4.21 - Large-Scale Industrial Coating Applications and Systems

PT202 Predictors of the coronary in-stent restenosis

This paper explores the effect that unions have on a firm's ability to reengineer manufacturing processes. We begin by exploring the various effects that a union may have in a manufacturing environment. Next, we briefly review how unions may affect managerial initiatives to reengineer processes and improve manufacturing performance. The third section analyzes an existing database to test for differences in cycle time and manufacturing performance between union and nonunion firms. Finally, we discuss the implications of the study for future operations strategy research and note how a different form of union‐management relationships is beginning to evolve.

Online Monitoring of a Sequencing Batch Reactor Treating Domestic Wastewater

Increased Activity of Prothrombinase Fgl-2 in Peripheral Blood Mononuclear Cells of Patients with B-Cell Lymphoma.

Al2O3/ZrO2-8Y2O3 and (Cr,Ti)AlSiN tool coatings to influence the temperature and surface quality in friction-spinning processes

Anomaly detection plays a critical role in large-scale industrial systems, where real-time streaming data is generated at high volumes. This research journal presents an in-depth study on developing an efficient anomaly detection algorithm specifically designed for such industrial systems. The goal is to identify abnormal patterns and deviations from normal behavior, enabling proactive maintenance, improved operational efficiency, and reduced downtime. The proposed algorithm leverages machine learning and stream processing techniques to handle the challenges associated with real- time streaming data analysis. The research covers algorithm design, implementation, evaluation, and performance analysis using large-scale datasets from industrial domains. Keywords: anomaly detection, real-time streaming data, large-scale industrial systems, machine learning, stream processing

Correlative Analyses of Cereblon (CRBN), cMYC, IRF4, BLIMP1, and XBP1 with Clinical Outcomes in Stratus (MM-010), a Phase 3b Study of Pomalidomide (POM) + Low-Dose Dexamethasone (LoDEX) in Patients (pts) with Relapsed/Refractory Multiple Myeloma (RRMM)

Feature models are frequently used to capture the knowledge about configurable software systems and product lines. However, feature modeling of large-scale systems is challenging as many models are needed for diverse purposes. For instance, feature models can be used to reflect the perspectives of product management, technical solution architecture, or product configuration. Furthermore, models are required at different levels of granularity. Although numerous approaches and tools are available, it remains hard to define the purpose, scope, and granularity of feature models. In this paper we thus present experiences of developing feature models for two large-scale industrial automation software systems. Specifically, we extended an existing feature modeling tool to support models for different purposes and at multiple levels. We report results on the characteristics and modularity of the feature models, including metrics about model dependencies. We further discuss lessons learned during the modeling process.

In this article, a correlation-aided neural network (CANN) framework is proposed for distributed process monitoring of large-scale industrial automation systems. First, to model the nonlinear data relationship of multiple subsystems, a feature space is learned for each subsystem by means of neural network-based nonlinear projection. Second, different from conventional distributed monitoring methods, a distributed learning method and a novel objective function are proposed, where the influence of uncertainties is decreased by considering the correlation of variables among multiple subsystems. Third, a process monitoring method is proposed based on the CANN framework. The impact of the fault is analyzed, and the optimal monitoring performance is achieved by considering the correlation of all subsystems. To further reduce communication overhead among multiple subsystems, a tradeoff between monitoring performance and communication overhead is made based on gradients of the CANN model. Theoretical analysis demonstrates the superiority of the proposed method. The case studies on a multizone heating, ventilation, and air-conditioning system and the Tennessee Eastman benchmark process are given to evaluate and compare the effectiveness of the proposed method.

Object-Relational Mapping (ORM) provides developers a conceptual abstraction for mapping the application code to the underlying databases. ORM is widely used in industry due to its convenience; permitting developers to focus on developing the business logic without worrying too much about the database access details. However, developers often write ORM code without considering the impact of such code on database performance, leading to cause transactions with timeouts or hangs in large-scale systems. Unfortunately, there is little support to help developers automatically detect suboptimal database accesses. In this paper, we propose an automated framework to detect ORM performance anti-patterns. Our framework automatically flags performance anti-patterns in the source code. Furthermore, as there could be hundreds or even thousands of instances of anti-patterns, our framework provides sup- port to prioritize performance bug fixes based on a statistically rigorous performance assessment. We have successfully evaluated our framework on two open source and one large-scale industrial systems. Our case studies show that our framework can detect new and known real-world performance bugs and that fixing the detected performance anti- patterns can improve the system response time by up to 98%.

