Search Results (34)

A Bayesian convolutional neural network (BCNN) quantifies prediction uncertainty by introducing randomness into weights or activations, which is important for safety-critical applications such as medical diagnosis and autonomous driving. However, BCNN inference typically relies on Monte Carlo sampling requiring multiple forward passes, leading to computation and energy consumption far beyond standard CNN hardware acceleration. FPGA, with its parallel processing, reconfigurability, and high-energy efficiency, are ideal platforms for dedicated BCNN accelerators. This paper designs and implements an FPGA acceleration method for BCNN-using high-level synthesis. First, convolution, pooling, and fully connected modules are individually optimized. Then, a mean/variance dual-path parallel expansion is adopted, combined with mixed-precision quantization and global scaling compensation, local reparameterization sampling, parameter reordering, and ping-pong buffering, achieving low resource usage and high-energy efficiency while enabling uncertainty evaluation. Experimental results on Bayes VGG16 show resource utilization of 24,776 LUT, 23,378 FF, 115 BRAM, and 129 DSP, with total power of 2.049 W. Compared with an unoptimized Bayesian implementation, the proposed design reduces inference latency to about one-third, and its latency is only 17% higher than that of the classical VGG16. Compared with PC-based floating-point models, the accuracy loss on four BCNN models (tested on CIFAR-10) is within 1%. The predictive entropy effectively distinguishes normal, noisy, and out-of-distribution (OOD) samples, validating the uncertainty quantification capability of the BCNN FPGA accelerator. Full article

To resolve the inherent conflict between high robustness and strict reversibility in frequency-domain image encryption, as well as to eliminate data expansion caused by floating-point errors, this paper presents a novel lossless frequency-domain image encryption scheme via 3D exponential hyper-chaos and integer lifting wavelet transform (ILWT). Firstly, a 3D hyper-chaotic exponential sine map (3D-HESM) is constructed by introducing nonlinear exponential coupling, providing a high-entropy keystream source with wider chaotic ranges than traditional maps. Secondly, to guarantee lossless reconstruction, the ILWT is employed to diffuse image coefficients in the frequency domain. By integrating modular arithmetic into the lifting steps, this transform confines coefficients within the finite integer ring, effectively solving the data expansion problem while maintaining perfect mathematical reversibility. Thirdly, an adaptive key generation protocol is designed by fusing SHA-512 with Singular Value Decomposition (SVD). Leveraging the geometric stability of singular values, this mechanism establishes a balance between extreme sensitivity to plaintext alterations and tolerance to channel noise. Experimental results and security analyses demonstrate that the proposed scheme achieves a vast key space and resists differential attacks. Furthermore, it exhibits superior robustness against data cropping and noise interference compared to state-of-the-art methods, validating its suitability for secure and lossless image transmission. Full article

As remarked by Boltzmann, the Second Law of Thermodynamics is notable for the fact that it is readily proved using elementary statistical arguments, but becomes harder and harder to verify the more precise the microscopic description of a system. In this article, we investigate one particular realization of the 2nd Law, namely Joule heating in a wire under electrical bias. We analyze the production of entropy in an exactly solvable model of a quantum wire wherein the conserved flow of entropy under unitary quantum evolution is taken into account using an exact formula for the entropy current of a system of independent quantum particles. In this exact microscopic description of the quantum dynamics, the entropy production due to Joule heating does not arise automatically. Instead, we show that the expected entropy production is realized in the limit of a large number of local measurements by a series of floating thermoelectric probes along the length of the wire, which inject entropy into the system as a result of the information obtained via their continuous measurements of the system. The decoherence resulting from inelastic processes introduced by the local measurements is essential to the phenomenon of entropy production due to Joule heating, and would be expected to arise due to inelastic scattering in real systems of interacting particles. Full article

