bluuebunny/crossref_metadata_2025 huggingface dataset:
Ceramic Nanomaterials in Caries Prevention: A Narrative Review
Authors: Nizami Mohammed Zahedul Islam , Xu Veena Wenqing , Yin Iris Xiaoxue , Lung Christie Ying Kei , Niu John Yun , Chu Chun Hung
Published: December 2022

Ceramic nanomaterials are nanoscale inorganic metalloid solids that can be synthesised by heating at high temperatures followed by rapid cooling. Since the first nanoceramics were developed in the 1980s, ceramic nanomaterials have rapidly become one of the core nanomaterials for research because of their versatility in application and use in technology. Researchers are developing ceramic nanomaterials for dental use because ceramic nanoparticles are more stable and cheaper in production than metallic nanoparticles. Ceramic nanomaterials can be used to prevent dental caries because some of them have mineralising properties to promote the remineralisation of tooth tissue. Ceramic minerals facilitate the remineralisation process and maintain an equilibrium in pH levels to maintain tooth integrity. In addition, ceramic nanomaterials have antibacterial properties to inhibit the growth of cariogenic biofilm. Researchers have developed antimicrobial nanoparticles, conjugated ceramic minerals with antibacterial and mineralising properties, to prevent the formation and progression of caries. Common ceramic nanomaterials developed for caries prevention include calcium-based (including hydroxyapatite-based), bioactive glass-based, and silica-based nanoparticles. Calcium-based ceramic nanomaterials can substitute for the lost hydroxyapatite by depositing calcium ions. Bioactive glass-based nanoparticles contain surface-reactive glass that can form apatite crystals resembling bone and tooth tissue and exhibit chemical bonding to the bone and tooth tissue. Silica-based nanoparticles contain silica for collagen infiltration and enhancing heterogeneous mineralisation of the dentin collagen matrix. In summary, ceramic nanomaterials can be used for caries prevention because of their antibacterial and mineralising properties. This study gives an overview of ceramic nanomaterials for the prevention of dental caries.
Investigation of the Effects of Pulse-Atomic Force Nanolithography Parameters on 2.5D Nanostructures’ Morphology
Authors: Pellegrino Paolo , Farella Isabella , Cascione Mariafrancesca , De Matteis Valeria , Bramanti Alessandro Paolo , Della Torre Antonio , Quaranta Fabio , Rinaldi Rosaria
Published: December 2022

In recent years, Atomic Force Microscope (AFM)-based nanolithography techniques have emerged as a very powerful approach for the machining of countless types of nanostructures. However, the conventional AFM-based nanolithography methods suffer from low efficiency, low rate of patterning, and high complexity of execution. In this frame, we first developed an easy and effective nanopatterning technique, termed Pulse-Atomic Force Lithography (P-AFL), with which we were able to pattern 2.5D nanogrooves on a thin polymer layer. Indeed, for the first time, we patterned nanogrooves with either constant or varying depth profiles, with sub-nanometre resolution, high accuracy, and reproducibility. In this paper, we present the results on the investigation of the effects of P-AFL parameters on 2.5D nanostructures’ morphology. We considered three main P-AFL parameters, i.e., the pulse’s amplitude (setpoint), the pulses’ width, and the distance between the following indentations (step), and we patterned arrays of grooves after a precise and well-established variation of the aforementioned parameters. Optimizing the nanolithography process, in terms of patterning time and nanostructures quality, we realized unconventional shape nanostructures with high accuracy and fidelity. Finally, a scanning electron microscope was used to confirm that P-AFL does not induce any damage on AFM tips used to pattern the nanostructures.
Cefiderocol against Multi-Drug and Extensively Drug-Resistant Escherichia coli: An In Vitro Study in Poland
Authors: Zalas-Więcek Patrycja , Płachta Katarzyna , Gospodarek-Komkowska Eugenia
Published: December 2022

Cefiderocol (CFDC) is a novel, broad-spectrum siderophore cephalosporin with potential activity against multi-drug (MDR) and extensively drug-resistant (XDR) Enterobacterales, including carbapenem-resistant strains. We assessed the in vitro susceptibility to CFDC of MDR, and XDR E. coli isolates derived from clinical samples of hospitalized patients. Disk diffusion (DD) and MIC (minimum inhibitory concentration) test strip (MTS) methods were used. The results were interpreted based on EUCAST (version 12.0 2022) recommendations. Among all E. coli isolates, 98 (94.2%) and 99 (95.2%) were susceptible to CFDC when the DD and MTS methods were used, respectively (MIC range: <0.016–4 µg/mL, MIC50: 0.19 µg/mL, MIC90: 0.75 µg/mL). With the DD and MTS methods, all (MIC range: 0.016–2 µg/mL, MIC50: 0.19 µg/mL, MIC90: 0.75 µg/mL) but three (96.6%) ESBL-positive isolates were susceptible to CFDC. Out of all the metallo-beta-lactamase-positive E. coli isolates (MIC range: 0.016–4 µg/mL, MIC50: 0.5 µg/mL, MIC90: 1.5 µg/mL), 16.7% were resistant to CFDC with the DD method, while 11.1% were resistant to CFDC when the MTS method was used. CFDC is a novel therapeutic option against MDR and XDR E. coli isolates and is promising in the treatment of carbapenem-resistant E. coli strains, also for those carrying Verona integron-encoded metallo-beta-lactamases, when new beta-lactam-beta-lactamase inhibitors cannot be used.
Multidrug-Resistant Gram-Negative Bacteria Contaminating Raw Meat Sold in Accra, Ghana
Authors: Baah Deric A. , Kotey Fleischer C. N. , Dayie Nicholas T. K. D. , Codjoe Francis S. , Tetteh-Quarcoo Patience B. , Donkor Eric S.
Published: December 2022

