Optimization Opportunities Research Articles

Development of the structure of a cyberphysical control system for the process of tailored induction heating of a steel billet

The article is devoted to the development of the structure of a cyberphysical control system for the process of zone induction heating, widely used in modern industrial production during the heat treatment of metal products for various purposes. An analysis of the range of products manufactured using induction heat treatment and the advantages of using induction heating systems has shown a wide range of applications of this method of heat treatment in modern production and great opportunities for automation and optimization of processes associated with induction heating of products. This fact indicates the relevance of the development of various kinds of intelligent control systems in relation to the processes of industrial induction heating of metal blanks and semi-finished products. The article describes the induction method of zone heat treatment of metals, its advantages over flame heat treatment methods and the range of products manufactured using it. A description of the billet-inductor system is presented, for which the structure of a cyberphysical control system for the process of zone induction heating has been developed. The technological requirement for the final temperature distribution on the surface of the workpiece and the factors influencing the deviation of the temperature distribution from that required in the process of zone induction heating are considered. A list of possible disturbing influences is given, requirements for the technological process are formulated, control principles are described and, based on this, the structure of the zone induction heating process control system, which belongs to the class of cyberphysical process control systems, is developed.

Changing or validating physician opioid prescribing behaviors through audit and feedback and academic detailing interventions in primary care.

In Ontario, Canada, province-wide initiatives supporting safer opioid prescribing in primary care include voluntary audit and feedback reports and academic detailing. In this process evaluation, we aimed to determine the fidelity of delivery and receipt of the interventions, the observed change strategies used by physicians, potential mechanisms of action, and how complementary the initiatives can be to each other. Semi-structured interviews were conducted with academic detailers and with physicians who received both interventions. Academic detailer interviews were coded using the Behavior Change Technique Taxonomy; physician interviews were coded to the Theoretical Domain Framework. Change strategies were summarized based on academic detailer intentions and physician-reported practice changes. Potential mechanisms of action were identified using the Theories and Techniques Tool and the literature. Patient partners informed the interpretation of results through ongoing group discussions of preliminary findings. Interviews were conducted with eight academic detailers and 12 physicians. Change strategies described by academic detailers to support physicians' opioid prescribing included problem solving, instructions on how to perform the behavior, adding objects to the environment, credible source, shaping knowledge, and social support. Physicians mentioned that academic detailing validated current opioid practices or increased their belief about capabilities and their intentions, mediated by increased skills and the impact of environmental context and resources. Potential mechanisms of action included behavioral regulation, behavioral cueing, and general attitudes/beliefs. On its own, receiving the audit and feedback report did not lead to changes in beliefs about prescribing practices; however, for some physicians, it provided validation and reassurance. Physicians saw unrealized potential for complementarity. New interventions are often implemented in a complex ecosystem with other competing interventions. In this study, we show how examining the fidelity of the intervention from initial design through to delivery can identify opportunities for potential optimization.

Logistics cost management in foundry production using the Activity – Based Costing method

The aim of the paper is to present the application of Activity-Based Costing (ABC) method in logistics cost management in foundry production and its practical use in differentiating logistics service levels. The study was carried out in a foundry specializing in custom and small batch production of castings. The implementation of the ABC method involved three key phases. Tools such as Pareto analysis and the Lorenz curve were used to analyse the customers, which enabled the classification of customers according to their contribution to profit. The main result of the research was the creation of a calculation formula allowing detailed allocation of logistics costs to individual activities. At the same time, the key benefits of the ABC method for logistics management of foundry production were identified. The method was also used to analyse customers, which led to differentiation of logistics service levels based on their economic benefits. The main benefit of the implementation of the ABC method was the accurate allocation of logistics costs to activities, which enabled a better understanding of the economic efficiency of logistics processes. However, the study was time and data intensive, with some of the data based on educated guesses, which may have affected the accuracy of the results. Moreover, the focus on custom foundry production limits the generalizability of the conclusions. However, the ABC method has opened up new opportunities for data automation and optimization of logistics processes through digital technology, which contributes to modernise logistics management in foundry production.

