Miracle-dz/UniFAR-FTUs huggingface dataset:
Fertility Life Table, Thermal Requirements, and Ecological Zoning of Anthonomus grandis grandis Boheman (Coleoptera: Curculionidae) in Brazil

Simple Summary The boll weevil is a key pest of cotton in Central and South America. Its thermal requirements, development time, oviposition, survival, adult longevity, and sex ratio were determined under laboratory conditions, and the data were used to construct a fertility life table. Based on the results and a GIS (Geographic Information System), the R0 was estimated for different Brazilian regions and represented on a map. This information can be useful for developing strategies to manage the pest in cotton crops. Abstract The boll weevil, Anthonomus grandis grandis Boh. , is the most important cotton pest in Central and South America. The biological characteristics and thermal requirements of boll weevils reared on an artificial diet were assessed at seven constant temperatures (18, 20, 22, 25, 28, 30, and 32 ± 1 °C) under laboratory conditions. These data were used to determine the ecological zoning for the pest in Brazil. The development time; oviposition period; the number of eggs produced; survival of eggs, larvae, and pupae; adult longevity; and sex ratio were recorded, and additional life table parameters were calculated. The total development duration ranged from 16.1 (32 °C) to 46.2 (18 °C) days. Temperature significantly affected the number of eggs laid per female (fecundity), with the highest number of eggs observed at 25 °C (251 ± 15.8). The parameters from the fertility life table indicated the greatest population growth at 25 °C and 28 °C. The net reproductive rate (R0) at these temperatures was 22.25 times higher than at 18 °C. Based on R0 and temperature, an ecological zoning of the pest was developed for Brazil. Brazilian regions with mean temperatures above 20 °C and below 30 °C are most favorable for the population growth of the boll weevil. The most suitable crop areas were found to be the north, midwest, and part of the northeast region, although the weevil can occur throughout Brazil if the host plants are available.
On the resolved scales in a turbulent boundary layer by tomographic PIV and PTV aided by VIC

Time-resolved tomographic PIV and tomographic PTV aided by VIC+ are applied in a turbulent boundary layer (Reθ = 2038) measurement, and the resulting small scales flow properties, i.e. vorticity and dissipation, are compared. The VIC+ technique was recently proposed and uses the concept of pouring time into space to increase reconstruction quality of instantaneous velocity and vorticity. The tomographic PTV particle track measurements are interpolated to a dense grid, making use of both particle velocity and Lagrangian acceleration. The existing timeresolved tomographic PIV measurements by Jodai and Elsinga (2016, J. Fluid Mech. 795:611-633) are reprocessed using tomographic PTV and VIC+. Comparison of the vortical structures by visualization of isosurfaces of vorticity magnitude shows that the two methods return similar coherent vortical structures, but their strength in terms of vorticity magnitude is increased when using VIC+. Moreover, the results obtained by VIC+ provide additional detailing of the vortex shapes, which suggests an improvement in spatial resolution. Further statistical evaluation shows that the RMS of vorticity fluctuations from tomographic PIV are approximately 40% lower in comparison to a reference profile available from a DNS simulation, while the VIC+ technique returns RMS vorticity fluctuations to within 10% of the DNS reference. The dissipation rate is approximated by VIC+ to within approximately 10% for 25 < y+ < 120, in comparison to a damping of 50% by tomographic PIV. It is concluded that when time-resolved tomographic measurements are available, the VIC+ technique can improve reconstruction quality of velocity and vorticity – also in actual experiments.
Hybrid forecast models for PM-10 prediction: A case study of Chiang Mai city of Thailand during high season

In this study, the forecast models including to ARIMA, NNs, hybrid of ARIMA-NNs and hybrid of NNs-ARIMA model are employed to predict the PM-10 level during the high season in Chiang Mai, in the province of northern Thailand. The k-folds cross validation technique is used in the experimental design in order to prevent the over-fitting which is generally existed with strong impact on the forecast model. The historical PM-10 data are taken as the input of the forecast model. The statistics test and parameter designed experiments are used to optimize the linear models while the back-propagation (BP) learning algorithm is used to train the neural networks. The average of root mean squared error (RMSE) and mean absolute error (MAE) are used to indicate their performances. The results indicate that the hybrid NNs-ARIMA model is highly able to predict the PM-10 over the rest.
Glycogen Metabolism and Its Role in Growth and Encystation in Entamoeba histolytica

