Search Results (143)

Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards. Full article

In the context of global climate change and the escalating energy crisis, the development of new energy vehicles (NEVs) has become a critical strategy for China to foster green transformation and achieve its carbon neutrality goals. This study focuses on A-share-listed NEV companies in China from 2015 to 2023, specifically those listed on the Shanghai or Shenzhen Stock Exchange and subject to domestic regulatory standards and disclosure requirements. These firms were selected due to the representativeness, availability, and quantifiability of their data. A super-efficient-network SBM model based on undesirable outputs and the Malmquist index were employed to assess the static and dynamic green technology innovation efficiency of 260 NEV enterprises. Additionally, the Tobit regression model was applied to analyze the influencing factors. The findings reveal that the overall green technology innovation efficiency of Chinese NEV enterprises is relatively low and has exhibited a declining trend over the years. Furthermore, the efficiency of enterprises in the western regions surpasses that of those in the eastern and central regions. Key factors, including government support, enterprise scale, and R&D investment, significantly inhibit the green technology innovation efficiency of firms. Based on these findings, this paper recommends prioritizing the innovation of core technologies, addressing regional disparities in development, and implementing tailored policies to enhance the green technology innovation efficiency and economic performance of NEV enterprises. Full article

Investigating valley-related physics in rare intrinsic ferromagnetic materials with high-temperature stability and viable synthesis methods is of vital importance for advancing fundamental physics and information technology. Through first-principles calculations, we forecast that monolayer TiAlTe₃ has superb structural stability, a ferromagnetic coupling mechanism deriving from direct-exchange and superexchange interactions, and a high magnetic transition temperature. We observed spontaneous valley polarization of 103 meV in the bottom conduction band when monolayer TiAlTe₃ is magnetized toward an out-of-plane orientation. Additionally, because of its powerful valley-contrasting Berry curvature, the anomalous valley Hall effect emerges under an in-plane electric field. The cooperation of ferromagnetic coupling, a high magnetic transition temperature, and spontaneous valley polarization makes monolayer TiAlTe₃ a promising room-temperature ferrovalley material for use in nanoscale spintronics and valleytronics. Full article

The cooperative localization of Unmanned Aerial Vehicles (UAVs) has emerged as a pivotal application in Internet of Things (IoT) tasks. However, the frequent exchange of localization data among UAVs leads to significant energy consumption and escalates the computational complexity involved in multi-UAV cooperative localization tasks. To address these challenges, this paper proposes a cooperative localization algorithm that integrates a biogeography optimization-based cluster networking and adaptive sampling-improved Nystrom super-multidimensional scaling (BOCN-ASNSMS). The proposed method leverages biogeography optimization (BO), prioritizing nodes with higher residual energy and density to serve as cluster heads, thereby optimizing energy usage. Subsequently, an improved adaptive sampling Nystrom super-multidimensional scaling algorithm is employed to dynamically select the kernel matrix row vectors. This selection process not only reduces data processing requirements but also enhances the accuracy of the similarity matrix approximation, thus diminishing computational complexity and achieving precise relative positioning of UAVs. Furthermore, Procrustes analysis and least squares methods are utilized to fuse coordinates across UAV clusters, aligning them into a unified coordinate system and converting them into absolute coordinates, which facilitates high-precision global localization. Theoretical analysis and simulation results underscore that the proposed algorithm substantially reduces computational complexity and energy consumption while enhancing localization accuracy, compared to conventional multi-UAV cooperative localization approaches. Full article

Organometallic rare-earth complexes have attracted considerable attention in recent years due to their unique structures and exceptional magnetic properties. In this study, we report the synthesis and magnetic characteristics of a family of monopentamethylcyclopentadienyl-coordinated trinuclear rare-earth hepta-chloride clusters [(Li(THF)(Et₂O))(Cp^*RE)₃(μ-Cl)₄(μ₃-Cl)₂(μ₄-Cl)] (RE₃: RE =Y, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu; Cp* = pentamethylcyclopentadienide). These clusters were synthesized by reacting LiCp* with RECl₃ in a 1:1 molar ratio within a mixed solvent system (THF: Et₂O = 1:9), resulting in high solubility in common organic solvents such as DCM, THF, and Et₂O. Magnetic studies conducted on these paramagnetic clusters reveal the coexistence of ferromagnetic and antiferromagnetic superexchange interactions in Gd₃. Additionally, Dy₃ exhibits both ferromagnetic and antiferromagnetic intramolecular dipolar interactions. Notably, slow magnetic relaxation was observed in Dy₃ below 23 K under a zero DC applied field with an energy barrier of 125(6) cm⁻¹. Full article

