Miracle-dz/UniFAR-FTUs huggingface dataset:
FLYER'S CORNER: SOME SIMPLE TRUTHS ABOUT SCHEDULED AIR TRAVEL

Airlines in a state of crisis need to take steps to ensure revenues keep coming in. Repeat business and eventually loyalty is critical in that respect. This paper considers airline customer buying behaviour and preferences and post-flight attitudes with a view to identifying what makes the air travel product more appealing to customers. A structured web-based purposedesigned instrument was used to collect travel and traveller data and perceptions and the findings are prioritised using a consensus decision making approach. A major issue is identified and a two-step model is developed to demonstrate its significance for airline customers’ loyalty. Specific airline customer communications and other priorities and preferences are identified. In addition, it is found that airline customers have a number of complaints, many of which are not communicated to the airline, and it is demonstrated that these complaints impact on loyalty. It is therefore argued that airlines and researchers alike need to study customer complaints in relation to loyalty in greater detail. In view of both the economic situation in Greece and airline deregulation, this research is of significance to Greek airlines seeking to “tie” their customers and maintain market presence.
ravel distance to outpatient substance use disorder treatment facilities for panish-speaking clients

Background: Travel distance and English proficiency skills are widely recognized factors associated with service access and treatment engagement. As Latino populations represent one of the most rapidly growing populations in the United States, methods are needed to better understand availability of linguistically appropriate services in Latino communities. Given regional variability in the density of Latino communities, the current study examines treatment access as travel distance to outpatient substance use disorder treatment facilities in one of the largest and most rapidly changing Latino communities in the United States – Los Angeles County, CA. Methods: Data from the 2010 U.S. Census and the National Survey of Substance Abuse Treatment Services were analyzed using a geographic information system approach to determine the street-level distance between treatment facilities with services in Spanish and Latino communities throughout L.A. County. This study used an innovative approach that included network analysis and spatial autocorrelation to identify “hot spots,” i.e. clusters of census tracts with high-density Latino populations that were relatively far from treatment services in Spanish. Results: The analysis identified several key hot spots with significantly large Latino populations and far street distances to the closest facility offering Spanish-language services. The average distance between these hot spots and the closest facilities was 2.74 miles (SD = 0.38), compared to a county average of 2.28 miles (SD = 2.60). In several key hot spots, the distance was greater than 3 miles. Conclusion: Despite the growing presence of Latinos in L.A. County in 2010, constrained access to services in Spanish was found in geographic locations highly represented by Latinos. The distances identified in this study are almost triple the 1-mile threshold representing reduced access to treatment as determined by other studies. Geographic information systems represent an innovative and user-friendly approach for effectively and efficiently identifying areas with the greatest service needs. This approach can inform policies to increase the capacity of ethnic minority communities to develop linguistically responsive social services.
Adaptation of the self-compassion scale for Bangladeshi culture

Self-compassion is a raising construct in the field of positive psychology. Evidence supported that self-compassion is an important factor in increasing psychological well-being and reducing symptoms of anxiety, depression and rumination. In the present study, authors determined the psychometric properties of the Bangla version of Self-Compassion Scale (SCS). The study was carried out in a sample of 160 adults, ranging from 18 to 28 years old. Although the original scale has 26 items, in item-total correlation analysis it was found that 24 items in the questionnaire were significantly correlated with total items, the rest two items consumed negative/low correlation (below 0.30). So, these two items were excluded from the Bangla version and 24 items were selected for the final version of the SCS. The internal consistency of the SCS was computed by Cronbach’s Alpha which was 0.83 and the split-half coefficient of the scale was 0.84. Thus the results support the Bangla SCS is suitable for use in Bangladeshi culture. Introduction Self-compassion is theme of Buddhist psychology for centuries and it has become the focus of eastern and western psychologies(1). The term “Self-compassion” derives from the word “compassion”, which refers to an understanding of another’s pain and the desire to alleviate that pain. Directing the same compassionate attitude towards oneself involves self-compassion. Self-compassion is defined as treating oneself kindly in moments of failure and acknowledging negative feelings with openness and acceptance(1). It works as buffer against negative coping such as self-criticism and acts as a protective factor against mental illness. It involves being kind, compassionate and understood toward oneself when one is emotionally distressed or when there is a feeling of incompetence or failure(2). There are three interrelated components of selfcompassion. These components might be of great help in times of suffering , if it is well trained. The three main elements include: (i) Self-kindness being supportive , kind and caring toward oneself rather than being harshly self-critical when individuals having a *Author for correspondence: <naherjamiun31@gmail.com>.
Response to Short Comment 1 (SC1) on “Rapid transition in winter aerosol composition in Beijing from 2014 to 2017: response to clean air actions” by H. Li et al

