Generic Methodology Research Articles

Probabilistic selection and design of concrete using machine learning

Abstract Development of robust concrete mixes with a lower environmental impact is challenging due to natural variability in constituent materials and a multitude of possible combinations of mix proportions. Making reliable property predictions with machine learning can facilitate performance-based specification of concrete, reducing material inefficiencies and improving the sustainability of concrete construction. In this work, we develop a machine learning algorithm that can utilize intermediate target variables and their associated noise to predict the final target variable. We apply the methodology to specify a concrete mix that has high resistance to carbonation, and another concrete mix that has low environmental impact. Both mixes also fulfill targets on the strength, density, and cost. The specified mixes are experimentally validated against their predictions. Our generic methodology enables the exploitation of noise in machine learning, which has a broad range of applications in structural engineering and beyond.

Renewable energy solutions based on artificial intelligence for farms in the state of Minas Gerais, Brazil: Analysis and proposition

Rural areas have great renewable energy potential. With an introduction to sustainable development goals, the smart farm concept presents a novel idea for providing energy in rural areas using artificial intelligence and renewable energy management. We proposed the following topics in this research: (i) methodologies for sizing generation systems by integer linear programming, (ii) use of analytic hierarchy process (AHP) to select the alternative source by financial, environmental, social, and physical criteria, and (iii) training of an artificial neural network (ANN) for process optimization based on the agroeconomic profile of farms. We applied the proposed generic methodology to farms in São Francisco do Glória, Brazil. The biomass, solar, and wind systems were indicated by AHP for implementation in 62.16, 15.32, and 22.52% of farms, respectively. The best configuration of ANN presented a maximum precision of 81.80 ± 3.36%. If the systems were to be implemented, 1.975 GWh yr−1 would be generated, 435.23 tonnes of CO2 would no longer be emitted per year, and a CO2 credit of 366.62 tonnes yr−1 would be injected into the national electric system. Public policies are necessary for this scenario to become a reality in Brazil, such as research incentives and market development.

Toward Performance Improvement of a Baculovirus–Insect Cell System under Uncertain Environment: A Robust Multiobjective Dynamic Optimization Approach for Semibatch Suspension Culture

The baculovirus expression vector system (BEVS) is one of the well-known versatile platforms for the recombinant protein/vaccine production. Mathematical modeling and optimization of a baculovirus–insect cell system can have significant industrial relevance as this reduces the number of expensive experiments and time involved in the experiment-based optimization. However, modeling and control of such a nonlinear system remains challenging due to the presence of uncertainties in the model. In this context, we propose a novel computational framework combining the principles of systems biology and dynamic optimization under uncertainty for optimizing a semibatch baculovirus–insect cell system. Toward this, first, a mathematical model replicating the dynamic experimental data on cell and virus growth was identified. Next, the proposed model was used for deterministic multiobjective dynamic optimization of the control variables, substrate, and multiplicity of infection (MOI) to achieve the conflicting objectives of productivity maximization and substrate minimization, simultaneously. Finally, based on the sensitivity analysis, six of the most influential parameters depicting model uncertainties have been considered for the robust multiobjective optimal control of the system. A comprehensive comparison displays up to 114% and 76% increases in the cell densities for the deterministic and stochastic semibatch processes, respectively, compared to the batch process. Semibatch operation also favors a minimum 40% reduction in MOI required to achieve the same level of infected cell density compared to the batch operation. This study provides a generic methodology for exhibiting a proof of concept that a semibatch suspension culture considering uncertainty in model parameters can give better productivity compared to a batch suspension culture for a BEVS.

