bluuebunny/crossref_metadata_2025 huggingface dataset:
S2NMF: Information Self-Enhancement Self-Supervised Nonnegative Matrix Factorization for Recommendation
Authors: Zhang Ronghua , Wang Yuanyuan , Liu Fangyuan , Liu Changzheng , Song Yaping , Yu Baohua
Published: August 2022

Nonnegative matrix factorization (NMF), which is aimed at making all elements of the factorization nonnegative and achieving nonlinear dimensional reduction at the same time, is an effective method for solving recommendation system problems. However, in many real-world applications, most models learn recommendation models under the supervised learning paradigm. Since the recommendation performance of NMF models relies heavily on initialization, the user-item interaction information is often very sparse. In many cases, supervised information about the data is difficult to obtain, resulting in a large number of existing models for supervised learning being inapplicable. To address this problem, we propose an information self-supervised NMF model for recommendation. Specifically, this model is based on the matrix factorization idea and introduces a self-supervised learning mechanism based on the NMF model to enhance the sparse data information of sparse data, and an easily extensible self-supervised NMF model was proposed. Furthermore, a corresponding gradient descent optimization algorithm was proposed, and the complexity of the algorithm was analysed. A large number of experimental results show that the proposed S2NMF has better performance.
Intelligent System Application in Clinical Management of Medical Teaching Based on Deep Reinforcement Learning
Authors: Zhu Min , Zhou Ju , Chen Liang , Zhao Xueping , Li Chunhui
Published: August 2022

When dealing with engineering projects, there are many kinds of planning schemes. There are many problems to be solved in the performance and economic conditions of these indicators and engineering contents we study. Recently, many researchers have made great achievements in optimizing various subject projects. One of its purposes is to optimize and improve intelligent multi-objectives to make it more effective. Therefore, it is of great significance to develop intelligent multi-objective projects in academia and more engineering fields. The purpose of this study is to make efforts to strengthen the learning and construction of rapidly developing multi-objective optimization programs, and closely link these programs with neural network, fuzzy technology, interactive technology, and give a lot of examples of multi-objective optimization improvement program methods, which can eliminate the uncertainty in the multi-objective project plan, The purpose is to solve some difficult problems of multi-objective optimization and give the best suggestions. In this study, the strengthening of learning construction is combined with the clinical experiment, and most of this teaching method is put in the clinical management experiment of medical teaching. At the end of this study, we compared the performance of deep reinforcement learning with its dual structure version and interleaved structure version in different medical teaching clinical management environments and make a detailed analysis. Deep reinforcement learning provides a new idea for the improvement of intelligent multi-objective optimization and clinical management of medical teaching.
Bayesian Hierarchical Compositional Models for Analysing Longitudinal Abundance Data from Microbiome Studies
Authors: Creus Martí I. , Moya A. , Santonja F. J.
Published: August 2022

Gut microbiome plays a significant role in defining the health status of subjects, and recent studies highlight the importance of using time series strategies to analyse microbiome dynamics. In this paper, we develop a Bayesian model for microbiota longitudinal data, based on Dirichlet distribution with time‐varying parameters, that take into account the compositional paradigm and consider principal balances. The proposed model can be effective for predicting the future dynamics of a microbial community in the short term and for analysing the microbial interactions using the value of the estimated parameters. The usefulness of the proposed model is illustrated with six different datasets, and a comparison study with four alternative models is provided.
Neutrophil to Lymphocyte Ratio in Epilepsy: A Systematic Review
Authors: Hosseini Samaneh , Mofrad Amir Mohammad Eghbalnejad , Mokarian Peyman , Nourigheimasi Shima , Azarhomayoun Amir , Khanzadeh Shokoufeh , Habibzadeh Saba , Ghaedi Arshin
Published: August 2022

This study was conducted to summarize the results of studies investigating the role of neutrophil to lymphocyte ratio (NLR) in epilepsy. The search was conducted on PubMed, Scopus, and Web of Science up to December 25, 2021. Finally, a total of seven studies were included in the review. The NLR in patients who were in the acute phase was higher than that of healthy. NLR in the patients who were in either acute or subacute phase was higher than in healthy controls. A significant difference in NLR levels between the acute and subacute phases was also noted. Epilepsy is one of the most important neurological diseases in the world, and millions of people around the world suffer from it, and a cheap and fast biomarker is needed for it. The interesting thing is that inflammation plays a role in epilepsy, and elevated NLR value can be a good biomarker of inflammation and, as a result, for epilepsy.
Robust Tracking Control of a Three-Phase Bidirectional Charger for Electric Vehicle
Authors: Choeung Chivon , Kry Meng Leang , Lee Young-Il
Published: August 2022

