Computational Tools Research Articles

Fuel Cell Electric Vehicle Characterisation under Laboratory and In-use Operation

Fuel Cell Electric Vehicles (FCEVs) are one of the main options considered as possible future alternatives to conventional vehicles. The paper evaluates a light-duty FCEV under both laboratory and on-road conditions. Hydrogen consumption and vehicle efficiency were measured over various driving and temperature conditions. Results show hydrogen consumption between 0.975kg/100km and 1.5kg/100km, close to the officially declared value. A new extended formulation of vehicle efficiency is proposed to consider different energy streams in the vehicle. Vehicle indicative efficiency remained between 50 and 60% on the majority of cases. The paper also investigates the feasibility of applying a fuel cell system of similar characteristics for commercial urban and rural applications, the Vehicle Energy Consumption Calculation TOol (VECTO), as a possible alternative solution to a battery electric equivalent vehicle.

Short- and long-term performance of risk calculation tools for mortality in patients with acute coronary syndrome

BackgroundThe mortality rate of acute coronary syndrome (ACS) remains high. Therefore, patients with ACS should undergo early risk stratification, for which various risk calculation tools are available. However, it remains uncertain whether the predictive performance varies over time between risk calculation tools for different target periods. This study aimed to compare the predictive performance of risk calculation tools in estimating short- and long-term mortality risks in patients with ACS, while considering different observation periods using time-dependent receiver operating characteristic (ROC) analysis.MethodsThis study included 404 consecutive patients with ACS who underwent coronary angiography at our hospital from March 2017 to January 2021. The ACTION and GRACE scores for short-term risk stratification purposes and CRUSADE scores for long-term risk stratification purposes were calculated for all participants. The participants were followed up for 36 months to assess mortality. Using time-dependent ROC analysis, we evaluated the area under the curve (AUC) of the ACTION, CRUSADE, and GRACE scores at 1, 6, 12, 24, and 36 months.ResultsSixty-six patients died during the observation periods. The AUCs at 1, 6, 12, 24, and 36 months of the ACTION score were 0.942, 0.925, 0.889, 0.856, and 0.832; those of the CRUSADE score were 0.881, 0.883, 0.862, 0.876, and 0.862; and those of the GRACE score 0.949, 0.928, 0.888, 0.875, and 0.860, respectively.ConclusionsThe ACTION and GRACE scores were excellent risk stratification tools for mortality in the short term. The prognostic performance of each risk score was almost similar in the long term, but the CRUSADE score might be a superior risk stratification tool in the longer term than 3 years.

Enhancing OpenEP: atrial conduction velocity and conduction velocity heterogeneity quantification through EP Workbench

Abstract Background Alterations in atrial conduction velocity have been implicated in the pathogenesis of atrial arrhythmias, with conduction velocity thought to be slower and more heterogeneous in areas promoting atrial fibrillation. However, clinical studies of conduction velocity are challenging since there are no available platforms that provide researchers and clinicians with the ability to quantify conduction velocity and analyse conduction heterogeneity from electroanatomic mapping data. Purpose In this paper we sought to address these limitations by enhancing the OpenEP Python library by (1) automating calculations using previously-published conduction velocity estimation algorithms; (2) providing an interactive graphical representing of conduction velocity and conduction heterogeneity and (3) implementing a histogram analysis tool to quantify conduction velocity and enable the identification of slow conduction regions. Method Two well-known and previously published methods for calculating cardiac conduction velocity, Triangulation and polynomial surface fitting, were implemented. Conduction velocity estimation was improved further by excluding regions with wave collisions and focal discharges. These regions were identified using the divergence of conduction velocity vector fields, with positive divergence representing focal sources and negative divergence representing areas of collision. Finally, calculated conduction velocities using electrogram data were interpolated over the surface mesh via Radial Basis Function (RBF) interpolation method. All algorithms were implemented in the OpenEP Python library (openep-py), with visualisation tools provided in the complementary graphical interface, EP Workbench. Results An extensible "Analysis" feature was created in openep-py, to provide conduction velocity and divergence calculations which are fully customisable through the graphical interface based on user preferences. The EP Workbench desktop application displays resulting cardiac surface maps to depict calculated conduction velocity and divergence fields, together with conventional activation and voltage maps (Figure 1). The histogram analysis tool can be used to identify regions of slow conduction velocity from electroanatomic mapping data. Using a threshold of <0.3m/s, two slow conducting channels are visualised on the posterior wall of a test case (Figure 2). The tool is not specific to conduction velocity and may also be used to quantify other data types in EP Workbench. Finally, computed conduction velocity and divergence values and surface maps are stored in the standard OpenEP data structure, allowing further analysis following data export. Conclusion We have extended OpenEP to provide a comprehensive set of tools for conduction velocity calculation and analysis which will facilitate investigation of the structural and functional basis of conduction velocity alterations in disease states.

