Miracle-dz/UniFAR-FTUs huggingface dataset:
Design of Experiments in Nonlinear Models: Asymptotic Normality, Optimality Criteria and Small-Sample Properties

Introduction. - Asymptotic designs and uniform convergence. Asymptotic properties of the LS estimator. - Asymptotic properties of M, ML and maximum a posteriori estimators. - Local optimality criteria based on asymptotic normality. - Criteria based on the small-sample precision of the LS estimator. - Identifiability, estimability and extended optimality criteria. - Nonlocal optimum design. - Algorithms-a survey. - Subdifferentials and subgradients. - Computation of derivatives through sensitivity functions. - Proofs. - Symbols and notation. - List of labeled assumptions. - References.
Effects of New Zealand Blackcurrant Extract on Sequential Performance Testing in Male Rugby Union Players

Previous studies on performance effects by New Zealand blackcurrant (NZBC) extract used mainly a single exercise task. We examined the effects of NZBC extract in a battery of rugby union–specific tests including speed, agility and strength testing. University male rugby union players (n = 13, age: 21 ± 2 years, height: 182 ± 6 cm, body mass: 87 ± 13 kg) completed two full familiarisations and two experimental visits in an indoor facility. The study had a double blind, placebo-controlled, randomised, crossover design. For the experimental visits, participants consumed NZBC extract (210 mg/day of anthocyanins for 7 days) or placebo with a 7-day wash-out. Testing order was the running-based anaerobic sprint test, the Illinois agility test, seated medicine ball (3 kg) throw, and handgrip strength. With NZBC extract, there may have been an effect for average sprint time to be faster by 1.7% (placebo: 5.947 ± 0.538 s, NZBC extract: 5.846 ± 0.571 s, d = −0.18 (trivial), p = 0.06). However, with NZBC extract there may have been reduced slowing of sprint 2 (d = −0.59 (moderate), p = 0.06) and reduced slowing for sprint 6 (d = −0.56 (moderate), p = 0.03). In the Illinois agility test, there may have also been an effect for the mean time to be faster by 1.6% (placebo: 18.46 ± 1.44 s, NZBC extract: 18.15 ± 1.22 s, d = −0.24 (small), p = 0.07). The correlation between the %change in average sprint time and %change in mean agility time was not significant (Pearson R2 = 0.0698, p = 0.383). There were no differences for the seated medicine ball throw distance (p = 0.106) and handgrip strength (p = 0.709). Intake of NZBC extract in rugby union players seems to improve tasks that require maximal speed and agility but not muscle strength. NZBC blackcurrant extract may be able to enhance exercise performance in team sports that require repeated movements with high intensity and horizontal change of body position without affecting muscle strength.
Liposomal Formulations Loaded with a Eugenol Derivative for Application as Insecticides: Encapsulation Studies and In Silico Identification of Protein Targets

A recently synthesized new eugenol derivative, ethyl 4-(2-methoxy-4-(oxiran-2-ylmethyl)phenoxy)butanoate, with a high insecticidal activity against Sf9 (Spodoptera frugiperda) insect cells, was encapsulated in the liposomal formulations of egg-phosphatidylcholine/cholesterol (Egg-PC:Ch) 70:30 and 100% dioleoylphosphatidylglycerol (DOPG), aiming at the future application as insecticides. Compound-loaded DOPG liposomes have sizes of 274 ± 12 nm, while Egg-PC:Ch liposomes exhibit smaller hydrodynamic diameters (69.5 ± 7 nm), high encapsulation efficiency (88.8 ± 2.7%), higher stability, and a more efficient compound release, thus, they were chosen for assays in Sf9 insect cells. The compound elicited a loss of cell viability up to 80% after 72 h of incubation. Relevantly, nanoencapsulation maintained the toxicity of the compound toward insect cells while lowering the toxicity toward human cells, thus showing the selectivity of the system. Structure-based inverted virtual screening was used to predict the most likely targets and molecular dynamics simulations and free energy calculations were used to demonstrate that this molecule can form a stable complex with insect odorant binding proteins and/or acetylcholinesterase. The results are promising for the future application of compound-loaded nanoliposome formulations as crop insecticides.
Accurate determination of the elastic moduli of optimized cantilever beams by efficient time-averaged ESPI system

