Conversion Model Research Articles

Interfacial Polymerization Fabricated Polydopamine Capsules as a Step Toward a Photothermal Protocell Model

AbstractPhotothermal is a significant solar energy conversion process on Earth, and photothermal‐responsive protocells are a potential model for prebiotic chemistry. Here, polydopamine microcapsules are fabricated via emulsion droplets‐templated interfacial polymerization and investigated their potential as synthetic protocell models for photothermal conversion and utilization. DNAzyme‐mediated dopamine peroxidation and polymerization occur at the water/oil interface of the emulsion droplets, facilitated by a biphasic microfluidic technique, leading to the formation of uniform polydopamine microcapsules, which retain structural integrity upon transfer into a continuous water phase. These microcapsules exhibit size‐dependent semi‐permeability and high photothermal capability due to their broad spectral absorbance. The photothermal‐mediated temperature rise within the microcapsules triggers DNA denaturation and rearrangement, and intermittent photothermal irradiation modulates reversible phase separation of DNA condensates, mimicking membrane‐less organelles. This work suggests that interfacial polymerization fabricated polydopamine capsules are a plausible photothermal protocell model that can demonstrate aspects of primitive abiotic cellularity.

Landscape-scale predictions of future grassland conversion to cropland or development.

Grassland conservation planning often focuses on high-risk landscapes, but many grassland conversion models are not designed to optimize conservation planning because they lack multidimensional risk assessments and are misaligned with ecological and conservation delivery scales. To aid grassland conservation planning, we developed landscape-scale models at relevant scales that predict future (2021-2031) total and proportional loss of unprotected grassland to cropland or development. We developed models for 20 ecoregions across the contiguous United States by relating past conversion (2011-2021) to a suite of covariates in random forest regression models and applying the models to contemporary covariates to predict future loss. Overall, grassland loss models performed well, and explanatory power varied spatially across ecoregions (total loss model: weighted group mean R2=0.89 [range: 0.83-0.96], root mean squared error [RMSE]=9.29ha [range: 2.83-22.77ha]; proportional loss model: weighted group mean R2=0.74 [range: 0.64-0.87], RMSE=0.03 [range: 0.02-0.06]). Amount of crop in the landscape and distance to cities, ethanol plants, and concentrated animal feeding operations had high variable importance in both models. Total grass loss was greater when there were moderate amounts of grass, crop, or development (∼50%) in the landscape. Proportional grass loss was greater when there was less grass (∼<30%) and more crop or development (∼>50%). Some variables had a large effect on only a subset of ecoregions, for example, grass loss was greater when ∼>70% of the landscape was enrolled in the Conservation Reserve Program. Our methods provide a simple and flexible approach for developing risk layers well suited for conservation that can be extended globally. Our conversion models can support conservation planning by enabling prioritization as a function of risk that can be further optimized by incorporating biological value and cost.

Experimental and Mathematical Investigation of Thermochemical Conversion for Horse Manure

Abstract Horse manure is one of the highest potential biowastes for heat and power generation. This article investigates the experimental and mathematical modeling of thermochemical conversion for horse manure. As one type of thermochemical conversions, the pyrolysis process was carried out at eight different heating rates on horse manure using three parameters: the extent of reaction, the rate change of the extent of reaction, and differential thermal analysis (DTA), all used to determine kinetic data that will be validated with a mathematical model. Slow pyrolysis: below 15 °C/min showed optimistic results of obtaining exothermic reaction over a wide range of temperature which makes it self-sustainable with steady heat generation. Also, low heating rates allowed a quasi-equilibrium state through slow heating with a minimum delay in response for any transient error that could be generated from differential thermal gravimetry (DTG) device.

A high-precision automatic extraction method for shedding diseases of painted cultural relics based on three-dimensional fine color model

