Miracle-dz/UniFAR-FTUs huggingface dataset:
An ecotourist by whose standards? Developing and testing the Ecotourist Identification Scale (EIS)

ABSTRACT Despite numerous attempts at operationalising ecotourism, none have explicitly incorporated the three tenets of ecotourism (i.e. nature, education, and sustainability) into scale development with the purpose of identifying ecotourists from non-ecotourists. Therefore, we used a combination of Churchill and Rossiter’s scale development frameworks to create the Ecotourist Identification Scale (EIS) as a tool for identifying ecotourists based on these three criteria. We split a sample of 786 U.S. travellers in half to perform a Principal Components Analysis for scale purification on one half followed by a Confirmatory Factor Analysis on the other. Results of the Confirmatory Factor Analysis indicated strong construct reliability for each component of the EIS based on factor loadings above 0.50, reliability coefficients exceeding 0.70, and Average Variance Explained above 50%. Furthermore, we employed Structural Equation Modeling to explore which aspects of ecotourism’s three tenets influence intention to engage in ecotourism by testing the relationship between EIS scale components and ecotourism intention. Results indicate interest in nature and environmental sustainability are significantly related to ecotourism intention while interest in education, socio-cultural sustainability, and economic sustainability are not, indicating discrepancies between the academic definition of ecotourism and the factors driving tourists to engage in ecotourism.
Toward an Online DoS/DDoS Classification: An Empirical Study for Network Intrusion Detection Systems

In recent years, Distributed Denial of Services (DDoS) attacks have caused significant losses to industry and government due to an increasing number of devices connected to the Internet. These devices use services-over-Internet more frequently with services characterized and provided seamlessly by 5G, Cloud and Edge Computing. According to Cisco Annual Internet Report, the frequency of DoS/DDoS attacks has increased more than 2.5 times over the last 3 years and the average size of attacks is increasing steadily and approaching 1 Gbps. Therefore, there are cyber threats continuing to grow even with the development of new protection technologies. Our work is strongly motivated from with the goal to study and evaluate four Machine Learning models toward development of an Online Network Intrusion Detection System (N-IDS). This article studies on the application on three feature selection algorithms combined with four machine learning models applied to N-IDS. We have implemented performance evaluation our proposed model on three up-to-date DoS/DDoS datasets. We have shown that Feature Importance and K-Nearest Neighbors’ algorithm (KNN) can give better results in all benchmark datasets than previous work and the empirical results of all four machine learning models and three feature selection algorithms are also presented in detail.
A compound deep learning model for long range forecasting in electricity sale.

Accurate prediction of electricity sale has a positive effect on power companies in rationally arranging power supply plans, scientifically optimizing power resource allocation, improving power management efficiency, saving energy and reducing consumption. Predicting future electricity sale based on historical electricity sale data can essentially be summarized as a time series forecasting problem. This paper proposes a fast and memory-efficient method, which adopts the expressive power of deep neural networks and the time characteristics of sequence-to-sequence structure (parallel convolution and recurrent neural network) for long range forecasting in electricity sale. Through a large number of experiments and evaluation of real-world datasets, the effectiveness of the proposed method is proved and verified in terms of prediction accuracy, time consuming and training speed.
Potential Use of the Pigments from Scytalidium cuboideum and Chlorociboria aeruginosa to Prevent ‘Greying’ Decking and Other Outdoor Wood Products

UV-light degradation of wood is one of the top reasons for consumer replacement of outdoor wooden structures. This type of degradation is seldom mechanical, and is instead often motivated by loss of aesthetics (graying). There are numerous commercial products available on the market that deal with this loss of color, many of which contain added pigments to ‘rejuvenate’ or ‘revitalize’ greyed wood. These pigments are almost uniformly synthetic. In contrast, pigments from wood decay fungi (spalting), which have been used in woodworking since the 1400s (intarsia), have remarkable optical (UV-light resistance) properties due to their naphthoquinonic configuration. In recent years the pigments made from these fungi have been extracted and tested across numerous substrates, from solar cells to textile dyes. In this work, researchers extracted pigments from Scytalidium cuboideum (red pigmentation) and Chlorociboria aeruginosa (blue-green pigmentation), solubilized the pigments in raw linseed oil, and tested the resulting solution on samples of Douglas-fir (Pseudotsuga menziesii) and western white pine (Pinus monticola). These mixtures were compared against a ‘stain and coat’ treatment (utilizing an aniline stain and coated with raw linseed oil), raw linseed oil, and untreated wood. The wood samples were then placed in an accelerated weathering machine (Q-UV) following the ASTM G154 standard, for 500 and 1000 h. The results showed that while no visible color change occurred to the wood when the pigmented oil was applied, the red pigment oil significantly lowered the coating degradation for both wood types at an exposure of 500 h. The results show the potential applications for fungal pigments in the wood coating industry, as it offers an increased coating service life. As there is a shift to renewable products, the pigments from wood decay fungi show potential as additives for wood coatings.
Comparative leaf anatomy in Ptilochaetoids clade (Malpighiaceae): A taxonomic and phylogenetic approach

