Barchan dune fields are a dominant landscape feature in SE Qatar and a key element of the peninsula’s geodiversity. The migration of barchan dunes is mainly controlled by dune size, wind pattern, vegetation cover and human impact. We investigate the variability of dune migration in Qatar over a time period of 50 years using high-resolution satellite and aerial imagery and explore its relation to the regional Shamal wind system, teleconnection patterns, and limitations in sand supply associated with the transgression of the Arabian Gulf. We detect strong size-dependent differences in migration rates of individual dunes as well as significant decadal variability on a dune-field scale, which was found to be correlating with the intensity of the North Atlantic Oscillation (NAO) and the Indian Summer Monsoon (ISM). High uncertainties associated with the extrapolation of migration rates back into the Holocene, however, do not permit to further specify the timing of the loss of sand supply and the onset of the mid-Holocene relative sea-level (RSL) highstand. For the youngest phase considered in this study (2006–2015), human impact is anticipated to have accelerated dune migration under a weakening Shamal regime through sand mining and excessive vehicle frequentation upwind of the core study area.

The application of non-uniform spiral gratings to control structure, topological parameters and propagation of orbital angular momentum (OAM) beams was studied experimentally with coherent near-infrared light. Adapted digital spiral grating structures were programmed into the phase map of a high-resolution liquid-crystal-on-silicon spatial light modulator (LCoS-SLM). It is shown that characteristic spatio-spectral anomalies related to Gouy phase shift can be used as pointers to quantify rotational beam properties. Depending on sign and gradient of spatially variable periods of chirped spiral gratings (CSG), variations of rotation angle and angular velocity as a function of propagation distance were measured. Propagation-dependent self-torque is introduced in analogy to known local self-torque phenomena of OAM beams as obtained by the superposition of temporally chirped or phase modulated wavepackets.

The objective of this study was to investigate the impact of athletes' respiratory muscle strength, as assessed through maximal inspiratory (MIP) and expiratory pressure (MEP), on aerobic endurance, as indicated by maximal oxygen consump-tion (Max VO2) kinetics. Respiratory muscle strength was assessed using a digital spirometer (Pony FX Cosmed, Italy), while maximal oxygen utilization capacity was measured using a breath-by-breath automatic portable gas analysis system (Cosmed K5, Italy). Statistical analysis was conducted utilizing SPSS 22.0. Based on the standardized regression coefficients (β), it was found that MEP (mean ± SD: 130.95 ± 42.82) and inspiratory diaphragmatic circumference values significantly influenced ventilatory equivalent (VE), oxygen consumption (VO2), and carbon dioxide pro-duction (VCO2). Conversely, the remaining predictor variables did not exhibit a sig-nificant effect on VE (mean ± SD: 134.80 ± 36.69), VO2 (mean ± SD: 3877.52 ± 868.47 ml), and VCO2 (mean ± SD: 4301.27 ± 1001.07 ml). In contrast, measurements of chest circumference (mean ± SD: 91.40 ± 10.72 cm), MEP, and diaphragmatic cir-cumference during inspiration (mean ± SD: 95.20 ± 10.21 cm) were found to signifi-cantly impact Max VO2 (mean ± SD: 58.52 ± 10.74 ml/kg/min), while the other pre-dictor variables did not demonstrate a significant effect on Max VO2. Conclusively, the study revealed that measured values of diaphragmatic circumference during inspiration and MEP exerted a notable influence on Max VO2, VE, VO2, and VCO2. Our findings underscore the importance of considering respiratory muscle strength in assessing and enhancing athletes' aerobic performance. These insights contribute to a deeper understanding of the interplay between respiratory function and exercise capacity, offering potential avenues for optimizing training regimens and perfor-mance outcomes in athletic contexts.

Recognition, decoding and tracking of vortex patterns is of increasing importance in many fields reaching from astronomical observation of distant galaxies to turbulence phenomena in liquids or gases. Currently, coherent light beams with orbital angular momentum (OAM) are of particular interest for optical communication, metrology, micro-machining or particle manipulation. One common task is to identify characteristic spiral patterns in pixelated intensity maps at real-world signal-to-noise ratios. Recently, Fast Fourier Transform (FFT) was applied to the quantitative analysis of spiral interference patterns of OAM beams. Our study considers this approach in more detail. It is demonstrated that specific information can be extracted by combining conformal mapping by polar transform with determining one- or two-dimensional angular frequency spectra either in spatially fixed or scanning mode. Results of proof-of-principle experiments are presented.

This paper proposes two max-pooling engines, named the RTB-MAXP engine and the CMB-MAXP engine, with a scalable window size parameter for FPGA-based convolutional neural network (CNN) implementation. The max-pooling operation for the CNN can be decomposed into two stages, i.e., a horizontal axis max-pooling operation and a vertical axis max-pooling operation. These two one-dimensional max-pooling operations are performed by tracking the rank of the values within the window in the RTB-MAXP engine and cascading the maximum operations of the values in CMB-MAXP engine. Both the RBM-MAXP engine and the CMB-MAXP engine were implemented using VHSIC Hardware Description Language (VHDL) and verified by simulations. They have been employed for and tested in our CNN accelerator targeting at the CNN model YOLOv4-CSP-S-Leaky for object detection.

In this article various heuristic approaches are discussed to solve the dark matter phenomenon by the concept of vacuum polarization. They are compared with a more fundamental approach, based upon an entropy model of the visible universe. They all make use of some kind of gravitational dipole. These dipoles seem to violate Einstein’s equivalence principle between inertial mass and gravitational mass. It is shown how the paradox can be solved by a quantum mechanical principle.

A structure based analysis of the pion’s decay path reveals that neutrinos can show up in three eigenstates. It requires a proper understanding of the nature of charged leptons, such as why the loss of binding energy stops the lepton generation at the tauon level. The analysis quantifies this binding energy in terms of the weak interaction strength as embodied by the weak interaction boson and the strength of the energetic background field as embodied by the Higgs boson. Next to this it is shown that a reconstruction of Fermi’s neutrino theory allows a quantitative assessment of the Fermi constant straightforwardly from the weak interaction strength. The article ends with the conclusion that neutrino oscillation is not a physical phenomenon, but, instead, a measurement interpretation induced from projecting the statistical behaviour of a multi-particle ensemble onto a single particle.

It is shown that the relationship between gravity and quantum physics can be described in terms of the symmetry break of space due to elementary constituents, dubbed as “darks”, which constitute a universal energetic background field that extends from the cosmological level down to the nuclear level. It requires (a) the awareness of the polarisable second elementary dipole moment of a recently discovered third Dirac particle type, next to the electron-type and the Majorana-type, and (b) the awareness that Einstein’s Lambda is not a constant of nature, but, instead, a covariant integration constant with a value that depends on the scope of the cosmological system under consideration, such as solar systems and galaxies, eventually showing up as the Cosmological Constant at the level of the universe. The relationship has been made explicit by relating two major gravitational constants quantaties (the gravitational constant and Milgrom’s acceleration constant) with a single nuclear quantity (the rest mass of the pion derived from the Higgs boson value).

It is shown that gravity and quantum physics can be unified upon the basis of a quark description in terms of a gravitational Dirac particle. It requires the awareness of a second elementary dipole moment (isospin) next to the angular moment (spin) of Dirac particles and the awareness of an (unbroken) omnipresent energetic cosmological background field. The unification has been made explicit by an expression that relates the two major gravitational constants of nature (the gravitational constant and Milgrom’s acceleration constant) with the two major nuclear constants of nature (the weak interaction boson and the Higgs boson).

Conceiving vacuum energy as gravitational particles subject to Heisenberg’s energy-time uncertainty, modelled as dipoles in a fluidal space at thermodynamic equilibrium, and interpreting the Bekenstein-Hawking entropy as the effective amount of spins of those dipoles enclosed within the event horizon of the universe, allows the calculation of Milgrom’s acceleration constant. The result is a quantum mechanical interpretation of gravity, and dark matter in particular.

In this article the possible impact on the present state of particle physics theory is discussed of two unrecognized theoretical elements. These elements are the awareness that (a) the quark is a Dirac particle with a polarisable dipole moment in a scalar field and that (b) Dirac’s wave equation for fermions, if derived from Einstein’s geodesic equation, reveals a scaling theorem for quarks. It is shown that recognition of these elements proves by theory quite some relationships that are up to now only empirically assessed, such as for instance, the mass relationships between the elementary quarks, the relationship between the bare mass and the constituent mass of quarks, the mass spectrum of hadrons and the mass values of the Z boson and the Higgs boson.

A calculation is presented for the mass relationships between electron, muon and the tau particle. It is shown that charged leptons with mass levels beyond the one of the tau particle cannot exist because of insufficient binding energy. The underlying mechanism of the relationship between the leptons is the isospin feature of Dirac particles, which appears being a substantial attribute next to nuclear angular spin. It is further shown that the energy of the elementary electric charge, such as embodied by the mass of an electron, is due to the interaction between the isospins of the pion’s quark and antiquark.

In an historic perspective on the development of the Standard Model of particle physics it is shown how mathematically driven axioms have masked the merits of a physically comprehensible structural view. It is concluded that the difference between the two approaches can be traced back to two major issues. Whereas in the Standard Model the quark is a Dirac particle with a single real dipole moment, the quark in the structural model, in confinement with other quarks, is a Dirac particle with two real dipole moments. The second issue is the view that empty space does not exist, but that space is filled with a polarisable energetic fluid. It is shown how recognition of these two issues pave a road to reconcile particle physics with gravity, in which the quark can be seen as a magnetic electron and in which the gluon, as the strong force carrier, can be seen as a massive photon.

Starting from an overview of neutrino problems and a simplified survey of Fermi’s neutrino theory, it is shown why neutrinos are left-handed and why they seem to show an oscillatory behaviour between their flavours. After addressing the question how to assess the naked mass of the true elementary particles, it is hypothesized that the elementary constituents of the nuclear background energy and the cosmological background energy are the same. This allows to derive the magnitude of the quark’s “naked” mass from the polarization of the vacuum.

It is shown that the Lambda component in the cosmological Lambda-CDM model can be conceived as vacuum energy, consisting of gravitational particles subject to Heisenberg’s energy-time uncertainty. These particles can be modelled as elementary polarisable Dirac-type dipoles (“darks”) in a fluidal space at thermodynamic equilibrium, with spins that are subject to the Bekenstein-Hawking entropy. Around the baryonic kernels, uniformly distributed in the universe, the spins are polarized, thereby invoking an increase of the effective gravitational strength of the kernels. It explains the dark matter effect of galaxies to the extent that a numerical value of Milgrom’s acceleration constant can be assigned by theory. Non-polarized vacuum particles beyond the baryonic kernels compose the dark energy at the cosmological level. The result is an interpretation of gravity at the quantum level in terms of quantitatively established shares in baryonic matter, dark matter and dark energy, which correspond with the values of the Lambda-CDM model.

