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This research examines the implementation of Time-To-Live (TTL) caching within a Content Delivery Network (CDN) model that incorporates API integration, structured to simulate a hierarchical configuration of CDN edge servers across Indonesia's administrative tiers. The analysis centers on the influence of TTL configurations on critical performance metrics—namely latency, cache hit ratio, throughput, and bandwidth consumption. Special focus is placed on scenarios in which a 1 MB data object originating from the Central Government (Level 1) is primarily accessed through edge servers positioned at the village level (Level 5). The simulation envisions a CDN architecture where in the Central Government functions as the Main Server/Origin Server, with edge servers extending across 38 provinces (Level 2), 514 regencies (Level 3), 7,277 districts (Level 4), and 83,763 villages (Level 5). [ABSTRACT FROM AUTHOR]

The emergence of large language models (LLMs) presents significant opportunities in accounting, including optimizing current processes, extracting new information, and updating accounting measurements. However, factors such as skill gaps, perceived complexity of integration, and cost constraints have limited the implementation of LLMs in accounting. This study provides an overview of mainstream LLM utilization methods, including user interface and application programming interface, and introduces a novel approach via robotic process automation integration. The advantages and limitations of each method are discussed, accompanied by a current analysis of the time, labor, and monetary costs involved in using LLMs for accounting tasks. To facilitate practical applications, three off-the-shelf examples are provided. This study contributes to the literature and practice by summarizing and comparing LLM implementation methods, responding to the challenges raised by researchers and stakeholders, and bridging the gap between technology innovation and its practical application in accounting. JEL Classifications: M41; O14; O33; D61. [ABSTRACT FROM AUTHOR]

Romana Rust and Inés Ariza illustrate how digital fabrication projects developed by Gramazio Kohler Research at ETH Zurich are using innovative software pipelines and producing more collaborative software and digitally driven techniques to illicit much more dynamic and sustainably effective interfaces between machines and humans for the construction industry. Rust is a computational architect and Head of Research and Innovation at vyzn AG, and Ariza is an architect and researcher within the NCCR Digital Fabrication consortium at Gramazio Kohler Research, and R&D Project Lead at MESH AG. [ABSTRACT FROM AUTHOR]

One of the most critical challenges for the success of the Metaverse is interoperability amongst its virtual platforms and worlds. In this context, application programming interfaces (APIs) are essential. This study analyzes a sample of 15 Metaverse platforms. In the first step, the availability of publicly accessible APIs was examined. For those platforms offering an API, i.e., Decentraland, Second Life, Voxels, Roblox, Axie Infinity, Upland, and VRChat, the available API contents were collected, analyzed, and presented in the paper. The results show that only a few Metaverse platforms offer APIs at all. In addition, the available APIs are very diverse and heterogeneous. Information is somewhat fragmented, requiring access to several APIs to compile a comprehensive data set. Thus, standardized APIs will enable better interoperability and foster a more seamless and immersive user experience in the Metaverse. [ABSTRACT FROM AUTHOR]

One of the most critical challenges for the success of the Metaverse is interoperability amongst its virtual platforms and worlds. In this context, application programming interfaces (APIs) are essential. This study analyzes a sample of 15 Metaverse platforms. In the first step, the availability of publicly accessible APIs was examined. For those platforms offering an API, i.e., Decentraland, Second Life, Voxels, Roblox, Axie Infinity, Upland, and VRChat, the available API contents were collected, analyzed, and presented in the paper. The results show that only a few Metaverse platforms offer APIs at all. In addition, the available APIs are very diverse and heterogeneous. Information is somewhat fragmented, requiring access to several APIs to compile a comprehensive data set. Thus, standardized APIs will enable better interoperability and foster a more seamless and immersive user experience in the Metaverse.

In recent years, technological advances have led to dramatic changes in the financial ecosystem and momentum in the traditional structure of the banking industry and the need to review the traditional business models of banks. Therefore, the current study has been conducted with a qualitative-quantitative approach to explain the open banking business model in the Middle East Bank. The statistical population of this research in the qualitative part consists of middle managers of various branches of the Middle East Bank active in the field of open banking and managers of fin-techs whose statistical sample size was equal to 10 people. Also, the quantitative population of this study is the managers and employees and natural and legal customers of 16 branches of the Middle East Bank in Tehran, which was determined based on Cochran's formula equal to 370 people. The data collection tool in this study is semi-structured in-depth qualitative interviews, the data of which were analyzed using the soft systems research method, and in the quantitative part, the data were collected through a researcher-made questionnaire were collected and then analyzed by using confirmatory factor analysis and structural equations. Based on this, 94 codes extracted from interviews with experts, were classified into 74 concepts, 16 subcategories and 3 categories of customer-oriented banking, API management and regulatory developments. Quantitative findings also confirm the positive and significant impact of these three components on the open banking business model of the Middle East Bank. [ABSTRACT FROM AUTHOR]

