Size-reduction heuristics for the unrelated parallel machines scheduling problem

In this paper we study the unrelated parallel machines problem where n independent jobs must be assigned to one out of m parallel machines and the processing time of each job differs from machine to machine. We deal with the objective of the minimisation of the maximum completion time of the jobs, usually referred to as makespan or C_max. This is a type of assignment problem that has been frequently studied in the scientific literature due to its many potential applications. We propose a set of metaheuristics based on a size-reduction of the original assignment problem that produce solutions of very good quality in a short amount of time. The underlying idea is to consider only a few of the best possible machine assignments for the jobs and not all of them. The results are simple, yet powerful methods. We test the proposed algorithms with a large benchmark of instances and compare them with current state-of-the-art methods. In most cases, the proposed size-reduction algorithms produce results that are statistically proven to be better by a significant margin.     

Do bugs foreshadow vulnerabilities? An in-depth study of the chromium project

As developers face an ever-increasing pressure to engineer secure software, researchers are building an understanding of security-sensitive bugs (i.e. vulnerabilities). Research into mining software repositories has greatly increased our understanding of software quality via empirical study of bugs. Conceptually, however, vulnerabilities differ from bugs: they represent an abuse of functionality as opposed to insufficient functionality commonly associated with traditional, non-security bugs. We performed an in-depth analysis of the Chromium project to empirically examine the relationship between bugs and vulnerabilities. We mined 374,686 bugs and 703 post-release vulnerabilities over five Chromium releases that span six years of development. We used logistic regression analysis, ranking analysis, bug type classifications, developer experience, and vulnerability severity metrics to examine the overarching question: are bugs and vulnerabilities in the same files? While we found statistically significant correlations between pre-release bugs and post-release vulnerabilities, we found the association to be weak. Number of features, source lines of code, and pre-release security bugs are, in general, more closely associated with post-release vulnerabilities than any of our non-security bug categories. In further analysis, we examined sub-types of bugs, such as stability-related bugs, and the associations did not improve. Even the files with the most severe vulnerabilities (by measure of CVSS or bounty payouts) did not show strong correlations with number of bugs. These results indicate that bugs and vulnerabilities are empirically dissimilar groups, motivating the need for security engineering research to target vulnerabilities specifically.     

A case study about the improvement of business process models driven by indicators

Organizations are increasingly concerned about business process model improvement in their efforts to guarantee improved operational efficiency. Quality assurance of business process models should be addressed in the most objective manner, e.g., through the application of measures, but the assessment of measurement results is not a straightforward task and it requires the identification of relevant indicators and threshold values, which are able to distinguish different levels of process model quality. Furthermore, indicators must support the improvements of the models by using suitable guidelines. In this paper, we present a case study to evaluate the BPMIMA framework for BP model improvement. This framework is composed of empirically validated measures related to quality characteristics of the models, a set of indicators with validated thresholds associated with modeling guidelines and a prototype supporting tool. The obtained data suggest that the redesign by applying guidelines driven by the indicator results was successful, as the understandability and modifiability of the models were improved. In addition, the changes in the models according to guidelines were perceived as acceptable by the practitioners who participated in the case study.     

Influence of photoperiod on carbon dioxide and methane emissions from two pilot-scale stabilization ponds

