This paper discusses an adaptive method of image steganography issues based on the application of a linear hash function over the GF (2) field to control the embedding process. The method uses staggered splitting of an image into 8 × 8-pixel blocks to provide blind steganography. Classification thresholds are defined as the percentiles of the distribution of gradients throughout the image, allowing for efficient load distribution between textured and smooth areas. Experiments on the BOSSBase, SIPI and Kaggle kits show that the method provides an actual capacity of up to 0.7 bpp at PSNR 47–50 dB and is resistant to statistical tests and RS analysis. At the same time, like other approaches based on modification of pixel differences, it remains vulnerable to modern stegoanalysis based on spatial rich models (SRMs). However, thanks to the modular structure of embedding control based on linear hash function, the proposed architecture allows direct integration with many modern adaptive strategies aimed at minimizing statistical anomalies.

This paper presents a comprehensive study aimed at systematically analyzing and evaluating natural language processing (NLP) techniques for military information operations, with a special focus on social media intelligence. Among an ever-growing complicated information environment, NLP methods like sentiment analysis, named entity recognition, and topic modeling have been essential in tracking online propaganda efforts, discovering emerging issues and threats globally with dialogues on military operations. These techniques make an impact on available decision making via situational awareness and getting the added extraction from volumes of unstructured data outputs thus increasing the overall strategic benefits to military organizations. There are technical and operational challenges concerning the use of NLP in a military context such as requirements for real-time data processing; language diversity; and maintaining data privacy while preserving ethical standards. To address these challenges, the study conducts an exhaustive survey of NLP methods, reviewing their range of applications, and highlights the relevance of several approaches for military information operations, with special emphasis on social media intelligence. The work further provides discussion on the comprehensive adoption of artificial intelligence (AI), edge computing, and multilingual NLP models for enhancing adaptability, efficiency, and transparency of the systems. It also extols the need for explainable AI (XAI) to improve accountability and trust by making term or even whole early warning systems derived from NLP analyses, transparent and interpretable for these military research applications with significant financial consequences. The paper also emphasizes the strategic importance of multilingual and multimodal analysis and the integration of specialized military lexicons to improve the contextual understanding of military discourse in social media environments. We also elucidate the important capabilities of NLP in enabling military operations to be responsive, rapid and data-driven while also adapting to the evolving nature of warfare. Key conclusions suggest that applying advanced NLP tools enhances situational awareness, enables timely threat detection, and supports more agile, data-informed decision-making within modern military operations. The paper shows a perspective to optimize NLP and AI technologies, leveraging various perspectives to benefit the operational needs of military and defense sectors in more data-rich environments.

The rapid digitalisation of the medical field has heightened concerns over protecting patients’ personal information during the transmission of medical images. This study introduces a method for securely transmitting X-ray images that contain embedded patient data. The proposed steganographic approach ensures that the original image remains intact while the embedded data is securely hidden, a critical requirement in medical contexts. To guarantee reversibility, the Interpolation Near Pixels method was utilised, recognised as one of the most effective techniques within reversible data hiding (RDH) frameworks. Additionally, the method integrates a statistical property preservation technique, enhancing the scheme’s alignment with ideal steganographic characteristics. Specifically, the “forest fire” algorithm partitions the image into interconnected regions, where statistical analyses of low-order bits are performed, followed by arithmetic decoding to achieve a desired distribution. This process successfully maintains the original statistical features of the image. The effectiveness of the proposed method was validated through stegoanalysis on real-world medical images from previous studies. The results revealed high robustness, with minimal distortion of stegocontainers, as evidenced by high PSNR values ranging between 52 and 81 dB.

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology.

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology.