The electronic tree of life (eToL): a net of long probes to characterize the microbiome from RNA-seq data

BackgroundMicrobiome analysis generally requires PCR-based or metagenomic shotgun sequencing, sophisticated programs, and large volumes of data. Alternative approaches based on widely available RNA-seq data are constrained because of sequence similarities between the transcriptomes of microbes/viruses and those of the host, compounded by the extreme abundance of host sequences in such libraries. Current approaches are also limited to specific microbial groups. There is a need for alternative methods of microbiome analysis that encompass the entire tree of life.ResultsWe report a method to specifically retrieve non-human sequences in human tissue RNA-seq data. For cellular microbes we used a bioinformatic 'net', based on filtered 64-mer sequences designed from small subunit ribosomal RNA (rRNA) sequences across the Tree of Life (the 'electronic tree of life', eToL), to comprehensively (98%) entrap all non-human rRNA sequences present in the target tissue. Using brain as a model, retrieval of matching reads, re-exclusion of human-related sequences, followed by contig building and species identification, is followed by confirmation of the abundance and identity of the corresponding species groups. We provide methods to automate this analysis. The method reduces the computation time versus metagenomics by a factor of >1000. A variant approach is necessary for viruses. Again, because of significant matches between viral and human sequences, a 'stripping' approach is essential. Contamination during workup is a potential problem, and we discuss strategies to circumvent this issue. To illustrate the versatility of the method we report the use of the eToL methodology to unambiguously identify exogenous microbial and viral sequences in human tissue RNA-seq data across the entire tree of life including Archaea, Bacteria, Chloroplastida, basal Eukaryota, Fungi, and Holozoa/Metazoa, and discuss the technical and bioinformatic challenges involved.ConclusionsThis generic methodology is likely to find wide application in microbiome analysis including diagnostics.

IQRe: An Integrated Cross Scale Urban Resilience Assessment framework

Developing successful urban resilience plans is a very challenging task due to the complex cross scale, multi sectoral dynamics of urban areas. This paper presents a novel integrated, multi sectoral, cross scale urban resilience assessment framework (iQRe) that facilitates bridging the spatial and sectoral gaps in the existing climate change adaption frameworks. The iQRe framework merges the IPCC AR5 risk assessment approach with the generic multi-criteria analysis methodology “SB Method” developed by iiSBE, creating a series of agile and quantitative climate impact chains that allow assigning a normalized numerical value to each of the 3 components needed to assess the climate risk (Hazard, vulnerability, and exposure). The Paper provide a sample application of the iQRe on a residential building.

Barriers to Transitioning to a Circular Food Economy in Mississippi, USA.

Background to the Study: The adoption of circular economy ideas to reduce waste and pollution has gained popularity in many industries, including the food business. Although there are multiple benefits to be gained from minimizing food waste through a circular economy approach, there are several barriers preventing economies from successfully transitioning. This research will explore the potential barriers to transitioning to a circular food economy in Mississippi, USA. Purpose: The purpose of this paper is to investigate the associated risks of transitioning to a circular food economy in Mississippi, USA. Design/methodology/approach: Over 120 questionnaire surveys were conducted professionally to assess the associated risks of transitioning to a circular food economy in Mississippi, USA. The questionnaire comprised two main sections each exploring different parts of the research question. Findings: The key findings of the statistical analysis indicated that there is a lack of industry-specific policies, regulations, and strong legislative frameworks. This paper will be very beneficial to industry practitioners and economic researchers with an interest in the circular economy. Research limitations/implications: There is a limitation of this article because it does not cover all the professional bodies in the food industry and data used in this publication were outsourced from qualified researchers. Practical and Social implications: The lack of a generic framework and methodology, key performance indicators, benchmark index, and economic performance measures has practical and social implications for society. Originality/value: This paper contributes with new outlooks aimed at evaluating the associated risks of a circular food economy for decision-makers. Keywords : Barriers, Circular, Economy, Food, Mississippi, Risk. DOI: 10.7176/JESD/13-24-01 Publication date: December 31 st 2022

