bluuebunny/crossref_metadata_2025 huggingface dataset:
A basic model for the nonlinear rheology of bijels
Authors: Ching Herman , Mohraz Ali
Published: January 2025

We examine the rheology of bicontinuous interfacially jammed emulsion gels (bijels) beyond the limit of linear viscoelasticity and propose a simple model that connects bijel rheology to anticipated microstructural changes in the continuous particle-laden interface. The basic elements of our model are constructed from a linear stress relaxation experiment, which suggests glasslike rheology mediated by α (out-of-cage) and β (in-cage) particle dynamics along the interface in this limit. Extending to medium amplitude oscillatory shear experiments then reveals signatures of nonlinearity, which we rationalize as the combined effect of shear-induced dilation of the interface and its simultaneous recompaction by interfacial tension, as well as potential buckling along portions of the interface under compression. Informed by these observations, we present a double Maxwell model with sigmoidal nonlinearities introduced to account for how interfacial dilation and recompaction affect the intercage particle dynamics (α relaxation) along the interface and buckling. This simple model successfully captures the general features of nonlinear rheology in bijels, indicating that their linear-to-nonlinear transition is associated with loss of compaction along the dilated and buckling along the compacted regions of the interface. Our results shed initial light on the microstructural origins of nonlinear rheology in bijels and the reconfigurability afforded in these systems by the balance of glassy particle dynamics and interfacial recompaction under shear deformation.
Assessing neonatal Sequential Organ Failure (nSOFA) scores in suspected late-onset neonatal sepsis among preterm infants: implications for morbidity and mortality
Authors: Kurul Şerife , Reijnierse Joyce J , Koppens Hugo J , Onland Wes , Simons Sinno H P , Reiss Irwin K M , Taal H Rob , Visser Douwe H
Published: December 2024

Background

The neonatal Sequential Organ Failure Assessment (nSOFA) score is an organ dysfunction score developed for predicting mortality risk in preterm neonates with proven late-onset neonatal sepsis (LONS) and necrotising enterocolitis. However, the utility of the nSOFA score in determining the risk of retinopathy of prematurity (ROP), bronchopulmonary dysplasia (BPD) or mortality in patients with suspected LONS is unknown.

Methods

We performed a dual-centre retrospective cohort study of preterm (gestational age <32 weeks) neonates suspected of LONS, from 2016 to 2020 at two neonatal intensive care units. The nSOFA scores (range 0–15) were calculated for each suspected LONS episode at various time points around the sepsis evaluation. A nSOFA burden score was calculated, by counting each time point the nSOFA score was ≥4 during all sepsis episodes (in the time period −6 to 48 hours). The association with 10-day sepsis-related mortality and severe ROP and BPD was assessed.

Results

A total of 1157 episodes of suspected LONS in 706 neonates occurred. The nSOFA was significantly associated with 10-day mortality at various time points. The nSOFA score 6 hours after drawing a blood culture (T6) was associated with 10-day sepsis-related mortality (adjusted OR (aOR) 1.31; 95% CI (1.22 to 1.40; p<0.001)), in a model corrected for gestational age, sex, age at evaluation and gestational age-adjusted birth weight. The nSOFA burden scores were positively associated with the risk for ROP (aOR 1.24; 95% CI 1.09 to 1.41; p=0.001) and BPD (aOR 1.30; 95% CI 1.13 to 1.50; p<0.001).

Conclusion

Our findings show that the nSOFA score in preterm neonates suspected of LONS is associated with subsequent mortality, ROP and BPD. Incorporating nSOFA scores may help to identify sepsis survivors at the highest risk of adverse outcomes, who may require more intensive monitoring and adapted therapy.
Preventing abusive head trauma: what do frontline healthcare workers (HCW) know about normal infant crying?
Authors: Guinness Freya , O'Neill Ciara , Keating Brian , Walsh Michelle , Sharif Farhana
Published: December 2024

It is well reported that one of the main precipitants of abusive head trauma (AHT) is frequent and consistent periods of crying. The cornerstones in the management of excessive infant crying are reassurance and education. Our study showed a knowledge deficit in frontline healthcare workers (HCW) understanding of normal infant crying. We received 122 survey responses. 40% of respondents did not feel that ‘walking away and leaving a baby for a limited time in a safe space’ was a valid method of soothing. This highlights the need for better HCW education on normal infant crying to help prevent AHT.
The Tolman VII space-time in the presence of charge and a cosmological constant
Authors: Ripple James , Agashe Anish
Published: December 2024

The Tolman VII space-time is one of the few physically acceptable exact solutions in general relativity. In this paper, we derive a generalised Tolman VII solution which includes a charge and a cosmological constant. We analyse the spatial geometry of the solution and present conditions for zero and non-zero spatial curvature. We show that for a particular value of the boundary, the Tolman VII space-time can be matched to the charged Nariai space-time. This represents a new class of interior Nariai solutions. Matching with the Reissner–Nordström-de Sitter space-time, we derive analytic expressions for the metric functions and the pressure. Using this, we show that the solution allows for trapped null geodesics for a broad range of values for the total charge, central density, and the cosmological constant. We investigate the physical properties and derive an equation of state for the fluid. We show that the fluid can be considered a polytrope with,

$$\Gamma \sim 2.5$$

Γ
∼
2.5

. Finally, we analyse the sound speed and energy conditions to conclude that only a subclass of the solution follows all the basic physical acceptability criteria.
Leptonic dipole operator with $$\Gamma _2$$ modular invariance in light of Muon $$(g-2)_\mu $$
Authors: Nomura Takaaki , Tanimoto Morimitsu , Wang Xing-Yu
Published: December 2024

