bluuebunny/crossref_metadata_2025 huggingface dataset:
Improving a Street-Based Geocoding Algorithm Using Machine Learning Techniques
Authors: Lee Kangjae , Claridades Alexis Richard C. , Lee Jiyeong
Published: August 2020

Address matching is a crucial step in geocoding; however, this step forms a bottleneck for geocoding accuracy, as precise input is the biggest challenge for establishing perfect matches. Matches still have to be established despite the inevitability of incorrect address inputs such as misspellings, abbreviations, informal and non-standard names, slangs, or coded terms. Thus, this study suggests an address geocoding system using machine learning to enhance the address matching implemented on street-based addresses. Three different kinds of machine learning methods are tested to find the best method showing the highest accuracy. The performance of address matching using machine learning models is compared to multiple text similarity metrics, which are generally used for the word matching. It was proved that extreme gradient boosting with the optimal hyper-parameters was the best machine learning method with the highest accuracy in the address matching process, and the accuracy of extreme gradient boosting outperformed similarity metrics when using training data or input data. The address matching process using machine learning achieved high accuracy and can be applied to any geocoding systems to precisely convert addresses into geographic coordinates for various research and applications, including car navigation.
A Methodology for Open Information Extraction and Representation from Large Scientific Corpora: The CORD-19 Data Exploration Use Case
Authors: Papadopoulos Dimitris , Papadakis Nikolaos , Litke Antonis
Published: August 2020

The usefulness of automated information extraction tools in generating structured knowledge from unstructured and semi-structured machine-readable documents is limited by challenges related to the variety and intricacy of the targeted entities, the complex linguistic features of heterogeneous corpora, and the computational availability for readily scaling to large amounts of text. In this paper, we argue that the redundancy and ambiguity of subject–predicate–object (SPO) triples in open information extraction systems has to be treated as an equally important step in order to ensure the quality and preciseness of generated triples. To this end, we propose a pipeline approach for information extraction from large corpora, encompassing a series of natural language processing tasks. Our methodology consists of four steps: i. in-place coreference resolution, ii. extractive text summarization, iii. parallel triple extraction, and iv. entity enrichment and graph representation. We manifest our methodology on a large medical dataset (CORD-19), relying on state-of-the-art tools to fulfil the aforementioned steps and extract triples that are subsequently mapped to a comprehensive ontology of biomedical concepts. We evaluate the effectiveness of our information extraction method by comparing it in terms of precision, recall, and F1-score with state-of-the-art OIE engines and demonstrate its capabilities on a set of data exploration tasks.
Nano-Scale Drinking Water Treatment Residuals Affect Arsenic Fractionation and Speciation in Biosolids-Amended Agricultural Soil
Authors: Mahdy Ahmed M. , Elkhatib Elsayed , Zhang Tiequan , Fathi Nieven O. , Lin Zhi-Qing
Published: August 2020

An incubation experiment was conducted to determine the effects of nanoscale drinking water treatment residuals (nWTRs) on arsenic (As) fractionation and speciation in agricultural soil amended with biosolids. The soils were treated with biosolids of 3% (w/w), along with nWTR application rates of 0, 0.25, 0.50, or 1.00% (w/w). The results revealed that the As adsorption rate increased with increasing the As treatment level from 50 to 800 mg/L. The maximum efficiency of As adsorption was 95%–98% in the soil treated with nWTRs of 1%, while the least As adsorption was 53%–91% in the soil treated with nWTRs of 0.25%. The overall As bioavailability in the biosolids-amended soil followed a descending order of nWTRs treatment: (0%) > 0.25% nWTRs, >0.50% nWTRs, and >1% nWTRs. The addition of nWTRs significantly changed As speciation in biosolids-amended soil. The X-ray absorption near-edge structure spectroscopy (XANES) and MINEQL+4.6 analyses showed that most of As was in a oxidized form of As5+ that likely incorporated in As pentoxide, and thus, with low mobility, bioavailability, and toxicity. This study demonstrated that nWTRs were effective in adsorbing and immobilizing As in biosolids-amended agricultural soils by forming stable As-nWTR surface complexes.
Influence of Blade Leading-Edge Shape on Rotating-Stalled Flow Characteristics in a Centrifugal Pump Impeller
Authors: Luo Hongying , Tao Ran , Yang Jiandong , Wang Zhengwei
Published: August 2020

