Cost and safety optimisation in 'Berlin' type retaining walls

Studies for earth retaining wall structures provide engineers with the values for the response of design characteristics which represents the stability and the required budget for the completion of a project. Fundamental theories guide engineers to combinations of design variables values. These values have a direct relation to the responses of the earth retaining wall structure. The requirement of this analysis is that there is no proven technique which ensures the best combination of design variables for the simultaneous optimisation of safety factor and overall cost of a project. This paper presents an integration of the desirability analysis which provides the multivariate optimisation with the performance of few experimental runs based on statistical tools and finite elements methodology. The methodology provides a 24% higher safety factor and 50% lower overall cost comparing to the results of an experienced foundation engineering company

Lean and green – a systematic review of the state of the art literature

The move towards greener operations and products has forced companies to seek alternatives to balance efficiency gains and environmental friendliness in their operations and products. The exploration of the sequential or simultaneous deployment of lean and green initiatives is the results of this balancing action. However, the lean-green topic is relatively new, and it lacks of a clear and structured research definition. Thus, this paper’s main contribution is the offering of a systematic review of the existing literature on lean and green, aimed at providing guidance on the topic, uncovering gaps and inconsistencies in the literature, and finding new paths for research. The paper identifies and structures, through a concept map, six main research streams that comprise both conceptual and empirical research conducted within the context of various organisational functions and industrial sectors. Important issues for future research are then suggested in the form of research questions. The paper’s aim is to also contribute by stimulating scholars to further study this area in depth, which will lead to a better understanding of the compatibility and impact on organisational performance of lean and green initiatives. It also holds important implications for industrialists, who can develop a deeper and richer knowledge on lean and green to help them formulate more effective strategies for their deployment

The Role of the Mucus Barrier in Digestion

Mucus forms a protective layer across a variety of epithelial surfaces. In the gastrointestinal (GI) tract, the barrier has to permit the uptake of nutrients, while excluding potential hazards, such as pathogenic bacteria. In this short review article, we look at recent literature on the structure, location, and properties of the mammalian intestinal secreted mucins and the mucus layer they form over a wide range of length scales. In particular, we look at the structure of the gel-forming glycoprotein MUC2, the primary intestinal secreted mucin, and the influence this has on the properties of the mucus layer. We show that, even at the level of the protein backbone, MUC2 is highly heterogeneous and that this is reflected in the networks it forms. It is evident that a combination of charge and pore size determines what can diffuse through the layer to the underlying gut epithelium. This information is important for the targeted delivery of bioactive molecules, including nutrients and pharmaceuticals, and for understanding how GI health is maintained

Lean screening for greener energy consumption in retrofitting a residential apartment unit

Buildings consume a large portion of the global primary energy. They are also key contributors to CO2 emissions. Greener residential buildings are part of the ‘Renovation Wave’ in the European Green Deal. The purpose of this study was to explore the usefulness of energy consumption screening as a part of seeking retrofitting opportunities in the older residential building stock. The objective was to manage the screening of the electromechanical energy systems for an existing apartment unit. The parametrization was drawn upon inspection items in a comprehensive electronic checklist—part of an official software—in order to incur the energy certification status of a residential building. The extensive empirical parametrization intends to discover retrofitting options while offering a glimpse of the influence of the intervention costs on the final screening outcome. A supersaturated trial planner was implemented to drastically reduce the time and volume of the experiments. Matrix data analysis chart-based sectioning and general linear model regression seamlessly integrate into a simple lean-and-agile solver engine that coordinates the polyfactorial profiling of the joint multiple characteristics. The showcased study employed a 14-run 24-factor supersaturated scheme to organize the data collection of the performance of the energy consumption along with the intervention costs. It was found that the effects that influence the energy consumption may be slightly differentiated if intervention costs are also simultaneously considered. The four strong factors that influenced the energy consumption were the automation type for hot water, the types of heating and cooling systems, and the power of the cooling systems. An energy certification category rating of ‘B’ was achieved; thus, the original status (‘C’) was upgraded. The renovation profiling practically reduced the energy consumption by 47%. The concurrent screening of energy consumption and intervention costs detected five influential effects—the automation type for water heating, the automation control category, the heating systems type, the location of the heating system distribution network, and the efficiency of the water heating distribution network. The overall approach was shown to be simpler and even more accurate than other potentially competitive methods. The originality of this work lies in its rareness, worldwide criticality, and impact since it directly deals with the energy modernization of older residential units while promoting greener energy performance

Taguchi-generalized regression neural network micro-screening for physical and sensory characteristics of bread

Generalized regression neural networks (GRNN) may act as crowdsourcing cognitive agents to screen small, dense and complex datasets. The concurrent screening and optimization of several complex physical and sensory traits of bread is developed using a structured Taguchi-type micro-mining technique. A novel product outlook is offered to industrial operations to cover separate aspects of smart product design, engineering and marketing. Four controlling factors were selected to be modulated directly on a modern production line: 1) the dough weight, 2) the proofing time, 3) the baking time, and 4) the oven zone temperatures. Concentrated experimental recipes were programmed using the Taguchi-type L9(34) OA-sampler to detect potentially non-linear multi-response tendencies. The fused behavior of the master-ranked bread characteristics behavior was smart sampled with GRNN-crowdsourcing and robust analysis. It was found that the combination of the oven zone temperatures to play a highly influential role in all investigated scenarios. Moreover, the oven zone temperatures and the dough weight appeared to be instrumental when attempting to synchronously adjusting all four physical characteristics. The optimal oven-zone temperature setting for concurrent screening-and-optimization was found to be 270–240 °C. The optimized (median) responses for loaf weight, moisture, height, width, color, flavor, crumb structure, softness, and elasticity are: 782 g, 34.8 %, 9.36 cm, 10.41 cm, 6.6, 7.2, 7.6, 7.3, and 7.0, respectively. Keywords: Industrial engineering, Food scienc