Sequence Of Processing Steps Research Articles

Cascadic degradation of selected polyolefin grades in a simulated closed-loop recycling process

Reintroducing recycled plastics into production processes can lead to irreversible degradation and changes in their properties, thereby limiting their recycling potential to a finite number of loops. This study investigates the impact of process-induced degradation resulting from multiple reprocessing cycles on specific material properties and aims to establish structure–property relationships. Four different polyolefins, including two polypropylenes, high-density polyethylene, and low-density polyethylene, were selected for the study. The materials underwent extrusion, pelletizing, injection molding, and milling before being reintroduced into the reprocessing cycle. This sequence of processing steps was repeated six times on each material. Various characterization techniques, including high-temperature gel permeation chromatography, melt mass flow rate, parallel-plate rheology, differential scanning calorimetry, gas chromatography–mass spectrometry, and optical defect control system, were performed to evaluate the molecular structure, rheological behavior, thermal stability, and the resulting contaminants and defects after each reprocessing step. The reprocessing of polypropylene resulted in a gradual decrease in the average molecular weights accompanied by a shift to lower viscosities and higher melt mass flow rates, whereas the polyethylene grades showed the opposite trend with a less pronounced effect in high-density polyethylene. The volatile organic compounds rose in polypropylene and sank in polyethylene after reprocessing. Additionally, all four materials exhibited an increase in degradation-related defects based on optical defect analysis.Graphical abstract

Representation of the program model using predicates

The object of research in this article is the process of modeling programs and their subsequent development. The purpose of this article is to develop a methodology for describing and building software models in the form of nondeterministic finite automat. To achieve this goal, a task was set to improve the method for describing such models using predicates based on the MODEL CHECKING technology. The result of this article is a method for describing and presenting program models directly according to the chosen algorithm using predicates. If the program algorithm is chosen and described correctly, the resulting model should also be correct. The model will be a non-deterministic state machine that will not require further checking, as provided by the MODEL CHECKING technology. Structurally, the model will represent a special database, the processing of which will allow turning the model into a program in any procedural programming language. When developing parallel programs that are widely used for control in aviation, land transport, military affairs, etc., two additional states of the automaton are introduced into the model, which take into account the features of such programs. Therefore, a state monitor is provided for access to shared resources and a state protocol to process parallel branches of the program. To describe the algorithm of the program, we propose to present it in the form of a connected sequence of certain actions using predicates with the use of extended temporal logic. This description covers both the logic of the program and its branches and the specific actions at each location of the program model. With the help of this methodology, a program model of a stack algorithm was developed, which is the main component for the future automated system of processing the description of program models. The program which was created according to this technology, is currently in the testing and verification stage. The sequence of processing steps of such a model is shown in the example of a floating-point constant translation program. This program is also created using this technology in the target language assembly, has been fully tested, and has shown its functionality. This description covers both the logic of the program with its branches and the specific actions at each location of the application model. Conclusions: with a correct description of the program algorithm, an adequate model of it is built, with the help of which the program itself is created in the target procedural programming language. Note that in the conditions of the rapid development of management and control automation systems in various spheres of human activity, research on the creation of reliable based on the description of their models is an urgent problem.004.414.23:510.637

A Taste for Sweet: Ethnoarchaeological Insights on Plant-Based Sweeteners of Prehistoric Southeastern Europe with Special Emphasis on Wild Pear Syrup

ABSTRACT Little is known about food sweeteners of prehistoric Europe other than honey. In this study, archaeobotanical evidence of wild pears is combined with an ethnographic study of wild pear syrup preparation in Greece to better understand potential sources of natural plant-based sweeteners used in prehistoric times in southeastern Europe. Participant observation and interviews were conducted in Kosmati, a village in the western Macedonia region of northern Greece, to investigate how a wild plant food resource can be converted into a sweet syrup. This study documents the sequence of processing steps required to transform wild pear fruit (Pyrus amygdaliformis Vill.) into syrup and other sweet foods. The study also documents cultural perceptions of wild versus cultivated fruit, contributing to wider discussions of how social differentiation can be expressed in nuanced ways through the selection of different natural sweeteners.

