Benchmark Analysis Research Articles

An Assessment of Container Seaport Efficiency Determinants

Maritime transport plays a pivotal role in the global economy, facilitating the majority of international trade and serving as a cornerstone for efficient and expansive logistics networks. The proliferation of economic globalisation has resulted in a significant upsurge in intercontinental transactions, thereby fostering the utilisation of ports and shipping enterprises as cost-effective and expeditious means of accessing a wide range of destinations in Europe, Asia, Africa, and North America. The objective of this study is to evaluate the significance of five exogenous variables, namely, GDP per capita, water depth, commodity-type diversification, management model, and European directional division, in relation to the performance of seaports. Measuring the impact of exogenous variables in seaport performance is crucial for understanding how external factors influence efficiency, enabling informed decision-making, and facilitating the development of targeted policies for sustainable and effective port operations. This assessment will be conducted using robust benchmarking analysis methods, specifically the nonparametric order-α model. Several findings suggest that there is a negative relationship between GDP per capita and the performance of seaports when GDP per capita reaches very high levels. However, seaports located in regions with lower GDP per capita tend to exhibit superior performance. The inefficiency of southern seaports is evident, whereas seaports located in Central/Eastern Europe exhibit superior performance, irrespective of their model orientation. These findings underscore the importance of considering economic context and regional factors in understanding seaport performance and highlight potential areas for improvement in southern seaports.

An autoencoder-based self-supervised learning for multimodal sentiment analysis

Representation learning is a crucial and challenging task within multimodal sentiment analysis. Effective multimodal sentiment representations contain two key aspects: consistency and difference. However, the state-of-the-art multimodal sentiment analysis approaches failed to capture the difference and consistency of sentiment information across diverse modalities. To address the multimodal sentiment representation problem, we propose an autoencoder-based self-supervised learning framework. In the pre-training stage, an autoencoder is designed for each modality, leveraging unlabeled data to learn richer sentiment representations for each modality through sample reconstruction and modality consistency detection tasks. In the fine-tuning stage, the pre-trained autoencoder is injected into MulT (AE-MT) and enhance the model's ability to extract deep sentiment information by incorporating a contrastive learning auxiliary task. Our experiments on the popular Chinese sentiment analysis benchmark (CH-SIMS v2.0) and English sentiment analysis benchmark (MOSEI) demonstrate significant gains over baseline models.

Enhanced Bearing Fault Diagnosis Through Trees Ensemble Method and Feature Importance Analysis

Abstract Purpose This research introduces a groundbreaking method for bearing defect detection. It leverages ensemble machine learning (ML) models and conducts comprehensive feature importance analysis. The key innovation is the training and benchmarking of three tree ensemble models—Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost)—on an extensive experimental dataset (QU-DMBF) collected from bearing tests with seeded defects of varying sizes on the inner and outer raceways under different operating conditions. Method The dataset was meticulously prepared with categorical variable encoding and Min–Max data normalization to ensure consistent class distribution and model accuracy. Implementing the ML models involved a grid search method for hyperparameter tuning, focusing on reporting the models’ accuracy. The study also explores applying ensemble methods and using supervised and unsupervised learning algorithms for bearing fault detection. It underscores the value of feature importance analysis in understanding the contributions of specific inputs to the model’s performance. The research compares the ML models to traditional methods and discusses their potential for advanced fault diagnosis in bearing systems. Results and Conclusions The XGBoost model, trained on data from actual bearing tests, outperformed the others, achieving 92% accuracy in detecting bearing health and fault location. However, a deeper analysis of feature importance reveals that the models weigh certain experimental conditions differently—such as sensor location and motor speed. This research’s primary novelties and contributions are comparative evaluation, experimental validation, accuracy benchmarking, and interpretable feature importance analysis. This comprehensive methodology advances the bearing health monitoring field and has significant practical implications for condition-based maintenance, potentially leading to substantial cost savings and improved operational efficiency.

