bluuebunny/crossref_metadata_2025 huggingface dataset:
Scale-Dependent Effects of Growth Stage and Elevational Gradient on Rice Phyllosphere Bacterial and Fungal Microbial Patterns in the Terrace Field
Authors: Wang Pei , Dai Jianping , Luo Luyun , Liu Yong , Jin Decai , Zhang Zhuo , Li Xiaojuan , Fu Wei , Tang Tao , Xiao Youlun , Hu Yang , Liu Erming
Published: January 2022

The variation of phyllosphere bacterial and fungal communities along elevation gradients may provide a potential link with temperature, which corresponds to an elevation over short geographic distances. At the same time, the plant growth stage is also an important factor affecting phyllosphere microorganisms. Understanding microbiological diversity over changes in elevation and among plant growth stages is important for developing crop growth ecological theories. Thus, we investigated variations in the composition of the rice phyllosphere bacterial and fungal communities at five sites along an elevation gradient from 580 to 980 m above sea level (asl) in the Ziquejie Mountain at the seedling, heading, and mature stages, using high-throughput Illumina sequencing methods. The results revealed that the dominant bacterial phyla were Proteobacteria, Actinobacteria, and Bacteroidetes, and the dominant fungal phyla were Ascomycota and Basidiomycota, which varied significantly at different elevation sites and growth stages. Elevation had a greater effect on the α diversity of phyllosphere bacteria than on that phyllosphere fungi. Meanwhile, the growth stage had a great effect on the α diversity of both phyllosphere bacteria and fungi. Our results also showed that the composition of bacterial and fungal communities varied significantly along elevation within the different growth stages, in terms of both changes in the relative abundance of species, and that the variations in bacterial and fungal composition were well correlated with variations in the average elevation. A total of 18 bacterial and 24 fungal genera were significantly correlated with elevational gradient, displaying large differences at the various growth stages. Soluble protein (SP) shared a strong positive correlation with bacterial and fungal communities (p < 0.05) and had a strong significant negative correlation with Serratia, Passalora, unclassified_Trichosphaeriales, and antioxidant enzymes (R > 0.5, p < 0.05), and significant positive correlation with the fungal genera Xylaria, Gibberella, and Penicillium (R > 0.5, p < 0.05). Therefore, it suggests that elevation and growth stage might alter both the diversity and abundance of phyllosphere bacterial and fungal populations.
Arginine Increases Tolerance to Nitrogen Deficiency in Malus hupehensis via Alterations in Photosynthetic Capacity and Amino Acids Metabolism
Authors: Chen Qi , Wang Yanpeng , Zhang Zhijun , Liu Xiaomin , Li Chao , Ma Fengwang
Published: January 2022

Arginine plays an important role in the nitrogen (N) cycle because it has the highest ratio of N to carbon among amino acids. In recent years, there has been increased research interest in improving the N use of plants, reducing the use of N fertilizer, and enhancing the tolerance of plants to N deficiency. Here, the function of arginine in the growth of apple (Malus hupehensis) under N deficiency was explored. The application of 100 μmol L–1 arginine was effective for alleviating N-deficiency stress. Exogenous arginine promoted the absorption and use of N, phosphorus (P), and potassium (K) under low N stress. The net photosynthetic rate, maximal photochemical efficiency of photosystem II, and chlorophyll content were higher in treated plants than in control plants. Exogenous arginine affected the content of many metabolites, and the content of many amino acids with important functions was significantly increased, such as glutamate and ornithine, which play an important role in the urea cycle. Half of the metabolites were annotated to specialized metabolic pathways, including the synthesis of phenolic substances, flavonoids, and other substances with antioxidant activity. Our results indicate that arginine promotes the plant photosynthetic capacity and alters amino acid metabolism and some antioxidants including phenolic substances and flavonoids to improve the tolerance of apple to N deficiency, possibly through the improvement of arginine content, and the absorption of mineral.
Red Light Regulates the Metabolite Biosynthesis in the Leaves of “Huangjinya” Through Amino Acid and Phenylpropanoid Metabolisms
Authors: Ma Qingping , Song Laichao , Niu Zhanhai , Li Jingshan , Wang Yu , Sun Haiwei , Ren Zhihong , Zhao Hongxia , Guo Shangjing , Ding Zhaotang
Published: January 2022

“Huangjinya” is a light-sensitive albino variety and is widely cultivated in China. It has been proved that red light could promote the vegetable growth of plants. However, the mechanism of “Huangjinya” in response to a red light is unclear. This study used high-throughput sequencing technology to analyze the transcriptome of tender shoots of “Huangjinya” under the white and red light supplement conditions. At the same time, liquid chromatography tandem mass spectrometry (LC-MS) was used to analyze metabolite changes under different light conditions. Transcriptome analysis revealed that a total of 174 differentially expressed genes (DEGs) were identified after the red light supplement. Kyoto encyclopedia of genes and genomes (KEGG) classification indicated that amino acid metabolism enriched the most DEGs. In addition, two phenylpropanoid metabolism-related genes and five glutathione S-transferase genes (CsGSTs) were found to be expressed differently. Metabolome analysis revealed that 193 differential metabolites were obtained. Being the same as transcriptome analysis, most differential metabolites were enriched in amino acids, sweet and umami tasting amino acids were increased, and bitter-tasting amino acids were decreased after the red light supplement. In summary, red light supplementary treatment may be propitious to the quality of “Huangjinya” due to its regulatory effect on amino acid metabolism. Also, CsGSTs involved phenylpropanoid metabolism contributed to tea quality changes in “Huangjinya.”
How Does Search for Meaning Lead to Presence of Meaning for Korean Army Soldiers? The Mediating Roles of Leisure Crafting and Gratitude
Authors: Lim Jung In , Yu Jason , Sohn Young Woo
Published: January 2022