Heterogeneous Edge Computing for Molecular Property Prediction with Graph Convolutional Networks

Graph-based neural networks have proven to be useful in molecular property prediction, a critical component of computer-aided drug discovery. In this application, in response to the growing demand for improved computational efficiency and localized edge processing, this paper introduces a novel approach that leverages specialized accelerators on a heterogeneous edge computing platform. Our focus is on graph convolutional networks, a leading graph-based neural network variant that integrates graph convolution layers with multi-layer perceptrons. Molecular graphs are typically characterized by a low number of nodes, leading to low-dimensional dense matrix multiplications within multi-layer perceptrons—conditions that are particularly well-suited for Edge TPUs. These TPUs feature a systolic array of multiply–accumulate units optimized for dense matrix operations. Furthermore, the inherent sparsity in molecular graph adjacency matrices offers additional opportunities for computational optimization. To capitalize on this, we developed an FPGA GFADES accelerator, using high-level synthesis, specifically tailored to efficiently manage the sparsity in both the graph structure and node features. Our hardware/software co-designed GCN+MLP architecture delivers performance improvements, achieving up to 58× increased speed compared to conventional software implementations. This architecture is implemented using the Pynq framework and TensorFlow Lite Runtime, running on a multi-core ARM CPU within an AMD/Xilinx Zynq Ultrascale+ device, in combination with the Edge TPU and programmable logic.

The Use of Artificial Intelligence in Patient Triage in Emergency Departments: an Integrative Review

Objective: This study aims to explore how AI has been applied in patient triage in emergency services, investigating its contributions to the efficiency of care, the challenges faced in implementation, and the opportunities for response time optimization and resource allocation. Method: An integrative literature review was carried out with studies published between 2020 and 2024. The research was guided by the PICo (Population, Interest and Context) model, focusing on patients treated in emergency services (P), the application of AI for triage (I) and the hospital and emergency context (Co). The search was carried out in databases such as PubMed, LILACS, SciELO and Scopus, applying the Boolean code "Artificial Intelligence" AND "Triage" AND "Emergency Services". After screening 214 initial articles, 12 studies were selected for final analysis, based on the PRISMA guidelines. Results and Discussion: The review pointed out that AI significantly improves response time and reduces the margin of error in patient classification, especially in high-demand situations. The use of AI stood out in identifying critical outcomes, such as the need for immediate care. During the COVID-19 pandemic, AI has proven to be essential in the remote triage of high-risk patients, ensuring efficient use of resources in overcrowded settings. However, implementation faces challenges, such as resistance from health professionals and the need for integration with existing health systems. Conclusion: In summary, the use of AI in emergency services has brought significant benefits, such as increased triage efficiency, improved diagnostic accuracy, and improved resource management. However, overcoming cultural and operational barriers and setting clear ethical guidelines are essential. The careful integration of AI, with continuous training and periodic assessments, is critical to transforming care and ensuring more effective and safer patient care.

Applying the Monte Carlo Method for Modeling Order Fulfillment with Consideration of Supply Risk

This paper presents the application of the Monte Carlo method for modelling order fulfilment, taking into account supply risks and delays. The method allows for the consideration of stochastic events and uncertainties in supply chains, which are becoming increasingly complex and vulnerable to various risks, such as production failures, transportation issues, and external factors. Using probabilistic distributions, the Monte Carlo method enables forecasting the frequency and impact of delays, supporting proactive decision-making in logistics management. The developed model assesses the likelihood of on-time order fulfilment under uncertainty, demonstrating the effectiveness of Monte Carlo simulations. The simulation results provide insights into delay patterns, risk factors, and potential strategies for minimizing them, creating opportunities for supply chain optimization.

Reducing Numerical Precision Requirements in Quantum Chemistry Calculations.

The abundant demand for deep learning compute resources has created a renaissance in low-precision hardware. Going forward, it will be essential for simulation software to run on this new generation of machines without sacrificing scientific fidelity. In this paper, we examine the precision requirements of a representative kernel from quantum chemistry calculations: the calculation of the single-particle density matrix from a given mean-field Hamiltonian (i.e., Hartree-Fock or density functional theory) represented in an LCAO basis. We find that double precision affords an unnecessarily high level of precision, leading to optimization opportunities. We show how an approximation built from an error-free matrix multiplication transformation can be used to potentially accelerate this kernel on future hardware. Our results provide a roadmap for adapting quantum chemistry software for the next generation of high-performance computing platforms.

