Precision agriculture management based on a surrogate model assisted multiobjective algorithmic framework.

Sustainable intensification needs to optimize irrigation and fertilization strategies while increasing crop yield. To enable more precision and effective agricultural management, a bi-level screening and bi-level optimization framework is proposed. Irrigation and fertilization dates are obtained by upper-level screening and upper-level optimization. Subsequently, due to the complexity of the problem, the lower-level optimization uses a data-driven evolutionary algorithm, which combines the fast non-dominated sorting genetic algorithm (NSGA-II), surrogate-assisted model of radial basis function and Decision Support System for Agrotechnology Transfer to handle the expensive objective problem and produce a set of optimal solutions representing a trade-off between conflicting objectives. Then, the lower-level screening quickly finds better irrigation and fertilization strategies among thousands of solutions. Finally, the experiment produces a better irrigation and fertilization strategy, with water consumption reduced by 44%, nitrogen application reduced by 37%, and economic benefits increased by 7 to 8%.

An aphid inspired metaheuristic optimization algorithm and its application to engineering.

The biologically inspired metaheuristic algorithm obtains the optimal solution by simulating the living habits or behavior characteristics of creatures in nature. It has been widely used in many fields. A new bio-inspired algorithm, Aphids Optimization Algorithm (AOA), is proposed in this paper. This algorithm simulates the foraging process of aphids with wings, including the generation of winged aphids, flight mood, and attack mood. Concurrently, the corresponding optimization models are presented according to the above phases. At the phase of the flight mood, according to the comprehensive influence of energy and the airflow, the individuals adaptively choose the flight mode to migrate; at the phase of attack mood, individuals use their sense of smell and vision to locate food sources for movement. Experiments on benchmark test functions and two classical engineering design problems, indicate that the desired AOA is more efficient than other metaheuristic algorithms.

Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition.

Intradialytic hypotension (IDH) is a serious complication of hemodialysis (HD), with an incidence of more than 20%. IDH induces ischemic organ damage and even reduces the ultrafiltration and duration of HD sessions. Frequent attacks of IDH are a risk factor for death in HD patients. Malnutrition is common in HD patients and is also associated with mortality. Although the link between IDH episodes and malnutrition has been observed in practice, it has not been supported by the data. To study the relationship, we propose a promising hybrid model called BSCWJAYA_KELM, which is a wrapper feature selection method based on a variant of the JAYA optimization algorithm (SCWJAYA) and Kernel extreme learning machine (KELM). In this paper, we verify the optimization capability of the SCWJAYA algorithm in the model by comparing experiments with some state-of-the-art methods for IEEE CEC2014, IEEE CEC2017, and IEEE CEC2019 benchmark functions. The prediction accuracy of BSCWJAYA_KELM is validated by the public datasets and the HD dataset. In the experiments on the HD dataset, 1940 HD sessions of 178 HD patients are analyzed by the developed BSCWJAYA_KELM model. The key indicators selected from vast amounts of data are serum uric acid, dialysis vintage, age, diastolic pressure, and albumin. The BSCWJAYA_KELM method is a stable and excellent prediction model that can achieve a more accurate prediction of IDH.

Directional mutation and crossover boosted ant colony optimization with application to COVID-19 X-ray image segmentation.

This paper focuses on the study of Coronavirus Disease 2019 (COVID-19) X-ray image segmentation technology. We present a new multilevel image segmentation method based on the swarm intelligence algorithm (SIA) to enhance the image segmentation of COVID-19 X-rays. This paper first introduces an improved ant colony optimization algorithm, and later details the directional crossover (DX) and directional mutation (DM) strategy, XMACO. The DX strategy improves the quality of the population search, which enhances the convergence speed of the algorithm. The DM strategy increases the diversity of the population to jump out of the local optima (LO). Furthermore, we design the image segmentation model (MIS-XMACO) by incorporating two-dimensional (2D) histograms, 2D Kapur's entropy, and a nonlocal mean strategy, and we apply this model to COVID-19 X-ray image segmentation. Benchmark function experiments based on the IEEE CEC2014 and IEEE CEC2017 function sets demonstrate that XMACO has a faster convergence speed and higher convergence accuracy than competing models, and it can avoid falling into LO. Other SIAs and image segmentation models were used to ensure the validity of the experiments. The proposed MIS-XMACO model shows more stable and superior segmentation results than other models at different threshold levels by analyzing the experimental results.

An optimized machine learning framework for predicting intradialytic hypotension using indexes of chronic kidney disease-mineral and bone disorders.

