Miracle-dz/UniFAR-FTUs huggingface dataset:
Variable fuzzy engineering method of comprehensive evaluation and its application

Variable fuzzy engineering method can scientifically determine the relative membership degrees of disquisitive indexes at level intervals,and reasonably determine the evaluation level of each sample by varying the model and its parameters.The method is applied to comprehensive evaluating the intensity performance of the high pressure vessel of spacecrafts,and the evaluation result is consistent with that of experts.The variable fuzzy engineering method can also be applied to evaluating products quality of other industries.At the same time,it's pointed out that the method of the extension multi-factor evaluation can not be applied to evaluating the intensity performance of the high pressure vessel of spacecrafts for the mistakes of its basic dependent function formula.
Soil metabolomics and bacterial functional traits revealed the responses of rhizosphere soil bacterial community to long-term continuous cropping of Tibetan barley

Continuous cropping often leads to an unbalanced soil microbial community, which in turn negatively affects soil functions. However, systematic research of how these effects impact the bacterial composition, microbial functional traits, and soil metabolites is lacking. In the present study, the rhizosphere soil samples of Tibetan barley continuously monocropped for 2 (CCY02), 5 (CCY05), and 10 (CCY10) years were collected. By utilizing 16S high-throughput sequencing, untargeted metabolomes, and quantitative microbial element cycling smart chips, we examined the bacterial community structure, soil metabolites, and bacterial functional gene abundances, respectively. We found that bacterial richness (based on Chao1 and Phylogenetic Diversity [PD] indices) was significantly higher in CCY02 and CCY10 than in CCY05. As per principal component analysis (PCA), samples from the continuous monocropping year tended to share more similar species compositions and soil metabolites, and exhibited distinct patterns over time. The results of the Procrustes analysis indicated that alterations in the soil metabolic profiles and bacterial functional genes after long-term continuous cropping were mainly mediated by soil microbial communities (P < 0.05). Moreover, 14 genera mainly contributed to the sample dissimilarities. Of these, five genera were identified as the dominant shared taxa, including Blastococcus, Nocardioides, Sphingomonas, Bacillus, and Solirubrobacter. The continuous cropping of Tibetan barley significantly increased the abundances of genes related to C-degradation (F = 9.25, P = 0.01) and P-cycling (F = 5.35, P = 0.03). N-cycling significantly negatively correlated with bacterial diversity (r = − 0.71, P = 0.01). The co-occurrence network analysis revealed that nine hub genera correlated with most of the functional genes and a hub taxon, Desulfuromonadales, mainly co-occurred with the metabolites via both negative and positive correlations. Collectively, our findings indicated that continuous cropping significantly altered the bacterial community structure, functioning of rhizosphere soils, and soil metabolites, thereby providing a comprehensive understanding of the effects of the long-term continuous cropping of Tibetan barley.
Rapidly Signal‐enhanced Metabolites for Atomic Scale Monitoring of Living Cells with Magnetic Resonance

Nuclear magnetic resonance (NMR) is widely applied from analytics to biomedicine although it is an inherently insensitive phenomenon. Overcoming sensitivity challenges is key to further broaden the applicability of NMR and, for example, improve medical diagnostics. Here, we present a rapid strategy to enhance the signals of C-labelled metabolites with parahydrogen and, in particular, C-pyruvate, an important molecule for the energy metabolism. We succeeded to obtain an average of 27 % C polarization of 1-C-pyruvate in water which allowed us to introduce two applications for studying cellular metabolism. Firstly, we demonstrate that the metabolism of 1-C-pyruvate can serve as a biomarker in cellular models of Parkinson’s disease and, secondly, we introduce the opportunity to combine real-time metabolic analysis with protein structure determination in the same cells. Based on the here presented results, we envision the use of our approach for future biomedical studies to detect diseases.
Effect of conductance linearity of Ag-chalcogenide CBRAM synaptic devices on the pattern recognition accuracy of an analog neural training accelerator

