The asymmetric skew divergence smooths one of the distributions by mixing it, to a degree determined by the parameter $\lambda$, with the other distribution. Such divergence is an approximation of the KL divergence that does not require the target distribution to be absolutely continuous with respect to the source distribution. In this paper, an information geometric generalization of the skew divergence called the $\alpha$-geodesical skew divergence is proposed, and its properties are studied.

Divergences have become a very useful tool for measuring similarity (or dissimilarity) between probability distributions. Depending on the field of application, a more appropriate measure may be necessary. In this paper we introduce a family of divergences called γ-Divergences. They are based on the convexity property of the functions that generate them. We demonstrate that these divergences verify all the usually required properties, and we extend them to weighted probability distribution. In addition, we define a generalised entropy closely related to the γ-Divergences. Finally, we apply our findings to the analysis of simulated and real time series.

Although Shannon mutual information has been widely used, its effective calculation is often difficult for many practical problems, including those in neural population coding. Asymptotic formulas based on Fisher information sometimes provide accurate approximations to the mutual information but this approach is restricted to continuous variables because the calculation of Fisher information requires derivatives with respect to the encoded variables. In this paper, we consider information-theoretic bounds and approximations of the mutual information based on Kullback--Leibler divergence and Rényi divergence. We propose several information metrics to approximate Shannon mutual information in the context of neural population coding. While our asymptotic formulas all work for discrete variables, one of them has consistent performance and high accuracy regardless of whether the encoded variables are discrete or continuous. We performed numerical simulations and confirmed that our approximation formulas were highly accurate for approximating the mutual information between the stimuli and the responses of a large neural population. These approximation formulas may potentially bring convenience to the applications of information theory to many practical and theoretical problems.

Today, we are all threatened by an unprecedented pandemic: COVID-19. How different is it from other coronaviruses? Will it be attenuated or become more virulent? Which animals may be its original host? In this study, we analyzed 377 publicly available complete genome sequences for the COVID-19 virus, the previously known flu-causing coronaviruses (HCov-229E, HCov-OC43, HCov-NL63 and HCov-HKU1) and the lethal, pathogenic P3/P4 viruses, SARS, MERS, Victoria, Lassa, Yamagata, Ebola, and Dengue. We found strong similarities between the current circulating COVID-19 and SARS and MERS, as well as COVID-19 in rhinolophines and pangolins. On the contrary, COVID-19 shares little similarity with the flu-causing coronaviruses and the other P3/P4 viruses. Strikingly, we observed divergence of COVID-19 strains isolated from human hosts has steadily increased from December 2019 to March 2020, suggesting COVID-19 is actively evolving in human hosts. From all existing human COVID-19 genome sequences, we calculated the first common model that represents the shared sequences of the human COVID-19 strains, which provides important information for vaccine and antibody development. Geographic and time-course analysis of the evolutionary trees of the human COVID-19 reveals possibly heterogeneous evolutional paths among strains from 21 countries. This finding has important implications to the management of COVID-19 and the development of vaccines.

Information geometry (IG) seeks to characterize the structure of statistical geodesic models from a differential geometric point of view. By considering families of probability distributions as manifolds with coordinate charts determined by the parameters of each individual model, the tools of differential geometry, such as divergences and metric tensors, provide effective means of studying their characteristics. The research undertaken in this paper presents a novel approach to the modelling study of information geometrics of a queueing system. In this context, the manifold of a stable M/G/1queue is characterised from the viewpoint of IG. The Fisher Information matrix (FIM) as well as the inverse of (FIM), (IFIM) of stable M/G/1 queue manifold are devised. In addition to that, new results that uncovered the significant impact of stability of M/G/1 queue manifold on the existence of (IFIM) are obtained. The Kullback’s divergence (KD), and J-divergence (JD).New result has been devised on the significant impact of both server utilization and squared coefficient of variation of the underlying M/G/1 queue manifold on both (KD) and (KD) are devised. Also, it is revealed that stable M/G/1 QM is developable (i.e., has a zero Gaussian curvature) and has a non-zero Ricci Curvature Tensor (RCT). Novel stability dynamics of M/G/1 queue manifold is revealed by discovering the mutual dual impact between the behaviour of (RCT) and the stability and the instability phases of the underlying M/G/1 queue manifold. Furthermore, a new discovery that presents the significant impact of stability of M/G/1 queue manifold and the continuity of the unique representation between M/G/1 queue manifold and Ricci Curvature Tensor (RCT). The information matrix exponential (IME) is devised. It is also shown that the obtained (IME) is unstable. Also, it is shown that stability of the devised (IME) enforces the instability of M/G/1 queue manifold. Unifying IG with Queueing Theory enables the study of dynamics of queueing system from a novel Riemannian Geometric (RG) point of view, leading to the analysis of the stable M/G/1 queue, based on the Theory of Relativity (TR).Extending the study over two new additional divergence measures, namely and together with a complete illustrative numerical results for all these measures including KD, JD. This links Queueing theory, IG with deep machine learning and metric learning. Furthermore, this reveals the revolutionary approach of queue learning. Full analytic study of Gaussian curvatures subject to both Angular and Monge techniques together with the overall stability dynamics impact on these curvatures. Full analytic study of Einestein Tensor and Stress Energy Tensor together with the overall stability dynamics impact on these curvatures. The inclusion of the definitions of Gaussian and Ricci, Ricci scalar curvatures and Einstein Tensor together with their physical interpretations; The proposed novel approach for the pioneer visualization of queueing systems via computational information geometry. The determination of new important links between classical queueing theory and other mathematical disciplines, such as IG, matrix theory Riemannian geometry and the THEORY OF RELATIVITY by providing for first time i) The full detailed derivations of the Gaussian curvature ii) The Ricci curvature tensor and iii) The full physical as well as the geometric interpretation of these new results. The provision of a novel link between Ricci Curvature (RCT) and the stability analysis of the stable M/G/1 QM. The full investigation of the newly introduced QT-IG unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them. The full investigation of the newly introduced (QIGU) unifiers together with the impact of stability/ instability of the underlying M/G/1 QM on them.

We propose that the coexistence of gapless Fermi arcs with the metal-insulator crossover of in-plane resistivity in strongly underdoped cuprates can be explained by the FL* fractionalized Fermi liquid nature of these systems and it is not due simply to disorder effects. The particle excitations of this FL* system derived from variants of the t-J model are: the gapless holon carrying charge with small Fermi momentum proportional to the doping, the gapful spinon carrying spin 1/2, gauge fluctuations coupling them and the hole as a spinon-holon bound state or resonance due to gauge binding with a Fermi surface respecting the topological Luttinger theorem. In our proposal Fermi arcs are determined by the hole resonance, whereas the metal-insulator crossover is dominated by spinon-spinon (with subleading holon-holon) gauge interactions and this dichotomy is able to explain their coexistence.

Nanoparticle-assisted polymerase chain reaction (nanoPCR) is a novel method for rapid detection of pathogens. A sensitive and specific multiple nanoPCR assay was developed for simultaneous detection of three subgroups avian leucosis virus (ALV). In this study, three pairs of primers that were designed based on gp85 gene sequences of subgroup A, B and J, optimizations were carried out for better performance of the nanoPCR assay and remarkably improved the detection limit of the assay. The detection limit of the assay was 101copies/μl, which was 100-fold more sensitive than conventional PCR assays. The nanoPCR assay amplified the specific 336 bp, 625 bp and 167 bp fragments of ALV-A, -B and –J respectively and no cross-reactivity were found with the genomic DNA or cDNA of Newcastle disease virus, avian infectious laryngotracheitis virus, Marek's disease virus, infectious bronchitis vaccine, infectious bursal disease virus and avian encephalomyelitis Virus. To evaluate the detection ability of the assay, 186 clinical samples were detected by nanoPCR and the detection results were further confirmed by sequencing. The sequencing results were 100% in coincidence with the nanoPCR detection results, suggesting the possibility of clinical application of the newly developed nanoPCR assay. Taking all the results together, a highly specific and sensitive nanoPCR was successfully developed, which could be useful for rapid diagnosis of ALV in poultry farming.

