Miracle-dz/UniFAR-FTUs huggingface dataset:
How Does Advertising Appeal Affect the Attitude of Brand Intelligent Extension?

Brand intelligent extension is an important strategy for enterprises to conform to the trend of The Times and realize long-term development. However, the launch of new products cannot be separated from the publicity and promotion of advertisements. Therefore, how to formulate advertising strategies for intelligent extension products has become an important issue. This study found that different advertising strategies should be adopted for different types of intelligent extension products through one study between 2×2 groups. (1) For prototype intelligent extension products, compared with warm advertising appeals, consumers have a more positive attitude towards competent advertising appeals; (2) For exemplar intelligent extension, consumers' attitudes towards competent or warm advertising appeals are at a high level, and there is no significant difference; (3) Pleasure and arousal play a mediating role, that is, the interaction between brand intelligent extension and advertising appeals has a positive impact on consumers' attitudes through pleasure and arousal. This study is of practical significance for enterprises to make effective advertising strategies for intelligent extension products.
Financial Advice and Gender: Wealthy Individual Investors in the UK

Abstract We examine how gender differences in investment risk tolerance, knowledge, confidence and portfolio cash allocations relate to the gender mix of investors and financial advisors among a sample of wealthy individuals in the UK. Our results demonstrate that gender effects are more nuanced than previously assumed. First, while even wealthy women consider themselves more conservative and allocate a higher proportion of their investable assets to cash than men, previous findings of lower investment knowledge and confidence do not extend to our sample. Second, having an advisor matters. Advised investors perceive themselves to have a higher risk tolerance and invest 10.6%-points more than self-directed investors. Finally, the investor-advisor gender combination matters, but only for female investors. Women with male advisors are more risk averse, feel less knowledgeable and less confident about their investment decisions. They also invest 11%-points less than women with female advisors. Indeed, female investors advised by women report the highest risk tolerance and make the lowest portfolio allocation to risk-free assets across the full sample, including men.
The Effect of Physical Activity and Red Dragon Fruit (Hylocereus polyrhizus) in Red Blood Cell and Hemoglobin in Trained People

BACKGROUND: Physical fitness depends on hematological parameters such as red blood cells and hemoglobin. Hemoglobin is needed in adequate quantities in trained athletes, especially in endurance sports. The effects of antioxidant supplementation are not yet fully known whether the body’s natural antioxidants that act as a defense system can overcome the increase in free radicals during physical exercise or whether additional supplements are needed. AIM: The purpose of this study was to find out the effect of physical activity and consumption of red dragon fruit on red blood cells and hemoglobin in trained people. METHODS: The subject of the study was 20 people, male, trained, divided into two groups randomly, each consisting of 10 people, namely, Group 1 (PA): A group that was given physical exercise and obtained a placebo; Group 2 (PA+RDF): A group was assigned physical activity and given red dragon fruit juice. Physical activity was performed in the form of treadmill exercises with an intensity of 80–85% of the maximum heart rate (MHR= 220 – age), frequency 3 times a week, duration 30 min, for 28 days. RESULTS: The results show that the increase in red blood cells and hemoglobin after intervention in PA+RDF group is higher than in the PA group, but based on statistic test, there is no significant difference to increase in red blood cells and hemoglobin between the two groups (p > 0.05). CONCLUSION: The physical activity and red dragon fruit can prevent the decrease of red blood cells and hemoglobin in trained people.
Forecasting the Impact of Population Growth on Robustness of Water Distribution Networks: A System Dynamics Approach

