Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW ON AUTOMATICALLY IDENTIFYING WILD ANIMALS IN CAMERA-TRAP IMAGES USING CONVOLUTIONAL NEURAL NETWORK

Ecologists also use camera traps to monitor wildlife populations. This procedure is utilized to deliver enormous informational indexes which can be hard to physically break down for research groups. Specialists are progressively recruiting open volunteers as individuals to aid the order of photographs. The quantity of camera trap examines makes it progressively hard to track down adequate volunteers to handle all activities in time. Advances, especially profound learning, in machine training, permit the exact programmed grouping of pictures. Preparing models with existing datasets of pictures arranged by residents' researchers and their ensuing execution in new investigations will significantly lessen human exertion. The precision of tests, nonetheless, relies upon the amount, quality and assortment of data accessible for preparing models, and the writing centers around ventures with a great many explicit marked pictures. Numerous camera trap ventures come up short on countless marked recordings, so current AI methods can not be utilized. Moreover, also projects with labeled images from similar environments fail to follow methods for profound learning because image classification models overfit different image backgrounds (i.e. positions of camera). We do not concentrate on automating camera trap image marking, but rather on speeding up this method in this article. In order to construct a scalable, speedy and accurate active learning system we combine artificial intelligence and human intelligence, to reduce the manual work necessary for identification and counting animals in camera-trap images. Our proposed system will match with only 14,100 manual labels the state-of-the-art data collection of 3,2 million, which means manual labeling has decreased by more than 99.5 percent.
Near-field-far-field transformation from nonredundant plane-polar data: effective modellings of the source

The paper provides a fast and accurate technique for the far-field evaluation from a nonredundant number of plane-polar near-field samples. By introducing very flexible modellings of the source, which fit very well a lot of actual sources, a new sampling technique is developed to recover the plane-rectangular data from the knowledge of the plane-polar ones. It must be stressed that the amount of required plane-polar data remains finite also when the radius of the scanning zone approaches infinity and it can be significantly lower than the amount needed by using a spherical modelling of the source. This result is based on an appropriate choice of the phase function extracted from the field expression and of the coordinates used to describe the plane and on the reduced volumetrical redundancy of the proposed source modellings. The effectiveness of the proposed technique is proved by numerical examples and its stability is verified by adding randomly distributed errors to the ideal near-field samples.
Comparing E-tivities, e-Moderation and the Five Stage Model to the Community of Inquiry Model for Online Learning Design

This paper explores the question as to whether learning design strategies of E-tivities, eModeration and the 5-Stage Model by Professor Gilly Salmon, might be practically aligned with the Community of Inquiry Model (CoI). This is relevant to explore as there is very little literature that firstly presents on the current research in to these 'Salmon Methodologies'. Nor is there any literature at all that marries these methods as a possible guide for catering to the complexities of Social Presence, Cognitive Presence and Teaching presence within the CoI model. This report will explore, analyse and compare these methods and their alignment to the three CoI presences. Also providing an argument for consideration of their use in CoI online learning design. Limitations in the research and application of both models are explored and recommendations for future research that would enable the appropriate testing of this idea are then finally presented.
Research on infrared fault warning method of hotline tap clamp of substation equipment based on hybrid segmentation

The occurrence of substation equipment faults is usually associated with the heating of equipment components. Hotline tap clamps of substation equipment are important parts for carrying load currents and key parts for thermal fault potential. Therefore, a new hybrid early warning method for infrared faults of hotline tap clamp of substation equipment is proposed. A two-dimensional Otsu algorithm is used for coarse segmentation of infrared images to reduce the complexity of subsequent fine segmentation. Since the CV (Chan-Vese model) model is not accurate enough for image segmentation with uneven grayscale. The differential information obtained by the Prewitt operator to detect the target edges is combined with CV model to improve the segmentation accuracy, and the fine segmentation of hotline tap clamp of substation equipment is achieved by the improved CV model. The temperature information is applied to the segmented images, and the fault warning of the hotline tap clamp of substation equipment is realized based on the relative temperature difference. The experimental results show that the method can improve the segmentation effect of infrared images and achieve the purpose of fault warning.
Analysis of the impact of a volumetric tariff for irrigation in Northern Italy through the “Inverse DiD” approach

