In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is...In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.展开更多
The Australian Shuffle consists of placing a deck of cards onto a table according to this rule: put the top card on the table, the next card on the bottom of the deck, and repeat until all the cards have been placed o...The Australian Shuffle consists of placing a deck of cards onto a table according to this rule: put the top card on the table, the next card on the bottom of the deck, and repeat until all the cards have been placed on the table. A natural question is “Where was the very last card placed located in the original deck?” Card trick magicians have known empirically for years that the fortieth card from the top of a standard fifty-two card deck is the final card placed by this shuffle. The moniker “Australian” comes from putting every other card “Down Under”. We develop a formula for the general case of N cards, and then extend that generalization further to cases involving the discard of k cards before or after putting one on the bottom of the deck. Finally, we discuss the connection of the Australian Shuffle and its generalizations to the famous Josephus problem.展开更多
Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military...Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.展开更多
To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM...To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.展开更多
DNA shuffling是蛋白质定向进化的一种常用策略,其优点是可以快速积累多突变效果,但同时由于突变数较多,其中真正发挥作用的突变及其结构基础往往不清楚。β-葡萄糖苷酶是纤维素高效降解的限速酶,良好的热稳定性是影响其实际催化效率的...DNA shuffling是蛋白质定向进化的一种常用策略,其优点是可以快速积累多突变效果,但同时由于突变数较多,其中真正发挥作用的突变及其结构基础往往不清楚。β-葡萄糖苷酶是纤维素高效降解的限速酶,良好的热稳定性是影响其实际催化效率的关键因素。该研究以DNA shuffling策略产生的热稳定性β-葡萄糖苷酶突变体Bgl3-6511(含60个突变,T_(50)值比野生型提高4.6℃)为研究对象,通过序列比对、定点突变和热稳定性测定,对其中的有益突变进行鉴定。结果显示,6个单点突变Y50F、R52H、R56K、V65I、T67A和P143A分别将该酶的T_(50)值提高2.9、4.2、1.5、2.8、3.2、1.2℃。同时,鉴定到5个有害突变将该酶的T_(50)值降低1.0~3.4℃。将获得单点有益突变进行组合,获得T_(50)值提高13.4℃的M6(Y50F/R52H/R56K/V65I/T67A/P143A),说明DNA shuffling策略积累的有害突变确实损害了性能优化。结构分析和分子动力学模拟显示,有益突变主要是通过增强分子内氢键、π-π键和稳定二级结构发挥作用。该研究对Bgl3-6511中的单点有益突变进行鉴定,对其结构基础进行了分析,并获得了热稳定性更加优良的突变酶,相关信息可为其他酶的分子改造提供有益借鉴。展开更多
The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitate...The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.展开更多
在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,...在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,或存在领域局限性,难以针对性解决该问题。为此,基于证券行业用户数据的特点,提出了一种基于用户关系的多分组归并算法(multi group merging algorithm,MGMA)。该算法通过有效分组和优化处理策略,显著提升计算效率,并降低计算资源消耗。实验表明,相较于无优化对照组,MGMA算法的数据倾斜率为20%,内存占用为72%,计算用时为61%,且上述3项指标均优于其他4种对比优化方法。展开更多
As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that a...As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that at least one machine is not eligible for at least one job.PBPMSP and scheduling problems with machine eligibility are frequently considered;however,PBPMSP with machine eligibility is seldom explored.This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition(CSFLA)to minimize makespan.In CSFLA,the initial population is produced in a heuristic and random way,and the competitive search of memeplexes comprises two phases.Competition between any two memeplexes is done in the first phase,then iteration times are adjusted based on competition,and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase.An adaptive population shuffling is given.Computational experiments are conducted on 100 instances.The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.展开更多
For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-block...For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-blocks are unchanged using block position shuffle.For this defect,this paper proposes a fast attack algorithm for JPEG image encryption based on inter-block shuffle and non-zero quantization discrete cosine transformation coefficient attack.The algorithm analyzes the position mapping relationship before and after encryption of image blocks by detecting the pixel values of an image by the designed plaintext image.Then the preliminary attack result of the image blocks can be obtained from the inverse mapping relationship.Finally,the final attack result of the algorithm is generated according to the numbers of non-zero coefficients in each 8×8 block of the preliminary attack result.Every 8×8 block position is related with its number of non-zero discrete cosine transform coefficients in the designed plaintext.It is verified that the main content of the original image could be obtained without knowledge of the encryption algorithm and keys in a relatively short time.展开更多
Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing ...Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.展开更多
文摘In this paper the geometric description and general theory of mechanical twinning are reviewed, the twins in general lattices and superlattices are summarized, and the kinetic process by which mechanical twins form is revisited. A case study of mechanical twinning of HfV2+Nb, (cubic) Laves phase, is presented and the synchroshear of selected atomic layers is proposed to explain the physical process of twin formation. If the twins form in this way, then long shear vectors and / or atomicshuffles are not really necessary.
