bluuebunny/crossref_metadata_2025 huggingface dataset:
In-Vitro Selection of Ceftazidime/Avibactam Resistance in OXA-48-Like-Expressing Klebsiella pneumoniae: In-Vitro and In-Vivo Fitness, Genetic Basis and Activities of β-Lactam Plus Novel β-Lactamase Inhibitor or β-Lactam Enhancer Combinations
Authors: Palwe Snehal , Bakthavatchalam Yamuna Devi , Khobragadea Kshama , Kharat Arun S. , Walia Kamini , Veeraraghavan Balaji
Published: November 2021

Ceftazidime/avibactam uniquely demonstrates activity against both KPC and OXA-48-like carbapenemase-expressing Enterobacterales. Clinical resistance to ceftazidime/avibactam in KPC-producers was foreseen in in-vitro resistance studies. Herein, we assessed the resistance selection propensity of ceftazidime/avibactam in K. pneumoniae expressing OXA-48-like β-lactamases (n = 10), employing serial transfer approach. Ceftazidime/avibactam MICs (0.25–4 mg/L) increased to 16–256 mg/L after 15 daily-sequential transfers. The whole genome sequence analysis of terminal mutants showed modifications in proteins linked to efflux (AcrB/AcrD/EmrA/Mdt), outer membrane permeability (OmpK36) and/or stress response pathways (CpxA/EnvZ/RpoE). In-vitro growth properties of all the ceftazidime/avibactam-selected mutants were comparable to their respective parents and they retained the ability to cause pulmonary infection in neutropenic mice. Against these mutants, we explored the activities of various combinations of β-lactams (ceftazidime or cefepime) with structurally diverse β-lactamase inhibitors or a β-lactam enhancer, zidebactam. Zidebactam, in combination with either cefepime or ceftazidime, overcame ceftazidime/avibactam resistance (MIC range 0.5–8 mg/L), while cefepime/avibactam was the second best (MIC: 0.5–16 mg/L) in yielding lower MICs. The present work revealed the possibility of ceftazidime/avibactam resistance in OXA-48-like K. pneumoniae through mutations in proteins involved in efflux and/or porins without concomitant fitness cost mandating astute monitoring of ceftazidime/avibactam resistance among OXA-48 genotypes.
Treatment of UTIs Due to Klebsiella pneumoniae Carbapenemase-Producers: How to Use New Antibiotic Drugs? A Narrative Review
Authors: Chapelle Caroline , Gaborit Benjamin , Dumont Raphaëlle , Dinh Aurélien , Vallée Maxime
Published: November 2021

Background: K. pneumoniae is one of the bacteria most frequently causing health care-associated urinary tract infections, and increasingly incriminating Klebsiella pneumoniae carbapenemase producers (KPCp). Most infections caused by KPCp are nosocomial and might cause serious issues, even leading to death in half of the reported cases. Our aim was to identify the best strategy, based on available scientific data, for the use of new antibiotic treatments to manage KPCp UTIs. Methods: this narrative review of the literature was performed according to the criteria of preferred reporting items for systematic review and meta-analyses statement (PRISMA) (2020). Results and Conclusions: KPCp-UTIs are a real challenge for physicians. While cefiderocol, meropenem-vaborbactam, ceftazidim-avibactam, and imipenem-relebactam represent a major step forward in the treatment of these UTIs, no guidelines are currently available, in view of choosing the most appropriate treatment, in each specific case.
Prevalence and Characterization of ESBL/AmpC Producing Escherichia coli from Fresh Meat in Portugal
Authors: Clemente Lurdes , Leão Célia , Moura Laura , Albuquerque Teresa , Amaro Ana
Published: November 2021

The present study aimed to characterize the extended-spectrum β-lactamases and plasmid-mediated AmpC β-lactamases (ESBL/PMAβ) among Escherichia coli producers isolated from beef, pork, and poultry meat collected at retail, in Portugal. A total of 638 meat samples were collected and inoculated on selective medium for the search of E. coli resistant to 3rd generation cephalosporins. Isolates were characterized by antimicrobial susceptibility testing, molecular assays targeting ESBL/AmpC, plasmid-mediated quinolone resistance (PMQR), and plasmid-mediated colistin resistance (PMCR) encoding genes. The highest frequency of E. coli non-wild type to 3rd generation cephalosporins and fluoroquinolones was observed in broiler meat (30.3% and 93.3%, respectively). Overall, a diversity of acquired resistance mechanisms, were detected: blaESBL [blaCTX-M-1 (n = 19), blaCTX-M-15 (n = 4), blaCTX-M-32 (n = 12), blaCTX-M-55 (n = 8), blaCTX-M-65 (n = 4), blaCTX-M-27 (n = 2), blaCTX-M-9 (n = 1), blaCTX-M-14 (n = 11), blaSHV-12 (n = 27), blaTEM-52 (n = 1)], blaPMAβ [blaCMY-2 (n = 8)], PMQR [qnrB (n = 27), qnrS (n = 21) and aac(6’)-Ib-type (n = 4)] and PMCR [mcr-1 (n = 8)]. Our study highlights that consumers may be exposed through the food chain to multidrug-resistant E. coli carrying diverse plasmid-mediated antimicrobial resistance genes, posing a great hazard to food safety and a public health risk.
Dietary Strawberries Improve Biomarkers of Antioxidant Status and Endothelial Function in Adults with Cardiometabolic Risks in a Randomized Controlled Crossover Trial
Authors: Basu Arpita , Izuora Kenneth , Betts Nancy M. , Ebersole Jeffrey L. , Scofield Robert Hal
Published: November 2021

