Current Models Research Articles

A Comprehensive Review of Preclinical Models for Polycystic Ovary Syndrome

Background: Polycystic ovary syndrome (PCOS) is a reproductive, metabolic, and endocrine disorder with unclear aetiology. PCOS, the most common cause of female reproductive and metabolic disorders, is known to affect more than one in ten women globally. PCOS and associated clinical manifestations are probably underdiagnosed despite their high occurrence. Objective: Alternative animal models have been employed to investigate the causes of PCOS or assess potential treatments. In light of this piece of information, it is challenging to create an animal model that accurately captures all components of this condition; nonetheless, the resemblance of an animal model's biology and/or biochemical characteristics to the phenotypes of PCOS in humans may boost its applicability. Result: The key characteristics of these models are closer to human situations when compared to women with PCOS, as shown by this comparison. The creation and testing of drugs for the treatment of PCOS are necessary. Conclusion:: The overview of PCOS, current preclinical models, and appropriate models chosen in different studies to mimic various phenotypes in PCOS studies are all covered in this review paper. Additionally, we have outlined the benefits and drawbacks of PCOS animal models.

Generalized Geographically Weighted Regression Model within a Modularized Bayesian Framework.

Geographically weighted regression (GWR) models handle geographical dependence through a spatially varying coefficient model and have been widely used in applied science, but its general Bayesian extension is unclear because it involves a weighted log-likelihood which does not imply a probability distribution on data. We present a Bayesian GWR model and show that its essence is dealing with partial misspecification of the model. Current modularized Bayesian inference models accommodate partial misspecification from a single component of the model. We extend these models to handle partial misspecification in more than one component of the model, as required for our Bayesian GWR model. Information from the various spatial locations is manipulated via a geographically weighted kernel and the optimal manipulation is chosen according to a Kullback-Leibler (KL) divergence. We justify the model via an information risk minimization approach and show the consistency of the proposed estimator in terms of a geographically weighted KL divergence.

An Exploration of Organoid Technology: Present Advancements, Applications, and Obstacles.

Organoids are in vitro models that exhibit a three-dimensional structure and effectively replicate the structural and physiological features of human organs. This work examines the potential applications of organoid technology, as well as the challenges and future directions associated with its implementation. The manuscript was put together by conducting a comprehensive literature review, which involved an in-depth evaluation of globally renowned scientific research databases. The field of organoids has generated significant attention due to its potential applications in tissue development and disease modelling, as well as its implications for personalised medicine, drug screening, and cell-based therapies. The utilisation of organoids has proven to be effective in the examination of various conditions, encompassing genetic disorders, cancer, neurodevelopmental disorders and infectious diseases. The exploration of the wider uses of organoids is still in its early phases. Research shall be conducted to integrate 3D organoid systems as alternatives for current models, potentially improving both fundamental and clinical studies in the future.

Modeling and Analysis of Turbidity Currents in a Reservoir with the Dredged Guiding Channel

Modeling and Analysis of Turbidity Currents in a Reservoir with the Dredged Guiding Channel

A novel ballistic model for the subthreshold behavior of nanosheet transistors

A novel ballistic model for the subthreshold current of nanosheet transistors is successfully developed based on the Landauer approach with the three-dimensional number of channels. The ballistic threshold voltage can also be achieved through the calculated free charge density induced by the three-dimensional density-of-states equal to the substrate doping concentration. It indicates that under the low drain voltage corresponding to the Fermi distribution function, the subthreshold current is mainly governed by the low contact potential. However, under the high drain voltage corresponding to the Fermi distribution function, the thermal voltage, instead of the contact potential between the source and drain, initiates the subthreshold current. Besides subthreshold current and threshold voltage, the ballistic conductance and subthreshold swing are also revealed in the subthreshold conduction. It indicates that the thin silicon, thin gate oxide, heavy substrate doping density, and high work function will alleviate the ballistic effects, decrease the subthreshold current/swing, and increase the threshold voltage/ballistic resistance.

