Global Trajectory Research Articles

Global research trajectories in gut microbiota and functional constipation: a bibliometric and visualization study

BackgroundFunctional constipation (FC) negatively impacts quality of life and is associated with gut microbiota (GM) imbalances. Despite the growing interest in this area, a thorough analysis of research trends is missing. This study uses bibliometric methods to assess the global research on GM’s role in FC, pinpointing key topics, impactful studies, and prominent researchers to guide future research and identify gaps.MethodsIn our study, we conducted a performance analysis and science mapping using bibliometric indicators such as publication trends, author and institutional contributions, productivity, impact, keyword analysis, and collaboration networks. We employed software tools like VOSviewer, Biblioshiny, CiteSpace, and SCImago Graphica to automate the assessment of metrics including country, institutional, and journal distribution, authorship, keyword frequency, and citation patterns.ResultsFrom 2013 to 2024, annual publications on GM and FC rose from 29 to 252, with a slight decrease to 192 in 2024. Average citations per publication peaked at 11.12 in 2021, declining to 6.43 by 2024. China led in research output (37.8%), followed by the United States (14.4%) and Japan (7.5%). Bibliometric analysis identified key authors like CHEN W and ZHANG H, with 30 and 27 articles, respectively. Jiangnan University and Harvard University were top contributors, with 131 and 81 articles. Keywords analysis revealed “constipation,” “gut microbiota,” and “probiotic” as central themes, with a shift toward “gut microbiota” and “intestinal flora” in recent years. This study provides a comprehensive overview of the research landscape, highlighting leading authors, institutions, and evolving research priorities in the field.ConclusionOur review synthesizes current GM and FC research, guiding future studies. It suggests exploring GM in various GI disorders, the impact of lifestyle and drugs on GM, advanced research techniques, and probiotics/prebiotics for FC. There’s also a focus on therapies targeting GM’s effect on the gut-brain axis, paving the way for improved FC management.

Book Review: Anne-Christin Mittwoch, Nachhaltigkeit und Unternehmensrecht, Schriften zum Unternehmens - und Kapitalmarkrecht 107, TÜBINGEN (MOHR SIEBECK) 2022.

The increasing acknowledgment that economic development is constrained by planetary boundaries, coupled with heightened societal awareness of climate change, places the transition to a sustainable economy at the forefront of today’s global political agendas. International climate policy commitments such as the Paris Climate Agreement and the UN 2030 Agenda for Sustainable Development are shaping the global trajectory of sustainability policy. In the European Union (EU), alongside treaty-based obligations (Art. 3 TEU), the European Green Deal, with its climate neutrality commitment, is laying the groundwork for this transformative change. Recognizing that a sustainable reorientation of the economy necessitates a shift in economic behavior, there is an increasing understanding that companies must play a central role in pursuing sustainability objectives. While public law instruments, such as direct regulatory controls on emissions, have a longstanding tradition of regulating commercial activity, the emerging discourse increasingly emphasizes the role of private law in fostering sustainable business conduct. The monograph "Nachhaltigkeit und Unternehmensrecht" (Sustainability and Company Law), authored by Anne-Christin Mittwoch, substantially contributes to this discussion by exploring to what extent company law is capable of facilitating sustainability objectives.

Trends of mapping knowledge structure and themes of cancer sonodynamic therapy: a text-mining study.

