Search Results (13,809)

The CPace protocol (Internet-Draft:draft-irtf-cfrg-cpace-14) is a password-authenticated key exchange optimized for simplicity. In particular, it involves only two messages exchanged in an arbitrary order. CPace combines a simple and elegant design with privacy guarantees obtained via strict mathematical proofs. In this paper, we go further and analyze its resilience against malicious cryptography implementations. While the clever design of CPace immediately eliminates many kleptographic techniques applicable to many other protocols of this kind, we point to the remaining risks related to kleptographic setups. We show that such attacks can break the security and privacy features of CPace. Thereby, we point to the necessity of very careful certification of the devices running CPace, focusing in particular on critical threats related to random number generators. Full article

The Lorentz-breaking theory not only modifies the geometric structure of curved spacetime but also significantly alters the quantum dynamics of bosonic and fermionic fields in black hole spacetime, leading to observable physical effects on Hawking temperature and Bekenstein–Hawking entropy. This study establishes the first systematic theoretical framework for entropy modifications of charged accelerating Anti-de Sitter black holes, incorporating gauge-invariant corrections derived from Lorentz-violating quantum field equations in curved spacetime. The obtained analytical expression coherently integrates semi-classical approximations with higher-order quantum perturbative contributions. Furthermore, the methodologies employed and the resultant conclusions are subjected to rigorous analysis, establishing their physical significance for advancing fundamental investigations into black hole entropy. Full article

China’s major function-oriented zoning (MFOZ) serves as a crucial policy instrument for functional regulation of land use, playing a significant role in the latest territorial spatial planning. Studies on the implementation of MFOZ have been conducted since its release in 2012, but there is a lack of comprehensive methods to assess the effectiveness of its implementation. In China, the newly initiated City Examination provides novel technical support for verifying MFOZ planning, addressing the gap in comprehensive evaluation methodologies and channels. This study proposes a comprehensive urban assessment framework and a major function classification approach based on City Examination data, enabling the identification of implementation deviations in MFOZ planning based on the current urban conditions reflected by City Examination. The methodology incorporates dimensionality reduction, multi-indicator clustering, entropy-weighted overlays, and natural break classification techniques and examines the degree of strategic deviation in China’s MFOZ through a comprehensive and systematic assessment. Due to the timeliness and long-term nature City Examination data, the method allows for the long-time dynamic tracking and evaluation of the real-time progress in MFOZ. Empirical analysis of Hubei Province revealed that 77.9% of its urban development aligns with the 2011 MFOZ scheme while demonstrating discernible deviation types and hierarchical discrepancies, with geographically clustered patterns observed among cities exhibiting such deviations. Full article

The precise measuring and control of fiber tension are critically important for enhancing structural and mechanical properties in spinning processes, as tension directly influences orientation, crystallinity, and mechanical properties. However, current tension measurement methods primarily operate offline and lack real-time measuring capabilities. A non-contact fiber tension detection system is introduced to investigate the effects of draw tension and its uniformity on the structure and mechanical properties of polyester fibers. During experiments conducted at a spinning speed of 1200 m/min across different draw ratios, the non-contact system demonstrated strong agreement with the contact tension detector. The results showed that increasing the tension from 34 cN to 164 cN reduced the monofilament diameter from 39.61 µm to 20.35 µm. Simultaneously, the orientation factor nearly tripled, while crystallinity increased from 55.72% to 77.39%. Mechanical testing revealed a 50.96% improvement in breaking strength, rising from 1.57 to 2.37 cN/dtex, accompanied by a significant decrease in elongation at break from 275.55% to 34.95%. However, tension fluctuations, characterized by an average fluctuation coefficient increase from 4.51% to 18.18%, caused diameter inconsistency. These fluctuations also reduced the orientation factor by 10.78%, lowered crystallinity, and substantially deteriorated mechanical properties. These findings underscore the critical importance of real-time, online tension monitoring for ensuring polyester fiber quality and performance during production. Full article

Methane concentration anomalies during coal mining operations are identified as important factors triggering major safety accidents. This study aimed to address the key issues of insufficient adaptability of existing detection methods in dynamic and complex underground environments and limited characterization capabilities for non-uniform sampling data. Specifically, an intelligent diagnostic model was proposed by integrating the improved Dung Beetle Optimization Algorithm (SGDBO) with Transformer-SVM. A dual-path feature fusion architecture was innovatively constructed. First, the original sequence length of samples was unified by interpolation algorithms to adapt to deep learning model inputs. Meanwhile, statistical features of samples (such as kurtosis and differential standard deviation) were extracted to deeply characterize local mutation characteristics. Then, the Transformer network was utilized to automatically capture the temporal dependencies of concentration time series. Additionally, the output features were concatenated with manual statistical features and input into the LSSVM classifier to form a complementary enhancement diagnostic mechanism. Sine chaotic mapping initialization and a golden sine search mechanism were integrated into DBO. Subsequently, the SGDBO algorithm was employed to optimize the hyperparameters of the Transformer-LSSVM hybrid model, breaking through the bottleneck of traditional parameter optimization falling into local optima. Experiments reveal that this model can significantly improve the classification accuracy and robustness of anomaly curve discrimination. Furthermore, core technical support can be provided to construct coal mine safety monitoring systems, demonstrating critical practical value for ensuring national energy security production. Full article

