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Spectroscopic analysis was employed to evaluate the quality of three bell pepper varieties within the 350–1150 nm wavelength range. Quality parameters such as firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins were measured. To enhance data reliability, principal component analysis (PCA) was used to identify and remove outliers. Raw spectral data were initially modeled using partial least squares regression (PLSR). To optimize wavelength selection, support vector machines (SVMs) were combined with genetic algorithms (GAs), particle swarm optimization (PSO), ant colony optimization (ACO), and imperial competitive algorithm (ICA). The most effective wavelength selection method was subsequently used for further analysis. Three modeling techniques—PLSR, multiple linear regression (MLR), and artificial neural networks (ANNs)—were applied to the selected wavelengths. PLSR analysis of raw data yielded a maximum R2 value of 0.98 for red pepper pH, while the lowest R2 (0.58) was observed for total phenols in yellow peppers. SVM-PSO was determined to be the optimal wavelength selection algorithm based on ratio of performance to deviation (RPD), root mean square error (RMSE), and correlation values. An average of 15 effective wavelengths were identified using this combined approach. Model performance was evaluated using root mean square error of cross-validation and coefficient of determination (R2). ANN consistently outperformed MLR and PLSR in predicting firmness, pH, soluble solids content, titratable acids, vitamin C, total phenols, and anthocyanins for all three varieties. R2 values for the ANN model ranged from 0.94 to 1.00, demonstrating its superior predictive capability. Based on these results, ANN is recommended as the most suitable method for evaluating the quality parameters of bell peppers using spectroscopic data.

The evaluation of energy input and output processes in agricultural systems is a crucial method for assessing sustainability levels within these systems. In this research, the investigation focused on the input and output energies and related indices in sunflower farms in Khoy County during the agricultural year 2017–2018. Data were collected from 140 sunflower producers through specialized questionnaires and face-to-face interviews. Additionally, artificial neural networks (ANNs), specifically the multilayer perceptron, were employed to predict the output energy. The results revealed that a substantial portion of the total input energy was attributed to chemical nitrogen fertilizer (43.98%), consumable fuel (25.74%), and machinery (8.42%). The energy efficiency (energy ratio) in these agroecosystems was relatively low, measured at 1.57 for seed and 7.96 for seed and straw. These values should be improved. The energy efficiency in seed production was computed at 0.06 MJ·ha−1, while, for the combined seeds and straw, it was 0.57 MJ·ha−1. In particular, seed energy efficiency represents approximately 11% of the overall biological energy efficiency, highlighting that a substantial 89% of the produced energy is associated with straw. The proper use of this straw is crucial, as its improper handling could lead to a drastic decrease in overall efficiency. Furthermore, the explanatory coefficient (R2) and the mean absolute percentage error (MAPE) to predict the output energy with the best neural network were 0.94, and 1.77 for the training data, 0.97 and 1.55 for the test data, and 0.9 and 2.08 for the validation data, respectively; additionally, 0.97 and 0.42 were obtained by an ANFIS.

Fereydounshahr experiences growing electricity shortfalls and an overreliance on polluting diesel generators. This analysis models renewable hybrid systems to electrify a local dairy operation using HOMER Pro 3.11 software. Despite higher upfront costs, adding just 5% solar photovoltaics maintained low net present costs while increasing clean energy penetration versus diesel generators alone. Furthermore, a combined diesel-solar-wind system reduced carbon dioxide and nitrogen oxide emissions by over 1%. Although diesel generators had the shortest payback period at 48 years, the capital recovery factor for a diesel–solar combination reached 62 years. Thus, tailored hybrid renewable systems could provide an affordable, low-emissions electricity solution for the Fereydounshahr livestock facility. With suitable wind and solar resources, the right policy incentives could also unlock substantial local renewable capacity to meet rising demand and mitigate dependence on imported, climate-harming fossil fuels.

