Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Electron Pair, the Single Bond C-C Rotational Energy Barrier and Other Molecular Mechanisms

To find evidence of the electron pair has proven to be a very difficult task. Bader et al. tried to unsuccessfully find evidence of the electron pair in the topological analysis of the Laplacian of the electron density of molecules. By using electron localization functions, Silvi et al. pointed out where these pairs might be in the molecule and represented them as attractors. Still, to locate the electron pair does not give answers to different molecular mechanisms. For instance, the mechanism of hindered rotation about the carbon-carbon single bond in ethane, which is of great interest and controversy. This phenomenon is not yet explained by Silvi’s most advanced molecular model (state of the art). A new alternative uses the relationship between the area of the electron density and the energy of the bond. This approach also provides the electron pair localization. Furthermore, by allowing the magnetic momenta of the bonding electrons to interact, an explanation of the rotational barrier appeared straightforwardly. Also, the model presented in this paper find bonding electrons not found by Silvi’s model. The results agree and/or complement the state of the art.
Senecio lautus complex in Australia. V. Taxonomic interpretations

Isolation mechanisms and the basis for taxonomic recognition in the Senecio lautus complex are discussed. Recognition is accorded to the following four Australian subspecies in this complex: alpinus Ali, dissectifolius Ali, maritimus Ali, and lanceolatus (Benth.) Ali. The synonymy and the geographical distribution of these taxa and the related forms are discussed in detail.
Phase Cancellation and Range Extension in Backscatter T2T Networks

Tag-to-tag (T2T) communications, in which two embedded backscatter tags directly communicate without the need of a reader, has recently seen progress in research. There are, however, challenges to overcome before this technology can take off. For example, state-of-the-art solutions to the phase cancellation problem that occurs in such networks are still trivial and sub-optimal, especially in terms of resource utilization. This thesis aims to address this issue by designing a T2T multi-hop network. This is done by (i) characterizing and analyzing such networks to assess the potential of multi-hop, and (ii) experimentally demonstrating the concept in the largest decode-and-relay multi-hop network so far. Findings show that multi-hop not only matches current phase cancellation solutions in terms of robustness, but also extends the range of communication by a factor of two with only four hops and enables connecting networks served by separate exciters.
Graph Planarity by Replacing Cliques with Paths

This paper introduces and studies the following beyond-planarity problem, which we call h-Clique2Path Planarity. Let G be a simple topological graph whose vertices are partitioned into subsets of size at most h, each inducing a clique. h-Clique2Path Planarity asks whether it is possible to obtain a planar subgraph of G by removing edges from each clique so that the subgraph induced by each subset is a path. We investigate the complexity of this problem in relation to k-planarity. In particular, we prove that h-Clique2Path Planarity is NP-complete even when h=4 and G is a simple 3-plane graph, while it can be solved in linear time when G is a simple 1-plane graph, for any value of h. Our results contribute to the growing fields of hybrid planarity and of graph drawing beyond planarity.
Solving the Distance-Based Critical Node Problem

In critical node problems, the task is to identify a small subset of so-called critical nodes whose deletion maximally degrades a network’s “connectivity” (however that is measured). Problems of this type have been widely studied, for example, for limiting the spread of infectious diseases. However, existing approaches for solving them have typically been limited to networks having fewer than 1,000 nodes. In this paper, we consider a variant of this problem in which the task is to delete b nodes so as to minimize the number of node pairs that remain connected by a path of length at most k. With the techniques developed in this paper, instances with up to 17,000 nodes can be solved exactly. We introduce two integer programming formulations for this problem (thin and path-like) and compare them with an existing recursive formulation. Although the thin formulation generally has an exponential number of constraints, it admits an efficient separation routine. Also helpful is a new, more general preprocessing procedure that, on average, fixes three times as many variables than before. Summary of Contribution: In this paper, we consider a distance-based variant of the critical node problem in which the task is to delete b nodes so as to minimize the number of node pairs that remain connected by a path of length at most k. This problem is motivated by applications in social networks, telecommunications, and transportation networks. In our paper, we aim to solve large-scale instances of this problem. Standard out-of-the-box approaches are unable to solve such instances, requiring new integer programming models, methodological contributions, and other computational insights. For example, we propose an algorithm for finding a maximum independent set of simplicial nodes that runs in time O(nm) that we use in a preprocessing procedure; we also prove that the separation problem associated with one of our integer programming models is NP-hard. We apply our branch-and-cut implementation to real-life networks from a variety of domains and observe speedups over previous approaches.
Fundamentals of Survey Sampling

