Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-harmonic electromagnetic field evaluation using a hybrid FEM/boundary method for open transmission line systems

This paper is a contribution to the electromagnetic field evaluation of open boundary time-harmonic eddy current problems, using a hybrid Finite Element/Boundary Fourier Transform method (FE/BFT). The finite element method applies inside a fictitious cylindrical surface where the conductors lie and the boundary method applies to the whole empty space outside the cylinder. The field descriptions, inside and outside, are matched on a set of discretization nodes over the fictitious boundary. The field over the boundary is handled in the spatial frequency domain by the use of a suitable discrete Fourier transform.
Multi-omics analysis reveals the pathogenesis of db/db mice diabetic kidney disease and the treatment mechanisms of multi-bioactive compounds combination from Salvia miltiorrhiza

Diabetic kidney disease (DKD) is a common diabetic complication. Salvia miltiorrhiza has significant therapeutic effects on diabetes complications, although the mechanism remains unclear. Here, biochemical indicators and pathological changes were used to screen out the optimal Salvia miltiorrhiza multi-bioactive compounds combination. Metabolomics, transcriptomics and proteomics were used to explore the pathogenesis of DKD. RT-PCR and parallel reaction monitoring targeted quantitative proteome analysis were utilized to investigate treatment mechanisms of the optimal Salvia miltiorrhiza multi-bioactive compounds combination. The db/db mice showed biochemical abnormalities and renal lesions. The possible metabolic pathways were steroid hormone biosynthesis and sphingolipid metabolism. The 727 differential genes found in transcriptomics were associated with biochemical indicators via gene network to finally screen 11 differential genes, which were mainly key genes of TGF-β/Smad and PI3K/Akt/FoxO signaling pathways. Salvia miltiorrhiza multi-bioactive compounds combination could significantly regulate the Egr1, Pik3r3 and Col1a1 genes. 11 differentially expressed proteins involved in the two pathways were selected, of which 9 were significantly altered in db/db mice compared to db/m mice. Salvia miltiorrhiza multi-bioactive compounds combination could callback Q9DBM2, S4R1W1, Q91Y97, P47738, A8DUK4, and A2ARV4. In summary, Salvia miltiorrhiza multi-bioactive compounds combination may ameliorate kidney injury in diabetes through regulation of TGF-β/Smad and PI3K/Akt/FoxO signaling pathways.
Signature construction and molecular subtype identification based on cuproptosis-related genes to predict the prognosis and immune activity of patients with hepatocellular carcinoma

Background Hepatocellular carcinoma (HCC) is one of the most common malignancies in the world, with high incidence, high malignancy, and low survival rate. Cuproptosis is a novel form of cell death mediated by lipoylated TCA cycle proteins-mediated novel cell death pathway and is highly associated with mitochondrial metabolism. However, the relationship between the expression level of cuproptosis-related genes (CRGs) and the prognosis of HCC is still unclear. Methods Combining the HCC transcriptomic data from The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus (GEO) databases, we identified the differentially expressed cuproptosis-related genes (DECRGs) and obtained the prognosis-related DECRGs through univariate regression analysis.LASSO and multivariate COX regression analyses of these DECRGs yielded four genes that were used to construct the signature. Next, we use ROC curves to evaluate the performance of signatures. The tumor microenvironment, immune infiltration, tumor mutation load, half-maximum suppression concentration, and immunotherapy effects were also compared between the low-risk and high-risk groups. Finally, we analyzed the expression level, prognosis, and immune infiltration correlation on the four genes that constructed the model. Results Four DECRGs s were used to construct the signature. The ROC curves indicated that signature can better assess the prognosis of HCC patients. Patients were grouped according to the signature risk score. Patients in the low-risk group had a significantly longer survival time than those in the high-risk group. Furthermore, the tumor mutation burden (TMB) values were associated with the risk score and the higher-risk group had a higher proportion of TP53 mutations than the low-risk group.ESTIMATE analysis showed significant differences in stromal scores between the two groups.N6-methyladenosine (m6A) and multiple immune checkpoints were expressed at higher levels in the high-risk group. Then, we found that signature score correlated with chemotherapeutic drug sensitivity and immunotherapy efficacy in HCC patients. Finally, we further confirmed that the four DECRGs genes were associated with the prognosis of HCC through external validation. Conclusions We studied from the cuproptosis perspective and developed a new prognostic feature to predict the prognosis of HCC patients. This signature with good performance will help physicians to evaluate the overall prognosis of patients and may provide new ideas for clinical decision-making and treatment strategies.
Family functioning and delinquency among Chinese adolescents: Mediating effects of positive behavior recognition according to the humanistic perspective

