Optimal Combination Research Articles

Aceite esencial de hojas de Minthostachys mollis[HBK] Griseb. del Ecuador: Extracción, composición química, capacidad antioxidante y actividad antimicrobiana

The aim of this study was to optimize by response surface design, the extraction of the leafessential oil (EO) from Minthostachys mollis [HBK] Griseb., grown in Ecuador, using steam distillation. The factors used were extraction time (XTIE) of 60, 105 and 150 min and plant material/water ratio (XRMA) of 1:3, 1:4 and 1:5. The optimal combination was reached with XRMA 1:5 and XTIE 150 min, obtaining a process yield of 0.67%. The chemical composition of the EO analyzed by GC-MS was determined, where the main compounds were carvacryl acetate (44.01%), carvacrol (16.51%) and menthone (8.20%). The antioxidant capacity of EO was evaluated using the FRAP and ABTS methodologies, with an IC50243.21 μmol Fe2+/g and 0.12 mg/mL, respectively. In addition, the antimicrobial activity of EO was found against Pseudomonas aeruginosa, Salmonella enterica, Escherichia coliand Staphylococcus aureus.

Optimization of mixed cargo allocation and stowage with heterogeneous vehicles

In order to solve the problem of low efficiency and high loading costs in business logistics and distribution operations, this paper classifies non-standard cargoes based on their spatial and temporal characteristics. Based on this, a dual-layer planning model for multi-vehicle cargoes loading and transportation is constructed to jointly optimize the loading process. This paper designs a space-inspired heuristic algorithm to solve the model and verifies the feasibility of the model. The results show that compared to a single vehicle type, the joint optimization of multi-vehicle combinations for loading and transportation has stronger adaptability and can better accommodate changes in cargo volume. Compared to the single vehicle type, not only did the loading rate increase by 18%, but also effectively reduced the transportation cost by 3%. Different proportions of spatial and temporal characteristic cargoes have different effects on the loading and transportation. Adjusting the proportion of cargoes that can be placed on both upper and lower layers to 50% can better utilize transportation resources such as vehicles. Increasing the proportion of urgent cargoes can improve the economic efficiency of loading and transportation.

Predictive models for treated clayey soils using waste powdered glass and expanded polystyrene beads using regression analysis and artificial neural network

AbstractWaste materials contribute to a wide range of environmental and economic problems. To minimize their effects, a safe strategy for reducing such negative impact is required. Recycling and reusing waste materials have proved to be effective measures in this respect. In this study, an eco-friendly treatment is investigated based on using waste powdered glass (WGP) and EPS beads (EPSb) as mechanical and chemical admixers in soils. For this purpose, Atterberg limit, standard proctor, free swell, and unconfined compression tests are performed on soil samples with different ratios of waste materials at their optimum moisture contents. The obtained test results indicate that adding WGP to cohesive soils increases the unconfined compressive strength (UCS) and reduces free swell (FS). In contrast, using EPSb reduces both FS and UCS of the treated soil samples. An optimum combination of both waste materials is determined for the improvement of the properties of high plasticity clay used in this study. Furthermore, multiple linear regression (MLR) and artificial neural network (ANN) methods are used to predict the FS and UCS of the clayey soils based on the data obtained here and the experimental test results reported in the literature. Once the FS and UCS values of untreated soil and additive percentages are defined as independent variables, both methods are shown to predict the FS and UCS values of the treated soil samples on a satisfactory level with the coefficient of correlation ($${R}^{2}$$ R 2 ) values greater than 0.926. Additionally, when only the index properties (liquid limit, plastic limit, and plasticity index) of the soil samples with waste materials are used as dependent variables, the $${R}^{2}$$ R 2 values obtained by the ANN method are 0.968 and 0.974 for FS and UCS, respectively. The results of the untreated soil samples' FS and UCS tests are known, and the linear regression and ANN techniques yield similar results. Lastly, the ANN method is used to predict the FS and UCS of the treated samples in accordance to the limited predictors (e.g., only the Atterberg limits of the soil sample).

