Computational Tools Research Articles

Bacteriophage λ exonuclease and a 5'-phosphorylated DNA guide allow PAM-independent targeting of double-stranded nucleic acids.

Sequence-specific recognition of double-stranded nucleic acids is essential for molecular diagnostics and in situ imaging. Clustered regularly interspaced short palindromic repeats and Cas systems rely on protospacer-adjacent motif (PAM)-dependent double-stranded DNA (dsDNA) recognition, limiting the range of targetable sequences and leading to undesired off-target effects. Using single-molecule fluorescence resonance energy transfer analysis, we discover the enzymatic activity of bacteriophage λ exonuclease (λExo). We show binding of 5'-phosphorylated single-stranded DNA (pDNA) to complementary regions on dsDNA and DNA-RNA duplexes, without the need for a PAM-like motif. Upon binding, the λExo-pDNA system catalytically digests the pDNA into nucleotides in the presence of Mg2+. This process is sensitive to mismatches within a wide range of the pDNA-binding region, resulting in exceptional sequence specificity and reduced off-target effects in various applications. The absence of a requirement for a specific motif such as a PAM sequence greatly broadens the range of targets. We demonstrate that the λExo-pDNA system is a versatile tool for molecular diagnostics, DNA computing and gene imaging applications.

Repurposing chemotherapy-induced peripheral neuropathy grading.

Chemotherapy-induced peripheral neuropathy (CIPN) is perceived differently by patients and physicians, complicating its assessment. Current recommendations advocate combining clinical and patient-reported outcomes measures, but this approach can be challenging in patient care. This multicenter European study aims to bridge the gap between patients' perceptions and neurological impairments by aligning both perspectives to improve treatment decision-making. Data were pooled from two prospective studies of subjects (n = 372) with established CIPN. Patient and physician views regarding CIPN were assessed using the National Cancer Institute Common Terminology Criteria for Adverse Events (NCI-CTCAE), Total Neuropathy Scale-clinical version (TNSc) items, and the disease-specific quality of life - Chemotherapy-Induced Peripheral Neuropathy questionnaire (QLQ-CIPN20) from the European Organization for Research and Treatment of Cancer (EORTC). To identify inherent neurotoxic severity patterns, we employed hierarchical cluster analysis optimized with k-means clustering and internally validated by discriminant functional analysis. Both NCI-CTCAE and TNSc demonstrated a significant difference in the distribution of severity grades in relation to QLQ-CIPN20 scores. However, a proportion of subjects with different neurotoxic severity grades exhibited overlapping QLQ-CIPN20 scores. We identified three distinct clusters classifying subjects as having severely impaired, intermediately impaired, and mildly impaired CIPN based on TNSc and QLQ-CIPN20 scores. No differences in demographics, cancer type distribution, or class of drug received were observed. Our results confirm the heterogeneity in CIPN perception between patients and physicians and identify three well-differentiated subgroups of patients delineated by degree of CIPN impairment based on scores derived from TNSc and QLQ-CIPN20. A more refined assessment of CIPN could potentially be achieved using the calculator tool derived from the cluster equations in this study. This tool, which facilitates individual patient classification, requires prospective validation.

A new approach for risk assessment of pulmonary toxicants: a preliminary study using didecyldimethylammonium chloride.

Current human risk assessments often rely on animal toxicity data to establish point of departure (POD) values, followed by the application of uncertainty factors. Consequently, there is growing interest in alternative toxicity testing methods that reduce reliance on animal models. In this study, we propose a novel approach for inhalation toxicity risk assessment that integrates in silico and in vitro methods. Human primary alveolar epithelial cells were exposed to aerosolized didecyldimethylammonium chloride (DDAC) to assess cytotoxicity. This was followed by transcriptome analysis and biological pathway investigation, utilizing adverse outcome pathway (AOP), to calculate the POD. Additionally, human DDAC exposure was simulated using a multiple-path particle dosimetry (MPPD) model to predict exposure levels in the human alveolar region via inhalation. The results from in silico and in vitro studies were then compared, and a comprehensive risk assessment was performed. The POD for AOP 452 key events-oxidative stress, inflammation, epithelial-mesenchymal transition (EMT), apoptosis, and autophagy-was found to range between 19.0 and 23.89ng/cm2, according to benchmark dose calculation tools. The estimated human exposure to DDAC in the alveolar region under actual exposure conditions was 0.164ng/cm2/day, resulting in a margin of exposure (MOE) ranging from 121 to 145, suggesting caution regarding DDAC inhalation exposure. This study presents a novel risk assessment method that compares estimated human inhalation exposure values to in vitro results, applying the human equivalent concentration concept. Our findings demonstrate the potential for conducting human risk assessments using both in silico and in vitro methods as alternatives to traditional in vivo studies.