Intradialytic hypotension (IDH) is the most common acute complication in hemodialysis (HD) sessions and is associated with increased morbidity and mortality in HD patients. To prevent the episode of IDH, it is critical to predict its occurrence. Chronic kidney disease-mineral and bone disorders (CKD-MBD) induce cardiac and vascular calcification, which impairs the compensatory mechanisms of blood pressure during HD. In this study, we proposed a feature selection framework called BSWEGWO_KELM to analyze 1940 records from 178 HD patients, which was based on an enhanced grey wolf optimization (GWO) algorithm and the kernel extreme learning machine (KELM). Then, global optimization experiments, together with feature selection experiments on public data sets and HD dataset, were performed to verify the effectiveness of the BSWEGWO_KELM method. The experimental results showed that the established BSWEGWO_KELM had the capability of screening out the key indicators such as dialysis vintage, mean arterial pressure (MAP), alkaline phosphatase (ALP), and intact parathyroid hormone (iPTH). Consequently, BSWEGWO_KELM can be applied as a practical and accurate method to predict IDH.

Horizontal and vertical search artificial bee colony for image segmentation of COVID-19 X-ray images.

The artificial bee colony algorithm (ABC) has been successfully applied to various optimization problems, but the algorithm still suffers from slow convergence and poor quality of optimal solutions in the optimization process. Therefore, in this paper, an improved ABC (CCABC) based on a horizontal search mechanism and a vertical search mechanism is proposed to improve the algorithm's performance. In addition, this paper also presents a multilevel thresholding image segmentation (MTIS) method based on CCABC to enhance the effectiveness of the multilevel thresholding image segmentation method. To verify the performance of the proposed CCABC algorithm and the performance of the improved image segmentation method. First, this paper demonstrates the performance of the CCABC algorithm itself by comparing CCABC with 15 algorithms of the same type using 30 benchmark functions. Then, this paper uses the improved multi-threshold segmentation method for the segmentation of COVID-19 X-ray images and compares it with other similar plans in detail. Finally, this paper confirms that the incorporation of CCABC in MTIS is very effective by analyzing appropriate evaluation criteria and affirms that the new MTIS method has a strong segmentation performance.

Detection of pulmonary embolism severity using clinical characteristics, hematological indices, and machine learning techniques.

Introduction: Pulmonary embolism (PE) is a cardiopulmonary condition that can be fatal. PE can lead to sudden cardiovascular collapse and is potentially life-threatening, necessitating risk classification to modify therapy following the diagnosis of PE. We collected clinical characteristics, routine blood data, and arterial blood gas analysis data from all 139 patients. Methods: Combining these data, this paper proposes a PE risk stratified prediction framework based on machine learning technology. An improved algorithm is proposed by adding sobol sequence and black hole mechanism to the cuckoo search algorithm (CS), called SBCS. Based on the coupling of the enhanced algorithm and the kernel extreme learning machine (KELM), a prediction framework is also proposed. Results: To confirm the overall performance of SBCS, we run benchmark function experiments in this work. The results demonstrate that SBCS has great convergence accuracy and speed. Then, tests based on seven open data sets are carried out in this study to verify the performance of SBCS on the feature selection problem. To further demonstrate the usefulness and applicability of the SBCS-KELM framework, this paper conducts aided diagnosis experiments on PE data collected from the hospital. Discussion: The experiment findings show that the indicators chosen, such as syncope, systolic blood pressure (SBP), oxygen saturation (SaO2%), white blood cell (WBC), neutrophil percentage (NEUT%), and others, are crucial for the feature selection approach presented in this study to assess the severity of PE. The classification results reveal that the prediction model's accuracy is 99.26% and its sensitivity is 98.57%. It is expected to become a new and accurate method to distinguish the severity of PE.

Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation.

