bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence Facial Recognition and Voice Anomaly Detection in the Application of English MOOC Teaching System
Authors: Li Fengkai , Zhang Xuan 
Published: February 2023

Abstract
As a direct and effective biometric technology that follows human life habits, facial recognition has gradually become a mainstream, stable and reliable recognition method in the process of further development of science and technology. Facial recognition is a kind of biometric authentication based on recognition technology that is an original biological characteristic. After collecting the biometric functions, use a computer for digital image processing and template matching to complete the process of facial recognition. In the face of MOOC, establish a high-quality resource sharing mechanism, use this opportunity to explore innovative education models, and truly feel the strong momentum of traditional Chinese higher education, which has greatly improved the quality of education in Chinese universities. In addition, in order to vigorously promote the internationalization of education in China, education scholars need to conduct a lot of in-depth research on MOOC. In this article, a demand-based testing method is used to establish a skin color distribution model for color image preprocessing, and then develop and construct according to the comprehensive analysis of the university English class in the MOOC platform implementation mechanism (including the establishment of basic principles of operation mechanism), thereby Become a guarantee and support for curriculum and education quality evaluation system. This article combines the characteristics of educational practice and MOOC education, takes college English courses as an example, studies its application mechanism, builds a MOOC platform, and continuously enhances students' interest in learning English, aiming to provide a practical reference for the reform of Chinese college English education.
Benchmarking universal quantum gates via channel spectrum
Authors: Gu Yanwu , Zhuang Wei-Feng , Chai Xudan , Liu Dong 
Published: February 2023

Abstract
Noise remains the major obstacle to scalable quantum computation. Quantum benchmarking
provides key information on noise properties and is an important step for developing more advanced
quantum processors. However, current benchmarking methods are either limited to a specific subset
of quantum gates or cannot directly describe the performance of the individual target gate. To
overcome these limitations, we propose channel spectrum benchmarking (CSB), a novel method to
infer the noise properties of the target gate, including process fidelity, stochastic fidelity, and some
unitary parameters, from the eigenvalues of its noisy channel. Our CSB method is insensitive to
state-preparation and measurement errors, and importantly, can benchmark universal gates and
is scalable to many-qubit systems. Unlike standard randomized schemes, CSB can provide direct
noise information for both target native gates and circuit fragments, allowing benchmarking and
calibration of frequently used modules in quantum algorithms like Trotterized Hamiltonian evolution
operator in quantum simulation.
Effects of lateralized interictal epileptiform discharges on the sleep architecture of people with epilepsy: a case-control study
Authors: Wu Meina , Wu Sangru , Wang Sihang , Lin Fang , Ji Xiaolin , Yan Jinzhu 
Published: February 2023

Abstract

Background: Disruption of sleep architecture occurs in patients with epilepsy, with interictal epileptiform discharge (IED) being one possible cause. IED lateralization may exert different effects on sleep architecture, and different types of IEDs may affect the sleep architecture of patients with epilepsy. Therefore, we investigated the sleep architecture of people with epilepsy to determine the effects of IED lateralization on sleep architecture.Methods: A total of 119 patients with epilepsy who sought medical attention at our hospital between March 2018 and March 2022 and 106 healthy control participants were selected for this study. Patients with epilepsy were grouped based on the IED source determined by nocturnal scalp electroencephalography as left-sided, right-sided, simultaneous bilateral, and alternating bilateral. Polysomnography was performed to compare the effects of IED lateralization on sleep architecture among the different groups. Normally distributed data were tested for differences using thettest; the Mann–Whitney U test was used to analyze data with a non-normal distribution or heterogeneity of variance. Count data were compared using the chi-squared test, and comparisons of multiple groups were performed using analysis of variance.Results: Compared with the control group, the epilepsy group showed a statistically significant decrease in the percentage of rapid eye movement (REM) sleep (R%) and REM sleep time. The left-sided and right-sided IED groups exhibited significant decreases in R%, and the left-sided IED group showed a significant decrease in REM sleep time. Multiple linear regression analysis showed that the IED index was correlated positively withcomorbid hypertension, non-REM stage 1 (N1) sleep time, and sleep latency.Conclusions: An increase in the IED index correlates positively with comorbid hypertension, N1 sleep time, and sleep latency. Therefore, a worthwhile consideration for neurologists to reduce the IED index in clinical practice is to adopt methods that include sleep improvement techniques for patients with epilepsy.
Mining wastes as CO oxidation catalysts from the Fe-skarns of Serifos Island, Cyclades, Greece
Authors: Mpiliou Maria , Kappis Konstantinos , Tombros Stylianos , Avgouropoulos George , Kokkalas Sotirios , Lampropoulou Paraskevi , Triantafyllidis Stavros , Li Haibin , Papavasiliou Joan 
Published: February 2023

