Web-based Software Application Research Articles

Reliability and Agreement of Free Web-Based 3D Software for Computing Facial Area and Volume Measurements

Background: Facial surgeries require meticulous planning and outcome assessments, where facial analysis plays a critical role. This study introduces a new approach by utilizing three-dimensional (3D) imaging techniques, which are known for their ability to measure facial areas and volumes accurately. The purpose of this study is to introduce and evaluate a free web-based software application designed to take area and volume measurements on 3D models of patient faces. Methods: This study employed the online facial analysis software to conduct ten measurements on 3D models of subjects, including five measurements of area and five measurements of volume. These measurements were then compared with those obtained from the established 3D modeling software called Blender (version 3.2) using the Bland–Altman plot. To ensure accuracy, the intra-rater and inter-rater reliabilities of the web-based software were evaluated using the Intraclass Correlation Coefficient (ICC) method. Additionally, statistical assumptions such as normality and homoscedasticity were rigorously verified before analysis. Results: This study found that the web-based facial analysis software showed high agreement with the 3D software Blender within 95% confidence limits. Moreover, the online application demonstrated excellent intra-rater and inter-rater reliability in most analyses, as indicated by the ICC test. Conclusion: The findings suggest that the free online 3D software is reliable for facial analysis, particularly in measuring areas and volumes. This indicates its potential utility in enhancing surgical planning and evaluation in facial surgeries. This study underscores the software’s capability to improve surgical outcomes by integrating precise area and volume measurements into facial surgery planning and assessment processes.

Fish Catch Survey and Analysis – An online application for deriving measures and indicators for fish stock assessment

As fish are a natural resource, it is important to balance their use and protection. To comprehend the status of fisheries and to keep sustainable harvest levels to prevent the fish population from declining over time, reliable data on both recent and historical catches are crucial. The monitoring and assessment of marine fishery resources have been the primary mandate of the Central Marine Fisheries Research Institute of the Indian Council of Agricultural Research (ICAR-CMFRI) since its inception in 1947. Accurate and consistent knowledge about how a fishery is doing, as well as what species and how many are being captured and where they are captured, requires precise data collection, fast and advanced reporting, processing and analysis, and efficient mechanisms to disseminate the results to enable near real-time assessment. Stratified multistage random sampling design (SMRSD), a unique and robust system developed by the institute for the estimation of fish catch and effort over a long period used multiple computer programs built in-house for processing data. The area covering the 1269 fish landing centres along the 6,068-km coastline of India is spatially stratified, with each maritime state divided into suitable nonoverlapping zones based on fishing intensity and geographical considerations. In order to collect data on a real-time basis from the fish landing centres using electronic devices and process it, ICAR-CMFRI developed a web based application software called Fish Catch Survey and Analysis. It is a user-friendly interface with a variety of data entry options, and a suite of analysis tools. It includes species-wise, gear-wise and catch-per-unit-effort analysis also. Researchers and decision-makers can develop appropriate measures and indicators for fish stock assessment using FCSA.

A045 Weight loss among severely obese individuals using nutritional plans generated by a digital, fully automated, web-based software application or by a physician. A proof-of-concept and first of its kind study

A045 Weight loss among severely obese individuals using nutritional plans generated by a digital, fully automated, web-based software application or by a physician. A proof-of-concept and first of its kind study

Accounting for the distributions of input quantities in the procedure for the measurement uncertainty evaluation when calibrating the goniometer