We have studied the leptonic EDM and the LFV decays relating with the recent data of anomalous magnetic moment of muon,

$$(g-2)_{\mu }$$

(
g
-
2
)

μ

in the leptonic dipole operator. We have adopted the successful

$$\Gamma _2$$

Γ
2

modular invariant model by Meloni–Parriciatu as the flavor symmetry of leptons. Suppose the anomaly of

$$(g-2)_{\mu }$$

(
g
-
2
)

μ

,

$$\Delta a_{\mu }$$

Δ

a
μ

to be evidence of New Physics (NP), we have related it with the anomalous magnetic moment of the electron

$$\Delta a_e$$

Δ

a
e

, the electron EDM

$$d_e$$

d
e

and the

$$\mu \rightarrow e \gamma $$

μ
→
e
γ

decay. We found that the NP contributions to

$$\Delta a_{e(\mu )}$$

Δ

a

e
(
μ
)

are proportional to the lepton masses squared likewise the naive scaling

$$\Delta a_\ell \propto m^2_\ell $$

Δ

a
ℓ

∝

m
ℓ
2

. The experimental constraint of

$$|d_e|$$

|

d
e

|

is much tight compared with the one from the branching ratio

$$\mathcal {B} (\mu \rightarrow e \gamma )$$

B
(
μ
→
e
γ
)

in our framework. Supposing the phase of our model parameter

$$\delta _{\alpha }$$

δ
α

for the electron to be of order one, we have estimated the upper-bound of

$$\mathcal {B}(\mu \rightarrow e \gamma )$$

B
(
μ
→
e
γ
)

, which is at most

$$10^{-21}-10^{-20}$$

10

-
21

-

10

-
20

. If some model parameters are real, leptonic EDMs vanish since the CP phase of the modular form due to modulus

$$\tau $$

τ

does not contribute to the EDM. However, we can obtain

$$\mathcal {B} (\mu \rightarrow e \gamma )\simeq 10^{-13}$$

B

(
μ
→
e
γ
)

≃

10

-
13

with non-vanishing

$$d_e$$

d
e

in a specific case. The imaginary part of a parameter can lead to

$$d_e$$

d
e

in the next-to-leading contribution. The predicted electron EDM is below

$$10^{-32}$$

10

-
32

e cm, while

$$\mathcal {B} (\mu \rightarrow e \gamma )$$

B
(
μ
→
e
γ
)

is close to the experimental upper-bound. The branching ratios of

$$\tau \rightarrow e\gamma $$

τ
→
e
γ

and

$$\tau \rightarrow \mu \gamma $$

τ
→
μ
γ

are also discussed.
Cosmological constant as an integration constant
Authors: Feng Justin C. , Chen Pisin
Published: December 2024

The discrepancy between the observed value of the cosmological constant (CC) and its expected value from quantum field theoretical considerations motivates the search for a theory in which the CC is decoupled from the vacuum energy. In this article, we consider the viability of theories in which the Einstein equations are recovered (without additional constraints) and in which the CC is regarded as an integration constant. These theories include trace-free Einstein gravity, theories constructed from the Codazzi equation (which includes Cotton gravity and a gauge-gravity inspired theory), and conformal Killing gravity. We remark on a recent debate regarding Cotton gravity and find that while the Codazzi equation of that theory is indeed underdetermined, the solutions of the Codazzi equation trivialize to

$$\lambda g_{ab}$$

λ

g

ab

on generic backgrounds, and that in principle, one can close the system with the divergence-free condition and an appropriate choice of initial data. We also propose a full variational principle (full in the sense that variations in all variables are considered) for each of the aforementioned theories that can incorporate the matter sector; in this manner, we can obtain the trace-free Einstein equations without a unimodular constraint. The resulting actions require additional (auxiliary) fields and are therefore only expected to be effective, but they may provide a useful starting point in bottom up approaches to constructing more fundamental theories.
Optimal Location Method of Urban and Rural Emergency Shelter Based on Improved Ant Colony Algorithm
Authors: Wang Mengfei
Published: December 2024

In order to realize the automatic location of urban and rural emergency shelters, the optimal location method of urban and rural emergency shelters based on the improved ant colony algorithm is proposed. Combined with the method of urban and rural land block grid space planning, the spatial grid planning of urban and rural distribution and the method of remote sensing image covering big data detection are adopted to extract the spatial grid geospatial detection model of urban and rural emergency shelters. The remote sensing images of urban and rural emergency shelters and the wireless sensor information sampling method of unmanned aerial vehicle (UAV) are adopted to establish the remote sensing images of urban and rural emergency shelters and the monitoring map database of UAV wireless sensor information. The improved ant colony optimization algorithm is adopted to optimize the path in the process of urban and rural emergency shelters’ site selection, and the overall geographical and geometric characteristics of urban and rural emergency shelters’ site selection are analyzed. The texture, color, shape and other characteristics related to the location of urban and rural emergency shelters are extracted, and the distributed optimization control method of dynamic ant colony is adopted to detect the distribution of geometric deformation characteristics and optimize the location of urban and rural emergency shelters. The location is realized by spatial enhancement of remote sensing dynamic information and the location of spatial feature points. The simulation results show that this method has a good positioning ability and a strong ability to optimize the location of emergency shelters in urban and rural areas.
Training Model of Students’ English Creative Thinking Ability Based on Big Data Analysis
Authors: Dejun Wang
Published: December 2024

