Component Of Process Research Articles

Understanding the dual impact of oat protein on the structure and digestion of oat starch at pre- and post-retrogradation stages.

Although extensive research has been conducted on the effects of protein-starch interaction on the gelatinization and microstructure of starch gels, the starch retrogradation process and protein component has been overlooked. Moreover, the dual regulatory effects of oat protein on starch gel microstructure and starch digestibility pre-and post-retrogradation still remains largely unexplored. In this study, the gelation behaviors, structure, and digestibility of oat protein-oat starch mixtures pre- and post-retrogradation were determined. The oat protein-starch samples exhibited lower setback values (314cP) and higher relaxation peak time T23 (363.62ms) compared to the oat starch samples (1994cP and 160.51ms), indicating that oat protein effectively delayed the short-term retrogradation and weakened the water-binding capacity of starch gel. Morphological and rheological analyses revealed that prior to retrogradation, oat protein promoted the formation of a looser network structure with weak shear resistance and consistency, while, after 7 d of retrogradation, an appropriate protein content (<30%) facilitated the formation of an orderly and uniform honeycomb porous gel structure. Furthermore, oat protein increased the resistant starch content (RS) from 18.16% to 32.66% before retrogradation. After retrogradation, oat protein slowed down the increase in RS content by inhibiting the formation of starch crystal structure. Noteworthy, different oat protein components play various roles in starch gelatinization and retrogradation process.

General design flow for waveguide Bragg gratings

Abstract Bragg gratings are crucial components in passive photonic signal processing, with wide-ranging applications including biosensing, pulse compression, photonic computing, and addressing. However, the design of integrated waveguide Bragg gratings (WBGs) for arbitrary wavelengths presents significant challenges, especially when dealing with highly asymmetric layer stacks and large refractive index contrasts. Convenient approximations used for fiber Bragg gratings generally break down in these cases, resulting in nontrivial design challenges. In this work, we introduce a general simulation and design framework for WBGs, which combines coupled mode theory with three-dimensional finite-element method eigenfrequency computations. This approach allows for precise design and optimization of WBGs across a broad range of device layer stacks. The design flow is applicable to further layer stacks across nearly all wavelengths of interest, given that the coupling between the forward and backward propagating mode is dominant.

Development of a service blueprint for blockchain services.

As blockchain has been actively applied in various services, a tool for visualizing the complex service processes reflecting the characteristics of blockchain has been required. A service blueprint is a tool to visualize all key systems and encounters in service delivery. Although several blueprints already exist, they have limitations to systematically visualize and analyze blockchain service processes. This study develops a Blockchain Service Blueprint (BSB) specialized in visualizing and analyzing blockchain service processes. A comprehensive literature review and an analysis of blockchain services were conducted to identify characteristics of blockchain services and limitations of existing blueprints. The BSB was developed based on the derived key components of blockchain service processes, so that it has the optimal structure with key elements to visualize complex processes of blockchain services. The usefulness of the BSB was verified by both comparisons with traditional blueprints and expert interviews. The proposed BSB can intuitively and clearly visualize a service process between customers and service providers in blockchain services. Using the Blockchain Service Blueprint (BSB), providers can identify and improve service processes to enhance sustainability, and this study offers researchers cases and a development process that demonstrate the BSB's effectiveness across various blockchain services.

What do pseudowords tell us about word processing? An overview

This article provides an overview of the use of pseudowords—letter strings that resemble real words by adhering to phonotactic and orthotactic rules (e. g., fambo follows the rules of English phonology and orthography, but it does not have an actual meaning)—in written word processing research, with a focus on readers in alphabetic languages. We review how pseudowords have been used in research to isolate specific features of words to examine the cognitive mechanisms underlying various aspects of their processing, including orthographic, phonological decoding, lexical-semantic, and syntactic components, as well as to the way those empirical observations have shaped theories and models of word recognition. The overview also considers their broader applications, such as in studying non-alphabetic scripts, speech processing, and language disorders like dyslexia. By providing a focused synthesis of empirical findings, this article underscores the critical insights that research using pseudowords offers into the interconnected nature of cognitive mechanisms in language processing.

