Mean Maximum Research Articles

Fault diagnosis of axial piston pump based on multi-source subdomain adaptation and sensor data fusion

The axial piston pump is the core component in hydraulic systems. Its condition monitoring and fault diagnosis are crucial to ensure the safe and reliable operation of hydraulic systems. However, most of the existing fault diagnosis methods for axial piston pumps use the same working condition data. In actual operation, axial piston pump often experience varying loads, and the collected data is typically correlated but has different distributions. Therefore, a transfer learning method of multi-source subdomain adaptation and sensor fusion (MSASF) is proposed for fault diagnosis of axial piston pump. The proposed MSASF has three modules, a shared feature extraction module, a domain-specific feature extraction module and an output decision module. Firstly, the adaptive weighted fusion of multi-sensor data features is realized by the shared feature extraction module and the common features of multi-source heterogeneous data are extracted. Secondly, in the domain-specific feature extraction module, multi-branch network is used to extract features of each pair of source and target domains and the local maximum mean difference is utilized to align the sub-domain distribution of each pair of source and target domains. Finally, in the output decision module, the distribution distance between each pair of source domain and target domain is calculated using maximum mean discrepancy to obtain its weighted score. Combined with the classification output of each source domain, the final diagnosis decision is made. A dataset was constructed for the axial piston pump design fault experiment, and four sets of transfer tasks were designed to compare with those of seven classic methods. The experimental results showed that the proposed MSASF method exhibits a superior domain adaptation effect and fault diagnosis performance.

Dual-weight Attention-based Multi-source Multi-stage Alignment Domain Adaptation for Industrial Fault Diagnosis

Abstract Cross-domain fault diagnosis is crucial for industrial applications with various and unknown operating conditions. However, due to the significant differences in the distribution of features in multiple source domains, it may lead to mutual interference of features between different domains and reduce the accuracy of diagnosis, which is a problem not considered by most current researches. In addition, most of the existing methods focus only on the extraction of low-frequency global information and cannot adequately deal with high-frequency local information. Consequently, this paper provides a multi-stage processing integrated dual-weight attention-based multi-source multi-stage aligned domain adaptation (DAMMADA) method. Global fault features that are shared by various subdomains are extracted by three domain-specific feature extractors from various domains. In a local feature extractor, the dual-weight attention module not only uses shared weights to aggregate local information, but it also uses contextual weights to improve local features. In terms of loss handling, multiple pseudo-labels are used to reduce the loss of the local maximum mean discrepancy (LMMD) in order to learn the domain-invariant characteristics after improving the high-frequency and low-frequency information extraction. To modify the classification boundaries, the pseudo-labels' mean square errors (MSE) are combined. Comprehensive experiments were carried out on two platforms for fault diagnosis of SCARA robots and bearings respectively, and the results demonstrated that DAMMADA is superior to other methods in terms of accuracy and its ability to suppress negative migration for cross-domain tasks.

Tool Wear Monitoring In Micro-Milling Based on Digital Twin Technology with an Extended Kalman Filter

In order to avoid catastrophic events that degrade the quality of machined products, such as tool breakage, it is vital to have a prognostic system for monitoring tool wear during the micro-milling process. Despite the long history of the tool wear monitoring field, creating such a system to track, monitor, and foresee the rapid progression of tool wear still needs to be improved in the application of micro-milling. On the other hand, digital twin technology has recently become widely recognized as significant in manufacturing and, notably, within the Industry 4.0 ecosystem. Digital twin technology is considered a potential breakthrough in developing a prognostic tool wear monitoring system, as it enables the tracking, monitoring, and prediction of the dynamics of a twinned object, e.g., a CNC machine tool. However, few works have explored the digital twin technology for tool wear monitoring, particularly in the micro-milling field. This paper presents a novel tool wear monitoring system for micro-milling machining based on digital twin technology and an extended Kalman filter framework. The proposed system provides wear progression notifications to assist the user in making decisions related to the machining process. In an evaluation using four machining datasets of slot micro-milling, the proposed system achieved a maximum error mean of 0.038 mm from the actual wear value. The proposed system brings a promising opportunity to widen the utilization of digital twin technology with the extended Kalman filter framework for seamless data integration for wear monitoring service.

