bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating for bias in healthcare algorithms: a sex-stratified analysis of supervised machine learning models in liver disease prediction
Authors: Straw Isabel , Wu Honghan
Published: September 2022

Objectives

The Indian Liver Patient Dataset (ILPD) is used extensively to create algorithms that predict liver disease. Given the existing research describing demographic inequities in liver disease diagnosis and management, these algorithms require scrutiny for potential biases. We address this overlooked issue by investigating ILPD models for sex bias.

Methods

Following our literature review of ILPD papers, the models reported in existing studies are recreated and then interrogated for bias. We define four experiments, training on sex-unbalanced/balanced data, with and without feature selection. We build random forests (RFs), support vector machines (SVMs), Gaussian Naïve Bayes and logistic regression (LR) classifiers, running experiments 100 times, reporting average results with SD.

Results

We reproduce published models achieving accuracies of >70% (LR 71.31% (2.37 SD) – SVM 79.40% (2.50 SD)) and demonstrate a previously unobserved performance disparity. Across all classifiers females suffer from a higher false negative rate (FNR). Presently, RF and LR classifiers are reported as the most effective models, yet in our experiments they demonstrate the greatest FNR disparity (RF; −21.02%; LR; −24.07%).

Discussion

We demonstrate a sex disparity that exists in published ILPD classifiers. In practice, the higher FNR for females would manifest as increased rates of missed diagnosis for female patients and a consequent lack of appropriate care. Our study demonstrates that evaluating biases in the initial stages of machine learning can provide insights into inequalities in current clinical practice, reveal pathophysiological differences between the male and females, and can mitigate the digitisation of inequalities into algorithmic systems.

Conclusion

Our findings are important to medical data scientists, clinicians and policy-makers involved in the implementation medical artificial intelligence systems. An awareness of the potential biases of these systems is essential in preventing the digital exacerbation of healthcare inequalities.
Establishing a colorectal cancer research database from routinely collected health data: the process and potential from a pilot study
Authors: Tamm Andres , Jones Helen JS , Perry William , Campbell Des , Carten Rachel , Davies Jim , Galdikas Algirdas , English Louise , Garbett Alex , Glampson Ben , Harris Steve , Khan Khurum , Little Stephanie , Malcomson Lee , Matharu Sheila , Mayer Erik , Mercuri Luca , Morris Eva JA , Muirhead Rebecca , Norris Ruth , O’Hara Catherine , Papadimitriou Dimitri , Peek Niels , Renehan Andrew , Roadknight Gail , Starling Naureen , Teare Marion , Turner Rachel , Várnai Kinga A , Wasan Harpreet , Woods Kerrie , Cunningham Chris
Published: September 2022

Objective

Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source.

Methods

Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically.

Results

Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed.

Discussion

Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer.

Conclusion

The data set has great potential to facilitate research into colorectal cancer.
Early detection of autism spectrum disorder in young children with machine learning using medical claims data
Authors: Chen Yu-Hsin , Chen Qiushi , Kong Lan , Liu Guodong
Published: September 2022

Objectives

Early diagnosis and intervention are keys for improving long-term outcomes of children with autism spectrum disorder (ASD). However, existing screening tools have shown insufficient accuracy. Our objective is to predict the risk of ASD in young children between 18 months and 30 months based on their medical histories using real-world health claims data.

Methods

Using the MarketScan Health Claims Database 2005–2016, we identified 12 743 children with ASD and a random sample of 25 833 children without ASD as our study cohort. We developed logistic regression (LR) with least absolute shrinkage and selection operator and random forest (RF) models for predicting ASD diagnosis at ages of 18–30 months, using demographics, medical diagnoses and healthcare service procedures extracted from individual’s medical claims during early years postbirth as predictor variables.

Results

For predicting ASD diagnosis at age of 24 months, the LR and RF models achieved the area under the receiver operating characteristic curve (AUROC) of 0.758 and 0.775, respectively. Prediction accuracy further increased with age. With predictor variables separated by outpatient and inpatient visits, the RF model for prediction at age of 24 months achieved an AUROC of 0.834, with 96.4% specificity and 20.5% positive predictive value at 40% sensitivity, representing a promising improvement over the existing screening tool in practice.

Conclusions

Our study demonstrates the feasibility of using machine learning models and health claims data to identify children with ASD at a very young age. It is deemed a promising approach for monitoring ASD risk in the general children population and early detection of high-risk children for targeted screening.
1 A double-blind randomised control trial of high-volume image guided injections in achilles and patellar tendinopathy in a young active population
Authors: Barker-Davies Robert M , Baker Polly , Watson James , Goodall Duncan , Wheeler Patrick C , Nicol Alastair M , Fong Daniel TP , Lewis Mark P , Bennett Alexander N
Published: September 2022

Background

Chronic tendinopathy is a significant burden in physically active populations. High-volume image-guided injection (HVIGI) proposes to strip away associated neovascularity, disrupt painful nerve ingrowth and facilitate rehabilitation.

