Miracle-dz/UniFAR-FTUs huggingface dataset:
Titanium or Biodegradable Osteosynthesis in Maxillofacial Surgery? In Vitro and In Vivo Performances

Osteosynthesis systems are used to fixate bone segments in maxillofacial surgery. Titanium osteosynthesis systems are currently the gold standard. However, the disadvantages result in symptomatic removal in up to 40% of cases. Biodegradable osteosynthesis systems, composed of degradable polymers, could reduce the need for removal of osteosynthesis systems while avoiding the aforementioned disadvantages of titanium osteosyntheses. However, disadvantages of biodegradable systems include decreased mechanical properties and possible foreign body reactions. In this review, the literature that focused on the in vitro and in vivo performances of biodegradable and titanium osteosyntheses is discussed. The focus was on factors underlying the favorable clinical outcome of osteosyntheses, including the degradation characteristics of biodegradable osteosyntheses and the host response they elicit. Furthermore, recommendations for clinical usage and future research are given. Based on the available (clinical) evidence, biodegradable copolymeric osteosyntheses are a viable alternative to titanium osteosyntheses when applied to treat maxillofacial trauma, with similar efficacy and significantly lower symptomatic osteosynthesis removal. For orthognathic surgery, biodegradable copolymeric osteosyntheses are a valid alternative to titanium osteosyntheses, but a longer operation time is needed. An osteosynthesis system composed of an amorphous copolymer, preferably using ultrasound welding with well-contoured shapes and sufficient mechanical properties, has the greatest potential as a biocompatible biodegradable copolymeric osteosynthesis system. Future research should focus on surface modifications (e.g., nanogel coatings) and novel biodegradable materials (e.g., magnesium alloys and silk) to address the disadvantages of current osteosynthesis systems.
Automatic classification of traffic incident's severity using machine learning approaches

During daily work at a Transport Management Centre (TMC), the operators have to record and process a large volume of traffic information especially incident records. Their tasks involve manual classification of the data and then decide appropriate operations to clear the incidents on time. A real-time automatic decision support system can minimise an operator's responded time and hence reduce congestion. Besides standard descriptions (e.g. incident location, date, time, lanes affected), severity is an important criteria that operators have to evaluate based on all available information before any control commands can be issued. The NSW TMC and the research organisation Data61 in Sydney have collaborated to discover and visualise frequent patterns in historical incident response records, leading to the automatic classification of severity levels among past incidents using advanced machine learning, active learning and outlier detection techniques. The experiments were executed using 4 years TMC's incident logs from 2011 to 2014 which includes >40,000 records. The classification model achieved nearly 90% accuracy in five-fold cross-validation and is expected to help the TMC to improve its procedures, response plans, and resource allocations.
Experimental and Numerical Study of Pressure Drop Characteristics of Soybean Grain under Vertical Pressure

The vertical pressure lead to increased airflow resistance through the grain bulk, which affected the efficiency of ventilation and drying. The effects of vertical pressures at 50, 150, and 250 kPa on the pressure drop characteristics of soybeans were studied using experiment and numerical simulation. The random packing and different compression states for soybean packed beds were generated by the Discrete Element Method (DEM). The Discrete Element Method (DEM) and Computational Fluid Dynamics (CFD) were coupled to investigate the radial velocity and pressure drop of soybean bulk. The simulation results showed that the radial porosity had an oscillating distribution, and the radial average dimensionless velocity was consistent with the distribution trend of porosity. The increase in vertical pressure causes a decrease in porosity and an increase in local velocity. The PathFinder code was used as a supplementary method to calculate the pore path and pore characterization parameters, and the resistance coefficient term in the Forchheimer equation was determined. The compression of soybeans measured by the experiment mainly occurred within two hours after loading. The pressure drop of soybeans increased with the vertical pressure, with the average pressure drop at vertical pressures of 50, 150, and 250 kPa being 36%, 57%, and 92% higher than the uncompressed state (0 kPa). The pressure drop of soybeans calculated by the DEM-CFD method and the Forchheimer equation under different vertical pressures were in close agreement with the experimental results, and an average relative difference was found to be less than 10%. These results provide guidance for estimating the pressure drop of soybeans at different grain depths.
Effect of Adiponectin Variant on Lipid Profile and Plasma Adiponectin Levels: A Multicenter Systematic Review and Meta-Analysis

