Miracle-dz/UniFAR-FTUs huggingface dataset:
Risk analysis of flooding caused by joint action of multivariate hydrological factors in river network area

Floods are often caused by various hydrological factors, while, traditional drainage criteria is usually defined based on the frequency analysis of rainstorm events. Such kind of drainage criteria based on univariate frequency analysis of rainfall does not reflect the joint action of multivariate hydrological factors on flooding, and it cannot measure the occurrence probability of flooding caused by various hydrological factors. Taking the drainage problem in river network area as an example, where the rainstorm and high water level in outside river are the two main factors causing flooding, the Gumbel-Hougard Copula were applied to construct the joint probability distribution model of rainstorm and water level in outside river. The occurrence probability of flooding under the two main factors were analyzed, and the joint probability and conditional probability of different combinations of various magnitude rainfall and water level were calculated. The results show that the flooding probability under joint action of multivariate hydrological factors can reflect the true occurrence probability of flooding, and flooding probability under different combinations of multivariate hydrological factors can be easily obtained by multivariate joint probability distribution model based on Copula method.
Structural, Biochemical, and Biophysical Characterization of Idelalisib Binding to Phosphoinositide 3-Kinase (cid:2) *

, , Background: Idelalisib is a PI3K (cid:2) inhibitor used to treat hematological malignancies. Results: Idelalisib is selective, noncovalent, reversible, and ATP-competitive. Conclusion: The crystal structure helps explain the potency and selectivity of idelalisib. The biophysical and biochemical data clarify the details of the inhibitor’s interactions with PI3K (cid:2) . Significance: Its use in humans makes it important to understand how idelalisib inhibits PI3K (cid:2) . Idelalisib (also known as GS-1101, CAL-101, IC489666, and Zydelig) is a PI3K (cid:2) inhibitor that has recently been approved for the treatment of several hematological malignancies. Given its use in human diseases, we needed a clear picture of how idelalisib binds to and inhibits PI3K (cid:2) . Our data show that idelalisib is a potent and selective inhibitor of the kinase activity of PI3K (cid:2) . A kinetic characterization clearly demonstrated ATP-competitive inhibition, and several additional biochemical and biophysical assays showed that the compound binds reversibly and nonco-valently to the kinase. A crystal structure of idelalisib bound to the p110
Prediction of Non-Transplantable Recurrence After Liver Resection for Solitary Hepatocellular Carcinoma

Purpose Using a combination model of preoperative imaging and clinical factors to predict non-transplantable recurrence (NTR) after liver resection and assist solitary hepatocellular carcinoma (HCC) patients in the selection of early treatment options. Patients and Methods A retrospective analysis was conducted on 253 solitary HCC patients who underwent radical resection and had preoperative MRI. NTR patients were defined as those exceeding the University of California, San Francisco (UCSF) criteria at the time of recurrence. Cox regression analysis was employed to identify preoperative factors associated with NTR based on clinical and tumor imaging characteristics. A risk scoring model (NTRScore) was developed and validated. Results Among the 253 patients, 86 (33.9%) experienced recurrence, and among those with recurrence, 34 patients (39.5%) developed NTR. In multivariate analysis, factors associated with NTR included alpha-fetoprotein (AFP) [>10 ng/mL] [HR: 3.42, 95% confidence interval (CI): 1.54–7.63, P: 0.003], arterial phase hyperenhancement (APHE) [HR: 2.23, 95% CI: 1.03–4.81, P: 0.041], washout[HR: 0.35, 95% CI: 0.15–0.84, P: 0.019], and capsule [HR: 0.44, 95% CI: 0.22–0.88, P: 0.021]. The β-coefficients of these variables were utilized to develop the weighted NTRScore(c-index 0.72, 95% CI: 0.65–0.79). The NTR occurrence increased across the three categories (low: 5.6%, medium: 13.6%, high: 35.1%, p < 0.001), and the Kaplan-Meier curves of recurrence-free survival(RFS) and overall survival(OS) show significant differences (p = 0.004 and p<0.001). Furthermore, the higher NTR categories may be associated with an increased risk of extrahepatic recurrence. Conclusion The NTRScore demonstrated strong discriminatory ability and may serve as a clinically useful tool to assist in risk stratification and potential to guide treatment and optimal surveillance for patients of solitary hepatocellular carcinoma within UCSF criteria.
Precise and rapid whole-head segmentation from magnetic resonance images of older adults using deep learning

