Performance Comparison Research Articles

Differences in second trimester risk estimates for trisomy 21 between Maglumi X3/Preaccu and Immulite/Prisca systems

Abstract Objectives Maternal serum alpha-fetoprotein (AFP), human chorionic gonadotropin (HCG) or free βHCG, and unconjugated estriol (uE3) concentrations are used to screen trisomy 21 in the second trimester. The performance of different analytical platforms has an impact on individual risk estimates. The aim of this study is to compare the multiple of median (MoM) values and risk estimates generated by Maglumi X3 analyzer/Preaccu software with the Immulite 2000 XPi device/Prisca software. Methods 164 pregnant women (including 20 pregnants with risk estimates above ≥1 in 250 for trisomy 21) analyzed with both platforms. Results Passing–Bablok indicated proportional bias (0.75 [95 % CI 0.70 to 0.82]) between AFP MoMs and both systematic (−0.20 [95 % CI –0.33 to −0.05]) and proportional (1.25 [95 % CI 1.06 to 1.44]) differences between the HCG/free βHCG MoMs, respectively. No significant differences (p=0.070) were present between calculated individual risks by both of the programmes (estimated median risk with Immulite/Prisca system was 1 in 1890 and 1 in 1220 with Maglumi X3/Preaccu system). The triple test result for three pregnant women was negative with the Prisca program, it was positive with the Preaccu. Conclusions Second trimester screening performance of Maglumi X3/Preaccu system achieves comparable performance. Determining regional median values before using will provide more accurate and reliable results.

Comparative performance study of active magnetic regenerative system using mono/hybrid nanofluids

Comparative performance study of active magnetic regenerative system using mono/hybrid nanofluids

Multimodal Fusion-Based Lightweight Model for Enhanced Generalization in Drug-Target Interaction Prediction.

Predicting drug-target interactions (DTIs) with precision is a crucial challenge in the quest for efficient and cost-effective drug discovery. Existing DTI prediction models often require significant computational resources because of the intricate and exceptionally lengthy protein target sequences. This study introduces MMF-DTI, a lightweight model that uses multimodal fusion, to improve the generalizability of DTI predictions without sacrificing computational efficiency. The MMF-DTI model combines four distinct modalities: molecular sequence, molecular properties, target sequence, and target function description. This approach is noteworthy because it is the first to use natural language-based target function descriptions in predicting DTIs. The effectiveness of MMF-DTI has been confirmed through benchmark data sets, demonstrating its comparable performance in terms of generalizability, especially in scenarios with limited information about the drug or target. Remarkably, MMF-DTI accomplishes this using only half of the parameters and 17% of the VRAM compared with previous state-of-the-art models. This allows it to function even in constrained computational environments. The combination of performance and efficiency highlights the potential of multimodal data fusion in improving the overall applicability of models, providing promising opportunities for future drug discovery endeavors.

Plasma p-tau immunoassays in clinical research for Alzheimer's disease.

The revised biomarker framework for diagnosis and staging of Alzheimer's disease (AD) relies on amyloid beta (Aβ) and tau pathologies as core markers, and markers for adjacent pathophysiology, such as neurodegeneration and inflammation. Many of the core fluid biomarkers are phosphorylated tau (p-tau) fragments, with p-tau217 showing a prominent association with Aβ and tau. While positron emission tomography (PET) imaging is well established, plasma p-tau assays are newer and likely to reduce the use of expensive, and less accessible cerebrospinal fluid and PET imaging tests, thereby promoting wider access to AD screening. There is a need for greater understanding of how the various plasma p-tau species reflect different pathological processes of AD and how different immunoassays perform. This review surveys the available immunoassays and highlights their strengths and limitations in different contexts of use. Assays need to be standardized to maximize their impact on AD clinical research, and patient diagnosis and management. HIGHLIGHTS: Different plasma phosphorylated tau (p-tau) species reflect different pathological processes of Alzheimer's disease (AD), with p-tau231 showing the greatest association with the earliest increases in brain amyloid beta (Aβ) accumulation, while p-tau217 shows greater association with both brain Aβ and early tau pathology, and other p-tau and tau fragment species show greater association with later stages of brain tau pathology. Plasma p-tau217 has proven to be an excellent biomarker for AD pathology due to its close association with both brain Aβ and tau pathology, as well as its large dynamic range. Many different assays with varying performance exist for the same p-tau species, with mass spectrometry assays performing uniformly well, and several immunoassays achieving comparable performance. "Round robin" head-to-head studies have been performed to compare different assays for several key plasma biomarkers, including p-tau181 and p-tau217, but additional head-to-head studies are needed, especially for new analytes and for measuring performance in diverse populations. Plasma immunoassays have the potential to increase accessibility of early diagnostic testing for a broad population, including diverse historically under-represented and under-served populations, due to the potential to be implemented globally, including in primary care settings; however, further research is needed to validate the optimal cutoffs for each assay for real-world clinical usage. Eventually, clinical implementation of a two-step workflow may allow standalone use of plasma testing in certain contexts, minimizing the need for confirmation with costly and less accessible cerebrospinal fluid/positron emission tomography testing.

