Incremental Approach Research Articles

Facilitating the learning and adaptation process of regional initiatives that transform towards health and wellbeing systems.

Introduction: Health-systems in western countries are under pressure due to increasing costs, changing health care demands, and a growing shortage of staff. Therefore, regional initiatives are searching for ways to integrate and reorganize services across domains. This is a complex process and requires an incremental approach based on reflection and adaptation. However, regional initiatives are still searching how to construct these processes. This study aimed to gain more understanding of how the reflection and adaptation process in regional initiatives could be facilitated.
 Methods and analysis: This study included an action research within a Dutch regional initiative, including the program managers, involved municipalities, province, health care insurer and the public health services. The partners reflected on transformation themes, identified in previous evaluation studies, by scoring them in questionnaires and discussing them in interviews. Finally, two reflection meetings were facilitated with representation of all partners, respectively on management and board level. The program manager was interviewed about the experiences with this action research. Notes of this interview and observations of the reflection sessions were analyzed inductively. To broaden the experiences within this one initiative, the experiences with the reflection process were reflected upon in semi-structured interviews with program managers from other regional initiatives (n = 11). The semi-structured interviews were coded and clustered inductively.
 Results: Based on the scoring in questionnaires and interviews a radar chart was shown in the reflection meetings. This highlighted that especially two themes (accountability and citizen participation) would need adaptation. Partners especially recognized the problems regarding accountability and agreed that further elaboration was needed. One of the aims of the reflection process was to create a collective feeling of ownership. Results show that external facilitation of the reflection process, which was objective and knowledge-based, helped to enhance the collective conversation. Reflecting with the program managers of the other initiatives learned that not all initiatives invested in a regional reflection and adaptation process. Lack of this investment was related to partners not prioritizing this process. Some program managers expected the knowledge-based reflection to be helpful in creating a common ground among the partners. The use of scoring, like in the questionnaires, or having actionable options after scoring the themes was expected to be helpful to be able to act upon the reflection.
 Lessons learned and next steps: This study described the experiences with facilitating reflection and adaptation processes of regional initiatives. It showed that knowledge-based reflection by an external facilitator was valued to enhance the collective conversation and common ground about transformation. Further research is needed to learn how this common ground could eventually result in adaptation of the initiatives and thus aid their transformation. This research identified the importance of creating a collective process amongst partners for learning and adaptation, though found that not all initiatives invested in this process due to other priorities. More awareness among partners of the initiatives is needed about the importance of this process for transformation and their role in this collective process.
 

A data-driven chance-constrained BESS planning in distribution networks by a decoupling solution method

In this study, a novel approach for the battery energy storage system (BESS) planning considering the integration of photovoltaic (PV) is proposed. A data-driven chance-constrained BESS programming model including the dynamic network reconfiguration (DNR) is developed. Then, a decoupling solution approach for sitting selection and sizing optimization is proposed to obtain optimal solution. According to the approach, comprehensive cost sensitivity factor (CCSF) is formulated for the sitting selection, and the particle swarm optimization (PSO) combined with the Minty algorithm and the switch operation time reduction algorithm is developed to obtain more accurate sizing. In addition, based on the scenarios generated by the spectral clustering approach, a continuous scenario increment approach is proposed to handle chance-constrained constraints. The numerical simulations on a practical 31-bus distribution network show the superiority of the proposed algorithm over existing methods.

A deep neural network with modified random forest incremental interpretation approach for diagnosing diabetes in smart healthcare

Artificial intelligence (AI) applications based on deep learning for diagnosing type-II diabetes are sometimes difficult to understand and communicate even as patients are eager to understand the rationale behind the diagnostic results. Accordingly, recent studies have used multiple simple rules to adequately explain the diagnostic process and results to patients. However, this can cause patient confusion as the rules vary. Hence, this study proposes a deep neural network (DNN) with random forest (RF) and modified random forest incremental interpretation (MRFII) approach for diagnosing diabetes. This method first entails constructing a DNN to predict the probability of a patient having diabetes. To make the prediction result explainable, an RF is built to explain the process and results in terms of multiple simple decision rules. Additionally, to eliminate patient confusion, the MRFII is proposed to sort and aggregate the decision rules for a specific patient. A certainty mechanism is also established to feed back the explanation results from RF to improve the effectiveness of the DNN. The proposed method was applied to a diabetes dataset from the National Institute of Diabetes and Digestive and Kidney Diseases, and the results showed that this approach provided a more concise and accurate explanation than existing explainable artificial intelligence (XAI) techniques for the same purpose.