RDM Compas: Building Competencies for the Professional Curation of Research Data

“RDM Compas: Research Data Management Competence Base”, consisting of an education and training centre with modular online trainings on the one hand, and a comprehensive knowledge base covering all topics of curation-specific RDM on the other hand. In addition, a certification option is envisioned for curation-specific RDM competencies. In the context of our presentation, due to the advanced state of work we want to focus on the knowledge base and present its structure and elements. The structural basis is a slightly simplified version of the Data Curation Lifecycle Model [2] offered by the UK Digital Curation Centre [3]. This lifecycle describes both the basic activities and the sequential process steps of data curation and therefore provides a helpful schema for teaching the necessary RDM competencies.

Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation.

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject's Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson's Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.

Advanced Metrics for Quantification of By‐Process Segregation beyond Ternary Systems

Ge‐rich GST (GGST) alloys have been shown to fulfill the tough temperature specifications required by automotive applications. Thus, they are the most promising materials for the embedded phase‐change memory market. In this respect, the characterization of the material properties once patterned and fully integrated is instrumental to improve the process and optimize the device performance. A description of the material behavior through a realistic sequence of process steps is still missing. Herein, a statistical‐based methodology capable to extract advanced metrics for the quantification of by‐process segregation beyond ternary systems is presented, capable to quantify thermal evolution of any out‐of‐stoichiometry and segregated material beyond ternary systems. The methodology to study the physical behavior of GGST as a function of a typical back‐end‐of‐line (BEOL) process is exploited, providing a further comparison between three different BEOL‐ended GGST.

Data-driven Prognostic Approaches for Semiconductor Manufacturing Process: A Review of Recent Works and Future Perspectives

The manufacturing process of semiconductor devices is one of the most complex processes in manufacturing industry. The devices fabrication is performed through hundreds of sequential process steps with different recipes. The level of complexity is also increasing due to the high demands in terms of feature size and number of devices. Maintaining high yield and good quality production are the main objectives of these industries. These objectives can be achieved by adopting efficient maintenance strategies. In this context, a suitable prognostic model is required in order to schedule the maintenance actions. Among the different prognostic approaches, data-driven ones received a lot of attention since they do not require any specific knowledge for modeling these complex processes. Although the advances in data-driven prognostic works, there is a real lack of survey papers that overview and discuss the existing approaches for this industry over the past 10 years. Therefore, this paper presents a systematic overview of data-driven prognostic for semiconductor manufacturing. It investigates the different used methods, the challenges of their application and the unexplored research areas.

Rapid Interactions of Widespread Brain Networks Characterize Semantic Cognition.

Language comprehension requires the rapid retrieval and integration of contextually appropriate concepts ("semantic cognition"). Current neurobiological models of semantic cognition are limited by the spatial and temporal restrictions of single-modality neuroimaging and lesion approaches. This is a major impediment given the rapid sequence of processing steps that have to be coordinated to accurately comprehend language. Through the use of fused functional magnetic resonance imaging and electroencephalography analysis in humans (n = 26 adults; 15 females), we elucidate a temporally and spatially specific neurobiological model for real-time semantic cognition. We find that semantic cognition in the context of language comprehension is supported by trade-offs between widespread neural networks over the course of milliseconds. Incorporation of spatial and temporal characteristics, as well as behavioral measures, provide convergent evidence for the following progression: a hippocampal/anterior temporal phonological semantic retrieval network (peaking at ∼300 ms after the sentence final word); a frontotemporal thematic semantic network (∼400 ms); a hippocampal memory update network (∼500 ms); an inferior frontal semantic syntactic reappraisal network (∼600 ms); and nodes of the default mode network associated with conceptual coherence (∼750 ms). Additionally, in typical adults, mediatory relationships among these networks are significantly predictive of language comprehension ability. These findings provide a conceptual and methodological framework for the examination of speech and language disorders, with additional implications for the characterization of cognitive processes and clinical populations in other cognitive domains.SIGNIFICANCE STATEMENT The present study identifies a real-time neurobiological model of the meaning processes required during language comprehension (i.e., "semantic cognition"). Using a novel application of fused magnetic resonance imaging and electroencephalography in humans, we found that semantic cognition during language comprehension is supported by a rapid progression of widespread neural networks related to meaning, meaning integration, memory, reappraisal, and conceptual cohesion. Relationships among these systems were predictive of individuals' language comprehension efficiency. Our findings are the first to use fused neuroimaging analysis to elucidate language processes. In so doing, this study provides a new conceptual and methodological framework in which to characterize language processes and guide the treatment of speech and language deficits/disorders.