Dual Transition of Net Zero Carbon and Digital Transformation: Case Study of UK Transportation Sector

The UK Government’s Building Information Modelling (BIM) mandate has encouraged the utilisation of BIM within the Transportation Sector (TS), with research demonstrating positive effects of BIM. However, BIM processes are incipient to TS project implementation across the UK. This paper is carried out to understand the current BIM usage within the UK’s TS and how BIM practises and workflows contribute towards the government’s NZC approach. We used research questions derived from the population, intervention, comparison and outcome (PICO) system and inclusion and exclusion criteria to screen irrelevant information from a Systematic Literature review with 18 pieces of literature. We identified six key drivers: carbon reduction and BIM, BIM in transportation design, BIM uptake and usage in transportation, BIM in transportation construction and Digital Twins and BIM. It was identified that, with the integration of the Carbon Calculator Tool into Civil 3D, structural and material data can be obtained and areas of Embodied Carbon hotspots can be identified to contribute to reduce overall carbon across a project, which requires further collaboration between software providers and industry leaders for further streamlining the process. A limitation of this research is the requirement for wider study of differing disciplines within the TS, more qualitative research and a lack of information regarding other Carbon Calculator Tools and how compatible they are with Civil 3D.

Using health literacy principles to improve understanding of evolving evidence in health emergencies: Optimisation and evaluation of a COVID-19 vaccination risk-benefit calculator

BackgroundRisk communication tools based on epidemiological models can help inform decision-making, but must be responsive to health literacy needs to be effective. To facilitate informed choice about risks and benefits of COVID-19 vaccination, an epidemiological model called the COVID-19 Risk Calculator (CoRiCal) tool was developed by a multi-disciplinary team. AimThis paper demonstrates how to use health literacy principles to improve consumer understanding of COVID-19 and vaccine effects, using a range of methods that could be applied to any health emergency. MethodsStage 1: Health literacy optimisation and user testing to reduce improve understandability (n = 19). Stage 2: Experiments to explore the effect of risk communication formats on perceived understanding including probability, graphs, evaluative labels and comparison risks (n = 207). Stage 3: Randomised controlled trial (n = 2005) with 4 arms: 1) standard government information; 2) standard CoRiCal output based on bar graphs; 3) animation explaining bar graphs in “x per million” format; 4) animation explaining bar graphs in “1 in x chance” format. The primary outcome was knowledge about COVID-19 risk. ResultsStage 1 reduced the complexity of the text and graphs. Stage 2 showed that different risk communication formats change perceived understanding, with a preference for evaluative labels across 2 experiments and some indication people with lower health literacy had a greater preference for bar graphs. Stage 3 showed both animations increased knowledge compared to standard government information. There was no difference between the probability formats, or by health literacy level. DiscussionThe results showed that simple explanations of complex epidemiological models improve knowledge about COVID-19 and vaccination. This demonstrates how health literacy design principles and short animations can be used to support informed decision making about health emergencies.

What's new in the latest editions of the Institution's carbon calculation tools?

IStructE Head of Climate Action, Will Arnold, explains what members can expect from the third editions of How to calculate embodied carbon and The Structural Carbon Tool.

In silico Approaches for Exploring the Pharmacological Activities of Benzimidazole Derivatives: A Comprehensive Review.

