Strong Adaptability Research Articles

Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities.

Previous health impact assessments of temperature-related mortality in Europe indicated that the mortality burden attributable to cold is much larger than for heat. Questions remain as to whether climate change can result in a net decrease in temperature-related mortality. In this study, we estimated how climate change could affect future heat-related and cold-related mortality in 854 European urban areas, under several climate, demographic and adaptation scenarios. We showed that, with no adaptation to heat, the increase in heat-related deaths consistently exceeds any decrease in cold-related deaths across all considered scenarios in Europe. Under the lowest mitigation and adaptation scenario (SSP3-7.0), we estimate a net death burden due to climate change increasing by 49.9% and cumulating 2,345,410 (95% confidence interval = 327,603 to 4,775,853) climate change-related deaths between 2015 and 2099. This net effect would remain positive even under high adaptation scenarios, whereby a risk attenuation of 50% is still insufficient to reverse the trend under SSP3-7.0. Regional differences suggest a slight net decrease of death rates in Northern European countries but high vulnerability of the Mediterranean region and Eastern Europe areas. Unless strong mitigation and adaptation measures are implemented, most European cities should experience an increase of their temperature-related mortality burden.

The Fungi–Bacteria Interaction Mechanism of Microbial Consortium During Efficient Lignin Degradation Based on Metabolomics Analysis

Microbial consortium degradation technology can improve the degradation efficiency and adaptability through fungi–bacteria synergism, but the mechanism of the fungi–bacteria interaction is still unclear, making it difficult to optimize the degradation process. The microbial consortium J-6, with high lignin degradation efficiency and strong environmental adaptability, was obtained in our previous research. In this study, the fungi–bacteria interacting mechanism of the microbial consortium J-6 was inferred based on metabolomics technology. The results showed that the positive interaction between fungi and bacteria could improve the efficiency of lignin degradation. The metabolites released by fungi, especially betanidin and ergosterol, had an impact on bacterial metabolism, promoted the degradation of macromolecules, and significantly increased the lignin degradation efficiency. Metabolites released by bacteria, especially L-phenylalanine and taurine, played a key role in fungal metabolism, leading to more complete degradation. The interaction mechanism of chemical currencies exchange between fungi and bacteria during lignin degradation obtained in this study can provide theoretical guidance for microbial consortium degradation technology.

From Mountains to Basins: Asymmetric Ecosystem Vulnerability and Adaptation to Extreme Climate Events in Southwestern China

The increasing frequency of both singular and compound extreme climate events driven by global warming has profoundly impacted terrestrial ecosystems. Using machine learning-based Random Forest algorithms and moving correlation analysis, this study quantifies the impacts of extreme climate indices (ECIs) on two ecological indicators (EIs), the NDVI and GPP, from 1982 to 2019. The results reveal that singular extreme climate events exert a more pronounced influence on ecosystems across Southwestern China (SWC) than compound ones. Specifically, the NDVI and GPP exhibited strong correlations with summer days (SU) and diurnal temperature range (DTR), with SU contributing positively (weight = 0.275 for the GPP and 0.238 for the NDVI) and DTR negatively (weight = 0.107 for the GPP and 0.130 for the NDVI). Regional analyses highlighted distinct spatial patterns: in mid–high-altitude areas (>1 km), including the Hengduan Mountains (HDMs) and Yunnan–Guizhou Plateau (YGP), extreme temperatures and precipitation significantly promoted vegetation growth, with rainfall day index (RDI), frost days (FD), extreme temperature index (ETI), SU, and DTR all having a strong influence (>0.1) on the GPP and NDVI. These areas showed strong adaptability to extreme climate, benefiting overall vegetation health. In contrast, ecosystems in low-altitude areas (<1 km) showed more variable responses. The Guangxi Basin (GXB) exhibited strong resistance to ECIs, with vegetation being almost unaffected by extreme precipitation and benefiting from continuous warming. Only consecutive wet days (CWD) and FD were significantly negatively correlated with EIs (p < 0.05), and their correlation weights were low (weights = 0.043 and 0.013). However, the vegetation in the Sichuan Basin (SCB) is more susceptible to climate extremes, which have particularly strong effects on the NDVI. SU, tropical nights (TR), ETI, and growing season length (GSL), which have positive effects on EIs in mid–high-altitude areas, show extremely significant negative correlations in the SCB (p < 0.001), and their weights account for one-third of the total (weights = 0.15, 0.11, 0.061 and 0.012, respectively). These findings underscore the heterogeneous responses of ecosystems to ECIs and emphasize the need for region-specific strategies in ecosystem management and disaster prevention amid climate change.

