Miracle-dz/UniFAR-FTUs huggingface dataset:
Staining of Callose Depositions in Root and Leaf Tissues

[Abstract] The plant cell wall is a physical barrier, which fulfills a plethora of functions, for example it can efficiently prevent pathogen’s entry into the cell. In addition, its changing composition contributes to plants inducible defense mechanisms. This layer of defense includes pathogen perception and is followed by the activation of defense responses resulting, among others, in a modification and remodeling of the cell wall. This relatively late defense response (hours or days after contact with pathogen) comprises the accumulation of polysaccharides, such as the 1,3-s-glucan callose, phenolic compounds and reactive oxygen species. Callose depositions occur during normal plant growth (e.g. in the phloem), they can be also a response to different stress stimuli. During the response to pathogen attack, callose depositions are essential part of cell wall reinforcement and are important for successful plant defense. Here, we describe a method to stain callose apposition spots, which can be used to quantify this defense response.
Global well-posedness of non-isothermal inhomogeneous nematic liquid crystal flows

In this paper, we consider the initial-boundary value problem to the non-isothermal incompressible liquid crystal system with both variable density and temperature. Global well-posedness of strong solutions is established for initial data being small perturbation around the equilibrium state. As the tools in the proof, we establish the maximal regularities of the linear Stokes equations and parabolic equations with variable coefficients and a rigid lemma for harmonic maps on bounded domains. This paper also generalizes the result in [ 5 ] to the inhomogeneous case.
A New Control Strategy for Bi-directional DC/DC Converter in DC Microgrid

The bi-directional DC / DC converter in DC microgrid is a typical nonlinear system which has large voltage disturbance during lead accumulator charging and discharging. In order to solve the problem of voltage disturbance, the linearization of the converter is realized by exact feedback linearization, and the sliding mode controller is designed by using exponential approximation law. The simulation results show that the method has fast response speed, strong anti-interference ability and good steady-state characteristics.
Organosolv Lignin for Non-Isocyanate Based Polyurethanes (NIPU) as Wood Adhesive

A non-isocyanate-based polyurethane (NIPU) wood adhesive was produced from organosolv lignin, which is a bio-sourced raw material, available in large quantities and produced as a by-product of the paper industry. The formulation of this new lignin-based NIPU adhesive, which is presented, was chemically characterised by Matrix-Assisted Laser Desorption Ionization Time of Flight (MALDI ToF) mass spectrometry and by Fourier Transform Infra-Red (FTIR) spectrometry analyses. The oligomers formed were determined and showed that the three species involved in the NIPU adhesive preparation were formed by the co-reaction of the three reagents used: lignin, dimethyl carbonate, and hexamethylene diamine. Linear and branched structures were both identified. Mechanical properties of the adhesive were determined using the Automated Bonding Evaluation System (ABES) and internal bond (IB) strength test of the laboratory particleboard bonded with it. The adhesive has shown satisfactory mechanical properties after hot pressing at 230°C. Such a temperature is used industrially in the most modern particleboard factories, but since it is hardly feasible for more conventional wood bonding equipment, the reactivity of the NIPU adhesive was successfully increased with the addition of a small percentage of a silane coupling agent. With the addition of the silane, the proposed NIPU adhesive could also be used at a hot-pressing temperature lower than 200°C.
Surface-modified Cellulose Nanofibril Surfactants for Stabilizing Oil-in-Water Emulsions and Producing Polymeric Particles

In this work, the surface of hydrophilic cellulose nanofibrils (CNFs) was modified precisely by varying amounts of cetyltrimethylammonium bromide (CTAB) to produce CNF-based particle surfactants. We found that a critical CTAB density was required to generate amphiphilic CTAB-grafted CNF (CNF-CTAB). Compared to pristine CNF, CNF-CTAB was highly efficient at stabilizing oil-in-water Pickering emulsions. To evaluate their effectiveness as particle surfactants, the surface coverage of oil-in-water emulsion droplets was determined by changing the CNF-CTAB concentration in the aqueous phase. Furthermore, styrene-in-water stabilized by CNF-CTAB surfactants was thermally polymerized to produce CNF-stabilized polystyrene (PS) particles, offering a great potential for various applications including pharmaceuticals, cosmetics, and petrochemicals.
Building Graduate Salary Grading Prediction Model Based on Deep Learning

Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to capture the correlation between the salary grades during training. Moreover, the model is pretrained using a stacked denoising autoencoder and the corresponding weights after pre-training are used as the initial weights of the neural network. To improve the performance of model, dropout and bootstrap aggregation are used. The experimental results are very encouraging. With the predictive salary grades for graduates, school’s researchers can have a clear understanding of salary trends in order to promote student employability and competitiveness.
Optimal Resource Allocation and Quality of Service Prediction in Cloud

