Development Of Modeling Tools Research Articles

ЗАСТОСУВАННЯ СУЧАСНИХ МЕТОДІВ І ТЕХНОЛОГІЙ МОДЕЛЮВАННЯ ПРОГРАМНОГО ЗАБЕЗПЕЧЕННЯ У ПІДГОТОВЦІ ІТ-ФАХІВЦІВ

Methodological approaches to software modeling are considered in the article. Proposed modeling methods and tools for software development for IT projects. The importance of understanding the basics of modeling and analytics for modern IT specialists is determined. The research is based on the analysis of modern methods and technologies, such as Model-Driven Development (MDD) and Unified Modeling Language (UML), which contribute to the effective creation and management of complex software systems. The focus is on the importance of visualizing requirements, optimizing design, and improving communication between team developers. In particular, it is emphasized that modeling helps to detect errors in the early stages, which significantly reduces the risks and costs associated with their correction in the later stages of software development. The study analyzes the impact of adaptive methodologies such as Agile and DevOps on modeling, noting its role in enabling flexibility and rapid response to changing requirements. The results of the study confirm that the mastery of modern approaches to modeling is a necessary component of the professional training of future IT specialists, which allows them to successfully adapt to the dynamic IT market. Modeling is one of the key stages of the software development process, which ensures the formalization of requirements, the structuring of system components, and the creation of a basis for further design and implementation. Modern modeling methods and technologies make it possible not only to increase the efficiency of the development process but also to ensure the flexibility of systems in response to dynamic changes in business requirements. The use of modern modeling methodology is an important stage in the educational process because it helps students not only understand the basics of design but also prepares them for the challenges of real projects in the field of information technologies.

Participatory Method to Business Model Innovation for Students

The past few years have been a period of intense transformation. Pandemics, innovative technology like chatbots, and the ever-evolving needs of society serve as a powerful illustration of the world's constant evolution. Equipping students with business model innovation (BMI) skills across disciplines is crucial as it fosters their ability to innovate not just products and processes, but entire businesses, which is essential in an everchanging environment. Furthermore, integrating remote participative modelling (PM) into the curriculum is essential as it enhances students’ online collaboration skills, vital in today's modern team dynamics. A previous study at a research-intensive university explored the usability of online platforms during BMI within a Business Engineering module focused on entrepreneurship and innovation. MURAL, a virtual whiteboard software application, was used in this study to facilitate remote PM sessions utilising the Business Model Canvas (BMC). While this study found that MURAL is easy-to-use within this specific context, a key limitation emerged. The built-in BMC template within MURAL was found to be insufficient for guiding students through a systematic transformation of a traditional business model into a new iteration. To address this limitation, this current study focuses on enhancing a well-established Business Model Development Tool (BMDT), the BMC, within the context of undergraduate engineering education. We propose a method, called EDU-BMI, specifically designed to support students during PM sessions focused on BMI activities within the MURAL platform. Drawing inspiration from PICO, an educational personal ideation companion, EDU-BMI incorporates features that address educational, social, and motivational aspects. The method is embedded within MURAL and validated through a two-group participant study. Observations of the modelling sessions were used to gather valuable insights into the method's effectiveness and will inform future teaching practices. Integrating EDU-BMI into MURAL has the potential to improve the effectiveness of remote PM sessions for students engaged in BMI activities. Accessibility for modellers will also be ensured by leveraging a free, online tool like MURAL. Future research will focus on evaluating the method in an undergraduate engineering education context, refining EDU-BMI based on the feedback received from participants.

Frequency-domain patterns in foot-force line-of-action: an emergent property of standing balance control.

