Imaginary dimensions in physics require an imaginary set of base Planck units and some negative parameter $c_n$ corresponding to the speed of light in vacuum $c$. The second, negative fine-structure constant $\alpha_2^{-1} \approx -140.178$ present in Fresnel coefficients for the normal incidence of electromagnetic radiation on monolayer graphene leads to the imaginary Planck units. Furthermore, it sets $c_n \approx -3.06 \times 10^8~\text{[m/s]}$. It follows that electric charges are the same in real and imaginary dimensions. Neutron stars and white dwarfs, objects emitting perfect black-body radiation, need energy exceeding their mass-energy equivalence ratios. Complex energies are defined in terms of real and imaginary Planck units. Their imaginary parts, inaccessible for direct observation, store the excess of these energies. It follows that black holes are fundamentally uncharged, charged micro neutron stars and white dwarfs with masses lower than $5.7275 \times 10^{-10}~[\text{kg}]$ are unphysical, and the radii of white dwarfs' cores are limited to $R_{\text{WD}} < 6.7933~G M_{\text{WD}}/c^2$. It is conjectured that the maximum atomic number $Z=238$. A black-body object is in the equilibrium of complex energies of masses, charges, and wavelengths if its radius $R_\text{eq} \approx 2.7665~G M_{\text{BBO}}/c^2$, which corrects the value of the photon sphere radius $R_{\text{ps}}=3G M/c^2$, taking into account the value(s) of the fine-structure constant(s), which is otherwise neglected in general relativity.

There must exist a reformulation of quantum field theory which does not refer to classical time. We propose a pre-quantum, pre-spacetime theory, which is a matrix-valued Lagrangian dynamics for gravity, Yang-Mills fields, and fermions. The definition of spin in this theory leads us to an eight dimensional octonionic space-time. The algebra of the octonions reveals the standard model; model parameters are determined by roots of the cubic characteristic equation of the exceptional Jordan algebra. We derive the asymptotic low energy value 1/137 of the fine structure constant, and predict the existence of universally interacting spin one Lorentz bosons, which replace the hypothesised graviton. Gravity is not to be quantized, but is an emergent four-dimensional classical phenomenon, precipitated by the spontaneous localisation of highly entangled fermions.

Deep neural network (DNN) watermarking is a potential approach for protecting the intellectual property rights of DNN models. Similar to classical watermarking techniques for multimedia content, the requirements for DNN watermarking include capacity, robustness, and transparency, and other factors. Studies have focused on robustness against retraining and fine-tuning. However, less important neurons in the DNN model may be pruned. Moreover, although the encoding approach in renders DNN watermarking robust against pruning attacks, the watermark is assumed to be embedded only into the fully connected layer in the fine-tuning model. In this study, we extended the method such that the model can be applied to any convolution layer of the DNN model and designed a watermark detector based on a statistical analysis of the extracted weight parameters to evaluate whether the model is watermarked. Using a nonfungible token mitigates the overwriting of the watermark and enables checking when the DNN model with the watermark was created.

Registration of the flow pattern fine structure and the matter distribution of a free falling liquid drop in a target fluid at rest in the impact mode of coalescence, when the kinetic energy of the drop exceeds its available potential surface energy (APSE), was carried out by photo and video recording. The main attention was paid to the study of the flow structure at the initial stage of the cavity formation. To carry out color registration, the flow pattern was illuminated by several matrix LED and fiber optic sources of constant light. The planning of experiments and interpretation of the results were based on the properties of the complete solutions of the fundamental equations of fluid mechanics system, including the transfer and conversion of energy processes. Complete solutions of the system of equations describe large-scale flow components that are waves or vortices as well as thin jets (ligaments, filaments, fibers, trickles). In experiments, the jets are accelerated by the converted available potential surface energy (APSE) when the free surfaces of merging fluids were eliminated. The experiments were performed with the coalescence of water, solutions of alizarin ink, potassium permanganate, copper sulphate or iron sulphate drops in deep water. In all cases, at the initial contact, the drop begins to lose its continuity and breaks up into a thin veil and jets, the velocity of which exceeds the drop contact velocity. Small droplets, the size of which grows with time, are thrown into the air from spikes at the jet tops. On the surface of the liquid, the fine jets leave colored traces that form linear and reticular structures. Part of the jets penetrating through the bottom and wall of the cavity forms an intermediate covering layer. The forming layer jets are separated by interfaces of the target fluid. The processes of molecular diffusion equalize the density differences and form an intermediate layer with sharp boundaries in the target fluid. All noted structural features of the flow are also visualized when a fresh water drop isothermally spreads in the same tap water. The fast-changing and finely structured diffuse boundary of merging fluids, which at the initial stage has a complex and irregular shape, is gradually smoothed out by molecular diffusion processes. The similar flow patterns were observed in all performed experiments, however the geometric features of the flow depend on the individual thermodynamic and kinetic parameters of the contacting fluids.

Our understanding and theoretical interpretation of observations in astrophysics and cosmology depends on our knowledge of the fundamental constants and their possible dependence on time and space. Atomic spectroscopy and radio astronomy give important information on the validity and stability of the fundamental constants. The possible dependence of the fine structure constant alpha on time and spatial direction is an active topic of research.Period doubling is a universal property of nonlinear dynamical systems, and the doubling is exact in principle. The value of the elementary charge squared can be calculated by the period doubling process from the Planck charge and thereby the value of alpha.If ‘old’ and ‘new’ electrons are identical, then the Planck charge, i.e. a set of natural constants, has remained constant over time. In this article we show that the value of alpha calculated from the Planck charge is 0.007 % smaller than the current accepted value of alpha.

Although the presence of dark energy is well established from various observations, its true nature is still not well understood. The cosmological constant term seems to be the preferred candidate. In earlier work we had a constant cosmological constant term to limit the sizes of large-scale structures at lower redshifts. In this work, we extend this to large scale structures at higher redshifts. Here we invoke a time varying cosmological constant to set constraints on sizes of galaxies at high redshifts and see that they are consistent with their observed sizes. The time-varying cosmological constant also provides a possible solution to the puzzle of structure formation of large disk galaxies (like the Wolfe disk) observed at very early stages of the Universe. Future observations of galaxies at even higher redshifts could support our approach.

In this paper we will provide a new equation that explains why there are three generations or families of leptons and quarks, respectively. We will also explain why all those particles have the known mass ratios amongst their three respective generations and flavors. We will also tackle the problem of Yukawa couplings being arbitrary parameters in the Standard Model Higgs mechanism, which is a long standing problem do to their formulaic dependence on the Higgs Vacuum Expectation Value (VEV). We will attempt to solve this problem and provide a strong argument through an equation for Yukawa couplings of all leptons and quarks via a new methodology that depends on the running of the fine-structure constant on the Q scale, quantum numbers and the Weinberg angle (also on the Q scale). We will also make predictions for all three left-chiral neutrino mass eigenstates and we will provide upper limits for the three right-chiral neutrino mass eigenstates

The problem of Yukawa couplings being arbitrary parameters in the Standard Model Higgs mechanism is a long standing one due to their formulaic dependence on the Higgs Vacuum Expectation Value. We will attempt to solve this problem and provide a strong argument that the Yukawa couplings of charged leptons and down type quarks are not arbitrary parameters in the SM. A new methodology for predicting the Yukawa couplings will be presented by using Compton wavelengths, the Rydberg Constant and g-factors of charged leptons instead of relying on the Higgs VEV. We will then proceed to rewrite this new method in terms of an empirical formula that depends on the running of the fine-structure constant on the Q scale, charge and lepton quantum numbers and g-factors to predict the values of the Yukawa couplings for all three generations of charged leptons and d-type quarks. We will also touch on the subject of neutrinos both as Majorana and Dirac fermions respectively and make a prediction for the lightest possible Majorana neutrino and the differences between Dirac neutrinos and anti-neutrinos. We conclude that the Yukawa couplings are not arbitrary parameters in the SM and that this new formula provides very accurate results.

In the Standard Model of elementary particles, the rest mass, magnetic moment and electric charge of the electron are considered to be natural constants. In this article it is proposed that these properties can be calculated in a simple way from the corresponding Planck units using the well-known period doubling phenomenon in nonlinear dynamical systems. Periods corresponding to the values of the electron and positron properties belong to a subset of stable periods. The periodic structures of the rest energy and magnetic moment consist of three internal degrees of freedom, whereas the Coulomb energy of the electric charge consists of four. The number of period doublings for the elementary charge determines the value of the fine structure constant alpha.

Henry’s constants for different existing compounds in water have great importance in transfer calculations. Measurement of these constants face different difficulties including high costs of experiment and low accuracy of measurement apparatus. Due to these facts, proposing a low cost and accurate approach becomes highlighted. To this end, adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) have been used as Henry’s constant predictor tools. The molecular structure of compounds has been used as inputs of models. After training the models, the visual and mathematical studies of outputs have been done. The coefficients of determination of LSSVM and ANFIS algorithms are 0.999 and 0.990 respectively. According to the comprehensiveness of databank and accurate prediction of algorithms, it can be concluded that LSSVM and ANFIS algorithms are accurate methods for prediction of Henry’s constant in wide range of chemical structure of compounds in water.

Prolonged high-intensity grinding can modify the crystal structure of solid substances and/or induce chemical reaction, which is referred to as mechanochemical reaction. Such reactions can exert positive influences on hydrometallurgical processes, therefore, many researchers have applied mechanochemical reactions for metals dissolution from minerals. The mechanism of mechanochemical reaction has been investigated using solid analyses and simulations. Structural changes caused by mechanochemical reaction are not yet sufficiently clarified because the ground samples are amorphous. The objective of this study was to analyze structural changes of cerianite in weathered residual rare earth ore by mechanochemical reduction. Structural change was analyzed by x-ray absorption near-edge structure and extended x-ray absorption fine structure analysis at the cerium L_III- and K-edges. These analyses revealed that the structural change of cerianite in this ore induced by mechanochemical reduction involved oxygen vacancy production. The process of the oxygen vacancy formation was closely coupled with the quantum effect of localization–delocalization of the 4f electron of cerium.

Herein is introduced a simple scalar field model derived from classical based kinetic energy, gravitational potential energy, and Special Relativity’s rest mass energy. By applying a classical orbit over the scalar field, relativistic effects are predicted. The scalar field is then applied to a classical model of the Hydrogen atom resulting in a relativistic effect equal to the binding energy of the Hydrogen atom. In addition, the model derives the fine structure constant due to the gravitational effect. The relativistic effects are then discretized in increments equal to the model’s gravitational induced constant. The discretization produces the Hydrogen atom spectral emissions and an angular moment equal to Planck’s reduced constant. The model is not presented as a replacement for current theory, rather it is for inspection and illustration of how a simplistic model may offer a fundamental bridge between the more complex, time proven theories of General Relativity and Quantum Mechanics.

A geometric Planck-scale model of quantum electrodynamics is tested against observations. Based on Dirac’s proposal to describe spin 1/2 particles as tethered objects, elementary fermions are conjectured to be fluctuating rational tangles with unobservable tethers. As Battey-Pratt and Racey showed, when such tangles propagate, they obey the free Dirac equation. Classifying rational tangles yields the observed spectrum of elementary fermions. Classifying deformations of tangle cores yields exactly three types of gauge interactions. They exchange three types of elementary gauge bosons and have the symmetry groups U(1), broken SU(2) and SU(3). The possible rational tangles for fermions, Higgs and gauge bosons allow only the observed Feynman diagrams. The complete Lagrangian of the standard model arises, including the Lagrangian of quantum electrodynamics. Over 50 tests of the tangle model are deduced. They include details on the perturbation expansion of the g-factor and the existence of limit values for electric and magnetic fields. Measurements agree with most tests, though this could change in the future. Some tests are genuine predictions that still need to be checked. In particular, the geometry of the process occurring at QED interaction vertices suggests an ab-initio estimate for the fine structure constant. In addition, the average geometric shapes of the elementary particle tangles suggest ab-initio lower and upper limits for the mass values of the electron and the other leptons.

