bluuebunny/crossref_metadata_2025 huggingface dataset:
Extraintestinal Manifestation of Yersinia pseudotuberculosis Bacteremia as Acute Hepatitis: Case Report and Review of the Literature
Authors: Lee Yun Jeong , Kim Jooyun , Jeon Ji Hoon , Seok Hyeri , Choi Won Sun , Chang Eun-Ah , Yim Hyung Joon , Park Dae Won
Published: September 2021

Yersinia pseudotuberculosis is a causative agent of foodborne zoonosis that usually causes self-limiting pseudoappendicitis. Y. pseudotuberculosis infection also causes systemic spread or extraintestinal manifestations in patients with predisposing conditions. Here, we present a case of acute hepatitis with Y. pseudotuberculosis bacteremia in a 30-year-old man. He was previously healthy without significant medical history other than obesity and current smoking. At the time of admission, he presented with high fever accompanied by chills, jaundice, abdominal pain, and watery diarrhea. Laboratory studies revealed leukocytosis and elevated liver function parameters. A stool culture showed no causative pathogens. Empiric antibiotic therapy with ceftriaxone and metronidazole was administered. Y. pseudotuberculosis was later isolated from the initial blood culture performed on the day of admission using MALDI-TOF mass spectrometry. Antibiotic treatment was continued based on the susceptibility testing results from MALDI-TOF MS and VITEk®2, as well as clinical and laboratory improvements. The patient was discharged on the tenth day of admission and remained healthy with no recurrence during the 12-month follow-up. Here, we review the literature on the systemic infection caused by Y. pseudotuberculosis, including extraintestinal manifestations. This case highlights that Y. pseudotuberculosis may be considered a differential causative organism in patients with acute colitis and hepatitis.
A New Lineage of Perch Rhabdovirus Associated with Mortalities of Farmed Perch
Authors: Pallandre Laurane , Lautraite Armand , Feuvrier Claudette , Pozet Françoise , Dacheux Laurent , Bigarré Laurent
Published: September 2021

A perhabdovirus was isolated from a mortality episode affecting a fish farm in 2019 in Western Europe. This virus was produced in cell culture and was readily detected by a species-specific real-time PCR assay. The near-complete sequence of the virus obtained showed some relatedness with viruses of the species Perhabdovirus perca. However, it was distinct enough from these viruses to form a separate genetic lineage. Multiple substitutions along the genome caused non-detection using a range of conventional PCRs previously shown to target four known genogroups of perhabdoviruses. However, various generic PCRs efficiently detected the isolated virus. The origin of this virus remains to be elucidated. It may have been introduced into the farm via wild genitors. This finding provides new evidence of the high genetic diversity of percid perhabdoviruses and the potential of new genotypes to emerge as threats for fish farming. Efforts to improve the existing diagnostic methods and control this large group of viruses are still needed.
Targeting and Understanding HIV Latency: The CRISPR System against the Provirus
Authors: Magro Gloria , Calistri Arianna , Parolin Cristina
Published: September 2021

The presence of latently infected cells and reservoirs in HIV-1 infected patients constitutes a significant obstacle to achieve a definitive cure. Despite the efforts dedicated to solve these issues, the mechanisms underlying viral latency are still under study. Thus, on the one hand, new strategies are needed to elucidate which factors are involved in latency establishment and maintenance. On the other hand, innovative therapeutic approaches aimed at eradicating HIV infection are explored. In this context, advances of the versatile CRISPR-Cas gene editing technology are extremely promising, by providing, among other advantages, the possibility to target the HIV-1 genome once integrated into cellular DNA (provirus) and/or host-specific genes involved in virus infection/latency. This system, up to now, has been employed with success in numerous in vitro and in vivo studies, highlighting its increasing significance in the field. In this review, we focus on the progresses made in the use of different CRISPR-Cas strategies to target the HIV-1 provirus, and we then discuss recent advancements in the use of CRISPR screens to elucidate the role of host-specific factors in viral latency.
Rediscovering Psilocybin as an Antidepressive Treatment Strategy
Authors: Zeiss Rene , Gahr Maximilian , Graf Heiko
Published: September 2021

There has recently been a renewal of interest in psychedelic research on the use of psilocybin in psychiatric treatment and, in particular, for the treatment of major depressive disorder (MDD). Several state-of-the-art studies have provided new insight into the mechanisms of action of psilocybin and its therapeutic potential. Nevertheless, many questions remain unanswered. With this review, we provide an overview of the current state of research on the potential mechanisms of psilocybin, its antidepressant potential, and the associated risks and adverse effects, to provide an update on a controversial topic discussed in psychopharmacology. A database search was conducted in Medline including articles on psilocybin over the period of the last 20 years. Despite the promising progress in understanding the mechanisms of psilocybin, the exact antidepressive mechanism and the role of the psychedelic experience remain elusive. The studies included in this review found high treatment effect sizes for psilocybin as an antidepressant. However, the results must be regarded as preliminary due to several limitations. Although the current studies observed no severe adverse events, several questions regarding safety and utility remain and must be subject of future research.
Silver Nanoparticles under Nanosecond Pulsed Laser Excitation as an Intensity Sensitive Saturable Absorption to Reverse Saturable Absorption Switching Material
Authors: Shiju Edappadikkunnummal , Sharafudeen Kaniyarakkal , Remya T. M. , Siji Narendran N. K. , Sudheesh Palengara , Sadasivan Nair Vijayakumar
Published: September 2021

