bluuebunny/crossref_metadata_2025 huggingface dataset:
Ischemia/Reperfusion Injury of Fatty Liver Is Protected by A2AR and Exacerbated by A1R Stimulation through Opposite Effects on ASK1 Activation
Authors: Alchera Elisa , Chandrashekar Bangalore R. , Clemente Nausicaa , Borroni Ester , Boldorini Renzo , Carini Rita
Published: November 2021

Hepatic ischemia/reperfusion injury (IRI) is aggravated by steatosis and is a main risk factor in fatty liver transplantation. Adenosine receptors (ARs) are emerging as therapeutic targets in liver diseases. By using cellular and in vivo systems of hepatic steatosis and IRI, here we evaluated the effects of pharmacological A2AR and A1R activation. The A2AR agonist CGS21680 protected the primary steatotic murine hepatocyte from IR damage and the activation of ASK1 and JNK. Such an effect was attributed to a phosphatidylinositol-3-kinase (PI3K)/Akt-dependent inhibition of ASK1. By contrast, the A1R agonist CCPA enhanced IR damage, intracellular steatosis and oxidative species (OS) production, thereby further increasing the lipid/OS-dependent ASK1-JNK stimulation. The CGS2680 and CCPA effects were nullified by a genetic ASK1 downregulation in steatotic hepatoma C1C7 cells. In steatotic mice livers, CGS21680 protected against hepatic IRI and ASK1/JNK activation whereas CCPA aggravated hepatic steatosis and IRI, and enhanced ASK1 and JNK stimulation. These results evidence a novel mechanism of CGS21680-mediated hepatoprotection, i.e., the PI3K/AKT-dependent inhibition of ASK1, and they show that CGS21680 and CCPA reduces and enhances the IRI of fatty liver, respectively, by preventing or increasing the activation of the cytotoxic ASK1/JNK axis. They also indicate the selective employment of A2AR agonists as an effective therapeutic strategy to prevent IRI in human fatty liver surgery.
Role of AHR Ligands in Skin Homeostasis and Cutaneous Inflammation
Authors: Fernández-Gallego Nieves , Sánchez-Madrid Francisco , Cibrian Danay
Published: November 2021

Aryl hydrocarbon receptor (AHR) is an important regulator of skin barrier function. It also controls immune-mediated skin responses. The AHR modulates various physiological functions by acting as a sensor that mediates environment–cell interactions, particularly during immune and inflammatory responses. Diverse experimental systems have been used to assess the AHR’s role in skin inflammation, including in vitro assays of keratinocyte stimulation and murine models of psoriasis and atopic dermatitis. Similar approaches have addressed the role of AHR ligands, e.g., TCDD, FICZ, and microbiota-derived metabolites, in skin homeostasis and pathology. Tapinarof is a novel AHR-modulating agent that inhibits skin inflammation and enhances skin barrier function. The topical application of tapinarof is being evaluated in clinical trials to treat psoriasis and atopic dermatitis. In the present review, we summarize the effects of natural and synthetic AHR ligands in keratinocytes and inflammatory cells, and their relevance in normal skin homeostasis and cutaneous inflammatory diseases.
Overexpression of Heat Shock Protein 70 Improves Cardiac Remodeling and Survival in Protein Phosphatase 2A-Expressing Transgenic Mice with Chronic Heart Failure
Authors: Yoon Somy , Gergs Ulrich , McMullen Julie R. , Eom Gwang Hyeon
Published: November 2021

Heat shock protein (HSP) 70 is a molecular chaperone that regulates protein structure in response to thermal stress. In addition, HSP70 is involved in post-translational modification and is related to the severity of some diseases. Here, we tested the functional relevance of long-lasting HSP70 expression in a model of nonischemic heart failure using protein phosphatase 2 catalytic subunit A (PP2CA)-expressing transgenic mice. These transgenic mice, with cardiac-specific overexpression of PP2CA, abruptly died after 12 weeks of postnatal life. Serial echocardiograms to assess cardiac function revealed that the ejection fraction (EF) was gradually decreased in transgenic PP2CA (TgPP2CA) mice. In addition, PP2CA expression exacerbated systolic dysfunction and LV dilatation, with free wall thinning, which are indicators of fatal dilated cardiomyopathy. Interestingly, simultaneous expression of HSP70 in double transgenic mice (dTg) significantly improved the dilated cardiomyopathy phenotype of TgPP2CA mice. We observed better survival, preserved EF, reduced chamber enlargement, and suppression of free wall thinning. In the proposed molecular mechanism, HSP70 preferentially regulates the phosphorylation of AKT. Phosphorylation of AKT was significantly reduced in TgPP2CA mice but was not significantly lower in dTg mice. Signal crosstalk between AKT and its substrates, in association with HSP70, might be a useful intervention for patients with nonischemic heart failure to suppress cardiac remodeling and improve survival.
Prediction of Coating Adhesion on Laser-Cleaned Metal Surfaces of Battery Cells Using Hyperspectral Imaging and Machine Learning
Authors: Vater Johannes Maximilian , Gruber Florian , Grählert Wulf , Schneider Sebastian , Knoll Alois Christian
Published: November 2021

