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Abstract Background Echinococcus granulosus sensu lato has a complex developmental biology with a variety of factors relating to both intermediate and final hosts. To achieve maximum parasite adaptability, the development of the cestode is dependent on essential changes in transcript regulation. Transcription factors (TFs) and miRNAs are known as master regulators that affect the expression of downstream genes through a wide range of metabolic and signaling pathways. In this study, we aimed to develop a regulatory miRNA-Transcription factor (miRNA-TF) network across early developmental stages of E. granulosus protoscoleces by performing in silico analysis, and to experimentally validate TFs expression in protoscoleces obtained from in vitro culture, and from in vivo experiments. Results We obtained list of 394 unique E. granulosus TFs and matched them with 818 differentially expressed genes which identified 41 predicted TFs with differential expression. These TFs were used to predict the potential targets of 31 differentially expressed miRNAs. As a result, eight miRNAs and eight TFs were found, and the predicted network was constructed using Cytoscape. At least four miRNAs (egr-miR-124a, egr-miR-124b-3p, egr-miR-745-3p, and egr-miR-87-3p) and their corresponding differentially expressed TFs (Zinc finger protein 45, Early growth response protein 3, Ecdysone induced protein 78c and ETS transcription factor elf 2) were highlighted in this investigation. The expression of predicted differentially expressed TFs obtained from in vitro and in vivo experiments, were experimentally validated by quantitative polymerase chain reaction. This confirmed findings of RNA-seq data. Conclusion miRNA-TF networks presented in this study control some of the most important metabolic and signaling pathways in the development and life cycle of E. granulosus, providing a potential approach for disrupting the early hours of dog infection and preventing the development of the helminth in the final host.

In the last decade, the adoption of technological tools in manufacturing industry, such as the use of the Internet of Things (IoT) and Machine Learning (ML), has led to the advent of the industry 4.0 (I4.0). In this scenario, intelligent devices can generate large volumes of data about industrial machinery and equipment that can be used to make maintenance more efficient. Prognostics and Health Management (PHM) is an emerging maintenance strategy that uses systems’ Condition Monitoring through IoT sensors installed on machinery to diagnose their faults or estimate their Remaining Useful Life (RUL). This study aims to conduct a Systematic Literature Review (SLR) on the use of ML techniques in the field of PHM of industrial mechanical systems and equipment. 50 studies resulted eligible for the above-mentioned SLR. Diagnostics and prognostics approach and the ML algorithm types used in the 50 analyzed papers have been analyzed together with the Key Performance Indicators (KPIs) used for their validation. From the analyses, it was found that Shallow Learning and Deep Learning (DL) algorithms are the most applied ones, while KPIs are used differently according to the type of task classification or regression. Moreover, results highlighted that many authors still use artificial datasets to test their algorithms, instead of datasets based on real data retrieved by their components. For the last type of datasets, this paper also introduces a schematic framework to standardize the step-by-step diagnostics and prognostics process carried out by the authors.

Water, sewerage, and gas infrastructures play a crucial role in optimising the housing quality of buildings and cities. On the other hand, water, sewer, and gas pipelines constantly need maintenance, checks, and repairs. These interventions require large budgets, and therefore scrupulous investment planning is necessary. In this study, Multiple Regression Analysis (MRA) is applied to estimate the urban renovation costs related to the works on water, sewage, and gas networks. The goal is to build a reliable cost estimator that is easy to apply and has a minimum number of explanatory variables. Four regressive models are tested: linear, linear-logarithmic, logarithmic-linear, and exponential. The analysis is implemented on two datasets of projects carried out in Italy: the first collects the data of 19 projects made in historical centres, while the second collects the data of 20 projects made in the peripheries. The variables that impact costs the most are selected. In terms of results, the estimated functions return an average error of 1.25% for historical centres and 1.00% for peripheral areas. The application shows that a differentiation of cost functions based on the urban context is relevant. Specifically, two different functions are detected: exponential for historical centres and linear for peripheral areas. In conclusion, we interpret that the exponential growth of costs in historical centres depends on a series of critical issues (logistical, architectural, etc.), present to a lesser extent in the peripheries, which complicate the execution of the interventions. The approach adopted, which led to the detection of cost functions differentiated based on the urban context, allows us to benefit from more accurate modelling that considers the places’ specificities.

