Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection of subtle deforestation due to logging using satellite remote sensing in wet and dry savanna woodlands of Southern Africa

Abstract Tropical deforestation through logging activities poses a direct threat to biodiversity. However, the detection of logging has remained a challenge. Based on study sites in Zimbabwe and Zambia, we tested whether the Normalized Difference Vegetation Index (NDVI) and the Coefficient of Variation in NDVI (CVNDVI) derived from high and medium spatial resolution satellite data could be used to detect logging in dry and wet miombo woodlands. Separately, we integrated NDVI and CVNDVI in logistic regression to test whether each can be used to successfully predict logging in the study sites. We tested whether the spatial resolution of satellite data has an effect in detection of logging using NDVI and CVNDVI derived from Landsat 8 and Worldview-2. Based on the ROC curves, we concluded that remotely sensed data could provide an effective predictive tool for detecting logging. However, in wet miombo woodlands the predictive power of remotely sensed data is weak.
Research on safety standardization management based on knowledge mapping

Continuous and effective management is the driving force to promote the development of our society and ensure the smooth progress of safety work, and analyzing its evolutionary trends and hot changes will promote the development of the field. Based on 2786 safety management-related literature in the field of engineering industry and engineering electronics and electrical appliances from 2021-2023 in the WOS database, we use CiteSpace to generate co-citation maps and keyword co-occurrence maps to analyze the existing safety management-related results in detail from three perspectives: accident causation analysis, hazard assessment, and accident prevention programs, and to analyze in depth safety in the form of visual analysis The hotspots in management-related fields are analyzed in depth in the form of visualization.
Development and Evaluation of a North America Ensemble Wildfire Air Quality Forecast: Initial Application to the 2020 Western United States “Gigafire”

Wildfires emit vast amounts of aerosols and trace gases into the atmosphere, exerting myriad effects on air quality, climate, and human health. Ensemble forecasting has been proposed to reduce the large uncertainties in the wildfire air pollution forecast. This study presents the development of a multi‐model ensemble (MME) wildfire air pollution forecast over North America. The ensemble members include regional models (GMU‐CMAQ, NACC‐CMAQ, and HYSPLIT), global models (GEFS‐Aerosols, GEOS5, and NAAPS), and global ensemble (ICAP‐MME). Performance of the ensemble forecast was evaluated with MAIAC and VIIRS‐SNPP retrieved aerosol optical depth (AOD) and AirNow surface PM2.5 measurements during the 2020 Western United States “Gigafire” events (August–September 2020). Compared to individual models, the ensemble mean significantly reduced the biases and produced more consistent and reliable forecasts during extreme fire events. For AOD forecasts, the ensemble mean was able to improve model performance, such as increasing the correlation to 0.62 from 0.33 to 0.57 by individual models compared to VIIRS AOD. The ensemble mean also yields the best overall RANK (a composite indicator of four statistical metrics) when compared to VIIRS and MAIAC AOD. For the surface PM2.5 forecast, the ensemble mean outperformed individual models with the strongest correlation (0.60 vs. 0.43–0.54 by individual models), lowest fractional bias (0.54 vs. 0.55–1.32), highest hit rate (87% vs. 40%–82%), and highest RANK (2.83 vs. 2.40–2.81). Finally, the ensemble shows the potential to provide a probability forecast of air quality exceedances. The exceedance probability forecast can be further applied to early warnings of extreme air pollution episodes during large wildfire events.
Masry: A Text-to-Speech System for the Egyptian Arabic

: This paper presents the improvement and evaluation of Masry , an end-to-end system planned to synthesize Egyptian Arabic speech. The proposed approach leverages the capable Tacotron speech synthesis models, counting Tacotron1 and Tacotron2, and integrated with progressed vocoders – Grifﬁn-Lim for Tacotron1 and HiFi-GAN for Tacotron2. By synthesizing waveforms from mel-spectrograms, Masry offers a comprehensive solution for generating natural and expressive Egyptian Arabic speech. To train and validate our system, we construct a dataset including a male speaker describing standard composing pieces and news content in Egyptian Arabic. The sampling rate of recorded data is 44100 Hz, guaranteeing constancy and richness within the synthesized speech output. The execution of our framework was fastidiously assessed through different measurements, with a speciﬁc center on the Mean Opinion Score (MOS). The experimental results demonstrated the prevalence of Tacotron2 over Tacotron1, yielding a MOS of 4.48 compared to 3.64. This emphasizes the system’s capacity to capture and duplicate the nuances of Egyptian Arabic speech more effectively. Besides, The assessment extended to include fundamental measurements such as word and character error rates (WER and CER). These metrics give a quantitative appraisal of the precision and exactness of the synthesized speech.
An investigation of robust indoor location system techniques for educational purposes

