Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Agglomeration on the Electrical Percolation of Polyimide/Graphene Nanocomposites

Electrically conductive polymer/graphene nanocomposites are multipurpose in nature and have a wide range of potential applications in electronics and electrical engineering. The conductivity of these composites depends on a range of parameters such as the choice of polymer matrix, graphene filler type, the processing methodology and the quality of dispersion. The optimization of the dispersion process of graphene in the polymer matrix is a major challenge in the development of nanocomposites and their integration into industrial applications. This paper investigates the efficacy of centrifugation as a means of isolating smaller graphene agglomerates from the supernatant after initially dispersing the material in polyamic acid using high shear mixing. The relationship between the centrifugation time and agglomerate size and its effect on the electrical percolation of the polymer nanocomposite is elucidated. A set of polyimide films prepared using varying centrifugation times and graphene nanoflake concentrations were prepared by thermal imidization and electrically characterized to assess the impact of the structure of the nanocomposite on the electrical percolation threshold. Graphene nanoflakes agglomerates were reduced in size by increasing the centrifugation time and this led to a decrease in the electrical conductivity of the final films. The electrical conductivity of polyimide graphene (PI/G) nanocomposites consistently increased with increasing filler concentration. PI/G prepared using 2 min centrifugation times exhibited an electrical conductivity of 2.7 × 10-3 S/m, while the corresponding PI/G prepared using 5min of centrifugation had an electrical conductivity of 0.16 × 10-3 S/m. The percolation phenomena were clearly observed for all nanocomposites as the conductivity rose from ~10-13 to ~10-3 S/m, while the percolation thresholds varied depending on the centrifugation time (agglomeration density).
Discrete-Time Impulsive Adaptive Dynamic Programming

In this paper, a new iterative adaptive dynamic programming (ADP) algorithm is developed to solve optimal impulsive control problems for infinite horizon discrete-time nonlinear systems. Considering the constraint of the impulsive interval, in each iteration, the iterative impulsive value function under each possible impulsive interval is obtained, and then the iterative value function and iterative control law are achieved. A new convergence analysis method is developed which proves an iterative value function to converge to the optimum as the iteration index increases to infinity. The properties of the iterative control law are analyzed, and the detailed implementation of the optimal impulsive control law is presented. Finally, two simulation examples with comparisons are given to show the effectiveness of the developed method.
Greedy Projected Gradient-Newton Method for Sparse Logistic Regression

Sparse logistic regression (SLR), which is widely used for classification and feature selection in many fields, such as neural networks, deep learning, and bioinformatics, is the classical logistic regression model with sparsity constraints. In this paper, we perform theoretical analysis on the existence and uniqueness of the solution to the SLR, and we propose a greedy projected gradient-Newton (GPGN) method for solving the SLR. The GPGN method is a combination of the projected gradient method and the Newton method. The following characteristics show that the GPGN method achieves not only elegant theoretical results but also a remarkable numerical performance in solving the SLR: 1) the full iterative sequence generated by the GPGN method converges to a global/local minimizer of the SLR under weaker conditions; 2) the GPGN method has the properties of afinite identification for an optimal support set and local quadratic convergence; and 3) the GPGN method achieves higher accuracy and higher speed compared with a number of state-of-the-art solvers according to numerical experiments.
Construction of a Modified Clip Cage and Its Effects on the Life-History Parameters of Sitobion avenae (Fabricius) and Defense Responses of Triticum aestivum

Simple Summary Clip cages are increasingly used in the study of plant–small insect interactions; however, the heavy weight and low light transmittance of clip cages cause damage to plants and affect insect growth and reproduction. This study aimed to develop a lightweight, transparent clip cage and investigate its effects on Sitobion avenae (Fabricius) and Triticum aestivum. The results indicate that the modified clip cages neither altered the S. avenae life-history parameters nor activated the T. aestivum defense responses and can, therefore, be used for plant–aphid interaction studies. Abstract Clip cages are commonly used to confine aphids or other small insects to a single leaf when conducting plant–small insect interaction studies; however, clip cages are usually heavy or do not efficiently transmit light, which has an impact on leaf physiology, limiting their application. Here, simple, lightweight, and transparent modified clip cages were constructed using punched clear plastic cups, cut transparent polyvinyl chloride sheets, nylon organdy mesh, and bent duck-bill clips. These cages can be clipped directly onto dicot leaves or attached to monocot leaves with bamboo skewers and elastic bands. The weight, production time, and aphid escape rates of the modified clip cages were 3.895 ± 0.004 g, less than 3 min, and 2.154 ± 0.323%, respectively. The effects of the modified clip cage on the growth, development, and reproduction of the English grain aphid (Sitobion avenae Fabricius) in comparison with the whole cage were studied. The biochemical responses of wheat (Triticum aestivum) to the cages were also investigated. No significant differences were observed in the life table parameters, nymph mortality, and adult fecundity in S. avenae confined to clip cages and whole cages, but the clip cages were more time efficient than whole cages when conducting life table studies. Moreover, the hydrogen peroxide accumulation, callose deposition, and cell necrosis in wheat leaves covered by empty clip cages and empty whole cages were similar, and significantly lower than treatments where the aphids were inside the clip cage. The results demonstrate that the modified clip cages had negligible effects on the plant and aphid physiology, suggesting that they are effective for studying plant–small insect interactions.
Open Innovation in Organization Through Stakeholder Capability During Pandemic (COVID-19): Indonesian SMEs Perspective