Rotating stall, which is a common phenomenon in turbomachinery, strongly relates to the flow rate condition. In centrifugal impellers, rotating stall was induced by the incidence angle on blade leading-edge at partial-load. The blade leading-edge shape also influences the rotating stall because of the subtle change of local flow-field. In this study, the influence of blade leading-edge shape on rotating-stalled flow characteristics was studied in a six-blade centrifugal pump impeller. The stall pattern was “alternating”: Three passages were stalled, three passages were well-behaved, and the stalled and well-behaved passages occurred alternately. The stalled flow characteristics can be studied without the interruption of stall cell movement. Four types of blade leading-edge (blunt, sharp, ellipse, and round) were numerically compared based on the initial typical impeller and the numerical–experimental verification. The numerical comparison shows that the leading-edge shape has a strong influence on the stalled flow pattern, velocity, pressure, turbulence kinetic energy, and flow-induced noise inside impellers. The blunt and sharp leading-edge impellers had a similar internal pattern; the ellipse and round leading-edge impellers were also similar in the internal flow-field. Pressure pulsation analysis showed more obvious differences among these impellers. The main frequency and the pulsation peak–peak values were completely different because of the slight leading-edge shape differences. It revealed the impact of leading-edge geometry on the transient flow-field change under the same incidence angle conditions. It also provided reference for influencing or controlling the rotating stall by blade profile design.
Association of Game Events with Facial Animations of Computer-Controlled Virtual Characters Based on Probabilistic Human Reaction Modeling
Authors: Alsaggaf Wafaa , Tsaramirsis Georgios , Al-Malki Norah , Khan Fazal Qudus , Almasry Miadah , Abdulhalim Serafi Mohamad , Almarzuqi Alaa
Published: August 2020

Computer-controlled virtual characters are essential parts of most virtual environments and especially computer games. Interaction between these virtual agents and human players has a direct impact on the believability of and immersion in the application. The facial animations of these characters are a key part of these interactions. The player expects the elements of the virtual world to act in a similar manner to the real world. For example, in a board game, if the human player wins, he/she would expect the computer-controlled character to be sad. However, the reactions, more specifically, the facial expressions of virtual characters in most games are not linked with the game events. Instead, they have pre-programmed or random behaviors without any understanding of what is really happening in the game. In this paper, we propose a virtual character facial expression probabilistic decision model that will determine when various facial animations should be played. The model was developed by studying the facial expressions of human players while playing a computer videogame that was also developed as part of this research. The model is represented in the form of trees with 15 extracted game events as roots and 10 associated animations of facial expressions with their corresponding probability of occurrence. Results indicated that only 1 out of 15 game events had a probability of producing an unexpected facial expression. It was found that the “win, lose, tie” game events have more dominant associations with the facial expressions than the rest of game events, followed by “surprise” game events that occurred rarely, and finally, the “damage dealing” events.
Association between Anatomical Variations and Maxillary Canine Impaction: A Retrospective Study in Orthodontics
Authors: Pasini Marco , Giuca Maria Rita , Ligori Sara , Mummolo Stefano , Fiasca Fabiana , Marzo Giuseppe , Quinzi Vincenzo
Published: August 2020

This study aims to evaluate whether or not there is a higher prevalence of skeletal abnormalities in subjects with maxillary canine impaction (MCI). This retrospective study was performed on 67 subjects with maxillary canine impaction (test group) and on 67 patients without dental displacement (control group). Sella turcica bridging (SB), ponticulus posticus (PP), atlas posterior arch deficiency (APAD) and the morphology of sella turcica and pterygopalatine fissure were evaluated on lateral cephalometric radiographs. Statistical analysis was performed using chi-square, Mann–Whitney test and multivariate logistic models; the level of significance was p < 0.05. Results showed that in the test and control groups 87% and 62.7% of patients had SB, respectively. PP was observed in 60% of patients in the test group and in 16.4% of patients in the control group. APAD was observed in 9% of test group and in 4.5% of the control group. Skeletal anomalies were significantly increased (p < 0.05) in subjects with MCI. A significant difference between the groups was observed in regards to the shape of the pterygopalatine fissure, found to be less wide and longer in the test group. SB, PP and APAD were higher in subjects with MCI; furthermore, an elongated pterygopalatine fissure was significantly associated with MCI.
A Tutorial for Feature Engineering in the Prognostics and Health Management of Gears and Bearings
Authors: Sim Jinwoo , Kim Seokgoo , Park Hyung Jun , Choi Joo-Ho
Published: August 2020

Gears and bearings are one of the major components of many machines, which can result in operation downtime or even catastrophic failure of a whole system. This paper addresses a tutorial for the features extraction and selection of the gears and bearings, which is known as feature engineering, a prerequisite step for the prognostics and health management (PHM) of these components. While there have been many new developments in this field, no studies have addressed the tutorial aspects of features engineering to aid engineers in solving problems by their own effort, which is of practical importance for successful PHM. The paper aims at helping beginners learn the basic concepts, and implement the algorithms using the public datasets as well as those made by the authors. Matlab codes are provided for them to implement the process by their own hands.
A Low-Cost, Small-Size, and Bluetooth-Connected Module to Detect Faults in Rolling Bearings
Authors: Raviola Erica , Fiori Franco
Published: August 2020

