bluuebunny/crossref_metadata_2025 huggingface dataset:
Graft Preservation Solution DuraGraft® Alleviates Vascular Dysfunction Following In Vitro Ischemia/Reperfusion Injury in Rats
Authors: Korkmaz-Icöz Sevil , Ballikaya Belinda , Soethoff Jasmin , Kraft Patricia , Sayour Alex Ali , Radovits Tamás , Loganathan Sivakkanan , Karck Matthias , Szabó Gábor , Veres Gábor
Published: October 2021

Vascular ischemia/reperfusion injury (IRI) in patients undergoing coronary artery bypass grafting can result in graft failure and the need for repeat revascularization procedures. DuraGraft® has been shown to protect structure and function in saphenous vein grafts against IRI. We compared the effect of DuraGraft® to saline solution on arterial grafts submitted to IRI. Rat thoracic aortic rings were harvested and immediately mounted in organ bath chambers (control, n = 7 rats) or underwent cold ischemic preservation either in saline (IR, n = 9 rats) or DuraGraft® (IR+Dura, n = 9 rats). Vascular function was measured ex vivo and immunohistochemistry was performed. Impaired maximum vasorelaxation (Rmax) to ACh in the IR-group compared to controls was ameliorated by DuraGraft®, indicating an improvement in endothelial function (Rmax to ACh (%): IR + Dura 73 ± 2 vs. IR 48 ± 3, p < 0.05). Additionally, decreased aortic ring sensitivity to ACh (pD2-value: -log 50% maximum response) seen after IR in the saline group was increased by DuraGraft® (pD2 to ACh: IR+Dura 7.1 ± 0.1 vs. IR 6.3 ± 0.2, p < 0.05). Impaired maximum contractile response to phenylephrine and high potassium chloride concentrations in the IR group compared to controls was significantly improved by DuraGraft®. DuraGraft® alleviates vascular dysfunction following IRI by reducing nitro-oxidative stress and the expression of ICAM-1, without leukocytes engagement.
Investigation on the Preparation of Rice Straw-Derived Cellulose Acetate and Its Spinnability for Electrospinning
Authors: Yan Juntao , Liu Jinhong , Sun Ya , Song Guangsen , Ding Deng , Fan Guozhi , Chai Bo , Wang Chunlei , Sun Linbing
Published: October 2021

Rice straw-derived cellulose (RSC) with purity of 92 wt.% was successfully extracted from rice straw by a novel and facile strategy, which integrated the C2H5OH/H2O autocatalytic process, dilute alkali treatment and H2O2 bleaching process. Influencing factors of the cellulose extraction were systematically examined, such as ethanol concentration, alkali concentration, H2O2 bleaching process and so on; the optimal extraction conditions of cellulose was determined. A series of rice straw-derived cellulose acetate (RSCA) with different degree of substitution (DS) were prepared by the acetylation reaction; the effects of Ac2O/cellulose ratio, reaction temperature and reaction time on the acetylation reaction were investigated. Results of FTIR and XRD analysis demonstrated that highly purified RSC and RSCA were prepared comparing with the commercial cellulose and cellulose acetate. Solubility analysis of RSCA with different DS indicated as-prepared RSCA with DS of 2.82 possessed the best solubleness, which was suitable for electrospinning. Moreover, the flexible RSCA fibrous membrane was easily fabricated by a facile electrospinning method. Our proposed method provided a strategy for realizing the high-value utilization of waste rice straw resource, as prepared RSC and RSCA can be used as chemical raw material, and electrospun RSCA fibrous membrane has various applications in medical materials, food packaging, water purification and so on.
The Spiritual Features of the Experience of qi in Chinese Martial Arts
Authors: Cibotaru Veronica
Published: October 2021

I argue in this article, grounding on a phenomenological analysis of the experience of qi in Chinese martial arts, that the experience of qi in this framework can share the features of a secular spiritual experience, in other words of a spiritual experience that is not religious, at least not necessarily. I put in evidence five features that can characterize the experience of qi in Chinese martial arts and that arguably pertain to spirituality: (1) the importance of individual experience; (2) self-transcendence and the quest for authenticity; (3) the connection with a transcendent dimension; (4) the importance of corporeity and at the same time the apprehension of a dimension which cannot be reduced to corporeity; (5) the use of imagination in order to grasp a transcendent dimension that presupposes the use of metaphors. Consequently, the experience of qi in Chinese martial arts suggests the possibility of a form of spirituality that is not necessarily bound to religion and that at the same time is not a mere rejection of traditional religions. At the same time, I argue that the experience of qi in Chinese martial and energetic arts reveals radical possibilities of human experience at the core of which are fundamental transcendental structures of human experience, i.e., the experience of our body and the experience of the world through our body. This suggests the idea that phenomenology has an important potential for the investigation of spirituality and opens towards a research field that can be deeper explored.
Metamaterial-Integrated High-Gain Rectenna for RF Sensing and Energy Harvesting Applications
Authors: Lee Woosol , Choi Suk-il , Kim Hae-in , Hwang Sunghyun , Jeon Saeyoung , Yoon Yong-Kyu
Published: October 2021

