Miracle-dz/UniFAR-FTUs huggingface dataset:
Pheromone dynamics in virgin and mated females of the sexually cannibalistic orb-web spider Argiope bruennichi (Araneidae)

Abstract. Female chemical signals are crucial for sexual communication in spiders and convey detailed information about females' mating status or age. However, evidence for chemical communication in spiders is largely behavioral. Only a few spider pheromones are known and quantitative chemical analyses of individual spiders are especially scarce. In the European wasp spider, Argiope bruennichi (Scopoli, 1772), females emit the volatile trimethyl methylcitrate, which attracts males from a distance. A recent study on the role of this pheromone in close-range male mate choice revealed that older virgin females approaching oviposition produce higher amounts of pheromone than younger ones, thereby increasing their attractiveness to males, and stop pheromone signaling shortly after copulation. Here we provide more detailed analyses of temporal dynamics in pheromone production in both virgin and mated female A. bruennichi. Our results corroborate previous observations indicating that male mating behavior in this species is guided by variation in female signaling effort.
Constraints on Light Gluinos from Tevatron Dijet

The effects of light, long-lived gluinos on 2 ! 2 processes at hadron colliders are examined. Such particles can mediate single squark resonant production via q˜ g ! ˜ ! q˜ g which would significantly modify the dijet data sample. We find that squark masses in the range 130 < m˜ q < 694,595,573 GeV are excluded for gluino masses of 0.4,1.3,5.0 GeV from existing UA2 and Tevatron data on dijet bump searches and angular distributions. Run II of the Tevatron has the capability of excluding this scenario for squark masses up to � 1 TeV.
Sizing, economic, and reliability analysis of photovoltaics and energy storage for an off‐grid power system in Jordan

Remote areas in Jordan often rely on expensive and polluting diesel generators to meet their electricity demand. This study investigates 100% renewable solutions to supply the electricity demand of off ‐ grid energy systems through optimal sizing of photovoltaics and energy storage systems. A linear programming approach is proposed to minimise the annualised cost of electricity supply including capital costs of equipment and their operation and maintenance costs. The optimisation determines the size of photovoltaics and energy storage required to satisfy electricity demand at every hour of a selected year. A Jordan campsite was used as a case study to assess and compare the performance of PV ‐ battery storage and PV ‐ hydrogen storage systems from economic and reliability perspectives. The results show that hydrogen storage was more economical for a 100% renewable energy system. However, introducing some diesel generation gave the battery system a significantly lower annualised cost of energy.
Optimal Design of PID Controller for an AVR System Using Flower Pollination Algorithm

In this presented work, terminal voltage and reactive power control at the different conditions of load changes of synchronous generator in the AVR system is presented. To improve the step response of the alternator terminal voltage proportional integral derivative controller is carried out with optimization technique as Flower Pollination Algorithm (FPA) to optimally tuned the parameters of PID controller. The AVR system response with proposed FPA-PID controller is found to be better and compared that of with controllers in literature (42 in number). The performance is compared in terms of peak value, settling time and performance indices like ITAE, ISE and IAE. The robustness analysis of the proposed FPA based PID controller is considered for 156 plant conditions and varying time constants parameters as ±20% system conditions. The system stability with proposed FPA optimized PID controller also analysed in the form of eigenvalue, damping factor and frequency of oscillations.
Identification of methylation pattern in the partial promoter of acetyl CoA carboxylase beta (ACACB) gene in White Leghorn line