“Mindset Matters”: Perseverance, a balanced approach and structured support as facilitators of whole foods plant-based adoption

Whole foods plant-based diets (WFPBD) show potential for preventing and addressing chronic diseases. However, concerns exist about their acceptability and feasibility. Research on firsthand WFPBD adoption experiences is limited but crucial for promoting dietary change. In a 12-week remotely delivered lifestyle modification program using an ad libitum WFPBD, twenty weight-loss seeking adults (ages 18–75) with overweight or obesity completed self-report surveys and semi-structured interviews via Zoom. The study aimed to explore: (1) experiences with WFPBD adoption; (2) factors that helped or hindered adherence; and (3) perceived salient benefits. Interviews were analyzed inductively through a conventional content analysis, and associations between variables examined with correlational analyses. Participants overall reported WFPBD adoption being a positive, new experience, with an equal number (30%) finding it easy/easier than expected as challenging. Key cited challenges included overwhelm, different eating habits in the household, and meal preparation. Key cited facilitators included adopting an incremental approach to dietary change, persisting after setbacks, and having simple go-to meals. Greater self-compassion and family support, and less sabotage from friends and family corresponded to greater dietary change (rs > 0.45, ps < .05). Participants valued accountability, structure, human support, nutrition psychoeducation and recipes in the program. Three categories emerged regarding perceived benefits of following the WFPBD: physical health benefits, improvements to eating habits, and greater perceived control/agency over health. Results indicate that future interventions should include psychological strategies alongside nutrition education to enhance self-efficacy, address household barriers, and combat feelings of overwhelm through sufficient structure, support, and meal preparation guidance. Messaging around WFPBD may benefit from discussing prevailing positive experiences with adoption, common benefits experienced, and options for an incremental approach given that feasibility and acceptability concerns may deter efforts.

Transaction-based lending and real earnings management

Purpose The purpose of this study is to examine how lenders alter their behavior when faced with real earnings management.Design/methodology/approach This study uses the incremental R-square approach as in Kim and Kross (2005) to examine how much lenders rely on income statement and balance sheet ratios as the degree of real earnings management increases.Findings As real earnings management affects mostly the income statement, the authors find that lenders rely less on income statement ratios in making credit decisions in the presence of real earnings management. The authors also find that lenders do not alter their reliance on balance sheet ratios when faced with real earnings management.Originality/value This paper is the first to study how lenders alter their reliance on financial statements in making credit decisions in the presence of real earnings management. The findings of this paper could help the regulators set standards to improve the usefulness of financial statements. The findings of this paper could also help practitioners (borrowers and lenders) understand how real earnings management affects credit decisions.

A Pragmatic Randomized clinical trial: twice-weekly vs. thrice-weekly Incident hemoDialysis in Elderly patients (PRIDE): study protocol.

The optimal frequency for hemodialysis in older adults with end-stage kidney disease (ESKD) has not been established. This study aims to investigate whether a twice-weekly dialysis schedule using an incremental approach can reduce hospitalization rates in the elderly with incident dialysis, compared with conventional thrice-weekly dialysis. We have designed a pragmatic randomized controlled trial to compare the effects of twice-weekly versus thrice-weekly hemodialysis in 428 ESKD individuals (dropout rate 20%) aged 60 years or older with residual kidney function (urine output, >500 mL/ day). The trial will be conducted across 18 referral hospital-based dialysis centers in Korea. Individual participants will be randomized to either a twice-weekly (with incremental approach) or thrice-weekly dialysis group and will be followed up for 24 months. The primary outcome of the study is all-cause hospitalization rate, while secondary outcomes include dialysis-specific hospitalization rates, mortality, quality of life, frailty, and cost-effectiveness. Participants have the flexibility to transfer to other dialysis centers as needed. The decision to increase dialysis frequency will be made by the treating physicians. The study is ongoing and will be completed in May 2026. This study will provide valuable insights into the benefits and risks of twice-weekly dialysis with an incremental approach in elderly with residual kidney function compared to thrice-weekly dialysis.