This paper presents a metamaterial (MTM)-integrated high-gain rectenna for RF sensing and energy harvesting applications that operates at 2.45 GHz, an industry, science, medicine (ISM) band. The novel MTM superstrate approach with a three-layered integration method is firstly introduced for rectenna applications. The integrated rectenna consists of three layers, where the first layer is an MTM superstrate consisting of four-by-four MTM unit cell arrays, the second layer a patch antenna, and the third layer a rectifier circuit. By integrating the MTM superstrate on top of the patch antenna, the gain of the antenna is enhanced, owing to its beam focusing capability of the MTM superstrate. This induces the increase of the captured RF power at the rectifier input, resulting in high-output DC power and high entire end-to-end efficiency. A parametric analysis is performed in order to optimize the near-zero property of the MTM unit cell. In addition, the effects of the number of MTM unit cells on the performance of the integrated rectenna are studied. A prototype MTM-integrated rectenna, which is designed on an RO5880 substrate, is fabricated and characterized. The measured gain of the MTM-integrated rectenna is 11.87 dB. It shows a gain improvement of 6.12 dB compared to a counterpart patch antenna without an MTM superstrate and a maximum RF–DC conversion efficiency of 78.9% at an input RF power of 9 dBm. This results in the improvement of the RF–DC efficiency from 39.2% to 78.9% and the increase of the output DC power from 0.7 mW to 6.27 mW (a factor of 8.96 improvements). The demonstrated MTM-integrated rectenna has shown outstanding performance compared to other previously reported work. We emphasize that the demonstrated MTM-integrated rectenna has a low design complexity compared with other work, as the MTM superstrate layer is integrated on top of the simple patch antenna and rectifier circuit. In addition, the number of MTM units can be determined depending on applications. It is highly envisioned that the demonstrated MTM-integrated rectenna will provide new possibilities for practical energy harvesting applications with improved antenna gain and efficiency in various IoT environments.
COVID-19 Testing and Diagnostics: A Review of Commercialized Technologies for Cost, Convenience and Quality of Tests
Authors: Benda Ashler , Zerajic Lukas , Ankita Ankita , Cleary Erin , Park Yunsoo , Pandey Santosh
Published: October 2021

Population-scale and rapid testing for SARS-CoV-2 continues to be a priority for several parts of the world. We revisit the in vitro technology platforms for COVID-19 testing and diagnostics—molecular tests and rapid antigen tests, serology or antibody tests, and tests for the management of COVID-19 patients. Within each category of tests, we review the commercialized testing platforms, their analyzing systems, specimen collection protocols, testing methodologies, supply chain logistics, and related attributes. Our discussion is essentially focused on test products that have been granted emergency use authorization by the FDA to detect and diagnose COVID-19 infections. Different strategies for scaled-up and faster screening are covered here, such as pooled testing, screening programs, and surveillance testing. The near-term challenges lie in detecting subtle infectivity profiles, mapping the transmission dynamics of new variants, lowering the cost for testing, training a large healthcare workforce, and providing test kits for the masses. Through this review, we try to understand the feasibility of universal access to COVID-19 testing and diagnostics in the near future while being cognizant of the implicit tradeoffs during the development and distribution cycles of new testing platforms.
Cardiac Diagnostic Feature and Demographic Identification (CDF-DI): An IoT Enabled Healthcare Framework Using Machine Learning
Authors: Kumar Deepak , Verma Chaman , Dahiya Sanjay , Singh Pradeep Kumar , Raboaca Maria Simona , Illés Zoltán , Bakariya Brijesh
Published: October 2021

The incidence of cardiovascular diseases and cardiovascular burden (the number of deaths) are continuously rising worldwide. Heart disease leads to heart failure (HF) in affected patients. Therefore any additional aid to current medical support systems is crucial for the clinician to forecast the survival status for these patients. The collaborative use of machine learning and IoT devices has become very important in today’s intelligent healthcare systems. This paper presents a Public Key Infrastructure (PKI) secured IoT enabled framework entitled Cardiac Diagnostic Feature and Demographic Identification (CDF-DI) systems with significant Models that recognize several Cardiac disease features related to HF. To achieve this goal, we used statistical and machine learning techniques to analyze the Cardiac secondary dataset. The Elevated Serum Creatinine (SC) levels and Serum Sodium (SS) could cause renal problems and are well established in HF patients. The Mann Whitney U test found that SC and SS levels affected the survival status of patients (p < 0.05). Anemia, diabetes, and BP features had no significant impact on the SS and SC level in the patient (p > 0.05). The Cox regression model also found a significant association of age group with the survival status using follow-up months. Furthermore, the present study also proposed important features of Cardiac disease that identified the patient’s survival status, age group, and gender. The most prominent algorithm was the Random Forest (RF) suggesting five key features to determine the survival status of the patient with an accuracy of 96%: Follow-up months, SC, Ejection Fraction (EF), Creatinine Phosphokinase (CPK), and platelets. Additionally, the RF selected five prominent features (smoking habits, CPK, platelets, follow-up month, and SC) in recognition of gender with an accuracy of 94%. Moreover, the five vital features such as CPK, SC, follow-up month, platelets, and EF were found to be significant predictors for the patient’s age group with an accuracy of 96%. The Kaplan Meier plot revealed that mortality was high in the extremely old age group (χ2 (1) = 8.565). The recommended features have possible effects on clinical practice and would be supportive aid to the existing medical support system to identify the possibility of the survival status of the heart patient. The doctor should primarily concentrate on the follow-up month, SC, EF, CPK, and platelet count for the patient’s survival in the situation.
A 0.3 V PNN Based 10T SRAM with Pulse Control Based Read-Assist and Write Data-Aware Schemes for Low Power Applications
Authors: Sheu Ming-Hwa , Tsai Chang-Ming , Tsai Ming-Yan , Hsia Shih-Chang , Morsalin S. M. Salahuddin , Lin Jin-Fa
Published: October 2021

