We study the propagation of a particular field that we call Gaussian-top-hat that presents self-focusing and maintains its shape for some propagation distances.

The two-parameter Wright special function is an interesting mathematical object that arises in the theory of the space and time-fractional diffusion equations. Moreover, many other special functions are particular instantiations of the Wright function. The article demonstrates finite representations of the Wright function in terms of sums of generalized hypergeometric functions, which in turn provide connections with the theory of the Gaussian, Airy, Bessel, and Error functions, etc. The main application of the presented results is envisioned in computer algebra for testing numerical algorithms for the evaluation of the Wright function.

In this paper, we present Binet's formulas, generating functions, and the summation formulas for generalized Pentanacci numbers, and as special cases, we investigate Pentanacci and Pentanacci-Lucas numbers with their properties. Also, we define Gaussian generalized Pentanacci numbers and as special cases, we investigate Gaussian Pentanacci and Gaussian Pentanacci-Lucas numbers with their properties. Moreover, we give some identities for these numbers. Furthermore, we present matrix formulations of generalized Pentanacci numbers and Gaussian generalized Pentanacci numbers.

The meta hybrid M06-2X functional combined with the 6-311++G(d,p) basis set are used to investigate the antioxidant activity of five benzoic acid derivatives naturally occurring in several plant food: gallic acid, para-hydroxybenzoic acid, protocatechuic acid, syringic acid and vanillic acid. To assess the antioxidant properties of these compounds, three mechanisms of free radicals scavenging are considered, namely the hydrogen atom transfer (HAT), stepwise electron transfer proton transfer (SET-PT) and sequential proton loss electron transfer (SPLET) models. The effect of polar environments on the antioxidant power of these compounds is mimicked in water and methanol using the IEF-PCM solvation method. Our findings suggest that HAT is the preferred mechanistic pathway in gas phase, while SPLET is favoured in polar mediums. Protocatechuic and gallic acids are the most active in gas and polar solutions respectively, whereas PHBA is the least active in all the environments considered. The O-H group in para position of the carboxylic group (O3-H for GA and O2-H for the rest) is confirmed to be the most reactive site in gas phase, while in solution it is either of the O1-H site (for PHBA, PCA, SA and VA) or O4-H groups (for GA). The “HOMO-rule” of free radical scavenging ability does not seem to account properly for the antioxidant properties of this set of chemicals.

This article investigates the vibration analysis of the mast on the bridge top of a 28-meter tugboat, in the quasi-static regime. The study aims to determine the natural frequencies using an equivalent dynamic model of the mast and considers the accelerations of different degrees of freedom of the vessel. Practical design and reinforcement strategies for the local bridge top structure are proposed, utilizing numerical and analytical tools. The investigation explores various alternatives to optimize the structural arrangement. By analyzing the modal behavior, insights are provided for efficient design and reinforcement. The results contribute to understand the mast's vibration characteristics, offering guidance for similar structures. The study employs numerical and analytical methods, enhancing the optimization of the mast's behavior. It determines natural frequencies, proposes practical design strategies, and explores alternatives for structural optimization. The findings provide insights for shipbuilding and mast design applications in other vessels and industries such as radar mast, compass deck and guyed mast.

At the LHC, the process of a Higgs boson decaying into bottom or charm quarks produced in association with a pair of top quarks, tt¯H, allows for an empirical exploration of the heavy-flavor quark Yukawa couplings to the Higgs boson. Accordingly, the cross sections for the tt¯ + heavy-flavor production without the appearance of the Higgs boson have been measured at the LHC in various phase spaces using data samples collected in pp collisions at s = 7, 8 and 13 TeV with the ATLAS and CMS experiments. Flavor ratios of cross sections of tt¯ + additional jets processes with different flavors are also measured. In this paper, the measured cross sections and ratios are reviewed and the prospects with more data are presented.

Human African Trypanosomiasis (HAT) commonly known as sleeping sickness, is a neglected tropical vector borne disease caused by trypanosome protozoa. It is transmitted by bites of infected tsetse fly. In this paper we first present the vector-host model which describes the general transmission dynamics of HAT. In the tsetse fly population, the HAT is modelled by three compartments while in the human population, the HAT is modelled by four compartments. The next generation matrix approach is used to derive the basic reproduction number, R₀, and also it is proved that if R₀ ≤ 1 the disease free equilibrium is globally asymptotically stable, which means the disease dies out. The disease persist in the population if the value of R₀ > 1. Furthermore, the optimal control model is determined by using the Pontryagin’s maximum principle with control measures such as education, treatment and insecticides used to optimize the objective function. The model simulations confirm that the use of the three control measures are very efficient and effective to eliminate HAT in Africa.

We have revised the problem of the motion of a heavy symmetric top. When formulating equations of motion of the Lagrange top with the diagonal inertia tensor, the potential energy has more complicated form as compared with that assumed in the literature on dynamics of a rigid body. Using the Liouville's theorem, we solve the improved equations in quadratures and present the explicit expressions for the resulting elliptic integrals.

Breast cancer pathogenesis, treatment, and patient outcomes are shaped by tumor-intrinsic genomic alterations that divide breast tumors into molecular subtypes. These molecular subtypes often dictate viable therapeutic interventions, and ultimately, patient outcomes. However, heterogeneity of therapeutic response may be a result of underlying epigenetic features that may further stratify breast cancer patient outcomes. In this review we examine non-genetic mechanisms that drive functional changes to chromatin in breast cancer to contribute to cell and tumor fitness, and highlight how epigenetic activity may inform therapeutic response. We conclude by providing perspectives on the future of therapeutic targeting of epigenetic enzymes, an approach which holds untapped potential to improve breast cancer patient outcomes.

This article describes an algorithm for estimation the variance of Gaussian noise. The data is smoothed using the Savitsky-Golay polynomial filter. Absolute differences between original and smoothed data are sorted in ascending order. The initial part of this sequence is selected for analysis. The result of calculation mean value of differences can be used to estimate the variance of the noise. By selecting points for analysis, the impact of cosmic ray noise and other artifacts can be reduced. The use of the proposed method for artificial and real spectra shows the ability to effectively estimate the noise variance. The algorithm contains no user-defined parameters.

In this review we extensively describe the main post-translational modifications that give rise to the multiple proteoforms characterized to date in the human salivary proteome and their potential role. Most of the data reported were obtained by our group in over twenty-five years of research carried out on human saliva mainly by applying a top-down strategy. At the beginning we describe the products generated by proteolytic cleavages, which can occur before and after secretion. In this section the most relevant families of salivary proteins are also described. Next, we report the current information concerning the human salivary phospho-proteome and the limited news available on sulfo-proteome. Three sections are dedicated to the description of the glycation and enzymatic glycosylation. Citrullination and N- and C- terminal PTMs and a miscellaneous of other modifications are described in the last two sections. Results highlighting the variation in the level of some proteoforms in local or systemic pathologies are also reviewed along the sections of manuscript to underline the impact and relevance of this information for the development of new diagnostic biomarkers useful in clinical practice.

The issue of women’s participation in top management and boardroom positions has received increasing attention in the academic literature and the press. However, the pace of advancement for women managers and directors continues to be slow and uneven. The novel framework of this study organizes the factors at the individual, organizational and public policy level that affect both career persistence and the advancement of women in top management positions; namely, factors affecting 1) career persistence (staying at the organization) and 2) career advancement or mobility (getting promoted within the organization). In the study location, Chile, only 32 percent of women “persist”, or have a career without interruptions, mainly due to issues with work–family integration and organizational environments with opaque and challenging working conditions. Women who “advanced” in their professional careers represent 30 percent of high management positions in the public sector and 18 percent in the private sector. Only 3 percent of general managers in Chile are women. Women in Chile have limited access and are still not integrated into business power networks. Our findings will enlighten business leaders and public policy-makers interested in designing organizations that retain and promote talented women in top positions.

China is out of extreme poverty in 2020 on schedule and one decade in advance to fulfill the UN 2030 Agenda for Sustainable Development Goals (SDGs), and became the first developing country to alleviate poverty in half century. Therefore, a large numbers of effective approaches are emerging, and the intellectual’s technology-led poverty reduction, which locally known as the “Straw Hat University”initiated by “Farmer's professor”mode, is the most tried and tested approach motivated by the intellectual’s “Serve the people” tradition and supported by the all nation. This research conduct case analysis with three most remarkable organic intellectuals as Agronomist Yuan longping, Mycologist Lin Zhanxi and Plant pathologist Zhu Youyong with their bridging gaps in food security, regional imbalance and ethnic disparity respectively to sort out the sustainable modules and universal experiences. The conclusion indicates that“Farmer's Professor” Initiated “Straw Hat University” is an effective approach to solve human beings’ development problems and benefit the livelihoods, especially in the under development regions; and the authentic down to earth experiments into productivity as well as the Intellectual property transformation is the perfect path to deploy offline and online resources building the effective production and supply chain to integrate industries by intellectual’s critical innovation.

Atmospheric correction is the processes of converting radiance measured at a spectral 1 sensor to the reflectance of the materials in a multispectral or hyperspectral image. This is an 2 important step for detecting or identifying the materials present in the pixel spectra. We present 3 two machine learning models for atmospheric correction trained and tested on 100,000 batches of 40 4 reflectance spectra converted to radiance using MODTRAN, so the machine learning model learns 5 the radiative transfer physics from MODTRAN. We created a theoretically interpretable Bayesian 6 Gaussian process model and a deep learning autoencoder treating the atmosphere as noise. We 7 compare both methods for estimating gain in the correction model to the well-know QUAC method 8 of assuming a constant mean endmember reflectance. Prediction of reflectance using the Gaussian 9 process model outperforms the other methods in terms of both accuracy and reliability.

In this study, we first present a time-fractional L$\hat{e}$vy diffusion equation of the exponential option pricing models of European option pricing and the risk-neutral parameter. Then, we modify a particular L$\hat{e}$vy-time fractional diffusion equation of European-style options. Introduce a more general model from the models based on the L$\hat{e}$vy-time fractional diffusion equation and review some recent findings regarding of the Europe option pricing of risk-neutral free.

Aiming at the removal of radioactive cobalt ions from water by graphene oxide (GO), the adsorption mechanism of Co2+ on graphene oxide was analyzed using the quantum chemical calculation software Gaussian 16 based on the density functional theory. The influence of material structure factors such as carboxyl group, hydroxyl group, epoxy group and graphene sheet as well as external environmental factors such as pH, temperature and interfering ions on the adsorption effect was determined, and the influence of external environment was verified through experiments. Through calculation and experiment, it was found that the existence of oxygen-containing functional groups on graphene oxide can improve the adsorption efficiency of the materialappropriately,and increasing pH under acidic conditions was also helpful to improve the adsorption effect, and the material had certain selectivity for Co2+, the adsorption capacity and selectivity could be further improved when it was modified by increasing hydroxyl group.