Ceramic samples of CuCr_1−xY_xO₂ (x = 0–0.02) were synthesized via the high temperature solid-state reaction method, and the influence of Y³⁺ doping on their microstructure and antiferromagnetic phase transitions was systematically investigated. Y³⁺ doping increased the unit cell volume from 130.928 ų for x = 0 to 131.147 ų for x = 0.0200, and the average grain size decreased from 3.38 μm for x = 0 to 4.27 μm for x = 0.0200. The Cr and Y elements maintained +3 valence, while the Cu element had +1 valence. All samples showed obvious paramagnetism when the temperature was higher than 140 K. When the temperature continued to decrease, the lattice expansion changed the bond length and bond angle of the Cr-O-Cr bond, resulting in a change in the superexchange interaction, and the magnetic susceptibility increased significantly, gradually showing antiferromagnetism. The T_N of the undoped sample was about 46 K, the T_N of the doped sample with x = 0.0175 was about 21 K, and the T_N of other doped samples was about 30 K. This result indicates that Y³⁺ doping enhanced the antiferromagnetism of the sample but also weakened its antiferromagnetic stability. Full article

Basic principles of ferroelectric activity induced by the noncollinear alignment of spins are reviewed. There is a fundamental reason why the inversion symmetry can be broken by certain magnetic order. This situation occurs when the magnetic order simultaneously involves ferromagnetic ($\mathit{F}$) and antiferromagnetic ($\mathit{A}$) counterparts, transforming under the spatial inversion $\mathcal{I}$ and time reversal $\mathcal{T}$ as $\mathcal{I}\mathit{F}=\mathit{F}$ and $\mathcal{IT}\mathit{A}=\mathit{A}$, respectively. The incompatibility of these two conditions results in breaking the inversion symmetry, which manifests itself in the electric polarization $\overrightarrow{P}$. The noncollinear alignment of spins is one of examples of such coexistence of $\mathit{F}$ and $\mathit{A}$. This coexistence principle imposes a constraint on possible dependencies of $\overrightarrow{P}$ on the directions of spins, which can include only “antisymmetric coupling” in the bond, ${\overrightarrow{\mathcal{P}}}_{ij}·[{\mathit{e}}_i\times {\mathit{e}}_j]$, and “single-ion anisotropy”, ${\mathit{e}}_i·$ $\overrightarrow{\mathbb{\Pi }}$ ${\mathit{e}}_i$. Microscopically, ${\overrightarrow{\mathcal{P}}}_{ij}$ can be evaluated in the framework of superexchange theory. For the single Kramers doublet, this theory yields ${\overrightarrow{\mathcal{P}}}_{ij}\sim {\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$, where ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ is the spin-dependent part of the position operator induced by the relativistic spin-orbit coupling. ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ remains invariant under spatial inversion, providing the microscopic reason why noncollinear alignment of spins can induce $\overrightarrow{P}$ even in centrosymmetric crystals. The symmetry properties of ${\overrightarrow{\mathit{r}}}_{ij}^{\phantom{0}}$ can be rationalized from the viewpoint of symmetry of Kramers states. Particularly, the commonly used Katsura–Nagaosa–Balatsky (KNB) rule $\overrightarrow{P}\propto {\overrightarrow{ϵ}}_{ji}\times [{\mathit{e}}_i\times {\mathit{e}}_j]$ (${\overrightarrow{ϵ}}_{ji}$ being the direction of the bond $ij$) can be justified only for relatively high symmetry of the bonds. The single-ion anisotropy vanishes for the spin ${\displaystyle \frac{1}{2}}$ or if magnetic ions are located in inversion centers, thus severely restricting the applicability of this microscopic mechanism. The properties of multiferroic materials are reconsidered from the viewpoint of these principles. A particular attention is paid to complications caused by possible deviations from the KNB rule. Full article

