bluuebunny/crossref_metadata_2025 huggingface dataset:
Recent Green Technologies in Natural Stilbenoids Production and Extraction: The Next Chapter in the Cosmetic Industry
Authors: Aneklaphakij Chaiwat , Chamnanpuen Phatthilakorn , Bunsupa Somnuk , Satitpatipan Veena
Published: September 2022

Stilbenoids are well-known phytoalexins in the group of polyphenolic compounds. Because of their potent bioactivities, including antioxidant, antityrosinase, photoprotective, and antibacterial activities, stilbenoids are utilized as pharmaceutical active ingredient in cosmetic products. Thus, the demand for stilbenoids in the cosmetic industry is increasing. The main sources of stilbenoids are plants. Although plants are green and sustainable source materials, some of them do not allow a regular and constant supply due to seasonal and geographic reasons. Stilbenoids typically have been extracted by conventional organic solvent extraction, and then purified by separation techniques. This method is unfriendly to the environment and may deteriorate human health. Hence, the procedures called “green technologies” are focused on novel extraction methods and sustainable stilbenoids production by using biotechnology. In this review, the chemical structures together with the biosynthesis and current plant sources of resveratrol, oxyresveratrol, and piceatannol are described. Furthermore, recent natural deep eutectic solvents (NADES) for green extraction as well as plant cell cultures for the production of those stilbene compounds are updated.
Thermal and Mechanical Characterization of Coir Fibre–Reinforced Polypropylene Biocomposites
Authors: Ichim Mariana , Stelea Lucia , Filip Ioan , Lisa Gabriela , Muresan Emil Ioan
Published: September 2022

In recent years, the growth of environmental awareness has increased the interest in the development of biocomposites which are sustainable materials with an excellent price–performance ratio and low weight. The current study aimed to obtain and characterize the biocomposites prepared by thermoforming using coir fibres as reinforcing material and polypropylene as matrix. The biocomposites were produced with different coir fibres/polypropylene ratios and were characterized by physical–mechanical indices, thermal analysis, crystallinity, attenuated total reflection-Fourier transform infrared spectroscopy analysis (ATR-FTIR), scanning electron microscopy (SEM), and chromatic measurements. Both tensile and bending strength of biocomposites decreased when the coir fibre content increased. The melting temperature of biocomposite materials has decreased with the increase of the coir fibre loading. Regarding the thermal stability, the weight loss and degradation temperature increased with decreasing coir fibre content. The ATR-FTIR and SEM analyses underlined the modifications that took place in the structure of the biocomposites by modifying the coir fibres/matrix ratio.
Experimental Investigation of the Performance of SBR and RSS Materials as a Sliding Bearing Element
Authors: Çalışkan Mehmet
Published: September 2022

Conventional bearing systems and materials, which are important components of machine elements, have often failed to fulfil requirements, especially in places where sealing is required. In such cases, sliding bearings with PTFE- and NBR-based polymer and elastomer materials operating in water or oil are preferred. In this study, the performance of two alternative rubber-based materials, synthetic styrene butadiene rubber (SBR) and natural rubber smoked sheet (RSS), as a sliding bearing was experimentally investigated in terms of frictional resistance using pin-on-disc and pin-on-cylinder devices. In the study, very low friction forces emerged at low sliding speeds compared to PTFE and NR operating in water. The friction force values and friction coefficients obtained are presented in diagrams and tables.
β-Phase-Induced Quasi-Cleavage Fracture Mechanism by Dual-Phase High-Strength Titanium Alloy at Elevated Temperature
Authors: Wei Wenlan , Qu Hao , Cheng Jiarui , Zhang Rui , Cao Yinping , Cui Lu
Published: September 2022

Dual-phase high-strength titanium alloy has the properties of high specific strength and good toughness, which have resulted in its gradual use in the fields of oil and gas well engineering. The elevated-temperature service environment of deep strata is its key research direction. In this paper, the strength and fracture mechanism of a new type of α + β-phase titanium alloy tubing material in its service-temperature range are studied. Its fracture mechanism changed at 130 °C to 150 °C, from normal-stress ductile fracture to quasi-cleavage fracture formed by β-phase voids, which induced microshear, which significantly reduced the elongation of the material and accelerated the rate of yield strength decline with temperature. This mechanism provides a new guiding idea for the design of the microstructure and element content of dual-phase high-strength titanium alloy. For titanium alloy materials in service within the temperature range of the fracture mechanism transition, which is between 130 °C and 150 °C, reducing the void-inducing factors in the β-phase or reducing the content of the β-phase to avoid microshear failure should be considered.
Ambient Pressure Synthesis of Re-Substituted MnGe and Its Magnetic Properties
Authors: Zhupanov Vladislav O. , Khalaniya Roman A. , Bogach Alexey V. , Verchenko Valeriy Yu. , Likhanov Maxim S. , Shevelkov Andrei V.
Published: September 2022