The impact of water-pricing policy in irrigated agriculture in Emilia Romagna (Italy) is evaluated through the analysis of farmers’ water use whereby a flat-rate tariff (20132015) is replaced by a two-tier scheme composed of a flat rate plus a volumetric tariff (2016-2018). The policy assessment is performed by an innovative ‘Inverse-DiD’ approach based on the reverse application of the Difference in Differences method. The results indicate that farmers reacted to volumetric pricing by reducing water use per hectare to 56%. The high responsiveness may be explained by the combined impact of volumetric metering itself and the small price increase from the previous flat rate (zero marginal price) to a moderate volumetric tariff (from 0.025 to 0.044).
Use of tunneling spectroscopy in high-temperature superconductivity

Superconductivity is one of the most interesting phenomena that has been observed in material in recent history. Despite being discovered in 1911, it was nearly 50 years before a universal theory was published describing the observation. The Bardeen, Cooper, and Schrieffer (BCS) Theory became the universally accepted basis for superconductivity. Among the many experimental techniques to test BCS theory, quasiparticle tunneling introduced by Giaever [1] was the most promising. He theorized that the derivative of the current-voltage
Ensemble Meta Classifier with Sampling and Feature Selection for Data with Multiclass Imbalance Problem

Ensemble learning by combining several single classifiers or another ensemble classifier is one of the procedures to solve the imbalance problem in multiclass data. However, this approach still faces the question of how the ensemble methods obtain their higher performance. In this paper, an investigation was carried out on the design of the meta classifier ensemble with sampling and feature selection for multiclass imbalanced data. The specific objectives were: 1) to improve the ensemble classifier through data-level approach (sampling and feature selection); 2) to perform experiments on sampling, feature selection, and ensemble classifier model; and 3) to evaluate the performance
Regional Sampling of Forest Canopy Covers Using UAV Visible Stereoscopic Imagery for Assessment of Satellite-Based Products in Northeast China

Canopy cover is an important parameter affecting forest succession, carbon fluxes, and wildlife habitats. Several global maps with different spatial resolutions have been produced based on satellite images, but facing the deficiency of reliable references for accuracy assessments. The rapid development of unmanned aerial vehicle (UAV) equipped with consumer-grade camera enables the acquisition of high-resolution images at low cost, which provides the research community a promising tool to collect reference data. However, it is still a challenge to distinguish tree crowns and understory green vegetation based on the UAV-based true color images (RGB) due to the limited spectral information. In addition, the canopy height model (CHM) derived from photogrammetric point clouds has also been used to identify tree crowns but limited by the unavailability of understory terrain elevations. This study proposed a simple method to distinguish tree crowns and understories based on UAV visible images, which was referred to as BAMOS for convenience. The central idea of the BAMOS was the synergy of spectral information from digital orthophoto map (DOM) and structural information from digital surface model (DSM). Samples of canopy covers were produced by applying the BAMOS method on the UAV images collected at 77 sites with a size of about 1.0 km2 across Daxing’anling forested area in northeast of China. Results showed that canopy cover extracted by the BAMOS method was highly correlated to visually interpreted ones with correlation coefficient (r) of 0.96 and root mean square error (RMSE) of 5.7%. Then, the UAV-based canopy covers served as references for assessment of satellite-based maps, including MOD44B Version 6 Vegetation Continuous Fields (MODIS VCF), maps developed by the Global Land Cover Facility (GLCF) and by the Global Land Analysis and Discovery laboratory (GLAD). Results showed that both GLAD and GLCF canopy covers could capture the dominant spatial patterns, but GLAD canopy cover tended to miss scattered trees in highly heterogeneous areas, and GLCF failed to capture non-tree areas. Most important of all, obvious underestimations with RMSE about 20% were easily observed in all satellite-based maps, although the temporal inconsistency with references might have some contributions.
Metrological Infrastructure of Standards and Standardization for Quality Management and Global Trade : The Indian Scenario