Background Adiponectin is a recognized antiatherogenic molecule; this study was aimed at clarifying the effects of adiponectin variants on lipid and adiponectin levels. Methods By searching PubMed and Cochrane databases for studies published before March 31, 2022, a total of 86,610 individuals were included in the analysis. Results Variants of rs2241766 and rs266729 were associated with decreased adiponectin and high-density lipoprotein cholesterol (HDL-C), as well as increased triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels. In contrast, the rs1501299 variant was correlated with increased adiponectin and HDL-C, as well as decreased TG, TC, and LDL-C levels. Subgroup analysis indicated that the significant effect of the rs2241766 and rs266729 variants on lipid profile was predominant in Chinese, while the significant effect of the rs1501299 variant on lipid profile was primarily in Caucasians. Moreover, a stronger effect of the rs2241766 and rs1501299 variants on LDL-C levels was observed in males, while a considerable effect of the rs266729 variant on LDL-C levels was observed in children. Conclusions The present study indicated that Chinese with the rs2241766 and rs266729 variants were at high risk of dyslipidemia, atherosclerosis, or coronary artery disease (CAD). Males with the rs2241766 variant were at high risk of CAD. Children with the rs266729 variant had a high risk to develop dyslipidemia, atherosclerosis, and even early onset of CAD in the future. These findings are beneficial to clinical physicians to choose different management strategies for cardiovascular disease (CVD) prevention.
Prokineticins as a Prognostic Biomarker for Low-Grade Gliomas: A Study Based on The Cancer Genome Atlas Data

Lower-grade glioma (LGG) is a crucial pathological type of glioma. Prokineticins have not been reported in LGG. Prokineticins as a member of the multifunctional chemokine-like peptide family are divided into two ligands: PROK1 and PROK2. We evaluated the role of PROK1 and PROK2 in LGG using TCGA database. We downloaded the datasets of LGG from TCGA and evaluated the influence of prokineticins on LGG survival by survival module. Correlations between clinical information and prokineticins expression were analyzed using logistic regression. Univariable survival and multivariate Cox analysis was used to compare several clinical characteristics with survival. Correlation between prokineticins and cancer immune infiltrates was explored using CIBERSORT and correlation module of GEPIA. We analyzed genes of PROK1 and PROK2 affecting LGG, screened differentially expressed genes (DEGs), interacted protein-protein with DEGs through the STRING website, then imported the results into the Cytospace software, and calculated the hub genes. To analyze whether hub genes and prokineticins are related, the relationship between PROK1 and PROK2 and hub genes was assessed and shown by heat map. In addition, gene set enrichment analysis (GSEA) was performed using the TCGA dataset. The univariate analysis using logistic regression and PROK1 and PROK2 showed opposite expression differences between tumor and normal tissues (p < 0.05). PRO1 and PROK2 expressions showed significant differences in tumor grade, age, Iiscitrate DeHydrogenase (IDH) status, histological type, and 1P/19q codeletion. Multivariate analysis revealed that the up-regulated PROK1 and PROK2 expression is an independent prognostic factor for bad prognosis. Specifically, prokineticin expression level has significant correlations with infiltrating levels of Th1 cells, NK CD 56bright cells, and Mast cells in LGG. We screened 21 DEGs and obtained 5 hub genes (HOXC10, HOXD13, SOX4, GATA4, HOXA9). GSEA-identified FCMR activation, creation of C4 and C2 activators, and CD22-mediated BCR regulation in gene ontology (GO) were differentially enriched in high PROK1 and PROK2 expression phenotype pathway, cytoplasmic ribosomal proteins, and ribosome and were differentially enriched in the low PROK1 and PROK2 expression phenotype pathway. Prokineticins are a prognostic biomarker and the correlation between hub genes and LGG requires further attention.
A Comparative Analysis of Multiple Linear Regression Models and Neural Networks for Stock Price Prediction - Take BYD as an Example

In the context of global efforts to combat the climate warming, energy saving and emission reduction, China has also put forward the goal of achieving carbon peak by 2030 and carbon neutrality by 2060, and the new energy industry has received key support and development, and is also favored by capital, so it is important to analyze and study the factors influencing stock prices, both for the industry and for investors. BYD is the leading company in the new energy industry, it is typical. Therefore, this paper chooses to take BYD as an example to analyze and study the influencing factors of stock price through BP neural network and multiple linear regression model, and finally finds that the company's stock price has a statistically significant correlation with USD/CNH exchange rate, CPI, P/E ratio and Dow Jones Industrial Index. The BP neural network has better explanation ability than the multiple linear regression model.
Implementation Aspects of Smart Grids Cyber-Security Cross-Layered Framework for Critical Infrastructure Operation