Due to their non-centrosymmetric structure, B20-type compounds have intriguing properties of chiral magnets and are the objects of study of topological spin textures. Among them is a high-pressure phase MnGe, which demonstrates properties of magnetic skyrmions. We report on the synthesis of an Mn1−xRexGe solid solution with the B20 structure, which can be prepared without the application of high pressure. Mn1−xRexGe (x = 0.169(6)) shows unconventional magnetic behavior, where the Neel temperature is only slightly reduced compared to a chiral-lattice helimagnet MnGe.
Diversity of Silica-Scaled Chrysophytes (Stramenopiles: Chrysophyceae) from Indonesian Papua
Authors: Gusev Evgeniy , Kapustin Dmitry , Martynenko Nikita , Kulikovskiy Maxim
Published: September 2022

The silica-scaled chrysophyte flora from Indonesian Papua was investigated by means of electron microscopy. A total of twenty-four taxa were recorded, including five belonging to Paraphysomonas, one to Chrysosphaerella, one to Spiniferomonas, fifteen to Mallomonas and two to Synura. Thirteen taxa were recorded for the first time in Indonesia. Comparison of the species composition of silica-scaled chrysophytes from different parts of the Island of New Guinea shows significant differences. A “living fossil” was discovered, Mallomonas preisigii, which was described previously from Eocene deposits in Northern Canada. Although the scales from Papua slightly differ from those of the fossil one, we believe they belong to the same morphospecies. Mallomonas preisigii can be considered as a paleoendemic species.
Radiographic Findings of Inflammatory Arthritis and Mimics in the Hands
Authors: Ezzati Fatemeh , Pezeshk Parham
Published: September 2022

Clinical presentation could be challenging in patients with arthralgia, and imaging plays an important role in the evaluation of these patients to make the diagnosis or narrow the differential diagnosis. Radiography of the hands is a commonly available imaging modality that can provide crucial information with regard to the pattern and pathology of the involved joints. It is important that radiologists and rheumatologists are familiar with the imaging findings of different rheumatic diseases to make the diagnosis in the early stages of disease to initiate treatment.
Critically Ill COVID-19 Patients Show Reduced Point of Care-Measured Butyrylcholinesterase Activity—A Prospective, Monocentric Observational Study
Authors: Espeter Florian , Künne David , Garczarek Lena , Kuhlmann Henning , Skarabis Annabell , Zivkovic Aleksandar R. , Brenner Thorsten , Schmidt Karsten
Published: September 2022

A biomarker for risk stratification and disease severity assessment in SARS-CoV-2 infections has not yet been established. Point of care testing (POCT) of butyrylcholinesterase (BChE) enables early detection of systemic inflammatory responses and correlates with disease severity in sepsis and burns. In acute care or resource-limited settings, POCT facilitates rapid clinical decision making, a particularly beneficial aspect in the management of pandemic situations. In this prospective observational study, POCT-measured BChE activity was assessed in 52 critically ill COVID-19 patients within 24 h of ICU admission and on the third and seventh day after ICU admission. Forty (77%) of these patients required venovenous extracorporeal membrane oxygenation (vvECMO). In critically ill COVID-19 patients, BChE activity is significantly decreased compared with healthy subjects, but also compared with other inflammatory conditions such as sepsis, burns, or trauma. POCT BChE activity reflects the severity of organ dysfunction and allows prediction of 28-day mortality in critically ill COVID-19 patients. Implementing early POCT BChE measurement could facilitate risk stratification and support admission and transfer decisions in resource-limited settings.
Usefulness and Limitations of Anti-S IgG Assay in Detecting Previous SARS-CoV-2 Breakthrough Infection in Fully Vaccinated Healthcare Workers
Authors: Spiteri Gianluca , Monaco Maria Grazia Lourdes , Caliskan Gulser , Carta Angela , Pezzani Maria Diletta , Lippi Giuseppe , Gibellini Davide , Verlato Giuseppe , Porru Stefano
Published: September 2022

Introduction: The anti-spike (S) IgG assay is the most widely used method to assess the immunological response to COVID-19 vaccination. Several studies showed that subjects with perivaccination infection have higher anti-S IgG titers. However, a cut-off has not yet been identified so far for distinguishing infected subjects after vaccination. This study thus evaluates the performance of the anti-S IgG assay in identifying subjects with breakthrough infections (BIs) and its potential usefulness for screening healthcare workers (HCWs). Methods: Out of 6400 HCWs of the University Hospital of Verona vaccinated with two doses of BNT162b2, 4462 never infected before subjects who had completed primary vaccination were tested for IgG anti-S 6 to 9 months after the second dose. Of these, 59 (1.3%) had a BI. The discriminant power of IgG anti-S in detecting previous breakthrough infection was tested by constructing receiver operating characteristic (ROC) curves. Results: The discriminant power for BI was rather good (area under the curve (AUC), 0.78) and increased with decreasing time elapsed between antibody titer assessment and previous SARS-CoV-2 infection. Accuracy (AUC) sensitivity increased from 0.78 (95% CI 0.70–0.85) for BI in the previous six months to 0.83 (95% CI 0.67–0.99) for those in the previous two months, and from 0.68 to 0.80, respectively. The specificity (0.86) and optimal cut-off (935 BAU/mL) remained unchanged. However, BI were rather rare (1.3%), so the positive predictive value (PPV) was low. Only 40 of the 664 HCWs with antibody titer > 935 BAU/mL had previously confirmed BI, yielding a PPV of only 6.0%. When adopting as cut-off the 90th percentile (1180 BAU/mL), PPV increased to 7.9% (35/441). Conclusions: The anti-S IgG assay displayed good sensitivity and specificity in discriminating subjects with BI, especially in recent periods. However, BIs were rare among HCWs, so that the anti-S IgG assay may have low PPV in this setting, thus limiting the usefulness of this test as a screening tool for HCWs. Further studies are needed to identify more effective markers of a previous infection in vaccinated subjects.
Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network
Authors: Zováthi Bendegúz H. , Mohácsi Réka , Szász Attila Marcell , Cserey György
Published: September 2022

