bluuebunny/crossref_metadata_2025 huggingface dataset:
Variability in intermediate predator hatching does not alter top-down effects of top predators
Authors: Davenport Jon M. , Burton David , Suchniak Logan , Anderson Thomas L.
Published: April 2024

Variability in phenological shifts (seasonal changes in biological events) has become more common with climate change. As phenological variability increases due to climatic factors, species interactions within diverse communities can be affected. Shifts in hatching phenology can lead to body size variability that can alter species interactions, especially within and across trophic levels in complex food webs. To understand the effects of hatching phenology and food web complexity on prey communities, we conducted an experiment with ectotherms from ponds by manipulating hatching synchrony of a prey species across three dates and food web complexity. We hypothesized that predation on prey would be highest in the lowest synchrony treatment due to predator satiation effects in high synchrony additions. Food webs with top predators present had the lowest survival rates for prey compared to other food webs. Prey size at metamorphosis was also highest in the top predator food webs, suggesting a thinning effect. Hatching synchrony treatments did not have a significant impact on prey survival or size at metamorphosis except in treatments where intermediate and top predators were absent. Overall, our results show that top-down effects of predators may be enough to suppress the influence of phenological shifts in prey.
Asymmetric influence-based superposed random walk link prediction algorithm in complex networks
Authors: Liu Shihu , Feng Xueli , Yang Jin
Published: April 2024

Random walk-based link prediction algorithms have achieved desirable results for complex network mining, but in these algorithms, the transition probability of particles usually only considers node degrees, resulting in particles being able to randomly select adjacent nodes for random walks in an equal probability manner, to solve this problem, the asymmetric influence-based superposed random walk link prediction algorithm is proposed in this paper. This algorithm encourages particles to choose the next node at each step of the random walk process based on the asymmetric influence between nodes. To this end, we fully consider the topological information around each node and propose the asymmetric influence between nodes. Then, an adjustable parameter is applied to normalize the degree of nodes and the asymmetric influence between nodes into transition probability. Based on this, the proposed new transition probability is applied to superposed random walk process to measure the similarity between all nodes in the network. Empirical experiments are conducted on 16 real-world network datasets such as social network, ecology network, and animal network. The experimental results show that the proposed algorithm has high prediction accuracy in most network, compared with 10 benchmark indices.
Kinetic modeling of a Sznajd opinion model on social networks
Authors: Liao Jie , Yang Xiongfeng
Published: April 2024

In this paper, we study a kinetic modeling of opinion formation on social networks in which the distribution function depends on both the opinion and the connectivity of the agents. The opinion exchange process is governed by a Sznajd-type model with three opinions, [Formula: see text], 0, and the social network is represented statistically with connectivity denoting the number of contacts of a given individual. It is commonly accepted that, in social networks, the opinion of the agents with a higher connectivity, i.e. a larger number of followers, is more convincing than that of the agents with a lower number of followers. By the kinetic modeling approach, we derived the asymptotic mean opinion of a social network in terms of the initial opinion and the connectivity of the agents. The present work extends the result in [N. Loy, M. Raviola and A. Tosin, Opinion polarisation in social networks, Phil. Trans. R. Soc. A 380, 20210158 (2021)] where only binary opinions, [Formula: see text], were considered, that indecisive people who cannot make a clear choice between [Formula: see text] are allowed in this study.
UNCERTAINTY, ANTICIPATION AND MACROECONOMIC FLUCTUATIONS
Authors: MA ZHENYU , WANG JUNBO
Published: April 2024

This paper examines the effects of anticipated and unanticipated uncertainty on macroeconomic fluctuations and investigates the optimal macroeconomic policy portfolio. Our empirical findings indicate that both anticipated and unanticipated uncertainty shocks result in a reduction in gross corporate output, while the negative impact of the former is relatively smaller. By developing a multi-sector DSGE model, we further find that anticipated uncertainty shocks lead to a relatively smaller rise in the risk premium for corporate lending, and a smaller decrease of corporate lending, investment, and output. The policy analysis reveals that monetary policy can effectively dampen the fluctuations of macroeconomic variables like output, but its impact on financial variables such as risk premiums and asset prices is limited. On the other hand, macroprudential policy can directly tackle the root causes of uncertainty shocks and alleviate their adverse effects. The combination of these two policies facilitates a balance between the policy objectives of managing systemic risks and mitigating macroeconomic fluctuations.
Dispersion and absorption effects in the linearized Euler–Heisenberg electrodynamics under an external magnetic field
Authors: Santos G. R. , Neves M. J.
Published: April 2024

