bluuebunny/crossref_metadata_2025 huggingface dataset:
Physalis peruviana L. Pulp Prevents Liver Inflammation and Insulin Resistance in Skeletal Muscles of Diet-Induced Obese Mice
Authors: Pino-de la Fuente Francisco , Nocetti Diego , Sacristán Camila , Ruiz Paulina , Guerrero Julia , Jorquera Gonzalo , Uribe Ernesto , Bucarey José Luis , Espinosa Alejandra , Puente Luis
Published: March 2020

A chronic high-fat diet (HFD) produces obesity, leading to pathological consequences in the liver and skeletal muscle. The fat in the liver leads to accumulation of a large number of intrahepatic lipid droplets (LD), which are susceptible to oxidation. Obesity also affects skeletal muscle, increasing LD and producing insulin signaling impairment. Physalis peruviana L. (PP) (Solanaceae) is rich in peruvioses and has high antioxidant activity. We assessed the ability of PP to enhance insulin-dependent glucose uptake in skeletal muscle and the capacity to prevent both inflammation and lipoperoxidation in the liver of diet-induced obese mice. Male C57BL/6J mice were divided into groups and fed for eight weeks: control diet (C; 10% fat, 20% protein, 70% carbohydrates); C + PP (300 mg/kg/day); HFD (60% fat, 20% protein, 20% carbohydrates); and HFD + PP. Results suggest that PP reduces the intracellular lipoperoxidation level and the size of LD in both isolated hepatocytes and skeletal muscle fibers. PP also promotes insulin-dependent skeletal muscle glucose uptake. In conclusion, daily consumption of 300 mg/kg of fresh pulp of PP could be a novel strategy to prevent the hepatic lipoperoxidation and insulin resistance induced by obesity.
2019 FDA TIDES (Peptides and Oligonucleotides) Harvest
Authors: Al Shaer Danah , Al Musaimi Othman , Albericio Fernando , de la Torre Beatriz G.
Published: March 2020

2019 has been an excellent year in terms of peptides and oligonucleotides (TIDES) approved by the FDA. Despite the drop in the number of total drugs approved by the FDA in 2019 in comparison with 2018 (48 vs. 59), the total number of TIDES authorized increased (seven vs. three). Year after year, TIDES are increasingly present in therapy, as imaging agents, theragnostic and constituent moieties of other complex drugs, such as antibody drug conjugates. This means a consolidation of these kinds of drugs in the pharmaceutical arena, paving the way in the coming years for the approval of others for diverse medical indications. Here the TIDES approved in 2019 are analyzed in terms of chemical structure, medical target, mode of action, and adverse effects.
In Silico Optimization of Fiber-Shaped Aerosols in Inhalation Therapy for Augmented Targeting and Deposition across the Respiratory Tract
Authors: Shachar-Berman Lihi , Bhardwaj Saurabh , Ostrovski Yan , Das Prashant , Koullapis Pantelis , Kassinos Stavros , Sznitman Josué
Published: March 2020

Motivated by a desire to uncover new opportunities for designing the size and shape of fiber-shaped aerosols towards improved pulmonary drug delivery deposition outcomes, we explore the transport and deposition characteristics of fibers under physiologically inspired inhalation conditions in silico, mimicking a dry powder inhaler (DPI) maneuver in adult lung models. Here, using computational fluid dynamics (CFD) simulations, we resolve the transient translational and rotational motion of inhaled micron-sized ellipsoid particles under the influence of aerodynamic (i.e., drag, lift) and gravitational forces in a respiratory tract model spanning the first seven bifurcating generations (i.e., from the mouth to upper airways), coupled to a more distal airway model representing nine generations of the mid-bronchial tree. Aerosol deposition efficiencies are quantified as a function of the equivalent diameter (dp) and geometrical aspect ratio (AR), and these are compared to outcomes with traditional spherical particles of equivalent mass. Our results help elucidate how deposition patterns are intimately coupled to dp and AR, whereby high AR fibers in the narrow range of dp = 6–7 µm yield the highest deposition efficiency for targeting the upper- and mid-bronchi, whereas fibers in the range of dp= 4–6 µm are anticipated to cross through the conducting regions and reach the deeper lung regions. Our efforts underscore previously uncovered opportunities to design the shape and size of fiber-like aerosols towards targeted pulmonary drug delivery with increased deposition efficiencies, in particular by leveraging their large payloads for deep lung deposition.
The Prescribing and Education of Naloxone in a Large Academic Medical Center
Authors: Miller Sabrina , Williams Lauren , Thompson Amy N.
Published: March 2020

