Early-stage Projects Research Articles

Describing, knotting, tying: developing emerging media documentary in a creative accelerator context

ABSTRACT This article discusses the experiences of six artists participating in a new Australian creative accelerator programme for emerging media documentary. Attending to the processes, perspectives and frames employed by these artists in developing their early-stage projects, the article explores the diverse practice-based methods employed in developing documentary for emerging media formats. The paper argues for the value of attending to process and practice, as key frameworks that set the context for eventual creative outcomes. In doing so, the paper aims to contribute to knowledge about methods to support artists in developing emerging media documentary in a creative accelerator context.

DIPG-05. CHARACTERIZATION OF THE EFFECTS OF H3K27M VARIANTS ON THE DNA-DAMAGE RESPONSE AND TUMOR EVOLUTION IN DIFFUSE MIDLINE GLIOMA

Abstract Diffuse midline gliomas (DMGs) are universally-fatal pediatric brain tumors driven by K27M alterations in histone 3 (H3K27M). Histone 3 is encoded by several variants, including the canonical H3.1, and H3.3 which replaces H3.1 at transcriptionally active and repetitive genomic regions. Both H3.3K27M and H3.1K27M are present as driver events in DMGs and recent single-cell-RNA and whole-genome sequencing experiments have demonstrated that they define distinct subsets of disease, with varying secondary genetic alterations and different gene-expression patterns. The cause of the divergence in routes of tumor evolution between these subsets remains an open question. Previous work by our group has shown that H3.3K27M DMGs show greater genomic instability than H3.1K27M tumors – with a greater number and higher complexity of genomic structural variants resulting from errors in double-stranded DNA break repair. This raises the possibility that histone variants differentially influence the generation of, or response to, DNA breaks. Here we present an early-stage project that aims to investigate whether H3K27M variants are sufficient to produce differences in genomic instability and in directing the path of tumor evolution. Using isogenic cell-line models expressing H3.1K27M or H3.3K27M, we aim to characterize whether H3K27M variants produce varying levels of replicative stress and P53 activation, leading to varying selective pressures for P53 loss. In turn, we seek to demonstrate that loss of P53 produces a greater growth advantage in H3.3K27M cells, and that P53 loss leads to increased tolerance of genomic instability in DMG models. Together, these experiments seek to provide new insight on the mechanisms of divergence between H3.1K27M and H3.3K27M-driven DMGs, and a better understanding of the sources of genomic instability in DMGs. Given that ionizing radiation remains the only approved treatment modality for DMGs, a greater understanding of DMG response to DNA-damage is critical to improving therapeutic outcomes.

What's the catch? Trade-off challenges in early design for sustainability

AbstractTrade-offs involving multiple criteria that cannot be satisfied at the same time are ubiquitous in engineering design activities. Navigating trade-off decisions can be challenging, especially when it comes to sustainability-related decisions in early-stage projects. Through a systematic literature review, we unravel the challenges related to sustainability trade-offs in technology development, concept design, and other front-end of innovation activities. The challenges, which were evaluated by experts from industry and academia, range from technical and organisational to psychological aspects.

The Translational Science Benefits Model, a new training tool for demonstrating implementation science impact: A pilot study.

Demonstrating the impact of implementation science presents a new frontier for the field, and operationalizing downstream impact is challenging. The Translational Science Benefits Model (TSBM) offers a new approach for assessing and demonstrating research impact. Here we describe integration of the TSBM into a mentored training network. Washington University's Clinical and Translational Science Awards TSBM team collaborated with a National Institute of Mental Health-supported training program, the Implementation Research Institute (IRI), a 2-year training institute in mental health implementation science. This partnership included three phases: (1) introductory workshop on research impact, (2) workshop on demonstrating impact, and (3) sessions to guide dissemination, including interactive tools and consultation with the TSBM research team. Fifteen IRI alumni were invited to participate in the pilot; six responded agreeing to participate in the training, develop TSBM case studies, and provide feedback about their experiences. Participants applied the tools and gave feedback on design, usability, and content. We present their case studies and describe how the IRI used the results to incorporate TSBM into future trainings. The case studies identified 40 benefits spanning all four TSBM domains, including 21 community, 11 policy, five economic, and three clinical benefits. Participants reported that TSBM training helped them develop a framework for talking about impact. Selecting benefits was challenging for early-stage projects, suggesting the importance of early training. The case studies showcased the institute's impact and the fellows' work and informed refinement of tools and methods for incorporating TSBM into future IRI training.