An active learning framework for the rapid assessment of galvanic corrosion

The current present in a galvanic couple can define its resistance or susceptibility to corrosion. However, as the current is dependent upon environmental, material, and geometrical parameters it is experimentally costly to measure. To reduce these costs, Finite Element (FE) simulations can be used to assess the cathodic current but also require experimental inputs to define boundary conditions. Due to these challenges, it is crucial to accelerate predictions and accurately predict the current output for different environments and geometries representative of in-service conditions. Machine learned surrogate models provides a means to accelerate corrosion predictions. However, a one-time cost is incurred in procuring the simulation and experimental dataset necessary to calibrate the surrogate model. Therefore, an active learning protocol is developed through calibration of a low-cost surrogate model for the cathodic current of an exemplar galvanic couple (AA7075-SS304) as a function of environmental and geometric parameters. The surrogate model is calibrated on a dataset of FE simulations, and calculates an acquisition function that identifies specific additional inputs with the maximum potential to improve the current predictions. This is accomplished through a staggered workflow that not only improves and refines prediction, but identifies the points at which the most information is gained, thus enabling expansion to a larger parameter space. The protocols developed and demonstrated in this work provide a powerful tool for screening various forms of corrosion under in-service conditions.

Zero-Resistance-Branch Electrical Drainage System to Solve the Adverse Influence of Stray Current on Buried Pipelines

The buried pipeline's ground potential may be seriously shifted by the stray current leaking from the rail transit, leading to the problem of severe electrochemical corrosion on buried pipeline. For this, a zero-resistance- branch electrical drainage system is proposed to solve the adverse influence of stray current on buried pipeline just like the APF technology in power systems, mainly consisting of a negative resistance compensating converter (NRCC) and a grounding electrode, which can directly drain the stray current to the soil without connecting the rail system. Moreover, the equivalent interference circuit model of stray current is established, and the corrosion rule is analyzed in detail. Furthermore, based on the NRCC, the zero-resistance active drainage control method is proposed to realize the absorption of stray current. As the optimization objective of the control method, the disturbance component of pipe-to-soil potential is regulated to be approximately 0, and the zero-resistance branch at the disturbance frequency can be realized without directly detecting the value of the stray current. Then, the stray current can be transferred to the new drainage branch to mitigate its electrochemical corrosion on buried pipelines. Finally, the experimental results verify the effectiveness of the proposed electrical drainage system.

Static and dynamic conductivity of amorphous nanogranular composites (CoTaNb)x(MgO)1-x

The paper presents the results of study of amorphous nanogranular composite films CoTaNbxMgO1−x, (21.73≤ x ≤73.27 at. %) with a thickness of 311–1040nm on a lavsan substrate with a thickness of about 20μm. The results of experimental studies of microstructure, static conductivity (at direct current), dynamic conductivity (50kHz – 15MHz), determined from measurements of the total impedance and phase angle, and dynamic microwave conductivity (8–12GHz), calculated from measurements microwave reflectance, were presented. The spectra of dynamic conductivity as a function of frequency, the dependence of static conductivity and the ratio of dynamic to static conductivity on the content of the metal phase and on the ratio of the sizes of granules to the spaces between granules were obtained. Using the mechanisms of closed and open circuit models of intragranular currents and an algorithm for calculating structural and conductive characteristics from measured values of microwave reflectance, the conductivity of granules was calculated and its contribution to the dynamic microwave conductivity of composites was assessed.

Weight Decay With Tailored Adam on Scale-Invariant Weights for Better Generalization.