: In the present scenario, cloud computing service provides on-request access to a collection of resources available in remote system that can be shared by numerous clients. Resources are in self-administration; consequently, clients can adjust their usage according to their requirements. Resource usage is estimated and clients can pay according to their utilization. In literature, the existing method describes the usage of various hardware assets. Quality of Service (QoS) needs to be considered for ascertaining the schedule and the access of resources. Adhering with the security arrangement, any additional code is forbidden to ensure the usage of resources complying with QoS. Thus, all monitoring must be done from the hypervisor. To overcome the issues, Robust Resource Allocation and Utilization (RRAU) approach is developed for optimizing the management of its cloud resources. The work hosts a numerous virtual assets which could be expected under the circumstances and it enforces a controlled degree of QoS. The asset assignment calculation is heuristic, which is based on experimental evaluations, RRAU approach with J48 prediction model reduces Job Completion Time (JCT) by 4.75 s, Make Span (MS) 6.25, and Monetary Cost (MC) 4.25 for 15, 25, 35 and 45 resources are compared to the conventional methodologies in cloud environment. a VM placement algorithm and it forecasts of VM runtimes to optimize asset and time in cloud environment. RRAU approach and J48 classifier reduces JCT 4.75 s, MS 6.25, and MC 4.25 for 15, 25, 35 and 45 resources in cloud environment. Based on the tabular and the graphical result, it be said the proposed performs better than the existing methods. a reduces JET 4.75 MS 6.25, and MC 4.25 for 15, 35 and 45 resources in cloud environment.
Machine Learning Regression Algorithm Predicts Multi-component Crystal Configuration Energy

Some machine learning algorithm tools, such as neural networks and Gaussian process regression, are increasingly being applied to the exploration of materials. Here, we have developed a form to use this nonlinear interpolation tool to describe properties that depend on the degrees of freedom in multi-component solids. A symmetrically adapted clustering function is used to distinguish different atomic order degrees. These features are used as the input of neural networks, Gaussian process regression and other algorithmic models, and some inherent properties of materials, such as formation energy, can be reproduced by the trained machine algorithm model. We use this technique to reproduce the expansion Hamiltonian of a synthetic cluster with multi-body interaction, and calculate the formation energy of ZrO based on first principles. The form proposed in this paper and the results shown that complex multi-body interactions can be approximated by nonlinear models involving smaller clusters. The training models used in this paper to predict energy include neural networks, Gaussian process regression, random forests, and support vectors regression, using MSE and coefficient of determination to evaluate the prediction results, and adding genetic algorithms in the feature selection process can remove some redundant features and improve the prediction efficiency and accuracy. The results show that the neural network is the best algorithm model which selected in this article, the prediction effect of support vector regression is relatively inferior.
Can Robots Refill a Supermarket Shelf?: Motion Planning and Grasp Control

Robots are becoming more widely used in the retail market, mostly for warehousing, but many in-store logistics processes are still difficult to automate. Both technological and methodological barriers hinder the use of robots for handling single products rather than the boxes containing them, as the experiences of the various editions of the Amazon Challenge demonstrated.
A study on application of the golden and Fibonacci geometry in design of fashion accessories

The paper presents a study of application of the golden and Fibonacci geometry in design of fashion accessories: bags, scarfs, small accessories, etc. The connection between the application of the golden and Fibonacci geometry in accessories design and sustainable fashion is one of main accents in the study. The application of golden and Fibonacci tilings, floral and animal ornaments, which are created on their base, and geometric forms in golden or Fibonacci proportions are shown. As expressors of beauty, aesthetics, and harmony, the creations on the base of the golden and Fibonacci geometry can be applied in design of fashion accessories directly, as frames, and as parts of new forms. Regardless of the way of the application, the new designs are in strong connections with the sustainable fashion: slow fashion, sustainable trends, zero waste cutting, minimal waste cutting, ecological printing, etc.
Temperature-Constrained Reliability Optimization of Industrial Cyber-Physical Systems Using Machine Learning and Feedback Control