Blockchain Technology in Lands Registration: A Systematic Literature Review

Blockchain technology (BT) is increasingly important in digital government as a means of efficient information management, decision making and an instrument for reform. This study presents a systematic review of BT's potential and application in land registration within low-income countries. The study uncovers diverse approaches to BT implementation that are influenced by local conditions and government structures. The study reveals that while there is a burgeoning interest in this field, actual implementations remain limited. The key barriers include resistance from government officials and a lack of local BT skills. Public blockchains have shown a high tendency for adoption, indicating a shift towards more transparent relationships between governments and citizens. The Hyperledger Fabric platform emerges as a popular choice due to its ability to provide secure, scalable, and robust solutions. However, there is a lack of clarity regarding the consensus mechanisms used, indicating a potential gap in current research practices. The study recommends an incremental approach to BT implementation, starting with non-threatening, transparent processes that could be expanded as part of broader government reform programs. Despite the potential of BT to revolutionize land registration systems and democratize tracking, it also poses a threat to existing power structures. Therefore, more robust empirical research is needed to evaluate the impacts and navigate the associated sociotechnical, legal, and institutional challenges. The study also proposes the establishment of a BT collaborative network among low-income countries to leverage shared experiences and develop a common framework for implementation. In the single instance where it was implemented in Georgia, public trust in government was restored. The study contributes to understanding how BT can be effectively harnessed to improve land registration systems in low-income countries.

Direct Determination of High-Affinity Binding Constants by Continuous Injection Isothermal Titration Calorimetry.

Isothermal titration calorimetry (ITC) is a method to determine thermodynamic values (ΔG, ΔH, and ΔS) for ligand-receptor binding in biological and abiological systems. It is challenging to directly determine subnanomolar dissociation constants using a standard incremental injection approach ITC (IIA-ITC) measurement. We recently demonstrated a continuous injection approach ITC (CIA-ITC) [ J. Phys. Chem. B 2021, 125, 8075-8087]enables the estimation of thermodynamic parameters in situ. In this work, we demonstrate a label-free and surface modification-free CIA-ITC to determine the complete binding thermodynamics of a ligand with a subnanomolar dissociation constant KD. The KD for desthiobiotin (DTB)-avidin binding was determined to be 6.5 pM with respect to the ligand by CIA-ITC, a quantity unsuccessfully measured with IIA-ITC and surface plasmon resonance spectroscopy (SPR). This value compares well with literature-reported spectroscopic determination of DTB-avidin binding. Criteria with respect to the concentration of the ligand and receptor and flow rate for obtaining true equilibrium dissociation constants without displacement titration are presented.

An incremental feature selection approach for dynamic feature variation

In numerous domains, there is ample evidence indicating that a significant portion of real-world data exhibits temporal variations, such as medical research and meteorological studies. Particularly in the era of big data, not only does the size of data change dynamically but also its dimensions at an unprecedented pace. Consequently, employing traditional methods to handle such dynamic data becomes highly impractical. To address this limitation, this paper proposes an incremental feature selection algorithm tailored for dynamic feature variations. For scenarios involving an increase in features, the novel algorithm efficiently identifies a target subset with effective features. In order to showcase the efficacy of the proposed algorithm, this paper conduct experiments using four commonly used classifiers and five UCI data sets. The experimental results further validate both the feasibility and efficiency of the new approach.

Decentralized and collaborative machine learning framework for IoT

Decentralized machine learning has recently been proposed as a potential solution to the security issues of the canonical federated learning approach. In this paper, we propose a decentralized and collaborative machine learning framework specially oriented to resource-constrained devices, usual in IoT deployments. With this aim we propose the following construction blocks. First, an incremental learning algorithm based on prototypes that was specifically implemented to work in low-performance computing elements. Second, two random-based protocols to exchange the local models among the computing elements in the network. Finally, two algorithmics approaches for prediction and prototype creation. This proposal was compared to a typical centralized incremental learning approach in terms of accuracy, training time and robustness with very promising results.

Supranational Self‐Empowerment Through Bricolage: The Role of the European Commission in EU Security and Defence

AbstractThis article provides a novel conceptualization of bricolage as a strategy for incremental supranational self‐empowerment. It argues that the cumulative effects of different bricolage tools employed by the Commission have been central for progressively strengthening its role in EU security and defence, which culminated in the establishment of the European Defence Fund (EDF) and the Commission's Directorate‐General Defence Industry and Space (DG DEFIS). Building communication upon communication, the Commission used discursive bricolage to set the conditions for employing existing EU financial and organizational resources to advance its interests. Specifically, with its incremental bricolage approach, the Commission has managed to mitigate sovereignty concerns of member states, progressively nudging them towards deeper integration. Overall, our article shows how the Commission has strengthened its influence through the cumulative bricolage tools even in the intergovernmental domains of security and defence.

