Shelf-life extension of refrigerated ready-to-eat harpiosquillid mantis shrimp meat using modified atmosphere packaging combined with epigallocatechin gallate

Metaheuristics optimization algorithms have established themselves as a new cornerstone in the area of complex, nonlinear, and high-dimensional problems of science and engineering. Most existing algorithms, however, still do not manage to balance exploration and exploitation and still experience stagnation and premature convergence. Though there have been many developments, most of the available algorithms are not yet optimized in terms of premature convergence and adaptive decision-making ability. This paper presents two smart reinforcement-based schemes, one Adaptive Intelligent Reinforced Walrus-Gazelle Optimizer (AIRE_WGO), and another Dual-Mode Adaptive Reinforced Switching Walrus-Gazelle Optimizer (DMARS_WGO). The suggested AIRE_WGO steps up the classical Walrus gazelle hybrid, where it implements Q-learning-based control of the agent behavior, adjusts its parameters in response to the current environment, and considers diversity-informed mutations in order to dynamically switch between global exploration and local exploitation. However, DMARS_WGO incorporates a dual-agent reinforcement framework that combines tabular Q-learning and deep Q-network (DQN) reasoning. When interacting with diverse populations, DMARS_WGO autonomously switches between its learning dominance as a result of real-time feedback about the population structure, the rate of improvement, and the level of stagnation. Moreover, the cross-agent knowledge-sharing process provides a two-way experience transfer between Q-learning and DQN modules, which strengthens cooperative intelligence and stability. Extensive tests of CEC2017 and CEC2022 benchmark suites and six engineering design problems prove that the proposed algorithms, especially DMARS_WGO, are more successful compared to nine recent state-of-the-art optimizers including Golden Jackal Optimization (GJO), Osprey Optimization Algorithm (OOA), Pelican Optimization Algorithm (POA), Rat Swarm Optimizer (RSO), Smell Agent Optimization (SAO), Channa argus Optimizer (CAO), Rüppell's Fox Optimizer (RFO), Mantis Shrimp Optimization Algorithm (MShOA), and Adaptive L-SHADE (ALSHADE). On CEC2017, DMARS_WGO achieved first rank in 26 out of 29 benchmark functions and obtained the best overall Friedman mean rank. On CEC2022, it ranked first in 8 out of 12 functions and achieved the best overall ranking among all compared algorithms. Similarly, across the six engineering design problems, DMARS_WGO secured first position in 4 out of 6 cases and achieved the best overall ranking performance. Wilcoxon Signed-Rank and Friedman mean-rank statistical tests prove that DMARS_WGO is significantly superior. Its ability to smartly self-adapt its search dynamics is pointed out in the results, and makes it a strong and robust optimizer of real-world engineering problems.

Inspired by the Bouligand helicoidal architecture of the dactyl club of the peacock mantis shrimp, this study employed direct ink writing (DIW) 3D printing to construct a three-level synergistic toughening system composed of nano-SiO2, microscale flake alumina, and a macroscale helicoidal structure. The effects of nano-SiO2 content, Bouligand helix angle, and flake alumina content on the flexural strength and fracture toughness of the composite ceramics were systematically investigated. The results showed that the optimal nano-SiO2 addition was 7 wt%, yielding a fracture toughness of 1.03 MPa·m1/2, which was 13% higher than that of pure alumina. The introduced intergranular glassy phase transformed the rigid grain-boundary bonding into a moderately strong gradient interface, resulting in higher fracture toughness for all SiO2-containing samples than for pure alumina. The Bouligand structure further increased the fracture toughness to a maximum of 1.45 MPa·m1/2 at a helix angle of 10°, representing a 39% improvement over the 0° sample. When microscale flake alumina was incorporated into the optimal matrix containing 7 wt% SiO2, the best overall mechanical performance was achieved at a flake alumina content of 5 wt%, where the flakes directly dissipated fracture energy through pull-out, fracture, and bridging mechanisms. The synergistic effect of the three structural levels was most pronounced at a helix angle of 20°, at which the sample containing 5 wt% flake alumina achieved a fracture toughness of 2.07 MPa·m1/2 with almost no loss in flexural strength, corresponding to a 113% improvement over the sample without flake alumina. These results demonstrate that three-level synergy can be achieved through nanoscale interfacial optimization, microscale energy dissipation by reinforcing phases, and macroscale crack deflection induced by the helicoidal structure, thereby providing important theoretical and experimental support for the multiscale design of high-performance bioinspired ceramic materials.

