Key Challenges Research Articles

Methods for the Analysis of Multiple Epigenomic Mediators in Environmental Epidemiology.

Epigenetic changes can be highly influenced by environmental factors and have in turn been proposed to influence chronic disease. Being able to quantify to which extent epigenomic processes are mediators of the association between environmental exposures and diseases is of interest for epidemiologic research. In this review, we summarize the proposed mediation analysis methods with applications to epigenomic data. The ultra-high dimensionality and high correlations that characterize omics data have hindered the precise quantification of mediated effects. Several methods have been proposed to deal with mediation in high-dimensional settings, including methods that incorporate dimensionality reduction techniques to the mediation algorithm. Although important methodological advances have been conducted in the previous years, key challenges such as the development of sensitivity analyses, dealing with mediator-mediator interactions, including environmental mixtures as exposures, or the integration of different omic data should be the focus of future methodological developments for epigenomic mediation analysis.

Career Consequences of Firm Heterogeneity for Young Workers: First Job and Firm Size

I study the long-term effects of landing a first job at a large firm versus a small one using Spanish social security data. Size could be a relevant employer attribute for inexperienced workers since large firms are associated with greater training, higher wages, and enhanced productivity. The key empirical challenge is selection into first jobs – for instance, more able people may land jobs at large firms. I address this challenge developing an instrumental-variables approach that, while keeping business-cycle conditions fixed, leverages variation in the composition of labor demand that labor-market entrants face. I find that initially matching with a larger firm substantially improves long-term outcomes such as lifetime income, and that these benefits persist through subsequent jobs. Additional results point to mechanisms related to search frictions and better skill-development at large firms. Together, these findings shed light on how heterogeneous firms persistently impact young workers' trajectories.

Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis

Predicting the therapeutic response to biologics before administration is a key clinical challenge in ulcerative colitis (UC). We previously reported a model for predicting the efficacy of vedolizumab (VDZ) for UC using a machine-learning approach. Ustekinumab (UST) is now available for treating UC, but no model for predicting its efficacy has been developed. When applied to patients with UC treated with UST, our VDZ prediction model showed positive predictive value (PPV) of 56.3% and negative predictive value (NPV) of 62.5%. Given this limited predictive ability, we aimed to develop a UST-specific prediction model with clinical features at baseline including background factors, clinical and endoscopic activity, and blood test results, as we did for the VDZ prediction model. The top 10 features (Alb, monocytes, height, MCV, TP, Lichtiger index, white blood cell count, MCHC, partial Mayo score, and CRP) associated with steroid-free clinical remission at 6 months after starting UST were selected using random forest. The predictive ability of a model using these predictors was evaluated by fivefold cross-validation. Validation of the prediction model with an external cohort showed PPV of 68.8% and NPV of 71.4%. Our study suggested the importance of establishing a drug-specific prediction model.

Explainability for Large Language Models: A Survey

Large language models (LLMs) have demonstrated impressive capabilities in natural language processing. However, their internal mechanisms are still unclear and this lack of transparency poses unwanted risks for downstream applications. Therefore, understanding and explaining these models is crucial for elucidating their behaviors, limitations, and social impacts. In this article, we introduce a taxonomy of explainability techniques and provide a structured overview of methods for explaining Transformer-based language models. We categorize techniques based on the training paradigms of LLMs: traditional fine-tuning-based paradigm and prompting-based paradigm. For each paradigm, we summarize the goals and dominant approaches for generating local explanations of individual predictions and global explanations of overall model knowledge. We also discuss metrics for evaluating generated explanations and discuss how explanations can be leveraged to debug models and improve performance. Lastly, we examine key challenges and emerging opportunities for explanation techniques in the era of LLMs in comparison to conventional deep learning models.

