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Energy efficiency is a major challenge in developing wireless sensor networks (WSNs). The cluster head (CH) can be selected at random or depending on one or more criteria that leads to increase the network lifespan directly. Nevertheless, the CH selection creates an optimization problem. For this purpose, a number of researches have been presented so far to select the optimum CH with the help of various optimization methods, but none of them effectively solves this problem. Therefore, a topological information embedded convolution neural network based energy alert‐cluster head selection in wireless sensor network (TIECNN‐EAC‐WSN) is proposed in this paper. In this method, cluster formation and CH selection is performed by topological information embedded convolution neural network (TIECNN). The CH selection is carried out by three features: energy stabilization, minimization of distance among nodes, and minimization of delay during data transmission. Then, the optimal route is selected by using improved manta ray foraging optimization (IMFO). The TIECNN‐EAC‐WSN approach is evaluated with some metrics, such as network lifetime, number of alive sensor node, and energy consumption with different scenrios. The stimulation results show that the proposed TIECNN‐EAC‐WSN method attains 23.20%, 27.22%, and 26.28% higher number of alive sensor node when compared with the existing models: energy‐aware optimization clustering for hierarchical routing in WSN (EAC‐HR‐WSN), energy‐aware clustering depending on fuzzy modeling in WSN utilizing modified invasive weed optimization (FMEAC‐WSN‐IWO), and quantum tunicate swarm approach–dependent energy‐aware clustering mode for WSN (QTSA‐EAC‐WSN), respectively. [ABSTRACT FROM AUTHOR]

Eagle rays, cownose rays and manta rays (order Myliobatiformes) have a slender tail that can be longer than the animal's body length, but its function and structure are unknown. Using histology, immunohistochemistry and three-dimensional imaging with micro-computed tomography scans, we describe the anatomy and function of the tail in Rhinoptera bonasus, the cownose ray. The tail is an extension of the vertebral column with unique morphological specializations. Along the tail behind the barb, vertebral centra are absent and neural and haemal arches fuse to form a solid mineralized structure that we describe for the first time and term caudal synarcual, which imparts passive stiffness to the tail, reducing bending. Two lateral line canals connected to an extensive tubule network extend along both sides of the tail. Tubules branch from the lateral line canal toward the dorsal and ventral tail surfaces, opening to the surrounding water via pores. A continuous neuromast is located within each lateral line canal, maintaining an uninterrupted structure along the entire tail. The complex lateral line mechanosensory system in the tail of R. bonasus supports the hypothesis that the tail functions like a hydrodynamic sensory antenna and may play an important role in their behavioural and functional ecology. [ABSTRACT FROM AUTHOR]

Mobulid rays (manta and devil rays) use a highly specialized filtering apparatus to separate plankton food particles from seawater. Recent studies have indicated that captive vortices form within the microscale pores of the filter, which enhance filtration efficiency through a novel mechanism referred to as ricochet separation. The high throughput and clog resistance of this filtration process have led to the development of several bioinspired engineered filtration systems. However, it is still unclear how changes to the filter morphology influence the surrounding flow patterns and filtration efficiency. We address this question by examining the flow fields around and filtering properties of mobulid filters with systematically varied morphologies, using a combination of computational fluid dynamics and experiments on physical models. While the pore size is the principal determinant of filtration efficiency in a sieve filter, we found that the captive vortices in a mobulid filter grow or shrink to fill the pore, and changes in the pore size have modest effects. By contrast, the filtration efficiency appears to be highly sensitive to the orientation of the filter lobes (microscale plate-like structures). These results provide a foundation for interpreting the morphological differences between species and also for generating optimized bioinspired designs. [ABSTRACT FROM AUTHOR]

The increasing demand for autonomous mobile robots in complex environments calls for efficient path-planning algorithms. Bio-inspired algorithms effectively address intricate optimization challenges, but their computational cost increases with the number of particles, which is great when implementing algorithms of high accuracy. To address such topics, this paper explores the application of the leader-based bat algorithm (LBBA), an enhancement of the traditional bat algorithm (BA). By dynamically incorporating robot orientation as a guiding factor in swarm distribution, LBBA improves mobile robot localization. A digital compass provides precise orientation feedback, promoting better particle distribution, thus reducing computational overhead. Experiments were conducted using a mobile robot in controlled environments containing obstacles distributed in diverse configurations. Comparative studies with leading algorithms, such as Manta Ray Foraging Optimization (MRFO) and Black Widow Optimization (BWO), highlighted the proposed algorithm's ability to achieve greater path accuracy and faster convergence, even when using fewer particles. The algorithm consistently demonstrated robustness in bypassing local minima, a notable limitation of conventional bio-inspired approaches. Therefore, the proposed algorithm is a promising solution for real-time localization in resource-constrained environments, enhancing the accuracy and efficiency in the guidance of mobile robots, thus highlighting its potential for broader adoption in mobile robotics. [ABSTRACT FROM AUTHOR]

Understanding the behaviour and ecological role of elasmobranchs is essential for their conservation, particularly in regions harbouring threatened and data-deficient species like the Inhambane coastline in southern Mozambique, southwestern Indian Ocean. This study employed long-life remote underwater video cameras (LL-RUV) to observe elasmobranch occurrence and behaviour at a rocky reef near Praia do Tofo known for frequent elasmobranch sightings. Between 2021 and 2024, 524 hours of LL-RUV data were collected, revealing the presence of 14 threatened and data-deficient elasmobranch species. Reef manta rays (Mobula alfredi), small eye stingrays (Megatrygon microps), and oceanic manta rays (Mobula birostris) were identified as the primary elasmobranch users of the studied cleaning stations (n = 81, n = 72 and n = 61 respectively), engaging in mutualistic interactions with cleaner fish and spending significant amounts of time at the studied reef. In contrast, spotted eagle rays (Aetobatus narinari) and blacktip sharks (Carcharhinus limbatus) were solely observed cruising over the reef without engaging in cleaning interactions (n = 40 and n = 27 respectively). In addition, this study provides evidence of intra- and inter-specific interactions between reef and oceanic manta rays, small eye stingrays and bowmouth guitarfish (Rhina ancylostoma) and co-occurrences of both manta ray species and smalleye stingrays with "hitchhiker" species including cobia (Rachycentron canadum) and remoras (Echeneis spp.). The use of LL-RUV cameras represents a significant advancement in marine research. These cameras enable continuous, non-intrusive, long-term monitoring of marine environments, capturing natural behaviours, while minimising disturbances caused by divers. As a result, more accurate observations of elasmobranch behaviour and cleaning station dynamics can be recorded, which might be missed during short-term surveys. This research highlights the potential of LL-RUV cameras as powerful tools for advancing our understanding of elasmobranch occurrence and behaviour and underscores their value in informing targeted and effective conservation strategies. Beyond elasmobranch research, LL-RUVs hold tremendous potential for studying a wide range of marine species and habitats, offering a versatile tool for ecological monitoring and conservation efforts across diverse marine ecosystems. [ABSTRACT FROM AUTHOR]