Condition monitoring techniques have been successfully applied to detect damaged bearings. However, the signal acquisition and the subsequent processing are typically outsourced to expensive data acquisition boards and complex software, resulting in expensive solutions. As a side effect, the integration of condition monitoring systems in wireless sensor networks can be tough to achieve. Aiming to overcome such issues, a low-cost and small-size electronic module to be placed in the proximity of the bearing to be monitored was developed. The acoustic signal delivered by the bearing is acquired, and the corresponding frequency spectrum is evaluated on-board. Based on that, the developed module automatically detects the presence of defects and notifies the remote controller via a wireless connection only when a fault is detected, thus avoiding the use of data cables whilst minimizing the amount of transferred data. Experimental tests carried out on the proposed system assessed the accuracy of the evaluated frequency spectrum, resulting in an amplitude error within ±0.6%, as well as the fault detection capability in the presence of environmental acoustic noise.
A Biomechanical Study of Various Fixation Strategies for the Treatment of Clavicle Fractures Using Three-Dimensional Upper-Body Musculoskeletal Finite Element Models
Authors: Shih Kao-Shang , Hsu Ching-Chi , Shih Bo-Yu
Published: August 2020

Plate or nail fixations have been applied to the repair of clavicle fractures. However, it is quite difficult to fairly evaluate the different clavicle fixation techniques owing to variations in the bone anatomy, bone quality, and fracture pattern. The purpose of this study was to investigate the biomechanical performances of different fixation techniques applied to a clavicle fracture using the finite element method. A simplified single-clavicle model and a complete human upper-body skeleton model were developed in this study. Three types of plate fixations, namely, superior clavicle plate, anterior clavicle plate, and clavicle anatomic spiral fixations, and one nail fixation, a titanium elastic nail fixation, were investigated and compared. The plate fixation techniques have a better fixation stability compared to the nail fixation technique. However, the nail fixation technique shows lower bone stress and can reduce the risk of a peri-implant fracture compared to the plate fixation techniques. Increasing the number of locking screws for the clavicle plate system can reduce the implant stress. Insertion of the bone plate into the anterior site of the clavicle or a multi-plane fixation is recommended to achieve the required biomechanical performance. A plate fixation revealed a relatively better fixation stability, and a nail fixation showed a lower risk of a peri-implant fracture.
Installation Time of an Initial Support for Tunnel Excavation upon the Safety Factors of Surrounding Rock
Authors: Zhang Yan-Jun , Su Kai , Zhu Hong-Ze , Qian Zhong-Dong , Wu He-Gao
Published: August 2020

In engineering practice, the initial support system is commonly installed in the vicinity of the tunnel face after excavation, whereas the self-capacity of rock mass will fail to be utilized and the cost of the initial support system will be expensive. In this study, a methodology is proposed to determine the appropriate timing of initial support installation to find out the balance of tunnel safety and construction cost. Firstly, the global safety factor is introduced as the critical indicator to evaluate tunnel stability. Then, the comprehensive graphic relationship between the global safety factor and the distance to the tunnel face is established. Once the global safety factor decreases to an admissible value, the stability of the surrounding rock is in a critical state and the corresponding distance is the recommended location for installing the initial support. In these procedures, the installation time of the initial support at the typical tunnel section can be quickly designed and fed back by a direct indicator during construction. Meanwhile, several cases with different conditions have been carried out to discuss the regularity of the method.
Wind-Energy-Powered Electric Vehicle Charging Stations: Resource Availability Data Analysis
Authors: Noman Fuad , Alkahtani Ammar Ahmed , Agelidis Vassilios , Tiong Kiong Sieh , Alkawsi Gamal , Ekanayake Janaka
Published: August 2020

The integration of large-scale wind farms and large-scale charging stations for electric vehicles (EVs) into electricity grids necessitates energy storage support for both technologies. Matching the variability of the energy generation of wind farms with the demand variability of the EVs could potentially minimize the size and need for expensive energy storage technologies required to stabilize the grid. This paper investigates the feasibility of using the wind as a direct energy source to power EV charging stations. An interval-based approach corresponding to the time slot taken for EV charging is introduced for wind energy conversion and analyzed using different constraints and criteria, including the wind speed averaging time interval, various turbines manufacturers, and standard high-resolution wind speed datasets. A quasi-continuous wind turbine’s output energy is performed using a piecewise recursive approach to measure the EV charging effectiveness. Wind turbine analysis using two years of wind speed data shows that the application of direct wind-to-EV is able to provide sufficient constant power to supply the large-scale charging stations. The results presented in this paper confirm that the potential of direct powering of EV charging stations by wind has merits and that research in this direction is worth pursuing.
Distribution of Harmful Algal Growth-Limiting Bacteria on Artificially Introduced Ulva and Natural Macroalgal Beds
Authors: Inaba Nobuharu , Kodama Isamu , Nagai Satoshi , Shiraishi Tomotaka , Matsuno Kohei , Yamaguchi Atsushi , Imai Ichiro
Published: August 2020

