Miracle-dz/UniFAR-FTUs huggingface dataset:
Ecological mechanisms regulating the dynamics of the field rotifer population in a subtropical lake: evidence from the density, reproduction, and morphology of a case rotifer, Brachionus angularis

Abstract Understanding the ecological mechanisms driving the dynamics of field rotifer populations remains a challenge in ecology. Brachionus angularis (Rotifera) is preferred in such studies because of their planktonic behavior, high fecundity, and absence of cryptic species. In this study, one-year zooplankton samples were collected twice a month from a subtropical lake and the roles of abiotic and biotic factors regulating the population dynamics of B. angularis were analyzed. The sampled B. angularis were composed of two discontinuous populations: ‘summer and autumn’ and ‘spring and winter’, segmented by high mixis rates. The ‘summer and autumn’ population had high egg ratios, but low egg volumes. Water temperature was the main factor driving temporal dynamics in density, thereby counteracting the negative effect of top-down force. Based on the synchronous dynamics of two fractions of chlorophyll-a, the densities of B. angularis, its potential competitors and predators, together with the reverse correlations between the egg ratio and population density, and Secchi-disk depth, we found that bottom-up and top-down forces were plausible factors regulating variations in population density in spring (March and April). However, the density peak of B. angularis in winter might be primarily ascribed to recruitment from the sediment. We further addressed the necessity for diverse evidence from the speciation of cryptic species, abundance, reproduction, and morphology, in understanding the population dynamics of a field rotifer population.
Occurrence, Demography, and Habitat Features of Pollicipes elegans (Pollicipedidae: Scalpelliformes), an Amphitropical Species at a Rocky Intertidal Shore, Costa Rica1

Abstract: Clumps of the gooseneck barnacle Pollicipes elegans were found in the central Pacific on a rocky shore of Costa Rica. This species is known to be abundant in northern and southern latitudes of the eastern tropical Pacific. This study describes aspects of its demography and size structure, as well as habitat features in Costa Rica. The clumps were generally on vertical rocky substrates, with a negative slope and an average height of 1.70 m above the Lowest Astronomical Tide. Two cohorts were found on the rocky shore in 2014. An important decline of abundance and density of clumps was found between July and October of 2014. Additional clumps were found in September 2016. We present hypotheses about how this species became established in Costa Rica and why a decline of the clumps was observed.
A checklist of the Argidae (Hymenoptera: Symphyta: Tenthredinoidea) of Iran

ABSTRACT The fauna of Iranian Argidae (Hymenoptera) is evaluated in this paper. In total, 21 species in five genera, Aproceros Malaise (one species), Aprosthema Konow (two species), Arge Schrank (14 species), Kokujewia Konow (one species) and Sterictiphora Billberg (two species), are recorded as definitely present in Iran. In addition, Sterictiphora furcata (Villers, 1789) is questionably present. The most relevant synonymies and distribution data are given. Arge pleuritica (Klug, 1834) is a new record for the fauna of Iran.
Are there differences in the frequency of micronuclei in Astyanax lacustris in relation to sex, mass and length?

Lucilene Finoto Viana ORCID: https://orcid.org/0000-0002-6654-0355 Universidade Estadual de Mato Grosso do Sul, Brasil E-mail: lucilenefinoto@hotmail.com Bruno do Amaral Crispim ORCID: https://orcid.org/0000-0003-0606-4905 Universidade Federal da Grande Dourados, Brasil E-mail: brunocrispim.bio@gmail.com Alexeia Barufatti ORCID: https://orcid.org/0000-0001-8789-2117 Universidade Federal da Grande Dourados, Brasil E-mail: alexeiabarufatti@ufgd.edu. br Sidnei Eduardo Lima-Junior ORCID: https://orcid.org/0000-0001-7906-9737 Universidade Estadual de Mato Grosso do Sul, Brasil E-mail: selimajunior@hotmail.com
Communicating Climate Change Risk: A Content Analysis of IPCC’s Summary for Policymakers