Electric vehicles are shaping the future of the automotive industry. The traction battery is one of the most important components of electric cars. To ensure that the battery operates safely, it is essential to physically and electrically separate the cells facing each other. Coating a cell with varnish helps achieve this goal. Current studies use a destructive method on a sampling basis, the cross-cut test, to investigate the coating quality. In this paper, we present a fast, nondestructive and inline alternative based on hyperspectral imaging and artificial intelligence. Therefore, battery cells are measured with hyperspectral cameras in the visible and near-infrared (VNIR and NIR) parts of the electromagnetic spectrum before and after cleaning then coated and finally subjected to cross-cut test to estimate coating adhesion. During the cross-cut test, the cell coating is destroyed. This work aims to replace cross-cut tests with hyperspectral imaging (HSI) and machine learning to achieve continuous quality control, protect the environment, and save costs. Therefore, machine learning models (logistic regression, random forest, and support vector machines) are used to predict cross-cut test results based on hyperspectral data. We show that it is possible to predict with an accuracy of ~75% whether problems with coating adhesion will occur. Hyperspectral measurements in the near-infrared part of the spectrum yielded the best results. The results show that the method is suitable for automated quality control and process control in battery cell coating, but still needs to be improved to achieve higher accuracies.
Sol–Gel Encapsulation of ZnAl Alloy Powder with Alumina Shell
Authors: Svetlizky David , Eliaz Noam
Published: November 2021

Additive manufacturing (AM), for example, directed energy deposition (DED), may allow the processing of self-healing metal–matrix composites (SHMMCs). The sealing of cracks in these SHMMCs would be achieved via the melting of micro-encapsulated low melting point particulates (LMPPs), incorporated into the material during AM, by heat treatment of the part during service. Zn-Al alloys are good candidates to serve as LMPPs, for example, when the matrix of the MMC is made of an aluminum alloy. However, such powders should first be encapsulated by a thermal and diffusion barrier. Here, we propose a sol–gel process for encapsulation of a custom-made ZA-8 (Zn92Al8, wt.%) core powder in a ceramic alumina (Al2O3) shell. We first modify the surface of the ZA-8 powder with (12-phosphonododecyl)phosphonic acid (Di-PA) hydrophobic self-assembled monolayer (SAM) in order to prevent extensive hydrogen evolution and formation of non-uniform and porous oxide/hydroxide surface layers during the sol–gel process. Calcination for 1 h at 500 °C is found to be insufficient for complete boehmite-to-γ(Al2O3) phase transformation. Thermal stability tests in an air-atmosphere furnace at 600 °C for 1 h result in melting, distortion, and sintering into a brittle sponge (aggregate) of the as-atomized powder. In contrast, the core/shell powder is not sintered and preserves its spherical morphology, with no apparent “leaks” of the ZA-8 core alloy out of the ceramic encapsulation.
Influence of Zirconium on the Corrosion Passivation of Titanium in Simulated Body Fluid
Authors: Alharbi Hamad F. , Bahri Yassir A. , Sherif El-Sayed M.
Published: November 2021

Pure Ti and 85%Ti-15%Zr alloy were sintered in a heat induction high frequency furnace. The corrosion behaviour of Ti and 85%Ti-15%Zr in simulated body fluid (SBF) was studied. The work was achieved using different techniques as the cyclic polarization (PCP), the impedance spectroscopy (EIS), and the change of the potentiostatic current with time (PCT) experiments. The morphology of the surfaces of Ti and Ti-Zr after being corroded in SBF for 72 were investigated by a scanning electron microscope (SEM) and the analysis for the layer formed on that surface was collected via energy dispersive X-ray (EDX). Results depicted that the 15% Zr present within Ti decreases the corrosion via increasing Ti corrosion resistance and minimizing its measured corrosion rate. PCP data proved that the addition of Zr reduced all anodic, cathodic and corrosion currents. EIS results confirmed that Zr has a positive impact on the reduction of corrosion resistance. PCT, SEM, and EDX examinations displayed that both Ti and Ti-Zr alloy don’t show pitting corrosion even after 72 h exposure to SBF.
Quality of End-of-Life Cancer Care in Canada: A 12-Year Retrospective Analysis of Three Provinces’ Administrative Health Care Data Evaluating Changes over Time
Authors: Khan Amanda Farah , Seow Hsien , Sutradhar Rinku , Peacock Stuart , Chan Kelvin Kar-Wing , Burge Fred , McGrail Kim , Raymakers Adam , Lawson Beverley , Barbera Lisa
Published: November 2021

This retrospective cohort study of cancer decedents during 2004–2015 examined end-of-life cancer care quality indicators (QIs) in the provinces of British Columbia (BC), Ontario, and Nova Scotia (NS). These included: emergency department use, in-patient hospitalization, intensive care unit admissions, physician house calls, home care visits, and death experienced in hospital. Ontario saw the greatest 12-year decrease in in-hospital deaths from 52.8% to 41.1%. Hospitalization rates within 30 days of death decreased in Ontario, increased in NS, and remained the same in BC. Ontario’s usage of aggressive end-of-life measures changed very little, while BC increased their utilization rates. Supportive care use increased in both NS and Ontario. Those who were male or living in a lower income/smaller community (in Ontario) were associated with a decreased likelihood of receiving supportive care. Despite the shift in focus to providing hospice and home care services, approximately 50% of oncology patients are still dying in hospital and 11.7% of patients overall are subject to aggressive care measures that may be out of line with their desire for comfort care. Supportive care use is increasing, but providers must ensure that Canadians are connected to palliative services, as its utilization improves a wide variety of outcomes.
Clinical Study of Sentinel Lymph Node Detection Using Photodynamic Eye for Abdominal Radical Trachelectomy
Authors: Harano Naomi , Sakamoto Masaru , Fukushima Souta , Iwai Shinnosuke , Koike Yuki , Horikawa Shingo , Suzuki Kayo , Narui Chikage , Matsuoka Kazuko , Ozeki Rinko , Iwaya Keiichi , Umayahara Kenji , Tanaka Tadao , Okamoto Aikou
Published: November 2021

