Reliable Applications Research Articles

Analytical solutions for autonomous differential equations with weighted derivatives

In this work, we introduce a new definition of weighted derivatives along with corresponding integral operators, which aim to facilitate the solution of both linear and non-linear differential equations. A significant finding is that the fractional derivative of Caputo–Fabrizio type is a special case within this framework, allowing us to build upon existing research in this area. Additionally, we provide closed-form analytical solutions for autonomous and logistic equations using our newly defined derivatives and integrals. We thoroughly explore the properties associated with these weighted derivatives and integrals. To demonstrate the reliability and practical applicability of our results, we include several examples and applications that highlight the effectiveness of our approach.

Balancing hydrophobicity and surface potential in bio-based bisfuran-containing polyamide coatings for enhanced long-term antibacterial efficacy and endotoxin adsorption

To develop antimicrobial protective materials based on renewable polymers, we synthesized a series of bio-based bisfuran-based polyamide (bFPA) polymers with various functional groups, including allyl quaternary ammonium cations, decyl quaternary ammonium cations, and sulfonate betaine. These bFPA-based coating materials, featuring excellent thermal stability (>230 °C) and biocompatibility, were facilely fabricated on polyurethane (PU) substrates by blending and crosslinking with unsaturated aliphatic PU resin. By altering the combination of different functional groups in bFPA polymers, we controlled the hydrophilicity/hydrophobicity and surface charge properties of the bio-based coating. Compared to pure PU coatings, the bFPA-based coatings with moderate hydrophilicity/hydrophobicity and surface potential significantly reduced the adhesion of model protein and bacteria by approximately 70 % and >99 %, respectively, demonstrating outstanding anti-protein and anti-bacterial adhesion properties. Even after seven cycles of use, the coatings maintained the ability to kill ∼80 % and >99 % of Gram-negative Escherichia coli and Gram-positive Staphylococcus aureus bacteria, respectively, indicating long-lasting antibacterial activity. Additionally, the optimized bFPA-based coatings effectively adsorbed ∼80 % of endotoxins from damaged Gram-negative bacteria through electrostatic interactions, thereby reducing the risk of inflammation and sepsis. The development of bio-based polyamide coatings with long-term antibacterial and endotoxin adsorption properties significantly advances their safe and reliable application of in the field of biomedical devices.

Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward

In a recent paper, Hong et al developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random forest models significantly improved the preoperative prediction and patient outcome management accuracy. By incorporating data from multiple centers, their model ensures standardization, reliability, and broad applicability, distinguishing it from the prior models. The present study highlights AI's potential in clinical decision support, aiding in the preoperative and postoperative management of gastric cancer patients. Our findings may pave the way for future prospective studies to further enhance AI-supported diagnoses in clinical practice.

Modification and Validation of the System Causability Scale Using AI-Based Therapeutic Recommendations for Urological Cancer Patients: A Basis for the Development of a Prospective Comparative Study

The integration of artificial intelligence, particularly Large Language Models (LLMs), has the potential to significantly enhance therapeutic decision-making in clinical oncology. Initial studies across various disciplines have demonstrated that LLM-based treatment recommendations can rival those of multidisciplinary tumor boards (MTBs); however, such data are currently lacking for urological cancers. This preparatory study establishes a robust methodological foundation for the forthcoming CONCORDIA trial, including the validation of the System Causability Scale (SCS) and its modified version (mSCS), as well as the selection of LLMs for urological cancer treatment recommendations based on recommendations from ChatGPT-4 and an MTB for 40 urological cancer scenarios. Both scales demonstrated strong validity, reliability (all aggregated Cohen’s K > 0.74), and internal consistency (all Cronbach’s Alpha > 0.9), with the mSCS showing superior reliability, internal consistency, and clinical applicability (p < 0.01). Two Delphi processes were used to define the LLMs to be tested in the CONCORDIA study (ChatGPT-4 and Claude 3.5 Sonnet) and to establish the acceptable non-inferiority margin for LLM recommendations compared to MTB recommendations. The forthcoming ethics-approved and registered CONCORDIA non-inferiority trial will require 110 urological cancer scenarios, with an mSCS difference threshold of 0.15, a Bonferroni corrected alpha of 0.025, and a beta of 0.1. Blinded mSCS assessments of MTB recommendations will then be compared to those of the LLMs. In summary, this work establishes the necessary prerequisites prior to initiating the CONCORDIA study and validates a modified score with high applicability and reliability for this and future trials.

