Search Results (59,032)

In this research, the effects of density and porosity on the mechanical properties of a stir-cast hybrid magnesium ZE41 alloy strengthened with 2% weight of silicon carbide (SiC) and boron carbide (B₄C) are assimilated. The experimental and theoretical densities of the ZE41 hybrid matrix were found and compared. From the results of density analysis, it can be inferred that the experimental density of hybrid matrix is smaller when compared to the pure ZE41 matrix. The percentage porosity of hybrid matrix was also analyzed, and it was observed that the hybrid matrix has a slight increase in porosity when compared to the pure ZE41 matrix. The ultimate strength and hardness of the ZE41 hybrid matrix have increased significantly due to its moderate density and acceptable porosity values. Full article

Improving fuel efficiency is a primary challenge in modern aviation, with aerodynamics serving as a key enabler. Aerodynamic friction drag accounts for more than 50% of total drag, highlighting a significant opportunity for efficiency gains through laminar flow, which reduces skin friction drag. In addition, increasing the wing aspect ratio while maintaining a constant lift coefficient to achieve maximum lift-to-drag ratio can further improve aerodynamic performance. However, evaluating laminar flow in isolation, without considering overall mass, system power requirements, or engine performance, can lead to an incomplete assessment of its true technological potential. In this study, a conceptual design methodology was applied to integrate laminar-flow technologies (natural and hybrid) across the wing, empennage, nacelle, and fuselage of a 2035 long-haul reference aircraft. Results indicate a potential for 16% block fuel reduction at the aircraft level, with wing aspect-ratio tailoring delivering up to 24% fuel savings. These findings will be refined through detailed disciplinary analyses in future work. Full article

The deep integration of digital and intelligent technologies is reshaping urban disaster emergency management capabilities; however, improvements in their effectiveness are constrained by complex, multidimensional factors. Identifying the key driving factors and their mechanisms is of great significance for enhancing urban disaster emergency response capabilities. Based on literature analysis and expert consultation, this paper constructs a framework of factors influencing the digital and intelligent empowerment of urban emergency management capabilities. By employing the IT2FS-DEMATEL-AISM multi-criteria hybrid decision-making method, an analytical framework comprising factor identification, relationship decomposition, and hierarchical evolution is established. The study found that 15 key factors, including the soundness of emergency management systems and the level of smart platform development, exert a significant influence on urban emergency management capabilities through direct or indirect mechanisms. Meanwhile, the institutional framework for emergency management serves as a deep-seated driving force, systematically promoting the deep integration of emergency management operations with digital and intelligent technologies. This, in turn, enhances the operational effectiveness of urban disaster emergency response and comprehensively strengthens the city’s overall disaster emergency management capabilities. Full article

Common wheat (Triticum aestivum ssp. vulgare) is one of the three major cereal crops cultivated worldwide and plays a key role in ensuring food safety. Adequate nitrogen supply is a key factor affecting the yield and functional properties of the grain of common wheat. Improving the efficiency of soil nitrogen use can be achieved through the application of appropriate mineral fertilizers and proper variety selection. The aim of this study was to determine the effect of residual nitrogen (N_res) remaining after maize cultivation on the functional properties of winter and spring wheat grain. The results of the present study clearly indicate that appropriate selection of the maize hybrid (preceding crop) and nitrogen fertilization strategy (residual nitrogen, N_res) can significantly enhance the antioxidant potential of grain in both forms of wheat (winter and spring). At the same time, our results highlight the practical importance of agronomic practices in improving the functional value of grain, both in terms of nutritional quality and health-promoting potential. Total polyphenol content in grain was stable, while antioxidant activity (ABTS⁺, DPPH) depended on genotype × fertilization interaction, particularly in winter wheat. These changes likely result from differences in polyphenol profile and the proportion of other antioxidants. Appropriate cultivar selection and nitrogen fertilization can enhance the antioxidant potential of wheat. No significant effect of either the preceding crop (maize) or its cultivar, or the form of nitrogen fertilizer, was found on the amino acid and total polyphenol content in winter and spring wheat grain. Population growth and the need to ensure adequate food supply highlight the importance of improving nitrogen management efficiency in agriculture by accounting for the amount and quality of residual soil nitrogen after the preceding crop. Full article

