Abstract: The emerging approaches to sustainable manufacturing are considered to be solution-oriented with the aim of addressing the environmental, economic and social issues holistically. However, the analysis of the interdependencies amongst the three sustainability dimensions has not been fully captured in the literature. In a recent review of approaches to sustainable manufacturing, two categories of techniques are identified: 1) Sustainable Product Development (SPD), and 2) Sustainability Performance Assessment (SPA) techniques. The challenges of the approaches are not only related to the arguments and misconceptions of the relationships between the techniques and sustainable development but also to the inability to capture and integrate the three sustainability dimensions. This requires a clear definition of some of the approaches and a road-map to the development of a holistic approach that supports sustainability decision-making. In this context, eco-innovation, social impact assessment, and life cycle sustainability analysis play an important role. This paper deployed an integrative approach that enabled amalgamation of sustainable manufacturing approaches and the theories of reciprocity and motivation into a holistic simulation-based impact analysis framework. The findings in this research have the potential to guide sustainability analysts to capture the aspects of the three sustainability dimensions into an analytical model. Additionally, the research findings presented can aid the construction of a holistic simulation model of a sustainable manufacturing and support effective decision-making.
Abstract: This paper presents an approach to the performance of the natural lighting, when the use of appropriated solar lighting systems on the roof is applied in cultural buildings such as museums and foundations. The roofs, as a part of contact between the building and the external environment, require special attention in projects that aim at energy efficiency, being an important element for the capture of natural light in greater quantity, but also for being the most important point of generation of photovoltaic solar energy, even semitransparent, allowing the partial passage of light. Transparent elements in roofs, as well as superior protection of the building, can also play other roles, such as: meeting the needs of natural light for the accomplishment of the internal tasks, attending to the visual comfort; to bring benefits to the human perception and about the interior experience in a building. When these resources are well dimensioned, they also contribute to the energy efficiency and consequent character of sustainability of the building. Therefore, when properly designed and executed, a roof light system can bring higher quality natural light to the interior of the building, which is related to the human health and well-being dimension. Furthermore, it can meet the technologic, economic and environmental yearnings, making possible the more efficient use of that primordial resource, which is the light of the Sun. The article presents the analysis of buildings that used zenith light systems in search of better lighting performance in museums and foundations: the Solomon R. Guggenheim Museum in the United States, the Iberê Camargo Foundation in Brazil, the Museum of Fine Arts in Castellón in Spain and the Pinacoteca of São Paulo.
Abstract: This paper presents an approach on the advantages of using adequate coverage in the zenithal lighting typology in various areas of architectural production, while at the same time to encourage to the design concerns inherent in this choice of roofing in Brazil. Understanding that sustainability needs to cover several aspects, a roofing system such as zenithal lighting system can contribute to the provision of better quality natural light for the interior of the building, which is related to the good health and welfare; it will also be able to contribute for the sustainable aspects and environmental needs, when it allows the generation of energy in semitransparent or opacity photovoltaic solutions and economize the artificial lightning. When the energy balance in the building is positive, that is, when the building generates more energy than it consumes, it may fit into the Net Zero Energy Building concept. The zenithal lighting systems could be an important ally in Brazil, when solved the burden of heat gains, participate in the set of pro-efficiency actions in search of "zero energy buildings". The paper presents comparative three cases of buildings that have used this feature in search of better environmental performance, both in light comfort and sustainability as a whole. Two of these buildings are examples in Europe: the Notley Green School in the UK and the Isofóton factory in Spain. The third building with these principles of shed´s roof is located in Brazil: the Ipel´s factory in São Paulo.
Abstract: There is a need for new systems that can be attached to drinking water treatment plants and have the required treatment capacity as well as the selectivity regarding components derived from anthropogenic activities. In a context of high volumes of water and low concentration of contaminants, adsorption/interchange processes are appealing since they meet the required features. Iron oxides such as siderite and molysite, which are respectively based on FeCO3 and FeCl3, can be found in nature. In this work, their observed performance, raw or roasted at different temperatures, as adsorbents of some inorganic anions is discussed. Roasted 1:1 FeCO3: FeCl3 mixture was very selective for arsenic and allowed a 100% removal of As from a 10 mg L-1 As solution. Besides, the 1:1 FeCO3 and FeCl3 mixture roasted at 500 ºC showed good selectivity for, in order of preference, arsenate, bromate, phosphate, fluoride and nitrate anions with distribution coefficients of, respectively, 4200, 2800, 2500 0.4 and 0.03 L g-1.
