Application Programming Research Articles

The role of SNAP and WIC in the bidirectional relationship between food insecurity and maternal depressive symptoms

BackgroundThe association between food insecurity and maternal depressive symptoms has been established by many cross-sectional and longitudinal studies however the understanding of the reciprocal relationship between them remains unclear. Further, previous research demonstrates that federal nutrition assistance decreases food insecurity and promotes maternal mental well-being but further research is needed to elucidate the moderating role of these programs in the association between food insecurity and maternal depressive symptoms. Therefore, the current study examined the bidirectional associations between maternal depression probability and food insecurity using cross-lagged models and then tested the main and moderating effects of SNAP and WIC. MethodsData were from the Future of Families and Child Wellbeing Study and the sample included 1948 mothers who participated in year 3 and year 5 of data collection. ResultsThe cross-lagged paths show that food insecurity at year 3 predicted maternal depression probability at year 5 and depression probability at year 3 predicted food insecurity at year 5. There was a significant moderating effect of WIC receipt on the association between food insecurity at year 3 and at year 5 such that when mothers with high food insecurity at year 3 received WIC, they were less food insecure at year 5. LimitationsThe study oversampled for unmarried mothers only two waves of data were used for the cross-lagged panel models. ConclusionsResults demonstrate the need to incorporate mental health services with the existing food assistance programs and the need to destigmatize the application process and program structure.

Building a Natural Language Interface for FHIR Clinical Terminology Server.

While Fast Healthcare Interoperability Resources (FHIR) clinical terminology server enables quick and easy search and retrieval of coded medical data, it still has some drawbacks. When searching, any typographical errors, variations in word forms, or deviations in word sequence might lead to incorrect search outcomes. For retrieval, queries to the server must strictly follow the FHIR application programming interface format, which requires users to know the syntax and remember the attribute codes they wish to retrieve. To improve its functionalities, a natural language interface was built, that harnesses the capabilities of two preeminent large language models, along with other cutting-edge technologies such as speech-to-text conversion, vector semantic searching, and conversational artificial intelligence. Preliminary evaluation shows promising results in building a natural language interface for the FHIR clinical terminology system.

Monte Carlo Workflow Unification for Nuclear Reactor Analysis with Metamodel-Driven Modeling

Monte Carlo (MC) transport codes are a cornerstone of nuclear reactor analysis frameworks, providing reference solutions and multigroup cross sections, and even as core components in multiphysics couplings. These applications can be seen in toolkits from the Nuclear Energy Advanced Modeling and Simulation program and the U.S. Nuclear Regulatory Commission’s BlueCRAB (Comprehensive Reactor Analysis Bundle). Contrary to their ubiquitousness in reactor physics modeling and simulation, popular MC codes, such as MCNP, Serpent, and KENO, are still reliant on antiquated textual input formats. These input languages use a plethora of keywords with terse syntax to specify all facets of a model, including its geometry, materials, physics settings, and tally options. This poses a steep learning curve and a poor user experience. Being tied to unique text-based input formats also significantly complicates programmatic input generation or modification that may be desired and/or required within a multiphysics framework. This work demonstrates how the development of programmatic interfaces for MC codes can support model unification and translation activities. Building on the Python application program interface (API) development of MCNPy, similar capabilities are being implemented for Serpent that are able to support model translation. In future work, the Serpent API capabilities will be made more robust and the work will be further expanded to include translations with KENO.

Navigating the spectrum of service in hospitality: hotel employees’ experiences toward guests with disabilities

ABSTRACT The primary goal of the present study was to investigate the experiences and challenges hotel employees face when interacting with guests with disabilities. The findings of this study, derived from data collected using Reddit’s Application Programming Interface, are not just significant but crucial and timely. Using Structural Topic Modelling and quadrant analysis, 12 topics emerged, each stratified based on topic frequency and sentiment scores. These topics delve into the daily interactions of hotel employees, the emotional and practical challenges they face, and the critical issues within the hotel industry, including disability accommodation, the role of service animals, and the complexities of reservation systems in accommodating special needs. This study also sheds light on the emotional toll accommodating guests with special needs entails on employees, emphasising the urgent need for better training and clear policies to support employees and guests with disabilities. The findings suggest that service interactions can be significantly improved by enhancing disability awareness and accommodation practices.

