Manta Ray Research Articles

Individual flexibility in group foraging behaviour of reef manta rays (Mobula alfredi)

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 statementReef 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.

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

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.

Characteristics, residency and site fidelity of photo-identified reef manta rays (Mobula alfredi) population in New Caledonia

Characteristics, residency and site fidelity of photo-identified reef manta rays (Mobula alfredi) population in New Caledonia

An automated metaheuristic-optimized approach for diagnosing and classifying brain tumors based on a convolutional neural network

Brain tumors must be classified to determine their severity and appropriate therapy. Applying Artificial Intelligence to medical imaging has enabled remarkable developments. The presented framework classifies patients with brain tumors with high accuracy and efficiency using CNN, pre-trained models, and the Manta Ray Foraging Optimization (MRFO) algorithm on X-ray and MRI images. Additionally, the CNN and Transfer Learning (TL) hyperparameters will be optimized through MRFO, resulting in improved performance of the pre-trained model. Two public datasets from Kaggle were used to build the models. The first dataset consists of two X-ray classes, while the 2nd dataset includes three contrast-enhanced T1-weighted MRI classes. First, a patient should be diagnosed as “Healthy” (or “Tumor”). When the scan returns the result “Healthy,” the patient has no abnormalities in their brain. If a scan reveals that the patient has a tumor, an MRI will be performed on them. After that, the type of tumor (i.e., meningioma, pituitary, and glioma) will be identified using the second proposed classifier. A comparative analysis of the models used in the two-class dataset showed that VGG16's pre-trained model outperformed other models. Besides, the Xception pre-trained model achieved the best results in the three-class dataset. A manual review of misclassifications was conducted to determine the reasons for the misclassifications and correct them. The evaluation of the suggested architecture yielded an accuracy of 99.96% for X-rays and 98.64% for T1-weighted contrast-enhanced MRIs. The proposed deep learning framework outperformed most current deep learning models.

Evidence of reproductive and feeding habitat for manta rays off Florida’s Atlantic coast

Evidence of reproductive and feeding habitat for manta rays off Florida’s Atlantic coast

Multi-step optimization of hybrid cooling array via integrating Taguchi method and long short-term memory neural network

In advanced aero-engines, the pursuit of higher propulsive efficiency and power output creates an imbalance in the supply and demand of cooling air. In response, this study establishes a multi-step optimization platform for the hybrid cooling array, with the dual objectives of improving the cooling efficiency and coolant utilization. The design space involves the impingement hole diameter, film hole pitch in the spanwise and streamwise directions, thickness of the cellular partition and film cooling plate, height of impingement channel, and configuration inclination. Furthermore, two types of coolant supply condition are incorporated into the study. To attain the dual objectives, we specify a fixed constraint on the coolant supply per unit area. Taguchi method is chosen for the initial design of partial factors that have a weak nonlinear relationship with the cooling effectiveness. Optimization is conducted by long short-term memory model combined with manta ray foraging optimization algorithm for the residual factors which maintain a strong nonlinear relationship with the cooling effectiveness. Using the Taguchi method, the optimal combination for maximum cooling performance and relative contribution level of initial design factors have been successfully determined, and a set of mechanism analyses are given accordingly. LSTM model achieved a high reconstruction precision for the laterally averaged cooling effectiveness (ηlat¯) of the hybrid cooling array, with mean absolute error of 0.31 % and 0.83 % for ηlat¯, determination coefficient of 0.9997 and 0.9988 for the area-averaged overall cooling effectiveness (ηov¯), in the training and testing group, respectively. Meanwhile, the optimized cooling array exhibits an encouraging performance, with 8.94 % and 10.37 % improvements on ηov¯ for two coolant supplies, respectively. It is believed that the proposed framework can be generalized to optimize other forms of hybrid cooling array.