This study examines the unique circumstances surrounding state-owned equity participation in non-state-controlled enterprises in China. Specifically, this study examines the impact of state-owned equity participation on the environmental, social, and governance (ESG) performance of non-state-controlled enterprises. Focusing on A-share listed firms on the Shanghai and Shenzhen Stock Exchanges and using data from 2013 to 2021, empirical testing shows that state-owned equity participation can significantly improve the ESG performance of non-state-controlled enterprises, with this conclusion remaining reliable after a series of robustness tests. Top management incentives is a mediating mechanism for state-owned equity participation in enhancing ESG performance. This study also finds that when state-owned equity participates in large enterprises or companies with a high degree of digital transformation, the effect on ESG performance is greater than that of small to medium-sized enterprises (SMEs) or enterprises with a low level of digital transformation. The findings of this study add to the current body of the existing field of research on the factors influencing corporate ESG performance and the impact of state-owned equity on corporate non-financial performance.

Wildlife conservation is an important component of environmental sustainability and can be improved by reviewing the performance of its three major models – common pool, top-down regulatory, and private resource -- under varying environmental and socioeconomic conditions. Generally, the private resource model is the most sustainable because it provides the best incentives to balance the needs of humans and wildlife, to maintain general wildlife habitat, and to adapt quickly to changing environmental and/or socioeconomic conditions. Top-down or “command and control” regulation, however, can be employed as a model of last resort if the private resource model shows signs of failing to protect specific species from local extirpation or extinction, which it is most likely to do for migratory species, species with close commercial substitutes, and species with no direct commercial value. Top-down regulators may also be needed to enforce property rights arrangements like catch shares and to monitor resources that remain in the common pool in the event that socioeconomic or environmental conditions change sufficiently to trigger the tragedy of the commons.

Protein translational modifications (PTMs) generate an enormous, but as yet undetermined, expansion of the expressed proteoforms. In this Viewpoint, we firstly differentiate the concepts of proteoform and peptidoform by reviewing and discussing previous literature. We show that the current PTM biological investigation and annotation largely follow a PTM site-specific rather than proteoform-specific approach. We further illustrate a potentially useful matching strategy in which a particular “modified peptidoform” is matched to the corresponding “unmodified peptidoform” as a reference for the quantitative analysis between samples and conditions. We suggest this strategy could provide directly relevant information for learning the PTM site-specific biological functions. Accordingly, we advocate for the wider use of the nomenclature “peptidoform” in the future bottom-up proteomic studies.

This paper aims to explore the effect of humble leadership on project success by integrating the mediating role of employee creativity. Top management support moderates the direct relationship (humble leadership and project management effectiveness) and indirect relationships through employee creativity. Time-lagged data were obtained from 332 persons working in the matrix organization across the information technology. The results showed that humble leadership enhance project management effectiveness by mediating and moderating processes. This study provides a solution to an underlying research question that has gone unanswered in prior studies. What are the strategies proposed for humble leadership in fostering the effectiveness of the project?

A deep review of the existing literature on the culpeo ecology is carried out, using scientific articles, book chapters and web resources. For information published before 1988, the synthesis made by a previous report was used. For subsequent information, bibliographic searches were carried out through the main servers, considering all of the generic names used to define the species so far. From this update, new general patterns on ecology, behavior and conservation concerns about culpeos are described. Gaps in current knowledge have been identified and new lines of research are proposed.Most of the studies focused on diet, conflicts with the species in livestock areas, and on the use of space and habitat. We found an incomplete, poor justification for all of the proposed subspecies and their supposed geographical distribution, as well as a scarcity of studies on genetic issues, population dynamics and conservation concerns. It is remarkable that vast regions in South America holding culpeos lacked basic information on the species.Diet studies describe a marked trend towards resources selection at the local level, which supports the view of the culpeo as a facultative trophic specialist. In addition, it has been confirmed that in the high Andes the culpeo is also a top predator that may regulate carnivorous communities, as well as that in arid environments culpeos can act as important seed dispersers. The assessment of the conservation status of the species differs among regions, although there is no sufficient information to reach clear conclusions in most cases. Even so, in Ecuador and Colombia the species has been listed as ‘Vulnerable’. Direct persecution and habitat alteration are considered to be the most important threats that the species is facing in many countries, although other risk factors such as climate change could also have serious consequences for the canid at the global scale.

Citation frequencies represent the most significant contributions in any respective field. This bibliometric analysis aimed to identify and analyze the 100 most-cited publications in the field of antibiotics and to highlight the trends of research in this field. “All databases” of Clarivate Analytics' Web of Science was used to identify and analyze the 100 publications. The articles were then cross-matched with Scopus and Google Scholar. The frequency of citation ranged from 940 to 11051 for the Web of Science, 1053 to 10740 for Scopus, and 1162 to 20041 for Google Scholar. Five hundred thirteen authors made contributions to the ranked list, and Robert E.W. Hancock contributed in six articles, which made it to the ranked list. Sixty-six scientific contributions originated from the United States of America. In contrast, five publications were linked to the University of Manitoba, Canada, that was identified as the educational organization, which made the most contributions (n=5). According to the methodological design, 26 of the most cited works were review-type closely followed by 23 expert opinions/perspectives. Eight articles were published in Nature journal, making it the journal with the most scientific contribution in this field. Correlation analysis between the publication age and citation frequency was found statistically significant (P = .012).

Recent progress in the fabricating flexible electronics has been developed significantly due to the increased interest of the flexible electronics which can be applied to enormous fields not only to conventional electronic devices but to bio/eco electronic devices. Flexible electronics can be applied to wide range of fields such as flexible display, flexible power storage, flexible solar cells, wearable electronics and healthcare monitoring devices. Recently, flexible electronics are being attached on the skin and even implanted into human body to monitor the bio-signals and for treatment purpose. To improve the electrical characteristic and the mechanical properties of flexible electronics, nanoscale fabrications using novel nano-materials are required. Advanced in nanoscale fabrication methods allow construction of the active materials that can combine with the ultra-thin soft substrate to form flexible electronics with high performances and reliability. In this review, wide range of nanoscale fabrication methods for flexible electronics classified in either top-down or bottom-up approaches such as conventional photolithography, soft lithography, nanoimprint lithography, growth, assembly and chemical vapor deposition(CVD) will be reported with specific fabrication processes and results. Here, our aim is to introduce various fabrication methods that can be used to fabricate the flexible electronics.

In a wide range of lymphoid neoplasms, the process of malignant transformation is associated to somatic mutations in B cells that affect the epigenetic machinery. Consequential alterations in histone modifications contribute to disease-specific changes in the transcriptional program. Affected genes commonly play important roles in cell cycle regulation, apoptosis-inducing signal transduction and DNA damage response, thus facilitating the emergence of malignant traits that impair immune surveillance and favor the emergence of different B-cell lymphoma subtypes. In the last two decades, the field has made a major effort to develop therapies that target these epigenetic alterations. In this review, we discuss which epigenetic alterations occur in non-Hodgkin B-cell lymphoma. Furthermore, we aim to present in close to comprehensive manner the current state-of-the-art in the preclinical and clinical development of epigenetic drugs. We focus on therapeutic strategies interfering with histone methylation and acetylation as these are most advanced in being deployed from the bench-to-bedside and have greatest potential to improve the prognosis of lymphoma patients.

Surface activity of humic acid (HA) and its six sub-fractions isolated from forest soil were characterized by surface tension measurements, dynamic light scattering, and laser doppler electrophoresis. The surface tension of HA sub-fractions decreased from 72.4 mN·m-1 to 36.8 mN·m-1 with the increasing concentration from 0 to 20000 mg·L-1. The critical micelle concentration (CMC) of HA and its sub-fractions ranged from 216 to 1024 mg·L-1 with the multiple linear regression equation of CMC = 18896–6.9×C–296×alkyl-C–331×aromatic-C–17019×H/C+4054×HB/HI (p < 0.05). The Z-average particle size ranges from 108.2 nm to 186.9 nm. The maximum particle size was 5000 nm after filtered with a membrane of 0.45 μm, indicating HA and its sub-fractions progressed self-assembly during 2000 mg·L-1, pH = 6.86. The number-base particle size distribution of HA sub-fractions was identified as four peaks by Gaussian model, and the aggregate sizes were mainly in six clusters including 2 ± 1 nm, 5 ± 2 nm, 10 ± 3 nm, 21 ± 8 nm, 40 ± 10 nm, and > 50 nm indicating multiple aggregations of different particle sizes in the HA fractions solution.

Physical-law based models are widely utilized in the aerospace industry. One such use is to provide flight dynamics models for use in flight simulators. For human-in-the-loop use, such simulators must run in real-time. Due to the complex physics of rotorcraft flight, to meet this real-time requirement, simplifications to the underlying physics sometimes have to be applied to the model, leading to model response errors in the predictions compared to the real vehicle. This study investigated whether a machine-learning technique could be employed to provide rotorcraft dynamic response predictions, with the ultimate aim of this model taking over when the physics-based model's accuracy degrades. In the current work, a machine-learning technique was employed to train a model to predict the dynamic response of a rotorcraft. Machine learning was facilitated using a Gaussian Process (GP) non-linear autoregressive model, which predicted the on-axis pitch rate, roll rate, yaw rate and heave responses of a Bo105 rotorcraft. A variational sparse GP model was then developed to reduce the computational cost of implementing the approach on large data sets. It was found that both of the GP models were able to provide accurate on-axis response predictions, particularly when the input contained all four control inceptors and one lagged on-axis response term. The predictions made showed improvement compared to a corresponding physics-based model. The reduction of training data to one-third (rotational axes) or one-half (heave axis) resulted in only minor degradation of the GP model predictions.

We provide a comprehensive framework for forecasting five minute load using Gaussian processes with a positive definite kernel specifically designed for load forecasts. Gaussian processes are probabilistic, enabling us to draw samples from a posterior distribution and provide rigorous uncertainty estimates to complement the point forecast, an important benefit for forecast consumers. As part of the modeling process, we discuss various methods for dimension reduction and explore their use in effectively incorporating weather data to the load forecast. We provide guidance for every step of the modeling process, from model construction through optimization and model combination. We provide results on data from the PJMISO for various periods in 2018. The process is transparent, mathematically motivated, and reproducible. The resulting model provides a probability density of five-minute forecasts for 24 hours.

Trees, often referred to as the "lungs of the Earth," hold immense significance for both the natural world and human society. Their contributions span a multitude of ecological, environmental, economic, and social domains, making them essential components of a healthy and thriving planet. Phytoplasma association not only compromise tree aesthetics but also reduce photosynthetic capacity and overall vigour. Phytoplasma diseases also lead to increased tree mortality, altering ecosystem dynamics.This review article provides an in-depth exploration of phytoplasma diseases affecting trees worldwide, encompassing their epidemiology, symptomatology, impact and management strategies.

