Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-derived Multispectral Indices and Machine Learning

9 The acquisition of phenological events play an integral part in investigating the effects of climate 10 change on forest dynamics and assessing the potential risk involved with the early onset of young 11 leaves. Large scale mapping of forest phenological timing using earth observation data, could 12 facilitate a better understanding of phenological processes due to an added spatial component. 13 The translation of traditional phenological ground observation data into reliable ground truthing 14 for the purpose of the training and validation of Earth Observation (EO) mapping applications is 15 a challenge. In this study, we explored the possibility of predicting high resolution phenological 16 phase data for European beech (Fagus sylvatica) with the use of Unmanned Aerial Vehicle 17 (UAV)-based multispectral indices and machine learning. Using a comprehensive feature 18 selection process, we were able to identify the most effective sensors, vegetations indices, 19 training data partitions, and machine learning models for phenological phase prediction. The best 20 performing model that generalised well over various sites was the model utilising the Green 21 Chromatic Coordinate (GCC) and Generalized Addictive Model (GAM) boosting. The GCC 22 training data was derived from the radiometrically calibrated visual bands from a multispectral 23 sensor and predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model 24 was capable in predicting phenological phases on unseen datasets within a RMSE threshold of 25 0.5. This research shows the potential of the interoperability among common UAV-mounted 26
GAP LINES REDUCTION FOR ENHACED TEMPORAL LANDSAT ETM+ IMAGERY USING MORPHOLOGICAL OPRATIONS

The failure of the Scanning Line Corrector of ETM+2004 of Landsat7 in imposed systematic data gap lines on retrieved the correct satellite images and rejected the ability to provide continuous information in different scientific fields. While a many processing algorithms proposed to fill out this gap lines, utmost of these algorithms are just only valid through the homogeneous image regions. When there are applied to process the heterogeneous ETM+landsat7 images, returning image pixels and image features can’t become correct and precise. In this study a gap filling algorithm has been proposed that is based on the morphologic operations of image. The operations used here are combination of the processes, dilation and erosion. The algorithm employs opening, closing morphologic operations of gap image methods to identify the properties and size the separated gab lines image for ETM+landsat7 images. Each band images has been passed through milt-image processing stages. The images that output of all stages are eight corrected images. Enhanced images are very clear and the gab lines completely disappear from all image bands. The algorithm succeeded in correction and filling gab lines for homogeneous and heterogeneous image areas.
An analysis of the impact of the environment on the transfer of training in the work environment: A systematic review

This study aimed to analyze the impact of the environment on the transfer of training in the work environment, as little has been done to explore the nature of the transfer of the learning work environment. This study used secondary sources. The information was outsourced from online journals (published peer-reviewed articles), published and unpublished dissertations, online sources, and textbooks relevant to the topic being studied. An employee's work environment consists of the physical and social conditions in which employees perform their daily duties and have an impact on how well a person puts their training to use. This study concludes that organizational level (characteristics of the work environment) significantly affect the transfer of training in the work environment positively or negatively. Trainees who worked in a more positive environment and who received more encouragement from their peers made the most progress on transferring training. However, peer-supported trainees in a negative environment were just as successful at transferring what they learned as those in a favourable one. Training transfer was highly correlated with aspects including supervisory assistance, work autonomy, and preferred support. There is a lack of theory on the transfer of training. This study will contribute to the theory by expanding the subject of the work environment about the transfer of training. Findings from this research will advance the science behind the work environment variables and transfer of training. This study will bring new knowledge of the work environment on the transfer of training and will further provide leads for future research. Since this study relied on secondary sources it was limited to the scholarly work that is available at the time the research was conducted. © 2022 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Spatial and Temporal Disparities of Leptospirosis Transmission in Sarawak (Malaysia), 2011-2018