In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.
Deep Reinforcement Learning with Corrective Feedback for Autonomous UAV Landing on a Mobile Platform
Authors: Wu Lizhen , Wang Chang , Zhang Pengpeng , Wei Changyun
Published: September 2022

Autonomous Unmanned Aerial Vehicle (UAV) landing remains a challenge in uncertain environments, e.g., landing on a mobile ground platform such as an Unmanned Ground Vehicle (UGV) without knowing its motion dynamics. A traditional PID (Proportional, Integral, Derivative) controller is a choice for the UAV landing task, but it suffers the problem of manual parameter tuning, which becomes intractable if the initial landing condition changes or the mobile platform keeps moving. In this paper, we design a novel learning-based controller that integrates a standard PID module with a deep reinforcement learning module, which can automatically optimize the PID parameters for velocity control. In addition, corrective feedback based on heuristics of parameter tuning can speed up the learning process compared with traditional DRL algorithms that are typically time-consuming. In addition, the learned policy makes the UAV landing smooth and fast by allowing the UAV to adjust its speed adaptively according to the dynamics of the environment. We demonstrate the effectiveness of the proposed algorithm in a variety of quadrotor UAV landing tasks with both static and dynamic environmental settings.
Dealing with Moments of Crisis Interculturally in Educational Virtual Exchanges: A Sino–Finnish Case Study
Authors: Peng Jun , Dervin Fred
Published: September 2022

Being able to deal with “moments of crisis” is crucial in intercultural communication. Using identities as an analytical lens, this paper examines different types of “moments of crisis” identified in educational virtual exchanges between Chinese and Finnish university students. The study shows that the participants use soothing and code-switching as strategies in these moments of crisis to “do” interculturality, i.e., to achieve interactional arrangements (e.g., moving the discussion forward) and self-other alignments (e.g., saving face). Focusing on soothing as a positive strategy and code-switching as an avoidance strategy in dealing with “moments of crisis”, we also scrutinize the emergence of the identities of mediator, fence-sitter, and facilitator in the students’ behaviors. Finally, students’ working experience, language use, and physical surroundings are identified as having a potential influence on their use of soothing and code-switching during the online intercultural interactions. Recommendations for preparing students for international virtual exchanges conclude the paper.
Mass Transport Limitations in Electrochemical Conversion of CO2 to Formic Acid at High Pressure
Authors: Chinnathambi Selvaraj , Ramdin Mahinder , Vlugt Thijs J. H.
Published: September 2022

Mass transport of different species plays a crucial role in electrochemical conversion of CO2 due to the solubility limit of CO2 in aqueous electrolytes. In this study, we investigate the transport of CO2 and other ionic species through the electrolyte and the membrane, and its impact on the scale-up process of HCOO−/HCOOH formation. The mass transport of ions to the electrode and the membrane is modelled at constant current density. The mass transport limitations of CO2 on the formation of HCOO−/HCOOH is investigated at different pressures ranges from 5–40 bar. The maximum achievable partial current density of formate/formic acid is increased with increasing CO2 pressure. We use an ion exchange membrane model to understand the ion transport behaviour for both the monopolar and bipolar membranes. The cation exchange (CEM) and anion exchange membrane (AEM) model show that ion transport is limited by the electrolyte salt concentrations. For 0.1 M KHCO3, the AEM reaches the limiting current density more quickly than the CEM. For the BPM model, ion transport across the diffusion layer on either side of the BPM is also included to understand the concentration polarization across the BPM. The model revealed that the polarization losses across the bipolar membrane depend on the pH of the electrolyte used for the CO2 reduction reaction (CO2RR). The polarization loss on the anolyte side decreases with an increasing pH, while, on the cathode side, it increases with increasing catholyte pH. With this combined model for the electrode reactions and the membrane transport, we are able to account for the various factors influencing the polarization losses in the CO2 electrolyzer. To complete the analysis, we simulated the full cell polarization curve and fitted with the experimental data.
Learning Balance Feature for Object Detection
Authors: Zhang Zhiqiang , Qiu Xin , Li Yongzhou
Published: September 2022