Click-Through Rate (CTR) prediction, estimating the probability of a user clicking on an item, is essential in industrial applications, such as online advertising. Many works focus on user behavior modeling to improve CTR prediction performance. However, most of those methods only model users' positive interests from users' click items while ignoring the context information, which is the display items around the clicks, resulting in inferior performance. In this paper, we highlight the importance of context information on user behavior modeling and propose a novel model named Deep Context Interest Network (DCIN), which integrally models the click and its display context to learn users' context-aware interests. DCIN consists of three key modules: 1) Position-aware Context Aggregation Module (PCAM), which performs aggregation of display items with an attention mechanism; 2) Feedback-Context Fusion Module (FCFM), which fuses the representation of clicks and display contexts through non-linear feature interaction; 3) Interest Matching Module (IMM), which activates interests related with the target item. Moreover, we provide our hands-on solution to implement DCIN on large-scale industrial systems. The significant improvements in both offline and online evaluations demonstrate the superiority of our proposed DCIN method. Notably, DCIN has been deployed on our online advertising system serving the main traffic, which brings 1.5% CTR and 1.5% RPM lift.

Difficult-to-reach program paths remain a fundamental challenge in search-based software testing (SBST), often resulting in limited structural coverage and substantial computational overhead. This work presents a methodological integration that combines abstract path modeling with stagnation-aware refinement to improve exploration of difficult-to-reach paths in search-based software testing (SBST). Rather than proposing a new SBST paradigm, the contribution lies in unifying (i) a reduced, count-based abstraction of control-flow behavior and (ii) an adaptive refinement mechanism triggered by stagnation, enabling targeted redirection toward underexplored execution regions. To address search stagnation, boundary constraints are adaptively introduced when progress plateaus, enabling targeted redirection toward underexplored execution regions while avoiding unnecessary computational cost. The proposed technique was empirically evaluated against baseline control strategies, dependency-driven methods, and conventional diversity-based approaches using multiple benchmark programs. Experimental results indicate that the proposed approach achieves substantially higher branch coverage, while reducing the average number of test executions by 73.4% (2,083 vs. 7,833) and decreasing execution time by 57.2%. While the empirical results indicate consistent efficiency gains across the evaluated benchmarks, the study is limited to established research programs rather than large-scale industrial systems. Consequently, claims regarding scalability and industrial applicability should be interpreted as indicative rather than conclusive. Nevertheless, the observed reductions in execution count and runtime suggest that the proposed heuristic has characteristics that are favorable for practical deployment, motivating further evaluation on larger and more heterogeneous software systems.

Architecture-based software reliability analysis methods shall help software architects to identify critical software components and to quantify their influence on the system reliability. Although researchers have proposed more than 20 methods in this area, empirical case studies applying these methods on large-scale industrial systems are rare. The costs and benefits of these methods remain unknown. On this behalf, we have applied the Cheung method on the software architecture of an industrial control system from ABB consisting of more than 100 components organized in nine subsystems with more than three million lines of code. We used the Littlewood/Verrall model to estimate subsystems failure rates and logging data to derive subsystem transition probabilities. We constructed a discrete time Markov chain as an architectural model and conducted a sensitivity analysis. This paper summarizes our experiences and lessons learned. We found that architecture-based software reliability analysis is still difficult to apply and that more effective data collection techniques are required.

In large-scale industrial recommendation systems, model checkpoints are instrumental in maintaining training goodput and numerical correctness during system failures and job preemptions. The increasing prevalence of multi-terabyte models has rendered frequent regular model checkpoints impractical, resulting in substantial lost progress when recovering from failures. As model sizes continue to grow, researchers and practitioners are compelled to investigate more efficient and scalable solutions. This paper presents DECK, a novel approach to delta model checkpointing designed for real-world industrial systems. Specifically, DECK focuses on extracting delta states with near-zero overhead, staging and streaming delta checkpoints without interrupting the training process, and merging delta checkpoints in an optimal and decoupled manner. Experimental results demonstrate that DECK achieves a 12-fold increase in checkpoint frequency while maintaining negligible impact on training throughput, thereby attaining state-of-the-art (SOTA) production performance.

High thermal conductive Al2O3@Al composites supported cobalt catalysts for Fischer-Tropsch synthesis