Purpose: the aim of the study is to compare the performance of different supply chain configurations adopting Additive Manufacturing. Five input factors have been varied with the aim of testing the response of the supply chain to different starting conditions. In order to evaluate the supply chain performance, a set of key performance indicators have been identified considering both manufacturing and logistic processes. Design/methodology/approach: A discrete event simulation model has been developed in order to reproduce the behavior of the players according to their role in the supply chain. Different supply chain configurations have been modelled to assess the performance of the solution combined with different input factors. Many scenarios have been tested with the aim of identifying suitable applications of the additive technology. Findings: in general, the decentralized configuration has better logistic performance than the centralized supply chain. In fact, it is more flexible, suitable for high service levels, and less affected by the variability of the demand. However, when the distances among players are very short and the average demand is low, the benefits in adopting a decentralized configuration are very limited. Concerning the performance of the production phase, the centralized structure allows providing a better capacity utilization, exploiting the potential of a High-cost machine with higher production camera volume and speed. Practical implications: the outcomes obtained allow deriving some useful guidelines, which could help practitioners to identify a suitable application of the additive technology. Originality/value: first, the model provides a quantitative evaluation. Moreover, the study analyzes the performance of the additive technology combined with different supply chain configurations. This is a strong point since it is well known that emerging manufacturing technologies can affect the structure and the performance of the whole supply chain.

The United Nations SDG6 goal of ensuring universal access to safe drinking water and sanitation by 2030 will require increased investment in the rehabilitation and maintenance of water infrastructure. In Italy, the water sector has not yet reached the performance of other European countries. The hierarchization of investments is essential for identifying priorities and efficiently allocating resources. This issue is part of the debate on the reconciliation of public and private needs in the management of water services. The present research proposes a model based on the analytic hierarchy process (AHP). Taking into account the design alternatives considered optimal that contribute to the resolution of territorial criticalities, the model organizes them in a ranking that indicates the chronological priorities to be respected in the investments to be made. The evaluation criteria are set in compliance with the norms defined by the National Authority (ARERA). The model is tested on a water manager in the Campania region. Among the main results, it is found that the two extremes of the ranking are shared between the two actors involved in the investment strategy (the private operator and public regulator). The model represents an effective tool for identifying shared planning strategies between public and private operators.

More than 60 years has passed since the installation of the first robot in an industrial context. Since then, industrial robotics has seen great advancements and, today, robots can collaborate with humans in executing a wide range of working activities. Nevertheless, the impact of robots on human operators has not been deeply investigated. To address this problem, we conducted an empirical study to measure the errors performed by two groups of people performing a working task through a virtual reality (VR) device. A sample of 78 engineering students participated in the experiments. The first group worked with a robot, sharing the same workplace, while the second group worked without the presence of a robot. The number of errors made by the participants was collected and analyzed. Although statistical results show that there are no significant differences between the two groups, qualitative analysis proves that the presence of the robot led to people paying more attention during the execution of the task, but to have a worse learning experience.