The discords concerning the measurement uncertainty evaluation in the Guide to the Expressing of Uncertainty in Measurement (GUM) and its Supplement 1 are considered. To overcome these discords, the authors of the paper propose to use the kurtosis method and the law of the propagation of the expanded uncertainty. Using the example of the goniometer calibration, the features of accounting for the distribution laws of input quantities in the procedure for the measurement uncertainty evaluation are shown. A model for direct measurements of the value of a reference measure of the angle using a goniometer is written, the procedures for the measurement uncertainty evaluation are described, and uncertainty budgets for each of the methods are given. An example of the measurement uncertainty evaluation when calibrating a digital goniometer using a 24-sided reference prism is described. An estimate of the expanded measurement uncertainty for this example was made based on the web-based software application NIST Uncertainty Machine, which showed a good agreement with the estimates obtained by the considered methods. The technology of applying this software application for the confidence level of 0,9545, which the software lacks, is shown. The estimates of the measurement uncertainty obtained by the proposed methods, Monte Carlo method and methodology of the Guide to the Expressing of Uncertainty in Measurement are compared.

Project-Based Entrepreneurship Learning for Vocational High School Students in Bekasi City

Entrepreneurial learning requires methods that generate students' intentions and motivation. The project-based practice method is an alternative method that is suitable for SMK students. This research uses descriptive qualitative method. Respondents were 7 students, 2 teachers and 2 principals. Data in the form of primary and secondary data, data taken through interviews and documentation. The data is processed through data collection, data reduction, data analysis and conclusions are drawn. Data is tested by triangulation technique. The research was conducted in 2 vocational schools in the city of Bekasi. The results showed that vocational students were able to make hand and face cleaning liquid products and create desktop and web-based PHP information system software applications. The project-based learning process is able to make students work and try to make a product so that they are expected to become reliable entrepreneurs

Bibliometric Analysis: Financial Literacy and Financial Behavior

Currently, there is no comprehensive scientific study of financial literacy and behavior. This is due to the presence of no trends in the field of research; instead, many exploratory, conceptual and empirical studies have been carried out. The current study intends to acquire trends by synthesizing different knowledge structures. A scientific search was performed to gather data for financial literacy and financial behavior. After searching Dimensions, a web-based software application created in the R language was used to gather information automatically. This software is part of a package called Bibliometrix, which also includes a web-based application called Biblioshiny. The two are used to find sources, countries, journals, authors, articles and themes. Analysis of social networks and joint quotations are also performed using automated workflows in the software. This study involved collecting data from 417 documents. Initially, this interdisciplinary field focused on the specific aspects of demographics and socio-economic status. However, as time progressed, it expanded to include behavioral aspects and even mental concepts that affected financial behavior. The study also outlines how the field’s intellectual and social ideas combine. This study uses bibliometric analysis to study important subjects. Because of this, future researchers can use its limitations to shape their ideas. Studying the literature through a systematic review will help lead to a more solid understanding of the material. Due to Dimensions' larger scope than journals, current studies utilize the database for science mapping. This information provides important data regarding financial literacy and behaviors. It also highlights the most important issues currently being addressed in the field. This data helps lead to future research projects that focus on new areas. This paper reveals the social and intellectual foundations of a particular academic or scientific field. It also details the collaborations, contexts and key themes that emerge in this particular field. After reading this paper, other researchers can make informed decisions about financial education and financial literacy policies. A large amount of work from different fields have been done across the country. This includes work from psychology, finance, economics and other fields. The study pulls apart the current literature and unites it into one cohesive body. It also explores how behavior and financial literacy are connected

Weather prediction performance evaluation on selected machine learning algorithms

Prediction of weather has been proved useful in the early warning on the impacts of weather on several areas of human livelihood. For example, the provision of decisions for autonomous transportation to reduce traffic congestion and accidents during the rainy season. However, providing the most accurate and effective forecasting model for weather forecasts has been a challenge. Hence, machine learning (ML) techniques and factors influencing weather prediction need to be investigated. Data scientists are yet to discover the best models for weather prediction. Therefore, this study compares three ML classification techniques for weather prediction. A web-based software application was developed using Flask App to demonstrate weather modeling using three ML models, and the data used for the study was obtained from Kaggle. For the weather prediction; a decision tree (DT), k-nearest neighbor (k-NN), and logistic regression (LR) classifier method were suggested, and comparisons were made between the three classifications techniques. The accuracy results show that with a 100% accuracy rate, the DL classifier outperforms the k-NN with a 78% accuracy rate and LR with a 93% accuracy rate. The results show that the application of ML models gives accurate results on weather prediction.

