bluuebunny/crossref_metadata_2025 huggingface dataset:
Autoimmune Hepatitis: Shifts in Gut Microbiota and Metabolic Pathways among Egyptian Patients
Authors: Elsherbiny Nahla M. , Rammadan Mohammed , Hassan Elham A. , Ali Mohamed E. , El-Rehim Abeer S. Abd , Abbas Wael A. , Abozaid Mohamed A. A. , Hassanin Ebtisam , Hetta Helal F.
Published: July 2020

Autoimmune hepatitis (AIH) is a chronic inflammatory disorder with complex immunopathogenesis. Dysbiosis has been linked to many autoimmune diseases, but its detailed role in autoimmune hepatitis (AIH) still needs rigorous evaluation, especially in Egypt. We aimed to identify the shift in the gut microbiota profile and resultant metabolic pathways in AIH Egyptian patients compared to healthy individuals. Stool samples were collected from 15 AIH-naive patients and from 10 healthy individuals. The V3-V4 hyper-variable regions in16S rRNA gene was amplified and sequenced using Illumina MiSeq platform. Significantly lower bacterial diversity in AIH patients was found compared to the controls. A phylum-level analysis showed the overrepresentation of Firmicutes, Bacteroides, and Proteobacteria. At the genus level, AIH-associated enrichment of Faecalibacterium, Blautia, Streptococcus, Haemophilus, Bacteroides, Veillonella, Eubacterium, Lachnospiraceae and Butyricicoccus was reported in contrast to Prevotella, Parabacteroides and Dilaster, which were significantly retracted in such patients. Overall, the predicted metabolic pathways associated with dysbiosis in AIH patients could orchestrate the potential pathogenic roles of gut microbiota in autoimmune disease, though not in a disease-specific manner, calling for future large-scale studies.
Protective Roles of Cytosolic and Plastidal Proteasomes on Abiotic Stress and Pathogen Invasion
Authors: Ali Md. Sarafat , Baek Kwang-Hyun
Published: July 2020

Protein malfunction is typically caused by abiotic stressors. To ensure cell survival during conditions of stress, it is important for plant cells to maintain proteins in their respective functional conformation. Self-compartmentalizing proteases, such as ATP-dependent Clp proteases and proteasomes are designed to act in the crowded cellular environment, and they are responsible for degradation of misfolded or damaged proteins within the cell. During different types of stress conditions, the levels of misfolded or orphaned proteins that are degraded by the 26S proteasome in the cytosol and nucleus and by the Clp proteases in the mitochondria and chloroplasts increase. This allows cells to uphold feedback regulations to cellular-level signals and adjust to altered environmental conditions. In this review, we summarize recent findings on plant proteolytic complexes with respect to their protective functions against abiotic and biotic stressors.
Changes in the Phenolic Compounds of Hop (Humulus lupulus L.) Induced by Infection with Verticillium nonalfalfae, the Causal Agent of Hop Verticillium Wilt
Authors: Kunej Urban , Mikulič-Petkovšek Maja , Radišek Sebastjan , Štajner Nataša
Published: July 2020

Phenolic compounds are involved in plant responses to various biotic and abiotic stress factors, with many studies suggesting their role in defense mechanisms against fungal pathogens. Soilborne vascular pathogen Verticillium nonalfalfae causes severe wilting and consequent dieback in a wide range of economically important crops, including hops (Humulus lupulus L.). In this study, we investigated the differential accumulation of phenolics in the susceptible “Celeia” and resistant “Wye Target” hop cultivars during the pathogenesis of Verticillium wilt. Quantitative polymerase chain reaction showed that colonization in the roots of both cultivars was intensive, but decreased continuously throughout the experiment in the resistant cultivar, while the relative fungal amount continuously increased in the stems of the susceptible cultivar. In response to colonization in the roots of the resistant cultivar, a significant increase in total flavanols was detected at three days postinoculation (dpi), suggesting a possible role in preventing fungus spread into the stems. The accumulation of phenolic compounds was less pronounced in the stems of the resistant cultivar since, compared to the latter, significant increases in flavonols at 3 and 15 dpi and hydroxycinnamic acids at 6 dpi were observed in the stems of the susceptible cultivar.
Large-Scale Plant Production of Lycium barbarum L. by Liquid Culture in Temporary Immersion System and Possible Application to the Synthesis of Bioactive Substance
Authors: Ruta Claudia , De Mastro Giuseppe , Ancona Simona , Tagarelli Anna , De Cillis Francesca , Benelli Carla , Lambardi Maurizio
Published: July 2020