This study aimed to assess the accuracy of predicting pelvic lymph node status using sentinel lymph node (SLN) biopsy with indocyanine green (ICG) and to examine the outcomes of SLN biopsy-guided abdominal radical trachelectomy (ART). Patients with stage IA2–IB2 cervical cancer from January 2009 to January 2021 were included. ICG was injected before ART and SLNs were identified, excised, and assessed intraoperatively using fast-frozen sections. Systemic pelvic lymphadenectomy was subsequently performed. The SLN detection rate, sensitivity, and false-negative rate were determined. Thirty patients desiring fertility preservation were enrolled, of whom 26 successfully completed ART and four underwent radical hysterectomies because of metastatic primary SLNs. Bilateral SLNs were identified in all patients. The sensitivity, false-negative rate, and negative predictive value were 100%, 7.7%, and 92.3%, respectively. Three (12%) patients were lost to follow-up: two relapsed and one died of tumor progression. Of the nine patients who tried to conceive after surgery, four achieved pregnancy and three delivered healthy live infants. In women with early-stage cervical cancer who desired to conserve fertility, SLN mapping with ICG had a very high detection rate, sensitivity, and low false-negative rate. SLN biopsy-guided ART is a feasible and accurate method for assessing pelvic node status.
Comparative Analysis of BIOCHIP Mosaic-Based Indirect Immunofluorescence with Enzyme-Linked Immunosorbent Assay for Diagnosing Myasthenia Gravis
Authors: Gambino Caterina Maria , Agnello Luisa , Lo Sasso Bruna , Scazzone Concetta , Giglio Rosaria Vincenza , Candore Giuseppina , Ciaccio Anna Maria , Di Stefano Vincenzo , Brighina Filippo , Vidali Matteo , Ciaccio Marcello
Published: November 2021

Background: The detection of anti-acetylcholine receptor (AChR) and anti-muscle-specific tyrosine kinase (MuSK) antibodies is useful in myasthenia gravis (MG) diagnosis and management. BIOCHIP mosaic-based indirect immunofluorescence is a novel analytical method, which employs the simultaneous detection of anti-AChR and anti-MuSK antibodies in a single miniature incubation field. In this study, we compare, for the first time, the BIOCHIP MG mosaic with conventional enzyme-linked immunosorbent assay (ELISA) in the diagnosis of MG. Methods: A total of 71 patients with MG diagnosis were included in the study. Anti-AChR and anti-MuSK antibodies were measured separately by two different ELISA and simultaneously by BIOCHIP. The results were then compared. Results: The overall concordance between ELISA and BIOCHIP for anti-AChR reactivity was 74%. Cohen’s kappa was 0.51 (95% CI 0.32–0.71), which corresponds to 90% of the maximum possible kappa (0.57), given the observed marginal frequencies. The overall concordance for anti-MuSK reactivity was 84%. Cohen’s kappa was 0.11 (95% CI 0.00–0.36), which corresponds to 41% of the maximum possible kappa (0.27). Conclusion: The overall concordance among assays is not optimal.
Current Insights: The Impact of Gut Microbiota on Postoperative Complications in Visceral Surgery—A Narrative Review
Authors: Lederer Ann-Kathrin , Chikhladze Sophia , Kohnert Eva , Huber Roman , Müller Alexander
Published: November 2021

Postoperative complications are a major problem occurring in up to 50% of patients undergoing major abdominal surgery. Occurrence of postoperative complications is associated with a significantly higher morbidity and mortality in affected patients. The most common postoperative complications are caused by an infectious genesis and include anastomotic leakage in case of gastrointestinal anastomosis and surgical site infections. Recent research highlighted the importance of gut microbiota in health and disease. It is plausible that the gut microbiota also plays a pivotal role in the development of postoperative complications. This narrative review critically summarizes results of recent research in this particular field. The review evaluates the role of gut microbiota alteration in postoperative complications, including postoperative ileus, anastomotic leakage, and surgical site infections in visceral surgery. We tried to put a special focus on a potential diagnostic value of pre- and post-operative gut microbiota sampling showing that recent data are inhomogeneous to identify a high-risk microbial profile for development of postoperative complications.
Improved Alzheimer’s Disease Detection by MRI Using Multimodal Machine Learning Algorithms
Authors: Battineni Gopi , Hossain Mohmmad Amran , Chintalapudi Nalini , Traini Enea , Dhulipalla Venkata Rao , Ramasamy Mariappan , Amenta Francesco
Published: November 2021