Strawberries, a popularly consumed berry fruit, are rich in bioactive compounds with antioxidant effects. In this study, we examined the effects of two dietary achievable doses of strawberries on the antioxidant status and biomarkers of endothelial function in adults with features of metabolic syndrome and a confirmed low baseline of fruit and vegetable intake. In a 14-week randomized controlled crossover study, participants were assigned to one of three groups for four weeks separated by a one-week washout period: control powder, one serving (low dose: 13 g strawberry powder/day), or 2.5 servings (high dose: 32 g strawberry powder/day). Blood samples and health data were collected at baseline and at the end of each four-week phase of intervention. Thirty-three participants completed all three phases of the trial. Significant increases were observed in serum antioxidant capacity and superoxide dismutase activity as well as decreases in lipid peroxidation after both low and high dose strawberry phases when compared with the control phase. Significant decreases were also observed in soluble vascular cell adhesion molecule-1 and tumor necrosis factor-α with the high dose strawberry phase. These data confirm that consuming strawberries for four weeks significantly improves antioxidant status, endothelial function, and inflammation in adults with cardiometabolic risks.
Dysregulated Autophagy Leads to Oxidative Stress and Aberrant Expression of ABC Transporters in Women with Early Miscarriage
Authors: Shahnawaz Saira , Nawaz Usman Shah , Zaugg Jonas , Hussain Ghulam , Malik Nadia , Dogar Muhammad Zahoor-ul-Hassan , Malik Shoaib Ahmad , Albrecht Christiane
Published: November 2021

Early miscarriage (EMC) is a devastating obstetrical complication. ATP-binding cassette (ABC) transporters mediate cholesterol transfer across the placenta and enhance cell survival by effluxing substrates from target cells in the presence of stressors. Recent evidence reports an intricate interplay between autophagy and ABC transporters. We hypothesized that dysregulated autophagy and oxidative stress (OS) in the placenta leads to abnormal expression of membrane transporters contributing to poor pregnancy survival in EMC. We determined mRNA and protein expression of autophagy genes (Beclin-1/Bcl-2/LC3I/LC3II/p62) and ABC transporters (ABCA1/ABCG1/ABCG2) in placentae from EMC patients (n = 20), term controls (n = 19), first trimester (n = 6), and term controls (n = 5) controls. Oxidative/antioxidant status and biomarkers of oxidative damage were evaluated in maternal serum and placentae from EMC and healthy controls. In EMC, placental expression of LC3II/LC3I as well as of the key autophagy regulatory proteins Beclin-1 and Bcl-2 were reduced, whereas p62 was increased. Both in the serum and placentae of EMC patients, total OS was elevated reflected by increased oxidative damage markers (8-OHdG/malondialdehyde/carbonyl formation) accompanied by diminished levels of total antioxidant status, catalase, and total glutathione. Furthermore, we found reduced ABCG1 and increased ABCG2 expression. These findings suggest that a decreased autophagy status triggers Bcl-2-dependent OS leading to macromolecule damage in EMC placentae. The decreased expression of ABCG1 contributes to reduced cholesterol export to the growing fetus. Increasing ABCG2 expression could represent a protective feedback mechanism under inhibited autophagy conditions. In conclusion, dysregulated autophagy combined with increased oxidative toxicity and aberrant expression of placental ABC transporters affects materno-fetal health in EMC.
Dietary Antioxidants in Age-Related Macular Degeneration and Glaucoma
Authors: Dziedziak Jacek , Kasarełło Kaja , Cudnoch-Jędrzejewska Agnieszka
Published: November 2021

Age-related macular degeneration (AMD) and glaucoma are ophthalmic neurodegenerative diseases responsible for irreversible vision loss in the world population. Only a few therapies can be used to slow down the progression of these diseases and there are no available treatment strategies for reversing the degeneration of the neural retina. In AMD, the pathological process causes the malfunction and damage of the retinal pigmented epithelium and photoreceptors in the macula. In glaucoma, damage of the retinal ganglion cells and their axons is observed and treatment strategies are limited to intraocular pressure lowering. Therefore, other prophylactic and/or therapeutic methods are needed. Oxidative stress is involved in the neurodegenerative process accompanying both AMD and glaucoma; therefore, the use of antioxidant agents would clearly be beneficial, which is supported by the decreased prevalence and progression of AMD in patients adherent to a diet naturally rich in antioxidants. Dietary antioxidants are easily available and their use is based on the natural route of administration. Many preclinical studies both in vitro and using animal models of retinal degeneration showed the efficacy of dietary antioxidants, which was further proved in clinical trials. Resveratrol is beneficial both in AMD and glaucoma animal models, but confirmed only among AMD patients. For AMD, carotenoids and omega-3 fatty acids were also proved to be sufficient in preventing neurodegeneration. For glaucoma, coenzyme Q10 and alpha-lipoic acid showed efficacy for decreasing retinal ganglion cell loss and inhibiting the accompanying destructive processes. Interestingly, the benefits of vitamins, especially vitamin E was not confirmed, neither in preclinical nor in clinical studies.
Glutathione Peroxidase in Stable Chronic Obstructive Pulmonary Disease: A Systematic Review and Meta-analysis
Authors: Zinellu Elisabetta , Zinellu Angelo , Pau Maria Carmina , Piras Barbara , Fois Alessandro G. , Mellino Sabrina , Carru Ciriaco , Mangoni Arduino A. , Pirina Pietro
Published: November 2021

