Digital Approach Research Articles

Digital Leadership: Predicting Team Dynamics, Communication Effectiveness, and Team Performance

Digitalization is essential in modern business organizations, where it transforms the operational framework and increases competitive advantage through the integration of advanced technologies. This transformation enables real-time data analysis, improves communication channels, and automates routine tasks, thereby driving innovation and efficiency that ultimately drives sustainable growth and success in a highly competitive market environment. The research observed the effect of digital leadership on team dynamics, communication effectiveness, and team performance. Data were collected by using a Likert scale questionnaire with 117 individual respondents across various business organizations in Yogyakarta and Banten, Indonesia. Then, the data obtained were analyzed using Structural Equation Modeling (SEM) in AMOS software. The results show that digital leadership has a significant effect on team dynamics and communication effectiveness. In addition, communication effectiveness has been proven to have a significantly positive effect on team performance. However, there is no direct relationship between team dynamics and performance. The results underscore the importance of strengthening digital leadership and communication effectiveness to improve team performance. Organizations should prioritize fostering a collaborative culture through digital platforms, effectively managing conflicts, and ensuring timely communication. Digital leadership should promote a participatory culture, handle conflicts adeptly, and maintain teams motivated through an effective digital approach. Lastly, organizations must provide effective digital communication tools and ensure team members understand how to use them.

Improving COVID-19 contact tracing and testing of exposed individuals in Cameroon using digital health technology: a cluster randomised trial

Contact tracing was described as a key strategy to contribute to controlling the spread of severe acute respiratory syndrome of Coronavirus 2 (SARS-CoV-2) but implementing it can be a challenge. Digitalisation of contact tracing is among the proposed solutions being explored in sub-Saharan African settings. We assessed the effectiveness of a digital tool to expand SARS-CoV-2 testing in exposed individuals in Cameroon. We conducted a cluster-randomised (1:1) trial in eight health districts, including 22 facilities and SARS-CoV-2 testing units, randomly assigned to a digital (intervention) or standard (control) contact tracing approach. The intervention consisted of a contact tracing module added to the digital platform "Mamal PRO" used for monitoring and coordination of Coronavirus Disease 2019 pandemic response in Cameroon. The primary outcome was the proportion of contacts declared by SAR-CoV-2 index patients who were successfully traced and tested for SARS-CoV-2 evaluated with a Poisson regression model with cluster adjustment. This study is registered with ClinicalTrials.gov (NCT05684887). Between October 18, 2022, and March 31, 2023, we enrolled 164 index patients in the intervention arm and 149 in the control arm, who identified 854 and 849 contacts, respectively. In the intervention arm, 93.8% (801/854) of identified contacts were successfully reached by the tracing unit versus 54.5% (463/849) in the control arm. The intervention significantly increased the likelihood of successfully tracing contacts (adjusted relative risks (RR) 1.72 [95% CI: 1.00-2.95], p=0.049). The median (interquartile range, IQR) time to successfully tracing contacts was 0 days [IQR: 0, 1] in the intervention and 1 day [IQR: 0, 2] in the control arm. In the intervention arm, 21.3% (182/854) of identified contacts received SARS-CoV-2 testing compared to 14.5% (123/849) in the control arm (adjusted RR 1.47 [95% CI: 0.44-4.90], p=0.530). Digitalising the contact tracing process improved exposure notification and facilitated the tracing of a greater number of contacts of individuals infected with SARS-CoV-2 in resource-limited settings. The study was funded by FIND, United Kingdom (FCDO 40105983), Switzerland (81066910), Netherlands (SDD 4000004160), Canada (DFATD 7429348), The Kingdom of Saudi Arabia (FIND-ACT-A DX PARTNERSHIP 20.08.2020), The Rockefeller Foundation (2020 HTH 059), Germany (BMZ Covid-19 Diagnostic and Surveillance Response 27.07.2021), Australia (DFAT 76442), Kuwait (M239/2020), The Government of Portugal and Partners (ANF, BCP, CGF, APIFARMA) and The BlackRock Foundation (Grant Agreement as of April 20, 2022).

