radiotherapy sensitization, and for developing new diagnostic and therapeutic strategies using GNP-PNPs.

Recent Progress in Block Copolymer Self-Assembly for the Fabrication of Structural Color Pigments.

The self-assembly of block copolymers (BCPs) into photonic materials has garnered increasing interest due to the versatility and ease of fabrication offered by the synthesized building blocks. BCPs are highly tunable, with their self-assembled structures' size being adjustable by modifying the block lengths, molecular weight(Mw), and polymer composition. This review provides a concise summary of the use of BCPs as photonic pigments, which generate color through structural manipulation rather than relying on chemical pigmentation. These photonic crystal pigments manipulate light behavior, including interference, diffraction, and diffusion, to generate specific colors. BCPs are categorized into two types: linear block copolymers (LBCPs) and brush block copolymers (BBCPs), each involving different monomers that form photonic crystals(PCs). The structural evolution and advancements of BCPs in various practical applications are also explored. It concludes by suggesting that structural color(SC) pigments based on eco-friendly PCs may replace traditional chemical ones in fields such as printing ink, biosensing, chemical sensing, and adaptive photonic materials.

A Cooperative Communication System With P-LDPC For Internet of Underwater Things

The acoustic communications are extremely challenging in underwater environments due to harsh conditions of this kind of nature. That medium's harshness affects the transmission reliability which in turn crucially requires an adoption of robust channel codes based on cooperative approaches to attain a more reliable data communication. This study proposes a cooperative communication system based on a protograph low-density parity check (P-LDPC) code. The system is elaborated in an Internet of underwater things (IoUT) scenario in which a relay node is acting as a helper that provides side information to destination. Compression and channel coding are offered by the system to cope with requirements of IoT in terms of reliable communication and compressed data between nodes. Furthermore, an algorithm for joint source channel coding is presented based on a modified version of offset min-sum. Moreover, layered decoding is Implemented in order to minimize number of iterations for the P-LDPC code. The results prove that proposed design can achieve a bit error rate (BER) of 5 × 10−6 at signal to noise ratio SNR = 8 dB for a coding rate of 1/3 and block length of 1500 bits. In addition, the proposed system shows a coding gain of 3.6 dB when compared with a non cooperative (no relay) approach. Besides, the proposed cooperative design can offer reduced computations versus the non cooperative system to achieve the same BER at a fixed SNR.

Evaluating The Performance of DWT-DCT Feature Extraction in Guitar Chord Recognition

This study presents advancements in audio signal processing techniques, specifically in enhancing the efficiency of guitar chord recognition. It is a continuation of the previous studies, which also aim at minimizing the feature extraction length with the intended performance. This study adopted two signal processing techniques that are common: Discrete Wavelet Transform (DWT) and Discrete Cosine Transform (DCT) for use in the feature extraction method. By conducting a systematic evaluation of two key parameters: frame blocking length and wavelet filter selection, a significant achievement could be achieved. The recognition system managed to obtain chord recognition with an accuracy of up to 91.43%, by using a feature extraction length of only three, which brought about smaller representation than the previous studies. The outcome of this study will help improve the data processing, which can be applied in real time, in this case in Field Programmable Gate Array (FPGA)-based chord recognition systems. Keywords: chord recognition, Discrete Wavelet Transform, Discrete Cosine Transform, feature extraction

Immersing Students in Classics

This lesson engages 7th grade students in a creative reading/writing project where they extend their knowledge of a text by utilizing block coding, sound recording, and Makey Makey kits which are "a digital toolkit that creates a closed-loop circuit by connecting alligator clips to conductive materials” (Turcotte, 2024, p. 1)—to create an interactive poster. This lesson requires students to move beyond comprehension to analysis and interpretation. This lesson could be adapted for any subject and grade level with the proper scaffolding and supports.