Artificial Fish Research Articles

Modeling rate of penetration using hybridization of Artificial Neural Network and Artificial Fish Swarm algorithm

This study aims to develop a robust and efficient approach for predicting the Rate of Penetration (ROP) by integrating the Artificial Fish Swarm Algorithm (AFSA) with the Back Propagation (BP) algorithm in an Artificial Neural Network (ANN). The integration of AFSA with Back Propagation is the primary novelty of this work, as it seeks to achieve optimal weight and bias values for the ANN, thereby enhancing prediction accuracy. A dataset of 1944 data points from a vertical well in southern Algeria was utilized. The performance of the developed models was evaluated using correlation coefficient and root mean square error (RMSE). The results demonstrated that the AFSA-ANN model significantly outperforms conventional ANN, Support Vector Regression (SVR), Random Forest regressor (RFR), and Bourgoyne and Young’s model in terms of accuracy. Additionally, the application of leverage method revealed that only 0.98% of the data falls outside the model’s applicability domain, indicating that the data and the model are statistically valid and reliable. The findings of this study have significant implications for the oil and gas field, providing a more accurate and reliable method for predicting ROP, which can lead to more efficient drilling operations and reduced costs.

Efficient Fault Detection and Analysis of Power System Distribution Networks by Integrating BP Data Mining

As the basic guarantee for people’s production and life, the safe operation of the power system has an important impact on the development and operation of society. To ensure the safe and stable operation of the power grid, predicting potential faults and taking reasonable preventive measures can effectively avoid the occurrence of power accidents. However, due to the difficulty in ensuring the prediction accuracy of traditional methods, there are issues of protection misoperation and rejection. Therefore, in order to achieve accurate prediction of power grid faults and avoid protection misoperation and rejection issues, a distribution network fault classification prediction model using a combination of three-layer data mining model (TLDM) and adaptive moment estimation (Adam) algorithm/random gradient descent algorithm improved backpropagation neural network (BPNN) is proposed. The implementation results showed that the classification accuracy of artificial fish school [Formula: see text] priori, [Formula: see text]-means clustering convolutional neural network model and TLDM for single-phase grounding faults was 93.2%, 91.5% and 96.6%, respectively. The classification accuracy for two-phase faults was 92.8%, 92.4% and 95.7%, respectively. The classification accuracy for two-phase grounding faults was 93.7%, 91.2% and 96.9%, respectively. The classification accuracy for three-phase faults was 93.3%, 92.1% and 97.1%, respectively. The TLDM had the highest classification accuracy. The average accuracy, average accuracy and average recall of the BPNN improved by the combination of the ADAM algorithm and random gradient descent algorithm were 94.1%, 90.9% and 88%, respectively, which were higher than the BPNN improved by the combination of ADAM algorithm and random gradient descent algorithm. The above results indicate that the proposed distribution network fault classification and prediction model has good performance and can achieve accurate prediction of distribution network faults.

Evaluating Sustainability: A Social Cost-Benefit Analysis of Shrimp Aquaculture Practices in Kerala

Kerala is blessed with an abundance of freshwater and brackish water rivers, estuaries, backwaters, and interconnecting backwaters. These bodies of water add to the inland production and make the land lush and fertile. As per the ‘State Wise Area Utilized and Production of Tiger Shrimp 2020-21’ data of MPEDA Kerala has 2813.85ha area under culture in hectares and 1128.98 estimated production in tonnes. In the last decades its development has attracted considerable attention because of its export potential. During the study period (2017-18), Kerala produced an estimated 2,952.56 metric tonnes of farmed shrimp annually, out of which 26.34 percent were produced scientifically and the remaining 73.66 percent were produced traditionally. State fishery sector is also promoting ‘one paddy one shrimp farming’ project as it is suitable for coastal wetland economy like Kerala. Consequently, shrimp aquaculture sector plays a major role in the coastal wetland economy of Kerala, which has 234 prawn filtration fields covering 12873ha and 65213ha of brackish water area. ‘Sustainable blue economy’ states that shrimp aquaculture contributes to food security, social status, livelihood for the poor, women's empowerment through employment, increased family income, and purchasing power to the Kerala economy, it is necessary to question the sustainability of the Kerala shrimp aquaculture sector, particularly in light of the micro plastic pollution caused by current shrimp aquaculture practices. Kerala is a top producer of shrimp raised in aquaculture due to its abundant backwater and estuary water bodies. There are claims that the amount of shrimp consumed domestically is also rising. Exposure to Micro plastics poses a severe risk to the health and well-being of regular shrimp users. Using Soya Bean Meal (SBM), a reasonably priced and sustainable substitute for artificial fish feed becomes necessary. In light of this, this paper conducts a sustainability analysis based on social cost-benefit analysis of the feeding practices currently used by the shrimp aquaculture industry in Kerala.

