Miracle-dz/UniFAR-FTUs huggingface dataset:
Genome-Wide SNP Markers Based on SLAF-Seq Uncover Genetic Diversity of Saccharina Cultivars in Shandong, China

China has an abundance of Saccharina cultivars. Most of them are in Shandong Province. These cultivars have been used for many years, and their genetic structure is likely to change. In this study, 13 Saccharina cultivars that are widely applicated in China were collected from Rongcheng and Changdao and sequenced using SLAF-seq technology. A total of 277.27M reads were obtained with a 12.98× sequencing depth, a mean 87.84% Q30 ratio, and a mean 48.49% GC content. A subset of >300,000 SNPs associated with minor allele frequency (MAF) > 0.05 and integrity > 0.5 was obtained. A phylogenetic tree and a population structure analysis based on the high-confidence SNP data showed that the 13 Saccharina cultivars could be divided into 2 groups. The average nucleic acid diversity (Pi), heterozygosity (Ho), and expected heterozygosity (He) of SNP markers in the populations were 4.38×10-5, 0.249, and 0.265, respectively. The average inbreeding coefficient is 0.198, which is high. The results could provide scientific basis for heterosis in Saccharina breeding and germplasm utilization.
The Comparative Efficacy of Non-ergot Dopamine Agonist and Potential Risk Factors for Motor Complications and Side Effects From NEDA Use in Early Parkinson's Disease: Evidence From Clinical Trials

Background/Objectives Non-ergot dopamine agonist (NEDA) are recommended as the first-line treatment for patients with early Parkinson's disease (PD) because of their efficacy in treating PD motor symptoms. However, systematic evaluations of the risk of motor complications induced by NEDA and risk factors potentially associated with motor complications are still lacking. Methods Medline, Embase, the Cochrane Central Register of Controlled Trials, and Web of Science were searched for potentially eligible randomized controlled trials. The incidence of motor complications (dyskinesia, motor fluctuations), impulsive-compulsive behaviors and adverse events and clinical disability rating scale (UPDRS) scores were evaluated using standard meta-analytic methods. Metaregression was conducted on the incidence of motor complications (dyskinesia) with treatment duration and NEDA dose as covariates. Results Patients treated with NEDA had significantly lower UPDRS total scores, motor scores and activity of daily living (ADL) scores than those receiving a placebo (weighted mean difference (WMD) −4.81, 95% CI −6.57 to −3.05; WMD −4.901, 95% CI −7.03 to −2.77; WMD −1.52, 95% CI −2.19 to −0.84, respectively). Patients in the NEDA and NEDA+open Levodopa (LD) groups had lower odds for dyskinesia than patients in the LD group (OR = 0.21, 95% CI: 0.15–0.29; OR = 0.31, 95% CI 0.24–0.42, respectively). Metaregressions indicated that the mean LD dose of the NEDA group increased, and the odds of developing dyskinesia increased (p = 0.012). However, the odds of developing dyskinesia in the NEDA group were not related to treatment duration (p = 0.308). PD patients treated with NEDA or NEDA+open LD had a lower risk of wearing-off implications than those treated with LD (all p < 0.05). No significant difference was found between the NEDA and placebo groups in impulsive-compulsive behavior development (p > 0.05). Patients in the NEDA group were more likely to suffer somnolence, edema, constipation, dizziness, hallucinations, nausea and vomiting than those in the placebo or LD group. Conclusion NEDA therapy reduces motor symptoms and improves ADLs in early PD. The odds of developing motor complications were lower with NEDA than with LD, and dyskinesia increased with increasing LD equivalent dose and was not influenced by NEDA treatment duration. Therefore, long-term treatment with an appropriate dosage of NEDA might be more suitable than LD for early PD patients. Registration PROSPERO CRD42021287172.
Intermediate Observer based Fault-Tolerant Control for Fuzzy Stochastic Systems with Actuator Fault*