Abstract
The Fe-skarns from the Greek Island of Serifos in Cyclades is utilized only for the exploitation of magnetite ores while a significantly vast amount of red and yellow hematite ores remained unexploited as mining wastes. The main aim of this study is the effective utilization of such mining wastes, as active catalytic materials for various environmental and energy applications, taking CO oxidation as a model reaction. The results from the characterization techniques have illustrated that the physicochemical characteristics of the hematite materials can positively be influenced by the implementation of a simple calcination step, while the characteristics of magnetite remained unaffected. The catalytic studies for CO oxidation reaction have shown that the increased surface area and concentration of oxygen vacancies of the calcined (yellow and red) mineral hematite from Serifos Fe-skarns mining wastes, act beneficial in the catalytic activity with respect to calcined magnetite catalyst. Moreover, the hematite materials can be utilized as suitable supports for Au/Fe2O3 catalysts, illustrating the dual role of natural hematites in catalytic applications.
Artificial Intelligence for Product Quality Inspection in Manufacturing Industry - Online Detection of Edge Defects on Inorganic Solid Material
Authors: Mazgualdi Choumicha El , Masrour Tawfik , Hassani Ibtissam El , Barka Noureddine , Khdoudi Abdelmoula 
Published: February 2023

Abstract
The detection and localization of small and tiny defects on high-resolution images is considered one of the main challenges in the field of computer vision. In the manufacturing industry, the production speed and cycle time are considered the major target of a production process. For such reason, automated quality detection is getting even more complexified by the need of performing defect detection on moving products. In this work, we investigate the performance of a small defect detection process on high-scale images by utilizing state-of-the-art object detection architectures and a set of the hardware setup. Taking as a target the detection of defects on moving products, and using a small training set and a procedure of data augmentation, we demonstrated that such a challenge can be solved using machine learning and artificial intelligence coupled with domain knowledge in machine vision hardware selection and design. The sections of this paper are organized into two parts, the first part describes the problem, the existing and related works, and a summary of the existing methodologies. The second part of the paper is centered on our case study for which we started by describing the adopted methodology, the vision system design, the data acquisition and processing, the model training, and the obtained results, then it is concluded with a discussion of the model outputs and the listing of challenges that still to be studied in future works.
Kinetic modeling and optimization of n-heptane catalytic isomerization using Box-Behnken experimental design
Authors: Parsafard Nastaran 
Published: February 2023

Abstract
The statistical analysis using response surface methodology approach was conducted for optimization of the kinetic conditions of the n-heptane isomerization reaction over Pt-HZSM-5/HMS catalysts. The conditions of the kinetic experiments were as H2 flow rate of 20-45 cc min−1, n-C7 flow rate of 2-4.5 cc h−1, the reaction temperatures in the range of 200-350 °C, and the weight percent of HZSM-5 of 10-40. The results show that the amounts of HZSM-5 into Pt-HMS structure helps the rate of n-heptane isomerization reaction. The surface and contour plots confirm that the reaction rates do not considerably change versus temperature and flow rates of n-heptane and H2. 0.24 mol g−1s−1 is the highest reaction rate obtained in the 4.5 cc h−1 n-heptane flow rate and 45 cc min−1 H2 flow rate. The modelling results show both power-law and Langmuir–Hinshelwood model are in agreement with the experimental data.
Evaluation of Prediction Performance of Vegetation Biomass Density for Two Different Case Study Areas in Turkey with Hybrid Wavelet and Artificial Neural Network Method
Authors: İşler B. , Aslan Z. , Sunar F. , Güneş A. , Feoli E. , Gabriels D. 
Published: February 2023

