Second-order Polynomial Research Articles

Inactivated lactobacillus plantarum promoted growth performance, intestine health and antioxidant capacity of juvenile largemouth bass, Micropterus salmoides

To evaluate the effects of inactivated Lactobacillus plantarum (LPM) on carnivorous fish, juvenile largemouth bass [initial body mass: (5.62±0.13) g] were fed five isonitrogenous and isoenergetic diets containing 0 (M0), 200 (M1), 400 (M2), 600 (M3), and 800 (M4) mg/kg LPM for 8 weeks. The results showed that in terms of growth, weight gain rate (WGR) and specific growth rate (SGR) showed significant second-order polynomial trend with increasing LPM supplementation. WGR and SGR of the M3 group were significantly increased by 14.18% and 5.62%, respectively, compared with the M0 group. Significant linear and second order polynomial trends were observed for feed conversion ratio (FCR), protein efficiency ratio (PER) and protein deposition rate (PDR) with the increase in dietary LPM. FCR was significantly reduced by 8.9% in the M2 group compared to the M0 group. In terms of intestinal health, intestinal amylase, intestinal villi number, length and width showed significant second order polynomial trends with increasing LPM supplementation. Significant linear and second order polynomial trends were observed for intestinal lipase activity, liver crude fat content with increasing dietary LPM. Dietary LPM supplementation of 600 mg/kg significantly increased the abundance of beneficial bacteria. In terms of antioxidant capacity, with the increase of LPM supplemental level, superoxide dismutase activity, malondialdehyde content of serum and liver, and liver Cu/Zn-SOD, Keap-1 and TGF-β1 mRNA expression levels showed significant linear and second-order polynomial trend, and Nrf2 mRNA expression level showed a significant second-order polynomial trend. Overall, the appropriate level of LPM (400–600 mg/kg) promoted the growth performance and feed utilization of largemouth bass by increasing the abundance of intestinal bacteria, improving the activities of digestive enzymes and antioxidant enzymes, and up-regulating the mRNA expressions of genes related to antioxidants.

Dietary supplementation with succinic acid improves growth performance and flesh quality of adult Nile tilapia (Oreochromis niloticus) fed a high-carbohydrate diet

To evaluate the effects of dietary supplementation with succinic acid on growth performance, flesh quality, glucose, and lipid metabolism of Nile tilapia (Oreochromis niloticus) fed a high-carbohydrate diet (HCD), five iso-nitrogenous and iso-lipidic diets were prepared as follows: HCD (control group) consisting of 55% corn starch and HCD supplemented with 0.5%, 1.0%, 2.0%, and 4.0% succinic acid, respectively. Tilapia with an initial body weight of 204.90 ± 1.23 g randomly assigned to 15 tanks with 3 replicates per group and 10 fish per tank fed for 8 weeks. Increasing dietary succinic acid supplementation resulted in significant second-order polynomial relationship in the weight gain rate (WGR), specific growth rate (SGR), feed conversion ratio (FCR), protein efficiency rate (PER), viscerosomatic index, condition factor, and contents of muscular crude lipid and glycogen (P < 0.05). The hepatosomatic index, mesenteric fat index, liver glycogen content and crude lipid contents of the whole-body and liver demonstrated significantly linear and second-order polynomial relationship (P < 0.05). Quadratic curve model analysis based on WGR, SGR, PER, and FCR demonstrated that optimal supplementation with succinic acid in the HCD of Nile tilapia ranged from 1.83% to 2.43%. Fish fed with 1.0% succinic acid had higher muscular hardness, increased the contents of alkali-soluble hydroxyproline in collagen, docosahexaenoic acid (DHA) and n-3 polyunsaturated fatty acid (n-3PUFA) in muscle, and lower total fatty acid content in muscle (P < 0.05) compared with the control group. Compared to the control group, dietary supplementation with 1.0% succinic acid significantly increased the contents of total bounding amino acid (arginine, histidine, isoleucine, lysine, methionine, alanine, proline), total flavor amino acid (free aspartic acid), the catalase (CAT) activity and total antioxidant capacity, and the mRNA relative expression levels of CAT, superoxide dismutase (SOD), and nuclearfactor erythroidderived 2-like 2 (Nrf2) in muscle (P < 0.05). Furthermore, succinic acid supplementation significantly up-regulated mRNA relative expression levels of glycolysis genes (hexokinase 2 [HK2], phosphofructokinase, muscle-A [PFKMA], and phosphofructokinase, muscle-B [PFKMB]), a key glycogen synthesis gene (glycogen synthase [GYS]), and lipid catabolism genes (carnitine palmitoyltransferase-1B [CPT1B], hormone sensitive lipase [HSL], and lipoprotein lipase [LPL]), while down-regulating the mRNA relative expression level of fatty acid synthase (FASN) in muscle (P < 0.05). In conclusion, dietary supplementation with 1.83% to 2.43% succinic acid improved muscle quality by increasing muscle antioxidant capacity and hardness, changing muscle amino acid and fatty acid composition, and regulating muscle glucose and lipid metabolism.

