Bed morphology changes at river contractions

Contraction scour is a significant problem of engineering concern which might occur when the stream encounters a reduction in flow area because of either natural constraints or hydraulic works. This paper is a part of ongoing investigations by the authors on the spatial and temporal variations in bed morphology at both short and long river contractions based on experimental investigations at laboratory scale. Experiments were carried out at University of Basilicata, Italy, in a 1 m wide and 20 m long rectangular channel. The working section was up to 16 m, depending on the length of the contraction model. Two nearly-uniform sediments were used as mobile bed, sand with median grain size d501.7 mm and gravel with d509.0 mm. The contraction length was either 0.5,1.0, 2.0 or 3.0 m. Runs were carried out under steady flow and clear-water approach flow conditions. Typically they were of long duration (up to 15 days) to achieve conditions of dynamic equilibrium. New results on the effects of contraction ratio, densimetric Froude number, and relative contraction length are given. Moreover, insights on the spatial and temporal changes in bed morphology are provided with emphasis on the thalweg profile at contracted sections and bed degradation processes downstream of the contracted area

Do sophisticated evolutionary algorithms perform better than simple ones?

Evolutionary algorithms (EAs) come in all shapes and sizes. Theoretical investigations focus on simple, bare-bones EAs while applications often use more sophisticated EAs that perform well on the problem at hand. What is often unclear is whether a large degree of algorithm sophistication is necessary, and if so, how much performance is gained by adding complexity to an EA. We address this question by comparing the performance of a wide range of theory-driven EAs, from bare-bones algorithms like the (1+1) EA, a (2+1) GA and simple population-based algorithms to more sophisticated ones like the (1+(λ,λ)) GA and algorithms using fast (heavy-tailed) mutation operators, against sophisticated and highly effective EAs from specific applications. This includes a famous and highly cited Genetic Algorithm for the Multidimensional Knapsack Problem and the Parameterless Population Pyramid for Ising Spin Glasses and MaxSat. While for the Multidimensional Knapsack Problem the sophisticated algorithm performs best, surprisingly, for large Ising and MaxSat instances the simplest algorithm performs best. We also derive conclusions about the usefulness of populations, crossover and fast mutation operators. Empirical results are supported by statistical tests and contrasted against theoretical work in an attempt to link theoretical and empirical results on EAs

Mining Version Histories for Detecting Code Smells

Code smells are symptoms of poor design and implementation choices that may hinder code comprehension, and possibly increase change- and fault-proneness. While most of the detection techniques just rely on structural information, many code smells are intrinsically characterized by how code elements change over time. In this paper, we propose HIST (Historical Information for Smell deTection), an approach exploiting change history information to detect instances of five different code smells, namely Divergent Change, Shotgun Surgery, Parallel Inheritance, Blob, and Feature Envy.We evaluate HIST in two empirical studies. The first, conducted on twenty open source projects, aimed at assessing the accuracy of HIST in detecting instances of the code smells mentioned above. The results indicate that the precision of HIST ranges between 72% and 86%, and its recall ranges between 58% and 100%. Also, results of the first study indicate that HIST is able to identify code smells that cannot be identified by competitive approaches solely based on code analysis of a single system’s snapshot. Then, we conducted a second study aimed at investigating to what extent the code smells detected by HIST (and by competitive code analysis techniques) reflect developers’ perception of poor design and implementation choices. We involved twelve developers of four open source projects that recognized more than 75% of the code smell instances identified by HIST as actual design/implementation problems

Genetic diversity and its impact on disease severity in respiratory syncytial virus subtype-A and -B bronchiolitis before and after pandemic restrictions in Rome

Objectives: To scrutinize whether the high circulation of respiratory syncytial virus (RSV) observed in 2021-2022 and 2022-2023 was due to viral diversity, we characterized RSV-A and -B strains causing bronchiolitis in Rome, before and after the COVID-19 pandemic. Methods: RSV-positive samples, prospectively collected from infants hospitalized for bronchiolitis from 2017-2018 to 2022-2023, were sequenced in the G gene; phylogenetic results and amino acid substitutions were analyzed. Subtype-specific data were compared among seasons. Results: Predominance of RSV-A and -B alternated in the pre-pandemic seasons; RSV-A dominated in 2021-2022 whereas RSV-B was predominant in 2022-2023. RSV-A sequences were ON1 genotype but quite distant from the ancestor; two divergent clades included sequences from pre- and post-pandemic seasons. Nearly all RSV-B were BA10 genotype; a divergent clade included only strains from 2021-2022 and 2022-2023. RSV-A cases had lower need of O2 therapy and of intensive care during 2021-2022 with respect to all other seasons. RSV-B infected infants were more frequently admitted to intensive care units and needed O2 in 2022-2023. Conclusions: The intense RSV peak in 2021-2022, driven by RSV-A phylogenetically related to pre-pandemic strains is attributable to the immune debt created by pandemic restrictions. The RSV-B genetic divergence observed in post-pandemic strains may have increased the RSV-B specific immune debt, being a possible contributor to bronchiolitis severity in 2022-2023

How to Escape Local Optima in Black Box Optimisation: When Non-elitism Outperforms Elitism