A recent line of work suggests that the net behavior of the foot-ground interaction force provides insight into quiet-standing-balance dynamics and control. Through human-subject experiments, Boehm et al. found that the relative variations of the center of pressure and force orientation emerge as a distinct pattern in the frequency domain, termed the "intersection-point (IP) height." Subsequent empirical and simulation-based studies showed that different control strategies are reflected in the distribution of intersection-point height across frequency. To facilitate understanding of the strengths and limitations of the intersection-point height in describing the dynamics and control of standing, the present work establishes a spectral-based method that also enables derivation of a closed-form estimate of the intersection-point height from any linear model of quiet stance. This new method explained observations from prior work, including how the measure captures aspects of control and physiological noise. The analysis presented herein highlights the utility of the frequency-dependent foot-force dynamics in probing the balance controller and provides a tool for model development and validation to further our understanding of the neuromotor control of natural upright posture in humans.NEW & NOTEWORTHY The present work details a closed-form analytical result that reveals a direct link between frequency-domain patterns in the foot-force line-of-action and the closed-loop frequency response function of human upright stance. The analytical method developed herein dramatically simplifies the identification of the intersection-point-height measure of standing balance, and further demonstrates that the net behavior of the foot-ground interaction force quantifies essential characteristics of the underlying neuromotor control of human quiet standing.

7.L. Practice session: Is there an investment case for health at older ages?

Abstract The global demographic landscape is undergoing significant transformation due to increases in longevity and declining birth rates. According to the United Nations, the world’s population aged 65 and over is projected to more than double, reaching over 1.5 billion by 2050, while global fertility rates are expected to decline to 2.4 births per woman by 2030 and continue decreasing to 2.2 births per woman by 2050. Ageing populations have frequently concerned decision-makers due to the potential implications of changes in population age-mix for labour markets and the economy, as well as for the sustainability of public programs and spending, particularly the health sector. For example, as the share of the population at older age increases, there are concerns over how to cope with the possible economic implications of having a smaller share of people at traditional working age (with potentially more people obtaining income security through both public and private pensions) and how to respond to greater health and long-term care needs. Without appropriate policy responses, there are worries that demographic changes will have inevitable consequences for households, public finances and possibly for economic growth and development. To support policy decisions related to population ageing it is important to have informative evidence and modelling tools that allow policymakers to better understand both the implications of population ageing as well as the consequences of policy action or inaction. To this end, this session presents three recently developed modelling tools that aim to address a number of policy relevant questions related to ageing: 1) How does ageing affect health financing and are health systems unsustainable? 2) How does ageing affect economic objectives and does health at older ages matter? 3) Can we develop economically relevant measures of health at older ages if older people do not have formal ties to the labour force? Using data and analyses from a range of European countries, we will demonstrate empirically that the way ageing affects health financing systems, economies, and societies more broadly reflects policy choices and is not an inevitable consequence of demographic changes. Key messages • Policymakers must prioritize informed policies to comprehensively understand the impact of aging on health financing and economies, facilitating the development of sustainable solutions. • Immediate action is imperative to address the implications of aging on labor markets and public spending, ensuring future economic stability and safeguarding societal well-being.

Simulation-based evaluation of the Pharmpy Automatic Model Development tool for population pharmacokinetic modeling in early clinical drug development.

The Pharmpy Automatic Model Development (AMD) tool automates the building of population pharmacokinetic (popPK) models by utilizing a systematic stepwise process. In this study, the performance of the AMD tool was assessed using simulated datasets. Ten true models mimicking classical popPK models were created. From each true model, dataset replicates were simulated assuming a typical phase I study design-single and multiple ascending doses with/without dichotomous food effect, with rich PK sampling. For every dataset replicate, the AMD tool automatically built an AMD model utilizing NONMEM for parameter estimation. The AMD models were compared to the true and reference models (true model fitted to simulated datasets) based on their model components, predicted population and individual secondary PK parameters (SP) (AUC0-24, cmax, ctrough), and model quality metrics (e.g., model convergence, parameter relative standard errors (RSEs), Bayesian Information Criterion (BIC)). The models selected by the AMD tool closely resembled the true models, particularly in terms of distribution and elimination, although differences were observed in absorption and inter-individual variability components. Bias associated with the derived SP was low. In general, discrepancies between AMD and true SP were also observed for reference models and therefore were attributed to the inherent stochasticity in simulations. In summary, the AMD tool was found to be a valuable asset in automating repetitive modeling tasks, yielding reliable PK models in the scenarios assessed. This tool has the potential to save time during early clinical drug development that can be invested in more complex modeling activities within model-informed drug development.

