Miracle-dz/UniFAR-FTUs huggingface dataset:
Theoretical Investigation on Rare Earth Elements of Y, Nd and La Atoms’ Adsorption on the Kaolinite (001) and (001¯) Surfaces

With the growing demand of rare earth elements, the recovery of rare earth elements is a major issue for researchers in related fields. Adsorption technology is one of the most effective and popular recovery methods. Therefore, the adsorption mechanism of Yttrium (Y), Neodymium (Nd), and Lanthanum (La) atoms on the kaolinite (001) and (001¯) surfaces was examined by density functional theory (DFT). The most stable adsorption sites on the kaolinite (001) surface for Y atoms was the bridge site, and the hollow site was the most favorable adsorption site for Nd and La atoms with high adsorption energy. However, the adsorption energies of kaolinite (001¯) surface sites for Y, Nd, and La atoms were much lower than the (001) surface sites, indicating that the adsorption capability of the hydroxylated (001) surface is stronger. The effects of coverage on adsorption position, energy, and structures were entirely investigated on top, bridge, and hollow sites of the kaolinite (001) surface from 0.11 to 1.0 monolayers (ML). The adsorption energy of Y, Nd, and La atoms on three kinds of sites increased with increasing of the coverage implied the stronger capability of surface adsorption. The recovery capability of kaolinite for the rare earth atoms was in the order of La > Nd > Y. The changes in the atomic structure, charge density, and electron density of states for Y, Nd, and La/kaolinite (001) before and after adsorption were also analyzed in depth.
Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom

Construction disputes are one of the main challenges to successful construction projects. Most construction parties experience claims—and even worse, disputes—which are costly and time-consuming to resolve. Lessons learned from past failure cases can help reduce potential future risk factors that likely lead to disputes. In particular, case law, which has been accumulated from the past, is valuable information, providing useful insights to prepare for future disputes. However, few efforts have been made to discover legal knowledge using a large scale of case laws in the construction field. The aim of this paper is to enhance understanding of the multifaceted legal issues surrounding construction adjudication using large amounts of accumulated construction legal cases. This goal is achieved by exploring dispute-related contract terms and conditions that affect judicial decisions based on their verdicts. This study builds on text mining methods to examine what type of contract conditions are frequently referenced in the final decision of each dispute. Various text mining techniques are leveraged for knowledge discovery (i.e., analyzing frequent terms, discovering pairwise correlations, and identifying potential topics) in text-heavy data. The findings show that (1) similar patterns of disputes have occurred repeatedly in construction-related legal cases and (2) the discovered dispute topics indicate that mutually agreed upon contract terms and conditions are import in dispute resolution.
The Pedagogy of Vulnerability and Marketing Education: Cultivating Self-expansion in a Time of Separation

Applying the pedagogy of vulnerability and self-expansion theory, the exercise known as the Know Me Activity (KMA) enhances self-expansion by encouraging connections between students and the instructor. The pedagogy of vulnerability is premised on risking self-disclosure as an act of courage. Self-expansion theory is grounded in personal relationships whereby individuals are motivated for personal growth and identify with others to gain access to their resources. In the context of marketing education, self-expansion stems from enhancing student self-awareness for personal growth as well as perceived vulnerability and relatedness of the instructor to encourage access to expertise. The KMA was conducted at three universities and assessed to determine its usefulness as a self-expansion exercise. Survey results indicate that students found the activity contained self-expansion characteristics, and they recommend it for future use. The results of the pretest and posttest analyses also demonstrated an increase in student self-awareness, perceived instructor vulnerability, and perceived instructor relatedness. Self-expansion activities enhance relationships by design and participating in them can lead to increased student effort and persistence. Marketing educators can, therefore, have confidence in utilizing the KMA as a means for mitigating social isolation and encouraging perseverance.
Traveling capillary waves on the boundary of a fluid disc

The equation for a traveling wave on the boundary of a two‐dimensional droplet of an ideal fluid is derived by using the conformal variables technique for free surface waves. The free surface is subject only to the force of surface tension and the fluid flow is assumed to be potential. We use the canonical Hamiltonian variables discovered and map the lower complex plane to the interior of a fluid droplet conformally. The equations in this form have been originally discovered for infinitely deep water and later adapted to a bounded fluid domain.The new class of solutions satisfies a pseudodifferential equation similar to the Babenko equation for the Stokes wave. We illustrate with numerical solutions of the time‐dependent equations and observe the linear limit of traveling waves in this geometry.
Global Hotspots for Future Absolute Temperature Extremes From CMIP6 Models

