bluuebunny/crossref_metadata_2025 huggingface dataset:
Controlling Unconventional Secretion for Production of Heterologous Proteins in Ustilago maydis through Transcriptional Regulation and Chemical Inhibition of the Kinase Don3
Authors: Hussnaetter Kai P. , Philipp Magnus , Müntjes Kira , Feldbrügge Michael , Schipper Kerstin
Published: March 2021

Heterologous protein production is a highly demanded biotechnological process. Secretion of the product to the culture broth is advantageous because it drastically reduces downstream processing costs. We exploit unconventional secretion for heterologous protein expression in the fungal model microorganism Ustilago maydis. Proteins of interest are fused to carrier chitinase Cts1 for export via the fragmentation zone of dividing yeast cells in a lock-type mechanism. The kinase Don3 is essential for functional assembly of the fragmentation zone and hence, for release of Cts1-fusion proteins. Here, we are first to develop regulatory systems for unconventional protein secretion using Don3 as a gatekeeper to control when export occurs. This enables uncoupling the accumulation of biomass and protein synthesis of a product of choice from its export. Regulation was successfully established at two different levels using transcriptional and post-translational induction strategies. As a proof-of-principle, we applied autoinduction based on transcriptional don3 regulation for the production and secretion of functional anti-Gfp nanobodies. The presented developments comprise tailored solutions for differentially prized products and thus constitute another important step towards a competitive protein production platform.
The Effect of Binder Loading on the Pore Size of 3D Printed PMMA
Authors: Riechmann Simon , Wunnicke Odo , Kwade Arno
Published: March 2021

Binder jetting is known to produce porous objects by depositing the binder selectively layer by layer on a powder bed. In this study, the pore size of printed parts and the correlating mechanical properties are investigated on a commercially available PMMA powder binder system. Pore sizes are measured via capillary flow porometry and mechanical properties via tensile tests. Porometry indicates that the pore size stays at a constant level of 22 µm at 5 to 10 wt% binder loading before decreasing to 6 µm at loadings of 30 wt% or higher. The results were compared with the mechanical testing and related to the agglomerate strength model of Rumpf. The highlights of the article are the application of a binder jetted part as a filter and the identification of a close relationship between porosity and mechanical strength, similar to phenomena in agglomeration science.
Structural Defects, Mechanical Behaviors, and Properties of Two-Dimensional Materials
Authors: Xiong Zixin , Zhong Lei , Wang Haotian , Li Xiaoyan
Published: March 2021

Since the success of monolayer graphene exfoliation, two-dimensional (2D) materials have been extensively studied due to their unique structures and unprecedented properties. Among these fascinating studies, the most predominant focus has been on their atomic structures, defects, and mechanical behaviors and properties, which serve as the basis for the practical applications of 2D materials. In this review, we first highlight the atomic structures of various 2D materials and the structural and energy features of some common defects. We then summarize the recent advances made in experimental, computational, and theoretical studies on the mechanical properties and behaviors of 2D materials. We mainly emphasized the underlying deformation and fracture mechanisms and the influences of various defects on mechanical behaviors and properties, which boost the emergence and development of topological design and defect engineering. We also further introduce the piezoelectric and flexoelectric behaviors of specific 2D materials to address the coupling between mechanical and electronic properties in 2D materials and the interactions between 2D crystals and substrates or between different 2D monolayers in heterostructures. Finally, we provide a perspective and outlook for future studies on the mechanical behaviors and properties of 2D materials.
The Optimal Order Newton’s Like Methods with Dynamics
Authors: Singh Manoj Kumar , Singh Arvind K.
Published: March 2021

In this paper, we have obtained three optimal order Newton’s like methods of order four, eight, and sixteen for solving nonlinear algebraic equations. The convergence analysis of all the optimal order methods is discussed separately. We have discussed the corresponding conjugacy maps for quadratic polynomials and also obtained the extraneous fixed points. We have considered several test functions to examine the convergence order and to explain the dynamics of our proposed methods. Theoretical results, numerical results, and fractal patterns are in support of the efficiency of the optimal order methods.
SWIFT Calibration of the Heston Model
Authors: Romo Eudald , Ortiz-Gracia Luis
Published: March 2021

