Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss Prediction of Ultrahigh Voltage Transmission Lines Based on EEMD–LSTM–SVR Algorithm

Line loss prediction of ultrahigh voltage transmission lines is the key for ensuring the safe, reliable, and economical operation of the power system. However, the strong volatility of line loss brings challenges to the prediction of transmission line loss. For more accurate prediction, this article uses ensemble empirical mode decomposition (EEMD) to decompose the line loss and proposes the EEMD–LSTM–SVR prediction model. First of all, this article performs feature engineering on power flow, electric energy, and meteorological data and extracts the exponentially weighted moving average (EWMA) feature from the line loss. After the integration of the time dimension, this article mines the curve characteristics from the time series and constructs a multidimensional input dataset. Then, through ensemble empirical mode decomposition, the line loss is decomposed into high-frequency, low-frequency, and random IMFs. These IMFs and the standardized multidimensional dataset together constitute the final input dataset. In this article, each IMF fusion dataset is sent to LSTM and support vector regression models for training. In the training process, the incremental cross-validation method is used for model evaluation, and the grid search method is used for hyperparameter optimization. After evaluation, the LSTM algorithm predicts high-frequency IMF1 and 2 and random IMF4 and 5; the SVR algorithm predicts low-frequency IMF6 and 7 and random IMF3. Finally, the output value of each model is superimposed to obtain the final line loss prediction value. Also, the comparative predictions were performed using EEMD–LSTM, EEMD–SVR, LSTM, and SVR. Compared with the independent prediction models EEMD–LSTM and EEMD–SVR, the combined EEMD–LSTM–SVR algorithm has a decrease in mean absolute performance error% by 2.2 and 25.37, respectively, which fully demonstrates that the combined model has better prediction effect than the individual models. Compared with that of SVR, the MAPE% of EEMD–SVR decreases by 11.16. Compared with that of LSTM, the MAPE% of EEMD–LSTM is reduced by 32.72. The results show that EEMD decomposition of line loss series can effectively improve the prediction accuracy and reduce the strong volatility of line loss. Compared with that of the other four algorithms, EEMD–LSTM–SVR has the highest R-square of 0.9878. Therefore, the algorithm proposed in this article has the best effectiveness, accuracy, and robustness.
Application of Metagenomic Next-Generation Sequencing in the Etiological Diagnosis of Infective Endocarditis During the Perioperative Period of Cardiac Surgery: A Prospective Cohort Study

Objective The present study aimed to prospectively evaluate the role of metagenomic next-generation sequencing (mNGS) in the etiological diagnosis of patients with perioperative infective endocarditis (IE). Methods From May 1st, 2019 to December 31st, 2020, a total of 99 patients with IE were enrolled in the present study according to the modified Duke criteria, etiological, and pathological results. 11 non-IE patients undergoing heart valve surgery in the same period were selected as the control group. A blood culture test was performed immediately after admission, and the valves harvested operatively were examined by blood culture and mNGS. Results In the IE group, there were 29 cases (29.3%) with positive blood culture, 16 cases (16.2%) with positive valve culture, and 85 cases (85.9%) with positive valve mNGS. Compared to culture-based detection, mNGS achieved better performance with a sensitivity, specificity, area under the curve (AUC) of 0.859, 0.727, and 0.793, respectively. The combined approach using culture and mNGS further improved the diagnostic accuracy (sensitivity 89.9%, specificity 72.7%, AUC 0.813). Preoperative white blood cell (P = 0.029) and neutrophils (P = 0.046) were identified as independent factors affecting the detection rate of mNGS. In the mNGS-positive group, 95 strains of pathogens were found and 10 cases were identified with mixed infection. There were 72 gram-positive bacteria and 14 gram-negative bacteria. mNGS positive group displayed higher species richness than mNGS negative group with enrichment of Streptococcus sanguis, Streptococcus buccalis, and Streptococcus griseus. Proteobacteria and Actinomycetes were enriched in mNGS negative group. Notably, six patients showed disconcordant results between culture and mNGS. Rothia aeria was identified in the blood culture, valve culture, and valve mNGS in one patient. Bartonella Quintana and Coxiella burnetii, which were fastidious intracellular bacteria, were found in two blood and valve culture-negative cases. Conclusions mNGS outperformed the conventional culture method and displayed high accuracy in detecting pathogens in IE patients. This study provided support for the use of mNGS in the etiological diagnosis of IE.
Development of Site-Specific Soil Design Basis Earthquake (DBE) Parameters for the Integrated Waste Treatment Unit (IWTU)