Two questions motivated this study: (a) Are global land regions exposed to future changes in absolute temperature extremes? (b) And to which degree? To answer these questions, we projected the trend in four indices, recommended by the Expert Team on Climate Change Detection and Indices (ETCCDI), through the 21st century. Observational (HadEX3) and reanalyses (ERA5, JRA‐55, NCEP‐R‐1, and NCEP‐R‐2) datasets were used to validate and correct the bias in the Coupled Model Intercomparison Project phase 6 (CMIP6) models. CMIP6 models were then used to evaluate the changes globally and in 44 reference regions under two shared socioeconomic pathways (SSPs): SSP2‐4.5 and SSP5‐8.5. Globally, the analysis showed that, under SSP5‐8.5, TXx and TNx would increase by 0.07°C/year, and TXn (TNn) would increase by 0.09 (0.08)°C/year, during the middle of the 21st century (2021–2050), compared with the reference period (1981–2010). These trends are larger during the end of the 21st century (2071–2100). The analysis also showed that all land reference regions are projected to indices growth up to 3.5°C in TXx; 2.8°C in TNx; 4.5°C in TXn; and 4°C in TNn, during the mid‐21st century under SSP5‐8.5. The latter growth in indices increased by 211% for TXx, 237% for TNx, 242% for TXn, and 300% for TNn during the end‐21st century. The global hotspots affected by absolute temperature extremes are in the Northern Hemisphere, including North America, Iceland, Central Asia, Tibetan Plateau, Russian Arctic, Siberia, Mediterranean, Sahara, and Arabian Peninsula. The study's findings help understand the evolution of climate extremes, which is essential for climate change mitigation and adaptation plans.
A Time-Varying Coupling Analysis of Expressway Traffic Volume and Manufacturing PMI

This study investigates the time-varying coupling relationship between expressway traffic volume and manufacturing purchasing manager index (PMI). First, for the traffic volume and manufacturing PMI time-series data, unit root stability test and Johansen cointegration test are applied to determine the stability of single sequence and the long-term stable correlation between variables, respectively. Then, a time-varying vector autoregressive model (TVP-VAR) is developed to quantify the time-varying correlation between variables. The time-varying parameters of TVP-VAR are estimated using the Markov chain Monte Carlo (MCMC) theory. Finally, the model is validated using examples from China. In the numeric example, three variables, i.e., expressway car traffic volume, expressway truck traffic volume, and manufacturing PMI, are selected for analysis. Results show that there is a positive interaction between expressway traffic volume (both car and truck) and manufacturing PMI. Express traffic volume slowly promotes the development of manufacturing industry. However, with the reform policy of road freight structure in China, the promotion effect of truck traffic on manufacturing PMI in the past two years has decreased significantly. Moreover, as affected by the China demand-led economic development model in recent years, the stimulus effect of manufacturing PMI on expressway passenger traffic volume has increased year by year. And, while the expressway freight structure remains stable, truck traffic volume is hardly affected by fluctuations in manufacturing PMI. These research results are helpful for policy makers to understand the time-varying coupling relationship between expressway traffic volume and manufacturing development and finally to improve the expressway management level.
Tourists’ perceptions of climate: Application of machine learning to climate and weather data from Chinese social media

Understanding tourists’ perceptions of climate is essential to improving tourist satisfaction and destination marketing. This paper constructs a sentiment analysis framework for tourists’ perceptions of climate using not only continuous climate data but also short-term weather data. Based on Sina Weibo, we found that Chinese tourists’ perceptions of climate change were at an initial stage of development. The accuracies of word segmentation between sentiment and nonsentiment words using ROST CM, BosonNLP, and GooSeeker were all high, and the three gradually decreased. The positively expressed sentences accounted for 79.80% of the entire text using ROST EA, and the sentiment score was 0.784 at the intermediate level using artificial neural networks. The results indicate that the perceived emotional map is generally consistent with the actual climate and that cognitive evaluation theory is suitable to study text on climate perception.
Conventional and Islamic stock market liquidity and volatility during COVID 19