An approach for semi-automated data quality assurance within BIM models

Abstract Successful Building Information Modelling (BIM) enabled projects that require large volumes of project data to be embedded within BIM models. However, with this wealth of data, relevance and accuracy have been identified as important issues affecting the BIM performance of the project. Currently, Quality Assurance (QA) in the industry has focused on geometric data, including scrutinising physical and spatial clashes. However, as BIM practices progress in the industry, the requirements for nongeometric model data and their quality have become more necessary. This study aimed to ascertain the feasibility of using visual programming for semi-automating the BIM QA process in a practical case study on using BIM in infrastructure projects. This paper outlines a generic semi-automated QA methodology and its application in a construction project case study. The validity of this method was tested and evaluated in practice through (n=2) workshops. The methodology was implemented within an integrated engineering consultancy, employing visual programming methodology to generate QA summaries and additionally highlight model elements with data quality issues based on a defined set of parameters. Based on the evaluation findings, the proposed process was feasible and provided a pathway for low-cost and low-skill QA of BIM model data within the architecture, engineering and construction (AEC) industry. The paper’s main scientific contribution is a conceptual framework for using visual programming to achieve automatic quality assurance.

Generic Modal Design Variables for Efficient Aerodynamic Optimization

Orthogonal modes are an effective method for aerodynamic shape optimization due to their excellent design space compactness; however, all existing methods are generated from a database of representative geometry. Moreover, application to high-fidelity design spaces is not possible because high-frequency shape components are insufficiently bounded, leading to nonsmooth and oscillatory geometries. In this work, a new generic methodology for generating orthogonal shape modes is presented based on a purely geometric derivation, eliminating the need for geometric training data. The new method is a further development of the gradient-limiting method developed previously for constraining the design space in a geometrically meaningful way to reduce the effective degrees of freedom and improve optimization convergence rate and final result. Here, the gradient-limiting methodology is reformulated by transforming the constraints directly onto design variables to produce orthogonal shape modes with equivalent constraints for ensuring smooth and valid iterates. The new generic methodology requires no training data, can be applied to arbitrary topologies using different boundary conditions, and naturally includes translational modes as part of the orthogonal basis. When applied to two standard aerodynamic test cases, the new method has superior performance compared to library-derived modes. Importantly, the optimization convergence rate is independent of the number of design variables, and the optimized objective at high design fidelities is greatly improved by avoiding local minima corresponding to spurious geometries. A nonstandard test case is demonstrated, for which traditional library modes are not useable due to nontrivial topology, and it is shown to benefit from the high-fidelity design space and translational mode made possible with the novel methodology.

Assessment of Yield and Nitrogen Utilization of the Mixed CRU and Urea in Wheat–Maize Production in a 5-Year Field Trial

To identify the general pattern of impact of the application of the mixed controlled-release urea (CRU) with urea (C-U) on grain yield, plant characteristics, NUE and soil nitrogen contents in wheat–maize production, a 5-year field trial with three release longevity and four ratios of CRU in C-U and common urea alone (U) was carried out in the North China Plain. Results with meta-analysis revealed that C-U had significant effects on grain yield and plant characteristics, NUE and soil NO3−-N contents in wheat–maize production positively with the release longevity of CRU and the ratio of CRU-N in C-U. The application of C-U with 60 d or 90 d CRU for wheat and maize had the best overall effects, while C-U treatment with 30 d CRU had a significant inhibitory effect. For maize, C-U with 30% CRU-N had the largest increase rate on yield, 1000-grain weight, plant height, dry weight and NUE by 5.13%, 1.61%, 3.70%, 11.33%, and 8.63%, respectively, while C-U with 40% CRU-N had the largest reduction soil NO3−-N. For wheat, the application of C-U with 40% CRU-N had a significant effect on yield, sterile spikelet number, and NUE by 4.45%, −9.76%, and −8.04%, respectively. To conclude, the use of the C-U with appropriate release longevity and the ratio of CRU has great potential to proliferate wheat–maize yields and reduce fertilizer loss especially for maize that not only provides an effective generic methodology for agriculture to improve measures but also ensures profitability.

