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Effective resource allocation in the agri-food sector is essential in mitigating environmental impacts and moving toward circular food supply chains. The potential of integrating life cycle assessment (LCA) with machine learning has been highlighted in recent studies. This hybrid framework is valuable not only for assessing food supply chains but also for improving them toward a more sustainable system. Yet, an essential step in the optimization process is defining the optimization boundaries, or minimum and maximum quantities for the variables. Usually, the boundaries for optimization variables in these studies are obtained from the minimum and maximum values found through interviews and surveys. A deviation in these ranges can impact the final optimization results. To address this issue, this study applies the Delphi method for identifying variable optimization boundaries. A hybrid environmental assessment framework linking LCA, multilayer perceptron artificial neural network, the Delphi method, and genetic algorithm was used for optimizing the pomegranate production system. The results indicated that the suggested framework holds promise for achieving substantial mitigation in environmental impacts (potential reduction of global warming by 46%) within the explored case study. Inclusion of the Delphi method for variable boundary determination brings novelty to the resource allocation optimization process in the agri-food sector. Integr Environ Assess Manag 2024;20:1759-1769. © 2024 SETAC.

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The viability of employing soft computing models for predicting the viscosity of engine lubricants is assessed in this paper. The dataset comprises 555 reports on engine oil analysis, involving two oil types (15W40 and 20W50). The methodology involves the development and evaluation of six distinct models (SVM, ANFIS, GPR, MLR, MLP, and RBF) to predict viscosity based on oil analysis results, incorporating metallic and nonmetallic elements and engine working hours. The primary findings indicate that the radial basis function (RBF) model excels in accuracy, consistency, and generalizability compared with other models. Specifically, a root mean square error (RMSE) of 0.20 and an efficiency (EF) of 0.99 were achieved during training and a RMSE of 0.11 and an EF of 1 during testing, utilizing a 35-network topology and an 80/20 data split. The model demonstrated no significant differences between actual and predicted datasets for average and distribution indices (with P-values of 1.00). Additionally, robust generalizability was exhibited across various training sizes (ranging from 50 to 80%), attaining a RMSE between 0.09 and 0.20, a mean absolute percentage error between 0.23 and 0.43, and an EF of 0.99. This study provides valuable insights for optimizing and implementing machine learning models in predicting the viscosity of engine lubricants. Limitations include the dataset size, potentially affecting the generalizability of findings, and the omission of other factors impacting engine performance. Nevertheless, this study establishes groundwork for future research on the application of soft computing tools in engine oil analysis and condition monitoring.

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The accurate estimation of in vitro ruminal biohydrogenation (BH) kinetics of fatty acids (FA) allows for a more accurate understanding of their dynamics and develop targeted strategies to enhance desirable FA bypass. This study comprises a comprehensive evaluation of 33 nonlinear regression models to determine the most suitable model for accurately estimating the in vitro BH kinetics of individual FA. The data set utilized in the present research originates from a recent investigation on the effects of micronization and vitamin E on the in vitro ruminal BH of rapeseed. For the nonlinear regression analysis, data comprising FA concentrations (expressed as g FA/100 g FA) at the conclusion of 2, 4, 8, 12, 24, and 48 h incubation periods were employed. The evaluation of nonlinear regression models focused on identifying the ideal model based on criteria including the highest R2 value, the lowest RMSE value, and statistically significant coefficients. The results pinpoint the Gompertz model as an effective choice for estimating the in vitro ruminal BH kinetics of upward-trending fatty acids, including intermediate unsaturated fatty acids and saturated end FA. Additionally, the first-order kinetic model of Ørskov and McDonald emerges as the preferred model for investigating the BH kinetics of downward-trending fatty acids, including oleic acid, linoleic acid, and alpha-linolenic acid. In summary, this rigorous evaluation led to the identification of the most appropriate model, one that not only exhibited an exceptional fit to the data but also provided profound insights into the intricate relationships between predictors and the dynamic behavior of FA. The established nonlinear regression models will serve as invaluable tools for future research investigating FA biohydrogenation kinetics.

