bluuebunny/crossref_metadata_2025 huggingface dataset:
Religiosity and Generosity: Multi-Level Approaches to Studying the Religiousness of Prosocial Actions
Authors: Herzog Patricia Snell , Strohmeier Amy , King David P. , Khader Rafia A. , Williams Andrew L. , Goodwin Jamie L. , Doan Dana R. H. , Moyo Bhekinkosi
Published: August 2020

This paper provides a meta-analysis of the intersection of (a) religiosity and spirituality with (b) generosity, philanthropy, nonprofits, and prosociality. The study is informed by three informational sources, chronologically: (1) informational interviews with scholars and practitioners based within and studying regions outside of the U.S. and Western Europe; (2) discovery search of purposefully selected extant publications, especially focusing on the last decade of contemporary scholarship; and (3) systematic search of relevant peer-reviewed publication outlets since 2010. Reviewed publications are categorized by level of analysis into macro, meso, and micro approaches. Across each level and source, publications are also geo-tagged for their geographic scope. Particular attention is paid to the under-studied world regions of Africa, Asia, Latin America, and the Middle East. The results reveal that Asia is the most studied and Latin America the least studied, and that meso-level approaches are the most common while micro-level are the least common. Additionally, a map of publication counts reveals within-region inequalities by country. Implications of the analysis are drawn for future studies, particularly ways to advance this interdisciplinary field.
Supply Risk Considerations for the Elements in Nickel-Based Superalloys
Authors: Helbig Christoph , Bradshaw Alex M. , Thorenz Andrea , Tuma Axel
Published: August 2020

Nickel-based superalloys contain various elements which are added in order to make the alloys more resistant to thermal and mechanical stress and to the adverse operating environments in jet engines. In particular, higher combustion temperatures in the gas turbine are important, since they result in higher fuel efficiency and thus in lower CO2 emissions. In this paper, a semi-quantitative assessment scheme is used to evaluate the relative supply risks associated with elements contained in various Ni-based superalloys: aluminium, titanium, chromium, iron, cobalt, niobium, molybdenum, ruthenium, tantalum, tungsten, and rhenium. Twelve indicators on the elemental level and four aggregation methods are applied in order to obtain the supply risk at the alloy level. The supply risks for the elements rhenium, molybdenum and cobalt are found to be the highest. For three of the aggregation schemes, the spread in supply risk values for the different alloy types (as characterized by chemical composition and the endurance temperature) is generally narrow. The fourth, namely the cost-share’ aggregation scheme, gives rise to a broader distribution of supply risk values. This is mainly due to the introduction of rhenium as a component starting with second-generation single crystal alloys. The resulting higher supply risk appears, however, to be acceptable for jet engine applications due to the higher temperatures these alloys can endure.
Validation of the SMOS Level 1C Brightness Temperature and Level 2 Soil Moisture Data over the West and Southwest of Iran
Authors: Jamei Mozhdeh , Mousavi Baygi Mohammad , Oskouei Ebrahim Asadi , Lopez-Baeza Ernesto
Published: August 2020

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) mission with the MIRAS (Microwave Imaging Radiometer using Aperture Synthesis) L-band radiometer provides global soil moisture (SM) data. SM data and products from remote sensing are relatively new, but they are providing significant observations for weather forecasting, water resources management, agriculture, land surface, and climate models assessment, etc. However, the accuracy of satellite measurements is still subject to error from the retrieval algorithms and vegetation cover. Therefore, the validation of satellite measurements is crucial to understand the quality of retrieval products. The objectives of this study, precisely framed within this mission, are (i) validation of the SMOS Level 1C Brightness Temperature (TBSMOS) products in comparison with simulated products from the L-MEB model (TBL-MEB) and (ii) validation of the SMOS Level 2 SM (SMSMOS) products against ground-based measurements at 10 significant Iranian agrometeorological stations. The validations were performed for the period of January 2012 to May 2015 over the Southwest and West of Iran. The results of the validation analysis showed an RMSE ranging between 9 to 13 K and a strong correlation (R = 0.61–0.84) between TBSMOS and TBL-MEB at all stations. The bias values (0.1 to 7.5 K) showed a slight overestimation for TBSMOS at most of the stations. The results of SMSMOS validation indicated a high agreement (RMSE = 0.046–0.079 m3 m−3 and R = 0.65–0.84) between the satellite SM and in situ measurements over all the stations. The findings of this research indicated that SMSMOS shows high accuracy and agreement with in situ measurements which validate its potential. Due to the limitation of SM measurements in Iran, the SMOS products can be used in different scientific and practical applications at different Iranian study areas.
A Framework to Predict High-Resolution Spatiotemporal PM2.5 Distributions Using a Deep-Learning Model: A Case Study of Shijiazhuang, China
Authors: Zhang Guangyuan , Lu Haiyue , Dong Jin , Poslad Stefan , Li Runkui , Zhang Xiaoshuai , Rui Xiaoping
Published: August 2020