The sustainable management of water resources can be pursued through effective tariff policies capable of discouraging water wastefulness. Increasing Block Tariffs (IBT) represent a method of pricing the water service which consists of providing various tariff ranges, with a unit cost that increases as consumption increases. The definition of the consumption ranges and the relative tariffs must guarantee the right balance between the needs of the users and the need to protect the resource according to 2030 Sustainable Development Goals (SDGs). This study proposes an optimization model useful for ensuring the tariff structure complies with the guidelines dictated by the Integrated Text for Water Services Tariffs (TICSI), an Italian standard that aims to rationalize and level out the fee structure at the national level. The purpose of the model is to guarantee the sustainability of the tariffs for users, protect less well-off households, and, at the same time, to ensure that the fees grow with consumption in an optimal way for the operator, in compliance with the economic constraints imposed by the national authority (ARERA). The model, which consists of a non-linear function capable of minimizing the difference between the tariffs before and after TICSI’ rules implementation, was tested through a case study. Specifically, the optimal water tariffs for each consumption range were defined for an operator in Southern Italy. The proposed model makes it possible to integrate EU guidelines relating to “polluter pays” and the protection of water resources more effectively into the national regulatory framework.

The Additive Manufacturing (AM) scheduling problem is becoming a very felt issue not only by the scholars but also by the practitioners who are looking to this new technology as a new integrated part of their traditional production systems. They need new scheduling models to adapt the traditional scheduling rules to the changed ones of the additive manufacturing. This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms.

The Superbonus is an Italian tax relief policy aimed at encouraging residential buildings’ energy and seismic efficiency. Only the energy part of the legislation is analyzed here. The tax deductions of the Superbonus exceed the nominal value of the project, making the interventions convenient even for economically disadvantaged citizens and small construction companies. However, the measure has only found wide diffusion in single-family housing, while it proceeds more slowly for multi-family buildings, where procedural complexities greatly amplify the risk of the financial failure of projects. The purpose of the paper is to analyze how urban planning and technical and administrative problems affect the Return on Investment (ROI) when the Superbonus is applied to multi-unit buildings. Therefore, a financial risk analysis is conducted from the perspective of an ordinary Energy Service Company (ESCo), which assumes the burden of carrying out energy efficiency measures. The property considered has all the requirements of an ordinary multi-family building for which the Superbonus is generally used. The works considered are also those carried out most frequently. The study shows that only three out of five energy interventions are financially sustainable. This result is in line with the data provided by the Italian Revenue Agency.

Cestode parasites cause neglected diseases, such as echinococcosis and cysticercosis, which represent a significant problem in human and animal health. Benzimidazoles and praziquantel are the only available drugs for chemotherapy and it is therefore important to identify new alternative drugs against cestode parasites. Histone deacetylases (HDACs) are validated drug targets for the treatment of cancer and other diseases, including neglected diseases. However, knowledge of HDACs in cestodes is very scarce. In this work, we investigated cestode HDACs as potential drug targets to develop new therapies against neglected diseases caused by cestodes. Here we showed the full repertoire of HDAC coding genes in several members of the class Cestoda. Between 6 and 7 zinc-dependent HDAC coding genes were identified in the genomes of species from Echinococcus, Taenia, Mesocestoides and Hymenolepis genera. We classified them as Class I and II HDACs and analyzed their transcriptional expression levels throughout developmental stages of Echinococcus spp. We confirmed for the first time the complete HDAC8 nucleotide sequences from Echinococcus canadensis G7 and Mesocestoides corti. Homology models for these proteins showed particular structural features which differentiate them from HDAC8 from Homo sapiens. Furthermore, we showed that Trichostatin A (TSA), a pan-HDAC inhibitor, decreases the viability of M. corti, alters its tegument and morphology and produces an increment of the total amount of acetylated proteins, including acetylated histone H4. These results suggest that HDAC from cestodes are functional and might play important roles on survival and development. The particular structural features observed in cestode HDAC8 proteins suggest that these enzymes could be selectively targeted. This report provides the basis for further studies on cestode HDAC enzymes and for discovery of new HDAC inhibitors for the treatment of neglected diseases caused by cestode parasites. Keywords: Cestode, Neglected Tropical Diseases, Echinococcus, Mesocestoides corti, Histone deacetylases, Trichostatin A

According to the international literature postures, exerted forces, manual handling and repetitive actions with upper limbs must be considered in order to estimate the workers’ exposure to biomechanical overload risk, but also a preventive ergonomic approach in the design phase is possible. Within the Industry 4.0, the digitalisation of manufacturing processes generate benefits in terms of production costs and time. Regarding the ergonomics, it is possible to set up a predictive model for the evaluation of biomechanical overload risk. This paper proposes an appraisal of a workplace design and ergonomics validation procedure based on simulation: data from assembly tasks simulation of Digital Human Models (DHM) can be used to assess the ergonomic indexes (OWAS, NIOSH, OCRA, EAWS, etc.). So, it is possible to preventively solve ergonomic risks during the design phase. A test case, regarding a real workplace of an assembly line of an important automotive Company, is also presented.