文摘The Australian Shuffle consists of placing a deck of cards onto a table according to this rule: put the top card on the table, the next card on the bottom of the deck, and repeat until all the cards have been placed on the table. A natural question is “Where was the very last card placed located in the original deck?” Card trick magicians have known empirically for years that the fortieth card from the top of a standard fifty-two card deck is the final card placed by this shuffle. The moniker “Australian” comes from putting every other card “Down Under”. We develop a formula for the general case of N cards, and then extend that generalization further to cases involving the discard of k cards before or after putting one on the bottom of the deck. Finally, we discuss the connection of the Australian Shuffle and its generalizations to the famous Josephus problem.
文摘Military image encryption plays a vital role in ensuring the secure transmission of sensitive visual information from unauthorized access.This paper proposes a new Tri-independent keying method for encrypting military images.The proposed encryption method is based on multilevel security stages of pixel-level scrambling,bitlevel manipulation,and block-level shuffling operations.For having a vast key space,the input password is hashed by the Secure Hash Algorithm 256-bit(SHA-256)for generating independently deterministic keys used in the multilevel stages.A piecewise pixel-level scrambling function is introduced to perform a dual flipping process controlled with an adaptive key for obscuring the spatial relationships between the adjacent pixels.Adynamicmasking scheme is presented for conducting a bit-level manipulation based on distinct keys that change over image regions,providing completely different encryption results on identical regions.To handle the global correlation between large-scale patterns,a chaotic index-map system is employed for shuffling image regions randomly across the image domain based on a logistic map seeded with a private key.Experimental results on a dataset of military images show the effectiveness of the proposed encryption method in producing excellent quantitative and qualitative results.The proposed method obtains uniform histogram distributions,high entropy values around the ideal(≈8 bits),Number of Pixel Change Rate(NPCR)values above 99.5%,and low Peak Signal-to-Noise Ratio(PSNR)over all encrypted images.This validates the robustness of the proposed method against cryptanalytic attacks,verifying its ability to serve as a practical basis for secure image transmission in defense systems.
基金supported by the National Natural Science Foundation of China under Grant No.12204062the Natural Science Foundation of Shandong Province under Grant No.ZR2022MF330。
文摘To enhance speech emotion recognition capability,this study constructs a speech emotion recognition model integrating the adaptive acoustic mixup(AAM)and improved coordinate and shuffle attention(ICASA)methods.The AAM method optimizes data augmentation by combining a sample selection strategy and dynamic interpolation coefficients,thus enabling information fusion of speech data with different emotions at the acoustic level.The ICASA method enhances feature extraction capability through dynamic fusion of the improved coordinate attention(ICA)and shuffle attention(SA)techniques.The ICA technique reduces computational overhead by employing depth-separable convolution and an h-swish activation function and captures long-range dependencies of multi-scale time-frequency features using the attention weights.The SA technique promotes feature interaction through channel shuffling,which helps the model learn richer and more discriminative emotional features.Experimental results demonstrate that,compared to the baseline model,the proposed model improves the weighted accuracy by 5.42%and 4.54%,and the unweighted accuracy by 3.37%and 3.85%on the IEMOCAP and RAVDESS datasets,respectively.These improvements were confirmed to be statistically significant by independent samples t-tests,further supporting the practical reliability and applicability of the proposed model in real-world emotion-aware speech systems.
文摘The counterflow burner is a combustion device used for research on combustion.By utilizing deep convolutional models to identify the combustion state of a counter flow burner through visible flame images,it facilitates the optimization of the combustion process and enhances combustion efficiency.Among existing deep convolutional models,InceptionNeXt is a deep learning architecture that integrates the ideas of the Inception series and ConvNeXt.It has garnered significant attention for its computational efficiency,remarkable model accuracy,and exceptional feature extraction capabilities.However,since this model still has limitations in the combustion state recognition task,we propose a Triple-Scale Multi-Stage InceptionNeXt(TSMS-InceptionNeXt)combustion state recognitionmethod based on feature extraction optimization.First,to address the InceptionNeXt model’s limited ability to capture dynamic features in flame images,we introduce Triplet Attention,which applies attention to the width,height,and Red Green Blue(RGB)dimensions of the flame images to enhance its ability to model dynamic features.Secondly,to address the issue of key information loss in the Inception deep convolution layers,we propose a Similarity-based Feature Concentration(SimC)mechanism to enhance the model’s capability to concentrate on critical features.Next,to address the insufficient receptive field of the model,we propose a Multi-Scale Dilated Channel Parallel Integration(MDCPI)mechanism to enhance the model’s ability to extract multi-scale contextual information.Finally,to address the issue of the model’s Multi-Layer Perceptron Head(MlpHead)neglecting channel interactions,we propose a Channel Shuffle-Guided Channel-Spatial Attention(ShuffleCS)mechanism,which integrates information from different channels to further enhance the representational power of the input features.To validate the effectiveness of the method,experiments are conducted on the counterflow burner flame visible light image dataset.The experimental results show that the TSMS-InceptionNeXt model achieved an accuracy of 85.71%on the dataset,improving by 2.38%over the baseline model and outperforming the baseline model’s performance.It achieved accuracy improvements of 10.47%,4.76%,11.19%,and 9.28%compared to the Reparameterized Visual Geometry Group(RepVGG),Squeeze-erunhanced Axial Transoformer(SeaFormer),Simplified Graph Transformers(SGFormer),and VanillaNet models,respectively,effectively enhancing the recognition performance for combustion states in counterflow burners.
