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The most significant architectural spaces in the world are now entirely empty of people. The data centres, telecommunications networks, distribution warehouses, unmanned ports and industrialised agriculture that define the very nature of who we are today are at the same time places we can never visit. Instead they are occupied by server stacks and hard drives, logistics bots and mobile shelving units, autonomous cranes and container ships, robot vacuum cleaners and internet-connected toasters, driverless tractors and taxis. This issue is an atlas of sites, architectures and infrastructures that are not built for us, but whose form, materiality and purpose is configured to anticipate the patterns of machine vision and habitation rather than our own. We are said to be living in a new geological epoch, the Anthropocene, in which humans are the dominant force shaping the planet. This collection of spaces, however, more accurately constitutes an era of the Post-Anthropocene, a period where it is technology and artificial intelligence that now computes, conditions and constructs our world. Marking the end of human-centered design, the issue turns its attention to the new typologies of the post-human, architecture without people and our endless expanse of machine landscapes.

In the current study, the Generalized Structure of the Group Method Of Data Handling (GSGMDH) is developed to overcome the main drawbacks of the classical GDMH. The performance of the GSGMDH was checked in two case studies for multi-step flood forecasting at the upstream station (i.e., Saint-Charles station) using the historical records of upstream stations (i.e., Nelson and Croche stations). The results revealed high accuracy in flood forecasting one to six hours ahead for all sample ranges and peak flows, with indices showing R: [0.993, 0.9995], NSE: [0.986, 0.999], RMSE: [0.416, 1.453], NRMSE: [0.0239, 0.152], MAE: [0.146, 0.761], MARE: [0.023, 0.156], and BIAS: [-0.058, 0.01]. Indeed, the descriptive performance of the developed model rates as Very Good for both R and NSE, and Good for NRMSE. The uncertainty analysis of the GSGMDH models demonstrates remarkable precision in flood forecasting, with relative differences between the minimum and maximum uncertainty ranges of less than 1% for both Nelson and Croche upstream stations. Specifically, U95 for Nelson is [0.148, 0.149], and for Croche, it is [0.166, 0.167]. Besides, The reliability analysis of the GSGMDH highlights its effective peak flow forecasting capabilities, with MARE values for various flow discharges remaining below 10% across different lead times, demonstrating the model's precision in predicting high-impact flood events. Moreover, a comparison between the developed GSGMDH and the traditional model reveals that the former surpasses the latter, achieving a maximum relative error of less than 7%, in contrast to the traditional GMDH's minimum MARE exceeding 12%.

This paper presents the research done to determine the functional relationship between process parameters and tensile strength for the fused deposition modelling (FDM) process using the group method for data modelling for prediction purposes. An initial test was carried out to determine whether part orientation and raster angle variations affect the tensile strength. It was found that both process parameters affect tensile strength response. Further experimentations were carried out in which the process parameters considered were part orientation, raster angle, raster width and air gap. The process parameters and the experimental results were submitted to the group method of data handling (GMDH), resulting in predicted output, in which the predicted output values were found to correlate very closely with the measured values. Using differential evolution (DE), optimal process parameters have been found to achieve good strength simultaneously for the response. The mathematical model of the response of the tensile strength with respect to the process parameters comprising part orientation, raster angle, raster width and air gap has been developed based on GMDH, and it has been found that the functionality of the additive manufacturing part produced is improved by optimizing the process parameters. The results obtained are very promising, and hence, the approach presented in this paper has practical application for the design and manufacture of parts using additive manufacturing technologies.

Accurate estimation of streamflow has a vital importance in water resources engineering, management and planning. In the present study, the abilities of group method of data handling-neural networks (GMDH-NN), dynamic evolving neural-fuzzy inference system (DENFIS) and multivariate adaptive regression spline (MARS) methods are investigated for monthly streamflow prediction. Precipitation, temperature and streamflows from Kalam and Chakdara stations at Swat River basin (mountainous basin), Pakistan, are used as inputs to the applied models in the form of different input scenarios, and models’ performances are evaluated on the basis of root mean square error (RMSE), mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE) and combined accuracy (CA) indexes. Test results of the Kalam Station show that the DENFIS model provides more accurate prediction results in comparison of GMDH-NN and MARS models with the lowest RMSE (18.9 m 3 /s), MAE (13.1 m 3 /s), CA (10.6 m 3 /s) and the highest NSE (0.941). For the Chakdara Station, the MARS outperforms the GMDH-NN and DENFIS models with the lowest RMSE (47.5 m 3 /s), MAE (31.6 m 3 /s), CA (26.1 m 3 /s) and the highest NSE (0.905). Periodicity (month number of the year) effect on models’ accuracies in predicting monthly streamflow is also examined. Obtained results demonstrate that the periodicity improves the models’ accuracies in general but not necessarily in every case. In addition, the results also show that the monthly streamflow could be successfully predicted using only precipitation and temperature variables as inputs.

