Multi-grade modelling - paperboard quality modeling

BackgroundThe software product lines (SPL) enable development teams to fully address a systematic reuse of shared assets to deliver a family of similar software products. Mobile applications are an obvious candidate for employing an SPL approach. This paper presents our research outcomes, based on empirical data from an industry-level development project. Two development teams were confronted with the same functionalities set to be delivered through a family of native mobile applications for Android and iOS.MethodsEmpirical data was gathered before, during and after a year of full-time development. The data demonstrate the impact of a SPL approach by comparing the SPL and non-SPL multiple edition development. One family of products (Android apps) was developed using an SPL approach, while another (iOS apps), functionally the same, was developed without employing an SPL approach. The project generated a volume of raw and aggregated empirical data to support our research questions.ResultsThe paper reports a positive impact of an SPL approach on product quality (internal and external) and feature output per week. As data shows, it also increases the delivery of functionalities (240% in 6 more editions), while investing the same amount of effort needed for a single-edition development. As a result of system-supported separation of development and production code, developers had a high confidence in further development. On the other hand, the second team delivered less new functionalities, only two new application editions, and lower software quality than the team that manages multi-edition development by employing an SPL approach.

Promoting reusability by managing software assets can greatly benefit companies that develop a family of similar products, where products are evolving from one another. One of the main goals of domain engineering is to identify and document the commonalities across the various products in a particular domain. Managing these common products or assets will keep the development cycle short for future products and help tame the large divergence seen in the development of different products. Guidant Corporation has been involved in the development of medical devices, such as cardiac rhythm management systems at CPI, which are complex real-time safety-critical systems. Cardiac rhythm management systems include implantable medical devices used to monitor human hearts and to provide appropriate therapy when needed. These devices and related products are constantly evolving as technology and market needs change. The systems being developed at CPI are thus a family of products and share many functionalities among them. There are several issues that need to be addressed in the domain analysis of a family of products.

A family of software products comprises similar products within a defined scope that share common characteristics, often due to reuse techniques applied during development. This paper introduces an approach that applies biological insights to map the landscape of a software product family, identifying potential gaps within its scope. Phylogenetics studies the gene similarity among groups of organisms to understand ancestry among species. Leveraging Phylogenetics in software, our approach offers a structured view of a product family, aiding in the discovery of unexplored areas fitting the scope of the family. Our approach creates a phylogenetic tree that enables to easily identify latent products (ancestors) that did not exist in the original family. Those ancestors can then be reconstructed from existing products (descendants). The product family evaluated is a set of industry-scale video game non-playable characters. We assess this approach through video game simulations and scope metrics to determine how closely the reconstructed products align with the family’s scope. The results confirm that the content generated with phylogenetics aligns better with the family scope than the state-of-the-art procedural content generation techniques using evolutionary algorithms. Phylogenetics enhances content generation by providing a framework to understand and expand the product family with new content.

This paper presents a feature modeling endeavour for a television set product family by a major consumer electronics company. This work consists of three stages. First, a feature model is constructed, based on the analysis of the product family requirements. The constructed model is supplemented with a feature glossary. FeatureIDE is used as the model editor. Feature attributes, not supported by FeatureIDE, are represented in the basic feature model by using additional features. Second, the feature model in XML is converted into the schema of the analysis tool, using a custom parser developed for this purpose. Third, the model is analyzed by well-known analysis operations. FAMA is used as the analysis tool. Performance results are obtained. Finally, lessons learned from the whole effort are discussed.