Sonodynamic therapy (SDT) is a non-invasive cancer treatment technique stemming from photodynamic therapy (PDT) and has garnered escalated interest among researchers in recent years. Numerous aspects of cancer SDT remain contentious, and the global research trajectory within this domain remains insufficiently explored. This study seeks to delineate the comprehensive knowledge framework, developmental trends, and pivotal research focal points concerning cancer SDT. The study retrieved documents on cancer SDT from the Web of Science Core Collection (WoSCC) database spanning from 1 January 2000 to 7 December 2023. Bibliometric visualization was carried out through the utilization of CiteSpace 6.2 R6, VOSviewer 1.6.20, and an online analytical platform. Several bibliometric techniques including co-authorship, co-citation, co-occurrence, cluster, as well as burst analysis were used. A total of 672 publications including 603 articles and 69 reviews were included. The annual publication count exhibited a steady increase over time, notably experiencing a surge, particularly in recent years. In terms of contributors, China has maintained its prominent position with the highest outputs and the most financial support. Chinese Academy of Sciences contributed the most articles. Materials Science was the most investigated research areas. Breast cancer emerged as the most extensively studied tumor, succeeded by sarcoma, hepatocellular carcinoma, melanoma, pancreatic cancer, glioma. According to co-cited references, "harnessing nanomaterial", "sonodynamic precision tumor therapy" and "metal-organic framework" denote the current and emerging research focuses within the field. In tandem with the results from keywords co-occurrence and burst, we identified the following research topics including mechanism of induced cell death (ferroptosis, immunogenic cell death), nano-related research (nanoplatform, nanozymes, nanomaterials, nanosheets, metal-organic frameworks (MOFs), nanocomposites, nanoparticles, nanosonosensitizers, liposomes, nanocarriers), combination therapies (chemodynamic therapy, immunotherapy, radiotherapy, photothermal therapy), and tumor microenvironment (hypoxia, singlet oxygen, oxidative stress), that may remain the research hotspots and receive sustained attention in the near future. For the first time, this bibliometric analysis not only presents a comprehensive portrayal of the knowledge framework, but also delineates shifts in research focal points related to cancer SDT within the last two decades. This systematic summarization offers a comprehensive and lucid comprehension of cancer SDT, providing valuable insights for further investigations in this domain.

A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

FROT: A Framework to comprehensively describe radiative contributions to temperature responses

Abstract Different human activities and associated emissions of CO2 and non-CO2 radiative forcing agents and feedbacks determine the final state of Earth’s climate. To understand and explain contributions to global temperature changes, many emission-based metrics have been employed, such as CO2-equivalent or -forcing equivalent. None of these metrics, however, include dynamic responses from Earth system feedbacks in terms of carbon and heat redistribution, known to play an increasingly important role in ambitious mitigation scenarios. Here we introduce a framework that allows for an assessment of such feedbacks in addition to CO2, non-CO2 anthropogenic forcing and natural external variability contributions. FROT (Framework for Radiative cOntributions to Temperature response) allows for an assessment of components of direct radiative impact to the system (climate forcing), as well as Earth system feedbacks concerning heat and carbon. The framework is versatile in terms of applications and allows for exploring individual components contributions to, for example, temperature stabilisation simulations, or comparisons in different models and scenarios, as it can reasonably explain their simulated temperature variability. Here, we apply FROT to both an intermediate complexity and a fully coupled Earth system model, as we simulate highly ambitious mitigation scenarios. Comparing temperature stabilisation scenarios, we can show that both net-zero CO2 emissions and small amounts of positive CO2 emissions could lead to a stable global temperature trajectory. Our assessment reveals that the effects of non-CO2 climate forcings, especially the development of sulphate aerosols in the atmosphere, and the dynamics of the carbon cycle, play a pivotal role in the final level of warming and in enabling a temperature stabilisation. Under highly ambitious climate mitigation scenarios it becomes crucial to include Earth system feedbacks, specifically ocean heat uptake, to understand interannual to decadal temperature development, since previously secondary processes now become increasingly dominant. Our framework offers the opportunity to do so.

An Analysis of the Global EV Market: Sustainable Pathways to Net Zero Emissions

The rising urgency to address climate change has positioned electric vehicles (EVs) as a critical solution for achieving the United Nations' net-zero carbon emission targets. As human activities increase greenhouse gas emissions, the average global temperature continues to rise, leading to severe natural disasters in many regions. This situation underscores the urgent need to limit pollutant emissions and pursue sustainable resource management. This study analyzes the global EV market's trajectory towards sustainability, focusing on the interplay of policy frameworks and consumer engagement strategies in major EV markets, including the USA, the European Union, and China. The transition to EVs must be considered in conjunction with policy frameworks and the broader social environment. Using mixed methods and statistical models, we assess how different policies influence EV adoption and their alignment with sustainability goals. As the concept of sustainable development becomes more ingrained in society, it is promising that, through sustained policy implementation and resource development, the shift from ICE vehicles to EVs will make a crucial contribution to reducing carbon emissions. The findings provide actionable insights for optimizing policies and refining marketing strategies, offering a comprehensive blueprint for guiding the global EV market towards a sustainable future.

