Ergonomic risk factors associated with muscuslokeletal disorders in computer workstation

Ergonomics Risk Factors (ERFs) at computer works are commonly related to Musculoskeletal Disorders (MSDs) such as repetitive movements, doing work in awkward postures and static postures while prolonged seating at works. The main objective of this study was to investigate the ergonomic risk factors associated with MSDs among employees in computer workstation. In this study, the data were obtained by structured interview using self-reported questionnaire and direct observation. The results show that there is significant association between neck and stress score with musculoskeletal symptoms and among office workers. As a conclusion, by assessing ERFs at workplace, the effectiveness of workplace interventions can be evaluated without waiting for changes in the prevalence of MSDs

Robust Approaches for Fuzzy Clusterwise Regression Based on Trimming and Constraints

Three different approaches for robust fuzzy clusterwise regression are reviewed. They are all based on the simultaneous application of trimming and constraints. The first one follows from the joint modeling of the response and explanatory variables through a normal component fitted in each cluster. The second one assumes normally distributed error terms conditional on the explanatory variables while the third approach is an extension of the Cluster Weighted Model. A fixed proportion of “most outlying” observations are trimmed. The use of appropriate constraints turns these problem into mathematically well-defined ones and, additionally, serves to avoid the detection of non-interesting or “spurious” linear clusters. The third proposal is specially appealing because it is able to protect us against outliers in the explanatory variables which may act as “bad leverage” points. Feasible and practical algorithms are outlined. Their performances, in terms of robustness, are illustrated in some simple simulated examples.Spanish Ministerio de Economía y Competitividad, grant MTM2017-86061-C2-1-P, and by Consejería de Educación de la Junta de Castilla y León and FEDER, grant VA005P17 and VA002G18

A Fuzzy Approach to Robust Clusterwise Regression

new robust fuzzy linear clustering method is proposed. We estimate coe cients of a linear regression model in each unknown cluster. Our method aims to achieve robustness by trimming a xed proportion of observations. Assignments to clusters are fuzzy: observations contribute to estimates in more than one single cluster. We describe general criteria for tuning the method. The proposed method seems to be robust with respect to di erent types of contamination

New bioprocess technologies underpinning future manufacture of magnetosome products

Magnetic support based separations in biotechnological applications was initiated in the late 1970’s. Since then, magnetic supports have been widely applied in the laboratory and increasingly at process scales in hugely diverse applications. To date most of these have employed artificial chemically synthesized magnetic particles, but interest in naturally occurring magnetic materials made biologically is growing. Magnetosomes are one such example. These are needle-like chains of single-domain permanently magnetic membranewrapped crystals that act as a compass to allow magnetotactic bacteria navigate along geomagnetic field lines in search of optimal environmental oxygen levels. Their unique characteristics convey numerous advantages over chemically manufactured magnetic particles in biomedical and biotechnological settings, but future widespread application requires the development of commercial scale intensified high-yielding manufacturing platforms for magnetosome-based products. Against the above the overall aim of this work has been to advance new bioprocess technologies underpinning future manufacture of magnetosome products. The starting point for this work was to develop a battery of flow cytometric tools for analysing the growth, viability, physiology of magnetotactic bacteria (Magnetospirillum gryphiswaldense MSR-1 was selected as a model organism) and their biomineralization of magnetic iron minerals. Specifically, methods for the determination of cellular concentration, cell size distribution, single-cell physiology and time dependent changes in intracellular PHA content and the chelatable iron pool were advanced. ii The next study was the development of a simple pH-stat fermentation strategy for production of M. gryphiswaldense MSR-1 and magnetosomes. Growth conditions were optimised with respect to biomass concentration, cellular magnetism (indicative of magnetosome production) and intracellular iron concentration using the previously developed flow cytometry analytics. High biomass and cellular iron contents of 4.2 g dry cell weight per litre and 33.1 milligrams per gram dry cell weight respectively were obtained. The final piece of work describes the systematic advance of a fully scalable platform for extraction, recovery and purification of magnetosomes. The approach comprises single pass disruption of exponential phase Magnetospirillum gryphiswaldense MSR-1 cells in a commercial high pressure homogenizer, recovery and partial purification of magnetosomes by high gradient magnetic fishing in an automated ‘state-of-the-art’ magnetic separator, and final purification by magnetic micellar aqueous two phase separation. A magnetosome yield of nearly 45% was achieved, with 98.5% and >99% removal of polyhydroxyalkanoate and protein respectivel

The application of multivariate statistical analysis and batch process control in industrial processes

To manufacture safe, effective and affordable medicines with greater efficiency, process analytical technology (PAT) has been introduced by the Food and Drug Agency to encourage the pharmaceutical industry to develop and design well-understood processes. PAT requires chemical imaging techniques to be used to collect process variables for real-time process analysis. Multivariate statistical analysis tools and process control tools are important for implementing PAT in the development and manufacture of pharmaceuticals as they enable information to be extracted from the PAT measurements. Multivariate statistical analysis methods such as principal component analysis (PCA) and independent component analysis (ICA) are applied in this thesis to extract information regarding a pharmaceutical tablet. ICA was found to outperform PCA and was able to identify the presence of five different materials and their spatial distribution around the tablet.Another important area for PAT is in improving the control of processes. In the pharmaceutical industry, many of the processes operate in a batch strategy, which introduces difficult control challenges. Near-infrared (NIR) spectroscopy is a non-destructive analytical technique that has been used extensively to extract chemical and physical information from a product sample based on the scattering effect of light. In this thesis, NIR measurements were incorporated as feedback information into several control strategies. Although these controllers performed reasonably well, they could only regulate the NIR spectrum at a number of wavenumbers, rather than over the full spectrum.In an attempt to regulate the entire NIR spectrum, a novel control algorithm was developed. This controller was found to be superior to the only comparable controller and able to regulate the NIR similarly. The benefits of the proposed controller were demonstrated using a benchmark simulation of a batch reactor.EThOS - Electronic Theses Online ServicePfizer IncorporatedUniversity UKUniversity of ManchesterGBUnited Kingdo

From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.publishedVersio

Fuzzy Logic

Fuzzy Logic is becoming an essential method of solving problems in all domains. It gives tremendous impact on the design of autonomous intelligent systems. The purpose of this book is to introduce Hybrid Algorithms, Techniques, and Implementations of Fuzzy Logic. The book consists of thirteen chapters highlighting models and principles of fuzzy logic and issues on its techniques and implementations. The intended readers of this book are engineers, researchers, and graduate students interested in fuzzy logic systems

Nature’s Optics and Our Understanding of Light

Optical phenomena visible to everyone abundantly illustrate important ideas in science and mathematics. The phenomena considered include rainbows, sparkling reflections on water, green flashes, earthlight on the moon, glories, daylight, crystals, and the squint moon. The concepts include refraction, wave interference, numerical experiments, asymptotics, Regge poles, polarisation singularities, conical intersections, and visual illusions