Virtual Cell Research Articles

Nondestructive EIS Testing to Estimate a Subset of Physics-based-model Parameter Values for Lithium-ion Cells

This paper is the final installment in a series of articles that collectively shows how to estimate parameter values for lumped-parameter physics-based models of lithium-ion cells without requiring cell teardown. In this paper, we leverage electrochemical impedance spectroscopy (EIS) to find estimates of all as-yet-unresolved parameter values. The characterization process regresses the measured cell impedance spectrum to exact analytic closed-form expressions of the frequency response of an extended Doyle–Fuller–Newman model to identify thirteen lumped parameters plus multiple reaction-rate constants. A nonlinear optimization algorithm performs the regression, and so it is important to provide reasonable initial parameter estimates and constraints, which we also discuss. As part of this process, the generalized distribution of realization times technique is used to isolate time constants from the two electrodes as well as to calibrate the laboratory EIS-test data. The overall methodology is studied on a virtual cell and on a laboratory cell (both having graphite//NMC chemistries). Parameter estimates found in the simulation study are highly accurate, leading us to have confidence in the values estimated for the physical cell as well.

A data-assimilation approach to predict population dynamics during epithelial-mesenchymal transition

Epithelial-mesenchymal transition (EMT) is a biological process that plays a central role in embryonic development, tissue regeneration, and cancer metastasis. Transforming growth factor-β (TGFβ) is a potent inducer of this cellular transition, comprising transitions from an epithelial state to partial or hybrid EMT state(s), to a mesenchymal state. Recent experimental studies have shown that, within a population of epithelial cells, heterogeneous phenotypical profiles arise in response to different time- and TGFβ dose-dependent stimuli. This offers a challenge for computational models, as most model parameters are generally obtained to represent typical cell responses, not necessarily specific responses nor to capture population variability. In this study, we applied a data-assimilation approach that combines limited noisy observations with predictions from a computational model, paired with parameter estimation. Synthetic experiments mimic the biological heterogeneity in cell states that is observed in epithelial cell populations by generating a large population of model parameter sets. Analysis of the parameters for virtual epithelial cells with biologically significant characteristics (e.g., EMT prone or resistant) illustrates that these sub-populations have identifiable critical model parameters. We perform a series of in silico experiments in which a forecasting system reconstructs the EMT dynamics of each virtual cell within a heterogeneous population exposed to time-dependent exogenous TGFβ dose and either an EMT-suppressing or EMT-promoting perturbation. We find that estimating population-specific critical parameters significantly improved the prediction accuracy of cell responses. Thus, with appropriate protocol design, we demonstrate that a data-assimilation approach successfully reconstructs and predicts the dynamics of a heterogeneous virtual epithelial cell population in the presence of physiological model error and parameter uncertainty.

3d-reconstruction of destructive process models using remote sensing by a group of unmanned aerial vehicles

The paper presents a novel method of volumetric reconstruction of transient destructive processes using remote sensing by a group of unmanned aerial vehicles. The study is based on the most common class of such processes like forest fires, where a fire front is a determinant, and its propagation reflects the dynamics of the process. The effects of wind, smoke and fire, turbulence and vibration, interference, distortion, and obstacles lead to uncertainty of observations, to overcome which fuzzy sets, soft sets and gray numbers were combined. A spatial model based on a recursive eight-fold subdivision of space as well as on a hierarchical structure of virtual cells is proposed, which allowed to resolve the contradictions between the accuracy and rate of reconstruction. The set of possible states of virtual cells is determined and the algorithm of their classification based on the use of a five-channel image recognition system containing infrared, two main, and two additional optical channels is proposed. An algorithm for calculating a 3D observation vector, presented by an array of confidence vectors, is proposed, which can be used to determine the gray fuzzy state of virtual cells allowing a combination of observations from different observers and refining them sequentially. The terrain where the process evolves is represented by a soft gray fuzzy set of virtual cells, which belong to a specific state at the consideration time, allowing identification of convincing, uncertain, suspicious, and negative components. The first one defines a stable core of the fire front while the second one represents its variation caused by uncertainty. The proposed method allows the reconstruction of transient spatially distributed processes of other classes, smoothing the effects of distortions and noise and ensuring acceptable performance.

Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Federated Deep Reinforcement Learning Approach

With the stringent requirement of receiving video from the unmanned aerial vehicle (UAV) from anywhere in the stadium of sports events and the significant-high per-cell throughput for video transmission to virtual reality (VR) users, a promising solution is a cell-free multi-group broadcast (CF-MB) network with cooperative reception and broadcast access-points (AP). To explore the benefit of broadcasting user-correlated decode-dependent video resources to spatially correlated VR users, the network should dynamically schedule the video and cluster APs into virtual cells for a different group of VR users with overlapped video requests. By decomposing the problem into scheduling and association sub-problems, we first introduce the conventional non-learning-based scheduling and association algorithms, and a centralized deep reinforcement learning (DRL) association approach based on the rainbow agent with a convolutional neural network (CNN) to generate decisions from observation. To reduce its complexity, we then decompose the association problem into multiple sub-problems, resulting in a networked-distributed Partially Observable Markov decision process (ND-POMDP). To solve it, we propose a multi-agent deep DRL algorithm. To jointly solve the coupled association and scheduling problems, we further develop a hierarchical federated DRL algorithm with scheduler as meta-controller, and association as the controller. Our simulation results show that our CF-MB network can effectively handle real-time video transmission from UAVs to VR users. Our proposed learning architecture is effective and scalable for a high-dimensional cooperative association problem with increasing APs and VR users. Also, our proposed algorithms outperform non-learning based methods with significant performance improvement.

Joint Channel Estimation and Data Detection in Cell-Free Massive MU-MIMO Systems

We propose a joint channel estimation and data detection (JED) algorithm for densely-populated cell-free massive multiuser (MU) multiple-input multiple-output (MIMO) systems, which reduces the channel training overhead caused by the presence of hundreds of simultaneously transmitting user equipments (UEs). Our algorithm iteratively solves a relaxed version of a maximum a-posteriori JED problem and simultaneously exploits the sparsity of cell-free massive MU-MIMO channels as well as the boundedness of QAM constellations. In order to improve the performance and convergence of the algorithm, we propose methods that permute the access point and UE indices to form so-called virtual cells, which leads to better initial solutions. We assess the performance of our algorithm in terms of root-mean-squared-symbol error, bit error rate, and mutual information, and we demonstrate that JED significantly reduces the pilot overhead compared to orthogonal training, which enables reliable communication with short packets to a large number of UEs.

Expanding a Regional Sickle Cell Disease Project ECHO® to Rapidly Disseminate COVID-19 Education.

PurposeHealthcare providers faced numerous knowledge gaps and challenges with adapting practice behaviors in light of the COVID-19 pandemic. In response, an established virtual sickle cell disease (SCD) telementoring program rapidly expanded from monthly evidence-based didactic sessions focused on medical and psychosocial complications of sickle cell disease, to additional supplemental COVID-19 sessions with emerging pandemic topics and forums for shared experiences to address this timely educational need among multidisciplinary healthcare providers.MethodsIn March 2020, the COVID-19 and Sickle Cell Disease Project ECHO® telementoring series was launched with a rapidly evolving curriculum of contemporary topics and case presentations. Topics included COVID-19 specific management for children and adults with sickle cell disease and strategies to adapt care and communication during the pandemic. Participants completed evaluations after each session.ResultsFrom March 2020 to February 2022, there were 20 COVID-19 and SCD Project ECHO® sessions held with an average of 43 participants per session, which is over a 170% increase from the average SCD ECHO monthly attendance pre-pandemic. Participants represented 21 states and 3 countries. A majority of participants (91%) self-reported significantly improved knowledge of COVID-19.ConclusionProject ECHO® is a successful educational strategy to diffuse knowledge using a virtual platform during a public health emergency, by facilitating shared learning among a community of practice that specializes in the management of sickle cell disease.

