Miracle-dz/UniFAR-FTUs huggingface dataset:
Can Artificial Intelligence Help in the Study of Vegetative Growth Patterns from Herbarium Collections? An Evaluation of the Tropical Flora of the French Guiana Forest

A better knowledge of tree vegetative growth phenology and its relationship to environmental variables is crucial to understanding forest growth dynamics and how climate change may affect it. Less studied than reproductive structures, vegetative growth phenology focuses primarily on the analysis of growing shoots, from buds to leaf fall. In temperate regions, low winter temperatures impose a cessation of vegetative growth shoots and lead to a well-known annual growth cycle pattern for most species. The humid tropics, on the other hand, have less seasonality and contain many more tree species, leading to a diversity of patterns that is still poorly known and understood. The work in this study aims to advance knowledge in this area, focusing specifically on herbarium scans, as herbariums offer the promise of tracking phenology over long periods of time. However, such a study requires a large number of shoots to be able to draw statistically relevant conclusions. We propose to investigate the extent to which the use of deep learning can help detect and type-classify these relatively rare vegetative structures in herbarium collections. Our results demonstrate the relevance of using herbarium data in vegetative phenology research as well as the potential of deep learning approaches for growing shoot detection.
Historical mitochondrial genome introgression confounds species delimitation: evidence from phylogenetic inference in the Odorrana grahami species complex

Abstract Species delimitation is essential to informing conservation policy and understanding ecological and evolutionary processes. Most of our recent gains in knowledge on animal diversity rely on morphological characteristics and mitochondrial (mt) DNA variation. Concordant results based on both have led to an unprecedented acceleration in the identification of new species and enriched the field of taxonomy. However, discordances are also found commonly between morphological and mtDNA evidence. This confounds species delimitation, especially when gene flow or mt genome introgression has occurred. Here, we illustrate how mt genome introgression among species of the Odorrana grahami complex confounds species delimitation using the combined evidence of morphological characters, mt variation, and thousands of nuclear single-nucleotide polymorphisms (SNPs) from genotyping-by-sequencing (GBS). Fifty-eight samples across the distribution of the O. grahami complex were included. The mtDNA matrilineal genealogy indicated 2 clades, with O. grahami and Odorrana junlianensis clustered together. In contrast, all nuclear evidence including gene trees, species trees, and genetic structure analyses based on GBS data support 3 species with distinct genetic clusters. These 3 distinct genetic clusters also correspond to distinct morphological characters. They affirm the distinct taxonomic entities of both O. grahami and O. junlianensis, as well as a third clade distinct from either. Which species the third clade belongs to remains unclear and will require further testing. The nuclear genomic loci contradict the COI evidence, with indications of rampant historical mt genome introgression among the species of the O. grahami complex. These discordant signals previously confused species delimitation efforts in this group. Based on these findings, we recommend the integration of independent data, especially nuclear genomic evidence, in species delimitation so as to be robust against the pitfalls of mt introgression.
Relationship between Peak Inspiratory Flow and Patient and Disease Characteristics in Individuals with COPD—A Systematic Scoping Review

Optimal delivery of medication via dry powder inhalers, the most commonly prescribed inhaler type, is dependent on a patient achieving a minimum level of inspiratory flow during inhalation. However, measurement of peak inspiratory flow (PIF) against the simulated resistance of a dry powder inhaler is not frequently performed in clinical practice due to time or equipment limitations. Therefore, defining which patient characteristics are associated with lower PIF is critically important to help clinicians optimize their inhaler choice through a more personalized approach to prescribing. The objective of this scoping review was to systematically evaluate patient and disease characteristics determining PIF in patients with chronic obstructive pulmonary disease (COPD). Medline, Cochrane and Embase databases were systematically searched for relevant studies on PIF in patients with COPD published in English between January 2000 and May 2021. The quality of evidence was assessed using a modified Grading of Recommendations Assessment, Development and Evaluation checklist. Of 3382 citations retrieved, 35 publications were included in the review (nine scored as high quality, 13 as moderate, nine as low, and four as very low). Factors correlating with PIF in >70% of papers included both patient characteristics (lower PIF correlated with increased age, female gender, shorter height, decreased handgrip and inspiratory muscle strength, and certain comorbidities) and disease characteristics (lower PIF correlated with markers of lung hyperinflation, lower peak expiratory flow [PEF] and increased disease severity). Other factors correlating with adequate/optimal or improved PIF included education/counseling and exercise/inspiratory muscle training; impaired physical function and errors in inhalation technique/non-adherence were associated with low/suboptimal PIF. In conclusion, clinicians should measure PIF against the simulated resistance of a particular device wherever possible. However, as this often cannot be done due to lack of resources or time, the patient and disease characteristics that influence PIF, as identified in this review, can help clinicians to choose the most appropriate inhaler type for their patients.
A Machine Learning-Based Classification and Prediction Technique for DDoS Attacks

