Meta-heuristic optimization algorithms based feature selection for joint moment prediction of sit-to-stand movement using machine learning algorithms.

The sit-to-stand (STS) movement is fundamental in daily activities, involving coordinated motion of the lower extremities and trunk, which leads to the generation of joint moments based on joint angles and limb properties. Traditional methods for determining joint moments often involve sensors or complex mathematical approaches, posing limitations in terms of movement restrictions or expertise requirements. Machine learning (ML) algorithms have emerged as promising tools for joint moment estimation, but the challenge lies in efficiently selecting relevant features from diverse datasets, especially in clinical research settings. This study aims to address this challenge by leveraging metaheuristic optimization algorithms to predict joint moments during STS using minimal input data. Motion analysis data from 20 participants with varied mass and inertia properties are utilized, and joint angles are computed alongside simulations of joint moments. Feature selection is performed using the Manta Ray Foraging Optimization (MRFO), Marine Predators Algorithm (MPA), and Equilibrium Optimizer (EO) algorithms. Subsequently, Decision Tree Regression (DTR), Random Forest Regression (RFR), Extra Tree Regression (ETR), and eXtreme Gradient Boosting Regression (XGBoost Regression) ML algorithms are deployed for joint moment prediction. The results reveal EO-ETR as the most effective algorithm for ankle, knee, and neck joint moment prediction, while MPA-ETR exhibits superior performance for hip joint prediction. This approach demonstrates potential for enhancing accuracy in joint moment estimation with minimal feature input, offering implications for biomechanical research and clinical applications.

An improved manta ray foraging optimization algorithm.

The Manta Ray Foraging Optimization Algorithm (MRFO) is a metaheuristic algorithm for solving real-world problems. However, MRFO suffers from slow convergence precision and is easily trapped in a local optimal. Hence, to overcome these deficiencies, this paper proposes an Improved MRFO algorithm (IMRFO) that employs Tent chaotic mapping, the bidirectional search strategy, and the Levy flight strategy. Among these strategies, Tent chaotic mapping distributes the manta ray more uniformly and improves the quality of the initial solution, while the bidirectional search strategy expands the search area. The Levy flight strategy strengthens the algorithm's ability to escape from local optimal. To verify IMRFO's performance, the algorithm is compared with 10 other algorithms on 23 benchmark functions, the CEC2017 and CEC2022 benchmark suites, and five engineering problems, with statistical analysis illustrating the superiority and significance of the difference between IMRFO and other algorithms. The results indicate that the IMRFO outperforms the competitor optimization algorithms.

Optimized fuzzy K-nearest neighbor approach for accurate lung cancer prediction based on radial endobronchial ultrasonography.

Radial endobronchial ultrasonography (R-EBUS) has been a surge in the development of new ultrasonography for the diagnosis of pulmonary diseases beyond the central airway. However, it faces challenges in accurately pinpointing the location of abnormal lesions. Therefore, this study proposes an improved machine learning model aimed at distinguishing between malignant lung disease (MLD) from benign lung disease (BLD) through R-EBUS features. An enhanced manta ray foraging optimization based on elite perturbation search and cyclic mutation strategy (ECMRFO) is introduced at first. Experimental validation on 29 test functions from CEC 2017 demonstrates that ECMRFO exhibits superior optimization capabilities and robustness compared to other competing algorithms. Subsequently, it was combined with fuzzy k-nearest neighbor for the classification prediction of BLD and MLD. Experimental results indicate that the proposed modal achieves a remarkable prediction accuracy of up to 99.38%. Additionally, parameters such as R-EBUS1 Circle-dense sign, R-EBUS2 Hemi-dense sign, R-EBUS5 Onionskin sign and CCT5 mediastinum lymph node are identified as having significant clinical diagnostic value.

Hydrodynamic analysis of fin-fin interactions in two-manta-ray schooling in the vertical plane.

