bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing PET Glycolysis with an Oyster Shell-Derived Catalyst Using Response Surface Methodology
Authors: Kim Yonghwan , Kim Minjun , Hwang Jeongwook , Im Eunmi , Moon Geon Dae
Published: February 2022

Polyethylene terephthalate (PET) waste was depolymerized into bis(2-hydroxyethyl) terephthalate (BHET) through glycolysis with the aid of oyster shell-derived catalysts. The equilibrium yield of BHET was as high as 68.6% under the reaction conditions of mass ratios (EG to PET = 5, catalyst to PET = 0.01) at 195 °C for 1 h. Although biomass-derived Ca-based catalysts were used for PET glycolysis to obtain BHET monomers, no statistical analysis was performed to optimize the reaction conditions. Thus, in this study, we applied response surface methodology (RSM) based on three-factor Box–Behnken design (BBD) to investigate the optimal conditions for glycolysis by analyzing the independent and interactive effects of the factors, respectively. Three independent factors of interest include reaction time, temperature, and mass ratio of catalyst to PET under a fixed amount of ethylene glycol (mass ratio of EG to PET = 5) due to the saturation of the yield above the mass ratio. The quadratic regression equation was calculated for predicting the yield of BHET, which was in good agreement with the experimental data (R2 = 0.989). The contour and response surface plots showed the interaction effect between three variables and the BHET yield with the maximum average yield of monomer (64.98%) under reaction conditions of 1 wt% of mass ratio (catalyst to PET), 195 °C, and 45 min. Both the experimental results and the analyses of the response surfaces revealed that the interaction effects of reaction temperature vs. time and temperature vs. mass ratio of the catalyst to the PET were more prominent in comparison to reaction time vs. mass ratio of the catalyst to the PET.
Microfluidic Rheometry and Particle Settling: Characterizing the Effect of Polymer Solution Elasticity
Authors: Faroughi Salah A. , Del Giudice Francesco
Published: February 2022

The efficient transport of solid particles using polymeric fluids is an important step in many industrial operations. Different viscoelastic fluids have been designed for this purpose, however, the effects of elasticity have not been fully integrated in examining the particle-carrying capacity of the fluids. In this work, two elastic fluid formulations were employed to experimentally clarify the effect of elasticity on the particle drag coefficient as a proxy model for measuring carrying capacity. Fluids were designed to have a constant shear viscosity within a specific range of shear rates, γ˙<50(1/s), while possessing distinct (longest) relaxation times to investigate the influence of elasticity. It is shown that for dilute polymeric solutions, microfluidic rheometry must be practiced to obtain a reliable relaxation time (as one of the measures of viscoelasticity), which is on the order of milliseconds. A calibrated experimental setup, furnished with two advanced particle velocity measurement techniques and spheres with different characteristics, was used to quantify the effect of elasticity on the drag coefficient. These experiments led to a unique dataset in moderate levels of Weissenberg numbers, 0<Wi<8.5. The data showed that there is a subtle reduction in the drag coefficient at low levels of elasticity (Wi<1), and a considerable enhancement at high levels of elasticity (Wi>1). The experimental results were then compared with direct numerical simulation predictions yielding R2=0.982. These evaluations endorse the numerically quantified behaviors for the drag coefficient to be used to compare the particle-carrying capacity of different polymeric fluids under different flow conditions.
Chitosan Nanoparticles-Based Ionic Gelation Method: A Promising Candidate for Plant Disease Management
Authors: Hoang Nguyen Huy , Le Thanh Toan , Sangpueak Rungthip , Treekoon Jongjit , Saengchan Chanon , Thepbandit Wannaporn , Papathoti Narendra Kumar , Kamkaew Anyanee , Buensanteai Natthiya
Published: February 2022

By 2050, population growth and climate change will lead to increased demand for food and water. Nanoparticles (NPs), an advanced technology, can be applied to many areas of agriculture, including crop protection and growth enhancement, to build sustainable agricultural production. Ionic gelation method is a synthesis of microparticles or NPs, based on an electrostatic interaction between opposite charge types that contains at least one polymer under mechanical stirring conditions. NPs, which are commonly based on chitosan (CS), have been applied to many agricultural fields, including nanopesticides, nanofertilizers, and nanoherbicides. The CS-NP or CS-NPs-loaded active ingredients (Cu, saponin, harpin, Zn, hexaconazole, salicylic acid (SA), NPK, thiamine, silicon, and silver (Ag)) are effective in controlling plant diseases and enhancing plant growth, depending on the concentration and application method by direct and indirect mechanisms, and have attracted much attention in the last five years. Many crops have been evaluated in in vivo or in greenhouse conditions but only maize (CS-NP-loaded Cu, Zn, SA, and silicon) and soybean (CS-NP-loaded Cu) were tested for manage post flowering stalk rot, Curvularia leaf spot, and bacterial pustule disease in field condition. Since 2019, five of eight studies have been performed in field conditions that have shown interest in CS-NPs synthesized by the ionic gelation method. In this review, we summarized the current state of research and provided a forward-looking view of the use of CS-NPs in plant disease management.
Safety and Protective Activities of Manufactured Alcohol-Free Beers
Authors: Merinas-Amo Tania , Del Río Celestino Mercedes , Font Rafael , Alonso-Moraga Ángeles
Published: February 2022

