Miracle-dz/UniFAR-FTUs huggingface dataset:
Coupling of Magneto–Hydrodynamics and Structural Models to Predict Wall Deformation due to Arcing

Electric arc discharges in low–voltage (LV) or medium–voltage (MV) power distribution devices can cause significant pressure rise. For example, a high amplitude pressure peak can damage the housing of a LV circuit breaker initiating cracks during short circuit interruption. In case of larger deformations or creation of additional gaps, the impact of the geometric changes on the pressure rise cannot be neglected. This article describes a new three–codes–coupling approach, wherein a magneto–hydrodynamics (MHD) model consisting of a fluid–flow solver and an electromagnetic solver are coupled with a structural dynamics solver to build a complex co–simulation model. This model can predict the deformation of structures under the influence of arcing pressure. The applicability of the model was tested with a setup, where an electric arc is ignited inside an arc chamber that has a flexible plate on one side. Predicted pressure rise and displacement results are in good agreement with test data. In a more complex setup, this approach was applied to model the bending of a flexible baffle plate in the venting path of a LV circuit breaker during short circuit interruption. Additional challenges such as contact motion and pre–stress analysis were resolved.
Crew Interaction in Extended Space Missions

Detection of the extent of common values in a cohesive space crew has become an important trend in modern space psychology. It is known from the works of Ch. Osgood that the semantic differential scale is a reliable way to obtain objective information on the emotional attitudes towards a topic of interest. Within the frame of the Russian space experiment “Interactions” on the International Space Station (ISS), a computerized survey, the Personal Self-Perception and Attitudes (PSPA), was developed for analyzing the subjects’ emotional attitudes toward their social environment. In the course of the PSPA procedure, the crewmembers rate each other and themselves (in the past, present, and future) using the criteria previously personally chosen. These criteria should be regarded as their personal values. A total of 30 subjects have already completed the study on board the ISS. The main tasks of the study are: (1) to define individual and group values and the extent of group identification reflected in sharing these values; (2) to determine the impact of cross-cultural factors on mutual perceptions and self-perceptions in space crews and with the Mission Control Center (MCC); (3) to study changes in the space crews’ group cohesiveness and structure as they are exposed to the stress of the extended space mission environment. The data obtained indicate an increase in a “psychological distance” between the crew and the MCC personnel versus increased crew cohesion. The results gained made it possible to identify the most significant categories of values common to the subjects from the professional cosmonaut group. The priority of these shared values for each subject is an important condition for the formation of a cohesive crew.
Proteomic Mapping of the Interactome of KRAS Mutants Identifies New Features of RAS Signalling Networks and the Mechanism of Action of Sotorasib

Simple Summary Cancer is caused by changes in DNA called mutations that alter the way proteins work. We know that RAS proteins are one of the most commonly mutated proteins in cancer cells. These proteins work like a light switch and mutant RAS remain in the ON position sending signals to the cell to keep dividing when they should not. Until recently, it was thought that all RAS mutations were equal, but differences among these mutants have been identified. Here we used a technique called proteomics to decipher the differences among RAS mutants. We find that each mutant binds a different set of proteins and can regulate different signals. We also find that a clinically approved drug that inhibits one RAS mutant regulates the interaction of RAS proteins with other proteins. Our findings extend our knowledge of how the RAS mutants work, which can potentially be used to improve cancer treatments. Abstract RAS proteins are key regulators of cell signalling and control different cell functions including cell proliferation, differentiation, and cell death. Point mutations in the genes of this family are common, particularly in KRAS. These mutations were thought to cause the constitutive activation of KRAS, but recent findings showed that some mutants can cycle between active and inactive states. This observation, together with the development of covalent KRASG12C inhibitors, has led to the arrival of KRAS inhibitors in the clinic. However, most patients develop resistance to these targeted therapies, and we lack effective treatments for other KRAS mutants. To accelerate the development of RAS targeting therapies, we need to fully characterise the molecular mechanisms governing KRAS signalling networks and determine what differentiates the signalling downstream of the KRAS mutants. Here we have used affinity purification mass-spectrometry proteomics to characterise the interactome of KRAS wild-type and three KRAS mutants. Bioinformatic analysis associated with experimental validation allows us to map the signalling network mediated by the different KRAS proteins. Using this approach, we characterised how the interactome of KRAS wild-type and mutants is regulated by the clinically approved KRASG12C inhibitor Sotorasib. In addition, we identified novel crosstalks between KRAS and its effector pathways including the AKT and JAK-STAT signalling modules.
The parallel biosynthesis routes of hyperoside from naringenin in Hypericum monogynum