Biohydrogen is a versatile energy carrier for the generation of electric energy from renewable sources. Hydrogenases can be used in enzymatic fuel cells to oxidize dihydrogen. The rate of electron transfer (ET) at the anodic side between the [NiFe]-hydrogenase enzyme distal iron-sulfur cluster and the electrode surface can be described by the Marcus equation. All parameters for the Marcus equation are accessible from DFT calculations. The distal cubane FeS-cluster has a three cysteine and one histidine coordination [Fe₄S₄](His)(Cys)₃ first ligation sphere. The reorganization energy (inner- and outer-sphere) is almost unchanged upon a histidine-to-cysteine substitution. Differences in rates of electron transfer between the wild-type enzyme and the all-cysteine mutant can be rationalized by a diminished electronic coupling between the donor and acceptor molecules in the [Fe₄S₄](Cys)₄ case. The fast and efficient electron transfer from the distal iron-sulfur cluster is realized by a fine-tuned protein environment which facilitates the flow of electrons. This study enables the design and control of electron transfer rates and pathways by protein engineering.

In a search for a solution of the galaxy rotation problem, a novel view on cosmological gravity is developed. In this view, the origin of gravity is traced back to the basic nuclear field of energy, spread by quarks as elementary pointlike sources. This gives a common basis for gravity, electromagnetism, the strong nuclear force and the weak nuclear force. After a review on gravimagnetism and the causality problem of gravity, a hypothetical concept for the gravity field is proposed, which solves the rotation problem without the need to accept dark matter as the deus ex machina.

Knowledge of the genetic diversity of the available germplasm is an important foundation for crop conservation, management, and improvement. Successful breeding strategies rely on a sound understanding of the genetic diversity of the crop germplasm. This study was conducted to evaluate and compare the genetic diversity of 49 soybean [Glycine max (L.) Merrill] genotypes using SSR markers as well as to evaluate parental potential of these genotypes in cultivar improvement. The bulk of the genotypes were developed in the national soybean breeding program in Zimbabwe between 1940 and 2013. The genetic diversity and relatedness were estimated using 30 SSR markers and produced 5 clusters that generally matched the pedigree information of the germplasm. The SSR analysis detected a total of 135 alleles. The polymorphic information content ranged from 0.10 to 0.77 with a mean of 0.45. The SSR primer, Satt012 was the most informative. The SSR marker analysis showed a wide genetic diversity in the germplasm. To date, there are no reports regarding the genetic diversity analysis of soybean germplasm in Zimbabwe. The genotypes, EL41 and EL7; EL41 and EL1; as well as EL41 and EL42 were the most divergent thus, indicating their potential use as parental lines in the genetic improvement of soybean in the region.

This dissertation is a rigorous study of ecology and macrocellular biology as a subfield of abstract algebra. We begin with the creation of an axiomatic paradigm, then move onto constructing a universal genetic code of biology. We use this to define increasingly complex algebraic structures (ecosystem, evolving populations, etc.). We prove a variety of theorems regarding to the members of the previous mathematical constructs, notably the following three: 1. There is one unique phenotypic representation of each organism. For example, if you subdivide any piece of genetic code into its phenotypic components, then two identical organisms have identical decomposed DNA 2. There are a finite number of indivisible phenotypic traits. 3. The three sophioid-definitions are equivalent: (a) dynamical evolutionary enlargement of the medial temporal lobe and frontal lobe, (b) reliance upon intelligence, (c) the existence of an intellectually- or socially-hierarchical society. Much has yet to be done on this work, but as a first draft, it stands as a jumping point for future exploits; I am working on an entirely revised second draft.

The mass of the nucleons is calculated from first principles by defining the quark as a non-gravitational particle, subject to Dirac’s equation with non-canonical gamma matrices. Unlike the canonical electron-type, this Dirac particle has two real dipole moments, which allows a structural modelling of hadrons. It is shown how such modelling reveals striking correspondences and differences between dually related particles and properties like electrons and quarks, photons and gluons, pions and nucleons, spin and isospin and protons and neutrons. It is a stepping stone to the actual calculation, which competes in precision with lattice QCD. The appendix contains a Lorentz covariance proof of hadrons composed by such particular Dirac-type quarks.

In the cone of positive quadratic forms C^{8 x 8}, it is shown that there exists in the neighbourhood of the quadratic form Q_{F_{22}} a large cluster of non-equivalent \Sigma_0-subcones of positive volume which are as well minimal as maximal.

It is shown that the four fundamental physical forces, i.e. weak interaction, strong interaction, electromagnetism and gravity, all have their origin in the quark as the single true elementary particle.This requires conceiving the quark as a Dirac particle in a pseudo-tachyon mode, which possesses two real dipole moments: the common one associated with its angular momentum and a second one that is polarisable in a scalar field. This Dirac particle carries a regular charge magnetic monopole without Dirac’s string, theorized by Comay. The boson carrier of its field of energy is the gluon showing an exponential decay of its spatial range because of the influence of an omni-present energetic background field, known as the Higgs field, in this article interpreted as the Lambda in Einstein’s Field Equation.

An analysis is presented of the possible existence of a second anomalous dipole moment of Dirac’s particle next to the one associated with the angular momentum. It includes a discussion why, in spite of his own derivation, Dirac has doubted about its relevancy. It is shown why since then it has been overlooked and why it has vanished from leading textbooks. A critical survey is given on the reasons of its reject, including the failure of attempts to measure and the perceived violations of time reversal symmetry and charge-parity symmetry. It is emphasized that the anomalous electric dipole moment of the pointlike electron (AEDM) is fundamentally different from the quantum field type electric dipole moment of an electron (eEDM) as defined in the standard model of particle physics. The analysis has resulted into the identification of a third type Dirac particle, next to the electron type and the Majorana particle. It is shown that, unlike as in the case of the electron type, its second anomalous dipole moment is real valued and is therefore subject to polarization in a vector field. Examples are given that it may have a possible impact in the nuclear domain and in the gravitational domain.

It is shown that the Lambda component in the cosmological Lambda-CDM model can be conceived as vacuum energy, consisting of gravitational particles subject to Heisenberg’s energy-time uncertainty. These particles can be modelled as elementary polarisable Dirac-type dipoles (“darks”) in a fluidal space at thermodynamic equilibrium, with spins that are subject to the Bekenstein-Hawking entropy. Around the baryonic kernels, uniformly distributed in the universe, the spins are polarized, thereby invoking an increase of the effective gravitational strength of the kernels. It explains the dark matter effect to the extent that the numerical value of Milgrom’s acceleration constant can be assessed by theory. Non-polarized vacuum particles beyond the baryonic kernels compose the dark energy. The result is a quantum mechanical interpretation of gravity in terms of quantitatively established shares in baryonic matter, dark matter and dark energy, which correspond with the values of the Lambda-CDM model..

An analysis is presented of the possible existence of a second anomalous dipole moment of Dirac’s particle next to the angular one. It includes a discussion why, in spite of his own derivation, Dirac has doubted about its relevancy. It is shown why since then it has been overlooked and why it has vanished from leading textbooks. A critical survey is given on the reasons of its reject, including the failure of attempts to measure and the perceived violations of time reversal symmetry and chargeparity symmetry. It is emphasized that the anomalous electric dipole moment of the pointlike electron (AEDM) is fundamentally different from the quantum field type electric dipole moment of an electron (eEDM) as defined in the standard model of particle physics and that its measurement requires different instrumentation. A proposal has been described how to prove or disprove its existence by experiment. Moreover, by reference from literature, the possible impact is discussed in the nuclear domain and in the gravitational domain.

In this article it is shown how Milgrom’s acceleration constant is related with the Ω_Λ, Ω_m and Ω_B values of the matter distribution within observable universe as defined in the Standard Model of Cosmology, established by the Planck Collaboration Group. The result is a two-parameter model for the observable universe. The two parameters are the age of the universe and Milgrom’s acceleration constant. It is shown that these are sufficient to calculate the amounts of matter and dark energy in the universe, as well as the contributions of dark matter and baryonic matter in the matter part. The numerical results are in agreement with those of the Lamda-CDM model.

By solving the weak field limit of Einstein’s Field Equation including the Cosmological Constant, under the constraint of spherical isotropy, it is shown that, at large cosmological distance, the gravitational force exceeds the one that is predicted by Newton’s gravity law, such that it corresponds with Milgrom’s MOND hypothesis. However, the resulting prediction that, at extremely large distances, gravity with some spatial periodicity turns on-and-off into antigravity marks a decisive difference.

In this article a simplified view is given on Verlinde’s entropic gravity. It reveals several shortcomings in the theory. This holds in particular for the inclusion of the dark matter effect in the derived modified expression for the Newtonian gravitational acceleration. Although the merit of the entropic view on gravity is recognized, the proposed explicit axiomatic hypothesis as a basis is criticized. There is no reason why it should be preferred above Einstein’s Field Equation.

It is shown that Verlinde’s hypothetical concepts of entropic gravity can be successfully applied to the author’s previous work on the modification of the Newtonian gravity by Einstein’s Cosmological Constant. It allows a theoretical justification for Milgrom’s empirical acceleration constant, thereby revealing some fundamental differences with Verlinde’s conclusions.

A structure-based view on mesons is given, based upon the concept of an archetype quark, described as a pointlike source producing an energy flux, the spatial description of which is derived from Dirac’s second dipole moment. This enables to conceive the archetype meson (pion) as a structure that behaves as a one-body anharmonic quantum mechanical oscillator. All mesons appear being excitations of the archetype, thereby allowing a calculation of the mass spectrum without the use of empirical parameters for the masses of the quark flavors. This includes a physically comprehensible analysis of the spin-spin interaction between quarks. It also provides a solution for the eta-etaprime puzzle. Next to this, it is shown that quite some particles that are presently regarded as elementary, have a common root and can be traced back to a few archetypes only.

The Mononuclear Phagocyte System includes monocytes, macrophages, some dendritic cells and multinuclear giant cells. These cell populations display marked heterogeneity depending on their differentiation from embryonic and bone marrow haematopoietic progenitors, tissue location and activation. They contribute to tissue homeostasis by interacting with local and systemic immune and non-immune cells, through trophic, clearance and cytocidal functions. During evolution they contributed to innate host defense before effector mechanisms of specific adaptive immunity emerged. Mouse macrophages appear at midgestation and are distributed throughout the embryo to facilitate organogenesis and clear cells undergoing programmed cell death. Yolk sac-, AGM- and foetal liver-derived tissue resident macrophages persist throughout postnatal and adult life, supplemented by bone marrow-derived blood monocytes, as required after injury and infection. Nobel awards to Elie Metchnikoff and Paul Ehrlich in 1908 drew attention to cellular phagocytic and humoral immunity, respectively. In 2011, prizes were awarded to Jules Hoffmann and Bruce Beutler for contributions to innate immunity and to Ralph Steinman for discovery of Dendritic Cells and their role in antigen presentation to T lymphocytes. We trace milestones in the history of mononuclear phagocyte research from the perspective of Nobel awards bearing directly and indirectly on their role in cellular immunity.

Genetic testing has expanded out of the research laboratory into medical practice and the direct-to-consumer market, and rapid analysis of the resulting genotype data can now have significant impact. We present a method for summarizing personal genotypes as ‘genotype fingerprints’ that meet these needs. Genotype fingerprints can be derived from any single nucleotide polymorphism (SNP)-based assay, and remain comparable as chip designs evolve to higher marker densities. We demonstrate that they support distinguishing types of relationships among closely related individuals and closely related individuals from individuals from the same background population, as well as high-throughput identification of identical genotypes, individuals in known background populations, and de novo separation of subpopulations within a large cohort through extremely rapid comparisons. While fingerprints do not preserve anonymity, they provide a useful degree of privacy by summarizing a genotype in a way that prevents reconstruction of individual marker states. Genotype fingerprints are therefore well-suited as a format for public aggregation of genetic information to support ancestry and relatedness determination without revealing personal health risk status.