This study developed a deep learning–based water level recognition model using Closed-Circuit Television (CCTV) footage. The model focuses on real-time water level recognition in agricultural reservoirs that lack automated water level gauges, with the potential for future extension to flood forecasting applications. Video data collected over approximately two years at the Myeonggyeong Reservoir in Chungcheongbuk-do, South Korea, were utilized. A semantic segmentation approach using the U-Net model was employed to extract water surface areas, followed by the classification of water levels using Convolutional Neural Network (CNN), ResNet, and EfficientNet models. To improve learning efficiency, water level intervals were defined using both equal spacing and the Jenks natural breaks classification method. Among the models, EfficientNet achieved the highest performance with an accuracy of approximately 99%, while ResNet also demonstrated stable learning outcomes. In contrast, CNN showed faster initial convergence but lower accuracy in classifying complex intervals. This study confirms the feasibility of applying vision-based water level prediction technology to flood-prone agricultural reservoirs. Future work will focus on enhancing system performance through low-light video correction, multi-sensor integration, and model optimization using AutoML, thereby contributing to the development of an intelligent, flood-resilient water resource management system. Full article

Spilled hydrocarbons released from oil pipeline accidents can result in long-term environmental contamination and significant damage to habitats. In this regard, evaluating actions in response to vulnerability scenarios is fundamental to emergency management and groundwater integrity. To this end, understanding the trajectories and their influence on the various parameters and characteristics of the contaminant’s fate through accurate numerical simulations can aid in developing a rapid remediation strategy. This paper develops a numerical model using the CactusHydro code, which is based on a high-resolution shock-capturing (HRSC) conservative method that accurately follows sharp discontinuities and temporal dynamics for a three-phase fluid flow. We analyze nine different emergency scenarios that represent the breaking of a diesel oil onshore pipeline in a porous medium. These scenarios encompass conditions such as dry season rupture, rainfall-induced saturation, and varying pipeline failure pressures. The influence of the spilled oil pressure and water saturation in the unsaturated zone is analyzed by following the saturation contour profiles of the three-phase fluid flow. We follow with the high-accuracy formation of shock fronts of the advective part of the migration. Additionally, the mass distribution of the expelled contaminant along the porous medium during the emergency is analyzed and quantified for the various scenarios. The results obtained indicate that the aquifer contamination strongly depends on the pressure outflow in the vertical flow. For a fixed pressure value, as water saturation increases, the mass of contaminant decreases, while the contamination speed increases, allowing the contaminant to reach extended areas. This study suggests that, even for LNAPLs, the distribution of leaked oil depends strongly on the spill pressure. If the pressure reaches 20 atm at the time of pipeline failure, then contamination may extend as deep as two meters below the water table. Additionally, different seasonal conditions can influence the spread of contaminants. This insight could directly inform guidelines and remediation measures for spill accidents. The CactusHydro code is a valuable tool for such applications. Full article

The shear-compression failure of key strata leads to stair-step collapse and severe mine pressure, posing significant safety risks in coal mines. Existing theories fail to account for the boundary conditions and breaking sizes of key strata, making accurate description of shear-compression failure difficult. A periodic breakage mechanics model for key strata was developed using Timoshenko Beam and Winkler Foundation Theory, incorporating transverse shear deformation. The deflection, rotation angle, bending moment, and shear force were calculated, and a shear-compression failure criterion function f(x) was derived. The main conclusions include the following: (1) shear-compression failure is influenced by the thickness–span ratio, cohesion, internal friction angle, and elastic modulus of the key strata, but not by the elastic foundation coefficient and shear modulus; (2) shear-compression failure occurs when the thickness–span ratio reaches 0.4; (3) when the internal friction angle is 25°, 30°, 35°, or 40°, shear-compression failure does not occur if cohesion exceeds 8.0, 7.5, 7.0, or 6.5 MPa, respectively, with a larger internal friction angle corresponding to a smaller critical cohesion; (4) when cohesion is 6 MPa, 8 MPa, 10 MPa, or 12 MPa, shear-compression failure does not occur if the internal friction angle exceeds 44°, 32°, 19°, or 8°, respectively, with larger cohesion correlating to a smaller critical internal friction angle; and (5) once cohesion or internal friction angle surpasses a critical value, the failure criterion approaches a constant value, preventing failure; the elastic modulus has a greater effect on shear-compression failure than the shear modulus, with higher elastic modulus increasing the likelihood of failure. Full article