The intensity and frequency of harmful algal blooms (HABs) have increased, posing a threat to human seafood resources due to massive kills of cultured fish and toxin contamination of bivalves. In recent years, bacteria that inhibit the growth of HAB species were found to be densely populated on the biofilms of some macroalgal species, indicating the possible biological control of HABs by the artificial introduction of macroalgal beds. In this study, an artificially created Ulva pertusa bed using mobile floating cages and a natural macroalgal bed were studied to elucidate the distribution of algal growth-limiting bacteria (GLB). The density of GLB affecting fish-killing raphidophyte Chattonella antiqua, and two harmful dinoflagellates, were detected between 106 and 107 CFU g−1 wet weight on the biofilm of artificially introduced U. pertusa and 10 to 102 CFU mL−1 from adjacent seawater; however, GLB found from natural macroalgal species targeted all tested HAB species (five species), ranging between 105 and 106 CFU g−1 wet weight in density. These findings provide new ecological insights of GLB at macroalgal beds, and concurrently demonstrate the possible biological control of HABs by artificially introduced Ulva beds.
Application of Artificial Neural Network for Prediction of Key Indexes of Corn Industrial Drying by Considering the Ambient Conditions
Authors: Li Bin , Li Chengjie , Huang Junying , Li Changyou
Published: August 2020

Uncontrollable ambient conditions are the main factors limiting the self-adaption control of an industrial drying system. To achieve the goal of accurate control of the drying process, the influence of the ambient conditions on the drying behavior should be taken into consideration when modeling the drying process. Present work introduced an industrial drying system with a loading capacity of 50 t, two artificial neural network prediction models with (IANN) and without (OANN) considering the ambient conditions were established using artificial neural network modeling approach. The ambient conditions on the moisture content (MC), exergy efficiency of the heat exchanger (ηex,h) and specific recovered radiant energy (Er) of the drying process were also investigated. The results showed that the ηex,h and Er increase with the increase of ambient temperature while the drying time decrease with the increase of the ambient temperature. The IANN model has a better prediction performance that that of OANN model. An optimal architecture of 9-2-12-3 artificial neuron network model was developed and the best prediction performance of the artificial neural network (ANN) model were found at a training epoch number of 30, and a momentum coefficient of 0.4, where the coefficient of determination of moisture content, exergy efficiency of heat exchanger, and the specific recovered radiant energy, respectively are 0.998, 0.992, and 0.980, indicating that the model has an excellent prediction performance and can be used in engineering practice.
Augmented Reality: A Systematic Review of Its Benefits and Challenges in E-learning Contexts
Authors: Alzahrani Nouf Matar
Published: August 2020

Augmented reality (AR) has received increasing attention in the research literature as a fundamental pedagogical tool that can enhance learning at most educational levels. In academic contexts, this technology permits the superimposing of three-dimensional images onto the real environment. Although AR has been found to enhance learning in the academic environment, no systematic review of it has been conducted to identify, evaluate, and summarize empirical findings on its advantages and challenges in e-learning contexts. Hence, a systematic review of the research literature was conducted on the use of AR in e-learning contexts, with a focus on the key benefits and challenges related to its adoption and implementation. Electronic searches on databases, including Springer, Science Direct, EBSCO, and Google Scholar, were performed to retrieve relevant journal articles; 28 studies were included after they were screened using the inclusion and exclusion criteria. The key benefits of using AR in e-learning included support of kinesthetic (tactile) learning, collaborative learning, distance/remote learning, learner-centered learning, and creative learning. Studies also reported that AR enhanced students’ engagement, motivation, attention/focus, interactivity, verbal participation, concentration, knowledge retention, and spatial abilities, as well as information accessibility. The findings suggest that challenges associated with AR in e-learning include information and cognitive overload, lack of experience in using the technology, resistance from teachers, complex technology, costly technology, and technical issues, such as connectivity problems.
Decoding Visual Motions from EEG Using Attention-Based RNN
Authors: Yang Dongxu , Liu Yadong , Zhou Zongtan , Yu Yang , Liang Xinbin
Published: August 2020