Comprehensive assessment of mRNA isoform detection methods for long-read sequencing data

The advancement of Long-Read Sequencing (LRS) techniques has significantly increased the length of sequencing to several kilobases, thereby facilitating the identification of alternative splicing events and isoform expressions. Recently, numerous computational tools for isoform detection using long-read sequencing data have been developed. Nevertheless, there remains a deficiency in comparative studies that systemically evaluate the performance of these tools, which are implemented with different algorithms, under various simulations that encompass potential influencing factors. In this study, we conducted a benchmark analysis of thirteen methods implemented in nine tools capable of identifying isoform structures from long-read RNA-seq data. We evaluated their performances using simulated data, which represented diverse sequencing platforms generated by an in-house simulator, RNA sequins (sequencing spike-ins) data, as well as experimental data. Our findings demonstrate IsoQuant as a highly effective tool for isoform detection with LRS, with Bambu and StringTie2 also exhibiting strong performance. These results offer valuable guidance for future research on alternative splicing analysis and the ongoing improvement of tools for isoform detection using LRS data.

SUSTAINABILITY TRANSPARENCY IN SOVEREIGN WEALTH FUNDS: BENCHMARK AND CASE STUDY APPROACH

The COVID-19 pandemic and the current food, energy, and safety crisis caused by the war in Ukraine strongly affected the existing gap in Sustainable Development Goals funding. Restoring global sustainability progress and the 2030 Agenda requires reshaping responsible investment architecture and rethinking the role of sovereign wealth funds as the largest investors in this process.The current study explores the ways in which the most transparent sovereign wealth funds act towards sustainability values and incorporate environmental, social, governance, and ethical criteria (ESGE) into their investment strategy, portfolio, and reporting. Also, 16 high-ranked funds by the Sustainability Transparency Index (Cavagnetto et al., 2022) were investigated by using the case study and benchmark analysis. Study results demonstrate the similarity of all funds disclosure within some questionnaire criteria like sustainability policy, scoring (voting) rules for decision-making, ESGE criteria, stakeholder engagements, and assurance of the financial information disclosed. Nevertheless, qualitative differences were identified between the sustainability transparency criteria of A and B-ranked funds. There is also huge variation in the implementation of funds’ ethical criteria, sustainability coherence, relevance of sustainability goals, disclosure of specific climate-related goals, and foundation membership criteria.Policy implications derived from the differences in sovereign funds' benchmarks are aimed at enhancing the sustainability context in funds’ operating frameworks, developing impact measurement metrics, prioritizing the most material stakeholder requests, sustainability verification procedures, and standardizing funds’ sustainability reporting. Specific channels, instruments, and regulatory measures towards greater funds’ sustainability transparency and their involvement in bridging the sustainable development funding gap may be the subject of further investigation.

Formalizing Multimedia Recommendation through Multimodal Deep Learning

Recommender systems (RSs) provide customers with a personalized navigation experience within the vast catalogs of products and services offered on popular online platforms. Despite the substantial success of traditional RSs, recommendation remains a highly challenging task, especially in specific scenarios and domains. For example, human affinity for items described through multimedia content (e.g., images, audio, and text), such as fashion products, movies, and music, is multi-faceted and primarily driven by their diverse characteristics. Therefore, by leveraging all available signals in such scenarios, multimodality enables us to tap into richer information sources and construct more refined user/item profiles for recommendations. Despite the growing number of multimodal techniques proposed for multimedia recommendation, the existing literature lacks a shared and universal schema for modeling and solving the recommendation problem through the lens of multimodality. Given the recent advances in multimodal deep learning for other tasks and scenarios where precise theoretical and applicative procedures exist, we also consider it imperative to formalize a general multimodal schema for multimedia recommendation. In this work, we first provide a comprehensive literature review of multimodal approaches for multimedia recommendation from the last eight years. Second, we outline the theoretical foundations of a multimodal pipeline for multimedia recommendation by identifying and formally organizing recurring solutions/patterns; at the same time, we demonstrate its rationale by conceptually applying it to selected state-of-the-art approaches in multimedia recommendation. Third, we conduct a benchmarking analysis of recent algorithms for multimedia recommendation within Elliot, a rigorous framework for evaluating recommender systems, where we re-implement such multimedia recommendation approaches. Finally, we highlight the significant unresolved challenges in multimodal deep learning for multimedia recommendation and suggest possible avenues for addressing them. The primary aim of this work is to provide guidelines for designing and implementing the next generation of multimodal approaches in multimedia recommendation.