In order to optimize the evaluation of students’ English creative thinking ability under the multimedia assisted teaching mode, a training model of students’ English Creative Thinking Ability Based on big data analysis is proposed. Establish a big data block fusion structure analysis model, analyze the fuzzy parameters of constraint indicators, obtain a quantitative table for the state allocation of evaluation constraint indicators, formulate an evaluation quantitative analysis scoring table, and use hierarchical structural reconstruction to construct the clustering model parameters for the evaluation of students’ English creative thinking ability training. Through the quantitative index feature analysis, the optimization design of teaching activities and teaching methods for the evaluation of the cultivation process of students’ English creative thinking ability are realized. In the multi-dimensional hierarchical structure parameter model, the parameter configuration of students’ English creative thinking ability training indicators is realized, and the evaluation characteristic index fusion clustering processing is carried out on the parameter configuration results to form a pair of classification prediction and index analysis model. According to the hierarchical distribution density and grid clustering of indicators, the evaluation of students’ English creative thinking ability under the multimedia-assisted teaching mode is realized. The results of empirical analysis show that the fuzzy parameter analysis ability of the constraint index of process evaluation using this method is strong, the evaluation results are accurate and reliable, and the reliability and confidence level of evaluation are improved.
Research on Robot SLAM Technology Based on Semantic Information in Dynamic Environment
Authors: Shengqian Li , Hao Tang , Xiaofan Zhang
Published: December 2024

Aiming at the problems of low positioning accuracy, poor map readability and weak robustness when the mobile robot implements the SLAM technology due to the existence of dynamic objects in the mobile robot working environment, an SLAM technology algorithm for mobile robot dynamic environment positioning based on semantic information is proposed. Firstly, the front end of the ORB-SLAM2 framework will be used, combined with the YOLO v4 target detection algorithm, to extract the ORB features of the input image. Meanwhile, the YOLO v4 target detection algorithm is used to obtain the dynamic and static areas of the object containing semantic information in the scene image, to obtain the preliminary semantic dynamic and static areas, and to perform semantic segmentation in the image; Then, the dynamic object region is selected by using the image polar equation, and the ORB feature points distributed on the dynamic object are eliminated. Finally, the processed feature points and adjacent key frames are used for inter-frame matching to estimate the camera pose to build a static environment map. The experiment used the open TUM dataset to compare the proposed algorithm with the traditional ORB-SLAM2 test results show that the proposed algorithm improves the pose estimation accuracy by 75% compared with the ORB-SLAM2 in the dynamic environment, and the map construction effect is significantly enhanced. The experimental results show that this algorithm can eliminate the influence of dynamic objects on SLAM technology, improve the positioning accuracy of the system, and expand the application field of the system.
Research on BP Neural Network Prediction of Financial Transformation and Upgrading Potential of Enterprises in the Digital and Intelligent Era
Authors: Jingnan Zuo
Published: December 2024

In order to reduce the operational risks of enterprises and help enterprises improve their core competitiveness, this paper studies the BP neural network prediction method for the financial transformation and upgrading potential of enterprises in the age of digital intelligence. Select the sample data of enterprises from Shanghai and Shenzhen Stock Exchanges, use the financial data of these enterprises to build an indicator of the potential for financial transformation and upgrading of enterprises, and use the nonparametric test method to eliminate the duplicate content in the indicator, and provide the tested indicators as input to the BP neural network model. Under the prediction of the model, obtain the potential for financial transformation and upgrading of different enterprises. The model test results show that the prediction method has a high fitting effect, which can divide the company’s financial transformation and upgrading potential into three states: poor, ordinary, and high, while maintaining accurate prediction ability.
Construction of College Students’ Music Interest Platform Based on Internet+
Authors: Yuan Fang
Published: December 2024

In the context of the gradual improvement of the degree of personalized development, College Students’ interest in music shows a diversified trend, which makes the construction of relevant music platforms fully consider the actual needs of individual users and recommend resources corresponding to their interests. This paper puts forward the research on the construction of College Students’ music interest platform based on Internet [Formula: see text]. On the premise of analyzing the value proposition of the cultural industry platform model and the emphasis on customer goals under the background of “Internet [Formula: see text]”, this paper designs a platform structure including resource management module, College Students’ user management module, resource recommendation module and resource startup module. The big data technology under the background of “Internet [Formula: see text]” is used to analyze the MIDI audio characteristics and user behavior characteristics of resources. The binary community is used to establish the matching relationship between the two, determine the recommended resources, and introduce the heat parameter to avoid the cold start problem. The test results show that the processing efficiency and response time of the design platform for user behavior and user requests can meet the needs of practical applications, and has high stability. The collection map, Download map and share map of the recommended results of the platform are the best outputs in the test stage.
Research on Marketing Prediction Model for Cross-Border E-Commerce Enterprises Based on Artificial Intelligence Algorithms
Authors: Kang Yuanhua
Published: December 2024