In the present work, the SWIFT method for pricing European options is extended to Heston model calibration. The computation of the option price gradient is simplified thanks to the knowledge of the characteristic function in closed form. The proposed calibration machinery appears to be extremely fast, in particular for a single expiry and multiple strikes, outperforming the state-of-the-art method we compare it with. Further, the a priori knowledge of SWIFT parameters makes a reliable and practical implementation of the presented calibration method possible. A wide range of stress, speed and convergence numerical experiments is carried out, with deep in-the-money, at-the-money and deep out-of-the-money options for very short and very long maturities.
DBTMPE: Deep Bidirectional Transformers-Based Masked Predictive Encoder Approach for Music Genre Classification
Authors: Qiu Lvyang , Li Shuyu , Sung Yunsick
Published: March 2021

Music is a type of time-series data. As the size of the data increases, it is a challenge to build robust music genre classification systems from massive amounts of music data. Robust systems require large amounts of labeled music data, which necessitates time- and labor-intensive data-labeling efforts and expert knowledge. This paper proposes a musical instrument digital interface (MIDI) preprocessing method, Pitch to Vector (Pitch2vec), and a deep bidirectional transformers-based masked predictive encoder (MPE) method for music genre classification. The MIDI files are considered as input. MIDI files are converted to the vector sequence by Pitch2vec before being input into the MPE. By unsupervised learning, the MPE based on deep bidirectional transformers is designed to extract bidirectional representations automatically, which are musicological insight. In contrast to other deep-learning models, such as recurrent neural network (RNN)-based models, the MPE method enables parallelization over time-steps, leading to faster training. To evaluate the performance of the proposed method, experiments were conducted on the Lakh MIDI music dataset. During MPE training, approximately 400,000 MIDI segments were utilized for the MPE, for which the recovery accuracy rate reached 97%. In the music genre classification task, the accuracy rate and other indicators of the proposed method were more than 94%. The experimental results indicate that the proposed method improves classification performance compared with state-of-the-art models.
Inhibitor Development against p7 Channel in Hepatitis C Virus
Authors: Wei Shukun , Hu Xiaoyou , Du Lingyu , Zhao Linlin , Xue Hongjuan , Liu Chaolun , Chou James J. , Zhong Jin , Tong Yimin , Wang Shuqing , OuYang Bo
Published: March 2021

Hepatitis C Virus (HCV) is the key cause of chronic and severe liver diseases. The recent direct-acting antiviral agents have shown the clinical success on HCV-related diseases, but the rapid HCV mutations of the virus highlight the sustaining necessity to develop new drugs. p7, the viroporin protein from HCV, has been sought after as a potential anti-HCV drug target. Several classes of compounds, such as amantadine and rimantadine have been testified for p7 inhibition. However, the efficacies of these compounds are not high. Here, we screened some novel p7 inhibitors with amantadine scaffold for the inhibitor development. The dissociation constant (Kd) of 42 ARD-series compounds were determined by nuclear magnetic resonance (NMR) titrations. The efficacies of the two best inhibitors, ARD87 and ARD112, were further confirmed using viral production assay. The binding mode analysis and binding stability for the strongest inhibitor were deciphered by molecular dynamics (MD) simulation. These ARD-series compounds together with 49 previously published compounds were further analyzed by molecular docking. Key pharmacophores were identified among the structure-similar compounds. Our studies suggest that different functional groups are highly correlated with the efficacy for inhibiting p7 of HCV, in which hydrophobic interactions are the dominant forces for the inhibition potency. Our findings provide guiding principles for designing higher affinity inhibitors of p7 as potential anti-HCV drug candidates.
Let Food Be Thy Medicine—Its Role in Crohn’s Disease
Authors: Wellens Judith , Vermeire Séverine , Sabino João
Published: March 2021

The food we eat is thought to play a role in both the increasing incidence as well as the course of Crohn’s disease. What to eat and what to avoid is an increasingly important question for both patients and physicians. Restrictive diets are widely adopted by patients and carry the risk of inducing or worsening malnutrition, without any guarantees on anti-inflammatory potential. Nevertheless, exploration of novel therapies to improve long-term management of the disease is desperately needed and the widespread use of exclusive enteral nutrition in the induction of paediatric Crohn’s disease makes us wonder if a similar approach would be beneficial in adult patients. This narrative review discusses the current clinical evidence on whole food diets in achieving symptomatic and inflammatory control in Crohn’s disease and identifies knowledge gaps with areas for future research.
Single- versus Dual-Targeted Nanoparticles with Folic Acid and Biotin for Anticancer Drug Delivery
Authors: Jurczyk Magdalena , Jelonek Katarzyna , Musiał-Kulik Monika , Beberok Artur , Wrześniok Dorota , Kasperczyk Janusz
Published: March 2021