Massively parallel implementation of iterative eigensolvers in large-scale plane-wave density functional theory

The Kohn-sham density functional theory (DFT) is a powerful method to describe the electronic structures of molecules and solids in condensed matter physics, computational chemistry and materials science. However, large and accurate DFT calculations within plane waves process a cubic-scaling computational complexity, which is usually limited by expensive computation and communication costs. The rapid development of high performance computing (HPC) on leadership supercomputers brings new opportunities for developing plane-wave DFT calculations for large-scale systems. Here, we implement parallel iterative eigensolvers in large-scale plane-wave DFT calculations, including Davidson, locally optimal block preconditioned conjugate gradient (LOBPCG), projected preconditioned conjugate gradient (PPCG) and the Chebyshev subspace iteration (CheFSI) algorithms, and analyze the performance of these algorithms in massively parallel plane-wave computing tasks. We adopt a two-level parallelization strategy that combines the message passing interface (MPI) with open multi-processing (OpenMP) parallel programming to handle data exchange and matrix operations in the construction and diagonalization of large-scale Hamiltonian matrix within plane waves. Numerical results illustrate that these iterative eigensolvers can scale up to 42,592 processing cores with high peak performance of 30% on leadship supercomputers to study the electronic structures of bulk silicon systems containing 10,648 atoms. Program summaryProgram Title: Plane wave density functional theory (PWDFT)CPC Library link to program files:https://doi.org/10.17632/c8v2mx5vn4.1Developer's repository link:https://bitbucket.org/berkeleylab/scalesLicensing provisions: BSD 3-clauseProgramming language: C++Nature of problem: PWDFT is used for electronic structure calculations based on Kohn-Sham density functional theory. The key challenge to address is a constrained energy minimization problem, which can also be formulated as a nonlinear eigenvalue problem. MPI/OpenMP-based approaches are employed to provide multi-core acceleration for the study of the chemical and material properties of larger-scale molecules and solids.Solution method: PWDFT implements self-consistent field (SCF) iterations and direct constrained minimization algorithms with various acceleration strategies. It is written in C++ and offers parallel acceleration based on MPI/OpenMP.

Data-driven sensor delay estimation in industrial processes using multivariate projection methods

A key step in preparing industrial data for multivariate statistical modelling of (batch-)continuous processes is the estimation of sensor delays along the production line, to use as a correction for understanding relationships between them. Without such a correction, the measurements collected from the sensors do not all relate to the same portion of material travelling through the plant, which is where from a physical point of view the correlations are expected to arise from. Most methods to do these corrections currently reported in literature are limited in that they optimize only correlations bivariately between the different sensors, despite the eventual goal of being understood as a multivariate process model. This work proposes several methods for multivariate sensor delay estimation, based on multivariate projection methods, and critically investigates these using simulated and empirical process data from two industrial facilities. The objective is to provide more clarity on if, how, and why taking into account these multivariate correlations using such projection methods is beneficial. One of the proposed methods, named PLS-SEQ, was shown to outperform bivariate sensor delay estimation, which is regarded as benchmark method in this work, for all datasets. The average reduction in delay estimation error achieved by using PLS-SEQ is between 6% and 64%. The other newly proposed multivariate projection-based methods did not show such unanimous improvement in accuracy, but helped identifying the presence of sensor clusters in industrial data as a novel key challenge for sensor delay estimation. The presented results illustrate that projection-based multivariate methods, in particular the newly proposed method PLS-SEQ, show promising potential for sensor delay estimation, outperforming the bivariate sensor delay estimation methods used as benchmark. Ultimately, using these methods can improve the predictive and descriptive quality of statistical models for key industrial processes.

