Barau's petrel, Pterodroma baraui, as a bioindicator of plastic pollution in the South-West Indian Ocean: A multifaceted approach.

Marine plastic pollution is well described by bioindicator species in temperate and polar regions but remains understudied in tropical oceans. We addressed this gap by evaluating the seabird Barau's petrel as bioindicator of plastic pollution in the South-West Indian Ocean. We conducted a multifaceted approach including necropsies of birds to quantify plastic ingestion; GPS tracking of breeding adults to identify their foraging areas; manta trawling of plastic debris to measure plastic pollution at sea and modelling of plastic dispersal. We developed a spatial risk index of seabird exposure to plastic ingestion. Seventy-one percent of the analysed birds had ingested plastic. GPS tracking coupled with manta trawling and dispersal modelling show that adults consistently foraged at places with high level of plastic concentration. The highest ingestion risk occurred in the northwest of Reunion Island and at latitude 30°S. Our findings confirm that Barau's petrel is a reliable bioindicator of plastic pollution in the region.

Characterizing offshore polar ocean soundscapes using ecoacoustic intensity and diversity metrics.

Polar offshore environments are considered the last pristine soundscapes, but accelerating climate change and increasing human activity threaten their integrity. In order to assess the acoustic state of polar oceans, there is the need to investigate their soundscape characteristics more holistically. We apply a set of 14 ecoacoustic metrics (EAMs) to identify which metrics are best suited to reflect the characteristics of disturbed and naturally intact polar offshore soundscapes. We used two soundscape datasets: (i) the Arctic eastern Fram Strait (FS), which is already impacted by anthropogenic noise, and (ii) the quasi-pristine Antarctic Weddell Sea (WS). Our results show that EAMs when applied in concert can be used to quantitatively assess soundscape variability, enabling the appraisal of marine soundscapes over broad spatiotemporal scales. The tested set of EAMs was able to show that the eastern FS, which is virtually free from sea ice, lacks seasonal soundscape dynamics and exhibits low acoustic complexity owing to year-round wind-mediated sounds and anthropogenic noise. By contrast, the WS exhibits pronounced seasonal soundscape dynamics with greater soundscape heterogeneity driven in large part by the vocal activity of marine mammal communities, whose composition in turn varies with the prevailing seasonal sea ice conditions.

Microplastic pollution in the North-east Atlantic Ocean surface water: How the sampling approach influences the extent of the issue.

A lack of standardization in monitoring protocols has hindered the accurate evaluation of microplastic (MP) pollution in the open sea and its potential impacts. As sampling techniques significantly influence the amounts of MPs contained in the sample, the aim of this study was to compare two sampling methods: Manta trawl (size selective approach) and grab sampling (volume selective approach). Both approaches were applied in the open sea surface waters of the North-east Atlantic Ocean. Onshore sample processing was carried out using the innovative tape lifting technique, which affords a series of advantages, including prevention of airborne contamination during analysis, without compromising integrity of the results. The results obtained indicated an MP concentration over four orders of magnitude higher using grab sampling compared to the Manta net approach (mean values equal to 0.24 and 4050 items/m3, respectively). Consequently, the sole quantification of MPs using results obtained with the Manta trawl resulted in a marked underestimation of abundance. Nevertheless, the grab sampling technique is intricately linked to a risk of collecting non-representative water volumes, consequently leading to an overestimation of MPs abundance and a significant inter-sample variability. Moreover, the latter method is unsuitable for use in sampling larger MPs or in areas with low concentrations of MP pollution. The optimal sampling method therefore is dependent on the specific objectives of the study, often resulting in a combination of size and volume selective methods. The results of this study have the potential to contribute to the standardization of monitoring protocols for microplastics, both during the sampling phase and sample processing.

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.

A single institution experience of the Manta closure device in endovascular aortic repair.