Alteration of land use and climate change are among the main variables affecting watershed hydrology. Characterizing the impacts of climate variation and land use alteration on water resources is essential in managing watersheds. Thus, in this research, streamflow and baseflow responses to climate and land use variation were modeled in two watersheds, the Upper West Branch DuPage River (UWBDR) watershed in Illinois and Walzem Creek watershed in Texas. The variations in streamflow and baseflow were evaluated using the Soil and Water Assessment Tool (SWAT) hydrological model. The alteration in land use between 1992 and 2011 was evaluated using transition matrix analysis. The non-parametric Mann-Kendall test was adopted to investigate changes in meteorological data from 1980-2017. Our results indicated that the baseflow accounted for almost 55.3% and 33.3% of the annual streamflow in the UWBDR and Walzem Creek watersheds, respectively. The contribution of both land use alteration and climate variability on the flow variation is higher in the UWBDR watershed. In Walzem Creek, the alteration in streamflow and baseflow appears to be driven by the effect of urbanization more than that of climate variability. The results reported herein are compared with results reported in recent work by the authors in order to provide necessary information for water resources management planning, as well as soil and water conservation, and to broaden the current understanding of hydrological components variation in different climate regions.

The estuarine exchange flow increases longitudinal dispersion of passive tracers and traps sinking particles, potentially creating an estuarine turbidity maximum (ETM): a localized maximum of suspended particulate matter concentration in an estuary. The ETM can have many implications - dead zones due to increased turbidity or hypoxia from organic matter decomposition, naval navigation challenges, and other water quality problems. Using timescales, we investigate how the interaction between exchange flow and particle sinking leads to ETMs by modeling a sinking tracer in an idealized box model of the Total Exchange Flow (TEF) first developed by Parker MacCready. Results indicate that the balance of particle sinking and vertical mixing is critical to determining ETM size and location. We then focus on the role of ecology in ETM formation through the use of The Peter-Parker Model: a new biophysical model which combines the TEF box model with a Nutrient-Phytoplankton-Zooplankton-Detritus (NPZD) model, the likes of which were first developed by Peter J.S. Franks. Detritus sinking rates similarly influence detritus peak concentration and location (an ETM), but detritus ETMs occur in a different location than the sinking tracer due to the influence of the biological factors which create a time lag of about 1 day. Lastly, we characterize the flow of the models with a dimensionless parameter that compares timescales and summarizes the dynamics of the sinking tracer in ETM formation and can be used across systems.

In this paper, we consider a manufacturer who produces and sells a kind of innovative product in the monopoly market environment. Because the life cycle of innovative product is usually shorter than its procurement lead time, one unique demand quantity (scenario) will occur in the selling season, thus there is only one chance for the manufacturer to determine both optimal production quantity and optimal sale price. Considering this one-time feature of such a decision problem, a price-setting newsvendor model for innovative products is proposed. Different to the existing price-setting newsvendor models, the proposed models determine the optimal production quantity and sale price based on some specific state (scenario) which is most applicable for the manufacturer. The theoretical analysis provides managerial insights into the manufacturers’ behaviors in a monopoly market of an innovative product and several phenomena in the luxury goods market are well explained.

This study addresses the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

Computer vision models of salient object detection attempt to mimic the ability of the human visual system to select relevant objects in images. To this end, the development of deep neural networks on high-end computers has recently made it possible to achieve high performance. However, it remains a challenge to develop deep neural network models of the same performance for devices with much more limited resources. In this work, we propose a new approach for a lightweight salient object detection neural network model, inspired by the cone and spatial opponent processes of the primary visual cortex (V1), that inextricably link color and shape in human color perception. Our proposed model, namely CoSOV1net, is trained from scratch, without using backbones from image classification or other tasks. Experiments, on the most widely used and challenging datasets for salient object detection, show that CoSOV1Net achieves competitive performance (i.e. Fβ=0.931 on the ECSSD dataset) with state-of-the-art salient object detection models, while having low number of parameters (1.14M), low FLOPS (1.4G) and high FPS (211.2) on GPU (nvidia Geforce RTX 3090 TI) compared to the state-of-the-art in the salient object detection or lightweight salient object detection task. Thus, CoSOV1net turns out to be a lightweight salient object detection that can be adapted to mobile environments and resource-constrained devices.

Additive Friction Extrusion Deposition (AFED) is a recently developed additive manufacturing technique that promises high deposition rates at low forces. Due to the novelty of the process, the underlying phenomena and their interactions are not fully understood, and in particular the processing strategy and tool design are still in their infancy. This work contributes to the state of the art of AFED through a comprehensive analysis of its working principles and an experimental program including a representative sample component. The working principle and process mechanics of AFED are broken down into their individual components. Forces, their origins and effects on the process are described, and measures of process efficiency and theoretical minimum energy consumption are derived. Three geometrical features of the extrusion die are identified as most relevant to the active material flow, process forces and deposition quality: the topography of the inner and outer circular surfaces and the geometry of its extrusion channels. Based on this, the experimental program investigates seven different tool designs in terms of efficiency, force reduction and throughput. The experiments using AA 6061-T6 as feedstock show that AFED is capable of both high material throughput (close to 550 mm³/s) and reduced substrate forces: For example, the forces for a run at 100 mm³/s remained continuously below 500 N, and for a run at 400 mm³/s below 3500 N. Material flow and microstructure of the AFED are assessed from the macrosections. Significant differences are found between the advancing and retracting sides for both process effects and material flow. Banded structures show strong similarities to other solid state processes. The fabrication of the sample components demonstrates that AFED is already capable of producing industrial grade components. In mechanical testing, interlayer bonding defects result in a more brittle failure behavior in the direction of the structure, in the microstructure show strong similarities to other solid-state processes. The manufacturing of the sample components demonstrates that AFED is already capable of producing industrial grade components. In mechanical tests, interlayer bonding defects result in a more brittle failure behavior in the direction of the structure, while in the horizontal direction of the structure mechanical properties corresponding to a T4 temper have been achieved.

Plant growth promoting rhizobacteria (PGPR) are a diverse group of microorganisms including genera such as Pseudomonas, Bacillus, Serratia, Enterobacter and Klebsiella. These bacteria have the ability to colonize roots externally and internally, and to directly enhance plant growth employing various mechanisms like phytohormone production and nutrient mineralization. These microbes can also promote plant growth indirectly by secreting antimicrobial compounds (e.g., siderophores, hydrogen cyanide) to antagonize root pathogens or by inducing defensive responses in the plant. Interestingly, there are also cases where production of antibiotic compounds such as 2,4-Diacetylphloroglucinol have the direct effect of enhancing plant growth and yield, irrespective of their biocontrol effects. The objective of this paper is to review publications showcasing PGPRs that have the capacity to both control root pathogens and stimulate plant growth, and to discuss what mechanisms might be involved.

The importance of detecting and diagnosing the imminence of aneurysm rupture before it occurs cannot be overstated. However, this process is often time-consuming and challenging, with variability in conclusions not uncommon among experts. Furthermore, current state-of-the-art models in medical image segmentation suffer from limited availability of large annotated datasets for training, exacerbated by an immense data imbalance. In most segmentation tasks, 3D segmentation is preferred due to its capacity to leverage more contextual information. However, these models require more data, which is constrained by the aforementioned issues. In the realm of current diagnosis models, those lacking geographic context are notably ineffective. To address these issues, the aim is to develop a model pipeline to accurately detect cerebral intracranial aneurysms and diagnose rupture imminence through 3D magnetic resonance angiography (MRA) and tabular input, augmented with the implementation of neuro-symbolic ai. The utilization of data fusion to tackle the lack of context in tabular models is proposed, leveraging extracted geographical features from a segmentation model. To enhance segmentation results and tackle the data imbalance in aneurysm segmentation, a two-stage model pipeline that extracts contextual geographic features before aneurysm segmentation is suggested. Segmentation results are additionally improved through ensemble techniques and novel preprocessing techniques. Finally, a neuro-symbolic aspect is introduced to enhance model diagnosis, interpretability and performance. The multi-step Integrated Neuro-Symbolic Analysis Pipeline for Cerebral Aneurysm Rupture Diagnosis is introduced.

Monkeypox is a zoonotic illness caused by the Orthopoxvirus monkeypox virus (MPXV). Since 1970, outbreaks of MPXV have occurred in several Sub-Saharan African countries. However, from May 2022 to April 2023, recent outbreaks of MPXV occurred in several countries outside of Africa, and these cases quickly spread to over 100 non-endemic countries on all continents. Because of this, in July 2022, World Health Organization declared monkeypox a Public Health Emergency of International. MPXV disproportionately affects men who have sex with men and members of the HIV-infected population. The current strategy for control and pre-exposure prophylaxis or post-exposure prophylaxis for people at high risk is vaccination. In this context, Peru has the fourth-highest number of MPXV cases in Latin America. Because of this, in this review, we describe public health indicators in Peru and reflect on the COVID-19 pandemic so that health authorities can join forces to identify and control MPXV transmission routes.

The change in both streamflow and baseflow in urban catchments has received significant attention in the latest decades as a result of their drastic variability. In this research, effects of climate variation and dynamics of land use are measured separately and in combination on streamflow and baseflow in the Little Eagle Creek (LEC) watershed (Indianapolis, Indiana). These effects are examined using land use maps, statistical tests, and hydrological modeling. Transition matrix analysis was used to investigate the change in land use between 1992 and 2011. Temporal trends and changes in meteorological data were evaluated from 1980-2017 using the Mann-Kendall test. Changes in streamflow and baseflow were assessed using the Soil and Water Assessment Tool (SWAT) hydrological model using multiple scenarios that varied in land use and climate change. Evaluation of the model outputs showed streamflow and baseflow in LEC are well represented using SWAT; however, comparing the calibration and validation period showed SWAT performs better for the calibration. During 1992-2011, roughly 30% of the watershed experienced change, typically cultivated agricultural areas became urbanized. Baseflow is significantly affected by the observed urbanization; however, the combination of land and climate variability has a larger effect on the baseflow in LEC. Generally, the variability in the baseflow and streamflow appears to be heavily driven by the response to climate change in comparison to variability due to altered land use. The results reported herein expand the current understanding of variation in hydrological components, and provides useful information for management planning regarding water resources, as well as water and soil conservation in urban watersheds in Indiana and beyond.

Vine growth habit (VGH) is a notable property of wild soybean plants that also holds a high degree of importance in the context of domestication as it can preclude the use of these wild cultivars for the breeding and improvement of domesticated soybean. Here, a bulked segregant analysis (BSA) approach was employed to study the genetic etiology of VGH in soybean plants by integrating linkage mapping and population sequencing approaches. To develop a recombinant inbred line (RIL) population, the cultivated Zhongdou41 (ZD41) soybean cultivar was bred with ZYD02787, a wild soybean accession. The VGH status of each line in the resultant population was assessed, ultimately leading to the identification of 6 and 9 QTLs from the BSA sequencing of the F4 population and F6- F8 population sequence mapping, respectively. One QTL shared across these analyzed generations was detected on chromosome 19. Three other QTLs detected by BSA-seq were validated and localized to 90.93 kb, 2.9 Mb, and 602.08 kb regions of chromosomes 6 and 13, respectively harboring 14, 53, and 4 genes. Four consistent VGH-related QTLs located on chromosomes 10, 13, and 19 were detected by both analytical approaches in a minimum of two environments, while an additional five loci on chromosomes 2, 10, and 18 were detected in at least two environments only via ICIM mapping. Of the detected loci, five had been reported previously whereas six represent novel QTLs. Together, these data offer new insight into the genetic basis for VGH in soybean plates, providing a rational basis to inform the use of wild accessions in future breeding efforts.