Background: Batoids possess a unique body plan associated with a benthic lifestyle that includes dorsoventral compression and anteriorly expanded pectoral fins that fuse to the rostrum. The family Myliobatidae, including manta rays and their relatives, exhibit further modifications associated with invasion of the pelagic environment, and the evolution of underwater flight. Notably, the pectoral fins are split into two domains with independent functions that are optimized for feeding and oscillatory locomotion. Paired fin outgrowth is maintained during development by Wnt3, while domain splitting is accomplished by expression of the Wnt antagonist Dkk1, which is differentially expressed in the developing anterior pectoral fins of myliobatids, where cephalic fins separate from pectoral fins. We examine the evolution of this unique feature in the cownose ray (Rhinoptera bonasus), a member of the genus that is sister to Mobula. Results: Here, we provide functional evidence that DKK1 is sufficient to initiate pectoral fin domain splitting. Agarose beads soaked in DKK1 protein were implanted in the pectoral fins of little skate (Leucoraja erinacea) embryos resulting in AER interruption. This disruption arrests fin ray outgrowth, resembling the myliobatid phenotype. In addition, fins that received DKK1 beads exhibit interruption of Axin2 expression, a downstream target of β-catenin-dependent Wnt signaling and a known AER marker. We demonstrate that Msx1 and Lhx2 are also associated with fin expansion at the AER. These results provide functional evidence for the underlying genetic pathway associated with the evolution of a novel paired fin/limb modification in manta rays and their relatives. We introduce the gas/brake pedal model for paired fin remodeling at the AER, which may have been co-opted from domain splitting in pelvic fins of cartilaginous fishes 370 million years earlier. Conclusions: The pectoral fins of manta rays and their relatives represent a dramatic remodel of the ancestral batoid body plan. The premiere feature of this remodel is the cephalic fins, which evolved via domain splitting of the anterior pectoral fins through inhibition of fin ray outgrowth. Here, we functionally validate the role of Dkk1 in the evolution of this phenotype. We find that introduction of ectopic DKK1 is sufficient to recapitulate the myliobatid pectoral fin phenotype in an outgroup lacking cephalic fins via AER interruption and fin ray truncation. Additional gene expression data obtained via in situ hybridization suggests that cephalic fin development may have evolved as a co-option of the pathway specifying claspers as modifications to the pelvic fins, the only other known example of domain splitting in vertebrate appendages. [ABSTRACT FROM AUTHOR]

Manta rays use wing-like pectoral fins for intriguing oscillatory swimming. It provides rich inspiration for designing potentially fast, efficient, and maneuverable soft swimming robots, which, however, have yet to be realized. It remains a grand challenge to combine fast speed, high efficiency, and high maneuverability in a single soft swimmer while using simple actuation and control. Here, we report leveraging spontaneous snapping stroke in the monostable flapping wing of a manta-like soft swimmer to address the challenge. The monostable wing is pneumatically actuated to instantaneously snap through to stroke down, and upon deflation, it will spontaneously stroke up by snapping back to its initial state, driven by elastic restoring force, without consuming additional energy. This largely simplifies designs, actuation, and control for achieving a record-high speed of 6.8 body length per second, high energy efficiency, and high maneuverability and collision resilience in navigating through underwater unstructured environments with obstacles by simply tuning single-input actuation frequencies. [ABSTRACT FROM AUTHOR]

Knowledge of killer whale (Orcinus orca) feeding ecology in tropical waters is scarce. In the southwest Indian Ocean, opportunistic sightings provide a valuable source of information to better understand their behaviour, diet and distribution. Here, we compile existing records of killer whale predation on elasmobranchs in the southwest Indian Ocean, including sightings of three undescribed prey species regionally: a giant manta ray (Mobula birostris), a sicklefin devil ray (Mobula tarapacana) and a tiger shark (Galeocerdo cuvier). Documenting such observations is important to increase knowledge on killer whale ecology in the tropics and the cascading impact they might have on prey populations. [ABSTRACT FROM AUTHOR]

Discovering the business process model from an organisation's records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models. [ABSTRACT FROM AUTHOR]

Observation of manta rays exiting water has been rarely reported, as there are various difficulties in observing and obtaining data on their behavior in a marine environment. Therefore, the movement mechanism of manta rays exiting water is still unclear. This paper proposes the idea of using CFD (based on Ansys Fluent, version 2022) to simulate the water-exit process of the manta ray. The study discusses the changes in the mechanical and kinematic parameters of the manta ray over time and obtains the evolution of vortex structures during the underwater movement phase of the manta ray. Time history variations of the mechanical and kinematics parameters in the vertical water-exit motion are discussed. The evolution of vortex structures during the underwater movement of the manta ray is obtained. The direction in which the manta ray approaches the free surface is the X-direction and the direction of its flapping motion is the Z-direction. VX and VZ are the velocities of the manta ray in the X- and Z-directions, respectively. FX and FZ represent the forces acting on the manta ray in the X- and Z-directions, respectively. The results indicate that the vertical water-exit of the manta ray mainly undergoes three stages: underwater acceleration, crossing the free surface, and aerial movement. During the underwater acceleration phase, the force FX of the manta ray fluctuates, but its average value is positive within one cycle. VX also shows a stepwise increase, while FZ and VZ exhibit periodic changes. During the stage of crossing the free liquid surface, FX first increases and then sharply decreases, VX also shows an increase and then decrease, FZ fluctuates greatly, producing a peak, and the swimming speed VZ of the manta ray is negative. During the aerial motion phase, FX is mainly affected by gravity, VX decreases linearly, FZ approaches 0, and VZ remains constant. During the process of swimming underwater, the tail vortex of the manta ray presents a double row staggered structure to generate thrust. Increasing the flapping frequency and decreasing the wave number can improve the swimming speed of the manta ray, and then increase its water-exit height. The findings may provide an important hydrodynamics basis for biomimetic trans-media vehicle designs. [ABSTRACT FROM AUTHOR]

