Androgenetic alopecia: a review

Purpose Androgenetic alopecia, commonly known as male pattern baldness, is the most common type of progressive hair loss disorder in men. The aim of this paper is to review recent advances in understanding the pathophysiology and molecular mechanism of androgenetic alopecia. Methods Using the PubMed database, we conducted a systematic review of the literature, selecting studies pub- lished from 1916 to 2016. Results The occurrence and development of androgenetic alopecia depends on the interaction of endocrine factors and genetic predisposition. Androgenetic alopecia is character- ized by progressive hair follicular miniaturization, caused by the actions of androgens on the epithelial cells of genetically susceptible hair follicles in androgen-dependent areas. Although the exact pathogenesis of androgenetic alopecia remains to be clari fi ed, research has shown that it is a polygenetic condition. Numerous studies have unequi- vocally identi fi ed two major genetic risk loci for androge- netic alopecia, on the X-chromosome AR ⁄ EDA2R locus and the chromosome 20p11 locus. Conclusions Candidate gene and genome-wide association studies have reported that single-nucleotide polymorphisms at different genomic loci are associated with androgenetic alopecia development. A number of genes determine the predisposition for androgenetic alopecia in a polygenic fashion. However, further studies are needed before the specific genetic factors of this polygenic condition can be fully explaine

Spare Parts Replacement Policy Based on Chaotic Models

Poisson point processes are widely used to model the consumption of spare parts. However, when the items have very low consumption rates, the historical sample sizes are too small. This paper presents a modelling technique for spare parts policies in the case of items with a low consumption rate. We propose the use of chaotic models derived from the well-known chaotic processes logistic map and Hénon attractor to assess the behaviour of a set of five medium voltage motors supplying four drives in the rolling mill of a steelmaking plant. Supported by the chaotic models, we conclude that the company needs an additional motor to ensure full protection against shortages

A framework to assess the sustainability of additive manufacturing for spare parts

Additive manufacturing (AM) is a promising technology for the optimization of the spare parts supply chain. A complete evaluation of whether it is advantageous to switch to this technology for spare parts management should include a comprehensive assessment of its sustainability in addition to its techno-economic viability. General analyses of the economic, environmental, and social impacts of AM have been conducted, but assessments of the sustainability effects of AM in the spare parts field is limited to specific industries. Thus, based on the literature, we designed a framework that can support a life cycle evaluation of the emerging application of AM technology. It represents a methodological approach that covers all the stages of the spare parts life cycle and the three dimensions of sustainability. It has been designed to support both researchers and practitioners who are considering AM for the manufacturing of spare parts. Copyright (C) 2022 The Authors

Conventional or additive manufacturing for spare parts management: An extensive comparison for Poisson demand

Due to the main peculiarities of spare parts, i.e. intermittent demands, long procurement lead times and high downtime costs when the parts are not available on time, it is often difficult to find the optimal inventory level. Recently, Additive Manufacturing (AM) has emerged as a promising technique to improve spare parts inventory management thanks to a ‘print on demand’ approach. So far, however, the impact of AM on spare parts inventory management has been little considered, and it is not yet clear when the use of AM for spare parts inventory management would provide benefits over Conventional Manufacturing (CM) techniques. With this paper we thus aim to contribute to the field of AM spare parts inventory management by developing decision trees that can be of support to managers and practitioners. To this aim, we considered a Poisson-based inventory management system and we carried out a parametrical analysis considering different part sizes and complexity, backorder costs and part consumption. Moreover, we evaluated scenarios where the order-up-to level is limited to resemble applications with a limited storage capacity. For the first time, the analysis was not limited to just one AM and one CM technique, but several AM and CM techniques were considered, also combined with different post-process treatments, for a total of nine different sourcing alternatives. In addition, the economic and technical performance of the different sourcing options were obtained thanks to an interdisciplinary approach, where experts from production economics and material science were brought together

On the Analysis of Effectiveness in a Manufacturing Cell: A Critical Implementation of Existing Approaches

Abstract OEE (Overall Equipment Effectiveness) is a widely used indicator in the evaluation of effectiveness of manufacturing systems. However, several authors published alternative approaches for its computation, complicating the implementation step for practitioners. This study analyses the literature regarding OEE, selects four main methodologies for its evaluation and examines the underlying differences between them. A real life case study is analysed to illustrate problems arising during data collection and the differences in results obtained, together with traceable conclusions for improving the performance of production systems, both in traditional and in innovative industrial plants, following Industry 4.0 principles

what is the role of post acute eeg in prediction of late neurological outcome in severe disorders of consciousness

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FlowSort-GDSS:a novel group multi-criteria decision support system for sorting problems with application to FMEA

Failure mode and effects analysis (FMEA) is a well-known approach for correlating the failure modes of a system to their effects, with the objective of assessing their criticality. The criticality of a failure mode is traditionally established by its risk priority number (RPN), which is the product of the scores assigned to the three risk factors, which are likeness of occurrence, the chance of being undetected and the severity of the effects. Taking a simple "unweighted" product has major shortcomings. One of them is to provide just a number, which does not sort failures modes into priority classes. Moreover, to make the decision more robust, the FMEA is better tackled by multiple decision-makers. Unfortunately, the literature lacks group decision support systems (GDSS) for sorting failures in the field of the FMEA. In this paper, a novel multi-criteria decision making (MCDM) method named FlowSort-GDSS is proposed to sort the failure modes into priority classes by involving multiple decision-makers. The essence of this method lies in the pair-wise comparison between the failure modes and the reference profiles established by the decision-makers on the risk factors. Finally a case study is presented to illustrate the advantages of this new robust method in sorting failures

Economic order quantity and storage assignment policies

The basic Harris’s lot size model dates back to 1913 (Harris, 1913), hence one century from its publication has been recently celebrated. Starting from the seminal work of Harris, a wide plethora of contributors has faced with the lot-sizing problem for fitting the basic model of the economic order quantity to several environments. In fact, the three key parameters constituting the basic model, i.e. the demand rate, the ordering costs, and the inventory holding costs, have been widely explored in order to relax the assumptions of the original model. However, to the best of the authors’ knowledge, the liaison between holding costs and warehouse management has not been completely addressed. The holding costs have been early considered for simplicity as primarily given by the cost of capital, and thus dependent solely on the average inventory on stock. Conversely, by including a more detailed supply chain costs contribution, the economic order quantity calculus appears depending on a recursive calculus process and on the storage assignment policy. In fact, different approaches of warehouse management, e.g. shared and dedicated storage, lead to highly variable distances to be covered for performing the missions. This leads to a total cost function, and consequently to optimum lot sizes, that are affected by the warehouse management. In this paper, this relationship has been made explicit in order to evaluate an optimal order quantity taking into account storage assignment policies