Adult-onset dementia disorders represent a challenge for modern medicine. Alzheimer’s disease (AD) represents the most diffused form of adult-onset dementias. For half a century, the diagnosis of AD was based on clinical and exclusion criteria, with an accuracy of 85%, which did not allow for a definitive diagnosis, which could only be confirmed by post-mortem evaluation. Machine learning research applied to Magnetic Resonance Imaging (MRI) techniques can contribute to a faster diagnosis of AD and may contribute to predicting the evolution of the disease. It was also possible to predict individual dementia of older adults with AD screening data and ML classifiers. To predict the AD subject status, the MRI demographic information and pre-existing conditions of the patient can help to enhance the classifier performance. In this work, we proposed a framework based on supervised learning classifiers in the dementia subject categorization as either AD or non-AD based on longitudinal brain MRI features. Six different supervised classifiers are incorporated for the classification of AD subjects and results mentioned that the gradient boosting algorithm outperforms other models with 97.58% of accuracy.
Contrast-Enhanced Ultrasound for Precise Sentinel Lymph Node Biopsy in Women with Early Breast Cancer: A Preliminary Study
Authors: Zhu Yangyang , Fan Xiao , Yang Dan , Dong Tiantian , Jia Yingying , Nie Fang
Published: November 2021

Background: Sentinel lymph node biopsy (SLNB), as a common method for axillary staging of early breast cancer, has gradually attracted people’s attention to the false-negative rate and postoperative complications. The aim of the study is to investigate the clinical value of preoperative contrast-enhanced ultrasound (CEUS) for intraoperative SLNB in early breast cancer patients. Methods: A total of 201 patients scheduled for SLNB from September 2018 to April 2021 were collected consecutively. Preoperative CEUS was used to identify sentinel lymph nodes (SLN) and lymphatic drainage in breast cancer patients. Results: The SLN identification rate of CEUS was 93.0% (187/201) and four lymphatic drainage patterns were found: single LC to single SLN (70.0%), multiple LCs to single SLN (8.0%), single LC to multiple SLNs (10.2%), and multiple LCs to multiple SLNs (11.8%). The Sen, Spe, PPV, NPV, AUC of CEUS, US and CEUS + US in diagnosis of SLNs were 82.7%, 80.4%, 73.8%, 87.4%, 0.815; 70.7%, 77.7%, 68.0%, 79.8%, 0.742; and 86.7%, 77.7%, 72.2%, 89.7%, 0.822, respectively. There was no statistically significant difference between the diagnostic performance of CEUS and CEUS + US (p = 0.630). Conclusions: CEUS can be used to preoperatively assess the lymphatic drainage patterns and the status of the SLNs in early breast cancer to assist precision intraoperative SLNB.
Rapid, Cheap, and Effective COVID-19 Diagnostics for Africa
Authors: Yusuf Lukman , Appeaning Mark , Amole Taiwo Gboluwaga , Musa Baba Maiyaki , Galadanci Hadiza Shehu , Quashie Peter Kojo , Aliyu Isah Abubakar
Published: November 2021

Background: Although comprehensive public health measures such as mass quarantine have been taken internationally, this has generally been ineffective, leading to a high infection and mortality rate. Despite the fact that the COVID-19 pandemic has been downgraded to epidemic status in many countries, the real number of infections is unknown, particularly in low-income countries. However, precision shielding is used in COVID-19 management, and requires estimates of mass infection in key groups. As a result, rapid tests for the virus could be a useful screening tool for asymptomatic virus shedders who are about to come into contact with sensitive groups. In Africa and other low- and middle-income countries there is high rate of COVID-19 under-diagnosis, due to the high cost of molecular assays. Exploring alternate assays to the reverse transcriptase polymerase chain reaction (RT-PCR) for COVID-19 diagnosis is highly warranted. Aim: This review explored the feasibility of using alternate molecular, rapid antigen, and serological diagnostic assays to accurately and precisely diagnose COVID-19 in African populations, and to mitigate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RT-PCR diagnostic challenges in Africa. Method: We reviewed publications from internet sources and searched for appropriate documents available in English. This included Medline, Google Scholar, and Ajol. We included primary literature and some review articles that presented knowledge on the current trends on SARS-CoV-2 diagnostics in Africa and globally. Results: Based on our analysis, we highlight the utility of four different alternatives to RT-PCR. These include two isothermal nucleic acid amplification assays (loop-mediated isothermal amplification (LAMP) and recombinase polymerase amplification (RPA)), rapid antigen testing, and antibody testing for tackling difficulties posed by SARS-CoV-2 RT-PCR testing in Africa. Conclusion: The economic burden associated COVID-19 mass testing by RT-PCR will be difficult for low-income nations to meet. We provide evidence for the utility and deployment of these alternate testing methods in Africa and other LMICs.
Digital Pathology Workflow Implementation at IPATIMUP
Authors: Eloy Catarina , Vale João , Curado Mónica , Polónia António , Campelos Sofia , Caramelo Ana , Sousa Rui , Sobrinho-Simões Manuel
Published: November 2021

