摘要：
Radon (Rn-222) released by the decay of Ra-226 in uranium ores may cause potential radiation risks in the water environment. However, the migration behaviour characterised by the low diffusion coefficient of radon in water and the slow change in concentration is still lacking systematic research. This study investigates the radon release behavior of uranium ore in water under different temperature conditions (20–32 °C). A closed-loop measurement system combined with a bubbling method was established, utilizing a RAD7 radon detector. A nonlinear model was developed to describe the time-dependent measured radon concentration, and the radon release rate was obtained through nonlinear fitting. Experiments were conducted under five temperature conditions ranging from 20 °C to 32 °C. The Results showed that the proposed model achieved high fitting accuracy (R 2 > 0.95) across all conditions. The radon release rate increased significantly with temperature, rising from 0.210 ± 0.006 mBq s −1 to 0.391 ± 0.019 mBq s −1 within the range of 20 °C–32 °C, an increase of 86 %. Further analysis shows that the radon release rate of uranium ore in water has a good linear relationship with temperature, indicating that temperature is an important factor affecting the radon release behavior. The methods and models proposed in this paper can effectively depict the radon release behavior of uranium ores in water environments and are suitable for related experimental designs to assess environmental radiation risks.
Radon (Rn-222) released by the decay of Ra-226 in uranium ores may cause potential radiation risks in the water environment. However, the migration behaviour characterised by the low diffusion coefficient of radon in water and the slow change in concentration is still lacking systematic research. This study investigates the radon release behavior of uranium ore in water under different temperature conditions (20–32 °C). A closed-loop measurement system combined with a bubbling method was established, utilizing a RAD7 radon detector. A nonlinear model was developed to describe the time-dependent measured radon concentration, and the radon release rate was obtained through nonlinear fitting. Experiments were conducted under five temperature conditions ranging from 20 °C to 32 °C. The Results showed that the proposed model achieved high fitting accuracy (R 2 > 0.95) across all conditions. The radon release rate increased significantly with temperature, rising from 0.210 ± 0.006 mBq s −1 to 0.391 ± 0.019 mBq s −1 within the range of 20 °C–32 °C, an increase of 86 %. Further analysis shows that the radon release rate of uranium ore in water has a good linear relationship with temperature, indicating that temperature is an important factor affecting the radon release behavior. The methods and models proposed in this paper can effectively depict the radon release behavior of uranium ores in water environments and are suitable for related experimental designs to assess environmental radiation risks.
作者机构：
College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, China;Hunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, 421002, China;[Xingqi Yue; Qingling Song] College of Computer Science and Technology, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, 421002, China<&wdkj&>Hunan Engineering Research Center of Cyberspace Security Technology and Applications, Hengyang Normal University, Hengyang, 421002, China
摘要：
Lightweight cryptography is crucial for securing data in resource-constrained IoT devices. However, most existing lightweight block ciphers suffer from slow diffusion, high hardware cost, or insufficient side-channel resistance, limiting their practical deployment. To address these challenges, this work proposes HDHL, a 64-bit block cipher that integrates a hybrid Generalized Feistel and Substitution-Permutation Network (GSP) structure. The design features a 64-bit sponge-based F -function for strong nonlinearity, lightweight AND-Rotation-XOR (AND-RX) operations for enhanced confusion, and an involutive linear layer that enables efficient encryption and decryption with a unified datapath. A compact LFSR-driven key schedule delivers four 16-bit sub-keys per round with near-ideal entropy. Experimental results show that HDHL achieves full diffusion in just two rounds, area occupies only 1756.71 gate equivalents (GE), and energy 7.82 μ J/bit at a 100 kHz test clock, outperforming CRAFT and SKINNY-64 in both area and energy. Security evaluation demonstrates strong resistance against differential, linear, integral, and algebraic attacks, with a seven-round differential probability below 2 − 70 . Fixed-random t -tests on a prototype ASIC confirm first-order side-channel resistance when lightweight masking or a two-share threshold implementation is applied. These results indicate that HDHL offers a balanced combination of security, efficiency, and implementation cost, making it a promising candidate for future low-power embedded applications.
Lightweight cryptography is crucial for securing data in resource-constrained IoT devices. However, most existing lightweight block ciphers suffer from slow diffusion, high hardware cost, or insufficient side-channel resistance, limiting their practical deployment. To address these challenges, this work proposes HDHL, a 64-bit block cipher that integrates a hybrid Generalized Feistel and Substitution-Permutation Network (GSP) structure. The design features a 64-bit sponge-based F -function for strong nonlinearity, lightweight AND-Rotation-XOR (AND-RX) operations for enhanced confusion, and an involutive linear layer that enables efficient encryption and decryption with a unified datapath. A compact LFSR-driven key schedule delivers four 16-bit sub-keys per round with near-ideal entropy. Experimental results show that HDHL achieves full diffusion in just two rounds, area occupies only 1756.71 gate equivalents (GE), and energy 7.82 μ J/bit at a 100 kHz test clock, outperforming CRAFT and SKINNY-64 in both area and energy. Security evaluation demonstrates strong resistance against differential, linear, integral, and algebraic attacks, with a seven-round differential probability below 2 − 70 . Fixed-random t -tests on a prototype ASIC confirm first-order side-channel resistance when lightweight masking or a two-share threshold implementation is applied. These results indicate that HDHL offers a balanced combination of security, efficiency, and implementation cost, making it a promising candidate for future low-power embedded applications.