Trace element contamination biomonitoring: A comparative study between the polychaetes Alitta virens and Hediste diversicolor

Trace elements (TEs) remain of significant toxicological concern as many are critical for global decarbonisation. TEs accumulate in sediments so benthic polychaetes (e.g. Hediste diversicolor and Alitta virens) are highly relevant for ecotoxicology. However, ecological/biological differences could influence TE accumulation and biomonitoring suitability. Exploiting multiple sympatric populations (Solent, UK), we measure sediment and tissue concentrations generating EFs (enrichment factors), AEIs (Adverse Effects Indexes) and tissue bioaccumulation factors. We also assess stable isotope compositions to elucidate diet influences. Despite diverse anthropogenic activity in the Solent, the majority of TEs present low levels of sediment contamination at the sites. For Ni, Pb and As, a combination of mean AEIs >1 and some sediment concentrations exceeding SQVs (Sediment Quality Values) indicate a slight toxicological risk. For Cu and Hg, high EFs and AEI scores confirm they are the greatest risk, thus requiring source identification/control. However, only mean As tissue concentrations reflect contaminated sites, therefore, identifying the As-source(s) is also a priority. Sediment and tissue concentration relationships were generally negative and not significant for both species. Although a significant negative relationship for Cd for A. virens requires further investigation, the lack of evidence for TE bioaccumulation from sediment may limit both species' biomonitoring suitability for low-contamination sites. Species differences in tissue concentration were also TE specific: H. diversicolor had significantly higher concentrations for Ag, Cu, Hg, Ni and Zn, whilst the reverse was true for Cd, Fe, Cr and As. Whilst ecological differences and that feeding sources are site and species-specific (as evidenced by C, N and S stable isotopes analysis) cannot be ignored, the diverse tissue concentrations strongly suggest different TE regulation strategies per species. Together these data will be important for ecotoxicologists and regulators to select the ‘best’ polychaete biomonitor and assess TE toxicity under future global decarbonisation trajectories for TE inputs.

Hybrid Mobile Robot Path Planning Using Safe JBS-A*B Algorithm and Improved DWA Based on Monocular Camera

This paper addresses the formidable challenge of enabling autonomous navigation in Mobile Robots (MRs), focusing on the development of advanced path planning strategies. Despite their pivotal role in diverse applications, they face challenges in dynamic settings due to limitation in existing Global Path Planning (GPP) and Local Path Planning (LPP) techniques. In response to this, we propose an innovative hybrid path planning approach that enhances the A* algorithm with a risk-aware heuristic function and integrates the Jump Point Search (JPS) technique for route optimization. Additionally, B-spline smoothing is employed for perceptually global trajectory refinement. Our approach also includes an innovative improvement to the Dynamic Window Approach (DWA) to align with the proposed enhanced A* algorithm for effective local navigation. Acknowledging the importance of high-quality input in path planning, we present substantial improvements to the IRDC-Net, a monocular-image semantic-segmentation model that we studied previously. Novel improvements include the integration of quantization and the Adam optimizer, along with the implementation of the Balanced Cross-Entropy loss function. These enhancements not only elevate the output quality of IRDC-Net but also reduce the model’s training parameters. The experimental results demonstrate the performance and viability of the proposed algorithm. Ultimately, the hybrid MR’s path planning algorithm exhibits proficiency across various tasks, particularly in addressing the challenge of evading moving obstacles to ensure the robot’s safety while adhering to the global path.

RS-SLAM: real time semantic slam with driverless car using LiDAR-camera-IMU sensing

Abstract Accurate and robust Simultaneous Localization and Mapping (SLAM) technology is a critical component of driverless cars, and semantic information plays a vital role in their analysis and understanding of the scene. In the actual scene, the moving object will produce the shadow phenomenon in the mapping process. The positioning accuracy and mapping effect will be affected. Therefore, we propose a semantic SLAM framework combining LiDAR, IMU, and camera, which includes a semantic fusion front-end odometry module and a closed-loop back-end optimization module based on semantic information. An improved image semantic segmentation algorithm based on Deeplabv3+ is designed to enhance the performance of the image semantic segmentation model by replacing the backbone network and introducing an attention mechanism to ensure the accuracy of point cloud segmentation. Dynamic objects are detected and eliminated by calculating the similarity score of semantic labels. A loop closure detection method based on semantic information is proposed to detect key semantic features and use threshold range detection and point cloud re-matching to establish the correct loop closure detection, and finally reduce the global cumulative error and improve the global trajectory accuracy using graph optimization to ultimately obtain the global motion trajectory and realize the construction of 3D semantic maps. We evaluated it on the KITTI dataset and collected a dataset for evaluation by ourselves, which includes four different sequences. The results show that the proposed framework has good positioning accuracy and mapping effect in large-scale urban road environments.