Methods

The objective was to investigate the efficacy of HVIGI with and without steroid relative to placebo. The study design was a three-arm block randomised control trial in a tertiary rehabilitation centre. Sixty-two participants were recruited between 25 May 2016 and 5 March 2020. Participants were men aged 18–55 with, Achilles or patellar tendinopathy of at least 6-months chronicity who had not improved with conservative management including shockwave, Ultrasound (US) evidence of neovascularisation, tendon thickening and echogenic changes.

Participants were randomly assigned to control (3ml subcutaneous 0.5% Bupivacaine) or HVIGI (10 ml 0.5% Bupivacaine and 30 ml normal saline US guided between tendon and underlying fatpad) or HVIGI with steroid (0.25 ml 100 mg/ml hydrocortisone). Clinicians and assessors were blinded. All participants were supervised through a pain-guided progressive loading programme for 6-months post injection. The primary outcome measures were the Victoria institute of sport assessments for Achilles and patellar tendinopathy (VISA-A and VISA-P) and visual analogue scale (VAS)-pain at 6-months.

Results

VISA improved by 22.8 ((95% CI) 10.4 to 35.3, effect size (ES) 1.51) in control (n=21), 18.6 (9.1 to 28.0, ES 1.31) in HVIGI (n=21) and 18.5 (3.4 to 33.6, ES 0.88) in HVIGI with steroid (n=20) groups. VAS-pain improved by 15 ((IQR) -38.75 to 8, ES 0.39) in control, 13 in HVIGI (-34.0 to 3.75, ES 0.47) and 27 (-38.0 to-1.0, ES 0.54) in HVIGI with steroid groups. Main effects for time were significant (p<0.001) but not group (p≥0.48) with no group-time interaction (p=0.71). One participant was lost to follow-up from each group for whom multiple imputation was used. No adverse events occurred.

Conclusions

Data does not demonstrate superiority of HVIGI over control injection. Pain-guided progressive loading remains the mainstay of treatment.
Low LDL-C level and intracranial haemorrhage risk after ischaemic stroke: a prospective cohort study
Authors: Xu Jie , Chen Zimo , Wang Meng , Mo Jinglin , Jing Jing , Yalkun Gulbahram , Dai Liye , Meng Xia , Li Hao , Li Zixiao , Wang Yongjun
Published: September 2022

Background and purpose

The Treat Stroke to Target trial has confirmed the benefit of targeting low-density lipoprotein cholesterol (LDL-C) of <1.8 mmol/L in patients who had an ischaemic stroke (IS). However, haemorrhagic risk brought by this target level (<1.8 mmol/L) or even lower level (<1.4 mmol/L) of LDL-C should also be concerned. In this study, we aimed to demonstrate whether low LDL-C could increase the intracranial haemorrhage risk following IS.

Methods

Patients who had an IS from China Stroke Center Alliance programme with complete baseline information were prospectively enrolled. 793 572 patients who had an IS were categorised into 6 groups according to LDL-C level (<1.40 mmol/L, 1.40–1.79 mmol/L, 1.80–2.59 mmol/L, 2.60–2.99 mmol/L, 3.00–4.89 mmol/L, ≥4.90 mmol/L). The study outcome was defined as intracranial haemorrhage identified during hospitalisation. Logistic regression model was used to examine the association between different LDL-C levels and risk of intracranial haemorrhage.

Results

Compared with patients of LDL-C=1.80–2.59 mmol/L, both subgroups of LDL-C<1.40 mmol/L and LDL-C=1.40–1.79 mmol/L showed significantly higher risk of intracranial haemorrhage (OR=1.26, 95% CI=1.18 to 1.35; OR=1.22, 95% CI=1.14 to 1.30, respectively). Interaction effect was found to exist between the subgroups of intravenous thrombolytic therapy (p=0.04), rather than the subgroups of age, sex and body mass index. Moreover, the sensitivity analyses indicated that even patients who had an IS with minor stroke still suffered from the increased intracranial haemorrhage risk related to low LDL-C level.

Conclusions

Among patients who had an IS, the low LDL-C level (<1.4 mmol/L or <1.8 mmol/L) at baseline is associated with increased risk of intracranial haemorrhage during acute stage. While actively lowering LDL-C level for patients who had an IS, clinicians should also concern about the haemorrhagic risk associated with low LDL-C level.
Challenges in fish aging: the role of otolith preparation technique and experience level in aging lake whitefish (Coregonus clupeaformis)
Authors: McKeefry Madeline N. , Tucker Stefan R. , Ransom Andrew L. , Kroeff Timothy G. , Forsythe Patrick S.
Published: September 2022