Investigating the Correlations between Patient Demographics and Complications Following Total Knee Arthroplasty: A Cross-Sectional Study

Introduction: This study investigates the relationship between patient age, gender, and various complications, such as pain, restricted range of motion (ROM), and hemorrhage following total knee arthroplasty. Materials and Methods: This cross-sectional study included 62 patients (Mean±SD age= 65.9±7.38 years; female participants=74.2%) who underwent total knee arthroplasty at Atieh Hospital in Tehran, Iran, between 2020 and 2021. Pain levels were assessed using the numerical pain rating score. Meanwhile, knee joint ROM was evaluated using a reliable mobile application. Hemorrhage was measured through knee joint drainage. Statistical analyses were performed to analyze the data. Results: Women reported higher pain levels and more restricted ROM in passive knee extension and active/passive knee flexion compared to men (P<0.05). However, no significant differences were observed between genders in active knee extension and hemorrhage. A significant inverse correlation was found between pain and ROM in passive knee extension (rho=-0.41, P≤0.001), as well as in active (rho=-0.5, P≤0.001) and passive knee flexion (rho= -0.59, P≤0.001). However, no significant relationship was observed between pain and active knee extension or hemorrhage. Age showed no statistically significant correlation with the other variables. Conclusion: This study highlights the association between higher pain levels and greater ROM restrictions in passive knee extension and active/passive knee flexion following total knee arthroplasty; however, patient age and gender were not found to be associated with postoperative hemorrhage. These findings underscore the importance of closely monitoring pain and ROM, particularly in women undergoing total knee arthroplasty.

Crosstalk Delay and Reliability Analysis of Carbon Nanotube On-Chip Interconnects: Modelling and Optimization using Metaheuristic Algorithm

In the field of VLSI technology, the miniaturization of ICs poses significant challenges to overall interconnect performance, particularly due to the rising concerns of crosstalk-induced reliability and durability issues. This work explores graphene-based randomly mixed carbon nanotube bundle (RMCB) interconnects as a viable solution for advanced reliable VLSI applications. Employing metaheuristic algorithms (Maximum Hole Degree, Particle Swarm Optimization, Stoyan and Yaskov Algorithm), this study seeks to optimize RMCB structures, maximizing carbon nanotube density within a fixed area. Notably, this study explores the effectiveness of algorithms’ performance in optimizing RMCB structures at a nano-technology node. Extensive signal integrity and reliability assessments, considering both rugged and pristine substrates, reveal that Stoyan and Yaskov (SY)-based optimization excels over PSO and MHD-based counterparts in terms of on-chip interconnect performance and reliability. The SY structure significantly dominates MHD-based (and PSO) counterparts by reducing crosstalk delay by 50% (30%), enhancing the average failure rate by 40% (37%), and improving electromigration reliability by 170% (63%).

HDRSA-Net: Hybrid dynamic residual self-attention network for SAR-assisted optical image cloud and shadow removal

Clouds and shadows often contaminate optical remote sensing images, resulting in missing information. Consequently, continuous spatiotemporal monitoring of the Earth’s surface requires the efficient removal of clouds and shadows. Unlike optical satellites, synthetic aperture radar (SAR) has active imaging capabilities in all weather conditions, supplying valuable supplementary information for reconstructing missing regions. Nevertheless, the reconstruction of high-fidelity cloud-free images based on SAR-optical data fusion remains challenging due to differences in imaging mechanisms and the considerable contamination from speckle noise inherent in SAR imagery. To solve the aforementioned challenges, this paper presents a novel hybrid dynamic residual self-attention network (HDRSA-Net), aiming to fully exploit the potential of SAR images in reconstructing missing regions. The proposed HDRSA-Net comprises multiple dynamic interaction residual (DIR) groups organized into an end-to-end trainable deep hierarchical stacked architecture. Specifically, the omni-dimensional dynamic local exploration (ODDLE) module and the sparse global context aggregation (SGCA) module are used to form a local–global feature adaptive extraction and implicit enhancement. A multi-task cooperative optimization loss function is designed to ensure that the results exhibit high spectral fidelity and coherent spatial structures. Additionally, this paper releases a large dataset that can comprehensively evaluate the reconstruction quality under different cloud coverages and various types of ground cover, providing a solid foundation for restoring satisfactory sensory effects and reliable semantic application value. In comparison to the current representative algorithms, the presented approach exhibits effectiveness and advancement in reconstructing missing regions with stability. The project is accessible at: https://github.com/RSIIPAC/LuojiaSET-OSFCR.