文摘在证券行业,用户数据处理和分析是核心技术,对业务决策和风险控制具有重要的影响。然而,证券公司庞大的用户数据规模和复杂的数据关系导致大数据计算面临Shuffle操作和数据倾斜问题。现有的Shuffle和数据倾斜优化方法或依赖于硬件升级,或存在领域局限性,难以针对性解决该问题。为此,基于证券行业用户数据的特点,提出了一种基于用户关系的多分组归并算法(multi group merging algorithm,MGMA)。该算法通过有效分组和优化处理策略,显著提升计算效率,并降低计算资源消耗。实验表明,相较于无优化对照组,MGMA算法的数据倾斜率为20%,内存占用为72%,计算用时为61%,且上述3项指标均优于其他4种对比优化方法。
基金supported by the National Natural Science Foundation of China(Grant Number 61573264).
文摘As a complicated optimization problem,parallel batch processing machines scheduling problem(PBPMSP)exists in many real-life manufacturing industries such as textiles and semiconductors.Machine eligibility means that at least one machine is not eligible for at least one job.PBPMSP and scheduling problems with machine eligibility are frequently considered;however,PBPMSP with machine eligibility is seldom explored.This study investigates PBPMSP with machine eligibility in fabric dyeing and presents a novel shuffled frog-leaping algorithm with competition(CSFLA)to minimize makespan.In CSFLA,the initial population is produced in a heuristic and random way,and the competitive search of memeplexes comprises two phases.Competition between any two memeplexes is done in the first phase,then iteration times are adjusted based on competition,and search strategies are adjusted adaptively based on the evolution quality of memeplexes in the second phase.An adaptive population shuffling is given.Computational experiments are conducted on 100 instances.The computational results showed that the new strategies of CSFLA are effective and that CSFLA has promising advantages in solving the considered PBPMSP.
文摘For traditional JPEG image encryption,block position shuffling can achieve a better encryption effect and is resistant to non-zero counting attack.However,the numbers of non-zero coefficients in the 8×8 sub-blocks are unchanged using block position shuffle.For this defect,this paper proposes a fast attack algorithm for JPEG image encryption based on inter-block shuffle and non-zero quantization discrete cosine transformation coefficient attack.The algorithm analyzes the position mapping relationship before and after encryption of image blocks by detecting the pixel values of an image by the designed plaintext image.Then the preliminary attack result of the image blocks can be obtained from the inverse mapping relationship.Finally,the final attack result of the algorithm is generated according to the numbers of non-zero coefficients in each 8×8 block of the preliminary attack result.Every 8×8 block position is related with its number of non-zero discrete cosine transform coefficients in the designed plaintext.It is verified that the main content of the original image could be obtained without knowledge of the encryption algorithm and keys in a relatively short time.
文摘Fabric dyeing is a critical production process in the clothing industry and heavily relies on batch processing machines(BPM).In this study,the parallel BPM scheduling problem with machine eligibility in fabric dyeing is considered,and an adaptive cooperated shuffled frog-leaping algorithm(ACSFLA)is proposed to minimize makespan and total tardiness simultaneously.ACSFLA determines the search times for each memeplex based on its quality,with more searches in high-quality memeplexes.An adaptive cooperated and diversified search mechanism is applied,dynamically adjusting search strategies for each memeplex based on their dominance relationships and quality.During the cooperated search,ACSFLA uses a segmented and dynamic targeted search approach,while in non-cooperated scenarios,the search focuses on local search around superior solutions to improve efficiency.Furthermore,ACSFLA employs adaptive population division and partial population shuffling strategies.Through these strategies,memeplexes with low evolutionary potential are selected for reconstruction in the next generation,while thosewithhighevolutionarypotential are retained to continue their evolution.Toevaluate the performance of ACSFLA,comparative experiments were conducted using ACSFLA,SFLA,ASFLA,MOABC,and NSGA-CC in 90 instances.The computational results reveal that ACSFLA outperforms the other algorithms in 78 of the 90 test cases,highlighting its advantages in solving the parallel BPM scheduling problem with machine eligibility.
