Amplitude Sensitivity Research Articles

Analysis of a highly temperature-sensitive gold-coated plasmonic biosensor for analyte detection

A temperature-sensitive gold coated PCF-based surface plasmon resonance (SPR) biosensor was examined for detecting analytes based on their refractive indices. The proposed sensor attained maximum amplitude sensitivities (AS) of 1432 RIU−1 and 1291 RIU−1, wavelength sensitivities (WS) of 13,500 nm/RIU and 13,000 nm/RIU in X and Y- polarized mode individually in RI spectrum of 1.34–1.4. Furthermore, the proposed sensor produced ultimate figure of merit (FOM) values of 546.93 RIU−1 and 521.4601 RIU−1 in x and y polarization modes. High birefringence of 1.5 × 10−3 was also computed for the suggested sensor. Additionally, the proposed sensor shows high temperature sensitivity of 4.5833 nm/°C with a comparable resolution of 2.18××10−2 °C and a maximum FOM value of 0.101 °C−1 in the detection range of −114 °C to 78 °C in the presence of ethanol as a sensing medium. The reported sensor therefore can be considered as a notable solution for any chemical or biological analytes detection.

Improvement of positive dysphotopsia with multifocal intraocular lenses by hormone replacement therapy

Introduction: The case of a patient with positive dysphotopsia (PD) affected by hormone replacement therapy (HRT) after multifocal intraocular lens (IOL) insertion is reported. Patient and Clinical Findings: A 66-year-old woman underwent bilateral cataract surgery and multifocal IOL implantation. Postoperatively, the uncorrected distance and near visual acuity improved bilaterally. The patient experienced mild PD symptoms in the left eye. She had been using an estradiol patch for menopausal disorders. 6 months postoperatively, the use of an estradiol patch was discontinued because of dermatitis and she noticed a marked worsening of her PD. Diagnosis, Intervention, and Outcomes: With the patient's cooperation, full-field electroretinography (ERG) and contrast sensitivity tests were performed before and after estradiol discontinuation. After the discontinuation, the amplitude of both rod and cone ERGs decreased, contrast sensitivity decreased, and PD deteriorated. There were no changes in anterior-segment condition and retinal shape. Conclusions: PD symptoms worsened after the discontinuation of estrogen hormone therapy. ERG amplitude and contrast sensitivity decreased when HRT was discontinued, suggesting that retinal function in the presence of estrogen receptors may be related to the development of PD.

Machine learning assisted vector atomic magnetometry

Multiparameter sensing such as vector magnetometry often involves complex setups due to various external fields needed in explicitly connecting one measured signal to one parameter. Here, we propose a paradigm of indirect encoding for vector atomic magnetometry based on machine learning. We encode the three-dimensional magnetic-field information in the set of four simultaneously acquired signals associated with the optical rotation of a laser beam traversing the atomic sample. The map between the recorded signals and the vectorial field information is established through a pre-trained deep neural network. We demonstrate experimentally a single-shot all optical vector atomic magnetometer, with a simple scalar-magnetometer design employing only one elliptically-polarized laser beam and no additional coils. Magnetic field amplitude sensitivities of about 100 {{{{{{{rm{fT}}}}}}}}/sqrt{{{{{{{{rm{Hz}}}}}}}}} and angular sensitivities of about 100 sim 200,mu rad/sqrt{{{{{{{{rm{Hz}}}}}}}}} (for a magnetic field of around 140 nT) are derived from the neural network. Our approach can reduce the complexity of the architecture of vector magnetometers, and may shed light on the general design of multiparameter sensing.

