Miracle-dz/UniFAR-FTUs huggingface dataset:
Redundancy analysis: A Swiss Army Knife for landscape genomics

Landscape genomics identifies how spatial and environmental factors structure the amount and distribution of genetic variation among populations. Landscape genomic analyses have been applied across diverse taxonomic groups and ecological settings, and are increasingly used to analyse datasets composed of large numbers of genomic markers and multiple environmental predictors. It is in this context that multivariate methods show their strengths. Redundancy analysis (RDA) is a constrained ordination that, in a landscape genomics framework, models linear relationships among environment predictors and genomic variation, effectively identifying covarying allele frequencies associated with the multivariate environment. RDA can be used at both individual and population levels, can include covariates to account for confounding factors and can be used to directly infer genotype–environment associations on the landscape. The modelling of both multivariate response and explanatory variables allows RDA to accommodate the genomic and environmental complexity found in nature, producing a powerful and efficient tool for landscape genomics. In this review, we outline the diverse uses of RDA in landscape genomics, including variable selection, variance partitioning, genotype–environment associations, and the calculation of adaptive indices and genomic offset. To illustrate these applications, we use a published dataset for lodgepole pine that includes genomic, phenotypic and environmental data. We provide an introduction to the statistical basis of RDA, a tutorial on its use and interpretation in landscape genomics applications, discuss limitations and provide guidelines to avoid misuse. This review and associated tutorial provide a comprehensive resource to the landscape genomics community to improve understanding of RDA as a modelling framework, and encourage the appropriate use of RDA across diverse landscape genomics applications. RDA is truly a Swiss Army Knife for landscape genomics: a multipurpose, adaptable and versatile approach to identifying, evaluating and forecasting relationships between genetic and environmental variation.
A Comparison of Employee’s Engagement Models with Emphasis on Human Resources in Iranian Organizations

Many organizations face the challenge of having employees with low sense of job belonging. The aim of this study was to compare employee’s belongingness models with emphasis on human resources in Iranian organizations. The method of research is qualitative comparative using content-oriented analysis approach. The statistical population includes all models of employee’s job belonging using purposeful sampling method for selection of samples. The method of data collection is documentary and method of data analysis was deductive content analysis. Findings show that the highest degree of similarity among employee’s job belongingness models is on emotional belonging, work quality, possibility of progressiveness and reward and benefits components. Also, out of the 14 selected models, about one third emphasize on role of effective leadership, customer orientation, and productivity dimensions to increase employee’s job belonging. Another finding of the study is lack of attention of all models to the role of components such as social and organizational cultures. According to the research findings, it is suggested to human resources policymakers in governmental and non-governmental organizations of Iran to pay more attention to components such as role of social culture and organizational culture to design a new model for their organizations.
Coal quality prediction based on multi-feature fusion of flotation foam images

Coal flotation monitoring cannot provide real-time feedback on the yield and ash of coal preparation products because it is influenced by the subjective nature of artificial judgment of coal preparation status and the lag of product quality testing of coal preparation. This paper aims to extract the texture, colour and shape features of floating foam images using various image processing methods, such as colour space, wavelet transform, greyscale co-occurrence matrix and edge operator, and to quantify the characterisation of various characteristic parameters on the basis of the indicative effect of floating foam characteristics on the quality of coal preparation products. The correlation between image features and the yield and ash of flotation products is studied, and a regression prediction model of coal preparation yield and ash was established by combining various image feature parameters using machine learning methods. Experimental results show that the proposed method can realise the real-time monitoring of coal mine flotation and effectively predict coal quality.
Galaxy Formation and Rotation as a 4D Relativistic Cloud-World Embedded in a 4D Conformal Background: A Cloud Theory

The recent observation of the G2 gas cloud orbit around the galactic centre has challenged the model of a mere supermassive black hole at the centre of our galaxy which should have destroyed it. In addition, the Planck Legacy 2018 (PL18) release has preferred a positively curved early Universe with a confidence level exceeding 99%. In this study, the collapse of a large gas cloud in the early Universe to form a galaxy is modelled based on extended field equations as a 4D relativistic CloudWorld that flows and spins through a 4D independent conformal background of an initial positive curvature considering the preference of the PL18 release. Owning to the curved background, this scenario of galaxy formation indicates that the core of the galaxy undergoes a forced vortex formation with a central event horizon leading to opposite traversable wormholes that are spatially shrinking through the conformal time. It reveals that the galaxy and its core are formed at the same process where the surrounding gas clouds form the spiral arms due to the frame-dragging induced by the fast-rotating core. Accordingly, the G2 gas cloud that only faced the drag effects could be explained if its orbit is around the wormhole but at a distance from the central event horizon. The formation of the galaxy and its core simultaneously could explain the formation of the supermassive compact galaxy cores with a mass of ~109 M⊙ at just 6% of the current Universe age and could resolve the black hole hierarchy problem.
Different Effects of Four Yogic Breathing Techniques on Mindfulness, Stress, and Well-being

