Miracle-dz/UniFAR-FTUs huggingface dataset:
Ordered vs. unordered samples: response to Bromaghin

To estimate population size from a genetic sample, we proposed an urn model that assumes sampling with replacement (Miller et al. 2005). This allows for a simple study design where only a single sampling session is necessary. Bromaghin (2007) suggests that the likelihood function upon which our method is based contains an error. He offers a likelihood function with an additional combinatorial term. As he notes, this produces the same estimator as our likelihood function because the additional term does not contain any parameters to be estimated. Which likelihood is correct — Dr Bromaghin’s or ours — depends on whether the sample is ordered or not. In Dr Bromaghin’s likelihood function, it is assumed that the sample has no order. If three individuals are sampled with the set of capture counts (2, 1, 3), this is the same as capture counts (1, 2, 3). The order the counts are listed in is arbitrary. Indeed, the combinatorial term that Dr Bromaghin introduces into the likelihood tallies the number of ways to reorder the capture counts. Alternatively, one may define the vector of capture counts according to the order in which individuals are observed as they are genotyped. This yields an ordered sample. With a sample ordered this way, our original likelihood is correct. Returning to the example above: the vector (2, 1, 3) indicates that the first individual genotyped is sampled twice in total, the second individual observed is sampled once, and the third individual observed is sampled three times total. Under this ordering rule, the sample (2, 1, 3) is different from the sample (1, 2, 3). Applying this ordering rule to Dr Bromaghin’s example in his Table 4, the row for capture history (3, 2) would need to be doubled to include (2, 3) and history (2, 1, 1) would need to triple to include (1, 2, 1) and (1, 1, 2). This results in a likelihood that sums to one. In our original study, we were not explicit about whether the sample should be ordered by the capture sequence or not. In fact, we had not considered that it was of consequence. In practice, we sometimes ordered our sample according to capture sequence (e.g. in the simulations) and in other cases, we chose an arbitrary order (e.g. in analysing published data sets). We therefore accept the suggested likelihood of Dr Bromaghin. Our likelihood is not incorrect, but we believe Dr Bromaghin’s is a more natural way to treat the data. Our main objective here is to point out that the difference between the two likelihoods is simply a matter of whether the samples are ordered or not. Because this distinction depends only on the sample — and not on the parameters in the model — it does not affect the estimation process.
Multi-Block Fault Detection for Plant-Wide Dynamic Processes Based on Fault Sensitive Slow Features and Support Vector Data Description

This study proposes a multi-block fault detection method based on fault-sensitive slow features for large-scale dynamic industrial processes. Firstly, slow feature analysis (SFA) can effectively extract the process dynamic information. However, the slowest changing features may not contain more fault information. Thus, through the analysis of $T^{2}$ statistic in SFA-based process monitoring model, a fault sensitivity coefficient is defined as a new slow feature sorting criterion to select the most sensitive slow features to fault in each variable direction. Then, considering the unknown characteristics of the fault in the real-time monitoring process, the monitoring model is established for each dimension of variables based on the multi-block strategy. Finally, the support vector data description is used as a fusing method to integrate the statistics calculated in each sub-block to obtain an intuitive detection result. The effectiveness and superiority of the proposed strategy are demonstrated by the experiments on Tennessee Eastman benchmark process and an actual blast furnace ironmaking process.
Involvement of polyamines in the control of fruitlet physiological abscission in grapevine (Vitis vinifera)