Abstract Whole-head segmentation from Magnetic Resonance Images (MRI) establishes the foundation for individualized computational models using finite element method (FEM). This foundation paves the path for computer-aided solutions in fields such as non-invasive brain stimulation. Most current automatic head segmentation tools are developed using healthy young adults. Thus, they may neglect the older population that is more prone to age-related structural decline such as brain atrophy. In this work, we present a new deep learning method called GRACE, which stands for General, Rapid, And Comprehensive whole-hEad tissue segmentation. GRACE is trained and validated on a novel dataset that consists of 177 manually corrected MR-derived reference segmentations that have undergone meticulous manual review. Each T1-weighted MRI volume is segmented into 11 tissue types, including white matter, grey matter, eyes, cerebrospinal fluid, air, blood vessel, cancellous bone, cortical bone, skin, fat, and muscle. To the best of our knowledge, this work contains the largest manually corrected dataset to date in terms of number of MRIs and segmented tissues. GRACE outperforms five freely available software tools and a traditional 3D U-Net on a five-tissue segmentation task. On this task, GRACE achieves an average Hausdorff Distance of 0.21, which exceeds the runner-up at an average Hausdorff Distance of 0.36. GRACE can segment a whole-head MRI in about 3 seconds, while the fastest software tool takes about 3 minutes. In summary, GRACE segments a spectrum of tissue types from older adults’ T1-MRI scans at favorable accuracy and speed. The trained GRACE model is optimized on older adult heads to enable high-precision modeling in age-related brain disorders. To support open science, the GRACE code and trained weights are made available online and open to the research community at https://github.com/lab-smile/GRACE.
Effectiveness of a Nursing Educational Intervention in Adults to Promote Control Behaviors Against Dengue: Protocol for a Randomized Controlled Trial

Background The increase in dengue cases can be attributed to social, demographic, environmental changes, or community-driven factors. In this regard, different strategies have been established in health education, using educational interventions as necessary tools for the reduction of the disease with the aim of reinforcing and stimulating the prevention and control of dengue. Objective This study aims to evaluate the effectiveness of a nursing educational intervention for dengue control. Methods A randomized controlled trial will be conducted with adults living in rural areas and participating in health promotion and disease prevention programs. We will enroll 116 adults. Adults will be randomized 1:1, with 58 adults assigned to the educational intervention group and 58 to the usual care group. Participants will receive 4 sessions over the course of a month, 1 week apart, and will be followed up for 1 month after the end of the educational intervention. Nursing Outcome Classification labels will be used to measure the outcomes: risk control (1902) and participation in health care decisions (1606). Results The participants in the intervention group are expected to achieve better dengue control behaviors than those in the usual care group. Conclusions Risk factors are fostered by the community, largely caused by artificial reservoirs or unprotected tanks in homes; also, the lack of information hinders the identification of symptomatology and the poor implementation of effective measures, and the development of standardized educational strategies can contribute to efficient and cost-effective control of the disease. Trial Registration ClinicalTrials.gov NCT05321264; https://clinicaltrials.gov/study/NCT05321264 International Registered Report Identifier (IRRID) PRR1-10.2196/54286
The application of peroxidase mimetic nanozymes in cancer diagnosis and therapy

In recent decades, scholarly investigations have predominantly centered on nanomaterials possessing enzyme-like characteristics, commonly referred to as nanozymes. These nanozymes have emerged as viable substitutes for natural enzymes, offering simplicity, stability, and superior performance across various applications. Inorganic nanoparticles have been extensively employed in the emulation of enzymatic activity found in natural systems. Nanoparticles have shown a strong ability to mimic a number of enzyme-like functions. These systems have made a lot of progress thanks to the huge growth in nanotechnology research and the unique properties of nanomaterials. Our presentation will center on the kinetics, processes, and applications of peroxidase-like nanozymes. In this discourse, we will explore the various characteristics that exert an influence on the catalytic activity of nanozymes, with a particular emphasis on the prevailing problems and prospective consequences. This paper presents a thorough examination of the latest advancements achieved in the domain of peroxidase mimetic nanozymes in the context of cancer diagnosis and treatment. The primary focus is on their use in catalytic cancer therapy, alongside chemotherapy, phototherapy, sonodynamic therapy, radiation, and immunotherapy. The primary objective of this work is to offer theoretical and technical assistance for the prospective advancement of anticancer medications based on nanozymes. Moreover, it is anticipated that this will foster the investigation of novel therapeutic strategies aimed at achieving efficacious tumor therapy.
Audit Quality dan Value Relevance of Accounting Information