Godunov-type solutions for free surface transient flow in pipeline incorporating unsteady friction

AbstractA finite-volume second-order Godunov-type scheme (GTS) combining the unsteady friction model (UFM) is introduced to simulate free surface flow in pipelines. The exact solution to the Riemann problem calculates the mass and momentum fluxes while considering the Brunone unsteady friction factor. One simple boundary treatment with double virtual cells is proposed to ensure the whole computation domain with second-order accuracy. Results of various transient free-surface flows achieved by the proposed models are compared with exact solution, experimental data, the four-point implicit Preissmann scheme solution, as well as predictions by the classic Method of Characteristics (MOC). Results show that the proposed second-order GTS UFMs are accurate, efficient, and stable even for Courant numbers less than one and sparse grid. The four-point implicit Preissmann scheme may produce severe numerical attenuation in the case of large time steps and unsuitable weighting factors, while the MOC scheme may produce severe numerical attenuation in the case of a low Courant number and could not maintain mass conservation. The numerical simulations considering the unsteady friction factor are closer to the measured water depth variations. The effect of unsteady friction becomes more important as the initial water depth difference increases significantly.

Comparative analysis of free cooling of photovoltaics – phase change versus evaporative cooling

Free cooling of PV cells is a common research topic; it lowers the operating temperature of PV cells, resulting in higher electricity production. This article investigated free cooling techniques for the wide range of thermal properties of the PCM layer installed on the backside of the PV cell. Two options were assumed: micro-encapsulated PCM in which heat is transferred by conduction only, and macro-encapsulated PCM in which heat transfer is enhanced by convection. The effect of free cooling with PCM was compared with the evaporative cooling technique. The thermal response of virtual PV cells (vPV) was evaluated with in-situ experiments. One vPV was upgraded with a micro-encapsulated 5.2 mm thick PCM layer, another with an evaporative layer. Multi-parametric approximation models of the PV cell temperature and combined surface heat transfer coefficients were developed. Approximation models of temperatures were used for the determination of overheating hours (OHH) for the reference and evaporatively cooled vPV, while combined surface heat transfer coefficient models were used in the developed numerical model of thermal response of vPV with micro- and macro-encapsulated PCM. The required thermal properties of PCM were determined to provide the same efficacy of free cooling as an evaporative cooling technique for three selected climate conditions (Stockholm, Ljubljana, and Athens). The study shows that the free cooling of PV cells with PCM could be as efficient as evaporative cooling, but only in the hottest and coldest observed climates if the thermal conductivity of PCM is above 1.8 W/mK for micro- and above 1.2 W/mK for macro-encapsulated PCM, and the latent heat capacity is above 250 kJ/kg. In the milder climate, free cooling will not be as efficient as evaporative cooling (for the defined ranges of thermal properties). It was determined that for such a climate, the additional heat transfer resistance of the PCM layer increases overheating of PV cells if the PCM's thermal conductivity is below 0.4 W/mK and the latent heat capacity is below 150 kJ/kg.

Derivation of a Human In Vivo Benchmark Dose for Bisphenol A from ToxCast In Vitro Concentration Response Data Using a Computational Workflow for Probabilistic Quantitative In Vitro to In Vivo Extrapolation.

