Search Results (56)

With the advent of large language models (LLMs), significant progress has been made in improving the fluency of machine translation (MT). However, hallucination remains a persistent challenge to translation accuracy, making Critical Error Detection (CED) increasingly important. In this paper, we introduce a simple yet effective approach, termed external knowledge-guided tuning, for the CED task. We focus on sentence-level CED, formulated as a binary classification task that determines whether an MT output contains critical errors. Although the task is binary, the data consist of diverse error cases, including issues related to toxicity, safety, named entities, sentiment, and numerical information, which may manifest as hallucination, mistranslation, or deletion. Our approach restructures model inputs in a cloze-style format and incorporates auxiliary descriptions, casting CED within a masked language modeling framework. By integrating additional contextual signals, including demonstration examples and outputs from commercial systems, our method guides the model to acquire task-specific knowledge and compare alternative MT outputs. Experimental results demonstrate the effectiveness of our approach, achieving state-of-the-art (SOTA) performance on the English–Czech language pair and a second-place ranking on English–German. We further provide a comprehensive analysis of the aggregated effects of external knowledge and examine the contribution of each component within the proposed framework. Our proposed method enables the model to internalize task-relevant knowledge through parameter updates within a prompt-based formulation, providing a principled way to incorporate external knowledge into CED and enhancing the model’s ability to identify critical errors in practice. Full article

Efficiently and accurately converting floating-point numbers to decimal strings remains a fundamental challenge in numerical computation, data serialization, and human–computer interaction. While modern algorithms such as Ryū, Dragonbox, and Schubfach rigorously satisfy the Steele–White criteria for correctness and minimal output length, their performance is frequently constrained by branch mispredictions, high-precision multiplication overhead, and suboptimal utilization of instruction-level parallelism. This paper introduces xjb, a novel floating-point–string conversion algorithm derived from Schubfach that systematically overcomes these bottlenecks. By restructuring the core computation to reduce instruction dependencies, adopting branchless decision logic, and exploiting SIMD instruction sets for decimal-to-ASCII formatting, xjb delivers state-of-the-art throughput across diverse hardware platforms. The algorithm requires only a single 64-by-128-bit multiplication for IEEE 754 binary64 conversions and a single 64-by-64-bit multiplication for binary32, drastically decreasing arithmetic complexity. Extensive benchmarking on AMD R7-7840H and Apple M1/M5 processors demonstrates that xjb consistently outperforms leading contemporary implementations. Notably, on the Apple M5, xjb achieves speedups of approximately 20% and 136% for binary64 and binary32 conversions, respectively, when compared to the highly optimized zmij library. The algorithm is fully compliant with the Steele–White principle; exhaustive validation over the entire binary32 space and extensive random testing across the binary64 range confirm both its theoretical soundness and practical robustness. Full article

Industrial Internet of Things (IIoT) and SCADA-connected networks are increasingly vulnerable to Distributed Denial of Service (DDoS) attacks, which can disrupt time-sensitive industrial processes and compromise operational continuity. Effective mitigation requires accurate and low-latency attack detection at the network edge, where industrial gateways operate under strict constraints in computation, memory, and energy. This study investigates Deep Reinforcement Learning (DRL) for real-time binary DDoS detection and proposes a detector based on Proximal Policy Optimisation (PPO) for deployment in resource-constrained IIoT environments. Four DRL agents, namely Deep Q-Network (DQN), Double DQN, Dueling DQN, and PPO, are trained and evaluated within a unified experimental pipeline incorporating automatic label mapping, numerical feature selection, robust scaling, and class balancing. Experiments are conducted on three representative benchmark datasets: CIC-DDoS2019, Edge-IIoTset, and CICIoT23. Performance is assessed using accuracy, precision, recall, F1-score, false positive rate, false negative rate, and CPU inference latency. The reward function is asymmetric: +1 for correct classification, −1 for false positive, and −2 for false negative, penalising missed attacks more heavily for IIoT safety. The results show that PPO provides a competitive accuracy–latency tradeoff across all three datasets, achieving the highest mean accuracy of 97.65% and ranking first on CIC-DDoS2019 with a score of 95.92%, while remaining competitive on Edge-IIoTset (99.11%) and CICIoT23 (97.92%). PPO also converges faster than the value-based baselines. Inference latency is below 0.8 ms per sample on a standard CPU (Intel i7-11800H), confirming real-time feasibility. To support practical deployment, the trained PPO policies are exported to ONNX format (≈9 KB per model), enabling lightweight and PyTorch-independent inference on industrial edge gateways. Full article