This study evaluates GNSS water-vapor tomography across the Lisbon metropolitan area and explores how increasing network density with low-cost receivers improves three-dimensional humidity fields for meteorological applications. Three configurations were tested for December 2022, a month characterized by several rainfall events, including a severe urban-impacting one: (i) a hybrid setup combining permanent and low-cost stations (TOMO_PL), (ii) a dense network of only low-cost stations (TOMO_L), (iii) a sparse arrangement using only permanent stations (TOMO_P). Tomographic water vapor density fields were compared with independent references from the Weather Research and Forecasting (WRF) model, ERA 5 reanalysis, and radiosonde data. All products show the expected exponential decline in water vapor with increasing altitude. Tomography consistently underestimates moisture in the lowest 2.0 to 2.5 km and tends to overestimate it at higher levels, with a weaker correlation above mid-tropospheric heights. Vertical RMSE remains below 2 g m⁻³ for all solutions, but TOMO_P performs the worst due to weak and uneven spatial geometry. Time–height analysis reveals that densified setups capture the changing moisture in the lower atmosphere, including increased near-surface humidity during December 11–13, when rainfall exceeded 120 mm in 24 h, although mid-level intrusions and dry layers observed by radiosondes are not captured. Mean PWV patterns show realistically low points over the Sintra mountain range and align best with TOMO_PL (spatial RMSE 0.6 g m⁻³, bias 0.4 g m⁻³, correlation 0.9), while TOMO_P creates artifacts that mimic mesoscale gradients. Categorized skill analysis shows the highest accuracy under high-moisture conditions and limited ability to detect dry conditions, with TOMO_PL showing the best overall performance against both ERA5 and WRF. Overall, low-cost densification significantly enhances boundary-layer humidity and PWV retrievals, supporting their use for urban heavy-rain monitoring and, with error-aware integration, for short-term forecasting. Full article

The accelerated growth of e-commerce has intensified the dual challenges of weak infrastructure and carbon emission pressures in last-mile delivery for rural and mountainous regions. As the World Bank calls for integrating carbon market development into national strategies, Truck-Drone Collaborative Delivery (TDCD) has emerged as a critical sustainable solution. However, existing research often overlooks the strict airspace regulations in sensitive border areas. Therefore, this paper proposes a Vehicle Routing Problem with Drones and Mobile Base Stations (VRPDBS) model that explicitly incorporates airspace constraints and mobile hub deployment. We introduce a quantified “Regional Flyability Factor” ($f_k$) to measure the impact of airspace restrictions on routing decisions and solve the problem using a hybrid metaheuristic algorithm. A case study based on real-world data from the Yanbian Korean Autonomous Prefecture reveals that strict airspace compliance imposes an absolute delivery delay of 4–5 h and an operational cost premium of up to 15%, an impact that can be effectively mitigated through a mobile base station mediation strategy. More importantly, multi-scenario sensitivity analysis under carbon price uncertainty indicates that although truck-dominant modes are cost-effective at current low carbon prices, drone-intensive configurations demonstrate superior economic robustness and environmental performance under high carbon price scenarios. This study not only provides a technical framework for green logistics planning in complex airspace but also offers strategic decision support for logistics enterprises to navigate long-term climate policy risks. Full article

Carbon quantum dots (CQDs) and stimuli-responsive hydrogels are advanced functional materials whose hybridization yields CQD-enhanced stimuli-sensitive hydrogels, opening new interdisciplinary avenues for smart material applications. This review systematically summarizes the latest advances in these composites, focusing on synthetic strategies, structure–property modulation mechanisms, and practical applications. Distinct from existing reviews that either investigate CQDs or hydrogels independently or discuss their composites in a single research field, this work features core novelties in integration strategy, application scope and critical analysis: it systematically compares the advantages, limitations and applicable scenarios of three typical CQD–hydrogel integration approaches (physical entrapment, in situ synthesis, covalent conjugation), comprehensively covers the multi-field application progress of the composites and conducts in-depth cross-field analysis of their common scientific issues and technical bottlenecks. By incorporating CQDs, the composites achieve remarkable performance optimizations: 40% improved mechanical toughness, sub-ppm-level heavy metal-sensing sensitivity, and over 80% organic dye photocatalytic degradation efficiency, addressing pure hydrogels’ inherent limitations of insufficient strength and single functionality. These enhancements enable sophisticated applications in biomedical field (real-time biosensing, controlled drug delivery), environmental remediation (pollutant detection/degradation), energy storage, and flexible electronics. The synergistic interplay between CQDs and hydrogels facilitates precise single/multi-stimulus responsiveness (pH, temperature, light), a pivotal advance for precision medicine and intelligent environmental monitoring. Despite promising progress, the large-scale practical application of CQD–hydrogel composites still faces prominent challenges: the difficulty in scalable fabrication with the uniform dispersion of CQDs in hydrogel matrices, poor long-term stability of most composites under physiological cyclic stress (service life < 6 months in practical tests), and low accuracy in discriminating multi-stimuli in complex real-world matrices. Future research should prioritize biomass-based eco-friendly CQD synthesis, machine learning-aided multimodal responsive systems, and 3D bioprinting for scalable manufacturing. Full article