Digital Methods to Promote Inclusive and Effective Learning in Schools: A Mixed Methods Research Study

Abstract This study investigates 14 digitally enhanced learning methods, shedding light on students with special educational needs (SEN) in inclusive digital learning environments. We seek to fill the gap in the literature by specifically investigating methods suitable for students with SEN. A survey among experts has been carried out to assess learning methods that are effectively applicable in inclusive digital learning environments. A mixed method: quantitative and qualitative data analysis with the use of a constant comparative method has been applied to synthesise and compare experts’ answers. Quantitative data analysis showed that cooperative learning, digital problem/project-based learning, and virtual exchange are the most suitable methods for all students, whereas digital problem/project-based learning, cooperative learning, and service-learning were agreed upon as the most appropriate for students with SEN. Answers to open questions, evaluated using a qualitative approach, showed that the effectiveness of digital approaches is heavily reliant on the skills, experience, willingness, confidence, and knowledge of teachers implementing them. Employing cooperative learning and digital problem/project-based learning, particularly by experienced and highly skilled teachers, has the potential to effectively support all students, including those with SEN, in digital learning environments.

The Development of digital tourism at the Tebing Tegallinggah temple tourist attraction in Bedulu Village, Gianyar Regency, Bali

Candi Tebing Tegallinggah is a tourist attraction located in Bedulu Village, Blahbatuh District, Gianyar Regency. Since it opened as a tourist attraction at the end of 2019, the level of tourist visits to Candi Tebing Tegallinggah is still relatively low, this is because access to information regarding the existence of Tebing Tegallinggah especially through online media is still very minimal. Story posts and reviews on social media regarding the existence of the Candi Tebing Tegallinggah are still very minimal, until March 2023 there were only 11 story posts and 26 reviews. Therefore, this research is very important to carry out to create a digital tourism website application in the development of the Candi Tebing Tegallinggah. Data collection in this research was carried out using in-depth interviews, observation, and document study methods. The data was analyzed descriptively and qualitatively using the digital tourism concept approach, the concept of potential tourist attractions, the concept of destination development, and the social action theory approach. The results of this research show that the potential tourist attraction of Tebing Tegallinggah is in the form of natural potential, cultural potential, and artificial potential. The digital tourism application for the Candi Tebing Tegallinggah tourist attraction takes the form of a website (https://canditebingtegallingah.id) which contains information regarding the location, history, and general description of the Candi Tebing Tegallinggah which contains virtual tour content. The development of Candi Tebing Tegallinggah as a tourist attraction has had a positive impact on the economy, socio-culture, and the surrounding environment. The application of digital tourism applications in developing the Candi Tebing Tegallinggah is expected to increase the number of tourist visits.

Repositioning Religious Organization Activism in The Digital Era

The rise of the digital world poses a challenge to the position of religious organizations. This is evident in the emergence of new authorities, both individuals and groups, outside traditional religious structures. Nahdlatul Ulama (NU), a mainstream traditional organization, has also been impacted by this shift. However, several studies indicate that NU has effectively adapted to various challenges and situations over time. This analysis focuses on how NU, as a representative of traditional mainstream Islamic organizations, has repositioned itself in response to the digital era, using the example of its activities in Mojokerto. This work addresses two key issues: first, how NU adapts to the presence of social media; second, what strategies NU employs to reposition itself in the face of these new challenges on social media. To explore these questions, this work employs a qualitative methodology, collecting data through interviews, observations, and documentation. The data is then analyzed and presented using a digital religion approach. The findings suggest that NU has repositioned itself in two main ways. First, through adaptation, by adopting and actively using social media platforms such as Facebook, Instagram, and YouTube via the organization’s official accounts. Second, through a form of resistance, by having NU activists engage on social media without prominently displaying their organizational affiliation.

Liquid biopsy in brain tumors: moving on, slowly.