Mechanical properties and damage plastic model for cement-soil mortar: Laboratory tests and numerical analysis

Cement-soil mortar is a composite material that provides an efficient and cost-effective solution for a wide range of construction applications. This study analyzes the mechanical properties of cement-soil mortar through experimental investigation and explores the application and parameter calibration of the Concrete Damage Plasticity (CDP) model in the finite element simulation of cement-soil mortar. Additionally, an innovative Initial Defect Generation (IDG) method is proposed to enhance the accuracy in simulation of failure mode. The research findings provide a simulation framework that balances simplicity and accuracy for cement-soil mortar. Uniaxial compressive tests are first conducted on cement-soil mortar specimens with water contents ranging from 40 % to 70 %, and cement-to-soil proportions from 30 % to 300 %. Based on the experimental results, the regression relationships correlating unconfined compressive strength (UCS) with elastic modulus, peak strain and stress-strain curves are established. Then, the simplified equations for calculating CDP model parameters from the UCS of cement-soil mortar are further proposed following damage mechanics. A parameter table summarizing the calculation methods for all relevant CDP parameters is provided to streamline the model calibration process. Simulations incorporating the simplified calibration method and IDG method successfully replicated the stress-strain responses and failure modes observed in uniaxial compressive strength tests of cement-soil specimens with varied strength. The results demonstrate the reliability and broad applicability of this simulation framework in predicting the mechanical performance of cement-soil mortar.

Large language models in pathology: A comparative study of ChatGPT and Bard with pathology trainees on multiple-choice questions

Large language models (LLMs), such as ChatGPT and Bard, have shown potential in various medical applications. This study aimed to evaluate the performance of LLMs, specifically ChatGPT and Bard, in pathology by comparing their performance with those of pathology trainees, and to assess the consistency of their responses. We selected 150 multiple-choice questions from 15 subspecialties, excluding those with images. Both ChatGPT and Bard were tested on these questions across three separate sessions between June 2023 and January 2024, and their responses were compared with those of 16 pathology trainees (8 junior and 8 senior) from two hospitals. Questions were categorized into easy, intermediate, and difficult based on trainee performance. Consistency and variability in LLM responses were analyzed across three evaluation sessions. ChatGPT significantly outperformed Bard and trainees, achieving an average total score of 82.2% compared to Bard's 49.5%, junior trainees' 45.1%, and senior trainees' 56.0%. ChatGPT's performance was notably stronger in difficult questions (63.4%–68.3%) compared to Bard (31.7%–34.1%) and trainees (4.9%–48.8%). For easy questions, ChatGPT (83.1%–91.5%) and trainees (73.7%–100.0%) showed similar high scores. Consistency analysis revealed that ChatGPT showed a high consistency rate of 80%–85% across three tests, whereas Bard exhibited greater variability with consistency rates of 54%–61%. While LLMs show significant promise in pathology education and practice, continued development and human oversight are crucial for reliable clinical application.

In-situ grown hexagonal rod-like ZIF-L(Zn/Co) variant on reduced graphene oxide (rGO) for the enhanced electrochemical sensing of acetaminophen

