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Sericulture has a pivotal position in the agricultural production of China. The number of silkworms is one of the key indicators in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms. Accurate detection of the number of silkworms can ensure the quality of sericulture products. However, manual counting has not fully met the needs of large-scale production in recent years. There is a high demand to combine AI technology for silkworm counting. In this study, silkworm detection and counting were studied from the perspective of deep learning. Several challenges were solved in the detection of silkworms. Since the silkworms belonged to the small targets, it was difficult to detect the fewer features; When the number of silkworms was high, serious masking occurred to cause interference in the detection of densely distributed silkworms; It was difficult to ensure that the clear images of silkworms were captured from the actual production. The blur images were easy to deteriorate the accuracy of detection. Two tasks were covered as follows. 1) The image dataset of silkworms was constructed; 2) MAM-YOLO, a silkworm detection and counting model was proposed using multi-dimensional perception. The pictures of silkworms were first taken from the Guangxi Sericulture Technology Promotion Station. These pictures of silkworms were preliminarily processed. The polygon annotation was then selected, according to the body and distribution of silkworms. The pictures of silkworms were annotated with the open-source tool (Labelme). Finally, the dataset was expanded using image transformation and noise addition. The silkworm picture dataset was obtained for the subsequent training, verification, and testing. An improved YOLOv8 (MAM-YOLO) was proposed to detect the silkworm, in order to realize the accurate detection and counting. A silkworm detection and counting model was proposed using space-to-depth transformation and image deblurring. The fine-grained detection performance of blurred images was improved in the MAMYOLO model. The space-to-depth transformed convolution was utilized instead of stride convolution for the pooling layer in the feature extraction. The fine-grained features were extracted from the images, in order to further improve the detection of the low-resolution and small targets. In addition, the impact of blurred images on the detection performance was reduced by introducing a deblurring module into the model. RCS-OSA module was added to the YOLOv8 model. The network feature extraction was improved to integrate the information of different sizes in the receptive fields. The detection head of the original network was improved into the multi-dimensional sensing head. The three-dimensional features of size, space, and channel were aggregated to enhance the detection of silkworm targets. The loss function was replaced with the EIOU loss function. The regression accuracy of the detection frame was improved to reduce the object occlusion. Experimental results show that the MAM-YOLO more accurately detected the silkworm targets. The detection speed of mAP@0.5:0.95 reached 74.8% and 22.2 frames per second, indicating the better functions of silkworm detection and counting with high precision and high rate. The detection requirements were fully met in the actual work of the Guangxi sericulture station. The results show that the silkworm detection and counting system of the technical indicators met the application requirements. The finding can provide the detection services for the actual production work of the silkworm breeding station. In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 dataset. It was found that the MAM-YOLO was also achieved in the better detection performance of wheat ears, particularly on the small targets similar to dense occlusion.Sericulture has a pivotal position in China's agricultural production. The number of silkworms is a key indicator in the process of silkworm breeding, which is crucial to the feeding and healthy growth of silkworms, and accurate detection of the number of silkworms can ensure the quality of sericulture products. The traditional way of counting silkworms manually is inefficient, and it needs to be combined with technology to intelligently complete this kind of heavy work. In this thesis, from the perspective of deep learning, a silkworm detection and counting method is studied, and several problems in silkworm detection are solved: silkworms belong to small targets, which have fewer features so that they are not easy to detect; silkworms are densely distributed, and when the number of silkworms is high, serious masking phenomenon occurs which causes interference in the detection; it is difficult to ensure that the images of silkworms obtained in the actual production are clear, and blur images will make the detection affected. The main research content of this thesis is as follows: This chapter completed two main tasks: 1) Completed the production of silkworm image data set; 2) MAM-YOLO, a silkworm detection and counting model based on multi-dimensional perception, was proposed.In this paper, pictures of silkworms were taken by workers from Guangxi Sericulture Technology Promotion Station. First, these pictures of silkworms were preliminically processed. Then, according to the characteristics of the body and distribution of silkworms, the polygon annotation method was selected, and the pictures of silkworms were annotated with the open-source tool Labelme. Finally, the data set is expanded by means of image transformation and noise addition, and finally the silkworm picture data set is obtained for subsequent training, verification and testing.This chapter proposes an improved silkworm detection method based on YOLOv8, MAM-YOLO, which realizes accurate detection and counting of silkworm. Secondly, aiming at the problems of loss of fine-grained information and poor detection performance of blurred images in the MAM-YOLO model, a silkworm detection and counting model based on spaceto-depth transformation and image deblurring is proposed. The model utilizes space-to-depth transformed convolution instead of stride convolution and pooling layer in the feature extraction process, so that fine-grained features in the image can be extracted, which further improves the detection capability for low-resolution and small targets. In addition, the impact of blurred images on the detection performance is reduced by introducing a deblurring module into the model. By adding RCSOSA module to YOLOv8 model, the capability of network feature extraction is improved, and the information of different size receptive fields is integrated. The detection head of the original network is improved into multi-dimensional sensing head, and the three-dimensional characteristics of size, space and channel are aggregated to enhance the detection ability of silkworm targets. By replacing the loss function with EIOU loss function, the regression accuracy of detection frame is improved and the problem caused by object occlusion is reduced. Experiments show that MAM-YOLO can more accurately detect silkworm targets, and the detection speed of mAP@0.5:0.95 reaches 74.8% and 22.2 frames per second, which realizes the functions of silkworm detection and counting with high precision and high rate, and can meet the detection requirements in the actual work of Guangxi sericulture station. results show that the silkworm detection and counting system of the technical indicators have met the application requirements, and can provide detection services for the actual production work of silkworm breeding station.In the end, a generalization experiment was conducted on the Global Wheat Challenge 2021 data set, and it was found that MAM-YOLO also showed a high improvement in the detection performance of wheat ears, proving that the proposed method can generalize to other small targets similar to dense occlusion. [ABSTRACT FROM AUTHOR]