This study investigates the interaction of a two-manta-ray school using computational fluid dynamics simulations. The baseline case consists of two in-phase undulating three-dimensional manta models arranged in a stacked configuration. Various vertical stacked and streamwise staggered configurations are studied by altering the locations of the top manta in the upstream and downstream directions. Additionally, phase differences between the two mantas are considered. Simulations are conducted using an in-house developed incompressible flow solver with an immersed boundary method. The results reveal that the follower will significantly benefit from the upstroke vortices (UVs) and downstroke vortices depending on its streamwise separation. We find that placing the top manta 0.5 body length (BL) downstream of the bottom manta optimizes its utilization of UVs from the bottom manta, facilitating the formation of leading-edge vortices (LEVs) on the top manta's pectoral fins during the downstroke. This LEV strengthening mechanism, in turn, generates a forward suction force on the follower that results in a 72% higher cycle-averaged thrust than a solitary swimmer. This benefit harvested from UVs can be further improved by adjusting the phase of the top follower. By applying a phase difference ofπ/3to the top manta, the follower not only benefits from the UVs of the bottom manta but also leverages the auxiliary vortices during the upstroke, leading to stronger tip vortices and a more pronounced forward suction force. The newfound interaction observed in schooling studies offers significant insights that can aid in the development of robot formations inspired by manta rays.

Optimizing the Probabilistic Neural Network Model with the Improved Manta Ray Foraging Optimization Algorithm to Identify Pressure Fluctuation Signal Features.

To improve the identification accuracy of pressure fluctuation signals in the draft tube of hydraulic turbines, this study proposes an improved manta ray foraging optimization (ITMRFO) algorithm to optimize the identification method of a probabilistic neural network (PNN). Specifically, first, discrete wavelet transform was used to extract features from vibration signals, and then, fuzzy c-means algorithm (FCM) clustering was used to automatically classify the collected information. In order to solve the local optimization problem of the manta ray foraging optimization (MRFO) algorithm, four optimization strategies were proposed. These included optimizing the initial population of the MRFO algorithm based on the elite opposition learning algorithm and using adaptive t distribution to replace its chain factor to optimize individual update strategies and other improvement strategies. The ITMRFO algorithm was compared with three algorithms on 23 test functions to verify its superiority. In order to improve the classification accuracy of the probabilistic neural network (PNN) affected by smoothing factors, an improved manta ray foraging optimization (ITMRFO) algorithm was used to optimize them. An ITMRFO-PNN model was established and compared with the PNN and MRFO-PNN models to evaluate their performance in identifying pressure fluctuation signals in turbine draft tubes. The evaluation indicators include confusion matrix, accuracy, precision, recall rate, F1-score, and accuracy and error rate. The experimental results confirm the correctness and effectiveness of the ITMRFO-PNN model, providing a solid theoretical foundation for identifying pressure fluctuation signals in hydraulic turbine draft tubes.

Application of the novel manta-ray foraging algorithm to optimize acidic peptidase production in solid-state fermentation using binary agro-industrial waste.

Peptidases, which constitute about 20% of the global enzyme market, have found applications in detergent, food and pharmaceutical industries, and could be produced on a large scale using low-cost agro-industrial waste. An acidophilic Bacillus cereus strain produced acidic peptidase on binary-agro-industrial waste comprising yam peels and fish processing waste at pH 4.5 with high catalytic activity. A five-variable central composite rotatable design of a response surface methodology was used to model bioprocess conditions for improved peptidase production in solid-state fermentation. Data generated was leveraged as the basis for applying the novel Manta-ray foraging optimization-linked feed-forward artificial neural network to predict bioprocess conditions optimally. Results obtained from the optimization experiments revealed a significant coefficient of determination of 0.9885 with low-performance error. The bioprocess predicted a peptidase activity of 1035.32 U/mL under optimized conditions set as 54.8 g/100 g yam peels, 23.85 g/100 g fish waste, 0.31 g/100 g CaCl2, 47.54% (v/w) moisture content, and pH 2. Peptidase activity was improved 5-fold, and was stable for 240 min between pH 2.5 and 3.5. Michaelis-Menten kinetics revealed a Km of 0.119 mM and a catalytic efficiency of 45462.19 mM-1 min-1. The bioprocess holds promise for sustainable enzyme-driven applications.

