bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Coupling Action of Load and Temperature on the Lubricity of Coke Powder
Authors: Xiang Jin , Cai Wenan , Zhang Haidong
Published: May 2023

By introducing the coke powder into the friction pair, the effects of load and temperature on the lubricity of coke powder at the frictional interface are studied. Before the test, the microcrystalline structures of coke powder at different temperatures are characterized by X-ray diffraction (XRD). After the test, the friction surfaces are observed by electron microscope and energy spectrum, the oxide compositions of friction surface are characterized by XRD, and the lubrication mechanism of coke powder is investigated. The results show that at RT (room temperature), as the load increases, the forming of the powder layer gradually deteriorates. When the load is 5 MPa, at a low temperature, the powder layer is thicker, and the coke powder exhibits better lubricity. In contrast, at a high temperature, affected by the temperature, the lubricity of coke powder declines, and the friction coefficient is higher.
Surface Smoothing by Gas Cluster Ion Beam Using Decreasing Three-Step Energy Treatment
Authors: Pelenovich Vasiliy , Zeng Xiaomei , Zhang Xiangyu , Fu Dejun , Lei Yan , Yang Bing , Tolstoguzov Alexander
Published: May 2023

A three-step treatment of Si wafers by gas cluster ion beam with decreasing energy was used to improve the performance of surface smoothing. First, a high energy treatment at 15 keV and an ion fluence of 2 × 1016 cm−2 was used to remove initial surface features (scratches). Next, treatments at 8 and 5 keV with the same fluences reduced the roughness that arose due to the formation of morphological features induced by the surface sputtering at the first high energy step. The surface morphology was characterized by the atomic force microscopy. The root mean square roughness Rq and 2D isotropic power spectral density functions were analyzed. For comparison, the smoothing performances of single-step treatments at 15, 8, and 5 keV were also studied. The lowest roughness values achieved for the single and three-step treatments were 1.06 and 0.65 nm, respectively.
Evaluation of Galvanic and Crevice Corrosion of Watch Case Middle (1.4435 Steel) and Bottom (Panacea® Steel) Assembly Supposed to Be in Prolonged Contact with the Skin
Authors: Reclaru Lucien , Cotrut Cosmin M. , Vranceanu Diana M. , Ionescu Florina
Published: May 2023

Today, laws protecting the population at the global level aim to minimize the induction risk of allergies to type IV contact dermatitis. In the European population, the prevalence of nickel allergy is at 10%–15% of adult females and 1%–3% of adult males. A total of 30% of nickel-sensitive people in the general population develop hand eczema. This study concerns the possibility of assembling a bottom of nickel-free austenitic steel (Panacea®) in a watch case middle made of a grade of austenitic steels, steel 316L (DIN 1.44359), to avoid the risks of Ni release and to develop a galvanic pile between these two dissimilar materials. Two types of methods were used: direct measurements and prediction techniques (mixed potentials theory). For the degradation of thbottom-middle watch assembly, Nielsen–Tuccillo tests were performed, and Ni release measurements according to EN 1811 completed the study. All direct electrochemical investigations and galvanic current prediction measurements show low current values of 40–400 nA. Measurements of nickel release of Panacea® and 316L reveal small quantities of nickel, much lower than the 0.5 µg/cm2 per week that the European legislation enforces. The nickel-free steel Panacea® in the work hardening conditions 280, 427, and 510 HV0.1s were also studied. The cation extractions reveal the large quantities released from Cr, Mo, Mn, and Fe, so there is a risk of toxicity in contact with the skin.
Systematic Review and Meta-Analysis Assessing Perioperative and Oncologic Outcomes in Patients Undergoing Urologic Procedures with a History of Prior Abdominal/Pelvic Surgery: Study Protocol
Authors: Eppler Michael B. , Sayegh Aref S. , Davis Ryan , Hemal Sij , Desai Mihir , Sotelo Rene , Gill Inderbir , Cacciamani Giovanni E.
Published: May 2023

Prior abdominal/pelvic surgery (PAS) has the potential to impact perioperative and oncologic outcomes in patients undergoing urologic surgery. There is a need to study outcomes in this population to determine if reoperation is safe and feasible. This review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and has been registered with PROSPERO (ID: CRD42022361935). The search for articles will be conducted in PubMed, Scopus, and Web of Science, and additional articles may be identified by reviewing the manuscripts of the included literature. Outcomes of interest will be used to determine if reoperation is safe and feasible in this population.
Strengthening the Security of Smart Contracts through the Power of Artificial Intelligence
Authors: Krichen Moez
Published: May 2023