Yogic breathing techniques are fundamental to the physical and mental practice of yoga. They are closely connected to meditation, which involves the observation of breath. There are many yogic techniques based on the active regulation of the breath. Breathing practices influence many processes in the body, e.g. heart rate variability, and the mind, e.g. relaxation and stress, through their impact on the autonomic nervous system. This study intended to investigate differential effects of four yogic breathing techniques: (1) ujjayi—relaxation through slowing down the breath, (2) paced breathing—enhancement of concentration by following a precise protocol of slowdown breathing, (3) kapalabhati—raising wakefulness by mild hyperventilation, and (4) alternate nostril breathing—balancing the autonomic nervous system by alternating breath between the two nostrils. This study was conducted on 36 participants, who learned each technique within two weeks of an eight-week program and practiced them daily. After each technique, mindfulness, perceived stress, and physical well-being were assessed based on questionnaires. Ujjayi breathing, showed a relaxing effect, reduced stress, increased peacefulness, and the feeling of being at ease/leisure. Paced breathing resulted in a greater awareness of inner experiences. Kapalabhati showed a significant increase in vitality and joy of life, and alternate nostril breathing showed no hypothesis-compliant changes. The findings of this study suggest several beneficial and differential effects of these breathing techniques; therefore, they could be employed as tools for self-regulation in therapeutic contexts.
Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing

Background Online media play an important role in public health emergencies and serve as essential communication platforms. Infoveillance of online media during the COVID-19 pandemic is an important step toward gaining a better understanding of crisis communication. Objective The goal of this study was to perform a longitudinal analysis of the COVID-19–related content on online media based on natural language processing. Methods We collected a data set of news articles published by Croatian online media during the first 13 months of the pandemic. First, we tested the correlations between the number of articles and the number of new daily COVID-19 cases. Second, we analyzed the content by extracting the most frequent terms and applied the Jaccard similarity coefficient. Third, we compared the occurrence of the pandemic-related terms during the two waves of the pandemic. Finally, we applied named entity recognition to extract the most frequent entities and tracked the dynamics of changes during the observation period. Results The results showed no significant correlation between the number of articles and the number of new daily COVID-19 cases. Furthermore, there were high overlaps in the terminology used in all articles published during the pandemic with a slight shift in the pandemic-related terms between the first and the second waves. Finally, the findings indicate that the most influential entities have lower overlaps for the identified people and higher overlaps for locations and institutions. Conclusions Our study shows that online media have a prompt response to the pandemic with a large number of COVID-19–related articles. There was a high overlap in the frequently used terms across the first 13 months, which may indicate the narrow focus of reporting in certain periods. However, the pandemic-related terminology is well-covered.
Modeling the factors that explain customer loyalty in retail banking

Literature suggests that achieving adequate customer loyalty is a significant determinant of growth and profitability. However, in South Africa, there is no evidence of a validated customer-loyalty-in-retail-banking scale. Thus, this study aimed to contribute to the literature by validating customer loyalty in retail banking as a six-factor structure comprising customer loyalty, service quality, customer commitment, trust, switching cost and customer satisfaction, which practitioners can use as a marketing guide to better understand customer loyalty. Data was collected from one sample only once, and the sample size was selected (N = 400). Descriptive and confirmatory factor analyses were undertaken to achieve the study’s objective. Confirmatory factor analysis results validated customer loyalty in retail banking as a six-factor structure that includes customer loyalty, service quality, customer commitment, trust, switching cost and customer satisfaction. The results show no serious multicollinearity between the latent factors and that acceptable internal-consistency reliability was returned for each factor. Moreover, the measurement model returned acceptable composite reliability together with construct, convergent and discriminant validity. Moreover, IFI, TLI, CFI, SRMR and RMSEA model fit index values suggest a good fitting model. Thus, the results concluded that this six-factor model is a reliable and valid instrument of customer loyalty in retail banking and is the first validated customer loyalty scale within the retail-banking context of South Africa. Retail banks are encouraged to use this instrument as a marketing guide in their quest to provide excellent banking services to their market segments, as well as build solid bank-customer relationships.
A Shielded-QMSIW-Based Self-Diplexing Antenna for Closely Spaced Bands and High Isolation