Abstract
Deterioration of natural resources such as vegetation, due to urbanization and increasing population density is evident in many areas of the world. For land use planning, it is vital to assess the plant density and forecast its future changes in light of vegetation-climate interactions given the current trend of global climate change. The purpose of this article is to show how we can detect the variation of vegetation density and forecast its future values with the Artificial Intelligence (AI) methods. As case studies, we selected 2 districts namely Alanya in Antalya province and Iznik in Bursa province of Turkey, that showed the highest and lowest land cover change between 2006 and 2018 respectively, according to the CORINE land cover classification. In the analysis, we have used satellite data (Enhanced Vegetation Index (EVI) and Land Surface Temperature (LST) data from MODIS/Terra satellite) and atmospheric data (archive precipitation and temperature measurements at meteorological stations) to define vegetation changes up to 2030.We have used ANN with original data and with the data obtained by the wavelet transform application (W-ANN). The average EVI value for 2030 was calculated as 0.22 with a 5.4% error probability for Iznik, and 0.28 with a 2% error probability for Alanya. By comparing the predicted values of W-ANN for 2030 with respect those of 2018, vegetation biomass density will decrease by 21.4% in Iznik, and 6.6% in Alanya. The results were also compared with the Landsat Normalized Difference Built-up Index (NDBI).
RNA methylation patterns mediated by m 6 A regulators are involved in the regulation of immune microenvironment in hepatocellular carcinoma
Authors: Gao Yifan , Liu Yangqing , Zhang Hui , Zhang Mengyi 
Published: February 2023

Abstract
Background: It has been reported that epigenetic regulation is emerging as a new regulatory pattern, especially for RNA N6-methyladenosine (m6A) modifications. It has been reported to play an important biological function in immunity. However, the role of m6A on the immune microenvironment in hepatocellular carcinoma (HCC) remains unclear. In this study, we systematically evaluated the RNA modification patterns mediated by 23 m6A modulators in HCC samples using the TCGA database.
Methods and results: The effects of m6A modification on the characteristics of immune microenvironment gene were investigated. Meanwhile, we characterized m6A phenotype-related immune genes. Our study further identified two distinct patterns of RNA modification mediated by 23 m6A modulators. They have different immune cell abundances, immune responses, and HLA genes.
Conclusion: In a word, our findings suggest that m6A modification plays a crucial role in regulating the immune microenvironment in HCC, providing a guiding significance in the selection of immunotherapy or target for treating HCC.
Feasibility of force myography for the direct control of an assistive robotic hand orthosis
Authors: Gantenbein Jessica , Ahmadizadeh Chakaveh , Heeb Oliver , Lambercy Olivier , Menon Carlo 
Published: February 2023

Abstract
Background: Assistive robotic hand orthoses can support people with sensorimotor hand impairment in many activities of daily living and therefore help to regain independence. However, in order for the users to fully benefit from the functionalities of such devices, a safe and reliable way to detect their movement intention for device control is crucial. Gesture recognition based on force myography measuring volumetric changes in the muscles during contraction has been previously shown to be a viable and easy to implement strategy to control hand prostheses. Whether this approach could be efficiently applied to intuitively control an assistive robotic hand orthosis remains to be investigated.
Methods: In this work, we assessed the feasibility of using force myography measured from the forearm to control a robotic hand orthosis worn on the hand ipsilateral to the measurement site. In ten neurologically-intact participants wearing a robotic hand orthosis, we collected data for four gestures trained in nine arm configurations, i.e., seven static positions and two dynamic movements, corresponding to typical activities of daily living conditions. In an offline analysis, we determined achieved classification accuracies for two binary classifiers (one for opening and one for closing) and further assessed the impact of individual training arm configurations on the overall performance.
Results: We achieved an overall classification accuracy of 92.9% (averaged over two binary classifiers, individual accuracies 95.5% and 90.3%, respectively) but found a large variation in performance between participants, ranging from 75.4% up to 100%. Further, we found that the number of training arm configurations could be reduced from nine to six without notably decreasing classification performance.
Conclusion: The results of this work support the general feasibility of using force myography as an intuitive intention detection strategy for a hand orthosis. Further, the findings also generated valuable insights into challenges and potential ways to overcome them in view of applying such technologies for assisting people with sensorimotor hand impairment during activities of daily living.
Prognostic Model Establishment and Immune Microenvironment Analysis of Lung Adenocarcinoma Based on Ferroptosis-Related Long Noncoding RNAs
Authors: Yuan Jianxu , Jiang Qing , Wang Jiawu , Fu Yongxin , Hua Zhengzhao , Yu Shengjie 
Published: February 2023

