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In this study, we optimized the traditional composition of AISI 8630 steel and evaluated its corrosion resistance through a series of tests. We conducted corrosion tests in a 3.5% NaCl solution and performed a 720 h fixed-load tensile test in accordance with the NACE TM-0177-2016 standard to assess sulfide stress corrosion cracking (SSCC). To analyze the corrosion products and the structure of the corrosion film, we employed X-ray diffraction and transmission electron microscopy. The corrosion rate, characteristics of the corrosion products, structure of the corrosion film, and corrosion resistance mechanism of the material were investigated. The results indicate that the optimized AISI 8630 material demonstrates excellent corrosion resistance. After 720 h of exposure, the primary corrosion products were identified as chromium oxide, copper sulfide, iron oxide, and iron–nickel sulfide. The corrosion film exhibited a three-layer structure: the innermost layer with a thickness of 200–300 nm contained higher concentrations of alloying elements and formed a dense, cohesive rust layer that hindered the diffusion of oxygen and chloride ions, thus enhancing corrosion resistance. The middle layer was thicker and less rich in alloying elements, while the outer layer, approximately 300–400 nm thick, was relatively loose. [ABSTRACT FROM AUTHOR]

Flavor plays a crucial role in the sensory perception of food and is a key determinant for consumer preference and choice. Therefore, flavor analysis methods are of paramount importance. Traditional methods for flavor analysis have limitations such as time-consuming and unable to handle large sample data. The emergence of machine learning is poised to address these problems. Machine learning possesses the capability to analyze and process vast amounts of data, identify complex patterns in high-dimensional variable spaces, autonomously learn useful information from known data, and automatically generate and optimize algorithms for prediction based on new data. The emergence of machine learning provides a new method for understanding the complex flavor characteristics of food. This article provides a comprehensive review of the advantages and disadvantages of traditional and novel machine learning methods as well as their various application scenarios in conjunction with analytical instruments such as electronic tongue, electronic nose, and gas chromatography-mass spectrometry (GC-MS). Additionally, it reviews the application of machine learning in food flavor analysis. Through research, it has been found that different scenarios of food flavor analysis require different machine learning methods. Machine learning holds significant potential for enhancing food quality, safety and consumer satisfaction. The combination of multiple machine learning models and analytical techniques will play a crucial role in food flavor analysis.

At present, the development of renewable energy has become an important part of the global sustainable energy strategy. Hydrogen is the cleanest energy in the world and is considered to be the most promising alternative energy. Industrial hydrogen production contains a large number of impurities. Therefore, the purification of hydrogen is an indispensable part of the use of hydrogen energy. Palladium and its alloy membranes are the most common materials for hydrogen separation, but they are too expensive and insufficient in yield. We need to find new hydrogen separation membranes with excellent performance. It is found that the hydrogen permeability coefficient of V/Nb and its alloy membranes is much larger than that of Pd, and the cost is lower than that of Pd metal, which is the best hydrogen separation membrane material to replace Pd metal. At present, there are many studies on V/Nb-based alloy membranes. This paper introduces the principle of hydrogen permeation of alloy membranes, the preparation methods of hydrogen separation membranes and their advantages and disadvantages, as well as the research status of V/Nb-based alloys in recent years, and looks forward to the future research and development trend of hydrogen separation membranes. [ABSTRACT FROM AUTHOR]

Copyright of Shipin Kexue/ Food Science is the property of Food Science Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract The near-infinite compositional space of high-entropy-alloys (HEAs) is a huge resource-intensive task for developing exceptional materials. In the present study, an algorithmic framework has been developed to optimize the composition of an alloy with chosen set of elements, aiming to maximize the hardness of the former. The influence of phase on hardness prediction of HEAs was thoroughly examined. This study aims to establish generalized prediction models that aren’t confined by any specific set of elements. We trained the HEA identification model to classify HEAs from non-HEAs, the multi-labeled phase classification model to predict phases of HEAs also considering the processing route involved in the synthesis of the alloy, and the hardness prediction model for predicting hardness and optimizing the composition of the given alloy. The purposed algorithmic framework uses twenty-nine alloy descriptors to compute the composition that demonstrates maximum hardness for the given set of elements along with its phase(s) and a label stating whether it is classified as HEA or not.

