bluuebunny/crossref_metadata_2025 huggingface dataset:
Polyphenols as Inhibitors of Antibiotic Resistant Bacteria—Mechanisms Underlying Rutin Interference with Bacterial Virulence
Authors: Ivanov Marija , Novović Katarina , Malešević Milka , Dinić Miroslav , Stojković Dejan , Jovčić Branko , Soković Marina
Published: March 2022

The rising incidence of antibiotic resistant microorganisms urges novel antimicrobials development with polyphenols as appealing potential therapeutics. We aimed to reveal the most promising polyphenols among hesperetin, hesperidin, naringenin, naringin, taxifolin, rutin, isoquercitrin, morin, chlorogenic acid, ferulic acid, p-coumaric acid, and gallic acid based on antimicrobial capacity, antibiofilm potential, and lack of cytotoxicity towards HaCaT, and to further test its antivirulence mechanisms. Although the majority of studied polyphenols were able to inhibit bacterial growth and biofilm formation, the most promising activities were observed for rutin. Further investigation proved rutin’s ability to prevent/eradicate Pseudomonas aeruginosa and MRSA urinary catheter biofilms. Besides reduction of biofilm biomass, rutin antibiofilm mechanisms included reduction of cell viability, exopolysaccharide, and extracellular DNA levels. Moderate reduction of bacterial adhesion to human keratinocytes upon treatment was observed. Rutin antivirulence mechanisms included an impact on P. aeruginosa protease, pyocyanin, rhamnolipid, and elastase production and the downregulation of the lasI, lasR, rhlI, rhlR, pqsA and mvfR genes. Rutin also interfered with membrane permeability. Polyphenols could repress antibiotic resistant bacteria. Rutin has shown wide antimicrobial and antibiofilm capacity employing a range of mechanisms that might be used for the development of novel antimicrobials.
Skin-on-a-Chip Technology: Microengineering Physiologically Relevant In Vitro Skin Models
Authors: Zoio Patrícia , Oliva Abel
Published: March 2022

The increased demand for physiologically relevant in vitro human skin models for testing pharmaceutical drugs has led to significant advancements in skin engineering. One of the most promising approaches is the use of in vitro microfluidic systems to generate advanced skin models, commonly known as skin-on-a-chip (SoC) devices. These devices allow the simulation of key mechanical, functional and structural features of the human skin, better mimicking the native microenvironment. Importantly, contrary to conventional cell culture techniques, SoC devices can perfuse the skin tissue, either by the inclusion of perfusable lumens or by the use of microfluidic channels acting as engineered vasculature. Moreover, integrating sensors on the SoC device allows real-time, non-destructive monitoring of skin function and the effect of topically and systemically applied drugs. In this Review, the major challenges and key prerequisites for the creation of physiologically relevant SoC devices for drug testing are considered. Technical (e.g., SoC fabrication and sensor integration) and biological (e.g., cell sourcing and scaffold materials) aspects are discussed. Recent advancements in SoC devices are here presented, and their main achievements and drawbacks are compared and discussed. Finally, this review highlights the current challenges that need to be overcome for the clinical translation of SoC devices.
Optical Design of a 4× Zoom Lens with a Stable External Entrance Pupil and Internal Stop
Authors: Qu Rui , Duan Jing , Liu Kai , Cao Jianzhong , Yang Jianfeng
Published: March 2022

Zoom lens with stationary external entrance pupil and internal stop is a type of special optical system that can be used in cascaded optics to meet the requirements of long focal length or variable magnification. We proposed a 4× zoom lens to improve the pupil walking limitation observed in the conventional design. Varifocal- and pupil-stable differential equations are presented and a paraxial design of the lens with two moving parts was developed. Moreover, the zoom lens, which functions in the visible waveband 500 nm~750 nm, is designed using seven types of common optical glasses, has a constant f-number of 10 and focal range of 100 mm~400 mm, and achieves pupil walking in the range −3.9 mm to +4.3 mm. The results demonstrate that the design had good image quality and tolerance characteristics. Owing to the limited pupil walking and zoom capability, the scheme is of considerable interest for application in electrical optical systems.
Chili Pepper AN2 (CaAN2): A Visible Selection Marker for Nondestructive Monitoring of Transgenic Plants
Authors: Lim Sun-Hyung , Kim Da-Hye , Cho Myeong-Cheoul , Lee Jong-Yeol
Published: March 2022