Elastic moduli, including Young’s modulus, shear modulus, bulk modulus, etc, are key parameters that are used to characterize the ability of a solid material to resist various types of deformation. The moduli can be extracted from the natural frequencies of a cantilever beam. In this paper, the relationships between moduli and natural frequencies, for the first time, are quantified by the finite element method. The optimized three-dimensional proportion of the cantilever beam is selected to be implemented simple error compensation. Experimentally, to precisely obtain the natural frequencies of the cantilever beam, an efficient time-averaged electronic speckle pattern interferometry(ESPI) system has been developed. The efficiency and precision are reflected in the following aspects: firstly, according to the slender character of the cantilever beam, a large shear optical path arrangement is designed to facilitate isolation from environmental interference; secondly, a resonance search method, based on the moiré effect is employed to recognize the natural frequencies accurately and efficiently; thirdly, a novel dynamic phase-shifting method is proposed based on the arrangement of the large shear optical path for clearer visualization of the mode shape of the cantilever beam. The proposed methods are verified by three kinds of common materials. The results suggest that Young’s modulus and shear modulus derived from natural frequencies are higher than the known value, and the error compensation can significantly reduce the calculation error. Furthermore, the experiments carried out on the woven carbon fiber reinforced plastic laminates illustrate the potential of the proposed methods in the evaluation of elastic moduli of composites. Given that the exciter attached to the specimen surfaces can be replaced with some special counterparts, the proposed ESPI system has considerable potential to test the objects loaded in some extreme environments, e.g. at high temperatures or underwater, where contact detection methods are difficult to be implemented.
Adaptive Neuroevolution With Genetic Operator Control and Two-Way Complexity Variation

Topology and weight evolving artificial neural network algorithms optimize the structure and weights of artificial neural networks (ANNs) simultaneously. The resulting networks are typically used as policy models for solving control and reinforcement learning (RL) type problems. This article presents a neuroevolution algorithm that aims to address the typical stagnation and sluggish convergence issues present in other neuroevolution algorithms. These issues are often caused by inadequacies in population diversity preservation, exploration/exploitation balance, and search flexibility. This new algorithm, called the adaptive genomic evolution of neural-network topologies (AGENT), builds on the neuroevolution of augmenting topologies (NEAT) concept. Novel mechanisms for adapting the selection and mutation operations are proposed to favorably control population diversity and exploration/exploitation balance. The former is founded on a fundamentally new way of quantifying diversity by taking a graph-theoretic perspective of the population of genomes and intergenomic differences. Further advancements to the NEAT paradigm occur through the incorporation of variable neuronal properties and new mutation operations that uniquely allow both the growth and pruning of ANN topologies during evolution. Numerical experiments with benchmark control problems adopted from the OpenAI Gym illustrate the competitive performance of AGENT against standard RL methods and adaptive HyperNEAT, and superiority over the original NEAT algorithm. Further parametric analysis provides key insights into the impact of the new features in AGENT. This is followed by evaluation on an unmanned aerial vehicle collision avoidance problem where maneuver planning models are learnt by AGENT with 33% reward improvement over 15 generations.
Advanced Quantum Communications: An Engineering Approach

The book provides an overview of the most advanced quantum informational geometric techniques, which can help quantum communication theorists analyze quantum channels, such as security or additivity properties. Each section addresses an area of major research of quantum information theory and quantum communication networks. The authors present the fundamental theoretical results of quantum information theory, while also presenting the details of advanced quantum ccommunication protocols with clear mathematical and information theoretical background. This book bridges the gap between quantum physics, quantum information theory, and practical engineering.
Improvements to propositional satisfiability search algorithms