Cancer is one of the major causes of death worldwide and its treatment remains very challenging. The effectiveness of cancer therapy significantly depends upon tumour-specific delivery of the drug. Nanoparticle drug delivery systems have been developed to avoid the side effects of the conventional chemotherapy. However, according to the most recent recommendations, future nanomedicine should be focused mainly on active targeting of nanocarriers based on ligand-receptor recognition, which may show better efficacy than passive targeting in human cancer therapy. Nevertheless, the efficacy of single-ligand nanomedicines is still limited due to the complexity of the tumour microenvironment. Thus, the NPs are improved toward an additional functionality, e.g., pH-sensitivity (advanced single-targeted NPs). Moreover, dual-targeted nanoparticles which contain two different types of targeting agents on the same drug delivery system are developed. The advanced single-targeted NPs and dual-targeted nanocarriers present superior properties related to cell selectivity, cellular uptake and cytotoxicity toward cancer cells than conventional drug, non-targeted systems and single-targeted systems without additional functionality. Folic acid and biotin are used as targeting ligands for cancer chemotherapy, since they are available, inexpensive, nontoxic, nonimmunogenic and easy to modify. These ligands are used in both, single- and dual-targeted systems although the latter are still a novel approach. This review presents the recent achievements in the development of single- or dual-targeted nanoparticles for anticancer drug delivery.
Snoring and Sleep-Related Symptoms: A Novel Non-Invasive 808 nm Wavelength Diode Laser Non-Ablative Outpatient Treatment. A Prospective Pilot-Study on 45 Patients
Authors: Fini Storchi Isabelle , Frosecchi Massimo , Bovis Francesca , Zekiy Angelina , Benedicenti Stefano , Amaroli Andrea , Convissar Robert A.
Published: March 2021

Background: Surgical treatments for snoring and sleep-related symptoms are invasive, may have complications, have inconstant success rates, and may have a significant relapse rate. Methods: We evaluated the effectiveness of a non-surgical, non-invasive outpatient 808 nm diode laser treatment. Forty-five patients with snoring and sleep disorders were treated with an Elexxion-Claros® 50W Diode Laser 808-nm using the specific SNORE3 application. The possible presence of obstructive sleep apnea syndrome in patients was diagnosed with Polysomnography. The Epworth sleepiness scale was reported pre- and post-laser treatments. Patients were classified according to both Friedman tongue position and Mallampati classification. During treatment, the pain reported by patients was measured on a visual analogue scale. Results: The following data improved significantly: visual analogue scale for the loudness of snoring, Epworth sleepiness scale, waking up during sleep because of snoring, dreaming during the night, dry mouth on awakening, daytime sleepiness, tiredness on awakening, Mallampati score, Friedman tongue position and degree of oropharynx at nose, oropharynx, hypopharynx, and larynx classification (p < 0.001). A cessation of choking in 89% of the patients and of restless legs syndrome in 83% of the patients (p ≤ 0.005 and p ≤ 0.025) was also noted after the laser treatments. All of the patients who reported suffering from headaches upon waking (10 subjects) reported the complete cessation of this phenomenon after laser therapy. Conclusions: diode laser treatment is a promising procedure not only in reducing the loudness of snoring, but also in raising the global quality of sleep, a statistically significant reduction of AHI, and a cessation of bothersome phenomenon related to apnea. Results were sustainable at one year post-treatment.
Applications of Clustering with Mixed Type Data in Life Insurance
Authors: Yin Shuang , Gan Guojun , Valdez Emiliano A. , Vadiveloo Jeyaraj
Published: March 2021

Death benefits are generally the largest cash flow items that affect the financial statements of life insurers; some may still not have a systematic process to track and monitor death claims. In this article, we explore data clustering to examine and understand how actual death claims differ from what is expected—an early stage of developing a monitoring system crucial for risk management. We extended the k-prototype clustering algorithm to draw inferences from a life insurance dataset using only the insured’s characteristics and policy information without regard to known mortality. This clustering has the feature of efficiently handling categorical, numerical, and spatial attributes. Using gap statistics, the optimal clusters obtained from the algorithm are then used to compare actual to expected death claims experience of the life insurance portfolio. Our empirical data contained observations of approximately 1.14 million policies with a total insured amount of over 650 billion dollars. For this portfolio, the algorithm produced three natural clusters, with each cluster having lower actual to expected death claims but with differing variability. The analytical results provide management a process to identify policyholders’ attributes that dominate significant mortality deviations, and thereby enhance decision making for taking necessary actions.
Twenty-Seven Years of Scatterometer Surface Wind Analysis over Eastern Boundary Upwelling Systems
Authors: Bentamy Abderrahim , Grodsky Semyon A. , Cambon Gildas , Tandeo Pierre , Capet Xavier , Roy Claude , Herbette Steven , Grouazel Antoine
Published: March 2021