This study aims to analyze the role of stakeholders who support the open innovation transition in SMEs. This is important because the innovation process has shifted from closed innovation to open innovation which requires good management of organizational capabilities, especially in managing stakeholders and resources owned by the organization. However, in the current pandemic situation, SMEs have challenges in adopting and implementing these open innovations. In this study, a conceptual research model was compiled and produced which elaborated several previous references, and then tested empirically on respondents, namely in SMEs in Indonesia. The responses involved in this study were 218 SMEs, but the complete response are 206 respondents. Data testing was carried out using the Partial Least Square-Structural Equation Modeling (PLS-SEM) statistical method. The results of hypothesis testing indicate that there are four variables that significantly strengthen the effect of closed innovation on open innovation, namely financial capability, network, knowledge management system, and organizational culture (p < 0.01). The moderating variable with the most dominant influence is financial ability ( $\beta =0.915$ , p < 0.01). Based on the results of this study, SMEs that have high financial capabilities or have advantages in terms of funding and financial management can make the transition from closed innovation to open innovation better or independently. There is one moderating variable that is not proven to be significant, namely technology. These findings can then be used to formulate appropriate policies to support the adoption of open innovation in the context of developing the ability of SMEs to survive during the pandemic.
ELBOW AND WRIST JOINT LOADING IN GYMNASTICS: INJURY RISK IN ROUND OFF TECHNIQUE SELECTION

The aim of the current study was to investigate key elbow and wrist joint injury risk factors during different round-off (RO) techniques performed by young female artistic gymnasts. Ten active female gymnasts performed 30 successful trials of RO from a hurdle step with three different hand positions (parallel (10), T-shape (10) and reverse (10)). Synchronized kinematic and kinetic data were collected for each trial. One-way ANOVA and effect-size statistics determined differences between each hand position. Significant differences (p<0 were found for peak VGRF, peak elbow and wrist compression force, and elbow internal adduction moment. Decrease in VGFR, elbow and wrist joint kinetics indicated that the T-shape technique may prevent elbow and wrist joint complex and reduces potential of injuries.
Investigation of Discharge Activity Between Rolling Drops on an Inclined Plane

Discharge activity associated with water droplets on a polymer insulator surface is thought to be the main mechanism of the early stage of hydrophobicity loss. The mutual positions and dynamics of the droplets affect the probability and the cause of the partial discharges. Therefore, this works aims to observe the dynamic behavior of droplets and the associated dynamics of surface discharge during the process of liquid runoff on silicon rubber surface in the presence of the tangential AC electric field. The research is carried out using an electrode system of two parallel plate electrodes with a sample between them, where NaC1 solution flows down on the surface of the sample in a form of separate droplets. Two ultra-sensitive cameras record droplet positions and weak partial discharges simultaneously, which makes it possible to know exactly what event caused each discharge. Two stages in the behavior of discharge activity are distinguished during the loss of hydrophobicity. At first, droplet mergings and multi-drop runoffs are observed. In the second stage, water channels form and break-up after the droplet merging. After the loss of hydrophobicity, discharge light is observed continuously at the ends of the formed conducting path. The revealed behavior of the discharge activity helps to clarify the scenario of the hydrophobicity loss. The accumulated intensity of the discharge light during accelerated aging of the sample can be further investigated as a quantitative parameter to evaluate how resistant material is to the loss of hydrophobicity under the action of surface partial discharges.
Uncertainties of the Pulsed Electroacoustic Method: Peak Positions of Embedded Charge Distributions