The advantages of the digital methodology are well known. In this paper, we provide a detailed description of the process for the digital transformation of the pathology laboratory at IPATIMUP, the major modifications that operate throughout the processing pipeline, and the advantages of its implementation. The model of digital workflow implementation at IPATIMUP demonstrates that careful planning and adoption of simple measures related to time, space, and sample management can be adopted by any pathology laboratory to achieve higher quality and easy digital transformation.
Chronic Autoimmune Gastritis: Modern Diagnostic Principles
Authors: Livzan Maria A. , Gaus Olga V. , Mozgovoi Sergei I. , Bordin Dmitry S.
Published: November 2021

This article summarizes and systematizes the available data from the literature on chronic autoimmune gastritis (CAG) in order to increase the awareness of specialists about the modern possibilities for diagnosing the disease, including its early stages. The clinical manifestation of the disease includes possible variants such as gastrointestinal, hematological (first of all, the formation of iron deficiency and B12-deficiency anemia), and neurological variants. Patients with chronic autoimmune gastritis are characterized by comorbidity with other autoimmune diseases. In this paper, data on the most informative serological markers for the diagnosis of CAG, as well as laboratory tests to detect micronutrient deficiencies, information on the characteristic changes in the gastric mucosa, and the prognosis of the disease, are presented. The diagnosis of CAG should be based on a multidisciplinary approach that combines a thorough analysis of a patient’s complaints with a mandatory assessment of nutritional status, as well as the results of serological, endoscopic, and histological research methods.
Diet in Scleroderma: Is There a Need for Intervention?
Authors: Burlui Alexandra Maria , Cardoneanu Anca , Macovei Luana Andreea , Rezus Ciprian , Boiculese Lucian Vasile , Graur Mariana , Rezus Elena
Published: November 2021

Systemic sclerosis (SSc) patients exhibit a plethora of risk factors for nutritional decline, including the presence of chronic inflammation and the progressive nature of disease-related multisystem involvement. The prevalence and consequences of nutritional decline in scleroderma are frequently underestimated, its management currently remaining a subject of debate. The main objective of the present study was to perform a detailed assessment of scleroderma patients’ diet as well as their eating habits and to describe the relationships with weight loss and malnutrition risk in the absence of professional nutritional counseling. Methods: We used a translated and validated version of the EPIC-Norfolk FFQ (European Prospective Investigation into Cancer and Nutrition Norfolk Food Frequency Questionnaire) to evaluate the patients’ diet and MUST (Malnutrition Universal Screening Tool) to investigate the risk of malnutrition. Disease activity was estimated using the EUSTAR-AI (European Scleroderma Trials and Research group Activity Index). Results: We included 69 patients with SSc, of which 42 underwent a detailed dietary assessment. Dietary factors were connected to body composition and digestive symptoms. We found high sodium intake and frequent suboptimal energy consumption in our study group, including patients with cardiopulmonary involvement. Liver transaminases were inversely correlated with the consumption of nuts and seeds. Malnutrition and weight loss were significantly associated with pulmonary hypertension, heart failure, albumin levels, and the extent of skin fibrosis, but not advanced age. Although the patients with EUSTAR-AI ≥ 2.5 were more frequently included in the moderate and high malnutrition risk categories, these results did not reach statistical significance. Conclusions: Currently, there is an unmet need for longitudinal and interventional research focusing on the long-term significance, ramifications, and management of nutritional impairment in SSc patients with various clinical manifestations. Our results indicate that scleroderma patients could benefit from personalized nutritional counseling in an interdisciplinary setting.
Belief and Possibility Belief Interval-Valued N-Soft Set and Their Applications in Multi-Attribute Decision-Making Problems
Authors: Ali Shahbaz , Kousar Muneeba , Xin Qin , Pamučar Dragan , Hameed Muhammad Shazib , Fayyaz Rabia
Published: November 2021

In this research article, we motivate and introduce the concept of possibility belief interval-valued N-soft sets. It has a great significance for enhancing the performance of decision-making procedures in many theories of uncertainty. The N-soft set theory is arising as an effective mathematical tool for dealing with precision and uncertainties more than the soft set theory. In this regard, we extend the concept of belief interval-valued soft set to possibility belief interval-valued N-soft set (by accumulating possibility and belief interval with N-soft set), and we also explain its practical calculations. To this objective, we defined related theoretical notions, for example, belief interval-valued N-soft set, possibility belief interval-valued N-soft set, their algebraic operations, and examined some of their fundamental properties. Furthermore, we developed two algorithms by using max-AND and min-OR operations of possibility belief interval-valued N-soft set for decision-making problems and also justify its applicability with numerical examples.
Random Integer Lattice Generation via the Hermite Normal Form
Authors: Hu Gengran , You Lin , Li Liang , Hu Liqin , Wang Hui
Published: November 2021

Lattices used in cryptography are integer lattices. Defining and generating a “random integer lattice” are interesting topics. A generation algorithm for a random integer lattice can be used to serve as a random input of all the lattice algorithms. In this paper, we recall the definition of the random integer lattice given by G. Hu et al. and present an improved generation algorithm for it via the Hermite normal form. It can be proven that with probability ≥0.99, this algorithm outputs an n-dim random integer lattice within O(n2) operations.
Invariance Properties of the Entropy Production, and the Entropic Pairing of Inertial Frames of Reference by Shear-Flow Systems
Authors: Niven Robert K.
Published: November 2021