Microwave imaging is emerging as an alternative to conventional medical diagnostic techniques. Traditional analytical and numerical methods fail to adequately address these fundamental challenges: they often rely on strict linear approximations or simplified physical models, leading to low reconstruction accuracy, poor robustness, and limited generalization ability in complex clinical scenarios. As a result, they cannot meet the high-precision requirements of practical stroke microwave imaging. To further improve the accuracy of microwave imaging algorithms in recognizing stroke regions and solving the backscattering problem, this study employs a combination of methods with deep learning. It presents the Multi-Scale Attention Network (MSA-Net) for microwave imaging. The network is based on the EGE-UNet network structure with improved multi-axis Hadamard attention, incorporating null-space pyramid pooling and introducing a deep supervisory mechanism to improve the network performance further. To combine microwave imaging with deep learning, firstly, a large amount of microwave data need to be simulated with HFSS, in which the simulation model is a human brain stroke model constructed by an HFSS simulation system. Secondly, the microwave data obtained from the simulation are converted into a tensor format. Then, the tensor data are input into the MSA-Net neural network, which generates a binary mask image that can be used to detect the size and location of the stroke. This study also prompts the model to converge faster by sparsifying the microwave data to improve training efficiency. The method has been tested using simulation data, and based on the comparison experiments with other networks, MSA-Net is more accurate in detecting the location and the bleed size. The experimental results show that the proposed method is superior for stroke imaging. The experimental results show that the proposed model achieves a 1.08 improvement in peak signal-to-noise ratio and a 0.017 reduction in learned perceptual image block similarity, fully validating the effectiveness of the structural optimization strategy proposed in this paper. Full article

Liquid metal cathodes, particularly liquid cadmium (Cd), are widely used in molten salt electrorefining and pyrochemical reprocessing of spent nuclear fuel due to their high electrical conductivity, strong affinity for actinides, and favorable alloying characteristics. During electrorefining, reduced metal species enter the liquid Cd phase and may exist as dissolved atoms, liquid alloys, or intermetallic compounds, all of which significantly influence deposition behavior, separation selectivity, and cathode performance. Although numerous experimental and theoretical studies have investigated metal solubility, alloy formation, and diffusion in liquid Cd systems, the current understanding remains fragmented. Thermodynamic phase behavior and mass transport kinetics are often discussed separately, and reported diffusion data show considerable discrepancies owing to variations in experimental techniques and interpretations. In addition, the relationship between phase stability, diffusion mechanisms, and electrochemical conditions in practical electrorefining environments has not yet been systematically clarified. This review aims to present an integrated thermodynamic–kinetic perspective on the behavior of metals in liquid Cd cathodes. Recent progress in dissolution behavior, alloy phase formation, and diffusion-controlled transport processes is critically summarized. The differences in solubility and precipitation behavior of actinides, rare-earth elements, and selected transition metals are analyzed in relation to binary phase diagrams and thermodynamic stability. Furthermore, experimental methods for determining diffusion coefficients, including capillary techniques and electrochemical approaches, are comparatively evaluated. By correlating thermodynamic phase stability with diffusion-driven mass transport, this work provides a coherent framework for understanding metal behavior in liquid Cd cathodes and offers insights for optimizing molten salt electrorefining and advanced nuclear fuel cycle technologies. Full article

Field-Programmable Gate Arrays (FPGAs) have become an important platform for accelerating real-time communication systems, and System-on-Chip (SoC) devices provide the flexibility to design and optimize architectures that support high data rates, different modulation formats, and channel equalization schemes. Selecting the appropriate architecture can be guided through Design Space Exploration (DSE) using high-level synthesis tools, which enables the identification of numerical representations that balance performance with reduced hardware resource consumption. Despite their relevance, recent developments in communication systems often overlook the impact of numerical precision in Digital Signal Processing algorithms, particularly the trade-offs between floating- and fixed-point arithmetic when targeting hardware implementations. In this work, two widely used blind equalization algorithms, the Constant Modulus Algorithm (CMA) and the Multi-Modulus Algorithm (MMA), were implemented on a low-cost Ultra96 SoC-FPGA to analyze the effect of a fixed-point representation. A multi-objective Design Space Exploration methodology was applied to minimize hardware utilization while maintaining reliable transmission performance. Resource consumption, latency, and throughput were measured across different binary formats using the Minimum Mean Square Error (MMSE) criterion. Parallelization techniques were incorporated to improve throughput. The DSE generated comprehensive performance surfaces quantifying latency, MMSE convergence, and FPGA resource utilization (DSP48E/FF/LUT/BRAM) across fixed-point formats, achieving optimal 4 MS/s throughput configurations. Although this throughput is naturally lower than the Gigabit speeds required in backbone optical networks, the results demonstrate the effectiveness of numerical representation optimization in resource-constrained SoC-FPGA devices, offering a practical approach for real-time Edge and IoT implementations where cost and hardware limitations are critical. Full article