Understanding consumer behavior and predicting market trends are critical for enterprises looking to advance their innovation in the rapidly changing world of Cross-border E-commerce (CBEC). To advance the logistical facility occurrence for consumers and optimize inventory organization, e-commerce businesses are focused on using Artificial Intelligence (AI) approach to amplify the accuracy of sales prediction. This study proposes a marketing prediction model that employs Enhanced Tabu Search Optimized Dynamic Gradient Boosting Machines (ETSO-DGBM) to aid CBEC businesses in building enhanced assessments. The CBEC enterprises’ data were collected and used for predicting their marketing approach. To advance the capability of the model to predict, pre-processing procedures, including normalization, are used to certify data stability and Independent Component Analysis (ICA) assists in extracting features. The result illustrates that the ETSO-DGBM marketing prediction model develops forecasting accuracy significantly. When assessing with traditional techniques, the proposed ETSO-DGBM model performs better. The method assists organizations in predicting their marketing strategy to gather the diverse demands of different segments by providing perceptive information about customer behavior. It uses AI approach to extend adaptable marketing programs for CBEC companies, facilitating informed decision-making and promoting expansion and profitability in international markets. It underscores the significance of AI in marketing strategies to handle cross-border customer behavior.
Effective field theory for a singlet scalar extension of the standard model and electroweak phase transition
Authors: Kang Sin Kyu
Published: December 2024

In this paper, we study an effective field theory (EFT) for a singlet scalar extension of the standard model (SM). The tree- and one-loop level effective actions are derived by integrating out the heavy singlet scalar in the Lagrangian for the UV theory and by applying to the functional method, respectively. In the UV model, the light scalar field identified with the SM Higgs is assumed to be massless in the symmetric phase. When the heavy singlet scalar acquires a vacuum expectation value, the [Formula: see text] symmetry is broken and a mass term of the light scalar is induced. In light of EFT we derive, the electroweak vacuum resulting in 126[Formula: see text]GeV Higgs mass can be attained when the induced squared-mass term of the light scalar in the scalar potential becomes negative. This condition can occur when the constant of the scalar coupling between the two different scalars is allowed to be negative. We investigate how the EFT can be constrained by theory and experiments. We also examine whether or not electroweak phase transition (EWPT) in this scenario can be a first order. Some numerical results leading to the first-order EWPT while satisfying the constraints studied are presented.
Nonautonomous Single Inertial Neuron Model: Coexisting Patterns, Hamilton Energy, and Analog Circuit Implementation
Authors: Zhao Shuang , Chuah Joon Huang , Khairuddin Anis Salwa Mohd , Zhang Yunzhen , Chen Chengjie
Published: December 2024

The main objective of this study is to investigate the chaotic dynamics and analog circuit implementation of a nonautonomous single inertial neuron. First, the dimensionless mathematical model of such neuron is established. The equilibria with three kinds of stabilities are then depicted, and system symmetry and Hamilton energy function are analyzed, respectively. In numerical simulations, by using the two-dimensional/one-dimensional bifurcation diagrams, Lyapunov exponent spectra, phase plots, and basins of attraction, bifurcation of coexisting bubbles as well as bi-stable patterns are revealed, where the generated coexisting attractors are symmetric about the origin. In addition, we confirm that the complexity of the basins of attraction deteriorates with the increase of the frequency parameter. Finally, analog circuit experiments with few analog circuit elements are deployed, via which the bi-stable patterns of the model are well verified.
3D Generating Surfaces in a Quartic Hamiltonian System with Three Degrees of Freedom – I
Authors: Katsanikas Matthaios , Montoya Francisco Gonzalez , Wiggins Stephen
Published: December 2024

In our earlier research [ Katsanikas & Wiggins ,  2021a , 2021b , 2023a , 2023b , 2024a , 2024b , 2024c ], we developed two new approaches for building up dividing surfaces in the phase space of Hamiltonian systems with three or more degrees of freedom. These surfaces were derived either from periodic orbits or from 2D or 3D generating surfaces in the phase space. Our previous work extended the realization of these dividing surfaces into more intricate forms, such as tori or cylinders, situated within the constant energy manifold of the Hamiltonian system. In those studies, we utilized the above-mentioned surfaces in the setting of a three-degrees-of-freedom quadratic normal form Hamiltonian system. This series of papers extends our findings to 3D generating surfaces for a three-degrees-of-freedom quartic normal form Hamiltonian system. The focus of this paper is to introduce the first method for constructing 3D generating surfaces.
3D Generating Surfaces in a Quartic Hamiltonian System with Three Degrees of Freedom – II
Authors: Katsanikas Matthaios , Montoya Francisco Gonzalez , Wiggins Stephen
Published: December 2024

In our previous studies [ Katsanikas & Wiggins ,  2021a ,  2021b , 2023a , 2023b , 2024a , 2024b , 2024c ], we presented two methods for building up dividing surfaces based on either periodic orbits or 2D/3D generating surfaces, specifically for Hamiltonian systems with three or more degrees of freedom. These papers extended these dividing surface constructions to allow for more complex forms, such as tori or cylinders, embedded within the energy surface of the Hamiltonian system. These studies were applied to a quadratic normal form Hamiltonian system with three degrees of freedom. This series of papers extends our findings to 3D generating surfaces for three degrees of freedom quartic Hamiltonian systems. This paper focuses on the second approach for constructing these 3D generating surfaces.
A Novel Gray-Scaled Threshold-Free Recursive Analysis Method: Revealing Tribological Behaviors and Identifying Frictional Running-In State
Authors: Sun Guo-Dong , Wang Hai-Sheng , Wang Yu-Ting , Jian Bo-Bo
Published: December 2024