This paper discusses the fault estimation and fault-tolerant control of fuzzy stochastic systems under disturbance. Firstly, an intermediate observer is devised to reconstruct the state and actuator fault. The difference from the classical intermediate observer is that this method introduces the output estimation error feedback into the observer design, so this method expands the design freedom. Then a fault-tolerant controller based on the information estimated by the observer is designed. Lastly, a simulation of cart model is provided to show the reliability of our method.
A Robust Point Cloud Registration Method for Structured Scenes Based on Plane Elements Using Global Information

Point cloud registration is a necessary step in the fields of mapping and positioning when a complete surface description of the scene needs to be constructed. This paper proposes a robust point cloud registration method based on plane elements, which uses plane information in the environment to solve the transform matrix. A combination of region grow and Random Sample Consensus (RANSAC) algorithm is employed to segment the planes from the scene. A novel metric based both on the plane area and the number of plane points is utilized to filter abundant planes in the environment. Coincidence degree for diffenent candidate transforms are calculated after down-sampling to determine the optimal result of the final transform matrix. Experimental results demonstrate that the success rate of the proposed method on Apartment and Stairs dataset are 95% and 96%, respectively. The success rate is at least 10% higher than the current commonly-used global and local registration methods. In conclusion, the proposed robust registration method has superior performance in structured scenes with small overlap for applications in different environment.
Learning Model Predictive Control Law for Nonlinear Systems

In this paper, the model predictive control(MPC) problem of nonlinear systems is studied. Since the traditional MPC algorithms require a lot of computing resources and time to solve optimal control problems, it is hard to use in practical systems with high sampling rate. To resolve this issue, this paper proposes a deep neural network-based learning model predictive control (LMPC) algorithm to improve the speed of computing the control laws. Because the use of deep neural network to learn the control law instead of solving optimization problems, the computation speed of MPC algorithm has been greatly improved. Simulation experiments verify the effectiveness of the proposed algorithm.
The Application of 1D-CNN in Microsoft Malware Detection

In the computer field, cybersecurity has always been the focus of attention. How to detect malware is one of the focuses and difficulties in network security research effectively. Traditional existing malware detection schemes can be mainly divided into two methods categories: database matching and the machine learning method. With the rise of deep learning, more and more deep learning methods are applied in the field of malware detection. Deeper semantic features can be extracted via deep neural network. The main tasks of this paper are as follows: (1) Using machine learning methods and one-dimensional convolutional neural networks to detect malware (2) Propose a machine The method of combining learning and deep learning is used for detection. Machine learning uses LGBM to obtain an accuracy rate of 67.16%, and one-dimensional CNN obtains an accuracy rate of 72.47%. In (2), LGBM is used to screen the importance of features and then use a one-dimensional convolutional neural network, which helps to further improve the detection result has an accuracy rate of 78.64%.
Research on Influential Factors in Stock Market Prediction with LSTM

Stock market prediction is the prediction of stock index or single stock price by time series data prediction algorithms such as machine learning or deep learning. It is used to assist stock market supervision or stock trading, etc. This paper studies the influential factors of stock market prediction by deep learning model long-short term memory (LSTM). Three kinds of influential factors are selected including historical Shanghai A-share index, the historical US NASDAQ index and the term frequency of "increase positions" and "decrease positions" in Weibo. And in order to see their performance, the influential factors are made into two kinds of groups. One is single factor group and the other is multiple factor group. The experiment is carried out on the data during January 1, 2018 to December 31, 2020. And the prediction results are measured by three evaluation metrics: mean absolute error MAE, root mean square error RMSE and mean absolute percentage error MAPE. The experiment results show that the three evaluation metrics of the prediction results of single factor group are better than the prediction results of multiple factor group in both terms of the prediction of stock index value and its daily rise and fall. This paper shows that in the research of stock market index trend prediction, it is not the more predictive factors, the better the prediction results.
The Impact of Media Attention on Enterprises’ Green Technology Innovation

