Scientific Data Research Articles

Using electrodialysis to increase the efficiency of sugar enterprises

The paper shows the efficiency of electromembrane treatment of semi-products of sugar production in order to increase the yield of sugar by reducing its losses in molasses. Based on the analysis of changes in the studied parameters of semi­products of sugar production during the electrodialysis process, scientific data were obtained on the effect of electromembrane treatment on the chemical composition and technological quality of purified semi­products of sugar production, as well as on decrease in calcium salts content in them. The purpose of the research is to create a highly efficient method for production of white sugar, allowing to increase the yield of sugar from raw materials and reduce the amount of excipients by means of electrodialysis with its subsequent practical implementation at industry enterprises. Under production conditions, it was found that electromembrane treatment of one batch (7 t) of purified juice allows to additionally obtain up to 35 kg of sugar, a batch of syrup – up to 130 kg, and soft sugar effluent of II crystallization – up to 255 kg. Electromembrane treatment of molasses makes it possible to: reduce the mass fraction of calcium salts to dry matters by 39 % of their initial content; increase molasses purity by 8.5 %; reduce the molasses-forming coefficient by 21 % by reducing the content of potassium and sodium. In this case, the estimated amount of additional sugar obtained from it will make 5.1 % by weight or 392.7 kg of sugar from one batch of processed molasses. The research will allow to develop scientific and technological aspects of purification of semi-products of sugar production using electromembrane treatment.

MORSE CODE-BASED AUTHENTICATION SYSTEM

To tackle analytically complex issues, data science is a multidisciplinary combination of data inference, algorithm development, and technology. Nearly every industry, including business, finance, healthcare, and education, uses data science to manage massive volumes of data. Practical uses include text prediction, image processing, identity recognition, audio processing for speech, stock movement, and cancer prediction. Given that the majority of individuals on the planet have issues with security and authentication, For those who use Morse Code for self-authentication, we can offer real-time eye tracing for password authentication. As we all know, the 21st century has seen significant support for advancements in authorization and authentication technology. Since the late 1990s, personal identification numbers, or PINs, have been extensively used for user authentication and security. These days, it is easy to crack PIN codes, so we would rather use a different strategy. On the other hand, PIN authentication using hands-off gaze-based PIN entry techniques offers a more secure password entry option because it leaves no physical traces behind. Key Words: Morse code, Face recognition, Pin Entry, Eye Blinks, Gaze based

Surface Elevation Change Monitoring Based on ICESat-2 Satellite Altimetry Data

The ICESat-2 (Ice, Cloud and Land Elevation Satellite-2), launched by NASA on September 18, 2018, offers higher precision in surface elevation monitoring. This study aims to investigate the surface elevation changes over different time periods in the Delong and Bulianta open-pit mines in Wulanmuren Township, Ordos City, using ICESat-2 laser data. The study utilizes the spatial distribution information of each photon from ATL03 (terrain elevation data) and the classification information of each photon from ATL08 (vegetation canopy height and surface elevation data) to extract surface photon elevation data for different periods in the same area. The precision of ICESat-2 satellite elevation data is validated using the on-board radar data from Henan Polytechnic University's Feima unmanned aerial vehicle D2000 in Jiaozuo City, Henan Province.Results indicate that in the No.1 area of the Delong open-pit mine, the surface subsided by approximately 16.3 meters between November 14, 2019, and November 10, 2021, due to soil removal operations. In the No.2 and No.3 areas of the Bulianta open-pit mine, the surface was uplifted by 3.1 meters and 13.6 meters, respectively, between May 16, 2019, and August 13, 2020, due to backfilling after mining. Additionally, the No.4 area was uplifted by about 10.5 meters between November 14, 2019, and November 10, 2021.Furthermore, the accuracy assessment yielded the following results: R2=0.956, RMSE=0.324, and MAE=0.105 meters, indicating that ICESat-2 data can be used as scientific data for inverting surface elevation changes.