The aim of this study is to introduce a novel threshold-free recursive analysis method for friction-induced signals and explore its potential application in the identification of running-in state. To address some issues from recursive analysis results being influenced by the recursive threshold, a grayscale threshold-free recursive metric is investigated to measure the recursive characteristics in a tribosystem. Additionally, the visualization and quantitative measure extraction of the gray-scaled recursive matrix are discussed. Afterward, the friction-induced torque signals throughout the running-in process are extracted and then characterized by the proposed method. The results demonstrate that the morphology and quantitative features of the gray-scale recursive plots effectively reveal tribological behaviors and are closely related to the frictional running-in states. Therefore, the proposed method can be considered a reliable approach for characterizing the recursive properties of a tribosystem and identifying running-in states.
Bifurcation and Exact Solutions of a Concatenation Physical Model
Authors: Jin Xianghua , Chen Guanrong , Li Jibin
Published: December 2024

To find the exact explicit solution of the concatenation model, the corresponding differential system of the amplitude component is used, which is a planar dynamical system with a singular straight line. In this paper, by using the techniques from dynamical systems and singular traveling wave theory developed by Li and Chen [ 2007 ], its corresponding traveling wave system is solved and analyzed, obtaining the corresponding phase portraits and showing the dynamical behavior of the amplitude component. Under different parameter conditions, exact explicit solitary wave solutions, periodic wave solutions, kink and anti-kink wave solutions, compacton solutions, as well as peakons and periodic peakons, are found explicitly.
ADVANCING SUSTAINABLE AGRICULTURE IN KSA USING DEEP LEARNING-DRIVEN AUTOMATED PEPPER LEAF DISEASE RECOGNITION MODEL
Authors: ALABDULKREEM EATEDAL , ALRUWAIS NUHA , MARAY MOHAMMED , ALABDAN RANA , ASKLANY SOMIA A. , MOHAMED ABDULLAH
Published: December 2024

Food security is a national strategic plan for Saudi Arabia and a part of Saudi Vision 2030. Focusing on an increased area under production to reduce import dependency on foods and, ultimately, food security has led to higher domestic production of food crops. Pepper is cultivated worldwide, and several farmer’s subsistence depends on these crops. Unfortunately, due to lower pepper productivity, farmers involved in pepper cultivation face enormous losses caused by various pepper diseases. These losses can be avoided if the disease is detected timely and accurate. The time needed for the procedure and improper detection cannot help reduce the losses and cannot be released from the diseases. Deep learning (DL)-based automated techniques are one of the most computing techniques can be used in environmental modeling for detecting plant disease can provide promising outcomes to the users for acquiring higher accuracy within a shorter time for recognizing pepper diseases. This study designs an Automated Pepper Leaf Disease Recognition and Classification using Optimal Deep Learning (APLDRC-ODL) technique to improve sustainable agriculture in KSA. The purpose of the APLDRC-ODL technique is to enhance crop productivity and reduce crop losses in KSA via the detection of pepper leaf diseases. The APLDRC-ODL technique involves a multi-faceted approach comprising median filtering (MF)-based noise elimination and Otsu thresholding-based segmentation as a preprocessing step. At the same time, complex and intrinsic features can be generated by the utilization of the capsule network (CapsNet) model. Meanwhile, the African vulture fractals optimization algorithm (AVOA) can be applied for the optimal hyperparameter selection process. Lastly, an extreme learning machine (ELM) classifier is utilized for the detection and classification of pepper diseases. An extensive set of experiments is performed to highlight the efficiency of the APLDRC-ODL technique under the pepper leaf disease dataset. The simulation process of the APLDRC-ODL technique highlighted a superior value of 98.32% compared to existing models.
A Fully Explicit–Explicit Staggered Algorithm for the Dynamic Response of Fluid-Saturated Porous Media
Authors: Zhou Qinglong , Chen Xinyi
Published: December 2024

The analysis of the dynamic response of fluid-saturated porous media under stress waves is of great practical value in the fields of geotechnical engineering, geophysics, earthquake engineering and marine engineering. In this research, a new fully explicit finite element algorithm for solving the coupled soil-pore fluid dynamic problem is developed based on the u–p formulation. The proposed method is a staggered algorithm that is realized with the central difference method used to solve the displacement u and the improved Euler method used to solve the fluid pressure p, it has a second-order theoretical accuracy since both the central difference method and the improved Euler method are second-order methods. The correctness and efficiency of the proposed method are investigated through two numerical examples. The computational results of the one-dimensional soil column model shows that the numerical solution of the proposed algorithm agree well with the theoretical analytical solution. The differences of the computational accuracy and efficiency between the proposed method and the other existing methods are also discussed in the numerical examples, and the results show that the proposed explicit method is higher in calculation efficiency than the implicit algorithm, and its accuracy is higher than that of the existing first-order explicit method.
Band Gap Analysis and Vibration Reduction of Metamaterial Periodic Timoshenko Beam with Inertial Amplification Mechanism
Authors: Shoaib Muhammad , Wu Zhijing , Liu Long , Jing Jiping , Li Fengming
Published: December 2024