This paper presents a robust control strategy for an electric vehicle's three-phase off-board bidirectional AC-DC battery charger. The conventional constant current (CC) and constant voltage (CV) charging mode are considered to provide a fast-charging performance for the batteries. The bidirectional charger also allows using of the vehicle as an energy storage system for the grid i.e., charging during the peak-off times and delivering the energy back to the grid during peak times of electrical consumption. In discharging mode, the bidirectional charger maintains constant active power flow to the grid with a given reference. For both cases, user of a robust state feedback controller with integral action is made in the DQ-synchronous frame. The set of stabilizing gains of this controller are determined by a linear matrix inequality (LMI)-based optimization so that the convergence time to steady stead is minimized in the occurrence of the parametric uncertainties of the L-filter. The efficacy of the proposed controller is verified through simulation and experimental results on 102.4 V Lithium iron phosphate (LiFePO4) batteries.
Influences of Airway Obstruction Caused by Adenoid Hypertrophy on Growth and Development of Craniomaxillofacial Structure and Respiratory Function in Children
Authors: Li Huina , Wang Hongwei , Hao Hengrui , An Hong , Geng Hongya
Published: August 2022

Adenoid hypertrophy (AH) is a common disease in otorhinolaryngology. Children with chronic snoring and hypoxia are susceptible to long-term nasal obstruction, while long-term open-mouth breathing may cause craniofacial bone development disorders and dull facial expressions, the so-called adenoid face. The purpose of this work is to analyze the influence of AH-induced airway obstruction (AO) on the growth and development of craniomaxillofacial structure and respiratory function (RF) in children. The clinical data of 56 AH children (observation group) and 42 healthy children with physical examination (control group) who visited the Hebei Eye Hospital during the same period were retrospectively analyzed. All children received acoustic rhinometry and X-ray cephalometric measurements. The upper airway structure, sleep disorder score, andA/Nvalue of nasopharyngeal lateral X-ray images were compared between cases and controls. For AH children, sleep tests were also performed to assess their RF. X-ray cephalometric measurements of facial morphology showed obvious vertical growth, mandibular retrognathia, and enlarged mandibular angle in AH children. AH mainly affects the size of the nasopharyngeal and oropharyngeal airway. AH children presented with higher nasal airway resistance (5.11±1.95 cmH2O/L min) and lower nasopharyngeal volume (NPV) (16.86±3.93 cm3) than controls. Of the AH children, 45 had abnormal RF, including 4 with obstructive sleep apnea syndrome. TheA/Nvalue of nasopharyngeal lateral X-ray images was significantly higher in AH children than in controls. Besides, worse sleep quality was found in AH children. The above differences were all of statistical significance. The above indicates that AH can affect the size of the nasopharyngeal and oropharyngeal airway, change children’s respiratory mode and RF, increase nasal resistance, and decrease NPV, resulting in upper respiratory tract stenosis, as well as craniomaxillofacial and oral malformations, which affects children’s normal growth and development.
Hazard Trend Identification Model Based on Statistical Analysis of Abnormal Power Generation Behavior Data
Authors: Xu Gaojun , Qian Xusheng , Li Xiaodong , Wu Weijiang
Published: August 2022

In order to solve the problem of abnormal data identification for key indicators with the deepening and development of power enterprise reform, this paper proposes a method of dangerous trend identification model based on a statistical analysis of abnormal power generation behavior data. The method includes a data access scheme, feature extraction scheme, and anomaly detection algorithm. The experimental results show that the proportion of users whose electricity consumption behavior conforms to the peak period electricity consumption > normal period electricity consumption > valley period electricity consumption exceeds 90%. More than 85% of users’ electricity consumption behavior is in line with the proportion of electricity consumption that is less than 0.25 in millet hours. The proportion of users whose fluctuation coefficient of electricity consumption in the valley period is less than 1 exceeds 85%, and 99.9% of users’ fluctuation coefficient of electricity consumption in the valley period is less than 5, which proves that abnormal power generation behavior data and abnormal power consumption data can bring early warning to some dangerous power consumption behaviors. The statistical analysis model of abnormal power generation behavior data can play a positive role in the identification of dangerous trends.
The Construction of Intelligent Supply Chain System for Agricultural Products Based on Improved Ant Colony Algorithm
Authors: Bian Xueyong , Tian Jie
Published: August 2022

With the rapid development of China itself, the supply chain system has become an effective tool to enhance economic competitiveness, and the intelligent supply chain system integrates innovative technologies such as the Internet, the Internet of Things, cloud computing, and big data into the industrial supply chain management. The ant colony algorithm shows super flexibility and robustness at the level of optimizing many combination problems; the intelligent supply chain architecture of agricultural products is essentially the rational allocation of resources, which is a dynamic combination of resources and tasks that combines a variety of combinations to select the most appropriate and optimal performance. In this paper, the architecture of the intelligent supply chain of agricultural products is studied in combination with the improved ant colony algorithm, and some improvement strategies are adopted to avoid falling into local optimization or local convergence in order to avoid falling into local optimization or local convergence. The improvements of the algorithm mainly include the following: improving the path search of individual ants, improving the pheromone update strategy, improving the selection probability, and improving the dynamic growth mechanism of ants. In this paper, the VRPTW mathematical model, the dataset TSPLIB, and the operation data of the intelligent supply chain of agricultural products are used for test verification; the results can be obtained by verifying the results of the experiment closer to the optimal solution, and the efficiency of the algorithm is significantly improved. Therefore, through the improvement and optimization of the ant colony algorithm, it is very suitable for solving and optimizing the intelligent supply chain system of agricultural products.
Friend Recommender System for Social Networks Based on Stacking Technique and Evolutionary Algorithm
Authors: Ghorbani Aida , Daneshvar Amir , Riazi Ladan , Radfar Reza
Published: August 2022