Novel Federated Decision Making for Distribution of Anti-SARS-CoV-2 Monoclonal Antibody to Eligible High-Risk Patients

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients’ data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients’ score and dose availability without sharing the patients’ data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death.

Found in translation: The role of response mappings for observing binding effects in localization tasks

ABSTRACT According to action control theories, response and stimulus’ features are integrated into event files. Repeating any of an event file’s components retrieves the previously bound information, causing benefits for full repetition, but interference for partial repetition. Yet, such “binding effects” are absent in localization performance. By assuming sequential processing steps until response execution as assumed in visual search, we hypothesized that, for localization, participants can execute their response without the need to process target features. Hence, post-selective processing might be crucial for binding effects to emerge. Here, participants localized coloured targets appearing on one of four corners of a touchpad in two response conditions, namely, directly tapping on the target (direct response mapping), and tapping on the corner diagonal opposite to the target (translational response mapping). Only the translational response mapping yielded binding effects between localization response and colour. The direct response mapping instead showed an effect that is better explained by (non-spatial) Inhibition of Return or related change benefit effects. We conclude that an arbitrary response mapping – based on a translation of a spatial feature into a non-direct spatial response – can lead to binding effects even in localization tasks.

Assessing the volatile composition of seaweed (Laminaria digitata) suspensions as function of thermal and mechanical treatments

Macroalgae are a rich source of dietary fibre, protein, lipids and bioactives. However, more research is needed to understand how processing methods impact macroalgae techno-functional properties. In this work, aqueous suspensions of the fibre-rich brown algae Laminaria digitata were prepared by conventional food processing methods. The impact of sequence of processing steps (thermal and mechanical treatments), and heating time (15, 30 and 45 min), on volatile aroma composition were investigated. Changes in volatile composition were monitored with headspace GC-FID/MS analysis. Our results showed that both parameters impacted the volatiles profile of the suspensions. From an overall volatile perspective, short heating times (90 °C for 15 min) led to similar profiles, independently of the sequence of thermal and mechanical treatments. However, longer heating times induced a larger release of several aldehydes, specific for each processing method. Our results bring new insights on the volatile composition of suspensions of Laminaria digitata, which are related to flavour properties, and aid design of food products containing seaweed.

Evolution of hole surface integrity in successive processing using sequential milling and laser shock peening

Part's surfaces are usually produced by successive/integration machining process and the performance of the part is directly tied to the generated surface integrity. Holes are commonly used in many critical engineering structures to connect parts, whose surface primarily characterizes a high risk of fretting or contact fatigue failure. In this paper, the evolution of metallurgical and mechanical surface integrity of Ti-6Al-4V alloy hole surface which machined using multi-step milling and successive laser shock peening (LSP) treatment was investigated. Firstly, a special workpiece with various hole surface pieces was designed and four different sequential milling processes were set to conduct the milling experiments. A comprehensive analysis of the evolution of surface roughness, microstructure, microhardness, and residual stress along the sequential processing steps was carried out. The results showed that the forms of surface integrity evolution could be classified into mono-evolution and coupling evolution, which were correlated to the combined effect of the milling parameters, cutter-workpiece engagement nature, milling sequence, milling mode, and the coolant/lubricant applied strategy. Mono-evolution was predominated in material removal sequences (milling sequences), while the coupling evolution was dependent on the radial depth of cut (DoC, ~50 μm). By comparison, coupling evolution was prominent in non-material removal related sequences (LSP treatment). Due to the conformal contact nature of tool-workpiece engagement and the flood cooling strategy, the microhardness in the subsurface layer was not significantly altered in milling sequences, except that the up-milling normally produced a surface with lower hardness and the down-milling produced almost unchanged surface hardness compared with bulk material. The residual stress was a result of the coupling effect of the residual stress field introduced in the previous and current machining sequences, whose in-depth distribution could be represented as an exponential decay function. The degree of influence of the machining sequences and their interactions on the final residual stresses could be evaluated by utilizing a set of weighting coefficients. This research indicated that understanding the effect of successive processing on the surface integrity evolution could help in planning of appropriate processes to achieve desirable surface integrity.

