Miracle-dz/UniFAR-FTUs huggingface dataset:
Wind power generation forecast by coupling numerical weather prediction model and gradient boosting machines in Yahyalı wind power plant

Forecasting of the wind speed and power generation for a wind farm has always been quite challenging and has importance in terms of balancing the electricity grid and preventing energy imbalance penalties. This study focuses on creating a hybrid model that uses both numerical weather prediction model and gradient boosting machines (GBM) for wind power generation forecast. Weather Research and Forecasting (WRF) model with a low spatial resolution is used to increase temporal resolutions of the computed new or existing variables whereas GBM is used for downscaling purposes. The results of the hybrid model have been compared with the outputs of a stand-alone WRF which is well configured in terms of physical schemes and has a high spatial resolution for Yahyalı wind farm over a complex terrain located in Turkey. Consequently, the superiority of the hybrid model in terms of both performance indicators and computational expense in detail is shown.
Dimensions of Motivation in Teaching: Relations with Social Support Climate, Teacher Efficacy, Emotional Exhaustion, and Job Satisfaction

This study explored how teachers' peer support climate (PSC) and supervisory support climate (SSC) were related to teacher self-efficacy (TSE), teacher job satisfaction (TJS), teacher emotional exhaustion (TEE), and motivation to quit the teaching profession (MQTP) among teachers in the Philippines. Participants were 457 teachers in the Central Visayas Region. Structural equation modeling (SEM) indicated that MQTP varies as to self-efficacy, emotional exhaustion, and job satisfaction. Responses among all constructs do not vary among novice and experienced teachers except on TJS. The findings of the research advocate the proposed model. The model can guide future researchers in developing countries like the Philippines to explain teachers’ attrition caused by social support, efficacy factors, burnout, and job satisfaction.
Cutting through the noise: reducing bias in motor adaptation analysis

During goal-directed movements, the magnitude of error correction by a person on a subsequent movement provides important insight into a person’s motor learning dynamics. Observed differences in trial-by-trial adaptation rates might indicate different relative weighting placed on the various sources of information that inform a movement, e.g. sensory feedback, control predictions, or internal model expectations. Measuring this trial-by-trial adaptation rate is not straightforward, however, since externally observed data are masked by noise from several sources and influenced by inaccessible internal processes. Adaptation to perturbation has been used to measure error adaptation as the introduced external disturbance is sufficiently large to overshadow other noise sources. However, perturbation analysis is difficult to implement in real-world scenarios, requires a large number of movement trials to accommodate infrequent perturbations, and the paradigm itself might affect the movement dynamics being observed. Here we focus on error adaptation during unperturbed and naturalistic movements. With increasing motor noise, the conventional estimation of trial-by-trial adaptation increases, a counterintuitive finding that is the consequence of systematic bias in the estimate due to noise masking the learner’s intention. We present an analytic solution relying on stochastic signal processing to reduce this effect of noise, producing an estimate of motor adaptation with reduced bias. The result is an improved estimate of trial-by-trial adaptation in a human learner compared to conventional methods. We demonstrate the effectiveness of the new method in analyzing simulated and empirical movement data under different noise conditions. The analytic approach is applicable across different types of movements in varied contexts and should replace the regression analysis method in future motor analysis studies.When a person makes a movement, a motor error is typically observed that then drives motor planning corrections on subsequent movements. This error correction provides insight into how the nervous system is operating, particularly in regard to how much confidence a person places in different sources of information such as sensory feedback or motor command reproducibility. Traditional analysis of movement has required carefully controlled laboratory conditions, limiting the usefulness of motor analysis in clinical and everyday environments. Here we present a new computational method that can be accurately applied to typical movements. Counterintuitive findings of the established approach are corrected by the proposed method. This method will provide a common framework for researchers to analyze movements while extending dynamic motor adaptation analysis capabilities to clinical and non-laboratory settings.
Enhancement of low‐fat Feta cheese characteristics using probiotic bacteria