In this paper, the effects of the Ohmic and magnetic density currents are investigated in the linearized Euler–Heisenberg (EH) electrodynamics (ED). The linearization is introduced through an external magnetic field, in which the vector potential of the EH ED is expanded around a magnetic background field that we consider uniform and constant in this paper. From the EH linearized equations, we obtain the solutions for the refractive index associated with the electromagnetic wave superposition, when the current density is ruled by the Ohm law, and in the second case, when the current density is set by an isotropic magnetic conductivity. These solutions are functions of the magnetic background [Formula: see text] of the wave propagation direction [Formula: see text]. It also depends on the conductivity and on the wave frequency. As a consequence, the dispersion and the absorption of plane waves change when [Formula: see text] is parallel to [Formula: see text] in relation to the case of [Formula: see text] perpendicular to [Formula: see text] in the medium. The characteristics of the refraction index are related to direction of [Formula: see text] and of the wave polarization, where there is an open discussion for the birefringence in this medium.
Asymptotically nonrelativistic string backgrounds
Authors: Ávila Daniel , Güijosa Alberto , Olmedo Rafael
Published: April 2024

In recent years, interesting curved-space extensions of nonrelativistic (NR) string theory have been very actively pursued, where the background has a structure that is a stringy generalization of Newton–Cartan geometry. Here we show that the natural black branes of the NR theory, sourced by the familiar repertoire of stringy objects, generally have a different structure. The black string is our main example. We find that the source distorts the background significantly, generating a large throat within which physics is in fact relativistic. It is only far away from the throat that the background approaches the string Newton–Cartan form. We show that exactly the same is true for the longitudinal RR-charged black brane. On the other hand, the transverse RR-charged black brane turns out to have a proper string Newton–Cartan structure everywhere, not just asymptotically.
Energies and a gravitational charge for massive particles in general relativity
Authors: Aoki Sinya , Onogi Tetsuya , Yamaoka Tatsuya
Published: April 2024

In this paper, we investigate relations or differences among various conserved quantities which involve the matter Energy Momentum Tensor (EMT) in general relativity. These quantities include the energy with Einstein’s pseudo EMT, the generalized Komar integral, or the ADM energy, all of which can be derived from Noether’s second theorem, as well as an extra conserved charge recently proposed in general relativity. For detailed analyses, we apply definitions of these charges to a system of free massive particles. We employ the post-Newtonian (PN) expansion to make physical interpretations. We find that the generalized Komar integral is not conserved at the first non-trivial order in the PN expansion due to non-zero contributions at spatial boundaries, while the energy with Einstein’s pseudo EMT at this order agrees with a total energy of massive particles with gravitational interactions through the Newtonian potential, and thus is conserved. In addition, this total energy is shown to be identical to the ADM energy not only at this order but also all orders in the PN expansion. We next calculate an extra conserved charge for the system of massive particles, at all orders in the PN expansion, which turns out to be a total number of particles. We call it a gravitational charge, since it is clearly different from the total energy. We finally discuss an implication from a fact that there exist two conserved quantities, energy and gravitational charge, in general relativity.
Interpretable Long-Term Forecasting Based on Dynamic Attention in Smart City
Authors: Ma Changxia , Xie Jun , Yang Lisha , Zhong Zhaoman , Zhao Xuefeng , Hu Wenbin
Published: April 2024

Accurate prediction is of great significance to the construction of a smart city. However, current models only focus on mining the relationship among sequences and ignore the influence of the predicted sequences on future predictions, so we propose a Dynamic Attention Neural Network (DANN) based on encoder-decoder, which combines encoder context vectors and newly generated decoder context vectors to jointly dynamically representation learning, then generates corresponding predicted values. DANN processes data via the Bi-directional Long Short-Term Memory (Bi-LSTM) network as the fundamental structure of the network between encoder and decoder. What’s more, in order to produce a new feature representation with low redundancy, gate mechanism network module is used to adaptively learn the interdependence of multivariate feature data. The relevant experiments show that compared with baseline models, DANN has the most stable long-term prediction performance, which reduces the problem of error accumulation to a certain degree.
BPNS: A Fast and Accurate Power Estimation Model for Embedded Devices
Authors: Cai Chenyu , Hu Biao , Zhao Mingguo
Published: April 2024

The surge in the intelligent information age has led to a growing need for enhanced computing power in smart mobile devices. However, the accompanying increase in computing power brings about a significant challenge — high power consumption. This poses a notable hurdle for mobile devices that prioritize sustainability performance. Effectively addressing power consumption estimation is crucial for overcoming this sustainability issue. In this paper, we propose using a branching parallel neural networks (BPNS) that consists of FCNN-LSTM and a attention mechanism to estimate the power consumption of a device. In addition, a data stabilization module is incorporated into the framework to improve the power estimation accuracy. We implement and assess the performance of BPNS for online estimation across diverse smart mobile devices, conducting a comparative analysis with commonly used neural networks. Notably, our approach achieves a deviation of 0.28W, 0.65W and 0.76W for online estimation on three smart mobile devices, and outperforms other methods in terms of estimation accuracy and computational cost.
Enhancing Stock Trading Strategies: Integrating Discrete Wavelet Transformation with Deep Q-Network
Authors: Wang Qi , Zhang Liang , Zeng Yanyu , Wu Shize , Yu Chuanwei , Sun Song
Published: April 2024