Breast cancer is one of the most dangerous diseases for women's health, and it is imperative to provide the necessary diagnostic assistance for it. The medical image processing technology is one of the most critical of all complementary diagnostic technologies. Image segmentation is the core step of image processing, where multilevel image segmentation is considered one of the most efficient and straightforward methods. Many multilevel image segmentation methods based on evolutionary and population-based methods have been proposed in recent years, but many have the fatal weakness of poor convergence accuracy and the tendency to fall into local optimum. Therefore, to overcome these weaknesses, this paper proposes a modified differential evolution (MDE) algorithm with a vision based on the slime mould foraging behavior, where the recently proposed slime mould algorithm (SMA) inspires it. Besides, to obtain high-quality breast cancer image segmentation results, this paper also develops an excellent MDE-based multilevel image segmentation model, the core of which is based on non-local means 2D histogram and 2D Kapur's entropy. To effectively validate the performance of the proposed method, a comparison experiment between MDE and its similar algorithms was first carried out on IEEE CEC 2014. Then, an initial validation of the MDE-based multilevel image segmentation model was performed by utilizing a reference image set. Finally, the MDE-based multilevel image segmentation model was compared with peers using breast invasive ductal carcinoma images. A series of experimental results have proved that MDE is an evolutionary algorithm with high convergence accuracy and the ability to jump out of the local optimum, as well as effectively demonstrated that the developed model is a high-quality segmentation method that can provide practical support for further research of breast invasive ductal carcinoma pathological image processing.

First Report of Severe Autoimmune Hemolytic Anemia During Eltrombopag Therapy in Waldenström Macroglobulinemia-Associated Thrombocytopenia.

Autoimmune thrombocytopenia (ITP) and autoimmune hemolytic anemia (AIHA) can be observed in Waldenström macroglobulinemia (WM). The autoimmune disorders are primarily mediated by autoimmune monoclonal gammopathy, but drug-induced hemolysis should also be considered. Herein, we presented the case of a 63-year-old female WM patient complicated with ITP, who was admitted to our department with a complaint of abdominal pain. After first half of bortezomib/dexamethasone/rituximab (BRD) chemotherapy, her platelet level recovered, but subsequently decreased to extremely low level (around 1-2×109/L), and the patient suffered from platelet transfusion refractoriness. During the management of refractory thrombocytopenia, the patient developed severe hemolytic anemia, and further tests confirmed warm AIHA. FcγRIIα polymorphism test showed that the patient had FcγRIIα-131RH, which implied that the AIHA may not be WM-related. Given the effects of ibrutinib in controlling WM, secondary AITP and AIHA, ibrutinib single treatment was started, which quickly corrected the thrombocytopenia within five days, but not hemolysis. With a relatively safe platelet level, eltrombopag was stopped, and the hemolysis relieved three days after eltrombopag withdrawal. This is the first report on eltrombopag-induced AIHA in the management of WM-associated ITP.

Ant colony optimization with Cauchy and greedy Levy mutations for multilevel COVID 19 X-ray image segmentation.

This paper focuses on the study of multilevel COVID-19 X-ray image segmentation based on swarm intelligence optimization to improve the diagnostic level of COVID-19. We present a new ant colony optimization with the Cauchy mutation and the greedy Levy mutation, termed CLACO, for continuous domains. Specifically, the Cauchy mutation is applied to the end phase of ant foraging in CLACO to enhance its searchability and to boost its convergence rate. The greedy Levy mutation is applied to the optimal ant individuals to confer an improved ability to jump out of the local optimum. Furthermore, this paper develops a novel CLACO-based multilevel image segmentation method, termed CLACO-MIS. Using 2D Kapur's entropy as the CLACO fitness function based on 2D histograms consisting of non-local mean filtered images and grayscale images, CLACO-MIS was successfully applied to the segmentation of COVID-19 X-ray images. A comparison of CLACO with some relevant variants and other excellent peers on 30 benchmark functions from IEEE CEC2014 demonstrates the superior performance of CLACO in terms of search capability, and convergence speed as well as ability to jump out of the local optimum. Moreover, CLACO-MIS was shown to have a better segmentation effect and a stronger adaptability at different threshold levels than other methods in performing segmentation experiments of COVID-19 X-ray images. Therefore, CLACO-MIS has great potential to be used for improving the diagnostic level of COVID-19. This research will host a webservice for any question at https://aliasgharheidari.com.

Online Determination of Graphene Lattice Orientation Through Lateral Forces.

Rapid progress in graphene engineering has called for a simple and effective method to determine the lattice orientation on graphene before tailoring graphene to the desired edge structures and shapes. In this work, a wavelet transform-based frequency identification method is developed to distinguish the lattice orientation of graphene. The lattice orientation is determined through the different distribution of the frequency power spectrum just from a single scan line. This method is proven both theoretically and experimentally to be useful and controllable. The results at the atomic scale show that the frequencies vary with the lattice orientation of graphene. Thus, an adjusted angle to the desired lattice orientation (zigzag or armchair) can easily be calculated based on the frequency obtained from the single scan line. Ultimately, these results will play a critical role in wafer-size graphene engineering and in the manufacturing of graphene-based nanodevices.