Background: Efforts to combat antimicrobial resistance (AMR) should be based on the One Health approach, involving human health, animal health, and the environment. In Ghana, previous studies on AMR have given little attention to animal source food, a major route of transmission of antibiotic-resistant zoonotic pathogens. The aim of this study was to investigate the occurrence of multidrug-resistant (MDR) bacteria in meat sold in Accra. Methods: This was a cross-sectional study in which 270 meat samples (90 each of beef, goat meat, and chicken) were collected, and investigated for contamination with multidrug-resistant bacteria. The bacteria were subjected to susceptibility testing against amikacin (30 µg), ampicillin (10 µg), amoxicillin-clavulanate (20/10 µg), cefuroxime (30 µg), ceftriaxone (30 µg), ceftazidime (30 µg), cefepime (30 µg), ciprofloxacin (5 µg), trimethoprim-sulfamethoxazole (1.25/23.75 µg), ertapenem (10 µg), meropenem (10 µg), imipenem (10 µg), tigecycline (15 µg), and gentamicin (10 µg). Results: Thirty-two different types of bacteria, totalling 558, were isolated, the predominant being Escherichia coli (44.6%), Aeromonas hydrophila (19.9%), Vibrio cholerae (3.4%), Aeromonas veronii (3.2%), and Klebsiella pneumoniae (3.1%). The prevalence of MDR among the contaminating bacteria was 14.9%. The MDR distribution among the predominant bacteria was Escherichia coli (18.7%), Aeromonas hydrophila (11.1%), Vibrio cholerae and Aeromonas veronii (0.0% each), and K. pneumoniae (5.6%). Moreover, 2.0% of the contaminating bacteria were extended-spectrum beta-lactamase (ESBL) producers, all of which occurred in the chicken samples, and their distribution was: Escherichia coli (1.3%), Klebsiella pneumoniae, Pantoea spp., Enterobacter cloacae, and Serratia plymuthica (0.2% each). Conclusions: The meat samples were heavily contaminated with Escherichia coli and Aeromonas hydrophila, and less frequently, with Vibrio cholerae, Klebsiella pneumoniae, and other organisms. The prevalence of multidrug-resistant bacteria was moderate (14.9%), while that of ESBL producers was low (2%).
Strategies of Influenza A Virus to Ensure the Translation of Viral mRNAs
Authors: Li Hui-Chun , Yang Chee-Hing , Lo Shih-Yen
Published: December 2022

Viruses are obligatorily intracellular pathogens. To generate progeny virus particles, influenza A viruses (IAVs) have to divert the cellular machinery to ensure sufficient translation of viral mRNAs. To this end, several strategies have been exploited by IAVs, such as host gene shutoff, suppression of host innate immune responses, and selective translation of viral mRNAs. Various IAV proteins are responsible for host gene shutoff, e.g., NS1, PA-X, and RdRp, through inhibition of cellular gene transcription, suppression of cellular RNA processing, degradation of cellular RNAs, and blockage of cellular mRNA export from the nucleus. Host shutoff should suppress the innate immune responses and also increase the translation of viral mRNAs indirectly due to the reduced competition from cellular mRNAs for cellular translational machinery. However, many other mechanisms are also responsible for the suppression of innate immune responses by IAV, such as prevention of the detection of the viral RNAs by the RLRs, inhibition of the activities of proteins involved in signaling events of interferon production, and inhibition of the activities of interferon-stimulated genes, mainly through viral NS1, PB1-F2, and PA-X proteins. IAV mRNAs may be selectively translated in favor of cellular mRNAs through interacting with viral and/or cellular proteins, such as NS1, PABPI, and/or IFIT2, in the 5′-UTR of viral mRNAs. This review briefly summarizes the strategies utilized by IAVs to ensure sufficient translation of viral mRNAs focusing on recent developments.
Roles of Glutamate Receptor-Like Channels (GLRs) in Plant Growth and Response to Environmental Stimuli
Authors: Yu Bo , Liu Nian , Tang Siqi , Qin Tian , Huang Junli
Published: December 2022

