Quality Of Rules Research Articles

Membrane-based conceptual design of reuse water production from candy factory wastewater.

Intense pressure on water resources has led to efforts to reuse reclaimed processing wastewater in the food industry. There are tight rules for water quality, but efficient separation technologies such as reverse osmosis possess good possibilities for water reuse. This study developed a membrane-based reuse water concept for wastewater from the candy industry emphasizing the pre-treatment stage in the concept to reduce fouling. The wastewater contained suspended solids, sugar compounds and the ingredients for candy gelation, which had a tendency to foul membranes, making pre-treatment essential for a successful concept. Cross-rotational ultrafiltration, which featured enhanced fouling prevention for membranes, functioned well for the removal of challenging substances. Conventional filtration technologies were impractical due to a low flux, even when the viscosity of the wastewater was reduced using surfactants. The wastewater had a high chemical oxygen demand, meaning that there was a strong fouling potential for reverse osmosis membranes, but also high osmotic pressure. A spiral wound reverse osmosis functioned well when the wastewater was pre-treated, and it produced good quality water with respect to all the other studied parameters except the chemical oxygen demand. However, chemical oxygen demand rejection was 99% since the concentration in the wastewater was originally very high.

Big data quality framework: a holistic approach to continuous quality management

Big Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is a very costly and time-consuming process, since excessive computing resources are required. Maintaining Quality through the Big Data lifecycle requires quality profiling and verification before its processing decision. A BDQ Management Framework for enhancing the pre-processing activities while strengthening data control is proposed. The proposed framework uses a new concept called Big Data Quality Profile. This concept captures quality outline, requirements, attributes, dimensions, scores, and rules. Using Big Data profiling and sampling components of the framework, a faster and efficient data quality estimation is initiated before and after an intermediate pre-processing phase. The exploratory profiling component of the framework plays an initial role in quality profiling; it uses a set of predefined quality metrics to evaluate important data quality dimensions. It generates quality rules by applying various pre-processing activities and their related functions. These rules mainly aim at the Data Quality Profile and result in quality scores for the selected quality attributes. The framework implementation and dataflow management across various quality management processes have been discussed, further some ongoing work on framework evaluation and deployment to support quality evaluation decisions conclude the paper.

OEN: Multi-center, international, real-world evidence studies performed using health records without data pooling—The use of a common data model and shared analytical methods.

e13554 Background: The value of real-world evidence derived from the care of patients managed outside the context of clinical trials is well recognised. However, the ability to link data from multiple centres, especially those from different countries, is complicated by complex legal and information governance differences. The Oncology Evidence Network is a collaboration of large hospital centres, with strong clinical informatics capabilities in six countries in Europe and Asia working with the support of an industrial partner to provide high quality, real world data reflecting routine clinical care. We have developed an efficient workflow based on a study-specific common data model (CDM) clinically validated at each site and analysed with a single analysis script, which embeds a set of data quality rules. Local implementation allows each centre to generate analytical outputs aligned across the different sites without the need for any patient level data to leave the participating site. This approach has been designed and tested in Epithelial Ovarian Cancer (EOC) patients. Methods: A CDM was agreed using expert advisors from each centre. Clinical alignment was achieved through iterative assessment of clinical vignettes, to ensure common definitions of clinical assessment, prognosis, and treatment algorithms in EOC patients. A data guide detailing variable level derivations and validation rules, general data coding principles, and conversions/codes from international coding systems was developed. The analysis scripts were implemented as a bespoke package (OpenOvary) in R. The package includes functions to validate the data against the CDM, and generate a standard output including tables, numerical summaries and Kaplan-Meier analysis of progression and overall survival. Results: 2,925 patient records from 6 centres across 6 countries were included in the study with 27 key data items curated by each centre. Treatment data is available detailing relevant surgical procedures and their outcomes, and regimens of SACT throughout patients’ care from diagnosis to death. Data completeness was generally high for key data items, with missing data ranging from 0-16% for FIGO stage at diagnosis and 0-14% for tumour morphology. The CDM and R script will be made publicly available for other centres to adopt and facilitate analysis of their local data. Conclusions: This collaboration has brought together a substantial body of data describing the care and outcomes for EOC patients. A CDM and flexible shared analysis approach enabled unified analysis and reporting whilst avoiding the transfer of patient level data and its pooling into a common database. The process of clinical and data alignment has generated a replicable model for rapid extension to other study centres to join the EOC study, or application to other disease areas.