Smart contracts (SCs) are digital agreements that execute themselves and are stored on a blockchain. Despite the fact that they offer numerous advantages, such as automation and transparency, they are susceptible to a variety of assaults due to their complexity and lack of standardization. In this paper, we investigate the use of artificial intelligence (AI) to improve SC security. We provide an overview of Smart Contracts (SCs) and blockchain technology, as well as a discussion of possible SC-based attacks. Then, we introduce various AI categories and their applications in cybersecurity, followed by a thorough analysis of how AI can be used to enhance SC security. We also highlight the open questions and future directions of research in this field. Our research demonstrates that AI can provide an effective defense against assaults on SCs and contribute to their security and dependability. This article lays the groundwork for future research in the field of AI for SC security.
Hot Springs Cyanobacteria Endowed with Biological Activities for Cosmetic Applications: Evaluation of On-Site Collected Communities and Isolated Strains
Authors: Biondi Natascia , Martina Maria Raffaella , Centini Marisanna , Anselmi Cecilia , Tredici Mario R.
Published: May 2023

Microbial communities growing around hot springs (bioglea), of which cyanobacteria are the main components, contribute to beneficial properties of thermal muds and often enter cosmetic treatment products. Bioglea is obtainable via alternative approaches to harvesting from nature: direct cultivation of whole communities and isolation and cultivation of the isolated strains. Cultivation represents a step towards industrial production, guaranteeing higher availability, quality and safety. In this work, the biochemical composition of natural and cultivated bioglea collected in different hot springs and of cyanobacterial strains isolated thereof was analysed. Lipophilic and hydrophilic extracts were tested for antimicrobial and radical scavenging activities and toxicity against Artemia salina. Higher antimicrobial activity was found in lipophilic than hydrophilic bioglea extracts and the opposite in isolates’ extracts. Thermal water extracts also showed some activity. No toxicity was observed. Bioglea radical scavenging activity positively correlated with carotenoids (lipophilic extracts) and phycobiliproteins (hydrophilic extracts). No correlation was observed for isolated strains. Cultivation at the 10 L scale of Chroococcidiopsis BIOG3 evidenced bioactivity changes with the growth phase and life cycle stage. From activity comparison, communities showed higher potential over single strains cultivation; however, further investigations should elucidate isolates’ full potential also by combining them into synthetic communities to simplify production and standardisation.
Differences in Perceived Stress and Depression among Weight (Dis)Satisfied Midwestern College Students during COVID-19
Authors: Harville Cedric , Santos Ellen
Published: May 2023

Background: Stress and depression are common mental health concerns among college students. Factors related to weight status and stigma are associated with poor mental health outcomes. We sought to describe the prevalence of weight dissatisfaction in relation to stress and depression among college students (n = 551). Methods: A cross-sectional study was conducted via a convenient sample between December 2020 and February 2021. Mean differences in the Perceived Stress Scale-10 scores and Center for Epidemiologic Studies Depression Scale scores were examined using a one-way analysis of variance. Associations between stress, depression, and weight dissatisfaction were measured by logistic regression. Results: Weight dissatisfied (75.1%) students had significantly higher mean depression scores compared to weight satisfied. The weight dissatisfied students were 1.05 times more likely to be depressed compared to those who were weight satisfied. Significant mean differences in stress and/or depression were found for weight dissatisfied students by gender, race, parental status, marital status, residence, and U.S. citizenship. Weight dissatisfaction was higher than that reported in the literature, possibly due to the influence of social isolation during the COVID-19 pandemic. Conclusions: Strategies to reduce the prevalence of weight dissatisfaction for improved mental health should be explored, particularly efforts to reduce weight stigmatization and expand access to mental health care.
Interface-Dominated Plasticity and Kink Bands in Metallic Nanolaminates
Authors: Arora Abhishek , Arora Rajat , Acharya Amit
Published: May 2023

The theoretical and computational framework of finite deformation mesoscale field dislocation mechanics (MFDM) is used to understand the salient aspects of kink-band formation in Cu-Nb nano-metallic laminates (NMLs). A conceptually minimal, plane-strain idealization of the three-dimensional geometry, including crystalline orientation, of additively manufactured NML is used to model NMLs. Importantly, the natural jump/interface condition of MFDM imposing continuity of (certain components) of plastic strain rates across interfaces allows theory-driven ‘communication’ of plastic flow across the laminate boundaries in our finite element implementation. Kink bands under layer parallel compression of NMLs in accord with experimental observations arise in our numerical simulations. The possible mechanisms for the formation and orientation of kink bands are discussed, within the scope of our idealized framework. We also report results corresponding to various parametric studies that provide preliminary insights and clear questions for future work on understanding the intricate underlying mechanisms for the formation of kink bands.
Theoretical Study on (n,n)-Nanotubes Rolled-up from B/N Substituted Me-Graphene
Authors: Luo Hong-Chao , Li Feng-Yin , Zhang Ya-Nan , Zhang Hong-Xing , Eglitis Roberts I. , Jia Ran
Published: May 2023

