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Worldwide, about one-third of food produced for human consumption is wasted, which includes byproducts from food processing, with a significant portion of the waste still being landfilled. The aim of this study is to convert chestnut shells (CNSs) from food processing into a valuable resource through bioprocesses. Currently, one of the highest barriers to bioprocess commercialization is low conversion of sugar from biomass, and KOH pretreatment was suggested to improve enzymatic digestibility (ED) of CNS. KOH concentration of 3% (w/w) was determined as a suitable pretreatment solution by a fundamental experiment. The reaction factors including temperature, time and solid/liquid (S/L) ratio were optimized (77.1 g/L CNS loading at 75 °C for 2.8 h) by response surface methodology (RSM). In the statistical model, temperature and time showed a relatively significant effect on the glucan content (GC) and ED, but S/L ratio was not. GC and ED of the untreated CNS were 45.1% and 12.7%, respectively. On the other hand, GC and ED of pretreated CNS were 83.2% and 48.4%, respectively, and which were significantly improved by about 1.8-fold and 3.8-fold compared to the control group. The improved ED through the optimization is expected to contribute to increasing the value of byproducts generated in food processing.

Since the heroic age of Antarctic exploration, the continent has been pressurized by multiple anthropogenic activities, today including research and tourism, which have led to the emergence of phenol pollution. Natural attenuation rates are very slow in this region due to the harsh environmental conditions; hence, biodegradation of phenol using native bacterial strains is recognized as a sustainable remediation approach. The aim of this study was to analyze the effectiveness of phenol degradation by a binary consortium of Antarctic soil bacteria, Arthrobacter sp. strain AQ5-06, and Arthrobacter sp. strain AQ5-15. Phenol degradation by this co-culture was statistically optimized using response surface methodology (RSM) and tolerance of exposure to different heavy metals was investigated under optimized conditions. Analysis of variance of central composite design (CCD) identified temperature as the most significant factor that affects phenol degradation by this consortium, with the optimum temperature ranging from 12.50 to 13.75 °C. This co-culture was able to degrade up to 1.7 g/L of phenol within seven days and tolerated phenol concentration as high as 1.9 g/L. Investigation of heavy metal tolerance revealed phenol biodegradation by this co-culture was completed in the presence of arsenic (As), aluminum (Al), copper (Cu), zinc (Zn), lead (Pb), cobalt (Co), chromium (Cr), and nickel (Ni) at concentrations of 1.0 ppm, but was inhibited by cadmium (Cd), silver (Ag), and mercury (Hg).

We propose a computational framework named iterative local adaptive majorize-minimization (I-LAMM) to simultaneously control algorithmic complexity and statistical error when fitting high-dimensional models. I-LAMM is a two-stage algorithmic implementation of the local linear approximation to a family of folded concave penalized quasi-likelihood. The first stage solves a convex program with a crude precision tolerance to obtain a coarse initial estimator, which is further refined in the second stage by iteratively solving a sequence of convex programs with smaller precision tolerances. Theoretically, we establish a phase transition: the first stage has a sublinear iteration complexity, while the second stage achieves an improved linear rate of convergence. Though this framework is completely algorithmic, it provides solutions with optimal statistical performances and controlled algorithmic complexity for a large family of nonconvex optimization problems. The iteration effects on statistical errors are clearly demonstrated via a contraction property. Our theory relies on a localized version of the sparse/restricted eigenvalue condition, which allows us to analyze a large family of loss and penalty functions and provide optimality guarantees under very weak assumptions (e.g., I-LAMM requires much weaker minimal signal strength than other procedures). Thorough numerical results are provided to support the obtained theory.

The study investigates the relationship between rubber crumb particle size and the abrasive behavior of polymer composites, using statistical optimization techniques. Polymer composites with different rubber crumb sizes were synthesized and subjected to abrasion tests. Taguchi’s L18 orthogonal array was employed to analyze the effects of filler particle size (2 levels), filler weight percentage, load, and time (3 levels each) on the specific wear rate of the developed composites. Analysis of variance (ANOVA) revealed that particle size significantly influenced abrasion resistance, with a P-value of 0.073 and an F-value of 3.8 from Taguchi’s Design of Experiments (DOE). Smaller particles exhibited milder abrasion, characterized by smoother surface interactions and lower material removal, while larger particles caused more aggressive abrasion due to rougher surfaces, larger contact areas, and higher stress concentrations. The findings highlight the pivotal role of rubber crumb particle size in tailoring the abrasive properties of polymer composites. This study provides a framework for optimizing material performance, offering significant potential for applications in sectors requiring durable and wear-resistant composites, such as automotive, construction, and aerospace.

This study presents a detailed statistical optimization methodology for improving the performance of high-strength concrete through the strategic integration of weathered crystalline rock (WCR) fine aggregate. Utilizing Taguchi L5 orthogonal array design, the research assessed the impact of varying WCR replacement levels (0%, 5%, 10%, 15% and 20%) on essential concrete properties, encompassing mechanical strength, fresh properties, durability metrics and impact resistance. The experimental program included 175 specimens tested across seven response variables to find out how they worked together in detail. By using signal-to-noise ratio analysis and grey relational analysis together, the best replacement strategies were found. For example, adding 5% WCR resulted in better multi-objective performance with a grey relational grade of 0.953. Using mechanical property correlations, advanced regression modeling was able to predict compressive strength very well (R² = 0.997). ANOVA statistical validation showed that all response variables had significant factor contributions (> 94%). The optimized mix had a compressive strength of 74.5 MPa, improved workability (66.3 mm slump) and impact resistance (778 blows). The results show that using WCR strategically can help meet both structural performance goals and sustainability goals at the same time. This sets a strong foundation for developing sustainable high-strength concrete for use in infrastructure.

