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This investigation aimed to extract and characterize the GCSM proteins, determine their solubility potential at two different temperatures and different solvents, and explore their functional properties. During the extraction, no water‐ or ethanol‐soluble protein was found. Most of the protein was extracted with KOH solution. GCSM showed major protein bands between 13,273 and 56,564 Da with an isoelectric point of 5.1. The results showed that extraction temperature and solvent affected the amount of protein extracted from GCSM. The highest protein yield (63.4%) was obtained with KOH at 55 °C. Fat content negatively affected the protein solubility. The highest protein purity (99.9%) was obtained with 6% of fat content and the lowest one with 19% of fat content. GCSM has a high glutamic acid content, followed by arginine and aspartic acid compared to the other amino acids. The essential amino acids make up about 30.0% of the total amino acid concentration in KOH‐soluble fractions. The results showed a denaturation temperature of GCSM protein ranging from 61.4 to 63.6 °C. Scanning electron microscopy reveals a microglobular protein structure. GCSM protein isolate showed lower (P < 0.05) water‐holding and oil‐holding capacity but similar gelation properties as soy protein. GCSM protein shows a high foaming capacity at high pH values and high emulsion stability. Practical Application The results of this investigation have a direct impact on the plant protein processing industry. This paper presents a new source of plant protein with a high foaming capacity in alkaline conditions with potential applications for human consumption and feed for aquaculture and animals. The results of this research may impact the cotton producers who can increase their income, and the aquaculture industry will have a cheaper source of protein that can partially substitute the expensive fishmeal. Cottonseed protein can be used to develop high protein extruded snacks and other functional foods, such as plant protein‐based food products.

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Manufacturing companies usually expect strategic improvements to focus on reducing both waste and variability in processes, whereas markets demand greater flexibility and low product costs. To deal with this issue, lean manufacturing (LM) emerged as a solution; however, it is often challenging to evaluate its true effect on corporate performance. This challenge can be overcome, nonetheless, by treating it as a multi-criteria problem using the Hesitant Fuzzy linguistic and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. In fact, the hesitant fuzzy linguistic term sets (HFLTS) is vastly employed in decision-making problems. The main contribution of this work is a method to assess the performance of LM applications in the manufacturing industry using the hesitant fuzzy set and TOPSIS to deal with criteria and attitudes from decision makers regarding such LM applications. At the end of the paper, we present a reasonable study to analyze the obtained results.

Today, in reliability analysis, the most used distribution to describe the behavior of electronic products under voltage profiles is the Weibull distribution. Nevertheless, the Weibull distribution does not provide a good fit to lifetime datasets that exhibit bathtub‐shaped or upside‐down bathtub–shaped (unimodal) failure rates, which are often encountered in the reliability analysis of electronic devices. In this paper, a reliability model based on the beta‐Weibull distribution and the inverse power law is proposed. This new model provides a better approach to model the performance and fit of the lifetimes of electronic devices. To estimate the parameters of the proposed model, a Bayesian analysis is used. A case study based on the lifetime of a surface mounted electrolytic capacitor is presented, the results showed that the estimation of the proposed model differs from the inverse power law–Weibull and that it affects directly the mean time to failure, the failure rate, the behavior, and the performance of the capacitor under analysis.