The Spanish region of Andalusia is the world-leading olive oil producer. Its olive-dominated landscapes are among the most biodiverse drylands of the globe and prospectively among the areas most affected by climate change. This analysis used physiologically based demographic modeling (PBDM) to assess the impact of climate change on the olive/olive fly system of Andalusia. The analysis was implemented on cloud computing, allowing PBDM models to be run from any computer connected to the internet, to interface with state-of-the-art climatic drivers, and to scale efficiently with increasing computational loads and user requests. Findings include that chilling required for olive blooming will decrease in large areas of the Andalusian provinces of Jaen, Cordoba, and Sevilla, with some areas not meeting the minimum chilling threshold and some accumulating no chilling by the end of the century under the high greenhouse gas (GHG) emission scenario. Olive blooming will occur up to five weeks earlier in the Jaen, Cordoba, Sevilla, and Granada provinces, but olive yield is expected to increase or remain stable. Olive fly infestation will decrease with climate change, with infestations below the reference economic threshold of 4 % towards the end of the century in some areas under high GHG emission scenario. Measures to adapt Andalusian olive systems to climate change include: selecting olive cultivars with lower chilling requirements; implementing cover crops to enhance water use efficiency under increased CO2 concentration and uncertain precipitation projections; and targeting the spring generation of the fly and diversifying the olive landscape to reduce infestation levels.

A Decision Support System (DSS) is a useful information systems tool that supports decision-making as it aids institutional admission committees in effective and efficient decision-making. Several types of admission DSS are said to have existed over time. However, for most of the admission DSS as seen from the literature reviewed, soft copies of data are obtained from a source and uploaded onto the system, whereas, the DSS can be made to obtain data directly from the source into itself, thereby speeding up system procedures. The objectives of this study, are to develop an Application Programming Interface (API) and use mathematical methods of weighted grade points for ranking and provision of information for sensitive admission analysis and decision-making. The method of inquiry used in this study was inductive, geared majorly towards consultations of research papers, relevant materials such as educational journals, publications, websites etc., and resource persons through oral interviews. This study adopts a qualitative approach, by studying admission DSS with the aim of improving the existing systems. A Use-case scenario was used as a design model and the system architectural model and program flow were demonstrated as well. Thus, the DSS developed in this study referred to as AARISYS (Automated Admission Ranking System), was implemented using scripting (PHP & JavaScript) and markup languages (HTML & CSS) with MySQL. The web-based system fetches Applicants' data from an external Database (source) via an API, store and assign rank scores. Validation and defect tests were also used and the system results were displayed. Thus, with the aid of API, admission DSS can now obtain data from sources by itself, in contrast to the methods of inputting soft copied data, as used by other DSS.

Purpose – The study aim is to evaluate the contribution of Blockchain technology (Cryptobanking) using expected operating model (EOM) to address the pain points in reconciliation at middle and back-office operational levels in assessing the significance of this technology on return on investment. Design/methodology/approach – A structured questionnaire was designed to collect primary data using a stratified sampling method from 120 respondents working in leading Investment banks operating in the geographical locality of urban Bangalore. Demographic variables, accounting variables, data reporting variables, approach variables, variables of EOM were considered to validate the hypothesis with the help of statistical tools, namely ANOVA, and Multiple Stepwise Regression Analysis. Findings – The results obtained confirm that there is significant difference in reconciliation with implementation of an innovative business process. Financial analysis is the highest predictor of ROI when integrated with technology as the adapted Blockchain innovation in reconciliation is the most influencing factor in enhancing, improving ROI playing a pivotal role in the Investment banks. Originality/value – Blockchain technology (Cryptobanking) facilitates in transforming the reconciliation process of these banks with improved operational efficiency. Blockchain and settlement platforms offer inter-organization solutions facilitating in the reconciliation of various transactions in real-time through a trust-based network in the form of digital settlements with better consortiums.

In order to gain a competitive edge in the banking industry, direct face-to-face interactions between customers and banks are being gradually replaced by virtual interactions. Instead, they communicate using electronic devices such as smartphones, tablets, and web applications. Transaction banking, which provides commercial services for banking products to small, medium, and large corporations, is a highly typical work unit in banking. Shipping and international payments, risk management for international trade, and other services are provided. We use the Service Oriented Modelling Architecture (SOMA) methodology to solve the problem of the transaction banking unit by relocating it from a system with general dependencies to a system that is independent of the entire service and operates on a small function scale. The study found that a decision as a service (DAAS) model with an Enterprise Service Bus (ESB), Business Process Management (BPM), and Business Rule Management (BRM) solution can provide guidelines for the design of decision rules pertaining to integrated or separate business modeling, thereby aiding business unit delivery.