Escaping local optima is one of the major obstacles to function optimisation. Using the metaphor of a fitness landscape, local optima correspond to hills separated by fitness valleys that have to be overcome. We define a class of fitness valleys of tunable difficulty by considering their length, representing the Hamming path between the two optima and their depth, the drop in fitness. For this function class we present a runtime comparison between stochastic search algorithms using different search strategies. The ((Formula presented.)) EA is a simple and well-studied evolutionary algorithm that has to jump across the valley to a point of higher fitness because it does not accept worsening moves (elitism). In contrast, the Metropolis algorithm and the Strong Selection Weak Mutation (SSWM) algorithm, a famous process in population genetics, are both able to cross the fitness valley by accepting worsening moves. We show that the runtime of the ((Formula presented.)) EA depends critically on the length of the valley while the runtimes of the non-elitist algorithms depend crucially on the depth of the valley. Moreover, we show that both SSWM and Metropolis can also efficiently optimise a rugged function consisting of consecutive valleys

Toxic Code Snippets on Stack Overflow

Online code clones are code fragments that are copied from software projects or online sources to Stack Overflow as examples. Due to an absence of a checking mechanism after the code has been copied to Stack Overflow, they can become toxic code snippets, e.g., they suffer from being outdated or violating the original software license. We present a study of online code clones on Stack Overflow and their toxicity by incorporating two developer surveys and a large-scale code clone detection. A survey of 201 high-reputation Stack Overflow answerers (33% response rate) showed that 131 participants (65%) have ever been notified of outdated code and 26 of them (20%) rarely or never fix the code. 138 answerers (69%) never check for licensing conflicts between their copied code snippets and Stack Overflow?s CC BY-SA 3.0. A survey of 87 Stack Overflow visitors shows that they experienced several issues from Stack Overflow answers: mismatched solutions, outdated solutions, incorrect solutions, and buggy code. 85% of them are not aware of CC BY-SA 3.0 license enforced by Stack Overflow, and 66% never check for license conflicts when reusing code snippets. Our clone detection found online clone pairs between 72,365 Java code snippets on Stack Overflow and 111 open source projects in the curated Qualitas corpus. We analysed 2,289 non-trivial online clone candidates. Our investigation revealed strong evidence that 153 clones have been copied from a Qualitas project to Stack Overflow. We found 100 of them (66%) to be outdated, of which 10 were buggy and harmful for reuse. Furthermore, we found 214 code snippets that could potentially violate the license of their original software and appear 7,112 times in 2,427 GitHub projects

Towards a Runtime Comparison of Natural and Artificial Evolution

Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrences of new mutations is much longer than the time it takes for a mutated genotype to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a stochastic process evolving one genotype by means of mutation and selection between the resident and the mutated genotype. The probability of accepting the mutated genotype then depends on the change in fitness. We study this process, SSWM, from an algorithmic perspective, quantifying its expected optimisation time for various parameters and investigating differences to a similar evolutionary algorithm, the well-known (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient

SARS-CoV-2 diagnostics in the virology laboratory of a University Hospital in Rome during the lockdown period

Italy was one of the most affected nations by coronavirus disease 2019 outside China. The infections, initially limited to Northern Italy, spread to all other Italian regions. This study aims to provide a snapshot of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) epidemiology based on a single-center laboratory experience in Rome. The study retrospectively included 6565 subjects tested for SARS-CoV-2 at the Laboratory of Virology of Sapienza University Hospital in Rome from 6 March to 4 May. A total of 9995 clinical specimens were analyzed, including nasopharyngeal swabs, bronchoalveolar lavage fluids, gargle lavages, stools, pleural fluids, and cerebrospinal fluids. Positivity to SARS-CoV-2 was detected in 8% (527/6565) of individuals, increased with age, and was higher in male patients (P <.001). The number of new confirmed cases reached a peak on 18 March and then decreased. The virus was detected in respiratory samples, in stool and in pleural fluids, while none of gargle lavage or cerebrospinal fluid samples gave a positive result. This analysis allowed to gather comprehensive information on SARS-CoV-2 epidemiology in our area, highlighting positivity variations over time and in different sex and age group and the need for a continuous surveillance of the infection, mostly because the pandemic evolution remains unknown

Tutorials at PPSN 2016

PPSN 2016 hosts a total number of 16 tutorials covering a broad range of current research in evolutionary computation. The tutorials range from introductory to advanced and specialized but can all be attended without prior requirements. All PPSN attendees are cordially invited to take this opportunity to learn about ongoing research activities in our field

On the Analysis of Trajectory-Based Search Algorithms: When is it Beneficial to Reject Improvements?

We investigate popular trajectory-based algorithms inspired by biology and physics to answer a question of general significance: when is it beneficial to reject improvements? A distinguishing factor of SSWM (strong selection weak mutation), a popular model from population genetics, compared to the Metropolis algorithm (MA), is that the former can reject improvements, while the latter always accepts them. We investigate when one strategy outperforms the other. Since we prove that both algorithms converge to the same stationary distribution, we concentrate on identifying a class of functions inducing large mixing times, where the algorithms will outperform each other over a long period of time. The outcome of the analysis is the definition of a function where SSWM is efficient, while Metropolis requires at least exponential time. The identified function favours algorithms that prefer high quality improvements over smaller ones, revealing similarities in the optimisation strategies of SSWM and Metropolis respectively with best-improvement (BILS) and first-improvement (FILS) local search. We conclude the paper with a comparison of the performance of these algorithms and a (1, λ ) RLS on the identified function. The algorithm favours the steepest gradient with a probability that increases with the size of its offspring population. The results confirm that BILS excels and that the (1, λ ) RLS is efficient only for large enough population sizes