In molecular phylogeny of Malpighiaceae, the Ptilochaetoide clade is composed of Dinemagonum, Dinemandra, Lasiocarpus and Ptilochaeta, found in the sub-desert and desert regions of South and Central America. The taxonomic relationship between the genera of the group is little discussed and phylogenetic studies point out to the need for an evolutionary approach using morphoanatomical characters. Thus, based on the recurrent contribution of leaf anatomy to taxonomic studies in Malpighiaceae, we characterized the leaf anatomy of seven species of the Ptilochaetoide clade and listed relevant anatomical patterns for their circumscription. For this, we used standard anatomical techniques and the anatomical data were discussed phylogenetically. The anatomical data such as: petiole/midrib contour, petiole/midrib vascular system conformation, presence/absence of trichomes and mesophyll organization were useful for the elaboration of the identification key. Character states: Plane-convex midrib contour, isobilateral mesophyll and absence of trichomes were considered informative for the subclade composed by Dinemandra and Dinemagonum, while in the other genera, no relevant morphotypes were recognized. The absence of accessory bundles, presence of druses and sheath extension are common features in the clade and contribute to the group description. These findings reaffirm the effectiveness of leaf anatomy in taxonomic and phylogenetic studies in Malpighiaceae.
Image Compression Based on Deep Learning: A Review

Image compression is an essential technology for encoding and improving various forms of images in the digital era. The inventors have extended the principle of deep learning to the different states of neural networks as one of the most exciting machine learning methods to show that it is the most versatile way to analyze, classify, and compress images. Many neural networks are required for image compressions, such as deep neural networks, artificial neural networks, recurrent neural networks, and convolution neural networks. Therefore, this review paper discussed how to apply the rule of deep learning to various neural networks to obtain better compression in the image with high accuracy and minimize loss and superior visibility of the image. Therefore, deep learning and its application to different types of images in a justified manner with distinct analysis to obtain these things need deep learning.
Influence of Service Innovations on Profit Growth: a Case of Motor Vehicle Retailers

Organisations implement innovation strategies to remain competitive and achieve improved business performance. Previous studies have focused more on product and technology innovations. The purpose of this article is to determine the influence of service innovations on the financial performance of organisations – specifically profit growth – using the theory of competitive advantage. A quantitative research method was employed to achieve the research objective, with linear regression analysis used to analyse the primary data. Cronbach values were calculated to determine the reliability and validity of the research instrument used to collect the primary data. The findings of the research on which this article is based, confirm the existence of a positive relationship between service innovation practices and profit growth. Recommendations are made to key stakeholders in the motor vehicle industry, and future research directions are outlined.
Efficient Multiple-Precision Posit Multiplier

Posit number system has been recently widely applied in many fields of applications. For different applications, the precision requirements are usually different. In addition, the transprecision computing paradigm, which is proposed for energy efficient computation, even requires different precision in each computation step. To support computations of various precision in a single hardware architecture, in this paper, a unified architecture of multiple-precision posit multiplier is proposed. The proposed posit multiplier supports the commonly used Posit(8, 0), Posit(16, 1), and Posit(32, 2) formats, where one Posit(32, 2), or two parallel Posit(16, 1), or four parallel Posit(8, 0) multiplications can be accomplished each time. Each module of the proposed posit multiplier is carefully tailored for resource sharing among three supported precision formats. Compared to the Posit(32, 2) multiplier, the proposed multiple- precision multiplier adds the support for parallel low-precision posit multiplications with only 12.8% more area and 15.4% more power. The proposed architecture can be used in posit-enabled general-purpose processor designs.
Dielectric investigation of the Sr3WO6 double perovskite at RF/microwave frequencies

This work reports the dielectric properties of Sr3WO6 (SWO) double perovskite at radio frequencies as a function of the temperature and a study in microwaves range to evaluate the material as a novel dielectric resonator. Thermo-activated charge transfer process for SWO ceramic was observed and two resistor–capacitor associations were fitted for the Nyquist diagram. SWO presented negative values of temperature coefficient of capacitance (TCC) and the activation energies of dielectric relaxation processes were measured by conductivity (1.35 eV), imaginary modulus (1.38 eV) and imaginary impedance (1.36 eV). For the microwave range, SWO shows high dielectric permittivity (13.57) and dielectric loss (0.0281). The dielectric resonator antenna (DRA) fabricated from SWO ceramic presented an operation frequency in 4.1 GHz and return loss below −40 dB. It was possible to evaluate the performance of the antenna from numerical simulation. From these results, the SWO based DRA shows good properties to be used as a novel microwave dielectric resonator.
Non‐stationary drivers on fish sampling efforts in Brazilian freshwaters

