A Framework for Genetic Algorithms Based on Hadoop

Genetic Algorithms (GAs) are powerful metaheuristic techniques mostly used in many real-world applications. The sequential execution of GAs requires considerable computational power both in time and resources. Nevertheless, GAs are naturally parallel and accessing a parallel platform such as Cloud is easy and cheap. Apache Hadoop is one of the common services that can be used for parallel applications. However, using Hadoop to develop a parallel version of GAs is not simple without facing its inner workings. Even though some sequential frameworks for GAs already exist, there is no framework supporting the development of GA applications that can be executed in parallel. In this paper is described a framework for parallel GAs on the Hadoop platform, following the paradigm of MapReduce. The main purpose of this framework is to allow the user to focus on the aspects of GA that are specific to the problem to be addressed, being sure that this task is going to be correctly executed on the Cloud with a good performance. The framework has been also exploited to develop an application for Feature Subset Selection problem. A preliminary analysis of the performance of the developed GA application has been performed using three datasets and shown very promising performance

Using Hadoop MapReduce for parallel genetic algorithms: A comparison of the global, grid and island models

The need to improve the scalability of Genetic Algorithms (GAs) has motivated the research on Parallel Genetic Algorithms (PGAs), and different technologies and approaches have been used. Hadoop MapReduce represents one of the most mature technologies to develop parallel algorithms. Based on the fact that parallel algorithms introduce communication overhead, the aim of the present work is to understand if, and possibly when, the parallel GAs solutions using Hadoop MapReduce show better performance than sequential versions in terms of execution time. Moreover, we are interested in understanding which PGA model can be most effective among the global, grid, and island models. We empirically assessed the performance of these three parallel models with respect to a sequential GA on a software engineering problem, evaluating the execution time and the achieved speedup. We also analysed the behaviour of the parallel models in relation to the overhead produced by the use of Hadoop MapReduce and the GAs’ computational effort, which gives a more machine-independent measure of these algorithms. We exploited three problem instances to differentiate the computation load and three cluster configurations based on 2, 4, and 8 parallel nodes. Moreover, we estimated the costs of the execution of the experimentation on a potential cloud infrastructure, based on the pricing of the major commercial cloud providers. The empirical study revealed that the use of PGA based on the island model outperforms the other parallel models and the sequential GA for all the considered instances and clusters. Using 2, 4, and 8 nodes, the island model achieves an average speedup over the three datasets of 1.8, 3.4, and 7.0 times, respectively. Hadoop MapReduce has a set of different constraints that need to be considered during the design and the implementation of parallel algorithms. The overhead of data store (i.e., HDFS) accesses, communication, and latency requires solutions that reduce data store operations. For this reason, the island model is more suitable for PGAs than the global and grid model, also in terms of costs when executed on a commercial cloud provider

Efficiency and Patient-Reported Outcome Measures From Clinic to Home: The Human Empowerment Aging and Disability Program for Digital-Health Rehabilitation