Pattern recognition using deep neural networks (DNN) has been implemented using resistive RAM (RRAM) devices. To achieve high classification accuracy in pattern recognition with DNN systems, a linear, symmetric weight update as well as multi-level conductance (MLC) behavior of the analog synapse is required. Ag-chalcogenide based conductive bridge RAM (CBRAM) devices have demonstrated multiple resistive states making them potential candidates for use as analog synapses in neuromorphic hardware. In this work, we analyze the conductance linearity response of these devices to different pulsing schemes. We have demonstrated an improved linear response of the devices from a non-linearity factor of 6.65 to 1 for potentiation and −2.25 to −0.95 for depression with non-identical pulse application. The effect of improved linearity was quantified by simulating the devices in an artificial neural network. The classification accuracy of two-layer neural network was seen to be improved from 85% to 92% for small digit MNIST dataset.
Optimal Active and Reconfigurable Meta-Sphere Design for Metamaterial-Enhanced Magnetic Induction Communications

The magnetic induction (MI) technique enables wireless communications in extreme environments. However, the existing MI systems rely on large antennas and high power, which is not suitable for Internet of Things (IoT) and other mobile devices. Although the new metamaterial-enhanced MI (<inline-formula> <tex-math notation="LaTeX">$\text{M}^{2}\text{I}$ </tex-math></inline-formula>) technique can theoretically enhance the MI signal by 30 dB, none of the existing real-world systems achieve the performance due to: 1) the inherent metamaterial loss is significant; 2) the complicated metamaterial structure is not optimized; and 3) an <inline-formula> <tex-math notation="LaTeX">$\text{M}^{2}\text{I}$ </tex-math></inline-formula> system with fixed parameters cannot keep the optimal performance in dynamic environments. In this article, an active and reconfigurable meta-sphere is designed, optimized, and implemented to approach the theoretical <inline-formula> <tex-math notation="LaTeX">$\text{M}^{2}\text{I}$ </tex-math></inline-formula> enhancement bound. Specifically, the new <inline-formula> <tex-math notation="LaTeX">$\text{M}^{2}\text{I}$ </tex-math></inline-formula> meta-sphere is realized by a spherical array of active and reconfigurable coil units. Given fixed meta-sphere size and power constraints, there exist complicated trade-offs among coil number, coil size, and the power consumption. Based on the theoretical design, the prototype of the active and reconfigurable <inline-formula> <tex-math notation="LaTeX">$\text{M}^{2}\text{I}$ </tex-math></inline-formula> meta-sphere system is implemented and tested. Significant power gain and range extension are proved through comprehensive analytical deduction, COMSOL Multiphysics-based simulations, and real-world experiments.
Impact of use & gratification theory and user generated content on purchase intention

Content Marketing is a marketing strategy which attracts, engages and retains an audience by creating and sharing relevant articles, videos, podcasts and other media. It also creates, publishes and distributes content for customers through online channel in order to induce their purchase intention. The major focus is on the content created by the marketer to attract the customers. This study uses ‘Use and Gratification Theory’ and ‘User Generated content’ to determine ‘Customer Engagement’ and ‘Purchase Intention’ of customers. The objectives of the study are to analyze the relationship among Use and Gratification factors, User Generated Content, Customer Engagement and Purchase Intention and to test whether Customer Engagement has a mediating effect between Use and gratification factors and User Generated Content (Independent Variable) and Purchase Intention (Dependent Variable). This study uses primary data to collect the information about digital content marketing through a standardized questionnaire focusing on Use and Gratification factors, User Generated Content, Customer Engagement and Purchase Intention.
Hybrid systems modeling and automated air traffic control for three-dimensional separation assurance