We study decision problems of the form: given a regular or linear context-free language L, is there a word of a given fixed form in L, where given fixed forms are based on word operations copy, marked copy, shuffle and their combinations. [ABSTRACT FROM AUTHOR]

This paper proposes a performance optimization strategy for Spark-based machine learning algorithms in Shuffle and memory data management modules. The Shuffle module is optimized by introducing Observer monitoring module in Spark cluster to achieve task status monitoring and dynamic ShuffleWrite task generation. Meanwhile, an adaptive caching mechanism for RDD data addresses the lack of in-memory data caching. The performance-optimized algorithm performs well in the experiments, with a clustering accuracy of 89% and a response time that is 5% faster than the Random Forest algorithm. In road network traffic state discrimination, the optimized algorithm’s classification decision F-measure value is as high as 99.53%, which is 5.32% higher than that before unoptimization, and the running time is 767 seconds less than that of the unoptimized algorithm when dealing with about 6,880,000 pieces of data, which significantly improves the efficiency and accuracy.

This work is a study on transient vibrational behaviour of a vehicle driveline system during tip-in/released/tip-out throttle. The considered vehicle is a passenger sedan, front-wheel drive, front-engine equipped with a four-stroke four-cylinder SI engine and a 5-speed manual transmission. The engine's model follows the mean value model including the effects both dynamics and combustion parameters including manifold/engine volume, air to fuel ratio, spark advance/retard and RPM on its transient indicator and brake torque behaviour. A primary fourteen-degrees-of-freedom lumped parameter torsional driveline model including, a multi-staged clutch with nonlinear clutch springs model and the gear backlash effect on idle as well as engaged gears is considered. This model is reduced to a six-degrees-of-freedom model. A rigid 2D model and magic formula are used for modelling of vehicle-tyre-road dynamics interaction. Using the overall model, the role of the driving style, the engine dynamics and longitudinal vehicle-road dynamic interaction in creating and intensifying the shuffle, clonk, and shunt, are studied and summarized, as the particular focus of this study. Although the results verify the other related researches on the importance of nonlinear parameters such as nonlinear stiffness, backlash and gear teeth impact, on the intensity of vibro-impact responses, they prove that engine's dynamics/combustion parameters such as engine volume, intake manifold volume and spark advance timing as well as driving style and also tyre-road parameters such as coefficient of friction and road grade have significant role in intensifying the vibro-impact responses. For example, (a) the smaller the engine, the larger amplitude the shuffle; (b) the sport driving style intensifies the both the clonk and the shuffle and (c) the more slippery road in time of the tip-out, the more strengthen the shuffle. The results of the overall driver/engine/driveline/tyre/road model provide more knowledge towards better understanding of vibro-impact response of the driveline. [ABSTRACT FROM AUTHOR]

Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In order to improve the efficiency of detecting surface defects on industrial parts, a target detection method based on improved YOLOv3 is proposed. The latest attention mechanism SA (Shuffle Attention) with channel shuffling is introduced and combined with the residual unit of the Darknet-53 backbone structure of the YOLOv3 model to form the SA residual block structure, which fully utilizes the feature channel information to obtain the YOLOv3-SA model. For different datasets, the input images are scaled at different scales, and the K-means method is used to cluster the real bounding boxes to improve detection efficiency. The experimental results show that the recall rate of the YOLOv3-SA model reaches 95.4%, and the mAP can be increased by up to 7 % compared to YOLOv3. [ABSTRACT FROM AUTHOR]

The Join task between Spark large tables takes a long time to run and produces a lot of disk I/O, network I/O and disk occupation in the Shuffle process. This paper proposes a lightweight distributed data filtering model that combines broadcast variables and accumulators using RoaringBitmap. When the data in the two tables are not exactly matched, the dimension table Key is collected through the accumulator, compressed by RoaringBitmap and distributed to each node using broadcast variables. The distributed fact table data can be pre-filtered on the local server, which effectively reduces the data transmission and disk reading and writing in the Shuffle phase. Experimental results show that this optimization method can reduce disk usage, shorten the running time and reduce network I/O and disk I/O for Spark Join tasks in the case of massive data, and the effect is more obvious when the two tables have a higher incomplete matching degree or a fixed matching degree but a larger amount of data. This optimization scheme has the advantages of being easy to use, being easy to maintain and having an obvious effect, and it can be applied to many development scenarios. [ABSTRACT FROM AUTHOR]

Abstract Penalty function method is popular for constrained evolutionary optimization. However, it is non-trivial to set a proper penalty factor for a constrained optimization problem. This paper takes advantage of co-evolution to adjust the penalty factor and proposes a two-stage adaptive penalty method. In the co-evolution stage, the population is divided into multiple subpopulations, each of which is associated with a penalty factor. Through the co-evolution of these subpopulations, the performance of penalty factors can be evaluated. Since different penalty factors are used, the subpopulations will evolve along different directions. Thus, exploration can be enhanced. In the shuffle stage, all subpopulations are merged into a population and the best penalty factor from the co-evolution stage is used to guide the evolution. In this manner, the information interaction among subpopulations can be facilitated; thus, exploitation can be promoted. By executing these two stages iteratively, the feasible optimum could be obtained finally. In the two-stage evolutionary process, the search algorithm is designed based on two trial vector generation strategies of differential evolution. Additionally, a restart mechanism is designed to help the population avoid stagnating in the infeasible region. Extensive experiments demonstrate the effectiveness of the proposed method.