A Benchmarking Study of Irrigation Advisory Platforms

In the contemporary agricultural landscape, agriculture faces four pressing demands: competitiveness, ensuring food security for a growing population, environmental sustainability, and providing farmers with acceptable living conditions. To meet this global challenge, digital technologies represent a major avenue for innovation and development towards modernized digital agriculture. In this context, irrigation advisory platforms have proven to be transformational tools for both farmers and policymakers, offering insights into the appropriate crop water requirements. This article presents a benchmarking analysis of around 20 professional irrigation advisory platforms. The methodology involves selecting 20 platforms based on accessibility ensuring geographical characteristic diversity. Our findings highlight key criteria shaping the ecosystem of such platforms, including the services offered and their objectives, the types of covered crops, the target users, the form, as well as the source, the availability of the platform, and the variety of data utilized. Lastly, we discuss the main conclusions drawn from our analysis and provide insights into the challenges and future perspectives of irrigation advisory platforms in enhancing agricultural practices and sustainability.

A Metastatic Cancer Expression Generator (MetGen): A Generative Contrastive Learning Framework for Metastatic Cancer Generation.

Despite significant advances in tumor biology and clinical therapeutics, metastasis remains the primary cause of cancer-related deaths. While RNA-seq technology has been used extensively to study metastatic cancer characteristics, challenges persist in acquiring adequate transcriptomic data. To overcome this challenge, we propose MetGen, a generative contrastive learning tool based on a deep learning model. MetGen generates synthetic metastatic cancer expression profiles using primary cancer and normal tissue expression data. Our results demonstrate that MetGen generates comparable samples to actual metastatic cancer samples, and the cancer and tissue classification yields performance rates of 99.8 ± 0.2% and 95.0 ± 2.3%, respectively. A benchmark analysis suggests that the proposed model outperforms traditional generative models such as the variational autoencoder. In metastatic subtype classification, our generated samples show 97.6% predicting power compared to true metastatic samples. Additionally, we demonstrate MetGen's interpretability using metastatic prostate cancer and metastatic breast cancer. MetGen has learned highly relevant signatures in cancer, tissue, and tumor microenvironments, such as immune responses and the metastasis process, which can potentially foster a more comprehensive understanding of metastatic cancer biology. The development of MetGen represents a significant step toward the study of metastatic cancer biology by providing a generative model that identifies candidate therapeutic targets for the treatment of metastatic cancer.

GradHC: highly reliable gradual hash-based clustering for DNA storage systems.

As data storage challenges grow and existing technologies approach their limits, synthetic DNA emerges as a promising storage solution due to its remarkable density and durability advantages. While cost remains a concern, emerging sequencing and synthetic technologies aim to mitigate it, yet introduce challenges such as errors in the storage and retrieval process. One crucial task in a DNA storage system is clustering numerous DNA reads into groups that represent the original input strands. In this paper, we review different methods for evaluating clustering algorithms and introduce a novel clustering algorithm for DNA storage systems, named Gradual Hash-based clustering (GradHC). The primary strength of GradHC lies in its capability to cluster with excellent accuracy various types of designs, including varying strand lengths, cluster sizes (including extremely small clusters), and different error ranges. Benchmark analysis demonstrates that GradHC is significantly more stable and robust than other clustering algorithms previously proposed for DNA storage, while also producing highly reliable clustering results. https://github.com/bensdvir/GradHC.

VitTCR: A deep learning method for peptide recognition prediction

This study introduces VitTCR, a predictive model based on the vision transformer (ViT) architecture, aimed at identifying interactions between Tcell receptors (TCRs) and peptides, crucial for developing cancer immunotherapies and vaccines. VitTCR converts TCR-peptide interactions into numerical AtchleyMaps using Atchley factors for prediction, achieving AUROC (0.6485) and AUPR (0.6295) values. Benchmark analysis indicates VitTCR's performance is comparable to other models, with further comparative studies suggested to understand its effectiveness in varied contexts. Additionally, integrating a positional bias weight matrix (PBWM), derived from amino acid contact probabilities in structurally resolved pMHC-TCR complexes, slightly improves VitTCR's accuracy. The model's predictions show weak yet statistically significant correlations with immunological factors like Tcell clonal expansion and activation percentages, underscoring the biological relevance of VitTCR's predictive capabilities. VitTCR emerges as a valuable computational tool for predicting TCR-peptide interactions, offering insights for immunotherapy and vaccine development.