The increasing volume of traffic in the air transportation is leading to excessive workload on air traffic controllers. Developing automated air traffic management (ATM) tools is a critical technology in reducing the workload of air traffic controllers and hence increasing the airspace capacity. The existing approaches to automated ATM either use overly-simplified air traffic and aircraft dynamics models to reduce computational complexity or end up being computationally intractable for large-scale ATM scenarios. This paper presents a new hybrid system description of modeling the decision process of the air traffic controllers in en-route and approach operations. The model is based on the domain expertise provided by the State Airport Authority and Air Navigation Service Provider (ANSP) of Turkey. The emulation of air traffic controller decision process in the hybrid model provides realistic conflict resolution maneuvers and separation assurance in 3D, while being computationally tractable. The algorithm has polynomial iteration complexity in the number of waypoints of the aircraft, which makes it scalable to large-scale ATM scenarios with more than 100 aircraft. The algorithm is validated on the real air traffic data over the Istanbul region extracted from the ALLFT+ dataset provided by EUROCONTROL, which includes over 7000 flights in a 96-hour period. The developed algorithm is also integrated into a Boeing 737-800 flight deck simulator with a custom radar display to demonstrate the applicability to existing avionics systems.
Optical Active Thermal Stable Nanocomposites Using Polybutadiene-Based Polyurethane and Graphene Quantum Dot-MnO2

Synthesis and characterization of new polybutadiene-based polyurethane, graphene quantum dot-MnO2 nanoparticles, and relative nanocomposites were set as the aim of current artwork. For this purpose, a one-pot polymerization approach was employed in preparation of polyurethane through the reaction of amine polyol and toluene diisocyanate (TDI) in presence of DBTDL catalyst. Nanocomposites were synthesized using 1 to 3 incorporation percent of graphene quantum dot-MnO2 nanoparticles in polymer matrix. 1H-NMR and FT-IR spectroscopies confirmed successful synthesis of reaction products including graphene quantum dot-MnO2, polyurethane, and nanocomposites. UV-vis and PL spectrophotometry techniques were applied for achieving optical information of samples. Optical properties of nanocomposites were reserved properly with no great quenching. Thermal stabilities, degradation rates, and thermal characteristics of polyurethane and nanocomposites were investigated using TGA/DTG and DSC analysis. Thermal stability showed direct relationship to nanoparticle content, and 3%wt nanocomposite showed improved thermal behaviour in comparison with pure PU. SEM, XRD, and AFM techniques proved successful nanocomposite synthesis with detecting nanoparticle species and fine nanoparticle dispersion with improved topographic and morphologic characteristics making GQD-MnO2 polyurethane nanocomposites a good candidate for using in optical active and thermal stable coatings.
Does it Pay to Invest in Dirty Industries? - New Insights on the Shunned-Stock Hypothesis

: This research examines whether stocks of firms operating in highly polluting industries (“dirty stocks”) are treated like sin stocks. We assume that investors shun dirty stocks based on non-pecuniary preferences and employ screening approaches that lead to the exclusion of entire industries. Using emission data of the U.S. Toxics Release Inventory, we show that dirty stocks are held in lower proportions by institutional investors and are followed by fewer financial analysts than other stocks. The shunning leads to an outperformance of dirty stocks in cross-sectional and time-series return analyses. These observations affect all firms within a dirty industry, regardless of whether they have high or low TRI emissions. This means that comparatively clean firms are shunned by capital market participants simply because of their industry affiliation, which can result in financing disadvantages and low incentives to improve sustainability performance. Thus, our findings contribute to the understanding of environmental preferences of investors and their consequences for asset pricing.
Multiobjective Design Optimization of Stator for Synchronous Generator Using Bat Algorithm and Analysis of Magnetic Flux Density Distribution

Abstract— In this study, we aimed to optimize 3000 kVA synchronous generator (SG) stator design to obtain the desired magnetic flux density distribution and efficiency. We used Maxwell simulations for experiments on some design parameters of stator (slot height and teeth width). Then second-order regression models are calculated that represent the relations between the factors (design parameters) and the measured performance criteria (called as the responses: stator-teeth flux density, stator-yoke flux density, and efficiency). These regression models are used at the multiobjective optimization phase. Bat algorithm (BA) is used for performing the multiobjective optimization. By combining Maxwell with regression modeling and BA, the efficiency of the SG is increased to 96.84% from 96.5% with a more acceptable magnetic flux density (between 1.65 and 1.70 T ranges). The stator-teeth flux density and stator-yoke flux density are calculated as 1.9 T and 2.07 T for the current SG, whereas these values are reduced to 1.647 and 1,634 T, respectively, for the optimized SG. Results of this study show how the numerical simulation can be successfully combined with the BA to improve the efficiency of the SG by providing the desired magnetic flux density distribution.
Extended state observer-based nonsingular terminal sliding mode controller for a DC-DC buck converter with disturbances: theoretical analysis and experimental verification