Optical nonlinearity involved switching draws an important consideration in nonlinear optical studies. Based on that, we explored nonlinear absorption processes in silver nanoparticles synthesized by liquid phase laser ablation technique employing a second harmonic wavelength (532 nm) of Q switched Nd:YAG laser pulses with 7 ns pulse width and 10 Hz repetition rates. The typical surface plasmon resonance induced absorption (~418 nm) confirmed the formation of Ag NPs. The Z-scan technique was used to study the nonlinear optical processes, employing the same laser system used for ablation. Our study reveals that there is an occurrence of a saturable to reverse saturable absorption switching activity in the Ag nanoparticles, which is strongly on-axis input intensity dependent as well. The closed aperture Z-scan analysis revealed the self-defocusing nature of the sample.
Composite Structure of Ag Colloidal Particles and Au Sinusoidal Nanograting with Large-Scale Ultra-High Field Enhancement for SERS Detection
Authors: Feng Ke , Chen Zhaoyi , Chen Zhibin , Shen Jinxing , Li Huanliang
Published: September 2021

In this study, a novel composite Surface-Enhanced Raman Scattering (SERS) substrate is proposed for ultrasensitive detection. Consisting of gold sinusoidal nanograting and silver colloidal nanoparticles (AgNPs-AuSG), this type of SERS substrate is easy for fabrication by maskless laser interference lithography, and capable of providing large-scale ultra-high field enhancement, attributed to localized surface plasmons (LSPs) and surface plasmon polaritons (SPPs). The enhancement factor (EF) of this composite substrate is as high as up to 10 orders of magnitude in the simulation experiment. Experimental results show that this large-area, productive SERS substrate of AgNPs-AuSG has realized sensitive TNT and RDX detection with the limit of detection (LOD) of 10−10 M, which may be a potential candidate for trace explosives detection.
Chemical Constituents of the Leaves of Diospyros kaki (Persimmon)
Authors: Kwon Jaeyoung , Park Jeong-Eun , Lee Jin-Su , Lee Jung-Hwan , Hwang Hoseong , Jung Sang-Hoon , Kwon Hak-Cheol , Jang Dae-Sik
Published: September 2021

Diospyros kaki (persimmon) leaves have long been utilized as traditional medicine for the treatment of ischemic stroke, angina, and hypertension and as a healthy beverage and cosmetic for anti-aging. This study aimed to isolate as many compounds as possible from an ethanol extract of the persimmon leaves to identify the biologically active compounds. The antioxidative effect of the ethyl acetate layer from the ethanol extract of the persimmon leaves was demonstrated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay and online high-performance liquid chromatography-2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (HPLC-ABTS) analysis. A new flavonoid, kaempferol-3-O-β-d-2″-coumaroylgalactoside (1), and a new natural compound, kaempferol-3-O-β-d-2″-feruloylglucoside (3) were isolated from the ethyl acetate layer, along with 25 previously known compounds, including fourteen flavonoids, one ionone, two coumarins, seven triterpenoids, and one acetophenone. Their structures were determined by the interpretation of spectrometric and spectroscopic data. All isolated compounds were rapidly evaluated using an online HPLC-ABTS assay, and of these, compounds 4–8, 11, 13, 15, and 16 clearly showed antioxidative effects. The amount of these compounds was 0.3–0.65% of the extract.
Response of Three Miscanthus × giganteus Cultivars to Toxic Elements Stress: Part 1, Plant Defence Mechanisms
Authors: Al Souki Karim Suhail , Liné Clarisse , Douay Francis , Pourrut Bertrand
Published: September 2021

Miscanthus × giganteus demonstrated good phytostabilization potentials in toxic element (TE) contaminated soils. However, information about its tolerance to elevated concentrations is still scarce. Therefore, an ex-situ pot experiment was launched using three cultivars (termed B, U, and A) grown in soils with a gradient Cd, Pb and Zn concentrations. Control plants were also cultivated in non-contaminated soil. Results show that the number of tillers per plant, stem diameter as well as leaf photosynthetic pigments (chlorophyll a, b and carotenoids) were negatively impacted by soil contamination. On the other hand, phenolic compounds, flavonoids, tannins, and anthocyanins levels along with the antioxidant enzymatic activities of superoxide dismutase, ascorbate peroxidase and glutathione reductase increased in the plants grown on contaminated soils. Altogether, these data demonstrate that miscanthus is impacted by concentrations of toxic elements yet is able to tolerate high levels of soil contamination. These results may contribute to clarifying the miscanthus tolerance strategy against high contamination levels and its efficiency in phytoremediation.
Genome-Wide Association Study of QTLs Conferring Resistance to Bacterial Leaf Streak in Rice
Authors: Xie Xiaofang , Zheng Yan , Lu Libin , Yuan Jiazheng , Hu Jie , Bu Suhong , Lin Yanyi , Liu Yinsong , Guan Huazhong , Wu Weiren
Published: September 2021