We have recently proposed a pre-quantum, pre-space-time theory as a matrix-valued Lagrangian dynamics on an octonionic space-time. This pre-theory offers the prospect of unifying the internal symmetries of the standard model with gravity. It can also predict the values of free parameters of the standard model, because these parameters arising in the Lagrangian are related to the algebra of the octonions which define the underlying non-commutative space-time on which the dynamical degrees of freedom evolve. These free parameters are related to the algebra $J_3(\mathbb O)$ [exceptional Jordan algebra] which in turn is related to the three fermion generations. The exceptional Jordan algebra [also known as the Albert algebra] is the finite dimensional algebra of 3x3 Hermitean matrices with octonionic entries. Its automorphism group is the exceptional Lie group $F_4$. These matrices admit a cubic characteristic equation whose eigenvalues are real and depend on the invariant trace, determinant, and an inner product made from the Jordan matrix. Also, there is some evidence in the literature that the groups $F_4$ and $E_6$ could play a role in the unification of the standard model symmetries, including the Lorentz symmetry. The octonion algebra is known to correctly yield the electric charge values (0, 1/3, 2/3, 1) for standard model fermions, via the eigenvalues of a $U(1)$ number operator, identified with $U(1)_{em}$. In the present article, we use the same octonionic representation of the fermions to compute the eigenvalues of the characteristic equation of the Albert algebra, and compare the resulting eigenvalues with the known mass ratios for quarks and leptons. We find that the ratios of the eigenvalues correctly reproduce the [square root of the] known mass ratios for quarks and charged leptons. We also propose a diagrammatic representation of the standard model bosons, Higgs and three fermion generations, in terms of the octonions, exhibiting an $F_4$ and $E_6$ symmetry. In conjunction with the trace dynamics Lagrangian, the Jordan eigenvalues also provide a first principles theoretical derivation of the low energy value of the fine structure constant, yielding the value $1/137.04006$. The Karolyhazy correction to this value gives an exact match with the measured value of the constant, after assuming a specific value for the electro-weak symmetry breaking energy scale.

We review time expansion wave theory and refine our model showing it to accurately match observed cosmological luminosity data. The theory augments general relativity to create a container within which quantum effects can be explained by treating time as an artefact of a fourth spatial dimension that we observe as the expansion of the universe. This leads us to explain mass creation, the speed of light limit, gravity, black holes without singularities and other macroscopic effects. Our model leads to an expansion velocity of 6.87 0.36 × 106 ms-1, which results in a Hubble expansion rate constant of 71.3 3.7 km/s/Mpc. Because of the finite window of time in which we exist, an extra a degree of freedom is available leading to interesting interpretations at the subatomic level. At this size range, then a spin is allowed within the time dimension explaining why wave particle duality occurs and explains the electromagnetic force and other quantum mechanical properties. It provides a solution to the double slit conundrum and can explain how quantum entangled partners can behave in a quantum way and pass seemingly time-defying information. The potential quantitative origin of the fine structure constant which governs the size of the electromagnetic force is also strongly indicated by this theory.

A rigorous model for the electron is presented by generalizing the Coulomb's Law or Gauss's Law of electrostatics, using a unified theory of electricity and gravity. The permittivity of the free-space is allowed to be variable, dependent on the energy density associated with the electric field at a given location, employing generalized concepts of gravity and mass/energy density. The electric field becomes a non-linear function of the source charge, where concept of the energy density needs to be properly defined. Stable solutions are derived for a spherically symmetric, surface-charge distribution of an elementary charge. This is implemented by assuming that the gravitational field and its equivalent permittivity function is proportional to the energy density, as a simple first-order approximation, with the constant of proportionality referred to as the Unifield Electro-Gravity (UEG) constant. The stable solution with the lowest mass/energy is assumed to represent a ``static'' electron without any spin. Further, assuming that the mass/energy of a static electron is half of the total mass/energy of an electron including its spin contribution, the required UEG constant is estimated. More fundamentally, the lowest stable mass of a static elementary charged particle, its associated classical radius, and the UEG constant are related to each other by a dimensionless constant, independent of any specific value of the charge or mass of the particle. This dimensionless constant is numerologically found to be closely related to the the fine structure constant. This possible origin of the fine structure constant is further strengthened by applying the proposed theory to successfully model the Casimir effect, from which approximately the same above relationship between the UEG constant, electron's mass and classical radius, and the fine structure constant, emerges.

Fine-tuning is an important technique in transfer learning that has achieved significant success in tasks that lack training data. However, as it is difficult to extract effective features for single-source domain fine-tuning when the data distribution difference between the source and the target domain is large, we propose a transfer learning framework based on multi-source domain called adaptive multi-source domain collaborative fine-tuning to address this issue. This approach utilizes multiple source domain models for collaborative fine-tuning, thereby improving the model's feature extraction capability in the target task. Specifically, this approach employs an adaptive multi-source domain layer selection strategy to customize appropriate layer fine-tuning schemes for the target task among multiple source domain models. The objective is to extract more efficient features. Furthermore, a novel multi-source domain collaborative loss function is designed to facilitate the precise extraction of target data features by each source domain model. Simultaneously, it works towards minimizing the output difference among various source domain models, thereby enhancing the adaptability of the source domain model to the target data. In order to validate the effectiveness of the proposed the adaptive multi-source domain collaborative fine-tuning framework, it is analyzed and compared with the most widely used fine-tuning methods by applying them to seven publicly available visual categorization datasets commonly employed in transfer learning. Experimental results demonstrate that, in comparison with the existing fine-tuning approaches, our method not only enhances the accuracy of feature extraction in the model but also provides precise layer fine-tuning schemes for the target task, resulting in state-of-the-art performance.

Since crystals are made of periodic structures in space, predicting their three period vectors starting from any values based on the inside interactions is a basic theoretical physics problem. For the general situation where crystals are under constant external stress, we derived dynamical equations of the period vectors in the framework of Newtonian dynamics, for pair potentials recently (doi:/10.1139/cjp-2014-0518). The derived dynamical equations show that the period vectors are driven by the imbalance between the internal and external stresses. This presents a physical process where when the external stress changes, the crystal structure changes accordingly, since the original internal stress can not balance the external stress. The internal stress has both a full kinetic energy term and a full interaction term. It is extended to many-body interactions in this paper. As a result, all conclusions in the pair-potential case also apply for many-body potentials.

The flux growth of InVO4 bulk single crystals has been explored for the first time. The reported eutectic composition at a ratio of V2O5:InVO4 = 1:1 could not be used as a self-flux, since no sign of melting was observed up to 1100 °C. Crystals of InVO4 of typical size 0.5×1×7 mm3 were obtained using copper pyrovanadate (Cu2V2O7) as a flux, using Pt crucibles. XRD confirmed the orthorhombic Cmcm structure. Rests of flux material were observed on the sample surface, with occasional traces of Pt indicating some level of reaction with the crucible. X-ray absorption spectroscopy showed that oxidation states of indium and vanadium ions are +3 and +5, respectively. The size and high quality of the obtained InVO4 crystals makes them excellent candidates for the further study of their physical properties.

To harness light-matter interaction at nano-/micro-scale better control tools have to be developed. Here, it is shown that by applying external electric and/or magnetic field, ablation of Si and glass under ultra-short (sub-1~ps) laser pulse irradiation can be controlled via the Lorentz force $\mathbf{F} = e\mathbf{E} + e[\mathbf{v}\times\mathbf{B}]$, where $\mathbf{v}$ is velocity of charge $e$, $\mathbf{E}$ is the applied electrical bias and $\mathbf{B}$ is the magnetic flux density. The external electric E-field was applied during laser ablation using suspended micro-electrodes above glass substrate with an air gap for the incident laser beam. The counter-facing Al-electrodes on Si surface were used to study debris formation patterns on Si. Debris was deposited preferentially towards the negative electrode in the case of glass and Si ablation. Also, an external magnetic field was applied during laser ablation of Si in different geometries and is shown to affect ripple formation. The vectorial nature of the Lorentz force widens application potential of surface modifications and debris formation in external E-/B-fields with potential applications in mass and charge spectroscopes.

Our existence depends on a variety of constants which appear to be extremely fine-tuned to allow for the existence of life as we know it. These include the number of spatial dimensions, the strengths of the forces, the masses of the particles, the composition of the Universe and others. On the occasion of the 300^th anniversary of the death of G.W. Leibniz we discuss the question of whether we live in the "Best of all possible Worlds". The hypothesis of a multiverse could explain the mysterious fine tuning of so many fundamental quantities. Anthropic arguments are critically reviewed.

In this paper, we suggest an expansion of the Planck distribution function derived from the Boltzmann principle. Thereby, we consider expanding Planck's law with a new distribution function, and discuss fine structure constant. Furthermore, using ideas applied to the expansion of the Planck distribution function, we show that Von Koch's inequality can be derived without using the Riemann Hypothesis, that is, the Riemann Hypothesis is true, and that the abc conjecture is not true. Furthermore, we define a generalization of Entropy and discuss that Entropy is relevant to dynamical systems described by logistic function models, such as the growth of bacteria and populations.

Flotation of small particles is one of the global challenges facing the mineral raw materials processing industry. Large amounts of non-ferrous and rare metals are lost in the flotation tailings in the form of mineral particles below 15 µm in size as a result of the low effectiveness of their capture by coarse bubbles generated in conventional flotation machines. The method of combined microflotation, which is being developed in recent years, uses not only coarse but also microbubbles (MB) produced in the stand-alone generator of air-in-water microdispersion, which serves as the flotation carriers. Depending on the dose of MB, the effect of their application may be both positive, but also negative. The theoretical analysis of various mechanisms of particle transfer onto the surface of coarse bubbles and further into the froth layer allowed to obtain the formula for the optimal MB dose f=2dddpρp, where dd is MB size; dp and ρp respectively are the size and the density of particles. Experiments performed on the copper ore flotation tailings at the Atalaya Mining (Spain) and Chaarat Kapan (Armenia) concentrators showed that, besides the optimal MB dose in the range of 1-3 ml/g, there is another optimal MB dose in the range of 10-20 ml/g, where the copper recovery increases by several percent compared to the reference test (f= 0). In the area between the optimal MB doses, the deep minimum in copper recovery is observed, which value is by several percent lower compared to the value in the reference test.

Scale inhibitors are deployed as preventive and rejuvenation operation in oil and gas industry when production operations are under threat or menace of scale blockage. The application of scale inhibitors is carried out through a method known as squeezing. In general, the squeeze process is governed by inhibitor-rock interaction which is described by adsorption/desorption isotherm. Most reservoirs produce loose sand grains or fine sand which float and flow within the pore spaces along with the squeezed scale inhibitors. Hypothetical reports have shown that not all scale inhibitors pumped into the formation adsorb onto the formation rock. A number of factors (irreversible adsorption, pH changes, competing ions, concentration and temperature) have been considered to affect the adsorption and return profile of these scale inhibitors. This review work examines the performances of most common scale inhibitors used in the oil and gas production activities, theoretical application in reservoirs and how loose fine sand grains affect the adsorption and desorption characteristics of squeezed scale inhibitors. Additionally, presented were overviews of previous reports on fine sand production and migration of fine sands through formation pores in reservoirs.

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows the superior performance of proposed method to the previous works on Stanford dog breeds datasets.

The fluid seepage in local-saturated zone of subgrade promotes the migration of fine particles in the filler, resulting in the change of pore structure and morphology of the filler and the deformation of solid skeleton, which affects the fluid seepage characteristics. Repeatedly, the muddy interlayer, mud pumping and other diseases are finally formed. Based on the theory of two-phase seepage, the theory of porous media seepage, and the principle of effective stress in porous media, a two-phase fluid-solid coupling mathematical model in local-saturated zone of subgrade considering the effect of fine particles migration is established. The mathematical model is numerically calculated with the software COMSOL Multiphysics○R, the two-phase seepage characteristics and the deformation characteristics of the solid skeleton in local-saturated zone of the subgrade are studied. The research results show that due to the continuous erosion and migration of fine particles in local-saturated zone of the subgrade, the volume fraction of fine particles first increases then decreases and finally becomes stable with the increase of time. And the volume fraction of fine particles for the upper part of the subgrade is larger than that for the lower part of the subgrade. The porosity, the velocity of fluid, the velocity of fine particles, and the permeability show a trend of increasing first and then stabilizing with time; the pore water pressure has no significant changes with time. The vertical displacement increase first and then decrease slightly with the increase of time, and finally tend to be stable. For a filler with a larger initial volume fraction of fine particles, the maximum value of the volume fraction of fine particles caused by fluid seepage is larger, and the time required to reach the maximum value is shorter. It can be concluded that in actual engineering, the volume fraction of fine particles in the subgrade filler should be minimized on the premise that the filler gradation meets the requirements of the specification.

These years have witnessed growing research on applying rheology in grinding and flotation treatment of finely disseminated ores. The slurry rheology has long been identified as the comprehensive effect of inter-particle interactions, including their aggregation (coagulation and flocculation) and dispersion states in slurry, which are more impactive under the fine-particle effect. In this regard, rheology has the potential to play a significant role in interpreting the flowing and deforming phenomenon of inter-particle aggregates, particle-bubble aggregates, and flotation froth. Though much attention has been paid to the rheological effect in industrial suspension, this has not been the case for mineral grinding and flotation for fine particles. The influential mechanism of rheology on the sub-processes of mineral processing has not been systemically concluded and revealed thoroughly, without which the underpinning mechanism for enhancing the processing efficiency has been difficult to discover. This paper reviews the current application and importance of rheology in the fine mineral processing, and the potential research direction in the field is proposed.

