bluuebunny/crossref_metadata_2025 huggingface dataset:
Leaf Area Calculation Models for Vines Based on Foliar Descriptors
Authors: Sala Florin , Dobrei Alin , Herbei Mihai Valentin
Published: November 2021

In the case of foliar area studies on vines, with a large number of determinations, a simple, fast, sufficiently accurate and low-cost method is very useful. The typology of leaves on the vine is complex, characterized by several descriptive parameters: median rib; secondary venations of the first and second order; angles between the median rib and the secondary venations; sinuses; length and width of the leaf. The present study aimed to evaluate models for calculating the leaf area based on descriptive parameters and KA (KA as the surface constant used to calculate the leaf area) for six vine cultivars, ‘Cabernet Sauvignon’ (CS), ‘Muscat Iantarnîi’ (MI), ‘Muscat Ottonel’ (MO), ‘Chasselas’ (Ch), ‘Victoria’ (Vi) and ‘Muscat Hamburg’ (MH). The determined KA surface constants had subunit values (0.91 to 0.97), except for the cultivars ‘Muscat Iantarnîi’ and ‘Muscat Ottonel’ where the surface constant KA2 (in relation to the second-order secondary venations) had supraunitary values (1.07 and 1.08, respectively). The determination of the leaf area was possible under different conditions of statistical accuracy (R2 = 0.477, p = 0.0119, up to R2 = 0.988, p < 0.001) in relation to the variety and parametric descriptors considered. The models obtained from the regression analysis facilitated a more reliable prediction of the leaf area based on the elements on the left side of the leaf, in relation to the median rib, compared to those on the right. The accuracy of the results was checked on the basis of minimum error (ME) and confirmed by parameters R2, p and RMSE.
Metabolic Changes in Seed Embryos of Hypoxia-Tolerant Rice and Hypoxia-Sensitive Barley at the Onset of Germination
Authors: Jayawardhane Jayamini , Wijesinghe M. K. Pabasari S. , Bykova Natalia V. , Igamberdiev Abir U.
Published: November 2021

Rice (Oryza sativa L.) and barley (Hordeum vulgare L.) are the cereal species differing in tolerance to oxygen deficiency. To understand metabolic differences determining the sensitivity to low oxygen, we germinated rice and barley seeds and studied changes in the levels of reactive oxygen species (ROS) and reactive nitrogen species (RNS), activities of the enzymes involved in their scavenging, and measured cell damage parameters. The results show that alcohol dehydrogenase activity was higher in rice than in barley embryos providing efficient anaerobic fermentation. Nitric oxide (NO) levels were also higher in rice embryos indicating higher NO turnover. Both fermentation and NO turnover can explain higher ATP/ADP ratio values in rice embryos as compared to barley. Rice embryos were characterized by higher activity of S-nitrosoglutathione reductase than in barley and a higher level of free thiols in proteins. The activities of antioxidant enzymes (superoxide dismutase, ascorbate peroxidase, monodehydroascorbate reductase, dehydroascorbate reductase) in imbibed embryos were higher in rice than in barley, which corresponded to the reduced levels of ROS, malonic dialdehyde and electrolyte leakage. The observed differences in metabolic changes in embryos of the two cereal species differing in tolerance to hypoxia can partly explain the adaptation of rice to low oxygen environments.
Cytotoxic Effects of Phytomediated Silver and Gold Nanoparticles Synthesised from Rooibos (Aspalathus linearis), and Aspalathin
Authors: Akinfenwa Akeem O. , Abdul Naeem S. , Docrat Fathima T. , Marnewick Jeanine L. , Luckay Robbie C. , Hussein Ahmed A.
Published: November 2021

The green chemistry approach has continuously been applied for the synthesis of functional nanomaterials to reduce waste, environmental hazards, and the use of toxic chemicals among other reasons. Bioactive natural compounds have been found great potential in this regard and are used to improve the stability, activity, and biodistribution of metal nanoparticles (MNPs). Aspalathin (ASP) from Aspalathus linearis (rooibos) has a well-defined pharmacological profile and functional groups capable of both reducing and capping agents in the synthesis of metallic nanoparticles (NP). This study provides the first report of the phytomediated synthesis of gold and silver nanoparticles (AuNPs/AgNPs) via ASP and the green rooibos (GR) extract. The study demonstrated a green chemistry approach to the biosynthesis of nanoparticles of GR-AuNPs, ASP-AuNPs, GR-AgNPs, and ASP-AgNPs. The results showed that GR and ASP could act both as reducing and stabilising agents in the formation of crystalline, with different shapes and dispersity of NPs in the ranges of 1.6–6.7 nm for AgNPs and 7.5–12.5 nm for the AuNPs. However, the ASP NPs were less stable in selected biogenic media compared to GR NPs and were later stabilised with polyethene glycol. The cytotoxicity studies showed that GR-AgNPs were the most cytotoxic against SH-SY5Y and HepG2 with IC50 108.8 and 183.4 μg/mL, respectively. The cellular uptake analysis showed a high uptake of AuNPs and indicated that AgNPs of rooibos at a lower dose (1.3–1.5 μg/mL) is favourable for its anticancer potential. This study is a contribution to plant-mediated metallic nanoparticles using a pure single compound that can be further developed for targeted drug delivery for cancer cells treatments in the coming years.
Genome-Wide Analysis of AAT Genes and Their Expression Profiling during Fiber Development in Cotton
Authors: Yang Dongjie , Liu Yuanyuan , Cheng Hailiang , Wang Qiaolian , Lv Limin , Zhang Youping , Zuo Dongyun , Song Guoli
Published: November 2021