Introduction The purpose of sampling theory is to develop methods of sample selection from a finite population and of estimation that provide estimates of the unknown population parameters, generally population total or population mean, which are precise enough for our purpose. Survey samples can broadly be categorized into two types: probability samples and nonprobability samples. Surveys based on probability samples are capable of providing mathematically sound statistical inferences about a larger target population. Inferences from probability-based surveys may, however, suffer from many types of bias.
The Threat of Malaria Cross-Infections in Human and Wild Primate

The plasmodia that cause malaria have been identified in over 170 contemporary animal species; of these, more than half afflict primates and are represented by over 40 different malarial plasmodia [2]. Al though it is common to make a distinction between “human” and “nonhuman” primate malaria, this is a false dichotomy. Malaria is best understood as four separate disorders affecting anthropoid1 primates as a whole. The plasmodia responsible for human malaria are only distantly related to one another, having closer genetic relationships to wild primate forms. Human Plasmodium falciparum is closely related to African ape P. reichenowi and several recently identified forms in Af rican apes. Human Plasmodium vivax is closely related to New World monkey P. simium, Old World monkey P. gonderi, and about a dozen plasmodia affecting Southeast Asian primates. Human P. malariae is virtually indistinguishable genetically from New World monkey P. brasilianum and African ape P. rodhaini. Human P. ovale is the only malaria form that has not yet been determined to have a wild primate relative; however, P. schwetzi is suspected and anthroponotic infections of a P. ovale-like parasite have been documented in chimpanzees.
Bayesian Neural Networks Uncertainty Quantification with Cubature Rules

Bayesian neural networks are powerful inference methods by accounting for randomness in the data and the network model. Uncertainty quantification at the output of neural networks is critical, especially for applications such as autonomous driving and hazardous weather forecasting. However, approaches for theoretical analysis of Bayesian neural networks remain limited. This paper makes a step forward towards mathematical quantification of uncertainty in neural network models and proposes a cubature-rule-based computationally-efficient uncertainty quantification approach that captures layer-wise uncertainties of Bayesian neural networks. The proposed approach approximates the first two moments of the posterior distribution of the parameters by propagating cubature points across the network nonlinearities. Simulation results show that the proposed approach can achieve more diverse layer-wise uncertainty quantification results of neural networks with a fast convergence rate.
Cost-effective priorities for the expansion of global terrestrial protected areas: Setting post-2020 global and national targets

Cost-effective zones for the expansion of global terrestrial protected areas are identified. Biodiversity loss is a social and ecological emergency, and calls have been made for the global expansion of protected areas (PAs) to tackle this crisis. It is unclear, however, where best to locate new PAs to protect biodiversity cost-effectively. To answer this question, we conducted a spatial meta-analysis by overlaying seven global biodiversity templates to identify conservation priority zones. These are then combined with low human impact areas to identify cost-effective zones (CEZs) for PA designation. CEZs cover around 38% of global terrestrial area, of which only 24% is currently covered by existing PAs. To protect more CEZs, we propose three scenarios with conservative, moderate, and ambitious targets, which aim to protect 19, 26, and 43% of global terrestrial area, respectively. These three targets are set for each Convention on Biological Diversity party with spatially explicit CEZs identified, providing valuable decision support for the post-2020 global biodiversity framework.
Let-7 derived from endometrial extracellular vesicles is an important inducer of embryonic diapause in mice