Chronic obstructive pulmonary disease (COPD) is a progressive disease that is characterized by a state of persistent inflammation and oxidative stress. The presence of oxidative stress in COPD is the result of an imbalance between pro-oxidant and antioxidant mechanisms. The aim of this review was to investigate a possible association between glutathione peroxidase (GPx), a key component of antioxidant defense mechanisms, and COPD. A systematic search for relevant studies was conducted in the electronic databases PubMed, Web of Science, Scopus, and Google Scholar, from inception to June 2021. Standardized mean differences (SMDs) were used to express the differences in GPx concentrations between COPD patients and non-COPD subjects. Twenty-four studies were identified. In 15 studies assessing whole blood/erythrocytes (GPx isoform 1), the pooled results showed that GPx concentrations were significantly lower in patients with COPD (SMD = −1.91, 95% CI −2.55 to −1.28, p < 0.001; moderate certainty of evidence). By contrast, in 10 studies assessing serum/plasma (GPx isoform 3), the pooled results showed that GPx concentrations were not significantly different between the two groups (very low certainty of evidence). The concentration of GPx-1, but not GPx-3, is significantly lower in COPD patients, suggesting an impairment of antioxidant defense mechanisms in this group.
Mitochondrial Uncoupling Proteins (UCPs) as Key Modulators of ROS Homeostasis: A Crosstalk between Diabesity and Male Infertility?
Authors: Monteiro Bruno S. , Freire-Brito Laís , Carrageta David F. , Oliveira Pedro F. , Alves Marco G.
Published: November 2021

Uncoupling proteins (UCPs) are transmembrane proteins members of the mitochondrial anion transporter family present in the mitochondrial inner membrane. Currently, six homologs have been identified (UCP1-6) in mammals, with ubiquitous tissue distribution and multiple physiological functions. UCPs are regulators of key events for cellular bioenergetic metabolism, such as membrane potential, metabolic efficiency, and energy dissipation also functioning as pivotal modulators of ROS production and general cellular redox state. UCPs can act as proton channels, leading to proton re-entry the mitochondrial matrix from the intermembrane space and thus collapsing the proton gradient and decreasing the membrane potential. Each homolog exhibits its specific functions, from thermogenesis to regulation of ROS production. The expression and function of UCPs are intimately linked to diabesity, with their dysregulation/dysfunction not only associated to diabesity onset, but also by exacerbating oxidative stress-related damage. Male infertility is one of the most overlooked diabesity-related comorbidities, where high oxidative stress takes a major role. In this review, we discuss in detail the expression and function of the different UCP homologs. In addition, the role of UCPs as key regulators of ROS production and redox homeostasis, as well as their influence on the pathophysiology of diabesity and potential role on diabesity-induced male infertility is debated.
Whey- and Soy Protein Isolates Added to a Carrot-Tomato Juice Alter Carotenoid Bioavailability in Healthy Adults
Authors: Iddir Mohammed , Pittois Denis , Guignard Cédric , Weber Bernard , Gantenbein Manon , Larondelle Yvan , Bohn Torsten
Published: November 2021

Recent findings suggested that proteins can differentially affect carotenoid bioaccessibility during gastro-intestinal digestion. In this crossover, randomized human trial, we aimed to confirm that proteins, specifically whey- and soy-protein isolates (WPI/SPI) impact postprandial carotenoid bioavailability. Healthy adults (n = 12 males, n = 12 females) were recruited. After 2-week washout periods, 350 g of a tomato-carrot juice mixture was served in the absence/presence of WPI or SPI (50% of the recommended dietary allowance, RDA ≈ 60 g/d). Absorption kinetics of carotenoids and triacylglycerols (TAGs) were evaluated via the triacylglycerol-rich lipoprotein (TRL) fraction response, at timed intervals up to 10 h after test meal intake, on three occasions separated by 1 week. Maximum TRL-carotenoid concentration (Cmax) and corresponding time (Tmax) were also determined. Considering both genders and carotenoids/TAGs combined, the estimated area under the curve (AUC) for WPI increased by 45% vs. the control (p = 0.018), to 92.0 ± 1.7 nmol × h/L and by 57% vs. SPI (p = 0.006). Test meal effect was significant in males (p = 0.036), but not in females (p = 0.189). In males, significant differences were found for phytoene (p = 0.026), phytofluene (p = 0.004), α-carotene (p = 0.034), and β-carotene (p = 0.031). Cmax for total carotenoids (nmol/L ± SD) was positively influenced by WPI (135.4 ± 38.0), while significantly lowered by SPI (89.6 ± 17.3 nmol/L) vs. the control (119.6 ± 30.9, p < 0.001). Tmax did not change. The results suggest that a well-digestible protein could enhance carotenoid bioavailability, whereas the less digestible SPI results in negative effects. This is, to our knowledge, the first study finding effects of proteins on carotenoid absorption in humans.
Prediction and Chemical Interpretation of Singlet-Oxygen-Scavenging Activity of Small Molecule Compounds by Using Machine Learning
Authors: Fujimoto Taiki , Gotoh Hiroaki
Published: November 2021

