Miracle-dz/UniFAR-FTUs huggingface dataset:
Season, anthocyanin supplementation, and flight training have mixed effects on the antioxidant system of migratory European Starlings

ABSTRACT Migratory birds engage in 2 periods of endurance flight annually as they travel between summer breeding and overwintering grounds, and such endurance flights likely incur oxidative costs. These costs may differ between fall and spring migration, especially for females who must prepare for breeding and egg laying in spring. The objective of this study of a migratory bird was to test proposed hypotheses about how key components of the female's antioxidant system differ in response to flight training in the fall and spring and to dietary antioxidant supplementation. We hand raised female European Starlings (Sturnus vulgaris) and fed them either a diet supplemented with dietary anthocyanins or a diet without added anthocyanins. We flew females in a wind tunnel for 15 days during fall and spring migration seasons and measured over time oxidative lipid damage (d-ROMs) and 3 components of the antioxidant system: nonenzymatic antioxidant capacity (OXY), uric acid, and glutathione peroxidase (GPx) activity. Prior to flight training, OXY and oxidative damage were lower in females during spring compared with fall, and females fed a low-antioxidant diet had consistently higher circulating uric acid. GPx activity decreased more in spring immediately after a long-duration flight. Females fed a high-antioxidant diet had a greater decrease in OXY after the 15-day flight training. Flight-trained females had higher circulating uric acid than untrained females immediately after the longest-duration flight and decreased GPx activity after the 15-day flight training. In sum, females upregulated enzymatic and nonenzymatic endogenous antioxidants in spring, and females fed a diet with less antioxidants appear to compensate by increasing circulating uric acid. Our findings emphasize the important role of dietary antioxidants for birds during migration, and similar flights in fall and spring likely represent distinct oxidative challenges in the life history of female birds. LAY SUMMARY Migratory birds incur oxidative costs during their long endurance flights, and these costs may differ between fall and spring migration, especially for females who must prepare for breeding and egg laying in spring. We fed female European Starlings diets with different quantities of anthocyanin, a dietary antioxidant, and then measured during both fall and spring migration periods how their antioxidant system responded to diet and two weeks of daily flight in a wind tunnel. Females upregulated some key components of their endogenous antioxidant system in spring and had lower oxidative damage than females in fall, and females fed less dietary antioxidants appear to have compensated by increasing circulating uric acid. Similar flights in fall and spring likely represent distinct oxidative challenges for female songbirds, and dietary antioxidants play an important role in modulating the antioxidant system.
Localization of Photoexcited Electrons and Holes on Low Coordinated Ti and O Sites in Free and Supported TiO2 Nanoclusters

Photocatalysis is being intensively studied for reactions such as water splitting and CO2 reduction, where absorption of light in a semiconductor such as TiO2 produces electrons and holes that can drive chemical reactions. Efficiencies on bulk materials, however, are too low to be of practical use. In recent years, low dimensional structures such as nanoclusters or nanotubes, displaying metal and oxygen coordination environments very different from the bulk, show promise for improved photocatalytic activities. Key to this is the presence of low coordinated metal and oxygen sites which can act as both charge carrier trapping sites and active sites with target molecules such as water or CO2. This paper presents the results of a density functional theory, with Hubbard U correction (DFT+U), study of electron and hole localization in free and metal oxide-supported TiO2 nanoclusters that display low coordinated titanium and oxygen sites. In free TiO2 nanoclusters, electrons and holes preferentially localize at .
A NEW COLOR DISTANCE MEASURE FORMULATED FROM THE COOPERATION OF THE EUCLIDEAN AND THE VECTOR ANGULAR DIFFERENCES FOR LIDAR POINT CLOUD SEGMENTATION