The main objective of this paper is to use deep neural networks to decode the electroencephalography (EEG) signals evoked when individuals perceive four types of motion stimuli (contraction, expansion, rotation, and translation). Methods for single-trial and multi-trial EEG classification are both investigated in this study. Attention mechanisms and a variant of recurrent neural networks (RNNs) are incorporated as the decoding model. Attention mechanisms emphasize task-related responses and reduce redundant information of EEG, whereas RNN learns feature representations for classification from the processed EEG data. To promote generalization of the decoding model, a novel online data augmentation method that randomly averages EEG sequences to generate artificial signals is proposed for single-trial EEG. For our dataset, the data augmentation method improves the accuracy of our model (based on RNN) and two benchmark models (based on convolutional neural networks) by 5.60%, 3.92%, and 3.02%, respectively. The attention-based RNN reaches mean accuracies of 67.18% for single-trial EEG decoding with data augmentation. When performing multi-trial EEG classification, the amount of training data decreases linearly after averaging, which may result in poor generalization. To address this deficiency, we devised three schemes to randomly combine data for network training. Accordingly, the results indicate that the proposed strategies effectively prevent overfitting and improve the correct classification rate compared with averaging EEG fixedly (by up to 19.20%). The highest accuracy of the three strategies for multi-trial EEG classification achieves 82.92%. The decoding performance for the methods proposed in this work indicates they have application potential in the brain–computer interface (BCI) system based on visual motion perception.
Cost-Sensitive Ensemble Feature Ranking and Automatic Threshold Selection for Chronic Kidney Disease Diagnosis
Authors: Imran Ali Syed , Ali Bilal , Hussain Jamil , Hussain Musarrat , Satti Fahad Ahmed , Park Gwang Hoon , Lee Sungyoung
Published: August 2020

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that, unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non tree-based classification models. The proposed approaches were also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation, it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.
Validation of Satellite and Merged Rainfall Data over Ghana, West Africa
Authors: Atiah Winifred Ayinpogbilla , Amekudzi Leonard Kofitse , Aryee Jeffrey Nii Armah , Preko Kwasi , Danuor Sylvester Kojo
Published: August 2020

In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.
Affective Normative Data for English Weather Words
Authors: Stewart Alan E.
Published: August 2020

The research in this article examines the emotional associations people have to common weather words and to selected terms that appear in weather communications (e.g., severe thunderstorm warning). A sample of 420 university students provided ratings for each term along four dimensions: 1. Valence (unhappy vs. happy), 2. Arousal (calm vs. excited), 3. Dominance (in control/dominant vs. controlled/passive), and 4. Surprise (unsurprising/predictable vs. surprising/unpredictable). The results of this research provide descriptive statistical data for the 141 weather words along the four dimensions. The author also examined the correlations of the four dimensions across the terms and observed a high degree of association between the rated arousal and surprise characteristics of terms. In addition, the results revealed the clustering of weather words according to shared similarities across the four affective dimensions (illustrating affective-based synonymy). The results of the research are significant because they reveal a deeper understanding of the subjective and emotional experiences of the atmosphere that people may have when describing the weather of a place. Similarly, the normative data from this research may be used in the analysis of weather- or climate-based communications to characterize the emotional significance or impact of a message.
Analysis of Cooling and Humidification Effects of Different Coverage Types in Small Green Spaces (SGS) in the Context of Urban Homogenization: A Case of HAU Campus Green Spaces in Summer in Zhengzhou, China
Authors: Li Huawei , Meng Handong , He Ruizhen , Lei Yakai , Guo Yuchen , Ernest Amoako-atta , Jombach Sandor , Tian Guohang
Published: August 2020

In the context of global warming, more and more cities are experiencing extreme Urban Heat Island (UHI) effects and extreme weather phenomena, but urban green spaces are proven to mitigate UHI. Most of UHI’s research focuses on the large scale and uses remote sensing methods, which do not reflect the dynamic characteristics in detail and do not detect internal influencing factors of the green space cooling effect. Therefore, this study focused on Small Green Spaces (SGS), carrying out the measurement of the meteorological parameters (temperature, relative humidity, wind direction, wind speed, photosynthetic radiation) of the 16 sites in four types of coverage (Impervious surface; Shrub-grass; Tree-grass; Tree-shrub-grass) in a university campus. At the same time, the coverage characteristic parameters, such as Canopy Density (CD), Leaf Area Index (LAI), Photosynthetically Active Radiation (PAR), Mean Leaf Angle (MLA), of each plot were analyzed and compared. The results showed that there were significant differences in temperature among different coverage types in SGS. The biggest difference was concentrated in the noon period when solar radiation is strongest during the day. The difference between the four types of coverage with vegetation at night was small. The maximum air temperature difference among the four types could reach 8.9 ℃ and the maximum relative humidity difference was 28.5%. The cooling effect of the multi-layer vegetation-covered (Tree-shrub-grass) area was the largest compared to the impervious surface, indicating that tree cover was the core factor affecting the temperature. Temperature and relative humidity had a close correlation with surface coverage types and some plant community characteristics (such as CD and LAI). The cooling and humidifying effects of plants were also related to PAR and leaf angle. The results provide suggestions for green space management and landscape design.
Modeling Emissions from Concentrated Sources into Large-Scale Models: Theory and apriori Testing
Authors: Paoli Roberto
Published: August 2020