Bacterial leaf streak (BLS) is a devastating rice disease caused by the bacterial pathogen, Xanthomonas oryzae pv. oryzicola (Xoc), which can result in severe damage to rice production worldwide. Based on a total of 510 rice accessions, trialed in two seasons and using six different multi-locus GWAS methods (mrMLM, ISIS EM-BLASSO, pLARmEB, FASTmrMLM, FASTmrEMMA and pKWmEB), 79 quantitative trait nucleotides (QTNs) reflecting 69 QTLs for BLS resistance were identified (LOD > 3). The QTNs were distributed on all chromosomes, with the most distributed on chromosome 11, followed by chromosomes 1 and 5. Each QTN had an additive effect of 0.20 (cm) and explained, on average, 2.44% of the phenotypic variance, varying from 0.00–0.92 (cm) and from 0.00–9.86%, respectively. Twenty-five QTNs were detected by at least two methods. Among them, qnBLS11.17 was detected by as many as five methods. Most of the QTNs showed a significant interaction with their environment, but no QTNs were detected in both seasons. By defining the QTL range for each QTN according to the LD half-decay distance, a total of 848 candidate genes were found for nine top QTNs. Among them, more than 10% were annotated to be related to biotic stress resistance, and five showed a significant response to Xoc infection. Our results could facilitate the in-depth study and marker-assisted improvement of rice resistance to BLS.
Responses of Ecological Stoichiometric Characteristics of Carbon, Nitrogen, and Phosphorus to Periodic Submergence in Mega-Reservoir: Growth of Taxodium distichum and Taxodium ascendens
Authors: Ding Dongdong , Liu Minghui , Arif Muhammad , Yuan Zhongxun , Li Jiajia , Hu Xin , Zheng Jie , Li Changxiao
Published: September 2021

Ecological stoichiometric studies can be useful for managing the deteriorated riparian zones of mega-reservoirs in which nutrients significantly impact the balanced vegetation cover. The present study aims to explore the effects of periodic submergence on the stoichiometric ecological characteristics of carbon (C), nitrogen (N), and phosphorus (P), as well as the growth conditions of two leading conifer species (Taxodium distichum and Taxodium ascendens) in the hydro-fluctuation zone of the Three Gorges Reservoir (TGR) region, China. The stoichiometrical contents of C, N, and P in fine roots, leaves, and branches, and the growth conditions of T. distichum and T. ascendens were measured in July 2019. The results showed that periodic submergence affected the stoichiometric characteristics and growth conditions of these two woody species, and the impact was restrained, but both grew well. The effects of inundation on the C, N, and P ecological stoichiometric characteristics differed in different parts of trees. In general, the C contents showed the following pattern: leaves > branches > fine roots. The N and P content showed the following pattern: leaves > fine roots > branches, while the C/N and C/P ratios showed an opposite trend to that of N and P. The N and P content in all parts of T. distichum (with means of 17.18 and 1.70 g/kg for leaves, 4.80 and 0.57 g/kg for branches, and 6.88 and 1.10 g/kg for fine roots, respectively) and T. ascendens (with means of 14.56 and 1.87 g/kg for leaves, 5.03 and 0.63 g/kg for branches, and 8.17 and 1.66 g/kg for fine roots, respectively) were higher than the national average level (with means of 14.14 and 1.11 g/kg for leaves, 3.04 and 0.31 g/kg for branches, and 4.85 and 0.47 g/kg for fine roots, respectively). Except for N and P contents in the leaves of T. distichum, there was a significant correlation between N and P elements in other parts (p < 0.05). Nevertheless, the N/P ratio (10.15, 8.52, 6.44, and 7.93, 8.12, 5.20 in leaves, branches, and fine roots of T. distichum and T. ascendens, respectively) was lower than the critical ratio of 14. The growth conditions of T. distichum and T. ascendens were significantly negatively correlated with their leaf C contents and significantly positively correlated with their fine root N and P contents. This study showed that T. distichum and T. ascendens could maintain their normal growth needs by properly allocating nutrients between different organs to adapt to the long periodic submergence in the hydro-fluctuation zone of the TGR region.
Photosynthetic Efficiency and Yield of Cucumber (Cucumis sativus L.) Grown under HPS and LED Lighting in Autumn–Winter Cultivation
Authors: Gajc-Wolska Janina , Kowalczyk Katarzyna , Przybysz Arkadiusz , Mirgos Małgorzata , Orliński Paweł
Published: September 2021