In recent years, with the development of 3D digitization of cultural relics, most cultural sites contain a large number of fine 3D data of cultural relics, especially complex geometric objects such as painted cultural relics. At present, how to automatically extract surface damage information from the fine 3D color model of painted cultural relics and avoid the loss of accuracy caused by reducing the dimension using conventional methods is an urgentproblem. In view of the above issues, this paper proposes an automatic and high-precision extraction method for cultural relics surface shedding diseases based on 3D fine data. First, this paper designs a 2D and 3D integrated data conversion model based on OpenSceneGraph, a 3D engine, which performs mutual conversion between 3D color model textures and 2D images. Second, this paper proposes a simple linear iterative clustering segmentation algorithm with an adaptive k value, which solves the problem of setting the superpixel k value and improves the accuracy of image segmentation. Finally, through the 2D and 3D integrated models, the disease is statistically analyzed and labeled on the 3D model. Experiments show that for painted plastic objects with complex surfaces, the disease extraction method based on the 3D fine model proposed in this paper has improved geometric accuracy compared with the current popular orthophoto extraction method, and the disease investigation is more comprehensive. Compared with the current 3D manual extraction method in commercial software, this method greatly improves the efficiency of disease extraction while ensuring extraction accuracy. The research method of this paper activates many existing 3D fine data of cultural protection units and converts conventional 2D data mining and analysis into 3D, which is more in line with the scientific utilization of data in terms of accuracy and efficiency and has certain scientific research value, leading value and practical significance.

The genetic link between anxiety and libido: libido as an anxiety conversion

Psychoanalysis in the study of the human psyche considers all processes in the psychodynamic paradigm. The term "psychodynamics" first appeared in the works of Freud. From the information given by Freud deduces the definition: psychodynamics is the mutual influence of unconscious (id, BSZ) and conscious (ego) processes in which BSZ is of priority importance [14]. The psychodynamic approach made it possible to reveal the economic features of mental processes. Psychic economism describes how psychic energy is involved in the processes of production/reproduction, distribution, transition and qualitative and quantitative changes. When describing psychodynamics and the economy of Freud focused on libido as the only psychic energy that can be invested in an object in a direct and sublimated form, and manifest itself in models of attraction or conversion, in symptoms. However, the study of mental processes in patients with borderline personality organization of psychogenic etiology with the help of hypnopsychoanalysis allowed us to change the view of libido as the primary and central mental energy. According to the data of psychocorrective work, we came to the conclusion that anxiety is a pre-libidinal energy that is first experienced by a child at the moment of birth and in the process of ontogenesis is able to convert into libido. This leads to the conclusion that libido is the result of the conversion of anxiety, or, in other words, the reactive formation of anxiety. For the first time, an attempt to give anxiety a basal status was made by Karen Horney [20]. However, her work describes the phenomenological aspects of anxiety. We find a broad study of anxiety in a phenomenological way both in early authors, among whom R. May is particularly noted [8], and in modern ones [5], [3], [7]. In turn, the report examines the economic aspect of anxiety in the psychodynamic approach, its genetic relationship with libido, and the ability of anxiety to convert into libido. The research methodology is based on the following: - analysis of sources that highlight the problem of anxiety; - philosophical justification of the singular and plural; - data obtained as a result of psychocorrective work using psychodynamic hypnotherapy

River Surface Velocity Measurement for Rapid Levee Breach Emergency Response Based on DFP-P-LK Algorithm.

In recent years, the increasing frequency of climate change and extreme weather events has significantly elevated the risk of levee breaches, potentially triggering large-scale floods that threaten surrounding environments and public safety. Rapid and accurate measurement of river surface velocities is crucial for developing effective emergency response plans. Video image velocimetry has emerged as a powerful new approach due to its non-invasive nature, ease of operation, and low cost. This paper introduces the Dynamic Feature Point Pyramid Lucas-Kanade (DFP-P-LK) optical flow algorithm, which employs a feature point dynamic update fusion strategy. The algorithm ensures accurate feature point extraction and reliable tracking through feature point fusion detection and dynamic update mechanisms, enhancing the robustness of optical flow estimation. Based on the DFP-P-LK, we propose a river surface velocity measurement model for rapid levee breach emergency response. This model converts acquired optical flow motion to actual flow velocities using an optical flow-velocity conversion model, providing critical data support for levee breach emergency response. Experimental results show that the method achieves an average measurement error below 15% within the velocity range of 0.43 m/s to 2.06 m/s, demonstrating high practical value and reliability.