In this paper, we consider the family of parameterized Mandelbrot-like sets generated as any point $c\in \mathbb{C}\setminus \left\{0\right\}$ of the complex plane belongs to any member of this family for a real parameter $t\ge 1$, provided that its corresponding orbit of 0 does not escape to infinity under iteration $f_c^n\left(0\right)={\left[f_c^{n-1}\left(0\right)\right]}^2+log{c}^t$; otherwise, it is not a member of this set. This classically means there is only a binary membership possibility for all points. Here, we call this type of fractal set a Mandelblog set, and then we introduce a membership function that assigns a degree to each c to be an element of a fuzzy Mandelblog set under the iterations, even if the orbits of the points are not limited. Moreover, we provide numerical examples and gray-scale graphics that illustrate the membership degrees of the points of the fuzzy Mandelblog sets under the effects of iteration parameters. This approach enables the formation of graphs for these fuzzy fractal sets by representing points that belong to the set as white pixels, points that do not belong as black pixels, and other points, based on their membership degrees, as gray-toned pixels. Furthermore, the membership function facilitates the direct proofs of the symmetry criteria for these fractal sets. Full article

We investigate the effect of general relativity on the Sitnikov problem. The Sitnikov problem is one of the simplest three-body problems, in which the two primary bodies (a binary system) have equal mass m and orbit their barycenter, while the third body is treated as a test particle under Newtonian gravity. The trajectory of the test particle is perpendicular to the orbital plane of the binary (along z-axis) and passes through the barycenter of the two primaries. To study the general relativistic contributions, we first derive the equations of motion for both the binary and the test particle based on the first post-Newtonian Einstein–Infeld–Hoffmann equation, and integrate these equations numerically. We examine the behavior of the test particle (third body) as a function of the orbital eccentricity of the central binary e, the dimensionless gravitational radius $\lambda$, which characterizes the strength of general relativistic effect, and the initial position of the test particle ${\overline{z}}_0$. Our numerical calculations reveal the following; as general relativistic effects $\lambda$ increase and the eccentricity e of the binary orbit grows, the distance $\overline{r}$ between the test particle and the primary star undergoes complicated oscillations over time. Consequently, the gravitational force acting on the test particle also varies in a complex manner. This leads to a resonance state between the position $\overline{z}$ of the test particle and the distance $\overline{r}$, causing the energy E of the test particle to become $E\ge 0$. This triggers the effective ejection of the test particle due to the gravitational slingshot effect. In this paper, we shall refer to this ejection mechanism of test particle as the “Sitnikov mechanism.” As a concrete phenomenon that becomes noticeable, the increase in general relativistic effects and the eccentricity of the binary orbit leads to the following: (a) ejection of test particles from the system in a shorter time, and (b) increasing escape velocity of the test particle from the system. As an astrophysical application, we point out that the high-velocity ejection of test particles induced by the Sitnikov mechanism could contribute to elucidating the formation processes of astrophysical jets and hyper-velocity stars. Full article

Wireless Sensor Networks (WSNs) consist of numerous battery-powered sensor nodes that operate with limited energy, computation, and communication capabilities. Designing routing strategies that are both energy-efficient and attack-resilient is essential for extending network lifetime and ensuring secure data delivery. This paper proposes Adaptive Federated Reinforcement Learning-Hunger Games Search (AFRL-HGS), a Hybrid Routing framework that integrates multiple advanced techniques. At the node level, tabular Q-learning enables each sensor node to act as a reinforcement learning agent, making next-hop decisions based on discretized state features such as residual energy, distance to sink, congestion, path quality, and security. At the network level, Federated Reinforcement Learning (FRL) allows the sink node to aggregate local Q-tables using adaptive, energy- and performance-weighted contributions, with Polyak-based blending to preserve stability. The binary Hunger Games Search (HGS) metaheuristic initializes Cluster Head (CH) selection and routing, providing a well-structured topology that accelerates convergence. Security is enforced as a constraint through a lightweight trust and anomaly detection module, which fuses reliability estimates with residual-based anomaly detection using Exponentially Weighted Moving Average (EWMA) on Round-Trip Time (RTT) and loss metrics. The framework further incorporates energy-accounted control plane operations with dual-format HELLO and hierarchical ADVERTISE/Service-ADVERTISE (SrvADVERTISE) messages to maintain the routing tables. Evaluation is performed in a hybrid testbed using the Graphical Network Simulator-3 (GNS3) for large-scale simulation and Kali Linux for live adversarial traffic injection, ensuring both reproducibility and realism. The proposed AFRL-HGS framework offers a scalable, secure, and energy-efficient routing solution for next-generation WSN deployments. Full article