The objective of this study was to evaluate the effect of the supplemental lighting of cucumber with sodium pressure lamps (HPSs) and light-emitting diodes (LEDs) on photosynthetic efficiency and yield in autumn–winter cultivation. Cucumber plants of the ‘Svyatogor’ F1 midi-cucumber parthenocarpic type cultivar were grown on mineral wool mats in three compartments, differing only in the type of light, i.e., (1) HPS top lighting (HPS) in the first compartment, (2) HPS top lighting and LED panel interlighting (HPS + LED) in the second compartment and (3) LED top lighting and inter-row LED panels (LED) in the third compartment. The photosynthetically active radiation was the same in each compartment. The study showed that the highest commercial yields of cucumber could be achieved under LED light (top and inter-row). The chlorophyll content in the leaf blade of younger leaves was higher in plants under LED lighting. This type of lighting also had a positive effect on the gas exchange of plants (net carbon assimilation, stomatal conductance, transpiration). LED and HPS + LED lighting increased the chlorophyll a fluorescence parameters, such as Fs, Fm’ and vitality index (PI), in both younger and older leaves, which also increased the fruit yield in the tested combinations.
Photocatalytic Degradation of Diazo Dye over Suspended and Immobilized TiO2 Catalyst in Swirl Flow Reactor: Kinetic Modeling
Authors: Jadaa Waleed , Prakash Anand , Ray Ajay K.
Published: September 2021

The degradation kinetics of Direct Blue 15 (DB15), a diazo dye, were studied over a suspended and immobilized TiO2 catalyst. For all experiments, the kinetics experiments were performed in a swirl flow photoreactor under the influence of UV light. The effect of different parameters: dye concentration, catalyst loading, and light intensity, on the DB15 kinetics was investigated. The kinetic rates were assessed using apparent (ka) approach, a single value of reaction rate (kr) and adsorption constant (K), and approach of kr as of variable. The DB15 mineralization was discussed as well. Using a dip-coating device, the P25 catalyst was deposited on a Pyrex glass. The thin film surface characterization was examined. The coated catalyst was evaluated by checking the effect of two variables: initial dye concentration and light intensity on the DB15 kinetics. In terms of the ka approach, the results demonstrated that DB15 degradation is described by the pseudo first-order kinetics model. The Langmuir-Hinshelwood (L-H) model was fitted well with the experimental data for the number of process variables. L-H constant kr was determined as a function of three parameters: initial dye concentration, catalyst loading, and light intensity. The ka values were evaluated and compared with experimental results. In terms of three variables, ka can be expressed as ka=0.15 [C]o−0.69 [W]0.73 I0.91 1+0.17 [C]o  while the empirical model results in the following expression, ka=0.77 [C]o−1.65 [W]0.73 Io0.89. It was observed that 83.64% mineralization was achieved after a period of 16 hrs. In terms of immobilized catalyst, the DB15 degradation kinetics was described by a pseudo first-order model for different dye concentrations. Meanwhile, a power-law model described the impact of light intensity on dye kinetics. In addition, the coated catalyst was successfully reusable with high efficiency for up to four cycles.
The Complexity of Medical Device Regulations Has Increased, as Assessed through Data-Driven Techniques
Authors: Arnould Arthur , Hendricusdottir Rita , Bergmann Jeroen
Published: September 2021

Medical device regulations are dynamic, as they need to cover an ever changing landscape. In Europe this has led to a new set of regulations (both for Medical Devices and In Vitro Diagnostics), which replaced the old rules. This study is interested in how the complexity of these medical regulations changed over time and if additional time-based metrics can be associated with any of the complexity metrics. Complexity is defined in terms of readability of the text and it is computed using established linguistic measures, as well as Halstead complexity scores. It was shown that the regulatory complexity of new EU medical device regulations was higher than their predecessors, especially when Halstead complexity measures were considered. The complexity metrics obtained for the new regulations were subsequently associated with the time it took to consider these regulations. Only very weak Pearson’s correlation coefficients were found between the complexity scores and the obtained response times for the new regulations. This could indicate that there are issues with how complexity is perceived by those that need to apply these regulations. Taking the complexity of regulations into account can greatly help with the development of more user friendly regulations. The results from the data-driven methods that are applied in this research indicate that governments could benefit from focusing on making regulations more accessible and utilitarian. This would improve the stakeholder adherence and facilitate effective implementation. This work also highlighted the need to develop more suitable methods to analyse regulatory text to further inform the wider research community.
Unravelling the Taphonomic Stories of Bird Bones from the Middle Pleistocene Layer VIII of Grotte Vaufrey, France
Authors: Rufà Anna , Laroulandie Véronique
Published: September 2021

In recent years, several studies have significantly changed our knowledge concerning the use of birds by Neanderthals. However, what remains to be clarified is the geographical and chronological variability of this human behaviour. The present case study provides new information on this topic/debate. The Grotte Vaufrey was discovered during the 1930s and was excavated during different periods. Work carried out by J.-P. Rigaud during the 1980s motivated many multidisciplinary studies in the cave, but accurate studies were not focused on avian remains. In this work, we provide new data on the bird remains from layer VIII (MIS 7), which is the richest among all the sequences and which has an important Mousterian component. Corvids are predominant in the assemblage and are associated with medium-sized birds and small Passeriformes, among others. Most of the remains present modern fractures, which hinder taphonomic interpretation. However, some alterations associated with raptor or mammalian carnivore activities, together with the anatomical representation and age profile, suggest a non-human accumulation of the majority of the bird remains, especially in the case of corvids that naturally died in the cave. However, at least some bones show evidence of anthropic activity, suggesting the occasional use of large- and medium-sized birds by human populations.
Southern African Soap Plants and Screening of Selected Phytochemicals and Quantitative Analysis of Saponin Content
Authors: Mohlakoana Mpho , Moteetee Annah
Published: September 2021