In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and the detection head to enable shallow features with more semantic information. However, FPN is insufficient for object detection on various scales, especially for small-scale object detection. One of the reasons is that the features are extracted at different network depths, which introduces gaps between features. That is, as the network becomes deeper and deeper, the high-level features have more semantics but less content description. This paper proposes a new method that includes a multi-scale receptive fields extraction module, a feature constructor module, and an attention module to improve the detection efficiency of FPN for objects of various scales and to bridge the gap in content description and semantics between different layers. Together, these three modules make the detector capable of selecting the most suitable feature for objects. Especially for the attention module, this paper chooses to use a parallel structure to simultaneously extract channel and spatial attention from the same features. When we use Adopting Adaptive Training Sample Selection (ATSS) and FreeAnchor as the baseline and ResNet50 as the backbone, the experimental results on the MS COCO dataset show that our algorithm can enhance the mean average precision (mAP) by 3.7% and 2.4% compared to FPN, respectively.
Pseudo-Signal Interference Regularity of Single-Frequency Electromagnetic Radiation to Stepped-Frequency Radar
Authors: Zhao Hong-Ze , Wei Guang-Hui , Pan Xiao-Dong , Du Xue , Lyu Xu-Xu
Published: September 2022

When typical radar equipment is subjected to single-frequency electromagnetic radiation, the radar display interface forms a pseudo-signal, resulting in the misjudgment of real targets. Based on the working principle of stepped-frequency radar ranging, the effect mechanism of radar equipment pseudo-signal interference is revealed. Taking a Ku band stepped-frequency ranging radar as the test object, the pseudo-signal interference effect test of single-frequency electromagnetic radiation is carried out in this study. The pseudo-signal level value of 6 dBmV is selected as the sensitive criterion of the pseudo-signal interference effect. Through experiments, the variation curves of the pseudo-signal level values of the sensitive frequency bands and the typical frequency points inside and outside the band with the field strength of the single-frequency interference are obtained. Based on the nonlinear distortion analysis of the receiving circuit, the variation laws of the pseudo-signal level values inside and outside the band are explained, respectively. The experimental results show that there are at least seven pseudo-signal interference-sensitive bands in the tested radar, and the first pseudo-signal strength is only related to the interference signal strength. The essence of the second type of pseudo-signal interference is intermodulation interference, and the pseudo-signal level is related to the interference signal and the useful signal strength.
A New Unsupervised Technique to Analyze the Centroid and Frequency of Keyphrases from Academic Articles
Authors: Miah Mohammad Badrul Alam , Awang Suryanti , Rahman Md Mustafizur , Hosen A. S. M. Sanwar , Ra In-Ho
Published: September 2022

Automated keyphrase extraction is crucial for extracting and summarizing relevant information from a variety of publications in multiple domains. However, the extraction of good-quality keyphrases and the summarising of information to a good standard have become extremely challenging in recent research because of the advancement of technology and the exponential development of digital sources and textual information. Because of this, the usage of keyphrase features for keyphrase extraction techniques has recently gained tremendous popularity. This paper proposed a new unsupervised region-based keyphrase centroid and frequency analysis technique, named the KCFA technique, for keyphrase extraction as a feature. Data/datasets collection, data pre-processing, statistical methodologies, curve plotting analysis, and curve fitting technique are the five main processes in the proposed technique. To begin, the technique collects multiple datasets from diverse sources, which are then input into the data pre-processing step by utilizing some text pre-processing processes. Afterward, the region-based statistical methodologies receive the pre-processed data, followed by the curve plotting examination and, lastly, the curve fitting technique. The proposed technique is then tested and evaluated using ten (10) best-accessible benchmark datasets from various disciplines. The proposed approach is then compared to our available methods to demonstrate its efficacy, advantages, and importance. Lastly, the results of the experiment show that the proposed method works well to analyze the centroid and frequency of keyphrases from academic articles. It provides a centroid of 706.66 and a frequency of 38.95% in the first region, 2454.21 and 7.98% in the second region, for a total frequency of 68.11%.
A Systematic Review and IoMT Based Big Data Framework for COVID-19 Prevention and Detection
Authors: Hamid Soomaiya , Bawany Narmeen Zakaria , Sodhro Ali Hassan , Lakhan Abdullah , Ahmed Saleem
Published: September 2022