This paper presents a general procedure to incorporate the effects emissions from localized sources, such as aircraft or ship engines, into chemical transport models (CTM). In this procedure, the species concentrations in each grid box of a CTM are split into plume or small-scale concentrations and background concentrations, respectively, and the corresponding conservation equations are derived. The plume concentrations can be interpreted as subgrid contributions for the CTM grid-box averaged concentrations. The chemical reactions occurring inside the plume are parameterized by introducing suitable “effective” reaction rates rather than modifying the emission indices of the species inside the plume. Various methods for implementation into large-scale models are discussed that differ by the accuracy of the description of plume process. The mathematical consistency of the method is verified on simple idealized setting consisting of a reactive plume in homogeneous turbulence.
Why Does Fog Deepen? An Analytical Perspective
Authors: Izett Jonathan G. , van de Wiel Bas J. H.
Published: August 2020

The overall depth of a fog layer is one of the important factors in determining the hazard that a fog event presents. With discrete observations and often coarse numerical grids, however, fog depth cannot always be accurately determined. To address this, we derive a simple analytical relation that describes the change in depth of a fog interface with time, which depends on the tendencies and vertical gradients of moisture. We also present a lengthscale estimate for the maximum depth over which mixing can occur in order for the fog layer to be sustained, assuming a uniform mixing of the vertical profiles of temperature and moisture. Even over several hours, and when coarse observational resolution is used, the analytical description is shown to accurately diagnose the depth of a fog layer when compared against observational data and the results of large-eddy simulations. Such an analytical description not only enables the estimation of sub-grid or inter-observation fog depth, but also provides a simple framework for interpreting the evolution of a fog layer in time.
SOC Estimation of a Rechargeable Li-Ion Battery Used in Fuel Cell Hybrid Electric Vehicles—Comparative Study of Accuracy and Robustness Performance Based on Statistical Criteria. Part II: SOC Estimators
Authors: Tudoroiu Roxana-Elena , Zaheeruddin Mohammed , Tudoroiu Nicolae , Radu Sorin-Mihai
Published: August 2020

The purpose of this paper is to analyze the accuracy of three state of charge (SOC) estimators of a rechargeable Li-ion SAFT battery based on two accurate Li-ion battery models, namely a linear RC equivalent electrical circuit (ECM) and a nonlinear Simscape generic model, developed in Part 1. The battery SOC of both Li-ion battery models is estimated using a linearized adaptive extended Kalman filter (AEKF), a nonlinear adaptive unscented Kalman filter (AUKF) and a nonlinear and non-Gaussian particle filter estimator (PFE). The result of MATLAB simulations shows the efficiency of all three SOC estimators, especially AEKF, followed in order of decreasing performance by AUKF and PFE. Besides, this result reveals a slight superiority of the SOC estimation accuracy when using the Simscape model for SOC estimator design. Overall, the performance of all three SOC estimators in terms of accuracy, convergence of response speed and robustness is excellent and is comparable to state of the art SOC estimation methods.
Noninvasive and Safe Cell Viability Assay for Breast Cancer MCF-7 Cells Using Natural Food Pigment
Authors: Yamashita Kyohei , Tagawa Ryoma , Higami Yoshikazu , Tokunaga Eiji
Published: August 2020

A dye exclusion test (DET) was performed to determine the viability of human breast cancer cells MCF-7, using natural food pigments as compared with trypan blue (TB), a typical synthetic dye for DET known to exhibit teratogenicity and cytotoxicity. We demonstrated that Monascus pigment (MP) is noninvasive to living cells and can effectively stain only dead cells. This study is the first verification of the applicability of MP to cancer cells. The appropriate MP concentration was 0.4% (0.02% as the concentration of pure MP) and all the dead cells were stained within 10 min. We found that the cell proliferation or the reduced nicotinamide adenine dinucleotide (NADH) activity of living cells was maintained over 48 h. Although 0.1% TB did not show an increase in dead cells, a marked decrease in NADH activity was confirmed. In addition, even when MP coexisted with cisplatin, staining of dead cells was maintained for 47 h, indicating stability to drugs (reagents). The cost of MP is estimated to be about 1/10 of TB. The fact that MP can be used as a cell viability determination reagent for Euglena and Paramecium, as shown in preceding papers, and also for MCF-7, as shown in this paper, indicates the possibility of application in more cells of different species.
Rapid Ex-Vivo Ciliogenesis and Dose-Dependent Effect of Notch Inhibition on Ciliogenesis of Respiratory Epithelia
Authors: Zahid Maliha , Feinstein Timothy N. , Oro Anthony , Schwartz Molly , Lee Alex D. , Lo Cecilia W.
Published: August 2020

