Miracle-dz/UniFAR-FTUs huggingface dataset:
The shear strain energy fluctuations caused by random porosities in graphene based on the stochastic finite element model

Strain-induced deformation is a promising strategy to modify and functionalize the material properties of graphene. However, the impacts of random porosities are inevitable and complicated in the microstructure. In order to quantify and analyze the effects of random porosities in graphene under shear stress, the energy fluctuations and the equivalent elastic modulus are computed and recorded based on the stochastic finite element model. The finite element computation is combined with the Monte Carlo stochastic sampling procedure to distribute and propagate the random porosities in pristine graphene. Two different boundary conditions are taken into consideration and compared. Furthermore, the probability statistics of shear strain energy and equivalent elastic modulus are provided based on the comparison with the results of pristine graphene. The inhomogeneous spatial randomness is founded in the statistic records of shear strain energy. The sensitivity to the graphene chirality and boundary conditions are also shown for the porous graphene. The work in this paper provides important references for strain-induced engineering and artificial functionalization through topological vacancy control in graphene.
Classification of Various Marijuana Varieties by Raman Microscopy and Chemometrics

The Raman analysis of marijuana is challenging because of the sample’s easy photo-degradation caused by the laser intensity. In this study, optimization of collection parameters and laser focusing on marijuana trichome heads allowed collecting Raman spectra without damaging the samples. The Raman spectra of Δ9-tetrahydrocannabinol (THC), cannabidiol (CBD), and cannabinol (CBN) standard cannabinoids were compared with Raman spectra of five different types of marijuana: four Sativa varieties (Amnesia Haze, Amnesia Hy-Pro, Original Amnesia, and Y Griega) and one Indica variety (Black Domina). The results verified the presence of several common spectral bands that are useful for marijuana characterization. Results were corroborated by the quantum chemical simulated Raman spectra of their acid-form (tetrahydrocannabinol acid (THCA), cannabidiol acid (CBDA)) and decarboxylated cannabinoids (THC, CBD, and CBN). A chemometrics-assisted method based on Raman microscopy and OPLS-DA offered good classification among the different marijuana varieties allowing identification of the most significant spectral bands.
Engineering Hydrogels for the Development of Three-Dimensional In Vitro Models

The superiority of in vitro 3D cultures over conventional 2D cell cultures is well recognized by the scientific community for its relevance in mimicking the native tissue architecture and functionality. The recent paradigm shift in the field of tissue engineering toward the development of 3D in vitro models can be realized with its myriad of applications, including drug screening, developing alternative diagnostics, and regenerative medicine. Hydrogels are considered the most suitable biomaterial for developing an in vitro model owing to their similarity in features to the extracellular microenvironment of native tissue. In this review article, recent progress in the use of hydrogel-based biomaterial for the development of 3D in vitro biomimetic tissue models is highlighted. Discussions of hydrogel sources and the latest hybrid system with different combinations of biopolymers are also presented. The hydrogel crosslinking mechanism and design consideration are summarized, followed by different types of available hydrogel module systems along with recent microfabrication technologies. We also present the latest developments in engineering hydrogel-based 3D in vitro models targeting specific tissues. Finally, we discuss the challenges surrounding current in vitro platforms and 3D models in the light of future perspectives for an improved biomimetic in vitro organ system.
Robust Anomaly Clue Localization of Multi-Dimensional Derived Measure for Online Video Services

