Compliant Contact Force Control for Aerial Manipulator of Adaptive Neural Network-Based Robust Control.

Aerial manipulators expand the application scenarios of manipulators into the air. To complete various operations, the contact force between the aerial manipulator and the target must be precisely controlled. In this study, we first established the mathematical models of the multirotor and the manipulator separately. Their mutual influence is regarded as each other's disturbance, and the overall linkage mechanism is established through analysis. Then, a robust sliding mode control strategy is developed for accurate trajectory tracking. The controller is derived from Lyapunov theory, which can ensure the stability of the closed-loop system. To compensate for the effect of system uncertainty, an adaptive radial basis function neural network is devised to approximate the part of the controller containing the model information. In addition, an impedance controller is designed to convert force control into position control to make the manipulator contact with the target compliantly. Finally, the simulation and experimental results indicate that the proposed method can guarantee the accuracy of the contact force and has good robustness.

Optoelectronic Torque Measurement System Based on SAPSO-RBF Algorithm.

The torque is a significant indicator reflecting the comprehensive operational characteristics of a power system. Thus, accurate torque measurement plays a pivotal role in ensuring the safety and stability of the system. However, conventional torque measurement systems predominantly rely on strain gauges adhered to the shaft, often leading to reduced accuracy, poor repeatability, and non-traceability due to the influence of strain gauge adhesion. To tackle the challenge, this paper introduces a photoelectric torque measurement system. Quadrants of photoelectric sensors are employed to capture minute deformations induced by torque on the rotational axis, converting them into measurable voltage. Subsequently, the system employs the radial basis function neural network optimized by simulated annealing combined with particle swarm algorithm (SAPSO-RBF) to establish a correlation between measured torque values and standard references, thereby calibrating the measured values. Experimental results affirm the system's capability to accurately determine torque measurements and execute calibration, minimizing measurement errors to 0.92%.

Study on the space field reconstruction method of the radial basis function of electromagnetic radiation under optimal parameters.

Electromagnetic radiation (EM) pollution has a certain impact on human life and health, and the reconstruction of the EM space field in this paper is of great practical significance for EM analysis and research. The radial basis function (RBF) sufficiently considers the influence of each sampling point and is more suitable for reconstructing the EM space field than other spatial interpolation methods. Currently, when RBF is used to reconstruct the EM space field, the optimal determination of the basis function and shape parameter (SP) is rarely considered. This ultimately leads to low reconstruction accuracy of the EM space field. Therefore, in this paper, the particle swarm optimization (PSO) is used to calculate the optimal SP of the RBF. On this basis, reliable EM space field reconstruction is performed, which helps people understand the EM distribution characteristics in actual situations from a visual perspective. The EM sampling data of a region on the Yunnan Normal University campus are used as the data source, and the RBF under the optimal parameters is used for EM reconstruction. The accuracy of its interpolation results is evaluated and compared and analyzed with inverse distance weighting (IDW) after distance index optimization. The results show that the RBF under optimal parameters reconstructs the EM space field with high accuracy and good effect, which can truly reflect the actual distribution of EM.

Electromagnetic radiation (EM) pollution has a great impact on the surrounding environment. Therefore, EM space field reconstruction can help us analyze the characteristics of the electromagnetic environment in a visual way. Radial Basis Function (RBF) is a method more suitable for EM space field reconstruction than other methods because it fully considers the influence of each sampling point. However, when currently using RBF to reconstruct the EM space field, few researchers consider how to choose the most appropriate basis function and shape parameter (SP). This results in low reconstruction accuracy. Therefore, this study uses particle swarm optimization (PSO) to find the optimal SP parameters for reliable EM space field reconstruction. The study used the EM sampling data of an area within the campus of Yunnan Normal University as the study material, and a parameter-optimized RBF method was adopted for the reconstruction of the EM space field. The reconstruction results were then evaluated for accuracy and compared and analyzed with the IDW method optimized with a distance index. Research results show that using RBF with optimal parameters to reconstruct the EM space field has high accuracy and can effectively reflect the actual EM distribution, thereby helping people better understand the characteristics of the electromagnetic environment.