Software Source Code: Theoretical Analyzing and Practical Reviewing Model

The high-quality software product is one of the most important goals of software engineering in general, source coding or implementation phase is one of the main steps of the software development process; when this phase is going in a good way and according to the source code quality standards and rules, the final product will be a high-quality product. This research focuses on the issues that can affect source code quality, such as errors, faults, defects, and failures; a practical system model will be provided (proposed) to predict some possible types of errors and defects and suggests some guidance and recommendations on source code error detection and analysis.

Application of openEHR archetypes to automate data quality rules for electronic health records: a case study

BackgroundEnsuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data.MethodsThe clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn’s DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs.ResultsWe used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness.ConclusionIt’s feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.

AN INTERACTIVE TOOL FOR EXTRACTING LOW-QUALITY SPREADSHEET TABLES AND CONVERTING INTO RELATIONAL DATABASE

Spreadsheets are contained critical information on various topics and most broadly utilized in numerous spaces. There are a huge amount of spreadsheets clients around the world. As a result of their convenience, support for announcing and portrayal as diagrams and graphs and gives their makers an enormous level of opportunity in encoding their data as it simple to utilize. Tables produce a large amount of spreadsheet data. The expansion in volume and complexity of tables has prompted expanded necessities to preserve this data and reuse it. However, spreadsheets are hard to arrange with other data sources. As a result, it makes data stored in spreadsheets with low-quality. We exhibited an automated extractor tool that gives the standard client a chance to concentrate on extracted relational tables from spreadsheets without experience in any programming language besides high-quality data extraction. The paper executed novel algorithms based on a heuristic approach for table extraction from a spreadsheet and implemented data improvement and quality rules using domain ontology for changing over between low-quality semi-structured data to high-quality relational data for reusability and integration as a Java program interfacing with SQL server database. The paper does experiments on 2 real public datasets. The percentage of improving the performance using the proposed approach on the 2 datasets are 100 % for extracting duplicated records and the percentage of successfully table identified are 100% and 85% respectively.

Modeling dynamic scenarios for safety, reliability, availability, and maintainability analysis

Safety analysis uses probability combinatorial models like fault tree and/or event tree. Such methods have static basic events and do not consider complex scenarios of dynamic reliability, leading to conservative results. Reliability, availability, and maintainability (RAM) analysis using reliability block diagram (RBD) experience the same limitations. Continuous Markov chains model dynamic reliability scenarios but suffer from other limitations like states explosion and restriction of exponential life distribution only. Markov Regenerative Stochastic Petri Nets oblige complex mathematical formalism and still subject to state explosions for large systems. In the design of complex systems, distinct teams make safety and RAM analyses, each one adopting tools better fitting their own needs. Teams using different tools turns obscure the detection of problems and their correction is even harder. This work aims to improve design quality, reduce design conservatism, and ensure consistency by proposing a single and powerful tool to perform any probabilistic analysis. The suggested tool is the Stochastic Colored class of Petri Nets, which supplies hierarchical organization, a set of options for life distributions, dynamic reliability scenarios and simple and easy construction for large systems. This work also proposes more quality rules to assure model consistency. Such method for probabilistic analysis may have the effect of shifting systems design from “redundancy, segregation and independency” approach to “maintainability, maintenance and contingency procedures” approach. By modeling complex human and automated interventional scenarios, this method reduces capital costs and keeps safety and availability of systems.

Enhancing the preservation of liposomes: The role of cryoprotectants, lipid formulations and freezing approaches

In the last decades, liposomes acquired a striking success in the biomedical field thanks to their biocompatibility and drug delivery ability. Many liposomal drug formulations have been already approved by the Food and Drug Administration (FDA) and used for the treatment of a wide range of pathologies with or without further engineering. Their clinical application requires strict compliance with high standard quality rules, and it is crucial to employ storage methods that do not affect the integrity of the vesicles and preventing the leakage of their cargo.In this work, the design of a suitable formulation for freeze-drying had been investigated for two different liposomes, DOPC-DOTAP and the PEGylated counterpart, DOPC-DOTAP-DSPE-PEG. The role of various cryoprotectants was evaluated paying attention to their ability to preserve the structural integrity of liposomes. At first, the study was focused on freezing and two methodologies were investigated, quenching in liquid nitrogen and shelf-ramped freezing. This analysis showed that the disaccharides (cellobiose, glucose, lactose, sucrose, and trehalose) and the polyol (mannitol) protected successfully the integrity of liposomes, while during the process, in the presence of a surfactant, liposomes were strongly damaged and fragmented by the ice crystals. Furthermore, the choice of the rate of freezing depended on the different compositions of the lipid bilayer. Finally, the effects of lyophilization on liposomes with and without additives were studied; cellobiose, lactose and trehalose showed encouraging results for the maintenance of the morpho-functional parameters of liposomes during the entire freeze-drying process.