Amino acid transporters (AATs) are a kind of membrane proteins that mediate the transport of amino acids across cell membranes in higher plants. The AAT proteins are involved in regulating plant cell growth and various developmental processes. However, the biological function of this gene family in cotton fiber development is not clear. In this study, 190, 190, 101, and 94 full-length AAT genes were identified from Gossypiumhirsutum, G. barbadense, G. arboreum, and G. raimondii. A total of 575 AAT genes from the four cotton species were divided into two subfamilies and 12 clades based on phylogenetic analysis. The AAT genes in the four cotton species were distributed on all the chromosomes. All GhAAT genes contain multiple exons, and each GhAAT protein has multiple conserved motifs. Transcriptional profiling and RT qPCR analysis showed that four GhATT genes tend to express specifically at the fiber initiation stage. Eight genes tend to express specifically at the fiber elongation and maturity stage, and four genes tend to express specifically at the fiber initiation and elongation stages. Our results provide a solid basis for further elucidating the biological function of AAT genes related to cotton fiber development and offer valuable genetic resources for crop improvement in the future.
Chitosan with Sulfonic Groups: A Catalyst for the Esterification of Caprylic Acid with Methanol
Authors: Castanheiro José
Published: November 2021

Esterification of caprylic acid with methanol was performed over chitosan with sulfonic acid groups, as a catalyst, at 60 °C. The sulfonic acid groups were introduced into chitosan (CH) by using chlorosulfonic acid. Catalysts were characterized by scanning electron microscopy (SEM), elemental analysis, thermogravimetric analysis (TGA), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), and acid–base titration. Catalytic activity increased with the amount of sulfonic acid groups present on chitosan. The 4–CH–SO3H catalyst (chitosan with sulfonic acid groups—sample 4 prepared) showed the highest activity of all materials. The esterification of caprylic acid with methanol was optimized using a 4–CH–SO3H catalyst. Under optimized reaction conditions, it was found that, at 60 °C, with 0.2 g of catalyst loading and with a molar ratio methanol to caprylic acid equal 1:95, a caprylic acid conversion of about 83%, after 4 h could be obtained. Catalytic stability of the 4–CH–SO3H material was evaluated through consecutive batch runs. After the second batch, the catalytic activity stabilized.
Preparation and Characterization of Highly Porous Polyacrylonitrile Electrospun Nanofibers Using Lignin as Soft Template via Selective Chemical Dissolution Technique
Authors: Ahmad Mohd Adib Tajuddin , Abdul Rahman Norizah
Published: November 2021

In this study, polyacrylonitrile (PAN) was mixed with a renewable polymer, lignin, to produce electrospun nanofibers by using an electrospinning technique. Lignin was utilized as a soft template that was removed from the nanofibers by using a selective dissolution technique to create porous PAN nanofibers. These nanofibers were characterized with Fourier transform infrared (FTIR), field emission scanning electron microscopy (FESEM), thermogravimetry analysis (TGA), X-ray diffraction (XRD), and Brunauer-Emmett-Teller (BET) to study their properties and morphology. The results showed that lignin can be homogeneously mixed into the PAN solution and successfully electrospun into nanofibers. FESEM results showed a strong relationship between the PAN: lignin ratio and the diameter of the electrospun fibers. Lignin was successfully removed from electrospun nanofibers by a selective chemical dissolution technique, which resulted in roughness and porousness on the surface of the nanofibers. Based on the BET result, the specific surface area of the PAN/lignin nanofibers was more than doubled following the removal of lignin compared to PAN nanofibers. The highest specific surface area of nanofibers after selective chemical dissolution was found at an 8:2 ratio of PAN/lignin, which was 32.42 m2g−1 with an average pore diameter of 5.02 nm. The diameter of electrospun nanofibers was also slightly reduced after selective chemical dissolution. Porous PAN nanofibers can be seen as the precursors to the production of highly porous carbon nanofibers.
Data-Driven Evaluation and Optimization of Agricultural Sustainable Development Capability: A Case Study of Northern Anhui
Authors: Luo Muchen , Liu Fan , Chen Jianqing
Published: November 2021