Distance-guided domain adaptation for bearing fault diagnosis under variable operating conditions

Transfer learning (TL) has demonstrated effective application in diagnosing bearing faults under varying operating conditions. The current TL methods achieve domain alignment by minimizing the variation in the marginal distribution of data between the source and target domains in the feature space. However, this approach yields incomplete similarity, leading to domain shift and a decrease in diagnostic performance. To overcome this problem, this paper proposes a new distance-guided domain adaptation method that consists of two modules: deep domain adaptive correlation alignment (Deep CORAL) combined with joint maximum mean discrepancy (JMMD) for guided domain adaptation. Deep CORAL employs nonlinear transformations to synchronize second-order statistical correlations across source and target domains, thus ensuring feature-level alignment between these domains. JMMD is utilized to align the joint distribution of input features and output labels within the activation layer in the deep network, thereby bolstering domain alignment. Building on this, we propose a network structure that merges ResNet and bidirectional long short-term memory, powered by wavelet kernels, serving as a feature extractor. This structure is designed to learn domain-invariant features and incorporates attention mechanisms to amplify important information while diminishing the impact of redundant data. An analysis of bearing experiments is used to demonstrate the effectiveness of this method, and the proposed method significantly outperforms several popular methods in diagnostic performance.

Short-term wind speed prediction of wind farm based on TSO-VMD-BiLSTM

Aiming at the random and intermittent characteristics of wind speed, a short-term wind speed prediction (SWSP) method based on TSO-VMD-BiLSTM is proposed in this article. Firstly, open-source historical data from a certain region in 2022, including wind speed, direction, pressure, and temperature is analyzed. The data is processed through variational mode decomposition (VMD) to fully extract feature data from historical wind speed records. Secondly, taking historical wind speed, direction, pressure, and temperature as inputs and wind speed as output, a SWSP model based on long short-term memory (LSTM) network is constructed. Thirdly, the tuna swarm optimization (TSO) algorithm is utilized for parameters optimization, and a bi-directional long short-term memory (BiLSTM) network is incorporated to enhance prediction accuracy for micrometeorological parameters. The proposed TSO-VMD-BiLSTM model is validated through comparison with other models, demonstrating its higher accuracy with the maximum absolute error of only 2.52 m/s, the maximum root mean square error of 0.81, the maximum mean absolute error of only 0.54, and the maximum mean absolute percentage error of 6.89%.

Experimental investigation on the influence of temperature on the fracture surface variations of granite after Brazilian splitting tests

Understanding the variation in the morphology of rock fracture surfaces after high-temperature is of great significance for evaluating the strength, deformation, and seepage characteristics of deep engineering rock masses. In this study, the variations in the granite fracture surface roughness after exposure to different temperatures (25–800 °C) were investigated, and the damage deterioration mechanism was revealed. The results showed that the maximum fluctuation (Z) and root mean square of the first derivative (Z2) of the granite fracture surface tended to increase with increasing temperature, whereas the fractal dimension (D) decreased, suggesting that the higher temperature contributed to greater roughness of the fracture surface. The anisotropy of the fracture surface was more evident at higher temperatures and had greater roughness in the 45°–75°, 105°–135°, and 155°–170° directions after different temperatures. Physicochemical reactions, such as dehydration, quartz phase transformation, and mineral oxidation, occurred in the granite owing to high temperatures. These reactions changed the crystalline state of the minerals and facilitated the increase and expansion of microcracks, thereby weakening the cementing property between mineral particles. Fracture surfaces with higher roughness were formed after tensile failure of the rock. The research results may provide theoretical references for the construction and operation of deep engineering.

Plasma and interstitial fluid antibiotic levels of subcutaneously implanted compounded florfenicol calcium sulfate beads in New Zealand White rabbits (Oryctolagus cuniculus).

