Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution characteristics of star ratings in online consumer reviews

Purpose The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers. Design/methodology/approach Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms. Findings The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews. Research limitations/implications The authors did not analyse data across demographic details because of access restriction policies of the websites. Practical implications Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously. Social implications This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers. Originality/value This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.
The inhibiting effect of photodynamic therapy and novel recombinant human endostatin on the n vivo growth of U251 human glioma xenografts

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Does experience with agricultural loans improve farmers' well-being? Evidence from Benin

PurposeThe purpose of this study is to examine the effect of loans provided by Benin's largest microlender.Design/methodology/approachA pipeline design and matching techniques contribute for identifying the causal effect. The loan treatment considered is loan experience. It includes program entry timing, take-up frequency and the amount obtained over six years. The study uses a cluster analysis to create comparison groups.FindingsExperience with agricultural loans has a statistically significantly positive effect on recipients' farm income, food security and women's empowerment. Though unusual, these positive results can be credited to the very low rate of loan repurposing and mostly to the lender's rigorous loan implementation strategy.Practical implicationsThe study results validate the hypothesis underlying development strategies that target women regarding loan programs. The study provides a simple yet valuable lesson for future credit impact evaluations: the context of the loan program as well as the evaluation indicators is essential.Social implicationsThis study’s findings suggest that microcredit, when offered judiciously and with support, can improve farmers' conditions.Originality/valueGiven the relatively long period studied, the analysis has been innovative in defining loan treatment and creating reliable treatment groups. Also to the best of the authors’ knowledge, this study is the first of its kind in Benin.
Stabilizing a Class of Switched Nonholonomic Mechanical Systems

Abstract: Structurally reconfigurable or variant robot systems can provide better mobility and environmental adaptability. In this paper it is shown that a class of nonholonomic constraints robot systems can be changed to a class of special linear time varying (LTV) systems, and by applying switching control strategy the control singular problems of nonholonomic systems caused by the local coordinates can be overcome, and provides a flexible approach of optimal motion planning for mobile robotic systems. For variant robot systems with switched discontinuous dynamics, it is shown that the switching control approaches can be used to stabilize a class of switched heterogeneous nonholonomic systems. Some numerical simulation results also demonstrate the effectiveness of the control strategy proposed in this paper.
Spatiotemporal Modelling of COVID-19 Infection Risk in Portugal

Since its outbreak, the SARS-CoV-2 pandemic has shown complex dynamics in both time and space. These dynamics are the result of a combination of factors, including the spatial distribution of the population’s social and economic levels and its mobility patterns within a given country. After assessing the risk of infection and associated uncertainty based on infection rates by municipality, one of the most important challenges now facing health authorities concerns the ability to predict second waves and interactions with epidemics of other viruses (e.g. influenza). We propose characterizing the local spatiotemporal behaviour of risk of infection based on existing historical data, and classifying the local spatiotemporal patterns of the time series, thus allowing the management of new waves by region. We combined functional data analysis with geostatistical simulation to model the spatiotemporal evolution of infection risk by COVID-19 in mainland Portugal. The daily number of infection data by municipality reported by the Portuguese Directorate-General for Health are used to build time series of infection since the beginning of the outbreak in Portugal. We employ a dimensionality reduction of these curves using functional principal component analysis. The objective of this step is twofold: detect municipalities with a similar temporal evolution, and get a small number of coefficients to describe the temporal pattern of the series. The low-dimension coefficients are then used as experimental data to map the infection time series spatially using geostatistical simulation. With this step, we recover high-resolution maps of COVID-19 infection risk at any time step, allowing the simultaneous modelling of time and space. With the resulting spatiotemporal models, authorities can identify locations where the disease exhibits similar behaviours and, therefore, devise mitigation actions based on previous experience. Also, the models may be used for short-term forecasting as a simple data-driven proxy of full SEIR (susceptible, exposed, infectious, recovered) models.
Frequency-Based Regularization for Improved Reliability Optimization of Antenna Structures

