Data and performance of an active-set truncated Newton method with non-monotone line search for bound-constrained optimization

In this data article, we report data and experiments related to the research article entitled “A Two-Stage Active-Set Algorithm for Bound-Constrained Optimization”, by Cristofari et al. (2017). The method proposed in Cristofari et al. (2017), tackles optimization problems with bound constraints by properly combining an active-set estimate with a truncated Newton strategy. Here, we report the detailed numerical experience performed over a commonly used test set, namely CUTEst (Gould et al., 2015). First, the algorithm ASA-BCP proposed in Cristofari et al. (2017) is compared with the related method NMBC (De Santis et al., 2012). Then, a comparison with the renowned methods ALGENCAN (Birgin and Martínez et al., 2002) and LANCELOT B (Gould et al., 2003) is reported

Hybridization of multi-objective deterministic particle swarm with derivative-free local searches

The paper presents a multi-objective derivative-free and deterministic global/local hybrid algorithm for the efficient and effective solution of simulation-based design optimization (SBDO) problems. The objective is to show how the hybridization of two multi-objective derivative-free global and local algorithms achieves better performance than the separate use of the two algorithms in solving specific SBDO problems for hull-form design. The proposed method belongs to the class of memetic algorithms, where the global exploration capability of multi-objective deterministic particle swarm optimization is enriched by exploiting the local search accuracy of a derivative-free multi-objective line-search method. To the authors best knowledge, studies are still limited on memetic, multi-objective, deterministic, derivative-free, and evolutionary algorithms for an effective and efficient solution of SBDO for hull-form design. The proposed formulation manages global and local searches based on the hypervolume metric. The hybridization scheme uses two parameters to control the local search activation and the number of function calls used by the local algorithm. The most promising values of these parameters were identified using forty analytical tests representative of the SBDO problem of interest. The resulting hybrid algorithm was finally applied to two SBDO problems for hull-form design. For both analytical tests and SBDO problems, the hybrid method achieves better performance than its global and local counterparts

A multi-objective DIRECT algorithm for ship hull optimization

The paper is concerned with black-box nonlinear constrained multi-objective optimization problems. Our interest is the definition of a multi-objective deterministic partition-based algorithm. The main target of the proposed algorithm is the solution of a real ship hull optimization problem. To this purpose and in pursuit of an efficient method, we develop an hybrid algorithm by coupling a multi-objective DIRECT-type algorithm with an efficient derivative-free local algorithm. The results obtained on a set of “hard” nonlinear constrained multi-objective test problems show viability of the proposed approach. Results on a hull-form optimization of a high-speed catamaran (sailing in head waves in the North Pacific Ocean) are also presented. In order to consider a real ocean environment, stochastic sea state and speed are taken into account. The problem is formulated as a multi-objective optimization aimed at (i) the reduction of the expected value of the mean total resistance in irregular head waves, at variable speed and (ii) the increase of the ship operability, with respect to a set of motion-related constraints. We show that the hybrid method performs well also on this industrial problem

Synthesis and characterisation of a new benzamide-containing nitrobenzoxadiazole as a GSTP1-1 inhibitor endowed with high stability to metabolic hydrolysis

The antitumor agent 6-((7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)thio)hexan-1-ol (1) is a potent inhibitor of GSTP1-1, a glutathione S-transferase capable of inhibiting apoptosis by binding to JNK1 and TRAF2. We recently demonstrated that, unlike its parent compound, the benzoyl ester of 1 (compound 3) exhibits negligible reactivity towards GSH, and has a different mode of interaction with GSTP1-1. Unfortunately, 3 is susceptible to rapid metabolic hydrolysis. In an effort to improve the metabolic stability of 3, its ester group has been replaced by an amide, leading to N-(6-((7-nitrobenzo[c][1,2,5]oxadiazol-4-yl)thio)hexyl)benzamide (4). Unlike 3, compound 4 was stable to human liver microsomal carboxylesterases, but retained the ability to disrupt the interaction between GSTP1-1 and TRAF2 regardless of GSH levels. Moreover, 4 exhibited both a higher stability in the presence of GSH and a greater cytotoxicity towards cultured A375 melanoma cells, in comparison with 1 and its analog 2. These findings suggest that 4 deserves further preclinical testing

