Optimal Conditioning Research Articles

Modification of steel slag and its application for phosphate and ammonia removal in aquatic products processing wastewater

ABSTRACT This study aims to modify different types of steel slag by heating, characterise the material and test its ability for adsorption of phosphate and ammonia from aquatic products processing wastewater. The materials were characterised by SEM, EDS, BET and FT-IR analyses. The effects of the type of steel slag, calcination temperature in steel slag modification, pH, contact time, adsorbent mass and initial pollutant concentration were investigated using the one-factor-at-a-time approach. The results show that the adsorption kinetic of the samples followed the pseudo-second-order model, whereas their adsorption isotherm fitted well with the monomolecular adsorptive Langmuir with a maximum phosphate adsorption capacity of 45.045 mgPO43-/g. Optimisation using Response Surface Methodology was conducted with the independent factors (adsorbent doses, contact time and calcination temperature of steel slag) and response (removal efficiency of phosphate). The temperature and dosage of the material significantly affected phosphate removal efficiency and optimisation conditions were suggested. Validation of one optimum condition at the calcination temperature of 801°C, the adsorbent dosage of 11.9 g/L and the contact time of 72 min using real aquatic products processing wastewater resulted in the removal efficiencies of 86.93, 87.59, 7.76 and 7.58% for phosphate, total phosphorus, ammonia and total nitrogen, respectively.

Plasma Functionalization for Enhanced Natural Dye Affinity on Bioplastics: A Novel Approach

ABSTRACTBioplastics offer biodegradability and reduced environmental impact but often use nondegradable synthetic dyes. This study introduces a novel application of glow discharge plasma (GDP) technology to modify cassava starch‐based bioplastic sheets, specifically optimizing conditions to enhance their hydrophilicity and adhesion for natural dyes. This research systematically tailors plasma treatment parameters to maximize the interaction between bioplastics and eco‐friendly natural dyes. Optimal conditions, identified as 30 W for 3 min using oxygen, air, and nitrogen plasma treatments, demonstrate significant advancement in sustainable material development. Analyses (ATR‐FTIR, EDX, XPS) showed increased concentration of polar functional groups and oxygen content, with FE‐SEM and water contact angle measurements revealing improved surface roughness and wettability, respectively. Oxygen plasma treatment yielded the highest hydrophilicity and crystallinity of the polymer. Tensile tests indicated increased strength and decreased elongation. UV‐Vis spectroscopy confirmed enhanced dye retention and reduced leaching. This study demonstrates GDP's potential to incorporate eco‐friendly natural dyes into bioplastics, thereby contributing to the development of more sustainable materials.

B-286 Single Experiment, Single Plate Solid Phase Extraction (SPE) Optimization using 47 Drug Analytes as an Example