In recent years, social networks have made significant progress and the number of people who use them to communicate is increasing day by day. The vast amount of information available on social networks has led to the importance of using friend recommender systems to discover knowledge about future communications. It is challenging to choose the best machine learning approach to address the recommender system issue since there are several strategies with various benefits and drawbacks. In light of this, a solution based on the stacking approach was put out in this study to provide a buddy recommendation system in social networks. Additionally, a decrease in system performance was caused by the large amount of information that was accessible and the inefficiency of some functions. To solve this problem, a particle swarm optimization (PSO) algorithm to select the most efficient features was used in our proposed method. To learn the model in the objective function of the particle swarm algorithm, a hybrid system based on stacking is proposed. In this method, two random forests and Extreme Gradient Boosting (XGBoost) had been used as the base classifiers. The results obtained from these base classifiers were used in the logistic regression algorithm, which has been applied sequentially. The suggested approach was able to effectively address this issue by combining the advantages of the applied strategies. The results of implementation and evaluation of the proposed system show the appropriate efficiency of this method compared with other studied techniques.
Nontherapeutic Risk Factors of Different Grouped Stage IIIC Breast Cancer Patients’ Mortality: A Study of the US Surveillance, Epidemiology, and End Results Database
Authors: Qiu Yue , Chen Hongye , Dai Yongjing , Bao Baoshi , Tian Lin , Chen Yuhui
Published: August 2022

Objectives. Stage IIIC breast cancer, as a local advanced breast cancer, has a poor prognosis compared with that of early breast cancer. We further investigated the risk factors of mortality in stage IIIC primary breast cancer patients and their predictive value. Methods. We extracted data from the US Surveillance, Epidemiology, and End Results (SEER) database of female patients with stage IIIC primary breast cancer (n = 1673) from January 2011 to December 2015. Results. Hormone receptor negativity (

P
≤
0.001

and

P
≤
0.001

, respectively), aggressive molecular typing (

P
≤
0.001

and

P
≤
0.001

, respectively), high T stage (

P
≤
0.001

and

P
≤
0.001

, respectively), a high number of positive lymph nodes (≥14) (

P
=
0.005

and

P
=
0.001

, respectively), and lymph node ratio (≥0.8148) (

P
≤
0.001

and

P
≤
0.001

, respectively) were associated with poor disease-specific survival. The indicators of disease-specific survival included estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio (

P
≤
0.001

,

P
≤
0.001

,

P
≤
0.001

,

P
≤
0.001

,

P
=
0.002

, and

P
≤
0.001

, respectively). Conclusion. Hormone receptor negativity, aggressive molecular typing, high T stage, high number of positive lymph nodes, and lymph node ratio are poor prognostic factors patients with stage IIIC primary breast cancer. The efficient indicators of disease-specific survival include estrogen receptor status, progesterone receptor status, molecular typing, T stage, number of positive lymph nodes, and lymph node ratio.
[Retracted] Magnetic Resonance Neuroimaging Contrast Agents of Nanomaterials
Authors: Guo Longjun , Xi Jianing , Teng Jiaqi , Wang Juan , Chen Yukun
Published: August 2022

Since the early 1980s when MRI imaging technology was put into clinical use, the number of MRI clinical tests has steadily increased by more than 10% every year. At the same time, exogenous MRI contrast agents have also been developed with the development of MRI technology. However, there are still challenges in the preparation of contrast agents for magnetic resonance imaging, such as how to prepare high‐efficiency contrast agents with high stability and low biological toxicity. In order to study the contrast agent with simple preparation method, low cost, and good imaging effect, a magnetic resonance contrast agent was prepared by magnetic nanoparticles. By acting on magnetic resonance imaging detection method, and using polymer ligands to synthesize magnetic nanoparticles, experiments and tests of P(MA‐alt‐VAc) polymer ligand‐modified magnetic nanoparticles were carried out. The experimental results showed that when nanoparticles containing different iron ion concentrations were incubated with DC 2.4 normal cells for 48 hours, the cell viability was still higher than 80% at concentrations up to 200 μm. It shows that the nanoparticle has high cell activity and good biological adaptability. The transverse relaxation (r2) value of the nanoparticles in aqueous solution at 37°C and 1.5 T magnetic field is 231.1 m-1 s-1, which is much higher than that of PTMP‐PMAA (r2 = 35.1 mM-1 s-1), which is also more than five times the relaxation of SHU‐555C (r2 = 44 mM-1 s-1). It shows that the nanoparticles prepared in this paper have good effect and can be used as a contrast agent in human brain for magnetic resonance imaging.
Microgrid Stability Improvement Using a Deep Neural Network Controller Based VSG
Authors: Ghodsi Mohammad Reza , Tavakoli Alireza , Samanfar Amin
Published: August 2022