POINT CLOUD EXPLOITATION FOR STRUCTURAL MODELING AND ANALYSIS: A RELIABLE WORKFLOW

none4noThe digitization and geometric knowledge of the historical built heritage is currently based on point cloud, that rarely or only partially is used as digital twin for structural analysis. The present work deals with historical artefacts survey, with particular reference to masonry structures, aimed to their structural analysis and assessment. In detail, the study proposes a methodology capable of employing semi-directly the original data obtained from the 3D digital survey for the generation of a Finite Element Model (FEM), used for structural analysis of masonry buildings. The methodology described presents a reliable workflow with twofold purpose: the improvement of the transformation process of the point cloud in solid and subsequently obtain a high-quality and detailed model for structural analyses. Through the application of the methodology to a case study, the method consistency was assessed, regarding the smoothness of the whole procedure and the dynamic characterization of the Finite Element Model. The main improvement in respect with similar or our previous workflows is obtained by the introduction of the retopology in data processing, allowing the transformation of the raw data into a solid model with optimal balancing between Level of Detail (LOD) and computational weight. Another significant aspect of the optimized process is undoubtedly the possibility of faithfully respecting the semantics of the structure, leading to the discretization of the model into different parts depending on the materials. This work may represent an excellent reference for the study of masonry artefacts belonging to the existing historical heritage, starting from surveys and with the purpose to structural and seismic evaluations, in the general framework of knowledge-based preservation of heritage.openLucidi, A.; Giordano, E.; Clementi, F.; Quattrini, R.Lucidi, A.; Giordano, E.; Clementi, F.; Quattrini, R

A convergent decomposition algorithm for support vector machines.

In this work we consider nonlinear minimization problems with a single linear equality constraint and box constraints. In particular we are interested in solving problems where the number of variables is so huge that traditional optimization methods cannot be directly applied. Many interesting real world problems lead to the solution of large scale constrained problems with this structure. For example, the special subclass of problems with convex quadratic objective function plays a fundamental role in the training of Support Vector Machine, which is a technique for machine learning problems. For this particular subclass of convex quadratic problem, some convergent decomposition methods, based on the solution of a sequence of smaller subproblems, have been proposed. In this paper we define a new globally convergent decomposition algorithm that differs from the previous methods in the rule for the choice of the subproblem variables and in the presence of a proximal point modification in the objective function of the subproblems. In particular, the new rule for sequentially selecting the subproblems appears to be suited to tackle large scale problems, while the introduction of the proximal point term allows us to ensure the global convergence of the algorithm for the general case of nonconvex objective function. Furthermore, we report some preliminary numerical results on support vector classification problems with up to 100 thousands variables

Minimization over the l1-ball using an active-set non-monotone projected gradient

The l1-ball is a nicely structured feasible set that is widely used in many fields (e.g., machine learning, statistics and signal analysis) to enforce some sparsity in the model solutions. In this paper, we devise an active-set strategy for efficiently dealing with minimization problems over the l1-ball and embed it into a tailored algorithmic scheme that makes use of a non-monotone first-order approach to explore the given subspace at each iteration. We prove global convergence to stationary points. Finally, we report numerical experiments, on two different classes of instances, showing the effectiveness of the algorithm

Fishing activities overlap with bottlenose dolphin core habitats of Ischia and Procida islands