Summary: Wireless sensor networks (WSNs) are becoming increasingly important and well liked for delivering pervasive computing environments for a range of applications. Extending the networking life lifetime in WSNs is an important issue that must be addressed. Effective techniques for conserving the WSN's limited energy resources must be developed. Cross‐layer protocols are employed in WSNs to solve network lifespan difficulties. This paper proposes a new cross‐layer–cross‐domain routing scheme with stages such as "(1) network association stage, (2) nearer node detection phase, and (3) consistent state phase." In the consistent stage, the optimal cluster head selection (CHS) is carried out by taking into account risk, delay, energy, trust, and distance. A new model called manta ray collided dwarf mongoose optimization (MRC‐DMO) is introduced to help with this. Furthermore, the routing is accomplished by dependable data communication. The results obtained establish the effectiveness of the MRC‐DMO scheme for SEACRCLCD in WSN over varied methods. [ABSTRACT FROM AUTHOR]

In this study, an overlapping grid was used to create a simplified model based on the pectoral fins of a manta ray. The oscillatory traveling wave equation is programmed and loaded on the pectoral fin model to characterize the motion state of the manta ray cross-section. It explored how wavelength and frequency affect the efficiency of flexible hydrofoil swimming. Results showed that flexible hydrofoils create reverse Kármán vortex streets during autonomous swimming. The size of these vortices, along with frequency and wavelength, impacts propulsive efficiency. When frequency is fixed, increasing wavelength increases turbulence, reducing efficiency but slightly boosting speed. Conversely, fixing wavelength and increasing frequency improves stability, enhancing efficiency and speed. This research offers new insights for designing biomimetic underwater propulsion systems in marine engineering. [ABSTRACT FROM AUTHOR]

If chafing behaviours on the bottom or rocks are common, interspecific events are rare and usually involve smaller ray-finned fishes rubbing on larger sharks or manta rays. Reports on interspecific chafing between fishes and reptiles are even less common, with just one report of great barracudas (Sphyraena barracuda) using swimming hawksbill (Eretmochelys imbricata) and green turtles (Chelonia mydas) as chafing surfaces in the Southwestern Atlantic. Here, we describe a chafing interaction between a great barracuda and a hawksbill turtle in the Red Sea. Our observation provides evidence that this interaction is probably not localised but may represent a more widespread behavioural pattern than previously thought. This chafing behaviour is suggested to remove external parasites, and our observation documents the behaviour of the scraper and the scraped animal, as well as the reaction of the parasites during the same event, clearly demonstrating that chafing behaviour aims at the removal of parasitic copepods (Caligidae). We also report the evasive behaviour of the copepods during the event, providing novel information on parasite removal in reef fishes. Highlights: Great barracudas scrape the carapace of sea turtles for parasite removal. First report of parasitic copepods actively evading dislodging attempts. [ABSTRACT FROM AUTHOR]

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively. [ABSTRACT FROM AUTHOR]

The rapid expansion of cloud computing is fueled by its vast processing capabilities and ability to support data-intensive applications, which highlights the urgent demand for advanced and reliable security solutions. Despite its benefits, the persistent security problems prevent wider use, particularly when it comes to handling sensitive data. To address these, effective intrusion detection is essential for maintaining data confidentiality and integrity. The intricacy of dynamic cloud environments surpasses the capacity of existing methodologies, which underscoring the necessity for advanced solutions. Therefore, a novel approach called Cloud Guard: Optimizing Intrusion Detection for Fortifying Privacy Defenses with an Optimized Self Adaptive Physics-Informed Neural Network (CG-FPD-SAPINN) is proposed in this paper to enhance the intrusion detection in cloud environments. This method leverages Self Adaptive Physics-Informed Neural Network (SAPINN) optimized with the Piranha Search Optimization Algorithm (PFOA). The process begins with data acquisition from the BOT-IoT dataset and proceeds through pre-processing and feature selection phases. Hierarchical Manta Ray Foraging Optimization (HMRFO) is used for optimal feature selection. Then, the SAPINN is used for classification, and its network's weights are optimized by PFOA to improve the efficacy of intrusion detection. The proposed CG-FPD-SAPINN technique is executed in Python using performance metrics, like accuracy, precision, recall, specificity, f-Score, computation time, error rate, latency, throughput, scalability, and resource consumption. The comparative analysis demonstrates that the CG-FPD-SAPINN method outperforms other existing approaches. It exhibits higher recall and Receiver Operating Characteristic (ROC) curve while achieving reduced computation time and latency. The proposed CG-FPD-SAPINN method provides a feasible solution for increasing the safety in cloud computing environments, particularly in intrusion detection. By using advanced techniques, such as SAPINN and PFOA, the method demonstrates better performance in detecting intrusions, thus addressing security concerns and facilitating the secure adoption of cloud technologies. [ABSTRACT FROM AUTHOR]

The remarkable mechanical properties of high-performance concrete (HPC) make it indispensable in a wide range of engineering applications. Predicting HPC’s compressive strength with precision is essential to guaranteeing its structural stability and longevity. The present work introduces a novel method for predicting HPC compressive strength (CS) by utilizing the Multi-layer Perceptron (MLP) model in conjunction with three distinct optimizers, namely Northern Goshawk Optimization (NGO), Manta Ray Foraging Optimization (MRFO), and Atom Search Optimization (ASO). An effective method for capturing complex relationships between input and output variables is the multi-layer perceptron (MLP) in artificial neural networks. Developing a dependable, robust predictive model is the goal of training the MLP model on a variety of datasets of HPC mixtures and CS. As evidenced by the MLNG model’s R2 value of 0.994 and RMSE of 1.3572, the joint efforts of MRFO, NGO, and ASO during the optimization phase improve the MLP model and produce promising results. These results clarify how different optimizers play a crucial role in improving the accuracy of HPC compressive strength predictions made with the MLP model. This information helps make better decisions regarding design and construction methods for HPC. [ABSTRACT FROM AUTHOR]

The large, metabolically expensive brains of manta and devil rays (Mobula spp.) may act as a thermogenic organ representing a unique mechanistic basis for cranial endothermy among fishes that improves central nervous system function in cold waters. Whereas early hominids in hot terrestrial environments may have experienced a thermal constraint to evolving larger brain size, cetaceans and mobulids in cold marine waters may have experienced a thermal driver for enlargement of a thermogenic brain. The potential for brain enlargement to yield the dual outcomes of cranial endothermy and enhanced cognition in mobulids suggests one may be an evolutionary by‐product of selection for the mechanisms underlying the other, and highlights the need to account for non‐cognitive functions when translating brain size into cognitive capacity. Computational scientific imaging offers promising avenues for addressing the pressing mechanistic and phylogenetic questions needed to assess the theory that cranial endothermy in mobulids is the result of temperature‐driven selection for a brain with augmented thermogenic potential. [ABSTRACT FROM AUTHOR]

This article discusses the use of citizen science to observe and document rare behaviors of the spotted eagle ray. The study highlights eight previously unreported resting behaviors of the species, collected from various locations including Australia, the Maldives, and Japan. The findings contribute to our understanding of the behavior and habitat use of this endangered species. The document also emphasizes the value of citizen science in documenting elasmobranch behavior and calls for further research in this area. Additionally, there is a list of references cited in a research article or document related to various species of rays and sharks, providing a comprehensive source for library patrons conducting research on these specific topics. [Extracted from the article]

