Search Results (4,506)

Modern computing platforms increasingly rely on random number generators (RNGs) for modeling probabilistic processes in simulation, probabilistic computing, and system validation. They are also essential for cryptographic operations such as key generation, authenticated encryption, and digital signatures. Deterministic Random Bit Generators (DRBGs), as specified in the National Institute of Standards and Technology (NIST) Special Publication (SP) 800-90A, provides a standardized method for expanding entropy into cryptographically strong pseudorandom sequences. This work presents the design and Field Programmable Gate Array (FPGA) implementation of a hash-based DRBG using Ascon-Hash256, a lightweight, quantum-resistant hash function from the NIST-standardized Ascon cryptographic suite. It implements hash-based derivation, instantiation, generation, and reseeding of the generator via iterative hash invocations and state updates. Leveraging Ascon’s sponge-based structure, the design achieves efficient entropy absorption and diffusion while maintaining an area-efficient FPGA architecture, making it well suited for resource-constrained platforms. The diffusion properties of the proposed DRBG are evaluated through avalanche and reproducibility analyses, confirming strong sensitivity to input variations and secure, repeatable operation. Moreover, Monte Carlo and stochastic-diffusion evaluation of the generated bitstreams demonstrates correct convergence and statistically consistent behavior. These results confirm that the proposed hash-based DRBG provides reproducible, hardware-efficient, and cryptographically secure random numbers suitable for next-generation neuromorphic, probabilistic computing systems, and Internet of Things (IoT) devices. Full article

Autonomous systems are expanding rapidly, driving a demand for robust perception technologies capable of navigating challenging, unstructured environments. While urban autonomy has made significant progress, off-road environments pose unique challenges, including dynamic terrain and limited communication infrastructure. This research addresses these challenges by introducing a novel three-part solution for off-road autonomous vehicles. First, we present a large-scale off-road dataset curated to capture the visual complexity and variability of unstructured environments, providing a realistic training ground that supports improved model generalization. Second, we propose a Confusion-Aware Loss (CAL) that dynamically penalizes systematic misclassifications based on class-level confusion statistics. When combined with cross-entropy, CAL improves segmentation mean Intersection over Union (mIoU) on the off-road test set from 68.66% to 70.06% and achieves cross-domain gains of up to ~0.49% mIoU on the Cityscapes dataset. Third, leveraging semantic segmentation as an intermediate representation, we introduce a spatial overlay video encoding scheme that preserves high-fidelity RGB information in semantically critical regions while compressing non-essential background regions. Experimental results demonstrate Peak Signal-to-Noise Ratio (PSNR) improvements of up to +5 dB and Video Multi-Method Assessment Fusion (VMAF) gains of up to +40 points under lossy compression, enabling efficient and reliable off-road autonomous operation. This integrated approach provides a robust framework for real-time remote operation in bandwidth-constrained environments. Full article

Industrial digitalization reshapes production processes and can potentially improve carbon productivity by optimizing factor allocation and energy efficiency. Using panel data for 30 Chinese provinces from 2012 to 2022, this study constructs a comprehensive industrial digitalization index with four dimensions and 13 indicators using the entropy method and examines its impact on carbon productivity (GDP per unit of CO₂ emissions). We employ the Dagum Gini coefficient and kernel density estimation to describe regional disparities and their evolution, a dynamic panel threshold model to test the nonlinear role of industrial transformation and upgrading, and a spatial Durbin model to identify spatial spillover effects. The results indicate that industrial digitalization has risen nationwide but remains uneven; industrial digitalization significantly enhances carbon productivity, with stronger effects in the eastern and western regions and in plain areas; the effect exhibits a double-threshold pattern with respect to industrial transformation and upgrading, implying a U-shaped relationship; and industrial digitalization generates positive spatial spillovers. These findings suggest that policy should coordinate digital infrastructure investment with industrial upgrading and regional collaboration to accelerate low-carbon, high-efficiency growth. Full article