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Oriented strand board (OSB) has become a popular building material for residential construction, but little research has been conducted on its use as a finish floor material. The study investigated the quality and performance of OSB as an alternative to traditional engineered wood products for finish floors. Four types of OSB finish floors using a mixture of garden and urban tree toppings were produced and evaluated, along with different types and levels of resin and mat moisture content. The finish floor panels were subjected to a battery of tests, including concentrated loading, indentation, falling ball impact resistance, abrasion resistance, and surface wettability. The findings showed that urea formaldehyde resin with garden tree toppings performed best in floor surface indentation, abrasion resistance, and falling ball indentation. The phenol formaldehyde resin with garden tree toppings, on the other hand, showed less moisture absorption and swelling during surface wetting tests and better resistance to force application in the concentrated loading test. Our qualitative comparison revealed that OSB finish floor production using 100% garden tree topping strands and 12% urea formaldehyde resin, along with 14% mat moisture content, produced the best results. The study provides valuable insights into the potential use of OSB as a sustainable and cost-effective finish floor material, using waste materials from urban and garden tree toppings.

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When considering options for future foods, cell culture approaches are at the fore, however, culture media to support the process has been identified as a significant contributor to the overall global warming potential (GWP) and cost of cultivated meat production. To address this issue, an artificial intelligence-based approach was applied to simultaneously optimize the GWP, cost, and cell growth rate of a reduced-serum culture media formulation for a zebrafish (ZEM2S cell line) cultivated meat production system. Response surface methodology (RSM) was used to design the experiments, with seven components - IGF, FGF, TGF, PDGF, selenium, ascorbic acid, and serum - selected as independent variables, given their influence on culture media performance. Radial basis function (RBF) neural networks and genetic algorithm (GA) were applied for prediction of dependent variables, and optimization of the culture media formulation, respectively. The results indicated that the developed RBF could accurately predict the GWP, cost and growth rate, with a model efficiency of 0.98. Subsequently, the three developed RBF neural networks predictive models were used as the inputs for a multi-objective genetic algorithm, and the optimal quantities of the independent variables were determined using a multi-objective optimization algorithm. The suggested RSM + RBF + GA framework in this study could be applied to sustainably optimize serum-free media development, identifying the combination of media ingredients that balances yield, environmental impact, and cost for various cultivated meat cell lines.

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Saffron (Crocus sativus L.) is the most expensive spice in the world, known for its unique aroma and coloring in the food industry. Hence, its high price is frequently adulterated. In the current study, a variety of soft computing methods, including classifiers (i.e., RBF, MLP, KNN, SVM, SOM, and LVQ), were employed to classify four samples of fake saffron (dyed citrus blossom, safflower, dyed fibers, and mixed stigma with stamens) and three samples of genuine saffron (dried by different methods). RGB and spectral images (near-infrared and red bands) were captured from prepared samples for analysis. The amount of crocin, safranal, and picrocrocin were measured chemically to compare the images' analysis results. The comparison results of the classifiers indicated that KNN could classify RGB and NIR images of samples in the training phase with 100% accuracy. However, KNN's accuracy for different samples in the test phase was between 71.31% and 88.10%. The RBF neural network achieved the highest accuracy in training, test, and total phases. The accuracy of 99.52% and 94.74% was obtained using the features extracted from RGB and spectral images, respectively. So, soft computing models are helpful tools for detecting and classifying fake and genuine saffron based on RGB and spectral images.

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A promising strategy for controlling repeated implantation failure (RIF) may be the use of hydroxychloroquine (HCQ). To the best of our knowledge, no systematic review has been conducted on the effects of hydroxychloroquine on pregnancy outcomes. A systematic research of the following electronic databases was conducted: Cochrane, EMBASE-Ovid, PubMed, Web of Science, and Scopus from inception to December 2021, using the following keywords [hydroxychloroquine] AND [infertility]. Fertilization and rate of live birth were significantly higher in the HCQ+ prednisone (PDN) group than in the PDN alone group. However, the abortion rate was not different between the two groups. The meta-analysis of two studies revealed no statistical significance between the PDN group and HCQ+PDN group regarding clinical pregnancy rate (OR=.14 [95%CI: 0.4-4.370]; heterogeneity; P=0.13; I2=54%; random effect model) and implantation rate (OR=1.99 [95%CI: 0.94-4.2]; heterogeneity; P=0.37; I2=0%; fixed-effect model). While HCQ may help improve fertilization and live birth rates, adding it to prednisone did not improve overall pregnancy outcomes. This systematic review should be used with caution due to the small size, study design, and difference in the studies' population.