More than twelve satellite scatterometers have operated since 1992 through the present, providing the main source of surface wind vector observations over global oceans. In this study, these scatterometer winds are used in combination with radiometers and synthetic aperture radars (SAR) for the better determination and characterization of high spatial and temporal resolution of regional surface wind parameters, including wind speed and direction, wind stress components, wind stress curl, and divergence. In this paper, a 27-year-long (1992–2018) 6-h satellite wind analysis with a spatial resolution of 0.125° in latitude and longitude is calculated using spatial structure functions derived from high-resolution SAR data. The main objective is to improve regional winds over three major upwelling regions (the Canary, Benguela, and California regions) through the use of accurate and homogenized wind observations and region-specific spatial and temporal wind variation structure functions derived from buoy and SAR data. The long time series of satellite wind analysis over the California upwelling, where a significant number of moorings is available, are used for assessing the accuracy of the analysis. The latter is close to scatterometer wind retrieval accuracy. This assessment shows that the root mean square difference between collocated 6-h satellite wind analysis and buoys is lower than 1.50 and 1.80 m s−1 for offshore and nearshore locations, respectively. The temporal correlation between buoy and satellite analysis winds exceeds 0.90. The analysis accuracy is lower for 1992–1999 when satellite winds were mostly retrieved from ERS-1 and/or ERS-2 scatterometers. To further assess the improvement brought by this new wind analysis, its data and data from three independent products (ERA5, CMEMS, and CCMP) are compared with purely scatterometer winds over the Canary and Benguela regions. Even though the four products are generally similar, the new satellite analysis shows significant improvements, particularly in the upwelling areas.
Learning from Nighttime Observations of Gas Flaring in North Dakota for Better Decision and Policy Making
Authors: Lu Rong , Miskimins Jennifer L. , Zhizhin Mikhail
Published: March 2021

In today’s oil industry, companies frequently flare the produced natural gas from oil wells. The flaring activities are extensive in some regions including North Dakota. Besides company-reported data, which are compiled by the North Dakota Industrial Commission, flaring statistics such as count and volume can be estimated via Visible Infrared Imaging Radiometer Suite nighttime observations. Following data gathering and preprocessing, Bayesian machine learning implemented with Markov chain Monte Carlo methods is performed to tackle two tasks: flaring time series analysis and distribution approximation. They help further understanding of the flaring profiles and reporting qualities, which are important for decision/policy making. First, although fraught with measurement and estimation errors, the time series provide insights into flaring approaches and characteristics. Gaussian processes are successful in inferring the latent flaring trends. Second, distribution approximation is achieved by unsupervised learning. The negative binomial and Gaussian mixture models are utilized to describe the distributions of field flare count and volume, respectively. Finally, a nearest-neighbor-based approach for company level flared volume allocation is developed. Potential discrepancies are spotted between the company reported and the remotely sensed flaring profiles.
Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms
Authors: Shafiei Somayeh B. , Durrani Mohammad , Jing Zhe , Mostowy Michael , Doherty Philippa , Hussein Ahmed A. , Elsayed Ahmed S. , Iqbal Umar , Guru Khurshid
Published: March 2021

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.
Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification
Authors: Yan Ming , Li Shuijing , Chan Chien Aun , Shen Yinghua , Yu Ying
Published: March 2021

The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.
Detection of Dental Caries and Cracks with Quantitative Light-Induced Fluorescence in Comparison to Radiographic and Visual Examination: A Retrospective Case Study
Authors: Oh Song Hee , Lee Sae Rom , Choi Jin Young , Choi Yong Suk , Kim Seong Hun , Yoon Hong Cheol , Nelson Gerald
Published: March 2021