Sustainable multiphase jet polishing of additively manufactured heat pipes utilizing recycled glass chips

Each year, a huge amount of waste glass chips is produced by machining, which is not recycled and has been treated as waste, resulting in the wasting of resources. However, the pristine physical properties of these micro-sized chips show great potential to be recycled as abrasives in jet polishing. In this work, machined glass chips were recycled and directly used as the abrasives in multiphase jet (MPJ) and abrasive water jet (AWJ) polishing for aluminum heat pipes of additive manufacturing with complex structures. Polishing performance of the recycled abrasives from waste glass was compared with those of commercial quartz abrasives with mesh sizes of 600, 800, 1000, 1200, and 3000. The results show that the recycled abrasives of glass chips have comparable performance to the commercial abrasives with mesh size of 1000. Computational fluid dynamic simulations were conducted on the two jet polishing to elucidate the fundamental material removal mechanisms. It is found that a higher drag force in the AWJ polishing could lead to uniform material removal and induce four times higher for material removal rate (MRR). The cost of setup, water and energy consumptions of MPJ polishing are only one tenth those of AWJ polishing. Energy dispersive spectroscopy reveals that there is no element contamination on the polished surface. This work proposes a novel approach of MPJ polishing for components produced by additive manufacturing with complicated structures. These findings provide new insights for the recycling use of waste chips generated in manufacturing, and suggest new solutions for saving energy and resources, as well as for clean and green production.

Classifying cognitive impairment based on FDG-PET and combined T1-MRI and rs-fMRI: An ADNI study.

Mild cognitive impairment (MCI) refers to a memory impairment among non-demented adults. It is a condition that increases the risk of dementia, notably due to Alzheimer's disease (AD). MCI is heterogeneous and there is a need for novel diagnostic approaches. Fluorodeoxyglucose positron emission tomography (FDG-PET) imaging provides robust AD biomarker characteristics, while anatomical and functional magnetic resonance imaging (MRI) offer complementary information. Classify MCI and cognitively normal (CN) adults using FDG-PET images; predict individuals with MCI that convert to AD dementia; determine if MRI can achieve comparable performance to FDG-PET classification. Four ADNI cohorts were created. Cohort 1: 805 participants (MCI n = 455; CN n = 350) that underwent FDG-PET. FDG-PET images were inputs to a one-channel 3-dimensional (3D) DenseNet deep learning model. Cohort 2: 348 participants (MCI n = 174; CN n = 174) with MRI and functional MRI. Cohort 3: overlapping cases from cohorts 1 and 2 (MCI n = 70; CN n = 70). Cohort 4: 336 participants (MCI-converters n = 168; MCI-stable n = 168) with FDG-PET from cohort 1. The one/two-channel models' inputs were T1-weighted MRI and/or amplitude of low-frequency fluctuations images, with classification metrics evaluated through 10-fold cross-validation. The FDG-PET model achieved 88.02%±3.82 accuracy for MCI versus CN classification, with 88.70%±4.70 sensitivity and 87.14%±5.03 specificity. Neither MRI model outperformed the FDG-PET model, as the highest MRI-based accuracy was 76.86%±1.95. The FDG-PET model achieved 63.23%±4.68 accuracy in classifying MCI-converters versus MCI-stable. FDG-PET images produced the highest accuracy in classifying MCI versus CN. While MRI-based approaches were inferior to FDG-PET, multi-contrast MRI still offers value for neurodegeneration classification.

Diagnostic performance of 5 FITs varied for detection of advanced colorectal neoplasia.