Greenhouse gas (GHG) emissions (CO(2), CH(4)) from pilot-scale algal and duckweed-based ponds (ABP and DBP) were measured using the static chamber methodology. Daylight and nocturnal variations of GHG and wastewater characteristics (e.g. chemical oxygen demand (COD), pH) were determined via sampling campaigns during midday (12:30-15:30) and midnight (00:30-03:30) periods. The results showed that under daylight conditions in ABP median emissions were -232 mg CO(2) m(-2) d(-1) and 9.9 mg CH(4) m(-2) d(-1), and in DBP median emissions were -1,654.5 mg CO(2) m(-2) d(-1) and 71.4 mg CH(4) m(-2) d(-1), respectively. During nocturnal conditions ABP median emissions were 3,949.9 mg CO(2) m(-2) d(-1), 12.7 mg CH(4) m(-2) d(-1), and DBP median emissions were 5,116 mg CO(2) m(-2) d(-1), 195.2 mg CH(4) m(-2) d(-1), respectively. Once data measured during daylight were averaged together with nocturnal data the median emissions for ABP were 1,566.8 mg CO(2) m(-2) d(-1) and 72.1 mg CH(4) m(-2) d(-1), whilst for DBP they were 3,016.9 mg CO(2) m(-2) d(-) and 178.9 mg CH(4) m(-2) d(-1), respectively. These figures suggest that there were significant differences between CO(2) emissions measured during daylight and nocturnal periods (p < 0.05). This shows a sink-like behaviour for both ABP and DBP in the presence of solar light, which indicates the influence of photosynthesis in CO(2) emissions. On the other hand, the fluxes of CH(4) indicated that DBP and ABP behave as net sources of CH(4) during day and night, although higher emissions were observed from DBP. Overall, according to the compound average (daylight and nocturnal emissions) both ABP and DBP systems might be considered as net sources of GHG.     

A VNS heuristic for escaping local extrema entrapment in normalized cut clustering

Normalized cut is one of the most popular graph clustering criteria. The main approaches proposed for its resolution are spectral clustering methods and a multilevel approach of Dhillon et al. (TPAMI 29:1944–1957, 2007), called graclus. Their aim is to obtain good solutions in a small amount of time for large instances. Metaheuristics are general frameworks for stochastic searches often employed in global optimization to improve the solutions obtained by other heuristics. Variable neighborhood search (VNS) is a metaheuristic which exploits systematically the idea of neighborhood change during the search. In this paper, we propose a VNS heuristic for normalized cut segmentation. Computational experiments show that in most cases this VNS heuristic improves significantly, and in moderate time, the solutions obtained by the current state-of-the-art algorithms, i.e., graclus and a spectral method proposed by Yu and Shi (ICCV, 2003).     

Modelling and characterization of AlN-actuated microcantilevers vibrating in the first in-plane mode

The characterization of the first in-plane mode of aluminum nitride-actuated piezoelectric microcantilevers was carried out by using electrical and optical techniques. The top electrode of the cantilever was specifically designed to allow for an efficient electrical actuation of these in-plane modes. In order to confirm the in-plane nature of the modal vibration, the detection of the electrically induced movement was performed optically with the help of a stroboscopic microscope. In parallel, resonances were also measured electrically by means of an impedance analyzer. The quality factor and the resonant frequencies of the in-plane modes were estimated from the corresponding measurement data when applying both detection techniques. Our results show quality factor values as high as 3,000 for the first in-plane mode in air.     

Does who you marry matter for your health? Influence of patients' and spouses' personality on their partners' psychological well-being following coronary artery bypass surgery.

Research suggests that presurgical personality attributes influence postsurgical well-being in both patients and their spouses in the context of coronary artery bypass grafting (CABG) surgery. The authors hypothesized that a spouse's characteristics would influence a partner's psychological well-being, regardless of whether he or she was the patient or the caregiver. In this study, 111 male patients and their caregiver spouses completed measures of neuroticism, optimism, perceived marital satisfaction, and depression prior to elective CABG. Follow-up was conducted at 18 months. As expected, higher caregiver presurgical neuroticism predicted higher patient depressive symptoms at follow-up, with caregiver's concurrent 18-month affect controlled for. Likewise, higher patient presurgical neuroticism predicted higher caregiver depressive symptoms at follow-up. Additionally, higher patient presurgical depressive symptoms and lower presurgical optimism contributed to greater caregiving burden. Relationship satisfaction moderated these effects. These results suggest that partners' personality traits are important determinants of both patients' and their caregiving spouses' well-being. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Establishing In-House Cutoffs of CSF Alzheimer’s Disease Biomarkers for the AT(N) Stratification of the Alzheimer Center Barcelona Cohort