Background: The recent exponential growth of Digital Health (DH) in the healthcare system provides a crucial transformation in healthcare, answering to alarming threats related to the increasing number of Chronic Neurological Diseases (CNDs). New long-term integrated DH-care approaches, including rehabilitation, are warranted to address these concerns. Methods: The Human Empowerment Aging and Disability (HEAD) rehabilitation program, a new long-term integrated care including DH-care system, was evaluated in terms of efficiency and patient-reported outcome measures (PROMs) in 107 CND patients (30 with Parkinson's Disease, PD; 32 with Multiple Sclerosis, MS; 45 with stroke in chronic stage). All participants followed 1-month of HEAD rehabilitation in clinic (ClinicHEAD: 12 sessions, 3/week), then 1:3 patient was consecutively allocated to 3-months telerehabilitation at home (HomeHEAD: 60 sessions, 5/week). Efficiency (i.e., adherence, usability, and acceptability) and PROMs (i.e., perceived functioning in real-world) were analyzed. Results: The rate of adherence to HEAD treatment in clinic (≥90%) and at home (77%) was high. Usability of HEAD system was judged as good (System Usability Scale, median 70.00) in clinic and even more at home (median 80.00). Similarly, administering the Technology Acceptance Model 3 questionnaire we found high scores both in clinic/at home (Usefulness, mean 5.39 ± 1.41 SD/mean 5.33 ± 1.29 SD; Ease of use, mean 5.55 ± 1.05 SD/ mean 5.45 ± 1.17 SD, External Control, mean 4.94 ± 1.17 SD/mean 5.07 ± 1.01 SD, Relevance, mean 5.68 ± 1.29 SD/mean 5.70 ± 1.13 SD and Enjoyment, mean 5.70 ± 1.40 SD/mean 6.01 ± 1.08 SD). After ClinicHEAD, participation and autonomy in daily routine was maintained or even ameliorated (PD and stroke > MS). Whereas, increased functionality and participation in the MS group was found only after HomeHEAD intervention. Discussion: Our results suggest that a tele-health-based approach is both feasible and efficient in providing rehabilitation care to CNDs from clinic to home. Increasing and maintaining participation as well as autonomy in daily routine are promising findings that open up scenarios for the continuity of care at home through DH-care for CNDs

TCO Optimization in Si Heterojunction Solar Cells on p-type Wafers with n-SiOx Emitter☆

Abstract Silicon heterojunction solar cells have largely demonstrated their suitability to reach high efficiencies. We have here focused on p-type c-Si wafers as absorber, considering that they share more than 90% of the solar cell market. To overcome some of the issues encountered in the conventional (n)a-Si:H/(p)c-Si configuration, we have implemented a mixed phase n-type silicon oxide (n-SiOx) emitter in order to gain from the wider bandgap and lower activation energy of this material with respect to (n)a-Si:H. The workfunction of the transparent conductive oxide layer (WTCO) plays also a key role, as it may induce an unfavourable band bending at the interface with the emitter. We have here focused on AZO, a promising alternative to ITO. Different layers with varying WTCO were prepared, by changing relevant deposition parameters, and were tested into solar cells. The experimental results have been explained with the aid of numerical simulations. Finally, for the n-SiOx/(p)c-Si heterojunction with optimized WTCO a potential conversion efficiency well over 23% has been estimated

MOLECULAR CHARACTERIZATION OF ANISAKID NEMATODES IN FISHES OF NORTHERN SARDINIAN SEA

The authors report results of analysis carried out during 2008-2010 for identification and molecular characterization of larval Anisakis nematodes isolated from fishes of the northern Sardinian sea

GrassPlot v. 2.00 – first update on the database of multi-scale plant diversity in Palaearctic grasslands

Abstract: GrassPlot is a collaborative vegetation-plot database organised by the Eurasian Dry Grassland Group (EDGG) and listed in the Global Index of Vegetation-Plot Databases (GIVD ID EU-00-003). Following a previous Long Database Report (Dengler et al. 2018, Phyto- coenologia 48, 331–347), we provide here the first update on content and functionality of GrassPlot. The current version (GrassPlot v. 2.00) contains a total of 190,673 plots of different grain sizes across 28,171 independent plots, with 4,654 nested-plot series including at least four grain sizes. The database has improved its content as well as its functionality, including addition and harmonization of header data (land use, information on nestedness, structure and ecology) and preparation of species composition data. Currently, GrassPlot data are intensively used for broad-scale analyses of different aspects of alpha and beta diversity in grassland ecosystems

Threatened and extinct amphibians and reptiles in Italian natural history collections are useful conservation tools