This study examines the invariance properties of the thermodynamic entropy production in its global (integral), local (differential), bilinear, and macroscopic formulations, including dimensional scaling, invariance to fixed displacements, rotations or reflections of the coordinates, time antisymmetry, Galilean invariance, and Lie point symmetry. The Lie invariance is shown to be the most general, encompassing the other invariances. In a shear-flow system involving fluid flow relative to a solid boundary at steady state, the Galilean invariance property is then shown to preference a unique pair of inertial frames of reference—here termed an entropic pair—respectively moving with the solid or the mean fluid flow. This challenges the Newtonian viewpoint that all inertial frames of reference are equivalent. Furthermore, the existence of a shear flow subsystem with an entropic pair different to that of the surrounding system, or a subsystem with one or more changing entropic pair(s), requires a source of negentropy—a power source scaled by an absolute temperature—to drive the subsystem. Through the analysis of different shear flow subsystems, we present a series of governing principles to describe their entropic pairing properties and sources of negentropy. These are unaffected by Galilean transformations, and so can be understood to “lie above” the Galilean inertial framework of Newtonian mechanics. The analyses provide a new perspective into the field of entropic mechanics, the study of the relative motions of objects with friction.
Synchronization of the Glycolysis Reaction-Diffusion Model via Linear Control Law
Authors: Ouannas Adel , Batiha Iqbal M. , Bekiros Stelios , Liu Jinping , Jahanshahi Hadi , Aly Ayman A. , Alghtani Abdulaziz H.
Published: November 2021

The Selkov system, which is typically employed to model glycolysis phenomena, unveils some rich dynamics and some other complex formations in biochemical reactions. In the present work, the synchronization problem of the glycolysis reaction-diffusion model is handled and examined. In addition, a novel convenient control law is designed in a linear form and, on the other hand, the stability of the associated error system is demonstrated through utilizing a suitable Lyapunov function. To illustrate the applicability of the proposed schemes, several numerical simulations are performed in one- and two-spatial dimensions.
On the Non-Adaptive Zero-Error Capacity of the Discrete Memoryless Two-Way Channel
Authors: Gu Yujie , Shayevitz Ofer
Published: November 2021

We study the problem of communicating over a discrete memoryless two-way channel using non-adaptive schemes, under a zero probability of error criterion. We derive single-letter inner and outer bounds for the zero-error capacity region, based on random coding, linear programming, linear codes, and the asymptotic spectrum of graphs. Among others, we provide a single-letter outer bound based on a combination of Shannon’s vanishing-error capacity region and a two-way analogue of the linear programming bound for point-to-point channels, which, in contrast to the one-way case, is generally better than both. Moreover, we establish an outer bound for the zero-error capacity region of a two-way channel via the asymptotic spectrum of graphs, and show that this bound can be achieved in certain cases.
Digital Competences of Students of Library Studies: Comparison of Research Results for 2018–2020
Authors: Cerny Michal
Published: November 2021

This study focuses on the analysis of changes in the digital competence profile of students of Information and Library Studies at Masaryk University in Czechia. As a research tool, we used the DigComp self-assessment questionnaire that students were asked to fill in after completing the course. Our research shows that students are insufficiently prepared for work as highly qualified information specialists. At the same time, we found that their competence profile remained very stable between 2018 and 2020. This finding indicates that students do not readily respond to new societal changes at the level of individual competences. The research results are based on data collected from 152 students during three runs of a compulsory course at the university. Information Science and Library Science students have long perceived their competences to be strongest in the domains of information and data literacy and communication and collaboration. Programming is the weakest competency among the competences, followed by solving technical problems and engaging in active citizenship through digital technologies. These findings can be used to innovate the curriculum to meet the demands of digitally competent information workers.
Worldwide Trends in Bilingual Education Research: A Half-Century Overview
Authors: Sánchez-Pérez María del Mar , Manzano-Agugliaro Francisco
Published: November 2021

Despite the wealth of studies on bilingual education, there is a dearth of meta-research on the worldwide development and trends of this area of investigation over the past few decades. The occupation of this gap allows scholars to take stock of current states of research, get overviews of the contributions made to the field, foresee future research trends, and identify research needs and gaps that may be addressed in future investigation. This study analyses the evolution and trends of bilingual education research during a 50-year period (1969–2018) from a bibliometric perspective. The results show a steady increase in the number of publications, and was exponential in the last decade, mainly in the form of research articles, which makes bilingual education a truly consolidated and increasingly evolving research field. The US is the leading country with respect to the number of publications, affiliations, and sponsors, followed, primarily, by some other North American (e.g., Canada), European (e.g., UK and Spain), and Asian (e.g., China) countries, as well as Australia. There is a large research network cluster led by the US involving intercontinental interaction among institutions from Europe, Asia, and, to a lesser extent, South America. However, a scant level of internationalisation of scholars publishing works on bilingual education was observed, with most author collaboration being limited to different US institutions. The most influential authors belong to institutions from the US, Canada, Spain, and Israel, and, to a lesser extent, Australia. The main research topics in the field depend on the contexts and include regulations of language institutions, bilingual education models, language skills, pedagogical strategies, education levels, and ages, among others. These results may contribute to the identification of new research needs and therefore, to the development of future directions in bilingual education research.
A Passive, Skin-Attachable Multi-Sensing Patch Based on Semi-Liquid Alloy Ni-GaIn for Wireless Epidermal Signal Monitoring and Body Motion Capturing
Authors: Lin Shipeng , Fang Jiming , Ye Tianchen , Tao Yan , Duan Shengshun , Wu Jun
Published: November 2021