Air-borne particulate matter, PM2.5 (PM having a diameter of less than 2.5 micrometers), has aroused widespread concern and is a core indicator of severe air pollution in many cities globally. In our study, we present a validated framework to predict the daily PM2.5 distributions, exemplified by a use case of Shijiazhuang City, China, based on daily aerosol optical depth (AOD) datasets. The framework involves obtaining the high-resolution spatiotemporal AOD distributions, estimation of the spatial distributions of PM2.5 and the prediction of these based on a convolutional long short-term memory (ConvLSTM) model. In the estimation part, the eXtreme gradient boosting (XGBoost) model has been determined as the estimation model with the lowest root mean square error (RMSE) of 32.86 µg/m3 and the highest coefficient of determination regression score function (R2) of 0.71, compared to other common models used as a baseline for comparison (linear, ridge, least absolute shrinkage and selection operator (LASSO) and cubist). For the prediction part, after validation and comparison with a seasonal autoregressive integrated moving average (SARIMA), which is a traditional time-series prediction model, in both time and space, the ConvLSTM gives a more accurate performance for the prediction, with a total average prediction RMSE of 14.94 µg/m3 compared to SARIMA’s 17.41 µg/m3. Furthermore, ConvLSTM is more stable and with less fluctuations for the prediction of PM2.5 in time, and it can also eliminate better the spatial predicted errors compared to SARIMA.
Facial Muscle Activity Recognition with Reconfigurable Differential Stethoscope-Microphones
Authors: Bello Hymalai , Zhou Bo , Lukowicz Paul
Published: August 2020

Many human activities and states are related to the facial muscles’ actions: from the expression of emotions, stress, and non-verbal communication through health-related actions, such as coughing and sneezing to nutrition and drinking. In this work, we describe, in detail, the design and evaluation of a wearable system for facial muscle activity monitoring based on a re-configurable differential array of stethoscope-microphones. In our system, six stethoscopes are placed at locations that could easily be integrated into the frame of smart glasses. The paper describes the detailed hardware design and selection and adaptation of appropriate signal processing and machine learning methods. For the evaluation, we asked eight participants to imitate a set of facial actions, such as expressions of happiness, anger, surprise, sadness, upset, and disgust, and gestures, like kissing, winkling, sticking the tongue out, and taking a pill. An evaluation of a complete data set of 2640 events with 66% training and a 33% testing rate has been performed. Although we encountered high variability of the volunteers’ expressions, our approach shows a recall = 55%, precision = 56%, and f1-score of 54% for the user-independent scenario(9% chance-level). On a user-dependent basis, our worst result has an f1-score = 60% and best result with f1-score = 89%. Having a recall ≥60% for expressions like happiness, anger, kissing, sticking the tongue out, and neutral(Null-class).
A Multi-Pump Magnetohydrodynamics Lab-On-A-Chip Device for Automated Flow Control and Analyte Delivery
Authors: Cardoso Rafael M. , Santos Robson O. dos , Munoz Rodrigo A. A. , Garcia Carlos D. , Blanes Lucas
Published: August 2020

This article shows the development of a computer-controlled lab-on-a-chip device with three magnetohydrodynamic (MHD) pumps and a pneumatic valve. The chip was made of a stack of layers of polymethylmethacrylate (PMMA), cut using a laser engraver and thermally bonded. The MHD pumps were built using permanent magnets (neodymium) and platinum electrodes, all of them controlled by an Arduino board and a set of relays. The implemented pumps were able to drive solutions in the open channels with a flow rate that increased proportionally with the channel width and applied voltage. To address the characteristic low pressures generated by this kind of pump, all channels were interconnected. Because the electrodes were immersed in the electrolyte, causing electrolysis and pH variations, the composition and ionic strength of the electrolyte solution were controlled. Additionally, side structures for releasing bubbles were integrated. With this multi-pump and valve solution, the device was used to demonstrate the possibility of performing an injection sequence in a system that resembles a traditional flow injection analysis system. Ultimately, the results demonstrate the possibility of performing injection sequences using an array of MHD pumps that can perform fluid handling in the 0–5 µL s−1 range.
Displacement Detection Decoupling in Counter-Propagating Dual-Beams Optical Tweezers with Large-Sized Particle
Authors: Zhu Xunmin , Li Nan , Yang Jianyu , Chen Xingfan , Hu Huizhu
Published: August 2020