An innovative and stable PNN based 10-transistor (10T) static random-access memory (SRAM) architecture has been designed for low-power bit-cell operation and sub-threshold voltage applications. The proposed design belongs to the following features: (a) pulse control based read-assist circuit offers a dynamic read decoupling approach for eliminating the read interference; (b) we have utilized the write data-aware techniques to cut off the pull-down path; and (c) additional write current has enhanced the write capability during the operation. The proposed design not only solves the half-selected problems and increases the read static noise margin (RSNM) but also provides low leakage power performance. The designed architecture of 1-Kb SRAM macros (32 rows × 32 columns) has been implemented based on the TSMC-40 nm GP CMOS process technology. At 300 mV supply voltage and 10 MHz operating frequency, the read and write power consumption is 4.15 μW and 3.82 μW, while the average energy consumption is only 0.39 pJ.
Joint Torque Prediction via Hybrid Neuromusculoskeletal Modelling during Gait Using Statistical Ground Reaction Estimates: An Exploratory Study
Authors: Lam Shui Kan , Vujaklija Ivan
Published: October 2021

Joint torques of lower extremity are important clinical indicators of gait capability. This parameter can be quantified via hybrid neuromusculoskeletal modelling that combines electromyography-driven modelling and static optimisation. The simulations rely on kinematics and external force measurements, for example, ground reaction forces (GRF) and the corresponding centres of pressure (COP), which are conventionally acquired using force plates. This bulky equipment, however, hinders gait analysis in real-world environments. While this portability issue could potentially be solved by estimating the parameters through machine learning, the effect of the estimation errors on joint torque prediction with biomechanical models remains to be investigated. This study first estimated GRF and COP through feedforward artificial neural networks, and then leveraged them to predict lower-limb sagittal joint torques via (i) inverse dynamics and (ii) hybrid modelling. The approach was evaluated on five healthy subjects, individually. The predicted torques were validated with the measured torques, showing that hip was the most sensitive whereas ankle was the most resistive to the GRF/COP estimates for both models, with average metrics values being 0.70 < R2 < 0.97 and 0.069 < RMSE < 0.15 (Nm/kg). This study demonstrated the feasibility of torque prediction based on personalised (neuro)musculoskeletal modelling using statistical ground reaction estimates, thus providing insights into potential real-world mobile joint torque quantification.
MNP-Enhanced Microwave Medical Imaging by Means of Pseudo-Noise Sensing
Authors: Ley Sebastian , Sachs Jürgen , Faenger Bernd , Hilger Ingrid , Helbig Marko
Published: October 2021

Magnetic nanoparticles have been investigated for microwave imaging over the last decade. The use of functionalized magnetic nanoparticles, which are able to accumulate selectively within tumorous tissue, can increase the diagnostic reliability. This paper deals with the detecting and imaging of magnetic nanoparticles by means of ultra-wideband microwave sensing via pseudo-noise technology. The investigations were based on phantom measurements. In the first experiment, we analyzed the detectability of magnetic nanoparticles depending on the magnetic field intensity of the polarizing magnetic field, as well as the viscosity of the target and the surrounding medium in which the particles were embedded, respectively. The results show a nonlinear behavior of the magnetic nanoparticle response depending on the magnetic field intensity for magnetic nanoparticles diluted in distilled water and for magnetic nanoparticles embedded in a solid medium. Furthermore, the maximum amplitude of the magnetic nanoparticles responses varies for the different surrounding materials of the magnetic nanoparticles. In the second experiment, we investigated the influence of the target position on the three-dimensional imaging of the magnetic nanoparticles in a realistic measurement setup for breast cancer imaging. The results show that the magnetic nanoparticles can be detected successfully. However, the intensity of the particles in the image depends on its position due to the path-dependent attenuation, the inhomogeneous microwave illumination of the breast, and the inhomogeneity of the magnetic field. Regarding the last point, we present an approach to compensate for the inhomogeneity of the magnetic field by computing a position-dependent correction factor based on the measured magnetic field intensity and the magnetic susceptibility of the magnetic particles. Moreover, the results indicate an influence of the polarizing magnetic field on the measured ultra-wideband signals even without magnetic nanoparticles. Such a disturbing influence of the polarizing magnetic field on the measurements should be reduced for a robust magnetic nanoparticles detection. Therefore, we analyzed the two-state (ON/OFF) and the sinusoidal modulation of the external magnetic field concerning the detectability of the magnetic nanoparticles with respect to these spurious effects, as well as their practical application.
Evolutionary Mahalanobis Distance-Based Oversampling for Multi-Class Imbalanced Data Classification
Authors: Yao Leehter , Lin Tung-Bin
Published: October 2021