BACKGROUND: Majority of EVAR procedures are performed with percutaneous arterial access, unless there is severe steno-occlusive disease in the common femoral arteries (CFA). We present our experience of using MANTA closure device with a retrospective evaluation of its safety and efficacy, in the elective setting, and in the emergent setting for ruptured aortic aneurysm. DESIGN AND METHODS: Between Feb 2021 and May 2023 a total of 75 EVAR procedures were closed with a Manta device. Data was collected prospectively and analysed retrospectively. In 75 patients, 128 CFAs were closed with a Manta closure device including 4 emergent ruptured aneurysms. RESULTS: 67 male and 8 female patients with a median age of 77 years had percutaneous EVAR using Manta as a closure device. 128 CFAs were closed with Manta closure device. 3% (4/128) had deployment failures, with three requiring surgical cut down and closure. In one patient, a second Manta device deployment achieved satisfactory haemostasis. Three deployments were complicated by pseudoaneurysms of the CFA, all requiring no further interventions/treatment. No death related to severe haemorrhage from device failure. The pre- and post-procedure CFA puncture site AP diameter was recorded in a total of 106 cases with appropriate follow-up. 66% of these (70/106) had no reduction in CFA diameter post Manta closure. 34% (36/106) had some reduction of vessel AP size CFA post EVAR. No adverse features or further treatment was required due to reduction of vessel diameter in these cases (ongoing yearly surveillance). CONCLUSIONS: Manta closure device is safe and easy to deploy with an overall success rate of 97%. There is a short learning curve. Ultrasound assessment and precise puncture at the healthy section of femoral artery are the key to achieve successful haemostasis with Manta closure device. Our findings suggest there is an association of non-clinically significant mild reduction in CFA vessel AP diameter post Manta closure device, which does not require further intervention.

Age and growth of the blue shark Prionace glauca (Linnaeus, 1758) in the Ecuadorian Pacific: Bayesian multi-models.

The blue shark Prionace glauca plays a critical role as a predator in marine ecosystems but is threatened by by-catch. To obtain more precise biological data, a Bayesian approach was used, and 536 vertebrae samples collected during 1 year at the landing stage called "Playita Mía" Manta, Ecuador, were analysed. The objective was to estimate the age and growth parameters of the species. The size of the specimens varied between 116 and 310 cm in total length (TL). Using a Bayesian approach based on the Markov Chain Monte Carlo (MCMC) method, growth parameters were evaluated. The von Bertalanffy model was the one that best fitted the data and provided more adequate estimates (females: L ∞ = 325.50 cm, L 0 = 53.23 cm, and k = 0.12 years; males: L ∞ = 331.47 cm, L 0 = 51.59 cm, k = 0.12 years -1; combined sexes: L ∞ = 329.65 cm, L 0 = 53.64 cm, k = 0.11 year-1). The results indicated that females and males have a similar growth, and that the species has a slow growth. Further studies using multi-model Bayesian approaches and covering a broader range of sizes in the Pacific Ocean are suggested. These studies will provide crucial information for the management and conservation of this species.

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.

Systematic review and meta-analysis comparing Manta device and Perclose device for closure of large bore arterial access.

Data comparing MANTA device with Perclose device for large bore arterial access closure is limited. We performed meta-analysis to compare safety and efficacy of the two devices in large (⩾14 Fr sheath) arteriotomy closure post-TAVR. Relevant studies were identified via PubMed, Cochrane, and EMBASE databases until June, 2022. Data was analyzed using random effect model to calculate relative odds of VARC-2 defined access-site complications and short-term (in-hospital or 30-day) mortality. A total of 12 studies (2 RCT and 10 observational studies) comprising 2339 patients were included. The odds of major vascular complications (OR 0.99, 95% CI 0.51-1.92; p = 0.98); life threatening and major bleeding (OR 0.77, 95% CI 0.45-1.33; p = 0.35); minor vascular complications (OR 1.37, 95% CI 0.63-2.99; p = 0.43); minor bleeding (OR 0.94, 95% CI 0.57-1.56; p = 0.82); device failure (OR 0.74, 95% CI 0.49-1.11; p = 0.14); hematoma formation (OR 0.76, 95% CI 0.33-1.75; p = 0.52); dissection, stenosis, occlusion, or pseudoaneurysm (OR 1.08, 95% CI 0.71-1.62; p = 0.73) and short-term mortality (OR 1.01, 95% CI 0.55-1.84; p = 0.98) between both devices were similar. MANTA device has a similar efficacy and safety profile compared to Perclose device.