Background Empirical research on the relationship between family functioning and delinquency has been sparse, although many studies have focused on the influence of family functioning on adolescent development. The current research aimed to fill this gap by exploring the influences of family functioning on adolescent delinquency and the mechanisms connecting the processes. Methods We derived the baseline data from a prospective observational school-based cohort Chengdu Positive Child Development (CPCD) project. Students responded to a questionnaire containing validated measures of family functioning, positive behavior recognition, and delinquent behavior. We utilized structural equation modeling and maximum likelihood estimation to test the relationships. Results Across 8811 Chinese adolescents, the incidence of delinquency behaviors among Chinese adolescents was relatively low. Family functioning and positive behavior recognition negatively predict delinquency (p < 0.001). Further, positive behavior recognition partially mediated the influence of family functioning on delinquency [p < 0.001, std. error = 0.01, 95% CI = (0.04, 0.07)]. Adolescents with better family functioning had little delinquency behavior, with positive behavior recognition and delinquency behavior negatively reinforcing each other. Conclusions This study demonstrated that family functioning was a protective factor against adolescent delinquency and revealed that positive behavior recognition was a critical mediating mechanism linking family functioning to delinquency.
Intermittent MEK inhibition for the treatment of metastatic uveal melanoma

Introduction Uveal melanoma (UM) is associated with poor outcomes in the metastatic setting and harbors activating mutations resulting in upregulation of MAPK signaling in almost all cases. The efficacy of selumetinib, an oral allosteric inhibitor of MEK1/2, was limited when administered at a continual dosing schedule of 75 mg BID. Preclinical studies demonstrate that intermittent MEK inhibition reduces compensatory pathway activation and promotes T cell activation. We hypothesized that intermittent dosing of selumetinib would reduce toxicity, allow for the administration of increased doses, and achieve more complete pathway inhibition, thus resulting in improved antitumor activity. Methods We conducted a phase Ib trial of selumetinib using an intermittent dosing schedule in patients with metastatic UM. The primary objective was to estimate the maximum tolerated dose (MTD) and assess safety and tolerability. Secondary objectives included assessment of the overall response rate (RR), progression-free survival (PFS) and overall survival (OS). Tumor biopsies were collected at baseline, on day 3 (on treatment), and between days 11-14 (off treatment) from 9 patients for pharmacodynamic (PD) assessments. Results 29 patients were enrolled and received at least one dose of selumetinib across 4 dose levels (DL; DL1: 100 mg BID; DL2: 125 mg BID; DL3: 150 mg BID; DL4: 175 mg BID). All patients experienced a treatment-related adverse event (TRAE), with 5/29 (17%) developing a grade 3 or higher TRAE. Five dose limiting toxicities (DLT) were observed: 2/20 in DL2, 2/5 in DL3, 1/1 in DL4. The estimated MTD was 150 mg BID (DL3), with an estimated probability of toxicity of 29% (90% probability interval 16%-44%). No responses were observed; 11/29 patients achieved a best response of stable disease (SD). The median PFS and OS were 1.8 months (95% CI 1.7, 4.5) and 7.1 months (95% CI 5.3, 11.5). PD analysis demonstrated at least partial pathway inhibition in all samples at day 3, with reactivation between days 11-14 in 7 of those cases. Conclusions We identified 150 mg BID as the MTD of intermittent selumetinib, representing a 100% increase over the continuous dose MTD (75 mg BID). However, no significant clinical efficacy was observed using this dosing schedule.
The role of allogeneic platelet-rich plasma in patients with diabetic foot ulcer: Current perspectives and future challenges