The potential contribution of polyamines (PAs) in the regulation of physiologically induced fruitlet abscission was investigated in cuttings from two cultivars of Vitis vinifera L. , Pinot noir (PN) and Merlot (MRT). Abscission was higher in MRT than in PN and was preceded by a decrease in free PA levels. This decline was more pronounced in inflorescences than in leaves of the sensitive cultivar. Soluble conjugated PA showed an opposite trend in both cultivars. This suggests a cause-effect relationship between free and/or conjugated PA levels in floral organs and susceptibility to abscission. Spermidine (Spd), but not putrescine (Put) or diaminopropane, supplied at 0.5-1 mM to the nutritive medium prior to the anthesis, increased free and conjugated PA levels in the inflorescences and markedly inhibited abscission. α-Difluoromethylarginine, an inhibitor of arginine decarboxylase, but not α-difluoromethylornithine, an inhibitor of ornithine decarboxylase, lowered PA levels and increased abscission. Treatment with cyclohexylamine or β-hydroxyethylhydrazine as potent inhibitors of Spd synthase and PA oxidases, respectively, reduced the Spd and/or spermine levels and enhanced free Put in the inflorescences, inducing an increased abscission of floral organs shortly after anthesis. These data suggest that PAs, particularly Spd, could be involved in the regulation of grapevine fruitlet physiological abscission.
Immersive Virtual Reality-Based Cardiopulmonary Resuscitation Interactive Learning Support System

Traditional cardiopulmonary resuscitation training does not allow learners to accurately grasp power, depth and frequency. Therefore, this study aims to improve traditional training and reduce training volume, weight and price by using VR-CPRs learning support system. This study also provide immersive learning at a price lower than that currently prevalent in the market to impart the same level of learning and make CPR training more common. Our system makes it easier for individuals to learn cardiopulmonary resuscitation, making cardiopulmonary resuscitation training more common. Our system includes video health education, assisted real-time interactive operation tools, learning tests and other self-learning functions. Learners can practice on their own after watching the training film. The system reverberates the learner’s power and frequency through a force-sensitive model to ensure that they are properly trained. Encourage users to learn CPR skills through immersive virtual reality settings that guide somatosensory interactions and support game-based learning. The study collected 100 college students who had never received CPR training. Before performing cardiopulmonary resuscitation on the force-sensitive model, participants were asked to complete a pre-test questionnaire to obtain pre-intervention learning outcomes. We introduced VR-CPR into CPR learning (intervention), assessed the outcome of the intervention, and asked participants to complete a post-test questionnaire. The study used VR-CPR to intensively study 85% of participants, indicating that the system is effective in promoting CPR learning, cultivating CPR learning interest, and improving public CPR ability.
Repeatability of Spatio-Temporal Gait Measurements in Parkinson’s Disease

Parkinson’s Disease is one of the most common neurodegenerative disorders. Its principal symptoms regard motor area, and gait is one of the most affected motor characteristics. In clinical environment Parkinsonian gait is often assessed through gait analysis. Opal System by APDM is a commercial device used to perform gait analysis with inertial measurements units (IMUs). In this study we evaluate repeatability of spatio-temporal gait measurements, assessed with Opal Instrumented Stand and Walk (ISAW) test on a cohort of forty-five Parkinsonian patients. Repeatability is assessed by means of Intraclass Correlation Coefficient (ICC) and Repeatability Limit (RL) for each variable. RL is then compared to the absolute value of difference (DoM) of PD patients’ measurements mean and normative mean of the same variable, in order to understand which variable can better characterize Parkinsonian gait with respect to normal gait. Results show that gait and turn measurements are more repeatable than sway and anticipatory postural adjustments variables, and they are proper indexes to better identify Parkinsonian walking features.
Improved quality of beneath-canopy grass in South African savannas: Local and seasonal variation

Abstract Questions: Do large trees improve the nutrient content and the structure of the grass layer in savannas? Does the magnitude of this improvement differ with locality (soil nutrients) and season (water availability)? Are grass structure and species composition beneath tree canopies influenced by soil fertility and season? Location: South Africa. Methods: We compared grass leaf nutrient contents and grass sward structure beneath and outside tree canopy areas in three savannas of different soil fertility during the dry and the wet seasons. Results: Grass nitrogen contents were twice as high during the wet season as compared to the dry season, being more strongly elevated underneath tree canopies during the wet season. Grasses had significantly less stem material and provided less dead material underneath trees on the high soil fertility site. Grass species composition differed significantly beneath and outside tree canopies, with more nutritious grass species found sub-canopy. Grass species richness was significantly lower beneath than outside of trees at the site of high soil fertility. Conclusions: Trees improve overall quality of savanna grasses by enhancing grass growth and nutrient uptake during the wet season, and by delaying grass wilting in the dry season. The positive effect of trees on the grass layer might attract grazing herbivores in otherwise nutrient-poor savannas. Hence, single standing large trees should be maintained to sustain high grass quality and, consequently, grazer populations in savanna habitats. Nomenclature: Trees: Palgrave (1983); Beentje et al. (1994) Grasses: Van Oudtshoorn (1999).
Viral burst size of heterotrophic prokaryotes in aquatic systems