The identification and quantitative determination of wool and fine animal fibers are of great interest in the textile field because of significant price differences between them and common adulterations in raw and processed textiles. Since animal fibers have remarkable similarities in their chemical and physical characteristics, specific identification methods have been studied and proposed following advances in analytical technologies. The identification methods of wool and fine animal fibers are reviewed in this paper and the results of relevant studies are listed and summarized, starting from classical microscopy methods which are still used today not only in Small to Medium Enterprises but also in large industries, research studies and quality control laboratories. Particular attention has been paid to image analysis, Nir spectroscopy and proteomics which constitute the most promising technologies of quality control in the manufacturing and trading of luxury textiles and can find application in forensic science and archeology.

Objective: We aimed to clarify the differences in the association between re-admission for heart failure (HF) and left atrial (LA) overload indices by sex in heart failure and a preserved ejection fraction (HFpEF). Methods: We analysed 898 HFpEF patients hospitalized for acute decompensated HF. Blood tests and transthoracic echocardiography were performed before discharge. The primary endpoint was re-admission for HF during the first year. Results: The ratio of diastolic elastance to arterial elastance (p=0.014), a relative index of LA pressure overload, in men and LA volume index (LAVI, p=0.020) in women were significant for re-admission for HF during the first year in the multivariable Fine-Gray analysis. Stroke volume (SV)/LA volume (LAV), another index for LAV overload, was not a significant prognostic factor of re-admission for HF during this time. Conclusion: LA overload was an important prognostic factor for HF re-readmission during the first year after enrolment in patients with HFpEF, but the indices relating to LA overload differed by sex.

We investigated preschool-aged children’s understanding of early fractional tasks and how that performance correlates with fine motor skills and use of gestures while counting. Participants were 33 preschoolers aged 4 to 5 in two Southeastern public elementary schools. Children were tested individually in an interview-like setting. Mathematics tasks were presented in a paper and pencil format and the Grooved Pegboard test assessed fine motor skills. Finally, utilization of gestures was evaluated by taking a behavioral rating of the child’s hand morphology, accuracy of gestures, and synchrony of gestures and spoken word while performing a counting task. Results indicate performance on fractional reasoning tasks significantly predicts both fine motor ability and accuracy of gestures.

Due to metal leaching and poor catalyst stability, the chemical industry’s fine chemical and pharmaceutical sectors have been historically reluctant to use supported transition metal catalysts to manufacture fine chemicals and active pharmaceutical ingredients. With the advent of new generation supported metal catalysts and flow chemistry, we argue in this study, this situation is poised to quickly change. Alongside heterogenized metal nanoparticles, both single-site molecular and single-atom catalyst will become ubiquitous. This study offers a critical outlook taking into account both technical and economic aspects.

Ultraviolet (UV) light with a wavelength of 254 nm has proven to be effective at inactivating microorganisms, and thus has been increasingly employed as a method of disinfection for indoor environments. Solar UV wavelengths (300 to 400 nm) are known to initiate the formation of secondary organic aerosol (SOA) particles from photo-oxidation of volatile organic compounds in the atmosphere, but germicidal wavelengths have not been extensively studied for indoor environments. In this work, toluene was exposed to 254 nm UV light in a laboratory photoreactor, with varying conditions of the air, the duration of UV exposure, and the duration of post-UV time. The number of particles formed in the fine particulate matter (PM2.5) size range was measured, and significant levels of particle formation were observed for UV exposure periods of as short as 5 minutes. The particle formation ranged from 2.4x10⁶ particles/m³ for 5 minutes of UV exposure, to 1449.8x10⁶ particles/m³ for 15 minutes of UV exposure. Particle formation was found to increase with increasing concentrations of gas phase toluene, and at relative humidity of approximately 20% and higher. Variations in the initial number of particles present did not appear to have a significant effect on the particle formation, suggesting that nucleation was not a controlling factor. However, tests in a commercial environment showed no significant detectable PM2.5 formation, indicating that SOA formation during the intermittent use of germicidal UV may not significantly affect indoor air quality.

We introduce new metric structures on a smooth manifold (called “weak" structures) that generalize the almost contact, Sasakian, cosymplectic, etc. metric structures (φ,ξ,η,g) and allow us to take a fresh look at the classical theory and find new applications. This assertion is illustrated by generalizing several well-known results. It is proved that any Sasakian structure is rigid, i.e., our weak Sasakian structure is homothetically equivalent to a Sasakian structure. It is shown that a weak almost contact structure with parallel tensor φ is a weak cosymplectic structure and an example of such a structure on the product of manifolds is given. Conditions are found under which a vector field is a weak contact vector field.

Low-cost monitors have made possible for the first time measurements of long-term (months to years) potential indoor exposures to fine particles. Indoor and outdoor measurements made over nearly 5 years (2017-2021) by the largest network of low-cost monitors in the United States (PurpleAir) are compared to the prevalence of adult smokers in 1650 Zip codes within the three West Coast states of California, Oregon, and Washington. The results show that mean indoor exposures above the 75th percentile of adult smoking prevalence are more than 50% higher than those below the 25th percentile. Mean outdoor concentrations are also elevated, but by a smaller amount (20%). Both comparisons are significant at the p&lt;0.001 level. The elevation of the PM2.5 concentrations with increasing smoking prevalence is evidence of environmental disparities in income, education, and other socioeconomic indices..

Vibro-fluidized dry coal beneficiation involves the unavoidable mixing of -1 mm coal dust into the fluidized bed, which compromises the stability and homogeneity of the bed density and reduces the effectiveness of fine-grained coal separation. In order to understand the features of coal powder separation and mixing (-1 mm) in a vibrating fluidized bed, as well as to achieve a uniform and stable bed density, a wide-grained dense medium consisting of -1 mm coal dust and 0.3-0.15 mm magnetite powder was homogeneously mixed in this work. The degree of density separation in the entire bed was examined, as well as the local mixing properties of the Wide-grained Dense Medium. It was discovered that when low vibrational energy was introduced at the same gas velocity, particle separation was enhanced in comparison to the usual fluidized bed. Little bubbles were found to ensnare fine coal dust and migrate upward, increasing density separation. The introduction of high vibrational energy leads to particle disorder within the bed and an increase in the level of particle mixing. The beneficial effects of vibration on the bed are diminished when the gas velocity is gradually increased under specified vibration circumstances. This causes big bubbles to develop more frequently, which increases particle mixing. Among them, the 1-0.5 mm fine-grained coal exhibits the best mixing state; it is less impacted by variations in air velocity and vibration levels, whereas the -0.5 mm coal dust is more affected by each of these variables. Furthermore, a wide range of air velocity regulation without a significant segregation state change was discovered to be achievable by the particle system under vibration circumstances of f=25Hz A=1mm and f=25Hz A=2mm.

The research was undertaken to study the way deformation behaves in ultrafine-grained (UFG) conducting Al-Zr alloys doped with Sc, Hf, and Yb. All in all, 8 alloys were studied with zirconium partially replaced by Sc, Hf, and/or Yb. Doping elements (X = Zr, Sc, Hf, Yb) in the alloys total 0.4 wt.%. The choice of doping elements is conditioned by possible precipitation of Al3X particles with L12 structure while annealing these alloys. Such particles provide higher thermal stability of a nonequilibrium UFG microstructure. Initial coarse-grained samples were obtained by induction casting. The UFG microstructure in the alloys was formed by Equal Channel Angular Pressing (ECAP) at 225°C. Superplasticity tests were carried out at temperatures ranging from 300 to 500 °C and strain rates varying between 3.310-4 and 3.310-1 s-1. The highest values of elongation to failure are observed in Sc-doped alloys. A UFG Al-0.2%Zr-0.1%Sc-0.1%Hf alloy has maximum ductility: at 450 °C and a strain rate of 3.310-3 s-1, relative elongation to failure reaches 765%. At the onset of superplasticity, stress–strain curves are characterized by a stage of homogeneous strain and a long stage of localized plastic flow. The dependence of homogeneous strain (eq) on test temperature in UFG Sc-doped alloys is increasing uniformly, which is not the case for other UFG alloys with eq(T) dependence peaking at 350-400 оС. Strain rate sensitivity coefficient of flow stress m is small and does not exceed 0.26-0.3 at 400-500 °C. In UFG alloys containing no scandium, m coefficient is observed to go down to 0.12-0.18 at 500 оС. It has been suggested that lower m values are driven by intensive grain growth and pore formation in large Al3X particles, which develop specifically at the ingot crystallization stage.

There has been considerable research on reconstructing 3D shapes from single-view images; however, preserving the detailed information of the input image remains difficult. In this paper, we propose the application of a gradient map to train a network, aimed at improving the visual quality of fine-grained details such as the thin and tiny components of generated shapes. Each gradient map was created from the original voxel data, and each value represented the amount of information per volume. Here, the gradient map was defined by several methods that mathematically quantify and represent the detailed structure of an object. By applying this map to the loss function in training, we could induce the network to intensively train partial details, such as thin and narrow parts. We demonstrated that the detailed information was well-recovered when a weight that is proportional to the gradient value was applied to the loss. Furthermore, it is expected that our method will contribute to the development of 3D technologies related to the construction of virtual space for simulation and new customer experience.

Text summarization remains a challenging task in the Natural Language Processing field despite the plethora of applications in enterprises and daily life. One of the common use cases is the summarization of web pages which has the potential to provide an overview of web pages to devices with limited features. In fact, despite the increasing penetration rate of mobile devices in rural areas, the bulk of those devices offer limited features in addition to the fact that these areas are covered with limited connectivity such as the GSM network. Summarizing web pages into SMS becomes, therefore, an important task to provide information to limited devices. This work introduces WATS-SMS, a T5-based French Wikipedia Abstractive Text Summarizer for SMS. It is built through a transfer learning approach. The T5 English pre-trained model is used to generate a French text summarization model by retraining the model on 25,000 Wikipedia pages then compared with different approaches in the literature. The objective is twofold: (1) to check the assumption made in the literature that abstractive models provide better results compared to extractive ones; and (2) to evaluate the performance of our model compared to other existing abstractive models. A score based on ROUGE metrics gave us a value of 52% for articles with length up to 500 characters against 34.2% for transformer-ED and 12.7% for seq-2seq-attention; and a value of 77% for articles with larger size against 37% for transformers-DMCA. Moreover, an architecture including a software SMS-gateway has been developed to allow owners of mobile devices with limited features to send requests and to receive summaries through the GSM network.

Is the extensive decline of insects partially due to an insect-specific feature of their functional organization that is disadvantageous for living in an industrialized environment? The unique way in which gases are supplied in insect tissues is such a special trait. It exposes cells directly to the gas phase, via tracheae that end in micro-tubes, the tracheoles, which have diameters in the same range as particles of ultra-fine dust transported by air of industrialized countries. Number and volume of these particles – calculated to be inhaled by honeybees, e.g., – are indicative to locally impede O2-uptake and CO2-release and thus to restrain physiological activities.

This article uses a contemporary and revelatory case study to explore the relationship between three conversations in the innovation literature: design thinking, creativity in strategy and, the emerging area of art-thinking. Businesses are increasingly operating in a VUCA environment where they need to design better experiences for their customers and better outcomes for their firm and the Arts are no exception. Innovation, or more correctly growth through innovation, is a top priority for business and although there is no single, unifying blueprint for success at innovation, design thinking is the process that is receiving most attention and getting most traction. Design thinking teaches businesses to think with the creativity and intuition of a designer; to show a deep understanding of; and have empathy with the user. But design thinking has limitations. By placing the consumer at the very heart of the innovation process, design thinking can often lead to more incremental than radical ideas. Now there is a new perspective emerging, art-thinking, in which the objective is not to design a journey from current scenario; A to improved position: B. Art thinking requires the creation of B and spends more time in the open ended, problem space, staking out possibilities and looking for uncontested space. In Dublin, we examine a case of the oldest, largest and most prestigious fine art gallery and studio where most of the country’s best-known and successful visual artists both make, exhibit and sell their art. Graphic Studio Dublin is primarily a printmaking studio, established by artists over 60 years ago. It has facilities for woodblock, lino-print, silkscreen, intaglio and carborundum etching spread over four floors of a centrally located studio where the artists have access 24/7. But two years ago it found itself on the brink of collapse having borrowed heavily to invest in new facilities during the period of Ireland’s economic collapse. Its loans were sold to a vulture-fund who were about to foreclose in a move that would have seen a generation of Irish Artists displaced. A new board of directors was empaneled and they introduced some art thinking principles to bring the organisation back from the brink. They used an art-thinking mindset and design thinking tools to restore the fortunes of this venerable, artist-led institution and it worked.

