Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigating the Impact of Trade Disruptions on Price Transmission in Commodity Markets: An Application of Threshold Cointegration

Threshold cointegration is introduced as an econometric technique to model the impact of trade disruptions on spatial price transmission in commodity markets so that market participants and policy makers can understand the global impact of trade disruptions on prices. The threshold cointegration technique that is employed is flexible in that it allows the number of thresholds and their location to be determined endogenously and the threshold variable to be exogenous to the system. We innovate on the threshold cointegration technique by selecting a measure of trade disruptions as the threshold variable. This innovation can be used for any commodity market that is spatially connected due to arbitrage; however, to illustrate its usefulness we apply the technique to trade disruptions for canola traded between Canada and China using weekly data between 2014 and 2019 and find that canola trade disruptions between Canada and China impacted global price transmission and resulted in market fragmentation.
​Assessment of Hydrocyanic Acid (HCN) Content during Different Stages of Growth in Johnson Grass (Sorghum helepensis)

Background: Johnson grass (Sorghum helepensis) is not a cultivated fodder crop but it is a wild variety seen on farm bunds after rainfall in which Sorghum vulgare (Gundrijowar) fodder crops were cultivated in kharif season in Saurashtra region of Gujarat state. At different stages of growth, Johnson grass contains cyanogenic glycosides of varying level which liberate hydrocyanic acid in rumen when it consumed by ruminant animals due to rumen microflora (Toxic level of HCN is 20 mg/ 100 gm dry matter). The current study aimed to estimate varying levels of HCN in Johnson grass to keep dairy farmers informed of safe level of HCN for harvesting. Methods: An investigation was carried out at Cattle Breeding Farm, Junagadh Agricultural University, Junagadh, Gujarat for three consecutive years 2013-2015 in kharif season. Non-cultivated (Sorghum helepensis) fodder samples were collected for quantitative HCN concentration from farm bunds nearby eight sown plots of Gundrijowar (Sorghum vulgare) from 15 DAS and sampling were continued at fortnightly intervals till the plots exhibit 25% flowering stage. Result: Result of obtained data in present study revealed that concentration of HCN in Johnson grass in kharif season decreased significantly (p less than 0.05) on attaining 25% flowering stage during the year 2013, 2014 and 2015. It was concluded that Green Johnson grass can be fed to ruminant animals safely at 25% flowering stage.
Evaluating hospitality employees' various relationships and the effects on organizational commitment

PurposeThe purpose of this study is to investigate how various relationships an employee builds within the organization affect their willingness to stay with the company. Specific research objective was to examine impact of social exchange on organizational commitment in the hospitality industry.Design/methodology/approachAn online survey using Qualtrics' panel database was used to collect data. The target was full-time employees in the hospitality industry. A total of 245 surveys were collected and used for data analysis. The results were analyzed using structural equation modeling.FindingsResults of hypotheses testing showed that internal service quality and perceived organizational support have positive relationships with organizational commitment.Originality/valueThese findings can help hospitality managers develop programs and interact with employees in order to increase commitment and a sense of belonging with the company.
Promising Bialkali Bismuthides Cs(Na, K)2Bi for High-Performance Nanoscale Electromechanical Devices: Prediction of Mechanical and Anisotropic Elastic Properties under Hydrostatic Tension and Compression and Tunable Auxetic Properties

Using first-principles calculations, we predict highly stable cubic bialkali bismuthides Cs(Na, K)2Bi with several technologically important mechanical and anisotropic elastic properties. We investigate the mechanical and anisotropic elastic properties under hydrostatic tension and compression. At zero pressure, CsK2Bi is characterized by elastic anisotropy with maximum and minimum stiffness along the directions of [111] and [100], respectively. Unlike CsK2Bi, CsNa2Bi exhibits almost isotropic elastic behavior at zero pressure. We found that hydrostatic tension and compression change the isotropic and anisotropic mechanical responses of these compounds. Moreover, the auxetic nature of the CsK2Bi compound is tunable under pressure. This compound transforms into a material with a positive Poisson’s ratio under hydrostatic compression, while it holds a large negative Poisson’s ratio of about −0.45 along the [111] direction under hydrostatic tension. An auxetic nature is not observed in CsNa2Bi, and Poisson’s ratio shows completely isotropic behavior under hydrostatic compression. A directional elastic wave velocity analysis shows that hydrostatic pressure effectively changes the propagation pattern of the elastic waves of both compounds and switches the directions of propagation. Cohesive energy, phonon dispersion, and Born–Huang conditions show that these compounds are thermodynamically, mechanically, and dynamically stable, confirming the practical feasibility of their synthesis. The identified mechanisms for controlling the auxetic and anisotropic elastic behavior of these compounds offer a vital feature for designing and developing high-performance nanoscale electromechanical devices.
Improving Energy Efficiency and QoS of LPWANs for IoT Using Q-Learning Based Data Routing