In recent years, the discovery of the two-dimensional (2D) intrinsically ferromagnetic monolayer CrI₃ has opened up promising avenues for the advancement of spintronic devices. Nevertheless, the relatively low Curie temperature poses a significant challenge for practical applications. Herein, we determine changes in the superexchange interaction of ferromagnetic coupling caused under pressure by using first-principles calculations and Monte Carlo simulations. Based on the superexchange interaction of ferromagnetic coupling, the effect of applying high pressure on the Curie temperature of monolayer CrI₃ is investigated. With a pressure coefficient of 2.0%, the Curie temperature is enhanced to 97.3 K, which is nearly double that of the monolayer CrI₃ without pressure. In addition, the direction of the easy magnetization axis changes from the out-of-plane to the in-plane one when the pressure coefficient is 1.2%. Meanwhile, the band gap of monolayer CrI₃ can be transformed from indirect to direct by applying high pressure. Our work enriches the process of modulating the magnetic and electronic properties of 2D monolayer materials. Full article

On the basis of a microscopic model and employing Green’s function technique, the effects of temperature, size, and ion doping on the magnetization and phonon energy of the A_1g mode in double perovskites Ba₂FeReO₆ and Sr₂CrReO₆—both in bulk and nanoscale samples—are investigated for the first time. The Curie temperature T_C and magnetization M decrease as nanoparticle size is reduced. Doping with rare-earth ions such as Sm, Nd, or La at the Ba or Sr sites further reduces M. This behavior originates from the compressive strain induced by the smaller ionic radii of the dopant ions compared to the host ions. As a result, the antiferromagnetic superexchange interaction between Fe or Cr and Re ions is enhanced, along with an increase in the magnetic moment of the Re ion. The dependence of the band gap energy of Sr₂CrReO₆ on temperature, size, and doping is also studied. Near the magnetic-phase-transition temperature T_C, anomalies in phonon energy and damping indicate strong spin–phonon coupling. The theoretical calculations show good qualitative agreement with experimental data. Full article

We investigated the geometry and electronic structure of the oxygen-bridged dicopper complex [Cu^II₂(NH₃)₄O₂]²⁺ and discussed how different DFT methods and basis sets, including dispersion corrections and dielectric media, affect the predicted structure and spin state. Our results showed that pure functionals yielded the closed-shell singlet character, whereas hybrid functionals presented a partial diradical character that coincided with increased spin contamination. Incorporating a polarizable continuum model further enhanced the diradical character and more closely reproduced the measured Cu–Cu distance with a bent Cu₂O₂ core. Analysis of the molecular orbitals and computed absorption spectra revealed how orbitals produce the key transition from ligand-to-metal charge transfer. These findings underscore how environmental effects influence the description of Cu₂O₂ chemistry. Full article

Lightweight remote sensing image super-resolution methods aim to enhance image resolution and recover fine details through lightweight neural networks. However, current lightweight methods still suffer from poor performance and unattractive details. DyLKANet introduces a novel lightweight architecture that utilizes a multi-level feature integration strategy to enhance information exchange between context-aware and large kernel attention mechanisms. The network comprises two core modules: the feature distillation and enhancement block for efficient feature extraction, and the context-aware attention-based feature fusion module for capturing global interdependencies. Experiments conducted on the UCMerced, AID, and DIV2K datasets reveal that DyLKANet achieves comparable performance while maintaining a low parameter count and computational complexity. Taking the 2× upscaling results on the UCMerced dataset as an example, specifically, DyLKANet improves PSNR by 0.212–3.544 dB, SSIM by 0.005–0.038, and reduces parameters by 18.79–95.46%. DyLKANet reduces FLops by 7.25–82.63%, making it a promising solution for remote sensing image super-resolution tasks in resource-constrained environments. Full article