Using the A-share industrial companies from 2015~2019 as samples, this paper takes financing constraints as the intermediary variable and the nature of enterprise property rights as the moderating variable to explore the impact of different types of media attention on enterprises' green technology innovation. The empirical results show: ①Media attention can promote the green technology innovation of enterprises. Total media attention, eight mainstream media attention, paper media attention, and online media attention can significantly improve enterprises' green technology innovation level. ②Financing constraints play a part of the intermediary role between the eight mainstream media attention and green technology innovation, while the intermediary role between total media attention, paper media attention, online media attention and green technology innovation is not significant. ③The nature of corporate property rights positively regulates the relationship between total media attention, eight mainstream media attention, paper media attention, online media attention and green technology innovation. This study contributes to a more profound and comprehensive understanding of the influence of the media on corporate green innovation behavior, and provides experience and reference for the research and practice of corporate governance and innovation effects that the media pay attention to.
Quantitative Analysis of PV Micro-inverters in terms of Full and Partial Irradiance

Performance and reliability of micro inverter in photovoltaics (PV) is most important to increase the life span of PV which also reduces its cost and maintenance. In this paper, detailed comparative analysis of various design cases of PV micro inverter have been given. The comparison is made in terms of voltage, current and total harmonic distortion (THD) by considering partial and full irradiance. The four cases have been categorized and investigated as single level, multilevel and cascaded system by Using MATLAB as a tool. Results reveal that partial shading does increase the harmonics with cascaded inverters; however the impact is limited as compared to single level micro inverter. The overall THD of cases and current, voltages increases under partial shading but remains within defined IEEE limits.
TQM Metrics in Mobile Applications Industry

: Development of mobile applications is a young market and open broadly to organizations and individuals for competition. Software organizations utilize total quality management (TQM) strategies progressively to enhance their operations and procedures as a part of every single zone of their business. This study focuses on the Total Quality Management (TQM) properties in mobile applications industry. It aims to explore the relation between mobile application development and TQM. It identifies the metrics and attributes that help to gain successful implementation of TQM tools in field of mobile applications platform. The importance of this paper comes from the significance knowledge which provides an experience of implementing TQM in field of mobile applications industry with an overview of mobile applications. It concludes with clarifying benefits of applying TQM strategies, and how TQM philosophy could be conducted successfully.
Linear Variational Autoencoder for Top-N Recommendation

Top-N recommendation is significant in various service-based platforms. Variational Autoencoders (VAEs) have been used in top-N recommendation in recent years for its effectiveness in collaborative filtering. Mult-VAE is such a variant that achieves great success, by adopting the multinomial likelihood, and an additional hyperparameter β on the KL divergence term of ELBO of VAE. However, Mult-VAE uses non-linear neural networks as encoder and decoder, to encode and reconstruct the user-item interaction data, which we prove unnecessary in sparse datasets because it will degrade the prediction accuracy in our experiments. Moreover, most variants of VAE-based collaborative filtering methods use the unnormalized user-item interaction data to make recommendations, which will hinder the learning process of the interaction data. In this paper, we propose Linear Variational Autoencoder (LVA), a linear version of Mult-VAE, considering additional normalization on user-item interaction data, for collaborative filtering under the implicit feedback setting. We verify its effectiveness in the experiments and prove that LVA achieves better or competitive performance over current state-of-the-art collaborative filtering methods, e.g, LightGCN, on four public real-world datasets.
Research on UAV autonomous deformation strategy based on deep learning