Improving part-load performance of combined-cycle gas turbines by optimizing variable geometry control strategy for compressor and power turbine combined adjustment

The variable geometry methods currently used in combined-cycle gas turbines are compressor variable inlet guide vanes (VIGV) or power turbine variable area nozzles (VAN). On this basis, this study presents the optimal variable geometry control strategy for compressor and power turbine combined adjustment ([Formula: see text]) using the Differential Evolutionary Algorithm with the LM2500+ gas turbine. The aim is to further improve the part-load performance of the combined-cycle gas turbine. Firstly, a part-load performance prediction model for variable geometry gas turbines is established based on the component method. Subsequently, a variable geometry gas turbine part-load performance optimization model is developed by combining the Differential Evolution Algorithm. Finally, the optimum combination of stagger angles for the compressor inlet vane and power turbine nozzle is calculated at each part-load condition. Compared to the VIGV and VAN control strategies, the [Formula: see text] control strategy proposed in this paper shows a higher stability margin and better economy. The [Formula: see text] control strategy maintains a constant exhaust temperature within a part load range from 20% to 100% with the stability margin exceeding 14%. In comparison with the VAN control strategy, the fuel flow rate decreases by 1.152% at 45% relative load power and by 3.435% at 20.0% relative load power with the [Formula: see text] control strategy.

Effects of Bed Width and Crop Row Spacing on Barnyardgrass (Echinochloa crus-galli) Emergence and Seed Production in Furrow-Irrigated Rice

Abstract Furrow-irrigated rice (Oryza sativa L.) has become a popular option for rice production in Arkansas. Highly troublesome weeds like barnyardgrass [Echinochloa crus-galli (L.) P. Beauv.] are a major problem for producers in all rice production systems. Cultural tactics should become a priority to enhance crop growth and competitiveness. This research aimed to determine the effects of bed width (irrigation furrow spacing) and crop row spacing manipulation on E. crus-galli emergence and seed production in a furrow-irrigated rice system. Three bed widths (76-, 97-, and 152-cm) (whole plot factor) were used, and plots were drill-seeded in four crop row spacings (subplot factor) (13-, 19-, 25-, and 38-cm). The widest width of 152-cm had a slight increase in E. crus-galli density in the early rice life cycle but, by the end of the season, did not differ from the narrower bed widths. Conversely, a decrease in E. crus-galli seed production was observed as the bed width increased. Similar rice canopy coverage and yields occurred between all three bed widths. As for crop row spacing, as the width increased, E. crus-galli density also increased. The 13-cm crop row spacing had the lowest preflood E. crus-galli density, preharvest panicle count, and seed production. No effect of crop row spacing was observed on rice canopy coverage; however, the 13-cm crop row spacing produced the greatest rice yield. The 13-cm crop row spacing paired with the 152-cm bed width may be the optimum combination of ecological strategies in furrow-irrigated rice to reduce E. crus-galli seed production while maintaining rice growth and yield.

Adventitious root culture dyeing and optimisation of dyeing parameters using roots of Gynochthodes umbellata (L.) Razafim and B. Bremer

This study offers a new source of natural dye. Taguchi's design of experiments was used to statistically and optimize the dyeing parameters of different fabrics. The results varied with different fabrics and the optimum conditions for cotton were found to be ultrasonication for 5 min, a material-to-liquor ratio of 10 mL dye to 100 mL water, a temperature of 60 ºC, and a pH of 8. For linen, the optimum values were ultra-sonication for 15 min, 15 mL dye to 100 mL water, pH of 8, and temperature of 80 ºC. Optimum combinations for silk were a temperature of 80 ºC, the material-to-liquor ratio of 20 mL of dye extract to 100 mL water, pH of 8, and ultrasonication for 15 min. The results indicated the commercial viability of dyeing fabrics using this procedure.

Study and Experiment on Screen Surface Homogenization Technology of Dislodged Material Based on Longitudinal Flow Threshing

Aiming at the problems of uneven distribution of dislodged material on the screen surface of longitudinal axial flow grain combine harvester, a large difference in material clearing time, and large clearing loss, a dislodged material homogenizing device that can realize dislodged material return and homogenization at the rear of longitudinal axial flow was developed. (1) The structure and motion parameters of the reflux plate were determined, and simulation tests were carried out to verify them; (2) A test bench was set up, and the Box-Behnken test method was adopted to determine the influence law of each factor on the operating effect and the optimal parameter combination, and the results showed that the tilt angle of the return plate, motor speed, and amplitude had a significant influence on the distribution uniformity of the material on the screen surface; it was determined that the optimal combination of the angle of the return plate configuration was 28.7°, the speed of the motor was 247 r/min, the amplitude of the return plate was 18.3 mm, and the seed contamination rate was 0.48%. The optimum combination was determined to be 28.7°, 247 r/min, 18.3 mm, and 0.48% impurity rate; (3) under the conditions of the field test validation, the validation error is less than 5%, proving that it can effectively improve the performance of the clearing and reduce the rate of impurity content.

Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation

Mechanical water detection is recognized as the most reliable and safe production technology for coal mines, mainly for the detection of water hazards in pre-mining operations. Intelligent water detectors are currently the main research direction in mechanical water detection, and the automatic installation of drilling bars is the key to achieving intelligent water detection. Improving the connection accuracy in the process of installing drilling bars is an important research topic for the improvement of control links. To improve the connection accuracy of the drilling bars at the time of supplying material, we used the modified Denavit–Hartenberg method to analyze the motion gestures of the supplied material device and the Lagrange equation to establish a dynamic analysis model. We aimed at better control precision by improving the sliding mode control algorithm and at increasing the convergence rate of tracking errors with a sliding controller based on an exponential approximation law and using saturated functions instead of the symbol functions in the reaching law to weaken the vibration in the control process. We then used particle swarm optimization (PSO) to find the optimum combination parameters of the sliding mode controllers and test the performance of the sliding mode controllers before and after PSO with MATLAB/Simulink. The results showed that the optimized controller has a strong resistance to parameter fluctuations, and the system responds quickly, achieves a good performance, and improves the convergence rate of tracking errors.

Tailoring recrystallization for optimum mechanical combination in Ni-rich medium-entropy alloy via simplified thermomechanical treatment

A simplified strategy is implemented to obtain the comprehensive strength-ductility synergy in the Ni40Fe30Co20Al10 medium-entropy alloy (MEA) through one-pass cold-rolling (∼80%) and annealing without pre-homogenization treatment. The MEA annealed at 800 ℃ (A800) consists of disordered face-centered-cubic (FCC) matrix (γ) with ordered FCC nanoscale precipitates (γ1’ and γ2’), displaying ultimate tensile strength (UTS) of 895 MPa coupled with uniform elongation (UE) of 29%. It is found the selective distribution behavior of nanoscale γ1’ and γ2’ as full recrystallization (FR) precipitates within partial recrystallization (PR) and FR grains, exhibiting multiple nested-pattern heterostructure. The coarse-grained MEA annealed at 1200 ℃ (A1200) possesses a superior UE of ∼70% and an UTS higher than that of the as-cast sample, which can be rationalized by the interweaved stacking faults/nanoscale deformation-twins and the continuously generated twin boundaries. Moreover, a new parameter k involving both UTS and UE is designed to unveil the salience of strength-ductility synergy. As compared with the reported MEAs within a similar yield strength range, A1200 MEA reveals the optimized k, and can be considered as an outstanding candidate for further thermomechanical treatment.

Manipulating the mechanical strength and thermal conductivity of a Mg–4Zn-0.6Zr alloy through Ca addition

In this study, the microstructure, mechanical properties, and thermal conductivity of as-extruded Mg–4Zn-0.6Zr-xCa (x = 0, 0.3, 0.6, 0.9 wt%) alloys were investigated. The results revealed a bimodal grain size distribution in all the alloys due to incomplete dynamic recrystallization (DRX), characterized by the coexistence of elongated deformed grains and equiaxed DRX grains. The bimodal grain size distribution enhanced the mechanical properties of the studied alloys. Furthermore, Ca alloying facilitated the formation of Ca2Mg6Zn3 phases, through which the DRX extent was also enhanced. The precipitation of secondary phases, along with the increased DRX induced by Ca addition, was beneficial in eliminating the lattice distortion of the alloys, resulting in improved thermal conductivity compared to the Ca-free Mg–4Zn-0.6Zr alloy. The optimum combination of mechanical properties and thermal conductivity was achieved in the Mg–4Zn-0.6Zr-0.6Ca alloy, with yielding strength, ultimate tensile strength, tensile fracture elongation, and thermal conductivity values of 271 MPa, 318 MPa, 17.6 %, and 123.9 W/(m‧K), respectively. This work demonstrates that Mg–Zn–Zr–Ca-based alloys can be developed with high strength and high thermal conductivity, significantly expanding the industrial application of magnesium alloys.

