Universal Parameter Research Articles

A Terrylene Bisimide based Universal Host for Aromatic Guests to Derive Contact Surface-Dependent Dispersion Energies.

π-π interactions are among the most important intermolecular interactions in supramolecular systems. Here we determine experimentally a universal parameter for their strength that is simply based on the size of the interacting contact surfaces. Toward this goal we designed a new cyclophane based on terrylene bisimide (TBI) π-walls connected by para-xylylene spacer units. With its extended π-surface this cyclophane proved to be an excellent and universal host for the complexation of π-conjugated guests, including small and large polycyclic aromatic hydrocarbons (PAHs) as well as dye molecules. The observed binding constants range up to 108 M-1 and show a linear dependence on the 2D area size of the guest molecules. This correlation can be used for the prediction of binding constants and for the design of new host-guest systems based on the herewith derived universal Gibbs interaction energy parameter of 0.31 kJ/molÅ2 in chloroform.

Data-driven unlocking volume conservation in single-crystal Ni-rich layered oxides

Our study redefines anisotropic lattice variation through volume conservation (VC) in single-crystal (SC) Ni-rich layered oxides to facilitate long-term cycling stability. Data-driven analysis of the correlation heatmap of the five covariate variables and cycling retention enables precise cut-off voltage modulation for high-energy-density Ni-rich cathodes. We establish the anisotropic variation between a and c lattice constants as critical, leading to interfacial microcracks formation in degrading cycling retention. A balanced use of this variation, verified through computational models, Li1−x[Ni10/12Co1/12Mn1/12]O2 (NCM) and Li1−x[Ni10/12Co1/12Ti1/12]O2 (NCT), creates a volumetric stress free state at the inflection point. Unfortunately, two-scale challenges, namely macroscopic and local structure lattice misfits, which trigger intragranular microcracks, remain unresolved. Formation energy diagrams show biphasic reaction causing macroscopic lattice misfit based on the lattice direction occurring below x = 0.75 in both cathode models. Its effect can be mitigated by doping Ti into the Ni-rich material. Atomistic misfits of in-plane and out-of-plane directions are observed. Furthermore, MO2 and LiO2 slabs anisotropically vary in the complexes for NCM and NCT. Moreover, Ti-doped layered oxide clearly reduces local structural misfits and anisotropy. The proposed VC holds potential as a universal parameter for durable Ni-rich layered oxide fabrication for lithium-ion batteries.

Quantifying distortion in time-correlated single photon counting: a universal parameter.

One major drawback of the classic time-correlated single photon counting (TCSPC) technique is pileup-related distortion. To keep it under a reasonable level, the maximum count rate has to be reduced, posing a serious limitation to the overall measurement speed. This means that there is an intrinsic trade-off between speed and distortion: either count rate is raised, but distortion is worsened, or distortion is minimized at the expense of speed. In both cases, it is impossible to precisely evaluate the degree of distortion introduced. Here comes our new, to the best of our knowledge, figure of merit, which is able to provide a numerical estimate of the distortion whatever the signal shape is, marking a real turning point in the way of doing TCSPC. In this article, this new parameter will be defined and its effectiveness will be demonstrated by means of mathematical computations.

Novel approach for designing order parameters of clathrate hydrate structures by graph neural network.

Clathrate hydrates continue to be the focus of active research efforts due to their use in energy resources, transportation, and storage-related applications. Therefore, it is crucial to define their essential characteristics from a molecular standpoint. Understanding molecular structure in particular is crucial because it aids in understanding the mechanisms that lead to the formation or dissociation of clathrate hydrates. In the past, a wide variety of order parameters have been employed to classify and evaluate hydrate structures. An alternative approach to inventing bespoke order parameters is to apply machine learning techniques to automatically generate effective order parameters. In earlier work, we suggested a method for automatically designing novel parameters for ice and liquid water structures with Graph Neural Networks (GNNs). In this work, we use a GNN to implement our method, which can independently produce feature representations of the molecular structures. By using the TeaNet-type model in our method, it is possible to directly learn the molecular geometry and topology. This enables us to build novel parameters without prior knowledge of suitable order parameters for the structure type, discover structural differences, and classify molecular structures with high accuracy. We use this approach to classify the structures of clathrate hydrate structures: sI, sII, and sH. This innovative approach provides an appealing and highly accurate replacement for the traditional order parameters. Furthermore, our method makes clear the process of automatically designing a universal parameter for liquid water, ice, and clathrate hydrate to analyze their structures and phases.