The number of sensing data are often imbalanced across data classes, for which oversampling on the minority class is an effective remedy. In this paper, an effective oversampling method called evolutionary Mahalanobis distance oversampling (EMDO) is proposed for multi-class imbalanced data classification. EMDO utilizes a set of ellipsoids to approximate the decision regions of the minority class. Furthermore, multi-objective particle swarm optimization (MOPSO) is integrated with the Gustafson–Kessel algorithm in EMDO to learn the size, center, and orientation of every ellipsoid. Synthetic minority samples are generated based on Mahalanobis distance within every ellipsoid. The number of synthetic minority samples generated by EMDO in every ellipsoid is determined based on the density of minority samples in every ellipsoid. The results of computer simulations conducted herein indicate that EMDO outperforms most of the widely used oversampling schemes.
CultReal—A Rapid Development Platform for AR Cultural Spaces, with Fused Localization
Authors: Morar Anca , Băluțoiu Maria-Anca , Moldoveanu Alin , Moldoveanu Florica , Butean Alex
Published: October 2021

Virtual and augmented reality technologies have known an impressive market evolution due to their potential to provide immersive experiences. However, they still have significant difficulties to enable fully fledged, consumer-ready applications that can handle complex tasks such as multi-user collaboration or time-persistent experiences. In this context, CultReal is a rapid creation and deployment platform for augmented reality (AR), aiming to revitalize cultural spaces. The platform’s content management system stores a representation of the environment, together with a database of multimedia objects that can be associated with a location. The localization component fuses data from beacons and from video cameras, providing an accurate estimation of the position and orientation of the visitor’s smartphone. A mobile application running the localization component displays the augmented content, which is seamlessly integrated with the real world. The paper focuses on the series of steps required to compute the position and orientation of the user’s mobile device, providing a comprehensive evaluation with both virtual and real data. Pilot implementations of the system are also described in the paper, revealing the potential of the platform to enable rapid deployment in new cultural spaces. Offering these functionalities, CultReal will allow for the fast development of AR solutions in any location.
The Requirement of Genetic Diagnostic Technologies for Environmental Surveillance of Antimicrobial Resistance
Authors: Caron Karine , Craw Pascal , Richardson Mark B. , Bodrossy Levente , Voelcker Nicolas H. , Thissen Helmut , Sutherland Tara D.
Published: October 2021

Antimicrobial resistance (AMR) is threatening modern medicine. While the primary cost of AMR is paid in the healthcare domain, the agricultural and environmental domains are also reservoirs of resistant microorganisms and hence perpetual sources of AMR infections in humans. Consequently, the World Health Organisation and other international agencies are calling for surveillance of AMR in all three domains to guide intervention and risk reduction strategies. Technologies for detecting AMR that have been developed for healthcare settings are not immediately transferable to environmental and agricultural settings, and limited dialogue between the domains has hampered opportunities for cross-fertilisation to develop modified or new technologies. In this feature, we discuss the limitations of currently available AMR sensing technologies used in the clinic for sensing in other environments, and what is required to overcome these limitations.
Deep Neural Networks for Detection and Location of Microseismic Events and Velocity Model Inversion from Microseismic Data Acquired by Distributed Acoustic Sensing Array
Authors: Wamriew Daniel , Pevzner Roman , Maltsev Evgenii , Pissarenko Dimitri
Published: October 2021

Fiber-optic cables have recently gained popularity for use as Distributed Acoustic Sensing (DAS) arrays for borehole microseismic monitoring due to their physical robustness as well as high spatial and temporal resolutions. As a result, the sensors record large amounts of data, making it very difficult to process in real-/semi-real-time using the conventional processing routines. We present a novel approach, based on deep learning, for handling the large amounts of DAS data in real-/semi-real-time. The proposed neural network was trained on synthetic microseismic data contaminated with real-ambient noise from field data and was validated using field DAS microseismic data obtained from a hydraulic fracturing operation. The results indicate that the trained network is capable of detecting and locating microseismic events from DAS data and simultaneously update the velocity model to a high degree of precision. The mean absolute errors in the event locations and the velocity model parameters are 2.04, 0.72, 2.76, 4.19 and 0.97 percent for distance (x), depth (z), P-wave velocity, S-wave velocity and density, respectively. In addition to automation and computational efficiency, deep learning reduces human expert data handling during processing, thus preserving data integrity leading to more accurate and reproducible results.
221 K Local Photothermal Heating in a Si Plasmonic Waveguide Loaded with a Co Thin Film
Authors: Ota Nana , Miyauchi Tomohiro , Shimizu Hiromasa
Published: October 2021