Carbon emissions prediction considering environment protection investment of 30 provinces in China.

In the backdrop of carbon peaking and carbon neutrality, carbon emissions have always been a major concern. The approach of the heterogeneity grey model is proposed, aiming to predict carbon emissions of 30 provinces in China. This model combines the manta ray foraging optimization algorithm to search for the optimal heterogeneity coefficient. By using the heterogeneity grey model, the carbon emissions are analyzed in 30 provinces of China from 2022 to 2030 considering different environmental protection investment scenarios. The results indicate that in 19 provinces from 2022 to 2030, there is a significant decrease in carbon emissions as government investment increases. In 11 provinces during the same period, there is a rising trend in carbon emissions with the increase of government investment. Hence, achieving a reduction in carbon emissions necessitates not only relying on government investment in environmental protection but also exploring alternative approaches to mitigate carbon emissions. The methodologies and conclusions proposed in this study can provide technical references and making decision references for provincial carbon emission efforts.

Hybrid Manta Ray Foraging Algorithm with Cuckoo Search for Global Optimization and Three-Dimensional Wireless Sensor Network Deployment Problem.

In this paper, a new hybrid Manta Ray Foraging Optimization (MRFO) with Cuckoo Search (CS) algorithm (AMRFOCS) is proposed. Firstly, quantum bit Bloch spherical coordinate coding is used for the initialization of the population, which improves the diversity of the expansion of the traversal ability of the search space. Secondly, the dynamic disturbance factor is introduced to balance the exploratory and exploitative search ability of the algorithm. Finally, the unique nesting strategy of the cuckoo and Levy flight is introduced to enhance the search ability. AMRFOCS is tested on CEC2017 and CEC2020 benchmark functions, which is also compared and tested by using different dimensions and other state-of-the-art metaheuristic algorithms. Experimental results reveal that the AMRFOCS algorithm has a superior convergence rate and optimization precision. At the same time, the nonparametric Wilcoxon signed-rank test and Friedman test show that the AMRFOCS has good stability and superiority. In addition, the proposed AMRFOCS is applied to the three-dimensional WSN coverage problem. Compared with the other four 3D deployment methods optimized by metaheuristic algorithms, the AMRFOCS effectively reduces the redundancy of sensor nodes, possesses a faster convergence speed and higher coverage and then provides a more effective and practical deployment scheme.

Design and Realization of a Novel Robotic Manta Ray for Sea Cucumber Recognition, Location, and Approach.

Sea cucumber manual monitoring and fishing present various issues, including high expense and high risk. Meanwhile, compared to underwater bionic robots, employing autonomous underwater robots for sea cucumber monitoring and capture also has drawbacks, including low propulsion efficiency and significant noise. Therefore, this paper is concerned with the design of a robotic manta ray for sea cucumber recognition, localization, and approach. First, the developed robotic manta ray prototype and the system framework applied to real-time target search are elaborated. Second, by improved YOLOv5 object detection and binocular stereo-matching algorithms, precise recognition and localization of sea cucumbers are achieved. Thirdly, the motion controller is proposed for autonomous 3D monitoring tasks such as depth control, direction control, and target approach motion. Finally, the capabilities of the robot are validated through a series of measurements. Experimental results demonstrate that the improved YOLOv5 object detection algorithm achieves detection accuracies (mAP@0.5) of 88.4% and 94.5% on the URPC public dataset and self-collected dataset, respectively, effectively recognizing and localizing sea cucumbers. Control experiments were conducted, validating the effectiveness of the robotic manta ray's motion toward sea cucumbers. These results highlight the robot's capabilities in visual perception, target localization, and approach and lay the foundation to explore a novel solution for intelligent monitoring and harvesting in the aquaculture industry.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs.