Levy BT, Xu Y, Daly JM, et al. Comparative performance of common fecal immunochemical tests: a cross-sectional study. Ann Intern Med. 2024;177:1350-1360. 39222513.

Deciphering white matter microstructural alterations in catatonia according to ICD-11: replication and machine learning analysis

AbstractCatatonia is a severe psychomotor disorder characterized by motor, affective and cognitive-behavioral abnormalities. Although previous magnetic resonance imaging (MRI) studies suggested white matter (WM) dysconnectivity in the pathogenesis of catatonia, it is unclear whether microstructural alterations of WM tracts connecting psychomotor regions might contribute to a better classification of catatonia patients. Here, diffusion-weighted MRI data were collected from two independent cohorts (whiteCAT/replication cohort) of patients with (n = 45/n = 13) and without (n = 56/n = 26) catatonia according to ICD-11 criteria. Catatonia severity was examined using the Northoff (NCRS) and Bush-Francis (BFCRS) Catatonia Rating Scales. We used tract-based spatial statistics (TBSS), tractometry (TractSeg) and machine-learning (ML) to classify catatonia patients from tractometry values as well as tractomics features generated by the newly developed tool RadTract. Catatonia patients showed fractional anisotropy (FA) alterations measured via TractSeg in different corpus callosum segments (CC_1, CC_3, CC_4, CC_5 and CC_6) compared to non-catatonia patients across both cohorts. Our classification results indicated a higher level of performance when trained on tractomics as opposed to traditional tractometry values. Moreover, in the CC_6, we successfully trained two classifiers using the tractomics features identified in the whiteCAT data. These classifiers were applied separately to the whiteCAT and replication cohorts, demonstrating comparable performance with Area Under the Receiver Operating Characteristics (AUROC) values of 0.79 for the whiteCAT cohort and 0.76 for the replication cohort. In contrast, training on FA tractometry resulted in lower AUROC values of 0.66 for the whiteCAT cohort and 0.51 for the replication cohort. In conclusion, these findings underscore the significance of CC WM microstructural alterations in the pathophysiology of catatonia. The successful use of an ML based classification model to identify catatonia patients has the potential to improve diagnostic precision.

Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models.

In deep learning, different kinds of deep networks typically need different optimizers, which have to be chosen after multiple trials, making the training process inefficient. To relieve this issue and consistently improve the model training speed across deep networks, we propose the ADAptive Nesterov momentum algorithm, Adan for short. Adan first reformulates the vanilla Nesterov acceleration to develop a new Nesterov momentum estimation (NME) method, which avoids the extra overhead of computing gradient at the extrapolation point. Then Adan adopts NME to estimate the gradient's first- and second-order moments in adaptive gradient algorithms for convergence acceleration. Besides, we prove that Adan finds an ϵ-approximate first-order stationary point within O(ϵ-3.5) stochastic gradient complexity on the non-convex stochastic problems (e.g., deep learning problems), matching the best-known lower bound. Extensive experimental results show that Adan consistently surpasses the corresponding SoTA optimizers on vision, language, and RL tasks and sets new SoTAs for many popular networks and frameworks, e.g., ResNet, ConvNext, ViT, Swin, MAE, DETR, GPT-2, Transformer-XL, and BERT. More surprisingly, Adan can use half of the training cost (epochs) of SoTA optimizers to achieve higher or comparable performance on ViT, GPT-2, MAE, etc, and also shows great tolerance to a large range of minibatch size, e.g., from 1 k to 32 k.

RepSGG: Novel Representations of Entities and Relationships for Scene Graph Generation.

Scene Graph Generation (SGG) has achieved significant progress recently. However, most previous works rely heavily on fixed-size entity representations based on bounding box proposals, anchors, or learnable queries. As each representation's cardinality has different trade-offs between performance and computation overhead, extracting highly representative features efficiently and dynamically is both challenging and crucial for SGG. In this work, a novel architecture called RepSGG is proposed to address the aforementioned challenges, formulating a subject as queries, an object as keys, and their relationship as the maximum attention weight between pairwise queries and keys. With more fine-grained and flexible representation power for entities and relationships, RepSGG learns to sample semantically discriminative and representative points for relationship inference. Moreover, the long-tailed distribution also poses a significant challenge for generalization of SGG. A run-time performance-guided logit adjustment (PGLA) strategy is proposed such that the relationship logits are modified via affine transformations based on run-time performance during training. This strategy encourages a more balanced performance between dominant and rare classes. Experimental results show that RepSGG achieves the state-of-the-art or comparable performance on the Visual Genome and Open Images V6 datasets with fast inference speed, demonstrating the efficacy and efficiency of the proposed methods.