In the educational process, quality has long been considered a critical component. It may get impacted due to different factors especially when talking about the COVID19 pandemic. The purpose of this investigation is to explore the impact and relationship between COVID-19 and the quality of education using bibliometric analysis. As face-to-face learning shifts to online learning, this research investigates how the COVID-19 epidemic has affected education quality, concentrating on Programme Educational Outcomes (PEOs), Programme Learning Outcomes (PLOs), and Course Learning Outcomes (CLOs). The research covers pandemic online education, literature, and trends. There are two important steps in any bibliometric analysis selecting keywords and databases. The initial set of keywords was selected by the authors' experience and then an online survey study through Google Forms was conducted among experts in the research area. This was followed by a search of the selected keywords in the databases consisting of PubMed, Web of Science (WoS), and SCOPUS. Therefore, the final data was collected from the SCOPUS database on August 24, 2021. Hence, 182 documents were considered for the current study. The document's data were analysed by using the “visualization of similarities” (VOS) Viewer software and the R-Tool of the Bibliometrix-package that was specifically designed for quantitative Bibliometrics research. Based on the obtained data, the results showed the quality of education that may get impacted by the changing of the education delivery from face-to-face to online-based learning. In conclusion, this study showed the importance of studying the correlation and impact of COVID-19 on the quality of education.

T cell function is determined by transcriptional networks that are regulated by epigenetic programming via posttranslational modifications (PTMs) of histone proteins and DNA. Bottom-up mass spectrometry (MS) can identify histone PTMs, whereas intact protein analysis with high-field Fourier transform ion cyclotron resonance MS (FTICR-MS) can detect species missed by bottom-up approaches. We used high-resolution reversed-phase liquid chromatography (RPLC) FTICR-MS, alternating electron transfer dissociation (ETD) and collision-induced dissociation (CID) on precursor ions to maximize fragmentation of uniquely modified species. First online RPLC separation sorted histone families then weak cation exchange hydrophilic interaction liquid chromatography (WCX-HILIC) separated species heavily clad in PTMs. Tentative PTM identifications were assigned by matching peptide masses to predicted theoretical masses that were verified with tandem MS. We used this innovative approach for Histone-intact protein PTM mapping (HiPTMap) and to quantify PTMs on core histones purified from CD8+ T cells directly isolated ex vivo post-influenza infection. Activation significantly reduced PTMs in vivo following influenza infection, histone maps changed as T cells migrated to infections, and T cells responding to secondary heterologous infections had significantly more PTMs enhancing transcriptional activation. Thus, HiPTMap identifies and quantifies PTMs on CD8+ T cell histones and determines their combinations in T cell states.

The Gaussian signaling strategy with power control for the Gaussian Z-interference channel with weak interference is reviewed in this paper. In particular, we study the various communication strategies that may arise at various points of the capacity region and identify the locations of the phase transitions between the various strategies. The Gaussian Z-interference channel with weak interference is known to have two critical points in its capacity region, where the slope of the region shows a sudden change. They occur at the points of the unconditional maximum rate for one of the users and the maximum rate that can be accommodated by the other user. In this paper, we discuss two additional critical points (locations of phase transitions) in the achievable region of this channel. At these points, we do not observe a discontinuous slope in the achievable rate region, but there is a discontinuity in the second derivative of the rate contour of the achievable region. This review paper is mainly based on some of our ITA (Information Theory and Applications Workshop, UCSD, San Diego, CA, USA) papers since 2011.

Motivated by the intriguing discovery of $X(6900)$ by LHCb Collaboration, we undertake a comprehensive study of the $s\bar{s}s\bar{s}$ tetraquark system in positive parity, employing the Gaussian expansion within the chiral quark model method. We consider two structures, diquark-antidiquark ($ss$-$\bar{s}\bar{s}$) structure and meson-meson ($s\bar{s}$-$s\bar{s}$)strcuture, encompassing all conceivable color and spin configurations. Despite the absence of bound states in our calculations, we have identified potential resonant states with $J^P=0^{+}$, namely $R(0,2150)$ and $R(0,2915)$, as well as a resonant state with $J^P=1^{+}$ denoted as $R(1,2950)$, and a resonant state with $J^P=2^{+}$ denoted as $R(2,2850)$, utilizing the real-scaling method. Through a comparison of their energies and widths, we propose that $R(0,2915)$ and $R(1,2950)$ may share characteristics with $X(6900)$, while $R(0,2150)$ could be a promising experimental candidate for $f_0(2100)$. We strongly advocate for experimental investigations to shed light on the existence and properties of these resonant states.

High PM10 concentrations are still a significant problem in many parts of the world. In many countries, including Poland, 50μg/m3 is the permissible threshold for a daily averaged PM10 concentration. The number of people affected by this threshold’s exceedance is challenging to estimate and requires high-resolution concentration maps. This paper presents an application of random forests for downscaling regional model air quality results. As policymakers and other end users are eager to receive a detailed resolution PM10 concentration maps, we propose a technique which utilizes the results of regional CTM (GEM-AQ, with 2.5km resolution) and local Gaussian plume model. As a result, we receive a detailed, 250-meter resolution PM10 distribution, which resembles the complex emission pattern in a foothill area in southern Poland. The random forest results are highly consistent with the GEM-AQ and observed concentration. We also discuss different strategies of data training random forest - using additional features and selecting target variables.

A class of models for non-Gaussian spatial random fields is explored for spatial field reconstruction in environmental and sensor network monitoring. The family of models explored utilises a class of transformation functions known as the Tukey g-and-h transformations to create a family of warped spatial Gaussian process models which can support various desirable features such as flexible marginal distributions, which can be skewed, leptokurtic and/or heavy-tailed. The resulting model is widely applicable in a range of spatial field reconstruction applications. To utilise the model in applications in practice, it is important to carefully characterise the statistical properties of the Tukey g-and-h random fields. In this work, we both study the properties of the resulting warped Gaussian processes as well as using the characterising statistical properties of the warped processes to obtain flexible spatial field reconstructions. In this regard, we derive five different estimators for various important quantities often considered in spatial field reconstruction problems. These include the multi-point Minimum Mean Squared Error (MMSE) estimators; the multiple point Maximum A-Posteriori (MAP) estimators; an efficient class of multiple-point linear estimators based on the Spatial-Best Linear Unbiased (S-BLUE) estimators; and two multi-point threshold exceedance based estimators, namely the Spatial Regional and Level Exceedance estimators. Simulation results and real data examples show the benefits of using the Tukey g-and-h transformation as opposed to standard Gaussian spatial random fields in a real data application for environmental monitoring.

The objective of this study was to correlate the binding of drugs on a very popular nanoparticulate polymeric matrix; PLGA nanoparticles with their main constitutional, electronic and physico-chemical descriptors. Gaussian Processes (GPs) was the artificial intelligence machine learning method that was utilized to fulfil this task. The method could successfully model the results where optimum values of the investigated descriptors of the loaded drugs were deduced. A percentage bias of 12.68 % ± 2.1 was obtained in predicting the binding energies of a group of test drugs. As a conclusion, GPs could successfully model the drugs-PLGA interactions associated with a good predicting power. The GPs-predicted binding energies (ΔG) can easily be projected to the drugs loading as was previously proven. Adopting the “Pharmaceutics Informatics” approach can save the pharmaceutical industry and the drug delivery scientists a lot of exerted resources, efforts and time.

We study a conformation of an albumin protein in the temperature range of 300K-315K, i.e. in the physiological range of temperature. Using simulations we calculate values of two backbone angles, that carry most of information about positioning of the protein chain in a conformational space. Given these, we calculate energy components of such protein. Further, using the Flory theory we determine the temperature in which investigated albumin chain model is closest to the free joined chain model. Near the Flory temperature, we study energy components and the conformational entropy, both derived from two angles that reflect most of the chain dynamics in a conformational space. We show that the conformational entropy is an oscillating function of time in considered range of temperature. Our finding is that, the only regular oscillation pattern appears near the Flory temperature.

The statistics of grain displacements probability distribution function (pdf) during the shear of a granular medium displays an unusual dependence with the shear increment upscaling as recently evinced [Phys. Rev. Lett. 115 238301 2015]. Basically, the pdf of grain displacements has clear nonextensive ($q$-Gaussian) features at small scales but approaches to Gaussian characteristics at large shear window scales -- the granulence effect. Here, we extend this analysis studying a larger system (more grains considered in the experimental setup) which exhibits a severe shear band fault during the macroscopic straining. We calculate the pdf of grain displacements and the dependency of the $q$-statistics with the shear increment. This analysis have shown a singular behavior of $q$ at large scales, displaying a non-monotonic dependence with the shear increment. By means of an independent image analysis, we demonstrate that this singular non-monotonicity could be associated with the emergence of a shear band within the confined system. We show that the exact point where the $q$-value inverts its tendency coincides with the emergence of a giant percolation cluster along the system, caused by the shear band. We believe that this original approach using Statistical Mechanics tools to identify shear bands can be a very useful piece to solve the complex puzzle of the rheology of dense granular systems.

In this article we derive a closed form expression for the symmetric logarithmic derivative of Fermionic Gaussian states. This provides a direct way of computing the quantum Fisher Information for Fermionic Gaussian states. Applications range from quantum Metrology with thermal states to non-equilibrium steady states with Fermionic many-body systems.

Lamellar eutectic structure of Al_0.7CoCrFeNi high-entropy alloy (HEA) is emerging as a promising candidate for structural applications because of its high strength-ductility combination. The alloy consists of a fine-scale lamellar fcc+B2 microstructure with high flow stresses >1500 MPa under quasi-static conditions. The response to shear loading was not investigated so far. This is the first report on the shear deformation of an eutectic structured HEA and effect of precipitation on shear deformation. The dynamic shear response (DSR) of the eutectic HEA was examined in two microstructural conditions, with and without the presence of L1₂ precipitates. A split-Hopkinson pressure bar (SHPB) was used to compress the hat-shaped specimens to study the local DSR of the alloy. The adiabatic shear bands (ASBs) in two different microstructural conditions were characterized after deformation at dynamic strain rates. The adiabatic shear localization occurs at low strains for the high strength material, and the eutectic microstructure does not delay cracking. The width of ASBs and the extent of plastic deformation around them has been correlated with the rate of straining. Dynamic recrystallization within ASBs and profuse twinning around it was observed. Local mechanical response of individual lamellae before and after shear deformation was examined using nano-indentation.