Incremental updating fuzzy tolerance rough set approach in intuitionistic fuzzy information systems with fuzzy decision

As information technology develops rapidly and data is constantly updated, efficient mining knowledge from dynamic intuitionistic fuzzy information systems with fuzzy decision (IFFD) is a significant topic. In the dynamic data environment, the corresponding IFFD is always changing with time when object sets of datasets may evolve in time. Fuzzy tolerance rough set (FTRS), as one of extended rough set models, has a strong capacity for the expression of information and better representation of uncertainty. Therefore, it is very effective and necessary to use FTRS to acquire valuable knowledge from dynamic IFFD. In this paper, we investigate the dynamic fuzzy tolerance rough set approach for IFFD. Firstly, a new fuzzy tolerance similarity is defined to describe fuzzy tolerance relation between objects in the IFFD. Second, we construct a fuzzy tolerance rough set model in the IFFD, discuss some properties, and propose the corresponding static algorithm. Subsequently, incremental approaches which update fuzzy tolerance rough approximations with the insertion and deletion of objects in the IFFD are investigated and the corresponding dynamic algorithms are also designed. Finally, the feasibility of the FTRS model and the effectiveness and efficiency of the dynamic algorithm in dynamic environments are validated through a series of comparative experiments on nine datasets.

A highly scalable CF recommendation system using ontology and SVD-based incremental approach

In recent years, the need of recommender systems has increased to enhance user engagement, provide personalized services, and increase revenue, especially in the online shopping industry where vast amounts of customer data are generated. Collaborative filtering (CF) is the most widely used and effective approach for generating appropriate recommendations. However, the current CF approach has limitations in addressing common recommendation problems such as data inaccuracy recommendations, sparsity, scalability, and significant errors in prediction. To overcome these challenges, this study proposes a novel hybrid CF method for movie recommendations that combines the incremental singular value decomposition approach with an item-based ontological semantic filtering approach in two phases, online and offline. The ontology-based technique is leveraged to enhance the accuracy of predictions and recommendations. Evaluating our method on a real-world movie recommendation dataset using precision, F1 scores, and mean absolute error (MAE) demonstrates that our system generates accurate predictions while addressing sparsity and scalability issues in recommendation system. Additionally, our method has the advantage of reduced running time.

External Synchronization of Distributed Redundant Flight Control Computers

&lt;div&gt;This work introduces a practical approach to external synchronization for flight control computers (FCCs) deployed in a decentralized fashion. The internal synchronization among the FCCs in distributed flight control systems needs to be extended for specific applications, necessitating an urgent need for an external synchronization mechanism. For instance, when the air data and attitude reference system (ADAHRS) and the flight control computer (FCC) are not synchronized, a dead time or time offset occurs between the time the ADAHRS transmits data and the time the FCC begins executing its control functions, which can impair flight control system performance or even cause system instability, particularly for the system with incremental control approaches, such as incremental nonlinear dynamic inversion (INDI). Therefore, an external synchronization technique that does not rely on establishing a global view of time that is accurately synchronized with an external reference device has been proposed and implemented for distributed flight control systems. The findings of the implemented system demonstrate that the suggested approach can synchronize the distributed FCCs to an external reference device with high synchronization precision and deal with varying clock drift rates.&lt;/div&gt;

Grammar of Impact Sensitivity: An Incremental Theory.

In this paper, we report the first attempt to quantify impact sensitivity using the second-order incremental approach based on the structural features of explosives. It has been found that impact height (h50) can be expressed via a multiplicative incremental exponential form, in which the exponents are characteristic coefficients of structural increments multiplied by their numbers in the molecule. The method was developed on a large array of experimental data (450 molecules and salts) of different energetic materials, namely, nitro compounds, peroxides, nitrogen-rich salts, heterocycles, etc., while testing of the model was performed for 170 compounds. The results demonstrate a noticeable correlation with the experimental h50 values. Thus, the corresponding R2 and RMSE for the training and test sets are 0.56 (12.5 J) and 0.63 (18.8 J), respectively. In this work, we use 53 individual structural increments, but their number can be extended, and the corresponding coefficients can be refined; this allows for increasing the prediction accuracy on-the-fly. The calculation algorithm is discussed, and the corresponding examples are presented. The performed machine-based regression analysis using genetic function approximation, multiple linear regression, and artificial neural network has proven the reasonability and informativity of the proposed incremental theory. Thus, the developed approach significantly extends our understanding of the impact sensitivity phenomenon and translates it into the category of one that can be calculated by a pocket calculator.