(a) Overview The authors used fine particle data collected in Beijing during the winters of 2014 and 2017 to understand changes in the chemical composition of the PM over a period when particle mass decreased significantly. The results here are consistent with past data, showing a large decrease in PM mass, but also revealing that this is largely because of decreases in sulfate, organics, and unidentified components, as a result of decreasing local and regional emissions. The authors find that meteorology also contributed to lower PM concentrations in 2017 but that this was a smaller factor.
A Survey on Machine Learning for Stock Price Prediction: Algorithms and Techniques

Stock market trading is an activity in which investors need fast and accurate information to make effective decisions. Since many stocks are traded on a stock exchange, numerous factors influence the decision-making process. Moreover, the behaviour of stock prices is uncertain and hard to predict. For these reasons, stock price prediction is an important process and a challenging one. This leads to the research of finding the most effective prediction model that generates the most accurate prediction with the lowest error percentage. This paper reviews studies on machine learning techniques and algorithm employed to improve the accuracy of stock price prediction.
Comparative toxicity study of two different synthesized silver nanoparticles on the bacteria Vibrio fischeri

A comparative evaluation of acute toxicity of synthesized nano-silvers using two different procedures (biological and chemical reduction methods) and effect of silver ions on the bacteria Vibrio fischeri were investigated. The bacterial light inhibition testas a toxicological endpoint was used by applying of a homemade luminometer. To compare the toxicity effects as a quantitative parameter, after 5 min and 30 min exposure times, nominal effective concentration (EC) of chemicals and susceptibility constant (Z-value) were calculated. After 5 and 30 min contact times, the EC50 values of two silver nanoparticles and the EC20 values were about similar. The EC values of nanoparticles were larger than those of the silver ions. The susceptibilities of V.fischeri (L/mg) to the silver ions were greater than those of the nano silvers. According to the EC and Z values, the toxicity of silvers decreased in the following order: silver ions >> silver nanoparticles from chemical reduction method ~ silver nanoparticles from biological method. Key words: Vibrio fischeri, toxicity, luminometer, bioluminescence, silver nano particles.
Association among self-esteem, aggression and psychological adjustment of orphans and family-reared children

The present study explored the relationship among self-esteem, aggression, and psychological adjustment between orphan children and family-reared children. The sample comprised of 160 adolescents. Out of the total sample, 80 were living in the orphanages; and 80 participants were living with their both parents. They were selected by employing purposive-incidental sampling technique. Participants responded to the index of the Personal Information Form (PIF), Bangla version of Rosenberg Self-Esteem scale, Bangla version of BussPerry Aggression Questionnaire, and Bangla version of Adolescent Personality Assessment Questionnaire. Results showed that key variables were significantly correlated with one another. Results indicated that the children in orphanages have lower self-esteem, are more aggressive, and have higher psychological adjustment problem than children living with their both parents. Results also showed that girls have lower self-esteem and they are less aggressive than boys. Self-esteem has been found to be an important predictor of psychological adjustment of the respondents. Therefore, the findings of present research will encourage our perceptive of self-esteem and will advance the understanding about the negative impact of aggression on children’s mental health.
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review

Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
Uncovering how area-based initiatives may stimulate leisure-time walking among adults in deprived areas: a realist review

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Logistic Regression Ensemble (LORENS) Applied to Drug Discovery

Logistic regression is one of the commonly used classification methods. It has some advantages, specifically related to hypothesis testing and its objective function. However, it also has some disadvantages in the case of high-dimensional data, such as multicolinearity, over-fitting, and a high computational burden. Ensemblebased classification methods have been proposed to overcome these problems. The logistic regression ensemble (LORENS) method is expected to improve the classification performance of basic logistic regression. In this paper, we apply it to the case of drug discovery with the objective of obtaining candidate compounds to protect the normal non-cancerous cells, which is considered to be a problem with a data-set of high dimensionality. The experimental results show that it performs well, with an accuracy of 69% and AUC of 0.7306.
Background reduction in the vector boson fusion produced Higgs → invisible search at Atlas using final state photons