Numerical Analysis of Solid-Core Photonic Crystal Fiber Based on Plasmonic Materials for Analyte Refractive Index Sensing

In this study, we presented a simple highly sensitive sensor based on commercially available solid-core photonic crystal fiber (PCF) and surface plasmon resonance (SPR) for measuring the refractive index (RI) of analytes. The numerical simulation based on the finite element method (FEM) has been examined to compute the optical properties such as confinement loss, power spectrum, and transmission intensity of the sensor. The most sensitive and inert plasmonic materials (gold and silver) have been assumed to be coated inside the fiber with the range of analyte RI from 1.32 to 1.40. The performance of the proposed sensor has been evaluated by tracing the several optical features like wavelength sensitivity, amplitude sensitivity, resolution of the sensor, and figure of merit. As a result, the comparative study between silver and gold elements has been carried out in which the maximum sensitivity received was 1.15 μm/RIU and 1.10 μm/RIU, respectively. Whereas, on the base of power spectrum, the obtained sensitivity was 513 μm/RIU for the gold layer. Moreover, the effect of other structural parameters (air holes and plasmonic layer thickness) on the sensing performance has been taken into an account. According to the simulation analysis and results, this sensor would have a great potential in various sensing applications of biomedical and liquid refractive index.

Wide refractive index detection range sensors based on D-shape photonic crystal fiber with a nanoscale gold wire

Optical sensing based on photonic crystal fiber (PCF) is attracting interests. Currently, PCF sensing suffered from the insensitivity with the changes of environmental surroundings with low refractive index. A D-shape PCF supporting plasmonic resonances based on gold nanowires is designed to demonstrated the sensitivity optical sensing in the refractive index range from 1.00 to 1.30. The finite element method is performed to optimize the device parameters, including the diameter of the large and tiny air holes, the size of the gold nanowire, and the space between the air holes. Utilizing plasmon-induced confined electrical around gold nanowires, the predicted device performance in spectral and amplitude sensitivities are up to 19,400 nm/RIU and 5.15 × 10−6 RIU, respectively. It is believed that the designed sensor can promote the development of applications in biological and pharmaceutical.

Characterization of incompletely coated D-shaped PCF-SPR refractive index sensors

To reduce the complexity of the sensor structure and improve the sensitivity of refractive index (RI) measurement, a D-shaped photonic crystal fiber (PCF) surface plasmon resonance (SPR) RI sensor with incomplete coating of gold-tantalum pentoxide (Au-Ta2O5) on the surface is proposed. When the coating width is 3.1 μm, the sensor can detect RI samples ranging from 1.35 to 1.41 in the ultra-wide near-infrared (NIR) region of 1100–2100 nm wavelength. Its maximum wavelength sensitivity is 20,000 nm RIU−1, maximum amplitude sensitivity is 2817.7 RIU−1, resolution is RIU, and figure of merit (FOM) is 333.3 RIU−1. This sensor can be widely used in biological tissue detection. For this type of incomplete coated D-shaped sensor, the coating material and structural parameters not only affect the performance of the sensor, but also change the RI detection range, which has been ignored or not mentioned in previous literature on PCF-SPR sensors. Therefore, the reasons for the change of RI detection interval and negative optimization caused by structural parameters were analyzed, and two different sensors with different coating widths were finally obtained, which can achieve high sensitivity sensing in their respective detection intervals. The detailed research results expand the understanding of the SPR phenomenon in D-shaped PCF and provide theoretical guidance for adjusting and improving the detection performance of PCF-SPR sensors.

Numerical investigation on the D-SPR-PQF — High Refractive Index and temperature sensor for transport fuel adulteration

This study presents a Surface Plasmonic Resonance based D-shape oval open loop Photonic Quasi-Crystal Fiber (SPR-PQF). There is necessary to concurrently measure the changes in transport fuels temperature and Refractive index for onsite fuel quality measurements. However, the sensing methods and material selectivity significantly impact the sensor performance. The Finite Element Method (FEM) is a good choice for solving the propagation characteristics of the modes using Maxwell equations. The composite coating of Silver (Ag) and Graphene Oxide (GO) in the open loop D-PQF-structure effectively tunes the SPR condition in the external sensing approach. This FEM-based numerical study reaches the high-temperature sensitivity (22 nm/ ∘C), wavelength Sensitivity (150000 nm/ RIU), amplitude Sensitivity (-6000/ RIU), Figure of Merit (FoM) (1457.85), and detection accuracy (0.66 mg/L, 1.4 mg/L) in the RI range of 1.4185 to 1.448. This sensitivity response is six times larger than the existing SPR PCF sensor in the literature. Hence, this sensor has enormous potential in the petrochemical sensing applications.