Digitalization of Financial Reports of Village-Owned Enterprises

This study aimed to describe to the optimization of the role of village-owned enterprises which will increase the village original income. Management of village potential becomes the basic needs of economic growth and equity. Therefore, it is necessary to maximize the role of village-owned enterprises to increase village original income. The research objectives are to design and create a web-based village-owned enterprises governance digitization system and to design and create a web-based village-owned enterprises financial reporting digitization system. The research population is the village-owned enterprises in Siberida District, Indragiri Hulu Regency. The research sample is Berkah Bersama village-owned enterprises. The data collection technique used primary data and secondary data, namely conducting interviews with the directors and employees of village-owned enterprises, as well as the public as users of information. While secondary data are in the form of Standard Operating Procedures (SOP), ministerial regulations, series 7 village fund guidebooks on village-owned enterprises. The conclusion of this study is the achievement of the result of web-based software application to design a financial management application through e-village budgeting that can help village-owned enterprises managers make transparent and accountable financial reporting.

Acute Toxicity Level of Pulutan (Urena lobata) Leaf Extract on Zebrafish (Danio rerio) and its Analysis by In Silico Study

Pulutan (Urena lobata) is one of medicinal plant used to treat some diseases traditionally and pre-clinical studies have showed its efficacy. However, the study about its safety has not been evaluated completely. An acute toxicity test has to be performed in order to screen for its safety. The objective of study to determine the acute toxicity level of Urena lobata (U. lobata) leaf extract on embryo phase, juvenile and adult of zebrafish (Danio rerio) and the role of its active constituents through in silico methods. This was an experimental laboratory study using embryo, juvenile and adult of zebrafish (Danio rerio). The leaf of U. lobata was extracted by decoction methods and the extract was diluted from 12000 mg/L to 500 mg/L. The animals were exposed to the extracts for 96 hours. Toxicity level of herbs was defined using lethal concentration-50 (LC-50) obtained through linear regression. In silico study was performed using a web-based software application (iLAB ACD). The LC-50 values of U. lobata leaf extract for embryo, juvenile and adult of zebrafish (Danio rerio) were 2548 mg/L, 8748 mg/L and 8088 mg/L, respectively. Acute toxicity level of U. lobata on embryo is higher compared to juvenile and adult of zebrafish. After identification of active compound and in silico study was performed, Stigmasterol and β-sitosterol in U. lobata showed high toxicity level based on LD-50 value. Level toxicity of U. lobata on zebrafish embryo was moderate (0.5-5.0 g/L) and it shows teratogenic effect, meanwhile, its effects on juvenile and adult fish were considered mild (5.0-15.0 g/L).

Abstract CT549: Randomized Phase II trial of two different nutritional approaches for patients receiving treatment for their advanced pancreatic cancer