Entamoeba histolytica is a parasitic protozoan that causes diarrheal disease in approximately 100 million people worldwide every year. E. histolytica has two forms, the growing trophozoite and the infectious cyst. Trophozoites colonizing the large intestine form cysts that are released into the environment. The ingestion of the cysts in contaminated food and water continues the disease cycle. Here, we investigated the role of glycogen in trophozoite growth and encystation. Glycogen is thought to provide precursors for the synthesis of chitin, a major component of the protective cyst wall. We propose that glycogen also serves as an energy source during metabolic adaptation to different nutrient environments. We examined the role of glycogen in E. histolytica by analyzing the growth and encystation of RNAi strains with reduced expression of the single gene-encoding glycogen synthase (GYS) or two of three genes encoding glycogen phosphorylase (PYG). The GYS RNAi strain had a greatly reduced glycogen accumulation, and both the GYS and PYG RNAi strains exhibited reduced growth in the glucose-poor medium. Both RNAi strains also showed reduced cyst production. Our results suggest glycogen synthesis and degradation are vital to the growth and adaptation of E. histolytica to a low-glucose environment such as that encountered in the large intestine.
Towards a quantum internet

A long-range quantum communication network is among the most promising applications of emerging quantum technologies. We discuss the potential of such a quantum internet for the secure transmission of classical and quantum information, as well as theoretical and experimental approaches and recent advances to realize them. We illustrate the involved concepts such as error correction, teleportation or quantum repeaters and consider an approach to this topic based on catchy visualizations as a context-based, modern treatment of quantum theory at high school.
Secrecy Rate Maximization in Gaussian MIMO Wiretap Channels

Secrecy rate maximization in Gaussian MIMO wiretap channels is considered. While the optimality of Gaussian signaling and a general expression for the secrecy capacity have been well established, closed-form solutions for the optimal transmit covariance matrix are known for some special cases only, while the general case remains an open problem. This chapter reviews known closed-form solutions and presents a numerical algorithm for the general case with guaranteed convergence to the global optimum. The known solutions include full-rank and rank-1 cases (which, when combined, provide a complete solution for the case of two transmit antennas), the case of identical right singular vectors for the eavesdropper and legitimate channels, and the cases of weak, isotropic, and omnidirectional eavesdroppers, which also provide lower and upper bounds to the general case. Necessary optimality conditions and a tight upper bound for the rank of the optimal covariance matrix in the general case are discussed. Sufficient and necessary conditions for the optimality of three popular signaling strategies over MIMO channels, namely, isotropic and zero-forcing signaling as well as water-filling over the legitimate channel eigenmodes, are presented. The chapter closes with a detailed description of a numerical globally convergent algorithm to solve the general case, and gives some illustrative examples.
Impact of Using Smartphone While Walking or Standing: A Study Focused on Age and Cognition

Background: Using smartphones during a task that requires upright posture is suggested to be detrimental for the overall motor performance. The aim of this study was to determine the role of age and specific aspects of cognitive function on walking and standing tasks in the presence of smartphone use. Methods: 51 older (36 women) and 50 young (35 women), mean age: 66.5 ± 6.3 and 22.3 ± 1.7 years, respectively, were enrolled in this study. The impact of using a smartphone was assessed during a dynamic (timed up and go, TUG) and a static balance test (performed on a force platform). Multivariate analyses of variance were applied to verify main effects of age, task, estimates of cognitive function and interactions. Results: Compared to young, older individuals exhibited a poorer performance on the dynamic and on the static test (age effect: p = 0.001 for both variables). Dual-tasking with a smartphone had a negative impact on both groups (task effect: p = 0.001 for both variables). The negative impact, however, was greater in the older group (age × task effect: p = 0.001 for both variables). Executive function and verbal fluency partially explained results of the dynamic and static tests, respectively. Conclusions: The negative impact of using a smartphone while performing tasks similar to daily activities is higher in older compared to young people. Subclinical deficits in distinct aspects of cognitive function partially explain the decreased performance when dual-tasking.
Hidden overdesign in building services: insights from two UK hospital case studies

This paper discusses margins in the context of building services, where the capacity in many buildings, such as hospitals, exceeds the requirements by huge amounts. This oversizing has a direct impact on building efficiency, capital, maintenance and operational costs, as well as environmental and societal impacts throughout its lifecycle. This paper analyses two in-depth case studies of oversized boiler and chiller systems within NHS hospitals, whereby a number of key factors leading to the oversized systems are identified. The excessive and uncoordinated use of margins that are applied during the various stages of building service projects for a variety of reasons, is one significant factor. The lack of requirements information such as energy demand profiles and the use of vague and unreliable data for initial project requirements specifications, is also a major contributing factor. The paper points to the need to develop robust processes that capture the scope and rationale for the margins applied, to communicate project assumptions and stakeholder requirements in a clear and unambiguous format and to develop systems for improved data capture and analysis.
A comparison of polynomial chaos and Gaussian process emulation for uncertainty quantification in computer experiments