Mantis shrimp saddle-mimetic amorphous calcium (zinc) phosphate/chitin scaffolds with superior mechanical properties and bioactivity for bone regeneration.

Effect of packaging conditions and chitooligosaccharides-catechin on shelf life extension of cooked Harpiosqullid mantis shrimp meat stored at refrigerated temperature

Effect of temperature on the swimming ability of mantis shrimp, Oratosquilla oratoria.

Bistable grasping devices exhibit high maneuverability and rapid responsiveness; however, existing bistable grippers often lack the capability for active adjustment of triggering forces and effective interaction with the environment during grasping. Inspired by the zebra mantis shrimp, this article proposes a bistable, multimode, and adaptive gripper named bistable multimode adaptive gripper (BMA) gripper. Through a bioinspired design of a multimode bistable actuator and underactuated fingers with modular fingertips, the proposed gripper achieves rapid grasping across diverse scenarios. A series of experiments were conducted to evaluate the performance and functionality of the BMA gripper. Experimental results demonstrate that the gripper can operate in three distinct grasping modes: fully active, active bistable, and passive bistable grasping, effectively interacting with the environment to achieve adaptive grasping. In the bistable grasping mode, the gripper requires a minimal energy of only 0.412 mJ for state transition and can switch from the open state to the closed state in as short as 0.116 s. In addition, the gripper achieves a maximum gripping force of 68.4 N at a gripping distance of 120 mm. These results confirm the significant potential of the BMA gripper for high-speed adaptive grasping tasks in various application scenarios.

Optical attenuation caused by absorption and scattering in turbid water significantly degrades underwater image quality, making reliable underwater imaging a challenging problem. Underwater polarization imaging has attracted increasing attention because of its ability to suppress scattered light and provide additional polarization cues. However, existing polarization-based enhancement approaches often adapt conventional underwater image enhancement strategies, and the multi-dimensional characteristics of polarization information are not always fully utilized, which may limit detail restoration in complex underwater environments. To address this issue, this paper proposes a bio-inspired underwater polarization image enhancement framework motivated by the polarization vision mechanism of marine organisms. Specifically, a two-stage architecture consisting of a Polarization Adversarial Network (PAN) and a Polarization Enhancement Network (PEN) is designed. The PAN incorporates a Bionic Antagonistic Module (BAM) to exploit complementary information among polarization channels, while Salient Feature Extraction (SFE) is introduced to reduce redundant feature interference. The subsequent PEN integrates a frequency-aware Mamba-based structure to enhance feature representation and improve detail reconstruction. Experiments on simulated underwater polarization datasets indicate that the proposed framework can effectively suppress backscattering and improve structural detail visibility in challenging underwater scenes, demonstrating competitive performance compared with representative traditional and learning-based methods.

The Atlantic sharpnose (Rhizoprionodon terraenovae), blacknose (Carcharhinus acronotus), blacktip (Carcharhinus limbatus), and bonnethead (Sphyrna tiburo) sharks are commonly encountered large mobile consumers found in the estuaries along the western North Atlantic coast. The bulk of the dietary data for these species has been coarsely recorded at a broad taxonomic level (e.g., “teleost fish”). Here, we used DNA metabarcoding of fecal DNA collected using non-lethal cloacal swabs to identify the species of prey contributing to the diets of these shark species and measure the degree of trophic overlap. Samples were collected from 24 Atlantic sharpnose, 33 blacknose, six blacktip, and 17 bonnethead sharks in the summer of 2020. Based on previous dietary research on these shark species, we targeted teleost fishes and crustaceans using two previously published primer sets. From the 80 sharks sampled off the coast of North Carolina, we identified 38 prey taxa, with 82% classified to the species level and all assigned to at least the genus and family levels. The most common prey taxa found in the diet of the bonnethead was Atlantic blue crab (Callinectes sapidus; 44.75%, based on percent of occurrence) followed by penaeid shrimp (Penaeus spp.; 24.41%), mantis shrimp (Squilla empusa; 20.34%), and spot (Leiostomus xanthurus; 4.75%). Atlantic sharpnose and blacknose sharks had the largest Levin’s niche overlap, with both species relying on the same two most frequently consumed prey taxa: penaeid shrimp (Atlantic sharpnose: 33.33%, percent of occurrence, and blacknose: 34.78%) and spot (Atlantic sharpnose: 32.70% and blacknose: 22.32%). Bonnetheads and blacktips had the least amount of overlap between all shark species, where blacktips primarily consumed menhaden (Brevoortia spp.; 58.62%) and penaeid shrimp (26.44%). Our findings highlight the value of DNA metabarcoding in refining our understanding of predator diets, moving beyond broad taxonomic classifications to identify species-level prey associations and trophic interactions. As coastal habitats undergo increasing alteration due to anthropogenic impacts, such information is crucial for fisheries management, helping to identify key prey dependencies and anticipate potential ecosystem shifts.