Wearable integrated systems that rely on liquid metal commonly require an extremely complicated, high-cost fabrication process, while lacking multiple sensing functions without conductive wires connected to external electronic systems. A multi-sensing wearable patch independent from sophisticated manufacturing method and excessive use of wires has yet to be developed. Herein, we introduce a wireless, battery-free, and skin-attachable patch with multiple sensing capacities, utilizing a low-budget, less time-consuming and design-customizable fabrication method. In an effort to achieve our goal, the general sensing system architecture is promoted, which consists of a semi-liquid alloy Ni-GaIn based strain sensor and a co-designed near-field-communication (NFC) tag integrating thermistor, photoresistor, as well as sensor interface circuits, enabling energy-autonomous power supply and wireless data transmission. In human volunteers, the patch was mounted on the skin surface to demonstrate real-time temperature and light intensity signal monitoring. Further evaluation of body motion capturing involved finger bending and swallowing, demonstrating the feasibility of practical applications in different scenarios. Continuous and simultaneous multi-type signal sensing using the wearable patch should enrich the dimensions of measurements of body response to daily activities, unveiling the potential for remote human health monitoring, advanced human–machine interfaces, and other applications of interest.
TRIG: Transformer-Based Text Recognizer with Initial Embedding Guidance
Authors: Tao Yue , Jia Zhiwei , Ma Runze , Xu Shugong
Published: November 2021

Scene text recognition (STR) is an important bridge between images and text, attracting abundant research attention. While convolutional neural networks (CNNS) have achieved remarkable progress in this task, most of the existing works need an extra module (context modeling module) to help CNN to capture global dependencies to solve the inductive bias and strengthen the relationship between text features. Recently, the transformer has been proposed as a promising network for global context modeling by self-attention mechanism, but one of the main short-comings, when applied to recognition, is the efficiency. We propose a 1-D split to address the challenges of complexity and replace the CNN with the transformer encoder to reduce the need for a context modeling module. Furthermore, recent methods use a frozen initial embedding to guide the decoder to decode the features to text, leading to a loss of accuracy. We propose to use a learnable initial embedding learned from the transformer encoder to make it adaptive to different input images. Above all, we introduce a novel architecture for text recognition, named TRansformer-based text recognizer with Initial embedding Guidance (TRIG), composed of three stages (transformation, feature extraction, and prediction). Extensive experiments show that our approach can achieve state-of-the-art on text recognition benchmarks.
Machine Learning (ML) Based Thermal Management for Cooling of Electronics Chips by Utilizing Thermal Energy Storage (TES) in Packaging That Leverages Phase Change Materials (PCM)
Authors: Chuttar Aditya , Banerjee Debjyoti
Published: November 2021

Miniaturization of electronics devices is often limited by the concomitant high heat fluxes (cooling load) and maldistribution of temperature profiles (hot spots). Thermal energy storage (TES) platforms providing supplemental cooling can be a cost-effective solution, that often leverages phase change materials (PCM). Although salt hydrates provide higher storage capacities and power ratings (as compared to that of the organic PCMs), they suffer from reliability issues (e.g., supercooling). “Cold Finger Technique (CFT)” can obviate supercooling by maintaining a small mass fraction of the PCM in a solid state for enabling spontaneous nucleation. Optimization of CFT necessitates real-time forecasting of the transient values of the melt-fraction. In this study, the artificial neural network (ANN) is explored for real-time prediction of the time remaining to reach a target value of melt-fraction based on the prior history of the spatial distribution of the surface temperature transients. Two different approaches were explored for training the ANN model, using: (1) transient PCM-temperature data; or (2) transient surface-temperature data. When deployed in a heat sink that leverages PCM-based passive thermal management systems for cooling electronic chips and packages, this maverick approach (using the second method) affords cheaper costs, better sustainability, higher reliability, and resilience. The error in prediction varies during the melting process. During the final stages of the melting cycle, the errors in the predicted values are ~5% of the total time-scale of the PCM melting experiments.
Adaptive Impedance Matching Scheme for Magnetic MIMO Wireless Power Transfer System
Authors: Lu Ziyang , Zhao Yubin , Liu Dunge
Published: November 2021

In coupled magnetic resonance (CMR) wireless energy transfer systems, the energy transfer power is low and the power transfer efficiency changes with the coil position. One reason for this reduction in power and efficiency is the impedance mismatching (IM) between the Tx and Rx coils; achieving impedance matching for multiple-input multiple-output (MIMO) CMR IM wireless power transmission (WPT) is quite complex due to the uncertainty in the number of coils and the interaction between coils. In this paper, we provide an analytical model of MIMO CMR which fully formulates the complex relationship between multiple Tx and Rx channels. Then, we design an impedance matching network (IMN) for MIMO CMR and derive an optimal IM solution. Base on this solution, we also develop an adaptive impedance matching scheme to control IMN, based on an automatic analysis of MIMO CMR system; the resulting control scheme achieves optimal values for transmission power and efficiency through IMN and coil selection. The simulation results indicate that the scheme is able to automatically adjust the impedance matching network according to the changes of the relative positions between Tx and Rx coils to achieve high energy transfer power and efficiency.
Effects of Using Vibrotactile Feedback on Sound Localization by Deaf and Hard-of-Hearing People in Virtual Environments
Authors: Mirzaei Mohammadreza , Kán Peter , Kaufmann Hannes
Published: November 2021