In this dissertation, we examine complete search algorithms for SAT, the satisfiability problem for propositional formulas in conjunctive normal form. SAT is NP-complete, easy to think about, and one of the most important computational problems in the field of Artificial Intelligence. From an empirical perspective, the central problem associated with these algorithms is to implement one that runs as quickly as possible on a wide range of hard SAT problems. This in turn require identifying a set of useful techniques and programming guidelines. Another important problem is to identify the techniques that do not work well in practice, and provide qualitative reasons for their poor performance whenever possible. This dissertation addresses all four of these problems. Our thesis is that any efficient SAT search algorithm should perform only a few key technique at each node of the search tree. Furthermore, any implementation of such an algorithm should perform these techniques in quadratic time total down any path of the search tree, and use only a linear amount of space. We have justified these claims by writing POSIT (for PrOpositional SatIsfiability Testbed), a SAT tester which runs more quickly across a wide range of hard SAT problems than any other SAT tester in the literature on comparable platforms. On a Sun SPARCStation 10 running SunOS 4.1. 3$\sb{-}$U1, POSIT can solve hard random 400-variable 3-SAT problems in about 2 hours on the average. In general, it can solve hard n-variable random 3-SAT problems with search trees of size $O(2\sp{n/18.7}). $ In addition to justifying these claims, this dissertation describes the most significant achievements of other researchers in this area, and discusses all of the widely known general techniques for speeding up SAT search algorithms. It should be useful to anyone interested in NP-complete problems or combinatorial optimization in general, and it should be particularly useful to researchers in either Artificial Intelligence or Operations Research.
The A-infinity operad and the moduli space of curves

The modular envelope of a cyclic operad is the smallest modular operad containing it. A modular operad is constructed from moduli spaces of Riemann surfaces with boundary; this modular operad is shown to be the modular envelope of the A-infinity cyclic operad. This gives a new proof of the result of Harer-Mumford-Thurston-Penner-Kontsevich that a cell complex built from ribbon graphs is homotopy equivalent to the moduli space of curves.
Application of univariate and multivariate statistical analyzes in clonal selection of Eucalyptus spp. for charcoal production

The aim of this study was to select superior materials of Eucalyptus spp. using univariate and multivariate statistical analyzes. Twenty-five genetic materials from Eucalyptus spp. collected in Itamarandiba, Minas Gerais were used. The properties of wood and charcoal were determined for all genetic materials, in addition to the gravimetric yield. Data were submitted to Scott-Knott hierarchical clustering algorithm as univariate analysis. For the multivariate approach, a combination between principal component analysis and hierarchical cluster analysis was used. Both analyzes were efficient in the selection of genetic materials for charcoal production. According to the Scott-Knott test, genetic materials 9 and 21 were the most suitable to produce charcoal. By means of the multivariate analyzes the most indicated were 9, 10 and 21. The Scott-Knott test allowed the visualization of the results of each quality parameter independently. On the other hand, the multivariate tools enabled the observation of the relation between the properties of wood and charcoal.
The Comprehensive Analysis of Hub Gene ARRB2 in Prostate Cancer

Methods The differential expressed genes (DEGs) were screened from the gene expression profile GSE30994 related to PRAD and then analyzed by protein-protein interaction (PPI) to screen the hub gene. Subsequently, the relation between hub gene and pan cancers, PRAD prognosis, and immunotherapy was analyzed. Besides, the effects of hub gene on the growth and metastasis of PRAD cell lines and inflammatory factors (IFs) were detected by functional experiments. Results 276 upregulated and 1,861 downregulated DEGs were analyzed from GSE30994 gene expression profiles. Through enrichment analysis, it was found that upregulated DEGs were significantly enriched in nitric oxide-mediated signal transduction, insulin signaling pathway, etc. Through PPI networks, ARRB2 was determined as the hub gene that was highly expressed in pan cancers, including PRAD, and contributed to poor prognosis of PRAD patients. Immunoassay showed that ARRB2 was associated with B cells, NK cells, endothelial cells, etc. and also connected with tumor-infiltrating lymphocytes (TILs). Next, the signature model analysis revealed that ARRB2 had a clinical value in predicting PRAD prognosis. In functional experiments, ARRB2 was highly expressed in PRAD cell lines, promoted PRAD cell growth and metastasis, and positively associated with IFs. Conclusion ARRB2 has a good prognostic ability in PRAD, and it could be a potential target of PRAD immunotherapy, which offers new directions for PRAD research.
Robust, risk-sensitive, and data-driven control of markov decision processes