As a kind of ultra-sensitive acceleration sensing platform, optical tweezers show a minimum measurable value inversely proportional to the square of the diameter of the levitated spherical particle. However, with increasing diameter, the coupling of the displacement measurement between the axes becomes noticeable. This paper analyzes the source of coupling in a forward-scattering far-field detection regime and proposes a novel method of suppression. We theoretically and experimentally demonstrated that when three variable irises are added into the detection optics without changing other parts of optical structures, the decoupling of triaxial displacement signals mixed with each other show significant improvement. A coupling detection ratio reduction of 49.1 dB and 22.9 dB was realized in radial and axial directions, respectively, which is principally in accord with the simulations. This low-cost and robust approach makes it possible to accurately measure three-dimensional mechanical quantities simultaneously and may be helpful to actively cool the particle motion in optical tweezers even to the quantum ground state in the future.
Reinforcement Learning-Based BEMS Architecture for Energy Usage Optimization
Authors: Park Sanguk , Park Sangmin , Choi Myeong-in , Lee Sanghoon , Lee Tacklim , Kim Seunghwan , Cho Keonhee , Park Sehyun
Published: August 2020

Currently, many intelligent building energy management systems (BEMSs) are emerging for saving energy in new and existing buildings and realizing a sustainable society worldwide. However, installing an intelligent BEMS in existing buildings does not realize an innovative and advanced society because it only involves simple equipment replacement (i.e., replacement of old equipment or LED (Light Emitting Diode) lamps) and energy savings based on a stand-alone system. Therefore, artificial intelligence (AI) is applied to a BEMS to implement intelligent energy optimization based on the latest ICT (Information and Communications Technologies) technology. AI can analyze energy usage data, predict future energy requirements, and establish an appropriate energy saving policy. In this paper, we present a dynamic heating, ventilation, and air conditioning (HVAC) scheduling method that collects, analyzes, and infers energy usage data to intelligently save energy in buildings based on reinforcement learning (RL). In this regard, a hotel is used as the testbed in this study. The proposed method collects, analyzes, and infers IoT data from a building to provide an energy saving policy to realize a futuristic HVAC (heating system) system based on RL. Through this process, a purpose-oriented energy saving methodology to achieve energy saving goals is proposed.
Study on the Influence of Image Noise on Monocular Feature-Based Visual SLAM Based on FFDNet
Authors: Cao Like , Ling Jie , Xiao Xiaohui
Published: August 2020

Noise appears in images captured by real cameras. This paper studies the influence of noise on monocular feature-based visual Simultaneous Localization and Mapping (SLAM). First, an open-source synthetic dataset with different noise levels is introduced in this paper. Then the images in the dataset are denoised using the Fast and Flexible Denoising convolutional neural Network (FFDNet); the matching performances of Scale Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF) and Oriented FAST and Rotated BRIEF (ORB) which are commonly used in feature-based SLAM are analyzed in comparison and the results show that ORB has a higher correct matching rate than that of SIFT and SURF, the denoised images have a higher correct matching rate than noisy images. Next, the Absolute Trajectory Error (ATE) of noisy and denoised sequences are evaluated on ORB-SLAM2 and the results show that the denoised sequences perform better than the noisy sequences at any noise level. Finally, the completely clean sequence in the dataset and the sequences in the KITTI dataset are denoised and compared with the original sequence through comprehensive experiments. For the clean sequence, the Root-Mean-Square Error (RMSE) of ATE after denoising has decreased by 16.75%; for KITTI sequences, 7 out of 10 sequences have lower RMSE than the original sequences. The results show that the denoised image can achieve higher accuracy in the monocular feature-based visual SLAM under certain conditions.
Towards a Multi-Enzyme Capacitive Field-Effect Biosensor by Comparative Study of Drop-Coating and Nano-Spotting Technique
Authors: Molinnus Denise , Beging Stefan , Lowis Carsten , Schöning Michael J.
Published: August 2020

Multi-enzyme immobilization onto a capacitive field-effect biosensor by nano-spotting technique is presented. The nano-spotting technique allows to immobilize different enzymes simultaneously on the sensor surface with high spatial resolution without additional photolithographical patterning. The amount of applied enzymatic cocktail on the sensor surface can be tailored. Capacitive electrolyte-insulator-semiconductor (EIS) field-effect sensors with Ta2O5 as pH-sensitive transducer layer have been chosen to immobilize the three different (pL droplets) enzymes penicillinase, urease, and glucose oxidase. Nano-spotting immobilization is compared to conventional drop-coating method by defining different geometrical layouts on the sensor surface (fully, half-, and quarter-spotted). The drop diameter is varying between 84 µm and 102 µm, depending on the number of applied drops (1 to 4) per spot. For multi-analyte detection, penicillinase and urease are simultaneously nano-spotted on the EIS sensor. Sensor characterization was performed by C/V (capacitance/voltage) and ConCap (constant capacitance) measurements. Average penicillin, glucose, and urea sensitivities for the spotted enzymes were 81.7 mV/dec, 40.5 mV/dec, and 68.9 mV/dec, respectively.
Silicone Rubber Based Highly Sensitive Fiber-Optic Fabry–Perot Interferometric Gas Pressure Sensor
Authors: Cheng Xin , Dash Jitendra , Gunawardena Dinusha , Htein Lin , Tam Hwa-Yaw
Published: August 2020

