Large-scale Environment Research Articles

Mars Exploration: Research on Goal-Driven Hierarchical DQN Autonomous Scene Exploration Algorithm

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration.

The fate of antibiotics and antibiotic resistance genes in Large-Scale chicken farm Environments: Preliminary view of the performance of National veterinary Antimicrobial use reduction Action in Guangdong, China

In 2018, China implemented the Veterinary Antimicrobial Use Reduction Action to curb the rapid development of antibiotic resistance (AR). However, the AR-related pollutions in animal farms after the reduction policy has been poorly investigated. Here, we performed a comprehensive investigation combining UPLC-MS/MS, metagenomic, and bacterial genomic analyses in eight representative large-scale chicken farms in Guangdong, China. Our results showed that antibiotics and ARGs contaminations were more severe in broiler farms than in layer farms. Notably, diverse tet(X) variants were prevalent in the chicken farms. These tet(X)s was carried by diverse E. coli lineages and obviously correlated with ISCR2 and IS1B transposases. The resistomes in chicken farms was significantly correlated with microbial community, and multiple factor analyses indicated that the joint effect of antibiotics-microbial community-MGEs was the most dominant driver of ARGs. Host tracking identified a variety of ARG bacterial hosts and the co-occurrence of ARGs-MRGs-MGEs. Source tracking indicated that the inherent component represented the main feature of resistomes in different hosts, while ARG transfer between the chicken gut and farm environments were frequent. A multiperspective evaluation of AR risk revealed that the early effect of antibiotic reduction was exhibited by the mitigation of maximum level of risky ARGs, prevalence of environmental AR pathogens, and HGT potential of ARGs mediated by phage structures. Overall, our findings provide insights into the antibiotic and ARG profiles in large-scale chicken farms with different rearing strategies and demonstrate a preliminary view of the performance of antibiotic reduction actions in China.

Implementation of Agent Systems in Big Data Management: Integrating Artificial Intelligence for Data Mining Optimization

Background: The rapid growth of data generated across various domains necessitates advanced methodologies for effective data management and extraction of meaningful insights. Traditional data processing techniques often struggle with the volume, variety, and velocity of big data. The integration of Agent Systems and Artificial Intelligence (AI) presents a promising approach to address these challenges by enhancing the efficiency and effectiveness of data mining processes. Objective: This study aims to explore the implementation of Agent Systems in big data management, focusing on how the integration of AI can optimize data mining operations. By leveraging the capabilities of intelligent agents, we seek to improve the accuracy, speed, and scalability of data analysis. Methods: A hybrid research methodology was employed, combining a systematic literature review with an empirical case study. The literature review analyzed previous research on Agent Systems, AI, and big data management to identify key trends and challenges. The empirical case study involved deploying an AI-integrated Agent System within a large-scale data environment to evaluate its performance. Key performance indicators (KPIs) such as processing time, accuracy, and scalability were measured and analyzed. Results: The findings indicate that the integration of AI within Agent Systems significantly enhances the data mining process. The system demonstrated a reduction in processing time by 40%, an increase in data analysis accuracy by 25%, and improved scalability, handling larger datasets more efficiently compared to traditional methods. These improvements were attributed to the autonomous and adaptive nature of agent systems, which enabled dynamic data processing and real-time decision-making. Conclusion: The study concludes that the implementation of AI-integrated Agent Systems in big data management offers substantial benefits, including optimized data mining performance. This integration facilitates more efficient and effective data analysis, which is crucial for organizations dealing with large volumes of data. Future research should focus on further refining these systems and exploring their application across different sectors.

Global preventive feedback of powerful radio jets on galaxy formation

Firmly anchored on observational data, giant radio lobes from massive galaxies hosting supermassive black holes can exert a major negative feedback effect, by endowing the intergalactic gas with significant magnetic pressure hence retarding or preventing gas accretion onto less massive halos in the vicinity. Since massive galaxies that are largely responsible for producing the giant radio lobes, this effect is expected to be stronger in more overdense large-scale environments, such as protoclusters, than in underdense regions, such as voids. We show that by redshift [Formula: see text] halos with masses up to [Formula: see text] are significantly hindered from accreting gas due to this effect for radio bubble volume filling fraction of [Formula: see text], respectively. Since the vast majority of the stars in the universe at [Formula: see text][Formula: see text] 2 to 3 form precisely in those halos, this negative feedback process is likely one major culprit for causing the global downturn in star formation in the universe. It also provides a natural explanation for the rather sudden flattening of the slope of the galaxy rest-frame UV luminosity function around [Formula: see text]. A cross-correlation between protoclusters and Faraday rotation measures may test the predicted magnetic field. Inclusion of this external feedback process in the next generation of cosmological simulations may be imperative.