Dynamic Global Vegetation Models (DGVMs) provide a state‐of‐the‐art process‐based approach to study the complex interplay between vegetation and its physical environment. For example, they help to predict how terrestrial plants interact with climate, soils, disturbance and competition for resources. We argue that there is untapped potential for the use of DGVMs in ecological and ecophysiological research. One fundamental barrier to realize this potential is that many researchers with relevant expertize (ecology, plant physiology, soil science, etc.) lack access to the technical resources or awareness of the research potential of DGVMs. Here we present the Land Sites Platform (LSP): new software that facilitates single‐site simulations with the Functionally Assembled Terrestrial Ecosystem Simulator, an advanced DGVM coupled with the Community Land Model. The LSP includes a Graphical User Interface and an Application Programming Interface, which improve the user experience and lower the technical thresholds for installing these model architectures and setting up model experiments. The software is distributed via version‐controlled containers; researchers and students can run simulations directly on their personal computers or servers, with relatively low hardware requirements, and on different operating systems. Version 1.0 of the LSP supports site‐level simulations. We provide input data for 20 established geo‐ecological observation sites in Norway and workflows to add generic sites from public global datasets. The LSP makes standard model experiments with default data easily achievable (e.g., for educational or introductory purposes) while retaining flexibility for more advanced scientific uses. We further provide tools to visualize the model input and output, including simple examples to relate predictions to local observations. The LSP improves access to land surface and DGVM modelling as a building block of community cyberinfrastructure that may inspire new avenues for mechanistic ecosystem research across disciplines. [ABSTRACT FROM AUTHOR]

The evolution of deep learning (DL) has made artificial intelligence image recognition a mature technology. Recently, the use of DL to identify diabetic retinopathy (DR) has been recognized as a major challenge. Retinal abnormalities caused by DR can damage the retina and thus cause permanent damage or even blindness. Therefore, the detection of diabetes symptoms at an early stage can help to considerably reduce the risk of blindness. The differences in environments, equipment, and photographers have led to the inconsistent specifications of images and have thus affected the efficiency of the training model for classifying the DR level. If low‐quality fundus images are removed through an image quality assessment (Eye‐Quality Library, EyeQ), the number of images of the trained model would considerably reduce, in turn affecting the reliability of the training model. To solve this problem, this paper proposes a preprocessing method to strengthen the image features. The results obtained in this study revealed that the preprocessing method could increase the amount of data available for the training model. Thus, this study improved the EfficientNet model for the enhancement of the classification performance of the DR level. The results also showed that an increase in the model accuracy from 0.7727 to 0.7920 for the classification of the different stages of DR. In addition, the results revealed that the revised EfficientNet could obtain better average area under the ROC curve among the five classes (0.926) than MobileNet (0.54) and the original EfficientNet (0.922). Finally, this study implemented the proposed system by using an application programming interface (API) to enable the users to upload a fundus image to the API and obtain the DR results. [ABSTRACT FROM AUTHOR]

ChatGPT could serve as a tool for text analysis within the field of Human–Computer Interaction, though its validity requires investigation. This study applied ChatGPT to: (1) textbox questionnaire responses on nine augmented-reality interfaces, (2) interview data from participants who experienced these interfaces in a virtual simulator, and (3) transcribed think-aloud data of participants who viewed a real painting and its replica. Using a hierarchical approach, ChatGPT produced scores or summaries of text batches, which were then aggregated. Results showed that (1) ChatGPT generated sentiment scores of the interfaces that correlated extremely strongly (r > 0.99) with human rating scale outcomes and with a rule-based sentiment analysis method (criterion validity). Additionally, (2) by inputting automatically transcribed interviews to ChatGPT, it provided meaningful meta-summaries of the qualities of the interfaces (face validity). One meta-summary analysed in depth was found to have substantial but imperfect overlap with a content analysis conducted by an independent researcher (criterion validity). Finally, (3) ChatGPT's summary of the think-aloud data highlighted subtle differences between the real painting and the replica (face validity), a distinction corresponding with a keyword analysis (criterion validity). In conclusion, our research indicates that, with appropriate precautions, ChatGPT can be used as a valid tool for analysing text data.

IntroductionMethods and tools evaluating treatment effect have been primarily developed for binary type of treatment. Yet, treatment is rarely binary outside the experimental setting, varies by dosage, frequency and time. Treatment is routinely adjusted, initiated or stopped when being administered over a period of time.MethodsBoth Gaussian Process (GP) regression and Bayesian additive regression tree (BART) have been used successfully for handling complex setting involving time-varying treatments that is either adaptive or non-adaptive. Here, we introduce an application programming interface (API) that implements both BART and GP for estimating averaged treatment effect (ATE) and conditional averaged treatment (CATE) for the two-stage time-varying treatment strategies.ResultsWe provide two real applications for evaluating comparative effectiveness of time-varying treatment strategies. The first example evaluates an early aggressive treatment strategies for caring children with newly diagnosed Juvenile Idiopathic Arthritis (JIA). The second evaluates the persistent per-protocol treatment effectiveness in a large randomized pragmatic trial. The examples demonstrate the use of the API calling from R and Python, for handling both non-adaptive or adaptive treatments, with presences of partially observed or missing data issues. Summary tables and interactive figures of the results are downloadable.