ARTIFICIAL INTELLIGENCE ADOPTION IN INVESTMENT MANAGEMENT COMPANIES

This analysis delves into the evolving landscape of artificial intelligence (AI) adoption within the financial services industry, juxtaposed against broader market trends. Drawing insights from industry experts and research findings, it examines key challenges and opportunities faced by financial institutions in leveraging AI technologies to drive innovation and competitive advantage. The study highlights the critical importance of data management strategies, cultural transformation, and talent development in facilitating successful AI implementation. It underscores the significance of striking a balance between centralization and federation in data management approaches, alongside the imperative of strengthening ethics and bias management practices. Furthermore, the analysis delves into the pivotal role of multidisciplinary AI teams, emphasizing the necessity of integrating diverse skill sets, including data scientists, business experts, and senior executives, to maximize the efficacy of AI initiatives. It also sheds light on regulatory developments, such as the Canadian government's Algorithmic Impact Assessment (AIA), aimed at fostering transparency and accountability in automated decision-making systems. Overall, this study provides valuable insights into the challenges and opportunities inherent in AI adoption within the financial services sector, offering recommendations to guide firms towards sustainable AI-driven growth and innovation.

Assessing Data Quality in the Age of Digital Social Research: A Systematic Review

While survey data has long been the focus of quantitative social science analyses, observational and content data, although long-established, are gaining renewed attention; especially when this type of data is obtained by and for observing digital content and behavior. Today, digital technologies allow social scientists to track “everyday behavior” and to extract opinions from public discussions on online platforms. These new types of digital traces of human behavior, together with computational methods for analyzing them, have opened new avenues for analyzing, understanding, and addressing social science research questions. However, even the most innovative and extensive amounts of data are hollow if they are not of high quality. But what does data quality mean for modern social science data? To investigate this rather abstract question the present study focuses on four objectives. First, we provide researchers with a decision tree to identify appropriate data quality frameworks for a given use case. Second, we determine which data types and quality dimensions are already addressed in the existing frameworks. Third, we identify gaps with respect to different data types and data quality dimensions within the existing frameworks which need to be filled. And fourth, we provide a detailed literature overview for the intrinsic and extrinsic perspectives on data quality. By conducting a systematic literature review based on text mining methods, we identified and reviewed 58 data quality frameworks. In our decision tree, the three categories, namely, data type, the perspective it takes, and its level of granularity, help researchers to find appropriate data quality frameworks. We, furthermore, discovered gaps in the available frameworks with respect to visual and especially linked data and point out in our review that even famous frameworks might miss important aspects. The article ends with a critical discussion of the current state of the literature and potential future research avenues.

Predicting an unfavorable outcome in patients with liver failure associated with obstructive jaundice syndrome: a prospective observational study

INTRODUCTION: Specialized scales developed to assess survival and mortality of patients after liver transplantation have not been validated when applied to liver failure in the setting of obstructive jaundice. OBJECTIVE: To determine the prognostic value of multiparametric scoring scales and to identify predictors of unfavorable outcome in liver failure due to obstructive jaundice. MATERIALS AND METHODS: 53 patients with confirmed liver failure due to obstructive jaundice of benign etiology who underwent biliary decompression were included in this prospective, observational, single-center study. Depending on the outcome two groups were distinguished: with favorable (n = 27, 51 %) and with unfavorable outcome (n = 26, 49 %). The following time points were defined for the dynamic evaluation of the course of the disease: day 1, day 3, day 7, and day 11 after decompression. Statistical processing of the data was performed using IBM SPSS Statistics 22 software for social science data analysis. RESULTS: The sensitivity and specificity of the studied scales in predicting liver failure due to obstructive jaundice were calculated on the basis of receiver operating characteristic (ROC) analysis. The AUROC for the SOFA was 0.862, for the MELD — 0.882, for the APACHE II — 0.864, for the Child-Turcotte-Pugh — 0.813. The sensitivity and specificity values for the SOFA were 78.9 and 77.8 %, for the MELD — 80.8 and 79.7 %, for the APACHE II — 57.7 and 81.9 %, and for the Child-Turcotte-Pugh — 75.1 and 70.9 %, respectively. Albumin was an independent predictor of unfavorable outcome (AUC ROC 0.909, p = 0.01), sensitivity 88.6 %, specificity 85.2 %. CONCLUSIONS: The scales presented in the study and the biomarker candidate “albumin” showed significant prognostic ability, but low values of sensitivity and specificity (less than 80 %) in some points of the study require the search for additional predictors of unfavorable outcome of liver failure due to obstructive jaundice.