Abstract Background SPE of biological matrices isolates analytes of interest from interfering matrix components that can affect LC-MS/MS quantitation. Optimal SPE conditions are dependent upon the sample matrix and analyte properties and typically require multiple experiments to determine optimal conditions for SPE extraction. Here, a 96-well method development plate was utilized to streamline method development for a panel of 47 clinical research drug analytes: 14 antipsychotics, 22 antidepressants and 11 anticonvulsants. Methods An SPE plate with four polymeric sorbents: reversed phase (X), strong and weak cation-exchange and weak anion-exchange mixed-mode phases (X-C, X-CW and X-AW) was used. The 47 drugs analytes were extracted on all four sorbents using three sets of conditions: acidic pretreatment/wash (aq) with basic elution (AB), neutral pretreatment/wash (aq) with neutral elution (NN), and basic pretreatment/wash (aq) with acidic elution (BA). A 50/50 methanol/water wash was also included. Elution used neutral or pH adjusted ethyl acetate/isopropanol or methanol/acetonitrile. A reagent blank, matrix blank, extracted serum samples spiked post-elution (n=2), and spiked serum (n=4) were extracted on each sorbent using each set of conditions. Optimal conditions were determined by calculating absolute percent recovery for each analyte. Results Recovery for antipsychotics was 85-97% using X-CW-AB, except Chlorpromazine (59%). It had better recovery (80%) using X-AB. Recovery for the antidepressants was 75-112% using X-C-AB, except Bupropion (35%) and Selegiline (10%). Bupropion had better recovery (83% and 95%) using X or X-CW and BA. Selegiline had better recovery using X, X-CW and X-AW with BA (83-92%). Anticonvulsants comprise zwitterionic, sulfonamides, neutral, acidic, and basic compounds with widely varying pKa and polarity. Targeting a single method for these analytes is difficult. The best recovery for anticonvulsants used X-CW-AB with several exceptions. Lacosamide, Felbamate, and Levetiracetam are neutral and among the most polar analytes and had 7-31% recovery under all SPE conditions tested. Zwitterionic Gabapentin and Pregabalin had 50-53% recovery from X-CW and X-C sorbents. The stronger protic elution (ammoniated methanol/acetonitrile vs ethyl acetate/isopropanol ) improved solubility for complete elution. Oxcarbazepine had 37% recovery from X-CW-AB, but recovery was 71% under BA conditions. Neutral, polar Zonisamide and Topiramate (Sulfonamide compounds) recovery was 25-26% using X-CW-AB. However, recovery was better (47% and 83%) under NN conditions, suggesting the analytes didn’t undergo complete elution from the X-CW sorbent. A stronger elution solvent would improve recovery from X-CW. Conclusions Most antipsychotics were recovered with X-CW-AB. Most antidepressants could be extracted using X-C-AB. Some analytes had higher recovery using hydrophobic interaction (X) SPE. A single mixed-mode SPE two-step elution protocol using a stronger elution solvent may be feasible. Anticonvulsants had mixed results. Some would require a stronger elution solvent. Some could be included in the other two panels to maximize recovery, but some had low recovery under all conditions tested. The method development plate screened 12 SPE methods for 47 analytes in a single experiment, greatly reducing the time and labor required to identify the best sorbent and starting conditions for SPE extraction optimization for drug analysis. This workflow could be implemented for other SPE method development projects.

Sequential Conditioning With FLAMSA Does Not Improve Outcomes of Allogeneic Stem Cell Transplantation in Chronic Myelomonocytic Leukemia Patients

As with myelodysplastic syndromes (MDS), effective treatment options for Chronic myelomonocytic leukemia (CMML) are limited, and the optimal treatment approach remains undefined. Allogeneic stem cell transplantation (allo-SCT) is potentially curative therapy for patients with CMML. Sequential conditioning with FLAMSA was initially developed for refractory acute myeloid leukemia (AML) and has since been applied in the treatment of MDS and CMML. Data on optimal allo-SCT conditioning in CMML Patients is scarce. This retrospective study from the Department of Stem Cell Transplantation at the University Medical Center Hamburg, Germany, compared allo-SCT outcomes in CMML patients across three conditioning regimes: Thiotepa-Busulfan (TB), Sequential FLAMSA-Busulfan Fludarabine (FLAMSA-FB), and Treosulfan-Fludarabine (Treo-Flu). Sixty-nine consecutive patients with CMML who underwent allo-SCT between the years 2006-2022 were included in the study. Twenty-two received TB, 27 received FLAMSA-FB, and 20 received Treo-Flu conditioning. Transplant sources included matched related donors (MRD, 8 patients), mismatched related donors (MMRD, 8 all in the TB group), matched unrelated donors (MUD, 31), and mismatched unrelated donors (MMUD, 22) with significant group variations (p<0.001). Most patients received anti-T lymphocyte Globulin (ATLG) for graft versus host disease (GVHD) prophylaxis (TB 68%, FLAMSA-FB 93%, Treo-Flu 85%, p=0.08). CPSS-Molecular score was comparable between the groups. One TB patient experienced primary graft failure, but engraftment times were comparable across the groups. Although not statistically significant, the TB group showed a trend towards improved 3-year OS rates (80%) compared to FLAMSA-FB (37%) and Treo-Flu (55%) (p=0.05). The TB group also displayed significantly higher 3-year PFS rates (80%) compared to FLAMSA-FB (33%) and Treo-Flu (both 39%), (p=0.02). No significant differences were observed in 3-year non-relapse mortality (NRM) across the TB (20%), FLAMSA-FB (30%), and Treo-Flu (26%) groups (p=0.8). Interestingly, no TB patients relapsed at 3 years, contrasting with the FLAMSA-FB (41%) and Treo-Flu groups (30%, p=0.02). Lastly, cumulative incidences of acute and chronic GVHD were similar across groups. Our analysis suggests FLAMSA-FB does not improve transplant outcomes, however TB represents the preferred conditioning regimen for CMML patients undergoing allo-SCT. It demonstrates notable advantages in relapse prevention and leads to improved OS and PFS compared to FLAMSA-FB and Treo-Flu protocols.