On the difficulty of validating molecular generative models realistically: a case study on public and proprietary data

While a multitude of deep generative models have recently emerged there exists no best practice for their practically relevant validation. On the one hand, novel de novo-generated molecules cannot be refuted by retrospective validation (so that this type of validation is biased); but on the other hand prospective validation is expensive and then often biased by the human selection process. In this case study, we frame retrospective validation as the ability to mimic human drug design, by answering the following question: Can a generative model trained on early-stage project compounds generate middle/late-stage compounds de novo? To this end, we used experimental data that contains the elapsed time of a synthetic expansion following hit identification from five public (where the time series was pre-processed to better reflect realistic synthetic expansions) and six in-house project datasets, and used REINVENT as a widely adopted RNN-based generative model. After splitting the dataset and training REINVENT on early-stage compounds, we found that rediscovery of middle/late-stage compounds was much higher in public projects (at 1.60%, 0.64%, and 0.21% of the top 100, 500, and 5000 scored generated compounds) than in in-house projects (where the values were 0.00%, 0.03%, and 0.04%, respectively). Similarly, average single nearest neighbour similarity between early- and middle/late-stage compounds in public projects was higher between active compounds than inactive compounds; however, for in-house projects the converse was true, which makes rediscovery (if so desired) more difficult. We hence show that the generative model recovers very few middle/late-stage compounds from real-world drug discovery projects, highlighting the fundamental difference between purely algorithmic design and drug discovery as a real-world process. Evaluating de novo compound design approaches appears, based on the current study, difficult or even impossible to do retrospectively.Scientific Contribution This contribution hence illustrates aspects of evaluating the performance of generative models in a real-world setting which have not been extensively described previously and which hopefully contribute to their further future development.

Tradeoff different construction project goals in using a novel multi-objective sea horse algorithm

Construction project managers aspire to achieve projects of the highest quality while minimizing time and cost. Presently, most trade-off analysis models focus predominantly on the traditional trifecta of time, cost, and quality, often overlooking other pivotal factors and project traits. This study introduces the Chaotic Adaptive Multi-Objective Sea Horse (CAMOSH), an innovative algorithm crafted to navigate the nuanced tradeoffs among time, cost, quality, risk, and environment (TCQRE) in comprehensive construction projects. CAMOSH leverages chaos sequences for population initialization and integrates an adaptive selection technique to harmonize exploitation and exploration during optimization. To facilitate the early-stage project implementation, the multiple-criteria decision analysis (MCDA) method is employed. When benchmarked against six esteemed algorithms across two construction projects, CAMOSH shows its superiority. Statistical evaluation underscores CAMOSH’s outstanding performance, evidenced by its top-ranking values in Diversity Measure (DM) and Hyper-Volume (HV) for both case studies. Furthermore, the algorithm consistently yields solutions with the most optimal Spacing Metric (SM) values and operates in the shortest time frames. These results collectively validate CAMOSH as an instrumental resource, equipping project managers to adeptly balance multifaceted construction project objectives and pinpoint the most effective project timeline.

Development of Locked-cycle piston press test procedure to simulate closed circuit HPGR comminution

The increased energy efficiency of HPGR-based circuits as an alternative to AG/SAG mill circuits has been well established. However, assessing the HPGR technology involves pilot-scale testing requiring large samples, which is expensive and may be prohibitive, particularly for early-stage projects. As a result, the uptake of HPGR technology has been slow, emphasizing the need for small-scale tests for sizing and selection. A reliable small-scale test for preliminary HPGR circuit design is essential in garnering the mining industry's interest in the broader adoption of HPGRs.Inspired by the Bond ball mill grindability test, a small-scale locked-cycle piston press test was developed to estimate the circulating load and circuit-specific energy consumption for closed-circuit HPGR operation. The test introduces the Piston Work Index, which describes the ore samples’ resistance to HPGR comminution and can be subsequently used to estimate the HPGR circuit-specific energy consumption. The test results indicated that the circuit-specific energy and circulating load obtained from the small-scale piston press test were within the ± 20% range of the locked-cycle pilot scale HPGR tests for a fine feed representing quaternary stage HPGR application.

