Miracle-dz/UniFAR-FTUs huggingface dataset:
A simplified 3D model for existing tunnel response to piles construction

Abstract The construction and loading of deep foundations (piles) of high-rise buildings causes a considerable effect in terms of stresses and deformation and requires assessing their impact on the response of adjacent tunnels to deformations, particularly for pile foundations, which are often constructed in locations very close to existing tunnels. The execution process for piles structures generates displacements, stresses, and forces, which are transferred through the piles’ soil surrounding a nearby existing tunnel. The research presented in this paper has led to a significantly improved understanding of pile-tunnel interaction problem. It is crucial for the analysis of the impact of the pile construction on an existing tunnel. The treated topic appears in a setting of an urban environment, where we construct numerous profound foundations, sometimes in contact or adjacent to a. In this paper, the response of the existing tunnel under constructed pile process is studied. Numerical modeling was carried out using Plaxis3D software in which the Mohr-Coulomb Model (MC) has been used for modeling, while the piles/ tunnels are modeled as a linear elastic material. Furthermore, a parametric study is conducted, and its cases are investigated. The displacements and the stresses generated on the tunnel lining decreases with the increase in relative distance between pile and tunnel (spacing), the location/length of the pile from the tunnel, the pile diameter, the number of piles. We have also identified two geometrical parameters of the tunnel: shape section and thickness lining which play a prominent role in the interaction between an existing tunnel and a new pile to excavate.
Applications of rough sets in big data analysis: An overview

Abstract Big data, artificial intelligence and the Internet of things (IoT) are still very popular areas in current research and industrial applications. Processing massive amounts of data generated by the IoT and stored in distributed space is not a straightforward task and may cause many problems. During the last few decades, scientists have proposed many interesting approaches to extract information and discover knowledge from data collected in database systems or other sources. We observe a permanent development of machine learning algorithms that support each phase of the data mining process, ensuring achievement of better results than before. Rough set theory (RST) delivers a formal insight into information, knowledge, data reduction, uncertainty, and missing values. This formalism, formulated in the 1980s and developed by several researches, can serve as a theoretical basis and practical background for dealing with ambiguities, data reduction, building ontologies, etc. Moreover, as a mature theory, it has evolved into numerous extensions and has been transformed through various incarnations, which have enriched expressiveness and applicability of the related tools. The main aim of this article is to present an overview of selected applications of RST in big data analysis and processing. Thousands of publications on rough sets have been contributed; therefore, we focus on papers published in the last few years. The applications of RST are considered from two main perspectives: direct use of the RST concepts and tools, and jointly with other approaches, i.e., fuzzy sets, probabilistic concepts, and deep learning. The latter hybrid idea seems to be very promising for developing new methods and related tools as well as extensions of the application area.
Functional oscillation of a multienzyme glucosome assembly during cell cycle progression

Glucose metabolism has been studied extensively to understand functional interplays between metabolism and a cell cycle. However, our understanding of cell cycle-dependent metabolic adaptation particularly in human cells remains largely elusive. Meanwhile, human enzymes in glucose metabolism are shown to functionally organize into three different sizes of a multienzyme metabolic assembly, the glucosome, to regulate glucose flux in a size-dependent manner. Here, using fluorescence single-cell imaging techniques, we discover that glucosomes spatiotemporally oscillate during a cell cycle in an assembly size-dependent manner. Importantly, their oscillation at single-cell levels is in accordance with functional contributions of glucose metabolism to cell cycle progression at a population level. Collectively, we demonstrate functional oscillation of glucosomes during cell cycle progression and thus their biological significance to human cell biology.
Management of traumatic brain injury: a narrative review of current evidence

Globally, approximately 70 million people sustain traumatic brain injury each year and this can have significant physical, psychosocial and economic consequences for patients, their families and society. The aim of this review is to provide clinicians with a summary of recent studies of direct relevance to the management of traumatic brain injury in order to promote best clinical practice. The use of tranexamic acid in the management of traumatic brain injury has been the focus of several studies, with one large randomised controlled trial suggesting a reduction in all‐cause mortality within 24 h of injury. The use of therapeutic hypothermia does not improve neurological outcomes and maintenance of normothermia remains the optimal management strategy. For seizure management, levetiracetam appears to be as effective as phenytoin, but the optimal dose remains unclear. There has been a lack of clear outcome benefit for any individual osmotherapy agent, with no difference in mortality or neurological recovery. Early tracheostomy (< 7 days from injury) for patients with traumatic brain injury is associated with a reduction in the incidence of ventilator‐associated pneumonia and duration of mechanical ventilation, critical care and hospital stay. Further research is needed in order to determine the optimal package of care and interventions. There is a need for research studies to focus on patient‐centred outcome measures such as long‐term neurological recovery and quality of life.
Analysis of the Auditor's Perspective on the Use of Big Data in Financial Statements: UTAUT Model Approach