The frequency of chronic cutaneous wounds are sharply increasing in aging populations. Patients with age-related diseases, such as diabetes, tumors, renal failure and stroke are prone to soft tissue and skin injury, compounded by slowed healing in aging. Imbalance of wound inflammation, loss of growth factor secretion, and impairment of tissue repair abilities are all possible reasons for failed healing. Therefore, it is vital to explore novel approaches to accelerate wound healing. Platelet-rich plasma (PRP) as a cell therapy has been widely applied for tissue repair and regeneration. PRP promotes wound healing by releasing antimicrobial peptides, growth factors and micro-RNAs. Medical evidence indicates that autologous platelet-rich plasma (au-PRP) can promote wound healing effectively, safely and rapidly. However, its clinical application is usually restricted to patients with chronic cutaneous wounds, generally because of other severe complications and poor clinical comorbidities. Allogeneic platelet-rich plasma (al-PRP), with abundant sources, has demonstrated its superiority in the field of chronic wound treatment. Al-PRP could overcome the limitations of au-PRP and has promising prospects in clinical applications. The aim of this review is to summarize the current status and future challenges of al-PRP in chronic cutaneous wound management. We also summarized clinical cases to further describe the application of al-PRP for chronic wounds in clinical practice.
Astragaloside IV supplementation attenuates cognitive impairment by inhibiting neuroinflammation and oxidative stress in type 2 diabetic mice

Although diabetic cognitive impairment is one of the most common complications of type 2 diabetes mellitus (T2DM), optimized therapeutic strategies are not available yet. Astragalosides IV (AS-IV) is a traditional Chinese medicine possessing diverse pharmacological properties including anti-inflammatory and antioxidant effects. However, the effects of AS-IV on diabetes-related cognitive impairment and its precise mechanisms remain largely unknown. T2DM mice, induced by a high-fat diet (HFD) and an intraperitoneal injection of low-dose streptozotocin (STZ) were administrated with AS-IV every other day for eight consecutive weeks. Learning and memory abilities were assessed subsequently using the Ymaze test and the anxious behavior was evaluated using an open field test. Then, the morphology and number of neurons and microglia were observed by HE staining or immunohistochemistry. Oxidative stress biomarkers and pro-inflammatory cytokines were determined using relevant kits. In addition, the expression levels of Nrf2, Keap1, HO-1, and NQO1 were determined by Western blot analyses. The results indicated that AS-IV administration significantly improved neuronal damage and cognitive deficit in T2DM mice. Meanwhile, oxidative stress and neuroinflammation were also ameliorated in T2DM mice, which might be attributed to the regulation of Nrf2/Keap1/HO-1/NQO1 pathway in T2DM mice. Taken together, these data suggested that AS-IV ameliorates cognitive impairment in T2DM mice by attenuating oxidative stress and neuroinflammation, possibly through modulating the Nrf2/Keap1/HO1/NQO1 pathway.
Upscaling in socio-environmental systems modelling: Current challenges, promising strategies and insights from ecology

Sustainability challenges in socio-environmental systems (SES) are inherently multiscale, with global-level changes emerging from socio-environmental processes that operate across different spatial, temporal, and organisational scales. Models of SES therefore need to incorporate multiple scales, which requires sound methodologies for transferring information between scales. Due to the increasing global connectivity of SES, upscaling – increasing the extent or decreasing the resolution of a modelling study – is becoming progressively more important. However, upscaling in SES models has received less attention than in other fields (e.g., ecology or hydrology) and therefore remains a pressing challenge. To advance the understanding of upscaling in SES, we take three steps. First, we review existing upscaling approaches in SES as well as other disciplines. Second, we identify four main challenges that are particularly relevant to upscaling in SES: 1) heterogeneity, 2) interactions, 3) learning and adaptation, and 4) emergent phenomena. Third, we present an approach that facilitates the transfer of existing upscaling methods to SES, using two good practice examples from ecology. To describe and compare these methods, we propose a scheme of five general upscaling strategies. This scheme builds upon and unifies existing schemes and provides a standardised way to classify and represent existing as well as new upscaling methods. We demonstrate how the scheme can help to transparently present upscaling methods and uncover scaling assumptions, as well as to identify limits for the transfer of upscaling methods. We finish by pointing out research avenues on upscaling in SES to address the identified upscaling challenges.
Why are NLP Models Fumbling at Elementary Math? A Survey of Automatic Word Problem Solvers