The opioid epidemic has led to increased needs for opioid reversal agents which require education and counseling for proper use. The purpose of this study was to evaluate outpatient naloxone prescribing and education practices at an academic medical center to understand the current state and inform quality improvement measures. This retrospective chart review study included 439 patients that were at least 18 years old and received an outpatient prescription for naloxone between 1 July 2017 and 30 June 2018. Descriptive and demographic data were collected. The primary endpoint was whether an indication for naloxone and education on administration were documented when naloxone was initially prescribed to patients. Overall, 39% of naloxone prescriptions did not have an indication for prescribing listed in the medical record. Of those with a documented indication, concomitant benzodiazepines and history of overdose or substance abuse were most common (22% and 14%). The average morphine milligram equivalents were 165. Additionally, 69% of dispenses did not have documentation that the patient or a caregiver received education regarding the use and administration of naloxone. These findings suggest that patients are receiving naloxone for appropriate indications. Documentation of medication education is needed to ensure it is occurring and that patients are informed.
Influence of Polymer Concentration and Nozzle Material on Centrifugal Fiber Spinning
Authors: Merchiers Jorgo , Meurs Willem , Deferme Wim , Peeters Roos , Buntinx Mieke , Reddy Naveen K.
Published: March 2020

Centrifugal fiber spinning has recently emerged as a highly promising alternative technique for the production of nonwoven, ultrafine fiber mats. Due to its high production rate, it could provide a more technologically relevant fiber spinning technique than electrospinning. In this contribution, we examine the influence of polymer concentration and nozzle material on the centrifugal spinning process and the fiber morphology. We find that increasing the polymer concentration transforms the process from a beaded-fiber regime to a continuous-fiber regime. Furthermore, we find that not only fiber diameter is strongly concentration-dependent, but also the nozzle material plays a significant role, especially in the continuous-fiber regime. This was evaluated by the use of a polytetrafluoroethylene (PTFE) and an aluminum nozzle. We discuss the influence of polymer concentration on fiber morphology and show that the choice of nozzle material has a significant influence on the fiber diameter.
Simulation and Experimental Investigation on Carbonized Tracking Failure of EPDM/BN-Based Electrical Insulation
Authors: Nazir Muhammad Tariq , Butt Faizan Tahir , Phung Bao Toan , Yeoh Guan Heng , Yasin Ghulam , Akram Shakeel , Bhutta Muhammad Shoaib , Hussain Shahid , Nguyen Tuan Anh
Published: March 2020

Ethylene propylene diene monomer (EPDM) is broadly employed as an insulating material for high voltage applications. Surface discharge-induced thermal depolymerization and carbon tracking adversely affect its performance. This work reports the electrical field modeling, carbon tracking lifetime, infrared thermal distribution, and leakage current development on EPDM-based insulation with the addition of nano-BN (boron nitride) contents. Melt mixing and compression molding techniques were used for the fabrication of nanocomposites. An electrical tracking resistance test was carried out as per IEC-60587. Simulation results show that contamination significantly distorted the electrical field distribution and induced dry band arcing. Experimental results indicate that electric field stress was noticed significantly higher at the intersection of insulation and edges of the area of contamination. Moreover, the field substantially intensified with the increasing voltage levels. Experimental results show improved carbonized tracking lifetime with the addition of nano-BN contents. Furthermore, surface temperature was reduced in the critical contamination flow path. The third harmonic component in the leakage current declined with the increase of the nano-BN contents. It is concluded that addition of nano-BN imparts a better tracking failure time, and this is attributed to better thermal conductivity and thermal stability, as well as an improved shielding effect to electrical discharges on the surface of nanocomposite insulators.
Melt Blending Modification of Commercial Polystyrene with Its Half Critical Molecular Weight, High Ion Content Ionomer, Poly(styrene–ran–cinnamic Acid) Zn Salt, toward Heat Resistance Improvement
Authors: Yu Zixin , Wang Jie , Li Peihua , Ding Dachuan , Zheng Xuan , Hu Chuanqun , Gao Zhinan , Hu Tao , Gong Xinghou , Wu Chonggang
Published: March 2020

A half-critical weight-average molecular weight ( M ¯ w ) (approximately 21,000 g mol−1), high-ion-content Zn-salt poly(styrene–ran–cinnamic-acid) (SCA–Zn) ionomer was successfully synthesized by styrene–cinnamic-acid (10.8 mol %) copolymerization followed by excess-ZnO melt neutralization. At 220 °C, the SCA–Zn’s viscosity was only approximately 1.5 magnitude orders higher than that of commercial polystyrene (PS) at 102 s−1, and the PS/SCA–Zn (5–40 wt %) melt blends showed apparently fine, two-phased morphologies with blurred interfaces, of which the 95/5 and 90/10 demonstrated Han plots suggesting their near miscibility. These indicate that any PS–(SCA–Zn) processability mismatch was minimized by the SCA–Zn’s half-critical M ¯ w despite its dense ionic cross-links. Meanwhile, the SCA–Zn’s Vicat softening temperature (VST) was maximized by its cross-linking toward 153.1 °C, from that (97.7 °C) of PS, based on its half-critical M ¯ w at which the ultimate glass-transition temperature was approximated. Below approximately 110 °C, the PS/SCA–Zn (0–20 wt %) were seemingly miscible when their VST increased linearly yet slightly with the SCA–Zn fraction due to the dissolution of the SCA–Zn’s cross-links. Nevertheless, the 60/40 blend’s VST significantly diverged positively from the linearity until 111.1 °C, revealing its phase-separated morphology that effectively enhanced the heat resistance by the highly cross-linked SCA–Zn. This work proposes a methodology of improving PS heat resistance by melt blending with its half-critical M ¯ w , high-ion-content ionomer.
Conjugated Copolymers through Electrospinning Synthetic Strategies and Their Versatile Applications in Sensing Environmental Toxicants, pH, Temperature, and Humidity
Authors: Veeramuthu Loganathan , Venkatesan Manikandan , Liang Fang-Cheng , Benas Jean-Sebastien , Cho Chia-Jung , Chen Chin-Wen , Zhou Ye , Lee Rong-Ho , Kuo Chi-Ching
Published: March 2020