This communication proposes a modified formulation of antenna parameter tuning problem. The main ingredient of the presented approach is a frequency-based regularization. It allows for smoothening the functional landscape of the assumed cost function, defined to encode the prescribed design specifications. The regularization is implemented as a special penalty term complementing the primary objective and enforcing the alignment of the antenna responses with the target operating frequency (or frequencies for multiband antennas). The result is an improved reliability and reduced cost of the optimization process, both highly desirable from the point of view of the efficacy of electromagnetic (EM)-driven design procedures. Furthermore, regularization makes the use of local routines sufficient even in situations where the global search is otherwise imperative (e.g., due to poor initial design). Our methodology is demonstrated using three microstrip antennas. The superior performance of the approach, both in terms of design reliability and in terms of the computational cost of the optimization process, is corroborated by comparisons with a conventional formulation.
The solvothermal synthesis of γ-AlOOH nanoflakes and their compression behaviors under high pressures

Boehmite, γ-AlOOH, has attracted increasing research enthusiasm due to its scientific and technological importance. The compression behaviors of γ-AlOOH are of vital relevance to the insight into geophysical and geochemical processes in Earth's mantle. In this work, free-standing γ-AlOOH nanoflakes with high purity and crystallinity have been synthesized via a template-free, one-step solvothermal alcoholysis strategy. The nanoflakes have high aspect ratio, with the lateral size ranging from 100 to 300 nm, and the thickness ranging from 20 to 45 nm. The band gap of the obtained γ-AlOOH nanoflakes is estimated to be 3.35 eV through UV-vis absorption spectra recorded at room temperature. A broad PL emission band can be observed in the 400 to 700 nm spectrum range, with the maximum at 480 nm. The compression behaviors of γ-AlOOH nanoflakes have been investigated by in situ high pressure synchrotron radiation angle dispersive X-ray diffraction up to 54.9 GPa and Raman scattering spectroscopy up to 22.4 GPa at room temperature. The compression is nearly linear and anisotropic, with b axis more compressible than a and c axes. The zero-pressure bulk modulus of γ-AlOOH nanoflakes is estimated to be 135.2 GPa, higher than the theoretical value of its bulk counterpart. The frequencies of the observed Al–O related Raman modes increase monotonously in a linear manner with increasing pressure. The mode Gruneisen coefficients are estimated, which may provide important information about the anharmonicity in the bonding nature of γ-AlOOH.
Empirical Analysis on the Influence of Business Environment on Foreign Direct Investment Inflow Based on the Panel Data on 26 Countries

With the intensification of global competition and development of investment theories, foreign direct investment (FDI) is no longer solely affected by economic factors. Many noneconomic factors, such as policies and institution, now play an important role in FDI inflow. As a composite indicator, business environment has attracted a growing attention from investors. From theoretical and empirical perspectives, this paper quantifies and qualifies the influence of business environment over the FDI under different conditions. The impact mechanism of business environment on the FDI was refined by decomposing business environment into multiple subfactors, and considering various factors of different economies, such as natural resources (NR), technological resources (TR), and political stability (PS). An empirical analysis was conducted on the panel data of 26 countries in 2005-2018. The results show that: the host country can attract more FDI inflow by improving business environment, NR, TR, and PS; excessively high NR and TR, to a certain extent, suppress the promotion effect of business environment on FDI; four subfactors of business environment, namely, the protection of small and medium investor (PI), cross-border trade (CT), electricity supply (ES), and insolvency (IN), have relatively high promotion effects on FDI inflow. The research results enrich the theories on FDI and business environment, and provide a reference for the design of innovative polices.
Optoelectronic Properties of Ultrathin Indium Tin Oxide Films: A First-Principle Study