Due to limited access to the tumor, there is an obvious clinical potential for liquid biopsy in patients with primary brain tumors. Here, we review current approaches, present limitations to be dealt with, and new promising data that may impact the field. The value of circulating tumor cell-free DNA (ctDNA) in the cerebrospinal fluid (CSF) for the noninvasive diagnosis of primary brain tumors has been confirmed in several reports. The detection of ctDNA in the peripheral blood is desirable for patient follow-up but requires ultrasensitive methods to identify low mutant allelic frequencies. Digital PCR approaches and targeted gene panels have been used to identify recurrent hotspot mutations and copy number variations (CNVs) from CSF or plasma. Tumor classification from circulating methylomes in plasma has been actively pursued, although the need of advanced bioinformatics currently hampers clinical application. The use of focused ultrasounds to open the blood-brain barrier may represent a way to enrich of ctDNA the peripheral blood and enhance plasma-based liquid biopsy. Monitoring CNVs and hotspot mutations by liquid biopsy is a promising tool to detect minimal residual disease and strengthen response assessment in patients with primary brain tumors. Novel methods to increase the relative and/or absolute amount of ctDNA can improve the clinical potential of plasma-based liquid biopsies.

TWIN-GPT : Digital Twins for Clinical Trials via Large Language Model

Clinical trials are indispensable for medical research and the development of new treatments. However, clinical trials often involve thousands of participants and can span several years to complete, with a high probability of failure during the process. Recently, there has been a burgeoning interest in virtual clinical trials, which simulate real-world scenarios and hold the potential to significantly enhance patient safety, expedite development, reduce costs, and contribute to the broader scientific knowledge in healthcare. Existing research often focuses on leveraging electronic health records (EHRs) to support clinical trial outcome prediction. Yet, trained with limited clinical trial outcome data, existing approaches frequently struggle to perform accurate predictions. Some research has attempted to generate EHRs to augment model development but has fallen short in personalizing the generation for individual patient profiles. Recently, the emergence of large language models has illuminated new possibilities, as their embedded comprehensive clinical knowledge has proven beneficial in addressing medical issues. In this paper, we propose a large language model-based digital twin creation approach, called TWIN-GPT . TWIN-GPT can establish cross-dataset associations of medical information given limited data, generating unique personalized digital twins for different patients, thereby preserving individual patient characteristics. Comprehensive experiments show that using digital twins created by TWIN-GPT can boost the clinical trial outcome prediction, exceeding various previous prediction approaches. Besides, we also demonstrate that TWIN-GPT can generate high-fidelity trial data that closely approximates specific patients, aiding in more accurate result predictions in data-scarce situations. Moreover, our study provides practical evidence for the application of digital twins in healthcare, highlighting its potential significance.

Digital twin-driven energy consumption management of integrated heat pipe cooling system for a data center

The energy consumption management (ECM) for the integrated heat pipe cooling (IHPC) systems has become a significant cost-cutting strategy, given the growing demand for the decreased cooling and maintenance costs in data centers. However, the traditional ECM strategies lack an integration with the real-time information and the automatic feedback control, causing the risks of system operation difficult to diagnose and the potential for energy saving hard to exploit. In this respect, a digital twin approach was proposed to efficiently and automatically implement the ECM strategy for an IHPC system. First, a digital twin architecture was established to enable seamless integration and real-time interaction between the physical system and the digital twin. Secondly, the digital twin models of monitoring, simulation, energy evaluation and optimization were developed to drive the corresponding services. Finally, the approach was verified on an IHPC system operating in a real-life data center. It is found that the approach can automatically detect and justify the abnormal states of the IHPC system. Moreover, the approach can reduce the power consumption by 23.63% while meeting the production requirements. The mean relative errors of the supply air temperature and the cooling capacity between the digital twin simulated and the on-site records are 1.43% and 1.46%, respectively. In summary, the proposed approach provides a digital twin workflow that can significantly improve the efficiency of the ECM strategy deployed on an IHPC system.