Abstract The objective of this study was to manufacture low‐fat Feta cheese (LFC) using different types of starter cultures, such as yogurt (Y) cultures (Streptococcus thermophilus and Lactobacillus bulgaricus), bifidobacterium (B) cultures (Bifidobacterium bifidum and Bifidobacterium longum), and mixed of them (Y + B) at different rates (0.4, 0.5, and 0.6%). The Y + B cultures improved the flavor and body and texture of LFC, especially at a ratio of 0.4 + 0.6% and 0.5 + 0.5%, which is similar to the typical full‐fat Feta cheese. Also, the LFC maintained a higher number of probiotics and lactic acid bacteria after 30 d of storage at a range of 5 to 7 log cfu/g.
Applying GIS and Text Mining Methods to Twitter Data to Explore the Spatiotemporal Patterns of Topics of Interest in Kuwait

Researchers have developed various approaches for exploring the spatial information, temporal patterns, and Twitter content in topics of interest in order to generate a better understanding of human behavior; however, few investigations have integrated these three dimensions simultaneously. This study analyzes the content of tweets in order to conduct a spatiotemporal exploration of the main topics of interest in Kuwait in order to provide a deeper understanding of the topics people think about, when they think about them, and where they tweet about them. To this end, we collect, process, and analyze tweets from nearly 120 areas in Kuwait over a 10-month period. The study’s results indicate that religion, emotions, education, and public policy are the most popular topics of interest in Kuwait. Regarding the spatiotemporal analysis, people post more tweets regarding religion on Fridays, a holy day for Muslims in Kuwait. Moreover, people are more likely to tweet about policy and education on weekdays rather than weekends. In contrast, people tweet about emotional expressions more often on weekends. From the spatial perspectives, spatial clustering in topics occurs across the days of the week. The findings are applicable to further topic analysis and similar research in other countries.
Factors Influencing Innovation in Advertisement: A Study on the Perspective of Bangladesh Market

The research has been conducted to find out the factors affecting the advertising creativity in the context of Bangladesh. The research is a Conclusive Causal Research, where we tried to find factors influencing advertising creativity in the Bangladeshi market. The study was conducted through an online questionnaire survey as well as face to face interview with the hardcopy of the questionnaire, both of which involved 210 participants. An in-depth analysis was run through SPSS V25.0 to analyze the acquired data. It was found that Motivation and Divergence greatly influences Innovation in Advertisement, however, the Bangladeshi market does not intend to follow the global Trend. In addition to this, we proposed what the customer base of the market demands when it comes to brand promotions, and how they perceive the recent changes as well as what makes the promotional campaigns innovative. It was found that the younger customers tend to be more inclined towards the uniqueness, novelty, and originality of today’s advertisements, while they do not indulge in being included in the campaigns. Moreover, with the industry being more competitive and diverse, the work environment is expected to be more challenging as well as rewarding that will bring out the creativity out of the marketers.
Modified window shape‐based crossover with enhanced bandwidth

In this paper, a wideband crossover is presented based on the typical 2×2 distributed window‐shaped structure. By loading extra stepped‐impedance open stub at each port, three reflection zeros could be generated within the operating band for bandwidth enhancement, while three transmission zeros are introduced for isolation improvement. Two additional transmission zeros at through port are also generated to enhance the out‐of‐band attenuation. By properly adjusting the physical width and length of the stubs, a wideband response with equal reflection ripples can be achieved. As an experimental verification, a prototype of the designed microstrip crossover at 2.4 GHz is implemented and fabricated. The measured results have a good agreement with those obtained from simulation. The measured fractional bandwidth of 1 dB insertion loss achieves 38.7% and the measured 15 dB return loss bandwidth is about 39.2%, while the measured 15 dB isolation bandwidth reaches 40.0%.
Concatenation Technique in Convolutional Neural Networks for COVID-19 Detection Based on X-ray Images

