Miracle-dz/UniFAR-FTUs huggingface dataset:
Tourist Police Service Quality and Service Culture Influence on Tourists’ Confidence

The travel and tourism sector is a significant economic contributor in Malaysia. Tourist police service quality plays an important role in promoting tourist confidence in the safety of a destination. However, there is less attention in tourism research focuses on tourist policing. Hence, this research fills the gap by examining the relationship between tourist police service quality and service culture influence on tourist confidence. The research is conducted in Penang Island, Malaysia. The dimension of tourist police service quality is conceptualized based on the SERVQUAL model with an additional factor – the service culture. The unit of analysis is international tourists. Based on the convenience sampling approach, a total of 150 completed responses was collected and analysed using the Partial Least Squares – Structural Equation Modelling (PLS-SEM) technique. This research finds that tourist confidence in the police is influenced by service quality factors: tangibles, responsiveness, and police service culture. The practical contribution of the research is on the utility of the finding for the Malaysian tourist police department to strategize their organization to achieve a better level of service quality, hence promotes tourist confidence.
SENTIMENT ANALYSIS FOR OPINIONS ON SOCIAL MEDIA: A SURVEY

With the rapid development of the Internet industry, an increasing number of social media platforms have been developed. These social media platforms have become the main channels for communication among most users. Opinions from social media platforms provide the most updated and inclusive information. Sentiments from opinions are a valuable data source for solving many issues. Therefore, sentiment analysis has developed into one of the most popular natural language processing fields. Hence, improving the performance of sentiment analysis methods or discovering new problems related to these methods is essential. In this context, we must be aware of the general information relevant to this area. This survey presents a summary of the necessary stages for building a complete model to be used in sentiment analysis. For each stage, we list the popular techniques that have been widely used in recent years. In addition, discussions and comparisons related to these methods are provided. Additionally, we discuss the challenges and possible research directions for future research in this field.
BIOPLASTICS FROM FISH SCALES AND WATER HYACINTH

Plastic is one product that is used in highly used in modern life for its durability and affordability. On the other hand, plastic poses a threat to the environment due to its non-renewable nature, making it difficult to decompose. Environmental pollution has increased due to the ease of transportation, single use in daily life and intensive use. Because of this, bioplastics have emerged as a solution and are thought to be more beneficial to the environment. Bioplastics are plastic materials produced from renewable biomass sources and are biodegradable. Recently, bioplastics production from fish wastes has been started. Cellulose from water hyacinth has been used to make polyhydroxybutyrate (PHB), a resource for bioplastics and also contributes to a more flexible. Also it reduces the usage of petroleum derived product by using bioplastics instead of plastic. For all of that reasons bioplastics usage has increased in our life.
A performance evaluation of convolutional neural network architecture for classification of rice leaf disease

Plant disease is a challenge in the agricultural sector, especially for rice production. Identifying diseases in rice leaves is the first step to wipe out and treat diseases to reduce crop failure. With the rapid development of the convolutional neural network (CNN), rice leaf disease can be recognized well without the help of an expert. In this research, the performance evaluation of CNN architecture will be carried out to analyze the classification of rice leaf disease images by classifying 5932 image data which are divided into 4 disease classes. The comparison of training data, validation, and testing are 60:20:20. Adam optimization with a learning rate of 0.0009 and softmax activation was used in this study. From the experimental results, the InceptionV3 and InceptionResnetV2 architectures got the best accuracy, namely 100%, ResNet50 and DenseNet201 got 99.83%, MobileNet 99.33%, and EfficientNetB3 90.14% accuracy.
Patients with metabolic syndrome present decreased cardiorespiratory fitness facing maximum progressive exercise

Background: Metabolic Syndrome represents a set of predisposing factors for the development of cardiovascular diseases and other pathophysiological repercussions such as decreased aerobic capacity, an important marker of mortality. Due to limitations in the measurement of VO2max, studies on the behavior of ventilatory parameters in submaximal phases of exercise are necessary so that it can be reproduced the patient’s general performance during maximum physical effort. Objective: Compare cardiorespiratory fitness between women with MetS and sedentary eutrophic women. Methods: 277 female individuals (42.1 ± 5.5 years) were evaluated, divided into two groups, Group 1 – Metabolic Syndrome (MetS =210) and Group 2 – Heath Control (HC = 67), all patients underwent the ergospirometric test, which consists of performing a graduated exercise with direct analysis of respiratory gases. The data were expressed as mean and standard deviation and the inferential analysis performed with the Test T student. For multivariate correlations, the linear regression model stepwise. For all tests, the level of significance adopted was 5%. Results: MetS Group showed loss when compared to the HC Group in weight, BMI and risk factors for MetS, P<0.05. In the Cardiopulmonary exercise test, had lower values of VO2peak (21.2±4.6; and 27.5±9.3 ml/kg/min, respectively, Interaction; P<0.05) and a lower value for VO2 at the anaerobic threshold (14.3±7.1; and 12.1±4.0; p<0.05), corretating directly with the BMI (R=-0.48; P=0.001) and CA (R=-0.46; P=0.001). Conclusion: Patientes with MetS have decreased cardiorespiratory efficiency compared to maximum progressive exercise.
High incidence of plant-animal mutualisms in the woody flora of the temperate forest of southern South America: biogeographical origin and present ecological significance