Nowadays, a general interest in improving health in order to achieve better conditions of life is increasing. Diet is a complex factor affecting health conditions. We analysed the biological activities of three types of alcohol-free lager beer (a blond, a pale-blond and a stout beer) as well as epicatechin gallate (ECG) as one of their most abundant phenols with the aim of revealing them as nutraceuticals. For that purpose, we carried out safety and protective assays of the tested substances in the well-known Drosophila melanogaster animal model. Moreover, chemoprevention studies on human leukaemia cells (HL-60) in an in vitro model were carried out to evaluate the viability and genomic damage potential of the studied compounds on the tumour cell line. Results suggest the safety properties of all compounds, although pale-blond and stout beer only showed genotoxic activity at the lowest concentrations assayed. Moreover, alcohol-free beers and phenols were able to protect against H2O2 oxidative damage as well as to induce an increase in longevity with an improvement of the quality of life in the in vivo animal model assayed. Promising results were obtained with the alcohol-free beers and ECG in the in vitro assays with human leukaemia cells as they inhibited the tumour cells’ growth, induced DNA damage and modified the methylation status of such a cancer cell line. To sum up, alcohol-free beers should be of interest not only because of their reduced calories and isotonic properties but because they can be recognised as nutraceutical substances.
Psychotherapy as a Polyphonic and Playful Conversation
Authors: Guerra Cátia , Pedrosa Raquel , Nunes Patrícia , Rebelo Joana , Osório Eva , Roma-Torres António
Published: February 2022

Since the emergence of hypnosis, we have witnessed a multiplication of psychotherapies, which have different backgrounds and aims. The omnipresence of psychotherapy leads us to an inevitable question: what is psychotherapy? In this article, we analyse the concept of mental disorder and how psychotherapy works, underlining three mechanisms: influence, polyphonic dialogue and play. Focusing on the therapeutic dialogue, we explore what is told during therapy and how, what is done while telling, and how dialogue can create new narratives and new meanings, highlighting the importance of influencing the patient on changing the symptomatic behaviour. We also consider how the multiple voices of the patient, therapist and others can generate an alternative to the monologic discourse of the disease. While the psychiatric illness may indicate a sterile dialogue which often expands the pathology, communicating in a different and active way can create new and healthier meanings. Therefore, one of the therapy’s aims is to influence the patient, throughout a dialogic and playful conversation, to gain freedom from disease.
Symbol Matters: A Sequential Mediation Model in Examining the Impact of Product Design with Buddhist Symbols on Charitable Donation Intentions
Authors: Qin Zhenzhen , Song Yao
Published: February 2022

Though religion-informed charitable behavior has gained much academic attention recently, there is an ambiguous relationship between individual donation intention and Buddhist determinants. Drawing on the theories of reasoned action and product semantics, this study tries to examine the effects of product design with Buddhist symbols on individual charitable donation behavior and the underlying mechanism. Some studies have argued for the positive effects of religious symbols on people’s behavioral reactions; however, limited prior research has addressed the impact of a touchable product, such as a donation box, on people’s charitable donation intentions from the perspective of Buddhism. The current study statistically shows that donation boxes with Buddhist symbols could significantly improve people’s charitable donation intention through two sequential mediators, namely, their perceived religiosity and product attitude. The study provides both theoretical contributions and practical implications for future research.
Multi-Species Inference of Exotic Annual and Native Perennial Grasses in Rangelands of the Western United States Using Harmonized Landsat and Sentinel-2 Data
Authors: Dahal Devendra , Pastick Neal J. , Boyte Stephen P. , Parajuli Sujan , Oimoen Michael J. , Megard Logan J.
Published: February 2022