Selecting transformed plants is generally time consuming and laborious. To develop a method for transgenic plant selection without the need for antibiotics or herbicides, we evaluated the suitability of the R2R3 MYB transcription factor gene CaAN2 from purple chili pepper (Capsicum annuum) for use as a visible selection marker. CaAN2 positively regulates anthocyanin biosynthesis. Transient expression assays in tobacco (Nicotiana tabacum) leaves revealed that CaAN2 actively induced sufficient pigment accumulation for easy detection without the need for a basic helix-loop-helix (bHLH) protein as a cofactor; similar results were obtained for tobacco leaves transiently co-expressing the anthocyanin biosynthesis regulators bHLH B-Peru from maize and R2R3 MYB mPAP1D from Arabidopsis. Tobacco plants harboring CaAN2 were readily selected based on their red color at the shoot regeneration stage due to anthocyanin accumulation without the need to impose selective pressure from herbicides. Transgenic tobacco plants harboring CaAN2 showed strong pigment accumulation throughout the plant body. The ectopic expression of CaAN2 dramatically promoted the transcription of anthocyanin biosynthetic genes as well as regulators of this process. The red coloration of tobacco plants harboring CaAN2 was stably transferred to the next generation. Therefore, anthocyanin accumulation due to CaAN2 expression is a useful visible trait for stable transformation, representing an excellent alternative selection system for transgenic plants.
Chemical Composition of Essential Oil from Four Sympatric Orchids in NW-Italy
Authors: Robustelli della Cuna Francesco Saverio , Cortis Pierluigi , Esposito Fabiana , De Agostini Antonio , Sottani Cristina , Sanna Cinzia
Published: March 2022

Orchidaceae is a flowering plant family worldwide distributed known for producing volatile organic compounds (VOCs) which can act as olfactory signals for pollinators. Despite the importance of VOCs in the different reproductive strategies, in the literature there are only a few publications on the characterization of orchids’ volatile profiles. In this study, the essential oils from fresh inflorescences of sympatric orchids Anacamptis morio, Himantoglossum robertianum, Ophrys sphegodes and Orchis purpurea, naturally growing in Piedmont (Italy) were isolated by steam distillation and characterized by GC/FID and GC/MS. A number of compounds were identified, with a peculiar distribution in the species: alcohols (range 16.93–50.60%), from which p-cresol (range 12.75–38.10%) was the most representative compound; saturated hydrocarbons (range 5.81–59.29%), represented by pentacosane (range 2.22–40.17%) and tricosane (range 0.78–27.48%); long-chain monounsaturated hydrocarbons (range 0.29–5.20%) represented by 9-pentacosene, 11-tricosene, and 1-heneicosene. The structure of positional isomers in linear alkenes was elucidated by derivatization with dimethyl disulfide and MS fragmentation patterns. Coumarin (68.84%) was the dominant compound in O. purpurea and was detected in lower concentrations (range 0.21–0.26%) in the other taxa. These volatile compounds may represent a particular feature of these plant species and play an essential role in pollinator interaction.
Determination of Mechanical Properties of Epoxy Composite Materials Reinforced with Silicate Nanofillers Using Digital Image Correlation (DIC)
Authors: Jelić Aleksandra , Sekulić Milica , Travica Milan , Gržetić Jelena , Ugrinović Vukašin , Marinković Aleksandar D. , Božić Aleksandra , Stamenović Marina , Putić Slaviša
Published: March 2022

In this study, silicate nanofillers; dicalcium silicate, magnesium silicate, tricalcium silicate, and wollastonite; were synthesized using four different methods and incorporated into the epoxy resin to improve its mechanical properties. Characterization of the newly synthesized nanofillers was performed using Fourier-transformation infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The purpose of this study was to analyze newly developed composite materials reinforced with silicate nanoparticles utilizing tensile testing and a full-field non-contact 3D Digital Image Correlation (DIC) method. Analysis of deformation and displacement fields gives precise material behavior during testing. Testing results allowed a more reliable assessment of the structural integrity of epoxy composite materials reinforced using different silicate nanofillers. It was concluded that the addition of 3% of dicalcium silicate, magnesium silicate, tricalcium silicate, and wollastonite lead to the increasement of tensile strength up to 31.5%, 29.0%, 27.5%, and 23.5% in comparison with neat epoxy, respectively. In order to offer more trustworthy information about the viscoelastic behavior of neat epoxy and composites, a dynamic mechanical analysis (DMA) was also performed and rheological measurements of uncured epoxy matrix and epoxy suspensions were obtained.
Polyacrylic Acid Nanoplatforms: Antimicrobial, Tissue Engineering, and Cancer Theranostic Applications
Authors: Arkaban Hassan , Barani Mahmood , Akbarizadeh Majid Reza , Pal Singh Chauhan Narendra , Jadoun Sapana , Dehghani Soltani Maryam , Zarrintaj Payam
Published: March 2022

Polyacrylic acid (PAA) is a non-toxic, biocompatible, and biodegradable polymer that gained lots of interest in recent years. PAA nano-derivatives can be obtained by chemical modification of carboxyl groups with superior chemical properties in comparison to unmodified PAA. For example, nano-particles produced from PAA derivatives can be used to deliver drugs due to their stability and biocompatibility. PAA and its nanoconjugates could also be regarded as stimuli-responsive platforms that make them ideal for drug delivery and antimicrobial applications. These properties make PAA a good candidate for conventional and novel drug carrier systems. Here, we started with synthesis approaches, structure characteristics, and other architectures of PAA nanoplatforms. Then, different conjugations of PAA/nanostructures and their potential in various fields of nanomedicine such as antimicrobial, anticancer, imaging, biosensor, and tissue engineering were discussed. Finally, biocompatibility and challenges of PAA nanoplatforms were highlighted. This review will provide fundamental knowledge and current information connected to the PAA nanoplatforms and their applications in biological fields for a broad audience of researchers, engineers, and newcomers. In this light, PAA nanoplatforms could have great potential for the research and development of new nano vaccines and nano drugs in the future.
UV-Cured Chitosan and Gelatin Hydrogels for the Removal of As(V) and Pb(II) from Water
Authors: Noè Camilla , Zanon Michael , Arencibia Amaya , López-Muñoz María-José , Fernández de Paz Nieves , Calza Paola , Sangermano Marco
Published: March 2022