Users of financial reports need relevant information as a basis for making decisions. The existence of an auditor helps investors in assessing the quality of accounting information. The research aims to examine the quality of audit reports as proxied by the size of the public accounting firm and audit opinions on the value relevance of accounting information. The research population is all sectors of business entities registered on the IDX in the 2018-2021 period. A total of 1,480 observations were used as samples in the research. Researchers use a fixed effect model in a panel data structure to test hypotheses. The research results show that auditor size increases the relevance of earnings value, but reduces the relevance of book value. Meanwhile, the audit opinion was found not to affect the relevance of accounting information. The research findings are interesting because audit opinions provided by Big 4 auditors increase the relevance of book value. This research is expected to help investors consider audit quality as an integral part of fundamental analysis, as well as contribute to the development of the topic of value relevance of accounting information and audit quality. Keywords: Audit, Financial Statement, Value Relevance
Hybrid model of unsupervised and supervised learning for multiclass sentiment analysis based on users’ reviews on healthcare web forums

Twitter has become a popular platform for sharing health information, including diabetes-related content. Recent research studies have shown that Twitter data can be used for various purposes such as monitoring illnesses, promoting health, analyzing sentiment, and potentially aiding in medical directing. However, detecting fitness-related tweets in the vast amount of data on Twitter can be difficult. This pilot study, therefore, aimed to classify patient text about drugs and disease-associated tweets into meaningful health-related segments. The unlabeled dataset is divided into several groups using an unsupervised learning technique called K-Means Clustering, using this first label the text and followed by a combination of neural networks and machine learning classifiers, they classified 32046 diabetes-related tweets and 161290 drug text lines into five groups. Approximately 66.38% of drug line text was classified as health-related, with 55.14% “treatment and medication”, 7.10% “prevention” and 4.14% “symptoms and causes”. Over 33% were categorized as “Other and News”. If we talk about the tweets as a dataset then the tweet was classified as health-related, with 44.30% “treatment and medication”, 7% “prevention” and 5.3% “symptoms and causes”. Over 56.10% were categorized as “Other and News. After this multiclass classification, we applied three machine learning and two deep learning models to find accuracy, precision, recall, and F1 scores. Drug review was used as a dataset then SVM and LR models provided an accuracy of 98% and when tweets were used as a dataset then LR models provided an accuracy of 97%. This research shows the importance of social media data in the decision-making system in the healthcare domain.
SHORT-TERM VARIABILITY OF COASTAL ZONE HYDRODYNAMICS UNDER AN EXTERNAL FORCING: OBSERVATIONS AT THE BLACK SEA RESEARCH SITE OF SIO RAS

Zatsepin A.G. , P.P. Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Kuklev S.B. , Southeren Branch of the P.P. Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Ostrovskii A.G. , Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Piotoukh V.B. , Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Podymov O.I. , Southeren Branch of the P.P. Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Silvestrova K.P., Shirshov Institute of Oceanology, RAS (SIO RAS), Russia Soloviev D.M. , Shirshov Institute of Oceanology, RAS (SIO RAS), Marine Hydrophysical Institute RAS, Russia Stanichny S.V. , Shirshov Institute of Oceanology, RAS (SIO RAS), Marine Hydrophysical Institute RAS, Russia
Familial Resilience in Crisis: Navigating the Mediating Landscape of Depressive Symptoms Between Uncertainty Stress and Suicide Behavior Among Chinese University Students

Background Previous findings indicate that stress has a profound influence on suicide behavior, but the potential mediating and moderating mechanisms are unknown between uncertainty stress and suicide behavior. The present study, therefore, examined the relationship between uncertainty stress and suicide behavior, the mediating effect of depressive symptoms, and the moderating effect of family relationship in a sample of university students in China. Methods 1828 university students were assessed anonymously by using the Uncertainty Stress Scale, Center for Epidemiologic Studies Depression Scale, Brief Suicidal Scale, and Family Relationship Scale between May to June in 2021. SPSS 26.0 was used for descriptive statistics and Spearman correlation analysis. PROCESS 3.5 was used to calculate the significance of the mediating and moderating effects of the variables. Results Moderated mediation model analyses showed that: (a) depressive symptoms partially mediated the link between uncertainty stress and suicide behavior (indirect effect = 0.14, 95%bootstrap CI = 0.10, 0.19). The indirect effect of the depressive symptoms accounted for 67.12% of the total variance in suicide behavior. (b) The indirect association between uncertainty stress and suicide behavior was moderated by family relationship. Specifically, the paths from uncertainty stress to depressive symptoms (interact effect = −0.06, P<0.001) and depressive symptoms to suicide behavior (interact effect = −0.08, P<0.01) were weakened in the context of higher family relationship. Conclusion Depressive symptoms play a crucial role in bridging uncertainty stress and suicide behavior, while the family relationship can buffer the mediation impact of depressive symptoms. These findings significantly contribute to the prevention and intervention of suicide in Chinese university students.
Short-Circuit Model for Type-IV Wind Turbine Generators With Decoupled Sequence Control