Mapping the terrain of global research trajectory of international doctoral students: a bibliometric review

ABSTRACT Significant changes in global doctoral education have occurred in recent decades. Many countries have adopted strategies to intensify and expand their doctoral education. Meanwhile, the number of doctoral students globally has increased, with a notable surge in the enrollment of international doctoral students (IDS). Previous studies have investigated IDS-related issues such as students’ motivations, learning experiences, and post-graduate development; yet a comprehensive review of the existing literature on IDS is lacking. This study reviews the IDS literature since the year 2000, aiming to unveil the landscape of contemporary IDS research within a fast-changing context of global doctoral education. Using bibliometric analysis and data retrieved from Web of Science, we examine trends in IDS publications, key authors, influential journals, geographic distribution by countries and institutions, and significant research themes within IDS studies. The concept of asymmetry is used to interpret the findings, which contribute to a critical understanding of the current landscape of IDS research. The findings serve as valuable references for examining the dynamic and changing context of IDS research, providing valuable insights for future studies.

ER-GET: Emotion Recognition Based on Global ECG Trajectory.

In recent years, the recognition of human emotions based on electrocardiogram (ECG) signals has been considered a novel area of study among researchers. Despite the challenge of extracting latent emotion information from ECG signals, existing methods are able to recognize emotions by calculating the heart rate variability (HRV) features. However, such local features have drawbacks, as they do not provide a comprehensive description of ECG signals, leading to suboptimal recognition performance. For the first time, we propose a new strategy to extract hidden emotional information from the global ECG trajectory for emotion recognition. Specifically, a period of ECG signals is decomposed into sub-signals of different frequency bands through ensemble empirical mode decomposition (EEMD), and a series of multi-sequence trajectory graphs is constructed by orthogonally combining these sub-signals to extract latent emotional information. Additionally, to better utilize these graph features, a network has been designed that includes self-supervised graph representation learning and ensemble learning for classification. This approach surpasses recent notable works, achieving outstanding results, with an accuracy of 95.08% in arousal and 95.90% in valence detection. Additionally, this global feature is compared and discussed in relation to HRV features, with the intention of providing inspiration for subsequent research.

Constructive Approximate Nash Equilibrium for In-Orbit Target Enclosing with Collision Avoidance and Full-state Constraint via Nonzero-Sum Differential Games

The problem of in-orbit cooperative target enclosing involving N thrust-limited satellites under collision avoidance and maneuver amplitude constraints is studied. In order to find global optimal trajectories for target enclosing task with all constraints above, by integrating the collision threat and maneuver boundaries into a novel nonlinear cost functional, the studied target enclosing problem is described as a nonlinear nonzero-sum differential game. Further, to avoid iterative calculations caused by traditional nonlinear-game-solving methods, an approximate solution which can be constructed directly is designed. Then the approximate Nash equilibrium strategies can be educed by the constructive approximate solution for the proposed nonzero-sum game. Analysis shows that the proposed control strategies can asymptotically approach the exact one and can ensure a zero-error tracking of the enclosing configuration. Simulation results illustrate lower time costs and better enclosing accuracy while the collision avoidance and maneuver amplitude constraints are satisfied simultaneously.

A multi-layer predictive energy management strategy for intelligent hybrid electric trucks collaborated with eco-driving control

Nowadays, hybrid electric trucks (HETs) are also urgently expected to further enhance energy efficiency with the development of intelligent driving system. A multi-layer predictive energy management strategy is designed for intelligent parallel HET to enhance fuel economy and computational efficiency. Depending on intelligently implementation of traffic prediction with navigation data, an online smart battery SOC generation is proposed to obtain an approximately global optimal SOC reference trajectory in the upper layer. In the bottom controller, a novel method, the tolerant sequential model predictive control (MPC) collaborated with sampling strategy, could achieve eco-driving in consideration of both driving comfort and safety with less computation time in autonomous driving scenario. Moreover, to simultaneously accomplish fuel economy and decrease computational burden, the integrated equivalent consumption minimization strategy with dynamic programming (ECMS-DP) solver for MPC scheme is presented to frozen SOC state and jointly control torque distribution and gear action. Numerical simulations demonstrate that the proposed multi-layer strategy has improved over 20 % fuel economy and decreased almost 50 % computational burden relative to traditional MPC benchmark strategy, which could exhibit great effectiveness and potential for practical implementation in real driving routes.