Co-designing interprofessional education in primary healthcare: an illustration from the Make My Day stroke prevention project

ABSTRACT The aim of this paper is to describe a research process of actively engaging stakeholders using co-design in the development of interprofessional education and a health intervention program targeting stroke prevention. Stakeholders included potential patients, healthcare professionals, and healthcare experts/researchers. Collaborating through co-design can be utilized in developing primary healthcare interventions including educational strategies for interprofessional learning. In this paper, an intervention in primary healthcare (Make My Day) will be used to illustrate how co-design was applied, partly as a method for developing educational resources together with stakeholders, and partly by engaging interprofessional healthcare teams in adapting intervention materials to address the needs of groups more specifically at risk of stroke in local contexts. There is a need to actively involve stakeholders, build on user experiences, and integrate interprofessional knowledge in the design and evaluation of health interventions. However, there is a lack of detailed accounts about how this can be accomplished. This study illustrates collaborative research process components and thus contributes with knowledge about how co-design methods can be applied in health intervention design as well as in interprofessional education within primary healthcare settings.

Relevant Information in TDD Experiment Reporting

Experiments are a commonly used method of research in software engineering (SE). Researchers report their experiments following detailed guidelines. However, researchers do not, in the field of test-driven development (TDD) at least, specify how they operationalized the response variables and, particularly, the measurement process. This article has three aims: (i) identify the response variable operationalization components in TDD experiments that study external quality; (ii) study their influence on the experimental results; (iii) determine if the experiment reports describe the measurement process components that have an impact on the results. We used two-part sequential mixed methods research. The first part of the research adopts a quantitative approach applying a statistical analysis of the impact of the operationalization components on the experimental results. The second part follows with a qualitative approach applying a systematic mapping study (SMS). The test suites, intervention types and measurers have an influence on the measurements and results of the statistical analysis of TDD experiments in SE. The test suites have a major impact on both the measurements and the results of the experiments. The intervention type has less impact on the results than on the measurements. While the measurers have an impact on the measurements, this is not transferred to the experimental results. On the other hand, the results of our SMS confirm that TDD experiments do not usually report either the test suites, the test case generation method, or the details of how external quality was measured. A measurement protocol should be used to ensure that the measurements made by different measurers are similar. It is necessary to report the test cases, the experimental task and the intervention type in order to be able to reproduce the measurements and statistical analyses, as well as to replicate experiments and build dependable families of experiments.

Determining the process components of impact assessment in health and social program implementation: A scoping review of theories, models and frameworks.

Health and social service research impact analysis play a pivotal role in demonstrating research value. Impact analysis of programs, interventions, or policies in real-world settings is complex. There are many implementation evaluation theories, models, and frameworks (TMF) and researchers find choosing one challenging. Our objective was to systematically scope TMFs, review and chart key components of the process of implementation impact analysis to identify gaps. A scoping review was undertaken and reported using PRISMA-ScR guidelines. Systematic literature searches were conducted for impact analysis and impact assessment TMFs in MEDLINE, SCOPUS databases, hand searches, and expert directed search (2010-2024). Peer-reviewed articles were eligible for inclusion if they described an implementation evaluation TMF in English and used in the real world. Data extracted by the study team was charted in an Excel spreadsheet. The review identified 71 relevant papers which included a theory (n=6), model (n=14), or framework (n=51). Most considered resources and/or results, whereas only 25% considered implementation process components. Ten frameworks were deemed comprehensive and covered at least two phases of implementation and five components. Most frameworks had not developed or tested practical tools to facilitate use of the framework. No frameworks were identified that incorporated all phases of implementation, nor key components of the process in each phase of implementation research. The findings highlight the need to identify key components and develop a taxonomy, glossary and tools to assess the process components of implementation in real world settings.