In this paper we present a Convolutional Neural Network consisting of NASNet and MobileNet in parallel (concatenation) to classify three classes COVID-19, normal and pneumonia, depending on a dataset of 1083 x-ray images divided into 361 images for each class. VGG16 and RESNet152-v2 models were also prepared and trained on the same dataset to compare performance of the proposed model with their performance. After training the networks and evaluating their performance, an overall accuracy of 96.91%for the proposed model, 92.59% for VGG16 model and 94.14% for RESNet152. We obtained accuracy, sensitivity, specificity and precision of 99.69%, 99.07%, 100% and 100% respectively for the proposed model related to the COVID-19 class. These results were better than the results of other models. The conclusion, neural networks are built from models in parallel are most effective when the data available for training are small and the features of different classes are similar.
The Visual Features of Emotional Faces That Predict Forced Choice Selection of Faces Download Text Copy to Clipboard

Emotional facial expressions are important visual communication signals that indicate a sender’s intent and emotional state to an observer. As such, it is not surprising that reactions to different expressions are thought to be automatic and independent of awareness. What is surprising, is that studies show inconsistent results concerning such automatic reactions, particularly when using different face stimuli. We argue that automatic reactions to facial expressions can be better explained, and better understood, in terms of quantitative descriptions of their visual features rather than in terms of the semantic labels (e.g. angry) of the expressions. Here, we focused on overall spatial frequency (SF) and localized Histograms of Oriented Gradients (HOG) features. We used machine learning classification to reveal the SF and HOG features that are sufficient for classification of the first selected face out of two simultaneously presented faces. In other words, we show which visual features predict selection between two faces. Interestingly, the identified features serve as better predictors than the semantic label of the expressions. We therefore propose that our modelling approach can further specify which visual features drive the behavioural effects related to emotional expressions, which can help solve the inconsistencies found in this line of research.
Influence of the COVID-19 Pandemic on Household Food Waste Behavior in Japan

The 2019 coronavirus disease (COVID-19) pandemic had various influences on people’s ordinary lives, including their thoughts and behaviors related to food consumption. Food waste has been cited as a serious issue with environmental, social, and economic consequences. In this study, we investigated how the COVID-19 pandemic altered the social consciousness and behavior related to food waste in Japan. We conducted a nationwide online-based survey and collected a cross-sectional dataset from 1959 adult respondents. The results showed that people in regions highly impacted by the pandemic reported a clearer understanding of the situation of their household food waste, more careful food preparation and purchasing, and were more strongly influenced to change their behaviors due to COVID-19. Further analyses revealed that thoughts and behaviors related to food waste significantly differed by sociodemographic characteristics, such as gender, household size, and employment status. This study also implied that the COVID-19 pandemic encouraged some improvements in peoples’ behaviors and thoughts with regard to food, such as paying attention to food waste, making efforts to reduce food waste, and attempting cooking by themselves at home.
Sociodemographic Predictors of Changes in Physical Activity, Screen Time, and Sleep Among Toddlers and Pre-Schoolers in Chile During the COVID-19 Pandemic

The aim was to examine the sociodemographic predictors associated with changes in movement behaviours (physical activity, screen time and sleep) among toddlers and pre-schoolers during the early stages of the coronavirus disease 2019 pandemic in Chile. Caregivers of 1- to 5-year-old children completed an online survey between March 30th and April 27th, 2020. Information about the child's movement behaviours before (retrospectively) and during the pandemic, as well as family characteristics were reported. In total, 3,157 participants provided complete data (mean children age: 3.1±1.38 years). During early stages of the pandemic, time spent in physical activity decreased, recreational screen time and sleep duration increased, and sleep quality declined. Toddlers and pre-schoolers with space to play at home and living in rural areas experienced an attenuated impact of the pandemic restrictions on their physical activity levels, screen time, and sleep quality. Older children, those whose caregivers had a higher educational level, and children living in apartments had greater changes, mainly a decrease in total physical activity and increase in screen time. This study has shown the significant impact of the pandemic restrictions on movement behaviours in toddlers and pre-schoolers in Chile.
J-aggregate Formation and Thermal Decay Kinetics of Photoinduced Merocyanine in Bilayer Membrane as a Function of the Mixing Ratio

