Engineering Decisions Research Articles

Data proficiency in MAE education: Insights from student perspectives and experiences

This paper explores how mechanical and aerospace engineering (MAE) students understand and improve their data proficiency throughout their engineering curriculum. Data is essential for engineering students to be proficient in handling, as it is involved in every aspect of engineering. With the growing ubiquity of data and data analysis in all engineering fields, engineering students need to learn and master data skills to be competitive in the current and future job market. However, there is a lack of research on how non-computer science or software engineering majors perceive data proficiency and how they seek opportunities to develop data skills, especially as it relates to specific subdomains. In this paper, we investigate how students perceive data proficiency and how they develop using interview data from N = 27 MAE students at a research institution in the southeastern United States. Using the How People Learn framework, we analyzed the data through thematic analysis methods with a postpositivist approach, considering the bounded context of this study. The results show that MAE students value data proficiency as a crucial skill for their future careers and recognize its importance in making evidence-based engineering decisions. The study also reveals that, even though data proficiency is often a “hidden competency,” MAE students intuitively find various ways to enhance their data skills. These findings may help engineering educators to tailor their instruction to their students’ needs, address misconceptions about data and data proficiency, and prepare a data-literate future engineering workforce.

Estimation of Reservoir Storage Capacity Using the Gould-Dincer Formula with the Aid of Possibility Theory

This paper presents a method for estimating reservoir storage capacity using the Gould–Dincer normal formula (G-DN), enhanced by the possibility theory. The G-DN equation is valuable for regional studies of reservoir reliability, particularly under climate change scenarios, using regional statistics. However, because the G-DN formula deals with measured data, it introduces a degree of uncertainty and fuzziness that traditional probability theory struggles to address. Possibility theory, an extension of fuzzy set theory, offers a suitable framework for managing this uncertainty and fuzziness. In this study, the G-DN formula is adapted to incorporate fuzzy logic, and the possibilistic nature of reservoir capacity is translated into a probabilistic framework using α-cuts from the possibility theory. These α-cuts approximate probability confidence intervals with high confidence. Applying the proposed methodology, in the present crisp case with the storage capacity D = 0.75, the value of the capacity C was found to be 1271×106 m3, and that for D = 0.5 was 634.5×106 m3. On the other hand, in the fuzzy case using the possibility theory, the value of the capacity for D = 0.75 is the internal [315,5679]×106 m3 and for D = 0.5 the value is interval [158,2839]×106 m3, with a probability of ≥95% and a risk level of α = 5% for both cases. The proposed approach could be used as a robust tool in the toolkit of engineers working on irrigation, drainage, and water resource projects, supporting informed and effective engineering decisions.

ANALYSIS OF THE POSSIBILITY OF USING AN AUTONOMOUS MEASUREMENT SYSTEM AND REGISTRATION OF THE ASSESSMENT OF THE LEVEL OF PROTECTION OF THE ARMOURED VEHICLE CREW

In the context of large-scale armed aggression by the Russian Federation and growing losses from mine injuries, there is a significant need to obtain objective information about the level of protection provided by military armoured vehicles. At the same time, Ukrainian defence enterprises are constantly working to develop the latest methods to improve the protection of vehicle crews from the threat of mines and explosives. There is an acute problem and an essential need to quickly improve the mine resistance of standard models of armoured vehicles and, as a result, the need to quickly determine the effectiveness of the measures taken and make engineering decisions. The basis for the development of modern mine protection equipment should be to identify the impact of the shock wave not only on the vehicle structure, but primarily on the crew members. It is possible to evaluate the results, conduct comprehensive research and provide reasonable recommendations for further work only on the basis of the results of tests (experimental physical modelling) during which physical factors affecting the crew are measured. It is impossible to carry out the necessary measurements without the use of modern sensors (sensors) in conjunction with an information and measurement system that will ensure the reception, processing and registration of information. The main criteria for selecting sensors are sensitivity, resolution, linearity, frequency response, temperature response, noise level and long-term stability.

