Miracle-dz/UniFAR-FTUs huggingface dataset:
Real-Time Personalised Pharmacokinetic-Pharmacodynamic Modelling in Propofol Anesthesia through Bayesian Inference

Pharmacological models describe a patient’s response to the administration of a medicinal drug based on parameters derived from population studies. However, considerable inter-patient variability exists, such that population models may underperform when used to predict the actual response of a specific individual. In applications which demand predictive accuracy—such as target-controlled infusion of anesthetic agents—modeling uncertainty may reduce system dependability and introduce clinical risk. Our work investigates the use of Bayesian inference, implemented through a particle filter algorithm, to refine a prior model of propofol pharmacokinetics-pharmacodynamics and estimate patient-specific parameters in real-time. We report here on an observational clinical study conducted on 40 adults undergoing general anesthesia, where we evaluated the performance of Bayesian inference-personalized models in forecasting forward trends of depth of anesthesia (Bispectral Index) measurements and compared it with that of a traditional population-based pharmacological model. Our results show a significant reduction in prediction error metrics for the patient-specific models. Our study demonstrates the viability and practical implementability of Bayesian inference as a tool for real-time intra-operative estimation of personalized pharmacological models in anesthesia applications.
Evaluating methods of oversampling and averaging resting-state electroencephalography data in classifying Parkinson’s disease *

Collecting resting-state electroencephalography (RSEEG) data is time-consuming and data sets are therefore often small. Because many machine learning (ML) algorithms work better with ample data, researchers looking to use RSEEG and ML to develop diagnostic models have used oversampling methods that may seem to contradict averaging methods used in conventional electroencephalography (EEG) research to improve the signal-to-noise ratio. Using eyes open (EO) and eyes closed (EC) recordings from 3 different research groups, we investigated the effect of different averaging and oversampling methods on classification metrics when classifying people with Parkinson’s disease (PD) and controls. Both EC and EO recordings were used due to differences found between these methods. Our results indicated that grouping 58 electrodes into regions-of-interest (ROI) based on anatomical location is preferable to using single electrodes. Furthermore, although recording EO data led to slightly better classification, the number of data points for each participant was reduced and recordings for three participants entirely lost during pre-processing due to a higher level of artefacts than in the EC data.Clinical relevance— RSEEG is a potential biomarker for the diagnosis and prognostication of PD, but for RSEEG to have clinical relevance, it is necessary to establish which averaging and oversampling of data most reliably segregates the classes for people with PD and controls. We found that using of ROIs and EC data performed the best, as EO data was often contaminated with artefacts.
A Machine Learning Approach to COVID-19 Detection via Graphene Field-Effect-Transistor (GFET)

In the wake of the COVID-19 pandemic, there has been a need for reliable diagnostic testing. However, state-of-the-art detection methods rely on laboratory tests and also vary in accuracy. We evaluate that the usage of a graphene field-effect-transistor (GFET) coupled with machine learning can be a promising alternate diagnostic testing method. We processed the current-voltage data gathered from the GFET sensors to assess information about the presence of COVID-19 in biosamples. We perform binary classification using the following machine learning algorithms: Linear Discriminant Analysis (LDA), Support Vector Machines (SVM) with the Radial Basis Function (RBF) kernel, and K-Nearest Neighbors (KNN) in conjunction with Principal Component Analysis (PCA). We find that LDA and SVM with RBF proved to be the most accurate in identifying positive and negative samples, with accuracies of 99% and 98.5%, respectively. Based on these results, there is promise to develop a bioelectronic diagnostic method for COVID-19 detection by combining GFET technology with machine learning.
Joint Imitation Learning of Behavior Decision and Control for Autonomous Intersection Navigation