In this study, new photocurable biobased hydrogels deriving from chitosan and gelatin are designed and tested as sorbents for As(V) and Pb(II) removal from water. Those renewable materials were modified by a simple methacrylation reaction in order to make them light processable. The success of the reaction was evaluated by both 1H-NMR and FTIR spectroscopy. The reactivity of those formulations was subsequently investigated by a real-time photorheology test. The obtained hydrogels showed high swelling capability reaching up to 1200% in the case of methacrylated gelatin (GelMA). Subsequently, the Z-potential of the methacrylated chitosan (MCH) and GelMA was measured to correlate their electrostatic surface characteristics with their adsorption properties for As(V) and Pb(II). The pH of the solutions proved to have a huge influence on the As(V) and Pb(II) adsorption capacity of the obtained hydrogels. Furthermore, the effect of As(V) and Pb(II) initial concentration and contact time on the adsorption capability of MCH and GelMA were investigated and discussed. The MCH and GelMA hydrogels demonstrated to be promising sorbents for the removal of heavy metals from polluted waters.
A Density-Dependent Modified Doraivelu Model for the Cold Compaction of Poly (Ether Ketone Ketone) Powders
Authors: Xu Fan , Wang Huixiong , Wu Xuelian , Ye Zihao , Liu Hong
Published: March 2022

The cold compaction of poly (ether ketone ketone) (PEKK) powder was studied by experiments and simulations based on the modified Doraivelu model. Although this model can successfully predict the compaction behavior of metal powders, discussion of the prediction of polymer powders is lacking. Based on the mechanical theory of metal plasticity, the modified Doraivelu model was established by introducing the material parameters m and n. The modified model can predict the compaction density of PEKK powder during cold compaction. A sub-increment method for this constitutive model was then established and implemented into a finite-element model by using the user-defined material subroutine UMAT in ABAQUS/Standard. Consequently, the material parameters of the modified Doraivelu model were identified by an inverse method using the experimental data and simulation results. It was found that when m = 0, n = 4, and the initial relative density was 0.4485, the simulation results were the closest to the experimental ones.
Advancing Self-Evaluative and Self-Regulatory Mechanisms of Scholarly Journals: Editors’ Perspectives on What Needs to Be Improved in the Editorial Process
Authors: Krapež Katarina
Published: March 2022

Meticulous self-evaluative practices in the offices of academic periodicals can be helpful in reducing widespread uncertainty about the quality of scholarly journals. This paper summarizes the results of the second part of a qualitative worldwide study among 258 senior editors of scholarly journals across disciplines. By means of a qualitative questionnaire, the survey investigated respondents’ perceptions of needed changes in their own editorial workflow that could, according to their beliefs, positively affect the quality of their journals. The results show that the most relevant past improvements indicated by respondents were achieved by: (a) raising the required quality criteria for manuscripts, by defining standards for desk rejection and/or shaping the desired qualities of the published material, and (b) guaranteeing a rigorous peer review process. Respondents believed that, currently, three areas have the most pressing need for amendment: ensuring higher overall quality of published articles (26% of respondents qualified this need as very high or high), increasing the overall quality of peer-review reports (23%), and raising reviewers’ awareness of the required quality standards (20%). Bivariate analysis shows that respondents who work with non-commercial publishers reported an overall greater need to improve implemented quality assessment processes. Work overload, inadequate reward systems, and a lack of time for development activities were cited by respondents as the greatest obstacles to implementing necessary amendments.
Blockchained Industry Information Handoff Based on Internet of Things Devices with Intelligent Customized Object Recognition
Authors: Jian Ming-Shen , Pan Chin-Ju
Published: March 2022

To determine the quality and safety of each product used in manufacturing, the exchange of measured data between machines, operators, production lines, and manufacturing companies is crucial. In this study, we developed a system with customized object recognition capability for the secure blockchain-based transfer of industry information through Internet of Things (IoT) devices. In the proposed system, product history data are transferred through blockchains through artificial intelligence (AI)-based object recognition. Individual objects are recognized and represented using a unique number sequence for use as a private key on a blockchain. The data history can be automatically secured, and all the data are traceable and trackable. The reliability and validity of the proposed system were verified using the Jetson Nano Developer Kit. The proposed AI-based system is a low-cost embedded system. Based on the open-source cloud computing platform, the required computing resources for blockchain computing and storage are available. In an experiment, the proposed system achieved >99% accuracy within 1 s. Furthermore, the computational cost of the proposed system was 10% that of traditional AI systems. The proposed device can be rapidly connected to IoT devices that require limited manual operation and can be adopted in manufacturing and production lines.
Spine-like Joint Link Mechanism to Design Wearable Assistive Devices
Authors: Kim Jung-Yeong , Cho Jung-San , Kim Jin-Hyeon , Kim Jin-Tak , Han Sang-Chul , Park Sang-Shin , Yoon Han-Ul
Published: March 2022