Digital phantom versus patient-specific radiation dosimetry in adult routine thorax CT examinations.

The aim of this study was to compare the organ doses assessed through a digital phantom-based and a patient specific-based dosimetric tool in adult routine thorax computed tomography (CT) examinations with reference to physical dose measurements performed in anthropomorphic phantoms. Two Monte Carlo based dose calculation tools were used to assess organ doses in routine adult thorax CT examinations. These were a digital phantom-based dosimetry tool (NCICT, National Cancer Institute, USA) and a patient-specific individualized dosimetry tool (ImpactMC, CT Imaging GmbH, Germany). Digital phantoms and patients were classified in four groups according to their water equivalent diameter (Dw). Normalized to volume computed tomography dose index (CTDIvol), organ dose was assessed for lungs, esophagus, heart, breast, active bone marrow, and skin. Organ doses were compared to measurements performed using thermoluminescent detectors (TLDs) in two physical anthropomorphic phantoms that simulate the average adult individual as a male (Alderson Research Labs, USA) and as a female (ATOM Phantoms, USA). The average percent difference of NCICT to TLD and ImpactMC to TLD dose measurements across all organs in both sexes was 13% and 6%, respectively. The average±1 standard deviation in dose values across all organs with NCICT, ImpactMC, and TLDs was±0.06 (mGy/mGy),±0.19 (mGy/mGy), and±0.13 (mGy/mGy), respectively. Organ doses decreased with increasing Dw in both NCICT and ImpactMC. Organ doses estimated with ImpactMC were in closer agreement to TLDs compared to NCICT. This may be attributed to the inherent property of ImpactMC methodology to generate phantoms that resemble the realistic anatomy of the examined patient as opposed to NCICT methodology that incorporates an anatomical discrepancy between phantoms and patients.

Theoretical screening of N-[5'-methyl-3'-isoxasolyl]-N-[(E)-1-(-2-thiophene)] methylidene]amine and its isoxazole based derivatives as donor materials for bulk heterojunction organic solar cells: DFT and TD-DFT investigation.

In the present work, the influence of aromatic ring substitution on a series of small-donor organic molecules (A, B, C, D, and E) with isoxazole cores was investigated for photovoltaic applications in organic solar cells. Frontier molecular orbital analysis, chemical reactivity descriptors, dipole moment, and population analysis showed that all the organic materials have intramolecular charge transfer abilities capable of donating electrons to the acceptor material (PCBM). The required photovoltaic parameters such as Voc, FF, Jsc, LHE, and other associated optoelectronic parameters are reported. The results demonstrate that aromatic ring substitution influences charge transfer and power conversion efficiencies of solar cells. That is, an increase in the aromatic character of a material increases its charge transfer, and as a result, its photovoltaic properties are increased. Additionally, all the investigated derivatives are good charge transporters with suitable electron reorganization energies, which are beneficial for minimizing energy loss. Hence, these organic derivatives with isoxazole backbones are promising materials and may provide fresh insights into the design of new materials for organic solar cell applications. All calculations were performed using DFT and the ORCA 4.1.0 program package as the main tool for geometry optimization and frequency calculations. The Avogadro 1.2.1 visualization tool was used to prepare all input files executed by ORCA 4.1.0. The BP86, B3LYP, and wB97M series of functionals coupled with the def2/TZVP basis set were employed for geometry optimization. All energy-related calculations were carried out using the M06-2x functional. Multiwfn version 3.7 was used for aromaticity and population analysis. Excited state and UV-visible spectra were simulated using the TD-DFT method at the CAM-B3LYP-D3, wB97X-D3, and PBE0-D3 coupled with the ma-def2-TZVP basis set. Moreover, solvent effects were incorporated using the SMD scheme as incorporated in the ORCA software. Lastly, the RIJCOSX approximations were used to speed up calculations while maintaining accuracy.