As the backbone of Industry 4.0, industrial cyber-physical systems (ICPSs) that are geographically dispersed, federated, cooperative, and security-critical systems become the center of interest from both industry and academia. In ICPS, there are huge amounts of devices, such as sensors and actuators, which are embedded and networked together to improve the performance of real-time monitoring and control. Reliability and temperature are two important concerns of these embedded and networked devices in ICPS due to their stringent requirement of reliable execution and long lifespan. In this article, we study the problem of maximizing soft-error reliability of CPU- and GPU-integrated embedded platforms deployed in ICPS under the temperature constraint. To speed up the estimation of soft-error rate (SER) and temperature, we train an artificial neural network (ANN) that is able to quickly and accurately derive the system’s SER and temperature. To solve the temperature-constrained reliability optimization problem, we propose a feedback control-based task scheduling scheme that adaptively determines the number of tasks admitted in the system and the number of replicas for the admitted tasks. We perform a series of simulation experiments to verify the efficacy of our scheme. The experimental results demonstrate that: 1) the estimated SER and temperature derived by our ANN-based method are very close to the ground-truth data and 2) our proposed feedback control-based task scheduling method can improve system reliability by up to 184.2% with a lower peak temperature when compared with one baseline and two state-of-the-art methods. Note to Practitioners—This article is motivated by the safety-critical industrial cyber-physical system (ICPS) applications necessitating reliable execution and long lifespan, which could be realized by increasing reliability and controlling operating temperature. Our goal is to improve the system reliability of CPU- and GPU-integrated multiprocessor systems-on-chip (MPSoCs) deployed in ICPS under the temperature constraint. Most of the existing papers target either reliability or temperature. A few recent papers have focused on reliability and temperature optimization simultaneously. However, they are not designed for ICPS and do not consider the widely accepted CPU- and GPU-integrated MPSoC platforms. This article proposes a machine learning-based approach that trains an artificial neural network (ANN) to facilitate the online estimation of system SER and temperature. Compared to the offline estimation using simulation tools, the online approach is more applicable to real-time ICPS applications. This article also designs a feedback control-based approach for improving system reliability and reducing peak temperature of the CPU- and GPU-integrated MPSoCs by determining the number of tasks to be admitted and the number of replicas for tasks.
Research on the Work Engagement of Hospitality Industry Staff from the Perspective of Tourism Management Informatization

With the development of information technology, the Internet and informatization have brought huge changes in tourism management. As the pillar industry of the tourism, the hospitality industry, are facing new requirements for transformation and optimization. How to retain talents and how to make talents willing to work are two important issues that hotels need to pay attention to in practice. Through a self-report questionnaire survey of 325 employees from hospitality industry, this paper use SPSS and AMOS tools to analyze the influencing factors of performance evaluation fairness (emloyee voice) and the employees’s work engagement, and constructs the impact mechanism model of them, meanwhile aim to give some helpful suggestions on indigenous research, and can probably enrich the informatization application in tourism management and innovation.
CFD analysis of the increase in ship resistance due to biofouling growth represented by roughness length scale

Fuel consumption is heavily influenced by ship resistance, which mostly consists of friction resistance. During her operation, a ship is susceptible to the attachment of biofouling to its hull. The arbitrary form of biofouling induces an increase in surface roughness, which is responsible for the increase in friction resistance. This research analyses the increase in skin friction resistance due to biofouling, that is represented by a few variations of roughness length scale, in various orders throughout the models. Two-dimensional models of flat plates are utilized to investigate this issue by means of computational fluid dynamics (CFD) analysis. This study uses models with 0.56 m length to be tested in five Reynolds numbers, to be compared to a similar experiment conducted by using wind tunnel. Other models include 30 m and 60 m length to be tested in two Reynolds numbers each. It is found that the increase in skin friction reaches 3.5 – 31.4% for 0.6 m, 68.1 – 206.9% for 30 m with extreme regular roughness and 117.3 – 129.3% for 30 m with extreme irregular roughness, and 59.5 – 179.6% for 60 m with extreme regular roughness and 123.5 – 128.9% for 60 m with extreme irregular roughness.
The behavior of solution function of the fractional differential equations using modified homotopy perturbation method

The discourse regarding fractional calculus, in particular those related to fractional differential equation, is still continue to attract researcher attention. Previous studies have elaborated on the variation of fractional differential equation models. This study aims to uncover the problem of convergence of the solution function sequence related to the order of fractional differential equation. Firstly, this study presents how to find a solution for the model by using a Modified Homotopy Perturbation Method as the improvement of Homotopy Perturbation Method. Furthermore, the solution function with the sequence of fractional order is drawn by Maple. Using the geometrical analysis, the result of this study shows that if fractional order sequence is convergent to α, then sequence of its solution function will be convergent to a solution function of fractional differential equation with order α.
A Cyber Kill Chain Approach for Detecting Advanced Persistent Threats