This paper develops a nonsingular terminal sliding mode controller (NTSMC) based on extended state observer (ESO) for a buck converter subject to load disturbances and input voltage variations. First, a novel modeling method is developed to transform the matched and mismatched disturbances caused by load resistance and input voltage variations into a uniform matched total disturbance. Second, an ESO is established to estimate the differential of the output voltage and the total disturbance. Third, a NTSMC based on ESO is designed to achieve faster tracking speed and better tracking accuracy around the expected voltage. In addition, the theoretical stability of the proposed controller is analyzed by a Lyapunov stability criterion. Finally, both simulation and experimental results show that the proposed NTSMC-ESO controller with the novel modeling method can attenuate the input voltage variation effectively and has a better load disturbance rejection ability than the reduced-order ESO based sliding mode controller. Abbreviations: ADRC: active disturbance rejection control; ESO: extended state observer; NTSMC: nonsingular terminal sliding mode control; RESO: reduced-order extended state observer; SMC: sliding mode control;
On the identity of west Saharan geckos of the Tarentola ephippiata complex (Squamata: Phyllodactylidae), with comments on an extreme case of syntopy with their close relative T. annularis

ABSTRACT Tarentola geckos have a widespread geographic distribution and occur both in the Palearctic and Afrotropical realms, as well as the Neotropical region. Particularly, across North Africa phenotypically similar and cryptic species can be found, like the west Saharan members of the T. ephippiata complex. However, the taxonomic relationships and phylogeographic patterns of these geckos are not fully understood. Here we show that some specimens of Tarentola geckos from Mauritania and southern Morocco previously identified as T. hoggarensis can actually be assigned to T. panousei, a taxon treated as a synonym until now. Because the corresponding type specimen has apparently been lost, we designate a neotype for T. panousei referring to a suitable specimen from the type locality. Based on a morphological examination of the neotype and comparative material we provide a detailed redescription of T. panousei and evaluation of its diagnostic characters. Moreover, we report on a syntopic occurrence of T. annularis and T. panousei on an isolated acacia tree in the Western Sahara and the (micro) habitat use of both species.
Laugh before You Study: Does Watching Funny Videos before Study Facilitate Learning?

Emotions exist widely in the entire process of learning and affect students’ motivation as well as academic performance. In multimedia learning, academics usually focus on the impact of teachers’ emotions or the emotional design of multimedia learning materials on students’ emotions and learning results. Few studies have investigated how to enhance learning by regulating students’ pre-learning emotions. This study focused on whether playing funny videos before learning could promote students’ positive emotions to enhance their motivation, satisfaction, and learning outcomes. We randomly divided 81 elementary school students into two groups: experimental group and control group. While the experimental group watched funny video clips, the control group watched neutral video clips before starting the video learning. The experimental group had more positive pre-learning emotions than the control group. After the course, the emotion of the experimental group declined while that of the control group enhanced. However, positive pre-learning emotions still promoted students’ understanding and transfer of learning materials. Moreover, no significant differences were observed between the two groups in learning motivation, satisfaction, and retention tests. Furthermore, this paper analyzed the causes of the experimental results and discussed the insights for teaching.
Accurate Steady-State Modeling and Design Based on State Trajectory Analysis for LCC Resonant Converter With Voltage Doubler Rectifier