Natural deep eutectic solvents for efficient recovery of bioactive compounds from by-product of industrial hemp processing: Pretreatment, modeling and optimization

Hempseed hull, an underutilized by-product of the hemp processing industry, is a valuable bio-based material. This study aims to optimize phenolic compound extraction by natural deep eutectic solvents (NADES) with enhanced antioxidant properties (DPPH, FRAP, ABTS). By utilizing a chemometric approach, the research was conducted in three stages: selecting high-extractability NADES, identifying crucial extraction variables via screening, and optimizing conditions using response surface methodology (RSM). Fractional factorial experimental design was employed to identify the important variables, while a face-centered central composite experimental design (CCD) was utilized in combination with response surface methodology (RSM) to determine the optimal extraction conditions. The optimal conditions were found using NADES based on L-proline and lactic acid (1:2 ratio) at 39°C for the liquid-solid ratio, 70°C for temperature, and 135 minutes for extraction time. By optimizing extraction conditions this study contributes to advancing our understanding of hempseed hulls' biochemical composition and its potential as bio-based material.

Automated multicolumn screening workflow in ultra-high pressure hydrophilic interaction chromatography for streamlined method development of polar analytes

The pharmaceutical industry is rapidly advancing toward new drug modalities, necessitating the development of advanced analytical strategies for effective, meaningful, and reliable assays. Hydrophilic Interaction Chromatography (HILIC) is a powerful technique for the analysis of polar analytes. Despite being a well-established technique, HILIC method development can be laborious owing to the multiple factors that affect the separation mechanism, such as the selection of stationary phase chemistry, mobile phase eluents, and optimization of column equilibration time. Herein, we introduce a new automated multicolumn and multi-eluent screening workflow that streamlines the development of new HILIC assays, circumventing the existing tedious ‘hit-or-miss’ approach. A total of 12 complementary columns packed with sub-2 µm fully porous and 2.7 µm superficially porous particles operated on readily available ultra-high pressure liquid chromatography (UHPLC) instrumentation across a diverse set of commercially available polar stationary phases were investigated. Different mobile phases with pH ranging from pH 3 to 9 were evaluated using different organic modifiers. The gradient and column re-equilibration were judiciously set to ensure a reliable assay screening framework that indicates promising conditions for subsequent method optimization to achieve resolution of challenging mixtures. This UHPLC screening system is coupled with a diode array and charged aerosol detectors (DAD, CAD and mass spectrometry) to ensure versatile detection for a variety of compounds. This fast-screening platform lays the foundation for a convenient generic workflow, accelerating the pace of HILIC method development and transfer across both academic and industrial sectors.

Contour Analysis for Heat Transfer Rate in a Wedge Geometry with Non-Uniform Shapes Nanofluid: Gradient Descent Machine Learning Technique