The salivary metabolome of children and parental caregivers in a large-scale family environment study

Human metabolism is complex, and is impacted by genetics, cohabitation, diet, health, and environmental inputs. As such, we applied untargeted LC-MS metabolomics to 1425 saliva samples from a diverse group of elementary school-aged children and their caregivers collected during the Family Life Project, of which 1344 were paired into caregiver/child dyads. We compared metabolomes within and between homes, performed population-wide “metabotype” analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, environmental tobacco smoke (ETS) exposure, metabolic regulation, and heavy metals. Children and caregivers had similar salivary metabolomes, and dyad explained most metabolomic variation. Our data clustered into two groups, indicating that “metabotypes” exist across large populations. Lastly, several metabolites—putative oxidative damage-associated or pathological markers—were correlated with the above-mentioned salivary biomeasures and heavy metals. Implications of the family environment’s effects on metabolomic variation at population, dyadic, and individual levels for human health are discussed.

The last of their kind: Is the genus Scutiger (Anura: Megophryidae) a relict element of the paleo-Transhimalaya biota?

The orographic evolution of the Himalaya-Tibet Mountain system continues to be a subject of controversy, leading to considerable uncertainty regarding the environment and surface elevation of the Tibetan Plateau during the Cenozoic era. As many geoscientific (but not paleontological) studies suggest, elevations close to modern heights exist in vast areas of Tibet since at least the late Paleogene, implicating the presence of large-scale alpine environments for more than 30 million years. To explore a recently proposed alternative model that assumes a warm temperate environment across paleo-Tibet, we carried out a phylogeographic survey using genomic analyses of samples covering the range of endemic lazy toads (Scutiger) across the Himalaya-Tibet orogen. We identified two main clades, with several, geographically distinct subclades. The long temporal gap between the stem and crown age of Scutiger may suggest high extinction rates. Diversification within the crown group, depending on the calibration, occurred either from the Mid-Miocene or Late-Miocene and continued until the Holocene. The present-day Himalayan Scutiger fauna could have evolved from lineages that existed on the southern edges of the paleo-Tibetan area (the Transhimalaya = Gangdese Shan), while extant species living on the eastern edge of the Plateau originated probably from the eastern edges of northern parts of the ancestral Tibetan area (Hoh Xil, Tanggula Shan). Based on the Mid-Miocene divergence time estimation and ancestral area reconstruction, we propose that uplift-associated aridification of a warm temperate Miocene-Tibet, coupled with high extirpation rates of ancestral populations, and species range shifts along drainage systems and epigenetic transverse valleys of the rising mountains, is a plausible scenario explaining the phylogenetic structure of Scutiger. This hypothesis aligns with the fossil record but conflicts with geoscientific concepts of high elevated Tibetan Plateau since the late Paleogene. Considering a Late-Miocene/Pliocene divergence time, an alternative scenario of dispersal from SE Asia into the East, Central, and West Himalaya cannot be excluded, although essential evolutionary and biogeographic aspects remain unresolved within this model.

Multi-area collision-free path planning and efficient task scheduling optimization for autonomous agricultural robots

Collision-free path planning and task scheduling optimization in multi-region operations of autonomous agricultural robots present a complex coupled problem. In addition to considering task access sequences and collision-free path planning, multiple factors such as task priorities, terrain complexity of farmland, and robot energy consumption must be comprehensively addressed. This study aims to explore a hierarchical decoupling approach to tackle the challenges of multi-region path planning. Firstly, we conduct path planning based on the A* algorithm to traverse paths for all tasks and obtain multi-region connected paths. Throughout this process, factors such as path length, turning points, and corner angles are thoroughly considered, and a cost matrix is constructed for subsequent optimization processes. Secondly, we reformulate the multi-region path planning problem into a discrete optimization problem and employ genetic algorithms to optimize the task sequence, thus identifying the optimal task execution order under energy constraints. We finally validate the feasibility of the multi-task planning algorithm proposed by conducting experiments in an open environment, a narrow environment and a large-scale environment. Experimental results demonstrate the method's capability to find feasible collision-free and cost-optimal task access paths in diverse and complex multi-region planning scenarios.