Introductory Overview of Layer Formation Techniques of Ag Nanowires on Flexible Polymeric Substrates

Ag nanowire electrodes are promising substitutes for traditional indium tin oxide (ITO) electrodes in optoelectronic applications owing to their impressive conductivity, flexibility, and transparency. This review provides an overview of recent trends in Ag nanowire electrode layer formation, including key developments, challenges, and future prospects. It addresses several challenges in integrating Ag nanowires into practical applications, such as scalability, cost-effectiveness, substrate compatibility, and environmental considerations. Additionally, drawing from current trends and emerging technologies, this review explores potential avenues for improving Ag nanowire layer-forming technologies, such as material advancements, manufacturing scalability, and adaptability to evolving electronic device architectures. This review serves as a resource for researchers, engineers, and stakeholders in nanotechnology and optoelectronics, and underscores the relationship between advancements in patterning and the application of Ag nanowire electrodes. Through an examination of key developments, challenges, and future prospects, this review contributes to the collective knowledge base and encourages continued innovation in the ever-evolving realm of Ag nanowire-based optoelectronics.

Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.

The field of implementation science was developed to address the significant time delay between establishing an evidence-based practice and its widespread use. Although implementation science has contributed much toward bridging this gap, the evidence-to-practice chasm remains a challenge. There are some key aspects of implementation science in which advances are needed, including speed and assessing causality and mechanisms. The increasing availability of artificial intelligence applications offers opportunities to help address specific issues faced by the field of implementation science and expand its methods. This paper discusses the many ways artificial intelligence can address key challenges in applying implementation science methods while also considering potential pitfalls to the use of artificial intelligence. We answer the questions of "why" the field of implementation science should consider artificial intelligence, for "what" (the purpose and methods), and the "what" (consequences and challenges). We describe specific ways artificial intelligence can address implementation science challenges related to (1) speed, (2) sustainability, (3) equity, (4) generalizability, (5) assessing context and context-outcome relationships, and (6) assessing causality and mechanisms. Examples are provided from global health systems, public health, and precision health that illustrate both potential advantages and hazards of integrating artificial intelligence applications into implementation science methods. We conclude by providing recommendations and resources for implementation researchers and practitioners to leverage artificial intelligence in their work responsibly. Artificial intelligence holds promise to advance implementation science methods ("why") and accelerate its goals of closing the evidence-to-practice gap ("purpose"). However, evaluation of artificial intelligence's potential unintended consequences must be considered and proactively monitored. Given the technical nature of artificial intelligence applications as well as their potential impact on the field, transdisciplinary collaboration is needed and may suggest the need for a subset of implementation scientists cross-trained in both fields to ensure artificial intelligence is used optimally and ethically.

PTET: A progressive token exchanging transformer for infrared and visible image fusion

Integrating complementary information from different modalities is one of the key challenges in image fusion. Most of the existing deep learning-based methods still rely on a one-off fusion layer to integrate the features extracted from two modalities into one. Such an information interaction pattern only considers significant feature integration but neglects the removal of hazardous information that is widely present in the source images. To overcome these limitations, we propose a progressive token exchanging Transformer for infrared and visible image fusion, named PTET. Different from the one-time fusion layer, we devise a progressive token exchange strategy to gradually transfer features from source images and remove harmful information simultaneously. A predictor is utilized to assess the saliency of Transformer tokens from both modalities. Afterwards, an exchanger is designed to perform beneficial token transfer and insignificant token elimination. Through the cascading layers, our network enhances the feature of fusion branch in a progressive manner. Innovative exchange loss and rank loss are introduced to constrain the fusion network. Extensive experiments on MSRS and LLVIP datasets demonstrate the superiority of our PTET compared to nine state-of-the-art alternatives. Visualization of token exchanging strategy and ablation study reveals the effectiveness of our designs.

Integrating Blockchain in Smart Grids for Enhanced Demand Response: Challenges, Strategies, and Future Directions

This research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DR’s efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs.