Real-time tracking is one of the most challenging problems in computer vision. Most Transformer-based trackers usually require expensive computational and storage power, which leads to the inability of these robust trackers to achieve real-time performance in resource-constrained devices. In this work, we propose a lightweight tracker, AnteaTrack. To localize the target more precisely, we develop a scaling-invariant max-filtering operator employing a sliding window combined with local max-pooling, which filters out the suspected target from the feature and performs an augmented representation while suppressing the background. For a more tight bounding box, we employ Pixel-Shuffle to increase the resolution of the feature map and get a more fine-grained representation of the target. In addition, AnteaTrack can run in real-time at 47 frames per second(FPS) on the CPU. We tested AnteaTrack on 5 datasets, and a large number of experiments have shown that AnteaTrack provides the most efficient solution compared to the same type of CPU real-time trackers. The code will be available at https://github.com/cnchange/AnteaTrack.

Vaccines decrease morbidity and mortality. Nevertheless, their benefits depend on public response. During COVID-19, vaccine hesitancy and refusal were rampant, threatening public health. A thorough understand-ing of opponents' arguments is required to address the diffusion of unreliable information on social media and prevent vaccine hesitancy from developing into vaccine refusal. Accordingly, the article examines vac-cine opponents' justifications on social media. Textual content analysis of reader comments on three health-related Israeli Facebook pages was conducted. Data collection encompassed the Israeli COVID-19 vaccination period from October 2020 to May 2022. The comments were analyzed according to the health beliefs model (HBM). We found that vaccine opponents were characterized by low perceptions of the severity of the disease combined with high perceptions of the damages of the vaccine; low perceived benefits of vac-cine compliance; vaccine hesitancy and fear along with public distrust as barriers to change; and call for ac-tion to resist the vaccine and spread related anti-establishment views on the web. As vaccination hesitancy was found to develop into public distrust in the state systems and escalate into conspiracy beliefs and an-ti-vaccination activism, its early detection may prevent future vaccine resistance.

The high radio frequency polarization imaging of non-thermal emission from active galactic nuclei (AGN) is a direct way to probe the magnetic field strength and structure in the immediate vicinity of supermassive black holes (SMBHs) and is crucial in testing the jet-launching scenario. To explore the the magnetic field configuration at the base of jets in blazars, we took advantage of the full polarization capabilities of the GMVA (Global Millimeter VLBI Array). With an angular resolution of ~50 micro-arcseconds at 86 GHz, one could reach scales down to ~900 Schwarzschild radii (for a 10^9 solar mass black hole). We present here the preliminary results of our study on the blazar BL Lac. Our results imply that on sub-mas scales the core and central jet of BL Lac is significantly polarized with two distinct regions of polarized intensity. We also noted a great morphological similarity between the 7mm/3mm VLBI images at very similar angular resolution.

The German wine sector has encountered new challenges in water management recently. To manage water resources responsibly, it is necessary to understand the relationship between the input of water and the output of wine, in terms of quantity and quality. The objectives of this study are to examine water use at the farm scale at three German wineries, and to develop and apply, for the first time, a quality-based indicator. Water use is analyzed in terms of wine production and wine-making over three years. After the spatial and temporal boundaries of the wineries and the water flows are defined, the farm water productivity indicator is calculated to assess water use at the winery scale. Farm water productivity is calculated using the AgroHyd Farmmodel modeling software. Average productivity on a quantity basis is 3.91 L wine per m3 of water. Productivity on a quality basis is 329.24 °Oechsle per m3 of water. Water input from transpiration for wine production accounts for 99.4–99.7% of total water input in the wineries, and, because irrigation is not used, precipitation is the sole source of transpired water. Future studies should use both quality-based and mass-based indicators of productivity.

The extensive use of polypropylene (PP) in various industries has heightened interest in devel-oping efficient methods for recycling and optimising its mixtures. This study focuses on formu-lating predictive models for the Melt Flow Rate (MFR) and shear viscosity of PP blends. The in-vestigation involved characterising various grades, including virgin homopolymers, copolymers, and post-consumer recyclates, in accordance with ISO 1133 standards. The research examined both binary and ternary blends, utilising traditional mixing rules and symbolic regression to predict rheological properties. High accuracy was achieved with the Arrhenius and Cragoe models, at-taining R² values over 0.99. Symbolic regression further enhanced these models, offering signif-icant improvements. To mitigate overfitting, empirical noise and variable swapping were in-troduced, increasing the models' robustness and generalisability. The results demonstrated that the developed models could reliably predict MFR and shear viscosity, providing a valuable tool for improving the quality and consistency of PP mixtures. These advancements support the de-velopment of recycling technologies and sustainable practices in the polymer industry by opti-mising processing and enhancing the use of recycled materials.

Ransomware constitutes a distinctive category of pernicious software that sequesters a user's digital assets by encryption, holding them hostage until a sum is extorted from the victim. These incursions have escalated to become among the most prevalent and significant threats confronting both individuals and corporate entities. In combatting this virulent program, dynamic analysis has been established as the favored detection modality. Such analyses typically hinge on Windows API calls, the conduits through which programs requisition services from the operating system. Yet, the superfluous and unrelated Windows API calls interjected by adversaries into the execution stream of suspect binaries precipitate an excessively noisy behavioral sequence, which impairs the performance of counter-ransomware mechanisms. The research outlined herein introduces a novel non-signature-based detection paradigm that harnesses efficacious Windows API call sequences through supervised machine learning strategies. An innovative Enhanced Min Max (EmRmR) filter technique is proposed, aiming to purge noisy features and isolate the most indicative feature subset that encapsulates the ransomware's true behavior. The EmRmR method, diverging from the traditional Min Max approach, circumvents the superfluous calculations that are a hallmark of the conventional algorithms, thereby necessitating a reduced number of evaluations. Additionally, a refinement procedure has been integrated to contract the program's call trace volume by discarding those Windows API calls lacking a robust correlation with ransomware's pivotal behavior. Subsequent to rigorous experimental analyses and juxtaposition with extant behavior-based detection methodologies, the proposed strategy has demonstrated its efficacy in differentiating ransomware behavior, delivering high detection precision alongside a diminution in false-positive occurrences.

Community peer review is a method that extends the ethics of consent into scientific practices. It gives communities affected by scientific research the ability to determine whether research may cause them harm and be part of determining how knowledge should best circulate to reduce or eliminate that harm. This paper introduces the method of community peer review by first looking at the concepts of consent and refusal, then outlining the steps to community peer review, using a case study of community meetings on a study of plastic ingestion by fish to elucidate the details of each step. Steps include: hiring a community member to the team; researching the social, cultural, and economic contexts of the community; identify the community; ensure skills for community conversation are in place; call the community meeting; conduct the community meeting; and analyze feedback for consent and refusal. Community peer review is premised on the idea that research is not inherently good and can cause harm, and that the best people to know whether and what kinds of harms are likely to occur are community members rather than researchers. The second premise is that the researcher’s “right” to research never supersedes a community’s right to not be harmed.

Research Question: To what extent is local flora influenced by ornithogenic soil biochemical-composition in the South Sandwich Islands, with special attention given to Zavodovski, and what are the down-stream effects on the geology of the islands? Hypothesis: Areas supplied by fluvial run-off from penguin colonies, especially crˆeches, host the most productive and species rich floral landscapes, even after controlling for geothermal activity.

We describe a novel machine that uses the greater-than-100% efficiency of air/ground-source heat pumps to recursively generate electricity via steam-powered generators, taking thermal energy from the ambient environment to convert into electrical power. The invention of a machines that generate clean power at low costs will be fundamental to the future of electricity. We estimate efficiency and calculate minimum efficiencies for unknown parts to consider the applicability of this machine to the real world.

DiFX is a software VLBI correlator currently adopted by several main correlation sites around the globe. After the launch of the RadioAstron Space-VLBI mission in 2011, an extension was necessary to handle processing of an orbiting antenna, to be correlated with supporting ground arrays. We present here a branch of the main DiFX distribution (2.4), recently uploaded on the publicly available repository, that MPIfR developed to process data of the three AGN-imaging RadioAstron key science projects. It can account for general relativistic correction of an orbiting antenna with variable position/velocity, providing a routine to covert the native RDF format to the more common M5B one. Also, a fringe-finding algorithm able to manage arbitrary large fringe-search window is included, allowing one to increase the search space normally adopted by common software packages like HOPS.

(1) Background: In the United Kingdom, Ireland, Iceland, France, Spain, Portugal, and Italy a subject participating in a (bio) medical experiment is termed a guinea pig. In Germany (Versuchskaninchen), The Netherlands, all of Scandinavia, and most of Eastern Europe, the term rabbit is used for such a subject. We have searched for potential differences in attitudes towards biomedical research by inhabitants of the respective European countries by making an analysis of frequency and motivation to participate in biomedical experiments on both sides of the European rabbit borderline. (2) Methods: We have performed an analysis of the use of experimental animals for research in European countries as well as country specific scientific output in PubMed indexed literature. Attitudes towards biomedical research in European countries was derived from EU questionnaires. (3) Results: In biomedical experiments with animals, more guinea pigs are used in laboratories in “rabbit countries” than in “guinea pig countries”. Inhabitants of “rabbit countries” have a higher participation rate in biomedical experiments and donation of blood than people from “guinea pig” countries. The reasons to participate in a medical experiment are not purely altruistic, especially in “rabbit countries”. (4) Conclusions: inhabitants of European countries in which a person who participates in a biomedical experiment is labeled as an (experimental) rabbit participate more in biomedical experiments as well as tissue and organ donation. Motives to do so are not just altruistic, because financial reasons also play a role.

Polyploidy induction is recognized as one of the major evolutionary processes presenting remark-able morphological, physiological, and genetic variations in plants. Soybean (Glycine max L.), also known as soja bean or soya bean, is an annual leguminous crop of the pea family (Fabaceae) that shares a paleopolypoidy history, dating back to approximately 56.5 million years ago with other leguminous crops such as cowpea and other Glycine specific polyploids. This crop has been documented as one of the polyploid complex species among legumes whose gene evolution and result-ant adaptive growth characteristics, following induced polyploidization has not been fully explored. Furthermore, there are no successfully established in vivo or in vitro based polyploidy in-duction protocols that have been reported so far, particularly, with the intention to develop mutant plants showing strong resistance to abiotic salinity stress. This review, therefore, describes the role of synthetic polyploid plant production in soybean for the mitigation against high soil salt stress levels, and how this evolving approach could be used to further enhance nutritional, pharmaceutical and economic industrial value of soybeans, including addressing challenges involved during the polyploidization process.