A chemically explainable machine learning model was constructed with a small dataset to quantitatively predict the singlet-oxygen-scavenging ability. In this model, ensemble learning based on decision trees resulted in high accuracy. For explanatory variables, molecular descriptors by computational chemistry and Morgan fingerprints were used for achieving high accuracy and simple prediction. The singlet-oxygen-scavenging mechanism was explained by the feature importance obtained from machine learning outputs. The results are consistent with conventional chemical knowledge. The use of machine learning and reduction in the number of measurements for screening high-antioxidant-capacity compounds can considerably improve prediction accuracy and efficiency.
The Potential of Dietary Antioxidants
Authors: Dini Irene
Published: November 2021

Oxidative stress happens when the levels of reactive species made from oxygen and nitrogen exceed the body’s antioxidant capacity [...]
A New Perspective on Robot Ethics through Investigating Human–Robot Interactions with Older Adults
Authors: van Maris Anouk , Zook Nancy , Dogramadzi Sanja , Studley Matthew , Winfield Alan , Caleb-Solly Praminda
Published: November 2021

This work explored the use of human–robot interaction research to investigate robot ethics. A longitudinal human–robot interaction study was conducted with self-reported healthy older adults to determine whether expression of artificial emotions by a social robot could result in emotional deception and emotional attachment. The findings from this study have highlighted that currently there appears to be no adequate tools, or the means, to determine the ethical impact and concerns ensuing from long-term interactions between social robots and older adults. This raises the question whether we should continue the fundamental development of social robots if we cannot determine their potential negative impact and whether we should shift our focus to the development of human–robot interaction assessment tools that provide more objective measures of ethical impact.
Personalization of a Human Body Model Using Subject-Specific Dimensions for Designing Clothing Patterns
Authors: Hynčík Luděk , Čechová Hana , Bońkowski Tomasz , Kavalířová Gabriela , Špottová Petra , Hampejsová Viola , Meng Hong
Published: November 2021

Virtual human body models contribute to designing safe and user-friendly products through virtual prototyping. Anthropometric biomechanical models address different physiques using average dimensions. In designing, e.g., personal protective equipment, orthopedic tools, or vehicle safety systems, biomechanical models with the correct geometry and shape shall play a role. The presented study shows the variations of subject-specific anthropometric dimensions from the average of the different population groups in the Czech Republic and China as a background for the need for personalized human body models. The study measures a set of dimensions used to design clothing patterns of Czech children, Czech adolescents, Czech adults, and Chinese adults and compares them to the corresponding age average, which is represented by a scaled anthropometric human body model. The cumulative variation of the dimensions used to design the clothing patterns increases the further the population group is from the average. It is smallest for the Czech adults at 7.54 ± 6.63%; Czech adolescents report 7.93 ± 6.25%; Czech children differ be 9.52 ± 6.08%. Chinese adults report 10.86 ± 11.11%. The variations from the average of the particular dimensions used to design clothing patterns prove the necessity of having personalized subject-specific models. The measured dimensions used to design the clothing patterns serve as the personalization of particular body segments and lead to a subject-specific virtual model. The developed personalization algorithm results in the continuous body surface desired for contact applications for assessing body behavior and injury risk under impact loading.
Resonant Airborne Acoustic Emission for Nondestructive Testing and Defect Imaging in Composites
Authors: Solodov Igor , Bernhardt Yannick , Kreutzbruck Marc
Published: November 2021

A new version of an acoustic emission mode which is different from its traditional counterpart is discussed in view of applications for nondestructive testing. It is based on the effect of acoustic waves generation from the defect area in ambient air by local standing wave vibration developed in this area at the defect resonant frequency. Another approach which does not require preliminary knowledge of local defect-resonance frequency is one that uses wideband acoustic activation by a noise-like input signal. The acoustic emission field from the defect area is a “fingerprint” of the radiation source, and thus is applicable to defect detection and imaging. This enables the use of commercial microphone scanning for detecting and imaging various defects in composites. An improvement in the acoustic-emission scanning mode based on a multiple-axis robot is studied to applications to complex shape components. A rapid, full-field imaging of the acoustic-emission field is implemented by means of an array of microphones (acoustic camera). Numerous case studies validate the potential of the resonant acoustic-emission modes for integration in the defect imaging system based on inexpensive, fully acoustic instrumental components.
Text-Based Emotion Recognition in English and Polish for Therapeutic Chatbot
Authors: Zygadło Artur , Kozłowski Marek , Janicki Artur
Published: November 2021

In this article, we present the results of our experiments on sentiment and emotion recognition for English and Polish texts, aiming to work in the context of a therapeutic chatbot. We created a dedicated dataset by adding samples of neutral texts to an existing English-language emotion-labeled corpus. Next, using neural machine translation, we developed a Polish version of the English database. A bilingual, parallel corpus created in this way, named CORTEX (CORpus of Translated Emotional teXts), labeled with three sentiment polarity classes and nine emotion classes, was used for experiments on classification. We employed various classifiers: Naïve Bayes, Support Vector Machines, fastText, and BERT. The results obtained were satisfactory: we achieved the best scores for the BERT-based models, which yielded accuracy of over 90% for sentiment (3-class) classification and almost 80% for emotion (9-class) classification. We compared the results for both languages and discussed the differences. Both the accuracy and the F1-scores for Polish turned out to be slightly inferior to those for English, with the highest difference visible for BERT.
Supercapacitors in Constant-Power Applications: Mathematical Analysis for the Calculation of Temperature
Authors: Pedrayes Joaquín F. , Melero Manuel G. , Norniella Joaquín G. , Cabanas Manés F. , Orcajo Gonzalo A. , González Andrés S.
Published: November 2021