Adaptive terminal sliding mode control of hypersonic vehicles based on ESO

PurposeThis paper aims to solve the uncertainty problem of hypersonic vehicle tracking control; an adaptive terminal sliding mode control (TSMC) method based on extended state observer (ESO) is proposed. The combination of adaptive techniques, TSMC and ESO offers an effective approach for managing uncertain systems.Design/methodology/approachThe dynamic model of a hypersonic vehicle is transformed into two control-oriented subsystems. The control system design incorporates an adaptive technique, an ESO and a TSMC. The ESO estimates the primary uncertainties, while the adaptive technique determines the upper limit of secondary uncertainties. These estimates are used for the design of the TSMC law. In addition, the filter is used to generate the reference trajectory to improve the dynamic performance of the system. The stability of the closed-loop system is proved by the Lyapunov stability theory.FindingsA robust control system for hypersonic vehicles is developed with guaranteed stability and strong adaptability to various uncertainties such as parameter variations, external disturbances and actuator faults. Furthermore, the proposed system demonstrates enhanced dynamic performance compared to observer-based sliding mode control. Specifically, for the velocity and altitude tracking control, the settling time of the proposed sliding mode control is approximately 100 s and 70 s shorter than that of the observer-based sliding mode control, respectively.Originality/valueDifferent from the single equivalent treatment, various uncertainties here are classified and treated with different strategies, which improves the disturbance rejection ability of the control system. This ability is of great significance for enhancing the autonomy, adaptability and reliability of hypersonic vehicles in extreme environments.

Characteristics of the macroinvertebrate community structure and their habitat suitability conditions in the Chishui River

IntroductionAs an important tributary of the upper Yangtze River without dams, the study of the macroinvertebrate community structure and habitat suitability conditions in the Chishui River holds significant implications for water ecological conservation and restoration.MethodsIn order to explore the species composition, community characteristics and their ecological needs of macroinvertebrates in the Chishui River, 26 sampling sites were deployed in the Chishui River in this study in March (dry season), July (wet season), and November (normal season) of 2023 and performed community structure analysis.ResultsA total of 153 macroinvertebrate taxa were identified, with 62 taxa recorded in the dry season, 46 in the wet season and 115 in the normal season. The assemblage was predominantly composed of aquatic insects from the EPT group (Ephemeroptera, Plecoptera and Trichoptera). The results of ANOSIM analysis indicate that the species composition of macroinvertebrates varies significantly across different water periods. The Shannon-Wiener diversity index and Margalef index were significantly higher during the normal season compared to the dry and wet seasons. In contrast, the Pielou index and Simpson index remained relatively stable, suggesting a higher level of evenness in community structure across the different water periods. Based on the habitat suitability curves, Corbicula fluminea and Heptagenia prefer environments with high flow velocities and substrate particle sizes (D50) smaller than 300 mm. Hydropsyche sp.1 and Baetis thrive in environments with medium to low water depths, high flow velocities, and D50 ranging from 100 to 300 mm. Caridina favor deeper waters, low flow velocities, and larger substrate particles. The GAM fitting results revealed that the number of macroinvertebrate taxa increased with rising pondus hydrogenii (pH) and electrical conductivity (EC) levels. Species richness initially declined and then rose within certain thresholds of total nitrogen (TN) and total phosphorus (TP) concentrations. The optimal conditions for macroinvertebrate communities in the Chishui River were determined to be a DO concentration of 9.8 mg/L and an NH3-N concentration of 0.12 mg/L.DiscussionOverall, the macroinvertebrate community structure in the Chishui River is significantly influenced by factors such as water quality, flow velocity, and substrate particle size, and it demonstrates strong adaptability to seasonal variations.