Comparative analysis of Benchmark and Aeon Blue Technologies for sustainable eFuel production: Integrating Direct Air Capture and Green Hydrogen approaches

Renewable Fuels of Non-Biological Origin (RFNBOs, a.ka. “eFuels”) are drop-in replacements for fossil fuels in the “hard-to-abate” sectors, and utilise existing distribution and storage infrastructure, but suffer from low synthetic efficiency. In particular, the low efficiency and high energy consumption of hydrogen synthesis by freshwater electrolysis remains a problem to be addressed. In the current study, we explore the potential of saltwater electrolysis (chloralkali) for the synthesis of eFuels generally, and specifically eMethanol. The study explores the use of chlorinated water in carbonate-loop pH-swing “cold capture” of carbon dioxide, and the resulting synergetic integration of the chloralkali process in eFuel syntheses. Doing so eliminates the high energy consumption of the calciner and the slaker of the benchmarked carbonate-loop thermal-swing method. It is replaced with a spontaneous chemical process in which chlorine is neutralized in the presence of carbonates. This allows the simultaneous solution of “The Chlorine Problem” of saltwater electrolysis, and a lower overall energy consumption of direct air capture when combined with green hydrogen synthesis. The only limitation on the comparison is that a large amount of carbon dioxide is over-captured when using chloralkali. That is, the ratio of hydrogen generated to carbon dioxide captured is fixed by the stoichiometry of chloralkali and not that of the eFuel. This results in an excess of carbon captured and thus a carbon-negative eFuel. We examine one configuration of secondary revenue and use it to inform the marginal cost of CO2 capture. In the system proposed by Aeon Blue Technologies (ABT), carbon dioxide is absorbed into caustic according to the standard method. Hydrogen gas is produced in a standard chloralkali electrolyzer, along with caustic for the contactor, and chlorine. Water oxidation by chlorine gives the strong acid, hydrochloric acid, and the weak acid, hypochlorous acid, which react directly with wet carbonate to release CO2. H2 and CO2 react in a methanol reactor to produce eFuel. H2 is the limiting reagent for eFuel synthesis, giving an excess of cold captured CO2. Thus, the current study underscores significant advancements in renewable fuel synthesis, particularly eMethanol, and evaluates the carbon capture and energy efficiency of a novel method. The simulation indicates that the marginal energy cost for a tonne of CO2 is ∼ 184kWh. This gives ∼ 76 % theoretical carbon capture efficiency and an overall process efficiency of 79.5 % (approximately a threefold improvement over the benchmark). The authors are unaware of a comparable method. The overall system yields a tonne of carbon-neutral e-methanol (eMeOH) while capturing an additional 3 tonnes of carbon dioxide, which means the modelled process captures over 300 % more carbon dioxide than is released upon combustion of the eFuel. In summary, this research contributes valuable insights into carbon-negative eFuel production, representing a significant scientific step in sustainable fuel production.

Neurotoxic effects of low dose ranges of environmental metal mixture in a rat model: The benchmark approach

Metals exert detrimental effects on various systems within the body, including the nervous system. Nevertheless, the dose-response relationship concerning the administration of low doses of metal mixtures remains inadequately explored. The assessment of neurotoxic effects of lead, cadmium, mercury, and arsenic mixture (MIX) administered at low dose ranges, was conducted using an in vivo approach. A subacute study was conducted on a rat model consisting of a control and five treatment groups subjected to oral exposure with gradually increasing doses (from MIX 1 to MIX 5). The results indicated that behavioural patterns in an already developed nervous system displayed a reduced susceptibility to the metal mixture exposure with tendency of higher doses to alter short term memory. However, the vulnerability of the mature brain to even minimal amounts of the investigated metal mixture was evident, particularly in the context of oxidative stress. Moreover, the study highlights superoxide dismutase's sensitivity as an early-stage neurotoxicity marker, as indicated by dose-dependent induction of oxidative stress in the brain revealed through Benchmark analysis. The narrowest Benchmark Dose Interval (BMDI) for superoxide dismutase (SOD) activity (1e-06 - 3.18e-05 mg As/kg b.w./day) indicates that arsenic may dictate the alterations in SOD activity when co-exposed with the other examined metals. The predicted Benchmark doses for oxidative stress parameters were very low, supporting “no-threshold” concept. Histopathological alterations were most severe in the groups treated with higher doses of metal mixture. Similarly, the brain acetylcholinesterase (AChE) activity demonstrated a dose-dependent decrease significant in higher doses, while BMDI suggested Cd as the main contributor in the examined metal mixture. These findings imply varying susceptibility of neurotoxic endpoints to different doses of environmentally relevant metal mixtures, advocating for risk assessment and regulatory measures to address metal pollution and enhance remediation strategies.