When we develop wearable assistive devices, comfort and support are two main issues that need to be considered. In conventional design approaches, the degree of freedom of the wearer’s joint movements tends to be oversimplified. Accordingly, the wearer’s motion becomes restrained and bone/ligament injuries might occur in case of an unexpected fall. To mitigate these issues, this paper proposes a novel joint link mechanism inspired by a human spine structure as well as functionalities. The key feature of the proposed spine-like joint link mechanism is that hemispherical blocks are concatenated via flexible synthetic fiber lines so that their concatenation stiffness can be adjusted according to a tensile force. This feature has a great potentiality for designing a wearable assistive device that can support aged people’s sit-to-stand action or augment spinal motion by regulating the concatenation stiffness. In addition, the concatenated hemispherical blocks enable the wearer to move his/her joint with full freedom, which in turn increases the wearer’s mobility and prevents joint misalignment. The experimental results with a testbed and a pilot wearer substantiated that the spine-like joint link mechanism can serve as a key component in the design of wearable assistive devices for better mobility.
SHEL5K: An Extended Dataset and Benchmarking for Safety Helmet Detection
Authors: Otgonbold Munkh-Erdene , Gochoo Munkhjargal , Alnajjar Fady , Ali Luqman , Tan Tan-Hsu , Hsieh Jun-Wei , Chen Ping-Yang
Published: March 2022

Wearing a safety helmet is important in construction and manufacturing industrial activities to avoid unpleasant situations. This safety compliance can be ensured by developing an automatic helmet detection system using various computer vision and deep learning approaches. Developing a deep-learning-based helmet detection model usually requires an enormous amount of training data. However, there are very few public safety helmet datasets available in the literature, in which most of them are not entirely labeled, and the labeled one contains fewer classes. This paper presents the Safety HELmet dataset with 5K images (SHEL5K) dataset, an enhanced version of the SHD dataset. The proposed dataset consists of six completely labeled classes (helmet, head, head with helmet, person with helmet, person without helmet, and face). The proposed dataset was tested on multiple state-of-the-art object detection models, i.e., YOLOv3 (YOLOv3, YOLOv3-tiny, and YOLOv3-SPP), YOLOv4 (YOLOv4 and YOLOv4pacsp-x-mish), YOLOv5-P5 (YOLOv5s, YOLOv5m, and YOLOv5x), the Faster Region-based Convolutional Neural Network (Faster-RCNN) with the Inception V2 architecture, and YOLOR. The experimental results from the various models on the proposed dataset were compared and showed improvement in the mean Average Precision (mAP). The SHEL5K dataset had an advantage over other safety helmet datasets as it contains fewer images with better labels and more classes, making helmet detection more accurate.
Hardware Selection and Performance of Low-Cost Fluorometers
Authors: Hixson Jase L. , Ward Adam S.
Published: March 2022

Access to and extensive use of fluorometric analyses is limited, despite its extensive utility in environmental transport and fate. Wide-spread application of fluorescent tracers has been limited by the prohibitive costs of research-grade equipment and logistical constraints of sampling, due to the need for high spatial resolutions and access to remote locations over long timescales. Recently, low-cost alternatives to research-grade equipment have been found to produce comparable data at a small fraction of the price for commercial equipment. Here, we prototyped and benchmarked performance of a variety of fluorometer components against commercial units, including performance as a function of tracer concentration, turbidity, and temperature, all of which are known to impact fluorometer performance. While component performance was found to be comparable to the commercial units tested, the best configuration tested obtained a functional resolution of 0.1 ppb, a working concentration range of 0.1 to >300 ppb, and a cost of USD 59.13.
Thermal Deformation Modeling for Phased Array Antenna Compensation Control
Authors: Liu Hui , Wang Wei , Tang Dafeng , Zhang Liyin , Wang Youming , Miao Enming
Published: March 2022

Thermal compensation control can correct errors caused by the thermal deformation of phased array antenna (PAA) panels. Thermal deformation of the panel is needed to calculate the compensation value. While the PAA is working, thermal deformation is unconditional to measure, but predicting it by temperature is feasible. However, thermal deformation is also affected by other factors, such as the structural shape, assembly method, and material parameters, and it is difficult to measure these parameters of PAA because of the complex structure. In contrast, the measurement method of the temperature and thermal deformation of the PAA in the laboratory is much easier. Therefore, a comprehensive influence parameters (CIPs)-finite element method (FEM) method was proposed in this study, it can extract the influence of above parameters on thermal deformation from temperature and thermal deformation measurement data and build a thermal deformation prediction model. Experiments have verified that the CIPs-FEM can greatly reduce the difficulty of thermal deformation modeling and have a high prediction accuracy.
An Atlas for the Inkjet Printing of Large-Area Tactile Sensors
Authors: Baldini Giulia , Albini Alessandro , Maiolino Perla , Cannata Giorgio
Published: March 2022