Robustness analysis of water distribution networks (WDNs) has recently come to prominence in the research literature. While the robustness analysis is garnering much attention, there is a knowledge gap surrounding the forecasting of robustness in response to changes in the parameters involved in the analysis, such as water demand and population growth. To fill this gap, this article looks at the robustness of WDNs through the lens of the system dynamics (SD) modeling approach. The objective of this article is to design a dynamic model for the robustness analysis of WDNs that can be updated as its constituent variables change over time. The propose SD modeling approach is developed for a real-world case study of an Australian town. Model simulations are performed for three scenarios designed to forecast the effects of different population growth rates on robustness. The results showed that uneven demand distribution forced by uneven population growth has a strong influence on the robustness of WDNs. Furthermore, the simulation results highlighted the importance of considering the future development pattern of suburbs at the inception of the city development planning.
Spectroscopic Investigation on the Interaction of Pb(II) with KeyholeLimpet Hemocyanin

The interaction between Pb2+ and Keyhole Limpet Hemocyanin (KLH) was investigated by fluorescence, Ultraviolet-visible (UV-Vis) absorption and Circular Dichroism (CD) spectroscopy. The experimental results showed that Pb2+ could quench the intrinsic fluorescence of KLH following static and dynamic quenching process. The number of binding sites n was approximately equal to 1. ΔH<0 and ΔS<0 indicated that a Pb-KLH complex was formed. Furthermore, the data of synchronous fluorescence, UV-Vis and CD spectra suggested that Pb2+ changed the micro-environment and conformation of KLH.
Direct Predictive Current Control of a New Five-Level Voltage Source Inverter

This article proposes a direct predictive current control approach to regulate the flying capacitor voltages along with the output currents in a new five-level voltage source inverter under a wide range of output frequencies and load power factors. Also, the proposed approach maintains the flying capacitor voltage ripples well below the desired limits (10%–15%) throughout the operating range. The sampled data model of a new five-level voltage source inverter is developed to predict the future behavior of the control variables such as output current, and flying capacitor voltage and its ripples. A three-objective cost function with reference and predicted control variables is formulated. The cost function is evaluated for all possible switching states, then an optimum switching state which gives the low cost value is selected. The optimum switching state is applied to the converter in one sampling interval. The feasibility of the proposed control method is verified on a new five-level voltage source inverter by using MATLAB/Simulink simulations and experimental studies on a scaled-down laboratory prototype with dSPACE Microlab Box. Furthermore, the performance of the proposed approach is analyzed in terms of voltage and current harmonic distortion, and flying capacitor voltage ripples at different current magnitudes, output frequencies, and load power factors.
Question Answering Systems: A Systematic Literature Review

Question answering systems (QAS) are developed to answer questions presented in natural language by extracting the answer. The development of QAS is aimed at making the Web more suited to human use by eliminating the need to sift through a lot of search results manually to determine the correct answer to a question. Accordingly, the aim of this study was to provide an overview of the current state of QAS research. It also aimed at highlighting the key limitations and gaps in the existing body of knowledge relating to QAS. Furthermore, it intended to identify the most effective methods utilized in the design of QAS. The systematic review of literature research method was selected as the most appropriate methodology for studying the research topic. This method differs from the conventional literature review as it is more comprehensive and objective. Based on the findings, QAS is a highly active area of research, with scholars taking diverse approaches in the development of their systems. Some of the limitations observed in these studies encompass the focused nature of current QAS, weaknesses associated with models that are used as building blocks for QAS, the need for standard datasets and question formats hence limiting the applicability of the QAS in practical settings, and the failure of researchers to examine their QAS solutions comprehensively. The most effective methods for designing QAS include focusing on syntax and context, utilizing word encoding and knowledge systems, leveraging deep learning, and using elements such as machine learning and artificial intelligence. Going forward, modular designs ought to be encouraged to foster collaboration in the creation of QAS.
Phytochemical and Biological Traits of Endemic Betonica bulgarica (Lamiaceae)

