Miracle-dz/UniFAR-FTUs huggingface dataset:
Abnormally High Water Temperature Prediction using Deep Learning Technology

ABSTRACT Yang, H. , 2020. Abnormally high water temperature prediction using deep learning technology. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1553–1557. Coconut Creek (Florida), ISSN 0749-0208. Recently, abnormally high water temperature (AHWT) phenomena have caused extensive damage to the maritime economy of Korea. These phenomena have caused the mass stranding of farmed fish around the Korean coast and have also caused illnesses by facilitating the propagation of Vibrio pathogens. To reduce damage by AHWT phenomena, it is necessary to respond as quickly as possible or to predict such events in advance. Therefore, in this study, a methodology using satellite big data and a deep-learning technology was proposed to forecast AHWT occurrences. First, the deep-learning model was trained using the long short-term memory (LSTM) architecture. Then, AHWT occurrences were predicted using the trained model. It is expected that the use of this methodology will effectively reduce the damage from AHWT phenomena and protect the maritime economy.
Paradoxical population resilience of a keystone predator to a toxic invasive species

Abstract Context. The invasive cane toad (Rhinella marina) has decimated populations of a keystone predator, the yellow-spotted monitor (Varanus panoptes), causing trophic cascades in Australian animal communities. Paradoxically, some V. panoptes populations coexist with toads. Demonstrating patterns in heterogeneous population-level impacts could reveal mechanisms that mediate individual effects, and provide managers with the ability to predict future impacts and assist in population recovery. Aims. The aim of the present study was to search for spatial patterns of population resilience of V. panoptes to invasive cane toads. Methods. Published literature, unpublished data, reports and anecdotal information from trained herpetologists were used to test the emerging hypothesis that resilient predator populations are mainly coastal, whereas non-resilient populations are mostly inland. Key results. Post-toad invasion data from 23 V. panoptes populations supported the idea that toad impacts on V. panoptes were heterogeneous; roughly half the populations could be designated as resilient (n = 13) and half as non-resilient (n = 10). Resilient populations had longer times since toad invasion than did non-resilient populations (39 versus 9 years respectively), supporting the idea that some recovery can occur. Non-resilient populations were exclusively inland (n = 10), whereas resilient populations were split between inland (n = 5) and coastal (n = 8) populations. Resilient inland populations, however, were mainly confined to areas in which decades had passed since toad invasion. Conclusions. The findings suggest that coastal V. panoptes populations fare much better than inland populations when it comes to surviving invading cane toads. Implications. Unambiguous recovery of monitor populations remains undemonstrated and will require long-term population monitoring before and after toad invasion.
The estimation of the digestibility and metabolizable energy content of ruminant feedingstuffs from the gas production when they are incubated with rumen liquor in vitro

SUMMARY A rapid method for measuring gas production during incubation of feedingstuffs with rumen liquor in vitro is described. Gas production in 24 h from 200 mg feed dry matter was well correlated with digestibility of organic matter, determined in vivo with sheep. Multiple regression analysis, when it included data from proximate analysis, resulted in an equation (R = 0·98) for prediction of metabolizable energy content, based on 30 experiments with rations varying in protein and crude fibre content, and 59 other experiments with concentrates. Energy content was in the range of 7·7–13·2 MJ ME/kg D.M. (± S.D. = 11·17 ± 1·08). The residual standard deviation of the equation was 0·25 MJ. Gas production was measured in calibrated syringes. The only chemical determinations needed are dry matter, protein and fat. Differences in activity between batches of rumen liquor are corrected by reference to gas production with standard feedingstuffs (hay meal and maize starch).
Adaptive Quaternion Tracking with Nonlinear Disturbance Rejection

This paper addresses the problem of attitude tracking for spacecraft in the presence of uncertainties in moments of inertia, environmental disturbances and reaction wheel friction. An adaptive quaternion tracking control is combined with a nonlinear extended state observer and the disturbances compensated for in each sampling period. The tracking controller is proved to asymptotically track a prescribed motion in the presence of these uncertainties. Simulations of a nano-spacecraft demonstrate a significant improvement in pointing accuracy, convergence and tracking error when compared to a conventional attitude controller. In addition the control is shown to provide a significant increase in pointing accuracy of a nano-spacecraft in the presence of reaction wheel friction. The proposed tracking control is completely deterministic, simple to implement, does not require knowledge of the uncertainties and does not suffer from chattering.
Development of a Low-cost Ultrasonic Sensor for Groundwater Monitoring in Coastal Environments: Validation using Field and Laboratory Observations