[Evaluation of the accuracy of modern intraocular lens calculation formulas when optical biometry is not possible]. / Otsenka tochnosti sovremennykh formul rascheta intraokulyarnykh linz pri nevozmozhnosti vypolneniya opticheskoi biometrii.

PURPOSE: This study evaluates the accuracy of modern intraocular lens (IOL) calculation formulas using axial length (AL) data obtained by ultrasound biometry (UBM) compared to the third-generation SRK/T calculator. MATERIAL AND METHODS: The study included 230 patients (267 eyes) with severe lens opacities that prevented optical biometry, who underwent phacoemulsification (PE) with IOL implantation. IOL power calculation according to the SRK/T formula was based on AL and anterior chamber depth obtained by UBM (Tomey Biometer Al-100) and keratometry on the Topcon KR 8800 autorefractometer. To adapt AL for new generation calculators - Barrett Universal II (BUII), Hill RBF ver. 3.0 (RBF), Kane and Ladas Super Formula (LSF) - the retinal thickness (0.20 mm) was added to the axial length determined by UBM, and then the optical power of the artificial lens was calculated. The mean error and its modulus value were used as criteria for the accuracy of IOL calculation. RESULTS: A significant difference (p=0.008) in the mean IOL calculation error was found between the formulas. Pairwise analysis revealed differences between SRK/T (-0.32±0.58 D) and other formulas - BUII (-0.16±0.52 D; p=0.014), RBF (-0.17±0.51 D; p=0.024), Kane (-0.17±0.52 D; p=0.029), but not with the LSF calculator (-0.19±0.53 D; p=0.071). No significant differences between the formulas were found in terms of mean error modulus (p=0.238). New generation calculators showed a more frequent success in hitting target refraction (within ±1.00 D in more than 95% of cases) than the SRK/T formula (86%). CONCLUSION: The proposed method of adding 0.20 mm to the AL determined by UBM allows using this parameter in modern IOL calculation formulas and improving the refractive results of PE, especially in eyes with non-standard anterior segment structure.

Quantifying the effect of the nitrogen biogeochemical processes on the distribution of ammonium in the riverbank filtration system.

Ammonium (NH4+) enrichment of riverbank filtration (RBF) systems is gaining popularity. However, most previous research has concentrated on NO3- removal efficiencies, while the mechanisms of NH4+ enrichment remain unknown. A nitrogen biogeochemical process model was developed for the quantitative analysis of NH4+ enrichment in the Kaladian well field in northwest Songyuan City, NE China. Data from laboratory experiments and in-situ monitoring were used to determine initial values and calibrate the thermodynamic/kinetic parameters representing nitrogen (N) biogeochemical reactions. (1) The NO3- from river was subjected to denitrification (DNF) and dissimilatory nitrate reduction to ammonium (DNRA) within 10-14 m of the shore, whereas the NH4+ in groundwater was caused by DNRA, organic nitrogen mineralization (MIN), and mixing with laterally recharged high NH4+ groundwater. (2) DNF and DNRA were regulated by hydrodynamic processes, with the ranges of these processes being more significant in the wet season due to a higher hydraulic gradient. MIN occurred widely throughout the water flow path, with temperature primarily controlling the rates of the three reactions. (3) DNRA activity was relatively higher in the wet season when the water temperature was higher within 10-14 m of the shore. In the wet season, DNRA contributed 25%-30% to NO3- reduction, which was higher than in the dry season (5%-10%). DNRA contributed at least 40% and 15% to NH4+ enrichment in the wet and dry seasons, respectively. (4). Organic N in media gradually released NH4+ into groundwater via MIN and desorption across the entire flow path, with contributions to NH4+ enrichment reaching 75% and 85%, respectively, in the wet and dry seasons.

Fast Distributed Model Predictive Control Method for Active Suspension Systems.

In order to balance the performance index and computational efficiency of the active suspension control system, this paper offers a fast distributed model predictive control (DMPC) method based on multi-agents for the active suspension system. Firstly, a seven-degrees-of-freedom model of the vehicle is created. This study establishes a reduced-dimension vehicle model based on graph theory in accordance with its network topology and mutual coupling constraints. Then, for engineering applications, a multi-agent-based distributed model predictive control method of an active suspension system is presented. The partial differential equation of rolling optimization is solved by a radical basis function (RBF) neural network. It improves the computational efficiency of the algorithm on the premise of satisfying multi-objective optimization. Finally, the joint simulation of CarSim and Matlab/Simulink shows that the control system can greatly minimize the vertical acceleration, pitch acceleration, and roll acceleration of the vehicle body. In particular, under the steering condition, it can take into account the safety, comfort, and handling stability of the vehicle at the same time.