The Internet of Medical Things (IoMT) is transforming modern healthcare systems by merging technological, economical, and social opportunities and has recently gained traction in the healthcare domain. The severely contagious respiratory syndrome coronavirus called COVID-19 has emerged as a severe threat to public health. COVID-19 is a highly infectious virus that is spread by person-to-person contact. Therefore, minimizing physical interactions between patients and medical healthcare workers is necessary. The significance of technology and its associated potential were fully explored and proven during the outbreak of COVID-19 in all domains of human life. Healthcare systems employ all modes of technology to facilitate the increasing number of COVID-19 patients. The need for remote healthcare was reemphasized, and many remote healthcare solutions were adopted. Various IoMT-based systems were proposed and implemented to support traditional healthcare systems with reaching the maximum number of people remotely. The objective of this research is twofold. First, a systematic literature review (SLR) is conducted to critically evaluate 76 articles on IoMT systems for different medical applications, especially for COVID-19 and other health sectors. Secondly, we briefly review IoMT frameworks and the role of IoMT-based technologies in COVID-19 and propose a framework, named ‘cov-AID’, that remotely monitors and diagnoses the disease. The proposed framework encompasses the benefits of IoMT sensors and extensive data analysis and prediction. Moreover, cov-AID also helps to identify COVID-19 outbreak regions and alerts people not to visit those locations to prevent the spread of infection. The cov-AID is a promising framework for dynamic patient monitoring, patient tracking, quick disease diagnosis, remote treatment, and prevention from spreading the virus to others. We also discuss potential challenges faced in adopting and applying big data technologies to combat COVID-19.
High-Performance and Robust Binarized Neural Network Accelerator Based on Modified Content-Addressable Memory
Authors: Choi Sureum , Jeon Youngjun , Seo Yeongkyo
Published: September 2022

The binarized neural network (BNN) is one of the most promising candidates for low-cost convolutional neural networks (CNNs). This is because of its significant reduction in memory and computational costs, and reasonable classification accuracy. Content-addressable memory (CAM) can perform binarized convolution operations efficiently since the bitwise comparison in CAM matches well with the binarized multiply operation in a BNN. However, a significant design issue in CAM-based BNN accelerators is that the operational reliability is severely degraded by process variations during match-line (ML) sensing operations. In this paper, we proposed a novel ML sensing scheme to reduce the hardware error probability. Most errors occur when the difference between the number of matches in the evaluation ML and the reference ML is small; thus, the proposed hardware identified cases that are vulnerable to process variations using dual references. The proposed dual-reference sensing structure has >49% less ML sensing errors than that of the conventional design, leading to a >1.0% accuracy improvement for Fashion MNIST image classification. In addition, owing to the parallel convolution operation of the CAM-based BNN accelerator, the proposed hardware achieved >34% processing-time improvement compared with that of the digital logic implementation.
A Second Order 1.8–1.9 GHz Tunable Active Band-Pass Filter with Improved Noise Performances
Authors: Colaiuda Davide , Leoni Alfiero , Ferri Giuseppe , Stornelli Vincenzo
Published: September 2022

In this paper, a novel active tunable band-pass filter with improved noise performances is presented. This filter is based on a negative resistance circuit (or active capacitance), where the gain obtained with a transistor is used to compensate for inductor losses. Moreover, the capacitance of the resonator is obtained through a voltage-controlled reverse-biased varactor, which allows for frequency tuning. Despite the active component, the proposed filter also has good noise performance. Measurements show a tunability range from 1.816 GHz to 1.886 GHz, with a bandwidth of 38 MHz. The insertion loss maximum value is 0.4 dB, while the noise figure value has a minimum value of 2.5 dB at the center frequency within the tunability range.
A Self-Tuned Method for Impedance-Matching of Planar-Loop Resonators in Conformable Wearables
Authors: Bing Sen , Chawang Khengdauliu , Chiao J.-C.
Published: September 2022

Loop structure has been used as a single resonator and in meta-materials. Variations from the loop structures such as split-ring resonators have been utilized as sensing elements in integrated devices for wearable applications or in array configurations for free-space resonance. Previously, impedance formula and equivalent circuit models have been developed for a single loop made of a conductor wire with a negligible wire diameter in the free space. Despite the features of being planar and small, however, the quality factors of single-loop resonators or antennas have not been sufficiently high to use them efficiently for sensing or power transfer. To investigate the limitation, we first experimentally examined the formula and equivalent circuits for a single loop made of planar metal sheets, along with finite element simulations. The loop performance factor was varied to validate the formula and equivalent circuits. Then a tuning element was utilized in the planar loop to improve resonance by providing distributed impedance-matching to the loop. The proposed tuning method was demonstrated with simulations and measurements. A new equivalent circuit model for the tuned loop resonator was established. Quality factors at resonance show significant improvement and the tuning can be done for a specific resonance order without changing the loop radius. It was also shown that the tuning method provided more robust performance for the resonator. The tuning mechanism is suitable for miniature planar device architectures in sensing applications, particularly for implants and wearables that have constraints in dimensions and form factors. The equivalent circuit model can also be applied for meta-materials in arrayed configurations.
Research on Adaptive Exponential Droop Control Strategy for VSC-MTDC System
Authors: Li Jianying , Yang Minsheng , Li Jianqi , Xiao Yunchang , Wan Jingying
Published: September 2022