Goji (Lycium barbarum L.) has recognized nutritive and antioxidant properties and many products are commercialized for health in food market. Besides its food use, goji has been the subject of more than 2000 years of traditional Chinese medicine, using berries, root bark, and leaves. Here, the potential of the liquid culture in temporary immersion system (TIS) by using the bioreactor PlantformTM was tested for the large-scale production of high-quality goji shoots and the subsequent production of total phenols and flavonoids. The three tested immersion cycles differently influenced the shoot quality in terms of proliferation and hyperhydricity. The best immersion cycle (time and frequency) was proven to have the shortest daily immersion time (6 min every 24 h) which ensured good levels of relative growth and multiplication rate, very limited onset of hyperydricity, and the longest shoots, promoting direct rooting after only 30 days of culture. In comparison with the semisolid culture, the TIS culture resulted in an increase of the total phenolic content (TPC) and in a lower value of the total flavonoid content (TFC). However, considering the higher quantity of biomass produced in the PlantformTM bioreactor, the difference in terms of TFC productivity between semisolid medium and TIS liquid culture was proven to be statistically equivalent.
Transcriptome Sequencing and Chemical Analysis Reveal the Formation Mechanism of White Florets in Carthamus tinctorius L.
Authors: Qiang Tingyan , Liu Jiushi , Dong Yuqing , Ma Yinbo , Zhang Bengang , Wei Xueping , Liu Haitao , Xiao Peigen
Published: July 2020

Carthamus tinctorius L. (safflower), an economic crop and herb, has been extensively studied for its diverse chemical constituents and pharmacological effects, but the mechanism of safflower pigments (SP) leading to different colors of florets has not been clarified. In the present study, we compared the contents of SP in two varieties of safflower with white and red florets, named Xinhonghua No. 7 (WXHH) and Yunhong No. 2 (RYH). The results showed the contents of SP in RYH were higher than WXHH. To investigate genes related to SP, we obtained six cDNA libraries of florets from the two varieties by transcriptome sequencing. A total of 225,008 unigenes were assembled and 40 unigenes related to safflower pigment biosynthesis were annotated, including 7 unigenes of phenylalanine ammonia-lyase (PAL), 20 unigenes of 4-coumarate-CoA ligase (4CL), 1 unigene of trans-cinnamate 4-monooxygenase (C4H), 7 unigenes of chalcone synthase (CHS), 4 unigenes of chalcone isomerase (CHI), and 1 unigene of flavanone 3-hydroxylase (F3H). Based on expression levels we selected 16 differentially expressed unigenes (DEGs) and tested them using reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR), which was consistent with the sequencing results. Consequently, we speculated that in WXHH, 3 PALs, 3 4CLs, 1 C4H, 1 CHS, and 1 CHI, which were down-regulated, and 1 F3H, which was up-regulated, may play a key role in the formation of white florets.
Defense Priming in Nicotiana tabacum Accelerates and Amplifies ‘New’ C/N Fluxes in Key Amino Acid Biosynthetic Pathways
Authors: Hanik Nils , Best Marcel , Schueller Michael J. , Tappero Ryan , Ferrieri Richard A.
Published: July 2020

In the struggle to survive herbivory by leaf-feeding insects, plants employ multiple strategies to defend themselves. One mechanism by which plants increase resistance is by intensifying their responsiveness in the production of certain defense agents to create a rapid response. Known as defense priming, this action can accelerate and amplify responses of metabolic pathways, providing plants with long-lasting resistance, especially when faced with waves of attack. In the work presented, short-lived radiotracers of carbon administered as 11CO2 and nitrogen administered as 13NH3 were applied in Nicotiana tabacum, to examine the temporal changes in ‘new’ C/N utilization in the biosynthesis of key amino acids (AAs). Responses were induced by using topical application of the defense hormone jasmonic acid (JA). After a single treatment, metabolic partitioning of recently fixed carbon (designated ‘new’ carbon and reflected as 11C) increased through the shikimate pathway, giving rise to tyrosine, phenylalanine and tryptophan. Amplification in ‘new’ carbon fluxes preceded changes in the endogenous (12C) pools of these AAs. Testing after serial JA treatments revealed that fluxes of ‘new’ carbon were accelerated, amplified and sustained over time at this higher rate, suggesting a priming effect. Similar results were observed with recently assimilated nitrogen (designated ‘new’ nitrogen reflected as 13N) with its partitioning into serine, glycine and glutamine, which play important roles supporting the shikimate pathway and downstream secondary metabolism. Finally, X-ray fluorescence imaging revealed that levels of the element Mn, an important co-factor for enzyme regulation in the shikimate pathway, increased within JA treated tissues, suggesting a link between plant metal ion regulation and C/N metabolic priming.
Evaluation of Alpha-Amylase Inhibitory, Antioxidant, and Antimicrobial Potential and Phytochemical Contents of Polygonum hydropiper L.
Authors: Nasir Abdul , Khan Mushtaq , Rehman Zainab , Khalil Atif Ali Khan , Farman Saira , Begum Naeema , Irfan Muhammad , Sajjad Wasim , Parveen Zahida
Published: July 2020