Plant glutamate receptor-like channels (GLRs) are the homologues of ionotropic glutamate receptors (iGluRs) that mediate neurotransmission in mammals, and they play important roles in various plant-specific physiological processes, such as pollen tube growth, sexual reproduction, root meristem proliferation, internode cell elongation, stomata aperture regulation, and innate immune and wound responses. Notably, these biological functions of GLRs have been mostly linked to the Ca2+-permeable channel activity as GLRs can directly channel the transmembrane flux of Ca2+, which acts as a key second messenger in plant cell responses to both endogenous and exogenous stimuli. Thus, it was hypothesized that GLRs are mainly involved in Ca2+ signaling processes in plant cells. Recently, great progress has been made in GLRs for their roles in long-distance signal transduction pathways mediated by electrical activity and Ca2+ signaling. Here, we review the recent progress on plant GLRs, and special attention is paid to recent insights into the roles of GLRs in response to environmental stimuli via Ca2+ signaling, electrical activity, ROS, as well as hormone signaling networks. Understanding the roles of GLRs in integrating internal and external signaling for plant developmental adaptations to a changing environment will definitely help to enhance abiotic stress tolerance.
Microbial Intervention: An Approach to Combat the Postharvest Pathogens of Fruits
Authors: Verma Sargam , Azevedo Lucas Carvalho Basilio , Pandey Jyoti , Khusharia Saksham , Kumari Madhuree , Kumar Dharmendra , Kaushalendra  , Bhardwaj Nikunj , Teotia Pratibha , Kumar Ajay
Published: December 2022

Plants host diverse microbial communities, which undergo a complex interaction with each other. Plant-associated microbial communities provide various benefits to the host directly or indirectly, viz. nutrient acquisition, protection from pathogen invaders, mitigation from different biotic and abiotic stress. Presently, plant-associated microbial strains are frequently utilized as biofertilizers, biostimulants and biocontrol agents in greenhouse and field conditions and have shown satisfactory results. Nowadays, the plant/fruit microbiome has been employed to control postharvest pathogens and postharvest decay, and to maintain the quality or shelf life of fruits. In this context, the intervention of the natural fruit microbiome or the creation of synthetic microbial communities to modulate the functional attributes of the natural microbiome is an emerging aspect. In this regard, we discuss the community behavior of microbes in natural conditions and how the microbiome intervention plays a crucial role in the postharvest management of fruits.
Intraspecific Variability of Wild-Growing Common Valerian (Valeriana officinalis L.)
Authors: Bączek Katarzyna Barbara , Kosakowska Olga , Boczkowska Maja , Bolc Paulina , Chmielecki Rafał , Pióro-Jabrucka Ewelina , Raj Kavana , Węglarz Zenon
Published: December 2022

Common valerian (Valeriana officinalis L.) is an important medicinal plant revealing sedative, hypotensive, anti-spasmodic and anxiolytic activity. The purpose of the study was to determine the intraspecific variability of the common valerian growing wild in Poland and the ‘Lubelski’ landrace, as to their developmental traits, chemical composition and selected genetic parameters. Both wild-growing populations (19) and the landrace (1) were evaluated under ex situ conditions. Observations of the underground organs parameters, both developmental and chemical (according to the European Pharmacopoeia) were carried out in the first year of the plant’s development, while the characteristics of the aboveground organs, followed by the sowing value of seeds (according to the International Seed Testing Association)—in the second year. The genetic analyses were performed using the NGS-DArT-seq method. Results indicate the presence of five different gene pools covering the regions of population’s origin, with a gene flow within and between them. A high level of developmental and chemical variabilities among the wild-growing populations was noticed, however without a clear relation to the region of the origin. The mass of underground organs ranged from 107.4 to 403.6 g FW × plant−1 with the content of sesquiterpenic acids at the level of 0.004–0.094%. Population no 18 was distinguished by the highest content of sesquiterpenic acids and the relatively high mass of underground organs, followed by the admixture of the gene pool, typical for the ‘Lubelski’ landrace. Unlike the ‘Lubelski’ landrace, the wild-growing populations were characterized by a high amount of an essential oils (3.90 to 10.04 mL/kg), which may be promising from the perspective of their potential use. In turn, the sowing value of the seeds obtained from the populations, expressed as the germinability, was rather low (25.25–62.25%).
Effective Resistance to Four Fungal Foliar Diseases in Samples of Wild Triticum L. Species from the VIR (N.I. Vavilov All-Russian Institute of Plant Genetic Resources) Collection: View from Vavilov’s Concepts of Plant Immunity
Authors: Tyryshkin Lev G. , Lysenko Natalia S. , Kolesova Maria A.
Published: December 2022