This study is to analyse the spatial distribution of leptospirosis and identify its high and low incidence clusters in Sarawak. The annual incidence rate at the district level was calculated using confirmed report of leptospirosis cases from year 2011 to 2018. Empirical Bayes estimation smoothing of relative risks was used to display the spatial distribution of leptospirosis across the study region. Moran's Global Index and Local Indicators of Spatial Association (LISA) were used to analyse the existence of global and local spatial autocorrelation. Data were analysed using ArcGIS and Geoda software at the district level. The annualised average incidence reported during the study period was 20.83 per 100,000 population, with the highest cases reported in year 2018 (n = 870). The Global Moran's Index revealed spatial clustering of leptospirosis incidence in 2012 (Moran's I : 0.23), 2013 (Moran's I : 0.33), and 2014 (Moran's I : 0.45), while 2011 (Moran's I : -0.01), 2015 and 2016 (Moran's I : 0.09), 2017 (Moran's I : 0.13), and 2018 (Moran's I : 0.04) showed random patterns. High incidence clusters of leptospirosis were primarily congested in the Southeast of Sarawak, involving districts such as Kapit, Belaga, Song, Tatau and Lubok Antu. Spatial and temporal patterns of leptospirosis incidence were heterogeneous across Sarawak. This study facilitates the implementation of targeted interventions and control measures for leptospirosis in Sarawak by identifying spatial cluster and outliers.
Exploring Late‐Time Cosmic Acceleration with Eos Parameterizations in Horava‐Lifshitz Gravity via Baryon Acoustic Oscillations

In this study, the framework of Horava‐Lifshitz gravity to model the Universe's dark matter and dark energy (DE) components is adopted. Specifically, two recent parametrizations for DE models: the CBDRM‐type and CADMM‐type parameterizations is considered. In the analysis, the Hubble parameter is explicitly expressed, denoted as , for these two distinct dark energy models. By doing so, investigate and quantify the accelerated cosmic expansion rate characterizing the late‐time Universe is aimed. This study uses a wide range of datasets. This dataset consists of recent measurements of baryon acoustic oscillations (BAOs) collected over a period of 22 years with the Cosmic Chronometers (CC) dataset, Type Ia supernovae (SNIa) dataset, the Hubble diagram of gamma‐ray bursts (GRBs), quasars (Q), and the latest measurement of the Hubble constant (R22). Consequently, a crucial aspect of this study by plotting the vs. H0 plane is presented. In the context of the ΛCDM model, after incorporating all the datasets, including the R22 prior, the following results: H0 = 71.674089 ± 0.734089 and is obtained. For the CBDRM model, H0 = 72.355058 ± 1.004604 and is found. In the case of the CADMM model, the analysis yields H0 = 72.347804 ± 0.923328 and . Cosmographic analyses for both of the proposed parameterizations in comparison to the ΛCDM paradigm are conducted. Additionally, Diagnostic tests to investigate the evolution of both models is applied. Finally, the Information Criteria test demonstrates that the ΛCDM model emerges as the preferred choice among the models is considered.
Alcohol Advertisements in Youth-Oriented Magazines: Persuasive Themes and Responsibility Messages

Abstract Every year in the United States, health and social problems result from adolescents' use and abuse of alcohol. Alcohol use is associated with high-risk health behaviors and is a leading cause of death among adolescents. Research indicates that alcohol advertising influences drinking intentions for children and adolescents. Using content analysis of 486 advertisements, the focus of this study was to analyze persuasive themes and responsibility messages in alcohol advertising from a popular youth-oriented magazine over a three-year period. Findings indicate that product quality, humor, sex appeal, romance/relationships, and hanging out/partying were the most frequently used persuasive themes. Nearly 75% of advertisements included a responsibility message, but less than 1% included statements warning against underage drinking or encouraging the use of a designated driver. Chi-square analysis indicated a relationship between persuasive themes and the presence of responsibility messages. Given the susceptibility of youth to the emotional appeals of advertising, health education implementing media literacy and advocacy strategies can inform adolescents of the persuasive techniques and mixed messages used in advertising, and help develop skills to decrease their susceptibility to them.
Stellar mass-metallicity relation throughout the large-scale structure of the Universe: CAVITY mother sample