The decay of a Higgs boson produced in Vector Boson Fusion into invisible particles has a very low (0.1%) branching ratio in the Standard Model. This makes it an interesting channel to search for new physics in, for example Higgs-Portal dark matter particles. To significantly improve the current upper limit on the H → inv. branching ratio of 28%, improving the suppression of background events is crucial. The present report shows how background suppression can be improved by a cut on the highest transverse photon momentum in an event. It is a report on my work in the DESY ATLAS group during the DESY Summer Student Programme 2016.
Failure Correction of a Linear Array of Coupled Parallel Vertical Dipole Antennas

In this paper, a cuckoo search algorithm based on the combined characteristics of the brood parasite behavior and Levy flights is applied to correct the radiation pattern of a linear antenna array composed of parallel dipoles with faulty elements. An effort is made to restore the radiation pattern similar to one without any faulty elements, and the difference in the values of side lobe level and wide null depth of both patterns, as well as the voltage standing wave ratio obtained from the new voltage excitations become diminished. The examples presented in this paper show the effectiveness of this algorithm in correcting the radiation pattern of a linear array of 36 and 120 dipole antennas with four and ten failed elements, respectively. The results show that the matching condition and the wide null control produced by Cuckoo Search algorithm are more efficient in comparison with the benchmark failure correction algorithm. The approach adopted herein may be applied to other array configurations as well. Keywords—array failure correction, cuckoo search algorithm, wide null depth, mutual coupling.
Richey, Michelle and Gonibeed, Aparna and Ravishankar, MN (2017)The Perils and Promises of Self-Disclosure on Social Media. Information Systems

In addition to their professional social media accounts, individuals are increasingly using their personal profiles and casual posts to communicate their identities to work colleagues. They do this in order to ‘stand out from the crowd’ and to signal attributes that are difficult to showcase explicitly in a work setting. Existing studies have tended to treat personal posts viewed in a professional context as a problem, since they can threaten impression management efforts. These accounts focus on the attempts of individuals to separate their life domains on social media. In contrast, we present the narratives of professional IT workers in India who intentionally disrupt the boundaries between personal and professional profiles in order to get noticed by their employers. Drawing on the dramaturgical vocabulary of Goffman (1959) we shed light on how individuals cope with increased levels of self-disclosure on social media. We argue that their self-presentations can be likened to post-modern performances in which the traditional boundaries between actor and audience are intentionally unsettled. These casual posts communicate additional personal traits that are not otherwise included in professional presentations. Since there are no strict boundaries between formal front-stage and relaxed back-stage regions in these types of performance, a liminal mental state is often used, which enables a better assessment of the type of information to present on social media.
Impacts of dynamic mechanical stretch on the expression of plasminogen activator inhibitor-1 (PAI-1) in human A549 cells

We investigated the expression of plasminogen activator inhibitor-1 (PAI-1) in A549 cells under cyclic mechanical stretch using cytomechanics. A cell stretch loading device was used to simulate mechanical ventilation conditions towards the in vitro cultured A549 cells, with stress loads of 10% and 20% and frequency of 0.3 Hz for 4, 12, and 24 h. Polymerase chain reaction and western blotting were then performed to measure the expression of PAI-1mRNA and protein; annexin V/propidium iodide flow cytometry was performed to detect apoptosis; an enzyme linked immunosorbent assay was performed to detect the expression of interleukin-8 in the supernatant; and electron microscopy was performed to observe changes in cell morphology. The expression of PAI-1mRNA and protein, cell early apoptosis rate, and interleukin-8 expression in the supernatant in each 20% stretch subgroup were increased compared to the corresponding control group, and cell damage was more obvious by electron microscopy observation. Mechanical stretch may damage A549 cells and upregulate PAI-1 expression.
MICROPROPAGATION AND EX SITU CONSERVATION OF ACHILLEA THRACICA VELEN

