Benefits and limits of quality cost concept applied to software industry

Quality Cost approach is important to be implemented for each product or project of any software company, wherever it’s possible, because it provides additional and more accurate information about costs, costs determined by level of product/project quality. In order to minimize the costs of the required quality level in software industry is important to find out a balance between prevention costs and failure costs. But, even if the prevention costs are very high, it doesn’t assure the elimination of all quality problems or it finally drives the product/project to an unacceptable price from the consumer point of view.failure costs, prevention costs, quality cost, software industry.

A BIOASSAY-GUIDED INVESTIGATION INTO THE CANCER CHEMOPREVENTIVE POTENTIAL OF SELECTED NON-DIETARY PLANTS AND SELECTED FLAVONOIDS

A selection of twelve non-dietary plants were subjected to Soxhlet extraction with n-hexane and methanol and the crude extracts were screened for Nrf2 induction potential using a AREc32 cell-based luciferase gene reporter assay. Screening for free-radical scavenging activity using the DPPH assay was also performed. The highest increase in Nrf2 induction was achieved by the methanol extract of Centaurea dichroa Boiss.& Heldr. (CD-Me, 250 µg/ml), with a 22.7-fold to control induction, followed by the n-hexane extract of Solanum anguivi Lam. (SA-He, 100 µg/ml), with 20.2-fold to control induction.The Nrf2/ARE signaling pathway was also up-regulated by two other methanol extracts, of Centaurea pamphylica Boiss. & Heldr. (CP-Me, 100 µg/ml) and Gardenia ternifolia Schumach. & Thonn. (GT-Me, 750 µg/ml), with 11.22-fold and 8.94-fold to control luciferase induction, respectively. The bioassay guided investigation led to further fractionation of the bioactive methanol extracts so that the less polar methanolic fractions F3 and F4 of CP-Me and GT-Me increased Nrf2 activity more than their respective crude extracts; up to 12.6 - 13.4-fold for CP-Me fractions, and up to 11.6 – 12.6-fold for GT-Me fractions. Moreover, compounds isolated from the bioactive fractions indicated flavonoid type structures, identifying sakuranetin for the first time in Gardenia ternifolia Schumach.& Thonn. Stachyose, mannitol and betulinic acid were also identified as precipitates from solvent extraction. Because of limited amount of material, various types of flavonoids such as flavones, flavanones and flavonols were purchased with the purpose of screening them for Nrf2 activity in AREc32 cells. The flavonoids alone increased the luciferase activity to no more than 3.1-fold (hesperetin, 40 µM), with most reaching slightly above 2-fold induction, indicating a possible synergy in the way of action of the natural products since mixtures of compounds showed higher bioactivity in the same assay. Fractions F3 of CP-Me and GT-Me showed the highest free radical-scavenging potential in the DPPH assay, with IC50 values of 0.072 mg/ml and 0.132 mg/ml respectively (IC50 exerted by positive control quercetin was 0.005 mg/ml). Finally, all flavonoids tested offered protection against oxidative stress induced by ethacrynic acid (ETA) in MCF-7 cells (LD50=68.5 µM), with the flavone velutin (2.5 µM) and flavanone sakuranetin (20 µM) increasing the LD50 of ETA more than 200 times, while all flavonoid pretreatment conditions generally increased the LD50 of ETA more than 9 times

Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms.

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimize the benefits of adding CAVs to existing multi-modal transport systems. Using a real-world scenario from the Leeds Metropolitan Area as a case study, we demonstrate an effective way of combining macro-level mobility simulations based on open data with global optimisation techniques to discover realistic optimal deployment strategies for CAVs. The macro-level mobility simulations are used to assess the quality of a potential multi-route CAV service by quantifying geographic accessibility improvements using an extended version of Dijkstra's algorithm on an abstract multi-modal transport network. The optimisations were carried out using several popular population-based optimisation algorithms that were combined with several routing strategies aimed at constructing the best routes by ordering stops in a realistic sequence

Comparative analysis of two asynchronous parallelization variants for a multi-objective coevolutionary solver.

We describe and compare two steady state asynchronous parallelization variants for DECMO2++, a recently proposed multi-objective coevolutionary solver that generally displays a robust run-time convergence behavior. The two asynchronous variants were designed as trade-offs that maintain only two of the three important synchronized interactions / constraints that underpin the (generation-based) DECMO2++ coevolutionary model. A thorough performance evaluation on a test set that aggregates 31 standard benchmark problems shows that while both parallelization options are able to generally preserve the competitive convergence behavior of the baseline coevolutionary solver, the better parallelization choice is to prioritize accurate run-time search adaptation decisions over the ability to perform equidistant fitness sharing

Autonomous supervision and optimization of product quality in a multi-stage manufacturing process based on self-adaptive prediction models.

