Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning

Emergent application domains (e.g., Edge Computing/Cloud/B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large Variability Models (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems — the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Funding for open access charge: Universidad de Málaga / CBUA. This work is supported by the European Union’s H2020 re search and innovation programme under grant agreement DAEMON H2020-101017109, by the projects IRIS PID2021-12281 2OB-I00 (co-financed by FEDER funds), Rhea P18-FR-1081 (MCI/AEI/ FEDER, UE), and LEIA UMA18-FEDERIA-157, and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación, Spain

Detecting Feature Influences to Quality Attributes in Large and Partially Measured Spaces using Smart Sampling and Dynamic Learning

Publicación Journal First siendo el original: Munoz, D. J., Pinto, M., & Fuentes, L. (2023). Detecting feature influences to quality attributes in large and partially measured spaces using smart sampling and dynamic learning. Knowledge-Based Systems, 270, 110558.Emergent application domains (e.g., Edge Computing/Cloud /B5G systems) are complex to be built manually. They are characterised by high variability and are modelled by large \textit{Variability Models} (VMs), leading to large configuration spaces. Due to the high number of variants present in such systems, it is challenging to find the best-ranked product regarding particular Quality Attributes (QAs) in a short time. Moreover, measuring QAs sometimes is not trivial, requiring a lot of time and resources, as is the case of the energy footprint of software systems -- the focus of this paper. Hence, we need a mechanism to analyse how features and their interactions influence energy footprint, but without measuring all configurations. While practical, sampling and predictive techniques base their accuracy on uniform spaces or some initial domain knowledge, which are not always possible to achieve. Indeed, analysing the energy footprint of products in large configuration spaces raises specific requirements that we explore in this work. This paper presents SAVRUS (Smart Analyser of Variability Requirements in Unknown Spaces), an approach for sampling and dynamic statistical learning without relying on initial domain knowledge of large and partially QA-measured spaces. SAVRUS reports the degree to which features and pairwise interactions influence a particular QA, like energy efficiency. We validate and evaluate SAVRUS with a selection of likewise systems, which define large searching spaces containing scattered measurements.Trabajo financiado por el programa de I+D H2020 de la UE bajo el acuerdo DAEMON 101017109, por los proyectos también co-financiados por fondos FEDER \emph{IRIS} PID2021-122812OB-I00, y \emph{LEIA} UMA18-FEDERIA-157, y la ayuda PRE2019-087496 del Ministerio de Ciencia e Innovación. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Extended Variability Models, Algebra, and Arithmetic

Although classic variability models have been traditionally used to specify members of a product-line, their level of expressiveness was quite limited. Several extensions have been proposed, like numerical features, complex cardinalities and feature and configuration attributes. However, modern tools often provide limited support to these extensions. Imposing variability modelling restrictions into general theories enables off-the-self automated reasoners to analyse extended variability models. While one could argue that those general theories are less reasoning efficient, in practice happen the same if we extend traditional solvers. In contrast, general theories provide new properties with the potential to a) improve reasoning efficiency above extending traditional solvers, and b) provide exotic analyses that uncover new properties of the variability models and feature and configuration spaces. Examples of this could be the functions commutativity property, (reasoning) functors composition, and the fundamental theorem of calculus applied to feature or configuration space.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes

Automatic analysis of variability is an important stage of Software Product Line (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model. In this paper, we make use of the Quality Variability Model (QVM), based on Category Theory (CT), to redefine reasoning operations. We start defining and composing the six most commonoperations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept,we present, implement and execute the operations as lambda reasoning for CQL IDE – the state-of-theart CT tool.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER funds LEIA UMA18-FEDERJA-15, MEDEA RTI2018-099213-B-I00 and Rhea P18-FR-1081 and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación

Transforming numerical feature models into propositional formulas and the universal variability language

Real-world Software Product Lines (SPLs) need Numerical Feature Models (NFMs) whose features have not only boolean values that satisfy boolean constraints but also have numeric attributes that satisfy arithmetic constraints. An essential operation on NFMs finds near-optimal performing products, which requires counting the number of SPL products. Typical constraint satisfaction solvers perform poorly on counting and sampling. Nemo (Numbers, features, models) is a tool that supports NFMs by bit-blasting, the technique that encodes arithmetic expressions as boolean clauses. The newest version, Nemo2, translates NFMs to propositional formulas and the Universal Variability Language (UVL). By doing so, products can be counted efficiently by #SAT and Binary Decision Tree solvers, enabling finding near-optimal products. This article evaluates Nemo2 with a large set of synthetic and colossal real-world NFMs, including complex arithmetic constraints and counting and sampling experiments. We empirically demonstrate the viability of Nemo2 when counting and sampling large and complex SPLs.Munoz, Pinto and Fuentes work is supported by the European Union’s H2020 research and innovation programme under grant agreement DAEMON 101017109, by the projects co-financed by FEDER, Spain funds LEIA UMA18-FEDERJA-15, IRIS PID2021- 122812OB-I00 (MCI/AEI), and the PRE2019-087496 grant from the Ministerio de Ciencia e Innovación. Funding for open access charge: Universidad de Málaga / CBUA

Implant Treatment by Guided Surgery Supporting Overdentures in Edentulous Mandible Patients