The flow of boundary layer over porous wedge nanofluids, hybrid nanofluids, and ternary hybrid nanofluids under constant transverse magnetic field and heat radiation have all been examined. The following cases are taken into consideration: Zirconium oxide + A A7072 + Polyethylene Glycol-water (Case-2); Magnesium oxide + Polyethylene Glycol-water + AA7072 + Zirconium oxide (Case-3); Polyethylene-water as base fluid; nanofluid, hybrid nanofluid, ternary hybrid nanofluid cases. The governing equations are solved using the shooting technique with the fourth-order Runge-Kutta method. The approximate relationship between temperature, velocity, heat transfer rate, and friction factor coefficient at the wedge is shown graphically for a range of values of the pertinent parameters. The flow of boundary layer over porous wedge nanofluids, hybrid nanofluids, and ternary hybrid nanofluids under constant transverse magnetic field and heat radiation have all been examined. Cases containing Polyethylene Glycol-Water + AA7072 (Case-1), Zirconium oxide + A A7072 + Polyethylene Glycol-water (Case-2), and Magnesium oxide + Polyethylene Glycol-water + AA7072 + Zirconium oxide (Case-3) are taken into consideration. Polyethylene water is used as the base fluid. The governing equations are solved using the shooting approach and Runge-Kutta fourth order. The approximate relationship between temperature, velocity, heat transfer rate, and friction factor coefficient at the wedge is shown graphically for a range of values of the pertinent parameters. Comparing Ternary hybrid nanofluid to nanofluid and hybrid nanofluid situations, it is discovered that the skin friction coefficient is higher. It has also been noted that ternary hybrid nanofluids have a higher Nusselt number than hybrid and nanofluids. When applied to specific dimensionless parameters, the basic linear regression machine learning technique using the gradient descent method accurately predicts the truth values. Finding the optimisation conditions values based on the key factors influencing the Response Function is easier with the RSM method's help. When applied to specific dimensionless parameters, simple linear regression machine learning using the gradient descent method accurately predicts the truth values.

Understanding the Impact of Conditioning Protocols for Water Electrolyzers on Durability

The global push towards climate neutrality has intensified research efforts on green hydrogen production technologies. Among them, proton exchange water electrolysis (PEMWE) stands out as a highly efficient technology for producing green hydrogen using renewable energies. Nevertheless, a significant reduction in the cost of PEMWE while maintaining its durability over its operational periods is imperative. The DOE’s Hydrogen Earthshot target aims to achieve $1 per 1kg of H2 production within the decade. Reducing the amount of expensive iridium catalysts also reduces the durability of PEMWE. Hence, understanding the degradation mechanisms of low Ir loading PEMWE holds utmost importance. One key step in the evaluation of PEMWE is the break-in or conditioning step. Ensuring optimal conditioning is crucial to guarantee the peak performance of membrane electrode assembly (MEA) before assessing the durability of the catalyst thereby ensuring representative and reproducible results.This research aims to comprehensively evaluate various conditioning protocols for PEMWE and their impact on durability. Utilizing a combination of electrochemical, electron, and X-ray characterization techniques. Accelerated stress tests (AST) will be conducted after each conditioning protocol to evaluate the degradation rate of the catalyst. Ex-situ characterization of the MEA post-conditioning as well as post-AST offers a comprehensive understanding of how the catalyst’s initial state, such as the iridium oxidation state, following the conditioning step directly influences the durability. The goal is to develop an optimal conditioning protocol for PEMWE, that considers the performance and durability challenges associated with low catalyst loading. Acknowledgment This research is supported by the U.S. Department of Energy (DOE) Hydrogen and Fuel Cell Technologies Office through the Hydrogen from Next-generation Electrolyzers of Water (H2NEW) consortium with support from Dave Peterson and McKenzie Hubert.

Evaluation of degree of blending and interaction between aged and virgin asphalt binders using rheological and fracture measurements

