Search Results (681)

In this work, an improved and scalable implementation of Cuppen’s algorithm for diagonalizing symmetric tridiagonal matrices is presented. This approach uses a hybrid-heterogeneous parallelization technique, taking advantage of GPU and CPU in a distributed hardware architecture. Cuppen’s algorithm is a theoretical concept and a powerful tool in various scientific and engineering applications. It is a key player in matrix diagonalization, finding its use in Functional Density Theory (FDT) and Spectral Clustering. This highly efficient and numerically stable algorithm computes eigenvalues and eigenvectors of symmetric tridiagonal matrices, making it a crucial component in many computational methods. One of the challenges in parallelizing algorithms for GPUs is their limited memory capacity. However, we overcome this limitation by utilizing multiple nodes with both CPUs and GPUs. This enables us to solve subproblems that fit within the memory of each device in parallel and subsequently combine these subproblems to obtain the complete solution. The hybrid-heterogeneous approach proposed in this work outperforms the state-of-the-art libraries and also maintains a high degree of accuracy in terms of orthogonality and quality of eigenvectors. Furthermore, the sequential version of the algorithm with our approach in this work demonstrates superior performance and potential for practical use. In the experiments carried out, it was possible to verify that the performance of the implementation that was carried out scales by 2× using two graphic cards in the same node. Notably, Symmetric Tridiagonal Eigenvalue Solvers are fundamental to solving more general eigenvalue problems. Additionally, the divide-and-conquer approach employed in this implementation can be extended to singular value solvers. Given the wide range of eigenvalue problems encountered in scientific and engineering domains, this work is essential in advancing computational methods for efficient and accurate matrix diagonalization. Full article

Coverage Path Planning (CPP) in unknown environments presents unique challenges that often require the system to maintain a symmetry between exploration and exploitation in order to efficiently cover unknown areas. This paper introduces latent imagination-based reinforcement learning (LIRL), a novel framework that addresses these challenges by integrating three key components: memory-augmented experience replay (MAER), a latent imagination module (LIM), and multi-step prediction learning (MSPL) within a soft actor–critic architecture. MAER enhances sample efficiency by prioritizing experience retrieval, LIM facilitates long-term planning via simulated trajectories, and MSPL optimizes the trade-off between immediate rewards and future outcomes through adaptive n-step learning. MAER, LIM, and MSPL work within a soft actor–critic architecture, and LIRL creates a dynamic equilibrium that enables efficient, adaptive decision-making. We evaluate LIRL across diverse simulated environments, demonstrating substantial improvements over state-of-the-art methods. Through this method, the agent optimally balances short-term actions with long-term planning, maintaining symmetrical responses to varying environmental changes. The results highlight LIRL’s potential for advancing autonomous CPP in real-world applications such as search and rescue, agricultural robotics, and warehouse automation. Our work contributes to the broader fields of robotics and reinforcement learning, offering insights into integrating memory, imagination, and adaptive learning for complex sequential decision-making tasks. Full article