Attention-based interactive multi-level feature fusion for named entity recognition

Named Entity Recognition (NER) is an essential component of numerous Natural Language Processing (NLP) systems, with the aim of identifying and classifying entities that have specific meanings in raw text, such as person (PER), location (LOC), and organization (ORG). Recently, Deep Neural Networks (DNNs) have been extensively applied to NER tasks owing to the rapid development of deep learning technology. However, despite their advancements, these models fail to take full advantage of the multi-level features (e.g., lexical phrases, keywords, capitalization, suffixes, etc.) of entities and the dependencies between different features. To address this issue, we propose a novel attention-based interactive multi-level feature fusion (AIMFF) framework, which aims to improve NER by obtaining multi-level features from different perspectives and interactively capturing the dependencies between different features. Our model is composed of four parts: the input, feature extraction, feature fusion, and sequence-labeling layers. First, we generate the original word- and character-level embeddings in the input layer. Then, we incorporate four parallel components to capture global word-level, local word-level, global character-level, and local character-level features in the feature extraction layer to enrich word embeddings with comprehensive multi-level semantic features. Next, we adopt cross-attention in the feature fusion layer to fuse features by exploiting the interaction between word- and character-level features. Finally, the fused features are fed into the sequence labeling layer to predict the word labels. We conducted generous comparative experiments on three datasets, and the experimental results showed that our model achieved better performance than several state-of-the-art models.

Pulsatile Ion Transport in Nanofiltration Membranes Coupled with Electrically Tunable Pore and Hydroxyl Electrostatic Interactions.

Pulsatile ion transport facilitates the adjusted transfer of substances, meeting the requirements for the gradient and timed separation of multiple components in membrane processes. Responsive nanofiltration membranes are thus currently receiving widespread attention but face limitations due to their narrow performance adjustment range. Herein, hydroxyl functional groups were introduced into electrically responsive nanofiltration membranes to broaden the adjustment range of separation performance through a combination of pore size sieving and functional group interactions, resulting in a greater change in rejection and flux compared to the original membrane. Membrane pore size is regulated by polypyrrole volume changes and becomes more variable when the cation's hydration radius is smaller. Although the hydroxyl group did not affect the charge transfer or volume change capacity of polypyrrole, it enhanced ion-pore interactions during ion transport, which was particularly pronounced in smaller nanochannels. The size effect of functional group interactions more strongly enhances the transmembrane energy barrier in the reduced state compared with the oxidized state, ultimately resulting in greater modulation of performance. This coupling strategy provides insights into the design of responsive membranes, offering the potential to achieve gradient separation of various solutes.

MEGA-GO: Functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network.

The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs. Furthermore, existing models have limitations in effectively predicting the function of newly sequenced proteins that are not included in protein interaction networks. This highlights the need for novel approaches integrating protein structure and sequence data. We introduce MEGA-GO, highlighting the capability of capturing diverse protein sequence length features from multiple scales. The unique graph adaptive neural network architecture of MEGA-GO enables a more nuanced extraction of graph structure features, effectively capturing intricate relationships within biological data. Experimental results demonstrate that MEGA-GO outperforms mainstream protein function prediction models in the accuracy of Gene Ontology (GO) term classification, yielding 33.4%, 68.9%, and 44.6% of Area Under the Precision-Recall Curve (AUPR) on Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) domains respectively. The rest of the experimental results reveal that our model consistently surpasses the state-of-the-art methods. The source code and implementation of MEGA-GO are available at https://github.com/Cheliosoops/MEGA-GO. The supplementary file can be found at Supplementary.

Exploring and validating associations between six systemic inflammatory indices and ischemic stroke in a middle-aged and old Chinese population