This study reports the synthesis of a high-entropy AlFeCuTiNi alloy via high-energy ball milling. The study investigates the effects of mechanical alloying time and sintering temperature on the microstructure, mechanical properties, wear, and corrosion behavior of the high-entropy AlFeCuTiNi alloy. XRD, SEM, and EDX analyses revealed that the mechanical alloying time and sintering temperature significantly affected the alloy’s homogeneity, phase structure, and oxide film stability. As the mechanical alloying time increases, the corrosion resistance of alloys sintered at 550 °C initially increases and then stabilizes. In samples sintered at 650 °C, corrosion resistance is generally higher. The highest corrosion resistance was achieved after 15 h of mechanical alloying and sintering at 650 °C. The study reveals that the best corrosion, wear, hardness, and wear density performance was observed in samples obtained at medium conditions, achieved after 20 h of mechanical alloying and sintering at 650 °C. These findings may contribute to optimizing production processes for sustainable material design. Moreover, this research highlights that high-entropy alloys and powder-metallurgy-based production methods enable industrial applications for energy-efficient, sustainable material design and contribute to sustainable production and circular-economy principles. Full article

The qEEG findings of subjects with Down syndrome (DS) have not been described in the context of bipolar montage. Resting-state EEG (rsEEG) with a bipolar montage was performed in 22 young adults (26.0 ± 1.2 years) with DS but without psychiatric or neurological pathology and matched control subjects of the same sex and age, and the results were conventionally and numerically analyzed. Channels were grouped into frontal, parieto-occipital, and temporal lobes. For every channel, the power spectrum was calculated and used to compute the area for the delta, theta, alpha and beta bands and was log-transformed. Shannon’s spectral entropy (SSE) and coherence by bands were computed. Finally, we also calculated the peak frequency distribution of the alpha band. qEEG revealed alterations in the rsEEG that were not detected visually. Subjects with DS showed a significant generalized increase in the power of the delta and theta bands, along with a decrease in the power of the alpha band in the posterior half of the scalp. This alpha activity also exhibited features corresponding to older euploid subjects, showing interhemispheric asynchrony in one-third of the individuals. The beta band power was significantly increased in the frontal lobes and adjacent regions, such as the parietal and mid-temporal regions. Individuals with DS showed a generalized decrease in parieto-occipital synchronization associated with intelligence quotient. Left temporal synchronization was also lower. The synchronization of specific channel pairs was greater in subjects with DS in the frontal lobe and much lower in the occipital and temporal regions. These results indicate that alterations in band structure and synchronization in subjects with DS are highly specific and can aid in the clinical evaluation of these individuals. Full article

Deep learning has achieved remarkable advancements in medical image segmentation, yet its generalization capability across unseen tasks remains a significant challenge. The variety of task objectives, disease-dependent labeling variations, and multi-center data contribute to the high uncertainty of task-specific models on unseen distributions. In this study, we propose PromptSeg, an innovative Transformer-based unified framework for universal 2D medical image segmentation. From an information-theoretic perspective, PromptSeg formulates the segmentation process as a conditional entropy minimization problem, utilizing visual prompts as side information to reduce the uncertainty of the target task. Guided by the information bottleneck principle, PromptSeg aims to utilize the provided visual prompts to filter out redundant noise and learn contextual representations, thereby breaking the restrictions of the task-specific paradigm. When faced with unseen datasets or segmentation targets, our method only requires a few annotated visual prompt pairs to extract task-specific semantics and segment the query images without retraining. Extensive experiments on CT and MRI datasets demonstrate that PromptSeg not only outperforms state-of-the-art methods but also exhibits strong multi-modality generalization capabilities. Full article