Ecological perspectives of endemic fish species under cascade hydropower development

Wang Y, Wang Q, Zhang W, Wang N, Cong N, Kattel G, Qi Y, Yao W. 2024. Ecological perspectives of endemic fish species under cascade hydropower development. Lake Reserv Manag. XXX–XXX. The upstream Lancang River basin (in Tibet) offers rich hydropower resources together with some of the most delicate freshwater fish species diversity in the world. However, a large dam construction scheme throughout the basin has threatened the fish species diversity and ecology. Our study focuses on the extent to which hydropower construction in the basin influences the river’s ecological status, including the aquatic environment, fish migration, and fish spawning sites. We surveyed the water quality, fish distribution, and fish spawning sites and estimated the fish spawning capacity in the basin. Our results indicate that the water quality prior to dam construction would meet China’s water standards. However, water quality would decline significantly following dam construction, exacerbating the rate of loss of 7 of the protected endemic fish species, as well as threatening the habitat of several other fish species. Our analysis shows a loss trend of fish populations and spawning capacity after damming the river. Cascade dams constructed in the upstream Lancang River basin would obstruct natural migration of fish and inundate their spawning sites, causing reproductive failure and population decline. Based on our analysis, and eco-hydraulic engineering principles, we recommend several ecological protection measures and management options in the upstream Lancang River basin, including the construction of artificial fish ladders, the implementation of ecological regulation, and a fish strategy that will reduce the negative effect of cascade dam constructions.

Optimization of household medical waste recycling logistics routes: Considering contamination risks.

The escalating generation of household medical waste, a byproduct of industrialization and global population growth, has rendered its transportation and logistics management a critical societal concern. This study delves into the optimization of routes for vehicles within the household medical waste logistics network, a response to the imperative of managing this waste effectively. The potential for environmental and public health hazards due to improper waste disposal is acknowledged, prompting the incorporation of contamination risk, influenced by transport duration, waste volume, and wind velocity, into the analysis. To enhance the realism of the simulation, traffic congestion is integrated into the vehicle speed function, reflecting the urban roads' variability. Subsequently, a Bi-objective mixed-integer programming model is formulated to concurrently minimize total operational costs and environmental pollution risks. The complexity inherent in the optimization problem has motivated the development of the Adaptive Hybrid Artificial Fish Swarming Algorithm with Non-Dominated Sorting (AH-NSAFSA). This algorithm employs a sophisticated approach, amalgamating congestion distance and individual ranking to discern optimal solutions from the population. It incorporates a decay function to facilitate an adaptive iterative process, enhancing the algorithm's convergence properties. Furthermore, it leverages the concept of crossover-induced elimination to preserve the genetic diversity and overall robustness of the solution set. The empirical evaluation of AH-NSAFSA is conducted using a test set derived from the Solomon dataset, demonstrating the algorithm's capability to generate feasible non-dominated solutions for household medical waste recycling path planning. Comparative analysis with the Non-dominated Sorted Artificial Fish Swarm Algorithm (NSAFSA) and Non-dominated Sorted Genetic Algorithm II (NSGA-II) across metrics such as MID, SM, NOS, and CT reveals that AH-NSAFSA excels in MID, SM, and NOS, and surpasses NSAFSA in CT, albeit slightly underperforming relative to NSGA-II. The study's holistic approach to waste recycling route planning, which integrates cost-effectiveness with pollution risk and traffic congestion considerations, offers substantial support for enterprises in formulating sustainable green development strategies. AH-NSAFSA offers an eco-efficient, holistic approach to medical waste recycling, advancing sustainable management practices.