The dual approach to climate change adaptation in official development assistance

ABSTRACT A dual approach is an effective method for formulating climate change adaptation measures. It combines both a top-down approach, whereby plans are formulated based on climate change projections, and a bottom-up approach, in which plans are formulated based on the situation in the target region. I analyse Japan’s advanced measures and a development case in Timor-Leste, where some level of adaptation effectiveness is expected. In Timor-Leste, the inadequacy of scientific data makes the top-down approach difficult, but the bottom-up approach based on thorough field surveys can promote climate-resilient development which can be expected to have high adaptation effects in such circumstances. To achieve this, it is necessary to complement scientific data with available data, further strengthen the current system in which each actor thinks independently and works and collaborates with others, and train facilitators who can understand scientific data.

Time series analysis and applications in data analysis, forecasting and prediction

Time series analysis is an essential field in data analysis, particularly within forecasting and prediction domains. Researching and building time series models play a crucial role in understanding and predicting the temporal dynamics of various phenomena. In mathematics, time series data is defined as data points indexed in chronological order and have a consistent time interval between consecutive observations. This can include data such as daily stock prices, annual national income, quarterly company revenue, and more. The advantage of time series data is that it can capture the state of a variable over time. In contrast, the world is constantly changing, and phenomena rarely remain static they typically exhibit variations over time. Therefore, time series data has highly practical applications and is used in various fields, including statistics, econometrics, financial mathematics, weather forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, telecommunications, and signal processing. ARIMA, which stands for Auto Regressive Integrated Moving Average, is a widely used time series forecasting method in data science. It is a popular model for analyzing and predicting time-dependent data points. ARIMA combines autoregression, differencing, and moving averages to capture different aspects of time series data. In this paper, we study ARIMA, which is a significant model for analyzing and predicting time series data.

Sonic vibration ameliorates inflammatory diseases via the up-regulation of IL-10

ABSTRACT Sonic vibration (SV), or vibroacoustic therapy, is applied to enhance local and systemic blood circulation and alleviate pain using low-frequency sine wave vibrations. However, there is limited scientific data on the mechanisms through which the benefits are achieved. In this study, we investigated the impact of SV on inflammatory responses by assessing cytokine secretion in both in vivo and in vitro models. After inducing inflammatory responses in mice and macrophages, we studied cytokine expression and the symptoms of inflammatory diseases in response to three frequencies (14, 45, or 90 Hz) of SV stimulation at 0.5 m/s2 of amplitude. The results showed that SV at 90 Hz significantly increased interelukin-10 (IL-10) secretion in mice who were administered lipopolysaccharides (LPS) and increased the expression of IL-10 transcripts in peritoneal exudate cells and macrophages. Furthermore, SV at 90 Hz improved LPS-induced lethality and alleviated symptoms in a colitis model. In conclusion, this study scientifically proves the anti-inflammatory effects of vibration therapy through its ability to increase IL-10 expression.

Cardiovascular Disease Prediction from Retinal Images using Machine Learning

The structure and function of the microvascular are significantly influenced by the key cardiovascular disease risk factors of hypertension and heart attacks. Images taken with a fundus camera can be used to spot irregularities in the blood vessels of retina that indicate the extent injury on blood vessels by hypertension and heart attacks. Using machine learning and AI techniques, detecting the preclinical signs that fall below the threshold of an observer. The proposed methodology aimed to investigate the effects of hypertension and heart attacks on morphological characteristics of retinal blood vessels. With a diagnosis of hypertension and heart attack, data scientists collect retinal images. Interference data is removed— information about structures other than that retinal vasculature using the vessel segmentation method, leaving only morphological details about the blood vessel of retina. Keywords— Heart Disease, Retinal Image, Microvasculature, Hypertension

The perpetual twilight of records: consentful recordkeeping as moral defence

AbstractIn this article, we examine the significance of establishing participatory and consentful recordkeeping practice in the face of ubiquitous use of records beyond their original intent. Among such secondary uses is the decontextualisation of data as part of the 'industrialisation' of access and use of ‘historical’ records within current transactional contexts, together with a wide range of data sharing practices arising from contemporary data science paradigms. To situate the call to action for consentful recordkeeping practice, we begin the article by exploring how human ability to navigate through the perpetual twilight of records becomes increasingly murky when a wholesale approach to data collection and governance is applied by machine learning practitioners. We then re-frame some classical archival principles to align them with participatory approaches; specifically, by expanding the scope of Jenkinsonian ‘moral defence’ as an imperative for proactive engagement with the Archival Multiverse. We then describe a case study of consentful recordkeeping in practice, using the example of the AiLECS Lab’s newly developed collection acquisition and management system. This principles-based framework informs our practices for collecting and curating datasets for machine learning research and development and aims to privilege the ongoing consent of those represented in records to their use. In the context of this work, our core premise is that technologies designed to prevent exploitation of children should aim to avoid underlying data practices that are themselves exploitative (of children or adults).