To determine antibiotic levels in plasma and interstitial fluid (ISF) after SC placement of compounded florfenicol (FF) calcium sulfate beads (CSBs) in New Zealand White rabbits (Oryctolagus cuniculus). 6 juvenile female rabbits (n = 5 treatment and 1 control). An ultrafiltration probe and CSBs were placed SC in 6 rabbits (n = 5 for FF CSBs and 1 for control CSBs). Plasma (3, 6, 12, 24, and 48 hours and 7, 14, and 21 days) and ISF (daily for 21 days) samples were collected, and FF was measured by HPLC for pharmacokinetic analysis. Hematology, biochemistry, and histopathology were assessed. Means ± SD for the area under the curve, maximum concentration, time of maximum concentration, terminal half-life, and mean residence time to the last data point for plasma and ISF were 16.63 ± 8.16 and 17,902 ± 7,564 h·µg/mL, 0.79 ± 0.38 and 245 ± 223 µg/mL, 2.90 ± 0.3 and 59 ± 40 hours, 30.81 ± 16.9 and 27.3 ± 9.39 hours, 23.4 ± 10 and 73.7 ± 13 hours, respectively. Plasma FF was < 2 µg/mL at all time points. The ISF FF remained > 8 μg/mL for 109.98 to 231.58 hours. One rabbit death occurred during treatment, but the cause of death was undetermined. Local tissue inflammation was present, but no clinically significant systemic adverse effects were found on hematology, biochemistry, or histopathology in the remaining rabbits. Florfenicol CSBs maintained antibiotic concentrations in ISF at levels likely to be effective against bacteria sensitive to > 8 µg/mL for 5 to 10 days while maintaining low (< 2 µg/mL) plasma levels. Florfenicol CSBs may be effective for local antibiotic treatment in rabbit abscesses.

High-performance Ga2O3 solar-blind UV photodetectors via film growth regulation and surface state engineering

Gallium oxide (Ga2O3) with a wide bandgap is emerging as one of the most promising candidates for solar-blind photodetectors (SBPDs). However, the practical application of Ga2O3-based SBPDs is impeded by substantial defects-related leakage currents, sluggish response speed, and high-cost, making their practical applications face great challenges. Here, we proposed strategic films growth regulation and surface state engineering to grow high-quality yet economical β-Ga2O3 films, thereby achieving high-performance SBPDs. High-resolution transmission electron microscopy revealed the presence of high-quality films with an extremely regular arrangement of β-Ga2O3 (2‾01) planes. The full width at half maximum and root mean square roughness values are 0.07° and 0.716 nm, respectively. The β-Ga2O3 films demonstrated a significant reduction in VO concentrations after O2/N2 plasma treatment. This process can effectively minimize thermal damage to the sample surface while retaining highly reactive free radicals. The photocurrents of devices can be further enhanced, leading to a larger PDCRs of 8.6 × 107, through N2 plasma treatment. Moreover, the films effectually suppress the persistent photoconductivity effect with τr and τd values of 5.31/0.97 s and 0.49/0.87 s before and after N2 plasma treatment, respectively. We have demonstrated the influence mechanism of O2/N2 plasma treatment on photoelectronic performance. This study provides a practical approach to growing high-quality β-Ga2O3 films and preparing high-performance but low-cost Ga2O3-based SBPDs.

Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties

Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.

Endovascular Treatment of Complex Aortic Aneurysms Using Inner Branch Device (IBEVAR): A Single Center Experience

Abstract Background Due to advances in devices and techniques, the endovascular approach using fenestrated or branched endografts has emerged as a valid and safe alternative for patients with complex aortic aneurysms, especially if considered at high risk for surgery. The use of inner branch devices (iBEVAR) combining benefits of fenestrations and outer branches could offer an ideal configuration in this context. Aims The aim of our study was to report our monocentric experience with iBEVAR in the treatment of complex aortic aneurysms. Methods A retrospective analysis of prospective data retrieved from 1 center between January 2020 and January 2024 was done. Endpoints were immediate technical success, postoperative morbidity, rate of re-intervention and mortality. Results During this period, 72 patients with a mean age of 68.5 years (range 41-88) were identified. The maximum preoperative mean aortic diameter was 56 mm (44-71 mm). The majority of patients was asymptomatic. For diffuse thoraco-abdominal aortic aneurysms, a 2-steps procedure was used. There were 34 off-the-shelf E-nside (47%) and the remaining 38 patients were treated with a custom-made Extra-Design graft (53%). Despite successful endograft deployment in all patients and branch catheterization in 69/72 cases (96%), the technical success was 94% due to an additional open conversion for ilio-mesenteric bypass. The 30-days mortality was 5.5%. During the post-operative period, 19% of patients presented complications with 1 case of mesenteric ischaemia requiring a visceral resection and 2 cases of paralytic ileus, treated conservatively. 5 cases (6.9) of spinal cord ischemia were observed. During the median follow-up, 22 patients (31%) had a re-intervention, for endoleaks, bridging-stent events or limbs problems. The overall target vessel patency was 94%. Conclusion The use of iBEVAR appears to be safe and effective for the treatment of complex aortic aneurysms with an acceptable rate of complications. Strict follow-up is mandatory and about one-third of patients required a re-intervention. Further patients and longer follow-up are needed.