Human survival depends on the sustainable development of agriculture. This study constructs a data-driven evaluation and optimization method of agricultural sustainable development capacity, aiming to better cope with challenges such as environmental pollution and excessive consumption of resources and energy, as well as improve agricultural economic level. Further, an evaluation index system was constructed based on comprehensive consideration of energy and resources utilization, environmental pollution, and agricultural economy. After simplifying and integrating the data, a data envelopment analysis model was constructed to quantitatively evaluate the capability for agricultural sustainable development and its changing trend. Moreover, its influencing factors were analyzed from the perspective of input, which provides accurate countermeasures for improving agricultural sustainable development ability, resource utilization efficiency, and process optimization. This study shows the realization process of the aforementioned method for the agricultural development of six cities in northern Anhui from 2010 to 2019. Our results suggest that the sustainable development ability of northern Anhui is weak, but overall, has a good development trend. Based on our results, some countermeasures were proposed to control production scale reasonably, reduce environmental load, and improve resource efficiency, which provides a reference for policymakers to guide and standardize the development of regional agriculture.
Optimization of No-Wait Flowshop Scheduling Problem in Bakery Production with Modified PSO, NEH and SA
Authors: Babor Majharulislam , Senge Julia , Rosell Cristina M. , Rodrigo Dolores , Hitzmann Bernd
Published: November 2021

In bakery production, to perform a processing task there might be multiple alternative machines that have the same functionalities. Finding an efficient production schedule is challenging due to the significant nondeterministic polynomial time (NP)-hardness of the problem when the number of products, processing tasks, and alternative machines are higher. In addition, many tasks are performed manually as small and medium-size bakeries are not fully automated. Therefore, along with machines, the integration of employees in production planning is essential. This paper presents a hybrid no-wait flowshop scheduling model (NWFSSM) comprising the constraints of common practice in bakeries. The schedule of an existing production line is simulated to examine the model and is optimized by performing particle swarm optimization (PSO), modified particle swarm optimization (MPSO), simulated annealing (SA), and Nawaz-Enscore-Ham (NEH) algorithms. The computational results reveal that the performance of PSO is significantly influenced by the weight distribution of exploration and exploitation in a run time. Due to the modification to the acceleration parameter, MPSO outperforms PSO, SA, and NEH in respect to effectively finding an optimized schedule. The best solution to the real case problem obtained by MPSO shows a reduction of the total idle time (TIDT) of the machines by 12% and makespan by 30%. The result of the optimized schedule indicates that for small- and medium-sized bakery industries, the application of the hybrid NWFSSM along with nature-inspired optimization algorithms can be a powerful tool to make the production system efficient.
The Policy of the Russian Orthodox Church on International Labor Migrants: How the Public Role Collides with Preaching the Word
Authors: Mitrofanova Anastasia , Riazanova Svetlana , Brega Aleksandr
Published: November 2021

The authors’ objective was to find out how and why the approach of the Russian Orthodox Church to sociocultural adaptation of predominantly Muslim international labor migrants has evolved from its initial stage to now. Research methodology is based on a critical analysis of various sources, on observations, and archival materials. The adaptation program of the Church was advertised as a secular project pursuing the goal of peacekeeping because of a tacit agreement on mutual non-proselytisation between the basic faith-based communities in Russia. The initiative, launched in December 2012, had to merge adaptation courses of all dioceses into a nationwide network that then was expected to become part of an all-Russian system of preparation for language and culture tests. The Church also planned to open its own network of testing centrescenters for migrants. The authors emphasize that, although some of the diocesan courses were successful, the initiative deteriorated due to many external and internal factors. One of them was that diocesan courses have proven to be unattractive for labor migrants; their curriculum was too thick and overloaded with information about Russian culture and Orthodox Christianity, while Muslim labor migrants preferred to adapt to their new environment with a mediation of their own networks. It is suggested by the authors that the main cause of the project’s non-fulfillment was an intra-church cleavage between the enthusiasts of adaptation who convinced that diocesan courses must aim at spreading the Orthodox faith to foreign workers, including Muslims, and the church officials who promoted secular curriculum and forbade preaching Christianity to labor migrants.
Designing a Model for Testing the Effectiveness of a Regulation: The Case of DORA for Insurance Undertakings
Authors: Grima Simon , Marano Pierpaolo
Published: November 2021

Technology is sometimes seen as a disruption that although provides opportunities for growth and development, also provides opportunities for deception, theft, and fraud. On the other hand, automation can make it easier to identify and protect from threats. Hence, a proposal was made by the European Commission to enact a digital operations resilience act. Therefore, our objective in this paper is to lay out the perceived characteristics of effective regulation by using DORA as our case study. We do this by carrying out a literature review and extracting using the thematic analysis approach propositions for these characteristics. Then, we test these using exploratory factor analysis and design a model for perceived effective regulation (PERM). We test the reliability and validity of this model by using the Cronbach alpha. Results show that according to our model, an effective regulation should have four characteristics, specifically “Flexibility and Integration”, “Proportionality and Cost”, “Reliability and Transparency”, and “Relevance and Timeliness”. Findings laid out in this paper and PERM can be used to test other proposed regulations to ensure that they are effective before being enacted and also to determine when there is a need for a revamp in specified areas of current regulations and requirements.
Bio-Inspired Conceptual Mechanical Design and Control of a New Human Upper Limb Exoskeleton
Authors: Zakaryan Narek , Harutyunyan Mikayel , Sargsyan Yuri
Published: November 2021