Chronobiological approach to study microsymbiont catalase activity in female reproductive tract

Catalase is a heme-containing enzyme belonging to protection factors that destroys peroxide compounds. The presence of catalase activity is an important ability of microorganisms that allows them to be protected from unfavorable factors as well as adapt to macroorganism conditions. Catalase along with superoxide dismutase plays an important role in pathogen resistance to phagocyte oxygen-dependent bactericidal mechanisms. The aim of the study was to investigate microsymbiont catalase activity from female reproductive tract in normocenosis and candidiasis dysbiosis using the chronobiological approach. The study was conducted on clinical isolates, isolated from female reproductive tract microsymbiocenosis. The catalase activity was determined by spectrophotometry based on 24 hour-long hydrogen peroxide reduction with 3-hours interval in winter season. Dynamic hydrogen peroxide was assessed in 3–5 experiment replicates. In some Lactobacillus spp., catalase was found containing no heme group — pseudocatalase. Chronobiological approach allowed to reveal enzyme activity from all microsymbionts. The dominant and associative microbiota isolated from healthy females was characterized by circadian (24 hours) rhythms of catalase activity early in the morning — 5 a.m. (р 0.05). Hydrogen peroxide decomposes spontaneously or via non-enzymatic catalysts, and microorganisms cope with this situation under such conditions. In microsymbionts characteristic of female reproductive tract dysbiosis, and usually found in large numbers along with decreased Lactobacillus spp. ultradian rhythms with 12- and 8-hour harmonics of catalase activity with acrophase were recorded in the morning (8 a.m.) and evening hours (8 p.m.). The minimum values of enzyme production in all cultures were recorded at 12 p.m. and 5 p.m. Therefore, the contribution of the rhythm of the studied parameter at varying degree of vaginal sterility reflects the adaptive pathogen capabilities to the conditions of existence and can be the basis for studying related regulatory mechanisms. Mesor and amplitude phase stability are universal rhythmometric parameters used to evaluate patient’s condition independent of species assignment.

A Different, Nondestructive Method of Investigating In Situ Degradation in Hybrid Perovskite Solar Cells

One of the most significant impediments to the upscaling of hybrid organic-inorganic perovskite (HOIP) solar cells is poor stability. So, to effectively establish precautionary strategies, it is necessary to have a comprehensive grasp of the mechanisms that contribute to their degradation. Conventional characterization techniques used for in situ degradation assessment of perovskite solar cells either track just universal parameters, which yields poor insights about localized degradation processes, or the technique itself induces degradation, which may confound results. Developed in this study is a nondestructive technique of analyzing in situ active layer degradation in HOIP solar cells’ active layer utilizing a combination of optical modelling and ageing device’s reflectance tracking. The optical dielectric functions of the solar cell’s functional layers were modelled using the layers’ transmitted and reflected light spectra. The transfer matrix approach was used to fit the reflectance spectrum of entire solar cell layer stacks utilizing the computed dielectric functions for individual materials and accounting for the possibility of lead iodide gradient layer formation in the model. Leveraging no other characterization method, the presence of a lead iodide gradient layer was identified within the solar cell layer stack, an unmistakable indicator of methyl ammonium lead iodide perovskite (MAPI) degradation, probing with only photons.