A numerical determination of complex solid gun propellant burn rates through closed bomb simulation

AbstractClosed bomb testing is a prominent means of characterizing the combustion behavior of solid gun propellants. This sub‐scale test allows the propellant to burn in a constant volume environment, where the resulting pressure‐time trace can be collected via a pressure transducer. Historically, numerical procedures have been developed to determine the burn rates of the gun propellants from these pressure‐time traces; however, no standardized procedure exists to determine the burn rates of grains with variable surface thermochemistry and ignition criteria. To address this capability gap, a non‐linearly constrained, multivariate optimization algorithm has been developed to decouple propellant grain surfaces and determine surface‐specific burn rates [1]. In this work, the optimization algorithm as well as the legacy Excel‐based Closed Bomb (XLCB) program [2] were used to determine the burn rates of homogeneous, deterred, and layered propellants from experimental data. Closed bomb simulations using these burn rates were then conducted with the two‐phase, multidimensional, interior ballistics solver, iBallistix [3]. The maximum mean error between the simulated and experimental pressure‐time curves was 6.8 % for the optimization algorithm and 23.8 % for XLCB, showing a marked improvement with our new approach. Furthermore, the approach discussed herein improves burn rate predictions of complex solid gun propellants when compared with legacy closed bomb data reduction analysis programs.

Development of multi-sensors fusion monitoring system for shaft wall deformation

Ensuring shaft safety is crucial for underground coal mining. The prevailing method for monitoring shaft deformation involves manual measurements conducted regularly, necessitating numerous surveyors to descend into the shaft. This approach is laborious and time-consuming, and its reliance on human intervention significantly affects accuracy. An emerging alternative employs an enhanced laser slam scanner comprising laser ranging and inertial navigation angle measurement modules. The accuracy of the inertial navigation angle often requires refinement through laser point cloud registration to mitigate errors. However, existing research has not explored the utilization of a single inertial navigation sensor within a shaft, which potentially influences measurement outcomes. To investigate the impact of inertial navigation angle measurement errors from lidar scanners on shaft interior measurements and propose corresponding error reduction methods, this study developed a multisensor fusion system for monitoring shaft wall deformations. Three experiments were conducted to assess the accuracy of this monitoring system: a laboratory experiment, a simulated experiment, and a real shaft experiment. The results demonstrated that the maximum mean absolute errors (MAEs) in the laboratory and simulated shaft experiments were 0.06 cm and 0.30 cm, respectively, validating the reliability of the point cloud processing algorithm. In the real shaft experiment, the maximum MAE was 0.48 cm, confirming the measurement accuracy of the monitoring system. Compared to manual measurements, the system reduced the measurement time by 48% and streamlined the process from 12 to 3 steps. These findings highlight the monitoring system’s centimeter-level accuracy, efficiency in reducing measurement time, simplification of the measurement process, and elimination of the need for human entry into the shaft.

