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Abstract The distorted Born iterative (DBI) method is considered to obtain images with high-contrast and resolution. Besides satisfying the Born approximation condition, the frequency-hopping (FH) technique is necessary to gradually update the sound contrast from the first iteration and progress to the actual sound contrast of the imaged object in subsequent iterations. Inspired by the fact that the higher the frequency, the higher the resolution. Because low-frequency allows for low-resolution object imaging, hence for high-resolution imaging requirements, using low-frequency to possess a high-resolution image from the first iteration will be less efficient. For an effective reconstruction, the object’s resolution at low frequencies should be small. And similarly, with high frequencies, the object resolution should be larger. Therefore, in this paper, the FH, and the resolution-turning (RT) technique are proposed to obtain object images with high-contrast and -resolution. The convergence speed in the initial iterations is rapidly achieved by utilizing low frequency in the frequency-turning technique and low image resolution in the resolution-turning technique. It is crucial to ensure accurate object reconstruction for subsequent iterations. The desired spatial resolution is attained by employing high frequency and large image resolution. The resolution-turning distorted Born iterative (RT-DBI) and frequency-hopping distorted Born iterative (FH-DBI) solutions are thoroughly investigated to exploit their best performance. This makes sense because if it is not good to choose the number of iterations for the frequency f 1 in FH-DBI and for the resolution of N 1 × N 1 in RT-DBI, then these solutions give even worse quality than traditional DBI. After that, the RT-FH-DBI integration was investigated in two sub-solutions. We found that the lower frequency f 1 used both before and after the RT would get the best performance. Consequently, compared to the traditional DBI approaches, the normalized error and total runtime for the reconstruction process were dramatically decreased, at 83.6% and 18.6%, respectively. Besides fast and quality imaging, the proposed solution RT-FH-DBI is promised to produce high-contrast and high-resolution object images, aiming at object reconstruction at the biological tissue. The development of 3D imaging and experimental verification will be studied further.

In pharmacokinetics, the clinical information collected from the patient is often much less than the complexity of the patient’s internal operations, hence the undetermined inverse problem has emerged as a challenge to solve it and find multiple possible point sets for considering the many possible implications of drug kinetics in the patient’s body. This paper suggests two enhanced schemes for the early cluster Newton method (CNM) to concomitantly explore a great solutions number for the inverse parameter determination in pharmacokinetics. The first scheme is the application of Tikhonov regularization to deal with the overdetermined system for hyperplane fitting in the CNM, and the second is an effective iterative strategy by tuning perturbation-level for the CNM. As a result of Tikhonov’s filtering operation, lower order singular values than the regularization parameter, that are to blame for the instability of the matrix equation, are efficiently eliminated. With perturbation-level tuning, following every iteration, as the point cluster (PoC) gets near the solution manifold (SoM), it is essential to lessen the level of perturbation in the patient’s clinical measurement data and this is suited for a numerical stabilization. Numerical simulation scenarios of two schemes have revealed that these suggested schemes can lower the iterations number and computed time, and PoC move more steadily towards the solutions manifold.

The precise evaluation of the Muskingum model (MM) parameters is quite critical for routing flood waves for achieving flood control in open channels. The MM is one of the popular techniques adopted for flood routing. Estimation of the MM parameters so as to provide the best fit for the observed and computed flow values is a global optimization problem. Several optimization techniques have been adopted in the past to serve this purpose, but efficient optimization algorithms are needed to overcome the local optima issues and improvement of accuracy. In this paper, the efficiency of three optimization algorithms, namely Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE), has been assessed in the evaluation of the Muskingum parameters. The sum of the square deviation of the observed outflow and computed outflow (SSQ) is considered an objective in this MM optimization problem. Also, a constraint is proposed in this paper to help the optimization algorithms in finding the optimal global solutions. The simulation results show that the sum of the square deviation of the observed outflow and computed outflow (SSQ) was the least for SaDE, followed by CMAES. HIGHLIGHTS Precise evaluation of Muskingum model (MM) parameters is quite critical for routing flood waves.; Efficient optimization algorithms are needed to overcome local optima issues in the estimation of the Muskingum parameters.; Jaya, Covariance Matrix Adaption-Evolution Strategy (CMAES) and self-adaptive differential evolution (SaDE) have been assessed.; SaDE shows the best performance followed by CMAES.;