Weight decay (WD) is a fundamental and practical regularization technique in improving generalization of current deep learning models. However, it is observed that the WD does not work effectively for an adaptive optimization algorithm (such as Adam), as it works for SGD. Specifically, the solution found by Adam with the WD often generalizes unsatisfactorily. Though efforts have been made to mitigate this issue, the reason for such deficiency is still vague. In this article, we first show that when using the Adam optimizer, the weight norm increases very fast along with the training procedure, which is in contrast to SGD where the weight norm increases relatively slower and tends to converge. The fast increase of weight norm is adverse to WD; in consequence, the Adam optimizer will lose efficacy in finding solution that generalizes well. To resolve this problem, we propose to tailor Adam by introducing a regularization term on the adaptive learning rate, such that it is friendly to WD. Meanwhile, we introduce first moment on the WD to further enhance the regularization effect. We show that the proposed method is able to find solution with small norm and generalizes better than SGD. We test the proposed method on general image classification and fine-grained image classification tasks with different networks. Experimental results on all these cases substantiate the effectiveness of the proposed method in help improving the generalization. Specifically, the proposed method improves the test accuracy of Adam by a large margin and even improves the performance of SGD by 0.84% on CIFAR 10 and 1.03 % on CIFAR 100 with ResNet-50. The code of this article is public available at xxx.

Denoised Non-Local Neural Network for Semantic Segmentation.

The non-local (NL) network has become a widely used technique for semantic segmentation, which computes an attention map to measure the relationships of each pixel pair. However, most of the current popular NL models tend to ignore the phenomenon that the calculated attention map appears to be very noisy, containing interclass and intraclass inconsistencies, which lowers the accuracy and reliability of the NL methods. In this article, we figuratively denote these inconsistencies as attention noises and explore the solutions to denoise them. Specifically, we inventively propose a denoised NL network, which consists of two primary modules, i.e., the global rectifying (GR) block and the local retention (LR) block, to eliminate the interclass and intraclass noises, respectively. First, GR adopts the class-level predictions to capture a binary map to distinguish whether the selected two pixels belong to the same category. Second, LR captures the ignored local dependencies and further uses them to rectify the unwanted hollows in the attention map. The experimental results on two challenging semantic segmentation datasets demonstrate the superior performance of our model. Without any external training data, our proposed denoised NL can achieve the state-of-the-art performance of 83.5% and 46.69% mean of classwise intersection over union (mIoU) on Cityscapes and ADE20K, respectively.

Dynamic Characterization of SynRM With Dual-Axis Hybrid Excitation Self-Commissioning

The synchronous reluctance machine (SynRM) has gained increasing attraction for future electric drive systems. The characterization of its magnetic and current dynamics is essential for high-performance model-driven control, which still remains a promising problem. In this paper, a dynamic characterization scheme for SynRM with dual axis hybrid excitation self-comissioning is proposed to characterize the magnetic and current dynamics. Inspired by separating dual-axis current variables, a compact magnetic circuit model is investigated with reciprocity analytics. Furthermore, an improved current dynamic model is derived with resulting analytical apparent and incremental inductances. On this basis, the characterized parameters involved in the magnetic circuit and current dynamic models can be accurately identified by combining the designed dual axis hybrid excitation method with effective parameter identification algorithm. The effectiveness and practicability of the proposed scheme are verified based on the development platform for commercial SynRM drive. This simple dynamic characterization scheme fits for online implementation on standard drives. Compared with the existed work, 38.2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> improvement of normalized modeling accuracy for magnetic circuit can be achieved. In the meanwhile, the normalized modeling errors of apparent inductances for current dynamics are strictly less than 6.91 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Multidimensional Data Processing With Bayesian Inference via Structural Block Decomposition.

