Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF MOMORDICA CHARANTIA FRUIT ETHANOLIC EXTRACT ON MALONDIALDEHYDE (MDA) LEVEL IN BLOOD OF SPRAGUE-DAWLEY RATS INDUCED BY 7,12-DIMETHYLBENZ[A]ANTHRACENE

Background: Cancer is a type of disease with a high rate of diagnostic cases. The real cause of cancer remains unknown. Free radicals can cause cancer by DNA’s gen mutation. 7, 12- Dimethylbenz[a]anthracene (DMBA) is often used for researching carcinogenesis. Many types of research have used natural substances as supportive cancer therapies including Momordica charantia (bitter melon). Malondialdehyde (MDA) is one of the oxidative damage biomarkers which can significantly increase, especially on breast cancer patients. The success of therapy on a breast cancer patient is shown by decreasing MDA levels in the blood. Aim: To investigate the effect of Momordica charantia fruit ethanolic extract on malondialdehyde levels in the blood of Sprague Dawley rats induced by 7,12- Dimethylbenz [a] anthracene. Methods: This study was a true experimental randomized post-test only with control group design on rats that divided into two groups: X1 given DMBA induction only and X2 given DMBA induction with Momordica charantia fruit ethanolic extract by feeding tube with 200mg/kgbw/day for 14 days. Results: The mean of MDA level in group X1 = 0,83±0,32 and in group X2 = 0,30±0,19. The result of the normality test of data using the Shapiro-Wilk test obtained abnormal distribution data for one of the groups. The result of independent-samples T-Test showed a significant difference with P = 0,001. Conclusion: There was an effect of Momordica charantia fruit ethanolic extract on MDA level in the blood of Sprague-Dawley induced by 7,12- Dimethylbenz[a]anthracene.
Wind turbine power control for coordinated control of wind farms

The new grid regulations require that a grid-connected wind farm acts as a single controllable power producer. To meet this requirement a traditional wind farm control structure, which allowed individual wind turbines to internally define their power production, has to be modified. In this paper the opportunity for wind turbine load reduction that arises from dynamic power control of wind turbines is studied. The wind farm controller design is proposed that utilizes coordinated power control of all wind turbines to achieve the wind farm regulation requirements and to minimize the wind turbine loads.
EXPERIMENTAL AND NUMERICAL INVESTIGATION OF FLUTTER PHENOMENON OF AN AITCRAFT WING (NACA 0012)

Floating is a dangerous phenomenon for aircrafts and usually ends with plane rupture. It can be used to characterize a dynamic instability of the structure. Floating phenomenon is usually due to an interaction between aerodynamics, elasticity and inertial forces. It appears at a determinate speed of flight called critical floating speed. The aim of the present study is to determine the floating critical speed of an aircraft wing in a subsonic fan by means of experimental tests and numerical simulations using the commercial Finite Element Analysis package ANSYS CFX. The obtained experimental results are in good agreement with those obtained using the numerical approach. DOI: http://dx.doi. org/10.5755/j01.mech. 23.4. 15175
The Effect of Bi-Level Positive Airway Pressure on Sleep Structure in Patients with Overlap Syndrome

Objective: The present study aimed to observe the effect of bi-level positive airway pressure on sleep structure in patients with overlap syndrome (OS). Method: Forty patients with OS were randomly divided into control and experimental groups, respectively, with 20 cases in each group. Patients in both the control and experimental groups were given anti-infection, dilating bronchi, expectoration, and oxygen inhalation treatment according to the conditions. Bi-level positive airway pressure was conducted in patients in the experimental group for approximately 10 hr a day over a one-week therapeutic course. Polysomnography, Sleep questionnaire survey was performed before treatment and after one week of treatment in both the control and experimental groups to measure sleep structure to investigate the drowsiness and quality of life of the patients. Results: (1) Prior to treatment, differences in the total sleep time, sleep latency, rapid eye movement (REM) latency, sleep efficiency, phase I, II, and III sleep, REM phase sleep and sleep questionnaire survey was not statistically significant between the two groups (P > 0.05). (2) One week after treatment, compared with the control group, the REM latency in the experimental group was shortened, the percentage of REM sleep and Rem sleep in total sleep time was prolonged, the second stage sleep time and the percentage of second stage sleep in total sleep time were shortened, and the scores of Epworth drowsiness, fatigue, dizziness, lack of energy and anxiety decreased in the sleep questionnaire. There was no significant difference in other indexes (P > 0.05). After treatment in the experimental group, REM latency was shortened, REM sleep time and the percentage of Rem sleep in total sleep time were prolonged, second-stage sleep time and second-stage sleep in total sleep time were shortened. Sleep questionnaire survey showed that Epworth drowsiness score, fatigue, dizziness, mental in concentration and anxiety symptoms decreased. There was no significant difference in other indexes (P > 0.05). The sleep questionnaire in the control group after treatment showed that the score of fatigue symptoms decreased, and the difference was statistically significant (P < 0.05). There was no significant difference in other indexes (P > 0.05). Conclusion: Bi-level positive airway pressure can potentially shorten REM latency, prolong REM sleep, and shorten phase II sleep, alleviate the symptoms of drowsiness, fatigue, dizziness, distraction and anxiety, and improve the quality of life in patients with OS.
Genomic Insights of Enterococcus faecium UC7251, a Multi-Drug Resistant Strain From Ready-to-Eat Food, Highlight the Risk of Antimicrobial Resistance in the Food Chain