When limited area models of the hydrodynamics of the atmosphere and of the ocean are 1 run over an extended time, variability unrelated to external "drivers" emerge - this variability is 2 colloquially named "hydrodynamical noise" or just "noise". This article summarises what we have 3 learned in the past few years about the properties of such noise, and the implications for numerical 4 experimentation and analysis. The presence of this noise can be identified easily in ensembles of 5 numerical simulations, and it turns out that the intensity of the noise is closely linked to the scale- 6 dependent "memory". At the system level, this "memory" term as given by Hasselmann’s Stochastic 7 Climate model plays a key role. In the case of marginal seas, the process of baroclinic instability 8 modulated by tides and the formation of seasonal thermoclines are significant aspects. Some more 9 general aspects are discussed, such as the applicability of the Stochastic Climate model to systems 10 outside of atmospheric and oceanic dynamics, the irreversibility of tipping points and the challenges 11 of detecting changes beyond a noise level and of the attribution of causes of change.

We are calling for a paradigm shift in engineering education. In times of the Fourth Industrial Revolution (“4IR”), a myriad of potential changes is affecting all industrial sectors leading to increased ambiguity that makes it impossible to predict what lies ahead of us. Thus, incremental culture change in education is not an option any more. The vast majority of engineering education and training systems, having remained mostly static and underinvested in for decades, are largely inadequate for the new 4IR labor markets. Some positive developments in changing the direction of the engineering education sector can be observed. Novel approaches of engineering education already deliver distinctive, student centered curricular experiences within an integrated and unified educational approach. We must educate engineering students for a future whose main characteristics are volatility, uncertainty, complexity and ambiguity. Talent and skills gaps across all industries are poised to grow in the years to come. The authors promote an engineering curriculum that combine timeless didactic tradition, such as Socratic inquiry, project-based learning and first-principles thinking with novel elements (e.g. student centered active and e-learning by focusing on the case study and apprenticeship pedagogical methods) as well as a refocused engineering skillset and knowledge. These capabilities reinforce engineering students’ perceptions of the world and the subsequent decisions they make. This 4IR engineering curriculum will prepare engineering students to become curious engineers and excellent communicators better navigating increasingly complex multistakeholder ecosystems.

In many countries, increasing concern for animal welfare is driving retailer commitments and government legislation that aim to improve the lives of farmed fish. One aspect of fish welfare involves stunning fish prior to slaughter. The feasibility of stunning depends on the species of fish and physical farm characteristics. In this article, we provide an overview of stunning before slaughter in European sea bass and sea bream aquaculture, one of the largest finfish farming industries in the developed world that does not yet stun most of its production. Sea bass and sea bream stunning necessitates the use of electrical stunning equipment aboard harvest vessels, often a significant distance from the shoreline; this presents an interesting engineering and policy challenge. Together, Türkiye, Greece, Spain, and Italy produced over 400,000 t of sea bass and sea bream in 2020. In Türkiye and Greece, farms are numerous and located very close to the shoreline. In Spain and Italy, farms are few and located far from the shoreline. The highest average production is found in farms from Türkiye (1,000 t) and Spain (1,300 t, and lower average production is found in Greece (300 t) and Italy (350 t). Producer progress towards the installation of electrical stunning appears comparatively well-developed for Türkiye, Spain, and Greece, though we emphasise that producers and other stakeholders require continued support to realise this opportunity. Producers in Italy appear slower to make progress on this aspect of animal welfare and may require additional support.

To understand the biological relevance and mode of action of artificial protein ligands, crystal structures with their protein targets are essential. Here, we describe and investigate all known crystal structures that contain a so-called “molecular tweezer” or one of its derivatives with an attached natural ligand on the respective target protein. The aromatic ring system of these compounds is able to include lysine and arginine side chains, supported by one or two phosphate groups that are attached to the half-moon shaped molecule. We analyse the corresponding crystal structures with 14-3-3 proteins in complex with mono- and diphosphate tweezers. Furthermore, we solved crystal structures of two different tweezer variants in complex with the enzyme 1-Pyrroline-5-carboxyl-dehydrogenase (P5CDH), and found that the tweezers are bound to a lysine and methionine side chain, respectively. The different binding modes and their implications for affinity and specificity are discussed, as well as the general problems in crystallizing protein complexes with artificial ligands.

TThe XPRIZE Foundation designs and operates multi-million-dollar, global competitions to incentivize the development of technological breakthroughs that accelerate humanity toward a better future. To combat the COVID-19 pandemic, the Foundation coordinated with several organizations to make available data sets about different facets of the disease and to provide the computational resources needed to analyze those data sets. This is paper is a case study of the requirements, design, and implementation of the XPRIZE Data Collaborative, a cloud-based infrastructure that enables the XPRIZE to meet its COVID-19 mission and host future data-centric competitions. We examine how a Cloud Native Application can use an unexpected variety of Cloud technologies, ranging from containers, serverless computing, to even older ones like Virtual Machines. We also search and document the effects that the pandemic had on application development in the Cloud. We include our experiences of having users successfully exercise the Data Collaborative, detailing the challenges encountered and areas for improvement and future work.

A FIB/STEM interfacial study was performed on a TBC/Ti₂AlC MAX phase system, oxidized in an aggressive burner rig test (Mach 0.3 at 1300°C for 500 h). The 7YSZ TBC, a-Al₂O₃ TGO, and MAXthal 211 Ti₂AlC base were variously characterized by TEM/STEM, EDS, SADP, and HRTEM. The YSZ was a mix of ‘clean’ featureless and ‘faulted’ high contrast grains. The latter exhibited ferro-elastic domains of high Y content tetragonal t'' variants. No martensite was observed. The TGO was essentially a duplex a-Al₂O₃ structure of inner columnar plus outer equiaxed grains. It maintained a perfectly intact, clean interface with the Ti₂AlC substrate. The Ti₂AlC substrate exhibited no interfacial Al-depletion zone, but rather numerous faults along the basal plane of the hexagonal structure. These are believed to offer a means of depleting Al by forming crystallographic, low-Al planar defects, proposed as Ti_2.5AlC_1.5. These characterizations support and augment prior optical, SEM, and XRD findings that demonstrated remarkable durability for the YSZ/Ti₂AlC MAX phase system in aggressive burner tests.

The skyrocketing growth in the number of Internet of Things (IoT) devices will certainly pose a huge traffic demand for fifth-generation (5G) wireless networks and beyond. In-band full-duplex (IBFD), which is theoretically expected to double the spectral efficiency of a half-duplex (HD) wireless channel and to connect more devices, has been considered as a promising technology to accelerate the development of IoT. To exploit the full potential of IBFD, the key challenge is how to handle network interference (including self-interference, co-channel interference and multiuser interference) more effectively. In this paper, we propose a simple yet efficient user grouping method, where a base station (BS) serves strong downlink users and weak uplink users and vice versa in different frequency bands, mitigating severe network interference. We aim to maximize a minimum rate among all users subject to bandwidth and power constraints, which is formulated as a highly nonconvex optimization problem. By leveraging inner approximation framework, we develop a very efficient iterative algorithm to solve this problem, which guarantees at least a local optimal solution. Numerical results are provided to show not only the benefit of using full-duplex raido at BS, but also the advantage of the proposed user grouping method.

Although the vegetative stage of indoor cannabis production can be relatively short in duration, there is a high energy demand due to higher light intensities (LI) than the clonal propagation stage and longer photoperiods than the flowering stage (i.e., 16 – 24 hours vs. 12 hours). While electric lighting is a major component of both energy consumption and overall production costs, there is a lack of scientific information to guide cultivators in selecting a LI that corresponds to their vegetative stage production strategies. To determine the vegetative plant responses to LI, clonal plants of ‘Gelato’ were grown for 21 days with canopy-level photosynthetic photon flux densities (PPFD) ranging between 135 and 1430 µmol·m^-2·s^-1 on a 16-hour photoperiod (i.e., daily light integrals of ≈ 8 to 80 mol·m^-2·d^-1). Plant height and growth index responded quadratically; the number of nodes, stem thickness, and aboveground dry weight increased asymptotically; and internode length and water content of aboveground tissues decreased linearly with increasing LI. Foliar attributes had varying responses to LI. Chlorophyll content index increased asymptotically, leaf size decreased linearly and specific leaf weight increased linearly with increasing LI. Generally, PPFD levels of ≈ 900 µmol·m^-2·s^-1 produced compact, robust plants that are commercially relevant, while PPFD levels of ≈ 600 µmol·m^-2·s^-1 promoted plant morphology with more open architecture – to increase airflow and reduce the potential foliar pests in compact (i.e., indica-dominant) genotypes.

Following legalization, cannabis has quickly become an important horticultural crop in Canada and increasingly so in other parts of the world. However, due to previous legal restrictions on cannabis research there are limited scientific data on the relationship between nitrogen (N), phosphorus (P), and potassium (K) supply (collectively: NPK) and the crop yield and quality. This study examined the response of a high delta-9-tetrahydrocannabinol (THC) Cannabis sativa cultivar grown in deep-water culture with different nutrient solution treatments varying in their concentrations (mg L^-1) of N (70, 120, 180, 250, 290), P (20, 40, 60, 80, 100) and K (60, 120, 200, 280, 340) according to a central composite design. Results demonstrated that inflorescence yield responded quadratically to N and P, with the optimal concentrations predicted to be 194 and 59 mg L^-1, respectively. Inflorescence yield did not respond to K in the tested range. These results can provide guidance to cultivators when formulating nutrient solutions for soilless cannabis production and demonstrates the utility of surface response design for efficient multi-nutrient optimization.

Most commercial Cannabis sativa L. (cannabis) genotypes are short-day plants and cultivators typically use a 12.0 h uninterrupted dark period to induce flowering; however, scientific information is lacking to prove this is the optimal dark period for all genotypes, and cultivar specific photoperiods may increase productivity. Tissue culture can be used for research requiring multiple treatments, proper replication, and in a controlled environment on a smaller scale compared to greenhouse and indoor facilities. To determine whether cannabis explants can flower under varied photoperiods in vitro, explants were grown under one of six photoperiod treatments: 12.0, 13.2, 13.8, 14.4, 15.0, and 16.0 h for four weeks. The percentage of flowering explants was highest under 12.0 and 13.2 h treatments. There were no treatment effects on the fresh weight, final height, or growth index of the explants. The results suggest an uninterrupted dark period of at least 10.8 h (i.e. 13.2 h photoperiod) is needed to induce the flowering of this genotype. In vitro flowering could provide a unique and high throughput approach to study floral/seed development and secondary metabolism in cannabis under highly controlled conditions. Further research should determine if this response is the same on a whole plant level.

In this work, the possibility of fabrication magneto-optical ceramics by vacuum sintering of compacts prepared by electrophoretic deposition (EPD) from suspensions of (Ho0.95La0.05)2O3 (HoLa) nanopowders made by self-propagating high-temperature synthesis and (Y0.9La0.1)2O3 (YLa) nanopowders made by laser synthesis was systematically investigated. The processes of the formation of bulk one- and two-layer compacts by the EPD method from non-aqueous suspensions of nanopowders was studied. The use of acetylacetone and polyvinyl butyral (PVB) was shown to make it possible to obtain stable alcohol suspensions of the nanopowders characterized by high zeta potential values (+29…+80 mV) and to carry out a stable EPD process. One-layer compacts were made from the YLa and HoLa nanopowders with a density of 30–43%. It was found out that the introduction of PVB into the suspension leads to a decrease in the mass and thickness of the green bodies deposited, but does not significantly affect their density. A possibility to make two-layers (YLa/HoLa) compacts with a thickness of up to 2.6 mm and a density of up to 46% was demonstrated. Sintering such compacts in a vacuum at a temperature of 1750°C for 10 hours leads to the formation of ceramics with a homogeneous boundary between the YLa/HoLa layers and thickness of the interdiffused ions layer of about 30 μm.