The dynamic response of the secondary circuit system in nuclear propulsion plants is critical to the power output, safety, and energy efficiency of nuclear-powered ships. High-fidelity thermo-hydraulic simulation models can accurately capture system transients but are computationally expensive and unsuitable for real-time applications. To address this limitation, this study proposes a reduced-order dynamic parameter prediction method that integrates high-fidelity simulation with deep learning. A multi-operating-condition simulation model of a typical nuclear-powered ship secondary circuit system is developed to generate time-series data covering load ramping and propulsion mode switching. Based on this dataset, a conventional recurrent neural network (RNN) and a multilayer long short-term memory (LSTM) network are constructed for multivariate autoregressive prediction of 17 key dynamic parameters, and their performances are systematically compared. Results show that the LSTM significantly outperforms the RNN in capturing long-term temporal dependencies, achieving average RMSE and MAPE values of 0.0228% and 0.365%, respectively. The proposed model completes 50-step-ahead prediction within 0.84 s, satisfying real-time requirements. The hybrid simulation-driven and data-driven framework provides a practical solution for intelligent monitoring and control optimization of nuclear-powered ship propulsion systems. Full article

The aviation industry is increasingly prioritizing sustainability, with significant focus on the development of Hybrid-Electric Aircraft (HEA). By integrating electric motors with conventional combustion engines, HEA systems offer substantial environmental benefits and operational efficiency improvements. However, the successful implementation of HEA technologies is contingent upon advancements in power converter systems. This paper addresses the critical need for sustainable aviation solutions by examining the challenges and opportunities associated with High-Efficiency Aviation Power (HEAP) technology. Specifically, the study investigates the role of power converters in Hybrid-Electric Aircraft Propulsion systems, with a particular emphasis on addressing key concerns such as weight reduction, compact design, and system reliability. A comparative analysis of three converter topologies is conducted: two established configurations serve as baseline references, while a third topology, a modular, fault-tolerant DC-DC converter, is proposed for the first time in the context of hybrid-electric aircraft. Its novelty lies in the system-level use of redundancy to offer an inherent architectural advantage against cosmic-ray-induced failures a critical aviation reliability challenge that existing converter topologies do not address through hardware redundancy. This qualitative reliability advantage is presented as an architectural feature, pending quantitative validation through future hardware testing and mean-time-between-failures (MTBF) analysis. This exploration is essential for identifying the most suitable configuration for HEA integration, with the goal of overcoming challenges related to lightweight design, high efficiency, and reliability. The findings contribute to the advancement of more sustainable and efficient aviation solutions by demonstrating the potential of the proposed converter architecture. Full article

DP-128 is a multitarget benzonaphthyridine-6-chlorotacrine hybrid molecule with potent in vitro anticholinesterase and Aβ42 and tau anti-aggregating activity. While often used as a reference protein aggregation inhibitor, its further development as an anti-Alzheimer agent is limited by significant cytotoxicity, suboptimal aqueous solubility and microsomal stability. Since these drawbacks might arise from its rather high lipophilicity, in this work we have developed a series of more polar analogues, designed by structural modifications at the benzonaphthyridine or 6-chlorotacrine moieties or within the eight-atom linker. Half of the new analogues are indeed slightly more soluble and clearly less cytotoxic than DP-128, display single-digit acetylcholinesterase inhibitory activity, and retain the Aβ42 and tau anti-aggregating potency of the lead, as well as favourable brain permeation and high plasma stability. While further optimization of microsomal stability is necessary for a potential therapeutic use of this class of compounds, hybrids 16 and 17, with similar or even higher Aβ42 and tau anti-aggregating activity and lower cytotoxicity than DP-128, might represent novel pharmacological tools for protein aggregation studies. Full article