Optimization of Temperature Field by Differentiating Number of Circumferential Control Zones in Local Post Weld Heat Treatment on 9%Cr Heat-Resistant Steel Pipe

Abstract This paper reports the optimal use of control zones to achieve required uniformity of temperature distribution in local postweld heat treatment of welds in 9% Cr heat-resistant large pipe system. In this research, local post weld heat treatment tests on temperature distribution with different control zones were carried out on 9%Cr steel pipes (OD 710 mm × 35 mm and OD 575 mm × 35 mm), which was further used for the development of the thermal analysis of the postweld heat treatment model via abaqus. The research results revealed (1) the effect of number of control zone on the uniformity of temperature distribution; (2) the effect of size of pipe diameter on the circumferential temperature and the through-thickness temperature gradients. This article discusses the possible reasons for the temperature difference at various positions of the pipe caused by the air flow inside the pipe with different control zones. Based on the obtained results, a practical method was designed for the selection the number of circumferential control zones on 9%Cr heat-resistant steel pipeline according to the required degree of temperature distribution uniformity. This paper contributes to the specific knowledge and the generic methodology.

Petroleum refinery effluent contribution to chemical mixture toxic pressure in the environment

Petroleum refinery effluents (PRE) are wastewaters from industries associated with oil refining. Within Europe, PREs are regulated through local discharge permits and receive substantial treatment before emission. After treatment, PREs can still contain low levels of various pollutants potentially toxic to organisms. Earlier work, including whole-effluent toxicity assessments, has shown that the toxicity of permitted PREs is often limited. However, the extent to which PREs contribute to chemical pollution already present in the receiving environment is unknown. Therefore, our study aimed to assess the contribution of PREs to mixture toxic pressure in the environment, using the multi-substance potentially affected fraction of species (msPAF) as an indicator. Based on measured chemical concentrations, compiled species sensitivity distributions (SSD) and a mechanistic solubility model, msPAF levels were estimated for undiluted effluents at discharge points and diluted effluents downstream in receiving waters. Median msPAF-chronic and msPAF-acute levels of PREs at discharge points were 74% (P50) and 40% (P95), respectively. The calculated msPAF levels were reduced substantially to <5% downstream for most effluents (82%), indicating low to negligible toxicity of PREs in receiving environments beyond the initial mixing zone. Regardless of differences in endpoints and locations, hydrocarbons (mainly total petroleum hydrocarbons) and inorganics (mainly ammonia) explained at least 85% of the mixture toxic pressure. The msPAF levels of PREs were on average 2.5–4.5 orders of magnitude lower than msPAF levels derived from background pollution levels, suggesting that PREs were minor contributors to the toxic pressure in the environment. This study presents a generic methodology for quantifying the potential toxic pressure of PREs in the environment, identifying hotspots where more effective wastewater treatment could be needed. We explicitly discuss the uncertainties for further refinement and development of the method.

Query-adaptive training data recommendation for cross-building predictive modeling

Predictive modeling in buildings is a key task for the optimal management of building energy. Relevant building operational data are a prerequisite for such task, notably when deep learning is used. However, building operational data are not always available, such is the case in newly built, newly renovated, or even not yet built buildings. To address this problem, we propose a deep similarity learning approach to recommend relevant training data to a target building solely by using a minimal contextual description on it. Contextual descriptions are modeled as user queries. We further propose to ensemble most used machine learning algorithms in the context of predictive modeling. This contributes to the genericity of the proposed methodology. Experimental evaluations show that our methodology offers a generic methodology for cross-building predictive modeling and achieves good generalization performance.