There is a close relationship between the metrological standardization system, the technological capability and the industrial development of a country. The more industrially advanced the country, the greater is the part played by its 'National Measurement System'. When India became independent in 1947 after nearly two hundred years of alien rule, this particular concept greatly influenced the technology policy decisions of the leaders of the national government who had the urge and vision to develop the country into a highly industrial state so that the people of the country could be offered a quality of life commensurate with its rich national resources, talents and latent potential. At the time of independence, India had a meagre metrological facility. To develop the proper metrological infrastructure the following steps were taken by the government: (a) adoption of the metric system for weights and measures and also in coinage system; (b) establishment of the Indian Standards Institution (ISI) which has been later renamed as Bureau of Indian Standards (BIS); and (c) setting-up of the National Physical Laboratory (NPL). These constituted the basic facilities essentially required for formulation of national standard, specifications for Indian products, and for calibration and testing facilities of the highest order of accuracy. Today, these facilities have been augmented and strengthened to grow up into a vast metrological infrastructure which caters for the assessment of the performance of Indian products and services taking into account of their 'Quality System' and 'Quality Assurance' aspect as advocated by the ISO-9000 and the EN-45000 series of international specifications, ultimately with the object to make Indian products competitive in quality and cost to the best of their counterparts in the world. Starting from an almost 'zero' position in 1947, today India has developed itself as a leader in technology and industry, exporting industrial products, services and technologies. The metrological infrastructures developed during this period has played an essential role in the country's achievements. Obviously, standardisation constitutes the 'heart' of metrology.
EVALUATION OF ANTIOXIDANT AND ANTIBACTERIAL ACTIVITIES OF DIFFERENT BANGLADESHI SPICES (TURMERIC, GARLIC AND GINGER)

Spices are commonly used in preparation of Asian food for fortification of taste, flavor, color and fragrance. They also impart many other properties which are beneficial for human health. Antioxidant and antibacterial properties of turmeric (Curcuma longa), garlic (Allium sativum) and ginger (Zingiber officinale) powders were determined in this study. The spice powders were extracted in ethanol, methanol and distilled water. Antioxidant property was determine by Hydrogen Peroxide (H2O2) scavenging assay and antibacterial activity by agar well diffusion assay against twenty four pathogenic bacteria e.g. Escherichia coli (n=4), Staphylococcus aureus (n=4), Pseudomonas spp. (n=4), Klebsiella spp. (n=4), Salmonella spp. (n=4) and Vibrio cholera (n=4). The highest antioxidant activity was observed for methanol extract (60.00 μg/mL) of ginger, % scavenging being 94.09 ± 3.63 %. The ethanol extract (100.00 μg/ml) of turmeric showed the strongest antibacterial activities against Pseudomonas spp. and Vibrio cholerae with zone of inhibition being 16.00 ± 1.00 mm. While the methanol extract (100.00 μg/ml) of garlic showed maximum zone of inhibition (15.67 ± 1.53 mm) against Klebsiella spp. , the ethanol extract of ginger (100.00 μg/ml) exhibited the maximum zone of inhibition (18.00 ± 1.00 mm) against Pseudomonas spp. This study may contribute to the global efforts towards screening compounds possessing antioxidant and antibacterial properties that may be used as precursor molecules for new drugs to be used for treatment of cancer and emerging drug resistant microorganisms.
Comment on gmd-2021-251 Anonymous Referee

This papers proposes an objective method to tune ESMs in a global approach. The authors proposes a mathematical equivalent to the way modellers tune their models so as to rationalize and automatize the tuning process, based on a Green’s functions approach. This is a very important topic for ESM developers that come to a limitation in the model developments due to the long process of human-made iterative tuning. This study is perfectly timely. The authors provides an example of application and discuss some of the assumptions made. However, some assumptions are not fully explained or would deserve more comments. I therefore recommend the manuscript to be accepted after minor revisions.
International Journal of Instruction