To identify new sources of effective resistance to four foliar diseases of wheat, 173 accessions of four wheat species, Triticum boeoticum, T. urartu, T. araraticum, and T. dicoccoides, from the VIR collection were tested at the juvenile and adult growth stages for resistance to leaf rust (Pt = Puccinia triticina), powdery mildew (Bgt = Blumeria graminis tritici), Septoria nodorum blotch (SNB), and dark-brown leaf spot blotch (HLB = Helminthospjrium leaf blotch). The accessions included new additions to the collection, some old samples that had never been tested before, as well as earlier tested samples noted for high levels of juvenile resistance to some fungal diseases. Natural populations of Puccinia triticina and Blumeria graminis f. sp. tritici, mixture of Parastagonospora nodorum and Bipolaris sorokiniana isolates were used to inoculate and to evaluate resistance to Pt, Bgt, SNB, and HLB, respectively. Two samples of T. boeoticum, three of T. urartu, and one of T. araraticum were resistant to leaf rust at both tested stages. Further tests (phytopathological and molecular analyses) excluded Lr9, Lr19, Lr24, Lr41, or Lr47 as single genes controlling resistance; hence, these accessions likely carry new effective leaf rust resistance genes. High level of Bgt resistance was identified in three entries of T. boeoticum, one of T. araraticum, and eleven of T. dicoccoides. All tested accessions were susceptible to HLB and SNB at both tested stages. Accessions identified as resistant are valuable plant material for introgressive hybridization in bread and durum wheat breeding. The results are discussed in the context of N.I. Vavilov’s concept of crop origin and diversity, and the laws of plant natural immunity to infectious diseases.
Natural Deep Eutectic Solvent-Based Matrix Solid Phase Dispersion (MSPD) Extraction for Determination of Bioactive Compounds from Sandy Everlasting (Helichrysum arenarium L.): A Case of Stability Study
Authors: Ivanović Milena , Krajnc Peter , Mlinarič Aleš , Razboršek Maša Islamčević
Published: December 2022

In the present study, vortex-assisted matrix solid-phase dispersion (VA-MSPD) extraction was used to isolate the major bioactive compounds from H. arenarium. To reduce the negative environmental impact of the conventionally used organic solvents, four different choline chloride-based natural deep eutectic solvents (NADES) were investigated as possible eluents. The most influential VA-MSPD extraction parameters: stationary phase (adsorbent), adsorbent/sample ratio, vortex time, and volume of extraction solvent were systematically optimized. Ultrasound-assisted extraction with 80% MeOH was used as the standard method for the comparison of results. The stability of the obtained extracts was studied over a period of 0 to 60 days at three different temperatures (−18 °C, 4 °C, and 25 °C). All extracts were evaluated both spectrophotometrically (determination of total phenolic content (TPC) and antioxidant activity by ABTS and FRAP assay) and chromatographically (HPLC-UV). NADES based on choline chloride and lactic acid (ChCl-LA) was selected as the most effective extractant, with a determined TPC value of its extract of 38.34 ± 0.09 mg GA/g DW (27% higher than the methanolic VA-MSPD extract) and high antioxidant activity. The content of individual phenolic compounds (chlorogenic acid, dicaffeoylquinic acid isomers, naringenin isomers, and chalcones) in the ChCl-LA extract, determined by HPLC-UV, was comparable to that of the conventionally obtained one. Moreover, the stabilization effect of ChCl-LA was confirmed for the studied compounds: chlorogenic acid, naringenin-4′-O-glucoside, tomoroside A, naringenin-5-O-glucoside, isosalipurposide, and naringenin. The optimum VA-MSPD conditions for the extraction of H. arenarium polyphenols were: florisil/sample ratio of 0.5/1, a vortex time of 2 min, and an elution volume of ChCl-LA of 10 mL.
Environmental Impact Assessment of a Solar Drying Unit for the Transformation of Food Waste into Animal Feed
Authors: Abeliotis Konstadinos , Chroni Christina , Lasaridi Katia , Terzis Evangelos , Galliou Fenia , Manios Thrassyvoulos
Published: December 2022

Food waste valorization via its transformation into animal feed is a viable alternative for improving food security and the diversion of organic waste from landfills. The manuscript presents the environmental impact assessment of the construction and operation of a novel solar food waste drying unit on the island of Crete in Greece, which is treating food waste from hotels. Life cycle assessment is utilized for the impact assessment. The results indicate a total carbon footprint of approximately 217.5 kg CO2 eq. per ton of treated food waste. In conclusion, the operation phase is the major contributor to the environmental impacts, due to the utilization of electricity.
Column Adsorption Studies for the Removal of Ammonium Using Na-Zeolite-Based Geopolymers
Authors: Sundhararasu Elavarasi , Runtti Hanna , Kangas Teija , Pesonen Janne , Lassi Ulla , Tuomikoski Sari
Published: December 2022

The aim of this study was to examine the removal of ammonium ions from a synthetic model solution by using Na-zeolite-based geopolymers. Na-zeolite (=analcime) is a residue from mining industry. Three adsorbents were prepared from Na-zeolite using different production steps and metakaolin as a blending agent. These novel adsorbents were investigated in a fixed-bed column system where the effects of different flow rates with the initial ammonium concentration of 40 mg/L were studied. The Thomas, Bohart–Adams and Yoon–Nelson breakthrough curve models fitted well with the experimental data with a high R2 value. After adsorption experiments, adsorbents were regenerated using a mixture of 0.2 M NaCl and 0.1 M NaOH as a regeneration agent; after that, adsorbents were reutilised for ammonium ion adsorption for three adsorption–regeneration cycles. The results of the experiment indicate that all the prepared analcime-based geopolymers are suitable adsorbents for the removal of ammonium ions and that capacity remains nearly constant for two of them during two adsorption–regeneration cycles.
High-Speed and High-Power Ferroelectric Switching Current Measurement Instrument for Materials with Large Coercive Voltage and Remanent Polarization
Authors: Yazawa Keisuke , Zakutayev Andriy , Brennecka Geoff L.
Published: December 2022