Abstract: Inspired by topology of humpback whale flippers, a meta-model is designed for wing planform design. The net is trained based on experimental data using cascade-forward artificial neural network (ANN) to investigate effects of the amplitude and wavelength of sinusoidal leading edge configurations on the wing performance. Afterwards, the trained ANN is coupled with a genetic algorithm method towards an optimum design strategy. Finally, flow physics of the problem for an optimized rectangular planform and also a real flipper geometry planform is simulated using Lam-Bremhorst low Reynolds number turbulence model with damping wall-functions resolving to the wall. Lift and drag coefficients and also details of flow are presented along with comparisons to available experimental data. Results show that the proposed strategy can be adopted with success as a fast-estimation tool for performance prediction of wing planforms with wavy leading edge at preliminary design phase.
Abstract: Underground mining is at the core of rapidly evolving metals and minerals sector due to the increasing mineral consumption globally. Even though the surface mines are still more abundant on earth, the scales of industry are slowly tipping towards underground mining due to rising depth and complexities of orebodies. Thus, the efficient and productive functioning of underground operations depends significantly on the synchronized performance of key elements such as operating site, mining equipment, manpower and mine services. Production drilling is the process of conducting long hole drilling for the purpose of charging and blasting these holes for the production of ore in underground metal mines. Thus, production drilling is the crucial segment in the underground metal mining value chain. This paper presents the process mapping tool to evaluate the production drilling process in the underground metal mining operation by dividing the given process into three segments namely Input, Process and Output. The three segments are further segregated into factors and sub-factors. As per the study, the major input factors crucial for the efficient functioning of production drilling process are power, drilling water, geotechnical support of the drilling site, skilled drilling operators, services installation crew, oils and drill accessories for drilling machine, survey markings at drill site, proper housekeeping, regular maintenance of drill machine, suitable transportation for reaching the drilling site and finally proper ventilation. The major outputs for the production drilling process are ore, waste as a result of dilution, timely reporting and investigation of unsafe practices, optimized process time and finally well fragmented blasted material within specifications set by the mining company. The paper also exhibits the drilling loss matrix, which is utilized to appraise the loss in planned production meters per day in a mine on account of availability loss in the machine due to breakdowns, underutilization of the machine and productivity loss in the machine measured in drilling meters per unit of percussion hour with respect to its planned productivity for the day. The given three losses would be essential to detect the bottlenecks in the process map of production drilling operation so as to instigate the action plan to suppress or prevent the causes leading to the operational performance deficiency. The given tool is beneficial to mine management to focus on the critical factors negatively impacting the production drilling operation and design necessary operational and maintenance strategies to mitigate them.
Abstract: Based on fast discrete cosine transform (FDCT), the authors present a high capacity and high perceived quality method for electrocardiogram (ECG) signal. By using a simple adjusting policy to the 1-dimentional (1-D) DCT coefficients, a large volume of secret message can be effectively embedded in an ECG host signal and be successfully extracted at the intended receiver. Simulations confirmed that the resulting perceived quality is good, while the hiding capability of the proposed method significantly outperforms that of existing techniques. In addition, our proposed method has a certain degree of robustness. Since the computational complexity is low, it is feasible for our method being employed in real-time applications.
Abstract: In this research, a multidimensional compromise optimization method is proposed for multidimensional decision making analysis in the development ranking of the Gulf Cooperation Council Countries and Turkey. The proposed approach presents ranking solutions resulting from different multicriteria decision analyses, which yield different ranking orders for the same ranking problem, consisting of a set of alternatives in terms of numerous competing criteria when they are applied with the same numerical data. The multiobjective optimization decision making problem is considered in three sequential steps. In the first step, five different criteria related to the development ranking are gathered from the research field. In the second step, identified evaluation criteria are, objectively, weighted using standard deviation procedure. In the third step, a country selection problem is illustrated with a numerical example as an application of the proposed multidimensional compromise optimization model. Finally, multidimensional compromise optimization approach is applied to rank the Gulf Cooperation Council Countries and Turkey.
Abstract: In this study, a predictive model for estimating the metabolism (MET) of human body was developed for the optimal control of indoor thermal environment. Human body images for indoor activities and human body joint coordinated values were collected as data sets, which are used in predictive model. A deep learning algorithm was used in an initial model, and its number of hidden layers and hidden neurons were optimized. Lastly, the model prediction performance was analyzed after the model being trained through collected data. In conclusion, the possibility of MET prediction was confirmed, and the direction of the future study was proposed as developing various data and the predictive model.