In this letter, a shielded quarter-mode substrate integrated waveguide (S-QMSIW)-based self-diplexing antenna for closely spaced bands and high isolation is proposed. A <inline-formula><tex-math notation="LaTeX">${\rm Y}$</tex-math></inline-formula>-shaped slot is engraved on the top of the metal to enhance the extra capacitance of the QMSIW cavity resonator; thus, it helps to reduce its size as well as achieve dual-band characteristics. The S-QMSIW resonator is then excited by two orthogonal microstrip feed lines in the full-wave electromagnetic simulator (high-frequency structure simulator) to operate at two distinct frequencies (3.47 and 3.84 GHz). A transmission zero is generated at 3.56 GHz to provide port isolation better than 47 dB. In addition, a lumped circuit model is predicted to validate the antenna structure. To demonstrate the proposed concept, an S-QMSIW-based self-diplexing antenna is designed, fabricated, and tested. The recorded peak gains of the antenna are 4.8 and 5.7 dBi at 3.47 and 3.84 GHz, respectively. At each band, the antenna front-to-back ratio is greater than 20 dB. The circuit size of the proposed antenna is very compact (0.15<inline-formula><tex-math notation="LaTeX">$\lambda _g^{2}$</tex-math></inline-formula>).
Machine Learning based Traffic Light Detection and IR Sensor based Proximity Sensing for Autonomous Cars

In this fast-moving digital world autonomous cars will be a norm in near future. One of the most critical features of any autonomous car is traffic light detection and recognition. The problem related to outdoor perception is a key issue faced by driver-assisted and autonomous cars, as the machine, unless taught, cannot detect traffic lights, road signs, obstacles, etc. in the direction of motion. Autonomous cars need to be capable of detecting traffic signals and recognizing their current status whereas Human beings can easily identify the relevant traffic signals. Traffic light detection and recognition needs to be integrated with the CPU of autonomous cars (which governs the car) leading to resolution of the traffic signal alignment issue. Additionally, solution is required for the detection of obstacles. Open CV2 module of machine learning works well for resolving traffic related problems. Furthermore, it has leveraged state-of-the-art detectors to recognize traffic lights from 2D camera images. Tesla is using the traffic light detection and recognition technology in their autonomous cars. Google has proposed prior map of the location of traffic signals (recognition of traffic lights) which makes it possible for autonomous cars to performance well in the night too. Another integral feature for an autonomous car is proximity sensing, it’s basically done for sensing any obstacle near the car. The car should act accordingly and well within time to avoid a collision when an obstacle is sensed in the direction it is moving. Proximity sensing is done using cameras, RADAR (Radio Detection And Ranging) and LiDAR sensors (Light Detection And Ranging); IR sensor can be used as a redundancy in case one of the above fails. Tesla, Google and BMW have come up with a software that uses cutting edge technology such as RADAR, high resolution 3-D cameras, ultrasonic sensors and LiDAR sensors. This paper talks about the traffic light detection robustly using opencv2 module, IR sensors for proximity sensing (nearby traffic, obstacles, pedestrians) interfaced with an Arduino Uno board and a computer through which the behaviour of the autonomous car can be monitored.
The effect of age on higher education teachers’ ICT use

Many researchers have explored instructional use of information communication technology (ICT) in higher education and uncovered several factors that affect its application in teaching. The effect of age on university teachers’ ICT use, however, has attracted much less attention. This study uses quantitative data from a survey of 401 teachers from the University of Ljubljana in Slovenia to reveal any differences in instructional ICT use in terms of age. The results show that age is not a factor in instructional ICT use, although some age-related differences appear in teachers’ personal ICT uses. The findings may inform planning for teacher support along with training and quality assurance at the university’s centre for ICT-supported teaching.
New spectrophotometric method for the determination of gabapentin in bulk and dosage forms using p-dimethylaminobenzaldehyde

ABSTRACT A new simple, accurate and economic spectrophotometric method based on azo dye derivatization for the determination of gabapentin (GBP) was developed. Critical factors were optimized. The method was validated and assay of dosage forms was done. Spot tests and TLC confirmed the formation of azo adduct. A 0.3 M NaNO2 solution using 2 M HCl was used for diazotization. The optimal temperature and time were 30°C and 10 min. Azo adducts were determined at 430 nm. Methanol was found to be the best solvent. Gabapentin coupled at a ratio of 1:1 with DMAB. The assays of GBP were linear over the range 1–6 µg/mL (r = 0.9973) and LOD of 0.8322 µg/mL. The methods were accurate (error < 2%) and precise (RSD < 0.5%). The methods were successfully applied to the assay of GBP in dosage forms and compared favorably with reference method (p > .05). The successful diazotization of gabapentin and the azo adduct formation with DMAB is reported for the first time.
The Role of Perceived Organisational Support on Organisational Justice and Turnover Intent among Private Secondary Schools in Jos, Plateau State