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.

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.

Clinical outcomes of MANTA closure device in percutaneous endovascular aortic aneurysm repair.

OBJECTIVE: The MANTA device is a plug-based vascular closure device (VCD) designed for large bore femoral arterial access site closure. It showed promising results in transcatheter aortic valve replacement cases. In this study, we report our results and evaluate the MANTA VCD in percutaneous endovascular aortic aneurysm repair (pEVAR). METHODS: All data of consecutive patients who underwent an elective pEVAR between October 2018 and December 2022 were retrospectively reviewed. In all patients at least one common femoral artery was intended to close with the MANTA VCD. Depending on the sheath size, the 14Fr or 18Fr MANTA VCD was used. On the preoperative computed tomography scan, the diameter of the common femoral artery (CFA) was measured and the amount of calcification based on the Peripheral Arterial Calcium Scoring System (PACSS) was scored. Primary outcome was procedural technical success. Procedural technical success was defined as placement of the MANTA closure device resulting in vascular closure with patent CFA, without requiring immediate open or endovascular surgery. The secondary outcomes were access site complications requiring reintervention and all-cause mortality at 30-day follow-up. RESULTS: In total, 152 consecutive patients underwent pEVAR with 291 common femoral artery closure procedures with the Manta VCD. Mean age was 74.1 ± 6.4 years, with a mean body mass index of 27.7 ± 4.4 kg/m2. The mean diameter of the CFA was 10.5 ± 1.9 mm. In 52.6% of the cases, there were no calcification on the preoperative computed tomography scan. The 18Fr and 14Fr Manta VCD were used 169 and 122 times, respectively. The technical success rate was 96.6%. Major vascular complications were reported in 4.5% of the cases, without any death-related events. CONCLUSIONS: This single-center retrospective cohort study analyzed the procedural technical success, major vascular complications and all-cause mortality at 30-day follow-up of the MANTA vascular closure device in 152 pEVAR patients with 291 common femoral artery closure procedures. The technical success rate was 96,6%. Major vascular complications were reported in 4.5% of the cases, without any death related events. We concluded that the MANTA device is a safe and feasible option with a high rate of technical success in patients undergoing pEVAR.

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.

A Comparative Analysis of the Early and Late Complication Rates and the Effect of Calcification on the Efficacy of MANTA and ProGlide Vascular Closure Devices.