This research was financially supported by the Centre for the Development of Industrial Technology (CDTI) of the Spanish Ministry of Science and Innovation under the grant agreement: TECNOMIFOOD project (CER-20191010); and by the ELINUT project (2021-2022 Food Industry Framework, Basque Government). This is contribution n degrees 1131 of AZTI.

Huperzine A (HupA) is a potent acetylcholinesterase (AChE) inhibitor of a great consideration as a prospective drug candidate for Alzheimer’s disease treatment. Production of HupA by endophytes offers an alternative challenge to reduce the massive plant harvest needed to meet the increasing demand of HupA. In the current study, some endophytic fungal and actinobacterial isolates from the Chinese herb, Huperzia serrata , underwent liquid fermentation, alkaloid extraction, and screening for AChE inhibition and HupA production. Among these isolates, Alternaria brassicae AGF041 strain was the only positive strain for HupA production with the maximum AChE inhibition of 75.5%. Chromatographic analyses verified the identity of the produced HupA. The HupA production was efficiently maximized up to 42.89 μg/g of dry mycelia, after optimization of thirteen process parameters using multifactorial statistical approaches, Plackett–Burman and central composite designs. The statistical optimization resulted in a 40.8% increase in HupA production. This is the first report to isolate endophytic actinobacteria with anti-AChE activity from H. serrata , and to identify an endophytic fungus A. brassicae as a new promising start strain for a higher HupA yield.

This work proposes a research method that is a scheme that can be universally applied in problems based on the selection of optimal parameters for metal forming processes. For this purpose, statistical data optimisation methods were used. The research was based on the analysis of the shear spinning tests performed in industrial conditions. The process of shear spinning was conducted on the components made of Inconel 625 nickel superalloy. It was necessary to select the appropriate experimental plan, which, by minimising the number of trials, allowed one to draw conclusions on the influence of process parameters on the final quality of the product and was the starting point for their optimisation. The orthogonal design 2III3−1 is the only design for three factors at two levels, providing non-trivial and statistically significant information on the main effects and interactions for the four samples. The samples were analysed for shape and dimensions using an Atos Core 200 3D scanner. Three-dimensional scanning data allowed the influence of the technological parameters of the process on quality indicators, and thus on the subsequent optimisation of the process, to be determined. The methods used proved to be effective in the design, evaluation and verification of the process.

This study used conservative one variable-at-a-time study and statistical surface response methods to increase the yields of an extracellular thermostable protease secreted by a newly identified thermophilic Bacillus subtilis BSP strain. Using conventional optimization techniques, physical parameters in submerged fermentation were adjusted at the shake flask level to reach 184 U/mL. These physicochemical parameters were further optimized by statistical surface response methodology using Box Behnken design, and the protease yield increased to 295 U/mL. The protease was purified and characterized biochemically. Both Ca 2+ and Fe 2+ increased the activity of the 36 kDa protease enzyme. Based on its strong inhibition by ethylenediaminetetracetate (EDTA), the enzyme was confirmed to be a metalloprotease. The protease was also resistant to various organic solvents (benzene, ethanol, methanol), surfactants (Triton X-100), sodium dodecyl sulfate (SDS), Tween 20, Tween-80 and oxidants hydrogen per oxide (H 2 O 2 ). Characteristics, such as tolerance to high SDS and H 2 O 2 concentrations, indicate that this protease has potential applications in the pharmaceutical and detergent industries.

The prevalence of multi-label (ML) data has witnessed a significant increase in numerous fields as big data technology continues to expand. However, these datasets often contain a high degree of redundancy and irrelevant attributes, which can negatively impact the efficiency and predictive performance of machine learning models. To address these challenges, this paper introduces PSI-MFS, a novel lightweight multi-objective feature selection (MLFS) approach that efficiently optimizes feature selection criteria while maintaining computational efficiency. Despite the availability of several MLFS approaches, many existing methods struggle to achieve optimal balance between computational efficiency and selection quality. PSI-MFS overcomes this by simultaneously optimizing three conflicting feature selection (FS) objectives: minimizing feature–feature redundancy (↓), maximizing feature–label relevancy (↑), and maximizing feature–label interaction (↑), using a preference selection index (PSI)-based optimization strategy. PSI-MFS provides a trade-off between feature correlation and classification performance while significantly reducing memory consumption and execution time. The time complexity of PSI-MFS is low, and experimental assessments on ten benchmark datasets demonstrate its superior or competitive performance compared to 11 state-of-the-art (SOTA) FS methods. The effectiveness of PSI-MFS is further validated through statistical analysis using Friedman’s test, which confirms its significant performance improvements. Notably, PSI-MFS achieves faster execution times and outperforms existing methods in 80% of cases, making it a robust and scalable solution for ML feature selection.