Context. Void galaxies are essential for understanding the physical processes that drive galaxy evolution because they are less affected by external factors than galaxies in denser environments, that is, in filaments, walls, and clusters. The stellar metallicity of a galaxy traces the accumulated fossil record of the star formation through the entire life of the galaxy. A comparison of the stellar metallicity of galaxies in various environments, including voids, filaments, walls, and clusters can provide valuable insights into how the large-scale environment affects the chemical evolution of the galaxy. Aims. We present the first comparison of the relation of the total stellar mass versus central stellar metallicity between galaxies in voids, filaments, walls, and clusters with different star formation history (SFH) types, morphologies, and colours for stellar masses between 108.0 to 1011.5 solar masses and redshift 0.01 < z < 0.05. We aim to better understand how the large-scale structure affects galaxy evolution by studying the stellar mass-metallicity relation of thousands of galaxies, which allows us to make a statistically sound comparison between galaxies in voids, filaments, walls, and clusters. Methods. We applied non-parametric full spectral fitting techniques (pPXF and STECKMAP) to 10 807 spectra from the SDSS-DR7 (987 in voids, 6463 in filaments and walls, and 3357 in clusters) and derived their central mass-weighted average stellar metallicity ([M/H]M). Results. We find that galaxies in voids have slightly lower stellar metallicities on average than galaxies in filaments and walls (by ∼0.1 dex), and they are much lower than those of galaxies in clusters (by ∼0.4 dex). These differences are more significant for low-mass (∼109.25 M⊙) than for high-mass galaxies, for long-timescale SFH (extended along time) galaxies than for short-timescale SFHs (concentrated at early times) galaxies, for spiral than for elliptical galaxies, and for blue than for red galaxies.
Psychosocial Factors in Brain Infections Research in the Last Decade: A Scoping Review

Background: Brain infections are serious neurological events that require immediate care, with around 171 districts of 19 states in India reporting cases every year. Along with the biomedical factors, psychosocial factors of health (BPS) are influential in the outcomes of brain infections as well. Materials and Methods: A scoping review was conducted to understand the psychosocial factors explored in brain infections in the last decade. Articles focusing on social, psychological, public health factors, sequelae, and rehabilitation of inflammatory conditions, both pathogenic and autoimmune were covered. The search was conducted using keywords related to brain infections in electronic databases: PubMed, EBSCO, ProQuest, Scopus, and Google Scholar. Prisma-ScR guidelines were used to screen articles and the identified factors were categorized under eight psychosocial factors using Arksey and O'Malley's framework of analysis. Results: From a total of 6012 documents retrieved, 11 articles met the criteria. Global burden associated with brain infections, disability and death, the vulnerable population at risk of developing brain infections, gaps in existing literature, pathways to care, mental health, cognitive difficulty associated with infections and their sequelae were the major psychosocial factors identified. Conclusions: The review focussed to understand the multitude of psychosocial factors causing delay and damage in brain infections in LMIC context. Along with biomedical factors, there exist several psychosocial factors that could potentially influence the outcome of treatment in brain infections. However, only few have been explored, suggesting the need for more studies to inform the care and sustainable interventions at the macro level to improve the outcomes and reduce the burden in brain infections.
Keep Various Trajectories: Promoting Exploration of Ensemble Policies in Continuous Control

The combination of deep reinforcement learning (DRL) with ensemble methods has been proved to be highly effective in addressing complex sequential decision-making problems. This success can be primarily attributed to the utilization of multiple models, which enhances both the robustness of the policy and the accuracy of value function estimation. However, there has been limited analysis of the empirical success of current ensemble RL methods thus far. Our new analysis reveals that the sample efficiency of previous ensemble DRL algorithms may be limited by sub-policies that are not as diverse as they could be. Motivated by these findings, our study introduces a new ensemble RL algorithm, termed \textbf{T}rajectories-awar\textbf{E} \textbf{E}nsemble exploratio\textbf{N} (TEEN). The primary goal of TEEN is to maximize the expected return while promoting more diverse trajectories. Through extensive experiments, we demonstrate that TEEN not only enhances the sample diversity of the ensemble policy compared to using sub-policies alone but also improves the performance over ensemble RL algorithms. On average, TEEN outperforms the baseline ensemble DRL algorithms by 41\% in performance on the tested representative environments.
The ?-EM algorithm and its basic properties