Abstract: Obesity may lead to growing serious health problems throughout the world. Vitamin D appears to play a role in cardiovascular and metabolic health. Vitamin D deficiency may add to derangements in human metabolic systems, particularly those of children. Childhood obesity is associated with an increased risk of chronic and sophisticated diseases. The aim of this study is to investigate associations as well as possible differences related to parameters affected by obesity and their relations with vitamin D status in obese (OB) and morbid obese (MO) children. This study included a total of 78 children. Of them, 41 and 37 were OB and MO, respectively. WHO BMI-for age percentiles were used for the classification of obesity. The values above 99 percentile were defined as MO. Those between 95 and 99 percentiles were included into OB group. Anthropometric measurements were recorded. Basal metabolic rates (BMRs) were measured. Vitamin D status is determined by the measurement of 25-hydroxy cholecalciferol [25- hydroxyvitamin D3, 25(OH)D] using high-performance liquid chromatography. Vitamin D status was evaluated as deficient, insufficient and sufficient. Values < 20.0 ng/ml, values between 20-30 ng/ml and values > 30.0 ng/ml were defined as vitamin D deficient, insufficient and sufficient, respectively. Optimal 25(OH)D level was defined as ≥ 30 ng/ml. SPSSx statistical package program was used for the evaluation of the data. The statistical significance degree was accepted as p < 0.05. Mean ages did not differ between the groups. Significantly increased body mass index (BMI), waist circumference (C) and neck C as well as significantly decreased fasting blood glucose (FBG) and vitamin D values were observed in MO group (p < 0.05). In OB group, 37.5% of the children were vitamin D deficient, and in MO group the corresponding value was 53.6%. No difference between the groups in terms of lipid profile, systolic blood pressure (SBP), diastolic blood pressure (DBP) and insulin values was noted. There was a severe statistical significance between FBG values of the groups (p < 0.001). Important correlations between BMI, waist C, hip C, neck C and both SBP as well as DBP were found in OB group. In MO group, correlations only with SBP were obtained. In a similar manner, in OB group, correlations were detected between SBP-BMR and DBP-BMR. However, in MO children, BMR correlated only with SBP. The associations of vitamin D with anthropometric indices as well as some lipid parameters were defined. In OB group BMI, waist C, hip C and triglycerides (TRG) were negatively correlated with vitamin D concentrations whereas none of them were detected in MO group. Vitamin D deficiency may contribute to the complications associated with childhood obesity. Loss of correlations between obesity indices-DBP, vitamin D-TRG, as well as relatively lower FBG values, observed in MO group point out that the emergence of MetS components starts during obesity state just before the transition to morbid obesity. Aside from its deficiency state, associations of vitamin D with anthropometric measurements, blood pressures and TRG should also be evaluated before the development of morbid obesity.
Abstract: South Africa is one of the few countries that have stopped using the same Enumeration Areas (EAs) for census enumeration and dissemination. The advantage of this change is that confidentiality issue could be addressed for census dissemination as the design of geographic unit for collection is mainly to ensure that this unit is covered by one enumerator. The objective of this paper was to evaluate the performance of automated zone design output areas against non-zone design developed geographies using the 2001 census data, and 2011 census to some extent, as the main input. The comparison of the Automated Zone-design Tool (AZTool) census output areas with the Small Area Layers (SALs) and SubPlaces based on confidentiality limit, population distribution, and degree of homogeneity, as well as shape compactness, was undertaken. Further, SPSS was employed for validation of the AZTool output results. The results showed that AZTool developed output areas out-perform the existing official SAL and SubPlaces with regard to minimum population threshold, population distribution and to some extent to homogeneity. Therefore, it was concluded that AZTool program provides a new alternative to the creation of optimised census output areas for dissemination of population census data in South Africa.
Abstract: Fish is a kind of food that contains many nutritions, one of those is the long chain of unsaturated fatty acids as omega-3 and omega-6 fatty acids and essential amino acid in enough amount for the necessity of our body. Like pelagic fish that found in the sea of Maluku. This research was done to identify fatty acids and amino acids composition in Moonfish (M. maculata) using transesterification reaction steps and Gas Chromatograph-Mass Spectrophotometer (GC-MS) and High-Performance Liquid Chromatography (HPLC). The result showed that fatty acids composition in Moonfish (M. maculata) contained tridecanoic acid (2.84%); palmitoleic acid (2.65%); palmitic acid (35.24%); oleic acid (6.2%); stearic acid (14.20%); and 5,8,11,14-eicosatetraenoic acid (1.29%) and 12 amino acids composition that consist of 7 essential amino acids, were leucine, isoleucine, valine, phenylalanine, methionine, lysine, and histidine, and also 5 non-essential amino acid, were tyrosine, glycine, alanine, glutamic acid, and arginine.Thus, these fishes can be used by the people to complete the necessity of essential fatty acid and amino acid.
