Miracle-dz/UniFAR-FTUs huggingface dataset:
Nocturnal variation of the zooplankton community in coral reef substrates at Isla del Coco National Park (Eastern Tropical Pacific)

Introduction: Zooplankton is a major link in coral reef food webs and a source of nutrients for corals. Demersal zooplankton in coral reefs makes diel migrations at night to avoid predation by fish and corals, increasing abundance and biomass at night. Objectives: To study the composition and variation of the demersal and pelagic zooplankton community at night and to increase the taxonomic knowledge of the benthic copepod fauna over different substrates of a coral reef. Methods: Traps made of plastic, with a 100 μm mesh size collector cup attached, were placed for 12 hours between 5-10 m depth over four substrates: (1) sand, (2) small and (3) big colonies of Porites lobata, and (4) dead and alive corals. Sampling started at dusk (1800 h), and the collector cups were collected and replaced at 2100 and 0000, and the last samples removed at down (0600 h). Results: The highest abundance of zooplankton was capture over P. lobata colonies between 0000 to 0600 h. Copepods (adults, copepodites, and nauplii) dominated the community structure and were more abundant during all the time intervals and substrates. Pelagic copepods were more abundant than the benthic, however, not more diverse. Among the benthic copepods, 20 new records of harpacticoids and calanoids were registered for Isla del Coco and Costa Rican waters. Appendicularians and decapod larvae were also abundant. Changes in coral reef community structure were related with time and not with the kind of substrate, although some taxa were found only in a specific time interval or substrate. Conclusions: Community structure and abundance of demersal and pelagic coral reef zooplankton in Chatham Bay were dominated by copepods, larvaceans and decapod larvae, similar to other coral reefs around the world. The highest abundance peak of organisms in the water column between 0000 to 0600 h could be related to diel migration.
Foreign Equity Ownership and Income Smoothing

We examine whether foreign investors influence a local firm's income smoothing, using a sample of Korean firms from 2000 to 2013. We hypothesize that given innate informational difficulties of overseas investments, foreign investors demand less noisy and more sustainable earnings, and to satisfy this demand, managers have strong incentives to smooth earnings. We find that foreign investors' ownership is positively related to the level of earnings smoothing. We also find that earnings smoothing improves earnings informativeness in the presence of high foreign investor ownership, consistent with the notion that foreign investors play an important role in local firms' information environments. JEL Classifications: M41; M43; J53.
Pasta de ajonjolí (Sesamum indicum) un alimento proteico para cerdos y lechones recién destetados con base a sus características químicas y a la respuesta fisiológica del aparato digestivo

La produccion pecuaria implica la transformacion de alimentos por el animal para satisfacer las necesidades proteicas del humano. Para que la produccion animal se vea favorecida economica y productivamente es necesario conocer las caracteristicas quimicas, nutritivas y digestibles de ingredientes alternativos para la alimentacion animal, ya que los costos por alimentacion son alrededor del 75% del total de la produccion. El presente trabajo tuvo como objetivo evaluar a la pasta de ajonjoli (PA) desde sus caracteristicas quimicas hasta su efecto como ingrediente proteico en dietas para cerdos sobre su digestibilidad ileal aparente (DIA) y estandarizada (DIE), y las adaptaciones morfofisiologicas del tracto gastrointestinal (TG) del lechon. El proyecto estuvo conformado por cuatro etapas experimentales: 1) Caracterizacion quimica de PA, 2) Digestibilidad de PA en lechones destetados; 3) Digestibilidad de PA en cerdos, y 4) Evaluacion del efecto de la PA sobre la morfofisiologia del TG de lechones destetados. La PA mostro alto contenido en proteina cruda (PC), arginina, aminoacidos azufrados, extracto etereo, fibra detergente neutra y acido fitico, y una baja actividad inhibitoria de tripsina en comparacion con las pastas de soya (PS) y canola (PCA). Las proteinas dominantes de la PA son del tipo globulinas y glutelinas basicas. Al realizar la separacion electroforetica de las proteinas de la PA se observo una gran dominancia de proteinas de bajo (<20 kDa) (30.2%) y medio (20 a 60 kDa) (53.4%) peso molecular. Al incluir PA en dietas y comparandose con dietas con PS se observo que la DIA y DIE de PC en cerdos fueron mayores en la PA, y para lechones la DIA y DIE de PC y aminoacidos no fue diferente entre ambas pastas, a excepcion de la lisina que fue mayor en la PS. Al evaluar los cambios morfofisiologicos del TG de lechones alimentados con dietas conteniendo PA, se encontro que estas dietas no afectaron la incidencia ni la severidad de las diarreas, el crecimiento de los organos digestivos, la actividad de proteasas pancreaticas ni la integridad intestinal. Bajo las condiciones del presente estudio, se concluye que la PA es una fuente de proteina alternativa para incluirse en dietas para lechones y cerdos.
Anthracnose Disease on Vegetables Crops in Serang Village, District of Karangreja, Purbalingga Regency