Recent proliferation of Internet of Things (IoT) demands large scale connectivity among smart IoT devices over a vast geographical area. However, limited radio range and lack of scalability of conventional wireless sensor networks do not allow a wide area connectivity among IoT devices. To address these challenges, Low-Power Wide-Area Networks (LPWANs) are emerging to provide long-range communication capability with low-power consumption of the end devices. Nevertheless, given the demand in delivering an increasingly large volume of data generated by IoT devices, the direct data transmission model is not suitable due to its poor network lifetime. Therefore, in this work, a multi-hop data routing method is proposed for LPWANs. Since multi-hop data transmission faces several challenges such as increased data latency, higher interference, and reduced data throughput (i.e., poor bandwidth utilization), we propose a reinforcement learning method to address those challenges. The proposed method updates the Q-matrix of the network at varying discrete time instants and selects relay devices in such a way that maximizes the cumulative reward value between selected device-gateway pairs. The applicability and effectiveness of the proposed method are illustrated over both simulated LPWAN testbed and real field data sets. The obtained results clearly demonstrate the improved network performance in terms of energy efficiency and QoS of the proposed method as compared to various existing methods.
Teaching Reading Through KWL (Know-Want to know-Learned) Strategy In Islamic Economics Text

KWL strategy is a reading strategy used by teachers or educators to improve the students' reading skills by activating their prior knowledge, encouraging their motivation, and making the student comprehend the text holistically. The purpose of this research is to (1) apply the K-W-L Strategy in Reading Islamic Economics Text (2) know How do the students apply the K-W-L strategy in reading Islamic economics text. This research is conducted in a descriptive qualitative design. The data is collected by observation and documents. Data analysis procedure based on Creswell, Rossman & Marshall, 1) Organizing and familiarizing 2) Coding and reducing 3) Interpreting and representing. The results present that the application of K-W-L strategy is made through three steps, namely 1) Pre-reading, where the teacher or researcher created a good condition of the classroom and introducing passage, and the students filling the chart in K column 2) Whilst reading, the teacher leads the student read the text and started to write down what they want to learn in W column 3) Post-reading in which the students started to comprehend the passage, make a conclusion and stated what they have learned from text based on their comprehension in L column. The study also concluded that the KWL strategy was appropriate in teaching reading on Islamic Economics Text, and it can be applied in reading for any level of English learners.
Comparison of the immediate effects of prefabricated soft medical insoles and custom-molded rigid medical insoles on plantar pressure distribution in athletes with flexible flatfoot: a prospective study

Background: The aims of this quasi-experimental study were to compare the effect of prefabricated soft medical insoles and custom-molded rigid medical insoles on plantar pressure distribution in athletes with flexible flatfoot. Methods: Eighteen amateur athletes with flexible flatfoot participated in this study. All participants received custom-molded rigid medical insoles and prefabricated soft medical insoles. Mean peak pressure (kPa), maximum force (N), and contact area (cm2) were calculated for the heel, medial and lateral midfoot, first metatarsal head, second and third metatarsal heads, fourth and fifth metatarsal heads, hallux, and other toe area using the Pedar-X system (Novel GmbH, Munich, Germany) immediately. Results: The use of both prefabricated and custom-molded insoles significantly reduced the mean pressure and force in the heel, second and third metatarsophalangeal joints (MTP2,3), the fourth and fifth metatarsophalangeal joints (MTP4,5), and increased the pressure in the medial midfoot compared to the shoe (P<0.001). No significant difference was observed between the two insoles (P>0.05). The contact area increased significantly in the medial midfoot (P<0.001) and MTP2,3 (P=0.02) and decreased in the hallux (P<0.001) and other toes (P=0.01) using both insoles compared to only shoes. Conclusions: The results showed that the use of a medical insole in athletes with flexible flatfoot could alter athletics’ plantar pressures immediately. Since both insoles had a similar effect on the distribution of plantar pressures, it seems that the prefabricated soft medical insole is a more suitable option for daily use in athletes to prevent injury because of its availability and cost-effectiveness. Level of Evidence: Level II.
Uniplanar Wideband High-Selectivity Filtering Full-Band-Isolation Baluns Using Novel Out-of-Phase Matching Phase Shifters

