Miracle-dz/UniFAR-FTUs huggingface dataset:
Post‐fledging interactions between the Common Cuckoo
 Cuculus canorus
 and its cavity‐nesting Common Redstart
 Phoenicurus phoenicurus
 host

Brood parasite–host interactions during the incubation and nestling stages have been well studied, but the post‐fledging period remains virtually unknown. Using radiotracking, we provide the first detailed data on post‐fledging interactions between the Common Cuckoo Cuculus canorus and its only regular cavity‐nesting host, the Common Redstart Phoenicurus phoenicurus. Cuckoos raised alone (‘solitary’) fledged at higher mass, with higher wing and tarsus length and started to fly at a younger age than Cuckoos raised alongside young Redstarts (‘mixed’). However, a further 23 fledging and post‐fledging parameters measured at five pre‐determined times (fledging, first‐flight, predation, starvation, independence) did not differ between solitary and mixed Cuckoos. In addition, none of the parameters measured during the post‐fledging period (growth, dispersal distances, number of flights) differed between solitary and mixed Cuckoos. Redstart fledglings from non‐parasitized broods (‘solitary’) showed generally similar fledging and post‐fledging parameters to fledglings reared alongside a Cuckoo (‘mixed’). Surprisingly, there were no significant differences in post‐fledging predation rate, starvation or overall survival rates between mixed and solitary Cuckoos or mixed and solitary Redstarts. Thus, during the post‐fledging period, mixed Cuckoo fledglings successfully compensated for the poorer performance experienced during the nestling stage whereas mixed and solitary Redstarts did not differ in any measured parameters. This suggests that the regular occurrence of mixed broods in this host–parasite system – which is unique among the many Cuckoo hosts – is evolutionarily stable for both hosts and parasites.
How Do You Categorize Yourself as a Sports Fan?: A New Scale of Sports Fan Social–Personal Identity Salience (FSPIS) and Its Consequences

Based on social identity, self-categorization, and optimal distinctiveness theories, this article argues that fans of team sports clubs (TSCs) may position themselves as members of their TSCs or as unique, individual sports fans. To date, no published, validated instrument has been designed to measure differences in sports fans’ personal and social identity orientation. We conducted three studies to test the validity and reliability of the Fan Social–Personal Identity Salience (FSPIS) scale. In the first two, exploratory factor analysis and confirmatory factor analysis were performed. The third study used a structural equation model to test the consequences of the new scale in an extension of the test of its nomological network validity. The data for the current research was collected using three distinct and separate empirical surveys of professional basketball fans in Israel. Our findings show that there is a moderate correlation between social and personal identities, indicating that a low level of social identity is not the same as personal identity. The FSPIS scale predicted fan optimism and satisfaction, which, in turn, partially mediated the effect on involvement. The uniqueness of the proposed scale is that it is a continuous scale that is able to capture mixed identity salience and variations in its magnitude.
Numerical Simulation of Single-Droplet Dynamics, Vaporization, and Heat Transfer from Impingement onto Static and Vibrating Surfaces

A numerical study is presented to examine the behavior of a single liquid droplet initially passing through air or steam, followed by impingement onto a static or vibrating surface. The fluid dynamic equations are solved using the Volume of Fluid method, which includes both viscous and surface tension effects, and the possibility of droplet evaporation when the impact surface is hot. Initially, dynamic behavior is examined for isothermal impingement of a droplet moving through air, first without and then with boundary vibration. Isothermal simulations are used to establish how droplet rebound conditions and the time interval between initial contact to detachment vary with droplet diameter for droplet impingement onto a stationary boundary. Heat transfer is then assessed for a liquid droplet initially at saturation temperature passing through steam, followed by contact with a hot vibrating boundary, in which droplet evaporation commences. The paper shows that, for droplet impingement onto a static boundary, the minimum impact velocity for rebound reduces linearly with droplet diameter, whereas the time interval between initial contact and detachment appears to increase linearly with droplet diameter. With the introduction of a vibrating surface, the minimum relative impact velocity for isothermal rebound is found to be higher than the minimum impact velocity for static boundary droplet rebound. For impingement onto a hot surface, in which droplet evaporation commences, it is shown that large-amplitude surface vibration reduces heat transfer, whereas low-amplitude high-frequency vibration appears to increase heat transfer.
A Mean Convolutional Layer for Intrusion Detection System