Optimal surgical scheduling necessitates a strategic balance between elective efficiency and responsiveness to stochastic emergency arrivals. This study evaluates a Genetic Algorithm alongside discretized variants of Particle Swarm Optimization, the Secretary Bird Optimization Algorithm, and the Mantis Shrimp Optimization Algorithm. These algorithms are assessed within a dynamic three-stage flexible flow shop model under no-buffer blocking constraints. Findings from 300 Monte Carlo replications demonstrate that while the Genetic Algorithm achieves peak global efficiency, discretized bio-inspired algorithms reach a comparable statistical efficiency frontier. Notably, the discretized Secretary Bird Optimization Algorithm facilitates superior emergency integration by maintaining natural capacity buffers, whereas the aggressive local optimization characteristic of alternative methods often triggers resource saturation in recovery units. These results indicate a potential recovery of 90 annual operating hours per theater.These results indicate a potential recovery of 90 annual operating hours per theater, representing a 6.7% increase in resource utilization efficiency. This improvement provides a critical data-driven capacity margin to mitigate the non-prioritized (Non-GES) surgical backlog in Chilean public hospitals.

To improve the impact resistance of composite materials, this study adopted the structures in the impact region of mantis shrimp appendages as a bionic prototype, designing a composite structure with a rigid outer layer and flexible sinusoidal inner layer. Meanwhile, bionic arrangement was conducted on the fibers in three directions (X-Y, X-Z, and Y-Z planes) within the flexible layer to regulate the crack propagation path during the impact process. Finite Element Method and low-velocity impact tests were carried out to verify the structural effectiveness, analyze the energy absorption mechanism, and investigate the failure modes. Relative to the basic rigid-flexible structure, the brick-and-mortar (Y-Z) and vertical-horizontal alternating fiber (X-Y) models show a 94% and 109% improvement in kinetic energy absorption efficiency, respectively. Additionally, the catastrophic damage in the impact center area caused by crack concentration is significantly reduced. This study confirms that the bionic 3D arrangement of fibers can realize interlayer connection, optimize crack distribution, and enhance energy dissipation, thereby improving the impact resistance of composite materials.

Biomimetic composites inspired by mantis shrimp for aerospace applications

Jet-type centrifugal pumps are widely used in daily water supply systems due to their excellent self-priming capabilities; however, their complex internal flow patterns can negatively affect performance and generate high noise levels. In this study, inspired by the physiological structure of the mantis shrimp, circular non-smooth surface structures (NSSS) were introduced at various locations within the jet throat, and numerical simulations were conducted using a geometric model of the pump body to optimize flow stability while achieving the dual goals of improving efficiency and reducing noise. The results indicate that introducing the NSSS effectively enhances flow stability within the pump, particularly at the front, middle, and rear sections of the throat. The pressure within the pump increased significantly, while turbulence intensity and energy loss were notably reduced, resulting in a 2.79% improvement in hydraulic efficiency. Additionally, the amplitude of broadband noise decreased by 13.04% at the front section of the throat. Further analysis revealed that the application of the NSSS not only reduced the internal noise levels but also shifted the high-noise regions, particularly within the injector’s throat area. These findings highlight the significant potential of NSSS for noise control and flow optimization in jet-type centrifugal pumps.