A high-speed and high-power current measurement instrument is described for measuring rapid switching of ferroelectric samples with large spontaneous polarization and coercive field. Instrument capabilities (±200 V, 200 mA, and 200 ns order response) are validated with a LiTaO3 single crystal whose switching kinetics are well known. The new instrument described here enables measurements that are not possible using existing commercial measurement systems, including the observation of ferroelectric switching in large coercive field and large spontaneous polarization Al0.7Sc0.3N thin films.
Thermal Strain Detection for Concrete Structure Cold Shrinkage under Stress Constraint with FBG
Authors: Yang Lubing , Li Chuan , Luo Chuan
Published: December 2022

Additional strain increments occur in concrete subject to stress constraints during cold shrinkage, resulting in irregular deformation and reducing the concrete structure’s stability. When an annular concrete structure is subjected to radial pressure, two tensile stress concentration zones will appear at the intersection of the inner wall and the diameter along the pressure direction. When exposed to low temperatures, the total strain in the tensile stress concentration zones is caused by the combined effect of applied stress strain and thermal strain. Then, the thermal strain of the structure can be obtained from the difference between the total strain and the applied stress strain. Gradient cooling was performed after applying radial pressure to the annular concrete using a counterforce device. The applied stress strain and total strain of the tensile stress concentration zones are measured by fiber Bragg grating (FBG) strain sensors fixed along the stress direction. According to the measurement results, the thermal strains of the concrete structure under the stress constraint are extracted to analyze the influence of the tensile stress constraint on the thermal strain of the concrete structure. In the temperature range of −40∘C∼20∘C, the thermal strains of the structure under radial pressures of 1500 N, 2000 N, and 3000 N are extracted, respectively. The thermal expansion coefficients are calculated based on the thermal strain of the structure. The free thermal expansion coefficient of concrete structures fluctuates around 11×10−6/∘C. When the temperature is reduced to 10∘C, the difference between the thermal expansion coefficient under the stress constraint and the free thermal expansion coefficient is the largest. When the temperature is reduced to −20∘C, the thermal expansion coefficients under each stress condition are close to the same. The results show that the stress confinement significantly inhibits the cold shrinkage of the concrete structure, and the inhibitory effect is gradually weakened when the temperature decreases.
Saliency-Based Rotor Spatial Position Displacement Self-Sensing for Self-Bearing Machines
Authors: Kang Ye gu , Fernandez Daniel , Reigosa David Diaz
Published: December 2022

Self-bearing machines do not contain physical bearings but magnetic bearings. Both rotor rotary and spatial positions displacement are required in these types of machines to control the rotor position while it is levitating. Self-bearing machines often use external sensors for x (horizontal) and y (vertical) spatial position measurement, which will result in additional cost, volume, complexity, and number of parts susceptible to failure. To overcome these issues, this paper proposes a xy-position estimation self-sensing technique based on both main- and cross-inductance variation. The proposed method estimates x and y position based on inductive saliency between two sets of three-phase coils. The proposed idea is applied on a combined winding self-bearing machine which does not require additional suspension force winding. No additional search coil placement for xy-position estimation is required. Therefore, the proposed algorithm can result in a compact size self-bearing machine that does not require external sensors for xy-position measurement and suspension force winding.
Blockchain and Secure Element, a Hybrid Approach for Secure Energy Smart Meter Gateways
Authors: Zakaret Carine , Peladarinos Nikolaos , Cheimaras Vasileios , Tserepas Efthymios , Papageorgas Panagiotis , Aillerie Michel , Piromalis Dimitrios , Agavanakis Kyriakos
Published: December 2022

This paper presents a new hybrid approach that is suitable for application to energy smart meter gateways, based on combining both blockchain and Secure Element (SE) technologies serving the roles of a secure distributed data storage system and an essential component for building a “root of trust” in IoT platforms simultaneously. Blockchain technology alone may not completely secure a transaction because it only guarantees data immutability, while in most cases, the data has to be also secured at the point of generation. The proposed combinational approach aims to build a robust root of trust by introducing the SE, which will provide IoT devices with trusted computed resources. The feasibility of the proposed method is validated by testing three different implementation scenarios, using different Secure Element systems (SES) combined with blockchain and LPWAN communication technologies to encrypt, transmit, and save data. This hybrid approach aids in overcoming the obstructions of using any one technology alone, and its use is demonstrated with a case study for an Energy Smart Metering gateway that enables the implementation of a local Peer to Peer energy trading scheme that is end-to-end secure and decentralized.
AWANet: Attentive-Aware Wide-Kernels Asymmetrical Network with Blended Contour Information for Salient Object Detection
Authors: Ullah Inam , Jian Muwei , Shaheed Kashif , Hussain Sumaira , Ma Yuling , Xu Lixian , Muhammad Khan
Published: December 2022