A simple, compact, and highly sensitive gas pressure sensor based on a Fabry–Perot interferometer (FPI) with a silicone rubber (SR) diaphragm is demonstrated. The SR diaphragm is fabricated on the tip of a silica tube using capillary action followed by spin coating. This process ensures uniformity of its inner surface along with reproducibility. A segment of single mode fiber (SMF) inserted into this tube forms the FPI which produces an interference pattern with good contrast. The sensor exhibits a high gas pressure sensitivity of −0.68 nm/kPa along with a low temperature cross-sensitivity of ≈ 1.1 kPa/°C.
Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network
Authors: Luo Honglin , Bo Lin , Peng Chang , Hou Dongming
Published: August 2020

Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.
A Heuristic Angular Clustering Framework for Secured Statistical Data Aggregation in Sensor Networks
Authors: Krishnasamy Lalitha , Dhanaraj Rajesh , Ganesh Gopal D. , Reddy Gadekallu Thippa , Aboudaif Mohamed , Abouel Nasr Emad
Published: August 2020

Clustering in wireless sensor networks plays a vital role in solving energy and scalability issues. Although multiple deployment structures and cluster shapes have been implemented, they sometimes fail to produce the expected outcomes owing to different geographical area shapes. This paper proposes a clustering algorithm with a complex deployment structure called radial-shaped clustering (RSC). The deployment structure is divided into multiple virtual concentric rings, and each ring is further divided into sectors called clusters. The node closest to the midpoint of each sector is selected as the cluster head. Each sector’s data are aggregated and forwarded to the sink node through angular inclination routing. We experimented and compared the proposed RSC performance against that of the existing fan-shaped clustering algorithm. Experimental results reveal that RSC outperforms the existing algorithm in scalability and network lifetime for large-scale sensor deployments.
Using a Sensory Learning Framework to Design Effective Curricula: Evidence from Indigenous Nutrition Education Programs
Authors: Green Kelly , Chenarides Lauren
Published: August 2020

As health disparities among Native Americans persist, promoting better health outcomes is of paramount concern among Indigenous populations. A variety of programs exist that try to alleviate problems resulting in higher rates of diet-related chronic diseases and premature death. For this study, we collaborated with an Indigenous-led nonprofit that implemented a series of nutrition education courses designed to empower community members to make healthier food choices. The theoretically based curriculum, which provided learners with information in the form of sensory-based modules, e.g., food preparation, food handling, cultural awareness, and practical cooking skills, was introduced in various communities in the Great Plains and Southwest. The nutrition education programs were modeled after a canonical educational learning model, Bloom’s Taxonomy, designed to provide participants with information and resources necessary to make healthier food choices across three cognitive domains (i.e., tiers). We used a mixed-methods approach, coupling Principal Components Analysis with a qualitative SWOT (Strengths, Weaknesses, Opportunities, Threats) analysis, to assess each program’s capacity to enhance learning retention, i.e., to assess the salience of information provided and the extent to which each program was more or less successful in participants’ learning. We found that course content and instruction are strongly correlated with program satisfaction. In addition, from the qualitative analysis, we found that as each successive module of the program challenged higher cognitive domains, participants were more likely to indicate satisfaction in the course material as well as state a desired change in their behavior, which we attribute to participants’ ability to synthesize and evaluate information. Aspects of this programming framework have the potential to be adapted to and integrated into other Native communities striving for the successful adoption of healthier diets.
How We Can Enhance Spectator Attendance for the Sustainable Development of Sport in the Era of Uncertainty: A Re-Examination of Competitive Balance
Authors: Sung Sang Hun , Hong Doo-Seung , Sul Soo Young
Published: August 2020

This study seeks to find factors that determine the mobilization of spectators in games of the Korean professional baseball league for sustainable development. In particular, it re-examines the uncertainty-of-outcome hypothesis by looking at the effect of competitive balance (CB) on seat occupancy in stadiums. Data for 2160 games over three years (2015–2017) from the archives of the Korea Baseball Organization (KBO) were analyzed. The results show that the CB of the home team, with the entire teams, affects seat occupancy rate (SOR). It is also shown that there exists an inverted U-shaped curvilinear relationship between CB and SOR. However, the winning percentage of the home team in the entire league had a significant impact on the SOR. This implies that the overall athletic performance of the home team in the league competing with all other teams is much more important than the home team’s competitive balance in the season played before the game, for attracting fans to the stadium.
Intellectual Capital of a Trading Company: Comprehensive Analysis Based on Reporting
Authors: Pirogova Oksana , Voronova Olga , Khnykina Tatyana , Plotnikov Vladimir
Published: August 2020