The COVID-19 pandemic has presented unprecedented challenges to healthcare systems worldwide. One of the key challenges in controlling and managing the pandemic is accurate and rapid diagnosis of COVID-19 cases. Traditional diagnostic methods such as RT-PCR tests are time-consuming and require specialized equipment and trained personnel. Computer-aided diagnosis systems and artificial intelligence (AI) have emerged as promising tools for developing cost-effective and accurate diagnostic approaches. Most studies in this area have focused on diagnosing COVID-19 based on a single modality, such as chest X-rays or cough sounds. However, relying on a single modality may not accurately detect the virus, especially in its early stages. In this research, we propose a non-invasive diagnostic framework consisting of four cascaded layers that work together to accurately detect COVID-19 in patients. The first layer of the framework performs basic diagnostics such as patient temperature, blood oxygen level, and breathing profile, providing initial insights into the patient's condition. The second layer analyzes the coughing profile, while the third layer evaluates chest imaging data such as X-ray and CT scans. Finally, the fourth layer utilizes a fuzzy logic inference system based on the previous three layers to generate a reliable and accurate diagnosis. To evaluate the effectiveness of the proposed framework, we used two datasets: the Cough Dataset and the COVID-19 Radiography Database. The experimental results demonstrate that the proposed framework is effective and trustworthy in terms of accuracy, precision, sensitivity, specificity, F1-score, and balanced accuracy. The audio-based classification achieved an accuracy of 96.55%, while the CXR-based classification achieved an accuracy of 98.55%. The proposed framework has the potential to significantly improve the accuracy and speed of COVID-19 diagnosis, allowing for more effective control and management of the pandemic. Furthermore, the framework's non-invasive nature makes it a more attractive option for patients, reducing the risk of infection and discomfort associated with traditional diagnostic methods.

The complete mitochondrial genome of the Reef Manta Ray, Mobula alfredi, from Hawaii.

We provide the complete mitochondrial genome of the reef manta ray, Mobula alfredi, using an ezRAD approach. The total length of the mitogenome was 18,166 bp and contained 13 protein-coding genes, 22 transfer RNAs genes, two ribosomal RNA genes, and one non-coding control region. The gene organization and length are similar to other Mobula species. This reference mitogenome that includes the control region is expected to be a valuable resource for molecular-based species identification, population genomics, and phylogeography.

Evaluation of SLS 3D-Printed Filter Structures Based on Bionic Manta Structures.

We report on additively manufactured filter systems based on bionic manta ray structures and evaluate their filter performance. The filters are periodic lamella structures produced by selective laser sintering using PA12 polyamide powder. Two different lamella types are investigated, which are derived from two manta ray genera, namely, Mobula tarapacana and Manta birostris. The precipitator efficiency of sand particles in water is determined for both flow directions, which are referred to as the "wing" and "spoiler" arrangements. With a flat filter design, more than 90% of sand particles can be removed from the water. The variation of the lamella distance reveals that the filter effect is based on the different dynamic flow of particles and water rather than filtering by the hole size. The successful transformation of the primary flat filter design into a round filter structure is demonstrated with precipitator efficiencies above 95% and a ratio of filtered to unfiltered water of 1:1 being achieved, depending of the gap between the filter and the surrounding pipe. A shortening of the filter structure results in an unaltered precipitator efficiency but a lower ratio of filtered water. These results reveal the peculiar possibility to produce 3D round-shaped filters based on manta ray structures with additive manufacturing, achieving good precipitator efficiencies.

Manta Ray Inspired Soft Robot Fish with Tough Hydrogels as Structural Elements.