This study investigated the effectiveness of climate change risk communication in terms of its theoretical potential to stimulate recipients’ awareness and behavioral change. We selected the summary for policy makers (SPM) of the most recent Intergovernmental Panel on Climate Change (IPCC) report in order to conduct a content analysis; the extended parallel process model and construal level theory served as conceptual lenses to perform the analysis. Specifically, we evaluated to what extent the SPM included informational elements of threat, efficacy and psychological distance related to climate change. The results showed that threat information was prominently present, but efficacy information was less frequently included, and when it was, more often in the latter parts of the SPM. With respect to construal level it was found that in the IPCC report concrete representations were used only sparingly. Theoretical relevance and implications for climate change risk communication with key audiences are discussed.
IMPAβ: Incremental Modal Pushover Analysis for Bridges

In the present study, the incremental modal pushover analysis (IMPA), a pushover-based approach already proposed and applied to buildings by the same authors, was revised and proposed for bridges (IMPAβ). Pushover analysis considers the effects of higher modes on the structural response. Bridges are structurally very different from multi-story buildings, where multimodal pushover (MPA) has been developed and is currently used. In bridges, consideration for higher modes is often necessary: The responses of some structural elements of the bridge (e.g., piers) influence the overall bridge response. Therefore, the failure of these elements can determine the failure of the whole structure, even if they give a small contribution total base shear. Incremental dynamic analysis (IDA) requires input accelerograms for high intensities, which are rare in the databases, while scaling of generated accelerograms with a simple increment of the scaling acceleration is not appropriate. This fact renders IDA, which is by its nature time-consuming, not straightforward. On the contrary, the change of input spectrum required by IMPA is simple. IMPAβ also utilizes a simple complementary method coupled to MPA, to obtain bounds at very high seismic intensities. Finally, the two incremental methods based on static nonlinear and dynamic nonlinear analyses are compared.
A Geofencing-based Recent Trends Identification from Twitter Data

For facilitating users from information overloading by finding recent trends in twitter, several techniques are proposed. However, most of these techniques need to process extensive data. Therefore, in this paper, a geofencing-based recent trends identification technique is proposed, which acquires data based on a geofence. Afterwards, they are cleaned and the weight of these tweet data is calculated. For that, the frequency of tweet texts and hashtags are taken into account along with a boosting factor. Thereafter, they are ranked to recommend recent trends to the user. This proposed technique is applied in developing a system using Java and python. It is compared with other relevant systems, where it demonstrates that the performance of the proposed system is comparable. Over and above, since the proposed system integrates geofencing feature, it is more preferable over other systems.
A Novel Higher Level Symmetrical and Asymmetrical Multilevel Inverter for Solar Energy Environment

ABSTRACT In this paper, a higher level symmetrical and asymmetrical multilevel inverter (MLI) with reduced device count is proposed for renewable energy systems. Multilevel inverters are recently gaining importance in renewable energy systems and other DC to AC conversion applications due to reducing voltage stress, low total harmonic distortion (THD) and high-quality output as compared to classical inverters. This paper present a basic cell unit of reduced device count (RDC) MLI with six switching devices is proposed. The topology has advantages of reduced device count over existing topologies for the same number of output levels, thus improving operating efficiency and reliability. Upon cascading two such basic cells with 12 devices synthesizes a 49 level output with an estimated THD of 1.63%. The other advantages accrued due to reduced device count are a reduction in switch driver circuits, less number of required dc voltage sources, less installation space and reduction in cost. The major benefit of the proposed topology is that it can deliver power from an asymmetrical source to load even under sudden variation in environmental conditions. The proposed MLI topology is simulated in MATLAB/Simulink environment and a laboratory prototype is developed and tested in the lab. The control paradigm is developed as the Simulink model, which is interfaced with dSPACE DS-1104 for generating control pulses to drive switches of proposed MLI topology. The experimental results validate the concept and paradigm of the proposed MLI topology.
Driver Identification Based on Wavelet Transform Using Driving Patterns