Evaluating the blending and interaction processes between aged and virgin asphalt binders is significant for enhancing the performance and longevity of asphalt pavements, particularly when using Reclaimed Asphalt Pavement (RAP). The primary aim of this research is to investigate the optimal conditioning time for diffusion process between virgin and aged binders. To simulate age-hardened RAP binder, asphalt binders were artificially aged in a forced draft oven at 163 °C for five hours, followed by additional aging at 85 °C and 110 °C over periods of 5, 7, and 10 days. The research assessed the degree of blending and interaction between aged and virgin binders in two phases using rheological and fracture measurements. In the first phase, the study explored the effects of various conditioning temperatures and durations (135 °C and 165 °C for 15, 30, and 60 minutes) on the diffusion and blending of the asphalt binders. Results indicated that the interaction between aged and virgin binders depends on the temperature and duration of conditioning; notably, complete blending was achieved at 165 °C after 60 minutes. The second phase investigated the impact of aged binder content on the rheological and fracture characteristics of the blended binders. Findings showed that fracture energy and peak load decrease with an increase in aged binder content. Specifically, increasing the aged binder content from 10 % to 30 % resulted in an approximate 23 % reduction in fracture-related parameters, while increasing the aged binder content from 50 % to 70 % led to an approximate 51 % reduction. Additionally, analysis of the complex shear modulus across different blends revealed an increase in modulus and high-temperature performance proportional to the percentage of aged binder, suggesting a hardening effect in the blended binder. Overall, this study underscores the significance of understanding the interactions between aged and virgin binders to optimize the diffusion and blending processes, ultimately enhancing the performance of asphalt pavements containing recycled materials.

Collision-Free Trajectory Planning Optimization Algorithms for Two-Arm Cascade Combination System

As a kind of space robot, the two-arm cascade combination system (TACCS) has been applied to perform auxiliary operations at different locations outside space cabins. The motion coupling relation of two arms and complex surrounding obstacles make the collision-free trajectory planning optimization of TACCS more difficult, which has become an urgent problem to be solved. For the above problem, this paper proposed collision-free and time–energy–minimum trajectory planning optimization algorithms, considering the motion coupling of two arms. In this method, the screw-based inverse kinematics (IK) model of TACCS is established to provide the basis for the motion planning in joint space by decoupling the whole IK problem into two IK sub-problems of two arms; the minimum distance calculation model is established based on the hybrid geometric enveloping way and basic distance functions, which can provide the efficient and accurate data basis for the obstacle-avoidance constraint condition of the trajectory optimization. Moreover, the single and bi-layer optimization algorithms are presented by taking motion time and energy consumption as objectives and considering obstacle-avoidance and kinematics constraints. Finally, through example cases, the results indicate that the bi-layer optimization has higher convergence efficiency under the premise of ensuring the optimization effect by separating variables and constraint terms. This work can provide theoretical and methodological support for the efficient and intelligent applications of TACCS in the space arena.

Optimizing Conditions to Combat Antibiotic Resistance in Pathogenic Bacteria

Background: Antimicrobial resistance (AMR) is a significant adaptive trait that allows pathogenic bacterial subpopulations to out-compete and out-survive their microbial neighbors and overcome host defenses. Specific Background: Despite extensive research, the influence of various environmental parameters on antibiotic sensitivity in pathogenic bacteria remains underexplored. Knowledge Gap: There is limited understanding of how temperature, pH, bacterial inoculum volume, and culture medium amount affect the antibiotic resistance of both gram-negative and gram-positive bacteria. Aims: To investigate the effects of these parameters on the antibiotic sensitivity of four standard laboratory strains: Escherichia coli, Proteus spp., Klebsiella spp., and Staphylococcus aureus. Results: Our findings indicate imipenem exhibited the highest sensitivity, with percentages varying significantly based on temperature (92% at 35-39°C), pH (83% at pH 6-8), inoculum volume (42% at 0.1-1.0 μL), and medium volume (67% at 15-35 ml). Conversely, antibiotics such as Piperacillin, Amoxicillin, Erythromycin, Tetracycline 30, and Cephalexin showed high resistance, with Tetracycline 10 being the most resistant. Novelty: This study highlights the significant impact of environmental conditions on bacterial antibiotic resistance, emphasizing the need for tailored antibiotic use based on specific bacterial characteristics and growth conditions. Implications: The results suggest that optimizing environmental parameters can enhance antibiotic efficacy and inform better clinical practices to combat AMR, thus improving treatment outcomes for bacterial infections. Highlights: Parameter Influence: Temperature, pH, inoculum, medium amount affect antibiotic sensitivity. Highest Sensitivity: Imipenem most effective across conditions. Tailored Use: Optimize conditions for better antibiotic efficacy. Keywords: Antimicrobial resistance, bacterial sensitivity, environmental parameters, Imipenem, pathogenic bacteria