Photothermal heaters are important devices for optical switches and memories based on the thermo-optic/magneto-optic effect and phase change materials. We demonstrated photothermal heating in Si plasmonic waveguides loaded with Co thin films by measuring the resistance change upon inputting transverse-magnetic (TM) mode light. Temperature rise is proportional to the light intensity with clear polarization dependence. The photothermal conversion efficiency was estimated at 36 K/mW and maximum temperature rise was estimated at 221 K at steady state upon the inputting 6.3 mW TM mode light for the 400 nm-wide, 8 µm-long and 189 nm-thick Co film deposited on the Si wire waveguide with 129 nm-thick SiO2 buffer layer. The method to increase the efficiency is discussed based on the experimental and simulation results considering the thickness of the SiO2 buffer layer, Co layer and Si core layer, waveguide width, and wavelength. Local photothermal heaters in this study can be applied to a variety of fields including optical switches/memories without electrical control signals in photonic integrated circuits, on-chip optical sensors, and a lab-on-a-chip in biology, chemistry, and medicine.
Diffractive Sensor Elements for Registration of Long-Term Instability at Writing of Computer-Generated Holograms
Authors: Shimansky Ruslan V. , Belousov Dmitrij A. , Korolkov Victor P. , Kuts Roman I.
Published: October 2021

The research and development of methods using of the specialized diffractive microstructure sensors embedded in the pattern of computer-generated holograms (CGH) manufactured on circular and X-Y laser writing systems is discussed. These microstructures consist of two parts: one of which is written before the CGH in the field of future hologram and the second one is written during the long-term writing of the CGH. The shift between the first and second part of the microstructure is the trace of the writing errors and allows one to determine and calculate the error of CGH fabrication along both orthogonal coordinates. The developed method is based on the principle of diffraction-based overlay with 1D and 2D built-in diffractive microstructure-sensors. Mathematical modeling and results of experimental test writings of such diffractive microstructure sensors are described. The efficiency of using these types of build-in sensors for the writing errors estimation for CGHs is demonstrated.
Indirect Measurement of Loading Forces with High-Speed Camera
Authors: Mendrok Krzysztof , Dworakowski Ziemowit , Dziedziech Kajetan , Holak Krzysztof
Published: October 2021

In the last few decades, there has been a significant increase in interest in developing, constructing, and using structural health monitoring (SHM) systems. The classic monitoring system should, by definition, have, in addition to the diagnostic module, a module responsible for monitoring loads. These loads can be measured with piezoelectric force sensors or indirectly with strain gauges such as resistance strain gauges or FBG sensors. However, this is not always feasible due to how the force is applied or because sensors cannot be mounted. Therefore, methods for identifying excitation forces based on response measurements are often used. This approach is usually cheaper and easier to implement from the measurement side. However, in this approach, it is necessary to use a network of response sensors, whose installation and wiring can cause technological difficulties and modify the results for slender constructions. Moreover, many load identification methods require the use of multiple sensors to identify a single force history. Increasing the number of sensors recording responses improves the numerical conditioning of the method. The proposed article presents the use of contactless measurements carried out with the help of a high-speed camera to identify the forces exiting the object.
SAR Image Change Detection via Multiple-Window Processing with Structural Similarity
Authors: Kang Minseok , Baek Jaemin
Published: October 2021

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.
A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling
Authors: Syed Abbas Shah , Sierra-Sosa Daniel , Kumar Anup , Elmaghraby Adel
Published: October 2021

Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.
Solid-Contact Ion-Selective Electrodes for Histamine Determination
Authors: Ma Siyuan , Wang You , Zhang Wei , Wang Ye , Li Guang
Published: October 2021

Solid-contact ion-selective electrodes for histamine (HA) determination were fabricated and studied. Gold wire (0.5 mm diameter) was coated with poly(3,4-ethlenedioxythiophene) doped with poly(styrenesulfonate) (PEDOT:PSS) as a solid conductive layer. The polyvinyl chloride matrix embedded with 5,10,15,20-tetraphenyl(porphyrinato)iron(iii) chloride as an ionophore, 2-nitrophenyloctyl ether as a plasticizer and potassium tetrakis(p-chlorophenyl) borate as an ion exchanger was used to cover the PEDOT:PSS layer as a selective membrane. The characteristics of the HA electrodes were also investigated. The detection limit of 8.58 × 10−6 M, the fast response time of less than 5 s, the good reproducibility, the long-term stability and the selectivity in the presence of common interferences in biological fluids were satisfactory. The electrode also performed stably in the pH range of 7–8 and the temperature range of 35–41 °C. Additionally, the recovery rate of 99.7% in artificial cerebrospinal fluid showed the potential for the electrode to be used in biological applications.
Ammonia Generation System for Poultry Health Research Using Arduino
Authors: Hofstetter Dan , Fabian Eileen , Lorenzoni A. Gino
Published: October 2021

