Miracle-dz/UniFAR-FTUs huggingface dataset:
Sustaining corporate performance through the happy worker influence

Happy workers tend to be more productive. It would be beneficial for managers to understand what factors tend to predict happiness to assess where the most potentially productive worker pools are located around the world. The current study aims to examine the relationship between multiple factors and happiness. Twelve countries have been selected and data relating to nine different factors of each country have been used to determine the sum impact. All variable factors have been statistically correlated to happiness to predict the factor(s) that could be linked strongly with happiness. An intensive statistical analysis has been used to evaluate the significance of these relations. Our paper concludes that happiness without carbon footprint factor is a more accurate measure of personal happiness. The study will benefit companies and human resource managers to understand the impact of norms and cultural diversity in influencing business operations.
Predicting COVID-19 pneumonia severity on chest X-ray with convolutional neural network: A retrospective study

Objectives: Radiological lung changes in COVID-19 infections present a noteworthy avenue to develop chest X-ray (CXR) -based testing models to support existing rapid detection techniques. The purpose of this study is to evaluate the accuracy of artificial intelligence (AI) -based screening model employing deep convolutional neural network for lung involvement. Material and Methods: An AI-based screening model was developed with state-of-the-art neural networks using Indian data sets from COVID-19 positive patients by authors of CAIR, DRDO, in collaboration with the other authors. Our dataset was comprised of 1324 COVID-19, 1108 Normal, and 1344 Pneumonia CXR images. Transfer learning was carried out on Indian dataset using popular deep neural networks, which includes DenseNet, ResNet50, and ResNet18 network architectures to classify CXRs into three categories. The model was retrospectively used to test CXRs from reverse transcriptase-polymerase chain reaction (RT-PCR) proven COVID-19 patients to test positive predictive value and accuracy. Results: A total of 460 RT-PCR positive hospitalized patients CXRs in various stages of disease involvement were retrospectively analyzed. There were 248 males (53.92%) and 212 females (46.08%) in the cohort, with a mean age of 50.1 years (range 12–89 years). The commonly observed alterations included lung consolidations, ground-glass opacities, and reticular–nodular opacities. Bilateral involvement was more common compared to unilateral involvement. Of the 460 CXRs analyzed, the model reported 445 CXRs as COVID -19 with an accuracy of 96.73%. Conclusion: Our model, based on a two-level classification decision fusion and output information computation, makes it a robust, accurate and reproducible tool. Based on the initial promising results, our application can be used for mass screening.
Effect of artificial weathering on the some surface properties of uv system parquet varnish applied to persian silk (Albizia julibrissin) wood

It is necessary to know the different properties of the wood material belonging to each tree species to be able to use it correctly and efficiently. This study was carried out to investigate the evaluation possibilities of persian silk (Albizia julibrissin) wood in UV system parquet industry. UV system parquet varnishes were applied to persian silk wood in 3 and 5 layers in a parquet factory according to industrial applications. The materials obtained later were exposed to accelerated UV aging processes using UVA-340 lamps for 252 and 504 hours. Color parameters (L*, a* and b*), glossiness values, konig pendulum hardness and surface adhesion resistance (MPa) tests were carried out before and after aging. It has been observed that these tests change with aging. As a result, persian silk (Albizia julibrissin) wood can be used in the parquet industry.
BIOLOGICAL ACTIVITIES AND PHYTOCHEMICAL SCREENING OF ETHANOL EXTRACTS FROM ADONIS PARYADRICA (RANUNCULACEAE)