The prediction of price trends in the stock market has always been a hot research topic in the financial field. However, due to the high instability and volatility of stock prices, it is very difficult to accurately predict stock trends. How to remove the noise of stock data, extract effective features, and pursue maximum value returns has always been a challenge. This paper proposes a hybrid model (DWT-DQN) that combines discrete wavelet transform with deep reinforcement learning to improve the accuracy and return rate of stock predictions. First, the model captures price fluctuation information on different scales by performing discrete wavelet transformation on the difference between long- and short-term moving averages of stock prices, and well extracts the changing characteristics of stock price data in the time domain and frequency domain. Then the feature data are input into the built DQN network for model training. The network can select the optimal trading action based on market status and historical experience and returns. At the same time, during the data sampling process, an attention mechanism is introduced to allow the model to further learn in the direction of maximizing returns. Through testing and verification on SSEC, HSI, NDX and SPX data sets, experiments show that the hybrid model proposed in this paper has excellent performance in terms of accuracy and return rate.
Integration of UML Class Diagrams Based on Semantics and Structure
Authors: Yuan Zhongchen , Hu Xingda , Zhang Gang , Ma Zongmin
Published: April 2024

As a high-level reuse, software design reuse has received more and more attention because of its important impact on the subsequent development stages. Usually, the design models are typically represented as some graphs or diagrams, in which Unified Modeling Language (UML) class diagram is so widely used in software design that it has become the de facto standard. There has been some research on the reuse of UML class diagrams so far, mainly focusing on matching and retrieval. However, it is worth noting that there are many similar class diagrams modeling the same object or some related class diagrams modeling different aspects of the same object in the reuse repository. As a matter of fact, the primary step to achieve a high-quality reuse is to have high-quality software artifacts, so the well-designed UML class diagrams become a necessary resource for software design reuse. Therefore, it is necessary to integrate these class diagrams so that they have stronger modeling ability, and eliminating redundancy is another benefit of the integration. Up to now, there has been little discussion about the integration of class diagrams, so we propose an integration approach based on semantics and structure in this paper. The equivalent elements that can identify the semantically merged parts are defined, and the possible conflict items are listed from both semantic and structural aspects. The integration procedure composed of three stages is proposed, in which an approach combining semantic common class diagrams (SCCDs) and structural common graph sequence (SCGS) is combined to determine the merged parts, the integration issue of heterogeneous class diagrams is considered from the proposed abstract models, and the conflict resolution for each conflict item is described by examples. The experimental results show the effectiveness of our proposed integration approach.
Combining Error Guessing and Logical Reasoning for Software Fault Localization via Deep Learning
Authors: Wang Rongcun , Fan Mingmei , Yan Yue , Jiang Shujuan
Published: April 2024

Automated fault localization has been extensively studied to improve the effectiveness of software debugging. Existing automated fault localization methods neglect the guidance of the simple and easily available debugging information on fault localization. To bridge manual fault localization with automated fault localization, we propose a fault localization approach combining error guessing and logical reasoning via deep learning. The proposed approach simulates the actual debugging process. Specifically, developers’ debugging experience and context dependencies between methods are mapped into two different types of coverage matrices. The constructed matrices are fed to a convolutional neural network (CNN) to predict whether a method is buggy or not. To validate the effectiveness of the proposed approach, we designed and constructed the empirical study on the widely used Defect4J datasets. With respect to the top-n ([Formula: see text]) metric, our approach outperforms the state-of-the-art DeepFL and other five methods including Ochai, Muse, MULTRIC, TraPT and FLUCSS. Particularly, compared with the above methods, our approach has an improvement of 5–182% for top-1. In terms of MFR and MAR, the proposed approach is slightly lower than the best DeepFL but better than the other five methods. The approach we presented achieving the unification of manual and automatic debugging can aid in the improvement of fault localization accuracy.
Semidualizing modules over numerical semigroup rings
Authors: Celikbas Ela , Geller Hugh , Kobayashi Toshinori
Published: April 2024

A semidualizing module is a generalization of Grothendieck’s dualizing module. For a local Cohen–Macaulay ring R, the ring itself and its canonical module are always realized as (trivial) semidualizing modules. Reasonably, one might ponder the question; when do nontrivial examples exist? In this paper, we study this question in the realm of numerical semigroup rings and, up to multiplicity 9, completely classify which of these rings possess a nontrivial semidualizing module. Using this classification, for each integer [Formula: see text], we construct a numerical semigroup ring of multiplicity n which admits a nontrivial semidualizing module.
New trends on the systems approach to modeling SARS-CoV-2 pandemics in a globally connected planet
Authors: Bertaglia Giulia , Bondesan Andrea , Burini Diletta , Eftimie Raluca , Pareschi Lorenzo , Toscani Giuseppe
Published: April 2024