In this work, the n,n-type nanotube systems rolled up from the B/N substituted Me-graphene (i.e., Me-CBNT and Me-CNN, respectively) were investigated with the aid of the density functional theory (DFT). Due to the lattice dynamic instabilities until n=10, the n,0 and n,m nanotube systems were not involved in this study. According to our calculations at the Perdew-Burke-Ernzerhof (PBE) level, the n,n Me-CBNT and Me-CNNT systems possess excellent mechanical strengths. The Young’s moduli of Me-CBNTs can reach 60% of single-walled carbon nanotubes (SWCNTs), while their mass densities are only around 70% of SWCNTs. Based on the fully relaxed geometric configurations at the PBE level, the electronic configurations of the related nanotubes were evaluated by using the global hybrid functional B3LYP with 36% Fock exchanges. The n,n Me-CBNTs are metallic, while the n,n Me-CNNTs are semiconductors with the inherent band gaps in the range of 3.08 eV to 3.31 eV. The Bloch flat bands appear on both sides of their Fermi levels, indicating the localized charge carriers. Their band edge arrangements imply that these materials are promising candidates for the photocatalytic water splitting reactions at certain pH values.
Analysis of Photo-Generated Carrier Escape in Multiple Quantum Wells
Authors: Guo Jiaping , Liu Weiye , Ding Ding , Tan Xinhui , Zhang Wei , Han Lili , Wang Zhaowei , Gong Weihua , Li Jiyun , Zhai Ruizhan , Jia Zhongqing , Ma Ziguang , Du Chunhua , Jia Haiqiang , Tang Xiansheng
Published: May 2023

Recent experiments have shown that more than 85% of photo-generated carriers can escape from multiple quantum wells (MQWs) sandwiched between p-type and n-type layers (PIN). In this work, we quantitatively analyze the relationship between the energy of carriers and the height of potential barriers to be crossed, based on the GaAs/InGaAs quantum well structure system, combined with the Heisenberg uncertainty principle. It was found that that the energy obtained by electrons from photons is just enough for them to escape, and it was found that the energy obtained by the hole is just enough for it to escape due to the extra energy calculated, based on the uncertainty principle. This extra energy is considered to come from photo-generated thermal energy. The differential reflection spectrum of the structure is then measured by pump–probe technology to verify the assumption. The experiment shows that the photo-generated carrier has a longer lifetime in its short circuit (SC) state, and thus it possesses a lower structure temperature than that in open circuit (OC). This can only explain a thermal energy reduction caused by the continuous carrier escape in SC state, indicating an extra thermal energy transferred to the escaping carriers. This study is of great significance to the design of new optoelectronic devices and can improve the theory of photo-generated carrier transports.
Comparing the Utility of Microsatellites and Single Nucleotide Polymorphisms in Conservation Genetics: Insights from a Study on Two Freshwater Fish Species in France
Authors: Prunier Jérôme G. , Veyssière Charlotte , Loot Géraldine , Blanchet Simon
Published: May 2023

Biodiversity is facing an unprecedented crisis and substantial efforts are needed to conserve natural populations, especially in river ecosystems. The use of molecular tools to guide conservation practices in rivers has grown in popularity over the last decades, but the amount of precision and/or biological information that would be gained by switching from the traditional short tandem repeats (STRs) to the increasingly used single nucleotide polymorphisms (SNPs) is still debated. Here, we compared the usefulness of STRs and SNPs to study spatial patterns of genetic variability in two freshwater fish species (Phoxinus dragarum and Gobio occitaniae) in southern France. SNPs were obtained from a pool-seq procedure and mapped to new genome assemblies. They provided much more precise estimates of genetic diversity and genetic differentiation than STRs, but both markers allowed the detection of very similar genetic structures in each species, which could be useful for delineating conservation units. While both markers provided similar outcomes, there were two discrepancies in genetic structures that could, nonetheless, be explained by unrecorded stocking events. Overall, we demonstrated that SNPs are not unconditionally superior to STRs in the context of large-scale riverscape genetic conservation, and that the choice of marker should primarily be based on research questions and resources available.
Recent Advances, Development, and Impact of Using Phase Change Materials as Thermal Energy Storage in Different Solar Energy Systems: A Review
Authors: Rashid Farhan Lafta , Al-Obaidi Mudhar A. , Dulaimi Anmar , Bahlol Haitham Y. , Hasan Ala
Published: May 2023

The efficient utilization of solar energy technology is significantly enhanced by the application of energy storage, which plays an essential role. Nowadays, a wide variety of applications deal with energy storage. Due to the intermittent nature of solar radiation, phase change materials are excellent options for use in several types of solar energy systems. This overview of the relevant literature thoroughly discusses the applications of phase change materials, including solar collectors, solar stills, solar ponds, solar air heaters, and solar chimneys. Despite the complexity of their availability and high costs, phase change materials are utilized in the majority of solar energy techniques because of the considerable technical improvements they provide. While numerous studies have investigated the progress of phase change materials used in solar energy applications such as photovoltaic systems, it is vital to understand the conceptual knowledge of employing phase change materials in various types of solar thermal energy systems. Investigations into the use of phase change materials in solar applications for the purpose of storing thermal energy are still being carried out to upgrade the overall performance. This paper briefly reviews recently published studies between 2016 and 2023 that utilized phase change materials as thermal energy storage in different solar energy systems by collecting more than 74 examples from the open literature. This study focuses on demonstrating the maturity of phase change materials and their integration into solar energy applications. Based on the findings, proposals for new research projects are made.
Cervical Cancer Detection Techniques: A Chronological Review
Authors: Mustafa Wan Azani , Ismail Shahrina , Mokhtar Fahirah Syaliza , Alquran Hiam , Al-Issa Yazan
Published: May 2023

