Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminate...
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Rivers are vital for sustaining human life as they foster social development, provide drinking water, maintain aquatic ecosystems, and offer recreational spaces. However, most rivers are being increasingly contaminated by pollutants from non-point sources, urbanization, and other sources. Consequently, real-time river water quality modeling is essential for managing and protecting rivers from contamination, and its significance is growing across various sectors, including public health, agriculture, and water treatment systems. Therefore, a real-time river water quality simulation toolbox was developed using machine learning (ML) and an application program interface (API). To create the toolbox, models that simulated water quality parameters such as chlorophyll a (Chla), dissolved oxygen (DO), total nitrogen (TN), total organic carbon (TOC), and total phosphorus (TP) at each point in the Nakdong River were constructed. The models were constructed using Artificial neural network (ANN), Random Forest (RF), support vector machines (SVM), and data from API. Subsequently, hyperparameter optimization was conducted to enhance the model's performance. During training, the models' performances were evaluated and compared based on the data sampling method and ML algorithms. Models trained with random sampling data outperformed those trained with time-series data. Among the algorithm models that used random sampling data, the RF exhibited the best performance. The average coefficient of determination (R2) values for each water quality simulation with randomly sampled data using RF for DO, TN, TP, Chl-a, and TOC were 0.79, 0.65, 0.74, 0.45, and 0.48, respectively. For ANN, they were 0.7, 0.51, 0.64, 0.35, and 0.35, respectively, and for SVM, they were 0.73, 0.51, 0.59, 0.21, and 0.3, respectively. The Chl-a and TOC models exhibited relatively poor performance, whereas the DO, TN, and TP models demonstrated superior performance. Diversifying the input data variables is necessary
A significant effort to upgrade the Program to Optimize Simulated Trajectories II (POST2), a heritage flight mechanics tool developed at NASA Langley Research Center, is ongoing to support current and future NASA miss...
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ISBN:
(数字)9781624107115
ISBN:
(纸本)9781624107115
A significant effort to upgrade the Program to Optimize Simulated Trajectories II (POST2), a heritage flight mechanics tool developed at NASA Langley Research Center, is ongoing to support current and future NASA missions. To meet mission requirements, it may be necessary for multiple specialized computational tools to interact to properly assess a system. An application programming interface for POST2 was developed to allow easier access for users and to enable communication between external applications. A demonstration of the POST2 application programming interface is presented by utilizing common engineering platforms such as MATLAB and Python.
Purpose This study aims to identify the developer's objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programmin...
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Purpose This study aims to identify the developer's objectives, current state-of-the-art techniques, challenges and performance evaluation metrics, and presents outlines of a knowledge-based application programming interfaces (API) recommendation system for the developers. Moreover, the current study intends to classify current state-of-the-art techniques supporting automated API recommendations. Design/methodology/approach In this study, the authors have performed a systematic literature review of studies, which have been published between the years 2004-2021 to achieve the targeted research objective. Subsequently, the authors performed the analysis of 35 primary studies. Findings The outcomes of this study are: (1) devising a thematic taxonomy based on the identified developers' challenges, where mashup-oriented APIs and time-consuming process are frequently encountered challenges by the developers;(2) categorizing current state-of-the-art API recommendation techniques (i.e. clustering techniques, data preprocessing techniques, similarity measurements techniques and ranking techniques);(3) designing a taxonomy based on the identified objectives, where accuracy is the most targeted objective in API recommendation context;(4) identifying a list of evaluation metrics employed to assess the performance of the proposed techniques;(5) performing a SWOT analysis on the selected studies;(6) based on the developer's challenges, objectives and SWOT analysis, presenting outlines of a recommendation system for the developers and (7) delineating several future research dimensions in API recommendations context. Research limitations/implications This study provides complete guidance to the new researcher in the context of API recommendations. Also, the researcher can target these objectives (accuracy, response time, method recommendation, compatibility, user requirement-based API, automatic service recommendation and API location) in the future. Moreover, the developers can ov
In this study, buckling restrained braces (BRBs) are optimized through application programming interface be-tween simulation and discrete optimization. It is aimed to maximize the energy dissipation capacity of BRBs c...
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In this study, buckling restrained braces (BRBs) are optimized through application programming interface be-tween simulation and discrete optimization. It is aimed to maximize the energy dissipation capacity of BRBs considering the American Institute of Steel Construction (ANSI/AISC 341). Unlike other studies, BRBs modeled in the finite element packaged software are directly linked to optimization algorithms. So, the geometric and material nonlinearities are also considered. Harmony Search Algorithm (HSA), which simulates the improvisa-tion musician performances in finding pleasing harmony, and Black Widow Optimization Algorithm (BWOA), which imitates inimitable paring attitude of black widow spiders, are taken as the optimizer tools of this study. They encoded in Microsoft Visual Basic programming language. Initially, the algorithmic performances of the HSA and BWOA are compared and evaluated on two benchmark structural engineering design problems. Af-terward, two different shaped BRBs are modeled in a finite element analysis (FEA) based software, namely ANSYS Workbench. Then, the obtained simulations are integrated with the HS and BWO algorithms throughout the application programming interface without identification of complex objective function and design con-straints in formulations. These are directly calculated by ANSYS Workbench over very simple formulas. So, the attained optimum designs of BRBs are investigated with a new approach. Furthermore, in order to see the su-preme algorithmic performances of the HSA and BWOA, all benchmark and BRB design problems are solved so-called well-established conventional standard Genetic Algorithm (sGA). Eventually, the proposed novel design methodology gives opportunity and eases to the designers since it provides convenience in solving complicated problems having nonlinear objective function and design constraints that are tiresome to encode.