Experimental study on the workability of sands conditioned with bentonite-silty clay modified slurry

With the widespread application of Earth Pressure Balance (EPB) shield technology, the generation of shield muck has been increasing yearly. This paper aims to investigate the effectiveness of bentonite-silty clay modified slurry (BSC) as a soil conditioner for enhancing the workability of sands during EPB shield tunneling, thus enabling the recycling and reuse of the discarded muck (waste silty clay). Standard slump tests were conducted on three typical sand specimens from Shenyang Metro Line 6. The influence of the types of conditioners and slurry injection ratio (SIR) on slump values were examined to determine an optimal conditioning scheme tailored to the specific formation conditions. Furthermore, the study explored the combined use of BSC and foam to improve workability, employing a three-factor four-level orthogonal experiment. Finally, the rheological parameters (yield stress) derived from the slump tests provide valuable insights for assessing material flow within the tunneling system. The results show that comparative analyses with pure bentonite slurries reveal that BSC is a suitable, economical, and effective alternative for soil conditioning. The particle size distribution of sand specimens significantly influences the conditioning process, necessitating adjustments to SIR and slurry viscosity for optimal results. When the slump value of slurry-conditioned soil falls within the range of 150–250 mm, the slump test can be effectively used to estimate its yield stress under atmospheric conditions. This study contributes to the development of sustainable and economical solutions for soil conditioning in urban tunnel projects, particularly by utilizing excavated materials effectively.

Optimization of Ultrasound-Assisted Deep Eutectic Solvent Extraction, Characterization, and Bioactivities of Polysaccharide from Pericarpium Citri Reticulatae.

The ultrasonic-assisted deep eutectic solvent method was used to extract the polysaccharides of Pericarpium Citri Reticulatae (PCRP), and the ultrasound-assisted DES extraction process was optimized by Box-Behnken response surface test using the extraction rate of the PCRP as an index; the in vitro activities of purified the PCRP(PCRPs-1) were investigated by determining the scavenging rate of DPPH• and ABTS•+ as well as by enzyme inhibition assay. The monosaccharide composition was analyzed by HPLC. The best process conditions for response surface optimization were a material-liquid ratio of 1:37g/mL, water content of 44%, time of 89min, and power of 320 W. The polysaccharide extraction rate was measured to be 5.41%, which was well optimized when compared with that of the ordinary aqueous extraction method of 3.92%. By α-glucosidase and α-amylase inhibition activity test, it showed that the PCRPs-1 had hypoglycemic activity. The DPPH radical scavenging activity test and ABTS + scavenging activity test indicated that the PCRPs-1 had good biological activity. Analysis of the monosaccharide fractions showed that the PCRPs-1 consisted of mannose, rhamnose, glucuronic acid, galacturonic acid, glucose, galactose, xylose, and arabinose, with molar ratios of 1:39.24:4.41:8.91:7.83:86.00:1.02:9.17. The activity studies showed that PCRPs-1 possessed certain hypoglycaemic and antioxidant activities.