The study is devoted to the analysis of the efficiency of use and the effectiveness of disclosing the intellectual capital (IC) of a trading company operating in the market of the Russian Federation. The subject of the research is an assessment of the quality of disclosure of information about the IC company involved in the creation of financial results of activities and the growth of the company’s value. The study examines the assessment of IC and the search for links between the involvement of IC in the formation of the financial result of a trading company and the degree of its reflection in the company’s annual reports. Methods of using intellectual value-added coefficients (VAIC) such as the trademark logo (written as ™), Calculated Intangible Value (CIV) and content analysis of the company’s annual reports are used to assess the IC and its elements. The influence of IC and its components, on the financial results of a trading company are also investigated and calculated using various methods. It is shown that there are no statistically significant relationships between the assessments of IC and its elements obtained using financial ratios, and those obtained using content analysis. This indicates that the opinions and assessments of the heads of a trading company regarding IC are formed regardless of the size of IC involved in the formation of economic results and testifies to the absence of an effective investment management policy in the studied company. Some of the results obtained confirm the trends in IC structures previously observed for companies in other industries. The results also indicate that the existing system for preparing annual reports does not sufficiently consider the size, dynamics and efficiency of using the intangible assets of a trading company. The results of this study are likely to be useful to management and academics.
Adjustments of Socially Vulnerable Populations in Galveston County, Texas USA Following Hurricane Ike
Authors: Fucile-Sanchez Emily , Davlasheridze Meri
Published: August 2020

The role of socio-demographic vulnerability to hazards is an increasingly important aspect for consideration in disaster mitigation and adaptation. This paper examines the spatial adjustments of populations to the 2008 Hurricane Ike by estimating the effects of damage on the changes of socially vulnerable populations pre- and post-Hurricane Ike. Multivariate regression models are used to understand household-level adjustments in different flood zones and inundation levels at the block-group level in Galveston county. In contrast to past literature that suggests that vulnerable populations remain or move into hazardous areas post-disaster, our results indicate that socially vulnerable populations have moved out of highly damaged areas. The tremendous investment opportunity post-disaster and the slow distribution of funds to recover public housing on Galveston Island provide potential explanation of the estimated adjustment patterns. Analyzing post disaster adjustments offers important insights into the “resilient” recovery of Galveston County post-Hurricane Ike. Our results also point to potential vulnerabilities that may arise in the future because of the change in community identity and the loss of social memory. Understanding disaster-driven changes in community make-up will help inform potential recovery trajectories from future catastrophes.
Sustainable Method Using Filtering Techniques for a Fermentation Process State Estimation
Authors: Sipos Anca
Published: August 2020

Winemaking is concerned about sustainable energy availability that implies new methods for process monitoring and control. The aim of this paper is to realize a comparative analysis of the possibilities offered using estimation techniques, balances, and filtering techniques such as the Kalman filter (KF) and the extended Kalman filter (EKF), to obtain indirect information about the alcoholic fermentation process during winemaking. Thus, an estimation solution of the process variables in the exponential growing phase is proposed, using an extended observer. In addition, two estimation solutions of this process with the EKF and an estimation of the decay phase of the fermentation process are presented. The difference between the two EKF variants consisted of taking into consideration the indicator of the integral of the error norm square for the second EKF, for which the performance criterion was the statistical average of this indicator. Results from the simulation of the estimation programs of the two EKF variants were more than satisfactory. This research provides a basis for using an EKF designed for advanced control of the alcoholic fermentation batch process as a knowledge-based system.
Cost of Climate Change: Risk of Building Loss from Typhoon in South Korea
Authors: Kim Ji-Myong , Son Seunghyun , Lee Sungho , Son Kiyoung
Published: August 2020

In recent years, natural disasters and climate abnormalities have increased worldwide. The Fifth Assessment Report (2014) of the Intergovernmental Panel on Climate Change warned of extreme rainfall events, warming and acidification, global mean temperature rises, and average sea level rises. In many countries, changes in weather disaster patterns, such as typhoons and heavy rains, have already led to increased damage to buildings. However, the empirical quantification of typhoon risk and building damage due to climate change is insufficient. The purpose of this study was to quantify the risk of building loss from typhoon pattern change caused by climate change. To this end, the intensity and frequency of typhoons affecting Korea were analyzed to examine typhoon patterns. In addition, typhoon risk was quantified using the Korean typhoon vulnerability function utilized by insurers, reinsurers, and vendors, the major users of catastrophe modeling. Hence, through this study, it is possible to generate various risk management strategies, which can be used by governments when establishing climate change policies and help insurers to improve their business models through climate risk assessment based on reasonable quantitative typhoon damage scenarios.
Optimization of Two Soil–Structure Interaction Parameters Using Dynamic Centrifuge Tests and an Analytical Approach
Authors: Kim Hyun-Uk , Ha Jeong-Gon , Ko Kil-Wan , Kim Dong-Soo
Published: August 2020