Structural Optimization of Chalcogenide Thin Films for Enhancing the Threshold Switching Performance.

This study conducts a comprehensive investigation into the physical properties of germanium telluride (GeTe, GT) thin films and their associated electronic devices to establish fundamental relationships that are essential for achieving reliable threshold switching (TS) characteristics. Chemical composition variations in GT thin films significantly influence their crystallinity, atomic vibration modes, chemical binding status, bandgap, and distribution of electronic traps. The increase in the tellurium (Te) content results in elevated Urbach energy, subsequently reducing the effective bandgap energy. Excluding GT8, trap energy increases proportionally with increasing Te content. Correlating electrical properties with physical characteristics reveals key conditions necessary for stable TS behavior: maintaining amorphous crystallinity, optimizing the chemical composition of the GT layer, and ensuring an appropriate bandgap and trap energy. These findings underscore the importance of precise chemical composition control for reliable TS performance. Insights from this study can guide nanoscale analyses of bonding structures in Te-based binary TS systems and present opportunities for material optimization across various applications.

Synthesis and Engineering of Hyaluronic Acid-Gelatin Hydrogels with Improved Cellular Attachment and Growth.

Injectable hydrogels are promising materials for cartilage regeneration in tissue engineering due to their tunable crosslinking rates, mechanical properties, and biodegradation profiles. This study investigates the chondrogenic potential of hyaluronic acid (HA) hydrogels crosslinked via tyramine (TA) moieties, with and without gelatin modified with TA (Gel-TA). Incorporating Gel-TA improved cell viability, spreading, and cartilage matrix deposition, particularly in medium and high molecular weight (MMW and HMW) HA-TA/Gel-TA hydrogels. Although the hydrogels' molecular weight did not significantly alter stiffness, MMW and HMW HA-TA/Gel-TA formulations exhibited enhanced functional properties such as slower degradation and superior cartilage matrix deposition. These attributes, coupled with Gel-TA's effects, underscore the importance of both molecular weight and biofunctional components in hydrogel design for cartilage regeneration. While low molecular weight (LMW) HA-TA hydrogels offered excellent injectability and supported high cell viability, they degraded rapidly and exhibited reduced cartilage matrix formation. Gel-TA enhanced cell adhesion and spreading by providing integrin-binding sites and promoted collagen type II deposition, crucial for cartilage regeneration. Moreover, the increased stiffness of MMW and HMW HA-TA/Gel-TA hydrogels facilitated extracellular matrix production. These findings show the potential of Gel-TA-modified HA-TA hydrogels for cartilage tissue engineering, with the opportunity for further optimization through the incorporation of bioactive components.

The role of artificial intelligence and machine learning in optimizing U.S. healthcare supply chain management

This paper aims to review the role of Artificial Intelligence and Machine Learning in managing the healthcare supply chain in the United States. Healthcare supply chains face several challenges such as fragmentation, lack of real-time visibility, and inventory management issues. However, there are solutions with the help of AI and machine learning, including the availability of predictive analytics to improve demand forecasting, optimization algorithms for inventory and logistics, and automated quality control. Application areas demand forecasting, supplier selection, logistics optimization, quality control, and real-time tracking. The applications of AI in healthcare supply chains have the potential to improve the healthcare supply chain in terms of reduced costs, increased efficiency, optimized decision-making, and better patient outcomes. However, the implementation experience has shown several challenges such as data quality, privacy concerns, regulatory compliance, and workforce adaptation within organizations. Successful implementations in various health organizations in the US give valuable insights into how AI could be well implemented. The future presents several opportunities for supply chain optimization with the rise of blockchain and Internet of Things (IoT) integration. For healthcare supply chains to adopt AI, organizations should have specific AI plans, start with pilot projects in high-impact areas, invest in data infrastructure, and ensure strong leadership support. As AI becomes increasingly critical for competitive advantage, it has the potential to create more resilient, efficient, and patient-centric supply chains in the US healthcare system.