A data-driven ensemble technique for the detection of false data injection attacks in the smart grid framework

The major component of the smart grid (SG) is the advanced metering infrastructure (AMI), which monitors and controls the existing power system and provides interactive services for invoicing and electricity usage management with the utility. Including a cyber-layer in the metering system allows two-way communication but creates a new opportunity for energy theft, resulting in significant monetary loss. This article proposes an approach to detecting abnormal consumption patterns using energy metering data based on the ensemble technique AdaBoost, a boosting algorithm. Different statistical and descriptive features are retrieved from metering data samples, which account for extreme conditions. The model is trained for malicious and non-malicious data for five different attack scenarios, which are analyzed on the Irish Social Science Data Archive (ISSDA) smart meter dataset. In contrast to prior supervised techniques, it works well even with unbalanced data. The efficacy of the proposed theft detection method has been evaluated by comparing the accuracy, precision, recall, and F1 score with the other well-known approaches in the literature.

INNBC DApp, a decentralized application to permanently store biomedical data on a modern, proof-of-stake (POS), blockchain such as BNB Smart Chain

BackgroundA blockchain can be described as a distributed ledger database where, under a consensus mechanism, data are permanently stored in records, called blocks, linked together with cryptography. Each block contains a cryptographic hash function of the previous block, a timestamp, and transaction data, which are permanently stored in thousands of nodes and never altered. This provides a potential real-world application for generating a permanent, decentralized record of scientific data, taking advantage of blockchain features such as timestamping and immutability.ImplementationHere, we propose INNBC DApp, a Web3 decentralized application providing a simple front-end user interface connected with a smart contract for recording scientific data on a modern, proof-of-stake (POS) blockchain such as BNB Smart Chain. Unlike previously proposed blockchain tools that only store a hash of the data on-chain, here the data are stored fully on-chain within the transaction itself as “transaction input data”, with a true decentralized storage solution. In addition to plain text, the DApp can record various types of files, such as documents, images, audio, and video, by using Base64 encoding. In this study, we describe how to use the DApp and perform real-world transactions storing different kinds of data from previously published research articles, describing the advantages and limitations of using such a technology, analyzing the cost in terms of transaction fees, and discussing possible use cases.ResultsWe have been able to store several different types of data on the BNB Smart Chain: raw text, documents, images, audio, and video. Notably, we stored several complete research articles at a reasonable cost. We found a limit of 95KB for each single file upload. Considering that Base64 encoding increases file size by approximately 33%, this provides us with a theoretical limit of 126KB. We successfully overcome this limitation by splitting larger files into smaller chunks and uploading them as multi-volume archives. Additionally, we propose AES encryption to protect sensitive data. Accordingly, we show that it is possible to include enough data to be useful for storing and sharing scientific documents and images on the blockchain at a reasonable cost for the users.ConclusionINNBC DApp represents a real use case for blockchain technology in decentralizing biomedical data storage and sharing, providing us with features such as immutability, timestamp, and identity that can be used to ensure permanent availability of the data and to provide proof-of-existence as well as to protect authorship, a freely available decentralized science (DeSci) tool aiming to help bring mass adoption of blockchain technology among the scientific community.