Citation: Sitanggang, N. , Luthan, P. L. A. , & Hamid K, A. (2021). Relationship between Total Personal Quality, Service Quality and Student Satisfaction on Higher Education System. International Journal of Instruction, 14(4), 357-372. https://doi.org/10.29333/iji.2021. 14421a Article submission code: 20200905030512 Received: 05/09/2020 Revision: 16/03/2021 Accepted: 08/04/2021 OnlineFirst: 02/08/2021
Solution of three-dimensionalmultiple scattering problems by themethod of difference potentials

We propose an algorithm based on the Method of Difference Potentials (MDP) for the numerical solution of multiple scattering problems in three space dimensions. The propagation of waves is assumed time-harmonic and governed by the Helmholtz equation. The latter is approximated with 6th order accuracy on a Cartesian grid by means of a compact finite difference scheme. The shape of the scatterers does not have to conform to the discretization grid, yet the MDP enables the approximation with no loss of accuracy. At the artificial outer boundary, which is spherical, the solution is terminated by a 6th order Bayliss–Gunzburger–Turkel (BGT) radiation boundary condition. The method enables efficient solution of a series of similar problems, for example, when the incident field changes while everything else stays the same, or when the type of the scattering changes (e.g., sound-soft vs. sound-hard) while the shape of the scatterer remains the same. © 2021 Elsevier B.V. All rights reserved.
International Journal of Instruction

Citation: Rodriguez-Hernandez, B. A. , & Silva-Maceda, G. (2021). Impact of instructional sequence to teach argumentative writing to disadvantaged students using the opinion article. International Journal of Instruction, 14(4), 103-118. https://doi.org/10.29333/iji.2021. 1447a Article submission code: 20201031141751 Received: 31/10/2020 Revision: 22/02/2021 Accepted: 20/03/2021 OnlineFirst: 08/07/2021
TRƯỜNG ĐẠI HỌC SƯ PHẠM TP HỒ CHÍ MINH

The Human rhinovirus 3C protease (HRV3C) is one of the most effective enzymes for removing fusion tag in purification process. This protease is often produced as fusion form GSTHRV3C but there is no study about the fusion form: GST-HRV3C-His. In this study, researchers conducted the purification GST-HRV3C-His expressed in E. coli,checked the activity and investigated its application. GST-HRV3C-His could be purified using His-tag column with 86.6% purity and GST column with 96.87%. The specific activity of GST-HRV3C-His was demonstrated to be about 4500 U/mg and its application in the purification of another proteins carrying HRV3Cspecific recognition sequence, LEVLFQGP based on His-tag or GST-tag was also proved in this study.
Soft-V oting-B ased SVM-KNN Algorithm and Its Application in Big Data Modeling

Recent years have witnessed the surge of interest of big data model predictions in various fields. How to improve the accuracy of big data models has become an important issue. Most of the existing big data models use a single algorithm or its improved version. Using the Support Vector Machine (SVM) algorithm and the K-Nearest Neighbor (KNN) algorithm as the base classifiers, this paper aims to the soft voting method in ensemble learning to establish a combined model of SVM-KNN. A comparative experiment on the proposed algorithm and the single algorithm is performed, which show that the SVM-KNN combined model has better performance in the four model evaluation indicators of accuracy, precision, recall, and F1 score.
THE CYBERNETIC ETHOS OF CRYPTOCURRENCIES: ECONOMIC AND SOCIAL DIMENSIONS