作者：
Caizhi Wu;Yipeng Zhao*;Liang Ma;Yicheng Wang;Zhiqiang Li
期刊：
Journal of Physics and Chemistry of Solids,2026年208:113216 ISSN：0022-3697
通讯作者：
Yipeng Zhao
作者机构：
[Caizhi Wu; Yipeng Zhao; Liang Ma; Yicheng Wang; Zhiqiang Li] College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang, 421008, China
通讯机构：
[Yipeng Zhao] C;College of Physics and Electronic Engineering, Hengyang Normal University, Hengyang, 421008, China
摘要：
Two-dimensional van der Waals heterojunctions are seen as a powerful strategy to tune the electronic properties and enhance their performance in devices. In this study, we have systematically investigated the electronic properties and energy band alignments of MoS 2 /MSe (M = In, Ga) heterojunctions using first-principles calculations. The MoS 2 /InSe heterojunction exhibits a type-I band alignment, whereas the MoS 2 /GaSe heterojunction presents a type-II band characteristic. Furthermore, the MoS 2 /InSe heterojunction achieves a transition from type-I to type-II energy band alignment in the strain range of −3 % to −4 % biaxial strain. In contrast, the MoS 2 /GaSe heterojunction consistently maintains the type-II energy band alignment. In addition, the tensile strain effectively enhanced the optical absorption of MoS 2 /GaSe in the visible spectral interval, and a red-shift/blue-shift of the absorption peaks with increasing tensile/compressive strains was observed. The MoS 2 /InSe heterojunction at −3 % and the MoS 2 /GaSe heterojunction at −2 % to −1 % strain intervals exhibited suitable band gaps with strong photocatalytic capabilities. These results demonstrate that the MoS 2 /MSe heterojunctions are expected to significantly improve the photocatalytic efficiency, suggesting the promising application prospect of MoS 2 /MSe heterojunctions in photocatalytic technology.
Two-dimensional van der Waals heterojunctions are seen as a powerful strategy to tune the electronic properties and enhance their performance in devices. In this study, we have systematically investigated the electronic properties and energy band alignments of MoS 2 /MSe (M = In, Ga) heterojunctions using first-principles calculations. The MoS 2 /InSe heterojunction exhibits a type-I band alignment, whereas the MoS 2 /GaSe heterojunction presents a type-II band characteristic. Furthermore, the MoS 2 /InSe heterojunction achieves a transition from type-I to type-II energy band alignment in the strain range of −3 % to −4 % biaxial strain. In contrast, the MoS 2 /GaSe heterojunction consistently maintains the type-II energy band alignment. In addition, the tensile strain effectively enhanced the optical absorption of MoS 2 /GaSe in the visible spectral interval, and a red-shift/blue-shift of the absorption peaks with increasing tensile/compressive strains was observed. The MoS 2 /InSe heterojunction at −3 % and the MoS 2 /GaSe heterojunction at −2 % to −1 % strain intervals exhibited suitable band gaps with strong photocatalytic capabilities. These results demonstrate that the MoS 2 /MSe heterojunctions are expected to significantly improve the photocatalytic efficiency, suggesting the promising application prospect of MoS 2 /MSe heterojunctions in photocatalytic technology.
期刊：
Dyes and Pigments,2026年244:113133 ISSN：0143-7208
通讯作者：
Chuang Yao
作者机构：
[Yingde Niu; Fei Gu; Jinshan Wang] School of Materials Science and Engineering, Yancheng Institute of Technology, Yancheng, 224051, PR China;[Chuang Yao] Chongqing Key Laboratory of Extraordinary Bond Engineering and Advance Materials Technology (EBEAM), Yangtze Normal University, Chongqing, 408100, PR China;[Zhixin Dai; Yanhong Deng] College of Physics and Electronics Engineering, Hengyang Normal University, Hengyang, 421002, PR China;[Jianfeng Zhang] School of Integrated Circuits, Guangdong University of Technology, Guangzhou, 510006, PR China;[Yang Jiang] School of Materials Science and Engineering, Yancheng Institute of Technology, Yancheng, 224051, PR China<&wdkj&>Chongqing Key Laboratory of Extraordinary Bond Engineering and Advance Materials Technology (EBEAM), Yangtze Normal University, Chongqing, 408100, PR China
通讯机构：
[Chuang Yao] C;Chongqing Key Laboratory of Extraordinary Bond Engineering and Advance Materials Technology (EBEAM), Yangtze Normal University, Chongqing, 408100, PR China
摘要：
Through-space charge transfer (TSCT) thermally activated delayed fluorescence (TADF) emitters typically exhibit relatively low radiative decay rate ( k r s ), which remains a formidable obstacle to further improving device efficiency and suppressing efficiency roll-off. Here, two novel deep-blue TSCT-TADF emitters (YCIT14 and YCIT15) were rationally developed. Both emitters feature 2,4-diphenyl-1,3,5-triazine (DPTRz) and 2,4,6-triphenyl-1,3,5-triazine (TPTRz) as acceptor (A) units, which are interconnected via a phenyl bridging structure with 9-phenyl-9H-carbazole (9-PhCz) donor (D) group. Through precise structural modulation, YCIT15 incorporates an extra phenyl spacer unit compared to YCIT14, establishing a well-defined V-shaped conformation that enforces intimate face-to-face alignment between the donor and acceptor moieties. This distinct spatial configuration significantly amplifies intramolecular D-A electronic coupling through enhanced orbital overlap. These interactions enable not only through-bond charge transfer (TBCT) but also TSCT between the D and A fragments. As a consequence, YCIT15 achieves a small singlet-triplet energy gap (Δ E ST ) and high efficiency. Both molecules exhibit TSCT and TBCT characteristics, accompanied by rapid reverse intersystem crossing rates ( k RISC >10 5 s −1 ) and high radiative decay rate ( k r s >10 7 s −1 ) in neat films. The doped organic light-emitting diodes (OLEDs) utilizing YCIT14 and YCIT15 as emitters show deep-blue electroluminescence (EL), with emission peaks at 448 nm and 428 nm, respectively.