In order to support the inertia of a microgrid, virtual synchronous generator control is a suitable control method. However, the use of the virtual synchronous generator control leads to unacceptable transient active power sharing, active power oscillations, and the inverter output power oscillation in the event of a disturbance. This study aims to propose a deep neural network controller which combines the features of a restricted Boltzmann machine and a multilayer neural network. To initialize a multilayer neural network in the unsupervised pretraining method, the restricted Boltzmann machine is applied as a very important part of the deep learning controller. The Lyapunov stability method is used to update the weight of the deep neural network controller. The proposed method performs power oscillation damping and frequency stabilization. The experimental and simulation results are presented to assess the usefulness of the suggested method in damping oscillations and frequency stabilization.
The Effect of the Environment on the Serviceability of the Cross‐Laminated Timber (CLT) Floor: Virtual Reality as a Research Tool
Authors: Huang Haoyu , Zhang Junhui , Uttley Jim , Chang Wen-Shao , Wang Brad Jianhe
Published: August 2022

The environment is one of the factors that may influence occupants’ perception of floor vibration and the assessment of floor serviceability. In this study, laboratory tests were conducted on a 3‐ply CLT floor. Occupants’ assessment of the floor serviceability under human‐induced vibration was investigated. Virtual reality (VR) technique was used as a research method, simulating two common environments in life. First, the correlation between the occupants’ annoyance rating and serviceability indicators (response factor and vibration dose value (VDV)) was compared with existing standards. The results show that the response factor method in ISO 10137:2007 is conservative for timber floors in both bedroom and gym environments. The VDV method in BS 6472‐1:2008 can generally reflect the vibration acceptability of timber floor vibration. Then, the effect of acceleration and environment on the floor serviceability assessment was investigated through statistical methods, respectively. A weak positive correlation between the annoyance rating and the acceleration was found. The effect of the environment on floor vibration assessment was found to be significant.
Does the Stock Market Affect the Real Economy? An Empirical Analysis Based on China’s Stock Market Liquidity
Authors: Wang Chao , Hao Jia
Published: August 2022

This article investigates the causal effect between the stock market and the real economy. Based on the data of China’s A‐share market from 1998 to 2017, this article uses the fixed‐effect model and other methods to find the causal effect from the perspective of the impact of illiquidity at the individual stock level on enterprise investment decision‐making. Empirical results show that stock illiquidity significantly decreases enterprise investment. Information asymmetry is one of the important channels between the stock market and the real economy. The financial constraints and agency conflicts caused by information asymmetry significantly affect the causal effect. Macroeconomic policies to alleviate market information asymmetry can effectively act on the real economy through the stock market. From the microperspective of studying the causal effect, this study controls the time effect and individual effect and further explores the influence mechanism between the stock market and the real economy. Research shows that macroeconomic policies can effectively affect the real economy through the stock market and provides decision‐making references for future macropolicy formulation.
A New Flexible Logarithmic‐X Family of Distributions with Applications to Biological Systems
Authors: Alkhairy Ibrahim , Faqiri Humaira , Shah Zubir , Alsuhabi Hassan , Yusuf M. , Aldallal Ramy , Makumi Nicholas , Riad Fathy H.
Published: August 2022

Probability distributions play an essential role in modeling and predicting biomedical datasets. To have the best description and accurate prediction of the biomedical datasets, numerous probability distributions have been introduced and implemented. We investigate a novel family of lifetime probability distributions to represent biological datasets in this paper. The proposed family is called a new flexible logarithmic‐X (NFLog‐X) family. The suggested NFLog‐X family is obtained by applying the T‐X method together with the exponential model having the PDF m(t) = e−t. Based on the NFLog‐X approach, a three parameters probability distribution, namely, a new flexible logarithmic‐Weibull (NFLog‐Wei) distribution is introduced. The method of maximum likelihood estimation is adopted for estimating the parameters of the NFLog‐X family. In the end, we examine three different biological datasets in order to give a thorough numerical research that illustrates the NFLog‐Wei distribution. Comparisons are made between the analytical goodness‐of‐fit metrics of the suggested distribution. We made comparison with the (i) alpha power transformed Weibull, (ii) exponentiated Weibull, (iii) Weibull, (iv) flexible reduced logarithmic‐Weibull, and (v) Marshall–Olkin Weibull distributions. After performing the analyses, we observe that the proposed method outclassed other competitive distributions.
Clear Lensectomy with Hydrus Stent in Black and Afro-Latino Patients: A 1-Year Retrospective Study
Authors: Laroche Daniel , Oseni Jessinta , Nkrumah Gideon , Ng Chester
Published: August 2022

Purpose. To determine the efficacy and safety of phacoemulsification, clear lensectomy, and the Hydrus microstent (Ivantis, Inc.) in Black and Afro-Latino glaucoma patients. Method. This is a retrospective nonrandomized study of 134 Black and Afro-Latino patients who underwent clear lensectomy with Hydrus stent implant for the treatment of glaucoma. For comprehensive analysis, patients were divided into mild, moderate, and advanced glaucoma. The evaluated parameters were reductions in the number of medications, intraocular pressure (IOP), mean deviation on visual field test, and visual acuity. Results. A total of 134 patients with 1-year follow-up were evaluated. At 1 year, the average number of medications significantly decreased from 2.5 ± 1.4 preoperatively to 0.43 ± 1.04 (

p
<
0.001

) and IOP decreased from 14.4 ± 3.9 to 13.8 ± 3.10 (

p
=
0.16

). 110 (82.1%) patients were medication-free at 1 year (n = 57, 83.8% mild glaucoma; n = 37, 92.5% moderate glaucoma; n = 16, 61.5% advanced glaucoma). There was stabilization of mean deviation on the visual field test (baseline, −8.28; 1 year, −8.28;