Let-7 in endometrial epithelial cell–derived extracellular vesicles induces embryonic diapause. Embryonic diapause is a maternally controlled phenomenon. The molecule controlling the onset of the phenomenon is unknown. We demonstrated that overexpression of microRNA let-7a or incubation with let-7g–enriched extracellular vesicles from endometrial epithelial cells prolonged the in vitro survival of mouse blastocysts, which developed into live pups after having been transferred to foster mothers. Similar to in vivo dormant blastocysts, let-7–induced dormant blastocysts exhibited low level of proliferation, apoptosis, and nutrient metabolism. Let-7 suppressed c-myc/mTORC1 and mTORC2 signaling to induce embryonic diapause. It also inhibited ODC1 expression reducing biosynthesis of polyamines, which are known to reactivate dormant embryos. Furthermore, the overexpression of let-7 blocked trophoblast differentiation and implantation potential of human embryo surrogates, and prolonged survival of human blastocysts in vitro, supporting the idea that embryonic diapause was an evolutionary conserved phenomenon. In conclusion, let-7 is the main factor inducing embryonic diapause.
Machine learning for the design of nanomaterials

In this article we review some machine learning methods for the design of nanomaterials. The first part will discuss how to use neural network to build a predictive model of the optical properties of a certain material or structure. The second part is dedicated to the optimization and reverse engineering of an optical material using generative networks.
CD44, TLR4, TREM-1/DAP12 in renal injury, inflammation and fibrosis

Acute kidney injury (AKI) is a common complication during systemic inflammatory response syndrome (SIRS), a potentially deadly clinical condition characterized by whole-body inflammatory state and organ dysfunction. CD44 is a ubiquitously expressed cell-surface transmembrane receptor with multiple functions in inflammatory processes, including sterile renal inflammation. The present study aimed to assess the role of CD44 in endotoxic shock-induced kidney inflammation and dysfunction by using CD44 KO and WT mice exposed intraperitoneally to LPS for 2, 4, and 24 hours . Upon LPS administration, CD44 expression in WT kidneys was augmented at all time-points. At 2 and 4 hours, CD44 KO animals showed a preserved renal function in comparison to WT mice. In absence of CD44, the pro-inflammatory cytokine levels in plasma and kidneys were lower, while renal expression of the anti-inflammatory cytokine IL-10 was higher. The cytokine levels were associated with decreased leukocyte influx and endothelial activation in CD44 KO kidneys. Furthermore, in vitro assays demonstrated a role of CD44 in enhancing macrophage cytokine responses to LPS and leukocyte migration. In conclusion, our study demonstrates that lack of CD44 impairs the early pro-inflammatory cytokine response to LPS, diminishes leukocyte migration/chemotaxis and endothelial activation, hence, delays endotoxic shock-induced AKI.
Directed self-assembly of a bio-inspired peptide into ring microstructures using micro-bubbles induced in thermo-optical tweezers

Molecular self-assembly has been recognized as an important technique for organizing building blocks into ordered structures with promising applications in various fields. Spontaneous self-assembly of diphenylalanine(FF) pep- tide derivatives which have remarkable sequence similarity with Alzheimer’s Aβ peptide, give rise to micro-rods and microtubes. But, the reported structures suffer from a lack of control. Herein, we report a new technique for laser-assisted self-assembly of diphenylanaline where we obtain stable annular ring microstructures using thermo-optical tweezers. In this method, a dense aqueous dispersion of the material which has high absorption at the laser wavelength is taken in a sample holder so that some material is adsorbed on the top surface. A hot spot is created on the top surface when the adsorbed material absorbs the high intensity at the focus of the laser beam, giving rise to a water vapor bubble. Due to Gibbs Marangoni convection, self-assembly of FF peptides occurs around the bubble in the form of rings. This method of fabricating rings is fast with complete control over the spatial location, size, and thickness of the rings. Interestingly, these self-assembled ring struc- tures display wave-guiding and spectrally asymmetric Fano resonances. We demonstrate the potential of such micro-structures in sensing by changes in their Fano spectral line and wave-guiding response on binding with Congo red dye, which is commonly used to stain amyloid proteins. Such intriguing waveguiding systems may have promising applications in biological and chemical sensing, precision diagnostics of various neurodegenerative diseases and in the fabrication of multifarious micro-optical devices.
Electrocorticographic signals classification for brain computer interfaces using stacked-autoencoders