Transcriptomics and Time-Course Proteomics Reveal Pclaf As an Inducer of Resistance Against Pevonedistat in Diffuse Large B-Cell Lymphoma

Neddylation, a post-translational modification which regulates the activity or function of proteins but does not mediate their degradation by the 26S proteasome, attaches ubiquitin-like protein NEDD8 (Neural- precursor cell- expressed developmentally downregulated 8) to the substrates via enzymatic cascades. Cullins are classical substrates and inhibiting neddylation could hamper the activation of Cullin-RING ligases (CRLs) and further impede the ubiquitination. Recently, it has been found that neddylation overactivation is prevalent in malignancies, and hence, targeting neddylation is an attractive antitumor strategy. Pevonedistat, a first-in-class small-molecule inhibitor specific for neddylation, exhibited good activity in preclinical models, but meanwhile its resistance was reported, which might be a dominant factor limiting its single-drug efficacy. Herein, we sought to elucidate the mechanism of Pevonedistat resistance in diffuse large B-cell lymphoma (DLBCL) and propose a novel combination strategy to improve therapeutic efficacy. Firstly, we constructed acquired Pevonedistat-resistant cell lines of DLBCL (OCI-LY8-Pevonedistat resistant, OCI-LY8-PR; OCI-LY3- Pevonedistat resistant, OCI-LY3-PR) by incremental concentration approach. The resistance to Pevonedistat was authenticated by resistance index and its specificity was substantiated by cross resistance test to its downstream proteasome inhibitors, MG132 and Bortezomib. Subsequently, we examined the phenotypic differences between the parental and resistant cells. Pevonedistat significantly inhibited neddylation modification of Cullin, accompanied with the enhanced apoptosis and cell cycle arrest at G2/M phase in parental cells, which, nevertheless, was inapparent in resistant cells. To unravel the mechanism of acquired resistance, we conducted transcriptomics between the parental and Pevonedistat-resistant cells, which suggested genes regulating cell cycle and DNA damage repair upregulated in resistant cells. For further exploration, we employed time-course proteomics, in which parental and resistant cells were treated without or with Pevonedistat for 2, 8, 24 hours and collected for mass spectrum, respectively. The cluster analysis of proteomics suggested that the proteins overexpressed in the resistant lines in all time points were significantly enriched in biological processes such as chromosome assembly and cell cycle, which were almost consistent with transcriptomics. However, the proteins highly expressed in the parental cells were mainly enriched in the metabolism of a variety of organic substances (Figure 1). Subsequently, we mainly targeted the upregulated proteins in the resistant cells, among which, PCNA clamp-associated factor (PCLAF), a PCNA-binding partner, was screened out for its great expression difference between resistant and parental cells. We confirmed PCLAF was upregulated in the resistant cells by immunoblotting and PCLAF overexpression entitled the parental cells resistance against Pevonedistat. We further explored the mechanism of PCLAF-mediated Pevonedistat resistance in DLBCL. The ubiquitination of PCLAF was hindered in Pevonedistat-resistant cells and the accumulation of PCLAF facilitated DNA damage repair and rescued DLBCL cells from cell cycle arrest after Pevonedistat treatment. These suggested that PCLAF mediated Pevonedistat resistance in DLBCL. Subsequently, we sought to propose a combination therapeutic regimen based on PCLAF-driven Pevonedistat resistance. Given PCLAF functions in DNA damage repair, via drug library screening, we found a DNA damage repair inhibitor, Niraparib tosylate, significantly ameliorated DLBCL cells' resistance against Pevonedistat. The efficacy of combination was validated both in vitro and in vivo. In conclusion, this study identified that PCLAF overexpression mediated the resistance against Pevonedistat in DLBCL and proposed a potential combination strategy to improve Pevonedistat resistance.

A Multiplier-Free Convolution Neural Network Hardware Accelerator for Real-Time Bearing Condition Detection of CNC Machinery

In various industrial domains, machinery plays a pivotal role, with bearing failure standing out as the most prevalent cause of malfunction, contributing to approximately 41% to 44% of all operational breakdowns. To address this issue, this research employs a lightweight neural network, boasting a mere 8.69 K parameters, tailored for implementation on an FPGA (field-programmable gate array). By integrating an incremental network quantization approach and fixed-point operation techniques, substantial memory savings amounting to 63.49% are realized compared to conventional 32-bit floating-point operations. Moreover, when executed on an FPGA, this work facilitates real-time bearing condition detection at an impressive rate of 48,000 samples per second while operating on a minimal power budget of just 342 mW. Remarkably, this system achieves an accuracy level of 95.12%, showcasing its effectiveness in predictive maintenance and the prevention of costly machinery failures.