Polygonum hydropiper L. is a traditionally used medicinal plant. The present study was designed to explore the α-amylase inhibitory, antioxidant, and antimicrobial activities of Polygonum hydropiper L. Polarity-based solvent extracts (n-hexane, acetone, chloroform, methanol, ethanol, and water) of Polygonum hydropiper leaves and stem were used. Antioxidant activity was assessed by free radical scavenging assay (FRAP) and 2,2-diphenylpicrylhydrazyl (DPPH) free radical scavenging activity methods. Quantitative phytochemical analyses suggested that the stem of Polygonum hydropiper L. contains higher levels of bioactive compounds than its leaves (p < 0.05). The results suggested that stem-derived extracts of Polygonum hydropiper L. are more active against bacterial species, including two Gram-positive and three Gram-negative strains. Moreover, our results showed that the bioactive compounds of Polygonum hydropiper L. significantly inhibit α-amylase activity. Finally, we reported the polarity-based solvent extracts of Polygonum hydropiper L. and revealed that the stem, rather than leaves, has a high antioxidant potential as measured by FRAP and DPPH assay with IC50 values of 1.38 and 1.59 mg/mL, respectively. It may also be deducted from the data that the Polygonum hydropiper L. could be a significant candidate, which should be subjected to further isolation and characterization, to be used as an antidiabetic, antimicrobial and antioxidant resource in many industries, like food, pharmaceuticals and cosmetics.
Melatonin Regulatory Mechanisms and Phylogenetic Analyses of Melatonin Biosynthesis Related Genes Extracted from Peanut under Salinity Stress
Authors: ElSayed Abdelaleim I. , Boulila Moncef , Rafudeen Mohammed S. , Mohamed Azza H. , Sengupta Sonali , Rady Mostafa , Omar Ahmad A.
Published: July 2020

Melatonin improves the tolerance of plants to various environmental stresses by protecting plant cells against oxidative stress damage. The objective of the current study was to determine whether exogenous melatonin (MT) treatments could help protecting peanut (Arachis hypogaea) seedlings against salinity stress. This was achieved by investigating enzymatic and non-enzymatic antioxidant systems and the expression of melatonin biosynthesis related genes in response to salinity stress with or without exogenous MT. The results showed a significant increase in the concentrations of reactive oxygen species (ROS) in peanut seedlings under salinity stress. The exogenous application of melatonin decreased the levels of ROS through the activation of antioxidant enzymes in peanut seedlings under salinity stress. Transcription levels of melatonin biosynthesis related genes such as N-acetylserotonin methyltransferase (ASMT1, ASMT2, ASMT3), tryptophan decarboxylase (TDC), and tryptamine 5-hydroxylase (T5H) were up-regulated with a 150 µM melatonin treatment under salinity stress. The results indicated that melatonin regulated the redox homeostasis by its ability to induce either enzymatic or non-enzymatic antioxidant systems. In addition, phylogenetic analysis of melatonin biosynthesis genes (ASMT1, ASMT2, ASMT3, TDC, T5H) were performed on a total of 56 sequences belonging to various plant species including five new sequences extracted from Arachis hypogaea (A. hypogaea). This was based on pairwise comparison among aligned nucleotides and predicted amino acids as well as on substitution rates, and phylogenetic inference. The analyzed sequences were heterogeneous and the A. hypogaea accessions were primarily closest to those of Manihot esculenta, but this needs further clarification.
Mathematical Model Describing HIV Infection with Time-Delayed CD4 T-Cell Activation
Authors: Toro-Zapata Hernán Darío , Trujillo-Salazar Carlos Andrés , Carranza-Mayorga Edwin Mauricio
Published: July 2020

A mathematical model composed of two non-linear differential equations that describe the population dynamics of CD4 T-cells in the human immune system, as well as viral HIV viral load, is proposed. The invariance region is determined, classical equilibrium stability analysis is performed by using the basic reproduction number, and numerical simulations are carried out to illustrate stability results. Thereafter, the model is modified with a delay term, describing the time required for CD4 T-cell immunological activation. This generates a two-dimensional integro-differential system, which is transformed into a system with three ordinary differential equations. For the new model, equilibriums are determined, their local stability is examined, and results are studied by way of numerical simulation.
Dust Suppression Analysis of a New Spiral Hopper Using CFD-DEM Simulations and Experiments
Authors: Yuan Jianming , Jin Chenglong , Ye Fangping , Hu Zhihui , Chen Huozhi
Published: July 2020

A new dust suppression hopper with a spiral guide plate embedded in the conventional hopper is proposed for the dust suppression of hopper transfer processes in this article. The Computational Fluid Dynamics-Discrete Element Method (CFD-DEM) coupling numerical method is used to investigate the particle motion and flow field distribution of the hopper transfer process. The experiment is undertaken to determine dust suppression performance. The results show that the maximum particle velocity for the spiral hopper is dropped by 1.6 m/s compared to the conventional hopper, which means the collision of the particles and the spiral hopper is weakened. The axial airflow velocity of the spiral is reduced. In addition, the maximum dust concentration of the spiral hopper inlet is reduced by 56.9% due to the impact velocity of particles is small, and the secondary fugitive dust is controlled inside a semi-closed space formed by the spiral guide plate. It is thus concluded that the spiral hopper provides an effective way in dust control.
Evaluation of the Interactive Effect Pretreatment Parameters via Three Types of Microwave-Assisted Pretreatment and Enzymatic Hydrolysis on Sugar Yield
Authors: Ethaib Saleem , Omar Rozita , Siti Mazlina Mustapa Kamal , Dayang Radiah Awang Biak
Published: July 2020