Numerical simulation has played a crucial role in modeling the behavior of natural gas hydrate (NGH). However, the existing numerical simulators worldwide have exhibited limitations in functionality, convergence, and computational efficiency. In this study, we present a novel numerical simulator, GPSFlow/Hydrate, for modeling the behavior of hydrate-bearing geologic systems and for addressing the limitations in the existing simulators. It is capable of simulating multiphase and multicomponent flow in hydrate-bearing subsurface reservoirs under ambient conditions. The simulator incorporates multiple mass components, various phases, as well as heat transfer, and sand is treated as an independent non-Newtonian flow and modeled as a Bingham fluid. The CH₄ or binary/ternary gas hydrate dissociation or formation, phase changes, and corresponding thermal effects are fully accounted for, as well as various hydrate formation and dissociation mechanisms, such as depressurization, thermal stimulation, and sand flow behavior. In terms of computation, the simulator utilizes a domain decomposition technology to achieve hybrid parallel computing through the use of distributed memory and shared memory. The verification of the GPSFlow/Hydrate simulator are evaluated through two 1D simulation cases, a sand flow simulation case, and five 3D gas production cases. A comparison of the 1D cases with various numerical simulators demonstrated the reliability of GPSFlow/Hydrate, while its application in modeling the sand flow further highlighted its capability to address the challenges of gas hydrate exploitation and its potential for broader practical use. Several successful 3D gas hydrate reservoir simulation cases, based on parameters from the Shenhu region of the South China Sea, revealed the correlation of initial hydrate saturation and reservoir condition with hydrate decomposition and gas production performance. Furthermore, multithread parallel computing achieved a 2–4-fold increase in efficiency over single-thread approaches, ensuring accurate solutions for complex physical processes and large-scale grids. Overall, the development of GPSFlow/Hydrate constitutes a significant scientific contribution to understanding gas hydrate formation and decomposition mechanisms, as well as to advancing multicomponent flow migration modeling and gas hydrate resource development. Full article

This study presents an innovative pipeline for processing, compressing, and remotely visualizing large-scale numerical simulations of fluid dynamics in a virtual wind tunnel (VWT), leveraging virtual and augmented reality (VR/AR) for enhanced analysis and high-end visualization. The workflow addresses the challenges of handling massive databases generated using Direct Numerical Simulation (DNS) while maintaining visual fidelity and ensuring efficient rendering for user interaction. Fully immersive visualization of supersonic (Mach number 2.86) spatially developing turbulent boundary layers (SDTBLs) over strong concave and convex curvatures was achieved. The comprehensive DNS data provides insights on the transport phenomena inside turbulent boundary layers under strong deceleration or an Adverse Pressure Gradient (APG) caused by concave walls as well as strong acceleration or a Favorable Pressure Gradient (FPG) caused by convex walls under different wall thermal conditions (i.e., Cold, Adiabatic, and Hot walls). The process begins with a .vts file input from a DNS, which is visualized using ParaView software. These visualizations, representing different fluid behaviors based on a DNS with a high spatial/temporal resolution and employing millions of “numerical sensors”, are treated as individual time frames and exported in GL Transmission Format (GLTF), which is a widely used open-source file format designed for efficient transmission and loading of 3D scenes. To support the workflow, optimized Extract–Transform–Load (ETL) techniques were implemented for high-throughput data handling. Conversion of exported Graphics Library Transmission Format (GLTF) files into Graphics Library Transmission Format Binary files (typically referred to as GLB) reduced the storage by 25% and improved the load latency by 60%. This research uses Unity’s Profile Analyzer and Memory Profiler to identify performance limitations during contour rendering, focusing on the GPU and CPU efficiency. Further, immersive VR/AR analytics are achieved by connecting the processed outputs to Unity engine software and Microsoft HoloLens Gen 2 via Azure Remote Rendering cloud services, enabling real-time exploration of fluid behavior in mixed-reality environments. This pipeline constitutes a significant advancement in the scientific visualization of fluid dynamics, particularly when applied to datasets comprising hundreds of high-resolution frames. Moreover, the methodologies and insights gleaned from this approach are highly transferable, offering potential applications across various other scientific and engineering disciplines. Full article