How to handle large multidimensional datasets, such as hyperspectral images and video information, efficiently and effectively plays a critical role in big-data processing. The characteristics of low-rank tensor decomposition in recent years demonstrate the essentials in describing the tensor rank, which often leads to promising approaches. However, most current tensor decomposition models consider the rank-1 component simply to be the vector outer product, which may not fully capture the correlated spatial information effectively for large-scale and high-order multidimensional datasets. In this article, we develop a new novel tensor decomposition model by extending it to the matrix outer product or called Bhattacharya-Mesner product, to form an effective dataset decomposition. The fundamental idea is to decompose tensors structurally in a compact manner as much as possible while retaining data spatial characteristics in a tractable way. By incorporating the framework of the Bayesian inference, a new tensor decomposition model on the subtle matrix unfolding outer product is established for both tensor completion and robust principal component analysis problems, including hyperspectral image completion and denoising, traffic data imputation, and video background subtraction. Numerical experiments on real-world datasets demonstrate the highly desirable effectiveness of the proposed approach.

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

This article presents a comprehensive and practical guide for practitioners and end-users working with Large Language Models (LLMs) in their downstream Natural Language Processing (NLP) tasks. We provide discussions and insights into the usage of LLMs from the perspectives of models, data, and downstream tasks. First, we offer an introduction and brief summary of current language models. Then, we discuss the influence of pre-training data, training data, and test data. Most importantly, we provide a detailed discussion about the use and non-use cases of large language models for various natural language processing tasks, such as knowledge-intensive tasks, traditional natural language understanding tasks, generation tasks, emergent abilities, and considerations for specific tasks. We present various use cases and non-use cases to illustrate the practical applications and limitations of LLMs in real-world scenarios. We also try to understand the importance of data and the specific challenges associated with each NLP task. Furthermore, we explore the impact of spurious biases on LLMs and delve into other essential considerations, such as efficiency, cost, and latency, to ensure a comprehensive understanding of deploying LLMs in practice. This comprehensive guide aims to provide researchers and practitioners with valuable insights and best practices for working with LLMs, thereby enabling the successful implementation of these models in a wide range of NLP tasks. A curated list of practical guide resources of LLMs, regularly updated, can be found at https://github.com/Mooler0410/LLMsPracticalGuide . An LLMs evolutionary tree, editable yet regularly updated, can be found at llmtree.ai .

Study of optical float-zone grown gallium oxide Schottky barrier diode

β−Ga2O3 based lateral Schottky barrier diodes are fabricated using Ni/Au as Schottky contact and Ti/Au as Ohmic contact. The Sn-doped β−Ga2O3 sample is grown by the optical float-zone technique. The effect of trenches, which are used for mesa isolation, on breakdown voltage is investigated. The device shows near-ideal characteristics in terms of built-in potential. The parasitic series and shunt resistances are extracted, and their dependency on temperature is established. Modeling of temperature-dependent reverse leakage current is demonstrated using the thermionic emission model, Poole–Frenkel emission model, and Fowler-Nordheim tunneling mechanism. The procedure to extract relevant parameters is explained in terms of bias and temperature dependency. The combined model shows excellent agreement with experimental data over a wide range of bias and temperature. The maximum electric field of 2.3 MV cm−1 is achieved.

Enhancing online health consultations through fuzzy logic-integrated attribute-based encryption system

Online health consultations are becoming more popular as a result of technological improvements. Patients routinely look for information about medical disorders online, which could jeopardize the privacy of medical records and increase the workload of healthcare professionals. Nonetheless, academics continue to be extremely concerned about issues related to the quality characteristics that relate to the current architectural models, such as energy consumption, latency, resource utilization, scalability, and packet loss. This method, however, also results in a significant strain being placed on medical experts who must sort through vast amounts of medical records to extract certain information. This paper presents a novel ciphertext policy attribute-based encryption method coupled with fuzzy logic to overcome these issues. This solution uses a hybrid structure of IPFS and blockchain to store data and enables complex bidirectional access control. Before being added to IPFS, medical records are encrypted. To ensure data integrity, related IPFS hash indexes are then added to the blockchain. Utilizing attribute-based technology, users’ data is encrypted to give them fine-grained bidirectional access control. A thorough security analysis proves the system’s resilience, especially when faced with chosen plaintext assaults inside the random oracle model. Tests for this study were conducted using 10–50 attribute sets. This paper’s technique solely makes use of a hash operation. All things considered; the study demonstrates that the proposed design is more efficient than earlier schemes. Thus, from the comparison study above, it can be concluded that the system presented in this work is more efficient. Results from simulations provide additional support for the suggested methodology by highlighting the improved computing efficiency of users as compared to baseline conventional systems. This study demonstrates how technological advancement and healthcare requirements can coexist harmoniously, paving the way for secure and effective online medical consultations that are powered by fuzzy logic.