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included “(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)”. Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease’s burden on women worldwide.
Liver Involvement in Patients with Systemic Sclerosis: Role of Transient Elastography in the Assessment of Hepatic Fibrosis and Steatosis
Authors: Cuomo Giovanna , Iandoli Carlo , Galiero Raffaele , Caturano Alfredo , Di Vico Claudio , Perretta Danilo , Adamo Pier Vincenzo , Ferrara Roberta , Rinaldi Luca , Romano Ciro , Sasso Ferdinando Carlo
Published: May 2023

Background Systemic sclerosis (SSc) is a rare, multisystemic disorder of connective tissue characterized by widespread inflammation, vascular abnormalities, and both skin and visceral organ fibrosis. Tissue fibrosis is the final phase of a complex biological process of immune activation and vascular damage. Objectives The aim of the study was to assess hepatic fibrosis and steatosis in SSc patients by transient elastography (TE). Methods Fifty-nine SSc patients fulfilling the 2013 ACR/EULAR classification criteria were recruited. Clinical and laboratory findings, modified Rodnan skin score (mRSS), activity index, videocapillaroscopy, echocardiography, and lung function data were analyzed. Liver stiffness (LS) was measured by transient elastography (TE), with 7 kPa used as the cut-off value for significant liver fibrosis. In addition, hepatic steatosis was evaluated by means of controlled attenuation parameter (CAP) findings. Specifically, CAP values ≥ 238 ≤ 259 dB/m were considered consistent with mild steatosis (S1), values ≥ 260 ≤ 290 dB/m were compatible with moderate steatosis (S2), and values ≥ 291 dB/m were indicative of severe steatosis (S3). Results The median age of patients was 51 years, with a median disease duration of 6 years. The median LS was 4.5 (2.9–8.3) kPa; 69.5% of patients had no evidence of fibrosis (F0); 27.1% displayed LS values between 5.2 and 7 kPa; and only 3.4% of patients had LS values > 7 kPa (F3). The median CAP value for liver steatosis was 223 dB/m (IQR: 164–343). Overall, 66.1% of patients did not show evidence of steatosis (CAP values < 238 dB/m); 15.2% showed values consistent with mild (S1) steatosis (CAP value ≥ 238 ≤ 259 dB/m); 13.5% had moderate (S2) steatosis (CAP value ≥ 260 ≤ 290 dB/m); and 5.1% were deemed to have severe steatosis (S3) due to CAP values ≥ 291 dB/m. Conclusions Although systemic sclerosis is associated with fibrosis of the skin and several organs, only 3.4% of our patient population showed evidence of marked liver fibrosis, which is the same prevalence as that expected in the general population. Therefore, fibrosis of the liver did not appear to be a significant concern in SSc patients, albeit moderate fibrosis could still be detected in a significant proportion of subjects. A prolonged follow-up may clarify whether liver fibrosis in SSc patients may still progress. Likewise, the prevalence of significant steatosis was low (5.1%) and depended on the same variables associated with fatty liver disease in the general population. TE was shown to be an easy and valuable method for detection and screening of hepatic fibrosis in SSc patients with no additional risk factors for liver disease and may be useful to assess the potential progression of liver fibrosis over time.
Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model
Authors: Uysal Fatih
Published: May 2023

Monkeypox, a virus transmitted from animals to humans, is a DNA virus with two distinct genetic lineages in central and eastern Africa. In addition to zootonic transmission through direct contact with the body fluids and blood of infected animals, monkeypox can also be transmitted from person to person through skin lesions and respiratory secretions of an infected person. Various lesions occur on the skin of infected individuals. This study has developed a hybrid artificial intelligence system to detect monkeypox in skin images. An open source image dataset was used for skin images. This dataset has a multi-class structure consisting of chickenpox, measles, monkeypox and normal classes. The data distribution of the classes in the original dataset is unbalanced. Various data augmentation and data preprocessing operations were applied to overcome this imbalance. After these operations, CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE-ResNet and Xception, which are state-of-the-art deep learning models, were used for monkeypox detection. In order to improve the classification results obtained in these models, a unique hybrid deep learning model specific to this study was created by using the two highest-performing deep learning models and the long short-term memory (LSTM) model together. In this hybrid artificial intelligence system developed and proposed for monkeypox detection, test accuracy was 87% and Cohen’s kappa score was 0.8222.
Electroencephalography-Based Depression Detection Using Multiple Machine Learning Techniques
Authors: Ksibi Amel , Zakariah Mohammed , Menzli Leila Jamel , Saidani Oumaima , Almuqren Latifah , Hanafieh Rosy Awny Mohamed
Published: May 2023

