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Software size estimation is a vital activity of software project planning and management. Early software size estimation is a challenging task due to the limited information available during the early phases of software development. The goal of this paper is to construct and validate early software size estimation models based on four analysis-to-design adjustment factor (ADAF)-adjusted analysis class diagram metrics (i.e. ADAF-adjusted number of classes, ADAF-adjusted number of attributes, ADAF-adjusted number of methods and ADAF-adjusted number of relationships) using stepwise multiple linear regression and leave-one-out cross-validation. Furthermore, the prediction accuracy of the best-performing proposed model is also compared with the model based on objective class points. The results of this comparison reveal that our proposed method reduces errors significantly (i.e. on average, 16% reduction in mean absolute residual and 24% reduction in mean squared error). [ABSTRACT FROM AUTHOR]

Software development requires adequate methods for requirements engineering, design, development, testing and maintenance. The more complex the system is, the more sophisticated methods should be applied. A significant part of software projects is short on appropriate requirements engineering, communication, development and testing method, furthermore, verification and validation processes; i.e., not the right method is applied, and the project turns into ad hoc design and development decisions. This paper introduces our quality assured model-driven requirements engineering and software development method. The method is based on the modeling of the software requirements in a way that these models can be used to automatically generate several artifacts during the engineering process. This method is continuously developed during the last 12 years driven by our software projects. In the last two years, we have developed new tool support and also reworked the method. In this paper, as an experience report, we introduce the method: a framework to specify software requirements with four domain-specific languages and automated solutions to process the models. We focus on the requirements engineering method of the whole development cycle. We also report the lessons learned and share our best practices on the field of model-driven requirements engineering and software development. [ABSTRACT FROM AUTHOR]

In this paper, a novel domestic code called KIANA was developed for the assessment of radiological impacts on the population in normal and accident conditions including design basis accident (DBA) and beyond DBA (BDBA) for the nuclear power plants. The validation process of the KIANA code was performed using the results of the DOZA_M radiological code, whose results are presented in the Final Safety Analysis Report (FSAR) of the Bushehr Nuclear Power Plant Unit One (BNPP-1). The calculations of KIANA are performed based on the Gaussian diffusion model. The developed KIANA code has the potential of calculating the concentration and radionuclide doses due to the pathways such as airborne, foodstuff, marine (both one and two boxes models), soils, animals, vegetation (with and without tritium) and other pathways without any restriction. In the current research, the individual dose from a cloud to the member of the public in the region of BNPP-1 in normal condition was calculated. Moreover, the total effective dose to the member of the public from the primary to the secondary leakage inside steam generators, large break loss-of-coolant accident (LBLOCA) and small break loss-of-coolant accident was calculated. Thyroid gland equivalent dose for the infant (1–8 years) in the case of LBLOCA at the BNPP in DBA conditions was also evaluated. Finally, the prevented dose at the initial stage for the whole body of adults after BDBA, prevented dose at the initial stage for the thyroid gland of children after BDBA and the effective dose during the first year after the accident (external body irradiation from presence in the area) in the case of BDBA are assessed. The KIANA simulation results showed a good agreement with the FSAR data of BNPP. [ABSTRACT FROM AUTHOR]

This paper develops a new theory and model for information and sensor validation. The model represents relationships between variables using Bayesian networks and utilizes probabilistic propagation to estimate the expected values of variables. If the estimated value of a variable differs from the actual value, an apparent fault is detected. The fault is only apparent since it may be that the estimated value is itself based on faulty data. The theory extends our understanding of when it is possible to isolate real faults from potential faults and supports the development of an algorithm that is capable of isolating real faults without deferring the problem to the use of expert provided domain-specific rules. To enable practical adoption for real-time processes, an any time version of the algorithm is developed, that, unlike most other algorithms, is capable of returning improving assessments of the validity of the sensors as it accumulates more evidence with time. The developed model is tested by applying it to the validation of temperature sensors during the start-up phase of a gas turbine when conditions are not stable; a problem that is known to be challenging. The paper concludes with a discussion of the practical applicability and scalability of the model. [ABSTRACT FROM AUTHOR]

It is certain that cloud users are eager to ascertain where their sensitive data are stored. Misplacing the data probably divulges secret information. Therefore, researchers have designed mechanisms of geolocation of cloud data to allow cloud users to get geolocation of cloud data. However, to seek an approach to seeing through frauds is still a challenge. Trusted computing technology offers an alternative. The current study endeavors to construct the method through combining the technology with established geolocation techniques of Internet hosts thoroughly. Our work is complementary to Gondree and Peterson's recent contributions in which a constraint-based cloud data geolocation solution is present. The current scheme offers a strong validation of geolocation of cloud data, and can see through deceits. In the scheme, we leverage an un-clonable and tamperproof device, Trusted Platform Module, which guarantees that cloud users can collect authentic geolocation evidences. We evaluate the original idea using a wide range of experiments. Results demonstrate that the scheme is feasible, and has a satisfactory performance in the city-level accuracy. [ABSTRACT FROM AUTHOR]