Horizontal and vertical PC 3 (2,500 yr) Soil Design Basis Earthquake (DBE) 5% damped spectra, corresponding time histories, and strain-compatible soil properties were developed for the Integrated Waste Treatment Unit (IWTU). The IWTU is located at the Idaho Nuclear Technology and Engineering Center (INTEC) at the Idaho National Laboratory (INL). Mean and 84th percentile horizontal DBE spectra derived from site-specific site response analyses were evaluated for the IWTU. The horizontal and vertical PC 3 (2,500 yr) Soil DBE 5% damped spectra at the 84th percentile were selected for Soil Structure Interaction (SSI) analyses at IWTU. The site response analyses were performed consistent with applicable Department of Energy (DOE) Standards, recommended guidance of the Nuclear Regulatory Commission (NRC), American Society of Civil Engineers (ASCE) Standards, and recommendations of the Blue Ribbon Panel (BRP) and Defense Nuclear Facilities Safety Board (DNFSB).
Electrochemical Properties of Diluted Al-Mg Alloys With Columnar-To-Equiaxed Transition

The objective of the present research is to study the corrosion susceptibility of two Al-Mg diluted alloys (Al-0.5wt.%Mg and Al-2wt.%Mg) with different grains structures obtained by directional solidification (columnar, equiaxed and columnar-to-equiaxed transition, CET) in 0.5% NaCl solution, at room temperature. The corrosion resistance is analyzed by potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) techniques in both longitudinal and transversal sections of the samples. The columnar grain zone presents higher corrosion resistance than the equiaxed grain zone. In addition, the transversal section shows higher corrosion resistance than the longitudinal section of the samples. Then, the Al-0.5wt. % Mg alloy displays higher corrosion resistance than the Al-2wt. % Mg alloy. The values of the polarization resistance are used as a basic criterion for the evaluation of the corrosion resistance of both alloys. In this way, when the polarization resistance decreases with the increasing in the distance from the base, the grain size, secondary dendritic arm spacings and hardness increases. In addition, when the polarization resistance increases, the critical temperature gradient decreases.
Triglyceride-Glucose Index and New-Onset Atrial Fibrillation in ST-Segment Elevation Myocardial Infarction Patients After Percutaneous Coronary Intervention

Background New-onset atrial fibrillation (NOAF) is associated with worse prognostic outcomes in cases diagnosed with ST-segment elevation myocardial infarction (STEMI) patients after percutaneous coronary intervention (PCI). The triglyceride-glucose (TyG) index, as a credible and convenient marker of insulin resistance, has been shown to be predictive of outcomes for STEMI patients following revascularization. The association between TyG index and NOAF among STEMI patients following PCI, however, has not been established to date. Objective To assess the utility of the TyG index as a predictor of NOAF incidence in STEMI patients following PCI, and to assess the relationship between NOAF and long-term all-cause mortality. Methods This retrospective cohort research enrolled 549 STEMI patients that had undergone PCI, with these patients being clustered into the NOAF group and sinus rhythm (SR) group. The predictive relevance of TyG index was evaluated through logistic regression analyses and the receiver operating characteristic (ROC) curve. Kaplan-Meier curve was employed to explore differences in the long-term all-cause mortality between the NOAF and SR group. Results NOAF occurred in 7.7% of the enrolled STEMI patients after PCI. After multivariate logistic regression analysis, the TyG index was found to be an independent predictor of NOAF [odds ratio (OR): 8.884, 95% confidence interval (CI): 1.570–50.265, P = 0.014], with ROC curve analyses further supporting the predictive value of this parameter, which exhibited an area under ROC curve of 0.758 (95% CI: 0.720–0.793, P < 0.001). All-cause mortality rates were greater for patients in the NOAF group in comparison with the SR group over a median 35-month follow-up period (log-rank P = 0.002). Conclusions The TyG index exhibits values as an independent predictor of NOAF during hospitalization, which indicated a poorer prognosis after a relatively long-term follow-up.
Special Collection: A pioneer in antiviral drug discovery and development: Commemorating John Martin-Original Article