p
=
1

). The most reported adverse effects were transient IOP spike and hyphema (n = 7, 5.2%; n = 3, 2.2%, respectively); both events were self-resolving. No decline in vision or sight-threatening complications were reported at 1 year, and no additional surgeries were required. Conclusion. This 1-year retrospective study demonstrated the efficacy and safety of clear lensectomy and Hydrus stent implantation in decreasing medication burden while maintaining lower IOP in Black and Afro-Latino glaucoma patients.
Application of Discriminative Training Algorithm Based on the Improved Gaussian Mixture Model in English Translation Evaluation
Authors: Wang Shengnan
Published: August 2022

English translation, also a kind of language conversion, refers to the activities of expressing each other in English to express another language or another language to express English. This paper aimed to evaluate and research methods of English translation based on the improved Gaussian mixture model (GMM). The paper proposed a simple evaluation and analysis of English translation using a discriminative training algorithm. In the experimental part, on the one hand, taking students, teachers, and translators as case study objects, through a questionnaire survey, it can be known that the number of students who scored more than 80 points in the English translation test is increasing every year, from 11.30% in the first year to 21.13% in the fourth year, an increase of nearly 10%. On the other hand, it can be seen from the speech translation experiment that the correct translation rate was 46.18% by using intelligent technology to distinguish translation. The experimental results showed that the discriminative training algorithm under the improved GMM algorithm is effective in the research of English translation evaluation.
An Evaluation of Replacing Soybean Meal with Proteolytic Soybean Meal in Low-Fish-Meal Diet on Growth Performance, Expression of Immune-Related Genes, and Resistance against Vibrio alginolyticus in White Shrimp (Litopenaeus vannamei)
Authors: Wang Weilong , Jiang Detian , Yi Ganfeng , Huang Xuxiong
Published: August 2022

A 56-day feeding trial was conducted to investigate the effects of dietary proteolytic soybean meal (PSM) on growth performance, immune-related genes, and resistance against Vibrio alginolyticus in Litopenaeus vannamei. Six dietary levels of PSM (0, 35, 45, 55, and 65 g/kg) were added to a basal diet. The results showed that juveniles fed more than 45 g/kg PSM exhibited significantly (

P
<
0.05

) promoted growth performance compared to the control. Furthermore, all PSM supplemented treatments showed significantly better performances in terms of feed conversion ratio (FCR), the protein efficiency ratio (PER), and the protein deposition ratio (PDR). Corresponding to the performances on growth and nutrient utilization, a significantly higher protease activity in hepatopancreas was also obtained in all PSM incorporations. Also, the activities of immune-related enzymes such as superoxide dismutase (SOD) and lysozyme in serum were significantly (

P
<
0.05

) elevated in shrimp fed with PSM. Notably, shrimp fed with the 65 g/kg PSM supplemented diet showed significantly (

P
<
0.05

) lower cumulative mortality compared to the control after challenging with Vibrio alginolyticus injection at 72 h. PSM supplementation significantly (

P
<
0.05

) upregulated expression levels of immune deficiency (IMD) and Toll-like receptor 2 mRNA in shrimp gill tissues directly or indirectly reflected their activation effect in shrimp innate immune response. In conclusion, the current study proved that partial replacement of soybean meal with PSM could result in better growth and immune status of L. vannamei.
Design and Implementation of Energy-Saving Logistics Management System for Route Optimization
Authors: Zhao Junqing , Tie Pengfei
Published: August 2022

In order to effectively solve the problem of vehicle routing, a design and implementation method of an energy-saving logistics management system oriented to routing optimization is proposed. From the perspective of optimal calculation, this research uses the improved Dixie algorithm and clustering algorithm to design and implement a logistics company’s distribution center location and distribution path planning system. First of all, the authors analyze the common models of the LRP problem in detail and give the mathematical model and calculation method of positioning rationing and the transportation route planning problem. Secondly, in view of the shortcomings of traditional evolutionary algorithms, the authors propose a series of improvement measures. The authors adopt a natural number coding scheme combined with an adaptive crossover mutation operator to improve the search ability of the solution space; the authors also introduce a penalty function to deal with constraints and take corresponding measures for illegal individuals generated in the evolution process, reducing premature convergence. Possibility. It has been verified that the design and development of the system saves investment costs for small and medium-sized logistics enterprises and reduces the cost of goods distribution by 80%. The effect is remarkable, which verifies the effectiveness, accuracy, and superiority of the algorithm.
Highway Temporal-Spatial Traffic Flow Performance Estimation by Using Gantry Toll Collection Samples: A Deep Learning Method
Authors: Niu Jun , He Jiami , Li Yurong , Zhang Saifei
Published: August 2022