First-principle density functional theory simulations have been performed to predict the electronic structures and optoelectronic properties of ultrathin indium tin oxide (ITO) films, having different thicknesses and temperatures. Our results and analysis led us to predict that the physical properties of ultrathin films of ITO have a direct relation with film thickness rather than temperature. Moreover, we found that a thin film of ITO (1 nm thickness) has a larger absorption coefficient, lower reflectivity, and higher transmittance in the visible light region compared with that of 2 and 3 nm thick ITO films. We suggest that this might be due to the stronger surface strain effect in 1 nm thick ITO film. On the other hand, all three thin films produce similar optical spectra. Finally, excellent agreement was found between the calculated electrical resistivities of the ultrathin film of ITO and that of its experimental data. It is concluded that the electrical resistivities reduce along with the increase in film thickness of ITO because of the short strain length and limited bandgap distributions.
Dimensions of Nepalese Bakery Products and its Impact on Customers’ Satisfaction

Kapil Khanal* Abstract This research tries to identify the dimensions of Nepalese bakery products and measure the impact of Nepalese bakery products on customers’ satisfaction. This research is based on primary data. To achieve the objective, quantitative and qualitative research designs have been used. Data were collected through personal interview, questionnaire and observation with 396 customers from Kathmandu Valley. Judgmental and purposive sampling technique was used in this procedure. Demographic analysis, percentages, arithmetic means, standard deviation and multiple regression equations were used to analyze and interpret the data. It is revealed that the price of bakery products have highest mean values where as location is least important than other dimensions. Among different dimensions of bakery products, price, quality and location have significant impact upon the customers’ satisfaction under one percent level of significant. Similarly, taste of bakery items and ambience of bakery shop are significant.
Public finance management system in modern conditions

The public finance management system is an important lever for equalizing financial and budgetary disproportions in the context of institutional changes. The paper aims to substantiate the directions of development of the public financial management system. Economic and statistical methods and correlation-regression analysis methods are used to determine the relationship between the GDP deflator and the share of revenues, expenditures, the general government budget deficit, and public debt in GDP, assessing the features of the public financial management system in Ukraine and EU countries. This study reveals that one of the main restraining factors in the public finance system development is a significant level of uncertainty in economic processes, which intensifies macroeconomic fluctuations, significant indicators of the share of public debt and budget deficit of the state administration sector pose risks to financial and economic stability; their potential negative impact on socio-economic processes is much more destructive than the pro-cyclical nature of fiscal policy. From this point of view, the public finance management system should be directed at optimizing financial and budgetary tools to prevent the growth of public debt and budget deficit in gross domestic product, which determines the importance of substantiating further development directions of the public financial management system. It is concluded that the mechanism of public financial management in recent years is quite rigid and restrictive, in the context of institutional change expands the tools of public financial management and increases its impact on socio-economic processes.
Simulation data driven weakly supervised adversarial domain adaptation approach for intelligent cross-machine fault diagnosis

In current research works, a number of intelligent fault diagnosis methods have been proposed with the assistance of domain adaptation approach, which attempt to distinguish the health modes for target domain data using the diagnostic knowledge learned from source domain data. An important assumption for these methods is that the label information for the source domain data should be known in advance. However, the high-quality condition monitoring data with sufficient label information is difficult to be acquired in the actual field, which can greatly hinder the effectiveness of domain adaptation–based fault diagnosis methods. The simulation model of the rotating machine is an effective approach to provide an insight into the characteristics of the mechanical equipment, which can also easily carry the sufficient label information for the mechanical equipment under various operating conditions. In this study, a simulation data–driven domain adaptation approach is proposed for the intelligent fault diagnosis of mechanical equipment. The simulation data from a rotor-bearing system are used to build the source domain data set, and the diagnostic knowledge learned from the simulation data is used to realize the healthy mode identification of mechanical equipment in the actual field. The proposed domain adaption approach consists of two parts. The first part is to achieve the conditional distribution alignment between source domain data and target domain supervised data in an alternative way. The second part is to achieve the marginal distribution alignment between source domain data and target domain unsupervised data in an adversarial training process. The proposed domain adaptation method is evaluated on two case studies, the diagnostic results on two case studies indicate that the proposed domain adaptation method is capable of realizing the fault diagnosis of mechanical equipment using the diagnostic knowledge learned from simulation data.
Higher Survival Rate Associated with Thymosin Alpha 1 use in Critical COVID-19: A Retrospective Multicenter Cohort Study