RBF-Based Camera Model Based on a Ray Constraint to Compensate for Refraction Error.

A camera equipped with a transparent shield can be modeled using the pinhole camera model and residual error vectors defined by the difference between the estimated ray from the pinhole camera model and the actual three-dimensional (3D) point. To calculate the residual error vectors, we employ sparse calibration data consisting of 3D points and their corresponding 2D points on the image. However, the observation noise and sparsity of the 3D calibration points pose challenges in determining the residual error vectors. To address this, we first fit Gaussian Process Regression (GPR) operating robustly against data noise to the observed residual error vectors from the sparse calibration data to obtain dense residual error vectors. Subsequently, to improve performance in unobserved areas due to data sparsity, we use an additional constraint; the 3D points on the estimated ray should be projected to one 2D image point, called the ray constraint. Finally, we optimize the radial basis function (RBF)-based regression model to reduce the residual error vector differences with GPR at the predetermined dense set of 3D points while reflecting the ray constraint. The proposed RBF-based camera model reduces the error of the estimated rays by 6% on average and the reprojection error by 26% on average.

A Novel Combined Method for Measuring the Three-Dimensional Rotational Angle of a Spherical Joint.

To improve the measurement accuracy of the three-dimensional rotation angle of a spherical joint, a novel approach is proposed in this study, which combines magnetic detection by a Hall sensor and surface feature identification by an eddy current sensor. Firstly, a permanent magnet is embedded in the ball head of a spherical joint, and Hall sensors are set and distributed in the ball socket to measure the variation in the magnetic flux density when the spherical joint rotates, which are related to the 3D rotation angle. In order to further improve the measurement accuracy and robustness, we also set grooves on the ball head and use eddy current sensors to synchronously identify the rotation angle of the ball head. After the combination of two signals is performed, a measurement model is established using the RBF neural network by training, and the real-time measurement of the 3D rotation angle of the spherical joint is realized. The feasibility and superiority of this method are validated through experiments. The experimental results indicate that the measurement accuracy is substantially promoted compared to the preliminary measurement scheme based on spherical coding; the average measurement error of the single axis is reduced by 9'9″. The root mean square errors for the measurements of the 3D rotation angles in this proposed method are as follows: pitch angle α has an error of 1'8″, yaw angle ß has an error of 2'15″, and roll angle γ has an error of 29'6″.

Adaptive Predefined-Time Sliding Mode Control for QUADROTOR Formation with Obstacle and Inter-Quadrotor Avoidance.

In this paper, aiming at the problem of control and obstacle avoidance in quadrotor formation when mathematical modeling is not accurate, the artificial potential field method with virtual force is used to plan the obstacle avoidance path of quadrotor formation to solve the problem that the artificial potential field method may fall into local optimal. The adaptive predefined-time sliding mode control algorithm based on RBF neural networks enables the quadrotor formation to track the planned trajectory in a predetermined time and also adaptively estimates the unknown interference in the mathematical model of the quadrotor to improve the control performance. Through theoretical derivation and simulation experiments, this study verified that the proposed algorithm can make the planned trajectory of the quadrotor formation avoid obstacles and make the error between the true trajectory and the planned trajectory converge within a predetermined time under the premise of adaptive estimation of unknown interference in the quadrotor model.

Production Change Optimization Model of Nonlinear Supply Chain System under Emergencies.