Gold coated surface plasmon resonance based biosensor: An hexagonal photonic crystal Fiber platform

We numerically analyze a dual-core gold coated photonic crystal fiber (PCF) biosensor based on surface plasmon resonance that can be used for remote sensing applications in place of the traditional prism-based biosensor. Chemically stable active plasmonic material gold (Au) are positioned outside the fiber structure as a sensing layer to facilitate the construction of the sensor. From finite element method (FEM) based simulation, it is shown that the proposed sensor has the maximum wavelength sensitivity of 5000 nm/RIU. In addition, the amplitude sensitivity of the sensor is 267.66 RIU−1 with the resolution of 2.00 × 10−5 RIU−1 having sensing range of analyte refractive index 1.30 to 1.40. The suggested two-ring dual-core PCF sensor can be used to detect biological analytes, organic compounds, biomolecules, and other unidentified analytes due to its high sensitivity, enhanced sensing resolution, and suitable linearity properties. The proposed sensor has high sensitivity and wide operating range; from visible to mid-infrared which enables the detection of various biomolecules with different absorption or fluorescence spectra. Overall, the unique combination of structural design and optical properties make hexagonal PCF biosensor a promising and innovative platform for advanced and high-performance biosensing applications.

Orthokeratology and Visual Motor Training for Juvenile Myopia

This study aimed to explore the effect of orthokeratology (Ortho-K) combined with visual motor training in juvenile myopia. We included 156 myopia patients, which were randomly divided into the combined (visual motor training+Ortho-K) and Ortho-K (only) groups. Corneal morphological changes (horizontal keratometry [HK], vertical keratometry [VK], central corneal thickness [CCT], and corneal endothelial cell density), regulatory function (positive and negative relative accommodation [PRA, NRA], regulatory amplitude, and regulatory sensitivity), ocular axis, visual acuity (naked visual acuity, Diopter), tear film stability (BUT test and SIT test), intraocular pressure, and safety before and after treatment were compared between groups. Moreover, we performed a Spearman correlation analysis on the effects of initial age and diopter on axial growth. After treatment, HK, VK of cornea, and CCT levels significantly decreased in both groups while no significant changes were found in endothelial cell density. In the combined group, there was a higher improvement in PRA, accommodation amplitude, and accommodation sensitivity, which could delay ocular axis growth and improve naked eye vision and diopters. Meanwhile, a significant negative correlation was found between patients’ initial age and axial growth. Moreover, BUT and SIT recovery in the combined group were relatively better after wearing Ortho-K. In conclusion, Ortho-K combined with visual motor training could better correct the vision of juvenile myopia patients and delay ocular axis growth. In addition, the earlier the intervention, the better the effect.

Classical interpretation for the influence of XUV pulse width on the streaking time delay and the oscillation amplitude of the momentum shift

We numerically investigate both the streaking time delay and the oscillation amplitude of the momentum shift of the photoelectron and justify them physically by developing a classical model based on the weak field approximation. The streaking time delay is insensitive to the extreme ultraviolet (XUV) pulse duration, while the oscillation amplitude obviously reduces as the XUV duration increases. This XUV duration dependence is attributed to the ionization probability of electron at initial times other than the peak of the XUV pulse. We propagate the classical electron trajectories originating at different initial times in the coupled Coulomb-laser (IR) potential and average the momentum shift for each trajectory over the width of the XUV pulse. By extracting the streaking time delay and the oscillation amplitude from this averaged momentum shift, the classical model results and the time-dependent Schrödinger equation results are found to be in good agreement. Both the insensitivity of the streaking time delay and the sensitivity of the oscillation amplitude on the XUV pulse width are well explained by our classical model considering initial ionization time average. Analytical estimation for the oscillation amplitude is obtained from the model of initial ionization time average.