Two important features of the points in the LiDAR point clouds are the spatial and the color features. The spatial feature is mostly used in the point cloud processing field due to its 3D informative and distinctive characteristic. The local geometric difference derived from the spatial features of the points is usually benefited by graph-based point cloud segmentation methods, because the geometric features of the local point groups are highly distinctive. In this paper, we use both the geometric and color differences of the adjacent local point groups at the impact rates 0.3, 0.5, and 0.7 and cooperate the Euclidean and the vector color differences within several averaging techniques for the color difference. The difference forms have been tested within a graph-based segmentation method on four point cloud segmentation datasets, two indoor and two outdoor, using their spatial and color information. The geometric mean as an averaging techniques increases the segmentation success for the all datasets except one outdoor when the color differences are used in the segmentation at the impact rate 0.3, while the harmonic mean increases the success for the all datasets the successes except the other outdoor at the same impact rate. According to the test results, the cooperating of the Euclidean and vector angular color difference measurements can considerable increase the segmentation success on the point clouds with color information in a high quality.
Research on impulse impedance model and protection optimization of transmission tower grounding device in mountainous area

The impulse impedance of the tower is the core parameter to evaluate the lightning protection performance of the transmission line. The high soil resistivity and the underground rock layers in the mountainous areas will induce large impulse impedance and further lead to frequent lightning striking accidents. Therefore, this paper proposes a circuit-ﬁnite element model (CFEM) and investigates the corresponding impedance-reduction optimization method of the grounding device in mountainous areas. The model takes into account the nonlinear characteristics of the soil. Based on the parallel-plate electrode and the concentric hemispheric test device, the nonlinear curves of the soil resistivity of ﬁve typical kinds of soils with the electric ﬁeld intensity are obtained. To justify the CFEM, the ﬁeld experiment of a single horizontal conductor is performed, with an average relative error of 4.67%. Based on the CFEM, this paper presents an impedance-reduction optimization method for the grounding device in the mountainous areas, while proposing the best design method and the optimal layout of the grounding device in the rocky zone. The research results can provide a reference for the design transformation and protection optimization of the transmission tower grounding device in mountainous
Promising Gains of 5G Networks with Enhancing Energy Efficiency Using Improved Linear Precoding Schemes

: Massive multiple-input-multiple-output (MIMO) technology offers high spectral efficiency (SE) using simple schemes while it is also especially expected to offer high energy efficiency (EE). Then to capture promising gains expected from the 5G networks, this work proposes two improved versions of famous linear precoding schemes, i.e., MRT (maximal-ratio-transmission) and ZF (zero-forcing). To enhance and evaluate the energy efficiency performance in massive multi-user mimo (MU-MIMO) system, we considered here imperfection base station (BS) channel state information. Additionally, our proposed improved versions of linear precoding schemes are taking into account power circuit consumption and transmit power. Simulation results have shown that the improved ZF precoding scheme achieves higher EE as compared to improved MRT precoding scheme, while the results showed that improved MRT precoding outperforms ZF precoding within small BS number of M-antenna deployment comparatively to number of K terminals (from 20 to approximately 40 antennas). Previous results had been further shown that without circuit power consideration, the energy-efficiency was increasing without limit. In contrast to this belief in the literature, our model shows that energy efficiency is not unbounded when BS antennas increase very-large. We found that around 100 active antennas serving tenth of autonomous users simultaneously showing that energy-efficiency cannot be optimal at low signal to noise ratio (SNR) region, but in region where an appropriate interference-canceling processing, ZF precoding is extremely suitable over an interference-limited processing, MRT precoding. Finally, the results demonstrated and confirmed that an efficient-energy can be achieved by allowing so more and more users to access the channel simultaneously, thanks to massive MIMO that illustrates 5G promising gain
Thirty states of renewability: Controversial energies and the politics of incumbent industry