In this study, a hybrid multi-layer scheme for reversible color image encryption is proposed, ensuring lossless reconstruction and strong cryptographic security concurrently. This method consists of three main stages. First, session-specific keys are generated using HKDF-SHA256 along with a timestamp-based mechanism to prevent replay attacks and support dynamic key management. Second, a four-layer confusion–diffusion structure is applied. It uses Gram–Schmidt orthogonal matrices, integer-based PWLCM chaotic mapping, the Hill cipher, and dynamically created S-Boxes. These operations rely on integer modular arithmetic ${\mathbb{Z}}_{256}$ and Q16.16 fixed-point precision. Finally, ChaCha20 stream encryption with HMAC-SHA256 authentication is used to secure data transmission in distributed environments. Experimental tests conducted on standard images show strong cryptographic performance, including near-ideal entropy (7.9993 bits), a significant avalanche effect ($NPCR99.6\mathrm{\%}$, $UACI33.4\mathrm{\%}$), and very low pixel correlation. The method achieves perfect lossless reconstruction and provides an effective key space $\ge 2¹²⁸$. These results confirm the suitability of the proposed scheme for secure image protection in applications requiring bit-exact recovery, such as medical imaging, digital forensics, and satellite communications. Full article

The secure management of classified documents containing sensitive information is critical for governments, military organizations, and the industry. Traditional data loss prevention (DLP) systems lack robustness against insider threats, particularly regarding access log integrity and tamper-proof auditing. To address log security, the previous literature has proposed multiple solutions, including private and hybrid blockchain models (e.g., Ethereum + MultiChain) to ensure audit trail integrity. However, hybrid architectures often face challenges such as unpredictable transaction costs (gas fees) and potential privacy risks when scaled for enterprise DLP logs. Conversely, private architectures may require higher resources, potentially causing bottlenecks on endpoints. In this paper, we propose an optimized Blockchain-Based Classified Document Management System (B2CDMS) utilizing a permissioned architecture. Our work demonstrates the challenges, advantages, and weak points of current solutions. We optimized a permissioned blockchain (BC) (Hyperledger Fabric v2.5) with an External Chaincode Builder using the Chaincode-as-a-Service (CCaaS) pattern. We compared our proposed private architecture with a hybrid architecture (Ethereum + MultiChain) and a public solution (Ethereum). We conducted a comprehensive analysis using pseudo Trellix ePolicy Orchestrator (ePO) Data Loss Prevention (DLP) logs. Experimental results on an Apple Silicon M4 (Apple Inc., Cupertino, CA, USA) testbed show that the proposed architecture achieves a throughput of 845.8 Transactions Per Second (TPS) with a sub-second latency of 55 ms, aiming to eliminate the bottlenecks of public blockchains. Furthermore, the system introduces a privacy-preserving hashing mechanism (i.e., committing only deterministic Secure Hash -bit (SHA-256) digests to the immutable ledger while keeping the actual sensitive Personally Identifiable Information (PII) strictly in off-chain databases) compliant with General Data Protection Regulation (GDPR). It ensures that classified document metadata remains immutable and secure against rogue access benefiting from admin privileges. This study concludes that permissioned blockchain architectures offer a scalable and resource-efficient solution for forensic evidence preservation throughout the classified document lifecycle. Full article