The Soft Yoke Mooring (SYM) system is a critical single-point mooring method for Floating Production Storage and Offloading systems (FPSOs) in shallow waters. Its articulated thrust roller bearing operates long-term in harsh marine environments, making it prone to failure and difficult to diagnose. To address the issues of non-stationary signals and fault features submerged in strong noise caused by the bearing’s non-rotational oscillatory motion, this paper proposes an adaptive improved diagnosis scheme based on Minimum Entropy Deconvolution (MED). By optimizing Finite Impulse Response (FIR) filter parameters to adapt to the oscillatory operating conditions and combining joint analysis of time-domain indicators and envelope spectra, precise identification of bearing faults is achieved. Research shows that this method effectively enhances fault impact components. After MED processing, the kurtosis value of the fault signal can be significantly increased from approximately 2.6 to over 8.6. Its effectiveness in noisy environments was verified through simulation. Experiments conducted on a 1:10 scale soft yoke model demonstrated that the MED denoising and filtering signal analysis method can effectively identify damage in the thrust roller bearing of the SYM system under marine conditions characterized by high noise and complex frequencies. This study provides an efficient and reliable method for fault diagnosis of non-rotational oscillatory bearings in complex marine environments, holding significant engineering application value. Full article

Nanoparticle-mediated photothermal conversion exploits the unique light-to-heat transduction properties of engineered nanomaterials to address challenges in energy, water, and healthcare. This review first examines fundamental mechanisms—localized surface plasmon resonance (LSPR) in plasmonic metals and broadband interband transitions in semiconductors—demonstrating how tailored nanoparticle compositions can achieve >96% absorption across 250–2500 nm and photothermal efficiencies exceeding 98% under one-sun illumination (1000 W·m⁻², AM 1.5G). Next, we highlight advances in solar steam generation and desalination: floating photothermal receivers on carbonized wood or hydrogels reach >95% efficiency in solar-to-vapor conversion and >2 kg·m⁻²·h⁻¹ evaporation rates; three-dimensional architectures recapture diffuse flux and ambient heat; and full-spectrum nanofluids (LaB₆, Au colloids) extend photothermal harvesting into portable, scalable designs. We then survey photothermal-enhanced thermal energy storage: metal-oxide–paraffin composites, core–shell phase-change material (PCM) nanocapsules, and MXene– polyethylene glycol—PEG—aerogels deliver >85% solar charging efficiencies, reduce supercooling, and improve thermal conductivity. In biomedicine, gold nanoshells, nanorods, and transition-metal dichalcogenide (TMDC) nanosheets enable deep-tissue photothermal therapy (PTT) with imaging guidance, achieving >94% tumor ablation in preclinical and pilot clinical studies. Multifunctional constructs combine PTT with chemotherapy, immunotherapy, or gene regulation, yielding synergistic tumor eradication and durable immune responses. Finally, we explore emerging opto-thermal nanobiosystems—light-triggered gene silencing in microalgae and poly(N-isopropylacrylamide) (PNIPAM)–gold nanoparticle (AuNP) membranes for microfluidic photothermal filtration and control—demonstrating how nanoscale heating enables remote, reversible biological and fluidic functions. We conclude by discussing challenges in scalable nanoparticle synthesis, stability, and integration, and outline future directions: multicomponent high-entropy alloys, modular photothermal–PCM devices, and opto-thermal control in synthetic biology. These interdisciplinary innovations promise sustainable solutions for global energy, water, and healthcare demands. Full article

Climate change and related weather extremes are increasingly having an impact on all aspects of life. The main objective of the research was to analyze the impact of the most important meteorological elements and the image data of various water bodies of the Kis-Balaton wetland, Hungary. The primary question was which meteorological elements have a positive or negative influence on vegetational surface cover. Drones have facilitated the visual surveying and monitoring of challenging-to-reach water bodies in the area, including a lake and multiple channels. The individual channels had different flow conditions. Aerial surveys were conducted monthly, based on pre-prepared flight plans. Images captured by a Mavic 3 drone flying at an altitude of 150 m and equipped with a multispectral sensor were processed. The time-series images were aligned and assembled into orthophotos. The image details relevant to the research were segregated and classified using Maximum Likelihood classification algorithm. The reliability of the image data used was checked by Shannon entropy and spectral fractal dimension measurements. The results of the classification were compared with the meteorological data collected by a QLC-50 automatic climate station of Keszthely. The investigations revealed that the surface cover of the examined water bodies was different in the two years but showed a kind of periodicity during the year. In those periods, where photosynthetic organisms multiplied in a higher proportion in the water body, higher monthly average air temperatures and higher monthly global solar radiation sums were observed. Full article