In modern manufacturing facilities, there are basically two essential phases for assuring high production quality with low (or even zero) defects and waste in order to save costs for companies. The first phase concerns the early recognition of potentially arising problems in product quality, the second phase concerns proper reactions upon the recognition of such problems. In this paper, we address a holistic approach for handling both issues consecutively within a predictive maintenance framework at an on-line production system. Thereby, we address multi-stage functionality based on (i) data-driven forecast models for (measure-able) product quality criteria (QCs) at a latter stage, which are established and executed through process values (and their time series trends) recorded at an early stage of production (describing its progress), and (ii) process optimization cycles whose outputs are suggestions for proper reactions at an earlier stage in the case of forecasted downtrends or exceeds of allowed boundaries in product quality. The data-driven forecast models are established through a high-dimensional batch time-series modeling problem. In this, we employ a non-linear version of PLSR (partial least squares regression) by coupling PLS with generalized Takagi–Sugeno fuzzy systems (termed as PLS-fuzzy). The models are able to self-adapt over time based on recursive parameters adaptation and rule evolution functionalities. Two concepts for increased flexibility during model updates are proposed, (i) a dynamic outweighing strategy of older samples with an adaptive update of the forgetting factor (steering forgetting intensity) and (ii) an incremental update of the latent variable space spanned by the directions (loading vectors) achieved through PLS; the whole model update approach is termed as SAFM-IF (self-adaptive forecast models with increased flexibility). Process optimization is achieved through multi-objective optimization using evolutionary techniques, where the (trained and updated) forecast models serve as surrogate models to guide the optimization process to Pareto fronts (containing solution candidates) with high quality. A new influence analysis between process values and QCs is suggested based on the PLS-fuzzy forecast models in order to reduce the dimensionality of the optimization space and thus to guarantee high(er) quality of solutions within a reasonable amount of time (→ better usage in on-line mode). The methodologies have been comprehensively evaluated on real on-line process data from a (micro-fluidic) chip production system, where the early stage comprises the injection molding process and the latter stage the bonding process. The results show remarkable performance in terms of low prediction errors of the PLS-fuzzy forecast models (showing mostly lower errors than achieved by other model architectures) as well as in terms of Pareto fronts with individuals (solutions) whose fitness was close to the optimal values of three most important target QCs (being used for supervision): flatness, void events and RMSEs of the chips. Suggestions could thus be provided to experts/operators how to best change process values and associated machining parameters at the injection molding process in order to achieve significantly higher product quality for the final chips at the end of the bonding process

On-line anomaly detection with advanced independent component analysis of multi-variate residual signals from causal relation networks.

Anomaly detection in todays industrial environments is an ambitious challenge to detect possible faults/problems which may turn into severe waste during production, defects, or systems components damage, at an early stage. Data-driven anomaly detection in multi-sensor networks rely on models which are extracted from multi-sensor measurements and which characterize the anomaly-free reference situation. Therefore, significant deviations to these models indicate potential anomalies. In this paper, we propose a new approach which is based on causal relation networks (CRNs) that represent the inner causes and effects between sensor channels (or sensor nodes) in form of partial sub-relations, and evaluate its functionality and performance on two distinct production phases within a micro-fluidic chip manufacturing scenario. The partial relations are modeled by non-linear (fuzzy) regression models for characterizing the (local) degree of influences of the single causes on the effects. An advanced analysis of the multi-variate residual signals, obtained from the partial relations in the CRNs, is conducted. It employs independent component analysis (ICA) to characterize hidden structures in the fused residuals through independent components (latent variables) as obtained through the demixing matrix. A significant change in the energy content of latent variables, detected through automated control limits, indicates an anomaly. Suppression of possible noise content in residuals—to decrease the likelihood of false alarms—is achieved by performing the residual analysis solely on the dominant parts of the demixing matrix. Our approach could detect anomalies in the process which caused bad quality chips (with the occurrence of malfunctions) with negligible delay based on the process data recorded by multiple sensors in two production phases: injection molding and bonding, which are independently carried out with completely different process parameter settings and on different machines (hence, can be seen as two distinct use cases). Our approach furthermore i.) produced lower false alarm rates than several related and well-known state-of-the-art methods for (unsupervised) anomaly detection, and ii.) also caused much lower parametrization efforts (in fact, none at all). Both aspects are essential for the useability of an anomaly detection approach

Potential identification and industrial evaluation of an integrated design automation workflow.

Purpose - The paper aims to raise awareness in the industry of design automation tools, especially in early design phases, by demonstrating along a case study the seamless integration of a prototypically implemented optimization, supporting design space exploration in the early design phase and an in operational use product configurator, supporting the drafting and detailing of the solution predominantly in the later design phase. Design/methodology/approach - Based on the comparison of modeled as-is and to-be processes of ascent assembly designs with and without design automation tools, an automation roadmap is developed. Using qualitative and quantitative assessments, the potentials and benefits, as well as acceptance and usage aspects, are evaluated. Findings - Engineers tend to consider design automation for routine tasks. Yet, prototypical implementations support the communication and identification of the potential for the early stages of the design process to explore solution spaces. In this context, choosing from and interactively working with automatically generated alternative solutions emerged as a particular focus. Translators, enabling automatic downstream propagation of changes and thus ensuring consistency as to change management were also evaluated to be of major value. Research limitations/implications - A systematic validation of design automation in design practice is presented. For generalization, more case studies are needed. Further, the derivation of appropriate metrics needs to be investigated to normalize validation of design automation in future research. Practical implications - Integration of design automation in early design phases has great potential for reducing costs in the market launch. Prototypical implementations are an important ingredient for potential evaluation of actual usage and acceptance before implementing a live system. Originality/value - There is a lack of systematic validation of design automation tools supporting early design phases. In this context, this work contributes a systematically validated industrial case study. Early design-phases-support technology transfer is important because of high leverage potential