The power system planning and protection studies are becoming more challenging due to the rapid increase in penetration levels of converter-interfaced renewables. Type-IV wind turbine generators (WTGs) and photovoltaic panels are interfaced to the grid through a full-scale converter, and their short-circuit current contributions are mainly designated by the converter control and associated current limits. This paper proposes a new phasor domain modeling approach for the wind parks (WPs) with Type-IV WTGs, using the concept of control-based equivalent circuits. The proposed model precisely represents the detailed electromagnetic transient (EMT)-type model in steady state, and is able to account for the fault-ride-through function of the WTG control as well as its specific decoupled sequence control scheme in addition to the traditional coupled control scheme. Although the collector grid and WTGs inside the WP are represented with their aggregated models, the overall reactive power control structure of the WP is preserved by taking the central WP controller into account. The accuracy of the proposed model is validated through detailed EMT simulations.
Assessment of Patient Matters in Healthcare Facilities

Background and Objectives: Ensuring the sustainability of healthcare facilities requires the evaluation of patient matters with appropriate methods and tools. The objective of this research is to develop a new tool for assessing patient matters as a component of social responsibility requirements that contribute to the sustainability of healthcare facilities. Materials and Methods: We carried out an analytical observational study in which, starting from the domains of the reference framework for the sustainability of health facilities (economic, environmental, social, provision of sustainable medical care services and management processes), we designed indicators that describe patient matters. To achieve this, we extracted from the scientific literature the most recent data and aspects related to patient matters that have been reported by representative hospitals from all over the world. These were organized into the four sequences of the quality cycle. We designed the method of evaluating the indicators based on the information couple achievement degree-importance of the indicator. In the experimental part of the study, we validated the indicators for the evaluation of patient matters and the evaluation method at an emergency hospital with an orthopedic profile. Results: We developed the patient matters indicator matrix, the content of the 8 indicators that make it up, questions for the evaluation of the indicators, and the evaluation grids of the indicators. They describe five levels for each variable of the achievement degree-importance couple. The practical testing of the indicators at the emergency hospital allowed the calculation of sustainability indicators and the development of a prioritization matrix for improvement measures. Conclusions: Indicators designed in this research cover social responsibility requirements that describe patient matters. They are compatible and can be used by health facilities along with other implemented national and international requirements. Their added value consists in promoting social responsibility and sustainable development of healthcare facilities.
Capitals as bad equilibria

Throughout the recent pandemic, governments used digital video technologies to facilitate social distancing during political meetings. In addition to enabling social distancing, a theoretical advantage of virtual political governance is that it has the potential to mitigate the hierarchical administrative relationship between capitals and regions and the differences in real estate prices and wealth that often follows from such hierarchical structures. However, hardly any governments are currently planning a long‐term transition to work‐from‐home digital governance. On the contrary, several countries are doubling down on the centralization model of government by building new capitals in new locations. This article proposes that in a time of digital alternatives, physically centralized “capitals” could be considered examples of bad equilibrium.
Sharp-Crested Breaking Surface Waves Observed from a Ship-Based Stereo Video System

AbstractA new ship-based stereo video system is used to observe breaking ocean waves (i.e., whitecaps) as three-dimensional surfaces evolving in time. First, the stereo video measurements of all waves (breaking and nonbreaking) are shown to compare well with statistical parameters from traditional buoy measurements. Next, the breaking waves are detected based on the presence of whitecap foam, and the geometry of these waves is investigated. The stereo measurements show that the whitecaps are characterized by local extremes of surface slope, though the larger-scale, crest-to-trough steepness of these waves is unremarkable. Examination of 103 breaking wave profiles further demonstrates the pronounced increase in the local wave steepness near the breaking crest, as estimated using a Hilbert transform. These crests are found to closely resemble the sharp corner of the theoretical Stokes limiting wave. Results suggest that nonlinear wave group dynamics are a key mechanism for breaking, as the phase speed of th.