3D Sensitivity Kernels With Full Attenuation Computed by a Combination of the Strong Stability Preserving Runge‐Kutta Method and the Scattering‐Integral Method

AbstractTo date developments of seismic attenuation models of the Earth have lagged those of velocity models. This is partly due to difficulties in isolating waveform perturbations caused by attenuation and velocity heterogeneities and partly due to different theories (e.g., ray theory, finite frequency theory) used in most previous and current studies to approximate sensitivity kernels and invert for the attenuation structure. We present in this paper a new method for computing the 3D waveform Fréchet kernels that account for full physical‐dispersion and dissipation attenuation. For solving the 3D isotropic anelastic wave equation described by the generalized Maxwell body model, we extend our previously proposed full waveform modeling method in Cartesian coordinates to that in spherical coordinates, which can provide stable numerical solutions even in the presence of strong attenuation. Then, we apply the scattering‐integral method for calculating 3D travel time and amplitude sensitivity kernels with respect to velocity and attenuation structures. We demonstrate the accuracy of our forward method and the effectiveness of the implementation of absorbing and free surface boundary conditions through numerical tests. Moreover, by choosing the Northwestern United States region as a realistic example, we verify the accuracy of the computed 3D sensitivity kernels through comparing the waveform measurements with predictions from the kernels. Finally, we discuss the importance of calculating full anelastic sensitivity kernels including both effects of physical dispersion and dissipation, where we specially explore the effect of scattering due to random velocity and attenuation heterogeneities on waveform measurements.

Integrating multi-influencing factor techniques and fuzzy methods to identify recommendation domains for out-scaling conservation agriculture in China

Climate-smart agriculture (CSA) is a global development strategy aimed to address the interlinked challenges of food security and climate change. Expanding the implementation of conservation agriculture (CA), a vital component of CSA, is essential for enhancing agricultural and food security resilience while sustainably managing arable land. However, the extensive heterogeneity of biophysical and socioeconomic conditions presents significant complexities in promoting CA adoption. Addressing these challenges, this study carried out a comprehensive theoretical investigation of biophysical and socioeconomic factors influencing CA adoption and performance, integrating stakeholder feedback to create a systematic and robust evaluation index system for assessing CA suitability. By integrating multi-influencing factor techniques and fuzzy logic methods, we spatially identified suitable areas for CA implementation in China, providing valuable insights for land use policy. The reliability of the models was verified through a sensitivity analysis using the map removal sensitivity analysis method and the extended Fourier amplitude sensitivity test. The results indicated that 29.78% of the cultivated land was unsuitable or marginally suitable for CA, while 29.30 and 40.92% were determined to be moderately suitable and suitable zones, respectively. Suitable cultivated land was primarily distributed in the northern arid and semi-arid regions, the Loess Plateau, the Huang-Huai-Hai Plain, and the Northeast China Plain. Conversely, unsuitable, and marginally suitable cultivated land was predominantly located in the Qinghai Tibet Plateau, Middle-lower Yangtze Plain, Sichuan Basin and surrounding areas, the Yunnan-Guizhou Plateau, and Southern China. The topographical index, annual mean precipitation, humidity index, and population density were identified as the most significant factors influencing CA suitability. The CA suitability maps generated in this study will guide the development and extension agents targeting CA to suitable locations with a high potential impact, thereby maximizing the likelihood of adoption and minimizing the risk of failure.