A new class of statistical algorithms is presented and examined. The method is called the α-EM algorithm. This novel algorithm contains the traditional EM algorithm as a special case of α = –1. The choice of the design parameter “ α” affects the eigenvalues of Hessian matrices for likelihood maximization. This causes much faster convergence than the traditional EM algorithm. Convergence theorems are given for the basic α-EM algorithm and its practical variants. Numerical evaluation shows fast convergence at nearly one-third the iteration counts and one-half the CPU time relative to the traditional method. © 2000 Scripta Technica, Syst Comp Jpn, 31(11): 12–23, 2000
Smart OMVI: Obfuscated Malware Variant Identification using a novel dataset

Cybersecurity has become a significant issue in the digital era as a result of the growth in everyday computer use. Cybercriminals now engage in more than virus distribution and computer hacking. Cyberwarfare has developed as a result because it has become a threat to a nation's survival. Malware analysis serves as the first line of defence against an attack and is a significant component of cybercrime. Every day, malware attacks target a large number of computer users, businesses, and governmental agencies, causing billions of dollars in losses. Malware may evade multiple AV software with a very minor, cunning tweak made by its designers, despite the fact that security experts have a variety of tools at their disposal to identify it. To address this challenge, a new dataset called the Obfuscated Malware Dataset (OMD) has been developed. This dataset comprises 40 distinct malware families having 21924 samples, and it incorporates obfuscation techniques that mimic the strategies employed by malware creators to make their malware variations different from the original samples. The purpose of this dataset is to provide a more realistic and representative environment for evaluating the effectiveness of malware analysis techniques. Different conventional machine learning algorithms including but not limited to Support Vector Machine (SVM), Random Forrest (RF), Extreme Gradient Boosting (XGBOOST) etc are applied and contrasted. The results demonstrated that XGBoost outperformed the other algorithms, achieving an accuracy of f 82%, precision of 88%, recall of 80%, and an F1-Score of 83%.
POLYMERS SEED COATING EFFECT WINTER CANOLA SEEDLING ESTABLISHMENT

One of the major constraints of winter canola (Brassica napus L.) production in southeastern regions of US is poor stand establishment. Improvement in stand establishment with polymer coated of seeds in some crops including spring type canola has been reported but in winter type canola seed it has not been evaluated. Therefore, effect of three polymers [CelGard (CP), Spectrum 511 (SP), and DiscoClear (DC)] each at three concentrations (0.33%, 0.66%, and 0.99%), with and without fungicide Helix, and Apron on seed of two winter cultivars (cvs.) ‘Abilene’ and 'Jetton’ at 0.75 and 1.5 planting depth in three soil types (Clay, Silt, and Sandy loam) were evaluated. Results showed that polymer CelGard in cv. Abilene and DiscoClear in cv. Jetton gave highest seedling establishment in all the three soils type. Seeds of both the cultivars when centricoated with Helix, gave higher seedling establishment (88.0% in cv. Abilene and 85.0% in cv. Jetton) than other polymer treatments without Helix. The seedling establishment was higher when coated seeds were planted at 0.75 depth than 1.5 . This study suggested that polymer coated canola seeds increased the seedling establishment in all the soil types and it was further enhanced when seeds were coated with polymer and fungicide together.
The impacts of psycho-organizational factors on acceptance of organizational change in the 4IR Era