Effect on combustion performance and emission behavior of diesel engine fueled with hybrid biodiesel produced from a novel ternary oil mixture incorporated with nano additive

ABSTRACT Global energy demand has been above 80% fossil fuels for decades, creating a greater and more complicated energy crisis. Biodiesel is a great alternate energy source for future energy demand. Producing biodiesel from a mixture of waste and non-edible oils may be a practical solution to address the growing global need of biodiesel. In this study, hybrid biodiesel is produced from a ternary oil mixture of Waste cooking oil, Ricinus communis oil, and Melia azedarach oil. The hybrid biodiesel blends are prepared by 10% blending (HB10), 20% blending (HB20) and 30% blending (HB30) and 30ppm multiwall carbon nanotube has been added in the blended fuel through sonicating process. At steady 1500rmp and variable load (0–100%), the engine behavior has been examined. The results show that the hybrid biodiesel blend (HB10) with 30ppm MWCNT has shown highest cylinder pressure of 70.9 bar, maximum mean gas temperature 1467.56°C. The engine shows 34.08% rise in net heat release and 9.13% rise in exhaust gas temperature as compare to diesel. The maximum rise in break thermal efficiency of 19.74% and minimum reduction in break specific fuel consumption of 13.79% is noticed in comparison to B00. The highest reduction of HC emission is found as 20.83% for HB20MWCNT30 and 19.34% for HB30MWCNT30 as compare to B00. The inclusion of multiwall carbon nanotube in hybrid biodiesel reduces the HC, CO, NOx and smoke emission and increases CO2 emission as compare to respective hybrid biodiesel blend and diesel. The hybrid biodiesel-diesel blend, with multiwall carbon nanotube can be utilized efficiently in diesel engine with improved overall engine behavior.

Elevation dependent climate assessment and its influence on snow cover variability in Hindu Kush Himalayan region using Google Earth Engine for the period of 2003–2021

The Hindu Kush Himalayan (HKH) regions are taken for this study, which is predominantly characterized by amplified climatic uncertainty and rapid snow melting. Because at the present there is a lack of studies estimating the elevation dependent and climate influenced snow cover variability in the HKH region. This present study examines elevation-dependent (2003–2021) distribution and trends in precipitation, temperature, and snow cover area (SCA) by employing numerous non-parametric statistical applications using the Google Earth Engine (GEE). The Modified Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover data is employed to estimate SCA along with the Climate Hazards Group Infrared Precipitation with Station Data version 2.0 (CHIRPS-V2.0) for precipitation and MODIS land surface temperature (LST). The study reveals statistically significant decreasing annual, seasonal, and monthly trends of SCA in most of the elevation zones typically during winter season (−0.256%/year), whereas temperature and precipitation exhibit strong spatial variability. Inclusive investigation portrays maximum mean annual and seasonal increased precipitation at the low-elevated regions, whereas seasonal disparities in temperature ostensible marginal decreased at high-elevation, albeit increased at lowland. Moreover, topographic importance on SCA distribution showcases maximum snow (43.4%) at north-facing less surface inclination with flat topography (14.6%). The comprehensive elevation wise analysis improves our understanding of the overall hydrological challenge and assist in the development of more reliable flood forecasting and water resource management.

Critical speed models of high-resolution speed-duration profiles describe peak running demands in soccer

The purpose of this study was to characterize critical speed (CS) models for summarizing high-resolution speed-duration profiles from GPS tracking data obtained from soccer players. GPS data from 15 male NCAA Division I soccer players were collected during practices and games over a 6-week period. Moving averages of the speed data were computed for each file for duration windows spanning 0.1 to 600 seconds at 0.1-second resolution. Speed-duration profiles for each session and for the entire sampling period (“global”) were generated for each player by selecting the maximal mean speeds for each duration. Four models were fit to the profiles: the two-parameter CS (CS2) model, the three-parameter CS (CS3) model, the omni-domain speed-duration (OmSD) model, and the five-parameter logistic (5PL) model. The 5PL, CS3, and OmSD models exhibited similar goodness of fits, and all outperformed the CS2 model. Similar CS estimates were obtained for each model, whereas maximum speed ( Smax) estimates were lower for OmSD compared to the 5PL. Players exhibited a range of parameter values for CS, D′, and Smax. Smax and CS estimated from session-specific speed-duration profiles were on average higher for games compared to practices. We conclude that CS models are useful for empirically describing speed-duration profiles and for assessing peak running demands for soccer practices and games. The proposed approach could help coaches design practice activities to better mimic game demands.

