Prioritization of noise abatement methods for controlling hospital noise pollution

Noise pollution in hospitals has increased over the last few years to a level that can threaten the health and productivity of staff and patient safety. There are many control measures to reduce hospital noise. However, there is still no consensus on the best measures. This study aims to prioritize the control measures for reducing hospital noise. The work is divided into three phases. The first phase identifies and categorizes noise sources in hospitals through a review of the state-of-the art literature using Scopus®, ProQuest, PubMed, Google Scholar, Embase,™ and Web of Science™. The second phase identifies possible strategies for reduction of hospital noise and the best criteria for their adoption using findings from the literature review and interviews with corresponding experts. The third phase uses Fuzzy Analytic Hierarchy Process (FAHP) method and the Technique for Order of Preference by Similarity to Ideal Solution (fuzzy TOPSIS) method to weigh the criteria and to prioritize the control measures. Based on the results, hospital noise sources were classified into four groups: outdoor noise sources (29.7%), noise produced by domestic facilities (20.8%), indoor noise from human activities (27.5%), and noise produced by diagnostic and treatment equipment (22%). The study further arrives at a set of 9 criteria and 22 alternatives ranked using FAHP and fuzzy TOPSIS. The criteria’s weights were determined using the FAHP method, with feasibility (0.175), effectiveness (0.143), and interference with staff activities (0.140) being the most important criteria. It was found that engineering controls such as substitution of noisy equipment (rank = 1), using acoustic enclosures (rank = 2), using double-glazed windows (rank = 2), and soundproofing walls, doors, and windows (rank = 3) have priority for reducing hospital noise

Case-based reasoning for product style construction and fuzzy analytic hierarchy process evaluation modeling using consumers linguistic variables

Key form features are relative to the style of a product and the expression style features depict the product description and are a measurement of attribute knowledge. The uncertainty definition leads to an improved and effective product style retrieval when combined with fuzzy sets. Firstly, a style knowledge and features database are constructed using fuzzy case based reasoning technology (FCBR). A similarity measurement method based on case-based reasoning and fuzzy model of the fuzzy proximity method may be defined by the Fuzzy Nearest-Neighbor (FNN) algorithm obtaining the style knowledge extraction. Secondly, the Linguistic Variables (LV) are used to assess the product characteristics to establish the product style evaluation database for simplifying the style presentation and decreasing the computational complexity. Thirdly, the model of product style feature set, extracted by FAHP and the final style related form features set, are acquired using LV. This research involves a case study for extracting the key form features of the style of high heel shoes. The proposed algorithms are generated by calculating the weights of each component of high heel shoes using FAHP with LV. The case study and results established that the proposed method is feasible and effective for extracting the style of the product

Plantar pressure image fusion for comfort fusion in diabetes mellitus using an improved fuzzy hidden Markov model

Diabetes mellitus is a clinical syndrome caused by the interaction of genetic and environmental factors. The change of plantar pressure in diabetic patients is one of the important reasons for the occurrence of diabetic foot. The abnormal increase of plantar pressure is a predictor of the common occurrence of foot ulcers. The feature extraction of plantar pressure distribution will be beneficial to the design and manufacture of diabetic shoes that will be beneficial for early protection of Diabetes mellitus patients. In this research, texture-based features of the Angular Second Moment (ASM), Moment of Inertia (MI), Inverse Difference Monument (IDM), and Entropy (E) have been selected and fused by using the an up-down algorithm. The fused features are normalized to predict comfort plantar pressure imaging dataset using an improved Fuzzy Hidden Markov Model (FHMM). In FHMM, type-I fuzzy set is proposed and Fuzzy Baum-Welch algorithm is also applied to estimate the next features. The results are discussed, and by comparing with other back-forward algorithms and different fusion operations in FHMM. Improved HMMs with up-down fusion using type-I fuzzy definition performs high effectiveness in prediction comfort plantar pressure distribution in an image dataset with an accuracy of 82.2% and the research will be applied to the shoe-last personalized customization in the industry