The invasion of exotic annual grass (EAG), e.g., cheatgrass (Bromus tectorum) and medusahead (Taeniatherum caput-medusae), into rangeland ecosystems of the western United States is a broad-scale problem that affects wildlife habitats, increases wildfire frequency, and adds to land management costs. However, identifying individual species of EAG abundance from remote sensing, particularly at early stages of invasion or growth, can be problematic because of overlapping controls and similar phenological characteristics among native and other exotic vegetation. Subsequently, refining and developing tools capable of quantifying the abundance and phenology of annual and perennial grass species would be beneficial to help inform conservation and management efforts at local to regional scales. Here, we deploy an enhanced version of the U.S. Geological Survey Rangeland Exotic Plant Monitoring System to develop timely and accurate maps of annual (2016–2020) and intra-annual (May 2021 and July 2021) abundances of exotic annual and perennial grass species throughout the rangelands of the western United States. This monitoring system leverages field observations and remote-sensing data with artificial intelligence/machine learning to rapidly produce annual and early season estimates of species abundances at a 30-m spatial resolution. We introduce a fully automated and multi-task deep-learning framework to simultaneously predict and generate weekly, near-seamless composites of Harmonized Landsat Sentinel-2 spectral data. These data, along with auxiliary datasets and time series metrics, are incorporated into an ensemble of independent XGBoost models. This study demonstrates that inclusion of the Normalized Difference Vegetation Index and Normalized Difference Wetness Index time-series data generated from our deep-learning framework enables near real-time and accurate mapping of EAG (Median Absolute Error (MdAE): 3.22, 2.72, and 0.02; and correlation coefficient (r): 0.82, 0.81, and 0.73; respectively for EAG, cheatgrass, and medusahead) and native perennial grass abundance (MdAE: 2.51, r:0.72 for Sandberg bluegrass (Poa secunda)). Our approach and the resulting data provide insights into rangeland grass dynamics, which will be useful for applications, such as fire and drought monitoring, habitat suitability mapping, as well as land-cover and land-change modelling. Spatially explicit, timely, and accurate species-specific abundance datasets provide invaluable information to land managers.
Multi-Temporal Variations in Surface Albedo on Urumqi Glacier No.1 in Tien Shan, under Arid and Semi-Arid Environment
Authors: Yue Xiaoying , Li Zhongqin , Li Huilin , Wang Feiteng , Jin Shuang
Published: February 2022

Surface albedo exerts substantial control over the energy available for glacier melting. For Urumqi Glacier No.1 in the Tien Shan Mountains, China, represented as a “summer accumulation” glacier, the variations in albedo driven by surface processes are complex and still poorly understood. In this study, we examined the interannual trends in ablation-period albedo from 2000 to 2021 using MOD10A1 products, evaluated the variation in bare-ice albedo retrieved from 13 end-of-summer Landsat images obtained between 2002 and 2019, and investigated the seasonal variation and diurnal cycle of surface albedo collected near the equilibrium line of the glacier by an AWS from September 2018 to August 2021. During the period of 2000–2021, the average ablation-period albedo presented a slight but not statistically significant downward trend, with a total decrease of 1.87%. Specifically, the decrease in glacier albedo was quicker in July than that in August, and there was a slight increase in May and June. The blackening phenomenon was shown on the east branch glacier, but not on the west branch glacier. For seasonal variability, a bimodal pattern was demonstrated, different from the unimodal seasonal variation in other midlatitude glaciers. The albedo peaks occurred in December and April or May. Under clear sky conditions, the diurnal cycle presented three patterns: a symmetric pattern, an asymmetric pattern, and a progressive decreasing pattern. Air temperature and solid precipitation are the main drivers of variations in glacier albedo, but in different periods of the ablation season, two climate variables affect albedo to varying degrees. The effect of surface albedo reduction enhanced glacier melting by about 20% over the past 20 years. The short-term increase in albedo caused by summer snowfall can considerably reduce glacier melting by as much as 80% in June.
Permafrost Ground Ice Melting and Deformation Time Series Revealed by Sentinel-1 InSAR in the Tanggula Mountain Region on the Tibetan Plateau
Authors: Wang Lingxiao , Zhao Lin , Zhou Huayun , Liu Shibo , Du Erji , Zou Defu , Liu Guangyue , Wang Chong , Li Yan
Published: February 2022

In this study, we applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) to monitor the ground surface deformation from 2017 to 2020 in the permafrost region within an ~400 km × 230 km area covering the northern and southern slopes of Mt. Geladandong, Tanggula Mountains on the Tibetan Plateau. During SBAS-InSAR processing, we inverted the network of interferograms into a deformation time series using a weighted least square estimator without a preset deformation model. The deformation curves of various permafrost states in the Tanggula Mountain region were revealed in detail for the first time. The study region undergoes significant subsidence. Over the subsiding terrain, the average subsidence rate was 9.1 mm/a; 68.1% of its area had a subsidence rate between 5 and 20 mm/a, while just 0.7% of its area had a subsidence rate larger than 30 mm/a. The average peak-to-peak seasonal deformation was 19.7 mm. There is a weak positive relationship (~0.3) between seasonal amplitude (water storage in the active layer) and long-term deformation velocity (ground ice melting). By examining the deformation time series of subsiding terrain with different subsidence levels, we also found that thaw subsidence was not restricted to the summer and autumn thawing times but could last until the following winter, and in this circumstance, the winter uplift was greatly weakened. Two import indices for indicating permafrost deformation properties, i.e., long-term deformation trend and seasonal deformation magnitude, were extracted by direct calculation and model approximations of deformation time series and compared with each other. The comparisons showed that the long-term velocity by different calculations was highly consistent, but the intra-annual deformation magnitudes by the model approximations were larger than those of the intra-annual highest-lowest elevation difference. The findings improve the understanding of deformation properties in the degrading permafrost environment.
Tropical Cyclone Intensity Estimation Using Himawari-8 Satellite Cloud Products and Deep Learning
Authors: Tan Jinkai , Yang Qidong , Hu Junjun , Huang Qiqiao , Chen Sheng
Published: February 2022