Many studies demonstrate that finding meaning in life reduces stress and promotes physical and psychological well-being. However, extant literature focuses on meaning in life among the general population (e.g., college students or office workers) in their daily lives. Thus, this study aimed to investigate the mechanisms of how individuals living in life-threatening and stressful situations obtain meaning in life, by investigating the mediating roles of leisure crafting and gratitude. A total of 465 Army soldiers from the Republic of Korea (ROK) participated in two-wave surveys with a 2-week interval. Structural equation modeling analyses indicated that the direct effects between the search for meaning, presence of meaning, leisure crafting, and gratitude were significant, except for the direct relationship between the search for meaning and the presence of meaning, and between leisure crafting and the presence of meaning. We tested indirect effects using a Monte Carlo approach and found that leisure crafting and gratitude sequentially mediated the relationship between the search for meaning and the presence of meaning. Our findings highlight the importance of the motivation behind searching for meaning, the proactive use of leisure time, and gratitude for individuals in stressful situations and controlled lifestyles. Finally, we discuss the implications and limitations of this research and future research directions.
What Matters in Online Education: Exploring the Impacts of Instructional Interactions on Learning Outcomes
Authors: Li Xing , Lin Xinyue , Zhang Fan , Tian Yuan
Published: January 2022

Instructional interactions, which includes student–student interaction (SS), student–teacher interaction (ST), and student–content interaction (SC), are crucial factors affecting the learning outcomes in online education. The current study aims to explore the effects of instructional interactions on individuals’ learning outcomes (i.e., academic performance and learning satisfaction) based on the Interactive Equivalence Theory by conducting two empirical studies. In Study 1, we explored the direct relationships between instructional interactions and learning outcomes. A quasi-experimental design was used to manipulate the two groups of subjects (n1= 192;n2= 195), and the results show that not all of the three types of interaction can significantly positively predict learning satisfaction, among which ST cannot significantly predict learning satisfaction. When the total amount of instructional interactions is constant, adjusting the relative level of the three types of instructional interactions can effectively improve the learning outcomes to some extent. We further probed into the mediating effects of task value and self-regulated learning on the relationships between instructional interactions and learning outcomes in Study 2. We conducted an online survey and collected 374 valid data. The results showed that task values mediated the relationship between SS and learning satisfaction. In addition, SC can not only directly affect learning satisfaction, but also affect it through task value and self-regulated learning respectively, orviachain mediations of both task value and self-regulated learning. Our findings enrich the previous instructional interactions research and provide reference for online education curriculum design.
COVID-19 Related Sentiment Analysis Using State-of-the-Art Machine Learning and Deep Learning Techniques
Authors: Jalil Zunera , Abbasi Ahmed , Javed Abdul Rehman , Badruddin Khan Muhammad , Abul Hasanat Mozaherul Hoque , Malik Khalid Mahmood , Saudagar Abdul Khader Jilani
Published: January 2022

The coronavirus disease 2019 (COVID-19) pandemic has influenced the everyday life of people around the globe. In general and during lockdown phases, people worldwide use social media network to state their viewpoints and general feelings concerning the pandemic that has hampered their daily lives. Twitter is one of the most commonly used social media platforms, and it showed a massive increase in tweets related to coronavirus, including positive, negative, and neutral tweets, in a minimal period. The researchers move toward the sentiment analysis and analyze the various emotions of the public toward COVID-19 due to the diverse nature of tweets. Meanwhile, people have expressed their feelings regarding the vaccinations' safety and effectiveness on social networking sites such as Twitter. As an advanced step, in this paper, our proposed approach analyzes COVID-19 by focusing on Twitter users who share their opinions on this social media networking site. The proposed approach analyzes collected tweets' sentiments for sentiment classification using various feature sets and classifiers. The early detection of COVID-19 sentiments from collected tweets allow for a better understanding and handling of the pandemic. Tweets are categorized into positive, negative, and neutral sentiment classes. We evaluate the performance of machine learning (ML) and deep learning (DL) classifiers using evaluation metrics (i.e., accuracy, precision, recall, and F1-score). Experiments prove that the proposed approach provides better accuracy of 96.66, 95.22, 94.33, and 93.88% for COVISenti, COVIDSenti_A, COVIDSenti_B, and COVIDSenti_C, respectively, compared to all other methods used in this study as well as compared to the existing approaches and traditional ML and DL algorithms.
The Role of the Digital Economy in Rebuilding and Maintaining Social Governance Mechanisms
Authors: Niu Fengjuan
Published: January 2022