The design of soft robots capable of navigation underwater has received tremendous research interest due to the robots' versatile applications in marine explorations. Inspired by marine animals such as jellyfish, scientists have developed various soft robotic fishes by using elastomers as the major material. However, elastomers have a hydrophobic network without embedded water, which is different from the gel-state body of the prototypes and results in high contrast to the surrounding environment and thus poor acoustic stealth. Here, we demonstrate a manta ray-inspired soft robot fish with tailored swimming motions by using tough and stiff hydrogels as the structural elements, as well as a dielectric elastomer as the actuating unit. The switching between actuated and relaxed states of this unit under wired power leads to the flapping of the pectoral fins and swimming of the gel fish. This robot fish has good stability and swims with a fast speed (∼10 cm/s) in freshwater and seawater over a wide temperature range (4-50 °C). The high water content (i.e., ∼70 wt %) of the robot fish affords good optical and acoustic stealth properties under water. The excellent mechanical properties of the gels also enable easy integration of other functional units/systems with the robot fish. As proof-of-concept examples, a temperature sensing system and a soft gripper are assembled, allowing the robot fish to monitor the local temperature, raise warning signals by lighting, and grab and transport an object on demand. Such a robot fish should find applications in environmental detection and execution tasks under water. This work should also be informative for the design of other soft actuators and robots with tough hydrogels as the building blocks.

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.

Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework's adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.

First photographic evidence of oceanic manta rays (Mobula birostris) at two locations in the Fiji islands.

Until the revision of the genus Manta in 2009, when a second manta species (Manta alfredi) was resurrected based on morphological and meristic data, all available records in Fijian literature were recorded as Manta birostris. Subsequently, documented sightings were recorded as M. alfredi. Another reclassification of the genus Manta was undertaken in 2018 when both manta ray species (Manta alfredi, Manta birostris) were moved to Mobula based on phylogenetic analysis. Here, we present the first unequivocal evidence of oceanic manta ray (Mobula birostris) occurrence in Fijian waters. In November 2018, two individuals were sighted foraging in Laucala Bay, a large lagoon adjacent to Suva, the capital city of Fiji. Subsequently, three more individuals were sighted in December 2018, two individuals in July 2020, at least six individuals were observed in November 2021, and eight individuals in May/June 2022, all foraging in the same geographical area. Unique ventral identification patterns could be obtained for nine individuals, and all nine individuals have been re-sighted since first identification, with one individual being documented in 2018, 2020, 2021 and 2022. Two additional individuals were recorded in the Yasawa Island Group in the west of Fiji while passing through and foraging in a channel between Drawaqa and Naviti Island in April and September 2020. We provide photographic identification of ten M. birostris individuals from two sites and discuss our findings in the context of local environmental parameters and other recorded sightings in the South Pacific region. In light of the global extinction risk of M. birostris and the recent reclassification from Vulnerable to Endangered on the Red List of Threatened Species, the expansion of their known distribution range to Fijian waters and the recurrence of individuals over consecutive years in the same location adds valuable information for the development of effective and data-driven conservation strategies.

MANTA and MANTA-RAy: Rationale and Design of Trials Evaluating Effects of Filgotinib on Semen Parameters in Patients with Inflammatory Diseases.