A systematic review of progress test as longitudinal assessment in Saudi Arabia

BackgroundThe rapid evolution of healthcare necessitates a new generation of doctors with strong critical thinking, problem-solving, and adaptability skills. This systematic review explores current longitudinal assessment practices in undergraduate medical education in Saudi Arabia with particular emphasis on progress test and its utility, advantages, and disadvantages.MethodsA systematic search was conducted across relevant databases to identify studies that discuss progress test in undergraduate medical education in Saudi Arabia. The studies were analyzed to evaluate their alignment with study objectives and to the demands of a changing healthcare landscape.ResultsThe review highlights the dynamic nature of progress test (PT) as a longitudinal assessment method in undergraduate medical education in Saudi Arabia. The advantages included yearly assessments and holistic feedback to enhance learning techniques and knowledge retention. Moreover, PT serves as both a summative and formative assessment, offering personalized learning experiences tailored to individual needs and pacing. Contrasting traditional assessments, PT promotes deep learning strategies and meaning-oriented approaches, fostering holistic understanding rather than rote memorization. Additionally, PT’s continuous nature allows for early identification of knowledge gaps and facilitates ongoing improvement.ConclusionThis systematic review provides a comprehensive overview of progress test as a longitudinal assessment method in undergraduate medical education. It identifies areas for improvement and informs the development of more effective assessment methods that better prepare future doctors for a future of innovation and personalized medicine.

Differences in Biogeographic Patterns and Mechanisms of Assembly in Estuarine Bacterial and Protist Communities.

As transitional ecosystems between land and sea, estuaries are characterized by a unique environment that supports complex and diverse microbial communities. A comprehensive analysis of microbial diversity and ecological processes at different trophic levels is crucial for understanding the ecological functions of estuarine ecosystems. In this study, we systematically analyzed the diversity patterns, community assembly, and environmental adaptability of bacterial and protist communities using high-throughput sequencing techniques. The results revealed a higher alpha diversity for the bacteria than for protists, and the beta diversity pattern was dominated by species turnover in both communities. In addition, the two community assemblages were shown to be dominated by deterministic and stochastic processes, respectively. Furthermore, our results emphasized the influence of the local species pool on microbial communities and the fact that, at larger scales, geographic factors played a more significant role than environmental factors in driving microbial community variation. The study also revealed differences in environmental adaptability among different microbial types. Bacteria exhibited strong adaptability to salinity, while protists demonstrated greater resilience to variations in dissolved oxygen, nitrate, and ammonium concentrations. These results suggested differences in environmental adaptation strategies among microorganisms at different trophic levels, with bacteria demonstrating a more pronounced environmental filtering effect.

A traffic prediction method for missing data scenarios: graph convolutional recurrent ordinary differential equation network

Traffic prediction plays an increasingly important role in intelligent transportation systems and smart cities. Both travelers and urban managers rely on accurate traffic information to make decisions about route selection and traffic management. Due to various factors, both human and natural, traffic data often contains missing values. Addressing the impact of missing data on traffic flow prediction has become a widely discussed topic in the academic community and holds significant practical importance. Existing spatiotemporal graph models typically rely on complete data, and the presence of missing values can significantly degrade prediction performance and disrupt the construction of dynamic graph structures. To address this challenge, this paper proposes a neural network architecture designed specifically for missing data scenarios—graph convolutional recurrent ordinary differential equation network (GCRNODE). GCRNODE combines recurrent networks based on ordinary differential equation (ODE) with spatiotemporal memory graph convolutional networks, enabling accurate traffic prediction and effective modeling of dynamic graph structures even in the presence of missing data. GCRNODE uses ODE to model the evolution of traffic flow and updates the hidden states of the ODE through observed data. Additionally, GCRNODE employs a data-independent spatiotemporal memory graph convolutional network to capture the dynamic spatial dependencies in missing data scenarios. The experimental results on three real-world traffic datasets demonstrate that GCRNODE outperforms baseline models in prediction performance under various missing data rates and scenarios. This indicates that the proposed method has stronger adaptability and robustness in handling missing data and modeling spatiotemporal dependencies.