To improve the autonomous deformation capability of morphing aircraft, a new method was proposed, which established an autonomous deformation strategy knowledge base based on deep learning. A longitudinal nonlinear dynamic model of variable-sweep-wing unmanned aerial vehicle (UAV) was obtained by using multi-rigid-body modeling. DATCOM was used to perform aerodynamic analysis, and then aerodynamic coefficient fitting formula was calculated. Based on prior knowledge, a deformation strategy was designed, which could generate training data for deep learning. A deep neural network (DNN), so-called autonomous deformation strategy knowledge base, was trained by using these data, which could output optimal sweep angle according to current flight states and flight mission. Simulation results show that this DNN gives morphing U A V a better speed maneuverability in deceleration task and a better directional maneuverability in turning task, which can enhance UA V's combat capability.
Development and Application of Intelligent Agricultural Machinery—A Review

With the rapid development of science and technology and the acceleration of urbanization, agricultural mechanization has become an indispensable condition for sustainable agricultural development. Automation is an important cornerstone to improve the rapid development of agricultural machinery in the direction of specialization, efficiency, automation, informatization and intelligence. It can not only help improve the quality and output of agricultural products, but also ensure the healthy and sustainable development of modern agriculture, and accelerate the process of agricultural modernization. This review systematically summarizes and analyzes the technological achievements of intelligent agricultural machinery in recent years, explores future opportunities and challenges, and provides the latest reference for researchers.
Simulation and Experimental Analysis of a Mechanical Flux Modulated Permanent Magnet Homopolar Inductor Machine

A permanent magnet homopolar inductor machine with a mechanical flux modulator (PMHIM-MFM) for flywheel energy storage system (FESS) is investigated. The no-load air-gap flux generated by the PM can be suppressed, and the no-load core loss can be reduced by using the MFM when the PMHIM-MFM works at an idling state, which helps to improve the energy transformation ratio of the FESS. First, the operation principle and flux modulation mechanism of the PMHIM-MFM are presented. Second, the influences of MFM shapes and structure parameters, moving distances, and the eccentricity on the electromagnetic characteristics of PMHIM-MFM are investigated based on the 3-D finite element method (FEM). Third, the simplified equivalent analysis model of the PMHIM-MFM is proposed to analyze the flux weakening ability and magnetic pull of the MFM. Fourth, the characteristics of the armature reaction of the PMHIM-MFM are investigated, and the load performance is studied. Finally, a prototype of the PMHIM-MFM is tested, and some theoretical analyses and FE calculations are validated.
Defense against Adversarial Attacks Using Image Label and Pixel Guided Sparse Denoiser

The Deep Neural Networks models have been extensively applied to many real-world areas due to their outstanding performance, especially to safety-critical applications such as autonomous vehicles, healthcare and face recognition. However, many types of research showed that they are easily disturbed by some carefully crafted tiny perturbations, which are called adversarial examples. Therefore, in this paper, a novel method is proposed as image preprocessing. When preprocessing, without discriminating the input images clean images or adversarial examples, the adaptive parameters adjust the images of label loss and pixel-level loss to jointly guide the model to generate reconstructed images and eliminate adversarial perturbation without affecting the images quality and classification result. Moreover, the model uses an architecture of a sparsity constraint that ensures that a neuron fires only for meaningful patterns, limiting the influence of adversarial perturbation on clean images. The defense proposed against attacks of FGSM, MI-FGSM, C&W, PGD, RAND+FGSM under the MNIST and CIFAR-10 datasets. The experimental results show that the target model can reach the average accuracy of 98% and 92% separately when the MNIST and CIFAR-10 datasets are attacked through the defense proposed, demonstrating the effectiveness of the proposed method.
Ring-Star based transmission over Fiber/FSO Links for Reliable Access Network Services

In this work, we propose and simulate an unidirectional Free Space Optical (FSO) or Optical Fiber (OF) link transmission with Wavelength Division Multiplexing (WDM) capability using ring and star topology. The data transmission is performed on the ring-based architecture while keeping the star-based architecture as a backup link and vice versa if fault occurs in OF or FSO links. For the proposed architecture, we compare FSO-WDM and OF-WDM network performance for ring & star-based architecture. The bit error rate (BER) performance is quite satisfactorily for feeder fiber (FF) length of $50\text{km}$ and distributed fiber (DF)/FSO link length of $200\mathrm{m}$. The architecture provides the reliable high-speed data transmission and may be useful for smart rural and urban communities.
Integration of CQCC and MFCC based Features for Replay Attack Detection