Water-based aerosol is widely used as an effective strategy in electro-optical countermeasure on the battlefield used to the preponderance of high efficiency, low cost and eco-friendly. Unfortunately, the stability of the water-based aerosol is always unsatisfactory due to the rapid evaporation and sedimentation of the aerosol droplets. Great efforts have been devoted to improve the stability of water-based aerosol by using additives with different composition and proportion. However, the lack of the criterion and principle for screening the effective additives results in excessive experimental time consumption and cost. And the stabilization time of the aerosol is still only 30 min, which could not meet the requirements of the perdurable interference. Herein, to improve the stability of water-based aerosol and optimize the complex formulation efficiently, a theoretical calculation method based on thermodynamic entropy theory is proposed. All the factors that influence the shielding effect, including polyol, stabilizer, propellant, water and cosolvent, are considered within calculation. An ultra-stable water-based aerosol with long duration over 120 min is obtained with the optimal fogging agent composition, providing enough time for fighting the electro-optic weapon. Theoretical design guideline for choosing the additives with high phase transition temperature and low phase transition enthalpy is also proposed, which greatly improves the total entropy change and reduce the absolute entropy change of the aerosol cooling process, and gives rise to an enhanced stability of the water-based aerosol. The theoretical calculation methodology contributes to an abstemious time and space for sieving the water-based aerosol with desirable performance and stability, and provides the powerful guarantee to the homeland security.

Copyright of New Building Materials / Xinxing Jianzhu Cailiao is the property of New Building Materials Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this study, we optimized the traditional composition of AISI 8630 steel and evaluated its corrosion resistance through a series of tests. We conducted corrosion tests in a 3.5% NaCl solution and performed a 720 h fixed-load tensile test in accordance with the NACE TM-0177-2016 standard to assess sulfide stress corrosion cracking (SSCC). To analyze the corrosion products and the structure of the corrosion film, we employed X-ray diffraction and transmission electron microscopy. The corrosion rate, characteristics of the corrosion products, structure of the corrosion film, and corrosion resistance mechanism of the material were investigated. The results indicate that the optimized AISI 8630 material demonstrates excellent corrosion resistance. After 720 h of exposure, the primary corrosion products were identified as chromium oxide, copper sulfide, iron oxide, and iron–nickel sulfide. The corrosion film exhibited a three-layer structure: the innermost layer with a thickness of 200–300 nm contained higher concentrations of alloying elements and formed a dense, cohesive rust layer that hindered the diffusion of oxygen and chloride ions, thus enhancing corrosion resistance. The middle layer was thicker and less rich in alloying elements, while the outer layer, approximately 300–400 nm thick, was relatively loose.

Ti–V-based hydrogen storage alloys have garnered interest owing to their salient advantages of reversible hydrogen absorption/desorption at room temperature and excellent kinetic properties. However, the complicated smelting technology of high-purity V metal renders expensive prices, hampering their large-scale production and application. Herein, to overcome this hurdle, abundant and inexpensive Mn and Fe are added to obtain Ti–V–Mn–Fe hydrogen storage alloys. The novel alloy compositions were obtained using a uniform design method. The hydrogen absorption/desorption behaviors of alloys prepared using arc melting technology were analyzed in detail. Systematic investigations show that the activation, kinetics, and thermodynamic properties of alloys are closely correlated with the Ti/(Mn + Fe) ratios. The designed alloy compositions have a multiphase structure with a body-centered cubic (BCC) main phase. The lattice parameters of the BCC phase increase with increasing Ti/(Mn + Fe) ratio, and the large lattice parameters of alloys are beneficial to facilitate the diffusion of hydrogen atoms, resulting in fast kinetics. A reasonable Ti/(Mn + Fe) ratio positively affects the hydrogen storage properties. Among them, the Ti 40 V 40 Mn 2 Fe 18 alloy with Ti/(Mn + Fe) = 2 achieved a hydrogen absorption capacity of 3.472 wt% at 298 K and an effective reversible hydrogen storage capacity of 1.485 wt% above 0.1 MPa at 363 K. This study provides a novel insight for designing Ti–V-based alloy compositions via the uniform design method. [Display omitted] • Adding Fe and Mn to obtain Ti–V–Mn–Fe alloys. • Introducing uniform design method for composition optimization. • The activation, kinetics, and thermodynamic properties of alloys were analyzed. • The optimized Ti 40 V 40 Mn 2 Fe 18 alloy exhibits superior hydrogen storage properties. [ABSTRACT FROM AUTHOR]