In southern Africa, several plants are used ethnobotanically as soap substitutes, however, this information resides in different literature sources. The foaming and cleansing properties of such plants are attributed mainly to the presence of saponins, but other compounds such as alkaloids and terpenoids are also implicated. This study aimed to compile a comprehensive list of plants used traditionally as soap substitutes in southern Africa and to assess the chemical properties of selected species. Qualitative phytochemical analysis was done using five solvents (ethanol, methanol, water, chloroform, and acetone) to determine the presence of saponins, alkaloids, and terpenoids in selected soap plants. Quantitative analysis of the saponin content was done employing spectrophotometric tests of methanol extracts. There are thirty-seven (37) known southern African soap plants from twenty-four (24) different families, with the Fabaceae having the highest number of species (eight). Saponin concentrations of nine previously unstudied selected soap plants are reported for the first time in this study, whereby Calodendrum capense had the highest saponin concentrations are at 107.89 ± 4.89 mg/g, followed by Noltea africana (52.65 ± 6.81 mg/g), Crinum bulbispermum (35.43 ± 4.25 mg/g), and Merwilla plumbea (25.59 ± 0.83 mg/g). The knowledge of plant composition gives a better understanding of plant chemistry and possible use of plants medicinally, industrially and as soap substitutes. Furthermore, this allows the verification and the justification of traditional plant use. Soap plants have been used traditionally for many years, the potential to commercialise the use of these plants has been realised with the increase in the use of organic products by conscious consumers hence, the purpose of this investigation can have bearing on future projects and products.
Semantic Description of Quality of Data in Sensor Networks
Authors: Vedurmudi Anupam Prasad , Neumann Julia , Gruber Maximilian , Eichstädt Sascha
Published: September 2021

The annotation of sensor data with semantic metadata is essential to the goals of automation and interoperability in the context of Industry 4.0. In this contribution, we outline a semantic description of quality of data in sensor networks in terms of indicators, metrics and interpretations. The concepts thus defined are consolidated into an ontology that describes quality of data metainformation in heterogeneous sensor networks and methods for the determination of corresponding quality of data dimensions are outlined. By incorporating support for sensor calibration models and measurement uncertainty via a previously derived ontology, a conformity with metrological requirements for sensor data is ensured. A quality description for a calibrated sensor generated using the resulting ontology is presented in the JSON-LD format using the battery level and calibration data as quality indicators. Finally, the general applicability of the model is demonstrated using a series of competency questions.
An Incremental Clustering Algorithm with Pattern Drift Detection for IoT-Enabled Smart Grid System
Authors: Jiang Zigui , Lin Rongheng , Yang Fangchun
Published: September 2021

The IoT-enabled smart grid system provides smart meter data for electricity consumers to record their energy consumption behaviors, the typical features of which can be represented by the load patterns extracted from load data clustering. The changeability of consumption behaviors requires load pattern update for achieving accurate consumer segmentation and effective demand response. In order to save training time and reduce computation scale, we propose a novel incremental clustering algorithm with probability strategy, ICluster-PS, instead of overall load data clustering to update load patterns. ICluster-PS first conducts new load pattern extraction based on the existing load patterns and new data. Then, it intergrades new load patterns with the existing ones. Finally, it optimizes the intergraded load pattern sets by a further modification. Moreover, ICluster-PS can be performed continuously with new coming data due to parameter updating and generalization. Extensive experiments are implemented on real-world dataset containing diverse consumer types in various districts. The experimental results are evaluated by both clustering validity indices and accuracy measures, which indicate that ICluster-PS outperforms other related incremental clustering algorithm. Additionally, according to the further case studies on pattern evolution analysis, ICluster-PS is able to present any pattern drifts through its incremental clustering results.
Hyperspectral Image Classification Using Deep Genome Graph-Based Approach
Authors: Tinega Haron , Chen Enqing , Ma Long , Mariita Richard M. , Nyasaka Divinah
Published: September 2021