Distributed network attacks are referred to, usually, as Distributed Denial of Service (DDoS) attacks. These attacks take advantage of specific limitations that apply to any arrangement asset, such as the framework of the authorized organization’s site. In the existing research study, the author worked on an old KDD dataset. It is necessary to work with the latest dataset to identify the current state of DDoS attacks. This paper, used a machine learning approach for DDoS attack types classification and prediction. For this purpose, used Random Forest and XGBoost classification algorithms. To access the research proposed a complete framework for DDoS attacks prediction. For the proposed work, the UNWS-np-15 dataset was extracted from the GitHub repository and Python was used as a simulator. After applying the machine learning models, we generated a confusion matrix for identification of the model performance. In the first classification, the results showed that both Precision (PR) and Recall (RE) are $\sim 89$ % for the Random Forest algorithm. The average Accuracy (AC) of our proposed model is ~89% which is superb and enough good. In the second classification, the results showed that both Precision (PR) and Recall (RE) are approximately 90% for the XGBoost algorithm. The average Accuracy (AC) of our suggested model is ~90%. By comparing our work to the existing research works, the accuracy of the defect determination was significantly improved which is approximately 85% and 79%, respectively.
Investigation of the Influence of Neutron Irradiation on Cladded Nuclear Reactor Pressure Vessel Steel Blocks by Magnetic Adaptive Testing

The influence of neutron irradiation degradation on reactor pressure vessel steel was investigated. Large blocks were irradiated by neutrons in the BR2 reactor at a low irradiation temperature. They were measured by a nondestructive magnetic method, magnetic adaptive testing, before and after the neutron irradiation. It was shown that the modification of the magnetic parameters due to the neutron irradiation was well detectable by this nondestructive method. It was also shown that the influence of neutron irradiation could be detected with a reasonable signal-to-noise ratio through the cladding. The present results are considered as a first step towards the application of magnetic adaptive testing in the nuclear industry.
DOES UNEMPLOYMENT LEAD TO THE BREAKDOWN OF A MARRIAGE? EVIDENCE FROM THE SLOVAK REPUBLIC

In a sample of 79 districts of the Slovak Republic for the period 1997 2019, we examine the connection between the unemployment rate and the crude divorce rate. Our findings reveal a negative and statistically significant relationship, which suggests a procyclical nature in terms of the economic cycle. The estimated coefficients are relatively consistent even after the addition of control variables considering socio economic factors or fixed effects. The results of the regression analysis show that an increase in the unemployment rate by one percentage point is accompanied by a decrease in the crude divorce rate by an average of 0.024 units. These findings support the assumptions of the theory of the high costs of divorce that spouses must bear. In a period of high unemployment, it is more acceptable for spouses to stay married than to bear the high costs associated with legal proceedings or the loss of part of the joint savings. The analysis of possible non-linear relationships between the unemployment rate and the crude divorce rate also showed a negative relationship. Quantile regression also points to the U-shaped relationship between variables and supports the opportunity theory. ii
3D Modeling of IP Effects on Electromagnetic Data in the Time Domain

We develop a new technique for forward modeling the threedimensional electromagnetic (EM) response of chargeable material in the time domain. The method can be applied to any type of transmitter (loop or grounded electrodes) or receiver (H or E field measurements) and operates directly in the time domain, requiring no transformations. A synthetic example of an airborne time domain survey passing above a vertical, polarizable dike is presented.
Active Valve Resonant Pulse Combustor for Pressure Gain Combustion Applications

This work is an experimental demonstration of a novel resonant pulse combustor technology for which sustained pressure gain can be achieved at thermal equilibrium. The resonant pulse combustor employs an actively driven air inlet valve with passively modulated fuel injection such that the device operating frequency is speciﬁed by the valve set-point frequency. This conﬁguration enables more control over the combustor operation compared to passive valve and valveless resonant pulse combustors and enables the durability necessary for consideration in practical applications. Combustor performance is characterized with measured stagnation pressure gain as a function of stagnation temperature and fuel mass ﬂux. Steady state stagnation pressure ratios between 1.005 to 1.025 are demonstrated for gaseous (ethylene) and liquid (gasoline) fuels at temperature ratios from 2.8 to 4.5. Baseline performance measurements for a well studied passive valve resonant pulse combustor are presented for comparison. The proposed active valve technology provides a direct path for translation from research type devices to practical gas turbine applications by addressing key challenges inherent of existing resonant pulse combustor technologies.
SREBP activation contributes to fatty acid accumulations in necroptosis