Optimization of Epigenetic Modifier Drug Combination for Synergistic Effect against Glioblastoma Multiform Cancer Cell Lines

Glioblastoma multiforme (GBM), is a frequent class of malignant brain tumors. Epigenetic therapy, especially with synergistic combinations is highly paid attention for aggressive solid tumors like GBM. Here, RSM optimization has been used to increase the efficient arrest of U87 and U251 cell lines due to synergistic effects. Cell lines were treated with SAHA, 5-Azacytidine, GSK-126, and PTC-209 individually and then RSM was used to find most effective combinations. Results showed that optimized combinations significantly reduce cell survival and induce cell cycle arrest and apoptosis in both cell lines. Expression of cyclin B1 and cyclin D1 were decreased while caspase3 increased expression.

N-GlycoPred: A hybrid deep learning model for accurate identification of N-glycosylation sites

Studies have shown that protein glycosylation in cells reflects the real-time dynamics of biological processes, and the occurrence and development of many diseases are closely related to protein glycosylation. Abnormal protein glycosylation can be used as a potential diagnostic and prognostic marker of a disease, as well as a therapeutic target and a new breakthrough point for exploring pathogenesis. To address the issue of significant differences in the prediction results of previous models for different species, we constructed a hybrid deep learning model N-GlycoPred on the basis of dual-layer convolution, a paired attention mechanism and BiLSTM for accurate identification of N-glycosylation sites. By adopting one-hot encoding or the AAindex, we specifically selected the optimum combination of features and deep learning frameworks for human and mouse to refine the models. Based on six independent test datasets, our N-GlycoPred model achieved an average AUC of 0.9553, which is 0.23% higher than MusiteDeep. The comparison results indicate that our model can serve as a powerful tool for N-glycosylation site prescreening for biological researchers.

Ultrasound-assisted extraction of sulfonamides from soil samples using natural deep eutectic solvents and their determination by liquid chromatography tandem mass spectrometry

In this work, several potential natural deep eutectic solvents (NADES) were evaluated as sustainable solvents in the development of an ultrasound (US)-assisted extraction method for sulfonamides from soil samples and final determination by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Choline chloride (ChCl) was used as hydrogen bond acceptor (HBA), and different carboxylic acids and alcohols were tested as hydrogen bond donors (HBDs) in different molar ratios in order to find the optimum combination able to form appropriated NADES for the extraction of sulfonamides. Among the different mixtures tested, the one consisting of ChCl:1,3-propanediol in a molar ratio of 1:4 showed the better physico-chemical properties and was selected for the US-assisted extraction of sulfonamides. Under optimum conditions, no matrix effect was observed and quantitative recoveries, ranging from 81.9 % to 103.9 % with relative standard deviations (RSDs) lower than 8 % were obtained, reaching limits of quantification within the range 8.5–31 µg Kg−1, depending upon the analyte. In addition, after evaluation by AGREEprep, Sample Preparation Metric of Sustainability (SPMS) and Blue Applicability Grade Index (BAGI) metrics tools, the proposed method was found to be superior to other published methods in terms of safety and environmental impact with good applicability potential.

Parameter optimization, microstructural and mechanical properties of fiber laser lap welds of DP1200 steel sheets

In this research, the effects of laser power, laser welding speed and laser incidence angle were evaluated in terms of weld geometry, microstructure, microhardness, fracture surfaces and tensile shear load in fiber laser welding joints of DP1200 steel sheets. Response Surface Methodology (RSM) was utilized to derive mathematical relationships between the process parameters and the tensile shear load after the tensile test. The optimum combination of process parameters was a laser power of 2800 W, a laser welding speed of 40 mm/s, and a laser incidence angle of 70°. A thermal camera was used to record the laser welding process, and the cooling rates were evaluated by analyzing this data. The influence of cooling rate upon microstructure phase formation for DP1200 steel is investigated, with continuous cooling transformation (CCT) diagrams. Also, post-welding stress and displacement were simulated by Simufact Welding software. At high heat input (55.79 J/mm) occurred coarse dendritic martensitic lath growth while at low heat input (37.6 J/mm) fine dendritic martensitic structure was observed in the weld zone was obtained owing to the high rate of cooling. The microstructure consists of full martensite at a higher than 10 °C/s cooling rate. The findings reveal that the phases in FZ and HAZ, the morphology and martensite/bainite constituents differed related to the heat input and laser incidence angle, which ultimately affects the joint’s microhardness, fracture dynamics and tensile shear load.

