Comparative Performance Analysis Research Articles

Performance-Based contracting of urban transport operation Services: Evidence from Porto’s Light-Rail

This paper develops a bonus/malus incentive model for contracting out the provision of urban railroad operation services, and testing parts of its performance using Monte Carlo methods. We also provide evidence on the performance measures of Metro do Porto’s (MdP) light-rail network operation, during the 2010–2016 term of a bonus/malus-based contract, incorporating some features of our model. Results document that the implementation of a performance-based contract with an embedded incentive bonus/malus mechanism may contribute to promoting ridership patronage, increasing the average ride, and ultimately promoting the overall economic operating efficiency of the system. The comparative operating performance analysis of MdP versus Metropolitano de Lisboa, a vertically integrated, governmentally-owned metro network, shows that MdP exhibits higher revenue, cost, and operating efficiency in the 2010–2016 period, and that the bonus/malus mechanism induces the private sub-concessionaire to respond more efficiently to changes in demand.

Comparative performance analysis of different hydrogen power composite cycles with waste heat recovery

ABSTRACT Hydrogen energy has been under close scrutiny by countries around the world. At present, the main technologies for utilizing hydrogen energy in transport power systems are hydrogen internal combustion engine (HICE) and proton exchange membrane fuel cell (PEMFC). Since both of them generate a large amount of waste heat, and waste heat recovery technology is one of the most promising means of utilizing waste heat to improve efficiency, this paper makes a comprehensive comparison of the two technologies in composite waste heat recovery (WHR) system in terms of modeled maximum efficiency, full operating condition efficiency and future predicted maximum efficiency. Based on the thermal efficiency analysis, the advantages, disadvantages, and development prospects of the two technologies in composite the WHR system are reevaluated. The results demonstrate that the efficiency of the HICE and PEMFC can be improved by 6.83% and 2.81%, with the use of WHR system, respectively. Under the full range of operating conditions, the HICE-WHR composite system has obvious efficiency advantage in middle-high load working condition, but PEMFC-WHR composite system has obvious efficiency advantage in low load working condition. Both of them will be very competitive and potential technology routes of hydrogen energy utilization for land transportation.

Comparative performance analysis of pyramid-shaped solar distiller augmented with phase change materials enriched with graphite nanoparticles: experimental study

Comparative performance analysis of pyramid-shaped solar distiller augmented with phase change materials enriched with graphite nanoparticles: experimental study

Building a ML-based QSAR model for predicting the bioactivity of therapeutically active drug class with imidazole scaffold

Human immunodeficiency virus, a retrovirus, causes AIDS, a chronic immune system disease. HIV interferes with the ability of our body to combat disease and infection by weakening our immune system. An essential enzyme necessary for HIV replication is reverse transcriptase (RT). RT inhibitors (RTIs) are a class of antiretroviral drugs that target HIV's RT enzyme, blocking its ability to convert viral RNA into DNA. The RT-1 enzyme has been found to be inhibited by imidazole. It attaches to the RT-1 enzyme's active site and prevents it from performing its usual activity. As a result, viral replication is inhibited, which can eventually aid in slowing the course of HIV and other retroviral diseases. A computational tool allows researchers to simulate and analyze the drug's behaviour in a virtual environment, providing valuable insights into its pharmacological properties, efficacy, and safety. QSAR modelling uses machine learning methods to create predictive models from datasets of chemical substances and the accompanying biological activity. Here, a comparative analysis of the model performances by four different algorithms for the Imidazole scaffold are reported. The algorithms of Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR) and Hist Gradient Boosting Regression (HGBR) have given promising results with the R2 value of 0.905, 0.993, 0.688 and 0.921 respectively for the train sets and for the test set 0.843, 0.977, 0.567 and 0.880. The best performed RFR model have been validated using developed RFR codes for randomly selected compounds and it shows the error percentage of about 0.151% only. From the R2 values, it is observed that the RFR and HGBR models show a better fit with the variables compared to the other models thereby making them the potential models for predicting the activity of novel anti-viral compounds.

