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Vegetative and productive response of olive trees under anti-insect nets
No full textAnti-insect netting can help control pests in many fruit tree crops while reducing the application of pesticides. In olive production, netting is used as a method of controlling the fly Bractrocera oleae Gmel., especially for table cultivars. In addition to preventing the direct damages to the fruits by excluding the insects from access to the canopy, the net can be useful in mitigating other abiotic stressors. For example, it can provide shade that protects fruits from excessive insolation leading to greater final growth. In this study we evaluated the shading effect of anti-insect nets on the vegetative and reproductive growth of the olive tree. In July 2021 and 2022, nets with a shading factor of 15% were installed in two treatments: before and after the pit hardening (15 days difference). Uncovered trees were used as control. In 2021, the trees did not show differences regarding the fruit yield, but the trees covered with the nets after the pit hardening had bigger fruits than the control without nets. The installation of nets after pit hardening also induced a higher number of nodes in the vegetative portion of 1-year-old mixed shoots during the period following the installation of the nets (from July to October 2021). In 2022 no differences were recorded among the applied treatments and the control, probably because of a low crop load of the trees. Further investigation is needed to understand the optimal installation time so as to not negatively affect vegetative-reproductive growth and flower induction
La valutazione economica dei software
No full textIl contributo analizza le metodologie, la base dati e i principali profili legati alla valutazione economica dei software nell'ambito dei processi di stima del valore aziendale
Modeling tuberculosis transmission with kernel functions: effects of vaccination and time delay
No full textThis study investigates the dynamics of tuberculosis (TB) transmission in high-density populations with suboptimal hygiene conditions, focusing on India as a representative setting. We develop a transmission model incorporating both weak and strong kernel functions to represent distributed time delays in TB latency, along with vaccination effects. By analyzing stability around the TB-free equilibrium with respect to the time delay arising from the latent phase, we identify critical bifurcation thresholds for local stability. The model exhibits a Hopf bifurcation at the tuberculosis-existence equilibrium when the latency delay exceeds this critical value, indicating oscillatory disease dynamics. Numerical simulations validate our analytical findings and provide insights into the interplay between vaccination parameters, latency periods, and TB control. The results offer valuable guidance for optimizing vaccination strategies and timing interventions in TB-endemic regions
Mapping European innovation and policy landscapes using deep learning: Technologies for sustainable wastewater management
No full textSustainable Development Goals (SDGs), established by the United Nations in 2015, chart a global course toward sustainability, with Goal 6 specifically targeting the optimization of water resources to ensure widespread access to clean water and sanitation. The objective of this SDG mandates innovative approaches to managing wastewater, which is a critical component for the preservation of public health and our environment. Recent advancements across the technological landscape, including the Internet of Things (IoT), Computer Vision, and Artificial Intelligence (AI), have ushered in a new era for wastewater management, offering unprecedented capabilities in monitoring, data collection, and analysis at scale. These technologies have the potential to revolutionize traditional practices, enhance process efficiency, and play a crucial role in achieving SDG 6. This chapter explores the evolution of wastewater management strategies within the context of the regulation framework of the European Union. The methodology employs a comprehensive analysis and empirical review of 5550 patent grants collected from the years 2016–23 using KeyBERT and topic modeling to identify and categorize relevant patents. We then assess the alignment of these patents with the Urban Wastewater Treatment Directive of 2014 using cosine similarity metrics to evaluate the contextual relevance and technological alignment between them and select the most relevant patents. The analysis reveals an interest in digital innovations, as evidenced by the surge in patent grants in wastewater management. However, there is a gap between the potential of these theoretical advancements and their real-world implementation since only 68 patents show a high similarity rating with the policy document. Even in those cases, there is a weak similarity between the patents and the policy document, with most patents from outside European regions. This gap signifies not just a technological challenge but also highlights areas requiring European policy intervention, cross-sector collaboration, and knowledge sharing to bridge the divide between innovation and application. The proposed framework in the chapter will serve as a template for stakeholders in the industry to better align innovations to the needs proposed by experts in the government and to better draft streamlined policies