Renewable energy advocates have positioned a wide array of technologically novel energy sources as fossil fuel alternatives. These efforts to usher in renewable energy transitions have long been shaped by definitional contestations. Political ecological scholarship tells us that such definitions are meaningful. Indeed, labeling energy sources as renewable has become a power-laden act, which may spark innovation, yet simultaneously creates openings for problematic classifications and unjust socio-ecological relations. However, we still know too little about how such classification politics are taking shape within green industrial policy formation; particularly, how they encounter incumbent industries and industrial regions. In this paper, we argue that these theoretical questions are crucial for an emerging industrial political ecology and explore three recent developments in the US context. A country that has notoriously avoided open and coordinated national industrial policy, the United States has approached the renewable energy economy in a similarly geographically fragmented fashion. We highlight a central yet under-examined tool in US energy-industrial policy: the renewable portfolio standard. Mandated by 30 states, renewable portfolio standards are the US’s central mechanism for renewable energy procurement—yet renewable portfolio standards diverge substantially from state to state in terms of the energy sources they classify and incentivize as renewable. We argue that industrial interests and state governments have together capitalized upon renewable portfolio standards’ malleability to support regionally significant sectors, including “dirty” industries and industrial wastes. These industries, often controversially, thereby position themselves for rebranding and new forms of value capture.
An Empirical Comparison of Resampling Ensemble Methods of Deep Learning Neural Networks for Cross-Project Software Defect Prediction

: Software defect prediction is one of the most important quality assurance activities during software development. This paper contributes empirical insights into the effectiveness of three resampling ensemble methods (bagging, boosting, and dagging) of Deep Learning Neural Networks (DLNN) for cross-project software defect prediction, compared to individual DLNN. An empirical study was conducted using five datasets. The results indicate that the bagging ensembles of DLNN offer only 0.24% increase in accuracy, on average, compared to the individual DLNN models, whereas the boosting and dagging ensembles degrade the accuracy. Furthermore, the results show that the three resampling ensembles of DLNN outperform the individual DLNN models in precision; with a maximum improved precision by 25.15% on average using the boosting ensembles. The results however indicate that none of the resampling ensembles improve the recall. Lastly, for a balanced performance in terms of both precision and recall, the results indicate improvements ranging from 0.98% on average by applying the bagging ensembles to 11.67% on average by applying the boosting ensembles.
Synthesis of polypyrrole/indium tin oxide nanocomposites via miniemulsion polymerization

Abstract Polypyrrole/indium tin oxide nanocomposites were synthesized via in situ miniemulsion polymerization of pyrrole monomer in the presence of indium tin oxide nanoparticles. Different nanocomposites were synthesized by different loadings of nano indium tin oxide. The morphology and nanoparticles distribution of the nanocomposites were characterized by electron microscopy. The results of XRD and TEM analysis showed that indium tin oxide nanoparticles were well placed in the polymeric structure of latex. FTIR analysis was used for the characterization of synthesized polypyrrole and its nanocomposites. TGA analysis was performed to investigate the thermal behavior of pristine polypyrrole and its nanocomposites. Conductivities of nanocomposites were measured by 4-point probe method and compared to the neat polymer.
Decomposition-based multi-objective optimization for energy-aware distributed hybrid flow shop scheduling with multiprocessor tasks

: This paper addresses the Energy-Aware Distributed Hybrid Flow Shop Scheduling Problem with Multiprocessor Tasks (EADHFSPMT) by considering two objectives simultaneously, i.e., makespan and total energy consumption. It consists of three sub-problems, i.e., job assignment between factories, job sequence in each factory, and machine allocation for each job. We present a mixed inter linear programming model and propose a Novel Multi-Objective Evolutionary Algorithm based on Decomposition (NMOEA/D). We specially design a decoding scheme according to the characteristics of the EADHFSPMT. To initialize a population with certain diversity, four different rules are utilized. Moreover, a cooperative search is designed to produce new solutions based on different types of relationship between any solution and its neighbors. To enhance the quality of solutions, two local intensiﬁcation operators are implemented according to the problem characteristics. In addition, a dynamic adjustment strategy for weight vectors is designed to balance the diversity and convergence, which can adaptively modify weight vectors according to the distribution of the non-dominated front. Extensive computational experiments are carried out by using a number of benchmark instances, which demonstrate the effectiveness of the above special designs. The statistical comparisons to the existing algorithms also verify the superior performances of the NMOEA/D.
An Artificial Intelligence-Based Smart System for Early Glaucoma Recognition Using OCT Images