Comparative performance analysis of Boruta, SHAP, and Borutashap for disease diagnosis: A study with multiple machine learning algorithms

ABSTRACT Interpretable machine learning models are instrumental in disease diagnosis and clinical decision-making, shedding light on relevant features. Notably, Boruta, SHAP (SHapley Additive exPlanations), and BorutaShap were employed for feature selection, each contributing to the identification of crucial features. These selected features were then utilized to train six machine learning algorithms, including LR, SVM, ETC, AdaBoost, RF, and LR, using diverse medical datasets obtained from public sources after rigorous preprocessing. The performance of each feature selection technique was evaluated across multiple ML models, assessing accuracy, precision, recall, and F1-score metrics. Among these, SHAP showcased superior performance, achieving average accuracies of 80.17%, 85.13%, 90.00%, and 99.55% across diabetes, cardiovascular, statlog, and thyroid disease datasets, respectively. Notably, the LGBM emerged as the most effective algorithm, boasting an average accuracy of 91.00% for most disease states. Moreover, SHAP enhanced the interpretability of the models, providing valuable insights into the underlying mechanisms driving disease diagnosis. This comprehensive study contributes significant insights into feature selection techniques and machine learning algorithms for disease diagnosis, benefiting researchers and practitioners in the medical field. Further exploration of feature selection methods and algorithms holds promise for advancing disease diagnosis methodologies, paving the way for more accurate and interpretable diagnostic models.

Comparative analysis of wheat and barley yield performance across temperate environments

ContextA comparative analysis of grain yield performance of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) under different environments constitutes a way to identify restrictions in yield formation between both crop species. However, most of the previous studies on these field crops have been carried out in Mediterranean environments. ObjectiveThis study aims to compare the grain yield performance of wheat and barley across a wide range of temperate environmental conditions. MethodsWheat and barley yield databases from the national network of cultivar evaluation of Argentina were assembled. The database included 191 wheat cultivars (with early-, mid- and late-flowering length cycle), 140 barley cultivars, and 44 combinations of sites and years of the Argentine wheat and barley belt region. Average heading date variation among environments and crops was no more than 10 days. Crop grain yield comparisons were carried out using a regression analysis, benchmarking within each site-year the grain yield of each cultivar against the average grain yield of all crop cultivars (i.e., environmental index). ResultsThere were no differences in grain yield between wheat and barley for all data, but the behavior between crop species changed with the length of the wheat cycle (i.e., early-, mid-, or late-flowering cultivars). Barley portrayed a greater grain yield than early-flowering wheat under low-yielding environments (< 6060 kg ha−1), but this advantage vanished as the yield environment improved. Mid-flowering wheat showed a similar grain yield to barley across temperate environments. Lastly, late-flowering wheat outperformed barley mainly in high-yielding environments (> 6138 kg ha−1). Grain number m−2 was the main numerical component that explained variability in grain yield. The relative contribution of the yield numerical components differed between species, having barley lower grain number m−2 but a greater grain weight than wheat. ConclusionsThe comparison of the grain yield performance between species represents a strategy to adjust the rotation system, being a critical factor in considering the variability in the crop growth cycle.

Comparative Analysis of Multimodal Large Language Model Performance on Clinical Vignette Questions

This study compares 2 large language models and their performance vs that of competing open-source models.

Evaluating reliability in wearable devices for sleep staging.

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

A novel MPPT technology based on dung beetle optimization algorithm for PV systems under complex partial shade conditions.