Review of emerging trends and projection of future developments in large language models research in ophthalmology

BackgroundLarge language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and...

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study.

According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? We used X's application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non-English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability. Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others' drug use, legal implications, and public health. This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

Public Perceptions of the Food and Drug Administration's Regulatory Authority Over Synthetic Nicotine on Twitter: Observational Study.

The Omnibus Budget Bill, known as H. R. 2471, passed through Congress on March 10, 2022, and was eventually signed by President Biden on March 15, 2022. This bill amended the Federal Food, Drug, and Cosmetic Act granting the Food and Drug Administration (FDA) regulatory authority over synthetic nicotine. This study aims to examine the public perceptions of the Omnibus Bill that regulates synthetic nicotine products as tobacco products on Twitter (rebranded as X). Through the X streaming application programming interface, we collected and identified 964 tweets related to the Omnibus Bill on synthetic nicotine between March 8, 2022, and April 13, 2022. The longitudinal trend was used to examine the discussions related to the bill over time. An inductive method was used for the content analysis of related tweets. By hand-coding 200 randomly selected tweets by 2 human coders respectively with high interrater reliability, the codebook was developed for relevance, major topics, and attitude to the bill, which was used to single-code the rest of the tweets. Between March 8, 2022, and April 13, 2022, we identified 964 tweets related to the Omnibus Bill regulating synthetic nicotine. Our longitudinal trend analysis showed a spike in the number of tweets related to the bill during the immediate period following the bill's introduction, with roughly half of the tweets identified being posted between March 8 and 11, 2022. A majority of the tweets (497/964, 51.56%) had a negative sentiment toward the bill, while a much smaller percentage of tweets (164/964, 17.01%) had a positive sentiment toward the bill. Around 31.43% (303/964) of all tweets were categorized as objective news or questions about the bill. The most popular topic for opposing the bill was users believing that this bill would lead users back to smoking (145/497, 29.18%), followed by negative implications for small vape businesses (122/497, 24.55%) and government or FDA mistrust (94/497, 18.91%). The most popular topic for supporting the bill was that this bill would take a dangerous tobacco product targeted at teens off the market (94/164, 57.32%). We observed a more negative sentiment toward the bill on X, largely due to users believing it would lead users back to smoking and negatively impact small vape businesses. This study provides insight into public perceptions and discussions of this bill on X and adds valuable information for future regulations on alternative nicotine products.

SLOPE: Safety LOg PEripherals implementation and software drivers for a safe RISC-V microcontroller unit

The focus of this manuscript is related to the main safety issues regarding a mixed criticality system running multiple concurrent tasks. Our concerns are related to the guarantee of Freedom of Interference between concurrent partitions, and to the respect of the Worst Case Execution Time for tasks. Moreover, we are interested in the evaluation of resources budgeting and the study of system behavior in case of occurring random hardware failures. In this paper we present a set of Safety LOg PEripherals (SLOPE): Performance Monitoring Unit (PMU), Execution Tracing Unit (ETU), Error Management Unit (EMU), Time Management Unit (TMU) and Data Log Unit (DLU); then, an implementation of SLOPE on a single core RISC-V architecture is proposed. Such peripherals are able to collect software and hardware information about execution, and eventually trigger recovery actions to mitigate a possible dangerous misbehavior. We show results of the hardware implementation and software testing of the units with a dedicated software library. For the PMU we standardized the software layer according to embedded Performance Application Programming Interface (ePAPI), and compared its functionality with a bare-metal use of the library. To test the ETU we compared the hardware simulation results with software ones, to understand if overflow may occur in internal hardware buffers during tracing. In conclusion, designed devices introduce new instruments for system investigation for RISC-V technologies and can generate an execution profile for safety related tasks.

Performance of Publicly Available Large Language Models on Internal Medicine Board-style Questions.