Resilient moduli characterization of cement-treated silt

The performance of a flexible pavement depends on the resilient modulus (MR) of subgrade soil. Thus, MR is a key design parameter for mechanistic-empirical pavement design of flexible pavements. Generally, the resilient modulus is determined by conducting repeated load triaxial (RLT) tests in the laboratory and has been used to characterize the subgrade soil behavior under repeated traffic loading conditions. The use of cement to stabilize natural subgrade soils is widely accepted by transportation agencies. Several research studies were conducted on the resilient behavior of cementtreated soils. However, limited research studies have been conducted on the resilient behavior of cement-treated silty soil. Therefore, the current research study assessed the resilient moduli properties of cement-treated silt. Cement-stabilized soil specimens were statically compacted and cured in a humid room for a stipulated curing period before conducting RLT tests. RLT tests were conducted on cement-treated specimens at different cement dosages and curing periods to study the effect of the cement dosage and curing time on the resilient modulus. Test results indicated that a significant improvement in performance was observed after cement treatment. The untreated soil specimens exhibited stresssoftening behavior with an increase in deviator stress, whereas the cement-treated specimens exhibited stress-hardening behavior. The resilient modulus was increased with an increase in cement dosage. Regression analyses were conducted on RLT test results using three-parameter universal model and model parameters were determined. It was observed that the three-parameter universal model exhibited an excellent fit with experimental data.

Secure Consensus Control of Multiagent Systems Under DoS Attacks: A Switching-Scheme-Based Active Defense Method.

This article studies the secure consensus control problem of multiagent systems (MASs) with a nonzero input leader subject to denial-of-service (DoS) attacks. The introduction of backup topologies makes it possible for MASs to actively defend against DoS attacks. Subsequently, a novel active defense method consisting of two types of state observers, an adaptive topology switching mechanism, and switching controllers is proposed, which can ensure the leader-follower bound consensus even if DoS attacks hinder the interaction between agents. Within such a defense framework, the switching mechanism, driven by the predefined performance index and designed monitoring function, can automatically search for a healthy communication graph among backup topologies. Concurrently, the observer-based switching control strategy will be modified to match the corresponding topology, in which the universal observer and controller parameters in different topologies are obtained by solving linear matrix inequalities. It should be highlighted that the developed defense scheme not only removes the limitations of existing results on the duration and frequency of DoS attacks but also ensures the same upper bound of consensus error before and after DoS attacks. Finally, several simulation examples for different systems illustrate the efficiency and superiority of the theoretical results.

A tractable methodology for assessing the pressure scaling of sooting processes in a counterflow diffusion flame from 1 to 6 bar

A parametric study using ethylene as fuel was undertaken which examines the efficacy of concentration based and soot production rate (SPR) based parameters for assessing the pressure scaling of sooting processes in a counterflow diffusion flame. Two experimental designs pertaining to constant flow residence times (Case A), and constant carbon mass flux (Case B) were respectively implemented for pressures between 1 to 6 bars and across peak temperatures ranging from ∼2300−2600 K for Yf=Yox=0.35, ∼2100−2400 K for Yf=Yox=0.30, and ∼1800−2100 K for Yf=Yox=0.25 flames. A diffuse line-of-sight based extinction imaging diagnostic was employed for the measurement of soot concentrations. The evaluation of SPR follow a transport based coupling of inferred gaseous precursors, temperatures and velocity fields from detailed 1D OpenSMOKE++ simulations and soot concentrations from extinction measurements. The use of concentration based parameters, namely peak (∼p2.5−4.5) and integrated soot volume fractions (∼p2.1−3.9), is noted to be dependent on the Case A or B with little information discernible about the pressure effects in an experimental setting. The use of production rate based parameters, namely mean SPR (∼p2.2−3.4) removes the disparity of pressure scaling exponents across cases A and B but inherits the dependency to peak flame temperature, and gaseous precursor concentrations. An empirical fit of local SPR to acetylene and pyrene concentrations reveals a universal Arrhenius activation energy parameter in the high-temperature region (1300–2000 K) of the studied flames. The global activation energy is noted to remain roughly constant across varying peak flame temperatures, fuel flux, flow residence times and pressure. Consequently, we propose a soot yield parameter (Ω), calculated as the mean soot production rate normalized to acetylene and pyrene mass fractions which is noted to universally scale with pressure as Ω∼p1.7.