Various vegetables are widely cultivated in Serang Village, District of Karangreja, Purbalingga Regency. This village is located at 1350 above sea level. Vegetable productions in Serang are often constrained by the presence of diseases caused by pathogenic fungi Colletotrichum sp. called anthracnose. However, there was no data regarding anthracnose attack in vegetable crops in Serang. This study aimed to find out the types of vegetable that were attacked by anthracnose disease as well as the intensity of the attack in a vegetable crops in Serang Village, Karangreja District, Purbalingga Regency. Vegetables with anthracnose were purposively selected from 10 different locations. The anthracnose disease was identified based on their symptoms and analyzed descriptively. The results showed that five different vegetable crops suffer from anthracnose, and the intensity of anthracnose ranges from 32% up to 52%. Those intensity percentages indicate that large part of vegetable crops in Serang Village was attacked by anthracnose and possibly cause a significant decrease in vegetable production in that village. Our results provide the first scientific data about anthracnose attack in Serang Village. The data is essential for the management of vegetable crops in Serang Village.
A Model-Based Deep Network for MRI Reconstruction Using Approximate Message Passing Algorithm

We propose a novel model-based network to reconstruct the magnetic resonance (MR) image. In this network, the Approximate Message Passing (AMP) algorithm is unrolled to solve the optimization problem of compressed sensing MR imaging, and several CNN blocks is embedded as de-aliasing steps. We relax the restriction on the parameter selection of AMP algorithm, and enable the parameters trainable in our proposed method. Each CNN block and AMP block is followed by a data consistency (DC) operation, which can efficiently accelerate the convergence of the reconstruction network. The trainable parameters of our DC share the weights and can automatically adapt to the error pattern. Experimental results show that the proposed method obtains faster convergence speed and achieves a new state-of-the-art MR image reconstruction performance.
A fine-grained analysis of students’ explanations based on their knowledge of the atomic structure

ABSTRACT The present work explores students’ repertoire of explicit knowledge fragments relevant to the atomic structure, seeking to answer which of those and through which routes are activated by students when constructing their explanations for some everyday situations. For this purpose, six tasks describing different situations were given to 225 students of the 10th and 11th grades of secondary schools from Northern Greece. Students’ responses were qualitatively analysed through a five-step scheme, where any discrete explicit knowledge fragments included in students’ explanations was extracted and categorised in relation to the atomic or subatomic characteristics. Extracted fragments of knowledge were also analysed according to their complexity and the ways that are organised when being activated by students. Implications for science education are analytically discussed.
Numerical solutions of SPDEs with boundary noise