Data curation has significant research implications irrespective of application areas. As most curated studies rely on databases for data extraction, the availability of data resources is extremely important. Taking a perspective from pharmacology, extracted data contribute to improved drug treatment outcomes and well-being but with some challenges. Considering available pharmacology literature, it is necessary to review articles and other scientific documents carefully. A typical method of accessing articles on journal websites is through long-established searches. In addition to being labor-intensive, this conventional approach often leads to incomplete-content downloads. This paper presents a new methodology with user-friendly models to accept search keywords according to the investigators' research fields for metadata and full-text articles. To accomplish this, scientifically published records on the pharmacokinetics of drugs were extracted from several sources using our navigating tool called the Web Crawler for Pharmacokinetics (WCPK). The results of metadata extraction provided 74,867 publications for four drug classes. Full-text extractions performed with WCPK revealed that the system is highly competent, extracting over 97% of records. This model helps establish keyword-based article repositories, contributing to comprehensive databases for article curation projects. This paper also explains the procedures adopted to build the proposed customizable-live WCPK, from system design and development to deployment phases. [ABSTRACT FROM AUTHOR]

Ransomware is a type of malware that employs encryption to target user files, rendering them inaccessible without a decryption key. To combat ransomware, researchers have developed early detection models that seek to identify threats before encryption takes place, often by monitoring the initial calls to cryptographic APIs. However, because encryption is a standard computational activity involved in processes, such as packing, unpacking, and polymorphism, the presence of cryptographic APIs does not necessarily indicate an imminent ransomware attack. Hence, relying solely on cryptographic APIs is insufficient for accurately determining a ransomware pre-encryption boundary. To this end, this paper is devoted to addressing this issue by proposing a Temporal Data Correlation method that associates cryptographic APIs with the I/O Request Packets (IRPs) based on the timestamp for pre-encryption boundary delineation. The process extracts the various features from the pre-encryption dataset for use in early detection model training. Several machine and deep learning classifiers are used to evaluate the accuracy of the proposed solution. Preliminary results show that this newly proposed approach can achieve higher detection accuracy compared to those reported elsewhere. [ABSTRACT FROM AUTHOR]

Introduction: In the field of Information Science, the Referential Database of the Database of Journal Articles in Information Science (BRAPCI) is a national reference. However, compared to other databases, BRAPCI provides limited bibliometric metrics. Objective: This search proposes a new web environment, named BRAPCI Explorer, aimed at bibliometric analyses. This would be achieved by requesting data through the BRAPCI database API, expanding and structuring the data visualization. Methodology: An R programming language code was developed to extract data from BRAPCI via API, organizing and structuring the data into an interactive website. This code generates a co-authorship network, tables of author and publication source frequency, a production graph per year, and calculates bibliometric indicators, including the quantity of distinct authors, average documents per author, co-authorship index, total different sources, average documents per source, and per year. Results: When testing the new environment, a search with the term "scientific production" yielded 3099 distinct documents produced by 3494 distinct authors in 51 distinct publication sources. BRAPCI Explorer was capable of generating the coauthorship network as well as preparing all tables and frequency graphs, in addition to calculating the indicators. Conclusion: It is concluded that BRAPCI Explorer contributes to a better visualization of the data provided by BRAPCI because it presents enhanced visualizations in the form of networks, graphs, and tables, along with bibliometric indicators, all in a single environment. [ABSTRACT FROM AUTHOR]

With advancements in new technologies in the industry, efforts have been made to adopt various smart devices during the life cycle of building construction. However, little attention has been paid to developing a work process model to maximize the benefits of smart technologies. While identifying the shortcomings of conventional quality inspection, this study configures a new quality inspection process model in collaboration with smart devices. Based on the proposal, the study developed a new tool that effectively improves the current quality inspection practice. The final goal of this research is to propose a novel inspection process model by developing an Application Programming Interface (API) system using Building Information Model (BIM) software. Additionally, to verify the applicability of the system, this study includes a case study on tile work and confirms the effects of a prototype of the proposed system through an expert survey. The survey results reveal that the proposed system is effective and practical. This research contributes to maximizing the effectiveness of using smart devices and supporting effective application in smart construction. [ABSTRACT FROM AUTHOR]