Recently developed hybrid models that stack 3D with 2D CNN in their structure have enjoyed high popularity due to their appealing performance in hyperspectral image classification tasks. On the other hand, biological genome graphs have demonstrated their effectiveness in enhancing the scalability and accuracy of genomic analysis. We propose an innovative deep genome graph-based network (GGBN) for hyperspectral image classification to tap the potential of hybrid models and genome graphs. The GGBN model utilizes 3D-CNN at the bottom layers and 2D-CNNs at the top layers to process spectral–spatial features vital to enhancing the scalability and accuracy of hyperspectral image classification. To verify the effectiveness of the GGBN model, we conducted classification experiments on Indian Pines (IP), University of Pavia (UP), and Salinas Scene (SA) datasets. Using only 5% of the labeled data for training over the SA, IP, and UP datasets, the classification accuracy of GGBN is 99.97%, 96.85%, and 99.74%, respectively, which is better than the compared state-of-the-art methods.
Development of a Low-Cost Open-Source Measurement System for Joint Angle Estimation
Authors: de Almeida Túlio Fernandes , Morya Edgard , Rodrigues Abner Cardoso , de Azevedo Dantas André Felipe Oliveira
Published: September 2021

The use of inertial measurement units (IMUs) is a low-cost alternative for measuring joint angles. This study aims to present a low-cost open-source measurement system for joint angle estimation. The system is modular and has hardware and software. The hardware was developed using a low-cost IMU and microcontroller. The IMU data analysis software was developed in Python and has three fusion filters: Complementary Filter, Kalman Filter, and Madgwick Filter. Three experiments were performed for the proof of concept of the system. First, we evaluated the knee joint of Lokomat, with a predefined average range of motion (ROM) of 60∘. In the second, we evaluated our system in a real scenario, evaluating the knee of a healthy adult individual during gait. In the third experiment, we evaluated the software using data from gold standard devices, comparing the results of our software with Ground Truth. In the evaluation of the Lokomat, our system achieved an average ROM of 58.28∘, and during evaluation in a real scenario it achieved an average ROM of 44.62∘. In comparing our software with Ground Truth, we achieved a root-mean-square error of 0.04 and a mean average percentage error of 2.95%. These results encourage the use of this system in other scenarios.
Space-Filling Curve Resistor on Ultra-Thin Polyetherimide Foil for Strain Impervious Temperature Sensing
Authors: Rager Korbinian , Jaworski David , von der Heide Chresten , Kyriazis Alexander , Sinapius Michael , Constantinou Iordania , Dietzel Andreas
Published: September 2021

Monitoring process parameters in the manufacture of composite structures is key to ensuring product quality and safety. Ideally, this can be done by sensors that are embedded during production and can remain as devices to monitor structural health. Extremely thin foil-based sensors weaken the finished workpiece very little. Under ideal conditions, the foil substrate bonds with the resin in the autoclaving process, as is the case when polyetherimide is used. Here, we present a temperature sensor as part of an 8 µm thick multi-sensor node foil for monitoring processing conditions during the production and structural health during the lifetime of a construction. A metallic thin film conductor was shaped in the form of a space-filling curve to suppress the influences of resistance changes due to strain, which could otherwise interfere with the measurement of the temperature. FEM simulations as well as experiments confirm that this type of sensor is completely insensitive to the direction of strain and sufficiently insensitive to the amount of strain, so that mechanical strains that can occur in the composite curing process practically do not interfere with the temperature measurement. The temperature sensor is combined with a capacitive sensor for curing monitoring based on impedance measurement and a half-bridge strain gauge sensor element. All three types are made of the same materials and are manufactured together in one process flow. This is the key to cost-effective distributed sensor arrays that can be embedded during production and remain in the workpiece, thus ensuring not only the quality of the initial product but also the operational reliability during the service life of light-weight composite constructions.
Recovery of Ionospheric Signals Using Fully Convolutional DenseNet and Its Challenges
Authors: Mendoza Merlin M. , Chang Yu-Chi , Dmitriev Alexei V. , Lin Chia-Hsien , Tsai Lung-Chih , Li Yung-Hui , Hsieh Mon-Chai , Hsu Hao-Wei , Huang Guan-Han , Lin Yu-Ciang , Tsogtbaatar Enkhtuya
Published: September 2021

The technique of active ionospheric sounding by ionosondes requires sophisticated methods for the recovery of experimental data on ionograms. In this work, we applied an advanced algorithm of deep learning for the identification and classification of signals from different ionospheric layers. We collected a dataset of 6131 manually labeled ionograms acquired from low-latitude ionosondes in Taiwan. In the ionograms, we distinguished 11 different classes of the signals according to their ionospheric layers. We developed an artificial neural network, FC-DenseNet24, based on the FC-DenseNet convolutional neural network. We also developed a double-filtering algorithm to reduce incorrectly classified signals. That made it possible to successfully recover the sporadic E layer and the F2 layer from highly noise-contaminated ionograms whose mean signal-to-noise ratio was low, SNR = 1.43. The Intersection over Union (IoU) of the recovery of these two signal classes was greater than 0.6, which was higher than the previous models reported. We also identified three factors that can lower the recovery accuracy: (1) smaller statistics of samples; (2) mixing and overlapping of different signals; (3) the compact shape of signals.
Sensor Size Effect on Rayleigh Wave Velocity on Cementitious Surfaces
Authors: Ospitia Nicolas , Aggelis Dimitrios G. , Lefever Gerlinde
Published: September 2021