The flora of the temperate forest of southern South America highly depends on animals for pollination and seed dispersal. This resembles more tropical settings and distinguishes it from other temperate floras. This high mutualism-dependency seems paradoxical and potentially “anachronic” in view of this flora’s present interaction with an impoverished fauna. We analyzed the frequency of biotic pollination and seed dispersal across woody genera classified according to growth form and biogeographical affiliation. We found that genera that probably originated in warm or tropical like environments of ancient Gondwana and the Neotropics, two of the main biogeographical sources of this flora, had high incidences of biotic pollination and dispersal. The rich endemic foristic element, mostly related phylogenetically to these two foristic sources, also exhibited a high frequency of mutualism-dependency. Many endemic and Neotropical genera showed distinctive reproductive traits associated with bird pollination, a type of mutualism nearly absent in taxa with other biogeographical affiliations. This suggests that many southern South American plant taxa may have evolved novel mutualistic associations in situ, probably before the onset of present cooler climatic conditions. We propose that, despite the current relictual status of many of these genera and their associated reproductive traits, dependence on mutualists is not necessarily “anachronic” in today’s cool climate and depauperate faunal environment. Our finding that the average southernmost distribution limit is similar both for genera that depend on mutualists for either pollination or dispersal and for those that do not support this view. The steep W-E rainfall gradient created by the rising of the Andes represents a more potent ecological filter than the N-S temperature gradient, sorting out forest plant taxa according to their dependence on hummingbirds for pollination and vertebrates for dispersal. Overall, we believe that plant-animal mutualisms in the temperate forest of southern South America are still well suited interactions, because they rely on animals that are abundant and show a high degree of residency despite being represented by a low number of species. However, present human-induced elevated rates of deforestation, fragmentation and habitat degradation may be particularly threatening for mutualist-dependent plants due to low redundancy in alternative animal partners.
Reply to comments of Anonymous Reviewer #1 (RC1)

1) In the Introduction section, authors should insist more on previous works in India dealing with CCN, since they practically refer only to CAIPEEX campaign (airborne measurements). Looking the literature, I can see several other works with ground instrumentation as well, some of them also cited later in the manuscript like Dumka et al. (2015), Jayachandran et al. (20020a, 2020b), etc. You may also see Jayachandran, et al., 2018 (Atm.Res.), Dipu et al. (2013, AtmEnv), Bhattu et al. (2014, AtmEnv).
IJEBR-07-2020-0496_proof 1142..1165

Purpose – The purpose of the paper is to theorize how to develop student entrepreneurs’ ability to reflect by means of a learning activity called the entrepreneurial diary, which seeks to develop self-regulated learners capable of intelligent entrepreneurial action. The importance of self-regulation in entrepreneurship is linked to the individual’s ability to make judgments under conditions of uncertainty, which requires reflective thinking. Design/methodology/approach – The paper builds on a synthesized conceptualization of three main literature strands, reflective thinking, cognitive-load theory and experiential entrepreneurship education. In addition to the synthesized conceptualization, it builds on some empirical insights derived froma venture creation master programme in which the learning activity has been developed and refined for the last seven years. Findings –Themain finding from the paper is the theoretical justification forwhy reflective thinking deserves an important place in the educational process and how the entrepreneurial diary as a learning activity can create a bridge between theory and practice in venture creation programmes that take an experience-based pedagogical approach. Furthermore, the study also provides some empirical insights of how students create self-awareness of their learning through the method and the metareflection reports. Self-awareness is foundational for developing conditional knowledge on why and when to make entrepreneurial decisions to balance the often action-oriented processes seen in venture creation programmes. Originality/value – The paper provides both a practical learning activity to be used in the entrepreneurial classroomand a theoretical contribution on how entrepreneurial experience is transformed into entrepreneurial knowledge to enhance students’ judgmental abilities to make entrepreneurial decisions in future entrepreneurial endeavours.
Morphology, sedimentology and origin of an anomalous cut-off along the Pravara river, deccan trap region, India