Graphene – one of the most regarded materials in the world of Flatland has a substantial role in sensing applications due to its exceptional properties. Combining graphene with MOF can effectively mitigate the limitations of MOF while synergistically enhancing their unique properties. In this research work, we present a new hybrid composite of Zeolite Imidazolate Framework-L made composite with reduced graphene oxide, ZIF-L(Zn/Co)/rGO (ZLG) and applied its electrocatalytic performance in the sensitive detection of acetaminophen (AP). The mixture was prepared via a simple in-situ solvothermal method whose physico-chemical nature was investigated in detail. The ZIF-L phase identification, morphological change of ZIF, confirmation of rGO incorporation, and chemical composition analysis were established using the XRD, SEM, Raman and XPS respectively. Additionally, the kinetics of electron transfer was studied by EIS. Thereafter, proper optimization of various sensor parameters such as pH, scan rate and analytical performance were executed. Preliminary sensing studies carried out by cyclic voltammetry revealed an enhancement in peak current from 0.48µA to 1.05µA upon incorporation of rGO into the ZIF-L(Zn/Co) hybrid. Compared with reported studies along a similar vein, from the differential voltammetric analysis the ZLG-modified GCE displays a high selectivity towards AP with a broad linear range of 1 µM – 2060 µM exhibiting a sensitivity and LOD of 8.145 µA/mM and 162 nM respectively. The real-time validation of the sensor in paracetamol tablets and biological samples of human blood and urine exhibited recovery values in the range of ∼ 94 % − 102 %. Hence, this suggests a reliable practical applicability of the sensor owing to the high catalytic, large surface area and increased conductivity of the nanocomposite.

Chemical-Physical Synergistic Assembly of MXene/CNT Nanocoatings in Silicone Foams for Reliable Piezoresistive Sensing in Harsh Environments.

Owing to their high sensitivity across a wide stress range, mechanical reliability, and rapid response time, flexible polymer foam piezoresistive sensors have been extensively used in various fields. The reliable application of these sensors under harsh environments, however,is severely limited by structural devastation and poor interfacial bonding between polymers and conductive nanoparticles. To address the above issues, robust MXene/CNT nanocoatings on the foam surface, where the chemical assembly of MXene nanosheets and the physical anchoring of CNTs lead to strong interfacial bonding, are designed and described, which endows foams with structural reliability and unexpected multi-functionalities without compromising their instinct properties. The optimized foam nanocomposites thus maintain outstanding wide-temperature flexibility (-60-210 °C) and elasticity (≈3% residual strain after 1000 cycles). Moreover, the nanocomposites display good sensitivity at a relatively wide stress range of 0-70% and remarkable stability under acidic and alkaline settings. Furthermore, the foams with exceptional fire resistance (UL-94 V-0 rating) can provide stable sensing behavior (over 300 cycles) even after being exposed to flames for 5 s, making them one of the most reliable sensing materials so far. Clearly, this work widens applications of flexible piezoresistive sensors based on silicone foam nanocomposites for various harsh environments.

Generalized bivariate Kummer-beta distribution with marginals defined on the unit interval.

In this paper, a generalized bivariate Kummer-beta distribution is proposed. The name derives from the fact that its particular cases include univariate Kummer-beta distributions. This distribution generalizes a number of existing bivariate beta distributions, including Nadarajah's bivariate distributions, Libby and Novick's bivariate beta distribution and a central bivariate Kummer-beta distribution. Various properties associated with this newly introduced distribution are derived. The derived properties include product moments, marginal densities, marginal moments, conditional densities, conditional moments, Rényi entropy and Shannon entropy. Motivated by possible applications in economics, genetics, hydrology, meteorology, nuclear physics, and reliability, we also derive distributions of the product and the ratio of the components following the proposed distribution. Parameter estimation by maximum likelihood method is discussed by deriving expressions for score functions. Inference based on maximum likelihood estimation supposes that the maximum likelihood estimators have zero bias and zero mean squared errors. A simulation study is performed to check this for finite samples.

From Code to Care: The Impact of Software Engineering and QA on Healthcare Delivery Systems

The rapid digitalization of healthcare has led to an unprecedented reliance on software systems to improve patient care, streamline operations, and ensure regulatory compliance. This article presents a comprehensive analysis of the critical role that software engineering and quality assurance (QA) play in three key areas of modern healthcare: Electronic Health Records (EHRs), telemedicine platforms, and medical device software. Through an examination of current practices, challenges, and case studies, we demonstrate how robust software engineering methodologies and rigorous QA processes contribute to the development of reliable, secure, and efficient healthcare applications. Our findings highlight the importance of data integrity, interoperability, user experience, and regulatory compliance in healthcare software development. Furthermore, we explore emerging trends such as artificial intelligence, blockchain, and the Internet of Medical Things (IoMT), discussing their implications for future software engineering and QA practices in healthcare. This article provides valuable insights for healthcare organizations, software developers, and policymakers, offering recommendations to enhance the quality and safety of software-driven healthcare solutions.