Research on Mixed Reality Hand Interaction Technology in Flight Simulation Teaching

Abstract The integration of mixed reality simulation training in institutional education and teaching processes can yield significant quality benefits. Non-wearable interactive methods represent a more natural form of hand interaction within flight cockpits, covering various emerging fields such as mixed reality, computer vision, and human-computer interaction. This paper is guided by a “human-centered” approach to natural human-computer interaction and proposes a virtual hand mixed reality interaction scheme based on hand natural feature points. This study introduces a hand detection, segmentation, and recognition algorithm based on skin colour and key hand feature points. Initially, the algorithm uses the Otsu adaptive threshold algorithm in the YCbCr space for skin colour detection to accommodate varying image brightness levels. Subsequently, it employs a hand Keypoint model for rapid hand detection within skin-like areas while excluding interference from other regions. During the process of hand tracking, optimization prediction is performed using the particle filter algorithm combined with the artificial fish swarm algorithm to achieve tracking accuracy within six pixels in both horizontal and vertical directions. Gesture recognition involves extracting Fourier descriptive contour features of the hand bone structure through identification of feature points. Furthermore, this study combines the optimization capabilities of the artificial fish swarm algorithm to enhance support vector machine model parameter optimization for improved recognition rates. Testing involved 800 processed images as part of a test set for gesture recognition; only five commonly used gestures within flight cockpits were recognized and classified with an accuracy rate reaching up to 98%. Finally, this research presents virtual representations of common operational gestures including natural hand states, control stick manipulation, and throttle handling positions, button activations, and rotational movements via head-mounted displays or screens. The proposed mixed reality-based hand gesture interaction system addresses challenges related to complex background interference during hand detection in cockpit environments as well as issues arising from lighting disturbances. Additionally it resolves problems associated with insufficient particles leading to tracking failures while significantly enhancing registration effects during tracking processes. Moreover it mitigates issues related to Fourier descriptive features being influenced by background variations or changes in pose along with limitations pertaining to expressing singular forms of gestures thereby improving overall classification accuracy.

Optimization design of composite cap-shape pillar structure based on gaussian process of combined kernel function

Abstract To quickly realize the optimal design of the cap-shaped structure, an optimization design method of composite cap-shaped pillar structure based on combined kernel function Gaussian process was proposed. First, the shortcomings of two kernel functions representing different linear characteristics of data in model construction were analyzed, on which a new combined kernel function was constructed. Based on the finite element numerical simulation results, a database with the web thickness, web height, fiber volume, and ultimate bearing capacity of the cap-shaped structure was constructed. Then, a Gaussian process model based on the combined kernel function was established and the model parameters were trained by the maximum likelihood method. Finally, taking the ultimate bearing capacity per unit volume as the optimization objective, the artificial fish swarm algorithm was used to quickly obtain the structure design parameters under the optimal objective function, and the simulation results were compared with the initial design parameters. The results showed that the method proposed in this paper can optimize the structure performance, improve the design efficiency, and meet the engineering design requirements.

Steam Pressure Control of Marine Boiler Based on AFSA-PSO-LSSVM

Abstract To solve the problem that the main steam pressure fluctuates greatly when the load of the combustion control system of the marine main boiler changes suddenly, present a combined inverse control system of Support vector machine and PID to control the steam pressure intelligently. The inverse model of the nonlinear system identified by LSSVM is used as the feedforward controller and the PID controller is used as the feedback controller. The hybrid intelligent algorithm of artificial fish swarm Algorithm and particle swarm optimization is used to optimize the parameters of the control system. The AFSA algorithm is used to search the initial values of the parameters and the PSO algorithm is locally updated. Finally, the mechanism of the steam pressure control system of the marine boiler is modeled and simulated, and the algorithm models of AFSA-PSO-LSSVM-PID, AFSA-LSSVM-PID, PSO-LSSVM-PID and LSSVM-PID are established. The experimental results show that the AFSA-PSO-LSSVM-PID model has a good dynamic, stability, anti-interference ability and robustness.

An optimized method for AUV trajectory model in benthonic hydrothermal area based on improved slime mold algorithm

The optimization of the desired autonomous underwater vehicle (AUV) trajectory modeling and AUV trajectory tracking control in the benthonic hydrothermal area were studied. In the conventional trajectory tracking model construction methods, the time points were roughly combined with the position points of the planned path, making it difficult to produce a smooth trajectory. Although the spline interpolation method was an ideal option for smooth curves, a great number of points were needed for a complex desired trajectory mode. In response to the demanding requirements of AUV trajectory tracking control in the benthonic hydrothermal area, an under-actuated test platform was first established, and the cubic spline interpolation was adopted to process the preset path points for a smooth desired trajectory. An improved slime mold algorithm (SMA) was put forward to optimize the interpolating points used in the trajectory modeling. The Levy flight technology and the compactness technique to speed up the search process and increase the search accuracy. The simulation experiments were conducted in comparison with the artificial fish swarm algorithm (AFSA), the particle swarm optimization (PSO), and the compact cuckoo search (CCS). The results showed that the improved SMA shortened the search process, effectively avoided the local extreme values, and generated a high-precision desired trajectory model in a shorter time. The pool test also verified the feasibility and effectiveness of the proposed method. The method proposed in this study can satisfy the modeling of benthonic hydrothermal trajectory with a fewer number of nodes, faster search progress and search accuracy.