Ongoing research attempts to benchmark large language models (LLM) against physicians' fund of knowledge by assessing LLM performance on medical examinations. No prior study has assessed LLM performance on internal medicine (IM) board examination questions. Limited data exists on how knowledge supplied to the models, derived from medical texts improves LLM performance. The performance of GPT-3.5, GPT-4.0, LaMDA and Llama 2, with and without additional model input augmentation, was assessed on 240 randomly selected IM board-style questions. Questions were sourced from the Medical Knowledge Self-Assessment Program released by the American College of Physicians with each question serving as part of the LLM prompt. When available, LLMs were accessed both through their application programming interface (API) and their corresponding chatbot. Mode inputs were augmented with Harrison's Principles of Internal Medicine using the method of Retrieval Augmented Generation. LLM-generated explanations to 25 correctly answered questions were presented in a blinded fashion alongside the MKSAP explanation to an IM board-certified physician tasked with selecting the human generated response. GPT-4.0, accessed either through Bing Chat or its API, scored 77.5-80.7% outperforming GPT-3.5, human respondents, LaMDA and Llama 2 in that order. GPT-4.0 outperformed human MKSAP users on every tested IM subject with its highest and lowest percentile scores in Infectious Disease (80th) and Rheumatology (99.7th), respectively. There is a 3.2-5.3% decrease in performance of both GPT-3.5 and GPT-4.0 when accessing the LLM through its API instead of its online chatbot. There is 4.5-7.5% increase in performance of both GPT-3.5 and GPT-4.0 accessed through their APIs after additional input augmentation. The blinded reviewer correctly identified the human generated MKSAP response in 72% of the 25-question sample set. GPT-4.0 performed best on IM board-style questions outperforming human respondents. Augmenting with domain-specific information improved performance rendering Retrieval Augmented Generation a possible technique for improving accuracy in medical examination LLM responses.

Sweetening the deal: an infodemiological study of worldwide interest in semaglutide using Google Trends extended for health application programming interface

BackgroundOff-label use of semaglutide for non-diabetic weight loss (which regulators have linked to social media promotion) created worldwide supply shortages. We evaluated worldwide semaglutide interest measured by online search behavior to gauge social media and conventional print media reporting’s effect on search interest.MethodsUsing Google Trends Extended for Health (GTEH) multiple sampling, we retrieved regional online interest (ROI) for all countries and extracted timelines and top search queries for January 2021–August 2023 for countries with median ROI ≥ 20 using the “semaglutide” topic. We obtained semaglutide media reporting from the ProQuest database. We estimated the effect of media and within-country semaglutide interest on between-country interest with Granger causality analysis. We determined changepoints for trends within each country with joinpoint regression. We determined prominent themes in search queries for each country with natural language processing thematic analysis.ResultsTwenty-seven countries were included. Most countries showed an increase in semaglutide interest over time, with Canada and the USA showing the largest sustained interest. Most of the search interest arose from 2022 onwards. Granger’s analysis showed that media coverage could only partially explain interest, and interest in some countries partially preceded interest in others, with the UK and Germany showing strong relationships between news reports and lagged search interest. Joinpoint analysis identified up to four significant within-country changepoints. Most countries showed significant positive weekly trends in 2021–2022, although uptrends in search interest varied considerably between countries. One episode of the Dr. Oz show (TV media event) coincided with strong peaks in numerous countries. Natural language processing of top search queries showed some agreement between countries and country-specific themes. Weight loss was a major theme in most countries, while a diabetes theme was generally absent or weak. Some countries (Australia, Chile, South Africa, UK) had themes for buying Ozempic from (named) local retailers, and Germany had a theme related to buying Ozempic without a script.ConclusionsGTEH data provided insights into global search interest in semaglutide and regional variation. Studies focusing on specific countries which include social media data can elucidate specific drivers behind the surge in off-label use of semaglutide.