Introduction: This study aimed to show the clinical outcomes of implants inserted by guided surgery supporting mandibular overdentures in edentulous patients. Patients and methods: Mandibular edentulous patients were diagnosed with an oral examination, cone-beam computerized tomography, and diagnostic casts for intermaxillary relations and treated with overdentures over two implants by guided surgery. After flapless surgery, implants were early loaded with an overdenture at 6 weeks. Results and discussion: Fourteen patients (nine females and five males) were treated with 28 implants. Four patients (28.6%) had a previous history of periodontitis. Five patients (35.7%) were smokers. Nine patients (64.3%) suffered from systemic diseases (i.e., diabetes, cardiovascular diseases). The clinical follow-up of the study was 44.7 ± 31.4 months. Clinical outcomes showed a global success of 100% of implants. Fourteen overdentures were placed in the patients over the implants. Mean marginal bone loss was 1.25 mm ± 0.95 mm. Four patients (28.6%) showed some kind of mechanical prosthodontic complications. Six implants (21.4%) were associated with peri-implantitis. Conclusions: This study indicates that treatment of mandibular edentulous patients with overdentures by guided surgery and early loading of implants placed appears to be a successful implant protocol

Long-term treatment outcomes of implant prostheses in partially and totally edentulous patients

Implant dental therapy is a clinical procedure used for treating patients with tooth loss with known clinical success. This clinical study aimed to evaluate the long-term clinical outcomes of dental implants in partially and totally edentulous patients. A total of 544 Microdent (Microdent SU, Implant Microdent System®, Santa Eulàlia de Ronçana Barcelona, Spain) screw implants were placed in 111 patients using a two-stage surgical technique and a conventional loading protocol (lasting 3 months). Implant and prosthetic clinical findings were evaluated during a 15-year follow-up. A total of 6 implants were lost during the healing period, and 124 prostheses were placed over the 538 implants that remained: 20 single crowns, 52 partially fixed bridges, 45 full-arch fixed restorations, and 7 overdentures. A total of 20 of these were lost during the follow-up period. The cumulative survival rate for all implants was 96.4%. The data underwent statistical analysis (significance level: p < 0.05). The mean marginal bone loss was 1.82 ± 0.54 mm, ranging from 1.2 to 3.1 mm. The most frequent complications were mechanical prosthodontic complications (16.2%). In all, 11.8% of implants showed periimplantitis as the primary biological complication. Dental implants inserted in both the maxillary and mandibular areas produce long-term favorable outcomes and stable tissue conditions when a delayed loading protocol is followed

Risk Factors for COVID-19 in Inflammatory Bowel Disease: A National, ENEIDA-Based Case–Control Study (COVID-19-EII)

(1) Scant information is available concerning the characteristics that may favour the acquisition of COVID-19 in patients with inflammatory bowel disease (IBD). Therefore, the aim of this study was to assess these differences between infected and noninfected patients with IBD. (2) This nationwide case-control study evaluated patients with inflammatory bowel disease with COVID-19 (cases) and without COVID-19 (controls) during the period March-July 2020 included in the ENEIDA of GETECCU. (3) A total of 496 cases and 964 controls from 73 Spanish centres were included. No differences were found in the basal characteristics between cases and controls. Cases had higher comorbidity Charlson scores (24% vs. 19%; p = 0.02) and occupational risk (28% vs. 10.5%; p < 0.0001) more frequently than did controls. Lockdown was the only protective measure against COVID-19 (50% vs. 70%; p < 0.0001). No differences were found in the use of systemic steroids, immunosuppressants or biologics between cases and controls. Cases were more often treated with 5-aminosalicylates (42% vs. 34%; p = 0.003). Having a moderate Charlson score (OR: 2.7; 95%CI: 1.3-5.9), occupational risk (OR: 2.9; 95%CI: 1.8-4.4) and the use of 5-aminosalicylates (OR: 1.7; 95%CI: 1.2-2.5) were factors for COVID-19. The strict lockdown was the only protective factor (OR: 0.1; 95%CI: 0.09-0.2). (4) Comorbidities and occupational exposure are the most relevant factors for COVID-19 in patients with IBD. The risk of COVID-19 seems not to be increased by immunosuppressants or biologics, with a potential effect of 5-aminosalicylates, which should be investigated further and interpreted with caution

Correction : Chaparro et al. Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain: Large-Scale Epidemiological Study. J. Clin. Med. 2021, 10, 2885

The authors wish to make the following corrections to this paper [...]

Incidence, Clinical Characteristics and Management of Inflammatory Bowel Disease in Spain : Large-Scale Epidemiological Study

(1) Aims: To assess the incidence of inflammatory bowel disease (IBD) in Spain, to describe the main epidemiological and clinical characteristics at diagnosis and the evolution of the disease, and to explore the use of drug treatments. (2) Methods: Prospective, population-based nationwide registry. Adult patients diagnosed with IBD-Crohn's disease (CD), ulcerative colitis (UC) or IBD unclassified (IBD-U)-during 2017 in Spain were included and were followed-up for 1 year. (3) Results: We identified 3611 incident cases of IBD diagnosed during 2017 in 108 hospitals covering over 22 million inhabitants. The overall incidence (cases/100,000 person-years) was 16 for IBD, 7.5 for CD, 8 for UC, and 0.5 for IBD-U; 53% of patients were male and median age was 43 years (interquartile range = 31-56 years). During a median 12-month follow-up, 34% of patients were treated with systemic steroids, 25% with immunomodulators, 15% with biologics and 5.6% underwent surgery. The percentage of patients under these treatments was significantly higher in CD than UC and IBD-U. Use of systemic steroids and biologics was significantly higher in hospitals with high resources. In total, 28% of patients were hospitalized (35% CD and 22% UC patients, p < 0.01). (4) Conclusion: The incidence of IBD in Spain is rather high and similar to that reported in Northern Europe. IBD patients require substantial therapeutic resources, which are greater in CD and in hospitals with high resources, and much higher than previously reported. One third of patients are hospitalized in the first year after diagnosis and a relevant proportion undergo surgery