INTRODUCTION: The phase 2 MANTA and MANTA-RAy studies were developed in consultation with global regulatory authorities to investigate potential impacts of filgotinib, a Janus kinase 1 preferential inhibitor, on semen parameters in men with active inflammatory diseases. Here we describe the methods and rationale for these studies. METHODS AND RATIONALE: The MANTA and MANTA-RAy studies included men (aged 21-65 years) with active inflammatory bowel disease (IBD) and rheumatic diseases, respectively. Participants had no history of reproductive health issues, and the following semen parameter values (≥ 5th percentile of World Health Organization reference values) at baseline: semen volume ≥ 1.5 mL, total sperm/ejaculate ≥ 39 million, sperm concentration ≥ 15 million/mL, sperm total motility ≥ 40% and normal sperm morphology ≥ 30%. Each trial included a 13-week, randomized, double-blind, placebo-controlled period (filgotinib 200 mg vs placebo, up to N = 125 per arm), for pooled analysis of the week-13 primary endpoint (proportion of participants with ≥ 50% decrease from baseline in sperm concentration). All semen assessments were based on two samples (≤ 14 days apart) to minimize effects of physiological variation; stringent standardization processes were applied across assessment sites. From week 13, MANTA and MANTA-RAy study designs deviated owing to disease-specific considerations. All subjects with a ≥ 50% decrease in sperm parameters continued the study in the monitoring phase until reversibility, or up to a maximum of 52 weeks, with standard of care as treatment. Overall conclusions from MANTA and MANTA-RAy will be based on the totality of the data, including secondary/exploratory measures (e.g. sperm motility/morphology, sex hormones, reversibility of any effects on semen parameters). CONCLUSIONS: Despite the complexities, the MANTA and MANTA-RAy studies form a robust trial programme that is the first large-scale, placebo-controlled evaluation of potential impacts of an advanced IBD and rheumatic disease therapy on semen parameters. TRIAL REGISTRATION: EudraCT numbers 2017-000402-38 and 2018-003933-14; ClinicalTrials.gov identifiers NCT03201445 and NCT03926195.

Filgotinib is a treatment for patients with ulcerative colitis and rheumatoid arthritis, and is being studied in other inflammatory diseases. Filgotinib works by blocking Janus kinase 1, an intracellular protein involved in inflammatory signalling processes. We designed the MANTA and MANTA-RAy trials with global health agencies to find out if filgotinib decreases the quality of semen in men with active inflammatory bowel disease (ulcerative colitis or Crohn's disease) (MANTA) or rheumatic disease (rheumatoid arthritis, psoriatic arthritis, ankylosing spondylitis or non-radiographic axial spondylitis) (MANTA-RAy). This paper describes the design of the two trials.Patients had normal sperm measurements and could not have had previous reproductive health issues. Nearly 250 patients were included in each trial. In both MANTA and MANTA-RAy, half of the patients were treated with 200 mg of filgotinib once a day for 13 weeks, and the other half with placebo. We determined if any patients had a decrease in number of sperm cells per millilitre (sperm concentration) by at least half after 13 weeks of treatment. We then monitored any patients who had such a decrease in sperm concentration for up to 52 weeks (while they received standard of care treatment) or until the decrease was reversed.The conclusions from the trials will be in a different paper and will be based on all the final data, including changes in sex hormones. This is the first large-scale clinical trial programme to measure the effect of a treatment on sperm in men with inflammatory bowel disease or rheumatic diseases.

Bio-Inspired Propulsion: Towards Understanding the Role of Pectoral Fin Kinematics in Manta-like Swimming.

Through computational fluid dynamics (CFD) simulations of a model manta ray body, the hydrodynamic role of manta-like bioinspired flapping is investigated. The manta ray model motion is reconstructed from synchronized high-resolution videos of manta ray swimming. Rotation angles of the model skeletal joints are altered to scale the pitching and bending, resulting in eight models with different pectoral fin pitching and bending ratios. Simulations are performed using an in-house developed immersed boundary method-based numerical solver. Pectoral fin pitching ratio (PR) is found to have significant implications in the thrust and efficiency of the manta model. This occurs due to more optimal vortex formation and shedding caused by the lower pitching ratio. Leading edge vortexes (LEVs) formed on the bottom of the fin, a characteristic of the higher PR cases, produced parasitic low pressure that hinders thrust force. Lowering the PR reduces the influence of this vortex while another LEV that forms on the top surface of the fin strengthens it. A moderately high bending ratio (BR) can slightly reduce power consumption. Finally, by combining a moderately high BR = 0.83 with PR = 0.67, further performance improvements can be made. This enhanced understanding of manta-inspired propulsive mechanics fills a gap in our understanding of the manta-like mobuliform locomotion. This motivates a new generation of manta-inspired robots that can mimic the high speed and efficiency of their biological counterpart.