Concrete properties and damage conditions are widely evaluated by ultrasonics. When access is limited, the evaluation takes place from a single surface. In this case, the sensor size plays a crucial role due to the “aperture effect”. While this effect is well documented regarding the amplitude or the frequency content of the surface (or Rayleigh) wave pulses, it has not been studied in terms of the wave velocity, although the velocity value is connected to concrete stiffness, porosity, damage degree, and is even empirically used to evaluate compressive strength. In this study, numerical simulations take place where sensors of different sizes are used to measure the surface wave velocity as well as its dependence on frequency (dispersion) and sensor size, showing the strong aperture effect and suggesting rules for reliable measurements on a concrete surface. The numerical trends are also validated by experimental measurements on a cementitious material by sensors of different sizes.
Staring Spotlight SAR with Nonlinear Frequency Modulation Signal and Azimuth Non-Uniform Sampling for Low Sidelobe Imaging
Authors: Xu Wei , Zhang Lu , Fang Chonghua , Huang Pingping , Tan Weixian , Qi Yaolong
Published: September 2021

In synthetic aperture radar (SAR) imaging, geometric resolution, sidelobe level (SLL) and signal-to-noise ratio (SNR) are the most important parameters for measuring the SAR image quality. The staring spotlight mode continuously transmits signals to a fixed area by steering the azimuth beam to acquire azimuth high geometric resolution, and its two-dimensional (2D) impulse response with the low SLL is usually obtained from the 2D weighted power spectral density (PSD) by the selected weighting window function. However, this results in the SNR reduction due to 2D amplitude window weighting. In this paper, the staring spotlight SAR with nonlinear frequency modulation (NLFM) signal and azimuth non-uniform sampling (ANUS) is proposed to obtain high geometric resolution SAR images with the low SLL and almost without any SNR reduction. The NLFM signal obtains non-equal interval frequency sampling points under uniform time sampling by adjusting the instantaneous chirp rate. Its corresponding PSD is similar to the weighting window function, and its pulse compression result without amplitude window weighting has low sidelobes. To obtain a similar Doppler frequency distribution for low sidelobe imaging in azimuth, the received SAR echoes are designed to be non-uniformly sampled in azimuth, in which the sampling sequence is dense in middle and sparse in both ends, and azimuth compression result with window weighting would also have low sidelobes. According to the echo model of the proposed imaging mode, both the back projection algorithm (BPA) and range migration algorithm (RMA) are modified and presented to handle the raw data of the proposed imaging mode. Both imaging results on simulated targets and experimental real SAR data processing results of a ground-based radar validate the proposed low sidelobe imaging mode.
Firmware Update Using Multiple Gateways in LoRaWAN Networks
Authors: Charilaou Christia , Lavdas Spyros , Khalifeh Ala , Vassiliou Vasos , Zinonos Zinon
Published: September 2021

The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway.
Low-Power Failure Detection for Environmental Monitoring Based on IoT
Authors: Liu Jiaxi , Gao Weizhong , Dong Jian , Wu Na , Ding Fei
Published: September 2021

Many environmental monitoring applications that are based on the Internet of Things (IoT) require robust and available systems. These systems must be able to tolerate the hardware or software failure of nodes and communication failure between nodes. However, node failure is inevitable due to environmental and human factors, and battery depletion in particular is a major contributor to node failure. The existing failure detection algorithms seldom consider the problem of node battery consumption. In order to rectify this, we propose a low-power failure detector (LP-FD) that can provide an acceptable failure detection service and can save on the battery consumption of nodes. From simulation experiments, results show that the LP-FD can provide better detection speed, accuracy, overhead and battery consumption than other failure detection algorithms.
An Absorbing Markov Chain Model to Predict Dairy Cow Calving Time
Authors: Maw Swe Zar , Zin Thi Thi , Tin Pyke , Kobayashi Ikuo , Horii Yoichiro
Published: September 2021

Abnormal behavioral changes in the regular daily mobility routine of a pregnant dairy cow can be an indicator or early sign to recognize when a calving event is imminent. Image processing technology and statistical approaches can be effectively used to achieve a more accurate result in predicting the time of calving. We hypothesize that data collected using a 360-degree camera to monitor cows before and during calving can be used to establish the daily activities of individual pregnant cows and to detect changes in their routine. In this study, we develop an augmented Markov chain model to predict calving time and better understand associated behavior. The objective of this study is to determine the feasibility of this calving time prediction system by adapting a simple Markov model for use on a typical dairy cow dataset. This augmented absorbing Markov chain model is based on a behavior embedded transient Markov chain model for characterizing cow behavior patterns during the 48 h before calving and to predict the expected time of calving. In developing the model, we started with an embedded four-state Markov chain model, and then augmented that model by adding calving as both a transient state, and an absorbing state. Then, using this model, we derive (1) the probability of calving at 2 h intervals after a reference point, and (2) the expected time of calving, using their motions between the different transient states. Finally, we present some experimental results for the performance of this model on the dairy farm compared with other machine learning techniques, showing that the proposed method is promising.
A Heterogeneous RISC-V Processor for Efficient DNN Application in Smart Sensing System
Authors: Zhang Haifeng , Wu Xiaoti , Du Yuyu , Guo Hongqing , Li Chuxi , Yuan Yidong , Zhang Meng , Zhang Shengbing
Published: September 2021