The main study question guiding this investigation is what impacts do psycho-organizational factors (self-efficacy, employee attitude towards organizational change, Leadership 4.0, and perceived organizational support) have on employee acceptance of the organizational change (EAOC) within businesses in the 4IR era? Hence, this paper evaluates the impacts of self-efficacy, employee attitude towards organizational change, Leadership 4.0, and perceived organizational support (POS) on employee acceptance of the organizational change (EAOC) within businesses in the 4IR era. The study’s sample was taken from ten (10) businesses in the Lagos and Oyo States of Nigeria. This paper applied a cross-sectional survey method, and questionnaires were randomly disseminated. However, out of the 250 questionnaires distributed, 246 were recovered, suitable for use, and analyzed with the Statistical Packages for Social Sciences (SPSS version 28). This paper showed that self-efficacy, attitudes towards organizational change, Leadership 4.0, and perceived organizational support significantly, jointly, and independently influence the sustainability of employee acceptance of organizational change within Nigeria’s work organizations in the 4IR era. Work organizations are urged to provide training and exposure to higher responsibilities via delegation of authority. This would help employees within Nigeria’s business organizations gain better self-competence and serve as proof of organizational support, leading to accepting change without much struggle. They should also ensure that employees know the need for these changes in the 4IR era. Further, the management needs to train and support employees in dealing with the challenges inspired by the 4IR. Besides, the management should adopt a leadership style, such as Leadership 4.0, for the conditions and contests from the 4IR. © 2022 by the authors. Licensee SSBFNET, Istanbul, Turkey. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Light scintillation in oceanic turbulence

The scintillation index of plane and spherical light waves as well as of a Gaussian beam, propagating in the clear-water weakly turbulent ocean, is revealed. The results are of utmost importance for underwater optical communications and sensing. An analysis of the threshold between the weak and strong regimes of oceanic turbulence is made, with the accent on the contribution from salinity-induced turbulence. It is found that strong oceanic turbulence can occur at distances as short as several meters, in striking contrast with atmospheric studies for which the typical distances are on the order of a kilometer.
A Preliminary Study on the Beetle Fauna in Peat Swamp Forest of Kota Samarahan, Sarawak, Malaysia

The largest peatland in Malaysia is in Sarawak with approximately 1.66 million ha (13%) of total area, distributed widely in Sibu division followed by Sri Aman, Miri, Kota Samarahan, Sarikei and Bintulu. Despite being the most diverse animal species, the study of insects in peat swamp forest, particularly beetles, is still in the incipient stage. Therefore, this study aimed to provide recent information on the beetle species composition in a peat swamp forest of Real Living Lab, UNIMAS (RLL) located in Kota Samarahan. Beetles were sampled for five consecutive days and nights within a seven-day sampling trip in August 2020. Three sampling methods were employed in this study, namely handpicking method (HPM), modified Pennsylvanian light trap (MPLT) and pitfall trapping (PFT). A total of 15 families representing 37 species and morphospecies with 185 individuals were successfully collected. The most speciose family from the beetle assemblages in RLL is Scarabaeidae with eight species collected (21.62%), followed by Curculionidae with six species (16.22%) and Staphylinidae with five species (13.51%). The most abundant family was also represented by Scarabaeidae with 64 individuals (34.59%), followed by Scolytidae with 27 individuals (14.59%) and Carabidae with 23 individuals (12.43%), respectively. This suggests that these families are good candidates as biodiversity indicator of peat swamp forests. This study is still in its preliminary stage; hence it is important to conduct further beetle samplings in future to better understand the potential of beetle as a bioindicator in the peat swamp habitat as an effort to conserve and protect the habitat and the biodiversity that came along with it.
Is Network Traffic Self-Similar or Multifractal?

This paper addresses the question of whether self-similar processes are sufficient to model packet network traffic, or whether a broader class of multifractal processes is needed. By using the absolute moments of aggregate traffic measurements, we conclude that measured local-area network (LAN) and wide-area network (WAN) traffic traces, with the sample means subtracted, are well modeled by random processes that are either exactly or asymptotically self-similar.