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by stromal fibrosis, hypoxia, and nutritional deprivation. PDAC tumors grow aggressively, diagnosis is typically made after metastasis and the disease remains associated with poor outcomes. The triplet chemotherapy regimen of gemcitabine, nab-paclitaxel with cisplatin was associated with a median overall survival of 16.4 months in patients with metastatic pancreatic cancer in the first-line setting (Jameson et al., 2020). Nutritional, metabolic interventions offer an opportunity to fundamentally change the tumor microenvironment and improve outcomes for patients. A medically supervised ketogenic diet (MSKD) defined as lower carbohydrate, lower protein, and higher fat can significantly reduce glucose and insulin and increase metabolically active ketone bodies. A ketogenic diet combined with triplet chemotherapy (gemcitabine, nab-paclitaxel, cisplatin) was shown to inhibit murine pancreatic KPC tumor growth and to triple the survival benefit of chemotherapy alone. The ketogenic diet combined with triple chemotherapy was associated with glucose depletion, altered TCA substrate usage, and NADH elevation. Methods: In this Phase II randomized clinical trial (NCT04631445), we are evaluating a medically supervised ketogenic diet (MSKD) versus a standard diet when combined with the triplet therapy in patients with treatment-naive advanced pancreatic cancer. The primary endpoint is progression free survival for triplet therapy while on MSKD or non-MSKD. Secondary endpoints include disease control rate (PR+ CR+ SD for at least 9 weeks), change in CA 19-9 (or CA125, or CEA if not expressers of CA 19-9), average insulin levels, HbA1c, body weight, a comparison of gut microbial diversity, changes in serum metabolites and quality of life via the EORTC QLQ-C30 assessment. Unlike prior ketogenic intervention studies, the MSKD is being supported by a continuous care nutrition intervention through Virta Health Corp, that offers tracking of daily ketone and glucose levels, a web-based software application, education, and communication with a remote care team to ensure sustained nutritional ketosis. A total of 40 patients with untreated metastatic PDAC are planned for enrollment, 20 randomized to each arm. The trial opened for accrual November 2020. Citation Format: Diana Hanna, Gayle S. Jameson, Drew W. Rasco, Angela Alistar, Richard C. Frank, Anthony B. El-Khoueiry, Julia E. Wiedmeier, Caroline Roberts, Brandon Fell, Sarah Hallberg, Denise Roe, Derek Cridebring, Joshua Rabinowitz, Stephen Thomas Gately, Daniel D. Von Hoff. Randomized Phase II trial of two different nutritional approaches for patients receiving treatment for their advanced pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr CT549.

Mathematical Modeling of Free Thyroxine Concentrations During Methimazole Treatment for Graves' Disease: Development and Validation of a Computer-Aided Thyroid Treatment Method.

BackgroundMethimazole (MMI) is the first-line treatment for patients with Graves’ disease (GD). While there are empirical recommendations for initial MMI doses, there is no clear guidance for subsequent MMI dose titrations. We aimed to (a) develop a mathematical model capturing the dynamics of free thyroxine (FT4) during MMI treatment (b), validate this model by use of numerical simulation in comparison with real-life patient data (c), develop the software application Digital Thyroid (DigiThy) serving either as a practice tool for treating virtual patients or as a decision support system with dosing recommendations for MMI, and (d) validate this software framework by comparing the efficacy of its MMI dosing recommendations with that from clinical endocrinologists.MethodsBased on concepts of automatic control and by use of optimization techniques, we developed two first order ordinary differential equations for modeling FT4 dynamics during MMI treatment. Clinical data from patients with GD derived from the outpatient clinic of Endocrinology at the Medical University of Graz, Austria, were used to develop and validate this model. It was subsequently used to create the web-based software application DigiThy as a simulation environment for treating virtual patients and an autonomous computer-aided thyroid treatment (CATT) method providing MMI dosing recommendations.ResultsBased on MMI doses, concentrations of FT4, thyroid-stimulating hormone (TSH), and TSH-receptor antibodies (TRAb), a mathematical model with 8 patient-specific constants was developed. Predicted FT4 concentrations were not significantly different compared to the available consecutively measured FT4 concentrations in 9 patients with GD (52 data pairs, p=0.607). Treatment success of MMI dosing recommendations in 41 virtually generated patients defined by achieved target FT4 concentrations preferably with low required MMI doses was similar between CATT and usual care. Statistically, CATT was significantly superior (p<0.001).ConclusionsOur mathematical model produced valid FT4 predictions during MMI treatment in GD and provided the basis for the DigiThy application already serving as a training tool for treating virtual patients. Clinical trial data are required to evaluate whether DigiThy can be approved as a decision support system with automatically generated MMI dosing recommendations.

NmfMapping: a cloud-based web application for non-negative matrix factorization of powder diffraction and pair distribution function datasets.