Glaucoma is a progressive and constant eye disease that leads to a deficiency of peripheral vision and, at last, leads to irrevocable loss of vision. Detection and identification of glaucoma are essential for earlier treatment and to reduce vision loss. This motivates us to present a study on intelligent diagnosis system based on machine learning algorithm(s) for glaucoma identification using three-dimensional optical coherence tomography (OCT) data. This experimental work is attempted on 70 glaucomatous and 70 healthy eyes from combination of public (Mendeley) dataset and private dataset. Forty-five vital features were extracted using two approaches from the OCT images. K-nearest neighbor (KNN), linear discriminant analysis (LDA), decision tree, random forest, support vector machine (SVM) were applied for the categorization of OCT images among the glaucomatous and non-glaucomatous class. The largest AUC is achieved by KNN (0.97). The accuracy is obtained on fivefold cross-validation techniques. This study will facilitate to reach high standards in glaucoma diagnosis.
Catalytic Pyrolysis of Municipal Solid Waste: Effects of Pyrolysis Parameters

Burning municipal solid waste (MSW) increases CO2, CH4, and SO2 emissions, leading to an increase in global warming, encouraging governments and researchers to search for alternatives. The pyrolysis process converts MSW to oil, gas, and char. This study investigated catalytic and noncatalytic pyrolysis of MSW to produce oil using MgO-based catalysts. The reaction temperature, catalyst loading, and catalyst support were evaluated. Magnesium oxide was supported on active carbon (AC) and Al2O3 to assess the role of support in MgO catalyst activity. The liquid yields varied from 30 to 54 wt% based on the experimental conditions. For the noncatalytic pyrolysis experiment, the highest liquid yield was 54 wt% at 500 °C. The results revealed that adding MgO, MgO/Al2O3, and MgO/AC declines the liquid yield and increases the gas yield. The catalysts exhibited significant deoxygenation activity, which enhances the quality of the pyrolysis oil and increases the heating value of the bio-oil. Of the catalysts that had high deoxygenation activity, MgO/AC had the highest relative yield. The loading of MgO/AC varied from 5 to 30 wt% of feed to the pyrolysis reactor. As the catalyst load increases, the liquid yield declines, while the gas and char yields increase. Copyright © 2021 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0).
Vocational Students' Perception of Online Learning during the Covid-19 Pandemic

COVID-19 pandemic impacts on vocational education. Lectures that were originally conducted face-to-face learning are diverted to online learning to avoid the spread of the pandemic. Online learning is very difficult to apply for courses conducted in the laboratory. This study discusses vocational students’ responses to the practice of online learning during the COVID-19 pandemic. Data were collected through a questionnaire created on Google form consisting of 20 questions. The questionnaire used a Likert scale to find out the attitudes and students’ perceptions of the implementation of online learning. The number of research respondents was 107 people consisting of 45 respondents from the West Aceh State Community Academy and 62 respondents from Lhokseumawe State Polytechnic, Aceh, Indonesia. The results of this study found that 59.81% of students disagree with online learning. The results also showed a score of 76.95% of the students agree that internet access is the main obstacle in online learning. However, students’ satisfaction with the current online learning system for students shows a score of 67.50%. Opinions related to online learning from 107 respondents showed that 45.42% of them less agree if online learning is still applied when the COVID-19 pandemic ends.
A rare variant in the IL22RA2 gene is associated with unexplained recurrent pregnancy loss in Han Chinese women