Markov Decision Processes (MDPs) model problems of sequential decision-making under uncertainty. They have been studied and applied extensively. Nonetheless, there are two major barriers that still hinder the applicability of MDPs to many more practical decision making problems: (1) The decision maker is often lacking a reliable MDP model. Since the results obtained by dynamic programming are sensitive to the assumed MDP model, their relevance is challenged by model uncertainty. (2) The structural and computational results of dynamic programming (which deals with expected performance) have been extended with only limited success to accommodate risk-sensitive decision makers. In this thesis, we investigate two ways of dealing with uncertain MDPs and we develop a new connection between robust control of uncertain MDPs and risk-sensitive control of dynamical systems. The first, approach assumes a model of model uncertainty and formulates the control of uncertain MDPs as a problem of decision-making under (model) uncertainty. We establish that most formulations are at least NP-hard and thus suffer from the ''curse of uncertainty. '' The worst-case control of MDPs with rectangular uncertainty sets is equivalent to a zero-sum game between the controller and nature. The structural and computational results for such games make this formulation appealing. By adding a penalty for unlikely parameters, we extend the formulation of worst-case control of uncertain MDPs and mitigate its conservativeness. We show a duality between the penalized worst-case control of uncertain MDPs with rectangular uncertainty and the minimization of a Markovian dynamically consistent convex risk measure of the sample cost. This notion of risk has desirable properties for multi-period decision making, including a new Markovian property that we introduce and motivate. This Markovian property is critical in establishing the equivalence between minimizing some risk measure of the sample cost and solving a certain zero-sum Markov game between the decision maker and nature, and to tackling infinite-horizon problems. An alternative approach to dealing with uncertain MDPs, which avoids the curse of uncertainty, is to exploit directly observational data. Specifically, we estimate the expected performance of any given policy (and its gradient with respect to certain policy parameters) from a training set comprising observed trajectories sampled under a known policy. We propose new value (and value gradient) estimators that are unbiased and have low training set to training set variance. We expect our approach to outperform competing approaches when there are few system observations compared to the underlying MDP size, as indicated by numerical experiments. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)
Towards an explanation for the 30 Dor (LMC) Honeycomb nebula – the impact of recent observations and spectral analysis

The unique Honeycomb nebula, most likely a peculiar supernova remnant, lies in 30 Doradus in the Large Magellanic Cloud (LMC). Due to its proximity to SN1987A, it has been serendipitously and intentionally observed at many wavelengths. Here, an optical spectral analysis of forbidden line ratios is performed in order to compare the Honeycomb high-speed gas with supernova remnants in the Galaxy and the LMC, with Galactic Wolf-Rayet nebulae and with the optical line emission from the interaction zone of the SS433 microquasar and W50 supernova remnant system. An empirical spatiokinematic model of the images and spectra for the Honeycomb reveals that its striking appearance is most likely due to a fortuitous viewing angle. The Honeycomb nebula is more extended in soft X-ray emission and could in fact be a small part of the edge of a giant LMC shell revealed for the first time in this short wavelength domain. It is also suggested that a previously unnoticed region of optical emission may in fact be an extension of the Honeycomb around the edge of this giant shell. A secondary supernova explosion in the edge of a giant shell is considered for the creation of the Honeycomb nebula. A microquasar origin of the Honeycomb nebula as opposed to a simple supernova origin is also evaluated.
A Dynamical Evolution Study of 40 2MASS Open Clusters