A cloud-hosted web-based software application, nmfMapping, for carrying out a non-negative matrix factorization of a set of powder diffraction or atomic pair distribution function datasets is described. This application allows structure scientists to find trends rapidly in sets of related data such as from in situ and operando diffraction experiments. The application is easy to use and does not require any programming expertise. It is available at https://pdfitc.org/.

A Geospatial Platform for Crowdsourcing Green Space Area Management Using GIS and Deep Learning Classification

Green space areas are one of the key factors in people’s livelihoods. Their number and size have a significant impact on both the environment and people’s quality of life, including their health. Accordingly, government agencies often rely on information relating to green space areas when devising suitable plans and mandating necessary regulations. At present, obtaining information on green space areas using conventional ground surveys faces a number of limitations. This approach not only requires a lengthy period, but also tremendous human and financial resources. Given such restrictions, the status of a green space is not always up to date. Although software applications, especially those based on geographical information systems and remote sensing, have increasingly been applied to these tasks, the capability to use crowdsourcing data and produce real-time reports is lacking. This is partly because the quantity of data required has, to date, prohibited effective verification by human operators. To address this issue, this paper proposes a novel geospatial platform for green space area management by means of GIS and artificial intelligence. In the proposed system, all user-submitted data are automatically verified by deep learning classification and analyses of the greenness areas on satellite imagery. The experimental results showed that the classification and analyses can identify green space areas at accuracies of 93.50% and 97.50%, respectively. To elucidate the merits of the proposed approach, web-based application software was implemented to demonstrate multimodal data management, cleansing, and reporting. This geospatial system was thus proven to be a viable tool for assisting governmental agencies to devise appropriate plans toward sustainable development goals.

Randomized phase II trial of two different nutritional approaches for patients receiving treatment for their advanced pancreatic cancer.

TPS637 Background: Pancreatic ductal adenocarcinoma (PDAC) is characterized by stromal fibrosis, hypoxia, and nutritional deprivation. PDAC tumors grow aggressively, diagnosis is typically made after metastasis and the disease remains associated with poor outcomes. The triplet chemotherapy regimen of gemcitabine, nab-paclitaxel with cisplatin was associated with a median overall survival of 16.4 months in patients with metastatic pancreatic cancer in the first-line setting (Jameson et al., 2020). Nutritional, metabolic interventions offer an opportunity to fundamentally change the tumor microenvironment and improve outcomes for patients. A ketogenic diet defined as lower carbohydrate, lower protein, and higher fat can significantly reduce glucose and insulin and increase metabolically active ketone bodies and has been evaluated in patients with a variety of solid tumors (Weber et al, 2020). Recently, a ketogenic diet combined with triplet chemotherapy was shown to inhibit murine pancreatic KPC tumor growth and significantly prolong animal survival over chemotherapy alone. Tumor growth inhibition was associated with glucose depletion, altered TCA substrate usage, and NADH elevation. Methods: In this Phase II randomized clinical trial (NCT04631445), we are evaluating a medically supervised ketogenic diet (MSKD) versus a standard diet when combined with the triplet therapy in patients with treatment-naive advanced pancreatic cancer. The primary endpoint is progression free survival for triplet therapy while on MSKD or non-MSKD. Secondary endpoints include disease control rate (PR+ CR+ SD for at least 9 weeks), change in CA 19-9 (or CA125, or CEA if not expressers of CA 19-9), average insulin levels, HbA1c, body weight, a comparison of gut microbial diversity, changes in serum metabolites and quality of life via the EORTC QLQ-C30 assessment. Unlike prior ketogenic intervention studies, the MSKD is being supported by a continuous care nutrition intervention through Virta Health Corp, that offers tracking of daily ketone and glucose levels, a web-based software application, education, and communication with a remote care team to ensure sustained nutritional ketosis. A total of 40 patients with untreated metastatic PDAC are planned for enrollment, 20 randomized to each arm. The trial opened for accrual November 2020. Clinical trial information: NCT04631445.