The significant development of Internet applications over the past 10 years has resulted in the rising necessity for the information network to be secured. An intrusion detection system is a fundamental network infrastructure defense that must be able to adapt to the ever-evolving threat landscape and identify new attacks that have low false alarm. Researchers have developed several supervised as well as unsupervised methods from the data mining and machine learning disciplines so that anomalies can be detected reliably. As an aspect of machine learning, deep learning uses a neuron-like structure to learn tasks. A successful deep learning technique method is convolution neural network (CNN); however, it is presently not suitable to detect anomalies. It is easier to identify expected contents within the input flow in CNNs, whereas there are minor differences in the abnormalities compared to the normal content. This suggests that a particular method is required for identifying such minor changes. It is expected that CNNs would learn the features that form the characteristic of the content of an image (flow) rather than variations that are unrelated to the content. Hence, this study recommends a new CNN architecture type known as mean convolution layer (CNN-MCL) that was developed for learning the anomalies’ content features and then identifying the particular abnormality. The recommended CNN-MCL helps in designing a strong network intrusion detection system that includes an innovative form of convolutional layer that can teach low-level abnormal characteristics. It was observed that assessing the proposed model on the CICIDS2017 dataset led to favorable results in terms of real-world application regarding detecting anomalies that are highly accurate and have low false-alarm rate as opposed to other best models.
The Impact of Fee and Liability Waivers on Patronage of Rattray Park in Ghana

The study assessed the relationship between fee and liability waivers introduced at Rattray Park vis-a-vis its patronage by low income earners within the Kumasi Metropolis. Barriers to park use as well as safety awareness were also analysed. The research design adopted as a guide was the survey study method. In all, 141 revellers were contacted through questionnaire administration within a 3-day period during the programme. Indepth Interview (IDI) schedules were also conducted with key stakeholders who were incharge of organising the waiver programmes. Quantitative data were analyzed using SPSS while qualitative data were manually transcribed. The study revealed that low income earners took advantage of the waiver programmes (attested by 44% of respondents) with p-value of 0.034 to make use of the park. Similarly, about 70% of respondents consideredinability to afford the cost of entry tickets a factor which hindered their recreational use of the park. The fee waiver concept was noted in the study as well-intended and a strategic recommendation suggested by park-goers signaled the need for boosting recreational activities in the park to engage visitors all day long for a richer experience. Keywords: Fee Waivers, Liability Waivers, Revellers, Rattray Park, Low Income Earners
A cellular automata approach of urban sprawl simulation with Bayesian spatially-varying transformation rules

ABSTRACT Incorporating spatial nonstationarity in urban models is essential to accurately capture its spatiotemporal dynamics. Spatially-varying coefficient methods, e.g. geographically weighted regression (GWR) and the Bayesian spatially-varying coefficient (BSVC) model, can reflect spatial nonstationarity. However, GWR possess weak ability eliminating the negative effects of non-constant variance because the method is sensitive to data outliers and bandwidth selection. We proposed a new cellular automata (CA) approach based on BSVC for multi-temporal urban sprawl simulation. With case studies in Hefei and Qingdao of China, we calibrated and validated two CA models, i.e. CABSVC and CAGWR, to compare their performance in simulating urban sprawl from 2008 to 2018. Our results demonstrate that CABSVC outperformed CAGWR in terms of FOM by ~2.1% in Hefei and ~3.6% in Qingdao during the calibration stage, and showed more accuracy improvement during the validation stage. The CABSVC model simulated urban sprawl more accurately than the CAGWR model in regions having similar proximity to the existing built-up areas, especially in less developed regions. We applied CABSVC to predict urban sprawl at Hefei and Qingdao out to 2028, and the urban scenarios suggest that the proposed model shows better performance and reduced bias in reproducing urban sprawl patterns, and extends urban simulation methods by accounting for spatial nonstationarity.
Biosynthesis of zinc oxide nanoparticles using bacteria: a study on the characterization and application for electrochemical determination of bisphenol A

Abstract Biosynthesis of zinc oxide nanoparticles was carried out by two bacterial strains namely Lactococcus lactis NCDO1281(T) and Bacillus sp PTCC 1538. The produced particles were characterized using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Dispersive X-ray Spectroscopy (EDX) analyses. Pure hexagonal crystalline structure of particles was confirmed by XRD spectra and FTIR analysis confirmed the synthesis of zinc oxide. The SEM analysis revealed that zinc oxide produced by B. sp was nano-rods with an average diameter of 99 nm, whereas L. lactis produced nano-spheres with diameter sizes in the range of 55–60.5 nm. Electrochemical sensors based on the biosynthesized nanoparticles and modified carbon paste electrodes were developed for determination of bisphenol A, among which zinc oxide from B.sp exhibited the better performance than the other one. Bisphenol A (BPA) oxidation peak current was about 144.849 µA at the surface of the electrode which was modified with these nanoparticles that is about 5 times more than that of bare electrode. In order to evaluate the stability of nanoparticles, the powders were tested again after six months, and this time the ZnO nanoparticles produced by L. lactis had a better performance than the zinc oxide from B.sp. BPA oxidation peak current was about 75.82 µA at the surface of the electrode that is about 2.7 times more than that of bare electrode.
Community attachment and emotional well-being: an empirical study of an online community for people with diabetes