Through-space charge transfer (TSCT) thermally activated delayed fluorescence (TADF) emitters typically exhibit relatively low radiative decay rate ( k r s ), which remains a formidable obstacle to further improving device efficiency and suppressing efficiency roll-off. Here, two novel deep-blue TSCT-TADF emitters (YCIT14 and YCIT15) were rationally developed. Both emitters feature 2,4-diphenyl-1,3,5-triazine (DPTRz) and 2,4,6-triphenyl-1,3,5-triazine (TPTRz) as acceptor (A) units, which are interconnected via a phenyl bridging structure with 9-phenyl-9H-carbazole (9-PhCz) donor (D) group. Through precise structural modulation, YCIT15 incorporates an extra phenyl spacer unit compared to YCIT14, establishing a well-defined V-shaped conformation that enforces intimate face-to-face alignment between the donor and acceptor moieties. This distinct spatial configuration significantly amplifies intramolecular D-A electronic coupling through enhanced orbital overlap. These interactions enable not only through-bond charge transfer (TBCT) but also TSCT between the D and A fragments. As a consequence, YCIT15 achieves a small singlet-triplet energy gap (Δ E ST ) and high efficiency. Both molecules exhibit TSCT and TBCT characteristics, accompanied by rapid reverse intersystem crossing rates ( k RISC >10 5 s −1 ) and high radiative decay rate ( k r s >10 7 s −1 ) in neat films. The doped organic light-emitting diodes (OLEDs) utilizing YCIT14 and YCIT15 as emitters show deep-blue electroluminescence (EL), with emission peaks at 448 nm and 428 nm, respectively.
摘要：
The RAD7 detector is widely used for measuring the radon exhalation rate from the surfaces of media such as soil, rocks, and building materials. However, during the measurement process, the accuracy of the results is prone to interference due to the instrument's inherent statistical errors and environmental noise. To reduce these measurement errors, the Kalman filtering was introduced in this study to correct the radon exhalation rate, which was obtained through data fitting of radon concentration measured by the RAD7 detector. Ten verified experiments were performed with a radon exhalation standard device. The experimental result shows that 80 % of the radon exhalation rate, corrected by Kalman filtering, significantly approached the theoretical value of the standard device, compared to the uncorrected experimental results. It confirms the effectiveness of the Kalman filtering in correcting RAD7 measurements, thereby enhancing the accuracy of radon exhalation rate measurements. The proposed method provides a reference technical pathway for improving the measurement accuracy of similar radon measurement instruments.
The RAD7 detector is widely used for measuring the radon exhalation rate from the surfaces of media such as soil, rocks, and building materials. However, during the measurement process, the accuracy of the results is prone to interference due to the instrument's inherent statistical errors and environmental noise. To reduce these measurement errors, the Kalman filtering was introduced in this study to correct the radon exhalation rate, which was obtained through data fitting of radon concentration measured by the RAD7 detector. Ten verified experiments were performed with a radon exhalation standard device. The experimental result shows that 80 % of the radon exhalation rate, corrected by Kalman filtering, significantly approached the theoretical value of the standard device, compared to the uncorrected experimental results. It confirms the effectiveness of the Kalman filtering in correcting RAD7 measurements, thereby enhancing the accuracy of radon exhalation rate measurements. The proposed method provides a reference technical pathway for improving the measurement accuracy of similar radon measurement instruments.
摘要：
Two-dimensional (2D) ferromagnetic materials integrating multiple functions are promising candidates for building magnetic and electronic nanodevices. Here, we predict a series of stable 2D multifunctional ferromagnetic monolayers VX (X = S, Se, Te) encompassing indirect semiconducting and half-metallic phases with sizable spin gaps. Due to the strong ferromagnetic coupling present in the VX monolayers, the magnetic transition temperatures ( Tc ) of VS, VSe, and VTe reach 369, 315, and 311 K, respectively. Furthermore, the magnetic and electronic properties of VX monolayers can be sensitively modulated via mechanical strain, while the VS and VSe monolayers further exhibit negative Poisson’s ratios. The VX monolayers thus represent an unusual family of 2D ferromagnetic materials with strong mechano-electromagnetic coupling that may serve as a building block for future multifunctional nanodevices.
摘要：
Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods.
Currently, Camouflaged Object Detection (COD) methods often rely on single-view feature perception, which struggles to fully capture camouflaged objects due to environmental interference such as background clutter, lighting variations, and viewpoint changes. To address this, we propose the multi-view collaboration network (MCNet), inspired by human visual strategies for complex scene analysis. MCNet incorporates multiple perspectives for enhanced feature extraction. The global perception module takes the original, far, and near views, using different large-kernel convolutions and multi-head attention mechanisms for global feature embedding. In parallel, the local perception module processes the tilted, projected, and color-jittered views, extracting fine-grained local features through multi-branch deep convolutions and dilated convolutions. To facilitate deep interaction between global and local features, we introduce the hybrid interactive module, which explores the correlation of multi-view feature information and adaptively fuses features. For feature decoding, the dynamic pyramid shrinkage module integrates dynamic gated convolutions with a pyramid shrinkage mechanism, progressively aggregating semantic features through a hierarchical shrinking strategy and group fusion strategy. Experimental results on popular COD benchmark datasets show that MCNet outperforms 18 state-of-the-art methods.