COVID-19 and long COVID-19 vulnerabilities may be caused indirectly by albumin binding deficiency (ABD) which can be corrected by the correct administration of human serum albumin (HSA). The liver is the primary site of nutrient regulation and fluid volume maintenance, control of both is by changes to albumin concentration. In healthy subjects the HSA lymphatic nutrient pump (HSALNP) ensures continual pumping of nutrients from the liver are appropriately distributed to organs. Nutrients are delivered to cells according to the availability of binding to HSA. The HSALNP therefore maintains the correct nutrients and colloidal pressure balance in all tissues independently. In unhealthy tissues, following COVID-19 infection, the passage of HSA/nutrients through the interstitial spaces and lymph will be impeded. Fluid therapy into the periphery leads to dilution of essential nutrients attached to the protein-carriers such as albumin. The levels of albumin being charged by the liver with nutrients is critical in maintaining immune stability by maintaining nutrient support and colloidal pressure of cellular structures. The site of HSA binding by the liver is of great importance and direct infusion of albumin into the Hepatic Portal Vein is the most appropriate method of maintaining colloid pressure and cellular nutrient levels.

Strigolactones (SLs) are a novel emerging plant hormones, which play important roles in regulating plant organ development and environmental stress tolerance. Even though the SL related genes have been identified and well characterized in some plants. The information of SL related genes in soybean is not fully established yet, especially in response to salt stress. In this study, we identified nine SL biosynthesis genes: two D27, two CCD7, two CCD8, and three MAX1, and seven SL signaling genes: two D14, two MAX2 and three D53 in soybean genome. We found that SL biosynthesis and signaling genes are conserved during evolution in different species. Syntenic analysis of these genes revealed their location on nine chromosomes as well as existence of ten pairs of duplication genes. Moreover, plant hormone and stress-responsive elements were identified in the promoter regions of SL biosynthesis and signaling genes. By using quantitative real-time PCR (qRT-PCR), we confirmed that SL genes have different tissue expression in roots, stems and leaves. Further, we also explored the expression profiles of SL biosynthesis and signaling genes under salt stress. These results suggested that SL signaling genes may play important regulatory roles in response to salt stress. In conclusion, we identified and provided valuable information on the soybean SL biosynthesis and signaling genes, and established a foundation for further functional analysis of soybean SL related genes in response to salt stress.

Self-assembly is a spontaneous process through which macroscopic structures are formed from basic microscopic constituents (e.g. molecules or colloids). By contrast, the formation of large biological molecules inside the cell (such as proteins or nucleic acids) is a process more akin to self-organization than to self-assembly, as it requires a constant supply of external energy. Recent studies have tried to merge self-assembly with self-organization by analyzing the assembly of self-propelled (or active) colloid-like particles whose motion is driven by a permanent source of energy. Here we present evidence that points to the fact that self-propulsion considerably enhances the assembly of polymers: self-propelled molecules are found to assemble into polymer-like structures, the average length of which increases towards a maximum as the self-propulsion force increases. Beyond this maximum, the average polymer length decreases due to the competition between bonding energy and disruptive forces that result from collisions. The assembly of active molecules might have promoted the formation of large pre-biotic polymers that could be the precursors of the informational polymers we observe nowadays.

Bacteriophages, viruses that are widespread throughout the world, are highly specific for bacteria, usually of a single species and often of a particular strain. After being discovered and isolated 100 years ago, their use, called phage therapy, was instituted in medicine two years later and quickly used around the world to treat various bacterial infections. In the West, phage therapy was overshadowed in the second half of the 20th century by antibiotic therapy, which was then thought to be the definitive solution. But because of the increase in bacterial resistance to antibiotics, the idea of using bacteriophages in medicine has been reawakened. The innumerable observations reported over the years in the literature constitute an invaluable experience. We and some of our colleagues have, in the last decade treated some patients compassionately. With the available documentation and our own experience we discuss the potential indications and limitations of phage therapy. The observation of the increasing number of therapeutic failures in the announced perspective of a post-antibiotic era, we believe, that the introduction of bacteriophages into the therapeutic arsenal seems conceivable today to two preconditions: that their production as biologic drug meets current regulatory standards and that the benefit-risk assessment was conducted in a modern setting. Phage therapy could be applied as a substitution or supplement to antibiotic therapy under multiple circumstances in different modes, precise indications and limits.

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a consecutive process monitoring is essential to meet the high-quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals to ensure the individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set and the prediction of untrained data is challenging. The aim of this paper is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the trained datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of the process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

Analysis of plesiosaur swim dynamics by means of a digital 3D armature (wireframe “skeleton”) of a pliosauromorph (“Ava”) demonstrates that: 1, plesiosaurs used all four flippers for primary propulsion; 2, plesiosaurs utilized all four flippers simultaneously; 3, respective pairs of flippers of Plesiosauridae, front and rear, traveled through distinctive, separate planes of motion, and; 4, the ability to utilize all four paddles simultaneously allowed these largely predatory marine reptiles to achieve a significant increase in acceleration and speed, which, in turn, contributed to their sustained dominance during the Mesozoic.

This article shortly introduces Gaussian processes as a new approach for modelling time series in the field of blazar physics. In the second part of the paper, recent results from an application of GP modelling to the multi-wavelength light curves of the blazar PKS 1502+106 are discussed.

3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in the recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundary for positioning. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude and longitude coordinate to determine the precise location of the fisheye image. The results are then compared with the conventional GNSS and advanced 3DMA GNSS positioning methods. The aims of the proposed algorithm are to increase positioning and heading accuracy in a rich urban environment.

Load variations in any power system result in losses escalation and voltage drops. With the sensible and optimal allocation of distributed generators (DGs), these problems could be considerably mitigated. It has been seen in the priorly existing methods that ideally allocation of DGs has been carried out during fixed loads and constant power requirements. However, in real scenarios the loads are always variable and allocation of DGs must be done in accordance to the variations of the connected load. Therefore, the current paper addressed the aforementioned problem by distinctive optimal allocation of DGs for each variability of 24hour load horizon. However, the single exclusive solution is considered among all allocations of 24 hours. The min-max regret concept has been utilized in order to deal with such methodology. Altogether, 24 scenarios are analyzed wherein each scenario corresponds to a specific hour of the respective day. The optimal allocation of DGs in terms of their optimal sizing and placement has been carried out by using three algorithms including battle royal optimization (BRO), accelerated particle swarm optimization (APSO) and genetic algorithm (GA). The multi-objective optimization problem is evaluated on the basis of minimum value criterion of the multi-objective index (MO). MO comprises of active and reactive power losses and voltage deviation. Hence, in order to find robustness of proposed technique, Conseil international des grands réseaux electriques (CIGRE’s) MV benchmark model incorporating 14 buses has been considerably used as a test network. In the end the results of three proposed algorithms have been compared.

Acoustic emission monitoring (AEM) has emerged as an effective technique for detecting wire-breaks resulting from, e.g., stress corrosion cracking, and its application on prestressed concrete bridges is increasing. The success of this monitoring measure depends crucially on a carefully designed sensor layout. For this the attenuation of elastic waves within the structure’s material is determined ideally in-situ through object-related measurements with a reproducible signal source, typically a rebound-hammer. This assumes that the attenuation coefficients derived from rebound-hammer tests are comparable to those from wire-breaks, thus allowing their results to be directly applied to wire-break detection without further adjustments. This study challenges this assumption by analysing attenuation behaviour through an extensive dataset. Employing time-domain and frequency analysis, the research generates attenuation profiles from laboratory experiments and in-situ measurements across various girders and bridge structures, extracting the slope and standard deviation of the residuals. While generally validating this approach, the findings highlight differences in attenuation behaviour from among wire-break signals and rebound-hammer impulses, whereby the latter potentially underestimates the relevant attenuation of wire-breaks by approximately 20 %. Consequently, a transfer factor is proposed to adjust object-related measurement results for wire-break scenarios, including a variance of 1.0 dB/m to cover a 95 % confidence interval for sample scattering. Moreover, the anisotropic attenuation behaviour across different structures was studied, showing that transverse attenuation consistently exceeds the longitudinal, significantly influenced by structural features such as voids. In prefabricated concrete bridges with in-situ-cast concrete slabs, transverse signal transmission remains unhindered across multiple elements. Finally, the results provide a valuable reference for the design of sensor layouts in bridge monitoring, particularly benefiting scenarios where direct in-situ experiences are lacking.

Reservoir computers (RCs) are a biology inspired computational framework for signal processing typically implemented using recurrent neural networks. Recent work has shown that Boolean networks (BN) can also be used as reservoirs. We analyze the performance of BN RCs, measuring their flexibility and identifying factors that determine effective approximation of Boolean functions that are applied in a sliding-window fashion over a binary signal, either non-recursively or recursively. We train and test BN RCs of different sizes, signal connectivity, and in-degree to approximate 3-bit, 5-bit and 3-bit recursive binary functions. We analyze how BN RC parameters and function average sensitivity, a measure of function smoothness, affect approximation accuracy as well as the spread of accuracies for a single reservoir. We found that approximation accuracy and reservoir flexibility are highly dependent on RC parameters. Overall, our results indicate that not all reservoirs are equally flexible and RC instantiation and training can be more efficient if this is taken into account. The optimum range of RC parameters opens up an angle of exploration for understanding how biological systems might be tuned to balance system restraints with processing capacity.

The feasibility of a steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) with a single flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e. different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode target for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an average offline 16-class accuracy of 66.8±16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.

In the last decade, readily available electronic devices have created unprecedented opportunities for teens to access a wide variety of information and media – both positive and negative – on the internet. Despite the increasing number of initiatives taking place worldwide intended to assess and mitigate the online risks encountered by children and adolescents, there is still a need for a better understanding of how adolescents use the internet and what consequences they may face. We conducted a cross sectional online survey of a convenience sample of 733 8th and 9th grade students in Utah. The survey contained eight questions regarding students’ exposure to three types of online risk scenarios: content risk, contact risk, and commercial risk. Independent variables included students’ online behaviors, use of social media and private messaging apps, and adult supervision of online activities. Logistic and negative binomial regression models indicated that female gender, social media use, and chatting with strangers were associated with exposure to multiple unsafe online scenarios. Our results provide critical information to practitioners involved in the development of educational initiatives by building a profile of potentially risky online behaviors and allow them to tailor their initiatives to meet the needs of more vulnerable populations.

Low Power Wide Area Networks (LPWAN) enable a growing number of Internet-of-Things (IoT) applications with large geographical coverage, low bit-rate, and long lifetime requirements. LoRa (Long Range) is a well-known LPWAN technology that uses a proprietary Chirp Spread Spectrum (CSS) physical layer, while the upper layers are defined by an open standard - LoRaWAN. In this paper, we propose a simple yet effective method to improve the Quality-of-Service (QoS) of LoRa networks by fine-tuning specific radio parameters. Through a Mixed Integer Linear Programming (MILP) problem formulation, we find optimal settings for the Spreading Factor (SF) and Carrier Frequency (CF) radio parameters, considering the network traffic specifications as a whole, to improve the Data Extraction Rate (DER) and to reduce the packet collision rate and the energy consumption in LoRa networks. The effectiveness of the optimization procedure is demonstrated by simulations, using LoRaSim for different network scales. In relation to the traditional LoRa radio parameter assignment policies, our solution leads to an average increase of 6% in DER, and a number of collisions 13 times smaller. In comparison to networks with dynamic radio parameter assignment policies, there is an increase of 5%, 2.8%, and 2% of DER, and a number of collisions 11, 7.8 and 2.5 times smaller than equal-distribution, Tiurlikova's (SoTa), and random distribution, respectively. Regarding the network energy consumption metric, the proposed optimization obtained an average consumption similar to Tiurlikova's, and 2.8 times lower than the equal-distribution and random dynamic allocation policies. Furthermore, we approach the practical aspects of how to implement and integrate the optimization mechanism proposed in LoRa, guaranteeing backward compatibility with the standard protocol.