Novel Class Discovery for Representation of Real-World Heritage Data as Neural Radiance Fields (Student Abstract)

Neural Radiance Fields (NeRF) have been extensively explored as a leading approach for modeling and representing 3D data across various domains. Their ability to capture arbitrary scale point clouds and generate novel views makes them particularly valuable for digitizing cultural heritage sites. However, despite their impressive rendering capabilities, prior methods have often overlooked a significant real-world challenge: handling open-world scenarios characterized by unstructured data containing multiple classes in a single set of unlabeled images. To address this challenge, we propose a novel method NCD-NeRF that leverages Novel-Class Discovery to effectively tackle the complexities inherent in real-world data with unlabeled classes while excelling in producing high-quality NeRF representation. To validate our approach, we conducted a benchmarking analysis using a custom-collected dataset featuring UNESCO World Heritage sites in India. We observe that our proposed NCD-NeRF can parallely discover novel classes and render high-quality 3D volumes.

Ascertaining the impact of e-service quality on e-loyalty for the e-commerce platform of liner shipping companies

Focusing on the e-commerce platforms provided by major global liner shipping companies (LSCs), this study explores how e-service quality (e-SQ) influences e-loyalty (e-LY) and establish benchmarks for individual customers. We initially employed structural equation modeling to develop a research framework, and then we transformed this framework into a network data envelopment analysis model. Our findings revealed that the e-SQ of LSCs has a significant positive impact on the e-satisfaction (e-SA) and e-LY of their customers. Additionally, we observed that trust plays a significant moderating role in the relationship between e-SA and e-LY. Through benchmark analysis, we found that for small-scale shippers, virtually no slack exists between their benchmark and actual perception. However, the slack increased as the scale of the shipper increased, implying that LSCs have provided an e-SQ that exceeds the expectations of their large-scale customers. Meanwhile, it’s crucial for LSCs to pay attention to specific aspects such as the architecture of e-commerce platforms and privacy protection because the slacks of these e-SQ items are relatively small. Furthermore, the large slacks in e-LY for large-scale shippers imply that their e-LY leaves much room for improvement compared with that of small-scale shippers. Overall, our study underscores the importance of maintaining or elevating e-SQ standards to reinforce e-LY while considering the mediating and moderating effects of e-SA and trust in the context of the e-commerce platforms provided by major LSCs.

Abstract 4946: ssTimiGP: Equip the TimiGP system with a single-sample module, facilitating the cell-cell interaction inference of the tumor microenvironment in any sample

Abstract Understanding the tumor microenvironment (TME) is essential in cancer treatment and risk assessment. While single-cell RNA-seq (scRNA-seq) offers in-depth insights into the TME, its expensive cost results in limited data sets and size. In contrast, microarray and RNA-seq provide rich datasets from larger cohorts, but they yield bulk data with lower resolution. To leverage the strengths of both approaches and comprehensively study the TME, we developed the computational framework TimiGP (Tumor Immune Microenvironment Illustration through Gene Pairs). It is inspired by the dynamic balance of the immune system and extended to the entire TME. TimiGP utilizes scRNA-seq to define cell type of interest, and bulk transcriptomic profiles to deduce clinically relevant cell-cell interaction networks and cell functions. Our recent release includes both prognosis and response modules to study the association between TME and the corresponding clinical outcomes. However, their applicability is constrained by the necessity for paired clinical information. In response to this limitation, we present a significant enhancement, a single sample module slightly modified from the TimiGP framework (ssTimiGP). This upgraded version enables TME analysis in any individual sample, requiring only bulk RNA-seq data and cell-type markers. It exports a cell-cell interaction network representing the relative expression of functional markers and the abundance of cells. This optimization expands the utility of TimiGP, making it more accessible across diverse samples and species. We have further expanded the application of ssTimiGP from solid tumors to liquid samples (e.g., peripheral blood). To validate its performance, we conducted a comparative analysis between the interactions of eight immune cell types observed in experimental results using FACS and the computational results derived from ssTimiGP based on paired RNA-seq data. As a result, ssTimiGP not only successfully identified interactions between different cell types that exhibited a significant correlation with experimental findings (e.g., CD4+ naïve T cells → Monocyte, R=0.75, p< 0.001, accuracy = 0.8), but also demonstrated its capability to achieve higher resolution in capturing interactions between immune cell subpopulations (e.g., CD4 naïve T cells → activated CD4+ memory T cells, R=0.57, p=0.008, accuracy = 1.0). In our benchmark analysis, ssTimiGP demonstrated superior performance compared to CIBERSORTx (e.g., CD4+ naïve T cells → Monocyte, R=0, Accuracy=0.3). In summary, ssTimiGP signifies an expansion of the TimiGP system, enabling the inference of the TME in individual solid tumors and liquid samples at various resolutions by integrating the strengths of both scRNA-seq and bulk RNA-seq. This approach offers substantial potential for advancing personalized cancer treatment. Citation Format: Chenyang Li, Jianjun Zhang, Chao Cheng. ssTimiGP: Equip the TimiGP system with a single-sample module, facilitating the cell-cell interaction inference of the tumor microenvironment in any sample [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4946.