Thermal radiation characteristic parameters of biomass mixed feedstock for concentrating solar gasification reaction

Solar energy and biomass are both important renewable energy, and the studies on them contribute to current goal of global carbon neutral. The solar gasification process directly employs solar radiation to drive the conversion from biomass to syngas via endothermic gasification reactions. This process relies on the biomass feedstock absorbing incident solar irradiation to sustain these reactions. The thermal radiation characteristics of biomass materials, such as spectral emissivity and absorptivity, determine its capacity for solar energy absorption versus losses from self-emission. Thus, these parameters are critical inputs for accurate thermal-balance modeling of the solar reactor. In this study, the reflectance of 9 biomass samples were experimentally measured in the main solar spectrum range of 200–2500 nm. Based on measurement results, the refractive index (n) and extinction coefficient (k) of each biomass materials are obtained via Kramers-Kronig relations. Besides, the dielectric function of the 9 biomass samples are also modeled using a 5-th Lorentz oscillator model and the model parameters are determined. The results indicate that the 5-th Lorentz oscillator model adequately characterizes the thermal radiation properties of diverse biomass feedstocks over the measured spectrum. This work contributes important spectral property data to enable accurate modeling and optimization of solar thermochemical conversion based on biomass gasification processes.

ChatGPT Goes to College: Exploring Student Perspectives on Artificial Intelligence in the Classroom

Background The emergence of artificial intelligence (AI) in higher education has sparked numerous discussions about its implications. ChatGPT, a prominent AI conversational model, has attracted significant attention for its ability to generate essays and formulate responses. Objective The current study sought to explore how and why students are using ChatGPT, and to examine their perceptions about ChatGPT and academic integrity. Method Students were surveyed about the frequency and motivation for ChatGPT use and their views on ChatGPT and academic misconduct. Exploratory factor analyses were conducted to examine patterns of correlations between each of the measures. Results Students primarily use ChatGPT for gathering information, motivated by its value and convenience rather than hedonic reasons, and can correctly identify academically unethical uses of the tool as cheating. Conclusion The current study presents comprehensive data on college students’ ChatGPT usage patterns, attitudes, and perceptions of cheating behavior. The outcomes of this research provide insight into how college students are currently interacting with AI tools. Teaching Implications Our findings offer practical insights for universities developing AI policies in the classroom, contributing to the ongoing discourse on AI’s role in higher education by providing accurate information about ChatGPT’s pervasiveness in academia.

Concrete Compressive Strength Prediction Using Combined Non-Destructive Methods: A Calibration Procedure Using Preexisting Conversion Models Based on Gaussian Process Regression

Non-destructive testing (NDT) techniques are crucial in making informed decisions about reconstructing or repairing building structures. The SonReb method, a combination of the rebound hammer (RH) and the ultrasonic pulse velocity (UPV) tests, is widely used for this purpose. To evaluate the compressive strength, CS, of the concrete under investigation, the ultrasonic pulse velocity Vp and the rebound index R must be mapped to the compressive strength CS using a suitable conversion model, the identification of which requires supplementing the NDT measurements with destructive-type measurements (DT) on a relatively large number of concrete cores. An approach notably indicated in all cases where the minimization of the number of cores is essential is to employ a pre-existing conversion model, i.e., a model derived from previous studies conducted in the literature, which must be appropriately calibrated. In this paper, we investigate the performance of Gaussian process regression (GPR) in calibrating the pre-existing SonReb conversion models, exploiting their ability to handle nonlinearity and uncertainties. The numerical results obtained using experimental data collected from the literature show that GPR calibration is very effective, outperforming, in most cases, the standard multiplicative and additive techniques used to calibrate the SonReb models.

Quantifying External Energy Inputs for Giant Planet Magnetospheres

AbstractThe long‐standing “energy crisis” at the giant planets refers to the anomalous heating of planetary thermospheres compared to the available energy from solar irradiance. The coupling between planetary magnetospheres and their upper atmospheres is thought to address these crises, though the sources and pathways of energy transport have not been fully explored at each system. In particular, the total available energy from the upstream solar wind at each planet has not been comprehensively quantified. Here we apply recently developed models of energy conversion by magnetic reconnection and the Kelvin‐Helmholtz instability to each of the Giant Planets, providing estimates of the average external energy inputs for each system between 1985 and 2020. We find that external energy associated with solar‐wind‐magnetospheric coupling significantly exceeds that from solar extreme ultraviolet photons. While internal energy sources are known to dominate at Jupiter and Saturn, external sources may be significant at Uranus and Neptune.