Safe operation, energy efficiency, versatility and kinematic compatibility are the most important aspects in the design of rehabilitation exoskeletons. This paper focuses on the conceptual bio-inspired mechanical design and equilibrium point control (EP) of a new human upper limb exoskeleton. Considering the upper limb as a multi-muscle redundant system, a similar over-actuated but cable-driven mechatronic system is developed to imitate upper limb motor functions. Additional torque adjusting systems at the joints allow users to lift light weights necessary for activities of daily living (ADL) without increasing electric motor powers of the device. A theoretical model of the “ideal” artificial muscle exoskeleton is also developed using Hill’s natural muscle model. Optimal design parameters of the exoskeleton are defined using the differential evolution (DE) method as a technique of a multi-objective optimization. The proposed cable-driven exoskeleton was then fabricated and tested on a healthy subject. Results showed that the proposed system fulfils the desired aim properly, so that it can be utilized in the design of rehabilitation robots. Further studies may include a spatial mechanism design, which is especially important for the shoulder rehabilitation, and development of reinforcement learning control algorithms to provide more efficient rehabilitation treatment.
Quantification of Wide-Area Norwegian Spring-Spawning Herring Population Density with Ocean Acoustic Waveguide Remote Sensing (OAWRS)
Authors: Duane Daniel , Godø Olav Rune , Makris Nicholas C.
Published: November 2021

Norwegian spring-spawning herring are a critical economic resource for multiple nations in the North Atlantic and a keystone species of the Nordic Seas ecosystem. Given the wide areas that the herring occupy, it is difficult to accurately measure the population size and spatial distribution. Ocean Acoustic Waveguide Remote Sensing (OAWRS) was used to instantaneously measure the areal population density of Norwegian herring over more than one thousand square kilometers in spawning grounds near Ålesund, Norway. In the vicinity of the Ålesund trench near peak spawning, significant attenuation in signal-to-noise ratio and mean sensing range was observed after nautical sunset that had not been observed in previous OAWRS surveys in the Nordic Seas or in other regions. We show that this range-dependent decay along a given propagation path was caused by attenuation through dense herring shoals forming at sunset and persisting through the evening for transmissions near the swimbladder resonance peak. OAWRS transmissions are corrected for attenuation in a manner consistent with waveguide scattering theory and simultaneous downward directed local line-transect measurements in the region in order to produce instantaneous wide-area population density maps. Corresponding measured reductions in the median sensing range over the azimuth before ambient noise limitation are shown to be theoretically predictable from waveguide scattering theory and observed population densities. Spatial-temporal inhomogeneities in wide-area herring distributions seen synoptically in OAWRS imagery show that standard sparsely spaced line-transect surveys through this region during spawning can lead to large errors in the estimated population due to spatial and temporal undersampling.
Quantifying Marine Plastic Debris in a Beach Environment Using Spectral Analysis
Authors: Guffogg Jenna A. , Blades Samantha M. , Soto-Berelov Mariela , Bellman Chris J. , Skidmore Andrew K. , Jones Simon D.
Published: November 2021

Marine plastic debris (MPD) is a globally relevant environmental challenge, with an estimated 8 million tons of synthetic debris entering the marine environment each year. Plastic has been found in all parts of the marine environment, including the surface layers of the ocean, within the water column, in coastal waters, on the benthic layer and on beaches. While research on detecting MPD using remote sensing is increasing, most of it focuses on detecting floating debris in open waters, rather than detecting MPD on beaches. However, beaches present challenges that are unique from other parts of the marine environment. In order to better understand the spectral properties of beached MPD, we present the SWIR reflectance of weathered MPD and virgin plastics over a sandy substrate. We conducted spectral feature analysis on the different plastic groups to better understand the impact that polymers have on our ability to detect synthetic debris at sub-pixel surface covers that occur on beaches. Our results show that the minimum surface cover required to detect MPD on a sandy surface varies between 2–8% for different polymer types. Furthermore, plastic composition affects the magnitude of spectral absorption. This suggests that variation in both surface cover and polymer type will inform the efficacy of beach litter detection methods.
Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite’s MWHS-2 Data Assimilation
Authors: Xu Dongmei , Zhang Xuewei , Li Hong , Wu Haiying , Shen Feifei , Shu Aiqing , Wang Yi , Zhuang Xiaoran
Published: November 2021

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.
DP-MVS: Detail Preserving Multi-View Surface Reconstruction of Large-Scale Scenes
Authors: Zhou Liyang , Zhang Zhuang , Jiang Hanqing , Sun Han , Bao Hujun , Zhang Guofeng
Published: November 2021