A word u is a shuffle of words v and w , which we denote by u ∈ v ⧢ w , if u can be obtained by mixing the letters from v and w in a way that preserves the left-to-right ordering of the letters from v and w. In case u ∈ v ⧢ v for some word v , the word u is called a shuffled-square. A word u is shuffled-square-free if it does not have a non-empty factor (i.e. , non-empty sequence of adjacent letters) that is a shuffled-square. Our contribution in this context is two-fold. First, we prove that there exist arbitrarily long shuffled-square-free words in any alphabet with six letters or more, thereby improving on a previous result of Guégan and Ochem. Furthermore, we show that recognizing shuffled-square-free words on arbitrary alphabets is NP -complete. [ABSTRACT FROM AUTHOR]

Since climate change has altered extreme precipitation and temperature patterns, further study of these patterns is essential. The examination of precipitation and temperature patterns is of great significance to water engineers, water resources management, and hydrological studies. Accordingly, this study explored the nonlinear dynamic patterns and their sources governing extreme precipitation and temperatures using multifractal, shuffling, surrogating techniques, and extreme climate indices. The temperature and precipitation data regarding central England (1931–2019) were collected and used for analysis. The results of extreme climate indices demonstrated climate change in the study area. Besides, the multifractal analysis indicated that all indices’ time series were characterized by multifractality. Despite the fact that multifractality of the maximum 1-day precipitation, minimum of maximum temperature, and maximum of maximum temperature was predominantly produced by correlation properties (long-range correlations between small and large local fluctuations), the multifractal characteristics of the warm nights were due to a probability density function (PDF) predominance. Moreover, multifractal properties of the diurnal temperature range, maximum 5-day precipitation, maximum of minimum temperature, minimum of minimum temperature, cool nights, and cool and warm days were produced by the identical extent of correlation properties and the PDF. HIGHLIGHTS Studying nonlinear dynamic patterns of temperatures and precipitation on a large scale.; Using extreme climate indices for the evaluation of temperatures and precipitation.; Employing MF-DFA to detect multifractal patterns in extreme temperatures and precipitation.; Revealing multifractal sources using surrogate and shuffling techniques.;

We define four new shuffling algorithms on permutations. For each algorithm, we characterize and enumerate the sets of permutations that are sorted after k iterations of the algorithm and we determine properties of the corresponding generating functions. For three of the algorithms, the sets of sortable permutations can be seen to be permutation classes for any nonnegative integer k. [ABSTRACT FROM AUTHOR]

Abstract Background SHuffle is a suitable Escherichia coli (E. coli) strain for high yield cytoplasmic soluble expression of disulfide-bonded proteins such as Insulin due to its oxidative cytoplasmic condition and the ability to correct the arrangement of disulfide bonds. Lispro is an Insulin analog that is conventionally produced in E. coli as inclusion bodies (IBs) with prolonged production time and low recovery. Here in this study, we aimed to optimize cultivation media composition for high cell density fermentation of SHuffle T7 E. coli expressing soluble Lispro proinsulin fused to SUMO tag (SU-INS construct) to obtain high cell density fermentation. Results Factors including carbon and nitrogen sources, salts, metal ions, and pH were screened via Plackett–Burman design for their effectiveness on cell dry weight (CDW) as a measure of cell growth. The most significant variables of the screening experiment were Yeast extract and MgCl2 concentration, as well as pH. Succeedingly, The Central Composite Design was utilized to further evaluate and optimize the level of significant variables. The Optimized media (OM-I) enhanced biomass by 2.3 fold in the shake flask (2.5 g/L CDW) that reached 6.45 g/L (2.6 fold increase) when applied in batch culture fermentation. The efficacy of OM-I media for soluble expression was confirmed in both shake flask and fermentor. Conclusion The proposed media was suitable for high cell density fermentation of E. coli SHuffle T7 and was applicable for high yield soluble expression of Lispro proinsulin.

The Join task between Spark large tables takes a long time to run and produces a lot of disk I/O, network I/O and disk occupation in the Shuffle process. This paper proposes a lightweight distributed data filtering model that combines broadcast variables and accumulators using RoaringBitmap. When the data in the two tables are not exactly matched, the dimension table Key is collected through the accumulator, compressed by RoaringBitmap and distributed to each node using broadcast variables. The distributed fact table data can be pre-filtered on the local server, which effectively reduces the data transmission and disk reading and writing in the Shuffle phase. Experimental results show that this optimization method can reduce disk usage, shorten the running time and reduce network I/O and disk I/O for Spark Join tasks in the case of massive data, and the effect is more obvious when the two tables have a higher incomplete matching degree or a fixed matching degree but a larger amount of data. This optimization scheme has the advantages of being easy to use, being easy to maintain and having an obvious effect, and it can be applied to many development scenarios.