The plant microbiome is involved in enhancing nutrient acquisition, plant growth, stress tolerance and reduces chemical inputs. The identification of microbial functional diversity offers the chance to comprehend and engineer them for various agricultural processes. Using a shotgun metagenomics technique, this study examined the functional diversity and metabolic potentials of microbial communities in the rhizosphere soybean. 18 genera were selected out of which six are prominent in sample AB, the prominent genera are Geobacter, Nitrobacter, Burkholderia, Candidatus, Bradyrhizobium and Streptomyces. Twenty-one functional categories were present with 14 of the functions being dominant. The dominant functions include carbohydrates, fatty acids, lipids and isoprenoids, amino acids and derivatives, sulfur metabolism, and nitrogen metabolism. Kruskal- Wallis test was used to test samples’ diversity differences. There was a significant difference in the diversity with p-value of 0.04. ANOSIM was used to analyse the similarities of the samples, p-values and R-values of the samples were 0.01 and 0.5835 respectively. Phosphorus with p-value of0.718 and 64.3% contribution was more prominent among the soil properties that have influence on functional diversity of the samples. Given the functional groups reported in this study, it is clear that soil characteristics had an impact on on the functions role of the rhizospheric microbiome of soybean

As breast cancer continues to take a devasting public health toll, most primary prevention approaches are targeted at individual actions. We have proposed, instead, developing systemic, population approaches to preventing the disease. We used a combined qualitative-quantitative methodology, Group Concept Mapping (GCM), to identify Importance and Feasibility ratings of systemic interventions across a wide spectrum of approaches and stakeholders. Participants (n=351) from across the state of California sorted 84 potential interventions into topical piles, and then rated each intervention on perceived Importance and Feasibility. Multidimensional scaling and cluster analysis identified eleven clusters or themes of interventions. Participants rated interventions on Importance and Feasibility differently depending on the region of the state in which they lived. The results of this study underscore the importance of sharing health information with and seeking public health solutions from community partners in general and from beyond the urban areas usually studied.

The development of compartmentalized neuron culture systems has been invaluable in the study of neuroinvasive viruses, including the alpha herpesviruses Herpes Simplex Virus 1 (HSV-1) and Pseudorabies Virus (PRV). This chapter provides updated protocols for assembling and culturing rodent embryonic superior cervical ganglion (SCG) and dorsal root ganglion (DRG) neurons in Campenot trichamber cultures. In addition, we provide several illustrative examples of the types of experiments that are enabled by Campenot cultures: 1. Using fluorescence microscopy to investigate axonal outgrowth/extension through the chambers, and alpha herpesvirus infection, intracellular trafficking, and cell-cell spread via axons. 2. Using correlative fluorescence microscopy and cryo electron tomography to investigate the ultrastructure of virus particles trafficking in axons.

Soybean (Glycine max L.) is an important crop that serves as a source of edible oil and protein. However, little is known about its molecular mechanism of adaptation to extreme environmental conditions. Based on the Arabidopsis thaliana sequence database and Phytozome, a soybean gene that was highly homogenous with the reduced induction of the non-photochemical quenching2 (AtRIQ2) gene, GmRIQ2-like (accession NO.: Glyma.04G174400), was identified in this study. The gene structure analysis revealed that GmRIQ2-like encoded a transmembrane protein. Elements of the promoter analysis indicated that GmRIQ2-like participated in the photosynthesis and abiotic stress pathways. The subcellular localization results revealed that the protein encoded by GmRIQ2-like was located in chloroplasts. The quantitative real-time (qRT)-PCR results revealed that GmRIQ2-like-overexpression (OE) and -knock-out (KO) transgenic soybean seedlings were cultivated successfully. The relative chlorophyll (Chl) and zeaxanthin contents and Chl fluorescence kinetic parameters demonstrated that GmRIQ2-like dissipated excess light energy by enhancing the non-photochemical quenching (NPQ) and reduced plant photoinhibition. These results suggested that GmRIQ2-like was induced in response to strong light and depressed Chl production involved in soybean stress tolerance. These findings indicate that the transgenic seedlings of GmRIQ2-like could be used to enhance strong light stress tolerance and protect soybean plants from photoinhibition damage. This study will serve as a reference for studying crop photoprotection regulation mechanisms and benefits the research and development of new cultivars.

To fill in the blanks for charophytes in the Caucasus to improve our knowledge of species distribution areas in Eurasia, a field survey and study of available specimens from Dagestan (North Caucasus, Russia) was conducted based on morphological observation and molecular genetic analysis. Seventeen new localities for six Chara species and one Tolypella species, with six new species and one new genus for the region studied, and one new species for the Caspian Sea region were found. Some species records changed the outline or filled in the gaps in the species distribution area. The presence of species with a distribution area mainly in the centre of Eurasia (C. globata, C. neglecta) with mainly Mediterranean – Middle East species (C. gymnophylla) is notable for this region, as well as for other studied regions of the Caucasus having a combination of species with different distribution areas. The frequent occurrence of C. gymnophylla points is similar to that in the Mediterranean region and the Middle East. Small brackish waterbodies at the coast of the Caspian Sea, freshwater mountain rivers, small waterbodies associated with them, and water reservoirs are the main habitats of charophytes in the regions. The habitat preference and distribution in the Caucasus allowed suggestion some species for protection. The absence of endemic species among charophytes from Dagestan and Caucasus contrasts with the flora of terrestrial magnoliophytes, which have a large number of endemic species.

We present preliminary results of JVLA wideband full polarization observations of a sample of Active Galactic Nuclei (AGN) with very high Rotation Measure (RM) values, sign of extreme environment. The polarization properties show a complex behaviour with the polarization angle (PA) and the fractional polarization (fp) that dramatically change within the wideband. The measured RM is not constant within the wide band. Its complex behaviour reflects the complexity of the medium with the presence of several Faraday components. The depolarization have been studied, modeling the Stokes parameters Q and U together with the polarization parameters (PA and fp) with wavelength by applying combinations of the simplest existing depolarisation models. With this JVLA study we could spectrally resolve multiple polarized components of unresolved AGN. These preliminary results reveal the complexity of these objects, still improvements have to be done on the depolairzation modeling to better understand the polarization structure of these sources.

Non-homologous end joining (NHEJ) is the main DNA repair mechanism for the repair of double strand breaks (DSBs) throughout the course of the cell cycle. DSBs are generated in developing B and T lymphocytes during V(D)J recombination to increase the repertoire of B and T cell receptors. DSBs are also generated during the class switch recombination (CSR) process in mature B lymphocytes, providing distinct effector functions of antibody heavy chain constant regions. Thus, NHEJ is important for both V(D)J recombination and CSR. NHEJ comprises core Ku70 and Ku80 subunits that form the Ku heterodimer, which binds DSBs and promotes the recruitment of accessory factors (e.g., DNA-PKcs, Artemis, PAXX, Mri) and downstream core factors (XLF, Lig4 and XRCC4). In recent decades, new NHEJ proteins have been reported, increasing complexity of this molecular pathway. Numerous in vivo mouse models have been generated and characterized to identify the interplay of NHEJ factors and their role in development of adaptive immune system. This review summarizes the currently available mouse models lacking one or several NHEJ factors, with a particular focus on early B cell development. We also underline genetic interactions and redundancy in the NHEJ pathway in mice.

Plants are subjected to abiotic stresses throughout their developmental period. Abiotic stresses include drought, salt, heat, cold, heavy metals, nutritional element and oxidative stresses. Improving plant response to various environmental stresses is critical for plant survival and perpetuation. The WRKY transcription factors have special structure (WRKY structural domains), which enable WRKY transcription factors to have different transcriptional regulatory functions. The WRKY transcription factors can not only regulate abiotic stresses response and plant growth and development by regulating phytohormone signalling pathways, but also promote or suppress the expression of downstream genes by binding to the W-box [TGACCA/TGACCT] in the promoters of their target genes. In addition, WRKY transcription factors not only interact with other families of transcription factors to regulate plant defence responses in abiotic stresses, but also self-regulate by recognizing and binding to W-boxes in their own target genes to regulate their defence responses to abiotic stresses. However, in recent years, research reviews on the regulatory roles of WRKY transcription factors in higher plants are scarce and shallow. In this review, we focus on the structure and classification of WRKY transcription factors, as well as the identification of their downstream target genes and molecular mechanisms involved in the response to abiotic stresses, which can improve the tolerance ability of plants in abiotic stress and we also look forward to their future research directions, with a view to providing theoretical support for the genetic improvement of crop abiotic stresses tolerance.

Autologous platelet-rich plasma (PRP) preparations are prepared at point of care. Centrifugation cellular density separation sequesters a fresh unit of blood into three main fractions: a platelet-poor plasma (PPP) fraction, a stratum rich in platelets (platelet concentrate) and, bioformulation dependent, also leukocyte and erythrocyte fractions. The employment of autologous platelet concentrates facilitates the biological potential to accelerate and support in numerous cellular activities that can lead to tissue repair, tissue regeneration, wound healing, and ultimately functional structural repair. Normally, after PRP preparation the PPP fraction is discarded. One of the less well known, but equally important features of PPP is that particular growth factors (GFs) are not abundantly present in PRP, as they reside outside the platelet alpha granules. Precisely, insulin-like growth factor-1 (IGF-1) and hepatocyte growth factor (HGF) are mainly present in the PPP fraction. In addition to their roles as angiogenesis activators, these plasma-based GFs are also known to inhibit inflammation, fibrosis, and they promote keratinocyte migration and support in tissue repair and wound healing. Additionally, PPP is known for the presence of exosomes and other macrovesicles, exerting cell-cell communication and cell signaling. Newly developed ultrafiltration technologies incorporate PPP processing methods by eliminating in a fast and efficient manner plasma water, cytokines, molecules, and plasma proteins with a molecular mass (weight) less than the pore size of the fibers. Consequently, a viable and viscous protein concentrate of functional, total proteins, like fibrinogen, albumin, and alpha-2-macroglobulin is created. Consolidating a small volume of high platelet concentrate with a small volume of highly concentrated protein-rich PPP creates a protein rich platelet concentrate (PRPC) biological preparation. After the activation of proteins, mainly fibrinogen, the PRPC matrix retains and facilitates interactions between invading resident cells, like macrophages, fibroblast, and mesenchymal stem cells (MSCs), and the embedded concentrated PRP cells and molecules. The administered PRPC biologic will ultimately undergo fibrinolysis, leading to a sustained release of concentrated cells and molecules that have been retained in the PRPC matrix until the matrix is dissolved. We will discuss the unique biological and tissue reparative and regenerative properties of PRPC matrix.