A Simple and Cost-Effective Extraction for Piscine Environmental DNA Metabarcoding Using Guanidine Hydrochloride Method.

Environmental DNA (eDNA) metabarcoding is a valuable tool for assessing aquatic biodiversity, but the high cost and complexity of DNA extraction pose challenges for widespread adoption, especially in developing countries. This study presents a cost-effective eDNA extraction method using a guanidine hydrochloride (GuHCl) buffer, proteinase-K digestion, and isopropanol precipitation to improve the detection of fish communities. Comparison with the Qiagen DNeasy Blood & Tissue Kit using MiFish universal primers showed that the GuHCl protocol detected more fish species in freshwater samples, with comparable performance in relative read abundance metrics. However, the GuHCl method exhibited higher PCR inhibition in brackish samples, likely due to salinity and natural inhibitors. The results suggest that the GuHCl-based method is a viable alternative, offering enhanced sensitivity for low-abundance species in freshwater samples and cost savings. This protocol facilitates large-scale eDNA metabarcoding for ecological studies and conservation management efforts.•The GuHCl protocol identified a greater diversity of fish species in freshwater samples than the Qiagen kit, but detected fewer species in brackish water samples.•Both extraction methods demonstrated robust positive correlations in metrics of relative read abundance.

Diagnostic accuracy of ultrasound classifications – O-RADS US v2022, O-RADS US v2020, and IOTA SR – in distinguishing benign and malignant adnexal masses: Enhanced by combining O-RADS US v2022 with tumor marker HE4

PurposeTo assess the diagnostic accuracy of O-RADS Ultrasound (O-RADS US) v2022, O-RADS US v2020, and IOTA SR, and to evaluate whether combining imaging findings with tumor markers enhances the diagnosis of adnexal masses. MethodsThis retrospective study, conducted between January 2018 and December 2023, included consecutive women with adnexal masses scheduled for surgery. Histopathologic results served as the reference standard. Risk factors for malignancy were identified using univariate and multivariate logistic regression analyses. ROC analysis was employed to assess diagnostic test performances, while Kappa statistics evaluated inter-reviewer agreement. ResultsA total of 613 women (mean age, 49.39 ± 12.81 years; range, 16–87 years) with pelvic masses were included. O-RADS US v2022 exhibited comparable performance to O-RADS US v2020, with areas under the curve (AUC) values of 0.940 and 0.937, respectively (p = 0.02, exceeding the adjusted significance level of 0.0167). Both O-RADS models outperformed the IOTA SR, which had an AUC of 0.862 (p < 0.0001 for both comparisons). Multivariate analysis revealed that O-RADS US v2022 [OR 9.148, 95 %CI (4.912–17.039), p < 0.001] and HE4 [OR 1.023, 95 %CI (1.010–1.036), p = 0.001] were significant factors associated with malignant lesions. Furthermore, the combination of O-RADS US v2022 and HE4 demonstrated an AUC of 0.98, significantly outperforming either O-RADS US v2022 alone (AUC = 0.94) or HE4 alone (AUC = 0.92). The Kappa values for O-RADS US v2022, O-RADS US v2020 and IOTA SR were 0.933, 0.891 and 0.923, respectively, indicating substantial inter-reader agreement. ConclusionsThe O-RADS US v2022 demonstrates comparable performance in predicting ovarian malignant lesions when compared to O-RADS US v2020, while surpassing the performance of IOTA SR. Additionally, the combination of O-RADS US v2022 and HE4 provides improved diagnostic effectiveness over using either O-RADS US v2022 or HE4 alone.