Engineering properties of bedrock materials at Badlands National Park were used to develop models for Park managers to assess slope erosion and stability for fossil resource protection. Six fully instrumented sites were used to document slope conditions. Bedrock consisted of Oligocene White River Group rocks. Bulk erosion rates correlated to grain size with silty-sandy materials producing higher mass erosion rates as a function of the silt-to-clay ratio and plastic index. Data indicated that as grain size decreased, plastic index increased leading to a decrease in erodibility. These parameters were used to construct a grain-size proxy, ψ, that was substituted for grain size, D, in Bagnold’s entrainment equation and provided significant improvement in calculation of critical entrainment velocities for fine-grained materials. Hydraulic analyses of slope and pediment surface processes indicated surface roughness was a controlling factor and materials washed from rough steep slopes were effectively transported across smooth low-angle pediments with slope-to-pediment angle ratios of nearly 6:1. Slope stability modeling of ten slopes produced high factors of safety for all slopes, even under saturated conditions and was attributable to clay cohesion. All results were used to construct models that predicted years until net slope erosion equaled 2.5 cm (1 inch). Using these results, Park managers were advised to visit erosion-prone sites on a 1 to 6 year schedule, based on site geology and slope aspect, to adequately protect critical fossil resources from destruction.

The two-stage direct thermal contact (2sDTC) process is a rapid dewatering approach for fluid fine tailings (FFT) that is integrated into an oil sands bitumen extraction plant to reduce tailings pond storage and freshwater usage. In the first stage, combustion gas directly contacts atomized FFT, vaporizing water and yielding dry solids and steam-rich hot gas. The second stage involves pond effluent water directly contacting the steam-rich hot gas, recovering heat and condensing moisture. Case studies confirm the technical feasibility of the integration. Benefits include producing a dry discharge, reducing tailings water discharge and conserving an equivalent to 0.2 barrels of freshwater per barrel of oil produced. Most importantly, these benefits incur no additional energy cost to the plant as the integration eliminates the energy penalty and CO2 emissions associated with FFT dewatering. Further capacity enhancement can be achieved by using centrifuge-concentrated FFT. The study reveals that FFT concentrated to ~50 wt% solids could still maintain its pump-ability and be accommodated by the 2sDTC process, leading to an integration which could dewater more FFT annually and conserve more freshwater ( 0.58 barrels per barrel of oil produced), with the sole energy requirement being the power to drive the centrifuge machinery.

CoO-ZnO-based composites have attracted considerable attention for the development of energy storage devices because of their multifunctional characterization and ease of integration with existing components. This paper reports the synthesis of CoO@ZnO (CZ) nanostructures on Ni foam by the CBD method for facile and eco-friendly supercapacitor applications. The formation of a CoO@ZnO electrode functioned with cobalt, zinc, nickel and oxygen groups was confirmed by X-ray diffraction, X-ray photoelectron spectroscopy, low and high-resolution of scanning electron microscopy, and transmission electron microscopy. The as-synthesized hierarchical nanocorn skeleton-like structure of CoO@ZnO-3h (CZ3h) electrode delivered a higher specific capacitance of 1136 F/g at a current density of 3 A/g with outstanding cycling stability, showing 98.3% capacitance retention over 3000 cycles in an aqueous 2 M KOH electrolyte solution. This retention was significantly better than that of other prepared electrodes, such as CoO (CO), ZnO (ZO), CoO@ZnO-1h (CZ1h), and CoO@ZnO-7h (CZ7h) (274, 383, 240 and 537 F/g, respectively). This superior capacitance was attributed to the ideal surface morphology of CZ3h, which is responsible for the rapid electron/ion transfer between the electrolyte and electrode surface area. The enhanced features of the CZ3h electrode highlight potential applications in high performance supercapacitors, solar cells, photocatalysis, and electrocatalysis.

In this study, the distribution patterns of southern horse mackerel are examined using commercial fine-scale resolution data. Using landings by size category and VMS data from the Portuguese commercial bottom trawl fishery, which consistently targets horse mackerel, this study provides a comprehensive analysis of horse mackerel age distributions spanning a decade (2010-2020). Importantly, the study addresses potential biases in commercial effort data and establishes the usefulness of commercial bottom-trawl gear as a suitable method for sampling and evaluate southern horse mackerel stock dynamics. Ordered regression models were applied to allow for the modelling of the distribution of multiple age categories and investigate spatio-temporal migrations off the Portuguese coast. Southern horse mackerel shows a widespread age distribution range and stable abundance with indications of seasonal and spatial patterns in the distribution of specific age groups. The insights derived from this research contribute valuable knowledge for understanding the dynamics and distribution patterns of fish populations.

Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.

Currently available nationwide prediction models for fine particulate matter (PM_2.5) lack prediction confidence intervals and usually do not describe cross validated model performance at different spatiotemporal resolutions and extents. We used 41 different spatiotemporal predictors, including data on land use, meteorology, aerosol optical density, emissions, wildfires, population, traffic, and spatiotemporal indicators to train a machine learning model to predict daily averages of PM_2.5 concentrations at 0.75 sq km resolution across the contiguous United States from 2000 through 2020. We utilized a generalized random forest model that allowed us to generate asymptotically-valid prediction confidence intervals and took advantage of its usefulness as an ensemble learner to quickly and cheaply characterize leave-one-location-out CV model performance for different temporal resolutions and geographic regions. Using a variable importance metric, we selected 8 predictors that were able to accurately predict daily PM_2.5, with an overall leave-one-location-out cross validated median absolute error of 1.20 ug/m³, an R² of 0.84, and confidence interval coverage fraction of 95%. When considering aggregated temporal windows, the model achieved leave-one-location-out cross validated median absolute errors of 0.99, 0.76, 0.63, and 0.60 ug/m³ for weekly, monthly, annual, and all-time exposure assessments, respectively. We further describe the model’s cross validated performance at different geographic regions in the United States, finding that it performs worse in the Western half of the country where there are less monitors. The code and data used to create this model are publicly available and we have developed software packages to be used for exposure assessment. This accurate exposure assessment model will be useful for epidemiologists seeking to study the health effects of PM across the continental United States, while possibly considering exposure estimation accuracy and uncertainty specific to the the spatiotemporal resolution and extent of their study design and population.

Background: Particulate matter and urban air pollution affect the human body and can lead to death. Epidemiological studies should consider the exposure to pollutants and the diverse responses of individuals depending on their sensitivity to the pollutants. Methods: In this study, air pollution measurements were obtained hourly at measuring stations operated by the national and local governments to increase the reliability of the measured values. A β-ray absorption method was used to analyze the measurements of fine dust and determine the particulate matter content. Results: The air pollution data were log-converted to enable the comparison of data from different time periods. The comparison was made, focusing on the period of Seoul's fine dust policy. As a result, the cancer incidence rate also decreased after the policy. The data on individual characteristics were obtained from a survey of Seoul citizens conducted from 2015 to 2016 and using indicators such as the quality of life and social trust of Seoul citizens. Concludions: The survey on the living environment and residence indicated that 80% of the heads of households were men. Women had a greater dissatisfaction than men with the residential, economic, and social environments. The survey questions on well-being were related to the internal and external environment such as air pollution, noise, and fine dust.

Learning dental procedures is a complex task involving the development of fine motor skills. The reported use of theories and/ or evidence for designing learning activities to develop the fine motor skills needed for dental practice is limited. The aim of this review is to explore the available body of knowledge related to learning fine motor skills in dentistry. Evidence from studies investigating motor skill learning highlights the negative impact of self-focus and self-regulation on learning outcomes, particularly during the early stages of learning. The development of activities and schedules that enable novices to demonstrate characteristics similar to experts, without the reported long period of ‘deliberate practice’, is clearly of value. Outcomes of learning implicitly are important in dentistry because working under stressful conditions is common, either during undergraduate study or in practice. It is suggested that learning implicitly in the simulation stage can reduce disrupted performance when transitioning to clinical settings. Therefore, further investigation of effective methods for learning dental fine motor skills is indicated, using approaches that result in robust performance, even under stressful conditions.

Background: Lung cancer is one of the leading causes of morbidity and mortality worldwide with 25% of deaths due to lung cancer occurring in Europe. This study therefore sought to assess the burden of lung cancer by country and to evaluate the magnitude of fine Particulate Matter (PM2.5) and cigarette smoking by country in Europe. Methods: An ecological study nested on the World Health Organization air pollution database 2016 was conducted. We sampled 30 European Countries, with a total of 1625 mean annual samples of Particulate Matter (PM2.5) collected from 1625 designated sites (n = 1625). We further used the ‘World Health Disease Rankings’ database to extract Lung Cancer Morbidity and Mortality Rate by country. We used SAS version 9.4 to indicate the distribution of PM2.5 and Lung Cancer Mortality Rate. Results: Lung cancer Relative Risk (RR) was 1.0 in all never- smokers. RR for Ex-smokers for Adeno carcinoma was 3.5 in males and 1.1 in females, small cell carcinoma was 16.2 in males and 3.8 in females. RR for current smokers for Adeno carcinoma was 8.0 in males and 4.1 in females, small cell carcinoma was 57.9 in males and 18.2 in females. Mean annual PM2.5 by country ranged from 6.01 to 37.28µg/m3 whereas lung cancer mortality rate by country ranged from 19.67 to 54.26 deaths per 100,000 population. Conclusion: Cigarette smoking and exposure to both second hand smoke and high concentration of PM2.5 resulted into increased burden of lung cancer in Europe. Countries should re-strategize to reduce the burden of lung cancer in Europe.

Previous studies have reported certain sex differences in motor performance precision. The aim of the present study was to analyse sex differences in fine motor precision performance for both hands in different tests conditions. 220 Spanish participants (ages: 12-95) performed fine motor tasks - tracing over the provided models – lines of 40 mm for both hands, two sensory conditions (PV – proprioceptive-visual; P – proprioceptive only) and three movement types (F – frontal, T – transversal and S - Sagittal). Differences in line length (the task focused on precision) were observed through MANOVA analysis for all test conditions, both sexes and different age groups. Sex differences in precision were observed in F and T movement types (statistically significance level and higher Cohens’ d was observed in condition with vision). No any statistically significant differences were observed in both hands and sensory conditions in sagittal type. Sex differences in fine motor precision were more frequently observed in the PV sensory condition in the frontal movement type and less in the sagittal one.

Crystal structures of simple metals and binary alloy phases based on the face-centered cubic (fcc) structure are analyzed within the model of Fermi sphere – Brillouin zone interactions to understand the stability of original cubic structure and derivative structures with distortions, superlattices and vacancies. Examination of the Brillouin-Jones configuration in relation to the nearly-free electron Fermi sphere for several representative phases reveals significance of the electron energy contribution to the phase stability. Representation of complex structures in the reciprocal space clarifies their relationship to the basic cubic cell.

Many studies discuss carbon-based materials because of the versatility of its element. They include different opinions for scientific problems and discuss fairly at convincing and compelling levels within the scope and application. A gas-state carbon atom converts into various states depending on its conditions of processing. The electron transfer mechanism in the gas-state carbon atom is responsible to convert it into various states, namely, graphite, nanotube, fullerene, diamond, lonsdaleite and graphene. The shape of ‘energy trajectory’ enables transferring electrons from the left- and right-sides of an atom is like a parabola. That ‘energy trajectory’ is linked to states (filled state and suitable nearby unfilled state) where force-exertion along the poles of transferring electrons is remained balance. So, the mechanism of originating different states of a gas-state carbon atom is under the involvement of energy first. This is not the case for atoms executing confined inter-state electron-dynamics as the force is involved first. Graphite-, nanotube- and fullerene-state atoms ‘partially evolve partially develop’ (form) their structures. These possess one-dimensional, two-dimensional and four-dimensional ordering of atoms, respectively. Their structural formation also comprises ‘energy curve’ having a shape-like parabola. Transferring suitable filled state electron to suitable nearby unfilled state is under a balance force exerting along the poles. The graphite structure under only attained-dynamics of atoms can also be formed but in two-dimension. Here, binding energy between graphite-state carbon atoms is for a small difference of exerting forces along their opposite poles. Structural formation in diamond, lonsdaleite and graphene atoms involve energy to gain required infinitesimal displacements of electrons through which they maintain orientationally-controlled exerting forces along dedicated poles. In this study, the growth of diamond is found to be south to east-west (ground) where atoms bound ground to south. Thus, diamond atoms merge for a tetra-electron ground to south topological structure. Lonsdaleite atoms merge for a bi-electron ground to just-south topological structure. The growth of graphene is found to be north to ground where atoms bound ground to north. Thus, graphene atoms merge for a tetra-electron ground to north topological structure. Glassy carbon exhibits layered-topological structure where, tri-layers of gas-, graphite- and lonsdaleite-state atoms successively bind in repetitive order. Nanoscale hardness is also sketched based on different force-energy behaviors of different state carbon atoms. Here, structure evolution in each carbon state atom explores its own science.