A computational workflow which integrates physiologically based kinetic (PBK) modelling; global sensitivity analysis (GSA), Approximate Bayesian Computation (ABC), Markov Chain Monte Carlo (MCMC) simulation and the Virtual Cell Based Assay (VCBA) for the estimation of the active, free in vitro concentration of chemical in the reaction medium was developed to facilitate quantitative in vitro to in vivo extrapolation (QIVIVE). The workflow was designed to estimate parameter and model uncertainty within a computationally efficient framework. The workflow was tested using a human PBK model for bisphenol A (BPA) and high throughput screening (HTS) in vitro concentration-response data, for estrogen and pregnane X receptor activation determined in human liver and kidney cell lines, from the ToxCast/Tox21 database. In vivo benchmark dose 10% lower confidence limits (BMDL10) for oral uptake of BPA (ng/kg BW/day) were calculated from the in vivo dose-responses and compared to the human equivalent dose (HED) BMDL10 for relative kidney weight change in the mouse derived by European Food Safety Authority (EFSA). Three from four in vivo BMDL10 values calculated in this study were similar to the EFSA values whereas the fourth was much smaller. The derivation of an uncertainty factor (UF) to accommodate the uncertainties associated with measurements using human cell lines in vitro, extrapolated to in vivo, could be useful for the derivation of Health Based Guidance Values (HBGV).

English Education Translation System Based on 5G Network Virtualization

In order to improve the effect of English education translation, this paper improves the 5G network virtualization algorithm and uses the typical correlation analysis method in data mining to extract the correlation relationship between the data content contained in the massive English translation user equipment. Furthermore, this paper adopts the similarity-based clustering method to realize the self-organization construction of English translation virtual community. On this basis, this paper proposes a spectrum allocation algorithm based on convex optimization, which effectively solves the problem of spectrum allocation in virtual cells. Finally, this paper constructs the functional architecture of the English education translation system based on 5G network virtualization and conducts experimental research. The experimental research results show that the English education translation system based on 5G virtualization constructed in this paper can play an important role in smart English teaching.

Etika Profesi Konselor dalam Layanan Bimbingan Konseling Virtual

The purpose of this study was to describe the professional ethics of counselors in virtual counseling guidance services. The method used in this research is a literature study with a qualitative descriptive approach. The data sources come from document studies in the form of books and previous research from various references. Furthermore, the data analysis technique used is descriptive analysis. The results of this study indicate that online counseling takes the form of virtual counseling technology, namely, websites, telephone or cell phones, email, video conferencing, chat, instant messaging, computers, and other social networks. The ethical aspects that must be obeyed by online counselors are building good relationships on the Internet, confidentiality when conducting consultations, legal aspects of telecommunications via the Internet, and licensing. Therefore, online counselors need to be professional in conducting virtual counseling by considering ethics.

Screening and bioavailability evaluation of anti-oxidative selenium-containing peptides from soybeans based on specific structures.

Our previous study has evaluated the antioxidant capacity and identified the sequences of soybean selenium-containing peptides. Herein, pharmacophore screening, gastrointestinal simulation and in vivo pharmacokinetics were performed to predict the potentials of selenium-containing peptides in terms of antioxidant activity, safety and bioavailability. A pharmacophore model with 6 structure features was constructed for virtual screening to determine the potential activities of 85 selenium sequences from soybean peptides. Strong reversing effects (p < 0.05) of the targeted sequences were observed in tumor necrosis factor-α (TNF-α)-induced inflammatory cytokines and adhesion factors burst in EA·hy926/Caco-2 co-culture cell models. Ser-Phe-Gln-SeMet (SFQSeM), a promising peptide selected from both virtual screening and cell models, was proved to be stable in the gastrointestinal tract and could be transported across the Caco-2 monolayer via the paracellular pathway. Additionally, SFQSeM showed a long residence time (89.42 ± 1.34 min) and half-life (81.60 ± 11.88 min) after consumption, and it induced lower liver alanine/aspartate transaminase (ALT/AST) and serum nitric oxide (NO) levels compared to Na2SeO3 and SeMet (p < 0.05). The potency of SFQSeM against oxidative stress as well as its oral bioavailability and low risk highlight its potential utility as an effective Se nutritional supplement.

Research on inheritance reconfiguration scheduling of virtual manufacturing cell based on improved genetic algorithm

Research on inheritance reconfiguration scheduling of virtual manufacturing cell based on improved genetic algorithm

Extension of the Virtual Cell Based Assay from a 2-D to a 3-D Cell Culture Model.