The modern automotive system, based on the convergence of information and communication technologies, is equipped with various functions to ensure vehicle safety and convenience of the driver. A driver-identification technology is an effective method to perform vehicle-theft detection. It can also provide customized driver-personalization services, such as healthcare or insurance. In this article, we propose and evaluate a driver-identification method based on wavelet transform by performing driving-pattern analysis for each driver. We compare the performances of three different machine-learning algorithms, namely Support Vector Machine (SVM), Random Forest, and XGBoost for performing driver identification. The proposed method is applicable to both binary and multiclass classifications for the driving data of five drivers. In the case of motorway, the XGBoost classifier identifies each driver and delivers an accuracy of up to 96.18% in binary classification and an accuracy of 91.6% in multiclass classification. Moreover, in the case of an urban road, the SVM classifier achieves an accuracy of up to 95.07% in binary classification and accuracy of 89.06% in multiclass classification. The proposed method provides a context for a better understanding of the association between driver behavior, which is an in-vehicle event, and mechanical reactions. Our results shall help researchers to broaden the understanding of driver identification using in-vehicle data.
The bioavailability of perfluoroalkyl substances (PFASs) and polycyclic aromatic hydrocarbons (PAHs) in soil to Eisenia fetida and Cucurbita pepo

Persistent organic pollutants (POPs) are organic chemicals of great concern because of their persistency, bioaccumulation and toxic effects both in the environment and to humans. Two groups of organic chemicals that are partly listed as POPs are perfluoroalkyl substances (PFASs) and polycyclic aromatic hydrocarbons (PAHs). PFASs are man-made organic chemicals that became of concern in recent decades due to their presence in wildlife and humans. PAHs are organic chemicals that are produced unintentionally as a result of incomplete combustion. The two main purposes of this master thesis were 1) to investigate the bioavailability of PFASs and PAHs in soil to earthworm (Eisenia fetida) and zucchini (Cucurbita pepo), and 2) to study the influence of PFASs on the availability of PAHs. Two different groups of soils (field-contaminated and spiked soils) were studied. The earthworm experiment was done in 7 weeks and the plant experiment in 12 weeks. Chemical analysis was done by liquid chromatography coupled to mass spectrometry (LC-MS\MS) and gas chromatography coupled to mass spectrometry (GC-MS\MS) for PFASs and PAHs, respectively. Bioaccumulation factors (BAFs) and biota-soil accumulation factors (BSAFs) were calculated for the uptake in E. fetida while bioconcentration factors (BCFs) were calculated for the uptake in C. pepo. The BAFs for PFASs were generally larger than BAFs for PAHs; therefore it can be said that PFASs are more bioaccumulative than PAHs. The BSAFs of PFASs in earthworms increased when the perfluorocarbon chain length increased, but the chain length had an inverse effect on BCF in zucchini, showing a decreased BCF when the chain length increased. The effect of the functional group of the PFASs on BSAF and BCF was studied, and no significant differences between the compounds with the same chain length but different functional groups could be observed. Moreover, BSAFs of PAHs had a slight tendency to increase with an increase in log KOW. The bioavailability of PAHs in two soils, one without PFASs and one with PFASs, was compared and it was shown that the PAH availability increased in the presence of PFASs.
Production of bioactive trastuzumab and chimeric anti-VEGF antibody in the cytoplasm of Escherichia coli