Charcot-Marie-Tooth (CMT) syndrome is the most common progressive human motor and sensory peripheral neuropathy. CMT type 1E is a demyelinating neuropathy affecting Schwann cells due to pmp22 mutations, modelized by Trembler-J mice. Curcumin, a natural polyphenol com-pound obtained from turmeric (Curcuma longa), exhibits dose- and time-varying antitumor, antioxidant and neuroprotective properties, however, the neurotherapeutic actions of curcumin still remain elusive. Here, we propose curcumin as a possible natural treatment capable of enhancing cellular detoxification mechanisms, resulting in an improvement of the neurodegenerative Trembler-J phenotype. Using a refined method for obtaining enriched Schwann cell cultures, we evaluated the neurotherapeutic action of low dose curcumin treatment on the PMP22 expression, and on the chaperones (HSF1 and Hsp27) and autophagy/mTOR (HDAC6 and ribosomes) pathways in Trembler-J and wild-type genotypes. In wild-type Schwann cells, the action of cur-cumin resulted in strong stimulation of the chaperone and macroautophagy pathway, whereas the modulation of ribophagy showed a mild effect. However, despite the promising neuroprotective effects for the treatment of neurological diseases, we report (demonstrate?) that the action of cur-cumin in Trembler-J Schwann cells could be impaired due to the irreversible impact of ethanol used as a common curcumin vehicle necessary for administration. These results contribute to expand our still limited understanding of PMP22 biology in neurobiology, and expose the intrinsic lability of the neurodegenerative Trembler-J genotype. Furthermore, they unravel interesting physiological mechanisms of cellular resilience relevant for to the pharmacological treatment of the neurodegenerative Tremble J phenotype with curcumin and ethanol. We conclude that the analysis of the effects of the vehicle itself is an essential and in-escapable step to comprehensibly assess the own effects and full potential of curcumin treatment for therapeutic purposes.

The current design process of a new aircraft includes several mod- eling and testing steps.The main purpose of the Ground Vibration Testing is to achieve experimental vibration data of the whole aircraft structure, in terms of frequencies, damping factors, and mode shapes to ensure that the aircraft will be free from any aeroelastic instability and safe to fly in the desired flight envelope. GVT results are also mainly used for validating and eventually im- proving FEM models of the aircraft examined. On the other hand, structural dynamic models are used to predict the flutter behavior and carefully plan the in-flight flutter tests, and lead to mitigation of the risks related to them. This paper aims to compare different aeroelastic methods outcomes achieved using two diverse commercial software: the widely used NASTRAN™ and the most recently developed Zonatech ZAERO. The flutter computation results are obtained by means of P-K, K, and G methods. The first case presented is the aeroelastic analysis of a forward-swept wing whereas the second case concerns the stabilator of a two-seater ultralight aircraft, for which an experimental campaign of vibrations test is also carried out. The structural mode shapes at each generated frequency mode are also visually presented.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged >65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

Due to a common mode of transmission through infected human blood, hepatitis C virus (HCV) and human immunodeficiency virus (HIV) coinfection is relatively prevalent. In alignment with this, HCV co-infection is associated with an increased size of the HIV reservoir in highly active antiretroviral therapy (HAART)-treated individuals. Hence, it is crucial to comprehend the physiological mechanisms governing the latency and reactivation of HIV in reservoirs. Consequently, our study delves into the interplay between HCV/HIV coinfection in liver cells and its impact on the modulation of HIV latency. We utilized the latently infected monocytic cell line (U1) and the latently infected T cell line (J-Lat) and found that mediators produced by the infection of hepatic stellate cells and hepatocytes with HIV and HCV, respectively, were incapable of inducing latency reversal under the studied conditions. This may favor the maintenance of the HIV reservoir size among latently infected mononuclear cells in the liver. Further investigations are essential to elucidate the role of the interaction between liver cells in regulating HIV latency and/or reactivation, providing a physiologically relevant model for comprehending reservoir microenvironments in vivo.

To extend the calculation power of tensor analysis, we introduce four new definition of tensor calculations. Some useful tensor identities have been proved. We demonstrate the application of the tensor identities in continuum mechanics: momentum conservation law and deformation superposition.

The transverse momentum distribution of J/ψ mesons produced in pp collisions at the center-of-mass energy 5 TeV, 7 TeV，and 13 TeV is described by the modified Hagedorn function which is based on the Tsallis function and Hagedorn function. The calculated results by the modified Hagedorn function are in accord with experimental data measured by the LHCb Collaboration at LHC. The related parameters are obtained and analyzed.

This case study examined changes in body composition, resting metabolic rate (RMR), aerobic capacity, and daily physical activity in a patient who had ulcerative colitis who underwent ileal pouch‒anal anastomosis (IPAA) surgery. Body composition, RMR, and VO2 peak were assessed prior to, 4, 8, and 16 weeks after IPAA surgery. Daily physical activity data was extracted from a wrist-worn activity tracker preoperatively and 16 months postoperatively. At baseline, total body mass was 95.3 kg; body fat, 11.6 %; lean body mass, 81.1 kg; RMR, 2,416 kcal/d; and V̇O2peak, 42.7 mL/kg/min. All values decreased from baseline at 4 weeks postoperatively, body mass was 85.2 kg (-10.5%); body fat, 10.9% (-6.0%); lean body mass, 73.1 kg (-9.9%); RMR 2,210 kcal/d (-8.5%) and V̇O2peak, 25.5 mL/kg/min (-40.3%). At 16 weeks postoperatively, most parameters were near their baseline levels (within 1%-7%); exceptions were V̇O2peak, which was 20.4% below baseline, and RMR, which increased to nearly 20% above baseline. After the patient had an IPAA surgery, his total and lean body masses, RMR, and aerobic capacity were markedly decreased. Daily physical activity decreased postoperatively and likely contributed to the decreased aerobic capacity, which may take longer to recover compared to body composition and RMR parameters.

Abstract: The second part of the paper develops the approach, suggested in the Entropy 2020, 22(1), 11; https://doi.org/10.3390/e22010011 , which relates ordering in physical systems to their symmetrizing. Entropy is frequently interpreted as a quantitative measure of “chaos” or “disorder”. However, the notions of “chaos” and “disorder” are vague and subjective to a much extent. This leads to numerous misinterpretations of entropy. We propose to see the disorder as an absence of symmetry and to identify “ordering” with symmetrizing of a physical system; in other words, introducing the elements of symmetry into an initially disordered physical system. We explore the initially disordered system of elementary magnets exerted to the external magnetic field H ⃗. Imposing symmetry restrictions diminishes the entropy of the system and decreases its temperature. The general case of the system of elementary magnets demonstrating the j-fold symmetry is treated. The interrelation T_j=T/j takes place, where T and T_j are the temperatures of non-symmetrized and j-fold-symmetrized systems of the magnets correspondingly.

Quantum physics through the lenses of Bayesian statistics consider probabilities to be degree of believes or knowledge and subjective. A Bayesian derivation of the probability density function in phase space is presented. The entropy proposed by Geiger and Kedem \cite{GeiKed2022a,GeiKed2022b} in phase space is expanded to define quantitatively interference and entanglement. Comparisons between each of these two quantities and the entropy are made. A brief presentation of entanglement in phase space to the spin degree of freedom and to mixed states completes the work.

Blast, caused by Magnaporthe oryzae, is one of the most destructive diseases affecting rice production. Understanding population dynamics of the pathogen avirulence genes is pre-required for breeding and then deploying new cultivars carrying promising resistance genes. The divergence and population structure of AvrPii was dissected in the populations of southern (Guangdong, Hunan, and Guizhou) and northern (Jilin, Liaoning, and Heilongjiang) China, via population genetic and evolutionary approaches. The evolutionary divergence between a known haplotype AvrPii-J and a novel one AvrPii-C was demonstrated by haplotype-specific amplicon-based sequencing and genetic transformation. The different avirulent performances of a set of seven haplotype-chimeric mutants suggested that the integrity of the full-length gene structures is crucial to express functionality of individual haplotypes. The population structure of the AvrPii family was shaped by balancing, purifying, and positive selection pressures in the Chinese populations. The AvrPii-J was recognized as the wild type that emerged before rice domestication. Considering higher frequencies of avirulent isolates were detected in Hunan, Guizhou, and Liaoning, the cognate resistance gene Pii could be continuously served as a basic and critical resistance resource in such regions. The unique population structures of the AvrPii family found in China have significant implications for understanding how the AvrPii family has kept an artful balance and purify among its members (haplotypes) those keenly interact with the cognate resistance gene under gene-for-gene relationships.