This study aims to evaluate the sugar yield from enzymatic hydrolysis and the interactive effect pretreatment parameters of microwave-assisted pretreatment on glucose and xylose. Three types of microwave-assisted pretreatments of sago palm bark (SPB) were conducted for enzymatic hydrolysis, namely: microwave-sulphuric acid pretreatment (MSA), microwave-sodium hydroxide pretreatment (MSH), and microwave-sodium bicarbonate (MSB). The experimental design was done using a response surface methodology (RSM) and Box–Behenken Design (BBD). The pretreatment parameters ranged from 5–15% solid loading (SL), 5–15 min of exposure time (ET), and 80–800 W of microwave power (MP). The results indicated that the maximum total reducing sugar was 386 mg/g, obtained by MSA pretreatment. The results also illustrated that the higher glucose yield, 44.3 mg/g, was found using MSH pretreatment, while the higher xylose yield, 43.1 mg/g, resulted from MSA pretreatment. The pretreatment parameters MP, ET, and SL showed different patterns of influence on glucose and xylose yield via enzymatic hydrolysis for MSA, MSH, and MSB pretreatments. The analyses of the interactive effect of the pretreatment parameters MP, ET, and SL on the glucose yield from SPB showed that it increased with the high MP and longer ET, but this was limited by low SL values. However, the analysis of the interactive effect of the pretreatment parameters on xylose yields revealed that MP had the most influence on the xylose yield for MSA, MSH, and MSB pretreatments.
Prediction of Cutting Material Durability by T = f(vc) Dependence for Turning Processes
Authors: Zajac Jozef , Duplak Jan , Duplakova Darina , Cizmar Peter , Olexa Igor , Bittner Anton
Published: July 2020

This article is focused on the prediction of cutting material durability by Taylor’s model. To create predictive models of the durability of cutting materials in the turning process, tools made of high-speed steel, sintered carbide without coating and with Titanium nitride (TiN) coating, cutting ceramics without coating and with TiN coating were applied. The experimental part was performed on reference material C45 using conventional lathe—type of machine SU50A and computer numerical control machine—CNC lathe Leadwell T-5 in accordance with International Organization for Standardization—ISO 3685. Implementation of the least-squares method and processing of regression analysis made predictions of cutting tool behaviour in the turning process. Using the method of regression analysis, a correlation index of 93.5% was obtained, indicating the functional dependence of the predicted relationship.
State Regulations and Divine Oppositions: An Ethnography of the Nine Emperor Gods Festival in Singapore
Authors: Chia Jie Lin
Published: July 2020

Studies of popular Chinese religions in Singapore have mostly focused on the relationship between Chinese religious practitioners and state regulations delimiting land for religious uses. Local scholars have also studied the state’s active construction of a domain within which local religions can operate, often rationalized as a means of maintaining harmonious relations between ethnic and religious groups. However, little attention has been paid to the symbolic spatial negotiations that exist between the gods and the Singaporean state. Through an ethnographic study of the Nine Emperor Gods Festival as organized by the Choa Chu Kang Dou Mu Gong (蔡厝港斗母宫), I analyze the tensions between the impositions of state authority upon the temple’s annual festival and the divine authority of the Nine Emperor Gods, as reproduced in the festival’s rituals and in the bodies of their spirit mediums. Borrowing Marshall Sahlins’ idea of inclusive “cosmic polities,” I argue that the Nine Emperor Gods, devotees, and state actors do not exist in separate “secular” and “divine” dimensions but rather, co-participate in the same complex society. By serving as a fertile ground upon which the divine bureaucracy of the Nine Emperor Gods is reproduced, the festival’s articulations of divine sovereignty provide a potent challenge to state-imposed imaginations of space and expand devotees’ understandings of agency from state-defined and into the larger cosmological order.
Automatic Updates of Transition Potential Matrices in Dempster-Shafer Networks Based on Evidence Inputs
Authors: Dunham Joel , Johnson Eric , Feron Eric , German Brian
Published: July 2020

Sensor fusion is a topic central to aerospace engineering and is particularly applicable to unmanned aerial systems (UAS). Evidential Reasoning, also known as Dempster-Shafer theory, is used heavily in sensor fusion for detection classification. High computing requirements typically limit use on small UAS platforms. Valuation networks, the general name given to evidential reasoning networks by Shenoy, provides a means to reduce computing requirements through knowledge structure. However, these networks use conditional probabilities or transition potential matrices to describe the relationships between nodes, which typically require expert information to define and update. This paper proposes and tests a novel method to learn these transition potential matrices based on evidence injected at nodes. Novel refinements to the method are also introduced, demonstrating improvements in capturing the relationships between the node belief distributions. Finally, novel rules are introduced and tested for evidence weighting at nodes during simultaneous evidence injections, correctly balancing the injected evidenced used to learn the transition potential matrices. Together, these methods enable updating a Dempster-Shafer network with significantly less user input, thereby making these networks more useful for scenarios in which sufficient information concerning relationships between nodes is not known a priori.
The Influence of Proprioceptive Training with the Use of Virtual Reality on Postural Stability of Workers Working at Height
Authors: Cyma-Wejchenig Magdalena , Tarnas Jacek , Marciniak Katarzyna , Stemplewski Rafał
Published: July 2020