Abstract Hyperoside is a bioactive flavonoid galactoside in both medicinal and edible plants. It plays an important physiological role in the growth of flower buds. However, the hyperoside biosynthesis pathway has not been systematically elucidated in plants, including its original source, Hypericaceae. Our group found abundant hyperoside in the flower buds of Hypericum monogynum, and we sequenced its transcriptome to study the biosynthetic mechanism of hyperoside. After gene screening and functional verification, four kinds of key enzymes were identified. Specifically, HmF3Hs (flavanone 3-hydroxylases) and HmFLSs (flavonol synthases) could catalyze flavanones into dihydroflavonols, as well as catalyzing dihydroflavonols into flavonols. HmFLSs could also convert flavanones into flavonols and flavones with varying efficiencies. HmF3′H (flavonoid 3′-hydroxylase) was found to act broadly on 4′-hydroxyl flavonoids to produce 3′,4′-diydroxylated flavanones, dihydroflavonols, flavonols, and flavones. HmGAT (flavonoid 3-O-galactosyltransferase) would transform flavonols into the corresponding 3-O-galactosides, including hyperoside. The parallel hyperoside biosynthesis routes were thus depicted, one of which was successfully reconstructed in Escherichia coli BL21(DE3) by feeding naringenin, resulting in a hyperoside yield of 25 mg/l. Overall, this research not only helped us understand the interior catalytic mechanism of hyperoside in H. monogynum concerning flower development and bioactivity, but also provided valuable insights into these enzyme families.
Liposome-Based Carriers for CRISPR Genome Editing

The CRISPR-based genome editing technology, known as clustered regularly interspaced short palindromic repeats (CRISPR), has sparked renewed interest in gene therapy. This interest is accompanied by the development of single-guide RNAs (sgRNAs), which enable the introduction of desired genetic modifications at the targeted site when used alongside the CRISPR components. However, the efficient delivery of CRISPR/Cas remains a challenge. Successful gene editing relies on the development of a delivery strategy that can effectively deliver the CRISPR cargo to the target site. To overcome this obstacle, researchers have extensively explored non-viral, viral, and physical methods for targeted delivery of CRISPR/Cas9 and a guide RNA (gRNA) into cells and tissues. Among those methods, liposomes offer a promising approach to enhance the delivery of CRISPR/Cas and gRNA. Liposomes facilitate endosomal escape and leverage various stimuli such as light, pH, ultrasound, and environmental cues to provide both spatial and temporal control of cargo release. Thus, the combination of the CRISPR-based system with liposome delivery technology enables precise and efficient genetic modifications in cells and tissues. This approach has numerous applications in basic research, biotechnology, and therapeutic interventions. For instance, it can be employed to correct genetic mutations associated with inherited diseases and other disorders or to modify immune cells to enhance their disease-fighting capabilities. In summary, liposome-based CRISPR genome editing provides a valuable tool for achieving precise and efficient genetic modifications. This review discusses future directions and opportunities to further advance this rapidly evolving field.
Identification of sources of sediment to Lake Samsonvale (North Pine Dam), southeast Queensland, Australia.

Lake Samsonvale (North Pine Dam, NPD) is a major reservoir, of approximately 22 km 2 with a capacity of 215 000 Ml and drainage area of 347 km2, which supplies drinking water to Brisbane, the capital city of Queensland in eastern Australia. Historically, there are large and persistent blooms of potentially toxic cyanobacteria in NPD, and it was hypothesized that their occurrence and biomass were related to input of sediment-boun d phosphorus from the drainage basin. The NPD drainage basin is ungauged and few estimates of (sub)drainage basin sediment loads exist. This paper develops a Bayesian approach utilizing endmember models to estimate, based on major and trace element geochemical signatures, the proportion of various drainage basin sediment sources in sediment samples taken from NPD. This approach not only allows for the incorporation of prior knowledge about the geo­ chemical composition of the sources (or endmembers), but also allows for correlation between spatially contiguous samples and the prediction of the sediment composition at unsampled locations.
The interaction of the five-factor personality traits and job embeddedness in explaining voluntary turnover: A necessary-condition perspective

ABSTRACT Job embeddedness is theorized and found to be negatively related with voluntary turnover. To provide an alternative to the dominant correlational understanding of causality in the theory and research on job embeddedness and to improve its ability to predict voluntary turnover, we apply the necessary-condition perspective to propose that a low level of job embeddedness provides a necessary but not sufficient reason for employees to leave their organizations. Specifically, we use the perspective to theorize and examine through a necessary condition analysis whether a low level of job embeddedness is a necessary condition for voluntary turnover, and logistic regression analysis to examine the moderating role of the five-factor personality traits on the negative relationship between job embeddedness and voluntary turnover. To cover a range of job positions and industries, we collected a sample of 478 employees ranging from staff to managers in various organizations and industries in Japan to test our hypotheses. Our findings suggest that a low level of job embeddedness is a necessary condition for voluntary turnover, as well as that extraversion and openness to experience amplified and agreeableness mitigated the negative relationship between job embeddedness on voluntary turnover. This study contributes to the literature by clarifying the role of job embeddedness in predicting voluntary turnover by using the necessary sufficient logic and delineating the moderating effects of personality traits between job embeddedness and turnover.
Center of Mass Estimation of Simple Shaped Magnetic Bodies Using Eigenvectors of Computed Magnetic Gradient Tensor