The moving particle semi-implicit (MPS) method is employed to investigate the dynamic response of a wave energy converter (WEC) buoy subjected to dam-break flows. The buoy is connected to a hydraulic power take-off (PTO) system equipped with an accumulator, enabling it to capture wave energy. First, the MPS method is validated by comparison with experimental results, demonstrating its accuracy in simulating violent interactions between dam-break flows and the buoy. Subsequently, numerical simulations are conducted to analyze the influence of different PTO forces and buoy positions on the heave motion, fluid forces and captured power of the buoy. The results indicate that PTO force exerts a significant influence on heave motion, captured power and vertical fluid force while having a relatively minor effect on the horizontal fluid force. In addition, the maximum power that the buoy can capture increases as its distance from the wall decreases. Notably, the maximum average captured power of the buoy located near a wall can be five times higher than that of a buoy far away from the wall, indicating that a vertical wall can significantly increase the efficiency of nearshore WEC devices. These findings could provide valuable insights for the design, optimization and operation of nearshore WEC devices. Full article

Ultrahigh dose rate (UHDR) radiotherapy, also known as FLASH radiotherapy (FLASH-RT), has shown potential for increasing tumor control while sparing normal tissue. In parallel, gold nanoparticles (GNPs) have been extensively explored as radiosensitizers due to their high atomic number and ability to enhance the generation of reactive oxygen species (ROS) through water radiolysis. In this study, we investigate the synergistic effects of UHDR electron beams and GNP-mediated radiosensitization using Monte Carlo (MC) simulations based on the Geant4-DNA code. A spherical water phantom with embedded GNPs of varying sizes (5–100 nm) was irradiated using pulsed electron beams (100 keV and 1 MeV) at dose rates of 60, 100, and 150 Gy/s. The chemical yield of ROS near the GNPs was quantified and compared to an equivalent water nanoparticle model, and the yield enhancement factor (YEF) was used to evaluate radiosensitization. Results demonstrated that YEF increased with smaller GNP sizes and at lower UHDR, particularly for 1 MeV electrons. A maximum YEF of 1.25 was observed at 30 nm from the GNP surface for 5 nm particles at 60 Gy/s. The elevated ROS concentration near GNPs under FLASH conditions is expected to intensify DNA damage, especially double-strand breaks, due to increased hydroxyl radical interactions within nanometric distances of critical biomolecular targets. These findings highlight the significance of nanoparticle size and beam parameters in optimizing ROS production for FLASH-RT. The results provide a computational basis for future experimental investigations into the combined use of GNPs and UHDR beams in nanoparticle-enhanced radiotherapy. Full article

COPA is a notable authenticated online cipher and was one of the winning proposals for the CAESAR competition. Current works describe how to break the existentially unforgeable under quantum chosen message attack (EUF-qCMA) of COPA. However, these works do not demonstrate the confidentiality of COPA in the quantum setting. This paper fills this gap, considers the indistinguishable under quantum chosen-plaintext attack (IND-qCPA) security for privacy, and presents the first IND-qCPA security analysis of COPA. In addition, in order to effectively avoid the problems of quantum existential forgery attack and quantum distinguishing attack, we introduce an intermediate state doubling-point technology into COPA, restrict the associated data non-emptiness, and present an enhanced variant, called COPA-ISDP, to support the IND-qCPA and EUF-qCMA security. Our work is of great significance, as it provides a simple and effective post-quantum secure design idea to resist Simon’s attack. Full article

This study investigates the processability—via Fused Deposition Modeling (FDM) 3D printing—and mechanical performance of biocomposites based on polylactic acid (PLA), polybutylene succinate (PBS), and their 50/50 wt% blend, each reinforced with hemp shive at 3 and 5 wt%. Blending PLA with PBS represents a straightforward and encouraging strategy to enhance both the printability and mechanical properties of the individual resins, expanding the range of their potential applications. The addition of hemp shive—a by-product of hemp processing—not only enhances the biodegradability of the composites but also improves their thermo-mechanical performance, as well as aligning with circular economy principles. The rheological characterization, performed on all the systems, evidenced that the PLA/PBS blend possesses viscoelastic properties well suited for FDM, enabling smooth extrusion through the nozzle, good shape stability after deposition, and effective interlayer adhesion. Moreover, the constrain effect of hemp shives within the polymer matrix reduced the extrudate swell, a key factor affecting the dimensional accuracy of the printed parts. Optimal processing conditions were identified at a nozzle temperature of 190 °C and a printing speed of 70 mm/s, providing a favorable compromise between print quality, final performances and production efficiency. From a mechanical perspective, the PLA/PBS blend exhibited an 8.6-fold increase in elongation at break compared to neat PLA, and its corresponding composite showed a ductility nearly three times higher than the PLA-based counterpart’s. In conclusion, the findings of this study provide new insights into the interplay between material formulation, rheological behavior and printing conditions, supporting the development of sustainable, hemp-reinforced biocomposites for additive manufacturing applications. Full article