In this study, secondary metabolite profiles of flower, leaf and root of Adonis paryadrica ethanolic extracts were performed by using a newly developed and validated LC-MS/MS method. For this purpose, the LC-MS/MS system was used for the quantitative and qualitative analysis of 37 phytochemicals. The main component of the flower, leaf and root extracts was the quinic acid (160, 111 and 64 mg/kg, respectively). Antioxidant activities were determined using methods such as DPPH (SC50, mg/mL), FRAP (μM TEAC), CUPRAC (μM TEAC), ABTS (SC50, mg/mL) and TPC (GAE, μg/mL). While the highest antioxidant activity value for FRAP, CUPRAC, ABTS and TPC methods was determined in the leaf part of the plant, this value was determined in the flower part for DPPH. The antibacterial effects of the plant extracts were determined by liquid microdilution and agar diffusion methods. As a result, antimicrobial effect of flowers, leaf and all sections of plant extracts were determined except plant root extracts which did not display any activity against bacteria. Antimicrobial activity of the plant flower extract was found to be higher especially against to P. vulgaris with 3.5 mg/mL MIC.
DOES SUKUK, DOMESTIC INVESTMENT, FOREIGN INVESTMENT, AND INFLATION CONTRIBUTE TO ECONOMIC GROWTH IN INDONESIA?

ABSTRAKIndikator yang menentukan keberhasilan pembangunan ekonomi adalah pertumbuhan ekonomi. Kemajuan dan kesejahteraan suatu negara dapat dilihat melalui pertumbuhan ekonominya. Pertumbuhan ekonomi Indonesia berfluktuasi setiap tahun dan dipengaruhi oleh banyak faktor. Penelitian ini bertujuan untuk mengetahui kontribusi sukuk jangka pendek dan jangka panjang, penanaman modal dalam negeri, penanaman modal asing, dan inflasi terhadap pertumbuhan ekonomi di Indonesia dari tahun 2011 hingga 2019. Metode yang digunakan dalam penelitian ini adalah Vector Error Correction Model (VECM). Data yang digunakan dalam penelitian ini adalah data sekunder time series dengan periode penelitian triwulanan dari tahun 2011 hingga 2019. Hasil pengujian menunjukkan bahwa variabel sukuk memiliki kontribusi negatif yang tidak signifikan terhadap pertumbuhan ekonomi dalam jangka pendek dan dalam jangka Panjang, sukuk berpengaruh positif signifikan terhadap pertumbuhan ekonomi. Variabel investasi dalam negeri dalam jangka pendek dan jangka panjang memberikan kontribusi positif terhadap pertumbuhan ekonomi. Variabel investasi dalam negeri jangka pendek dan jangka panjang memberikan kontribusi positif yang tidak signifikan terhadap pertumbuhan ekonomi. Dan variabel inflasi memiliki kontribusi yang negatif dan tidak signifikan terhadap pertumbuhan ekonomi dalam jangka pendek dan jangka panjang.Kata Kunci: Sukuk, Penanaman Modal Dalam Negeri, Penanaman Modal Asing, Inflasi, Pertumbuhan Ekonomi. ABSTRACTThe indicator for determining the success of economic development is economic growth. The progress and welfare of a country can be seen through its economic growth. Indonesia’s economic growth fluctuates every year and is influenced by many factors. This study aims to determine the short-term and long-term contributions of Sukuk, domestic investment, foreign investment, and inflation to economic growth in Indonesia from 2011 to 2019. This method used in this research is the Vector Error Correction Model (VECM). The data used in this study are secondary time series data with a quarterly research period from 2011 to 2019. The test results show that the sukuk variable has an insignificant negative contribution to economic growth in the short term and the long term sukuk has a significant positive contribution to economic growth. Domestic investment variables in the short and long term contribute positively to economic growth. The short-term and long-term domestic investment variables have an insignificant positive contribution to economic growth. And the inflation variable has a negative and insignificant contribution to economic growth in the short and long term. Keywords: Sukuk, Domestic Investment, Foreign Investment, Inflation, Economic Growth.
Factors influencing biometric technology adoption: Empirical evidence from Nigeria

An exploratory investigation of factors influencing adoption of biometric technologies among public and private organizations in Nigeria is carried out in this study. Based on a modified technology-organization-environment (TOE) theoretical framework, an empirical analysis using data from questionnaires surveyed on these organizations was performed. The results show that compatibility, organizational size, top management support, trust between organizations, competitive pressure and uncertainty have significant influence on biometric technology adoption among these organizations. However, there is not enough evidence to support complexity, relative advantage, cost, employee resistance, technical knowledge and government support as having significant effect on biometric technology adoption. The implication of this study is that decision-makers in organizations need to pay more attention to the factors influencing biometric technology adoption in order to prevent fraud, curb insecurity and restrict unauthorized access to their digital and physical assets.
Multiscale Analysis of the Amyloid Degradation Toxicity Hypothesis of Alzheimer’s Disease