The BioRECIPE Knowledge Representation Format.

The BioRECIPE (Biological system Representation for Evaluation, Curation, Interoperability, Preserving, and Execution) knowledge representation format was introduced to standardize and facilitate human-machine interaction while creating, verifying, evaluating, curating, and expanding executable models of intra- and intercellular signaling. This format allows a human user to easily preview and modify any model component, while it is at the same time readable by machines and can be processed by a suite of model development and analysis tools. The BioRECIPE format is compatible with multiple representation formats, natural language processing tools, modeling tools, and databases that are used by the systems and synthetic biology communities.

Modeling of Wind Power Plants and Their Impact on Economic and Environmental Development

Abstract This research addresses the environmental dynamics of wind power plants and their impact on the economy and environment. A system dynamics framework is used as a tool for model development since it accommodates relationships between complex and nonlinear variables affecting the wind power plants and their impact on the economy and environment. The scientific contribution of this research is the creation of scenario modeling that describes the interrelationships of variables and parameters affecting wind power plants and their impact. By changing the structure of the model, projections on the future of wind power plant generation can be estimated. Several scenarios being developed include adding turbines in the Sidrap and Jeneponto regencies to increase the fulfillment ratio of wind energy and scenarios to reduce CO2 emissions using solid direct air capture (S-DAC). The data and information used in this research come from the Central Statistics Agency, articles on wind energy power plants, and data from related previous studies. These models and scenarios can be applied in other regions by adjusting the parameter values of the case study model. Total wind energy depends on density, wind speed, blade cross-sectional area, and the efficiency of the Betz limit. With the addition of 25 turbines in Sidrap and 20 turbines in Jeneponto, the average fulfillment ratio is estimated to increase by around 7% due to increased production. Meanwhile, total CO2 emissions are estimated to decrease by approximately 45% due to solid direct air capture.

The optimal experiment? Influence of solution strategies on model-based optimal experimental design

The optimal experiment? Influence of solution strategies on model-based optimal experimental design

Crop Yield Prediction using Machine Learning

Weather profoundly impacts agricultural outcomes, making accurate crop prediction vital for farmers' decision-making. This abstract presents a comprehensive overview of weather-based crop prediction, emphasizing its significance, key components, and methodologies. The process begins with the collection and analysis of historical weatherdata encompassing variables such as temperature, precipitation, humidity, and sunlight. Utilizing Python programming and data visualization libraries like Pandas and Matplotlib facilitates the exploration and visualization of this data, revealing trends and patterns. Machine learning algorithms, including regression and ensemble methods, are employedto develop predictive models. These models leverage historical weather data to forecast future crop yields accurately. Python's extensive libraries, such as Scikit-learn and TensorFlow, offer robust tools for model development and evaluation. Incorporating advanced technologies like remote sensing and satellite imagery further refines the prediction process. These tools provide real-time insights into crop health and growth, enhancing the precision of forecasts. Ultimately, weather-based crop prediction serves as a valuable decision support tool for farmers, enabling informed choices regarding planting, irrigation, and harvesting practices. By harnessing historical weather data, machine learning algorithms, and innovative technologies, stakeholders can optimize agricultural productivity, mitigate risks, and contribute to global food security

Multidimensional Model of Information Struggle with Impulse Perturbation in Terms of Levy Approximation

The focus of this research was on building a decision support system for a model that characterizes the conflict interaction of n-dimensional complex systems with non-trivial internal structures. The interpretation of the new model was focused on information warfare as the impact of rare events that quickly change certain perceptions of a large number of people. Consequently, the support for various ideas experiences stochastic jumps, a phenomenon observable through a non-classical Levy approximation scheme. The essence of our decision support system lies in its ability to navigate the complex dynamics of conflict interaction among multifaceted systems. Through the utilization of advanced modeling techniques, our aim is to illuminate the complicated interplay of factors influencing information warfare and its cascading effects on societal perceptions and behaviors. Key components of our decision support system encompass model development, simulation capabilities, data integration, and visualization tools. The significance of our work lies in its potential to inform policy formulation, conflict resolution strategies, and societal resilience in the face of information warfare. By providing decision-makers with actionable intelligence and foresight into emerging threats and opportunities, our decision support system serves as a valuable tool for navigating the complexities of modern conflict dynamics. In conclusion, developing a decision support system for modeling conflict interaction in complex systems represents an essential step toward enhancing our understanding of information warfare and its consequences. Through interdisciplinary collaboration and innovative modeling techniques, we aim to provide stakeholders with the insights and capabilities needed to navigate the developing landscape of conflict and ensure the stability and resilience of society.