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Optimizing reverse osmosis desalination from brackish waters: Predictive approach employing response surface methodology and artificial neural network models

A mathematical model frame has been developed to optimize the performance of reverse osmosis (RO) desalination in brackish waters with medium to high salt concentrations using response surface methodology (RSM) and artificial neural network (ANN), specifically catered towards efficiently operating RO desalination systems. The RSM model aims at predicting RO performance with second-order polynomials based on 15 experimental datasets. Inputs for RSM model include feed TDS concentration, feed water flow rate, and concentration-to-desalination ratio, while the outputs consider rejection, membrane flux, performance index, and specific energy consumption. The Genetic Algorithm optimized back-propagation Neural Network was employed to update network weights and biases, leading to an optimal ANN network structure of 3–10-4. A ratio of 70 % training, 15 % validation, and 15 % test data was chosen to establish an ANN model that predicts the performance of RO desalination. The ANN model demonstrated significantly higher prediction accuracy than the RSM model, with impressive correlation coefficients of 0.9936 for the performance index and 0.9932 for SEC prediction, and low RMSE of 0.85 for rejection. To maximize the performance index and minimize specific energy consumption, a two-step optimization method was further set up by applying the RSM model to initially determine optimal process conditions followed by the ANN model to enhance the accuracy of performance forecasts. Optimal process conditions were proposed for specific feed TDS scenarios, which involved setting a feed TDS concentration limit to meet drinking water standards.

Upper limit of mineral-associated organic carbon in temperate and sub-tropical soils: How far is it?

Soil organic carbon (SOC) is the main terrestrial C pool and is roughly divided into stable mineral-associated organic carbon (MAOC) and particulate organic matter carbon (POC). However, the sequestration capacity of MAOC remains unquantified. Currently, soils are considered to have a limited capacity to accumulate MAOC, primarily due to the presence of fine fractions, i.e. silt and clay particles. However, it is unlikely that saturation will occur in all soils due to land use. Therefore it is necessary to investigate whether soils from different latitudes and their silt and clay fractions that comprise MAOC are saturated. We selected 71 soil samples that were physically fractionated from a Mexican inventory of vegetation with a wide range of SOC (4–186 g kg−1 soil) and clay (126–777 g kg−1 soil) contents. Soils were grouped into non-volcanic soils and allophanic Andosols. In the first group, we found a positive and highly significant linear relationship between SOC and MAOC contents, but no upper limit was detected. We found a common slope of 0.86 (R2 = 0.97, p < 0.01), i.e. 86% of the SOC was contained in MAOC irrespective of soil types and management, a value very similar to that in other studies. Conversely, for allophanic Andosols, which had SOC content twice that of non-volcanic soils, a second-order polynomial function (R2 = 0.98, p < 0.01) was established, the upper limit of which exceeded 186 g SOC kg−1 for a MAOC of 134 g kg−1. This was also supported by an increasing polynomial function of particulate organic carbon (POC) as the SOC increased. In both types of soils, maximum MAOCs were found between 700 and 800 g kg−1 of silt and clay in medium-textured soils. The amount of silt and clay particles was limited by SOC accumulation in the silt fraction as clay became saturated. These results are influenced by the mass proportion of silt and clay that follows a unique pattern in allophanic Andosols and non-volcanic soils. An investigation of a comprehensive database comprising 21,315 soil profiles sampled 0–30 cm across Mexico showed that most soils are below 186 g SOC kg−1 the bending point above which the upper limit does exist.