By combining the inertial amplification (IA) mechanism and Bragg scattering mechanisms, a metamaterial periodic Timoshenko beam with IA is designed to enhance the capability of flexural vibration reduction in the low-frequency range. The band structures of flexural vibration band gap (BG) are calculated using the transfer matrix method (TMM). The results based on the Euler–Bernoulli and Timoshenko beam theory are compared to show the necessity of using the Timoshenko beam model for small slenderness ratio beams. The formation mechanism of the BG of the proposed periodic Timoshenko beam with the IA mechanism is revealed theoretically. The vibration experiments with different excitation approaches are carried out to validate the theoretical results. The effects of the beam and IA mechanism parameters on the BG properties are investigated and the rules of parameter influences are discovered. It can be found that increasing the Segment II length, decreasing the Segment I and Segment III length and thickness, and increasing the IA mass and angle are beneficial for reducing low-frequency vibrations. This research will contribute to the development of innovative smart materials and structures for vibration and noise reduction.
Recurrent Neural Network Solver for Structural Motion Differential Equations
Authors: Zhao Duoyang , Zeng Sen
Published: December 2024

In the field of structural dynamic differential equations, although traditional numerical solution methods are mature, they generally have problems with high computational costs and numerous constraints. These constraints include whether the system is linear, sensitivity to time step size, limited ability to handle high-frequency responses, and limited applicability to nonlinear systems and complex dynamic environments. Therefore, this paper attempts to use a recurrent neural network (RNNs) to solve dynamic differential equations. Unlike intelligent models that require a large amount of sample data, this solver does not need samples. Instead, it constrains the loss function using the physics-informed neural network method and provides high-precision predictive solutions based on input parameters and memory parameters. The solver is applied to single-degree-of-freedom and multi-degree-of-freedom systems, including undamped and damped free vibrations as well as undamped and damped harmonic load problems. The feasibility of the model is validated based on the average relative error, with results showing that the average relative error between the model predictions and theoretical values is [Formula: see text], with a few cases reaching 1%. The relationship between network structure, learning rate, and model performance is also explored. Additionally, common activation functions perform poorly when applied to this problem, so this paper constructs a new activation function, verifying its effectiveness and efficiency through the solver and analyzing the factors influencing the model’s performance.
Mutual Information-Based Generalisation Gap Analysis Using Deep Learning Model
Authors: Bhuyan Hemanta Kumar , Unhelkar Bhuvan , Shankar S. Siva , Chakrabarti Prasun
Published: December 2024

Most deep learning models face difficulties in analysing image information due to the concept of information bottlenecks and their corresponding methodologies. But, the information bottleneck is used for discarding redundant data and trying to maximise in favour of data directly relevant to the task-oriented information. However, managing information bottlenecks is challenging in the learning model process. Although convolutional neural networks are designed for small-scale processing, their inductive bias makes it difficult to learn contextual features. Thus, we have considered the theoretical learning model to justify the advantages of information bottleneck in deep learning model. We tried to use a fundamental information bottleneck in the vision transformer model. The channel density module cleans up task-related data, while the collected image representations are encouraged to be diverse through local connections in cumulative local transformer blocks. We considered the encoder and decoder methods that analyse the information bottleneck techniques in the deep learning model. This paper presents a rigorous learning theory that mathematically links information bottlenecks for generalisation errors, demonstrating the usefulness of information bottlenecks in deep learning. Our approach suggests that limiting information bottlenecks is crucial for managing errors in deep learning techniques. We conducted experiments across various mathematical models and learning environments to test the validity of our new mathematical insights. In many cases, generalisation errors correspond to unwanted information at hidden levels. We have considered boundary approaches using various scaling parameters and dimensions for the degree of information bottleneck. As per the estimation loss and error by different correlation approaches using generalisation gap methods, we found Spearman correlation having loss (0.86) and error (0.758), whereas Pearson correlation having loss (0.85) and error (0.76), respectively. We also considered outputs for model compression metrics and analysed them through comparative performance.
Principle-Based Standards and Analyst Forecasts: Evidence from China
Authors: Wang Ruzhou , Ma Chensong , Wang Xuanli
Published: December 2024

Synopsis

The research problem

This paper investigated the impact of principle-based accounting standards on analyst forecasts.

Motivation

The theoretical and practical debates over whether accounting standards should be more principle-based or rule-based have persisted to this day. Existing research primarily focused on the impact of principle-based standards on managers, auditors, and investors in developed economies. However, whether principle-based standards have played the anticipated positive role in emerging markets with relatively weaker institutional environments—and particularly, how they affect another important participant in financial markets, namely, analysts—is the research gap we wished to further explore.

The test hypotheses

We tested the following hypotheses: (a) principle-based accounting standards are associated with higher analyst forecast errors; (b) managers’ earnings management incentives and a weak institutional environment exacerbate the negative impact of principle-based standards on analyst forecasts; and (c) analysts’ limited attention and effort aversion exacerbate the negative impact of principle-based standards on analyst forecasts.

Target population

Financial analysts, accounting standards setting bodies such as IASB and accounting regulators in emerging markets.