Conjugated copolymers (CCPs) are a class of polymers with excellent optical luminescent and electrical conducting properties because of their extensive π conjugation. CCPs have several advantages such as facile synthesis, structural tailorability, processability, and ease of device fabrication by compatible solvents. Electrospinning (ES) is a versatile technique that produces continuous high throughput nanofibers or microfibers and its appropriate synchronization with CCPs can aid in harvesting an ideal sensory nanofiber. The ES-based nanofibrous membrane enables sensors to accomplish ultrahigh sensitivity and response time with the aid of a greater surface-to-volume ratio. This review covers the crucial aspects of designing highly responsive optical sensors that includes synthetic strategies, sensor fabrication, mechanistic aspects, sensing modes, and recent sensing trends in monitoring environmental toxicants, pH, temperature, and humidity. In particular, considerable attention is being paid on classifying the ES-based optical sensor fabrication to overcome remaining challenges such as sensitivity, selectivity, dye leaching, instability, and reversibility.
Coupling Effect of Molecular Chain Displacement and Carrier Trap Characteristics on DC Breakdown of HDPE/LDPE Blend Insulation
Authors: Li Zhonglei , Fan Mingsheng , Zhong Zhuoyan , Du Boxue
Published: March 2020

This work focuses on the coupling effect of molecular chain displacement and trap characteristics on direct current (DC) breakdown properties of high density/low density polyethylene (HDPE/LDPE) blend insulation. Frequency domain spectroscopy (FDS) and isothermal discharge current (IDC) are used to characterize the dielectric relaxation and trap characteristics of HDPE/LDPE blends. A DC breakdown model is proposed to reveal the mechanisms of the molecular chain displacement and carrier trap on the DC breakdown strength. The dielectric relaxation α and δ present segmental motions and thermal ion polarization behaviours of HDPE/LDPE blends, respectively. α dielectric relaxation strength (Δεα) increases as the amount of HDPE increases from 0 to 5 wt%, and then declines with a further increase of HDPE content to 20 wt%. According to the velocity equation, the increase of Δεα will increase the molecular chain displacement, resulting in a larger free volume, which will provide electrons with larger free path λ to form hot electrons. A positive correlation exists between the activation energy of the dielectric relaxation process δ and trap density, and the increase of δ dielectric relaxation strength (Δεδ) will adversely affect the breakdown strength of the specimen. HDPE/LDPE blends with 15 wt% HDPE content have lower Δεα and lowest Δεδ, which decreases the mean free path λ of molecular chain and thermal ion polarization. At the same time, it has the highest deep trap density, thus increasing the probability of hot electrons being captured and improving the DC breakdown strength. It is concluded the breakdown of the dielectric is synergistically affected by the molecular chain displacement and carrier trap.
Phosphonium-Based Porous Ionic Polymer with Hydroxyl Groups: A Bifunctional and Robust Catalyst for Cycloaddition of CO2 into Cyclic Carbonates
Authors: Lei Yizhu , Wan Yali , Zhong Wei , Liu Dingfu , Yang Zhou
Published: March 2020

The integration of synergic hydrogen bond donors and nucleophilic anions that facilitates the ring-opening of epoxide is an effective way to develop an active catalyst for the cycloaddition of CO2 with epoxides. In this work, a new heterogeneous catalyst for the cycloaddition of epoxides and CO2 into cyclic carbonates based on dual hydroxyls-functionalized polymeric phosphonium bromide (PQPBr-2OH) was presented. Physicochemical characterizations suggested that PQPBr-2OH possessed large surface area, hierarchical pore structure, functional hydroxyl groups, and high density of active sites. Consequently, it behaved as an efficient, recyclable, and metal-free catalyst for the additive and solvent free cycloaddition of epoxides with CO2. Comparing the activity of PQPBr-2OH with that of the reference catalysts based on mono and non-hydroxyl functionalized polymeric phosphonium bromides suggested that hydroxyl functionalities in PQPBr-2OH showed a critical promotion effect on its catalytic activity for CO2 conversion. Moreover, PQPBr-2OH proved to be quite robust and recyclable. It could be reused at least ten times with only a slight decrease of its initial activity.
Progress in Polymeric Nano-Medicines for Theranostic Cancer Treatment
Authors: Ali Imran , Alsehli Mosa , Scotti Luciana , Tullius Scotti Marcus , Tsai Shang-Ting , Yu Ruei-Siang , Hsieh Ming Fa , Chen Jung-Chih
Published: March 2020