Aiming at the problem that the upstream manufacturer cannot accurately formulate the production plan after the link of the nonlinear supply chain system changes under emergencies, an optimization model of production change in a nonlinear supply chain system under emergencies is designed. Firstly, based on the structural characteristics of the supply chain system and the logical relationship between production, sales, and storage parameters, a three-level single-chain nonlinear supply chain dynamic system model containing producers, sellers, and retailers was established based on the introduction of nonlinear parameters. Secondly, the radial basis function (RBF) neural network and improved fast variable power convergence law were introduced to improve the traditional sliding mode control, and the improved adaptive sliding mode control is proposed so that it can have a good control effect on the unknown nonlinear supply chain system. Finally, based on the numerical assumptions, the constructed optimization model was parameterized and simulated for comparison experiments. The simulation results show that the optimized model can reduce the adjustment time by 37.50% and inventory fluctuation by 42.97%, respectively, compared with the traditional sliding mode control, while helping the supply chain system to return the smooth operation after the change within 5 days.

Design of Closed-Loop Control Schemes Based on the GA-PID and GA-RBF-PID Algorithms for Brain Dynamic Modulation.

Neurostimulation can be used to modulate brain dynamics of patients with neuropsychiatric disorders to make abnormal neural oscillations restore to normal. The control schemes proposed on the bases of neural computational models can predict the mechanism of neural oscillations induced by neurostimulation, and then make clinical decisions that are suitable for the patient's condition to ensure better treatment outcomes. The present work proposes two closed-loop control schemes based on the improved incremental proportional integral derivative (PID) algorithms to modulate brain dynamics simulated by Wendling-type coupled neural mass models. The introduction of the genetic algorithm (GA) in traditional incremental PID algorithm aims to overcome the disadvantage that the selection of control parameters depends on the designer's experience, so as to ensure control accuracy. The introduction of the radial basis function (RBF) neural network aims to improve the dynamic performance and stability of the control scheme by adaptively adjusting control parameters. The simulation results show the high accuracy of the closed-loop control schemes based on GA-PID and GA-RBF-PID algorithms for modulation of brain dynamics, and also confirm the superiority of the scheme based on the GA-RBF-PID algorithm in terms of the dynamic performance and stability. This research of making hypotheses and predictions according to model data is expected to improve and perfect the equipment of early intervention and rehabilitation treatment for neuropsychiatric disorders in the biomedical engineering field.

[Reasonability of accounting for gender in intraocular lens power calculation]. / O tselesoobraznosti ucheta gendernoi prinadlezhnosti pri raschete opticheskoi sily intraokulyarnykh linz.

PURPOSE: The study assesses the influence of gender on the accuracy of intraocular lens (IOL) power calculation by formulas SRK/T, Barrett Universal II (BUII), Ladas super formula (LSF), Hill RBF (RBF) and Kane. MATERIAL AND METHODS: The study enrolled 214 patients (106 men and 108 women) who underwent cataract phacoemulsification (PE). Optical biometry was performed on IOL-Master 500. IOL power calculation was performed either adjusting for gender (formulas SRK/T, BUII, LSF) or without such adjustment (formulas RBF, Kane). Calculation error (CE) was assessed one month after PE by comparing the achieved (autorefractometer Topcon-8800) and target spherical equivalent of refraction. RESULTS: Significant differences were found in mean IOL CE with gender-unspecific formulas (SRK/T, BUII, LSF) and no differences in gender-specific calculators (RBF, Kane). The Kane formula demonstrated the lowest CE between men and women (-0.01±0.43 versus -0.09±0.41 D; p=0.158), while the SRK/T formula had the highest CE (0.02±0.46 versus -0.21±0.44 D, respectively; p<0.001). Presence of a significant correlation between CE and gender was found for all formulas except Kane (R2=0.005, p=0.158). CONCLUSION: Patient's gender has a significant impact on IOL calculation accuracy. Using gender-responsive formulas could help achieve better refractive results with PE. The present study showed Kane formula to have the least CE dependence from gender. However, the CE difference (less than 0.25 D) was lower than the value of division (0.5D) in modern IOL models.

Workflow for automatic renal perfusion quantification using ASL-MRI and machine learning.