Galerkin finite element method is a technique for approximating solutions to stochastic partial differential equations (SPDEs) that has been extensively studied in the literature. In this thesis, we extend the scheme to solve the case where noise enters through the boundary of the domain. We prove that the optimal convergence rate is achieved for semi-linear parabolic SPDEs with random Neumann boundary conditions. Considering the advectiondiffusion equation with boundary noise, we show that solutions are useful for simulating solute dynamics in arteries where the vessel walls are treated as flexible. We also investigate SPDEs with Dirichlet boundary noise. The solutions do not exist in a Sobolev space but in a weighted Sobolev space. For the one-dimensional heat equation with white noise, we show that a numerical scheme that combines Galerkin finite element method in space and discontinuous Galerkin stepping in time converges at an optimal rate.
Sex-specific effects of DDT resistance in flies

In D. melanogaster, resistance to DDT is conferred by the upregulation of a cytochrome P450 enzyme, CYP6G1. Resistant flies have tandemly duplicated Cyp6g1 alleles that possess the LTR (Long Terminal Repeat) of an Accord retrotransposon inserted in the cis-regulatory region, 291bp upstream of the transcription start site. This DDT resistance allele (DDT-R) has been shown to have pleiotropic fitness benefits for female flies in at least one genetic background and with evidence of sexually antagonistic selection at this locus. In this thesis, I first review the role of transposable elements in conferring insecticide resistance and the evidence to date regarding the pleiotropic effects of DDT-R in D. melanogaster. By conducting life history and behavioural tests on flies of two genetic backgrounds I examine the sex-specific effects of expressing DDT-R in the absence of DDT. Finally I develop a single locus population genetics model based on these sex-specific effects and test the model using replicate laboratory populations. The first main finding is that DDT-R incurred a male mating cost that depended on the genetic background in which DDT-R was found and that this cost coincided with strong epistasis between genetic background and DDT-R that influenced male size (Chapter 3). Following on from this result, it was confirmed that the effect of DDT-R on male size does contribute to lowered mating success but does not fully explain this fitness cost (Chapter4). Additionally, resistant males were found to have a lowered rate of courtship behaviour driven by aborted chasing of females and lower male-male aggression than susceptible males (Chapter 4). Fitness assays in wild caught strain females revealed that DDT-R confers a fecundity increase but unlike previous work, no offspring viability increases were detected (Chapter 5). Thus as with male costs, specific pleiotropic female fitness benefits to resistance depend on genetic background. Modelling of DDT-R using a simple single-locus approach (Chapter 6) provides, for the first time, a unifying explanation for past and present DDT-R frequencies in nature and in old laboratory populations. The model is consistent with an old origin for the original DDT-R mutation held at low equilibrium frequency through balancing selection of a sexually antagonistic nature. It is also consistent with continued near fixation of DDT-R long after discontinued use and matches empirical observations in laboratory populations of the Canton-S background.
Coastal and seasonal hydrodynamics and morphodynamics of the Mekong Delta

Coastal retreat problems occur in many deltas over the world. Coastal features are not constant over time and are affected by sea level rise, river runoff, sediment supply, wave and tidal energy, underlying geology and climate. In addition, human activities profoundly influence the coastal processes as a result of changing natural patterns of runoff, littoral sediment supply and construction and reconstruction of engineering works.
Interactions between a H2 Molecule and Carbon Nanostructures: A DFT Study

On a long path of finding appropriate materials to store hydrogen, graphene and carbon nanotubes have drawn a lot of attention as potential storage materials. Their advantages lie at hand since those materials provide a large surface area (which can be used for physisorption), are cheap compared to metal hydrides, are abundant nearly everywhere, and most importantly, can increase safety to existing storage solutions. Therefore, a great variety of theoretical studies were employed to study those materials. After a benchmark study of different van-der-Waals corrections to Generalized Gradient Approximation (GGA), the present Density Functional Theory (DFT) study employs Tkatchenko–Scheffler (TS) correction to study the influence of vacancy and Stone–Wales defects in graphene on the physisorption of the hydrogen molecule. Furthermore, we investigate a large-angle (1,0) grain boundary as well as the adsorption behaviour of Penta-Octa-Penta (POP)-graphene.
COVID-19 will stimulate a new coronavirus research breakthrough: a 20-year bibliometric analysis