From the latter half of the last decade, there 001 has been growing interest in developing algo-002 rithms for automatically solving mathematical 003 word problems (MWP). It is an exciting lan-004 guage problem which demands not only sur-005 face level text pattern recognition but requires 006 coupling with mathematical reasoning as well. 007 In spite of the dedicated effort, we are still 008 miles away from building robust representa-009 tions of elementary math word problems. In 010 this paper, we critically examine the various 011 models that have been developed for solving 012 word problems, their pros and cons and the 013 challenges ahead. In the last two years, a lot of 014 deep learning models have come out with com-015 peting results on benchmark datasets. We take 016 a step back and analyse why, in spite of this, 017 the predominantly used experiment and dataset 018 designs are a stumbling block and provide a 019 road-map for the future. 020
Flagging Comprehensibility Issues in Hindi Text with Question Answering

There is a critical need for checking the qual-001 ity of translations while localizing important 002 content across all the industries. This paper 003 presents question-answering based techniques 004 to check the comprehensibility of a text trans-005 lation. The viability of the method is evalu-006 ated using text translated from English to Hindi, 007 where we see comprehensibility issues identi-008 fied with up to 87% accuracy. 009
ZEBRA: Anonymous Credentials with Practical On-chain Verification and Applications to KYC in DeFi

—ZEBRA is an Anonymous Credential (AC) scheme, supporting auditability and revocation, that provides practical on-chain verification for the first time. It realizes efficient access control on permissionless blockchains while achieving both privacy and accountability. In all prior solutions, users either pay exorbitant fees or lose privacy since authorities granting access can map users to their wallets. Hence, ZEBRA is the first to enable DeFi platforms to remain compliant with imminent regulations without compromising user privacy. We evaluate ZEBRA and show that it reduces the gas cost incurred on the Ethereum Virtual Machine (EVM) by 11 . 8 × when compared to Coconut [NDSS 2019], the state-of-the-art AC scheme for blockchains. This translates to a reduction in transaction fees from 94 USD to 8 USD on Ethereum in August 2022. However, 8 USD is still high for most applications, and ZEBRA further drives down credential verification costs through batched verification. For a batch of 512 layer-1 and layer-2 wallets, the gas cost is reduced by 35 × and 641 × on EVM, and the transaction fee is reduced to just 0 . 23 USD and 0 . 0126 USD on Ethereum, respectively. For perspective, these costs are comparable to the minimum transaction costs on Ethereum.
The Journal of Financial Research

Using 13,233 acquisitions from 57 countries, we examine merger and acquisition (M&A) decisions made by busy boards. We find that few busy acquirers originate from emerging markets and that they tend to undertake cross‐border mergers, favor public targets, finance with cash and equity, pursue nondiversifying mergers, avoid targets with multiple bidders, and long‐term underperform relative to nonbusy acquirers. Importantly, we discover a nonlinear relation between an acquirer’s board busyness and merger announcement returns. We find that the labor market penalizes directors who approve bad acquisitions but does not reward them for good mergers. We find a similar nonlinear relation between an acquirer’s board busyness and its long‐term performance along with a suggestion of an optimal board busyness. JEL Classification: G3, G34
Molecular branch of a small highly elongated Fermi gas with an impurity: Full three-dimensional versus effective one-dimensional description

We consider an impurity immersed in a small Fermi gas under highly-elongated harmonic confinement. The impurity interacts with the atoms of the Fermi gas through an isotropic short-range potential with three-dimensional free-space s-wave scattering length $a_{3\text{d}}$. We investigate the energies of the molecular branch, i.e., the energies of the state that corresponds to a gas consisting of a weakly-bound diatomic molecule and "unpaired" atoms, as a function of the s-wave scattering length $a_{3\text{d}}$ and the ratio $\eta$ between the angular trapping frequencies in the tight and weak confinement directions. The energies obtained from our three-dimensional description that accounts for the dynamics in the weak and tight confinement directions are compared with those obtained within an effective one-dimensional framework, which accounts for the dynamics in the tight confinement direction via a renormalized one-dimensional coupling constant. Our theoretical results are related to recent experimental measurements.
An Empirical Study of Document-to-document Neural Machine Translation