Innovative integration of multi-scale residual networks and MK-MMD for enhanced feature representation in fault diagnosis

This article proposes a method based on multi-scale expansion of residual neural networks (ResNets) to address challenges in the operation of rotating components, such as bearings and gears, under complex conditions where they are often affected by environmental noise. This interference leads to weaker fault characteristics, making feature selection difficult and increasing the presence of extraneous information features. To tackle this issue, the proposed method first employs a multi-scale feature ResNet to extract features from vibration signals of rotating machinery. The method decomposes the signal into multiple sub-signals of different scales, extracting local features at each scale. It then uses residual connections to combine these local features to obtain a global feature representation. Furthermore, the article introduces a construction of the maximum mean discrepancy (MMD) and minimization of entropy boundaries to adapt to the differences between two domains. The method utilizes multiple kernel functions to calculate distances between data at different scales and combines these distances to obtain a comprehensive measure. By employing the MMD and minimization of entropy boundary approach, the method can more accurately determine whether signals at different scales belong to the same category, thereby improving diagnostic accuracy and robustness. Experimental results demonstrate the effectiveness of the proposed method in unsupervised cross-domain fault diagnosis tasks. Future work will focus on further optimizing the architecture of ResNets, enhancing feature extraction capabilities, and exploring advanced data augmentation methods to further improve the model’s generalization performance.

An I–V characteristic reconstruction-based partial shading diagnosis and quantitative evaluation for photovoltaic strings

The partial shading condition (PSC) is the most common abnormality occurred in photovoltaic (PV) systems. Accurate quantitative evaluation of the shaded area and the severity of the shading is of potential importance in optimizing the maintenance strategy for PV systems. In this paper, we propose a PSC diagnosis and quantitative evaluation method by analyzing the measured string current–voltage (I–V) characteristic obtained from the PV inverter with the I–V scanning function, which includes pre-diagnosis of the system abnormality based on the operational power deviation, the diagnosis of PSCs based on the derivatives characteristics of the PV string, and the quantitative evaluation based on the I–V characteristic reconstruction. The quantitative evaluation of PSCs is the main focus in this paper, where the I–V characteristics of the unshaded PV modules in the partially shaded PV string are reconstructed according to different mismatch levels, respectively. The number of shaded PV modules and the corresponding severity of the partial shadings are estimated according to the reconstructed I–V characteristics. The simulation and experimental results verify that both the proposed diagnosis and quantitative evaluation method is effective with decent accuracy, especially for severe mismatch conditions. Experimental results show that the maximal mean absolute error of the quantified shaded area and quantified shaded rate are approximately 1.4446 units of 1/3 PV modules and 0.026, respectively.

Abstract PO5-06-03: Impact of curative-intent treatment on survival for metastatic breast cancer limited to distant lymph nodes

Abstract BACKGROUND: Metastatic breast cancer limited to distant lymph nodes is classified as stage IV but potentially represents an anatomically distinct entity amenable to curative treatment. Its optimal management is unclear, with existing studies limited by small sample sizes and short follow-up durations. We sought to investigate the impact of curative-intent treatment on breast cancer-specific survival (BCSS) and overall survival (OS) for patients with this diagnosis. METHODS: Three cohorts of patients diagnosed with breast cancer from 2010 to 2019 were identified from the Surveillance, Epidemiology, and End Results 17 registries database. The curative cohort consisted of patients with metastatic breast cancer involving only distant lymph nodes who received a combination of chemotherapy, surgery to the primary site, and radiotherapy (curative-intent treatment). The palliative cohort included patients who underwent chemotherapy but not curative-intent treatment. The N3c cohort was comprised of patients with ipsilateral supraclavicular lymph node metastases but no distant metastases who received curative-intent treatment. Weighting based on covariate balancing propensity scores was performed to balance age, sex, race, median household income, year of diagnosis, T stage, histology, pathological grade, and ER/PR/HER2 status between the cohorts. Weight-adjusted multivariable Cox regression models with the same covariates were then used to compare the BCSS and OS of the three cohorts. RESULTS: A total of 2,089 patients with a median follow-up of 37 months were eligible for analysis, of whom 577 (27.6%) were in the curative cohort, 905 (43.3%) were in the palliative cohort, and 607 (29.1%) were in the N3c cohort. After propensity score weighting, the maximal standardized mean difference for all covariates was less than 0.1. Five-year BCSS for patients in the weight-adjusted curative, palliative, and N3c cohorts were 63.0% (95% CI 58.1-68.3), 46.7% (42.4-51.4), and 66.4% (61.4-71.8), respectively; and five-year OS were 57.9% (53.0-63.2), 41.2% (37.2-45.7), and 62.2% (57.3-67.6), respectively. Compared to the palliative cohort, the curative cohort exhibited better BCSS (HR 0.47, 95% CI 0.38-0.57; P&amp;lt; 0.001) and OS (HR 0.48, 95% CI 0.40-0.58; P&amp;lt; 0.001). In contrast, the N3c cohort had similar BCSS (HR 0.93, 95% CI 0.73-1.17; P=0.53) and OS (HR 0.96, 95% CI 0.77-1.19; P=0.68) to the curative cohort. CONCLUSIONS: Patients treated curatively for metastatic breast cancer limited to distant lymph nodes had higher BCSS and OS than those receiving palliative chemotherapy and similar BCSS and OS to patients receiving curative-intent treatment for ipsilateral supraclavicular lymph node metastases. These results support the use of aggressive multimodal therapy for select stage IV breast cancer patients with only distant nodal metastases and, if confirmed, suggest that a substantial portion of such patients may be undertreated. Citation Format: Yang Xu. Impact of curative-intent treatment on survival for metastatic breast cancer limited to distant lymph nodes [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-06-03.