PurposeThe purpose of this study is to investigate how community attachment to an OHC reduces the OHC users' emotional distress and therefore improves their emotional well-being.Design/methodology/approachA survey study was conducted in one of the largest online health communities (OHC) for people with diabetes.FindingsOHC participants are likely to experience reduced emotional distress when they have developed an attachment to the community. This attachment is, in turn, positively associated with the normative expectations of reciprocity and the affective feeling of gratitude. However, some commonly used behavioral measures of community participation, such as visit frequency and membership tenure, have little to do with either community attachment or reduced emotional distress.Research limitations/implicationsThe research highlights the pivotal role of community attachment in appraising the much-debated benefits of OHCs. However, the cross-sectional survey study has its limitations in terms of establishing causality.Practical implicationsOHC managers need to look beyond some of the commonly used metrics, such as monthly visits and number of new postings, and focus on fostering a sense of attachment among existing users in order to fulfill the OHC's potential of emotional support. Our study implies that design features facilitating reciprocation and gratitude expression among users can lead to a strong emotional bond.Originality/valueThis is one of the first studies on the antecedents of community attachment and the relationship between community attachment and emotional distress in the context of OHC.
Electromagnetic Drift Waves at Substorm Onset: Theory and Observations

The onset of a modest substorm observed by THEMIS is studied and interpreted using a new model of current sheet instability. The wave‐like structuring of the first observed auroral arc is characterized using auroral imagers. Similarly, the magnetic fluctuations measured by the THEMIS satellites, located in the near‐Earth current sheet (10 RE) and magnetically connected to the arc, are determined. Both sets of measurements show that oscillatory components with periods from 7 to 20 s are detected at the very early phase of the onset. This is compared with the predictions of a recent theoretical model (Tsareva et al., 2019, https://doi.org/10.1017/S002237781900028X) that describes the kinetic instability of electromagnetic drift waves in 2‐D Harris sheet configurations. They are in a reasonable agreement with the observations since the measured magnetic fluctuations are characterized by frequencies, growth rates, and polarization that are consistent with the model. It is also remarkable that the oscillations are observed when the ion perpendicular velocity (drift velocity) exceeds 90–100 km/s. The theory indeed shows that the drift is the main trigger of the instability, with a possible threshold at 80–100 km/s. This suggests that above a threshold of magnetic stress, and thus ion drift, the current sheet equilibrium is affected by the generation of electromagnetic drift waves.
Effect of Natural Forest Fires on Regional Weather Conditions in Siberia

Effects of forest fires on regional weather conditions were analyzed for Central and Eastern Siberia after warm and dry weather conditions in summer 2019 using COSMO-Ru (COnsortium for Small-scale MOdeling; Ru—Russia) and COSMO-RuART (ART—Aerosols and Reactive Trace gases) model systems. Four series of numerical experiments were conducted (one control experiment and three forest fire experiments assuming total vegetation destruction within the burned areas) to evaluate possible effects of forest fires on surface albedo and vegetation properties as well as their influence on air chemistry and aerosol concentration in the atmosphere. The modeling results showed significant influence of forest fires on regional weather conditions that occurred over large areas situated even away from burnt regions. Decreased surface albedo and reduced latent heat fluxes due to fire-induced destruction of forest cover lead to higher near-surface air temperature and lower air humidity in both burned and surrounding unburned forest areas. On the other hand, reduced incoming solar radiation due to smoke from forest fire plumes decreased land surface temperatures and increased thermal atmospheric stability resulting in reduced regional precipitation.
Current and Future Trends and Issues Facing Technology and Engineering Education in the United States

Determining trends and issues is important for the health of any profession. The purpose of this research was to determine the current and future trends and issues facing technology and engineering education (TEE) in the United States (U.S.). The researchers used a three-round Modified-Delphi method to solicit information from technology and engineering education stakeholders across the U.S. In the first round, participants listed what they felt were current trends, future trends, current issues, and future issues facing TEE. The second round was designed to prioritize trends and issues. In the third round, participants were presented with one table for each current and future trend and issue and asked to identify if they felt each was essential or non-essential for technology and engineering leaders to address. Two hundred sixty-eight participants responded in the third round, and the resulting trends and issues were categorized into eight themes: 1) teacher shortage; 2) secondary and university TEE programs; 3) funding programs and teachers; 4) curriculum; 5) technology and engineering education identity and relevance; 6) collaborative efforts; 7) teacher certification and development; and 8) student-centered foci.
Temporal dynamics in the effectiveness of seed dispersal by birds visiting a tropical tree