The LCC series–parallel resonant converter with a voltage doubler rectifier, widely used in X-ray machines, is required to meet an extremely wide output range. However, state-of-the-art models are based on fundamental harmonic approximation and cannot maintain good accuracy over the entire output range. The state trajectory method has the potential to achieve a full range of high accuracy without high complexity, but it only applies to simple topologies, like LCC with a full-bridge rectifier. For LCC with a voltage multiplier, the capacitors in the multiplier are involved in the resonance, which can be modeled as a five-element resonant circuit. Moreover, the conduction sequence of components in the minor mode is different from that in the major mode, which is usually ignored in the steady-state model. The influence of the output capacitance of power transistors on the state trajectory is so unclear that the model accuracy decreases considerably at high frequencies. In this article, the equivalent parallel resonant capacitance is derived through the current distribution. The steady-state model of the LCC converter with a voltage doubler rectifier is developed for both major mode and minor mode. The deviation of the state trajectory and modeling accuracy induced by the output capacitor is investigated. Based on the proposed model and device stress analysis, the design procedure for LCC under a wide operation range is presented. The simulations and experiments verify the accuracy of the model and the validity of the design method. The simulation mismatches are less than 2.3% over the entire output range. The maximum experimental mismatch is 15%.
Electromagnetic Transparent Antenna with Slot-Loaded Patch Dipoles in Dual-Band Array

A novel electromagnetic transparent low-band (LB) antenna is given in the paper, with a good impedance matching in the band of 0.69-0.96 GHz (VSWR<1.5). The key feature lies in its great patch dipoles with low-scattering property. The transparent performance is achieved by loading slots on the dipoles, instead of inserting chokes. Dagger-like slot plays an important role in both impedance matching improvement and scattering suppression. It introduces an extra resonance point in the operating band, so that the problems brought by slot loading can be alleviated. Besides, a dual-band shared-aperture array was designed and fabricated for validation. Both simulated and measured results have proven that the LB antenna has satisfactory impedance characteristics within the operating band, and achieves electromagnetic transparency in the band of 1.7-2.4 GHz.
Mobile Device Association and Resource Allocation in HCNs With Mobile Edge Computing and Caching

To meet the need of computation-sensitive (CS) and high-rate (HR) communications, the mobile edge computing and caching framework has been widely regarded as a promising solution. When such a framework is implemented in heterogeneous cellular networks (HCNs), it is a key and open topic how to assign mobile edge computing and caching servers to mobile devices (MDs) with CS and HR communications. In this article, under the resource partitioning and resource constraints, joint MD association and resource allocation is performed to minimize the total delay of MDs. In such a formulated problem, the MD association and resource allocation parameters are in a coupling form. In addition, it is also in nonlinear mixed-integer one and hard to tackle. To solve it, we try to develop an alternating optimization algorithm according to the coalitional game and convex optimization theorems. As for the designed algorithm, we provide some detailed convergence, stability, parallel implement, and computation complexity analyses. At last, in the simulation, we introduce other association algorithms for comparison, and investigate the impacts of MD density, time, and frequency partitioning factors on the delay of MDs. Simulation results show that the designed algorithm can achieve lower network delay than existing algorithms in general.
Collaborative untangling of positionality, ownership, and answerability as white researchers in indigenous spaces

Decentering whiteness and decolonizing educational research is not a simple matter for researchers who may (initially) be unaware of their social locations. This paper begins by describing the three authors’ individual work within indigenous populations and subsequently explores the impact of their critical journey together as a community of scholars working to decolonize their research practice. Following an overview of their individual stories, the authors share principles of ethical scholarly engagement within indigenous communities, particularly positionality, ownership, and answerability, that they hope will be valuable to others who may embark upon similar critically reflective journeys. Alongside each of these principles, they pose “unanswered questions” so as not to uphold their journey as a model, but rather to illuminate how this work is messy, challenging, eye-opening, unsettling, and ongoing. The authors conclude that it is the “methodological responsibility” of white scholars to do the ethical work to understand, untangle, and dismantle potentially harmful dispositions, orientations, and practices before engaging with indigenous communities, and suggest that such transformation may be most powerfully pursued in a purposeful and collaborative space.