In order to accurately predict the short-time traffic flow of highways, to achieve the purpose of alleviating highway traffic congestion, saving travel time, and reducing energy waste and pollution, this paper considers the spatiotemporal characteristics of the road network, uses the advantages of long short-term memory network (LSTM) to analyze time series data, divides time intervals to collect traffic flow, and substitutes it into the model. Make traffic flow predictions. Three evaluation indicators, mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE), were used for training and testing on the section k602 + 630-k636 + 090 of Chang-Jiu Expressway. It is divided into average data with intervals of 1, 3, 5, and 10 minutes for prediction. The results show that each index obtained by dividing the scale of 1 minute is the smallest, which proves that the suitable prediction scale of road traffic flow is 1 minute.
High Dimensional Data Processing Based on Optimized DPC Algorithm
Authors: Yu Peiji , Wang Peiyuan , Ji Hongtao
Published: August 2022

With the rapid growth of science and technology, machine learning and big data analysis have developed more and more difficult. Similarly, data also become difficult to process and classify due to the fact that the data dimension is becoming larger and larger. Furthermore, aiming at the defect due to the amount of data the clustering using speedy examine, search, and discovery of density peaks (also known as DPC) does not adjust to data sets with large dimensions (high-dimensional). Therefore, in this paper, we suggest an optimization procedure, which we pronounce as t-dpc, and is founded on the t-sne dimension lessening technique, and which can also optimize the technique for estimation of the Gaussian kernel function, using unified measurement criteria in solving density. In the simulation based experiments, using two different data sets i.e., the UCI standard data set, and the artificial data set, the proposed DPC procedure is associated with the classical t-dpc algorithm. The empirical evaluation and investigational outcomes illustrate that the proposed method of t-dpc not merely acclimatizes to the high-dimensional data sets, nevertheless it also increases the effectiveness of the classical DPC technique.
L5-S1 Discoligamentous Distraction Injury following Bilateral Total Hip Arthroplasty Using an Anterior Approach: A Case Report
Authors: Gronki Ferda , Alfieri Alex , Kalberer Fabian , Meier Christoph , Wahl Peter
Published: August 2022

Although acute spinal injury is rarely reported to complicate total hip arthroplasty (THA), its consequences can be devastating. We present a case of L5-S1 discoligamentous dissociation following single-stage bilateral THA performed sequentially through an anterior approach with a traction hemi-table applied on the operated leg. Internal fixation L5-S1 was required, resulting in prolonged recovery, but without relevant long-term sequelae. Pre-operative assessment of the spine is recommended when considering these procedures to evaluate the risk for this potentially severe complication. Particularly, lateral position may be favoured to avoid extension stresses of the spine. An anterior approach may have to be avoided, as it requires hyperextending the hip. A staged procedure may also be preferred to a single-anaesthesia bilateral procedure, limiting duration of potentially harmful positioning.
A Case of Verrucous Carcinoma Treated by Combination of Radiotherapy and Mohs’ Chemosurgery
Authors: Kawaguchi Kozo , Kurihara Yuichi , Akashi Michiaki , Nakahara Takeshi
Published: August 2022

Verrucous carcinoma (VC) is a rare subtype of squamous cell carcinoma. VC is histologically a benign tumor, but it grows significantly and eventually forms a huge mass. Many different treatments are known, but the first-line treatment is surgical resection. VC has strong local infiltration and frequently recurs, making its local control very difficult in unresectable cases. We present a rare case of VC that could be treated with combined radiotherapy and Mohs’ chemosurgery, as a new option for unresectable VC.
Outcomes of Neonates with a 10-min Apgar Score of Zero: A Systematic Review and Meta-Analysis
Authors: Khorram Bita , Kilmartin Keira C. , Dahan Maya , Zhong You Jia , Abdelmageed Wael , Wintermark Pia , Shah Prakesh S.
Published: August 2022

Introduction: The Apgar score is a standardized method of assessing the primary adaptation and clinical status of a neonate after birth. Our objective was to systematically review and meta-analyze the survival and the survival without moderate-to-severe neurodevelopmental impairment (NDI) of neonates with a 10-min Apgar score of zero. Methods: Six electronic databases were searched for reports published until November 2021 of neonates with a 10-min Apgar score of zero. Risk of bias was assessed using the Newcastle-Ottawa scale for cohort studies and the Joanna Briggs Institute Critical Appraisal Checklist for case series/reports. Meta-analyses of the proportion of outcomes were conducted using a random-effects model for studies published after year 2000 and reporting >5 neonates. Meta-regression using the median year of the study period and subgroup analyses by treatment with therapeutic hypothermia and by gestational age were conducted. Results: Twenty-eight studies of 820 neonates with moderate risk of bias were included. Survival was 40% (95% confidence interval 30–50%, 16 studies, 646 neonates, I2 = 83%), and it increased by 2.3% per year (95% CI 1.3–3.2%, p < 0.001). Survival without moderate-to-severe NDI was 19% (95% confidence interval 11–27%, 13 studies, 211 neonates, I2 = 62%). Survival was higher for neonates who received therapeutic hypothermia and for those with a gestational age ≥32 weeks compared to <32 weeks. Conclusion: Approximately 2 in 5 neonates with a 10-min Apgar score of zero survived, and 1 in 5 survive without moderate-to-severe NDI survived. Survival has improved over the years, especially since the era of therapeutic hypothermia.
The Development and Validation of the First German Open Scale of Social Information Processing
Authors: Niestroj Sophie Charlotte , Steden Sarah , Boecker Maren , Brodkin Edward Stuart , Konrad Kerstin
Published: August 2022