Exploring the Opportunities of Applying RFID Technology in Smart Agriculture

This paper explores the opportunities of applying RFID (radio-frequency identification) technology in smart agriculture. With the projected increase in global population and the resulting higher demand for food production, the agricultural industry is under pressure to find more efficient and sustainable ways to meet these needs. Radio-frequency identification (RFID) technology has been recently adopted in the agriculture industry as a means of improving efficiency and productivity towards achieving smart agriculture. RFID technology is revolutionizing smart agriculture, offering automation, real-time monitoring and data-driven decision-making. Integrating RFID tags with plants and soil samples optimizes resource utilization, crop health management, traceability and quality assurance. RFID in smart agriculture offers opportunities for resource optimization, crop health management, traceability and automation. Leveraging RFID technology, farmers can elevate productivity, reduce costs, improve sustainability and meet global demands. Adopting RFID in agriculture revolutionizes traditional practices, enabling efficient and sustainable farming. RFID-enabled traceability enhances food safety, quality assurance and transparency in the food supply chain. The objective of this paper is to explore the potential of RFID technology in smart agriculture and highlight the significance of effective RFID antenna design for optimizing food production, addressing global challenges and fostering a more efficient, sustainable and resilient agricultural sector.

Strategic risk management in outsourced business processes: Navigating geopolitical and cybersecurity challenges

Outsourcing has become increasingly important for organizations around the world because the practice offers optimization opportunities for the organizations. However, the increase in the magnitude of outsourcing has brought new complex hazards that need organizations to be aware and ready to tackle. The hazards include operational disruptions due to data breaches and harm to brands. This research was aimed at comprehending the strategic risk management within the realm of outsourced processes with the intention of geopolitical and cybersecurity obstacles. The study sought to establish a framework that will help organizations to improve their risk management practices within the context of outsourcing. The study employed qualitative and quantitative methods to examine the operational, financial, geopolitical, and cybersecurity hazards of outsourcing. The qualitative component included in-depth interviews with key stakeholders in outsourcing and the quantitative component included surveys. The thematic analysis and inferential statistics indicated a strong association between geopolitical factors, cybersecurity threats, and strategic risk management. The recommends an inclusion of the geopolitical risk assessment as an integral part of strategic planning in the outsourcing practice. Also, the study recommends enhancement of cybersecurity processes to avert financial and operational risk.

Methods for refining the depth map obtained from depth sensors

Depth maps are essential in applications such as robotics, augmented reality, autonomous vehicles, and medical imaging, providing critical spatial information. However, depth maps from sensors like time-of-flight (ToF) and structured light systems often suffer from low resolution, noise, and missing data. Addressing these challenges, this study presents an innovative method to refine depth maps by integrating high-resolution color images. The proposed approach employs both hard- and soft-decision pixel assignment strategies to adaptively enhance depth map quality. The hard-decision model simplifies edge classification, while the soft-decision model, integrated within a Markov Random Field framework, improves edge consistency and reduces noise. By analyzing discrepancies between edges in depth maps and color images, the method effectively mitigates artifacts such as texturecopying and blurred edges, ensuring better alignment between the datasets. Key innovations include the use of the Canny edge detection operator to identify and categorize edge inconsistencies and anisotropic affinity calculations for precise structural representation. The soft-decision model introduces advanced noise reduction techniques, improving depth map resolution and preserving edge details better than traditional methods. Experimental validation on Middlebury benchmark datasets demonstrates that the proposed method outperforms existing techniques in reducing Mean Absolute Difference values, especially in high-upscaling scenarios. Visual comparisons highlight its ability to suppress artifacts and enhance edge sharpness, confirming its effectiveness across various conditions. This approach holds significant potential for applications requiring high-quality depth maps, including robotics, augmented reality, autonomous systems, and medical imaging. By addressing critical limitations of current methods, the study offers a robust, versatile solution for depth map refinement, with opportunities for real-time optimization in dynamic environments.