Anomaly clue localization of multi-dimensional derived measure is vitally important for the reliability of online video services. In this paper, we propose RobustSpot, an end-to-end framework for localizing the clues to anomalous multi-dimensional derived measures. RobustSpot integrates two novel indicators, i.e., “Anomaly Degree” and “Contribution Ability”, with a simple yet effective method, weighted association rule mining (WARM), to automatically mine the hidden relationships across data dimensions for localizing the most likely clues to the root cause. Using 135 real-world cases collected from a top-tier global online video service provider <inline-formula><tex-math notation="LaTeX">$H$</tex-math><alternatives><mml:math><mml:mi>H</mml:mi></mml:math><inline-graphic xlink:href="zhang-ieq1-3155500.gif"/></alternatives></inline-formula> with 170+ million monthly active users, we demonstrate that RobustSpot achieves high accuracy (Top-5 accuracy of 98%), significantly outperforming state-of-the-art methods. The average localization time of RobustSpot is 1.83s, which is satisfying in our scenario. We have open-sourced the implementation of RobustSpot as well as the data used in the evaluation experiments.
AIRCRAFT PAVEMENT LOADING: STATIC AND DYNAMIC

The subject of pavement loading by aircraft is treated by presenting a series of tables and figures designed to cover the essentials of pavement loading through the various phases of aircraft operation. The phases include static, slow taxi, steady-state turns at various speeds and turn radii, takeoff roll, roughness, landing impact, and braking. Figures and tables that present DC-8 responses at a number of international airports are also included. /Author/
Body Language and Gestures as a Lingua Franca among Multilingual Interlocutors: Multimodal Transcription Study

Lingua franca is known as the language bridge between two different people who have different native languages. The theory stated that humans communicate by using vocal organs and the whole part of the body. This study was conducted to seek out whether non-verbal language could be considered lingua franca. This study used multimodal transcription analysis with Jefferson and Modada transcript notation and has decided on several data clips from YouTube. The study examines the gestures and other non-verbal languages to seek out whether multilingual interlocutors could receive the implied message delivered by body language. The results show that the hearer was trying to understand the speaker’s gesture. The speaker does not speak English fluently, so he was struggling to find perfect diction. At the end of the clip, the speaker finally succeeds in portraying his thoughts through some gestures. In addition, the interlocutors were taking place in the action of delivering and receiving implied meaning through gestures and body language. Even though there was already a lingua franca (e.g., English), body language adds meaning through the message. Furthermore, this study was conducted in order to give a different insight into the term
Lead Lag Controller of TCSC Optimized by Bees Algorithm for Damping Low Frequency Oscillation Enhancement in SMIB

Power system is often vulnerable to low frequency electromechanical oscillations due to the interconnected configuration. A common lead-lag controller is used for one of the FACTS devices known as Thyristor Controlled Series Compensator (TCSC) as supplementary controller for damping purpose in order to improve transient stability and power oscillation damping of the system. As Bees Algorithm (BA) optimized the parameters of the TCSC lead-lag controller, thus its named is TCSC-BALL. In this study, the optimization problem is formulated as a constrained optimization with the main objective is to move the system eigenvalues to the left as far as possible in order to improve the system stability. Then, the system is simulated in MATLAB by using The Phillips-Heffron model for single machine infinite bus (SMIB) with responses of increases in mechanical power at t=1 second. The performance is observed in terms of electromechanical eigenvalues position on s-plane and damping responses of low-frequency oscillations where the system implemented with the TCSC-BALL controller given better results as compared to the system without and with the inclusion of conventional Power System Stabilizer (CPSS).
Open-Set Fault Diagnosis via Supervised Contrastive Learning With Negative Out-of-Distribution Data Augmentation

Fault diagnosis in an open world refers to the diagnosis tasks that need to cope with previously unknown faults in the online stage. It faces a great challenge yet to be addressed—that is, the online data of unknown faults may be classified as normal samples with a high probability. In this article, we develop an effective solution for this challenge by using supervised contrastive learning to learn a discriminative and compact embedding for the known normal situation and fault situations. Specifically, in addition to contrasting a given sample with other instances as is the case in conventional contrastive learning methods, our training scheme contrasts the normal samples with negative augmentations of themselves. The negative out-of-distribution data is generated by the Soft Brownian Offset sampling method to simulate the previously unknown faults. Computational experiments are conducted on the Tennessee Eastman Process benchmark dataset and a practical plasma etching process dataset. The proposed method achieves significant improvement compared with four existing methods under three open-set fault diagnosis circumstances, i.e., balanced open-set fault diagnosis, imbalanced fault diagnosis, and few-shot fault diagnosis. This demonstrates its great potentials in real world fault diagnosis applications.
Extraintestinal Pathogenic Escherichia coli: Beta-Lactam Antibiotic and Heavy Metal Resistance