This article reviews computational research on benzimidazole derivatives. Cytotoxicity for all compounds against cancer cell lines was measured and the results revealed that many compounds exhibited high inhibitions. This research examines the varied pharmacological properties like anticancer, antibacterial, antioxidant, anti-inflammatory and anticonvulsant activities of benzimidazole derivatives. The suggested method summarises In silico research for each activity. This review examines benzimidazole derivative structure-activity relationships and pharmacological effects. In silico investigations can anticipate structural alterations and their effects on these derivative's pharmacological characteristics and efficacy through many computational methods. Molecular docking, molecular dynamics simulations and virtual screening help anticipate pharmacological effects and optimize chemical design. These trials will improve lead optimization, target selection, and ADMET property prediction in drug development. In silico benzimidazole derivative studies will be assessed for gaps and future research. Prospective studies might include empirical verification, pharmacodynamic analysis, and computational methodology improvement. This review discusses benzimidazole derivative In silico research to understand their specific pharmacological effects. This will help scientists design new drugs and guide future research. Latest, authentic and published reports on various benzimidazole derivatives and their activities are being thoroughly studied and analyzed. The overview of benzimidazole derivatives is more comprehensive, highlighting their structural diversity, synthetic strategies, mechanisms of action, and the computational tools used to study them. In silico studies help to understand the structure-activity relationship (SAR) of benzimidazole derivatives. Through meticulous alterations of substituents, ring modifications, and linker groups, this study identified the structural factors influencing the pharmacological activity of benzimidazole derivatives. These findings enable the rational design and optimization of more potent and selective compounds.

Refining cPRA calculation to improve HLA compatibility assessment in organ transplantation: A detailed picture of the Paris waiting list.

The determination of panel reactive antibodies (cPRA) scores plays a critical role in assessing the immunological compatibility between organ transplant recipients and potential donors. Traditional cPRA methods focus on a limited number of HLA loci using physical cytotoxicity tests. However, advancements such as the Luminex single antigen (LSA) assay, which uses mean fluorescence intensity (MFI) of individualised HLA antigens for antibody evaluation, provide a foundation for a more precise assessment. We developed cPRAdictor, a novel cPRA calculation tool using a large series of HLA-type individuals in France with NGS. cPRAdictor was applied to a cohort of 5962 kidney transplant candidates in Paris. We analysed how extending the range of HLA specificities could affect cPRA values. Implementing cPRAdictor revealed and allowed quantification of the significant discrepancies in cPRA values that appeared when HLA loci C and DP, and antigen-specific antibodies were taken into account. Notably, over 43% of the immunised transplant candidates showed an increase in calculated cPRA values when considering C/DP loci and antigen-specific antibodies, negatively impacting their eligibility and prioritisation in the transplantation programme. These findings highlight the necessity of revisiting cPRA calculation methodologies to include a broader spectrum of immunological data, as more exhaustive and precise information regarding anti-HLA antibodies in patients' sera and donor and recipient HLA typing are available prospectively. This will strongly improve both accuracy and equity at the organ allocation step, especially for highly sensitised candidates for whom organ offers are very limited in number.

Strategy for increasing the productivity of low-permeability reservoirs at the U-XIV horizon of the field in the Mangystau region

This article discusses the actual problems of developing residual reserves in mature fields with The article analyzes modern problems of development of residual oil reserves in mature fields with low reservoir properties. The field under study is characterized by multiple productive formations, tectonic disturbances and a variety of lithological characteristics in the intervals of the Jurassic productive strata. The field is characterized by structural complexity, heterogeneity of productive strata, various degrees of reserves development and a variety of residual balance reserves. The purpose of the work was to identify areas with low reservoir properties and assess the possibilities of optimizing their exploitation to improve the efficiency of oil production. To analyze current and predicted oil recovery factors (ORF), a calculation tool was developed using methods for evaluating displacement characteristics, including the Sazonov, Kambarov, Pirverdyan, Sipachev, Nazarov, Abyzbayev and Gaisin methods. These methods take into account various dependencies of the development parameters, which allows for more accurate prediction of oil recovery factors in the studied areas. The work also includes ranking of areas by residual recoverable reserves, production rates and projected growth of the oil recovery factor. Promising areas were identified for the construction of sector geological and hydrodynamic models (GHDMs), which allows for a more detailed assessment of opportunities to improve development efficiency. GHDMs were built and adapted for two selected areas with low reservoir properties, taking into account well parameters, development history, and reservoir filtration characteristics.