Tursiops truncatus – the common bottlenose dolphin – is a species of conservation interest, listed in Annex II and IV of Habitat Directive (92/43/CEE) that requires strict protection and the creation of specially protected areas for conservation, managed according to the ecological requirements of the species, within the “Nature 2000” network. A local population of bottlenose dolphins has been monitored over fifteen years in the sea waters around Ischia and Procida Islands in the frame of the Ischia Dolphin Project, an ongoing long-term research program on Tyrrhenian cetaceans. The study area lies partially within the boundaries of "Regno di Nettuno" Marine Protected Area (MPA), which is classified by IUCN as an Important Marine Mammal Area (IMMA), where pods of cetacean key species such as common dolphin (Delphinus Delphis), bottlenose dolphin, and fin whale (Balaenoptera physalus) live. Investigating habitat exploitation by bottlenose dolphins is crucial for conserving this protected species. Between 2004 and 2018, 1186 surveys were performed, resulting in 91 encounters with the species. To investigate bottlenose dolphins' habitat exploitation, we combined both behavioral observations and spatial analysis. Kernel Density Estimation and Hotspot analysis allowed to delineate fine-scale areas of higher concentration of critical activities (feeding, socializing/mating, resting) and interactions with fisheries (gillnets and trawlers). Results show a vital region for feeding, resting, social cohesion, and mating, i.e. essential habitat for bottlenose dolphins. Unfortunately, these critical habitats are only partially protected by the zonation of the MPA, because it overlaps with human activities, especially fishing. Although the influence of fisheries on dolphins' behavior and movements needs further investigation, the results thus far collected suggest that effective management measures should take into account the human-animal conflict that can arise in these critical areas

Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals

Modeling the arrival process to an Emergency Department (ED) is the first step of all studies dealing with the patient flow within the ED. Many of them focus on the increasing phenomenon of ED overcrowding, which is afflicting hospitals all over the world. Since Discrete Event Simulation models are often adopted to assess solutions for reducing the impact of this problem, proper nonstationary processes are taken into account to reproduce time–dependent arrivals. Accordingly, an accurate estimation of the unknown arrival rate is required to guarantee the reliability of results. In this work, an integer nonlinear black–box optimization problem is solved to determine the best piecewise constant approximation of the time-varying arrival rate function, by finding the optimal partition of the 24 h into a suitable number of not equally spaced intervals. The black-box constraints of the optimization problem make the feasible solutions satisfy proper statistical hypotheses; these ensure the validity of the nonhomogeneous Poisson assumption about the arrival process, commonly adopted in the literature, and prevent mixing overdispersed data for model estimation. The cost function of the optimization problem includes a fit error term for the solution accuracy and a penalty term to select an adequate degree of regularity of the optimal solution. To show the effectiveness of this methodology, real data from one of the largest Italian hospital EDs are used

Behavioral Restriction Determines Left Attentional Bias: Preliminary Evidences From COVID-19 Lockdown

During the COVID-19 lockdown, individuals were forced to remain at home, hence severely limiting the interaction within environmental stimuli, reducing the cognitive load placed on spatial competences. The effects of the behavioral restriction on cognition have been little examined. The present study is aimed at analyzing the effects of lockdown on executive function prominently involved in adapting behavior to new environmental demands. We analyze non-verbal fluency abilities, as indirectly providing a measure of cognitive flexibility to react to spatial changes. Sixteen students (mean age 20.75; SD 1.34), evaluated before the start of the lockdown (T1) in a battery of psychological tasks exploring different cognitive domains, have been reassessed during lockdown (T2). The assessment included the modified Five-Point Test (m-FPT) to analyze non-verbal fluency abilities. At T2, the students were also administered the Toronto Alexithymia Scale (TAS-20). The restriction of behaviors following a lockdown determines increased non-verbal fluency, evidenced by the significant increase of the number of new drawings. We found worsened verbal span, while phonemic verbal fluency remained unchanged. Interestingly, we observed a significant tendency to use the left part of each box in the m-FPT correlated with TAS-20 and with the subscales that assess difficulty in describing and identifying feelings. Although our data were collected from a small sample, they evidence that the restriction of behaviors determines a leftward bias, suggesting a greater activation of the right hemisphere, intrinsically connected with the processing of non-verbal information and with the need to manage an emotional situation