Lake whitefish ( Coregonus clupeaformis (Mitchill, 1818)) is an important commercial and recreational species in the Great Lakes. Precise age estimates are important for management, and two widely used techniques for otolith preparation are thin-section and crack-and-burn, which have not been compared for lake whitefish. Sagittal otoliths were collected from 92 lake whitefish in Green Bay and Lake Michigan and aged using thin-section and crack-and-burn techniques. Otoliths were aged independently by three individuals (two novices and one expert) to assess repeatability in estimated ages. Our investigation highlights the inherent difficulty of aging lake whitefish, where thin-section produced significantly older estimated ages (6–30 years) compared to crack-and-burn (5–26 years). Percent agreement of estimated ages between preparation techniques was low for all readers within ±0 years but increased when tolerance buffers were applied. Mean coefficient of variation values from both experience levels (>10.8%) exceeded the acceptable range reported in the literature (5%–7%); however, species longevity and nature of the structure must be considered when establishing target values. Variation in estimated ages is attributed to the experience level and interpretation of structural features. Species-specific training and establishing an objective framework to identify annuli will improve precision metrics.
Inference of neutrino nature and Majorana CP phases from $$\mathbf{0}{\nu \beta \beta }$$ decays with inverted mass ordering
Authors: Huang Guo-yuan , Nath Newton
Published: September 2022

Whether the neutrino mass ordering is normal or inverted remains an experimentally open issue in neutrino physics. The knowledge of neutrino mass ordering has great importance for neutrinoless double-beta ($$ 0\nu \beta \beta $$

0
ν
β
β

) decay experiments, which can establish the nature of massive neutrinos, i.e., whether they are Dirac or Majorana fermions. Recently, the KamLAND-Zen 800 measurement has reached for the first time the parameter space of the inverted ordering with a vanishing lightest neutrino mass. By assuming the inverted ordering, we attempt to derive the physical information of the neutrino nature and Majorana CP phases from a negative or positive observation of $$ 0\nu \beta \beta $$

0
ν
β
β

decays in the near future. Moreover, the possibility of extracting the nuclear matrix element in the case of a positive observation is also examined.To avoid the ambiguity from unknown priors of neutrino masses, we adopt the maximum likelihood method instead of the Bayesian approach usually considered in previous works.
Quaternion-valued exponential matrices and its fundamental properties
Authors: Zahid Muhammad , Younus Awais , Ghoneim Mohamed E. , Yassen Mansour F. , Haider Jamil Abbas
Published: September 2022

Quaternion differential equations (QDEs) are a new kind of differential equations which differ from ordinary differential equations. Our aim is to get the exponential matrices for the QDE which is useful for finding the solution of quaternion-valued differential equations, also, we know that linear algebra is very useful to calculate the exponential for a matrix but the solution of QDE is not a linear space. Due to the noncommutativity of the quaternion, the solution set of QDE is a right free module. For this, we must read some basic concepts on Quaternions such as eigenvalues, eigenvectors, Wronskian and the difference between quaternion and complex eigenvalues and eigenvectors; by using the right eigenvalue method for quaternions we developed a fundamental matrix which is useful to construct the exponential matrices which perform a great role in solving the QDEs.
Irreversibility analysis on the radiative buoyancy flow toward stagnation point through water conveying three kinds of nanoparticles past a heated vertical flat plate with the ramification of Hall effects
Authors: Khan Umair , Zaib Aurang , Ishak Anuar , Waini Iskandar , Alzahrani Fatimah Mohammed , Katubi Khadijah Mohammedsaleh
Published: September 2022

Recent advancements in nanotechnology have created a tremendous platform for the development of the improved performance of ultrahigh coolants known as nanofluids for several industrial and engineering technologies. The present research peruses an inspection of irreversibility analysis of mixed convective flow near a stagnation point provoked by ternary hybrid nanoparticles through a vertical heated flat plate with the Hall effects. Water conveying alumina (Al2O3), silver (Ag) and titanium oxide (TiO2) nanoparticles experiencing convectively heated as appropriate in the engineering or industry are investigated. The leading equations are non-dimensionalized using relevant similarity variables and then numerically cracked via utilizing the bvp4c solver. The impressions of different pertinent parameters on the axial velocity, transverse velocity and temperature profile along with heat transfer and drag force are discussed carefully. Double solutions are observed in the opposing flow; however, a single solution is obtained for the assisting flow. Also, the results indicate that due to nanofluid, the velocity boundary layer thicknesses decrease and the thermal boundary layer width upsurges. Further, the flow and the characteristics of heat transfer can be controlled using a magnetic field.
A Widespread Assessment and Open Issues on Image Captioning Models
Authors: Padate Roshni , Jain Amit , Kalla Mukesh , Sharma Arvind
Published: September 2022

Automated generation of image captions is a demanding AI crisis as it necessitates the exploitation of numerous methods from diverse computer science fields. Deep learning (DL) approaches have revealed marvelous results in a lot of diverse appliances. On the other hand, data augmentation in DL that imitates the quantity and the variety of training data without the need of gathering additional data is a hopeful area in machine learning (ML). Producing textual descriptions for a specified image is a demanding task using the computer. This survey makes a critical analysis of about 65 papers regarding image captioning. More particularly, varied performance measures that are contributed in diverse articles are analyzed. In addition, a comprehensive study is made regarding the maximal performances and varied features deployed in each work. Moreover, chronological analysis and dataset analysis are done and finally, the survey extends with the determination of varied research challenges, which might be productive for the analysts to endorse enhanced upcoming works on image captioning.
Impaired hemodynamic response to exercise in patients with peripheral artery disease: evidence of a link to inflammation and oxidative stress
Authors: Craig Jesse C. , Hart Corey R. , Layec Gwenael , Kwon Oh Sung , Richardson Russell S. , Trinity Joel D.
Published: September 2022