Evaluation of output factors of different radiotherapy planning systems using Exradin W2 plastic scintillator detector.

This study aims to evaluate the output factors (OPF) of different radiation therapy planning systems (TPSs) using a plastic scintillator detector (PSD). The validation results for determining a practical field size for clinical use were verified. The implemented validation system was an Exradin W2 PSD. The focus was to validate the OPFs of the small irradiation fields of two modeled radiation TPSs using RayStation version 10.0.1 and Monaco version 5.51.10. The linear accelerator used for irradiation was a TrueBeam with three energies: 4, 6, and 10MV. RayStation calculations showed that when the irradiation field size was reduced from 10 × 10 to 0.5 × 0.5cm2, the results were within 2.0% of the measured values for all energies. Similarly, the values calculated using Monaco were within approximately 2.0% of the measured values for irradiation field sizes between 10 × 10 and 1.5 × 1.5cm2 for all beam energies of interest. Thus, PSDs are effective validation tools for OPF calculations in TPS. A TPS modeled with the same source data has different minimum irradiation field sizes that can be calculated. These findings could aid in verification of equipment accuracy for treatment planning requiring highly accurate dose calculations and for third-party evaluation of OPF calculations for TPS.

Multilevel Intervention Stepped Wedge Designs (MLI-SWDs).

Multilevel interventions (MLIs) hold promise for reducing health inequities by intervening at multiple types of social determinants of health consistent with the socioecological model of health. In spite of their potential, methodological challenges related to study design compounded by a lack of tools for sample size calculation inhibit their development. We help address this gap by proposing the Multilevel Intervention Stepped Wedge Design (MLI-SWD), a hybrid experimental design which combines cluster-level (CL) randomization using a Stepped Wedge design (SWD) with independent individual-level (IL) randomization. The MLI-SWD is suitable for MLIs where the IL intervention has a low risk of interference between individuals in the same cluster, and it enables estimation of the component IL and CL treatment effects, their interaction, and the combined intervention effect. The MLI-SWD accommodates cross-sectional and cohort designs as well as both incomplete (clusters are not observed in every study period) and complete observation patterns. We adapt recent work using generalized estimating equations for SWD sample size calculation to the multilevel setting and provide an R package for power and sample size calculation. Furthermore, motivated by our experiences with the ongoing NC Works 4 Health study, we consider how to apply the MLI-SWD when individuals join clusters over the course of the study. This situation arises when unemployment MLIs include IL interventions that are delivered while the individual is unemployed. This extension requires carefully considering whether the study interventions will satisfy additional causal assumptions but could permit randomization in new settings.

Short-flow-time expansion of quark bilinears through next-to-next-to-leading order QCD

The gradient-flow formalism proves to be a useful tool in lattice calculations of quantum chromodynamics. For example, it can be used as a scheme to renormalize composite operators by inverting the short-flow-time expansion of the corresponding flowed operators. In this paper, we consider the short-flow-time expansion of five quark bilinear operators, the scalar, pseudoscalar, vector, axialvector, and tensor currents, and compute the matching coefficients through next-to-next-to-leading order QCD. Among other applications, our results constitute one ingredient for calculating bag parameters of mesons within the gradient-flow formalism on the lattice.

ARIP: A Tool for Precise Interatomic Contact Area and Volume Calculation in Proteins.