ВКЛАД ПРОФЕССОРА В. П. РЕУТОВА В РАЗВИТИЕ УЧЕНИЯ О СТРУКТУРЕ ПРАВА И СОВРЕМЕННОСТЬ

the axial idea is discussed, uniting the scientific work of Professor of the Department of Theory and History of State and Law of PSU – PSNRU V. P. Reutov in the late period of his work, namely, the determining role of the proper legal functions of law (regulatory-static, regulatory-dynamic, protective, integrative-social) for the structure of the organic system of legal norms, which has objective, universal parameters for all legal systems. The presence of constitutive (substantive and procedural) law, public and private legal principles is present in the structure of any legal system, while the sectoral principle, as well as the distribution of norms among institutions, is conventional and subjective. The inconsistency of the dualism of the legal system and the system of laws is shown, the promising heuristic value of the teachings of V. P. Reutov for the development of the russian theory of state and law is noted.

Network dynamics in hybrid microgrid and its implications on stability analysis

Abstract Dynamic load is one of the significant factor affecting the stability of hybrid microgrid (MG), owing to their frequency and voltage dependency. As compared with the static load, a small variation in voltage or frequency significantly influences the stability of MG having dynamic load. Therefore, it necessitates considering the dynamics of load for stability analysis of an MG. Moreover, the stability issues become more critical for the islanded operation. This paper investigates the small signal (SS) stability of a hybrid MG utilizing composite load model (CLM) to include the load dynamics. This paper also investigates the critical universal droop control parameters and other internal controller parameters that influence MG stability. Eigenvalue analysis is used to identify the respective stability domain of different control parameters. The effectiveness of the proposed approach is tested by comparing the dynamics of CLM with constant power and impedance load. In addition, a memetic algorithm and SQP-GS is used to find the optimal values of different key parameters. The simulation results confirmed the effectiveness of the proposed SS dynamic model of hybrid MG and the efficacy of the optimization algorithm for tuning control parameters, which improved the dynamic performance of the hybrid MG.

Entropy scaling framework for transport properties using molecular-based equations of state

Modeling transport properties is important for many applications. In this work, an entropy scaling framework for modeling transport properties using molecular-based equations of state (EOS) is presented. It can be applied for modeling the viscosity, thermal conductivity, and self-diffusion coefficients. The framework is formulated in a general way such that it can be coupled with various EOS. It is demonstrated that the model, when coupled to molecular-based EOS, provides not only good descriptions of existing data but also reasonable predictions in a wide range of states covering liquid, gaseous, supercritical, and meta-stable regions. Moreover, the model can be used for reliably predicting transport properties of mixtures. The universal parameters of the model were fitted to computer experiment data of the Lennard-Jones fluid. This procedure provides inherently a robust form of the basic scaling function. Thereby, only few data points are required for the determination of the component-specific model parameters. The applicability of the developed framework is demonstrated for model fluids as well as for a wide variety of real substances including non-polar, polar, and associating pure fluids and mixtures.

Molecular Simulation of Nitrogen Adsorption in Multidimensional Nanopores and New Insights into the Inversion of Pore Size Distribution for Gas Shale