The last years have experienced an effervescence in the field of monetary innovation, concerning both complementary currencies and cryptocurrencies. The scenario of innovation has been intensively investigated with regard to economic and socio-political aspects. Against the peculiar multidimensionality of the phenomenon, the paper argues that the analysis should take the opportunity of grasping a cobelonging between the economic and the social. Whether they seem related to a proliferation of new forms of sociality (as in many experiences of complementary currencies) or to a disquieting desocialization (as in certain domains of the cryptocurrencies’ world), the social dimensions of the new monies can be fruitfully analyzed by focusing on how they are consonant with certain basic conceptions of economic life. After a brief discussion of this point with regard to complementary currencies, the above-mentioned theoretical approach is used to investigate the cybernetic ethos of cryptocurrencies. The analysis shows that the socio-technical imaginaries of some cryptocurrencies (with particular regard to Bitcoin) call into question the relationship between human and non-human agency and are complicit with certain ideas of economic life, one of whose main traits concerns the demand for unconditionally “assuring” the economic and for denying the dimension of uncertainty.
Plots with matching circles for optimizing the performance of a low‐noise amplifier

Plots for optimizing the performance of LNA are presented here. From the study of the interaction between the input/output stability and the conjugate-match conditions, four modified stability circles which are designated as matching circles are analytically developed for the input/ouptut networks. The available matching area is specified by using the modified determinations of the stability criteria. © 1993 John Wiley & sons, Inc.
Investigating the role of the carbon storage regulator A (CsrA) in Leptospira spp.

Carbon Storage Regulator A (CsrA) is a well-characterized post-transcriptional global regulator that plays a critical role in response to environmental changes in many bacteria. CsrA has been reported to regulate several metabolic pathways, motility, biofilm formation, and virulence-associated genes. The role of csrA in Leptospira spp. , which are able to survive in different environmental niches and infect a wide variety of reservoir hosts, has not been characterized. To investigate the role of csrA as a gene regulator in Leptospira, we generated a L. biflexa csrA deletion mutant (ΔcsrA) and csrA overexpressing Leptospira strains. The ΔcsrA L. biflexa displayed poor growth under starvation conditions. RNA sequencing revealed that in rich medium only a few genes, including the gene encoding the flagellar filament protein FlaB3, were differentially expressed in the ΔcsrA mutant. In contrast, 575 transcripts were differentially expressed when csrA was overexpressed in L. biflexa. Electrophoretic mobility shift assay (EMSA) confirmed the RNA-seq data in the ΔcsrA mutant, showing direct binding of recombinant CsrA to flaB3 mRNA. In the pathogen L. interrogans, we were not able to generate a csrA mutant. We therefore decided to overexpress csrA in L. interrogans. In contrast to the overexpressing strain of L. biflexa, the overexpressing L. interrogans strain had poor motility on soft agar. The overexpressing strain of L. interrogans also showed significant upregulation of the flagellin flaB1, flaB2, and flaB4. The interaction of L. interrogans rCsrA and flaB4 was confirmed by EMSA. Our results demonstrated that CsrA may function as a global regulator in Leptospira spp. under certain conditions that cause csrA overexpression. Interestingly, the mechanisms of action and gene targets of CsrA may be different between non-pathogenic and pathogenic Leptospira strains.
A Novel Multiple Person Pose Estimation Optimization Model Utilizing Genetic Algorithm

For traditional human pose estimation models rely on a large amount of human body feature information, this paper proposes an optimization model using genetic algorithm to solve the problem of multiple person body part assembly. Different from other human body parts assembly method. The method proposed in this paper depends on the joints position information, namely the sum of the connection distances between the joints as the objective function, and finds the optimal value to obtain the best human pose assembly information. The simulation results show that compared with the traditional OpenPose model, the model proposed in this paper can obtain the same human skeleton using less position information.
The International Review of Financial Consumers

In this paper we develop an analytical framework using the household utility maximization approach to model stability conditions to avoid household debt overhang. Our theoretical framework suggests that household debt stability is a function of five factors, namely the rate of interest, period of lending, income growth, loan-to-income ratio, and households’ disutility from borrowing. Further, we apply our analytical model to the case of India and estimate household debt stability conditions for Indian households under various scenarios to estimate the ceiling borrowing ratios below which households can avoid the risk of running into a debt overhang problem.