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Bioplastic biodegradation showed varying behavior during the process of biodegradation. The First-order and Gompertz models are the most prevalent models for monitoring biodegradation in an anaerobic digestion (AD) process, which do not suit adequately bioplastics fermentation modeling. This research aimed at studying the kinetics of methane production during AD of starch-based bioplastic by using a large library of non-linear regressions (NLRs) and an artificial neural network (ANN). Although 26 NLR models (25 were outlined in the AD literature + 1 modified by authors) have been analyzed, 9 of them were proper predictors for the whole AD process for methane production. In the end M9, which has been proposed by authors, was selected owing to the simplicity of regression as well as good statistical criteria. Moreover, MLP-ANN could outperform the NLR model and has been selected as the superior model that can define the kinetics of bioplastic AD.

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In recent decades, soil contamination with heavy metals has become an environmental crisis due to their long-term stability and adverse biological effects. Therefore, bioremediation is an eco-friendly technology to remediate contaminated soil, which the efficiency requires further research. This study was designed to comparatively investigate two strategies: bioaugmentation by using a cyanobacterial species (Oscillatoria sp.) and bioaugmentation-assisted phytoremediation by using Oscillatoria sp. and purslane (Portulaca oleracea L.) for the bioremediation of soil contaminated by heavy metals (Cr (III), Cr (VI), Fe, Al, and Zn). Various quantities of biochar (0.5, 2, and 5% (w/w)) were used as an amendment in the experiments to facilitate the remediation process. The results of the bioaugmentation test showed that applying biochar and cyanobacteria into contaminated soil significantly increased the chlorophyll a, nitrogen, and organic carbon contents. In contrast, the extractable fractions of Cr (III), Cr (VI), Zn, Al, and Fe declined compared with those of the control treatment. The highest reduction content (up to 87 %) in the extractable portion was obtained for Cr (VI). The development of longer root and hypocotyl lengths and vigour index from lettuces and radish seeds grown in the remediated soil confirmed the success of remediation treatments. Moreover, the findings of the bioaugmentation-assisted phytoremediation test displayed a reduction in the bioavailable fraction of Cr (III), Cr (VI), Zn, Al, and Fe. Cr (III) presented the highest reduction (up to 90 %) in metal bioavailability. With cyanobacteria inoculation and biochar addition, the shoot and root lengths of purslane grew 4.6 and 3-fold while the heavy metal accumulation decreased significantly. Besides, these treatments enhanced the tolerance index (TI) quantities of purslane whereas diminished its bioaccumulation coefficient (BAC) and bioconcentration factor (BCF) values. For all heavy metals (except Zn), translocation factor (TF) and BAC values were found to be less than 1.0 at all treatments, indicating the successful phytoextraction by the purslane. These results suggest that the purslane can be considered an excellent phytoextracting agent for soils contaminated with heavy metals.

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The present study seeks to investigate the use of husbandry waste and sawdust in the construction of degradable pots as a suitable alternative to plastic pots. Six mixture ratios of cattle manure and sawdust (85:15, 80:20, 75:25, 70:30, 65:35, and 60:40) were used along with three types of natural binders (sheep's wool, cornstarch, and sheep's wool:cornstarch) in phase I of the project. Phase I was replicated in triplicate to identify the best composition for each binding agent. International standards dictate that evaluations of biological pots include investigations into thickness swelling, internal bonding, and water absorption. Mean comparison of the resultant factorial data using the Tukey and TOPSIS methods indicated that production of bio-pots with a mixture of 80% manure and 20% sawdust may provide the best results for all three pot types. Phase II of the project involved using field experiments and cultivation of tomato plants in direct comparison to a commercial sample pot. The optimal pots for each binding agent in phase I were used in the evaluation. Field tests showed pots produced with 80% manure and 20% sawdust using cornstarch for binding adhesion performed best in terms of degradability and physical and mechanical properties.