A point-of-research decision in synovial tissue engineering: Mesenchymal stromal cells, tissue derived fibroblast or CTGF-mediated mesenchymal-to-fibroblast transition

Rheumatoid arthritis (RA) and osteoarthritis (OA) are prevalent inflammatory joint diseases characterized by synovitis, cartilage, and bone destruction. Fibroblast-like synoviocytes (FLSs) of the synovial membrane are a decisive factor in arthritis, making them a target for future therapies. Developing novel strategies targeting FLSs requires advanced in vitro joint models that accurately replicate non-diseased joint tissue. This study aims to identify a cell source reflecting physiological synovial fibroblasts. Therefore, we newly compared the phenotype and metabolism of “healthy” knee-derived FLSs from patients with ligament injuries (trauma-FLSs) to mesenchymal stromal cells (MSCs), their native precursors. We differentiated MSCs into fibroblasts using connective tissue growth factor (CTGF) and compared selected protein and gene expression patterns to those obtained from trauma-FLSs and OA-FLSs. Based on these findings, we explored the potential of an MSC-derived synovial tissue model to simulate a chronic inflammatory response akin to that seen in arthritis. We have identified MSCs as a suitable cell source for synovial tissue engineering because, despite metabolic differences, they closely resemble human trauma-derived FLSs. CTGF-mediated differentiation of MSCs increased HAS2 expression, essential for hyaluronan synthesis. It showed protein expression patterns akin to OA-FLSs, including markers of ECM components and fibrosis, and enzymes leading to a shift in metabolism towards increased fatty acid oxidation. In general, cytokine stimulation of MSCs in a synovial tissue model induced pro-inflammatory and pro-angiogenic gene expression, hyperproliferation, and increased glucose consumption, reflecting cellular response in human arthritis. We conclude that MSCs can serve as a proxy to study physiological synovial processes and inflammatory responses. In addition, CTGF-mediated mesenchymal-to-fibroblast transition resembles OA-FLSs. Thus, we emphasize MSCs as a valuable cell source for tools in preclinical drug screening and their application in tissue engineering.

London Bridge: A History of Urban Survival

United Kingdom artifacts have long held fascination in other parts of the world. However, none as large as the London Bridge have managed to evolve, transform, and survive for 2,000 years. The 1831 version of the bridge was purchased in 1968 and moved to the desert of Arizona in the United States where it serves as a major tourist attraction. How that came to occur is only the end story of how a bridge became more a symbol of national heritage than a mere passage across the Thames, how its iconic status drove its evolution, how economic growth impacted its viability, how unfortunate engineering decisions led to its earlier than necessary replacement, and how the timing of reconstruction shaped its composition. This paper traces the history of the 1831 London Bridge and argues that it is a historical accident the bridge was even offered for sale to the Americans.

Integrated BIM-GA approach for slab formwork design optimization

Purpose Formwork design and construction for reinforced concrete buildings take significant time, effort and money. Construction procedures are time-consuming for designers and costly for the contractor. Poor engineering decisions have led to several workplace accidents in the construction industry. This paper aims to present an integrated building information modeling – genetic algorithm (BIM-GA) model to automate formwork design, 3D visualization and optimization. Design/methodology/approach Data are precisely extracted from a 3D structural model and used to optimize formwork design based on available formwork components and prices. Optimization models are made using GA approach. A library of 3D formwork components was modeled and stored using Revit. The optimized design solution thereafter would be visualized automatically in Revit to readily acquire formwork quantities schedules and shop drawings. Findings A case study illustrating the proposed approach demonstrated that using BIM will reduce formwork design, quantification and drawing time by more than 50% of the traditional approach with safer design and accurate results due to process automation and optimize cost for the given data. Originality/value This research introduces an innovative integrated BIM and GA model for the optimization and automation of slab formwork design, which has significantly benefited the construction industry. The utilization of GA in the optimization process allows for the attainment of an optimal formwork design, ultimately leading to a reduction in construction cost and time.

Identification of Local Overpotentials and Their Spatial Distributions in Polymer Electrolyte Water Electrolyzers