A set of analytical equations for the calculation of the temperature in supercapacitors operating in constant-power applications is presented in this paper. Although the main operation modes of supercapacitors are constant-current and constant-power charge and discharge, this study was focused on the latter, since both sources and loads act as constant-power systems in a wide range of power conversion facilities. The starting point of this study is the classical supercapacitor model based on electrical and thermal parameters provided by manufacturers or also obtained by experimental means. The proposed mathematical analysis is based on the so-called incomplete gamma function that presents two major advantages over previously existing methods. Firstly, it is not necessary to solve any differential equations system by means of numerical methods, which reduces the required computational effort. Secondly, no simplifications to relief the calculations are made in the computation of any variable. The new formulation renders valid solutions even for high-power demand situations. Moreover, the temperature of the supercapacitor can be expressed as a function of time or any other electrical variable in the charging and discharging processes. Therefore, the proposed formulas are especially remarkable for the electrical and thermal dimensioning of supercapacitors.
Examining Post-Fire Perceptions of Selected Mitigation Strategies after the 2016 Horse River Wildland Fire in Alberta, Canada
Authors: Hassan Quazi K. , Rahaman Khan Rubayet , Ahmed M. Razu , Hossain Sheikh M.
Published: November 2021

Our aim was to study post-fire perceptions of selected mitigation strategies for wildland fire-induced risks proposed in a previous scientific study for the communities situated within the forested areas. Consequently, we considered engaging relevant professionals in the Regional Municipality of Wood Buffalo (RMWB), Alberta who experienced the costliest wildland fire occurrences in Canadian history known as the 2016 Horse River Fire (HRF). To meet our goal, we formulated a questionnaire based on the scientific evidence presented in a previous study and conducted a structured survey. Our results revealed that 24 professionals participated in the survey during the June 2020–April 2021 period, providing a 32% response rate. We observed that a high percentage of the participants agreed (i.e., between 63% and 80%) with the proposed wildland fire-induced risk mitigation strategies, including the presence of no to little vegetation in the 30 m buffer zone from the wildland–urban interface (WUI), extending the 30 m buffer zone to 70 m from the WUI, constructing a 70 m width ring road around the communities, and parking lots of the social infrastructures in the fringe of the communities encountering to the forest. We also found other views, including the use of non-combustible and fire-resistant construction materials, and developing the 70 m buffer zone as a recreational space.
High-Capacity and High-Quality Reversible Data Hiding Method Using Recurrent Round-Trip Embedding Strategy in the Quotient Image
Authors: Lee Chin-Feng , Wu Hua-Zhe
Published: November 2021

In previous research, scholars always think about how to improve the information hiding algorithm and strive to have the largest embedding capacity and better image quality, restoring the original image. This research mainly proposes a new robust and reversible information hiding method, recurrent robust reversible data hiding (triple-RDH), with a recurrent round-trip embedding strategy. We embed the secret message in a quotient image to increase the image robustness. The pixel value is split into two parts, HiSB and LoSB. A recurrent round-trip embedding strategy (referred to as double R-TES) is designed to adjust the predictor and the recursive parameter values, so the pixel value carrying the secret data bits can be first shifted to the right and then shifted to the left, resulting in pixel invariance, so the embedding capacity can be effectively increased repeatedly. Experimental results show that the proposed triple-RDH method can effectively increase the embedding capacity up to 310,732 bits and maintain a certain level of image quality. Compared with the existing pixel error expansion (PEE) methods, the triple-RDH method not only has a high capacity but also has robustness for image processing against unintentional attacks. It can also be used for capacity and image quality according to the needs of the application, performing adjustable embedding.
Application of Spinel and Hexagonal Ferrites in Heterogeneous Photocatalysis
Authors: Bielan Zuzanna , Dudziak Szymon , Kubiak Adam , Kowalska Ewa
Published: November 2021

Semiconducting materials display unique features that enable their use in a variety of applications, including self-cleaning surfaces, water purification systems, hydrogen generation, solar energy conversion, etc. However, one of the major issues is separation of the used materials from the process suspension. Therefore, chemical compounds with magnetic properties have been proposed as crucial components of photocatalytic composites, facilitating separation and recovery of photocatalysts under magnetic field conditions. This review paper presents the current state of knowledge on the application of spinel and hexagonal ferrites in heterogeneous photocatalysis. The first part focuses on the characterization of magnetic (nano)particles. The next section presents the literature findings on the single-phase magnetic photocatalyst. Finally, the current state of scientific knowledge on the wide variety of magnetic-photocatalytic composites is presented. A key aim of this review is to indicate that spinel and hexagonal ferrites are considered as an important element of heterogeneous photocatalytic systems and are responsible for the effective recycling of the photocatalytic materials.
BiTTM: A Core Biterms-Based Topic Model for Targeted Analysis
Authors: Wang Jiamiao , Chen Ling , Li Lei , Wu Xindong
Published: November 2021