To solve the problem of large DC voltage deviation caused by the power fluctuations and poor power distribution characteristics of converters in a voltage source converter multi-terminal DC (VSC-MTDC) system based on traditional droop control, this paper proposes an adaptive exponential droop control strategy. This strategy introduces the relative power deviation factor of the converter, and replaces the traditional linear droop control curve with a nonlinear exponential curve. Under different working conditions, the converter adaptively adjusts the droop control coefficient of the converter according to the relative power deviation factor to realize stability for the DC voltage and a reasonable power distribution for the MTDC system. A simulation model of a three-terminal VSC-MTDC was established in MATLAB/Simulink, and the feasibility and effectiveness of the proposed strategy were verified.
JR-TFViT: A Lightweight Efficient Radar Jamming Recognition Network Based on Global Representation of the Time–Frequency Domain
Authors: Lang Bin , Gong Jian
Published: September 2022

Efficient jamming recognition capability is a prerequisite for radar anti-jamming and can enhance the survivability of radar in electronic warfare. Traditional recognition methods based on manually designed feature parameters have found it difficult to cope with the increasingly complex electromagnetic environment, and research combining deep learning to achieve jamming recognition is gradually increasing. However, existing research on radar jamming recognition based on deep learning can ignore the global representation in the jamming time–frequency domain data, while not paying enough attention to the problem of lightweighting the recognition network itself. Therefore, this paper proposes a lightweight jamming recognition network (JR-TFViT) that can fuse the global representation of jamming time–frequency domain data while combining the advantages of the Vision Transformer and a convolutional neural network (CNN). The global representation and local information in the jamming time–frequency data are fused with the assistance of the multi-head self-attention (MSA) mechanism in the transformer to improve the feature extraction capabilities of the recognition network. The model’s parameters are further decreased by modifying the standard convolutional operation mechanism and substituting the convolutional operation needed by the network with Ghost convolution, which has less parameters. The experimental results show that the JR-TFViT requires fewer model parameters while maintaining higher recognition performance than mainstream convolutional neural networks and lightweight CNNs. For 12 types of radar jamming, the JR-TFViT achieves 99.5% recognition accuracy at JNR = −6 dB with only 3.66 M model parameters. In addition, 98.9% recognition accuracy is maintained when the JR-TFViT parameter number is further compressed to 0.67 M.
Reconfigurable Intelligent Surface Physical Model in Channel Modeling
Authors: Liu Yiping , Dou Jianwu , Cui Yijun , Chen Yijian , Yang Jun , Qin Fan , Wang Yuxin
Published: September 2022

Reconfigurable intelligent surfaces (RISs) are one of the potential technologies for 6th generation (6G) mobile communication systems with superior electromagnetic (EM) wave-steering capability to effectively control the phase, amplitude, and polarization of the incident EM wave. An implementation-independent physical RIS model with key EM characteristics is especially crucial to RIS channel modeling considering the trade-off between complexity and accuracy. In this paper, a reflective RIS physical model is proposed to facilitate channel modeling in a system simulation. Based on the impinging EM wave of the last bounce to the RIS, the scattering field intensity of the target point is obtained using geometric optics and the electric field surface integration method of physical optics. The feasibility of the model is verified by a comparison of the simulation and test results.
Multi-Class Pixel Certainty Active Learning Model for Classification of Land Cover Classes Using Hyperspectral Imagery
Authors: Yadav Chandra Shekhar , Pradhan Monoj Kumar , Gangadharan Syam Machinathu Parambil , Chaudhary Jitendra Kumar , Singh Jagendra , Khan Arfat Ahmad , Haq Mohd Anul , Alhussen Ahmed , Wechtaisong Chitapong , Imran Hazra , Alzamil Zamil S. , Pattanayak Himansu Sekhar
Published: September 2022

An accurate identification of objects from the acquisition system depends on the clear segmentation and classification of remote sensing images. With the limited financial resources and the high intra-class variations, the earlier proposed algorithms failed to handle the sub-optimal dataset. The building of an efficient training set iteratively in active learning (AL) approaches improves classification performance. The heuristics-based AL provides better results with the inheritance of contextual information and the robustness to noise variations. The uncertainty exists pixel variations make the heuristics-based AL fail to handle the remote sensing image classification. Previously, we focused on the extraction of clear textural pattern information by using the extended differential pattern-based relevance vector machine (EDP-AL). This paper extends that work into the novel pixel-certainty activity learning (PCAL) based on the information about textural patterns obtained from the extended differential pattern (EDP). Initially, distributed intensity filtering (DIF) is used to eliminate noise from the image, and then histogram equalization (HE) is used to improve the image quality. The EDP is used to merge and classify different labels for each image sample, and this algorithm expresses the textural information. The PCAL technique is used to classify the HSI patterns that are important in remote sensing applications using this pattern collection. Pavia University and Indian Pines (IP) are the datasets used to validate the performance of the proposed PCAL (PU). The ability of PCAL to accurately categorize land cover types is demonstrated by a comparison of the proposed PCAL with existing algorithms in terms of classification accuracy and the Kappa coefficient.
Operator-Based Fractional-Order Nonlinear Robust Control for the Spiral Heat Exchanger Identified by Particle Swarm Optimization
Authors: Dong Guanqiang , Deng Mingcong
Published: September 2022