ABSTRACT Gonzaga, B.A.; Alves, D.C.L. ; Albuquerque, M.G. ; Espinoza, J.M.A.; Almeida, L.P.M. , and Weschenfelder, J., 2020. Development of a low-cost ultrasonic sensor for groundwater monitoring in coastal environments: Validation using field and laboratory observations. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1001–1005. Coconut Creek (Florida), ISSN 0749-0208. This study presents the development and validation of a low-cost ultrasonic sensor (HCSR 04), coupled to an open source Arduino microcontroller platform, for groundwater monitoring in coastal environments. Given the fact that low-lying coastal zones are regions naturally prone to flooding (due to oceanic forcing and pluviosity) and densely occupied, monitoring groundwater variations is crucial for the management of flood-related events. A groundwater monitoring well was built, (made of a PVC pipe with 100 mm in diameter and 2 m long) and installed on the ground of a dune field, located in Cassino beach, southern of Brazil. The system was deployed for 84 consecutive days and programmed to perform water level measurements every 10 min. In laboratory, three units with the same hardware setup of the unit installed in the field (ultrasonic sensor coupled to the Arduino microcontroller) were installed on a 20 liter graduated PVC tube. Varying water levels were tested in the laboratory along the time (changing 1 cm every 2 minutes during 4 hours) in order to reproduce the synthetic signal. Eighty measurements were obtained for each sensor, a total of 240 for each validation process. For the laboratory validation, the results for nonparametric statistical tests (Kruskal and Wallis, and Dunn) presented a correlation of 99%, with Root mean square error (RMSE) 0.4113 and bias 0.0418. For the field tests, 3262 data collected by an ultrasonic sensor during 240 hours showed a means registration of 0.543 m. The minimum and maximum cataloged is 0.40 and 0.70 m, respectively, with 0.1076 m of standard deviation. Groundwater classical measurements, using a measuring tape, showed a mean and standard deviation of 0.544 and 0.1069 m, respectively. These preliminary tests showed that the developed monitoring system performs observations with an accuracy and precision within standard methods, therefore can be applied to monitoring changes in the level of groundwater.
Fine-grained Recognition of 3D Shapes Based on Multi-view Recurrent Neural Network

Multi-view convolutional neural network (MVCNN) has been proved more accurate and faster than those methods based on 3D modelling descriptors in 3D shapes recognition tasks. In this work, we improved MVCNN from three aspects. First, MVCNN was trained and verified on ModelNet dataset, in which views were rendered from 12 or 20 cameras at fixed positions. Since there is a big gap between this setting and real applications, we made another synthetic multi-view dataset MV3D which renders 100 views for each object at free positions. Second, a recurrent neural network (RNN) was adopted to fuse multi-view features instead of max-pooling operation, which is named as MVRNN. RNN has no requirement on the amount of views to fuse and won't loss appearance information. And at last, a loss function taking the discrimination of fused features into consideration was defined to train the MVRNN. The new loss function can improve the discrimination of fused features and improve the recognition accuracy. Comparison was carried out on both ModelNet dataset and MV3D dataset, and the results shown that MVRNN achieves higher accuracy.
Optimization and Reconstruction of EPMA Image Based on SAMP Algorithm

This paper proposes an algorithm -- Fourier's sparse adaptive matching pursuit algorithm based on the Sparse Adaptive Matching Pursuit (SAMP) algorithm. SAMP is a popular algorithm for image reconstruction. However, it demonstrates low reconstruction accuracies for electron probe images based on our experiments. This paper enhances SAMP by adjusting the algorithm parameters according to the characteristics of electron probe images. An optimal step-size for SAMP is selected through experiments. A best performed matrix, Fourier measurement matrix, has been identified after evaluation of various measurement matrices. The new algorithm is constructed based on these findings. The simulation results show that the proposed algorithm can achieve super resolution recovery requirements and provides a significantly higher reconstruction quality for electronic probe images than the original SAMP algorithm and other relevant algorithms.
A Deep Learning Approach for Estimation of the Nearshore Bathymetry

ABSTRACT Benshila, R. ; Thoumyre, G. ; Al Najar, M. ; Absessolo, G. ; Almar, R. ; Bergsma, E. ; Hugonnard, G. ; Labracherie, L. ; Lavie, B. ; Ragonneau, T. ; Simon, E. ; Vieuble, B. , and Wilson D. , 2020. A deep learning approach for estimation of the nearshore bathymetry. In: Malvárez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1011-1015. Coconut Creek (Florida), ISSN 0749-0208. Bathymetry is an important factor in determining wave and current transformation in coastal and surface areas but is often poorly understood. However, its knowledge is crucial for hydro-morphodynamic forecasting and monitoring. Available for a long time only via in-situ measurement, the advent of video and satellite imagery has allowed the emergence of inversion methods from surface observations. With the advent of methods and architectures adapted to big data, a treatment via a deep learning approach seems now promising. This article provides a first overview of such possibilities with synthetic cases and its potential application on a real case.
The Application of Phase Synchronization in Continuous Neural Signals