Abstract: Seed dispersal effectiveness (SDE) is related to the role of seed dispersal agents in realizing the reproductive potential of plants through seed dispersal and subsequent plant recruitment. The SDE of a given seed disperser may vary spatially and temporally, with important implications for our understanding of the mutualistic relationships involving plants and frugivores. Here we observed 22 frugivorous bird species visiting an individual tree (Cabralea canjerana) over a 9-y period in a fragment of Atlantic forest in south-eastern Brazil to document the temporal variation in SDE. The quantitative (that takes into account the frequency of feeding visits to the focal plant and the number of fruits removed per visit) and qualitative (that considers the probability of seed deposition on a suitable site for plant recruitment) components of SDE varied in two and one order of magnitude both among bird species and among years within bird species, respectively. As a result, the SDE of bird species fluctuates over the years, especially for a migratory species that is the main seed disperser of C. canjerana (Chivi vireo Vireo chivi) whose quantitative component (i.e. the product of visit and fruit intake rates) varied from 3.0 to 7.1. This study illustrates the dynamic nature of the seed-dispersal environment to which a plant is subjected to over the course of its life, which has important consequences for the plant's individual fitness.
Laryngeal Squamous Cell Carcinoma: Potential Molecular Mechanism and Prognostic Signature Based on Immune-Related Genes

Background Immune-related genes (IRGs) are closely related to the incidence and progression of tumors, potentially indicating that IRGs play an important role in laryngeal squamous cell carcinoma (LSCC). Material/Methods An RNA sequencing dataset containing 123 samples was collected from The Cancer Genome Atlas. Based on immune-related differentially expressed genes (IRDEGs), a potential molecular mechanism of LSCC was explored through analysis of information in the Gene Ontology (GO) resource and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interactions (PPIs). A regulatory network of transcriptional regulators and IRDEGs was constructed to explore the underlying molecular mechanism of LSCC at the upstream level. Candidates from IRDEGs for signature were screened via univariate Cox analysis and using the least absolute shrinkage and selection operator (LASSO) technique. The IRDEG signature of LSCC was constructed by using a multivariate Cox proportional hazards model. Results GO and KEGG analysis showed that IRDEGs may participate in the progression of LSCC through immune-related reactions. PPI analysis demonstrated that, among the IRDEGs in LSCC, the Kininogen 1; C-X-X motif chemokine ligand 10; elastase, neutrophil expressed; and LYZ genes are hub genes in the development of LSCC. At the upstream level, SPI1, SP140, signal transducer and activator of transcription 4, zinc finger E-box binding homeobox, and Ikaros family zinc finger 2 are the hub transcriptional regulators of IRDEGs. The risk score based on the IRDEG signature was able to distinguish prognosis in patients with LSCC and represents an independent prognostic risk factor for LSCC. Conclusions From the perspective of IRGs, we first constructed an IRDEG signature related to the prognosis of LSCC, which can be used as a novel marker to predict prognosis in patients with LSCC.
A Preprocessing Strategy for Denoising of Speech Data Based on Speech Segment Detection

In this paper, we propose a preprocessing strategy for denoising of speech data based on speech segment detection. A design of computationally efficient speech denoising is necessary to develop a scalable method for large-scale data sets. Furthermore, it becomes more important as the deep learning-based methods have been developed because they require significant costs while showing high performance in general. The basic idea of the proposed method is using the speech segment detection so as to exclude non-speech segments before denoising. The speech segmentation detection can exclude non-speech segments with a negligible cost, which will be removed in denoising process with a much higher cost, while maintaining the accuracy of denoising. First, we devise a framework to choose the best preprocessing method for denoising based on the speech segment detection for a target environment. For this, we speculate the environments for denoising using different levels of signal-to-noise ratio (SNR) and multiple evaluation metrics. The framework finds the best speech segment detection method tailored to a target environment according to the performance evaluation of speech segment detection methods. Next, we investigate the accuracy of the speech segment detection methods extensively. We conduct the performance evaluation of five speech segment detection methods with different levels of SNRs and evaluation metrics. Especially, we show that we can adjust the accuracy between the precision and recall of each method by controlling a parameter. Finally, we incorporate the best speech segment detection method for a target environment into a denoising process. Through extensive experiments, we show that the accuracy of the proposed scheme is comparable to or even better than that of Wavenet-based denoising, which is one of recent advanced denoising methods based on deep neural networks, in terms of multiple evaluation metrics of denoising, i.e., SNR, STOI, and PESQ, while it can reduce the denoising time of the Wavenet-based denoising by approximately 40–50% according to the used speech segment detection method.