Fractional-order calculus and derivative is extended from integral-order calculus and derivative. This paper investigates a nonlinear robust control problem using fractional order and operator theory. In order to improve the tracking performance and antidisturbance ability, operator- and fractional-order-based nonlinear robust control for the spiral counter-flow heat exchanger described by the parallel fractional-order model (PFOM) is proposed. The parallel fractional-order model for the spiral counter-flow heat exchanger was identified by particle swarm optimization (PSO) and the parameters of a fractional-order PID (FOPID) controller were optimized by the PSO. First, the parallel fractional-order mathematical model for a spiral counter-flow heat exchanger plant was identified by PSO. Second, a fractional-order PID controller and operator controller for the spiral heat exchanger were designed under the identified parallel fractional-order mathematical model. Third, the parameters of the operator and fractional-order PID were optimized by PSO. Then, tracking and antidisturbance performance of the control system were analyzed. Finally, comparisons of two control schemes were performed, and the effectiveness illustrated.
Three Soil Bacterial Communities from an Archaeological Excavation Site of an Ancient Coal Mine near Bennstedt (Germany) Characterized by 16S r-RNA Sequencing
Authors: Ehrhardt Linda , Günther P. Mike , Böhme Manfred , Köhler J. Michael , Cao Jialan
Published: September 2022

This metagenomics investigation of three closely adjacent sampling sites from an archaeological excavation of a pre-industrial coal mining exploration shaft provides detailed information on the composition of the local soil bacterial communities. The observed significant differences between the samples, reflected in the 16S r-RNA analyses, were consistent with the archaeologically observed situation distinguishing the coal seam, the rapidly deposited bright sediment inside an exploration shaft, and the topsoil sediment. In general, the soils were characterized by a dominance of Proteobacteria, Actinobacteria, Acidobacteria, and Archaea, whereas the coal seam was characterized by the highest proportion of Proteobacteria; the topsoil was characterized by very high proportions of Archaea—in particular, Nitrosotaleaceae—and Acidobacteria, mainly of Subgroup 2. Interestingly, the samples of the fast-deposited bright sediment showed a rank function of OTU abundances with disproportional values in the lower abundance range. This could be interpreted as a reflection of the rapid redeposition of soil material during the refilling of the exploration shaft in the composition of the soil bacterial community. This interpretation is supported by the observation of a comparatively high proportion of reads relating to bacteria known to be alkaliphilic in this soil material. In summary, these investigations confirm that metagenomic analyses of soil material from archaeological excavations can provide valuable information about the local soil bacterial communities and the historical human impacts on them.
Mismatch between Annual Tree-Ring Width Growth and NDVI Index in Norway Spruce Stands of Central Europe
Authors: D’Andrea Giuseppe , Šimůnek Václav , Castellaneta Maria , Vacek Zdeněk , Vacek Stanislav , Pericolo Osvaldo , Zito Rosa Giada , Ripullone Francesco
Published: September 2022

Presently, the forests of one of the most economically important tree species in Europe—Norway spruce [Picea abies (L.) Karst.]—have been disrupted and are in rapid decline due to a combination of several natural factors: extreme drought, heatwaves, and secondary damage caused by bark beetle outbreaks. The vulnerability of these forests has increased considerably over the past decade, and remote sensing methods can theoretically improve the identification of endangered forest stands. The main objective was to determine the relationship between remotely sensed characteristics of vegetation (using the normalized difference vegetation index—NDVI) and annual tree-ring growth in 180 trees through precipitation and air temperature. The research was conducted at six research plots in lowland spruce forests (319–425 m a.s.l.) in the central Czech Republic. No significant correlation between NDVI and annual ring width was observed. The primary factor limiting radial growth was lack of precipitation in the growing season; subsequently, spruce trees reacted negatively to air temperatures. A higher correlation with NDVI was observed on sites susceptible to drought, but overall, NDVI and RWI did not show similarities. This result describes that NDVI is a poor indicator for identifying low radial growth in Norway spruce stands on non-native localities in the studied area.
The Quality of Blended Cotton and Denim Waste Fibres: The Effect of Blend Ratio and Waste Category
Authors: Kanan Mohammad , Wannassi Bechir , Barham Ahmad S. , Ben Hassen Mohamed , Assaf Ramiz
Published: September 2022