Coproduction of Integrated Weather and Climate Services to Support Net-Zero Energy Transitions

Abstract Weather and climate services (W&amp;CS) are key to supporting the transition to net-zero emissions in the energy sector. These services are utilized to increase energy system resilience, enhance renewable energy deployment, and enable the uptake of energy-efficiency measures and innovations. As energy systems become increasingly dependent on and affected by weather and climatic conditions, integrating weather and climate data into energy management systems is essential. This paper addresses the gap in comprehensive guidance for developing integrated W&amp;CS to support net-zero energy transitions, drawing upon a report by the World Meteorological Organization’s Services Commission Study Group on Integrated Energy Services. We present a framework for coproduction of W&amp;CS, exploring how the uptake of W&amp;CS for energy transitions can be enabled through the evaluation of socioeconomic benefits, harnessing business models, identification of key policies, and capacity development measures. To support the uptake of W&amp;CS for net-zero energy transitions, we recommend a deeper understanding of user needs and requirements; continuous advancements in the science and technology of W&amp;CS; effective integration of weather and climate data within energy conversion models; improved accessibility and sharing of meteorological and, especially, energy data; promotion of coproduction approaches; exploration of novel applications of W&amp;CS in the energy sector; refinement of business models for sustainable W&amp;CS delivery; execution of capacity-building activities; and enhanced communication among stakeholders and strengthened collaborative efforts. These steps are crucial for realizing the full potential of W&amp;CS in driving the energy sector toward a sustainable, net-zero future.

An optimal dispatch strategy of off-grid park integrated energy system considering source volatility

An off-grid integrated energy system (IES) with hydrogen storage at park-level is proposed, utilizing wind, solar and natural gas as the main energy supply to replace fossil fuels, in order to overcome the insufficient consideration of energy source conversion and information exchange in the traditional energy system. Firstly, an IES schematic consisted with typical devices is proposed, and all the components are modelled in detail. Then, based on the renewable energy (RE) input requirements and the facility parameters, an optimal dispatch model for multi-energy conversion based on hydrogen storage has been demonstrated. In addition, an improved particle swarm optimization algorithm is also introduced to improve the strategy. Combined with the environmental characteristics, the multi-objective solution with the lowest economic cost and environmental cost, the highest energy supply reliability and energy efficiency as the optimization objectives is determined. Finally, a case study is conducted using the practical data of the Chongli Large-Scale Wind-Solar Complementary Coupled Hydrogen Production System Application Demonstration Project to validate the feasibility of the study under real conditions. The result shows that the RE consumption rate is greater than 99 % under the premise of considering the environmental cost and ensuring the operation reliability, which can effectively improve the operation economy and user economy of the power grid

Optimization of High-Temperature CO2 Capture by Lithium Orthosilicate-Based Sorbents Using Response Surface Methodology

The major challenge in the current context of the rising world energy demand is to limit the global temperature increase for mitigating climate change. This goal requires a large reduction of CO2 emissions, mainly produced by power generation and industrial processes using fossil fuels. In this study, a novel methodology for K2CO3-doped Li4SiO4 sorbents production for CO2 capture at high temperatures was adopted based on the Design of Experiments (DoE). This innovative approach systematically tested different synthesis (temperature and K2CO3 content) and adsorption conditions (sorption temperature and CO2 concentration), allowing for the assessment of individual and interactive effects of process parameters. The Response Surface Methodology (RSM) was employed to obtain non-linear predictive models of CO2 uptake and Li4SiO4 conversion. The results of RSM analysis evidenced a maximum adsorption capacity of 196.4 mg/g for a sorbent produced at 600 °C and with 36.9 wt% of K2CO3, tested at 500 °C and 4 vol% of CO2. Whereas at 50 vol% of CO2, the best uptake of 295.6 mg/g was obtained with a sorbent synthesized at 600 °C, containing less K2CO3 (17.1 wt%) and tested at a higher temperature (662 °C). These findings demonstrate that K2CO3-doped Li4SiO4 sorbents can be tailored to maximize CO2 capture under various operating conditions, making them suitable for use in industrial processes.