It has been known that photomerocyanine (PMC) compounds having two long alkyl chains form aJ-aggregate (head-to-tail) or H-aggregate (side-by-side) in bilayer membranes depending on the length and position of the introduced alkyl chain in the spiropyran moiety. The objective of this study is to obtain definiteinformation about the relationship between the formation and thermal decaykinetics forPMC in bilayermembranes, focusing attention on the characteristics of the J-aggregate (J-PMC). In this study, 8-[docosanoyloxymethyl]spiro[2H-1-benzopyran-2,2'-indoline] (SP1822) and dioctadecyldimethylammomum bromide (2C18N+2C1) were used as the PMC and bilayer matrices, respectively. The effects of molar mixing ratio (R = [SP1822]/ [2C18N+2C1]) on the J-aggregated PMC formation and thermal decay kinetics were then scrutinized. As a result, as R or the aggregate dimension increased, the formation efficiencyof J-PMC increased whilethe thermal decoloration ratefor J-PMC tended to decrease with increasing R. Therefore, it wasinferredthat the incorporationofPMC into the bilayer lowered the entropy of the bilayer and the thermal J-PMC decoloration reaction was governed by an entropy-driven process through transient disorder.
Correlation effects in the spectra of polyenes

A Hamiltonian of the Pariser–Parr–Pople form is employed to investigate the effect of correlation on the π‐electron spectrum of polyenes. Two limiting cases for the electron–electron interaction (short‐ and long‐range limit) are considered, and it is shown that they yield descriptions corresponding to the standard valence‐bond (Dirac–Heisenberg) and molecular‐orbital models, respectively. The intermediate, chemically most interesting, range is examined in detail by means of a full configuration‐interaction treatment with an exponential model potential that includes a variable effective range parameter. It is shown that correlation effects become more important as the effective range of the interaction decreases. The states of polyenes are classified as covalent or noncovalent, and it is found that the former are much more sensitive to correlation than the latter. Configuration interaction through double excitations yields a qualitatively correct ordering for all states in the chemical range, but triple an.
Challenges of IT Governance in the Financial Sector: A Study from Saudi Arabia

The financial sector plays a significant role in the economics of all nations. The Saudi Arabian economy ranks amongst the top twenty in the world. Consequently, the financial sector occupies a critical position within Saudi Arabia. Contemporary digital technology has rendered the financial sector heavily dependent upon information technology (IT) as a means of enhancing and extending its operational scope. Hence, efficient governance is required to streamline and utilize information technology. Moreover, the pivotal importance of the financial sector requires that IT governance challenges have to be swiftly identified. However, there is a manifest absence of scholarship regarding IT governance issues not only in the widest sense, but also specifically in the context of the financial sectors. This research seeks to identify the inherent challenges associated with IT governance in Saudi Arabia's financial sector with a view to generalizing any findings and contributing to future research in this field.
The Conditions of the Global Digital Working Class: The Continuing Relevance of Friedrich Engels to Theorising Platform Labour