The growth of biomedical engineering has made depression diagnosis via electroencephalography (EEG) a trendy issue. The two significant challenges to this application are EEG signals’ complexity and non-stationarity. Additionally, the effects caused by individual variances may hamper the generalization of detection systems. Given the association between EEG signals and particular demographics, such as gender and age, and the influences of these demographic characteristics on the incidence of depression, it would be preferable to include demographic factors during EEG modeling and depression detection. The main objective of this work is to develop an algorithm that can recognize depression patterns by studying EEG data. Following a multiband analysis of such signals, machine learning and deep learning techniques were used to detect depression patients automatically. EEG signal data are collected from the multi-modal open dataset MODMA and employed in studying mental diseases. The EEG dataset contains information from a traditional 128-electrode elastic cap and a cutting-edge wearable 3-electrode EEG collector for widespread applications. In this project, resting EEG readings of 128 channels are considered. According to CNN, training with 25 epoch iterations had a 97% accuracy rate. The patient’s status has to be divided into two basic categories: major depressive disorder (MDD) and healthy control. Additional MDD include the following six classes: obsessive-compulsive disorders, addiction disorders, conditions brought on by trauma and stress, mood disorders, schizophrenia, and the anxiety disorders discussed in this paper are a few examples of mental illnesses. According to the study, a natural combination of EEG signals and demographic data is promising for the diagnosis of depression.
CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images
Authors: Iqbal Uzair , Imtiaz Romil , Saudagar Abdul Khader Jilani , Alam Khubaib Amjad
Published: May 2023

The early diagnosis of infectious diseases is demanded by digital healthcare systems. Currently, the detection of the new coronavirus disease (COVID-19) is a major clinical requirement. For COVID-19 detection, deep learning models are used in various studies, but the robustness is still compromised. In recent years, deep learning models have increased in popularity in almost every area, particularly in medical image processing and analysis. The visualization of the human body’s internal structure is critical in medical analysis; many imaging techniques are in use to perform this job. A computerized tomography (CT) scan is one of them, and it has been generally used for the non-invasive observation of the human body. The development of an automatic segmentation method for lung CT scans showing COVID-19 can save experts time and can reduce human error. In this article, the CRV-NET is proposed for the robust detection of COVID-19 in lung CT scan images. A public dataset (SARS-CoV-2 CT Scan dataset), is used for the experimental work and customized according to the scenario of the proposed model. The proposed modified deep-learning-based U-Net model is trained on a custom dataset with 221 training images and their ground truth, which was labeled by an expert. The proposed model is tested on 100 test images, and the results show that the model segments COVID-19 with a satisfactory level of accuracy. Moreover, the comparison of the proposed CRV-NET with different state-of-the-art convolutional neural network models (CNNs), including the U-Net Model, shows better results in terms of accuracy (96.67%) and robustness (low epoch value in detection and the smallest training data size).
Comprehensive In Silico Characterization of the Coding and Non-Coding SNPs in Human Dectin-1 Gene with the Potential of High-Risk Pathogenicity Associated with Fungal Infections
Authors: Al-nakhle Hakeemah H. , Khateb Aiah M.
Published: May 2023

The human C-type lectin domain family 7 member A (CLEC7A) gene encodes a Dectin-1 protein that recognizes beta-1,3-linked and beta-1,6-linked glucans, which form the cell walls of pathogenic bacteria and fungi. It plays a role in immunity against fungal infections through pathogen recognition and immune signaling. This study aimed to explore the impact of nsSNPs in the human CLEC7A gene through computational tools (MAPP, PhD-SNP, PolyPhen-1, PolyPhen-2, SIFT, SNAP, and PredictSNP) to identify the most deleterious and damaging nsSNPs. Further, their effect on protein stability was checked along with conservation and solvent accessibility analysis by I-Mutant 2.0, ConSurf, and Project HOPE and post-translational modification analysis using MusiteDEEP. Out of the 28 nsSNPs that were found to be deleterious, 25 nsSNPs affected protein stability. Some SNPs were finalized for structural analysis with Missense 3D. Seven nsSNPs affected protein stability. Results from this study predicted that C54R, L64P, C120G, C120S, S135C, W141R, W141S, C148G, L155P, L155V, I158M, I158T, D159G, D159R, I167T, W180R, L183F, W192R, G197E, G197V, C220S, C233Y, I240T, E242G, and Y3D were the most structurally and functionally significant nsSNPs in the human CLEC7A gene. No nsSNPs were found in the predicted sites for post-translational modifications. In the 5′ untranslated region, two SNPs, rs536465890 and rs527258220, showed possible miRNA target sites and DNA binding sites. The present study identified structurally and functionally significant nsSNPs in the CLEC7A gene. These nsSNPs may potentially be used for further evaluation as diagnostic and prognostic biomarkers.
Challenges and Limitations of Endocrine Toxicity Evaluation in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy—Retrospective Study from a Tertiary-Level Hospital in Romania
Authors: Coniac Simona , Costache Outas Mariana Cristina , Pirvu Edvina-Elena , Patru Raluca-Ileana , Gainariu Estera , Aldea Ciprian , Iorga Polixenia Georgeta , Ambroci Mihaela , Liscu Horia-Dan , Miron Andreea-Iuliana , Badiu Corin
Published: May 2023