Classifying variability modeling techniques

Introduction Raman spectroscopy has been shown to accurately classify tissue pathology in a variety of conditions and organ systems. Much of this work has been performed using Raman microspectrometers on ex vivo tissue sections with long acquisition times, and measurements can take many hours. To enable translation of this technology to a clinical setting, either as an adjunct for pathologists or an in vivo probe for point of care testing, measurement times must be reduced. By using Renishaw’s Streamline™ technologywhich combines a line focusing technique and pixel binning, it is possible to collect Raman spectral measurements much faster without compromising signal to noise. This study aims to assess the ability of this technique to accurately classify tissue pathology, using an oesophageal model. Method Tissue was collected from the oesophagus in patients undergoing endoscopy of open surgery on the oesophagus. Specimens were collected from patients with Barrett’s oesophagus (BO), dysplasia and adenocarcinoma, and snap frozen in liquid nitrogen. 8 µm tissue sections were prepared onto calcium fluoride slides, with contiguous sections stained with haematoxylin and eosin (H&E) for histological comparison. Raman spectra were collected across homogeneous regions of tissue pathology, using Streamline™ acquisitions of 60 s per pixel, at 1.1 µm spatial resolution. Classification models were constructed to discriminate pathology subtypes. Results Advanced multivariate statistic analysis tools were used to develop pathology classification models, which were then tested using leave-one-out cross-validation. The sensitivity and specificity of this pathology classification model using Raman Spectroscopy to discriminate dysplasia/adenocarcinoma from Barrett’s oesophagus produced sensitivity and specificities >75% and >75%, respectively. Conclusion By combining multivariate statistical analysis with Streamline TM Raman acquisition of spectral data, we have demonstrated good sensitivities and specificities. This study illustrates the potential of non-invasive rapid Raman spectral mapping measurements and development of a robust and validated oesophageal classification model that are able to classify tissue pathology both providing a clinical tool for pathologists and clinicians. Disclosure of interest None Declared. Reference Shetty G, et al . Raman spectroscopy: elucidation of biochemical changes in carcinogenesis of oesophagus. Br J Cancer. 2006;94(10):1460–4

In this work, we propose the use a multivariate analysis tool, called principal components analysis (PCA), to address the problem of Students' Evaluation of Teaching Effectiveness (SETE). We conducted a research with Engineering Students in an undergraduate course. The values obtained after collecting research data were transformed from a 3D array to a 2D array performing an average of students' responses. The PCA was applied in order to take same intrinsic information of the dataset collected. The Cronbach's α validates the PCA application in the dataset. The results show that our study allows an analysis of how students perceive different disciplines about different criteria, which may serve as an indicator for an educational assessment area.

Independent component analysis (ICA) is a multivariate data analysis tool. The basic principle of ICA is the assumption of independency of the source data. On the separation of the data source, ICA algorithm searches for a demixing matrix that will maximize the independency. This searching process is mostly done in iterative way and involving high order statistics. This process is time consuming. For a certain application, such as speech, where the source signal has its power at the lower frequency, we can reduce the data length by removing the high frequency component. Wavelet decomposition is a popular method for this purpose. In this paper, we propose the data reduction using wavelet as a preprocessing of ICA to speed up the ICA computation. We investigate Haar, Daubechies 2, Daubechies 3, and Daubechies 4 wavelet as the wavelet analysis. We further investigate the computation time as the function of level of decomposition of the wavelet. In this study, we found that Haar wavelet at third level of decomposition gave the biggest advantage of computation speed, which is about 40-50%.

Review for "Cell culture process metabolomics together with multivariate data analysis tools opens new routes for bioprocess development and glycosylation prediction"

Software Product Lines (SPL) are systematic approach to develop families of similar software products by explicating their commonalities and variability, e.g., in a feature model. Using techniques from model-driven development, it is then possible to automatically derive a concrete product from a given configuration (i.e., selection of features). However, this is problematic for interactive applications with complex user interfaces (UIs) as automatically derived UIs often provide limited usability. Thus, in practice, the UI is mostly created manually for each product, which results in major drawbacks concerning efficiency and maintenance, e.g., when applying changes that affect the whole product family. This paper investigates these problems based on real-world examples and analyses the development of product families from a UI perspective. To address the underlying challenges, we propose the use of abstract UI models, as used in HCI, to bridge the gap between automated, traceable product derivation and customized, high quality user interfaces. We demonstrate the feasibility of the approach by a concrete example implementation for the suggested model-driven development process.

Real estate price prediction is crucial for informed decision making in the dynamic real estate sector. In recent years, machine learning (ML) techniques have emerged as powerful tools for enhancing prediction accuracy and data-driven decision making. However, the existing literature lacks a cohesive synthesis of methodologies, findings, and research gaps in ML-based real estate price prediction. This study addresses this gap through a comprehensive literature review, examining various ML approaches, including neural networks, ensemble methods, and advanced regression techniques. We identify key research gaps, such as the limited exploration of hybrid ML-econometric models and the interpretability of ML predictions. To validate the robustness of regression models, we conduct generalization testing on an independent dataset. Results demonstrate the applicability of regression models in predicting real estate prices across diverse markets. Our findings underscore the importance of addressing research gaps to advance the field and enhance the practical applicability of ML techniques in real estate price prediction. This study contributes to a deeper understanding of ML’s role in real estate forecasting and provides insights for future research and practical implementation in the real estate industry.