The aim of the study was to assess the impact of proprioceptive training with the use of virtual reality (VR) on the level of postural stability of high–altitude workers. Twenty-one men working at height were randomly assigned to the experimental group (EG) with training (n = 10) and control group (CG) without training (n = 11). Path length of the displacement of the center of pressure (COP) signal and its components in the anteroposterior and medial–lateral directions were measured with use of an AccuGaitTM force plate before and after intervention (6 weeks, 2 sessions × 30 min a week). Tests were performed at two different platform heights, with or without eyes open and with or without a dual task. Two–way ANOVA revealed statistically significant interaction effects for low–high threat, eyes open-eyes closed, and single task-dual task. Post-training values of average COP length were significantly lower in the EG than before training for all analyzed parameters. Based on these results, it can be concluded that the use of proprioceptive training with use of VR can support, or even replace, traditional methods of balance training.
Sensor Node Activation Using Bat Algorithm for Connected Target Coverage in WSNs
Authors: Kim Jaemin , Yoo Younghwan
Published: July 2020

This paper proposes a sensor node activation method using the nature-inspired algorithm (NIA) for the target coverage problem. The NIAs have been used to solve various optimization problems. This paper formulates the sensor target coverage problem into an object function and solves it with an NIA, specifically, the bat algorithm (BA). Although this is not the first attempt to use the BA for the coverage problem, the proposed method introduces a new concept called bat couple which consists of two bats. One bat finds sensor nodes that need to be activated for sensing, and the other finds nodes for data forwarding from active sensor nodes to a sink. Thanks to the bat couple, the proposed method can ensure connectivity from active sensor nodes to a sink through at least one communication path, focusing on the energy efficiency. In addition, unlike other methods the proposed method considers a practical feature of sensing: The detection probability of sensors decreases as the distance from the target increases. Other methods assume the binary model where the success of target detection entirely depends on whether a target is within the threshold distance from the sensor or not. Our method utilizes the probabilistic sensing model instead of the binary model. Simulation results show that the proposed method outperforms others in terms of the network lifetime.
Modeling Fabric Movement for Future E-Textile Sensors
Authors: Ketola Roope , Mishra Vigyanshu , Kiourti Asimina
Published: July 2020

Studies with e-textile sensors embedded in garments are typically performed on static and controlled phantom models that do not reflect the dynamic nature of wearables. Instead, our objective was to understand the noise e-textile sensors would experience during real-world scenarios. Three types of sleeves, made of loose, tight, and stretchy fabrics, were applied to a phantom arm, and the corresponding fabric movement was measured in three dimensions using physical markers and image-processing software. Our results showed that the stretchy fabrics allowed for the most consistent and predictable clothing-movement (average displacement of up to −2.3 ± 0.1 cm), followed by tight fabrics (up to −4.7 ± 0.2 cm), and loose fabrics (up to −3.6 ± 1.0 cm). In addition, the results demonstrated better performance of higher elasticity (average displacement of up to −2.3 ± 0.1 cm) over lower elasticity (average displacement of up to −3.8 ± 0.3 cm) stretchy fabrics. For a case study with an e-textile sensor that relies on wearable loops to monitor joint flexion, our modeling indicated errors as high as 65.7° for stretchy fabric with higher elasticity. The results from this study can (a) help quantify errors of e-textile sensors operating “in-the-wild,” (b) inform decisions regarding the optimal type of clothing-material used, and (c) ultimately empower studies on noise calibration for diverse e-textile sensing applications.
LSTM-Based VAE-GAN for Time-Series Anomaly Detection
Authors: Niu Zijian , Yu Ke , Wu Xiaofei
Published: July 2020

Time series anomaly detection is widely used to monitor the equipment sates through the data collected in the form of time series. At present, the deep learning method based on generative adversarial networks (GAN) has emerged for time series anomaly detection. However, this method needs to find the best mapping from real-time space to the latent space at the anomaly detection stage, which brings new errors and takes a long time. In this paper, we propose a long short-term memory-based variational autoencoder generation adversarial networks (LSTM-based VAE-GAN) method for time series anomaly detection, which effectively solves the above problems. Our method jointly trains the encoder, the generator and the discriminator to take advantage of the mapping ability of the encoder and the discrimination ability of the discriminator simultaneously. The long short-term memory (LSTM) networks are used as the encoder, the generator and the discriminator. At the anomaly detection stage, anomalies are detected based on reconstruction difference and discrimination results. Experimental results show that the proposed method can quickly and accurately detect anomalies.
Cantilever Type Acceleration Sensors Made by Roll-to-Roll Slot-Die Coating
Authors: Lee Sang Hoon , Lee Sangyoon
Published: July 2020