Neuro-degenerative diseases can break brain's common output pathways of peripheral nerves and muscles in an individual, inhibiting his ability to perform daily tasks. Brain Computer Interfaces BCI make decoding-encoding of brain signals into control instructions for external devices. This work proposes the use of stacked autoencoders and a softmax layer for classification of visual stimuli from Electrocorticographic (ECoG) signals as an input to the BCI control system. Experimental results show that the proposed method has a good classification performance (average accuracy across subjects 0.95 +/- 0.05), compared to state-of-the-art approaches as Support Vector Machines SVM. Furthermore, the proposed network architecture allows analysis of the weights learned by the classifier making it possible to obtain insights of what signal features the classifier uses to discriminate the visual stimulus.
Effect of vertical induced strain on growth kinetics of self-assembled epitaxially grown InAs surface quantum dots

Over the last decade InAs Stranski–Krastanov (S-K) quantum dots (QDs) grown on GaAs substrate have been widely explored for optoelectronic devices. In the recent past, the uncapped surface QDs are getting much attention for sensing application with adequate sensitivity to lower molar concentration of contaminants. Though non-uniform size distribution is an inherent property of the self-assembled S-K growth process, sensitivity of the surface QDs is significantly affected by this. Here, we have grown the surface QDs upon buried QD layer with a low barrier layer thickness to reduce the non-uniformity. In general, the 2D to 3D transition of the InAs QDs occurs only above 1.7 monolayer (ML). However, the 3D transition may be possible even at a lower monolayer coverage with the residual strain induced from the underlying QDs. In this study, particularly we have grown InAs surface QD layer at 1.6 ML coverage above the 2.7 ML buried QD layer with 8 nm thick GaAs spacer. The impact of vertically induced strain of the underneath InAs QD layer on the growth of surface dots has been investigated. The morphology of surface QDs is observed through Atomic force microscopy (AFM), which indicates the formation of uniform QDs with lower defects. The low temperature photoluminescence (PL) spectroscopy provides the evidence of the wave function overlap between the buried and surface QDs.
Nutrient enrichment alters the magnitude and timing of fungal, bacterial, and detritivore contributions to litter breakdown

Microbial decomposers and metazoan detritivores rely on and affect processing rates of detrital C in streams. Nutrient enrichment can affect the biomass of both groups, but we lack a quantitative understanding of how these groups affect breakdown rates of C under elevated nutrient conditions. We quantified the relative contribution of decomposers (fungi, bacteria) and detritivores (shredders) to breakdown rates in a reference vs a nutrient-enriched stream. We measured breakdown rates and litter-associated shredder, fungal, and bacterial biomass on Rhododendron maximum L. and Acer rubrum L. and modeled the contribution of these groups over the decay sequence for R. maximum and for a single time period for A. rubrum. We used published metabolic parameters for fungi and bacteria under high- and low-nutrient conditions and shredder feeding rates based on organisms and leaf litter from our study area. Breakdown rates of both litter types were higher (∼4–5×) in the treatment than reference stream and were associated with large changes in biomass of fungi, bacteria, and shredders. Changes in microbial metabolic parameters (growth rate, growth efficiency) affected estimates of mass loss. Fungal and shredder contributions were roughly equivalent and greater than bacterial contributions under reference and treatment conditions, but nutrient enrichment accelerated the colonization sequence of fungi and shredders, and shredders had relatively greater effects on mass loss in later stages of breakdown. Our results suggest facilitation of detritivores by decomposers and large combined effects on breakdown with nutrient enrichment. Decomposer contributions could exceed those of detritivores with changes in microbial metabolic rates. Large increases in breakdown rates could be attributed to biotic response to nutrient enrichment, are sensitive to microbial metabolic rates, and have the potential to alter residence times of litter in streams.
Automatic classification of retinal OCT images based on convolutional neural network