(1) Background: The endocrine system has become a prominent target to autoimmune damage during treatment with immune checkpoint inhibitors (ICIs) in cancer patients. Real-world data regarding endocrine immune-related adverse events (irAEs) are needed to explore their impact in cancer patients. An analysis was conducted to evaluate endocrine irAEs caused by ICIs, besides the challenges and limitations of daily medical practice in oncology in Romania. (2) Methods: This was a retrospective cohort study of lung cancer patients treated with ICIs at Coltea Clinical Hospital, Bucharest, Romania, from 1 November 2017 to 30 November 2022. Endocrine irAEs were identified through endocrinological assessment and were distinguished as any occurring endocrinopathy during treatment with ICIs and related to immunotherapy. Descriptive analyses were performed. (3) Results: Of 310 cancer patients treated with ICIs, we identified 151 with lung cancer. From this cohort, 109 NSCLC patients qualified for baseline endocrine estimation and 13 patients (11.9%) developed endocrine irAEs, such as hypophysitis (4.5%), thyroid disorder (5.5%) and primary adrenal insufficiency (1.8%), with one or more endocrine glands being affected. There might be a correlation between endocrine irAEs and duration of ICI treatment. (4) Conclusions: Early diagnosis and adequate management of endocrine irAEs may be challenging in lung cancer patients. A high incidence of endocrine irAEs is expected with the growing use of ICIs, and because not all endocrine events are immune-related, cooperation between oncologists and endocrinologists is crucial in the management of these patients. More data are needed to confirm the correlation between endocrine irAEs and the efficacy of ICIs.
Application of Bispectral Index System (BIS) Monitor to Ambulatory Pediatric Dental Patients under Intravenous Deep Sedation
Authors: Chen Shih-Chia , Chen Chun-Yu , Shen Shih-Jyun , Tsai Yung-Fong , Ko Yu-Chen , Chuang Li-Chuan , Lin Jr-Rung , Tsai Hsin-I
Published: May 2023

Purpose Intravenous sedation has been well accepted to allow dental restoration in uncooperative children while avoiding aspiration and laryngospasm; however, intravenous anesthetics such as propofol may lead to undesired effects such as respiratory depression and delayed recovery. The use of the bispectral index system (BIS), a monitoring system reflective of the hypnotic state, is con-troversial in the reduction in the risk of respiratory adverse events (RAEs), recovery time, the in-travenous drug dosage, and post-procedural events. The aim of the study is to evaluate whether BIS is advantageous in pediatric dental procedures. Methods A total of 206 cases, aged 2–8 years, receiving dental procedures under deep sedation with propofol using target-controlled infusion (TCI) technique were enrolled in the study. BIS level was not monitored in 93 children whereas it was for 113 children, among which BIS values were maintained between 50–65. Physiological variables and adverse events were recorded. Statistical analysis was conducted using Chi-square, Mann Whitney U, Independent Samples t and Wilcoxon signed tests, with a p value of <0.05 considered to be statistically significant. Results Although no statistical significance in the post-discharge events and total amount of propofol used was observed, a clear significance was identified in periprocedural adverse events (hypoxia, apnea, and recurrent cough, all p value < 0.05) and discharge time (63.4 ± 23.2 vs. 74.5 ± 24.0 min, p value < 0.001) between these two groups. Conclusions The application of BIS in combination with TCI may be beneficial for young children undergoing deep sedation for dental procedures.
Online Hybrid Neural Network for Stock Price Prediction: A Case Study of High-Frequency Stock Trading in the Chinese Market
Authors: Li Chengyu , Shen Luyi , Qian Guoqi
Published: May 2023

Time-series data, which exhibit a low signal-to-noise ratio, non-stationarity, and non-linearity, are commonly seen in high-frequency stock trading, where the objective is to increase the likelihood of profit by taking advantage of tiny discrepancies in prices and trading on them quickly and in huge quantities. For this purpose, it is essential to apply a trading method that is capable of fast and accurate prediction from such time-series data. In this paper, we developed an online time series forecasting method for high-frequency trading (HFT) by integrating three neural network deep learning models, i.e., long short-term memory (LSTM), gated recurrent unit (GRU), and transformer; and we abbreviate the new method to online LGT or O-LGT. The key innovation underlying our method is its efficient storage management, which enables super-fast computing. Specifically, when computing the forecast for the immediate future, we only use the output calculated from the previous trading data (rather than the previous trading data themselves) together with the current trading data. Thus, the computing only involves updating the current data into the process. We evaluated the performance of O-LGT by analyzing high-frequency limit order book (LOB) data from the Chinese market. It shows that, in most cases, our model achieves a similar speed with a much higher accuracy than the conventional fast supervised learning models for HFT. However, with a slight sacrifice in accuracy, O-LGT is approximately 12 to 64 times faster than the existing high-accuracy neural network models for LOB data from the Chinese market.
Digital Competence in University Lecturers: A Meta-Analysis of Teaching Challenges
Authors: Liesa-Orus Marta , Lozano Blasco Raquel , Arce-Romeral Lorena
Published: May 2023

