Miracle-dz/UniFAR-FTUs huggingface dataset:
Isolation and Identification of Pennogenin Tetraglycoside from Cestrum nocturnum (Solanaceae) and Its Antifungal Activity against Fusarium kuroshium, Causal Agent of Fusarium Dieback

Antifungal assay-guided fractionation of the methanolic crude extract of Cestrum nocturnum (Solanaceae), popular known as ‘lady of the night’, led the isolation and identification of the steroidal saponin named pennogenin tetraglycoside, which was identified for the first time in this plant species by spectroscopic means. The crude extract, fractions and pennogenin tetraglycoside exhibited mycelial growth inhibition of Fusarium solani and F. kuroshium. F. solani is a cosmopolitan fungal phytopathogen that affects several economically important crops. However, we highlight the antifungal activity displayed by pennogenin tetraglycoside against F. kuroshium, since it is the first plant natural product identified as active for this phytopathogen. This fungus along with its insect symbiont known as Kuroshio shot hole borer (Euwallacea kuroshio) are the causal agents of the plant disease Fusarium dieback that affects more than 300 plant species including avocado (Persea americana) among others of ecological relevance. Scanning electron microscopy showed morphological alterations of the fungal hyphae after exposure with the active fractions and 12 phenolic compounds were also identified by mass spectrometry dereplication as part of potential active molecules present in C. nocturnum leaves.
An improved parallel pre-synchronization control for multiple virtual synchronous generators

Parallel running multiple virtual synchronous generator connected to the electricity grid will make the micro grid showed weak characteristic of the grid. The strong coupling relationship between the phase-locked loop and the nonnegligible grid impedance will cause the instability of grid-connected inverter. In order to solve the grid connection problem, a pre-synchronization control based on virtual synchronous generator and double second order generalized integral frequency lock-loop is constructed. The output frequency of the virtual synchronous generator as the input of the frequency locked loop. And the output speed compensation of the frequency locked loop is imported into the virtual synchronous generators active power-frequency loop to correct the output frequency of the virtual synchronous generator. Thus, an automatically regulating frequency loop is formed. And the adaptive digital filter is used to filter the harmonic and noise of the input signal, which improves the frequency detection accuracy and has excellent dynamic and steady performance. Finally, through theoretical analysis and simulation results, the effectiveness of the pre-synchronization control strategy combined with the virtual synchronous generator algorithm and the frequency lock loop for the grid connection of the virtual synchronous generator is verified.
MECHANICAL MODEL AND DATA PROCESSING OF LOAD MEASUREMENT TEST FOR THE AIRPLANE'S WING STRUCTURE

The structure is often directly designed and processed into a sensor measurement system to realize single independent loading measurement when load measurement test is done. The relation equation regarding the strain output of electrical bridge versus the load input is established through indoor calibration test. Thereby the load time history of structural members in the actual service is obtained. But in the real situation, it is difficult or not permitted to design and process a lot of large sized structural members into sensors. The mechanical model of the load measurement test for the large sized structural members of airplane′s wing is investigated where an Yun 7 airplane is taken for example. The relation equation of the load input and strain output for the structural members of the airplane′s wing is created by using multiple linear regression. And, based on this, the load time course for the measurement cross section of the airplane′s wing and tail in the actual flying is obtained.
Creating functionally graded concrete materials with varying 3D printing parameters

ABSTRACT The varying physical property in a functionally graded material can be tailored to its specific requirements while using material resources effectively. 3D concrete printing can produce robust and lightweight functionally graded material for the construction industry. In this study, an approach was developed to create an objective tailored functionally graded concrete material by examining the filament material property correlations with the printing parameters. It was found that the physical property of the printed material is closely correlated to the flow rate and travel speed of the printer. Results obtained from the experiment show that the optimised structure achieved an approximately 50% improvement in the strength-to-weight ratio. This preliminary examination of the 2D optimised concrete structure opens the realm of possibility for future work in 3D optimised functionally graded concrete.
Consensus of Second-order Heterogeneous Linear Multi-agent Systems

This paper explores the leader-following consensus of second-order heterogeneous linear multi-agent systems with the directed communication topology. Firstly, we propose a general consensus algorithm which composed of different gain matrices and generic communication matrices, which increases the system freedom degree. Then, we adopt the state linear transformation method to convert the consensus study to a partial variable stability study of the corresponding auxiliary system, thereby reducing the computation complexity of the research problem. On this basis, we get a sufficient and necessary consensus standard. According to this criterion, by solving bilinear matrix inequality, we propose an algorithm for solving the protocol gain matriies. Finally, two sets of simulations prove the effectiveness of the algorithm proposed in this paper.
Material State Identification of the Conveyor Belt based on Inception-v4