Magnetically recoverable nanoparticle organic hybrid materials as kinetic hydrate inhibitors

Gas hydrates pose significant risks in the oil and gas industry because of their tendency to form blockages in transportation pipelines under low-temperature and high-pressure conditions. Conventional thermodynamic hydrate inhibitors require high dosages, with associated substantial costs and environmental concerns. Kinetic hydrate inhibitors (KHIs), which operate at much lower dosages, offer a more sustainable solution but are often limited by environmental compatibility and recovery problems. In this study, we introduce a novel class of magnetically recoverable KHIs utilizing nanoparticle organic hybrid materials (NOHMs), named NOHM-I-PNIPAM, featuring a tunable Fe3O4 core-SiO2 shell structure with poly(N-isopropylacrylamide) (PNIPAM) as the organic component. The chemical properties of NOHM-I-PNIPAM were characterized using TEM, FT-IR, and XPS. The hydrate kinetic inhibition performance was evaluated with a CH4 (90 %) + C2H6 (7 %) + C3H8 (3 %). Various grafting densities of PNIPAM on NOHMs and weight fractions of NOHM-I-PNIPAM were used to explore NOHM-I-PNIPAM’s role in the formation kinetics of natural gas hydrates under different cooling rates. Notably, the NOHM-I-PNIPAM (1.0), when utilized at a 2 wt% dosage, exhibited the most effective inhibition performance in terms of the onset temperature of CH4 (90 %) + C2H6 (7 %) + C3H8 (3 %) hydrate. Considering the effective concentration of PNIPAM in NOHM-I-PNIPAM, the inhibition performance was found to be comparable to that of conventional PNIPAM. Results showed that NOHM-I-PNIPAM is a promising KHI due to its combined properties of magnetic separation and comparable kinetic inhibition performance. Experimentally, NOHM-I-PNIPAM demonstrated stability over long-term cycles and was effectively magnetically separated. Hence, we believe that this work can provide valuable insights into the potential of NOHMs as recoverable and reusable KHIs for industrial applications for flow assurance.

CPPF++: Uncertainty-Aware Sim2Real Object Pose Estimation by Vote Aggregation.

Object pose estimation constitutes a critical area within the domain of 3D vision. While contemporary state-of-the-art methods that leverage real-world pose annotations have demonstrated commendable performance, the procurement of such real training data incurs substantial costs. This paper focuses on a specific setting wherein only 3D CAD models are utilized as a priori knowledge, devoid of any background or clutter information. We introduce a novel method, CPPF++, designed for sim-to-real category-level pose estimation. This method builds upon the foundational point-pair voting scheme of CPPF, reformulating it through a probabilistic view. To address the challenge posed by vote collision, we propose a novel approach that involves modeling the voting uncertainty by estimating the probabilistic distribution of each point pair within the canonical space. Furthermore, we augment the contextual information provided by each voting unit through the introduction of N-point tuples. To enhance the robustness and accuracy of the model, we incorporate several innovative modules, including noisy pair filtering, online alignment optimization, and a tuple feature ensemble. Alongside these methodological advancements, we introduce a new category-level pose estimation dataset, named DiversePose 300. Empirical evidence demonstrates that our method significantly surpasses previous sim-to-real approaches and achieves comparable or superior performance on novel datasets.

Experimental and Numerical Investigation of Steel Panel Links with Different Trapezoidal Corrugations

A novel type of steel panel link with a trapezoidal corrugated section is developed, which is called the corrugated steel panel (CSP) link, aiming to improve both the strength and energy dissipation capacity of the steel links. Quasi-static tests were carried out on two specimens with different corrugation inclination angles to investigate their seismic performance. Test results indicated that Specimen CSP-V (corrugation inclination angle of 90°) and Specimen CSP-H (corrugation inclination angle of 0°) exhibited distinct failure modes due to the differing out-of-plane constraints provided by their corrugations. Numerical analysis was conducted using the general finite element program, ABAQUS, to supplement the test results. Analytical findings suggested that CSPs with a 90° corrugation inclination angle could provide superior ductility, both yield and peak strengths demonstrated a strong linear correlation with the aspect ratio (H/L ratio). For CSPs with a 0° inclination angle, superior strength and stiffness were observed, the relationship between lateral strength and the H/L ratio was better described by a power-law correlation. Furthermore, to achieve comparable seismic performance, the H/L ratio of CSPs with a 0° inclination angle should be at least twice that of CSPs with a 90° inclination angle.