Deciphering Alternaria metabolic responses in microbial confrontation via an integrated mass spectrometric targeted and non-targeted strategy

Microbial confrontation is ubiquitously present in nature, such as mycoparasitism, competition and antibiosis between biocontrol agents and microbial pathogens. However, the internal metabolic responses of fungal pathogens under microbial interaction scenario have been scarcely investigated. In this study, we set up an integrated mycotoxin analysis and non-targeted metabolomics workflow to decipher metabolic changes of Alternaria pathogens when confronted with selected Trichoderma strains, as well as mycotoxin metabolization in the Trichoderma spp. Results demonstrated that Trichoderma spp. significantly influenced mycotoxin production and whole metabolome of Alternaria pathogens when in cocultivation, and one Trichoderma strain could metabolize alternariol into its hydroxylated form. These differential metabolites revealed fungal physiological alternations in various confrontation conditions. In all, a MS-based strategy was proposed to investigate microbial metabolic profiles under fungal/fungal and fungal/mycotoxin cocultivation, and this generic methodology would be significant for understanding the occurrence and change of food contaminants in microbial interactions.

Identification of Predictive Nursing Workload Factors: A Six Sigma Approach

A balanced nursing workload is crucial for patient and staff safety. Although there are several nursing planning models, there is no generic methodology to identify critical workload factors and their relative impact on different healthcare environments. We propose Six Sigma (SS) as a generic methodology and its DMAIC (Define, Measure, Analyze, Improve, Control) framework to identify statistically proven factors that affect nursing workload (NW) in any healthcare environment. Additionally, using a regression model, we estimated their relative importance. For our case study, we found that the number of patients per ward, the number of times medication was administered per shift, the number of nurses and the type of shifts were significant factors in predicting nursing workload. Using their relative importance as input for the nursing planning process, we improved the nursing assignment process performance from 0.09 to 1.05, with an increase in the sigma level from −0.34 to 2.97. Also, we reached the 55% target for the percentage of NW, from a baseline of 50.3%. We also reached the percentage target of NW set by the management of 55%, from the baseline of 50.3%. This study shows that SS can be used effectively to estimate the importance of the main factors that affect nursing workload, providing a methodology to improve the nurse–patient assignment process.

Evolution in fluctuating environments: A generic modular approach.

Evolutionary processes take place in fluctuating environments, where carrying capacities and selective forces vary over time. The fate of a mutant type and the persistence time of polymorphic states were studied in some specific cases of varying environments, but a generic methodology is still lacking. Here, we present such a general analytic framework. We first identify a set of elementary building blocks, a few basic demographic processes like logistic or exponential growth, competition at equilibrium, sudden decline, and so on. For each of these elementary blocks, we evaluate the mean and the variance of the changes in the frequency of the mutant population. Finally, we show how to find the relevant terms of the diffusion equationfor each arbitrary combination of these blocks. Armed with this technique one may calculate easily the quantities that govern the evolutionary dynamics, like the chance of ultimate fixation, the time to absorption, and the time tofixation.

The Three-Phase Methodology for IoT Project Development

The development of IoT solutions requires the expertise from professionals from several areas. This multidisciplinary aspect can be better served with an integrated process to structure the development of these solutions. There are several software development methodologies, such as Ignite and ELDAMeth, that have been adapted for this end. However, most lack some fundamental functionalities. The Three-Phase Methodology for Project Development (TpM-Pro) was originally developed as an IoT teaching methodology that proved ideal for solution development. It is a generic, agile, interactive, technology-independent and incremental methodology that splits the development of solutions into three distinct phases: Business, Requirements, and Implementation. This segmentation has proved invaluable and can become a standard for IoT solution development. In this paper, we present the TpM-Pro, gauge it against five other methodologies proposed for IoT solution development and detail its application on a corporate IoT project.