The present study was initiated with an objective of identifying methylation pattern in the partial promoter of Acetyl CoA carboxylase beta (ACACB) gene in White Leghorn IWK line. To understand the epigenetic regulation of gene expression, birds with highest and lowest expression at 18th week and 40th week age were chosen. Epigenetic profile of the minimal promoter indicated that there were seven CpG islands in this region, positioned at 75, 87, 155, 269, 284, 311 and 426 positions of the 555 bp promoter region. Among these, except the position 75, the remaining positions showed cis-acting transcription factors. There was negative relationship between highest and lowest expressed birds with the per cent methylation in White Leghorn IWK line. The mean methylation per cent was found to be 42.85 and 21.42 at 18th and 40th weeks of age, respectively. The results indicated decrease in methylation per cent with age. The overall mean methylation per cent of 32.14 was observed.
Ring shape Golden Ratio multicellular structures are algebraically afforded by asymmetric mitosis and one to one cell adhesion

Golden Ratio proportions are found throughout the world of multicellular organisms but the underlying mechanisms behind their appearance and their adaptive value if any remain unknown. The Golden Ratio is a real-valued number but cell population counts are whole numbered. Binet's formula connects the Golden Ratio to the whole numbered Fibonacci sequence (fn+1 = fn + fn–1 where f1 = 1 and f2 = 2), so we seek a cellular mechanism that yields Fibonacci cell kinetics. Drawing on Fibonacci’s description of growth patterns in rabbits, we develop a matrix model of Fibonacci cell kinetics based on an asymmetric pause between mitoses by daughter cells. We list candidate molecular mechanisms for asymmetric mitosis such as epigenetically asymmetric chromosomal sorting at anaphase due to cytosine-DNA methylation. A collection of Fibonacci-sized cell groups produced each by mitosis needs to assemble into a larger multicellular structure. We find that the mathematics for this assembly are afforded by a simple molecular cell surface configuration where each cell in each group has four cell to cell adhesion slots. Two slots internally cohere a cell group and two adhere to cells in other cell groups. We provide a notation for expressing each cell’s participation in dual Fibonacci recurrence relations. We find that single class of cell to cell adhesion molecules suffices to hold together a large assembly of chained Fibonacci groups having Golden Ratio patterns. Specialized bindings between components of various sizes are not required. Furthermore, the notation describes circumstances where chained Fibonacci-sized cell groups may leave adhesion slots unoccupied unless the chained groups anneal into a ring. This unexpected result suggests a role for Fibonacci cell kinetics in the formation of multicellular ring forms such as hollow and tubular structures. In this analysis, a complex molecular pattern behind asymmetric mitosis coordinates with a simple molecular cell adhesion pattern to generate useful multicellular assemblies. Furthermore, this reductively unifies two of the hypothesized evolutionary steps: multicellularity and cellular eusociality.
An Assessment of m-Health Effect on Covid-19 Management Using PLS Modeling Approach

Introduction: The aim of the present study was to investigate the different roles of m-Health in pandemic management using the Partial Least Square (PLS) modeling technique. Owing to the limited existing literature regarding theorizing and the lack of the default model in predicting the role of m-Health in pandemic management, this method was used for exploratory modeling.Material and Methods: The PLS model was performed with smart-PLS software for the following steps: estimating weight ratios, considering weight ratios as input, estimating parameters, model-fitting and testing hypotheses. In addition, Factor scores in regression equations were used to estimate structural parameters. PLS algorithm, Cronbach's alpha, and Composite Reliability were used for the measurement and reliability evaluation model Goodness-of-fit. In addition, the R2 index was used to evaluate the model adequacy. Bootstrapping was used for significant coefficients. The Goodness-of-fit of the model was examined via the Standardized Root Mean Square Residual (SRMR) criterion.Results: It is determined the measurement models goodness-of-fit which the alpha values were as follows: diagnosis construct=0.786, follow-up=0.772, treatment=0.796, health care providers=0.704 and education=0.839 with more than 0.7 for all measures for Composite Reliability, the structural model measures such as R2 were higher than 0.6 for all areas and the overall model goodness-of-fit was -0.007 for SRMR, the five hypotheses developed in the model were confirmed according standardized coefficients more than 1.96 for all paths. Furthermore, the proposed model concerning the positive and significant role of m-Health in diagnosis, treatment and follow-up, education and health providers during the pandemic era was approved.Conclusion: The results of the present study can be used as a theoretical basis in developing models related to the role of m-Health in pandemic management. Also, health policymakers and practitioners could use the results to manage current and post-coronary conditions and to promote services based on various m-Health apps.
Estimation of protein dynamic states with single molecule fluorescence data analysis at microsecond scale