ABSTRACT The coronavirus pandemic has impacted several stock markets worldwide. Among other countries, the US has endured a significant impact of the pandemic, with over 605,000 deaths. This study investigates the effect of COVID-19 on the liquidity and volatility of the US conventional and Islamic stock indexes to assess their efficiency in a linear and nonlinear frammeworks. We use different stock market data (stock prices and trading volumes) and COVID-19 statistics. In particular, we specify liquidity and volatility differently, compute the speed of COVID-19 transmission, rely on robust linear and nonlinear regressions before and during the pandemic to determine different forms of coronavirus effects. The study finds that both liquidity and volatility (regardless of the proxy under consideration) exhibit an important time variation. Second, we find that the variation of contamination and death speeds related to the pandemic has been nonlinearly driving market trading, liquidity, and volatility, suggesting a significant reaction of stock market to exogenous news related to the coronavirus outbreak and inefficiency of these markets. Considering this dependency is crucial to improving the forecasting of stock market dynamics. Further, we find that Islamic funds are not more resilient than conventional funds towards the pandemic.
The Immediate Effect of Muscle Energy Technique and Whole Body Vibration on Hamstring Muscle Flexibility and Stiffness in Healthy Young Females

409 SUMMARY Background. High prevalence of hamstring injury is attributable to the lack of efficient flexibility, and different methods are suggested for increasing its flexibility. Therefore, the present study investigated two methods in healthy young females, Muscle Energy Technique (MET) and Whole Body Vibration (WBV) on flexibility and Hamstring Muscle Stiffness. Materials and methods. Fifty-six young females were included in the sample with a mean age of 23.82 ± 3.17 years and were randomly placed in one of two treatment groups. The participants were treated 3 times with the Post Isometric Relaxation technique in the MET group. In the WBV group, in three 30-second sets at 30 Hz and 2 mm amplitude, each participant was subjected to vibration. Active Knee Extension (AKE) and Modified Sit & Reach (Mod S & R) tests measured Hamstring flexibility, and the Stiffness evaluation was carried out using the Biodex system 3. Results. The evaluation results revealed a significant difference between pre-intervention and post-intervention (P = 0.000), but there was no significant difference between the two treatment groups in terms of post-intervention test results (P > 0.000). Conclusions. The results showed that a single WBV and MET session increased flexibility and decreased the stiffness of the hamstring, but there was no substantial difference in the efficacy of these methods.
Physical layer key generation against MIMO eavesdropper by exploiting full-duplex technology

Generating a shared secret key from physical layer is an interesting topic with practical value. Inspired by the encouraging progress on full-duplex radio, a novel mechanism aiming at high and steady key generation rate with low cost is proposed in this paper. Legitimate users simultaneously send random bit sequences to actively interfere with each other. They extract those mutually jammed bits to form a secret key. A special digital modulation scheme, called Random Manchester coding is proposed. The proposed scheme achieves three goals. The first and the most important one is to prevent a MIMO eavesdropper from separating the superposed signal; the second one is to detect denial of service and key compromise attack to defend against an active attacker; the third one is to achieve design goal on low power radiation, computational complexity and memory cost. Theoretical analysis, numerical simulations and concept-proof experiments validate the effectiveness of our proposed scheme. Our solution is promising to facilitate key generation applications of nearby wireless devices such as ubiquitous smartphones, wearable devices.
Distributed model predictive control for nonholonomic multivehicle formation tracking

A distributed model predictive control strategy is proposed for the formation tracking problem of nonholonomic multivehicle systems, where each vehicle is modeled as a unicycle and is affected by disturbances. We design the optimization problems that followers need to solve, and the cooperation among the followers are achieved by the coupling costs between the followers. Meanwhile, the tightened constraints are used to ensure the actual tracking errors satisfy the constraints. The recursive feasibility of the optimization problems and the closed‐loop stability of the tracking error systems are established. Finally, the effectiveness of the proposed strategy is verified via an example which contains three followers.
Histological evaluation of the intestine of broiler chickens: comparison of three sampling methods