Development and application of a population pharmacokinetic model repository for caffeine dose tailoring in preterm infants

ABSTRACT Background Considerable interindividual variability for the pharmacokinetics of caffeine in preterm infants has been demonstrated, emphasizing the importance of personalized dosing. This study aimed to develop and apply a repository of currently published population pharmacokinetic (PopPK) models of caffeine in preterm infants to facilitate model-informed precision dosing (MIPD). Research design and methods Literature search was conducted using PubMed, Embase, Scopus, and Web of Science databases. Relevant publications were screened, and their quality was assessed. PopPK models were reestablished to develop the model repository. Covariate effects were evaluated and the concentration–time profiles were simulated. An online simulation and calculation tool was developed as an instance. Results Twelve PopPK models were finally included in the repository. Preterm infants’ age and body size, especially the postnatal age and current weight, were identified as the most clinically critical covariates. Simulated blood concentration–time profiles across these models were comparable. Caffeine citrate-dose regimen should be adjusted according to the age and body size of preterm infants. The developed online tool can be used to facilitate clinical decision-making. Conclusions The first developed repository of PopPK models for caffeine in preterm infants has a wide range of potential applications in the MIPD of caffeine.

Basis-Set Limit CCSD(T) Energies for Large Molecules with Local Natural Orbitals and Reduced-Scaling Basis-Set Corrections.

The calculation of density-based basis-set correction (DBBSC), which remedies the basis-set incompleteness (BSI) error of the correlation energy, is combined with local approximations. Aiming at large-scale applications, the procedure is implemented in our efficient local natural orbital-based coupled-cluster singles and doubles with perturbative triples [LNO-CCSD(T)] scheme. To this end, the range-separation function, which characterizes the one-electron BSI in space, is decomposed into the sum of contributions from individual localized molecular orbitals (LMOs). A compact domain is constructed around each LMO, and the corresponding contributions are evaluated only within these restricted domains. Furthermore, for the calculation of the complementary auxiliary basis set (CABS) correction, which significantly improves the Hartree-Fock (HF) energy, the local density fitting approximation is utilized. The errors arising from the local approximations are examined in detail, efficient prescreening techniques are introduced to compress the numerical quadrature used for DBBSC, and conservative default thresholds are selected for the truncation parameters. The efficiency of the DBBSC-LNO-CCSD(T) method is demonstrated through representative examples of up to 1000 atoms. Based on the numerical results, we conclude that the corrections drastically reduce the BSI error using double-ζ basis sets, often to below 1 kcal/mol compared to the reliable LNO-CCSD(T) complete basis set references, while significant improvements are also achieved with triple-ζ basis sets. Considering that the calculation of the DBBSC and CABS corrections only moderately increases the wall-clock time required for the post-HF steps in practical applications, the proposed DBBSC-LNO-CCSD(T) method offers a highly efficient and robust tool for large-scale calculations.

Numerical modelling of global/local mechanisms and sensitivity analysis for the seismic vulnerability assessment of glass curtain walls

Façades represent complex and vulnerable components for buildings, due to the intrinsic fragility and brittleness of glass, and require specific design and calculation tools, both for ordinary and extreme design loads. In this context, the availability of consolidated and strategic modelling strategies is of utmost importance for structural safety considerations and optimal decisions. In this paper, the attention is focused on the assessment of different modelling strategies for the global and local behaviour analysis of Glass Curtain Walls (GCWs) under in-plane seismic loads. Benefits and limitations are discussed for a more refined (but expensive, “MREF”) and a simplified (and computationally efficient, but weak in capturing local phenomena, “MSIMP”) model assemblies developed in ABAQUS and SAP2000, respectively. Careful consideration is given for typical mechanisms, local responses, key performance indicators for the façade components (i.e., stress and deformation peaks) for a reference GCW under monotonic and cyclic loads, as well as for major effects due to general input parameters (i.e., contact mechanisms, clearance, friction, bare frame features, secondary components, etc.), based on a parametric sensitivity investigation. The parametric comparative results are critically discussed, with the support of an experimental prototype test from literature, and the most influencing parameters are emphasized, to improve and facilitate the development of further design and modelling optimization strategies.

Harnessing Stadium Roofs for Community Electrical Power: A Case Study of Rome’s Olympic Stadium Title

Within a city, there is a lack of space for the installation of photovoltaic panels, especially in cities with significant artistic heritage. Hence, there is a need to identify new spaces for the installation of renewable energy systems capable of supplying part of the city’s energy demand. Large infrastructures for public use such as stadiums, because of their characteristics, can become an essential resource for surrounding communities by installing photovoltaic panels on their roofs. This innovative approach can supply renewable electricity to the local community, aligning with the concept of renewable energy communities (RECs). This study focuses on the Olympic Stadium in Rome, exploring a new way to produce and share the electricity generated. An energy simulation of the photovoltaic plant was carried out by means of a transient calculation tool System Advisor Model (SAM). Then, the energy output from photovoltaics was correlated with the stadium, streetlight, and household electrical energy demands. The results highlight the suitability of the photovoltaic plant and the energy, economic, and environmental advantages derived from its exploitation.