The study of coherent vortices remains an urgent field of singular optics of vortex beams. In this paper, coherent properties of partially coherent vortex Bessel—Gaussian optical beams propagating through a uniform medium (free space) or a turbulent atmosphere are examined theoretically. The consideration is based on analytical solution of the equation for the transverse second-order mutual coherence function of the field of a partially coherent optical radiation in a turbulent atmosphere. For the partially coherent Bessel—Gaussian beam, the second-order mutual coherence function of the source field is taken as a Gaussian Shell-model. In this approximation, we analyze the behavior of the coherence degree and the integral coherence scale of these beams as a function of the propagation pathlength, propagation conditions, and beam parameters, such as the radius of the Gauss factor of the beam, parameter of the Bessel factor of the beam, topological charge, and correlation width of the source field of partially coherent radiation. In addition, the integral coherence scale of the partially coherent vortex Bessel—Gaussian beam is compared with that of the partially coherent Gaussian beam. It is found that as a partially coherent vortex Bessel—Gaussian beam propagates through a turbulent atmosphere, there appear not two (as might be expected: one due to atmospheric turbulence and another due to partial coherence of the source field), but only one ring dislocation of the coherence degree (due to the simultaneous effect of both these factors on the optical radiation). In addition, it is shown that the dislocation of the coherence degree that affects significantly the beam coherence level is formed only for beams, for which the coherence width of the source field is larger than the diameter of the first Fresnel zone.

CO2 has characteristic properties and reactions at converter smelting temperature, and the chemical reaction between CO2 and elements such as C and Si in the molten pool has bubble proliferation and cooling effects, which can effectively improve the kinetic and thermodynamic conditions of converter smelting. Here, an experimental study and industrial test on the application of CO2 in converter smelting were carried out. The smelting effects of Mode-1 and Mode-2 with total CO₂ injection amounts of 229 Nm³ and 196 Nm³, respectively, were compared, and the changes in molten steel and slag compositions, dust removal, and gas are analyzed. The test results show that converter top and bottom blowing CO₂ technology (COMI-B)technology had significant metallurgical advantages over the N-Mode; the dephosphorization rate increased by 4.2%, slag (FeO) content was reduced by 2.04%, end point nitrogen content of molten steel was reduced by 20%, gas recovery increased by 8.29 Nm³/t, and soot production reduced by 14.7%. The results of the study provide reference for the application of COMI-B technology in converters in the iron and steel industry and develop a new path for resource utilization of CO₂.

This paper investigates the potential of utilising multilevel quasi-interpolation techniques on Chebyshev sparse grids for complex numerical computations. The paper starts by laying down the motivations for choosing Chebyshev sparse grids and quasi-interpolation methods with Gaussian kernels. It delves into the practical aspects of implementing these techniques. Various numerical experiments are performed to evaluate the efficiency and limitations of the multilevel quasi-sparse interpolation methods with dimensions two dimension and three dimension. The work ultimately aims to provide a comprehensive understanding of the computational efficiency and accuracy achievable through this approach, comparing its performance with traditional methods.

The abilities of quantitative description of noise are restricted due to its origin and only the statistical and spectral analysis methods can be applied while an exact time evolution cannot be defined or predicted. It emphasizes the challenges faced in many applications including communication systems where noise can play, on one hand, a vital role impacting signal-to-noise ratio but possessing, on the other hand, unique properties such as infinite entropy (infinite information capacity), exponentially decaying correlation function and so on. Despite the deterministic nature of chaotic systems, the predictability of chaotic signals is limited for a short time window putting them close to random noise. In this article, we propose and experimentally verify an approach to achieve Gaussian-distributed chaotic signals by processing the outputs of chaotic systems. The mathematical criteria, on which the main idea of the study is based on, is the Central Limit Theorem which states that the sum of a large number of independent random variables with similar variances approaches a Gaussian distribution. The study involves more than 40 mostly three-dimensional continuous-time chaotic systems (Chua’s, Lorenz’s, Sprott’s, memristor-based, etc.) whose output signals are analyzed according to criteria that encompass the probability density functions of a chaotic signal itself and its envelope and phase, statistical and entropy-based metrics such as skewness, kurtosis, and entropy power. We found that two chaotic signals of Chua’s and Lorenz’s systems exhibited superior performance across the chosen metrics. Furthermore, our focus extended to determining the minimum number of independent chaotic signals necessary to yield a Gaussian-distributed combined signal. The analytic and experimental results indicate that the sum of at least three non-Gaussian chaotic signals closely approximates Gaussian distribution. It allows generation of reproducible Gaussian-distributed deterministic chaos by modeling simple chaotic systems.

Femur length (FL) is a biometric measurement of ultrasound images used to identify the growth patterns and abnormalities of the foetus. Automatic segmentation of FL can improve the efficiency and convenience of inexperienced users. The femur region in a low-cost ultrasound image is a small area with low contrast intensity compared with the non-femur region. This study proposed a fully automated framework for segmenting and measuring the FL in a portable ultrasound image. We utilized statistical modeling, employing Gaussian mixture functions for thresholding to extract the femur area from the background. We also used the localizing region-based active contour method to segment the femur region accurately. We also proposed a refinement step in this framework to obtain a more accurate femur area in the ultrasound image. Because the length and position of the femur are the main factors affecting foetal biometric measurement accuracy, we not only evaluated segmentation results using region-based methods, such as sensitivity, specificity, and dice similarity. We also applied the FL measurement approach to evaluate the angle and length accuracy relative to the ground truth. In terms of region-based segmentation performance, the proposed method provided the best average performance with a sensitivity of 0.78, specificity of 0.87, and dice similarity of 0.81. For FL measurement performance, the proposed framework achieved an average angle accuracy of 0.99, distance accuracy of 0.88, and length accuracy of 0.93.

We have implemented quantum modeling mainly based on Bohmian Mechanics to study time series that contain strong coupling between their events. Compared to time series with normal densities, such time series are associated with rare events. Hence, employing Gaussian statistics drastically underestimates the occurrence of their rare events. The central objective of this study is to investigate the effects of rare events in the probability densities of time series from the point of view of quantum measurements. For this purpose, we first model the non-Gaussian behavior of time series using the multifractal random walk (MRW) approach. Then, we examine the role of the key parameter of MRW, λ, which controls the degree of non-Gaussianity, in quantum potentials derived for time series. Our Bohmian quantum analysis shows that the derived potential takes some negative values in high frequencies (its mean values), then substantially increases, and the value drops again for rare events. Thus, rare events can generate a potential barrier in the high-frequency region of the quantum potential, and the effect of such a barrier becomes prominent when the system transverses it. Finally, as an example of applying quantum potential beyond the microscopic world, we compute quantum potentials for the S&P financial market time series to verify the presence of rare events in the non-Gaussian densities and demonstrate deviation from the Gaussian case.

The Gol'denveizer problem of a torus was studied analytically by Audoly and Pomeau (2002), and the accuracy of the Audoly and Pomeau linear law was confirmed numerically by Sun (2021). However, the law does not include the major radius R of the torus. To find the influence of the major radius, we used finite element numerical simulation to simulate different cases, and we propose a modified Audoly and Pomeau linear law for vertical deformation, which includes R. A linear law of horizontal deformation is presented as well. Our studies show that the Audoly and Pomeau linear law has high accuracy. With modified vertical and horizontal deformation, a displacement-compatible relation between them is formulated.

This paper presents the application of optimal control problem in modeling of stirred tank heater temperature control. The analysis of the open loop system shows that the system is not efficient without a controller. Linear Quadratic Gaussian (LQG) and Linear Quadratic Integral (LQI) controllers are used to increase the performance of the system. Comparison of the closed loop system with the proposed controllers have been done with Matlab/Simulink Toolbox and a promising results have been analyzed.

We proposed a deep learning based chemometric data analysis technique. We trained L2 regularized sparse autoencoder end-to-end for reducing the size of the feature vector to handle the classic problem of curse of dimensionality in chemometric data analysis. We introduce a novel technique of automatic selection of nodes inside hidden layer of an autoencoder through pareto optimization. Moreover, linear regression, ϵ-SVR , and Gaussian process regressor are applied on the reduced size feature vector for the regression. We evaluated our technique on orange juice and wine dataset and results are compared against state-of-the-art methods. Quantitative results are shown on Normalized Mean Square Error (NMSE) and the results show considerable improvement in the state-of- the-art.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

Top-down models are defined by hardware architects to provide information on the utilization of the different hardware components. The target is to isolate the users from the complexity of the hardware architecture while giving them insight into how efficiently the code is using the resources. In this paper, we explore the applicability of 4 top-down models defined for different hardware architectures powering state-of-the-art HPC clusters (Intel Skylake, Fujitsu A64FX, IBM Power9, and Huawei Kunpeng 920) and propose a model for AMD Zen 2. We study a parallel CFD code used for scientific production to compare these 5 Top-Down models. We evaluate the level of insight achieved, the clarity of the information, the ease of use, and the conclusions that each one allows us to reach.

The effect of filling velocity on positive macrosegregations in large size steel ingots was studied. Macrosegregation and macro-/micro-structure were characterized on the hot-tops and a portion of the upper section of two ingots. The measurements revealed that segregation features in the two ingots varied as a function of the alloying elements, and that the severity of positive macrosegregation in the casting body was reduced when the filling rate was increased. It was also found that at the higher filling rate, grain morphologies in the first solidified zones of the ingot changed from columnar to equiaxe, and secondary dendrite arm spacing (SDAS) became slightly smaller in the intermediate and final solidified zones. The experimental findings were analyzed in the framework of diffusion and convection controlled solidification, as well as liquid metal flow theories. The solute dependence of segregation features was related to the difference in solid-liquid partition coefficient and diffusion capability of each element in the liquid iron. Calculation of Reynolds numbers (Re) during the filling process, for both ingots, showed that higher filling velocity caused more instable movement of the liquid metal in the initial solidification stage, resulting in the modification of grain morphology, as well as accelerated solidification rate.

In the broader landscape of cryptocurrency risk management, this study delves into the nuanced estimation of Value at Risk (VaR) for a uniformly weighted portfolio of cryptocurrencies, employing the bivariate Normal Inverse Gaussian distribution renowned for its semi-heavy tails. Utilizing high-frequency data spanning from January 1st, 2017, to October 25th, 2022, with a primary focus on Bitcoin and Ethereum, our research seeks to accentuate the resilience of VaR methodology as a paramount risk assessment tool. The essence of our investigation lies in advancing the comprehension of VaR accuracy by quantitatively comparing observed returns of both cryptocurrencies with their corresponding estimated values, with a central theme being the endorsement of the Normal Inverse Gaussian distribution as a potent model for risk measurement, particularly in the domain of high-frequency data. To bolster the statistical reliability of our results, we adopt a forward test methodology, showcasing not only a contribution to the evolution of risk assessment techniques in Finance but also underscoring the practicality of sophisticated distributional models in econometrics. Our findings not only contribute to the refinement of risk assessment methods but also highlight the applicability of such models in precisely modeling and forecasting financial risk within the dynamic realm of cryptocurrencies, epitomized by the case study of Bitcoin and Ethereum.