This paper investigates the various methods used in robust indoor location systems for educational purposes. The most important uses of localization namely navigation and tracking are addressed. The difference between slow tracking and fast tracking is discussed. A performance evaluation of indoor geolocation systems for example RF and Ultrasonic indoor location systems is done. These indoor location techniques can be used in robots and mobile machines with various applications. I. INTRODUCTION The process of location can be interpreted as computing the coordinates of an object in a given coordinate system (1). The most important uses of localization are navigation and tracking, while location and position information can also enable a huge amount of secondary applications. These possibilities include using the location information for customizing products, services; improving ways of data communications, building modern homes and offices, applications in emergency situations and in the context of a manufacturing, facilitating the movement of parts, the location of parts and an improvement in the overall efficiency of the production network. Measuring the distance between the object to be located and the reference station is a basic task in geolocation systems. Measurement can be mainly divided into three categories: fine precision measurement, intermediate precision measurement and coarse measurement. The type of precision in the measurement used in a research problem depends on the characteristics and requirements of the system under study. Coarse measurements are acceptable for use in long distance measurement systems, with errors in the order of a few meters. The Global Positioning System (GPS) is a well- known geolocation system using coarse measurements and was developed in the late 1980's. In GPS systems satellites are used as the reference points and distances (or pseudo ranges) between these satellites and the unknown points are measured. Using exact satellite locations, obtained from orbital ephemeris data, the coordinates of the point to be localized are computed via matrix computations. (1). Recently, intermediate precision measurement systems with errors limited to centimeters have attracted more attention. These systems can facilitate Slow and Fast tracking. Slow tracking means tracking a body moving at slow speed. It is mainly used in the tracking of personal, equipment and assets or the tracking of a part or tool in an automated manufacturing environment. Indoor geolocation systems are the most widely used application for Slow tracking measurements. Indoor geolocation systems can be placed in
The application of depletion curves for parameterization of subgrid variability of snow

Parameterization of subgrid‐scale variability in snow accumulation and melt is important for improvements in distributed snowmelt modelling. We have taken the approach of using depletion curves that relate fractional snow‐covered area to element‐average snow water equivalent to parameterize the effect of snowpack heterogeneity within a physically based mass and energy balance snowpack model. Comparisons of parameterization outputs with distributed model outputs and observations show performance comparable to the distributed model and reasonable performance relative to observations for time series modelling of snow water equivalent and snow‐covered area. Examination of the relationship between the shapes of the depletion curves and parametric distributions shows that the shapes of dimensionless depletion curves depend primarily on the coefficient of variation and to a lesser extent on the shape of the snow distribution function. The methods presented here are a generalization of several previously used methods to estimate depletion curve shapes. Comparison of several years of observed depletion curves from the study basin show that the shapes of the depletion curves change little from year to year. Copyright © 2004 John Wiley & Sons, Ltd.
Students' Perception and Motivation In Learning English Towards The Use of Quizizz For Efl Students

This research aimed to discover the students’ perception and motivation in learning English towards the use of quizizz features for EFL students. The method used in this research was a descriptive qualitative approach. Participants of this research were the students of Bina Sarana Informatika University, and the subject of this research was 42 students of 12.1A. 21 class 2022/ 2023. Data collection techniques used were the questionnaire and interview results. Data analysis techniques used were basic statistical techniques of percentages using the Likert-scale questionnaire, and the data was supported from the interview’s results as the secondary data. The results of this research indicated intrinsic and extrinsic factors that influence students to participate in the learning English towards the use of quizizz features which include: 1) Intrinsic factors (challenging, curiosity, learning new things, improving their knowledge and English skill), 2) Extrinsic factors (getting a good mark, gaining good Grade Point Average, and getting experience of using quizizz features). It showed that the EFL Students have a good perception and good motivation in learning English towards the use of quizizz features for EFL students. In conclusion, learning English towards the use of quizizz features for EFL students is good, and this quizizz features can be considered as English learning and practice media, which will be better to combine with the conventional ways in the classroom.
Emotion Recognition on Social Media Using Natural Language Processing (NLP) Techniques

In recent years, text has been the main form of communication on social media platforms such as Twitter, Reddit, Facebook, Instagram and YouTube. Emotion Recognition from these platforms can be exploited for all sorts of applications. Through the means of a review of the current literature, it was found that Transformer-based deep learning models show very promising results when trained and fine-tuned for emotion recognition tasks. This paper provides an overview of the architecture for three of the most popular Transformer-based models, BERT Base, DistilBERT, and RoBERTa. These models are also fine-tuned using the “Emotions” dataset; a data corpus composed of English tweets annotated in six (6) different emotions, and the performance of the models is evaluated. The results of this experiment showed that while all of the models demonstrated excellent emotion recognition capabilities by obtaining over 92% F1-score, DistilBERT could be trained in nearly half of the time compared to the other models. Thus, the use of DistilBERT for emotion recognition tasks is encouraged.
High-Power GaN SBD Limiter for Sub-6G With Fast Response and Recovery