The purpose of this study is to determine the perception of the existence of big data on auditors supported by the use of the UTAUT Model. Empirical evidence will be tested on performance expectations, effort expectations, social influences, facilitating conditions, and trust that are predicted to influence behavioral intentions. This research was conducted using quantitative methods on 70 samples of auditor respondents who work in Public Accounting Firms in Indonesia. The analytical method used in this study is PLS-SEM with behavioral intention as the dependent variable. This study found that the variables of independent performance expectations, business expectations, social influences, and facilitation conditions had no significant effect on behavioral intentions. While the trust variable has a significant positive effect. The abstract needs to summarize the content of the paper.
Technology Acceptance Model, Social Cognitive Theory and Remote Audit in Predicting Auditor Attitude toward Technology during Pandemic Covid-19

In this era of industry 4.0, all fields have become digital, including accounting and auditing. Auditors must keep up with the times by adopting audit technology, one of which is audit software. In addition, the current Covid-19 pandemic condition requires auditors to carry out remote audits. Remote audit itself must be supported by the auditor's ability to operate audit software. Our motivation for conducting this research is to find out what factors influence a financial auditor to adopt technology, namely software audits amid the pressure of the Covid-19 pandemic. This study is a quantitative study, we use the approach of the Technology Acceptance Model and Social Cognitive Model as well as remote audit to predict auditors' attitude toward using technology. Our study uses a sample of auditors working in public accounting firms in Jakarta, Indonesia. Methods of data collection by distributing questionnaires to respondents. The analytical method used is the structural equation modeling partial least square, we use the Smart PLS statistical software version 3. The results of your research state that technology self-efficacy, social participation, perceived ease of use, and remote audit have a significant effect on auditors' attitude toward using technology, while perceived usefulness has no significant effect
Improving the Sustainable Usage Intention of Mobile Payments: Extended Unified Theory of Acceptance and Use of Technology Model Combined With the Information System Success Model and Initial Trust Model

Under the background of global cross-border mobile commerce (m-commerce) integration, the importance of cross-border payment research is becoming increasingly prominent and urgent. The important value of this study is to empirically research the influence power of key elements in using two different mobile payment (m-payment) platforms in Korea. The extended unified theory of acceptance and use of technology (UTAUT2) has been widely applied in various studies because of its strong interpretive power. In Korea, there are a few empirical studies on Chinese users. Based on a survey of 908 Chinese participants (486 WeChat Pay’s Chinese users and 465 Kakao Pay’s Korean users) in Korea, this study is one application extending UTAUT2 by incorporating multi-group and multi-model constructs: UTAUT2, information system success (ISS) model, and an initial trust model (ITM), considering a multi-group analysis with some mediating variables (payment difference). By comparing the two different payment platforms’ characters, this manuscript provides a set of targeted measures to ensure Chinese WeChat Payment platform decision-makers create effective long-term strategic policies for cross-border m-payments in Korea, and eventually, benefit cross-border m-commerce and economic cooperation in Southeast Asia.
Correlation Between Previous Caesarean Section and Adverse Maternal Outcomes Accordingly With Robson Classification: Systematic Review and Meta-Analysis

Background: The increasing rates of Caesarean section (CS) beyond the WHO standards (10–15%) pose a significant global health concern. Objective: Systematic review and meta-analysis to identify an association between CS history and maternal adverse outcomes for the subsequent pregnancy and delivery among women classified in Robson classification (RC). Search Strategy: PubMed/Medline, EbscoHost, ProQuest, Embase, Web of Science, BIOSIS, MEDLINE, and Russian Science Citation Index databases were searched from 2008 to 2018. Selection Criteria: Based on Robson classification, studies reporting one or more of the 14 adverse maternal outcomes were considered eligible for this review. Data Collection: Study design data, interventions used, CS history, and adverse maternal outcomes were extracted. Main Results: From 4,084 studies, 28 (n = 1,524,695 women) met the inclusion criteria. RC group 5 showed the highest proportion among deliveries followed by RC10, RC7, and RC8 (67.71, 32.27, 0.02, and 0.001%). Among adverse maternal outcomes, hysterectomy had the highest association after preterm delivery OR = 3.39 (95% CI 1.56–7.36), followed by Severe Maternal Outcomes OR = 2.95 (95% CI 1.00–8.67). We identified over one and a half million pregnant women, of whom the majority were found to belong to RC group 5. Conclusions: Previous CS was observed to be associated with adverse maternal outcomes for the subsequent pregnancies. CS rates need to be monitored given the prospective risks which may occur for maternal and child health in subsequent births.
Extending Association Rule Mining to Microbiome Pattern Analysis: Tools and Guidelines to Support Real Applications