In order to address the shuffle problem of the electric powertrain system that occurs at the moment of torque reversing, a multibody dynamics model of the powertrain system, with the measured motor torque applied as the input loading, has been established to analyze the generating mechanism of the rotating speed ripple of the drive system which is regarded the root of shuffle. The influence on speed ripple from cumulative gap size and motor torque has been investigated. The model was validated by showing good agreement between the simulated speed response and the measured data. Perturbance on each backlash was performed in the simulation to reveal the sensitivity of the speed ripple on the size of the backlash. Much higher speed-to-gap sensitivities have been observed for the low-speed engagement pairs than the high-speed engagement pairs, indicating that compressing the backlashes of the former could achieve more NVH (noise, vibration, harshness, i.e., NVH) performance improvement. [ABSTRACT FROM AUTHOR]

We consider a Markov chain that iteratively generates a sequence of random finite words in such a way that the nth word is uniformly distributed over the set of words of length 2n in which n letters are a and n letters are b: at each step an a and a b are shuffled in uniformly at random among the letters of the current word. We obtain a concrete characterization of the Doob-Martin boundary of this Markov chain and thereby delineate all the ways in which the Markov chain can be conditioned to behave at large times. Writing N(u) for the number of letters a (equivalently, b) in the finite word u, we show that a sequence (un ) n∈ℕ of finite words converges to a point in the boundary if, for an arbitrary word ν, there is convergence as n tends to infinity of the probability that the selection of N(ν) letters a and N(ν) letters b uniformly at random from un and maintaining their relative order results in ν. We exhibit a bijective correspondence between the points in the boundary and ergodic random total orders on the set {a1, b1, a2, b2, …} that have distributions which are separately invariant under finite permutations of the indices of the a's and those of the b's. We establish a further bijective correspondence between the set of such random total orders and the set of pairs (μ, ν) of diffuse probability measures on [0,1] such that ½(μ + ν) is Lebesgue measure: the restriction of the random total order to {a1, b1,…, an, bn } is obtained by taking X1,…, Xn (resp. Y1,… ,Yn ) i.i.d. with common distribution μ (resp. ν), letting (Z1,…, Z2n) be {X1, Y1,…, Xn , Yn } in increasing order, and declaring that the kth smallest element in the restricted total order is ai (resp. bj ) if Zk = Xi (resp. Zk = Yj ).

Background: SHuffle is a suitable Escherichia coli (E. coli) strain for high yield cytoplasmic soluble expression of disulfide-bonded proteins such as Insulin due to its oxidative cytoplasmic condition and the ability to correct the arrangement of disulfide bonds. Lispro is an Insulin analog that is conventionally produced in E. coli as inclusion bodies (IBs) with prolonged production time and low recovery. Here in this study, we aimed to optimize cultivation media composition for high cell density fermentation of SHuffle T7 E. coli expressing soluble Lispro proinsulin fused to SUMO tag (SU-INS construct) to obtain high cell density fermentation. Results: Factors including carbon and nitrogen sources, salts, metal ions, and pH were screened via Plackett–Burman design for their effectiveness on cell dry weight (CDW) as a measure of cell growth. The most significant variables of the screening experiment were Yeast extract and MgCl2 concentration, as well as pH. Succeedingly, The Central Composite Design was utilized to further evaluate and optimize the level of significant variables. The Optimized media (OM-I) enhanced biomass by 2.3 fold in the shake flask (2.5 g/L CDW) that reached 6.45 g/L (2.6 fold increase) when applied in batch culture fermentation. The efficacy of OM-I media for soluble expression was confirmed in both shake flask and fermentor. Conclusion: The proposed media was suitable for high cell density fermentation of E. coli SHuffle T7 and was applicable for high yield soluble expression of Lispro proinsulin. [ABSTRACT FROM AUTHOR]