Urban road collapses are characterized by sudden occurrence and strong spatial heterogeneity, posing substantial challenges for proactive infrastructure management. Susceptibility mapping can provide spatially explicit evidence to support targeted inspection and early-warning strategies. Using Futian District, Shenzhen (China) as a case study, a total of 315 road collapse events recorded during 2019–2023 were compiled to develop an integrated framework for urban road collapse relative susceptibility mapping based on multi-source remote sensing and urban spatial data. First, an indicator-based susceptibility index (SI) was constructed using eight conditioning factors, including PS-InSAR-derived deformation, topographic–hydrological conditions, and distance-based infrastructure variables (distance to underground utilities, metro lines, and roads). Factor weights were determined by coupling the Analytic Hierarchy Process (AHP) with the Entropy Weight Method (EWM), producing a comprehensive SI for historical collapse locations. Subsequently, a set of 17 remote-sensing predictors, including Sentinel-2 spectral bands, Sentinel-2 GLCM texture features, and Sentinel-1 SAR backscatter variables, was used to train a Random Forest model to predict SI and generate continuous susceptibility maps at the urban road-network scale. The influence of neighborhood window size on predictive performance was systematically evaluated. Results show that the Random Forest model performed best at the 5 × 5 window scale (R² = 0.70, RMSE = 0.0172, MAE = 0.0122), outperforming both pixel-based inputs (1 × 1) and larger windows. Uncertainty analysis further indicated that the 5 × 5 RF configuration yielded the most stable and spatially coherent predictions, whereas overly small windows and less robust learners produced more fragmented or higher-uncertainty susceptibility patterns. Spatiotemporal analysis indicates that susceptibility patterns remained broadly stable from 2019 to 2023, with moderate susceptibility accounting for 50.82–57.89% and high susceptibility for 21.94–23.30%, while very high susceptibility consistently remained below 1%. Overall, this study demonstrates that integrating multi-source remote sensing with scale-optimized machine learning provides an effective approach for fine-scale susceptibility mapping of urban road collapses, offering practical guidance for differentiated monitoring and risk prevention along critical road corridors. Full article

This article introduces Memetic/Metaphorical Digital Twins (MDTs) as a novel extension of Digital Twin typologies by twinning conceptual schemes, complementing Industrial, Human, and Cognitive Digital Twins. MDTs embed cultural, organizational, and semiotic knowledge into digital frameworks, enabling the recombination and evolution of knowledge structures across disciplines. Drawing on Schlaile’s economic perspectives and Mavromatidis’s architectural lens of entropy and constructal thermodynamics, this study demonstrates how MDTs can address systemic challenges in communication, knowledge transfer, and design. A Digital Community Platform, under development for supporting decentralized Personal Knowledge Management Systems (PKMS), provides the operational foundation, integrating iterative KM cycles to support knowledge co-creation. Its logic and logistics substitute the traditional document paradigm with a memetic approach by utilizing memes as replicable, adaptive knowledge units, thereby mimicking biological evolution and ecosystem resilience in digital platform environments. It aims to offer distributed, decentralized, bottom-up, affordable, knowledge-worker-centric applications prioritizing personalization, mobility, generativity, and entropy reduction; its mission is to serve a knowledge-co-creating community characterized by highly diverse individual Abilities, Contexts, Means, and Ends (ACME) facing increasingly volatile, uncertain, complex, and ambiguous futures (VUCA). A Boundary Object Taxonomy to Omnify Memetic Storytelling (BOTTOMS) is proposed to further structure atomic units of meaning—such as memes, mythemes, narratemes, and reputemes—into a unified framework for authorship and dissemination. The article situates MDTs within a design science research paradigm, outlines current implementation progress, and identifies future developments, including AI-supported curation, personalized metrics, and expanded boundary objects. Together, these contributions position MDTs as a universal framework for adaptive, transdisciplinary knowledge co-creation. Full article