Seasonal predictions of summer compound humid heat extremes in the southeastern United States driven by sea surface temperatures

Humid heat extreme (HHE) is a type of compound extreme weather event that poses severe risks to human health. Skillful forecasts of HHE months in advance are crucial for developing strategies to enhance community resilience to extreme events1,2. This study demonstrates that the frequency of summertime HHE in the southeastern United States (SEUS) can be skillfully predicted 0–1 months in advance using the SPEAR (Seamless system for Prediction and EArth system Research) seasonal forecast system. Sea surface temperatures (SSTs) in the tropical North Atlantic (TNA) basin are identified as the primary driver of this prediction skill. The responses of large-scale atmospheric circulation and winds to anomalous warm SSTs in the TNA favor the transport of heat and moisture from the Gulf of Mexico to the SEUS. This research underscores the role of slowly varying sea surface conditions in modifying large-scale environments, thereby contributing to the skillful prediction of HHE in the SEUS. The results of this study have potential applications in the development of early warning systems for HHE.

Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network

High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model’s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 × 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model’s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.

Evolution of central galaxy alignments in simulations

Context. Observations suggest that red central galaxies align closely with their group galaxies and the large-scale environment. This finding was also replicated in simulations, which added information about the alignment of the stars that form the galaxies with the dark matter in the halo they inhabit. These results were obtained for the present Universe. Our study aims to build upon previous findings by examining the evolution of central galaxy alignment with the environment, as well as the alignment between the stellar and dark matter components. Aims. Based on previous studies, in this work, we describe the evolution of the alignment of bright central galaxies over time and try to understand the process leading to the current observed alignment. Methods. By employing the merger trees from the simulation, we tracked the alignment evolution of the central galaxy sample at z = 0 used in a previous study, the results of which correspond to observations. In particular, we exploited the anisotropic correlation function to study the alignment of the central galaxies with their environment and the probability distribution of the angle between the axes of the shape tensor calculated for each component to deepen the analysis of the stellar and dark matter components. Results. We provide a description of the evolution of alignment in bright central galaxies with a focus on the distinctions between red and blue galaxies. Furthermore, we find that the alignment of the dark matter halo differs from that of the stellar material within it. According to these findings, the assembly process and mergers influenced the evolution of the alignment.

MUSE Analysis of Gas around Galaxies (MAGG) - VI. The cool and enriched gas environment of z≳3 Lyα emitters

We present a novel dataset that extends our view of the cosmic gas around $z 3-4$ Lyalpha emitters (LAEs) in the Muse Analysis of Gas around Galaxies (MAGG) survey by tracing a cool and enriched gas phase through 47 absorbers identified in newly obtained VLT/X-shooter near-infrared quasar spectra. Jointly with the more ionized gas traced by systems and the neutral from previous work, we find that LAEs are distributed inside cosmic structures that contain multiphase gas in composition and temperature. All gas phases are a strong function of the large-scale galaxy environment: the and the strength and kinematics positively correlate with the number of associated galaxies, and it is $ times more likely to detect metal absorbers around groups of LAEs than isolated ones. Exploring the redshift evolution, the covering factor of around groups of LAEs and isolated ones remains approximately constant from $z to $z but the one of around group galaxies drops by $z Adding the cool enriched gas traced by the absorbers to the results that we obtained for the and gas, we put forward a picture in which LAEs lie along gas filaments that contain high column-density systems and are enriched by strong and absorbers. While the gas appears to be more centrally concentrated near LAEs, weaker systems instead trace a more diffuse gas phase extended up to larger distances around the galaxies.

Star formation properties of z ∼ 1 galaxy clusters and groups from Horizon Run 5

ABSTRACT Quiescent galaxies are predominantly observed in local galaxy clusters. However, the fraction of quiescent galaxies in high-redshift clusters significantly varies among different clusters. In this study, we present the results of an analysis of the star formation (SF) properties of $z \sim 0.87$ clusters and groups from a cosmological hydrodynamical simulation Horizon Run 5. We investigate the correlation between the quiescent galaxy fraction (QF) of these model clusters/groups and their various internal or external properties. We find that halo mass is one of the most important characteristics as higher mass clusters and groups have higher QFs. We also find that other properties such as stellar-mass ratio and Friends-of-Friends fraction, which measures the proportion of the area around a cluster occupied by dense structures, may mildly affect the QFs of clusters and groups. This may indicate that the evolutionary history as well as the large-scale environment of clusters and groups also play a certain role in determining the SF status of high-redshift galaxy clusters and groups.