An ammonia gas (NH3) generator was developed to maintain a set concentration of ammonia gas in a controlled environment chamber to study poultry physiological responses to sustained elevated levels of ammonia gas. The goal was to maintain 50 parts per million (ppm) of ammonia gas in a 3.7 m × 4.3 m × 2.4 m (12 ft × 14 ft × 8 ft) controlled environment chamber. The chamber had a 1.5 m3/s (3000 cfm) recirculation system that regulated indoor temperature and humidity levels and a 0.06 m3/s (130 cfm) exhaust fan that exchanged indoor air for fresh outdoor air. The ammonia generator was fabricated by coupling ultrasonic humidifiers with an Arduino-based microcontroller and a metallic oxide MQ-137 ammonia gas sensor. Preliminary evaluation under steady conditions showed the average MQ-137 gas sensor accuracy was within 1.4% of the 65.4 ppm concentration measured using a highly accurate infrared gas analyzer. Further evaluation was performed for a setpoint concentration of 50 ppm where ammonia generator reservoirs were filled with 2% or 10% ammonia liquid. For the system tested, it was found that two generators operating at the same time filled with 3.8 L (1.0 gallon) of 2% ammonia cleaning liquid each (7.6 L or 2.0 gallons total) could maintain a gas level of 49.45 ± 0.79 ppm in the chamber air for a duration of 30 h before refilling was required. One generator filled with 3.8 L of 10% ammonia cleaning liquid could maintain 51.24 ± 1.53 ppm for 195 h. Two ammonia generators were deployed for a six-week animal health experiment in two separate controlled environment chambers. The two ammonia generators maintained an average ammonia concentration of 46.42 ± 3.81 ppm and 45.63 ± 4.95 ppm for the duration of the experiment.
Photothermocapillary Method for the Nondestructive Testing of Solid Materials and Thin Coatings
Authors: Zykov Aleksandr , Vavilov Vladimir , Kuimova Marina
Published: October 2021

The photothermocapillary (PTC) effect is a deformation of the free surface of a thin liquid layer on a solid material that is caused by the dependence of the coefficient of surface tension on temperature. The PTC effect is highly sensitive to variations in the thermal conductivity of solids, and this is the basis for PTC techniques in the non-destructive testing of solid non-porous materials. These techniques analyze thermal conductivity and detect subsurface defects, evaluate the thickness of thin varnish-and-paint coatings (VPC), and detect air-filled voids between coatings and metal substrates. In this study, the PTC effect was excited by a “pumped” Helium-Neon laser, which provided the monochromatic light source that is required to produce optical interference patterns. The light of a small-diameter laser beam was reflected from a liquid surface, which was contoured by liquid capillary action and variations in the surface tension. A typical contour produces an interference pattern of concentric rings with a bright and wide outer ring. The minimal or maximal diameter of this pattern was designated as the PTC response. The PTC technique was evaluated to monitor the thickness of VPCs on thermally conductive solid materials. The same PTC technique has been used to measure the thickness of air-filled delaminations between a metal substrate and a coating.
Corona Discharge Characteristics under Variable Frequency and Pressure Environments
Authors: Bas-Calopa Pau , Riba Jordi-Roger , Moreno-Eguilaz Manuel
Published: October 2021

More electric aircrafts (MEAs) are paving the path to all electric aircrafts (AEAs), which make a much more intensive use of electrical power than conventional aircrafts. Due to the strict weight requirements, both MEA and AEA systems require to increase the distribution voltage in order to limit the required electrical current. Under this paradigm new issues arise, in part due to the voltage rise and in part because of the harsh environments found in aircrafts systems, especially those related to low pressure and high-electric frequency operation. Increased voltage levels, high-operating frequencies, low-pressure environments and reduced distances between wires pose insulation systems at risk, so partial discharges (PDs) and electrical breakdown are more likely to occur. This paper performs an experimental analysis of the effect of low-pressure environments and high-operating frequencies on the visual corona voltage, since corona discharges occurrence is directly related to arc tracking and insulation degradation in wiring systems. To this end, a rod-to-plane electrode configuration is tested in the 20–100 kPa and 50–1000 Hz ranges, these ranges cover most aircraft applications, so that the corona extinction voltage is experimentally determined by using a low-cost high-resolution CMOS imaging sensor which is sensitive to the visible and near ultraviolet (UV) spectra. The imaging sensor locates the discharge points and the intensity of the discharge, offering simplicity and low-cost measurements with high sensitivity. Moreover, to assess the performance of such sensor, the discharges are also acquired by analyzing the leakage current using an inexpensive resistor and a fast oscilloscope. The experimental data presented in this paper can be useful in designing insulation systems for MEA and AEA applications.
Investigation of Heterogeneity Sources for Occupational Task Recognition via Transfer Learning
Authors: Hajifar Sahand , Lamooki Saeb Ragani , Cavuoto Lora A. , Megahed Fadel M. , Sun Hongyue
Published: October 2021