Viral burst size (BS), i.e. the number of viruses released during cell lysis, is a critical parameter for assessing the ecological and biogeochemical role of viruses in aquatic systems. Burst size is typically estimated by enumerating the viral particles in bacteria using transmission electron microscopy. Here, we review the average BS reported for different aquatic systems, present several hypotheses on the control of the BS and evaluate whether there are relationships between BS and bacterial activity parameters across systems. Based on reports from a variety of different aquatic environments, we calculated a mean BS of 24 and 34 for marine and freshwater environments, respectively. Generally, the BS increased with the trophic status of the environment and with the percentage of infected cells in marine populations. When diel dynamics were investigated or averages from large-scale environments were used, BS was positively related to bacterial production but no trend was detectable across systems. The across systems' finding that BS was significantly related to the frequency of infected cells (FIC) could be due to co-infection or superinfection. At any given site, BS seems to be influenced by a number of factors such as the size of the host cell and the viruses, the metabolic activity of the host and phage and host diversity. Thus, based on the available data collected over the past two decades on a variety of aquatic systems, some relations between BS and bacterial variables were detectable.
Male and Female Reproductive Success in a Threatened Polygynous Species: The Strange-Tailed Tyrant, Alectrurus risora

Abstract. The Strange-tailed Tyrant (Alectrurus risora) is an endangered obligate grassland bird that inhabits savannas, wet grasslands and marshes in southern Paraguay and northeastern Argentina. We evaluated the extent of social polygyny, main measures of reproduction (clutch size, hatching success, and chick survival), and factors that influence nest success in this species. We also estimated the reproductive success of females and males by measuring the number and fate of nesting attempts by banded females and the number of females per a male's territory. More than 80% of the males were polygynous. Males defended contiguous territories of 2-2.5 ha that included the territories of up to four females. Females built the nest, incubated the eggs, and brooded and fed the chicks. On average, successful nests fledged 2.3 chicks. Nest survival over the entire cycle was 0.23 and decreased with nest age and time of breeding. Most females made two or three nesting attempts per breeding season and bred in the same area for 2 or 3 consecutive years. In contrast, males rarely were seen in the same area more than 1 year, suggesting sexual differences in mortality. As a result of this, the reproductive succcess of females and males was similar. Our findings indicate that although males are highly polygynous and nest success is low, the high turnover of males in successive breeding seasons and the high probability of females' renesting within and in successive breeding seasons reduce the variance in reproductive success of both sexes.
BREEDING BIOLOGY OF GRAY-CROWNED TYRANNULET IN THE MONTE DESERT, ARGENTINA

Abstract We present information from 75 nests of Gray-crowned Tyrannulet (Serpophaga griseiceps) found in open Prosopis woodlands of the central Monte desert between 1995 and 1997 and compare it with information corresponding to other species of the genus. Breeding occurred from October to January. Nests are small open cups. Both parents participated in nest building, which lasted 4–7 days. In the Prosopis woodland, 98% of the nests were built in chañar (Geoffroea decorticans), which also is commonly used as a nest plant by S. subcristata in east-central Argentina. Mean clutch size did not vary among years nor within the breeding season, and it was similar to that observed in other Serpophaga. Both male and female shared the 13–15 day incubation period. Hatching was asynchronous. Nestling period lasted 13–14 days, during which both parents reared the chicks. Nesting success (26%) appeared to be less than that previously reported for Nearctic open-nesters (50–60%), and Neotropical open-nesters in dry (50%) and wet tropics (35%). Egg and nestling predation were the main cause of nest failure.
An Efficient Model for Predicting Brain Tumor using Deep Learning Techniques