Closing knowledge gaps in species taxonomy and distribution (i.e. Linnean and Wallacean shortfalls, respectively) require spatially distributed high‐quality data. However, studies on terrestrial taxa have shown that occurrence data are biased owing to higher sampling efforts towards areas with greater accessibility, research infrastructure and attractiveness. Here, we tested whether these biasing factors are also important drivers of freshwater fish species research by assessing fishes’ sampling efforts in Brazil, a continental‐sized and megadiverse country. We hypothesized that the influence of biasing factors is scale‐dependent, with differential effects across regions, that is that they present non‐stationarity.
Ion properties in a Hall current thruster operating at high voltage

Operation of a 5 kW-class Hall current Thruster for various voltages from 400 V to 800 V and a xenon mass flow rate of 6 mg s−1 have been studied with a quasi-neutral hybrid model. In this model, anomalous electron transport is fitted from ion mean velocity measurements, and energy losses due to electron–wall interactions are used as a tuned parameter to match expected electron temperature strength for same class of thruster. Doubly charged ions production has been taken into account and detailed collisions between heavy species included. As the electron temperature increases, the main channel of Xe2+ ion production becomes stepwise ionization of Xe+ ions. For an applied voltage of 800 V, the mass utilization efficiency is in the range of 0.8–1.1, and the current fraction of doubly charged ions varies between 0.1 and 0.2. Results show that the region of ion production of each species is located at the same place inside the thruster channel. Because collision processes mean free path is larger than the acc.
Quantitative genetics of extreme insular dwarfing: The case of red deer on Jersey

The Island Rule—that is, the tendency for body size to decrease in large mammals and increase in small mammals on islands has been commonly evaluated through macroecological or macroevolutionary, pattern‐orientated approaches, which generally fail to model the microevolutionary processes driving either dwarfing or gigantism. Here, we seek to identify which microevolutionary process could have driven extreme insular dwarfism in the extinct dwarf red deer population on the island of Jersey.
“Marriage was not an option”: ethnoreligious mixed marriage in Israel

ABSTRACT This study explores the way Jewish women negotiate their crossing ethnoreligious borders in a deeply divided society through 15 in-depth interviews. Despite social, economic and political exclusion of Palestinians in Israel, in certain circumstances, the ethnic and religious borders are weakening. The core findings have led to three conclusions. First, ethnic mixed marriage occurs between Palestinian men and Jewish women from diverse groups with heterogeneity in their socioeconomic features, which places in doubt previous discourse that this kind of marriage appears among people from marginalized groups in Israel. Second, the ethnic hierarchical relationship between the two groups carries over into their intimate relationships. Third, the rigid borders of ethnoreligious belonging to the Israeli society and the extent of belonging to the Jewish mainstream differ among Jewish immigrants from different origins, produce different types of negotiations among the Jewish women while they are crossing the ethnoreligious borders by marrying Palestinian men.
Supplemental state observer‐based sliding mode control for a dynamic system

A supplemental state observer-based sliding mode control (SSOBSMC) for a dynamic system is proposed in this paper. First, a supplemental state vector is formulated including system output and supplemental output. A supplemental state observer (SSOB) is proposed to estimate the unavailable states. Furthermore, the SSOBSMC consisting of SSOB and sliding mode control (SMC) is proposed to perform state estimation and robust tracking control for the dynamic system with some unavailable states. To demonstrate the application of the proposed methods, a mass-spring-damper (MSD) system is used as an application example to perform numerically. From the simulation results, the SSOB shows a good ability for state estimation, while the SSOBSMC simultaneously demonstrates accuracy in state estimation ability as well as a robust tracking control performance for the non-linear MSD system. The major contributions of this paper are the proposed SSOB that can accurately estimate the unavailable states for the non-linear dynamic system with time-varying disturbances; and the SSOBSMC, which simultaneously displays accuracy in state estimation ability and robust tracking control performance.
Attached to Virtual Dreams

Abstract Maladaptive daydreaming describes excessive fantasy activity that interferes with an individual's life. Surprisingly, the precursors of maladaptive daydreaming and its role in excessive involvement in virtual worlds have been scarcely investigated. In the current study, we examined the relationships among attachment styles, maladaptive daydreaming, and problematic social media use (PSMU) in a sample of community-dwelling adults. Eight hundred seventy-seven participants between 18 and 68 years old were recruited via an online survey and asked to fill out self-reported measures on attachment styles, maladaptive daydreaming, and PSMU. Mediation analyses showed that maladaptive daydreaming is a significant mediator in the relationships between preoccupied and fearful attachment styles and PSMU, suggesting that maladaptive daydreaming partly explains the established link between insecure attachment styles and excessive use of social media. Individuals with PSMU fostered by maladaptive daydreaming may benefit from clinical interventions that promote the use of adaptive regulatory strategies to develop feelings of security and self-confidence that may serve to reduce the excessive involvement in social media.
Soil Aggregate Stability: Its Evaluation and Relation to Organic Matter Constituents and Other Soil Properties