A Robust Multi-Camera Vehicle Tracking Algorithm in Highway Scenarios Using Deep Learning

In intelligent traffic monitoring systems, the significant distance between cameras and their non-overlapping fields of view leads to several issues. These include incomplete tracking results from individual cameras, difficulty in matching targets across multiple cameras, and the complexity of inferring the global trajectory of a target. In response to the challenges above, a deep learning-based vehicle tracking algorithm called FairMOT-MCVT is proposed. This algorithm con-siders the vehicles’ characteristics as rigid targets from a roadside perspective. Firstly, a Block-Efficient module is designed to enhance the network’s ability to capture and characterize image features across different layers by integrating a multi-branch structure and depth-separable convolutions. Secondly, the Multi-scale Dilated Attention (MSDA) module is introduced to improve the feature extraction capability and computational efficiency by combining multi-scale feature fusion and attention mechanisms. Finally, a joint loss function is crafted to better distinguish between vehicles with similar appearances by combining the trajectory smoothing loss and velocity consistency loss, thereby considering both position and velocity continuity during the optimization process. The proposed method was evaluated on the public UA-DETRAC dataset, which comprises 1210 video sequences and over 140,000 frames captured under various weather and lighting conditions. The experimental results demonstrate that the FairMOT-MCVT algorithm significantly enhances multi-target tracking accuracy (MOTA) to 79.0, IDF1 to 84.5, and FPS to 29.03, surpassing the performance of previous algorithms. Additionally, this algorithm expands the detection range and reduces the deployment cost of roadside equipment, effectively meeting the practical application requirements.

Advancing Global Pose Refinement: A Linear, Parameter-Free Model for Closed Circuits via Quaternion Interpolation.

Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform paths characterizes this error. In response to this challenge, we propose a linear, parameter-free model that uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established by the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation, with substantial pose differences and efficient pose optimization in larger circuits.

Bibliometric analysis of research trends in biogranulation technology for wastewater treatment.

Inadequate management and treatment of wastewater pose significant threats, including environmental pollution, degradation of water quality, depletion of global water resources, and detrimental effects on human well-being. Biogranulation technology has gained increasing traction for treating both domestic and industrial wastewater, garnering interest from researchers and industrial stakeholders alike. However, the literature lacks comprehensive bibliometric analyses that examine and illuminate research hotspots and trends in this field. This study aims to elucidate the global research trajectory of scientific output in biogranulation technology from 1992 to 2022. Utilizing data from the Scopus database, we conducted an extensive analysis, employing VOSviewer and the R-studio package to visualize and map connections and collaborations among authors, countries, and keywords. Our analysis revealed a total of 1703 journal articles published in English. Notably, China emerged as the leading country, Jin Rencun as the foremost author, Bioresource Technology as the dominant journal, and Environmental Science as the prominent subject area, with the Harbin Institute of Technology leading in institutional contributions. The most prominent author keyword identified through VOSviewer analysis was "aerobic granular sludge," with "sequencing batch reactor" emerging as the dominant research term. Furthermore, our examination using R Studio highlighted "wastewater treatment" and "sewage" as notable research terms within the field. These findings underscore a diverse research landscape encompassing fundamental aspects of granule formation, reactor design, and practical applications. This study offers valuable insights into biogranulation potential for efficient wastewater treatment and environmental remediation, contributing to a sustainable and cleaner future.

Future scenarios of commercial freight shipping in the Euro-Asian Arctic

As climate warms and modern technologies advance, the Artic waters may offer new opportunities for shipping, notably in the Euro-Asian Arctic. This paper presents five alternative scenarios for commercial destination and transit shipping in the region until 2050. Using a pluralistic backcasting approach to foresight, these scenarios were co-created by the authors of this paper together with thirteen experts in relevant fields from seven different countries. The scenario-building exercise integrated global and regional factors and demonstrated that the future of commercial shipping in the Arctic is subject to vast uncertainties in global politics and global development trajectory alongside the sea ice conditions and technological progress. While the current volumes of commercial shipping in the Euro-Asian Arctic are insignificant, its future will largely depend on the development of these factors and how they will interface with each other. Plausible futures of commercial shipping in the region range from extensive international transit shipping through the Northern Sea Route to restricted shipping by vessels with Arctic flags only or even no shipping, to shipping over the transpolar route. The scenarios presented here can be used to inform national policymaking as well as to support strategic decision-making within corporate entities operating in related industries.

