New Synthetic Endocannabinoid as Anti-Inflammaging Cosmetic Active: an In Vitro Study on a Reconstructed Skin Model

Endocannabinoids have been recently appointed as interesting cosmetic actives in regulating inflammaging, a state of chronic low-grade inflammation, known for being involved in many senescence\u2019s manifestations, included skin aging. The aim of this study was to assess the anti-inflammaging activity of a new synthetic endocannabinoid, Isopalmide\uae, on a reconstructed skin model, on which inflammaging has been reproduced through UVA radiation and light mechanical stress. We tested Isopalmide\uae both as a single active and conveyed in a cosmetic product, in comparison with Anandamide, a well-known natural endocannabinoid with anti-inflammatory action. The anti-inflammaging activity of topically applied products has been assessed, after 6 hours of treatment post-irradiation, through the transcriptional modification of genes involved in the NF-\u3baB pathway and the epigenetic pathway targeting miRs as potential biomarkers of inflammaging: miR-21, miR-126 and miR-146a. The results confirmed the anti-inflammatory action of Anandamide which inhibits NF-\u3baB, while Isopalmide\uae showed its anti-inflammaging activity through the establishment of an inflammatory/anti-inflammatory balance by maintaining NF-\u3baB inactive in the cytoplasm and active in the nucleus. The anti-inflammaging activity was shown also by the cosmetic product containing Isopalmide

Multiparametric Analysis of Factors Associated With Eosinophilic Chronic Rhinosinusitis With Nasal Polyps

Introduction: Previous studies have reported a diverse range of threshold values for blood eosinophilia. In addition, a single predictive biomarker for eosinophilic chronic rhinosinusitis (CRS) with nasal polyps (ECRSwNP) has not yet been identified. Objectives: The aim of this study is to compare the clinical characteristics of ECRSwNP and non-ECRSwNP to evaluate the preoperative risk of tissue eosinophilia of chronic rhinosinusitis with nasal polyps (CRSwNP) through a multiparametric statistical analysis. Methods: One hundred ten patients with evidence of chronic polypoid rhinosinusitis were included in this study and clinical records were retrospectively reviewed. Eosinophilic CRSwNP was diagnosed based on the presence of at least 10 eosinophils per high-power field. The demographic and clinical features of ECRSwNP and non-ECRSwNP are described. The values of blood eosinophilia as predictors of tissue eosinophilia have been identified using receiver operating characteristic curves. As the predictive value of the identified cutoff through regression analysis was low, we evaluated whether other risk factors could be statistically associated with ECRSwNP, and from this, a new predictive model was proposed for the identification of eosinophilic nasal polyps before surgery. Results: We found that the best method for predicting ECRSwNP is based on a model having asthma, blood eosinophil percentage, posterior ethmoid value in Lund-Mackay score, and modified Lund-Kennedy score as explanatory variables. Conclusions: This study provides new data for a better understanding of the polypoid CRS endotypes, and the proposed model allows the endotype to be identified preoperatively

Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy

Nowadays, last-mile logistics represents the least efficient stage of supply chains, covering up to 28% of the total delivery cost and causing significant environmental emissions. In the last few years, a wide range of collaborative economy business models has emerged across the globe, rapidly changing the way services were traditionally provided and consumed. Crowd logistics (CL) is a new strategy for supporting fast shipping services, entrusting the management of the last-mile delivery to the crowd, i.e., normal people, who agree to deliver goods to customers located along the route they have to travel, using their own transport means, in exchange for a small reward. Most existing studies have focused on evaluating the opportunities and challenges provided by CL through theoretical analysis and literature reviews, while others have proposed models for designing such emerging distribution networks. However, papers analyzing real successful applications of CL worldwide are lacking, despite being in high demand. This study attempted to fill this gap by providing, at first, an overview of real CL applications around the globe to set the stage for future successful implementations. Then, the implementation potential of CL in northern Italy was assessed through a structured questionnaire delivered to a panel of 214 people from the Alma Mater Studiorum University of Bologna (Italy) to map the feasibility of a crowd-based system in this area. The results revealed that about 91% of the interviewees were interested in using this emerging delivery system, while the remaining respondents showed some concern about the protection of their privacy and the safeguarding of the goods during transport. A relevant percentage of the interviewees were available to join the system as occasional drivers (ODs), with a compensation policy preference for a fixed fee per delivery rather than a variable reward based on the extra distance traveled to deliver the goods