To tackle the shortcomings of the Dung Beetle Optimization (DBO) Algorithm, which include slow convergence speed, an imbalance between exploration and exploitation, and susceptibility to local optima, a Somersault Foraging and Elite Opposition-Based Learning Dung Beetle Optimization (SFEDBO) Algorithm is proposed. This algorithm utilizes an elite opposition-based learning strategy as the method for generating the initial population, resulting in a more diverse initial population. To address the imbalance between exploration and exploitation in the algorithm, an adaptive strategy is employed to dynamically adjust the number of dung beetles and eggs with each iteration of the population. Inspired by the Manta Ray Foraging Optimization (MRFO) algorithm, we utilize its somersault foraging strategy to perturb the position of the optimal individual, thereby enhancing the algorithm's ability to escape from local optima. To verify the effectiveness of the proposed improvements, the SFEDBO algorithm is utilized to optimize 23 benchmark test functions. The results show that the SFEDBO algorithm achieves better solution accuracy and stability, outperforming the DBO algorithm in terms of optimization results on the test functions. Finally, the SFEDBO algorithm was applied to the practical application problems of pressure vessel design, tension/extension spring design, and 3D unmanned aerial vehicle (UAV) path planning, and better optimization results were obtained. The research shows that the SFEDBO algorithm proposed in this paper is applicable to actual optimization problems and has better performance. [ABSTRACT FROM AUTHOR]

Lung cancer starts in the lungs and spreads to other organs in the body. Premature identification can only help the doctor to make an exact diagnosis and it may save the life of patients. Numerous studies have been conducted in this area, but none of them attains the accuracy outcomes. To overcome this drawback, a deep multi-scale three dimensional convolutional neural network optimized with the manta ray foraging optimization algorithm is proposed in this article for lung cancer classification on CT images (LCCT-DMS3DCNN-MRFOA) which effectively classifies the lung cancer as benign or malignant. The simulation is executed in MATLAB. The ELCAP dataset is utilized to confirm the performance of the proposed LCCT-DMS3DCNN-MRFOA approach. The efficiency of the LCCT-DMS3DCNN-MRFOA approach attains 14.21%, 22.96% and 24.94% higher accuracy; 12.59%, 11.71% and 11.55% higher AUC and 59.83%, 53.05% and 61.41% lower computational time compared with existing methods. [ABSTRACT FROM AUTHOR]

Interval Type-2 Fuzzy Logic Control (IT2FLC) possesses a high control ability in a way that it can optimally handle the presence of uncertainty in a system dynamic. However, the design of such a control scheme is a challenging task due to its complex structure and nonlinear behavior. A Manta Ray Foraging Optimization (MRFO) is a promising algorithm that can be applied to optimize the control design. However, MRFO still suffers the local optima problem due to unbalance exploration-exploitation of the MRFO agents and hence limiting the performance of the desired control. In this paper, Standard, Quasi, Super, and Quasi-Reflected opposition strategies are integrated into the MRFO structure. Each strategy enhances the exploration-exploitation capability and offers different approaches of varying agent's step size relative to the algorithm's iteration. The proposed opposition-based MRFO (OMRFO) algorithms are applied to optimize the IT2FLC control design for a laboratory-scaled inverted pendulum system. Moreover, as the algorithms are also promising strategies to other problems, they are applied to solve 50D of 30 IEEE CEC14 benchmark functions representing problems with different features. Performance analysis of the algorithms is statistically conducted using Wilcoxon sign rank and Friedman tests. The result shows that the performance of MRFO and Quasi-Reflected-OMRFO are equal, while all other OMRFO variants show a significant improvement and better rank over the MRFO. The Super and Quasi OMRFO-IT2FLC schemes acquired the best responses for the cart and pendulum, respectively. [ABSTRACT FROM AUTHOR]

Highly biosimilar manta ray morphological and kinematic models are first established, followed by a numerical study of the hydrodynamic characteristics of two manta rays swimming in tandem under the same/reverse phase-difference conditions based on the immersed boundary method. The results show that, compared to single swimming, under the same/reverse phase-difference conditions, the propulsive performance of leader manta rays are all significantly improved only when the tandem spacing is small (thrust increase up to 4.27%, efficiency increase up to 4.76%), while the propulsive performance curves of follower show fluctuating variation with the change of the spacing. It is especially worth noting that when the tandem spacing is not conducive to improving the propulsive performance, it can be improved by adjusting the motion phase difference, to realize that the wake between the individuals can be converted from collision interference to absorption and fusion, and to realize that the propulsive performance can be changed from inferior to superior. The findings of this paper provide a new idea for improving the underwater vehicle group propulsion performance based on coupled regulation of formation and motion parameters. [ABSTRACT FROM AUTHOR]

A three-dimensional smoothed-particle hydrodynamics (SPH) method is used to study the moving boundary problem of a swimming manta ray, focusing on Eulerian and Lagrangian coherent structures. The manta ray's boundary motion is predefined by a specific equation. The calculated hydrodynamic results and Eulerian coherent structures are compared with data from the literature. To improve computational stability and efficiency, the δ + -SPH model used in this study incorporates tensile instability control and an improved adaptive particle-refinement technique. By comparing and analyzing the Eulerian and Lagrangian coherent structures, the relationship between these vortex structures and hydrodynamic force generation is examined, revealing the jet mechanism in the manta ray's wake. The SPH method presented herein is robust and efficient for calculating biomimetic propulsion problems involving moving boundaries with large deformations, and it can accurately identify vortex structures. The approach of this study provides an effective simulation tool for investigating biomimetic propulsion problems such as bird flight and fish swimming. [ABSTRACT FROM AUTHOR]

The field of bioinspired underwater robots aims to replicate the capabilities of marine animals in artificial systems. Stingrays have emerged as highly promising species to be mimicked because of their flat body morphology and size. Furthermore, they are considered high-performance species due to their maneuverability, propulsion mode, and sliding efficiency. Designing and developing mechanisms to imitate their pectoral fins is a challenge for underwater robotic researchers mainly because the locomotion characteristics depend on the coordinated movement of the fins. In the state of the art, several mechanisms were proposed with 2 active rotation degrees of freedom (DoFs) to replicate fin movement. In this paper, we propose adding an additional active DoF in order to improve the realism in the robotic manta ray movement. Therefore, in this article, we present the mechanical design, modeling, and kinematics analysis of a 3-active-and-rotational-DoF pectoral fin inspired by the Mobula Alfredi or reef manta ray. Additionally, by using the kinematics model, we were able to simulate and compare the behaviour of both mechanisms, that is, those with 2 and 3 DoFs. Our simulation results reveal an improvement in the locomotion, and we hypothesized that with the third DoF, some specific missions, such as hovering or fast emergence to the surface, will have a better performance. [ABSTRACT FROM AUTHOR]