Curing deformation compensation in bonding assembly for high-precision linear displacement encoder sensors

Abstract Linear displacement encoder sensors extensively appear in high-precision instruments, in which the core scale is usually bonded with the scale base by adhesives. However, the curing shrinkage of the adhesives often causes a warping deformation of the moving ruler, which in turn leads to a decrease in measurement accuracy. To improve the accuracy of the bonding assembly, this paper proposes a novel deformation compensation method. A force control fixture is first designed, and a tensile force is applied to tense the scale base. The scale is then bonded to the scale base, and the tensile force is maintained until the adhesive is cured. The optimal tensile force is determined to minimize the warping deformation by combining a finite-element simulation and an artificial fish swarm algorithm. The optimal result is verified experimentally, where the warping deformation is measured using a homemade three-dimensional digital image correlation system. The experimental results demonstrate that the warping deformation of the moving ruler is reduced by 89.2% using the proposed method.

Maintenance strategy for urban micro wind farm considering maintenance route and resource allocation

Emerging urban wind farms (UMWF) are becoming increasingly prevalent, and optimizing maintenance strategy for UMWF has become crucial for reducing costs and improving wind power efficiency. This research formulates an optimization problem of UMWF by considering the maintenance route and resources. To solve this problem, firstly, the Efficient Maintenance Value (EMF) is proposed as an objective function to measure the maintenance value, based on which a novel Random Variable Neighborhood Descent and Cuckoo Search-based Hybrid Discretized Artificial Fish Swarm Algorithm (RVNDCS-HDAFSA) is proposed to search the optimal maintenance strategy. Experiments are conducted to verify the local search capabilities of the hybrid algorithm, and extensive comparison experiments with the other algorithms are conducted at different scales to validate the effectiveness of the RVNDCS-HDAFSA algorithm; the result of the comparison experiments and the ANOVA shows that the HDAFSA demonstrates a superior capability in solving the medium-scale and large-scale problems compared to the existing algorithm. In conclusion, the practical application experiments validate the effectiveness of our proposed approach.

Position estimation of acoustic elements based on improved delay estimation algorithm

Array signal processing is extensively utilized in the field of underwater acoustics (UWA). The majority of existing array signal processing algorithms require precise array position information to optimize their functionality. However, the intricate nature of the UWA environment introduces challenges such as the influence of water flow, which may result in deviations from the predetermined array positions. Consequently, this can amplify errors in array processing algorithms. Therefore, a high-precision array positioning method is needed to estimate the actual position of the array. The effectiveness of current array localization algorithms employing delay differential matching depends significantly on the accuracy of delay estimation and the formulation of the ambiguity function. In response to these crucial factors, this paper presents an algorithm for estimating array element positions that leverages an improved approach to delay estimation. Firstly, we propose the ρ-PHAT algorithm enhanced by the artificial fish swarm algorithm (AFSA-PHAT), significantly improving delay estimation accuracy, particularly in low signal-to-noise ratio (SNR) conditions. Compared to the traditional ρ-PHAT algorithm, this approach achieves a 3 dB increase in precision and a reduction in the root-mean-square error (RMSE). Additionally, a novel method is introduced for constructing the ambiguity function, which focuses on minimizing the acoustic complexity to encompass only direct and surface-reflected sounds. This improvement makes it particularly suitable for hydrophone arrays deployed near the sea surface. Computer simulations and experimental results validate that the algorithm, incorporating the aforementioned improvements, achieves enhanced accuracy in position estimation, reduced RMSE, and increased robustness.

Induction of Pluripotent Stem Cells from Muscle Cells of Large Yellow Croaker (Larimichthys Crocea) Via Electrotransfection.