This paper presents an accurate and robust dense 3D reconstruction system for detail preserving surface modeling of large-scale scenes from multi-view images, which we named DP-MVS. Our system performs high-quality large-scale dense reconstruction, which preserves geometric details for thin structures, especially for linear objects. Our framework begins with a sparse reconstruction carried out by an incremental Structure-from-Motion. Based on the reconstructed sparse map, a novel detail preserving PatchMatch approach is applied for depth estimation of each image view. The estimated depth maps of multiple views are then fused to a dense point cloud in a memory-efficient way, followed by a detail-aware surface meshing method to extract the final surface mesh of the captured scene. Experiments on ETH3D benchmark show that the proposed method outperforms other state-of-the-art methods on F1-score, with the running time more than 4 times faster. More experiments on large-scale photo collections demonstrate the effectiveness of the proposed framework for large-scale scene reconstruction in terms of accuracy, completeness, memory saving, and time efficiency.
Semi-Automated Semantic Segmentation of Arctic Shorelines Using Very High-Resolution Airborne Imagery, Spectral Indices and Weakly Supervised Machine Learning Approaches
Authors: Aryal Bibek , Escarzaga Stephen M. , Vargas Zesati Sergio A. , Velez-Reyes Miguel , Fuentes Olac , Tweedie Craig
Published: November 2021

Precise coastal shoreline mapping is essential for monitoring changes in erosion rates, surface hydrology, and ecosystem structure and function. Monitoring water bodies in the Arctic National Wildlife Refuge (ANWR) is of high importance, especially considering the potential for oil and natural gas exploration in the region. In this work, we propose a modified variant of the Deep Neural Network based U-Net Architecture for the automated mapping of 4 Band Orthorectified NOAA Airborne Imagery using sparsely labeled training data and compare it to the performance of traditional Machine Learning (ML) based approaches—namely, random forest, xgboost—and spectral water indices—Normalized Difference Water Index (NDWI), and Normalized Difference Surface Water Index (NDSWI)—to support shoreline mapping of Arctic coastlines. We conclude that it is possible to modify the U-Net model to accept sparse labels as input and the results are comparable to other ML methods (an Intersection-over-Union (IoU) of 94.86% using U-Net vs. an IoU of 95.05% using the best performing method).
First Results on Wake Detection in SAR Images by Deep Learning
Authors: Del Prete Roberto , Graziano Maria Daniela , Renga Alfredo
Published: November 2021

Spaceborne synthetic aperture radar (SAR) represents a powerful source of data for enhancing maritime domain awareness (MDA). Wakes generated by traveling vessels hold a crucial role in MDA since they can be exploited both for ship route and velocity estimation and as a marker of ship presence. Even if deep learning (DL) has led to an impressive performance boost on a variety of computer vision tasks, its usage for automatic target recognition (ATR) in SAR images to support MDA is still limited to the detection of ships rather than ship wakes. A dataset is presented in this paper and several state-of-the-art object detectors based on convolutional neural networks (CNNs) are tested with different backbones. The dataset, including more than 250 wake chips, is realized by visually inspecting Sentinel-1 images over highly trafficked maritime sites. Extensive experiments are shown to characterize CNNs for the wake detection task. For the first time, a deep-learning approach is implemented to specifically detect ship wakes without any a-priori knowledge or cuing about the location of the vessel that generated the wake. No annotated dataset was available to train deep-learning detectors on this task, which is instead presented in this paper. Moreover, the benchmarks achieved for different detectors point out promising features and weak points of the relevant approaches. Thus, the work also aims at stimulating more research in this promising, but still under-investigated, field.
P-Band InSAR for Geohazard Detection over Forested Terrains: Preliminary Results
Authors: Xu Yuankun , Lu Zhong , Kim Jin-Woo
Published: November 2021

Decorrelation of X, C, and L-band InSAR (Interferometric Synthetic Aperture Radar) over densely vegetated regions is a common obstacle for detecting ground deformation beneath forest canopies. Using long-wavelength P-band SAR sensors (wavelength of 69.72 cm), which can penetrate through dense forests and collect relatively consistent signals from ground surface, is one potential solution. Here, we experimented using the NASA JPL (Jet Propulsion Laboratory)’s P-band AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface) radar system to collect repeat-pass P-band SAR data over densely vegetated regions in Oregon and California (USA), and generated by far the first P-band InSAR results to test the capability of P-band InSAR for geohazard detection over forested terrains. Our results show that the AirMOSS P-band InSAR could retain coherence two times as high as the L-band satellite ALOS-2 (Advanced Land Observing Satellite-2) data, and was significantly more effective in discovering localized geohazards that were unseen by the ALOS-2 interferograms over densely vegetated areas. Our results suggest that the airborne P-band InSAR could be a revolutionary tool for studying geohazards under dense forest canopies.
The Impact of Attention Mechanisms on Speech Emotion Recognition
Authors: Chen Shouyan , Zhang Mingyan , Yang Xiaofen , Zhao Zhijia , Zou Tao , Sun Xinqi
Published: November 2021