The interrelationship between Lean 4.0 and value co-creation: an empirical study in the dairy sector

Purpose This study aims to examine the impact of Lean 4.0 practices on value co-creation in the dairy ecosystem. Design/methodology/approach Data collection were carried out through a questionary application with 126 professionals linked to the dairy ecosystem, including milk producers, milk cooperatives and milk transporters. The data were analyzed using Cluster Analysis, Mann-Whitney test and Chi-Square test. Findings A strong relation was found between the use of Lean 4.0 tools and the increase in operational performance, in addition to milk quality. Moreover, it can be noted that the use of digital technologies from Industry 4.0 has a strong relation with dairy production optimization, in other words, it is possible to be more efficient in the dairy process via Lean 4.0 adoption. Research limitations/implications The study is limited to analyzing the Brazilian dairy ecosystem. The results presented may not reflect the characteristics of the other countries. Practical implications Once the potential empirical impacts of the relation between Lean 4.0 and value co-creation are elucidated, it is possible to direct strategies for decision-making and guide efforts by researchers and professionals to deal with the waste mitigation present in the dairy sector. Social implications Lean 4.0 proves to be a potential solution to improve the operational performance of the dairy production system. Lean 4.0, linked to value co-creation, allows the integration of the production sector with consumers, through smart technologies, so new services and experiences can be provided to the consumer market. Additionally, the consumer experience can be stimulated based on Lean 4.0, once the quality specification is highlighted based on data science and smart management control. Originality/value To the best of the authors’ knowledge, this is the first study that analyzes the interrelationship between the Lean 4.0 philosophy and the value co-creation in the dairy ecosystem. In this sense, the study reveals the main contributions of this interrelation to the dairy sector via value co-creation, which demonstrates a new perspective on the complementarity of resources, elimination of process losses and new experiences for the user through digital technologies integrated with the Lean Thinking approach.

Taylor Approximation of Inventory Policies for One-Warehouse, Multi-Retailer Systems with Demand Feature Information

We consider a distribution system in which retailers replenish perishable goods from a warehouse, which, in turn, replenishes from an outside source. Demand at each retailer depends on exogenous features and a random shock, and unfulfilled demand is lost. The objective is to obtain a data-driven replenishment and allocation policy that minimizes the average inventory cost per time period. The extant data-driven methods either cannot guarantee a feasible solution for out-of-sample feature observations or generate one with excessive computational time. We propose a policy that resolves these issues in two steps. In the first step, we assume that the distributions of features and random shocks are known. We develop an effective heuristic policy by using Taylor expansion to approximate the retailer’s inventory cost. The resulting solution is closed-form, referred to as Taylor Approximation (TA) policy. We show that the TA policy is asymptotically optimal in the number of retailers. In the second step, we apply the linear quantile regression and kernel density estimation to the TA solution to obtain the data-driven policy called Data-Driven Taylor Approximation (DDTA) policy. We prove that the DDTA policy is consistent with the TA policy. A numerical study shows that the DDTA policy is very effective. Using a real data set provided by Fresh Hema, we show that the DDTA policy reduces the average cost by 11.0% compared with Hema’s policy. Finally, we show that the main results still hold in the cases of correlated demand features, positive lead times, and censored demand. This paper was accepted by J. George Shanthikumar, data science. Funding: Y. Yang acknowledges financial support from the NSFC [Grants 72125004, 71821002]. W. Zhou acknowledges financial support from the NSFC [Grants 72192823, 71821002]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.04241 .

A review on cardiovascular risk in rheumatoid arthritis

A systemic inflammatory disease known as rheumatoid arthritis (RA) is distinguished by excessive cardiovascular disease (CVD) morbidity and death. Traditional CV risk factors may partially contribute to CV disease in RA. Shared inflammatory mediators, post-translational modifications of peptides/proteins and subsequent immune responses, changes in the composition and function of lipoproteins, increased oxidative stress, and endothelial dysfunction are some of the mechanisms that link RA and CVD. The detailed pathogenetic pathway by which this association between RA and CVD might be explained is still not entirely known. It is crucial for controlling cardiovascular risk in people with RA. Optimizing care of traditional risk factors in addition to those inherent to RA is necessary to lessen the burden caused by CVD. The potential effect of planned Cardiac risk management in these individuals is highlighted by findings for under diagnosis and inadequate treatment of conventional CVD risk factors in RA. Present cardiovascular standards suggest RA patients to be examined for and treated for CVD risk factors without appropriate treatment goals. Utilizing potent anti-rheumatic medications that can reduce disease activity and treating the conventional CV risk factors should both be part of the therapy of CV risk in RA. There is currently insufficient scientific data to develop therapy targets for RA-related CVD risk factors. Thus, more study is required on the traditional CVD risk factor screening and management in RA patients.