High-Sensitivity and Wide Detection-Range Refractive-Index Sensor Based on Amplitude Change in Slotted Photonic Crystal Nanobeam Cavity

High Q-factor is essential for the realization of high sensitivity photonic crystal-based sensors. The Q-factor is usually optimized for a specific refractive-index (RI) value of the ambient background. However, a small change in the RI reduces the Q value, and therefore limits the performance of the sensor to a narrow RI range. Here, we report a high-sensitivity RI sensor with a wide detection-range based on amplitude change of the fundamental mode in slotted photonic crystal nanobeam cavity. Both wavelength and amplitude sensitivity of 333 nm/RIU and 188/RIU are realized in the RI range from 1.3 to 1.6, respectively. To the best of our knowledge, this represents the widest sensing range ever reported in photonic crystal cavities. Owing to the wide sensing range and the insignificance of the Q value, this approach would find applications in various research areas in integrated lab-on-chip systems for optofluidic- and bio-sensing applications.

Prediction and optimization of exergetic efficiency of reactive units of a petroleum refinery under uncertainty through artificial neural network-based surrogate modeling

Process uncertainties have been a big challenge to the stable operation and control of process industries. The current study is based on the use of an artificial intelligence model as a surrogate in the online optimization of process conditions of reactive units of a petroleum refinery under uncertainty. Initially, a steady-state Aspen model was used to perform exergy analysis for quantifying the exergy efficiency, irreversibility, and improvement potential of the plant. The process model was then transformed to a dynamic mode by inserting ±5 % uncertainty in process conditions, i.e., mass flow rate, pressure, and temperature, to generate a dataset of 216 samples. An artificial neural network (ANN) model was developed using the dataset to predict exergy efficiency. The ANN model was used as a surrogate in GA and PSO environments to achieve higher exergy efficiency under uncertainty. The optimized process condition derived through GA and PSO based approaches were fed to Aspen model for cross-validation. The Fourier Amplitude Sensitivity Test (FAST) was performed to identify the most influential process conditions in terms of their effect on process exergy efficiency. The overall plant had an exergy efficiency, irreversibility, and improvement potential of 50.57 %, 34,955.55 kW, and 17,276.98 kW, respectively. The correlation coefficient of ANN model was 0.97432. The use of ANN as a surrogate in the optimization frameworks outperformed the standalone (SA) Aspen model of the process in attaining the highest exergy efficiency. Overall the performance of both the GA and the PSO based approaches were comparable. Based on sensitivity analysis, inlet temperatures of reactors were the most sensitive variables affecting the process exergy efficiency. The current study helps in laying a foundation for the simulation of Refinery 4.0.

An asymmetric slotted structured twin core photonic crystal fiber based highly sensitive surface plasmon resonance refractive index biosensor

A butterfly-shaped core photonic crystal fibers with less asymmetric rectangular and circular air holes are proposed, and their properties are numerically analyzed. The sensor construction is reduced to only seven air holes and uses surface plasmons stimulated inside gold (Au) strips for simplicity in manufacture and high sensitivity. Gold is employed as an effective plasmonic metal throughout this design to create resonant effects and is located away from the proposed structure to make production easier. Maximum amplitude sensitivity for the proposed sensor is 228.89 RIU−1, maximum wavelength sensitivity is 88,000 nm/RIU, and maximum wavelength resolution is 1.14 × 10−6RIU−1 for y-polarized mode where the figure of merit is 65.78 RIU−1 with a linearity of 0.9905. The proposed sensor can detect the RI of unspecified analytes between 1.27 and 1.40, allowing for the analysis of many different types of analytes, such as viruses, cancer cells, sugars, proteins, DNA/RNA and many more.

Design and analysis of a highly sensitive SPR based PCF biosensor with double step dual peak shift sensitivity

This paper introduces a comprehensive study of a quad-cluster multi-functional Photonic Crystal Fiber (PCF) sensor where gold and Aluminum doped with zinc oxide (AZO) were used as plasmonic materials. A maximum Amplitude Sensitivity (AS) of 5336 RIU−1 and Wavelength Sensitivity (WS) of 40,500 nm/RIU in y pol was obtained incorporating Gold as plasmonic material. When AZO was included as the plasmonic material, AS of 3763 RIU−1 & WS of 9100 nm/RIU for y polarization were determined. The RI detecting range was increased from 1.32 to 1.43 to 1.19–1.42 after using AZO instead of Au that opens up a new horizon for detection. A novel detection technique, ‘Double Step Dual Peak Shift Sensitivity (DS-DPSS)’ was proposed in sensing temperature where highest sensitivity of 1.05 nm/°C having resolution of 0.095 °C for x pol. was achieved. Due to its diverse functionality, the suggested sensor represents a significant advancement in the detection of numerous analytes in biochemical applications.