An exaggerated mean arterial blood pressure (MAP) response to exercise in patients with peripheral artery disease (PAD), likely driven by inflammation and oxidative stress and, perhaps, required to achieve an adequate blood flow response, is well described. However, the blood flow response to exercise in patients with PAD actually remains equivocal. Therefore, eight patients with PAD and eight healthy controls completed 3 min of plantar flexion exercise at both an absolute work rate (WR) (2.7 W, to evaluate blood flow) and a relative intensity (40%WRmax, to evaluate MAP). The exercise-induced change in popliteal artery blood flow (BF, Ultrasound Doppler), MAP (Finapress), and vascular conductance (VC) were quantified. In addition, resting markers of inflammation and oxidative stress were measured in plasma and muscle biopsies. Exercise-induced ΔBF, assessed at 2.7 W, was lower in PAD compared with controls (PAD: 251 ± 150 vs. Controls: 545 ± 187 mL/min, P < 0.001), whereas ΔMAP, assessed at 40%WRmax, was greater for PAD (PAD: 23 ± 14 vs. Controls: 11 ± 6 mmHg, P = 0.028). The exercise-induced ΔVC was lower for PAD during both the absolute WR (PAD: 1.9 ± 1.6 vs. Controls: 4.7 ± 1.9 mL/min/mmHg) and relative intensity exercise (PAD: 1.9 ± 1.8 vs. Controls: 5.0 ± 2.2 mL/min/mmHg) trials (both, P < 0.01). Inflammatory and oxidative stress markers, including plasma interleukin-6 and muscle protein carbonyls, were elevated in PAD (both, P < 0.05), and significantly correlated with the hemodynamic changes during exercise ( r = −0.57 to −0.78, P < 0.05). Thus, despite an exaggerated ΔMAP response, patients with PAD exhibit an impaired exercise-induced ΔBF and ΔVC, and both inflammation and oxidative stress likely play a role in this attenuated hemodynamic response.
Software Engineering Code Workshop Based on B-RRT

∗

FND Algorithm for Deep Program Understanding Perspective
Authors: Xu Aiqiao
Published: September 2022

Developers will perform a lot of search behaviors when facing daily work tasks, searching for reusable code fragments, solutions to specific problems, algorithm designs, software documentation, and software tools from public repositories (including open source communities and forum blogs) or private repositories (internal software repositories, source code platforms, communities, etc.) to make full use of existing software development resources and experiences. This paper first takes a deep programmatic understanding view of the software development process. In this paper, we first define the software engineering code search task from the perspective of deep program understanding. Secondly, this paper summarizes two research paradigms of deep software engineering code search and composes the related research results. At the same time, this paper summarizes and organizes the common evaluation methods for software engineering code search tasks. Finally, the results of this paper are combined with an outlook on future research.
Breast Sentinel Lymph Node Cancer Detection from Mammographic Images Based on Quantum Wavelet Transform and an Atrous Pyramid Convolutional Neural Network
Authors: Qasim Mohammed N. , Mohammed Tareq Abed , Bayat Oguz
Published: September 2022

This study proposes an optimal approach to reduce noise in mammographic images and to identify salt-and-pepper, Gaussian, Poisson, and impact noises to determine the exact mass detection operation after these noise reductions. It therefore offers a method for noise reduction operations called quantum wavelet transform filtering and a method for precision mass segmentation called the image morphological operations in mammographic images based on the classification with an atrous pyramid convolutional neural network (APCNN) as a deep learning model. The hybrid approach called a QWT-APCNN is evaluated in terms of criteria compared with previous methods such as peak signal-to-noise ratio (PSNR) and mean-squared error (MSE) in noise reduction and accuracy of detection for mass area recognition. The proposed method presents more performance of noise reduction and segmentation in comparison with state-of-the-art methods. In this paper, we used the APCNN based on the convolutional neural network (CNN) as a new deep learning method, which is able to extract features and perform classification simultaneously, but it is intended as far as possible, empirically for the purpose of this research to be able to determine breast cancer and then identify the exact area of the masses and then classify them according to benign, malignant, and suspicious classes. The obtained results presented that the proposed approach has better performance than others based on some evaluation criteria such as accuracy with 98.57%, sensitivity with 90%, specificity with 85%, and also ROC and AUC with a rate of 86.77.
A
K
-Means Algorithm-Based Method in IOT Applications for Evaluating the Effectiveness of Physical Exercise in Preventing Youth Rebellion
Authors: Yin Wei
Published: September 2022

The purpose of this research was to determine the effectiveness of physical exercise in preventing youth rebellion in China. It was determined by using a