This study develops a Constraint-Driven Model of Intelligence to explain the emergence of structured meaning in complex systems, reconciling probability and cybernetics. It applies a conceptual–analytic procedure, conducted entirely through logical reasoning and theoretical analysis, without empirical measurement, data acquisition, experimental manipulation, or statistical testing, and is therefore methodologically separate from empirical artificial intelligence research. Phenomena such as model collapse are cited as theoretical instances for epistemic argumentation, without asserting empirical verification. Building on Émile Borel’s Infinite Monkey Theorem, which demonstrates the theoretical inevitability of order in unbounded stochastic processes, and Gregory Bateson’s principle of negative explanation, which defines structure as the result of systematically eliminated alternatives, the analysis formalizes how constraints break ergodicity and generate asymmetry. Shannon’s entropy quantifies the informational effects of constraints, while Simon’s bounded rationality and Turing’s algorithmic limits show how cognitive and computational boundaries produce tractable outcomes. Applied to modern AI, the model accounts for model collapse in recursive training, showing that the loss of asymmetric constraints produces low-entropy, repetitive outputs, demonstrating the epistemic necessity of constraint regulation. Comparing probabilistic and cybernetic accounts of emergence, the study shows that structured intelligence arises not from stochastic exploration alone, but from bounded, recursive, selective processes. This model is transdisciplinary, formalizing how constraints from socioeconomic pressures to subcultural circulation shape diversity, innovation, and functional asymmetry, establishing a generalizable cybernetic epistemology for the generation of structured intelligence and meaning across domains. By formalizing these concepts through set-theoretic derivations and integrative synthesis, this non-empirical model advances a cybernetic epistemology, separate from quantitative AI evaluations or experimental designs. Full article

Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based model interpretation, and two-way fixed effects (TWFE) regression analysis. Using provincial data for China (2003–2022), we first construct a composite EPI with the entropy weight method, then apply a Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters for EPI forecasting. SHAP is used to interpret feature contributions to model-predicted EPI, and TWFE regression is used to provide complementary panel-data evidence on factor–EPI associations. The results show that the SSA-LSTM model outperforms benchmark machine learning and deep learning models in out-of-sample prediction performance. SHAP-based interpretation indicates that variables such as GDP, energy intensity, and power generation per capita contribute strongly to prediction variation, with notable regional heterogeneity. TWFE results are broadly consistent with several key patterns identified in the SHAP analysis. Overall, the proposed framework provides an accurate and interpretable provincial energy poverty prediction approach and offers a useful empirical reference for energy poverty monitoring and policy discussion. Full article

Sustainable tourist route design is a critical challenge in industrial heritage planning. While prior tourism routing algorithms predominantly minimize physical distance, and conventional heritage planning focuses on the static preservation of abandoned sites, both lack the multi-objective adaptability required for “living” industrial landscapes. In such dynamic environments, active production, tourism, and ecological conservation intricately coexist. To address this gap, this study proposes a novel, data-driven route planning framework, taking the Tianjin Changlu Salt Field as a case study. The genuine novelty lies in integrating multi-objective network optimization with spatial design implementation. The site is abstracted into a topological network comprising 13 nodes and 19 edges. Multi-attribute edge weights—incorporating spatial distance, travel time, landscape attractiveness, and ecological sensitivity—are quantified using entropy weighting fused with subjective preferences. Using the Floyd–Warshall algorithm, three theme-based touring routes are generated. Unlike traditional methods, this workflow actively translates algorithmic outputs into concrete spatial strategies, such as bypassing ecologically sensitive zones and transforming production facilities into perceptible landscape nodes. Comparative evaluations demonstrate that these optimized routes achieve higher comprehensive utility than baseline and designer-generated schemes, offering a pioneering, reproducible paradigm for the sustainable renewal of living industrial heritage. Full article

In this study, the continuous strengthening behavior of CoCrNi medium-entropy alloy at 1.2–4.2 nm twin spacings was revealed by molecular dynamics simulation. It was found that the yield strength increased linearly with the decrease in twin spacing, up to 12.526 GPa, and there was no softening inflection point. The strengthening mechanism is mainly due to the effective obstruction of coherent twin boundaries (TBs) to the dislocation slip, especially the stair-rod and Lomer–Cottrell lock structures generated by ISF and ESF stacking faults when crossing the interface. These structures significantly enhance the work-hardening capacity of the alloy by inducing dislocation stacking, although the very dense twin boundary will reduce the dislocation growth rate by limiting dislocation propagation. This precise interface control provides an important atomic-scale basis for the design of novel high-strength and high-work-hardening alloys. Full article