The digital transformation has impacted society at different levels, mainly on the economic and governance levels. This paper investigates the impact of the digital economy on social governance mechanisms. Additionally, it captures the indirect effects or mediating forces such as social reforms and a sustainable digital economy. The study followed a positivism philosophy, and it is survey research influencing cross-sectional study. The unit of analysis in the current paper was employees from four different professions as economists, financial analysts, managers, and teachers. The random sampling technique was used as a sampling type, and a questionnaire was used for data collection. Structural equation modeling (SEM) was carried out as a data analysis technique. The research findings revealed that the digital economy has a favorable impact on the social governance mechanism. Likewise, the digital economy positively affects social reforms and a sustainable digital economy. Social reforms also proved to link with a sustainable digital economy positively. The output of the indirect effects and structural model confirmed that social reform played a partial mediation role between the digital economy and sustainable digital economy. Moreover, a sustainable digital economy confirmed a partial mediation between the digital economy and the social governance mechanism. Finally, analysis confirmed a serial mediation among digital economy, social reforms, sustainable digital economy, and social governance mechanism. Therefore, policymakers and government agents should improve the digital economy to have a strong social governance mechanism.
Quantum Propensity in Economics
Authors: Orrell David , Houshmand Monireh
Published: January 2022

This paper describes an approach to economics that is inspired by quantum computing, and is motivated by the need to develop a consistent quantum mathematical framework for economics. The traditional neoclassical approach assumes that rational utility-optimisers drive market prices to a stable equilibrium, subject to external perturbations or market failures. While this approach has been highly influential, it has come under increasing criticism following the financial crisis of 2007/8. The quantum approach, in contrast, is inherently probabilistic and dynamic. Decision-makers are described, not by a utility function, but by a propensity function which specifies the probability of transacting. We show how a number of cognitive phenomena such as preference reversal and the disjunction effect can be modelled by using a simple quantum circuit to generate an appropriate propensity function. Conversely, a general propensity function can be quantized,viaan entropic force, to incorporate effects such as interference and entanglement that characterise human decision-making. Applications to some common problems and topics in economics and finance, including the use of quantum artificial intelligence, are discussed.
Evaluating a Speech-Specific and a Computerized Step-Training-Specific Rhythmic Intervention in Parkinson's Disease: A Cross-Over, Multi-Arms Parallel Study
Authors: Rösch Anne Dorothée , Taub Ethan , Gschwandtner Ute , Fuhr Peter
Published: January 2022

Background:

Recent studies suggest movements of speech and gait in patients with Parkinson's Disease (PD) are impaired by a common underlying rhythmic dysfunction. If this being the case, motor deficits in speech and gait should equally benefit from rhythmic interventions regardless of whether it is a speech-specific or step-training-specific approach.

Objective:

In this intervention trial, we studied the effects of two rhythmic interventions on speech and gait. These rhythmic intervention programs are similar in terms of intensity and frequency (i.e., 3x per week, 45 min-long sessions for 4 weeks in total), but differ regarding therapeutic approach (rhythmic speech vs. rhythmic balance-mobility training).

Methods:

This study is a cross-over, parallel multi-arms, single blind intervention trial, in which PD patients treated with rhythmic speech-language therapy (rSLT; N = 16), rhythmic balance-mobility training (rBMT; N = 10), or no therapy (NT; N = 18) were compared to healthy controls (HC; N = 17; matched by age, sex, and education: p > 0.82). Velocity and cadence in speech and gait were evaluated at baseline (BL), 4 weeks (4W-T1), and 6 months (6M-T2) and correlated.

Results:

Parameters in speech and gait (i.e., speaking and walking velocity, as well as speech rhythm with gait cadence) were positively correlated across groups (p < 0.01). Statistical analyses involved repeated measures ANOVA across groups and time, as well as independent and one-samples t-tests for within groups analyses. Statistical analyses were amplified using Reliable Change (RC) and Reliable Change Indexes (RCI) to calculate true clinically significant changes due to the treatment on a patient individual level. Rhythmic intervention groups improved across variables and time (total Mean Difference: 3.07 [SD 1.8]; 95% CI 0.2–11.36]) compared to the NT group, whose performance declined significantly at 6 months (p < 0.01). HC outperformed rBMT and NT groups across variables and time (p < 0.001); the rSLT performed similarly to HC at 4 weeks and 6 months in speech rhythm and respiration.