Optimizing estimated ultimate recovery in horizontal wells through data-driven analytics

The oil and gas industry has undergone significant changes in completion operation design and execution, largely due to the advancements in machine learning techniques. In our study, we analyzed a dataset of approximately 200 horizontal wells from the Wolfcamp formations, categorized into two groups based on productivity and classification as parent versus child (infill) wells. Using response surface methodology (RSM), a data analytics technique that models the relationship between explanatory and response variables with up to second-order polynomial terms, we illustrate how to optimize horizontal well performance, specifically focusing on Estimated Ultimate Recovery (EUR). We selected a pool of 5 input variables for analysis, including initial oil production of 180 days, injected fluid volume in barrels, number of perforations in each cluster, volume of Hydrochloric Acid injected during frack job in gallons, and volume of injected Linear Gel in gallons.The selections were made in collaboration with industry professionals and validated using data analytics-based methods that rank variable importance. By pinpointing potential strategies to achieve a globally optimal EUR through the adjustment of completion variables (perforations and hydrochloric acid should be set at their data-recorded maximum values of 7.69 and 10.82 on a logarithmic scale), the study aims to simplify the challenging task of maximizing EUR from producing wells, offering an additional avenue for estimating reserves. It emphasizes the adaptability of this approach in allowing different completion designs to be dynamically tailored for optimizing EUR, each with its unique set of inputs. Ultimately, these diverse designs can be synthesized through meta-analysis. In summary, the study provides a practical framework for EUR estimation, readily applicable by operators in unconventional exploration, representing a transformative milestone in the industry. The subsequent phase involves collecting additional data from this specific field area and employing a range of models to improve precision and specificity.

Evaluating hydrothermal liquefaction hydrochar from sewage sludge as a phosphorus resource through struvite production

Hydrothermal liquefaction (HTL) has attracted considerable attention for valorizing sewage sludge to biofuels while the solid residue, hydrochar, has received little attention for its final application. In the current work hydrochar recovered from hot separation in a pilot-scale HTL plant is evaluated for its nutrient value, in particular as a sustainable Phosphorus (P) resource. Following thorough characterization of the hydrochar, P recovery as struvite (NH4MgPO4·6H2O) is investigated to produce a fertilizer with a lower heavy metal content that could be used in current agricultural practices. P extraction using inorganic (hydrochloric and sulphuric) and organic (citric) acids is evaluated by applying 0.1–1 mol/L of acids for 1–12 h with a liquid/solid ratio of 10 mL/g. A second-order polynomial model was established by response surface methodology (RSM) to predict the P extraction with different acids. The optimum operating conditions for maximum P extraction (94 %) are suggested as 0.7 M of acid strength (H2SO4) and 6.5 h of extraction time. Addition of citric acid (CA) during the struvite precipitation from acid-leached P solution was found to improve the struvite purity from 36.6 % to 54 % with a reduction in heavy metal content. Furthermore, morphological analysis validated the formation of struvite. Upon comparison with the regulations, hydrochar was found to exceed some of the heavy metal limits but was low in polyaromatic hydrocarbons (PAHs), per- and polyfluorinated substances (PFAS) and showed low toxicity to Aliivibrio fischeri. P extraction and struvite precipitation are shown to reduce heavy metal contents below regulatory limits with P recoveries from 40 % to 67 %. Thus, as an alternative to direct land application of hydrochar, harvested struvite from hydrochar could be applied as a fertilizer with no apparent environmental risk.

Accelerometer-based swimming metabolism of smallmouth bass (Micropterus dolomieu).

Bioenergetics is informative for a range of fundamental and applied resource management questions, but findings are often constrained by a lack of ecological realism due to the challenges of remotely estimating key parameters such as metabolic rate. To enable field applications, we conducted a calibration study with smallmouth bass (Micropterus dolomieu, 0.7-2 kg) surgically implanted with accelerometer transmitters and exposed to a ramp-Ucrit swimming protocol in a swim tunnel respirometer across a range of water temperatures (6, 12, 18, and 24°C). There was an exponential increase in fish acceleration with swimming speed, and acceleration per speed was higher in smaller fish and female fish, and at colder temperatures. Mass-specific fish metabolic rate (MO2; mg O2 kg-1 h-1) increased with swimming speed, acceleration, and temperature, and decreased with fish mass, which when combined were strong predictors of MO2. Maximum metabolic rate (MMR) was estimated to peak at 22°C, but maximum sustained swimming speed (Ucrit) remained high at c. 90-100 m s-1 above 20°C, based on second-order polynomial functions. Aerobic scope (AS) estimates peaked at 20°C (>90% AS at 17-24°C; >50% AS at 11-28°C). Males exhibited marginally higher MMR, AS, and Ucrit than females at higher temperatures. Larger fish generally exhibited higher Ucrit, but smaller fish had a marginally broader performance range (AS, Ucrit) among temperatures, benefiting from higher MMR despite a steeper increase in resting metabolic rate with temperature. These findings enable field studies to estimate metabolic metrics of smallmouth bass in situ to characterize their ecological energetics and inform bioenergetics models.