Induced pluripotent stem cells (iPSCs) are a new type of pluripotent cells reprogrammed from somatic cells back into an embryonic-like pluripotent state of stem cells to study development, disease and potential gene therapies. The induction and regulation mechanisms of iPSCs in fish are still unclear. By using the transfection technique, we investigated the crucial function of the OSKMNL factor co-expression for somatic reprogramming in the muscle cell line of large yellow croaker (Larimichthys crocea) (LYCMs) and successfully established a stable iPSCs line (Lc-OSNL-iPSCs). Stable culturing of iPSCs with high alkaline phosphatase activity and a stable karyotype was achieved. The qRT-PCR and immunofluorescence labeling results revealed that Lc-OSNL-iPSCs displayed a high expression level of pluripotent marker genes such as Nanog, Oct4, and Sox2. There were significant differences between Lc-OSNL-iPSCs, Lc-OSKMNL-iPSCs, and LYCMs, and the expression of several genes in maintaining cell pluripotency was up-regulated when the pluripotency signal pathway of stem cells was activated. The technical system for inducing iPSCs of Larimichthys crocea was constructed in this study. This system can serve as a basic model to understand germ cell differentiation mechanism, gender control, genetics, and breeding of large yellow croaker and a platform for studying iPSCs in fish. Interestingly, the acquired iPSCs serves as a useful material for the directional induction of muscle stem cells, thereby establishing the groundwork for obtaining "artificial fish" in the future.

БОЛЕЗНИ ПРОХОДНЫХ ЛОСОСЁВЫХ РЫБ ПРИ ИСКУССТВЕННОМ ВОСПРОИЗВОДСТВЕ В УСЛОВИЯХ КРАЙНЕГО СЕВЕРА ДАЛЬНЕГО ВОСТОКА

Replenishment of fish stocks by increasing the release of juveniles during artificial fish farming is currently an urgent problem in the Far North of the Far East. In 1995-2003; 2021, an epizootological survey of natural spawning grounds and fish farming enterprises located in the northern part of the Okhotsk Sea basin was conducted: Ola, Arman, Taui, Yana, Iret, Ola experimental production acclimatization base, Armansky, Taui, Yansky salmon hatcheries. The aim is to study bacterial and protozoal diseases of passing salmonids in artificial fish farming. The novelty of the research is that for the first time in the waters of the northern part of the Okhotsk Sea basin, bacterial and parasitic diseases were detected in passing salmon fish in natural reservoirs during anadromous migration and artificial fish farming. In pink salmon, chum salmon, coho salmon, sockeye salmon, Aeromonas hydrophila culture isolated from blood and internal organs turned out to be virulent. Aeromonosis was established for the first time in passing salmon fish of natural populations. As a result of bacteriological studies, Aeromonas hydrophila bacteria were isolated from different batches of fertilized eggs, yolk sac of larvae, ascitic fluid of the abdominal cavity of juveniles. Due to the detection of the invasion of juvenile fish Trichodina truttae and Myxosoma cerebralis, which amounted to 10.0-46.5%, trichodyniosis and myxosomiasis were registered. The ways of transmission of infection to fish farms have been established. Fertilized eggs are infected from producers on spawning rivers during the selection of sexual products. The epizootological situation of aeromonosis, trichodyniosis and myxozomiasis is recognized as unfavorable. The method of diagnosis of bacterial diseases of salmonids at fish-breeding enterprises of the region has been improved.

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

Introducing Artificial Intelligence (AI), Swarm Intelligence (SI) and Bio-Inspired Algorithms Concepts to Elementary and Secondary (K-12) Education Using Block-Based Programming Environments: A Simplified Simulation Inspired by Artificial Fish Swarm Optimization Algorithm (AFSO)

Artificial neural network for geometric design and optimization of three-stage segmented thermoelectric generators

The quest for greener power generation is a major driver for the development of thermoelectric generators. Segmented thermoelectric generators are an effective means to significantly improve the efficiency. Given the complexity of multiple geometric parameters in a three-stage segmented thermoelectric generator, the conventional practice is to use software for numerical simulation and geometric optimization before manufacturing. While this method saves time and increases production efficiency, it lacks reproducibility. However, artificial neural networks offer a solution to this problem by further reducing the computational time required. In this work, artificial neural networks are used to replace these processes by using optimization algorithms to guide the neural network in calculating the optimal thermoelectric conversion efficiency. First, a dataset of 3000 three-stage segmented thermoelectric generator configurations is generated using a COMSOL simulation model. A backpropagation neural network is then constructed and trained on this dataset, using particle swarm optimization to adjust the network weights and computational parameters. The trained neural network accurately predicted the conversion efficiency of three-stage segmented thermoelectric generators over various temperature ranges. When compared to COMSOL calculations, the values are generally within the 95% confidence interval of the backpropagation neural network predictions. Finally, genetic algorithms and artificial fish swarm algorithms are used to drive the neural network to calculate the optimal thermoelectric conversion efficiency. Compared to COMSOL, the thermoelectric conversion efficiency increased by 2.48% and 3.61%, respectively. Despite the initial dataset construction time, the artificial neural network can determine the optimal geometric structure and conversion efficiency of a three-stage segmented thermoelectric generator within 5 min. This method is expected to significantly improve the design and production efficiency of three-stage segmented thermoelectric generators.

Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems

Application of Multi-objective Optimization Algorithm based on Artificial Fish School Algorithm in Financial Investment Portfolio Problems

Evolutionary Niche Artificial Fish Swarm Algorithm for Dynamic Subgroup Size Adjustment in Robot Swarms

Evolutionary Niche Artificial Fish Swarm Algorithm for Dynamic Subgroup Size Adjustment in Robot Swarms

Optimization of renewable energy project portfolio selection using hybrid AIS-AFS algorithm in an international case study

In the coming decade, as restrictions on fossil fuel usage become more stringent, investment in renewable energy projects presents an increasingly appealing opportunity. Evaluating investment attractiveness involves considering both profitability and investment risk. This study proposes a multi-objective mathematical model for identifying the optimal Renewable Energy Project Portfolio (REPP), aiming to maximize net present value while minimizing investment risk. The key innovation of this model is its incorporation of project lifetime and workforce employment considerations to discern the best REPP. To optimize the objective functions of this mathematical model, a hybrid meta-heuristic algorithm combining Artificial Immune System (AIS) and Artificial Fish Swarm (AFS) algorithms is introduced. Genuine data from a varied spectrum of renewable energy projects spanning 20 countries has been meticulously collected. The proposed model is optimized using this dataset, considering portfolio sizes of 3, 5, 10, and 15. The numerical results indicate that, at a specific investment risk threshold, the proposed hybrid algorithm outperforms both AIS and AFS in terms of profitability. Furthermore, the assessment of the geographical distribution of selected projects reveals a deliberate effort to avoid concentration in any specific region, demonstrating a commitment to identifying optimal investment opportunities globally. This research advances the understanding of renewable energy project portfolio optimization, providing valuable insights for investors, policymakers, and sustainable development practitioners.

Design of an intelligent Near-Infrared responsive antibacterial Nano-Platform based on MoS2/Cu3Mo2O9

In this era of high drug resistance, there is an urgent need for novel antimicrobial agents and strategies to achieve smarter sterilization effects. However, developing an environmentally friendly, controllable, and self-cleaning smart antimicrobial material remains a formidable challenge. In this work, a smart antimicrobial nanoplatform composed of Cu3Mo2O9 and MoS2 has been successfully designed and synthesized. The computational findings reveal that, owing to the interfacial coupling between the two substances at the solid junction, this architecture can effectively absorb near-infrared light, significantly reduce migration resistance, and improve carrier separation efficiency. Empirical observations demonstrate that with a sample concentration of 100 μg/mL, under near-infrared (NIR) laser irradiation, the microenvironmental temperature proximal to the bacteria can be swiftly elevated from 28.6 °C to in excess of 50 °C within a mere 6 min. The overall photothermal conversion efficiency is 32.6 %, and it exhibits an active oxygen level nearly 5 times higher than that of Cu3Mo2O9. Furthermore, the permeability of the cell membrane was greatly improved with the increased temperature, which is beneficial for the generated reactive oxygen species (ROS) entering the bacterial cells. After synergistic antimicrobial treatment, the antibacterial rate against Escherichia coli can reach over 99 %. More importantly, this nano-platform features a unique ’activation’ mechanism that uses NIR as an ’activation switch’ to selectively toggle between ’passive defense,’ ’secondary sterilization,’ and ’active attack’ antibacterial modes as needed. Upon interaction with aqueous environments, its reusability and favorable biocompatibility with terrestrial and aquatic life promise to facilitate the intelligent eradication of deleterious microorganisms in aquatic systems via the diurnal cycle of sunrise and sunset. This is highly significant for the ongoing management and maintenance of public resources such as amusement parks, artificial fish ponds, and urban water features.