Convolutional neural network (CNN) based methods, such as the convolutional encoder–decoder network, offer state-of-the-art results in monaural speech enhancement. In the conventional encoder–decoder network, large kernel size is often used to enhance the model capacity, which, however, results in low parameter efficiency. This could be addressed by using group convolution, as in AlexNet, where group convolutions are performed in parallel in each layer, before their outputs are concatenated. However, with the simple concatenation, the inter-channel dependency information may be lost. To address this, the Shuffle network re-arranges the outputs of each group before concatenating them, by taking part of the whole input sequence as the input to each group of convolution. In this work, we propose a new convolutional fusion network (CFN) for monaural speech enhancement by improving model performance, inter-channel dependency, information reuse and parameter efficiency. First, a new group convolutional fusion unit (GCFU) consisting of the standard and depth-wise separable CNN is used to reconstruct the signal. Second, the whole input sequence (full information) is fed simultaneously to two convolution networks in parallel, and their outputs are re-arranged (shuffled) and then concatenated, in order to exploit the inter-channel dependency within the network. Third, the intra skip connection mechanism is used to connect different layers inside the encoder as well as decoder to further improve the model performance. Extensive experiments are performed to show the improved performance of the proposed method as compared with three recent baseline methods. [ABSTRACT FROM AUTHOR]

Abstract When embedding secret message into image by steganography with matrix encoding, there are still no effective methods to recover the stego key because it is difficult to statistically distinguish the stego coefficient sequences selected by true and false keys. Therefore, this paper proposes a method for recovering the stego key of a typical JPEG (Joint Photographic Experts Group) image steganography—F5 which composes of the check matrix and shuffling key. Firstly, the check matrix is recovered based on the embedding ratio estimated by quantitative steganalysis. The shuffling key is then recovered based on the distribution difference between the bit sequences extracted by the true and false shuffling keys. Additionally, the cardinality of the shuffling key space is significantly reduced by examining the extracted encoding parameter and message length. Experimental results show that the proposed method can recover the stego key accurately and efficiently, even when the existing Xu’s method fails for the high or very low embedding ratio.

Though research suggests that basketball players spend approximately 31% of game actions shuffling laterally, limited data are available on the kinetic factors that separate fast and slow shufflers. The purpose of this study was twofold: (1.) Examine the reliability of kinetic metrics from a single-leg Lateral Countermovement Jump (LCMJ) (2.) Determine if kinetic metrics from the LCMJ can stratify above (i.e., “fast”) or below (i.e., “slow”) median shuffling performance. Twenty professional basketball players participated in the reliability study (21.7 ± 3.5 years, 1.98 ± 0.1 m; 89.9 ± 10.9 kg). One hundred seven professional and thirty-three collegiate basketball players (N = 140) (22.7 ± 3.5 years, 2.0 ± 0.1 m; 98.4 ± 11.9 kg) participated in the experimental study examining the ability of LCMJ kinetics to stratify shuffling performance. Reliability was assessed using Bland–Altman plots, coefficients of variation (CVs), typical errors (TEs), and intraclass correlation coefficients (ICCs). Anthropometric and LCMJ kinetic differences between fast and slow shufflers were assessed with an independent t-test. Four kinetic metrics (peak vertical force, peak lateral force, relative lateral force, and lateral impulse) met within- and between-session reliability thresholds (CV < 10% and ICC > 0.70). Faster shufflers generated significantly more relative lateral force than their slower counterparts (9.51 ± 0.8 Nx/kg vs. 8.9 ± 0.9 Nx/kg, %Diff 6.3, p < 0.00007, ES = 0.70). Basketball practitioners who have access to triaxial force plates may consider adding the LCMJ into their testing battery, as relative lateral force is a reliable metric that can stratify fast and slow shufflers.