Abstract: The field of wireless sensor networks (WSN) engages a lot of associates in the research community as an interdisciplinary field of interest. This type of network is inexpensive, multifunctionally attributable to advances in micro-electromechanical systems and conjointly the explosion and expansion of wireless communications. A mobile ad hoc network is a wireless network without fastened infrastructure or federal management. Due to the infrastructure-less mode of operation, mobile ad-hoc networks are gaining quality. During this work, we have performed an efficient performance study of the two major routing protocols: Ad hoc On-Demand Distance Vector Routing (AODV) and Dynamic Source Routing (DSR) protocols. We have used an accurate simulation model supported NS2 for this purpose. Our simulation results showed that AODV mitigates the drawbacks of the DSDV and provides better performance as compared to DSDV.
Abstract: Integrated resilience engineering (IRE) is capable of returning banking systems to the normal state in extensive economic circumstances. In this study, information system of a large bank (with several branches) is assessed and optimized under severe economic conditions. Data envelopment analysis (DEA) models are employed to achieve the objective of this study. Nine IRE factors are considered to be the outputs, and a dummy variable is defined as the input of the DEA models. A standard questionnaire is designed and distributed among executive managers to be considered as the decision-making units (DMUs). Reliability and validity of the questionnaire is examined based on Cronbach's alpha and t-test. The most appropriate DEA model is determined based on average efficiency and normality test. It is shown that the proposed integrated design provides higher efficiency than the conventional RE design. Results of sensitivity and perturbation analysis indicate that self-organization, fault tolerance, and reporting culture respectively compose about 50 percent of total weight.
Abstract: Datasets or collections are becoming important assets by themselves and now they can be accepted as a primary intellectual output of a research. The quality and usage of the datasets depend mainly on the context under which they have been collected, processed, analyzed, validated, and interpreted. This paper aims to present a collection of program educational objectives mapped to student’s outcomes collected from self-study reports prepared by 32 engineering programs accredited by ABET. The manual mapping (classification) of this data is a notoriously tedious, time consuming process. In addition, it requires experts in the area, which are mostly not available. It has been shown the operational settings under which the collection has been produced. The collection has been cleansed, preprocessed, some features have been selected and preliminary exploratory data analysis has been performed so as to illustrate the properties and usefulness of the collection. At the end, the collection has been benchmarked using nine of the most widely used supervised multiclass classification techniques (Binary Relevance, Label Powerset, Classifier Chains, Pruned Sets, Random k-label sets, Ensemble of Classifier Chains, Ensemble of Pruned Sets, Multi-Label k-Nearest Neighbors and Back-Propagation Multi-Label Learning). The techniques have been compared to each other using five well-known measurements (Accuracy, Hamming Loss, Micro-F, Macro-F, and Macro-F). The Ensemble of Classifier Chains and Ensemble of Pruned Sets have achieved encouraging performance compared to other experimented multi-label classification methods. The Classifier Chains method has shown the worst performance. To recap, the benchmark has achieved promising results by utilizing preliminary exploratory data analysis performed on the collection, proposing new trends for research and providing a baseline for future studies.
Abstract: In this paper, a proper approach is taken to assess a set of the most effective rotor design parameters for an external-rotor permanent magnet assisted synchronous reluctance motor (PMaSynRM) and therefore to tackle the design complexity of the rotor structure. There are different advantages for introducing permanent magnets into the rotor flux barriers, some of which are to saturate the rotor iron ribs, to increase the motor torque density and to improve the power factor. Moreover, the d-axis and q-axis inductances are of great importance to simultaneously achieve maximum developed torque and low torque ripple. Therefore, sensitivity analysis of the rotor geometry of an 8-pole external-rotor permanent magnet assisted synchronous reluctance motor is performed. Several magnetically accurate finite element analyses (FEA) are conducted to characterize the electromagnetic performance of the motor. The analyses validate torque and power factor equations for the proposed external-rotor motor. Based upon the obtained results and due to an additional term, permanent magnet torque, added to the reluctance torque, the electromagnetic torque of the PMaSynRM increases.