Communication networks in power systems are a major part of the smart grid paradigm. It enables and facilitates the automation of power grid operation as well as self-healing in contingencies. Such dependencies on communication networks, though, create a roam for cyber-threats. An adversary can launch an attack on the communication network, which in turn reflects on power grid operation. Attacks could be in the form of false data injection into system measurements, flooding the communication channels with unnecessary data, or intercepting messages. Using machine learning-based processing on data gathered from communication networks and the power grid is a promising solution for detecting cyber threats. In this paper, a co-simulation of cyber-security for cross-layer strategy is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection as well as identification of anomalies in the operation of the power grid. The framework is implemented on the IEEE 118-bus system. The system is constructed in Mininet to simulate a communication network and obtain data for analysis. A distributed three controller software-defined networking (SDN) framework is proposed that utilizes the Open Network Operating System (ONOS) cluster. According to the findings of our suggested architecture, it outperforms a single SDN controller framework by a factor of more than ten times the throughput. This provides for a higher flow of data throughout the network while decreasing congestion caused by a single controller’s processing restrictions. Furthermore, our CECD-AS approach outperforms state-of-the-art physics and machine learning-based techniques in terms of attack classification. The performance of the framework is investigated under various types of communication attacks.
P1111: COMPARATIVE EFFICACY AND SAFETY OF TISAGENLECLEUCEL AND AXICABTAGENE CILOLEUCEL IN RELAPSED/REFRACTORY FOLLICULAR LYMPHOMA

Background: Background: The chimeric antigen receptor T-cell (CAR-T) therapies, tisagenlecleucel (tisa-cel) and axicabtagene ciloleucel (axi-cel) have demonstrated clinical benefits in treating relapsed/refractory follicular lymphoma (r/r FL). Aims: Aims: To compare the efficacy and safety outcomes of tisa-cel and axi-cel in r/r FL using matching-adjusted indirect comparison (MAIC). Methods: Methods: Individual The MAIC results indicated that tisa-cel and axi-cel were comparable in response rates and PFS, while tisa-cel was associated with better safety outcomes than axi-cel. These results confirm and reinforce the relevance of CAR-T therapies for r/r FL. Future analyses using IPD from both trials and real-world data are warranted.
The Role of Audit Report Lag in Mediating the Effect of Auditor Switching and Financial Distress on Financial Statement Fraud

This paper examines the effect of auditor switching and financial distress on financial statement fraud with audit report lag as the intervening variable. This research used 26 fraud companies and 45 non-fraud companies listed by Indonesia Stock Exchange that break the article VIII.G. 7 and IX.E. 2 issued by Financial Services Authority in 2020. This quantitative research used Partial Least Square (PLS) with WarpPLS 7.0 tools. We conclude that financial distress and audit report lag directly affect Financial Statement fraud. It also shows that audit report lag partially mediates the relationship between financial distress and financial statement fraud
The Influence of Competitive Level on Stretch-Shortening Cycle Function in Young Female Gymnasts

This cross-sectional study investigated how stretch-shortening cycle (SSC) function and kinetic variables differed between young female gymnasts of varying competitive levels. Drop jump (DJ) force–time profiles were examined in 118 female gymnasts, sub-divided by competitive level (n = 21 recreational, n = 41 regional and n = 50 elite). DJ force–time data were analyzed to calculate performance and kinetic variables. Participants’ SSC function was categorized as poor, moderate, or good, depending on the presence of an impact peak and spring-like behavior. A high proportion of gymnasts across each group were categorized as having “good” or “moderate” SSC function (i.e., >94.8%), with a trend of increasingly better SSC function observed with competitive level. Significant differences in reactive strength index, contact time, time of landing peak force, relative propulsive peak force, impulse, and ratio of braking: propulsive impulse were found between the elite and recreational group (p < 0.05). While SSC function was generally good to moderate, elite gymnasts had a more desirable kinetic jump-landing strategy than recreational level gymnasts. Drop jump kinetic variables appear to distinguish between elite and recreational gymnasts but not between regional standard gymnasts. Practitioners should consider the kinetic profile of gymnasts when benchmarking and setting training objectives.
Data-limited stock assessment of red mullet (Mullus barbatus) from the Iskenderun Bay, the north-eastern Mediterranean Sea

Stock size and status of red mullet Mullus barbatus were assessed via virtual population analysis and yield prediction analysis using on-board observed length-frequency catch data from the north-eastern Mediterranean Sea. The von Bertalanffy growth parameters obtained were L∞ = 35.4 cm, k = 0.42, t0 = 0.64, tanc = 0.59 and Φ′ = 2.72. Trawl-type selectivity ogive was calculated as 1.57 years corresponding to 17.1 cm TL. The virtual population analysis of M. barbatus indicated that the minimum and maximum fishing mortalities were 0.01 yr–1 and 11.1 yr–1 for the mid-length groups for 8.5 cm and 21.5 cm respectively. The estimated lowest mean biomass and yield were 3.541 t and 31 t at length 8.5, and the highest mean biomass and yield were 13.880 t at length 22.5 cm and 46.086 t at length 21.5 cm respectively. Biological reference points were FMSY = 5.85, F0.5 = 2.3, EMSY = 0.89, E0.5 = 0.77. The values for the current fishing mortality coefficient (Fcur) and the current exploitation rate (Ecur) calculated were 5.3 and 0.89 respectively. The present biological reference points indicate that the stock of M. barbatus in the investigated area is overexploited status and needs urgent management considerations.
Active Underwater Target Detection Using a Shallow Neural Network With Spectrogram-Based Temporal Variation Features