This meta-analysis (random effects) studies the self-perceived digital competence of university lecturers in university teaching, using 7470 lecturers from Europe and Latin America collected in K = 31 samples, with teaching experience of between 6 and 15 years. The effect size obtained from a moderate random effects model of r = −0.21 with a 99% confidence interval is significant, negative, and moderate, confirming the low competence level. The meta-regression results show that the area of knowledge plays an important role. The systematic review of the literature shows that the perception of ICTs is positive, while the level of competence is low, and there are institutional and training challenges to be solved.
From Resistance to Resilience: Teachers’ Adaptation Process to Mediating Digital Devices in Pre-COVID-19, during COVID-19, and Post-COVID-19 Classrooms in Nepal
Authors: Joshi Dirgha Raj , Khanal Jeevan , Dhakal Ram Hari
Published: May 2023

This qualitative study explores the implementation and adoption process of the use of digital devices and tools in teaching and learning before, during, and after the COVID-19 pandemic in Nepal. Using Rogers’ diffusion of innovation theory as a framework, the study examines the adoption and adaptation of digital devices by in-service secondary mathematics teachers (n = 62) and the teachers’ perceptions of and preferences for instructional modalities. The findings suggest that, despite the increased reliance on digital devices during the pandemic, there is a lower likelihood of them being used in face-to-face classrooms in developing countries, such as Nepal. The adoption of online learning had not yet reached the adoption stage, even after the onset of the COVID-19 pandemic. Prior to the pandemic, online learning was not widely adopted by teachers in developing countries societies. The study also provides important insights into the challenges of and opportunities provided by using digital devices in post-COVID-19 classrooms, and its implications for policymakers and educators in Nepal.
Educators’ Construction of a Sense of Belonging in ECEC: An Australian Case Study
Authors: Keary Anne , Zheng Haoran , Garvis Susanne
Published: May 2023

The involvement of refugee families in early childhood education and care (ECEC) is a complex issue in many countries. In this paper, we explore how early childhood (EC) educators construct refugee families’ sense of belonging in two metropolitan and one regional EC setting in Victoria, Australia. We undertook a multiple case study, analyzing interview data. We implemented Bourdieu’s notion of cultural capital and identity capital, drawing on the Provision Articles from the Convention on the Rights of the Child (1989) and the Australian Early Years Learning Framework 2.0. Our study revealed the tensions between creating a sense of belonging and recognizing the identity and cultural capital of refugee children and their families as they access ECEC programs. We conclude with suggestions for ways forward to navigate this space, including the need for greater awareness of research on the importance of a sense of belonging for refugee families in ECEC settings.
A 6G-Enabled Lightweight Framework for Person Re-Identification on Distributed Edges
Authors: Peng Xiting , Wang Yichao , Zhang Xiaoyu , Yang Haibo , Tang Xiongyan , Bai Shi
Published: May 2023

In the upcoming 6G era, edge artificial intelligence (AI), as a key technology, will be able to deliver AI processes anytime and anywhere by the deploying of AI models on edge devices. As a hot issue in public safety, person re-identification (Re-ID) also needs its models to be urgently deployed on edge devices to realize real-time and accurate recognition. However, due to complex scenarios and other practical reasons, the performance of the re-identification model is poor in practice. This is especially the case in public places, where most people have similar characteristics, and there are environmental differences, as well other such characteristics that cause problems for identification, and which make it difficult to search for suspicious persons. Therefore, a novel end-to-end suspicious person re-identification framework deployed on edge devices that focuses on real public scenarios is proposed in this paper. In our framework, the video data are cut images and are input into the You only look once (YOLOv5) detector to obtain the pedestrian position information. An omni-scale network (OSNet) is applied through which to conduct the pedestrian attribute recognition and re-identification. Broad learning systems (BLSs) and cycle-consistent adversarial networks (CycleGAN) are used to remove the noise data and unify the style of some of the data obtained under different shooting environments, thus improving the re-identification model performance. In addition, a real-world dataset of the railway station and actual problem requirements are provided as our experimental targets. The HUAWEI Atlas 500 was used as the edge equipment for the testing phase. The experimental results indicate that our framework is effective and lightweight, can be deployed on edge devices, and it can be applied for suspicious person re-identification in public places.
Output Feedback Control of Sine-Gordon Chain over the Limited Capacity Digital Communication Channel
Authors: Andrievsky Boris , Orlov Yury , Fradkov Alexander L.
Published: May 2023