This paper presents the fabrication by means of roll-to-roll slot-die coating and characterization of air gap-based cantilever type capacitive acceleration sensors. As the mass of the sensor moves in the opposite direction of the acceleration, a capacitance change occurs. The sensor is designed to have a six layers structure with an air gap. Fabrication of the air gap and cantilever was enabled by coating and removing water-soluble PVA. The bottom electrode, the dielectric layer, and the sacrificial layer were formed using the roll-to-roll slot-die coating technique. The spacer, the top electrode, and the structural layer were formed by spin coating. Several kinds of experiments were conducted for characterization of the fabricated sensor samples. Experimental results show that accelerations of up to 3.6 g can be sensed with an average sensitivity of 0.00856 %/g.
IR-UWB Pulse Generation Using FPGA Scheme for through Obstacle Human Detection
Authors: Tantiparimongkol Lalida , Phasukkit Pattarapong
Published: July 2020

This research proposes a scheme of field programmable gate array (FPGA) to generate an impulse-radio ultra-wideband (IR-UWB) pulse. The FPGA scheme consists of three parts: digital clock manager, four-delay-paths stratagem, and edge combiner. The IR-UWB radar system is designed to detect human subjects from their respiration underneath the rubble in the aftermath of an earthquake and to locate the human subjects based on range estimation. The proposed IR-UWB radar system is experimented with human subjects lying underneath layers of stacked clay bricks in supine and prone position. The results reveal that the IR-UWB radar system achieves a pulse duration of 540 ps with a bandwidth of 2.073 GHz (fractional bandwidth of 1.797). In addition, the IR-UWB technology can detect human subjects underneath the rubble from respiration and identify the location of human subjects by range estimation. The novelty of this research lies in the use of the FPGA scheme to achieve an IR-UWB pulse with a 2.073 GHz (117 MHz–2.19 GHz) bandwidth, thereby rendering the technology suitable for a wide range of applications, in addition to through-obstacle detection.
On Data-Driven Sparse Sensing and Linear Estimation of Fluid Flows
Authors: Jayaraman Balaji , Mamun S M Abdullah Al
Published: July 2020

The reconstruction of fine-scale information from sparse data measured at irregular locations is often needed in many diverse applications, including numerous instances of practical fluid dynamics observed in natural environments. This need is driven by tasks such as data assimilation or the recovery of fine-scale knowledge including models from limited data. Sparse reconstruction is inherently badly represented when formulated as a linear estimation problem. Therefore, the most successful linear estimation approaches are better represented by recovering the full state on an encoded low-dimensional basis that effectively spans the data. Commonly used low-dimensional spaces include those characterized by orthogonal Fourier and data-driven proper orthogonal decomposition (POD) modes. This article deals with the use of linear estimation methods when one encounters a non-orthogonal basis. As a representative thought example, we focus on linear estimation using a basis from shallow extreme learning machine (ELM) autoencoder networks that are easy to learn but non-orthogonal and which certainly do not parsimoniously represent the data, thus requiring numerous sensors for effective reconstruction. In this paper, we present an efficient and robust framework for sparse data-driven sensor placement and the consequent recovery of the higher-resolution field of basis vectors. The performance improvements are illustrated through examples of fluid flows with varying complexity and benchmarked against well-known POD-based sparse recovery methods.
Cross-Machine Fault Diagnosis with Semi-Supervised Discriminative Adversarial Domain Adaptation
Authors: Wang Xiaodong , Liu Feng , Zhao Dongdong
Published: July 2020

Bearings are ubiquitous in rotating machinery and bearings in good working conditions are essential for the availability and safety of the machine. Various intelligent fault diagnosis models have been widely studied aiming to prevent system failures. These data-driven fault diagnosis models work well when training data and testing data are from the same distribution, which is not easy to sustain in industry since the working environment of rotating machinery is often subject to change. Recently, the domain adaptation methods for fault diagnosis between different working conditions have been extensively researched, which fully utilize the labeled data from the same machine under different working conditions to address this domain shift diploma. However, for a target machine with seldom occurred faulty data under any working conditions, the domain adaptation approaches between working conditions are not applicable. Hence, the cross-machine fault diagnosis tasks are recently proposed to utilize the labeled data from related but not identical machines. The larger domain shift between machines makes the cross-machine fault diagnosis a more challenging task. The large domain shift may cause the well-trained model on source domain deteriorates on target domain, and the ambiguous samples near the decision boundary are prone to be misclassified. In addition, the sparse faulty samples in target domain make a class-imbalanced scenario. To address the two issues, in this paper we propose a semi-supervised adversarial domain adaptation approach for cross-machine fault diagnosis which incorporates the virtual adversarial training and batch nuclear-norm maximization to make the fault diagnosis robust and discriminative. Experiments of transferring between three bearing datasets show that the proposed method is able to effectively learn a discriminative model given only a labeled faulty sample of each class in target domain. The research provides a feasible approach for knowledge transfer in fault diagnosis scenarios.
A Virtual Reality Muscle–Computer Interface for Neurorehabilitation in Chronic Stroke: A Pilot Study
Authors: Marin-Pardo Octavio , Laine Christopher M. , Rennie Miranda , Ito Kaori L. , Finley James , Liew Sook-Lei
Published: July 2020