Urbanisation reshapes Land Cover and Land Use (LCLU) by driving deforestation, wetland loss, and the conversion of natural and agricultural areas into built environments. However, integrated analyses of LCLU change in response to climate variability in topographically complex, rapidly urbanising African cities remain limited. Therefore, this study examined 2000–2024 LCLU changes in hilly Gasabo District (Kigali, Rwanda) using 30 m Landsat imagery and a Random Trees classifier (92.7% accuracy, 70/30 train-test split), with 2032 projections via a population-driven hybrid trend model. Population estimates/projections 320,516 in 2002 to 967,512 in 2024, 1.41 million by 2032, were derived from Rwanda’s census data and exponential growth modelling (calibrated to 5.05% annual growth). Rapid population growth has driven a 539% expansion of Built-up Areas, accompanied by notable declines in cropland and Forest. Local climate trends (Meteo Rwanda stations) aligned with global datasets (ERA5-Land and CHIRPS): rainfall fluctuation and temperature rose, with strong correlations between population-driven Built-up Areas expansion. From 2024 to 2032, LCLU projections indicate that Built-up Areas will continue to expand by 29.5%. Cropland was forecast to decline to 15.9%, while Forest loss slowed to 5.7%. MLR analysis revealed strong correlations between population-driven expansion of Built-up Areas, cropland/forest loss, warming, and rainfall fluctuations in Gasabo. An ARDL model was applied to address multicollinearity among LCLU predictors, which limited the interpretation of individual coefficients, and confirmed the core MLR correlation trends, with statistically significant (p < 0.05) coefficients. The results highlight the need for data-driven spatial planning in Gasabo (stricter zoning, high-rise buildings, targeted reforestation, climate-resilient green infrastructure) to mitigate population and urbanisation-driven environmental degradation. Full article

Low earth orbit (LEO)-enhanced global navigation satellite systems (GNSSs) (LeGNSSs) have emerged as a promising paradigm for next-generation precise point positioning (PPP). However, the highly dynamic nature of LEO satellites results in significant ionospheric variations with more frequent cycle slips. Thus, identifying fractional cycle slips and evaluating false alarms present significant challenges. In this paper, we propose an ionospheric preprocessing generalized combination (IPGC) method to improve the reliability of cycle slip detection. The ionospheric delay in the carrier phase is mitigated using the NeQuick model. Additionally, a set of specifically designed coefficients is used to combine LEO and GNSS observations, which increases the sensitivity of cycle slip detection. The simulation results indicate that the proposed method can effectively eliminate ionospheric interference of up to 4 cycles in LEO satellite cycle slip detection and can accurately detect all combinations of cycle slips with a maximum deviation of $0.14$ cycles. Compared with solutions without cycle slip repair, this method accelerates the positioning convergence time by $0.96/0.89/1.2$ min on the north/east/up (NEU) components, and the reconvergence efficiency is increased by factors of 10, $5.5$, and 2, respectively. Even with an elevated cutoff angle of ${40}^{\circ }$, the system achieves centimeter-level positioning accuracy ($0.38/1.08/1.86$ cm). These results confirm the effectiveness of the proposed method in LEO satellite cycle slip detection, providing key algorithmic guidance for the practical implementation of PPP in hybrid constellation systems. Full article

Snow density plays a crucial role in water resource estimation, runoff forecasting, and early warning of natural disasters such as avalanches and blizzards. This study uses optical satellite multispectral reflectance data to retrieve snow density, providing a novel perspective for snow density retrieval research. Supported by auxiliary data including CanSWE in situ measurements, Sentinel-2 satellite data, and ERA5-Land reanalysis data, this study constructs a hybrid model (Snow_ACMix) that integrates the strengths of the multi-head attention mechanism and convolutional neural networks, realizing direct snow density retrieval from multispectral satellite reflectance data for the first time. This research was primarily conducted in Canada and Alaska. For the Canadian region, the model achieves a mean absolute error (MAE) of 0.034 g/cm³, a root mean square error (RMSE) of 0.051 g/cm³, and a coefficient of determination (R²) of 0.547. For the Alaska region, the model yields an MAE of 0.020 g/cm³, an RMSE of 0.029 g/cm³, and an R² of 0.803. Feature and module ablation experiments are carried out, and one-shot transfer learning is adopted to perform snow density retrieval in the Alaska region. The spatial transfer prediction results show an MAE of 0.027 g/cm³, an RMSE of 0.038 g/cm³, and an R² of 0.747, which verify the model’s excellent spatial generalization ability and superior performance in data-scarce regions. The advantages and limitations of the Snow_ACMix model are investigated through comparative validation across different land cover types, regions, time periods, and against ERA5 data. The Snow_ACMix model achieves favorable retrieval performance in mountainous areas, and its practical application capability is verified by snow density retrieval in the Silver Star Mountain region. However, the model still has limitations: it is vulnerable to the effects of wet snow, resulting in large fluctuations in retrieval results in wet snow regions. Full article