CD4 count recovery is the main goal for an HIV patient who initiated ART. Some patients respond positively to ART and attain CD4 count recovery, some patients might fail to recover their CD4 count due to non-adherence, treatment resistance, and virological failure, leading to HIV-related complications and death. The purpose of this study is to find the determinants of death in patients who failed to recover their CD4 count after initiating antiretroviral therapy. The data used in this study was obtained from KwaZulu-Natal, South Africa, where 2528 HIV-infected patients with a baseline CD4 count of 200 cells/ were initiated on ART. We used a Fine-Gray sub-distribution hazard and cumulative incidence function to estimate potential confounding factors of death, where CD4 count recovery was a competing event for failure due to death. Patients who were not suffering from tuberculosis were 1.33 times at risk of dying before attaining CD4 count recovery [aSHR 1.33; 95% CI (0.96-1.85] compared to those who had no tuberculosis. Rural patients had a higher risk of not recovering and leading to death [aSHR 1.97; 95%CI (1.57-2.47)] than those from urban areas. The patient’s tuberculosis status, viral load, regimen, baseline CD4 count, and location were significant contributors to death before CD4 count recovery. Intervention programs targeting HIV testing in rural areas for early ART initiation and promoting treatment adherence are recommended.

Self-compacting concrete (SCC) is a high-quality construction solution combining high fluidity, passing and filling ability with improved mechanical properties and durability. In the present study, the effect of incorporating alternative waste materials, such as two by-products of the ce-ment industry, namely cement kiln dust (CKD) and by-pass dust (BPD), into SCC as partial re-placement for traditional filler material was investigated. The produced compositions were compared with reference mixtures containing only marble powder (MP) as a filler. A series of tests including specific test methods for wet SCC, compressive, flexural and tensile splitting strength tests as well as drying shrinkage determination were conducted to evaluate the quality of the produced SCC in terms of fresh and hardened SCC properties. The use of alternative fine filler materials resulted in a high-performance sustainable SCC of low cement content. Specifi-cally, incorporating CKD into the SCC enhanced its rheological behavior and improved slightly its mechanical properties, while the use of BPD led to SCC mixtures of adequate rheological characteristics but with highly upgraded mechanical and physical properties

Drought poses a significant constraint on rice production, and in this study, we have discovered a novel drought-sensitive mutant, designated as dsm3, arising from the progenies of indica rice variety Zhonghui8015 treated with EMS. Under drought stress conditions, dsm3 exhibited characteristic withered leaf tips, accompanied by increased levels of MDA and H2O2, reduced Pn, and decreased activity of POD and SOD. Genetic analysis revealed that the withered leaf tip phenotype was governed by a single recessive gene, designated as Osdsm3.To begin with, Osdsm3 was initially mapped to the short arm of chromosome 1 through a cross involving dsm3 and 02428. Subsequently, utilizing a population of 2591 F2 individuals, we narrowed down the location of Osdsm3 to a 78Kb interval, encompassing 13 open reading frames (ORFs). Sequencing analysis unveiled a mutation (1275G→A) in the exon of the candidate gene (LOC_Os01g10680), leading to premature translation termination. Moreover, a quantitative RT-PCR assay demonstrated high expression of OsDSM3 in the panicle and sheath, with a significant upregulation of drought stress-related genes under drought conditions. Phylogenetic analyses indicated that Osdsm3 shares evolutionary homology with UNE1, an intracellular transport protein found in Arabidopsis thaliana. Subcellular studies further confirmed that OsDSM3 resides in the cytoplasm. In conclusion, the forthcoming cloning of Osdsm3 holds promise for delving deeper into the molecular mechanisms governing rice drought resistance.

Air pollution is a leading cause of death in the United States, and is associated with adverse health outcomes, including increased vulnerability to coronavirus disease 2019 (COVID-19). The AirBeam2 was used to measure particulate matter with a diameter of 2.5 micrometers or smaller (PM2.5), to investigate differences between indoor and ambient levels at seven private homes in New York during and after the COVID-19 lockdown. Measurements taken in 2020 winter, spring and fall and in fall 2022 showed that 90% of the time, indoor PM2.5 levels exceeded outdoor levels both during and after the COVID-19 lockdown, p = 0.03, and exceeded safety levels. Higher indoor PM2.5 levels attributed to little or no ventilation from cooking and smoke in the kitchen and basements, were significantly greater in fall than in winter. Higher ambient PM2.5 levels were attributed to vehicular traffic at a street-facing sampling site. PM2.5 sources identified in this study may help in devising control strategies to improve indoor air quality (IAQ), and consequently alleviate respiratory health effects. These findings may be used as a basis for in-house modifications including natural ventilation and use of air purifiers to reduce exposures, mitigate future risks, and prevent potential harm to vulnerable residents.

The cast grains of the 7055 aluminum alloy were refined by adding Sm + Er, and the proper heat treatment procedure was utilized to further precipitate the rare earth phase in order to increase the alloy's strength and toughness. The grain size, microstructure and phase were characterized by optical microscopes (OM), Scanning electron microscopy - energy spectrum (SEM-EDS), XRD diffractometer (XRD). The macroscopic hardness, yield strength and tensile strength of alloy materials were measured by hardness meter and universal electronic tensile machine. The results shown that the as-cast sample and the heat treatment sample all exist Al10Cu7Sm2 and Al8Cu4Er rare earth phase; But after heat treatment, the volume percentage of the rare earth phase dramatically increased and the dispersion was more unified. When 0.3wt.%Sm and 0.1wt.%Er were added, the grain size could be refined to 53μm. With the increase of the total content of rare earth elements, the refining effect first increased and then decreased. Under 410 oC solid solution for 2h + 150oC aging for 12h, the macroscopic hardness, yield strength, tensile strength and elongation of 0.3wt.%Sm + 0.1wt.%Er+7055 as-cast sample were 155.8HV, 620.5Mpa, 658.1Mpa, and 11.90%, respectively.

Stable isotope concentrations in the soil, rain and ground water have been used to trace the water extraction zones of plants in different environments. The need to identify the plant water use by plants in afforestation programs to control desertification increases the importance of sap water partitioning of plants in sand dune areas. However, the introduction of new plant covers exerts pressure on the water resources and can affect the local soil water conditions. In this study, we analyzed the isotope concentrations in rain, soil, sap, and ground water after the summer of 2010. Two experimental plots established in the Hailiutu catchment (Shaanxi province, northwest China) were selected to gather the water samples between September and October 2010. One plot is dominated by Salix bushes (Salix psammophila C. Wang \& Chang Y. Yang) and the other by the tree species Willow (Salix matsudana Koidz.). The total precipitation at the experimental site was 401 mm/yr during 2010, while 88.7 mm was collected in total for the period September to October. Willow trees transpired 12.82 kg/d being almost three times larger than Salix shrubs (4.57 kg/d). Despite the transpiration rates of both plant species and the few rain events in the region, the soil water beneath the plant covers is not depleted. Stable isotope signature of soil water beneath both covers shows the fractionation front in Salix at 20 cm depth and at Willow at 40 cm depth. However, soil water signature is closer to the groundwater than the collected rain water.

Population is the main driver of land systems and environmental change. However, the population is usually treated as a variable that only considers the population number, and the multidimensional population structure is largely ignored. There has been a systematic transition of population structure in the past several decades, such as the changing household structure, the increasing aging population and divorce rate, and human migration. All these have direct or indirect impacts on land use and environmental issues. Here, we will take China as an example to examine the relationship between household dynamics and land use change in China by exam-ining changes in household structure and land use change in China between 1980 and 2020. The results show:1) the number of households increased by 130.95% from 1980 to 2020, while the population only increased by 42.83%. The size of households decreased from 4.41 to 2.92 in China from 1982 to 2020. The household dynamics vary from province to province, which is affected by urbanization rate and economic development. 2) Birth rates, divorce rates, population aging, and migration all affect household structure, which directly or indirectly affects changes in land use systems. 3) The changes in China's land use are interlinked and interact with changes in household structure, evident in the increase in residential land use and the abandonment of arable land. The rising household number increased the area of urban and rural settlement, leading to the fragmentation of cultivated land and the reduction of ecological land. To regulate land use change for sustainable development, future land use planning should take into effect of household dynamics, and reduce the negative effects of household dynamics on land systems and environmental change.

The modern material design aims to achieve multifunctionality by integrating structures in a diverse range, resulting in simple materials with embedded functions. Biological materials and organisms are typical examples of this concept, where complex functionalities are achieved through a limited material base. This review highlights the inspiration from multifunctional organisms and materials in nature, emphasizing their structural and functional integration. In particular, we discuss complex hierarchical architectures ranging from the nanoscale to the macroscale, to achieve multifunctional properties such as extreme pressure, impact and wear resistance, shape memory and self-healing, adhesiveness and anti-fouling, sensing-actuating and sensing-camouflage, and extreme environmental survival. These strategies of integrating structure and function are critical for promoting innovations and breakthroughs in the modern engineering materials and applications.

Most non-infectious diseases are associated with dysfunction of proteins or protein complexes. Аssociation between sequence and structure is analyzed since a long time, and analysis of sequence organization in domains and motifs is actual research area. A mathematical method is proposed here to identify the hierarchical organization of protein sequences. The method is based on pentapeptide as a unit of protein sequences. This method was applied on a non-homologous dataset of protein sequences. The analysis revealed 11 hierarchical levels of protein sequence organization, showing the relationship of these multiple fragments of sequences. Using different examples, we illustrated how the fragments of the spatial structure of protein correspond to the elements of the hierarchical structure of the protein sequence. A hierarchical structure is observed in the protein sequence. This methodology is an interesting basis for mathematically based classification of elements of spatial organization of proteins. Elements of the hierarchical structure of different levels of the hierarchy can be used for biotechnological and medical problems.

The high prevalence of thyroid nodules and increased availability of neck ultrasound have led to an increased incidence of diagnostic thyroid fine needle aspirations, with approximately 20% yielding indeterminate results. The recent availability of molecular tests has helped guide the clinical management of these cases. This paper aims to review and compare three main commercially available molecular cytology platforms in the U.S. – Afirma GSC, Thyroseq GC, and ThyGeNEXT+ThyraMIR. Sequential improvements of the Afirma GSC and Thyroseq GC tests have led to higher positive and negative predictive values, sensitivity, and specificity. Comparative studies revealed similar diagnostic performance between these tests, with considerations for factors such as cost and processing time. Thyroseq GC provides detailed genomic information and specific management recommendations. ThyGeNEXT+ThyraMIR, though less studied, presents promising results, particularly in miRNA analysis for weak driver mutations. Challenges in interpreting results include variations in reporting and the evolving nature of testing platforms. Questions persist regarding cost-effectiveness and the utility of ultrasound characteristics in selecting candidates for molecular testing. While molecular testing has primarily served diagnostic purposes, advancements in understanding genetic alterations now offer therapeutic implications. FDA-approved options target specific genetic alterations, signaling a promising future for tailored treatments.

The characteristics of response to stressful situations depend on a complex of factors such as personality traits, emotional regulation, health state, features of the nervous system, motor functions and proprioception. In cases of disruption of the brain's dopamine system due to disease or chemotherapy, hormone discharge due to stress, changes in emotions and proprioception may occur. The aim of our study was to find the relationship of stress resilence in verbal test and individual differences performed in proprioceptive test in onco-patients, sportsmen and group of control; as well as to observe the differences between these groups in the verbal stress resistance and individual differences proprioceptive tests. The results showed that onco-patients were less resistant as per verbal stress resistance outcomes for a major part of variables compared to other groups. The sportsmen were superior in verbal Risk and Control dimensions compared to other groups; but less in Positive Affectiveness. The proprioceptive test perfomance revealed the only statistically significant difference in performance in the dimension related to Mood, showing more Pessimism in the sportsmen group, but more Optimism in the oncopatients at temperamental (or intrisinc) level. The obtained results can be used by psychologists, therapists, coach and healthcare when needed to attend the specific needs of public, being pateints or sportsmen, etc.

Accurate and efficient medicinal plant image classification is of utmost importance as these plants produce a wide variety of bioactive compounds that offer therapeutic benefits. With a long history of medicinal plant usage, different parts of plants, such as flowers, leaves, and roots, have been recognized for their medicinal properties and are used for plant identification. However, leaf images are extensively used due to their convenient accessibility and are a major source of information. In recent years, transfer learning and fine-tuning, which use pre-trained deep convolutional networks to extract pertinent features, has emerged as an extremely effective approach for image identification problems. This study is leveraging the power of three component deep convolutional neural networks, namely VGG16, VGG19 and DenseNet201, to derive features from the input images of the medicinal plant dataset, containing leaf images of 30 classes. The models were compared and ensembled to make four hybrid models to enhance the predictive performance by utilizing the averaging and weighted averaging strategies. Quantitative experiments were carried out to evaluate the models on the Mendeley medicinal leaf dataset. The resultant ensemble of VGG19+DensNet201 with fine-tuning showcases enhanced capability in identifying medicinal plant images with an improvement of 7.43% and 5.8% compared with VGG19 and VGG16. Furthermore, VGG19+DensNet201 can outperform its standalone counterparts by achieving an accuracy of 99.12% on the test set. A thorough assessment with metrics such as accuracy, recall, precision, and f1-score firmly establishes the effectiveness of the ensemble strategy.