K

-means algorithm based on vignettes, which were collected from various Chinese cities. The analysis indicated that physical exercise had a significant effect on lowering youth rebellion risk, while it had no significant effect on increasing youth rebellion risk. This study suggested that physical exercise can be implemented as a preventive measure in preventing the occurrence and growth of youth rebellion. This article provides an assessment of physical exercise as it relates to lessening the occurrence and growth of adolescent violence, specifically among Chinese youths. It was found that out of the five types of physical exercise (endurance, strength, flexibility, balance, and coordination), endurance exercises were the most effective at reducing youth violence. Also, it was discovered that there was no significant correlation between youth specific types of physical exercise and its effectiveness on lowering youth violence. Adolescence is a critical period in the development of Chinese young people. It is also a time period when many Chinese youths have their first exposure to violent behaviors. Between 1985 and 1999, Chinese adolescent violent crimes have increased five times in number. Furthermore, research has shown that between 26% and 66% of male college students in China exhibit aggressive behavior.
Construction and Evaluation of a Preoperative Prediction Model for Lymph Node Metastasis of cIA Lung Adenocarcinoma Using Random Forest
Authors: Zhang Chuhan , Xu Shun , Jiang Youhong , Jiang Changrui , Li Shangxin , Wang Zhitong , Dong Yan , Jin Feng , Zhao Dan , Zhao Yating
Published: September 2022

Background. Lymph node metastasis (LNM) is the main route of metastasis in lung adenocarcinoma (LA), and preoperative prediction of LNM in early LA is key for accurate medical treatment. We aimed to establish a preoperative prediction model of LNM of early LA through clinical data mining to reduce unnecessary lymph node dissection, reduce surgical injury, and shorten the operation time. Methods. We retrospectively collected imaging data and clinical features of 1121 patients with early LA who underwent video-assisted thoracic surgery at the First Hospital of China Medical University from 2004 to 2021. Logistic regression analysis was used to select variables and establish the preoperative diagnosis model using random forest classifier (RFC). The prediction results from the test set were used to evaluate the prediction performance of the model. Results. Combining the results of logistic analysis and practical clinical application experience, nine clinical features were included. In the random forest classifier model, when the number of nodes was three and the

n

-tree value is 500, we obtained the best prediction model (

accuracy
=
0.9769

), with a positive prediction rate of 90% and a negative prediction rate of 98.69%. Conclusion. We established a preoperative prediction model for LNM of early LA using a machine learning random forest method combined with clinical and imaging features. More excellent predictors may be obtained by refining imaging features.
Metamaterial-Loaded 16-Printed Log Periodic Antenna Array for Microwave Imaging of Breast Tumor Detection
Authors: Syed Avez , Sheikh Muntasir , Islam Mohammad Tariqul , Rmili Hatem
Published: September 2022

This article presents printed log periodic antennas with metamaterials for use in microwave imaging. A single layer of epsilon negative (ENG) metamaterial (MTM) array (1 × 6) of the unit cell is on the radiating patch. Adding a single negative metamaterial structure enhances the properties of far-field antennas, such as radiation pattern and gain, both of which are vital for breast imaging. Two frequency bands exhibit negative permittivity: 3–3.3 GHz and 3.6–4.5 GHz. In the operating band, the proposed antennas have achieved a maximum gain of 5.5 dBi and impedance bandwidth of 3 GHz (2–5 GHz) with a reflection coefficient less than −10 dB. At the lowest operating frequency of 2 GHz, the electrical dimensions of this designed antenna are 0.34λ × 0.26λ × 0.01λ. All 16 transceiver antennas are arranged vertically in a circular pattern around the phantom, each acting as a transmitter and the rest as receivers. The system design is carried out with the electromagnetic simulators CST and HFSS. After receiving the extracted data, the data are postprocessed using the MATLAB software and the delay multiply and sum (DMAS) imaging algorithm. Based on the reconstructed image, it is evident that the MTM-loaded antenna-based imaging system can detect many undesired tumors inside the breast phantom.
Preparation and Mechanical Properties of High Silicon Molybdenum Cast Iron Materials: Based on Deep Learning Model
Authors: Dong Fuyuan , Zhang Mingxu , Peng Fan , Zhang Jianglong
Published: September 2022

In the production and life of machines, wear is the main form of material failure. Many workpieces and equipment will produce friction and wear during use, causing energy and material consumption, resulting in huge economic losses. In the process of using engineering equipment, in order to improve the service life of the workpiece and improve the wear-resistant material, the research of the material itself must be closely linked with the wear mechanism. Among the antiwear materials, the application of high-chromium cast iron is the most common. The reason is that high-chromium cast iron has a series of advantages in performance, manufacturing technology, and price. However, high-chromium cast iron itself has high C content and high brittleness, resulting in defects in toughness and poor corrosion resistance. When working in harsh environments such as mechanical extrusion, high-strength wear, and chemical corrosion, cracking and deformation are prone to occur, corrosion and peeling, and another damage failure. High chromium cast iron must have a relatively stable structure, good strength and toughness, excellent wear resistance, and corrosion resistance. In this study, the static corrosion performance of high-chromium cast iron was tested to study the effects of Mo and V elements addition and content changes on corrosion resistance and vanadium, molybdenum, and subcritical treatment processes on the microstructure and properties of high-chromium cast iron, in order to obtain better performance. Lower cost high chromium cast iron castings. As a wear-resistant part, high-chromium cast iron castings are widely used in wear and can cope with harsh working environments, but their excellent hardness conditions bring certain defects in toughness and at the same time lack good corrosion resistance, in mechanical extrusion., high-strength wear, chemical corrosion, and other harsh environments are prone to damage and failure. Therefore, high-chromium cast iron should have dual properties, not only high hardness but also suitable for mud, sand, chemicals, and other media corrosion and wear resistance.
Predictive Models for Knee Pain in Middle-Aged and Elderly Individuals Based on Machine Learning Methods
Authors: Liu Lu , Zhu Min-min , Cai Lin-lin , Zhang Xiao
Published: September 2022