This paper presents a critical analysis of the literature and perspective research ideas for modeling the epidemics caused by the SARS-CoV-2 virus. It goes beyond deterministic population dynamics to consider several key complexity features of the system under consideration. In particular, the multiscale features of the dynamics from contagion to the subsequent dynamics of competition between the immune system and the proliferating virus. Other topics addressed in this work include the propagation of epidemics in a territory, taking into account local transportation networks, the heterogeneity of the population and the study of social and economic problems in populations involved in the spread of epidemics. The overall content aims to show how new mathematical tools can be developed to address the above topics and how mathematical models and simulations can contribute to the decision making of crisis managers.
Quantum gravity in flat spacetime
Authors: Mäkelä Jarmo
Published: April 2024

Inspired by Einstein’s Strong Principle of Equivalence, we consider the effects of quantum mechanics to the gravity-like phenomena experienced by an observer in a uniformly accelerating motion in flat spacetime. Among other things, our model of quantum gravity, derived from the first principles, predicts the Unruh effect, and a discrete area spectrum for spacelike two-surfaces.
A TEMPERATURE-CONTROLLED ON-CHIP HIGH-THROUGHPUT SINGLE-CELL CULTURE PLATFORM
Authors: ABEDINI-NASSAB ROOZBEH , POURYOSEF MAHRAD , AKRAMI SEYED MOHAMMAD REZA
Published: April 2024

Modern lab-on-a-chip and microfluidic technologies offer opportunities in developing portable cell culture platforms for growing and analyzing the behavior of living cells. We have proposed the magnetomicrofluidic chips, as a novel single-cell analysis tool, composed of microfluidic microchambers and microchannels overlaid on magnetic thin films designed to precisely transport magnetized single cells and magnetic particles with the help of hybrid hydrodynamic and magnetic forces. In order to provide the optimal temperature for the cells to grow on the chip, here we propose an on-chip microincubator at the single-cell level, without the need for a conventional incubator. An electronic circuit is used to read the chip temperature and control it with a feedback system. We perform finite element methods analysis to study the chip temperature profile, based on which the final chip is designed. We show that experimental results agree well with the simulation results. Using the proposed chip and the on-chip incubator, we transport thousands of single cells into the microchambers, culture them, and demonstrate their proper proliferation. The introduced chip can be used in crucial applications, such as drug screening, in which long-term study of the cells under a microscope, at the single-cell level, is required.
Trends by adaptive fourier decomposition and application in prediction
Authors: Hon Chitin , Liu Zige , Qian Tao , Qu Wei , Zhao Jiman
Published: April 2024

In this paper, we introduce the concept of trend function made from adaptive Fourier decomposition. Through applying a data preprocessing method, we achieve trend functions of the rational function type. The trend functions are divided into different levels to describe the tendency of the signal or time series in question. They are, in particular, smooth, and are free from the Gibbs phenomenon as usually introduced by the Fourier type methods. To justify the role of the trend function concept, an interpolation result is proved. An effective application of trend function in predicting stochastic time series is presented based on real COVID-19 outbreak data of China. The algorithm aspect is fully discussed.
Dynamic analysis of the IEASWR information propagation model with Holling-type II functional response
Authors: Mei Xuehui , Ren Jingjing , Xia Yang , Jiang Haijun
Published: April 2024

Noting that the gap between individual communication abilities is amplified in the network era, we classify spreaders into super-spreaders and ordinary spreaders by the Pareto principle. In addition, when facing negative information, individuals with higher knowledge levels will recognize it. Based on these, in this study, an improved Ignorants-Hesitant-Super-spreaders-Ordinary spreaders-Wise-Removers (IEASWR) information propagation model was developed. Meanwhile, in view of the gradual saturation, density dependence and nonlinear relationship of the propagation process, the Holling-type II functional response function is introduced to information propagation modeling for the first time making it more realistic. First, the basic reproduction number [Formula: see text] is deduced. Then, the global asymptotic stability of the information-free/prevailing equilibria is proved by constructing the Lyapunov function. Furthermore, using the Pontryagin maximum principle, a real-time optimization guidance measure is proposed to achieve the correct guidance of negative information within the expected time. Finally, the validity of the theory is verified through numerical simulation.
A technique for preserving network structure in randomized Hi-C data
Authors: Sizovs Andrejs , Melkus Gatis , Rucevskis Peteris , Silina Sandra , Lace Lelde , Celms Edgars , Viksna Juris
Published: April 2024

Chromatin interaction data are frequently analyzed as a network to study several aspects of chromatin structure. Hi-C experiments are costly and there is a need to create simulated networks for quality assessment or result validation purposes. Existing tools do not maintain network properties during randomization. We propose an algorithm to modify an existing chromatin interaction graph while preserving the graphs most basic topological features — node degrees and interaction length distribution.