Alzheimer's disease (AD) is the most common cause of dementia and affects millions of people around the world. Neuronal death in AD is initiated by the toxic action of oligomeric amyloid-β (Aβ) peptides. The formation of membrane channels by Aβ is a primary molecular action and does not require any other proteins. Channels are formed by short amyloid fragments faster and more frequently than by full-length peptides. The channel formation is dependent on an electrostatic interaction between a positively charged peptide and a negatively charged membrane. Negative membranes can be found in several locations of a cell – the inner leaflet of plasma membrane, mitochondria, and lysosomes, which all are well-known cellular targets in AD. Considering that the amyloid enters a cell by endocytosis and is exposed to lysosomal enzymes, we propose the amyloid degradation toxicity hypothesis. Endopeptidases degrade the endocytosed peptide. Produced fragments form membrane channels, which can transfer various ions (including protons) and even relatively large compounds. The neutralization of lysosomal content inactivates enzymes, which fails the whole system of recycling cellular content, including autophagy. The permeabilization of lysosomes could also lead to cell death through necrotic and apoptotic mechanisms. We discuss several mechanisms that describe how amyloid degradation products reach plasma and mitochondrial membranes, and form membrane channels. The pathogenesis of AD is discussed at various levels in a context of how the primary molecular mechanism of membrane channel formation could progress into the disease state. The discussion starts at the molecular level and extends to why the development of a disease takes years and is closely associated with aging. The proposed hypothesis offers an interpretation to several clinical observations such as the involvement of iron metabolism and an inverse association between developing Alzheimer's disease and cancer. Predictions about potential biomarkers and effectiveness of future treatments are discussed.
The Information Content of Trump Tweets and the Currency Market

Using textual analysis, we identify the set of Trump tweets that contain information on macroeconomic policy, trade, or exchange rate content. We then analyze the effects of Trump tweets on the intraday trading activity of foreign exchange markets, such as trading volume, volatility, and FX spot returns. We find that Trump tweets reduce speculative trading, with a corresponding decline in trading volume and volatility, and induce a bias reflecting Trump’s (optimistic) views on the U.S. economy. We rationalize these results within a model of Trump tweets revealing economic content as a public signal that reduces disagreement among speculators.
The Challenge of Environmental Samples for PCR Detection of Phytopathogenic Bacteria: A Case Study of Citrus Huanglongbing Disease

Huanglongbing (HLB) is the most devastating citrus disease and is associated with three bacterial species of the genus ‘Candidatus Liberibacter’ transmitted by insect vectors. The early detection of HLB is based on PCR methods, and it is one of the cornerstones for preventing incursion into disease-free countries. However, the detection of phytopathogenic bacteria with PCR-based methods is problematic in surveys that include a variety of samples of different origins. Here, we first report the proportion of amplifications obtained by two standardized real-time PCR methods for the diagnosis of HLB in various environmental samples that include plants, psyllid vectors, and parasitic wasps of the psyllids. The results of 4915 samples showed that 9.3% of them were amplified by the first rapid screening test and only 0.3% by the more specific tests. Most of the amplifications were associated with parasitic wasps. We designed the primers external to the target regions of both real-time PCR protocols to determine if amplifications belonged to one of three ‘Ca. Liberibacter’ species associated with HLB. The bioinformatic analysis of the sequences obtained with these primers revealed that all these amplifications came from the presence of other prokaryotic organisms in the samples. The primers developed in this study overcome the problem of undesired amplification in environmental samples. Thus, they could be used in future survey protocols to prevent the eradication of negative trees and the generation of unjustified alarms.
A critical review of research on technological pedagogical and content knowledge (TPACK) in language teaching