Betonica bulgarica is an endemic species distributed in Bulgaria. The chemical composition of the essential oil analysed by GC–MS (Gas chromatography–mass spectrometry) and the content of trace elements analysed by ICP–MS (Inductively coupled plasma mass spectrometry) were determined. Additionally, a study on the types and distribution of trichomes was done using a microscope with a camera. The essential oil was characterized using a high concentration of sesquiterpene hydrocarbons, whose major compounds are β-caryophyllene (17.4%), germacrene D (9.9%), and β-bourbonene (6.7%). The contents of manganese (177.2 µg/g) and strontium (156.8 µg/g) were highest among the investigated micronutrients. Two types of trichomes were identified on the adaxial and abaxial epidermises of the leaves of B. bulgarica—covering and glandular. Peltate stacked glandular trichomes with a four-celled head of type B were observed on the leaf surface.
In Vitro Evaluation of the Antifungal Activity of Ghassoul-Based Formulations with Oregano and Thyme Essential Oils against Penicillium sp.

The essential oils from aromatic plants are today considered a suitable tool to protect stored grains from fungal attacks. The purpose of this work is to study the effect of formulations of thyme and oregano essential oil (EO) adsorbed on purified (Gh-P) and sulfuric acid-activated (Gh-A) ghassoul on the biological activity of fungal pathogens. Purified and activated ghassoul were characterized by XRD and FTIR, and EOs used in this study were issued from two medicinal plants known in Morocco and commercially available. Their chemical compositions were analyzed by the GC-MS technique. The main constituents of thyme EO were thymol (67.13%), ρ-cymene (4.85%), Z-caryophyllene (1.77%), and γ-terpinene (2.74%). Oregano EO contained carvacrol (59.82%), γ-terpinene (10.85%), and α-pinene (9.89%). This work focused on the study of the antifungal activity of EOs mixed with purified and sulfuric acid-activated ghassoul, in order to look for new natural bioactive products and assess their antifungal activity. Penicillium sp. was used as a pathogen agent for biological activity on Czapek agar medium. The results showed that the active ghassoul formulations had significant antifungal activity against Penicillium sp. Gh-A-thyme, Gh-A-thymol, and Gh-A-oregano had an inhibitory potential of more than 75% and excelled to retain it over time even after five months. On the other hand, the three purified ghassoul formulations (Gh-P-thyme, Gh-P-thymol, and Gh-P-oregano) showed an initial inhibitory power of less than 22%, which was decreasing over time.
Blockchain-Based Stochastic Energy Management of Interconnected Microgrids Considering Incentive Price

Decomposing the large distribution grids into interconnected microgrids (MGs) can potentially enhance the power system's efficiency, sustainability, resiliency, and reliability. However, energy management within the entire network would be more complicated and challenging. This article develops a novel energy management framework for interconnected MGs based on a blockchain technology. Utilizing the blockchain technology can potentially enhance the system security, and also reduce the system risks, mitigate financial fraud, and cut down the operational cost. A priority list is first defined to get into an efficient energy tradeoff within the interconnected MGs. Moreover, the incentive contract is proposed to provide a price discount for a party that purchases more power from one sub-MG. A stochastic framework based on the unscented transform technique is also established to manage the uncertainties associated with hourly load demands and output power of renewable energy sources. The proposed model is formulated as a mixed-integer linear programming problem and solved through the blockchain-based energy/power management algorithm. The case study includes residential, industrial, and commercial MGs—namely, three residential, one commercial, and one critical load (hospital). The simulation results show the high efficiency and effectiveness of the proposed model and validate its economic and reliability merits.
Nonsmooth Extremum Seeking Control With User-Prescribed Fixed-Time Convergence

This article introduces a new class of nonsmooth extremum seeking controllers (ESCs) with convergence bounds given by class-$\mathcal {K}\mathcal {L}$ functions that have a uniformly bounded settling time. These ESCs are characterized by nominal average systems that render uniformly globally fixed-time stable (UGFxTS), the set of minimizers of the response map of a stable nonlinear plant. Given that, under suitable tuning of the parameters of the controllers, the ESCs inherit the convergence properties of their average systems, the proposed dynamics can achieve a better transient performance compared to the traditional ESCs based on gradient descent or Newton flows. Moreover, for the case when the plant is a static map, the convergence time of the proposed algorithms can be prescribed a priori by the users for all initial conditions without the need of retuning the gain of the learning dynamics of the ESC. Since autonomous feedback controllers with fixed-time convergence properties are necessarily non-Lipschitz continuous, standard averaging and singular perturbation tools, traditionally used in ESC, are not applicable anymore. We address this issue by using averaging and singular perturbation tools for nonsmooth and set-valued systems, which further allows us to consider ESCs modeled by discontinuous vector fields that are typical in fixed-time and finite-time optimization problems.
Streamlined analysis of short-, medium-, and long-chain free fatty acids in dairy products