River meanders have always been an intriguing subject in fluvial geomorphology because of their ubiquity, dynamism, remarkable forms and practical consequences of their movement. Sometimes a relatively straight channel flowing over bedrock may develop a lone meander bend cut-off which is very out of the place from the surrounding area. The occurrence of a sudden bend along a river may not be a meandering bend but may be manifestation of sudden change in the river dynamism due to many reasons, such as, lithology, change in rainfall regime, tectonics etc. The formation of such features highlights the behavior of river in the past. One such striking feature has been observed along the River Pravara in the Deccan Trap Region, Maharashtra, India. Rivers in Deccan Trap Region do not meander and form cut-offs by rule. It is rocky country where rivers flow in deeply incised bedrock. Hence, the observed feature displays a striking anomaly in this region. Hence, an attempt has been made in the present paper to evaluate the mode of formation of this single cut-off along this channel. Morphological and sedimentological data were generated and analyzed for the channel loop and the link channel to understand the competence of the river in the past and present which were directly or indirectly responsible for the development of this channel anomaly in this reach. Based on the results of the analysis and intensive field observations, it has been inferred that this is a classic example of natural morphological adjustment of a river when a set of events occurred, first retardation of vertical erosion encountering bedrock followed by series of floods to induce the channel to divert from the original path to resume the present course. Presence of a tributary further aided to the process of the loop development. The study can provide additional knowledge to the studies involving anomalous channel cut-offs at any part of the world.
The Effect of Circulating Oxidized LDL and High Sensitivity C-Reactive Protein on Coronary Heart Disease Susceptibility in a South Indian Population.

The mechanism by which circulating ox-LDL and inflammatory indicators such as high sensitivity C-reactive protein may exert their impact on the development of CHD is still poorly understood. The study aims to measure the levels of circulating oxidized LDL and high sensitive C - reactive protein in CHD subjects. This cross-sectional study was conducted in the Department of Cardiology, General Medicine, and Master health check-up OP of SRM Medical college hospital and research centre, Tamil Nadu, India. A total of 182 subjects in which 91 CHD subjects and 91 healthy control in the age group of 30 to 55. ox-LDL and hs-CRP were measured by ELISA method and Lipid Profile is measured using Auto Analyzer AU480. Statistical analysis was done using the student ‘t’ test and Pearson correlation analysis was done for the comparison between two groups. The mean level ox-LDL and hs-CRP in CHD were elevated and statistically significant (p-value <0.001) compared to healthy controls. Ox-LDL was positively correlated with and hs-CRP. This study brings new insights that Ox-LDL and CRP may play a direct role in promoting the inflammatory component of atherosclerosis. Furthermore, more emphasis should be placed on these oxidative stress indicators in the prevention and treatment of CHD.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation

Network monitoring and measurement have always been critical components of network management. Recent developments in sketch-based monitoring techniques and the deployment opportunities arising from the increasing programmability of network elements (e.g., programmable switches, Smart-NICs, and software switches) have made the possibility of accurate, detailed, network-wide telemetry tantalizingly within reach. However, the wide heterogeneity of the programmable hardware and dynamic changes in both resources available and resources needed for monitoring over time make existing approaches to network-wide monitoring impractical. We present HeteroSketch, a framework that consists of two main components: (1) a proﬁling tool that automatically quantiﬁes the capabilities of arbitrary hardware by predicting their performance for sketching algorithms, and (2) an optimization framework that decides placement of measurement tasks and resource allocation for devices to meet monitoring goals while considering heterogeneous device capabilities. HeteroSketch enables optimized deployments for large networks (> 40,000 nodes) using a novel clustering approach and enables prompt responses to network topology, trafﬁc, query, and resource dynamics. Our evaluation shows that HeteroSketch reduces resource overheads by 20 − 60% compared to prior art, while maintaining monitoring performance, coverage, and accuracy.
Non-Volatile Memory Accelerated Posterior Estimation

Bayesian inference allows machine learning models to express uncertainty. Current machine learning models use only a single learnable parameter combination when making predictions, and as a result are highly overconfident when their predictions are wrong. To use more learnable parameter combinations efficiently, these samples must be drawn from the posterior distribution. Unfortunately computing the posterior directly is infeasible, so often researchers approximate it with a well known distribution such as a Gaussian. In this paper, we show that through the use of high-capacity persistent storage, models whose posterior distribution was too big to approximate are now feasible, leading to improved predictions in downstream tasks.
CORRELATION BETWEEN LEIDENFROST TEMPERATURE, COOLING CAPACITY AND MACHINING TEMPERATURE: AN EXPERIMENTAL STUDY OF CUTTING FLUIDS