This study develops an objective deep-learning-based model for tropical cyclone (TC) intensity estimation. The model’s basic structure is a convolutional neural network (CNN), which is a widely used technology in computer vision tasks. In order to optimize the model’s structure and to improve the feature extraction ability, both residual learning and attention mechanisms are embedded into the model. Five cloud products, including cloud optical thickness, cloud top temperature, cloud top height, cloud effective radius, and cloud type, which are level-2 products from the geostationary satellite Himawari-8, are used as the model training inputs. We sampled the cloud products under the 13 rotational angles of each TC to augment the training dataset. For the independent test data, the model shows improvement, with a relatively low RMSE of 4.06 m/s and a mean absolute error (MAE) of 3.23 m/s, which are comparable to the results seen in previous studies. Various cloud organization patterns, storm whirling patterns, and TC structures from the feature maps are presented to interpret the model training process. An analysis of the overestimated bias and underestimated bias shows that the model’s performance is highly affected by the initial cloud products. Moreover, several controlled experiments using other deep learning architectures demonstrate that our designed model is conducive to estimating TC intensity, thus providing insight into the forecasting of other TC metrics.
Data-Driven Selection of Land Product Validation Station Based on Machine Learning
Authors: Li Ruoxi , Tao Zui , Zhou Xiang , Lv Tingting , Wang Jin , Xie Futai , Zhai Mingjian
Published: February 2022

Validation is a crucial technique used to strengthen the application capabilities of earthobservation satellite data and solve the quality problems of remote-sensing products. Observing land-surface parameters in the field is one of the key steps of validation. Therefore, the demand for long-term stable validation stations has gradually increased. However, the current location-selection procedure of validation stations lacks a systematic and objective evaluation system. In this research, a data-driven selection of a land product validation station (DSS-LPV) based on Machine Learning is proposed. Firstly, we construct an evaluation indicator system in which all factors affecting the location of validation stations are divided into surface characteristics, atmospheric conditions and the social environment. Then, multi-scale evaluation grids are constructed and indicators are allocated for spatial evaluation. Finally, four Machine Learning (ML) methods are used to learn the established reliable stations, and different data-driven scoring models are constructed to explore the intrinsic relationship between evaluation indicators and station locations. In this article, the reliability of DSS-LPV is effectively validated by the example of China using the national-level land product validation station that has been established. After a comparison between the four ML models, the random forest (RF) with the highest accuracy was selected as the modeling method of DSS-LPV. The correlation between the regression value of test stations and the target value is 0.9133. The average score of test stations is 0.8304. The test stations are generally located within the calculated hot-spot area of the score density map, which means that it is highly consistent with the location of the built stations. Research results indicate that DSS-LPV is an effective method that can provide a reasonable geographical distribution of the stations. The location-selection results can provide scientific decision-making support for the construction of land product validation stations.
Estimating Reservoir Release Using Multi-Source Satellite Datasets and Hydrological Modeling Techniques
Authors: Shen Youjiang , Liu Dedi , Jiang Liguang , Tøttrup Christian , Druce Daniel , Yin Jiabo , Nielsen Karina , Bauer-Gottwein Peter , Wang Jun , Zhao Xin
Published: February 2022

Reservoir release is an essential variable as it affects hydrological processes and water availability downstream. This study aims to estimate reservoir release using a satellite-based approach, specially focusing on the impacts of inflow simulations and reservoir water storage change (RWSC) on release estimates. Ten inflow simulations based on hydrological models and blending schemes are used in combination with three RWSC estimates based on two satellite-based approaches. A case study is performed at the Ankang reservoir, China. The results demonstrate that release estimates show high skill, with normalized root-mean-square error (NRMSE) less than 0.12 and Kling-Gupta Efficiency (KGE) over 0.65. The performance of release estimates is varying with and influenced by inflow simulations and RWSC estimates, with NRMSE ranging from 0.09–0.12 and KGE from 0.65–0.74. Based on time-varying Bayesian Model Averaging (BMA) approaches and synthetic aperture radar (SAR) satellite datasets, more accurate inflow and RWSC estimates can be obtained, thus facilitating substantially release estimates. With multi-source satellite datasets, temporal scale of reservoir estimates is increased (monthly and bi-weekly), acting as a key supplement to in situ records. Overall, this study explores the possibility to reconstruct and facilitate reservoir release estimates in poorly gauged dammed basins using hydrological modeling techniques and multi-source satellite datasets.
DSSM: A Deep Neural Network with Spectrum Separable Module for Multi-Spectral Remote Sensing Image Segmentation
Authors: Zhu Hongming , Tan Rui , Han Letong , Fan Hongfei , Wang Zeju , Du Bowen , Liu Sicong , Liu Qin
Published: February 2022