Due to the great threat of torpedo against surface warships, an efficient hydroacoustic countermeasure system must output realtime strategies that can accommodate varied antagonizing scenarios. Aiming at the decision-making problem in the anti-torpedo hydroacoustic countermeasure cases, this paper proposes an intelligent adversarial strategy based on the game theory. By discretizing the strategy space of both sides, a matrix game model is established with the payoff characterized by the capture probability of attacking torpedo. An improved simplex algorithm is then developed to get the mixed-strategy Nash equilibrium of the game model, with which preferred avoiding strategies can be obtained to minimize the risk of being captured by the attacking torpedo. Based on the probabilistic view of preferred adversarial strategy, the launching strategy of floating acoustic decoy can be achieved, and the evasion trajectory of warship can be confirmed simultaneously. Numerical simulation demonstrates that the proposed method can generate realtime intelligent antagonizing strategy, and consequently, can improve the survival rate of surface warships in the of anti-torpedo countermeasure scenarios.

This paper explores the use of different deep learning models for predicting precipitation in 56 meteorological stations in Jilin Province, China. The models used include Stacked-LSTM, Transformer, and SVR, and Gaussian noise is added to the data to improve their robustness. Results show that the Stacked-LSTM model performs the best, achieving high prediction accuracy and stability. The study also conducts variable attribution analysis using LightGBM and finds that temperature, dew point, precipitation in previous days, and air pressure are the most important factors affecting precipitation prediction, which is consistent with traditional meteorological theory. The paper provides detailed information on the data processing, model training, and parameter settings, which can serve as a reference for future precipitation prediction tasks. The findings suggest that adding Gaussian noise to the dataset can improve the model's generalization ability, especially for predicting days with zero precipitation. Overall, this study provides useful insights into the application of deep learning models in precipitation prediction and can contribute to the development of meteorological forecasting and applications.

Background: Functional Movement Screening (FMS) allows for rapid assessment of an individual’s physical activity level and timely detection of sports injury risk. However, traditional functional movement screening often requires on-site assessment by experts, which is time-consuming and prone to subjective bias. Therefore, the study of automated functional movement screening has become increasingly important. Methods: In this study, we propose an automated assessment method for FMS based on the improved Gaussian Mixture Model (GMM). First, the oversampling of minority samples is conducted, the movement features are manually extracted from the FMS dataset collected with two Azure Kinect depth sensors, then we train the Gaussian mixture model with different scores (1 point, 2 points, 3 points) of feature data separately, finally, we conducted FMS assessment by the Maximum Likelihood estimation. Results: The improved GMM has a higher scoring accuracy (Improved GMM:0.8) compared to other models (Traditional GMM=0.38, Adaboost.M1=0.7, Naïve-Bayes=0.75), and the scoring results of improved GMM have a high level of agreement with the expert scoring (kappa=0.67). Conclusions: The results show that the proposed method based on the improved Gaussian mixture model can effectively perform the FMS assessment task and it is potentially feasible to use depth cameras for FMS assessment.

Weaving is an ancient and effective structural forming technique characterized by the ability to convert two-dimensional ribbons to three-dimensional structures. However, most 3D structures woven from straight ribbons have topological defects. Baek et al. proposed a method to weave smoother continuous 3D surface structures using naturally curved (in-plane) ribbons, obtained a new surface structure with relatively continuous variation of Gaussian curvature, and analyzed its geometric properties. We believe that this new 3D surface structure with smooth geometric properties must correspond to new mechanical properties. To this end, we investigated a 3D surface structure using naturally curved (in-plane) ribbon weaving, and the results of calculations and experiments show that such structures have better buckling stability than those woven with straight ribbons. It is observed that the number of ribbons influences the buckling behavior of different types of woven structures.

This work addresses the problem of sampling from Gaussian probability distributions by means of uniform samples obtained deterministically and directly from space-filling curves (SFCs), a purely topological concept. To that end, the well-known inverse cumulative distribution function method is used, with the help of the probit function,which is the inverse of the cumulative distribution function of the standard normal distribution. Mainly due to the central limit theorem, the Gaussian distribution plays a fundamental role in probability theory and related areas, and that is why it has been chosen to be studied in the present paper. Numerical distributions (histograms) obtained with the proposed method, and in several levels of granularity, are compared to the theoretical normal PDF, along with other already established sampling methods, all using the cited probit function. Final results are validated with the Kullback-Leibler and two other divergence measures, and it will be possible to draw conclusions about the adequacy of the presented paradigm. As is amply known, the generation of uniform random numbers is a deterministic simulation of randomness using numerical operations. That said, sequences resulting from this kind of procedure are not truly random. Even so, and to be coherent with the literature, the expression ”random number” will be used along the text to mean ”pseudo-random number”.

In this work, Gaussian Tribonacci functions are defined and investigated on the set of real numbers $\mathbb{R},$ \textit{i.e}., functions $f_{G}$ $:$ $\mathbb{R}\rightarrow \mathbb{C}$ such that for all $% x\in \mathbb{R},$ $n\in \mathbb{Z},$ $f_{G}(x+n)=f(x+n)+if(x+n-1)$ where $f$ $:$ $\mathbb{R}\rightarrow \mathbb{R}$ is a Tribonacci function which is given as $f(x+3)=f(x+2)+f(x+1)+f(x)$ for all $x\in \mathbb{R}$. Then the concept of Gaussian Tribonacci functions by using the concept of $f$-even and $f$-odd functions is developed. Also, we present linear sum formulas of Gaussian Tribonacci functions. Moreover, it is showed that if $f_{G}$ is a Gaussian Tribonacci function with Tribonacci function $f$, then $% \lim\limits_{x\rightarrow \infty }\frac{f_{G}(x+1)}{f_{G}(x)}=\alpha \ $and\ $\lim\limits_{x\rightarrow \infty }\frac{f_{G}(x)}{f(x)}=\alpha +i,$ where $% \alpha $ is the positive real root of equation $x^{3}-x^{2}-x-1=0$ for which $\alpha >1$. Finally, matrix formulations of Tribonacci functions and Gaussian Tribonacci functions are given. In the literature, there are several studies on the functions of linear recurrent sequences such as Fibonacci functions and Tribonacci functions. However, there are no study on Gaussian functions of linear recurrent sequences such as Gaussian Tribonacci and Gaussian Tetranacci functions and they are waiting for the investigating. We also present linear sum formulas and matrix formulations of Tribonacci functions which have not been studied in the literature.

Deep coal beds have been suggested as possible usable underground geological locations for carbon dioxide storage. Furthermore, injecting carbon dioxide into coal beds can improve the methane recovery. Due to importance of this issue, a novel investigation has been done on adsorption of carbon dioxide on various types of coal seam. This study has proposed four types of Gaussian Process Regression (GPR) approaches with different kernel functions to estimate excess adsorption of carbon dioxide in terms of temperature, pressure and composition of coal seams. The comparison of GPR outputs and actual excess adsorption expresses that proposed models have interesting accuracy and also the Exponential GPR approach has better performance than other ones. For this structure, R2=1, MRE=0.01542, MSE=0, RMSE=0.00019 and STD=0.00014 have been determined. Additionally, the impacts of effective parameters on excess adsorption capacity have been studied for the first time in literature. According to these results, the present work has valuable and useful tools for petroleum and chemical engineers who dealing with enhancement of recovery and environment protection.

The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters which fit the shape of peakedness in bimodality on real line. The special values of peakedness parameters of the distribution are combination of half Laplace and half normal distributions on real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data.

Sustainable food systems imply the re-use of biowastes and water. In this paper we characterize 30 top soil improvers of anthropogenic, animal and green waste origin, and 11 irrigation waters from rivers, channels, and civil wastewater treatment plants (cWWTPs) for the presence of antimicrobials. Liquid chromatography coupled to hybrid High Resolution Mass Spectrometry (LC-HRMS/MS) was applied to determine 50 different drugs belonging to the classes of sulfonamides (11), tetracyclines (7), fluoroquinolones (10), macrolides (12), amphenicols (3), pleuromutilins (2), diaminopyrimidines (1), rifamycines (1) and lincosamides (1). Biowastes from cWWTPs and animal manure, slurry and litter revealed the highest loads for sulfonamides, tetracyclines, fluoroquinolones, and macrolides (80, 470, 885, and 4,487 ng/g wet weight, respectively) with nor- and cipro-floxacin as marker of anthropogenic sources. In composts and digestates antimicrobials were almost below the determination limits. Re-used waters for irrigation in open field lettuce production resulted contaminated in the range of 12-221 ng/L for sulfonamides, tetracyclines, and fluoroquinolones, against very few detects in channels and surface waters. Antimicrobials Hazard Index (HI) based on Predicted No Effect Concentration for Antimicrobial Resistance (PNEC_AMR) was largely >100 in contaminated top soil improvers from urban and animal sources. Accounting for worst-case inputs from top soil improvers and irrigation water and dilution factors of amended soil, fluoroquinolones only showed a HI around 1 in open fields for lettuce production. The origin of top soil improvers plays a pivotal role for safe and sustainable leafy vegetable productions, to abate the risk of AMR onset in food-borne diseases, as well the transfer of AMR elements to human gut flora.

Phenotypic plasticity is widely acknowledged as one of the most common solutions for coping with novel environmental conditions following climate change. However, it is less known wheth-er current amounts of trait plasticity, which is sufficient for matching with contemporary climate, will be adequate when global temperatures exceed historical levels. We addressed this issue by ex-ploring the responses of functional and structural leaf traits in Iris pumila clonal individuals to ex-perimentally increased temperature (~1.5°C), using an open top chamber (OTC) design. We de-termined phenotypic values of specific leaf area, leaf dry matter content, specific leaf water con-tent and leaf thickness in leaves sampled from the same clone inside and outside of the OTC de-ployed on it, over seasons and years within two natural populations. We analyzed the data by a repeated multivariate analysis of variance, which is primarily focused on profiles (reaction norms, RNs) of a variable gathered from the same individual at several different time points. We found that the mean RNs of all analyzed traits were parallel regardless of experienced temperatures, but differed in the level and the shape. The populations RNs were similar as well. Since the amount of plasticity in analyzed leaf trait was adequate for coping with elevated temperatures inside the OTCs, we predict that it will be also sufficient for responding to increased temperatures if they exceed the 1.5°C target.