Impact of degradation mechanisms at the cathode/electrolyte interface of garnet-based all-solid-state batteries

All-solid-state batteries have the potential to improve the safety, energy-, and power density of lithium-ion batteries. However, the limited stability of rigid solid-solid interfaces remains a key challenge. The cathode/electrolyte interface is particularly prone to degradation during high-temperature sintering and electrochemical cycling, forming secondary phases that impede charge transport and limit cell performance. Experimental analysis of these phases is challenging since they result in thin resistive films that are sensitive to typical characterization techniques. In this study, we use structure-resolved electrochemical simulations to investigate the impact of resistive phases at the cathode/electrolyte interface on cell performance and identify dominant degradation mechanisms. We extend our simulation framework with a novel resistive film model that accounts for the additional charge transfer resistance at the interface based on interphase properties. Our approach combines continuum simulations with insights from density functional theory and experimental data, including secondary ion mass spectrometry measurements. This allows us, for the first time, to assess the impact of resistive films on the degradation of full-cell performance.

RTHEN: Unsupervised deep homography estimation based on dynamic attention for repetitive texture image stitching

Homography estimation is regarded as one of the key challenges in image alignment, where the goal is to estimate the projective transformation between two images on the same plane. Unsupervised learning methods are gradually becoming popular due to their excellent performance and lack of need for labeled data. However, in regional scenes with repeated textures, there may be ambiguity in the correspondence between local features, affecting homography estimation accuracy. This paper proposes a new unsupervised deep homography method RTHEN to solve such problems. In order to effectively obtain repeated texture features, a multi-scale Feature pyramid Siamese network (FPSN) is designed. Specifically, we dynamically allocate the weights of recited texture features through a dynamic attention module and introduce a channel attention module to provide rich contextual information for repeated texture areas. We propose a hard triplet loss function based on overlap constraints to optimize the matching results. At the same time, we collected a repetitive texture image dataset (RTID) for homography estimation training and evaluation. Experimental results show that our method outperforms existing learning methods in repetitive texture scenes and offers competitive performance with state-of-the-art traditional methods.

Applications and challenges of Silicon Carbide (SiC) MOSFET technology in electric vehicle propulsion systems: A review

Silicon Carbide (SiC) MOSFET technology plays a pivotal role in the drive systems of electric vehicles (EVs), offering key applications and facing significant challenges. This paper provides an overview of the fundamental principles and characteristics of SiC MOSFETs, highlighting the unique properties of SiC materials. It delves into the critical applications of SiC MOSFETs in electric vehicle drive systems, including motor drives and inverters, and analyzes the advantages of using SiC MOSFETs for efficient energy conversion at high temperatures. The paper also discusses the key challenges faced by SiC MOSFET technology, such as stability issues under high-temperature environments, avalanche breakdown and tolerance issues affecting power supply reliability, and manufacturing cost and consistency issues that limit widespread application. Innovative solutions to these challenges are explored, including strategies for improving stability under high temperatures, techniques for suppressing avalanche effects and enhancing power supply reliability, and innovations in manufacturing processes to reduce costs and improve consistency. The paper concludes by summarizing the crucial role of SiC MOSFET technology in electric vehicle drive systems, emphasizing the importance of innovative solutions to address its challenges, and looking forward to its continued contribution to technological advancements in the field of electric vehicles.

Theoretical study of N2 adsorption and dissociation on Ir/Cu loaded Ir(100) catalyst

Electrocatalytic nitrogen reduction (NRR) is a promising method for NH3 synthesis. However, the design of catalysts with high activity for N2 dissociation remains a key challenge. Herein, we have designed several catalysts based on Ir, including pure Ir(100), and Ir(100) with Ir (Cu) atom loaded on it (denoted as Ir(a)@Ir(100) and Cu(a)@Ir(100)), to study the reactivity of N2 dissociation. The results showed that Ir(a)@Ir(100) and Cu(a)@Ir(100) can effectively activate NN bond with ultralow dissociation barriers of 0.31 eV and 0.61 eV. However, the adsorption strength of N2 is significantly poor on Ir(a)@Ir(100) (−0.24 eV) compared to that on Cu(a)@Ir(100) (−0.62 eV). This can be interpreted from the electronic properties: The Ir-5d states can hybridize with N-2π* states significantly near the Fermi level, which is absence for Cu-3d states. Therefore, the loaded Cu atom on Cu@Ir(100) can effectively decrease the occupation of N2 antibonding orbitals (ICOHP = −7.68) compared to the situation on Ir@Ir(100) (ICOHP = −7.35). Therefore, Cu(a)@Ir(100) can be screened as the favorable candidate although a little higher dissociation barrier of N2 (0.61 eV), compared to the situation on Ir(a)@Ir(100) (0.31 eV). However, a barrier of 0.61 eV can also be easily overcome at room temperature as 0.31 eV on Ir(a)@Ir(100). We firmly believe that this work can not only open a novel way for the design of Ir-based catalysts, but also provide a promising strategy of N2 dissociation for experimental works.