Modern autonomous driving systems face substantial challenges when navigating dense intersections due to the high uncertainty introduced by other road users. Due to the complexity of the task, the autonomous vehicle needs to generate policies at multiple levels of abstraction. However, previous deep imitation learning methods focused on learning control policies while using simple rule-based behavior models. To bridge this gap and achieve human-like driving, we develop a hierarchy of high-level behavior decision and low-level control, where both policies are jointly learned from human demonstrations based on imitation learning. Over 60 hours of driving data from 10 drivers at six intersections was collected. The proposed method is extensively evaluated in challenging intersection scenarios. Empirical results demonstrate the method's superior performance over baselines in terms of task completion and control quality. We demonstrate the importance of learning human-like behavior decisions as well as joint learning of behavior and control policies. The capability of imitating different driving styles is also illustrated.
Fiber reinforced composite bridge as a replacement for missing upper permanent lateral incisor – a case report

Abstract Hypodontia of upper lateral incisors is significant aesthetic problem in young people. Ideal solution to this problem would be an implant placement, but there is a problem of unfinished growth and development. It is therefore necessary to have a temporary solution in order to maintain normal stomatognathic system functions and solve aesthetic problems until the final restoration is placed. The aim of this study was to show the possibility of treating hypodontia of permanent upper lateral incisors by creating adhesive bridge in one visit. A 15-year-old patient was referred to the Department for Prosthodontics, School of Dental Medicine in Belgrade with hypodontia of permanent upper lateral incisors. The patient had braces until 14 years of age after which she was rehabilitated with partial denture that was neither aesthetically nor functionally acceptable solution for her and significantly affected her emotional security. The adhesive bridge was made using GC composite fibers and a set of GC composite materials for the purpose of temporary replacement of missing teeth. This treatment provided good functional and aesthetic results. The space for the placement of two endosseous implants is preserved and a period of adaptation positively influenced the patient’s social development in sensitive adolescent age.
A Prompt Learning-Based Framework for Similar Case Matching

Similar Case Matching (SCM) is a vital component of legal retrieval and plays a crucial role in intelligent legal systems. This task endeavors to ascertain the similarity between two cases. Recently, SCM methods have utilized a pre-trained language model (PLM) to encode legal case representations, following the traditional pre-train and fine-tune approach. How- ever, due to the inconsistency between the task objective of PLM and case matching, we argue that this approach does not effectively leverage the rich semantic and linguistic knowledge accumulated during pre-training. The emergence of a novel paradigm known as pre-train and prompt has remarkably succeeded in the field of natural language processing. In this paper, we explore the application of this innovative paradigm to establish a prompt learning-based framework for similar case matching. Our framework guides the pre-trained model to identify sentences in the cases that contain important information and align them using templates designed based on human prior knowledge. Finally, the case matching module generates the matching results, with the identified and aligned feature sentences serving as evidence supporting the matching results. The experimental results demonstrate the effectiveness of our framework, providing higher-quality supporting evidence for the SCM task. Additionally, our work provides fresh insights and contributes to the ongoing research in SCM tasks.
Exploring Self-regulated Learning Support for AI Related Work in an Insurance Firm

Self-regulated learning needs to be enhanced and supported for the sustainability of e-learning and skill development particularly in the context of workplace implementing artificial intelligence (AI). This study explores the support mechanism for SRL experienced by insurance agents in an insurance firm adopting AI technologies. The case study involves seven participants who were purposively selected based on the criteria of the study. Data were analysed using thematic analysis, and then triangulated and checked by members for trustworthiness and credibility. The themes that emerged are (i) training and development for agent; (ii) self-learning initiatives; (iii) knowledge sharing; and (iv) adaptation to change. To conclude, SRL practices require strong support from organisation in order to provide stimulating learning environment. In addition, individual agency would empower SRL and make learning more engaging and effective.
Seismic passive earth resistance in submerged soils using modified pseudo-dynamic method with curved rupture surface