This review aims to discuss the inkjet printing technique as a fabrication method for the development of large-area tactile sensors. The paper focuses on the manufacturing techniques and various system-level sensor design aspects related to the inkjet manufacturing processes. The goal is to assess how printed electronics simplify the fabrication process of tactile sensors with respect to conventional fabrication methods and how these contribute to overcoming the difficulties arising in the development of tactile sensors for real robot applications. To this aim, a comparative analysis among different inkjet printing technologies and processes is performed, including a quantitative analysis of the design parameters, such as the costs, processing times, sensor layout, and general system-level constraints. The goal of the survey is to provide a complete map of the state of the art of inkjet printing, focusing on the most effective topics for the implementation of large-area tactile sensors and a view of the most relevant open problems that should be addressed to improve the effectiveness of these processes.
High Specificity of Single Inertial Sensor-Supplemented Timed Up and Go Test for Assessing Fall Risk in Elderly Nursing Home Residents
Authors: Dierick Frédéric , Stoffel Pierre-Loup , Schütz Gaston , Buisseret Fabien
Published: March 2022

The Timed Up and Go test (TUG) is commonly used to estimate the fall risk in the elderly. Several ways to improve the predictive accuracy of TUG (cameras, multiple sensors, other clinical tests) have already been proposed. Here, we added a single wearable inertial measurement unit (IMU) to capture the residents’ body center-of-mass kinematics in view of improving TUG’s predictive accuracy. The aim is to find out which kinematic variables and residents’ characteristics are relevant for distinguishing faller from non-faller patients. Data were collected in 73 nursing home residents with the IMU placed on the lower back. Acceleration and angular velocity time series were analyzed during different subtasks of the TUG. Multiple logistic regressions showed that total time required, maximum angular velocity at the first half-turn, gender, and use of a walking aid were the parameters leading to the best predictive abilities of fall risk. The predictive accuracy of the proposed new test, called i + TUG, reached a value of 74.0%, with a specificity of 95.9% and a sensitivity of 29.2%. By adding a single wearable IMU to TUG, an accurate and highly specific test is therefore obtained. This method is quick, easy to perform and inexpensive. We recommend to integrate it into daily clinical practice in nursing homes.
Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques
Authors: Yasser Ibrahim , Khalifa Fahmi , Abdeltawab Hisham , Ghazal Mohammed , Sandhu Harpal Singh , El-Baz Ayman
Published: March 2022

Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.
Robust Multi-Scenario Speech-Based Emotion Recognition System
Authors: Zhu-Zhou Fangfang , Gil-Pita Roberto , García-Gómez Joaquín , Rosa-Zurera Manuel
Published: March 2022

Every human being experiences emotions daily, e.g., joy, sadness, fear, anger. These might be revealed through speech—words are often accompanied by our emotional states when we talk. Different acoustic emotional databases are freely available for solving the Emotional Speech Recognition (ESR) task. Unfortunately, many of them were generated under non-real-world conditions, i.e., actors played emotions, and recorded emotions were under fictitious circumstances where noise is non-existent. Another weakness in the design of emotion recognition systems is the scarcity of enough patterns in the available databases, causing generalization problems and leading to overfitting. This paper examines how different recording environmental elements impact system performance using a simple logistic regression algorithm. Specifically, we conducted experiments simulating different scenarios, using different levels of Gaussian white noise, real-world noise, and reverberation. The results from this research show a performance deterioration in all scenarios, increasing the error probability from 25.57% to 79.13% in the worst case. Additionally, a virtual enlargement method and a robust multi-scenario speech-based emotion recognition system are proposed. Our system’s average error probability of 34.57% is comparable to the best-case scenario with 31.55%. The findings support the prediction that simulated emotional speech databases do not offer sufficient closeness to real scenarios.
Voltage and Current Sensor Fault Diagnosis Method for Traction Converter with Two Stator Current Sensors
Authors: Tao Hongwei , Peng Tao , Yang Chao , Gao Jinqiu , Yang Chunhua , Gui Weihua
Published: March 2022

The traction converter is one of the key components of high-speed trains. Current and voltage sensor faults in the converter may lead to feedback values deviation and system degradation, which will bring security risks to the train. This paper proposes a real-time fault diagnosis method for grid current, DC-link voltage and stator current sensor faults in the traction converter with two stator current sensors, which can not only detect and locate faults but also identify the types of faults. Moreover, the faults considered in this paper are incipient. First, the DC-link model is established, and the fault is detected by the residual of the DC-link voltage. Next, the differential of DC-link voltage residual is calculated, which is applied to fault location. Then, according to the change of the differential values, different fault types are determined. Finally, the hardware-in-the-loop (HIL) platform is built and the effectiveness and accuracy of the proposed method are verified by the HIL tests.
A Contact-Sensitive Probe for Biomedical Optics
Authors: Renna Marco , Peruch Adriano , Sunwoo John , Starkweather Zachary , Martin Alyssa , Franceschini Maria Angela
Published: March 2022