A Pathfinding Algorithm for Large-Scale Complex Terrain Environments in the Field

Pathfinding for autonomous vehicles in large-scale complex terrain environments is difficult when aiming to balance efficiency and quality. To solve the problem, this paper proposes Hierarchical Path-Finding A* based on Multi-Scale Rectangle, called RHA*, which achieves efficient pathfinding and high path quality for large-scale unequal-weighted maps. Firstly, the original map grid cells were aggregated into fixed-size clusters. Then, an abstract map was constructed by aggregating equal-weighted clusters into rectangular regions of different sizes and calculating the nodes and edges of the regions in advance. Finally, real-time pathfinding was performed based on the abstract map. The experiment showed that the computation time of real-time pathfinding was reduced by 96.64% compared to A* and 20.38% compared to HPA*. The total cost of the generated path deviated no more than 0.05% compared to A*. The deviation value is reduced by 99.2% compared to HPA*. The generated path can be used for autonomous vehicle traveling in off-road environments.

Poleward migration of tropical cyclones over the western North Pacific in the CMIP6-HighResMIP models constrained by observations

Tropical cyclones (TCs) have experienced poleward migration in recent years, but whether this exists in future projections with high-resolution climate models remains unclear. This study investigates the poleward migration of TCs over the western North Pacific (WNP) using CMIP6-HighResMIP models. We first assess the model performance in TC genesis frequency and latitude, which differ greatly from the observations, especially in winter and spring due to misinterpretation of extratropical storms. In this study, we put forward a revised constrained detection method based on the sea surface temperature (SST) and the atmospheric conditions to resolve this bias. Results indicate that the revised detection method has good performance in capturing the annual cycle of TC genesis frequency and latitude. Future projections constrained by this method show that the latitude of TC genesis and lifetime maximum intensity (LMI) both undergo a poleward shift, with the former being more significant. Spatial changes in the dynamic potential genesis index and large-scale environment could explain this shift. The regional changes of Hadley circulation and the role of global warming and internal variability are also discussed.

Fast supermassive black hole growth in the SPT2349--56 protocluster at z=4.3

Large-scale environment is one of the main physical drivers of galaxy evolution. The densest regions at high redshifts (i.e. $z>2$ protoclusters) are gas-rich regions characterised by high star formation activity. The same physical properties that enhance star formation in protoclusters are also thought to boost the growth of supermassive black holes (SMBHs), most likely in heavily obscured conditions. We aim to test this scenario by probing the active galactic nucleus (AGN) content of SPT2349--56:\ a massive, gas-rich, and highly star-forming protocluster core at $z=4.3$ discovered as an overdensity of dusty star-forming galaxies (DSFGs). We compare our results with data on the field environment and other protoclusters We observed SPT2349--56 with Chandra (200 ks) and searched for X-ray emission from the known galaxy members. We also performed a spectral energy distribution fitting procedure to derive the physical properties of the discovered AGNs. In the X-ray band, we detected two protocluster members:\ C1 and C6, corresponding to an AGN fraction among DSFGs in the structure of $ This value is consistent with other protoclusters at $z=2-4$, but higher than the AGN incidence among DSFGs in the field environment. Both AGNs are heavily obscured sources, hosted in star-forming galaxies with $ M_ odot $ stellar masses. We estimate that the intergalactic medium in the host galaxies contributes to a significant fraction (or even entirely) to the nuclear obscuration. In particular, C1 is a highly luminous ($L_X=2 and Compton-thick ($N_H=2 cm^ $) AGN, likely powered by a $M_ BH M_ odot $ SMBH, assuming Eddington-limited accretion. Its high accretion rate suggests that it is in the phase of efficient growth that is generally required to explain the presence of extremely massive SMBHs in the centres of local galaxy clusters. Considering SPT2349--56 and DRC, a similar protocuster at $z=4$, and under different assumptions on their volumes, we find that gas-rich protocluster cores at $z enhance the triggering of luminous (log$ L_X lunit =45-46$) AGNs by three to five orders of magnitude with respect to the predictions from the AGN X-ray luminosity function at a similar redshift in the field environment. We note that this result is not solely driven by the overdensity of the galaxy population in the structures. Our results indicate that gas-rich protoclusters at high redshift boost the growth of SMBHs, which will likely impact the subsequent evolution of the structures. Therefore, they stand as key science targets to obtain a complete understanding of the relation between the environment and galaxy evolution. Dedicated investigations of similar protoclusters are required to definitively confirm this conclusion with a higher statistical significance.

