A Review of Energy Supply for Biomachine Hybrid Robots

Vertex-based parameters of hierarchal lattice tube with an application of metric dimension

Architectural metamaterials that span different length scales and are either self-similar or dissimilar to one another make up hierarchical lattices. Comparing hierarchical lattices to traditional ones reveals that they offer superior and customizable properties, which allows for a wide variety of material property manipulation and optimization. Each computer network can be represented as a graph, where nodes alternate as vertices and links are edges. The recent advanced topic of resolvability parameters of a graph involves shaping the entire structure to obtain each nodes' specific position. In this article, we computed the metric, fault metric, and partition dimension of the hierarchal lattic tube. The application of the metric dimension is also covered in this paper.

AI-driven predictive models for sustainability

This research presents an AI-driven, explainable energy management model that aligns with Net Zero sustainability objectives by optimizing energy consumption, enhancing predictive accuracy, and ensuring transparency. The model integrates machine learning algorithms, like Gradient Boosting Machines (GBM) and Random Forests, and utilizes techniques like SHAP and LIME for interpretability. Data was split 70/30 for training and validation, with 10-times validation to avoid overfitting, achieving a Mean Absolute Error (MAE) of 1.26-1.53 and Root Mean Squared Error (RMSE) of 1.97-2.06. The model's predictive accuracy reached an R2 of 0.92, with precision and recall scores of 85-90% and 80-88%, respectively, demonstrating significant improvements over traditional methods. Sensitivity analysis revealed high influence from temperature and historical consumption data, requiring careful monitoring. This model performed robustly across diverse scenarios, reducing CO₂ emissions by 30% and cutting costs by 18%, highlighting its adaptability in real-world applications. Conclusions affirm that the explainable AI model advances sustainable energy management by providing reliable, actionable insights, aligning with Net Zero goals, and supporting informed decision-making through enhanced transparency and accuracy.

Animal-Morphing Bio-Inspired Mechatronic Systems: Research Framework in Robot Design to Enhance Interplanetary Exploration on the Moon

Over the past 50 years, the space race has potentially grown due to the development of sophisticated mechatronic systems. One of the most important is the bio-inspired mobile-planetary robots, actually for which there is no reported one that currently works physically on the Moon. Nonetheless, significant progress has been made to design biomimetic systems based on animal morphology adapted to sand (granular material) to test them in analog planetary environments, such as regolith simulants. Biomimetics and bio-inspired attributes contribute significantly to advancements across various industries by incorporating features from biological organisms, including autonomy, intelligence, adaptability, energy efficiency, self-repair, robustness, lightweight construction, and digging capabilities-all crucial for space systems. This study includes a scoping review, as of July 2024, focused on the design of animal-inspired robotic hardware for planetary exploration, supported by a bibliometric analysis of 482 papers indexed in Scopus. It also involves the classification and comparison of limbed and limbless animal-inspired robotic systems adapted for movement in soil and sand (locomotion methods such as grabbing-pushing, wriggling, undulating, and rolling) where the most published robots are inspired by worms, moles, snakes, lizards, crabs, and spiders. As a result of this research, this work presents a pioneering methodology for designing bio-inspired robots, justifying the application of biological morphologies for subsurface or surface lunar exploration. By highlighting the technical features of actuators, sensors, and mechanisms, this approach demonstrates the potential for advancing space robotics, by designing biomechatronic systems that mimic animal characteristics.

Recent advances in neurotechnology-based biohybrid robots

Biohybrid robots retain the innate biological characteristics and behavioral traits of animals, making them valuable in applications such as disaster relief, exploration of unknown terrains, and medical care. This review aims to comprehensively discuss the evolution of biohybrid robots, their key technologies and applications, and the challenges they face. By analyzing studies conducted on terrestrial, aquatic, and aerial biohybrid robots, we gain a deeper understanding of how these technologies have made significant progress in simulating natural organisms, improving mechanical performance, and intelligent control. Additionally, we address challenges associated with the application of electrical stimulation technology, the precision of neural signal monitoring, and the ethical considerations for biohybrid robots. We highlight the importance of future research focusing on developing more sophisticated and biocompatible control methods while prioritizing animal welfare. We believe that exploring multimodal monitoring and stimulation technologies holds the potential to enhance the performance of biohybrid robots. These efforts are expected to pave the way for biohybrid robotics technology to introduce greater innovation and well-being to human society in the future.