Introduction: This study introduces the first German Open Scale of Social Information Processing (GOSSIP) and evaluates its psychometric properties. Even though social information processing (SIP) and its important role in developmental psychopathology is a rising field of interest, model-based standardized assessment tools are still scarce. Methods: GOSSIP was developed to assess SI processes in boys and girls aged eight to 21 years. First, 61 vignettes (combinations of pictures and short written descriptions of the situation depicted) were evaluated by an expert group and piloted with 48 healthy participants (aged 8–21). The best-rated vignettes were then implemented in a Web-based application. 191 participants completed GOSSIP. Of those, 76 answered additional questionnaires to assess their social skills and psychopathology. Internal consistencies for the emotional and cognitive GOSSIP scales were determined. Latent profile analysis (LPA) was used to identify subgroups of children and adolescents characterized by specific SIP profiles (i.e., patterns of emotional, cognitive, and behavioral responses to the GOSSIP). Furthermore, the external validity of the participants’ attribution tendencies in GOSSIP was evaluated in real life by smartphone-based Ecological Momentary Assessments (EMA). Results: The internal consistencies for the emotional and cognitive scales (angry, ashamed, physical aggression, pro-social response, revenge, and outcome expectancy) were good to excellent. The scales of hostile interpretation, relation aggression, sadness, and exclusion showed borderline/acceptable internal consistency. Correlation analysis confirmed convergent validity with self-reported social skills and external validity with ratings of aggressive and pro-social behaviors. The LPA revealed three profiles as the best fit of the data. The first group is named “aggressors,” the second “social-emotional group,” and a third named “ashamed-internalizing group.” However, no significant association was found between the attribution tendencies derived from GOSSIP and EMA data. Discussion/Conclusion: GOSSIP is the first model-based German Web-based assessment for several SIP mechanisms that showed overall adequate psychometric properties. GOSSIP can be used to classify individuals into SIP profiles that differ in terms of their cognitive and emotional response tendencies and therefore could contribute to the development of targeted interventions. Integrating assessments of emotional responses into GOSSIP revealed an important role of “shame” in SIP and the development of psychopathology. Furthermore, the lack of external validity between GOSSIP and EMA calls into question how attribution tendencies are best assessed in future studies.
Bronchoscopic Journey of in vivo Real-Time Microscopic Imaging in ILD: A Case Series
Authors: Wijmans Lizzy , Kalverda Kirsten , de Bruin Daniel , Brinkman Paul , van den Berk Inge , Roelofs Joris J.T.H. , Jonkers René , Bonta Peter I. , Annema Jouke
Published: August 2022

Background: Patients with interstitial lung diseases (ILDs) frequently present with nondiagnostic high-resolution CT (HRCT) scan and bronchoalveolar lavage (BAL) results, resulting in the need for invasive surgical or cryo-lung biopsy that is associated with significant morbidity. Confocal laser endomicroscopy (CLE) and optical coherence tomography (OCT) are high-resolution laser and light-based techniques that provide real-time imaging of the alveolar compartment during bronchoscopy with a different depth and field of view. Objectives: The aim of the study was to correlate OCT and CLE imaging to HRCT imaging in ILD. Methods: This is a retrospective case series of 20 ILD patients who underwent alveolar CLE and OCT imaging during a standard bronchoscopy with BAL, followed by a lung biopsy when indicated. CLE and OCT imaging were compared to four main HRCT patterns and histology. The final diagnosis was based on the multidisciplinary discussion diagnosis. Results: Bronchoscopic CLE and OCT imaging were feasible and safe and provided additional high-detailed anatomical information compared to the HRCT. Bronchoscopic real-time CLE was capable of identification of “alveolar cells” (ground glass opacities) and lung fibrosis (increased alveolar elastin fibers). Bronchoscopic real-time OCT allowed for visualization of “patchy fibrotic disease”, “honeycombing” (microcysts), and mucosal granulomas in the airways. Conclusions: Bronchoscopic CLE and OCT of the alveolar compartment is feasible and safe and enables minimally invasive, high-resolution detection of specific ILD features with the potential to improve ILD diagnostics and monitoring and decrease the need for surgical or cryo-lung biopsies.
Experience of a Bespoke Hyperkalaemia Clinic to Facilitate Prescribing of Renin-Angiotensin-Aldosterone System Inhibitors in Patients with Heart Failure with Reduced Ejection Fraction
Authors: Ali Ibrahim , Green Darren , Kalra Paul , Kalra Philip A.
Published: August 2022