The aim of this study was to present an optimal diagnostic protocol by comparing and analyzing a conventional examination and the quantitative light-induced fluorescence (QLF) technique. Selected were 297 teeth of 153 patients to take QLF images and bitewing radiographs. Occlusal dental caries, proximal dental caries and cracks were evaluated and scored using QLF, X-ray and/or visual criteria. The sensitivity, specificity, and area under the curve (AUC) of a receiver operating characteristic analysis were calculated. Two fluorescence parameters (|ΔFmax| and ΔRmax) were utilized to evaluate the fluorescence pattern according to the severity of lesions based on QLF or X-ray criteria. QLF showed higher scores for detecting occlusal dental caries and cracks than the conventional method. ΔRmax increased more clearly than ΔFmax did with occlusal dental caries. The |ΔFmax| values of occlusal dental caries, proximal dental caries and cracks showed good AUC levels (0.84, 0.81 and 0.83, respectively). The ΔRmax of occlusal dental caries showed the highest AUC (0.91) and the ΔRmax of proximal dental caries showed a fail level (0.59) compared to bitewing radiographs. The QLF image could visualize and estimate the degree of occlusal dental caries or cracks. Consequently, the QLF technique may be an adjunct tool to conventional methods for the detection of occlusal caries and peripheral cracks.
A New Super Resolution Framework Based on Multi-Task Learning for Remote Sensing Images
Authors: Yan Li , Chang Kun
Published: March 2021

Super-resolution (SR) algorithms based on deep learning have dominated in various tasks, including medical imaging, street view surveillance and face recognition. In the remote sensing field, most of the current SR methods utilize the low-resolution (LR) images that directly bicubic downsampled the high-resolution (HR) images as not only train set but also test set, thus achieving high PSNR/SSIM scores but showing performance drop in application because the degradation model in remote sensing images is subjected to Gaussian blur with unknown parameters. Inspired by multi-task learning strategy, we propose a multiple-blur-kernel super-resolution framework (MSF), in which a multiple-blur-kernel learning module (MLM) optimizes the parameters of the network transferable and sensitive for SR procedures with different blur kernels. Besides, to simultaneously exploit the prior of the large-scale remote sensing images and recurrent information in a single test image, a class-feature capture module (CCM) and an unsupervised learning module (ULM) are leveraged in our framework. Extensive experiments show that our framework outperforms the current state-of-the-art SR algorithms in remotely sensed imagery SR with unknown Gaussian blur kernel.
Multi-Probe Measurement System Based on Single-Cut Transformation for Fast Testing of Linear Arrays
Authors: Rodríguez Varela Fernando , López Morales Manuel José , Tena Sánchez Rubén , Muriel Barrado Alfonso Tomás , de la Fuente González Elena , Posada Quijano Guillermo , Zarzuelo Torres Carlos , Sierra Pérez Manuel , Sierra Castañer Manuel
Published: March 2021

This paper introduces a near-field measurement system concept for the fast testing of linear arrays suited for mass production scenarios where a high number of nominally identical antennas needs to be measured. The proposed system can compute the radiation pattern, directivity and gain on the array plane, as well as the array complex feeding coefficients in a matter of seconds. The concept is based on a multi-probe antenna array arranged in a line which measures the near field of the antenna under test in its array plane. This linear measurement is postprocessed with state-of-the-art single-cut transformation techniques. To compensate the lack of full 3D information, a previous complete characterization of a “Gold Antenna” is performed. This antenna is nominally identical to the many ones that will be measured with the proposed system. Therefore, the data extracted from this full characterization can be used to complement the postprocessing steps of the single-cut measurements. An X-band 16-probe demonstrator of the proposed system is implemented and introduced in this paper, explaining all the details of its architecture and operation steps. Finally, some measurement results are given to compare the developed demonstrator with traditional anechoic measurements, and show the potential capabilities of the proposed concept to perform fast and reliable measurements.
Treatment with Oral Ondansetron for Ultramarathon-Associated Nausea: The TOO FUN Study
Authors: Pasternak Andrew V. , Fiore David , Islas Arthur , Toti Sarah , Hoffman Martin D.
Published: March 2021