Capacitive proximity sensing is widespread in our everyday life, but no sensor for biomedical optics takes advantage of this technology to monitor the probe attachment to the subject’s skin. In particular, when using optical monitoring devices, the capability to quantitatively measure the probe contact can significantly improve data quality and ensure the subject’s safety. We present a custom novel optical probe based on a flexible printed circuit board which integrates a capacitive contact sensor, 3D-printed optic fiber holders and an accelerometer sensor. The device can be effectively adopted during continuous monitoring optical measurements to detect contact quality, motion artifacts, probe detachment and ensure optimal signal quality.
A Study on the Impacts of Slot Types and Training Data on Joint Natural Language Understanding in a Spanish Medication Management Assistant Scenario
Authors: Roca Surya , Rosset Sophie , García José , Alesanco Álvaro
Published: March 2022

This study evaluates the impacts of slot tagging and training data length on joint natural language understanding (NLU) models for medication management scenarios using chatbots in Spanish. In this study, we define the intents (purposes of the sentences) for medication management scenarios and two types of slot tags. For training the model, we generated four datasets, combining long/short sentences with long/short slots, while for testing, we collect the data from real interactions of users with a chatbot. For the comparative analysis, we chose six joint NLU models (SlotRefine, stack-propagation framework, SF-ID network, capsule-NLU, slot-gated modeling, and a joint SLU-LM model) from the literature. The results show that the best performance (with a sentence-level semantic accuracy of 68.6%, an F1-score of 76.4% for slot filling, and an accuracy of 79.3% for intent detection) is achieved using short sentences and short slots. Our results suggest that joint NLU models trained with short slots yield better results than those trained with long slots for the slot filling task. The results also indicate that short slots could be a better choice for the dialog system because of their simplicity. Importantly, the work demonstrates that the performance of the joint NLU models can be improved by selecting the correct slot configuration according to the usage scenario.
A Low-Cost, 3D-Printed Biosensor for Rapid Detection of Escherichia coli
Authors: Malhotra Samir , Pham Dang Song , Lau Michael P. H. , Nguyen Anh H. , Cao Hung
Published: March 2022

Detection of bacterial pathogens is significant in the fields of food safety, medicine, and public health, just to name a few. If bacterial pathogens are not properly identified and treated promptly, they can lead to morbidity and mortality, also possibly contribute to antimicrobial resistance. Current bacterial detection methodologies rely solely on laboratory-based techniques, which are limited by long turnaround detection times, expensive costs, and risks of inadequate accuracy; also, the work requires trained specialists. Here, we describe a cost-effective and portable 3D-printed electrochemical biosensor that facilitates rapid detection of certain Escherichia coli (E. coli) strains (DH5α, BL21, TOP10, and JM109) within 15 min using 500 μL of sample, and costs only USD 2.50 per test. The sensor displayed an excellent limit of detection (LOD) of 53 cfu, limit of quantification (LOQ) of 270 cfu, and showed cross-reactivity with strains BL21 and JM109 due to shared epitopes. This advantageous diagnostic device is a strong candidate for frequent testing at point of care; it also has application in various fields and industries where pathogen detection is of interest.
Object-Based Reliable Visual Navigation for Mobile Robot
Authors: Wang Fan , Zhang Chaofan , Zhang Wen , Fang Cuiyun , Xia Yingwei , Liu Yong , Dong Hao
Published: March 2022

Visual navigation is of vital importance for autonomous mobile robots. Most existing practical perception-aware based visual navigation methods generally require prior-constructed precise metric maps, and learning-based methods rely on large training to improve their generality. To improve the reliability of visual navigation, in this paper, we propose a novel object-level topological visual navigation method. Firstly, a lightweight object-level topological semantic map is constructed to release the dependence on the precise metric map, where the semantic associations between objects are stored via graph memory and topological organization is performed. Then, we propose an object-based heuristic graph search method to select the global topological path with the optimal and shortest characteristics. Furthermore, to reduce the global cumulative error, a global path segmentation strategy is proposed to divide the global topological path on the basis of active visual perception and object guidance. Finally, to achieve adaptive smooth trajectory generation, a Bernstein polynomial-based smooth trajectory refinement method is proposed by transforming trajectory generation into a nonlinear planning problem, achieving smooth multi-segment continuous navigation. Experimental results demonstrate the feasibility and efficiency of our method on both simulation and real-world scenarios. The proposed method also obtains better navigation success rate (SR) and success weighted by inverse path length (SPL) than the state-of-the-art methods.
Intelligent Reflecting Surfaces Beamforming Optimization with Statistical Channel Knowledge
Authors: Souto Victoria Dala Pegorara , Souza Richard Demo , Uchôa-Filho Bartolomeu F. , Li Yonghui
Published: March 2022