The nonlinear and intermittent nature of Photovoltaic (PV) systems introduces dynamic disturbances that negatively impact the stability of the DC bus voltage (Vdc) between PV sources and shunt active power filters (SAPFs). These fluctuations pose significant challenges to the performance of SAPFs, especially when the reference DC bus voltage (Vdc*) is constant and not adapted to the instantaneous operating conditions. In this study, a Perturb and Observe (P&O) algorithm is employed within the PV subsystem to perform Maximum Power Point Tracking (MPPT), further contributing to the time-varying behavior of Vdc. To address this problem, this paper proposes a real-time optimization strategy based on the Mantis Shrimp Optimization Algorithm (MShOA) for continuous Vdc* adjustment. This method relies on real-time Total Harmonic Distortion (THD) feedback to dynamically determine the optimal Vdc*, thereby improving harmonic mitigation and maintaining voltage stability. Simulation results demonstrate that the proposed MShOA-based approach effectively reduces THD from 3.59% to 2.85% obtained with conventional methods to 2.33% before PV injection, and maintains 4.19% after PV injection, remaining within the IEEE 519 - 92 standard limits. To confirm its superiority, a comparison with the Whale Optimization Algorithm (WOA) was performed, which achieved 2.65% before and 5.78% after PV injection. These findings validate the higher accuracy, faster convergence, and better adaptability of the proposed MShOA in ensuring robust voltage regulation and improved power quality under PV injection conditions.

Ultraviolet (UV) curable resins are widely used in photopolymerization-based 3D printing due to their rapid curing and compatibility with high-resolution processes. However, their brittleness and limited mechanical performance restrict their applicability, particularly in impact-resistant high-performance 3D-printed structures. Inspired by the mantis shrimp's exceptional energy absorption and impact resistance, attributed to its helicoidal fiber architecture, we developed a Bouligand flax fiber-reinforced composite laminate. By constructing biomimetic helicoidal composites based on Bouligand arrangements, the mechanical performance of flax fiber-reinforced UV-curable resin was systematically investigated. The influence of flax fiber orientation was assessed using mechanical testing combined with the digital image correlation (DIC) method. The results demonstrate that a 45° interlayer angle of flax fiber significantly enhanced the fracture energy of the resin from 1.67 KJ/m2 to 15.41 KJ/m2, an increase of ~823%. Moreover, the flax fiber-reinforced helicoidal structure markedly improved the ultimate tensile strength of the resin, with the 90° interlayer angle of flax fiber exhibiting the greatest enhancement, increasing from 5.32 MPa to 19.45 MPa.

The demand for localization using sky-polarized light has spurred significant advancements in polarization sensors. However, there is still a notable shortage of polarization sensors that are compact, lightweight, and highly integrated. Therefore, there is an urgent need to find a manufacturing process that can produce high-quality, efficient polarization sensors at a lower cost. In this manuscript, we explore the design and implementation of a miniature bionic compound eye polarization (MBCEP) sensor inspired by the structure of the mantis shrimp's compound eyes. The curved microlens arrays (CMLAs), a key component, are fabricated using ultraprecision machining and hot embossing technologies. The core element, a CMOS sensor integrated with metamaterials, employs nanoimprint lithography (NIL) and an aluminum thermal evaporation process to integrate the periodic arrays of metallic nanowires onto the surface of the CMOS sensor. A Field-Programmable Gate Array (FPGA) serves as the processor, capable of real-time computation and image output of polarization data. The MBCEP sensor is compact and lightweight, measuring only 84 × 82 × 34 mm and weighing approximately 65 g. Additionally, a polarization distribution model of the sky has been established. A testing system was established for calibrating and measuring the fundamental performance of the MBCEP sensor. Experimental results demonstrate that the sensor can accurately perform polarization imaging and capture polarized light information. In solar position tests, after systematic calibration, the MBCEP sensor effectively captures polarized information from the sky, ultimately achieving precise measurements of the sun's position.

As the casting industry transitions towards digitalization and intelligent systems, precise control of the return water temperature in the cooling system during deep well casting has become a critical factor in enhancing casting quality. However, traditional numerical simulation methods struggle to handle the complex, dynamically coupled scenarios involving multiple process parameters. This paper proposes a hybrid model, MShOA-CNN–LSTM-Attention, which integrates convolutional neural networks (CNN), long short-term memory networks (LSTM), and an attention mechanism for high-precision prediction of return water temperature. First, an input matrix is constructed using cooling system parameters, melt state data, and control instructions (valve opening) collected from industrial sites. The model employs CNN to capture spatial correlations among the input features, LSTM to identify long-term dependencies in the time series, and the attention mechanism to assign weights, focusing on critical stages of the process. Additionally, the mantis shrimp optimization algorithm (MShOA) is applied to optimize the key hyperparameters in the model. Validation with actual production data shows that the proposed model significantly outperforms comparison models in predicting return water temperature, with the coefficient of determination (R2) improving by 4.5% and the mean absolute error decreasing by 1.17. The model also demonstrates superior performance in time series prediction, and its results provide valuable guidance for the dynamic regulation of subsequent cooling water control.