The article is devoted to the introduction of digital watermarks, which formthe basis for copyright protection systems. Methods in this area are aimed at embedding hidden markers that are resistant to various container transformations. This paper proposes a method for embedding a digital watermark into bitmap images using Lagrange interpolation and the Bezier curve formula for five points, called Lagrange interpolation along the Bezier curve 5 (LIBC5). As a means of steganalysis, the RS method was used, which uses a sensitive method of double statistics obtained on the basis of spatial correlations in images. The output value of the RS analysis is the estimated length of the message in the image under study. The stability of the developed LIBC5 method to the detection of message transmission by the RS method has been experimentally determined. The developed method proved to be resistant to RS analysis. A study of the LIBC5 method showed an improvement in quilting resistance compared to that of the INMI image embedding method, which also uses Lagrange interpolation. Thus, the LIBC5 stegosystem can be successfully used to protect confidential data and copyrights.

. The purpose of the article is an experimental study of the impact of the wheelsets of a mainline diesel locomotive on the railway track on straight sections of the track. The measurements were performed on a vibration measuring device consisting of MV25 DV type sensors and an oscillation converter for a digital signal. It is proved that the indicators of dynamic characteristics comply with regulatory requirements. As a result of the conducted research, digital data was collected from the ADC and general monitoring was carried out. Measurement and signal processing are carried out using special software of a personal computer such as a “Notebook”. Vertical static load of a wheelset of a railway rolling stock unit on rails: the load of a railway rolling stock unit on rails attributed to one wheelset, taking into account the actual location of the center of gravity of the superstructure

Производство электрической энергии в Казахстане осуществляют 158 электрических станций различной формы собственности. Единым системным оператором всей энергосистемы с 1996 года определено АО «Казахстанская компания по управлению электрическими сетями» (Kazakhstan Electricity Grid Operating Company) «KEGOC». В электроэнергетике и энергетических системах (EPESs) происходит трансформация для обеспечения чистой распределенной энергии для устойчивого глобального экономического роста. Интернет вещей (IoT) находится на переднем крае этой трансформации, предоставляя такие возможности, как мониторинг в реальном времени, ситуационная осведомленность и разведка, контроль и кибербезопасность, для преобразования существующих EPES в интеллектуальные EPES с поддержкой кибербезопасности, которые являются более эффективными, безопасными, надежными, устойчивыми и устойчивыми. Кроме того, оцифровка экосистемы электроэнергетики с использованием интернета вещей улучшает видимость активов, оптимальное управление распределенной генерацией, устранение потерь энергии и создание экономии. Интернет вещей оказывает значительное влияние на EPESs и предлагает несколько возможностей для роста и развития. Существует несколько проблем, связанных с развертыванием интернета вещей для EPESs. Необходимо разработать жизнеспособные решения для преодоления этих проблем, чтобы обеспечить непрерывный рост интернета вещей для EPESs. Достижения в области вычислительного интеллекта могут привести к созданию интеллектуальной системы интернета вещей путем эмуляции биологических нервных систем с помощью когнитивных вычислений, потоковой передачи и распределенной аналитики, в том числе на пограничном уровне и на уровне устройств. В этом обзорном документе дается оценка роли, воздействия и проблем Интернета вещей в преобразовании электроэнергетики и энергетических систем.

This article examines the structures of one of the types of pyramidal networks - parallel-hierarchical networks. The presented material has a dual purpose - applied and cognitive. The first one is of great importance in increasing the "intelligence" of specialized computer tools using a bionic approach. The second one opens completely new possibilities for a deeper understanding of the structure of the brain from the perspective of the cybernetic approach.

This research paper presents the results of obtaining carbon films on various substrates (quartz, mica, and silicon) at temperatures from 0 °C (initial) to 800 °C through plasma chemical vapor deposition. The carbon films obtained on various films were studied using the method of electron paramagnetic resonance (EPR). EPR measurements were carried out on twenty samples at a perpendicular and parallel arrangement of the sample plane concerning the orientation of the magnetic field. When measuring the resonance conditions by changing the magnetic field, an EPR signal appeared in all of the deposited samples. The paper presents a general view of the EPR spectrum in all of the samples, including the signal intensity, g-factor, line widths, and normalized signal intensity of the carbon films on various substrates at temperatures from 0 °C to 800 °C. Studies show that with an increase in temperature, the normalized intensity of the EPR signal line increases during the deposition of a carbon in all deposited substrates (quartz, mica, and silicon) using the method of plasma decomposition of a mixture of methane and hydrogen.