In this article, we propose an active target detector by using a shallow neural network (NN) with novel features under small sonar data, where deep learning (DL) models are restricted. The features are contrived by considering differences between target and clutter signals in the temporal variation of amplitude and frequency when using an active ping; these are extracted from preprocessed spectrograms of beam time series. The classification ability of the features is demonstrated through comparison with that of prevalent timbral features in the training data set that reflect the human auditory system. Finally, the detection performance of the shallow NN trained using the proposed features is compared with those from the fine-tuned DL models. During the generalization test, the DL models miss a significant number of targets in the test data set—including target signals with patterns unseen during training—and the VGG16 used for detailed comparison has a detection probability of 0.69. The probability increases to 0.86 via the shallow NN, with remarkably enhanced target detection. This demonstrates the competitive performance and generalization ability of the shallow NN, using features that consider active sonar returns under a lack of data.
Excited-state palladium-catalysed reductive alkylation of imines: scope and mechanism

Palladium catalysis induced by visible-light irradiation is a promising tool for promoting unusual chemical transformations. We describe the development of excited-state palladium-catalyzed reductive alkylation of imines with alkyl bromides. The new methodology shows broad functional group tolerance and can additionally be applied in the direct three-component reaction of aldehydes, anilines, and alkyl bromides to give the alkyl amines under mild reaction conditions. Time-resolved photoluminescence experiments allowed the determination of the excited-state reaction kinetics and indicate that the reaction is proceeding via the inner-sphere electron transfer mechanism.
The Breakdown of the Distinction Between the Public, Secular Private, and Religious Spheres in Education Law and Policy

Background: Over the past decade, courts increasingly have considered cases that involve clashes between public, secular private, and religious institutions in education. Such clashes appear to have intensified as recently as the 2019–2020 Supreme Court term, and the confirmation of Associate Justice Amy Coney Barrett to the Court in 2020 suggests that issues centered on these institutions will continue to receive significant judicial attention. While these recent cases have focused on a range of education law and policy issues, some have focused on arguably the most fundamental legal issues applicable to such schools—the instances in which the legal distinctions between public, secular private, and religious schools are strong or weak. Purpose: This study examines three recent, major federal cases as both historical and legal cases to highlight the restructuring of the distinctions among public, secular private, and religions schools in the institutional setting of the courts. I examine how the courts have historically structured these distinctions; how these three recent cases have restructured these distinctions; and the education law and policy implications moving forward. Research Design: This article is a legal analysis and historical case study. Findings: Three recent and high-profile education cases reflect a spectrum of how the highest courts have restructured distinctions between public, secular private, and religious schools in a short period. In some instances, courts have blurred the legal distinctions between these types of schools by allowing religious schools to receive governmental support even in situations in which states have directly attempted to exclude institutions like them from receiving such support. In other instances, courts have strengthened these distinctions by differentiating how public, secular private, and religious schools are treated with respect to their abilities to discriminate. Conclusion: Taken together, the three cases underscore the intensifying attention of the courts to restructuring the distinctions between the public, secular private, and religious spheres in education. These distinctions reflect judicial engagement with major educational and political goals, such as pluralism, communality, and discrimination, and are grounded in a long history of courts’ involvement in this field. Especially in a field characterized by highly politicized debates, attention to the distinction between the public, secular private, and religious spheres in education is critical for understanding how and why fundamental educational policy decisions have been and continue to be made.
Impact of COVID-19 on labor’s motivational factors and construction productivity

Abstract Many studies were conducted to identify the factors that affect productivity in the construction industry. This study aims to identify the motivational and demotivational factors that affect construction labor productivity due to the COVID-19 pandemic occurrence. The required data was collected by interviewing construction workers on site. A factor analysis was conducted to allocate deep perception amongst the surveyed factors and to identify fewer essential factors. These essential factors are ranked by using relative importance index (RII). The study revealed that the most important motivational factor during the pandemic period is ranked as job security, followed by monetary benefits, and work satisfaction. Rework, a lack of recognition for efforts, and a lack of communication were identified as major demotivators. Spearman’s correlation coefficient was used to evaluate the level of agreement of study participant categories. The analysis revealed that the carpenters’ perspective regarding the motivators and demotivators' rank is different than other craftsman types, and there is a strong correlation between civil work craftsmen (masons and carpenters) and between electro-mechanical workers (electricians and welders). This study's motivational ranking is different than what recorded in normal conditions, and it recommends construction managers modify construction workers' motivational schemes.