When timescale of the molecule kinetics faster than the bin time of millisecond order, the commercial single- molecule fluorescence resonance energy transfer (smFRET) data analysis tools may not clearly separate different states. We proposed a novel method to obtain the analysis result from smFRET raw data in microsecond scale. We introduced Akaike information criterion and non-stationarity measure in the fitting procedure to give an objective estimate about the number of states and populations. The results were used as initial values to make parameter estimation about equilibrium populations and transition rates from the photon trajectories based on an iterative optimizing procedure.
The effect of moment of inertia on the speed of swung implements

The maximum swing speed of an implement is an important performance parameter in many sports. It is understood that moment of inertia (MOI) has an effect upon the swing speed of an implement and numerous studies have found a similar rate of swing speed decay (n). These studies considered different movements which suggested that skill was less important than physique to the relationship between swing speed and MOI. The aim of this project was to quantify this relationship and to determine whether the physical characteristics of a participant can be used to predict their swing speed performance. A series of eight visually identical rods with varied MOI were swung in a heavily restricted, maximal motion and trials were recorded with a motion capture system. The results found that swing speed decreased as MOI increased. It was also found that if n was assumed to be constant, the maximum work done by a participant was strongly and significantly related to their swing speed. The relationship between work done and swing speed was used to create a model to predict swing speed for an implement with a specific MOI. This model was validated for a new set of participants performing the same restricted motion and all measured data fell within the confidence intervals of the predictions. The ecological validity of the model was tested in an analysis of the swing speed of tennis groundstrokes. An impact model was used to analyse the effect of changing MOI on ball speed. It was discovered that there is an optimum MOI that produces a maximum ball speed and that this optimum MOI is dependent upon n. This makes the customisation of equipment a realistic possibility. A simple method for measuring n in a non-laboratory environment is proposed that will enable the customisation process to take place.
Development of Chinese Danmaku Video Sites based on Self-Directed Learning (CDSDL) Model for Undergraduate Students in TAM Model

In an increasingly complex and uncertain learning environments, self-directed learning is an essential skill for living and working.The notion of self-directed learning emerged from the adult education profession, and experts emphasize the need of examining that the self-directed learning competence may be developed among university students.However, little research has been conducted to investigate the factors that influence Chinese Danmaku video sites for self-directed learning strategies intention and adoption as sustainability. The current study seeks to determine whether and to what extent the factors influence Chinese Danmaku video sites for self-directed learning intention and adoption as sustainability in relation to the two stages. And, it presents a research framework for combining the technology acceptance model (TAM) for adoption with Facilitating conditions (FC), individual cultural characteristics, social influence (SI) and the task fit technology model (TTF) for utility. In addition, to encourage higher education universities to ensure their long-term viability (sustainability) by using Chinese Danmaku video sites for self-directed learning, student satisfaction measures when students succeed in their studies and are satisfied with their overall experience.
Modeling with Homophily Driven Heterogeneous Data in Gossip Learning