One of the most popular applications of Artificial Intelligence that has seen an immense growth in the digital era is Deep Learning Techniques, where the system studies and improves its performance through progressive learning without any explicit programming. Deep learning is widely used in numerous applications one of them being medical analysis. Feature extraction and Image classification are considered to be the most popularly used approaches done using deep learning process. Prior to this, some neural networks were used for image Classification. A significant, practical deep learning model is proposed which uses back propagation neural network classifier to predict brain stroke through CT/MRI scan images. The performance and accuracy of the proposed model is evaluated and compared with existing models and it produces high sensitivity, specificity, precision and accuracy.
Development of Data Transmission Model for Under Water Communication using Li-Fi Technology

In recent years the submerged acoustic correspondence assumes an essential job in the field of remote correspondence. Underwater acoustic communication is used for the monitoring of obstacles and to protect the endangered species. In the earlier systems, electromagnetic waves were used, which was found to be with less efficiency. The proposed system is devoid of all such deformities and has better efficiency. This system presents the design and implementation of underwater communication through Li-Fi. The data or information is sent from the transmitter submarine to the receiver submarine. The system develops voice signal, text and image which are transmitted using light waves of low noise. Initially, the voice signal is converted into digital values and these digital data obtained are transmitted as light waves of receiver submarines. The basic objective of the project is transmitting and receiving images using Visible Light Communication (VLC) with more accuracy. The software design was implemented in MATLAB code and Graphical User Interface.
Inducible Expression of Plasma Membrane H⁺-ATPase in the Roots of Figleaf Gourd Plants under Chilling Root Temperature

Figleaf gourd plants were grown under different root temperatures and the acclimation process of the roots was investigated. Conditioning of the roots by a gradual decrease (2°C h -1 ) in root temperature to 6°C considerably lowered the electrolyte leakage of the plant leaves compared to non-conditioned roots. A root temperature of 6°C was critical for the increase in H + -ATPase (EC 3.6.1.35) activity in the plasma membranes of the roots and this increase was abolished by the addition of cycloheximide in the root medium. The H + -transport rate of the plasma membranes was also increased by low root temperature. Increases in the amount of the plasma membrane H + -ATPase gene transcription and translation in the plant roots under chilling root temperature were demonstrated by Northern and Western blot analysis, respectively. A correlation between the leaf temperature, measured by an infra-red thermometer, and the H + -ATPase activity suggests the involvement of H + -ATPase in the root acclimation process.
Reducing Impact of CNFET Process Imperfections on Shape of Activation Function by Using Connection Pruning and Approximate Neuron Circuit

Deep Neural Networks (DNNs) based on Carbon nanotube field effect transistor (CNFET) technology can leverage the potential energy benefits of CNFET based technology in comparison to conventional Si technology. However, like other emerging materials based technologies, the current fabrication processes for CNFETs lack the quality, resulting in CNFETs suffering from process imperfections, consequently degradation in circuit-level performance. Such imperfections will cause timing failure and distort the shape of non-linear activation functions, which are vital in DNN, leading to significant degradation in classification accuracy. We utilize pruning of synaptic weights which combined with proposed approximate neuron circuit significantly reduces the chance of timing failure, and achieve better frequency of operation (speed), even using highly imperfect process. In our example, the proposed configuration with approximate neuron and pruning at a high imperfect process $(PCNT_{open}=\ 40\%)$, in comparison to base configuration of precise neuron and no pruning with ideal process $(PCNT_{open}=\ 0\%)$, achieves peak accuracy only 0.19% less, but significant energy-delay-product (EDP) advantage (56.7% less), at no area penalty.
Purple and Yellow Nutsedge (Cyperus rotundus and C. esculentus) Response to Postemergence Herbicides in Cotton