A co-formulated, one pill once a day antiretroviral regimen (single-tablet regimen), containing efavirenz, emtricitabine, and tenofovir disoproxyl fumarate (Atripla), revolutionized the antiretroviral therapy landscape. Single-tablet regimens provide not only dosing convenience but help optimize adherence and persistence with antiretroviral therapy to achieve durably suppressed viremia with both individual and societal benefits. Given the many excellent options available now, single-tablet regimens are the preferred choice for initiating antiretroviral therapy in almost all patients with rare exceptions for drug interactions and pregnancy, and for simplification of more complex antiretroviral therapy to a singletablet regimen. In this special commemorative article, we celebrate this astounding advancement in antiretroviral therapy, championed by John C. Martin while CEO of Gilead Sciences, and its transformative impact on HIV care nationally and globally.
Special Collection: A pioneer in antiviral drug discovery and development: Commemorating John Martin-Review

Tenofovir-based pre-exposure prophylaxis (PrEP) revolutionized the global HIV prevention landscape. Prior to the proofof concept trial in 2010, which demonstrated that tenofovir (TFV) could prevent sexual transmission of HIV, prevention options were largely limited to behavior change, condoms, and circumcision. Several subsequent studies evaluating oral tenofovir disoproxil fumarate (TDF) or the TDF/emtricitabine (FTC) combination as PrEP for HIV prevention provided evidence for regulatory approval and inclusion in national and international guidelines. By 2021, 1.5 million people had initiated oral tenofovir-based PrEP, contributing to declines in HIV incidence in some regions. Here we reflect on how oral tenofovir-based PrEP became an important component of combination HIV prevention programs across the globe.
Studies in protein targeting to the endoplasmic reticulum

Soluble proteins can be targeted and inserted into the endoplasmic reticulum (ER) co-translationally or post-translationally. Co-translational translocation requires a ribonucleoprotein complex, signal recognition particle (SRP), to target the ribosome-nascent chain complex to the ER-localised translocon. The targeting and insertion of membrane-bound proteins into the ER is more complex. One class of membrane proteins have the transmembrane domain near their C-terminus (tail-anchor proteins), and whilst the other classes of membrane proteins have had their mode of insertion elucidated, the insertion mechanism of these proteins remains unknown. This thesis deals with two areas of protein targeting in Saccharomyces cerevisiae. First the mode of insertion of Ufelp, a tail-anchor protein, was examined by use of a variety of reporters. This work identified a region N-terminal to the transmembrane domain that is required for correct localisation of the protein. Second, the role of the SRP component, Srp72p was examined. Srp72p is essential for SRP function, but its role has not been determined. A screen was set up to identify conditional mutants of SRP72. In addition, deletions and point mutations were created to analyse the roles of conserved residues within the protein. Mutations affecting a conserved region of the protein towards the C-terminus were found to confer slight defects in SRP-dependent translocation. Intracellular localisation of the mutant proteins was identical to that of the wild-type protein. A mini-screen performed on two mutants identified multicopy suppressors of translocation defects. Finally, preliminary comparison of the binding affinities of SRP containing mutated Srp72p or wild-type SRP with the SRP receptor revealed a subtle difference.
Virtual worlds: Not the final frontier for games-based nursing education