Classification of Fall Out Boy Eras

This paper explored the use of machine learning techniques to differentiate between two different musical eras of the same rock band, including the technique of Logistic Regression. Logistic regression (LR) is a widely used statistical modeling method for binary classification in supervised machine learning. It is often used to predict whether a given event belongs to one of two categories. The process helps data scientists understand which variables are good predictors of class membership. Applications of logistic regression include loan classification in the financial industry and predicting susceptibility to disease in the medical field. In this particular project, a dataset was constructed using data from Spotify and Genius consisting of songs and lyrics written by the band Fall Out Boy. A logistic regression model was developed from scratch to classify the songs and lyrics into one of two eras of the band: before their 2009 hiatus and afterward. The study aimed to determine if a computer could differentiate between the two eras. The model was also tested against other binary classification algorithms, including Random Forest and Support Vector Machines.

Analysis of the Impact of Graduate Scholarships on the Employment System Using the C5.0 Algorithm

In order to explore the relevant mechanisms of graduate students obtaining scholarships and employment, this study starts from the perspective of data science and utilizes the C5.0 algorithm to collect 9 basic attributes of students, thereby determining the input variables of the decision tree. Employment is taken as the output variable. The employment situation of the graduates in 2021 is used as the training set, and the situation of the graduates in 2022 is used as the test set. This opens the black box of the mechanism of scholarship acquisition and student employment. The study indicates that the publication of papers, whether a student cadre, and academic conference papers are the three most critical indicators for employment

Natrix natrix after dark: citizen science sheds light on the common grass snake's nightlife.

Activity patterns in animals are often species-specific, and can be generally categorized as diurnal, crepuscular, or nocturnal. Understanding these patterns provides insight into ecological adaptations and behaviors. The common grass snake (Natrix natrix), one of the most common and widespread European snake species, is traditionally considered diurnal, with scarce evidence of its crepuscular and nocturnal activity. We aimed to document the distribution, environmental conditions, and potential phenotype associations of nighttime activity in N. natrix. We used citizen science data from iNaturalist (1992-2022), Observation.org (2012-2022), together with personal field observations (2010-2023) to collect 127 crepuscular and nocturnal activity records. Most observations occurred between May and August, coinciding with the peak activity period of grass snakes across their distribution range. Statistical analyses revealed no significant difference in mean daily temperatures between crepuscular and nocturnal observations. However, striped individuals displayed nocturnal activity at higher temperatures, consistent with their distribution in warmer regions, but failed to register any difference when tested on a geographic subsample, that accounted for sympatry of the phenotypes. Surprisingly, we found no significant impact of moon presence or moonlight on nighttime activity or age class, contrary to expectations based on other snake species' responses. While our study reveals that nocturnal activity in the common grass snake is geographically widespread, further research is warranted to understand its drivers and ecological implications. This study highlights the value of citizen science platforms for biological and ecological research, offering unparalleled spatial and temporal coverage by their users. In conclusion, our work extends the knowledge of nocturnal behavior in N. natrix and underlines the critical role of citizen science in discovering behavioral aspects of common and widespread species.

Electronic Visualization Laboratory's 50th Anniversary Retrospective: Look to the Future, Build on the Past

Abstract September 2023 marks the 50th anniversary of the Electronic Visualization Laboratory (EVL) at University of Illinois Chicago (UIC). EVL's introduction of the CAVE Automatic Virtual Environment in 1992, the first widely replicated, projection-based, walk-in, virtual-reality (VR) system in the world, put EVL at the forefront of collaborative, immersive data exploration and analytics. However, the journey did not begin then. Since its founding in 1973, EVL has been developing tools and techniques for real-time, interactive visualizations—pillars of VR. But EVL's culture is also relevant to its successes, as it has always been an interdisciplinary lab that fosters teamwork, where each person's expertise contributes to the development of the necessary tools, hardware, system software, applications, and human interface models to solve problems. Over the years, as multidisciplinary collaborations evolved and advanced scientific instruments and data resources were distributed globally, the need to access and share data and visualizations while working with colleagues, local and remote, synchronous and asynchronous, also became important fields of study. This paper is a retrospective of EVL's past 50 years that surveys the many networked, immersive, collaborative visualization and VR systems and applications it developed and deployed, as well as lessons learned and future plans.