Potential Model Research Articles

Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes. Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024. Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves. This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators. The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways. PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.

Identification and Validation of a Novel PANoptosis-related Gene Signatured for Osteosarcoma as Prognostic Model.

To construct and validate a prognostic model for osteosarcoma prognostication and therapeutic potential of PANoptosis- related genes. Observational study. Place and Duration of the Study: Department of Orthopaedics, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China, from August 2021 to January 2024. Transcriptomic data from the GEO and TARGET databases were utilised to construct and validate a prognostic model for osteosarcoma. The analysis involved the use of the LASSO Cox-regression method with the Glmnet R package to identify key PANoptosis-related genes. Differential gene expression analysis was conducted using the Limma R package, and model validation was performed using Kaplan-Meier survival analysis and time-dependent ROC curves. This model, derived from five key PANoptosis-related genes, demonstrated significant predictive capability for patient survival across training and validation cohorts. Further analysis confirmed the model's effectiveness and identified metastasis stage and risk scores as the robust independent prognostic indicators. The prognostic model offers a novel tool for osteosarcoma prognostication and underscores the therapeutic potential of targeting PANoptosis-related pathways. PANoptosis-related genes, Osteosarcoma, Prognosis, Bioinformatics, Tumour micro-environment.

Study of A Heterojunction Double Gate Ferroelectric p-n-i-n Tunnel FET combining analytical modeling and TCAD simulation

A short channel Heterojunction Double Gate Ferroelectric p-n-i-n Tunnel Field Effect Transistor structure is proposed in this article to alleviate undesirable ambipolarity and Miller Capacitance and has a steeper subthreshold swing with improvement in ON state current compared to other conventional TFET structures. This work develops a physics based relevant analytical model of surface potential with the effect of gate fringing field and inclusive source and channel depletion region, drain current model, terminal charge and capacitance model to investigate its transient performance for the impact of ferroelectric polarization according to the Miller Ferroelectric polarization model. The proposed device is also validated using the SILVACO ATLAS device simulator, which yields a good agreement with the model, establishing its reliability and acceptability. The device achieves a steeper subthreshold of 31.3 mV/decade, an improved ON current∼5.9 x 10−4A/μmand enhanced transconductance∼0.105 ms as well as a very low energy delay product (EDP)∼4.07 x 10−3Js with hysteresis free operation at a low supply voltage∼0.5 V in a 40 nm technology node, making it desirable for ultra-low power analog and logic applications.

Computational modeling of cardiac electrophysiology applied to a swine model of Long QT Syndrome

Abstract Background Computational models are pivotal for understanding electrophysiological aspects of cardiac diseases. Despite pigs' increased use in research, limited in vitro porcine data impedes accurate mathematical modeling. In this context, we recently developed a novel knock-in porcine model of long QT syndrome type 8 (LQT8) [1]. Purpose To develop a novel computational model for electrophysiology and Ca2+ handing in wild-type and LQT8 swine ventricular cardiomyocytes. Methods We designed a mathematical model of wild-type swine action potential (AP) based on a wide range of cellular electrophysiological data. Using the ORd model [2] as a foundation, we re-parametrized main ionic currents using the Nelder-Mead algorithm. We then modified the model to simulate LQT8, evaluating its utility in exploring arrhythmogenic mechanisms and pharmacological approaches. For the L-type Ca Channel (LTCC), we replaced the original HH formulation with a Markov model based on [3], which was subsequently modified to account for a population of mutant p.G406R channels. Selected biomarkers for validation were AP duration at 20, 50, and 90% of repolarization, maximum upstroke velocity, AP amplitude and resting membrane potential. To assess the robustness of the model, we generated a random population of 500 models accounting for biological variability by sampling key parameters in the range [-20 to +20%] of their original values, using the Latin Hypercube Sampling. Simulations were executed using MATLAB R2023a. Numerical integration was performed utilizing the MATLAB function ode15s. Results The mathematical model accurately reproduced a wide range of experimental data, including steady-state curves of the main ionic currents, kinetic properties of LTCC, APD rate dependency, maximum upstroke velocity and Ca2+transient morphology. The model successfully simulated AP prolongation in LQT8. Also, it replicated key features of the cellular phenotype such as steeper APD rate adaptation, increased duration and amplitude of the Ca2+ transients, increased CaMKII-mediated late Na+ current, pathologically decreased upstroke velocity with increasing pacing frequency, due to a CaMKII-mediated mechanism. As a proof-of-concept, the model was utilized to investigate arrhythmogenic mechanisms and pharmacological approaches for LQT8. According to model’s prediction, early afterdepolarizations (EADs) in LQT8 can stem from three distinct mechanisms: improper kinetics of late Na+ current, improper kinetics of LTCC, and enhanced late-systolic SR Ca release. Notably, the model also predicts that full CaMKII inhibition effectively eliminates EADs for all three mechanisms, showcasing the potential efficacy of this pharmacological intervention. Conclusions A novel mathematical swine ventricular myocyte model explores arrhythmogenic mechanisms and therapeutic interventions, aiding LQT8 research and clinical translation in cardiac disorders.