The presence of multi-drug resistant (MDR) bacteria in ready-to-eat foods comprises a threat for public health due to their ability to acquire and transfer antibiotic-resistant determinants that could settle in the microbiome of the human digestive tract. In this study, Enterococcus faecium UC7251 isolated from a fermented dry sausage was characterized phenotypically and genotypically to hold resistance to multiple antibiotics including aminoglycosides, macrolides, β-lactams, and tetracyclines. We further investigated this strain following a hybrid sequencing and assembly approach (short and long reads) and determined the presence of various mobile genetic elements (MGEs) responsible of horizontal gene transfer (HGT). On the chromosome of UC7251, we found one integrative and conjugative element (ICE) and a conjugative transposon Tn916-carrying tetracycline resistance. UC7251 carries two plasmids: one small plasmid harboring a rolling circle replication and one MDR megaplasmid. The latter was identified as mobilizable and containing a putative integrative and conjugative element-like region, prophage sequences, insertion sequences, heavy-metal resistance genes, and several antimicrobial resistance (AMR) genes, confirming the phenotypic resistance characteristics. The transmissibility potential of AMR markers was observed through mating experiments, where Tn916-carried tetracycline resistance was transferred at intra- and inter-species levels. This work highlights the significance of constant monitoring of products of animal origin, especially RTE foodstuffs, to stimulate the development of novel strategies in the race for constraining the spread of antibiotic resistance.
A failure detection method of remote disaster recovery and backup system

Failure detection is one of the basic functions of building a reliable disaster recovery backup system. Aiming at the application-level disaster recovery backup failure detection problem, this paper analyzes the remote disaster recovery center architecture and failure detection hierarchy, and predicts the arrival time of cross-domain heartbeat information through the back propagation neural network based on particle swarm optimization (PSO-BP). When the actual timeout is reached, the active Auxiliary Detection (AD) is used to improve the correctness of failure detection, and finally the effectiveness of method PSO-BP-AD is verified through simulation.
Deep Adversarial Data Augmentation for Fabric Defect Classification With Scarce Defect Data

Fabric defect classification is a crucial and challenging task for fabric production quality guarantee. In recent years, many deep neural network-based methods have been proposed and shown promising performance on this task. However, it would be laborious and time-consuming to collect enough defect images to satisfy high-quality training because defects are too rare in factories. In this article, we propose a deep adversarial data augmentation method named DefectTransfer to address the defect data scarcity issue. Since the defect may happen anywhere on the background texture with any size, we consider the position and size of a defect should not be fully linked to the background texture in the network training. Based on this assumption, we design a cut–paste approach to augment the defect images by cutting out defects and pasting them on defect-free images. The defects are randomly transformed with scaling, rotating, and moving before the paste operation. To make the network training more efficient, we further propose an adversarial transformation algorithm that adjusts the pasted defects targeting the weakness of the classification network. The high diversity of the adversarial synthetic defect images forces the network to learn more discriminative category features. Experimental results show that our method can achieve comparable performance with recent fabric defect classification methods with only 1% fabric defect data on the ZJU-Leaper dataset. DefectTransfer also largely surpasses traditional augmentation methods even without manually annotated masks.
How Supportive Leadership Promotes Employee Innovation under Uncertainty: Evidence from Chinese E-Commerce Industry

Innovative behavior (IB) is an important issue in academic and other sectors. The increasing uncertainty caused by COVID-19 has resulted in rising job insecurity for employees in the e-commerce industry. This has jeopardized career sustainability (CS). Numerous studies have explored the influence of supportive leadership (SL) on IB, but so far there is still a dearth of research investigating the role of CS. In addition, CS must be considered because the perceived sustainability of a career has an impact on individual innovation. Therefore, based on job demands-resources (JDR) theory, we analyzed the effects of SL on IB as well as the roles of CS in IB. The mediating role of employee’s perceived occupational sustainability was explored. This study investigates the associations between supportive leadership style (job resource) and employee innovative behavior (job demand). In total, 308 valid samples were collected from China. Structural equation modeling examines the construct validity and path relationships. The results show that in China’s e-commerce industry, under the uncertainty brought about by the COVID-19 pandemic, only when employees perceive CS can SL promote the transformation of job resources into workplace IB. That is, CS completely mediates SL and IB. This provides new information for the management of employee behavior in the current special period. The result revealed that SL improves IB through CS. Theoretically, this study fills the gap and establishes a theoretical framework for SL and IB. Practically, we offer guidance for enterprises and managers in that they should provide their employees with work resources which are good for employee CS so as to promote employees’ IB.
Use of physical testing data for the accurate prediction of the ultimate compressive strength of steel stiffened panels