Nausea and vomiting are common for runners during ultramarathons and often contribute to non-finishes. We aimed to determine the efficacy of ondansetron, a commonly used antiemetic, to treat nausea and vomiting in runners during an ultramarathon. Runners who had a previous history of frequent nausea or vomiting during races and entered in 160, 80, and 55 km ultramarathons in 2018 and 2019 were randomized in a double-blind fashion to 4 mg ondansetron or placebo capsules to use if they developed nausea or vomiting during the race with the ability to take three additional doses. Study participants completed a post-race online survey to assess medication use and efficacy. Of 62 study participants, 31 took either ondansetron (20) or placebo (11). In this small study, there were no group differences in those reporting any improvement in nausea and vomiting (p = 0.26) or in the amount of improvement (p = 0.15). We found no evidence that ondansetron capsules improve nausea and vomiting during ultramarathons.
Examining Soil Erosion Responses to Grassland Conversation Policy in Three-River Headwaters, China
Authors: Zhao Yuting , Pu Yanfei , Lin Huilong , Tang Rong
Published: March 2021

Soil erosion in the Three-River Headwaters (TRH) region has continued to intensify in recent decades due to human activities and climate change. To reverse this situation, the Chinese central government has launched the Subsidy and Incentive System for Grassland Conservation (SISGC). As a sign of the effectiveness of SISGC implementation, the dynamic changes of soil erosion can provide timely feedback for decision makers and managers. The Revised Universal Soil Loss Equation (RUSLE) model was used to simulate the spatial distribution of soil erosion before and after SISGC implementation, and Mann–Kendall (MK) test to reveal the effect of policy implementation. The results showed that: (1) the soil erosion in the TRH was mainly mild (83.83% of the total eroded area), and the average soil erosion rate and the total erosion were 13.63 t ha−1 y−1 and 323.58 × 106 t y−1 respectively before SISGC implementation; (2) SISGC implementation has curbed soil erosion. After SISGC implementation, the total soil erosion decreased by 3.80%, which showed obvious differences between grassland types; (3) The influences of SISGC were mainly because it has increased vegetation cover, further decreasing soil erosion. However, soil erosion in Alpine grassland has deteriorated, indicating direct targeted policymaking should be on the agenda. Furthermore, SISGC should be continued and grassland-type-oriented to restore the grassland ecosystem.
A Concretizing Research on Making Higher-Education Sustainability Count
Authors: Usak Muhammet , Hsieh Ming Yuan , Chan Yung-Kuan
Published: March 2021

In order to manage the worrying predicament of declining global birth rates, a majority of higher-education institutions have commenced to institute a series of diversified strategies in order to make higher-education sustainability count. The aim of this research is to present the results of a study with the goal of helping educational institutions achieve their sustainability. As a succession of quantitative and qualitative analyses and measurements, there are four valuable and contributive findings in this research. First, total tuition and miscellaneous revenues (TTMR), total university–industry cooperative revenues (TUCIR) and total grants from government (TGG) of higher-education governance (HEG) were able to directly and effectively increase higher-education sustainable value. This definitively indicates that current higher-education students and staff, as well as higher-education experts and professionals, indeed agree that the tuition and miscellaneous fees, university–industry cooperative, and government grant revenues have become the most critical determinants of university environment social governance (UESG). This is because the majority of higher-education institutions do need these revenue resources to advance a succession of environmental protections, social responsibilities, and governance performance for current higher-education institution sustainability development. Second, comprehensive scale of QS rankings (CS-QS) was the most important key determinant to evaluate the university social responsibility development (USRD), which means the majority of higher-education institutions have to pay more attention on the comprehensive scale of QS rankings (CS-QS) in order to make more sustainability count. The third finding is the number of student’s publications (NSP), which was the most crucial key determinant to assay return on investments (ROI), meaning current higher-education students, staff, experts, and professionals consent to higher-education institutions having to establish an evaluated system for student’s publications to stimulate students to publish their diversified studying outcomes in order to make students recognize their growth after taking courses in higher education. The last finding is total tuition and miscellaneous revenues (TTMR) was the most momentous key determinant to control high education governance (HEG), which means current higher-education students, staff, experts, and professionals are concerned with the various governance performances of their tuition and miscellaneous fees. As for the future direction, there are still some evaluated criteria and assessed methods to be comprehensively considered and employed for inducing in-depth conclusions and findings with higher research reliability and validity without time limitations and resource restrictions.
Effects of Technological Progress from Different Sources on Haze Pollution in China
Authors: Zhao Yuan , Zhang Tian , Wu Ting , Xu Shujing , Yang Shuwang
Published: March 2021