The amount of data generated by vehicles has increased in recent years. Automotive manufacturers employ data processing and analysis to gain insights from the data they collect from vehicles. Contextually enriching ve...
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ISBN:
(纸本)9789531842716
The amount of data generated by vehicles has increased in recent years. Automotive manufacturers employ data processing and analysis to gain insights from the data they collect from vehicles. Contextually enriching vehicle-generated data with information describing location, weather and traffic is a way to generate even more insights into driver behaviour profiling and transportation sustainability. As the contextually enriched automotive data is usually stored in big data storage platforms, a middleware solution is needed to provide an abstraction layer for the stored data. application programming interfaces (APIs) are commonly used as a bridge between the data consumers and the collected data. This paper describes one such API for advanced analytics of contextually enriched automotive data. The collection, contextual enrichment and data model of the data offered by the API is shown, along with the APIs architecture and available functionalities. To show the usability of the API, two use cases from the automotive domain are demonstrated: (i) contextually enriched automotive data visualization;and (ii) eco-efficient driving pattern evaluation.
Context: From the past few years, application programming interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects t...
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Context: From the past few years, application programming interface (API) is widely used for mobile- and web-based application developments. Software developers can integrate third-party services into their projects to achieve their development goals efficiently using APIs;however, with the rapid increase in the number of APIs, the manual selection of Mashup-oriented API is becoming more difficult for the developer. Objective: In the COVID-19 pandemic, everyone wants an update about the latest Standard Operating Procedures (SOPs) and the latest information on COVID-19. Additionally, a software developer wants to develop an application that provides the SOPs and latest information of COVID-19;a developer can add these functionalities into an application using COVID-19-based APIs. Moreover, the current work aims at proposing a COVID-19-based API recommendation system for the developers. Method: In this study, we propose a COVID-19-based API recommendation system for developers. The recommendation system takes a developer query as input and recommends top-3 APIs and supported features, which help the developer during software development. Furthermore, the proposed COVID-19-based API recommendation system ensures the maximum participation of the developers by validating the recommended APIs and recommendation system from the expert developers using research questionnaires. Results: Additionally, the proposed COVID-19-based API recommendation system's output is validated by expert developers and evaluated on 120 expert developers' queries. In addition, experiment results show that single value decomposition achieves better prediction. Conclusion: We conclude that it is significantly important to recommend APIs along with supported features to the developer for project development, and future work is needed to take more developer's queries also to build Integrated Development Environment for the developers.
This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it inv...
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This paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.
ABSTRACTABSTRACTThis paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic a...
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ABSTRACTABSTRACTThis paper presents a C++ application programming interface for a co-evolutionary algorithm for solution and scenario generation in stochastic problems. Based on a two-space biased random-key genetic algorithm, it involves two types of populations that are mutually impacted by the fitness calculations. In the solution population, high-quality solutions evolve, representing first-stage decisions evaluated by their performance in the face of the scenario population. The scenario population ultimately generates a diverse set of scenarios regarding their impact on the solutions. This application allows the straightforward implementation of this algorithm, where the user needs only to define the problem-dependent decoding procedure and may adjust the risk profile of the decision-maker. This paper presents the co-evolutionary algorithm and structures the interface. We also present some experiments that validate the impact of relevant features of the application.
The purpose of this study is to perform a synthesis of API research. The study took stock of literature from academic journals on APIs with their associated themes, frameworks, methodologies, publication outlets and l...
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The purpose of this study is to perform a synthesis of API research. The study took stock of literature from academic journals on APIs with their associated themes, frameworks, methodologies, publication outlets and level of analysis. The authors draw on a total of 104 articles from academic journals and conferences published from 2010 to 2018. A systematic literature review was conducted on the selected articles. The findings suggest that API research is primarily a theoretical and largely focuses on the technological dimensions such as design and usage;thus, neglecting most of the social issues such as the business and managerial applications of APIs, which are equally important. Future research directions are provided concerning the gaps identified.
作者:
Wulf, JochenBlohm, IvoUniv St Gallen
Inst Informat Management Mueller Friedberg Str 8 CH-9000 St Gallen Switzerland Univ St Gallen
Inst Informat Management Data Sci & Management St Gallen Switzerland
While many firms in recent years have started to offer public application programming interfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and th...
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While many firms in recent years have started to offer public application programming interfaces (APIs), firms struggle with shaping digital platform strategies that align API design with aspired business goals and the demands of external developers. We address the lack of theory that explains the performance impacts of three API archetypes (professional, mediation, and open asset services). We couple survey data from 152 API product managers with manually coded API design classifications. With this data, we conduct cluster and regression analyses that reveal moderating effects of two value creation strategies (economies of scope in production and innovation) on the relationships between API archetype similarity and two API performance outcomes: return on investment and diffusion. We contribute to IS literature by developing a unifying theory that consolidates different theoretical perspectives on API design, by extending current knowledge on the performance effects of API design, and by empirically studying the distinct circumstances under which digital platforms facilitate economies of scope in production or in innovation. Our results provide practical implications on how API providers can align API archetype choice with the value creation strategy and the API's business objective.
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