Much of the energy consumed by machining materials is converted into heat, which causes several technical and economic problems for the process. The cutting fluid application by the conventional method forms a vapor film of low thermal conductivity, which prevents direct contact between the fluid and the heated surface; the so-called Leidenfrost effect, which reduces cooling efficiency. Studies of this phenomenon applied to the machining of materials are still very restricted and scarce. In this sense, the present work experimentally studied the correlation of parameters Leidenfrost temperature, cooling capacity and machining temperatures, applied to the SAE 52100 steel turning process with conventional lubri-coolant, with different cutting fluids. The thermal properties of the studied cutting fluids were taken from the technical-scientific literature. An experimental apparatus was developed for measuring and acquiring machining temperatures. Synthetic cutting fluids were shown to have a better cooling capacity, followed by semi-synthetic fluids and emulsions. There is no relationship between Leidenfrost temperature, cooling capacity, and machining temperatures for the different types of cutting fluids studied.
flux-data-qaqc: A Python Package for Energy Balance Closure and Post-Processing of Eddy Flux Data

Eddy Covariance (EC) systems provide continuous direct measurements of turbulent and radiative heat and energy fluxes between the atmosphere and land surface (Baldocchi et al., 1988). Evapotranspiration (ET) estimates from EC systems are highly valuable for many scientific disciplines, water policy decisions, and operational applications such as irrigation management. Although the EC technique is considered accurate under certain atmospheric and spatial conditions, inherent theoretical and practical limitations of the technique are magnified by data uncertainty from post-processing methods. Error can result from the well-documented energy balance closure problem resulting from inherent limitations in the technique but also from data processing errors, instrumentation errors, and other sources (Foken, 2008; Stoy et al., 2013). Several approaches for post-processing and correcting EC-based ET estimates exist, notably those implemented by major flux measurement networks and datasets like FLUXNET2015 (Pastorello et al., 2020). However, post-processing routines often differ between individual site teams, data networks, and end users, and in some cases post-processing decisions are difficult to reproduce.
Anticipating Attention: On the Predictability of News Headline Tests

Abstract Headlines play an important role in both news audiences' attention decisions online and in news organizations’ efforts to attract that attention. A large body of research focuses on developing generally applicable heuristics for more effective headline writing. In this work, we measure the importance of a number of theoretically motivated textual features to headline performance. Using a corpus of hundreds of thousands of headline A/B tests run by hundreds of news publishers, we develop and evaluate a machine-learned model to predict headline testing outcomes. We find that the model exhibits modest performance above baseline and further estimate an empirical upper bound for such content-based prediction in this domain, indicating an important role for non-content-based factors in test outcomes. Together, these results suggest that any particular headline writing approach has only a marginal impact, and that understanding reader behavior and headline context are key to predicting news attention decisions.
Facial Age Estimation Using Convolutional Neural Networks Based on Inception Modules

- Automatic age estimation has been used in many social network applications, practical commercial applications, and human – computer interaction visual-surveillance biometrics. However, it has rarely been explored. In this paper, we propose an automatic age estimation system, which includes face detection and convolutional deep learning based on an inception module. The latter is a 22-layer-deep network that serves as the particular category of the inception design. To evaluate the proposed approach, we use 4,000 images of eight different age groups from the Adience age dataset. k-fold cross-validation (k = 5) is applied. A comparison of the performance of the proposed work and recent related methods is presented. The results show that the proposed method significantly outperforms existing methods in terms of the exact accuracy and off-by-one accuracy. The off-by-one accuracy is when the result is off by one adjacent age label to the above or below. For the exact accuracy, the age label of “60+” is classified with the highest accuracy of 76%.
Rebalancing Strategy for Bike-Sharing Systems Based on the Model of Level of Detail

Traveling by bike-sharing systems has become an indispensable means of transportation in our daily lives because green commuting has gradually become a consensus and conscious action. However, the problem of “difficult to rent or to return a bike” has gradually become an issue in operating the bike-sharing system. Moreover, scientific and systematic schemes that can efficiently complete the task of rebalancing bike-sharing systems are lacking. This study aims to introduce the basic idea of the k-divisive hierarchical clustering algorithm. A rebalancing strategy based on the model of level of detail in combination with genetic algorithm was proposed. Data were collected from the bike-sharing system in Ningbo. Results showed that the proposed algorithm could alleviate the problem of the uneven distribution of the demand for renting or returning bikes and effectively improve the service from the bike-sharing system. Compared with the traditional method, this algorithm helps reduce the effective time for rebalancing bike-sharing systems by 28.3%. Therefore, it is an effective rebalancing scheme.