Computer supported cooperative work (CSCW) environments enable users to interact with each other by using computers to conveniently share relevant data across the user interface. Awareness is an essential requirement in CSCW to convey precise information about the context in which the work in group is taking place, contributing thus to collaboration between users. In this kind of environments, we need to go beyond traditional human–computer interaction to embrace human–computer–human interaction (HCHI). It is necessary to devise flexible mechanisms to support HCHI in dealing with the diversity of contexts and group concerns. Furthermore, these mechanisms should endeavor to provide a seamless integration with current development techniques. This work presents a multi-purpose framework to include group awareness in HCHI systems in a seamless way. Proposed framework is based on the Dichotomic View of Plasticity approach. An experiment was conducted with two different versions of a specific groupware platform: the original platform and a new version of it, extended by means of the proposed framework. The goal was twofold: (i) to verify the benefits of applying this framework and (ii) to validate, in terms of user satisfaction, the improvement regarding groupware features introduced in the extended version. The results of the experiment backed up our hypothesis by showing that proposed framework is able to add awareness support to existing human–computer–Human (HCH) interfaces in a seamless way. It is also showed that added awareness components effectively contributed to achieve a higher level of collaboration among users. [ABSTRACT FROM AUTHOR]

As one of the few irreversible protein posttranslational modifications, proteolytic cleavage is involved in nearly all aspects of cellular activities, ranging from gene regulation to cell life-cycle regulation. Among the various protease-specific types of proteolytic cleavage, cleavages by casapses/granzyme B are considered as essential in the initiation and execution of programmed cell death and inflammation processes. Although a number of substrates for both types of proteolytic cleavage have been experimentally identified, the complete repertoire of caspases and granzyme B substrates remains to be fully characterized. To tackle this issue and complement experimental efforts for substrate identification, systematic bioinformatics studies of known cleavage sites provide important insights into caspase/granzyme B substrate specificity, and facilitate the discovery of novel substrates. In this article, we review and benchmark 12 state-of-the-art sequence-based bioinformatics approaches and tools for caspases/granzyme B cleavage prediction. We evaluate and compare these methods in terms of their input/output, algorithms used, prediction performance, validation methods and software availability and utility. In addition, we construct independent data sets consisting of caspases/granzyme B substrates from different species and accordingly assess the predictive power of these different predictors for the identification of cleavage sites. We find that the prediction results are highly variable among different predictors. Furthermore, we experimentally validate the predictions of a case study by performing caspase cleavage assay. We anticipate that this comprehensive review and survey analysis will provide an insightful resource for biologists and bioinformaticians who are interested in using and/or developing tools for caspase/granzyme B cleavage prediction. [ABSTRACT FROM AUTHOR]

MetaMap is a widely used named entity recognition tool that identifies concepts from the Unified Medical Language System Metathesaurus in text. This study presents MetaMap Lite, an implementation of some of the basic MetaMap functions in Java. On several collections of biomedical literature and clinical text, MetaMap Lite demonstrated real-time speed and precision, recall, and F1 scores comparable to or exceeding those of MetaMap and other popular biomedical text processing tools, clinical Text Analysis and Knowledge Extraction System (cTAKES) and DNorm. [ABSTRACT FROM AUTHOR]

Radiomics refers to the extraction and analysis of advanced quantitative imaging from medical images to diagnose and/or predict diseases. In the dentistry field, the bone data from mandibular condyles could be computationally analyzed using the voxel information provided by high-resolution CBCT scans to increase the diagnostic power of temporomandibular joint (TMJ) conditions. However, such quantitative information demands innovative computational software, algorithm implementation, and validation. Our study's aim was to compare a newly developed BoneTexture application to two-consolidated software with previous applications in the medical field, Ibex and BoneJ, to extract bone morphometric and textural features from mandibular condyles. We used an imaging database of HR-CBCT TMJs scans with an isotropic voxel size of 0.08 mm3. A single group with 66 distinct mandibular condyles composed the final sample. We calculated 18 variables for bone textural features and 5 for bone morphometric measurements using the Ibex, BoneJ and BoneTexture applications. Spearman correlation and Bland–Altman plot analyses were done to compare the agreement among software. The results showed a high Spearman correlation among the software applications (r = 0.7–1), with statistical significance for all variables, except Grey Level Non-Uniformity and Short Run Emphasis. The Bland–Altman vertical axis showed, in general, good agreement between the software applications and the horizontal axis showed a narrow average distribution for Correlation, Long Run Emphasis and Long Run High Grey Level Emphasis. Our data showed consistency among the three applications to analyze bone radiomics in high-resolution CBCT. Further studies are necessary to evaluate the applicability of those variables as new bone imaging biomarkers to diagnose bone diseases affecting TMJs. [ABSTRACT FROM AUTHOR]