Multiple-antibiotic-resistant (MAR) extra-intestinal pathogenic Escherichia coli (ExPEC) represents one of the most frequent causes of human nosocomial and community-acquired infections, whose eradication is of major concern for clinicians. ExPECs may inhabit indefinitely as commensal the gut of humans and other animals; from the intestine, they may move to colonize other tissues, where they are responsible for a number of diseases, including recurrent and uncomplicated UTIs, sepsis and neonatal meningitis. In the pre-antibiotic era, heavy metals were largely used as chemotherapeutics and/or as antimicrobials in human and animal healthcare. As with antibiotics, the global incidence of heavy metal tolerance in commensal, as well as in ExPEC, has increased following the ban in several countries of antibiotics as promoters of animal growth. Furthermore, it is believed that extensive bacterial exposure to heavy metals present in soil and water might have favored the increase in heavy-metal-tolerant microorganisms. The isolation of ExPEC strains with combined resistance to both antibiotics and heavy metals has become quite common and, remarkably, it has been recently shown that heavy metal resistance genes may co-select antibiotic-resistance genes. Despite their clinical relevance, the mechanisms underlining the development and spread of heavy metal tolerance have not been fully elucidated. The aim of this review is to present data regarding the development and spread of resistance to first-line antibiotics, such as beta-lactams, as well as tolerance to heavy metals in ExPEC strains.
The Effect of Sleep Duration on Short-term Memory

Sleeping time is usually associated with short-term memory, while the mechanisms under this relationship are less understood. This study examined the role of sleeping time in this relationship with cross-sectional data. 24 Chinese between 18 and 30 years old were recruited. Their sleeping time was recorded, and PsychoPy collected the data of memory test. The results showed that (1) the Sleeping time is positively correlated with sleep duration, (2) participants were more likely to remember the words related to emotions. These findings suggest that proper sleeping time can help improve the efficiency of productivity.
Low-cost Y-type zeolite/carbon porous composite from coal gasification fine slag and its application in the phenol removal from wastewater: fabrication, characterization, equilibrium, and kinetic studies

As an industrial solid waste, coal gasification fine slag (CGFS), which consists of many elements, such as silicon, aluminum, and carbon, could be used as an important resource. Therefore, this solid waste was used as a raw material to prepare high-value-added adsorption material for the treatment of industrial wastewater in this study. A hydrothermal synthesis method was applied to convert CGFS into a Y-type zeolite/carbon porous composite. The effects of time and temperature on the synthesis were studied. XRD, SEM, and other techniques were used to analyze the material and its physicochemical properties. Additionally, the adsorption performance of the material for phenol was studied. The results showed that the composite has better adsorption capacity for phenol than CGFS. The Freundlich model and pseudo-second-order kinetics well fitted the adsorption behavior of the composite, which demonstrated that the adsorption of phenol was dominated by chemical adsorption.
Trace-Level Determination of Polycyclic Aromatic Hydrocarbons in Dairy Products Available in Spanish Supermarkets by Semi-Automated Solid-Phase Extraction and Gas Chromatography–Mass Spectrometry Detection