Intelligent Reflecting Surfaces (IRSs) are emerging as an effective technology capable of improving the spectral and energy efficiency of future wireless networks. The proposed scenario consists of a multi-antenna base station and a single-antenna user that is assisted by an IRS. The large number of reflecting elements at the IRS and its passive operation represent an important challenge in the acquisition of the instantaneous channel state information (I-CSI) of all links as it adds a very high overhead to the system and requires equipping the IRS with radio-frequency chains. To overcome this problem, a new approach is proposed in order to optimize beamforming at the BS and the phase shifts at the IRS without considering any knowledge of I-CSI but while only exploring the statistical channel state information (S-CSI). We aim at maximizing the user-achievable rate subject to a maximum transmit power constraint. To achieve this goal, we propose a new two-phase framework. In the first phase, both the beamforming at the BS and IRS are designed based only on S-CSI and, in the second phase, the previously designed beamforming pair is used as an initial solution, and beamforming at the BS and IRS is designed only by considering the feedback of the SNR at UE. Moreover, for each phase, we propose new methods based on Genetic Algorithms. Results show that the developed algorithms can approach beamforming with I-CSI but with significantly reduced channel estimation overhead.
Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing
Authors: Yang Liping , Driscol Joshua , Sarigai Sarigai , Wu Qiusheng , Lippitt Christopher D. , Morgan Melinda
Published: March 2022

Water features (e.g., water quantity and water quality) are one of the most important environmental factors essential to improving climate-change resilience. Remote sensing (RS) technologies empowered by artificial intelligence (AI) have become one of the most demanded strategies to automating water information extraction and thus intelligent monitoring. In this article, we provide a systematic review of the literature that incorporates artificial intelligence and computer vision methods in the water resources sector with a focus on intelligent water body extraction and water quality detection and monitoring through remote sensing. Based on this review, the main challenges of leveraging AI and RS for intelligent water information extraction are discussed, and research priorities are identified. An interactive web application designed to allow readers to intuitively and dynamically review the relevant literature was also developed.
HPLC–NMR-Based Chemical Profiling of Matricaria pubescens (Desf.) Schultz and Matricaria recutita and Their Protective Effects on UVA-Exposed Fibroblasts
Authors: Ignatiadou Maria-Elena , Kostaki Maria , Kabouche Zahia , Chatzopoulou Paschalina , Rallis Michail Christou , Karioti Anastasia
Published: March 2022

The present study aimed to investigate the chemical profile and the protective activity on fibroblasts of two Matricaria species: M. pubescens, which grows wild in Algeria, and M. recutita, which is cultivated in Greece. A comparative phytochemical investigation using High-Performance Liquid Chromatography, coupled with Photodiode Array Detection and Mass Spectrometry (HPLC–PDA–MS) combined with Nuclear Magnetic Resonance (NMR), was performed for the identification of the main constituents in the flowerheads of these medicinal plants. In M. pubescens more than 25 compounds were identified and/or isolated; among them are quercetagenin-3-O-glucopyranoside, reported for the first time in Matricaria sp., and two polyamines previously reported in other Asteraceae species. In M. recutita, which is the officially recognized species in Europe, 19 constituents were identified. To minimize time analysis, the structure elucidation was based on a multi-analytical approach directly on subfractions. Two representative polar extracts from each species were characterized chemically and further screened for their protective effects on 3T3 fibroblasts. The cells were exposed to a mild toxic dose of UVA light (6 J/cm2), in the presence of different concentrations of the extracts. Both M. recutita and M. pubescens extracts were effective. The methanolic extract was the best protective agent at lower concentrations (0.1 to 10 μg/mL), and hydromethanolic was best at higher ones (100–200 μg/mL). M. recutita exhibited the most enhanced cell viability in relation to those not exposed to UV control; it ranged from of 28 to 49% higher viability, depending on the dose, leading to the conclusion that the latter seems to exhibit potent cytoprotective activity and significant regeneration activity.
µQuEChERS Combined with UHPLC-PDA as a State-of-the-Art Analytical Approach for Quantification of Chlorpropham in Potato
Authors: Gomes Telma M. , Perestrelo Rosa , Câmara José S.
Published: March 2022