Transporter proteins, P-glycoprotein (P-gp) and P4ATPase-CDC50, are responsible for the transport of Miltefosine drug across cell membrane of a protozoan parasite Leishmania major. Mutations or change in activity of these proteins may lead to emergence of resistance in the parasite. Owing to the structural and functional importance of these transporter proteins, in this ppaper, we have tried to decipher the evolutionary divergence of these Miltefosine transporter proteins across different forms of life including Protists, Fungi, Plants and Animals. We retrieved 96, 207, and 189 sequences of P-gp, P4ATPase and CDC50 proteins respectively, across diverse variety of organisms for the conserved analysis. Phylogenetic trees were constructed for these three transporter proteins based on Bayesian posterior probability inference. The evolutionary analysis concluded that these proteins remain highly conserved throughout the species diversity but still substantial differences in the proteins for host (Homo sapiens) and parasite (L. major) were observed which have led in targeting these Miltefosine transporter proteins in a parasite specific manner.

Upper confidence bound multi-armed bandit algorithms (UCB) typically rely on concentration in- equalities (such as Hoeffding’s inequality) for the creation of the upper confidence bound. Intu- itively, the tighter the bound is, the more likely the respective arm is or isn’t judged appropriately for selection. Hence we derive and utilise an optimal inequality. Usually the sample mean (and sometimes the sample variance) of previous rewards are the information which are used in the bounds which drive the algorithm, but intuitively the more infor- mation that taken from the previous rewards, the tighter the bound could be. Hence our inequality explicitly considers the values of each and every past reward into the upper bound expression which drives the method. We show how this UCB method fits into the broader scope of other information theoretic UCB algorithms, but unlike them is free from assumptions about the distribution of the data, We conclude by reporting some already established regret information, and give some numerical simulations to demonstrate the method’s effectiveness.

The effect of divergence of chute sidewalls with three different baffle block geometries namely USBR, trihedral and semicircular blocks, as well as the depth and dimensions of the scour hole downstream of the chute were studied using a physical model. For this purpose, 9 models of baffled chutes were designed and constructed with divergence ratios of 1.45, 1.75, and 2.45 and without divergence (with a divergence ratio of 1). Comparing the results on the effect of block geometry at different divergence ratios revealed that the use of blocks proposed in this study instead of standard USBR blocks reduced the mean and maximum scour hole by 50%. For a given block geometry, the mean depth, maximum depth, and length of scour hole were reduced by 75%, 58%, and 50%, respectively.

The significance of Kashmir conflict as one of the world’s longest running conflicts has frequently drawn the attention of both communication and international relations’ scholars which makes it an important issue from the perspective of interdisciplinary approach. The researchers have attempted to assemble a brief history of the Indian held Jammu and Kashmir Conflict from the time of partition in 1947 to the developments which took place till 2019 in the light of the available literature. The paper provides substantial historical context of the important events like instrument of accession, UN resolutions on Kashmir conflict, Simla Accord, Gawkadal Massacre , Lahore Declaration, Kargil War, killing of Burhan Wani, Uri attack and Article 370’s abrogation .

Self-compacting concrete is essential for vertical structures due to high reinforcement and usage of pumps for transporting the concrete at the working site. SCC is mainly influenced by self-curing for arid areas. To explore the SCC (Self-Compacting Concrete) mixture and condition influencing the self-curing condition were determined using the experimental procedure. Determination of various parameters was done using T50 slump flow, V-funnel, L-box-box, J ring test, compressive strength, splitting tensile strength, and flexural strength test. After the experimental investigation, it shows that if the dose if the dosage of superplasticizer (SNF%) increases, it will improve the concrete mixture's different properties, such as flowability and ability to pass. Higher water-to-cement ratios (w/c) generally result in enhanced flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) demonstrates favourable results across all parameters, indicating excellent flowability and stability. Mixture 9 (SNF 0.35, w/c 0.5) also shows favourable flowability and stability characteristics. Lower concentrations, such as 0.5%, may have a positive effect up to a certain point but might not provide sustained improvement over the curing period. On the other hand, concentrations of 1% and 2% demonstrated consistent and significant enhancement in strength throughout the 28 days. It is found that the higher concentrations of PEG 4000 may be more effective in improving the material strength.

The article introduces the reader to the new thermodynamic potential of classical thermodynamics, called the J-potential. For the case of a homogeneous system, it vanishes, but it perfectly serves multiphase (heterogeneous) systems. The J-potential is especially important for the thermodynamics of capillary systems in colloidal science. The J-potential simplifies the derivation of the Neumann and Young equations, and has found application in the thermodynamics of thin films and the theory of strength of materials. In this work, for example, it was used to study the dependence of the chemical potential of a substance of a small bubble on the bubble size.

Polymethyl methacrylate (PMMA) polymer is widely used in various fields today. In order to thoroughly reveal the structural impact performance of PMMA materials, this paper is based on the comprehensive Johnson-Cook constitutive model and damage failure model, and accurately confirm the J-C constitutive and damage failure model parameters of PMMA through material test data, The dynamic process of steel bullet impacting PMMA plate structure is analyzed by finite element software ABAQUS. The calculation results show that the numerical simulation results in this paper are in good agreement with the experimental test data. Therefore, the feasibility and accuracy of the impact analysis of PMMA structures based on J-C constitutive and damage failure models in this paper are studied. Finally, based on the J-C constitutive model and damage failure model, the variation of the residual velocity of the bullet with the PMMA plate thickness is analyzed in depth, that is, the results show that the residual velocity of the bullet has a certain linear relationship with the plate thickness.

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

In the realm of molecular science, intricate relationships between molecular structures and their biomedical and pharmacological characteristics have been revealed through empirical experiments. This exploration hinges on the application of numerical descriptors, known as Topological Indices, which illuminate the inherent properties of diverse molecular structures. With particular significance in the medical and pharmaceutical domains, these indices facilitate the prediction of biological features for new chemical compounds and drugs by quantifying weighted entropies. In the context of this paper, we delve into the concept of graph entropy, weaving it intricately with the topological properties of the crystalline framework of the copper oxide molecule, denoted as Cu\textsubscript{2}O$[i,j,t]$. Our primary objective is to unravel the mathematical symphony underlying the structural intricacies of Cu\textsubscript{2}O$[i,j,t]$ and to imbue it with the conceptual essence of entropy. We accomplish this through the computation of entropy, leveraging various topological indices, including weight. In addition to our analytical journey, we present a graphical comparison that juxtaposes the computed indices and entropies, shedding light on the interplay between mathematical analysis and the structural elegance of Cu\textsubscript{2}O$[i,j,t]$. This endeavor contributes to a deeper understanding of the material's multifaceted applications, spanning domains from chemical sensors to solar cells, photocatalysis, and batteries, where Cu\textsubscript{2}O's crystallographic structure plays a pivotal role.

This work focuses on the well-known issue of mass conservation in the context of the finite element technique for computational fluid dynamic simulations. Specifically, non-conventional finite element families for solving Navier-Stokes equations are investigated to address the mathematical constraint of incompressible flows. Raviart-Thomas finite elements are employed for the achievement of a discrete free-divergence velocity. In particular, the proposed algorithm projects the velocity field into the discrete free-divergence space by using the lowest-order Raviart-Thomas element. This decomposition is applied in the context of the projection method, a numerical algorithm employed for solving Navier-Stokes equations. Numerical examples validate the approach’s effectiveness, considering different types of computational grids. Additionally, the presented paper considers an interface advection problem using marker approximation, in the context of multiphase flow simulations. Numerical tests, equipped with an analytical velocity field for the surface advection, are presented to compare exact and non-exact divergence-free velocity interpolation.