Boosted by the exponential growth of microbiome-based studies, analyzing microbiome patterns is now a hot-topic, finding different fields of application. In particular, the use of machine learning techniques is increasing in microbiome studies, providing deep insights into microbial community composition. In this context, in order to investigate microbial patterns from 16S rRNA metabarcoding data, we explored the effectiveness of Association Rule Mining (ARM) technique, a supervised-machine learning procedure, to extract patterns (in this work, intended as groups of species or taxa) from microbiome data. ARM can generate huge amounts of data, making spurious information removal and visualizing results challenging. Our work sheds light on the strengths and weaknesses of pattern mining strategy into the study of microbial patterns, in particular from 16S rRNA microbiome datasets, applying ARM on real case studies and providing guidelines for future usage. Our results highlighted issues related to the type of input and the use of metadata in microbial pattern extraction, identifying the key steps that must be considered to apply ARM consciously on 16S rRNA microbiome data. To promote the use of ARM and the visualization of microbiome patterns, specifically, we developed microFIM (microbial Frequent Itemset Mining), a versatile Python tool that facilitates the use of ARM integrating common microbiome outputs, such as taxa tables. microFIM implements interest measures to remove spurious information and merges the results of ARM analysis with the common microbiome outputs, providing similar microbiome strategies that help scientists to integrate ARM in microbiome applications. With this work, we aimed at creating a bridge between microbial ecology researchers and ARM technique, making researchers aware about the strength and weaknesses of association rule mining approach.
Ethnobotanical Documentation of Medicinal Plants Used by the Indigenous Panay Bukidnon in Lambunao, Iloilo, Philippines

The Panay Bukidnon is a group of indigenous peoples living in the interior highlands of Panay Island in Western Visayas, Philippines. Little is known about their ethnobotanical knowledge due to limited written records, and no recent research has been conducted on the medicinal plants they used in ethnomedicine. This study aims to document the medicinal plants used by the indigenous Panay Bukidnon in Lambunao, Iloilo, Panay Island. Semi-structured interviews were conducted with 75 key informants from June 2020 to September 2021 to determine the therapeutic use of medicinal plants in traditional medicine. A total of 131 medicinal plant species distributed in 121 genera and 57 families were used to address 91 diseases in 16 different uses or disease categories. The family Fabaceae was best represented with 13 species, followed by Lamiaceae with nine species and Poaceae with eight species. The leaf was the most frequently used plant part and decoction was the most preferred form of preparation. To evaluate the plant importance, use value (UV), relative frequency citation (RFC), relative important index (RI), informant consensus factor (ICF), and fidelity level (FL) were used. Curcuma longa L. had the highest UV (0.79), Artemisia vulgaris L. had the highest RFC value (0.57), and Annona muricata L. had the highest RI value (0.88). Diseases and symptoms or signs involving the respiratory system and injury, poisoning, and certain other consequences of external causes recorded the highest ICF value (0.80). Blumea balsamifera (L.) DC. and Chromolaena odorata (L.) R.M. King & H. Rob were the most relevant and agreed species for the former and latter disease categories, respectively. C. odorata had the highest FL value (100%) and was the most preferred medicinal plant used for cuts and wounds. The results of this study serve as a medium for preserving cultural heritage, ethnopharmacological bases for further drug research and discovery, and preserving biological diversity.
Breast Cancer Classification Based on Tumor Budding and Stem Cell-Related Signatures Facilitate Prognosis Evaluation