Aim. This study used machine learning methods to develop a prediction model for knee pain in middle-aged and elderly individuals. Methods. A total of 5386 individuals above 45 years old were obtained from the National Health and Nutrition Examination Survey. Participants were randomly divided into a training set and a test set at a 7 : 3 ratio. The training set was used to create a prediction model, whereas the test set was used to validate the proposed model. We constructed multiple predictive models based on three machine learning methods: logistic regression, random forest, and Extreme Gradient Boosting. The model performance was evaluated by areas under the receiver (AUC), sensitivity, specificity, positive predictive value, and negative predictive value. Additionally, we created a simplified nomogram based on logistic regression for better clinical application. Results. About 31.4% (1690) individuals were with self-reported knee pain. The logistic regression showed that female gender (odds ratio

OR

=
1.28

), pain elsewhere (

OR
=
4.64

), and body mass index (

OR
=
1.05

) were significantly associated with increased risk of knee pain. In the test set, the logistic regression (

AUC
=
0.71

) showed similar but slightly higher accuracy than the random forest (

AUC
=
0.70

), while the performance of the Extreme Gradient Boosting model was less reliable (

AUC
=
0.59

). Based on mean decrease accuracy, the most important first five predictions were pain elsewhere, waist circumference, body mass index, age, and gender. Additionally, the most important first five predictions with the highest mean decrease Gini index were pain elsewhere, body mass index, waist circumference, triglycerides, and age. The nomogram model showed good discrimination ability with an AUC of 0.75 (0.73-0.77), a sensitivity of 0.72, specificity of 0.71, a positive predictive value of 0.45, and a negative predictive value of 0.88. Conclusion. This study proposed a convenient nomogram tool to evaluate the risk of knee pain for the middle-aged and elderly US population in primary care. All the input variables can be easily obtained in a clinical setting, and no additional radiologic assessments were required.
Sustainable Machining Using Eco-Friendly Cutting Fluids: A Review
Authors: Kumar Pankaj , Jain Amit Kumar , Chaurasiya Prem Kumar , Tiwari Damodar , Gopalan Anitha , Arockia Dhanraj Joshuva , Solomon Jenoris Muthiya , Sivakumar Aravinth , Velmurugan Karthikeyan , Fefeh Rushman Johannex
Published: September 2022

The base fluids of eco-friendly cutting fluids should contain some vegetable oil. Due to their eco-friendly, thermal, and tribological qualities, these types of machining fluids are becoming increasingly popular among researchers throughout the world. The authors summarized published research studies on the use of eco-friendly machining fluids (including nanofluids) in various metal removal processes such as turning, milling, grinding, and drilling in this review paper. The impact of several biodegradable oil-based machining fluids on performance metrics such as surface irregularity, machining force, tool wear, power demand, and temperature-induced during the machining process is also explored in this paper. According to the previous and current literature, using an eco-friendly nano machining fluid results in better surface features, less cutting tool wear, lower cutting force, less power required, and lower machining zone temperature during the machining operation due to superior cooling and lubrication properties.
Strategies for Ideological and Political Education in Colleges and Universities Based on Deep Learning
Authors: Sun Ying
Published: September 2022

Ideological and political education in colleges and universities is routinely burdened with the job of building morality and cultivating people, which is related to the cultivation of college students’ ideals and beliefs, spiritual pursuits, and political literacy. Based on self-determination theory (SDT), this paper modeled different learning motivations in the early stage of ideological and political courses and analyzed the learning motivation of different student groups combining the Gaussian mixture model (GMM) and stacked autoencoder (SAE). Meanwhile, the study in this paper compared the participation characteristics of different learning motivation clusters, the differences between the ideological and political course performances of students with different learning motivations, and the potential link between learning motivation and learners’ educational level. The experimental results show that students with extrinsic motivation will have better performance in the courses. The strength of extrinsic motivation is positively correlated with students’ academic performance, and 70% of students with intrinsic motivation achieve excellent results. In addition, the χ2 test result of the two courses selected is 6.442, which confirms the effectiveness of the clustering model proposed in this paper from the side and provides effective theoretical support for the implementation and reform of ideological and political education strategies.
Optimization of Intelligent Distribution of Distribution Network in the Presence of Distributed Sources
Authors: Song Youle , Nie Ding , Fan Litao
Published: September 2022