Public opinion bunching storage model for dense graph data in social networks1

Graph data storage has a promising prospect due to the surge of graph-structure data. Especially in social networks, it is widely used because hot public opinions trigger some network structures consisting of massively associated entities. However, the current storage model suffers from slow processing speed in this dense association graph data. Thus, we propose a new storage model for dense graph data in social networks to improve data processing efficiency. First, we identify the public opinion network formed by hot topics or events. Second, we design the germ elements and public opinion bunching mapping relationship based on equivalence partition. Finally, the Public Opinion Bunching Storage(POBS) model is constructed to implement dense graph data storage effectively. Extensive experiments on Twitter datasets demonstrate that the proposed POBS performs favorably against the state-of-the-art graph data models for storage and processing.

A model for visual evacuation of people considering the effects of gravity

This paper improves the visual change-based personnel evacuation model by considering the evacuees’ gravity. Specifically, first, the new model incorporates the gravity formula in the model’s mechanic part to consider the influence of gravity. Second, the new model involves rules for determining the visual range of personnel moving in the stairwell. Third, the proposed model investigates the influence of the angle and width of the stairwell, the number of people, and other factors during personnel evacuation under the influence of gravity. The model is developed in Python and is compared with actual results, revealing that the proposed model is more realistic considering the evacuation time compared to current models. Indeed, under a fixed number of people, when the stairwell angle is less than 34°, the evacuation time decreases as the angle increases, and when the stairwell angle exceeds 34°, the evacuation time is almost unchanged. Additionally, under a fixed number of evacuees, the evacuation time decreases as the width of the stairwell increases, and due to stairwell width space redundancy, the evacuation time tends to stabilize. The results of the new model research provide reference for the design of building safety evacuation, thereby improving the safety of buildings.

Consequence of Red Blood Cells Deformability on Heat Sink Effect of Blood in a Three-Dimensional Bifurcated Vessel.

Several diseases like Sickle Cell Anemia, Thalassemia, Hereditary Spherocytosis, Malaria, and Micro-angiopathic Hemolytic Anemia can alter the normal shape of red blood cells (RBCs). The objective of this study is to gain insight into how a change in RBC deformability can affect blood heat transfer. The heat sink effect in a bifurcated vessel with two asymptotic cases (case 1: deformable and case 2: nondeformable RBCs) is being studied during hyperthermia treatment in a three-dimensional bifurcated vessel, whose wall is being subjected to constant heat flux boundary condition. Euler-Euler multiphase method along with the granular model and Kinetic theory is used to include the particle nature of RBCs during blood flow in the current model. To enhance the efficiency of the numerical model, user-defined functions (UDFs) are imported into the model from the C++ interface. The numerical model used is verified with the experimental results from (Carr and Tiruvaloor, 1989, "Enhancement of Heat Transfer in Red Cell Suspensions In Vitro Experiments," ASME J. Biomech. Eng., 111(2), pp. 152-156; Yeleswarapu et al. 1998, "The Flow of Blood in Tubes: Theory and Experiment," Mech. Res. Commun., 25(3), pp. 257-262). The results indicate that the deformability of RBCs can change both the flow dynamics and heat sink effect in a bifurcated vessel, which subsequently affects the efficacy and efficiency of the thermal ablation procedure. Both spatial and transient Nusselt numbers of blood flow with deformable RBCs are slightly higher compared to the one with nondeformable RBCs.