Machine learning prediction and characterization of sigma-free high-entropy alloys

Precipitation hardening provides a great potential to achieve superior strength-ductility trade-off in high-entropy alloys (HEAs). However, the formation of some precipitates such as brittle sigma phases may significantly deteriorate the ductility. Empirical criteria such as PSFE, VEC, and Md were proposed to predict the formation of undesirable phases during aging, nevertheless, they are insufficient to describe their stability and are not dependable for the noted purpose. Accordingly, for the first time, machine-learning models were conducted for this prediction. A feature selection based on Pearson correlation and mutual information has been used to improve the performance of models. Linear regression, second-order polynomial regression, decision tree, and neural network are conducted by machine learning methods in this paper. Among the implemented methods, the neural network had the best performance which improved the accuracy of the prediction by ∼20% compared to the conventional methods. Additionally, a new parameter based on linear regression, which is more reliable and user-friendly than thermodynamic parameters was developed in this investigation. The results showed that some elements such as Cr, Mo, and V may promote sigma precipitation. A HEA with a sigma-free microstructure was developed for validation of the model. We strongly believe that this HEA has a great potential for overcoming the strength-ductility trade-off because of not only the lack of sigma precipitate but also the presence of NiAl precipitate. The results are a step forward in designing alloys with the potential of microstructure engineering to achieve superior properties.

Optimization of Combined Microwave and Ultrasound Extractions of Cannabinoids Compounds from Cannabis indica L. (Blueberry Cultivar) Using Response Surface Methodology

Microwave and ultrasound are novel technologies that are widely used for extracting bioactive compounds from plant materials. In this study, combined extraction techniques, microwave and ultrasound, were applied to extract cannabinoids, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), from Cannabis indica L. (Blueberry cultivar). Three independent variables including solid to liquid ratio (X1, A: 1:10-1:30 w/v), microwave extraction time (X2, B: 5-20 min), and ultrasound extraction time (X3, C: 10-30 min) were optimized using central composite design (CCD). The experimental data obtained was fitted to a second-order polynomial equation using multiple regression analysis and additionally analyzed using appropriate statistical methods (analysis of variance, ANOVA). The optimum conditions were determined according to the solid to liquid ratio (X1, A) of 1:22 (w/v), microwave extraction time (X2, B) of 5 min and ultrasound extraction time (X3, C) of 14 min. Under these conditions, the experimental CBD and THC content was 0.298±0.001 mg/g dry weight and 91.35±0.35 mg/g dry weight, respectively. The experimentally-achieved values were in accordance with those estimated by the CCD model, suggesting the applicability of the utilized model and the favorable result of CCD’s application in the optimization of the combined microwave and ultrasound extraction.

Development and Application of an IoT-Based System for Soil Water Status Monitoring in a Soil Profile.

Soil water content (θ), matric potential (h) and hydraulic conductivity (K) are key parameters for hydrological and environmental processes. Several sensors have been developed for measuring soil θ-h-K relationships. The cost of such commercially available sensors may vary over several orders of magnitude. In recent years, some sensors have been designed in the framework of Internet of Things (i.e., IoT) systems to make remote real-time soil data acquisition more straightforward, enabling low-cost field-scale monitoring at high spatio-temporal scales. In this paper, we introduce a new multi-parameter sensor designed for the simultaneous estimation of θ and h at different soil depths and, due to the sensor's specific layout, the soil hydraulic conductivity function via the instantaneous profile method (IPM). Our findings indicate that a second-order polynomial function is the most suitable model (R2 = 0.99) for capturing the behavior of the capacitive-based sensor in estimating θ in the examined soil, which has a silty-loam texture. The effectiveness of low-cost capacitive sensors, coupled with the IPM method, was confirmed as a viable alternative to time domain reflectometry (TDR) probes. Notably, the layout of the sensor makes the IPM method less labor-intensive to implement. The proposed monitoring system consistently demonstrated robust performance throughout extended periods of data acquisition and is highly suitable for ongoing monitoring of soil water status.