Conversations in the wild: Data collection, automatic generation and evaluation

The aim of conversational speech processing is to analyze human conversations in natural settings. It finds numerous applications in personality traits identification, speech therapy, speaker identification and verification, speech emotion detection, and speaker diarization. However, large-scale annotated datasets required for feature extraction and conversational model training only exist for a handful of languages (e.g. English, Mandarin, and French) as the gathering, cleaning, and annotation of such datasets is tedious, time-consuming, and expensive. We propose two scalable, language-agnostic algorithms for automatically generating multi-speaker, variable-length, spontaneous conversations. These algorithms synthesize conversations using existing non-conversational speech datasets. We also contribute the resulting datasets (283 hours, 50 speakers). As a comparison, we also gathered the first spontaneous conversational dataset for Urdu (24 hours, 212 speakers) from public talk shows. Using speaker diarization as an example, we evaluate our datasets and report the first baseline diarization error rates (DER) for Urdu (25% for synthetic dataset-based models, and 29% for natural conversations). Our conversational speech generation technique allows training speaker diarization pipelines without the need for preparing huge conversational repositories.

Improved multi-input parameter optimization method for camera colorimetric characterization

In order to improve the accuracy of camera colorimetric characterization, a multi-input parameter optimization method was proposed in this paper. The input parameters of the traditional camera characterization method were generally RGB values; in the proposed method, the luminance parameter L was introduced in addition to RGB values, and the four-input parameters of RGBL were used as input parameters for the conversion model. In the experiment, 549 colors were uniformly selected from the Munsell Book of Color (Matte Edition), and the RGBL values and corresponding CIEXYZ values of the selected colors were measured by a spectroradiometer and three cameras, including an imaging luminance meter, respectively. Then, a polynomial model and a backpropagation (BP) neural network model were employed to establish the improved color conversion model with RGBL four-input parameters, which was compared with three-input parameter models to verify the effectiveness of the proposed method. Experimental results show that the proposed method can significantly improve the conversion accuracy and reduce the color difference with a maximum reduction of 57.7% in CIELAB.

Языковая компрессия на уровне словообразования в английском экономическом дискурсе

The purpose of the study is to identify the features of the structure and semantics of word-formation compressions in English economic discourse. Clarifying the meaning of the term "linguistic compression" gives the author the opportunity to determine the means of expressing compression at the word-formation level in the economic sphere. The research has a scientific novelty, which consists in the fact that as a result of the first comprehensive analysis of the means of compressive word formation in English economic discourse, productive word-formation models were identified and described, as well as the originality of their structure and content was determined. As a result of the study, the predominance of models of compressive word formation formed by word composition was revealed, among which the uncomplicated N + N model, the substantive conversion model V + Prep (Adv), complex phrasal words (collapsed linguistic units) and compound words. It is noted that abbreviation in English economic discourse is a fairly effective way to implement linguistic compression due to the fact that it is characterized by the highest degree of compression. The predominant are initial three-component abbreviations with a substantial basic component, truncated syllabic type abbreviations (apocopa) and graphic abbreviations, characterized by rich semantic content in combination with a compressed form of expression.

Encountering Octavius: Reading Minucius Felix within the Stage Model of Conversion

Minucius Felix’s Octavius (ca. 200 CE) is a remarkable document in the history of pagan-Christian interaction in Late Antiquity. The nature of the philosophical exchange between the principal interlocutors makes the Octavius ripe for analysis with tools from contemporary conversion studies. In particular, here I argue that the Octavius—especially in its depiction of the encounter between Octavius, advocate for Christianity, and Caecilius, Roman pagan, a candidate for conversion—aligns remarkably well with Lewis Rambo’s influential Stage Model for Conversion. The text and order of the Octavius will be interpreted according to Rambo’s model for encounter between advocate and prospective convert. The implication of this alignment will be evidence that the Octavius represents a good faith exchange between pagan and Christian in the third century CE. This essay will, further, serve librarians and information professionals in providing resources for patrons interested in Roman Christian-pagan interaction and Early Christianity. The Octavius is unique in offering one of the very few direct Christian-pagan exchanges in Latin. This essay also directs to further reading in Late Antiquity, Early Christianity, Ancient Philosophy, and Conversion Studies.