Polycyclic aromatic hydrocarbons (PAHs) have been classified as priority pollutants by the U.S. Environmental Protection Agency (EPA) and the European Commission on the grounds of their carcinogenic, mutagenic and teratogenic properties. Because of their ubiquity in industrial processes and the environment, PAHs can reach milk and dairy products and, eventually, humans. In this work, a new method was developed to detect and quantify sixteen of the EPA’s priority PAHs in commercial milk and dairy products. The method involves liquid–liquid extraction (LLE) followed by semi-automated solid-phase extraction (SPE) to clean up and preconcentrate the analytes prior their detection and quantification by gas chromatography–mass spectrometry (GC–MS). The proposed method provided high precision (relative standard deviation < 11.5%), recoveries of 80–107% and low detection limits (1–200 ng/kg). The method was applied to analyze 30 dairy products, the majority of which contained some PAH at concentrations from 7.1 to 1900 ng/kg. The most-detected analytes were the lighter PAHs (naphthalene, acenaphthylene, fluorene and phenanthrene). None of the samples, however, contained more than four PAHs.
A Conceptual Framework for Assessing Strategic Information Systems Planning (SISP) Success in the Current Dynamic Environments

Within the current dynamic, increasingly globalized and digitalized environments, Strategic Information Systems Planning (SISP) is recognized as one of the most important tasks for better management and supporting strategic use of IS/IT. However, undertaking SISP process in today's dynamic environments is difficult because organisations need to consider and take multiple planning perspectives, including managerial, environmental and organisational factors simultaneously, not to deal with only one important perspective. To facilitate organisational performance and sustain competitive advantage through SISP success, various factors and their relationship regarding SISP success, organisational performance and competitive advantage need to be well identified and understood. This paper proposes a model to show relationship between seven primary factors and SISP success; SISP success and its outcomes; and the factors and SISP's outcomes. Further research is planned to undertake the survey of top 1,000 Korean large organisations to examine the relationships and test the proposed research hypothesis.
A Self-Tuning LCC/LCC System Based on Switch-Controlled Capacitors for Constant-Power Wireless Electric Vehicle Charging

In this paper, a self-tuning LCC/LCC wireless power transfer system based on switch-controlled capacitors is proposed to maintain high power factor on the primary inverter side and fixed output power on the secondary side against self- or mutual inductance variation of the magnetic coupler. The PI control on primary side and the method of gradient descent on the secondary side are proposed based on the proposed normalized mistuned LCC/LCC circuit model. Only two switch-controlled capacitors on each primary and secondary side are used to implement the control scheme without the Wifi communication or parameter identification. A 3 kW experiment setup was built in the lab and two different magnetic shields, namely ferrite and nanocrystalline, were tested on the secondary pad. According to the experiment results, the proposed system is proven to be able to maintain high power factor (>0.9) and desired DC output power against 52% mutual inductance variation and 12% self-inductance variation of the secondary pad. The proposed system can be applied as a wireless charger for electric vehicles or a high power wireless charger test bench.
Higher Granular Sectoral Demand Forecast Under Data Scarcity: An Integrated Physics-Based Top–Down and Bottom–Up Approach

The proliferation of renewable distributed energy resources and transportation electrification has created new challenges for the utilities in integrating these new technologies. Consequently, the utilities and electric distribution companies need to adapt their current business models to maintain service reliability, while higher granular data scarcity hinders accurate decision-making processes. In this context, this article proposes an integrated top–down bottom–up approach that aims at estimating the essential higher granular demand data through a two-layer physics-based framework. In the top–down model, various economic-demographic regression models are developed to unlock higher granular demand in geographic areas and technology/policy adoptions, while in the bottom–up layer, our in-house-developed physics-based building simulator is employed to simulate sectoral disaggregated demand patterns. The simulation error is modeled by the least-squares estimation of a Fourier series, extreme gradient boosting (XGBoost) algorithm, and long short-term memory (LSTM) network, where the numerical results show the superiority of the LSTM in capturing the simulation errors. To reveal the merits of the proposed hierarchical framework, the state of New Jersey in the US is chosen as the test case directed by the Energy Master Plan to pursue a set of targets by 2021, which is the target year in this article, while data scarcity is in place.
Transmission Line Vibration Damper Detection Using Multi-Granularity Conditional Generative Adversarial Nets Based on UAV Inspection Images