In the extra-thick coal seams and multi-layered hard roofs, the longwall hydraulic support yielding, coal face spalling, strong deformations of goaf-side entry, and severe ground pressure dynamic events typically occur at the longwall top coal caving longwall faces. Based on the Key strata theory an overburden caving model is proposed here to predict the multilayered hard strata behaviour. The proposed model together with the measured stress changes in coal seam and underground observations in Tongxin coal mine provides a new idea to analyse stress changes in coal and help to minimise rock bursts in the multi-layered hard rock ground. Using the proposed primary Key and the sub-Key strata units the model predicts the formation and instability of the overlying strata that leads to abrupt dynamic changes to the surrounding rock stress. The data obtained from the vertical stress monitoring in the 38 m wide coal pillar located adjacent to the longwall face indicates that the Key strata layers have a significant influence on ground behaviour. Sudden dynamically driven unloading of strata was caused by the first caving of the sub-Key strata while reloading of the vertical stress occurred when the goaf overhang of the sub-Key strata failed. Based on this findings several measures were recommended to minimise the undesirable dynamic occurrences including pre-split of the hard Key strata by blasting and using the energy consumption yielding reinforcement to support the damage prone gate road areas. Use of the numerical modelling simulations was suggested to improve the key theory accuracy.

This paper analyzes the experience of first-person shooter (FPS) players when the game difficulty is adjusted by personalizing their audio cue settings, considering the balance between player performance, modeled using Gaussian Process Regression (GPR), and prior data serving as designer preference. In addition, we investigate the reason why player experience changes according to in-game audio cues. Previous studies have proposed various dynamic difficulty adjustment (DDA) methods for FPS games. However, few studies have considered the role of audio cues in the player experience. This paper compares the player experience of personalized enemy audio cue volume setting (GPR-DDA) and that of predetermined setting in an FPS game. Two comprehensive experimental phases, involving 80 participants, are conducted to assess the efficacy of GPR-DDA. The experience of our players is measured using questions taken from the Game User Experience Satisfaction Scale (GUESS) questionnaire and a final survey asking their open-ended feedback. A large language model (LLM) is used to analyze the natural language expressions of the players according to their native languages. To effectively have an LLM assist a limited number of qualified human evaluators in the classification of player responses, we develop an original procedure for this task. The GUESS results show that GPR-DDA can improve the player experience, and, in addition, the high consistency in the classification results over multiple runs of the selected LLM, as well as the similarity between its results and those of our human evaluators, reflect the reliability of the proposed LLM-assisted procedure.

This study presents an advanced data augmentation framework to enhance diagnostics in centrifugal pumps using vibration data, with a focus on practical industrial applications. Initially, Gaussian noise and signal stretching were employed to generate synthetic data, addressing the common issue of insufficient defect data in industrial settings. These methods simulate varied operating conditions and different rotating speeds. Recognizing the limitations of traditional approaches, we further integrated advanced models—Long Short-Term Memory (LSTM), Autoencoder (AE), and Generative Adversarial Networks (GANs) to augment the dataset comprehensively. This enhanced approach increases the robustness and accuracy of machine-learning models for fault detection and classification. Experimental results show significant improvements in diagnostic algorithm performance, reducing false positives and increasing fault detection rates. The study offers a complete framework for incorporating both traditional and advanced data augmentation techniques into predictive maintenance protocols, ensuring reliable operation of centrifugal pumps in diverse industrial environments.

Interactions between mobile robots and human operators in common areas require a high safety especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection the crossings of planned trajectories, their uncertainty based on model fluctuations, system noise and sensor noise,play an outstanding role. This paper discusses the calculation of expected areas of interactions du ring human-robot navigation with respect to fuzzy and noisy information. Expected crossing points of possible trajectories are nonlinearily associated with positions and orientations of robot and human. The nonlinear transformation of a noisy system input, such as directions of motion of human and robot, to a system output, the expected area of intersection of their trajectories, is done by two methods: statistical linearization and the sigma-point-transformation. For both approaches fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from given output distribution parameters.

Machine learning forks into three main branches such as supervised learning, unsupervised learning, and reinforcement learning where reinforcement learning is much potential to artificial intelligence (AI) applications because it solves real problems by progressive process in which possible solutions are improved and finetuned continuously. The progressive approach, which reflects ability of adaptation, is appropriate to the real world where most events occur and change continuously and unexpectedly. Moreover, data is getting too huge for supervised learning and unsupervised learning to draw valuable knowledge from such huge data at one time. Bayesian optimization (BO) models an optimization problem as a probabilistic form called surrogate model and then directly maximizes an acquisition function created from such surrogate model in order to maximize implicitly and indirectly the target function for finding out solution of the optimization problem. A popular surrogate model is Gaussian process regression model. The process of maximizing acquisition function is based on updating posterior probability of surrogate model repeatedly, which is improved after every iteration. Taking advantages of acquisition function or utility function is also common in decision theory but the semantic meaning behind BO is that BO solves problems by progressive and adaptive approach via updating surrogate model from a small piece of data at each time, according to ideology of reinforcement learning. Undoubtedly, BO is a reinforcement learning algorithm with many potential applications and thus it is surveyed in this research with attention to its mathematical ideas. Moreover, the solution of optimization problem is important to not only applied mathematics but also AI.

Musculoskeletal models (MSKMs) are used to estimate the muscle and joint forces involved in human locomotion, often associated with the onset of degenerative musculoskeletal pathologies (e.g. osteoarthritis). Subject-specific MSKMs offer more accurate predictions than their scaled-generic counterparts. This accuracy is achieved through time-consuming personalisation of models and manual tuning procedures that suffers from potential repeatability errors, hence limiting the wider application of this modelling approach. In this work we have developed a methodology for identifying and ranking the muscles that are more important to the determination of the joint forces, thus producing reduced but still accurate representation of the musculoskeletal system in shorter timeframes. The methodology hinges on Sobol's sensitivity analysis (SSA) for ranking the muscle importance. The thousands of data points required for SSA are generated using Gaussian Process emulators, a Bayesian technique to infer the input-output relationship between nonlinear models from a limited number of observations. Results show that there is a pool of muscles whose personalisation has little effects on the model predictions. Furthermore, joint forces in subject generic and subject generic models are influenced by different set of muscles, suggesting the existence of a model specific component of the sensitivity analysis.

This paper presents a novel method to extract local features, which instead of calculating local extrema computes global maxima in a discretized scale-space representation. To avoid obtaining precise scales by interpolation and to achieve perfect rotation invariance, two essential techniques, increasing the width of kernels in pixel and utilizing disk-shaped convolution template are adopted in this method. Since the size of a convolution template is finite and finite templates can introduce computational error into convolution, we sufficiently discuss this problem and work out an upper bound of the computational error. The upper bound is utilized in the method to ensure that all features obtained are computed under a given tolerance. Besides, the technique of relative threshold to determine features is adopted to reinforce the robustness for the scene of changing illumination. Simulations show that this new method attains high performance of repeatability in various situations including scale change, rotation, blur, JPEG compression, illumination change and even viewpoint change.

Many scientific and technological problems are studied with the help of computer codes that simulate the phenomena of interest rather than via traditional laboratory experiments. Such models play an important role in neuroscience where they are used to mimic brain function from the sub-cellular to the macroscopic level. Exploration with computer models carries with it a number of statistical challenges: where to sample the input space for the simulator, how to make sense of the data that is generated, how to estimate unknown parameters in the model, how to validate a model. The simulator setting also has some unique problems and possibilities. This review paper describes statistical research on these issues and how that work might be applied to neural simulations.

A diffusion welding of coarse-grained and ultrafine-grained (UFG) specimens of titanium near-α alloy Ti-5Al-2V used in nuclear power engineering was made by Spark Plasma Sintering. The failure of the welded specimens in the conditions of hot salt corrosion and of electrochemical corrosion were shown to have preferentially intercrystalline character. In the case of presence of macrodefects, crevice corrosion of the welded joints was observed. The resistance of the alloys against the intercrystalline corrosion was found to be determined by the concentration of vanadium at the titanium grain boundaries, by the size and volume fraction of the β-phase particles and by the presence of micro- and macropores in the welded joints. The specimens of the welded joints of the UFG alloy have higher hardness, hot salt corrosion resistance and the electrochemical corrosion.

This research aims to develop top-emission OLEDs (Organic Light light-emitting diodes) that exhibit enhanced electro-optical properties regarding color purity, luminance, efficiency, stability, and longer lifetime. To achieve this aim, we have used silicon wafer substrates with a thickness of 675 m. Top-emission OLED fabrication on silicon wafer substrates requires careful material selection and layer configuration. This device structure is described as follows: Silicon/Al (100 nm)/ HATCN (10 nm)/ Dic-TRZ: Ir(mppy)3 (40 nm)/ TPBi (30 nm)/ LiF 1.2 nm / Al 19.9 nm. LiF/Al combination is used as the semi-transparent cathode. Research on top-emission OLEDs has demonstrated the importance of silicon wafer substrates in enhancing electro-optical properties. The optical properties of top-emission OLEDs are significantly influenced by factors such as the semitransparent cathode layer's thickness and the anode layer's surface roughness. Study findings encompass the impact of microcavity on TEOLEDs, dopant concentration on device performance, and device thermal conductivity and lifetime. To create a highly efficient top-emission OLED on a silicon substrate, we meticulously fine-tuned parameters such as the thickness of the cathode Al layer (19.9 nm), the organic layer thickness (HIL - HATCN 10 nm, HTL - NPB 30 nm, ETL - TPBi 30 nm, EML - DIC–TRZ: Ir(mppy)3 40 nm), and the dopant concentration. Electro-optical performance has been significantly improved through optimization, especially in devices with high dopant concentrations. The 38% dopant concentration experiments resulted in a peak current density of 201.97 mA/cm2, a luminance of 4776 cd/m2, and an efficiency of 2.36 cd/A at 9 V. In addition, the high dopant concentration has resulted in a narrow spectral range, indicating a strong microcavity effect. Additionally, Si-TEOLEDs were tested for lifetime under these specific conditions, exhibiting a half-life of 420 hours.

The distribution and variation of top-of-atmosphere longwave cloud radiative forcing (LCRFTOA) has drawn a significant amout of attention due to its importance in understanding the energy budget. Advancements in sensor and data processing technology, as well as a new generation of geostationary satellites, such as the FengYun-4A (FY-4A), allow for high spatial resolutions that are crucial for real-time radiation monitoring. Nevertheless, there is a distinct lack of official top-of-atmosphere outgoing longwave radiation products under clear-sky conditions (OLRclear). Consequently, this study addresses the challenge of constructing LCRFTOA data with high spatiotemporal resolution over the full disk region of FY-4A. After simulating the influence of atmospheric parameters on OLRclear based on SBDART radiation transfer model (RTM), we developed a model for estimating OLRclear using infrared channels from the advanced geosynchronous radiation imager (AGRI) onboard the FY-4A satellite. The OLRclear results showed an RMSE of 5.05 W/m2 and MBE of 1.59 W/m2 compared to ERA5. Besides, the calculated LCRFTOA results were validated against instantaneous, daily average, and monthly average ERA5 and CERES LCRFTOA products, supporting the validity of the algorithm proposed in this paper. Finally, the changes in LCRFTOA due to variaty cloud heights (high, medium, and low cloud) were analyzed. This study provides the basis for comprehensive studies on the characteristics of top-of atmosphere radiation.