A study of data heterogeneity in federated learning

Data-driven artificial intelligence algorithms cannot do without large amounts of training data. However, the characteristics of data privacy and decentralization make constructing large-scale training data costly, which restricts the further application of artificial intelligence algorithms in different downstream fields. To address the above problems, federated learning has gradually attracted more and more research interests in recent years, which aims to utilize decentralized private data for model training while preserving privacy. However, the non-independent and homogeneous distribution of data across devices causes federated learning to face problems such as data imbalance and label bias, which in turn affects the generalization performance of the model. The problem of data heterogeneity has become a major key challenge in federated learning, and this paper aims to explore the impact of data heterogeneity on federated learning and to synthesize recent research results in this area. By analyzing different solution approaches from the aspects of adaptive data distribution, adding regularization terms, contrastive Learning, and multi-task learning, a comprehensive overview is provided for researchers. This paper further summarizes the existing challenges of data heterogeneity in the research field of federated learning and discusses its potential development directions.

Constructing Innovative Covalent and Noncovalent Compound Libraries: Insights from 3D Protein-Ligand Interactions.

Noncovalent interactions between small-molecule drugs and protein targets assume a pivotal role in drug design. Moreover, the design of covalent inhibitors, forming covalent bonds with amino acid residues, requires rational reactivity for their covalent warheads, presenting a key challenge as well. Understanding the intricacies of these interactions provides a more comprehensive understanding of molecular binding mechanisms, thereby guiding the rational design of potent inhibitors. In this study, we adopted the fragment-based drug design approach, introducing a novel methodology to extract noncovalent and covalent fragments according to distinct three-dimensional (3D) interaction modes from noncovalent and covalent compound libraries. Additionally, we systematically replaced existing ligands with rational fragment substitutions, based on the spatial orientation of fragments in 3D space. Furthermore, we adopted a molecular generation approach to create innovative covalent inhibitors. This process resulted in the recombination of a noncovalent compound library and several covalent compound libraries, constructed by two commonly encountered covalent amino acids: cysteine and serine. We utilized noncovalent ligands in KLIFS and covalent ligands in CovBinderInPDB as examples to recombine noncovalent and covalent libraries. These recombined compound libraries cover a substantial portion of the chemical space present in the original compound libraries and exhibit superior performance in terms of molecular scaffold diversity compared to the original compound libraries and other 11 commercial libraries. We also recombined BTK-focused libraries, and 23 compounds within our libraries have been validated by former researchers to possess potential biological activity. The establishment of these compound libraries provides valuable resources for virtual screening of covalent and noncovalent drugs targeting similar molecular targets.

Blood and urine multi-omics analysis of the impact of e-vaping, smoking, and cessation: from exposome to molecular responses