Life cycle assessment (LCA) of biodegradable linear low-density polyethylene (LLDPE) manufactured in India

The global plastic crisis requires urgent attention, as highlighted by a comprehensive review of LCA studies on plastics. Mismanagement of plastic waste has worsened this crisis, affecting all life forms and contaminating natural resources. Plastic production leads to Green House Gas (GHG) emissions. Plastic consumption has resulted in pollution across land, water, and even the air, with microplastics entering the human posing health risks like cancer and allergies. The article stresses the importance of LLDPE and explores the potential of bioplastics. Identifying research gaps led to the initiation of an innovative cradle-to-grave LCA investigation. NICHEM Solutions pioneered a three-stage process to manufacture three products: 1) a novel additive, 2) biodegradable LLDPE granules from the additive, and 3) biodegradable LLDPE film from these granules. Utilizing GaBi software and adhering to ISO 14040 guidelines, this study assessed the environmental and health impacts associated with all three products. Additive production had the highest environmental impact, followed by granule and film formation. Material production led to the overall environmental footprint, with LLDPE film production exhibiting notable effects. Transportation impacts were minimal but presented opportunities for optimization. Results revealed significant marine aquatic impacts across various processes, with global warming and human toxicity also identified as concerns. The study also evaluated alternative energy sources, finding natural gas to be the most favorable overall, followed by solar energy and biogas. Conversely, coal and grid-based electricity usage had the most detrimental effects on global warming and marine aquatic impact. Disposal methods such as landfilling and incineration were shown to exacerbate environmental impacts across multiple categories.

Commentary on Von Sneidern et al's "Evaluation and Treatment of Acute Facial Palsy: Opportunities for Optimization at a Single Institution."-Bridging the Gap Between Guidelines and Practice.

Commentary on Von Sneidern et al's "Evaluation and Treatment of Acute Facial Palsy: Opportunities for Optimization at a Single Institution."-Bridging the Gap Between Guidelines and Practice.

Evaluation and Treatment of Acute Facial Palsy: Opportunities for Optimization at a Single Institution.

Introduction: Most patients with acute facial palsy initially present to emergency departments (EDs), where clinical practice guidelines (CPGs) recommend steroids, eye care, and follow-up with facial nerve specialists. Objective: To evaluate adherence to the American Academy of Otolaryngology-Head and Neck Surgery (AAO-HNS) Bell's palsy (BP) CPGs within EDs at a single academic institution. Method: We conducted a retrospective review of all patients diagnosed with acute facial palsy in the EDs of an academic tertiary care center between June 1, 2021, and June 1, 2022. Results: A total of 270 patients met inclusion criteria; most were male (n = 150, 55.9%), diagnosed with BP (n = 243, 90.0%), and presented to community-based emergency rooms (n = 170, 62.96). Although most patients received steroid treatment (n = 243, 90.0%), only 61.5% (n = 166) received the AAO-HNS-recommended course. Older patients and those who received steroids were more likely to receive imaging (p < 0.001, p = 0.03). Treatment with the AAO-HNS-recommended steroid regimen was associated with a higher likelihood of receiving laboratory evaluation (p = 0.02). Providers often advised follow-up; however, only 12.2% of patients were discharged with an electronic referral order. Conclusion: There are opportunities to standardize the treatment of patients in the ED presenting with acute facial palsy according to AAO-HNS CPGs.

A new process route for the additive manufacturing of a high nitrogen containing martensitic stainless steel - A feasibility study

High-nitrogen martensitic stainless steels, such as X30CrMoN15 (0.3 to 0.5 mass% nitrogen), exhibit an excellent combination of strength and corrosion resistance, making them well-suited for applications in the medical technology and aerospace industry. The qualification of these steels for additive manufacturing (AM) could generate new application areas where AM, due to its process-specific advantages, could offer added value compared to conventional manufacturing methods. However, the laser powder bed fusion (PBF-LB/M) of high-nitrogen alloyed steels is challenging due to the high tendency for gas pore formation, resulting from the limited nitrogen solubility in the steel melt. In this work, a new process route for AM of a high nitrogen containing X50CrMoV15 martensitic stainless steel is presented, which consists of a process combination of powder nitriding, PBF-LB/M and subsequent hot isostatic pressing (HIP) with integrated quenching. Gas nitriding is used to achieve a nitrogen content in the starting powder that exceeds the maximum solubility in the melt. Although the nitrogen content decreases during the PBF-LB/M process, the high solidification and cooling rates prevent the melt from reaching equilibrium nitrogen levels, resulting in a nitrogen content above the solubility limit in the final PBF-LB/M state. The pores formed during the process are closed through HIP, which also allows hardening via integrated gas quenching. With an additional cryogenic treatment, the process produces a fully dense steel with 75% martensitic structure and 0.246 mass% nitrogen. Further optimization opportunities have been identified and are discussed.

