Small Solar Panels Can Drastically Reduce the Carbon Footprint of Radio Access Networks

The limited power requirements of new generations of base stations (BSs) make the use of renewable energy sources, solar in particular, extremely attractive for mobile network operators. Exploiting solar energy implies a reduction of the network operation cost as well as of the carbon footprint of radio access networks, but previous research works indicate that the area of the solar panels that are necessary to power a standard macro BS is large, so large to make the solar panel deployment problematic, especially within urban areas. In this paper we use a modeling approach based on Markov reward processes to investigate the possibility of combining small area solar panels with a connection to the power grid to run a macro BS. By so doing, it is possible to increase the amount of renewable energy used to run a radio access network, while also reducing the cost incurred by the network operator to power its base stations. We assume that energy is drawn from the power grid only when needed to keep the BS operational, or during the night, that corresponds to the period with lowest electricity price. This has advantages in terms of both cost and carbon footprint. We show that solar panels of the order of 1-2 kW peak, i.e., with a surface of about 5-10 m2, combined with limited capacity energy storage (of the order of 10-15 kWh, corresponding to about 3-5 car batteries), and a smart energy management policy, can lead to an effective exploitation of renewable energy

On the Use of Small Solar Panels and Small Batteries to Reduce the RAN Carbon Footprint

The limited power requirements of new generations of base stations make the use of renewable energy sources, solar in particular, extremely attractive for mobile network operators. Exploiting solar energy implies a reduction of the network operation cost as well as of the carbon footprint of radio access networks. However, previous research works indicate that the area of the solar panels that are necessary to power a standard macro base station (BS) is large, making the solar panel deployment problematic, especially within urban areas.In this paper we use a modeling approach based on Markov reward processes to investigate the possibility of combining a connection to the power grid with small area solar panels and small batteries to run a macro base station. By so doing, it is possible to exploit a significant fraction of renewable energy to run a radio access network, while also reducing the cost incurred by the network operator to power its base stations. We assume that energy is drawn from the power grid only when needed to keep the BS operational, or during the night, which corresponds to the period with lowest electricity price. The proposed energy management policies have advantages in terms of both cost and carbon footprint. Our results show that solar panels of the order of 1-2 kW peak, i.e., with a surface of about 5-10 m2, combined with limited capacity energy storage (of the order of 1-5 kWh, corresponding to about 1-2 car batteries) and a smart energy management policy, can lead to an effective exploitation of renewable energy

Heavy metal load and effects on biochemical properties in urban soils of a medium-sized city, Ancona, Italy

none6noUrban soils are often mixed with extraneous materials and show a high spatial variability that determine great differences from their agricultural or natural counterparts. The soils of 18 localities of a medium-sized city (Ancona, Italy) were analysed for their main physicochemical and biological properties, and for chromium (Cr), copper (Cu), cobalt (Co), lead (Pb), nickel (Ni), zinc (Zn), and mercury (Hg) total content, distribution among particle-size fractions, and extractability. Because of the absence of thresholds defining a hot spot for heavy metal pollution in urban soils, we defined a “threshold of attention” (ToA) for each heavy metal aiming to bring out hot spot soils where it is more impellent to intervene to mitigate or avoid potential environmental concerns. In several city locations, the soil displayed sub-alkaline pH, large contents of clay-size particles, and higher TOC, total N, and available P with respect to the surrounding rural areas, joined with high contents of total heavy metals, but low availability. The C biomass, basal respiration, qCO2, and enzyme activities were compared to that detected in the near rural soils, and results suggested that heavy metals content has not substantially compromised the soil ecological services. We conclude that ToA can be considered as a valuable tool to highlight soil hot spots especially for cities with a long material history and, for a proper risk assessment in urban soils, we suggest considering the content of available heavy metals (rather than the total content) and soil functions.openSerrani D.; Ajmone-Marsan F.; Corti G.; Cocco S.; Cardelli V.; Adamo P.Serrani, D.; Ajmone-Marsan, F.; Corti, G.; Cocco, S.; Cardelli, V.; Adamo, P

On the Limit Performance of Floating Gossip

In this paper we investigate the limit performance of Floating Gossip, a new, fully distributed Gossip Learning scheme which relies on Floating Content to implement location-based probabilistic evolution of machine learning models in an infrastructure-less manner. We consider dynamic scenarios where continuous learning is necessary, and we adopt a mean field approach to investigate the limit performance of Floating Gossip in terms of amount of data that users can incorporate into their models, as a function of the main system parameters. Different from existing approaches in which either communication or computing aspects of Gossip Learning are analyzed and optimized, our approach accounts for the compound impact of both aspects. We validate our results through detailed simulations, proving good accuracy. Our model shows that Floating Gossip can be very effective in implementing continuous training and update of machine learning models in a cooperative manner, based on opportunistic exchanges among moving users

Mitogenome information in cattle breeding and conservation genetics: Developments and possibilities of the SNP chip

In contrast to nuclear markers routinely used for genomic selection, mitogenome information has been underutilized for breeding and biodiversity management of cattle populations. Our main goal was to promote the efficient use of mitogenome SNPs contained in commercial high-throughput SNP arrays. In collaboration with NEOGEN Genomics (Lincoln, NE, USA), we integrated 310 SNPs into the commercial GGP Bovine 100K SNP array. In doing so, we demonstrated how mitogenome SNPs can be used in high-throughput arrays to (i) analyze population structure and diversity, (ii) classify bovine haplogroups and identify introgression and/or upgrading, (iii) screen and identify pedigree defects, (iv) impute mitogenome information on maternal lineages to increase statistical power in estimating the effects of mitogenome variation on quantitative production traits, and (v) identify deleterious mutations found in humans. In addition, we have developed protocols and pipelines inte-grated with the Magellan v2.0 software to enable efficient and routine use of mitogenome information in cattle breeding and genetic diversity management. Finally, we have highlighted some other interesting opportunities for the use of mitogenome information in the near future

What Can Genetics Do for the Control of Infectious Diseases in Aquaculture?