Background: Clinical diagnosis of Alzheimer’s disease (AD) increasingly incorporates CSF biomarkers. However, due to the intrinsic variability of the immunodetection techniques used to measure these biomarkers, establishing in-house cutoffs defining the positivity/negativity of CSF biomarkers is recommended. However, the cutoffs currently published are usually reported by using cross-sectional datasets, not providing evidence about its intrinsic prognostic value when applied to real-world memory clinic cases. Methods: We quantified CSF Aβ1-42, Aβ1-40, t-Tau, and p181Tau with standard INNOTEST^® ELISA and Lumipulse G^® chemiluminescence enzyme immunoassay (CLEIA) performed on the automated Lumipulse G600II. Determination of cutoffs included patients clinically diagnosed with probable Alzheimer’s disease (AD, n = 37) and subjective cognitive decline subjects (SCD, n = 45), cognitively stable for 3 years and with no evidence of brain amyloidosis in 18F-Florbetaben-labeled positron emission tomography (FBB-PET). To compare both methods, a subset of samples for Aβ1-42 (n = 519), t-Tau (n = 399), p181Tau (n = 77), and Aβ1-40 (n = 44) was analyzed. Kappa agreement of single biomarkers and Aβ1-42/Aβ1-40 was evaluated in an independent group of mild cognitive impairment (MCI) and dementia patients (n = 68). Next, established cutoffs were applied to a large real-world cohort of MCI subjects with follow-up data available (n = 647). Results: Cutoff values of Aβ1-42 and t-Tau were higher for CLEIA than for ELISA and similar for p181Tau. Spearman coefficients ranged between 0.81 for Aβ1-40 and 0.96 for p181TAU. Passing–Bablok analysis showed a systematic and proportional difference for all biomarkers but only systematic for Aβ1-40. Bland–Altman analysis showed an average difference between methods in favor of CLEIA. Kappa agreement for single biomarkers was good but lower for the Aβ1-42/Aβ1-40 ratio. Using the calculated cutoffs, we were able to stratify MCI subjects into four AT(N) categories. Kaplan–Meier analyses of AT(N) categories demonstrated gradual and differential dementia conversion rates (p = 9.815⁻²⁷). Multivariate Cox proportional hazard models corroborated these findings, demonstrating that the proposed AT(N) classifier has prognostic value. AT(N) categories are only modestly influenced by other known factors associated with disease progression. Conclusions: We established CLEIA and ELISA internal cutoffs to discriminate AD patients from amyloid-negative SCD individuals. The results obtained by both methods are not interchangeable but show good agreement. CLEIA is a good and faster alternative to manual ELISA for providing AT(N) classification of our patients. AT(N) categories have an impact on disease progression. AT(N) classifiers increase the certainty of the MCI prognosis, which can be instrumental in managing real-world MCI subjects.     

Arabinoxylan and Pectin Metabolism in Crohn’s Disease Microbiota: An In Silico Study

Inflammatory bowel disease is a chronic disorder including ulcerative colitis and Crohn’s disease (CD). Gut dysbiosis is often associated with CD, and metagenomics allows a better understanding of the microbial communities involved. The objective of this study was to reconstruct in silico carbohydrate metabolic capabilities from metagenome-assembled genomes (MAGs) obtained from healthy and CD individuals. This computational method was developed as a mean to aid rationally designed prebiotic interventions to rebalance CD dysbiosis, with a focus on metabolism of emergent prebiotics derived from arabinoxylan and pectin. Up to 1196 and 1577 MAGs were recovered from CD and healthy people, respectively. MAGs of Akkermansia muciniphila, Barnesiella viscericola DSM 18177 and Paraprevotella xylaniphila YIT 11841 showed a wide range of unique and specific enzymes acting on arabinoxylan and pectin. These glycosidases were also found in MAGs recovered from CD patients. Interestingly, these arabinoxylan and pectin degraders are predicted to exhibit metabolic interactions with other gut microbes reduced in CD. Thus, administration of arabinoxylan and pectin may ameliorate dysbiosis in CD by promoting species with key metabolic functions, capable of cross-feeding other beneficial species. These computational methods may be of special interest for the rational design of prebiotic ingredients targeting at CD.