Peanuts, owing to their composition of complex carbohydrates, plant protein, unsaturated fatty acids, and essential minerals (magnesium, iron, zinc, and potassium), hold significant potential as a vital component of the human diet. Additionally, their low water requirements and nitrogen fixation capacity make them an appropriate choice for cultivation in adverse environmental conditions. The germination ability of seeds profoundly impacts the final yield of the crop; assessing seed viability is of extreme importance. Conventional methods for assessing seed viability and germination are both time-consuming and costly. To address these challenges, this study investigated Visible–Near-Infrared Spectroscopy (Vis/NIR) in the wavelength range of 500–1030 nm as a nondestructive and rapid method to determine the viability of two varieties of peanut seeds: North Carolina-2 (NC-2) and Spanish flower (Florispan). The study subjected the seeds to three levels of artificial aging through heat treatment, involving incubation in a controlled environment at a relative humidity of 85% and a temperature of 50 °C over 24 h intervals. The absorbance spectra noise was significantly mitigated and corrected to a large extent by combining the Savitzky–Golay (SG) and multiplicative scatter correction (MSC) methods. To identify the optimal wavelengths for seed viability assessment, a range of metaheuristic algorithms were employed, including world competitive contest (WCC), league championship algorithm (LCA), genetics (GA), particle swarm optimization (PSO), ant colony optimization (ACO), imperialist competitive algorithm (ICA), learning automata (LA), heat transfer optimization (HTS), forest optimization (FOA), discrete symbiotic organisms search (DSOS), and cuckoo optimization (CUK). These algorithms offer powerful optimization capabilities for effectively extracting relevant wavelength information from spectral data. Results revealed that all the algorithms demonstrated remarkable accuracy in predicting the allometric coefficient of seeds, achieving correlation coefficients exceeding 0.985 and errors below 0.0036, respectively. In terms of execution time, the ICA (2.3635 s) and LCA (44.9389 s) algorithms exhibited the most and least efficient performance, respectively. Conversely, the FOA and the LCA algorithms excelled in identifying the least number of optimal wavelengths (10 wavelengths). Subsequently, the seeds were classified based on the wavelengths selected via the FOA (10 wavelengths) and (DSOS (16 wavelengths) methods, in conjunction with logistic regression (LR), decision tree (DT), multilayer perceptron (MP), support vector machine (SVM), k-nearest neighbor (K-NN), and naive Bayes (NB) classifiers. The DSOS–DT and FOA–MP methods demonstrated the highest accuracy, yielding values of 0.993 and 0.983, respectively. Conversely, the DSOS–LR and DSOS–KNN methods obtained the lowest accuracy, with values of 0.958 and 0.961, respectively. Overall, our findings demonstrated that Vis/NIR spectroscopy, coupled with variable selection algorithms and learning methods, presents a suitable and nondestructive approach for detecting seed viability.

In this study, the micro electro-polishing method was employed to improve the surface quality of microbeams machined by the micro WEDM method and to remove the recast layer. This approach changes the dimensions of the microbeams, as a result of the electrochemical corrosion, in addition to the elimination of the recast layer. To diminish the impact of this process on the dimensional deviation of the fabricated microbeams, the influence of the micro electro-polishing process parameters such as voltage, duration, cathode diameter, and electrolyte composition on the dimensional deviation of microbeams was studied using the Taguchi method. The optimum values of process parameters were determined by the S/N ratios analysis, and the order of parameters importance was determined through analysis of variance of the S/N ratios. It was found that the optimal levels of the process parameters are voltage of 2 V, process duration of 20 s, cathode diameter of 50 mm, and electrolyte composition of 25-5-40 ml (sulfuric-phosphoric-water) within the range of experiments. By using the optimum values of the parameters, the dimensional deviations were found to be 5.23 times lower compared to the average of the results. The importance of process parameters was found to follow this order: electropolishing duration, electrolyte composition, cathode diameter, and process voltage.

Metal-based microelectromechanical systems are widely used in applications such as micro-energy harvesters, micro-heat exchangers and micro-electromagnetic that require high strength and flexibility. In the fabrication of such systems, micro wire electrical discharge machining (MicroWEDM) is majorly used. This paper studies the effect of the MicroWEDM process parameters on the dimensional deviation of machined MEMS structures including microcantilevers and micro-beams using the Taguchi method. Using optimal levels of the parameters including pulse duration (0.8 µs), cutting speed (8.4 mm/min), voltage (17 V) and wire tension (0.5 kg), the dimensional deviation is reduced about 8.65 times compared with the average of experiments results. The order of effect importance of the process parameters on the dimensional deviation of microstructures obtained by the ANOVA analysis of S/N ratios is as follows: pulse duration, wire tension, process voltage and cutting speed. Dimensional deviation of the micro-features was reduced to 1 μm using the optimal levels of the process parameters.

The Micro wire electrical discharge machining (Micro wire-EDM) process is a modern machining process with various applications in manufacturing micro-parts. The recast layer remaining on the machined surfaces is an inevitable complication of this process. This layer can subsequently affect the machined parts performance. To optimize the recast layer in the micro wire-EDM of the molybdenum microparts, effect of the process parameters on the distribution amplitude of the recast layer was investigated using the Taguchi method. The obtained results showed that the optimal levels of the micro wire-EDM process parameters for achieving the optimal distribution amplitude of the recast layer are as follows: spark pulse-on time of 0.2 μs, cutting speed of 7 mm/min, process voltage of 17 V, and wire tension of 0.45 kg. Also the importance order of the effect of the process parameters on the distribution amplitude of recast layer is as follows: spark pulse-on time, process voltage, cutting speed, and wire tension.