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        <title>Cybernetics and Information Technologies Feed</title>
        <link>https://reference-global.com/journal/CAIT</link>
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            <title>Cybernetics and Information Technologies Feed</title>
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        <copyright>All rights reserved 2026, Bulgarian Academy of Sciences, Institute of Information and Communication Technologies</copyright>
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        <item>
            <title><![CDATA[Segmentation-Driven Morphology-Guided Attention Mechanism for Rhegmatogenous Retinal Detachment Classification]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0030</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Rhegmatogenous Retinal Detachment (RRD) is a vision-threatening condition that requires accurate classification to prevent vision loss. The existing Deep Learning (DL) models for RRD primarily rely on generic feature extraction from fundus images and often overlook subtle morphological variations across RD subtypes. To address this issue, an effective integrated model was proposed by combining EfficientNetB3-based DeepLabV3+ segmentation with morphological feature extraction and a morphological-guided attention mechanism referred to as Morpho-RRDNet. The segmentation stage isolates detachment zones by enabling RDD-relevant morphometric descriptors, including area percentage, centroid location, and convexity. These features were fused with deep spatial embeddings using a Morphological-Guided Attention Mechanism (MGAM) to focus mainly on RRD-specific regions while suppressing irrelevant regions. Finally, a Light Gradient Boosting Model (LightGBM) was introduced to classify the RRD and non-RRD classes. The proposed Morpho-RRDNet achieved competitive results, including accuracy (99.22%), sensitivity (100%), specificity (99.20%), and F1-score (85.71%), when compared to the existing optimized Ensemble Network.]]></description>
            <category>ARTICLE</category>
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        <item>
            <title><![CDATA[Evaluating the Impact of Hyperparameters Using U-Net Networks with Transfer-Learning for Cerebellum Segmentation from Ultrasound Scans]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0031</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The cerebellum is an essential part of the human brain, responsible for motor learning, balance, and coordination. Therefore, cerebellar growth in the fetal brain is identified by its shape on ultrasound images. To overcome the limitations of the current clinical process, several computerized approaches have been proposed in the literature. This research paper proposes cerebellum segmentation from 2D ultrasound images of the fetal brain using U-Net, Attention U-Net, and transfer-learning-based U-Net models, namely VGG16-U-Net, ResNet50-U-Net, and EfficientNetB1-U-Net, with (1) four different evaluation parameters: Dice coefficient, Intersection over Union (IoU), Precision, and loss, (2) three different loss functions, namely Binary Cross-Entropy (BCE) loss, Dice loss, and Tversky loss, (3) three batch sizes – 8, 16, and 32. Lastly, the effectiveness of the trained models for cerebellum segmentation is assessed for real-time data. This research aims to segment the cerebellum in second-trimester fetal brain ultrasound images to predict prenatal neurodevelopmental disorders.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Automated Digital Filter Synthesis with Canonical Signed Digit via Genetic Algorithms]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0034</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Finite Impulse Response (FIR) digital filters are widely used in signal processing to remove noise and extract frequency-domain features. Genetic Algorithms (GAs) have been employed to synthesize FIR filters by optimizing the coefficients in their transfer function. A notable approach is to evolve FIR filter coefficients in the Canonical Signed Digit (CSD) representation, which simplifies hardware implementation by replacing multipliers with shifts and additions. However, its crossover and mutation operators frequently produce offspring that violate CSD constraints, requiring repeated retries or coefficient resets. We propose modified crossover and mutation operators that guarantee CSD-compliant offspring by construction. We compare our method against the original CSD-based GA and a real-valued GA baseline across five low-pass filter design problems, varying filter order and coefficient word length. Results show that our method consistently outperforms the original CSD-based approach.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[PT-CPABKS – Secure Multi-User Keyword Search in Cloud Using Policy-Tiered CP-ABE with Optimized Indexing]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0028</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Cloud computing faces several challenges in balancing data confidentiality with efficient retrieval and fine-grained access control. Conventional searchable encryption models enable keyword search but lack fine-grained access control, whereas traditional model-based solutions enforce policies at the cost of computational and communication overhead. To address these drawbacks, this manuscript proposes a Policy-Tiered Ciphertext-Policy Attribute-Based Keyword Search (PT-CPABKS) model that integrates Ciphertext-Policy Attribute-Based Encryption (CP-ABE), Advanced Encryption Standard with Galois/Counter Model (AES-GCM), and Public-key Encryption with Keyword Search (PEKS) with a policy-tiered index structure. The proposed model supports secure multi-keyword search, enforces fine-grained access policies, and optimizes the encryption, decryption, and key generation process by minimizing per-attribute operations. The incorporation of the policy-tiered index reduces redundant attribute processing and accelerates query processing. The proposed model achieved a competitive performance by obtaining an overall execution time of 544 ms when compared with CP-ABE and MRSE models.