Global change impacts on mineral nutritional quality of cereal grains: Coordinated datasets and analyses to advance a systems-based understanding

Global change impacts on mineral nutritional quality of cereal grains: Coordinated datasets and analyses to advance a systems-based understanding

Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review.

A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy. Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model. A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions. There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland. https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.

Establishing a nomogram to predict refracture after percutaneous kyphoplasty by logistic regression.

Several studies have examined the risk factors for post-percutaneous kyphoplasty (PKP) refractures and developed many clinical prognostic models. However, no prior research exists using the Random Forest (RF) model, a favored tool for model development, to predict the occurrence of new vertebral compression fractures (NVCFs). Therefore, this study aimed to investigate the risk factors for the occurrence of post-PKP fractures, compare the predictive performance of logistic regression and RF models in forecasting post-PKP fractures, and visualize the logistic regression model. We collected clinical data from 349 patients who underwent PKP treatment at our institution from January 2018 to December 2021. Lasso regression was employed to select risk factors associated with the occurrence of NVCFs. Subsequently, logistic regression and RF models were established, and their predictive capabilities were compared. Finally, a nomogram was created. The variables selected using Lasso regression, including bone density, cement distribution, vertebral fracture location, preoperative vertebral height, and vertebral height restoration rate, were included in both the logistic regression and RF models. The area under the curves of the logistic regression and RF models were 0.868 and 0.786, respectively, in the training set and 0.786 and 0.599, respectively, in the validation set. Furthermore, the calibration curve of the logistic regression model also outperformed that of the RF model. The logistic regression model provided better predictive capabilities for identifying patients at risk for post-PKP vertebral fractures than the RF model.

VSCode-Antimony: a source editor for building, analyzing, and translating antimony models.

Developing biochemical models in systems biology is a complex, knowledge-intensive activity. Some modelers (especially novices) benefit from model development tools with a graphical user interface. However, as with the development of complex software, text-based representations of models provide many benefits for advanced model development. At present, the tools for text-based model development are limited, typically just a textual editor that provides features such as copy, paste, find, and replace. Since these tools are not "model aware," they do not provide features for: (i) model building such as autocompletion of species names; (ii) model analysis such as hover messages that provide information about chemical species; and (iii) model translation to convert between model representations. We refer to these as BAT features. We present VSCode-Antimony, a tool for building, analyzing, and translating models written in the Antimony modeling language, a human readable representation of Systems Biology Markup Language (SBML) models. VSCode-Antimony is a source editor, a tool with language-aware features. For example, there is autocompletion of variable names to assist with model building, hover messages that aid in model analysis, and translation between XML and Antimony representations of SBML models. These features result from making VSCode-Antimony model-aware by incorporating several sophisticated capabilities: analysis of the Antimony grammar (e.g. to identify model symbols and their types); a query system for accessing knowledge sources for chemical species and reactions; and automatic conversion between different model representations (e.g. between Antimony and SBML). VSCode-Antimony is available as an open source extension in the VSCode Marketplace https://marketplace.visualstudio.com/items?itemName=stevem.vscode-antimony. Source code can be found at https://github.com/sys-bio/vscode-antimony.