Over the past few years, deep learning algorithms have held immense promise for better multi-spectral (MS) optical remote sensing image (RSI) analysis. Most of the proposed models, based on convolutional neural network (CNN) and fully convolutional network (FCN), have been applied successfully on computer vision images (CVIs). However, there is still a lack of exploration of spectra correlation in MS RSIs. In this study, a deep neural network with a spectrum separable module (DSSM) is proposed for semantic segmentation, which enables the utilization of MS characteristics of RSIs. The experimental results obtained on Zurich and Potsdam datasets prove that the spectrum-separable module (SSM) extracts more informative spectral features, and the proposed approach improves the segmentation accuracy without increasing GPU consumption.
A Machine Learning Framework for the Classification of Natura 2000 Habitat Types at Large Spatial Scales Using MODIS Surface Reflectance Data
Authors: Sittaro Fabian , Hutengs Christopher , Semella Sebastian , Vohland Michael
Published: February 2022

Anthropogenic climate and land use change is causing rapid shifts in the distribution and composition of habitats with profound impacts on ecosystem biodiversity. The sustainable management of ecosystems requires monitoring programmes capable of detecting shifts in habitat distribution and composition at large spatial scales. Remote sensing observations facilitate such efforts as they enable cost-efficient modelling approaches that utilize publicly available datasets and can assess the status of habitats over extended periods of time. In this study, we introduce a modelling framework for habitat monitoring in Germany using readily available MODIS surface reflectance data. We developed supervised classification models that allocate (semi-)natural areas to one of 18 classes based on their similarity to Natura 2000 habitat types. Three machine learning classifiers, i.e., Support Vector Machines (SVM), Random Forests (RF), and C5.0, and an ensemble approach were employed to predict habitat type using spectral signatures from MODIS in the visible-to-near-infrared and short-wave infrared. The models were trained on homogenous Special Areas of Conservation that are predominantly covered by a single habitat type with reference data from 2013, 2014, and 2016 and tested against ground truth data from 2010 and 2019 for independent model validation. Individually, the SVM and RF methods achieved better overall classification accuracies (SVM: 0.72–0.93%, RF: 0.72–0.94%) than the C5.0 algorithm (0.66–0.93%), while the ensemble classifier developed from the individual models gave the best performance with overall accuracies of 94.23% for 2010 and 80.34% for 2019 and also allowed a robust detection of non-classifiable pixels. We detected strong variability in the cover of individual habitat types, which were reduced when aggregated based on their similarity. Our methodology is capable to provide quantitative information on the spatial distribution of habitats, differentiate between disturbance events and gradual shifts in ecosystem composition, and could successfully allocate natural areas to Natura 2000 habitat types.
Mid-State Kalman Filter for Nonlinear Problems
Authors: Liu Zhengwei , Chen Ying , Lu Yaobing
Published: February 2022

When tracking very long-range targets, wide-band radars capable of measuring targets with high precision at ranges have severe measurement nonlinearities. The existing nonlinear filtering technology, such as the extended Kalman filter and untracked Kalman filter, will have significant consistency problems and loss in tracking accuracy. A novel mid-state Kalman filter is proposed to avoid loss and preserve the filtering consistency. The observed state and its first-order state derivative are selected as the mid-state vector. The update process is transformed into the measurement space to ensure the Gaussian measurement distribution and the linearization of the measurement equation. In order to verify the filter performance in comparison, an iterative formulation of Cramér-Rao Low Bound for the nonlinear system is further derived and given in this paper. Simulation results show that the proposed method has excellent performance of high filtering accuracy and fast convergence by comparing the filter state estimation accuracy and consistency.
Cybersickness and Its Severity Arising from Virtual Reality Content: A Comprehensive Study
Authors: Oh Heeseok , Son Wookho
Published: February 2022

Virtual reality (VR) experiences often elicit a negative effect, cybersickness, which results in nausea, disorientation, and visual discomfort. To quantitatively analyze the degree of cybersickness depending on various attributes of VR content (i.e., camera movement, field of view, path length, frame reference, and controllability), we generated cybersickness reference (CYRE) content with 52 VR scenes that represent different content attributes. A protocol for cybersickness evaluation was designed to collect subjective opinions from 154 participants as reliably as possible in conjunction with objective data such as rendered VR scenes and biological signals. By investigating the data obtained through the experiment, the statistically significant relationships—the degree that the cybersickness varies with each isolated content factor—are separately identified. We showed that the cybersickness severity was highly correlated with six biological features reflecting brain activities (i.e., relative power spectral densities of Fp1 delta, Fp 1 beta, Fp2 delta, Fp2 gamma, T4 delta, and T4 beta waves) with a coefficient of determination greater than 0.9. Moreover, our experimental results show that individual characteristics (age and susceptibility) are also quantitatively associated with cybersickness level. Notably, the constructed dataset contains a number of labels (i.e., subjective cybersickness scores) that correspond to each VR scene. We used these labels to build cybersickness prediction models and obtain a reliable predictive performance. Hence, the proposed dataset is supposed to be widely applicable in general-purpose scenarios regarding cybersickness quantification.
A New Design of the Dual-Mode and Pure Longitudinal EMAT by Using a Radial-Flux-Focusing Magnet
Authors: Zhang Xu , Li Weiwen , Li Bo , Tu Jun , Liao Chunhui , Wu Qiao , Feng Sheng , Song Xiaochun
Published: February 2022