High quality coke is a key raw material for the metallurgical industry. The characteristics of the coal have a significant influence on the parameters of the coke produced and, consequently, on the valuation of coal deposits and the economic assessment of mining projects. Predicting the quality of coking coal allows for the optimisation of production processes, including the planning and management of operations and the early detection of quality problems. In this study, using the principles of a smart mine, it is proposed to determine the quality of coal based on the combination of mining and geological conditions of mineral deposits and its quality indicators. Possible interrelationships between the quality of the coal in the deposit and the characteristics of the final product have been identified. A neural network is used to determine the priority of individual indicators that have a significant impact on the quality of coking coal. An important part of the research is its practical implementation in the conditions of the Jastrzębska Spółka Węglowa SA. Qualitative and quantitative parameters of coking coals were obtained for each mine of the region by the method of sampling and statistical processing of data such as: degree of metamorphism, thickness, deviation of volatile substances, presence of phosphorus, ash content, etc. For their evaluation, the Group Method of Data Handling was used to compare the factors of quality indicators depending on the priority of influence on the final characteristics of the coking coal. Based on the results obtained, it is shown that not all coal quality indicators have a significant impact on the quality of the final product. The study shows that it is possible to predict the main indicators (CRI – Coke Reactivity Index, CSR – Coke Strength after Reaction) of coke quality using neural networks based on a larger number of coal quality parameters and to eliminate parameters that have virtually no influence on the value of the final product. This method can also be used to improve the results of economic valuation of a deposit and to better plan exploration and mining operations.

Accurate determination of the viscosity of carbon dioxide (CO2) mixed with nitrogen (N2) is vital for enhanced oil recovery (EOR) and carbon capture, utilisation and storage (CCUS/CCS). The determination of this important thermophysical property is usually through costly and time‐consuming experiments, which is not ideal for field recovery planning and rapid decision‐making. On the other hand, the conventional modelling relies largely on equations of state (EoS) and empirical correlations, which can be inaccurate for CO2–N2 viscosity, particularly near supercritical conditions due to simplifying assumptions and limited transferability. Consequently, machine‐learning (ML) methods have gained popularity for fast and accurate prediction. Hence, in this study, ~3036 literature data points spanning pressures of 0.00127–160.99 MPa and temperature of 66.55–575.15 K were collected, cleaned and pre‐processed. Then, using pre‐processed data, several ML models, including gradient boosting (GB), extreme gradient boosting (XGBoost), LightGBM, CatBoost, random forest, three multilayer perceptron artificial neural networks (MLP‐ANNs), a stacking ensemble and a group method of data handling (GMDH) were developed. The developed models were benchmarked to predict CO2–N2 viscosity as a function of temperature, pressure and the mole fractions of CO2–N2 in the mixture. The analysis of the results indicate that the GB achieved the best performance with a correlation coefficient (R2) of 0.9933 ± 0.0011, root mean square error (RMSE) of 4.83 ± 0.39 μPa·s and mean absolute error (MAE) of 2.34 ± 0.10 μPa·s (mean ± 95% CI) for the test dataset, outperforming all other ML models and the utilised literature correlations. In addition, based on GMDH, two practical explicit equations within temperature ranges of T < 300 K and T > 300 K that predict the experimental viscosity with high accuracy were proposed. The sensitivity analysis also shows that the pressure has the highest positive impact, while temperature exhibited a comparably strong negative effect on viscosity.