Background COVID-19 is currently rampant in China, causing unpredictable harm to humans. This study aimed to quantitatively and qualitatively investigate the research trends on coronaviruses using bibliometric analysis to identify new prevention strategies. Methods All relevant publications on coronaviruses were extracted from 2000–2020 from the Web of Science database. An online analysis platform of literature metrology, bibliographic item co-occurrence matrix builder (BICOMB) and CiteSpace software were used to analyse the publication trends. VOSviewer was used to analyse the keywords and research hotspots and compare COVID-19 information with SARS and MERS information. Results We found a total of 9,760 publications related to coronaviruses published from 2000 to 2020. The Journal of Virology has been the most popular journal in this field over the past 20 years. The United States maintained a top position worldwide and has provided a pivotal influence, followed by China. Among all the institutions, the University of Hong Kong was regarded as a leader for research collaboration. Moreover, Professors Yuen KY and Peiris JSM made great achievements in coronavirus research. We analysed the keywords and identified 5 coronavirus research hotspot clusters. Conclusions We considered the publication information regarding different countries, institutions, authors, journals, etc. by summarizing the literature on coronaviruses over the past 20 years. We analysed the studies on COVID-19 and the SARS and MERS coronaviruses. Notably, COVID-19 must become the research hotspot of coronavirus research, and clinical research on COVID-19 may be the key to defeating this epidemic.
Effect of Organic Cultivation of Capsicum annuum L. on Soil Microbial Properties under Open-Field and Shade-House Conditions

Two bell pepper (Capsicum annuum L.) varieties, viz., California Wonder and Gangavati Local, were raised under nine completely organic nutrient sources, along with recommended package of practices, and, under completely inorganic nutrient sources. Irrespective of the variety and growing environment, there was substantial increase in total bacterial count (22.97% and 24.98%), population of fungi (20.23% and 20.23%), actinomycetes (36.89% and 36.83%) and mycorrhiza (44.63% and 29.40%) in open-field and shade-house conditions, respectively, in all the nutrient combinations where organic sources were used, compared to the inorganic treatment. All organic nutrient sources used were found to be similar in their effect on soil microbes.
Burning biases: Mitigating cognitive biases in fire engineering

Fire engineering has developed into a mainstream engineering discipline within the building design process. Building fire codes are increasingly complex, comprising thousands of requirements regarding a wide range of topics that must be considered. Fire engineers are required to possess increasingly complex knowledge about a variety of subjects, along with expertise in their application. This has been magnified with the proliferation of performance‐based methods using a range of computational tools. This coupled with increased project performance pressures, raises the potential for errors in judgment. Errors in judgment may be caused by limitations in a given resource (e.g. time, information available, knowledge, etc) and/or neglect/over‐focus on specific information (at the expense of other and more relevant information) through cognitive biases. This paper initially provides a broad overview of general decision‐making, including the use of heuristics and cognitive biases. Examples of cognitive biases are presented which may be linked to errors in fire engineer decision‐making. This study considers several fire engineering decision contexts where cognitive biases may exist which are associated with fire code application, modeling/calculations, probabilistic risk assessments, general fire engineering practice, and perceptions based on experience. Potential measures to mitigate some of these biases and prompt better decision‐making are discussed. Those that may benefit from awareness of such biases and mitigation measures include not only practicing fire engineers, but also building developers, fire code committees, evacuation/fire/structural fire modeling developers, approving authorities, and fire engineering researchers/students.
Indigenous AM fungi and Azotobacter isolates, and their screening from citrus seedlings at different levels of inorganic fertilizers application.