The representation of binary data in a compact, printable, efficient, and often human-readable format is essential in numerous computing applications, mainly driven by the limitations of systems and communication protocols not designed to handle arbitrary 8-bit binary data. This paper provides a comprehensive survey and an extensive characterization of printable encoding schemes, tracing their evolution from historical methods to contemporary solutions for representing, storing, and transmitting binary data using restricted character sets. The review includes a foundational analysis of fundamental character encodings, proposes a layered model for the classification of printable encodings, and examines various schemes based on their numerical bases, alphabets, and functional characteristics. Algorithms, key design trade-offs, the impact of relevant standards, security implications, performance considerations, and human factors are systematically discussed, aiming to offer a detailed understanding of the current context and open challenges. Full article

The ground settlement induced by shield tunnel construction should be carefully monitored and controlled during construction as a compulsory measurement to ensure construction safety. In the existing literature, gap parameter theory is adopted to predict ground settlement; however, the influence of slurry grouting on ground settlement during the construction process has been ignored. Regarding this drawback, a novel optimized gap parameter theory is proposed and combined with 3D finite element analysis to investigate ground settlement caused by shield tunnel excavation. Considering that construction technology plays an important role in ground settlement, numerical studies are carried out to investigate the sensitivities of the grouting filling ratio, pressure of the tunnel face, and the strata conditions in ground settlement. The practical engineering of Wuhan Metro Line 7 is introduced to verify the superiority of the proposed method. The results show that the proposed method can reflect ground settlement well, compared to the existing methods and the measured data. Then, 3D finite element analysis and orthogonal test are adopted to conduct sensitivity analyses of the grouting fill rate, support pressure ratio, and strata conditions. The results illustrate that the grouting filling rate has the most obvious impact on ground settlement, while the support pressure ratio and strata conditions also have a certain impact on ground settlement. Taking the binary structure stratum of the terrace geological environment of the Yangtze River in Wuhan as the research object, this study employs a three-dimensional numerical simulation approach to analyze six distinct binary structure stratum models. The parameter value ranges, considering formation conditions, are determined through integrated theoretical analysis. Finally, based on the deviation analysis results between the optimized gap parameter theory and numerical simulation, it is concluded that there is no significant difference in the surface settlement values obtained from the two methods. To summarize, the proposed optimized gap parameter theory, combined with the corresponding numerical simulation technology, provides a good tool for the control of ground settlement caused by shield tunnel excavation in complex strata, such as binary structure strata. Full article

Background: Electronic Health Records (EHRs) encompass valuable data essential for disease prediction. The application of artificial intelligence (AI), particularly deep learning, significantly enhances disease prediction by analyzing extensive EHR datasets to identify hidden patterns, facilitating early detection. Recently, numerous foundation models pretrained on extensive data have demonstrated efficacy in disease prediction using EHRs. However, there remains some unanswered questions on how to best utilize such models, especially with very small fine-tuning cohorts. Methods: We utilized Med-BERT, an EHR-specific foundation model, and reformulated the disease binary prediction task into a token prediction task and a next visit mask token prediction task to align with Med-BERT’s pretraining task format in order to improve the accuracy of pancreatic cancer (PaCa) prediction in both few-shot and fully supervised settings. Results: The reformulation of the task into a token prediction task, referred to as Med-BERT-Sum, demonstrated slightly superior performance in both few-shot scenarios and larger data samples. Furthermore, reformulating the prediction task as a Next Visit Mask Token Prediction task (Med-BERT-Mask) significantly outperformed the conventional Binary Classification (BC) prediction task (Med-BERT-BC) by 3% to 7% in few-shot scenarios with data sizes ranging from 10 to 500 samples. These findings highlight that aligning the downstream task with Med-BERT’s pretraining objectives substantially enhances the model’s predictive capabilities, thereby improving its effectiveness in predicting both rare and common diseases. Conclusions: Reformatting disease prediction tasks to align with the pretraining of foundation models enhances prediction accuracy, leading to earlier detection and timely intervention. This approach improves treatment effectiveness, survival rates, and overall patient outcomes for PaCa and potentially other cancers. Full article

The study of the fundamental principles of gelation of colloidal nanoparticles (NPs) advances the understanding of the formation of colloidal systems of living organisms. In this paper, the effect of particle size for a binary system of oppositely charged latexes on the experimental parameters of the system, including the gelation region, rheological parameters and cluster size, is considered for the first time. It is shown that the gelation regions in the phase diagrams for asymmetric particles are symmetric with respect to the ratio of charge and surface area of the particles. It was found that asymmetric particles form denser gels compared with the same concentration of symmetrical particles. This work provides insight into the gelation of asymmetric NPs, which is important for numerous applications, including their utilization in colloidal gels as ink for additive manufacturing and as scaffolds for cell growth, as well as understanding the fundamental aspects of the formation of bio-colloids. Full article