A digital twin design and implementation approach for industrial application leveraging programmable logic controllers

A digital twin design and implementation approach for industrial application leveraging programmable logic controllers

P-601 Monitoring reproductive hormones during ovarian stimulation: performance and usability of a self-operated at-home urine test

Abstract Study question How effective and user-friendly is a non-invasive, patient-operated urine test (Kinder) in tracking reproductive hormone levels during ovarian stimulation (OS) treatment in an IVF cycle? Summary answer Urinary E1-3G correlates well with blood E2 levels and ovarian follicle development. Patients found it easy and stress-free to perform the urine test at home. What is known already Monitoring blood reproductive hormone levels during OS is a widely accepted practice to ensure the optimal ovarian response to gonadotropin treatment, timing of oocyte maturation triggering and mitigating hyperstimulation risk. However, conventional blood testing is invasive, necessitating patients to travel to clinics for blood sampling, adding to their overall burden. Previous studies have revealed a robust correlation between blood E2 levels, and its corresponding urinary metabolites E1-3G. Kinder is a portable device designed for quantifying urinary E1-3G, allowing patients to conduct tests in the privacy of their homes, and for many obviating the need to travel substantial distances for monitoring. Study design, size, duration This is a prospective, single-cohort, study of Caucasian patients (28-34 years), conducted at Fertility Specialists of Western Australia in Perth, Australia, where patients may travel vast distances for treatment. Twenty-five patients were enrolled from Aug 2022-Aug 2023, and performed home urine tests utilizing the Kinder device in parallel with clinic scheduled blood (ELISA) and ultrasound tests. The primary end point of the study was the correlation between urinary and blood reproductive hormone concentrations (E1-3G). Participants/materials, setting, methods Spearman and Pearson correlation coefficients (CC) and 95% confidence intervals were calculated for E1-3G and E2, and E1-3G/E2 and follicle development (total volume of ovarian follicles by ultrasound). To assess patient acceptability patient-reported outcomes (PRO) were assessed using the State Trait Anxiety Inventory (STAI) questionnaire. Further, patient-reported experiences (PRE) were evaluated through the System Usability Scale (SUS) questionnaire, along with assessing their likelihood to recommend Kinder to their friends or family undergoing IVF treatment. Main results and the role of chance Upon analysing 100 concurrent urine and blood samples from 25 patients, a robust correlation was found between E1-3G and E2 levels (Spearman CC: 0.818, 95% CI: 0.680-0.904; Pearson CC: 0.847, 95% CI: 0.712-0.939). Additionally, we observed a moderate correlation between E1-3G levels and ovarian volume (Spearman CC: 0.701, 95% CI: 0.556-0.805; Pearson CC: 0.670, 95% CI: 0.519-0.781, n = 72), resembled the correlation between E2 levels and ovarian volume (Spearman CC: 0.874, 95% CI: 0.804-0.920; Pearson CC: 0.849, 95% CI: 0.770-0.902, n = 75). PRE: 22 out of 23 patients who completed the survey confirmed that they would definitely, or probably, recommend Kinder. The mean SUS score (91.4) signified an excellent user experience. PRO: Anxiety levels were not affected by use of Kinder (STAI score below threshold of 42.38) in 18/20 patients who completed three STAI surveys (first one before and second one after the initial Kinder test; third one after the third/fourth Kinder test). Although 2/20 patients reported a STAI score of 49 in the final survey, suggesting increased anxiety, both affirmed in the PRE survey that they “will definitely recommend Kinder”, suggesting that the elevated STAI score can be attributed to the overall IVF treatment rather than the use of Kinder itself. Limitations, reasons for caution The sample size is relatively small, necessitating further multi-centre studies with a larger sample size to validate the findings. Wider implications of the findings The results suggest an acceptable interchangeability between at-home urine E1-3G and at-clinic blood E2 monitoring hormone levels during OS treatment. A digital health approach, combining tele-counselling, remote at-home hormone monitoring, and follicle tracking, could reduce patient burden and save many patients the inconvenience of travelling large distances for cycle monitoring. Trial registration number ACTRN12622000488707