Although deep learning-based techniques for salient object detection have considerably improved over recent years, estimated saliency maps still exhibit imprecise predictions owing to the internal complexity and indefinite boundaries of salient objects of varying sizes. Existing methods emphasize the design of an exemplary structure to integrate multi-level features by employing multi-scale features and attention modules to filter salient regions from cluttered scenarios. We propose a saliency detection network based on three novel contributions. First, we use a dense feature extraction unit (DFEU) by introducing large kernels of asymmetric and grouped-wise convolutions with channel reshuffling. The DFEU extracts semantically enriched features with large receptive fields and reduces the gridding problem and parameter sizes for subsequent operations. Second, we suggest a cross-feature integration unit (CFIU) that extracts semantically enriched features from their high resolutions using dense short connections and sub-samples the integrated information into different attentional branches based on the inputs received for each stage of the backbone. The embedded independent attentional branches can observe the importance of the sub-regions for a salient object. With the constraint-wise growth of the sub-attentional branches at various stages, the CFIU can efficiently avoid global and local feature dilution effects by extracting semantically enriched features via dense short-connections from high and low levels. Finally, a contour-aware saliency refinement unit (CSRU) was devised by blending the contour and contextual features in a progressive dense connected fashion to assist the model toward obtaining more accurate saliency maps with precise boundaries in complex and perplexing scenarios. Our proposed model was analyzed with ResNet-50 and VGG-16 and outperforms most contemporary techniques with fewer parameters.
Offline User Authentication Ensuring Non-Repudiation and Anonymity
Authors: Chang Ya-Fen , Tai Wei-Liang , Fung Ka-Ho
Published: December 2022

User authentication is the key to ensuring that only authorized users can deal with specific affairs and access services. Applications or systems possessing different properties or requirements need different authentication schemes. For example, some institutions or companies need executives to manage or inspect their corresponding departments while the inspected department should not know who the executives are but only can verify their legitimacy. This paper designs a non-repudiation and anonymity-ensured user authentication system to meet the mentioned special requirements. We also propose a user authentication scheme to ensure that the designed system can work as claimed. In the system, a department is equipped with an authentication device, namely the department authentication device, to authenticate an executive while the executive’s identity is not revealed to the department and only the department’s authentication device can identify the executive for non-repudiation. An executive is equipped with an authentication device to have himself/herself authenticated by the department’s authentication device. Moreover, authentication data stored in an executive’s authentication device does not need to be updated even when management personnel changes are made.
Polarization Super-Resolution Imaging Method Based on Deep Compressed Sensing
Authors: Xu Miao , Wang Chao , Wang Kaikai , Shi Haodong , Li Yingchao , Jiang Huilin
Published: December 2022

The division of focal plane (DoFP) polarization imaging sensors, which can simultaneously acquire the target’s two-dimensional spatial information and polarization information, improves the detection resolution and recognition capability by capturing the difference in polarization characteristics between the target and the background. In this paper, we propose a novel polarization imaging method based on deep compressed sensing (DCS) by adding digital micromirror devices (DMD) to an optical system and simulating the polarization transmission model of the optical system to reconstruct high-resolution images under low sampling rate conditions. By building a simulated dataset, training a polarization super-resolution imaging network, and showing excellent reconstructions on real shooting scenes, compared to current algorithms, our model has a higher peak signal-to-noise ratio (PSNR), which validates the feasibility of our approach.
Explainable AI in Scene Understanding for Autonomous Vehicles in Unstructured Traffic Environments on Indian Roads Using the Inception U-Net Model with Grad-CAM Visualization
Authors: Kolekar Suresh , Gite Shilpa , Pradhan Biswajeet , Alamri Abdullah
Published: December 2022

The intelligent transportation system, especially autonomous vehicles, has seen a lot of interest among researchers owing to the tremendous work in modern artificial intelligence (AI) techniques, especially deep neural learning. As a result of increased road accidents over the last few decades, significant industries are moving to design and develop autonomous vehicles. Understanding the surrounding environment is essential for understanding the behavior of nearby vehicles to enable the safe navigation of autonomous vehicles in crowded traffic environments. Several datasets are available for autonomous vehicles focusing only on structured driving environments. To develop an intelligent vehicle that drives in real-world traffic environments, which are unstructured by nature, there should be an availability of a dataset for an autonomous vehicle that focuses on unstructured traffic environments. Indian Driving Lite dataset (IDD-Lite), focused on an unstructured driving environment, was released as an online competition in NCPPRIPG 2019. This study proposed an explainable inception-based U-Net model with Grad-CAM visualization for semantic segmentation that combines an inception-based module as an encoder for automatic extraction of features and passes to a decoder for the reconstruction of the segmentation feature map. The black-box nature of deep neural networks failed to build trust within consumers. Grad-CAM is used to interpret the deep-learning-based inception U-Net model to increase consumer trust. The proposed inception U-net with Grad-CAM model achieves 0.622 intersection over union (IoU) on the Indian Driving Dataset (IDD-Lite), outperforming the state-of-the-art (SOTA) deep neural-network-based segmentation models.
Multi-Beam Conformal Array Antenna Based on Highly Conductive Graphene Films for 5G Micro Base Station Applications
Authors: Zheng Bin , Li Xiangyang , Rao Xin , Li Na
Published: December 2022