Dual core photonic crystal fiber based plasmonic refractive index sensor with ultra-wide detection range.

In this study, an ultra-wide range plasmonic refractive index sensor based on dual core photonic crystal fiber is suggested and analyzed numerically. The proposed design achieves fabrication feasibility by employing external sensing mechanism in which silver is deposited onto the flat outer surface of the fiber as plasmonic material. A thin layer of titanium oxide (TiO2) is considered on top of the silver layer for preventing its oxidation problem. The sensor attains identification of a vast array of analytes consisting a wide range of refractive indices of 1.10 - 1.45. It achieves a maximum spectral sensitivity of 24300 nm/RIU along with its corresponding resolution of 4.12 × 10-6 RIU. The maximum figure of merit of the sensor is 120 RIU-1. The sensor also supports amplitude interrogation approach and exhibits a maximum amplitude sensitivity of 172 RIU-1. The impact of the design parameters such as radius of air holes, polishing distance, thickness of silver and titanium oxide layers are investigated thoroughly. An ultra-wide detection range with high sensitivity, fabrication feasibility, and easy application make the sensor a potential candidate for detection of a wide array of bio-originated materials, chemicals, and other analytes.

Benefit of aerosol reduction to winter wheat during China's clean air action: A case study of Henan Province

A strongly declining aerosol radiative effect has been observed in China since 2013 after implementing the clean air action, yet its impact on wheat (Triticum aestivum L.) production remains unclear. We use satellite measures and a biophysical crop model to assess the impact of aerosol-induced radiative perturbations on winter wheat production in the agricultural belt of Henan province from 2013 to 2018. After calibrating parameters with the extended Fourier Amplitude Sensitivity Test (EFAST) and the generalized likelihood uncertainty estimation (GLUE) method, the DSSAT CERES-Wheat model was able to simulate crop biomass and yield more accurately. We found that the aerosol negatively impacted wheat biomass by 21.87% and yield by 22.48% from 2006 to 2018, and the biomass effects from planting to anthesis were more significant compared to anthesis to maturity. Due to the strict clean air action, under all-sky conditions, the surface solar shortwave radiation (SSR) in 2018 increased by about 7.08% over 2006-2013 during the wheat growing seasons. As a result of the improvement of crop photosynthesis, winter wheat biomass and yield increased by an average of 5.46% and 2.9%, respectively. Our findings show that crop carbon uptake and yield will benefit from the clean air action in China, helping to ensure national food and health security.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.

A comparative analysis of a D-shaped photonic crystal sensor based on graphene plasmonic

In this paper, a D-shaped photonic crystal fiber (PCF) sensor based on surface plasmon resonance has been presented. In the proposed structure, Graphene, Gold, and Silver have been used in different configurations in order to promote the sensitivity of the sensor. According to the numerical simulations performed by the finite element method (FEM) and considering the refractive index of the analyte from 1.36 to 1.40, the maximum wavelength sensitivity of 12600 nm RIU−1 has been obtained, which is related to the structure when the Gold layer sandwiched between two Graphene layers. This is while, the maximum amplitude sensitivity of the proposed sensor is 5708 (RIU−1), which is related to the configuration that Graphene has been placed under the Silver layer. On the other hand, the proposed structure is easy to use because there is no need to fill the fiber holes with the analyte and it can be easily placed on the D-shaped surface of the sensor. The proposed sensor is appropriate to sense the biological variation because of showing strong surface plasmon resonances. The effect of the fabrication tolerance on the proposed PCF is also studied. It is expected that the proposed model will be compatible with the bio-engineering approaches.