Uniplanar wideband high-selectivity filtering full-band-isolation baluns are presented in this article utilizing two different types of out-of-phase matching phase shifters. To obtain high-selectivity performance, half-wavelength open-circuit stubs are introduced. In addition, a single resistor is used for favorable output matching and high full band isolation. By using parallel-coupled ${n}$ -line technology, the bandwidth of the proposed balun can be controlled. Furthermore, the cascaded coupled-line sections and matching phase shifters are introduced to extend the impedance transforming. For demonstration, two prototypes with center frequency of 3.5 GHz were designed, fabricated, and measured. As expected, measurements show that the proposed baluns have the advantages of bandwidth control, high full-band isolation, impedance transforming, DC-blocking function, stable phase/amplitude imbalances, wideband out-of-band harmonic suppression and high selectivity.
Induction of Endoplasmic Reticulum Stress-Mediated Apoptosis by Aminosteroid RM-581 Efficiently Blocks the Growth of PC-3 Cancer Cells and Tumors Resistant or Not to Docetaxel

Aminosteroid derivative RM-581 was previously identified as an endoplasmic-reticulum (ER) stress inducer with potent in vitro and in vivo anticancer activities. We report its evaluation in androgen-independent prostate cancer (PC-3) cells. RM-581 efficiently blocks PC-3 cell proliferation with stronger activity than that of a selection of known antineoplastic agents. This later also showed a synergistic effect with docetaxel, able to block the proliferation of docetaxel-resistant PC-3 cells and, contrary to docetaxel, did not induce cell resistance. RM-581 induced an increase in the expression level of ER stress-related markers of apoptosis, potentially triggered by the presence of RM-581 in the ER of PC-3 cells. These in vitro results were then successfully translated in vivo in a PC-3 xenograft tumor model in nude mice, showing superior blockade than that of docetaxel. RM-581 was also able to stop the progression of PC-3 cells when they had become resistant to docetaxel treatment. Concomitantly, we observed a decrease in gene markers of mevalonate and fatty acid pathways, and intratumoral levels of cholesterol by 19% and fatty acids by 22%. Overall, this work demonstrates the potential of an ER stress inducer as an anticancer agent for the treatment of prostate cancers that are refractory to commonly used chemotherapy treatments.
THE ROLE OF FACE GAINING ON MILLENNIALS’ OUTBOUND TRAVEL INTENTION IN MAINLAND CHINA

As a typical cultural concept being deeply rooted in Chinese society, “face” regulates many social behaviors in China. However, research on the social aspect of “face” is limited in tourism studies. This study applies an extended TPB model incorporating face gaining for examining millennials’ outbound travel intention from mainland China. By analyzing data from 350 Chinese millennial tourists, we find that face gaining has an indirect impact on outbound travel intention through attitude (ATT), subjective norm (SN) and perceived behavioral control (PBC). Based on the findings, the research provides some insights regarding “face gaining” in travel behavior, and destination marketing on Chinese millennials.
Entrepreneurial perceptions of students regarding business professional career: The study on gender differences in Latvia

DOI: 10.14254/2071789X.2021/14-3/12 ABSTRACT. This study seeks to investigate the entrepreneurial perceptions among the students regarding their professional careers analyzing macro, micro and entrepreneurial education factors in Latvia, including the analysis of gender differences. The research is based on the analysis of data collected through a survey. The paper uses statistical analysis involving the measures of descriptive statistics (absolute and relative frequencies, means and respective standard deviations) and inferential statistics to examine the differences within an original dataset of 360 young people in Latvia. The results reveal several statistically significant gender differences. This research contributes to enriching literature and also adds value to studies on entrepreneurial perceptions divided by gender. Understanding the factors which shape entrepreneurship education, macrofactors and microfactors affecting young people’s decisions and perceptions, education systems and public policies would allow adjusting and designing policies aiming to boost entrepreneurship education and enhance entrepreneurship as a career choice and balance it in a gender context.
The heterogeneity spillover impact of U.S. permanent and temporary monetary shocks on China’s economy

Abstract This study adopts the New Keynesian theoretical model to analyse the heterogeneity spillover effect of U.S. permanent and temporary monetary policy shock on China’s economy through an exchange rate channel. It also employs the Bayesian technique to estimate SVAR model and obtain two main results. First, the permanent increase in the nominal interest rate in the U.S. causes Chinese yuan appreciation and U.S. dollar depreciation, which has a negative spillover impact on China’s economy and leads to the decline in China’s real output. Second, the temporary increase in the nominal interest rate in the U. S. leads to Chinese yuan depreciation, which has a positive spillover impact on China’ s macroeconomy and leads to the rise of China's real output.
An Investigation of English-Majored Students’ Learning Styles and Their Preferable Interaction Activities: A Case Study in the Mekong Delta of Vietnam