While most of the existing topic models perform a full analysis on a set of documents to discover all topics, it is noticed recently that in many situations users are interested in fine-grained topics related to some specific aspects only. As a result, targeted analysis (or focused analysis) has been proposed to address this problem. Given a corpus of documents from a broad area, targeted analysis discovers only topics related with user-interested aspects that are expressed by a set of user-provided query keywords. Existing approaches for targeted analysis suffer from problems such as topic loss and topic suppression because of their inherent assumptions and strategies. Moreover, existing approaches are not designed to address computation efficiency, while targeted analysis is supposed to provide responses to user queries as soon as possible. In this paper, we propose a core BiTerms-based Topic Model (BiTTM). By modelling topics from core biterms that are potentially relevant to the target query, on one hand, BiTTM captures the context information across documents to alleviate the problem of topic loss or suppression; on the other hand, our proposed model enables the efficient modelling of topics related to specific aspects. Our experiments on nine real-world datasets demonstrate BiTTM outperforms existing approaches in terms of both effectiveness and efficiency.
Applying Heuristics to Generate Test Cases for Automated Driving Safety Evaluation
Authors: Stepien Leonard , Thal Silvia , Henze Roman , Nakamura Hiroki , Antona-Makoshi Jacobo , Uchida Nobuyuki , Raksincharoensak Pongsathorn
Published: November 2021

Comprehensive safety evaluation methodologies for automated driving systems that account for the large complexity real traffic are currently being developed. This work adopts a scenario-based safety evaluation approach and aims at investigating an advanced methodology to generate test cases by applying heuristics to naturalistic driving data. The targeted requirements of the generated test cases are severity, exposure, and realism. The methodology starts with the extraction of scenarios from the data and their split in two subsets—containing the relatively more critical scenarios and, respectively, the normal driving scenarios. Each subset is analysed separately, in regard to the parameter value distributions and occurrence of dependencies. Subsequently, a heuristic search-based approach is applied to generate test cases. The resulting test cases clearly discriminate between safety critical and normal driving scenarios, with the latter covering a wider spectrum than the former. The verification of the generated test cases proves that the proposed methodology properly accounts for both severity and exposure in the test case generation process. Overall, the current study contributes to fill a gap concerning the specific applicable methodologies capable of accounting for both severity and exposure and calls for further research to prove its applicability in more complex environments and scenarios.
Robustness of PET Radiomics Features: Impact of Co-Registration with MRI
Authors: Stefano Alessandro , Leal Antonio , Richiusa Selene , Trang Phan , Comelli Albert , Benfante Viviana , Cosentino Sebastiano , Sabini Maria G. , Tuttolomondo Antonino , Altieri Roberto , Certo Francesco , Barbagallo Giuseppe Maria Vincenzo , Ippolito Massimo , Russo Giorgio
Published: November 2021

Radiomics holds great promise in the field of cancer management. However, the clinical application of radiomics has been hampered by uncertainty about the robustness of the features extracted from the images. Previous studies have reported that radiomics features are sensitive to changes in voxel size resampling and interpolation, image perturbation, or slice thickness. This study aims to observe the variability of positron emission tomography (PET) radiomics features under the impact of co-registration with magnetic resonance imaging (MRI) using the difference percentage coefficient, and the Spearman’s correlation coefficient for three groups of images: (i) original PET, (ii) PET after co-registration with T1-weighted MRI and (iii) PET after co-registration with FLAIR MRI. Specifically, seventeen patients with brain cancers undergoing [11C]-Methionine PET were considered. Successively, PET images were co-registered with MRI sequences and 107 features were extracted for each mentioned group of images. The variability analysis revealed that shape features, first-order features and two subgroups of higher-order features possessed a good robustness, unlike the remaining groups of features, which showed large differences in the difference percentage coefficient. Furthermore, using the Spearman’s correlation coefficient, approximately 40% of the selected features differed from the three mentioned groups of images. This is an important consideration for users conducting radiomics studies with image co-registration constraints to avoid errors in cancer diagnosis, prognosis, and clinical outcome prediction.
Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
Authors: Mukhamediev Ravil I. , Symagulov Adilkhan , Kuchin Yan , Zaitseva Elena , Bekbotayeva Alma , Yakunin Kirill , Assanov Ilyas , Levashenko Vitaly , Popova Yelena , Akzhalova Assel , Bastaubayeva Sholpan , Tabynbaeva Laila
Published: November 2021