Some cast metallic alloys for ultra-high-temperature structural applications can have better mechanical properties compared with Ni-based superalloys. Research on the directional solidification (DS) of such alloys is limited. The production of DS components of these alloys with “tailor-made” microstructures in different parts of the component has not been considered. This paper attempts to address these issues. A bar of the RCCA/RM(Nb)IC with nominal composition 3.5Al–4Crc6Ge–1Hf–5Mo–36Nb–22Si–1.5Sn–20Ti–1W (at.%) was directionally grown using OFZ processing, where the growth rate R increased from 1.2 to 6 and then to 15 cm/h. The paper studies how the macrosegregation of the elements affected the microstructure in different parts of the bar. It was shown that the synergy of macrosegregation and growth rate produced microstructures from the edge to the centre of the OFZ bar and along the length of the OFZ bar that differed in type and chemical composition as R increased. Contamination with oxygen was confined to the “root” of the part of the bar that was grown with R = 1.2 cm/h. The concentrations of elements in the bar were related (a) to each of the parameters VEC, Δχ, and δ for different sections, (i) across the thickness and (ii) along the length of the bar, or to each other for different sections of the bar, and demonstrated the synergy and entanglement of processing, parameters, and elements. In the centre of the bar, the phases were the Nb_ss and Nb₅Si₃ for all R values. In the bar, the silicide formed with Nb/(Ti + Hf) less or greater than one. There was synergy of solutes in the solid solution and the silicide for all R values, and synergy and entanglement of the two phases. Owing to the synergy and entanglement of processing, parameters, elements, and phases, properties would “emerge” in each part of the bar. The creep and oxidation properties of the bar were calculated as guided by the alloy design methodology NICE. It was suggested that, in principle, a component based on a metallic UHTM with “functionally graded” composition, microstructure and properties could be directionally grown. Full article

Against the backdrop of the global transition towards clean energy, deep-sea wind-power hydrogen production integrates offshore wind with green hydrogen technology. Addressing the technical coupling complexity and the impact of uncertain hydrogen prices, this paper develops a capacity optimization model. The model incorporates floating wind turbine output, the technical distinctions between alkaline (ALK) electrolyzers and proton exchange membrane (PEM) electrolyzers, and the synergy with energy storage. Under three hydrogen price scenarios, the results demonstrate that as the price increases from 26 CNY/kg to 30 CNY/kg, the optimal ALK capacity decreases from 2.92 MW to 0.29 MW, while the PEM capacity increases from 3.51 MW to 5.51 MW. Correspondingly, the system’s Net Present Value (NPV) exhibits an upward trend. To address the limitations of traditional methods in handling multi-dimensional benefit correlations and information ambiguity, a comprehensive benefit evaluation framework encompassing economic, technical, environmental, and social synergies was constructed. Sensitivity analysis indicates that the comprehensive benefit level falls within a relatively high-efficiency interval. The numerical characteristics, an entropy value of 3.29 and a hyper-entropy of 0.85, demonstrate compact result distribution and robust stability, validating the applicability and stability of the proposed offshore wind–hydrogen benefit assessment model. Full article