Prediction of chemical toxicity is very useful in risk assessment. With the current paradigm shift towards the use of in vitro and in silico systems, we present herein a theoretical mathematical description of a quasi-diffusion process to predict chemical concentrations in 3-D spheroid cell cultures. By extending a 2-D Virtual Cell Based Assay (VCBA) model into a 3-D spheroid cell model, we assume that cells are arranged in a series of concentric layers within the sphere. We formulate the chemical quasi-diffusion process by simplifying the spheroid with respect to the number of cells in each layer. The system was calibrated and tested with acetaminophen (APAP). Simulated predictions of APAP toxicity were compared with empirical data from in vitro measurements by using a 3-D spheroid model. The results of this first attempt to extend the VCBA model are promising - they show that the VCBA model simulates close correlation between the influence of compound concentration and the viability of the HepaRG 3-D cell culture. The 3-D VCBA model provides a complement to current in vitro procedures to refine experimental setups, to fill data gaps and help in the interpretation of in vitro data for the purposes of risk assessment.

Machine Learning Enables Radio Resource Allocation in the Downlink of Ultra-Low Latency Vehicular Networks

Autonomous driving and intelligent transportation demand ultra-low latency and high reliability communication in future vehicular networks. Proactive wireless communication can facilitate minimal latency by open-loop communication, which discards traditional feedback control mechanisms. However, appropriate radio resource allocation in such proactive mobile networks has not been fully studied due to lacking channel state information (CSI) and the alleviation of multiple access interference (MAI) in multiple virtual cells. This paper aims to ensure the reliability of downlink communication by a novel radio resource allocation scheme in proactive vehicular networks with ultra-low latency. We regard data transmission success rate as the reliability indicator and propose a joint radio resource allocation model based on the “generalized closed-loop”, where anchor node (AN) uses the radio resource utilization information (RRUI) from the vehicle in the immediate past uplink as a guide to assist resource allocation. Subsequently, we study the radio resource allocation model solution on the vehicle side and the network side respectively. On the vehicle side, vehicles use the local or global data transmission experience to select the radio resource with the best quality as the RRUI. On the network side, according to the latest RRUI of vehicle and resource occupancy information, deep reinforcement learning is proposed to make appropriate radio resource allocation decisions. Simulations demonstrate the effectiveness of the intelligent joint radio resource allocation scheme under the cooperation between vehicles and AN. When the resource load rate reaches 40%, the joint radio resource allocation scheme can achieve a data transmission success rate of more than 98%.

Preventive maintenance planning considering machines’ reliability using group technology

PurposeThe purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.Design/methodology/approachSimilarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.FindingsUsing similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.Practical implicationsThe proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.Originality/valueThis paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

Dependence of the Heterogeneity of Grain Boundaries on Adjacent Grains in Perovskites and Its Impact on Photovoltage.

In polycrystalline perovskites, grain boundaries (GBs) that isolate grains determine the optoelectronic properties of a semiconductor, and hence affect the photovoltaic performance of a solar cell. Photocurrent and photovoltage are affected by the microscopic structure of perovskites but are difficult to quantify on the intragrain length scale and are often treated as homogeneous within the photoactive layer. Here, the nanoscale through-film and lateral photoresponse of large-grained perovskite are studied by photoconductive atomic force microscopy. Photocurrent collection along GBs relies on the formation of adjacent grains, exhibiting GB to GB heterogeneity. Regarding to the spatially correlated heterogeneity, the photovoltage of grains deduced from the photoresponse curves at specific positions is larger than that of GBs by up to 0.4 V, suggesting that the photovoltage loss mainly originates from the shunting of GBs through the whole perovskite layer. These spatial heterogeneities are alleviated by depositing a capping layer onto the perovskite layer, highlighting the role of the inserted layer between the perovskite and electrode in real solar cells. This research reveals the heterogeneity of GBs and its influence on photovoltage that actually occurs in virtual solar cells, which is crucial for optimizing perovskite-based solar cells.