Research on the monitoring system for induced voltage and ground current of 27.5 kV cable sheath in railways

PurposeThe purpose of this study is to address the deficiency in safety monitoring technology for 27.5 kV high-voltage cables within the railway traction power supply by analyzing the grounding methods employed in high-speed railways and developing an effective monitoring solution.Design/methodology/approachThrough establishing a mathematical model of induced potential in the cable sheath and analyzing its influencing factors, the principle of grounding current monitoring is proposed. Furthermore, the accuracy of data collection and alarm function of the monitoring equipment were verified through laboratory simulation experiments. Finally, through practical application in the traction substation of the railway bureau on site, a large amount of data were collected to verify the stability and reliability of the monitoring system in actual environments.FindingsThe experimental results show that the designed monitoring system can effectively monitor the grounding current of high-voltage cables and respond promptly to changes in cable insulation status. The system performs excellently in terms of data collection accuracy, real-time performance and reliability of alarm functions. In addition, the on-site trial results further confirm the accuracy and reliability of the monitoring system in practical applications, providing strong technical support for the safe operation of high-speed railway traction power supply systems.Originality/valueThis study innovatively develops a 27.5kV high-voltage cable grounding current monitoring system, which provides a new technical means for evaluating the insulation status of cables by accurately measuring the grounding current. The design, experimental verification and application of this system in high-speed railway traction power supply systems have demonstrated significant academic value and practical significance, contributing innovative solutions to the field of railway power supply safety monitoring.

Two state model for the ML-BOP potential

The coarse-grained machine-learning derived ML-BOP model [Chan et al., Nature Commun. 10, 379 (2019)] provides a monoatomic representation of the water-water interaction potential in which orientational interactions are included as three-body contributions. Despite its simplicity, the model reproduces the phase diagram of water and its anomalies. Here, we show that a two-state Gibbs free energy expression – fitted simultaneously on the temperature and pressure dependence of the density and internal energy – predicts the existence of a liquid–liquid critical point, with critical parameters consistent with previous estimates. We also show that in this model: (i) while the low density liquid is pre-empted by crystal nucleation, the high-density liquid and its spinodal are accessible in numerical studies down to 100 K; (ii) crystallisation requires the presence of a local low density region. Thus, for densities larger than the critical density, spinodal decomposition (or nucleation of the low-density liquid) is a pre-requisite for ice nucleation.

Computational Modeling of Sodium-Ion-Channel-Based Glucose Sensing Biophysics to Study Cardiac Pacemaker Action Potential

Elevated blood glucose levels, known as hyperglycemia, play a significant role in sudden cardiac arrest, often resulting in sudden cardiac death, particularly among those with diabetes. Understanding the internal mechanisms has been a challenge for healthcare professionals, leading many research groups to investigate the relationship between blood glucose levels and cardiac electrical activity. Our hypothesis suggests that glucose-sensing biophysics mechanisms in cardiac tissue could clarify this connection. To explore this, we adapted a single-compartment computational model of the human pacemaker action potential. We incorporated glucose-sensing mechanisms with voltage-gated sodium ion channels using ordinary differential equations. Parameters for the model were based on existing experimental studies to mimic the impact of glucose levels on pacemaker action potential firing. Simulations using voltage clamp and current clamp techniques showed that elevated glucose levels decreased sodium ion channel currents, leading to a reduction in the pacemaker action potential frequency. In summary, our mathematical model provides a cellular-level understanding of how high glucose levels can lead to bradycardia and sudden cardiac death.

Computational modelling of mouse atrio ventricular node action potential and automaticity.