This paper presents a detailed thermodynamic and mathematical modeling study of the oxygenation processes in stationary water bodies, focusing on improving oxygen transfer efficiency, an essential factor in sustaining aquatic ecosystem health. The study employed mathematical models implemented in MATLAB R2024a to simulate the influence of temperature, bubble size, and mass transfer parameters. Key parameters, such as dissolved oxygen concentration, volumetric mass transfer coefficient (ak_L), and water temperature, were evaluated under different operational scenarios. The oxygenation system was powered by solar energy and included rotating fine-bubble generators mounted on a floating platform. Mathematical modeling carried out in MATLAB validated the theoretical models, showing how environmental factors such as temperature and bubble size influence oxygen dissolution. Initial experimental data, including dissolved oxygen levels (C₀ = 3.12 mg/dm³), saturation concentrations at various temperatures (Cs = 8.3 mg/dm³ at 24 °C; Cs = 7.3 mg/dm³ at 30 °C), and a mass transfer coefficient of ak_L = 0.09 s⁻¹, were used to support the model accuracy. The results highlight the potential of digitally controlled energy-efficient aeration technologies for applications in lake restoration, aquaculture, and sustainable water management. This paper introduces a coupled approach to oxygen transfer and temperature evolution validated experimentally, which has rarely been detailed in the literature. The novelty of this study lies in the combined thermodynamic modeling and exergy–entropy analysis along with real-time tracking, showing the relevance of energy-optimized, digitally monitored oxygenation platforms powered by solar energy. Full article

These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases. Full article

With the increasing demands of urban populations, achieving a balance between the supply and demand in the spatial allocation of urban green park spaces (UGSs) is essential for effective urban planning and improving residents’ quality of life. The study of UGS supply and demand balance has become a research hotspot. However, existing studies of UGS supply and demand balance rarely simultaneously improve the supply side, demand side, and transportation methods that connect the two, nor do they conduct a comprehensive, multi-dimensional supply and demand evaluation. Therefore, this study evaluates the accessibility of UGS within Hefei’s built-up areas, focusing on age-specific demands for UGS and incorporating various travel modes, including walking, cycling, driving, and public transportation. An improved two-step floating-catchment area (2SFCA) method is applied to evaluate the accessibility of UGS in Hefei’s built-up areas. This evaluation combines assessments using the Gini coefficient, Lorenz curve, location entropy, and local spatial autocorrelation analysis, utilizing the ArcGIS 10.8 and GeoDa 2.1 platforms. Together, these methods enable a supply–demand balance analysis of UGSs to identify areas needing improvement and propose corresponding strategies. The research results indicate the following: (1) from a regional perspective, there are significant disparities in the accessibility of UGS within Hefei’s urban center, with the old city showing more imbalance than the new city. Areas with high demand and low supply are primarily concentrated in the old city, which require future improvement; (2) in terms of travel modes, higher-speed travel (such as driving) offers better and more equitable accessibility compared to slower modes (such as walking), highlighting transportation as a critical factor influencing accessibility; (3) regarding population demand, there is an overall balance in the supply of UGS, with local imbalances observed in the needs of residents across different age groups. Due to the high specific demand for UGS among older people and children, the supply and demand levels in these two age groups are more consistent. This study offers valuable insights for achieving the balanced, efficient, and sustainable development of the social benefits of UGS. Full article

The Chinese government’s recent low-income housing scheme aims to tackle housing challenges faced by the urban floating population. A notable shift in this initiative is the focus on the job–housing balance. This study proposes that the spatial interaction between land designated for affordable rental housing and land for commercial facilities serves as a fundamental metric for evaluating this equilibrium, providing insights into the effectiveness of China’s nascent affordable housing efforts. Drawing on post-2021 data, when China’s revamped affordable housing policy took effect, our research examines the spatial distribution of affordable rental housing and commercial service land in Shanghai. By employing coupled coordination models and local entropy metrics, we delve into the supply equilibrium and pragmatic interrelation of these land types. Our findings reveal localized clustering in the spatial arrangement of rental and commercial land within Shanghai. Zones in the urban core exhibit a supply balance, while the peripheries display diminishing accessibility between these land types. Core urban areas have a lower supply balance but higher accessibility, whereas urban fringes face both low supply balance and low accessibility. These study outcomes have significant implications for strategic planning and the construction of affordable rental housing. Full article

Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems. Full article

Chaotic maps have been widely studied in the field of cryptography for their complex dynamics. However, chaos-based cryptosystems have not been widely used in practice. One important reason is that the following requirements of practical engineering applications are not taken into account: computational complexity and difficulty of hardware implementation. In this paper, based on the demand for information security applications, we modify the local structure of the three-dimensional Intertwining Logistic chaotic map to improve the efficiency of software calculation and reduce the cost of hardware implementation while maintaining the complex dynamic behavior of the original map. To achieve the goal by reducing the number of floating point operations, we design a mechanism that can be decomposed into two processes. One process is that the input parameters value of the original system is fixed to $2^k$ by Scale index analysis. The other process is that the transcendental function of the original system is replaced by a nonlinear polynomial. We named the new map as “Simple intertwining logistic”. The basic chaotic dynamic behavior of the new system for controlling parameter is qualitatively analyzed by bifurcation diagram and Lyapunov exponent; the non-periodicity of the sequence generated by the new system is quantitatively evaluated by using Scale index technique based on continuous wavelet change. Fuzzy entropy (FuzzyEn) is used to evaluate the randomness of the new system in different finite precision digital systems. The analysis and evaluation results show that the optimized map could achieve the designed target. Then, a novel scheme for generating pseudo-random numbers is proposed based on new map. To ensure its usability in cryptographic applications, a series of analysis are carried out. They mainly include key space analysis, recurrence plots analysis, correlation analysis, information entropy, statistical complexity measure, and performance speed. The statistical properties of the proposed pseudo random number generator (PRNG) are tested with NIST SP800-22 and DIEHARD. The obtained results of analyzing and statistical software testing shows that, the proposed PRNG passed all these tests and have good randomness. In particular, the speed of generating random numbers is extremely rapid compared with existing chaotic PRNGs. Compared to the original chaotic map (using the same scheme of random number generation), the speed is increased by 1.5 times. Thus, the proposed PRNG can be used in the information security. Full article

The degree of coordination between the supply and demand for urban green spaces serves as a vital metric for evaluating urban ecological development and the well-being of residents. An essential principle in assessing this coordination is the precise quantification of both the demand and supply of green spaces, as well as the differential representation of their spatiotemporal structures. This study utilizes the entropy weight method (EWM) and principal component analysis (PCA) to comprehensively measure supply indicators for green space quantity and quality in the central urban area of Shenyang, China. To establish reliable and quantifiable demand indicators, mobile signaling spatial-temporal data are corrected by incorporating static population cross-sectional data. The Gaussian two-step floating catchment area method (Ga2SFCA) is employed to calculate the accessibility of green spaces in each community with ArcGIS 10.2 software, while the Gini coefficient is utilized to assess the equity of green space distribution within the study area. This study employs location entropy to determine the levels of supply and demand for green spaces in each subdistrict. Furthermore, the priority of community-scale green space regulation is accurately determined by balancing vulnerable areas of green space supply and replenishing green space resources for the ageing population. The findings suggest a Gini coefficient of 0.58 for the supply and demand of green spaces in Shenyang’s central metropolitan region, indicating a relatively low level of equalization in overall green space allocation. Based on location entropy, the classification of supply and demand at the street level yields the following outcomes: balanced areas comprise 21.98%, imbalanced areas account for 26.37%, and highly imbalanced regions represent 51.65%. After eliminating the balanced regions, the distribution of the elderly population is factored in, highlighting the spatial distribution and proportions of communities with distinct regulatory priorities: Level 1 (S1) constitutes 7.4%, Level 2 (S2) accounts for 60.9%, and Level 3 (S3) represents 31.7%. Notably, the communities in the S1 category exhibit spatial distribution characteristics of aggregation within the inner ring and the northern parts of the third ring. This precise identification of areas requiring urgent regulation and the spatial distribution of typical communities can provide reliable suggestions for prioritizing green space planning in an age-friendly city. Full article