Finding the proper entropy functional associated with the inelastic Boltzmann equation for a granular gas is a yet unsolved challenge. The original H-theorem hypotheses do not fit here and the H-functional presents some additional measure problems that are solved by the Kullback–Leibler divergence (KLD) of a reference velocity distribution function from the actual distribution. The right choice of the reference distribution in the KLD is crucial for the latter to qualify or not as a Lyapunov functional, the “homogeneous cooling state” (HCS) distribution of the freely cooling system being a potential candidate. Due to the lack of a formal proof, the aim of this work is to support this conjecture aided by molecular dynamics simulations of inelastic hard disks and spheres in a wide range of values for the coefficient of restitution (α). Our results reject the Maxwellian distribution as a possible reference, whereas reinforce the HCS one. Moreover, the KLD is used to measure the amount of information lost on using the former rather than the latter, and reveals a nonmonotonic dependence with α. Additionally, a Maxwell-demon-like velocity-inversion experiment highlights the microscopic irreversibility of the granular gas dynamics.

This work aims to generate some types of M_(j )–neighborhoods. Relationships among these M_(j )-neighborhoods and some other types of neighborhoods are discussed. Additionally, M_(j )-lower, M_(j )-upper approximations and M_(j )-accuracy in terms of M_(j )-neighborhoods are presented. Their properties, with some examples, are investigated. Finally, We addressed the comparisons among M_(j )approach and other approaches.

DNA double-strand breaks (DSBs) trigger the Ataxia telangiectasia mutated (ATM)-dependent DNA damage response (DDR), which consists of histone H2AX, MDC1, RNF168, 53BP1, PTIP, RIF1, Rev7, and Shieldin. Early stages of B and T lymphocyte development are dependent on recombination activating gene (RAG)-induced DSBs that form the basis for further V(D)J recombination. Non-homologous end joining (NHEJ) pathway factors recognize, process, and ligate DSBs. Based on numerous loss-of-function studies, DDR factors were thought to be dispensable for the V(D)J recombination. In particular, mice lacking Mediator of DNA Damage Checkpoint Protein 1(MDC1) possessed nearly wild-type levels of mature B and T lymphocytes in the spleen, thymus, and bone marrow. NHEJ factor XRCC4-like factor (XLF)/Cernunnos is functionally redundant with ATM, histone H2AX, and p53-binding protein 1 (53BP1) during the lymphocyte development in mice. Here, we genetically inactivated MDC1, XLF, or both MDC1 and XLF in murine vAbl pro-B cell lines and, using chromosomally integrated substrates, demonstrated that MDC1 stimulates the V(D)J recombination in cells lacking XLF. Moreover, combined inactivation of MDC1 and XLF in mice resulted in synthetic lethality. Together, these findings suggest that MDC1 and XLF are functionally redundant during the mouse development, in general, and the V(D)J recombination, in particular.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.

The genus Rhodotorula includes basidiomycetous oleaginous yeast species. R. babjevae can produce compounds of biotechnological interest such as lipids, carotenoids and biosurfactants from low value substrates such as lignocellulose hydrolysate. High-quality genome assemblies are needed to develop genetic tools and to understand fungal evolution and genetics. Here, we combined short- and long-read sequencing to resolve the genomes of two R. babjevae strains, CBS 7808 (type strain) and DBVPG 8058 at chromosomal level. Both genomes have a size of 21 Mbp and a GC content of 68.2%. Allele frequency analysis indicated tetraploidy in both strains. They harbor 21 putative chromosomes with sizes ranging from 0.4 to 2.4 Mb. In both assemblies, the mitochondrial genome was recovered in a single contig, which shared 97% pairwise identity. The pairwise identity between the majority of chromosomes ranges from 82% to 87%. We found indications for strain-specific extrachromosomal endogenous DNA. 7,591 protein-coding genes and 7,607 associated transcripts were annotated in CBS 7808 and 7,481 protein-coding genes and 7,516 associated transcripts in DBVPG 8058. CBS 7808 has accumulated a higher number of tandem duplications than DBVPG 8058. We identified large translocation events between putative chromosomes and a high genetic divergence between the two strains.

In this paper, we report orthogonal fuzzy versions of some celebrated iterative mappings. We provide various concrete conditions on the real valued functions J,S:(0,1]→(−∞,∞) for the existence of fixed-points of (J,S)-fuzzy iterative mappings. We obtain many fixed point theorems in orthogonal fuzzy metric spaces. We apply (J,S)-fuzzy version of Banach fixed point theorem to show the existence and uniqueness of the solution. We support these results with several non-trivial examples and applications to Volterra-type integral equations and fractional differential equations.

In recent years, the research community has been interested in members of the Acinetobacter genus mainly because of their role as causative agents of nosocomial infections. However, this rich-in-species genus has been proven to play a significant role in different biotechnological processes, such as bioremediation and fermented foods production. To partially fill the lack of information on Acinetobacter’s dualistic nature, in this review, based on literature data, we attempt to summarize the available information on the different roles the members of the genus play by considering their genetic constitution and metabolic properties. We found pieces of evidence of genetic divergence between the pathogenic and non-pathogenic species and strains, which can be explained by their high adaptability to the different ecological niches. In turn, this adaptability could result from intrinsic genetic variability due to mechanisms of horizontal genetic transfer, as well as high mutability determined by the expression of error-prone DNA polymerases. Yet, further studies are needed, especially whole-genome sequencing of non-pathogenic isolates, which for the moment are relatively scarce.

The populations of clearwing moth borers in Korea have recently exploited and severe damage on pin oaks (Quercus palustris Munchh.). We conducted the field monitoring and the molecular analyses to identify them in an integrated manner. Morphological examination and molecular analyses on COI-gene based intra-and inter-specific genetic divergences (GD) were revealed that the borers were recognized as two invasive species, Sphecodoptera sheni and Paranthrenella pinoakula sp. nov.. The maximum intraspecific GD was found to be 1.9%, whereas the minimum interspecific GD was confirmed as 8.1%, with a distinct barcoding gap. Both MJ network and NJ tree were also showed that 18 haplotypes (HAPs) were detected from the 52 COI sequences. The borers were revealed total 17 HAPs: i) H1-H7 were detected from all seven regions with S. sheni; ii) Wonju and Goyang populations of S. sheni were revealed as more than three HAPs; iii) H7 was closely connected with H8 of Chinese population of S. sheni; iv) H9-H10 were detected as the other samples from Wonju population with P. pinoakula sp. n., and closely located with congeneric species. Maximum likelihood tree also revealed that P. pinoacula sp. n. nested within the congeneric species by genetically separating from the S. sheni.

Most existing flexible count regression models allow only approximate inference. This work proposes a new framework to provide an exact and flexible alternative for modeling and simulating count data with various types of dispersion (equi-, under- and overdispersion). The new method, referred as “balanced discretization”, consists in discretizing continuous probability distributions while preserving expectations. It is easy to generate pseudo random variates from the resulting balanced discrete distribution since it has a simple stochastic representation in terms of the continuous distribution. For illustrative purposes, we have developed the family of balanced discrete gamma distributions which can model equi-, under- and overdispersed count data. This family of count distributions is appropriate for building flexible count regressionmodels because the expectation of the distribution has a simple expression in terms of the parameters of the distribution. Using the Jensen–Shannon divergence measure, we have shown that under equidispersion restriction, the family of balanced discrete gamma distributions is similar to the Poisson distribution. Based on this, we conjecture that while covering all types of dispersion, a count regression model based on the balanced discrete gamma distribution will allow recovering a near Poisson distribution model fit when the data is Poisson distributed.

This paper considers estimating the ratio of two distributions with different parameters and common supports. We consider a Bayesian approach based on the Log--Huber loss function which is resistant to outliers and useful to find robust M-estimators. We propose two different types of Bayesian density ratio estimators and compare their performance in terms of Bayesian risk function with themselves as well as the usual plug-in density ratio estimators. Some applications such as classification and divergence function estimation are addressed.