Necroptosis is a type of programmed cell death. It is characterized by membrane permeabilization and is associated with a strong inflammatory response due to the release of intracellular components due to compromised membrane integrity. We recently showed that the accumulation of very long chain fatty acids (VLCFAs) contributes to membrane permeabilization during necroptosis. However, the mechanisms that result in the accumulation of these cytotoxic lipids remain unknown. Using comparative transcriptomics, we found that sterol regulatory element-binding protein 1 (SREBP1) is activated and that its downstream gene targets result in the accumulation of VLCFAs during necroptosis. We demonstrated that activation of SREBP1 during necroptosis exacerbates cell death. On the contrary, inactivation of SREBP1 reversed the accumulation of VLCFAs, and restored cell death and membrane permeabilization during necroptosis. Collectively, our results highlight a role for SREBP1 in regulating lipid changes during necroptosis and suggest SREBP1 as a potential target for therapeutics to reduce membrane permeabilization during necroptosis.
Trust, Media Credibility, Social Ties, and the Intention to Share towards Information Verification in an Age of Fake News

Social media is now the primary form of communication between internet users and has soared in popularity, which has directly impacted the spread of the phenomenon of fake news. Fake news is not only a widespread phenomenon; it is also problematic and dangerous for society. The aim of this study is to understand the phenomenon of fake news better. The study utilised a structural modelling equation in order to identify how Polish society perceives the problem of fake news and assess the extent to which it trusts content that is published on the internet. The key goal was to determine what factors have the most significant influence on the verification of information being viewed on the internet. By deploying the partial least squares method of validation, SmartPLS3 software was used to process the survey results. The strongest positive effect on information verification behaviour was found to be fake news awareness, which was followed by the intention to share information. The research did not consider any clear connections that may exist between the nature of fake news and its recipient; however, much of the fake news that appears on the internet is political in nature. The study can be used by news reporting companies and provides preliminary information for developers responsible for running social media sites as well as users who want to combat and limit the spread of fake news online. This study expands on the available literature related to fake news by identifying the effects on information verification behaviour of fake news awareness and the intention to share data.
Making model judgments ROC(K)-solid: Tailored cutoffs for fit indices through simulation and ROC analysis in structural equation modeling

Researchers commonly evaluate the fit of latent-variable models by comparing canonical fit indices (χ2, CFI, RMSEA, SRMR) against fixed cutoffs derived from simulation studies. However, the performance of fit indices varies greatly across empirical settings, and fit indices are susceptible to extraneous influences other than model misspecification. This threatens the validity of model judgments using fixed cutoffs. As a solution, methodologists have proposed four principal approaches to tailor cutoffs and the set of fit indices to the specific empirical setting at hand, which we review here. Extending this line of research, we then introduce a refined approach that allows (1) generating tailored cutoffs while also (2) identifying well-performing fit indices in the given scenario. Our so-called simulation-cum-ROC approach combines a Monte Carlo simulation with receiver operating characteristic (ROC) analysis. The Monte Carlo simulation generates distributions of fit indices under different assumptions about the population model that may have generated the data. ROC analysis helps evaluate the performance of fit indices in terms of their ability to discriminate between correctly specified and misspecified analysis models and allows selecting well-performing ones. It further identifies cutoffs for these fit indices that minimize Type I and Type II errors. The simulation-cum-ROC approach provides an alternative to fixed cutoffs, allows for more valid decisions about accepting or rejecting a model, and improves prior approaches to tailored cutoffs. We provide a shiny app that makes the application of our approach easy.
A 3D Matching Method to Compare a Scan to Its Reference using 3D Registration and Monte Carlo Metropolis Hastings Optimization for Industrial Inspection Applications

Currently in industry, inspection tasks are essential to ensure a product efficacity and reliability. Some automated tools to inspect, i.e. to detect defect exist, but they are not adapted to an industrial inspection application. Most of industrial inspection is human made. In this article, we propose a new algorithm to match a 3D point-cloud to its 3D reference to track visual defects. First, we reconstruct a 3D model of an object using Iterative Closest Points (ICP) algorithm. Then, we propose an ICP initialization based on a Monte Carlo Metropolis-Hasting optimization to match a partial point-cloud to its model. We applied our algorithm to the data measured from a Time-of-Flight sensor and a RGB camera. We present the results and performance of this approach for objects of different complexities and sizes. The proposed methodology shows good results and adaptability compared to a state-of-the-art method called Go-ICP.
Determinants Of Enterprise Resource Planning System Implementation In Ethiopia