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[An Explainable Deep Learning Approach for Distinguishing Cyber Attacks and Sensor Faults in Critical IoT Systems]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0027</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The increasing rate of cyber-attacks in critical Internet of Things (IoT) systems requires robust and interpretable detection systems. In this paper, an interpretable deep learning approach has been introduced to detect various types of cyber-attacks and distinguish them from sensor faults. The proposed model uses Convolutional Neural Networks-Bidirectional Long Short-Term Memory (CNN-BiLSTM) to extract features. The attention mechanism has also been employed to improve the interpretability of the model. In addition, the SHAP approach has been used to interpret the model’s predictions by measuring the contribution of features. The performance of the model has been evaluated using the TON_IoT dataset with an accuracy of 97.3% for multi-class classification and 94.25% for binary classification. The results show that the proposed approach offers high detection accuracy and interpretability of the results.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Construction of the Principle of Adaptive Position-Trajectory Control of Moving Objects (On the Example of a Multi-Link Industrial Robot Manipulator)]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0033</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This article presents the principle of adaptive position-trajectory control for moving objects, illustrated using a multi-link industrial robot manipulator. The principle is described through several stages derived from its main control objectives. A SCARA robot with adaptive position-trajectory control is chosen as the study object, and mathematical models are provided to compute its kinematic structure and dynamic characteristics. These models make it possible to determine the manipulator’s position in coordinates, the trajectory and position of the end-effector, and to analyze real-time trajectory generation. Based on the proposed principle, boundary conditions for the robot’s dynamic properties are formulated, enabling the development of control models, algorithms, and robust-adaptive position-trajectory control structures. The approach follows a classical PD controller framework using filtered and approximated tracking errors. Computer visualization of the robust-adaptive control structures is used to examine dynamic behavior. The proposed structures ensure high-accuracy and high-speed motion along specified trajectory positions.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Malware Classification Using Dynamic Cluster Adaptive Aggregation Graph Neural Network with Prototypical Loss Function]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0024</link>
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            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Malware classification is the process of categorizing malicious software into specific families based on its structural and behavioral features. However, malware classification remains challenging due to high code similarity across different families, leading to suboptimal performance. To address this issue, this research proposes a Dynamic Cluster Adaptive Aggregation Graph Neural Network with Prototypical Loss Function (DCAAGNN-PLF) for effective malware classification. DCAA is incorporated in the GNN to adaptively group similar nodes that effectively capture both global and local structural variations. PLF ensures robust intra-class clustering while enhancing inter-class separation in the feature space that minimizes misclassification. Min-max normalization scales all features to a common range that avoids larger numerical values and enhances model performance. Therefore, the proposed DCAAGNN-PLF achieves better accuracies of 99.90% and 99.25% on the Malimg dataset and the Microsoft BIG2015 dataset, respectively, compared to existing techniques such as Convolutional Neural Network (ConvNet).]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Scalable Framework for Developing Viable Ontology-Centric Systems]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0029</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0029</guid>
            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The paper addresses the question of creating long-living software systems. One of the possible challenging comprehensive solutions for this might be to provide a viable framework which supports their complete life-cycle. The concept and architecture of a scalable framework for creating and maintaining software ontology-centric systems are proposed. The basis for the framework is the 2-level approach to the formation of system components of all types. It allows one to separate component models (ontologies) from components themselves. The 3-layer architecture of the framework is proposed. An implementation of the framework concept as a cloud platform was created. Several knowledge portals have been developed with its use. This evaluation showed that the proposed solution is a promising way for the organization of an ecosystem for collaborative development of viable information and software resources.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[A Multi-Layered Defense AES-GCM and Dragonfly-ECC Based Protocol for Mitigating Spoofing Attacks in DNS Systems]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0025</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0025</guid>