ABSTRACT The sparsity of examination of seismic passive earth pressure acting on retaining wall holding soil backfill with full submergence, which is more common in waterfront areas, can be noticed from the literature. In the current study, a closed-form solution to compute the seismic passive earth pressure on nonvertical rigid retaining wall retaining a backfill with full submergence is proposed using the modified pseudo-dynamic approach. A nonlinear rupture surface (logarithmic spiral + straight line) in a submerged backfill of viscoelastic nature has been assumed. The presented modified pseudo-dynamic method overcomes the limitations of the existing pseudo-dynamic method for submerged soils. The proposed methodology has been thoroughly validated with the available literature. The influences of seismic acceleration coefficients, excess pore water pressure ratio, wall inclination, and soil and wall friction angles have been studied. It has been noticed that the consideration of excess pore pressure ratio leads to significant decrease in seismic passive resistance of the soil which in turn lead to extra hydraulic pressure acting on the wall in submerged backfill. There is a 57% decrease in seismic passive earth pressure coefficient as the wall inclination changes from −15° to 15°.
Situational effects of marketing strategies on financial performance

While a plethora of studies have investigated market orientation (MO) and entrepreneurial orientation (EO), prior research is unclear about their interaction and has largely neglected possible contingencies of their performance. Against this background, based on the results of Bhattacharya et al. (2019), we investigate the effects of MO and EO and their interaction on financial performance, and the role of the situation and how it may have a different impact on the performance effect of each strategy. To analyze our conceptual model, we use consistent computer-assisted test analysis (CATA) procedures on annual reports and ad-hoc announcements of 292 companies. We assess the market situation using the risk-optimism framework of Pröllochs and Feuerriegel (2020), which classifies internal and external situations based on capabilities and opportunities or threats. Our findings provide important contributions to our understanding of MO and EO, and valuable recommendations for management. Thus, we found a positive performance-enhancing effect of EO in the context of risk. Even if the negative effect of risk predominates, EO can help to reduce losses.
Outcomes After Respiratory Extracorporeal Life Support in Teens and Young Adults: An Extracorporeal Life Support Organization Registry Analysis*

OBJECTIVES: A recent study from Germany found that survival after respiratory extracorporeal life support (ECLS) was lower among patients 10–20 years old than 20–30 years old. The objective of this study was to compare survival between teenage and young adult patients who receive respiratory ECLS. DESIGN: Retrospective cohort study. SETTING: Extracorporeal Life Support Organization registry, an international prospective quality improvement database. PATIENTS: All patients ages 16–30 years cannulated for respiratory indications from 1990 to 2020 were included. Patients were divided into two groups, teens (16–19 yr old) and young adults (20–30 yr old). INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Primary outcome was survival to discharge. Variables were considered for the multivariate logistic regression model if there was both a statistically significant difference (p ≤ 0.05) and a clinically meaningful absolute difference between the groups. A total of 5,751 patients were included, of whom 1,653 (29%) were teens and 4,098 (71%) were young adults. Survival to discharge was higher in young adults than teens, 69% versus 63% (p < 0.001). Severity of illness was higher among teens; however, survival within each stratum defined by Pao2/Fio2 ratio was higher in young adults than in teens. Use of venoarterial ECLS was higher in teens than in young adults, 15% versus 7%, respectively. Teens were more likely to receive high-frequency oscillatory ventilation and this therapy was associated with a longer time from admission to ECLS initiation. After adjusting for variables that differ significantly between the groups, the odds ratio for survival in young adults compared with teens was 1.14 (95% CI, 1.004–1.3). CONCLUSIONS: In this large multicenter retrospective study, mortality was higher in teens than in young adults who received respiratory ECLS. This difference persisted after adjusting for multiple variables and the mechanism underlying these findings remains unclear.
Outcomes of Digit Replantation in Children: A Systematic Review