Beyond Tweets: Mass and Group Convergence Communication in Yogyakarta Fandom

Social media has revolutionized communication, creating new spaces for interaction and expression. This study examines the Stray Kids Yogyakarta community on Twitter (@straykids_YK), a fanbase for the South Korean idol group Stray Kids, established in December 2017. Although interaction on Twitter is limited, community organizers try to increase engagement by providing information about upcoming events and joint projects with other fanbase accounts, hoping to increase community awareness. This research, based on qualitative and case study methods, explores the role of the Stray Kids Yogyakarta community through interviews with administrators and community members. The study revealed that the Stray Kids Yogyakarta community thrives through active communication on WhatsApp, Instagram, and in-person meetings, despite limited Twitter engagement. Interviews highlighted that while Twitter needs improvement, the community is vibrant and well-connected, showing strong adaptability and dedication among fans. This indicates the community's ability to maintain robust connections and engagement across multiple platforms.

Knowledge graph-based thought: a knowledge graph-enhanced LLM framework for pan-cancer question answering.

In recent years, large language models (LLMs) have shown promise in various domains, notably in biomedical sciences. However, their real-world application is often limited by issues like erroneous outputs and hallucinatory responses. We developed the knowledge graph-based thought (KGT) framework, an innovative solution that integrates LLMs with knowledge graphs (KGs) to improve their initial responses by utilizing verifiable information from KGs, thus significantly reducing factual errors in reasoning. The KGT framework demonstrates strong adaptability and performs well across various open-source LLMs. Notably, KGT can facilitate the discovery of new uses for existing drugs through potential drug-cancer associations and can assist in predicting resistance by analyzing relevant biomarkers and genetic mechanisms. To evaluate the knowledge graph question answering task within biomedicine, we utilize a pan-cancer knowledge graph to develop a pan-cancer question answering benchmark, named pan-cancer question answering. The KGT framework substantially improves the accuracy and utility of LLMs in the biomedical field. This study serves as a proof of concept, demonstrating its exceptional performance in biomedical question answering.

A deep learning method based on multi-scale fusion for noise-resistant coal-gangue recognition

Coal-gangue recognition technology plays an important role in the intelligent realization of integrated working faces and coal quality improvement. However, the existing methods are easily affected by high dust, noise, and other disturbances, resulting in unstable recognition results that make it difficult to meet the needs of industrial applications. To realize accurate recognition of coal-gangue in noisy environments, this paper proposes an end-to-end multi-scale feature fusion convolutional neural network (MCNN-BILSTM) based gangue recognition method, which can automatically learn and fuse complementary information from multiple signal components of vibration signals. It combines traditional filtering methods and the idea of multi-scale learning, which can expand the breadth and depth of the feature learning process. the breadth and depth of the feature learning process. Moreover, to strengthen the expression of key features, a feature weighting method based on the attention mechanism is combined to give adaptive weights to different features. Finally, the experimental platform of a tail beam of coal-gangue impact hydraulic support is built, and several comparative experiments are carried out. The comprehensive comparison experiments show that the method shows strong adaptability, robustness, and noise resistance under various complex noise environments, and is suitable for complex practical industrial sites.