Monoclonal antibody (mAb) is a very useful tool in not only medical field but also in research field. However, the current cost of its production is very high as most mAbs are produced in mammalian cell system. Escherichia coli is a good alternative but has many bottlenecks. Recently, it was demonstrated that biological active full-length Immunoglobulin G (IgG) could be produced in the reductive cytoplasm of an engineered E. coli strain called SHuffle. In this study, we investigated the effect of temperature and induction level on the production of full-length humanized anti-Her2 and chimeric anti-VEGF IgGs expressed as cyclonal. The solubility as well as the assembly of the IgG molecules was examined by western blot analysis. It was found that expression at 30 °C with 0.1 mM IPTG induction was the most suitable for trastuzumab. In contrast, the full-length expression of chimeric anti-VEGF required induction with 1 mM IPTG at 30 °C. , however, 1 mM IPTG induction resulted in more full-length IgG. In conclusion, the present study indicated that E coli could be successfully used for the full-length expression of IgG by suitably modifying the expression conditions.
Research Data Management as an Integral Part of the Research Process of Empirical Disciplines Using Landscape Ecology as an Example

Research Data Management (RDM) is regarded as an elementary component of empirical disciplines. Taking Landscape Ecology in Germany as an example the article demonstrates how to integrate RDM into the research design as a complement of the classic quality control and assurance in empirical research that has, so far, generally been limited to data production. Sharing and reuse of empirical data by scientists as well as thorough peer reviews of knowledge produced by empirical research requires that the problem of the research in question, the operationalized definitions of the objects of investigation and their representative selection are documented and archived as well as the methods of data production including indicators for data quality and all data collected and produced. On this basis, the extent to which this complemented design of research processes has already been realized is demonstrated by research projects of the Chair of Landscape Ecology at the University of Vechta, Germany. This study is part of a joined research project on Research Data Management funded by the German Federal Ministry of Education and Research.
Peningkatan Akurasi Klasifikasi Sentimen Ulasan Makanan Amazon dengan Bidirectional LSTM dan Bert Embedding

Sudah memasuki revolusi industri 4.0 dengan infrastuktur internet semakin memadai dan biaya lebih murah mengakibatkan banyak masyarakat menggunakan layanan pada internet. Sehingga organisasi bisnis terdisrupsi untuk merambah ke media online. Seperti Amazon perusahaan e-commerce meliputi Costumer to Costumer maupun Business to Business , salah satu produk yang dipasarkan adalah makanan. Untuk menaikkan pemasukannya maka perusahaan harus mengerti kebutuhan pembeli. Sehingga dilakukan analisis sentimen konsumen namun proses ini memerlukan waktu lama sehingga dibuat secara otomatis menggunakan metode kecerdasan buatan. Dalam hasil penelitian tentang analisis sentimen pada dataset Amazon Fine Food Review menggunakan metode deep learning Bidirectional Long Short-Term Memory dengan penghasil vektor kata Bidirectional Encoder Representations from Transformers mampu menghasilkan akurasi yang lebih baik daripada menggunakan smetode machine learning Logistic Regression dengan pembobotan kata Mutual Information dan Bag of Words serta model deep learning Convolutional Neural Network dan Long Short-Term Memory dengan penghasil vektor kata Word2Vec dan GloVe pada konfigurasi ukuran embedding dan jumlah dataset paling besar yaitu 300 dan 85.000 sebesar 93 %.
Allosteric Structural Alterations and Auto-regulation of Rab5 GEF Activity in Rabex5

Intracellular trafficking depends on the function of Rab GTPases, whose activation is regulated by guanine exchange factors (GEFs). The Rab5 GEF, Rabex5, was previously proposed to be autoinhibited by its C-terminus. Here, we studied full-length Rabex5 and Rabaptin5 proteins as well as domain deletion Rabex5 mutants using hydrogen deuterium exchange mass spectrometry. We generated a structural model of Rabex5, using chemical crosslinking mass spectrometry and integrative modeling techniques. Our results are inconsistent with the previous model of autoinhibition. By correlating structural changes with nucleotide exchange activity for each construct, we uncovered new auto-regulatory roles for the Ubiquitin binding domains and the Linker connecting those domains to the catalytic core of Rabex5. Our results suggest a more complex auto-regulation mechanism than previously thought and imply that Ubiquitin binding serves not only to position Rabex5 but to also control its Rab5 GEF activity through allosteric structural alterations.
Combining symbiotic simulation systems with enterprise data storage systems for real-time decision-making