Solar power is a renewable energy source, and its efficient development and utilization are important for achieving global carbon neutrality. However, partial shading conditions cause the output of PV systems to exhibit nonlinear and multipeak characteristics, resulting in a loss of output power. In this paper, we propose a novel Maximum Power Point Tracking (MPPT) technique for PV systems based on the Dung Beetle Optimization Algorithm (DBO) to maximize the output power of PV systems under various weather conditions. We performed a performance comparison analysis of the DBO technique with existing renowned MPPT techniques such as Squirrel Search Algorithm, Cuckoo search Optimization, Horse Herd Optimization Algorithm, Particle Swarm Optimization, Adaptive Factorized Particle Swarm Algorithm and Gray Wolf Optimization Hybrid Nelder-mead. The experimental validation is carried out on the HIL + RCP physical platform, which fully demonstrates the advantages of the DBO technique in terms of tracking speed and accuracy. The results show that the proposed DBO achieves 99.99% global maximum power point (GMPP) tracking efficiency, as well as a maximum improvement of 80% in convergence rate stabilization rate, and a maximum improvement of 8% in average power. A faster, more efficient and robust GMPP tracking performance is a significant contribution of the DBO controller.

Comparative analysis of thermal performance in heat pipe heat exchangers employing two distinct convective heat transfer media

Comparative analysis of thermal performance in heat pipe heat exchangers employing two distinct convective heat transfer media

Comparative analysis of direct laryngoscopy and video laryngoscopy performance by medical students in simulated airway management scenarios

This study aims to compare the effectiveness of video laryngoscopes and Macintosh laryngoscopes for intubation by novices in a mannequin model.This study was conducted at our institution- Karuna Medical College, Hospital. A total of 50 house surgeons were included in this study. All 50 participants intubated on the airway trainer using a video laryngoscope and a conventional method using a Macintosh laryngoscope. Group A: Video laryngoscopy, Group B: Conventional Laryngoscopy. The following parameters such as – time taken to visualize vocal cords, time taken to intubate, POGO scoring, ease of intubation, etc. were recorded.Video laryngoscope performed better in terms of time-related metrics, glottis visibility, and injury prevention to lip and teeth. Both devices had a similar ease of intubation and number of attempts. The results provide evidence supporting the advantages of the video laryngoscope in certain aspects of intubation procedures. The video laryngoscope had several benefits over the Macintosh laryngoscope, such as top quality visualization of airway structures and an easier intubation process for novices. The video laryngoscope provides superior visualization of the larynx, requires fewer external adjustments, and reduces the number of intubation attempts compared to direct laryngoscopy using a Macintosh blade.

A Type-2 Fuzzy u-Control Chart Considering Probability-Based Average Run Length

Fuzzy sets are an emerging trend in shaping the development of control charts for statistical process control. The sets are germane to vague data that comes from incomplete or inaccurate measurements. Nevertheless, fuzzy sets are inadequate in some areas of industry since their membership functions are crisp numbers. The fuzzy sets are not fully able to compute higher levels of uncertainty, which might degrade the performance of the analysis. Therefore, type-2 fuzzy sets are proposed to be merged with control charts since these sets are hypothesized to be more capable of detecting a defect in process control. This paper aims to develop interval type-2 fuzzy u (IT2Fu) charts as a new approach to detecting defects. In addition, this paper presents a comparative analysis of performances between traditional u-control charts, type-1 fuzzy u-control charts, and type-2 fuzzy u-control charts. 23 samples of lubricant data with 48 subgroups were examined to identify the defects. The output showed that all of the control charts produced almost similar results except for data 14, which is “out of control” in IT2Fu-control charts but “in control” in traditional u-control charts and “rather in control” in type-1 fuzzy u-control charts. Furthermore, the performances of the charts were compared using a probability-based average run length (ARL), where probability type 1 error is computed. It was found that the ARL value of the IT2Fu-control chart showed the lowest value among the three types of charts. The analysis indicated that the IT2Fu-control chart outperformed the traditional u-control chart and the type-1 fuzzy u-control chart. The results obtained seem to support the idea that IT2Fu-control charts are more sensitive compared to type 1 fuzzy u-control charts and traditional u-control charts, so that IT2Fu-control charts are able to adequately support incomplete and vague data on process control.