The purpose of this study were: 1) to compare the aggregate stability of individual aggregate size fractions, 2) to determine the interrelationship and efficiency of several aggregate stability indices, and 3) to determine the relationship and importance of organic matter and other soil constituents on aggregate stability. To compare the aggregate stability of individual aggregate size fractions, a mathematical model was developed to estimate the breakdown of individual aggregate size fractions in the wet-sieving (using nested sieves) method. This model was validated and calibrated by comparing the estimation values to the actual aggregate breakdown values by paired sample t-test, linear regression and prediction error sum of squares. The average percentage of stable aggregates for all aggregate size fractions were represented in an index called average intact aggregates (AlA). Factor analysis was used to determine the interrelationship and efficiency of several aggregate stability indices. Aggregate stability of eight soil samples were measured with eight indices: percentage of water-stable aggregates >0.3 mm (WSA >0.3) and >0.5 mm (WSA >0.5), AlA, water-dispersible clay (WDC), waterdispersible clay and silt (WDCS), mean weight diameter after wet-sieving (MWDw), turbidity percentage (TP), and clay ratio (CR).
A multi‐class COVID‐19 segmentation network with pyramid attention and edge loss in CT images

Abstract At the end of 2019, a novel coronavirus COVID‐19 broke out. Due to its high contagiousness, more than 74 million people have been infected worldwide. Automatic segmentation of the COVID‐19 lesion area in CT images is an effective auxiliary medical technology which can quantitatively diagnose and judge the severity of the disease. In this paper, a multi‐class COVID‐19 CT image segmentation network is proposed, which includes a pyramid attention module to extract multi‐scale contextual attention information, and a residual convolution module to improve the discriminative ability of the network. A wavelet edge loss function is also proposed to extract edge features of the lesion area to improve the segmentation accuracy. For the experiment, a dataset of 4369 CT slices is constructed, including three symptoms: ground glass opacities, interstitial infiltrates, and lung consolidation. The dice similarity coefficients of three symptoms of the model achieve 0.7704, 0.7900, 0.8241 respectively. The performance of the proposed network on public dataset COVID‐SemiSeg is also evaluated. The results demonstrate that this model outperforms other state‐of‐the‐art methods and can be a powerful tool to assist in the diagnosis of positive infection cases, and promote the development of intelligent technology in the medical field.
Automated Residential Energy Audits Using a Smart WiFi Thermostat-Enabled Data Mining Approach

Smart WiFi thermostats, when they first reached the market, were touted as a means for achieving substantial heating and cooling energy cost savings. These savings did not materialize until additional features, such as geofencing, were added. Today, average savings from these thermostats of 10–12% in heating and 15% in cooling for a single-family residence have been reported. This research aims to demonstrate additional potential benefit of these thermostats, namely as a potential instrument for conducting virtual energy audits on residences. In this study, archived smart WiFi thermostat measured temperature data in the form of a power spectrum, corresponding historical weather and energy consumption data, building geometry characteristics, and occupancy data were integrated in order to train a machine learning model to predict attic and wall R-Values, furnace efficiency, and air conditioning seasonal energy efficiency ratio (SEER), all of which were known for all residences in this study. The developed model was validated on residences not used for model development. Validation R-squared values of 0.9408, 0.9421, 0.9536, and 0.9053 for predicting attic and wall R-values, furnace efficiency, and AC SEER, respectively, were realized. This research demonstrates promise for low-cost data-based energy auditing of residences reliant upon smart WiFi thermostats.
Athermal electric field effects in flash sintered zirconia

We propose an innovative methodology to quantify the dependence of grain growth with respect to current density during flash sintering of zirconia (3YSZ) (field set under a nominal current limit of 150 mA mm−2. This was achieved by using a notch (0.5 mm deep and 1.5 mm wide) located in the middle of a dog-bone specimen, allowing an uneven current distribution (ranging from 3 to 670 mA mm−2) while maintaining a fairly homogeneous temperature (1420–1500 °C). Microstructure observations nearby the notch tip confirmed localised overheating. The modelling approach decoupled thermal and electrical field effects identifying the long questioned athermal electric field effects in flash sintering. Increased current density seems to weakly restrain grain growth, to accelerate densification, to promote defects healing (i.e. notch, cracks and pores) and inter-particle bond formation. These results could rationalise the athermal effects occurring during FS and flash joining.