Natural gas hydrate has gained significant attention in recent years. To safely and sustainably exploit the natural gas from gas hydrate-bearing sediments, it is crucial to understand the long-term mechanical characteristics of the hydrate reservoir. In this study, the influence of hydrate and fine particles on the strain rate dependence of hydrate-bearing sediments under plane strain conditions has been studied. The experimental results show that the strain rate dependency of the mechanical properties of hydrate-bearing sediments is positively correlated with hydrate saturation instead of the morphology of hydrate in sediments. The residual strength of hydrate-bearing sediments is primarily controlled by the hydrate saturation and is independent of the strain rate. Changes in hydrate saturation, and fines content, can affect the relationship between the strain rate and shear band angle. Finally, the local volumetric expansion effect of hydrate-bearing sediments without fines content is more significant and shows a strong strain rate dependence characteristic. Overall, this study provides valuable insights into the long-term mechanical characteristics of hydrate reservoirs. These insights can contribute to the development of a constitutive model of hydrate-bearing sediments with time-dependence in the future, and which is meaningful to the exploitation of natural gas hydrate.

This paper presents a study of an automated system for identifying planktic foraminifera at the species level. The system uses a combination of deep learning methods, specifically Convolutional Neural Networks (CNNs), to analyze digital images of foraminifera taken at different illumination angles. The dataset is composed of 1437 groups of sixteen grayscale images, one group for each foraminifer, that are then converted to RGB images with various processing methods. These RGB images are fed into a set of CNNs, organized in an Ensemble Learning (EL) environment. The ensemble is built by training different networks using different approaches for creating the RGB images. The study finds that an ensemble of CNN models trained on different RGB images improves the system's performance compared to other state-of-the-art approaches. The proposed system was also found to outperform human experts in classification accuracy.

An effective method for coating textile fabrics with porous materials is proposed, and the removal rates of nitrogen oxides (NOx), sulfur oxides (SOx), and fine dust particles in the coated textile fabrics are evaluated. The textile fabrics made of polyester are used to effectively reduce fine dust particles through static electricity. Zeolite and coconut shell activated carbon are used as porous material to reduce SOx and NOx, respectively. The effects of the epoxy content and dilution solution types on the SOx removal rate of textile fabrics coated with zeolite are evaluated to determine the optimum coating conditions. In addition, the effects of external environmental conditions, such as washing and freeze thawing, on the SOx and NOx removal rates of the textile fabrics coated with porous materials using the optimum coating conditions are examined. The test results show that the SOx removal rate of textile fabrics coated with zeolite decreases with the increase in the epoxy content. The decrease is 2.9 times larger for textile fabrics coated using deionized water than those coated using isopropyl alcohol. After one wash, the SOx removal rate decreases dramatically. However, the decrease is reduced by 16% when the epoxy content ratio is increased by 0.5%. The effects of washing and freeze thawing on the SOx and NOx removal rates of textile fabrics coated using the deionized water diluted with the epoxy content ratio of 2% are minimal. Consequently, to maintain stable SOx and NOx removal rates under external environmental conditions such as washing and freeze thawing, 98% deionized water dilution and 2% epoxy content ratio are required for the optimum coating of textile fabrics with zeolite and coconut shell activated carbon.

Glacier dynamics play an important role in the mass balance of many glaciers, ice caps and ice sheets. In this study we exploit Radarsat-2 (RS-2) Wide Fine (WF) data to determine the surface speed of Svalbard glaciers in the winters of 2012/2013 and 2013/2014 using Synthetic Aperture RADAR (SAR) offset and speckle tracking. The RS-2 WF mode combines the advantages of the large spatial coverage of the Wide mode (150 x 150 km) and the high pixel resolution (9m) of the Fine mode and thus has a major potential for glacier velocity monitoring from space through offset and speckle tracking. Faster flowing glaciers (1.95 m d^-1 - 2.55 m d^-1) which are studied in detail are Nathorstbreen, Kronebreen, Kongsbreen and Monacobreen. Using our Radarsat-2 WF dataset, we compare the performance of two SAR tracking algorithms, namely the GAMMA Remote Sensing Software and a custom written MATLAB script (GRAY method) that has primarily been used in the Canadian Arctic. Both algorithms provide comparable results, especially for the faster flowing glaciers and the termini of slower tidewater glaciers. A comparison of the WF data to RS-2 Ultrafine and Wide mode data reveals the superiority of RS-2 WF data over the Wide mode data.

Micro satellites must survive severe mechanical conditions during their launch phase. Therefore, structural design requirements are imposed on the micro satellites. These requirements are the strength requirement and the stiffness requirement. Usually, the structural design of a micro satellite is performed using the internal stress analysis and the natural frequency analysis, which are based on a Finite Element Method (FEM). The validity of this structural design is evaluated through vibration tests. In an early stage of development, which has a FEM model of a satellite in the process of creation, presumption of the natural frequency of a satellite may be difficult. In this study, a simple method for determining the longitudinal and lateral minimum natural frequencies of the micro satellites during the ascent phase was clarified. The structure of the micro satellites used in this research is made of aluminum alloy, and they have a monocoque structure. The Young's modulus and moment of inertia of area used to calculate the minimum natural frequencies were determined using the area ratio of the monocoque structure to the entire satellite. When this method is used, the estimated minimum natural frequencies of the longitudinal direction and the lateral direction are in agreement with the measured values acquired by the vibration tests. In order to shorten the process of micro satellites development, this paper describes a practical estimation method of the minimum natural frequency for micro satellites.

The crystal structure of Pb₁₀(PO₄)₆O, the proposed matrix for the potential room-temperature superconductor LK-99, Pb_10-xCu_x(PO₄)₆O (x = 0.9-1.0), has been reinvestigated by single-crystal X-ray diffraction using crystals prepared by Merker and Wondratschek (The crystal structure is trigonal, P, a = 9.8109(6), c = 14.8403(12) Å, V = 1237.06(15), R₁ = 0.0413 using 3456 unique observed reflections. The crystal structure of Pb₁₀(PO₄)₆O is a superstructure with regard to the ‘standard’ P6₃/m apatite structure type. The doubling of the c parameter is induced by the ordering of ‘additional’ O’ atoms within the structure channels running parallel to the c axis and centered at (00z). The O’ atoms form short bonds to the Pb1 atoms, resulting in splitting the Pb1 site into two, Pb1A and Pb1B. The structural distortions are further transmitted to the Pb phosphate framework formed by four Pb2 sites and PO₄ groups. The structure data previously reported by Krivovichev and Burns (Z. Kristallogr. 2003, 218, 357-365) may either correspond to the Pb₁₀(PO₄)₆O_x(OH)_2-x (x ~ 0.4) member of the Pb₁₀(PO₄)₆O - Pb₁₀(PO₄)₆(OH)₂ solid solution series, or to the high-temperature polymorph of Pb₁₀(PO₄)₆O (with the phase with doubled c parameter being the low-temperature polymorph).

Nickel nitrate diammine, Ni(NH₃)₂(NO₃)₂, can be synthesised from the thermal decomposition of the nickel nitrate hexammine, Ni[(NH₃)₆](NO₃)₂. The hexammine decomposes in 2 distinct steps; the first releases 4 equivalents of ammonia while the second involves the release of NO_x, N₂ and H₂O to yield NiO. The intermediate diammine compound can be isolated following the first deammoniation step or synthesised as a single phase from the hexammine under vacuum. Powder X-ray diffraction (PXD) experiments have allowed the structure of Ni(NH₃)₂(NO₃)₂ to be solved for the first time. The compound crystallises in orthorhombic space group Pca2₁ (a = 11.0628 (5) Å, b = 6.0454 (3) Å, c = 9.3526 (4) Å; Z = 4) and contains 11 non-hydrogen atoms in the asymmetric unit. Fourier Transform Infrared (FTIR) spectroscopy demonstrates that the bonding in the ammine is consistent with the structure determined by PXD.

A monocular vision pose estimation and identification algorithm used on a small spacecraft for future orbital servicing is studied in this paper. A tracker spacecraft equipped with a short-range vision system is proposed to recover the 3D structural model of a space target in orbit and automatically identify its solar panels and main body using only visual information from an onboard camera. The proposed reconstruction and identification framework is tested using structure-from-motion and point cloud identification methods. The Efficient Perspective-n-Points (EPnP) descriptor is used for pose estimation. Triangulated points are used for component segmentation by means of orientation histogram descriptors. Experimental results based on laboratory images of a spacecraft model show the effectiveness and robustness of our approach.

In this article we review the advances that have been made on the understanding of the high-pressure structural, vibrational, and electronic properties of wolframite-type oxides since the first works in the early 1990s. Mainly tungstates, which are the best known wolframites, but also tantalates and niobates, with an isomorphic ambient-pressure wolframite structure, have been included in this review. Apart from estimating the bulk moduli of all known wolframites; the cation-oxygen bond distances and their change with pressure have been correlated with their compressibility. The composition variations of all wolframites have been employed to understand their different structural phase transitions to post-wolframite structures as a response to high pressure. The number of Raman modes and band gap energy changes have been also analyzed in the basis of these compositional differences. The reviewed results are relevant for both fundamental science and for the development of wolframites as scintillating detectors. The possible next research venues of wolframites have also been evaluated.

Accurate and robust deep learning models for facial expression recognition are challenging to achieve, given the diversity of human faces and variations in images, including different facial poses and lighting conditions. In this work, we proposed a clock-driven convolutional Spiking Neural Network (SNN) and STDP fine-tune architecture, meticulously calibrated its hyperpa-rameters, and experimented with various optimization methods. The best model resulted was trained and evaluated on the Fer2013 and FER+ database, obtaining an accuracy of 61.87% and 79.97% without requiring auxiliary training data or face registration. To our best knowledge, the proposed SNN achieved comparable accuracy to CNNs of similar depth and possessed advantages of low energy consumption and high computational efficiency. The computational efficiency of the proposed SNN is approximately three times that of CNNs. Along with this, we introduced the very recent cumulative spike guided encoder visualization technique and revealed the strong encoding capability of the proposed SNN.

Few-shot fine-grained image classification (FSFGIC) methods refer to machine learning methods which aim to classify images (e.g., bird species, flowers, and airplanes) belonging to subordinate object categories of the same entry-level category with only a few samples. It is worth to note that feature representation learning is used not only to represent training samples, but also to construct classifiers for performing various FSFGIC tasks. In this paper, starting from the definition of FSFGIC, a taxonomy of feature representation learning for FSFGIC is proposed. According to this taxonomy, we discuss key issues on FSFGIC (including data augmentation, local or/and global deep feature representation learning, class representation learning, and task specific feature representation learning). The existing popular datasets and evaluation standards are introduced. Furthermore, a novel classification performance evaluation mechanism is designed with a 0.95 confidence interval for judging whether the classification accuracy obtained by a certain specified method is good or bad. Moreover, current challenges and future trends of feature representation learning on FSFGIC are elaborated.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

This study explores the capital structure determinants of Korean firms over an extensive period (1995-2021), encompassing both stable and crisis conditions. Employing a dynamic panel data model and the Generalized Method of Moments (GMM) estimation, we address the endogeneity issue introduced by the inclusion of lagged dependent variables. Our research integrates firm-specific internal factors with macroeconomic external variables to provide a comprehensive understanding of the influence of varying economic environments on capital structure. Our study suggests that in times of economic stability, the capital structure decisions of a firm are more influenced by internal factors such as profitability. However, in periods of economic downturns, it's the external macroeconomic market conditions that tend to have a greater impact on these decisions. It's also noteworthy that both book leverage (BL) and market leverage (ML) exhibit quicker adjustments during stable periods as opposed to periods of crisis. This indicates a higher agility of firms in adapting their capital structures in stable normal conditions. Our findings contribute to existing literature by offering a holistic view of capital structure determinants in Korean firms. They underscore the necessity of adaptable financial strategies that account for both internal dynamics and external economic conditions. This study fills a gap in current research, presenting new insights into the dynamics of capital structure in Korean firms and suggesting a multifaceted approach to understanding capital structure in diverse economic contexts.

That form and function relates, is the maxim to anatomy and physiology. Yet form-function relations can be difficult to establish. Human subjects with excessive trabeculated myocardium in the left ventricle, for example, are diagnosed with non-compaction cardiomyopathy, but the extent of trabeculations may be without relation to ejection fraction. Rather than rejecting a relation between form and function, we may ask whether the salient function is assessed; is there a relation to electrical propagation, mean arterial blood pressure, propensity to form blood clots, or all? And how should extent of trabeculated muscle be assessed? While reviewing literature on trabeculated muscle, we applied Tinbergen’s four types of causation - how does it work, why does it work, how is it made, and why did it evolve - to better parse what is meant by form and function. The paper is structured around cases that highlight advantages and pitfalls of applying Tinbergen’s questions. It further uses the evolution of lunglessness in amphibians to argue lung reduction can impact on chamber septation, and it considers the evolution of an arterial outflow in fishes to argue that reductions in energy consumption may drive structural changes with little consequences to function. Concerning trabeculations, we argue they relate to pump function in the embryo in the couple of weeks before the onset of coronary circulation. In fetal and postnatal stages, a spectrum of trabeculated-to-compact myocardium makes no difference to cardiac function and in this period form and function may appear unrelated.