Development of a prediction model of conversion to Alzheimer’s disease in people with mild cognitive impairment: the statistical analysis plan of the INTERCEPTOR project

BackgroundIn recent years, significant efforts have been directed towards the research and development of disease-modifying therapies for dementia. These drugs focus on prodromal (mild cognitive impairment, MCI) and/or early stages of Alzheimer’s disease (AD). Literature evidence indicates that a considerable proportion of individuals with MCI do not progress to dementia. Identifying individuals at higher risk of developing dementia is essential for appropriate management, including the prescription of new disease-modifying therapies expected to become available in clinical practice in the near future. MethodsThe ongoing INTERCEPTOR study is a multicenter, longitudinal, interventional, non-therapeutic cohort study designed to enroll 500 individuals with MCI aged 50–85 years. The primary aim is to identify a biomarker or a set of biomarkers able to accurately predict the conversion from MCI to AD dementia within 3 years of follow-up. The biomarkers investigated in this study are neuropsychological tests (mini-mental state examination (MMSE) and delayed free recall), brain glucose metabolism ([18F]FDG-PET), MRI volumetry of the hippocampus, EEG brain connectivity, cerebrospinal fluid (CSF) markers (p-tau, t-tau, Aβ1-42, Aβ1-42/1–40 ratio, Aβ1-42/p-Tau ratio) and APOE genotype. The baseline visit includes a full cognitive and neuropsychological evaluation, as well as the collection of clinical and socio-demographic information. Prognostic models will be developed using Cox regression, incorporating individual characteristics and biomarkers through stepwise selection. Model performance will be evaluated in terms of discrimination and calibration and subjected to internal validation using the bootstrapping procedure. The final model will be visually represented as a nomogram.DiscussionThis paper contains a detailed description of the statistical analysis plan to ensure the reproducibility and transparency of the analysis. The prognostic model developed in this study aims to identify the population with MCI at higher risk of developing AD dementia, potentially eligible for drug prescriptions. The nomogram could provide a valuable tool for clinicians for risk stratification and early treatment decisions.Trial registrationClinicalTrials.gov NCT03834402. Registered on February 8, 2019

A new SPT-CPT correlation by statistical analysis based on a database from Anhui, China

ABSTRACT The standard penetration test (SPT) and cone penetration test (CPT) are the 2 most commonly used in situ tests in geotechnical investigations. In this study, various existing SPT–CPT correlations were assessed based on the database of Anhui Province, China, using data from 802 pairs of SPT–CPT measurements. The results showed that the existing correlations from other regions were not suitable for the geological conditions in Anhui Province. A new SPT–CPT conversion model based on the S curve was proposed through statistical analysis. This model introduced the soil behaviour index (Ic ) to SPT–CPT correlations to involve the influence of soil types on data interpretation. Comparative analysis demonstrated that the proposed correlation had the highest accuracy compared to correlations found in the literature, with the highest R2 value and lowest RMSE. Also, to validate the proposed model further, an additional set of investigation data was collected in Wuhu, Anhui, to verify the correlation. The correlation was successfully applied to predict a range of soil properties using the transformed N60 values. The validation analysis confirmed the highest accuracy of the correlation and its ability to effectively predict soil properties, thereby assisting in geotechnical design.

Model and performance analysis of energy conversion in functionally graded flexoelectric semiconductor nanostructures

Energy systems converting efficiently from surrounding environment for powering semiconductor electronic components at nanoscale has attracted intense research interests. Due to the strong size dependency, flexoelectricity has been demonstrated as an excellent candidate implemented as nanoscale piezoelectric semiconductor energy harvesters. In this work, through a judicious exploitation of structural symmetry and nonuniformity, we theoretically study the energy conversion behaviors of a novel asymmetric nanodisk model composed of functionally graded (FG) flexoelectric semiconductor (FS) (FG-FS) materials while being loaded with thickness-extensional mechanical vibration. In numerical analysis, the material parameters have been treated with a general variation method under the Cartesian coordinate systems for simplicity. Taking into accounts of the nonuniform structural form and multi-field coupling features, we derived an octic governing equation with variable coefficients for the current analyzed FG-FS nanodisk structure, which is, however, almost impossible to obtain analytical solutions. To successfully address this issue and also further explore the improved performance and new phenomena induced by the FG type of non-uniform structural profile, we specially proposed an efficient tensor algorithm and reduced the octic governing equation into one quartic equation with variable coefficients and other four common quadratic equations. Later, we explicitly explored the effect of each physical parameter on the energy conversion performance of the FS energy harvester by studying the variations of the output electric power density and the energy conversion efficiency. Results illustrated that the electromechanical energy conversion behaviors of the studied FG-FS nanodisk can be properly tuned by not only the related material parameters but also their gradations. We believe our work have the potential to pave new way for the designs and manufacture of novel nano-electronic semiconductor components for the future practical applications.