Background: Cilia are actin based cellular protrusions conserved from algae to complex multicellular organisms like Homo sapiens. Respiratory motile cilia line epithelial cells of the tracheobronchial tree, beat in a synchronous, metachronal wave, moving inhaled pollutants and pathogens cephalad. Their role in both congenital disorders like primary ciliary dyskinesia (PCD) to acquired disorders like chronic obstructive pulmonary disease (COPD) continues to evolve. In this current body of work we outline a protocol optimized to reciliate human nasal epithelial cells and mouse tracheal cells in vitro. Using this protocol, we knocked down known cilia genes, as well as use a small molecule inhibitor of Notch, N-[N-(3,5-Difluorophenacetyl)-L-alanyl]-S-phenylglycine t-butyl Ester (DAPT), to assess the effect of these on ciliogenesis in order to show the validity of our protocol. Methods: Tracheas were harvested from wild-type, adult C57B6 mice, pronase digested and sloughed off epithelial cells grown to confluence in stationary culture on rat-tail collagen coated wells. Upon reaching confluence, collagen was digested and cells placed suspension culture protocol to reciliate the cells. Using this suspension culture protocol, we employed siRNA gene knockdown to assay gene functions required for airway ciliogenesis. Knock down of Dynein axonemal heavy chain 5 (Dnah5), a ciliary structural protein, was confirmed using immunostaining. Mouse tracheal cells were treated in suspension with varying doses of DAPT, an inhibitor of Notch, with the purpose of evaluating its effect and dose response on ciliogenesis. The optimum dose was then used on reciliating human nasal epithelial cells. Results: siRNA knockdown of Foxj1 prevented ciliation, consistent with its role as a master regulator of motile cilia. Knockdown of Dnai1 and Dnah5 resulted in immotile cilia, and Cand1 knockdown, a centrosome protein known to regulate centrosome amplification, inhibited airway ciliogenesis. Dnah5 knockdown was confirmed with significantly decreased immunostaining of cilia for this protein. Inhibiting Notch signaling by inhibiting gamma secretase with DAPT enhanced the percentage of ciliation, and resulted in longer cilia that beat with higher frequency in both mouse and human airway epithelia. Conclusions: Modifying existing reciliation protocols to suit both human nasal epithelial and mouse tracheal tissue, we have shown that knockdown of known cilia-related genes have the expected effects. Additionally, we have demonstrated the optimal dosage for significantly improving reciliation of airway epithelia using DAPT. Given that cilia length and function are significantly compromised in COPD, these findings open up interesting avenues for further exploration.
A Transmembrane Histidine Kinase Functions as a pH Sensor
Authors: Bortolotti Ana , Vazquez Daniela Belén , Almada Juan Cruz , Inda Maria Eugenia , Drusin Salvador Iván , Villalba Juan Manuel , Moreno Diego M. , Ruysschaert Jean Marie , Cybulski Larisa Estefania
Published: August 2020

The two-component system DesK-DesR regulates the synthesis of unsaturated fatty acids in the soil bacteria Bacillus subtilis. This system is activated at low temperature and maintains membrane lipid fluidity upon temperature variations. Here, we found that DesK—the transmembrane histidine kinase—also responds to pH and studied the mechanism of pH sensing. We propose that a helix linking the transmembrane region with the cytoplasmic catalytic domain is involved in pH sensing. This helix contains several glutamate, lysine, and arginine residues At neutral pH, the linker forms an alpha helix that is stabilized by hydrogen bonds in the i, i + 4 register and thus favors the kinase state. At low pH, protonation of glutamate residues breaks salt bridges, which results in helix destabilization and interruption of signaling. This mechanism inhibits unsaturated fatty acid synthesis and rigidifies the membrane when Bacillus grows in acidic conditions.
Synthesis and Characterization of Oxidized Polysaccharides for In Situ Forming Hydrogels
Authors: Muhammad Muhammad , Willems Christian , Rodríguez-Fernández Julio , Gallego-Ferrer Gloria , Groth Thomas
Published: August 2020

Polysaccharides are widely used as building blocks of scaffolds and hydrogels in tissue engineering, which may require their chemical modification to permit crosslinking. The goal of this study was to generate a library of oxidized alginate (oALG) and oxidized hyaluronic acid (oHA) that can be used for in situ gelling hydrogels by covalent reaction between aldehyde groups of the oxidized polysaccharides (oPS) and amino groups of carboxymethyl chitosan (CMC) through imine bond formation. Here, we studied the effect of sodium periodate concentration and reaction time on aldehyde content, molecular weight of derivatives and cytotoxicity of oPS towards 3T3-L1 fibroblasts. It was found that the molecular weights of all oPs decreased with oxidation and that the degree of oxidation was generally higher in oHA than in oALG. Studies showed that only oPs with an oxidation degree above 25% were cytotoxic. Initial studies were also done on the crosslinking of oPs with CMC showing with rheometry that rather soft gels were formed from higher oxidized oPs possessing a moderate cytotoxicity. The results of this study indicate the potential of oALG and oHA for use as in situ gelling hydrogels or inks in bioprinting for application in tissue engineering and controlled release.
Gene Regulatory Network Analysis of Perivascular Adipose Tissue of Abdominal Aortic Aneurysm Identifies Master Regulators of Key Pathogenetic Pathways
Authors: Piacentini Luca , Chiesa Mattia , Colombo Gualtiero Ivanoe
Published: August 2020