Technological progress has always been regarded as an important factor affecting haze pollution. A large number of academic studies have focused on the effect of technological progress on haze pollution, but there are few discussions on the effects of technological progress from different sources. In view of this, a dynamic panel model is constructed, and a systematic generalized method of moments (GMM) method is applied to empirically test the overall impact of technological progress from different sources on haze pollution and the regional heterogeneity of the impact. The results show that the overall and regional impact of technological progress from different sources on haze pollution is entirely different. Among them, for the whole country, independent innovation has a significant inhibitory effect on haze pollution, and technology introduction has aggravated haze pollution to a certain extent. At the regional level, all types of technological progress in the east can effectively reduce haze, the central region having haze reduction results consistent with the overall national level, and in the west, independent innovation and direct introduction can effectively reduce haze, while reverse technology spillover is ineffective. Therefore, policy recommendations such as improving the ability of independent innovation, improving the quality of technology introduction, and coordinating regional technology against haze are put forward.
Compatibility of Automated Vehicles in Street Spaces: Considerations for a Sustainable Implementation
Authors: Soteropoulos Aggelos , Berger Martin , Mitteregger Mathias
Published: March 2021

Automated Vehicles (AVs) will bring a fundamental change in the mobility sector in the coming years. Whereas many studies emphasize opportunities with AVs, studies on the impacts of AVs on travel behavior particularly show an overall increase in traffic volume. This increase could impair the needs of other uses and users within street spaces and decrease the permeability of the street space for pedestrians and cyclists. However, only a few studies, so far, have looked at the changes of traffic volume due to AVs at the street level, and to what extent these impair the needs of other uses and users within different street spaces was not in the focus at all. This paper investigates the compatibility of AVs in street spaces, building on different modeling results of scenarios with AVs based on the Multi-Agent Traffic Simulation (MATSim) framework. Using the so-called compensatory approach and the whole street network of Vienna, Austria, as a case study, we examine how compatible AVs and their related changes in traffic volume are with the needs of other uses and users, i.e., pedestrians and cyclists, within different street spaces, by specifically considering the various characteristics of the latter. Results show that the effects of AVs on the compatibility of street spaces would be unevenly distributed across the city. For Shared Automated Vehicles (SAVs), a deterioration in compatibility is observable, especially in inner-city dense areas, because of an increase in traffic volume and an already high amount of competing uses. In contrast, especially (on main roads) in the outskirts, improvements in compatibility are possible. This particularly applies to SAVs with a stop-based service. However, private AVs interlinked with an overall capacity increase would lead to a deterioration in compatibility, especially in parts of the higher-level street network that already have incompatible traffic volumes, further increasing the separating or barrier effect of such streets. The results can provide insights for policymakers and stakeholders about where and how to facilitate AVs, to reach an implementation that is compatible with the different uses and needs of users within street spaces: While SAVs should be implemented particularly in the outskirts, as a complement for public transport, an implementation of AVs in the lower-level street network in inner parts of the city should not be facilitated, or it should at least be linked to measures that make street spaces more compatible with the needs of pedestrians and cyclists, e.g., implementation of walking and cycling infrastructure.
Mitigating the Impact of Touristification on the Psychological Carrying Capacity of Residents
Authors: Kwon Yoonku , Kim Jihyun , Kim Jiyoung , Park Chan
Published: March 2021

This study explores the impact of touristification on the residents of the Seochon and Bukchon areas of Seoul, Korea. Touristification refers to changing an urban space to promote tourism; however, this process displaces the original residents and affects the commercial and social fabric of neighborhoods. We examine the psychological carrying capacity of local residents to adapt to touristification, and present ways to mitigate the negative effects of touristification. First, a semantic differential scale was used to elicit adjectives to assess the carrying capacity of residents to adapt. This was correlated with a classification of the residents’ awareness of the changes. Second, a space improvement index was developed to verify whether an improvement in the physical space will change the psychological carrying capacity of residents. A space improvement simulation indicated the changes in carrying capacity based on the improvement of space. Finally, we established the key factors for each space type and proposed strategies to mitigate the impact of touristification.
Inclusion, Dyslexia, Emotional State and Learning: Perceptions of Ibero-American Children with Dyslexia and Their Parents during the COVID-19 Lockdown
Authors: Forteza-Forteza Dolors , Rodríguez-Martín Alejandro , Álvarez-Arregui Emilio , Menéndez Álvarez-Hevia David
Published: March 2021