Introduction: Recurrent pregnancy loss (RPL) occurred in ~1-2% of reproductive women. Previous studies have implicated that altered expression and polymorphisms of interleukin-related genes might be involved in RPL. The aim of this study was to explore the potential presence of interleukin-22 receptor subunit alpha-2 (IL22RA2) mutations in Han Chinese women with unexplained recurrent pregnancy loss (URPL). Material and methods: A total of 328 Han Chinese women with URPL were analyzed for the potential mutations in the IL22RA2 gene by sequencing all of the exons. Furthermore, 615 Han Chinese control women without miscarriage history were also analyzed. Evolutionary conservation and in silico analyses were performed to predict the potential pathogenicity of IL22RA2 mutations. Results: A rare variant, p.Arg204* (c.610C>T), was identified in 4 out of 328 URPL samples. In contrast, this rare variant was absent in 615 Han Chinese control women without miscarriage history and had a significantly higher mutation frequency when compared with 10588 Chinese control samples from The China Metabolic Analytics Project (ChinaMAP), 4245 East Asians, or 60051 individuals across the world from the Exome Aggregation Consortium (ExAC) database. Evolutionary conservation analysis indicated this rare variant was highly conserved from Human to Zebrafish, in silico analysis implicated this rare variant might be pathogenic. Conclusions: An enrichment of a potentially pathogenic rare variant in the IL22RA2 gene was observed in URPL patients, for the first time, implicating this rare variant might be associated with the development of URPL. However, further functional Pr ep rin t assays would be performed to confirm the potential role of this rare variant in URPL.
Determining the Overpotential for a Molecular Electrocatalyst

“The additional potential (beyond the thermodynamic requirement) needed to drive a reaction at a certain rate is called the overpotential. ”1 Over the last decade there has been considerable interest in the design and testing of molecular electrocatalysis for the interconversion of renewable energy and chemical fuels.2-5 One of the primary motivations for such research is the replacement of expensive and rare precious metal catalysts, such as platinum, with cheaper, more abundant metals.2,6-8 To become competitive with current electrocatalytic energy conversion technologies, new catalysts must be robust, fast, and energy-efficient. This last feature, the energy-efficiency, is dependent upon the overpotential. For molecular catalysts, the determination and reporting of overpotentials can be complicated by the frequent dependence on assumptions, especially when working in nonaqueous solvents. As overpotentials become lower, the meaningful comparison of molecular catalysts will require improved accuracy and precision. The intended purpose of this viewpoint is to provide a clear and concise description of overpotential and recommendation for its determination in molecular electrocatalysis. This material is based upon work supported as part of the Center for Molecular Electrocatalysis, an Energy Frontier Research Center funded by the US Department of Energy, Office of Science, Office of Basic Energy Sciences.
Analysis of Micro-Doppler Signatures of Small UAVs Based on Doppler Spectrum

Most of the investigations on the micro-Doppler (MD) effect caused by a small unmanned aerial vehicle (UAV) have been conducted using joint time–frequency (JTF) images rather than the Doppler spectrum. On the other hand, several researchers utilized the Doppler spectrum instead of JTF images to observe the MD signature of a small UAV, and found the relationship between the spectral distribution of a small UAV and its physical specifications. However, the studies using the Doppler spectrum still lack concrete and theoretical foundations of the MD effects of a small UAV, focusing mainly on phenomena identified by measurement data. In this article, we establish the theoretical foundation connecting the MD signatures and motion dynamics of small UAVs based on the Doppler spectrum, and analyze their spectral distribution using simulations and measured data. In addition, experimental analysis is conducted using the data measured from various types of small UAVs considering the translational motion and aspect change. In contrast to already existing investigations, we completely explain and predict the changes on the Doppler spectrum relative to the physical specifications of a small UAV (e.g., blade length and rotor rotation rate). In particular, we show that the Doppler spectrum, compared to the JTF images, is a considerably simple and useful tool for analyzing the MD effects of small flying UAVs. The analysis results reveal that the MD features obtained from the measured echoes of small UAVs have considerable potential for detection and classification of small UAVs.