As a promising optical method used in a variety of applications surface plasmon resonance (SPR) sensors are employed over a wide range of boundaries. This research proposes a highly sensitive SPR based sensor with a novel hybrid structure using transition metal dichalcogenides (e.g. WSe2, PtSe2) along with black phosphorene (BP) through comprehensive numerical study. To analyze and evaluate the performances of the proposed sensor, the widely used transfer matrix method (TMM) was used. The performances of the sensor were measured in terms of reflectivity, sensitivity, detection accuracy (DA), and figure of merit (FOM). The sensor structure was optimized by changing different structural parameters of the hybrid architecture to obtain better performances. The results revealed that insertion of PtSe2 with WSe2 and BP over a gold layer of the conventional structure improved the performance of the sensor and the maximum sensitivity of the sensor was measured as 200 deg/RIU with a FOM of 17.70 RIU−1. As well, the light penetration through the optimized sensor is investigated using the finite element method (FEM) based software. With this kind of high sensing capabilities, it may be convinced that the proposed sensor can be applied in different fields of biosensing to detect liquid biological and biochemical samples or analytes.

The detection of microorganisms like Pseudomonas are very important as they trigger an infection in human blood, lungs, and different parts of the body causing various ailments. In this paper, a surface plasmon resonance (SPR) biosensor based on photonic crystal fiber (PCF) has been proposed to detect the presence of Pseudomonas bacteria with attractive performance characteristics. The sensor is designed using a simple circular lattice of PCF, coated with a thin chemically stable gold layer. The performance investigation of the sensor is numerically carried out by using a finite element (FE) based simulation tool where the highest wavelength and amplitude sensitivity are found as 20,000 nm/RIU and 1380 RIU−1, respectively. The sensor shows an excellent spectral resolution of the highest value of $5.26\times 10^{-6}$ RIU, ensuring the capability of identifying a very small change in analyte refractive index (RI) within the range of 1.33 to 1.42. The performance investigation is also carried out altering the diameter of air holes, pitch, and gold layer thickness to explore the variation in phase matching conditions due to the change in structural parameters. As the sensor is adept at detecting the sample with high sensitivity and sensing resolution, the proposed sensor can be highly efficient in detecting Pseudomonas bacteria as well as other organic compounds, and biological analytes.

Rapid increase in scholarly publications on the web has posed a new challenge to the researchers in finding highly relevant and important research articles associated with a particular area of interest. Even a highly relevant paper is sometimes missed especially for novice researchers due to lack of knowledge and experience in finding and accessing the most suitable articles. Scholarly recommender system is a very appropriate tool for this purpose that can enable researchers to locate relevant publications easily and quickly. However, the main downside of the existing approaches is that their effectiveness is dependent on priori user profiles and thus, they cannot recommend papers to the new users. Furthermore, the system uses both public and non-public metadata and therefore, the system is unable to find similarities between papers efficiently due to copyright restrictions. Considering the above challenges, in this research work, a novel hybrid approach is proposed that separately combines a Content Based Filtering (CBF) recommender module and a Collaborative Filtering (CF) recommender module. Unlike previous CBF and CF approaches, public contextual metadata and paper-citation relationship information are effectively incorporated into these two approaches separately to enhance the recommendation accuracy. In order to verify the effectiveness of the proposed approach, publicly available datasets were employed. Experimental results demonstrate that the proposed approach outperforms the baseline approaches in terms of standard metrics (precision, recall, F1-measure, mean average precision, and mean reciprocal rank), indicating that the proposed approach is more efficient in recommending scholarly publications.