Adopted methodology

We used ordinary least squares regressions, archival data, and textual analysis methods to obtain the initial data.

Analyses

First, based on firms’ annual reports, we used text analysis methods to construct a firm-level principle-based measurement. Then, we examined the relationship between the degree of a firm’s reliance on principle-based standards and the analyst forecast errors. After obtaining the baseline result, we further discussed the possible mediating effects of accounting information quality and analysts’ subjective mindsets. Finally, we discussed the impact of principle-based standards on other forecasting attributes beyond analyst forecast errors.

Findings

Using a sample of Chinese listed firms, we found that analysts’ forecast errors are larger for firms that rely more on principle-based standards. Based on the “input-processing-output” model, we found that in the input stage, principle-based standards may become a tool for managers to manipulate accounting earnings in firms with stronger earnings management incentives or that are in weaker institutional environments, leading to higher analyst forecast error, with accounting information quality as an important mediator. During processing, analysts’ limited attention and effort aversion exacerbate the adverse impact of principle-based standards on forecast quality, while analysts’ experience can mitigate this effect. For output results, principle-based standards are also associated with higher forecast dispersion, longer forecast lag, and more forecast revisions. Collectively, our research uncovered some dark sides of principle-based standards in weak institutional environments.
Enhancing structural, thermal, and electrical properties of plasticized lithium-based PVdF-HFP polymer electrolytes for flexible energy storage applications
Authors: Gebreyesus Merhawi Abreha , Mettu Maheshwar Reddy , Mallikarjun Anna , Mahendrakar Sangeetha , Siva Kumar J.
Published: December 2024

Lithium ion-conducting plasticized polymer electrolyte membranes were prepared using poly (vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP) as the host polymer, lithium triflate (LiTf) as the dopant salt, and ethylene carbonate (EC) as the plasticizer, with varying compositions accomplished using the solution casting technique. X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and differential scanning calorimetry (DSC) analyses validate the presence of substantial structural alterations and the formation of a complex in the plasticized polymer electrolytes. The electrochemical impedance spectroscopy (EIS) analysis demonstrates an increase in ionic conductivity by about two orders of magnitude after the addition of EC. Estimates of ionic transference numbers derived from the direct current (DC) polarization approach indicate that charge transport in plasticized polymer electrolytes is mostly ionic. We examined the discharge characteristics of our flexible electrochemical storage device (FESD) over 25 h. The mean discharge current is 1.4 mA with a 0.04 C rating.
PUBLIC CREDIT GUARANTEES AND BANKING SYSTEM: THE ITALIAN CASE DURING COVID-19 EMERGENCY
Authors: BANFI ALBERTO , MARCHESI ALBERTO , PAMPURINI FRANCESCA
Published: December 2024

This paper presents an in-depth examination of the impact of public support to small and medium Italian enterprises during the COVID-19 pandemic. The country’s banking system was entrusted a key role in implementing extraordinary measures of financial support, such as moratoria and public guarantees. This support prevented the sudden drop in revenues and cash flows — caused by the pandemic and the related severe restrictive measures adopted by the Italian Government — from compromising the liquidity and solvency of a huge number of firms, thus preventing the occurrence of a chain of insolvencies, which would have seriously deteriorated Italian economy. To perform these tasks, Italian banks had to cope with commitments which severely tested their organizational structure, due to a large number of operations and the complexity and urgency these tasks entailed, especially in the first months of the emergency period. The study, in a five-year perspective, analyses — in terms of intensity and pervasiveness — the set of such initiatives, both at the entire Italian banking system level and at the specific cooperative banks level, the latter characterized by greater proximity to potentially more fragile customers. The aim of this work is to verify whether the effectiveness of the Italian banking system in this key role of accomplishing the transmission of economic policy decisions, aimed at promoting and sustaining real economy during a particularly difficult period, can be confirmed.
Early Identification of Myocardial Microstructural Alterations in Hypertrophic Cardiomyopathy with in Vivo Cardiac Diffusion-Tensor Imaging
Authors: Dong Zhixiang , Tang Yun , Sun Peng , Yin Gang , Zhao Kankan , Ma Xuan , Yang Shujuan , Wang Jiaxin , Xiang Xiaorui , Yang Kai , Lu Minjie , Chen Xiuyu , Zhu Yanjie , Zhao Shihua
Published: January 2025

In hypertrophic cardiomyopathy, myocardial microstructural alterations could be detected by in vivo cardiac diffusion-tensor imaging even before the appearance of cardiac MRI findings of hypertrophy or fibrosis.
Supplementary Figure S7 from N-Linked Fucosylated Glycans Are Biomarkers for Prostate Cancer with a Neuroendocrine and Metastatic Phenotype
Authors: Ippolito Joseph E. , Hartig Jordan P. , Bejar Kaitlyn , Nakhoul Hani , Sehn Jennifer K. , Weimholt Cody , Grimsley Grace , Nunez Elena , Trikalinos Nikolaos A. , Chatterjee Deyali , Kim Eric H. , Mehta Anand S. , Angel Peggi M. , Troyer Dean A. , Leach Robin J. , Corey Eva , Wu Jennifer D. , Drake Richard R.
Published: January 2025