A web-based tool for assessing the condition of benthic diatom assemblages in streams and rivers of the conterminous United States.

Benthic diatom assemblages are known to be indicative of water quality but have yet to be widely adopted in biological assessments in the United States due to several limitations. Our goal was to address some of these limitations by developing regional multi-metric indices (MMIs) that are robust to inter-laboratory taxonomic inconsistency, adjusted for natural covariates, and sensitive to a wide range of anthropogenic stressors. We aggregated bioassessment data from two national-scale federal programs and used a data-driven analysis in which all-possible combinations of 2-7 metrics were compared for three measures of performance. After ranking the best-performing MMIs, we selected the final MMIs by evaluating stress-response relations in independent regional datasets of diatom samples paired with measures of several water-quality stressors, including herbicides and streamflow flashiness. Each regional MMI performed well at calibration sites and represented diverse aspects of the structure and function of diatom communities. Most metrics included in the best MMIs were modeled to account for natural variation including climate, topography, soil characteristics, lithology, and groundwater influence on streamflow. MMI performance improved with higher numbers of component metrics, but this effect diminished beyond six metrics. Component metrics of MMIs were associated with a broad suite of measured stressors in every region, including salinity, nutrients, herbicides, and streamflow flashiness. We provide a web-based software application that allows users in the conterminous United States to apply our MMIs to their own datasets and compare MMI scores from their sites to a broader regional context.

Adaptive Remanufacturing – Decision Model for the Intelligent Maintenance of Production Resources

The life cycle extension of capital goods holds considerable ecological and economic potential in terms of improved resource efficiency in view of increasing resource scarcity and rising environmental awareness. The innovative approach of Adaptive Remanufacturing (AdR) allows to raise these potentials. The objective of this article is the detailed introduction of the structure and functioning of the AdR decision model. AdR describes a proactive, intelligent maintenance strategy based on structured decisions about the optimal time and scope of maintenance and remanufacturing measures. The basis of assessment are the remanufacturing-relevant components of a capital good. To protect these components from critical wear and tear, component conditions are monitored, and sensor measurement data are transferred into the decision model as dynamic input. Based on condition monitoring and defined threshold values per sensor, critical states of the monitored components are predicted. These forecasts trigger the decision process to identify and suggest suitable measures and the optimal point in time for their application based on a catalogue of measures, which serves as static input for the decision model. From this catalogue, feasible measures are preselected according to their technical suitability for specific components and wear mechanisms. To determine the optimal measure for a given component and wear, measures are assessed in terms of benefits regarding the state of a component as well as economic and ecological efforts. The decision model is implemented in a web-based software application. As a main result, it provides key figures regarding short- and long-term expenses, the CO2 consumption, and the effective duration per measure. Moreover, a prioritised measure recommendation is issued. The AdR decision model thus enables the substantiated selection of the optimal maintenance or remanufacturing measure per component.

Web-based software applications for frailty assessment in older adults: a scoping review of current status with insights into future development

BackgroundA crucial aspect of continued senior care is the early detection and management of frailty. Developing reliable and secure electronic frailty assessment tools can benefit virtual appointments, a need especially relevant in the context of the COVID-19 pandemic. An emerging effort has targeted web-based software applications to improve accessibility and usage. The objectives of this scoping review are to identify and evaluate web-based frailty assessment tools currently available and to identify challenges and opportunities for future development.MethodsWe conducted a review with literature (e.g., using MEDLINE databases) and Google searches (last updated on October 10, 2021). Each of the identified web applications were assessed based on eight featured categories and assigned a rating score accordingly.ResultsTwelve web-based frailty assessment applications were found, chiefly provided by the USA (50%) or European countries (41%) and focused on frailty grading and outcome prediction for specific patient groups (59%). Categories that scored well among the applications included the User Interface (2.8/3) and the Cost (2.7/3). Other categories had a mean score of 1.6/3 or lower. The least developed feature was Data Saving.ConclusionsWeb-based applications represent a viable option for remote frailty assessments and multidisciplinary integrated care of older adults. Despite the available web-based frailty assessments on the Internet, many missed certain needed features for professional use in healthcare settings. This situation calls for fully comprehensive web-based applications, taking into consideration a number of key functions linking graphical user interface and functionalities, and paying special attention to secure data management.