The lack of medical therapy to treat abdominal aortic aneurysm (AAA) stems from our inadequate understanding of the mechanisms underlying AAA pathogenesis. To date, the only available treatment option relies on surgical intervention, which aims to prevent AAA rupture. Identifying specific regulators of pivotal pathogenetic mechanisms would allow the development of novel treatments. With this work, we sought to identify regulatory factors associated with co-expressed genes characterizing the diseased perivascular adipose tissue (PVAT) of AAA patients, which is crucially involved in AAA pathogenesis. We applied a reverse engineering approach to identify cis-regulatory elements of diseased PVAT genes, the associated transcription factors, and upstream regulators. Finally, by analyzing the topological properties of the reconstructed regulatory disease network, we prioritized putative targets for AAA interference treatment options. Overall, we identified NFKB1, SPIB, and TBP as the most relevant transcription factors, as well as MAPK1 and GSKB3 protein kinases and RXRA nuclear receptor as key upstream regulators. We showed that these factors could regulate different co-expressed gene subsets in AAA PVAT, specifically associated with both innate and antigen-driven immune response pathways. Inhibition of these factors may represent a novel option for the development of efficient immunomodulatory strategies to treat AAA.
Fast Adhesion of Gold Nanoparticles (AuNPs) to a Surface Using Starch Hydrogels for Characterization of Biomolecules in Biosensor Applications
Authors: Heredia Frances L. , Resto Pedro J. , Parés-Matos Elsie I.
Published: August 2020

Gold nanoparticles (AuNPs) are the most thoroughly studied nanoparticles because of their remarkable optical properties. Color changes in assays that use AuNPs can be easily observed with the naked eye, resulting in sensitive colorimetric methods, useful for detecting a variety of biological molecules. However, while AuNPs represent an excellent nano-platform for developing analytical methods for biosensing, there are still challenges that must be overcome before colloidal AuNPs formulation can be successfully translated into practical applications. One of those challenges is the ability to immobilize AuNPs in a solid support. There are many difficulties with controlling both the cluster size and the adhesion of the coatings formed. In addition, many of the techniques employed are expensive and time-consuming, or require special equipment. Thus, a simple and inexpensive method that only requires common lab equipment for immobilizing AuNPs on a surface using Starch Hydrogels has been developed. Starch hydrogels confer a 400% increase in stability to the nanoparticles when exposed to changes in the environment while also allowing for macromolecules to interact with the AuNPs surface. Several starch derivatives were tested, including, dextrin, beta-cyclodextrin and maltodextrin, being dextrin the one that conferred the highest stability. As a proof-of-concept, a SlipChip microfluidic sensor scheme was developed to measure the concentration of DNA in a sample. The detection limit of our biosensor was found to be 25 ng/mL and 75 ng/mL for instrument and naked eye detection, respectively.
The Involvement of Exosomes in Glioblastoma Development, Diagnosis, Prognosis, and Treatment
Authors: Bălașa Adrian , Șerban Georgiana , Chinezu Rareş , Hurghiș Corina , Tămaș Flaviu , Manu Doina
Published: August 2020

Brain tumours are a serious concern among both physicians and patients. The most feared brain tumour is glioblastoma (GBM) due to its heterogeneous histology, substantial invasive capacity, and rapid postsurgical recurrence. Even in cases of early management consisting of surgery, chemo-, and radiotherapy, the prognosis is still poor, with an extremely short survival period. Consequently, researchers are trying to better understand the underlying pathways involved in GBM development in order to establish a more personalised approach. The latest focus is on molecular characterisation of the tumour, including analysis of extracellular vesicles (EVs), nanostructures derived from both normal and pathological cells that have an important role in intercellular communication due to the various molecules they carry. There are two types of EV based on their biogenesis, but exosomes are of particular interest in GBM. Recent studies have demonstrated that GBM cells release numerous exosomes whose cargo provides them the capacity to facilitate tumour cell invasion and migration, to stimulate malignant transformation of previously normal cells, to increase immune tolerance towards the tumour, to induce resistance to chemotherapy, and to enhance the GBM vascular supply. As exosomes are specific to their parental cells, their isolation would allow a deeper perspective on GBM pathogenesis. A new era of molecular manipulation has emerged, and exosomes are rapidly proving their value not only as diagnostic and prognostic markers, but also as tools in therapies specifically targeting GBM cells. Nonetheless, further research will be required before exosomes could be used in clinical practice. This review aims to describe the structural and functional characteristics of exosomes and their involvement in GBM development, diagnosis, prognosis and treatment.