Polymer electrolyte water electrolyzers (PEWEs) are pivotal in the transition to a clean energy economy, promising green and efficient hydrogen production. Understanding the complexities of overpotentials within PEWEs is crucial for optimizing their performance and efficiency1. For PEWEs to attain the highest possible system efficiency, considering a specific material set and operating parameters, meticulous design is imperative. The goal is to achieve a high reaction rate that is not only optimal but also uniform across the entire active surface area of the electrodes. This uniformity should ideally be maintained with minimal sensitivity to variations in operating conditions.This study focuses on the contribution of different overpotential categories and reveals their spatial variations through in-situ analysis to understand and overcome the issues caused by non-uniformity in reaction. Employing a segmented cell and printed circuit board (PCB) approach2, current density distribution (CDD) and distributed area-specific resistance (DASR) measurements are obtained, enabling a detailed examination of the mass transport overpotentials (as shown in Figure 1). Typically, in the fuel cell community, modeling mass transport limitations is thought of as diffusion-limited transport3. In the anode porous transport layer (PTL), oxygen accumulation (i.e. product transport limitation) can cause membrane dry-out and an increase in ASR4. The experimental results elucidate the intricate relationship between ohmic and mass transport losses, emphasizing the deep coupling induced by the two-phase nature of the system. Figure 1 shows that, as mass transport limitation grows, the highest increase in overpotential comes from mass transport-coupled ohmic losses. Beyond conventional diffusion limited mass transport losses, bubble dynamics significantly impact local hydration and ASR, highlighting the need for a holistic approach in device optimization5.This research contributes to the understanding of PEWE dynamics, providing a basis for informed engineering decisions. By unraveling the intricate interdependencies between overpotentials, this work empowers the efficient utilization of expensive catalyst materials. Moreover, it offers insights into mitigating the challenges associated with bubble removal, accomplishing this with lower pumping requirements that scale non-linearly. These insights enrich both theoretical understanding and are paramount for enhancing system efficiency and economic viability of PEWEs, thereby advancing the prospect of sustainable hydrogen production. Reference N. Danilovic and I. Zenyuk, Electrochem. Soc. Interface, 30 (2021).F. H. Roenning, A. Roy, D. S. Aaron, and M. M. Mench, J. Power Sources, 542, 231749 (2022) https://linkinghub.elsevier.com/retrieve/pii/S037877532200742X.A. Z. Weber et al., J. Electrochem. Soc., 161, F1254–F1299 (2014) https://iopscience.iop.org/article/10.1149/2.0751412jes.C. Immerz, B. Bensmann, P. Trinke, M. Suermann, and R. Hanke-Rauschenbach, J. Electrochem. Soc., 165, F1292–F1299 (2018).P. Satjaritanun et al., iScience, 23, 101783 (2020) https://doi.org/10.1016/j.isci.2020.101783. Figure 1

Prediction of remaining useful life for rolling bearing based on ISOMAP and multi-head self-attention with gated recurrent unit

The remaining useful life of rolling element bearing affects the reliability and stability of the machine. Accurately predicting the remaining useful life of rolling element bearings is always necessary to make maintenance decisions in practical engineering. However, the remaining useful life prediction methods using traditional machine learning are incompetent for the task of training without labels, which consumes computational cost, financial resources, and labor. Therefore, a gated recurrent unit model based on feature dimensionality reduction and the multi-head self-attention mechanism (MHGRU) is established, and a homology algorithm is proposed to train the model and make the prediction. The isometric feature mapping algorithm and a homology algorithm are used in the model training, which incorporates the advantages of appreciable computational efficiency and preservation of automatic labeling for training in engineering. First, 24 basic features are extracted from the life-cycle vibration signals of rolling element bearings to reconstruct fusion features by the isometric feature mapping algorithm, which aims to reduce the feature dimension and improve computational efficiency. Since the multi-head self-attention mechanism in the MHGRU has the ability to comprehensively highlight the attention coefficient for long-term fusion features at different moments, it gives the gated recurrent unit considerable performance in reducing the computational complexity of extremely long time series and improving the accuracy of prediction results. In addition, two open-source IEEE PHM Challenge and XJTU-SY bearing datasets sampled by the sensor are adopted to assess the prediction performance of the MHGRU with homology algorithm. Finally, some existing remaining useful life prediction approaches of rolling element bearings are used for comparison. The experimental results show that MHGRU is suitable for the remaining useful life prediction of rolling element bearings and is superior to other models.