Although cloud storage is widely trusted by users and enterprises, externally stored encrypted and fragmented data remain vulnerable to reconstruction and inference attacks following partial exposure. Existing decoy-based defenses often rely on static configurations or randomly generated artifacts that can be filtered during adversarial analysis. This paper presents an Artificial Intelligence (AI)-controlled modular decoy generation method to enhance reconstruction resistance in distributed storage systems. The method operates as a system-agnostic post-fragmentation layer and does not require modification of encryption or storage architecture. Given encrypted fragments as input, decoys are generated using a supervised Extreme Gradient Boosting (XGBoost) regression model that adapts decoy quantity based on system telemetry and resource conditions. Decoys maintain statistical alignment with real encrypted fragments in size and Shannon entropy characteristics. To address scalability, the method is evaluated across small, medium, and large deployments comprising up to 413 externally exposed fragments and compared against fixed-ratio (10%, 20%) and randomized baselines. Experimental evaluation demonstrates increased adversarial uncertainty without altering legitimate reconstruction procedures or encryption mechanisms. Kolmogorov–Smirnov analysis indicates no statistically significant difference between AI-generated decoys and real fragments, whereas baseline decoys produce significant deviations in size and entropy distributions, supporting reconstruction resistance at scale in multi-cloud environments. Full article

With China’s urban growthism past its peak, urban development has shifted from incremental expansion to inventory quality improvement. Renovating historical and cultural blocks—a core area for urban quality enhancement—makes exploring their integration with surroundings highly significant. Existing studies on historical district research mainly focus on single-dimensional research such as protection policies, spatial structure analysis, and quality evaluation, lacking a systematic and quantitative evaluation of the spatial integration degree between historical and cultural blocks and their surrounding areas. To improve research on the integrated development of historical and cultural districts and their surrounding areas, this study employs deep learning and machine learning techniques to process street view images from 2721 data points in 2024, investigating the integration of Wuhan Hankou’s historical and cultural districts with their surrounding areas. The spatial integration degree between a historical and cultural district and its surroundings refers to the coordinated development level in terms of history and culture, spatial ecology, and transportation infrastructure. Specifically, the DeepLab v3+ model processes the blocks’ street view images to generate indicator data (Green Visual Index, Sky Visibility Index, Road Area Index, Spatial Enclosure Index, Color Richness (Wheel), Color Richness (Entropy), Spatial Accessibility Index, Vehicle Disturbance Index, Traffic Sign, which is used to quantify the historical culture, spatial ecology, and transportation facilities of historical and cultural blocks and their surrounding areas. The Coupling Coordination Degree model evaluates spatial integration, while the Geodetector Model quantitatively analyzes interactions between spatial integration and driving factors here. The results show that the spatial interaction and dependence between the Hankou Historical and Cultural District and its surrounding areas are relatively high, but spatial coordination is insufficient; the integration remains at a primary stage with structural contradictions. SVI, SEI, and RAI have a significant impact on integration, while Spatial Accessibility Index, Green Visual Index, and CRW have a moderate influence, and CRE, Vehicle Disturbance Index, and Traffic Signs have a relatively weak impact. Among them, SVI exhibits the strongest interactive effect with other indicators and plays a leverage role in improving integration. Full article

Targeting resource-constrained Internet of Things (IoT) devices, this paper proposes Stuck-at-Fault Physical Unclonable Function (SAF-PUF), a lightweight Resistive Random-Access Memory (RRAM)-based PUF that exploits the intrinsic addresses of manufacturing-induced SAF defects as a stable entropy source. By using the coordinates of Stuck-at-1 (SA1) cells to seed a 32-bit Linear Feedback Shift Register (LFSR), SAF-PUF generates robust, variable-length responses with zero Bit Error Rate (BER) across a wide temperature range from −40 °C to 125 °C, without any error-correction circuitry. Experimental results based on 100,000 Challenge–Response Pairs (CRPs) demonstrate strong resilience against machine learning (ML) attacks, with prediction accuracies of logistic regression (LR), support vector machines (SVM), neural networks (NN) and convolutional neural networks (CNNs) remaining close to 50%. Moreover, a “use-then-conceal” mechanism is introduced to enhance post-authentication security, enabling response obfuscation with minimal cell reconfiguration. These features make SAF-PUF a high-security, low-overhead hardware root of trust suitable for IoT applications. Full article