The response of the structure subjected to an earthquake load is greatly affected by the properties of the structure and soil so it is very important to accurately determine the characteristics of the structure and soil for analysis. However, studies on the effective profile depth where soil properties are determined, have been conducted in the presence of restricted conditions (i.e., surface foundation, linear soil properties), and without any considerations on damping. In case of the effective height of structure that affects its rocking behavior, it was only theoretically or empirically determined. In addition, most previously published studies on soil–structure interaction (SSI) focused on limited effects and parameters (e.g., rocking behavior, embedment effect, effective profile depth, spring constant, and damping coefficient) and not on comprehensive SSI parameters. Furthermore, no detailed validation procedure has been set in place which made it difficult to validate the SSI parameters. Since the effective height of structure and effective profile depth are the basis of all the input parameters of SSI analysis, it is important to validate and determine them. Therefore, in this study, the procedure used to optimize the two SSI parameters was established based on an analytical approach that considered all the possible SSI parameters that were investigated from conventional codes and studies and physical model tests. As a result of this study, the optimum values of the effective height of the structure and effective profile depth were respectively determined according to (a) the height from the bottom part of the foundation to the center of the mass of the superstructure, and according to (b) the depth at values equal to four times the radius of the foundation.
A New Multi-Sensor Fusion Target Recognition Method Based on Complementarity Analysis and Neutrosophic Set
Authors: Gong Yuming , Ma Zeyu , Wang Meijuan , Deng Xinyang , Jiang Wen
Published: August 2020

To improve the efficiency, accuracy, and intelligence of target detection and recognition, multi-sensor information fusion technology has broad application prospects in many aspects. Compared with single sensor, multi-sensor data contains more target information and effective fusion of multi-source information can improve the accuracy of target recognition. However, the recognition capabilities of different sensors are different during target recognition, and the complementarity between sensors needs to be analyzed during information fusion. This paper proposes a multi-sensor fusion recognition method based on complementarity analysis and neutrosophic set. The proposed method mainly has two parts: complementarity analysis and data fusion. Complementarity analysis applies the trained multi-sensor to extract the features of the verification set into the sensor, and obtain the recognition result of the verification set. Based on recognition result, the multi-sensor complementarity vector is obtained. Then the sensor output the recognition probability and the complementarity vector are used to generate multiple neutrosophic sets. Next, the generated neutrosophic sets are merged within the group through the simplified neutrosophic weighted average (SNWA) operator. Finally, the neutrosophic set is converted into crisp number, and the maximum value is the recognition result. The practicality and effectiveness of the proposed method in this paper are demonstrated through examples.
Limnological Differences in a Two-Basin Lake Help to Explain the Occurrence of Anatoxin-a, Paralytic Shellfish Poisoning Toxins, and Microcystins
Authors: Smith Zacharias J. , Conroe Douglas E. , Schulz Kimberly L. , Boyer Gregory L.
Published: August 2020

Chautauqua Lake, New York, is a two-basin lake with a deeper, cooler, and less nutrient-rich Northern Basin, and a warmer, shallower, nutrient-replete Southern Basin. The lake is populated by a complex mixture of cyanobacteria, with toxigenic strains that produce microcystins, anatoxins, and paralytic shellfish poisoning toxins (PSTs). Samples collected from 24 sites were analyzed for these three toxin classes over four years spanning 2014–2017. Concentrations of the three toxin groups varied widely both within and between years. During the study, the mean and median concentrations of microcystins, anatoxin-a, and PSTs were 91 and 4.0 μg/L, 0.62 and 0.33 μg/L, and 32 and 16 μg/L, respectively. Dihydro-anatoxin was only detected once in Chautauqua Lake, while homo-anatoxin was never detected. The Northern Basin had larger basin-wide higher biomass blooms with higher concentrations of toxins relative to the more eutrophied Southern Basin, however blooms in the North Basin were infrequent. Chlorophyll concentrations and toxins in the two basins were correlated with different sets of environmental and physical parameters, suggesting that implementing controls to reduce toxin loads may require applications focused on more than reductions in cyanobacterial bloom density (e.g., reduction of phosphorus inputs), and that lake limnological factors and morphology are important determinants in the selection of an appropriate management strategy. Chautauqua Lake is a drinking water source and is also heavily used for recreation. Drinking water from Chautauqua Lake is unlikely to be a significant source of exposure to cyanotoxins due to the location of the intakes in the deeper North Basin, where there were generally low concentrations of toxins in open water; however, toxin levels in many blooms exceeded the US Environmental Protection Agency’s recreational guidelines for exposure to cyanotoxins. Current cyanotoxin monitoring in Chautauqua Lake is focused on microcystins. However, the occurrence of blooms containing neurotoxic cyanotoxins in the absence of the microcystins indicates this restricted monitoring may not be sufficient when aiming to protect against exposure to cyanotoxins. The lake has a large number of tourist visitors; thus, special care should be taken to prevent recreational exposure within this group.
Insight into Unprecedented Diversity of Cyanopeptides in Eutrophic Ponds Using an MS/MS Networking Approach
Authors: Kust Andreja , Řeháková Klára , Vrba Jaroslav , Maicher Vincent , Mareš Jan , Hrouzek Pavel , Chiriac Maria-Cecilia , Benedová Zdeňka , Tesařová Blanka , Saurav Kumar
Published: August 2020