Abstract Greenhouse studies were conducted to evaluate the nature of the cotton postemergence (POST) herbicides followed by (fb) MSMA postemergence-directed (LAYBY) for foliar and tuber reduction of yellow and purple nutsedge when applied to nutsedge at two different application timings. Trifloxysulfuron at 4 and 6 g ai/ha fb MSMA LAYBY reduced 10- to 15- and 20- to 30-cm purple and yellow nutsedge root and shoot dry weights by at least 56%. However, the effect of weed size at the time of application was significant for trifloxysulfuron at 6 g/ha for percent root and shoot reductions in yellow nutsedge and percent root reduction in purple nutsedge. Significance of herbicide rate was only observed for percent shoot and root reduction of 10- to 15-cm yellow nutsedge. Trifloxysulfuron treatments reduced purple and yellow nutsedge shoot and root dry weights equivalent to treatments involving glyphosate POST fb MSMA LAYBY. MSMA at 1,120 and 2,240 g/ha and glufosinate POST fb MSMA LAYBY were effective for reducing purple and yellow nutsedge shoot dry weights, although percent reduction was influenced by nutsedge height at herbicide application. Treatments involving pyrithiobac POST fb MSMA LAYBY slightly increased 10- to 15-cm yellow nutsedge root dry weights. MSMA at either rate produced additive responses when included in tank mixtures with trifloxysulfuron at either rate or pyrithiobac POST fb MSMA LAYBY in yellow nutsedge. Other tank mixes or sequential combinations did not cause additive or synergistic responses. Nomenclature: Glufosinate; glyphosate; MSMA; pyrithiobac; trifloxysulfuron; purple nutsedge, Cyperus rotundus L. CYPRO; yellow nutsedge, Cyperus esculentus L. CYPES;cotton, Gossypium hirsutum L
Bayesian Autoencoders for Drift Detection in Industrial Environments

Autoencoders are unsupervised models which have been used for detecting anomalies in multi-sensor environments. A typical use includes training a predictive model with data from sensors operating under normal conditions and using the model to detect anomalies. Anomalies can come either from real changes in the environment (real drift) or from faulty sensory devices (virtual drift); however, the use of Autoencoders to distinguish between different anomalies has not yet been considered. To this end, we first propose the development of Bayesian Autoencoders to quantify epistemic and aleatoric uncertainties. We then test the Bayesian Autoencoder using a real-world industrial dataset for hydraulic condition monitoring. The system is injected with noise and drifts, and we have found the epistemic uncertainty to be less sensitive to sensor perturbations as compared to the reconstruction loss. By observing the reconstructed signals with the uncertainties, we gain interpretable insights, and these uncertainties offer a potential avenue for distinguishing real and virtual drifts.
Environment Monitoring System for Agricultural Application using IoT and Predicting Crop Yield using Various Data Mining Techniques

Nowadays, Internet is facing an exponential increase in the number of electronic devices interfaced through it. Previously, there is a limit of connecting only mobile and PCs (personal computer) with the internet but now millions of devices can be connected using the concept of IoT (internet of things). Hence, IoT is able to communicate data between machine to machine and the data that is previously available to private server now easily available to the internet so that it can be accessed efficiently. This paper show the increase in the usage of IoT in monitoring like applications. Smart farming is the technique which intend to provide all the necessary resources for the specified amount of time. These resources required are light intensity, ambient temperature required, relative humidity, soil moisture content, pH reading of the soil. The central idea is to sense all these parameters one by one and take the final decision accordingly. A sensor node should be developed for sensing all the required resources parameters and subsequently send the data to the cloud for further processing. After getting all the required resources values our final aim is to predict the crop production using various data mining techniques. As the human lives are primarily depended on the food resources, the agricultural process need to be efficient and this efficiency can be enhanced if there is an accurate amount of resources. By getting the accurate amount of resources, Data mining techniques such as Random forest, KNN, SVM are used to analyze the crop production in advance such that farmer always have an upper hand on it and by comparing with the previous trend he will be able to detect which kind of parameter is accurate and which is not. A User defined site is designed to monitor all these values of agricultural process.