The atrioventricular node (AVN) is a crucial component of the cardiac conduction system. Despite its pivotal role in regulating the transmission of electrical signals between atria and ventricles, a comprehensive understanding of the cellular electrophysiological mechanisms governing AVN function has remained elusive. This paper presents a detailed computational model of mouse AVN cell action potential (AP). Our model builds upon previous work and introduces several key refinements, including accurate representation of membrane currents and exchangers, calcium handling, cellular compartmentalization, dynamic update of intracellular ion concentrations, and calcium buffering. We recalibrated and validated the model against existing and unpublished experimental data. In control conditions, our model reproduces the AVN AP experimental features, (e.g. rate=175bpm, experimental range [121, 191]bpm). Notably, our study sheds light on the contribution of L-type calcium currents, through both Cav1.2 and Cav1.3 channels, in AVN cells. The model replicates several experimental observations, including the cessation of firing upon block of Cav1.3 or INa,r current. If block induces a reduction in beating rate of 11%. In summary, this work presents a comprehensive computational model of mouse AVN cell AP, offering a valuable tool for investigating pacemaking mechanisms and simulating the impact of ionic current blockades. By integrating calcium handling and refining formulation of ionic currents, our model advances understanding of this critical component of the cardiac conduction system, providing a platform for future developments in cardiac electrophysiology. KEY POINTS: This paper introduces a comprehensive computational model of mouse atrioventricular node (AVN) cell action potentials (APs). Our model is based on the electrophysiological data from isolated mouse AVN cells and exhibits an action potential and calcium transient that closely match the experimental records. By simulating the effects of blocking specific ionic currents, the model effectively predicts the roles of L-type Cav1.2 and Cav1.3 channels, T-type calcium channels, sodium currents (TTX-sensitive and TTX-resistant), and the funny current (If) in AVN pacemaking. The study also emphasizes the significance of other ionic currents, including IKr, Ito, IKur, in regulating AP characteristics and cycle length in AVN cells. The model faithfully reproduces the rate dependence of action potentials under pacing, opening the possibility of use in impulse propagation models. The population-of-models approach showed the robustness of this new AP model in simulating a wide spectrum of cellular pacemaking in AVN.

Characterization and prediction of demand response potential of air conditioning systems integrated with chilled water storage

Chilled water storage offers a cost-effective and convenient solution for load flexibility of air-conditioning systems. However, its impacts on system flexibility and energy efficiency have not been comprehensively explored. In this paper, demand response (DR) potential delivered by chilled water storage was explored and predicted through both experimental and simulation studies. The initial experimental investigation focused on the DR potential at various cold charging and discharging proportions of chilled water storage. Subsequently, a virtual platform, validated through experimental data, was utilized to analyze load flexibility and energy efficiency of the system across different baseline loads, storage characteristics, and heat pump characteristics. Prediction models for DR potential in different cold charging and discharging scenarios were further developed based on the simulation results. The experimental and simulation results demonstrated effective load manage performance through adjusting charging and discharging proportions, even though the flexibility may sacrifice energy efficiency in cold discharging scenarios. In addition, the developed prediction models confirmed their feasibility with the coefficient of determination (R2) exceeding 0.80 in charging scenarios and 0.90 in discharging scenarios. The analysis also revealed significant benefits from storing supply water at lower temperatures and selecting heat pumps with partial-load efficiency curves characterized by higher Pearson Correlation Coefficients.

TH-graphyne: a new porous bidimensional carbon allotrope.

Graphyne and two-dimensional porous carbon-based materials have garnered significant attention due to their interesting structural characteristics and essential properties for new technological applications. Within this scope, this work investigates the structural, thermal, electronic, optical, and mechanical properties of a novel two-dimensional allotrope that combines triangular (T) and hexagonal (H) rings, connected by acetylenic linkages (graphyne-like), thus named TH-graphyne (TH-GY). This study comprehensively characterizes the proposed system's behavior using density functional theory, ab initio molecular dynamics, and classical reactive molecular dynamics simulations. Our results confirm the structural stability of TH-GY. AIMD simulations demonstrate the material's thermal stability at elevated temperatures, while phonon dispersions indicate its dynamical stability. Electronic band structure calculations show that the system is metallic. The analysis of optical properties reveals intense activity in the visible and UV regions, with pronounced anisotropy. A machine learning interatomic potentials model was developed for TH-GY and used to determine the mechanical behavior of the system, which exhibits Young's modulus ranging from 263 to 356 GPa, highlighting its flexibility. Classical reactive MD simulations elucidate the fracture behavior of TH-GY, revealing distinct fracture patterns and mechanical anisotropy.