Hypoglycemic TCM formulas (Huangqi-Gegen drug pair) have the potential as an Alzheimer's disease

BackgroundAlzheimer's disease (AD) is a neurological disorder. There is a considerable unmet medical need among those suffering from it. Hypothesis and purposeGiven the link between type-2 diabetes mellitus (T2DM) and AD, hypoglycemic traditional Chinese medicine formulas (TCMFs) may be a treatment for AD. We investigated the possibility of identifying anti-AD medicines in hypoglycemic TCMFs and presented another option for the screening of AD medications. Study design and methodsParalysis of the transgenic Caenorhabditis elegans (C. elegans) strain CL4176 (caused by amyloid beta (Aβ)1–42 aggregates) was used to evaluate the anti-AD effect. The toxicity and neurodegeneration induced by neuronal expression of Aβ in the transgenic C. elegans strain CL2355 were determined using a 5-hydroxytryptamine (5-HT) assay. The transgenic Aβ-expressing strain CL 2006 and transgenic tau-expressing strain BR5270 were used to explore the effect of TCMFs on protein expression in C. elegans using ELISAs. Then, network pharmacology was used to determine the mechanism of action. The Traditional Chinese Medicine Inheritance Support System platform was used to investigate prescription patterns, core drugs, and optimum combinations of hypoglycemic TCMFs for AD. ResultsSixteen hypoglycemic TCMFs prolonged the PT50 (half paralysis time) of the CL4176 strain of C. elegans, reduced the percentage of worms paralyzed. The results of network pharmacology showed that prostaglandin-endoperoxide synthase 2 (PTGS2) and acetylcholine esterase (AChE) are main targets of hypoglycemic TCMFs. Enriched pathway analysis showed that the cholinergic receptor-related pathway was the core pathway of hypoglycemic TCMFs. According to the “four qi and five flavors” system of TCM theory, the main pharmacological qualities were “cold” and “sweet.” Through the analysis by TCMISS, we found that Huangqi-Gegen drug pair as the significant Chinese herbs of hypoglycemic TCMFs. The Huangqi–Gegen pairing had the most robust therapeutic effect when delivered at a 2:1 (v/v) ratio. It reduced the paralysis caused by 5-HT, decreased protein expression of AChE and PTGS2, and reduced Aβ deposition in the brain of the CL2006 strain of C. elegans. ConclusionsHuangqi–Gegen is a promising treatment of AD, and its mechanism may be induced by suppressing the protein production of AChE and PTGS2, reducing 5-HT intake, and then decreasing Aβ deposition.

OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2∘ under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

Characterization, performance evaluation and optimization of wheat straw – bagasse blended fuel pellets

This study was carried out to assess the fuel pellets produced from wheat straw and sugarcane bagasse. The wheat straw and bagasse were blended into four ratios including; 10:90, 30:70, 70:30 and 90:10 (wheat straw: bagasse) and developed into fuel pellets. The fuel pellets were characterized to determine the moisture content, volatile matter, fixed carbon, ash content, calorific value, bulk density and mechanical durability. The ignition time, burning rate and specific fuel consumption of the wheat straw – bagasse blended fuel pellets were studied at varying blend ratios (10:90, 30:70, 70:30 and 90:10), moisture contents (9.1%, 10.6%, 12.6% and 14.7%) and raw material particle sizes (2 mm, 4 mm, 6 mm and 10 mm). Results indicated that the wheat straw: bagasse blend ratios containing more proportion of bagasse (30:70 and 10:90) recorded a shorter ignition time, higher burning rate and lower specific fuel consumption. Larger raw material particle sizes exhibited shorter ignition time, higher burning rate and specific fuel consumption. Moreso, the fuel pellets with low moisture contents also recorded shorter ignition time, higher burning rate and lower specific fuel consumption. It was concluded that fuel pellets with high quantity of bagasse, large particle sizes and low moisture content demonstrated favorable combustion characteristics. Response surface methodology was used in the optimization so as to determine the optimum combination of blending ratio, moisture content and raw material particle size that would result in the lowest ignition time, highest burning rate and lowest specific fuel consumption. Results indicated that an optimum combination of a wheat straw: bagasse blend ratio of 10:90, moisture content of 14.70% and a particle size of 10.00 mm resulted in the lowest ignition time, highest burning rate and lowest specific fuel consumption.