Wind turbine with line-side PMSG FED DC-DC converter for voltage regulation

This article represents a novel study of the design and analysis of a wind turbine system that includes a line-side permanent magnet synchronous generator (PMSG) with an ultra-step-up DC-DC converter for voltage regulation. Integrating renewable energy sources such as wind power into the grid requires efficient and reliable power conversion systems to handle fluctuating power and ensure a stable power supply. The wind turbine system utilizes a PMSG, which offers several advantages over traditional induction generators, including higher efficiency, reduced maintenance, and better power quality. The line-side configuration allows for increased control and flexibility, allowing the system to respond dynamically to grid conditions. This wind turbine system involves the integration of a grid-side PMSG-fed DC-DC converter between the PMSG and the grid. The converter enables a seamless flow of electricity between the wind turbine and the grid. By actively controlling the intermediate circuit voltage, the converter efficiently regulates the output voltage of the wind turbine and thus enables constant power generation regardless of fluctuating wind speeds. The simulation outcomes illustrate the efficacy of the proposed system in achieving voltage regulation and seamless integration with the grid. Performance is evaluated under various operating conditions and compared to conventional wind turbines.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

The structural, optical and photovoltaic properties of Zn-doped MAPbI2Br perovskite solar cells

The spin coating method was used to deposit MAPbI2Br films on FTO-glass substrates. Zn2+ (zinc) doping was used for these films at intensity rates of 2% and 4%, respectively. XRD analysis proved that MAPbI2Br films had a cubic structure and a crystalline character. 2% Zn doping into the MAPbI2Br film had a modest large grain size (38.09 nm), Eg (1.95 eV), high refractive index (2.66), and low extinction coefficient (1.67), according to XRD and UV-vis analyses. To facilitate and enhance carrier transit, at contacts as well as throughout the bulk material, the perovskite's trap-state densities decreased. The predicted MAPbI2Br valence and conduction band edges are -5.44 and -3.52, respectively. The conduction band (CB) edge of the film that was exposed to Zn atoms has been pressed towards the lower value, assembly it a better material for solar cells. EIS is particularly useful for understanding charge carrier transport, recombination mechanisms, and the influence of different interfaces within the device structure. Jsc is 11.09 mA cm-2, Voc is 1.09, PCE is 9.372% and FF is 0.777. The cell made with the 2% Zn doped into the MAPbI2Br film demonstrated a superior device.

Enhancing grid-connected photovoltaic system performance with novel hybrid MPPT technique in variable atmospheric conditions

This paper proposes an innovative approach to improve the performance of grid-connected photovoltaic (PV) systems operating in environments with variable atmospheric conditions. The dynamic nature of atmospheric parameters poses challenges for traditional control methods, leading to reduced PV system efficiency and reliability. To address this issue, we introduce a novel integration of fuzzy logic and sliding mode control methodologies. Fuzzy logic enables the PV system to effectively handle imprecise and uncertain atmospheric data, allowing for decision-making based on qualitative inputs and expert knowledge. Sliding mode control, known for its robustness against disturbances and uncertainties, ensures stability and responsiveness under varying atmospheric conditions. Through the integration of these methodologies, our proposed approach offers a comprehensive solution to the complexities posed by real-world atmospheric dynamics. We anticipate applications in grid-connected PV systems across various geographical locations and climates. By harnessing the synergistic benefits of fuzzy logic and sliding mode control, this approach promises to significantly enhance the performance and reliability of grid-connected PV systems in the presence of variable atmospheric conditions. On the grid side, both PSO (Particle Swarm Optimization) and GA (Genetic Algorithm) algorithms were employed to tune the current controller of the PI (Proportional-Integral) current controller (inverter control). Simulation results, conducted using MATLAB Simulink, demonstrate the effectiveness of the proposed hybrid MPPT technique in optimizing the performance of the PV system. The technique exhibits superior tracking efficiency, achieving a convergence time of 0.06 s and an efficiency of 99.86%, and less oscillation than the classical methods. The comparison with other MPPT techniques highlights the advantages of the proposed approach, including higher tracking efficiency and faster response times. The simulation outcomes are analyzed and demonstrate the effectiveness of the proposed control strategies on both sides (the PV array and the grid side). Both PSO and GA offer effective methods for tuning the parameters of a PI current controller. According to considered IEEE standards for low-voltage networks, the total current harmonic distortion values (THD) obtained are considerably high (8.33% and 10.63%, using the PSO and GA algorithms, respectively). Comparative analyses with traditional MPPT methods demonstrate the superior performance of the hybrid approach in terms of tracking efficiency, stability, and rapid response to dynamic changes.