Recently, micro base station antennas have begun to play a more important role in 5G wireless communication, with the rapid development of modern smart medical care, the Internet of things, and portable electronic devices. Meanwhile, in response to the global commitment to long-term carbon neutrality, graphene film has received significant attention in the field of antennas due to its low carbon environmental impact and high electrical conductivity properties. In this work, a conformal array antenna based on highly conductive graphene films (CGF) is proposed for 5G millimeter-wave (MMW) applications. The proposed antenna consists of three antenna arrays, with eight patch elements in each array, operating at 24 GHz, with linear polarization. Each antenna array’s current amplitude distribution coefficient is constructed by synthesizing a series-feeding linear array using the Chebyshev method. The measurement results demonstrated that the proposed CGF antenna exhibits a peak realized gain higher than 8 dBi in the bandwidth of 23.0–24.7 GHz. The proposed antenna achieves three independent beams from bore-sight to ±37° in conformal installations, with a cylinder radius of 30 mm, showing excellent beam-pointing performance. These characteristics indicate that the CGF can be used for the design of MMW micro base station antennas, fulfilling the requirements of the conformal carrier platform for a lightweight and compact antenna.
Exploring the Quality of Dynamic Open Government Data Using Statistical and Machine Learning Methods
Authors: Karamanou Areti , Brimos Petros , Kalampokis Evangelos , Tarabanis Konstantinos
Published: December 2022

Dynamic data (including environmental, traffic, and sensor data) were recently recognized as an important part of Open Government Data (OGD). Although these data are of vital importance in the development of data intelligence applications, such as business applications that exploit traffic data to predict traffic demand, they are prone to data quality errors produced by, e.g., failures of sensors and network faults. This paper explores the quality of Dynamic Open Government Data. To that end, a single case is studied using traffic data from the official Greek OGD portal. The portal uses an Application Programming Interface (API), which is essential for effective dynamic data dissemination. Our research approach includes assessing data quality using statistical and machine learning methods to detect missing values and anomalies. Traffic flow-speed correlation analysis, seasonal-trend decomposition, and unsupervised isolation Forest (iForest) are used to detect anomalies. iForest anomalies are classified as sensor faults and unusual traffic conditions. The iForest algorithm is also trained on additional features, and the model is explained using explainable artificial intelligence. There are 20.16% missing traffic observations, and 50% of the sensors have 15.5% to 33.43% missing values. The average percent of anomalies per sensor is 71.1%, with only a few sensors having less than 10% anomalies. Seasonal-trend decomposition detected 12.6% anomalies in the data of these sensors, and iForest 11.6%, with very few overlaps. To the authors’ knowledge, this is the first time a study has explored the quality of dynamic OGD.
Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering
Authors: Ramos-Romero Carlos , Asensio César , Moreno Ricardo , de Arcas Guillermo
Published: December 2022

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.
Graph Attention Interaction Aggregation Network for Click-Through Rate Prediction
Authors: Zhang Wei , Kang Zhaobin , Song Lingling , Qu Kaiyuan
Published: December 2022

Click-through rate prediction is a critical task for computational advertising and recommendation systems, where the key challenge is to model feature interactions between different feature domains. At present, the main click-through rate prediction models model feature interactions in an implicit way, which leads to poor interpretation of the model, and the interaction between each pair of features may introduce noise into the model, thus limiting the predictive ability of the model. In response to the above problems, this paper proposes a click-through rate prediction model (GAIAN) based on the graph attention interactive aggregation network, which explicitly obtains cross features on the graph structure. Our specific method is to design a feature interactive selection mechanism to select cross features that are beneficial to model prediction, reducing model noise and reducing the risk of model overfitting. On this basis, the bilinear interaction function is integrated into the aggregation strategy of the graph neural network, and the fine-grained intersection features are extracted in a flexible and explicit way, which makes graph neural networks more suitable for modeling feature interactions and enhances the interpretability of the model. Compared with several other state-of-the-art models on the Criteo and Avazu datasets, the experimental results show the superiority of the model.
Feature Consistent Point Cloud Registration in Building Information Modeling
Authors: Jiang Hengyu , Lasang Pongsak , Nader Georges , Wu Zheng , Tanasnitikul Takrit
Published: December 2022

Point Cloud Registration contributes a lot to measuring, monitoring, and simulating in building information modeling (BIM). In BIM applications, the robustness and generalization of point cloud features are particularly important due to the huge differences in sampling environments. We notice two possible factors that may lead to poor generalization, the normal ambiguity of boundaries on hard edges leading to less accuracy in transformation; and the fact that existing methods focus on spatial transformation accuracy, leaving the advantages of feature matching unaddressed. In this work, we propose a boundary-encouraging local frame reference, the PyramidFeature(PMD), consisting of point-level, line-level, and mesh-level information to extract a more generalizing and continuous point cloud feature to encourage the knowledge of boundaries to overcome the normal ambiguity. Furthermore, instead of registration guided by spatial transformation accuracy alone, we suggest another supervision to extract consistent hybrid features. A large number of experiments have demonstrated the superiority of our PyramidNet (PMDNet), especially when the training (ModelNet40) and testing (BIM) sets are very different, PMDNet still achieves very high scalability.
No-Reference Video Quality Assessment Using the Temporal Statistics of Global and Local Image Features
Authors: Varga Domonkos
Published: December 2022