This paper does not aim at introducing a novel 001 method for document NMT. Instead, we head 002 back to the original transformer model with 003 document-level training and hope to answer 004 the following question: Is the capacity of 005 current models strong enough for document-006 level NMT? Interestingly, we observe that 007 the original transformer with appropriate train-008 ing techniques can achieve strong results for 009 document translation, even with a length of 010 2000 words. We evaluate this model and sev-011 eral recent approaches on nine document-level 012 datasets and two sentence-level datasets across 013 six languages. Experiments show that the orig-014 inal Transformer model outperforms sentence-015 level models and many previous methods in 016 a comprehensive set of metrics, including 017 BLEU, four lexical indices, three newly pro-018 posed assistant linguistic indicators, and hu-019 man evaluation. 020
Prompt Combines Paraphrase: Enhancing Biomedical “Pre-training, Prompt and Predicting” Models by Explaining Rare Biomedical Concepts

Prompt-based fine-tuning for pre-trained mod-001 els has proven resultful in general domains for 002 few-shot learning in downstream tasks. As to 003 the biomedical domain, rare biomedical en-004 tities, which are quite ubiquitous in health-005 care contexts, can affect the performance of 006 pre-trained models, especially in low-resource 007 scenarios. We propose a simple yet effective 008 approach to helping models understand rare 009 biomedical words during tuning with prompt. 010 Experiments demonstrate that our method can 011 achieve up to 5% improvement in biomedical 012 tasks without any additional parameters or train-013 ing steps in few-shot vanilla prompt settings. 014
A Meta-transfer Learning framework for Visually Grounded Compositional Concept Learning

Humans acquire language in a compositional 001 and grounded manner. They can describe 002 their perceptual world using novel compo-003 sitions from already learnt elementary con-004 cepts. However, recent research shows that 005 modern neural networks lack such composi-006 tional generalization ability. To address this 007 challenge, in this paper, we propose MetaVL , 008 a meta-transfer learning framework to train 009 transformer-based vision-and-language (V&L) 010 models using optimization-based meta-learning 011 method and episodic training. We carefully 012 created two datasets based on MSCOCO and 013 Flicker30K to specifically target novel compo-014 sitional concept learning. Our empirical results 015 have shown that MetaVL outperforms baseline 016 models in both datasets. Moreover, MetaVL 017 has demonstrated higher sample efficiency com-018 pared to supervised learning, especially under 019 the few-shot setting. 020
Exploring Differences in Novice and Experienced Teachers’ Self-efficacy: A Mixed Methods Study 1

Self-efficacy refers to teachers’ beliefs in their own abilities. Although there are many studies on teacher self-efficacy, there is a need for more research on teachers’ self-efficacy in light of their experience. This study explored differences between novice and experienced teachers’ self-efficacy. Data were collected qualitatively and quantitatively through interviews and questionnaires. Content analysis was used to analyze qualitative data, and the quantitative data were analyzed via Wilcoxon test. The findings indicated that the experienced teachers held more efficacious beliefs as compared to the novice teachers. While the novice teachers were more concerned about syllabus-related issues than about shaping their efficacy, the experienced teachers held more general views about considering students’ needs as a factor in shaping their efficacy beliefs. The study concludes with implications for more attention to self-efficacy in pre-service and in-service teacher education settings. profesores principiantes estaban más preocupados por los problemas relacionados con el programa de estudios, los profesores experimentados tenían puntos de vista más generales sobre la importancia de considerar las necesidades de los estudiantes como un factor que configura sus creencias de eficacia. El estudio concluye con implicaciones para una mayor atención a la autoeficacia en los entornos de formación docente antes y durante el servicio.