Arbuscular mycorrhizal fungi (AMF) and Azotobacter chroococcum association in citrus orchards grown in nine different plantation areas of north-western Himalayan region (NWHR) of India was investigated. Twelve AMF species of genera viz., Glomus, Gigaspora, Scutellospora and Entrophospora and two strains of Azotobacter chroococcumviz. , Azotobacter strain-I (AZ1) and Azotobacter strain-II (AZ2) were isolated and identified. The species of Glomus namely G. fasciculatum (Thaxter) Gerdemann and Trappe and G. mosseae (Nicol. Gerd.) Gerdemann and Trappe were the most abundant in the majority of the experimental sites. The mycorrhizal spore population was maximum at Daad and minimum at Dashlera. Root colonization percentage oscillated between 24.6 and 30.5%. Inoculation study was carried out with the aim to select the most efficient native AMF species and Azotobacter strain for sustainable citrus nursery management. The impact of two most frequently occurring AM species viz., G. fasciculatum (Thaxter) Gerdemann & Trappe and G. mosseae (Nicol. & Gerd.) Gerdemann & Trappe and Azotobacter strains viz., AZ1 and AZ2 at alone and in combination with different levels of N and P was evaluated on growth and development status of citrus seedlings under controlled atmospheric conditions. The significant increase in growth parameters namely, height, diameter, root length and leaf area was more evident for the seedlings inoculated with G. fasciculatum (Thaxter) Gerdemann & Trappe followed by G. mosseae (Nicol. & Gerd.) Gerdemann & Trappe, AZ1 alone and in combination with 2/3rd dose of N+P.
Encoding Temporal Information For Automatic Depression Recognition From Facial Analysis

Depression is a mental illness that may be harmful to an individual’s health. Using deep learning models to recognize the facial expressions of individuals captured in videos has shown promising results for automatic depression detection. Typically, depression levels are recognized using 2D-Convolutional Neural Networks (CNNs) that are trained to extract static features from video frames, which impairs the capture of dynamic spatio-temporal relations. As an alternative, 3D-CNNs may be employed to extract spatiotemporal features from short video clips, although the risk of overfitting increases due to the limited availability of labeled depression video data. To address these issues, we propose a novel temporal pooling method to capture and encode the spatio-temporal dynamic of video clips into an image map. This approach allows fine-tuning a pre-trained 2D CNN to model facial variations, and thereby improving the training process and model accuracy. Our proposed method is based on two-stream model that performs late fusion of appearance and dynamic information. Extensive experiments on two benchmark AVEC datasets indicate that the proposed method is efficient and outperforms the state-of-the-art schemes.
Machine Learning Approach towards Tomato Leaf Disease Classification

India in an agricultural country and the detection of diseases in first stage is very important to increase the crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. In this paper, to detect symptoms of disease, we have developed a module that classifies the plant leaf disease automatically. This paper presents a performance measure for different feature extraction techniques for tomato leaf disease detection including GLCM, Gabor and SURF and classification techniques including decision trees, SVM, KNN and Naïve Bayes. The dataset contains 500 images of tomato leaves with seven symptoms of diseases. We have modeled a system for automatic feature extraction and classification. We have evaluated the performance of the system using different performance measures to conclude with appropriate features set and classification technique for tomato leaf disease classification. The experimental results validate that Gabor features effectively recognizes different types of tomato leaf diseases. Accuracy of SVM is better as compared to other classification techniques but the execution time is more.
Federated Deep Reinforcement Learning for Internet of Things With Decentralized Cooperative Edge Caching

Edge caching is an emerging technology for addressing massive content access in mobile networks to support rapidly growing Internet-of-Things (IoT) services and applications. However, most current optimization-based methods lack a self-adaptive ability in dynamic environments. To tackle these challenges, current learning-based approaches are generally proposed in a centralized way. However, network resources may be overconsumed during the training and data transmission process. To address the complex and dynamic control issues, we propose a federated deep-reinforcement-learning-based cooperative edge caching (FADE) framework. FADE enables base stations (BSs) to cooperatively learn a shared predictive model by considering the first-round training parameters of the BSs as the initial input of the local training, and then uploads near-optimal local parameters to the BSs to participate in the next round of global training. Furthermore, we prove the expectation convergence of FADE. Trace-driven simulation results demonstrate the effectiveness of the proposed FADE framework on reducing the performance loss and average delay, offloading backhaul traffic, and improving the hit rate.