An increasing onus on elasmobranch management by regional bodies has been hindered by a lack of data on abundance, distribution and fisheries, especially in data‐poor areas like the eastern Atlantic Ocean. From 2015 through 2017, 204 baited remote underwater videos (BRUV) were deployed in Cabo Verde around the eastern islands of Sal, Boavista and Maio and the remote offshore reef João Valente to establish a baseline of elasmobranch abundance. Over 200 hours of footage revealed 215 individual elasmobranchs, spanning 14 species from 6 taxonomic families. The abundance of elasmobranchs was highest in Maio, the island with the smallest human population, followed by Boavista and Sal. Smaller‐bodied meso‐predatory species such as the common smoothhound (Mustelus mustelus) and the Atlantic weasel shark (Paragaleus pectoralis) constituted the majority of observations in Maio and Boavista. Inversely, Carcharhinus spp. were observed in considerably greater abundance in Sal, and there was notably lower abundance of small‐bodied sharks at sites with high large‐bodied Carcharhinid abundance. Species richness was consistent with abundance estimates across islands, with Boavista and Sal recording the highest species diversity, followed by Maio. Results suggest that amongst Cabo Verde's eastern islands, there exists a high relative diversity and abundance of coastal elasmobranchs compared to populations in West Africa. Nonetheless, there is evidence of exploitation of higher trophic levels species. This trend is most notable in the decreasing abundance of Carcharhinids with increasing proximity to the capital city Praia, suggesting that fishing efforts from the capital are negatively affecting the abundance of large‐bodied, higher‐trophic predators. [ABSTRACT FROM AUTHOR]

To maximise energy efficiency, manta ray (Mobula alfredi, M. birostris) foraging and cleaning behaviours are thought to often be mutually exclusive, whereby individuals will only forage when prey density thresholds are met and will only clean when foraging is too energetically costly (i.e., thresholds are not met). Here, snorkel surveys and remote camera cleaning station footage show reef manta rays (M. alfredi) undertaking repetitive and short-term movements between surface-feeding and cleaning station visits around D'Arros Island, Seychelles. These observations demonstrate that foraging and cleaning behaviours are not mutually exclusive even when prey densities are high. At D'Arros Island, the proximity of cleaning stations to highly productive foraging areas may afford individuals the opportunity to undertake non-foraging activity without incurring significant energy loss from the shifts in behaviour. These data inform a more nuanced understanding of this species' use of key habitats. [ABSTRACT FROM AUTHOR]

This study compared earthquake location estimation using grid search and manta ray foraging optimization algorithm for synthetic and real earthquakes data from Van, Turkey. Both locating methods worked well, and they achieved similar results. The horizontal coordinates (latitude and longitude) of the earthquake were obtained successfully with both methods, from the inversion of the arrival times calculated from the noisy and noise-free synthetic earthquake data. However, there was some deviation in depth parameter for the noisy data. The location parameters obtained from the inversion of the real earthquake data using grid search and manta ray foraging optimization methods were in accordance with the solutions presented in previous studies. The depth parameters for the Van earthquakes did not fully match those in the previous studies, possibly due to differences in crustal velocity models. The depth parameters obtained for both Van earthquakes using both methods performed in this study are self-consistent at around 24 km. In addition, Disaster and Emergency Management Presidency and German Research Centre seismology centres also reached depth solutions near those in this study. The grid search method has some disadvantages compared with the manta ray foraging method, as it must be applied gradually, and delays reaching a solution. The manta ray foraging method is an easy, fast way to determine the kinematic location of earthquake hypocentres. [ABSTRACT FROM AUTHOR]

Reef manta rays (Mobula alfredi) face threats from human exploitation and activity in several regions of the world and crucial information on the ecology of the species is needed. The species is observed at several sites in all parts of the archipelago of New Caledonia where anthropogenic influence is presumed to be minimal. This study is the first to investigate the population of New Caledonia and focuses on its characteristics and its habitat use. Photographs of reef manta rays were collected directly from the authors (14.4%) between 2017 and 2020 and gathered from recreational divers and snorkelers (85.6%) from 11 sites around New Caledonia. The authors used the unique ventral coloration patterns of the manta rays that were clearly identifiable from 1741 of these photographs to identify 391 individuals and record their physical characteristics (sex, injuries, and colour morph) and resighting rates. These results highlight the widespread distribution of the species in the archipelago (11 sites) with little connectivity between all aggregations sites, with only 5.4% of the individuals observed at more than one site. Strong and long-term site fidelity was recorded at all studied sites through re-sighting rates (52.2% overall) and residency analysis. The population also records the world highest known proportion of melanistic manta rays (43%) to date, and a balanced male: female ratio (1.0:1.15). The analysis of injuries reported that 44.8% of all reef manta rays identified (n = 391) had noticeable wounds or injuries with no significant difference in the proportions of males and females injured. From these injured individuals only 9.8% of injuries judged to be of anthropogenic origin and 29.7% from attempted predation. Our study complements and correlates previous findings on this population that revealed strong site fidelity and low connectivity. The different characteristics of this population of reef manta rays also emphasize that concerns about species in New Caledonia are limited and that such favourable context needs to be preserved as reference for conservation. [ABSTRACT FROM AUTHOR]

Summary: In this proposed strategy, an effective routing strategy is introduced for routing based on Rendezvous Points assisted fuzzy inference logics. First, sensor nodes in the cluster are created using the mean‐shift clustering mechanism (MS‐ClusT). A cluster head is selected for the clusters to gather aggregated data from the nodes of clusters. The cluster head selection is done using the Manta Ray optimization‐assisted modified network‐based fuzzy inference logic (MMFiL) approach. Rendezvous points are then selected for routing to minimize the delay and energy consumption for visiting all cluster heads. The rendezvous points are used to define the shortest route for routing. The shortest path from clusters to the base station by selecting rendezvous points is implemented using an integrated neural network with an Archer‐Fish optimization algorithm (InnAFo). In the end, the performance attained by the model is compared with the results of the classical strategy and returns a 0.976563 packet delivery rate and 0.029297 loss rate in 500 nodes. Furthermore, the throughput attained by the proposed model is 0.992874 Mbps, and the delay is 67.30 ms at 500 nodes. The analysis of the proposed model will result in less time needed for cluster head selection. [ABSTRACT FROM AUTHOR]