Dynamical traceback age of the Octans young stellar association

Context. Octans is one of the most distant (d ∼ 150 pc) young stellar associations of the solar neighbourhood, and it has not yet been sufficiently explored. Its age is still poorly constrained in the literature and requires further investigation. Aims. We take advantage of the state-of-the-art astrometry delivered by the third data release of the Gaia space mission combined with radial velocity measurements obtained from high-resolution spectroscopy to compute the 3D positions and 3D spatial velocities of the stars and derive the dynamical traceback age of the association. Methods. We created a clean sample of cluster members by removing potential outliers from our initial list of candidate members. We then performed an extensive traceback analysis using different subsamples of stars, different metrics to define the size of the association, and different models for the Galactic potential to integrate the stellar orbits in the past. Results. We derive a dynamical age of $ 34^{+2}_{-2} $ Myr that is independent from stellar models and represents the most precise age estimate currently available for the Octans association. After correcting the radial velocity of the stars for the effect of gravitational redshift, we obtain a dynamical age of $ 33^{+3}_{-1} $ Myr, which is in very good agreement with our first solution. This shows that the effect of gravitational redshift is small for such a distant young stellar association. Our result is also consistent with the less accurate age estimates obtained in previous studies from lithium depletion (30–40 Myr) and isochrones (20–30 Myr). By integrating the stellar orbits in time, we show that the members of Octans and Octans-Near had different locations in the past, which indicates that the two associations are unrelated despite the close proximity in the sky. Conclusions. This is the first reliable and precise dynamical age result for the Octans young stellar association. Our results confirm that it is possible to derive precise dynamical ages via the traceback method for ∼30 Myr old stellar clusters at about ∼150 pc with the same precision level that has been achieved in other studies for young stellar groups within 50 pc of the Sun. This represents one more step towards constructing a self-consistent age scale based on the 3D space motion of the stars in the young stellar clusters of the solar neighbourhood.

Effect of heat stress on semen characteristics and genetics in Thai native grandparent roosters

Grandparent roosters are crucial in poultry breeding programs and significantly influence future bird generations' genetic makeup and performance. However, these roosters face considerable challenges from heat stress, which can adversely affect their reproductive performance, semen quality, and overall health and welfare. Our study aimed to investigate the effects of heat stress on the genetics of semen characteristics, identify the appropriate temperature and humidity indices (THI), and determine the threshold point of heat stress to prevent thermal stress. We analyzed data from 3,895 records of 242 Thai native grandparent roosters in conjunction with the THI using 7 THI functions and the regression method. The threshold point of heat stress, genetic parameters, rate of decline of semen characteristics per level of THI, estimated breeding values and selection index values were analyzed using the multivariate test-day model in the AIREML and BLUPF90 programs. Based on the regression coefficient and statistical criteria of the lowest -2logL and AIC values, the results showed that a THI of 78 was considered the threshold point of heat stress. The estimated heritability values ranged from 0.023 to 0.032, 0.066 to 0.069, 0.047 to 0.057, and 0.022 to 0.024 for mass movement, semen volume, sperm concentration, and the semen index, respectively. The reduction rates of mass movement, semen volume, sperm concentration, and semen index at a THI of 78 were -0.009, -0.003, -0.170, and -0.083 per THI, respectively. The genetic correlations among the semen traits were moderately to strongly positive and ranged from 0.562 to 0.797. The genetic correlations between semen traits and heat stress were negative and ranged from -0.437 to -0.749. The permanent environmental correlations among the semen traits (0.648-0.929) were positive and greater than the genetic correlations. Permanent environmental correlations between semen traits and heat stress were negative and ranged from -0.539 to -0.773. The results of the selection indices showed that the higher the selection intensity was, the greater the degree to which the selection index corresponded to genetic progress. The recommendation for animal genetic selection is that the top 10% is appropriate because it seems most preferred. Therefore, using a multivariate test-day model and selection index for the high genetic potential of semen traits and heat tolerance in Thai native grandparent roosters makes it possible to achieve genetic assessment in a large population.