BackgroundInflammation and maladaptive immune mechanisms have been substantiated as integral components in the critical pathological processes of the injury cascade in ischemic stroke (IS). This study aimed to explore the associations between six systemic inflammatory indices and IS in a Chinese population.MethodsThis was a case-control study based on the retrospective review of electronic medical records from two hospitals in Shandong Province, China. Systemic inflammatory indices, including the systemic inflammation response index (SIRI), systemic immune inflammation index (SII), pan-immune-inflammation value (PIV), neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR), and lymphocyte monocyte ratio (LMR), were calculated. Logistic regression models and classification analyses were employed to evaluate associations and discriminatory abilities.ResultsIn total, 9392 participants aged 40–83 years old were included in the discovery (3620 pairs of IS-present cases and healthy controls) and validation (1076 pairs of IS-present cases and IS-absent controls with IS mimics) datasets. After adjusting for potential confounding factors, IS was found to be associated with all six systemic indices in the discovery dataset, including SIRI (odd ratio [OR] 8.77, 95% confidence interval [CI] 7.48–10.33), SII (1.03, 1.01–1.04), PIV (1.01, 1.01–1.01), NLR (2.23, 2.08–2.39), PLR (1.01, 1.01–1.01), and LMR (0.77, 0.75–0.78). Notably, only LMR exhibited significant associations with IS in both discovery and validation datasets (0.88, 0.83–0.93), suggesting an independent protective role of this index. SIRI, SII, PIV, NLR, and LMR showed good discriminative ability between IS patients and healthy controls in the discovery dataset (AUCs > 0.70). However, they performed poorly in distinguishing IS patients from IS mimics in the validation dataset (AUCs < 0.60).ConclusionThis study provides valuable insights into the associations between systemic inflammatory indices and IS, offering potential implications for risk stratification. While these inflammatory indices are potential indicators for distinguishing IS from healthy conditions, additional biomarkers may be needed when differentiating IS from other chronic inflammatory conditions in clinical practice.

Beyond facilitating unisensory processing: Crossmodal associative memory training further modulates sensory integration.

Audiovisual associative memory and audiovisual integration involve common behavioral processing components and significantly overlap in their neural mechanisms. This suggests that training on audiovisual associative memory may have the potential to improve audiovisual integration. The current study tested this hypothesis by applying a 2 (group: audiovisual training group, unimodal control group) × 2 (time: pretest, posttest) design. The audiovisual training group received an adaptive audiovisual associative memory training protocol, whereas the active control group undertook an adaptive unimodal item memory training protocol. The training duration for both protocols was seven days, with one and a half hours per day. Before and after training, both groups underwent testing for audiovisual integration through event-related potential (ERP) measures in a detection task that required rapid detection of auditory, visual, or audiovisual stimuli. Behavioral results revealed a training-general effect, as both groups exhibited faster response times in unimodal stimulus detection after 7-day training. EEG results revealed a consistent pattern in which the amplitudes of both visual and auditory evoked components decreased following the training. Moreover, a training-specific effect was found for sensory integration, with the effect of audiovisual integration being modulated in the earlier stage of processing for the audiovisual training group. Such a modulation was not found in the unimodal control group. These findings indicate that training on higher cognitive functions could penetrate to both unisensory processing and the integration of sensory information, which may potentially aid in the development of more targeted and efficient interventions for perceptual processes.

Multiclass Synthetic Accessibility Prediction.

Evaluating synthetic accessibility of in silico molecules is an integral component of the drug discovery process. While the application of machine learning models to predict whether small molecules are easy or hard to synthesize has gained attention recently, predetermined thresholds and data set imbalances present challenges for these binary classification approaches. In this study, we introduce a novel multiclass fold-ensembled classification approach to predict the minimum number of steps needed to synthesize a small molecule. By ensembling the base models trained on multiple stratified subsampled folds, this approach effectively mitigates the impact of class imbalance through probability aggregation or voting aggregation strategies. Additionally, we propose fuzzy evaluation metrics that account for practical tolerances in predictions, providing a more flexible and realistic assessment of model performance. Through experimentation on two reaction benchmark data sets, we demonstrate the effectiveness of our model in a multiclass synthetic accessibility prediction task and the superiority of our proposed method over six existing models in binary synthetic accessibility prediction tasks.

Mast cells: key players in digestive system tumors and their interactions with immune cells.

Mast cells (MCs) are critical components of both innate and adaptive immune processes. They play a significant role in protecting human health and in the pathophysiology of various illnesses, including allergies, cardiovascular diseases and autoimmune diseases. Recent studies in tumor-related research have demonstrated that mast cells exert a substantial influence on tumor cell behavior and the tumor microenvironment, exhibiting both pro- and anti-tumor effects. Specifically, mast cells not only secrete mediators related to pro-tumor function such as trypsin-like enzymes, chymotrypsin, vascular endothelial cell growth factor and histamine, but also mediators related to anti-tumor progression such as cystatin C and IL-17F. This dual role of mast cells renders them an under-recognized but very promising target for tumor immunotherapy. Digestive system tumors, characterized by high morbidity and associated mortality rates globally, are increasingly recognized as a significant healthcare burden. This paper examines the influence of mast cell-derived mediators on the development of tumors in the digestive system. It also explores the prognostic significance of mast cells in patients with various gastrointestinal cancers at different stages of the disease. Additionally, the article investigates the interactions between mast cells and immune cells, as well as the potential relationships among intratumoral bacteria, immune cells, and mast cell within digestive system microenvironment. The aim is to propose new strategies for the immunotherapy of digestive system tumors by targeting mast cells.