作者机构：
[Li, Lang] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
通讯机构：
[Li, L ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.
关键词：
Side-channel analysis;Sample correlation locally;Deep learning;Kernel density estimation;Profiling analysis
摘要：
Label distribution learning techniques can significantly enhance the effectiveness of side-channel analysis. However, this method relies on using probability density functions to estimate the relationships between labels. The settings of parameters play a crucial role in the impact of the attacks. This study introduces a non-parametric statistical method to calculate the distribution between labels, specifically employing smoothing with the Gaussian kernel function and adjusting bandwidth. Then, the aggregation of the results from each label processed by the Gaussian kernel facilitates a hypothesis-free estimation of the label distribution. This method accurately represents the actual leakage distribution, speeding up guess entropy convergence. Secondly, we exploit similarities between profiling traces, proposing an analysis scheme for sample correlation locally of label distribution learning. Furthermore, Signal to-Noise Ratio (SNR) is employed to re-extract and reduce dataset dimensions to 500 power consumption points, resulting in noise reduction. Our results showcase the successful training of 800 profiling traces using our method for sample correlation locally of label distribution learning, with the findings indicating its exceptional performance.
Label distribution learning techniques can significantly enhance the effectiveness of side-channel analysis. However, this method relies on using probability density functions to estimate the relationships between labels. The settings of parameters play a crucial role in the impact of the attacks. This study introduces a non-parametric statistical method to calculate the distribution between labels, specifically employing smoothing with the Gaussian kernel function and adjusting bandwidth. Then, the aggregation of the results from each label processed by the Gaussian kernel facilitates a hypothesis-free estimation of the label distribution. This method accurately represents the actual leakage distribution, speeding up guess entropy convergence. Secondly, we exploit similarities between profiling traces, proposing an analysis scheme for sample correlation locally of label distribution learning. Furthermore, Signal to-Noise Ratio (SNR) is employed to re-extract and reduce dataset dimensions to 500 power consumption points, resulting in noise reduction. Our results showcase the successful training of 800 profiling traces using our method for sample correlation locally of label distribution learning, with the findings indicating its exceptional performance.
摘要：
Deep learning-based side-channel attacks (DL-SCA) are favored for their strong key recovery capabilities. However, their implementation is based on the attacker being able to manipulate a cloned device to build an attack model, which means that the attacker needs to know secret information in advance. The non-profiled side-channel attacks (NP-SCA) methods can complete the key recovery without knowing the secret information. Differential Deep Learning Analysis (DDLA) is the first NP-DLSCA method proposed in CHES2019, and several improved versions appeared later. In these methods, the bad quality of the raw traces, such as noise, random delay, etc., is often ignored, which limits the efficiency of key recovery. In this work, the conditional generative adversarial network (CGAN) is introduced and a novel framework NPSCA-CGAN is proposed to optimize traces in non-profiled SCA scenarios. We apply CGAN in non-profiled attacks and use plaintext to do trace labeling that optimizes the raw traces by training the generator to learn the label traces. The convolutional module and plaintext feature are added to the generator network to adapt various countermeasures. Moreover, a new traces quality evaluation metric average relative signal-to-noise ratio (AR-SNR) is proposed for non-profiled attack scenarios, which can directly reflect the performance of the traces in practical attack. The method is applied to unprotected, unaligned, and masked traces respectively. The experimental results indicate that it can enormously optimize the quality of the traces and improve the efficiency of non-profiled side-channel attacks.
摘要：
Constructing high-efficiency through-space charge-transfer (TSCT)-type thermally activated delayed fluorescence (TADF) blue emitters that simultaneously exhibit a high radiative decay rate ( k r s ) and a high reverse intersystem crossing rate ( k RISC ) remains a significant challenge. Herein, spatially confined “X”-shaped TSCT-TADF emitters are developed, which consist of planar 3,6-di-tert-butyl-9H-carbazole (tBuCz) donors, 2,4,6-triphenyl-1,3,5-triazine (for DTRZ-tBuCz) and 2,6-difluorobenzonitrile (for DFBN-tBuCz) acceptors, as well as phenyl bridging groups. Both emitters exhibit face-to-face donor/acceptor alignments and efficient intramolecular TSCT. Interestingly, the two emitters display strong intramolecular donor/acceptor interactions based on the “X”-shaped structure, which opens efficient intramolecular multi-channel TSCT, improving the k r s and k RISC values. In the 15 wt% and 20 wt% doped films, DTRZ-tBuCz and DFBN-tBuCz exhibit blue TADF with high fluorescence efficiency (Φ PL ) of 89 % and 91 %, accompanied by k r s / k RISC values of 2.32 × 10 7 s −1 /4.2 × 10 5 s −1 and 1.05 × 10 7 s −1 /9.03 × 10 5 s −1 , respectively. Organic light-emitting diode (OLED) fabricated with DFBN-tBuCz emitter achieve current efficiencies (CE) and external quantum efficiencies (EQE) reaching 34.1 cd/A and 18.7 %, along with low efficiency roll-offs. This study reveals that increasing intramolecular TSCT channels by constructing spatially confined molecules is an effective approach to simultaneously enhance the k r s and k RISC of TSCT-TADF emitters, which is of great significance for the development of blue TSCT-TADF OLEDs.