APPLICATIONS OF ENVIRONMENT-BASED DESIGN (EBD) METHODOLOGY

A product’s environments play a significant role in its development. In other words, any alteration in the environment surrounding a product leads to changes in its features. Hence, having a systematic procedure to analyze the product’s environments is a crucial need for industries. Environment-Based Design (EBD) methodology describes the environment of the product (excluding the product itself) and presents a rational approach to analyze it. In order to achieve an efficient product design and development process, EBD utilizes different tools. Recursive Object Model (ROM) diagram, Cause and Effect Analysis, Life Cycle Analysis, Asking Right Question and Answering are EBD’s major tools and technics. In this research, we aim to represent EBD’s capabilities for product evolution analysis, complex products development and human-centered products development. In order to demonstrate EBD’s competences for product evolution analysis, we conduct a case study of braking systems evolution analysis through analyzing the environments around them. Afterward, we perform environment analysis for aerospace design methodology in order to propose a novel design methodology for the aerospace industries. Finally, we propose a course scheduling model based on environment analysis of the academic schedules and we verify our model using Concordia University’s courses

MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS

Esta Tesis propone el uso de técnicas multidisciplinares como una alternativa viable a los procedimientos actuales de evaluación del calzado los cuales, normalmente, consumen muchos recursos humanos y técnicos. Estas técnicas son Realidad Virtual, Biomecánica Computacional y Redes Neuronales Artificiales. El marco de esta tesis es el análisis virtual del confort mecánico en el calzado, es decir, el análisis de las presiones de confort en el calzado y su principal objetivo es predecir las presiones ejercidas por el zapato sobre la superficie del pie al caminar mediante la simulación del contacto en esta interfaz. En particular, en esta tesis se ha desarrollado una aplicación software que usa el Método de los Elementos Finitos para simular la deformación del calzado. Se ha desarrollado un modelo preliminar que describe el comportamiento del corte del calzado, se ha implementado un proceso automático para el ajuste pie-zapato y se ha presentado una metodología para obtener una animación genérica del paso de cada individuo. Además, y con el fin de mejorar la aplicación desarrollada, se han propuesto nuevos modelos para simular el comportamiento del corte del calzado al caminar. Por otro lado, las Redes Neuronales Artificiales han sido aplicadas en esta tesis a la predicción de la fuerza ejercida por una esfera, que simulando un hueso, empuja a una muestra de material. Además, también han sido utilizadas para predecir las presiones ejercidas por el corte del calzado sobre la superficie del pie (presiones dorsales) en un paso completo. Las principales contribuciones de esta tesis son: el desarrollo de un innovador simulador que permitirá a los fabricantes de calzado realizar evaluaciones virtuales de las características de sus diseños sin tener que construir el prototipo real, y el desarrollo de una también innovadora herramienta que les permitirá predecir las presiones dorsales ejercidas por el calzado sobre la superficie del pie al caminar.Rupérez Moreno, MJ. (2011). MULTIDISCIPLINARY TECHNIQUES FOR THE SIMULATION OF THE CONTACT BETWEEN THE FOOT AND THE SHOE UPPER IN GAIT: VIRTUAL REALITY, COMPUTATIONAL BIOMECHANICS, AND ARTIFICIAL NEURAL NETWORKS [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/11235Palanci

Handling a large number of preferences in a multi-level decision-making process

The complexity of a decision is related to the number of persons that are involved, as well as to the diversity of their preferences based on their knowledge, experience or area of expertise. Consequently, it is a challenge to adequately handle a large number of heterogeneous preferences considering that all the participants are considered to be an important source of information to make better motivated decisions. Addressing this challenge constitutes the main motivation in this dissertation because these days decision makers seem to be increasingly interested in the opinions (or preferences) given by persons around a community (and sometimes around the world) through different sources including social media channels. This PhD study provides a set of tools that helps a decision maker to make better motivated decisions by a proper handling of a large number of preferences, identifying and evaluating relevant preferences and handling multiple perspectives. Herein, by 'preference' is meant a greater interest expressed by an individual for a particular alternative over others; by 'relevant' is meant a variety of preferences which are significant (or important) to a particular person acting as a decision maker; and by 'perspective' is understood a position (e.g., social, technical, financial or environmental) adopted by a decision maker when expressing his/ her preferences or constraints

Post-Series Design: a tool for catalysing the diffusion of personalisable design.