Digital approach to estimate clinical images of the cervix with ImageJ software

Digital approach to estimate clinical images of the cervix with ImageJ software

Implementing learning analytics interventions to support student agency in knowledge building

ABSTRACT Research and development of Learning Analytics (LA) have created new ways to support students’ learning. However, our understanding of teachers’ roles when implementing LA in classroom practices remains nascent. This study investigates how teachers can implement LA to support students’ agency in directing their own inquiry, when engaging in a digital pedagogy approach known as Knowledge Building (KB) on an online platform called Knowledge Forum (KF). Together with a teacher experienced in KB and KF, we co-constructed interventions anchored on two easy-to-use LA tools in KF, the Scaffold Tracker and Word Cloud. These LA interventions were enacted with a Grade 5 class of 18 students and another Grade 6 class of 18 students. Three teacher roles were identified: detecting behaviour unproductive to collaboration, mediating between LA and student self-assessment, and framing student-generated lines of inquiry in actionable ways. We coded KF notes for the discourse moves they reflected and found that after the teacher implemented the LA interventions, notes that sustained inquiry increased sharply with students’ focus shifting from individual to collective knowledge and from superficial explanations to deep understandings. We discuss the implications of these findings on the development of ways to bridge LA research and practice.

Evaluating cell type deconvolution in FFPE breast tissue: application to benign breast disease

Abstract Transcriptome profiling using RNA sequencing (RNA-seq) of bulk formalin-fixed paraffin-embedded (FFPE) tissue blocks is a standard method in biomedical research. However, when used on tissues with diverse cell type compositions, it yields averaged gene expression profiles, complicating biomarker identification due to variations in cell proportions. To address the need for optimized strategies for defining individual cell type compositions from bulk FFPE samples, we constructed single-cell RNA-seq reference data for breast tissue and tested cell type deconvolution methods. Initial simulation experiments showed similar performances across multiple commonly used deconvolution methods. However, the introduction of FFPE artifacts significantly impacted their performances, with a root mean squared error (RMSE) ranging between 0.04 and 0.17. Scaden, a deep learning-based method, consistently outperformed the others, demonstrating robustness against FFPE artifacts. Testing these methods on our 62-sample RNA-seq benign breast disease cohort in which cell type composition was estimated using digital pathology approaches, we found that pre-filtering of the reference data enhanced the accuracy of most methods, realizing up to a 32% reduction in RMSE. To support further research efforts in this domain, we introduce SCdeconR, an R package designed for streamlined cell type deconvolution assessments and downstream analyses.

Digital First Primary Care for those with multiple long-term conditions: a rapid review of the views of stakeholders.

General practices are facing challenges such as rising patient demand and difficulties recruiting and retaining general practitioners. Greater use of digital technology has been advocated as a way of mitigating some of these challenges and improving patient access. This includes Digital First Primary Care, when a patient's first contact with primary care is through a digital route, either through a laptop or smartphone. The use of Digital First Primary Care has been expedited since COVID-19. There is little evidence of staff experiences of using Digital First Primary Care with more complex patients, such as those with multiple long-term conditions. To understand the experiences of those with multiple long-term conditions of Digital First Primary Care from the perspectives of healthcare professionals and stakeholders. This was a qualitative evaluation, comprised of four distinct work packages: Work package 1: Locating the study within the wider context, engaging with literature, and co-designing the study approach and research questions with patients. Work package 2: Interviews with health professionals working across general practice and key expert topic stakeholders, including academics and policy-makers. Work package 3: Analysis of data and generation of themes, and testing findings with patients. Work package 4: Synthesis, reporting and dissemination. The study commenced in January 2021 and in total 28 interviews were conducted with 14 health professionals and 15 stakeholders between January and August 2022. From the perspective of health professionals, Digital First Primary Care approaches could enable patients to speak with a clinician more quickly than traditional approaches. Those with multiple long-term conditions could submit healthcare readings from home, though health professionals felt patients may struggle navigating digital systems not designed to capture the nuances associated with living with multiple conditions. Clinicians expressed preferences for seeing patients face-to-face, particularly those with multiple long-term conditions, to identify non-verbal cues about a patient's health. Digital First Primary Care approaches provided an opportunity for clinicians to engage with the carers of patients living with multiple long-term conditions, yet there were concerns around obtaining consent and confidentiality. There remain debates among stakeholders about the nature and extent to which Digital First Primary Care impacts on staff workload. At the time of data collection, general practices were facing considerable pressure to deliver care and respond to the COVID-19 pandemic. While it was originally intended that the study would include interviews with patients with multiple long-term conditions and their carers, none of the general practices that took part in the study were willing and/or able to recruit patients and carers in the time available. The rapid implementation of Digital First Primary Care, at a time of immense pressures, meant there has been little time for considering the impact on patients, including those with multiple long-term conditions. The impacts on care continuity depended largely on how surgeries implemented their approaches. Staff and stakeholders felt that Digital First Primary Care, as an additional route for accessing primary care, could be useful for patients with multiple long-term conditions but not at the expense of face-to-face consultations. Future research obtaining patient and carer views of digital-first approaches, understanding the impacts on carers and how approaches are designed with patients with more complex conditions in mind, is essential. This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: 16/138/31) and is published in full in Health and Social Care Delivery Research; Vol. 12, No. 21. See the NIHR Funding and Awards website for further award information.