Human activity recognition has been extensively used for the classification of occupational tasks. Existing activity recognition approaches perform well when training and testing data follow an identical distribution. However, in the real world, this condition may be violated due to existing heterogeneities among training and testing data, which results in degradation of classification performance. This study aims to investigate the impact of four heterogeneity sources, cross-sensor, cross-subject, joint cross-sensor and cross-subject, and cross-scenario heterogeneities, on classification performance. To that end, two experiments called separate task scenario and mixed task scenario were conducted to simulate tasks of electrical line workers under various heterogeneity sources. Furthermore, a support vector machine classifier equipped with domain adaptation was used to classify the tasks and benchmarked against a standard support vector machine baseline. Our results demonstrated that the support vector machine equipped with domain adaptation outperformed the baseline for cross-sensor, joint cross-subject and cross-sensor, and cross-subject cases, while the performance of support vector machine equipped with domain adaptation was not better than that of the baseline for cross-scenario case. Therefore, it is of great importance to investigate the impact of heterogeneity sources on classification performance and if needed, leverage domain adaptation methods to improve the performance.
Improving R Peak Detection in ECG Signal Using Dynamic Mode Selected Energy and Adaptive Window Sizing Algorithm with Decision Tree Algorithm
Authors: Abdullah Al Zubaer Md. , Thapa Keshav , Yang Sung-Hyun
Published: October 2021

R peak detection is crucial in electrocardiogram (ECG) signal analysis to detect and diagnose cardiovascular diseases (CVDs). Herein, the dynamic mode selected energy (DMSE) and adaptive window sizing (AWS) algorithm are proposed for detecting R peaks with better efficiency. The DMSE algorithm adaptively separates the QRS components and all non-objective components from the ECG signal. Based on local peaks in QRS components, the AWS algorithm adaptively determines the Region of Interest (ROI). The Feature Extraction process computes the statistical properties of energy, frequency, and noise from each ROI. The Sequential Forward Selection (SFS) procedure is used to find the best subsets of features. Based on these characteristics, an ensemble of decision tree algorithms detects the R peaks. Finally, the R peak position on the initial ECG signal is adjusted using the R location correction (RLC) algorithm. The proposed method has an experimental accuracy of 99.94%, a sensitivity of 99.98%, positive predictability of 99.96%, and a detection error rate of 0.06%. Given the high efficiency in detection and fast processing speed, the proposed approach is ideal for intelligent medical and wearable devices in the diagnosis of CVDs.
A Frequency-Programmable Miniaturized Radio Frequency Transmitter for Animal Tracking
Authors: Lu Jun , Li Huidong , Tian Chuan , Myjak Mitchell J. , Xiao Jie , Bellgraph Brian J. , Cartmell Samuel S. , Deng Zhiqun Daniel
Published: October 2021

In animal tracking applications, smaller transmitters can reduce the impact of the transmitter on the tagged animal and thus provide more accurate data about animal behavior. By combining a novel circuit design and a newly developed micro-battery, we developed frequency-programmable and more powerful radio frequency transmitters that are about 40% smaller and lighter in weight than the smallest commercial counterpart for animal monitoring at the time of development. The new radio frequency transmitter has a miniaturized form factor for studying small animals. Designs of two coding schemes were developed: one transmits unmodulated signals (weight: 152 mg; dimensions: Ø 2.95 mm × 11.22 mm), and the other transmits modulated signals (weight: 160 mg; dimensions: Ø 2.95 mm × 11.85 mm). To accommodate different transmitter life requirements, each design can be configured to transmit in high or low signal strength. Prototypes of these transmitters were evaluated in the laboratory and exhibited comparable or longer service life and higher signal strength compared to their smallest commercial counterparts.
Implementation of Facility Management for Port Infrastructure through the Use of UAVs, Photogrammetry and BIM
Authors: Jofré-Briceño Constanza , Muñoz-La Rivera Felipe , Atencio Edison , Herrera Rodrigo F.
Published: October 2021

The maintenance of port infrastructures presents difficulties due to their location: an aggressive environment or the variability of the waves can cause progressive deterioration. Maritime conditions make inspections difficult and, added to the lack of use of efficient tools for the management of assets, planning maintenance, important to ensure operability throughout the life cycle of port infrastructures, is generally not a priority. In view of these challenges, this research proposes a methodology for the creation of a port infrastructure asset management tool, generated based on the Design Science Research Method (DSRM), in line with Building Information Modeling (BIM) and digitization trends in the infrastructure sector. The proposal provides workflows and recommendations for the survey of port infrastructures from UAVs, the reconstruction of digital models by photogrammetry (due to scarce technical documentation), and the reconstruction of BIM models. Along with this, the bidirectional linking of traditional asset management spreadsheets with BIM models is proposed, by visual programming, allowing easy visualization of the status and maintenance requirements. This methodology was applied to a port infrastructure, where the methodology demonstrated the correct functionality of the asset management tool, which allows a constant up-dating of information regarding the structural state of the elements and the necessary maintenance activities.
Coresets for the Average Case Error for Finite Query Sets
Authors: Maalouf Alaa , Jubran Ibrahim , Tukan Murad , Feldman Dan
Published: October 2021