Polyhedral oligomeric silsesquioxane (POSS) is a suitable nanoscale reinforcement for thermosetting polymers, such as epoxy resin systems in order to modify its mechanical, thermal, and chemical properties. The inclusion of POSS in the epoxy resin at higher loading (greater than 1 wt.%), however, introduces the ductility during the fracture behavior of these nanocomposites. Consequently, the J-integral is used to quantify the fracture behavior of these materials and characterize the crack growth resistance curve against stable crack growth. A range of nanocomposites is prepared by adding 0.5, 1, 3, 5, and 8 wt.% of glycidyl POSS into DGEBF epoxy resin cured with an amine-based curing agent. From the fracture toughness experiment the load-displacement result confirms that when the POSS reinforcement is greater than 1 wt.%, the fracture behavior of the nanocomposite changes from brittle to ductile. For both brittle and ductile nanocomposites, the addition of POSS molecules improves the crack initiation toughness. The development of POSS–POSS compliant domains are reported previously, is attributed for this change in the failure behavior. The fractured images of POSS–epoxy nanocomposites, obtained using scanning electron microscopy, show that the increase in fracture resistance at higher values of POSS loading occurs due to the extensive shear yielding. Meanwhile, the increased fracture toughness at lower values of POSS loading occurs due to crack pinning and crack deflection.

Capulus danieli, a distinct member of Capulidae, exhibits a unique ecological behaviour by attaching and drilling onto the pecten, distinguishing itself from other gastropods. Herein, we present the first characterisation of the mitochondrial genome of Capulus danieli. The results showed that the genome spans 15600 base pairs, with an A + T content of 71.12% and a G + C content of 28.88%. Within this genetic makeup, we identified 13 protein-coding genes, 22 transfer RNA genes, and 2 ribosomal RNA genes. Phylogenetic analysis revealed a close evolutionary relationship between Capulus danieli and members of the Ficoidea superfamily. The divergence time estimation suggested that Capulus danieli diverged approximately 52.29 million years ago. These findings significantly contribute to our understanding of the evolutionary history and genetic architecture of Capulus danieli, shedding light on its unique ecological adaptations to the marine environment.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

This study delves into the complex dynamics of Underwater Wireless Optical Communication (UWOC) in varied Harbor water conditions. Focusing on four distinct types of Harbor water, the research employs the Monte Carlo method to simulate photon transmission and analyze critical UWOC characteristics. Key parameters such as the transmitted full divergence angle, received aperture, and Field of View (FOV) are meticulously evaluated for their impact on communication efficacy. The study first explores the relationship between laser power variation, attenuation length, and divergence angle, revealing how different water qualities influence received power and time delay spread. Notably, the received normalized power loss and time delay spread are found to be more significantly affected by communication distance than water quality. In addition, under the condition of small received aperture and FOV, the influence on normalized power loss and time spread is more obvious in Harbor water. The findings offer novel insights into the challenges of UWOC in Harbor waters, particularly in terms of light scattering and absorption properties, and contribute to the optimization of communication systems in such complex underwater environments. Furthermore, the analysis of time pulse spread in turbid water quality is of great significance for the analysis and improvement of underwater wireless optical timing, ranging and positioning.

The recent years have been marked by rapid growth in the field of "ESG finance," leading to the emergence and expansion of ESG ratings and rating agencies. This study seeks to examine how financial investors respond to updates in ESG ratings provided by two prominent ESG rating agencies: MSCI and Refinitiv. Specifically, the goal is to determine whether any potential positive or negative alterations in a company's sustainability ratings directly affect its market value. The chosen methodology for this investigation is the Event Study, a model employed to assess the influence of economic events on corporate valuations by analyzing their Cumulated Average Abnormal Returns (CAARs). Over 840 instances of rating updates (events) were analyzed using a sample of 75 companies across various industries, all listed on major stock exchanges. The findings indicate that shifts in sustainability ratings, as evaluated by the two rating agencies, do not have a significant impact on companies' market capitalization. Moreover, these outcomes remain consistent over time, suggesting that financial markets are not assigning increasing significance to ESG ratings. Potential explanations are offered and discussed in relation to existing literature on the subject.

Spatial variation of indoor and outdoor PM2.5 within three states for a 5-year period is studied using regulatory and low-cost PurpleAir monitors. Most of these data were collected in an earlier study1 investigating the relative contribution of indoor-generated and outdoor-infiltrated particles to indoor exposures. About 260 regulatory monitors and ~10,000 outdoor and ~4,000 indoor PurpleAir monitors are included. Daily mean PM2.5 concentrations, correlations, and coefficients of divergence (COD) are calculated for pairs of monitors at distances ranging from 0 (collocated) to 200 km. We use a transparent and reproducible open algorithm that avoids use of the proprietary algorithms provided by the manufacturer of the sensors in PurpleAir PA-I and PA-II monitors. The algorithm is available on the PurpleAir API website under the name “PM2.5_alt”. This algorithm is validated using several hundred pairs of regulatory and PurpleAir monitors separated by up to 0.5 km. The PM2.5 spatial variation outdoors is homogeneous with high correlations to at least 10 km, as shown by the COD index under 0.2. There is also a steady improvement in outdoor PM2.5 concentrations with increasing distance from the regulatory monitors. The spatial variation of indoor PM2.5 indoors is not homogeneous even at distances <100 m. We document substantial differences between Federal Reference Methods (FRM) and Federal Equivalent Methods (FEM).

In many Bayesian computations, first, we obtain the expression of the joint distribution of all the unknown variables given the observed data. In general, this expression is not separable in those variables. Thus, obtaining their marginals for each variable and computing the expectations are difficult and costly. This problem becomes even more difficult in high dimensional quandaries, which is an important issue in inverse problems. We may then try to propose a surrogate expression with which we can do approximate computations. Often a separable expression approximation can be useful enough. The Variational Bayesian Approximation (VBA) is a technique that approximates the joint distribution $p$ with an easier, for example separable, one $q$ by minimizing Kullback–Leibler Divergence $KL(q|p)$. When $q$ is separable in all the variables, the approximation is also called Mean Field Approximation (MFA) and so $q$ is the product of the approximated marginals. A first standard and general algorithm is alternate optimization of $KL(q|p)$ with respect to $q_i$. A second general approach is its optimization in the Riemannian manifold. However, in this paper, for practical reasons, we consider the case where $p$ is in the exponential family and so is $q$. For this case, $KL(q|p)$ becomes a function of the parameters $\thetab$ of the exponential family. Then, we can use any other optimization algorithm to obtain those parameters. In this paper, we compare three optimization algorithms: standard alternate optimization, a gradient-based algorithm and a natural gradient algorithm and study their relative performances on three examples.

The Musa textilis var. Abuab has high fiber quality (FQ) but has low resistance against abaca bunchy top virus (AbBTV); the Musa balbisiana var. Pacol, has low FQ but high resistance against AbBTV. Their backcrosses (BC2 and BC3) possess both desirable traits. Analysis using RNA-seq showed that the regulatory divergence of Abuab and Pacol is largely explained by cis differences with 27.4% and 22.3% if we are to assess it using BC2 and BC3, respectively. Cis differences between the two genotypes are significantly reduced from BC2 to BC3 due to changes in genomic constitution. Trans, on the other hand, is robust to changes in allelic composition. All these are attributed to the loss of heterozygosity in the BC3 relative to BC2. Further analysis showed that both backcrosses exhibited genome-wide preferential expression of Pacol- over Abuab-specific alleles, despite the wider genetic presence of the latter in the hybrids. The ratio of the two genotype-specific expressed transcripts and the ratio of their corresponding genetic make-up are significantly disproportionate, a phenomenon which we refer here as “genome–transcriptome incongruence”. We also observed preferential expression switching in which several genes prefer Abuab- (or Pacol-) specific allele in the BC2 but switched to Pacol- (or Abuab-) specific allele in the BC3 genome.