Conclusions:

Speech and gait deficits in PD may share a common mechanism in the underlying cortical circuits. Further, rSLT was more beneficial to dysrhythmic PD patients than rBMT, likely because of the nature of the rhythmic cue.
Urban River Interventions in São Paulo Municipality (Brazil): The Challenge of Ensuring Justice in Sociotechnical Transitions
Authors: Travassos Luciana , Momm Sandra
Published: January 2022

The paper aims to discuss the sociotechnical transitions regarding urban rivers policy in São Paulo Municipality by focusing on programs and projects conducted since 2000. Accordingly, we use a theoretical reflection on sociotechnical transitions and just transitions in interventions related to water and cities. Our work is based on a documental analysis of programs and projects for urban rivers in the municipality conducted via theoretical discussion. The primary focus is on the current sociotechnical regime, the channeling of streams and construction of road systems on its banks, and disputes and pressures brought by the technological landscape and niches, which lead to the construction of linear parks and leisure areas along with the bodies of water. It also shows how the issue of justice has been losing ground in this transition, which although is “in the making,” already presents many factors of injustice. This is due to the low presence of the theme of precarious settlements, in innovative speeches and practices, and the different treatment given by the programs and projects for rivers in the consolidated middle- and upper-class regions and for those located on the peripheries.
Triggermuscle: Exploring Weight Perception for Virtual Reality Through Adaptive Trigger Resistance in a Haptic VR Controller
Authors: Stellmacher Carolin , Bonfert Michael , Kruijff Ernst , Schöning Johannes
Published: January 2022

It is challenging to provide users with a haptic weight sensation of virtual objects in VR since current consumer VR controllers and software-based approaches such as pseudo-haptics cannot render appropriate haptic stimuli. To overcome these limitations, we developed a haptic VR controller named Triggermuscle that adjusts its trigger resistance according to the weight of a virtual object. Therefore, users need to adapt their index finger force to grab objects of different virtual weights. Dynamic and continuous adjustment is enabled by a spring mechanism inside the casing of an HTC Vive controller. In two user studies, we explored the effect on weight perception and found large differences between participants for sensing change in trigger resistance and thus for discriminating virtual weights. The variations were easily distinguished and associated with weight by some participants while others did not notice them at all. We discuss possible limitations, confounding factors, how to overcome them in future research and the pros and cons of this novel technology.
Adaptive Playback Control: A Framework for Cinematic VR Creators to Embrace Viewer Interaction
Authors: Tong Lingwei , Lindeman Robert W. , Regenbrecht Holger
Published: January 2022

Content creators have been trying to produce engaging and enjoyable Cinematic Virtual Reality (CVR) experiences using immersive media such as 360-degree videos. However, a complete and flexible framework, like the filmmaking grammar toolbox for film directors, is missing for creators working on CVR, especially those working on CVR storytelling with viewer interactions. Researchers and creators widely acknowledge that a viewer-centered story design and a viewer’s intention to interact are two intrinsic characteristics of CVR storytelling. In this paper, we stand on that common ground and propose Adaptive Playback Control (APC) as a set of guidelines to assist content creators in making design decisions about the story structure and viewer interaction implementation during production. Instead of looking at everything CVR covers, we set constraints to focus only at cultural heritage oriented content using a guided-tour style. We further choose two vital elements for interactive CVR: the narrative progression (director vs. viewer control) and visibility of viewer interaction (implicit vs. explicit) as the main topics at this stage. We conducted a user study to evaluate four variants by combining these two elements, and measured the levels of engagement, enjoyment, usability, and memory performance. One of our findings is that there were no differences in the objective results. Combining objective data with observations of the participants’ behavior we provide guidelines as a starting point for the application of the APC framework. Creators need to choose if the viewer will have control over narrative progression and the visibility of interaction based on whether the purpose of a piece is to invoke emotional resonance or promote efficient transfer of knowledge. Also, creators need to consider the viewer’s natural tendency to explore and provide extra incentives to invoke exploratory behaviors in viewers when adding interactive elements. We recommend more viewer control for projects aiming at viewer’s participation and agency, but more director control for projects focusing on education and training. Explicit (vs. implicit) control will also yield higher levels of engagement and enjoyment if the viewer’s uncertainty of interaction consequences can be relieved.
Pore-Scale Modeling of Mineral Growth and Nucleation in Reactive Flow
Authors: Starchenko Vitalii
Published: January 2022

A fundamental understanding of mineral precipitation kinetics relies largely on microscopic observations of the dynamics of mineral surfaces exposed to supersaturated solutions. Deconvolution of tightly bound transport, surface reaction, and crystal nucleation phenomena still remains one of the main challenges. Particularly, the influence of these processes on texture and morphology of mineral precipitate remains unclear. This study presents a coupling of pore-scale reactive transport modeling with the Arbitrary Lagrangian-Eulerian approach for tracking evolution of explicit solid interface during mineral precipitation. It incorporates a heterogeneous nucleation mechanism according to Classical Nucleation Theory which can be turned “on” or “off.” This approach allows us to demonstrate the role of nucleation on precipitate texture with a focus at micrometer scale. In this work precipitate formation is modeled on a 10 micrometer radius particle in reactive flow. The evolution of explicit interface accounts for the surface curvature which is crucial at this scale in the regime of emerging instabilities. The results illustrate how the surface reaction and reactive fluid flow affect the shape of precipitate on a solid particle. It is shown that nucleation promotes the formation of irregularly shaped precipitate and diminishes the effect of the flow on the asymmetry of precipitation around the particle. The observed differences in precipitate structure are expected to be an important benchmark for reaction-driven precipitation in natural environments.
Improvisational Movement to Improve Quality of Life in Older Adults With Early-Stage Dementia: A Pilot Study
Authors: Thumuluri Deepthi , Lyday Robert , Babcock Phyllis , Ip Edward H. , Kraft Robert A. , Laurienti Paul J. , Barnstaple Rebecca , Soriano Christina T. , Hugenschmidt Christina E.
Published: January 2022