Sound source localization is important for spatial awareness and immersive Virtual Reality (VR) experiences. Deaf and Hard-of-Hearing (DHH) persons have limitations in completing sound-related VR tasks efficiently because they perceive audio information differently. This paper presents and evaluates a special haptic VR suit that helps DHH persons efficiently complete sound-related VR tasks. Our proposed VR suit receives sound information from the VR environment wirelessly and indicates the direction of the sound source to the DHH user by using vibrotactile feedback. Our study suggests that using different setups of the VR suit can significantly improve VR task completion times compared to not using a VR suit. Additionally, the results of mounting haptic devices on different positions of users’ bodies indicate that DHH users can complete a VR task significantly faster when two vibro-motors are mounted on their arms and ears compared to their thighs. Our quantitative and qualitative analysis demonstrates that DHH persons prefer using the system without the VR suit and prefer mounting vibro-motors in their ears. In an additional study, we did not find a significant difference in task completion time when using four vibro-motors with the VR suit compared to using only two vibro-motors in users’ ears without the VR suit.
Fatigue Stress Estimation for Submerged and Sub-Soil Welds of Offshore Wind Turbines on Monopiles Using Modal Expansion
Authors: Henkel Maximilian , Weijtjens Wout , Devriendt Christof
Published: November 2021

The design of monopile foundations for offshore wind turbines is most often driven by fatigue. With the foundation price contributing to the total price of a turbine structure by more than 30%, wind farm operators seek to gain knowledge about the amount of consumed fatigue. Monitoring concepts are developed to uncover structural reserves coming from conservative designs in order to prolong the lifetime of a turbine. Amongst promising concepts is a wide array of methods using in-situ measurement data and extrapolating these results to desired locations below water surface and even seabed using models. The modal decomposition algorithm is used for this purpose. The algorithm obtains modal amplitudes from acceleration and strain measurements. In the subsequent expansion step these amplitudes are expanded to virtual measurements at arbitrary locations. The algorithm uses a reduced order model that can be obtained from either a FE model or measurements. In this work, operational modal analysis is applied to obtain the required stress and deflection shapes for optimal validation of the method. Furthermore, the measurements that are used as input for the algorithms are constrained to measurements from the dry part of the substructure. However, with subsoil measurement data available from a dedicated campaign, even validation for locations below mud-line is possible. After reconstructing strain history in arbitrary locations on the substructure, fatigue assessment over various environmental and operational conditions is carried out. The technique is found capable of estimating fatigue with high precision for locations above and below seabed.
Experimental Investigation of EU-DEMO Breeding Blanket First Wall Mock-Ups in Support of the Manufacturing and Material Development Programmes
Authors: Ghidersa Bradut-Eugen , Abou Sena Ali , Rieth Michael , Emmerich Thomas , Lux Martin , Aktaa Jarir
Published: November 2021

This paper presents the testing campaign of the two First Wall mock-ups in the HELOKA facility, one mock-up having a 3 mm thick Oxide Dispersion Strengthened (ODS) steel layer on its surface and the other featuring a tungsten functionally graded cover. Special consideration is given to the diagnostics used for these tests, in particular, the measurement of the surface temperature of the tungsten functionally graded layer with an infrared camera. Additionally, the paper looks into the uncertainty associated with the calorimetric evaluation of the applied heating power for these experiments.
Photovoltaic Energy All-Day and Intra-Day Forecasting Using Node by Node Developed Polynomial Networks Forming PDE Models Based on the L-Transformation
Authors: Zjavka Ladislav
Published: November 2021

Forecasting Photovoltaic (PV) energy production, based on the last weather and power data only, can obtain acceptable prediction accuracy in short-time horizons. Numerical Weather Prediction (NWP) systems usually produce free forecasts of the local cloud amount each 6 h. These are considerably delayed by several hours and do not provide sufficient quality. A Differential Polynomial Neural Network (D-PNN) is a recent unconventional soft-computing technique that can model complex weather patterns. D-PNN expands the n-variable kth order Partial Differential Equation (PDE) into selected two-variable node PDEs of the first or second order. Their derivatives are easy to convert into the Laplace transforms and substitute using Operator Calculus (OC). D-PNN proves two-input nodes to insert their PDE components into its gradually expanded sum model. Its PDE representation allows for the variability and uncertainty of specific patterns in the surface layer. The proposed all-day single-model and intra-day several-step PV prediction schemes are compared and interpreted with differential and stochastic machine learning. The statistical models are evolved for the specific data time delay to predict the PV output in complete day sequences or specific hours. Spatial data from a larger territory and the initially recognized daily periods enable models to compute accurate predictions each day and compensate for unexpected pattern variations and different initial conditions. The optimal data samples, determined by the particular time shifts between the model inputs and output, are trained to predict the Clear Sky Index in the defined horizon.