Training deep learning models on data distributed and local to edge devices such as mobile phones is a prominent recent research direction. In a Gossip Learning (GL) system, each participating device maintains a model trained on its local data and iteratively aggregates it with the models from its neighbours in a communication network. While the fully distributed operation in GL comes with natural advantages over the centralized orchestration in Federated Learning (FL), its convergence becomes particularly slow when the data distribution is heterogeneous and aligns with the clustered structure of the communication network. These characteristics are pervasive across practical applications as people with similar interests (thus producing similar data) tend to create communities. This paper proposes a data-driven neighbor weighting strategy for aggregating the models: this enables faster diffusion of knowledge across the communities in the network and leads to quicker convergence. We augment the method to make it computationally efficient and fair: the devices quickly converge to the same model. We evaluate our model on real and synthetic datasets that we generate using a novel generative model for communication networks with heterogeneous data. Our exhaustive empirical evaluation verifies that our proposed method attains a faster convergence rate than the baselines. For example, the median test accuracy for a decentralized bird image classifier application reaches 81% with our proposed method within 80 rounds, whereas the baseline only reaches 46%.
Preparation of Highly Textured Bi and MnBi Films by the Pulsed Laser Deposition Method

Textured Bi and MnBi/Bi thin films are prepared by the pulsed laser deposition method. The highly c-axis textured MnBi films are obtained by annealing the bilayer consisting of textured Bi and Mn films. The coercivities of the MnBi/Bi film are 1.5 T and 2.35 T at room temperature and at 373 K, respectively, showing a positive temperature coefficient. Microstructural investigations show that the textured MnBi film results from the orientated growth induced by the textured Bi under-layer.
Field Resistance of American Sycamore ‘Davis’ to Canker Pathogens

American sycamores (Platanus occidentalisL.) are found in many ecosystems and planted in urban landscapes worldwide. The trees are highly susceptible to anthracnose and canker pathogens, causing leaf blight and branch dieback. On The Ohio State University campus in Columbus, Ohio, an American sycamore was observed to thrive among many symptomatic sycamores. The healthy tree, subsequently protected as cultivar ‘Davis,’ was vegetatively propagated and tested for field resistance to natural infection of canker pathogens compared to the wildtype. Incidence and severity of leaf necrosis, incidence of dieback, and tree death were evaluated for 2 consecutive seasons. The incidence of leaf necrosis was disconnected from the incidence of dieback and tree mortality, as little to no leaves were produced on the wildtype trees. By the end of the second season, 7 out of 12 wildtype trees were dead, while all 12 ‘Davis’ trees were alive. Several canker pathogens were recovered from both ‘Davis’ and the wildtype, includingApiognomonia plataniandDiaporthe eres. The latter had not been previously reported on American sycamore. Pathogenicity tests confirmed thatD. eresis indeed pathogenic on sycamores and also that ‘Davis’ is significantly more resistant than wildtype to canker development and should be preferred over the wildtype in the urban landscape.
Apple tree disease detection using VGG16 & Inceptionv3

- In recent years, the term Precision agriculture has gained tremendous attention due to its ability to improve crop yield while reducing its cost. This can be achieved by applying modern Machine learning and Computer vision techniques. In this paper, we propose a comprehensive study of the following deep learning models VGG16 and InceptionV3. Our approach involves training these models individually on a data-set of apple-tree leaf images, which include healthy and diseased trees,and evaluating their performance on a validation set. We also conduct experiments to compare the performance of the two models and find a distinguishable difference between their Validation accuracy. Our results show that both VGG16 and InceptionV3 achieve high accuracy in detecting apple tree diseases, with InceptionV3 outperforming VGG16 slightly. We believe that our method can be applied in real-world scenarios to help farmers detect and manage diseases in their apple trees, leading to higher crop yield and reduced costs.
AN INDUSTRIAL CASE STUDY ON THE COMBINED IDENTIFICATION AND OFFSET-FREE MODEL PREDICTIVE CONTROL OF A CHEMICAL PROCESS *

For three decades, model predictive control (MPC) has been the flagship advanced control method in the chemical process industries. However, most implementations still use heuristic methods for designing MPC estimators, especially for offset-free MPC implementations. In this paper, we present a recently developed maximum likelihood-based method for the identification of linear augmented disturbance models for use in offset-free MPC. This method provides noise covariances that are used to derive Kalman filters and moving horizon estimators, forgoing the need for manual design and tuning of the estimator. The method is extended to handle closed-loop plant data. We also discuss design strategies for safe and inexpensive identification experiments. The proposed identification method and estimator design are evaluated in industrial-scale, real-world case study of a process at Eastman Chemical’s Kingsport plant.
Late Aptian paleoclimate reconstruction of Brazilian 1 equatorial margin : inferences from palynology 2