Figure S7. Example of adenocarcinoma with NE features.
Supplementary Figure S4 from Loss of PI5P4Kα Slows the Progression of a Pten Mutant Basal Cell Model of Prostate Cancer
Authors: Triscott Joanna , Lehner Marika , Benjak Andrej , Reist Matthias , Emerling Brooke M. , Ng Charlotte K.Y. , de Brot Simone , Rubin Mark A.
Published: January 2025

Figure S4. Top ten differentially expressed genes in scRNA-Seq clusters.
Figure S1 from c‐Myc, AKT, Hsc70, and the T-Box Transcription Factor TBX3 Form an Important Oncogenic Signaling Axis in Breast Cancer
Authors: Ncube Stephanie M. , Nagarajan ArulJothi , Lang Dirk , Sinkala Musalula , Burmeister Carly A. , Serala Karabo , Blackburn Jonathan , Prince Sharon
Published: January 2025

Figure S1: Establishment of FLAG-TBX3 ER+ breast cancer cells for mass spectrometry.
Table S12 from The Distinct Roles of NEIL1 and XPA in Limiting Aflatoxin B1–Induced Mutagenesis in Mice
Authors: Luzadder Michael M. , Minko Irina G. , Vartanian Vladimir L. , Davenport Marten , Fedorov Lev M. , McCullough Amanda K. , Lloyd R. Stephen
Published: January 2025

Mutation frequencies in target regions in aflatoxin-exposed mice
Table S11 from The Distinct Roles of NEIL1 and XPA in Limiting Aflatoxin B1–Induced Mutagenesis in Mice
Authors: Luzadder Michael M. , Minko Irina G. , Vartanian Vladimir L. , Davenport Marten , Fedorov Lev M. , McCullough Amanda K. , Lloyd R. Stephen
Published: January 2025

Mutation frequencies in target regions in unexposed mice
The role of reactive oxygen species in the pathogenesis of alopecia areata: a systematic review and meta-analysis.
Authors: Perlmutter Jonah , Akouris Polycronis P. , Fremont Sierra , Yang Brian , Toth Evan , Eze Michael , Wiseman Marni
Published: January 2025

Background: Alopecia areata (AA) is a T-cell-mediated autoimmune disease that significantly
impacts patient quality of life. The breakdown of hair follicle immune privilege underlies AA
pathogenesis. However, the precise mechanism of this breakdown remains unclear. This
study investigates the potential role of reactive oxygen species in AA pathogenesis.

Summary: A systematic review and meta-analysis were conducted on observational studies and
randomized controlled trials from 2000 to 2024. Studies included AA patients and measured
oxidative stress index (OSI), malondialdehyde (MDA), superoxide dismutase (SOD), glutathione
peroxidase (GSH-Px), or paraoxonase-1 (PON1). Extracted data were analyzed using the
Cochrane risk-of-bias tool and random-effects models.The review included 21 studies with 743 AA patients. OSI was elevated in AA patients (effect size = 1.58, 95% CI [0.31–2.68], p = 0.00068). MDA levels were also elevated (effect size = 1.60, 95% CI [0.43–2.6], p = 0.00023), while SOD (effect size = -0.97, 95% CI [-1.65 to -0.30], p = 0.00066) and GSH-Px (effect size = -1.41, 95% CI [-2.28 to -0.53], p = 0.00068) activities were reduced. PON1 levels showed no significant difference (effect size = -3.56, 95% CI [-8.63 to 1.51], p = 0.051).

Key Messages: The elevated OSI, MDA, and decreased antioxidant activity in AA patients
suggest a substantial role for reactive oxygen species and oxidative stress in AA
pathogenesis, highlighting oxidative stress as a potential target for therapeutic intervention.
These results underscore the importance of oxidative stress in AA and support further research
into antioxidant-based therapies.
Ophthalmological and Orthoptic Findings in Down Syndrome: Is Genotype-Phenotype Correlation Possible?
Authors: Koyuncuoglu Melisa Akgoz , Taylan Sekeroglu Hande , Urel Demir Gizem , Simsek Kiper Ozlem , Karakaya Jale , Utine Gulen Eda
Published: January 2025

Purpose: This is a preliminary study to investigate a feasible genotype-phenotype correlation by defining ophthalmological findings in different genotypes of Down syndrome (DS). Methods: The study included 62 eyes of 31 DS patients. Patients were further subgrouped according to cytogenetic forms of DS. A comprehensive ophthalmological examination was performed and, biometric, keratometric, and pachymetric parameters were evaluated. Results: The mean best-corrected visual acuity (BCVA) of trisomy 21 was 0.41±0.14 (0.1-0.6), and was 0.6±0.09 (0.5-0.7) for both mosaic and translocation trisomy 21 (p=0.004). While 6 of the trisomy 21 patients (24%), 2 of the mosaic patients (66.7%), and all of the three translocation type patients had a normal accommodation response, the remaining patients had accommodation lags (p=0.013). Conclusions: Lens opacities and fundus abnormalities were more common in trisomy 21 (p<0.001). The angle kappa was larger in trisomy 21 and smallest in the translocation trisomy 21 (p=0.014). K2 and corneal apex curvature were highest in trisomy 21 (p=0.05 and p=0.006, respectively). BCVA and accommodation response were reduced whereas lenticular opacities and fundus abnormalities were more common in trisomy 21. In addition, central cornea was steeper and angle kappa was larger. Further studies with larger cohorts would display differences among subgroups of cytogenetic abnormality.