Advanced efficient energy management strategy based on state machine control for multi-sources PV-PEMFC-batteries system

This article offers a PV-PEMFC-batteries energy management strategy (EMS) that aims to meet the following goals: keep the DC link steady at the standard value, increase battery lifespan, and meet power demand. The suggested multi-source renewable system (MSRS) is made to meet load demand while using extra power to fill batteries. The major energy source for the MSRS is photovoltaic, and fuzzy logic MPPT is used to guarantee that the PV operates at optimal efficiency under a variety of irradiation conditions. The suggested state machine control consists of 15 steps. It prioritizes the proton exchange membrane fuel cell (PEMFC) as a secondary source for charging the battery when power is abundant and the state of charge (SOC) is low. The MSRS is made feasible by meticulously coordinating control and power management. The MSRS is made achievable by carefully orchestrated control and electricity management. The efficacy of the proposed system was evaluated under different solar irradiance and load conditions. The study demonstrates that implementing the SMC led to an average improvement of 2.3% in the overall efficiency of the system when compared to conventional control techniques. The maximum efficiency was observed when the system was operating under high load conditions, specifically when the state of charge (SOC) was greater than the maximum state of charge (SOCmax). The average efficiency achieved under these conditions was 97.2%. In addition, the MSRS successfully maintained power supply to the load for long durations, achieving an average sustained power of 96.5% over a period of 7.5 s. The validity of the modeling and management techniques mentioned in this study are confirmed by simulation results utilizing the MATLAB/Simulink (version: 2016, link: https://in.mathworks.com/products/simulink.html ) software tools. These findings show that the proposed SMC is effective at managing energy resources in MSRS, resulting in improved system efficiency and reliability.

Merge-and-Split Graph Convolutional Network for Skeleton-Based Interaction Recognition

We introduce an innovative approach to address a significant challenge in interaction recognition, specifically the capture of correlation features between different interaction body parts. These features are often overlooked by traditional graph convolution networks commonly used in interaction recognition tasks. Our solution, the Merge-and-Split Graph Convolutional Network, takes a unique perspective, treating interaction recognition as a global problem. It leverages a Merge-and-Split Graph structure to effectively capture dependencies between interaction body parts. To extract the essential interaction features, we introduce the Merge-and-Split Graph Convolution module, which seamlessly combines the Merge-and-Split Graph with Graph Convolutional Networks. This fusion enables the extraction of rich semantic information between adjacent joint points. In addition, we introduce a Short-term Dependence module designed to extract joint and motion characteristics specific to each type of interaction. Furthermore, to extract correlation features between different hierarchical sets, we present the Hierarchical Guided Attention Module. This module plays a crucial role in highlighting the relevant hierarchical sets that contain essential interaction information. The effectiveness of our proposed model is demonstrated by achieving state-of-the-art performance on 2 widely recognized datasets, namely, the NTU60 and NTU120 interaction datasets. Our model's efficacy is rigorously validated through extensive experiments, and we have made the code available for the research community at https://github.com/wanghq05/MS-GCN/.

Spatial preference behavior of robo-pigeons induced by electrical stimulus targeting fear nuclei

BACKGROUND

Numerous studies have confirmed that stimulating the mid-brain motor nuclei can regulate movement forcibly for robo-pigeons, but research on behavior modulation using non-motor nuclei is scarce.

OBJECTIVE

In this study, we constructed a spatial preference behavior by stimulating the stratum griseum periventriculare (SGP), a nucleus correlated with fear and escape, for robo-pigeons.

METHODS

The study was carried out in a square-enclosed experimental field, with a designated box serving as the 'safe' area for the robo-pigeons. If the robo-pigeon exits this area, the SGP will be stimulated. After a brief training period, the robo-pigeons will have a clear spatial preference for the box.

RESULTS

The result from five pigeons has shown that, after simple training, the animals develop a spatial preference for the box. They can quickly return to the box in any situation when the SGP is stimulated, with a success rate exceeding 80% (89.0 ± 6.5%). Moreover, this behavior is highly stable and remains consistent, unaffected by changes in the location of the box or the interference box.

CONCLUSION

The results prove that using the electrical stimulus could enable animals to accomplish more complex tasks. It may offer a novel approach to regulating pigeon behavior and further advance the study of cyborg animals.