Web-based software application design for solar PV system sizing

The solar photovoltaic (PV) energy source systems generally rely on the availability of sunlight, its duration, and the capacity of storage devices if it is not a grid-tie system. The components of the PV sources come in different sizes and capacities, depending on the various applications and available products in the market. Therefore, sizing of PV components becomes important to the functionality and reliability of solar PV sources. This work is aimed at the development of a web-based software application designed for sizing the capacity of solar PV source components that meet required energy demand. A description of photovoltaic system components, available types, and sizing techniques are discussed. Parameter evaluation algorithms with flowcharts were developed for PV components. Consequently, web-based software was developed and simulated for a different case study. The results described the estimated load, average daily load, ratings of PV system components such as inverter, battery, solar panel, and charge controller. The cost estimates of each component, the total estimated cost of the project, and the specification of components’ purchasing store are similarly presented. Thus, the developed application can be applied to size different ranges of microgrid systems meant for several applications.

DSI Strategy Canvas: Modelling the Digital Social Innovation Strategy

Humanity’s main challenges are to solve social needs, such as: eradicating hunger and poverty; reduce racial and gender inequalities; promote environmentally sustainable behaviour, etc. Social needs motivate the raise of social innovations promoted by the most different actors, from government, companies, the third sector, to individual entrepreneurs and communities. To increase the scalability and impact of social innovation, it is generally necessary to use digital technologies. Thus, the concept of Digital Social Innovation (DSI) emerges, which is a type of social innovation mediated by digital technology, through which innovators, users and the community cooperate to co-create knowledge and solutions to meet social needs. This work reports the application of the scientific method for the development and evaluation of a practical and lean method to modelling viable and sustainable strategies of DSI enterprises. The result of this work consisted of a tool called DSI Strategy Canvas, which is based on determinants that lead to the achievement of the goals of the DSI and indicators that measure and evaluate the performance of the DSI. DSI Strategy Canvas is concerned with product strategy which involves product vision, target market, ecosystem integration, value delivery, revenue streams, risk and growth estimates, scalability, and product evaluation. For technological support to the DSI Strategy Canvas tool, a web-based software application was developed, which was used by social entrepreneurs in evaluating the proposed approach.

Screen Printed Antennas on Fiber-Based Substrates for Sustainable HF RFID Assisted E-Fulfilment Smart Packaging.

Intelligent packaging is an emerging technology, aiming to improve the standard communication function of packaging. Radio frequency identification (RFID) assisted smart packaging is of high interest, but the uptake is limited as the market needs cost-efficient and sustainable applications. The integration of screen printed antennas and RFID chips as smart labels in reusable cardboard packaging could offer a solution. Although paper is an interesting and recyclable material, printing on this substrate is challenging as the ink conductivity is highly influenced by the paper properties. In this study, the best paper/functional silver ink combinations were first selected out of 76 paper substrates based on the paper surface roughness, air permeance, sheet resistance and SEM characterization. Next, a flexible high frequency RFID chip (13.56 MHz) was connected on top of screen printed antennas with a conductive adhesive. Functional RFID labels were integrated in cardboard packaging and its potential application as reusable smart box for third party logistics was tested. In parallel, a web-based software application mimicking its functional abilities in the logistic cycle was developed. This multidisciplinary approach to developing an easy-scalable screen printed antenna and RFID-assisted smart packaging application is a good example for future implementation of hybrid electronics in sustainable smart packaging.