Porous structures and micropatterning surfaces play a crucial role in the development of highly sensitive force sensors. However, achieving these two conductive architectures typically requires complex materials synthesis and expensive manufacturing processes. In this study, we introduce a novel conductive composite film featuring a porous/micropyramid hybrid conductive architecture, which is achieved through a straightforward process of materials mixing and one-step screen printing. By utilizing a deep eutectic solvent in the ink component, micropores are induced in the printed composite, while the mesh of the screen mask acts as a template, resulting in a micropyramid film surface. We have successfully realized highly sensitive flexible force sensors with multifunctional capabilities for perceiving normal force and shear force.

The cuttlebone-like structure is a complex porous bionic structure with asymmetric sinusoidal S-shaped wall structure connecting laminar septa, and studies have shown that the cuttlebone-like structure has light weight, high strength and excellent energy absorption capability. It has become an important research to investigate the mechanical mechanism of cuttlebone-like structure and to design a bionic structure beyond nature structure based on it. In order to investigate the mechanical properties of the static compression and shearing processes of the cuttlebone-like structure, this paper establishes theoretical formulas and analyzes the influence of dimensionless parameters such as height-to-thickness ratio $\lambda$, period-to-thickness ratio $\xi$ and amplitude to period ratio $\eta$ on the compression and shearing processes with the help of ABAQUS finite element software.The parameter sensitivity analysis method was used to compare the important influence degree of each dimensionless parameter on the mechanical properties of the structure and to determine the relative optimal parameters of the structural mechanical properties under compression and shearing. Based on the relatively optimal parameter of the cuttlebone-like structure, the cross-sectional geometry of the structure is improved to build a new cuttlebone-like structure.The experimental results showed that the new cuttlebone-like structure with revised cross-section improved the mechanical properties of the cuttlebone-like structure.The study results are of theoretical guidance to enhance the mechanical properties and geometric design of cuttlebone-like structures.

Most fully developed galaxies have vivid spiral structure, but the formation and evolution of spiral structure is still a mystery that is not fully understood in astrophysics. We find that the currently used equations of galactic dynamics contain some unreasonable components. In this paper, the following three working assumptions are introduced to simplify the galactic structural equations. 1. In the research of large-scale structure, the retarded potential of the gravitational field should be taken into account. The propagating time of the gravitational field from center to border is longer than the revolution periods of the stars near the center of galaxy. Newton's gravitational potential is unreasonable for such case, and the weak field and low velocity approximation of Einstein's field equation should be adopted. 2. The stars in a fully developed galaxy should be zero-pressure and inviscid fluid, and the equation of motion is different from that of ordinary continuum mechanics. Stars move along geodesics. 3. The structure of the galaxy is only related to the total mass density distribution. The equation of state of dark halo is different from that of ordinary luminous interstellar matter, so their trajectories are also very different. Dark halo and ordinary matter in galaxy are automatically separated. The total mass density distribution can be presupposed according to the observation data, and then it can be determined by comparing the solution of the equations with the observed data. These assumptions and treatments are supported by theory and observation. The variables of the equations of simplified galactic dynamics are separated from each other, and the equations are well-posed and can be solved according to a definite procedure. Therefore, this simplified dynamic equation system provides a more reasonable and practical framework for the further study of galactic structure, and can solve many practical problems. Besides, it is closely related to the study of dark energy and dark matter.

Abstract:The objects of fine-grained image categories(e.g., bird species) are various subclass under different categories. Because the differences between subclass are very subtle and most of them are concentrated in multiple local areas, the task of fine-grained image recognition is very challenging. At the same time, some fine-grained networks tend to focus on a certain region when judging the target category, resulting in the lack of other auxiliary regional features. To this end, Inception V3 is used as the backbone network, and an enhanced and complementary fine-grained image classification network is designed. While adopting the method of reinforcement learning to obtain more detailed fine grain image features, the complementary network can obtain the complementary discriminant area of the target through the method of attention erasure to increase the network's perception of the overall target. Finally, experiments are conducted on CUB-200-2011, FGVC Aircraft and Stanford dogs three open datasets. The experimental results show that the proposed model has better performance.

The rapidly evolving field of artificial intelligence (AI) continues to witness the introduction of innovative open-source pre-trained models, fostering advancements in various applications. One such model is Llama 2, an open-source pre-trained model released by Meta, which has garnered significant attention among early adopters. In addition to exploring the foundational elements of the Llama v2 model, this paper investigates how these early adopters leverage the capabilities of Llama 2 in their AI projects. Through a qualitative study, we delve into the perspectives, experiences, and strategies employed by early adopters to leverage Llama 2's capabilities. For the purpose of data analysis, the capabilities inherent in the Llama 2 model were employed to conduct keyword extraction from the context of the early adopters' case studies. The findings shed light on the model's strengths, weaknesses, and areas of improvement, offering valuable insights for the AI community and Meta to enhance future model iterations. Additionally, we discuss the implications of Llama 2's adoption on the broader open-source AI landscape, addressing challenges and opportunities for developers and researchers in the pursuit of cutting-edge AI solutions. The present study constitutes an early exploration of the Llama 2 pre-trained model, holding promise as a foundational basis for forthcoming research investigations.

The study aims to identify the fine particulate matter (PM2.5) hazard area, mitigation method, and possible sustainable development in a changing global climate. The critical environmental hazards are artificial light at night (ALAN) and air pollution with ambient PM2.5. People use nighttime outdoor environments for their needs, and the nocturnally migrating birds are attracted to urban ALAN during seasonal migration, which could increase the birds' exposure to PM2.5. A comparative study examines PM2.5 concentrations and the spatial correlation between ALAN and PM2.5 within urban versus rural areas. The author used the nighttime data of the artificial light on the Earth's surface and the PM2.5 level of concentration to estimate the extent of air pollution associated with PM2.5 in the ground-level atmosphere. The results can assist in determining the required PM2.5 control areas and designing and executing environmental conservation planning. Furthermore, the results of this study are not only beneficial to understanding accurately the regional differences of spatiotemporal PM2.5 emission dynamics and helpful for proposing alleviation policies in air pollution control and providing scientific support for regional sustainable development in changing climate. The integrated hazards of ALAN and air pollution are most significant and likely to increase within the urban and decrease within rural areas. This study was undertaken by the first author and built upon the context of the academic, scientific, and technological challenges to identify the PM2.5 concentration in urban and rural areas and the expected outcomes.

Low-cost particulate matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and their cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors but some models of sensors also report particle number concentrations (PNC) segregated into different PM size bins. In this study, 8 units of each Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors to different particle size distributions to quantify and better understand the low cost sensors performance across a range of relevant environmental ranges. The PNC reported by the sensors were analysed. This study showed that there is added value in directly using the PNC reported by the sensors instead of the mass concentrations, which could aid the efforts to calibrate these sensors to a known accuracy. It demonstrated that all sensors tested here could track the fine temporal variation of PNC, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it showed that particle size distribution and composition are more impactful on the sensors measurement than relative humidity.

Physical distancing and wearing a face mask are key interventions to prevent COVID-19. While this remains difficult to practice for millions of firefighters in fire engines responding to emergencies, the delayed forthcoming of evidence on the physical effectiveness of such safety interventions in this setting presents a major problem. In this field experimental study, we provided initial evidence to close this gap. We examined total aerosol burden in the cabin of a fire engine whilst manipulating crew size, natural ventilation, use of FFP2 respirators and use of SCBA full-face masks during 15-minute driving periods. At the same time, we controlled for crew activity and speaking, vehicle speed, cabin air temperature, pressure and humidity. Limiting the crew size, using FFP2 respirators and not donning SCBA full-face masks was associated with a reduction of the arithmetic mean of total aerosol burden of up to 49%. Natural ventilation as tested in this study was associated with both an increase and a decrease of total aerosol burden. This study provided initial evidence on the physical effectiveness of safety interventions in fire engines to reduce potential airborne transmission of SARS-CoV-2 through aerosols. More research about the physical and clinical effectiveness of such safety interventions is needed.

Plants produce above- and below-ground biomass. However, our understanding of both production and decomposition of below-ground biomass is poor, largely because of the difficulties of accessing study materials. Below-ground organic matter decomposition studies are scanty and especially rare in the tropics. Here, we used a litter bag experiment to quantify the mass loss and nutrients dynamics of decomposing twigs and fine roots from an arbuscular mycorrhizal fungal associated tree, Parashorea chinensis, in a tropical rain forest in Southwest China. Overall, twig litter decomposed 1.9 times faster than fine roots (decay rate (k) twig=0.255, root=0.134). The difference in decomposition rates can be explained by a difference in phosphorus (P) concentration, availability and use by decomposers or C quality. Both materials showed an increase in N concentration, with final measurements still higher than initial levels. This suggests N may not be available due to microbial immobilization. Both carbon and nitrogen dynamics were significantly predicted by mass loss and showed a negative and positive relationship, respectively. Our study results imply that fine roots carbon and nitrogen contribute more to soils organic matter and enlarge the resident time. Therefore, better understanding of carbon cycle requires better understanding of mechanisms governing below ground biomass decomposition.

In recent decades, perovskite-type compounds (ABB′O3) have been exhaustively studied due to their unique ferroelectric properties. In this work, a systematic study aiming to prepare fine particles in the binary system SrZrO₃–SrTiO₃ was conducted under hydrothermal conditions in a KOH (5 M) solution at 200 °C for 4 h under a constant stirring speed of 130 rpm. The precursors employed were SrSO₄ powder (&lt; 38 μm size) and coprecipitated hydrous gels of Zr(OH)₄•9.64 H₂O (Zr-gel) and Ti(OH)₄•4.5H₂O (Ti-gel), which were mixed according to the stoichiometry of the SrZr_1-xTixO₃ in the compositional range of 0.0&gt;x&gt;100.0 mol% Ti⁴⁺. The XRD results revealed the formation of two crystalline phases rich in Zr⁴⁺, an orthorhombic structured SrZr0.93Ti0.07O3 and a cubic structured SrZr0.75Ti0.25O3 within the compositional range of 0.1–0.5 mol of Ti4+. A cubic perovskite-structured solid solution, SrTi1-xZrxO3, was preferentially formed within the compositional range of 0.5&gt;x&gt;0.1 mol% Ti⁴⁺. The SrZrO₃ and SrZr_0.93Ti_0.07O₃-rich phases had particle sizes averaging 3 m with a cubic morphology. However, a remarkable reduction in the particle size occurred on solid solutions prepared with hydrous Ti-gel over contents of 15 mol% Ti⁴⁺ in the reaction media, resulting in the formation of nanosized particles agglomerates with cuboidal shape self-assembled via a 3D hierarchical architecture, the sizes of these particles varied in the range between 141.0–175.5 nm. The limited coarsening of the particles is discussed based on the Zr-gel and Ti-gel dehydration capability differences that occurred under hydrothermal processing.

Alunogen, Al2(SO4)3·17H2O, occurs as an efflorescent in acid mine drainage, low-temperature fumarolic or pseudofumarolic (at coal fires) terrestrial environments. It is considered as one of the main Al-sulfates of Martian soils demanding comprehensive crystal chemical data of natural terrestrial samples. However, structural studies of natural alunogen were carried out in 1970s without localization of H atoms and have not previously been performed for samples from geothermal fields, despite the fact that these environments are considered as proxies of the Martian conditions. The studied alunogen sample comes from Verkhne-Koshelevsky geothermal field (Koshelev volcano, Kamchatka, Russia). Its chemical formula is somewhat dehydrated, Al2(SO4)3×15.8 H2O. The crystal structure was solved and refined to R1 = 0.068 based on 5112 unique observed reflections with I > 2σ(I). Alunogen crystalizes in P-1 space group, a = 7.4194(3), b = 26.9763(9), c = 6.0549(2) Å, α = 90.043(3), β = 97.703(3), γ = 91.673(3) °, V = 1200.41(7) Å3, Z = 2. The crystal structure consists of isolated SO4 tetrahedra, Al(H2O)6 octahedra and H2O molecules connected by hydrogen bonds. The structure refinement includes Al, S and O positions that are similar to previous structure determinations and thirty-four H positions localized for the natural sample first. The study also shows the absence of isomorphic substitutions in the composition of alunogen despite the iron-enriched environment of mineral crystallization. The variability of the alunogen crystal structure is reflected in the number of “zeolite” H2O molecules and their splitting. The structure complexity of alunogen and its modifications ranges from 333-346 bits/cell for models with non-localized H atoms to 783-828 bits/cell for models with localized H atoms. The higher values correspond to higher hydration state of alunogen.