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Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it is logical that a small increment in wind speed will result in significant growth in generated power. Shrouding a wind turbine is an ordinary way to exceed the Betz limit, which accelerates the wind flow through the rotor plane. Several layouts of shrouds are developed by researchers. Recently an innovative controllable duct is developed by the authors of this work that can vary the shrouding angle, so its performance is different in each opening angle. As a wind tunnel investigation is heavily time-consuming and has a high cost, therefore just four different opening angles have been assessed. In this work, the performance of the turbine was predicted using multiple linear regression and an artificial neural network in a wide range of duct opening angles. For the turbine power generation and its rotor angular speed in different wind velocities and duct opening angles, regression and an ANN are suggested. The developed neural network model is found to possess better performance than the regression model for both turbine power curve and rotor speed estimation. This work revealed that in higher ranges of wind velocity, the turbine performance intensively will be a function of shrouding angle. This model can be used as a lookup table in controlling the turbines equipped with the proposed mechanism.

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Dissolved oxygen (DO) as one of the most fundamental parameters of water quality plays a vital role in aquatic life. This study was conducted to predict DO, biological oxygen demand (BOD), and chemical oxygen demand (COD) in an intensive rainbow trout rearing system with different biomass (B). The multilayer perceptron (MLP) and the radial basis function (RBF) neural networks were employed for evaluating the impacts of food parameters (crude protein (CP), consumed feed (CF)), fish parameters (different values of B, and weight gain (WG)), and water quality parameters including temperature (T) and flow rate (Q) on variation of DO, BOD, and COD concentrations. This study's results showed that although both MLP and RBF neural networks are capable to estimate DO, BOD, and COD concentrations, RBF neural network showed better performance compared to MLP neural network. The results of sensitivity analysis indicated that the parameter CF has the highest effect on DO concentration estimation. Independent variables CF, CP, WG, and B showed the highest to the lowest rank of impacts on BOD estimation, respectively. The results also illustrated a decreasing trend of the effects on the estimation error of COD changes simulation by all independent variables, including B, T, WG, CF, CP, and Q, respectively. RBF neural network based on better stability and generalization ability with average root mean square error (RMSE) and mean absolute percentage error (MAPE) values of less than 0.12 and 3% was superior to MLP in DO, BOD, and COD concentration prediction. Moreover, CF was identified as the most effective factor in estima12tion process. Based on the present study results, there are direct relationships between DO, BOD, and COD concentrations and water quality parameters, fish parameters, and food parameters. Food parameters relative to fish and water quality parameters imposed the greatest effects. Improvement in feeding process such as application of intelligence feeding methods and change in fish diet and feeding time can considerably reduce losses in production system. Graphical abstract.

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In the present study, the effect of ultrasound irradiation on the transesterification parameters, biodiesel properties, and its combustion profiles in the diesel engine was investigated. Moreover, date seed oil (DSO) was firstly utilized in the ultrasound-assisted transesterification reaction. DSO was extracted from Zahidi type date (Phoenix dactylifera) and was esterified to reduce its Free Fatty Acid (FFA) content. Biodiesel yield was optimized in both heating methods, so that the yield of 96.4% (containing 93.5% ester) at 60 °C, with 6 M ratio of methanol/oil, 1 wt% of catalyst (NaOH) and at 90 min of reaction time was reported. The ultrasound irradiation did not influence the reaction conditions except reaction time, reduced to 5 min (96.9% yield and 91.9% ester). The ultrasonic irradiation also influenced on the physicochemical properties of DSO biodiesel and improved its combustion in the diesel engine. The analysis results related to the engine and gas emission confirmed that the ultrasound-assisted produced biodiesel has lower density and viscosity, and higher oxygen content facilitating injection of fuel in the engine chamber and its combustion, respectively. Although, B40 (biodiesel blend consisting of 40% biodiesel and 60% net diesel fuel) as a blend of both fuels presented higher CO2 and lower CO and HC in the emissions, the DSO biodiesel produced by ultrasound irradiation presented better specifications (caused about 2-fold improvement in emissions than that of conventional method). The findings of the study confirmed the positive effect of the ultrasound irradiation on the properties of the produced biodiesel along with its combustion properties in the diesel engine, consequently reducing air pollution problems.