The production of disulfide bond-containing recombinant proteins in Escherichia coli has traditionally been done by either refolding from inclusion bodies or by targeting the protein to the periplasm. However, both approaches have limitations. Two broad strategies were developed to allow the production of proteins with disulfide bonds in the cytoplasm of E. coli : i) engineered strains with deletions in the disulfide reduction pathways, e.g. SHuffle, and ii) the co-expression of oxidative folding catalysts, e.g. CyDisCo. However, to our knowledge, the relative effectiveness of these strategies has not been properly evaluated. Here, we systematically compare the purified yields of 14 different proteins of interest (POI) that contain disulfide bonds in their native state when expressed in both systems. We also compared the effects of different background strains, commonly used promoters, and two media types: defined and rich autoinduction. In rich autoinduction media, POI which can be produced in a soluble (non-native) state without a system for disulfide bond formation were produced in higher purified yields from SHuffle, whereas all other proteins were produced in higher purified yields using CyDisCo. In chemically defined media, purified yields were at least 10x higher in all cases using CyDisCo. In addition, the quality of the three POI tested was superior when produced using CyDisCo.

Transgenesis in numerous eukaryotes has been facilitated by the use of site-specific integrases to stably insert transgenes at predefined genomic positions (landing sites). However, the utility of integrase-mediated transgenesis in any system is constrained by the limited number and variable expression properties of available landing sites. By exploiting the nonstandard recombination activity exhibited by a phiC31 integrase mutant, we developed a rapid and inexpensive method for isolating landing sites that exhibit desired expression properties. Additionally, we devised a simple technique for constructing arrays of transgenes at a single landing site, thereby extending the utility of previously characterized landing sites. Using the fruit fly Drosophila melanogaster, we demonstrate the feasibility of these approaches by isolating new landing sites optimized to express transgenes in the nervous system and by building fluorescent reporter arrays at several landing sites. Because these strategies require the activity of only a single exogenous protein, we anticipate that they will be portable to species such as nonmodel organisms, in which genetic manipulation is more challenging, expediting the development of genetic resources in these systems.

We give a combinatorial proof of Stanley's shuffle theorem by using the insertion lemma of Haglund, Loehr and Remmel. Based on this combinatorial construction, we establish several refinements of Stanley's shuffle theorem. [ABSTRACT FROM AUTHOR]

In large-scale data-parallel analytics, shuffle, namely the cross-network read and the aggregation of partitioned data between tasks with data dependencies, usually bring in large overhead. To reduce shuffle overhead, we present SCache , an open-source plug-in system that particularly focuses on shuffle optimization. SCache adopts heuristic pre-scheduling combining with shuffle size prediction to pre-fetch shuffle data and balance load on each node. Meanwhile, SCache takes full advantage of the system memory to accelerate the shuffle process. We also propose a new performance model called Framework Resources Quantification (FRQ) model to analyze DAG frameworks and evaluate the SCache shuffle optimization. The FRQ model quantifies the utilization of resources and predicts the execution time of each phase of DAG jobs. We have implemented SCache on both Spark and Hadoop MapReduce. The performance of SCache has been evaluated with both simulations and testbed experiments on a 50-node Amazon EC2 cluster. Those evaluations have demonstrated that, by incorporating SCache, the shuffle overhead of Spark can be reduced by nearly 89%, and the overall completion time of TPC-DS queries improves 40% on average. On Apache Hadoop MapReduce, SCache optimizes end-to-end Terasort completion time by 15%. • SCache decouples the shuffle write and read from both map and reduce tasks. • SCache pre-schedules the reduce tasks without launching them and pre-fetches the shuffle data. • The FRQ model quantifies the utilization of resources and predicts the execution time of each phase of DAG jobs. • The shuffle overhead of Spark reduces by nearly 89%. • The overall completion time of TPC-DS queries improves 40%. [ABSTRACT FROM AUTHOR]

