Miracle-dz/UniFAR-FTUs huggingface dataset:
Communications Security in Industry X: A Survey

Industry 4.0 is moving towards deployment using 5G as one of the main underlying communication infrastructures. Thus, the vision of the Industry of the future is getting more attention in research. Industry X (InX) is a significant thrust beyond the state-of-the-art of current Industry 4.0, towards a mix of cyber and physical systems through novel technological developments. In this survey, we define InX as the combination of Industry 4.0 and 5.0 paradigms. Most of the novel technologies, such as cyber-physical systems, industrial Internet of things, machine learning, advances in cloud computing, such as edge and fog computing, and blockchain, to name a few, are converged through advanced communication networks. Since communication networks are usually targeted for security attacks, these new technologies upon which InX relies must be secured to avoid security vulnerabilities propagating into InX and its components. Therefore, in this article, we break down the security concerns of the converged InX-communication networks into the core technologies that tie these, once considered distinct, fields together. The security challenges of each technology are highlighted and potential solutions are discussed. The existing vulnerabilities or research gaps are brought forth to stir further research in this direction. New emerging visions in the context of InX are provided towards the end of the article to provoke further curiosity of researchers.
Benchmarking of parton distributions and their uncertainties

The proper treatment of uncertainties associated to the fit o f Parton Distribution Functions (PDF) has become a subject of great interest in the last few years. A simple way of understanding differences between available approaches to parton fits is to fix s me hypothesis (say, experimental data, QCD parameters, input parameterizations, error trea tment), and check what is the effect of the remaining assumptions. Such studies were previously done in the framework of the first HERA–LHC workshop [1]. In the following we will discuss three benchmark fits. The firs t one is presented in Sect. 2. It is based on the H12000 parton fit [2], and it compares a new ve rsion of this fit, in which uncertainty bands are determined [3, 4] using a Monte Carlo m ethod, to the reference fit, where uncertainty bands are obtained using the standard Hessian m ethod. The main motivation of this benchmark is to study the impact of possible non-Gaussian be haviour of the data and, more generally, the dependence on the error treatment. The second benchmark is presented in Sect. 3. It is based on th e study performed by S. Alekhin and R. Thorne in Ref. [1], which compared the fits by their respective groups to a common reduced set of data with common assumptions, and also to their respective reference (global) fits. This comparison is extended here in two ways. F irst, the comparison is extended to include an NNPDF fit to the same reduced set of data with the s ame assumptions, and the NNPDF1.0 reference fit [5]. Second, results are also compare d to a fit based on the recent MSTW 2008 [6, 7] analysis. As in the Thorne benchmark fit, this uses slightly different data sets and assumptions; it is furthermore modified to use the sa me input parameterization and improved treatment of uncertainties as MSTW. The main purpo se f these comparisons is to answer the questions (a) to which extent fit results from vari ous groups obtained using different methodologies still differ from each other when common or si milar assumptions and a common or similar reduced dataset are used and (b) how the fits to the r educed dataset by each group compare to the fit to the full dataset. The third benchmark, discussed in Sect. 4, is a further elabo ration on the benchmark presented in Sect. 2, extended to include the NNPDF fit, which als o uses a Monte Carlo approach. The main purpose of this benchmark is to compare two fits (H1 an d NNPDF) which have the same error treatment but different parton parameterizatio ns. The inclusion in this benchmark of the NNPDF fit is also interesting because it allows a comparis on of a fit based on a very consistent set of data coming from the H1 collaboration only, to fits which include all DIS data sets, which are less compatible than the H1 sets alone.
Islamic Crowdfunding: A Twitter Sentiment Analysis

This research aims to measure public sentiment related to Islamic crowdfunding on the Twitter social media platform. The research method involves the extraction and classification of tweet data using a Python Library called VADER (Valence Aware Dictionary and Sentiment Reasoner). The research utilized tweet data posted in the past one year. The results showed fluctuations and decreases in the number of tweets discussing Islamic crowdfunding. Word cloud analysis on keywords shows that in positive sentiments, words such as "crowdfunding platform," "crowdfunding impact," "crowdfunding," "Islamic finance," and "inclusivity Islamic finance" dominate the visualization. Overall, the majority of tweets expressed a positive attitude towards Islamic crowdfunding, with 44.0% positive sentiment. A total of 47.2% of tweets showed neutral sentiment, while 8.7% showed negative sentiment. These results illustrate that people generally give positive support to the concept of Islamic crowdfunding, although there is still a small proportion of tweets that express less favorable views. This research provides valuable insights into people's perceptions and responses to Islamic crowdfunding in cyberspace.
Evaluation of the RNA Silencing Suppression Activity of Three Cherry Virus F-Encoded Proteins