Computer simulation of real world phenomena is now ubiquitous in science, because experimentation in the field can be expensive, time-consuming, or impossible in practice. Examples include climate science, where future climate is examined under global warming scenarios, and cosmology, where the evolution of galaxies is studied from the beginning of the universe to present day. Combining complex mathematical models and numerical procedures to solve them in a computer program, these simulators are computationally expensive, in that they can take months to complete a single run. The practice of using a simulator to understand reality raises some interesting scientific questions, and there are many sources of uncertainty to consider. For example, the discrepancy between the simulator and the real world process. The field of uncertainty quantification is concerned with the characterisation and reduction of all uncertainties present in computational and real world problems. A key bottleneck in any uncertainty quantification analysis is the cost of evaluating the simulator. The solution is to replace the expensive simulator with a surrogate model, which is computationally faster to run, and can be used in subsequent analyses. Polynomial chaos and Gaussian process emulation are surrogate models developed independently in the engineering and statistics communities respectively over the last 25 years. Despite tackling similar problems in the field, there has been little interaction and collaboration between the two communities. This thesis provides a critical comparison of the two methods for a range of criteria and examples, from simple test functions to simulators used in industry. Particular focus is on the approximation accuracy of the surrogates under changes in the size and type of the experimental design. It is concluded that one method does not unanimously outperform the other, but advantages can be gained in some cases, such that the preferred method depends on the modelling goals of the practitioner. This is the first direct comparison of polynomial chaos and Gaussian process emulation in the literature. This thesis also proposes a novel methodology called probabilistic polynomial chaos, which is a hybrid of polynomial chaos and Gaussian process emulation. The approach draws inspiration from an emerging field in scientific computation known as
New update rules based on Kullback-Leibler, gamma, and Renyi divergences for nonnegative matrix factorization

—Three new update rules based on Kullback-Leibler divergence, γ -divergence and R´enyi divergence for nonnegative matrix factorization are presented in this paper. An important advantage of these update rules is that they are globally convergent in the sense that any sequence of solutions contains at least one convergent subsequence and the limit of any convergent subsequence is a stationary point of the corresponding optimization problem.
Sliding Mode Control of FMII Systems: Handling Rice Fading Issues Under 2-D Frame

The <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>th order Rice model can effectively describe imperfect transmission, including amplitude attenuation, time delays, and stochastic disturbance, and is a popular channel fading model in one-dimensional (1-D) systems. However, due to the state evolution feature of two-dimensional (2-D) systems, the previous state signals of 2-D systems are not as directly determinable as those of 1-D systems such that the construction of the conventional and obvious <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>th order Rice model become difficulty for 2-D systems. Motivated by these, this work will investigate the sliding mode control (SMC) problem of the 2-D Fornasini–Marchesini (FMII) system under Rice fading channels. First, the previous <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-step state signals are defined according to the evolution feature of the FMII model. Afterwards, a new applicable <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>th-order Rice fading model for 2-D FMII systems is constructed, which not only has 2-D structural characteristics but also has similar properties to 1-D cases. A 2-D-type sliding function involving the predefined previous <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-step state signals and channel attenuation coefficients is designed, which exhibits a similar structural feature to the FMII model. A feasible SMC law is designed via the available fading states to achieve the ultimately uniformly boundedness in the mean square of the closed-loop system and the reachability of the sliding domain around the specified sliding surface. Furthermore, an optimization-solving algorithm is introduced to minimize the convergent bound. Finally, an example is applied to demonstrated the proposed results.
Less conservative output‐feedback tracking control design for polynomial fuzzy systems

In this study, the problem of output-feedback tracking control for polynomial-fuzzy-model-based systems is studied. In this problem, the objective is to drive the system states of a non-linear plant to follow the states of a given reference model using a fuzzy controller. The design problem is formulated as a sum-of-squares (SOS) optimisation, which can be solved efficiently using semidefinite programming. Here, unlike previous SOS-based tracking control approaches, Lyapunov matrix depends on all of the system states, which decreases the conservatism of the stability criteria. Moreover, the SOS conditions are derived to guarantee the system stability subject to a given H ∞ performance. Two simulation examples are given to demonstrate the effectiveness and benefits of the proposed method.
The unidirectionality of semantic changes in grammaticalization: an experimental approach to the asymmetric priming hypothesis