Abstract: Visual comfort is one of the major parameters that can be taken to measure the human comfort in any environment. If the provided illuminance level in a working environment does not meet the workers visual comfort, it will lead to eye-strain, fatigue, headache, stress, accidents and finally, poor productivity. However, improvements in lighting do not necessarily mean that the workplace requires more light. Unnecessarily higher illuminance levels will also cause poor visual comfort and health risks. In addition, more power consumption on lighting will also result in higher energy costs. So, during this study, visual comfort and the illuminance requirement for the workers in textile/apparel industry were studied to perform different tasks (i.e. cutting, sewing and knitting) at their workplace. Experimental studies were designed to identify the optimum illuminance requirement depending upon the varied fabric colour and type and finally, energy saving potentials due to controlled illuminance level depending on the workforce requirement were analysed. Visual performance of workers during the sewing operation was studied using the ‘landolt ring experiment’. It was revealed that around 36.3% of the workers would like to work if the illuminance level varies from 601 lux to 850 lux illuminance level and 45.9% of the workers are not happy to work if the illuminance level reduces less than 600 lux and greater than 850 lux. Moreover, more than 65% of the workers who do not satisfy with the existing illuminance levels of the production floors suggested that they have headache, eye diseases, or both diseases due to poor visual comfort. In addition, findings of the energy analysis revealed that the energy-saving potential of 5%, 10%, 24%, 8% and 16% can be anticipated for fabric colours, red, blue, yellow, black and white respectively, when the 800 lux is the prevailing illuminance level for sewing operation.
Abstract: Electricity prices have sophisticated features such as
high volatility, nonlinearity and high frequency that make forecasting
quite difficult. Electricity price has a volatile and non-random
character so that, it is possible to identify the patterns based on the
historical data. Intelligent decision-making requires accurate price
forecasting for market traders, retailers, and generation companies.
So far, many shallow-ANN (artificial neural networks) models have
been published in the literature and showed adequate forecasting
results. During the last years, neural networks with many hidden
layers, which are referred to as DNN (deep neural networks) have
been using in the machine learning community. The goal of this
study is to investigate electricity price forecasting performance of the
shallow-ANN and DNN models for the Turkish day-ahead electricity
market. The forecasting accuracy of the models has been evaluated
with publicly available data from the Turkish day-ahead electricity
market. Both shallow-ANN and DNN approach would give successful
result in forecasting problems. Historical load, price and weather
temperature data are used as the input variables for the models.
The data set includes power consumption measurements gathered
between January 2016 and December 2017 with one-hour resolution.
In this regard, forecasting studies have been carried out comparatively
with shallow-ANN and DNN models for Turkish electricity markets
in the related time period. The main contribution of this study
is the investigation of different shallow-ANN and DNN models
in the field of electricity price forecast. All models are compared
regarding their MAE (Mean Absolute Error) and MSE (Mean Square)
results. DNN models give better forecasting performance compare to
shallow-ANN. Best five MAE results for DNN models are 0.346,
0.372, 0.392, 0,402 and 0.409.
Abstract: Load forecasting has become crucial in recent years
and become popular in forecasting area. Many different power
forecasting models have been tried out for this purpose. Electricity
load forecasting is necessary for energy policies, healthy and reliable
grid systems. Effective power forecasting of renewable energy load
leads the decision makers to minimize the costs of electric utilities
and power plants. Forecasting tools are required that can be used
to predict how much renewable energy can be utilized. The purpose
of this study is to explore the effectiveness of LSTM-based neural
networks for estimating renewable energy loads. In this study, we
present models for predicting renewable energy loads based on
deep neural networks, especially the Long Term Memory (LSTM)
algorithms. Deep learning allows multiple layers of models to learn
representation of data. LSTM algorithms are able to store information
for long periods of time. Deep learning models have recently been
used to forecast the renewable energy sources such as predicting
wind and solar energy power. Historical load and weather information
represent the most important variables for the inputs within the
power forecasting models. The dataset contained power consumption
measurements are gathered between January 2016 and December
2017 with one-hour resolution. Models use publicly available data
from the Turkish Renewable Energy Resources Support Mechanism.
Forecasting studies have been carried out with these data via deep
neural networks approach including LSTM technique for Turkish
electricity markets. 432 different models are created by changing
layers cell count and dropout. The adaptive moment estimation
(ADAM) algorithm is used for training as a gradient-based optimizer
instead of SGD (stochastic gradient). ADAM performed better than
SGD in terms of faster convergence and lower error rates. Models
performance is compared according to MAE (Mean Absolute Error)
and MSE (Mean Squared Error). Best five MAE results out of
432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting
performance of the proposed LSTM models gives successful results
compared to literature searches.
Abstract: Safety is often seen as a requirement or a performance indicator through the design process, and this does not always result in optimally safe products or systems. This paper suggests integrating the best safety practices with the design process to enrich the exploration experience for designers and add extra values for customers. For this purpose, the commonly practiced safety standards and design methods have been reviewed and their common blocks have been merged forming Safety Cube. Safety Cube combines common blocks for design, hazard identification, risk assessment and risk reduction through an integral approach. An example application presents the use of Safety Cube for design of machinery.