Alzheimer's disease has profound effects on quality of life, affecting not only cognition, but mobility and opportunities for social engagement. Dance is a form of movement that may be uniquely suited to help maintain quality of life for older adults, including those with dementia, because it inherently incorporates movement, social engagement, and cognitive stimulation. Here, we describe the methods and results of the pilot study for the IMOVE trial (NCT03333837, www.clinicaltrials.gov), a clinical trial designed to use improvisational dance classes to test the effects of movement and social engagement in people with mild cognitive impairment (MCI) or early-stage dementia. The pilot study was an 8-week investigation into the feasibility and potential effects of an improvisational dance intervention on people with MCI or early-stage dementia (PWD/MCI) and their caregivers (CG). The pilot aimed to assess changes in quality of life, balance, mood, and functional brain networks in PWD/MCI and their CG. Participants were recruited as dyads (pairs) that included one PWD/MCI and one CG. Ten total dyads were enrolled in the pilot study with five dyads assigned to the usual care control group and five dyads participating in the dance intervention. The intervention arm met twice weekly for 60 min for 8 weeks. Attendance and quality of life assessed with the Quality of Life in Alzheimer's disease (QoL-AD) questionnaire were the primary outcomes. Secondary outcomes included balance, mood and brain network connectivity assessed through graph theory analysis of functional magnetic resonance imaging (fMRI). Class attendance was 96% and qualitative feedback reflected participants felt socially connected to the group. Increases in quality of life and balance were observed, but not mood. Brain imaging analysis showed increases in multiple brain network characteristics, including global efficiency and modularity. Further investigation into the positive effects of this dance intervention on both imaging and non-imaging metrics will be carried out on the full clinical trial data. Results from the trial are expected in the summer of 2022.
What Will Happen to This Dog? A Qualitative Analysis of Rehoming Organisations' Pre-adoption Dog Behaviour Screening Policies and Procedures
Authors: Griffin Karen E. , John Elizabeth , Pike Tom , Mills Daniel S.
Published: January 2022

Rehoming organisations often undertake some type of behaviour evaluation to determine dogs' suitability for rehoming and/or the type of suitable home. Assessments can carry considerable weight in determining dogs' fates. Although evaluation of the validity and reliability of any test is important, a more fundamental consideration is if the nature of the information sought and the weight given to this in organisations' decision making is of more than anecdotal value. Therefore, this study's aim was to conduct a qualitative analysis of organisations' pre-adoption dog behaviour screenings and potential justifications, comparing this with the available scientific evidence. A written enquiry was sent electronically to rehoming organisations in the UK and US from February 2016-July 2017. Of 73 respondents, the majority conducted assessments for all dogs. Using a thematic analysis, nine themes and 71 sub-themes emerged concerning the types of information respondents aim to gather from assessments. The majority of respondents used, at least partially,pass/failscoring, i.e., certain outcomes would lead to dogs being deemed unadoptable. Forty-one sub-themes and one theme were identified as potentially leading to a dog being deemed unadoptable. The evidence base for these factors was identified from the scientific literature relating to: increased risk for relinquishment, impact on a dog's quality of life, and human safety risk. Evidence supported 10 factors: “aggression towards people”, “aggression towards cats or other animals”, “aggression towards dogs”, “biting or snapping”, “resource guarding”, “activity level or exercise needs”, “destructiveness”, “housetrained”, “fearfulness”, and “knowledge of basic commands and/or general training”. Of those, seven were associated only with relinquishment risk, two (“resource guarding”, “knowledge of basic commands”) with human safety risk, and one (“fearfulness”) with both. Thus, for >85% of characteristics organisations deemed important for dogs' adoptability, scientific evidence to support this is lacking. More research is needed to investigate the value of behaviour assessments, especially concerning the assessment of factors that could pose a public safety risk. However, given the current lack of scientific support for many decisions regarding dogs' rehoming suitability and recognised pressure on resources, it is suggested that organisations should focus on pre-adoption adopter education and post-adoption support.
Mach 4 Simulating Experiment of Pre-Cooled Turbojet Engine Using Liquid Hydrogen
Authors: Taguchi Hideyuki , Harada Kenya , Kobayashi Hiroaki , Hongoh Motoyuki , Masaki Daisaku , Nishida Shunsuke
Published: January 2022