Man-made shallow fishponds in the Czech Republic have been facing high eutrophication since the 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physicochemical properties of water and its aquatic community composition, leading to harmful algal bloom formation. In our current study, we characterized the phytoplankton community across three eutrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprising non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography-tandem mass spectrometry (LC-MS/MS) data coupled with a dereplication strategy. This MS networking approach, coupled with dereplication, on the online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin, and epidolastatin 12. We applied the binary logistic regression to estimate the CNPs producers by correlating the GNPS data with the species abundance. The usage of the GNPS web platform proved a valuable approach for the rapid and simultaneous detection of a large number of peptides and rapid risk assessments for harmful blooms.
Understanding the Mechanisms Underlying Host Restriction of Insect-Specific Viruses
Authors: Elrefaey Ahmed ME , Abdelnabi Rana , Rosales Rosas Ana Lucia , Wang Lanjiao , Basu Sanjay , Delang Leen
Published: August 2020

Arthropod-borne viruses contribute significantly to global mortality and morbidity in humans and animals. These viruses are mainly transmitted between susceptible vertebrate hosts by hematophagous arthropod vectors, especially mosquitoes. Recently, there has been substantial attention for a novel group of viruses, referred to as insect-specific viruses (ISVs) which are exclusively maintained in mosquito populations. Recent discoveries of novel insect-specific viruses over the past years generated a great interest not only in their potential use as vaccine and diagnostic platforms but also as novel biological control agents due to their ability to modulate arbovirus transmission. While arboviruses infect both vertebrate and invertebrate hosts, the replication of insect-specific viruses is restricted in vertebrates at multiple stages of virus replication. The vertebrate restriction factors include the genetic elements of ISVs (structural and non-structural genes and the untranslated terminal regions), vertebrate host factors (agonists and antagonists), and the temperature-dependent microenvironment. A better understanding of these bottlenecks is thus warranted. In this review, we explore these factors and the complex interplay between ISVs and their hosts contributing to this host restriction phenomenon.
Changes in MicroRNA Expression during Rabbit Hemorrhagic Disease Virus (RHDV) Infection
Authors: Hukowska-Szematowicz Beata , Maciejak-Jastrzębska Agata , Blatkiewicz Małgorzata , Maciak Karolina , Góra Monika , Janiszewska Joanna , Burzyńska Beata
Published: August 2020

Current knowledge on the role of microRNAs (miRNAs) in rabbit hemorrhagic disease virus (RHDV) infection and the pathogenesis of rabbit hemorrhagic disease (RHD) is still limited. RHDV replicates in the liver, causing hepatic necrosis and liver failure. MiRNAs are a class of short RNA molecules, and their expression profiles vary over the course of diseases, both in the tissue environment and in the bloodstream. This paper evaluates the expression of miRNAs in the liver tissue (ocu-miR-122-5p, ocu-miR-155-5p, and ocu-miR-16b-5p) and serum (ocu-miR-122-5p) of rabbits experimentally infected with RHDV. The expression levels of ocu-miR-122-5p, ocu-miR-155-5p, and ocu-miR-16b-5p in liver tissue were determined using reverse transcription quantitative real-time PCR (RT-qPCR), and the expression level of circulating ocu-miR-122-5p was established using droplet digital PCR (ddPCR). The expression levels of ocu-miR-155-5p and ocu-miR-16b-5p were significantly higher in the infected rabbits compared to the healthy rabbits (a fold-change of 5.8 and 2.5, respectively). The expression of ocu-miR-122-5p was not significantly different in the liver tissue from the infected rabbits compared to the healthy rabbits (p = 0.990), while the absolute expression level of the circulating ocu-miR-122-5p was significantly higher in the infected rabbits than in the healthy rabbits (p < 0.0001). Furthermore, a functional analysis showed that ocu-miR-155-5p, ocu-miR-16b-5p, and ocu-miR-122-5p can regulate the expression of genes involved in processes correlated with acute liver failure (ALF) in rabbits. Search tool for the retrieval of interacting genes/proteins (STRING) analysis showed that the potential target genes of the three selected miRNAs may interact with each other in different pathways. The results indicate the roles of these miRNAs in RHDV infection and over the course of RHD and may reflect hepatic inflammation and impairment/dysfunction in RHD.
Performance Study of an Integrated Solar Water Supply System for Isolated Agricultural Areas in Thailand: A Case-Study of the Royal Initiative Project
Authors: Imjai Thanongsak , Thinsurat Kamon , Ditthakit Pakorn , Wipulanusat Warit , Setkit Monthian , Garcia Reyes
Published: August 2020