Abstract 3551: Continued analysis of extensive data towards Genome in a Bottle benchmarks for a new tumor normal pair

Abstract Here, we detail in-progress genome-scale measurements from a variety of technologies of the first tumor normal benchmark from the Genome in a Bottle (GIAB) consortium. We created the first broadly-consented tumor cell line from a pancreatic ductal adenocarcinoma with matched normal pancreatic and duodenal tissue. Data is being collected from a large homogeneous batch of the tumor cell line and paired normal tissues. As we receive data we make it publicly available on the NCBI hosted GIAB FTP site, https://ftp.ncbi.nlm.nih.gov/ReferenceSamples/giab/data_somatic/. When complete, this dataset will include WGS measurements from Illumina, ONT, Hi-C, Bionano, single cell WGS, PacBio HiFi and Onso, and Element. Analysis and development of a variant calling benchmark using this data is ongoing in collaboration with an open working group of the GIAB consortium. Initial Hi-C and optical mapping data from the tumor cell line indicates substantial aneuploidy from translocations that cause large deletions. We found roughly 17 large inversions and translocations and 16 chromosomes with extensive loss of heterozygosity due to missing >30% of one copy, in addition to a few smaller duplications. Low coverage single cell sequencing that provides ploidy estimates across chromosomes showed that most of the large deletions appear in all or nearly all cells with some variation in a few cells. These observations are consistent with bulk WGS analyses from two batches of cells. Additionally, many of the observed large deletions correspond to deletions seen in the population of TCGA chromosomal Loss of Heterogeneity samples. Examining somatic SNVs in non-repetitive regions, we find that close to 60% occur in almost all cells in diploid regions, 30% occur in almost all cells in haploid regions, and 5% in only some cells in diploid and haploid regions, respectively. We take these results to indicate the cell line is relatively homogeneous and stable with most CNVs being deletions. As such, we plan to explore using long reads, ultralong reads, and Hi-C data to generate a near-complete genome assembly of the dominant tumor clone as well as a complete diploid assembly of the normal. With this personalized genome assembly, we will explore aligning tumor and normal reads to each haplotype of the normal to characterize somatic variants, including variants in minor clones. Building on the methods GIAB and T2T have developed to polish diploid assemblies of GIAB’s normal genomes and mosaic variant characterization, we will develop benchmarks for somatic variants against the normal assembly as well as standard references. Citation Format: Justin Wagner, Jennifer McDaniel, Gail L. Rosen, Nathan D. Olson, Vaidehi Patel, Chunlin Xiao, Andrew Liss, Justin Zook. Continued analysis of extensive data towards Genome in a Bottle benchmarks for a new tumor normal pair [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3551.

Thermogravimetric analysis and kinetic modeling of the pyrolysis of different biomass types by means of model-fitting, model-free and network modeling approaches