This study evaluated the impact of 21 agrochemical treatments (T0–T20), including combinations of amino acids, plant growth regulators, micronutrients, and vitamins, on the growth and yield performance of sesame (Sesamum indicum L.). Key traits assessed were plant height (PH), leaves per plant, chlorophyll index (CI), capsules per plant, seeds per capsule, 1000-seed weight, and seed yield. Multivariate analyses, including principal component analysis (PCA), K-means clustering, and Agglomerative Hierarchical Clustering, were used to interpret trait variation. PCA indicated that PC1 and PC2 accounted for 50.47% and 19.36% of the total variability, respectively, with PH, CI, and seed yield being the major contributors. K-means and Agglomerative Hierarchical Clustering grouped treatments into two clusters, identifying T14 and T18 as the most effective combinations. T18 produced the highest yield (7.56 q/ha), followed by T14 (7.13 q/ha), both of which significantly outperformed the control (3.89 q/ha). These findings highlight that targeted formulations containing tryptophan, gibberellic acid, boron, and B vitamins improve photosynthesis, biomass allocation, and yield potential. Accordingly, T14 and T18 treatment applications are recommended for field use.. KEYWORDS :Agrochemicals, Chlorophyli Index, Growth hormones, Multivariate tools, Sesame, Soil health

Carfilzomib (Cfz) is an anti-cancer drug related to cardiorenal adverse events, with cardiovascular and renal complications limiting its clinical use. Despite the important progress concerning the discovery of the underlying causes of Cfz-induced nephrotoxicity, the molecular/biochemical background is still not well clarified. Furthermore, the number of metabolomics-based studies concerning Cfz-induced nephrotoxicity is limited. A metabolomics UPLC-HRMS-DIA methodology was applied to three bio-sample types i.e., plasma, kidney, and urine, obtained from two groups of mice, namely (i) Cfz (8 mg Cfz/ kg) and (ii) Control (0.9% NaCl) (n = 6 per group). Statistical analysis, involving univariate and multivariate tools, was applied for biomarker detection. Furthermore, a sub-study was developed, aiming to estimate metabolites' correlation among bio-samples, and to enlighten potential mechanisms. Cfz mostly affects the kidneys and urine metabolome. Fifty-four statistically important metabolites were discovered, and some of them have already been related to renal diseases. Furthermore, the correlations between bio-samples revealed patterns of metabolome alterations due to Cfz. Cfz causes metabolite retention in kidney and dysregulates (up and down) several metabolites associated with the occurrence of inflammation and oxidative stress.

Software and System Product Lines (SSPL) are the state-of-the-art for systematically reusing a common set of core assets in the development of similar products in a product family. A large number of SSPL success stories have been published in the last decade and commercial tool support is also available. SSPLs promise to reduce cost, to shorten time-to-market for new features, and to increase product quality by systematically reusing core assets in the development of three or more systems. However, an open challenge is SSPL engineering for safety-relevant systems such as automotive, avionic, or industrial automation systems. Safety-relevant systems have to be developed, analyzed, and certified according to safety standards such as IEC 61508. These standards demand the application of safety analyses such as Fault Tree Analysis and Failure Mode and Effect Analysis. Starting the safety analysis of each product variant of a SSPL from scratch is complex and very time-consuming. However, there are only few convincing cases, where SSPL approaches have been followed in safety engineering. To pave the way for a broader adoption of SSPL approaches, this paper reports practical experiences with industrial-strength methods and tools along an adaptive cruise control SSPL. The paper demonstrates how commercial tools can be used (i) to analyze safety-related aspects already in the architectural design, (ii) to model the results as component integrated component fault trees (C2FT), and (iii) to systematically reuse C2FT in the safety analysis of a concrete product. The results of the case study show that C2FT (i) can be easily integrated into a feature-oriented development process of SSPL, (ii) facilitate early consideration of safety in domain engineering, and (iii) reduce effort and complexity of safety analyses in application engineering.

Food composition database reliability in calculations of diet offers