On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm

Efficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products.

Waste-derived activated carbons for effective adsorptive removal of strontium, barium, and binary pollutants: A response surface methodology study

A double-pronged approach to pollution management is proposed by sustainably managing solid wastes and converting them to activated carbons that are then utilized for water treatment. In this study, gas-to-liquids (GTL) derived biosolids, carboard and their mixed samples are used to produce the activated carbons. In a laboratory batch study, a Box-Behnken experimental design was used with four factors and three levels to optimize the removal of single component strontium and barium, and the binary system of pollutants from an aqueous solution. The design incorporated response surface modeling (RSM) techniques with a total of 29 different experimental data points collected and analyzed in this study. The study was conducted considering four parameters: the initial pH of the solution (ranging from 4.0 to 8.0), temperature (ranging from 20 to 40 ℃), the percentage of cardboard (fixed at 0 %, 50 %, and 100 %), and the amount of adsorbent (between 0.05 and 0.5 g). These factors were assigned three levels, represented as −1, 0, and 1. A second-order polynomial regression equation was then developed to estimate the responses. The statistical significance of the independent variables and their interactions was assessed using analysis of variance (ANOVA) with a 95 % confidence level (α = 0.05). The results revealed that only temperature and dosage show significant effects on the responses and that optimum values of the selected parameters were obtained by solving the prediction equations, which were validated with less than 4 % error %. In an attempt to optimize the factors, a pH of 5.5, temperature of 40 ℃ and dose of 0.3 g is found for all three samples. Validation results for optimization also proved that varying the percentage cardboard showed little difference in the percentage removal of all the pollutants. The results from this study can be directly applied for any such systems trying to optimize these parameters and the prediction equations can be utilized effectively.

Optimizing operational parameters for highly-concentrated sodium hydroxide production via electrodialysis with bipolar membranes from concentrated brines

Sodium hydroxide necessitates the implementation of cleaner production methods that deviate from the conventional practices in the chlor-alkali industry. New sustainable sources such as concentrated brines, which are typically regarded as waste, are currently being investigated for the production of sodium hydroxide using Electrodialysis with Bipolar Membranes (EDBM). This process has the advantages of not producing gas by-products, reducing operational risks, and providing value to waste brines after converting them into acids or alkali solutions. However, the production of sodium hydroxide via EDBM using brines is limited by the low concentration of the final base (e.g. typically below 2 M), which increases the cost of transport. This study aims to overcome this limitation by optimizing parameters such as salt-to-base volume ratio, initial salt concentration, and current density values. Notably, this research is one of the few to employ Response Surface Methodology with a Central Composite Design to maximize the final sodium hydroxide concentration; two models using second-order polynomial equations were proposed. Results showed the possibility to achieve 5.3 M NaOH under a volume ratio range of 9.8:1–10:1, a current density range of 490 A/m−2 to 500 A/m−2, and an initial salt concentration in the range of 6 M NaCl. Moreover, the energetic analysis showed values of specific energy consumption in a range of 2.5 kWh kgNaOH-1-3.5 kWh kgNaOH-1 and specific productivity of the process up to 3.08 tonNaOH year−1 m−2, which are moderate in comparison with values previously presented in literature. Finally, an economic analysis showed that energy was the most significant cost component, making up 89 % of the total process cost, which was estimated to be 0.69 € kgNaOH-1 (concentration 21.2 % w/w). Overall, these high concentration values open a real chance to shift towards sustainable production of NaOH by means of EDBM from brines, highlighting the novelty of this study.