Numerical simulation of the pedestrian comfort of the microdistrict

This article is concerned with the questions of assessment of microdistrict pedestrian comfort associated with wind influences. The application of the generic methodology developed by the authors for assessing the wind influences in respect of the assigned task is presented. Researched are the questions of pedestrian comfort assessment in a microdistrict that has been already built with heightened comfort requirements (this is the concept “Yard without Cars”) and originally disadvantageous location in terms of aerodynamics which heavily reduces the possibilities of comfort compensation by modifying the microdistrict layout during the design phase. A 3D computer model of the facility is built. The numerical simulation is carried out using the ANSYS software. The numerical grid parameters, boundary commissions, and computation technology are set out. The streamlines for various wind directions are obtained. For a pedestrian comfort assessment, the stream velocity modulus distributions are analyzed in relation to the flow field. Using the Qcrit parameter distribution, the stream velocity distribution may be obtained at any values of wind velocity. On the basis of the obtained results, the pedestrian comfort of the space in the territory of a residential complex was researched at any wind direction.

Techno-economic assessment of Carnot batteries for load-shifting of solar PV production of an office building

This study presents a techno-economic assessment of Carnot batteries for load-shifting of solar PV production of an office building considering variable electricity production, demand and pricing. The building has an annual electricity demand of 2600 MWh and a maximum power demand of 0.564 MW. The Carnot battery studied is based on a subcritical Rankine cycle with sensible thermal storage. A generic Carnot battery model and sizing methodology are presented and applied to the case study. It is found that implementing a Carnot battery is not cost effective for the studied case. Larger system sizes increase the integration of solar PV production, but decrease the net present value. Increasing the thermodynamic efficiency of the Carnot battery is beneficial to improve the financial feasibility and to reach higher levels of solar PV integration. The system investment cost should reduce minimally with a factor 9.4 and 4.1 to make the base and ideal system viable respectively. Alternatively, a minimum electricity tax or 326 €/MWh and 122 €/MWh could be implemented. The Carnot battery is not financially competitive with Li-ion batteries under the current scenario. The ideal Carnot battery becomes competitive at charging durations starting from 7 h.

A generic methodology to efficiently integrate weather information in short-term Photovoltaic generation forecasting models

The power output of Photovoltaic (PV) plants is weather-dependent, which results in inherent uncertainties about future production. This raises technical challenges for grid operators, especially in power systems with high PV penetration, and also financial losses when PV generation is traded on electricity markets. Accurate forecasts for the next hours or days contribute to alleviating these impacts. The literature features a plethora of forecasting models, among which outstanding approaches combine heterogeneous sources of inputs like measurements, weather forecasts and satellite images. The integration of such inputs into forecast models can take two forms: either as explanatory features, or as state features that condition the model training through a local regression approach. With the latter, physics-based information can be included within statistical regression tools to derive optimised models w.r.t. weather input. These models are then extended to integrate spatio-temporal information from satellite observations. We investigate these approaches with the objective of deriving the mathematical foundations of a generic methodology to integrate weather information into PV forecasting models. The paper assesses the influence of weather information integration strategies on forecasting performances for two state-of-the-art short-term forecasting models, belonging respectively to linear and non-linear families. Lastly, general guidelines for forecasters are derived regarding the procedure to follow when dealing with several sources of information. Evaluations are performed on real-world datasets composed of nine PV plants.

A generic sizing methodology for thermal management system in fuel cell vehicles using pinch analysis

The escalating transport demand over the past decade has intensified the need to replace internal combustion (IC) engines with better alternatives like fuel cells that can simultaneously provide superior performance and diminished emissions. The radiators that are used in fuel cell-based electric vehicles are at least twice the size of IC engine radiators. This leads to vehicle overloading and an increase in fuel consumption. The undertaken study proposes a reduction in radiator size by storing thermal energy in phase change materials. This paper presents a generalized sizing method for the recommended thermal management system, using a simple mathematical approach based on Pinch Analysis. The optimization studies show the possibility to bring down the minimum radiator area requirement in a fuel cell vehicle like the 2022 Toyota Mirai by 5.5 times by using around 13 kg of Paraffin Wax along with it. The proposed methodology is realized by analyzing a few representative parameters like ambient temperature, drag area and curb weight of the vehicle, maximum allowable temperature rise in fuel cells, etc. In addition, the study also estimates the minimum required radiator areas for a few commercial fuel cell vehicles in the Indian environment.