Non-Abelian braiding of Fibonacci anyons with a superconducting processor

Quantum many-body systems with a non-Abelian topological order can host anyonic quasiparticles. It has been proposed that anyons could be used to encode and manipulate information in a topologically protected manner that is immune to local noise, with quantum gates performed by braiding and fusing anyons. Unfortunately, realizing non-Abelian topologically ordered states is challenging, and it was not until recently that the signatures of non-Abelian statistics were observed through digital quantum simulation approaches. However, not all forms of topological order can be used to realize universal quantum computation. Here we use a superconducting quantum processor to simulate non-Abelian topologically ordered states of the Fibonacci string-net model and demonstrate braidings of Fibonacci anyons featuring universal computational power. We demonstrate the non-trivial topological nature of the quantum states by measuring the topological entanglement entropy. In addition, we create two pairs of Fibonacci anyons and demonstrate their fusion rule and non-Abelian braiding statistics by applying unitary gates on the underlying physical qubits. Our results establish a digital approach to explore non-Abelian topological states and their associated braiding statistics with current noisy intermediate-scale quantum processors.

A Digital Approach to via Edge Roughness Characterization and Quantification

This paper introduces a new digital integration that combines edge diffractometry with convolutional neural networks (CNN) for via metrology and inspection. The beam propagation method (BMP) was used to simulate the interferogram generated by edge diffractometry to characterize via edge roughness (VER). A comprehensive database was established to link different fringe patterns to VER for CNN training. The well-trained CNN-based methodology provided a fast and accurate assessment of VER, with a root mean squared error (RMSE) of 0.073 and an average mean absolute deviation ratio (MADR) of 2.274%. In addition, the proposed digital approach was compared to the multilayer perceptron machine (MLP) in terms of computational efficiency and predictive accuracy. The proposed digital integration greatly improved the accuracy and speed of VER measurement, characterization, and quantification, potentially enhancing device yield and reliability. The successful application of this digital approach could open up possibilities for various types of via or pattern metrology.

Quantitative digital image correlation approach for the monitoring of fatigue damage development in 10CrMo9-10 steel in the as-received state and after extended service

In this paper, the quantitative Digital Image Correlation (DIC) approach was used to study the long-time degradation of two different states of 10CrMo9-10 (10H2M) power engineering steel. The specimens subjected to cyclic loading (R = 0) were monitored by using the DIC technique. The data obtained from DIC measurements were presented in form of strain distributions for specified, independent points within the strain gauge of specimens. Furthermore, the strain profiles were extracted for the particular stages of fatigue damage development (FDD). The presented methodology provides a different approach of DIC application, in which, the data could be treated more quantitative than qualitative.