The algorithm is implemented in Python and its open-source code as well as the data to reproduce the results are available on Github.
Gene representation bias in spatial transcriptomics
Authors: Li Xinling , Qiu Peng
Published: April 2024

For sequencing-based spatial transcriptomics data, the gene-spot count matrix is highly sparse. This feature is similar to scRNA-seq. The goal of this paper is to identify whether there exist genes that are frequently under-detected in Visium compared to bulk RNA-seq, and the underlying potential mechanism of under-detection in Visium. We collected paired Visium and bulk RNA-seq data for 28 human samples and 19 mouse samples, which covered diverse tissue sources. We compared the two data types and observed that there indeed exists a collection of genes frequently under-detected in Visium compared to bulk RNA-seq. We performed a motif search to examine the last 350 bp of the frequently under-detected genes, and we observed that the poly (T) motif was significantly enriched in genes identified from both human and mouse data, which matches with our previous finding about frequently under-detected genes in scRNA-seq. We hypothesized that the poly (T) motif may be able to form a hairpin structure with the poly (A) tails of their mRNA transcripts, making it difficult for their mRNA transcripts to be captured during Visium library preparation.
Application of Improved SPH Method in Solving Time Fractional Schrödinger Equation
Authors: Ma Luyang , Imin Rahmatjan
Published: April 2024

In this paper, a pure meshless method for solving the time fractional Schrödinger equation (TFSE) based on KDF-SPH method is presented. The method is used for the first time to numerically solve the TFSE. The method utilizes the finite difference method (FDM) to approximate the time fractional-order derivative defined in the Caputo sense. The spatial derivatives are discretized by the KDF-SPH meshless method. Expressions for the kernel approximation and the particle approximation are provided. To ensure the validity and flexibility of the numerical calculations, we conducted numerical simulations of one- and two-dimensional linear/nonlinear time Schrödinger equations (1D/2D TFLSE/TF-NLSE) in both bounded and unbounded regions. We also examined nonlinear time fractional Schrödinger equations that lack analytical solutions and compared our method with other meshless methods. Numerical results show that the proposed method can approximate to the second-order precision in space, which verifies the effectiveness and accuracy of the proposed method.
Direct FE2 Based on Single-Integration Point for Modeling Damage Evolution of Materials with Micro-Cracks
Authors: Cui Yehui , Afrasiabi Mohamadreza , Zhang Zhilang , Bambach Markus
Published: April 2024

The Direct FE2 method is a recently developed concurrent multi-scale simulation approach with a monolithic solution scheme, where the macroscopic and microscopic models are solved in a unit iteration process. The conventional Direct FE2 using quadrilateral element has some limitations in determining the failure state of a macro-element. In this work, we develop a new Direct FE2 method based on the Single-integration Point triangle element (Direct SP-FE2) and apply it to simulate the micro-crack-induced fracture problems for the first time. The cohesive element is inserted into a representative volume element (RVE), so that the failure process of a macro-element can be determined by the micro-crack propagation on RVE. Accordingly, the relationship between macro-deformations and microstructures can be established directly, thus avoiding complex derivation and utilization of a multi-scale damage constitutive model. In Direct SP-FE2, the failure state of a macro-element can be directly predicted by the RVE at the single-integration point inside, avoiding the uncertainty associated with predictions by multiple RVEs. As a result, compared to the conventional Direct FE2 with a quadrilateral element, the Direct SP-FE2 shows advantages in capturing complex geometric boundaries and predicting the damage evolution process and failure state more accurately. Various numerical examples are conducted to comprehensively validate the effectiveness of the present method in describing the influence of micro-cracks on macro-structure deformations. For the macro-damage behaviors, the simulation results by the Direct SP-FE2 show good agreement with the direct numerical simulation (DNS) results of the full FE model at a significantly lower computational time. With the developed Direct SP-FE2, a variety of complex micro-crack fracture problems in composite materials can be successfully modeled by manipulating the interior RVE structure.
Evolutionary Probabilistic Analysis of Non-Smoothly Damped Stochastic Oscillator Under Modulated Random and Harmonic Excitations
Authors: Er Guo-Kang , Luo Jie , Iu Vai Pan
Published: April 2024

This paper concerns the evolutionary transitional probability density functions (PDFs) and the mean upcrossing rates (MCRs) of responses of the non-smoothly damped stochastic oscillator under both modulated random and harmonic excitations. The damping expressed by a non-smooth function of velocity is considered in the stochastic dynamical systems. A procedure is presented by making the time-related weighted residue of the formulated Fokker–Planck–Kolmogorov (FPK) equation vanish in the weak sense. Thus, a group of stochastic ordinary differential equations regarding the undetermined parameters in the assumed solution can be formulated and solved numerically. Three cases are studied under different conditions. The achieved evolutionary transitional PDF and MCR solutions attest to the outstanding effectiveness of the presented procedure even in the tail ends of the PDF solutions. Moreover, the achieved solutions of the stochastic dynamical systems are found to have significant non-Gaussian behaviors due to the interaction of the random and harmonic excitations and the system nonlinearity. Notably, the presented procedure greatly improves the running efficiency when compared with Monte Carlo simulation.
Dynamical Model of HIV Infection with Homeostatic Growth of CD4+ T Cells and Immune Response
Authors: Prakash Surya , Umrao Anuj Kumar , Srivastava Prashant K.
Published: April 2024