Tea processing steps affect chemical compositions, enzyme activities, and antioxidant and anti‐inflammatory activities of coffee leaves

AbstractCoffee leaves have been processed into a tea‐like beverage; however, how tea processing steps affect the chemical compositions and the bioactivities remain unknown. In the present study, the impacts of tea processing steps on the proximate compositions, phytochemicals, enzyme activities, and the antioxidant and anti‐inflammatory activities of coffee leaves were investigated. The results showed that although soluble protein, soluble sugar, and phenolics were increased during withering, they were gradually decreased in the sequential processing steps associated with the decreases in the antioxidant activity and nitric oxide inhibitory activity in lipopolysaccharide (LPS)‐induced RAW264.7 cells. Coffee leaves treated with the kill‐green process possessed greater phenolics and antioxidant activity compared with the no kill‐green counterparts due to the lower polyphenol oxidase and peroxidase activities found in the no kill‐green coffee leaves. Coffee leaves significantly inhibited the expressions of proteins (tumor necrosis factor‐alpha [TNF‐α] and prostaglandin E2 [PGE‐2]) proteins and genes (inducible nitric oxide synthase [iNOS] and cyclooxygenase 2 [COX‐2]); however, tea processing steps have lesser effects on the anti‐inflammatory activity. Our study indicated that kill‐green is the crucial processing step to affect the phenolic composition and bioactivity of coffee leaf tea processed with the kill‐green process; however, for the coffee leaves processed without the kill‐green process, rolling and fermentation are the key influencing steps.

Generalized Input-Output Hidden-Markov-Models for Supervising Industrial Processes

Often in industrial processes, batches of material are processed sequentially and repeatedly through a deterministic sequence of process steps. The possibly large number of sensor measurements collected throughout such industrial processes require supervisory modeling techniques that allow for characterizing the operating conditions (or states) of these process steps. This would allow for inference and predictions over the operating conditions of the current and upcoming process steps (e.g., abnormal behavior, failures, etc.) which may significantly assist the operators of the process. Traditionally, such inference questions in repeated processes can be handled in a rather straightforward manner via Input-Output Hidden Markov Models (IOHMM). However, standard IOHMM are limited to repeated and identical processes, while industrial processes may comprise multiple non-identical process steps and possibly with non-standard interdependencies between process steps. For this reason, in this paper we propose a generalization of standard IOHMM that is more appropriate for modeling industrial processes. Furthermore, we derive the update recursions for training such models and we show analytically that such update recursions guarantee an increase in the likelihood of the observation sequences.

Offline segmentation of spatio-temporal order trajectories by mixed-integer linear programming for determining process times in production systems

Real-time indoor positioning systems in manufacturing systems are used to track production orders. This generates spatio-temporal trajectories which can be segmented to determine process times. We present formulations of the offline segmentation problem as mixed-integer linear programs (MILPs) that utilize the sequence of processing steps from ERP systems. The MILP formulations are compared with online heuristics in terms of their accuracy and computational effort on data generated with features from a real job shop. We show that in terms of accuracy our offline segmentation formulations outperform the online heuristics with increasing measurement errors, justifying their higher computational effort.

Synthesis and Characterization of Low-Cost Wood based Biosorbent

The purpose of this study is to prepare and characterize natural wood-based biosorbents from India's Northwest region. Biosorbents were prepared using banana pith, pineapple and bamboo stems. The preparation procedure for the biosorbents involved sequential processing steps such as washing, drying, grinding and chemical treatment using phosphoric acid and NaOH. Finally, the prepared biosorbents were sieved to different mesh sizes ranging from 80 to 230 meshes. Biosorbent characterization were carried out using BET, SEM, FTIR and TGA. Prior to chemical treatment, the biosorbents were evaluated with BET method and the surface areas obtained are 3.574, 5.678 and 9.072 m2/g for banana pith, bamboo and pineapple stem based biosorbents respectively. However, chemical treatment method enhances the BET surface area to 100.16 and 116.01 m2/g for banana pith and bamboo stem based biosorbents respectively. The average particle sizes of the biosorbents were determined as 30, 63, and 78 (µm) for biosorbents prepared from pineapple stem, banana pith, and bamboo stem respectively. TGA provided the measurement of the mass variation of the biosorbent with variation in temperature and time. For a heating rate of 10 °C/min, TGA results indicated that the maximum temperature to withstand were 65, 129, and 200 °C for biosorbents prepared with pineapple stem, banana pith and bamboo stem respectively. The obtained characterization results for the biosorbents are indicative of their promising application towards the removal of heavy metals from wastewater streams.