The decarburization of heat and transport using electric vehicles (EVs) and heat pumps (HPs) needs an important investment in low-voltage (LV) networks. This article proposes an optimization approach to estimate the availability of headroom for domestic EV charging and optimization on LV networks under various penetrations of heat pumps. The proposed optimization approach is called dynamic differential annealed optimization (DDAO). The objective of the proposed approach is to maximize the hosting capability of low-voltage networks for EVs and HPs by optimizing the flexibility of EV charging. The DDAO is used to optimize different aspects of EV charging and LV networks. The DDAO approach is implemented in three steps: (a) HP headroom assessment, (b) EV optimization, and (c) validation. The performance of the proposed method is validated on a MATLAB platform and compared with the existing Binary Spring Search Algorithm (BSSA), Manta Ray Foraging Optimization Algorithm (MRFO), and Chaos Game Optimization methods. In comparison to the total prices of 3.5£ in BSSA, 4.5£ in MRFO, and 5.5£ in CGO, the proposed DDAO method achieves a lower total price of 3.2£. This suggests that the DDAO method is more cost-effective than the existing techniques. [ABSTRACT FROM AUTHOR]

Scientists, hunters and business entrepreneurs in the Carolinas all had mutual interests in giant manta rays (Mobula birostris) during the early decades of the 1900s. Eastern-seaboard coastal communities called them devil fish, because of the horn-shaped fins on their head. Although the Ocean Leather Company in Morehead City primarily processed shark-skin leather, it also experimented with the skins of rays and other sea animals for the manufacture of a great variety of consumer products. Articles were written for scientific journals and ray specimens were contributed to national institutions like the American Museum of Natural History. Local fishermen, along with celebrities like the US president, Teddy Roosevelt, harpooned devil fish in Cape Lookout while marvelling at their grace and strength, breaching up to six feet above the water's surface. Beaufort planter William Elliott presented many accounts of this fantastic sea creature, with vivid stories of enslaved African harpooners jumping off boats onto the backs of giant manta rays. This research combines historical accounts and images, newspaper advertisements and talks at local explorer clubs to illustrate case studies of the community's obsession with collecting, cooking, hunting and conquering rays as an important component of maritime leisure and environmental history. It concludes by addressing international examples of subsistence, recreational and industrial fishing, and its impacts on manta rays. [ABSTRACT FROM AUTHOR]

Summary: The wireless network resource allocation is a NP‐hard combinatorial optimization problem. This paper proposes a new optimization method using the manta ray foraging optimization (MRFO) based on spiral modeling to solve the wireless network resource problem. In order to reduce the computational complexity and ensure the optimal performance of the allocation scheme, a MRFO algorithm based on spiral modeling and mutation strategy is proposed. In the first stage, spiral modeling is introduced to narrow the exploration area, while the mutation strategy of the genetic algorithm enhances the ability of the algorithm to jump out of the local optimal. In the second stage, a binary MRFO algorithm for using different transfer functions is proposed to solve the mixed integer programming problem. The experimental results show that IMRFO and BIMRFO have high comprehensive performance advantages, achieve better effects than the other optimization algorithms, which lead to faster convergence, faster accuracy, and lower complexity. [ABSTRACT FROM AUTHOR]

Plant diseases significantly impact crop yields and financial stability, especially in Asia where rice is a staple crop. This study introduces the Automated Deep Learning with Wavelet Neural Network (ADLWNN) model to effectively identify and classify rice plant diseases. The ADLWNN model integrates the VGG-16 Convolutional Neural Network (CNN) for feature extraction from rice plant images. VGG-16, with its deep architecture of thirteen convolutional layers and two fully connected layers, is fine-tuned for binary classification by reinitializing the final SoftMax layers. Hyperparameter tuning is achieved through the Manta Ray Foraging Optimization (MRFO) algorithm, which mimics the foraging behavior of manta rays using techniques like somersault foraging and cyclone foraging to optimize the model parameters. For robust recognition, the Wavelet Neural Network (WNN) is employed, which decomposes input signals into simpler wavelet components for precise pattern identification. The WNN's wavelet analysis, combined with the optimized features extracted by VGG-16, enhances the model's classification capability. Simulation results on a rice plant image dataset show that the ADLWNN model achieves a remarkable 98.18% accuracy, outperforming existing methods in sensitivity, specificity, precision, and F-score. This comprehensive approach demonstrates the ADLWNN model's effectiveness in automated rice disease diagnosis, offering a valuable tool for safeguarding crop yields. [ABSTRACT FROM AUTHOR]

Water–air trans-media unmanned vehicle is a kind of aircraft, which can freely fly in the air, sail in the water and pass through free surface. For trans-media aircrafts, the development process from air–surface to air–underwater and from submarine-launched drive to autonomous drive is investigated. By analyzing the characteristic of manta ray, flying fish and existing aircraft, this paper proposes a new water–air trans-media unmanned vehicle with flat dish-airfoil-shaped main body and telescopic NACA-type wing. Then the numerical method to calculate the lift and drag forces is established and validated by the results of classic NACA cases. On this basis, the flow field around the new vehicle is numerically simulated, and its lift–drag performances in different media (air and water) and ground effect are analyzed, comparing it with a model inspired by the Blackwing Unmanned Aerial Vehicle (UAV). The findings illustrate the superior performance of the new vehicle in terms of lift and drag forces, offering an innovative design framework for water–air trans-media UAV applications. [ABSTRACT FROM AUTHOR]

Flexibility in animal foraging strategies can increase overall feeding efficiency for individuals. For example, group foraging can increase the efficiency of resource exploitation; conversely solo foraging can reduce intraspecific competition, particularly at low resource densities. The cost–benefit trade-off of such flexibility is likely to differ within and among individuals. Reef manta rays (Mobula alfredi) are large filter-feeding elasmobranchs that often aggregate to feed on ephemeral upwellings of zooplankton. Over three years in the Maldives, we free-dived to film 3106 foraging events involving 343 individually identifiable M. alfredi. Individuals fed either solo or in groups with a clear leader plus between one and eight followers. M. alfredi were significantly more likely to forage in groups than solo at high just prior to high tide and when aggregations were larger. Within aggregations, individuals foraged in larger groups when more food was available, and when the overall aggregations were relatively large suggesting that foraging in large groups was more beneficial when food is abundant, and the costs of intraspecific competition were outweighed by the efficiency resulting from group foraging strategies. Females, the larger sex, were more likely to lead foraging groups than males. The high within-individual variance (over 70%), suggested individuals were unpredictable across all foraging behaviours, thus individual M. alfredi cannot be classified into foraging types or specialists. Instead, each individual was capable of considerable behavioural flexibility, as predicted for a species reliant on spatially and temporally ephemeral resources. Significance statement: Reef manta rays (Mobula alfredi), listed as "Vulnerable" on the IUCN Red List, are at risk from targeted and by-catch fisheries due to their slow life history and aggregative behaviour. M. alfredi feed together in aggregations on short-lived glut of microscopic zooplankton. Over three years in the Maldives, we filmed 3106 foraging events involving 343 individually identifiable M. alfredi. Manta rays were more likely to forage in groups than solo just prior to high tide and when aggregations were larger, attracted by the influx of zooplankton. Foraging groups included more individuals when plankton was more abundant. However, individuals flipped between solo and group foraging and did not specialise. Foraging groups were most often led by females, the larger sex. Individuals were very flexible in how they foraged, which makes sense for a species that relies on a food source that varies enormously in when, where and for how long it is available. Understanding manta ray foraging behaviour will help conservation management efforts and predict their responses to climate change. [ABSTRACT FROM AUTHOR]