Three new coordination polymers, {[M(nbpdc)(DMF)(H₂O)₂]·H₂O}_∞ (M = Co and Ni) and [Zn(nbpdc)(DMF)(H₂O)]_∞, were synthesized from 2-nitrobiphenyl-4,4′-dicarboxylate (nbpdc²⁻). The isomorphous Co(II) and Ni(II) compounds exhibited a two-dimensional coordination network in which the chains with single-water bridges and the chains with single-nbpdc²⁻ bridges intersected each other by sharing the metal ions. The coordination networks were connected with uncoordinated water molecules through hydrogen bonds. The rarely identified single-water-bridged coordination chain was reinforced by water-based intrachain hydrogen bonds. The single-water bridges mediated modest antiferromagnetic superexchange in both Co(II) and Ni(II) compounds and afforded a spin-canting structure for the Co(II) compound at low temperatures. Water molecules played a distinct structural role in the Zn(II) compound, which was a one-dimensional coordination polymer with single-nbpdc²⁻ bridges. Instead of bridging metal ions, each water molecule was coordinated to one metal ion and hydrogen-bonded to the coordination spheres of other two metal ions, resulting to an infinite ladderlike hydrogen-bonding motif. The ladders interlinked the nbpdc-bridged chains into a three-dimensional supramolecular architecture featuring the 5-conneted {4⁴.6⁴} net. Full article

In recent years, with the development of deep learning technologies, Vision Transformers combined with Convolutional Neural Networks (CNNs) have made significant progress in the field of single-image super-resolution (SISR). However, existing methods still face issues such as incomplete high-frequency information reconstruction, training instability caused by residual connections, and insufficient cross-window information exchange. To address these problems and better leverage both local and global information, this paper proposes a super-resolution reconstruction network based on the Parallel Connection of Convolution and Swin Transformer Block (PCCSTB) to model the local and global features of an image. Specifically, through a parallel structure of channel feature-enhanced convolution and Swin Transformer, the network extracts, enhances, and fuses the local and global information. Additionally, this paper designs a fusion module to integrate the global and local information extracted by CNNs. The experimental results show that the proposed network effectively balances SR performance and network complexity, achieving good results in the lightweight SR domain. For instance, in the 4× super-resolution experiment on the Urban100 dataset, the network achieves an inference speed of 55 frames per second under the same device conditions, which is more than seven times as fast as the state-of-the-art network Shifted Window-based Image Restoration (SwinIR). Moreover, the network’s Peak Signal-to-Noise Ratio (PSNR) outperforms SwinIR by 0.29 dB at a 4× scale on the Set5 dataset, indicating that the network efficiently performs high-resolution image reconstruction. Full article

This research centers on one of northern China’s most crucial economic regions—the Beijing–Tianjin–Hebei urban agglomeration. This paper primarily addresses the present circumstances, developments, and obstacles pertaining to industrial green development and industrial innovation in the region, with a particular focus on its role in fostering integrated economic and environmental growth. This study utilizes a global super-efficiency SBM model and a coupled coordination model, along with a panel data analysis technique, to determine the extent of green development, innovation, and green innovation collaboration in the Beijing–Tianjin–Hebei urban agglomeration cluster between 2018 and 2022. The study revealed that, despite notable advancements in industrial green development in the Beijing–Tianjin–Hebei urban agglomeration in recent years, the disparity in urban development persists, with some cities exhibiting a relatively low input–output ratio for green innovation. There is a pressing need to enhance overall efficiency through policy guidance and technical support. Furthermore, the study underscores the significance of bolstering regional collaboration and facilitating the sharing of resources and technological exchange to attain harmonized regional development. Full article

In comparison to borehole heat exchangers that rely on forced convection, super-long thermosyphons offer a more efficient approach to extracting shallow geothermal energy. This work conducted field tests on a super-long flexible thermosyphon (SFTS) to evaluate its heat transfer characteristics. The tests investigated the effects of cooling water temperature and the inclination angle of the condenser on the start-up characteristics and steady-state heat transfer performance. Based on the field test results, the study proposed a dynamic heat transfer modeling method for SFTSs using the equivalent thermal conductivity (ETC) model. Furthermore, a full-scale 3D CFD model for geothermal extraction via SFTS was developed, taking into account weather conditions and groundwater advection. The modeling validation showed that the simulation results aligned well with the temperature and heat transfer power variations observed in the field tests when the empirical coefficient in the ETC model was specified as 2. This work offers a semi-empirical dynamic heat transfer modeling method for geothermal thermosyphons, which can be readily incorporated into the overall simulation of a geothermal system that integrates thermosyphons. Full article