T-ReX: The Tarantula—Revealed by X-Rays

The Tarantula Nebula (30 Doradus, 30 Dor) is the most important star-forming complex in the Local Group, offering a microscope on starburst astrophysics. At its heart lies the exceptionally rich young stellar cluster R136, containing the most massive stars known. Stellar winds and supernovae have carved 30 Dor into an amazing display of arcs, pillars, and bubbles. We present first results and advanced data-processing products from the 2 Ms Chandra X-ray Visionary Project, “The Tarantula—Revealed by X-rays” (T-ReX). The 3615 point sources in the T-ReX catalog include massive stars, compact objects, binaries, bright pre-main-sequence stars, and compact young stellar (sub)clusters in 30 Dor. After removing point sources and excluding the exceptionally bright supernova remnant N157B (30 Dor B), the global diffuse X-ray maps reveal hot plasma structures resolved at 1–10 pc scales, with an absorption-corrected total-band (0.5–7 keV) X-ray luminosity of 2.110 × 1037 erg s−1. Spatially resolved spectral modeling provides evidence for emission lines enhanced by charge-exchange processes at the interfaces. We identify a candidate for the oldest X-ray pulsar detected to date in 30 Dor, PSR J0538-6902, inside a newly resolved arcuate X-ray wind nebula, the Manta Ray. The long temporal baseline of T-ReX allowed monitoring of dozens of massive stars, several showing periodic variability tied to binary orbital periods, and captured strong flares from at least three low-mass Galactic foreground stars.

Cost-Effective Planning of Hybrid Energy Systems Using Improved Horse Herd Optimizer and Cloud Theory under Uncertainty

In this paper, an intelligent stochastic model is recommended for the optimization of a hybrid system that encompasses wind energy sources, battery storage, combined heat and power generation, and thermal energy storage (Wind/Battery/CHP/TES), with the inclusion of electric and thermal storages through the cloud theory model. The framework aims to minimize the costs of planning, such as construction, maintenance, operation, and environmental pollution costs, to determine the best configuration of the resources and storage units to ensure efficient electricity and heat supply simultaneously. A novel meta-heuristic optimization algorithm named improved horse herd optimizer (IHHO) is applied to find the decision variables. Rosenbrock’s direct rotational technique is applied to the conventional horse herd optimizer (HHO) to improve the algorithm’s performance against premature convergence in the optimization due to the complexity of the problem, and its capability is evaluated with particle swarm optimization (PSO) and manta ray foraging optimization (MRFO) methods. Also, the cloud theory-based stochastic model is recommended for solving problems with uncertainties of system generation and demand. The obtained results are evaluated in three simulation scenarios including (1) Wind/Battery, (2) Wind/Battery/CHP, and (3) Wind/Battery/CHP/TES systems to implement the proposed methodology and evaluate its effectiveness. The results show that scenario 3 is the best configuration to meet electrical and thermal loads, with the lowest planning cost (12.98% less than scenario 1). Also, the superiority of the IHHO is proven with more accurate answers and higher convergence rates in contrast to the conventional HHO, PSO, and MRFO. Moreover, the results show that when considering the cloud theory-based stochastic model, the costs of annual planning are increased for scenarios 1 to 3 by 4.00%, 4.20%, and 3.96%, respectively, compared to the deterministic model.

Lift–Drag Performance of a New Unmanned Aerial Vehicle in Different Media and Ground Effect

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.

Performance optimization and energy minimization of cloud data center using optimal switching and load distribution model

Cloud computing is an effective computing methodology used in all stages of business. Most of the Cloud Data Centers (CDC) operates on the basis of peak load and huge scales. Hence, it necessitates saving the energy in CDC. This study introduces an energy-efficient strategy based on the fat tree. Here, Taylor-based Manta-Ray Foraging Optimization (Taylor-MRFO) is developed by combining the Taylor series with Manta Ray Foraging Optimization (MRFO) to distribute the load in a CDC. In load distribution, the cloud data switching to the preferred mode is done by the Actor critic neural network (ACNN). Furthermore, the developed Taylor-MRFO+ACNN provided a better outcome than the conventional approaches with the least energy consumption of 0.4930, least load of 0.3631, and least fitness of 0.4343. For setup-1, when the population size is 15, the load value obtained by the proposed method is 23.43 %, 10.19 %, 7.18 %, 5.31 %, 4.43 %, and 2.58 % higher when compared to the existing approaches namely, Artificial Bee colony(ABC), Efficient Load Optimization and Resource Minimization (ELORM), Adaptive Parameter- Ant Colony Optimization (AP-ACO), Multi-Objective Memetic Algorithm-Adaptive Plant Intelligent Behavior Optimization (MOMA-APIBO), Cooling Control Algorithm (CCA), and Minimum Total Power (MinPR).