Owing to the rapid growth of the Internet of Things (IoT) and mobile devices, big data has been generated, and a cloud computing system is required to manipulate it. The main issue in this system is the access delay (AD) when the IoT and mobile devices offload their workload on the servers and the workload balance among them. Mobile edge computing (MEC), which has a sufficient distribution of edge servers (ESs) and resources, has been developed to overcome this problem. The placement of ESs in MEC is an NP-hard problem and a multi-objective optimization (MOO) problem that aims to reduce the AD between mobile users and ES and balance the workload among these servers. In this paper, we propose a new hybrid natural-inspired algorithm by combining the manta ray foraging algorithm and the uniform crossover operator to find the optimal edge server placement (ESP) in MEC and minimize the AD between mobile users and the ES. Although the standard manta ray foraging optimization (MRFO) algorithm can balance diversification and intensification, it suffers from premature convergence. We invoke a uniform crossover operator in the standard MRFO algorithm to overcome this problem. In addition, the standard MRFO algorithm is designed to solve continuous optimization problems and is unsuitable for solving discrete problems. However, ESP is a discrete optimization problem. We modified the operators in the standard MRFO algorithm to solve the ESP problem, achieving an optimal solution in terms of minimizing the AD between BSs and ESs and balancing the workload among ESs. The proposed algorithm is then tested on the Shanghai Telecom dataset and compared with five other algorithms. The results show that the proposed algorithm achieves the least AD, and the workload balance reaches about 52% compared with the other algorithms. [ABSTRACT FROM AUTHOR]

Size-based particle filtration has become indispensable in numerous biomedical and environmental applications. In this study, bioinspired by the filter-feeding mechanism (lobe filtration) of manta rays, we designed a U-shaped biomimetic gill rake filter that combined lobe filtration and Dean flow to filter monodisperse suspensions, bi-disperse suspensions and yeast cells. Compared with other equipment using the inertial focusing technology, our equipment can perform high-throughput (up to 8 mL min-1) and high-efficiency filtration of particles (maximum filtration efficiencies of 96.08% and 97.14% for 10 and 15 µm monodisperse suspensions at the optimum flow rate of 6 mL min-1). The complex velocity field of the micro-fluidic flow within the filter is numerically simulated, and in combination with experiments, a threshold for the flow rate is identified. When the inlet flow rate exceeds the threshold value, the efficiency of particle filtration is increased rapidly. Afterwards, by analysing the filtration mechanism, we develop three novel filtration processes. The equilibrium positions of the particles and yeast cells in the main channel are close to the outer wall at high flow rate, which diminishes the likelihood of particles and yeast cells entering the side channel. This configuration establishes a self-cleaning mechanism, ensuring prolonged and efficient operation of the filter with high-throughput processing. Furthermore, the influence of the filter lobe angle and channel width on the filtration efficiency and outlet flow rate ratio are explored, and an optimisation plan is prepared. [ABSTRACT FROM AUTHOR]

Brain tumor is an anomalous growth of glial and neural cells and is considered as one of the primary causes of death worldwide. Therefore, it is essential to identify the tumor as soon as possible for reducing the mortality rate throughout the world. However, the classification of brain tumor is a challenging task due to presence of irrelevant features that cause misclassification during detection. In this research, the adaptive manta ray foraging optimization (AMRFO) is proposed for performing an effective feature selection to avoid the problem of overfitting while performing the classification. The adaptive control parameter strategy is incorporated in the AMRFO for enhancing the search process while selecting the feature subset. The linear intensity distribution information and regularization parameter-based intuitionistic fuzzy C-means algorithm namely LRIFCM is used to perform the segmentation of tumor regions. Next, LeeNET, gray-level co-occurrence matrix, local ternary pattern, histogram of gradients, and shape features are used to extract essential features from the segmented regions. Further, the attention-based long short-term memory (ALSTM) is used to classify the brain tumor types according to the features selected by AMRFO. The datasets utilized in this research study for the evaluation of AMRFO-ALSTM method are BRATS 2017, BRATS 2018, and Figshare brain datasets. Segmentation and classification are the two different evaluations examined for the AMRFO-ALSTM. The structural similarity index measure, Jaccard, dice, accuracy, and sensitivity are utilized during segmentation evaluation, while accuracy, specificity, sensitivity, precision, and F1-score are used during classification evaluation. The existing researches namely, transformer-enhanced convolutional neural network, Chan Vese (CV)-support vector machine, CV-K-nearest neighbor, deep convolutional neural network (DCNN), and salp water optimization with deep belief network are used to compare with the AMRFO-ALSTM. The accuracy of AMRFO-ALSTM for Figshare brain dataset is 99.80 which is a greater achievement when compared to the DCNN. [ABSTRACT FROM AUTHOR]

Many shark and ray species show affinity to specific sites, making these areas critical for their survival. These include cleaning stations: locations on reefs where cleaner fish remove parasites and clean wounds, which is important for maintaining health. Cleaning stations also function as social gathering sites, or resting points, where courtship and mating can occur. In this study, we identify an aggregation site for the shortfin devil ray, Mobula kuhlii (Family Mobulidae) within the Aliwal Shoal Marine Protected Area in KwaZulu-Natal (KZN), South Africa, and document their behavior. Remote underwater video was used to collect footage of M. kuhlii being cleaned by blue streak cleaner wrasse, Labroides dimidiatus. Generalized additive models (GAMs) were used to assess environmental predictors of M. kuhlii presence on Aliwal Shoal. Mixed models were used to assess the same environmental predictors and their correlation with mean M. kuhlii cleaning duration and number of L. dimidiatus bites per second at the identified cleaning station site. M. kuhlii were present in 56% of observation days, with group numbers up to > 150 individuals. Sea surface temperature was a significant predictor for M. kuhlii presence, while a north to south current was significantly associated with longer mean cleaning durations. These results support findings of mobulid studies in KZN that show increased habitat use during summer temperatures (24–25 °C) and suggest these sites to be important for individual health and social interaction. We hope these findings can be used for development of location-specific management plans to safeguard this Endangered species. [ABSTRACT FROM AUTHOR]