The present paper reports the theoretical results on the thermal performance of proposed Integrated Hybrid Nanofluid Hemi-Spherical Fin Model assuming a combination of Fe₃O₄-Ni/C₆H₁₈OSi₂ hybrid nanofluid. The model leverages the concept of symmetrical geometries and optimized nanoparticle shapes to enhance the heat flux, with a focus on symmetrical design applications in thermal engineering. The simulations are carried out by assuming a silicone oil as a base fluid, due to its exceptional stability in hot and humid conditions, enriched with superparamagnetic Fe₃O₄ and Ni nanoparticles to enhance the heat transfer capabilities, with the aim of contributing to the field of nanotechnology, electronics and thermal engineering, The focus of this work is to optimize the heat dissipation in systems that require high thermal efficiency and stability such as automotive cooling systems, aerospace components and power electronics. In addition, the study explores the influence of key parameters such as heat transfer coefficients and thermal conductivity that play an important role in improving the thermal performance of cooling systems. The overall thermal performance of the model is evaluated based on its heat flux and thermal efficiency. The study also examines the impact of the shape optimized nanoparticles in silicone oil by incorporating shape-factor in its modelling equations and proposes optimization of parameters to enhance the overall thermal performance of the system. Darcy’s flow model is used to analyse the key parameters in the system and study the thermal behaviour of the hybrid nanofluid within the fin by incorporating natural convection, temperature-dependent internal heat generation, and radiation effects. By using the similarity approach, the governing equations were reduced to non-linear ordinary differential equations and numerical solutions were obtained by using four-stage Lobatto-IIIA numerical technique due to its robust stability and convergence properties. This enables a systematic investigation of various influential parameters, including thermal conductivity, emissivity and heat transfer coefficients. Additionally, it stimulates interest among researchers in applying mathematical techniques to complex heat transfer systems, thereby contributing towards the development of highly efficient cooling system. Our findings indicate that there is a significant enhancement in the heat flux as well as improvement in the thermal efficiency due to the mixture of silicone oil and shape optimized nanoparticles, that was visualized through comprehensive graphical analysis. Quantitatively, the proposed model displays a maximum thermal efficiency of 57.5% for lamina shaped nanoparticles at $N_c$ = 0.5, $N_r$ = 0.2, $N_g$ = 0.2 and ${\Theta }_a$ = 0.4. The maximum enhancement in the heat flux occurs when $N_c$ doubles from 5 to 10 for $m_2$ = 0.2 and $N_r$ = 0.1. Optimal thermal performance is found for $N_c$, $N_r$ and $m_2$ values in the range 5 to 10, 0.2 to 0.4 and 0.4 to 0.8 respectively. Full article

In this paper, we introduce the skew-symmetric generalized normal and the skew-symmetric generalized t distributions, which are skewed extensions of symmetric special cases of generalized skew-normal and generalized skew-t distributions, respectively. We derive key distributional properties for these new distributions, including a recurrence relation and an explicit form for the cumulative distribution function (cdf) of the skew-symmetric generalized t distribution. Numerical examples including a simulation study and a real data analysis are presented to illustrate the practical applicability of these distributions. Full article

Boolean functions are fundamental building blocks in both discrete mathematics and computer science, with applications spanning from cryptography to coding theory. Bent functions, a subset of Boolean functions with maximal nonlinearity, are particularly valuable in cryptographic applications. This study introduces a novel equivalence relation among all Boolean functions and presents an algorithm to generate bent functions based on this relation. We systematically generated a collection of 10,000 bent functions over eight variables, all originating from the same equivalence class, and analyzed their structural complexity through rank determination. Our findings revealed the presence of at least five distinct affine classes of bent functions within this collection. By employing this construction, we devised an algorithm to generate a filter function capable of combining Boolean functions. This filter function can be dynamically adjusted based on a key, offering potential applications in symmetric cipher design, such as enhancing security or improving efficiency. Full article

The trampoline is a popular piece of sports equipment both for recreational use and for Olympic trampolining as a competitive sport. Maintaining body position during jumps is influenced by sensory inputs (visual, auditory, and somatosensory) and symmetrical muscle activity that help athletes to perform consecutive jumps as vertically as possible. To evaluate the effects of these inputs, 15 male and 15 female students (with an average age of 24.4 years, height of 174.3 cm, and average weight of 69.7 kg) performed 10 consecutive straight jumps under four sensory conditions: (1) looking at the edge of the trampoline, (2) without sight, (3) without hearing, and (4) without hearing or sight. Using insoles with integrated pressure sensors (Pedar^®, novel GmbH, Munich, Germany), the contact forces on the trampoline during the jump were measured separately for the left and right feet. The results showed that the lack of visual input significantly shortened flight times and increased the asymmetry of ground reaction forces between the left and right legs. For example, in the second series without vision, the average normalized force difference between the legs increased by 0.33 G compared to the control condition. An ANOVA revealed significant differences in the ground reaction forces between sensory conditions, with vision playing a key role in maintaining body control. These results provide practical insights for coaches looking to improve jumping performance and address asymmetries during training by focusing on sensory feedback strategies. Full article