Tuning physicomechanical properties of PEG-interpenetrated anionically modified semi-IPN cryogels functionalized with carboxylate groups

Anionically modified interpenetrated polymer network (semi-IPN) cryogels containing linear polyethylene glycol chains (PEG) were prepared via in situ synthesis and directed assembly of the negatively charged copolymer poly(acrylamide-co-sodium acrylate) (PAN) network template. The influence of reaction conditions; the concentration of linear PEG chains, and polymerization temperature on the elasticity and swelling of these IPNs was analyzed in detail. The interpenetrating structure of PEG chains and PAN host network have been confirmed by FTIR, TGA and XRD analysis. The equilibrium water content, oscillatory swelling/deswelling kinetics, and water transport mechanism were analyzed as a function of PEG content. The results showed that semi-IPN gels exhibited different swelling behavior as the reaction conditions varied. Addition of 13.9% (w/v) PEG resulted in a 25-fold increase in the swelling ratio of semi-IPN hydrogels. By performing compression tests on semi-IPN gels to determine the change of compression properties, the effective cross-linking density of semi-IPN PAN/PEG was expressed as a second-order polynomial depending on the amount of PEG. The swollen modulus of semi-IPN cryogels was much higher than that of semi-IPN hydrogels for all PEG concentrations. Semi-IPNs showed reversible on-off switching with remarkable sensitivity to external pH stimulus. Structural parameters, degree of swelling, volume fraction of cross-linked polymer in the swollen state, and diffusion coefficients due to pH-induced swelling of semi-IPN gels were determined. The presence of linear PEG chains in the structure promoted swelling by both increasing the swelling rate and significantly reducing the time required to reach the equilibrium-state. The swelling process was Fickian diffusion, and the diffusion coefficients for swelling of PEG-interpenetrated semi-IPNs increased with PEG content. The effectiveness of removing methylene blue (MB) dye from aqueous solutions by semi-IPNs was evaluated. Batch adsorption experiments were performed as a function of the gelation temperature, amount of PEG, contact time, and initial dye concentration. The adsorption isotherm and kinetics were described well by Langmuir isotherm model and pseudo-second-order model, respectively. Adsorption of MB was a multi-step process involving surface adsorption followed by intra-particle diffusion. Semi-IPN PAN/PEG cryogels prepared under cryocondition with high adsorption capacities and fast adsorption rates have been developed as new adsorbents effective in removing dyes and other toxic substances with higher mechanical strength in water and wastewater treatment.

A physical tire model for real-time simulations

We present a novel tire brush model with carcass flexibility, primarily designed for real-time applications while preserving the physical significance of its parameters. The tire’s geometry is discretized and represented using a series of ribs, each intersecting the local road surface independently, on the other hand carcass in-plane fore-aft displacement, in-plane deflection, and out-of-plane sidewall torsion are approximated through a second-order polynomial. Forces and torques originating from the tire–road contact area are described using brush mechanics, where bristles’ kinematics is directly linked to carcass deformation. Extensive effort is dedicated to understanding the possible sources of instability, as well as describing and implementing a robust numerical scheme to effectively solve the nonlinear system of equations arising from the modeling. Timing performance results are provided to demonstrate the suitability of the presented model for demanding hard real-time simulations. Finally, validation of the presented tire model is carried out by fitting experimental data and comparing it with the state-of-the-art Magic Formula model, proving its reliability and accuracy in reproducing tire behavior.

Indicators of health groups of pupils of general education organizations of the Russian Ministry of Defense (2010–2022)

BACKGROUND: Currently, there are 33 educational organizations of the Russian Ministry of Defense in Russia with a total staff of about 18 thousand students studying around the clock&#x0D; AIM: to assess the dynamics of the health status of students in educational institutions of the Russian Ministry of Defense for 12 years from 2010/2011 to 2021/2022 academic year.&#x0D; MATERIALS AND METHODS: We studied reports on Form 13К/МЕД, prepared by employees of educational organizations of the Russian Ministry of Defense, and calculated the dynamics of the level of assessments of the health status of students. The article presents arithmetic averages and their errors; the development of health groups was assessed using time series analysis, for which a second-order polynomial series was used.&#x0D; RESULTS: In general educational institutions, there were 25.3 ± 0.9% of pupils with health group I, 60.7 ± 0.6% with group II, and 14.0 ± 1.2% with group III. In recent years, there has been a tendency to increase the number of pupils with health groups I and II and decrease with group III. These results indicated a properly organized educational process and optimization of medical support for students. The most positive dynamics of health status were observed in pupils aged 10–12 years, less pronounced — in pupils aged 16–18 years.&#x0D; CONCLUSIONS: In some educational organizations of the Russian Ministry of Defense during the period studied, there were not entirely favorable trends in the organization of medical support for students, which aims at more personalized work with students.