Though a previous study in Sudan confirmed the existence of a living hybrid of an oceanic manta ray (Mobula birostris) and a reef manta ray (M. alfredi), courtship behaviors between the two closely related species have never been documented. Here we report the first observation of courtship behaviors between two male M. birostris and a female M. alfredi at a manta ray cleaning station on a shallow coral reef seamount in Misool, Raja Ampat, Indonesia. The observed courtship event lasted for approximately 45 min and involved one male oceanic manta actively chasing the mature reef manta female around the cleaning station and repeatedly bumping her on the left wing tip with his right cephalic lobe, while the second male continuously flanked the female to seemingly prevent her retreat from the cleaning station and potential evasion of the pursuing male. Notably, the female exhibited a rapid coloration change, likely indicative of courtship-related stress, transitioning from the striking pale hue she exhibited throughout the courtship activity to a normal chevron pattern once the males departed. [ABSTRACT FROM AUTHOR]

The selection of attributes has become a crucial research focus in the domains of pattern recognition, machine learning, and big data analysis. In essence, the contemporary challenge revolves around reducing dimensionality while maintaining both a quick response time and improved classification performance. Metaheuristics algorithms (MAs) have emerged as pivotal tools in addressing this issue. Firstly, the problem of attribute selection was approached using the manta ray foraging optimization (MRFO) approach, but the majority of MAs suffer from a problem of convergence toward local minima. To mitigate this challenge, an enhanced variant of MRFO, known as MRFOSCA, employs trigonometric operators inspired by the sine cosine algorithm (SCA) to tackle the feature selection problem. The k-nearest neighbor (k-NN) technique is employed for feature-set selection. Additionally, the statistical significance of the proposed algorithms is assessed using the nonparametric Wilcoxon's rank-sum test at a 5% significance level. The outcomes are assessed and compared against some well-known MAs, including the original MRFO and SCA, as well as Harris Hawks optimizer, dragonfly algorithm, grasshopper optimizer algorithm, whale optimizer algorithm, salp swarm algorithm, and grey wolf optimizer. The experimental and comparison analyses validate the pretty effective performance of the proposed methods on low- and high-dimensional datasets by providing the highest accuracy in 85% of the feature selection benchmarks. [ABSTRACT FROM AUTHOR]

Investigating the spatiotemporal ecology of elasmobranchs is an important precursor to their effective management. Understanding long-term patterns in the movement and habitat use of threatened species can improve management plans so that they yield increased conservation benefits. We investigated the spatiotemporal and environmental drivers that underpin the abundance and distribution of elasmobranchs around reef habitats in southern Mozambique to highlight reefs that are important (“hotspots”) to the regional elasmobranch community. Visual belt transects (n = 738), supported by video recordings, were completed on 16 reef sites off the coast of southern Mozambique from 2018 to 2022. Nine elasmobranch species were encountered annually (Carcharhinus amblyrhynchos, Triaenodon obesus, Stegostoma tigrinum, Neotrygon caeruleopunctata, Pateobatis jenkinsii, Taeniurops meyeni, Mobula kuhlii, Mobula alfredi, Mobula birostris) and 11 individual environmental and spatiotemporal parameters (horizontal water visibility, tidal range and state, moon illumination, temperature on the reef, cloud cover, time of day, day of the year, transect distance from shore, transect depth, and the region that the transect occurred in) were measured. All species, (bar P. jenkinsii) were significantly more abundant around certain reefs in the sampled region. Total counts for most species were highest in the austral summer however two species’ (M. birostris and S. tigrinum) were most abundant in the winter months. The tidal state, tidal range, and moon illumination correlated significantly with the numbers of each of the nine elasmobranch species. Non-Metric Multidimensional Scaling (NMDS) indicated that species’ responses to the measured parameters grouped taxonomically. Environmental influences resulted in strong seasonal patterns of reef use by large-bodied and pelagic elasmobranch species (e.g. manta rays). The measured environmental parameters also resulted in daily, monthly, and seasonal patterns of abundance of reef-resident stingray and shark species. Banning extraction of elasmobranch species around the reefs where they aggregate and reflecting species distributions within fisheries regulations may significantly benefit the regional elasmobranch community. [ABSTRACT FROM AUTHOR]

Mobulid rays are medium‐ to large‐sized pelagic‐dwelling planktivorous elasmobranchs that are circumglobally distributed. Brazil has a high diversity of Mobula species, with five of the nine globally valid species and one additional putative new species.Brazil represents a substantial knowledge gap concerning the ecology, distribution and threats to mobulid rays. In global distribution analyses, several species have been underrepresented along the Brazilian coast.The dataset is composed of information from peer‐reviewed and grey literature, as well as data from scientific expeditions, citizen science collaboration, social media and local news.We found 6,450 records of manta and devil rays along the Brazilian coast, including Mobula tarapacana, Mobula mobular, Mobula thurstoni, Mobula hypostoma, Mobula birostris and the putative new species (Mobula cf. birostris).Our results revealed alarmingly high captures, such as 4.5 tons of M. hypostoma in a single fishery cruise and 809 individuals of Mobula spp. from a single fleet during one year in the Southeastern region.These findings evidence the fisheries' threats to mobulid species in Brazil and highlight previously unnoted species records, such as the occurrence of M. hypostoma on the entire Brazilian continental shelf, including estuaries and M. tarapacana in coastal regions.Integrative information on the occurrence, distribution, and threats to mobulid rays on local and global scales is crucial to supporting strategies for their effective management and conservation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Mobula hypostoma (Bancroft, 1831), Atlantic Pygmy Devil Rays, were observed during megafauna surveys in the shallow coastal waters off the Republic of the Congo. These are the first records of this species in its natural habitat in Congolese waters, which may well offer an important habitat for these coastal batoids, but further research is needed. There is an urgent need for management measures to protect M. hypostoma throughout its African range, particularly in view of the recent increase in fishing activities within the region. [ABSTRACT FROM AUTHOR]

Recent encounters with sicklefin (Mobula tarapacana) and bentfin (Mobula thurstoni) devil rays in the Chagos Archipelago provide the first confirmed observations of live specimens of these species in this region. Examination of illegal fishing photo archives collected during enforcement revealed these endangered species, and spinetail devil rays (Mobula mobular), are being caught within the archipelago's vast no‐take marine protected area. Future cooperation between authorities and mobulid ray experts is crucial to improve the availability and accuracy of enforcement data and improve management of illegal fishing and mobulid ray conservation activities. [ABSTRACT FROM AUTHOR]