Cherry virus F (CVF) is a newly emerged sweet cherry virus. CVF has been identified in a small number of countries and it has not been associated with discrete symptomatology. RNA silencing is a natural defense mechanism of plants against invaders that degrades viral RNA in a sequence-specific manner. As a counter-defense, plant viruses encode one or more RNA silencing suppressors (RSSs) interfering with the silencing pathway via several mechanisms. To identify putative RSSs, the three proteins (MP, CPL, CPS) encoded by the RNA2 of CVF were selected and separately cloned into the binary vector pART27. The clones were used for transient expression experiments in Nicotiana benthamiana leaves, using co-agroinfiltration with a GFP-expressing vector. In both CPL and CPS, a rapid decrease in fluorescence was recorded, comparable to the negative control, whereas the MP of CVF retained the GFP’s fluorescence for a few days longer even though this was observed in a small number of infiltrated leaves. Further experiments have shown that the protein was not able to inhibit the cell-to-cell spread of the silencing signal; however, a putative interference with systemic silencing was recorded especially when the induction was carried out with double-stranded GFP RNA. Overall, our results indicate that the MP of CVF is putatively implicated in the suppression of RNA silencing, though further experimentation is needed to unveil the exact mode of action.
Cassava Leaf Disease Prediction Using Efficientnet-B0 Model

The FAO estimates that 60 percent of the world’s population makes their living from agriculture. The rapid increase in the global populations demand for food is also quite fast. In this case, plant diseases pose a substantial threat to the agricultural industry. Therefore, deep learning algorithms are applied to spot them at an early stage as a move towards protecting farmers against such losses while increasing crop yield. We applied CNNs in developing a technique for identifying different diseases of cassava leaf which lead to low yields. We created a cost-effective model that will help farmers to save costs and specialize in farming operations. Early diagnosis of these diseases is proposed by EfficientNet-B0, which may serve well since they provide a remedy for minor cases of cassava leaf illnesses. This may lead to better cassava crop health, and therefore more food security especially in some particularly vulnerable places.
On the Upstream Track Deflection of Tropical Cyclones Past a Mountain Range: Idealized Experiments

AbstractUpstream track deflection of a propagating cyclonic vortex past an isolated mountain range is investigated by using idealized simulations with both boundary layer turbulent mixing and cloud effects. The westbound vortex past a shorter mountain range may experience an earlier northward deflection prior to landfall. The vortex then takes a sudden southward turn as it gets closer to the mountain range, in response to the effects of the stronger northerly wind over the mountain due to the effects of channeling flow. The vortex may deflect southward when approaching a longer mountain range and then rebound northward upstream of the mountain ridge. The southward deflection is primarily induced by the convergence (stretching) effect due to the combination of the speedy core at the southwestern flank of the vortex and a northerly jet between the vortex and the mountain. The vortex then rebounds northward to pass over the mountain as the speedy core rotates counterclockwise to the eastern flank of the vort.
Statistical Analysis of Factors for Changes in Student Behavior in Solo Raya as a Catalyst for Empowerment of Waste Banks: The Role of Agents of Change

Overcoming the substantial waste issue in Indonesia poses a significant challenge. The establishment of waste banks is identified as a potential solution, yet there needs to be more active community engagement in becoming waste bank patrons. Hence, this study aims to identify factors influencing individuals' behaviors in supporting waste bank empowerment. The research adopts the Theory of Planned Behavior, encompassing six variables: attitudes, subjective norms, knowledge, financial motives, intentions, and behavior. Questionnaires were distributed via social media for data collection, with 200 respondents selected through purposive sampling, primarily active students at the University in Solo Raya the analysis employed multiple linear analysis to assess the significance of the regression model, coefficient of determination, and the partial influence of each variable. Findings indicate that subjective norms and financial motives positively influence intentions, though not significantly. Conversely, attitude and knowledge variables significantly and positively impact intentions. Additionally, the intention variable demonstrates a significant positive effect on behavior supporting waste bank empowerment.
Natural variability in yield and properties of Balanites aegyptiaca (L.) Delile kernel oil from different locations in Nigeria

Balanites aegyptiaca known as the desert date is an underutilized and neglected wild fruit tree/shrub of the arid and semi-arid zones with high economic potential. This study, therefore, was conducted to investigate the yield, physico-chemical and phytochemical properties, proximate and mineral composition of desert date kernel oil from different locations. Oil was extracted using the soxhlet apparatus, while hexane served as solvent. Standard procedures were used to determine the yield and properties of desert date kernel oil. The data obtained were subjected to the analysis of variance (p< 0.05). Oil yield (%) was found to range from 21.09±1.04-43.95±1.85 and varied significantly among the locations. Acid and saponification values (mg KOH/g) increased significantly from 2.11±0.07 to 1.36±0.07 and 76.35±0.22 to 179.85±0.90, respectively. The refractive index ranged from 1.36±0.15 to 1.48±0.06, while the iodine value (mg KOH/g) varied significantly from 67.07±1.53 to 85.33±2.52. Saponins, tannins, and phenol were discovered to vary significantly among the locations, and range from 3.82±0.30 -5.83±0.45 (mg/100 g), 1.31±0.10 - 1.68±0.03 (mg/100 g) and 1.51±0.05 - 2.09±0.06 (mg/100 g), respectively. The broad variability observed in the properties of desert date kernel oil among locations indicates the feasibility of improving kernel oil yield and properties through selection
Has a High Dose of Vitamin D3 Impacted Health Conditions in Older Adults?—A Systematic Review and Meta-Analysis Focusing on Dose 100,000 IU