INTEGRATED 3D SURVEY FOR THE DOCUMENTATION AND VISUALIZATION OF A ROCK-CUT UNDERGROUND BUILT HERITAGE

Abstract. The research presented here is part of an on-going research focused on the 3D documentation of rock-cut Underground Built Heritage with a complex morphology and characterised by narrow spaces, the Hypogeum of Calaforno (province of Ragusa, Sicily). It is one of the most interesting prehistoric monuments in Sicily in terms of size and unique rock-cut architecture. Various digital techniques have been tested over the years on the site, to represent its spatiality, such as Laser Scanner and Structure from Motion. The proposed methodological approach for the knowledge and the documentation of this archaeological site is based on an interdisciplinary approach involving archaeological and engineering disciplines. This paper focuses on the use of expeditious techniques such as iMMS (indoor Mobile Mapping Systems) based on SLAM (Simultaneous Localization and Mapping) and on the comparison of different surveying equipment in order to verify data quality and accuracy, as well as the inherent advantages of using one technology over another in relation to the characteristics of the site. Through the global verification of TLS and SLAM model reliability, we maintain that such research can contribute to enriching the protocols surrounding the archaeological investigation of sites characterized by complex morphology, irregular surfaces, narrow spaces, specific chromatic features, scarce or total lack of lighting, and physical obstacles

A two-step methodology for product platform design and assessment in high-variety manufacturing

The delayed product differentiation (DPD) recently rose as a hybrid production strategy able to overcome the main limits of make to stock (MTS) and make to order (MTO), guaranteeing the management of high variety and keeping low storage cost and quick response time by using the so-called product platforms. These platforms are a set of sub-systems forming a common structure from which a set of derivative variants can be efficiently produced. Platforms are manufactured and stocked following an MTS strategy. Then, they are customized into different variants, following an MTO strategy. Current literature proposes methods for platform design mainly using optimization techniques, which usually have a high computational complexity for efficiently managing real-size industrial instances in the modern mass customization era. Hence, efficient algorithms need to be developed to manage the product platforms design for such instances. To fill this gap, this paper proposes a two-step methodology for product platforms design and assessment in high-variety manufacturing. The design step involves the use of a novel modified algorithm for solving the longest common subsequence (LCS) problem and of the k-medoids clustering for the identification of the platform structure and the assignment of the variants to the platforms. The platforms are then assessed against a set of industrial and market metrics, i.e. the MTS cost, the variety, the customer responsiveness, and the variants production cost. The evaluation of the platform set against such a combined set of drivers enhancing both company and market perspectives is missing in the literature. A real case study dealing with the manufacturing of a family of valves exemplifies the efficiency of the methodology in supporting companies in managing high-variety to best balance the proposed metrics

Ergonomic Design of an Adaptive Automation Assembly System

Ergonomics is a key factor in the improvement of health and productivity in workplaces. Its use in improving the performance of a manufacturing process and its positive effects on productivity and human performance is drawing the attention of researchers and practitioners in the field of industrial engineering. This paper proposes an ergonomic design approach applied to an innovative prototype of an adaptive automation assembly system (A3S) equipped with Microsoft Kinect™ for real-time adjustment. The system acquires the anthropometric measurements of the operator by means of the 3-D sensing device and changes its layout, arranging the mobile elements accordingly. The aim of this study was to adapt the assembly workstation to the operator dimensions, improving the ergonomics of the workstation and reducing the risks of negative effects on workers’ health and safety. The case study of an assembly operation of a centrifugal electric pump is described to validate the proposed approach. The assembly operation was simulated at a traditional fixed workstation and at the A3S. The shoulder flexion angle during the assembly tasks at the A3S reduced between 18% and 47%. The ergonomic risk assessment confirmed the improvement of the ergonomic conditions and the ergonomic benefits of the A3S