At present, DC grid fault current analyses are usually based on numerical analyses and/or simulations. To make fault current analyses more practical and convenient, this study proposes a fast fault current level assessment methodology that is applicable when a symmetrical monopole DC (SMDC) system encounters a pole-to-ground (PTG) fault. To further clarify how PTG faults develop in SMDC systems, fault current analysis is implemented in the frequency domain rather than the time domain. Specifically, we find the key factors that impact the high-frequency domain, as these factors have a high correlation with fault currents in the initial milliseconds. Based on simplified SMDC fault circuits and a high-frequency characteristic analysis, a practical index to assess PTG faults is ultimately proposed. It is proved that the index can be used for various SMDC systems with different topologies. With the proposed method, the topology of a DC grid can be optimized to decrease the fault current level, which can help with the design and construction of practical DC grid projects. Full article

The RSA cryptosystem has been a cornerstone of modern public key infrastructure; however, recent advancements in quantum computing and theoretical mathematics pose significant risks to its security. The advent of fully operational quantum computers could enable the execution of Shor’s algorithm, which efficiently factors large integers and undermines the security of RSA and other cryptographic systems reliant on discrete logarithms. While Grover’s algorithm presents a comparatively lesser threat to symmetric encryption, it still accelerates key search processes, creating potential vulnerabilities. In light of these challenges, there has been an intensified focus on developing quantum-resistant cryptography. Current research is exploring cryptographic techniques based on error-correcting codes, lattice structures, and multivariate public key systems, all of which leverage the complexity of NP-hard problems, such as solving multivariate quadratic equations, to ensure security in a post-quantum landscape. This paper reviews the latest advancements in quantum-resistant encryption methods, with particular attention to the development of robust trapdoor functions. It also provides a detailed analysis of prominent multivariate cryptosystems, including the Matsumoto–Imai, Oil and Vinegar, and Polly Cracker schemes, alongside recent progress in lattice-based systems such as Kyber and Crystals-DILITHIUM, which are currently under evaluation by NIST for potential standardization. As the capabilities of quantum computing continue to expand, the need for innovative cryptographic solutions to secure digital communications becomes increasingly critical. Full article

After the end of IPv4 addresses, the Internet is moving towards IPv6 address architecture quickly with the support of virtualization techniques worldwide. IPv4 and IPv6 protocols will co-exist long during the changeover process. Some attacks, such as MITM attacks, do not discriminate by appearance and affect IPv4 and IPv6 address architectures. In an MITM attack, the attacker secretly captures the data, masquerades as the original sender, and sends it toward the receiver. The receiver replies to the attacker because the receiver does not authenticate the source. Therefore, the authentication between two parties is compromised due to an MITM attack. The existing authentication schemes adopt complicated mathematical procedures. Therefore, the existing schemes increase computation and communication costs. This paper proposes a lightweight and authentic end-to-end communication model to detect MITM attacks using a pre-shared symmetric key. In addition, we implement and analyze the performance of our proposed security model on Linux-based virtual machines connected to large-scale hybrid IPv4-IPv6 virtual networks. Moreover, security analyses prove the effectiveness of our proposed model. Finally, we compare the performance of our proposed security model with existing models in terms of computation cost and communication overhead. Full article

Organic solid-state lasers are highly promising devices known for their low-cost fabrication processes and compact sizes and the tunability of their emission spectrum. These lasers are in high demand across various industries including biomedicine, sensors, communications, spectroscopy, and military applications. A key requirement for light-emitting materials used in a light-amplifying medium is a low threshold value of the excitation energy of the amplified spontaneous emission (ASE). A newly synthesized non-symmetric red-light-emitting laser dye, Ethyl 2-(2-(4-(bis(2-(trityloxy)ethyl)amino)styryl)-6-tert butyl-4H-pyran-4-ylidene)-2-cyanoacetate (KTB), has shown great promise in meeting this requirement. KTB, with its attached bulky trityloxyethyl groups, has the ability to form amorphous thin films from a solution using a wet-casting method. Recent experiments have demonstrated that KTB exhibits a low ASE threshold value. This study focused on investigating the optical and amplified spontaneous emission properties of KTB in poly(N-vinylcarbazole) (PVK), polysulfone (PSU), and polystyrene (PS) matrices at various concentrations. The results showed that as the concentration of the dye increased, a redshift of the photoluminescence and ASE spectra occurred due to the solid-state solvation effect. The lowest ASE threshold value of 9 µJ/cm² was achieved with a 20 wt% concentration of KTB in a PVK matrix, making it one of the lowest excitation threshold energies reported to date. Full article