In the post antimicrobial era, increasing attention is paid towards using bacteriophage (phage in short) therapy to control antibiotic-resistant bacteria. The first step in phage therapy applications is isolating highly efficient lytic phages or phage cocktails from various sources. When a double-layer- agar with around 0.7% agar in top agar is employed, it results in a low number of phage isolation with a poor resolution, and in many cases, you miss the phage. To address this problem, a low concentration of agar in top agar is examined for better phage isolation. Here, our results proved the efficiency of isolating phage upon formulating a double-layer agar with 0.3% agar in top agar. A sewage sample was collected then phages were isolated, purified, and spotted on a top layer agar with 0.3% agar. The results showed the possibility of isolating a higher number of phages on 0.3% top agar than 0.7%. The finding advocates using 0.3% top agar for the double-layer agar, as it will provide fast, better, and easy phage screening and isolation.

In the steeply inclined and extremely thick coal seams (SIETCS) mined using the horizontal sublevel top-coal caving (HSTCC) method, the uncertainty of gas emission is a safety threat to the mining operations. In order to reduce the occurrence of accidents, the determination of gas emission is crucial. In this paper, we first proposed a prediction model for workers at the floor coalbed to calculate gas emission on site. We then put forward a finite element numerical simulation for researchers to predict gas emission from the floor coalbed. At last, we measured gas emitted from the floor coalbed of SIETCS in Wudong Coal Mine in a specific mining period and used the data to verify the applicability and accuracy of these two gas-emission prediction methods. The results showed that the gas emission from Wudong Coal Mine was 1.08 m³/min calculated based on the prediction model and 1.07 m³/min obtained using the user-defined integration method. Both methods have their own advantages, disadvantages and applicable objects, and are important in predicting gas emission from SIETCS mined using HSTCC method.

Evaluating aviation emissions and examining the dispersion properties of contaminants are crucial for understanding atmospheric pollution. To more properly assess the pollutant emissions and dispersion of aircraft during the landing and take-off (LTO) cycle, and to more effectively address air pollution surrounding the airport resulting from flight operations, this study assessed emissions throughout the LTO phase utilizing Quick Access Recorder (QAR) data in conjunction with the first-order approximation method. An improved Gaussian diffusion model for mobile point sources is employed to examine the diffusion characteristics of contaminants. Additionally, CFD calculation outcomes for various exhaust velocities and wind speeds were utilized to validate the trustworthiness of the improved Gaussian model. The discussion also encompasses the influence of diffusion time, wind direction, wind speed, temperature gradient, and particle deposition on the concentration distribution of contaminants. The findings indicated that the Gaussian diffusion model aligned with the results of the CFD calculations. The diffusion distribution of contaminants around airports varies over time and is significantly influenced by atmospheric environmental factors, including wind direction, wind speed, and atmospheric stability.

Let a,b,c be positive integers such that a²+b²=c², 2|b,gcd(a,b)=1. In 1956, Jesmanowicz conjectured that for any positive integer w, the only solution of (aw)^x+(bw)^y=(cw)^z in positive integers is (x, y, z) = (2, 2, 2). In this paper, based on Gaussian integer ring, we show that Je$\acute{s}$manowicz' conjecture is true for any positive integer

Optical communications are described and analyzed by shaped beams; we challenge the ‎effects of parameters that impact the profile of a Flat Top Gaussian (FTG) beam. When ‎the laser beam propagates throughout the atmosphere, it can be influenced by different ‎optical phenomena including scattering, absorption, and turbulence due to changes in the ‎scintillation index and the forms of intensity that are displayed in the source and receiver ‎planes. In this project, the FTG laser beam that propagates through a weak turbulent ‎region is numerically investigated using open-source software. This simulation will be ‎performed according to a mathematical model based on the split-step beam propagation ‎method. Intensity distributions at the source plane and the received average intensity in ‎atmospheric turbulence are calculated, and additional contour is in the transducer plane. ‎The scintillation index, structure constant, source size, and other parameters, are applied ‎in the Rytov method to quantify the weak turbulent model. Moreover, these parameters ‎are analyzed in near-field propagation. Also, the effects of the beam’s scintillation and ‎beam wander are determined. All results simulated are discussed and compared with the ‎TEM00 Gaussian beam. Finally, these results are compared to measurements in the ‎experimental part of the work.

Due to the high computational burden and the high nonlinearity of the responses, crashworthiness optimizations are notoriously hard-to-solve challenges. Among various approaches, methods like the Successive Response Surface Method (SRSM) have stood out for their efficiency in enhancing baseline designs within a few iterations. However, these methods have limitations that restrict their application. Their minimum iterative resampling required is often computationally prohibitive. Furthermore, surrogate models are conventionally constructed using Polynomial Response Surface (PRS), a method that is poorly versatile, prone to overfitting, and incapable of quantifying uncertainty. Furthermore, the lack of continuity between successive response surfaces results in suboptimal predictions. This paper introduces the Multi-Fidelity Successive Response Surface (MF-SRS), a Gaussian process-based method, which leverages a nonlinear multi-fidelity approach for more accurate and efficient predictions compared to SRSM. After initial testing on synthetic problems, this method is applied to a real-world crashworthiness task: optimizing a bumper crossmember and crash box system. Results, benchmarked against SRSM and the Gaussian Process Successive Response Surface (GP-SRS) – a single-fidelity Gaussian process-driven extension of SRSM, show that MF-SRS offers distinct advantages. Specifically, it improves upon the specific energy absorbed optimum value achieved by SRSM by 14.1 %, revealing its potential for future applications.

Oriented object detection is a challenging task in scene text detection and remote sensing image analysis, which has attracted extensive attention in recent years with the development of deep learning. Currently, mainstream oriented object detectors are based on preset anchor boxes. This method increases the computational load of the network and cause a large amount of anchor box redundancy. To solve this problem, we propose anchor-free oriented object detection based on the Gaussian centerness(AOGC), a single-stage anchor-free detection method. Our method uses contextual attention FPN(CAFPN) to obtain the contextual information of the target. Then we design a label assignment method for oriented objects. Finally, we develop a Gaussian kernel-based centerness branch, which can effectively determine the significance of different anchors. AOGC achieves mAP of 74.30% on the DOTA-1.0 datasets and 89.80% on the HRSC2016 datasets, respectively. AOGC exhibits superior performance to other methods in oriented anchor-free object detection methods.

The treatment of insulin-dependent diabetes mellitus is characterized by artificial supplementation of pancreatic β-cell ability to regulate sugar levels in the blood. Even though various insulin analogs are crucial for reasonable glycemic control, understanding the dynamic mechanism of the insulin analogs may help to improve the best-protracted insulin analog to assist people with Type 1 Diabetes (T1D) to live comfortably while maintaining tight glycemic control. Here we present the high-resolution crystal structure of NN304, known as insulin detemir, to 1.7 A resolution at cryogenic temperature. We computationally further investigated our crystal structure's monomeric-dimeric conformation and dynamic profile by comparing it with a previously available detemir structure (PDB ID: 1XDA). Our structure (PDB ID: 8HGZ) obtained at elevated pH provides a distinct alternative dimeric conformation compared to the previous structure, suggesting it might induce an intermediate state in the dissociation pathway of the insulin detemir’s hexamer:dihexamer equilibrium. Combined with orientational cross-correlation analysis by Gaussian Network Model (GNM), this alternate oligomeric conformation offers the distinct cooperative motions of a protracted insulin analog that has not been previously observed.

Gol'denveizer's problem of a torus has been analyzed by Audoly and Pomeau (2002) and Sun (2021). However, all of the investigations of Gol'denveizer's problem of an elastic torus have been linear. In this paper, the finite element method is used to more accurately address this problem. Furthermore, Sun (2021) cannot be solved by nonlinear analysis. We research the nonlinear mechanical properties of Gol'denveizer's problem of circular and elliptic tori, and relevant nephograms are given. We study the buckling of Gol'denveizer's problem of an elastic torus, and propose failure patterns and force-displacement curves of tori in the nonlinear range. Investigations reveal that circular tori have more rich buckling phenomena as the parameter a increases. Gol'denveizer's problem of the buckling of an elliptic torus is analyzed, and we find a new buckling phenomenon called a "skirt." As a/b increases, the collapse load of an elliptic torus of the Gol'denveizer problem is enhanced gradually.

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

Imperceptible vibratory noise stimulation has shown to be an effective means of improving stability for both whole body postural control and simple motor control tasks. While the physiological mechanism affording this improvement is uncertain, it is suspected that sensory noise stimulation may elicit a stochastic resonance-like effect within the somatosensory system. A stochastic resonance effect describes the phenomenon in which noise added to a non-linear system improves signal detection rather than degrading it. One hallmark of stochastic resonance is the existence of an optimal noise level which elicits the best system performance. There is disagreement in the literature regarding the presence of an optimal stimulation level for motor stability in humans. The goals of this study were to: 1) determine optimal stimulation level as a function of an individual’s sub-sensory threshold level, and 2) to determine whether performance of a force stability task was significantly better when subjects received stimulation at this identified optimal level compared to other sub-sensory threshold stimulation levels. Eighteen (18) participants completed an isometric finger flexion task with visual feedback while receiving noise stimulation scaled to varying percentages of their individual sub-sensory threshold level. Performance for this force stabilization task was quantified as the root-mean-square (RMS) error between the target force and the actual generated force values. Despite controlling all other signal properties and varying only amplitude, optimal noise stimulation values still varied widely across participants (10-100% sub-sensory threshold level). Statistical modeling revealed a significant improvement in task performance with optimal noise stimulation compared to other sub-sensory stimulation levels (p ≤ 0.019) with estimated marginal mean differences in force errors ranging from 0.13 to 0.23 N. Moderate significant Spearman correlations (r_s = 0.49 and r_s = 0.56, respectively) were found between finger flexion maximal voluntary contraction (MVC) and sub-sensory threshold level and MVC and optimal stimulation level. A strong, significant Spearman correlation (r_s = 0.65) was observed between sub-sensory threshold level and optimal stimulation level. Although these correlations do not provide a means to predict optimal stimulation level as a function of these other measures, optimal stimulation level appears to increase with sub-sensory threshold and MVC.