We investigate the dynamical evolution of 40 open clusters (OCs) by means of their astrophysical parameters derived from field-decontaminated 2MASS photometry. We find a bifurcation in the planes core radius vs. age and cluster radius vs. age, in which part of the clusters appear to expand with time probably due to the presence of stellar black holes while others seem to shrink due to dynamical relaxation. Mass functions (MFs) are built for 3$/$4 of the sample (31 OCs), which are used to search for indications of mass segregation and external dynamical processes by means of relations among astrophysical, structural and evolutionary parameters. We detect a flattening of MF slopes ocurring at the evolutionary parameters $\tau_{core}\leq 32$ and $\tau_{overall}\leq 30$, respectively. Within the uncertainties involved, the overall MF slopes of 14 out of 31 OCs with $m_{overall} > 500~M_{\odot}$ are consistent with Kroupa's initial mass function, implying little or no dynamical evolution for these clusters. The remaining 17 OCs with MF slopes departing from that of Kroupa show mild/large scale mass segregation due to dynamical evolution.
Methane Biogas Production in Malaysia: Challenge and Future Plan

Biomethane is a sustainable energy that is produced from an organic and renewable resource. As the second-largest oil palm producer in the world, palm oil mill effluent (POME) is the primary source of biomethane generation in Malaysia. POME is the by-product of palm oil extraction and is extensively employed as a feedstock for the production of biomethane. Malaysia has an equatorial environment with humid and hot weather; this climate is conducive to the cultivation of numerous agricultural crops. A considerable number of agricultural wastes and residues are produced by agricultural crops, however, only 27% of them are used as fuel or to create useable products. Several publications have been published on the production of biomethane from POME; nevertheless, additional research is required on the use of other bioresources and technologies for biomethane production in Malaysia. In addition, there is a lack of comprehensive information on the future development of biomethane production in Malaysia; thus, to fill this gap, this review paper focuses on the challenges and future of Malaysia, which puts an emphasis on POME and also includes other alternative options of bioresources that can be the future feedstock for biomethane production in Malaysia. To the best of our knowledge, this is the first paper to provide a comprehensive overview of the biogas trend in Malaysia in terms of challenges and current biomethane development, as well as detailed information on a number of leading companies that are currently active in Malaysia biogas industry.
Mathematics Learning: Misconceptions, Problems and Methods of Making Mathematics Learning Fun

This article is related to the fallacy regarding learning mathematics in the current situation of school-level education. The article aims to dig out the meaning of learning mathematics, misconceptions, problems, and the particular ways of making mathematics learning fun in basic level school mathematics. The study is mainly based on the critical review of different related literature. Also, the in-depth interview with the basic level school teachers regarding the problem in learning mathematics, misconceptions about learning mathematics, and the approaches used to make mathematics learning effective and interesting were used to make the result more appropriate. Thus, the study utilized the systematic-descriptive approach combining the result of the different kinds of related literature and the concerned teachers' views and their experiences about mathematics learning problems, misconceptions, and ways to make mathematics learning enjoyable. This article presents the prevailing context of mathematics learning, major misconceptions about mathematics learning, problems of mathematics learning, and ways to make mathematics learning fun. It ascertains the major mathematics learning problems based on three aspects curriculum, teachers, and the students. Also, it helps to deal with misconceptions about learning mathematics by utilizing the most effective intervention techniques.
Non-parametric correlational analysis of the relationship between capacity building and the performance of poultry farming cooperatives: case of Gitega province in Burundi

Today, there is a strong emphasis by the Burundian government on developing and strengthening the cooperative movement. Since 2017 the Burundian government allocates 10,000,000 BIF (around 5,000 USD) to each Burundian hill’s cooperative. Burundi is made up with 2,638 hills. Therefore, there is a very dire need to evaluate the performance of these mushrooming cooperatives. This study set out to statistically analyze the relationship between capacity building and the performance of poultry farming cooperatives in Gitega province, Burundi. From a population of 3252 poultry farming cooperative members, a sample size of 356 was obtained using the Slovin’s formula. The study used a probability sampling technique where simple random sampling method was used to select respondents. From 356 questionnaires that were distributed, 352 were returned to the researcher. The findings revealed that there was no relationship between capacity building and the performance of poultry farming cooperatives in Gitega province, rs=. 054, n=352, p>0.01. The null hypothesis failed, therefore, to be rejected.