Background: Older adults are prone to vitamin D3 (VD3) deficiency, which may impair their health. A high dose of VD3 (HDVD3 = 100,000 IU) could improve their 25-hydroxyvitamin D3 [25(OH)D] level and health outcomes. However, evidence for such a beneficial effect of HDVD3 in older adults coming from clinical trials is mixed. Objective: To review the literature on the efficacy of a single dose of 100,000 IU of VD3 in older people. Methods: We searched PubMed/Medline, Science Direct, and NIH’s clinical trials registry for clinical studies on the effect of a single high dose of VD3 on various health outcomes in older people. We also performed a meta-analysis using the standardized mean difference to assess the effect of VD3 on its blood level. Due to expected high heterogeneity, its amount (i.e., tau2) was estimated using the DerSimonian-Laird estimator. To estimate tau2, the Q-test for heterogeneity and the I2 statistic were calculated. Results: Search results identify 13 studies that reported diverse health outcomes, such as lung and cardiovascular function, skin cancer progression, intensive care unit mortality, immune system response, and bone density. The meta-analysis showed a significant increase in 25(OH)D blood levels after treatment in 10 studies, with an average standardized mean difference of 2.60 ng/mL (95% CI: 2.07 to 3.13). Their results suggested that a single high dose of VD3 may benefit intensive care unit patients and skin cancer patients in remission. However, evidence for other beneficial health effects of HDVD3 was mixed due to high heterogeneity among studies. Conclusions: A single high dose of VD3 may positively affect some health outcomes in older people, possibly due to its pleiotropic and immunomodulatory effects. However, the evidence needs to be more extensive and consistent, and more rigorous studies are required to confirm the benefits and safety of VD3 high doses in older patients.
Class imbalance aware drift identification model for detecting diverse attack in streaming environment

Detecting fraudulent transactions in a streaming environment presents several challenges including the large volume of data, the need for real-time detection, and the potential for data drift. To address these challenges a robust model is needed that utilizes machine learning techniques to classify transactions in real-time. Hence, this paper proposes a model for detecting fraudulent transactions in a streaming environment using xtream gradient boost (XGBoost), cross-validation and class imbalance aware drift identification (CIADI) model. The performance of the proposed method is evaluated using datasets named credit card and Network Security Laboratory (NSL-KDD) dataset. The results demonstrate that the model can effectively detect fraudulent transactions with high accuracy, recall, and F-measure. The results show that the proposed CIADI model attained 95.63% for the credit card dataset which is higher accuracy in comparison to the generative-adversarial networks (GAN), network-anomaly-detection scheme-based on feature-representation and data-augmentation (NADS-RA) and feature-aware XGBoost (FA-XGB). Further the proposed CIADI model attained 98.5% for the NSL-KDD dataset which is higher accuracy in comparison to the NADS-RA, stacked-nonsymmetric deep-autoencoder (sNDAE) and convolutional neural-network (CNN). This study suggests that the proposed method can be an effective model for detecting fraudulent transactions in streaming environments.
Link Prediction Based on Community Information and Its Parallelization

Link prediction refers to predicting the likelihood of the existence of an unknown link or a future link based on the observed information. It plays an important role in complex network analysis. Classical similarity indices based on common neighbor nodes consider that each common neighbor has the same effect to the link likelihood. However, in real networks, the contribution of common neighbor nodes belonging to different communities may be different. This paper proposes a new link prediction algorithm with an adjustable parameter based on community information (CI). Applying the proposed algorithm to nine similarity indices, a family of CI-based indices, referred to as CI forms, is proposed. We empirically investigate the impact of the CI on the accuracy of link prediction with nine classical indices. The experiments on ten real-world networks show that, compared with tradition local indices, the proposed CI forms have better overall prediction performance. Furthermore, a parallelization algorithm is developed to apply the proposed CI-based link prediction algorithm to large-scale complex networks using Spark GraphX. The experiment results show that the proposed parallel algorithm significantly improves the computing efficiency of link prediction.
Deviation among Ghanaian Basic School Pupils Use of English in the Classroom: A case study of Bono and Ahafo Regions