In order to improve the intelligent distribution effect of the distribution network in the presence of distributed generation, this study combines intelligent algorithms to analyze the intelligent distribution of the distribution network in the presence of distributed generation. Moreover, this study establishes an objective function composed of system network loss expectation, voltage stability index, and reactive power compensation equipment investment. In addition, this study improves the artificial bee colony algorithm through tabu search algorithm and genetic algorithm and obtains an improved TS-IABC algorithm. Finally, this study compares and tests the performance of different algorithms in the form of functions and simulations. The simulation results show that the optimal intelligent distribution method of the distribution network in the presence of distributed power generation proposed in this study has a good effect and can promote the intelligent distribution of the distribution network.
Algorithm Design for Sharing Orchestral Network Teaching Resources Using New Media Platform
Authors: Li Ruipu
Published: September 2022

The new educational media platforms were introduced to address the problems related to sharing teaching content and promoting students’ learning abilities. However, there are several issues with distributing educational resources on new media platforms, both globally and domestically. In terms of resources, teaching resources are scarce and individual and lack systematization, which affects the implementation and development of our orchestral education and teaching in new media. Regarding technology, there are currently vacancies in data linkage and exchange between different databases when the resource sharing platform is used and the existing system is in operation. The packet loss rate created by the existing technology is far too high. To address this problem, this paper designs a new platform as well as an algorithm, applies to online teaching resource distribution based on a new media platform, plans the teaching resource attributes of orchestral music majors, and studies the optimal solution for the resource sharing game model regarding load balancing and honesty allocation principles in a distributed scenario of heterogeneous networks. Experiments show that our strategy reduces network demand by a significant amount. The results made the designed algorithm applicable to be created, developed, and deployed practically.
Impact of Authentic Inquiry on Undergraduate Students’ Self-Reported Understanding of Scientific Practices
Authors: Macik Maria , Ben Wu X. , Sandoval Carolyn
Published: September 2022

Science literacy, and specifically the understanding of scientific practices, has been identified as an important outcome for college students. Educational researchers have investigated various instructional strategies in search of effective methods for fostering students’ understanding of scientific practices. One such instructional practice that can be implemented in large-enrollment science courses is authentic inquiry. To explore the effects of authentic inquiry projects on students’ learning of scientific practices, we analyzed qualitative data from a student survey over several semesters of an introductory ecology course. The qualitative data gave insight into the mechanisms which influenced students’ learning and skill acquisition. Qualitative results support the finding that the authentic inquiry project contributed to students’ learning of scientific practices, and students identified several aspects of the inquiry project that contributed to their interest and learning. Findings of this study contribute to filling the research gap on the relationship between scientific practices and students learning and can be useful for instructors seeking practical strategies for implementing authentic inquiry into their large-enrollment science courses.
[Retracted] Changes and Influencing Factors of Stress Disorder in Patients with Mild Traumatic Brain Injury Stress Disorder
Authors: Xu Chunmiao , Li Qiang , Gao Yin , Huo Hongliang , Zhang Weixin
Published: September 2022

Traumatic brain injury (TBI) is a brain injury caused by motor vehicle accidents, falls from heights, sports, and combat. Posttraumatic stress disorder (PTSD) is a complex mental disorder caused by physical and psychological trauma, which manifests itself with symptoms such as anxiety, depression, and cognitive dysfunction. How its symptoms arise and what factors influence it are not fully understood nor can it be predicted. In order to better understand the changes after stress disorder in TBI patients and the influencing factors of PTSD, this paper analyzed the changes and influencing factors of stress disorder in patients with mild traumatic brain injury stress disorder. In this paper, the Wechsler Memory Scale and functional magnetic resonance imaging were first used to study the memory impairment and functional changes of corresponding brain regions in patients with TBI stress disorder, and then, the Pittsburgh Sleep Quality Index Scale and the pain Visual Analogue Scale were used to study the influencing factors of PTSD. The results of the study showed that PTSD patients reduced and enhanced regional brain functional activity and impaired memory function in the resting state. Male gender, age under 45 years, no hemiplegia, and good sleep quality were protective factors for PTSD in TBI patients. The need for drug‐assisted sleep, severe headache, and moderate headache was the risk factor for PTSD in TBI patients.
Survival Prediction of Children Undergoing Hematopoietic Stem Cell Transplantation Using Different Machine Learning Classifiers by Performing Chi-Square Test and Hyperparameter Optimization: A Retrospective Analysis
Authors: Ratul Ishrak Jahan , Wani Ummay Habiba , Nishat Mirza Muntasir , Al-Monsur Abdullah , Ar-Rafi Abrar Mohammad , Faisal Fahim , Kabir Mohammad Ridwan
Published: September 2022