Constructing high-efficiency through-space charge-transfer (TSCT)-type thermally activated delayed fluorescence (TADF) blue emitters that simultaneously exhibit a high radiative decay rate ( k r s ) and a high reverse intersystem crossing rate ( k RISC ) remains a significant challenge. Herein, spatially confined “X”-shaped TSCT-TADF emitters are developed, which consist of planar 3,6-di-tert-butyl-9H-carbazole (tBuCz) donors, 2,4,6-triphenyl-1,3,5-triazine (for DTRZ-tBuCz) and 2,6-difluorobenzonitrile (for DFBN-tBuCz) acceptors, as well as phenyl bridging groups. Both emitters exhibit face-to-face donor/acceptor alignments and efficient intramolecular TSCT. Interestingly, the two emitters display strong intramolecular donor/acceptor interactions based on the “X”-shaped structure, which opens efficient intramolecular multi-channel TSCT, improving the k r s and k RISC values. In the 15 wt% and 20 wt% doped films, DTRZ-tBuCz and DFBN-tBuCz exhibit blue TADF with high fluorescence efficiency (Φ PL ) of 89 % and 91 %, accompanied by k r s / k RISC values of 2.32 × 10 7 s −1 /4.2 × 10 5 s −1 and 1.05 × 10 7 s −1 /9.03 × 10 5 s −1 , respectively. Organic light-emitting diode (OLED) fabricated with DFBN-tBuCz emitter achieve current efficiencies (CE) and external quantum efficiencies (EQE) reaching 34.1 cd/A and 18.7 %, along with low efficiency roll-offs. This study reveals that increasing intramolecular TSCT channels by constructing spatially confined molecules is an effective approach to simultaneously enhance the k r s and k RISC of TSCT-TADF emitters, which is of great significance for the development of blue TSCT-TADF OLEDs.
期刊：
Expert Systems with Applications,2025年272:126693 ISSN：0957-4174
通讯作者：
Chen, WH
作者机构：
[Yan, Li; Chen, Wenhui; Zhao, Huihuang; Yang, Yanqing; Wang, Weijie] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;[Chen, Wenhui; Yang, Yanqing] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.;[Zhao, Huihuang] Hunan Univ, Natl Engn Lab Robot Visual Percept & Control Techn, Hengyang, Peoples R China.
通讯机构：
[Chen, WH ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Peoples R China.
关键词：
Time series floating point data;Lossless compression;Internet of things;Compression algorithm;Heuristic genetic algorithm
摘要：
The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floating-point lossless compression algorithms often struggle to deliver exceptional performance on diverse datasets, highlighting their inherent limitations. To address this issue, we propose a novel method called the Heuristic Genetic Algorithm Parameter Optimizer for Lossless Compression of Time Series Floating Point Data (HGA-ACTF). This method features a highly effective parameter optimizer designed specifically for compression algorithms that utilize leading zeros. The combination of our parameter optimizer and the HGA-ACTF algorithm strategy has been proven to outperform existing leading compression algorithms across multiple fields. This approach not only enhances the compression ratio but also significantly reduces both compression and decompression times. In our comparative study, we evaluated the HGA-ACTF algorithm against eleven well-performing algorithms and a variant of the algorithm, integrating our parameter optimizer and algorithmic strategy into other adaptable algorithms, and demonstrating notable improvements. Experimental results indicate that the HGA-ACTF algorithm achieves an average compression ratio improvement of 38.87%, with some datasets showing improvements of up to 54.36%. Our approach effectively addresses the transmission and storage of time series data, significantly reducing the overhead associated with data processing. The code can be found at https://github.com/wwj10/HGA-ACTF .
The processing of large volumes of time series data across various fields presents significant challenges, particularly when it comes to effectively managing floating-point numbers. Current dual precision floating-point lossless compression algorithms often struggle to deliver exceptional performance on diverse datasets, highlighting their inherent limitations. To address this issue, we propose a novel method called the Heuristic Genetic Algorithm Parameter Optimizer for Lossless Compression of Time Series Floating Point Data (HGA-ACTF). This method features a highly effective parameter optimizer designed specifically for compression algorithms that utilize leading zeros. The combination of our parameter optimizer and the HGA-ACTF algorithm strategy has been proven to outperform existing leading compression algorithms across multiple fields. This approach not only enhances the compression ratio but also significantly reduces both compression and decompression times. In our comparative study, we evaluated the HGA-ACTF algorithm against eleven well-performing algorithms and a variant of the algorithm, integrating our parameter optimizer and algorithmic strategy into other adaptable algorithms, and demonstrating notable improvements. Experimental results indicate that the HGA-ACTF algorithm achieves an average compression ratio improvement of 38.87%, with some datasets showing improvements of up to 54.36%. Our approach effectively addresses the transmission and storage of time series data, significantly reducing the overhead associated with data processing. The code can be found at https://github.com/wwj10/HGA-ACTF .
摘要：
Unlike conventional single-atom catalysts (SACs), ferroelectric materials provide a novel approach to controlling catalytic activity through ferroelectric polarization switching. Herein, utilizing ab initio calculations, we investigated the effect of the polarization switching on the catalytic activities of oxygen reduction (ORR) and oxygen evolution reactions (OER) in ferroelectric SACs with transition-metal atoms anchored on the ferroelectric In 2 Se 3 monolayer. The polarization switching not only enables effective control of the reaction overpotentials but also the corresponding potential limiting steps, thereby activating and enhancing catalytic performance. Notably, reorienting the polarization direction at the specific reaction step can reactivate the stuck catalytic reduction and further improve the activity of specific TM-In 2 Se 3 with poor catalytic activity in both upward and downward polarization. Multilevel corrections involving overpotentials, orbital populations, and d-band centers demonstrate that the modulation of catalytic activity through polarization switching originates from the adjustable d-band centers of the supported metal atoms. These findings demonstrate that ferroelectricity switching is a highly promising avenue for improving OER and ORR activity.