Abstract
Background: Lung cancer is a common malignant tumor, which is divided into many subtypes. Lung adenocarcinoma (LUAD) is a most common subtype. More and more studies have confirmed that ferroptosis is involved in the occurrence and development of lung cancer. In this paper, we studied the prognostic ferroptosis-related long noncoding RNAs (FRLs) to build a LUAD-related prognosis model.
Methods: We first downloaded the relevant data of 598 patients from the TCGA-LUAD dataset of The Cancer Genome Atlas, and then randomly divided them into training group and testing group in a 1:1 ratio. After that, we used Pearson correlation analysis and univariate Cox regression analysis to determine the FRLs related to prognosis. Then, according to the least absolute shrinkage and selection operator (LASSO) algorithm, the risk model was constructed using the optimized prognostic FRLs subset. We further used the receiver operating characteristic (ROC) curve and survival analysis to evaluate the performance of our model, meanwhile, Cox regression analysis was performed to analyze the risk score (RS). Finally, we also carried out gene set enrichment analysis (GSEA) , and differential analysis of immune-related genes and m6a-related genes.
Results: In this study, we identified a total of 34 FRLs associated with the prognosis of lung adenocarcinoma, and established a prognostic model with 7 of them. Kaplan-Meier analysis showed that relevant characteristics of patients in high-risk group were correlated with poorer prognosis. The AUC value of our model was quite ideal, indicating that it could accurately predict the prognosis of LUAD patients. Further GSEA results showed that FRLs of individuals in high-risk groups were mainly enriched in cell cycle and related regulatory pathways, while those in low-risk groups were mainly enriched in immune-related pathways. We also employed immune function analysis and immune checkpoints expression analysis, and found that CCR, check-point, HLA, T cell co−inhibition, T cell co−stimulation and Type II IFN Reponse had significant differences between two groups, while most immune checkpoints had higher expression levels in low-risk groups.
Conclusion: Our research has proved that FRls could indeed be used as a prognostic feature to build a prognostic model of lung adenocarcinoma. On the basis of this theory, it is of great significance and value to further study new treatment methods.
Preliminary Investigation Into Prospective Applications of Nanosilver, Produced Using Teff (Eragrostis tef) Flour Extract
Authors: Konwarh Rocktotpal , Workie Abie , Moges Abyalew , Seifu Daniel , Elias Dereje , Kebede Nahil , Tsigemariam Yosef , Singh Anand Pratap 
Published: February 2023

Abstract
Silver-based nanomaterials have etched an indelible mark in multiple domains. The green synthesis of silver nanoparticles has received significant attention over the last few years vis-à-vis the conventional use of toxic chemicals and reagents in the preparatory stages. In this milieu, the work reported here highlights the use of an aqueous extract of teff (Eragrostis tef) flour to prepare silver nanoparticles (TSNPs). The compositional abundance of various phytochemicals, sunlight-induced bio-reduction of silver ions, and subsequent stabilization of the nanostructures by teff’s biomolecules were adroitly exploited. UV-visible spectroscopic analysis was employed to track the evolution of the TSNPs over time and their month-long storage stability. Exhibiting λmax at around 426 nm and energy gap (as revealed by Tauc’s plot) of 2.26 eV, the silver nanomaterial was employed for methylene blue dye degradation (50% degradation in less than 50 min) and DPPH scavenging (IC50 = 243.42 µL containing 410 µg of TSNPs), attesting their catalytic and anti-oxidant potency. On the other hand, anticoagulant action and a concentration-dependent variation were noted for radicle length post germination of Cicer arientinum seeds, treated with the TSNPs. The TSNPs could have profound implications in multiple domains.
“As long as that place stays open, I’ll stay alive”: Accessing injectable opioid agonist treatment during dual public health crises
Authors: Jaffe Kaitlyn , Blawatt Sarah , Lehal Eisha , Lock Kurt , Easterbrook Adam , MacDonald Scott , Harrison Scott , Lajeneusse Julie , Byres David , Schechter Martin , Oviedo-Joekes Eugenia 
Published: February 2023