Background: Children have been suggested to benefit from digit replantation due to a greater neurogenerative capacity. We aimed to conduct a systematic review on digit replantation in children to provide a comprehensive overview of survival rates and functional outcomes. Methods: A systematic literature search was conducted on Ovid MEDLINE, Embase, and the Cochrane Controlled Register of Trials for studies published between 1980 and 2023. We included peer-reviewed studies reporting on digit survival rates in pediatric patients under the age of 18 years who underwent single or multiple digit replantations distal to the metacarpophalangeal joint. Preoperative, intraoperative, and postoperative outcomes were extracted, and pooled estimates were derived using univariable analysis. Results: Twenty-two studies reporting on 761 patients and 814 digit replantations were included in our study. Most replantations occurred in the index (n = 74), Tamai zone I (n = 168), and from clean-cut injuries (n = 190). The mean survival rate was 76% (n = 618/814), with a mean range of motion at the distal interphalangeal joint ranging from 64 degrees to 90 degrees and two-point discrimination ranging from 3.8 mm to 6.4 mm. Compared with clean-cut injuries, digit replantations from avulsion [odds ratio (OR), 0.81; 95% confidence interval (CI), 0.74–0.89] or crush (OR, 0.71; 95% CI, 0.59–0.82) injuries were associated with a lower odds of survival. Digit replantations performed with two venous (OR, 1.43, 95% CI; 1.28–1.59) or arterial anastomoses (OR, 1.65; 95% CI, 1.48–1.81) were associated with a higher odds of survival. Conclusions: Our systematic review suggests that digit replantation may be a viable option in children. Further research is required to explore functionality after digit replantation in diverse pediatric populations.
Mindfulness-Based Interventions for Postpartum Depression: A Systematic Review and Meta-Analysis

Background: We aimed to investigate the intervention effect of mindfulness-based interventions (MBIs) in patients with postpartum depression. Methods: The method of computer and manual keyword retrieval was used to search PubMed, Web of Science, Cochrane Library. Literature included in the study was assessed for quality and meta-analysis was performed using RevMan 5.3 software. Results: Twelve articles were finally included in the study and the meta-analysis showed that 6 articles used the Edinburgh Postnatal Depression Scale (EPDS) to compare MBIs with conventional therapy, and the statistical heterogeneity between the combined results was low (P=0.18, I2=32%). The level of depression in postpartum depression patients was lower in the MBIs group than in the conventional group [MD=3.13, 95%CI (2.57, 3.70), P<0.00001]. Based on the Beck Depression Inventory (BDI), the comparison between MBIs and conventional therapy had low statistical heterogeneity between the combined results (P=0.56, I2=0%). The level of depression in patients with postpartum depression who received MBIs was significantly lower than in the conventional care group [MD=5.89, 95%CI (4.88, 6.91), P<0.00001]. Subgroup analysis showed that the best intervention duration for MBIs for postpartum depression was within 4 weeks (SMD=−1.785), each session ≦60 minutes (SMD=−1.435), and participants had to complete the best three times per week (SMD=−2.185). Conclusion: MBIs can alleviate depression in women, thereby facilitating their adjustment to new life. It is recommended to practice mindfulness meditation for 30 minutes per day.
Mapping submarine glacial landforms using acoustic methods

The mapping of submarine glacial landforms is largely dependent on marine geophysical survey methods capable of imaging the seafloor and sub-bottom through the water column. Full global coverage of seafloor mapping, equivalent to that which exists for the Earth's land surface, has, to date, only been achieved by deriving bathymetry from radar altimeters on satellites such as GeoSat and ERS-1 (Smith & Sandwell 1997). The horizontal resolution is limited by the footprint of the satellite sensors and the need to average out local wave and wind effects, resulting in a cell size of about 15 km (Sandwell et al. 2001). A further problem in high latitudes is that the altimeter data are extensively contaminated by the presence of sea ice, which degrades the derived bathymetry (McAdoo & Laxon 1997). Consequently, the satellite altimeter method alone is not suitable for mapping submarine glacial landforms, given that their morphological characterization usually requires a much finer level of detail. Acoustic mapping methods based on marine echo-sounding principles are currently the most widely used techniques for mapping submarine glacial landforms because they are capable of mapping at a much higher resolution. Although the accuracy and resolution of echo-sounding methods are continually being improved, the portion of the world's ocean floor that has been acoustically surveyed is increasing only slowly. This lack of coverage is particularly true for those areas of the oceans covered by sea ice and infested with icebergs, where glacial landforms are an abundant component of continental shelf and fjord morphology. This is illustrated by the fact that only about 11% of the Arctic Ocean had been mapped using modern multibeam sonar technology by 2012 when the latest International Bathymetric Chart of the Arctic Ocean (IBCAO) was compiled (Jakobsson et al. 2012). A similar estimate of the mapped portion of the seafloor …
Tectonic history of the Ordos Block and Qinling Orogen inferred from crustal thickness