Natural history museums are irreplaceable tools to study and preserve the biological diversity around the globe and among the primary actors in the recognition of species and the logical repositories for their type specimens. In this paper we surveyed the consistency of the preserved specimens of amphibians and reptiles housed in the major Italian scientific collections, and verified the presence of threatened species according to the IUCN Red List, includ-ing the Extinct (EX), Extinct in the Wild (EW), Critically Endangered (CR), Endangered (EN), and Vulnerable (VU) categories. Altogether, we analyzed 39 Italian zoological collections. We confirmed the presence of one extinct reptile (Chioninia coctei) and five extinct or extinct in the wild amphibian species (Atelopus longirostris, Nectophrynoides asperginis, Pseudophilautus leucorhinus, P. nasutus, and P. variabilis). Seven CR amphibians, fourteen CR reptile species and the extinct skink C. coctei are shared by more than one institution. Museums which host the highest number of threatened and extinct amphibian species are respectively Turin (17 CR and 1 EX), Florence (13 CR and 1 EX), and Trento (15 CR and 1 EW), while for reptiles the richest museums are those from Genoa (15 CR and 1 EX), Florence (11 CR and 1 EX), and Pisa (7 CR). Finally, we discussed the utility of natural history museums and the strategies to follow for the implementation of their functionality. © Firenze University Press

Familial aggregation of MATRICS Consensus Cognitive Battery scores in a large sample of outpatients with schizophrenia and their unaffected relatives

Background The increased use of the MATRICS Consensus Cognitive Battery (MCCB) to investigate cognitive dysfunctions in schizophrenia fostered interest in its sensitivity in the context of family studies. As various measures of the same cognitive domains may have different power to distinguish between unaffected relatives of patients and controls, the relative sensitivity of MCCB tests for relative-control differences has to be established. We compared MCCB scores of 852 outpatients with schizophrenia (SCZ) with those of 342 unaffected relatives (REL) and a normative Italian sample of 774 healthy subjects (HCS). We examined familial aggregation of cognitive impairment by investigating within-family prediction of MCCB scores based on probands' scores.Methods Multivariate analysis of variance was used to analyze group differences in adjusted MCCB scores. Weighted least-squares analysis was used to investigate whether probands' MCCB scores predicted REL neurocognitive performance.Results SCZ were significantly impaired on all MCCB domains. REL had intermediate scores between SCZ and HCS, showing a similar pattern of impairment, except for social cognition. Proband's scores significantly predicted REL MCCB scores on all domains except for visual learning.Conclusions In a large sample of stable patients with schizophrenia, living in the community, and in their unaffected relatives, MCCB demonstrated sensitivity to cognitive deficits in both groups. Our findings of significant within-family prediction of MCCB scores might reflect disease-related genetic or environmental factors

The association between insight and depressive symptoms in schizophrenia: Undirected and Bayesian network analyses

Background. Greater levels of insight may be linked with depressive symptoms among patients with schizophrenia, however, it would be useful to characterize this association at symptom-level, in order to inform research on interventions. Methods. Data on depressive symptoms (Calgary Depression Scale for Schizophrenia) and insight (G12 item from the Positive and Negative Syndrome Scale) were obtained from 921 community-dwelling, clinically-stable individuals with a DSM-IV diagnosis of schizophrenia, recruited in a nationwide multicenter study. Network analysis was used to explore the most relevant connections between insight and depressive symptoms, including potential confounders in the model (neurocognitive and social-cognitive functioning, positive, negative and disorganization symptoms, extrapyramidal symptoms, hostility, internalized stigma, and perceived discrimination). Bayesian network analysis was used to estimate a directed acyclic graph (DAG) while investigating the most likely direction of the putative causal association between insight and depression. Results. After adjusting for confounders, better levels of insight were associated with greater self-depreciation, pathological guilt, morning depression and suicidal ideation. No difference in global network structure was detected for socioeconomic status, service engagement or illness severity. The DAG confirmed the presence of an association between greater insight and self-depreciation, suggesting the more probable causal direction was from insight to depressive symptoms. Conclusions. In schizophrenia, better levels of insight may cause self-depreciation and, possibly, other depressive symptoms. Person-centered and narrative psychotherapeutic approaches may be particularly fit to improve patient insight without dampening self-esteem