Dual dimensional fuzzy testing based on upper confidence limits for supplier selection

Many corporations purchase components from suppliers, which can reduce operating costs and enable firms to focus their resources on core advantages. Studies have indicated that process quality and manufacturing time performance are two crucial indicators for supplier selection. We used the process quality index and a manufacturing time performance index to create a dual dimensional fuzzy supplier selection model. First, the upper confidence limits of these two indices were derived, and a fuzzy membership function based on these limits was constructed. Based on the fuzzy test rules for process quality and manufacturing time performance, we divided the fuzzy supplier selection matrix into nine evaluation zones. Using the upper confidence limits of these two indices, we created evaluation coordinates and assigned weights based on the location of the coordinates. Then, the total of all the weights was employed to form a supplier selection index for which a higher value means a higher ranking. The use of confidence limits decreased the chance of misjudgment resulting from sampling errors while the fuzzy test rules increased the applicability of the model. Consequently, the proposed model can be used to select suppliers efficiently so as to form partnerships in which corporations and suppliers can grow together.

Assessment of drinking water quality within amalgamated territorial communities

One of the main problems of rural residential areas is to provide the local population with quality drinking water, which is a major factor influencing their health. To solve this problem, it is necessary to implement effective measures not only at the state level but also at the regional and local levels. The main purpose of decentralization of power is to transfer the power and appropriate financial resources to the lowest levels of local self-government, which makes it possible for created amalgamated communities to use these resources to solve the priority issues. However, to determine the ecological state of rural settlements in the subsystem of drinking water quality, it is necessary to assess its quality in the territory where the community is situated. The investigation was carried out in 15 rural settlements of the Teterivka amalgamated community of the amalgamated Zhytomyr district. 36 water samples were selected from the sources of non-centralized water supply. Water quality classes were determined following DSTU (National Standards of Ukraine) 4808:2007 “Sources of Centralized Drinking Water Supply. Hygienic and Environmental Requirements for Water Quality and Selection Rules”. As a result, it has been established that nitrates make the largest contribution to the pollution of drinking water. In 47% of the selected samples, the nitrate content on average exceeded the norm; and in 46.7% of the investigated settlements, water belongs to quality class 4, defined as “mediocre”, “partially potable” of undesirable quality. A critical situation was recorded in the villages of Mykhailivka, Nova Rudnia and Staroshiika, where the nitrate content in all selected samples of the well water exceeded the norm. On average, 61% of the selected water samples did not meet the standard and were below the standard. According to the pH indicator, the quality classes vary from “excellent”, very pure water grading to the class of “good”, pure water of the desired quality to “satisfactory”, slightly contaminated water of acceptable quality. The total iron content exceeded the standard in 5.6% of the selected samples.

DaQL 2.0: Measure Data Quality based on Entity Models

Abstract In order to make good decisions, the data used for decision-making needs to be of high quality. As the volume of data continually increases, ensuring high data quality is a big challenge nowadays and needs to be automated with tools. The goal of the Data Quality Library (DaQL) is to provide a tool to continuously ensure and measure data quality as proposed in [5]. In this paper, we present the current status of the development of the new DaQL version 2.0. The main contribution of DaQL 2.0 is the possibility to define data quality rules for complex data objects (called entities), which represent business objects. In contrast to existing tools, a user does not require detailed knowledge about the database schema that is observed.

Simple Modification for an Apriori Algorithm With Combination Reduction and Iteration Limitation Technique

Apriori algorithm is one of the methods with regard to association rules in data mining. This algorithm uses knowledge from an itemset previously formed with frequent occurrence frequencies to form the next itemset. An a priori algorithm generates a combination by iteration methods that are using repeated database scanning process, pairing one product with another product and then recording the number of occurrences of the combination with the minimum limit of support and confidence values. The a priori algorithm will slow down to an expanding database in the process of finding frequent itemset to form association rules. Modification techniques are needed to optimize the performance of a priori algorithms so as to get frequent itemset and to form association rules in a short time. Modifications in this study are obtained by using techniques combination reduction and iteration limitation. Testing is done by comparing the time and quality of the rules formed from the database scanning using a priori algorithms with and without modification. The results of the test show that the modified a priori algorithm tested with data samples of up to 500 transactions is proven to form rules faster with quality rules that are maintained.Keywords: Data Mining; Association Rules; Apriori Algorithms; Frequent Itemset; Apriori Modified;