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

Comparative Performance Analysis of Filtering Methods for Removing Baseline Wander Noise from an ECG Signal

TARGETING ECO-INNOVATION PERFORMANCE AMONG EU COUNTRIES: CLUSTER OUTCOME

An indispensable tool for enhancing economic growth and protecting the environment is eco-innovation, which integrates innovation and sustainable development. By utilizing innovation to address social, economic, and environmental issues, eco-innovation is essential to achieving sustainable development goals. One significant strategy for addressing environmental issues and promoting economic expansion is eco-innovation. Optimizing resource use and promoting efficiency across various sectors, eco-innovation holds significant importance in modern conditions. Eco-innovation represents a proactive approach and leads to positive environmental outcomes. The performance of eco-innovation is a key determinant of its effectiveness in achieving sustainable development goals. Undoubtedly, the European Union’s (EU) policy plan of the past few decades strongly emphasizes eco-innovation. Through initiatives such as the Eco-Innovation Scoreboard, which provides a comprehensive evaluation of eco-innovation performances across EU member states, the EU has been at the forefront of fostering eco-innovation. Eco-Innovation Scoreboards consist of indicators designed to measure various aspects of eco-innovation, used to assess the overall eco-innovation performance of EU member states and to track progress over time. This study examines the average value of eco-innovation performances across EU member states using data from the Eco-innovation Scoreboard. It also presents a comparative analysis of eco-innovation performances across EU member states over two different periods, 2013-2017 and 2018-2022, using indicators including eco-innovation input, eco-innovation activities, eco-innovation output, resource efficiency outcomes, and socio-economic outcomes. Cluster analysis groups EU member states based on their eco-innovation performances, allowing for a comparative assessment of different clusters. The examination evaluates the effectiveness of EU member states in combining innovative practices with sustainable development by focusing on cluster outcomes. The results of the analysis show dynamic changes in eco-innovation performance among EU countries, indicating migration between clusters over time. The observed trends suggest an evolution of priorities and strategies in eco-innovation, which requires adaptive policy responses. Policymakers are advised to adopt flexible and adaptive strategies that take into account the changing performance landscape of eco-innovation and that capitalize on the strengths recognized within each cluster while overcoming identified weaknesses.

Using Generative AI to Improve the Performance and Interpretability of Rule-Based Diagnosis of Type 2 Diabetes Mellitus

Introduction: Type 2 diabetes mellitus is a major global health concern, but interpreting machine learning models for diagnosis remains challenging. This study investigates combining association rule mining with advanced natural language processing to improve both diagnostic accuracy and interpretability. This novel approach has not been explored before in using pretrained transformers for diabetes classification on tabular data. Methods: The study used the Pima Indians Diabetes dataset to investigate Type 2 diabetes mellitus. Python and Jupyter Notebook were employed for analysis, with the NiaARM framework for association rule mining. LightGBM and the dalex package were used for performance comparison and feature importance analysis, respectively. SHAP was used for local interpretability. OpenAI GPT version 3.5 was utilized for outcome prediction and interpretation. The source code is available on GitHub. Results: NiaARM generated 350 rules to predict diabetes. LightGBM performed better than the GPT-based model. A comparison of GPT and NiaARM rules showed disparities, prompting a similarity score analysis. LightGBM’s decision making leaned heavily on glucose, age, and BMI, as highlighted in feature importance rankings. Beeswarm plots demonstrated how feature values correlate with their influence on diagnosis outcomes. Discussion: Combining association rule mining with GPT for Type 2 diabetes mellitus classification yields limited effectiveness. Enhancements like preprocessing and hyperparameter tuning are required. Interpretation challenges and GPT’s dependency on provided rules indicate the necessity for prompt engineering and similarity score methods. Variations in feature importance rankings underscore the complexity of T2DM. Concerns regarding GPT’s reliability emphasize the importance of iterative approaches for improving prediction accuracy.