            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Domain Name System (DNS) is the way that Internet domain names are located and translated into IP addresses. The security architecture of the original design suffers from a lack of strength, as it was designed without built-in security. Among the most pervasive threats is DNS spoofing, in which attackers inject fraudulent responses that redirect users to malicious websites, thereby compromising both security and data integrity. This threat necessitates a defense mechanism that embeds protection directly within the DNS communication workflow. We propose a multi-layered defense approach that protects the communication channel between server and client without needing alteration to the network infrastructure. It combines the Dragonfly PAKE protocol with AES-GCM authenticated encryption to secure DNS responses. Practical experiments through a virtual platform showed that the proposed method is able to thwart attacks better than DoH and DNSSEC. The novelty of this work lies in combining Dragonfly-ECC key exchange with AES-256-GCM encryption in a protocol designed for DNS response protection, while employing HKDF-based nonce derivation that avoids transmitting nonces over the network. The security analysis based on SVO logic verified the integrity and reliability of the protocol.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Hybrid Image Encryption Scheme Using SPECK Block Cipher and Salsa20-Based DNA-Enhanced Key Generation]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0026</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0026</guid>
            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[This paper presents a new hybrid image encryption algorithm, which combines the SPECK block cipher with a Salsa20-based key generation process. Although these factors have been integrated in the past, what is unique about our work is that DNA encoding and transformation are applied only to the key stream, not to the image data itself. The method produces a non-linear, irreversible key obfuscation layer that enhances the system’s resistance to key-based attacks at a relatively small cost to the overall computation of the main image encryption operation. The system will have the key generated by Salsa20 to 128 bits, converted by DNA complement and reverse operations, and finally loaded into the SPECK cipher to be encrypted. The approach provides a high-security level but with memory-efficient computation, and, therefore, it is resource-constrained and can be applied to IoT devices. The system shows robust results in NPCR (&gt;99.6%), UACI (~33%), and entropy (7.99).]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Interpretable Region-Attribute Graph Matching Framework for Structure-Aware and Explainable Content-Based Image Retrieval]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0032</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0032</guid>
            <pubDate>Wed, 09 Sep 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[In recent years, Content-Based Image Retrieval (CBIR) has advanced significantly with Deep Learning (DL), yet most of the existing models rely on global descriptors and attention-based local features, which struggle to capture explicit structural relationships within images. These models often operate as black boxes and provide limited interpretability; they exhibit reduced robustness under viewpoint and geometric variations. To address these limitations, an Interpretable Region-Attribute Graph Network (IR-AGN) is proposed in this research. Specifically, each image is decomposed into semantically meaningful regions, where each node encodes visual and semantic descriptors while edges represent geometric, appearance, and contextual relations. Consequently, a joint node-edge graph matching strategy computes similarity by aligning visual descriptors and relational structures between query and retrieval images. The results of the proposed IR-AGN model exhibit a higher mean Average Precision (mAP) of 95.19% on the Revisited Paris (RParis) dataset, when compared with the existing Multi-Layer Orientation Histogram (MLOH) model.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Privacy-Preserving Federated Learning with Galois Automorphism-Driven Linear Transformation with Brakerski-Fan-Vercauteren for Medical Data]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0012</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0012</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Healthcare data is frequently fragmented over diverse organizations because of its extremely complex and confidential nature. However, the existing Federated Learning (FL) approach through a central server creates various challenges within healthcare, such as privacy vulnerabilities and regulatory compliance. Thus, this research proposes the privacy-preserving FL approach with Fully Homomorphic Encryption (FHE) for protecting the patient’s sensitive data in medical records. In the proposed framework, the Galois Automorphism-driven Linear Transformation with Brakerski-Fan-Vercauteren, named GALT-BFV, is proposed for improving the medical data privacy and security. Moreover, this research introduces the pre-trained model of XceptionNet for training the local and global models in FL. Finally, the Federated Proximal (FedProx) approach is introduced for the aggregation of local and global models. The experimental discoveries establish that the proposed GALT-BFV method reaches better accuracies of 0.98 and 0.88 on Coronavirus Disease 2019 (COVID-19) X-ray and brain tumor Magnetic Resonance Imaging (MRI) datasets, compared to previous approaches.