In the block chain privacy protection technology, the decentralized coin mixing mechanism, CoinJoin and CoinShuffle, has the problem of low security or efficiency because the agent node cannot be trusted and the node need to pass encryption layer by layer, respectively. Therefore, this paper proposed TTShuffle, a two-tier privacy protection mechanism. Firstly, it divided all mixed currency nodes into different groups, and shuffled the first layer in the group through layers of encryption to protect the sensitive data of intra-group transactions; Then, each group sent a node as the representative of the group, and carried out the second layer of inter-group shuffling to protect the sensitive data of all groups to obtain the final complete transaction content. Through security and experimental analysis, it shows that the mixing time of the 40 participants in TTShuffle is less than 10 s, which improves the mixing efficiency, reduces the mixing time and ensures the privacy security of the mixing process compared with the CoinShuffle mechanism. [ABSTRACT FROM AUTHOR]

Given a set A ⊆ S n of m permutations of { 1 , 2 , ... , n } and a distance function d , the median problem consists in finding the set M (A) of all the permutations that are the "closest" of this set A. In this article we study the automedian case of the problem, i.e. when A = M (A) , under the Kendall- τ distance. We show that automedian sets of permutations are closed under the direct sum operation and also, when some balancing properties are imposed on these sets, under the shuffle operation. These results allow us to derive a parallel algorithm that computes the medians of any separable set of permutations in O (k ! + m n) , where k is the length of its longest inseparable component. [ABSTRACT FROM AUTHOR]

Information security has immense importance in today's world of digital communication. Sharing information between intended parties while keeping it secret from any outsider is a challenging task. Cryptography provides the security of information by keeping the information secret among only the communicating parties. In this paper, a new cryptographic algorithm is propounded, which is a block cipher algorithm. A positive integer, n, two n × 256 tables of ASCII characters, and a string, Mapper, are used as shared private keys in the proposed method. Every row of the key tables and Mapper contains a random permutation of all the ASCII characters. In the beginning of encryption of a block, an Exclusive Or (XOR) operation is done between the block and a random array of integers. Then, a 3-round substitution operation is done using the key tables and Mapper. We found that the encryption and the decryption speed of the proposed algorithm is faster than the encryption–decryption speed of Advanced Encryption Standard (AES) and Data Encryption Standard (DES). The strength of the proposed cipher was analyzed in the light of some popular attacks, and we found that the system is strong enough against those attacks. We hope that if integrated with the modern cryptography protocols, this method will enhance modern digital communication security. [ABSTRACT FROM AUTHOR]

Microbial production of antibodies offers the promise of cheap, fast, and efficient production of antibodies at an industrial scale. Limiting this capacity in prokaryotes is the absence of the post-translational machinery, present in dedicated antibody producing eukaryotic cell lines, such as B cells. There has been few and limited success in producing full-length, correctly folded, and assembled IgG in the cytoplasm of prokaryotic cell lines. One such success was achieved by utilizing the genetically engineered Escherichia coli strain SHuffle with an oxidative cytoplasm. Due to the genetic disruption of reductive pathways, SHuffle cells are under constant oxidative stress, including increased levels of hydrogen peroxide (H2O2). The oxidizing capacity of H2O2 was linked to improved disulfide bond formation, by expressing a fusion of two endoplasmic reticulum-resident proteins, the thiol peroxidase GPx7 and the protein disulfide isomerase, PDI. In concert, these proteins mediate disulfide transfer from H2O2 to target proteins via PDI-Gpx7 fusions. The potential of this new strain was tested with Humira, a blockbuster antibody usually produced in eukaryotic cells. Expression results demonstrate that the new engineered SHuffle strain (SHuffle2) could produce Humira IgG four-fold better than the parental strain, both in shake-flask and in high-density fermentation. These preliminary studies guide the field in genetically engineering eukaryotic redox pathways in prokaryotes for the production of complex macromolecules. Key points: • A eukaryotic redox pathway was engineered into the E. coli strain SHuffle in order to improve the yield of the blockbuster antibody Humira. • The best peroxidase-PDI fusion was selected using bioinformatics and in vivo studies. • Improved yields of Humira were demonstrated at shake-flask and high-density fermenters. [ABSTRACT FROM AUTHOR]