Speech emotion recognition (SER) plays an important role in real-time applications of human-machine interaction. The Attention Mechanism is widely used to improve the performance of SER. However, the applicable rules of attention mechanism are not deeply discussed. This paper discussed the difference between Global-Attention and Self-Attention and explored their applicable rules to SER classification construction. The experimental results show that the Global-Attention can improve the accuracy of the sequential model, while the Self-Attention can improve the accuracy of the parallel model when conducting the model with the CNN and the LSTM. With this knowledge, a classifier (CNN-LSTM×2+Global-Attention model) for SER is proposed. The experiments result show that it could achieve an accuracy of 85.427% on the EMO-DB dataset.
Removal of Pyridine, Quinoline, and Aniline from Oil by Extraction with Aqueous Solution of (Hydroxy)quinolinium and Benzothiazolium Ionic Liquids in Various Ways
Authors: Zhang Zhaojin , Li Yinan , Gao Jing , Yohannes Alula , Song Hang , Yao Shun
Published: November 2021

Based on above background, quinolinium, 8-hydroxy-quinolinium, and benzothiazolium ionic liquids, containing the acidic anions of methanesulfonate ([CH3SO3]−), phosphate ([H2PO4]−), p-toluenesulfonate ([p-TSA]−), and bisulfate ([HSO4]−) were synthesized. After comparison, the aqueous solution of benzothiazole bisulfate [HBth][HSO4] was selected as the most ideal extractant for removing pyridine and aniline. Meanwhile, benzothiazole bisulfate [HBth][HSO4] solution was found as the best one for removing quinoline from simulated oil. Then, the single stage extraction and two-step extraction were used in the extraction for the simulated oil containing pyridine, quinoline or aniline, and their mixture, respectively. Their denitrogenation performance on their N-removal effect was compared on the basis of structural features, and main extraction conditions were further investigated, including mass ratio of IL to water, mass ratio of IL to oil, and temperature. Furthermore, the extraction process was described by two kinetic equations. Recovery and reuse of IL were realized by back-extraction and liquid-liquid separation, and a related mechanism was speculated, according to all the experimental results. Finally, based on the developed method for preparing complex adsorbent tablets, corresponding immobilized IL was used to remove target objects, by solid phase extraction, in order to extend separation ways, which was more easily recovered after extraction.
A Scintillating One-Dimensional Coordination Polymer Based on Cadmium(II), N,N′-(1,4-Phenylenedicarbonyl)diglycinate, and 2,2′-Bipyridine: Crystal Structure, Hirshfeld Surface Analysis, and Luminescence Lifetime Properties †
Authors: Pook Niels-Patrick
Published: November 2021

In recent years, several coordination polymers of different dimensions and metal–organic frameworks were tested and expected to be good candidates for closing the gap between organic and plastic scintillators on the one hand side and inorganic scintillators on the other hand side. In the present work, we report the synthesis and characterization of a novel one-dimensional scintillating coordination polymer based on cadmium(II), N,N′-(1,4-phenylenedicarbonyl)di-glycinate, and 2,2′-bipyridine. Crystals could be obtained from water–methanol solutions and the structure was determined by single-crystal diffraction. The coordination polymer exhibits scintillation under X-ray excitation and laser as well as UV-light induced photoluminescence with fast decay times. Photoluminescence and X-ray excited optical luminescence (XEOL) properties and decay times were performed using a two-dimensional photon counting streak camera system with a time resolution up to 20 ps. The non-covalent interactions and supramolecular assemblies as a potential multiplier of the scintillating effect were investigated with the aid of a Hirshfeld surface analysis. The quality and phase purity of the used crystals and pellets was clarified by powder diffraction and Rietveld refinement.
Acute and Long-Term Effects of Attentional Focus Strategies on Muscular Strength: A Meta-Analysis
Authors: Grgic Jozo , Mikulic Ivan , Mikulic Pavle
Published: November 2021

This review aimed to perform a meta-analysis examining the following: (a) acute effects of adopting an internal focus vs. external focus of attention on muscular strength; and (b) long-term effects of adopting an internal focus vs. external focus of attention during resistance training on gains in muscular strength. We searched through five databases to find eligible studies. Random-effects meta-analyses of standardized mean differences were conducted to analyze the data. Ten studies were included. In the meta-analysis for the acute effects, there was a significant positive effect of external focus on muscular strength (standardized mean difference: 0.34; 95% confidence interval: 0.22, 0.46). In the meta-analysis for the long-term effects, there was no significant difference between training with an internal focus and external focus on muscular strength gains (standardized mean difference: 0.32; 95% confidence interval: –0.08, 0.73). In the subgroup analysis for lower-body exercises, we found a significant positive effect of training with an external focus on muscular strength gains (standardized mean difference: 0.47; 95% confidence interval: 0.07, 0.87). In summary, our findings indicate an acute increase in muscular strength when utilizing an external focus of attention. When applied over the long-term, using an external focus of attention may also enhance resistance training-induced gains in lower-body muscular strength.
Current Ice Adhesion Testing Methods and the Need for a Standard: A Concise Review
Authors: Bleszynski Monika , Clark Edward
Published: November 2021