A Combinatorial Strategy for API Completion: Deep Learning and Heuristics

Remembering software library components and mastering their application programming interfaces (APIs) is a daunting task for programmers, due to the sheer volume of available libraries. API completion tools, which predict subsequent APIs based on code context, are essential for improving development efficiency. Existing API completion techniques, however, face specific weaknesses that limit their performance. Pattern-based code completion methods that rely on statistical information excel in extracting common usage patterns of API sequences. However, they often struggle to capture the semantics of the surrounding code. In contrast, deep-learning-based approaches excel in understanding the semantics of the code but may miss certain common usages that can be easily identified by pattern-based methods. Our insight into overcoming these challenges is based on the complementarity between these two types of approaches. This paper proposes a combinatorial method of API completion that aims to exploit the strengths of both pattern-based and deep-learning-based approaches. The basic idea is to utilize a confidence-based selector to determine which type of approach should be utilized to generate predictions. Pattern-based approaches will only be applied if the frequency of a particular pattern exceeds a pre-defined threshold, while in other cases, deep learning models will be utilized to generate the API completion results. The results showed that our approach dramatically improved the accuracy and mean reciprocal rank (MRR) in large-scale experiments, highlighting its utility.

PSIII-30 Development of an application programming interface (API) to automate downloading and processing of precision livestock data

Abstract Advancements in technology have ushered in a new era of sensor-based measurement and management of livestock production systems. These sensor-based technologies have the ability to automatically monitor feeding, growth, and enteric emissions for individual animals across confined and extensive production systems. One challenge with sensor-based technologies is the large amount of data generated, which can be difficult to access, process, visualize, and monitor information in real time to ensure equipment is working properly and animals are utilizing it correctly. A solution to this problem is the development of application programming interfaces (APIs) to automate downloading, visualizing, and summarizing datasets generated from precision livestock technology. For this methods paper, we develop three APIs and accompanying processes for rapid data acquisition, visualization, systems tracking, and summary statistics for three technologies (SmartScale, SmartFeed, and GreenFeed) manufactured by C-Lock Inc (Rapid City, SD). Program R markdown documents and example datasets are provided to facilitate greater adoption of these techniques and to further advance precision livestock technology. The methodology presented successfully downloaded data from the cloud and generated a series of visualizations to conduct systems checks, animal usage rates, and calculate summary statistics. These tools will be essential for further adoption of precision technology. There is huge potential to further leverage APIs to incorporate a wide range of datasets such as weather data, animal locations, and sensor data to facilitate decision-making on times scales relevant to researchers and livestock managers.

A Case Study in Migrating Open Orders

ABSTRACT Migrating from Innovative’s Sierra to OCLC WorldShare during the COVID-19 quarantine was the incentive to try a novel approach to transferring open serial and monograph orders using WorldShare’s application programming interfaces (APIs). At a time when staff training and supervision would have been difficult or impossible, and in the absence of a standard procedure for migrating acquisitions data to WorldShare, we were nevertheless able to accomplish this task with a small team working remotely. Instead of recreating orders manually in the new system, we took advantage of the APIs and the flexible structure of order records in WorldShare to systematically and reliably migrate data exported from Sierra.

Wiener Filter with Convolutional Neural Network for Noise Removal in API-Based AI Models

This research aims to develop a robust Application Program Interface (API)-Based Artificial Intelligence (AI) system for effective noise removal from audio signals, enhancing speech quality and intelligibility in noisy environments to be fed into different AI models to assess the applicant interview. The proposed methodology combines sophisticated signal processing techniques and noise reduction algorithms with AI models trained on clean voice data and noise patterns. To achieve this goal, we leverage two key components: the Wiener filter and a Convolutional Neural Network (CNN). The Wiener filter serves as the foundational noise reduction technique, exploiting statistical properties of the signal and the noise to suppress unwanted noise components effectively. Concurrently, CNN is integrated to classify the clean and noisy audio. In this research, the best optimizers selected, including Adam, SGD, RMSprop, Adagrad, and Adadelta are evaluated to identify the most suitable classification. The optimizers evaluated through cross-validation and hold-out validation in the same batch size (25) and epoch (25) were used. The study demonstrates that the Adam optimizer yields the best results. The epoch was optimized to 35, 75, 105, and 125 and epoch of 105 was selected with accuracy of 99.52%, Recall of 100%, F1-Score of 99.50%, and ROC_AUC of 99.99% for cross-validation while Accuracy of 98.79%, Recall of 99.21%, F1-Score of 98.81%, and ROC_AUC of 99.54% for hold-out validation, significantly improving AI model performance. Lastly, we ensured the batch size parameter was suitable for our model by tuning it with different settings (25, 50, 75, and 125) using the optimized optimizer and epoch. The batch size of 25 yielded the best accuracy. The modeled CNN also included kernel regularization L2 to avoid overfitting.