Low-temperature nitrogen adsorption is a widely used method for the research and evaluation of gas shale’s pore structure. The existing interpretation method, utilizing gas adsorption isotherms to obtain pore size distribution (PSD), is always based on the one-dimensional geometry model, while the void space of gas shale has strong multi-dimensional characteristics. It is necessary to investigate the nitrogen condensation and evaporation behavior in multidimensional structures. In this study, a series of two-dimensional and three-dimensional models based on ink-bottle pores were constructed. A hybrid molecular simulation approach combining grand canonical Monte Carlo (GCMC) and molecular dynamics (MD) is proposed to simulate the low-temperature nitrogen adsorption isotherms. Three aspects have been analyzed in detail. Compared with the conventional understanding that the threshold of cavitation in the ink-bottle pore only relates to throat diameter, this study discloses a wider and more comprehensive range of conditions of cavitation that considers both the throat length and diameter. As pore spaces of shale samples consist of many complex interconnected pores, the multi-stage ink-bottle pore model is more suitable than the single ink-bottle pore model to similarly reproduce the wider cavitation pressure range. A more universal parameter is proposed that quantitatively unifies the influence of cavity diameter and length on condensation pressure and has good applicability in cavities with different shapes. This work quantitatively studies the nitrogen adsorption isotherms of three-dimensional complex nanopore structures using molecular simulation and provides a reasonable explanation for the low-temperature nitrogen adsorption isotherms of gas shale.

Dark matter, deep space frequencies and new era of space communication

Dark matter is the cosmological link that stayed universe to multiuniverse. All universal parameters directly or indirectly show that the dark matter is the universe hidden energy or soul. We have no confusion about the existence of Dark matter. As it is the universe linker we can make a communication through it. May be higher intelligence out of universe( Alien) are working on it as every time we are receiving unidentified signal from outer space. Previous paper had shown that how the dark matter is established and expanded on the universe. This paper is showing about deep space frequencies, energy distribution with quantum fluctuation and dark matter communication. And at last is discussed for making a quantum mechanical signal for long distance like galaxy to galaxy communication. By dark matter communication we can make stronger of our deep space communication that can lead of our future space communication.

Defining generation parameters with an adaptable data-driven approach to construct typical meteorological year weather files

Weather files play a critical role in building energy simulation by providing crucial information on the environmental condition. Typical meteorological year (TMY) weather files are often used in building energy simulation to approximate the long-term performance. These files are generated using long-term weather data and are meant to reflect the typical weather conditions within the dataset. Common generation methods used to construct TMY weather files typically rely on expert-defined universal generation parameters. However, the weather parameters and weighting factors used to generate these files, termed generation parameters, often overlook variations in climate and applications. This study introduces a data-driven approach to define the generation parameters used in the Sandia method to produce TMY weather files. The study aims to enhance the generation method by providing a framework to define the generation parameters which considers data availability, climate and application.The proposed methodology employs machine learning and feature importance to define the generation parameters used to construct TMY weather files. The generated TMY weather files are evaluated for a Montreal case study using building energy demand. The results show an improvement in the representativeness of the long-term average building energy demand for the weather file generated using the model-defined generation parameters.

Universal adversarial backdoor attacks to fool vertical federated learning

Vertical federated learning (VFL) is a privacy-preserving distribution learning paradigm that enables participants, owning different features of the same sample space to train a machine learning model collaboratively while retaining their data locally. This paradigm facilitates improved efficiency and security for participants such as financial or medical fields, making VFL an essential component of data-driven Artificial Intelligence systems. Nevertheless, the partitioned structure of VFL can be exploited by adversaries to inject a backdoor, enabling them to manipulate the VFL predictions. In this paper, we aim to investigate the vulnerability of VFL in the context of binary classification tasks. To this end, we define a threat model for backdoor attacks in VFL and introduce a universal adversarial backdoor (UAB) attack to poison the predictions of VFL. The UAB attack, consisting of universal trigger generation and clean-label backdoor injection, is incorporated during the VFL training at specific iterations. This is achieved by alternately optimizing VFL sub-problems' universal trigger and model parameters. Our work distinguishes itself from existing studies on designing backdoor attacks for VFL, as those require the knowledge of auxiliary information that is not accessible within the split VFL architecture. In contrast, our approach does not require additional data to execute the attack. On the real-world datasets, our approach surpasses existing state-of-the-art methods, achieving up to 100% backdoor task performance while maintaining the main task performance. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL. Our results in this paper make a major advance in revealing the hidden backdoor risks of VFL, hence paving the way for the future development of secure VFL applications such as finance.