Cotton is one of the most-used natural fibres in the world due to its relative comfort and strength compared with other natural fibres. However, the processing of cotton for manufacturing products consumes a lot of water, while harvesting cotton uses significant amounts of pesticides. One solution to this ecological problem is to recycle cotton waste. This study investigated the effect of blending virgin cotton with two categories of denim cotton waste—sourced during the winding and dyeing processes and used in various ratios—on the quality of blended denim cotton. The study was realised in collaboration with a large manufacturer of denim fabrics in Tunisia and a producer of preparation machines in Italy and aimed to use an adequate process to recycle cotton yarn waste and to obtain fibres with acceptable quality. The research aimed at providing a solution to the great demand for denim with the use of reclaimed fabric, which accompanies the increased need for denim with a fancy effect and the obligation of denim producers to follow environmental standards required by many brands The results show that it is possible to obtain a good quality of blend yarn using virgin cotton and cotton waste even when the waste content exceeds 50%. These results are significant for textile mills. Reprocessing fibres from denim colour-processing waste has a lot of advantages, including reductions in wastewater treatment and the consumption of energy, chemicals and water. In addition, the process eliminates the need for the dyeing and finishing processes of these coloured fibres.
On the Wake Dynamics of an Oscillating Cylinder via Proper Orthogonal Decomposition
Authors: Eiximeno Benet , Miró Arnau , Cajas Juan Carlos , Lehmkuhl Oriol , Rodriguez Ivette
Published: September 2022

The coherent structures and wake dynamics of a two-degree-of-freedom vibrating cylinder with a low mass ratio at Re=5300 are investigated by means of proper orthogonal decomposition (POD) of a numerical database generated using large-eddy simulations. Two different reduced velocities of U*=3.0 and U*=5.5, which correspond with the initial and super-upper branches, are considered. This is the first time that this kind of analysis is performed in this kind of system in order to understand the role of large coherent motions on the amplification of the forces. In both branches of response, almost 1000 non-correlated in-time velocity fields have been decomposed using the snapshot method. It is seen that a large number of modes is required to represent 95% of the turbulent kinetic energy of the flow, but the first two modes contain a large percentage of the energy as they represent the wake large-scale vortex tubes. The energy dispersion of the high-order modes is attributed to the cylinder movement in the inline and cross-stream directions. Substantially different POD modes have been found in the two branches. While the first six modes resemble those observed in the static cylinder or in the initial branch of a one-degree of freedom cylinder in the initial branch, the modes not only contain information about the wake vortexes in the super-upper branch but also about the formation of the 2T vortex pattern and the Taylor–Görtler structures. It is shown that the 2T vortex pattern is formed by the interplay between the Taylor–Görtler stream-wise vortical structures and the cylinder movement and is responsible for the increase in the lift force and larger elongation in the super-upper branch.
Memory-Enhancing Effect of 8-Week Consumption of the Quercetin-Enriched Culinary Herbs-Derived Functional Ingredients: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial
Authors: Wattanathorn Jintanaporn , Somboonporn Woraluck , Thukham-Mee Wipawee , Sungkamnee Sudarat
Published: September 2022

Due to great demand for memory enhancers, the memory-enhancing effects and the possible underlying mechanisms of the functional ingredients derived from the combined extract of Polygonum odoratum and Morus alba were investigated. A total of 45 participants randomly received either a placebo or the developed herbal supplement at a dose of 50 or 1500 mg/day. The consumption was done once daily for 8 weeks. Working memory was assessed via both an event-related potential and computerized battery tests at baseline and at the end of the 8-week study period. Acetylcholinesterase (AChE) and monoamine oxidase type A and type B (MAO-A, MAO-B) levels were also measured at the end of the study. The subjects who consumed the supplement containing a developed functional ingredient at a dose of 1500 mg/day showed reduced latencies but increased amplitudes of N100 and P300. An improvement in working memory and the suppression of AChE, MAO-A, and MAO-B activities were also observed. Therefore, this study clearly demonstrates the cognitive enhancing effect of the developed herbal congee, which may be associated with the suppressions of AChE and both types of MAO.
The Use of Durum Wheat Oil in the Preparation of Focaccia: Effects on the Oxidative Stability and Physical and Sensorial Properties
Authors: Vurro Francesca , Summo Carmine , Squeo Giacomo , Caponio Francesco , Pasqualone Antonella
Published: September 2022

Durum wheat oil is an innovative oil that could be considered the “second life” of durum wheat milling by-products. In this study, we proposed the use of this oil in the reformulation of a traditional Italian greased flat bread, namely focaccia, whose typical sensorial features are due to the presence of relevant amounts of oil in its formulation. The chemical, physical, and sensorial features of focaccia with durum wheat oil (DWO) were compared with those of focaccia prepared with olive oil (OO) and sunflower oil (SO). The results showed the prevalence of polyunsaturated fatty acids in DWO, followed by SO. DWO was more resistant to oxidation than SO (induction time 86.2 and 66.3 min, respectively), due to its higher content of tocotrienols (1020 and 70.2 mg/kg in DWO and SO, respectively), but was less resistant than OO, richer in monounsaturated fatty acids, and contained phenolic compounds. The volatile oxidation markers, namely hexanal and nonanal, were less prevalent in OO and DWO than in SO. Texture and color were positively influenced by the use of durum wheat oil, allowing the nutritional improvement of this flat bread in a sustainable and circular manner.