The use of unmanned aerial vehicles (UAVs) in various spheres of human activity is a promising direction for countries with very different types of economies. This statement refers to resource-rich economies as well. The peculiarities of such countries are associated with the dependence on resource prices since their economies present low diversification. Therefore, the employment of new technologies is one of the ways of increasing the sustainability of such economy development. In this context, the use of UAVs is a prospect direction, since they are relatively cheap, reliable, and their use does not require a high-tech background. The most common use of UAVs is associated with various types of monitoring tasks. In addition, UAVs can be used for organizing communication, search, cargo delivery, field processing, etc. Using additional elements of artificial intelligence (AI) together with UAVs helps to solve the problems in automatic or semi-automatic mode. Such UAV is named intelligent unmanned aerial vehicle technology (IUAVT), and its employment allows increasing the UAV-based technology efficiency. However, in order to adapt IUAVT in the sectors of economy, it is necessary to overcome a range of limitations. The research is devoted to the analysis of opportunities and obstacles to the adaptation of IUAVT in the economy. The possible economic effect is estimated for Kazakhstan as one of the resource-rich countries. The review consists of three main parts. The first part describes the IUAVT application areas and the tasks it can solve. The following areas of application are considered: precision agriculture, the hazardous geophysical processes monitoring, environmental pollution monitoring, exploration of minerals, wild animals monitoring, technical and engineering structures monitoring, and traffic monitoring. The economic potential is estimated by the areas of application of IUAVT in Kazakhstan. The second part contains the review of the technical, legal, and software-algorithmic limitations of IUAVT and modern approaches aimed at overcoming these limitations. The third part—discussion—comprises the consideration of the impact of these limitations and unsolved tasks of the IUAVT employment in the areas of activity under consideration, and assessment of the overall economic effect.
Prescribed Performance Control for the Upper-Limb Exoskeleton System in Passive Rehabilitation Training Tasks
Authors: Zhao Zhirui , Xiao Jichun , Jia Hongyun , Zhang Hang , Hao Lina
Published: November 2021

In this study, a model-free adaptive sliding mode control method was developed in combination with the prescribed performance method. On this basis, this study attempted to fulfill the joint position tracking trajectory task for the one-degree of freedom (DOF) upper-limb exoskeleton in passive robot-assisted rehabilitation. The proposed method is capable of addressing the defect of the initial error in the controller design and the application by adopting a tuning function, as compared with other prescribed performance methods. Moreover, the method developed here was not determined by the dynamic model parameters, which merely exploit the input and output data. Theoretically, the stability exhibited by the proposed controller and the tracking performance can be demonstrated. From the experimental results, the root mean square of the tracking error is equal to 1.06 degrees, and the steady-state tracking error converges to 1.91 degrees. These results can verify the expected performance of the developed control method.
Response Statistics of a Shape Memory Alloy Oscillator with Random Excitation
Authors: Guo Rong , Liu Qi , Li Junlin , Xu Yong
Published: November 2021

This paper aimed to explore analytically the influences of random excitation on a shape memory alloy (SMA) oscillator. Firstly, on the basis of the deterministic SMA model under a harmonic excitation, we introduce a stochastic SMA model with a narrow-band random excitation. Subsequently, a theoretical analysis for the proposed SMA model was achieved through a multiple-scale method coupled with a perturbation technique. All of the obtained approximate analytical solutions were verified by numerical simulation results, and good agreements were observed. Then, effects of the random excitation and the temperature value on the system responses were investigated in detail. Finally, we found that stochastic switch and bifurcation can be induced by the random fluctuation, which were further illustrated through time history and steady-state probability density function. These results indicate that the random excitation has a significant impact on dynamics of the SMA model. This research provides a certain theoretical basis for the design and vibration control of the SMA oscillator in practical application.
Deep Convolutional Neural Network with KNN Regression for Automatic Image Annotation
Authors: Bensaci Ramla , Khaldi Belal , Aiadi Oussama , Benchabana Ayoub
Published: November 2021

Automatic image annotation is an active field of research in which a set of annotations are automatically assigned to images based on their content. In literature, some works opted for handcrafted features and manual approaches of linking concepts to images, whereas some others involved convolutional neural networks (CNNs) as black boxes to solve the problem without external interference. In this work, we introduce a hybrid approach that combines the advantages of both CNN and the conventional concept-to-image assignment approaches. J-image segmentation (JSEG) is firstly used to segment the image into a set of homogeneous regions, then a CNN is employed to produce a rich feature descriptor per area, and then, vector of locally aggregated descriptors (VLAD) is applied to the extracted features to generate compact and unified descriptors. Thereafter, the not too deep clustering (N2D clustering) algorithm is performed to define local manifolds constituting the feature space, and finally, the semantic relatedness is calculated for both image–concept and concept–concept using KNN regression to better grasp the meaning of concepts and how they relate. Through a comprehensive experimental evaluation, our method has indicated a superiority over a wide range of recent related works by yielding F1 scores of 58.89% and 80.24% with the datasets Corel 5k and MSRC v2, respectively. Additionally, it demonstrated a relatively high capacity of learning more concepts with higher accuracy, which results in N+ of 212 and 22 with the datasets Corel 5k and MSRC v2, respectively.
Bladeless Heart Pump Design: Modeling and Numerical Analysis
Authors: Markučič Vedrana , Šavar Mario , Krizmanić Severino , Biočina Bojan
Published: November 2021

In this paper, the design of a new type of heart pump is presented. The uniqueness of this pump concept is that the mechanical energy is transferred to the fluid by the rotation of flat disks without blades. Both theoretical and numerical analysis are used to determine the pump design parameters. The pump design parameters are calculated using the Navier-Stokes equations. The application of computational fluid dynamics is used to define the geometric design. The pump head and flow have to be within strictly defined limits to ensure normal blood circulation. The negative impact of the pump on the blood must be minimal (no stagnation and recirculation zones, shear stress in the acceptable range). It is also important to achieve the smallest possible pump volume. For the pump operating point of ∆p = 65 mmHg, Q = 5.43 L/min, and ω = 6000 rpm, the design parameters are inner radius R1 = 12 mm, outer radius R2 = 15 mm, the distance between disk of h = 1 mm, and the number of disks is n = 6. The shear stress in the rotor is in range 46–108 Pa and the pump residence time is 0.0194 s.
Light-Convolution Dense Selection U-Net (LDS U-Net) for Ultrasound Lateral Bony Feature Segmentation
Authors: Banerjee Sunetra , Lyu Juan , Huang Zixun , Leung Hung Fat Frank , Lee Timothy Tin-Yan , Yang De , Su Steven , Zheng Yongping , Ling Sai-Ho
Published: November 2021