On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

AbstractArtificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations and typical flap boxes, which make the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows us to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging.

The Microbiome of Peri-Implantitis: A Systematic Review of Next-Generation Sequencing Studies.

(1) Introduction: Current evidence shows that mechanical debridement augmented with systemic and topical antibiotics may be beneficial for the treatment of peri-implantitis. The microbial profile of peri-implantitis plays a key role in identifying the most suitable antibiotics to be used for the treatment and prevention of peri-implantitis. This systematic review aimed to summarize and critically analyze the methodology and findings of studies which have utilized sequencing techniques to elucidate the microbial profiles of peri-implantitis. (2) Results: Fusobacterium, Treponema, and Porphyromonas sp. are associated with peri-implantitis. Veillonella sp. are associated with healthy implant sites and exhibit a reduced prevalence in deeper pockets and with greater severity of disease progression. Streptococcus sp. have been identified both in diseased and healthy sites. Neisseria sp. have been associated with healthy implants and negatively correlate with the probing depth. Methanogens and AAGPRs were also detected in peri-implantitis sites. (3) Methods: The study was registered with the International Prospective Register of Systematic Reviews (PROSPERO) (CRD42023459266). The PRISMA criteria were used to select articles retrieved from a systematic search of the Scopus, Cochrane, and Medline databases until 1 August 2023. Title and abstract screening was followed by a full-text review of the included articles. Thirty-two articles were included in the final qualitative analysis. (4) Conclusions: A distinct microbial profile could not be identified from studies employing sequencing techniques to identify the microbiome. Further studies are needed with more standardization to allow a comparison of findings. A universal clinical parameter for the diagnosis of peri-implantitis should be implemented in all future studies to minimize confounding factors. The subject pool should also be more diverse and larger to compensate for individual differences, and perhaps a distinct microbial profile can be seen with a larger sample size.

Observational constraints on anisotropic cosmological model in Lyra’s manifold

In the present study, we have explored an axially symmetric Bianchi I universe with Lyra’s geometry. We assumed variable displacement vector as component of Lyra’s geometry in the Einstein’s field equations. We developed and presented exact solutions of the field equations of proposed model. The parameters of model have been extracted using latest observational finding of OHD, Pantheon and BAO data sets. The best fit values are estimated using MCMC method. Features like deceleration parameter, particle horizon, age of universe and jerk parameter are explored. Further Om and statefinder diagnosis of the assumed model are also discussed for geometrical comparison with the standard ΛCDM model.

A real-time visual compass from two planes for indoor unmanned aerial vehicles (UAVs)

Improving the accuracy of pose determination of UAVs in a Global Positioning System-denied (GPS-denied) indoor environment, visual Simultaneous Localization And Mapping (vSLAM) techniques have attracted more attention from scholars. RGB-D (Red Green Blue-Depth) camera is a widely used visual sensor as it can recognize unknown environments and behave accordingly, which is valuable for UAVs. However, high computational resources are often required in featureless and uncharacteristic indoor environments to achieve the robust performance of pose decisiveness. To solve this challenge, this paper presents a novel real-time visual compass to estimate the three Degree-of-Freedom (DoF) relative orientations of an RGB-D camera, where the surface-normals-based RANdom Sample Consensus Model (RANSAC) is integrated. In addition, the Manhattan World (MW) assumption is exploited to simplify perception tasks by giving a way of probabilistically modeling background clutter. The effectiveness of the proposed estimator is comprehensively evaluated based on depth datasets from different indoor man-made scenes. And a universal parameter setting of the model is determined. The experiments demonstrated the effectiveness of the visual compass with an average absolute rotation error under 2°. Additionally, the average time needed by the algorithm to process an image frame is under 20 ms, satisfying real-time requirements. The robust achievement by both accuracy and timing indicates that our outperformed state-of-the-art approaches can be more conducive to indoor UAV design.