Reliability service-based strategies for improving electricity retailer competitiveness using a novel heuristic algorithm

Liberalizing China's electricity sales market has led to fierce competition among electricity retailers. Providing customized electricity sales services is beneficial for electricity retailers to capture market share and gain a competitive edge. Therefore, a reliability service-based strategy is proposed to improve the electricity retailer competitiveness, which attracts industrial users by providing a continuous electricity supply. Specifically, the electricity retailer designs an electricity price package based on supply service reliability and a compensation model to cope with supply interruptions, considering the incremental cost approach and industrial users' different reliability needs. In addition, a user power shift model is constructed to evaluate user satisfaction, including electricity consumption, electricity cost, and reliability satisfaction. Furthermore, a decision-making optimization model for electricity retailers is formulated to maximize competitiveness and solved by the Growth Optimizer (GO). Experiments based on power grid company show that the proposed model can effectively improve the competitiveness of electricity retailers. Comparison with other heuristic algorithms also proves the superiority of the GO algorithm in terms of solution speed and quality.

Protecting children from unhealthy food marketing: a comparative policy analysis in Australia, Fiji and Thailand.

Restrictions on marketing of unhealthy foods and beverages to children is a globally recommended policy measure to improve diets and health. The aim of the analysis was to identify opportunities to enable policy learning and shift beliefs of relevant actors, to engender policy progress on restrictions on marketing of unhealthy foods to children. We drew on the Advocacy Coalition Framework to thematically analyse data from qualitative policy interviews conducted Australia (n = 24), Fiji (n = 10) and Thailand (n = 20). In all three countries two clear and opposing advocacy coalitions were evident within the policy subsystem related to regulation of unhealthy food marketing, which we termed the 'strengthen regulation' and 'minimal/self regulation' coalitions. Contributors to policy stasis on this issue were identified as tensions between public health and economic objectives of government, and limited formal and informal spaces for productive dialogue. The analysis also identified opportunities for policy learning that could enable policy progress on restrictions on marketing of unhealthy foods to children as: taking an incremental approach to policy change, defining permitted (rather than restricted) foods, investing in new public health expertise related to emerging marketing approaches and scaling up of monitoring of impacts. The insights from this study are likely to be relevant to many countries seeking to strengthen regulation of marketing to children, in response to recent global recommendations.

An incremental singular value decomposition approach for large-scale spatially parallel & distributed but temporally serial data – applied to technical flows

The article presents a strategy and its algorithm to compile a simulation-accompanying, incremental Singular Value Decomposition (SVD) for time-evolving, spatially parallel discrete data sets. The framework addresses state-of-the-art PDE solvers for computational science and engineering applications. An important characteristic of such applications is that the spatial size of the data is often time-invariant and significantly exceeds the temporal size due to the large computational grid in 3D applications. Typical examples, which are also considered in this article, relate to results extracted from unsteady flow simulations. Herein, the flow data, which progresses over time, is frequently calculated spatially parallel based on domain decomposition strategies, which allow to parallelize the simulation on distributed memory machines following a Single Instruction Multiple Data (SIMD) concept.With a view to the memory-efficient reuse of (compressed) simulation results and their CPU time-saving, sufficiently accurate generation, the paper scrutinizes the efficiency of incremental/parallel SVD approaches for such simulation examples. To improve the computational efficiency, the introduction of a bunch matrix is proposed, which enables the aggregation of multiple time steps and SVD updates, and significantly increases the efficiency. The suggested strategy is verified and validated by simple 2D laminar single-phase flows and subsequently applied to more complex 2D and 3D turbulent two-phase flows. Emphasis is given to (a) the accuracy of SVD-based reconstruction, (b) the physical realizability of the reconstructed quantities, (c) the independence of domain partitioning, (d) an efficient snapshot bunching, and (e) related implementation aspects. In addition, the influence of lower and (adaptive) upper rank thresholds on the effort and accuracy is evaluated. A final application renders the practical benefits of the approach and refers to a merchant ship in head waves at Re = 1.4×107 and Fn = 0.26. The simulation involves 2880 processor cores and the related full-rank snapshot matrix has (108×104) entries. With a numerical overhead of O(10%), this snapshot matrix can be incrementally generated and compressed by O(95%). The compression is accompanied by only small errors in the integral force and local wave elevation of O(10−2%). This qualifies the method for an efficient subsequent data processing.