Virtual worlds present frontiers of promise for the ever evolving venture of pedagogical development, trial and embracement. Of late there have been large pushes into these worlds in terms of health-based education for students and early practitioners. Virtual worlds seem to be the next logical jump into nursing education and can offer a range of simulation benefits. But these worlds do not appeal to all students, can be complex and expensive to develop and interact within. Other game-like avenues exist though and have not been explored thoroughly enough to date. Such genres like puzzles games, management style games and surprisingly first person shooters already have titles and game mechanics which have been somewhat adapted to nursing education but could easily be more thought out and developed to suit. This paper outlines the two major gaming audience types to be considered and then explores a range of options for nursing education beyond virtual worlds.
Analysis of the Impact of Gamification on Learning Efficiency on the Example of a Mobile Application

The article analyses the impact of gamification on the effectiveness of the learning process with the use of the original mobile application. Gamification methods and gamification assessments attract more and more attention from both researchers and practitioners. We present the impact of learning rules and regulations of handball on the future referees of handball and people from the handball community (players, activists, sympathizers) from the implemented mobile application in comparison with the traditional method, i.e., from books or printed regulations. The results of the knowledge test for the users of the application were much better than traditional education. The analysis showed that in the first test, the users using the application and traditional learning achieved the same results (52%). In contrast, in the second test, the users using the application achieved 73% in comparison with the group not using the app (59%). Such a result may indicate that learning from the application has a good impact on the user. The adoption of gamification in learning and teaching is very popular and stimulating student motivation, involvement, and social influence.
SERRE SUBCATEGORIES OF ARTINIAN MODULES

We study subcategories of the category of artinian modules. We prove that all wide subcategories of artinian modules are Serre subcategories. We also provide the bijection between the set of Serre subcategories of artinian modules and the set of specialization closed subsets of the set of closed prime ideals of some completed ring.
Genetic Transformation of Arachis hypogaea Using Novel Genes Conferring Fungal Resistance-A Review

Peanut (Arachis hypogaea L.) or the common ‘peanut’ is a worldwide popular, affordable food containing high protein, calories, vitamins, and minerals. Several biotic and abiotic stresses are responsible for reaching the expected production of peanuts worldwide. Especially, the fungi are the major constraints that not only hamper the production but also that is deadly health hazardous for both human consumption and poultry-livestock. Approaches from various dimensions like cultural management, diseases free cultivar development, hybridization, tissue culture, and genetic transformations have been tried to overcome such challenges. This review epitomizes the total scenario from the plant physiological basis of fungal diseases to the peanut development approaches, which aimed to develop a concrete understanding of sustainable management of peanut production. Comparisons of Genetic Engineering methods such as Agrobacterium-mediated and direct gene gun (particle bombardment- mediated) with traditional hybridization have been compiled here, furthermore, candidate genes transformed to achieve fungus-resistance in peanuts have been listed up to provide an overview. Along with, the limitations of transformation attempts and the techniques for improvisation of transformation techniques have been discussed in sustainable peanut production. This study provides, comprehensive information on fungal-resistant peanut development so that, further research in this arena could be guided in an integrated way, which may serve for the thrust of sustainable improvisation in peanut cultivation.
Miniaturized Radio Frequency Choke Using Modified Stubs for High Isolation in MIMO Systems

In this paper, a miniaturized radio frequency choke (RFC) using modified stubs is proposed to improve isolation characteristics in a multiple-input-multiple-output (MIMO) antenna system. The proposed RFC, based on the LC resonance, is designed to suppress the leakage current that leads to the degradation of antenna diversity performances in the MIMO antenna configuration. The proposed RFC is composed of two open stubs that are implemented on the top of the ground plane and miniaturized by adding a slit structure on the ground plane. The MIMO antennas are also designed to verify isolation performance in the LTE 2300 band (2,300? 2,400 MHz). The MIMO antennas perform well with low reflection coefficient characteristics and high isolation characteristics in the whole LTE 2300 band. To evaluate the isolation in the MIMO system, the envelope correlation coefficient (ECC) is calculated, and the value is less than 0.08. The achieved ECC is regarded as a reasonable result for improving isolation performance in the frequency range of 2,300? 2,400 MHz; also, radiation patterns of antenna elements are maintained regardless of the presence of RFC.
A Mathematical Model for Undergraduate Suicide Ideation Detection