The pulsed electroacoustic (PEA) method allows for nondestructive measurements of internal charge distributions in dielectric materials. These measurements have been paramount in understanding and mitigating charge accumulation, aging, and electrostatic discharge in materials for various applications. This study aims to examine more closely the uncertainties of pulsed electroacoustic measurements. The first few moments of a charge distribution are directly related to the magnitude, peak position, full-width-at-half-maximum, and skewness. The uncertainty in the magnitude of the charge distribution is often quite large, but the peak position can be determined with a precision of $\lt 1 {\mu} \text{m}$. This has been demonstrated in our lab with repeated PEA measurements of polyether-etherketone (PEEK) with internal charge present. This precision is further validated with measurements of PEEK irradiated with differing doses of 50 keV incident electrons, resulting in peak positions that differ by only a few ${\mu} \text{m}$. A final test is given by measurements monitoring the slow migration of the charge distributions in these irradiated samples over several months. The measured shifts in the peak position of the charge distributions are $\leq 1 {\mu} \text{m}$. Though the spatial resolutions of PEA measurements are typically $\sim 10 {\mu} \text{m}$, as defined by the fullwidth-at-half-maximum of the leading interfacial peak, the precision of the peak position can be more than an order of magnitude greater. The statistical analysis of the repeated measurements to determine uncertainties, as well as the validation measurements, demonstrate the high precision determination of the peak position of embedded charge distributions.
The complete mitochondrial genome of Periphyllus diacerivorus Zhang, 1982 (Hemiptera: Aphididae: Chaitophorinae)

Abstract We assembled and annotated the complete mitochondrial genome of Periphyllus diacerivorus, which is the first complete mitogenome in subfamily Chaitophorinae. The circular mitogenome of P. diacerivorus is 16,148 bp long with an A + T content of 84.1%. It contains 13 protein-coding genes (PCGs), 22 transfer RNA genes (tRNAs), two ribosomal RNAs (rRNAs), a large control region, and a special repeat region. All PCGs initiate with ATN and terminate with TAA or TAG except for cox1 and nad4, which are terminated with an incomplete stop codon T--. All tRNAs display the typical clover-leaf secondary structure except for trnS (AGN). The repeat region between trnE and trnF is 970 bp long, including 2.18 repeat units. In the phylogenetic analysis tree, P. diacerivorus clusters with Mindarus keteleerifoliae.
Trends and Emerging Technologies for the Development of Electric Vehicles

In response to severe environmental and energy crises, the world is increasingly focusing on electric vehicles (EVs) and related emerging technologies. Emerging technologies for EVs have great potential to accelerate the development of smart and sustainable transportation and help build future smart cities. This paper reviews new trends and emerging EV technologies, including wireless charging, smart power distribution, vehicle-to-home (V2H) and vehicle-to-grid (V2G) systems, connected vehicles, and autonomous driving. The opportunities, challenges, and prospects for emerging EV technologies are systematically discussed. The successful commercialization development cases of emerging EV technologies worldwide are provided. This review serves as a reference and guide for future technological development and commercialization of EVs and offers perspectives and recommendations on future smart transportation.
Reconfigurable Millimeter-wave Power Amplifiers in GaN and SOI using Passive Load Modulation

5G applications in millimeter-wave bands have challenging requirements for power amplifiers (PAs) in handsets, picocells, and even basestations, and GaN in basestations. We propose passive outphasing load modulation (POLM) as a generalizable tuning approach that can be applied to various device technologies. Load modulation for high average efficiency as well as static tuning in the presence of load variation is described. The application of POLM to CMOS SOI and GaN PAs at 28 GHz is discussed and verified through measurements of different classes of operation and broadband waveforms.
Warm-Cool Emotions of LED Lightings around 5,000K

In this study, warm-cool feelings of near-white lightings were investigated after the subjects’ eyes being adapted to 5,000K. The psychophysical experiment was carried out in a dark room using 5 channels LED lighting booth. In the experiment, total of 48 test lightings around 5,000K were generated using the booth. Total of 20 subjects evaluated warm-cool feelings of each test lighting. The results showed that lower CCT tended to have ‘Warm’ feeling, while higher CCT tended to arouse ‘Cool’ feeling. When CIECAM02 H is in the range of 0-100 and 380-400, ‘Warm’ feeling was strongly evoked and when H is in the range of 250-300, ‘Cool’ feeling was strongly aroused. Also, when test lightings have same hue, the one having higher chroma aroused stronger feeling on either ‘Warm’ or ‘Cool’. Emotion model test results showed Koo’s model, which is based on lighting colors, showed better performance than the model developed based on color patches.
A New Culture Method for the Detection of Non-Tuberculous Mycobacteria in Water Samples from Heater–Cooler Units and Extracorporeal Membrane Oxygenation Machines