In this letter, a multistage high-power RF limiter based on a thin-barrier AlGaN/GaN Schottky barrier diode (SBD) has been proposed and demonstrated in 2–6 GHz. By multifinger device layout design with increased total anode width, the ON resistance of the diode reduces drastically and the maximum RF current can be increased, resulting in an increase of the power capacity of the limiter. In addition, benefiting from the thin-barrier epitaxial design and plasma-damage-free SBD device fabrication, the response time and recovery time of the limiter can be reduced, as well. By incorporating the fabricated devices with a well-designed matching circuit, the RF limiter obtains high incident power above 50 W in continuous wave (CW) mode, over 100 W in pulse mode, a fast recovery time of 35 ns, and spike leakage free at 4 GHz. The simulation and measurement results are well-agreed with each other. This demonstration shows that the GaN-SBD-based limiter is promising for future high-power and high-frequency applications.
Predictive influence of locus of control on adult learners’ academic attitude and engagement

Purpose: The primary purpose of this study was to empirically examine the predictive influence of locus of control (internal and external) on adult learners’ academic attitude and engagement using the theory of andragogy. Design/Methodology/Approach: The study adopted a quantitative research approach and data were collected from 216 adult learners participating in literacy programmes in Oyo State, Southwest Nigeria. The study's data collection tool was a structured questionnaire that focused on four domains: demographic parameters, locus of control (internal and external), academic attitude and academic engagement. A partial Least Square-Structural Equation Modelling (PLS-SEM) analysis with SmartPLS software was used to analyze the collected data. Findings: The findings of the study indicated that adult learners' academic attitude significantly influenced their academic engagement. It was revealed that the internal locus of control had a significant positive impact on the academic attitude and engagement of the adult learners. Furthermore, the relationship between academic attitude and engagement was moderated by internal locus of control. However, the external locus of control had no significant effect on the academic attitude and engagement of adult learners. Conclusion: Interventions aimed at improving adult learners' academic attitude and engagement should take into account the role of the internal locus of control. Strategies such as goal-setting and self-reflection critical to developing a stronger internal locus of control may be effective in promoting positive academic attitudes and engagement among adult learners. Practical Implications: This study’s results have implications for both literacy organizers and facilitators regarding support strategies to maximize learners’ engagement, motivation and effective learning.
Effects of seasonality on the productivity of pastoral goat herds in northern Kenya

Under semi-arid rangeland conditions in northern Kenya, the main factor influencing the productivity of small ruminant flocks is climatic seasonality. In pastoral production systems, alternatives to herd mobility as an efficient adaptive management strategy to overcome nutritional deficits are few. One possible intervention is to manipulate the total seasonal nutrient requirements of the herd through controlled seasonal breeding, which commonly is not practised by pastoralists. The restriction of breeding as a management strategy to match periods of critical nutrient demands with seasonal feed supply in pastoral goat flocks has so far received little attention in research. In order to gain a clearer understanding of the merits and demerits of controlled seasonal breeding, a systematic breeding programme in a herd of Small East African goats was initiated for a period of four years (1984-1988) in Isiolo District, northern Kenya. The study was undertaken to (1) assess the effect of seasonal forage supply on various parameters determining pastoral goat flock performance, and (2), using these baseline data, to test the hypothesis that a seasonally restricted breeding regime can increase flock productivity. A total of 145 does of the Small East African type were maintained under simulated pastoral management conditions and used for a total of 381 exposures which were distributed among 18 consecutive breeding groups consisting of approximately 18 does each. The experimental design resulted in six different, consecutive mating periods or seasons per year, which were replicated three times during the course of the experiment. A total of 8547 recordings were obtained on survival, liveweight, and milk production of does; 9837 observations were available on survival and liveweight development of youngstock. Detailed statistical analyses were performed on all traits relevant to assessing overall biological herd productivity, including: survival of kids and does; reproductive performance of does; growth performance of kids and body weight development of does; and milk production. A steady-state herd model was developed and used to assess overall flock productivity for each of the six consecutive two-month breeding seasons. The procedure is based on a stage-specific description of population dynamics and uses non-linear programming to derive the steady-state herd structure and culling policy that maximizes overall energetic efficiency of the herding enterprise. Mating season had no statistically significant effect on reproduction traits, most likely due to the large variability in within-season environmental conditions among the three production cycles. Differences in kid survival among mating seasons were marked. The results …
AI-based Proactive Storage Failure Management in Software-Defined Data Centres