Extracting features from sensing data on edge devices is a challenging application for which deep neural networks (DNN) have shown promising results. Unfortunately, the general micro-controller-class processors which are widely used in sensing system fail to achieve real-time inference. Accelerating the compute-intensive DNN inference is, therefore, of utmost importance. As the physical limitation of sensing devices, the design of processor needs to meet the balanced performance metrics, including low power consumption, low latency, and flexible configuration. In this paper, we proposed a lightweight pipeline integrated deep learning architecture, which is compatible with open-source RISC-V instructions. The dataflow of DNN is organized by the very long instruction word (VLIW) pipeline. It combines with the proposed special intelligent enhanced instructions and the single instruction multiple data (SIMD) parallel processing unit. Experimental results show that total power consumption is about 411 mw and the power efficiency is about 320.7 GOPS/W.
ECE Program Supports and Teacher-Perceived Support from Families: Are They Connected?
Authors: Schock Natalie , Jeon Lieny
Published: September 2021

According to the Conservation of Resources theory of stress, early care and education (ECE) teachers who receive greater tangible and interpersonal supports from their workplaces will be more positive and effective in their roles. This may translate to them perceiving or eliciting greater support from families, which is a key component to family engagement, a growing area of study in the ECE landscape. This study explores whether four program-level supports (benefits, professional development supports, teacher social supports, program-level family involvement activities) are associated with teacher-perceived support from families. The hypothesis was that all four will be positively associated. This study uses survey data from 102 preschool teachers and 13 preschool program directors in urban areas of two US states. We use ordinary least squares regression with cluster-robust standard errors and a stepwise build-up modeling procedure to determine associations between independent and dependent variables. While teacher social supports had the expected positive association with teacher-perceived support from families, family involvement activities were negatively associated. Our findings suggest that programs looking to improve family engagement may consider interpersonal/cultural supports for teachers and the larger school community. All else equal, simply offering more family involvement activities may not improve engagement culture.
The Application of Human Capital Theory and Educational Signalling Theory to Explain Parental Influences on the Chinese Population’s Social Mobility Opportunities
Authors: Hung Jason , Ramsden Mark
Published: September 2021

Existing studies argue life chances are, in part, vertically reproduced. Such a statement is applicable to the Chinese contexts as, but not limited to, parental hukou status, to some extent, determines the life chances their children receive. In this essay, the author would like to introduce human capital theory (HCT) and educational signalling theory (EST), and assess how the applications of each of these two models can enrich the understanding of vertical reproduction of individuals’ social mobility opportunities. The author would also present the limitations of each of these two models when addressing relevant Chinese contexts.
Participation and Iterative Experiments: Designing Alternative Futures with Migrants and Service Providers
Authors: Pollini Alessandro , Caforio Alessandro
Published: September 2021

The current crisis of participation and crisis of engagement of Western societies also affects the application and the validity of the participatory design methodology. Traditional techniques to collaboratively design future solutions with the users and bring their knowledge to the technology application might not no longer be sufficient for the complex nature of our societies. This paper describes the applied research and design of a Digital Companion with AI-based profiling, needs matching, and service-access supporting chatbots. The objective of the Digital Companion is to enhance both the effectiveness of the services currently provided to migrants and refugees by local public administration and organizations, and the life quality of the migrants themselves. The adoption of participatory, iterative, and experimental co-creation approaches allowed to contribute to the innovation of the participatory design framework at a theoretical and methodological level, as is required in order to turn participation into action, to go beyond the mere public consultation and to find new pathways for involving all the citizens (not only migrants or local service providers) as a first step to build a successful project.
The Collaborative Process of Sustainable Innovations under the Lens of Actor–Network Theory
Authors: Aka Kadia Georges , Labelle François
Published: September 2021

The development of sustainable innovation (SI) is complex and risky due to the characteristics and diversity of actors involved in its process. Little is known about the collaborative process underlying this development. The objective of the paper is to explore the collaborative mechanisms and dynamics that influence the process and characteristics of sustainable innovations. The translation approach of the actor–network theory is applied to shed light on the collaborative process of two cases of sustainable innovations within small- and medium-sized enterprises. The sociotechnical graph method is used as a methodology to track the mechanisms and compare the dynamics of their processes. The results reveal that the governance characteristic of sustainable innovations and the moment of mobilization are essential aspects of the collaborative processes. They show that, depending on the intensity and systemic impacts of SI, attraction and retention are important mechanisms in the construction of the governance characteristics of SI. A manager who uses these mechanisms during the mobilization of actors, having resources related to the governance characteristics, succeeds in sustainable innovation development. The paper contributes to the literature on sustainability management by linking the ‘becoming’ of sustainable innovations to their collaborative processes. It also informs managers on how to manage the collaborative process of sustainable innovations by relying on a translation approach.