Adsorptive removal of perfluorooctanoic acid from aqueous matrices using peanut husk-derived magnetic biochar: Statistical and artificial intelligence approaches, kinetics, isotherm, and thermodynamics

Removal of perfluorooctanoic acid (PFOA) from water matrices is crucial owing to its pervasiveness and adverse ecological and human health effects. This study investigates the adsorptive removal of PFOA using magnetic biochar (MBC) derived from FeCl3-treated peanut husk at different temperatures (300, 600, and 900 °C). Preliminary experiments demonstrated that MBC600 exhibited superior performance, with its characterization confirming the presence of γ-Fe2O3. However, efficient PFOA removal from water matrices depends on determining the optimum combination of inputs in the treatment approaches. Therefore, optimization and predictive modeling of the PFOA adsorption were investigated using the response surface methodology (RSM) and the artificial intelligence (AI) models, respectively. The central composite design (CCD) of RSM was employed as the design matrix. Further, three AI models, viz. artificial neural network (ANN), support vector machine (SVM), and adaptive neuro-fuzzy inference system (ANFIS) were selected to predict PFOA adsorption. The RSM-CCD model applied to optimize three input process parameters, namely, adsorbent dose (100-400 mg/L), pH (3-10), and contact time (20-60 min), showed a statistically significant (p<0.05) effect on PFOA removal. Maximum PFOA removal of about 98.3% was attained at the optimized conditions: adsorbent dose: 400 mg/L, pH: 3.4, and contact time: 60 min. Non-linear analysis showed PFOA adsorption was best fitted by pseudo-second-order kinetics (R2=0.9997). PFOA adsorption followed Freundlich isotherm (R2=0.9951) with a maximum adsorption capacity of ∼307 mg/g. Thermodynamics and spectroscopic analyses revealed that PFOA adsorption is a spontaneous, exothermic, and physical phenomenon, with electrostatic interaction, hydrophobic interaction, and hydrogen bonding governing the process. A comparative analysis of the statistical and AI models for PFOA adsorption demonstrated high R2 (>0.99) for RSM-CCD, ANN, and ANFIS. This research demonstrates the applicability of the statistical and AI models for efficient prediction of PFOA adsorption from water matrices using MBC (MBC600).

Screening of immune-related lncRNAs in lung adenocarcinoma and establishing a survival prognostic risk prediction model.

This study aimed to improve lung adenocarcinoma (LUAD) prognosis prediction based on a signature of immune-related long non-coding RNAs (lncRNAs). LUAD samples from the TCGA database were divided into the immunity_H group and the immunity_L group. Differentially expressed RNAs (DERs) between the two groups were identified. Optimized immune-related lncRNAs combination was obtained using LASSO Cox regression. A prognostic risk prediction (RS) model was built and further validated in the training and validation datasets. A network among lncRNAs in the RS model, their co-expressed DERs, and the related KEGG pathways were established. Critical lncRNAs were validated in LUAD tissue samples. In total, 255 DERs were obtained, and 11 immune-related lncRNAs were significantly related to prognosis. Six lncRNAs were demonstrated as an optimal combination for building the RS model, including LINC00944, LINC00930, LINC00607, LINC00582, LINC00543, and LINC00319. The KM curve and ROC curve revealed the RS model to be a reliable indicator for LUAD prognosis. LINC00944 and LINC00582 showed a co-expression relationship with the MS4A1. LINC00944, LINC00582, and MS4A1 were successfully validated in LUAD samples. We have established a promising LUAD patient survival prediction model based on six immune-related lncRNAs. For LUAD patients, this prognostic model could guide personalized treatment.

Analysis of Carbon Black Proportion on Durability and Wear Behavior of EPDM Rubber

This study aims at understanding the effect of carbon black on mechanical properties of EPDM rubber. For this we select combination of carbon blacks N330 and N550 with standard curing procedure to minimize the wear rate and maximize durability. The EPDM rubbers were characterized using hardness, tensile, wear and durability tests. The coefficient of friction (COF), volume loss and wear rate of the EPDM rubbers were determined. It was found that with increasing CB content all above characteristics were reduced. Result shows that by using optimum combination of carbon black we can enhance wear performance and durability of EPDM rubber.