Coreset is usually a small weighted subset of an input set of items, that provably approximates their loss function for a given set of queries (models, classifiers, hypothesis). That is, the maximum (worst-case) error over all queries is bounded. To obtain smaller coresets, we suggest a natural relaxation: coresets whose average error over the given set of queries is bounded. We provide both deterministic and randomized (generic) algorithms for computing such a coreset for any finite set of queries. Unlike most corresponding coresets for the worst-case error, the size of the coreset in this work is independent of both the input size and its Vapnik–Chervonenkis (VC) dimension. The main technique is to reduce the average-case coreset into the vector summarization problem, where the goal is to compute a weighted subset of the n input vectors which approximates their sum. We then suggest the first algorithm for computing this weighted subset in time that is linear in the input size, for n≫1/ε, where ε is the approximation error, improving, e.g., both [ICML’17] and applications for principal component analysis (PCA) [NIPS’16]. Experimental results show significant and consistent improvement also in practice. Open source code is provided.
Modern Types of Axicons: New Functions and Applications
Authors: Khonina Svetlana N. , Kazanskiy Nikolay L. , Khorin Pavel A. , Butt Muhammad A.
Published: October 2021

Axicon is a versatile optical element for forming a zero-order Bessel beam, including high-power laser radiation schemes. Nevertheless, it has drawbacks such as the produced beam’s parameters being dependent on a particular element, the output beam’s intensity distribution being dependent on the quality of element manufacturing, and uneven axial intensity distribution. To address these issues, extensive research has been undertaken to develop nondiffracting beams using a variety of advanced techniques. We looked at four different and special approaches for creating nondiffracting beams in this article. Diffractive axicons, meta-axicons-flat optics, spatial light modulators, and photonic integrated circuit-based axicons are among these approaches. Lately, there has been noteworthy curiosity in reducing the thickness and weight of axicons by exploiting diffraction. Meta-axicons, which are ultrathin flat optical elements made up of metasurfaces built up of arrays of subwavelength optical antennas, are one way to address such needs. In addition, when compared to their traditional refractive and diffractive equivalents, meta-axicons have a number of distinguishing advantages, including aberration correction, active tunability, and semi-transparency. This paper is not intended to be a critique of any method. We have outlined the most recent advancements in this field and let readers determine which approach best meets their needs based on the ease of fabrication and utilization. Moreover, one section is devoted to applications of axicons utilized as sensors of optical properties of devices and elements as well as singular beams states and wavefront features.
Air-Writing Character Recognition with Ultrasonic Transceivers
Authors: Saez-Mingorance Borja , Mendez-Gomez Javier , Mauro Gianfranco , Castillo-Morales Encarnacion , Pegalajar-Cuellar Manuel , Morales-Santos Diego P.
Published: October 2021

The interfaces between users and systems are evolving into a more natural communication, including user gestures as part of the interaction, where air-writing is an emerging application for this purpose. The aim of this work is to propose a new air-writing system based on only one array of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand marker with each transceiver. After acquiring the track, different deep learning algorithms, such as long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder (ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has been shown how these algorithms provide high accuracy, where the best result is extracted from the ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work to evaluate the proposed system in a real scenario to demonstrate its high performance regarding data acquisition and classification.
Speckle-Based Sensing of Microscopic Dynamics in Expanding Polymer Foams: Application of the Stacked Speckle History Technique
Authors: Zimnyakov Dmitry , Alonova Marina , Ushakova Ekaterina , Volchkov Sergey , Ushakova Olga , Klimov Daniil , Slavnetskov Ilya , Kalacheva Anna
Published: October 2021

Microscopic structural rearrangements in expanding polylactide foams were probed using multiple dynamic scattering of laser radiation in the foam volume. Formation and subsequent expansion of polylactide foams was provided by a rapid or slow depressurization of the “plasticized polylactide–supercritical carbon dioxide” system. Dynamic speckles induced by a multiple scattering of laser radiation in the expanding foam were analyzed using the stacked speckle history technique, which is based on a joint mapping of spatial–temporal dynamics of evolving speckle patterns. A significant decrease in the depressurization rate in the case of transition from a rapid to slow foaming (from 0.03 MPa/s to 0.006 MPa/s) causes dramatic changes in the texture of the synthesized stacked speckle history maps. These changes are associated with transition from the boiling dynamics of time-varying speckles to their pronounced translational motions and are manifested as significant slopes of individual speckle traces on the recovered stacked speckle history maps. This feature is interpreted in terms of the actual absence of a new cell nucleation effect in the expanding foam upon slow depressurization on the dynamic scattering of laser radiation.