Unified scheme design and control optimization of flapping wing for next-generation manta ray robot

An efficient global optimization (EGO) algorithm has been used in the geometry design and motion for a manta ray-like flapping wing to optimize the best swimming performance. To this end, we combine a three-dimensional simulation using STAR-CCM+ with this framework to generate the optimal flapping wing geometry that maximizes the lift-to-drag ratio. The drag and moment coefficients between the original and optimized flapping wings were further compared in a servo-driven towing tank to validate the optimization effects. In addition, by associating the force experiments in a water tunnel, we successfully predict the motion trajectory for the largest thrust with the same EGO algorithm. A free-swimming test is carried out to verify the optimization. This study gives a unified scheme design optimization for the next-generation manta ray robot.

Effects of motion parameters on the propulsion characteristics of flexible pectoral fins in bio-manta robots

Manta rays have emerged as excellent bionic objects due to their efficient and flexible propulsion. A thorough understanding of the propulsion characteristics of manta ray robot pectoral fins is highly important for optimizing pectoral fin design and guidance strategies. Based on the silhouette and kinematics of the manta ray, we designed flexible pectoral fins and a 2DoF bionic mechanism that couples the flapping and pitching movements. Blade element theory was used to conduct a dynamic analysis of the bio-manta pectoral fins. The Strouhal number is used as a key dimensionless number that affects the hydrodynamic characteristics of a bio-manta robot during periodic flapping, as well as the flexibility of the pectoral fins and the angle of attack. We describe a series of hydrodynamic experiments conducted to analyze the single and interactive effects of the motion parameters of pectoral fin propulsion and regression analysis. The results show that when St is [0.2,0.4], the CT (thrust coefficient) of the flexible pectoral fin increases rapidly. When St increased beyond 0.4, the growth trend slows and remains essentially unchanged after 0.8. The CT reaches its peak, the propulsion efficiency is highest when St is between [0.4,0.6]. The angle of attack and the CT show a negative inverse quadratic trend, reaching a peak at 6.5°, and their influence is weaker than that of the frequency. The flexibility and CT showed a cubic trend, and the influence of flexibility was more significant when swimming at medium and low speeds. When swimming slowly, flexibility has a significant interactive effect on the frequency and angle of attack. However, these interactive effects reduce at high speeds. When the bio-manta robot starts at 0 speed, it should maneuver at high frequency and large amplitude while ensuring sufficient structural strength. After reaching a certain speed, the device maintains a large amplitude and low-frequency flapping operation to continuously drive propulsion.

Occurrence, distribution and threats to mobulid rays in Brazil: A review and updated database

Abstract 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.

Cutting Energy Consumption Modeling by Considering Tool Wear and Workpiece Material Properties for Multi-Objective Optimization of Machine Tools

The increasing demand for energy is leading to global depletion of fossil fuels and growing environmental pressures, which are issues that need to be addressed. Machine tools are basic energy-consuming equipment in manufacturing systems. However, existing theoretical models ignore tool wear as well as workpiece material properties. This makes it difficult to further improve the accuracy of the model. Therefore, this study begins with the point of view of energy dissipation in the metal material removal process. A milling power model for computer numerical control (CNC) machines, considering tool wear and workpiece material properties during machining, is established. At the same time, milling is taken as the research object. A multi-objective cutting parameter optimization model is established to ensure the surface quality of the workpiece. In addition, the cutting energy consumption is taken into account in the developed models. Based on the multi-objective manta ray foraging optimization algorithm (MOMRFO), the Pareto-optimal solution set under multiple cutting conditions is solved. Finally, the experimental results of optimized parameters are compared with empirical parameters. The average prediction accuracy of the proposed energy consumption prediction model is above 91%. The experiments show that machining quality improves by optimizing the cutting parameters, with SEC, MRR, and Ra increasing by more than 44%, 53%, and 38%, respectively. The goals of reducing energy consumption and increasing productivity are achieved.

Development and 3-D Path-Following Control of an Agile Robotic Manta With Flexible Pectoral Fins.