The interplay patterns of amino acid residues are pivotal in determining the tertiary structure and flexibility of proteins, which in turn are intricately linked to their functionality and interactions with other molecules. Here, we introduce ARIP, a novel tool designed to identify contact residues within proteins. ARIP employs a modified version of the dr_sasa algorithm and an atomic overlap weighted algorithm to directly calculate the contact area and volume between atoms based on their van der Waals radius. It also allows for the selection of solvent radii, recognizing that not every atom in proteins can interact with water molecules. The solvent parameters were derived from the analysis of approximately 5000 protein and nucleic acid structures with water molecules determined using X-ray crystallography. One advantage of the modified algorithm is its capability to analyze multiple models within a single PDB file, making it suitable for molecular dynamic capture. The contact volume is symmetrically distributed between the interacting atoms, providing more informative results than contact area for the analysis of intra- and intermolecular interactions and the development of scoring functions. Furthermore, ARIP has been applied to four distinct cases: capturing key residue-residue contacts in NMR structures of P4HB, protein-drug binding of CYP17A1, protein-DNA binding of SPI1, and molecular dynamic simulations of BRD4.

Contribution of advanced windows and façades to buildings decarbonization: A comprehensive review

On a global level the building sector consumes about 45.00% of energy consumption, contributes about 40.00% to emission, uses 30.00% of water and impacts the environment by generating 30.00% of waste. Although windows are important components of a building that provide natural lighting and ventilation and establish necessary contact with the external environment necessary for healthy indoor ambient, they permit entry of undesirable solar heat in summer and allow escaping heat from the indoor ambient in cold seasons, which aggravate the building needs for energy and increase its contribution to atmospheric emissions. The present investigation provides a review on research, development, and applications of advanced windows in the building sector. The introduction highlights the importance and contribution of advanced glazing technology to improving energy, comfort, and thermal performance of buildings. The review includes natural illumination and ventilation, thermal comfort and discusses the effects of window to wall ratio on natural illumination and ventilation of windows and façades. The review also covers recent developments in glazed windows and façades including performance enhancements by using reflective solar films, vacuum glazing, windows with filling materials, windows with water flow, window with phase change material, window with stagnant inert gas filling, ventilated windows and façades and windows with aerogel. A special section was also included on smart glazing for windows and façades showing the new tendencies and applications in the building industry. Since commercial programs and open access codes are handy tools for simulation and performance calculations a section is dedicated to these codes. The conclusion section contains the most relevant conclusion of the review as well as future trends in research and developments in the area. The topics included in this review can be helpful for experienced and young researchers, practicing engineers and general readers interested in windows and façades.

Mengukur Pengaruh Kualitas Pelayanan Terhadap Kepuasan dan Loyalitas Pengguna Perpustakaan UMS

This research aims to evaluate the impact or effects of effect of Service, information control, and the Library as a Place on the satisfaction and loyalty of library users. The samples in this test were active students and users who utilized library services at the Muhammadiyah University of Surakarta. Sampling was done using a purposive sampling technique to obtain a total of 150 respondents. The data management is carried out using the SmartPLS 3.0 statistical calculation tool and it can be stated that the effect of Service, Information Control, and Library as Place has a significant effect and has a positive impact on user satisfaction and loyalty so the hypothesis can be accepted.

Uniform Modeling of Heat Production Costs in Single-Family and Multi-Family Houses

To ensure a fair and extensive comparison of different heating systems four representative buildings including their heat demand for space heating and domestic hot water were defined. A model for the heat production costs has been developed based on existing methods like the VDI 2067 part 1 [1] and the simplified approach for the calculation of the "Levelised Cost of Heat" (LCoH) of the IEA SHC Task 54 "Price Cost reduction in solar thermal systems" [2]. The developed model includes the costs of purchase, installation, operation, maintenance and disposal and was implemented in an EXCEL calculation tool which enables the calculation of the total life cycle costs (LCC), the levelized cost of heat (LCoH) and the CO2 emissions over the lifetime of the system.

Stratified homotopy theory of topological ∞-stacks: A toolbox

We exploit the theory of ∞-stacks to provide some basic definitions and calculational tools regarding stratified homotopy theory of stratified topological stacks.

Idiopathic Pulmonary Fibrosis: From Common Microscopy to Single-Cell Biology and Precision Medicine.