Wire rope isolator (WRI) devices are widely used in vibration reduction industrial equipment, and stiffness is the key parameter that determines isolation effectiveness. WRI devices show slight nonlinearity under small loads, and the manufacturers generally only provide the initial parameters. To investigate the mechanical behavior changes in the WRI devices under repeated loads, five types of WRI specimens were tested under various amplitudes, loading speeds, and preloads. The test results of large symmetrical compression and tension loads showed that the WRI devices demonstrated stable hysteresis curves under repeated loads, while the hysteresis curves were independent of the loading speed. The test results of small cyclic loads with large preloads show that the stiffness of the WRI specimen follows the logarithmic law, with the cycle number under various loading conditions. Particularly, the stiffness of the specimen increases by about 10–30% after 50 cycles. The initial stiffness K_a decreases linearly with the preloads, while the decrease is quadratic in relation to the cyclic load. The hardening coefficient C_a shows a positive correlation with the loading capacity of the WRI devices, while it shows a negative correlation with the preload and cyclic load amplitudes. It is recommended to consider the stiffness increase in the WRI devices during the evaluation of isolation effectiveness. Full article

The need to refuel future missions to Mars and the Moon via in situ resource utilization (ISRU) requires the development of LOX/LCH4 engines, which are complex and expensive to develop and improve. This paper discusses how the use of digital engineering—specifically physics-based modeling (PBM)—can aid in developing, testing, and validating a LOX/LCH4 engine. The model, which focuses on propulsion performance and heat transfer through the engine walls, was created using Siemens’ STAR-CCM+ CFD tool. Key features of the model include Eulerian multiphase physics (EMP), complex chemistry (CC) using the eddy dissipation concept (EDC), and segregated solid energy (SSE) for heat transfer. A comparison between the complete GRI 3.0 and Lu’s reduced combustion mechanisms was performed, with Lu’s mechanism being chosen for its cost-effectiveness and similar output to the GRI mechanism. The model’s geometry represents 1/8th of the engine’s volume, with a symmetric rotational boundary. The performance of this engine was investigated using NASA’s chemical equilibrium analysis (CEA) and STAR-CCM+ simulations, focusing on thrust levels of 125 lbf and 500 lbf. Discrepancies between theoretical predictions and simulations ranged from 1.4% to 28.5%, largely due to differences in modeling assumptions. While NASA CEA has a zero-dimensional, steady-state approach based on idealized conditions, STAR-CCM+ accounts for real-world factors such as multiphase flow, turbulence, and heat loss. For the 125 lbf case, a 9.2% deviation in combustion chamber temperature and a 15.0% difference in thrust were noted, with simulations yielding 113.48 lbf compared to the CEA’s 133.52 lbf. In the 500 lbf case, thrust reached 488 lbf, showing a 2.4% deviation from the design target and an 8.6% increase over CEA predictions. Temperature and pressure deviations were also observed, with the highest engine wall temperature at the nozzle throat. Monte Carlo simulations revealed that substituting LNG for LCH4 affects combustion dynamics. The findings emphasize the need for advanced modeling approaches to enhance the prediction accuracy of rocket engine performance, aiding in the development of digital twins for the CROME. Full article