What constitutes “institutional arrangements” for Member State reporting within the UNFCCC and Paris Agreement?

All Member States are expected to put in place sustainable institutional arrangements for implementation of the United Nations Framework Convention for Climate Change (UNFCCC) and the Paris Agreement, and to describe these in their reporting transparency obligations. Institutional arrangements provide the mechanism by which work gets visioned, planned, coordinated, implemented, monitored and reported on and are therefore fundamental to implementing the UNFCCC and the Paris Agreement. Without sustainable institutional arrangements, implementation of Nationally Determined Contributions (NDCs) will be jeopardised. Consequently, it is important to clearly identify what constitutes institutional arrangement, both from the perspective of reporting of what is in place and what is optimal for just transitions to achieve UNFCCC and Paris Agreement goals. The Consultative Group of Experts (CGE) and UNFCCC recommend five separate components for describing institutional arrangements for Member States to support their measurement, verification and reporting (MRV), as well as for climate action transparency and support. These components are: i) organization mandates; (ii) expertise; (iii) data flows; (iv) systems and tools; and (v) stakeholder engagement. In this study, we propose that describing institutional arrangements is akin to describing the components of an organisation (e.g. a government, ministry or agency). The Star Organisational Model, with its five inter-linked components of strategy, structure, processes, people and rewards is a useful additional approach for defining what constitutes institutional arrangements. Establishing sustainable institutional arrangements is challenging for developing countries, and yet critical for implementing the UNFCCC-Paris Agreement at country level. By applying the Star model to six developing countries to ascertain its usefulness for reporting on institutional arrangements related to MRV, we conclude that the Star Model offers a valuable additional method for more effectively delineating and documenting institutional arrangements, including describing existing organisational arrangements and envisioning ideal setups, in the context of the UNFCCC and Paris Agreement agendas.

Accelerating machine learning queries with linear algebra query processing

The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-making. As two primary components in traditional predictive pipelines, data processing, and model predictions often operate in separate execution environments, leading to redundant engineering and computations. Additionally, the diverging mathematical foundations of data processing and machine learning hinder cross-optimizations by combining these two components, thereby overlooking potential opportunities to expedite predictive pipelines. In this paper, we propose an operator fusion method based on GPU-accelerated linear algebraic evaluation of relational queries. Our method leverages linear algebra computation properties to merge operators in machine learning predictions and data processing, significantly accelerating predictive pipelines by up to 317x. We perform a complexity analysis to deliver quantitative insights into the advantages of operator fusion, considering various data and model dimensions. Furthermore, we extensively evaluate linear algebra query processing and operator fusion utilizing the widely-used Star Schema and TPC-DI benchmarks. Through comprehensive evaluations, we demonstrate the effectiveness and potential of our approach in improving the efficiency of data processing and machine learning workloads on modern hardware.

Presentation and analysis of the Geotechnical Coastal Vulnerability Index and validation of its application to coastal erosion problems

This study aims to construct a coastal vulnerability assessment conceptual framework to improve the outcomes of Coastal Vulnerability Index (CVI) for local scale areas. Consequently, a new CVI was created adapted to the specific conditions of the area using seven variables. The new index was named Geotechnical Coastal Vulnerability Index (GCVI) due to the incorporation of two new geotechnical variables: (1) Coastal geotechnical properties and (2) Median grain size distribution. Furthermore, Fuzzy Analytic Hierarchy Process (FAHP) and Principal Component Analysis (PCA) were applied in the methodology to assign weight factors to each variable. For the verification of the GCVI predictions, a validation approach is applied using two different variables (rate of Historical shoreline movement and rate of Bed level change). GCVI results for both FAHP and PCA indicates that the greatest part of the study’s area shoreline is under the regime of high and very high vulnerability. Comparison between the GCVIFAHP and GCVIPCA results indicates higher rates in the high and very high vulnerability classes for the GCVIFAHP, while the GCVIPCA shows lower rates in the same classes. Both analytical methods were used for the validation of the GCVI results and the comparison between them showed that the PCA was more efficient than the FAHP since it was coincided better with the rates of historical shoreline movement and bed level change.