The impact of high voltage cold plasma (HVCP) and modified atmosphere packaging (MAP) combined with chitooligosaccharide-catechin conjugate (COS-CAT) on extending the shelf-life of steam-cooked Harpiosquillid mantis shrimp (SC-HMS) meat during refrigerated storage at 4°C was examined. SC-HMS samples were treated with COS-CAT at 600ppm and packed under MAP (CO2:N2:O2 = 60:30:10) without and with the exposure to HVCP for 2.5min (CP2.5-600) and 5min (CP5-600). Air-packaged (AP) SC-HMS was spoiled before 9 days. However, the CP2.5-600 sample had significantly retarded growth of mesophilic bacteria, psychrophilic bacteria, and Pseudomonas spp. No Shewanella spp. and Vibrio spp. were found in all SC-HMS samples without and with treatments at day 0. The CP2.5-600 sample also showed the lowest total volatile base nitrogen, trimethylamine nitrogen contents, peroxide value, and thiobarbituric acid reactive substances up to 21 days of storage. The CP2.5-600 treatment retained MUFA and PUFA and lowered the formation of methylamine up to 21 days. Thus, the incorporation of COS-CAT in combination with HVCP for an appropriate time could maintain textural property, reduce the changes in fatty acid and volatile profiles, and extend its shelf-life up to 21 days at 4°C. PRACTICAL APPLICATIONS: The combined treatment of High Voltage Cold Plasma (HVCP) and Chitooligosaccharide-Catechin (COS-CAT) conjugate is a useful method to significantly extend the shelf-life of steam-cooked mantis shrimp. By using the optimal combination (COS-CAT at 600ppm + HVCP for 2.5min), the HMS meat's freshness is maintained for up to 21 days under refrigerated storage. This is achieved by retarding the growth of spoilage bacteria (Pseudomonas spp.) and keeping the eating quality, thus decreasing the loss in market value. This technology can be implemented in the seafood industry to improve the quality and market reach of cooked crustaceans.

Mercury (Hg) contamination in marine species is a critical environmental and public health issue, particularly for commercially important resources like mantis shrimp, Squilla mantis. This study assessed Hg levels in muscle and gonadal tissues of 64 female S. mantis from the Adriatic Sea, assessed into pre-spawning, spawning, and post-spawning reproductive stages. Hg concentrations in S. mantis varied across reproductive stages, with the highest levels in muscle during spawning and the lowest during post-spawning. An opposite trend was found in gonadal tissue. Muscle consistently contained more Hg than gonads, and significant positive linear correlations between the two tissues were observed across reproductive stages. Hg accumulation in muscle was also related to carapace length and gonad weight in specific stages. Hg concentrations measured in muscle and gonad tissues (0.110±0.006 and 0.035±0.001mgkg-1 wet weight, respectively) were below the maximum limits established by the European Union for seafood safety (0.5mgkg-1).

Orientation technology serves as the core foundation supporting for numerous navigation applications in the Internet of Underwater Things (IoUT). However, the unavailability of underwater GPS makes it challenging to mitigate orientation drift in small autonomous underwater vehicle (AUV). Inspired by the visual mechanism of mantis shrimp, underwater polarization navigation technology demonstrates significant potential for long-range and high-precision orientation tasks in AUVs, owing to its unique advantages of electromagnetic interference resistance and absence of cumulative error. However, factors such as bubble scattering, wave disturbances, and overexposure from intense light in complex underwater environments compromise the symmetry of the refractive polarization pattern. This causes the fitted solar meridian to deviate from the ideal reference direction, thereby significantly degrading the heading resolution accuracy and system reliability. To address the aforementioned polarization pattern degradation, this paper proposes an intelligent underwater IoUT orientation method based on polarization pattern restoration. By constructing a multi-step polarization feature reasoning mechanism, physical constraints are embedded into the reconstruction process to restore the polarization distribution characteristics compromised by interference. Furthermore, a line-symmetric polarization enhancement attention module and a solar meridian cosine loss function are designed, achieving an end-to-end solution from polarization information restoration to accurate solar meridian extraction. Additionally, a specialized underwater polarization pattern dataset is constructed for network training and performance evaluation. Simulation and AUV deployment experiments demonstrate that this method effectively suppresses polarization-based orientation errors in real underwater environments, offering a novel solution framework and practical reference for polarization-based orientation technology in complex underwater conditions. The code is available at https://github.com/zbdxdh/UPPRTE_PART.