Cancer is a life-threatening disease killing millions of people globally. Among various medical treatments, nano-medicines are gaining importance continuously. Many nanocarriers have been developed for treatment, but polymerically-based ones are acquiring importance due to their targeting capabilities, biodegradability, biocompatibility, capacity for drug loading and long blood circulation time. The present article describes progress in polymeric nano-medicines for theranostic cancer treatment, which includes cancer diagnosis and treatment in a single dosage form. The article covers the applications of natural and synthetic polymers in cancer diagnosis and treatment. Efforts were also made to discuss the merits and demerits of such polymers; the status of approved nano-medicines; and future perspectives.
Improvement of Mechanical and Self-Healing Properties for Polymethacrylate Derivatives Containing Maleimide Modified Graphene Oxide
Authors: Lee Won-Ji , Cha Sang-Ho
Published: March 2020

In this paper, a self-healable nanocomposite based on the Diels-Alder reaction is developed. A graphene-based nanofiller is introduced to improve the self-healing efficiency, as well as the mechanical properties of the nanocomposite. Graphene oxide (GO) is modified with maleimide functional groups, and the maleimide-modified GO (mGO) enhanced the compatibility of the polymer matrix and nanofiller. The tensile strength of the nanocomposite containing 0.030 wt% mGO is improved by 172%, compared to that of a polymer film incorporating both furan-functionalized polymer and bismaleimide without any nanofiller. Moreover, maleimide groups of the surface on mGO participate in the Diels-Alder reaction, which improves the self-healing efficiency. The mechanical and self-healing properties are significantly improved by using a small amount of mGO.
Sample Generation with Self-Attention Generative Adversarial Adaptation Network (SaGAAN) for Hyperspectral Image Classification
Authors: Zhao Wenzhi , Chen Xi , Chen Jiage , Qu Yang
Published: March 2020

Hyperspectral image analysis plays an important role in agriculture, mineral industry, and for military purposes. However, it is quite challenging when classifying high-dimensional hyperspectral data with few labeled samples. Currently, generative adversarial networks (GANs) have been widely used for sample generation, but it is difficult to acquire high-quality samples with unwanted noises and uncontrolled divergences. To generate high-quality hyperspectral samples, a self-attention generative adversarial adaptation network (SaGAAN) is proposed in this work. It aims to increase the number and quality of training samples to avoid the impact of over-fitting. Compared to the traditional GANs, the proposed method has two contributions: (1) it includes a domain adaptation term to constrain generated samples to be more realistic to the original ones; and (2) it uses the self-attention mechanism to capture the long-range dependencies across the spectral bands and further improve the quality of generated samples. To demonstrate the effectiveness of the proposed SaGAAN, we tested it on two well-known hyperspectral datasets: Pavia University and Indian Pines. The experiment results illustrate that the proposed method can greatly improve the classification accuracy, even with a small number of initial labeled samples.
A Survey of Using Swarm Intelligence Algorithms in IoT
Authors: Sun Weifeng , Tang Min , Zhang Lijun , Huo Zhiqiang , Shu Lei
Published: March 2020

With the continuing advancements in technologies (such as machine to machine, wireless telecommunications, artificial intelligence, and big data analysis), the Internet of Things (IoT) aims to connect everything for information sharing and intelligent decision-making. Swarm intelligence (SI) provides the possibility of SI behavior through collaboration in individuals that have limited or no intelligence. Its potential parallelism and distribution characteristics can be used to realize global optimization and solve nonlinear complex problems. This paper reviews representative SI algorithms and summarizes their applications in the IoT. The main focus consists in the analysis of SI-enabled applications to wireless sensor network (WSN) and discussion of related research problems in the WSN. Also, we concluded SI-based applications in other IoT fields, such as SI in UAV-aided wireless network. Finally, possible research prospects and future trends are drawn.
Biometric Identity Based on Intra-Body Communication Channel Characteristics and Machine Learning
Authors: Khorshid Ahmed E. , Alquaydheb Ibrahim N. , Kurdahi Fadi , Jover Roger Piqueras , Eltawil Ahmed
Published: March 2020

In this paper, we propose and validate using the Intra-body communications channel as a biometric identity. Combining experimental measurements collected from five subjects and two multi-layer tissue mimicking materials’ phantoms, different machine learning algorithms were used and compared to test and validate using the channel characteristics and features as a biometric identity for subject identification. An accuracy of 98.5% was achieved, together with a precision and recall of 0.984 and 0.984, respectively, when testing the models against subject identification over results collected from the total samples. Using a simple and portable setup, this work shows the feasibility, reliability, and accuracy of the proposed biometric identity, which allows for continuous identification and verification.
Improved Denoising of Structural Vibration Data Employing Bilateral Filtering
Authors: Liu Ning , Schumacher Thomas
Published: March 2020