Long-term life prediction of wearable CFRP lower limb exoskeleton suit by applying a multi-scale FEA and time-temperature superposition

This study explores the application of Carbon Fiber Reinforced Plastics (CFRP) in lightweight design for wearable exoskeleton suits, crucial for success in industrial settings. We aimed to predict the lifespan of the exoskeleton in terms of temperature and time rather than merely confirming its ability to withstand loads. The material’s viscoelastic behavior, establishing a master curve for long-term creep compliance, was characterized using Dynamic Mechanical Analysis (DMA). We also performed mechanical tests under various temperature conditions to measure strength in the load and fiber directions and carried out statistical analysis. From the test results, we constructed the master curves considering the probability of predicting the long-term strength variations. Subsequently, A multi-scale model assessed the exoskeleton frame’s failure, revealing no failures in the current model under given load conditions, with the highest failure index of 0.7 for matrix tensile failure. Combining the predicted long-term strength results, the lifespan prediction for matrix tensile failure under a reference temperature of 50°C was approximately five years. The findings and methodology of this study can serve as a guide for predicting long-term performance and lifespan when applying CFRP to exoskeletons.

Multi-stage micrite diagenesis in the late Jurassic of the Eastern Paris Basin: petrophysical and mechanical properties for engineering purposes

Abstract The Calcaires du Barrois Formation is a succession of dominantly micritic limestone of Kimmeridgian to Tithonian age, outcropping in the eastern part of the Paris Basin. This is an active karstic aquifer of interest to Andra (French National Agency for Radioactive Waste Management), who are studying the feasibility of a deep geological repository of radioactive waste in an underground research laboratory located approximately 450 m below the surface. Surface installations of the Industrial Centre for Geological Disposal project are planned in the upstream recharge zone of the aquifer. It is of primary interest to characterize the Calcaires du Barrois Formation to provide guidelines for the planning and sizing of these facilities, to minimize the impact on the aquifer system. An integrated study was designed for this purpose, linking petrography (thin section and scanning electron microscopy), carbon and oxygen stable isotope geochemistry, X-ray diffraction, petrophysics and geomechanics. It is based on the analysis of three key cored wells penetrating the formation at different relative depths. The Calcaires du Barrois have undergone several stages of diagenesis that have defined the current properties. Unconformities associated with the Jurassic–Cretaceous transition led to prolonged early subaerial exposures during which freshwater flowed efficiently through the upper half of the formation. Through mineralogical stabilization, among other processes, microporosity was preserved in micrites in this interval consisting of clean limestone with thin marl layers. The lower half of the formation, more argillaceous, was not or was only slightly affected by this early meteoric diagenesis, and recrystallization and cementation of micrites occurred during burial diagenesis, involving chemical compaction. Later, during the return to the surface associated to the Cenozoic orogens, another phase of meteoric diagenesis affected the uppermost few metres below the outcropping portions of the formation, but without modifying significantly the previously acquired petrophysical properties. Consequently, an intra-formational boundary was progressively developed at around 75 m (from the top reference). This boundary separates (1) a lower half of the Calcaires du Barrois with dense and tight micrites, showing high Young's modulus values, and a moderate intensity of fractures, from (2) an upper half with microporous micrites showing low Young's modulus values, and almost devoid of fractures. A transitional zone of about 30 m thick, with intermediate properties, sitting above this boundary and below the only thin metre-scale macroporous grainstone level of the formation, accommodated most of the deformation linked to the Cenozoic Western European orogens and is intensively fractured. The current hydrogeological model considers a purely sedimentological boundary to delimit two sub-aquifers within the Calcaires du Barrois Formation. This will have to be reappraised since it is here demonstrated that the real boundary is located significantly higher in the formation and is inherited from a multi-stage diagenetic history. These findings will complement and influence planning for the Industrial Centre's project.