Synchronization is an important feature in the exchange of information and the establishment of interconnections between the different regions of the brain. Phase-based synchronization is a very effective method in the analysis of neural signal synchronization. In this paper, the method of phase-based synchronization was used to calculate the phase lock value (PLV) of the local field potential(LFP) signal between the prefrontal cortex (PFC)and striatum(STR) in the monkey. We found that the phase synchronism in the beta frequency band was clearly different for different test conditions and different test stages between the two brain regions of the monkey. The beta-band LFP signal was more synchronous. The beta-band LFP signal was important in the exchange of information between two different brain regions.
Synthesis of Spinach Phosphoribulokinase and Ribulose 1,5-Bisphosphate in Escherichia coli

Phosphoribulokinase catalyses the reaction which supplies the CO2-acceptor of the photosynthetic carbon reduction cycle, D-ribulose 1,5-bisphosphate (RuBP). In plants, this enzyme is inactivated in the dark by oxidation of two cysteinyl residues, Cys16 and Cys55, on the same subunit. A cDNA for spinach phosphoribulokinase was isolated by amplification from leaf RNA and cloned into a bacterial expression plasmid to give plasmid pMMPRK. Escherichia coli carrying pMMPRK grown in rich medium accumulated both the enzyme phosphoribulokinase and its reaction product RuBP. This accumulation of RuBP severely retarded growth of the cells on minimal media. The enzyme was isolated from cell extracts and shown to be kinetically similar to authentic phosphoribulokinase isolated from spinach leaves. Cys55 was altered by site-directed mutagenesis to explore the role of this residue in catalysis and regulation. Alteration of Cys55 to Ser or Gly caused a 10-fold reduction in Vmax and altered Km(ATP). These results are consistent with Cys55 being located at the ATP-binding site but the side chain of this residue is clearly not essential for catalysis. Loss of the Cys55 thiol group accounts for most, but not all, of the loss of activity which occurs during dark inactivation of phosphoribulokinase.
Tabletop teleporter: evaluating the immersiveness of remote board gaming

Communication with remote persons over a video link is common today, e.g. to connect with family members abroad, particularly during the COVID-19 pandemic. However, social activities such as board games are rarely shared in this way, as common video chat software does not support this scenario well. However, interactive tabletops provide inherent support for natural tangible interaction with items on the tabletop surface. We present the Tabletop Teleporter, a setup designed to merge two remote locations into a single shared interaction space. We evaluate the system using a board game, focusing on the perceived immersion and connectedness of participants. Our evaluation shows that most measures for the social quality of a remotely shared game are not significantly different from one played with co-located participants, and that players prefer our setup over a pure videochat scenario.
Population genetic structure of the Canterbury mudfish (Neochanna burrowsius): Biogeography and conservation implications

Abstract The population genetic structure of the Canterbury mudfish (Neochanna burrowsius) (Phillipps), a threatened endemic freshwater fish of New Zealand, was investigated using DNA sequence data derived from the mitochondrial D‐loop region. Samples were obtained from eight sites throughout the Canterbury region of New Zealand. Comparison of 845 bp from the tRNA‐pro end of the D‐loop from the 79 fish sequenced revealed low genetic diversity. Only 13 (1.5%) of the 845 bp analysed were found to be variable and only nine unique haplotypes were revealed. DNA distance estimates and phylogenetic reconstruction of these haplotypes demonstrated that there is little divergence between populations. Despite this, the population structure was significant, with just one haplotype (9) detected at more than one site in the wild. This genetic structuring is attributed to recent dispersal from a small historical population and periodic bottlenecks within individual populations. DNA divergence estimates show a significant subdivision between northern and southern populations, indicating a historical separation. It is recommended that future conservation management priorities take into account this genetic structuring in order to adequately conserve the limited genetic diversity within this species.
Seasonal community structure of macromycetes in Veracruz, Mexico

A study of phenological patterns in macromycete communities in Veracruz, Mexico was carried out in order to understand changes in community structure across regions of different vegetation types. Previous studies suggest that similarities in community composition occur when there are similarities in certain geographical and climatological characteristics, however they do not address functional groups or seasonal changes across regions. Macromycete communities in Veracruz showed similar species distribution patterns, but individual assemblages changed structure seasonally, changes that were strongly correlated with rainfall. Interestingly, the number of functional groups (species performing similar ecological functions) was not determined by rainfall, but the distribution of species within functional groups was determined by rainfall. Temperature did not appear to play a role in structuring community diversity at this regional scale. However, temperature and other environmental factors such as pH or light may be the mechanism triggering phenological patterns, and influencing the species pool at localized scales. This work brings new light to fungal community diversity patterns in a largely unknown group of species.
Applying Machine Learning to a Conventional Data Processing Task: A Quantitative Evaluation