Abstract Research on TPACK has been burgeoning recently. Some review studies in the field of educational technology have indicated issues and trends in TPACK research conducted in the past decades. However, a review of TPACK research specific to language teachers’ knowledge about language teaching with technology has been lacking. To fill this research gap, the present review study intends to understand the landscape of TPACK research on language teachers published from 2011 to 2019. The analysis results reveal that 51 studies were identified with a publication peak in 2015, mostly conducted in Asia and the Middle East. The 51 articles were categorized into four areas: (a) exploring TPACK, (b) assessing TPACK, (c) developing TPACK, and (d) applying TPACK. More specifically, research on exploring TPACK suggests that while teachers had varying levels of confidence in their TPACK competence, their TPACK indicated the dominant use of technology in traditional teacher-centered teaching. Research on assessing TPACK shows that survey items were contextualized to lessen the difficulty of distinguishing between the seven sub-domains of TPACK. However, the difficulty remained perhaps due to a possibility that the seven sub-domains may not appear workable in practice. This issue needs further research. Research on developing TPACK reveals that effective interventions involved language teachers’ understanding of the TPACK framework, modeling from teacher educators or experienced teachers, and language teachers’ engagement in designing lessons collaboratively. Finally, research on applying TPACK indicates that TPACK-informed language learning courses and platforms were perceived to be helpful and effective. These findings may provide researchers with some possible directions.
CYP1A1 GENE POLYMORPHISM AND NASOPHARYNGEAL CARCINOMA IN ALGERIAN POPULATION

CYP1A1 polymorphisms are known to play a crucial role in the development and metastasis of malignant diseases including nasopharyngeal cancer (NPC). Individual susceptibility to cancer from environmental agents may be influenced by polymorphic metabolic gene (CYP1A1). The present study was carried out to analyze the relationship between CYP1A1 genetic polymorphism and the risk of developing nasopharyngeal cancer (NPC). The study involved 70 patients with nasopharyngeal carcinoma, compared with 100 healthy controls. Genomic DNA samples were assayed for restriction fragment length polymorphisms in the CYP1A1 by PCR amplification followed by digestion with MspI and BsrDI. The frequency comparison between the two groups was determined by the χ(2) test. Showed that the distribution of CYP1A1 mt/mt allele frequency was found to be significantly different between controls (6%) and cancer cases (11%) along with twice increase of the NPC risk in Algeria (OR=2.5, CI 95% [1.72-2.30]) Our results are in line with those found for other populations and subsequent they emphasize the importance of the involvement of this gene in the development of the NPC detoxification process, which exhibited more and more strong association with environmental factors.
Biochemical factors associated with mango mealy bug (Drosicha mangiferae G.) infestation in different mango cultivars at Malda, West Bengal (India)

Five different popular mango cultivars (Fazli, Guti, Lakhanbhog, Gopalbhog and Langra) of Malda, West Bengal, India were screened for the mango mealy bug (Drosicha mangiferae G.) population in panicle and trunk throughout the pest infestation period during 2018 and 2019. The average pest number at peak time (13 Standard Meteorological Week or SMW) of infestation indicated that maximum pest number of 132.57 and 28.05 was noted in the 30 cm branch of inflorescence and 10 cm2 area of the trunk in Fazli cultivar. Whereas, minimum number of 20.27/ 30 cm branch of inflorescence and 6.36 in the same area of trunk was found in Langra. Study of various biological parameters of mealy bugs also indicated the the order of infestation of the five cultivars as Fazli>Guti>Lakhanbho g>Gopalbhog>Langra. Analysis of various biochemical parameters revealed that there was a significant positive correlation between pest density phagostimulants like sugar, protein, moisture and nitrogen. Significant negative correlation was observed between pest number and alkaloid, flavonoid, phenol, tannin, ascorbic acid as well as crude fibre suggesting their pivotal role in low pest incidence. By considering the adjusted R2 in stepwise multiple regression model, it was noticed that tannin was the most significant factor (39.2 %) followed by nitrogen (15.5 %), flavonoid (11.4 %), phenol (7.7 %), ascorbic acid (6.9 %) and protein (6.2 %) to influence pest number. Factors like moisture (1.0 %), sugar (2.7 %), antioxidant (1.0 %) had minor contributions. Therefore, it can be concluded that a combination of factors influence pest density and a single factor is not adequate to affect the pest population. The study is helpful in understanding the host preference of mealy bug.
Electricity Consumption Forecasting Based on a Bidirectional Long-Short-Term Memory Artificial Neural Network