Ice accretion is a serious problem in cold climates, causing automobile and airplane accidents, as well as severe economic losses throughout various sectors. To combat these issues, many solutions have been developed, such as de-icing materials, which can delay or prevent the adhesion of ice to a surface through chemical, temperature, or physical means. To effectively assess the properties of a de-icing material, ice adhesion testing must be conducted, of which there are numerous types, each with their own characteristics. Unfortunately, the same material, tested with different methods, may provide very different ice adhesion values. This makes it difficult to properly characterize a material’s de-icing properties and compare values across the literature. In this review, we identified the main ice adhesion testing methods and compared ice adhesion values for a particular material with different testing methods. We then discussed some of the main issues with current ice testing methods and identified some of the main factors that may affect ice adhesion values, namely ice quality and the use of a mold, which may significantly affect the final ice adhesion results. Finally, we proposed a new, simple standard testing method, in an attempt to eliminate some of the issues with current ice testing methods.
Comparison of the Deep Learning Performance for Short-Term Power Load Forecasting
Authors: Son Namrye
Published: November 2021

Electricity demand forecasting enables the stable operation of electric power systems and reduces electric power consumption. Previous studies have predicted electricity demand through a correlation analysis between power consumption and weather data; however, this analysis does not consider the influence of various factors on power consumption, such as industrial activities, economic factors, power horizon, and resident living patterns of buildings. This study proposes an efficient power demand prediction using deep learning techniques for two industrial buildings with different power consumption patterns. The problems are presented by analyzing the correlation between the power consumption and weather data by season for industrial buildings with different power consumption patterns. Four models were analyzed using the most important factors for predicting power consumption and weather data (temperature, humidity, sunlight, solar radiation, total cloud cover, wind speed, wind direction, humidity, and vapor pressure). The prediction horizon for power consumption forecasting was kept at 24 h. The existing deep learning methods (DNN, RNN, CNN, and LSTM) cannot accurately predict power consumption when it increases or decreases rapidly. Hence, a method to reduce this prediction error is proposed. DNN, RNN, and LSTM were superior when using two-year electricity consumption rather than one-year electricity consumption and weather data.
Cost-Effective Inspection of Rebar Spacing and Clearance Using RGB-D Sensors
Authors: Yuan Xinxing , Moreu Fernando , Hojati Maryam
Published: November 2021

The quality assurance of constructing reinforced concrete (RC) structures in compliance with their design plays a key role in the durability, serviceability, and sustainability of the built RC elements. One area of concern in the quality control of constructing RC structures is examining the position and dimension of the rebars before pouring fresh concrete. Currently, this is accomplished by visual inspection and individually by hand with limited time available between construction stages. Over the past decades, structural health and monitoring during the construction period has applied remote sensing technologies. However, little research has focused on the use of such technologies to inspect and evaluate rebar placement prior to concrete pouring as quality control. In this study we develop an algorithm that facilitates inspecting the positions of rebars and the cover of concrete using a new-generation low-cost RGB-D sensor to find incorrect rebar placement. The proposed method is evaluated using a typical 5 × 5 two-layer rebar cage in the laboratory by comparing the proposed technique with traditional inspection methods. The results show that the RGB-D sensor can achieve cost-effective inspection for rebar spacing and clearance with an acceptable tolerance. The evaluation of rebar spacing results shows that the maximum standard deviation for rebar spacing is 0.34 inch (8.64 mm) between longitudinal rebar 2 and 3, which is the same as the rebar construction and traditional tape measurement results. The concrete cover estimation results show that the maximum standard deviation for rebar cage concrete cover is 0.19 inch (4.83 mm) for longitudinal rebar 3. The issues of new RGB-D sensor scan settings and the test results will be helpful for practitioners in improving construction quality.
Medfly Phenotypic Plasticity as A Prerequisite for Invasiveness and Adaptation
Authors: Lemic Darija , Bjeliš Mario , Ninčević Pave , Živković Ivana Pajač , Popović Luka , Gašparić Helena Virić , Benitez Hugo A.
Published: November 2021