In this work, we propose and investigate a human immunodeficiency virus (HIV) infection model that considers CD4[Formula: see text] T cell homeostasis and the cytotoxic T lymphocyte (CTL) response. The local stability of the disease-free and endemic equilibrium point is established. Further, the global stability of the disease-free and endemic equilibrium points is investigated. Under specific parametric conditions, it is shown that the model exhibits backward, forward (transcritical) bifurcation, Hopf and Hopf–Hopf bifurcation. We have further considered a time lag in the model to represent the time delay between CD4[Formula: see text] T cell infection and the viral particle production and performed the stability and bifurcation analysis for the delay model. We conduct comprehensive numerical experiments to visualize the dynamical behavior of the HIV model and validate our findings.
Influence of the presence of a pathogen and leachate recirculation on a bacterial competition
Authors: El Hajji Miled
Published: April 2024

In this paper, we proposed and analyzed a five-dimensional system of ordinary differential equations modeling the competition of two competing bacteria in a chemostat under the influence of the leachate recirculation and in the presence of a pathogen associated only with the bacteria 1. We suppose that the nutriment is present into two forms, soluble and insoluble nutriment, and both two forms are continuously added to the chemostat. The proposed model takes the form of an “SI” epidemic model and uses general increasing growth functions and general increasing incidence rate. It admits multiple equilibria that we give the conditions under which we assure both the existence and the local stability of each equilibrium point. The possibility of periodic trajectory was excluded, and the uniform persistence of both types of bacteria was proved. Finally, several numerical examples confirming the theoretical findings are given.
Research on the application of WO3 gas sensor based on noble metals modification in cable fire detection
Authors: Yao Dongting , Qin Qiming , Lu Zhenkun , Pan Xiang , Xia Youbin , Cheng Yin
Published: April 2024

In the monitoring of cable fire, it is of great significance to be able to conduct early fire warning, which requires that sensors have high sensitivity to the cable vapors at low temperatures. In this study, WO3 thick films modified with Au, Pd, and Pt single or bimetallic nanoparticles were fabricated via magnetron sputtering. Gas sensors utilizing these materials exhibited significant response to the low-temperature vapor emitted by YJV22 and YJY23 cables. Among them, Pd/WO3-20 demonstrated a response value of 20 to the vapor of 81[Formula: see text]C YJV22 cables, marking a remarkable 6.3-fold increase compared to pure WO3, and the response time was 13 s. Similarly, Pt/WO3-10 exhibited a response value of 11.11 to the vapor of 104[Formula: see text]C YJY23 cables, representing a 6.9-fold enhancement over pure WO3 with a response time of 12 s. The heating temperature of cables remains below their critical threshold, indicating that it is in the early stages of cable fires, the findings hold significant implications for early warning systems designed to detect cable fires promptly.
Carbon-coated TiNb2O7 microspheres with partial nitridation for high-performance lithium storage
Authors: Lei Yi , Yuan Qing , Zhao Yan , Wang Lei , Liu Zhongzhu , Monteiro Robson , Parreira Luanna , Ribas Rogerio , Guo Aimin , Lin Chunfu
Published: April 2024

TiNb2O7 has been applied to lithium-ion batteries owing to its large capacity and inherent safety, but suffers from its poor rate capability. Here, TiNb2O7 is modified through a combination of crystal-structure modification and conductive-phase coating, and C–NH3–TiNb2O7 with Ti2Nb[Formula: see text]O[Formula: see text]-type TiNb2O7 and Ti[Formula: see text][Formula: see text]Nb[Formula: see text]N/carbon double coatings is successfully synthesized. The unique Ti2Nb[Formula: see text]O[Formula: see text]-type structure of TiNb2O7 owns a large interlayer spacing, leading to fast Li[Formula: see text] diffusivity and remarkable intercalation-pseudocapacitive behavior. The conductive Ti[Formula: see text][Formula: see text]Nb[Formula: see text]N/carbon coatings result in good electrical conduction among the TiNb2O7 microspheres. Consequently, C–NH3–TiNb2O7exhibits excellent rate capability of TiNb2O7 (large capacity percentage of 73.9% at 5C versus 0.5C). Besides the fast electrochemical kinetics, C–NH3–TiNb2O7 further exhibits large reversible capacities (237 mAh g[Formula: see text]at 0.1C and 146 mAh g[Formula: see text]at 5C) and outstanding cyclability (93.1% capacity retention after 500 cycles at 5C). These desirable electrochemical properties fully demonstrate that the C–NH3–TiNb2O7 anode material can be suitable for high-performance Li[Formula: see text]-storage.
A new extended multivariable Horn function
Authors: Gupta Priyanka , Joshi Sunil , Mittal Ekta , Gour Murli Manohar
Published: April 2024

This study presents a new multivariable fourth class of Horn function. Additionally, for the new extended multivariable fourth-type Horn function, we create bilateral generating functions and particular generating functions for this multivariable fourth-type Horn function. Numerous multilinear and multilateral generating functions are also generated for these functions
Integration of conventional well logs and core samples to predict porosity of tight reservoir: a case study from Ordos Basin
Authors: Wang Xia , Fu Guomin , Fan Bojiang , Wang Shuai , Feng Luping , Peng Cheng
Published: April 2024