A droplet state prediction method for inkjet printing under small sample conditions based on the two-stage TrAdaBoost.R2 algorithm

Inkjet printing is regarded as a new generation of green intelligent manufacturing technology. However, precise control of the state of droplets in inkjet printing is critical and costly. In this study, a voltage-driven signal-based ink droplet state prediction model suitable for a small sample data environment is proposed. Seven public ink datasets and self-collected experimental data are grouped together, and the decision tree, AdaBoost.R2, and two-stage TrAdaBoost.R2 algorithms are used, respectively. The two-stage TrAdaBoost.R2 model shows excellent prediction performance in the case of insufficient data by transferring knowledge of different inks. In particular, it achieves mean square errors of 0.00219 for droplet volume predictions and 0.0645 for velocity predictions. Similarly, the mean absolute percentage errors are 0.91% for droplet volume and 1.37% for velocity. Furthermore, the two-stage TrAdaBoost.R2 model demonstrates strong generalization ability and adaptability to maintain high prediction accuracy under different conditions. The results indicate that the two-stage TrAdaBoost.R2 effectively mitigates the prediction performance issues caused by data scarcity, paving the way for technological advancements in the field of inkjet printing.

Influence of transmitting and receiving telescope parameters on measurement resolution of fiber laser doppler vibrometer

<sec>In a laser Doppler vibrometer (LDV), the laser Doppler effect is used to real-time acquire target displacement, velocity, and acceleration. Fiber optic laser vibrometers have received widespread attention in recent years due to their strong environmental adaptability and high integration advantages. With the expansion of detection target distance, higher requirements have been put forward for the measurement resolution of laser vibrometers. The LDV system typically employs a transceiver integrated telescope structure for laser emission and target return light reception. The aperture and focal length of the transceiver telescope determine its basic structure, directly affecting the emission and reception efficiency of laser energy. Additionally, the speckle effect generated by the scattering of rough targets affects the coupling of light energy entering the fiber optic for interference, thereby influencing the LDV measurement resolution.</sec><sec>Based on relevant theories such as Gaussian beam waist transmission, rough target generation, Fresnel diffraction integration, and fiber optic coupling, a transceiver integrated fiber optic laser vibrometer optical field transmission model is established. Numerical simulation and analysis of the emission transmission process of ideal Gaussian laser and the coupling process of surface target echo reception are conducted. Based on the assumption of laser vibrometer speckle noise limitation, an evaluation scheme for the instrument’s noise baseline under rough target return light conditions is proposed. Numerical simulation experiments are conducted for a typical fiber LDV application scenario with an alignment distance of 1 km, a single-mode fiber mode field radius of 5 μm, and a laser wavelength of 1550 nm. The results indicate that the focal length and aperture of the transceiver telescope determine the distribution of system energy utilization and further affect the instrument’s noise baseline. Simulation results show that when the F-number of the transceiver lens reaches 3.3, LDV achieves the highest system energy utilization at this focal length, verifying the correctness of the simulation model. The simulation results can serve as a basis for the design of transceiver lenses for fiber optic laser vibrometers, laser anemometers, and other devices.</sec>

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction accuracy of blood glucose quantitative models. Few studies discuss prediction accuracy in the case of a large time interval span between modeling and prediction. This paper proposes an automatic optimal threshold baseline removal algorithm based on variational mode decomposition (AOT-VMD), which can adaptively eliminate high-frequency noise and baseline interference for each decomposed IMF modal component and reduce the baseline difference of PPG signals from different days. Furthermore, a fuzzy integral multi-model decision fusion algorithm based on error weight is proposed. The fuzzy integral operator is introduced to make the features with large contributions in each sub-model maintain a high-weight value in the overall prediction mechanism, which improves the prediction accuracy of blood glucose. In this paper, a self-developed portable PPG glucose meter is used to collect PPG signals, and the true blood glucose values for 8 consecutive days are collected by CGM. The proposed algorithm is used to build a model with the first day's data and predict the blood glucose values for the remaining 7 days. The experimental results show that the AOT-VMD preprocessing algorithm and the quantitative regression algorithm of the fuzzy integral multiple model decision fusion algorithm proposed in this paper perform well in measurement accuracy and time adaptability compared with the traditional methods. In addition, the proposed method requires less invasive calibration samples in the modeling stage, achieving high-precision prediction for a long period. 100% of the samples are located in areas A and B of the Clarke area in this experiment, and the algorithm has strong time generalization ability. This innovative method can promote the development of a home blood glucose noninvasive detector.