Pesticides are chemicals used in agriculture to prevent insects, fungi, weeds, and other pests, from damaging crops. In addition, some types of pesticides are used after harvest as sprout suppressant agents help keeping the quality parameters of crops during storage. Nonetheless, its presence, even at trace levels, in food products is becoming a big challenge regarding human health. The current work aimed to develop and validate a sensitive and high-throughput analytical approach, based on a state-of-the-art microextraction technique—µQuEChERS, combined with ultra-high performance liquid chromatography equipped with a photodiode array detection system (UHPLC-PDA) to quantify isopropyl-N-(3-chlorophenyl) carbamate (chlorpropham), commonly used as efficient sprout suppressant stored potatoes, in raw and cooked potatoes cultivated in different geographical regions of Madeira Island (Portugal). Good results were obtained in terms of figures of the merit of the method, with correlation coefficients (R2) higher than 0.999 and recoveries between 94.5% to 125%. Method limit of detection (LOD) and limit of quantification (LOQ) were 0.14 μg/Kg and 0.43 μg/Kg, respectively, which are much lower than the accepted and legislated requirements by the European Union, which is 20 μg/Kg for chlorpropham. The concentration of chlorpropham in raw potatoes is significantly higher when compared to cooked samples, which revealed that the thermic treatment during cooking had a significant effect on its degradation. A significant decrease (90%, on average) was observed on chlorpropham levels.
Immigrants’ Length of Residence and Stalking Victimization in Canada: A Gendered Analysis
Authors: Braimah Joseph A. , Kyeremeh Emmanuel , Kwon Eugena , Antabe Roger , Sano Yujiro , Stoner Bradley P.
Published: March 2022

Although previous studies have explored the role of gender on stalking victimization, we know very little about how female and male immigrants are exposed to stalking victimization over time after their arrival to their host society. To address this void in the literature, we use the 2014 Canada General Social Survey to compare stalking victimization among native-born individuals, recent immigrants (those who have been in Canada for fewer than 10 years), and established immigrants (those who have been in Canada for 10 years or more) separately for women and men. Applying gender-specific complementary log-log models, we find that female (OR = 0.63, p < 0.05) and male (OR = 0.46, p < 0.01) recent immigrants are less likely to experience stalking victimization than their native-born counterparts. We also find that female established immigrants (OR = 0.65, p < 0.05) are less likely to experience stalking victimization than their native-born counterparts although there is no significance difference for male established immigrants (OR = 1.01, p > 0.05). Overall, this study points to the importance of understanding the intersection between immigrants’ length of residence and gender in the context of stalking victimization in Canada. Based on these findings, we discuss several implications for policymakers and directions for future research.
Thermoelectric Properties of Pnma and Rocksalt SnS and SnSe
Authors: Flitcroft Joseph M. , Pallikara Ioanna , Skelton Jonathan M.
Published: March 2022

Thermoelectric materials convert waste heat to electricity and are part of the package of technologies needed to limit global warming. The tin chalcogenides SnS and SnSe are promising candidate thermoelectrics, with orthorhombic SnSe showing some of the highest figures of merit ZT reported to date. As for other Group IV chalcogenides, SnS and SnSe can form rocksalt phases under certain conditions, but the thermoelectric properties of these phases are largely unexplored. We have applied a fully ab initio modelling protocol to compare the ZT of the orthorhombic and rocksalt phases of SnS and SnSe. Electronic structures from hybrid density-functional theory were used to calculate the three electrical transport properties, including approximate models for the electron relaxation times, and lattice dynamics calculations were performed to model the phonon spectra and lattice thermal conductivities. We obtained good estimates of the ZT of the well-studied orthorhombic phases. The rocksalt phases were predicted to show larger electrical conductivities and similar Seebeck coefficients to the orthorhombic phases, resulting in higher thermoelectric power factors, but these were offset by larger thermal conductivities. These results therefore motivate further investigation of the recently discovered “π-cubic” phases of SnS and SnSe, which are based on distorted rocksalt supercells, to establish their thermoelectric performance.
The Impacts of Establishing Pilot National Parks on Local Residents’ Livelihoods and Their Coping Strategies in China: A Case Study of Qilianshan National Park
Authors: Peng Jian , Xiao Honglin , Wang Rui , Qi Yuanyuan
Published: March 2022

National Parks are a category of protected areas that emphasizes the sustainable use of park resources. China is a latecomer regarding the establishment of a national park system. In 2013, the Chinese administrative authorities announced the establishment of its own national park system to better protect the country’s natural heritage and the integrity of its large ecosystems. Since 2015, ten pilot national parks have been designated to explore a pathway to implement the national park system better. Local communities are among the most critical stakeholders in establishing and managing a national park. Park management wouldn’t be successful without the local residents’ support and active involvement. Since national parks are positioned in China as protected areas with the highest priority to nature protection, their impacts on the local people, either inhabiting the park or living nearby, are unprecedented in the country. The park–people relationship is not a new topic in national park research; however, in the context of China’s social, economic, and political framework, very little is known about the livelihood impacts of establishing a national park on the local residents and what strategies those residents should adapt to cope. In this study, the authors attempt to reveal the livelihood impacts of the national park creation in China on the local residents and their adaptation approaches by taking northwestern China’s Qilianshan National Park (QNP) as a case. The study results indicate that the establishment of QNP and its policies have significantly impacted the livelihoods of the local residents. The more they rely on the park resources, the greater the magnitude of the effect has been, whether they reside inside or outside the park. Overall, the negative livelihood impacts exceed the positive ones at present. Still, most of the local residents wish to sustain their current livelihoods if the park’s impacts can be made more manageable for them. Rarely do the local residents try to find alternative livelihoods unless they absolutely cannot overcome the negative impacts caused by the park’s policies.