Background: Identification of cancer biomarkers that are differentially expressed (DE) under two biological conditions is an important task in many microarray studies. There exist several methods in the literature in this regards and most of these methods designed especially for unpaired samples, which does not satisfy the requirements of paired samples where the gene expressions are taken from the same patients before and after treatment. Furthermore, the traditional biomarker identification methods based on either p-values or fold change (FC) values. However, sometimes, p-value based results do not comply with FC based results due to the smaller variance of gene expressions. There are some methods that combine both p-values and FC values to solve this problem. But, these methods also show weak performance for small-sample case in presence of outlying expressions. To overcome this problem, in this paper an attempt is made to develop a hybrid robust SAM-FC approach by combining rank of FC values and rank of p-values based on SAM statistic using minimum β-divergence method, which is designed for paired samples. This method introduces a weight function known as β-weight function. This weight function produces larger weights corresponding to usual/normal expressions and smaller weights for unusual/outlying expressions. The β-weight function plays the significant role on the performance of the proposed method. Results: The proposed method uses β-weight function as a measure of outlier detection by setting β=0.2. We unify both classical and robust estimates using β-weight function such that maximum likelihood estimators (MLEs) are used in absence of outliers and minimum β-divergence estimators are used in presence of outliers to obtain reasonable p-values and FC values in the proposed method. We examined the performance of proposed method in a comparison of some popular methods (t-test, SAM, LIMMA, Wilcoxon, WAD, RP and FCROS) using both simulated and real gene expression profiles for both small-and large-sample cases. From the simulation and a real spike in data analysis results we observed that the proposed method outperforms other methods for small-sample case in presence of outliers and it keeps almost equal performance with other robust methods (Wilcoxon, RP and FCROS) otherwise. From a head-and-neck cancer (HNC) dataset the proposed method identified 2 genes (CYP3A4, NOVA1) that are significantly enriched in linoleic acid metabolism, drug metabolism, steroid hormone biosynthesis and metabolic pathways. The survival analysis through Kaplan-Meier curve revealed that combined effect of these 2 genes has prognostic capability and they might be promising biomarker of HNC. Moreover, we retrieved the 12 candidate drugs based on gene interaction from glad4u and drug bank databases. Conclusion The identified drugs showed statistical significance and critical role of the proteins indicate that these proteins might be therapeutic target in cancer. Thus, elucidating the associations between the drugs identified in the present study require further investigations.

The ability to self-direct a research program determines graduate degree completion. Yet, research on incompletion of graduate physics programs assume students’ present level of self-direction adequate and neglects to recognize a lack of self-directed learning as key. One theoretical mathematical physicist focused on changing this challenge of physics graduate education by promoting self-directed learning through the type research flow that has been found to bring the greatest satisfaction to researchers with respect to their insights. This he provided through his space, time, open mindedness and theoretical contributions with his students and in collaboration with his colleagues. A self-directed learner himself, David J. Rowe developed methods of mentoring for encouraging physics graduate students to recognize symmetry as valuable in identifying solutions to problems quickly—helping these students take the lead in finding insightful resolutions to complex, multidimensional, mathematical physics uncertainties. How Rowe set about supporting self-directed learning in his graduate physics education interactions will be examined with the use of narrative research to interpret the texts and conversations with the author he made available. His techniques will be presented and recommendations made regarding how Rowe’s work in this regard can be modeled to improve self-direction in STEM graduate education.

This paper analyzes low-Earth orbit (LEO) satellite downlinks when an airborne interference source moves parallel to the satellite trajectory by considering the relative angle differences be-tween the satellites and the interference sources. To make the experimental interference situations more like actual environments, the LEO trajectories are obtained from two-line element set (TLE) data. Airborne interference sources with various altitudes move parallel to the LEO trajectories, and the jamming to signal (J/S) ratio is calculated based on the relative angle differences between the ground station, the LEO satellite, and the interference source. In order to calculate the relative angle difference psi, the coordinates of the satellite and the interference source are converted from the World Geodetic System 1984 (WGS84) to the ground station-centered east-north-up (ENU) system. By applying the relative angle difference psi, we can obtain the sidelobe gain of the ground station antenna in the direction from which the interference comes. The results of the study confirm that, from a J/S ratio perspective, the distances between the ground station, LEO satellite, and airborne interference source are important, and in particular, the relative angle difference psi between the interference source and the satellite is more critical factor.

Aluminium alloys are used for fabrication of fuel clad of research grade nuclear reactors as well as of several types of core components of high flux research reactors. In order to carry out design and safety analysis of these components, their mechanical and fracture properties are required by the designer. In this work, experiments have been conducted on tensile specimens machined from an aluminium alloy block to evaluate the material stress-strain curve. For evaluation of fracture properties, experiments have been conducted on disc shaped compact tension specimens in order to determine the fracture toughness of aluminium alloy. From crystal plasticity based finite element analysis and experimental data, a material damage parameter for prediction of crack initiation has been evaluated. The variation of the parameter for various values of initial a/W ratios of the disc-shaped CT specimens have been studied. It was observed that the damage parameter is independent of crack geometry and hence, it qualifies as transferable material property which can be used to predict crack initiation in a component of this material subjected to different kinds of loading conditions. In addition, the JR curves of the disc-shaped CT specimens have also been evaluated from experiments. The material damage parameter has also been used to study the crack initiation in single crystal fracture specimens with two different orientations. It was observed that the [111] orientation is more susceptible to crack initiation and propagation compared to the [100] orientation as the damage parameter is high in the ligament of the specimen ahead of the crack-tip for the same level of load. The results of the experiments and the material damage parameter shall be helpful for integrity analysis of fuel clad of research reactors as well as components of high flux research reactors.

Recently, there appeared in this journal (Beh and Lombardo \textbf{2022}, {\it Symmetry}, 14, 1103) a paper that showed how to perform a correspondence analysis on a two-way contingency table where Bowker's statistic lies at the numerical heart of this analysis. Thus, we showed how this statistic can be used to visually identify departures from perfect symmetry. Interestingly, Bowker's statistic is a special case of the symmetry-version of the Cressie-Read family of divergence statistics. Therefore, this paper presents a new framework for visually assessing departures from perfect symmetry using a second-order Taylor series approximation of the Cressie-Read family of divergence statistics.

Lung cancer is a prevalent malignancy that impacts individuals of all genders and is often diagnosed late due to delayed symptoms. To catch it early, researchers are developing algorithms to study lung cancer images. One such method uses computer aided detection to classify as benign or malignant from the histopathological images. Standard histopathological images were used from a Lung and Colon Cancer Histopathological Image Dataset (LC25000) which contains two classes of benign and malignant of 5000 each. Images were preprocessed and features extracted using Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Feature selection methods used are KL Divergence and Invasive Weed Optimization (IWO). Seven different classifiers like SVM, KNN, Random Forest, Decision Tree, Softmax Discriminant, Multilayer Perceptron, and BLDC were used to analyze and classify the images as benign or malignant. Results were compared using standard metrics, and kappa analysis assessed classifier agreement. The Decision Tree Classifier with GWO feature extraction achieved good accuracy of 85.01% without Feature selection and Hyperparameter Tuning approaches. Furthermore, we present a methodology to enhance the accuracy of the classifiers by employing hyperparameter tuning algorithms based on Adam and RAdam. By combining features from GWO and IWO, and using the RAdam algorithm, the Decision Tree classifier achieves the commendable accuracy of 91.57%

Noisy data is always encountered in real applications, such as bioinformatics, neuroimage and remote sensing. Existing methods mainly consider linear or generalized linear errors-in-variables regression, while relatively little attention is paid for the multivariate response case, and how to evaluate the estimation performance under perturbed covariates is still an open question. In this paper, we consider the information-theoretic limitations of estimating a low-rank matrix in the multi-response errors-in-variables regression model. By application of the information theory and statistical techniques on concentration inequalities, the minimax lower bound is provided in terms of the squared Frobenius loss, which recaptures the rate provided under the clean covariate assumption in previous literatures. Hence our result further indicates that though under the more realistic errors-in-variables situation, no more samples are required so as to achieve a rate-optimal estimation.