In this era of COVID19, proper diagnosis and treatment of pneumonia are very important. Chest X-Ray (CXR) image analysis plays a vital role in the reliable diagnosis of pneumonia. An experienced radiologist is required for this. However, even for an experienced radiographer, it is quite challenging and time-consuming to diagnose accurately due to the fuzziness of CXR images. Also, identification can be erroneous due to the involvement of human judgement. Hence, an authentic and automated system can play an important role here. In this era of cutting-edge technology, deep learning (DL) is highly used in every sector. There are several existing methods to diagnose pneumonia but they have accuracy problems. In this study, an automatic pneumonia detection system has been proposed by applying the extreme learning machine (ELM) on the Kaggle CXR images (Pneumonia). Three models have been studied: classification using extreme learning machine (ELM), ELM with a hybrid convolutional neural network-principal component analysis (CNN-PCA) based feature extraction, and CNN-PCA-ELM with the CXR images which are contrast-enhanced by contrast limited adaptive histogram equalization (CLAHE). Among these three proposed methods, the final model provides an optimistic result. It achieves the recall score of 98% and accuracy score of 98.32% for multiclass pneumonia classification. On the other hand, a binary classification achieves 100% recall and 99.83% accuracy. The proposed method also outperforms the existing methods. The outcome has been compared using several benchmarks that include accuracy, precision, recall, etc.

In this article, a simple controller design with the fractional order calculations applies to unstable cascade processes with a significant time delay. Series cascade control system consists of two loops in which the inner loop is designed using the IMC principle based on the synthesis method. The primary controller is designed with the FOPI-FOPD controller to ensure stable and satisfactory closed-loop performances for the unstable processes. For the primary controller, five tuning parameters are involved, which tuned using the centroid of the convex stability region method. Different examples are given to illustrate the superiority of the proposed design over some existing designs. Results obtained from simulation reveals that with the proposed control design, enhanced closed-loop control performances are obtained for nominal and perturbed conditions.

Solar energy is the kind of alternative energy that a photovoltaic (PV) system may generate. The PV system depends on the environmental conditions present at the moment, such as the humidity level, surface temperature of the PV system, and wind speed. These are the most important characteristics to consider when calculating the amount of electricity generated by the PV system. Therefore, the calculation of power production is not a simple task. In the meanwhile, we are working on developing the machine learning algorithm that will be used to estimate electricity production using the fundamental characteristics that are now accessible. Now, we begin the process of machine learning by developing a time series model since the essential parameters will change over time. We get preliminary information from four distinct locations around the country. We arrived at this conclusion by using the ANN and regression modeling approaches for best to create the most significant overall performance across all cities.

Dextran sulfate (DXS) is a hydrophilic, non-toxic, biodegradable, biocompatible and safe biopolymer. These biomedically relevant characteristics make DXS a promising building block in the development of nanocarrier systems for several biomedical applications, including imaging and drug delivery. DXS polyanion can bind with metal oxide nanomaterials, biological receptors and therapeutic drug molecules. By taking advantage of these intriguing properties, DXS is used to functionalize or construct nanocarriers for specific applications. In particular, the diagnostic or therapeutic active agent-loaded DXS nanoparticles are prepared by simple coating, formation of polyelectrolyte complexes with other positively charged polymers or through self-assembly of amphiphilic DXS derivatives. These nanoparticles show a potential to localize the active agents at the pathological site and minimize undesired side effects. As DXS can recognize and be taken up by macrophage surface receptors, it is also used as a targeting ligand for drug delivery. Besides as a nanocarrier scaffold material, DXS has intrinsic therapeutic potential. DXS binds to thrombin, acts as an anticoagulant and exhibits an inhibitory effect against coagulation, retrovirus, scrapie virus and human immunodeficiency virus (HIV). Herein, biomedical applications involving the use of DXS as nanocarriers for drugs, biomolecules, and imaging agents have been reviewed. A special focus has been made on strategies used for loading and delivering of drugs and biomolecules meant for treating several diseases, including cancer, inflammatory diseases and ocular disease. [ABSTRACT FROM AUTHOR]