Pengembangan Sistem Seleksi Proposal Penelitian Berbasis Web Service Menggunakan REST API

Web service-based application development has become a vital component of the modern in-formation technology ecosystem. The implementation of web services facilitates interoperability among diverse systems and platforms. REST API (REpresentational State Transfer Application Programming Interface) is one of the most popular architectures for creating efficient and scalable web services. This study examines the implementation of REST API in the development of web service-based applications. The case study focuses on the creation of a Web service-based Research Proposal Selection System. The REST API implementation method in this study adheres to the system development life cycle (SDLC) to ensure that the developed API meets business needs while being efficient, secure, and reliable. The analysis phase is conducted to gather both functional and non-functional REST API requirements. During the design phase, the REST API architecture is designed in detail, including how data will be transferred and processed, and how the API will interact with databases and other systems. Furthermore, in the implementation phase, the REST API is developed, ensuring that each endpoint operates correctly. In developing the Proposal Selection System, the implementation of web services integrates data from various existing sys-tems, including employee, academic, and research data. The system encompasses various features, such as proposal import, proposal mapping, proposal review, proposal assessment, proposal se-lection, and reporting. The research findings indicate that implementing the REST API simplifies the development of web service applications. The test results reveal that the average response time is relatively stable, measuring 31 ms without iterations and 29 ms with five iterations.

Linking regional investments and revenues at the provincial level to investment loan decisions by local government banks in Indonesia

This study aims to analyze how two different types of investments (local domestic and foreign direct investments at the provincial level) and revenues (booked by the provincial governments, general allocation funds, special allocation funds, local taxes, and retribution) affect investment loan decisions by local government banks in Indonesia. The study uses panel data applying ordinary least squares and multiple linear regression. Thus, 144 data were sampled from 2013 to 2021 from 16 local government banks out of Java Island in 21 provinces in Indonesia. The study found that local domestic investment at the provincial level affects investment loan decisions by local government banks. In contrast, foreign direct investment did not affect lending decisions, indicating that local domestic investment contributes to the real local economy at the provincial level. Different results were found in provincial revenues in the form of general and special allocation funds, which negatively affected loan investment decisions, possibly due to provincial revenues utilized to cover the financial deficit and capital expenditure spent chiefly on imported goods. Additionally, local taxes at the provincial level also negatively affect investment loan decisions, possibly due to fluctuations in local tax collection during COVID-19. However, the study found that local retribution contributes to predicting loan investment decisions, suggesting revenue collection by the governments considering local economic conditions. The study findings suggest that provincial governments should direct investments that can impact the local economy and spend their revenues on goods and services that can drive local economic growth. AcknowledgmentThe investment loan made by local government banks, which is associated with local investment and revenue, is part of a research grant project from the Economics and Business Faculty of Hasanuddin University. This project is a result of collaboration with national and international researchers. In carrying out this research, some inputs from people working in local government banks, financial service authorities, and provincial and regency governments are addressed.Thanks to Ahmadi Usman for secondary data and Syahidah Ulhaq for some application programs enabling mapping literature, as well as Israa Natiq Jabar for supervising the result and applied some inputs in the section of research method.

Analysis of human errors in the operation of various treatment planning systems over a 10-year period.