Utilization of random vector functional link integrated with manta ray foraging optimization for effluent prediction of wastewater treatment plant.

An innovative predictive model was employed to predict the key performance indicators of a full-scale wastewater treatment plant (WWTP) operated with an activated sludge treatment process. The data-driven model was obtained using data gathered from Cairo, Egypt. The proposed model consists of Random Vector Functional Link (RVFL) Networks incorporated with Manta Ray Foraging Optimizer (MRFO). RVFL is used as an advanced Artificial Neural Network (ANN) that avoids the common conventional ANN problems such as overfitting. MRFO is employed to determine the best RVFL parameters to maximize the prediction accuracy of the model. The developed MRFO-RVFL is compared with conventional RVFL to figure out the role of MRFO as an optimization tool to enhance model performance. Both models were trained and tested using experimental data measured during a long period of 222 days. This study aims to provide an accurate prediction of the most widely treated effluent indicators of BOD5 and TSS in the wastewater treatment plants. In this study, ten well-known influent wastewater parameters, BOD5, TSS, and VSS, influent flow rate, pH, ambient temperature, F/M ratio, SRT, WAS, and RAS, the output BOD5 and TSS were modeled and predicted using the integrated MRFO-RVFL algorithms and compared with the standalone RVFL model. The performance of the models was evaluated using different assessment measures such as R2, RMSE, and others. The obtained results of R2 and RMSE for the MRFO-RVFL model were 0.924 and 3.528 for BOD5 and 0.917 and 6.153 for TSS, which were much better than the results of conventional RVFL with 0.840 and 6.207 for BOD5 and 0.717 and 10.05 for TSS. Based on the obtained results, the selective model (MRFO-RVFL) exhibited a higher performance and validity to predict the TSS and optimal BOD5.

Improved manta ray foraging optimization for multi-level thresholding using COVID-19 CT images.

Coronavirus disease 2019 (COVID-19) is pervasive worldwide, posing a high risk to people's safety and health. Many algorithms were developed to identify COVID-19. One way of identifying COVID-19 is by computed tomography (CT) images. Some segmentation methods are proposed to extract regions of interest from COVID-19 CT images to improve the classification. In this paper, an efficient version of the recent manta ray foraging optimization (MRFO) algorithm is proposed based on the oppositionbased learning called the MRFO-OBL algorithm. The original MRFO algorithm can stagnate in local optima and requires further exploration with adequate exploitation. Thus, to improve the population variety in the search space, we applied Opposition-based learning (OBL) in the MRFO's initialization step. MRFO-OBL algorithm can solve the image segmentation problem using multilevel thresholding. The proposed MRFO-OBL is evaluated using Otsu's method over the COVID-19 CT images and compared with six meta-heuristic algorithms: sine-cosine algorithm, moth flame optimization, equilibrium optimization, whale optimization algorithm, slap swarm algorithm, and original MRFO algorithm. MRFO-OBL obtained useful and accurate results in quality, consistency, and evaluation matrices, such as peak signal-to-noise ratio and structural similarity index. Eventually, MRFO-OBL obtained more robustness for the segmentation than all other algorithms compared. The experimental results demonstrate that the proposed method outperforms the original MRFO and the other compared algorithms under Otsu's method for all the used metrics.

The geographic distribution of reef and oceanic manta rays (Mobula alfredi and Mobula birostris) in Australian coastal waters.

The known distribution of manta rays in Australian waters is patchy, with records primarily centred around tourism hotspots. We collated 11,614 records of Mobula alfredi from photo-ID databases (n = 10,715), aerial surveys (n = 378) and online reports (n = 521). The study confirms an uninterrupted coastal distribution from north of 26°S and 31°S on the west and east coasts, respectively. More southerly M. alfredi records relate to warm-water events with a southernmost extent at 34°S. Coastal sightings of Mobula birostris were rare (n = 32), likely reflecting a preference for offshore waters, but encompass a wider latitudinal extent than M. alfredi of 10-40°S.