PURPOSE: Clinical applicability of renal arterial spin labeling (ASL) MRI is hampered because of time consuming and observer dependent post-processing, including manual segmentation of the cortex to obtain cortical renal blood flow (RBF). Machine learning has proven its value in medical image segmentation, including the kidneys. This study presents a fully automatic workflow for renal cortex perfusion quantification by including machine learning-based segmentation. METHODS: Fully automatic workflow was achieved by construction of a cascade of 3 U-nets to replace manual segmentation in ASL quantification. All 1.5T ASL-MRI data, including M0 , T1 , and ASL label-control images, from 10 healthy volunteers was used for training (dataset 1). Trained cascade performance was validated on 4 additional volunteers (dataset 2). Manual segmentations were generated by 2 observers, yielding reference and second observer segmentations. To validate the intended use of the automatic segmentations, manual and automatic RBF values in mL/min/100 g were compared. RESULTS: Good agreement was found between automatic and manual segmentations on dataset 1 (dice score = 0.78 ± 0.04), which was in line with inter-observer variability (dice score = 0.77 ± 0.02). Good agreement was confirmed on dataset 2 (dice score = 0.75 ± 0.03). Moreover, similar cortical RBF was obtained with automatic or manual segmentations, on average and at subject level; with 211 ± 31 mL/min/100 g and 208 ± 31 mL/min/100 g (P < .05), respectively, with narrow limits of agreement at -11 and 4.6 mL/min/100 g. RBF accuracy with automated segmentations was confirmed on dataset 2. CONCLUSION: Our proposed method automates ASL quantification without compromising RBF accuracy. With quick processing and without observer dependence, renal ASL-MRI is more attractive for clinical application as well as for longitudinal and multi-center studies.

Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part I: Femoral-Tibial Joint.

Osteoarthritis (OA) is a chronic, progressive disease which has over 300 million cases each year. Some of the main symptoms of OA are pain, restriction of joint motion and stiffness of the joint. Early diagnosis and treatment can prolong painless joint function. Vibroarthrography (VAG) is a cheap, reproducible, non-invasive and easy-to-use tool which can be implemented in the diagnostic route. The aim of this study was to establish diagnostic accuracy and to identify the most accurate signal processing method for the detection of OA in knee joints. In this study, we have enrolled a total of 67 patients, 34 in a study group and 33 in a control group. All patients in the study group were referred for surgical treatment due to intraarticular lesions, and the control group consisted of healthy individuals without knee symptoms. Cartilage status was assessed during surgery according to the International Cartilage Repair Society (ICRS) and vibroarthrography was performed one day prior to surgery in the study group. Vibroarthrography was performed in an open and closed kinematic chain for the involved knees in the study and control group. Signals were acquired by two sensors placed on the medial and lateral joint line. Using the neighbourhood component analysis (NCA) algorithm, the selection of optimal signal measures was performed. Classification using artificial neural networks was performed for three variants: I-open kinetic chain, II-closed kinetic chain, and III-open and closed kinetic chain. Vibroarthrography showed high diagnostic accuracy in determining healthy cartilage from cartilage lesions, and the number of repetitions during examination can be reduced only to closed kinematic chain.

A Novel Fuzzy Controller for Visible-Light Camera Using RBF-ANN: Enhanced Positioning and Autofocusing.

To obtain high-precision for focal length fitting and improve the visible-light camera autofocusing speed, simultaneously, the backlash caused by gear gaps is eliminated. We propose an improved RBF (Radical Basis Function) adaptive neural network (ANN) FUZZY PID (Proportional Integral Derivative) position closed-loop control algorithm to achieve the precise positioning of zoom and focus lens groups. Thus, the Levenberg-Marquardt iterative algorithm is used to fit the focal length, and the improved area search algorithm is applied to achieve autofocusing and eliminate backlash. In this paper, we initially adopt an improved RBF ANN fuzzy PID control algorithm in the position closed-loop in the visible-light camera position and velocity double closed-loop control system. Second, a similar triangle method is used to calibrate the focal length of the visible-light camera system, and the Levenberg-Marquardt iterative algorithm is used to fit the relation of the zoom potentiometer code values and the focal length to achieve the zoom position closed-loop control. Finally, the improved area search algorithm is used to achieve fast autofocusing and acquire clear images. The experimental results show that the ITAE (integrated time and absolute error) performance index of the improved RBF ANN fuzzy PID control algorithm is improved by more than two orders of magnitude as compared with the traditional fuzzy PID control algorithm, and the settling time is 6.4 s faster than that of the traditional fuzzy PID control. Then, the Levenberg-Marquardt iterative algorithm has a fast convergence speed, and the fitting precision is high. The quintic polynomial fitting results are basically consistent with the sixth-degree polynomial. The fitting accuracy is much better than that of the quadratic polynomial and exponential. Autofocusing requires less than 2 s and is improved by more than double that of the traditional method. The improved area search algorithm can quickly obtain clear images and solve the backlash problem.