Parametric and Nonparametric Approaches of Reid Vapor Pressure Prediction for Gasoline Containing Oxygenates: A Comparative Analysis Using Partial Least Squares, Nonlinear, and LOWESS Regression Modelling Strategies with Physical Properties

This study provides insights into the challenges involved in predicting the Reid vapor pressure (RVP) of gasoline-oxygenate blends (GOB), which is an important indicator of fuel quality and compliance with environmental and performance standards. Given the enormous variety of gasoline compositions and ratios available, there is a significant demand for a fast, straightforward, and cost-effective technique to predict RVP without relying on costly instruments or complicated spectral measurements that involve numerous input variables. A comparative performance analysis has been performed for different regression modelling strategies for predicting RVP in GOB, which is valuable for researchers and practitioners in the petroleum industry for saving time and money. Parametric and nonparametric approaches were compared using partial least squares regression (PLSR), nonlinear regression (NLR), and nonparametric regression (NPR) models. Locally weighted scatterplot smoothing (LOWESS) approach was applied to the NPR model. The gasoline’s physical characteristics (distillation curves and density) formed the basis for the analysis of these models’ performances. Acceptable error metrics have been reached for root mean square error of calibration and prediction (RMSEC and RMSEP) values, for the PLSR, NLR, and NPR models, which are 4.790, 6.235, 4.739, 6.149, 3.968, and 6.029, respectively, which are close for those reported in literature. The NPR model eliminates parametric constraints and allows for a different kind of data structure to emerge. The established models here demonstrate a sound ability to overcome barriers by omitting the use of inconvenient spectral measurements to save expense and simplify data calibration, making them a promising approach for RVP detection of GOB. This finding aids in the development of more accurate RVP prediction models and contributes to the optimization of fuel formulations.

Attention Mechanism with Spatial-Temporal Joint Deep Learning Model for the Forecasting of Short-Term Passenger Flow Distribution at the Railway Station

Accurate understanding of passenger flow distribution is crucial for effective station crowd management. However, due to the complexity and randomness of passenger flow and the unclear spatial-temporal correlation between functional areas within the station, predicting the spatiotemporal distribution dynamics of inflow and future short-term distribution trends is challenging. Emerging deep learning models offer valuable insights for accurately predicting passenger flow distribution. Thus, we propose a deep learning architecture, named “ST-Bi-LSTM,” which combines a bidirectional long short-term memory network with a spatial-temporal attention mechanism. Initially, we outline the methodologies of Bi-LSTM, the DeepWalk-based spatial attention mechanism, and the temporal attention mechanism. The spatial attention mechanism is employed to extract station spatial network topology information and enhance the representation of passenger flow characteristics in highly correlated areas during the forecasting process. Simultaneously, the temporal attention Bi-LSTM is utilized for capturing temporal correlations. The architecture comprises four branches dedicated to station real-time video monitoring data, spatial network topology, function area attributes, and train timetables. Subsequently, leveraging in-station CCTV data, passenger travel behavior data, and train timetables, we apply the architecture to the Tianjin West High-Speed Railway Station. We conduct a comparative analysis of the prediction performance and time complexity of the proposed architecture against existing baseline models, demonstrating superior performance and robustness exhibited by the ST-Bi-LSTM model (achieving a reduction in RMSE of over 10%). This study facilitates the transition of station management from passive response to active prediction of station passenger flow dynamics.

High-Performance Multi-GPU Analytic RI-MP2 Energy Gradients.