A Network Dynamic of Experimental Learning Cycle for Early-Stage Project Success

Early-stage project success does not depend exclusively on network activities but on a dynamic that can switch cognitive structures to behave in ways consistent with the expectations of network needs. By using a quasi-field experiment, this study finds that two types of networks determine the relationship between network embeddedness and early-stage project success via two distinct mechanisms of the experimental learning cycle: Knowledge embeddedness in knowledge networks increases early-stage project success and this relationship is mediated by failure analysis of the experimental learning cycle; social embeddedness in social networks increases early-stage project success and this relationship is mediated by tolerance for failure of the experimental learning cycle. This study sketches out a simple network dynamic, “What–Who–How”, for early-stage project success and illuminates the idea of Learning by Networking.

Advancements in Artificial Intelligence-Based Decision Support Systems for Improving Construction Project Sustainability: A Systematic Literature Review

This paper aims at evaluating the current state of research into artificial intelligence (AI)-based decision support systems (DSS) for improving construction project sustainability. The literature was systematically reviewed to explore the use of AI in the construction project lifecycle together with the consideration of the economic, environmental, and social goals of sustainability. A total of 2688 research papers were reviewed, and 77 papers were further analyzed, and the major tasks of the DSSs were categorized. Our review results suggest that the main research stream is dedicated to early-stage project prediction (50% of all papers), with artificial neural networks (ANNs) and fuzzy logic (FL) being the most popular AI algorithms in use. Hybrid AI models were used in 46% of all studies. The goal for economic sustainability is the most considered in research, with 87% of all papers considering this goal, and there is evidence given of a trend towards the environmental and social goals of sustainability receiving increasing attention throughout the latter half of the decade.

How to Build a Customized Scoring Tool to Evaluate and Select Early-stage Projects

Overview: In the early stages of technology or innovation projects, companies often need to make decisions about which projects to pursue when only scant information is available. In this article, we provide guidance on how to make the best use of the information available by assessing projects against several appropriate factors and allocating scores. Although companies have used this type of scoring method for many years, they have paid little attention to how to best design and configure scoring tools. We explain how to design a scoring tool specifically tailored for a particular situation and present the example of Grundfos to illustrate the efficacy of developing a customized scoring method that includes identifying factors under Opportunity and Feasibility. We suggest how to choose the factors to ensure that the scoring is as consistent and objective as possible and how to treat the inevitable uncertainty arising from early-stage projects.

Sensors and digital medicine in orthopaedic surgery.

Digital health principles are starting to be evident in medicine. Orthopaedic trauma surgery is also being impacted —indirectly by all other improvements in the health ecosystem but also in particular efforts aimed at trauma surgery. Data acquisition is changing how evidence is gathered and utilized. Sensors are the pen and paper of the next wave of data acquisition. Sensors are gathering wide arrays of information to facilitate digital health relevance and adoption. Early adaption of sensor technology by the nonlegacy health environment is what has made sensor driven data acquisition so palatable to the normal health care system. As it applies to orthopaedic trauma, current sensor driven diagnostics and surveillance are nowhere near as developed as in the larger medical community. Digital health is being explored for health care records, data acquisition in diagnostics and rehabilitation, wellness to health care translation, intraoperative monitoring, surgical technique improvement, as well as some early-stage projects in long-term monitoring with implantable devices. The internet of things is the next digital wave that will undoubtedly affect medicine and orthopaedics. Internet of things (loT) devices are now being used to enable remote health monitoring and emergency notification systems. This article reviews current and future concepts in digital health that will impact trauma care.