Application of Intelligent Analysis based on Engineering Management and Decision making for Economic Development of Regional Enterprise

Application of Intelligent Analysis based on Engineering Management and Decision making for Economic Development of Regional Enterprise

An approach for strength development assessment of cement-stabilized soils with various sand and fine contents

Cement stabilization is a well-established ground improvement technique. However, there have been limited investigations aimed at the effect of heterogeneous soil types with varying amounts of coarse-grained (sand) and fine-grained (silt and clay) substances, on the strength enhancement of cement-stabilized soils. In this cement stabilization research study, sand (S) and clayey silt (F) were mixed together to have a variety between coarse- and fine-grained fractions at 100, 75, 50, 25, and 0 % by dry weight, designated as the ratio of S:F=100:0, 75:25, 50:50, 25:75, and 0:100, respectively. The water content was prepared at the range of 1.25–2.50 optimal water content to simulate field applications. The strength enhancement of cement-stabilized soils was influenced by the fine content, water content, and cement content. The soil–water to cement ratio (s-w/c) was effectively incorporate the impact of both water and cement contents on the strength enhancement, for a given a specific fine content. The generalized correlation between unconfined compressive strength, qu and s-w/c could be represented as a power function: qu = M/(s-w/c)N. In this equation, M and N are constants that are primarily influenced by the fine content. The shear strength ratio versus fine content chart was proposed as a means to assess the effect of fine content on the strength of cement-stabilized soil with varying s-w/c. A stepwise approach for assessing the strength enhancement of cement stabilization in soil based on physical characteristics (i.e., plasticity index and fine content) was proposed and validated. The robustness of the proposed approach was realized by the low mean absolute percent error, (MAPE<7.0 %) and high coefficient of determination (R2 > 0.95) for measured and predicted strengths comparison. This technique serves as a valuable tool for mix design, specifically in relation to clay mineral and fine content. It supports in making engineering decision regarding the appropriate amount of water and cement needed to achieve strength requirements throughout the requisite curing period, while minimizing the number of repetitions needed.

Modelling Technical Capacity of Industrial Machines Suppliers’ Selection Post Engineering and Economic Considerations

After considering engineering, economics, and supply due-date strategic decisions as the death knell for the selection of industrial machinery suppliers, the technical capability of the suppliers is one of the strategic decisions for consideration. To achieve this, necessary attributes of this strategic decision were identified in this study. These attributes include the quality of mechanics used, quality of staff used, level of research work done, level of quality control, and quality of companies patronizing the vendor. They were modelled and integrated (logic) for decision-making and then evaluated using a case study of procuring a Cocoa Liquor Press for extracting butter from the cocoa liquor using three vendors V1, V2, and V3 that passed the three strategic decision death knells. The overall performance indices of all three vendors as per their strength on the considered strategic decision (technical quality of the supplier) are 1.71, 1.66, and 1.63 which are 43.2%, 33.25%, and 32.6% respectively by percentage. While their weaknesses are 3.28, 3.34, and 3.37 which are 65.6%, 66.8%, and 67.2%. Vendor one (V1) having a performance index of 1.71 strength and 3.28 weakness (43.2% strength and 65.8% weakness) post engineering and economic consideration death-knell was found to be best suitable and was selected.

Implications of vertical stability control on the SPARC tokamak

To achieve its performance goals, SPARC plans to operate in equilibrium configurations with a strong elongation of κareal∼1.75 , which in turn will destabilize the n = 0 vertical instability. However, SPARC also features a relatively thick conducting wall that is designed to withstand disruption forces, leading to lower vertical instability growth rates than usually encountered. In this work, we use the TokSyS framework to survey families of accessible shapes near the SPARC baseline configuration, finding maximum growth rates in the range of γ≲100 s−1. The addition of steel vertical stability plates has only a modest ( ∼25% ) effect on reducing the vertical growth rate and almost no effect on the plasma controllability when the full vertical stability system is taken into account, providing flexibility in the plate conductivity in the SPARC design. Analysis of the maximum controllable displacement on SPARC is used to inform the power supply voltage and current limit requirements needed to control an initial vertical displacement of 5% of the minor radius. From the expected spectra of plasma disturbances and diagnostic noise, requirements for filter latency and vertical stability coil heating tolerances are also obtained. Small modifications to the outboard limiter location are suggested to allow for an unmitigated vertical disturbance as large as 5% of the minor radius without allowing the plasma to become limited. Further, investigations with the 3D COMSOL code reveal that strategic inclusion of insulating structures within the VSC supports are needed to maintain sufficient magnetic response. The workflows presented here help to establish a model for the integrated predictive design for future devices by coupling engineering decisions with physics needs.