An electromagnetic acoustic transducer (EMAT) is suitable for measuring the propagation time more accurately without causing abrasion to the transducer during testing due to the principle of its excitation. This work designs a flux-concentrating EMAT with a radial-flux-focusing permanent magnet to significantly enhance static magnetic field strength. Through theoretical analysis and finite element simulation, two kinds of coils are designed according to the concentration areas of the horizontal and vertical components of the magnetic field. One is used to generate pure longitudinal waves, and the other is used to generate both longitudinal waves and shear waves. The experimental comparison shows that the amplitudes of the pure longitudinal wave and the dual-mode wave excited by the two kinds of coils with the radial-flux-focusing magnet are more than two times higher than those with the ordinary magnet. Therefore, the flux-concentrating EMAT with the appropriate coil provides an insight into realizing more accurate detection where longitudinal wave detection is required.
Lean Neural Networks for Autonomous Radar Waveform Design
Authors: Baietto Anthony , Boubin Jayson , Farr Patrick , Bihl Trevor J. , Jones Aaron M. , Stewart Christopher
Published: February 2022

In recent years, neural networks have exploded in popularity, revolutionizing the domains of computer vision, natural language processing, and autonomous systems. This is due to neural networks ability to approximate complex non-linear functions. Despite their effectiveness, they generally require large labeled data sets and considerable processing power for both training and prediction. Some of these bottlenecks have been mitigated by recent increased availability of high-quality data sets, improvements in neural network development software, and greater hardware support. Due to algorithmic bloat, neural network inference times and imprecision make them undesirable for some problems where fast classical algorithm solutions already exist, other classes of algorithms, such as convex optimization, with non-trivial execution times could be reduced using neural solutions. These algorithms could be replaced with light-weight neural networks, benefiting from their high degree of parallelization and high accuracy when properly trained. Previous work has explored how low size, weight, and power (low SWaP) neural networks and neuromorphic computing can be used to improve autonomous radar waveform design techniques that currently rely on convex optimization. Autonomous radar waveform design helps meet the need for interference mitigation caused by an ever-growing number of consumer and commercial technologies which pollute the radio frequency (RF) spectrum. Spectral notching, a radar waveform design technique, augments transmitted radar waveforms to avoid frequencies with excessive interference while maintaining the integrity of the waveform. In this paper, we extend that work, demonstrating that lean neural networks and specialized hardware can improve inference time for waveform design without sacrificing accuracy. Our lean neural solution incorporates problem-specific information into the layer structures and loss functions to decrease network size and improve accuracy. We provide model outcomes implemented on radio frequency system on a chip (RFSoC) hardware that support our simulation results. Our neural network solution decreases inference time on traditional CPU hardware by 1057× and on GPU hardware accelerators by 883× while maintaining 99% cosine similarity.
Micropreconcentrators: Recent Progress in Designs and Applications
Authors: Stolarczyk Agnieszka , Jarosz Tomasz
Published: February 2022

The detection of chemicals is a fundamental issue of modern civilisation, however existing methods do not always achieve the desired sensitivity. Preconcentrators, which are devices that allow increasing the concentration of the intended analyte via e.g., adsorption/desorption, are one of the solutions for increasing the sensitivity of chemical detection. The increased detection sensitivity granted by preconcentration can be used to miniaturise detection instruments, granting them portability. The primary goal of this review is to report on and briefly explain the most relevant recent developments related to the design and applications of preconcentrators. The key design elements of preconcentrators and the emerging area of liquid-phase preconcentrators are briefly discussed, with the most significant applications of these devices being highlighted.
A Fabricated Force Glove That Measures Hand Forces during Activities of Daily Living
Authors: Austin Edward F. , Kearney Charlotte P. , Chacon Pedro J. , Winges Sara A. , Acharya Prasanna , Choi Jin-Woo
Published: February 2022

Understanding hand and wrist forces during activities of daily living (ADLs) are pertinent when modeling prosthetics/orthotics, preventing workplace-related injuries, and understanding movement patterns that make athletes, dancers, and musicians elite. The small size of the wrist, fingers, and numerous joints creates obstacles in accurately measuring these forces. In this study, 14 FlexiForce sensors were sewn into a glove in an attempt to capture forces applied by the fingers. Participants in this study wore the glove and performed grasp and key turn activities. The maximal forces produced in the study were 9 N at the distal middle finger phalanx and 24 N at the distal thumb phalanx, respectively, for the grasp and key turn activities. Results from this study will help in determining the minimal forces of the hand during ADLs so that appropriate actuators may be placed at the appropriate joints in exoskeletons, orthotics, and prosthetics.
Mechanism Analysis and Self-Adaptive RBFNN Based Hybrid Soft Sensor Model in Energy Production Process: A Case Study
Authors: Du Junrong , Zhang Jian , Yang Laishun , Li Xuzhi , Guo Lili , Song Lei
Published: February 2022