Most pathogens are capable of infecting multiple host species and such cross-species transmission (CST) can have dramatic consequences, as highlighted by recent disease emergence events affecting human, animal and plant health. Understanding the ecological and evolutionary factors that constrain or facilitate the ability of disease agents to infect and establish in a novel host is therefore a timely and important research area. Previous work across different pathogens, including rabies virus (RABV), found that increased evolutionary distance between hosts reduces the frequency of cross-species transmission and of permanent host shifts. However, whether this effect of host relatedness still holds for transmission among recently diverged hosts, and the importance of this effect relative to other predictors, is not well understood. We addressed this question by quantifying the CST frequency of RABV between North American bat species within the genus Myotis, using a multi-decade data set containing 128 nucleoprotein (N) sequences from ten host species. For comparison, we also conducted an equivalent analysis of a RABV dataset from North American bat species comprising nine genera. We found that at the within genus scale, host relatedness failed to explain the frequency of CST events. However, CST frequency increased with overlap in species’ host range. Moreover, we found evidence of CST occurring among a higher proportion of species, and CST more frequently resulting in sustained transmission in the novel host in the Myotis dataset compared to the multi-genus dataset. Our results suggest that among recently diverged species, the ability to infect a novel host is no longer restricted by physiological barriers but instead is limited by physical contact. Our results improve predictions of where future CST events for RABV might occur and clarify the relationship between host divergence and pathogen emergence.

A framework for optimal sensor placement (OSP) for virtual sensing using the modal expansion technique and taking into account uncertainties is presented based on information and utility theory. The OSP maximizes a utility function that quantifies the expected information gained from the data for reducing the uncertainty of quantities of interest (QoI) predicted at the virtual sensing locations. The utility function is extended to make the OSP design robust to uncertainties in structural model and modelling error parameters, resulting in a multidimensional integral of the expected information gain over all possible values of the uncertain parameters and weighted by their assigned probability distributions. Approximate methods are used to compute the multidimensional integral and solve the optimization problem that arises. The Gaussian nature of the response QoI is exploited to derive useful and informative analytical expressions for the utility function. A thorough study of the effect of model, prediction and measurement errors and their uncertainties, as well as the prior uncertainties in the modal coordinates on the selection of the optimal sensor configuration is presented, highlighting the importance of accounting for robustness to errors and other uncertainties.

In this paper, we derive Jeffreys divergence, generalized Fisher divergence and corresponding De Bruijn identities on space-time random field. First, we determine the relation between Fisher information on the space-time random field in one of the space-time points and the ratio of Jeffreys divergence on a space-time random field at distinct space-time positions to the square of coordinate difference. In addition, we also find identities between the partial derivative of the Jeffreys divergence and the generalized Fisher divergence with respect to space-time variables, i.e. the De Bruijn identities, between two space-time random fields obtained by different parameters under the same Fokker-Planck equations. At the end of this paper, we present three examples of the Fokker-Planck equations on space-time random fields, identify their density functions, and derive the Jeffreys divergence, generalized Fisher information, generalized Fisher divergence, and accompanying De Bruijn identities.

For any odd positive integer $x$, define $(x_n)_{n\geqslant 0} $ and $(a_n )_{n\geqslant 1} $ by setting $x_{0}=x, \,\, x_n =\cfrac{3x_{n-1} +1}{2^{a_n }}$ such that all $x_n $ are odd. The 3x+1 problem asserts that there is an $x_n =1$ for all $x$. Usually, $(x_n )_{n\geqslant 0} $ is called the trajectory of $x$. In this paper, we concentrate on $(a_n )_{n\geqslant 1} $ and call it the E-sequence of $x$. The idea is that, we consider any infinite sequence $(a_n )_{n\geqslant 1} $ of positive integers and call it an E-sequence. We then define $(a_n )_{n\geqslant 1} $ to be $\Omega-$convergent to $x$ if it is the E-sequence of $x$ and to be $\Omega-$divergent if it is not the E-sequence of any odd positive integer. We prove a remarkable fact that the $\Omega-$divergence of all non-periodic E-sequences implies the periodicity of $(x_n )_{n\geqslant 0} $ for all $x_0$. The principal results of this paper are to prove the $\Omega-$divergence of several classes of non-periodic E-sequences. Especially, we prove that all non-periodic E-sequences $(a_n )_{n\geqslant 1} $ with $\mathop {\overline {\lim } }\limits_{n\to \infty } \cfrac{b_n }{n}>\log _23$ are $\Omega-$divergent by using the Wendel's inequality and the Matthews and Watts's formula $x_n =\cfrac{3^n x_0 }{2^{b_n }}\prod\limits_{k=0}^{n-1} {(1+\cfrac{1}{3x_k })} $, where $b_n =\sum\limits_{k=1}^n {a_k } $. These results present a possible way to prove the periodicity of trajectories of all positive integers in the 3x + 1 problem and we call it the E-sequence approach.

Phylostratigraphic analysis is a way to look anew on phylogenetic data in the evolutionary aspect. It allows counting the evolutionary age based on the analysis of genes, their orthologs and finding the last common ancestor. We performed phylostratigraphic analysis of Arabidopsis thaliana genes associated with several types of abiotic stresses (heat, cold, water-related, light, osmotic, salt, and oxidative) determined by the Gene Ontology annotation. Comparison of the distributions of ages of genes associated with stresses of different type has shown the heat stress to involve older genes while the light stress – younger genes. At the same time, all types of stress are characterized by a significantly higher proportion of old genes (common to all eukaryotes) compared to the whole set of A.thaliana genes. This can be explained by the involvement of basic molecular processes in plant cells into the stress response. Reconstruction and graphical analysis of the gene network of the heat stress educed several clusters associated with different response functions. Some of these clusters contain only ancient genes. The results obtained show that the phylostratigraphic analysis reveals the fundamental features of the organization of gene networks and their evolution.

Efficient textual data distributions (TDD) alignment and generation are open research problems in textual analytics and NLP. It is presently difficult to parsimoniously and methodologically confirm that two or more natural language datasets belong to similar distributions, and to identify the extent to which textual data possess alignment. This study focuses on addressing a segment of the broader problem described above by applying multiple supervised and unsupervised machine learning (ML) methods to explore the behavior of TDD by (i) topical alignment, and (ii) by sentiment alignment. Furthermore we use multiple text generation methods including fine-tuned GPT-2, to generate text by topic and by sentiment. Finally we develop a unique process driven variation of Kullback-Leibler divergence (KLD) application to TDD, named KL Textual Distributions Contrasts (KL-TDC) to identify the alignment of machine generated textual corpora with naturally occurring textual corpora. This study thus identifies a unique approach for generating and validating TDD by topic and sentiment, which can be used to help address sparse data problems and other research, practice and classroom situations in need of artificially generated topic or sentiment aligned textual data.

Phylogenomics has enriched our understanding that the Tree of Life can have network-like or reticulate structures among some taxa and genes. Non-vertical modes of evolution—such as hybridization/introgression and horizontal gene transfer—deviate from a strictly bifurcating tree model, causing non-treelike patterns. Here, we present a brief overview of a phylogenomic workflow for inferring organismal histories and compare methods for detecting reticulate evolution. We discuss how the timing of coalescent events can help disentangle introgression from incomplete lineage sorting and how horizontal gene transfer events can help determine the relative timing of speciation events. In doing so, we identify pitfalls of certain methods and discuss how to extend their utility across the Tree of Life. Workflows, methods, and future directions discussed herein underscore the need to embrace reticulate evolutionary patterns for understanding the timing and rates of evolutionary events, providing a clearer understanding of life’s history.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

Cancer cells are adept at reprogramming energy metabolism and the precise manifestation of this metabolic reprogramming exhibits heterogeneity across individuals (and from cell to cell). In this study, we analyzed the metabolic differences between interpersonal heterogeneous cancer phenotypes. We used divergence analysis on gene expression data of 1156 breast normal and tumor samples from The Cancer Genome Atlas (TCGA) and integrated this information with a genome-scale reconstruction of human metabolism to generate personalized, context-specific metabolic networks. Using this approach, we classified the samples into four distinct groups based on their metabolic profiles. Enrichment analysis of the subsystems indicated that amino acid metabolism, fatty acid oxidation, citric acid cycle, androgen and estrogen metabolism and ROS detoxification distinguished these four groups. Additionally, we developed a workflow to identify potential drugs that can selectively target genes associated with the reactions of interest. MG-132 (a proteasome inhibitor) and OSU-03012 (a celecoxib derivative) were the top-ranking drugs identified from our analysis and known to have anti-tumor activity. Our approach has the potential to provide mechanistic insights into cancer-specific metabolic dependencies, ultimately enabling the identification of potential drug targets for each patient independently, contributing to a rational personalized medicine approach.