Though machine learning (ML) can be applied to a wide spectrum of applications, it has been hardly used and evaluated in the context of conventional data processing tasks. Such conventional data processing tasks are characterized by a set of calculations that follow strict rules, such as in accounting or banking applications. This paper quantitatively evaluates how software which is automatically generated by ML methods and tools compares to software programmed by hand. The assessment of poker hands according to Texas Hold'em rules is a representative example for conventional data processing tasks, because of the various exceptions how to assess and compare hands. For some hand values, the rank (two, three, . king, ace) of the cards is relevant and the suit (club, diamond, heart, spade) irrelevant, and vice versa. This paper shows how an accuracy of 100% can be achieved for assessing poker hands according to Texas Hold'em rules, with a small set of labeled training data compared to the number of possible hands. We also evaluate quantitatively the effect of the labeling quality on accuracy.
The effects of lime addition on aquatic macrophytes in hard water: in situ and microcosm experiments

1. Aquatic macrophytes are abundant in ponds and canals that are constructed in semi-arid regions for water storage and conveyance, as well as in lakes that are culturally enriched. 2. Addition of Ca(OH) 2 to two hardwater ponds at 250 or 275 mg L -1 caused an immediate eradication of submersed aquatic plants. Although these ponds are well-buffered (alkalinity: 2.57-3.94 mequiv L -1 ; pH: 8.1-9.0), lime addition caused an immediate increase in pH of 0.2-3 units. 3. Application of 135 mg L -1 Ca(OH) 2 for 24 h or 210 mg L -1 Ca(OH) 2 for 65 h to two irrigation canals had no effect on macrophyte biomass at the lower concentration and duration, but resulted in the elimination of aquatic macrophytes 1 month after the higher concentration, longer duration treatment. 4. Unlike the macrophyte control achieved following application of 210-275 mg L -1 Ca(OH) 2 to ponds or canals, microcosm experiments in which lime formulation [slaked lime (Ca(OH) 2 ), calcite (CaCO 3 ), or a 1 : 1 mixture] and concentrations (up to 1500 mg L -1 ) were manipulated failed to elicit a consistent change in macrophyte biomass. Macrophytes in microcosms treated for the short-term (23-33 days) with ≥ 200 mg L -1 Ca(OH) 2 or a mixed Ca(OH) 2 /CaCO 3 formulation always lost pigmentation, but biomass was not consistently reduced. 5. Declines in macrophyte biomass following treatment of ponds and canals may have been triggered by a short-term rise in pH which, in these relatively warm (22-23 °C) alkaline (2.28-3.94 mequiv L -1 ) systems, would have resulted in low concentrations of free CO 2 and bicarbonate for photosynthesis.
GIUPlayer: A Gaze Immersive YouTube Player Enabling Eye Control and Attention Analysis

We developed a gaze immersive YouTube player, called GIUPlayer, with two objectives: First to enable eye-controlled interaction with video content, to support people with motor disabilities. Second to enable the prospect of quantifying attention when users view video content, which can be used to estimate natural viewing behaviour. In this paper, we illustrate the functionality and design of GIUPlayer, and the visualization of video viewing pattern. The long-term perspective of this work could lead to the realization of eye control and attention based recommendations in online video platforms and smart TV applications that record eye tracking data.
Designing for conflict: a design space for multi-viewer support in future display networks

Public display networks are increasingly seeking to provide personalised content to viewers. A key challenge in this area is to how to support conflicting personalisation requirements from multiple concurrent viewers. In this paper we present a four dimensional design space that describes a wide range of approaches to addressing this issue. This design space can be used by developers and researchers to understand how systems can be engineered to deliver an increase in the effective communications bandwidth to viewers while meeting their conflicting content requirements. This is the first attempt at systematically examining the challenges and design choices in this important emerging area.
Molecular Cloning and Functional Characterization of Rat Δ-6 Fatty Acid Desaturase

Abstract Mammalian cDNA fragments putatively encoding amino acid sequences characteristic of the fatty acid desaturase were obtained using expressed sequence tag (EST) sequence informations. These fragments were subsequently used to screen a rat liver cDNA library, yielding a 1573-bp clone. Expression of DNA fragment containing either of two possible open reading frames (nucleotide numbers 97-1431 and 148-1431) of the isolated clone in yeast led to the accumulation of γ-linolenic acid in the presence of exogenous linoleic acid. In this system, the addition of α-linolenic acid also resulted in the accumulation of its Δ-6 desaturated product whereas dihomo-γ-linolenic acid failed to be a substrate. These results indicate that the protein encoded by the rat cDNA is Δ-6 fatty acid desaturase, and the first 17 amino acids corresponding to the coding region 97-147 of the clone are not required to function in yeast.