One of the main security challenges when federating separate Internet of Things (IoT) administrative domains is effective Identity and Access Management, which is required to establish trust and secure communication between federated IoT devices. The primary goal of the work is to develop a “lightweight” protocol to enable authentication and authorization of IoT devices in federated environments and ensure the secure communication of IoT devices. We propose a novel Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI) which takes advantage of the unique fingerprint of IoT devices based on their configuration and additional hardware modules, such as Physical Unclonable Function, to provide flexible authentication and authorization based on Distributed Ledger technology. Moreover, LAAFFI supports IoT devices with limited computing resources and devices not equipped with secure storage space. We implemented a prototype of LAAFFI and evaluated its performance in the Hyperledger Fabric-based IoT framework. Three main metrics were evaluated: latency, throughput (number of operations or transactions per second), and network resource utilization rate (transmission overhead introduced by the LAAFFI protocol). The performance tests conducted confirmed the high efficiency and suitability of the protocol for federated IoT environments. Also, all LAAFFI components are scalable as confirmed by tests. We formally evaluated LAAFFI security using Verifpal as a formal verification tool. Based on the models developed for Verifpal, we validated their security properties, such as message secrecy, authenticity, and freshness. Our results show that the proposed solution can improve the security of federated IoT environments while providing zero-day interoperability and high scalability. Compared to existing solutions, LAAFFI is more efficient due to the use of symmetric cryptography and algorithms adapted for operations involving IoT devices. LAAFFI supports multiple authorization mechanisms, and since it also offers authentication and accountability, it meets the requirements of Authentication, Authorization and Accounting (AAA). It uses Distributed Ledger (DL) and smart contracts to ensure that the request complies with the policies agreed between the organizations. LAAFFI offers authentication of devices belonging to a single organization and different organizations, with the assurance that the encryption key will be shared with another device only if the appropriate security policy is met. The proposed protocol is particularly useful for ensuring the security of federated IoT environments created ad hoc for special missions, e.g., operations conducted by NATO countries and disaster relief operations Humanitarian Assistance and Disaster Relief (HADR) involving military forces and civilian services, where immediate interoperability is required. Full article

Background: The accurate segmentation of the appendix with well-defined boundaries is critical for diagnosing conditions such as acute appendicitis. The manual identification of the appendix is time-consuming and highly dependent on the expertise of the radiologist. Method: In this study, we propose a fully automated approach to the detection of the appendix using deep learning architecture based on the U-Net with specific training parameters in CT scans. The proposed U-Net architecture is trained on an annotated original dataset of abdominal CT scans to segment the appendix efficiently and with high performance. In addition, to extend the training set, data augmentation techniques are applied for the created dataset. Results: In experimental studies, the proposed U-Net model is implemented using hyperparameter optimization and the performance of the model is evaluated using key metrics to measure diagnostic reliability. The trained U-Net model achieved the segmentation performance for the detection of the appendix in CT slices with a Dice Similarity Coefficient (DSC), Volumetric Overlap Error (VOE), Average Symmetric Surface Distance (ASSD), Hausdorff Distance 95 (HD95), Precision (PRE) and Recall (REC) of 85.94%, 23.29%, 1.24 mm, 5.43 mm, 86.83% and 86.62%, respectively. Moreover, our model outperforms other methods by leveraging the U-Net’s ability to capture spatial context through encoder–decoder structures and skip connections, providing a correct segmentation output. Conclusions: The proposed U-Net model showed reliable performance in segmenting the appendix region, with some limitations in cases where the appendix was close to other structures. These improvements highlight the potential of deep learning to significantly improve clinical outcomes in appendix detection. Full article

We consider authenticating multicast messages in the case of extremely narrow communication channels, such as underwater acoustic communication, with devices such as mobile sensors creating a self-organizing autonomous network. Channel characteristics in this scenario prevent the application of digital signatures (and asymmetric cryptography in general), as it would consume too much of the available bandwidth. As communication is relatively sparse, standard symmetric methods such as TESLA have limited application in this scenario as well. Driven by real-world requirements, we focus on tiny networks of only a few nodes. This paper discusses two issues: (a) strategies of key predistribution enabling flexible creation of multicast groups; (b) authenticating multicast messages in a way that prevents an attacker impersonating the sender by subverting one or more receiver nodes and learning the symmetric keys stored by these nodes. For tiny networks, we show that scalable and asymptotically efficient solutions might be useless, and that specially tailored combinatorial approaches may confer some advantage. Full article