This paper evaluates the performance of integration of CQCC and MFCC based features for automatic speaker verification (ASV) system. The detection of replay attack is challenging. For the detection of spoofing attacks, it is important to focus on front-end processing i.e., feature extraction. This paper discusses feature extraction techniques, LPC, MFCC and CQCC. The performance of baseline $\text{CQCC}+\text{GMM},\ \text{LPC}+\text{GMM}$, and $\text{MFCC}+\text{GMM}$ is evaluated on ASVspoof 2017 version 2 dataset. Further, integration of CQCC and MFCC showed improved performance resulting in EER 10.18%.
Refractometry using Large Index Contrast Narrow Width Micro-ring Resonator

Exploiting the large field confinement of transverse magnetic mode in low refractive index region, we proposed and analyze refractometer using narrow width, high index contrast micro-ring resonator. For the waveguide width of 60 nm, a sensitivity of 610 nm/RIU is achieved with figure of merit 228.4 RIU−1 and limit of detection $4.4\times 10^{-3}$ in the range of 1.33-1.37 of refractive indices.
Learning behavior, strategy and performance : a structural equation modeling study

The theme for this study is organisational learning and strategy, and more specifically about the relationship between learning behaviour, strategy and performance. There were to research questions. The first is regarding the relationship between learning behaviour and performance, with the research question being: Does increased use of learning behaviour in a business simulation have an impact on the overall performance in the business simulation? The second research question is regarding the relationship between learning behaviour, strategy and performance, with strategy divided in two variables, quality factors and implementation factors. The research question is: Does increased use of learning behaviour increase the use of both implementation factors and quality factors? And will an increased level of implementation factors and quality factors lead to improved performance? Based on theory and research from among others Argyris and Schon (1996), Amy Edmondson (1999), Hanne Iversen (2006) and Henri Mintzberg (2000, 2007), two theoretical models was developed. The models was tested empirically using Structural Equation Modelling, on a sample of mid level managers participating in a strategy simulation After modifying the second model, the empirical findings are in accordance with the hypothesised models, and the relationship between learning behaviour and performance, as well as learning behaviour, strategy and performance is confirmed in the simulation.
Innovation and Design Processes : Towards a Model of Social Responsibility

The design practice today is rapidly evolving due to complex and integrated driving forces that lead to a new holistic design method based on a collaborative and socially responsible approach. Due to the depletion of physical resources, we have to replace the current processes based on material consumption with new processes supplied by goods that are limitless and can be used by a large number of people without causing exhaustion. The main value of today design is then human capability to produce ideas since they are non rival and non expensive goods. The main factor of economic growth is technological progress that works at the rhythm of decoupling to increase productivity by reducing raw materials consumption. The paper proposes an organic method for urban and architectural design based on the limitlessness of human creativity and on the intangibility of scarce natural resources, implemented in a local design experience.
Modelling and Calibration of a High-frequency Polarization Sensor: Applying to Underwater Variable Ambient Light Environment

With the advantages of full autonomy and no accumulative error, polarization navigation is an effective means to remedy the defects of existing underwater navigation, such as errors divergence and low reliability. However, the research on polarization navigation applied to underwater scene is not as mature as that applied to atmospheric environment because of the complexity of underwater environment. In order to increase the environmental adaptability of polarization navigation and applied it to underwater scene, inspired by the polarization vision of mantis shrimp, this paper designed a high-frequency polarization sensor. Considering the influence of fluctuant ambient light intensity underwater, a sensor model considering the influence of light intensity variation, a method to obtain polarization information, and a calibration method considering light intensity are proposed. The sensor model and calibration method were validated in the indoor underwater environment.