S U M M A R Y The Ordos Block and the Qinling Orogen in central China are key regions that have been affected by Indo-Eurasian collision, convergence of the North China, South China and Siberian cratons, and subduction of the Pacific oceanic plate during the Phanerozoic. Investigation of the crustal structure in this area can therefore provide significant insights into the interactions among these tectonic blocks and plates. The Chinese Academy of Geological Sciences deployed ca. 249 portable broad-band seismic stations in this area from 2011 to 2016, and we retrieved P-wave receiver functions (RFs) from these new stations, and also from all other available permanent and portable seismic stations in China. Crustal thicknesses beneath the stations were retrieved using 1-D S-wave velocity models, which were obtained by linearized inversion of the RFs with initial reference models from regionalized S-wave velocities of surface wave inversions. We demonstrate with synthetic tests that this inversion scheme is superior to a jointly linearized inversion of RFs and surface waves. The results show that crustal thicknesses generally correlate with tectonic domains in the study region. Thus, thick crust is present beneath the Daba Shan region and the area close to the northeastern Tibetan Plateau, intermediate-thickness crust is present beneath the Ordos Block and the central North China Craton, and thin crust exists beneath the North China Plain. However, some notable exceptions exist. The crust beneath the central Qinling is thinner than that beneath the neighbouring Daba Shan region and even the Weihe Basin, implying that subduction and collision of the North China and South China cratons in the Palaeozoic did not produce a thick crust beneath the suture. The relatively thin crust beneath the eastern and central Qinling must be denser than that beneath neighbouring areas. All the basins around the Ordos Block have been considered to be graben. However, our results show that the crust beneath the basins along the southern and northern margins of the Ordos Block is thicker or the same thickness as the crust beneath the flanks of the basins, which suggests these basins are (or were) foreland basins.
The Effects of Staff Education on Ventilator-Associated Pneumonia in the Intensive Care Unit: A Literature Review

Author(s): Braun, Suzanne Ellen | Abstract: Background: Mechanically ventilated patients in the ICU are at a high risk for ventilator-associated pneumonia (VAP), and the rate of this hospital-acquired infection (HAI) is problematic. Nursing interventions utilize evidence-based practice (EBP) VAP prevention guidelines to decrease the incidence. The purpose of this paper is to examine the effects of staff education on the reduction of VAP in the critical care setting.Methods: Literature search was performed on two databases, CINAHL and PubMed. Search results were narrowed based on type of research article, relevance, and publication date, and eventually three studies were chosen. Results: In two of the studies, VAP incidence was shown to have decreased after staff education and one study had insignificant results, although there was a trend towards decreased VAP incidence. Length of stay significantly decreased post-intervention in two studies. Nurse adherence/concordance significantly increased in all studies.Conclusions: These findings show that staff education may decrease VAP incidence in ICUs, but further research needs to be performed to confirm these findings, as well as to determine the best form of education. Nurses are the primary individuals to prevent mechanically ventilated patients from acquiring VAP, and thus the next step would be focusing on how to improve long-term nurse adherence to preventative interventions and how to implement them into bedside practice.