Despite hard sensors can be easily used in various condition monitoring of energy production process, soft sensors are confined to some specific scenarios due to difficulty installation requirements and complex work conditions. However, industrial process may refer to complex control and operation, the extraction of relevant information from abundant sensors data may be challenging, and description of complicated process data patterns is also becoming a hot topic in soft-sensor development. In this paper, a hybrid soft sensor model based mechanism analysis and data-driven is proposed, and ventilation sensing of coal mill in a power plant is conducted as a case study. Firstly, mechanism model of ventilation is established via mass and energy conservation law, and object-relevant features are identified as the inputs of data-driven method. Secondly, radial basis function neural network (RBFNN) is used for soft sensor modeling, and genetic algorithm (GA) is adopted for quick and accurate determination of the RBFNN hyper-parameters, thus self-adaptive RBFNN (SA-RBFNN) is proposed to improve the soft sensor performance in energy production process. Finally, effectiveness of the proposed method is verified on a real-world power plant dataset, taking coal mill ventilation soft sensing as a case study.
Cross-Modal Object Detection Based on a Knowledge Update
Authors: Gao Yueqing , Zhou Huachun , Chen Lulu , Shen Yuting , Guo Ce , Zhang Xinyu
Published: February 2022

As an important field of computer vision, object detection has been studied extensively in recent years. However, existing object detection methods merely utilize the visual information of the image and fail to mine the high-level semantic information of the object, which leads to great limitations. To take full advantage of multi-source information, a knowledge update-based multimodal object recognition model is proposed in this paper. Specifically, our method initially uses Faster R-CNN to regionalize the image, then applies a transformer-based multimodal encoder to encode visual region features (region-based image features) and textual features (semantic relationships between words) corresponding to pictures. After that, a graph convolutional network (GCN) inference module is introduced to establish a relational network in which the points denote visual and textual region features, and the edges represent their relationships. In addition, based on an external knowledge base, our method further enhances the region-based relationship expression capability through a knowledge update module. In summary, the proposed algorithm not only learns the accurate relationship between objects in different regions of the image, but also benefits from the knowledge update through an external relational database. Experimental results verify the effectiveness of the proposed knowledge update module and the independent reasoning ability of our model.
Small-Pore Zeolite Membranes: A Review of Gas Separation Applications and Membrane Preparation
Authors: Cao Zishu , Anjikar Ninad , Yang Shaowei
Published: February 2022

There have been significant advancements in small-pore zeolite membranes in recent years. With pore size closely related to many energy- or environment-related gas molecules, small-pore zeolite membranes have demonstrated great potential for the separation of some interested gas pairs, such as CO2/CH4, CO2/N2 and N2/CH4. Small-pore zeolite membranes share some characteristics but also have distinctive differences depending on their framework, structure and zeolite chemistry. Through this mini review, the separation performance of different types of zeolite membranes with respect to interested gas pairs will be compared. We aim to give readers an idea of membrane separation status. A few representative synthesis conditions are arbitrarily chosen and summarized, along with the corresponding separation performance. This review can be used as a quick reference with respect to the influence of synthesis conditions on membrane quality. At the end, some general findings and perspectives will be discussed.
Changed for the Better: The Modifiable Maltreatment Factors Framework
Authors: Feely Megan
Published: February 2022

Rates of child maltreatment in the U.S. have been relatively stagnant since the 1990s. This lack of progress suggests that prevention efforts, which have primarily focused on individual behaviors, have been unsuccessful. Building on existing research, this conceptual paper identifies the need to refocus prevention efforts on changeable and causal risk factors. The Modifiable Maltreatment Factors (MMF), a new framework to classify risk factors for maltreatment, is introduced. Use of the MMF in social work education and child protective services work could increase the understanding of macro factors in child maltreatment and the possibilities of policy change and community organizing in maltreatment prevention.
Containing the Risk of Phosphorus Pollution in Agricultural Watersheds
Authors: Wildemeersch Matthias , Tang Shaohui , Ermolieva Tatiana , Ermoliev Yurii , Rovenskaya Elena , Obersteiner Michael
Published: February 2022

Phosphorus (P) is an essential nutrient to boost crop yields, but P runoff can cause nutrient over-enrichment in agricultural watersheds and can lead to irreversible effects on aquatic ecosystems and their biodiversity. Lake Erie is one prominent example as this watershed has experienced multiple episodes of harmful algal blooms over the last decades. Annual P loads crucially depend on yearly weather variations, which can create the risk of years with high runoff and excessive nutrient loads. Here we apply stochastic modeling to derive sustainable management strategies that balance crop yield optimization with environmental protection, while accounting for weather variability as well as weather trends as a result of climate change. We demonstrate that ignoring annual weather variations results in mitigation efforts for environmental pollution that are largely insufficient. Accounting explicitly for future variations in precipitation allows us to control the risk of emissions exceeding the P target loads. When realistic risk targets are imposed, we find that a package of additional measures is required to avoid P over-enrichment in the Lake Erie watershed. This package consists of a substantial reduction of P inputs (approximately 30% for different accepted risk levels), adoption of cover crops throughout the near- and mid-century, and cultivation of less nutrient-intensive crops (30% more soy at the expense of corn). Although climate change reinforces these conclusions, we find that the accepted risk level of exceeding P target loads is the predominant factor in defining a sustainable nutrient management policy.
Effectively Recycling Swine Wastewater by Coagulation–Flocculation of Nonionic Polyacrylamide
Authors: Lee Wan-Chen , Chang Chih-Cheng
Published: February 2022