The genus Achillea (Asteraceae) comprises more than 130 perennial species most of which grow wild in Northeastern Europe and Northwestern Asia. In this study, Achillea thracica Velen. (in vivo plants) was collected at its natural habitat in Thracian lowland, Manole village, Plovdiv, Bulgaria, and propagated successfully in vitro on standard cultivation MS medium enriched in Gamburg vitamins (MSB5 medium). The plants were successfully ex vitro adapted and transferred to the experimental field in Lozen mountain. A. thracica is a Bulgarian endemic species containing a variety of biologically active compounds: monoterpenes, sesquiterpenes and flavonoids. The aim of this study was to investigate the content of total phenols and flavonoids and the total antioxidant activity in in vivo, in vitro and ex vitro adapted plants. Significant total phenolic content was observed in in vivo and ex vitro grown plants (48.02±4.06 and 40.02±0.49 mg gallic acid (GA). g -1 dry weight (DW), respectively). Interestingly, a higher content of flavonoids was observed in in vitro and ex vitro plants (10.62±0.28 and 12.49±0.46 mg quercetin (Q). g -1 DW, respectively). On the other hand, significant increase in the total antioxidant activity was observed in in vitro, followed by in vivo plants (83.39±0.54 and 73.18±2.3 mM tocopherol.g -1 DW, respectively). The present work offers an effective alternative method for propagation and preservation of the important medicinal plant A. thracica, and in parallel the modulation of its biological activity was followed during the different cultivation conditions.
Exploring Linkage Among Experiential Value, Image of Destination, and Intention to Revisit in Nature Tourism

Natural based tourism is one of the tourist activities offered by tourism destination managers and it is increasingly in demand by tourists. The study of tourist visits to natural tourism destinations is still limited. Exploration of experiential value, image of destination and intention to revisit natural tourism destinations is examined in this study. The samples in this study were 180 respondents with the purposive sampling technique used. The research sample was domestic tourists who had visited natural tourism destinations in Palembang during May 2019. Data collected through questionnaires to respondents and further processed using SPSS 23 and AMOS 22 to determine the minimum requirements and hypotheses testing. Results of analysis explain the value of experience has a significant and positive effect on the image of tourism destination. Experiential value of tourist has a significant and positive effect on the intention to revisit. However, the image of tourism destination has not a significant and positive effect on the intention to revisit. Therefore, results of the study can provide an understanding of visiting intentions and can be a basis for destination managers and related parties to pay attention to marketing natural tourism destinations.
An Approach to Semantic and Structural Features Learning for Software Defect Prediction

Research on software defect prediction has achieved great success at modeling predictors. To build more accurate predictors, a number of hand-crafted features are proposed, such as static code features, process features, and social network features. Few models, however, consider the semantic and structural features of programs. Understanding the context information of source code files could explain a lot about the cause of defects in software. In this paper, we leverage representation learning for semantic and structural features generation. Specifically, we first extract token vectors of code files based on the Abstract Syntax Trees (ASTs) and then feed the token vectors into Convolutional Neural Network (CNN) to automatically learn semantic features. Meanwhile, we also construct a complex network model based on the dependencies between code files, namely, software network (SN). After that, to learn the structural features, we apply the network embedding method to the resulting SN. Finally, we build a novel software defect prediction model based on the learned semantic and structural features (SDP-S2S). We evaluated our method on 6 projects collected from public PROMISE repositories. The results suggest that the contribution of structural features extracted from software network is prominent, and when combined with semantic features, the results seem to be better. In addition, compared with the traditional hand-crafted features, the F-measure values of SDP-S2S are generally increased, with a maximum growth rate of 99.5%. We also explore the parameter sensitivity in the learning process of semantic and structural features and provide guidance for the optimization of predictors.
Automatic Defect Detection for Mango Fruit Using Non-Extensive Entropy with Gaussian Gain

Quality inspection process of agricultural products recently becomes a crucial part of food industries. As the amount of these products gets larger, manual quality control based on traditional visual inspection performed by human can be tedious, time-consuming, labor-intensive, and inconsistent. Machine leaning can be used to automate the quality inspection, which will make this process more rapid, consistent, accurate and cost-e cient. This work focuses on identifying defects on mango surfaces. This work applies non-extensive entropy with Gaussian modeling, which is an unsupervised automated texture defect detection technique and therefore it does not require any training image samples in advance. The numerical results from this approach are shown to be e ective in detecting various defects, such as cracks, dark spots and bruises from mango image samples. This work also investigates di erent window sizes used in entropy computation, which will a ect the trade-o between computational speed and detection accuracy.
Envelope Modulation Spectrum (EMS)

The Envelope Modulation Spectrum (EMS) is a representation of the slow amplitude modulations in a signal and the distribution of energy in the amplitude fluctuations across designated frequencies, collapsed over time [1]. It has been shown to be a useful indicator of atypical rhythm patterns in pathological speech. The original speech segment (see Figure 2), x0[n], is first filtered into 9 octave bands with center frequencies of approximately 30, 60, 120, 240, 480, 960, 1920, 3840, and 7680 Hz, using eight-order Butterworth filters (see Figure 3). Let hi[n] denote the filter associated with the ith octave. The octave band filtered signals xi[n] is then denoted by,