With the continuous advancement of data acquisition and signal processing, sensors, and wireless communication, copious research work has been done using vibration response signals for structural damage detection. However, in actual projects, vibration signals are often subject to noise interference during acquisition and transmission, thereby reducing the accuracy of damage identification. In order to effectively remove the noise interference, bilateral filtering, a filtering method commonly used in the field of image processing for improving data signal-to-noise ratio was introduced. Based on the Gaussian filter, the method constructs a bilateral filtering kernel function by multiplying the spatial proximity Gaussian kernel function and the numerical similarity Gaussian kernel function and replaces the current data with the data obtained by weighting the neighborhood data, thereby implementing filtering. By processing the simulated data and experimental data, introducing a time-frequency analysis method and a method for calculating the time-frequency spectrum energy, the denoising abilities of median filtering, wavelet denoising and bilateral filtering were compared. The results show that the bilateral filtering method can better preserve the details of the effective signal while suppressing the noise interference and effectively improve the data quality for structural damage detection. The effectiveness and feasibility of the bilateral filtering method applied to the noise suppression of vibration signals is verified.
Machine Learning: New Potential for Local and Regional Deep-Seated Landslide Nowcasting
Authors: van Natijne Adriaan L. , Lindenbergh Roderik C. , Bogaard Thom A.
Published: March 2020

Nowcasting and early warning systems for landslide hazards have been implemented mostly at the slope or catchment scale. These systems are often difficult to implement at regional scale or in remote areas. Machine Learning and satellite remote sensing products offer new opportunities for both local and regional monitoring of deep-seated landslide deformation and associated processes. Here, we list the key variables of the landslide process and the associated satellite remote sensing products, as well as the available machine learning algorithms and their current use in the field. Furthermore, we discuss both the challenges for the integration in an early warning system, and the risks and opportunities arising from the limited physical constraints in machine learning. This review shows that data products and algorithms are available, and that the technology is ready to be tested for regional applications.
PACO: Python-Based Atmospheric Correction
Authors: de los Reyes Raquel , Langheinrich Maximilian , Schwind Peter , Richter Rudolf , Pflug Bringfried , Bachmann Martin , Müller Rupert , Carmona Emiliano , Zekoll Viktoria , Reinartz Peter
Published: March 2020

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range.
Fluorescence Sensing Platforms for Epinephrine Detection Based on Low Temperature Cofired Ceramics
Authors: Baluta Sylwia , Malecha Karol , Świst Agnieszka , Cabaj Joanna
Published: March 2020

A novel fluorescence-sensing pathway for epinephrine (EP) detection was investigated. The ceramic-based miniature biosensor was developed through the immobilization of an enzyme (laccase, tyrosinase) on a polymer—poly-(2,6-di([2,2′-bithiophen]-5-yl)-4-(5-hexylthiophen-2-yl)pyridine), based on low temperature cofired ceramics technology (LTCC). The detection procedure was based on the oxidation of the substrate, i.e., in the presence of the enzyme. An alternative enzyme-free system utilized the formation of a colorful complex between Fe2+ ions and epinephrine molecules. With the optimized conditions, the analytical performance illustrated high sensitivity and selectivity in a broad linear range with a detection limit of 0.14–2.10 nM. Moreover, the strategy was successfully used for an EP injection test with labeled pharmacological samples.
Radiation Angle Estimation and High-Precision Pedestrian Positioning by Tracking Change of Channel State Information
Authors: Komamiya Wataru , Tang Suhua , Obana Sadao
Published: March 2020

P2V (pedestrian-to-vehicle) communication, in which a pedestrian’s mobile device notifies its position to nearby vehicles in order to prevent pedestrian accidents, has attracted much research interest recently, but its performance largely depends on the precision of pedestrians’ positioning. Pedestrian positioning is generally performed by using GPS (Global Positioning System), and its precision may greatly degrade in urban canyons. To improve positioning precision of pedestrians, it was proposed that vehicles around pedestrians be used as anchors beside satellites. In this method, a pedestrian device overhears V2V (vehicle-to-vehicle) communication signals which carry the vehicle position and calculates a pedestrian’s position using pedestrian–vehicle distance/angle information estimated from CSI (channel state information) of the V2V signals. However, angle estimation typically depends on the number of antennas of the pedestrian device. In this paper, we propose a new method to estimate signal radiation angle by tracking temporal change of CSI caused by vehicle movement during signal transmission and investigate its application to precise pedestrian positioning. Three-dimensional ray tracing simulations confirm that compared to the base method using eight antennas, the proposed method with a single antenna reduces average angle error from 13 degrees to 3.9 degrees, and reduces average positioning error from 2.49 m to 0.78 m.
Ambiguity-Free Optical–Inertial Tracking for Augmented Reality Headsets
Authors: Cutolo Fabrizio , Mamone Virginia , Carbonaro Nicola , Ferrari Vincenzo , Tognetti Alessandro
Published: March 2020

The increasing capability of computing power and mobile graphics has made possible the release of self-contained augmented reality (AR) headsets featuring efficient head-anchored tracking solutions. Ego motion estimation based on well-established infrared tracking of markers ensures sufficient accuracy and robustness. Unfortunately, wearable visible-light stereo cameras with short baseline and operating under uncontrolled lighting conditions suffer from tracking failures and ambiguities in pose estimation. To improve the accuracy of optical self-tracking and its resiliency to marker occlusions, degraded camera calibrations, and inconsistent lighting, in this work we propose a sensor fusion approach based on Kalman filtering that integrates optical tracking data with inertial tracking data when computing motion correlation. In order to measure improvements in AR overlay accuracy, experiments are performed with a custom-made AR headset designed for supporting complex manual tasks performed under direct vision. Experimental results show that the proposed solution improves the head-mounted display (HMD) tracking accuracy by one third and improves the robustness by also capturing the orientation of the target scene when some of the markers are occluded and when the optical tracking yields unstable and/or ambiguous results due to the limitations of using head-anchored stereo tracking cameras under uncontrollable lighting conditions.
An Empirical Study on Transmission Beamforming for Ultrasonic Guided-Wave Based Structural Health Monitoring
Authors: Cantero-Chinchilla Sergio , Aranguren Gerardo , Malik Muhammad Khalid , Etxaniz Josu , Martín de la Escalera Federico
Published: March 2020