Recycling swine wastewater is an environmental and economic issue for promoting the sustainable development of the pig industry worldwide. The application of a flocculant, non-ionic polyacrylamide (NPAM) for treating the contaminants in wastewater was trialed in this study. Firstly, the optimal pH value for the coagulation–flocculation of NPAM was adjusted by hydrochloric acid and sodium hydroxide. The viscosity of the flocculant solution was examined by a rotational viscometer and the morphology of the flocculant on the glass surface was examined by an optical microscope and an atomic force microscope. The result showed that a pH value of 11 or more was best for NPAM coagulation–flocculation. Subsequently, the swine wastewater from the anoxic reactor of a three-stage manure treatment system was adjusted by a pH adjuster, calcium hydroxide, followed by the coagulation–flocculation of NPAM. The quality of the final, treated water was examined by a regular wastewater analysis. The results showed that the removal rates for copper ions, zinc ions, NH4+–N, total phosphate (TP), and total nitrogen (TN) were 96.3%, 97.8%, 99.2%, 94.9%, and 99.1%, respectively. Our study concluded that this water recycling method combining the existing organic fertilizer production and power generation enhanced the recycling strategy for swine wastewater treatment and could further the sustainable development of the pig industry.
Transport Accessibility and Tourism Development Prospects of Indigenous Communities of Siberia
Authors: Kuklina Maria , Dirin Denis , Filippova Viktoriya , Savvinova Antonina , Trufanov Andrey , Krasnoshtanova Natalia , Bogdanov Viktor , Kobylkin Dmitrii , Fedorova Alla , Itegelova Anna , Batotsyrenov Eduard
Published: February 2022

This paper aims to identify details of interaction and mutual influence of tourism and transport infrastructure development using a combination of sociological and statistical methods. For the analysis, five key sites were selected in several parts of Siberia, differing in natural, socio-economic, and ethnocultural quality, but possessing the presence of compact indigenous communities that reproduce traditional forms of both cultural life and economic activity. While each community has its own unique culture and resources for development, several similar characteristics were revealed. It has been revealed that tourists themselves and workers of the tourism sector, including local residents, are unequivocal supporters of transportation system development. A significant part of the population, recognizing the need and advantages of having a well-functioning transport infrastructure, expresses a negative attitude towards easy accessibility of the territory for outsiders who violate the rules and regime for usage of natural resources and augment economic and cultural competition for the local population. Understanding key processes of interrelations, both explicit and hidden, concomitant to socioeconomic development of remote indigenous communities, with the simultaneous development of tourism and transport, allows the authors to propose a model outline of attitudes of local communities towards tourist and transport perspectives in the territory of their residence.
Global Warming Potential of Organic Strawberry Production under Unheated High Tunnels in Kentucky, USA
Authors: Clark Sean , Mousavi-Avval Seyed Hashem
Published: February 2022

The global warming potential (GWP) of organic strawberries (Fragaria × ananassa) grown under high tunnels in Kentucky, USA, was assessed using life cycle assessment (LCA) methodology. The site, part of the Berea College Farm, had been under organic crop management for two decades. The GWP was calculated as 0.57 kg CO2-eq per kg of strawberries with the combined impact of the aluminum and plastic manufacturing accounting for 44% of the total and the direct production activities, including labor, accounting for another 28%. The average yields of 18,990 kg/ha of fresh fruit over the two years (2020–2021) were comparable to those typically reported in the southeastern USA for conventional production, but opportunities to increase strawberry yields in high tunnels without increasing inputs should be explored to reduce the GWP. Future research should also measure the GWP of production in controlled-environment agriculture (CEA) systems, particularly plant factories with artificial lighting (PFALs), to compare the greenhouse gas emissions of strawberries grown with these technologies to those produced using the simple, high-tunnel method.
Developing Preservice Chemistry Teachers’ Engagement with Sustainability Education through an Online Project-Based Learning Summer Course Program
Authors: Paristiowati Maria , Rahmawati Yuli , Fitriani Ella , Satrio Justinus A. , Putri Hasibuan Nur Azizah
Published: February 2022

The aim of this research was to develop the sustainability competencies of preservice chemistry teachers’ through the use of a project-based learning model. Preservice chemistry teachers were engaged in a summer course program in collaboration with national and international universities. The summer course program was conducted online due to the COVID-19 pandemic. The research involved 26 preservice chemistry teachers from a pedagogical university in Jakarta, Indonesia, which joined with other university students from other universities in Indonesia, America, Thailand, and Malaysia. We used a qualitative methodology. Data were collected through interviews, questionnaires, observations, preservice chemistry teachers’ portfolios, and reflective journals. The data were coded into themes and interpreted to reveal that all students engaged successfully in developing their sustainability perspectives, environmental awareness, project development engagement, communication, and collaboration skills. Meanwhile, the preservice chemistry teachers engaged in developing their project in an online summer course program within the framework of sustainability.