BackgroundCurrently no satisfactory pharmaceutical intervention is available for COVID-19. This retrospective study aimed to determine the therapeutic effect of thymosin alpha1 in critical COVID-19. ResultsWe enrolled 109 critically ill severe acute respiratory syndrome-related coronavirus-2 RNA positive patients from 15 hospitals. The mortality rate in critical patients treated with thymosin alpha1 was 11%, compared to 56% in critical patients not treated with thymosin alpha1. With confounding factors adjusted in multivariate logistic regression, thymosin alpha1 treatment was identified as a protective factor for critical COVID-19. ConclusionOur observation advocates the treatment of critical COVID-19 with thymosin alpha1.
Systematic Model-Based Steady State and Dynamic Optimization of Combined Cooling and Antisolvent Multistage Continuous Crystallization Processes

Currently, one of the key challenges in the pharmaceutical industry is the transformation of traditional batch production methods into robust continuous processes with the intention of reducing manufacturing costs and time and improving product quality. Crystallization is by far the most important purification technology in Pharma, as more than 80% of the active pharmaceutical ingredients (API) require at least one crystallization step. A successful crystallization process requires tight control over crystal size, shape and polymorphic purity. A rigorous and systematic methodology is presented to design and optimize multistage combined cooling and antisolvent continuous (mixed-suspension, mixed-product removal- MSMPR) crystallizers. The crystallization of acetylsalicylic acid (API) in ethanol (solvent) and water (anti-solvent) is used as a case study. A predictable and validated mathematical model of the system, which consists of a one-dimensional population balance model, was used to develop several optimizations strategies. Firstly, the attainable region of the mean particle size was determined for both minimum and maximum attainable crystal size. The method helped identify the most suitable number of stages and total residence time or volume for a cascade of continuous crystallizers. This was followed by a steady state optimization which helped determine the optimal operating temperatures and antisolvent flowrates. To minimize the startup time, a series of dynamic optimization strategies were implemented, assuming starting from empty vessels. The optimal dynamic profiles of the temperature and antisolvent flow rate, at different crystallization steps, were identified using a systematic and rigorous approach allowing a reduction in the startup time by 31%.
Hitchhiking Experiences and Perception of Affective Label Polarity in Social Networking Sites—Potential Memetic Implications for Digital Visual Content Management

Digital Visual Content (DVC), as sets of meanings, constitutes a pictorial representation of phenomena and may lead users of Social Networking Sites (SNS) to specific behavioral activities, i.e., connected with the expression of emotions. The goal of this research paper was to create a methodology of analysis and visualization of SNS users’ emotional responses to the hitchhiker archetype, built on the basis of a semantic decomposition of photos shared by hitchhikers in a closed group of Facebook users. Hitchhiker, as an archetype of authentic and sustainable tourism, was used as an illustrative example. ABSA (Aspect-Based Sentiment Analysis) methodology was used for a semantic decomposition of 300 randomly selected photos, applying the theoretical framework of information ecology. As a result, collections of single meanings (aspects) were distinguished and grouped into four main denotation groups: “Hitchhiker environment”, “Activities of hitchhikers”, “Season” and “Heritage”. Having created a holistic view of the archetype from aspects appearing at different frequencies, it was assumed that the archetype could be viewed as a meme. Given this memetic archetype and the emotional response associated with it, it could be considered a subject of cultural selection, conditioned by the influence of several factors, e.g., user demographics. The results allow for the development of the premises for research related to the dynamics, evolutionary analysis, and visualization of memes as well as emotional responses of visual content recipients.