Reevaluating soil amplification using multi-spectral HVSR technique in La Chana Neighborhood, Granada, Spain

This work presents a thorough reevaluation of soil amplification in the La Chana neighborhood of Granada through a pioneering application of the horizontal-to-vertical spectral ratio technique on seismic noise data using various spectral approaches. The research recycles old seismic noise data recorded at 34 stations with 2 Hz instruments in the year 2010, supplemented with additional measurements recorded with broadband seismometers at nearby locations in the years 2013 and 2017. Initial traditional processing identifies a narrowband dominant frequency around 1.5 Hz, attributed to artificial or anthropogenic sources. To address this, the Maximum Entropy Algorithm was implemented to smooth the spectral response below 1 Hz, and filter out frequency peaks with very narrow spectral bands, while preserving the narrowband frequency around 1.5 Hz in some records. The Thomson Multitaper method further refined the spectral ratio, emphasizing the detection and suppression of narrow frequency bands that may be related to industrial activity. The results demonstrated the reappearance of the 1.5 Hz frequency, but this time without narrow bandwidths, indicating its possible correlation with the natural ground movement. Fundamental periods, ranging from 0.45 s to 0.88 s, suggest a diverse lithological composition, indicating the presence of layers of sands, clays, conglomerates, and carbonates over a basement that represents the main impedance contrast in the area. The multispectral approach surpasses conventional methods in precision and reliability, providing valuable insights for earthquake risk assessment, urban planning, and engineering decisions in seismically active regions.

Codes and standards for structural design - developments and future potential

To date our built environment is broadly developed and maintained on the basis of structural design standards. Most design standards contain simplified semi-probabilistic safety concepts that help daily structural engineering decision making using simple calculus. In this paper research about the rational basis for the calibration of these simplified code formats is reviewed and the potential for further developments is presented.

Mapping of Atmospheric Corrosion of Steel in Southeast India Using Inverse Distance Interpolation Technique

Reinforced concrete and steel structural elements undergo premature degradation and lose strength primarily due to corrosion. Corrosion is an electrochemical phenomenon and its severity depends on several environmental factors. Experimental data on the corrosion of steel is important for making engineering decisions toward improving the service life of civil infrastructures. No recent dataset on atmospheric steel corrosion in an Indian coastal environment is found to be available in the literature, and hence this research attempts to address some of the literature gap. This paper presents an experimental study conducted to determine the corrosion rate of TMT, high-chromium steel, and stainless steel rebars exposed in actual coastal and inland regions for a period of 1 y. The site locations were located in southeastern parts of Tamil Nadu state in India. Based on the first-year corrosion rate, the corrosion rate after extended exposure (10 y) was determined based on ISO 9224 recommendations. The atmospheric steel corrosion map of Tamil Nadu state was developed using inverse distance interpolation technique. Microstructural studies indicated the formation of lepidocrocite (γ-FeOOH) phase composition in the rust products collected from coastal regions.

Advanced Uncertainty Quantification for Flood Inundation Modelling

Flood hazards present a significant risk to the UK, with homes, businesses and critical infrastructure exposed to a mixture of fluvial, surface water and coastal flooding. Climate change is expected to influence river flows, changing the frequency and magnitude of future flood events. Flood hazard assessments are used by decision-makers to implement policies and engineering interventions to reduce the impacts of these flood events. Probabilistic flood modelling can explore input and parameter uncertainties in flood models to fully quantify inundation uncertainty. However, probabilistic methods require large computational costs—limiting their application. This paper investigates a range of advanced uncertainty quantification methods (traditional Monte Carlo (FMC), Kriging and multi-fidelity Monte Carlo (MFMC)) to reduce the dichotomy between accuracy and costs. Results suggest that Kriging can reduce computational costs by 99.9% over FMC. The significantly increased efficiency has the potential to improve future policy and engineering decisions, reducing the impacts of future flood events.