Developing of Authentic Assessment Model Learning to Appreciate Drama Texts: A Pilot Test with Senior High School Students

This research is motivated by a lack of teacher attention in managing literary appreciation learning with evidence of the lack of completeness of the assessment tools in the teacher's lesson plans. Therefore, the purpose of this study was to develop an authentic assessment model to measure the achievement of basic competence (KD) in learning the ability to appreciate literary texts (drama) for the class XI 2013 curriculum at the SMA/MA/SMK level, which focused on psychomotor and affective domains which were the focus of learning achievement on literary appreciation by first perform needs analysis, KD analysis, material analysis, and student analysis. To achieve the objectives of this study, the theory of assessment and authentic assessment was used; learning theory and development of learning device models; literary and educational theory; and learning theory of the ability to appreciate literary texts. The design implications for the development of this model refer to the 4-D model suggested by Thiagarajan, Semel and Semel (1974) which consists of 4 stages, namely define, design, develop and disseminate. The type of research is R D development research. The data of this research consists of two groups, namely qualitative data in the form of assessment model development tools, and quantitative data in the form of achievement scores of performance tasks with authentic instruments whose limited trials and dissemination were carried out in Senior High School in Padang city. The result of the research is an assessment model that is based on social cognitive learning theory, constructivism, and meaningful learning, learning syntax that focuses on students with representative assessment tools. The results of testing the assessment tool which consisted of 10 types of instruments were concluded to be able to measure the level and type of appreciation of students' literature with a "Good" qualification.

Simulating the Impact of Ore and Water Quality on Flotation Recovery during the Life of a Mine

Blending of different ore types in the concentrator feed contributes significantly to maintaining a high recovery of valuable minerals with required grades in the concentrate. It is feasible to develop an ore-blending scheme over the life of a mine already in the design phase of the plant. In addition to ore characteristics, water quality is known to impact mineral recovery. A blending plan could also be developed for the different water streams of a future concentrator. This paper describes a novel modeling and simulation approach to predict metallurgical response combining ore types and water quality. The model is based on kinetic laboratory flotation test data, and it was tested on a case study. As a result, rougher flotation grade-recovery curves dependent on ore types and water quality are presented over the predicted life of the mine. The simulation results can be exploited in project design to maximize the recovery of valuable minerals and to ensure environmentally sound and profitable mining operations. Overall, the developed modeling tool can be applied widely for minerals processed by using froth flotation and water types available for kinetic laboratory flotation tests.

Efficiency Maps of Shifted Inductances Axes Permanent Magnet Synchronous Motors

In this contribution, a tool developed for the study of the efficiency maps of shifted inductances axes permanent magnet synchronous motors (SIAPMSMs) is presented and made available to the readers. The research builds upon a shared foundation that has already been established in previous works focused on the power capability of synchronous machines. This contribution is an extension of previous works dedicated to the power capabilities of shifted inductances axes synchronous motors where the losses were not considered. In this new contribution, the SIAPMSM models, which include the electromagnetic losses (joule and iron losses), are discussed. The mechanical losses are not included. The classical permanent magnet synchronous machines can be considered a particular case of a SIAPMSM. They will be used to validate the developed tools. The validation study is conducted by comparing the power capabilities and the efficiency maps of SIAPMSMs and classical PMSMs obtained by the newly developed modeling tool and a previously developed modeling tool.

AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning

AnimalAccML: An open-source graphical user interface for automated behavior analytics of individual animals using triaxial accelerometers and machine learning

Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1

Free AccessAboutSectionsView PDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareShare onFacebookTwitterLinked InEmail Go to SectionFree Access HomeService ScienceVol. 15, No. 1 Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1Guest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif BenjaafarPublished Online:13 Mar 2023https://doi.org/10.1287/serv.2023.0317Rapid developments in city infrastructure and technologies are creating numerous opportunities and inspiring innovative and emerging urban services. Among these innovations, complex systems of urban transportation and logistics have embraced advances and have been reshaped significantly. They enable innovative new urban services, which are now booming and changing everyday life for urban residents.This special issue of Service Science explores perspectives on innovation in transportation-enabled urban services. We hope that the special issue will enhance the understanding of the planning, operation, and management of such services. Contributions are expected to demonstrate rigorous model development, economic/econometric analysis, and decision-making tools based on optimization and/or data-driven approaches. Research in this special issue addresses the novel challenges and opportunities that arise in these transportation/logistics systems and relevant transportation-enabled urban services.The articles in the special issue represent an exciting diversity in research settings, methodologies, and applications. The articles are composed into two parts. Part 1 of the special issue includes four papers. In what follows, we provide a brief description of the papers in Part 1. The introduction of papers in Part 2 is rendered to the journal issue scheduled for September 2023.Yan et al. (2023) study an incentive mechanism for private parking-sharing programs in an imperfect information setting. They propose a matching-and-pricing mechanism for a driver’s demand-reporting problem in parking-sharing programs in which owners share their private parking slots with other drivers. They generate a driver-slot matching solution and show that, under the assumption that drivers do not know with certainty whether other drivers will show up to compete for the parking slots, the mechanism proposed induces drivers to truthfully report their private information about the travel plans and guarantees some desirable properties.Schwamberger et al. (2023) study the dynamic customer contacting for e-fulfillment in times of crisis. Specifically, the outbreak of the coronavirus disease 2019 pandemic increased demand for online grocery orders for both click & collect (C&C) and attended home delivery (AHD) and outstripped delivery capacity by a wide margin. They investigate a new demand management concept of proactively contacting customers and develop a decision policy for the C&C fulfillment method to address the problem of when to contact customers. They also extend this approach to the AHD setting and propose a three-step procedure to solve this problem.van der Gaast and Arslan (2023) explore the last-mile logistics operations of instant grocery delivery services. They introduce an instant delivery problem to replicate and examine two widely adopted strategies in the rapid delivery market: the personal shopper system (PSS) and the inventory owned delivery (IOD) system. They propose a tailored rolling horizon framework that utilizes column generation to browse updated delivery plans for arriving customer orders. Computational studies show that the PSS is a highly competitive strategy compared with IOD, particularly when dealing with small-sized customer orders.Ke and Mo (2023) study a one-way car-sharing system with both station-based and free-floating service types, which apply the two types in different parts of the service area. They establish a mixed-integer linear-programming model to maximize the operator profit, considering both strategic decisions of service type and operational decisions of relocation and trip selection. They propose a heuristic algorithm to effectively solve the model. Computational results demonstrate that the integrated system achieves a larger profit and higher demand fulfillment rate compared with the single station-based or free-floating system.ReferencesKe H, Mo Y (2023) Station-based or free-floating? An integrated carsharing system with dual service types. Service Sci. 15(1):58–76.Google ScholarSchwamberger J, Fleischmann M, Strauss A (2023) Feeding the nation—dynamic customer contacting for e-fulfillment in times of crisis. Service Sci. 15(1):22–40.Google Scholarvan der Gaast JP, Arslan AM (2023) Personal shopper systems in last-mile logistics. Service Sci. 15(1):41–57.Google ScholarYan P, Cai X, Chu F, Ni D, He H (2023) An incentive mechanism for private parking-sharing programs in an imperfect information setting. Service Sci. 15(1):3–21.Google Scholar Back to Top Next FiguresReferencesRelatedInformation Volume 15, Issue 1March 2023Pages 1-78, C3 Article Information Metrics Information Published Online:March 13, 2023 Copyright © 2023, INFORMSCite asGuest Editors: Niels Agatz, Soo-Haeng Cho, Hai Wang, Editor-in-Chief: Saif Benjaafar (2023) Introduction to the Special Issue on Innovation in Transportation-Enabled Urban Services, Part 1. Service Science 15(1):1-2. https://doi.org/10.1287/serv.2023.0317 PDF download

Progress in and Prospects of Genome Editing Tools for Human Disease Model Development and Therapeutic Applications.

Programmable nucleases, such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats (CRISPR)/Cas, are widely accepted because of their diversity and enormous potential for targeted genomic modifications in eukaryotes and other animals. Moreover, rapid advances in genome editing tools have accelerated the ability to produce various genetically modified animal models for studying human diseases. Given the advances in gene editing tools, these animal models are gradually evolving toward mimicking human diseases through the introduction of human pathogenic mutations in their genome rather than the conventional gene knockout. In the present review, we summarize the current progress in and discuss the prospects for developing mouse models of human diseases and their therapeutic applications based on advances in the study of programmable nucleases.