For the uneven phenomenon of materials during material transportation on the factory assembly line, this paper proposes an improved model based on Inception-v4 to realize the identification and classification of three material states (more materials, normal materials, and fewer materials). In the image processing stage, the Farneback algorithm is used to capture the moving area, which makes the training of the model focus on effective information. In addition, considering the color bias of materials, the RGB enhancement strategy is proposed. By processing the image data in RGB channels, different weights are given to realize the enhancement of image features. In the network training and testing stage, we use Inception-v4 as the baseline model. The output of the model is modified for the classification task, and a new loss function is designed. In addition, considering the limited sample size, we pre-trained the model on ImageNet. Through experimental comparison, the improved algorithm proposed in this paper has better performance.
Passenger Flow Prediction of Scenic Spot Using a GCN–RNN Model

The prediction and control of passenger flow in scenic spots is very important to the traffic management and safety of scenic spots. This study aims to predict the passenger flow of a scenic spot based on the passenger flow of the bus and subway stations around the scenic spots. We propose a passenger flow prediction model based on graph convolutional network–recurrent neural network (GCN–RNN). First, a “graph” is constructed according to the geographical relationship between the scenic spot and the surrounding bus and subway stations. Then, characteristics of surrounding areas of bus and subway stations are constructed based on the crowd behavior analysis, and these are then used as the node-information of the “graph”. Last, the GCN–RNN model is used to extract the temporal and spatial characteristics of the passenger flow data of the scenic spot to realize the prediction. The experimental results show that the proposed model is effective in passenger flow prediction in scenic spots.
Brain Network Associated with Three Types of Remote Associations: Graph Theory Analysis

ABSTRACT Remote association, the ability to form new relations between independent elements, was assessed using the Remote Associates Test (RAT). The Chinese RAT (CRAT) includes the Chinese radical RAT (CRRAT), Chinese word RAT (CWRAT), and Chinese compound RAT (CCRAT). Behavioral research indicates that an individual’s performance on the CRAT reflects different ways of remote associations, although there are few studies on brain networks. Therefore, this study analyzed the correlation between structural network connectivity and CRAT performance in 52 adults. The findings showed that CCRAT performance had a positive correlation with clustering coefficients, local efficiency, and global efficiency, but a negative correlation with the characteristic path length. CWRAT performance was positively correlated with normalized clustering coefficients and small-world-ness, and CRRAT performance was positively correlated with characteristic path length. This study enriches our understanding of the process of remote association by exploring different remote associations from a brain network perspective.
Global emissions of perfluorocyclobutane (PFC-318, <i>c</i>-C<sub>4</sub>F<sub>8</sub>) resulting from the use of hydrochlorofluorocarbon-22 (HCFC-22) feedstock to produce polytetrafluoroethylene (PTFE) and related fluorochemicals

Abstract. Emissions of the potent greenhouse gas perfluorocyclobutane (c-C4F8, PFC-318, octafluorocyclobutane) into the global atmosphere inferred from atmospheric measurements have been increasing sharply since the early 2000s. We find that these inferred emissions are highly correlated with the production of hydrochlorofluorocarbon-22 (HCFC-22, CHClF2) for feedstock (FS) uses, because almost all HCFC-22 FS is pyrolyzed to produce (poly)tetrafluoroethylene ((P)TFE) and hexafluoropropylene (HFP), a process in which c-C4F8 is a known by-product, causing a significant fraction of global c-C4F8 emissions. We find a global emission factor of ∼0.003 kg c-C4F8 per kilogram of HCFC-22 FS pyrolyzed. Mitigation of these c-C4F8 emissions, e.g., through process optimization, abatement, or different manufacturing processes, such as refined methods of electrochemical fluorination and waste recycling, could reduce the climate impact of this industry. While it has been shown that c-C4F8 emissions from developing countries dominate global emissions, more atmospheric measurements and/or detailed process statistics are needed to quantify c-C4F8 emissions at country to facility levels.
The genome sequence of the angle shades moth, Phlogophora meticulosa (Linnaeus, 1758)

We present a genome assembly from an individual female Phlogophora meticulosa (the angle shades; Arthropoda; Insecta; Lepidoptera; Noctuidae). The genome sequence is 539 megabases in span. The majority of the assembly, 95.17%, is scaffolded into 31 chromosomal pseudomolecules, with the Z sex chromosome assembled. Some unassigned scaffolds are identified as belonging to the W chromosome based on half-depth coverage and comparison to other Noctuidae W chromosomes. The mitochondrial genome was also assembled and is 15.4 kilobases in length.
Bureaucratic squires: A critical analysis of the construction of charismatic top-leaders