Scraping Effect of Dam-Overtopping Debris Flow—A Case Study of Chutou Gully ‘8.20’ in Miansi Town, Wenchuan County

ABSTRACT After the May 12 earthquake in Wenchuan, numerous debris flow disasters occurred due to heavy rainfall. After the earthquake, disaster prevention and mitigation projects, such as the sand dam, played an essential role in safety. However, when the outflow of solid material exceeds the designed storage capacity of the sand dam, the debris flow overtopping the dam intensifies the scraping effect on the source material of the gully channel. This paper takes the ”8.20” debris flow in the Chutou gully as the research object. A transport mode of the debris flow after dam overtopping is studied through comprehensive field investigation, theoretical analysis, and indoor model tests. A quantitative evaluation model is established for the scraping depth and scraping volume of dam-overtopping debris flow. The study results showed that once the debris flow overtops a dam, the loose source carried by the channel is involved in the entire migration process, enlarging the fluid scale and intensifying the damage along the channel. The results agree with the measurements and observations, with maximum relative errors of 5.81% and 14.70%, respectively. This shows model validity. Furthermore, the test results show that the debris flow scarping phenomenon in the γ=[1.4 t/m3, 1.6 t/m3] interval is the most significant. Overall, this study will help improve future engineering decisions, design strategies, and finding the best design solutions to minimize the risk of dam-overtopping debris flow disasters.

Effect of geotechnical soil properties on CBR value: A review

This review paper presents the current research state of the relationship between geotechnical soil properties and the California bearing ratio (CBR) value. Geotechnical considerations are pivotal in civil engineering projects to prevent collapse and settlement failures, underscoring the importance of understanding detailed soil properties. A CBR test used for assessing stiffness modulus and shear strength guides the overlaying layer-thickness calculation during pavement design. Although expensive and complex, researchers have explored correlations and machine-learning algorithms for CBR prediction from soil properties. This study delves into the varying influence of properties such as compaction (optimum moisture content and maximum dry density) and index (liquid, plastic, and shrinkage limits) properties. The relevance of such properties to CBR is explored, examining their significance and potential interactions. This review highlights the multi-faceted effects of soil properties on CBRvalue, enabling informed pavement engineering decisions.

Navigating the Complex ESG Accounting Landscape: Engineering a Method Selection Framework

Environmental, social, and governance accounting (ESGA) aids organizations in achieving their sustainability goals through continuous improvement. Suitable method selection is crucial to prevent rework, additional expenses, trivial outcomes, and reduced confidence in sustainability practices. The current ESGA method selection process lacks comprehensive consideration of alternatives and criteria, occasionally resulting in suboptimal choices. This work aims to achieve optimal ESGA by engineering a method selection framework. The research approach is based on the design cycle, where engineering decisions are informed by empirical evidence. The main findings are that the framework, which includes a decision model and a supporting information system, can reduce the chances of organizations selecting an unsuitable method, whilst sparing decision-making managers time and effort. Firstly, the reusable elements of the framework can help managers of any organization select a suitable method more efficiently since they do not have to produce these elements themselves. Secondly, the results demonstrate how selection frameworks and tools can aid organizations in navigating the complex ESG accounting landscape. Lastly, this study lowers the barrier for organizational impact management; in particular, for measuring and reporting ESG impact, which is a rigorous assessment of the organization’s progress towards sustainable development goals.

Modeling Spatiotemporal Heterogeneity of Customer Preferences With Small-Scale Aggregated Data: A Spatial Panel Modeling Approach

Abstract Customer preferences are found to evolve over time and correlate with geographical locations. Studying the spatiotemporal heterogeneity of customer preferences is crucial to engineering design as it provides a dynamic perspective for understanding the trend of customer preferences. However, existing choice models for demand modeling do not take the spatiotemporal heterogeneity of customer preferences into consideration. Learning-based spatiotemporal data modeling methods usually require large-scale datasets for model training, which are not applicable to small aggregated data, such as the sale records of a product in several regions and years. To fill this research gap, we propose a spatial panel modeling approach to investigate the spatiotemporal heterogeneity of customer preferences. Product and regional attributes varying in time are included as model inputs to support demand forecasting in engineering design. With case studies using the dataset of small SUVs and compact sedans in China's automotive market, we demonstrate that the spatial panel modeling approach outperforms other statistical spatiotemporal data models and non-parametric regression methods in goodness of fit and prediction accuracy. We also illustrate a potential design application of the proposed approach in a portfolio optimization of two vehicles from the same producer. While the spatial panel modeling approach exists in econometrics, applying this approach to support engineering decisions by considering spatiotemporal heterogeneity and introducing engineering attributes in demand forecasting is the contribution of this work. Our paper is focused on presenting the approach rather than the results per se.