Quantitative ultrasound for steatosis assessment using Hepatoscope®: Confounding technical factors

Quantitative ultrasound (QUS) is a well-suited modality to address large-scale screening and monitoring of liver steatosis as it is low-cost, non-invasive, and point-of-care. However, technical and biological confounders lead to difficulties in establishing universal cutoff values for diagnosis. Hepatoscope® remedies this issue as it integrates a unique QUS measurement technique enabling the simultaneous measurements of the backscattering coefficient, the attenuation coefficient and the speed of sound. Such measurements are performed in a large two-dimensional region of interest (ROI) and in a free breathing regime such that a relatively large portion of the liver is sampled. We studied the influence of the size of the ROI, its depth and the number of measurements on the QUS parameters with Hepatoscope® both in vitro on four calibrated phantoms and clinically on 60 participants referred to a hepatology consultation for chronic liver disease. We demonstrated that using large ROIs consistently improves the system reliability and the clinical applicability. We did not observe any systematic effect related to the depth of the ROI. We also showed that averaging consecutive measurements leads to better system reliability and better clinical applicability.

Evaluation and mitigation of cognitive biases in medical language models.

Increasing interest in applying large language models (LLMs) to medicine is due in part to their impressive performance on medical exam questions. However, these exams do not capture the complexity of real patient-doctor interactions because of factors like patient compliance, experience, and cognitive bias. We hypothesized that LLMs would produce less accurate responses when faced with clinically biased questions as compared to unbiased ones. To test this, we developed the BiasMedQA dataset, which consists of 1273 USMLE questions modified to replicate common clinically relevant cognitive biases. We assessed six LLMs on BiasMedQA and found that GPT-4 stood out for its resilience to bias, in contrast to Llama 2 70B-chat and PMC Llama 13B, which showed large drops in performance. Additionally, we introduced three bias mitigation strategies, which improved but did not fully restore accuracy. Our findings highlight the need to improve LLMs' robustness to cognitive biases, in order to achieve more reliable applications of LLMs in healthcare.

Metal Ion-Condensed DNA Nanoparticle Library: Phase Separation and Transition and Antisense Therapy Applications.

DNA condensation has long been investigated as a fundamental cellular activity and is known to be driven by the mediation of diverse condensing agents. The phase behaviors of DNA during condensation are particularly interesting because the complicated molecular structure of natural nucleotides fundamentally allows electrostatic, coordinate covalent, and various other secondary interactions with the condensing agents. Recently, metal ion (Mn+)-induced DNA condensation has emerged as a powerful approach to synthesizing nanoparticulate DNA structures suitable for therapeutic gene delivery. However, how the DNA phase changes during Mn+-induced DNA condensation has rarely been observed and is not understood yet. In this study, a library of Mn+-condensed DNA nanoparticles (Mn+-CDNPs) was established using 30 different types of Mn+s, and their phase behaviors during condensation were elucidated using spherical nucleic acids (SNAs) as electron microscopic labels. Importantly, the phase transition and separation of DNA were demonstrated to be driven by the Mn+s into either the growth of individual DNA particles or the fission of bulky DNA aggregates. Pt2+ and Eu3+ were chosen as model systems for the demonstration. The hard and soft acid nature of Mn+ is presumably the underlying driving force of these phase transitions. In addition, the Mn+-controlled anticancer therapeutic efficiency of the Mn+-CDNP library as a state-of-the-art gene delivery platform was demonstrated even for unmodified antisense oligonucleotides in association with the potential toxicity of the Mn+s released from the Mn+-CDNPs. This comprehensive study of the Mn+-dependent condensation of nucleic acids provides profound insights into the chemistry of the nucleic acid-Mn+ interactions and the reliable theragnostic applications of Mn+-CDNPs as functional nucleic acid nanostructures.

Evaluating pain in non-verbal critical care patients: a narrative review of the critical care pain observation tool and Its clinical applications.