Top-leaders continuously struggle to cultivate their image and duly face a challenge in fulfilling their role: establishing authority among their senior followers, while maintaining productive social distance to enhance charismatic appeal. Considering this challenge, rather than focusing on leader–follower relationship, this study critically spotlights the workings of top-leader assistants, referred to as squires (Weber & Moore, 2014). Accordingly, we inquire how squires construct the charisma of top-leaders within a bureaucratic setting. Through an extensive ethnographic study in Israeli military headquarters, alongside 28 semi-structured interviews with Generals, staff officers, and personal assistants of Generals, we identified two primary strategies by which squires enhance their top-leader’s charismatic appeal: daily aggrandizing rituals and wrapping. Daily rituals that occur in meetings and visitations enhance the leader’s aura as the embodiment of authority. Wrapping is diverse acts in which squires broker the leaders’ environment and access to subalterns, thus allowing leaders to focus on their primary agenda, while elevating their perceived status. These strategies allow dyadic performance of top-leaders and squires. They intricately utilize formal and informal interactions to sustain effective distance from followers, to foster disciplined selves among senior followers. Implications of the findings regarding the role of squires as power brokers and their equivalent roles in contemporary organizations are discussed, alongside implications regarding leadership and bureaucracy research.
Performance Analysis Of Various Anti-Reset Windup Algorithms For A Flow Process Station

The study was aimed to develop the various aspects of Anti reset windup or Integral windup and also the different algorithms available to eliminate the phenomenon of windup. Different open loop responses were obtained from a Flow process Station using MATLAB and SIMULINK and VI Microsystems process control software. The open loop responses were evaluated and different system models were generated using the two point method. The system models were found to follow a decreasing order of Gain values and an increasing order of Td and T values. A SIMULINK model was obtained to implement Back calculation combined with Conditional Integration. The models for the system obtained were simulated using the SIMULINK model and a PID controller and the closed loop responses were generated. The closed loop responses using a PID controller with Back calculation and Conditional integration were found to follow the set point as expected.
A Quasi-Experimental Investigation of Adult Student Enrollment Responses to the Tennessee Reconnect Grant

ABSTRACT In this study I deployed quasi-experimental methods to explore enrollment responses to a unique statewide college promise program for adult students attending college within the two-year public postsecondary education sector in Tennessee. State policymakers implemented Tennessee Reconnect in 2018 to encourage adult students without a college degree to enroll in college and pursue a postsecondary credential or degree. There is little research quantitatively exploring how promise programs affect adult student enrollment in contrast with nationwide trends. For this study, I used differences-in-differences to determine the effect of Tennessee Reconnect on total, part-time, full-time, male, and female adult student enrollment at public two-year postsecondary institutions in the first two years of the program. Findings revealed a significant increase in enrollment in response to the policy across all adult student categories in the first year. While overall adult student enrollment increased in the second year of the program, only part-time students and male students saw significant increases compared to the nationwide sample. Findings are encouraging regarding adult student responses to a free college program and how states can leverage policies to potentially increase enrollment among individuals who may not have otherwise pursued postsecondary education, such as adult students.
In vitro and in vivo antimicrobial activity of cinnamaldehyde and derivatives towards the intestinal bacteria of the weaned piglet

Abstract Cinnamaldehyde has an effective antimicrobial activity and therefore it received interest as a feed additive in swine nutrition. However, its high reactivity to amino acid residues might affect its efficacy and digestive processes. As an alternative, 20 chemical derivatives were evaluated. The in vitro antimicrobial activities were tested in an in vitro fermentation model. Thereof three compounds were selected and fed to newly weaned piglets. Five dietary treatments were replicated in six pens of four pigs per pen; i.e. control, cinnamaldehyde at 100 and 400 mg/kg, and 2-methoxycinnamaldehyde and 4-methoxycinnamaldehyde, both equimolar to 400 mg/kg cinnamaldehyde. In vitro results showed that 4-nitrocinnamaldehyde has the highest antimicrobial activity; however, this compound is carcinogenic and was not further issued. Cinnamaldehyde had the second-highest activity, particularly against coliform bacteria and Escherichia coli (E. coli), followed by 4-methoxycinnamaldehyde, 2-methoxycinnamaldehyde and hydrocinnamaldehyde. All other derivatives showed lower potency, but they were consistently more bactericidal against coliform bacteria and E. coli as compared to Gram-positive bacteria. At pH 7, aldehydes showed stronger bactericidal activity than their corresponding carboxylic acids, which was not the case at pH 5, suggesting a different mode of action. In the in vivo trial, no significant improvements in animal performance or antimicrobial effects were observed. To conclude, apart from 4-nitrocinnamaldehyde, none of the derivatives showed higher antimicrobial potency than cinnamaldehyde. Three selected compounds from in vitro trials failed to demonstrate major positive outcomes in the in vivo trial. Highlights 4-Nitrocinnamaldehyde has the highest antimicrobial activity in vitro. Cinnamaldehyde had the second-highest activity, followed by 4-methoxycinnamaldehyde, 2-methoxycinnamaldehyde. No significant improvements in animal performance or antimicrobial effects were observed.
Hyperspectral Image Classification With Contrastive Self-Supervised Learning Under Limited Labeled Samples