Theory of piezotronic and piezo-phototronic effects on high performance perovskite solar cells

Piezotronic and piezo-phototronic effects can effectively modulate the Schottky barrier height (SBH) of the metal-semiconductor contact and the built-in electric field. Most models focus on the built-in electric field of perovskite solar cells, and polarization-modulated SBH is missing. We investigated piezotronic and piezo-phototronic effects on the Schottky barrier and built-in electric field of perovskite solar cells. A model of the surface potential induced by polarization was developed. The analytical and numerical results are calculated for perovskite solar cells. We found that the piezo-phototronic effect can significantly increase the performance of perovskite solar cells by reducing the height of the Schottky barrier. This model not only provides a fundamental physical picture of the piezotronic and piezo-phototronic effect on perovskite solar cells, but also paves a new path to develop high-efficiency perovskite solar cells.

Information-theoretic measures and Compton profile of H atom under finite oscillator potential

Information-theoretic measures for nl (2L) states of a H atom (with n=1−10 and l=0−2 , where n and l denote principal and angular momentum quantum numbers) have been investigated within a quantum dot by utilizing the Ritz variational principle, with the help of a Slater-type basis set. A well-established two-parameter (depth and width) model of finite oscillator potential is used to simulate the dot environment. The variationally optimized position (r)-space wave function is utilized to determine the momentum (p)-space wave function, leading to the generation of p-space radial density distribution. We explore the impact of cavity parameters on quantum information theoretic measures, such as Shannon (S) and Fisher information (I) entropy, in the ground as well as the excited state. The results of S were also used to test the Bialynicki–Birula–Mycielski inequality, related to the entropic uncertainty principle for the confined H atom. Some simple new fitting laws pertaining to S and I have been proposed. Furthermore, the p-space radial density is employed to derive the Compton profile of the confined H atom. Possible tunability of S,I and Compton profiles with respect to the parameters is noted.

Development of a cross-latitude applicable predictive model for solar radiation acquisition potential of urban residential blocks

Existing research has revealed that predictive models can be developed based on urban morphology indicators to assess the potential of solar radiation acquisition (SRA) on building surfaces and provide much faster results compared to simulation software. However, such existing models can only be utilized in specific cities or regions, which restricts their popularity. To fill this scientific gap, this study develops a cross-latitude predictive model for assessing the SRA of buildings. Building solar radiation acquisition capacity, selected key morphology indicators, and cosine of latitude (cosφ) are employed as dependent and independent variables in the model to discuss the interaction between the urban form and the SRA based on the analysis of sixty sample urban residential blocks across nine cities in China with varying latitudes. The obtained results are indicative of relatively high prediction accuracy for the solar radiation acquisition capacity of buildings (with an adjusted R2 of 0.931 for roofs, and 0.962 for exterior walls). As a result, the proposed model has the potential to serve as a valuable tool for incorporating solar radiation considerations into sustainable urban design practices.

Learning together: Towards foundation models for machine learning interatomic potentials with meta-learning

The development of machine learning models has led to an abundance of datasets containing quantum mechanical (QM) calculations for molecular and material systems. However, traditional training methods for machine learning models are unable to leverage the plethora of data available as they require that each dataset be generated using the same QM method. Taking machine learning interatomic potentials (MLIPs) as an example, we show that meta-learning techniques, a recent advancement from the machine learning community, can be used to fit multiple levels of QM theory in the same training process. Meta-learning changes the training procedure to learn a representation that can be easily re-trained to new tasks with small amounts of data. We then demonstrate that meta-learning enables simultaneously training to multiple large organic molecule datasets. As a proof of concept, we examine the performance of a MLIP refit to a small drug-like molecule and show that pre-training potentials to multiple levels of theory with meta-learning improves performance. This difference in performance can be seen both in the reduced error and in the improved smoothness of the potential energy surface produced. We therefore show that meta-learning can utilize existing datasets with inconsistent QM levels of theory to produce models that are better at specializing to new datasets. This opens new routes for creating pre-trained, foundation models for interatomic potentials.

Soil organic carbon sequestration potential explained by mineralogical and microbiological activity using spectral transfer functions