Climate Change Impacts on Legume Physiology and Ecosystem Dynamics: A Multifaceted Perspective

As valuable sources of plant-based protein, leguminous vegetables (grain legumes) are essential for global food security and contribute to body growth and development in humans as well as animals. Climate change is a major challenge for agriculture development that creates major problems for the growth and development of plants. However, legume productivity is threatened by climate change factors, including rising temperatures, shifting precipitation patterns, increased atmospheric carbon dioxide levels, intensified extreme events, and altered pest/pathogen activity. This review synthesizes approximately 136 studies to assess the climate effects on major legume crops. Under all the global emissions trajectories, the mean temperatures are projected to rise beyond the optimal legume growing thresholds by 2050, carrying yield reductions between 10 and 49% for beans, soybeans, cowpeas, and lentils without adaptation measures. The elevated carbon dioxide may transiently enhance the yields up to 18%, but the benefits dramatically decline above 550 ppm and cannot offset the other climate impacts. Altered rainfall along with recurrent drought and heat waves are also expected to decrease the legume crop yields, seed quality, and soil nitrogen levels worldwide. Furthermore, the proliferation of legume pests and fungal diseases poses significant risks, amplified by climate shifts in 84% of the reviewed studies. These multifaceted impacts threaten the productivity gains in leguminous vegetables essential to sustainably meeting the global protein demand. Realizing resilience will require the accelerated development of heat/drought-tolerant legume varieties, enhanced climate-informed agronomic practices, strong policy interventions, and social safety nets explicitly supporting legume producers, in addition to the policies/steps that governments are taking to address the challenges of the climate crisis. This review highlights the essential adaptations and mechanisms required for legume crops to thrive and fulfill their significant roles in global nutrition. It explores how these crops can be improved to better withstand the environmental stresses, enhance their nutritional profiles, and increase their yields. Additionally, the review discusses the importance of legumes in sustainable agriculture and food security, emphasizing their potential to address the future challenges in feeding the growing global population. By focusing on these critical aspects, the review aims to underscore the importance of legumes in ensuring a healthy and sustainable food supply.

A LOCAL PATH PLANNING ALGORITHM FOR AVOIDING OBSTACLES IN THE FRENET FRAME

This paper presents a local path planning algorithm in the coordinate system of the roadbed. The algorithm is based on varying initial trajectory points using the potential field method and ensuring the smooth resulting path in a new coordinate system. This algorithm is executed by minimizing an objective functional. The problem is solved with application to path planning for an unmanned transport platform: it is necessary to change the vehicle’s global smooth trajectory points in real time while maintaining smoothness and avoiding emerging obstacles. Compared to the Cartesian coordinate system, the new coordinate system is advantageous in terms of the execution time of the algorithm. The algorithm is implemented in Python. With a planning horizon being specified, this approach can be combined with various path-following algorithms having no obstacle avoidance methods. Computer simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Design of Entire-Flight Pinpoint Return Trajectory for Lunar DRO via Deep Neural Network

Lunar DRO pinpoint return is the final stage of manned deep space exploration via a lunar DRO station. A re-entry capsule suffers from complicated dynamic and thermal effects during an entire flight. The optimization of the lunar DRO return trajectory exhibits strong non-linearity. To obtain a global optimal return trajectory, an entire-flight lunar DRO pinpoint return model including a Moon–Earth transfer stage and an Earth atmosphere re-entry stage is constructed. A re-entry point on the atmosphere boundary is introduced to connect these two stages. Then, an entire-flight global optimization framework for lunar DRO pinpoint return is developed. The design of the entire-flight return trajectory is simplified as the optimization of the re-entry point. Moreover, to further improve the design efficiency, a rapid landing point prediction method for the Earth re-entry is developed based on a deep neural network. This predicting network maps the re-entry point in the atmosphere and the landing point on Earth with respect to optimal control re-entry trajectories. Numerical simulations validate the optimization accuracy and efficiency of the proposed methods. The entire-flight return trajectory achieves a high accuracy of the landing point and low fuel consumption.