The development of reliable structural health monitoring techniques is enabling a healthy transition from preventive to condition-based maintenance, hence leading to safer and more efficient operation of different industries. Ultrasonic guided-wave based beamforming is one of the most promising techniques, which supports the monitoring of large thin-walled structures. However, beamforming has been typically applied to the post-processing stage (also known as virtual or receiver beamforming) because transmission or physical beamforming requires complex hardware configurations. This paper introduces an electronic structural health monitoring system that carries out transmission beamforming experiments by simultaneously emitting and receiving ultrasonic guided-waves using several transducers. An empirical characterization of the transmission beamforming technique for monitoring an aluminum plate is provided in this work. The high signal-to-noise ratio and accurate angular precision of the physical signal obtained in the experiments suggest that transmission beamforming can increase the reliability and robustnessof this monitoring technique for large structures and in real-world noisy environments.
Expanding Bias-instability of MEMS Silicon Oscillating Accelerometer Utilizing AC Polarization and Self-Compensation
Authors: Zhao Yang , Xia Guoming , Shi Qin , Qiu Anping
Published: March 2020

This paper presents a MEMS (Micro-Electro-Mechanical System) Silicon Oscillating Accelerometer (SOA) with AC (alternating current) polarization to expand its bias-instability limited by the up-converted 1/f noise from front-end transimpedance amplifier (TIA). In contrast to the conventional DC (direct current) scheme, AC polarization breaks the trade-off between input transistor gate size and white noise floor of TIA, a relative low input loading capacitance can be implemented for low noise consideration. Besides, a self-compensation technique combining polarization source and reference in automatic-gain-control (AGC) is put forward. It cancels the 1/f noise and drift introduced by the polarization source itself, which applies to both DC and AC polarization cases. The experimental result indicates the proposed AC polarization and self-compensation strategy expand the bias-instability of studied SOA from 2.58 μg to 0.51 μg with a full scale of ± 30 g, a 155.6 dB dynamic range is realized in this work.
The Development of Straw-Based Biomass Power Generation in Rural Area in Northeast China—An Institutional Analysis Grounded in a Risk Management Perspective
Authors: Watanabe Reeko , Watanabe Tsunemi
Published: March 2020

Given a lack of consideration for the role and importance of stakeholders and the importance of stakeholders in the operation of biomass power plants in China, a comprehensive analysis oriented toward stakeholder risk management is needed to further develop the country’s biomass energy industry. Accordingly, we analyzed institutional factors that contribute to or constrain progress in biomass power generation in China. Data were collected from 275 straw suppliers (farmers) living around a biomass power plant, 15 middlemen, five power plant managers, and five local government officers. Interviews were held with all the participants, but questionnaires were additionally administered to the straw suppliers. Results showed that: (1) risk transfer in the biomass supply chain is one of the reasons why farmers are unwilling to supply straw; (2) middlemen are vital intermediaries between biomass power plant managers and farmers as a middleman-based biomass supply system is necessary to guarantee the quantity of straw supply, and; (3) the institutional structure that underlies the Chinese biomass energy industry is immature.
Engineers Changing the World: Education for Sustainability in Romanian Technical Universities—An Empirical Web-Based Content Analysis
Authors: Alexa Lidia , Maier Veronica , Șerban Anca , Craciunescu Razvan
Published: March 2020

Without a doubt, we are living in interesting times, characterized by both continuous economic development and improved standard of living, but also uncertainty, increased pollution, and environmental degradation, which means that, now, more than ever, global and consistent action is needed in order to create a more sustainable future. In this context, education in general and higher education in particular, face both, a significant challenge and a substantial role due to their formative function both in terms of mindset and practical tools. The main objective of our research was to explore the way technical universities in Romania have integrated into their curricula courses that aim at shaping sustainability competencies in engineering students. The study was carried out based on an exploratory empirical content analysis of the technical universities’ curricula, in order to identify the courses, including sustainable development (SD)-related topics. The analysis covered 255 bachelor programs and 394 master programs with a total of 25,920 courses, both mandatory and optional. The results revealed that there are differences in approaching sustainability education between the universities and also faculties within the universities included in the sample, revealing a rather siloed approach.
Assessment of the Overtourism Phenomenon Risk in Tunisia in Relation to the Tourism Area Life Cycle Concept
Authors: Widz Monika , Brzezińska-Wójcik Teresa
Published: March 2020