Idiopathic pulmonary fibrosis is a chronic, progressive, and usually fatal lung disease of unknown etiology and is included in the large, complex, and heterogeneous group of interstitial lung diseases (ILD). IPF is characterized by an aberrant activation of epithelial cells and fibroblasts and excessive accumulation of extracellular matrix, resulting in the progressive destruction of the lung architecture. Here we approach the fascinating historical evolution of our knowledge about this disorder from the past century, when most efforts were focused on ILD classification and terminology, until the current century, when the explosive progress in molecular genetics, epigenetics, and multiomics, as well as the development of complex computational tools, resulted in a huge advance in unveiling the pathogenic mechanisms of the disease. Recent advances in artificial intelligence, machine and deep learning, and artificial neural networks show promising results in identifying lung fibrotic patterns in high-resolution computed tomography or helping to create a tissue genomic classifier for specific diagnosis. Finally, incipient approaches to precision medicine are ongoing in the area of pharmacogenetics.

Machine Learning Memory Kernels as Closure for Non-Markovian Stochastic Processes.

Finding the dynamical law of observable quantities lies at the core of physics. Within the particular field of statistical mechanics, the generalized Langevin equation (GLE) comprises a general model for the evolution of observables covering a great deal of physical systems with many degrees of freedom and an inherently stochastic nature. Although formally exact, GLE brings its own great challenges. It depends on the complete history of the observables under scrutiny, as well as the microscopic degrees of freedom, all of which are often inaccessible. We show that these drawbacks can be overcome by adopting elements of machine learning from empirical data, in particular coupling a multilayer perceptron (MLP) with the formal structure of GLE and calibrating the MLP with the data. This yields a powerful computational tool capable of describing noisy complex systems beyond the realms of statistical mechanics. It is exemplified with a number of representative examples from different fields: from a single colloidal particle and particle chains in a thermal bath to climatology and finance, showing in all cases excellent agreement with the actual observable dynamics. The new framework offers an alternative perspective for the study of nonequilibrium processes opening also a new route for stochastic modeling.

Performance gap analysis for Korean building energy efficiency certification

Performance gap between predicted and measured building energy use can be caused by simplifications and assumptions introduced in the building energy modeling process, uncertainties in building operation such as heating and cooling setpoint temperature, operational hours, occupant behavior, heat generated from lights and equipment, etc. In South Korea, the building energy efficiency certification system (BEEC) evaluates annual energy use intensity (EUI, kWh/m2/yr) and assigns ten certificate levels using ECO2, a EUI calculation tool developed by the Korean government that is based on ISO 52016 and DIN V 18599. Firstly, we collected information on 158 non-residential BEEC-certified buildings and their actual energy usage data. Subsequently, we analyzed the performance gap between predicted and measured EUI for 158 non-residential buildings in terms of building types, heating and cooling degree days, chiller types, gross floor area, window-wall ratio, and envelope U-value. The study reveals that the performance gap is significant, showing mean absolute error ranging from 21.3 to 417.4 kWh/m2/yr, mean biased error from −3.2 to 73.4 %, and the coefficient of variance of the root mean squared error from 57.0 to 304.9 %, respectively. It is also noteworthy that even individual building’s annual consumption during eight years (2014–2021) is annually fluctuating, indicating that even the performance gap is a moving target. This study reassures that building energy certification in the design stage should be understood as a rating, not for a prediction because of many unpredictable and stochastic behavior of building energy systems and occupants.