Unlike conventional single-atom catalysts (SACs), ferroelectric materials provide a novel approach to controlling catalytic activity through ferroelectric polarization switching. Herein, utilizing ab initio calculations, we investigated the effect of the polarization switching on the catalytic activities of oxygen reduction (ORR) and oxygen evolution reactions (OER) in ferroelectric SACs with transition-metal atoms anchored on the ferroelectric In 2 Se 3 monolayer. The polarization switching not only enables effective control of the reaction overpotentials but also the corresponding potential limiting steps, thereby activating and enhancing catalytic performance. Notably, reorienting the polarization direction at the specific reaction step can reactivate the stuck catalytic reduction and further improve the activity of specific TM-In 2 Se 3 with poor catalytic activity in both upward and downward polarization. Multilevel corrections involving overpotentials, orbital populations, and d-band centers demonstrate that the modulation of catalytic activity through polarization switching originates from the adjustable d-band centers of the supported metal atoms. These findings demonstrate that ferroelectricity switching is a highly promising avenue for improving OER and ORR activity.
作者机构：
College of Computer Science and Technology, Hengyang Normal University, Hengyang, China;Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China;[Yezhou (59366878200); Lang (36070638700); Yu (57202403406)] College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
通讯机构：
[Lang Li] C;College of Computer Science and Technology, Hengyang Normal University, Hengyang, China<&wdkj&>Hunan Provincial Key Laboratory of Intelligent Information Processing and Application, Hengyang Normal University, Hengyang, China
摘要：
Deep learning algorithms are increasingly employed to exploit side-channel information, such as power consumption and electromagnetic leakage from hardware devices, significantly enhancing attack capabilities. However, relying solely on power traces for side-channel information often requires adequate domain knowledge. To address this limitation, this work proposes a new attack scheme. Firstly, a Convolutional Neural Network (CNN)-based plaintext-extended bilinear feature fusion model is designed. Secondly, multi-model intermediate layers are fused and trained, yielding in the increase of the amount of effective information and generalization ability. Finally, the model is employed to predict the output probability of three public side-channel datasets (e.g. ASCAD, AES
$$\_$$
HD, and AES
$$\_$$
RD), and analyze the recovery key guessing entropy for each key to efficiently assess attack efficiency. Experimental results showcase that the plaintext-extended bilinear feature fusion model can effectively enhance the Side-Channel Attack (SCA) capabilities and prediction performance. Deploying the proposed method, the number of traces required for a successful attack on the ASCAD
$$\_$$
R dataset is significantly reduced to less than 914, representing an 70.5% reduction in traces compared to the network in Convolutional Neural Network-Visual Geometry Group (CNNVGG16) with plaintext, which incorporating plaintext features before the fully connected layer. Compared to existing solutions, the proposed scheme requires only 80% of the power traces for the attack mask design using only 75 epochs. As a result, the power of the proposed method is well proved through the different experiments and comparison processes.
通讯机构：
[Deng, YH ] H;[Xiang, HY ] N;Hengyang Normal Univ, Coll Phys & Elect Engn, Hengyang 421002, Hunan, Peoples R China.;Nanjing Univ Sci &Technol, Inst Optoelect & Nanomat, Sch Mat Sci & Engn, MIIT Key Lab Adv Display Mat & Devices, Nanjing 210094, Jiangsu, Peoples R China.
关键词：
perovskite/organic hybrid white light-emitting diodes (WLEDs);LiF interlayer;supplementary emission layer;recombination region;Commission Internationale de L'Eclairage(CIE)
摘要：
Perovskite light-emitting diodes (PeLEDs) have great potential in solid-state lighting and display fields due to their advantages of narrow emission spectrum, excellent optoelectronic properties, simple preparation process, and low cost. However, the compatibility of solvents used in the full solution process substantially hindered the development of multilayer white PeLEDs. Although mixing perovskite and organic materials can avoid this problem, it remains challenging to manufacture white PeLEDs that are close to the Commission Internationale de L'Eclairage (CIE) coordinate of (0.33,0.33). In this paper, the perovskite/organic hybrid white light-emitting diodes composed of a bottom blue perovskite light-emitting unit prepared through a solution method and an organic light-emitting unit fabricated by thermal evaporation are reported. To enhance carrier transport and adjust the recombination region, we employed several strategies: improving the perovskite surface with phenylethylammonium bromide additive, optimizing the thickness of the organic red emission layer, inserting a lithium fluoride layer, and incorporating a blue supplementary emission layer. The best white PeLED shows a maximum luminance of 2281 cd/m2, a maximum external quantum efficiency of 2.64%, a CIE coordinate close to the equal-energy white point of (0.33,0.34), and a correlated color temperature of 5206 K. The results presented in this paper provide a feasible method for obtaining white PeLEDs with excellent CIE coordinates.
通讯机构：
[Wei, XL ; Cao, JX] X;Xiangtan Univ, Dept Phys, Xiangtan 411105, Peoples R China.;Xiangtan Univ, Hunan Prov Key Lab Smart Carbon Mat & Adv Sensing, Xiangtan 411105, Peoples R China.;Hengyang Normal Univ, Coll Phys & Elect Engn, Hengyang 421002, Peoples R China.