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[AI Integration in the Public Sector: A Systematic Review of Risks and Opportunities]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0013</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0013</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The development of Artificial Intelligence (AI) and e-Government is a paradigm shift in the administration of the people. Although it supports optimization of service delivery, various challenges are also brought to the fore. This scoping review examines the challenges, threats, and regulations surrounding the implementation of AI in the government sector. Following PRISMA guidelines, fifty peer-reviewed articles published between 2021 and 2025 were reviewed. The analysis found four major groups of challenges: technical (32%), organizational (28%), ethical and social (25%), and regulatory and policy-related (15%). The most common identified obstacles included the lack of skills and talent (92%), integrating with legacy systems (85%), and algorithmic bias (75%). The authors suggest a multi-layered AI governance framework, which includes international and national regulation, organizational processes, and managerial practices. It provides strategic suggestions, which are aimed at promoting the effective and responsible use of AI in e-Government.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Robust and Efficient Detection of Large Language Model-Generated Text via the Multi-Feature Accurate Detection (MFAD) Approach]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0019</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0019</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Recently, Large Language Models (LLMs) have been able to generate text that closely resembles human writing, raising concerns about academic misuse and misinformation. Existing detection approaches often depend on a single type of feature, require direct access to the underlying models, and are sensitive to variations in text length and paraphrasing. To address these issues, this paper proposes a Multi-Feature Accurate Detection (MFAD) approach that integrates handcrafted statistical and syntactic features with deep semantic features based on Global Vectors for Word Representation (GloVe) embeddings, Convolutional Neural Networks (CNNs), and Bidirectional Long Short-Term Memory (BiLSTM). The results of the experiments on Human ChatGPT Comparison Corpus (HC3) demonstrate that MFAD achieves 98% accuracy, 96.5% precision, 97.5% recall, 97% F1-score, with a minimum False Positive Rate (FPR) of 0.01 across multiple domains. Additionally, MFAD demonstrates strong cross-model generalizability across LLMs such as GPT-4, Gemini, and Claude-4, and exhibits resilience to text length variations and paraphrasing.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Topology Aware Evaluation of SDN Controller Performance and Placement Strategies in Star, Linear, and Ring Networks]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0023</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0023</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Software-defined networking allows centralized and flexible control of a network, but its performance largely depends on the controller and its location in the network. The current work presents an experimental study of the Software-Defined Networking (SDN) controller performance considering various placement methods using star, linear, and ring topologies. Placement methods, including centrality-based, greedy, clustering, metaheuristic, capacitated, and baseline approaches, are analyzed with four popular controllers: Beacon, RYU, OpenDaylight, and Open Network Operating System (ONOS). Experiments are conducted using Mininet-based emulation, and findings indicate that controller performance varies based on location within the network. Among the evaluated techniques, the partition-based approach is identified as balanced controller placement in linear and ring topologies, given stable latency and throughput values comparable to other optimized strategies. Overall, the lowest latency is produced by RYU, the maximum throughput by OpenDaylight, and the intermediate performance by ONOS and Beacon.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[PAPR Reduction in MIMO-OFDM Using Refracted Opposition-Based Archerfish Hunting Optimization Algorithm]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0017</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0017</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The combination of the Multiple-Input Multiple-Output technique with Orthogonal Frequency Division Multiplexing (OFDM) is a widely used technique to improve Quality of Service (QoS) in wireless communication. However, a high Peak-to-Average Power Ratio (PAPR) in OFDM leads to signal distortion when passing through a High-Power Amplifier (HPA) into its nonlinear operating region. Moreover, this distortion degrades the OFDM system performance by increasing the Bit Error Rate (BER). To address these problems, a Refracted Opposition-Based Archerfish Hunting Optimization (ROB-AHO) Algorithm is proposed to minimize PAPR in OFDM systems. The AHO Algorithm dynamically adapts to various scenarios, and the proposed ROBL helps AHO select the most suitable phase factors to minimize PAPR efficiently across operating scenarios. Experimental results demonstrate that the ROB-AHO achieved a BER of 3!!!x.7; ×!!!x 10;−1 for Signal-to-Noise Ratio (SNR) at 10 dB that outperforms prior methods, namely the Asymmetrical Auto Encoder (AAE).]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Comparative Analysis of Population Initialization Strategies in Metaheuristic Optimization]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0016</link>