The MED_EAT-IT approach: A modelling study to develop feasible, sustainable and nutritionally targeted dietary patterns based on the Planetary health diet

In 2019, the EAT-Lancet Commission introduced the Planetary Health Diet (PHD), a guide for creating 2500 kcal/day country-specific sustainable diets that promote health while reducing the environmental impact associated with food systems. The PHD was previously adapted to the Italian food context, resulting in the EAT-IT dietary pattern. However, this adaptation revealed several challenges in terms of nutritional adequacy, feasibility, and environmental impact. This study reports on strategies to improve the previous pattern and align it more closely with the Mediterranean Diet, resulting in the MED_EAT-IT pattern. The study also explores feasible strategies for adapting this pattern to different energy targets, enhancing its scalability and promoting personalized approaches. For the optimization of this pattern, a specific calculation tool was developed to introduce variation to the pattern, considering realistic and feasible serving sizes and frequency of consumption. This tool integrates a defined food ontology, food composition data, and two environmental impact metrics (Carbon and Water Footprint). To optimize nutritional adequacy, several adaptations of the amount within the different food groups were made, for instance by increasing cereals and animal source by 25.5% kcal/day and 36.2% kcal/day respectively compared to EAT-IT. The resulting 2500 kcal/die pattern meets all nutritional requirements except for vitamin D and does not hamper the possibility to limit environmental impact (Carbon Footprint increased only by 12.2% but Water Footprint decreased by 6.3%). Lower energy targets were achieved by modulating amounts of the different food groups to ensure nutritional adequacy. The strategies and tools proposed here could aid in optimizing dietary plans, evaluating their potential for environmental impact reduction, and identifying issues that could hinder their adoption. Furthermore, the analyses carried out pave the way for the potential future development of new or improved foods that may contribute to the optimization of nutritional and environmental impact of diets.

Computational methods for processing and interpreting mass spectrometry-based metabolomics.

Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.

Northern Nevada Public Health: Utilizing the Public Health Workforce Calculator and Workforce Capacity Self-assessment Tools to Develop a Framework for Workforce Investment.

Health departments nationally are critically understaffed and lack infrastructure support. By examining current staffing and allocations through a Foundational Public Health Services (FPHS) lens at the Northern Nevada Public Health (NNPH), there is an opportunity to make a strong case for greater investment if current dedicated full-time equivalents are inadequate and to guide which investments in public health workforce are prioritized. To assess the use of the Public Health Workforce Calculator (calculator) and other tools to identify and prioritize FPHS workforce needs in a field application. Field application of the calculator in conjunction with the use of FPHS workforce capacity self-assessment tools. NNPH. NNPH and Public Health Foundation (PHF). From June 2022 through April 2023, PHF collaborated with NNPH, serving Washoe County, to provide expertise and assistance as NNPH undertook an assessment of its workforce needs based upon the FPHS model. Comparison of the calculator output with FPHS workforce capacity self-assessment tools. The calculator and the FPHS capacity self-assessment process yielded complementary FPHS workforce capacity gap data. The use of a structured and transparent process, coupled with additional tools that included prioritizing needs, provided a viable and sustainable process for public health workforce investment planning. NNPH successfully utilized the results to bolster a supplemental funding request and a state public health appropriation. The use of the calculator and an FPHS workforce capacity self-assessment in a facilitated and structured process such as that used by NNPH to identify staffing priorities may hold promise as an approach that could be used to support decision-making and justification for infrastructure resources when funding for public health increases in the future.

Advancements in artificial intelligence-driven techniques for interventional cardiology.

This paper aims to thoroughly discuss the impact of artificial intelligence (AI) on clinical practice in interventional cardiology (IC) with special recognition of its most recent advancements. Thus, recent years have been exceptionally abundant in advancements in computational tools, including the development of AI. The application of AI development is currently in its early stages, nevertheless new technologies have proven to be a promising concept, particularly considering IC showing great impact on patient safety, risk stratification and outcomes during the whole therapeutic process. The primary goal is to achieve the integration of multiple cardiac imaging modalities, establish online decision support systems and platforms based on augmented and/or virtual realities, and finally to create automatic medical systems, providing electronic health data on patients. In a simplified way, two main areas of AI utilization in IC may be distinguished, namely, virtual and physical. Consequently, numerous studies have provided data regarding AI utilization in terms of automated interpretation and analysis from various cardiac modalities, including electrocardiogram, echocardiography, angiography, cardiac magnetic resonance imaging, and computed tomography as well as data collected during robotic-assisted percutaneous coronary intervention procedures. Thus, this paper aims to thoroughly discuss the impact of AI on clinical practice in IC with special recognition of its most recent advancements.