Generalized multiscale homogenization approach to flexoelectric heterogeneous materials considering strain gradient contributions
No full textFlexoelectricity, the electromechanical coupling induced by strain gradients, has gained increasing attention due to its relevance in the design of nanoscale devices, sensors, and energy-harvesting systems. Accurate prediction of effective properties in flexoelectric composites is crucial for guiding materials design; however, most existing studies consider only the flexoelectric tensor while neglecting the combined role of higher-order couplings. In this work, we present a generalized methodology based on the two-scale asymptotic homogenization method to evaluate the effective behavior of flexoelectric materials. The proposed framework explicitly incorporates the contributions of the flexoelectric tensor μijkl, the strain-gradient elasticity tensor gijklmn, and the strain-gradient coupling tensor rijklm, allowing for a consistent treatment of higher-order interactions. As a particular case, explicit solutions are derived for stratified (multilayered) structures, which serve as a benchmark for more complex microstructures. Numerical examples illustrate the impact of microstructural symmetries, such as cubic, tetragonal, and isotropic arrangements, as well as the influence of the length-scale parameter and the constituent volume fractions on the effective tensors. The results demonstrate that second-order homogenization captures the interplay between microstructure, length scale, and electromechanical coupling, thereby providing a rigorous foundation for the design and optimization of advanced multifunctional materials
Sustainable Development: Official Statistics and Thematic Indicators as a Compass to Understand Complexity
No full textThis volume offers a comprehensive analysis of sustainable development paths, based on UN 2030 SDGs, using a deep, comparative, and standardized analysis of nine key indicators from official statistics. The book offers an introductory chapter that sets the stage and provides background for what is to come, followed by a chapter covering each of the nine indicators selected. The indicators' trends were evaluated over time (1960-2020) using World Bank data. The authors analyzed the apparent and latent dynamics of the nine indicators country by country, covering at least 100 countries per indicator. Offered here are descriptive statistics, figures, maps, graphs, bivariate correlations, and multivariate statistical tools in order to clarify the role of each indicator in specific sustainable development paths all over the world. The book provides novel tools for teaching as well as for academics and practitioners
A Complex Network-Based Approach for Detecting and Characterizing Power Neurons in Drosophila
No full textConnectome analysis investigates the connections in the brain to understand how brain regions communicate with each other and how brain structure relates to its function. In recent years, researchers have reconstructed the structural connectome of several organisms, the most complex being Drosophila melanogaster. Two research groups have reconstructed the larval and adult connectomes of this organism and have applied network analysis to learn more about the Drosophila brain and its behavior. In this paper, we aim to continue the work of these two research groups at the larval and adult stages. Specifically, we construct several derived network representations and define a set of techniques that use the main concepts and measures of complex network analysis to extract new knowledge about Drosophila connectomes at the larval and adult stages. First, we conduct an Exploratory Data Analysis on the larval and adult connectomes to detect similarities and differences between them. Then, we define the concept of power neurons and illustrate an approach to detect them. Next, we demonstrate that power neurons represent a limited set of highly interconnected neurons that form a backbone and that, given their peculiar connectivity properties, may play a strategic role in brain functions. Finally, we extract a set of connectome motifs that allow us to learn about various features characterizing power neurons. We demonstrate that complex network analysis can allow the extraction of relevant knowledge about connectomes. Furthermore, we show that a very small number of power neurons can strongly influence all other neurons in the Drosophila brain
Clustering analysis for indoor temperature and energy pattern identification through long-term monitoring data in a university classroom
