Dynamic simulation driven design and management of production facilities in agricultural/food industry

An industrial plant in the agro-food sector can be considered a complex system as it is composed of numerous types of machines and it is characterized by a strong variation (seasonality) in the agricultural production. Whenever the dynamic behavior of the plants during operation is considered, system and design complexities increase. Reliable operation of food processing farms is primarily dependent on perfect balance between variable supply and product storage at each given time. To date, the classical modus operandi of food processing management systems is carried out under stationary and average conditions. Moreover, most of the systems installed for agricultural and food industries are sized using average production data. This often results in a mismatch between the actual operation and the expected operation. Consequently, the system is not optimized for the needs of a specific company. Also, the system is not flexible to the evolution that the production process could possibly have in the future. Promising techniques useful to solve the above-described problems could possibly be borrowed from demand side management (DSM) in smart grid systems. Such techniques allow customers to make dynamically informed decisions regarding their energy demand and help the energy providers in reducing the peak load demand and reshape the load profile. DSM is successfully used to improve the energy management system and we conjecture that DSM could be suitably adapted to food processing management. In this paper we describe how DSM could be exploited in the intelligent management of production facilities serving agricultural and food industry. The main objective is, indeed, to present how methods for modelling and implementing the dynamic simulation used for the optimization of the energy management in smart grid systems can be applied to a fruit and vegetables processing plant through a suitable adaptation

Improving Multi-Objective Test Case Selection by Injecting Diversity in Genetic Algorithms

A way to reduce the cost of regression testing consists of selecting or prioritizing subsets of test cases from a test suite according to some criteria. Besides greedy algorithms, cost cognizant additional greedy algorithms, multi-objective optimization algorithms, and Multi-Objective Genetic Algorithms (MOGAs), have also been proposed to tackle this problem. However, previous studies have shown that there is no clear winner between greedy and MOGAs, and that their combination does not necessarily produce better results. In this paper we show that the optimality of MOGAs can be significantly improved by diversifying the solutions (sub-sets of the test suite) generated during the search process. Specifically, we introduce a new MOGA, coined as DIV-GA (DIversity based Genetic Algorithm), based on the mechanisms of orthogonal design and orthogonal evolution that increase diversity by injecting new orthogonal individuals during the search process. Results of an empirical study conducted on eleven programs show that DIV-GA outperforms both greedy algorithms and the traditional MOGAs from the optimality point of view. Moreover, the solutions (sub-sets of the test suite) provided by DIV-GA are able to detect more faults than the other algorithms, while keeping the same test execution cost

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

On the Runtime Analysis of the Clearing Diversity-Preserving Mechanism

Clearing is a niching method inspired by the principle of assigning the available resources among a niche to a single individual. The clearing procedure supplies these resources only to the best individual of each niche: the winner. So far, its analysis has been focused on experimental approaches that have shown that clearing is a powerful diversity-preserving mechanism. Using rigorous runtime analysis to explain how and why it is a powerful method, we prove that a mutation-based evolutionary algorithm with a large enough population size, and a phenotypic distance function always succeeds in optimising all functions of unitation for small niches in polynomial time, while a genotypic distance function requires exponential time. Finally, we prove that with phenotypic and genotypic distances clearing is able to find both optima for Twomax and several general classes of bimodal functions in polynomial expected time. We use empirical analysis to highlight some of the characteristics that makes it a useful mechanism and to support the theoretical results

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

Discovery and Preliminary Characterization of Translational Modulators that Impair the Binding of eIF6 to 60S Ribosomal Subunits