Many changes have been observed in the Vietnamese education context. As a result, higher education institutions implemented many innovations in English teaching and learning, such as new course books, new programs, and new teaching techniques. Somehow, it creates several challenges for English as a foreign language (EFL) students and their motivation for learning. Learning styles have been considered as one of the course factors.The current study attempted to investigate English-majored students’ learning styles and their preferred interaction activities in EFL classes. The study was administered as a concurrent nested design in which the qualitative strand is embedded within the prominence of the quantitative one. There were 191 students, studying English high-quality programs in a public tertiary institution in the Mekong Delta of Vietnam, who were recruited in this research. the results from questionnaires including thirty-one close-ended items and two open-ended ones showed that among the students, there are more visual learning styles than the counterparts, including auditory and kinesthetic ones. In terms of interaction activities in EFL classes, games are dominant. Based on the findings, some discussions and implications are presented at the end of the paper.
Machine Learning for Diagnosis of Event Synchronization Faults in Discrete Manufacturing Systems

Common in discrete manufacturing, timed event systems often have strict synchronization requirements for healthy operation. Discrete event system methods have been used as mathematical tools to detect known faults, but do not scale well for problems with extensive variability in the normal class. A hybridized discrete event and data-driven method is suggested to supplement fault diagnosis in the case where failure patterns are not known in advance. A unique fault diagnosis framework consisting of signal data from programmable logic controllers, a Timed Petri Net of the normal process behavior, and machine learning algorithms is presented to improve fault diagnosis of timed event systems. Various supervised and unsupervised machine learning algorithms are explored as the methodology is implemented to a case study in semiconductor manufacturing. State-of-the-art classifiers such as artificial neural networks, support vector machines, and random forests are implemented and compared for handling multi-fault diagnosis using programmable logic controller signal data. For unsupervised learning, classifiers based on principal component analysis utilizing major and minor principal components are compared for anomaly detection. The rule-based random forest and extreme random forest classifiers achieve excellent performance with a precision and recall score of 0.96 for multi-fault classification. Additionally, the unsupervised learning approach yields anomaly detection rates of 98% with false alarms under 3% with a training set 99% smaller than the supervised learning classifiers. These results obtained on a real use case are promising to enable prognostic tools in industrial automation systems in the future
Physical Discomfort Caused by the Computer Workstation Accessories

: In nature of office environment, most of workers are suffering Musculoskeletal Disorder (MSD) such as neck pain, carpal pain and slip disc. The purpose of this study is to identify the computer workstation accessories, which can affect the physical discomfort. This study was conducted at one of social security agency in Kuala Lumpur, Malaysia. Data was obtained by distributed an adopted questionnaire. The total number of population is 290 workers and the sample size are 165 workers. The result was analysed by using reliability analysis, frequency analysis and multiple regression analysis. Result shows the value of R2 is 65.5% means the variance in physical discomfort can be predicted from the variables of chair design, keyboard and mouse, work surface, breaks time and support accessories. The F-value is 60.432 and its significant is (Sig. = 0.000). It indicates that computer workstation accessories have a moderate association with the physical discomfort of workers. The result also at significant (Sig.=0.00) for keyboard and mouse, break and support accessories as compared to other variable. It indicate these accessories affecting physical discomfort. For the recommendation, guideline or good practice of using computer workstation accessories and enough accessories should be provided in order to avoid a physical discomfort. The employees also can be trained in ergonomic practice as to comfortable use of keyboard and mouse. The body part discomfort levels also could be attributed to rest breaks. For future research, others computer workstation accessories desk and laptop also can be used for the future research.
A Wearable Heartbeats Classification System Based on A New Method: Selective-Mask Artificial Neural Network

The electrocardiograph (ECG) signal is an essential biomedical human body signal that shows heart activity and can diagnose cardiovascular diseases. Many researchers investigate heartbeats detection and classification based on ECG to achieve a high-performance method. The main problem with improving performance is increasing the computation, such as in many existing methods. In this paper, a new artificial neural network (ANN) method named Selective-Mask Artificial Neural Network (SMANN) is proposed to improve the performance with low computational processes. Furthermore, A new mixture of features from reused the QRS-detection stage features and the others features from the RR-interval and between-RR are used to decrease the computation for features extraction. The proposed method performance evaluation is based on the MIT-BIH Arrhythmia Database using MATLAB program for software evaluation moreover a hardware implementation. The proposed method’s promising results show high accuracy of 99.9224 %, and the total classification errors for the SMANN are 80 comparing with the 583 errors for the same data with traditional ANN. The method with low error assists the clinical decision-maker in diagnosing the long-time ECG signals or the real-time monitoring. It was implemented as a prototype wearable system using Node-MCU with the internet of things (IoT). The system can operate online patient monitoring and offline for heartbeats detection and classification.