STUDY OF HEAT AND MASS TRANSFER IN WOOD-COMPOSITE MATERIALS BY MEANS OF COMPUTATIONAL FLUID DYNAMICS

Wood-composite materials (WCM), undergoes complex heat and mass transfer processes during its conditioning. Understanding these interactions is crucial for optimizing the conditioning process and ensuring high quality of the final product. This study focuses on the problem of modeling the interactions that occur during WCM conditioning, taking into account their characteristics that depend on temperature, moisture content, and density of the material. In addition, various input parameters are taken into account in the modeling process to set the optimal conditioning conditions. Despite the availability of these parameters in the literature based on the results of real experiments, consolidating this data in computer modeling is still a major problem. To resolve this issue, this study focuses on modeling the heat and mass transfer processes during WCM cooling using computational fluid dynamics (CFD) software such as SolidWorks Flow Simulation. By using the capabilities of CFD, it is relatively possible to utilize technical parameters involved in the WCM conditioning process with relative simplicity. The paper provides a detailed description of the created 3D model of the WCM-conditioning chamber and its main components. In addition, the paper describes the various input parameters used in the modeling process, including temperature, moisture content, and density of WCM. In addition, the operation of the fans was set up and the initial values of temperature, moisture content, and air velocity were set. Based on the simulation, the results obtained include the distribution of the temperature field and moisture content on the surface of the WCM at different material densities and air conditioning parameters. Additionally, the trajectory of air masses and their velocity within the 3D model was obtained. In general, the obtained results provide a better understanding of the influence of various input parameters on the process of heat and mass transfer in WCM during their cooling. Given the comprehensive analysis, this research this work can be useful for improvement of WCM production processes and quality control of final products. In particular, it emphasizes the importance of further research in this area and the need for computer modeling of such processes to further develop effective engineering solutions.

Garlic straw-derived composite hydrogel via electron beam irradiation for adsorptive removal of methylene blue

The study presents the development and optimization of a novel garlic straw-based composite hydrogel synthesized using electron beam irradiation for adsorption of common harmful organic pollutants like methylene blue dye (MB). The composite hydrogel was prepared from garlic straw (GS) mixed with (polyvinyl alcohol/Pectin) (PVA/Pt) copolymers using an eco-friendly technique of electron beam (EB) radiation for crosslinking. The synthesis process and parameters were optimized. The total PVA/Pt content 20wt% of composition 70/30 includes 20wt% GS at 35 kGy was chosen as the most suitable condition for optimization. The structure, morphology, and thermal properties were studied using different instruments. The resulting (PVA/Pt)-GS composite hydrogel exhibited excellent adsorption capacity for MB, showcased through comprehensive investigations of isotherms, kinetics, and thermodynamics. The maximum adsorption capacity of PVA/Pt-GS for MB was 505 mg/g at pH 9. Isotherm studies revealed the adsorption mechanism, while kinetic analyses provided insights into the adsorption rate. Thermodynamic investigations elucidated the spontaneity and feasibility of the adsorption process.

Optimisation of POME biodiesel with isobutanol additive to cater UN sustainable development goal on affordable and clean energy

Biodiesel is a suitable alternative to solve global pollution and declining non-renewable resources. This aligns with the sustainable climate goals and policies to cater to SDG 7. Sustainable technology and solutions have to be fostered. The higher blends of biodiesel will result in engine performance and emissions degradation. Therefore, this research aims to improve the engine performance and emissions of diesel engines operating with B20 POME biodiesel and isobutanol. The Central Composite Design (CCD) model was used to construct the RSM model to determine the optimised condition of the engine testing. Engine performance and emissions of diesel engines were tested on the Yanmar TF120M at 50 % load. Six fuel samples, diesel, B20 Palm Oil Methyl Ester (POME) biodiesel blended fuel, and another four B20 POME biodiesel blends added with 5–20 % in volume percentage of isobutanol, were tested. The optimised RSM obtained a desirability of 0.7. The optimal conditions for the engine testing were at 1868 rpm and a 10 % isobutanol additive percentage. The brake power, BSFC, and BTE of the B95IBU5 and B90IBU10 show an improvement compared to the B20. The exhaust emission shows the lowest CO emission for B90IBU10 at 50 % load. The lowest NOx emission was obtained by B95IBU5 and B90IBU10, with an improvement of 1.8 % and 2.4 %, respectively. Therefore, from this study, it can be concluded that a lower percentage of isobutanol additive of 5–10 % is a promising additive for biodiesel blended fuels.