Advanced Distribution System Optimization: Utilizing Flexible Power Buses and Network Reconfiguration

The increasing integration of distributed generation (DG) and the rise of microgrids have reshaped the operation of distribution systems, introducing both challenges and opportunities for optimization. This study presents a methodology that combines network reconfiguration with the integration of buses with flexible power in order to improve the efficiency and cost-effectiveness of distribution networks. Flexible buses, which aggregate multiple microgrids or controllable distributed resources, function as control points that can dynamically adjust active and reactive power within predefined limits. This capability allows for more precise management of power flows, enabling the system to respond to fluctuations in generation and demand. The proposed optimization framework aims to minimize the total operational costs, including power losses and the use of flexible power, while adhering to system constraints. The methodology is evaluated through case studies on two distribution systems: the Kumamoto and IEEE-33 systems. The results indicate a 43.9% reduction in power losses for the Kumamoto system and a 66.6% reduction for the IEEE-33 system, along with notable cost savings in both cases. These outcomes demonstrate the potential benefits of incorporating flexible power buses in modern radial distribution networks, showing their role in adapting to various operational scenarios and supporting the integration of distributed generation and microgrids.

Peer-to-Peer Transactive Energy Trading of Smart Homes/Buildings Contributed by A Cloud Energy Storage System

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed.

CT Surveillance for Local Recurrence After Pancreatic Cancer Resection: Evaluation of Imaging Findings From the SAR Disease-Focused Panel Consensus Statement.

BACKGROUND. A Society of Abdominal Radiology (SAR) Pancreatic Ductal Adenocarcinoma (PDAC) Disease-Focused Panel (DFP) consensus statement described findings suspicious for local recurrence (LR) on surveillance imaging after PDAC resection. OBJECTIVE. The purpose of this study was to evaluate interreader agreement and predictive utility of potential imaging findings of LR on serial surveillance CT examinations after the Whipple procedure for PDAC, using the SAR PDAC DFP consensus statement. METHODS. This retrospective study included 126 patients (mean age, 68.5 ± 10.3 [SD] years; 72 men, 54 women) who underwent the Whipple procedure for PDAC between January 2009 and December 2014. Three radiologists independently reviewed baseline and subsequent postoperative contrast-enhanced abdominopelvic CT examinations performed within 2 years after surgery, evaluating features in the SAR PDAC DFP consensus statement relating to surgical bed stranding, surgical bed soft tissue, vessel encasement, main pancreatic duct dilatation, and ascites. Interreader agreement was calculated. The reference standard for LR development within 2 years after surgery incorporated all available information. The frequencies of imaging features were calculated for the recurrence examinations (i.e., the first surveillance examinations indicating LR). For baseline postoperative examinations, associations of the imaging features with eventual LR development were assessed by multivariable logistic regression analysis. RESULTS. LR developed within 2 years after surgery in 81 of the 126 patients. For both the baseline and subsequent examinations, agreement for stranding and agreement for soft-tissue morphology were poor, for vessel encasement was fair, for soft tissue and ascites were moderate, and for main pancreatic duct dilatation was substantial. On recurrence examinations, across readers, new or increased stranding was present in 27-77%; new or increased soft tissue, 80-88%; soft tissue with vessel encasement and luminal narrowing, 36-59%; new or increased main pancreatic duct dilatation, 25-26%; and new or increased ascites, 20-23%. On baseline postoperative examinations, independent predictors of eventual LR were soft tissue for all three readers (OR = 2.78-6.85) and stranding for reader 1 (OR = 3.59); main pancreatic duct dilatation and ascites were not independent predictors of LR for any reader. CONCLUSION. This study highlights the role of soft tissue, particularly when associated with vessel encasement and luminal narrowing, in raising suspicion for LR after PDAC resection. CLINICAL IMPACT. This study supports the SAR PDAC DFP consensus statement, while highlighting opportunities for continued optimization.