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            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Metaheuristic algorithms depend on population initialization to ensure an effective balance between exploration and exploitation in complex search spaces. Randomly initialized populations result in uneven coverage and premature convergence, especially in high-dimensional or multimodal problem landscapes. In this study, we investigate traditional and seven advanced population initialization techniques, including quasi-random sequence, chaotic map, opposition-based, knowledge-based, hybrid, and machine learning-based approaches. We discuss their theoretical foundations, computational complexity, and practical effectiveness. Findings suggest quasi-random initialization improves convergence for medium-dimensioned problems, chaotic population initialization improves convergence for multimodal search landscapes, and machine learning techniques adapt to very-high-dimensional optimization problems. Advanced techniques increase computational cost but significantly improve convergence rate and algorithm robustness. Such insights can assist researchers and practitioners in selecting appropriate initialization techniques for the complexity of their problems, and also indicate the direction of future research into intelligent and adaptive initialization techniques.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Cross-Dataset DDoS Intrusion Detection Using Hybrid Welch-Point-Biserial Feature Selection and DenseMLP]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0020</link>
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            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Distributed Denial-of-Service (DDoS) attacks are one of the major threats in the cybersecurity domain. Lightweight feature selection is vital for improving DDoS attack detection efficiency and computational efficiency by selecting the most relevant features. Recently, numerous machine learning and deep learning models have been developed to detect various types of DDoS attacks; however, their performance is often hindered by the presence of irrelevant features, which can lead to increased false positives and longer processing times. To address this problem, we propose the WPBS-MLP Intrusion Detection System (IDS). The WPBS (Welch’s t-Test and Point Biserial Test) feature selection method is integrated with an optimized MLP classifier to detect DDoS attacks. It was evaluated on the CICDDoS2019, CICIDS2018, and CICIDS2017 publicly available intrusion detection datasets. The WPBS method selected 39 significant features from the CICDDoS2019 dataset, and these 39 features are retained and considered for experimentation w.r.t all datasets. The DenseMLP model achieved a range of 99.59% to 100% accuracy for binary classification and 84% to 100% accuracy for low-rate and high-rate attack detection across the three datasets. Further, the Wilcoxon statistical test provided validation evidence that the proposed WPBS-MLP-based IDS model showed superior performance compared to the existing research studies.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Bernoulli Trial (BT) Collision Operator Scheme Family in the Direct Simulation Monte-Carlo (DSMC): Derivation and Evaluation]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0011</link>
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            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[The Direct Simulation Monte-Carlo (DSMC) method is based on splitting the rarefied gas process evolution into two ballistic and collision steps within a time step. The collision step is the more complicated, requiring a rigorous theoretical analysis to discover its relation to the fundamental kinetic equations. In this work, we present a systematic derivation and examination of the Bernoulli‑Trial (BT) family of collision schemes. The master Kac equation describes the binary collision interactions as a stochastic process lying in the background of the DSMC probabilistic rules, and it serves as a starting point for our derivation of the BT family. This study is limited to the analysis of four BT members, including Simplified Bernoulli Trials (SBT), Generalized Bernoulli Trials (GBT), Symmetrized Simplified Bernoulli Trials (SSBT), and Symmetrized Generalized Bernoulli Trials (SGBT). These schemes are implemented in the DSMC code, and a simple relaxation problem is simulated to determine optimal values for the pair‑selection parameter Nsel.]]></description>
            <category>ARTICLE</category>
        </item>
        <item>
            <title><![CDATA[Supercomputer HEMUS: Benchmark Results and Performance Optimization]]></title>
            <link>https://reference-global.com/article/10.2478/cait-2026-0022</link>
            <guid isPermaLink="false">/article/10.2478/cait-2026-0022</guid>
            <pubDate>Sat, 13 Jun 2026 00:00:00 GMT</pubDate>
            <description><![CDATA[Supercomputers and advanced High-Performance Computing (HPC) systems are needed to address large scientific problems, AI models training or industrial/engineering tasks. One of the important steps during the installation, configuration and tuning stages, is to perform comprehensive benchmarking and obtain insights about the best ways of exploiting the systems capabilities. HEMUS is modern petascale heterogeneous HPC system. Benchmarking insights and optimization strategies from HEMUS are applicable to a broad class of contemporary HPC systems. In this paper we present results from general benchmarks, together with optimization strategies applied and the obtained conclusions. We also present benchmarks related to software simulation of quantum computing algorithms, as well as simulation using low-discrepancy sequences, as they cover a significant portion of the expected workload on the system. The results highlight the importance of careful tuning and optimization, demonstrating that significant performance gains can be achieved, with the proposed approaches broadly applicable across comparable HPC systems and workloads.]]></description>
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