This study seeks to investigate the function of Perceived Organisational Support on the association between Organisational Justice and Turnover Intent. To achieve this purpose, three objectives were established: firstly to examine the relationship between Organisational Justice and Turnover Intent, secondly to examine the relationship between Perceived Organisational Support and Turnover Intent, and lastly to examine the role of Perceived Organisational Support on the relationship between Organisational Justice and Turnover Intent.A survey research design was used to collate data from a population of 1162 teachers of private secondary schools. The Taro Yameneformular was used to determine the sample size of 298 teachers randomly selected from private secondary schools in Jos. The data was cleaned, coded, and properly analysed using multiple regression analysis. The results of the findings indicated that there was a positive relationship between Organisational Justice and Turnover Intent, secondly there exist a negative relationship between Perceived Organisational Support and Turnover Intent and lastly, Perceived Organisational Support did not moderate the relationship between Organisational Justice and Turnover Intent. In conclusion, Organisational Justice should be planted in the perceptions of teachers as it helps to prevent turnover intent or outright turnover of teachers which could further help to enhance the performance of students in WASSCE. Also, the management of educational institutions should focus more on distributive and informational justice to further reduce Turnover Intent to the bearest minimum.
Investigation the effects of synthetic cannabinoid-AKB48 on DNA methylation via the regulation of cannabinoid receptor gene specific methylation in vitro

: Synthetic cannabinoids have become a significant public health problem in the world because of inexpensive, easy to obtain, difficult to detect by many screening methods, constantly new compounds are produced and marketed, and so on. Despite their effect on ∆ 9-tetrahydrocannabinol (THC) like properties on cannabinoid receptors, they exert a very strong effect as opposed to THC and could cause serious side effects to death. Studies in the literature on causative epigenetic mechanisms of long-term usage of drugs like cocaine, cannabis, and heroin are available however, few studies have been conducted on synthetic cannabinoids, which are very common in use. Also, recently the endocrine disrupting effects of such substances have come into prominence. We aimed to examine potential effects of AKB48, one of the important synthetic cannabinoids, on cytotoxicity, global methylation and CpG island promoter methylation of cannabinoid receptor genes ( CB1 and CB2 ) and gene expressions of DNA methyltransferase enzymes ( DNMT1 and MGMT ), CB1 and CB2 genes in prostate adenocarcinoma (PC-3) cells following 24 h exposure. In MTT assay, IC 50 value was calculated as 343.8 µM for AKB48 in PC-3 cells. Percentage of 5-methylcytosine (5-mC%) levels were significantly decreased by 27% at 100 μM concentration. No significant alteration was shown in the promoter methylation of CB1 gene compared to the control. These results will contribute to literature for determination of potential effects of synthetic cannabinoids on epigenetic modifications in the tissues of endocrine system.
The Impacts of COVID-19 on Collegiate Student-Athlete Training, Health, and Well-Being

ABSTRACT Introduction The purpose of this study was to determine the impact of COVID-19 and stay-at-home (SAH) orders on collegiate student-athletes’ training, nutrition, sleep habits, and mental health and to identify disparities between sexes and competitive divisions. Methods Collegiate student-athletes (n = 401; age, 20 ± 2 yr) completed an 84-question anonymous survey regarding demographics, sport/exercise training, nutrition, sleep habits, and mental health. Response frequencies were calculated for each question, and χ2 analyses were used to determine statistical significance (α = 0.05). Results Although 80.7% of respondents indicated training for their sport, only 38.7% could fully perform their training programs. More D1 versus D3 athletes reported they could perform their training plan as written (D1: 44.4% [n = 83] vs D3: 27.3% [n = 50]; P < 0.01), but there were no differences between sexes. Cardiovascular exercise was the most common mode (87.5%) followed by resistance exercise (78.4%). Although there were no differences for cardiovascular exercise, more males (87.5%) than females (74.8%) indicated resistance training (P < 0.01). Average number of meals consumed per day remained similar before and during SAH, but females reported consuming less food and perceived increased healthfulness of their diets. Although most athletes did not use nutritional supplements, rates were higher among D3 and females. Respondents reported longer sleep durations but increased sleep disturbances, negative psychological states, and overall concerns during SAH. Maintaining fitness and sport-specific skills (~70.0%) were the most common concerns. In addition, ~60.6% of females and 41.9% of males indicated increased mental health concerns. Conclusions Our findings suggest that while attempting to be diligent with training during SAH, many student-athletes reported difficulties regarding limited equipment, motivation, and mental health concerns such as heightened anxiety. Many of these difficulties were division- and sex-specific. Discussions between coaches and student-athletes regarding SAH training and mental stressors may aid in determining student-athletes’ readiness to return to sport.
Image processing method for automatic measurement of number of DNA breaks

The number of double - strand breaks can be evaluated from the change of average DNA length. The average DNA length is measured by the single - molecule observation method using fluorescence microscope. The measurement of DNA length in the microscope images is done manually by experienced operators and it is time consuming in many experiments. An image processing method using OpenCV library to measure length of DNA in fluorescence microscope images is developed in this paper. An automation of measurement us-ing deep learning is also proposed.