For reservoirs of oil wells with no cored data, predicting porosity from wireline logs and core samples is an effective approach. Integration of conventional well logs and core samples to predict porosity with high accuracy is particularly challenging owing to complex logging responses of tight sandstone. Therefore, a novel prediction workflow based on a linear interpolation algorithm (LIA) is described to estimate porosity from well logs in the present study. Based on core reposition, porosity correction under overburden pressure, core–log data matching and calculation of shale content, two multi-regression formulae to estimate porosity values are obtained using the nearest neighbour algorithm and LIA respectively. The formulae are applied to the tight sandstone in the Chang 9 member of the Yanchang Formation in the NE Wuqi Oilfield, Ordos Basin. The comparison results indicate that the porosity predicted from the formula obtained by the LIA is in better agreement with the measured porosity, showing a better prediction effect. The application example demonstrates that the LIA formula is applicable for core porosity prediction in the study region. This approach can also be applied for porosity prediction in other oil regions that have similar geological background.
Portable Instrumentation for Ambient Ionization and Miniature Mass Spectrometers
Authors: Smith Barry L. , Hankinson Thomas , Maher Simon
Published: April 2024

We critically evaluate the current status of portable mass spectrometry (pMS), particularly where this aligns with ambient ionization. Assessing the field of pMS can be quite subjective, especially in relation to the portable aspects of design, deployment, and operation. In this review, we discuss what it means to be portable and introduce a set of criteria by which pMS and ambient ionization sources can be assessed. Moreover, we consider the recent literature in terms of the most popular and significant advances in portable instrumentation for ambient ionization and miniature mass spectrometers. Finally, emerging trends and exciting future prospects are discussed and some recommendations are offered.
The Interstellar Medium in Dwarf Irregular Galaxies
Authors: Hunter Deidre A. , Elmegreen Bruce G. , Madden Suzanne C.
Published: April 2024

Dwarf irregular (dIrr) galaxies are among the most common type of galaxy in the Universe. They typically have gas-rich, low-surface-brightness, metal-poor, and relatively thick disks. Here, we summarize the current state of our knowledge of the interstellar medium (ISM), including atomic, molecular, and ionized gas, along with their dust properties and metals. We also discuss star-formation feedback, gas accretion, and mergers with other dwarfs that connect the ISM to the circumgalactic and intergalactic media. We highlight one of the most persistent mysteries: the nature of pervasive gas that is yet undetected as either molecular or cold hydrogen, the “dark gas.” Some highlights include the following:
▪Significant quantities of Hi are in far-outer gas disks.▪Cold Hi in dIrrs would be molecular in the Milky Way, making the chemical properties of star-forming clouds significantly different.▪Stellar feedback has a much larger impact in dIrrs than in spiral galaxies.▪The escape fraction of ionizing photons is significant, making dIrrs a plausible source for reionization in the early Universe.▪Observations suggest a significantly higher abundance of hydrogen (H2 or cold Hi) associated with CO in star-forming regions than that traced by the CO alone.
Soluble Human Lectins at the Host–Microbe Interface
Authors: Peiffer Amanda L. , Dugan A.E. , Kiessling L.L.
Published: April 2024

Human lectins are integral to maintaining microbial homeostasis on the skin, in the blood, and at mucosal barriers. These proteins can recognize microbial glycans and inform the host about its microbial status. In accordance with their roles, their production can vary with tissue type. They also can have unique structural and biochemical properties, and they can influence microbial colonization at sites proximal and distal to their tissue of origin. In line with their classification as innate immune proteins, soluble lectins have long been studied in the context of acute infectious disease, but only recently have we begun to appreciate their roles in maintaining commensal microbial communities (i.e., the human microbiota). This review provides an overview of soluble lectins that operate at host–microbe interfaces, their glycan recognition properties, and their roles in physiological and pathological mechanisms.
Genomic Interactions Between Mycobacterium tuberculosis and Humans
Authors: Palittapongarnpim Prasit , Tantivitayakul Pornpen , Aiewsakun Pakorn , Mahasirimongkol Surakameth , Jaemsai Bharkbhoom
Published: April 2024

Mycobacterium tuberculosis is considered by many to be the deadliest microbe, with the estimated annual cases numbering more than 10 million. The bacteria, including Mycobacterium africanum, are classified into nine major lineages and hundreds of sublineages, each with different geographical distributions and levels of virulence. The phylogeographic patterns can be a result of recent and early human migrations as well as coevolution between the bacteria and various human populations, which may explain why many studies on human genetic factors contributing to tuberculosis have not been replicable in different areas. Moreover, several studies have revealed the significance of interactions between human genetic variations and bacterial genotypes in determining the development of tuberculosis, suggesting coadaptation. The increased availability of whole-genome sequence data from both humans and bacteria has enabled a better understanding of these interactions, which can inform the development of vaccines and other control measures.
The Psychology of Guilty Plea Decisions
Authors: Helm Rebecca K.
Published: April 2024