We investigate perovskite oxides from different aspects, namely their pseudoharmonic dynamical properties, their dynamical properties when strong anharmonicity exists and the intriguing functionalities arising from domain walls. Taking these viewpoints together yields a rather complex picture of this material class which was not anticipated in previous approaches. It opens pathways to novel applications and reveals the rich ground states beyond the fictitious belief in ‘simplicity of perovskites and such structures’.

P-glycoprotein (P-gp), a membrane-bound transporter, can eliminate xenobiotics by transporting them out of the cells or blood-brain barrier (BBB) at the expense of ATP hydrolysis. Thus, P-gp mediated efflux plays a pivotal role in altering the absorption and disposition of a wide range of substrates. Nevertheless, the mechanism of P-gp substrate efflux is rather complex since it can take place through active transport and passive permeability in addition to multiple P-gp substrate binding sites. A nonlinear quantitative structure-activity relationship (QSAR) model was developed in this study using the novel machine learning-based hierarchical support vector regression (HSVR) scheme to explore the perplexing relationships between descriptors and efflux ratio. The predictions by HSVR were found to be in good agreement with the observed values for the molecules in the training set (n = 50, r² = 0.96, q²_CV = 0.94, RMSE = 0.10, s = 0.10) and test set (n = 13, q² = 0.80–0.87, RMSE = 0.21, s = 0.22). When subjected to a variety of statistical validations, the developed HSVR model consistently met the most stringent criteria. A mock test also asserted the predictivity of HSVR. Consequently, this HSVR model can be adopted to facilitate drug discovery and development.

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

Human Activity Recognition (HAR) technology enables continuous behavior monitoring, particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR, were employed for classification. Results demonstrate the ear sensor's efficacy, with deep learning models achieving 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital signs monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.

The facultative loss of muscle mass and function during aging (sarcopenia) poses a serious threat to our independence and health. Much of what we know about sarcopenia derive from studies on small rodents which serve as models for the human condition. Here we used outbred male rats to study the natural history of sarcopenia with the aim to compare behavioral motor deficits with degree of muscle wasting and to find processes underlying the preclinical phase of sarcopenia. Selected metrics were validated in female rats. We used the soleus muscle because of its long duty cycles in everyday life and significance for postural control. The results show that gait and coordination remain intact through middle age (12-18 months; 40%-60% of median lifespan) when muscle mass relative to body weight is largely maintained. The muscle, however, show multiple signs of remodeling with a shift in myofiber-type composition towards type I, increased number of hybrid fibers and fibers expressing embryonic myosin. In parallel with the shift in fiber type prevalence, fiber type clustering increased. The latter combined with a transcriptional upregulation of nicotinic acetylcholine receptor subunit  (CHRN ), NCAM and Myogenin (MYOG) suggests a remodeling driven by myofiber denervation. Additional stigmata were increased number of central nuclei, transcriptional upregulation of Smad3, FBXO32, and MuRF1mRNAs; unaltered density of satellite cells (SC) and Catenin mRNA. In early (25-month-old) and advanced (30-month-old) aging, gait and coordination deteriorate as loss of muscle mass progresses. In late middle age and early aging because of a type II atrophy (>50%), followed by type I atrophy (>50%). Number of myofiber did not correlate with this process. At advance age, the atrophy is accompanied by a decrease in SC and Catenin mRNA while several transcripts upregulated earlier-on were downregulated. In contrast, glial-derived nerve growth factor (GDNF) mRNA abundance peaked at endpoint. We conclude that sarcopenia in the rat as in humans, has a long preclinical phase where the muscle undergoes extensive remodeling to preserve muscle mass and function. The data supports the notion that myofiber denervation is the instigating mechanism, initially affecting large type II motor units (MUs) and at more advanced stage also the smaller type I MUs.

Obstructive sleep apnea (OSA) is a chronic condition characterized by intermittent hypoxia (IH) and sleep fragmentation (SF). In murine models, chronic SF can impair endothelial function and induce cognitive declines. These deficits are likely mediated, at least in part, by alterations in Blood-Brain Barrier (BBB) integrity. Male C57Bl/6J mice were randomly assigned to SF or sleep control (SC) conditions for 4 or 9 weeks, and in a subset 2 or 6 weeks of normal sleep recovery. Presence of inflammation and microglia activation were evaluated. Explicit memory function was assessed with the novel object recognition (NOR) test, while BBB permeability was determined by systemic dextran-4kDA-FITC injection and Claudin 5 expression. SF exposures resulted in decreased NOR performance and in increased inflammatory markers and microglial activation as well as enhanced BBB permeability. Explicit memory and BBB permeability were significantly associated. BBB permeability remained elevated after 2 weeks of sleep recovery (p<0.01) and returned to baseline values only after 6 weeks. Chronic SF exposures mimicking the fragmentation of sleep that characterizes patient with OSA elicits evidence of inflammation in brain regions and explicit memory impairments in mice. Similarly, SF is also associated with increased BBB permeability, the magnitude of which is closely associated with cognitive functional losses. Despite normalization of sleep patterns, BBB functional recovery is a protracted process that merits further investigation.

While deforestation rates decline globally they are rising in the Western Amazon. Artisanal-scale gold mining (ASGM) is a large cause of this deforestation and brings with it extensive environmental, social, governance, and public health impacts, including large carbon emissions and mercury pollution. Underlying ASGM is a broad network of factors that influence its growth, distribution, and practices such as poverty, flows of legal and illegal capital, conflicting governance, and global economic trends. Despite its central role in land use and land cover change in the Western Amazon and the severity of its social and environmental impacts, it is relatively poorly studied. While ASGM in Southeastern Peru has been quantified previously, doing so is difficult due to the heterogeneous nature of the resulting landscape. Using a novel approach to classify mining that relies on a fusion of CLASlite and the Global Forest Change dataset, two Landsat-based deforestation detection tools, we sought to quantify ASGM-caused deforestation in the period 1984–2017 in the southern Peruvian Amazon and examine trends in the geography, methods, and impacts of ASGM across that time. We identify nearly 100,000 ha of deforestation due to ASGM in the 34-year study period, an increase of 21% compared to previous estimates. Further, we find that 10% of that deforestation occurred in 2017, the highest annual amount of deforestation in the study period, with 53% occurring since 2011. Finally, we demonstrate that not all mining is created equal by examining key patterns and changes in ASGM activity and techniques through time and space. We discuss their connections with, and impacts on, socio-economic factors, such as land tenure, infrastructure, international markets, governance efforts, and social and environmental impacts.

The experiment was conducted in three locations Jari, Chefa and Sirinka of Eastern Amhara to select the best performing varieties, in terms of biomass yield, chemical composition, haulm yield, seed yield and other agronomic characteristics of Glycine max (L.) Merrill grown under the rain- fed condition of lowland areas of Eastern Amhara in a randomized complete block design with tree replications. Twelve released soybean varieties were Afgat ,Belesa-95, Boshe , Cheri , Dhidhessa, Gishama , Gizo, Korme, Pawe-03, katta, Wegayen and Wollo were used as a treatments. The seeds were planted in 40 cm between rows and 10 cm between plants on a plot size of 3.2m*4m. Spacing between blocks and plots were 1 and 0.5m, respectively. The seed rate was 60 kg/ha and a fertilizer rate 100 kg/ha NPS was applied during seed planting. The combined analysis of variance over two years at location Jari for dry matter yield of varieties Afgat, Gizo, Pawe-03, Wogayen and Wollo were significantly higher as compared to other soybean varieties. The combined analysis across locations at Jari and Sirinka (2019-second year) showed that varieties Afgat, Gishama, Gizo, Pawe-03, Wogayen and Wollo had higher dry matter yield and varieties Gishama (3.97 t/ha), Gizo (3.60 t/ha), Pawe-03 (4.04 t/ha) and Wogayen (3.36 t/ha) had higher haulm yield as compared with other varieties. The combined analysis across locations at Jari and Sirinka (2019) showed that varieties Pawe-03(2951 kg/ha), Gizo (2862 kg/ha), Afgat (2859 kg/ha), Gishama (2654 kg/ha),Wollo (2461 kg/ha) and Wogayen (2404 kg/ha) had higher seed yield as compared with other varieties. The variety Wollo gave higher crud protein content in two locations (Jari and Sirinka) .Therefore, varieties Afgat, Pawe-03 and Wollo were recommends for the given areas of Jari , Chefa, Sirinka and could be produced in similar environments for the best of produced optimal amount of dry matter, haulm and seed yield and good protein supplement for production of ruminants.Thus, further researches will be needed to investigate on the utilization of livestock.

Commercial development of tidal stream energy is hampered by technical and financial challenges, and impeded by uncertainty about potential environmental effects that drive environmental risk assessments and permitting (consenting) processes. The effect of greatest concern for operational tidal stream energy devices is the potential for marine animals to collide with turbine blades, resulting in injury or death. Due to the turbulent and often turbid waters that frequently characterize tidal turbine sites, there is an absence of empirical evidence about collisions with marine animals. This paucity of observations often leads to risk-averse permitting decisions that further restrict the deployment of tidal energy devices that are needed to collect this evidence. This paper relies on the framework of stressors and receptors that is used widely in marine energy studies and outlines a stepwise probabilistic methodology that applies existing knowledge to further elucidate the risk to marine animals from operational tidal turbines. A case study using striped bass from the Bay of Fundy, Canada, accompanies the methodology, to partially demonstrate its application.

This article develops a multi-perspective view on motivations and methods for tobamovirus purification through the ages, and presents a novel efficient, easy-to-use approach that can be well-adapted to different species of native and functionalized virions. We survey the various driving forces prompting researchers to enrich tobamoviruses: from the search for the causative agents of mosaic diseases in plants to their increasing recognition as versatile nanocarriers in biomedical and engineering applications. Best practice and also rarely applied options for the serial processing steps required for successful isolation of tobamoviruses are then reviewed. Adaptations for distinct particle species, pitfalls and 'forgotten' or underrepresented technologies are considered as well. The article is topped off with our own development of a method for virion preparation, rooted in historical protocols. It combines selective re-solubilization of polyethylene glycol (PEG) virion raw precipitates with density step gradient centrifugation in biocompatible iodixanol formulations, yielding ready-to-use particle suspensions. This newly established protocol and some considerations for perhaps worthwile further developments could serve as putative stepping stones towards preparation procedures appropriate for routine practical uses of the multivalent soft-matter nanorods.

Rising antimicrobial resistance (AMR) in Salmonella serotypes host-adapted to cattle is of increasing concern to the beef and dairy industry. The bulk of the existing literature focuses on AMR post-slaughter. In comparison, the understanding of AMR in Salmonella among pre-harvest cattle is still limited, particularly in Texas, which ranks top five in beef and dairy exports in the United States; inherently, the health of Texas cattle has nationwide implications for the health of the United States beef and dairy industry. In this study, long-read whole genome sequencing and bioinformatic methods were utilized to analyze antimicrobial resistance genes (ARGs) in 98 isolates from beef and dairy cattle in the Texas Panhandle. Fisher exact tests and elastic net models accounting for population structure were used to infer associations between ARGs and antimicrobial phenotypic profiles and metadata. Gene mapping was also performed to assess the role of mobile genetic elements in harboring ARGs. ARG were found to be statistically different between the type of cattle operation and Salmonella serotypes. Beef operations were statistically significantly associated with more resistance genes compared to dairy operations. Salmonella Heidelberg, followed by Salmonella Dublin isolates, were associated with the most ARGs. Additionally, specific classes of ARGs were only present within mobile genetic elements.