Enterprise resource planning system is the type of software that organizations use to manage day-to-day business operations. The effective implementation of Enterprise Resource Planning (ERP) enables business firms to boost their overall performance. So, studying key determinants of ERP system implementation is a valuable issue. This study aimed to identify determinants of Enterprise resource planning implementation of Ethiotelecom Corporation of Ethiopia. The quantitative research approach was employed. To achieve the objective of this study, an explanatory research design where the effect caused by the independent variables on the dependent variable is observed through regression analysis. The simple random sampling technique was used to select respondents. A multiple regression analysis models was employed by the researcher. Accordingly, the result of regression analysis showed that top management support, conflict resolution, business process reengineering, and knowledge transfer have positive and statistically significant effects but independent variables i.e., lack of end-user involvement and lack of technical support to ERP system has a negative and statistically significant effect on ERP implementation. Based on the findings of the study, the researcher forwarded possible recommendations for Ethiotelecom Corporation to work on statistically significant variables due to fact that they have significant influence in improving ERP system effective implementation an organization.
Smoking Obstructive Sleep Apnea: Arguments for a Distinctive Phenotype and a Personalized Intervention

Background: This is the first study that aims to define smoking, with obstructive sleep apnea (OSA), as a phenotype (SOSA). Moreover, we wanted to demonstrate the deleterious effects of the continuation of smoking on OSA. Methods: The cross-sectional study highlighted four dimensions of SOSA: the demographic and anthropometric features, the symptoms, the comorbidities, and the sleep study parameters. This study compared these characteristics between current smokers (CS), those who have never smoked (NS), and ex-smokers (ES) with OSA. Results: More men (83.95% in CS, versus 66.67% in NS) and an earlier onset of OSA (average age = 50.05 in CS, versus 52.26 in NS, p = 0.04) were recorded among CS. The distinguishing symptom of CS was daytime sleepiness, with an Epworth score that was significantly higher than in NS. Chronic obstructive pulmonary disease (COPD) was significantly more prevalent in CS (38.27%) than in NS (1.51%) (p < 0.001). The severity of OSA, consisting of a higher apnea-hypopnea index, a higher oxygen desaturation index, and a longer time spent below 90% oxygen saturation during sleep was significantly influenced by smoking. Conclusions: The SOSA phenotype includes younger male patients with a higher waist circumference, suggesting central obesity. They have a higher prevalence of COPD and a greater severity of OSA, in correlation with the number of pack-years of smoking.
Nonlinear plasmon-exciton infrared photodetector operating in the two-photon absorption mode

Semiconductor quantum dots (QDs) are known for their high capacity to nonlinear interaction with light via two-photon absorption (TPA). This allows them to absorb efficiently the infrared photons with energies lower than the bandgap energy. In addition, the TPA can be further enhanced due to interaction of QD excitons with plasmons of metal nanoparticles making it possible to design highly efficient optoelectronic devices with a nonlinear response to irradiation. To achieve this goal, we have fabricated the nonlinear photodetectors based on the QDs and silver nanoplates (SNPs) which combine both mentioned effects and demonstrate a highly efficient nonlinear photocurrent response at the excitation in the nearinfrared region of optical spectrum. In this study, we compared the photodetectors efficiency enhancement in hybrid devices based on the CdSe QDs and SNPs designed by the different ways. In one case, the SNPs were deposited on the top of 10 layers of QDs, and in the other, they were placed between these layers. We have demonstrated that both types of hybrid photodetectors operate in the two-photon regime. At the same time, we have found that the two-photon absorption efficiency was significantly higher in the sample where the SNPs were located between the QD-layers.
Spectral replacement using machine learning methods for continuous mapping of Geostationary Environment Monitoring Spectrometer (GEMS)

Abstract. Earth radiance in the form of hyperspectral data contains useful information on atmospheric constituents and aerosol properties. The Geostationary Environment Monitoring Spectrometer (GEMS) is an environmental sensor measuring such hyperspectral data in the ultraviolet and visible (UV/VIS) spectral range over the Asia-Pacific region. After successful completion of the in orbit test of GEMS in October 2020, bad pixels are found as a remaining calibration issue to be updated with follow-up treatment. Currently, one-dimensional interpolation in the spatial direction is performed in operation to replace the erroneous pixels of GEMS, which causes high interpolation error for a wider defect area on a detector array. To resolve the issue, this study suggests machine learning methods with artificial neural network (ANN) and multivariate linear regression (Linear) to fill in a spectral gap of defective spectra. The machine learning models are trained with normal measurements to emulate spectral relations between input and output radiances in a spectrum. For efficient training, dimensionality reduction for the input radiances is applied with principal component analysis (PCA) prior to the training process. The results show that the defect area at the wavelengths of strong absorption lines is better replaced with PCA-ANN with the error of 5 %, while PCA-Linear is better for reproducing radiances having strong correlation with input radiances. The shorter the spectral range of output radiances is, the smaller the prediction error is with PCA-Linear (0.5–5 %). Spectral and spatial discontinuity caused by real bad pixels can be significantly improved with the trained machine learning models especially for wide defect areas. This study verifies that spectral relations of radiances in the UV/VIS spectrum are successfully reproduced with a simple machine learning model, which has high potential to be investigated further for enhancing measurement quality of environmental satellite measurements.