The hypothesis of this study was that different plant hosts of the medfly Ceratitis capitata may cause variability as a prerequisite for its invasiveness. The main objective was to determine population variability based on medfly wing shape in three favorable medfly host plants (peach, fig and mandarin) from different agroecological growing areas with different pest management practices, and to evaluate phenotypic plasticity as a basis for future expansion into new areas and new hosts. Using geometric morphometric methods across 14 specific landmarks on the medfly wings, 10 populations were tested from infested peach, fig and mandarin fruits, as well as laboratory-grown sterile populations. The studies led to the following main findings: (1) all of the medfly populations that were studied exhibited sexual dimorphism in wing shape; (2) the hosts in which the medfly develops influence wing shape and condition its variability; (3) there is significant variability between laboratory mass-reared sterile and wild individuals in male and female populations; (4) a high phenotypic plasticity of medfly populations was observed along the study sites. Even the low but clearly detected variability between different agroecological conditions and localized variability indicate genotypic stability and high phenotypic plasticity, which can be considered as a prerequisite for medfly invasiveness and dispersal to new areas.
Proactive Personality and Creative Performance: Mediating Roles of Creative Self-Efficacy and Moderated Mediation Role of Psychological Safety
Authors: Choi Suk Bong , Ullah S.M. Ebrahim , Kang Seung-Wan
Published: November 2021

Previous research has suggested that employees’ proactive personality can enhance their creative performance. However, studies that address the underlying mechanism of the effect of proactive personality on creative performance have been scarce. Additionally, contextual factors that could serve as useful conditions have received insufficient academic attention. Therefore, the present study proposed that creative self-efficacy serves as a mediator and psychological safety serves as a moderator in the positive relationship between employees’ proactive personality and their creative performance. The results of a large-scale survey revealed that a proactive personality was positively associated with creative performance. Furthermore, we found that the positive relationship between a proactive personality and creative performance was positively mediated by creative self-efficacy and further strengthened by psychological safety. Additionally, we found that psychological safety influenced the mediating role of creative self-efficacy in the link between employees’ proactive personality and creative performance. We have discussed theoretical and practical implications with future research directions.
Supply Chains’ Failure in Workers’ Rights with Regards to the SDG Compass: A Doughnut Theory Perspective
Authors: Lotfi Maryam , Walker Helen , Rendon-Sanchez Juan
Published: November 2021

Many supply chains have pledged to prevent the violation of workers’ rights as part of social sustainability in their far-flung supply chains. This paper provides a way to understand why supply chains fail to overcome the violation of workers’ rights by mapping the UN SDGs onto the social foundations of the doughnut model, with respect to workers’ rights in supply chains. We develop the sustainable supply chain doughnut model with regards to the SDGs, through which we investigate workers’ rights violations. Examples from both UK-based and world-wide supply chains illustrates our conceptual model. Supply chains have shortfalls in all aspects of the social foundation when it comes to workers as one of their main stakeholders. Until supply chains are successful in overcoming shortfalls across all elements of the social foundation, moving to the next layer of the doughnut framework is impossible, which is the safe and just space for all humans, including workers. This ‘safe and just space’ seems out of reach despite international efforts such as the SDGs. The resulting conceptual model can be the foundation for descriptive, instrumental, and normative research on workers’ rights in the supply chain as part of the social sustainability.
UAV Behavior-Intention Estimation Method Based on 4-D Flight-Trajectory Prediction
Authors: Zhang Honghai , Yan Yongjie , Li Shan , Hu Yuxin , Liu Hao
Published: November 2021

Aiming at the limitation of the traditional four-dimensional (4-D) trajectory-prediction model of unmanned aerial vehicles (UAV), a 4-D trajectory combined prediction model based on a genetic algorithm is proposed. Based on historical flight data and the UAV motion equation, the model is weighted dynamically by a genetic algorithm, which can predict UAV trajectory and the time of entering the protection zone instantly and accurately. Then, according to the number of areas where the tangent line of the current trajectory point intersects with the collision area, alarm area, alert area, and the time of entering the protection zone, the UAV’s behavior intention can be estimated. The simulation experiments verify the dangerous behaviors of UAV under different danger levels, which provides reference for the subsequent maneuvering strategies.
Future Policy and Technological Advancement Recommendations for Enhanced Adoption of Electric Vehicles in South Africa: A Survey and Review
Authors: Moeletsi Mokhele Edmond
Published: November 2021

There are major concerns globally on the increasing population of internal combustion engine (ICE) vehicles and their environmental impact. The initiatives for the advancement of alternative propulsion systems, such as electric motors, have great opportunities, but are marked by a number of challenges that require major changes in policies and serious investment on the technologies in order to make them viable alternative mobility sources around the world. South Africa has struggled a lot in adopting electric vehicles among all the emerging countries. This is mostly attributed to a non-conducive environment for electric vehicle adoption. This study administered a survey consisting of Likert-scale questions in the Gauteng Province to gather information on people’s views on some of the major concerns around electric vehicle technology. The survey results demonstrated that Gauteng residents perceive electric vehicle price as the main constraint towards adoption of the technology and introduction of government policy towards addressing this challenge would be helpful. Some of the suggested interventions, such as the rollout of purchasing subsidies and tax rebates, received a high level of satisfaction among the respondents. Future initiatives that tackle issues of charging infrastructure network also received high satisfaction. Thus, there is a need for all stakeholders in the South African automotive industry to improve the enabling environment for the adoption of electric vehicles.