This paper examines the conditions of the global digital class of platform labourers by drawing on the theoretical paradigm proposed by Engels in his pioneering contribution, The Conditions of the Working Class in England (CWC). Using a host of empirical sources – surveys, oral narrations, medical and legal journals, and journalistic accounts – the paper develops a political-economic understanding of the working conditions of contemporary crowdworkers while paying close attention to the national and gendered disparities within them. Following Engels’s dialectical mode of presentation in the CWC, the paper proposes a framework that contextualizes the lived experiences of crowdworkers in relation to: 1) the technological infrastructure of platforms, 2) emerging contractual and managerial modes of exploitation, 3) the gendered and racial articulation of labour extraction via Engels’s notion of inter-worker competition, and 4) the macro dynamics of “surplus population” that push workers into precarious employment. The paper argues that the four qualitative attributes of capitalist labour identified in the CWC have experienced quantitative transformation under digital capitalism and at the core remain fundamental to a theoretical appreciation of the impact of digital capital on the lived experiences of the global digital working-class.
Linking Soil Acidity to P Fractions and Exchangeable Base Cations under Increased N and P Fertilization of Mono and Mixed Plantations in Northeast China

Atmospheric N deposition is increasing worldwide, especially in China, significantly affecting soil health, i.e., increasing soil acidification. The northern region of China is considered to be one of the N deposition points in Asia, ranging from 28.5 to 100.4 N ha−1yr−1. Phosphorus (P) is the limiting factor in the temperate ecosystem and an important factor that makes the ecosystem more susceptible to N-derived acidification. However, it remained poorly understood how the soil acidification process affects soil P availability and base cations in the temperate region to increased N deposition. To address this question, in May 2019, a factorial experiment was conducted under N and P additions with different plantations in Maoershan Experimental Forest Farm, Northeast China, considering species and fertilization as variables. The effective acidity (EA) increased by N and NP fertilizations but was not significantly affected by P fertilization. Similarly, the pH, base saturation percentage (BS%), calcium (Ca2+), and magnesium (Mg2+) were decreased under N addition, while the Al:Ca ratio increased, whereas NaHCO3 inorganic phosphorus (Pi) and NaOH organic phosphorus (Po) significantly decreased under N enrichments. However, NaOH Pi increased in N-enriched plots, while H2O Pi and NaHCO3 Pi increased under the P addition. Thus, the results suggest that the availability of N triggers the P dynamics by increasing the P uptake by trees. The decrease in base cations, Ca2+, and Mg2+ and increase in exchangeable Fe3+ and Al3+ ions are mainly responsible for soil acidification and lead to the depletion of soil nutrients, which, ultimately, affects the vitality and health of forests, while the P addition showed a buffering effect but could not help to mitigate the soil acidity.
Tuning Structural, Electronic, and Magnetic Properties of C Sites Vacancy Defects in Graphene/MoS2 van der Waals Heterostructure Materials: A First-Principles Study

In this work, we systematically studied the structure, and electronic and magnetic properties of van der Waals (vdWs) interface Graphene/MoS2 heterostructure (HS-G/MoS2) and C sites vacancy defects in HS-G/MoS2 materials using first-principles calculations. By the structural analysis, we found that nondefects geometry is more compact than defects geometries. To investigate the electronic and magnetic properties of HS-G/MoS2 and C sites vacancy defects in HS-G/MoS2 materials, we have studied band structure, density of states (DOS), and partial density of states (PDOS). By analyzing the results, we found that HS-G/MoS2 is metallic in nature but C sites vacancy defects in HS-G/MoS2 materials have a certain energy bandgap. Also, from the band structure calculations, we found that Fermi energy level shifted towards the conduction band in vacancy defects geometries which reveals that the defected heterostructure is n-type Schottky contacts. From DOS and PDOS analysis, we obtained that the nonmagnetic HS-G/MoS2 material changes to magnetic materials due to the presence of C sites vacancy defects. Right 1C atom vacancy defects (R-1C), left 1C atom vacancy defects (L-1C), centre 1C atom vacancy defects (C-1C), and 2C (1C right and 1C centre) atom vacancy defects in HS-G/MoS2 materials have magnetic moments of −0.75 µB/cell, −0.75 µB/cell, −0.12 µB/cell, and +0.39 µB/cell, respectively. Electrons from 2s and 2p orbitals of C atoms have main contributions for the magnetism in all these materials.