Computed Magnetic Gradient Tensor (CMGT) includes the first derivatives of three components of magnetic field of a body. At the eigenvector analysis of Gravity Gradient Tensors (GGT) for a line of poles and point pole, the eigenvectors of the largest eigenvalues (first eigenvectors) point precisely toward the Center of Mass (COM) of a body. However, due to the nature of the magnetic field, it is shown that these eigenvectors for the similar shaped magnetic bodies (line of dipoles and point-dipole), in CMGT, are not convergent to COM anymore. Rather, in the best condition, when there is no remanent magnetization and the body is in the magnetic poles, their directions are a function of data point locations. In this study, by reduction to the pole (RTP) transformation and calculation of CMGT, a point is estimated that its horizontal components are exactly the horizontal components of the COM and its vertical component is a fraction of the COM vertical component. These obtained depth values are 0.56 and 0.74 of COM vertical components for a line of dipoles and point-dipole, respectively. To reduce the turbulent effects of noise, “Moving Twenty five Point Averaging” method and upward continuation filter are used. The method is tested on solitary and binary simulated data for bodies with varying physical characteristics, inclinations and declinations. Finally, it is imposed on two real underground examples; an urban gas pipe and a roughly spherical orebody and the results confirm the methodology of this syudy.
News Categorisation Based on Pre-Trained Transformer Models

-The rise of digital journalism, the amount of news and the continuous number of people accessing these contents, often generates that third parties, through web platforms and social networks, have the opportunity to persuade readers with content that alters their opinion or behaviour on a topic, so it is necessary to classify news using Natural Language Processing (NLP) techniques. This work seeks to experiment with pre-trained Transformer models using transfer learning and fine tuning to obtain a model capable of determining whether a news item is satire, opinion or information. To do so, we use a labelled dataset of news in English presented for the SemEval 2023 campaign, translating it into Spanish to experiment also in this language. We use pre-trained Transformer models for text classification tasks in the mentioned languages, thus, we compare several models and their predictions using evaluation metrics. The results give indications of the goodness of the models considering the subjective type of news, in the case of satire and opinion, and objective for information, thus contributing to future research related to text classification, specifically news categorisation.
Transition metal-doped V-shaped RuO2 103 nanotwins as highly active electrocatalysts for enhanced oxygen evolution in acidic media

Efficient and stable catalysts for the oxygen evolution reaction (OER) in acidic environments are essential for hydrogen production through electrolysis. The paper reported the transition metal-doped RuO2 103 nanotwins (RuO2 103-NTs), as cost-effective and high-performance catalysts for acidic OER. The MRu-VO defect models were constructed by introducing transition metal (Ag, Mn, Fe, Co, Cu, and Zn) doped surfaces. Thereinto, the AgRu-VO doped structure exhibited remarkable OER activity in acidic conditions, achieving a reduced overpotential of 0.37 eV. Furthermore, we investigated the synergistic effect of the Ag and oxygen vacancies (VO) in conjunction with grain boundaries. Above results revealed that an increase in the content of Ag and VO resulted in a decrease in structural stability, leading to a more disordered structure. By analyzing the adsorption energy, energy band center and charge transfer in bimetallic-doped NTs, we observed that the presence of MRu-AgRu-VO (M = Mn, Co, Fe) significantly promotes the positive shift of d band center and the increase of charge transfer. This effect ultimately led to a decrease in the overpotential of the rate-determining step to 0.27 eV, thereby enhancing the intrinsic activity of RuO2.
The extent of coaching behaviour and practices in supervision processes of PhD students

The extent to which coaching behaviour is practiced in PhD supervision is unknown. Supervision and elements of supervision have been studied extensively but not coaching and supervision. The aim of this study was to assess the extent to which coaching behaviours were practiced by supervisors while engaging with PhD students during supervision. A cross sectional survey using a self-administered structured questionnaire was used on an online platform (REDCap). Descriptive and correlational statistics were used to determine the extent to which each coaching and supervision phenomenon were practiced. Three hundred and eighty students registered for a PhD for longer than six months in a Faculty of Health Sciences were invited to participate in the study and 76 participated. Low to moderate levels in coaching behaviour and practice, personal support, supervisor availability and research academic support are reported. Personal support and the level of satisfaction with supervision were significant predictors of coaching behaviour and practice. This study established the extent of different supervisory behaviours and coaching behaviour and practice among supervisors. Coaching in this study was not practised largely with a third of the students interviewed experiencing coaching behaviour and practice.
Preliminary Insights into Enhancing Human-Robot Teamwork