Full text linkApproximately 10% of Italy’s building stock is owned by local public authorities, which are generally characterized by a low level of digitalization. The lack of a common repository and data leads to poor energy efficiency, management, and Indoor Environmental Quality (IEQ). The present research, based on a post-occupancy evaluation using real-time data collection essential for verifying building performance and operation, has a twofold objective: the first is the assessment over time of the IEQ and energy efficiency under real operation conditions; the second is the characterization, through clustering methods, of indoor air temperatures and energy needs. The assessment has been performed in a representative classroom of Politecnico di Milano University using a sensor network for IEQ and energy monitoring. The data collection shows that, during the winter season, the temperature falls outside comfort class II for 50% of the occupied hours. In summer, overheating is detected for 35% of the occupied hours. The clustering analysis successfully identified daily operational patterns for both key variables, air temperature and energy. Four clusters corresponding to the winter, summer, and intermediate seasons were identified from indoor temperature data, yielding a Silhouette Score of 0.488. Concurrently, three clusters corresponding to heating, cooling, and ventilation-only modes were identified with a Silhouette Score of 0.8015. The present work confirms that continuous monitoring and clustering analysis represent a valuable methodology for pattern identification within large operational datasets, enabling the quantification of typical operational modes, thereby establishing a foundation for advanced diagnostics and appropriate control strategies
Diet-Driven Modulation of Antibiotic Resistance Genes and Microbial Risk During the Bioconversion of Agro-Industrial Residues by Hermetia illucens
Full text linkBackground: Hermetia illucens larvae provide a sustainable bioconversion pathway that transforms agro-industrial residues into protein- and nutrient-dense biomass and frass, suitable for animal feed and soil amendment, respectively. Nevertheless, the potential spread of antibiotic resistance (AR) genes and pathogenic microorganisms poses biosafety concerns. This study examined the impact of four residue-based diet formulations; peas and chickpea (D1), peas and wheat (D2), onion and wheat (D3), and wheat with digestate (D4), on microbial safety during the bioconversion process. Methods: Enterococcus spp. (viable counts), Salmonella spp. (presence/absence), and 13 AR genes associated with resistance to tetracyclines, macrolide-lincosamide-streptogramin B, β-lactams, vancomycin, and aminoglycosides were quantified in single substrates, diets, larvae, and frass using qPCR. Results: Principal component analysis revealed diet-driven AR gene profiles. D1 lowered the levels of the greatest number of tested AR genes, particularly erm(B), tetracycline, and β-lactam genes in frass, as well as tet(O) and vanB in mature larvae. In contrast, D2 increased the AR gene levels in frass. All diets except D4 eliminated Salmonella spp. Enterococcus spp. loads varied by diet and larval stage, with D2 reducing counts in frass. Conclusions: Diet composition directly shapes microbial dynamics and AR gene dissemination, indicating that legume-based substrates may enhance biosafety in bioconversion systems
Microhaplotypes in forensic genetics: From exploration to application in degraded DNA specimens
Full text linkMicrohaplotypes have emerged as powerful forensic markers over the past decade. This paper sets out the development of a MPS panel of microhaps and its potential for application to identification, analysis of degraded DNA, ancestry inference, and identification of close biological relationships. To make it more effective when dealing with fragmented DNA, the MPS assay was designed to ensure a reduced amplicon size of less than 140 bp. After MPS assay validation, a panel of 76 microhaps, comprised of 299 different SNPs and spread across the autosomal human genome, was established. A total of 102 Italian individuals were analyzed to estimate the genotype and haplotype frequencies. The effective number of alleles at each locus (Ae) for the Italian population ranges from 1.926 to 6.187, with 59 MHs that have values greater than 3.0. The matching probability (PI) ranges from 0.055 to 0.345 and the cumulative PI value is 11.763E-66. Complete and reliable profiles were obtained with as little as 0.05 ng. The MHs panel was then validated on real forensic specimens chosen on the basis of their DNA content and degradation level. The majority of the casework samples analyzed showed complete or nearly complete MH profiles even in degraded samples. To assess the informative power of MH profiles in forensic casework, probabilistic genotyping on partial MH profiles has been used. The resulting likelihood ratio values range from 7.84E+09 to 2.70E+34, thus defining an extremely strong support for the hypothesis that the genetic profile in a casework sample comes from the reference sample. Pairwise kinship simulations using allele frequencies from Italian population samples showed that full- and half-sibling relationships can be readily distinguished from unrelated individuals. For evaluation of the 76 MH panel's utility for ancestry informativeness, PCA and STRUCTURE analyses are also presented comparing the newly collected sample from Ancona Italy with the 26 populations of the 1000 Genomes Project. The results of the analysis confirmed the effectiveness of these short microhaplotypes in typing, with high sensitivity, samples with highly degraded DNA typically encountered in forensic cases