The VLSI industry is facing parasitic effects that trouble development in the nanoscale domain. However, instead of replacing the traditional MOSFET design, it would be more advantageous to apply different doping profiles and discerning which deal with specific parasitic effects the best. With a review of Gaussian doping, Uniform doping, and Delta doping profiles and analysis of the FET technology characteristics that use these doping profiles, a comparison can be made among them for integrated circuit design engineers. These doping profiles are compared based on how well they perform against non-ideal and ideal environments. Also, both digital and analog performance are measured to ensure the uniqueness of each doping profile that is present. After getting a list of benefits from each doping profile, it is derived to determine which doping profile works best against a host of parasitic effects and what type of application do these doping profiles have

In this paper, a method is developed for presenting a novel virtual torque sensor based on precise model and position measurements avoids the need of traditional strain gauges and amplifiers. More specifically, the harmonic drive compliance model and the Gaussian process regression (GPR) technique are used together to achieve virtual torque sensor measurement. While the harmonic drive compliance model provides the analytic part, the Gaussian process regression method is used to reconstruct the unmolded part based on motor-side and link-side joint angles as well as motor current. After an automatic offline calibration, the method allows for a lean online implementation. The virtual torque sensor measurement is compared with measurements of a commercial torque sensor, and the results have attested the effectiveness of the proposed method.

The objective of parsing is to transform a natural language sentence it in to a standard order. and in a same way a sentence is tokenized with an appropriate format. There are certain English grammar evaluation rules and the parsing approach which is to be followed for the proper formation of a particular sentence syntactically and semantically using the parsing approach. A sentence in English language is the main element in the semantic parser, which creates a parse tree with the help of applying semantic dating technique to a number of phrases. A parser divides a token into smaller components by applying sets of guidelines that characterize and a series of the tokens to determine its structure of the language, which specified by grammar. The illustration provides easy records on grammatical connections, which can simply know and put into practice with those who have no prior knowledge of the language, such as those who need to obtain textual family members. The semantic family members represent the relationships of a number of the words in the sentence. We advocate utilizing our parser to acquire the tagged sets as well as a context-free layout grammatical representation for the source form. All pronouns, adverbs, singular, plural, nouns, verbs, people, adjectives, tenses and other words are kept in a database.

The Asian world is home to a multitude of venomous and dangerous snakes, which are attributed to various medical effects used in the preparation of traditional snake tinctures and alcoholics, like the Japanese snake wine, named Habushu. The aim of this work was to perform the first quantitative proteomic analysis of the Protobothrops flavoviridis pit viper venom. Accordingly, the venom was analyzed by complimentary bottom-up and top-down mass spectrometry techniques. The mass spectrometry-based snake venomics approach revealed that more than half of the venom is composed of different phospholipases A2 (PLA2). The combination with an intact mass profiling led to the identification of the three main Habu PLA2s. Furthermore, nearly one-third of the total venom consists of snake venom metalloproteinases and disintegrins, and several minor represented toxins families were detected: CTL, CRISP, svSP, LAAO, PDE and 5’-nucleotidase. Finally, the venom of P. flavoviridis contains certain bradykinin-potentiating peptides and related peptides, like the svMP inhibitors pEKW, pEQW, pEEW and pENW. In preliminary MTT cytotoxicity assays the highest cancerous-cytotoxicity of the crude venom was measured against human neuroblastoma SH-SY5Y cells and shows in some fractions disintegrin-like effects.

In this work is proposed a new unsupervised method to evaluate the behavior of urban green areas in presence of heatwave scenarios analyzing the NDVI, NDMI and LST satellite data. To evaluate how these three indices characterize urban green areas during periods of heat waves, a reclassification of the data raster of each index is carried out. Rather than using standard classifications of the three indices, an unsupervised classification approach is proposed that uses the Elbow method to determine the optimal number of classes and the Jenks thematic classification method. Each class is assigned a Gaussian fuzzy set with means and standard deviations equal to that of the associated thematic class. The green urban areas are classified using zonal statistics operators and the degree of membership in the corresponding fuzzy set is used to assess the reliability of the classification. Finally, for each type of greenery the frequencies of belonging to the NDVI, NDMI and LST classes are analyzed to evaluate the behavior of the type of urban greenery during heatwaves. The framework was tested in an urban area consisting of the city of Naples (Italy). The results show that some types of greenery, such as deciduous forests and olive groves, are not very vulnerable to heatwave scenarios, unlike uncultivated areas which, instead, show critical behaviors during heatwaves.

Molecular-scale density fluctuations at equilibrium produce cavities in a liquid and can be analyzed to shed light on the statistics of the number of molecules occupying observation volumes of increasing radius. An information theory approach led to the conclusion that these probabilities should follow a Gaussian distribution function. This prediction has been confirmed by computer simulations in different liquid models if the size of the observation volume is not large. The reversible work of cavity creation is exactly linked to the probability of finding zero molecules in a given observation volume. The Gaussian formula for the latter probability is scrutinized to arrive at the enthalpy and entropy changes associated with cavity creation. The reversible work of cavity creation has a purely entropic origin due to the solvent-excluded volume effect produced by the inaccessibility of a given region of the configurational space. The consequent structural reorganization of liquid molecules leads to exactly compensating enthalpy and entropy changes. These results are in line with those obtained in a direct statistical mechanical study by Lee.

Due to the highly nonlinear, multi-stage, and strongly time-varying marine lysozyme fermentation process, it is difficult to assure the stability and prediction accuracy of the traditional single global soft sensor model on a global scale. This study innovatively proposed a soft sensor model based on an improved seagull optimization algorithm (ISOA) combined with Gaussian process regression (GPR) weighted ensemble learning. First, the sample data set is divided into multiple local sample subsets by the improved density peak clustering algorithm (ADPC). Second, the Gaussian process regression model is optimally altered with an improved seagull optimization algorithm for the purpose of establishing the corresponding sub-prediction model. Finally, the prediction model's fusion strategy is ultimately determined depending on the degree of connection between the test samples and a subset of local pieces. Simulation results show that the proposed soft sensor model, which searches GPR based on ISOA and combines various sub-models, can predict the key biochemical parameters of the marine lysozyme fermentation process better with less prediction error under the condition of fewer training data, and it can be expanded to the soft sensor models of general nonlinear systems, according to simulation results.

This article describes techniques employed in the production of a synthetic dataset of driver telematics emulated from a similar real insurance dataset. The synthetic dataset generated has 100,000 policies that included observations about driver’s claims experience together with associated classical risk variables and telematics-related variables. This work is aimed to produce a resource that can be used to advance models to assess risks for usage-based insurance. It follows a three-stage process using machine learning algorithms. The first stage is simulating values for the number of claims as multiple binary classifications applying feedforward neural networks. The second stage is simulating values for aggregated amount of claims as regression using feedforward neural networks, with number of claims included in the set of feature variables. In the final stage, a synthetic portfolio of the space of feature variables is generated applying an extended SMOTE algorithm. The resulting dataset is evaluated by comparing the synthetic and real datasets when Poisson and gamma regression models are fitted to the respective data. Other visualization and data summarization produce remarkable similar statistics between the two datasets. We hope that researchers interested in obtaining telematics datasets to calibrate models or learning algorithms will find our work valuable.

The eruption of COVID-19 patients in 215 countries worldwide has urged for robust predictive methods that can detect as early as possible the size and duration of the contagious disease and also providing precision predictions. In much recent literature reported on COVID-19, one or more essential parts of such investigation were missed. One of the crucial elements for any predictive method is that such methods should fit simultaneously as much data as possible; these data could be total infected cases, daily hospitalized cases, cumulative recovered cases, and deceased cases, and so on. Other crucial elements include sensitivity and precision of such predictive methods on the amount of data as the contagious disease evolved day by day. To show the importance of these aspects, we have evaluated the standard SIRD model and a newly introduced Gaussian-SIRD model on the development of COVID-19 in Kuwait. It is observed that the SIRD model quickly picks up the main trends of COVID-19 development, but the Gaussian-SIRD model provides precise prediction a longer period of time.

In this paper, closed forms of the summation formulas for generalized Fibonacci numbers are presented. As special cases, we give summation formulas of Fibonacci, Lucas, Pell, Pell-Lucas, Jacobsthal, Jacobsthal-Lucas numbers.

We consider rotational hypersurface in the four dimensional Euclidean space. We calculate the mean curvature and the Gaussian curvature, and some relations of the rotational hypersurface. Moreover, we define the third Laplace-Beltrami operator and apply it to the rotational hypersurface.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

In this paper, the neural network is developed for solving the inverse photoacoustic problem aiming to estimate thermo-elastic properties and thickness of a semiconductor sample. The idea was that these sample properties be estimated from the phase characteristic because the phase measurements are more sensitive. The neural network has been trained on a large basis of photoacoustic phases calculated from a theoretical Si n-type model in the range of 20Hz to 20kHz, on which random Gaussian noise has been applied. It is shown that high accuracy and precision could be reached in solving of inverse photoacoustic problem using only phase measurement.

Plasma technology shows tremendous potential for revolutionizing oncology research and treatment. Reactive oxygen and nitrogen species, electromagnetic emissions generated through gas plasma jets, have attracted significant attention due to their selective cytotoxicity towards cancer cells. To leverage the full potential of plasma medicine, researchers have explored the use of mathematical models and various subsets of machine learning, such as reinforcement learning, and deep learning. This review emphasizes the significant application of AI algorithms in the adaptive plasma system, paving the way for precision and dynamic cancer treatment. Realizing the full potential of AI in plasma medicine, requires research efforts, data sharing and interdisciplinary collaborations. Unravelling the complex mechanisms, developing real-time diagnostics, and optimizing AI models will be crucial to harness the true power of plasma technology in oncology. The integration of personalized and dynamic plasma therapies, alongside AI and diagnostic sensors, presents a transformative approach to cancer treatment with the potential to improve outcomes globally.

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing, data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai'i island, using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that, while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m x 2 m M. polymorpha presence dataset and a 30 m x 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

In the broad field of Inverse Analysis and Structural Identification, it is nowadays of a large interest the study of Gaussian Processes, as a reliable and efficient optimisation method, particularly helpful toward the identification of a global optimum point, under the conditions of complicated functions to be optimised. In the present contribution, a specific case study is considered, focusing on a historical road reinforced concrete arched bridge, located in Northern Italy, employing dynamic modal properties, deciphered from in-situ measurements, previously acquired under operational traffic conditions, by a standard wired accelerometer system, placed at the deck level. Aiming at the identification and diagnosis of the bridge structure, three main methodological steps are herein considered: the adoption of a FEM model of the structure (in the linear dynamic framework), the definition of an appropriate discrepancy function, based on measured and numerically computed quantities (natural frequencies and mode shapes of the bridge), and the investigation of such a discrepancy function, toward a consistent selection of an optimisation strategy for the identification of sought parameters (Young’s moduli and mass densities of diverse elements of the structure). The presented developments, within the framework of methodological optimisation approach by Gaussian Processes, and achieved results display a rather efficient perspective, with reference to the considered case study, toward inverse analysis for structural diagnosis, in the context of strategic (historical) infrastructures.