This article presents a field-performance investigation on an Integrated Solar Water Supply System (SWSS) at two isolated agricultural areas in Thailand. The two case-study villages (Pongluek and Bangkloy) have experienced severe draughts in recent decades, and, therefore, water supply has become a major issue. A stand-alone 15.36 kW solar power and a 15 kW solar submersible pump were installed along with the input power generated by solar panels supported by four solar trackers. The aim is to lift water at the static head of 64 and 48 m via a piping length of 400 m for each village to be stored in 1000 and 1800 m3 reservoirs at an average of 300 and 400 m3 per day, respectively, for Pongluek and Bangkloy villages. The case study results show that the real costs of electricity generated by SWSS using solar photovoltaic (PV) systems intergraded with the solar tracking system yield better performance and are more advantageous compared with the non-tracking system. This study illustrates how system integration has been employed. System design and commercially available simulation predictions are elaborated. Construction, installation, and field tests for SWSS are discussed and highlighted. Performances of the SWSS in different weather conditions, such as sunny, cloudy, and rainy days, were analysed to make valuable suggestions for higher efficiency of the integrated solar water supply systems.
DEM-CFD Simulation and Experiments on the Flow Characteristics of Particles in Vortex Pumps
Authors: Gao Xiongfa , Shi Weidong , Shi Ya , Chang Hao , Zhao Ting
Published: August 2020

Due to their outstanding anti-clogging ability, vortex pumps have been gradually promoted and applied in recent years. However, when transporting sewage containing solids, they will still encounter problems such as partial clogging, overwork wear, etc., therefore, it is particularly important to master the flow characteristics of solid particles in the vortex pump. In this paper, the Discrete Element Model-Computational Fluid Dynamics (DEM-CFD) coupled calculation method is introduced into the numerical simulation of vortex pumps and particles with diameters of 1, 2 and 3 mm and concentrations of 1% and 5%, were subjected to numerical simulation and study of the flow characteristics of the particles, then rapeseed was used to represent solid particles in tests. It was obvious that the CFD results were in good agreement with the experimental results, whereby the high speed photography experimental results of the pump inlet section show that the experimental results are consistent with the numerical simulation results. The results show that there are three typical movement tracks of solid particles in the vortex pump: in Track A particles flow through the impeller and enter the volute by the through flow, in Track B particles go directly into the volute through the lateral cavity under the influence of circulation flow and in Track C the particles enter the impeller from the front cover end area of the impeller blade inlet and then into the volute through the back half area of blade. It can be found that the particles are mainly distributed at the back of the volute.
Toward Group Applications: A Critical Review of the Classification Strategies of Lithium-Ion Batteries
Authors: Li Ran , Zhang Haonian , Li Wenrui , Zhao Xu , Zhou Yongqin
Published: August 2020

To solve the problems of the decreased reliability and safety of battery pack due to the inconsistency between batteries after single batteries are grouped is of great significance to find an appropriate sorting method of single batteries. This study systematically reviews the available literature on battery sorting applications for battery researchers and users. These methods can be roughly divided into three types: direct measurement, sorting based on the model, and sorting based on the material chemistry of batteries. Among them, direct measurement is about the direct measurement of the state parameters of batteries using some professional instruments or testing tools to sort and group batteries with similar or close parameters. Sorting based on the model classifies batteries into groups by establishing a battery equivalent model and carrying out model identification and parameter estimation with machine learning or artificial intelligence algorithm. Sorting based on the material chemistry of batteries is to explore some characteristics related to the chemical mechanism inside the battery. On the basis of reading extensive literature, the methods for classification of battery are provided with an in-depth explanation, and each corresponding strengths and weaknesses of these methods are analyzed. Finally, the future developments of advanced sorting algorithms and batteries prospect.
Mental Disease as Developmental Regress of a Person in the Creative Work of M. Foucault
Authors: Izutkin Dmitriy , Gomozov Georgiy
Published: August 2020

The article is devoted to the philosophical notion of the mental illness as developmental regress of a personality in the work of one of the remarkable philosophers of the XX century, Paul-Michel Foucault “Mental Illness and Personality”. The chapter “Illness and Evolution” describes the pathological process as a particular way of illness with its sources.
Federal Internal Financial Audit Standard “Implement Internal Financial Audit Results”
Authors: Shinkareva Olga
Published: August 2020

The article is devoted to the analysis of the new federal standard of internal financial audit “Implementation of internal financial audit results”, approved by Order of the Ministry of Finance of the Russian Federation № 91n of 22.05.2020, which will affect medical institutions as recipients of budgetary funds. The article considers the main provisions of this standard, reveals the peculiarities of drawing up and submitting an opinion on the results of the audit event, as well as the peculiarities of approval of decisions taken by the head of the institution on the results of the internal financial audit and monitoring the implementation of measures to minimize budget risks. Features of the preparation and submission of annual reports on the results of the internal financial audit entity.