Data Dependencies Extended for Variety and Veracity: A Family Tree

Besides the conventional schema-oriented tasks, data dependencies are recently revisited for data quality applications, such as violation detection, data repairing and record matching. To address the variety and veracity issues of big data, data dependencies have been extended as data quality rules to adapt to various data types, ranging from (1) categorical data with equality relationships to (2) heterogeneous data with similarity relationships, and (3) numerical data with order relationships. In this survey, we briefly review the recent proposals on data dependencies categorized into the aforesaid types of data. In addition to (a) the concepts of these data dependency notations, we investigate (b) the extension relationships between data dependencies, e.g., conditional functional dependencies (CFDs) extend the conventional functional dependencies (FDs). It forms a family tree of extensions, mostly rooted in FDs, helping us understand the expressive power of various data dependencies. Moreover, we summarize (c) the discovery of dependencies from data, since data dependencies are often unlikely to be manually specified in a traditional way, given the huge volume and high variety of big data. We further outline (d) the applications of the extended data dependencies, in particular in data quality practice. It guides users to select proper data dependencies with sufficient expressive power and reasonable discovery cost. Finally, we conclude with several directions of future studies on the emerging data.

Contracts to Govern the Transition towards Sustainable Production: Evidence from a Discrete Choice Analysis in the Durum Wheat Sector in Italy

The increasing request for food sustainability is affecting the pasta sector in Italy. This phenomenon introduces different sources of uncertainties that, in turn, put pressure on all the stages of the supply chain, with a consequent emerging need for a higher level of coordination. Based on the Transaction Costs Theory approach, the paper is aimed at verifying whether contract design—revolving around the negotiation of contractual attributes with different functions in terms of safeguard, adaptability, and coordination—plays a crucial role in aligning sources of uncertainty surrounding transactions with the allocation of property and decision rights. To this aim, a sample of durum wheat producers is interviewed for expressing their preferences about some contractual features, such as price, production and quality rules, sustainable environmental techniques, and advisory services. Using a discrete choice analysis through a multinomial logit model, results reveal that, thanks to the presence of attributes able to ensure coordination and adaptability, contracts are able to steer towards elements of sustainability related to food quality and safety, whereas further efforts are needed to share environmental goals with farmers.

Potable Water Hygiene from the Stagnation of Water Point of View

Potable and hygienically suitable water supply is important for both residential and commercial buildings. The contribution focuses on potable water hygiene and quality from the water stagnation point of view. Stagnation of water is very dangerous phenomena because it causes the change in water quality. Proper routing and arrangement of cold and hot water pipes can significantly reduce the risk of water stagnation. In the paper, the classical trunk-and-branch installation system with the hygienically more suitable alternatives are compared. The paper also provides the definition of potable water, basic factors affecting its quality and basic rules for its control.

Explicit aspects extraction in sentiment analysis using optimal rules combination

Aspect extraction represents a core task of aspect-based sentiment analysis. This study presents a supervised aspect extraction algorithm for explicit aspect extraction from formal and informal texts. To accomplish the new algorithm, 126 aspect extraction rules are combined to cover both formal and informal texts, because customer reviews are a mix of formal and informal texts. These 126 rules include certain dependency-based rules and pattern-based rules from previous studies, in addition to newly developed rules intended to overcome prior rules’ weaknesses. In addition, many aspect extraction rules have remained unexplored by previous studies. However, many of these 126 rules are irrelevant and should be removed. Thus, a prober selection of the included rules is required. Therefore, in this study we also improved the Whale Optimization Algorithm (WOA) to address rules selection problem with an improved algorithm called improved WOA (IWOA). Two major improvements were included into IWOA. The first improvement is the development of a new update equation based on Cauchy mutation to improve WOA population diversity. The second improvement is the development of a new local search algorithm (LSA) to solve WOA local optima. The IWOA algorithm is applied on the full set of rules to select best rules subset and remove low quality rules. Finally, a new pruning algorithm (PA) has been developed to remove incorrect aspects and retain correct aspects. The Results on seven benchmark datasets demonstrate that IWOA+PA outperforms all other state-of-the-art baseline works and most recent works.