Neurocomputational Models of Language Processing

Efforts to understand the brain bases of language face the Mapping Problem: At what level do linguistic computations and representations connect to human neurobiology? We review one approach to this problem that relies on rigorously defined computational models to specify the links between linguistic features and neural signals. Such tools can be used to estimate linguistic predictions, model linguistic features, and specify a sequence of processing steps that may be quantitatively fit to neural signals collected while participants use language. Progress has been helped by advances in machine learning, attention to linguistically interpretable models, and openly shared data sets that allow researchers to compare and contrast a variety of models. We describe one such data set in detail in the Supplemental Appendix .

A Neural Dynamic Model of the Perceptual Grounding of Spatial and Movement Relations.

How does the human brain link relational concepts to perceptual experience? For example, a speaker may say "the cup to the left of the computer" to direct the listener's attention to one of two cups on a desk. We provide a neural dynamic account for both perceptual grounding, in which relational concepts enable the attentional selection of objects in the visual array, and for the generation of descriptions of the visual array using relational concepts. In the model, activation in neural populations evolves dynamically under the influence of both inputs and strong interaction as formalized in dynamic field theory. Relational concepts are modeled as patterns of connectivity to perceptual representations. These generalize across the visual array through active coordinate transforms that center the representation of target objects in potential reference objects. How the model perceptually grounds or generates relational descriptions is probed in 104simulations that systematically vary the spatial and movement relations employed, the number of feature dimensions used, and the number of matching and nonmatching objects. We explain how sequences of decisions emerge from the time- and state-continuous neural dynamics, how relational hypotheses are generated and either accepted or rejected, followed by the selection of new objects or the generation of new relational hypotheses. Its neural realism distinguishes the model from information processing accounts, its capacity to autonomously generate sequences of processing steps distinguishes it from deep neural network accounts. The model points toward a neural dynamic theory of highercognition.

Identification of Abusive Behavior Towards Religious Beliefs and Practices on Social Media Platforms

The ubiquitous use of social media has enabled many people, including religious scholars and priests, to share their religious views. Unfortunately, exploiting people’s religious beliefs and practices, some extremist groups intentionally or unin-tentionally spread religious hatred among different communities and thus hamper social stability. This paper aims to propose an abusive behavior detection approach to identify hatred, violence, harassment, and extremist expressions against people of any religious belief on social media. For this, first religious posts from social media users’ activities are captured and then the abusive behaviors are identified through a number of sequential processing steps. In the experiment, Twitter has been chosen as an example of social media for collecting dataset of six major religions in English Twittersphere. In order to show the performance of the proposed approach, five classic classifiers on n-gram TF-IDF model have been used. Besides, Long Short-term Memory (LSTM) and Gated Recurrent Unit (GRU) classifiers on trained embedding and pre-trained GloVe word embedding models have been used. The experimental result showed 85%accuracy in terms of precision. However, to the best of our knowledge, this is the first work that will be able to distinguish between hateful and non-hateful contents in other application domains on social media in addition to religious context.

Transforming the practice of medicine through team science

BackgroundThe translation of biomedical research discoveries into clinical practice is marked by extended timelines (averaging 17 years) and multiple sequential process steps. However, even after a drug, device, diagnostic tool or unique therapeutic procedure successfully navigates through clinical testing to approval, real barriers remain in applying and scaling the innovation in practice.MethodsMayo Clinic initiated the Transform the Practice programme to facilitate multidisciplinary team and convergence science to continuously reinvent solutions to address unmet patient needs and accelerate the application of next-generation healthcare solutions. During a 5-year period, 24 programme teams received financial resources, barrier-removing engagement from clinical and research leadership, and enhanced administrative support, including dedicated project managers.ResultsThe approach created value in facilitating consistent progress toward project objectives and resulted in multiple publications, new extramural funding sources, and implementation of new tests and services into the clinical practice. This report describes the concentrated institutional effort to accelerate the discovery–translation–application continuum in an academic medical centre and highlights successful applications and persistent obstacles.ConclusionsThe Transform the Practice approach is effective in moving high-potential research discoveries closer to implementation in the clinical practice. Its concepts, including the application of structured project management methodology, may be quickly integrated to shorten an organisation’s time to implementing its most important discoveries.