Assessing pain in critically ill patients who cannot communicate verbally poses significant challenges. Traditional self-report measures are ineffective for these patients, making the need for reliable observational tools crucial. To evaluate the effectiveness, reliability, and clinical applicability of the Critical Care Pain Observation Tool (CPOT) in various intensive care unit (ICU) settings and to explore potential innovations for improving its use and integration into clinical practice. A narrative review evaluated the Critical Care Pain Observation Tool (CPOT) for non-communicative ICU patients, comparing it to the Behavioral Pain Scale (BPS) and the FLACC scale. The review assessed CPOT's effectiveness across different ICU settings, identified limitations and challenges, and explored potential enhancements such as electronic scoring, additional physiological indicators, and improved training protocols. The CPOT has been validated as an effective pain assessment tool for non-verbal ICU patients. It evaluates pain through facial expressions, body movements, muscle tension, and ventilator compliance. The CPOT shows superior sensitivity at 76.5% compared to 62.7% for the BPS and offers a more comprehensive assessment of pain indicators like muscle tension and ventilator compliance than the FLACC scale. Despite its strengths, the CPOT has limitations, including inter-rater variability and challenges in certain patient populations. Barriers to implementation include resource constraints and the need for extensive training. The Critical Care Pain Observation Tool (CPOT) is a highly effective instrument for assessing pain in non-verbal ICU patients, demonstrating superior accuracy and reliability compared to other tools like the Behavioral Pain Scale (BPS) and FLACC scale. Its detailed approach, covering facial expressions, body movements, muscle tension, and ventilator compliance, offers a detailed measure of pain. However, challenges such as inter-rater variability and limitations in specific patient populations highlight the need for ongoing refinement and research.

Different copula types and reliability applications for a new fisk probability model

In this research endeavor, the authors introduce a novel lifetime probability model. This distribution is meticulously examined and characterized, offering insights into its behavior and applicability in various contexts. The proposed new density function of this distribution has various heavy tail forms that are useful in the field of reliability, insurance and statistical modeling. This allows for the representation and modeling of a wide range of data sets that are diverse in their form and nature. The new distribution is characterized by having different patterns of risk or failure rates. The researchers extend the new distribution to the bivariate domain through different methods, including the Morgenstern-Farley-Gumbel distribution, the modified Morgenstern-Farley-Gumbel distribution, the famous Clayton mathematical versions, and the Rennie versions. These extensions enhance the usefulness of the proposed distribution in modeling multivariate age and reliability data and dependencies between variables. The study presents some statistical modeling experiments on reliability data and some important comparisons are presented within the framework of some statistical comparison criteria.

Assessment of the efficiency of image analyzer applications in determining substrate quality for vegetable seedling development

The aim of this study is to evaluate the efficiency of images analyzer applications in determining substrate quality for vegetable seedling development. Two leaf coverage determiner apps using digital images, Canopeo and GreenTest, were tested and their values were compared with results obtained from NDVI, fresh mass, dry mass, and humidity in a substrate testing experiment for lettuce seedling development. The experiment, employing a completely redomized design with 4 treatments (control, witness, humus and organic compost) and 4 repetitions, took place in a greenhouse in Londrina, Paraná, Brazil, in April 2022. Results indicated that the Canopeo app demonstrated greater sensitivity in distinguishing between treatments and exhibited a very strong correlation with other scientifically employed analyses. In contrast, GreenTest showed moderate correlation and lower sensitivity in the analyses. In conclusion, Canopeo proves to be a reliable application for determining leaf coverage and analyzing the performance of vegetable seedlings.

Laser-Induced Self-Limiting Welding of Ag Nanowires with High Mechanical and Electrical Performance.

The high-efficiency and high-precision welding technology of Ag nanowires is of great engineering significance for the integration of new-generation micro- and nanodevices, and the mechanical behavior of its interconnect joints is also essential for their reliable application, especially for some flexible device. In this paper, based on the nano-focusing and localized plasma enhancement properties of Ag nanowires under laser irradiation, Ag nanowire self-limiting joints with mechanical and electrical properties comparable to those of the base material are obtained. At the same time, the local plasma enhancement characteristics of Ag nanowire joints are scanned and analyzed with nanoscale resolution by using cathodoluminescence technology, and the local multi-physical field coupling regulation mechanism of Ag nanowire joints induced by different laser parameters is systematically investigated by combining theoretical simulations. Meanwhile, based on the in situ laser welding and nanomechanical tensile experiments, the mechanical properties of single Ag nanowires and their welded joints are systematically analyzed, and the characteristics of the interfacial atomic behaviors and the evolution process during the welding and tensile processes are investigated.