Feature-based multi-class classification and novelty detection for fault diagnosis of industrial machinery

Given the strategic role that maintenance assumes in achieving profitability and competitiveness, many industries are dedicating many efforts and resources to improve their maintenance approaches. The concept of the Smart Factory and the possibility of highly connected plants enable the collection of massive data that allow equipment to be monitored continuously and real-time feedback on their health status. The main issue met by industries is the lack of data corresponding to faulty conditions, due to environmental and safety issues that failed machinery might cause, besides the production loss and product quality issues. In this paper, a complete and easy-to-implement procedure for streaming fault diagnosis and novelty detection, using different Machine Learning techniques, is applied to an industrial machinery sub-system. The paper aims to offer useful guidelines to practitioners to choose the best solution for their systems, including a model hyperparameter optimization technique that supports the choice of the best model. Results indicate that the methodology is easy, fast, and accurate. Few training data guarantee a high accuracy and a high generalization ability of the classification models, while the integration of a classifier and an anomaly detector reduces the number of false alarms and the computational time

Unsupervised fault detection and prediction of remaining useful life for online prognostic health management of mechanical systems

Predictive maintenance allows industries to keep their production systems available as much as possible. Reducing unforeseen shutdowns to a level that is close to zero has numerous advantages, including production cost savings, a high quality level of both products and processes, and a high safety level. Studies in this field have focused on a novel approach, prognostic health management (PHM), which relies on condition monitoring (CM) for predicting the remaining useful life (RUL) of a system. However, several issues remain in its application to real industrial contexts, e.g., the difficulties in conducting tests simulating each fault condition, the dynamic nature of industrial environments, and the need to handle large amounts of data collected from machinery. In this paper, a data-driven methodology for PHM implementation is proposed, which has the following characteristics: it is unsupervised, i.e., it does not require any prior knowledge regarding fault behaviors and it does not rely on pre-trained classification models, i.e., it can be applied "from scratch"; it can be applied online due to its low computational effort, which makes it suitable for edge computing; and, it includes all of the steps that are involved in a prognostic program, i.e., feature extraction, health indicator (HI) construction, health stage (HS) division, degradation modelling, and RUL prediction. Finally, the proposed methodology is applied in this study to a rotating component. The study results, in terms of the ability of the proposed approach to make a timely prediction of component fault conditions, are promising

real time assistance to manual assembly through depth camera and visual feedback

Abstract The current fourth industrial revolution significantly impacts on production processes. The personalized production paradigm enables customers to order unique products. The operators assemble an enormous component variety adapting their process from product to product with limited learning opportunities. Digital technologies are increasingly adopted in production processes to improve performance and quality. Considering this framework, this research proposes a hardware/software architecture to assist in real-time operators involved in manual assembly processes. A depth camera captures human motions in relation with the workstation environment whereas a visual feedback guides the operator through consecutive assembly tasks. An industrial case study validates the architecture

LOW COST TECHNIQUES FOR THE DIGITAL SURVEY OF A MINOAN ARCHITECTURE IN THE ARCHEOLOGICAL SITE OF PHAISTOS (CRETE)

Abstract. The paper shows the results of an experimentation on the use of low cost tools such as action cameras for the photogrammetric surveying of relevant archaeological sites characterized by the presence of narrow and complex rooms. The archaeological site chosen for this experimentation is the South-Western Quarter, also known as Quartiere Levi, of the Minoan Palace of Phaistos (Crete), one of only two cases of buildings surviving up to the third floor in the Aegean world. The research foresaw the setting up of a pipeline aimed at obtaining a complete scaled, photorealistic and navigable 3D model, with a considerable economy in terms of work time and number of photographs. For this purpose, many efforts have been paid on solving all the issues related to the complexity of the site and on comparing the performances of traditional (Canon EOS 70D) and action (GoPro Black Hero 6) cameras as well as of two of the current most used software in the field