Utilization of Bidirectional Reflectance Distribution Function (BRDF) model parameters obtained from the multi-angular remote sensing is one of the approaches for the retrieval of vegetation structural information. In this research, the potential of multi-angular vegetation indices, formulated by the combination of multi-spectral reflectance from different view angles, for the retrieval of forest above ground biomass was assessed. This research was implemented in the New England region with the availability of a high quality forest inventory database. The multi-angular vegetation indices were generated by the simulation of the Moderate Resolution Imaging Spectroradiometer (MODIS) BRDF/Albedo Model Parameters Product (MCD43A1 Version 6) based BRDF parameters. The effects of seasonal (spring, summer, autumn, and winter) composites of the multi-angular vegetation indices on above ground biomass, angular relationship of the spectral reflectance with above ground biomass, and the interrelationships between the multi-angular vegetation indices were analyzed. Among the existing multi-angular vegetation indices, only the Nadir BRDF-adjusted NDVI ( and Hot-spot incorporated NDVI ( showed significant relationship (more than 50%) with the above ground biomass. This research proposed two more sensitive vegetation structural indices, Fore-scattering Back-scattering NDVI and Vegetation Structure Index (VSI). The Fore-scattering Back-scattering NDVI showed higher sensitivity (R² = 0.62, RMSE = 52.46) towards the above ground biomass than existing multi-angular vegetation indices. Furthermore, the VSI performed in the most efficient way explaining 64% variation of the above ground biomass, suggesting that the right choice of the spectral channel and observation geometry should be considered for improving the estimates of the above ground biomass. In addition, the right choice of seasonal data (summer) was found to be important for estimating the forest biomass while other seasonal data were either insensitive or pointless. The promising results shown by the VSI suggest that it could be an appropriate candidate for monitoring vegetation structure from the multi-angular satellite remote sensing.

Image segmentation is a new challenge prob- lem in medical application. The use of medical imaging has become an integral part of research, as it allows us to see inside the human body without surgical intervention. Many researcher have studied brain segmentation. One stage method is used to segment the brain tissues. In this paper, we proposed the multi-stage generative ad- versarial network to solve the problem of information loss in the one-stage. We utilize the coarse-to-fine to improve brain segmentation using multi-stage generative adversar- ial networks (GAN). In the first stage, our model generated a coarse outline for (i) background and (ii) brain tissues. Then, in the second stage, the model generated outline for (i) white matter (WM), (ii) gray matter (GM) and (iii) cerebrospinal fluid (CSF). A good result can be achieved by fusing the coarse outline and refine outline. We conclude that our model is more efficient and accu- rate in practice for both infant and adult brain segmenta- tion. Moreover, we observe that multi-stage model is faster than prior models. To be more specific, the main goal of multi-stage model is to see the performance of the model in a few shot learning case where a few labeled data are available. For medical image, this proposed model can work in a wide range of image segmentation where the convolution neural networks and one-stage methods have failed.

Hepatitis C virus (HCV) is a major medical health burden and leading cause of chronic liver disease and cancer worldwide. More than 58 million people are chronically infected with HCV, with 1.5 million new infections occurring each year. An effective HCV vaccine is a major public health and medical need as recognized by the World Health Organization. However, due to the high variability of the virus and its ability to escape the immune response, HCV rapidly accumulates mutations making a vaccine a formidable challenge. An effective vaccine must elicit broadly neutralizing antibodies (bnAbs) in a consistent fashion. After decades of studies from basic research through clinical development, the antigen of choice is considered to be the E1E2 envelope glycoprotein due to conserved, broadly neutralizing antigenic domains located in the constituent subunits of E1, E2, and the E1E2 heterodimeric complex itself. The challenge has been elicitation of robust humoral and cellular responses leading to broad virus neutralization due to the relatively low immunogenicity of this antigen. In view of this challenge, structure-based vaccine design approaches to stabilize key antigenic domains have been hampered due to the lack of E1E2 atomic-level resolution structures to guide them. Another challenge has been development of a delivery platform in which a multivalent form of the antigen can be presented in order to elicit a more robust anti-HCV immune response. Recent nanoparticle vaccines are gaining prominence in the field due to their ability to facilitate a controlled multivalent presentation and trafficking to lymph nodes, where they can interact with both the cellular and humoral components of the immune system. This review focuses on recent advances in understanding the E1E2 heterodimeric structure to facilitate a rational design approach and the potential for development of a multivalent nanoparticle-based HCV E1E2 vaccine. Both aspects are considered important in the development of an effective HCV vaccine that can effectively address viral diversity and escape.

Soil structure is a sensitive and dynamic soil physical property which responds rapidly to different tillage systems, and thus it requires constant monitoring and evaluation. The visual evaluation of soil structure (VESS) and subsoil visual evaluation of soil structure (SubVESS) methods were used to assess the soil structure quality of clayey soils subjected to different tillage systems. The tillage systems were no-tillage (NT), conventional tillage (CT) and virgin land (VG). The study was conducted at Tshivhilwi and Dzingane in Thohoyandou, Vhembe District, Limpopo Province, South Africa. The NT system was practised for 8 and 40+ years at Tshivhilwi and Dzingahe, respectively. The results showed no impact of tillage system on both VESS and SubVESS at Tshivhilwi. The soil structure quality at Tshivhilwi as determined by VESS and SubVESS were found to be poor. However, at Dzingahe, both the VESS and SubVESS responded to the impact of tillage. At Dzingahe VESS showed a fair (Sq = 2.25) soil structural quality in the NT system, poor (Sq = 3.57) in the CT system and moderately poor (Sq = 3.05) in the VG. Similarly, at the same location the SubVESS scores were moderately good in the NT system, moderately poor for CT system and fair in the VG. The differences in the response of the VESS and SubVESS at the two locations were attributed to the differences in the duration at which the NT systems were practised. The VESS and SubVESS results were supported by selected measured soil physicochemical properties such as bulk density and porosity. In conclusion, the findings of this study showed that the VESS and SubVESS were able to effectively differentiate the impact of tillage systems on soil structural quality in clayey soils where NT was practised for a long period (40+ years) while it could not where NT was practised for a few years (8 years).

Multipactor is a common discharge phenomenon occurred in space microwave systems. The surface microstructure has been verified to be effective to mitigate multipactor. In this work, we design a square coaxial low-pass filter (SCLPF) with dielectric sheets loaded to check the multipactor dependence on the structure parameters of the loaded dielectric sheets, and further model groove structures on the sensitive area surface to lower the risk of multipactor. Simulation results indicate that the SCLPF loaded with alumina and PTFE exhibits good operational characteristics, and the multipactor threshold is significantly improved after introducing the surface grooves. Then we investigate the effects of three typical groove parameters, groove depth, groove number, and aspect ratio, on the multipactor threshold of the SCLPF device. The results show that the multipactor threshold raise at first as the groove number and groove depth increase, and then the threshold reaches a saturation status. For a deeper analysis of multipactor, we discuss how the grooves shelter the secondary electrons, and further mitigate the electron avalanche. Furthermore, the mechanisms of threshold saturation under the effect of groove parameters are analyzed in detail, and a contour map for the multipactor threshold of PTFE-loaded SCLPFs is given, which makes significant sense for predicting the multipactor threshold of the devices with its sensitive surface being grooved. The regularity of modulating the multipactor threshold by the groove structures obtained in this study is of engineering significance for suppressing multipactor in microwave devices in practical applications.

With the rapid development of the Internet industry, the problem of information overload has arisen due to the abundance of information available online. Recommendation algorithms, as the core of recommendation systems, have been attracting much attention and are a hot topic of research for many experts and scholars. The classical recommendation algorithms are mainly divided into three major categories: collaborative filtering recommendation algorithms, content-based recommendation algorithms, and hybrid-based recommendation algorithms. Although these algorithms are widely used in various fields, with the proliferation of information, these traditional recommendation algorithms are no longer able to meet the needs of the times. To address this issue, recommendation systems have been developed to provide users with personalized and relevant information or products. Despite the wide use of recommendation algorithms, such as collaborative filtering, content-based filtering, and hybrid approaches, traditional recommendation algorithms have limitations and are no longer suitable for meeting the demands of the times. This paper proposes a new recommendation algorithm, SFRRG, that fuses structure and feature information in graph neural networks to improve the performance of the recommendation system in rating prediction. The effectiveness of the proposed algorithm is demonstrated through experiments on various data sets and compared with existing recommendation algorithms.

The determination of the three-dimensional structure is a key step for the identification of new targets as well as the rational design of bioactive compounds. Herein, we report the synthesis and the structural elucidation of novel tetrameric macrocycles. Measurements were taken in solid and solution states with the help of X-ray scattering and NMR spectroscopy. The investigations made will help to find diverse applications for this new, promising compound class.

Due to the vast conformational space proteins can adopt, accurate and efficient prediction of protein structure remains still a challenging task, coupled with the intricacies of interatomic interactions and the limitations of current computational models in effectively navigating this complex molecular landscape. Additionally, the lack of comprehensive experimental data for all protein structures further exacerbates the difficulty in reliable machine learning-based prediction of the three-dimensional conformations of the proteios building block of life. Geometrically, Cartesian coordinate system (CCS, X, Y and Z) and spherical coordinate system (SCS, ρ, θ and φ) are two interconvertible coordinate systems, and are like two sides of one coin. Since the beginning of Protein Data Bank (PDB) in 1971, CCS has been the default approach to specify atomic positions with X, Y and Z in PDB. In this manuscript, therefore, I present a novel method for the reversible spherical geometric conversion of protein backbone structure coordinate matrices to three independent vectors: ρ, θ and φ. This reversible conversion facilitates lossless extraction of essential structural features from protein backbone structural data, enabling the development of advanced novel algorithms for protein structure prediction in future. In short, this inter-atomic SCS approach offers a comprehensive yet efficient means of representing protein backbone geometry, leveraging spherical coordinates to capture spatial relationships in a compact and intuitive inter-atomic manner, and to provide a robust framework for reversible feature extraction for the ongoing efforts in advancing the field of protein structure prediction, the holy grail of computational structural biology.

This paper introduces an innovative approach to numerically model Structure-Soil-Structure Interaction (SSSI) by integrating the Boundary Element Method (BEM) and the Finite Element Method (FEM) in a coupled manner. To assess the accuracy of the proposed method, a comparative study is undertaken, comparing its outcomes with those generated by the conventional FEM technique. Alongside accuracy, the computational efficiency aspect is crucial for the analysis of large-scale SSSI problems. Hence, the computational performance of the coupled BEM-FEM method undergoes a thorough examination and is compared with that of the standalone FEM method. The results from these comparisons illustrate the superior capabilities of the proposed method in comparison to the FEM method. The novel approach provides more reliable results compared to traditional FEM methods, serving as a valuable tool for engineers and researchers involved in structural analysis and design.

This paper investigates the hydroelastic responses of offshore floating solar photovoltaic farms (OFPVs) to minimize structural motion. The OFPV usually occupies a large sea space in the order of hectares and due to its huge structural length-to-thickness ratio, the structural deformation under wave action has to be taken into consideration. The flexible deformation of the structure under hydrodynamic loading is termed the hydroelastic response. Due to the relatively long structural length with respect to the encountering wavelength, the diffraction and radiation of waves have to be taken into account to accurately represent the hydrodynamic loadings on the floating platform. The numerical model is first validated by comparing the eigenvalues and eigenvectors of the OFPV obtained from the proposed numerical scheme with their counterparts obtained from an established finite element software. This is followed by the investigation of the hydroelastic response of various OFPVs designed in varying layout configurations. The various layout configurations are obtained by altering the floating modular units’ dimensions as well as the spacing of the OFPVs when deployed adjacent to each other. The optimal configuration that gives the best performance in terms of the overall smallest response, known as compliance, is then suggested. The results suggest that a longish OFPV layout has a lower hydroelastic response and the motion could be further reduced by increasing the global flexural stiffness via layout arrangement.

In this research, a design method for the graded triply periodic minimal surface (TPMS) structures with lightweight, high-strength, and high sound absorption properties is proposed. The graded TPMS structures are controlled by linear, quadratic, and sine functions. Uniform TPMS and graded TPMS were manufactured by laser powder bed fusion (LPBF) with AlSi7Mg powder, and an acoustic impedance tube test, compression test, and digital image correlation (DIC) test were ap-plied to obtain the sound absorption and compression properties. The sound absorption coefficient of a uniform Gyroid increases as the number of layers and surface thickness increase, and it in-creases as element size decreases. The sound absorption peak shifts to low frequencies as the number of layers increases. The sound absorption property of Type-Ⅰ graded TPMS is superior to the Type-Ⅱ, and the G-LinearⅠ has the highest sound absorption properties among the three functions. It reveals that the graded TPMS has superior sound absorption performance at low and medium frequencies. The compression and DIC results of graded TPMS also show excellent mechanical properties and energy absorption characteristics.