Smart logistics nodes:concept and classification

This paper presents the Smart Logistics Node concept, which combines the physical infrastructure of logistics nodes with digital systems to enhance collaboration. The Smart Logistics Node benefits from data sharing, supporting infrastructure, and Connected and Automated Transport (CAT) technologies. Based on a literature review on logistics nodes and CAT, we propose a general classification of Smart Logistics Nodes distinguishing upon the node function, degree of organisational (de-)centralisation, digital integration, and infrastructure support for automated driving. Then, we classify sixteen logistics nodes and find that high digital integration is common while automation is lacking. Further automation entails mixed traffic on public roads and requires organisational changes that do not always align with current business models. Our work supports the adoption of emerging technology at logistics nodes and the comparability of business cases. Ultimately, node authorities can use our concept and classification to draw a roadmap to develop CAT capabilities.</p

Curation of a reference database of COI sequences for insect identification through DNA metabarcoding: COins

DNA metabarcoding is a widespread approach for the molecular identification of organisms. While the associated wet-lab and data processing procedures are well established and highly efficient, the reference databases for taxonomic assignment can be implemented to improve the accuracy of identifications. Insects are among the organisms for which DNA-based identification is most commonly used; yet, a DNA-metabarcoding reference database specifically curated for their species identification using software requiring local databases is lacking. Here, we present COins, a database of 5' region cytochrome c oxidase subunit I sequences (COI-5P) of insects that includes over 532 000 representative sequences of &gt;106 000 species specifically formatted for the QIIME2 software platform. Through a combination of automated and manually curated steps, we developed this database starting from all COI sequences available in the Barcode of Life Data System for insects, focusing on sequences that comply with several standards, including a species-level identification. COins was validated on previously published DNA-metabarcoding sequences data (bulk samples from Malaise traps) and its efficiency compared with other publicly available reference databases (not specific for insects). COins can allow an increase of up to 30% of species-level identifications and thus can represent a valuable resource for the taxonomic assignment of insects' DNA-metabarcoding data, especially when species-level identification is needed https://doi.org/10.6084/m9.figshare.19130465.v1

Identification of bolted joint properties through substructure decoupling

Substructure decoupling techniques, defined in the frame of Frequency Based Substructuring, allow to identify the dynamic behaviour of a structural subsystem starting from the known dynamics of the coupled system and from information about the remaining components. The problem of joint identification can be approached in the substructuring framework by decoupling jointed substructures from the assembled system. In this case, information about the coupling DoFs of the assembled structure is necessary and this could be a problem if the interface is inaccessible for measurements. Expansion techniques can be used to obtain the dynamics on inaccessible (interface) DoFs starting from accessible (internal) DoFs. A promising technique is the System Equivalent Model Mixing (SEMM) that combines numerical and experimental models of the same component to obtain a hybrid model. This technique has been already used in an iterative coupling–decoupling procedure to identify the linear dynamic behaviour of a joint, with a Virtual Point description of the interface. In this work, a similar identification procedure is applied to the Brake Reus Beam benchmark to identify the linear dynamic behaviour of a three bolted connection at low levels of excitation. The joint is considered as a third independent substructure that accounts for the mass and stiffness properties of the three bolts, thus avoiding singularity in the decoupling process. Instead of using the Virtual Point Transformation, the interface is modelled by performing a modal condensation on remote points allowing deformation of the connecting surfaces between subcomponents. The purpose of the study is to highlight numerical and ill-conditioning problems that may arise in this kind of identification

Factors affecting soil invertebrate biodiversity in agroecosystems of the Po Plain area (Italy)

Soil is a fundamental component of the biosphere, whose properties and quality are affected by human activities, such as agriculture. Soil health is fundamental for different ecosystem services and soil biota has a crucial role in maintaining it. Elucidating how different crops and agricultural practices affect soil invertebrates communities is of relevance. In the present study, a DNA metabarcoding approach was adopted to evaluate the effects of different biotic and abiotic factors, including agricultural practices, on the composition and diversity of soil invertebrate communities of different agro-ecosystems (Po Plain-Italy). At this aim, the DNA markers and the more effective primers in retrieving soil metazoan communities were established. Bulk soil samples from different agro-ecosystems (i.e., cornfield, alfalfa, paddy fields, vineyard, stable meadow, woodland) were collected and, processed for obtaining 18S rRNA and coi sequences (raw reads analyzed using QIIME2 and R). Soil physical and chemical parameters were measured for each soil sample (e.g., pH, carbon-nitrogen ratio, texture, porosity) and metadata on farms management were retrieved. The most efficient primer pairs in recovering soil metazoans were M620F/M1260R for 18S rRNA, and mlCOIintF/jgHCO2198R for coi gene. Soil communities resulted dominated by Nematoda, Arthropoda, Annelida, Rotifera and Tardigrada. The most diverse invertebrate communities have been found in the soil of stable meadows and woodlands, while cornfields showed the lowest level of diversity. The diversity of soil invertebrate communities (Hill numbers) was positively correlated with the level of porosity and carbon-nitrogen ratio, while it was negatively correlated with the phosphate abundance. This pattern probably reflects the negative effect of excessive fertilization with phosphates on soil fauna, while the abundance of organic matter and microhabitats were found to enhance the presence of more complex communities. Other soil properties were correlated only with specific taxa (e.g., pH was negatively correlated with the diversity of Annelida and Rotifera)