The isolation of non-tuberculous mycobacteria (NTM) from cultures is particularly laborious due to the potential overgrowth of coexisting non-acid fast bacilli. To reduce the overgrowth of these non-mycobacterial organisms, a decontamination step with NaOH or cetylpyridinium chloride is highly recommended before plating the samples on the culture medium. However, due to their toxicity, decontamination solutions tend to decrease NTM recovery from clinical and environmental samples. Here, we tested an alternative method for NTM recovery based on the use of NTM Elite agar, a selective medium that does not require a decontamination step. Using NTM Elite agar, we were able to detect non-tuberculous mycobacteria in 27.7% (30/108) of water samples analyzed. The average time to NTM detection was 18 days, but some strains required longer to grow, perhaps due to the stressful environmental conditions (periodical disinfection of devices). NTM Elite agar’s effectiveness in inhibiting background flora was proven by the isolation of NTM from samples with and without background flora, showing no statistically significant differences in detection rates for different total viable counts of background flora (p = 0.4989). In conclusion, our findings indicate that effective NTM recovery from HCU- and ECMO-derived water samples can be achieved via filtration and direct culture of the filters on NTM Elite agar. This simple procedure can speed up laboratory work and provide an improved method, successfully resulting in low contamination and high detection rate, in addition to being less time-consuming. Its sensitivity and lack of a decontamination step make this protocol particularly useful for monitoring the effectiveness of device disinfection in hospital settings, even in the presence of low NTM loads. Reading timeframes should probably be extended to 7 weeks (i.e., well beyond the standard 4 weeks advised by the manufacturer), in order to isolate even the slow-growing mycobacteria. However, an extended incubation period is not necessary for exclusion of M. chimaera contamination of the devices, as M. chimaera isolation times do not generally exceed 3 weeks.
Predicting the Trending Research Topics by Deep Neural Network Based Content Analysis

Tracking the trends and taking early steps accordingly is important in academia, as well as in other domains such as technology and finance. In this work, we focus on the problem of predicting the trending research topics from a collection of academic papers. Previous efforts model the problem in different ways and mostly apply classical approaches such as correlation analysis and clustering. There are also several recent neural model based solutions, however they rely on feature vectors and additional information for the trend prediction. In this work, given a collection of publications within the observation time window, we predict whether the use of a keyword will increase, decrease or be steady for the future time window (prediction window). As the solution, we propose a family of deep neural architectures that focus on generating summary representations for paper collections under the query keyword. Due to the sequence based nature of the data, Long Short-Term Memory (LSTM) module plays a core role, but it is combined with different layers in a novel way. The first group of proposed neural architectures consider each paper as a sequence of keywords and use word embeddings to construct paper collection representations. In this group, the proposed architectures differ from each other in the way year based and overall summary representations are constructed. In the second group, each paper is directly represented as a vector and the use of different paper embedding techniques are explored. The analyses of the models are performed on a variety of paper collections belonging to different academic venues, obtained from Microsoft Academic Graph data set. The experiments conducted against baseline methods show that proposed deep neural based models achieve higher trend prediction performance than the baseline models on the overall. Among the proposed models, paper embedding based models provide better results for most of the cases.
Graph‐based Bayesian network conditional normalizing flows for multiple time series anomaly detection

Various devices and sensors of cyber‐physical systems interact with each other in time and space, and the generated multiple time series have implicit correlations and highly nonlinear relationships. Determining how to model the multiple time series and capture dependencies through extracting features is the key to anomaly detection. In this paper, we propose a graph‐based Bayesian network conditional normalizing flows model for multiple time series anomaly detection, Bayesian network conditional normalizing flows (BNCNF). It applies a Bayesian network to model the causal relationships of multiple time series and introduces a spectral temporal dependency encoder to obtain the representations of interdependency between multiple time series. The representations are introduced as conditional information into the normalizing flows for density estimation, and the data corresponding to low density is judged as anomalies. The experiment are conducted on SWaT, WADI, and SMD datasets, the F1 score reaches 0.95, 0.92, and 0.97 on the three datasets, respectively. The results show that BNCNF has better performance in anomaly detection compared with the current mainstream methods.
The chase procedure and its applications

The goal of this thesis is not only to introduce and present new chase-based algorithms, but also to investigate the differences between the main existing chase procedures. In order to achieve this, first we will investigate and do a clear delimitation between the existing chase algorithms based on their termination criteria. This will give a better picture of which chase algorithm can be used for different dependency classes. Next, we will investigate the data exchange, data repair and data correspondence problems and show how the chase algorithm can be used to characterize different types of solutions. For the later two problems, we will also investigate the data complexity of solution-existence and solution-check problems. Further, we will introduce a new chase based algorithm which computes representative solutions under constructible models, a new closed world semantics. This new semantics is, in our view, appropriate to be used as a closed world semantics in data exchange. We will also show that the conditional table computed by this chase algorithm can help to get both possible and certain answers for general queries. And finally, we will investigate strong representation systems and strong data exchange representation system. We will prove, by introducing a new chase based algorithm, that mappings specified by source-to-target second order dependencies and target richly acyclic TGD’s are strong data exchange representation systems for the class of first order queries.