In many jurisdictions, most convictions result from guilty pleas. This reality means that most convictions in these jurisdictions are reached not as the result of the decision-making of judges or juries but as the result of the decision-making of prosecutors (who often have discretion to offer incentives that can encourage defendants to plead guilty) and defendants (who must decide whether to plead guilty). These decisions can be psychologically complex and driven by a range of tactical and normative considerations. This article provides an overview of what we know about how prosecutors and defendants make decisions relating to guilty pleas, examines how modern psychological theory can help us understand these decisions better, and discusses directions for future research in this area. This future research will be important in more effectively evaluating the extent to which convictions obtained via guilty plea are consistent with normative legal goals.
Degradation Processes in Current Commercialized Li-Ion Batteries and Strategies to Mitigate Them
Authors: Kraytsberg Alexander , Ein-Eli Yair
Published: April 2024

Lithium-ion batteries (LIBs) are now widely exploited for multiple applications, from portable electronics to electric vehicles and storage of renewable energy. Along with improving battery performance, current research efforts are focused on diminishing the levelized cost of energy storage (LCOS), which has become increasingly important in light of the development of LIBs for large transport vehicles and power grid energy storage applications. Since LCOS depends on the battery's lifetime, understanding the mechanisms responsible for battery degradation and developing strategies to increase the lifetime of LIBs is very important. In this review, the latest developments related to the performance and degradation of the most common LIBs on the market are reviewed. The numerous processes underlying LIB degradation are described in terms of three degradation loss modes: loss of lithium inventory (LLI), active positive electrode material loss and degradation, and active negative electrode material loss and degradation. A strong emphasis is placed on the most recent strategies and tactics for LIB degradation mitigation.
Aerobic exercise training combined with local strength exercise restores muscle blood flow and maximal aerobic capacity in long-term Hodgkin lymphoma survivors
Authors: Santos Luciana De Souza , Rehder Marília Harumi Higuchi dos Santos , Negrao Marcelo Vailati , Goes-Santos Beatriz R. , Toshi Dias Edgar , Paixão Camila Jordão , Urias Ursula , Giannetti Natali Schiavo , Hajjar Ludhmila A. , Filho Roberto Kalil , Negrão Carlos E.
Published: April 2024

Long-term Hodgkin lymphoma (HL) survivors have impaired muscle blood flow responses during mental stress and decreased maximal aerobic capacity. Supervised aerobic exercise training combined with local strength exercises restores muscle blood flow responses during mental stress and maximal aerobic capacity in these survivors. These findings provide evidence of safety and effectiveness of exercise training in HL survivors. Moreover, they highlight the importance of exercise training in the treatment of this set of patients.
Therapy-naïve malignancy causes cardiovascular disease: a state-of-the-art cardio-oncology perspective
Authors: Ogilvie Leslie M. , Coyle-Asbil Bridget , Brunt Keith R. , Petrik Jim , Simpson Jeremy A.
Published: April 2024

Cardiovascular disease (CVD) and cancer are the leading causes of mortality worldwide. Although generally thought of as distinct diseases, the intersectional overlap between CVD and cancer is increasingly evident in both causal and mechanistic relationships. The field of cardio-oncology is largely focused on the cardiotoxic effects of cancer therapies (e.g., chemotherapy, radiation). Furthermore, the cumulative effects of cardiotoxic therapy exposure and the prevalence of CVD risk factors in patients with cancer lead to long-term morbidity and poor quality of life in this patient population, even when patients are cancer-free. Evidence from patients with cancer and animal models demonstrates that the presence of malignancy itself, independent of cardiotoxic therapy exposure or CVD risk factors, negatively impacts cardiac structure and function. As such, the primary focus of this review is the cardiac pathophysiological and molecular features of therapy-naïve cancer. We also summarize the strengths and limitations of preclinical cancer models for cardio-oncology research and discuss therapeutic strategies that have been tested experimentally for the treatment of cancer-induced cardiac atrophy and dysfunction. Finally, we explore an adjacent area of interest, called “reverse cardio-oncology,” where the sequelae of heart failure augment cancer progression. Here, we emphasize the cross-disease communication between malignancy and the injured heart and discuss the importance of chronic low-grade inflammation and endocrine factors in the progression of both diseases.
Gonadal sex and chromosome complement influence the gut microbiome in a mouse model of allergic airway inflammation
Authors: Ekpruke Carolyn Damilola , Alford Rachel , Parker Erik , Silveyra Patricia
Published: April 2024

Recently, the gut microbiome and its role in chronic respiratory disease have been the subject of extensive research and the establishment of its involvement in immune functions. Using the FCG mouse model, our findings revealed the influence of gonads and sex chromosomes on the microbial community structure before and after exposure to HDM. Our data provide a potential new avenue to better understand mediators of sex disparities associated with allergic airway inflammation.