In dairy products free fatty acids (FFAs) with fewer than ten carbon atoms can impart both desirable and undesirable (rancid) flavor notes, while long-chain FFAs are responsible for a soapy taste. The determination of FFAs in milk, cheese and other dairy products is particularly complicated owing to two main factors, i.e. FFAs (2 to 20 carbon atoms) represent <0.5% of total fat and many of the short-chain FFAs are extremely volatile. The best approach is to analyze short-chain FFAs (<C10) separately from the long-chain FFAs. Short-chain FFA can be extracted in soap form with water and then upon acidification of the extract short chain FFAs can be extracted into ether for GC analysis. Long chain FFA can be extracted with a mixture of ether:heptane (1:1, v/v), resulting in an extract that contains the whole range of lipids including FFAs. The present paper will describe a streamlined approach (utilizing an aminopropyl bonded phase column) for the analysis of total FFAs in dairy products.
Préparation et études des propriétés des films magnétiques nanostructures pour des applications en dispositifs magnéto-acoustiques et spintroniques

Aujourd'hui, les structures basees sur les materiaux ferromagnetiques sont largement utilisees pour differentes applications: memoires magneto-resistives a acces non sequentiel, capteurs magnetiques et egalement nouveaux composants electroniques et dipositifs spintroniques. La tendance generale de l'electronique moderne est une reduction de la dimension des elements a l'echelle submicronique. Ainsi, les nanostructures magnetiques sont d'un grand interet et leurs methodes de fabrication et proprietes sont etudiees activement.Le but principal de ce travail est la preparation et la recherche experimentale et theorique des proprietes de nanostructures magnetiques pour applications aux composants magneto-resistifs et phononiques. La lithographie a sonde locale (SPL) et la lithographie par faisceau d’electrons (EBL) ont ete utilisees pour la fabrication des nanostructures. De premiers pas ont egalement ete realises en fabrication des cristaux phononiques sensibles au champ magnetique.
Intrusion Location Technology of Sagnac Distributed Fiber Optical Sensing System Based on Deep Learning

For distributed fiber optical sensing based on Sagnac effect, the intrusion is usually located by notch frequency. However, the notch spectrum is the comprehensive result of the intrusion, so when multiple disturbances simultaneously intrude from different positions of the sensing fiber, it is impossible to establish a mathematical expression between the intrusion position and the notch frequency, this leads to the problem of multi-point intrusion localization. Therefore, in this paper, deep learning technology is used to locate multiple disturbing points in Sagnac distributed optical fiber sensing system, and the related specific technologies of deep learning applying to sagnac distributed optical fiber sensing are studied. First, according to the characteristics of the system, a network structure based on the regression probability distribution is proposed, second, a loss function is constructed. The results show that the trained model can realize the positioning of multiple and single intrusion points.
The Role of Engineering Thermodynamics in Explaining the Inverse Correlation between Surface Temperature and Supplied Nitrogen Rate in Corn Plants: A Greenhouse Case Study