This study investigated a pre-cooled turbojet engine for a Mach 5 class hypersonic transport aircraft. The engine was demonstrated under takeoff and Mach 2 flight conditions, and a Mach 5 propulsion wind tunnel test is planned. The engine is composed of a pre-cooler, a core engine, and an afterburner. The engine was tested under simulated Mach 4 conditions using an air supply facility. High-temperature air under high pressure was supplied to the engine components through an airflow control valve and an orifice flow meter, and liquid hydrogen was supplied to the pre-cooler and the core engine. The results confirmed that the starting sequence of the engine components was effective under simulated Mach 4 conditions using liquid hydrogen fuel. The pre-cooling effect caused no damage to the rotating parts of the core engine in the experiment.
Correction: Li et al. Quantitative Evaluation of China’s Pork Industry Policy: A PMC Index Model Approach. Agriculture 2021, 11, 86
Authors: Li Youzhu , He Rui , Liu Jinsi , Li Chongguang , Xiong Jason
Published: January 2022

The authors found some omissions and errors in the original paper [...]
Re-Estimation of Agricultural Production Efficiency in China under the Dual Constraints of Climate Change and Resource Environment: Spatial Imbalance and Convergence
Authors: Mo Binbin , Hou Mengyang , Huo Xuexi
Published: January 2022

Climate change and farmland environmental pollution have put greater pressure on the sustainability of agricultural production. Based on the provincial panel data of mainland China from 1978 to 2018, climate variables such as precipitation, temperature, and sunshine hours are included into the input indicators, and agricultural non-point source pollution and carbon emissions are taken as undesirable outputs, the agricultural production efficiency (APE) under the dual constraints of climate change and the resource environment was estimated by the super slacks-based measure (SBM)-undesirable model. On the basis of the trajectory of the imbalanced spatiotemporal evolution of APE shown by Kernel density estimation and the standard deviational ellipse (SDE)–center of gravity (COG) transfer model, the spatial convergence model was used to test the convergence and differentiation characteristics of APE. Under the dual constraints, APE presents a “bimodal” distribution with a stable increase in fluctuation, but it is still at a generally low level and does not show polarization, among which the APE in the northeast region is the highest. The COG of APE tends to transfer towards the northeast, and the coverage of the SDE is shrinking, so the overall spatial pattern is characterized by a tendency of clustering towards the north in the north-south direction and a tendency of imbalance in the east-west direction. APE has significant spatial convergence, and there is a trend of “latecomer catching-up” in low-efficiency regions. The introduction of spatial correlation accelerates the convergence rate and shortens the convergence period. The convergence rate is the highest in the central and western regions, followed by that in the northeastern region, and the convergence rate is the lowest in the eastern region. In addition, the convergence rate in different time periods has a phase change. The process of improving the quality and efficiency of agricultural production requires enhancing the adaptability of climate change, balancing the carrying capacity of the resource environment, and strengthening inter-regional cooperation and linkage in the field of agriculture.
Impacts of Shade Trees on the Adjacent Cacao Rhizosphere in a Young Diversified Agroforestry System
Authors: Schmidt Jennifer E. , Firl Alana , Hamran Hamran , Imaniar Nur Insana , Crow Taylor M. , Forbes Samantha J.
Published: January 2022

Cacao agroforestry systems offer the potential to diversify farmer income sources, enhance biodiversity, sequester carbon, and deliver other important ecosystem services. To date, however, studies have emphasized field- and system-scale outcomes of shade tree integration, and potential impacts on the rhizosphere of adjacent cacao trees have not been fully characterized. Interactions at the root–soil interface are closely linked to plant health and productivity, making it important to understand how diverse shade tree species may affect soil fertility and microbial communities in the cacao rhizosphere. We assessed the impacts of neighboring shade tree presence and identity on cacao yields and physical, chemical, and biological components of the cacao rhizosphere in a recently established diversified agroforestry system in South Sulawesi, Indonesia. Stepwise regression revealed surprising and strong impacts of microbial diversity and community composition on cacao yields and pod infection rates. The presence of neighboring shade trees increased nitrogen, phosphorus, and pH in the rhizosphere of nearby cacao trees without yield losses. Over a longer time horizon, these increases in rhizosphere soil fertility will likely increase cacao productivity and shape microbial communities, as regression models showed nitrogen and phosphorus in particular to be important predictors of cacao yields and microbiome diversity and composition. However, neither presence nor identity of shade trees directly affected microbial diversity, community composition, or field-scale distance-decay relationships at this early stage of establishment. These results highlight locally specific benefits of shade trees in this agroecological context and emphasize the rhizosphere as a key link in indirect impacts of shade trees on cacao health and productivity in diversified systems.
Machine Learning Approach to Simulate Soil CO2 Fluxes under Cropping Systems
Authors: Adjuik Toby A. , Davis Sarah C.
Published: January 2022