Research on Aerial Autonomous Docking and Landing Technology of Dual Multi-Rotor UAV.

This paper studies the cooperative control of multiple unmanned aerial vehicles (UAVs) with sensors and autonomous flight capabilities. In this paper, an architecture is proposed that takes a small quadrotor as a mission UAV and a large six-rotor as a platform UAV to provide an aerial take-off and landing platform and transport carrier for the mission UAV. The design of a tracking controller for an autonomous docking and landing trajectory system is the focus of this research. To examine the system's overall design, a dual-machine trajectory-tracking control simulation platform is created via MATLAB/Simulink. Then, an autonomous docking and landing trajectory-tracking controller based on radial basis function proportional-integral-derivative control is designed, which fulfills the trajectory-tracking control requirements of the autonomous docking and landing process by efficiently suppressing the external airflow disturbance according to the simulation results. A YOLOv3-based vision pilot system is designed to calibrate the rate of the aerial docking and landing position to eight frames per second. The feasibility of the multi-rotor aerial autonomous docking and landing technology is verified using prototype flight tests during the day and at night. It lays a technical foundation for UAV transportation, autonomous take-off, landing in the air, and collaborative networking. In addition, compared with the existing technologies, our research completes the closed loop of the technical process through modeling, algorithm design and testing, virtual simulation verification, prototype manufacturing, and flight test, which have better realizability.

A new combination model using decomposition ensemble framework and error correction technique for forecasting hourly PM2.5 concentration.

PM2.5 pollutants are seriously harmful to human health, which is of great significance for the forecasting of PM2.5 concentration. To accurately forecast hourly PM2.5 concentration, a new combination model based on agreement index variational mode decomposition (AIVMD), radial basis function neural network (RBF), induced ordered weighted averaging (IOWA) operator, long short-term memory neural network (LSTM) and error correction (EC), named AIVMD-RBF-IOWA-LSTM-EC, is proposed, which uses decomposition ensemble framework and error correction technique. Taking the reduction of reconstruction error in the process of VMD as the goal, an adaptive method to determine the mode number of VMD by agreement index (AI), named AIVMD, is proposed. Firstly, PM2.5 concentration data are decomposed into simple intrinsic mode function components (IMFs) by AIVMD to reduce the complexity of the data. Secondly, LSTM and RBF models are established for each IMF component, and the prediction results of each model are combined separately. Thirdly, an error correction model based on RBF is established to correct the prediction results. The predicted values of error are not only used to correct the prediction results, but also can be used as the induced value of IOWA operators to solve the weight allocation problem. Finally, the IOWA operator is used to weight the error correction prediction results, and the final result is obtained. To solve the problem that the forecasting accuracy of the combination model based on IOWA operators is low when the complementarity between single models is poor, a combination forecasting method with complementary disadvantage based on IOWA operators is proposed, which effectively improves the robustness of the model. A formula for calculating the proportion of complementary points is given. By solving the formula, the complementarity of the models can be judged, and the method of calculating the weight of the combined model can be selected accordingly. The proposed model is used to forecast PM2.5 concentration in Xi'an, and compared with the predicted results of contrast models. The results show that the proposed model has a great advantage in short-term forecasting of PM2.5 concentration.

Suitability evaluation of potential arable land in the Mediterranean region.