Modeling the relationship between postharvest storage conditions and grapefruit quality

The postharvest loss in quality of grapefruit during storage can be quantified by determining the change of the quality traits of grapefruit, including total soluble solids (TSS), titratable acidity (TA), TSS/TA, weight loss (WL), and the development of decay (DC) and chilling injury (CI). The objective of this study was, therefore, to develop models that encompass the relationship between both storage conditions and duration and the quality traits of grapefruit to predict the loss of grapefruit quality during storage. The models of TSS, TA and TSS/TA were developed with experimental data collected during the 2007–08 and 2008-09 growing seasons that were obtained from the literature. The models for WL, DC, and CI were developed with experimental data collected in Florida during the 2004–05 and 2005-06 growing seasons. The WL, DC and CI models were applied to determine storage life. The results indicated that there was a second-order polynomial relationship between storage duration and TSS and linear relationships between storage duration and TA and TSS/TA. A longer storage duration resulted in a higher TSS, a lower TA and a higher TSS/TA. There were linear relationships between storage duration and WL, thermal time above a threshold temperature of 0 °C and DC and thermal time below 10 °C and CI, with higher WL, DC and CI resulting from longer storage duration and larger number of thermal times. Model performance for simulating TSS, TA, TSS/TA for storage temperature between 2 °C and 11 °C was good. There is good performance for simulating WL for storage temperature between 4 °C and 12 °C, DC for storage temperature of 21.1 °C, and CI for storage temperature of 4.4 °C. Following model development, it was applied to determine the optimum storage life. Optimum storage condition was a storage temperature of 7.6 °C for approximately 20 d, resulting in less than 5 % of the stored fruits lost due to unacceptable quality based on predictions for WL, DC and CI.

PHB Production by Bacillus megaterium LSRB 0103 Using Cornstarch and Urea.

Polyhydroxybutyrates (PHBs) are biopolymers that are good green alternative for synthetic carbon-based polymers, and are also one of the most researched members of the Polyhydroxyalkanoates (PHA) family. In this study, a gram-positive bacterial strain Bacillus megaterium LSRB 0103 was isolated from Pallikaranai Marshland, Chennai, India. Primary screening using Sudan Black dye revealed the presence of intracellular PHB granules. Minimal Davis Media (MDM) which was used or PHB production gave a yield of 0.7107g/L. Subsequently, using response surface methodology (RSM), a central composite design (CCD) model was designed for media optimization having cornstarch, urea, and pH as independent variables. The findings of the CCD model were fitted into a second-order polynomial equation. The RSM model predicted the maximum PHB yield of 0.93g/L, at these independent variable levels, cornstarch, 5g/L; urea, 2.1g/L; and pH 7.0; while the experimental PHB yield was 0.94g/L, with a percentage error of 1.1%. This study is the first-time report of production of PHB by Bacillus megaterium using cornstarch and urea as substrate.

ENHANCED SURFACE QUALITY AND STRENGTH OF FDMed SPECIMENS USING BBD AND BIO-INSPIRED ALGORITHMS

This research investigated and optimized the parameters of the FDM process by employing bio-inspired algorithms for determining the optimal parameter settings in terms of surface quality and mechanical performance. Four important process parameters including layer thickness (0.11–0.33[Formula: see text]mm), part orientation (0–90∘), raster width (0.2–0.56[Formula: see text]mm), and the raster angle (0–60∘) at three variation levels were selected for fabricating the specimens (ABS material P430) using the statistical Box–Behnken design. ANOVA analysis and multiple regression analysis were used to fit the experimental data to a second-order polynomial equation. Through, the RSM analysis, the layer thickness is the key important factor that accounts for all of the responses. The fracture behavior of specimens was examined using a scanning electron microscope (SEM). From the SEM analysis, a substantial amount of plastic deformation on the fracture surface indicative of craze cracking is visible from a 0∘ orientation, indicating a totally ductile fracture mechanism. Then, three swarm intelligence algorithms such as Tasmanian Devil Optimization (TDO), Remora Optimization Algorithm (ROA), Tuna Swarm Optimization (TSO) were implemented to optimize the input parameters that would lead to minimum surface roughness and maximum tensile strength. Experimental data and predicted values varied between 1.64% and 1.84%, as shown by verification experiments.