: The number of cybersecurity incidents is on the rise despite sig-nificant investment in security measures. The existing conventional security approaches have demonstrated limited success against some of the more complex cyber-attacks. This is primarily due to the sophistication of the attacks and the availabilityof powerful tools. Interconnected devices such as the Internet of Things (IoT) are also increasing attack exposures due to the increase in vulnerabilities. Over the last few years, we have seen a trend moving towards embracing edge technologies to harness the power of IoT devices and 5G networks. Edge technology brings processing power closer to the network and brings many advantages, including reduced latency, while it can also introduce vulnerabilities that could be exploited. Smart cities are also dependent on technologies where everything is interconnected. This interconnectivity makes them highly vulnerable to cyber-attacks, especially by the Advanced Persistent Threat (APT), as these vulnerabilities are amplified by the need to integrate new technologies with legacy systems. Cybercriminals behind APT attacks have recently been targeting the IoT ecosystems, prevalent in many of these cities. In this paper, we used a publicly availabledataset on Advanced Persistent Threats (APT) and developed a data-driven approach for detecting APT stages using the Cyber Kill Chain. APTs are highly sophisticated and targeted forms of attacks that can evade intrusion detection systems, resulting in one of the greatest current challenges facing security professionals. In this experiment, we used multiple machine learning classifiers, such as Naïve Bayes, Bayes Net, KNN, Random Forest and Support Vector Machine (SVM). We used Weka performance metrics to show the numeric results. The best performance result of 91.1% was obtained with the Naïve Bayes classifier. We hope our proposed solution will help security professionals to deal with APTs in a timely and effective manner.
A Bidding-Based Peer-to-Peer Energy Transaction Model Considering the Green Energy Preference in Virtual Energy Community

The depletion of existing resources and environmental problems led to an increase in distributed energy resources (DERs) and the accordingly emergence of the prosumer. This change has created an issue of power system operations and the coordinating entities of distribution networks. Besides, environmental concerns and the global policy of supporting renewable energy resources has given some electricity consumers a better perception and a willingness to pay (WTP) for green energy at a higher cost. This article presents a bidding-based peer-to-peer (P2P) energy transaction optimization model that considers these green energy preferences based on prosumer and consumer’s intention. The objective of the model, which deals with energy transactions between prosumers and consumers within the virtual energy community, is to minimize community operating costs taking into account the depreciation costs of energy storage units while maximizing the social welfare for encouraging the energy trading among consumers and prosumers. The weighted social-welfare terms in the objective function provides diverse community operations reflecting the goal of the energy trading operator (community operator). Case studies were conducted according to the presence and absence of energy transactions and energy storages and the green energy preference. Results of the case study showed that the benefits of prosumers and community have occurred in energy transactions and CO2 emission is reduced according to green energy preference.
Phase-Amplitude Least-Squares Reverse Time Migration With a Simultaneous-Source Based on Sparsity Promotion in the Time-Frequency Domain

Least-squares reverse time migration (LSRTM) aims to produce a high-quality migration image of complex geological structures. However, the weaker deep seismic reflections are often masked by the overlying strata in migration images. Therefore, it is difficult for the LSRTM to image the deeper structures. This letter proposes a phase-amplitude LSRTM (PA-LSRTM) in the time-frequency domain to improve the migration image of deep seismic reflections and subsalt structures. The PA-LSRTM is formulated as an inverse problem that minimizes the time-frequency phase-amplitude difference between the predicted and observed data. The seismic data was initially transformed into the time-frequency domain to establish a PA-LSRTM misfit. An amplitude factor was then introduced in the time-frequency misfit to weaken the weights of amplitude components. In this case, it emphasized the similarity of phase components. Furthermore, the sparsity promotion method was combined with a simultaneous source technique to increase the computational efficiency and reduce the crosstalk noise. The PA-LSRTM with sparsity promotion (SPA-LSRTM) misfit can finally be solved using a Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). The numerical and marine field data tests demonstrate that the SPA-LSRTM can effectively produce a high-resolution image of deep structures.
Preparation and Rheology of Magnetorheological Fluid Using Six Kinds of Fumed Silica as Stabilizing Additives

In this study, magnetorheological fluid (MRF) was prepared with several kinds of hydrophobic fumed silica in order to improve MRF stability while maintaining desirable fluidity. We conducted steady flow measurements and oil separation tests under gravity and centrifugal conditions. The linkage of fumed silica particles was also observed morphologically by transmission electron microscopy. We found that the MRF sedimentation stability correlated with viscosity in the lower shear rate range. The oil separation ratio improved with the addition of the fumed silicas examined in this study. The surface characteristics of the silica particles affected the stabilization ability. The addition of hydrophobic fumed silica can desirably increase MRF magnetic properties.
Preliminary Study on Spatial and Temporal Distribution and Behavior of Tourists in Beihai Park in Winter

Tourists are the main users of the park, whose spatial and temporal distribution and behavioral characteristics are the most real reflection of the construction of the park environment. Tourists in Beihai Park were selected as the research object. Through field investigation and interview investigation, the main behavior characteristics of tourists in different time and space in winter were studied. Finally, from the perspectives of tourists relation with time and space, we analyze the existing problems, understand the causes of these phenomena, and then put forward the corresponding countermeasures and suggestions, and make suggestions on the construction and management of such parks.