Abstract: The main contribution of this paper is to introduce a new iterative training algorithm for restricted Boltzmann machines. The proposed learning path is inspired from online sequential extreme learning machine one of extreme learning machine variants which deals with time accumulated sequences of data with fixed or varied sizes. Recursive least squares rules are integrated for weights adaptation to avoid learning rate tuning and local minimum issues. The proposed approach is compared to one of the well known training algorithms for Boltzmann machines named “contrastive divergence”, in term of time, accuracy and algorithmic complexity under the same conditions. Results strongly encourage the new given rules during data reconstruction.

Radar transmit signal design is a critical factor for the radar performance. In this paper, we investigate the problem of radar signal waveform design under the small signal power conditions for detecting a doubly spread target, whose impulse response can be modeled as a random process, in a colored noise environment. The doubly spread target spans multiple range bins (range-spread) and its impulse response is time-varying due to fluctuation (hence also Doppler-spread), such that the target impulse response is both time-selective and frequency-selective. Instead of adopting the conventional assumption that the target is wide-sense stationary uncorrelated scattering,we assume that the target impulse response is both wide-sense stationary in range and in time to account for the possible correlation between the impulse responses corresponding to close range intervals. The locally most powerful detector, which is asymptotically optimal for small signal cases, is then derived for detecting such targets. The signal waveform is optimized to maximizing the detection performance of the detector or equivalently maximizing the Kullback-Leibler divergence. Numerical simulations validate the effectiveness of the proposed waveform design for the small signal power conditions and performance of optimum waveform design are shown in comparison to the frequency modulated waveform.

In this paper a robust version of the Wald test statistic for composite likelihood is considered by using the composite minimum density power divergence estimator instead of the composite maximum likelihood estimator. This new family of test statistics will be called Wald-type test statistics. The problem of testing a simple and a composite null hypothesis is considered and the robustness is studied on the basis of a simulation study. Previously, the composite minimum density power divergence estimator is introduced and its asymptotic properties are studied.

This article presents a novel resolution of the Collatz Conjecture, centered on the concept of Generative Completeness of the inverse Collatz function. We introduce and rigorously prove that for all N ∈ ℕ⁺, there exists a minimal generator m_N = 1 such that all positive integers up to N can be generated through successive applications of the inverse Collatz function G. This key property, which we term "Generative Completeness", forms the cornerstone of our proof. Building upon this foundation, we establish several crucial results: The boundedness of all Collatz sequences The existence and uniqueness of cycles in Collatz sequences The nature of the unique cycle as {1, 4, 2} We then present three distinct approaches to resolving the Collatz Conjecture, all fundamentally rooted in the Generative Completeness property. These diverse methods not only prove the conjecture but also provide deep insights into the structure of Collatz sequences.

The 3 31 nucleotide minihelix tRNA theorem describes evolution of type I and type II tRNAs to the last nucleotide. In databases, type I and type II tRNA V loops (V for variable) were improperly aligned, but alignment based on the theorem is accurate. Type II tRNA V arms were a 3’-acceptor stem (initially CCGCCGC) ligated to a 5’-acceptor stem (initially GCGGCGG). The type II V arm evolved to form a stem-loop-stem. In Archaea, tRNALeu and tRNASer are type II. In Bacteria, tRNALeu, tRNASer and tRNATyr are type II. The positioning of the type II V arm is determined by the number of unpaired bases just 5’ of the Levitt base (Vmax). For Archaea, tRNALeu has 2 unpaired bases, and tRNASer has 1 unpaired base. For Bacteria, tRNATyr has 2 unpaired bases, tRNALeu has 1 unpaired base and tRNASer has 0 unpaired bases. Thus, the number of type II tRNAs is limited by the possible set points of the arm. From analysis of aminoacyl-tRNA synthetase structures, contacts to type II V arms appear to adjust allosteric tension communicated primarily via tRNA to aminoacylating and editing active sites. To enhance allostery, it appears that type II V arm end loop contacts may tend to evolve to V arm stem contacts.

The aim of this article is to establish a new class of unified integrals associated with the generalized Lommel-Wright functions, which are expressed in terms of Wright Hypergeometric function.Some integrals involving trigonometric,generalized Bessel function and Struve functions are also indicated as special cases of our main results.further, with the help of our main results and their special cases, we obtain two reduction formulas for the Wright hypergeometric function.

We know that the classical Mittag-Leffler function play an important role as solution of fractional order differential and integral equations. We introduce the j-generalized p - k Mittag-Leffler function. We evaluate the second order differential recurrence relation and four different integral representations and introduce a homogeneous linear differential equation whose one of the solution is the j-generalized p-k Mittag-Leffler function. Also we evaluate the certain relations that exist between j-generalized p - k Mittag-Leffler function and Riemann-Liouville fractional integrals and derivatives. We evaluate Mellin-Barnes integral representation of j-generalized p-k Mittag-Le er Function. The relationship of j-generalized p-k Mittag-Leffler Function with Fox H-Function and Wright hypergeometric function is also establish. we obtained its Euler transform, Laplace Transform and Mellin transform. Finally we derive some particular cases.

The paper explores the Kullback-Leibler divergence formalism (KLDF) applied to the stable MG1 queue manifold. More potentially, both service time probability and cumulative functions which make KLDF exact are obtained. The credibility of KLDF is justified through consistency axioms. Some potential applications of Kullback-Leibler divergence to Biometry are presented. The paper concludes with closing remarks combined with some challenging open problems and the next phase of research.

Since the seminal work of Kolmogorov, probability theory has been based on measure theory, where the central components are so-called probability measures, defined as measures with total mass equal to 1. In Kolmogorov’s theory, a probability measure is used to model an experiment with a single outcome that will belong to exactly one out of several disjoint sets. In this paper, we present a different basic model where an experiment results in a multiset, i.e. for each of the disjoint sets we get the number of observations in the set. This new framework is consistent with Kolmogorov’s theory, but the theory focuses on expected values rather than probabilities. We present examples from testing Goodness-of-Fit, Bayesian statistics, and quantum theory, where the shifted focus gives new insight or better performance. We also provide several new theorems that address some problems related to the change in focus.

\textsc{Background} Often, in biology, we are faced with the problem of exploring relevant unknown biological hypotheses in the form of myriads of combination of factors that might be affecting the pathway under certain conditions. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses for investigation. The search and wet lab testing of these combinations costs a lot in terms of time, investment and energy. In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but for a majority, it is still not known which higher ($\geq 2$) order combinations might be playing a greater role in the pathway. \textsc{Results} The pipeline provides a prioritised list of important $2^{nd}$ order combinations of a range of family of genes involved in the Wnt pathway. More specifically, it reveals the various unexplored FZD-WNT combinations that have been untested till now in the pathway. In relation to ETC-1922159 affected combinations, the down-regulation of LGR-RNF family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 combination. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. \textsc{Conclusion} A pipeline has been developed to prioritise an $n^{th}$ order combination of factors that affect a signaling pathway. It takes into account the sensitivity indices computed from variance based (SOBOL) and density-kernel based (HSIC) methods to estimate the influence of each factor or combination of factors. These are then fed as feature vectors into a powerful support vector ranking algorithm that produces a ranked list of the interactions/combinations.