The Diagnosis-Effective Sampling of Application Traces

Distributed tracing is cutting-edge technology used for monitoring, managing, and troubleshooting native cloud applications. It offers a more comprehensive and continuous observability, surpassing traditional logging methods, and is indispensable for navigating modern complex software architectures. However, the sheer volume of generated traces is staggering in distributed applications, and the direct storage and utilization of every trace is impractical due to associated operational costs. This entails a sampling strategy to select which traces warrant storage and analysis. Historically, sampling methods have included a rate-based approach, often relying heavily on a manual configuration. There is a need for a more intelligent approach, and we propose a hierarchical sampling methodology to address multiple requirements concurrently. Initial rate-based sampling mitigates the overwhelming volume of traces, as no further analysis can be performed on this level. In the next stage, more nuanced analysis is facilitated based on the previous foundation, incorporating information regarding trace properties and ensuring the preservation of vital process details even under extreme conditions. This comprehensive approach not only aids in the visualization and conceptualization of applications but also enables more targeted analysis in later stages. As we delve deeper into the sampling hierarchy, the technique becomes tailored to specific purposes, such as the simplification of application troubleshooting. In this context, the sampling strategy prioritizes the retention of erroneous traces from dominant processes, thus facilitating the identification and resolution of underlying issues. The focus of this paper is to reveal the impact of sampling on troubleshooting efficiency. Leveraging intelligent and explainable artificial intelligence solutions enables the detection of malfunctioning microservices and provides transparent insights into root causes. We advocate for using rule-induction systems, which offer explainability and efficacy in decision-making processes. By integrating advanced sampling techniques with machine-learning-driven intelligence, we empower organizations to navigate the complexities of large-scale distributed cloud environments effectively.

Federated Learning-Based Security Attack Detection for Multi-Controller Software-Defined Networks

A revolutionary concept of Multi-controller Software-Defined Networking (MC-SDN) is a promising structure for pursuing an evolving complex and expansive large-scale modern network environment. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment against potential vulnerabilities that lead to misuse and malicious activities on data planes. The security holes in the MC-SDN significantly impact network survivability, and subsequently, the data plane is vulnerable to potential security threats and unintended consequences. Accordingly, this work intends to design a Federated learning-based Security (FedSec) strategy that detects the MC-SDN attack. The FedSec ensures packet routing services among the nodes by maintaining a flow table frequently updated according to the global model knowledge. By executing the FedSec algorithm only on the network-centric nodes selected based on importance measurements, the FedSec reduces the system complexity and enhances attack detection and classification accuracy. Finally, the experimental results illustrate the significance of the proposed FedSec strategy regarding various metrics.

Research on Path Planning Algorithm of Driverless Ferry Vehicles Combining Improved A* and DWA.

In view of the fact that the global planning algorithm cannot avoid unknown dynamic and static obstacles and the local planning algorithm easily falls into local optimization in large-scale environments, an improved path planning algorithm based on the integration of A* and DWA is proposed and applied to driverless ferry vehicles. Aiming at the traditional A* algorithm, the vector angle cosine value is introduced to improve the heuristic function to enhance the search direction; the search neighborhood is expanded and optimized to improve the search efficiency; aiming at the problem that there are many turning points in the A* algorithm, a cubic quasi-uniform B-spline curve is used to smooth the path. At the same time, fuzzy control theory is introduced to improve the traditional DWA so that the weight coefficient of the evaluation function can be dynamically adjusted in different environments, effectively avoiding the problem of a local optimal solution. Through the fusion of the improved DWA and the improved A* algorithm, the key nodes in global planning are used as sub-target punctuation to guide the DWA for local planning, so as to ensure that the ferry vehicle avoids obstacles in real time. Simulation results show that the fusion algorithm can avoid unknown dynamic and static obstacles efficiently and in real time on the basis of obtaining the global optimal path. In different environment maps, the effectiveness and adaptability of the fusion algorithm are verified.

An unusually prolonged Pacific-North American pattern promoted the 2021 winter Quad-State Tornado Outbreaks

This study examines the large-scale atmosphere-ocean environments that led to the winter tornado outbreak across the Ohio Valley on 10–11 December 2021, also known as the Quad-States Tornado Outbreaks. Here, we show that the Quad-States Tornado Outbreaks occurred under an exceptionally strong and prolonged negative Pacific-North American pattern (PNA), which developed around December 1 and persisted for a month. This unusual PNA produced a strong atmospheric ridge along the south and eastern US seaboard, which in turn helped warm the Gulf of Mexico and produced large-scale environments conducive for tornadogenesis across the Ohio Valley. Further analysis shows that a broad region across the Ohio Valley is particularly vulnerable to extensive winter tornado outbreaks during long-lived negative PNA, whereas a limited region in the central US is exposed to winter tornado activity during short-lived negative PNA. Finally, although the PNA is a mode of internal variability that occurs with or without El Niño—Southern Oscillation, the occurrence of prolonged negative PNA is more frequent during La Niña than during El Niño.

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.