Cigarette smoking is a major preventable cause of morbidity and mortality. While quitting smoking is the best option, switching from cigarettes to non-combustible alternatives (NCAs) such as e-vapor products is a viable harm reduction approach for smokers who would otherwise continue to smoke. A key challenge for the clinical assessment of NCAs is that self-reported product use can be unreliable, compromising the proper evaluation of their risk reduction potential. In this cross-sectional study of 205 healthy volunteers, we combined comprehensive exposure characterization with in-depth multi-omics profiling to compare effects across four study groups: cigarette smokers (CS), e-vapor users (EV), former smokers (FS), and never smokers (NS). Multi-omics analyses included metabolomics, transcriptomics, DNA methylomics, proteomics, and lipidomics. Comparison of the molecular effects between CS and NS recapitulated several previous observations, such as increased inflammatory markers in CS. Generally, FS and EV demonstrated intermediate molecular effects between the NS and CS groups. Stratification of the FS and EV by combustion exposure markers suggested that this position on the spectrum between CS and NS was partially driven by non-compliance/dual use. Overall, this study highlights the importance of in-depth exposure characterization before biological effect characterization for any NCA assessment study.

A hybrid heterogeneous framework for medical waste disposal evaluation by fusing group BWM and regret-rejoice MABAC

This article attempts to develop a hybrid heterogeneous framework to evaluate the disposal modes for emergency medical waste (EMW) by fusing GBWM (group best-worst method), RT (regret theory), and MABAC (multi-attributive border approximation area comparison). This hybrid framework tries to address three key challenges: (i) determining criteria weights, (ii) computing the regret-rejoice evaluation matrix, and (iii) identifying the optimal alternative. To address the first challenge, this paper innovates an innovative interval-valued intuitionistic fuzzy (IVIF) GBWM, which considers multiplicatively consistent IVIF preference relation, to efficiently determine global criteria weights (GCWs). For the second challenge, RT is incorporated to compute the regret-rejoice evaluation matrix using experts' heterogeneous evaluations which consist of intervals, triangular fuzzy numbers, hesitant fuzzy linguistic term sets and IVIF values. Regarding the third challenge, MABAC is applied to sort alternatives based on the obtained regret-rejoice evaluation matrix. Afterwards, the validity of the hybrid framework is verified by an illustrative example, and its advantages are demonstrated with some worthy sensitivity and comparative analyses. Some innovations of the proposed framework are highlighted below: (i) The IVIF GBWM streamlines calculation load, since it derives GCWs by solving the group weight assignment model only once; (ii) The introduction of RT into MABAC assists decision-makers in mitigating regret psychology, enhancing the quality of decision outcomes; (iii) The proposed framework’s practicality is enhanced by leveraging heterogeneous evaluations which capture the inherent complexities of actual decision-making problems.

Machine learning enhanced evaluation of semiconductor quantum dots

A key challenge in quantum photonics today is the efficient and on-demand generation of high-quality single photons and entangled photon pairs. In this regard, one of the most promising types of emitters are semiconductor quantum dots, fluorescent nanostructures also described as artificial atoms. The main technological challenge in upscaling to an industrial level is the typically random spatial and spectral distribution in their growth. Furthermore, depending on the intended application, different requirements are imposed on a quantum dot, which are reflected in its spectral properties. Given that an in-depth suitability analysis is lengthy and costly, it is common practice to pre-select promising candidate quantum dots using their emission spectrum. Currently, this is done by hand. Therefore, to automate and expedite this process, in this paper, we propose a data-driven machine-learning-based method of evaluating the applicability of a semiconductor quantum dot as single photon source. For this, first, a minimally redundant, but maximally relevant feature representation for quantum dot emission spectra is derived by combining conventional spectral analysis with an autoencoding convolutional neural network. The obtained feature vector is subsequently used as input to a neural network regression model, which is specifically designed to not only return a rating score, gauging the technical suitability of a quantum dot, but also a measure of confidence for its evaluation. For training and testing, a large dataset of self-assembled InAs/GaAs semiconductor quantum dot emission spectra is used, partially labelled by a team of experts in the field. Overall, highly convincing results are achieved, as quantum dots are reliably evaluated correctly. Note, that the presented methodology can account for different spectral requirements and is applicable regardless of the underlying photonic structure, fabrication method and material composition. We therefore consider it the first step towards a fully integrated evaluation framework for quantum dots, proving the use of machine learning beneficial in the advancement of future quantum technologies.

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.