The vibration dampers can eliminate the galloping phenomenon of transmission lines caused by the wind. The detection of vibration dampers based on visual technology is an important issue. Current CNN-based methods struggle to meet the requirements of real-time detection. Therefore, the current vibration damper detection work has mainly been carried out manually. In view of the above situation, we propose a vibration damper detection-image generation model called DamperGAN based on multi-granularity Conditional Generative Adversarial Nets. DamperGAN first generates a low-resolution detection result image based on a coarse-grained module, then uses Monte Carlo search to mine the latent information in the low-resolution image, and finally injects this information into a fine-grained module through an attention mechanism to output high-resolution images and penalize poor intermediate information. At the same time, we propose a multi-level discriminator based on the multi-task learning mechanism to improve the discriminator’s discriminative ability and promote the generator to output better images. Finally, experiments on the self-built DamperGenSet dataset show that the images generated by our model are superior to the current mainstream baselines in both resolution and quality.
Heterogeneous Dendrimer-Based Catalysts

The present review compiles the advances in the dendritic catalysis within the last two decades, in particular concerning heterogeneous dendrimer-based catalysts and their and application in various processes, such as hydrogenation, oxidation, cross-coupling reactions, etc. There are considered three main approaches to the synthesis of immobilized heterogeneous dendrimer-based catalysts: (1) impregnation/adsorption on silica or carbon carriers; (2) dendrimer covalent grafting to various supports (silica, polystyrene, carbon nanotubes, porous aromatic frameworks, etc.), which may be performed in a divergent (as a gradual dendron growth on the support) or convergent way (as a grafting of whole dendrimer to the support); and (3) dendrimer cross-linking, using transition metal ions (resulting in coordination polymer networks) or bifunctional organic linkers, whose size, polarity, and rigidity define the properties of the resulted material. Additionally, magnetically separable dendritic catalysts, which can be synthesized using the three above-mentioned approaches, are also considered. Dendritic catalysts, synthesized in such ways, can be stored as powders and be easily separated from the reaction medium by filtration/centrifugation as traditional heterogeneous catalysts, maintaining efficiency as for homogeneous dendritic catalysts.
A study of the temporal and spatial variations in the suitability of the environment in Chinese cities for tourism and in strategies for optimizing the environment

ABSTRACT With the aim of guaranteeing the interests and health of tourists, this study comprehensively evaluates the suitability of the environment in different cities in China for tourism by considering the impacts of climate, vegetation and environmental factors on health. The Universal Thermal Climate Index (UTCI), total precipitation (PREC), Vegetation Leaf Area Index (LAI), concentration of particulate matter with a diameter of less than 2.5 μm (PM2.5), and the downward UV radiation at the surface (UVB) are used as indices. In addition, the Delphi–AHP–TOPSIS algorithm is introduced and is used for the comprehensive evaluation of the environmental suitability of 684 cities in China for tourism in 2019. It is found that Chinese cities vary greatly in terms of the suitability of their environments for tourism. Among the factors that affect the environmental suitability, the LAI is important, with an increase in the LAI generally being advantageous. In contrast, an increase in the PM2.5 concentration is a key factor that limits the environmental suitability of an area. Thus, urban landscaping and control of the atmospheric quality are essential and effective ways of improving the suitability of the environment of cities in China for tourists.
Application of Problem-Based Learning Model to Students' Mathematic Communication Ability

This article describes applying the Problem-Based Learning (PBL) model on Mathematical Communication Ability. The research in this journal is an experimental study using pretest-posttest control group design with a population of seventh-grade students of SMP Negeri in Kuantan Singingi Regency, divided into three school levels: high, medium, and low schools. From each school, two classes were randomly selected as the experimental class and the control class. The research instrument was the Mathematical Communication Ability test questions. The data collection technique is done by testing. Data were analyzed using t-test and one-way ANOVA. The results showed an effect of PBL application on mathematical communication skills in terms of all students. The effect of PBL application on mathematical communication skills in terms of school-level shows that for the high school level, students are better than students using conventional learning, while at the medium and low school level, there is no significant effect.