Pulp screen plugging characteristics

Aperture plugging is a phenomenon that limits both the capacity and efficiency of pulp screens, which are critical components of the papermaking process. An understanding of how plugs are created and how they can be avoided can enhance the manufacture of paper products, providing energy savings, increased productivity, improved product quality, and higher levels of paper recycling. This work considers the creation and dispersion of plugs in a small, industrial screen. Flow resistance provides a means of assessing the presence of plugs and their evolution through creation and dispersion. A structured means of plug dispersion was formulated, from which floc strength could be inferred. These novel measurements provide insight not only into the factors that control plug creation, but into their character. Long-fiber (softwood) plugs were found to form and consolidate quickly and to achieve high strength. Their low porosity limited flow through the slot soon after creation. Short-fiber (hardwood) plugs formed more slowly, and they were more porous and weaker.

Salience maps for judgments of frontal plane distance, centroids, numerosity, and letter identity inferred from substance-invariant processing.

A salience map is a topographic map that has inputs at each x,y location from many different feature maps and summarizes the combined salience of all those inputs as a real number, salience, which is represented in the map. Of the more than 1 million Google references to salience maps, nearly all use the map for computing the relative priority of visual image components for subsequent processing. We observe that salience processing is an instance of substance-invariant processing, analogous to household measuring cups, weight scales, and measuring tapes, all of which make single-number substance-invariant measurements. Like these devices, the brain also collects material for substance-invariant measurements but by a different mechanism: salience maps that collect visual substances for subsequent measurement. Each salience map can be used by many different measurements. The instruction to attend is implemented by increasing the salience of the to-be-attended items so they can be collected in a salience map and then further processed. Here we show that, beyond processing priority, the following measurement tasks are substance invariant and therefore use salience maps: computing distance in the frontal plane, computing centroids (center of a cluster of items), computing the numerosity of a collection of items, and identifying alphabetic letters. We painstakingly demonstrate that defining items exclusively by color or texture not only is sufficient for these tasks, but that light-dark luminance information significantly improves performance only for letter recognition. Obviously, visual features are represented in the brain but their salience alone is sufficient for these four judgments.

Neuroinflammation: A Driving Force in the Onset and Progression of Alzheimer's Disease.

Background/Objectives: The goal of this commentary is to highlight several key components of the inflammatory process as it relates to amyloid toxicity in Alzheimer's disease (AD), including the role of neuroinflammatory factors and peripheral inflammatory events. Methods: Google Scholar and PubMed were used to find articles with the following keywords: Alzheimer's disease, amyloids, neuroinflammation, peripheral inflammation, microglia, cytokines, and treatments. Sources that were case reports, not peer-reviewed, or older than 30 years were excluded. Abstracts were reviewed first for their relevance before the full text was considered. Methods sections were reviewed to ensure the interventional papers included were randomized controlled trials, meta-analyses, or systematic reviews; however, several literature reviews were also included due to the relevance of their background information. Results: Based on the literature review, we chose to concentrate on microglia, cytokine signaling, and peripheral inflammation markers. We found that microglia activation and subsequent microglia-driven inflammation play a pivotal role in the pathomechanism of AD. Additionally, cytokines such as interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-a) appear to contribute to amyloid accumulation and cell damage. Finally, the increased permeability of the blood-brain barrier (BBB) allows for the peripheral inflammatory process to contribute to the inflammation of the central nervous system (CNS) and amyloid-beta (Aβ) accumulation. Conclusions: Current evidence suggests that the immune system plays a pivotal role in the pathogenesis of AD, both in the CNS and the periphery.