With the growing number of datasets to describe greenhouse gas (GHG) emissions, there is an opportunity to develop novel predictive models that require neither the expense nor time required to make direct field measurements. This study evaluates the potential for machine learning (ML) approaches to predict soil GHG emissions without the biogeochemical expertise that is required to use many current models for simulating soil GHGs. There are ample data from field measurements now publicly available to test new modeling approaches. The objective of this paper was to develop and evaluate machine learning (ML) models using field data (soil temperature, soil moisture, soil classification, crop type, fertilization type, and air temperature) available in the Greenhouse gas Reduction through Agricultural Carbon Enhancement network (GRACEnet) database to simulate soil CO2 fluxes with different fertilization methods. Four machine learning algorithms—K nearest neighbor regression (KNN), support vector regression (SVR), random forest (RF) regression, and gradient boosted (GB) regression—were used to develop the models. The GB regression model outperformed all the other models on the training dataset with R2 = 0.88, MAE = 2177.89 g C ha−1 day−1, and RMSE 4405.43 g C ha−1 day−1. However, the RF and GB regression models both performed optimally on the unseen test dataset with R2 = 0.82. Machine learning tools were useful for developing predictors based on soil classification, soil temperature and air temperature when a large database like GRACEnet is available, but these were not highly predictive variables in correlation analysis. This study demonstrates the suitability of using tree-based ML algorithms for predictive modeling of CO2 fluxes, but no biogeochemical processes can be described with such models.
Characterization of the Antibacterial Activity of an SiO2 Nanoparticular Coating to Prevent Bacterial Contamination in Blood Products
Authors: Fonseca Sahra , Cayer Marie-Pierre , Ahmmed K. M. Tanvir , Khadem-Mohtaram Nima , Charette Steve J. , Brouard Danny
Published: January 2022

Technological innovations and quality control processes within blood supply organizations have significantly improved blood safety for both donors and recipients. Nevertheless, the risk of transfusion-transmitted infection remains non-negligible. Applying a nanoparticular, antibacterial coating at the surface of medical devices is a promising strategy to prevent the spread of infections. In this study, we characterized the antibacterial activity of an SiO2 nanoparticular coating (i.e., the “Medical Antibacterial and Antiadhesive Coating” [MAAC]) applied on relevant polymeric materials (PM) used in the biomedical field. Electron microscopy revealed a smoother surface for the MAAC-treated PM compared to the reference, suggesting antiadhesive properties. The antibacterial activity was tested against selected Gram-positive and Gram-negative bacteria in accordance with ISO 22196. Bacterial growth was significantly reduced for the MAAC-treated PVC, plasticized PVC, polyurethane and silicone (90–99.999%) in which antibacterial activity of ≥1 log reduction was reached for all bacterial strains tested. Cytotoxicity was evaluated following ISO 10993-5 guidelines and L929 cell viability was calculated at ≥90% in the presence of MAAC. This study demonstrates that the MAAC could prevent bacterial contamination as demonstrated by the ISO 22196 tests, while further work needs to be done to improve the coating processability and effectiveness of more complex matrices.
Air Ambulance: Antimicrobial Power of Bacterial Volatiles
Authors: Lammers Alexander , Lalk Michael , Garbeva Paolina
Published: January 2022

We are currently facing an antimicrobial resistance crisis, which means that a lot of bacterial pathogens have developed resistance to common antibiotics. Hence, novel and innovative solutions are urgently needed to combat resistant human pathogens. A new source of antimicrobial compounds could be bacterial volatiles. Volatiles are ubiquitous produced, chemically divers and playing essential roles in intra- and interspecies interactions like communication and antimicrobial defense. In the last years, an increasing number of studies showed bioactivities of bacterial volatiles, including antibacterial, antifungal and anti-oomycete activities, indicating bacterial volatiles as an exciting source for novel antimicrobial compounds. In this review we introduce the chemical diversity of bacterial volatiles, their antimicrobial activities and methods for testing this activity. Concluding, we discuss the possibility of using antimicrobial volatiles to antagonize the antimicrobial resistance crisis.
Throwaway Shadows Using Parallel Encoders Generative Adversarial Network
Authors: Javed Kamran , Ud Din Nizam , Hussain Ghulam , Farooq Tahir
Published: January 2022

Face photographs taken on a bright sunny day or in floodlight contain unnecessary shadows of objects on the face. Most previous works deal with removing shadow from scene images and struggle with doing so for facial images. Faces have a complex semantic structure, due to which shadow removal is challenging. The aim of this research is to remove the shadow of an object in facial images. We propose a novel generative adversarial network (GAN) based image-to-image translation approach for shadow removal in face images. The first stage of our model automatically produces a binary segmentation mask for the shadow region. Then, the second stage, which is a GAN-based network, removes the object shadow and synthesizes the effected region. The generator network of our GAN has two parallel encoders—one is standard convolution path and the other is a partial convolution. We find that this combination in the generator results not only in learning an incorporated semantic structure but also in disentangling visual discrepancies problems under the shadow area. In addition to GAN loss, we exploit low level L1, structural level SSIM and perceptual loss from a pre-trained loss network for better texture and perceptual quality, respectively. Since there is no paired dataset for the shadow removal problem, we created a synthetic shadow dataset for training our network in a supervised manner. The proposed approach effectively removes shadows from real and synthetic test samples, while retaining complex facial semantics. Experimental evaluations consistently show the advantages of the proposed method over several representative state-of-the-art approaches.
Deep Learning-Based Water Crystal Classification
Authors: Thi Hien Doan , Andres Frederic , Quoc Long Tran , Emoto Hiro , Hayashi Michiko , Katsumata Ken , Oshide Takayuki
Published: January 2022