This study conducted high-resolution paleoclimatic analyses based on the identification 11 of palynological groups from the late Aptian age (Biozone Sergipea veriverrucata) in 12 the Bragança and Codó formations within the Bragança-Viseu, São Luís, and Parnaíba 13 basins. The analysis comprised 40 palynological samples, with 200 palynomorphs per 14 slide counted when possible. Bioclimatic analysis was mainly supported by the 15 identification of botanical affinities, and ecological and climatic parameters such as 16 wet/arid trend (Fs/X), Shannon-Wiener diversity, and indicator species analysis 17 (IndVal) were used. Statistical analyses such as principal component and cluster 18 analyses were employed to support the paleoclimatic interpretations. The study 19 recognized 69 genera distributed among the main groups of living plants, including 20 bryophytes, ferns, lycophytes, gymnosperms, and angiosperms. It was possible to 21 attribute botanical affinity in 94.2% of the taxa, and nine genera occurred in all sections 22 studied: Afropollis, Araucariacites, Callialasporites, Cicatricosisporites, Classopollis, 23 Cyathidites, Deltoidospora, Equisetosporites, and Verrucosisporites, with Classopollis 24
A Systematic Procedure for Assigning Uncertainties to Data Evaluations

In this report, an algorithm that automatically constructs an uncertainty band around any evaluation curve is described. Given an evaluation curve and a corresponding set of experimental data points with x and y error bars, the algorithm expands a symmetric region around the evaluation curve until 68.3% of a set of points, randomly sampled from the experimental data, fall within the region. For a given evaluation curve, the region expanded in this way represents, by definition, a one-standard-deviation interval about the evaluation that accounts for the experimental data. The algorithm is tested against several benchmarks, and is shown to be well-behaved, even when there are large gaps in the available experimental data. The performance of the algorithm is assessed quantitatively using the tools of statistical-inference theory.
Lateral Heterogeneity Analysis Using Multiwell ZVSP Unsupervised Machine Learning Classification

In 3D and 2D surface seismic interpretation, seismic waveform shapes and attributes can identify facies and reservoir parameters laterally with more details than traditional amplitude mapping. Herein, a method on 1D zero offset VSP (ZVSP) data was adapted, giving a unique perspective of lateral heterogeneity analysis using VSP seismic waveform shapes and attributes. The downgoing wavefield of VSP measures seismic wave variation in the vertical direction. When enough VSPs are covering an area, we can combine them to get an insight into both vertical and lateral variations. An unsupervised machine learning clustering algorithm based on K-means and self-organizing maps (SOM) was used to group the waveforms based on their shape similarity and attributes (frequency spectrum). The algorithm produced a cluster map, a probability map, and a typical wavelet for each cluster. These were then used to analyze the vertical and lateral heterogeneity from well to well based on VSP waveform attributes. The used example data were an open dataset, the Poseidon 3D data from the NW Shelf, Australia (Browse Basin), provided by GEO Science Australia. Six wells were available with VSP datasets. This technique can be of use to incorporate additional attributes from VSP into extensive 3D subsurface interpretations. For precautions, the VSP measurement or data preconditioning must be done reliably prior to clustering. Such A method may function well for vertical well ZVSP where variation was noticed because of the vertical seismic ray path. In this study, the application of VSP data has been extended from the conventional single well-to-well basis. The value of integrating VSP characterization has been investigated from various wells and numerous measurements to discern both vertical and lateral heterogeneity in a studied area.
Promoting and Restricting Marriage Migrations: When Marriages Are Not Such a Private Matter

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