Simplified approach to estimating the loading response of traffic infrastructures

Various numerical methods are commonly used to model the loading response of traffic infrastructures. However, the broader implementation of these methods into practice is typically limited by the high computing cost or technical complexity of the models. Computationally lighter methods are especially needed when a substantial number of calculations must be performed. Hence, this paper introduces a new simplified approach to estimating the loading response of different traffic infrastructures, e.g. roads and railways. This approach is based on the novel approximate solution for the vertical and shear stress fields caused by a uniformly distributed circular surface load. The presented theory has been validated using versatile field measurement data from different types of road structures under varied loading conditions. In the verification calculations, both the vertical stresses within the structure and the deflection of the road surface were examined. The findings indicate that the model closely aligns with the behavior observed in actual structures. The approach presented in this paper enables a quick and easy determination of the loading response of multi-layer traffic infrastructures, providing a new practical calculation tool for researchers and practitioners.

Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4.

We developed a scoring system for assessing glaucoma risk using demographic and laboratory factors by employing a no-code approach(automated coding) using ChatGPT-4. Comprehensive health checkup data were collected from the Korea National Health and Nutrition Examination Survey. Using ChatGPT-4, logistic regression was conducted to predict glaucoma without coding or manual numerical processes, and the scoring system was developed based on the odds ratios (ORs). ChatGPT-4 also facilitated the no-code creation of an easy-to-use risk calculator for glaucoma. The ORs for the high-risk groups were calculated to measure performance. ChatGPT-4 automatically developed a scoring system based on demographic and laboratory factors, and successfully implemented a risk calculator tool. The predictive ability of the scoring system was comparable to that of traditional machine learning approaches. For high-risk groups with 1-2, 3-4, and 5 + points, the calculated ORs for glaucoma were 1.87, 2.72, and 15.36 in the validation set, respectively, compared with the group with 0 or fewer points. This study presented a novel no-code approach for developing a glaucoma risk assessment tool using ChatGPT-4, highlighting its potential for democratizing advanced predictive analytics, making them readily available for clinical use in glaucoma detection.

Comparison of Monte Carlo Tools for Displacement Calculations for Protons and Heavy Ions Irradiation on Iron and EUROFER97

Abstract Ion irradiation of structural materials for the current and future generation nuclear reactors is a widespread technique used to simulate neutron irradiation in the context of material science research. During the designing phase of irradiation experiments Monte Carlo transport codes are often employed to quantify the radiation exposure, by estimating the displacements per atom (dpa) quantity. From the available displacement calculation codes, four popular choices were selected: cern-fluka, mcnp, phits, and srim. These codes will be used to simulate the irradiation effects of proton and Fe ions on pure iron (G379) and EUROFER97 alloy (a common material in fusion applications) targets. Results coming from the different software are presented in the form of damage profiles and of number of displacements as a function of the primary beam energy. This study identifies discrepancies between the codes' outputs and provides optimized simulation setup parameters.

Structure-based Virtual Screening and Molecular Dynamic Simulation Approach for the Identification of Terpenoids as Potential DPP-4 Inhibitors.

Diabetes mellitus is a metabolic disorder where insulin secretion is compromised, leading to hyperglycemia. DPP-4 is a viable and safer target for type 2 diabetes mellitus. Computational tools have proven to be an asset in the process of drug discovery. In the present study, tools like structure-based virtual screening, MM/GBSA, and pharmacokinetic parameters were used to identify natural terpenoids as potential DPP-4 inhibitors for treating diabetes mellitus. Structure-based virtual screening, a cumulative mode of elimination technique, was adopted, identifying the top five potent hit compounds depending on the docking score and nonbonding interactions. According to the docking data, the most important contributors to complex stability are hydrogen bonding, hydrophobic interactions, and Pi-Pi stacking interactions. The dock scores ranged from -6.492 to -5.484 kcal/mol, indicating robust ligand-protein interactions. The pharmacokinetic characteristics of top-scoring hits (CNP0309455, CNP0196061, CNP0122006, CNP0 221869, CNP0297378) were also computed in this study, confirming their safe administration in the human body. Also, based on the synthetic accessibility score, all top-scored hits are easily synthesizable. Compound CNP0309455 was quite stable during molecular dynamic simulation studies. Virtual database screening yielded new leads for developing DPP-4 inhibitors. As a result, the findings of this study can be used to design and develop natural terpenoids as DPP-4 inhibitors for the medication of diabetes mellitus.