Першопринципні методи обчислення лінійного коефіцієнта термічного розширення квазібінарних евтектичних систем

To calculate the linear coefficient of thermal expansion (LCTE) and its temperature dependence, a combination of the method of a priori pseudopotential and quasi-harmonic approximation (author's methods) is used. After approximating the results obtained for metal-like materials (carbides, borides, silicides), the LCTE is presented in an analytical form. In the case of quasi-binary eutectic systems based on carbides, borides, silicides, to estimate the interaction energy of the elements of two components, the concept of a virtual crystal (with a virtual cell) along the line of contact of two components is introduced. A virtual cell is assigned a volume average between the volume of a unit cell of two components, taking into account their concentration ratio. The components that make up the eutectic retain their crystal structure, their LCTE can be estimated as for pure components. Without taking into account the influence of interphase interaction, the LCTE of the eutectic system is determined using the rule of mixtures based on the LCTE components, taking into account their volume fraction. Taking into account the influence of the interface on thermal expansion is estimated by the virtual cell assigned to it. To determine the LCTE of the eutectic system, a ratio is proposed that connects the LCTE components and the docking boundaries through the concentration ratio. This method more realistically describes the structure of a quasi-binary eutectic. There is a consistency between the calculated and experimental data. Keywords: electron-ion system energy, interatomic interaction potential, quasiharmonic approximation, linear coefficient of thermal expansion, eutectic temperature.

Analysis of Area Spectral &amp; Energy Efficiency in a CoMP-Enabled User-Centric Cloud RAN

In this paper, we consider coordinated multipoint transmission (CoMP) by using distributed data base stations (DBSs) in a User-centric Cloud-based Radio Access Network (UCRAN). The creation of non-overlapping virtual cells (or service zones) centered around a user, contrary to traditional cellular architecture, shifts the mode of operation from base station centric “always ON” cells to user-centric “on-demand cells.” In contrast to existing literature, we show that CoMP is only beneficial in network architectures where cell-edge users are present. In a UCRAN architecture, where the cell-edge users are virtually removed due to non-overlapping user-centric cells, the performance gain provided by enabling CoMP is insignificant. We analyze this by performing a comparative performance analysis of UCRAN with different joint transmission schemes of CoMP. We also provide analytical expressions for network-wide coverage probability, area spectral efficiency, and energy efficiency by taking advantage of the stochastic geometry tools. Additionally, we investigate the impact of new degrees of freedom such as the size of the service zones and density of data base station on the spectral and energy efficiencies of CoMP-enabled UCRAN. Extensive Monte Carlo simulations validate the proposed analytical framework as well as provide useful insights into the design of next-generation cellular architectures.

Virtual cell model for osmotic pressure calculation of charged biomolecules.

The osmotic pressure of dilute electrolyte solutions containing charged macro-ions as well as counterions can be computed directly from the particle distribution via the well-known cell model. Originally derived within the Poisson-Boltzmann mean-field approximation, the cell model considers a single macro-ion centered into a cell, together with counterions needed to neutralize the total cell charge, while it neglects the phenomena due to macro-ion correlations. While extensively applied in coarse-grained Monte Carlo (MC) simulations of continuum solvent systems, the cell model, in its original formulation, neglects the macro-ion shape anisotropy and details of the surface charge distribution. In this paper, by comparing one-body and two-body coarse-grained MC simulations, we first establish an upper limit for the assumption of neglecting correlations between macro-ions, and second, we validate the approximation of using a non-spherical macro-ion. Next, we extend the cell model to all-atom molecular dynamics simulations and show that protein concentration-dependent osmotic pressures can be obtained by confining counterions in a virtual, spherical subspace defining the protein number density. Finally, we show the possibility of using specific interaction parameters for the protein-ion and ion-ion interactions, enabling studies of protein concentration-dependent ion-specific effects using merely a single protein molecule.