Scoliosis is a widespread medical condition where the spine becomes severely deformed and bends over time. It mostly affects young adults and may have a permanent impact on them. A periodic assessment, using a suitable modality, is necessary for its early detection. Conventionally, the usually employed modalities include X-ray and MRI, which employ ionising radiation and are expensive. Hence, a non-radiating 3D ultrasound imaging technique has been developed as a safe and economic alternative. However, ultrasound produces low-contrast images that are full of speckle noise, and skilled intervention is necessary for their processing. Given the prevalent occurrence of scoliosis and the limitations of scalability of human expert interventions, an automatic, fast, and low-computation assessment technique is being developed for mass scoliosis diagnosis. In this paper, a novel hybridized light-weight convolutional neural network architecture is presented for automatic lateral bony feature identification, which can help to develop a fully-fledged automatic scoliosis detection system. The proposed architecture, Light-convolution Dense Selection U-Net (LDS U-Net), can accurately segment ultrasound spine lateral bony features, from noisy images, thanks to its capabilities of smartly selecting only the useful information and extracting rich deep layer features from the input image. The proposed model is tested using a dataset of 109 spine ultrasound images. The segmentation result of the proposed network is compared with basic U-Net, Attention U-Net, and MultiResUNet using various popular segmentation indices. The results show that LDS U-Net provides a better segmentation performance compared to the other models. Additionally, LDS U-Net requires a smaller number of parameters and less memory, making it suitable for a large-batch screening process of scoliosis without a high computational requirement.
FPGA Implementation of IEC-61131-3-Based Hardware Aided Counters for PLC
Authors: Chmiel Miroslaw , Czerwinski Robert , Malcher Andrzej
Published: November 2021

The article discusses counters defined in the IEC 61131-3 standard. The possible implementations of standard counters function blocks in FPGAs are presented. First, counters are implemented as classical hardware-based modules. Second, counters are designed as the FPGA built-in memory blocks with a single common executing unit. These solutions are compared to each other and compared with counters realized in commercially available PLCs like Siemens SIMATIC S7 controllers. The structure of integrated hardware–software CPU with counters is presented. The paper presents how the designer can take advantage of the specific features of the FPGA devices to optimize both the utilization of resources and speed of realization of the particular blocks. Experimental results prove the high efficiency of the proposed solutions.
A Zeroth-Order Adaptive Learning Rate Method to Reduce Cost of Hyperparameter Tuning for Deep Learning
Authors: Li Yanan , Ren Xuebin , Zhao Fangyuan , Yang Shusen
Published: November 2021

Due to powerful data representation ability, deep learning has dramatically improved the state-of-the-art in many practical applications. However, the utility highly depends on fine-tuning of hyper-parameters, including learning rate, batch size, and network initialization. Although many first-order adaptive methods (e.g., Adam, Adagrad) have been proposed to adjust learning rate based on gradients, they are susceptible to the initial learning rate and network architecture. Therefore, the main challenge of using deep learning in practice is how to reduce the cost of tuning hyper-parameters. To address this, we propose a heuristic zeroth-order learning rate method, Adacomp, which adaptively adjusts the learning rate based only on values of the loss function. The main idea is that Adacomp penalizes large learning rates to ensure the convergence and compensates small learning rates to accelerate the training process. Therefore, Adacomp is robust to the initial learning rate. Extensive experiments, including comparison to six typically adaptive methods (Momentum, Adagrad, RMSprop, Adadelta, Adam, and Adamax) on several benchmark datasets for image classification tasks (MNIST, KMNIST, Fashion-MNIST, CIFAR-10, and CIFAR-100), were conducted. Experimental results show that Adacomp is not only robust to the initial learning rate but also to the network architecture, network initialization, and batch size.
A Study on the Anomaly Detection of Engine Clutch Engagement/Disengagement Using Machine Learning for Transmission Mounted Electric Drive Type Hybrid Electric Vehicles
Authors: Ji Yonghyeok , Jeong Seongyong , Cho Yeongjin , Seo Howon , Bang Jaesung , Kim Jihwan , Lee Hyeongcheol
Published: November 2021

Transmission mounted electric drive type hybrid electric vehicles (HEVs) engage/disengage an engine clutch when EV↔HEV mode transitions occur. If this engine clutch is not adequately engaged or disengaged, driving power is not transmitted correctly. Therefore, it is required to verify whether engine clutch engagement/disengagement operates normally in the vehicle development process. This paper studied machine learning-based methods for detecting anomalies in the engine clutch engagement/disengagement process. We trained the various models based on multi-layer perceptron (MLP), long short-term memory (LSTM), convolutional neural network (CNN), and one-class support vector machine (one-class SVM) with the actual vehicle test data and compared their results. The test results showed the one-class SVM-based models have the highest anomaly detection performance. Additionally, we found that configuring the training architecture to determine normal/anomaly by data instance and conducting one-class classification is proper for detecting anomalies in the target data.