Early-stage Project Outcome Prediction Considering Human Factors

In the early stages of a project, project managers need a way to connect concrete actions to the factors that affect project success. This study aims to upgrade project management methodology by using machine learning technologies to predict project results. Using a new deep learning model called “deep tensor,” we predict project results at the time of completion—including quality, cost, and delivery time—by evaluating the project’s state in its earliest stage using various types of project knowledge assets. The prediction results suggest that the predictive accuracy of the deep tensor model is more accurate than that of the random forest or multiple regression model. The way to use this model to recommend specific advice by using the factors that most influenced the model’s predictions is also presented. This research provides a method for sharing difficult-to-share knowledge across projects and will be useful for early, tangible improvement measures in the project execution phase.

Comparison of interpretation uncertainty in spatial domains using portable x-ray fluorescence and ICP data

Spatial domains defining either geological models or mineral estimation envelopes are among the few components of the mining life cycle that are not quantitatively assessed to communicate uncertainty or error in mineral resource projects. Recent work has investigated the use of Bayesian approximation methods to assess interpretation uncertainty of the classification of drill hole intercepts to spatial domain categories. A fundamental assumption is that the spatial domains being tested are used for geostatistics and that the spatial domains contain data that is homogenous to satisfy an assumption of stationarity. A binomial or multinomial model of data used to generate the spatial domains can be trained and simulated to assess the model's ability to predict categories. Categorical subjective geological logging and multielement data have shown suitable data inputs to assess interpretation uncertainty at two case studies. In early-stage projects laboratory multielement data may not be available or economically feasible. Portable X-Ray Fluorescence (pXRF) is a cost and time-effective method of providing multielement data at lower precision and accuracy. A case study had both Inductively coupled plasma mass spectrometry (ICP) and pXRF data. Bayesian approximation models were created from both data sets to assess interpretation uncertainty. Visual assessment of uncertainty band graphs and statistical testing using the Bayesian correlated t-test shows that the two data sets' interpretation uncertainty models are practically equivalent and that pXRF data can be used in early stage projects to assess interpretation uncertainty.

An early-stage project complexity assessment tool for the AEC industry

PurposeComplex projects require careful management. They may expose stakeholders to greater risks and place additional demands upon resources. In the initial stages of project development, however, little may be known about the nature and magnitude of such complexity. This paper aims to ensure that this gap is at least subjectively assessed and addressed.Design/methodology/approachResearch, using focus group workshops with a convenience sample of construction professionals, was carried out to test the validity of a Project Early Stage Complexity Assessment Tool (PESCAT). The PESCAT concept brings together selected complexity theories and uses subjectively based measures for assessment.FindingsThe findings confirm that an early-stage project complexity assessment tool is practicable and can contribute to project management practice in the construction industry. It should be applied by individual project stakeholders using small teams of experienced staff. PESCAT explores project complexity through “filter” sets of perspective factors which should be customised to reflect the stakeholder’s role and processes in a project.Originality/valueIn the initial complexity assessment model, resolution space and uncertainty were tested as project complexity parameters. However, in subsequent versions of the tool, four measures (differentiation and differentiation uncertainty; interdependency and interdependency uncertainty) are used in an innovative way that focuses users’ attention more clearly and points to a more targeted approach for addressing project complexity. The value of our model lies in its practical application and the project management benefits it can deliver.

Does Competition Affect the Likelihood, Speed and Tenor of Mandatory R&D Disclosures?

The information about R&D activities can shift the competitive landscape and carry a substantial strategic value for firms. However, most of our understanding of R&D disclosures is either for early-stage projects (i.e., patents) or when firms voluntarily share their information. Mandatory disclosures of development projects, such as clinical drug trials, demanded by regulators often put firms in a paradoxical situation - on the one hand, they face regulatory pressures to comply, and on the other hand, they fear informing competitors of internal developments. By integrating the theories of knowledge management and strategic responses to institutional forces, we propose that firms under intense competition would be reluctant to comply and share the results of their clinical trials. However, when they do share their results, they would do so quickly to deter competition. Further, firms would also use rhetoric in their scientific publications linked to the clinical trials to paint a favorable picture to competitors. We test our theoretical model with 7,731 completed clinical trials under a US Act (FDAAA 801) purview between the years 2008-2019 (both inclusive) and find confirming results overall. We discuss the broader implications of our findings on both theory and practice.