This article presents a novel algorithm for the calculation of analytic energy gradients from second-order Møller-Plesset perturbation theory within the Resolution-of-the-Identity approximation (RI-MP2), which is designed to achieve high performance on clusters with multiple graphical processing units (GPUs). The algorithm uses GPUs for all major steps of the calculation, including integral generation, formation of all required intermediate tensors, solution of the Z-vector equation and gradient accumulation. The implementation in the EXtreme Scale Electronic Structure System (EXESS) software package includes a tailored, highly efficient, multistream scheduling system to hide CPU-GPU data transfer latencies and allows nodes with 8 A100 GPUs to operate at over 80% of theoretical peak floating-point performance. Comparative performance analysis shows a significant reduction in computational time relative to traditional multicore CPU-based methods, with our approach achieving up to a 95-fold speedup over the single-node performance of established software such as Q-Chem and ORCA. Additionally, we demonstrate that pairing our implementation with the molecular fragmentation framework in EXESS can drastically lower the computational scaling of RI-MP2 gradient calculations from quintic to subquadratic, enabling further substantial savings in runtime while retaining high numerical accuracy in the resulting gradients.

CfCV: Towards algorithmic debiasing in machine learning experiment

Algorithmic failure in application leading to unfairness or bias typically results from data-inconsistent, diversity biases, inclusion biases, and under-representation, among many others, leading to imbalances in representation for training and validation. Cross-validation (CV) techniques are relevant in addressing this inconsistency from training to validation point. As a result, the essence of CV is to validate algorithm abilities to predict new data that are not part of the training set and to prompt issues such as overfitting or selection bias. This study considered the linkage of Inclusion, Participation, and Reciprocity (IPR) in data splitting for a sensitive attribute and caters for population grouping representativeness in splitting. The study remodified the Pre-In-Post (P-I-P) processing approach to accommodate various sensitive attribute levels within any training set and performed simulation experiments and real-life applications. It then conducted a comparative performance analysis with the two most notable CV techniques. The study innovative approach (CfCV) outperformed the existing CVs - Vfold and HoldOut; in experiments [RMSECfCV = 0.88; RMSEVfold = 0.98; RMSEHoldout = 4.69 | AccuracyCfCV = 99.75%; AccuracyVfold = 99.50%; AccuracyHoldOut = 85.50%] and applications [RMSECfCV = 0.59; RMSEVfold = 1.61; RMSEHoldout = 1.96 | AccuracyCfCV = 84%; AccuracyVfold = 81%; AccuracyHoldOut = 52%]. This study recommends the adoption of IPR in data splitting for machine experiments built for human-machine intelligence systems and concludes that machine learning experiments would be fairer if the concept of IPR formed the foundation of the human-machine intelligence framework.

Vehicle Sideslip Angle estimation under critical road conditions via nonlinear Kalman filter-based state-dependent Interacting Multiple Model approach

The knowledge of Vehicle Sideslip Angle (VSA) can play an essential role in active safety vehicle control systems. However, due to the high costs of sensing instruments, this information is difficult to be directly measured onboard of series production vehicles, restricting de facto its application in practice. It follows that there is a need for online VSA estimation methods only based on available measurements from low-cost sensors. From this perspective, this work proposes a strategy based on Interacting Multiple Model (IMM) filters, which does not require tyre–road friction coefficient knowledge. By integrating the available onboard sensor data, the IMM estimates relevant information in different driving conditions leveraging a 2-Degrees Of Freedom (DOF) single-track vehicle model embedding a Dugoff tyre representation. Two alternative IMM algorithms based on the Extended (EKF) and Unscented Kalman filter (UKF) are developed. Moreover, while usually the transition probabilities among models in classical IMMs are fixed and set on prior information and/or dedicated analysis, here these conservative hypotheses are relaxed introducing a state-dependent Markov transition matrix based on a novel model switching algorithm. The effectiveness of the new proposed methods is evaluated on extensive non-trivial simulation scenarios through a Monte Carlo analysis exploiting an accurate 15-DOF vehicle model via a purposely designed high-fidelity co-simulation platform embedding the dSPACE software Automotive Simulation Model (ASM). Results provide a meaningful comparative performance analysis between the IMMEKF and IMMUKF solutions, as well as with respect to traditional IMM based on constant probabilities transition matrix, blue in both the EKF and UKF configuration. Finally, the developed IMM-based estimation strategy is tested in two realistic driving scenarios to assess the VSA estimation accuracy in case of abrupt changes in road surface conditions.