Nitrogen stress plays a critical role in corn yield reduction. Thermal remote sensing has many applications: as an assessment tool for urban heat island, as an ecological indicator of ecosystem development, and as a water-stress-detection tool. In this study, it was hypothesized that corn crops supplied with optimum or high rates of nitrogen would have lower surface temperatures compared to corn grown under nitrogen-stressed conditions. Two experiments were conducted in the greenhouse at the University of Guelph, Canada, from the period between 2015 and 2016, involving three rates of nitrogen (high, medium, and low rates) supplied to corn plants after seed emergence. Leaf and whorl temperatures were collected by using a high-resolution thermal camera, an infrared handheld point measurements gun, and a type T thermocouple, respectively. An approximate difference of 2 °C was observed in temperatures between plants receiving high and low rates of nitrogen. These results supported the hypothesis that nitrogen-stressed plants have higher temperatures compared to less stressed plants, at a 0.05 significance level. This study investigated the application of the exergy destruction principle through thermal remote sensing, to detect crop stress at early growth stages under greenhouse conditions, to increase the production and reduce the harmful environmental impact.
230: Premature Closure Bias During the COVID-19 Pandemic: A Precautionary Case Report

INTRODUCTION: During the height of the COVID-19 epidemic, avoidance of premature closure bias is key Other causes of respiratory failure, particularly in the setting of unusual radiographic findings, must remain on the differential This case describes an unusual etiology of respiratory failure requiring multidisciplinary management METHODS: A 14-year-old, previously healthy male with three-day history of cough presents with sudden onset of dyspnea, chest pain and emesis He is transferred to the emergency department of a tertiary care children's hospital with concerns for COVID-19 infection He is hypoxic and orthopneic with an urticarial rash Initial chest radiograph demonstrates a large cystic, right lower pulmonary lobe lesion with leftward mediastinal shift He requires initiation of noninvasive respiratory support and is admitted to the intensive care unit For further characterization of the cystic lesion, additional imaging is obtained Chest computed tomography reveals a 14x17x11 cm well-defined right lower lobe lesion with air-fluid level and intraluminal hyper-attenuated serpiginous structure consistent with echinococcal hydatid cyst with endocyst rupture History later reveals he had lived on a farm in the Middle East until 3 years prior to admission, and more recently had accompanied his father to animal trading markets Anti-helminthic therapy is initiated Percutaneous aspiration of cyst with drain placement is performed to offset mass effect He remains intubated post-procedure and has worsening hypoxia, hemoptysis and hemodynamic instability requiring dual vasoactive support On hospital day 3, patient undergoes cyst resection and closure of multiple bronchopleural fistulas He is extubated shortly thereafter and transferred to the medical floor for continued management RESULTS: Echinococcal disease with hydatid cyst formation is common in the Middle East, Africa and Eastern Europe, yet rare in North America Mass effect of larger cysts and inflammatory response resulting from spillage of cyst contents may lead to hemodynamic instability Surgical resection is the definitive therapy This case highlights the importance of avoiding premature closure bias, as misdiagnosis may threaten ultimate outcome and delay necessary, multidisciplinary interventions
A Cluster-Based Model for Charging a Single-Depot Fleet of Electric Vehicles

This paper presents an operating model of a station that charges a single-depot and homogeneous fleet of electric vehicles (EV) performing deliveries. Both the charging station and the fleet of EVs are owned by a single delivery company. The operating model, whose goal is to find the optimal EV battery charging schedule, is based on a clustering technique that keeps track of the number of EVs with a specific battery state of energy (SoE), while considering battery degradation, variable (dis)charging efficiency and nonlinear charging speed. The proposed operating model can be used both for the day-ahead scheduling and for the intraday model-predictive-control-based adjustments. Due to its relatively low capacity as compared to other market participants, the charging station is considered to be a price taker in both markets. The price uncertainty is considered using the robust uncertainty budget. The paper also evaluates inefficiency of a commonly used charging policy, i.e., the baseline model, where every delivery vehicle is charged to at least a predetermined SoE before its departure. The presented model is evaluated using multiple case studies and sensitivity analysis. As opposed to the non-clustered baseline model, the proposed approach scales well for very large fleets. Our analysis confirms that the results of the baseline model depend on the preset SoE at departure, while the proposed model provides optimal solution without assumptions on the departing SoE.