Today a range of increasingly mainstream Digital Fabrication tools help designers not only in prototyping, but also in the production of final parts for consumer products. These hardware tools, while still have significant limitations, they already offer new levels of morphological freedom and logistical flexibility, which allows the efficient production of personalisable products – supposing advanced software tools of Parametric Design. However, since DF, PD and personalisation are still marginal, one may suspect that the Design profession has a shortage of adequate capabilities. Therefore, this contribution proposes a conceptual tool focused on valorising the previous hardware and software tools to achieve meaningfully personalisable products. The proposed canvas tool is structured specifically to facilitate opportunity identification and conceptual design, based on a set of key advantages (variabilities) derived from numerous case studies of existing personalisable products realised with DF. The new approach and tool have been experimented with a class of product design students, but it also aims to facilitate product development at enterprises, coherently with the emerging Industry 4.0 paradigm

Subjective assessment of university classroom environment

[EN] Research into the design of learning environments is warranted as the classroom space impacts on students' wellbeing and learning performance. Studies on subjective evaluation of classrooms usually focus on the influence of more objective aspects like temperature, light, sound, etc., based on concepts or attributes defined by experts. Thus, the attributes used to find relations with design parameters might not be recognised by users, thereby conditioning the evaluation process itself. This paper aims to analyse students' affective response to a university classroom in their own words, and then, after obtaining the semantic space, to identify the design elements that generate a positive affective response. This analysis was carried out implementing the Semantic Differential method in the framework of Kansei Engineering. A sample of 918 university students was assessed in situ in 30 university classrooms. The results show that students' affective structure in relation to their classroom comprises six independent factors: functionality and layout, cosy and pleasant, concentration and comfort, modern design, daylight and outward facing, and artificial lighting. From these factors, efforts to improve the classroom environment should be directed mainly towards two aspects: improving classroom functionality-layout, which is significantly related to the work space allocated to students; and the sensation of cosy-pleasant which is generated by all the classroom design parameters, but in particular, those that refer to the relationship of the classroom with the outdoor environment.This research was supported by Ministerio de Economia y Competitividad, Spain (project TIN2013-45736-R)Castilla-Cabanes, N.; Llinares Millán, MDC.; Bravo, JM.; Blanca Giménez, V. (2017). Subjective assessment of university classroom environment. Building and Environment. 122:72-81. https://doi.org/10.1016/j.buildenv.2017.06.004S728112

An efficient local binary pattern based plantar pressure optical sensor image classification using convolutional neural networks

The objective of this study was to design and produce highly comfortable shoe products guided by a plantar pressure imaging data-set. Previous studies have focused on the geometric measurement on the size of the plantar, while in this research a plantar pressure optical imaging data-set based classification technology has been developed. In this paper, an improved local binary pattern (LBP) algorithm is used to extract texture-based features and recognize patterns from the data-set. A calculating model of plantar pressure imaging feature area is established subsequently. The data-set is classified by a neural network to guide the generation of various shoe-last surfaces. Firstly, the local binary mode is improved to adapt to the pressure imaging data-set, and the texture-based feature calculation is fully used to accurately generate the feature point set; hereafter, the plantar pressure imaging feature point set is then used to guide the design of last free surface forming. In the presented experiments of plantar imaging, multi-dimensional texture-based features and improved LBP features have been found by a convolution neural network (CNN), and compared with a 21-input-3-output two-layer perceptual neural network. Three feet types are investigated in the experiment, being flatfoot (F) referring to the lack of a normal arch, or arch collapse, Talipes Equinovarus (TE), being the front part of the foot is adduction, calcaneus varus, plantar flexion, or Achilles tendon contracture and Normal (N). This research has achieved an 82% accuracy rate with 10 hidden-layers CNN of rotation invariance LBP (RI-LBP) algorithm using 21 texture-based features by comparing other deep learning methods presented in the literature