Background Tumor budding (TB) is emerging as a prognostic factor in multiple cancers. Likewise, the stemness of cancer cells also plays a vital role in cancer progression. However, nearly no research has focused on the interaction of TB and tumor stemness in cancer. Methods Tissue microarrays including 229 cases of invasive breast cancer (BC) were established and subjected to pan-cytokeratin immunohistochemical staining to evaluate molecular expression. Univariate and multivariate analyses were applied to identify prognostic factors of BC, and the Chi-square test was used for comparison of categorical variables. Results High-grade TB was significantly associated with T stage, lymph node metastasis, tumor node metastasis (TNM) stage, epithelial-mesenchymal transition, and poor disease-free survival (DFS) of BC patients. We also found that the prognostic value of TB varied widely among different subtypes and subgroups. Cox regression analysis then showed that TB grade was an independent prognostic factor. Moreover, cancer stem cell (CSC) markers CD44 and ALDH1A1 were significantly higher in high-grade TB tumors. Consequently, patients were classified into high CSC score subgroup and low CSC score subgroups. Further research found that CSC scores correlated with clinicopathological features and DFS of BC patients. Based on TB grade and CSC scores, we classified BC patients into TBlow-CSCslow (type I), TBlow-CSCshigh (type II), TBhigh-CSCslow (type III), and TBhigh-CSCshigh (type IV) subgroups. Survival analysis showed that patients in the type I subgroup had the best DFS, whereas those in the type IV subgroup had the worst DFS. Finally, a TB-CSC-based nomogram for use in BC was established. The nomogram was well calibrated to predict the probability of 5-year DFS, and the C-index was 0.837. Finally, the area under the curve value for the nomogram (0.892) was higher than that of the TNM staging system (0.713). Conclusion The combination of TB grade with CSC score improves the prognostic evaluation of BC patients. A novel nomogram containing TB grade and CSC score provides doctors with a candidate tool to guide the individualized treatment of cancer patients.
The Importance of Biobehavioral Research to Examine the Physiological Effects of Racial and Ethnic Discrimination in the Latinx Population

A growing body of research is documenting how racial and ethnic populations embody social inequalities throughout the life course. Some scholars recommend the integration of biospecimens representing the hypothalamic-pituitary-adrenal axis, neurological and endocrinological processes, and inflammation to capture the embodiment of inequality. However, in comparison to other racial and ethnic groups, there has been little research examining how Hispanic/Latinx persons embody racial and ethnic discrimination, much less resulting from institutional and structural racism. We provide a rationale for expanding biobehavioral research examining the physiological consequences of racism among Latinx persons. We identify gaps and make recommendations for a future research agenda in which biobehavioral research can expand knowledge about chronic disease inequities among Latinx populations and inform behavioral and institutional interventions. We end by cautioning readers to approach the recommendations in this article as a call to expand the embodiment of racism research to include the diverse Latinx population as the United States addresses racial inequity.
Chloroplast Genome Evolution in Four Montane Zingiberaceae Taxa in China

Chloroplasts are critical to plant survival and adaptive evolution. The comparison of chloroplast genomes could provide insight into the adaptive evolution of closely related species. To identify potential adaptive evolution in the chloroplast genomes of four montane Zingiberaceae taxa (Cautleya, Roscoea, Rhynchanthus, and Pommereschea) that inhabit distinct habitats in the mountains of Yunnan, China, the nucleotide sequences of 13 complete chloroplast genomes, including five newly sequenced species, were characterized and compared. The five newly sequenced chloroplast genomes (162,878–163,831 bp) possessed typical quadripartite structures, which included a large single copy (LSC) region, a small single copy (SSC) region, and a pair of inverted repeat regions (IRa and IRb), and even though the structure was highly conserved among the 13 taxa, one of the rps19 genes was absent in Cautleya, possibly due to expansion of the LSC region. Positive selection of rpoA and ycf2 suggests that these montane species have experienced adaptive evolution to habitats with different sunlight intensities and that adaptation related to the chloroplast genome has played an important role in the evolution of Zingiberaceae taxa.
Excitatory-Inhibitory Homeostasis and Diaschisis: Tying the Local and Global Scales in the Post-stroke Cortex

Maintaining a balance between excitatory and inhibitory activity is an essential feature of neural networks of the neocortex. In the face of perturbations in the levels of excitation to cortical neurons, synapses adjust to maintain excitatory-inhibitory (EI) balance. In this review, we summarize research on this EI homeostasis in the neocortex, using stroke as our case study, and in particular the loss of excitation to distant cortical regions after focal lesions. Widespread changes following a localized lesion, a phenomenon known as diaschisis, are not only related to excitability, but also observed with respect to functional connectivity. Here, we highlight the main findings regarding the evolution of excitability and functional cortical networks during the process of post-stroke recovery, and how both are related to functional recovery. We show that cortical reorganization at a global scale can be explained from the perspective of EI homeostasis. Indeed, recovery of functional networks is paralleled by increases in excitability across the cortex. These adaptive changes likely result from plasticity mechanisms such as synaptic scaling and are linked to EI homeostasis, providing a possible target for future therapeutic strategies in the process of rehabilitation. In addition, we address the difficulty of simultaneously studying these multiscale processes by presenting recent advances in large-scale modeling of the human cortex in the contexts of stroke and EI homeostasis, suggesting computational modeling as a powerful tool to tie the meso- and macro-scale processes of recovery in stroke patients.