ABSTRACT A symbiotic simulation system (S3) enables interactions between a physical system and its computational model representation. To support operational decisions, an S3 uses real-time data from the physical system, which is gathered via sensors and saved in an enterprise data storage system (EDSS). Both real-time and historical data are then used as inputs to the different components of an S3. This paper proposes a generic system architecture for an S3 and discusses its integration within EDSSs. The paper also reviews the literature on S3 and analyses how these systems can be used for real-time decision-making.
Effect of Irrigation Systems and Water Regime on Growth, Yield Components, Water Use Efficiency and Water Productivity of Guava (PSIDIUM GUAVAJA L.) Grown in Clay Soil

Field experiments were conducted during two successive growing seasons ( 2017/2018 and 2018/2019) in Guava orchard at Kafr Al Dawar district, Beheira Governorate, Egypt, to investigate the effect of water regime levels: 100% (E1), 80% (E2) and 60% (E3) of crop evapotranspiration (ETc) under two irrigation systems: furrow irrigation (I1) and surface drip irrigation (I2) on water use efficiency, growth parameters, yield components, and economic analysis of four years old guava trees grown in clay soil. The field experiments were implemented in a randomized complete block design with three replicates. The obtained results indicated that the highest shoot length, number of leaves/m and leaf area were obtained due to T2E1 treatment (drip irrigation with 100% ETc), followed by I2E2 treatment (drip irrigation with 80 % ETc). The average highest guava yield of the two growing seasons was 26.64 tons/ha which was obtained as a result of I2E1 treatment (drip irrigation with 100% of ETc). The lowest guava yield (20.24 and 17.90 tons/ha) were obtained due to I1E3 treatment (furrow irrigation with 60% of ETc) for the two growing seasons, respectively. Yield components such as number of fruits/m, length of fruit (cm), diameter of fruit (cm) , fruit size (cm) , fruit weight (gm.) and yield (kg/tree) were significantly effected (P˂ 0.05) by water regime levels and irrigation systems. Water use efficiency (WUE) of drip-irrigated treatments was higher than that obtained from furrow irrigated treatments in the two growing seasons. WUE increased from 2.37 to 3.34 kg.m for drip irrigated treatment, and from 1.63 to 2.18 kg.m for furrow irrigated treatments. Fruit quality such as TSS (%), acidity (%) and TSS / Acidity ratio were not significantly affected by irrigation systems and water regime. For the economical results, the maximum value of net return was due to I2E1(24411 LE/fed) treatment for the two growing seasons and the minimum value of total return was the result of T1E3 (6344 LE/fed)treatment for the two growing seasons. The maximum values of water productivity were obtained under T2E1 (5.56 LE/m) and I2E2 (5.25 LE/m) treatments in the two growing seasons.
“Now I know that meritocracy and the American dream are myths”: employing critical hope in a teacher pipeline program serving minoritized high school students

Abstract The purpose of this teacher action research project is to examine the perspectives of high school youth as they experience an intensive, 45-hours summer course geared toward developing and strengthening students critical consciousness as future teachers. This research study centers on a teacher pipeline program called the Future Educators Program (FEP) focused on preparing a diverse group of students interested in the teaching profession. Employing tenets of critical hope, such as hokey hope and Socratic hope, the teacher-researcher seeks to examine how students make sense of educational inequality as it relates to race and class. Findings from this study illustrate the ways participants debunk misconceptions linked to hokey hope and exercise Socratic hope in their critical self-reflections with regards to their lived and racialized experiences. The author concludes with implications for the recruitment and retention of minoritized youth as future teachers for social justice.
Improving Tourist Arrival Prediction: A Big Data and Artificial Neural Network Approach

Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.