Background: Renin-angiotensin-aldosterone system inhibitors (RAASi) improve prognosis in patients with heart failure with reduced ejection fraction (HFrEF), but suboptimal dosing or discontinuation of these medications often occurs due to RAASi-associated hyperkalaemia. We established a nephrology-led hyperkalaemia clinic to oversee prescribing of patiromer, an oral potassium binder, to facilitate RAASi optimization. Methods: The clinic was established in July 2019 at a nephrology tertiary centre in the UK. Patients with HFrEF who were unable to increase RAASi dosage due to hyperkalaemia were referred to the clinic, where all patients commenced patiromer 8.4 g daily. RAASi adjustments were deferred to the referring teams. Study outcomes included the percentage of patients who achieved a RAASi dose increase and the proportion of patients with normokalaemia at follow-up. Outcomes were evaluated until 1 May 2021. Results: A total of 34 patients were reviewed in the clinic between July 2019 and December 2020. Mean age was 71.6 years (±10.6 years), 56% had diabetes, and 71% had chronic kidney disease stages 3a-5; mean estimated glomerular filtration rate was 56 mL/min/1.73 m2 (±21 mL/min/1.73 m2). During follow-up, 13 patients discontinued patiromer (6 of whom did so due to gastrointestinal side effects) and were discharged; 2 patients died from non-hyperkalaemia-related illness; one switched to an alternative potassium binder. Over a mean follow-up of 13.4 months (±5.8 months), 17 of the 20 patients (85%) who continued with a potassium binder achieved a RAASi dose increase, with 4 patients (20%) receiving maximal dosages. This was attained by achieving normokalaemia during follow-up. No patients required magnesium supplementation. Of the 19 patients on patiromer, 12 continued this therapy for more than 12 months and 4 received it safely for 20 months. Discussion/Conclusion: Patiromer prescribing in a nephrology-led hyperkalaemia clinic facilitated RAASi up-titration in patients with HFrEF by controlling potassium levels.
Retinal Vascular Caliber in Coronary Heart Disease and Its Risk Factors
Authors: Liang Changsen , Gu Chen , Wang Ning
Published: August 2022

Background: Many clinical and experimental articles have suggested that the retinal vascular diameter can be used as a predictor of coronary heart disease (CHD). However, the results and reliability of the prediction are still controversial. Objective: A meta-analysis of observational study was conducted to clarify the relationships of retinal vessel caliber with CHD and CHD risk factors. Method: PubMed and Embase were searched for all observational studies on the relationship of retinal vessel caliber with CHD and CHD risk factors from 2001 to 2021. The meta-analysis has been registered in PROSPERO (registration number is CRD42021267154). Result: A total of 14 articles were selected for the inclusion in this meta-analysis. In the primary outcome, smaller retinal arteriolar caliber was related to CHD, and the results achieved statistical significance (MD: −5.55, 95% CI: −8.07 to −3.02, p < 0.0001), while there was no significant difference in vein caliber between CHD and healthy people (MD: 1.10, 95% CI: −3.55 to 5.76, p = 0.64 > 0.05). Smaller retinal arteriolar caliber was related to increasing age, male sex, bigger BMI, and hypertension. Bigger retinal arteriolar caliber was related to current smoking. Smaller retinal venule caliber was related to increasing age and hypertension. Bigger retinal venule caliber was related to current smoking, bigger BMI, and diabetes. Conclusion: Smaller retinal arteriolar caliber was related to CHD, while there was no significant difference in venule caliber between CHD and healthy people. Retinal vascular caliber also independently related to CHD risk factors (e.g., age, gender, smoke, BMI, hypertension, and diabetes).
Development of a Root Caries Prediction Model in a Population of Dental Attenders
Authors: Fee Patrick A. , Cassie Heather , Clarkson Jan E. , Hall Andrew F. , Ricketts David , Walsh Tanya , Goulão Beatriz
Published: August 2022

Root caries prevalence is increasing as populations age and retain more of their natural dentition. However, there is generally no accepted practice to identify individuals at risk of disease. There is a need for the development of a root caries prediction model to support clinicians to guide targeted prevention strategies. The aim of this study was to develop a prediction model for root caries in a population of regular dental attenders. Clinical and patient-reported predictors were collected at baseline by routine clinical examination and patient questionnaires. Clinical examinations were conducted at the 4-year timepoint by trained outcome assessors blind to baseline data to record root caries data at two thresholds – root caries present on any teeth (RC > 0) and root caries present on three or more teeth (RC ≥ 3). Multiple logistic regression analyses were performed with the number of participants with root caries at each outcome threshold utilized as the outcome and baseline predictors as the candidate predictors. An automatic backwards elimination process was conducted to select predictors for the final model at each threshold. The sensitivity, specificity, and c-statistic of each model’s performance was assessed. A total of 1,432 patient participants were included within this prediction model, with 324 (22.6%) presenting with at least one root caries lesion, and 97 (6.8%) with lesions on three or more teeth. The final prediction model at the RC >0 threshold included increasing age, having ≥9 restored teeth at baseline, smoking, lack of knowledge of spitting toothpaste without rinsing following toothbrushing, decreasing dental anxiety, and worsening OHRQoL. The model sensitivity was 71.4%, specificity 69.5%, and c-statistic 0.79 (95% CI: 0.76, 0.81). The predictors included in the final prediction model at the RC ≥ 3 threshold included increasing age, smoking, and lack of knowledge of spitting toothpaste without rinsing following toothbrushing. The model sensitivity was 76.5%, specificity 73.6%, and c-statistic 0.81 (95% CI: 0.77, 0.86). To the authors’ knowledge, this is the largest published root caries prediction model, with statistics indicating good model fit and providing confidence in its robustness. The performance of the risk model indicates that adults at risk of developing root caries can be accurately identified, with superior performance in the identification of adults at risk of multiple lesions.