The present study aimed to summarize and report data on errors related to treatment planning, which were collected by medical physicists. The following analyses were performed based on the 10-year error report data: (1) listing of high-risk errors that occurred and (2) the relationship between the number of treatments and error rates, (3) usefulness of the Automated Plan Checking System (APCS) with the Eclipse Scripting Application Programming Interface and (4) the relationship between human factors and error rates. Differences in error rates were observed before and after the use of APCS. APCS reduced the error rate by ~1% for high-risk errors and 3% for low-risk errors. The number of treatments was negatively correlated with error rates. Therefore, we examined the relationship between the workload of medical physicists and error occurrence and revealed that a very large workload may contribute to overlooking errors. Meanwhile, an increase in the number of medical physicists may lead to the detection of more errors. The number of errors was correlated with the number of physicians with less clinical experience; the error rates were higher when there were more physicians with less experience. This is likely due to the lack of training among clinically inexperienced physicians. An environment to provide adequate training is important, as inexperience in clinical practice can easily and directly lead to the occurrence of errors. In any environment, the need for additional plan checkers is an essential factor for eliminating errors.

WP6.6 - Sentiment Analysis of Social Media Posts by Medical Professional in the UK

Abstract Aims Notoriously described as being controversial in nature, we aim to objectively assess the language used amongst medical professionals in social media (SM). Methods Data was extracted from the Reddit SM website under the sub-page "r/DoctorsUK" forum, through a data-extraction code written in the python language. This was then analysed through a Natural Language Toolkit algorithm to classify language used into positive, negative and neutral sentiments with an overall polarity score ranging from -1 noting most negative to 1 being most positive. Results A total of 992 articles were examined over a 24-day period, as limited by Reddit data application programme interface (API) protocols. A total of 210 positive, 218 negative and 564 neutral articles were noted with an average of 43 articles posted per day. Headlines sentiment on average was overall marginally positive at 0.0154. Comment tree within each headline was also examined, noting a more positive 0.173. Through regression analysis, it was noted that headline and comment sentiments was directly correlated, and this was statistically significant(P=1.24*10-10). It was also noted that more negative headlines had higher “likes” and were therefore featured more prominently within the forum. Conclusions Although SM posts are overall positive in sentiment, it is noted that more negative sentiments are often up-voted and therefore featured more prominently due to their more provocative nature and emotions incited amongst readers. As SM posts elicits strong psychological and psychosocial reactions from a community, there is an urgency to more accurate data driven models in developing future policies to maintain an effective professional morale within the NHS.

Designing and delivering climate training for natural resource managers: Increasing climate literacy and action through education and engagement

AbstractResponding to climate impacts and expanding adaptation efforts necessitates getting the right knowledge and tools in the hands of land managers and decision‐makers. In 2022–2023, several regional US Geological Survey Climate Adaptation Science Centers partnered with the US Fish and Wildlife Service (FWS) Science Applications Program on the first targeted climate training series designed for the FWS Grassland Ecosystem Team. This training spanned multiple months and formats with self‐paced virtual lessons, webinars, and an in‐person workshop. As the FWS Grassland Ecosystem Team is tasked with conservation planning for grassland birds and other species, the focus of the workshop was an interactive collaborative activity incorporating species adaptive capacity assessments, future climate projections, and adaptation menus into the decision‐making process. Herein, we describe the methods used to design and deliver the training series, as well as lessons learned for future climate literacy programs aimed at natural resource managers.

Application AVATAR (Accounting Virtual Report) on PT Pelindo Terminal Petikemas

This research discusses the application of asset maintenance at PT Pelindo Terminal Petikemas. This research aims to determine the asset maintenance system at PT Pelindo Terminal Petikemas, as well as to create a web-based asset maintenance application program at PT Pelindo Terminal Petikemas. The theoretical framework of this research involves information system analysis, by examining the information needed by management, related functions, documents used, as well as document flowcharts. MySql is used as a database, with the PHP programming language. The research results show that the recording process at PT Pelindo Terminal Petikemas has been computerized but still uses Microsoft Excel. The weakness of this method is that it is difficult to look back at archived data for the previous year because you need to search for the file first and there is a risk of losing the file. Therefore, the application called AVATAR (Accounting Virtual Report) in this research is expected to help produce asset maintenance reports more effectively and efficiently, thus providing benefits for the company.