The COVID-19 pandemic has transformed educational processes. This has had major consequences for students and, in particular, for those with special education needs. Dyslexic students suffer from widespread educational and legal invisibility, and information on their situation and that of their families during this health crisis is lacking. This article presents the results of an exploratory study based on two online surveys taken by parents (n = 327) and children with dyslexia (n = 203) through the Spanish Dyslexia Federation (acronym in Spanish “FEDIS”), the Dyslexia and Family Association (acronym in Spanish “DISFAM”), and the Ibero-American Organisation for Specific Learning Difficulties (acronym in Spanish “OIDEA”). Data were collected in May–July 2020. The results offer a comprehensive viewpoint (family and children) on the aspects that have helped and hindered learning, such as teacher and family support, emotional state, use of ICT, and the importance of the voluntary/association network. The study provides evidence of how lockdown and school closures have created additional difficulties for learning but also how certain educational processes have been bolstered with the support of technological resources that should serve as benchmarks for education policy and classroom practice.
Examining the Variables Leading to Apparent Incongruity between Antimethanogenic Potential of Tannins and Their Observed Effects in Ruminants—A Review
Authors: Verma Supriya , Taube Friedhelm , Malisch Carsten S.
Published: March 2021

In recent years, several secondary plant metabolites have been identified that possess antimethanogenic properties. Tannin-rich forages have the potential to reduce methane emissions in ruminants while also increasing their nutrient use efficiency and promoting overall animal health. However, results have been highly inconclusive to date, with their antimethanogenic potential and effects on both animal performance and nutrition being highly variable even within a plant species. This variability is attributed to the structural characteristics of the tannins, many of which have been linked to an increased antimethanogenic potential. However, these characteristics are seldom considered in ruminant nutrition studies—often because the analytical techniques are inadequate to identify tannin structure and the focus is mostly on total tannin concentrations. Hence, in this article, we (i) review previous research that illustrate the variability of the antimethanogenic potential of forages; (ii) identify the source of inconsistencies behind these results; and (iii) discuss how these could be optimized to generate comparable and repeatable results. By adhering to this roadmap, we propose that there are clear links between plant metabolome and physiology and their antimethanogenic potential that can be established with the ultimate goal of improving the sustainable intensification of livestock.
A Spatial Decision Support System for Multifunctional Landscape Assessment: A Transformative Resilience Perspective for Vulnerable Inland Areas
Authors: Cerreta Maria , Panaro Simona , Poli Giuliano
Published: March 2021

The concept of transformative resilience has emerged from the recent literature and represents a way to interpret the potential opportunities for change in vulnerable territories, where a socioeconomic change is required. This article extends the perspective of transformative resilience to an assessment of the landscape multifunctionality of inland areas, exploring the potential of identifying a network of synergies among the different municipalities that is able to trigger a process of territorial resilience. A spatial decision support system (SDSS) for multifunctionality landscape assessment aims to help local actors understand local resources and multifunctional values of the Partenio Regional Park (PRP) and surrounding municipalities, in the South of Italy, stimulating their cooperation in the management of environmental and cultural sites and the codesign of new strategies of enhancement. The elaboration of spatial indicators according to Landscape Services classification and the interaction between the “Analytic Network Process” (ANP) method, spatial weighted overly and geographic information system (GIS) support the identification of a preferable scenario able to activate a transformative resilience strategy in selected vulnerable inland areas, which can be scaled up in other similar contexts.
Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine
Authors: Owolabi Taoreed O. , Abd Rahman Mohd Amiruddin
Published: March 2021

Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications.
A Recent Overview of Producers and Important Dietary Sources of Aflatoxins
Authors: Pickova Darina , Ostry Vladimir , Malir Frantisek
Published: March 2021

Aflatoxins (AFs) are some of the most agriculturally important and harmful mycotoxins. At least 20 AFs have been identified to this date. Aflatoxin B1 (AFB1), the most potent fungal toxin, can cause toxicity in many species, including humans. AFs are produced by 22 species of Aspergillus section Flavi, 4 species of A. section Nidulantes, and 2 species of A. section Ochraceorosei. The most important and well-known AF-producing species of section Flavi are Aspergillus flavus, A. parasiticus, and A. nomius. AFs contaminate a wide range of crops (mainly groundnuts, pistachio nuts, dried figs, hazelnuts, spices, almonds, rice, melon seeds, Brazil nuts, and maize). Foods of animal origin (milk and animal tissues) are less likely contributors to human AF exposure. Despite the efforts to mitigate the AF concentrations in foods, and thus enhance food safety, AFs continue to be present, even at high levels. AFs thus remain a current and continuously pressing problem in the world.