Severe impairment of limb movement after stroke can be challenging to address in the chronic stage of stroke (e.g., greater than 6 months post stroke). Recent evidence suggests that physical therapy can still promote meaningful recovery after this stage, but the required high amount of therapy is difficult to deliver within the scope of standard clinical practice. Digital gaming technologies are now being combined with brain–computer interfaces to motivate engaging and frequent exercise and promote neural recovery. However, the complexity and expense of acquiring brain signals has held back widespread utilization of these rehabilitation systems. Furthermore, for people that have residual muscle activity, electromyography (EMG) might be a simpler and equally effective alternative. In this pilot study, we evaluate the feasibility and efficacy of an EMG-based variant of our REINVENT virtual reality (VR) neurofeedback rehabilitation system to increase volitional muscle activity while reducing unintended co-contractions. We recruited four participants in the chronic stage of stroke recovery, all with severely restricted active wrist movement. They completed seven 1-hour training sessions during which our head-mounted VR system reinforced activation of the wrist extensor muscles without flexor activation. Before and after training, participants underwent a battery of clinical and neuromuscular assessments. We found that training improved scores on standardized clinical assessments, equivalent to those previously reported for brain–computer interfaces. Additionally, training may have induced changes in corticospinal communication, as indexed by an increase in 12–30 Hz corticomuscular coherence and by an improved ability to maintain a constant level of wrist muscle activity. Our data support the feasibility of using muscle–computer interfaces in severe chronic stroke, as well as their potential to promote functional recovery and trigger neural plasticity.
Numerical Sensitivity Analysis for Dielectric Characterization of Biological Samples by Open-Ended Probe Technique
Authors: Cavagnaro Marta , Ruvio Giuseppe
Published: July 2020

Dielectric characterization of biological tissues has become a fundamental aspect of the design of medical treatments based on electromagnetic energy delivery and their pre-treatment planning. Among several measuring techniques proposed in the literature, broadband and minimally-invasive open-ended probe measurements are best-suited for biological tissues. However, several challenges related to measurement accuracy arise when dealing with biological tissues in both ex vivo and in vivo scenarios such as very constrained set-ups in terms of limited sample size and probe positioning. By means of the Finite Integration Technique in the CST Studio Suite® software, the numerical accuracy of the reconstruction of the complex permittivity of a high water-content tissue such as liver and a low water-content tissue such as fat is evaluated for different sample dimensions, different location of the probe, and considering the influence of the background environment. It is found that for high water-content tissues, the insertion depth of the probe into the sample is the most critical parameter on the accuracy of the reconstruction. Whereas when low water-content tissues are measured, the probe could be simply placed in contact with the surface of the sample but a deeper and wider sample is required to mitigate biasing effects from the background environment. The numerical analysis proves to be a valid tool to assess the suitability of a measurement set-up for a target accuracy threshold.
An Innovative Multi-Model Neural Network Approach for Feature Selection in Emotion Recognition Using Deep Feature Clustering
Authors: Asghar Muhammad Adeel , Khan Muhammad Jamil , Rizwan Muhammad , Mehmood Raja Majid , Kim Sun-Hee
Published: July 2020

Emotional awareness perception is a largely growing field that allows for more natural interactions between people and machines. Electroencephalography (EEG) has emerged as a convenient way to measure and track a user’s emotional state. The non-linear characteristic of the EEG signal produces a high-dimensional feature vector resulting in high computational cost. In this paper, characteristics of multiple neural networks are combined using Deep Feature Clustering (DFC) to select high-quality attributes as opposed to traditional feature selection methods. The DFC method shortens the training time on the network by omitting unusable attributes. First, Empirical Mode Decomposition (EMD) is applied as a series of frequencies to decompose the raw EEG signal. The spatiotemporal component of the decomposed EEG signal is expressed as a two-dimensional spectrogram before the feature extraction process using Analytic Wavelet Transform (AWT). Four pre-trained Deep Neural Networks (DNN) are used to extract deep features. Dimensional reduction and feature selection are achieved utilising the differential entropy-based EEG channel selection and the DFC technique, which calculates a range of vocabularies using k-means clustering. The histogram characteristic is then determined from a series of visual vocabulary items. The classification performance of the SEED, DEAP and MAHNOB datasets combined with the capabilities of DFC show that the proposed method improves the performance of emotion recognition in short processing time and is more competitive than the latest emotion recognition methods.
Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples
Authors: Rasti Behnood , Ghamisi Pedram , Seidel Peter , Lorenz Sandra , Gloaguen Richard
Published: July 2020

Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.
WiFi Based Fingerprinting Positioning Based on Seq2seq Model
Authors: Sun Haotai , Zhu Xiaodong , Liu Yuanning , Liu Wentao
Published: July 2020

Indoor positioning technologies are of great use in GPS-denied areas. They can be partitioned into two types of systems—infrastructure-free based and infrastructure-dependent based. WiFi based indoor positioning system is somewhere between the infrastructure-free and infrastructure-dependent systems. The reason is that in WiFi based systems, Access Points (APs) as pre-installed infrastructures are necessary. However, the APs do not need to be specially installed, because WiFi APs are already widely deployed in many indoor areas, for example, offices, malls and airports. This feature makes WiFi based indoor positioning suitable for many practical applications. In this paper, a seq2seq model based, deep learning method is proposed for WiFi based fingerprinting. The model can learn from different length of training sequences, and thus can exploit the context information for positioning. The context information denotes the information contained in the sequence, which can help finding the correspondences between RSS fingerprints and the coordinate positions. A simple example piece of context information is human walking routine (such as no sharp turns). The proposed method shows an improvement with an open source dataset, when compared against deep learning based counterpart methods.
Advances on Measuring Deep-Seated Ground Deformations Using Robotized Inclinometer System
Authors: Allasia Paolo , Godone Danilo , Giordan Daniele , Guenzi Diego , Lollino Giorgio
Published: July 2020

In the field of geo-hazards and geo-engineering, monitoring networks represent a key element for the geological risk assessment and the design and management of large infrastructures construction. In the last decade, we have observed a strong development on remote sensing techniques but just small changes in the subsoil observations. However, this type of measurement is very important to have a three-dimensional representation of the studied area, since the surface measurements often represent a sum of deformations that develop in a complex way in the subsoil. In this paper, we present a robotic inclinometer system developed to acquire deep-seated ground deformations in boreholes. This instrumentation combines advantages offered by manual inclinometer measurements with a robotized approach that improves the results in term of accuracy, revisiting time, and site accessibility. The Automated Inclinometer System (AIS) allows one to explore automatically all the length of the monitored borehole using just one inclinometer probe with a semi-wireless system. The paper presents the system and a detailed dataset of measurements acquired on three inclinometer tubes installed for the monitoring of the construction phase of the new Line C Metro of Rome. The dataset was acquired in real monitored site and undisturbed conditions and can represent a benchmark for modern inclinometer measurements.
A Single-Dataset-Based Pre-Processing Joint Domain Localized Algorithm for Clutter-Suppression in Shipborne High-Frequency Surface-Wave Radar
Authors: Guo Liang , Zhang Xin , Yao Di , Yang Qiang , Bai Yang , Deng Weibo
Published: July 2020

Due to the motion of the platform, the spectrum of first-order sea clutter will widen and mask low-velocity targets such as ships in shipborne high-frequency surface-wave radar (HFSWR). Limited by the quantity of qualified training samples, the performance of the generally used clutter-suppression method, space–time adaptive processing (STAP) degrades in shipborne HFSWR. To deal with this problem, an innovative training sample acquisition method is proposed, in the area of joint domain localized (JDL) reduced-rank STAP. In this clutter-suppression method, based on a single range of cell data, the unscented transformation is introduced as a preprocessing step to obtain adequate homogeneous secondary data and roughly estimated clutter covariance matrix (CCM). The accurate CCM is calculated by integrating the approximate CCM of different range of cells. Compared with existing clutter-suppression algorithms for shipborne HFSWR, the proposed approach has a better signal-to-clutter-plus-noise ratio (SCNR) improvement tested by real data.
Improving Object Tracking by Added Noise and Channel Attention
Authors: Fiaz Mustansar , Mahmood Arif , Baek Ki Yeol , Farooq Sehar Shahzad , Jung Soon Ki
Published: July 2020

CNN-based trackers, especially those based on Siamese networks, have recently attracted considerable attention because of their relatively good performance and low computational cost. For many Siamese trackers, learning a generic object model from a large-scale dataset is still a challenging task. In the current study, we introduce input noise as regularization in the training data to improve generalization of the learned model. We propose an Input-Regularized Channel Attentional Siamese (IRCA-Siam) tracker which exhibits improved generalization compared to the current state-of-the-art trackers. In particular, we exploit offline learning by introducing additive noise for input data augmentation to mitigate the overfitting problem. We propose feature fusion from noisy and clean input channels which improves the target localization. Channel attention integrated with our framework helps finding more useful target features resulting in further performance improvement. Our proposed IRCA-Siam enhances the discrimination of the tracker/background and improves fault tolerance and generalization. An extensive experimental evaluation on six benchmark datasets including OTB2013, OTB2015, TC128, UAV123, VOT2016 and VOT2017 demonstrate superior performance of the proposed IRCA-Siam tracker compared to the 30 existing state-of-the-art trackers.