Tunisia is a destination where organised mass tourism has prevailed since 1985. This trend is still being observed, despite the unstable geopolitical situation in North Africa. Current reports from booking portals indicate that this country will be one of the most popular tourist destinations in 2020. Therefore, the aim of the study was to determine the prospects for sustainable development in Tunisia in 2020–2025 as means to prevent the negative effects of overtourism. The research was conducted in three stages: (1) analysis of the phases of tourism development in Tunisia from 1960 to 2019 in relation to the Tourism Area Life Cycle concept, (2) identification of the destination’s evolution in 2015–2019 with the method of trend function exploration, and (3) an attempt to assess the risk of overtourism in Tunisia in light of Tourism Carrying Capacity on the basis of the Tourism Intensity Index and Tourism Density index. The study results revealed three phases of development in Tunisia, i.e. exploration, involvement, and development. The verification of the trend function indicated that Tunisia would enter the consolidation phase in 2020. The highest risk of overtourism is estimated for three governorates—Tunis, Sousse, and Monastir.
Comparison of Three Availability Systems with Warm Standby Components, Detection Delay, and General Repair Times
Authors: Yen Tseng-Chang , Chen Wu-Lin , Wang Kuo-Hsiung
Published: March 2020

This study analyzes three availability systems with warm standby units, fault detection delay, and general repair times. The failure times and repair times of failed components were assumed to follow exponential and general distributions, respectively. The detection delay times were assumed to be exponentially distributed. This study exploited the supplementary variable technique to develop a recursive method for deriving the steady-state availability for three systems. By using extensive numerical computations, we compared three systems in terms of system availability based on specific values given to the system parameters. The state transition rate diagrams of the three systems revealed the symmetry property approximately. The three systems were ranked based on the system availability and the cost/benefit for the three various repair time distributions: exponential, three-stage Erlang, and deterministic, where the benefit was system availability.
Combined Use of Graphical and Statistical Approaches for Analyzing Historical Precipitation Changes in the Black Sea Region of Turkey
Authors: Cengiz Taner Mustafa , Tabari Hossein , Onyutha Charles , Kisi Ozgur
Published: March 2020

Many statistical methods have been developed and used over time to analyze historical changes in hydrological time series, given the socioeconomic consequences of the changes in the water cycle components. The classical statistical methods, however, rely on many assumptions on the time series to be examined such as the normality, temporal and spatial independency and the constancy of the data distribution over time. When the assumptions are not fulfilled by the data, test results are not reliable. One way to relax these cumbersome assumptions and credibilize the results of statistical approaches is to make a combined use of graphical and statistical methods. To this end, two graphical methods of the refined cumulative sum of the difference between exceedance and non-exceedance counts of data points (CSD) and innovative trend analyses (ITA)-change boxes alongside the classical statistical Mann–Kendall (MK) method are used to analyze historical precipitation changes at 16 stations during 1960–2015 in the Black Sea region of Turkey. The results show a good match between the results of the graphical and statistical methods. The graphical CSD and ITA methods, however, are able to identify the hidden trends in the precipitation time series that cannot be detected using the statistical MK method.
Analysis of Nitrate Pollution Pathways on a Vulnerable Agricultural Plain in Slovenia: Taking the Local Approach to Balance Ecosystem Services of Food and Water
Authors: Curk Miha , Glavan Matjaž , Pintar Marina
Published: March 2020

Groundwater pollution with nitrate of agricultural origin is a major problem in many countries. A great deal of effort is focused on finding ways to reduce leaching from agricultural land. In this study, different land management scenarios were evaluated with the SWAT model in order to determine which are the most effective in reducing nitrate leaching on specific soil types in the Krška kotlina alluvial plain (Slovenia). The area is very important both for agriculture production and drinking water resources. The model was calibrated for three soil moisture field trial sites, each representing one major soil type of the area. Simulated soil moisture values were in good agreement with the observed values (PBIAS (percent bias) ±25%). Of the nine land management scenarios that were evaluated, vegetable rotation caused the most nitrate leaching on all soil types, but it fared better on Cambisol than on Fluvisol. Orchards on the other hand leached the least amount of nitrate, but also fared better on Cambisol. Presented studies should be considered as a preliminary stage in the study of nitrate pollution in the investigated area. Results show that nitrate leaching varies for different land management scenarios on different soil types. Further work should concentrate on field trials to evaluate the impacts of reduced fertilization on nitrate leaching and both crop yield and quality on different soil types.
1-D Vertical Flux Dynamics in a Low-Gradient Stream: An Assessment of Stage as a Control of Vertical Hyporheic Exchange
Authors: Harris F. Claire , Peterson Eric W.
Published: March 2020

This study characterized vertical one-dimensional (1-D) flux rates in the top 150 cm of the streambed and explored the relationship between stage and 1-D vertical flux rates in a low-gradient stream. Six multi-level samplers were installed along a 25 m stretch of the thalweg spaced at 5 m intervals. Each sampler recorded temperature at five separate depths (30, 60, 90, and 150 cm) from February 2009 to March 2010. Temperature and stage data were collected at 15 min intervals. For the midpoint between sensors, 1-D flux rates were calculated using the 1-D conduction-advection-dispersion equation utilized in the VFLUX MATLAB program. The dominant flux direction at a depth of 15 cm was downward (negative), while the average flux directions at depths of 45, 75, and 120 cm were upward (positive). Variable flux directions above 15 cm indicated hyporheic exchange with background upwelling groundwater between 15 to 120 cm. Flux rates approach zero in the summer, representing limited hyporheic exchange. Relationships between stage and flux at the near-surface streambed (15 cm) were weak, but statistically significant, with Spearman’s rank correlations for all six multi-level samplers ranging from −0.032 to 0.369 with an average of 0.085.
Soil Temperature Dynamics at Hillslope Scale—Field Observation and Machine Learning-Based Approach
Authors: Nanda Aliva , Sen Sumit , Sharma Awshesh Nath , Sudheer K. P.
Published: March 2020