Comparative Efficiency and Performance of Glass Design Methods

Comparative Efficiency and Performance of Glass Design Methods

Research on Stiffness Analysis and Technology of the Heavy Spidle Top

Background: The spindle top is an important component used to withstand the shaft workpiece on machine tools so that the spindle can meet high efficiency and high precision requirements. However, the selection principles under various load conditions are not stipulated in use. In addition, material selection, manufacturing, heat treatment technology, etc., are of practical significance for the production of high hardness, high wear resistance, and high precision spindle tops. Objectives: The spindle top type, material selection principles, heat treatment, cold working, and other manufacturing processes are given. Provide a reference for highperformance and top-notch design and manufacturing. Methods: The model of the spindle top will be created in UG software, then using ANSYS finite element analysis software to analyze stiffness of spindle top whose height-to-diameter ratios are 1:4 and 1:7 types in a variety of different load cases. The design and manufacturing process of the spindle top is analyzed and expounded from the selection and performance comparison of metal materials, heat treatment of different materials, cold manufacturing technology, and other aspects. Results: The deformation laws of different types of spindle tops are obtained. According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The defects that are easy to occur in the technology are obtained and the preventive and solution measures are put forward. Conclusion: According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The material selection, heat treatment technology, and other technical research on the spindle top provide the necessary basis for the production of the spindle top.

Comparative Performance of Scenario Superposition by Sequential Bayesian Update for Tsunami Risk Evaluation

Comparative Performance of Scenario Superposition by Sequential Bayesian Update for Tsunami Risk Evaluation

CC4S: Encouraging Certainty and Consistency in Scribble-Supervised Semantic Segmentation.

Deep learning-based solutions have achieved impressive performance in semantic segmentation but often require large amounts of training data with fine-grained annotations. To alleviate such requisition, a variety of weakly supervised annotation strategies have been proposed, among which scribble supervision is emerging as a popular one due to its user-friendly annotation way. However, the sparsity and diversity of scribble annotations make it nontrivial to train a network to produce deterministic and consistent predictions directly. To address these issues, in this paper we propose holistic solutions involving the design of network structure, loss and training procedure, named CC4S to improve Certainty and Consistency for Scribble-Supervised Semantic Segmentation. Specifically, to reduce uncertainty, CC4S embeds a random walk module into the network structure to make neural representations uniformly distributed within similar semantic regions, which works together with a soft entropy loss function to force the network to produce deterministic predictions. To encourage consistency, CC4S adopts self-supervision training and imposes the consistency loss on the eigenspace of the probability transition matrix in the random walk module (we named neural eigenspace). Such self-supervision inherits the category-level discriminability from the neural eigenspace and meanwhile helps the network focus on producing consistent predictions for the salient parts and neglect semantically heterogeneous backgrounds. Finally, to further improve the performance, CC4S uses the network predictions as pseudo-labels and retrains the network with an extra color constraint regularizer. From comprehensive experiments, CC4S achieves comparable performance to those from fully supervised methods and shows promising robustness under extreme supervision cases.

Numerical investigation of combustion characteristics of hydrogen-enriched low calorific value landfill gas for energy applications

Landfill gas is regarded as a promising renewable energy resource. However, Low calorific value landfill gases with less than 40 vol% CH4 are not well suited for energy applications and therefore disposed by flaring or direct emission to atmosphere causing environmental problems. In the present work, the potential of using hydrogen-enriched low calorific value landfill gas for energy applications has been explored through numerical investigation of combustion characteristics of different blends of fuels. The ignition delay of fuel mixtures decreased with the increase of hydrogen fraction (H2 enrichment from 0 to 50 vol%) at stoichiometric conditions. At high initial pressure conditions, ignition delay showed a weak relationship with the pressure. The laminar burning velocity increased with the increment of hydrogen fraction for the range of equivalence ratios considered (φ = 0.7 – 1.4) and also with the increase of initial temperature. However, the laminar burning velocity decreased significantly with the increasing initial pressure and hydrogen enrichment has insignificant effect at high initial pressures. The sensitivity analysis shows that the main elementary reactions inhibit the laminar burning velocity more with increasing initial pressure. The combustion performance of low calorific value landfill gas with 30 vol% CH4 enriched with 30 vol% hydrogen shows comparable performance to biogas with 60 vol% CH4 for a range of operating conditions. The numerical investigation results show that hydrogen-enriched low calorific value landfill gas could be used for thermal applications with the use of suitably designed combustion systems operating at low initial pressures and relatively high initial temperatures.