Young adults of undergraduate age range are the most vulnerable group of global suicide death according to the world health organization. Student’s academic and social media activities provides plethora of opportunities to determine the students who are at risk of developing depression or suicide ideation. These opportunities have been overlooked as most research has been based on predicting individuals who are at immediate risk of suicide attempt or death using their (the students) social media texts streams and suicide notes. This research approach is reactive rather than proactive and based on that has failed as suicide is a cumulative process that is developed over a long period of time (Ren, Kang, & Quan, 2016). The aim of this thesis is to build a mathematical model that will predict students who are at risk of depression or suicide ideation using a proactive approach of suicide prevention which involves applying the model to the result gotten from machine learning prediction.
Abnormal Event Detection Using CCTV Camera

:- Abnormal event detection, human behavior detection, as well as object recognition plays a vital role in the creation of a smart CCTV system. These systems make it possible to detect abnormal events in an environment, abnormal behaviors by humans and the state of alert in the environment. Machine Vision property along with Machine Learning are used in these systems to detect as well as identify the particular anomalies that arise in the video feed from the CCTV. Frame by frame processing is commonly used and Supervised Learning is the commonly used training method for these systems. However, since the anomalies are of many different kinds and also because it is not feasible to pre-detect and train all types of anomalies, supervised learning is being replaced by unsupervised learning and semi - supervised learning for training the system. This system provides a means of minimising or removing the human workload that has to be put on to manually detect and create an alert on detection of an abnormality in the live feed provided by the CCTV. Also the system increases the storage efficiency by storing only the abnormal events in original quality and storing the normal scenarios in low quality for archiving. Also this system provides an extension of creating a distributed abnormality classification system, where only the abnormal events are sent on to different dedicated systems to classify the abnormality.
Robust Speaker Verification Using Population-Based Data Augmentation

Speaker recognition under environments with a low signal-to-noise ratio (SNR) and high reverberation level has always been challenging. Data augmentation can be applied to simulate the adverse environments that a speaker recognition system may encounter. Typically, the augmentation parameters are manually set. Recently, automatic hyper-parameter optimization using population-based learning has shown promising results. This paper proposes a population-based searching strategy for optimizing the augmentation parameters. We refer to the resulting augmentation as population-based augmentation (PBA). Instead of finding a fixed set of hyper-parameters, PBA learns a scheduler for setting the hyper-parameters. This strategy offers a considerable computation advantage over the grid search. We obtained high-performance augmentation policies using a population of six networks only. With PBA, we achieved an EER of 3.98% on the VOiCES19 evaluation set.
Augmented Intelligence for National Security and Development

Augmented Intelligence is a system model which centre on the partnership between human general intelligence & common sense with Artificial Intelligence’s exceptional processing speed and computation power to get better cognitive performance. This study can shed light on Augmented Intelligence and how it can be implemented so that we have heroes working at the peak human level possible, suffer fewer casualties, and also have equal footing in terms of technology and military strength. Nepal being open bordered country has a great threat from the border issues and with ever-rising crime and challenges in various cities creating intra threat. The study analyses to overcome this geographical challenge, operational problems, the rise of conflicts, and establish better policies, faced by security personnel with technology that actually empowers all the brave soldiers and Police officers to maintain security from both external and internal risk. In the ever-evolving technology limitations of human beings can be catastrophic, but replacing it all with Artificial Intelligence is not a practical solution as well because it would be giving the third entity all the ability that might revolt against humans themselves. The article can be summed up as, research on the practicality of Artificial Intelligence and Augmented Intelligence in national security and figure out a way for its legal adaptation. The availability of various case studies, surveys, research on various existing technology, and research papers published in renowned journals and publications has made this study possible.