The broad and powerful pectoral fins of manta rays are crucial to their efficient and maneuverable swimming. However, very little is currently known about the pectoral-fin-driven 3-D locomotion of manta-inspired robots. This study is focused on the development and 3-D path-following control of an agile robotic manta. First, a novel robotic manta with 3-D mobility is constructed, of which the distinctive pectoral fins provide the only propulsion. Specifically, the unique pitching mechanism is detailed in which the time-coupled coordination movement of the pectoral fins is applied. Second, based on a 6-axis force measuring platform, the propulsion characteristics of the flexible pectoral fins are analyzed. Then, the force-data-driven 3-D dynamic model is further established. Third, a control scheme combined with a line-of-sight (LOS) guidance system and a sliding-mode fuzzy controller is conceived, addressing the 3-D path-following task. Finally, various simulated and aquatic experiments are conducted, demonstrating the superior performance of our prototype and the effectiveness of the proposed path-following scheme. This study will hopefully generate fresh insights into the updated design and control of agile bioinspired robots performing underwater tasks in dynamic environments.

First Documented Courtship Behavior between Mobula birostris and M. alfredi at a Coral Reef Cleaning Station in Misool, Raja Ampat

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.

Environmental factors modulate the distribution of elasmobranchs in southern Mozambique

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.

Productivity prediction of a spherical distiller using a machine learning model and triangulation topology aggregation optimizer

Solar stills offer a sustainable and environmentally friendly solution to water scarcity in remote areas, but their limited productivity hinders their wider adoption. This study proposes innovative modifications to the spherical solar distiller to address this challenge. We introduce a rotating spherical ball within the distiller and investigate its impact on productivity at various speeds (0–2 rpm) with and without a wick. Additionally, we explore the effectiveness of preheating feed water to different temperatures (45–70 °C) and its interaction with the rotating ball mechanism. Moreover, six machine learning models were employed to predict the water productivity of the distillers under different working conditions. The employed models were standalone long short-term memory (LSTM), LSTM optimized by reptile search algorithm, LSTM optimized by grey wolf optimizer, LSTM optimized by dwarf mongoose optimization algorithm, LSTM optimized by manta ray foraging optimizer, LSTM optimized by triangulation topology aggregation optimizer. The results showcased that with an optimal rotation speed of 0.5 rpm and 1 rpm for configurations with and without wick, respectively, we achieved productivity increases of 62 % and 55 %. Notably, preheating feed water to 65 °C further boosted the new distiller performance, surpassing the conventional solar still by 91 %, achieving an impressive output of 6000–6200 mL/m2.day compared to 3000–3250 mL/m2.day for the conventional distiller. Moreover, the thermal efficiency of the new distiller configuration reached 62 %, almost doubling that of the conventional distiller (32 %). Moreover, the triangulation topology aggregation optimizer outperformed other models in predicting water productivity with a high R2 range of 0.953–0.999.

Optimizing power system stability: A hybrid approach using manta ray foraging and Salp swarm optimization algorithms for electromechanical oscillation mitigation in multi‐machine systems

AbstractThis paper emphasizes the significance of ensuring adequate damping of electromechanical oscillations in power systems to ensure stable operation. Power System Stabilizers (PSSs) are influential in enhancing system damping and refining dynamic characteristics during transient conditions. However, the efficacy of PSSs is notably contingent on parameter values, particularly in the case of lead‐lag PSSs. In response to this challenge, the paper introduces a Tilt‐Integral‐Derivative (TID)‐based PSS, optimized through a novel optimization algorithm called Hybrid Manta Ray Foraging and Salp Swarm Optimization Algorithms (MRFOSSA). The MRFOSSA algorithm demonstrates robustness and enhanced convergence, validated through benchmark function tests, and outperforms competing algorithms. These superior characteristics of MRFOSSA were employed in optimal tuning of TID‐PSSs to uphold the stability of multi‐machine power systems. The MRFOSSA algorithm demonstrates robustness and enhanced convergence, outperforms competing algorithms in the optimal tuning of TID‐PSS within the Western System Coordinating Council (WSCC)‐3‐machines 9‐bus test system. In summary, the proposed TID‐PSS, coupled with the MRFOSSA algorithm, presents a promising avenue for enhancing power system stability.