The neglected zoonotic disease alveolar echinococcosis (AE) is caused by the metacestode stage of the tapeworm parasite Echinococcus multilocularis. MicroRNAs (miRNAs) are small non-coding RNAs with a major role in regulating gene expression in key biological processes. We analyzed the expression profile of E. multilocularis miRNAs throughout metacestode development in vitro, determined the spatial expression of miR-71 in metacestodes cultured in vitro and predicted miRNA targets. Small cDNA libraries from different samples of E. multilocularis were sequenced. We confirmed the expression of 37 miRNAs in E. multilocularis being some of them absent in the host, such as miR-71. We found a few miRNAs highly expressed in all life cycle stages and conditions analyzed, whereas most miRNAs showed very low expression. The most expressed miRNAs were miR-71, miR-9, let-7, miR-10, miR-4989 and miR-1. The high expression of these miRNAs was conserved in other tapeworms, suggesting essential roles in development, survival, or host-parasite interaction. We found highly regulated miRNAs during the different transitions or cultured conditions analyzed, which might suggest a role in the regulation of developmental timing, host-parasite interaction, and/or in maintaining the unique developmental features of each developmental stage or condition. We determined that miR-71 is expressed in germinative cells and in other cell types of the germinal layer in E. multilocularis metacestodes cultured in vitro. MiRNA target prediction of the most highly expressed miRNAs and in silico functional analysis suggested conserved and essential roles for these miRNAs in parasite biology. We found relevant targets potentially involved in development, cell growth and death, lifespan regulation, transcription, signal transduction and cell motility. The evolutionary conservation and expression analyses of E. multilocularis miRNAs throughout metacestode development along with the in silico functional analyses of their predicted targets might help to identify selective therapeutic targets for treatment and control of AE.

Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.

Echinococcosis represents a major public health problem worldwide and is considered a neglected disease by the World Health Organization. The etiological agents are Echinococcus tapeworms, which display elaborate developmental traits that imply a complex control of gene expression. MicroRNAs (miRNAs), a class of small regulatory RNAs, are involved in the regulation of many biological processes such as development and metabolism. They act through the repression of messenger RNAs (mRNAs) usually by binding to the 3' untranslated region (3'UTR). Previously, we described the miRNome of several Echinococcus species and found that miRNAs are highly expressed in all life cycle stages, suggesting an important role in gene expression regulation. However, studying the role of miRNAs in helminth biology remains a challenge. To develop methodology for functional analysis of miRNAs in tapeworms, we performed miRNA knockdown experiments in primary cell cultures of Echinococcus multilocularis, which mimic the development of metacestode vesicles from parasite stem cells in vitro. First, we analysed the miRNA repertoire of E. multilocularis primary cells by small RNA-seq and found that miR-71, a bilaterian miRNA absent in vertebrate hosts, is one of the top five most expressed miRNAs. Using genomic information and bioinformatic algorithms for miRNA binding prediction, we found a high number of potential miR-71 targets in E. multilocularis. Inhibition of miRNAs can be achieved by transfection of antisense oligonucleotides (anti-miRs) that block miRNA function. To this end, we evaluated a variety of chemically modified anti-miRs for miR-71 knockdown. Electroporation of primary cells with 2'-O-methyl modified anti-miR-71 led to significantly reduced miR-71 levels. Transcriptomic analyses showed that several predicted miR-71 targets were up-regulated in anti-miR-treated primary cells, including genes potentially involved in parasite development, host parasite interaction, and several genes of as yet unknown function. Notably, miR-71-silenced primary cell cultures showed a strikingly different phenotype from control cells and did not develop into fully mature metacestodes. These findings indicate an important function of miR-71 in Echinococcus development and provide, for the first time, methodology to functionally study miRNAs in a tapeworm.