Extraction of Polyphenols from Cydonia Oblonga Leaves and Their Effect on Mitochondrial Membrane Stability

Cydonia oblonga Mill. (quince) is a fruit tree native to Azerbaijan, Dagestan, Turkmenistan, and Iran, known by various names including Baher Dana in Urdu and Behi in Hindi. Widely cultivated in Portugal, Turkey, Central Asia, India, and Europe, quince cultivars vary in flowering time and yield. High-yielding cultivars such as Aurora, Zoloto Skif, and Sophia show strong environmental adaptation. New cultivars such as Zolotistaya (28.4 mg/100 g vitamin C) and Padarok (32.3 mg/100 g) are rich in vitamin C and catechins. Phenolic compounds in quince leaves, particularly tannins (11%), have antidiarrheal properties. Extraction methods showed high phenolic content when the leaves were dried at optimal temperatures and extracted using ethanol systems. Quince polyphenols exhibit antioxidant and membrane stabilizing effects, which were observed in mitochondria under Fe2+/ascorbate stress, with inhibition values up to 90.3% at 30 μg/mL PF-4 extract. The study concludes that quince leaf extracts have significant antioxidant potential for pharmaceutical applications.

Succinic acid synthesis regulated by succinyl-coenzyme A ligase (SUCLA) plays an important role in root response to alkaline salt stress in Leymus chinensis.

Alkaline salts have more severe adverse effects on plant growth and development than neutral salts do. However, the adaptive mechanisms of plants to alkaline salt stress remain poorly understood, especially at the molecular level. The Songnen Plain in northeast China is composed of typical 'soda' saline-alkali soil, with NaHCO3 and Na2CO3 as the predominant alkaline salts (pH≥9.2). Leymus chinensis can grow on this saline-alkali land, showing strong adaptability. We investigated the role of succinic acid and genes regulating its synthesis in the response to alkaline salt stress in L. chinensis roots. Compared to the neutral salt (NaCl) and high pH treatments, the alkaline salt (NaHCO3 and Na2CO3) treatment specifically caused changes in 11 organic acids, of which the increase in succinic acid was the greatest. The exogenous addition of succinic acid alleviates the damage of alkaline salt to L. chinensis roots. Further, two genes encoding succinyl-coenzyme A ligase (SUCLA) subunits that regulate succinic acid synthesis, LcSUCLAα and LcSUCLAβ, were identified; these genes interact and were localized within mitochondria. Overexpression of LcSUCLAα and LcSUCLAβ caused an increase in succinic acid and enhanced tolerance of NaHCO3 in transgenic rice seedlings. These results suggest that LcSUCLAα and LcSUCLAβ may be involved in the response to alkaline salt stress through the regulation of succinic acid synthesis.

Isolation and characterization of a novel S1-gene insertion porcine epidemic diarrhea virus with low pathogenicity in newborn piglets