Dietaryindex: a user-friendly and versatile R package for standardizing dietary pattern analysis in epidemiological and clinical studies

BackgroundFew standardized and open-source tools exist for calculating multiple dietary pattern indexes from dietary intake data in epidemiological and clinical studies. ObjectivesThe primary aim is to develop and validate dietaryindex, a user-friendly and versatile R package that standardizes the calculation of dietary indexes. MethodsDietaryindex can calculate multiple dietary indexes of high interest in research, including the Healthy Eating Index (HEI)-2020, the Alternative HEI 2010, the Dietary Approaches to Stop Hypertension Index, the Alternate Mediterranean Diet Score, the Dietary Inflammatory Index, the American Cancer Society 2020 dietary index, and the Planetary Health Diet Index from the EAT-Lancet Commission. The package includes generic dietary index calculation functions that accept any dietary assessment with preprocessed serving sizes of food groups and nutrients as defined by the research group that developed each index. For ease of use and to eliminate any need for data preprocessing, dietaryindex also offers 1-step functions that directly reference common datasets and tools, including the National Health and Nutrition Examination Survey (NHANES), the Automated Self-Administered 24-h Dietary Assessment Tool (ASA24), the Diet History Questionnaire III (DHQ3), and the Block Food Frequency Questionnaire. At least 2 independent researchers validated the serving size definitions and scoring algorithms of dietaryindex. ResultsIn our validation process, dietaryindex demonstrated full accuracy (100%) in all generic functions with 2-decimal rounding precision when compared with hand-calculated results. Similarly, using NHANES 2017–2018 data and ASA24 and DHQ3 example data, the HEI-2015 outputs from dietaryindex aligned (99.95%–100%) with results using the Statistical Analysis System codes from the National Cancer Institute. ConclusionsDietaryindex is a user-friendly, versatile, and validated informatics tool for standardized dietary index calculations. We have open-sourced all validation files and codes with detailed tutorials on GitHub (https://github.com/jamesjiadazhan/dietaryindex).

State of Exception or the New Normal? An Evaluation of Pandemic Tracking Technologies and Their Implications for Citizenship

This article forms a critical examination of how the increased digitalization of daily life, exemplified by pandemic digital technologies of tracking apps and digital Covid-19 passports, affects ethical principles of privacy, subjectivity and autonomy, fundamental in a traditional notion of citizenship. The pandemic crisis has been an example of the increasing use of automated technologies across various domains, where society has become deeply reliant on these technologies. I argue that in ethical terms, the pandemic crisis has moved the borders of what is accepted by governments and citizens and have normalised far-reaching tools of registration, calculation, and surveillance. The pandemic state of exception has necessitated and legalised a temporary suspension of normal civil rights and replaced them with spatial ordering, automated surveillance, and the application of advanced and intrusive digital monitoring tools. Based on an empirical account of tracking apps I discuss the consequences for citizenship based on two theoretical figures: Michel Foucault´s concept of “bio-politics” and the concept of “a state of exception” as theorized by German political philosopher Carl Schmidt. Where traditional concepts of citizenships have focused on individual rights, responsibilities and articipation in civic life, bio-politics emphasizes a management and regulation of populations with less focus on individual agency and more on statistical analysis and control mechanisms. Technologies and discourses of automation are inter-related; digital innovation legitimize and strengthen an adherent automation of political logics, framed as a politics of necessity, where calculative predictions and technological facts leave little room for political subjectivity and values. In the concluding section it is discussed whether the rapid automation catalyzed by the pandemic was a state of emergency, necessitated by a pandemic threat, or rather represents a more profound change, a new normal for citizenship.

A Fast and Accurate Tool for Calculation of Characteristics of Capacitive Micromachined Ultrasound Transducers (CMUT)

A Fast and Accurate Tool for Calculation of Characteristics of Capacitive Micromachined Ultrasound Transducers (CMUT)