摘要：
Two-dimensional black arsenic phosphorus has attracted significant interest due to its extraordinary electronic, optical, and transport properties. Therefore, in this work, we go through all the possibilities, including 3297 nonrepetitive configurations, and demonstrate the lowest energy structure of the As x P 1– x ( x = 0.4) monolayer by first-principles calculations. Our results indicate that both single-layer and bilayer As 4 P 6 host direct and indirect bandgap semiconductors with bandgaps of 1.94 and 1.26 eV, respectively, which exhibit good light adsorption within the visible light and infrared region. Moreover, both single-layer and bilayer As 4 P 6 possess high electron and hole mobilities (up to 2.6 × 10 4 cm 2 v –1 s –1 ), which also exhibit extreme carrier anisotropy originating from their high in-plane lattice anisotropy. Furthermore, bilayer As 4 P 6 exhibits exceptional device characteristics including a lower threshold voltage, higher on-state current, and higher conductance. In addition, the transmission coefficient spectrum of bilayer As 4 P 6 is three times greater than that of the monolayer owing to an increased number of electronic channels. Additionally, the extinction ratio of single-layer As 4 P 6 exhibits high anisotropy, indicating enhanced polarization sensitivity in the zigzag direction. Our findings provide two excellent candidate materials for the application of optoelectronic devices.
关键词：
out-of-time-ordered correlato;quantum chaos;quantum Rabi model
摘要：
Quantum chaos is an intriguing topic and has attracted a great deal of interests in quantum mechanics and black hole physics. Recently, the exponential growth of out-of-time-ordered correlator (OTOC) has been proposed to diagnose quantum chaos and verify the correspondence principle. Here, good correspondence is found between the linear entanglement entropy and the semiclassical phase space structures in the anisotropic quantum Rabi model. The Loschmidt echo in the chaotic sea decays more faster than that in the stable island. However, the OTOCs grow exponentially at early times for the initial states centered both in the chaotic and stable regions. The exponential growth of the OTOC is attributed to quantum collapse that provides a novel mechanism of yielding exponential growth of the OTOC in quantum systems. Moreover, the quantum collapse effect is more obvious for the initial states centered in the chaotic one. The results show that in the anisotropic quantum Rabi model, the linear entanglement entropy, and Loschmidt echo are more effective than OTOC for diagnosing quantum chaotic signals.
摘要：
Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments.
Deep learning-assisted template attack (DLATA) is a novel side-channel attack (SCA) method proposed by Lichao Wu at CHES2022. It utilizes a triplet network to assist template attacks (TA), avoiding the redundant training and hyperparameter tuning required in traditional DL-based SCA methods. However, the training of the triplet network requires a large number of power samples due to its unique structure. We propose a new optimization scheme, in which the transfer learning (TL) technology is used to train multiple models on several similar datasets with fewer power traces, to mitigation the problem. The approach allows us to leverage pre-trained models to product a new mode on the another target dataset by fine-tuning weights so that significantly reduce the training cost for the triplet network while maintaining attack effectiveness. We remould the structure and dimensionality of similar datasets so that the models trained on them can perform effective transfer learning for training on the target dataset. Concretely, some of parameters and features obtained from pretraining can be used directly for the target task, while the rest only require fine-tuning. Evaluation and experimental validation on the public ASCAD dataset demonstrate that our method achieves or even surpasses the performance of the original method with a 90% reduction in the training set. These findings highlight the effectiveness of the proposed TL strategy in achieving robust attack performance in low-sample training environments.
作者机构：
[Zhou, Wang; Han, Miaomiao; Liu, Jilei; Gao, Peng; Tang, Rui; Mo, Ying] Hunan Univ, Coll Mat Sci & Engn, Hunan Joint Int Lab Adv Mat & Technol Clean Energy, Hunan Prov Key Lab Adv Carbon Mat & Appl Technol, Changsha 410082, Hunan, Peoples R China.;[Wang, Dan] Hengyang Normal Univ, Univ Hunan Prov, Key Lab Micronano Energy Mat & Applicat Technol, Hengyang 421002, Hunan, Peoples R China.;[Wang, Dan] Hengyang Normal Univ, Coll Phys & Elect Engn, Hengyang 421002, Hunan, Peoples R China.;[Chen, Shi] Univ Macau, Inst Appl Phys & Mat Engn, Joint Key Lab, Minist Educ, Taipa 999078, Macao, Peoples R China.;[Yoshii, Takeharu; Wakabayashi, Keigo; Nishihara, Hirotomo] Tohoku Univ, Inst Multidisciplinary Res Adv Mat, 2-1-1 Katahira,Aoba Ku, Sendai, Miyagi 9808577, Japan.
通讯机构：
[Liu, JL ] H;Hunan Univ, Coll Mat Sci & Engn, Hunan Joint Int Lab Adv Mat & Technol Clean Energy, Hunan Prov Key Lab Adv Carbon Mat & Appl Technol, Changsha 410082, Hunan, Peoples R China.