Abstract
Background
Since the onset of the COVID-19 pandemic, overdose rates in North America have continued to rise, with more than 100,000 drug poisoning deaths in the past year. Amidst an increasingly toxic drug supply, the pandemic disrupted essential substance use treatment and harm reduction services that reduce overdose risk for people who use drugs. In British Columbia, one such treatment is injectable opioid agonist treatment (iOAT), the supervised dispensation of injectable hydromorphone or diacetylmorphine for people with opioid use disorder. While evidence has shown iOAT to be safe and effective, it is intensive and highly regimented, characterized by daily clinic visits and provider-client interaction—treatment components made difficult by the pandemic.
Methods
Between April 2020 and February 2021, we conducted 51 interviews with 18 iOAT clients and two clinic nurses to understand how the pandemic shaped iOAT access and treatment experiences. To analyze interview data, we employed a multi-step, flexible coding strategy, an iterative and abductive approach to analysis, using NVivo software.
Results
Qualitative analysis revealed the ways in which the pandemic shaped clients’ lives and the provision of iOAT care. First, client narratives illuminated how the pandemic reinforced existing inequities. For example, socioeconomically marginalized clients expressed concerns around their financial stability and economic impacts on their communities. Second, clients with health comorbidities recognized how the pandemic amplified health risks, through potential COVID-19 exposure or by limiting social connection and mental health supports. Third, clients described how the pandemic changed their engagement with the iOAT clinic and medication. For instance, clients noted that physical distancing guidelines and occupancy limits reduced opportunities for social connection with staff and other iOAT clients. However, pandemic policies also created opportunities to adapt treatment in ways that increased patient trust and autonomy, for example through more flexible medication regimens and take-home oral doses.
Conclusion
Participant narratives underscored the unequal distribution of pandemic impacts for people who use drugs but also highlighted opportunities for more flexible, patient-centered treatment approaches. Across treatment offers, pandemic-era changes that increase client autonomy and ensure equitable access to care are to be continued and expanded, beyond the duration of the pandemic.
Interfacial engineered design of upcycled graphene and hemp fiber reinforced polypropylene compounds as an injection grade for overmoulding process with bio-based prepregs
Authors: Aliyeva Nargiz , Yagci Yavuz Emre , Sas Hatice S. , Okan Burcu Saner 
Published: February 2023

Abstract
The design of lightweight composite structures is a principal keystone in improving part performance by reducing adverse environmental impacts and adopting energy-efficient manufacturing technologies. This study replaced long glass fiber reinforced homoPP compounds as an injection grade with short hemp fiber reinforced homopolymer polypropylene (homoPP) by incorporating graphene nanoplatelets (GNP) produced from the source of recycled carbon black coming from pyrolysis of waste tires by providing 15% lightweighting. With new compound formulation by adjusting the amounts of compatibilizer and GNP, injection moulding process was integrated with overmoulding process by using bio-based UD prepregs to enhance the adhesion of injected part and interfacial interaction by decreasing the stress concentrations in the structure. This novel hybrid composite design having 40% hemp fiber, 1.0wt% GNP and 2.7wt% compatibilizer provided to improve flexural modulus and strength by 169% and 67.9%, respectively, compared to neat homoPP. For overmoulding process, bio-based natural fibers reinforced UD tapes were used as an insert and combined with injected hemp fiber reinforced composites with the enhancement of 211% and 93.6% in tensile modulus and strength compared to neat homoPP. This work achieved to convert conventional composite structures into recyclable and sustainable thermoplastic composites having multi-scale reinforcements with tailorable functionality.