The accurate forecasting of the hourly month-ahead electricity consumption represents a very important aspect for non-household electricity consumers and system operators, and at the same time represents a key factor in what regards energy efficiency and achieving sustainable economic, business, and management operations. In this context, we have devised, developed, and validated within the paper an hourly month ahead electricity consumption forecasting method. This method is based on a bidirectional long-short-term memory (BiLSTM) artificial neural network (ANN) enhanced with a multiple simultaneously decreasing delays approach coupled with function fitting neural networks (FITNETs). The developed method targets the hourly month-ahead total electricity consumption at the level of a commercial center-type consumer and for the hourly month ahead consumption of its refrigerator storage room. The developed approach offers excellent forecasting results, highlighted by the validation stage’s results along with the registered performance metrics, namely 0.0495 for the root mean square error (RMSE) performance metric for the total hourly month-ahead electricity consumption and 0.0284 for the refrigerator storage room. We aimed for and managed to attain an hourly month-ahead consumed electricity prediction without experiencing a significant drop in the forecasting accuracy that usually tends to occur after the first two weeks, therefore achieving a reliable method that satisfies the contractor’s needs, being able to enhance his/her activity from the economic, business, and management perspectives. Even if the devised, developed, and validated forecasting solution for the hourly consumption targets a commercial center-type consumer, based on its accuracy, this solution can also represent a useful tool for other non-household electricity consumers due to its generalization capability.
Exploring the potential of two-aged white spruce plantations for the production of sawlog volume with simulations using SORTIE-ND

The main objective for even-aged plantation (EAP) management of producing sawlog material has driven practices towards low initial planting densities and lower post thinning densities. For semi-shade tolerant species, the resulting stand density potentially leaves enough growing space for the introduction of a second cohort of trees in the understory, making it a two-aged plantation (TAP). TAP could have many silvicultural benefits, especially in sensitive areas where intensive treatments associated with EAP are incompatible with local management objectives. White spruce (Picea glauca) is a good candidate species for modeling TAP because it is the most widely planted tree species in Canada and has proven tolerance to understory planting. SORTIE-ND, a single-tree spatially explicit growth model was used to explore the yield of variable density and rotation length scenarios when each white spruce cohort is introduced mid rotation, compared to traditional even-aged management. All TAP scenarios tested produced more sawlog volume and more merchantable volume than equivalent densities of EAPs. The lowest density tested, 400 stems ha-1 planted every 35 years, had the highest sawlog yields (3.23 m3 ha-1 yr-1). Considering smaller size products changes the optimum TAP scenario but maintains the advantage over EAPs.
Informational Behavior in the COVID-19 Pandemic: Psychological Predictors

The core problem of the COVID-19 pandemic for psychologists is to find out how people cope with the stress of isolation and the threat of fatal disease. The scale of the pandemical impact on the psychological well-being of an individual has still no knowledge and psychological predictors which the impact depends on need to be identified. This paper presents an empirical study of informational behavior and its psychological predictors in the pandemic. The research was held online in April-May 2020. The total amount of 165 participants, aged from 18 to 66. The subjects were chosen from a randomly selected sample. The participants were asked to estimate their informational consumption in pandemic. Tolerance to ambiguity, hardiness and anxiety was studied in groups distinguished according to changes in informational consumption. The findings of this study indicate a significant correlation between informational behavior and psychological characteristics related to coping with stress. Besides the analysis proved a negative correlation between reactive and personal anxiety and tolerance to ambiguity, hardiness and its components. We have confirmed that increased informational comsumption can be considered as a coping strategy for overcoming the pandemic social isolation among respondents with low hardiness and tolerance to ambiguity. Stable and decreased informational consumption indicates that respondents with high hardiness and tolerance to ambiguity and low state and trait anxiety don’t need to consume information for coping with difficulties of pandemic self-isolation. Future work will concentrate on expanding the list of psychological predictors of informational behavior and studying the features of their interaction in different situations.