With the digitalization of mechatronic systems in the conditions of a shortage of available bandwidth of digital communication channels, the problem of ensuring the transfer of information between various components of the system can arise. This problem can be especially challenging in the observation and control of spatially distributed objects due to the complexity of their dynamics, wide frequency band, and other factors. In such cases, a useful approach is to employ smart sensors, in which the measurement results are encoded for transmission over a digital communication channel. Specifically, the article is focused on the transmission of measurement data for the control of energy for a spatially-distributed sine-Gordon chain. The procedures for binary coding of measurements by first- and full-order coder-decoder pairs are proposed and numerically investigated, for each of which the use of stationary and adaptive coding procedures is studied. The procedures for estimating the state of the circuit when measuring outputs are studied, and for each of them, the accuracy of not only estimating the state but also controlling the system by output with the help of an observer is considered. The results of comparative modeling are presented, demonstrating the dependence of the accuracy of estimation and control on the data transfer rate.
Design of Self-Calibration Comparator for 12-Bit SAR ADCs
Authors: Tang Junlong , Wang Yaodong , Gu Hongbo , Zou Wanghui
Published: May 2023

A novel self-calibration comparator for a 12-bit 2.5 MSPS successive approximation register analog-to-digital converter (SAR ADC) applied in a touch microcontroller unit (MCU) with small area, high precision, fast response speed, and low-voltage detection is proposed in this paper. A combination of input/output offset storage (IOS/OOS) and an offset trimming circuit was employed to reduce the offset of the cascade preamplifier and the operational transconductance amplifier (OTA), a novel offset trimming circuit with a 5-bit digital controller was designed to further reduce the residual offset voltage, and an improved self-calibration technology was also implemented to compensate the conversion error in SAR ADC system to a minimum. Simulation and measured results show that the input-referred offset calibrating range is ±9.15 mV at 0.61 mV/step, the low-voltage detection of SAR ADC is realized by compensating the conversion error to a minimum, and the effective number of bits (ENOB) and figure of merit (FoM) at 5 V supply and 2.5 M/s rate in the 12-bit SAR ADC with a 95 nm CMOS are 11.33 bits and 726.6 fJ/conversion-step, respectively. The proposed self-calibration comparator applied in the SAR ADC system can automatically eliminate the offset voltage caused by nonidealities and meet the requirements of the touch MCU.
Enhancing Photovoltaic Efficiency with the Optimized Steepest Gradient Method and Serial Multi-Cellular Converters
Authors: Fekik Arezki , Azar Ahmad , Hameed Ibrahim , Hamida Mohamed , Amara Karima , Denoun Hakim , Kamal Nashwa
Published: May 2023

Many methods have been developed to aid in achieving the maximum power point (MPP) generated by PV fields in order to improve photovoltaic (PV) production. The optimized steepest gradient technique (OSGM), which is used to extract the maximum power produced by a PV field coupled to a multicell series converter, is one such promising methodology. The OSGM uses the power function’s first and second derivatives to find the optimal voltage (Vpv) and converge to the voltage (Vref) that secures the MPP. The mathematical model was developed in Matlab/Simulink, and the MPPT algorithm’s performance was evaluated in terms of reaction time, oscillations, overshoots, and stability. The OSGM has a faster response time, fewer oscillations around the MPP, and minimal energy loss. Furthermore, the numerical calculation of the gradient and Hessian of the power function enables accurate modeling, improving the system’s precision. These findings imply that the OSGM strategy may be a more efficient way of obtaining MPP for PV fields. Future research can look into the suitability of this method for different types of PV systems, as well as ways to improve the algorithm’s performance for specific applications.
An Efficient Classification of Rice Variety with Quantized Neural Networks
Authors: Tasci Mustafa , Istanbullu Ayhan , Kosunalp Selahattin , Iliev Teodor , Stoyanov Ivaylo , Beloev Ivan
Published: May 2023

Rice, as one of the significant grain products across the world, features a wide range of varieties in terms of usability and efficiency. It may be known with various varieties and regional names depending on the specific locations. To specify a particular rice type, different features are considered, such as shape and color. This study uses an available dataset in Turkey consisting of five different varieties: Ipsala, Arborio, Basmati, Jasmine, and Karacadag. The dataset introduces 75,000 grain images in total; each of the 5 varieties has 15,000 samples with a 256 × 256-pixel dimension. The main contribution of this paper is to create Quantized Neural Network (QNN) models to efficiently classify rice varieties with the purpose of reducing resource usage on edge devices. It is well-known that QNN is a successful method for alleviating high computational costs and power requirements in response to many Deep Learning (DL) algorithms. These advantages of the quantization process have the potential to provide an efficient environment for artificial intelligence applications on microcontroller-driven edge devices. For this purpose, we created eight different QNN networks using the MLP and Lenet-5-based deep learning models with varying quantization levels to be trained by the dataset. With the Lenet-5-based QNN network created at the W3A3 quantization level, a 99.87% classification accuracy level was achieved with only 23.1 Kb memory size used for the parameters. In addition to this tremendous benefit of memory usage, the number of billion transactions per second (GOPs) is 23 times less than similar classification studies.