Advancements in robotics, artificial intelligence, and other automation have highlighted the need for humans to work together with machines in a more flexible and collaborative fashion than previously possible. To formulate effective human-robot teams, it is critical to understand which factors play important roles in enhancing human-robot teamwork. To gain preliminary insights into key factors of effective human-robot teams, we carried out an experiment using an enhanced version of the “Lunar Lander” game, where the goal is to safely land a spacecraft on the moon’s surface in concert with an AI teammate. We specifically attempted to observe some patterns of communication across high-performing teams in the experiment. Due to the limited number of experimental participants, the results of the experiment did not definitively identify the factors that account for effective teams. Instead, the experiment indicated potential avenues to further investigate, including intent-oriented communication and trust in teams with human and non-human entities. This paper presents findings from the experiment and discusses future work to extend the scope of the study to include teleoperation of unmanned vehicles with communication delay.
Functional MRI Lateralization [M1] of dlPFC and Implications for Transcranial Magnetic Stimulation (TMS) Targeting

The present study investigates a potential method of optimizing effective strategies for the functional lateralization of the dorsolateral prefrontal cortex (dlPFC) while in a scanner. Effective hemisphere lateralization of the dlPFC is crucial for lowering the functional risks connected to specific interventions (such as neurosurgery and transcranial magnetic stimulation (TMS), as well as increasing the effectiveness of a given intervention by figuring out the optimal location. This task combines elements of creative problem solving, executive decision making based on an internal rule set, and working memory. A retrospective analysis was performed on a total of 58 unique participants (34 males, 24 females, Mage = 42.93 years, SDage = 16.38). Of these participants, 47 were classified as right-handed, 7 were classified as left-handed, and 4 were classified as ambidextrous, according to the Edinburgh Handedness Inventory. The imaging data were qualitatively judged by two trained, blinded investigators (neurologist and neuropsychologist) for dominant handedness (primary motor cortex) and dominant dorsolateral prefrontal cortex (dlPFC). The results demonstrated that 21.4% of right-handed individuals showed a dominant dlPFC localized to the right hemisphere rather than the assumed left, and 16.7% of left-handers were dominant in their left hemisphere. The task completed in the scanner might be an efficient method for localizing a potential dlPFC target for the purpose of brain stimulation (e.g., TMS), though further study replications are needed to extend and validate these findings.
Machine learning classification models for the patients who have heart failure

Heart failure is a cardiovascular disease with significant morbidity and mortality, affecting a growing number of people worldwide [1]. The aim of this paper is to predict the probability of survival of patients by looking at their various characteristics, diseases, and lifestyles in the most successful way by using various machine learning methods. The 299 patients in the data set we use, had left ventricular systolic dysfunction in 2015 and are classified as New York Heart Association (NYHA) class III and IV. The probability of survival of patients is estimated by applying various machine learning methods on the data set. In this study, there are two versions. In the first version of the study, Principal Component Analysis (PCA) is used to reduce the size of the data set. The performance of the machine learning algorithms is then evaluated using a variety of metrics. In the second version, the data set is only subjected to machine learning techniques, and performance is then assessed. Accuracy, Matthews correlation coefficient (MCC), sensitivity, specifity, score, receiver operating characteristic-area under the curve (ROC-AUC), and precision-recall area under the curve (PR-AUC) values are calculated to measure success. Comparing the two versions reveals that all machine learning algorithms in general have performed better in the second version without PCA. In the second version, the CatBoost algorithm gave the most successful result. Patients with heart failure can have their mortality status predicted using machine learning techniques. The goal of this paper is to look at a variety of characteristics in order to assess the patient's mortality status. The condition of the patient can be improved by selecting the proper treatment based on the mortality situation.
Comparison of feature-based sentence ranking methods for extractive summarization of Turkish news texts

Document summarization is the task of generating a shorter form of document with important information content. Automatic text summarization has been developed for this process and is still widely used. It is divided into two main parts as extractive summarization and abstractive summarization. In this study, we used sentence ranking methods for extractive summarization for Turkish news text within the scope of the experimental study. We used different summarization rates, 20%, 30%, 40%, 50% and 60%. Summarization results were evaluated with the ROUGE ve BLEU metrics. We proposed new methods based on major vowel harmony and minor vowel harmony features. We obtained high evaluation results in both ROUGE ve BLEU metrics with major vowel harmony and minor vowel harmony features. Additionally, we studied a hybrid model using major vowel harmony and minor vowel harmony rules together. We obtained the best results with major vowel harmony, minor vowel harmony, and hybrid model (major vowel harmony and minor vowel harmony together). We compared the three proposed methods with the BERTurk model prepared for Turkish based on Google BERT. The results obtained gave very close results to this state-of-the-art method and showed that it is worth developing.