Bone marrow transplant (BMT) is an effective surgical treatment for bone marrow-related disorders. However, several associated risk factors can impair long-term survival after BMT. Machine learning (ML) technologies have been proven useful in survival prediction of BMT receivers along with the influences that limit their resilience. In this study, an efficient classification model predicting the survival of children undergoing BMT is presented using a public dataset. Several supervised ML methods were investigated in this regard with an 80-20 train-test split ratio. To ensure prediction with minimal time and resources, only the top 11 out of the 59 dataset features were considered using Chi-square feature selection method. Furthermore, hyperparameter optimization (HPO) using the grid search cross-validation (GSCV) technique was adopted to increase the accuracy of prediction. Four experiments were conducted utilizing a combination of default and optimized hyperparameters on the original and reduced datasets. Our investigation revealed that the top 11 features of HPO had the same prediction accuracy (94.73%) as the entire dataset with default parameters, however, requiring minimal time and resources. Hence, the proposed approach may aid in the development of a computer-aided diagnostic system with satisfactory accuracy and minimal computation time by utilizing medical data records.
Intelligent Dynamic Spectrum Allocation in MEC-Enabled Cognitive Networks: A Multiagent Reinforcement Learning Approach
Authors: Lei Chan , Zhao Haitao , Zhou Li , Zhang Jiao , Wang Haijun , Chen Haitao
Published: September 2022

Making effective use of scarce spectrum resources, along with efficient computational performance, is one of the key challenges for future wireless networks. To tackle this issue, in this paper, we focus on the intelligent dynamic spectrum allocation (DSA) in a mobile edge computing (MEC) enabled cognitive network. And our objective is to optimize the spectrum utilization and load balance among idle channels. Since users can only acquire part of environment information in a decentralized way, we model such a problem as decentralized partially observed Markov decision process (Dec-POMDP) and design the corresponding evaluating metric to encourage users sense and access spectrum properly. Then, we propose a QMIX-based DSA method with centralized training decentralized execution (CTDE) structure to tackle it. In the training phase, the users offload the computational tasks to the MEC server to obtain the optimal distributed DSA strategies, through which the users select the optimal channel locally in the execution phase. Simulation results show that, using the proposed algorithm, users can independently capture spectrum holes, and hence improve the spectrum utilization while balancing the load on available channels.
Matemáticas para la profesión
Authors: Soto Mónica
Published: September 2022

Vincular los ámbitos de la educación y el trabajo es una tarea compleja. La necesidad de dialogar entre ambos campos se sigue encontrando con perspectivas simplistas que a la mirada de Spinosa (2006) asocian el trabajo al empleo y la educación a las aulas, trivializando la relación entre la acción y la reflexión. Continuar leyendo...
Convolution Neural Networks and Targeted Fluorescent Nanoparticles to Detect and ICDAS Score Caries
Authors: Jones Kai A. , Jones Nathan , Tenuta Livia Maria Andalo , Bloembergen Wendy , Flannagan Susan E. , González-Cabezas Carlos , Clarkson Brian , Pan Li-Chi , Lahann Joerg , Bloembergen Steven
Published: September 2022

Previous work has shown targeted fluorescent starch nanoparticles (TFSNs) can label the subsurface of carious lesions and assist dental professionals in the diagnostic process. In this study, we aimed to evaluate the potential of using artificial intelligence (AI) to detect and score carious lesions using ICDAS in combination with fluorescent imaging following application of TFSNs on teeth with a range of lesion severities, using ICDAS-labeled images as the reference standard. A total of 130 extracted human teeth with ICDAS scores from 0 to 6 were selected by a calibrated cariologist. Then, the same surface was imaged with a stereomicroscope under white light illumination, without visible fluorescence, and blue light illumination with an orange filter following application of the TFSNs. Both sets of images were labeled by another blinded ICDAS-calibrated cariologist to demarcate lesion position and severity. Convolutional neural networks, state-of-the-art models in imaging AI, were trained to determine the presence, location, ICDAS score (severity), and lesion surface porosity (as an indicator of activity) of carious lesions, and tested by 30 k-fold validation for white light, blue light, and the combined image sets. The best models showed high performance for the detection of carious lesions (sensitivity 80.26%, PPV 76.36%), potential for determining the severity via ICDAS scoring (accuracy 72%, SD 5.67%), and the detection of surface porosity as an indicator of the activity of the lesions (accuracy 90%, SD 7.00%). More broadly, the combination of targeted biopolymer nanoparticles with imaging AI is a promising combination of novel technologies that could be applied to many other applications.
Quando a hipermodernidade guia à cegueira: desassossegos em Saramago
Authors: D'Antony Jean Paul
Published: September 2022

Neste artigo, nosso intuito é traçar um breve, mas não raso, mapeamento de alguns pontos do romance Ensaio sobre a cegueira (1995), de José Saramago, a fim de estabelecer um diálogo que nos permita problematizar as identidades desfeitas, cambiantes e modificadas intensamente por um mundo que se apresenta branco demais. Uma vez que na obra em questão se observa uma aparente neutralização das diferenças e homogeneização do tempo e do espírito através da uniformidade acarretada pela cegueira, nossa pesquisa se manterá imersa no paradoxo e desassossegado conceito da hipermodernidade (LIPOVETSKY, 2004), bem como do efêmero (LIPOVETSKY, 1989), da Identidade (Bauman, 2005), entre outras abordagens teóricas que ajudam a assimilar o inesgotável corpo literário-filosófico do romance saramaguiano.