This work aims at comparing the ability of 7 modeling approaches to simulate the pyrolysis kinetics of spruce wood, wheat straw, swine manure, miscanthus and switchgrass. Measurements were taken using a thermogravimetric analyzer (TGA) with 4 heating rates comprised between 5 and 30 K min−1. The obtained results were processed using 3 isoconversional methods (Kissinger–Akahira–Sunose (KAS), Ozawa–Flynn–Wall (OFW) and Friedman), 1-step and 3-step Kissinger models, as well as an advanced fitting method recently proposed by Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424). Seventeen reaction models were considered to derive rate constant parameters, which were used to simulate the variation of the fuel conversion degree α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha$$\\end{document} as a function of the temperature T\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T$$\\end{document}. To complement this benchmarking analysis of the modeling approaches commonly used to simulate biomass pyrolysis, a network model, the bio-CPD (chemical percolation devolatilization), was additionally considered. The suitability of each model was assessed by computing the root-mean-square deviation between simulated and measured α=f(T)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha =f(T)$$\\end{document} profiles. As highlights, the model-free methods were found to accurately reproduce experimental results. The agreement between simulated and measured data was found to be higher with the Friedman model, followed by the KAS, FWO, 3-step, and 1-step Kissinger models. As for the bio-CPD, it failed to predict measured data as well as the above-listed models. To conclude, although it was less efficient than the Friedman, KAS or OFW models, the fitting approach from Bondarchuk et al. [1] (Molecules 28:424, 2023, https://doi.org/10.3390/molecules28010424) still led to satisfactory results, while having the advantage of not requiring the selection of a reaction model a priori.

Whole-core analysis of Watts bar benchmark with three-dimensional MOC code STREAM3D

This paper presents a high-fidelity simulation of the Organization for Economic Co-operation and Development (OECD) Nuclear Energy Agency (NEA) 3D whole-core Watts Bar benchmark using the UNIST in-house STREAM3D (Steady State and Transient Reactor Analysis code with Method of Characteristics) neutronic code. The benchmark encompasses various whole-core exercises, including single physics problems, multi-physics simulations, and depletion problems. When comparing parameters during the zero-power physics tests, including ITC, DBW, CRW, and criticality tests, STREAM3D results indicate a strong agreement with the measured data and KENO-VI. The comparison with the MC21/CTF code in 3D HFP BOC condition demonstrated strong agreement, with only a 0.42% difference in the normalized radial power distribution, a 0.38 K difference in the RMS of the assembly coolant exit temperature, and a mere 4 ppm difference in CBC.

Imputation of data Missing Not at Random: Artificial generation and benchmark analysis

Experimental assessment of different missing data imputation methods often compute error rates between the original values and the estimated ones. This experimental setup relies on complete datasets that are injected with missing values. The injection process is straightforward for the Missing Completely At Random and Missing At Random mechanisms; however, the Missing Not At Random mechanism poses a major challenge, since the available artificial generation strategies are limited. Furthermore, the studies focused on this latter mechanism tend to disregard a comprehensive baseline of state-of-the-art imputation methods. In this work, both challenges are addressed: four new Missing Not At Random generation strategies are introduced and a benchmark study is conducted to compare six imputation methods in an experimental setup that covers 10 datasets and five missingness levels (10% to 80%). The overall findings are that, for most missing rates and datasets, the best imputation method to deal with Missing Not At Random values is the Multiple Imputation by Chained Equations, whereas for higher missingness rates autoencoders show promising results.

The formation of nickelalactone in CO2/C2H4 coupling reaction: A benchmark, dispersion correction, and energy decomposition analysis

The oxidative coupling of CO2 and ethylene for synthesizing acrylic acid and its derivatives is considered one of the “dream reactions” in catalysis. The formation of acrylic acid by CO2/C2H4 coupling is hardly possible due to the endothermic nature of the reaction. This work reports a comprehensive study of the nickelalactone formation from CO2/C2H4 coupling over Ni-catalysts. We have evaluated the accuracy of the DLPNO-CCSD(T) method in determining the barrier heights and the reaction energetics for the nickelalactone formation. Extrapolation to the complete basis set limit was performed using two-point extrapolation methods. It was found that the DLPNO-CCSD(T) method, in combination with the complete basis set cc-pVTZ/cc-pVQZ extrapolation, gives the error within 1.0 kcal/mol. To gain a molecular understanding of the formation of acrylic acid by CO2/C2H4 coupling, the distortion-interaction model was used as an energy decomposition method to analyze the inner- and outer-sphere reaction transition states, considering two competing pathways. It was found that i) the inner-sphere reaction is superior to the outer-sphere reaction owing to the lower distortion energy and stronger interactions, ii) three prominent pairs of orbital interactions exist between the Ni-C2H4 complex and the CO2 molecule, and iii) the dispersion correction is crucial to obtaining reliable non-covalent interaction energies.