ABSTRACT Physical testing data can be used to predict the ultimate compressive strength of steel stiffened panels. Moreover, useful empirical formulae have been developed by fitting curves to data from relevant testing databases. A representative example is the Paik–Thayamballi formula, which is based on physical testing data available until 1997 and is a closed-form function of plate and column slenderness ratios. Since 1997, high-precision data-acquisition equipment and large-scale physical models have been used to generate databases contained advanced testing data of modern materials such as AH32 high-tensile steel made by the thermo-mechanical control process (TMCP) technology together with modern fabrication technologies, e.g., flux-cored arc welding technique, under a strict control of welding parameters, e.g., current, voltage, speed and heat input, to achieve a required weld leg length. It is therefore important to determine if these advanced testing data are compatible with the established empirical formulae. This paper describes benchmark studies which were conducted to determine such compatibility, with a focus on the Paik–Thayamballi formula, and summarises key findings and insights obtained from the present study.
Validation of a Commercial Indirect ELISA Kit for the Detection of Bovine alphaherpesvirus 1 (BoHV-1)-Specific Glycoprotein E Antibodies in Bulk Milk Samples of Dairy Cows

Simple Summary Recent studies have assessed the feasibility of using commercial indirect enzyme-linked immunosorbent assays (ELISA) on bulk milk (BM) samples. Today is a need to validate a diagnostic method for granting and maintaining a correct surveillance strategy for infectious bovine rhinotracheitis (IBR). However, the poor availability of reference materials for diagnostic tests on milk samples and, the low sensitivity of blocking glycoprotein E (gE) ELISAs using milk as a matrix, represent limitations for developing new assays to control infectious diseases in livestock. This study aimed to validate a commercial indirect ELISA kit to detect antibodies to gE of Bovine alphaherpesvirus 1 (BoHV-1) from BM samples, according to the OIE Manual of Diagnostic Tests and Vaccines for Terrestrial Animals. The samples were collected from an IBR outbreak. The findings demonstrated high diagnostic performances. Furthermore, the validated kit is an easy-to-use and economical method to discriminate between BoHV-1 infected or immunised animals with gE-deleted markers vaccines using BM samples. Moreover, milk sampling represents a fast and non-invasive procedure for animals because it avoids negative effects, such as stress, and reduces the total cost of surveillance. Abstract In this study, we validated a commercial indirect enzyme-linked immunosorbent assay (ELISA) to detect antibodies to glycoprotein E (gE) of Bovine alphaherpesvirus 1 (BoHV-1) in bulk milk (BM) samples using the OIE Manual of Diagnostic Tests and Vaccines for Terrestrial Animals. The assay performance characteristics were evaluated using a panel of positive (n = 36) and negative (n = 80) samples with known infectious bovine rhinotracheitis (IBR) status. The assay showed adequate repeatability (within-run and between-run), with a coefficient of variability (CV%) of replicates below 30%; only two 1:40 diluted samples had a CV% above 20%. Additionally, an agreement analysis of the qualitative results of replicates led to a Gwet’s agreement coefficient of 0.99 (95% confidence interval (CI): 0.96–1.00, p < 0.001). The estimated diagnostic sensitivity (DSe) and diagnostic specificity (DSp) were 100% (95% CI: 90.3–100%) and 97.5% (95% CI: 91.3–99.7%), respectively. Overall, a good level of agreement was observed between the assay results and the true IBR status of samples (weighted Cohen’s κ: 0.96, 95% CI: 0.78–1.00). The findings demonstrate that the indirect ELISA kit validated here is an easy-to-use and economical method to differentiate infected and gE-deleted marker vaccine-immunised animals using BM samples.
Climate policies and nationally determined contributions: reconciling the needed ambition with the political economy