A Rule-Based Data Quality Assessment System for Electronic Health Record Data.

Rule-based data quality assessment in health care facilities was explored through compilation, implementation, and evaluation of 63,397 data quality rules in a single-center case study to assess the ability of rules-based data quality assessment to identify data errors of importance to physicians and system owners. We applied a design science framework to design, demonstrate, test, and evaluate a scalable framework with which data quality rules can be managed and used in health care facilities for data quality assessment and monitoring. We identified 63,397 rules partitioned into 28 logic templates. A total of 819,683 discrepancies were identified by 4.5% of the rules. Nine out of 11 participating clinical and operational leaders indicated that the rules identified data quality problems and articulated next steps that they wanted to take based on the reported information. The combined rule template and knowledge table approach makes governance and maintenance of otherwise large rule sets manageable. Identified challenges to rule-based data quality monitoring included the lack of curated and maintained knowledge sources relevant to data error detection and lack of organizational resources to support clinical and operational leaders with investigation and characterization of data errors and pursuit of corrective and preventative actions. Limitations of our study included implementation within a single center and dependence of the results on the implemented rule set. This study demonstrates a scalable framework (up to 63,397 rules) with which data quality rules can be implemented and managed in health care facilities to identify data errors. The data quality problems identified at the implementation site were important enough to prompt action requests from clinical and operational leaders.

Price-quality trade-off in procurement auctions with an uncertain quality threshold

Procuring high-quality goods at reasonable prices is a critical public sector concern. Yet, procurers often do not advertise clear weighting rules for price and quality but set a flexible quality threshold upon participation. This paper studies endogenous entry and bidding behavior in such procurement auctions. Using unique data on pharmaceutical procurement in Guangdong, China, we find that a high-quality index acts as a signal of high production costs. We also observe that high-quality bidders are more likely to withdraw from bidding because of their prior commitment to quality. The procurers who use quality indicators in price auctions need a carefully considered quality threshold. Our results have important implications for price-quality trade-offs in procurements that have uncertain quality thresholds.

Changing Nature of Organic Carbon over the United States.

Total organic carbon (TOC) mass concentrations are decreasing across the contiguous United States (CONUS). We investigate decadal trends in organic carbon (OC) thermal fractions [OC1 (volatilizes at 140 °C), OC2 (280 °C), OC3 (480 °C), OC4 (580 °C)] and pyrolyzed carbon (PC), reported at 121 locations in the Interagency Monitoring of Protected Visual Environments (IMPROVE) network from 2005 to 2015 for 23 regions across the CONUS. Reductions in PC and OC2 drive decreases in TOC (TOC = OC1 + OC2 + OC3 + OC4 + PC) mass concentrations. OC2 decreases by 40% from 2005 to 2015, and PC decreases by 34%. The largest absolute mass decreases occur in the eastern United States, and relative changes normalized to local concentrations are more uniform across the CONUS. OC is converted to organic mass (OM) using region- and season-specific OM:OC ratios. Simulations with GEOS-Chem reproduce OM trends and suggest that decreases across the CONUS are due to aerosol liquid water (ALW) chemistry. Individual model species, notably aerosol derived from isoprene oxidation products and formed in ALW, correlate significantly (p < 0.05) with OM2, even in arid regions. These findings contribute to literature that suggests air quality rules aimed at SO2 and NOx emissions induce the cobenefit of reducing organic particle mass through ALW chemistry, and these benefits extend beyond the eastern United States.

Application of machine learning techniques to the flexible assessment and improvement of requirements quality

It is already common to compute quantitative metrics of requirements to assess their quality. However, the risk is to build assessment methods and tools that are both arbitrary and rigid in the parameterization and combination of metrics. Specifically, we show that a linear combination of metrics is insufficient to adequately compute a global measure of quality. In this work, we propose to develop a flexible method to assess and improve the quality of requirements that can be adapted to different contexts, projects, organizations, and quality standards, with a high degree of automation. The domain experts contribute with an initial set of requirements that they have classified according to their quality, and we extract their quality metrics. We then use machine learning techniques to emulate the implicit expert’s quality function. We provide also a procedure to suggest improvements in bad requirements. We compare the obtained rule-based classifiers with different machine learning algorithms, obtaining measurements of effectiveness around 85%. We show as well the appearance of the generated rules and how to interpret them. The method is tailorable to different contexts, different styles to write requirements, and different demands in quality. The whole process of inferring and applying the quality rules adapted to each organization is highly automated.