Heterogeneity of Treatment Effects in Internet- and Mobile-Based Interventions for Depression

While the effects of internet- and mobile-based interventions (IMIs) for depression have been extensively studied, no systematic evidence is available regarding the heterogeneity of treatment effects (HTEs), indicating to what extent patient-by-treatment interactions exist and personalized treatment models might be necessary. To investigate the HTEs in IMIs for depression as well as their efficacy and effectiveness. A systematic search in Embase, MEDLINE, Central, and PsycINFO for randomized clinical trials and supplementary reference searches was conducted on October 13, 2019, and updated March 25, 2022. The search string included various terms related to digital psychotherapy, depression, and randomized clinical trials. Titles, abstracts, and full texts were reviewed by 2 independent researchers. Studies of all populations with at least 1 intervention group receiving an IMI for depression and at least 1 control group were eligible, if they assessed depression severity as a primary outcome and followed a randomized clinical trial (RCT) design. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses reporting guidelines. Risk of bias was evaluated using the Cochrane Risk of Bias Tool. HTE was investigated using logarithmic variance ratios (lnVR) and effect sizes using Hedges g. Three-level bayesian meta-regressions were conducted. Heterogeneity of treatment effects was the primary outcome of this study; magnitudes of treatment effect sizes were the secondary outcome. Depression severity was measured by different self-report and clinician-rated scales in the included RCTs. The systematic review of 102 trials included 19 758 participants (mean [SD] age, 39.9 [10.58] years) with moderate depression severity (mean [SD] in Patient Health Questionnaire-9 score, 12.81 [2.93]). No evidence for HTE in IMIs was found (lnVR = -0.02; 95% credible interval [CrI], -0.07 to 0.03). However, HTE was higher in more severe depression levels (β̂ = 0.04; 95% CrI, 0.01 to 0.07). The effect size of IMI was medium (g = -0.56; 95% CrI, -0.46 to -0.66). An interaction effect between guidance and baseline severity was found (β̂ = -0.24, 95% CrI, -0.03 to -0.46). In this systematic review and meta-analysis of RCTs, no evidence for increased patient-by-treatment interaction in IMIs among patients with subthreshold to mild depression was found. Guidance did not increase effect sizes in this subgroup. However, the association of baseline severity with HTE and its interaction with guidance indicates a more sensitive, guided, digital precision approach would benefit individuals with more severe symptoms. Future research in this population is needed to explore personalization strategies and fully exploit the potential of IMI.

NASA's Use of MBSE and SysML Modeling to Architect the Future of Human Exploration

AbstractOne of the key roles of National Aeronautics and Space Administration (NASA) is to help mitigate the risk and lower expenses associated with space exploration, science, and discovery to the point where industry and international partners are willing and able to profitably take on larger, more complex missions. To do this, the Agency must undertake a transformation to a more modern integrated Digital Engineering approach to mission definition and planning. This paper highlights NASA's journey in understanding what this Digital Engineering Transformation means for the Agency, the benefits of this transformation to human exploration definition and planning, and the benefits to the current Artemis campaign engineering capability portfolio.

A Technical Approach to the Digital Signature of MBSE Models

AbstractTo fully realize the benefits of Model‐Based Systems Engineering (MBSE), users of an MBSE model need to be able to verify its authenticity and integrity. A digital signature is a common cryptographic technique that enables users to sign digital content and verify the integrity of the signed content. This enables users to verify that the signed content is truly from the author who signed the content and is a common practice in digital documentation. MBSE models have unique qualities that separate themselves from other digital documentation, thus specific digital signature approaches need to be implemented for MBSE models.This paper describes an approach to apply digital signatures to MBSE models. The approach explores some characteristics of MBSE models and enables the digital signing of a portion of a model using a signer's digital certificate. The approach allows for the verification of the signed model content against the signature and indicates if information is altered from what the signer intended. This paper captures the technical challenges and lessons learned applying this approach as a prototype to an existing MBSE modeling tool. These findings from this paper can be used to guide the development of a digital signature capability in MBSE modeling tools.