Much of the earth’s surface is covered by water. As was pointed out in the 2020 edition of the World Water Development Report, climate change challenges the sustainability of global water resources, so it is important to monitor the quality of water to preserve sustainable water resources. Quality of water can be related to the structure of water crystal, the solid-state of water, so methods to understand water crystals can help to improve water quality. As a first step, a water crystal exploratory analysis has been initiated with the cooperation with the Emoto Peace Project (EPP). The 5K EPP dataset has been created as the first world-wide small dataset of water crystals. Our research focused on reducing the inherent limitations when fitting machine learning models to the 5K EPP dataset. One major result is the classification of water crystals and how to split our small dataset into several related groups. Using the 5K EPP dataset of human observations and past research on snow crystal classification, we created a simple set of visual labels to identify water crystal shapes, in 13 categories. A deep learning-based method has been used to automatically do the classification task with a subset of the label dataset. The classification achieved high accuracy when using a fine-tuning technique.
Comparative Study of Machine Learning Classifiers for Modelling Road Traffic Accidents
Authors: Bokaba Tebogo , Doorsamy Wesley , Paul Babu Sena
Published: January 2022

Road traffic accidents (RTAs) are a major cause of injuries and fatalities worldwide. In recent years, there has been a growing global interest in analysing RTAs, specifically concerned with analysing and modelling accident data to better understand and assess the causes and effects of accidents. This study analysed the performance of widely used machine learning classifiers using a real-life RTA dataset from Gauteng, South Africa. The study aimed to assess prediction model designs for RTAs to assist transport authorities and policymakers. It considered classifiers such as naïve Bayes, logistic regression, k-nearest neighbour, AdaBoost, support vector machine, random forest, and five missing data methods. These classifiers were evaluated using five evaluation metrics: accuracy, root-mean-square error, precision, recall, and receiver operating characteristic curves. Furthermore, the assessment involved parameter adjustment and incorporated dimensionality reduction techniques. The empirical results and analyses show that the RF classifier, combined with multiple imputations by chained equations, yielded the best performance when compared with the other combinations.
Application of Biology to Cultural Heritage
Authors: Macedo Maria Filomena , Miller Ana Zélia , Pinheiro Ana Catarina , Portugal António
Published: January 2022

This Special Issue of the Applied Sciences, entitled “Application of Biology to Cultural Heritage” aimed to cover all the latest outstanding progress of biological and biochemical methods developed and applied to cultural heritage [...]
Advanced Tensor Solution to the Problem of Inter-Domain Routing with Normalized Quality of Service
Authors: Lemeshko Oleksandr , Papan Jozef , Yevdokymenko Maryna , Yeremenko Oleksandra
Published: January 2022

The advanced tensor solution to the problem of inter-domain routing with normalized Quality of Service under hierarchical coordination in a communication network is proposed in the paper. The novelty of the method based on the tensor model lies in the more flexible load balancing over the network due to the presence of requirements to average end-to-end delay of packets. The framework of the method comprises a decomposed flow-based routing model that includes the inter-domain routing interaction conditions and ensures the normalized Quality of Service derived from the tensor model. Considering the mentioned above, the advanced inter-domain Quality of Service routing task was formulated in the optimization form, using the quadratic optimality criterion. The conducted analysis of the numerical research results confirmed the efficiency and adequacy of the proposed method when the desired solutions were obtained during the finite number of iterations under a provision of the normalized Quality of Service. It should be noted that the reduced number of such iterations during the operation of the method will decrease the amount of service traffic transmitted over the network needed for obtaining the final solution in the process of inter-domain routing with normalized Quality of Service.
Numerical Investigation of Excitation of Various Lamb Waves Modes in Thin Plastic Films
Authors: Kazys Rymantas Jonas , Sestoke Justina , Zukauskas Egidijus
Published: January 2022

Ultrasonic-guided waves are widely used for the non-destructive testing and material characterization of plates and thin films. In the case of thin plastic polyvinyl chloride (PVC), films up to 3.2 MHz with only two Lamb wave modes, antisymmetrical A0 and symmetrical S0, may propagate. At frequencies lower that 240 kHz, the velocity of the A0 mode becomes slower than the ultrasonic velocity in air which makes excitation and reception of such mode complicated. For excitation of both modes, we propose instead a single air-coupled ultrasonic transducer to use linear air-coupled arrays, which can be electronically readjusted to optimally excite and receive the A0 and S0 guided wave modes. The objective of this article was the numerical investigation of feasibility to excite different types of ultrasonic-guided waves, such as S0 and A0 modes in thin plastic films with the same electronically readjusted linear phased array. Three-dimensional and two-dimensional simulations of A0 and S0 Lamb wave modes using a single ultrasonic transducer and a linear phased array were performed. The obtained results clearly demonstrate feasibility to excite efficiently different guided wave modes in thin plastic films with readjusted phased array.