Current Status and Future Trends in the Operation and Maintenance of Offshore Wind Turbines: A Review

Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.

Building National Capacity for Dementia Caregiving Research: The NIA Edward R. Roybal Centers

The NIA Edward R. Roybal Centers for Translational Research in the Behavioral and Social Sciences of Aging aim to translate and integrate basic behavioral and social research findings into principle-driven interventions aimed at innovatively improving both the lives of older people and the capacity of institutions to adapt to societal aging. Newly funded Centers focus on interventions to promote caregiving mastery, integrate the use of technology in care support to improve assessments and interventions in care provision, develop behavioral interventions to reduce isolation and promote social connectedness in caregivers, promote health in racial/ethnic minorities, and apply insights from data science and behavioral economics to improve palliative care delivery and long-term support facilities for persons with dementia and their caregivers. Center leaders will present an overview of their cutting-edge, early-stage research projects based on the NIH Stage model conceptual framework and discuss implications for improving care of caregivers and patients.

Building National Capacity for Dementia Caregiving Research: The NIA Edward R. Roybal Centers

More than 5.8 million Americans are currently living with Alzheimer’s disease and related dementias, and they are cared for by over 16 million people at an estimated annual cost of $290 billion. The need for innovative, evidence-based interventions to support these patients and caregivers is critical to addressing this problem. The goal of the NIA Edward R. Roybal Centers for Translational Research in the Behavioral and Social Sciences of Aging is to translate and integrate basic behavioral and social research findings into interventions aimed at innovatively improving both the lives of older people and the capacity of institutions to adapt to societal aging. The Roybal Centers will develop research within the conceptual framework of the multidirectional and translational NIH Stage Model to produce these implementable, principle-driven behavioral interventions. The program has now funded new Centers, and each has a unique focus. Areas of concentration include promoting caregiving mastery (Emory University), integrating the use of technology in care support to improve assessments and interventions in care provision (Oregon Health & Science University), developing behavioral interventions to reduce isolation and promote social connectedness in caregivers (University of Rochester), promoting health in racial/ethnic minorities (University of Illinois at Chicago), and using insights from data science and behavioral economics to improve palliative care delivery and long-term support facilities for persons with dementia and their caregivers (University of Pennsylvania). Center leaders will present an overview of their cutting-edge, early-stage research projects and discuss implications for improving care of caregivers and patients.

Stage Gate Decision Making: A Scoping Review of Technology Strategic Selection Criteria for Early-Stage Projects

Innovation is a hybrid concept that has evolved over time and adapts itself to changing condition. It plays a major role in the growth and economic competitiveness of firms, and consequently, firms invest in innovation to build knowledge and thus increase competitive advantage. Front end of innovation (FEI) projects, and in particular technology development projects, are therefore a fundamental component of innovation and a crucial factor in developing new competitive advantage. To effectively and objectively manage and evaluate these early-stage technology projects, which are by nature uncertain, a number of firms deploy some form of stage gate processes, utilizing strategic decision criteria. Via a descriptive scoping literature review, we identify 46 articles, which contain 473 uniquely identified strategic selection criteria that can fit in nine categories. We find that technical feasibility is the most frequently occurring unique selection criteria, whereas the market category is the largest category by size of unique individual criteria with 23.3% of the share total. In this research, we contribute to the on-going discussion about early-stage technology projects and their effective evaluation using strategic technology selection criteria. The summary of criteria provided with definitions may serve as guidelines for technology and innovation managers.

The role of geographical clusters in the success of reward-based crowdfunding campaigns

This article studies whether some salient characteristics of the geographical area in which a crowdfunding campaign is launched affect its success. Drawing on a data set of 792 crowdfunded projects, we conduct an empirical study aimed at studying the role played by geographical clusters (GCs) in fostering the crowdfunding of new entrepreneurial ventures. The results offer insights into the phenomenon of crowdfunding and shed light on the role of GCs in the success of reward-based crowdfunding campaigns of early-stage entrepreneurial projects.