Abstract — Proactive failure management is essential to alleviate potential risks of service unavailability and downtime in Software-Defined Data Centres (SDDCs). Artificial Intelligence (AI) models enable proactive failure management by predicting and addressing potential failures before they actually occur. This paper proposes an AI-based Proactive Storage Failure Management (APSFM) solution for intelligent data centre management. The proposed solution includes a four-stage framework that employs AI models to predict failures efficiently. The study uses Random Forest and Artificial Neural Network models as examples to predict disk failures by employing Self-Monitoring, Analysis, and Reporting Technology (SMART) attributes. The experimental results have shown that both models can achieve high prediction performance.
Error Resilience in Deep Neural Networks Using Neuron Gradient Statistics

Modern deep neural networks (DNNs) are deployed across a wide range of applications, from medical robotics to autonomous driving, where safety and reliability are key concerns. The complexity, speed, and low-power operation of the underlying hardware makes them vulnerable to soft errors that corrupt the results of computations and memory accesses. Existing approaches to error resilience are either expensive in terms of overhead, require DNN retraining or applicable to only specific hardware domains. In contrast, we present a novel error resilience approach that does not require DNN retraining and scales across computation as well as weight parameter errors. In the proposed methodology, the statistics of gradients of neuron output values relative to adjacent neurons in an ordering of neurons allow tight theoretically grounded thresholding of neuron outputs to diagnose erroneous neuron outputs. These are then set to zero (suppressed) for error resilience. A low-overhead error diagnosis module is used for this purpose and is designed using gradient statistics collected across the training dataset of the DNN. Our approach is compared against state of the art error resilience techniques and validated on multiple datasets, networks and error scenarios as well a hardware test case.
Protective properties of AgNPs green-synthesized by Abelmoschus esculentus on retinal damage on the virtue of its anti-inflammatory and antioxidant effects in diabetic rat

Abstract Eye retinopathy means damage to the retina of the eye, which can have various causes, both congenital and acquired. Diabetes is one of the important causes of eye retinopathy. Retinopathy can develop slowly or quickly, get better on its own, or lead to permanent damage. No treatment is recommended in the early and mild stages. However, close monitoring is essential. Severe form of the disease may require treatment. Recently, the researchers have focused on new options for the treatment of the retinal damages. Present investigation discloses the silver nanoparticles (AgNPs) biosynthesizing capability of the leaves of pharmacologically important Abelmoschus esculentus. Rapid, cost-effective, one-step process of formulation has been achieved. New genre AgNPs were characterized by involving ultraviolet- visible spectroscopy, Fourier transform infrared, and field emission scanning electron microscopy analysis. Effect of AgNPs@Abelmoschus esculentus was assessed on the retinal injury of diabetic rats in this study. After inducing the diabetes by STZ, all rats were separated in to seven different groups (n = 20) including control, diabetic retinopathy group receiving saline solution, and AgNPs@Abelmoschus esculentus treated group receiving AgNPs@Abelmoschus esculentus (20, 40, and 80 µg/kg) for a duration of 8 weeks. After completion of the treatment protocol, the body weight and blood glucose were determined. Leukocytosis, retinal vascular permeability, fundus photography, and retinal vessel diameter, the levels of superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH), and concentrations of IL10, NF-κB, and TNFα in the retina were assessed. AgNPs@Abelmoschus esculentus in all doses reduced significantly (p ≤ 0.01) the weight, glucose, NF-κB, and TNFα concentrations, retinal leukocytosis, and vascular permeability and increased the concentrations of SOD, CAT, GSH, and IL10. Thus, the present research concludes that AgNPs@Abelmoschus esculentus effectively manages the diabetic retinopathy at the best.
Primary source of dietary protein is correlated with differences in the intestinal microbiome diversity

In spite of the recent popularization of the vegan diet, there is currently a deficit of studies that compare the impact and associated health benefits of dietary protein source, one of the main differentiators between vegan and non-vegan diets, on the intestinal microbiome. The objective of this study was therefore to correlate observed changes in the intestinal microbiome to changes in dietary protein sources. We hypothesized that changes in dietary protein source are associated with changes in the composition of the intestinal microbiome. The participant changed their diet over four separate one-week periods to include plant proteins only, plant proteins and dairy proteins, animal proteins only, and finally animal proteins and dairy proteins. The participant followed their respective dietary treatments for one week. Bacterial 16S dual-index sequencing and bioinformatic analysis identified seven genera and two families of bacteria. Of these, Bifidobacterium, Bacteroides, Lactobacillum, and Clostridium exhibited population trends correlated with changes in diet. Moreover, a comparison of α-diversity indicated a statistically significant change over the course of the experiment. Although the results of this experiment cannot be generalized due to the sample size, the hypothesis of this experiment was validated: changes in dietary protein source are correlated with changes in the diversity of the intestinal microbiome. However, we recommend further research into the impacts of variance in dietary protein source on the microbiome, especially by implementing longer-term dietary treatments.