关键词：
Hard carbon;Surface chemistry;C-H bond;Initial coulombic efficiency;Adsorption capacity
摘要：
Controlling surface chemistry is critically important for improving the initial Coulombic efficiency (ICE) and adsorption capacity of hard carbon anode used in Li/Na/K-ion batteries. However, accurately identifying the types and concentrations of hydrogen/oxygen terminated functional groups (HTFG/OTFGs) and distinguishing their functionalities remain challenge. Herein, we quantitatively investigated the surface chemistry on hard carbon via ultra-high temperature programed desorption measurements, and uncovered the role of HTFG/OTFGs in influencing ICE and adsorption capacity in Li/Na/K-ions cells. The C–H group is found to be dominant species on the surface of hard carbon, and presents a positive correlation with ICE values and adsorption capacity. The low reactivity of C–H group with both electrolyte salt and solvent results in the formation of thinner and highly conducive solid electrolyte interphase (SEI) layer, which benefit for the enhanced ICE and improved Li/Na/K-ions diffusion across SEI layer. Additionally, the pimping trapping effect of C–H groups allows the adsorbed Li/Na/K-ions to migrate into graphitic interlayer quickly, enhancing the slope capacity. By fabricating a C–H group-rich surface chemistry on hard carbon, a high ICE value and satisfactory specific capacity have been realized. These findings enrich our understanding of the surface chemistry-induced interfacial reaction, which effectively guides the rational design of high-performance hard carbon.
Controlling surface chemistry is critically important for improving the initial Coulombic efficiency (ICE) and adsorption capacity of hard carbon anode used in Li/Na/K-ion batteries. However, accurately identifying the types and concentrations of hydrogen/oxygen terminated functional groups (HTFG/OTFGs) and distinguishing their functionalities remain challenge. Herein, we quantitatively investigated the surface chemistry on hard carbon via ultra-high temperature programed desorption measurements, and uncovered the role of HTFG/OTFGs in influencing ICE and adsorption capacity in Li/Na/K-ions cells. The C–H group is found to be dominant species on the surface of hard carbon, and presents a positive correlation with ICE values and adsorption capacity. The low reactivity of C–H group with both electrolyte salt and solvent results in the formation of thinner and highly conducive solid electrolyte interphase (SEI) layer, which benefit for the enhanced ICE and improved Li/Na/K-ions diffusion across SEI layer. Additionally, the pimping trapping effect of C–H groups allows the adsorbed Li/Na/K-ions to migrate into graphitic interlayer quickly, enhancing the slope capacity. By fabricating a C–H group-rich surface chemistry on hard carbon, a high ICE value and satisfactory specific capacity have been realized. These findings enrich our understanding of the surface chemistry-induced interfacial reaction, which effectively guides the rational design of high-performance hard carbon.
作者机构：
[Wang, Fangyan; Jiao, Ge; Yue, Guowen] Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Hunan, Peoples R China.;[Jiao, Ge] Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Hunan, Peoples R China.;[Jiao, Ge] Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang 421002, Hunan, Peoples R China.
通讯机构：
[Jiao, G ] H;Hengyang Normal Univ, Coll Comp Sci & Technol, Hengyang 421002, Hunan, Peoples R China.;Hengyang Normal Univ, Hunan Prov Key Lab Intelligent Informat Proc & App, Hengyang 421002, Hunan, Peoples R China.;Hengyang Normal Univ, Hunan Engn Res Ctr Cyberspace Secur Technol & Appl, Hengyang 421002, Hunan, Peoples R China.
关键词：
Camouflaged object detection;Self-supervised learning;Reconstruction;Adapter;Segment anything model
摘要：
Current Camouflaged Object Detection (COD) methods primarily rely on a direct mapping from image to mask. However, due to the inherent semantic and structural gap between the image and its corresponding mask, the learned feature representations often exhibit poor generalization ability. To address this issue, we propose a novel intra-domain dual reconstruction framework, termed InDReCT, which reformulates the image-to-mask prediction as a cross-domain transfer task by simultaneously reconstructing both the input image and its corresponding mask. Within this framework, semantic knowledge is transferred through two reconstruction processes from different domains: image reconstruction (appearance domain) and mask reconstruction (structure domain), and is eventually integrated back into the image-to-mask prediction task. This dual reconstruction mechanism implicitly guides the network to extract hidden appearance semantics from image-to-image reconstruction and explicit structural information from mask-to-mask reconstruction, thereby enhancing the model’s generalization capability. Extensive experiments on three benchmark COD datasets and four downstream tasks demonstrate that InDReCT consistently outperforms state-of-the-art methods in both detection accuracy and generalization ability. Notably, on the widely-used COD10K dataset, InDReCT achieves a Mean E-measure ( E m ) of 95.6 %, surpassing the latest state-of-the-art model CamoDiffusion by 1.6 %. Code and models will be publicly available at: https://github.com/KungFuProgrammerle/InDReCT .
Current Camouflaged Object Detection (COD) methods primarily rely on a direct mapping from image to mask. However, due to the inherent semantic and structural gap between the image and its corresponding mask, the learned feature representations often exhibit poor generalization ability. To address this issue, we propose a novel intra-domain dual reconstruction framework, termed InDReCT, which reformulates the image-to-mask prediction as a cross-domain transfer task by simultaneously reconstructing both the input image and its corresponding mask. Within this framework, semantic knowledge is transferred through two reconstruction processes from different domains: image reconstruction (appearance domain) and mask reconstruction (structure domain), and is eventually integrated back into the image-to-mask prediction task. This dual reconstruction mechanism implicitly guides the network to extract hidden appearance semantics from image-to-image reconstruction and explicit structural information from mask-to-mask reconstruction, thereby enhancing the model’s generalization capability. Extensive experiments on three benchmark COD datasets and four downstream tasks demonstrate that InDReCT consistently outperforms state-of-the-art methods in both detection accuracy and generalization ability. Notably, on the widely-used COD10K dataset, InDReCT achieves a Mean E-measure ( E m ) of 95.6 %, surpassing the latest state-of-the-art model CamoDiffusion by 1.6 %. Code and models will be publicly available at: https://github.com/KungFuProgrammerle/InDReCT .