Eukaryotic initiation factor 6 (eIF6) is necessary for the nucleolar biogenesis of 60S ribosomes. However, most of eIF6 resides in the cytoplasm, where it acts as an initiation factor. eIF6 is necessary for maximal protein synthesis downstream of growth factor stimulation. eIF6 is an antiassociation factor that binds 60S subunits, in turn preventing premature 40S joining and thus the formation of inactive 80S subunits. It is widely thought that eIF6 antiassociation activity is critical for its function. Here, we exploited and improved our assay for eIF6 binding to ribosomes (iRIA) in order to screen for modulators of eIF6 binding to the 60S. Three compounds, eIFsixty-1 (clofazimine), eIFsixty-4, and eIFsixty-6 were identified and characterized. All three inhibit the binding of eIF6 to the 60S in the micromolar range. eIFsixty-4 robustly inhibits cell growth, whereas eIFsixty-1 and eIFsixty-6 might have dose- and cell-specific effects. Puromycin labeling shows that eIF6ixty-4 is a strong global translational inhibitor, whereas the other two are mild modulators. Polysome profiling and RT-qPCR show that all three inhibitors reduce the specific translation of well-known eIF6 targets. In contrast, none of them affect the nucleolar localization of eIF6. These data provide proof of principle that the generation of eIF6 translational modulators is feasible

First-Hitting Times Under Additive Drift

For the last ten years, almost every theoretical result concerning the expected run time of a randomized search heuristic used drift theory, making it the arguably most important tool in this domain. Its success is due to its ease of use and its powerful result: drift theory allows the user to derive bounds on the expected first-hitting time of a random process by bounding expected local changes of the process -- the drift. This is usually far easier than bounding the expected first-hitting time directly. Due to the widespread use of drift theory, it is of utmost importance to have the best drift theorems possible. We improve the fundamental additive, multiplicative, and variable drift theorems by stating them in a form as general as possible and providing examples of why the restrictions we keep are still necessary. Our additive drift theorem for upper bounds only requires the process to be nonnegative, that is, we remove unnecessary restrictions like a finite, discrete, or bounded search space. As corollaries, the same is true for our upper bounds in the case of variable and multiplicative drift

On the Analysis of Simple Genetic Programming for Evolving Boolean Functions

This work presents a first step towards a systematic time and space complexity analysis of genetic programming (GP) for evolving functions with desired input/output behaviour. Two simple GP algorithms, called (1+1) GP and (1+1) GP*, equipped with minimal function (F) and terminal (L) sets are considered for evolving two standard classes of Boolean functions. It is rigorously proved that both algorithms are efficient for the easy problem of evolving conjunctions of Boolean variables with the minimal sets. However, if an extra function (i.e. NOT) is added to F, then the algorithms require at least exponential time to evolve the conjunction of n variables. On the other hand, it is proved that both algorithms fail at evolving the difficult parity function in polynomial time with probability at least exponentially close to 1. Concerning generalisation, it is shown how the quality of the evolved conjunctions depends on the size of the training set s while the evolved exclusive disjunctions generalize equally badly independent of s

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

Hacia la implementación de un Marco de Seguridad de la Información en la Municipalidad de General Pico

La seguridad de la información es una cuestión clave en los nuevos paradigmas de la administración pública local. La gestión municipal moderna está basada esencialmente en una gran utilización de recursos tecnológicos que son explotados para llevar adelante un modelo de negocios que se apoya en el valor de los activos de información almacenados, procesados y transmitidos en consideración de crecientes requerimientos legales y normativos. El objetivo fundamental de este proyecto es el reconocimiento de las principales debilidades y vulnerabilidades asociadas a las operaciones de la Municipalidad de General Pico y la implementación de contramedidas destinadas a mitigarlas. Como valor agregado se comenzarán a establecer los basamentos políticos necesarios para dar soporte institucional a los instrumentos desarrollados en las distintas etapas del proyecto. La propuesta trasciende aspectos técnicos y pretende establecer un marco de colaboración y aprendizaje que enriquezca a todos los involucrados en base a la generación de transferencia de conocimientos en el área de seguridad. En este sentido es importante destacar que además de resolver su problemática tecnológica, la Municipalidad estará generando recursos humanos valiosos para servir a sus intereses de manera idónea y con elevados niveles de calidad. Este modelo de colaboración trae aparejado el desarrollo de recursos locales a la vez que favorece el desarrollo tecnológico de organizaciones similares en la región.Sociedad Argentina de Informática e Investigación Operativ