Soil temperature plays an important role in understanding hydrological, ecological, meteorological, and land surface processes. However, studies related to soil temperature variability are very scarce in various parts of the world, especially in the Indian Himalayan Region (IHR). Thus, this study aims to analyze the spatio-temporal variability of soil temperature in two nested hillslopes of the lesser Himalaya and to check the efficiency of different machine learning algorithms to estimate soil temperature in the data-scarce region. To accomplish this goal, grassed (GA) and agro-forested (AgF) hillslopes were instrumented with Odyssey water level and decagon soil moisture and temperature sensors. The average soil temperature of the south aspect hillslope (i.e., GA hillslope) was higher than the north aspect hillslope (i.e., AgF hillslope). After analyzing 40 rainfall events from both hillslopes, it was observed that a rainfall duration of greater than 7.5 h or an event with an average rainfall intensity greater than 7.5 mm/h results in more than 2 °C soil temperature drop. Further, a drop in soil temperature less than 1 °C was also observed during very high-intensity rainfall which has a very short event duration. During the rainy season, the soil temperature drop of the GA hillslope is higher than the AgF hillslope as the former one infiltrates more water. This observation indicates the significant correlation between soil moisture rise and soil temperature drop. The potential of four machine learning algorithms was also explored in predicting soil temperature under data-scarce conditions. Among the four machine learning algorithms, an extreme gradient boosting system (XGBoost) performed better for both the hillslopes followed by random forests (RF), multilayer perceptron (MLP), and support vector machine (SVMs). The addition of rainfall to meteorological and meteorological + soil moisture datasets did not improve the models considerably. However, the addition of soil moisture to meteorological parameters improved the model significantly.
An Observational Process Ontology-Based Modeling Approach for Water Quality Monitoring
Authors: Wang Xiaolei , Wei Haitao , Chen Nengcheng , He Xiaohui , Tian Zhihui
Published: March 2020

The increasing deterioration of aquatic environments has attracted more attention to water quality monitoring techniques, with most researchers focusing on the acquisition and assessment of water quality data, but seldom on the discovery and tracing of pollution sources. In this study, a semantic-enhanced modeling method for ontology modeling and rules building is proposed, which can be used for river water quality monitoring and relevant data observation processing. The observational process ontology (OPO) method can describe the semantic properties of water resources and observation data. In addition, it can provide the semantic relevance among the different concepts involved in the observational process of water quality monitoring. A pollution alert can be achieved using the reasoning rules for the water quality monitoring stations. In this study, a case is made for the usability testing of the OPO models and reasoning rules by utilizing a water quality monitoring system. The system contributes to the water quality observational monitoring process and traces the source of pollutants using sensors, observation data, process models, and observation products that users can access in a timely manner.
The Effect of Hydraulic Conditions in Barbotage Reactors on Aeration Efficiency
Authors: Kujawiak Sebastian , Makowska Małgorzata , Mazurkiewicz Jakub
Published: March 2020

Barbotage reactors such as airlift reactors (ALR) and bubble column reactors (BCR), due to their two-phase flow systems, were investigated in many research papers. In their basic design variants, they are typically used to lift, mix, and aerate liquids, while, when equipped with additional elements in hybrid variants, their individual properties, i.e., lifting, mixing, and aeration of liquids, can significantly change with the same reactor geometry. The object of this study was to develop a hybrid barbotage reactor in various structural design variants. The structure consisted of a barbotage column of 50 mm in diameter, used to transport a water–air mixture outside the reactor (so-called external loop). The installation was additionally equipped with a nozzle in order to improve mixture aeration and circulation efficiency. The nozzle was mounted at various heights of the column pump segment. Additionally, the reactor was equipped with s moving bed in two variants (20% and 40% reactor capacity) in order to determine its effect on the mixture aeration and circulation conditions. Based on the measurement results, aeration curves were prepared for various structural design and column packing variants of the reactor. Properties of the two-phase mixture were determined for both parts—ALR and BCR. Technological and energy parameters of the aeration process were calculated, and the results obtained for the individual structural design variants were compared. It was found that, for the most advantageous design, in terms of aeration efficiency, the aeration nozzle should be placed in the mid-length of the pump segment of the barbotage column, irrespective of the hybrid reactor packing rate with the moving bed. The reactor packing with the moving bed resulted in a decreased mean water velocity in the reactor. For most analyzed structural design variants, the respective packing with the moving bed had no significant effect on aeration efficiency. Only for one structural design variant did the lack of packing significantly improve oxygen levels by as much as approximately 41%.