After the growth-promoting antibiotics prohibition, intestinal health became an increasing concern worldwide in poultry farming. The intestinal histological evaluation is an inexpensive technique that brings relevant information, but in poultry, the immediate process of intestinal post-mortem autolysis interferes directly on the samples quality for histological analysis hindering a precise diagnosis. This study aimed to standardize a technique for broilers’ intestines sample collection and fixation for histological analysis. Seven broiler chickens received a standard diet until 23 days of age when they were euthanized. Fragments of duodenum, jejunum, and ileum were collected using three methods: intestine strips, transverse section, and Swiss roll and posteriorly fixed in 10% buffered formalin and bouin solution. Tissue samples were submitted for histological (number of villi and viable villi per field) and morphometrical (villi height, crypt depth and villi:crypt ratio) evaluations and the results analyzed statistically. A significant high number of villi and viable villi per field in all regions was observed in the Swiss roll method. In the duodenum (p= 0.0066) and jejunum (p= 0.0058) an interaction between the Swiss roll method and the fixative buffered formalin was observed in the viable and number of villi per field, respectively. Regarding the morphometrical analysis significant differences were observed, in the jejunum villi height sampling by the methods Swiss roll (1,157.66 ± 148.25 μm, p= 0.0015) that showed the highest mean. Deeper crypt depths were observed in the jejunum (156.59 ± 15.68 μm, p= 0.0002) and ileum (131.13 ± 15.01 μm, p= 0.0006) collect by the Swiss roll method. An interaction between the bouin fixative was also observed in the jejunum (p= 0.0223) for this variable. Duodenum sampling by transversal section (12.68 ± 1.45 μm, p= 0.0076) was the only segment that had a significant difference for villi:crypt ratio, showing the highest mean. It can be concluded that the Swiss roll technique was the best method for morphometrical evaluation of the chickens’ intestines, since the highest counts of villi per field and viable villi per field were obtained, while buffered formalin was considered as the best fixative.
Deep Learning-Based UAV Detection in Pulse-Doppler Radar

With the popularity of unmanned aerial vehicles (UAVs), how to conduct automatic and effective detection to prevent unauthorized flying has become an important issue. The conventional constant false alarm rate (CFAR) detector based on radar signal has shown advantages in moving target detection. However, the CFAR-based detectors are strongly dependent on some manual experience, such as the ambient noise distribution estimation and the detection windows' size selection, and usually suffered poor performance on small UAV detection due to the weak signal. Inspired by the success of deep learning (DL) on natural scene object detection, this article tries to explore a DL-based method for UAV detection in pulse-Doppler radar. Concretely, we propose a convolutional neural network (CNN) with two heads: one for the classification of the input range-Doppler map patch into target present or target absent and the other for the regression of offset between the target and the patch center. Then, based on the output of the network, a nonmaximum suppression (NMS) mechanism composed of probability-based initial recognition, distribution density-based recognition, and voting-based regression is developed to reduce false alarms as well as control the false alarms. Finally, experiments on both simulated data and real data are carried out, and it is shown that the proposed method can locate the target more accurately and achieve a much lower false alarm rate at a comparable detection rate than CFAR.
Robust optimal transition maneuvers control for tail‐sitter unmanned aerial vehicles

Optimal transition has been widely studied for tail‐sitter unmanned aerial vehicles (UAVs). However, since the derived optimal control can only be applied in an open‐loop form, unmodeled dynamics and external disturbances cannot be restrained. Aiming at this problem, this article presents a method to obtain optimal transition maneuvers in the presence of these uncertainties. The approach includes two steps: nonlinear trajectory optimization and robust trajectory tracking. For the trajectory optimization, the minimum altitude variation front and back transition trajectories are derived. Different from existing optimal transition studies, considerable actuator margins are reserved in this step. Thus the tail‐sitter possesses control efforts to perform closed‐loop corrections for uncertainties. For the trajectory tracking, a robust controller in the form of feedforward plus feedback is proposed. The feedforward term directly employs the derived optimal control to improve the dynamic response of the system. The feedback term adopts linear control and acceleration‐based compensator to place desired poles and restrain all uncertainties. To the authors' knowledge, this article is the first time to study the tail‐sitter robust transition control method considering the transition optimality and actuator margins. It is proved that the tracking errors converge into a specified neighborhood of the origin in a finite time. Simulation studies corroborate the performance of the optimal transition trajectories and the robust trajectory tracking controller.
Using a distributed systems laboratory to facilitate students' cognitive, metacognitive and critical thinking strategy use

Background: Recent research in online settings reports that supporting self-regulated learning (SRL) strategy use could lead to greater online academic success. A growing number of studies have started to investigate SRL supports in online environments recently, which indicates a great interest in this matter. Though several systems for automatic assessment of programming have been developed, there is hardly any study that has investigated how an automated assessment tool for distributed programming could facilitate students' SRL strategies. Objectives: This study examined the ways our online Distributed Systems Laboratory (DSLab) tried to enhance students' SRL strategies in an authentic long-term online educational experience. Methods: We applied an experimental research design, involving 111 university students who performed a programming assignment using DSLab. A customized questionnaire was used to collect data from all students. Results and Conclusions: The statistical analyses revealed that DSLab tool managed to facilitate students' cognitive and meta-cognitive strategy use to a certain extent and critical thinking strategy use to a fairly large extent. Implications: Though more experimental results are needed to delve more deeply into these findings, this study provides relevant implications for online distributed (or general) programming course teachers who seek to increase students' SRL strategies in this field.