The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly. Of late, Python and R provide diverse packages for handling missing data. In this study, an imputation algorithm, cumulative linear regression, is proposed. The proposed algorithm depends on the linear regression technique. It differs from the existing methods, in that it cumulates the imputed variables; those variables will be incorporated in the linear regression equation to filling in the missing values in the next incomplete variable. The author performed a comparative study of the proposed method and those packages. The performance was measured in terms of imputation time, root-mean-square error, mean absolute error, and coefficient of determination $\\lpar {\\bi R}^2\\rpar $(R2). On analysing on five datasets with different missing values generated from different mechanisms, it was observed that the performances vary depending on the size, missing percentage, and the missingness mechanism. The results showed that the performance of the proposed method is slightly better.

The natural gas compressibility factor indicates the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial method based on the group method of data handling (GMDH) is presented to determine this critical parameter for different natural gases at different conditions, using corresponding state principles. The accuracy of the proposed method is evaluated through graphical and statistical analyses. The method shows promising results considering the accurate estimation of natural gas compressibility. The evaluation reports 2.88% of average absolute relative error, a regression coefficient of 0.92, and a root means square error of 0.03. Furthermore, the equations of state (EOSs) and correlations are used for comparative analysis of the performance. The precision of the results demonstrates the model's superiority over all other correlations and EOSs. The proposed model can be used in simulators to estimate natural gas compressibility accurately with a simple mathematical equation. This model outperforms all previously published correlations and EOSs in terms of accuracy and simplicity.

In this paper, gamma attenuation has been utilised as a veritable tool for non-invasive estimation of the thickness of scale deposits. By simulating flow regimes at six volume percentages and seven scale thicknesses of a two phase-flow in a pipe, our study utilised a dual-energy gamma source with Ba-133 and Cs-137 radioisotopes, a steel pipe, and a 2.54 cm × 2.54 cm sodium iodide (NaI) photon detector to analyse three different flow regimes. We employed Fourier transform and frequency characteristics (specifically, the amplitudes of the first to fourth dominant frequencies) to transform the received signals to the frequency domain, and subsequently to extract the various features of the signal. These features were then used as inputs for the group method for data Hiding (GMDH) neural network framework used to predict the scale thickness inside the pipe. Due to the use of appropriate features, our proposed technique recorded an average root mean square error (RMSE) of 0.22, which is a very good error compared to the detection systems presented in previous studies. Moreover, this performance is indicative of the utility of our GMDH neural network extraction process and its potential applications in determining parameters such as type of flow regime, volume percentage, etc. in multiphase flows and across other areas of the oil and gas industry.

[Purpose/Significance] The development of open science still faces many data ethical problems, and there is an urgent need to explore the ethical guidelines for data handling that are compatible with the open science innovation ecology. The establishment of ethical guidelines is essential to create a favorable open science innovation ecology for data handling that match the construction of open science innovation ecology, and guide the participants in the open science innovation ecology to act in accordance with legal regulations and ethical norms in the process of data creation, sharing, dissemination, and utilization, and restraining data handling behaviors for the good and benefit of society. [Method/Process] This study first uses literature research and content analysis, based on relevant laws, guidelines, draft frameworks, policies and typical data management models on data handling ethics, from a universal perspective, based on the practice of data acquisition, storage, management, use and destruction, for social data in a broad sense, while taking into account a part of scientific research data, correlating and integrating different guidelines, seeking common themes and contents among existing guidelines, and summarizing the general dimensions and highly recognized guidelines. This study summarizes the general dimensions of ethical guidelines for data handling and the contents of the guidelines with high recognition. Second, this study combines the concept of open science and the needs of national social development, and distills the needs of ethical guidelines for data handling in the open science innovation ecosystem from the latest domestic planning policies and the connotation of open science. Finally, this study synthesizes and compares the two results through matrix analysis, and analyzes the framework of ethical guidelines for data handling in the construction of open science innovation ecology from the perspectives of commonality and characteristics, and synergy between guideline contents and policies and concepts. [Results/Conclusions] The existing ethical guidelines for data handling have high applicability in the open science innovation ecology, but still need dynamic expansion. Combining the concept of open science and the development needs of national society, this study proposes a framework of ethical guidelines for data handling in the open science innovation ecology, including three dimensions and 15 elements of long-term governance, coordination and order, and systematic science, aiming to serve the further development of the text of ethical guidelines for data handling adapted to the development of the open science innovation ecology in China and to provide some reference for data management in institutions.