This paper presents a novel reliability-based parametric methodology for quantifying the reliability of cost estimates for new composite aircraft components. In recent years, the aircraft production sector has increased its attention on optimizing their composite manufacturing operations. It has become clear that a key factor in the success of these operations is the consideration that not only technical factors, but also economic ones as well are relevant. Composite manufacturing variability is greatly influenced by many of these factors, and since manufacturing with composites is significantly more difficult than with more traditional materials, there are many sources of uncertainty that could influence the reliability of manufacturing cost estimates for new composite aircraft components. Therefore, it is worth considering these sources of uncertainty during the cost estimation process and to quantify the reliability of the cost estimates. To demonstrate the proposed methodology, a numerical example featuring a real-life composite aircraft component from a Boeing 787, with real-life data, is presented. Results show that the proposed methodology can quantify the uncertainty associated with cost estimates for new composite aircraft components in an effective manner, thereby supporting engineers in optimising the cost of their designs, helping them avoid errors in budget definition, and enabling them to allocate resources more efficiently. [ABSTRACT FROM AUTHOR]

Assessing the riskiness of investments in civil works is an integral part of the decision-making process. The main limitation is the absence, both in the regulatory landscape and in the literature of the sector, of threshold values that can guide the analyst in expressing an assessment on the acceptance of the investment risk. The aim of the paper is to define a risk management model that overcomes this gap by introducing acceptability and tolerability thresholds for project risk. The idea is to jointly use: (i) the As Low As Reasonably Practicable (ALARP) logic, from which the concepts threshold of acceptability and tolerability of risk derive, for the first time applied to assess the project risk in the civil field; (ii) the Capital Asset Pricing Model (CAPM) and statistical methods to define an innovative methodology for estimating the aforementioned threshold values. According to the proposed approach, these risk limit values can be specified according to both the investment sector and the socio-economic context of the project. The implementation of the methodology in the civil company sector in Europe allows to validate the described model. The elaborations show that the financial performance of the project is widely acceptable if the Expected Internal Rate of Return is greater than 7.8%; unacceptable if the expected rate of return is less than 5.6%; and tolerable as an ALARP if the expected rate is between 5.6% and 7.8%. The estimated acceptability and tolerability thresholds can provide the economic operator with a more immediate and consistent evaluation of the triangular balance of risks, costs, and benefits. This allows the decision-making process to become more rational and transparent.

The uncertainty in the supply chains (SCs) for manufacturing and services firms is going to be, over the coming decades, more important for the companies that are called to compete in a new globalized economy. Risky situations for manufacturing are considered in trying to individuate the optimal positioning of the order penetration point (OPP). It aims at defining the best level of information of the client’s order going back through the several supply chain (SC) phases, i.e. engineering, procurement, production and distribution. This work aims at defining a system dynamics model to assess competitiveness coming from the positioning of the order in different SC locations. A Taguchi analysis has been implemented to create a decision map for identifying possible strategic decisions under different scenarios and with alternatives for order location in the SC levels. Centralized and decentralized strategies for SC integration are discussed. In the model proposed, the location of OPP is influenced by the demand variation, production time, stock-outs and stock amount. Results of this research are as follows: (i) customer-oriented strategies are preferable under high volatility of demand, (ii) production-focused strategies are suggested when the probability of stock-outs is high, (iii) no specific location is preferable if a centralized control architecture is implemented, (iv) centralization requires cooperation among partners to achieve the SC optimum point, (v) the producer must not prefer the OPP location at the Retailer level when the general strategy is focused on a decentralized approach.