The ability of soil to sequester carbon and reduce atmospheric CO2 concentrations is limited and depends on the soil minerals and their interaction with the microbiota. Microbial activities are closely associated with the types and amounts of soil organic matter (SOM) and clay minerals that have functional groups that interact with energy in Vis NIR-SWIR and Mid-IR wavelengths. The main objective of this research was to determine, based on these spectral ranges, the relation between mineralogical and organic compounds, as their sequestration and specialization in soils from Brazil. It was possible to map microbiological activity by spectral transfer functions and digital soil mapping reaching R2 from 0.77 to 0.85. Multiple regression equations were constructed to quantify enzymatic activity, microbial biomass carbon (MBC), particulate organic matter (POM), and resistant forms of carbon, and SOM associated with the mineral fraction (MAOM). All these properties were detected by specific bands obtained with the recursive feature elimination (RFE) algorithm, reaching correlations from 0.64 to 0.98 in specific ranges. The prediction model of the carbon sequestration potential was adjusted with microbiological and mineralogical variables from Vis-NIR-SWIR and the Mid-IR spectral range. A SARAR double autoregressive model was adjusted with r 0.61 and to a spatial error model (SEM) with r 0.7. The explanatory variables were associated with kaolinite, hematite, goethite, gibbsite, and the abundance of fungi, actinomycetes, vesico-arbuscular mycorrhizal fungi, enzymatic activity of beta-glucosidase, urease and phosphatase, and POM. Among the microbiological variables, the general abundance of fungi was the most important, in contrast to enzymatic activity that was the least important. The interaction between the different maps constructed and historical land use allowed the identification of areas that contribute to sequestering new carbon and could be the key to climate change mitigation strategies.

Climatic conditions and landscape diversity predict plant–bee interactions and pollen deposition in bee‐pollinated plants

Climate change, landscape homogenization, and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant–pollinator interactions and pollinator's plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant–bee interactions in 68 Scandinavian plant communities in landscapes of varying land‐cover heterogeneity along a latitudinal temperature gradient of 4–8°C, and estimated pollen deposition as the number of pollen grains on flowers of the bee‐pollinated plants Lotus corniculatus and Vicia cracca. We show that plant–bee interactions, and the pollination potential for these bee‐pollinated plants increase with landscape diversity, annual mean temperature, and plant abundance, and decrease with distances to sand‐dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant–bee interactions that incorporate pollinator‐specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.

A gyrokinetic simulation model for 2D equilibrium potential in the scrape-off layer of a field-reversed configuration

The equilibrium potential structure in the scrape-off layer (SOL) of the field-reversed configuration (FRC) can be affected by the penetration of edge biasing applied at the divertor ends. The primary focus of the paper is to establish a formulation that accurately captures both parallel and radial variations of the two-dimensional (2D) potential in SOL. The formulation mainly describes a quasi-neutral plasma with a logical sheath boundary. A full-f gyrokinetic ion model and a massless electron model are implemented in the GTC-X code to solve for the self-consistent equilibrium potential, given fixed radial potential profiles at the boundaries. The first essential point of this 2D model lies in its ability to couple radial and parallel dynamics stemming from resistive currents and drag force on ions. The model successfully recovers the fluid force balance and continuity equations. These collisional effects on 2D potential mainly appear through the density profile changes, modifying the potential through electron pressure gradient. This means an accurate prescription of electron density and temperature profiles is important in predicting the potential structure in the FRC SOL. The Debye sheath potential and the potential profiles applied at the boundaries can be additional factors contributing to the 2D variations in SOL. This comprehensive full-f scheme holds promise for future investigations into turbulent transport in the presence of the self-consistent 2D potential together with the non-Maxwellian distributions and open boundary conditions in the FRC SOL.

Interacting dust grains in complex plasmas: Ion wake formation and the electric potential

Dust grains have been used as minimally invasive probes to determine plasma parameters including the plasma density, temperature, and electric field in a plasma discharge. However, the dust grains in a plasma generate local potential disturbances due to the collection of charge and the subsequent electrostatic interactions between the dust and charged plasma particles. Dust grains in close proximity to one another exhibit interesting non-reciprocal interactions and self-organize into structures such as one-dimensional filamentary chains, two-dimensional “zigzags,” and three-dimensional helices, among others. The formation of these structures suggests that although the dust grains may be less invasive than traditional plasma probes, the disturbance to the local plasma environment introduced by dust grains is non-trivial. Commonly used analytic forms of the electric potential describing complex plasmas have failed to resolve the near-dust region, and as a result are insufficient to provide insight about the formation of complex dust structures. Here, we use an N-body simulation to compute the electric potential from ion densities near various dust grain configurations. We provide an alternative description to the standard analytic model for the electric potential of dust and ion wakes based on a Gaussian shaped cloud of ions. The electric potential obtained from simulations is used to identify minimum energy configurations for two and three dust grains. It is further demonstrated that the minimum potential region identified for N dust grains and their associated ion wakes does not predict the minimum-energy configuration of N + 1 dust grains.