Introduction The Perclose ProGlide and, more recently, MANTA Large-Bore Closure Device are commonly used vascular closure devices (VCDs) for managing large-bore vascular access haemostasis. The extent of calcification in the common femoral artery (CFA) plays a crucial role in choosing between these devices. ProGlide may face challenges with anterior calcification, while MANTA may have issues with posterior calcification. Our study compared their effectiveness, adjunct usage, calcification impact and early/late complications. Methods A retrospective analysis of procedures involving large-bore CFA access from 2017 to 2022 was conducted. Closure was grouped according to VCD as Group A (ProGlide) and Group B (MANTA). Calcification was designated as anterior and posterior and combined on pre-operative computed tomography angiography along 10 mm segments with 0.625 mm slice thickness. The success of haemostasis was graded as Grade 1 (haemostasis without adjuncts), Grade 2 (haemostasis with adjuncts) and Grade 3 (failed haemostasis needing rescue); Grades 1 and 2 were pooled as successful haemostasis. Statistical analysis was undertaken in Minitab 21 for Windows, particularly analysing calcification and its impact on the success of haemostasis. Results We evaluated 370 large-bore CFA accesses, distributed across two groups: Group A(64.9%, n=243) and Group B (35.1%, n=127), for a total of 205 endovascular procedures (93.1% (191) EVAR and 5.3% (11) TEVAR). The mean age was 74.9±8 years, predominantly males (88.2%, n=181). The average body mass index (BMI) was 28±5.8, with 20.9% (43) individuals having diabetes and 18.5% (37) current smokers. The mean sheath size OD was 16±2.5, with 4.5% (11) re-do groins in Group A and 6.2% (8) in Group B. Successful haemostasis was achieved in 91.8% (n=223) in Group A (44.8%, n=109 Grade 2) and 90.5% (n=115) in Group B (21%, n=27 Grade 2). Rescue operations were needed in 8.2% (20) in Group A and 9.1% (12) in Group B. Pseudoaneurysms developed more commonly in Grade 2 haemostasis with 9.9% (11) in Group A and 1.6% (2) in Group B (p=0.3). Anterior calcification was observed in 14.8% (36) in Group A and 18.8% (24) in Group B. In comparison, posterior calcification was present in 62.5% (152) in Group A and 66.9% (85) in Group B. Notably, calcification did not significantly impact haemostasis (p=0.79). Additional VCD deployment was necessary due to device failure in 4.5% (11) cases in Group A and 1.5% (2) cases in Group B. Conclusion The overall success rate was comparable between the two groups. However, Group A required more adjuncts to achieve successful haemostasis. The site of calcification did not impact the efficacy of closure devices. Pseudoaneurysm formation was more frequent when adjuncts were needed.

Vascular Complications Following Transcatheter Aortic Valve Implantation, Using MANTA (Collagen Plug-Based) versus PROSTAR (Suture-Based), from a French Single-Center Retrospective Registry.

TAVI requires a large-bore arteriotomy. Closure is usually performed by the suture system. Some studies report a vascular complication rate of up to 21%. MANTA is a recently developed percutaneous closure system dedicated to large caliber vessels based on an anchoring system. Early studies report a lower rate of vascular complications with MANTA devices. This single-center retrospective study included all patients who underwent femoral TAVI at the Brest University Hospital from 20 November 2019 to 31 March 2021. The primary endpoint is the rate of vascular complications (major and minor) pre and post-TAVI procedure. In total, 264 patients were included. There were no significant differences in vascular complications (major and minor) between the two groups (13.6% in the MANTA group versus 21.2% in the PROSTAR group; p = 0.105), although there was a tendency to have fewer minor vascular complications in the Manta group (12.1% versus 20.5%; p = 0.067). Manta was associated with a lower rate of bleeding complications (3.8% versus 15.2%; p = 0.002), predominantly minor complications with fewer closure failures (4.5% versus 13.6%; p = 0.01), less use of covered stents (4.5% versus 12.9%; p = 0.016), and with no difference in the need for vascular surgery compared to the Prostar group (1.5% versus 2.3%; p = 0.652). On the other hand, Manta was associated with a higher rate of femoral stenosis (4.5% versus 0%; p = 0.013) without clinical significance (1.5% versus 0%; p = 0.156). The Manta and Prostar devices are equivalent in terms of vascular complications. The Manta, compared to the Prostar, is associated with fewer bleeding complications.

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.

Percutaneous VA-ECMO from Cannulation to Decannulation: Novel Use of a Vascular Closure Device in Pediatrics.

VA-ECMO can be lifesaving in cardiogenic shock in children. While surgical vascular repair is the current standard of care for decannulation, it comes with notable risks. We present a series of eight patients who underwent decannulation with a collagen plug-based vascular closure device (MANTA) for the common femoral artery. Seven of the patients were successfully decannulated without access site-related vascular complications. One required conversion to surgical cut-down with arterial repair due to device failure. This series demonstrates the successful use of the MANTA device in percutaneous VA-ECMO decannulation in the pediatric population, while highlighting potential technical challenges for success.

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.