Countries have pledged to stabilize global warming at a 1.5–2°C increase. Either target requires reaching net zero emissions before the end of the century, which implies a major transformation of the economic system. This study reviews the literature on how policymakers can design climate policies and their nationally determined contributions to reach zero‐net emissions before the end of the century in a socially and politically acceptable manner. To get the ambition right, policymakers can use sectoral roadmaps with targets and indicators that track progress toward zero emissions (e.g., regarding renewable power or reforestation). Indeed, monitoring economy‐wide emissions‐reductions alone would not ensure that short‐term action contributes meaningfully to the long‐term decarbonization goal. To get the political economy right, climate policies can be designed so that they contribute to nonclimate objectives and create broad coalitions of supporters. For instance, carbon taxes revenues can fund social assistance and infrastructure investment, while reducing tax evasion and informality. To minimize social and economic disruptions and avoid stranded assets, policymakers can also start with a low carbon price level and use complementary policies. Designed at the sector level, complementary policies such as performance standards or feebates for cars, building norms, or moratoriums on new coal power plants can be negotiated in partnership with local stakeholders and trigger a transition to zero carbon that does not directly affect existing polluting capital. WIREs Energy Environ 2017, 6:e256. doi: 10.1002/wene.256
Adaptive Multiple-Model-Based Fault-Tolerant Control for Non-minimum Phase Hypersonic Vehicles With Input Saturations and Error Constraints

In this article, an adaptive multiple-model-based fault-tolerant controller is developed for nonminimum phase air-breathing hypersonic vehicles (HSVs) in the presence of parametric uncertainties, elevator faults, input saturations, and time-varying error constraints. Compared with the existing works, the elevator-to-lift couplings are taken into account, which makes the HSV longitudinal models exhibit unstable internal-dynamics that impedes the applicability of common nonlinear control methods for control design. In order to solve this problem, a two-layer cascade control strategy is proposed in which the external inputs control the external states and the external state deviations control the internal states: 1) A multiple-model linear quadratic control strategy, based on gap metric, is proposed to guarantee the stability of the internal dynamics; 2) a fault-tolerant control scheme, based on tan-barrier Lyapunov functions, is developed by using a low-pass filter and an auxiliary system in conjunction with adaptive backstepping design. In the control law, the uncertain parameters are replaced by their estimates updated by adaptive laws. Additionally, the stability of the whole system is rigidly proved through standard Lyapunov approach, while the other states and signals in the closed-loop system are guaranteed to be bounded. Simulation results are provided to illustrate the effectiveness of the proposed adaptive multiple-model-based fault-tolerant controller.
Deep learning in hospitality and tourism: a research framework agenda for future research

Purpose This study aims to provide a systematic review of the existing literature on the applications of deep learning (DL) in hospitality, tourism and travel as well as an agenda for future research. Design/methodology/approach Covering a five-year time span (2017–2021), this study systematically reviews journal articles archived in four academic databases: Emerald Insight, Springer, Wiley Online Library and ScienceDirect. All 159 articles reviewed were characterised using six attributes: publisher, year of publication, country studied, type of value created, application area and future suggestions (and/or limitations). Findings Five application areas and six challenge areas are identified, which characterise the application of DL in hospitality, tourism and travel. In addition, it is observed that DL is mainly used to develop novel models that are creating business value by forecasting (or projecting) some parameter(s) and promoting better offerings to tourists. Research limitations/implications Although a few prior papers have provided a literature review of artificial intelligence in tourism and hospitality, none have drilled-down to the specific area of DL applications within the context of hospitality, tourism and travel. Originality/value To the best of the authors’ knowledge, this paper represents the first theoretical review of academic research on DL applications in hospitality, tourism and travel. An integrated framework is proposed to expose future research trajectories wherein scholars can contribute significant value. The exploration of the DL literature has significant implications for industry and practice, given that this, as far as the authors know, is the first systematic review of existing literature in this research area.
Optimal placing and sizing of parking lots including different levels of charging stations in electric distribution networks

ABSTRACT Background: Low emission, high efficiency, and convenience of using plug-in electric vehicles (PEVs) are considered as significant benefits of PEVs. However, installation of parking lots (PLs) in order to supply PEVs causes some financial and technical challenges for electric distribution networks. The changes in system reliability, power loss, voltage drop, and costs associated with the installation of PLs are considered as some of the aforementioned challenges. Therefore, optimal placing and sizing of PEV PLs including different levels of charging stations ((CS); slow, medium, and fast) are presented in this paper. Since PLs have the potential to exchange electricity by electric network, they can be taken into account as distributed generations (DGs) and their installation can be considered from the perspective of installation of DGs. An objective function including system reliability, power loss, voltage drop, and PL cost/revenue is proposed for the optimal planning. Results: Genetic algorithm is employed to solve the optimisation problem. Simulation is carried out on a 33-bus radial distribution network. For the planning purposes of PLs, three different levels of CSs (slow, medium, and fast) in PLs are considered. The effect of increasing the penetration of PEVs in PLs is also examined on planning the PLs. The effect of different dispatch times on the selection of different CS levels (slow, medium, and fast) is investigated as well. Furthermore, the effect of applying tariffs and incentives for the customers is analysed for the selection of CS levels. Finally, the effect and importance of combinations of the CSs with different levels are investigated.