The advancement of laser-induced graphene (LIG) has significantly enhanced the development of wearable and flexible electronic devices. Due to its exceptional physical, chemical, and electronic properties, LIG has emerged as a highly effective active material for wearable sensors. However, despite the wide range of materials suitable as precursors for LIG, the scarcity of stretchable and biocompatible polymers amenable to laser graphenization has remained a persistent challenge. In this study, laser-induced graphene (LIG) was fabricated directly on biocompatible and flexible cross-linked PDMS/PEG (with M_n (PEG) = 400 g/mol) composites for the first time, enabling their application in wearable sensors. The addition of PEG compensates for the low carbon content in PDMS, enabling efficient laser graphenization. Laser parameters were systematically optimized to achieve high-quality graphene, and a comprehensive characterization with varying PEG content (10–40 wt.%) was conducted using multiple analytical techniques. Tensile tests revealed that incorporating PEG significantly enhanced elongation at break, reaching 237% for PDMS/40 wt.% PEG while reducing Young’s modulus to 0.25 MPa, highlighting the excellent flexibility of the substrate material. Surface analysis using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Raman spectroscopy demonstrated the formation of high-quality few-layer graphene with the fewest defects in PDMS/40 wt.% PEG composites. Nevertheless, the adhesion of electrical contacts to LIG that was directly induced on PDMS/PEG proved to be challenging. To overcome this challenge, we produced devices by means of laser induction on polyimide and transfer to PDMS/PEG. We demonstrate the practical utility of such devices by applying them to monitor limb motion in real time. The sensor showed a stable and repeatable piezoresistive response under multiple bending cycles. These results provide valuable insights into the fabrication of biocompatible LIG-based flexible sensors, paving the way for their broader implementation in medical and sports technologies. Full article

Wastewater containing heavy metals can come from a variety of sources and is extremely toxic and hard to break down. Conventional treatment methods can easily result in secondary pollution and are expensive. The research on magnetic chitosan composites, a new adsorbent in the treatment of heavy metal wastewater, is methodically reviewed in this paper. It offers a theoretical foundation for the creation of more environmentally friendly and effective wastewater treatment technology by examining its preparation and modification technology, adsorption mechanism, and application performance. This paper provides a summary of the technology used to prepare and modify magnetic chitosan composites. Both the cross-linking and co-precipitation methods are thoroughly examined. A summary of the fundamental process of heavy metal ion adsorption is provided, along with information on the chemical and physical impacts. Of these, chemical adsorption has been shown to work well with the majority of heavy metal adsorption systems. According to application research, magnetic chitosan exhibits good adaptability in real-world industrial wastewater treatment and has outstanding adsorption performance for various heavy metal ion types and multi-metal coexistence systems (including synergistic/competitive effects). Lastly, the optimization of the material preparation and modification process, the mechanism influencing the various coexisting ion types, and the improvement of regeneration ability should be the main areas of future development. Full article

In socio-economically disadvantaged communities, the challenges faced by children with special needs are often overshadowed by more visible issues such as poverty, family instability, and substance abuse. Children, especially those with special needs, are particularly vulnerable in these settings as they are disproportionately impacted by intersecting adversities, including neglect, exploitation, and limited access to education and healthcare. These adversities create a vicious cycle, where disability exacerbates financial hardship, and in turn, economic deprivation negatively impacts early childhood development, further entrenching disability. Conventional models, which require physical presence and focus primarily on diagnosis and treatment within clinical settings, often fail to address the broader social, environmental, and contextual complexities of disability. We propose an Information Technology-based Exit Pathway as an innovative, scalable solution to disrupt this cycle. Anchored in the five pillars of the Community-Based Rehabilitation (CBR) matrix of Health, Education, Livelihood, Social, and Empowerment, the model envisions a multi-level digital platform that facilitates coordinated support across individual, familial, educational, community, regional, and national levels. By improving access to services, fostering inclusive networks, and enabling early intervention, the proposed approach aims to promote equity, social inclusion, and sustainable development for children with special needs in marginalized communities. Full article