The focus of this study is to discuss some deviations and innovations basic school pupils in the Bono and Ahafo regions of Ghana use in their classrooms and how these deviations are applied to their communication in the classroom. A descriptive survey design was adopted to help achieve these objectives. A qualitative approach was adopted to help explain the study better. A total population of 9,219 pupils and a sample size of 400 pupils were observed for the study. The findings of the study revealed that in the area of phonology, pupils replace the inter-dental fricative /ð/ with a voiced alveolar stop /d/. They also used the voiceless dental fricative /θ/ in place of /t/ a voiceless alveolar in their speech. Again, most pupils also use the trill /r/ in place of the /l/ sound both in writing and articulation. Under semantics, pupils usually transliterate statements from their L1 into the target language (English) and tend to use hyperbole for emphasis. Furthermore, over-generalization of syntactic rules was found as one of the innovations pupils make in classroom communication. Spelling pronunciation was also found as part of the innovations basic school pupils use in classroom communication.
Relationship between Mathematical Parameters of Modified Van der Pol Oscillator Model and ECG Morphological Features

The mathematical model describes the electrical and mechanical activity of the cardiac conduction system thought set of differential equations. By changing the value of parameters included in these equations, it is possible to change the amplitude and the period of ECG waves. Although this model is a powerful tool for modeling the electrical activity of the heart, its use is often limited to those familiar with the differential equations that describe the system. The purpose of this work is to provide a system that allows generating an ECG signal using Ryzhii model without knowing the details of differential equations. First, we provide the relationships between the ECG wave features and the model parameters; then we generalize them through fitting neural networks. Finally, putting in series fitting neural network and heart model, we provide a system that allows generating a synthetic signal by setting as input only the morphological ECG feature. We computed numerical simulation in Simulink environment and implemented the fitting neural networks in Matlab. Results show that non-linear trends characterize the correlation functions between ECG morphological features and model parameters and that the fitting neural networks can generalized this trend by providing the model parameters given in input the respectively ECG feature.
Effect of Flaxseed Supplementation in the Maternal Diet During Early Lactation on the Oxidative Status of Sows and Growth of Piglets

The present study was conducted on 21 crossbred Large White Yorkshire sows to assess the effect of flaxseed supple mentation on oxidative stress in sows and growth of piglets. All sows were equally allocated into three groups. Group 1 served as the control and followed their normal feeding schedule. Group 2 and group 3, in addition to their normal feed ing schedule, were supplemented with flaxseed at a rate of 0.5% and 1.0% of the dry matter, respectively. Blood samples were collected 15 days prior to farrowing, on the day of farrowing (day 0), at weekly intervals until day 28 of lactation and at monthly intervals during gestation to assess the oxidative status in each sow. Body weight and weight gain in each piglet was measured during lactation period. The assessment of oxidative stress in blood was done through estimations of malondialdehyde (MDA), end product of lipid peroxidation. No changes (P>0.05) in MDA concentrations were mani fested at any of the days measured during lactation and gestation in sows of all the groups. Similarly, there was no effect of flaxseed treatment on superoxide dismutase (SOD) levels in all sows at any of the days measured during lactation period (P>0.05) as well as during gestation period (days 30-90). In the current farrowing, the piglets exhibited a steady increase (P<0.05) in bodyweight as well as weight gain in all groups from day 0 until day 28 of lactation. No effect (P>0.05) of flax seed fed to sows on litter weight and weight gain was noticed until day 21 of lactation, However, the effect of treatment was evinced on day 28 of lactation compared to control group. In conclusion, flaxseed supplementation had no impact on MDA and SOD concentrations in sows; nevertheless a treatment effect was noticed on body weight and weight gain in piglets.
Effectiveness of Lumbar Stabilization Exercise in Improving Lumbar Spine Endurance by Using Sorenson’s Test and EMG Analysis for People with Chronic Low Back Ache

Background: Strength endurance in lower back region is the most important factor for chronic low - back pain.There are several treatments for strengthening low back muscle. Strength and endurance that have proven benefitin CLBP patients; however, there are very few studies that focus on lumbar stabilization exercises.Purpose: The intention of this study was to improve the strength and endurance more effectively than usualprograms by performing lumbar stabilization exercises.Materials And Methods: This Quasi-Experimental study had 40 participants with persistent LBP, aged 25 to50, divided into lumbar stability (n=20) and lumbar strengthening (n=20) groups. sEMG measured strengthendurance, NPRS scale measured pain, before and after intervention.Results: Lumbar stabilization exercises (Group A) were significantly more effective than static stretching andstrengthening exercises (Group B) in treating persistent low back pain, with p<0.001 in outcome measures.Conclusion: Lumbar stabilization exercises have been demonstrated to be more effective than conventionaltherapies in patients with CLBP.