During acquisition, storage, and transmission, the quality of digital videos degrades significantly. Low-quality videos lead to the failure of many computer vision applications, such as object tracking or detection, intelligent surveillance, etc. Over the years, many different features have been developed to resolve the problem of no-reference video quality assessment (NR-VQA). In this paper, we propose a novel NR-VQA algorithm that integrates the fusion of temporal statistics of local and global image features with an ensemble learning framework in a single architecture. Namely, the temporal statistics of global features reflect all parts of the video frames, while the temporal statistics of local features reflect the details. Specifically, we apply a broad spectrum of statistics of local and global features to characterize the variety of possible video distortions. In order to study the effectiveness of the method introduced in this paper, we conducted experiments on two large benchmark databases, i.e., KoNViD-1k and LIVE VQC, which contain authentic distortions, and we compared it to 14 other well-known NR-VQA algorithms. The experimental results show that the proposed method is able to achieve greatly improved results on the considered benchmark datasets. Namely, the proposed method exhibits significant progress in performance over other recent NR-VQA approaches.
Analysis of Signal Processing Methods to Reject the DC Offset Contribution of Static Reflectors in FMCW Radar-Based Vital Signs Monitoring
Authors: Mercuri Marco , Torfs Tom , Rykunov Maxim , Laureti Stefano , Ricci Marco , Crupi Felice
Published: December 2022

Frequency-modulated continuous wave (FMCW) radars are currently being investigated for remote vital signs monitoring (measure of respiration and heart rates) as an innovative wireless solution for healthcare and ambient assisted living. However, static reflectors (furniture, objects, stationary body parts, etc.) within the range or range angular bin where the subject is present contribute in the Doppler signal to a direct current (DC) offset. The latter is added to the person’s information, containing also a useful DC component, causing signal distortion and hence reducing the accuracy in measuring the vital sign parameters. Removing the sole contribution of the unwanted DC offset is fundamental to perform proper phase demodulation, so that accurate vital signs monitoring can be achieved. In this work, we analyzed different DC offset calibration methods to determine which one achieves the highest accuracy in measuring the physiological parameters as the transmitting frequency varies. More precisely, by using two FMCW radars, operating below 10 GHz and at millimeter wave (mmWave), we applied four DC offset calibration methods to the baseband radar signals originated by the cardiopulmonary activities. We experimentally determined the accuracy of the methods by measuring the respiration and the heart rates of different subjects in an office setting. It was found that the linear demodulation outperforms the other methods if operating below 10 GHz while the geometric fitting provides the best results at mmWave.
Design of Broadband High-Gain Fabry–Pérot Antenna Using Frequency-Selective Surface
Authors: Sheng Xianjun , Lu Xiaolong , Liu Ning , Liu Yunhong
Published: December 2022

In this paper, a broadband high-gain Fabry–Pérot (F-P) antenna composed of the air-loaded slot-coupled broadband microstrip antenna and the frequency selective surface (FSS) based positive gradient reflection phase structure is proposed. Taking advantage of the superposition effect of multiple reflections and transmissions occurring between layer structures, the gain enhancement was realized. Meanwhile, by cascading the single-layer FSS and the dielectric substrate, the positive gradient reflection phase over a wider frequency range was achieved. Simulated results show that the resonant frequency of the designed F-P antenna is 10 GHz, the impedance matching band (S11 < −10 dB) ranges from 8.3 GHz to 11.25 GHz with a bandwidth of 29.5%, and the antenna gain is improved significantly in the range of 8.1 GHz~11.25 GHz with a gain bandwidth of 31.5%. For further verification, a prototype was fabricated, and the experimental and simulated results are in good agreement.
Potentiometric Hydrogen Sensor with 3D-Printed BaCe0.6Zr0.3Y0.1O3-α Electrolyte for High-Temperature Applications
Authors: Hinojo Antonio , Lujan Enric , Nel-lo Marc , Abella Jordi , Colominas Sergi
Published: December 2022

Hydrogen is expected to play an important role in the near future in the transition to a net-zero economy. Therefore, the development of new in situ and real-time analytical tools able to quantify hydrogen at high temperatures is required for future applications. Potentiometric sensors based on perovskite-structured solid-state electrolytes can be a good option for H2 monitoring. Nevertheless, the geometry of the sensor should be designed according to the specific necessities of each technological field. Conventional shaping processes need several iterations of green shaping and machining to achieve a good result. In contrast, 3D printing methods stand out from conventional ones since they simplify the creation of prototypes, reducing the cost and the number of iterations needed for the obtainment of the final design. In the present work, BaCe0.6Zr0.3Y0.1O3-α (BCZY) was used as a proton-conducting electrolyte for potentiometric sensors construction. Two different shapes were tested for the sensors’ electrolyte: pellets (BCZY-Pellet) and crucibles (BCZY-Crucible). Ceramics were shaped using extrusion-based 3D printing. Finally, parameters, such as sensitivity, response time, recovery time and the limit of detection and accuracy, were evaluated for both types of sensors (BCZY-Pellet and BCZY-Crucible) at 500 °C.