Why is semantic change in grammaticalization typically unidirectional? It is a well-established finding that in grammaticalizing constructions, more concrete meanings tend to evolve into more schematic meanings. Jäger & Rosenbach (2008) appeal to the psychological phenomenon of asymmetric priming in order to explain this tendency. This article aims to evaluate their proposal on the basis of experimental psycholinguistic evidence. Asymmetric priming is a pattern of cognitive association in which one idea strongly evokes another (i.e. paddle strongly evokes water), while that second idea does not evoke the first one with the same force (water only weakly evokes paddle). Asymmetric priming would elegantly explain why semantic change in grammaticalization tends to be unidirectional, as in the case of English be going to, which has evolved out of the lexical verb go. As yet, empirical engagement with Jäger & Rosenbach's hypothesis has been limited. We present experimental evidence from a maze task (Forster et al. 2009), in which we test whether asymmetric priming obtains between lexical forms (such as go) and their grammaticalized counterparts (be going to). On the asymmetric priming hypothesis, the former should prime the latter, but not vice versa. Contrary to the hypothesis, we observe a negative priming effect: speakers who have recently been exposed to a lexical element are significantly slower to process its grammaticalized variant. We interpret this observation as a horror aequi phenomenon (Rohdenburg & Mondorf 2003).
Optimal Operation of a Microgrid by Planning Diesel Generators and Microturbines

Optimal operation of a microgrid is one of the important requirements. The reduction of the loss power of the microgrid supports satisfying the above mission. The paper proposes a solution to optimize the location and capacity of distributed energy sources such as diesel generators (DG) and microturbines (MT) in the microgrid to ensure the minimum active and reactive loss powers. An improved artificial bee colony (ABC) algorithm is proposed to find the solution of the optimal location and capacity of the DGs and MTs of the islanded microgrid with 33 buses. The improved ABC algorithm-based achievements are compared with those obtained using an ABC algorithm, a genetic algorithm (GA), and a particle swarm optimization (PSO) algorithm to validate the proposal.
Stabilization of Polytopes for Fully Actuated Euler-Lagrange Systems

Given an Euler-Lagrange system and a convex polytope in its output space, we design a switched feedback controller that drives the output to the polytope. On the polytope, the system output tracks assigned trajectories or follows assigned paths. The study of this problem is motivated by industrial applications such as robotic painting, welding and three dimensional printing. Many engineering systems, such as robotic manipulators, can be modelled with Euler-Lagrange equations, and many engineered surfaces, designed using software, are naturally modelled as convex polytopes. We use feedback linearization to decompose the design problem into two subproblems; stabilizing the polytope surface, and controlling its motion along the surface. The first subproblem, known as the design of the transversal controller, leverages the fact that a polytope can be represented as a finite union of facets. The controller determines the closest facet to the system output and stabilizes that facet by stabilizing its corresponding hyperplane via feedback linearization. The transversal dynamics can be stabilized using linear controllers. At the boundary of a facet, we propose a switching law that ensures weak invariance of the polytope for the closed-loop system. The second subproblem, known as the design of the tangential controller, enforces desired dynamics while the system output is restricted to the polytope. We investigate control specifications such as following a predefined path on the surface and tracking a trajectory that moves along the surface. The separation of the transversal and tangential control design phases is possible because feedback linearization decouples the transversal and tangential dynamic subsystems. This approach to control design is demonstrated experimentally on a four degree-offreedom robotic manipulator. The experimental implementation is made robust to modelling uncertainty via Lyapunov re-design methods.
Model selection: Using information measures from ordinal symbolic analysis to select model sub-grid scale parameterizations

AbstractThe use of information measures for model selection in geophysical models with subgrid parameterizations is examined. Although the resolved dynamical equations of atmospheric or oceanic global numerical models are well established, the development and evaluation of parameterizations that represent subgrid-scale effects pose a big challenge. For climate studies, the parameters or parameterizations are usually selected according to a root-mean-square error criterion, that measures the differences between the model state evolution and observations along the trajectory. However, inaccurate initial conditions and systematic model errors contaminate root-mean-square error measures. In this work, information theory quantifiers, in particular Shannon entropy, statistical complexity and Jensen-Shannon divergence, are evaluated as measures of the model dynamics. An ordinal analysis is conducted using the Bandt-Pompe symbolic data reduction in the signals. The proposed ordinal information measures are examin.
Stationary distributions for a class of Markov-modulated tandem fluid queues

ABSTRACT We consider a model consisting of two fluid queues driven by the same background continuous-time Markov chain, such that the rates of change of the fluid in the second queue depend on whether the first queue is empty or not: when the first queue is nonempty, the content of the second queue increases, and when the first queue is empty, the content of the second queue decreases. We analyze the stationary distribution of this tandem model using operator-analytic methods. The various densities (or Laplace–Stieltjes transforms thereof) and probability masses involved in this stationary distribution are expressed in terms of the stationary distribution of some embedded process. To find the latter from the (known) transition kernel, we propose a numerical procedure based on discretization and truncation. For some examples we show the method works well, although its performance is clearly affected by the quality of these approximations, both in terms of accuracy and run time.