Infectious diseases place an economic burden on aquaculture and a limitation to its growth. An innovative approach to mitigate their impact on production is breeding for disease resistance: selection for domestication, family-based selection, marker-assisted selection, and more recently, genomic selection. Advances in genetics and genomics approaches to the control of infectious diseases are key to increasing aquaculture efficiency, profitability, and sustainability and to reducing its environmental footprint. Interaction and co-evolution between a host and pathogen can, however, turn breeding to boost infectious disease resistance into a potential driver of pathogenic change. Parallel molecular characterization of the pathogen and its virulence and antimicrobial resistance genes is therefore essential to understand pathogen evolution over time in response to host immunity, and to apply appropriate mitigation strategies

Multiple-breed genomic evaluation by principal component analysis in small size populations

In this study, the effects of breed composition and predictor dimensionality on the accuracy of direct genomic values (DGV) in a multiple breed (MB) cattle population were investigated. A total of 3559 bulls of three breeds were genotyped at 54 001 single nucleotide polymorphisms: 2093 Holstein (H), 749 Brown Swiss (B) and 717 Simmental (S). DGV were calculated using a principal component (PC) approach for either single (SB) or MB scenarios. Moreover, DGV were computed using all SNP genotypes simultaneously with SNPBLUP model as comparison. A total of seven data sets were used: three with a SB each, three with different pairs of breeds (HB, HS and BS), and one with all the three breeds together (HBS), respectively. Editing was performed separately for each scenario. Reference populations differed in breed composition, whereas the validation bulls were the same for all scenarios. The number of SNPs retained after data editing ranged from 36 521 to 41 360. PCs were extracted from actual genotypes. The total number of retained PCs ranged from 4029 to 7284 in Brown Swiss and HBS respectively, reducing the number of predictors by about 85% (from 82% to 89%). In all, three traits were considered: milk, fat and protein yield. Correlations between deregressed proofs and DGV were used to assess prediction accuracy in validation animals. In the SB scenarios, average DGV accuracy did not substantially change when either SNPBLUP or PC were used. Improvement of DGV accuracy were observed for some traits in Brown Swiss, only when MB reference populations and PC approach were used instead of SB-SNPBLUP (+10% HBS, +16%HB for milk yield and +3% HBS and +7% HB for protein yield, respectively). With the exclusion of the abovementioned cases, similar accuracies were observed using MB reference population, under the PC or SNPBLUP models. Random variation owing to sampling effect or size and composition of the reference population may explain the difficulty in finding a defined pattern in the results

Mitochondrial DNA diversity of five Italian autochtonous donkey breeds

AbstractTo investigate the mitochondrial DNA diversity of five Italian donkey breeds (Amiata, Martinafranca, Romagnolo, Asinara, and Ragusano), we sequenced the HVR I region (D-loop, 288 bp) and cytochrome b gene (274 bp) in 121 individuals. In the D-loop we found nineteen mutations corresponding to fourteen different haplotypes, while in cyt b coding gene only six mutations were found, originating five different haplotypes. In particular, three mutations out of six were non-synonymous, causing an aminoacidic substitution. About the D-loop region, the value of nucleotide diversity (π) observed within breeds was relatively low, but not far from values detected in other European breeds. Phylogenetic and network analyses disclosed the presence of two divergent maternal lineages within Italian donkeys. These haplogroups correspond to the well known lineages of ancestors (Equus asinus somaliensis and E. a. africanus), as donkeys were domesticated from distinct wild subspecies living in Eastern Africa regions. ..

Genome-Wide DNA Methylation and Gene Expression Profiles in Cows Subjected to Different Stress Level as Assessed by Cortisol in Milk

Dairy cattle health, wellbeing and productivity are deeply affected by stress. Its influence on metabolism and immune response is well known, but the underlying epigenetic mechanisms require further investigation. In this study, we compared DNA methylation and gene expression signatures between two dairy cattle populations falling in the high- and low-variant tails of the distribution of milk cortisol concentration (MC), a neuroendocrine marker of stress in dairy cows. Reduced Representation Bisulfite Sequencing was used to obtain a methylation map from blood samples of these animals. The high and low groups exhibited similar amounts of methylated CpGs, while we found differences among non-CpG sites. Significant methylation changes were detected in 248 genes. We also identified significant fold differences in the expression of 324 genes. KEGG and Gene Ontology (GO) analysis showed that genes of both groups act together in several pathways, such as nervous system activity, immune regulatory functions and glucocorticoid metabolism. These preliminary results suggest that, in livestock, cortisol secretion could act as a trigger for epigenetic regulation and that peripheral changes in methylation can provide an insight into central nervous system functions