The existing cultivated land in the Mediterranean region faces great pressure from various sources. A suitability evaluation of potential arable land is urgent for helping adaptation measures to mitigate the impacts of climate change and human pressure on agricultural production in the Mediterranean region. We integrated 15 biophysical and socio-economic factors from GIS and remote sensing data to perform a suitability evaluation of potential arable land in the Mediterranean region using analytical hierarchy process and radial basis function artificial neural network methods. Moreover, we analyzed the gap between potential arable land and existing cultivated land and compared the evaluation results between the analytical hierarchy process and artificial neural network methods. The results show that the suitability index of potential arable land based on artificial neural network with 6 neurons has the best correlation with average yield and average harvested area. The land area with a suitability grade over medium level accounts for 62.95% of the potential arable land area, of which 45.71% is uncultivated land. Cyprus, France, Greece, Italy, Lebanon, Portugal, Spain and Turkey have great opportunities for agricultural development. Radial basis function artificial neural network outperforms analytical hierarchy process, has better verification results, and requires less input. This study provides an initial insight into the agricultural land suitability of 16 countries around the Mediterranean Sea and introduces a research idea for agricultural land suitability evaluation.

Analysis of algal bloom intensification in mid-Ganga river, India, using satellite data and neural network techniques.

River Ganga is one of the most significant rivers in the country. This river is the adobe for numerous aquatic species and microorganisms. The color of the river suddenly changed to green due to the rise of algal bloom in the Varanasi and nearby regions of the river Ganga during May-June 2021. These algal blooms can be detrimental to the aquatic animals of the river. This study analyzes the occurrence and the possible reasons for the algal bloom generation in the river for the considered stretch. Several factors like nutrient accumulation in the river through agricultural run-off, warm river temperature, low flow condition of the river, thermal stratification, and less turbid river water can be considered as possible reasons for algal bloom development. In this work, the optical remote sensing-based Sentinel 2 datasets have been used for the duration of mid-May 2021 to mid-June 2021. These datasets have been processed in the Google Earth Engine (GEE) platform, and chlorophyll concentration has been calculated using different satellite-based indices or band ratios. The chlorophyll concentration measurements have quantified the algal bloom growth. These indices or band ratios have been analyzed using several artificial neural network (ANN) architectures like multilayer perceptron (MLP) and radial basis function (RBF) along with the in situ values. It has been found that chlorophyll concentration has been highest for the mid-June 2021 time period in the considered river stretch.

A comparison of SRK/T formula with Hill RBF 2 and Barrett Universal II in the calculation of intraocular lens power.

OBJECTIVE: To compare the accuracy of SRK/T, Barrett Universal II and Hill radial basis activation function-2 formulas in intraocular lens power calculation using different axial lengths. METHODS: The retrospective study was conducted at the Lahore General Hospital, Lahore, Pakistan, and comprised data from June to December 2020 of patients who underwent phacoemulsification with non-toric, monofocal intraocular lens implantation. Data was sorted in 3 groups on the basis of axial length; group 1 22-25mm, group 2>25mm, and group 3 <22mm. Intraocular lens power was calculated using SRK/T with IOL Master, while online calculators were used for Barrett Universal II and Hill radial basis activation function-2 formulas. Data was analysed using SPSS 21. RESULTS: Of the 100 patients, 47(347%) were males and 53(53%) were females. There were 49(49%) diabetics, and 57(57%) were right eyes. There were 77(77%) patients with mean age 62.38±9.5 in group 1, 17(17%) patients with mean age 52.59±12.78 in group 2, and 6(6%) patients with mean age 61.33+7.61 years in group 3. Mean axial length in group 1 was 23.55±0.81mm with anterior chamber depth of 3.1± 0.37mm. In group 2, mean axial length was 27.54±2.8mm, with anterior chamber depth of 3.4±0.15mm. In group 3, mean axial length was 21.74mm, with anterior chamber depth of 3.14±0.44mm. Mean prediction error of SRK/T versus Barrett Universal II was 0.092±0.041D (p=0.078), SRK/T versus Hill radial basis activation function-2 was 0.066±0.037D (p=0.221) and Barrett Universal versus Hill radial basis activation function-2 was -0.025±0.019D (p=0.553). Mean prediction error of group 1 versus group 2 was -0.105±0.14D, group 2 versus group 3 was 0.046±0.216D and group 2 versus group 3 was 0.151±0.243D (p=1.0). In 74% eyes, absolute prediction error was within ±0.5D in group 1, 64% in group 2 and 50% in group 3 for all formulas. CONCLUSIONS: SRK/T formula was found to be as reliable as Barrett Universal II and Hill radial basis activation function-2 in terms of calculating intra ocular lens power for all axial lengths.