Morphological and molecular diversity analysis vis-a-vis phylogenetic correlation among pomegranate (Punica granatum L.) varieties

Abstract Identification of different pomegranate (Punica granatum L.) genotypes using morphological descriptors and further substantiated through molecular means is essential for a breeding program. Characterization using molecular markers has an advantage over its morphological counterpart due to its low expense, quick and reliable results, and the requirement of less sophisticated equipment. In this study, eight different pomegranate cultivars were characterized based on their morphological and molecular characters in sub-tropical regions between 2018 and 2019. Morphological experiments were conducted in a randomized block design replicated thrice for each cultivar, whereas RAPD markers were employed for molecular studies. Principal component analysis revealed significant morphological variation (79.61%) among the cultivars. A high level of polymorphism, similar to PCA, was observed with RAPD analysis (80.49%). Some discrepancies between morphological and molecular results were noted in UPGMA cluster analysis. However, eight primers (total = 41) distinctively separated the Bhagwa variety from others, with the maximum similarity coefficient value (ca. 0.54) and superior fruit quality distinguished in PCA analysis with the maximum mean Euclidean distance (ca. 16). Hence, RAPD markers could be used along with morphological markers for a quick screening of germplasms in further crop improvement programs.

Learning-based double layer control method of yaw stability simulation research for rear wheel independent drive electric tractor plowing operation

Complex field environments and variable soil conditions make tractor plowing operations susceptible to maneuvering instability, especially challenging in yaw stability. A learning-based double layer control (LDLC) method of yaw stability for rear wheel independent drive electric tractor (ET) plowing operation is proposed in this paper in order to solve this problem. Firstly, a sliding window self-learning traction resistance predictor is proposed based on Gaussian process regression (GPR) theory to predict the future traction resistance of plowing operation. Then, a discrete nonlinear model predictive control (NMPC) supervisor yaw stability controller is designed based on the dynamics model of the plowing unit and the tire-soil model to solve for the additional yaw torque required to maintain the yaw stability of the tractor. Finally, considering the additional yaw moment and driver demand torque, the subordinate controller composed of a particle swarm optimization algorithm optimally allocates the driving torque of the two driving wheels. Hardware-in-the-loop test results show that the maximum mean tracking errors of yaw rate and sideslip angle are 0.002 rad/s and 0.01 rad for the LDLC controlled ET during straight line and continuous steering under normal plowing operation, the maximum mean tracking errors of yaw rate and sideslip angle are 0.007 rad/s and 0.012 rad for straight line and continuous steering plowing operations under harsh plowing operation. The tracking accuracy is significantly higher than traditional cascade PID and equal distribution control methods. The proposed LDLC method effectively ensures the yaw stability of the ET during straight line and continuous steering under normal and harsh plowing operation conditions.