Machine learning-based modeling and fogging prevention strategies for ice rink environments

An ice rink designed as a specialized public building for ice sports, requires a precise indoor thermal environment to function optimally. Excess moisture in this environment leads to fogging issues. This study focuses on investigating an ice rink in Taiwan, employing machine learning-based modeling for fogging prevention. Utilizing logistic regression, we develop a fogging probability model, which serves as a pivotal advanced warning system for critical operational decision-making. Subsequently, we employ Deep Neural Networks (DNN) to predict the supply air temperature and humidity of two Air Handling Units (AHUs), extending this methodology to model the air conditions within the ice rink arena. The models achieve an impressive R2 of 0.94. Leveraging this predictive capacity, our study estimates fogging occurrences through a fogging line, exhibiting high classification accuracy of 96 %. Additionally, clustering analysis utilizing the k-means algorithm is conducted, serving as the initial condition for optimization. The optimization phase, employing a genetic algorithm, results in fogging prevention rates between 72 % and 85 % across clusters. This research indicates the efficacy of a machine learning-based approach in addressing fogging challenges, providing insight into effective preventive measures to maintain an optimal ice rink environment.

On Enhanced KKT Optimality Conditions for Smooth Nonlinear Optimization

On Enhanced KKT Optimality Conditions for Smooth Nonlinear Optimization

Cartilage Warping in Rhinoplasty: An Assessment of Temperature, Carving, and Suturing Conditions

Abstract Goals/Purpose Rhinoplasty is often contingent on the successful harvesting and allocation of cartilage in order to build the nasal framework. While autologous and cadaveric cartilage grafts have been utilized, it remains unclear how factors, such as suturing and carving techniques, as well as temperature, impact its short- and long-term warping potential. Our systematic review seeks to objectively assess how various carving (i.e. peripheral versus central, eccentric versus concentric), suture patterns (i.e. counter-balancing, oppositional), and temperature conditions impact the warping potential of cartilage grafts in rhinoplasty. In conducting this study, we hope to determine the optimal conditions for stable cartilage constructs in rhinoplasty. Methods/Technique A systematic literature review was conducted using a combination of terms including, “warp,” “rhinoplasty,” “cartilage,” “suture,” “temperature,” “carving,” and “cutting.” Removal of duplicates was performed, followed by further screening by abstract and full text. To reduce the possibility of bias, a standardized form was used for data extraction; variables including author, year of publication, journal of publication, study design, conditions, and results were recorded. Results/Complications A total of eleven studies were included in the analysis. Cartilage types included fresh frozen cadaveric graft, autologous costal cartilage, and bovine rib cartilage. Conditions associated with reduced warping include central and concentric based carving patterns, oppositional suturing; Peripherally and eccentric cut cartilage, as well as increased time, were associated with a higher degree of warping. Conclusion To the authors’ knowledge, our systematic analysis is the first to summarize the impact of various conditions on cartilage warping in rhinoplasty. Moreover, we determine the protective nature of certain carving and suturing methods - particularly central, concentric carving, and oppositional suturing - in minimizing cartilage warping. With this information, we hope to guide plastic surgeons in optimizing conditions to potentially prevent cartilage warping, and ultimately, provide reliable and sustainable results for patients undergoing rhinoplasty.

Computation cost reduction in 3D shape optimization of nanophotonic components

Inverse design methodologies effectively optimize many design parameters of a photonic device with respect to a primary objective, uncovering locally optimal designs in a typically non-convex parameter space. Often, a variety of secondary objectives (performance metrics) also need to be considered before fabrication takes place. Hence, a large collection of optimized designs is useful, as their performance on secondary objectives often varies. For certain classes of components such as shape-optimized devices, the most efficient optimization approach is to begin with 2D optimization from random parameter initialization and then follow up with 3D re-optimization. Nevertheless, the latter stage is substantially time- and resource-intensive. Thus, obtaining a desired collection of optimized designs through repeated 3D optimizations is a computational challenge. To address this issue, a machine learning-based regression model is proposed to reduce the computation cost involved in the 3D optimization stage. The regression model correlates the 2D and 3D optimized structural parameters based on a small dataset. Using the predicted design parameters from this model as the initial condition for 3D optimization, the same optima are reached faster. The effectiveness of this approach is demonstrated in the shape optimization-based inverse design of TE0-TE1 mode converters, an important component in mode-division multiplexing applications. The final optimized designs are identical in both approaches, but leveraging a machine learning-based regression model offers a 35% reduction in computation load for the 3D optimization step. The approach provides a more effective means for sampling larger numbers of 3D optimized designs.