ABSTRACT Porcine epidemic diarrhea virus (PEDV) causes diarrhea and vomiting in piglets, leading to a mortality rate of 100%. Due to the high frequency of mutation, it is important to monitor the evolution of PEDV and develop potential vaccine candidates. In this study, two PEDV strains (ZJ2022 and ZQ2022) were identified by PCR. These strains were subsequently isolated, and their genome sequences, growth characteristics, and pathogenicity were compared. Phylogenetic and recombination analyses revealed that both strains belonged to GIIa-subgroup, and ZQ2022 was identified as a recombinant strain derived from ZJ2022. Further sequence analysis showed that the ZJ2022 strain had a modified top region of the S1 protein due to a three amino acid insertion (T380_Y380insGGE) in the S1 gene. According to the virus growth curve, ZJ2022 exhibited better cellular adaptation than ZQ2022, with higher viral titers from 8 hpi to 24 hpi. Additionally, ZQ2022 exhibited a high level of pathogenicity, causing severe diarrhea in piglets at 36 hpi and a 100% mortality rate by 96 hpi. In contrast, ZJ2022 showed lower pathogenicity, inducing severe diarrhea in piglets at 60 hpi, with a mortality rate of 60% at 96 hpi and 100% at 120 hpi. In summary, our findings provided evidence of the undergoing mutations in Chinese PEDV strains. Furthermore, the S gene insertion strain ZJ2022 exhibited strong cellular adaptability and low pathogenicity, making it a potential candidate strain for vaccine development.

Design and Application of Agricultural Integrated Management System Based on TLINK

With the continuous advancement of science and technology, the era of the Internet of Everything has arrived. Agricultural management is becoming more and more intelligent and refined. Agricultural Internet of Things technology is vital in data collection, transmission, user management, etc. To achieve a highly integrated and low-cost smart agriculture with data display. This paper designs an intelligent agricultural management system based on low-cost ESP8266 WIFI Internet of Things technology, aiming to transform traditional agricultural management into a smart, remote, and data-user collaborative mode. The system integrates sensor nodes such as temperature, humidity, light, and carbon dioxide to replace traditional industrial sensors for real-time agricultural environmental data collection, which can realise the automation of irrigation, ventilation, temperature control, lighting, etc. It can start the ecological control system through local voice control. WIFI data transmission, data management through the TLINK cloud platform, PC or mobile phone APP, online remote data viewing and control at any time for agricultural management. The experimental results show that the system has good stability, reliability, high integration, strong environmental adaptability, accurate data, and less packet loss. It dramatically reduces the cost of intelligent agricultural management and construction difficulty, bringing users higher economic benefits.

Analysis of laser spot centroiding errors for laser acquisition system in gravitational wave detection missions.

In space-based gravitational wave detection, establishing ultra-long-distance and ultra-high-precision laser links between satellites is achieved through the laser acquisition and tracking system. The laser spot centroid positioning method, which offers low computational complexity and strong adaptability to beam shape, is currently the core measurement method during the laser acquisition phase. However, due to various interference factors encountered in practical tests, this algorithm often falls short of meeting the extremely high requirements. To address this challenge, this paper first defines the specific performance criteria for the centroid positioning method based on the needs of laser acquisition in gravitational wave detection. It then comprehensively analyzes how detector noise, window truncation effects, and beam wavefront aberrations impact the accuracy of angular measurements. Using derived analytical expressions, an improved centroiding algorithm is proposed to mitigate the effects of detector noise and wavefront aberrations simultaneously. Numerical simulations are conducted to design the specific parameters for the algorithm and the system, resulting in the ability to achieve an angular measurement accuracy of 60 nrad at the telescope front end.

Design of Multimodal Obstacle Avoidance Algorithm Based on Deep Reinforcement Learning

The navigation obstacle avoidance method based on deep reinforcement learning has stronger adaptability and better performance compared to traditional algorithms in complex unknown dynamic environments, and has been widely developed and applied. However, when using multimodal information input, deep reinforcement learning strategy networks extract features that differ significantly between simulated and real world environments, resulting in poor algorithm output strategies and difficulty in transferring models obtained from simulation training to actual environments. To address the aforementioned issues, this article utilizes image segmentation to narrow the gap in environmental features, integrates multimodal information, and designs a deep reinforcement learning multimodal local obstacle avoidance algorithm, MMSEG-PPO, based on proximal strategy optimization algorithms. The algorithm is then ported to practical environments for deployment and testing. The experiment shows that the algorithm proposed in this article reduces the gap between the simulation environment and the actual environment, and has better performance and generalization when transplanted to the real world environment.