We present an automatic classification algorithm for retinal optical coherence tomography (OCT) images based on convolution neural network (CNN). This algorithm inherently contains feature extraction and classification, thus avoiding the design feature extractor manually. Firstly, we processed the OCT images to focus on and determine the pathological area of the retinal OCT images, and to speed up the training of the network. Then we input the original images to crop them, which can effectively prevent the noise introduced in the processes of image processing and changing the pixels in the original image. Secondly, we augmented the OCT images in the source data set to obtain sufficient images, and to alleviate the impact of a relatively small number of target classification images on the model accuracy and generalization ability. Our method was introduced the random translation in image cropping and horizontal flipped to augment the OCT images. Then we applied two methods to build two data sets used to train the network, and we divided each of the data sets into a training set and a validation set. Next, we designed an efficient classification network and trained it with the two training sets respectively, to acquire the two models that can classify OCT images. The results indicate that the network trained by the augmented data can classify images more effectively. In our classification algorithm, the accuracy, the sensitivity and the specificity are 93.43%, 91.38%, and 95.88%, respectively.
Autonomous navigation for a holonomic drive robot in an unknown environment using a depth camera

The present paper explores the implementation of the RRT* path planning algorithm aided with a depth sensor in a physical robot for path planning and re-planning in a partially-known or unknown environment, the robot is capable of omnidirectional motion and aims to move from a starting location to a goal location in different environments. The proposed algorithm allows the robot to move through a map while avoiding collision by detecting unknown obstacles and updating the map for further planning and motion if required. The implementation and experimental results are presented for indoor environments with partial or non-knowledge of the environment in order to achieve autonomous navigation for a holonomic drive robot in an unknown environment using a depth camera as an optical sensing device.
Scattering electromagnetic eigenstates of a two-constituent composite where both the electric permittivity and the magnetic permeability are nonuniform

A theory is developed for calculating the electromagnetic (EM) eigenstates of Maxwell’s equations for a two-constituent composite where the magnetic permeability as well as the electric permittivity have different uniform values in the two constituents. The physical electric field E(r) produced in the system by a given source current density is expanded in this set of bi- orthogonal eigenstates for any position r. Once all these eigenstates are known for a given host and a given microstructure then calculation of E(r) only involves performing three-dimensional integrals of known functions and solving sets of linear algebraic equations.
Knowledge Sharing Challenges in Subsidiary-to-Subsidiary IT Infrastructure Outsourcing: A case study on a financial services provider

While prior studies have focused on information technology (IT) outsourcing challenges in general, those studies have not adequately addressed knowledge sharing challenges facing subsidiary-to-subsidiary IT infrastructure outsourcing relationships. This qualitative case study explores the major knowledge sharing challenges experienced in a subsidiary-to-subsidiary IT infrastructure outsourcing relationship at a large South African financial services provider. Using the knowledge-sharing success model, thematic analysis of semi structured interview data from twenty-one IT staff representing both the consumer and the IT services provider reveals three main interrelated knowledge-sharing challenges areas: service delivery quality, information sharing, and communication. The study extends upon the existing knowledge-sharing success model by including the role of management support in managing knowledge sharing challenges effectively. Future research should investigate the impact of service delivery quality, information sharing, and communication challenges in other types of IT outsourcing relationships.