Hyperspectral image (HSI) classification is an active research topic in remote sensing. Supervised learning-based methods have been widely used in HSI classification tasks due to their powerful feature extraction capabilities for cases of sufficiently labeled samples. However, practical applications often have limited samples with accurate labels due to the high cost of labeling or unreliable visual interpretation. We introduce a contrastive self-supervised learning (SSL) algorithm to achieve HSI classification for problems with few labeled samples. First, a new HSI-specific augmentation module is developed to generate sample pairs. Then, a contrastive SSL model based on Siamese networks is used to extract features from these easily accessible sample pairs. Finally, the labeled samples are taken to fine-tune the parameters of the classification model to boost classification performance. Tests of the contrastive self-supervised algorithm have been performed on two widely used HSI datasets. The experimental results reveal that the proposed algorithm requires a few labeled samples to achieve superior performance.
Enterprise Governance of Information Technology: Achieving Alignment and Value, Featuring COBIT 5

Featuring numerous case examples from companies around the world, this second edition integrates theoretical advances and empirical data with practical applications, including in-depth discussion on the COBIT 5 framework which can be used to build, measure and audit enterprise governance of IT approaches. At the forefront of the field, the authors of this volume draw from years of research and advising corporate clients to present a comprehensive resource on enterprise governance of IT (EGIT). Information technology (IT) has become a crucial enabler in the support, sustainability and growth of enterprises. Given this pervasive role of IT, a specific focus on EGIT has arisen over the last two decades, as an integral part of corporate governance. Going well beyond the implementation of a superior IT infrastructure, enterprise governance of IT is about defining and embedding processes and structures throughout the organization that enable boards and business and IT people to execute their responsibilities in support of business/IT alignment and value creation from their IT-enabled investments. Featuring a variety of elements, including executive summaries and sidebars, extensive references and questions and activities (with additional materials available on-line), this book will be an essential resource for professionals, researchers and students alike
Robot Grasp with Multi-object Detection based on RGB-D Image

An autonomous grasping system based on the method of depth learning is built in this paper, which is composed of a depth camera, a two-finger parallel gripper and a six-degree-of-freedom mechanical arm. To solve the poor performances of recognition and location of the grasping system in complex scenes with multiple irregular objects of different size, a multi-object grasping detection method based on RGB-D images is proposed. This method uses the combination of FPN and RPN methods to design the grasp proposal network, which can simultaneously use the shallow and deep information in the network to improve the detection effect of objects with different sizes, especially small objects. Secondly, the ROI Align method is introduced as a regional feature aggregation method, and the bilinear interpolation method can significantly improve the detection accuracy of the grasping model. Finally, the multi-object dataset containing small objects is used to test the proposed model, and the result illustrates the generalization ability of the proposed model. Experimental results show that the accuracy of the method proposed in this paper achieves 97% accuracy on single-object recognition and 92% accuracy on multi-object recognition. The robot grasping system based on Kinect camera and UR5 robotic arm achieved 91.0% grasping success rate on single-object, and 89% grasping success rate on multi-object. Comparing with some popular grasping methods, the results show the effectiveness and superiority of the proposed method in this paper.
Microphone Array Speaker Tracking Using Layered Sampling Particle Filtering

A new sound source localization and tracking method based on microphone array is presented for speaker tracking problem in noisy and reverberant environments.In the framework of particle filtering,the echo-free onset signal is extracted according to the echo-avoidance model of the precedence effect,and likelihood function is constructed by the output power of beamformer.Considering the effects of different frequency components on localization and tracking,layered sampling approach is used to improve the computational efficiency of particle filter.The results of simulation data and real data show that the proposed method can reliably track the speaker even in moderately reverberant rooms.