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy's components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm−3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys. [ABSTRACT FROM AUTHOR]

The work is aimed at research of technology of lightweight concrete with porous aggregate with improved strength characteristics to expand the field of its application in reinforced concrete structures. The lack of demand for lightweight concrete in structural construction is due to its low reliability in view of the increased ultimate deformation of compression and tension. However, as recent studies show, when taking into account the ultimate compressive strain of lightweight concrete, it is quite possible to ensure the reliability of such structures. In order to expand the scope of application of light concretes in bearing layers of multilayered enclosing and bearing reinforced concrete structures, our purpose was to determine the features of the lightweight concrete composition selection technique with improved characteristics using a porous aggregate of modified zeolite-alkaline charge. Empirical equations of dependence of lightweight concrete strength on concrete density and aggregate density as well as dependence of lightweight concrete strength on coarse aggregate volume concentration and aggregate strength were established to determine rational compositions of lightweight concrete. The possibility of producing structural lightweight concrete of strength classes B15 to B27.5, with the frost-resistance mark F₁ 150 with the use of porous aggregate modified zeolite-alkaline batch is shown.

Al–Cu–Mg–Fe–Ni alloys are widely used in high temperature structural materials such as automobile engine cylinders and pistons, owning to their good high temperature mechanical properties. In this work, the effect of Fe and Ni content, solution treated, and aged process on the microstructure and mechanical properties of Al–Cu–Mg–Fe–Ni alloys were investigated by optical microscopy (OM), scanning electron microscopy (SEM), hardness and tensile tests. Transmission electron microscope (TEM) and high–resolution TEM were used to determine the existence of S phase and Al9FeNi precipitates. The solution treatment followed by aged process is found to be of significant benefit in improving the tensile properties both at room– and elevated temperature.

As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions. [ABSTRACT FROM AUTHOR]

As greener and carbon-friendly material, geopolymer grouting material has a broad application potential in the field of road grouting and is expected to replace cement grouting material in the future. In order to explore the effects of the kinds and dosages of each composition on the performance of geopolymer grouting materials, three kinds of silica-aluminum sources (fly ash, slag powder and coal gangue powder) were used to prepare road geopolymer grouting materials by different alkali activators. The mix proportion range of road geopolymer grouting materials was optimized by single factor test. X-ray diffraction (XRD), field emission scanning electron microscopy (FE-SEM), energy dispersive spectroscopy (EDS) and Fourier transform infrared spectroscopy ( FTIR) were used to analyze the phase composition, micro morphology, energy spectrum and chemical bond composition of road geopolymer grouting materials. The results show that road geopolymer grouting material with the best performance is prepared with fly ash and slag powder as raw materials, KOH and sodium silicate solution as composite alkali activator. The recommended mix proportion range of road geopolymer grouting material is: fly ash and slag powder mass ratio is 4 : 6, sodium silicate dosage is 10% ~ 15% (mass fraction), KOH dosage is 5% ~ 11% (mass fraction), water-cement ratio is 0. 50 ~ 0. 65. The fly ash and slag powder are dissolved and regenerated by alkali activation to form amorphous gel products such as C-(A)-S-H, and finally formed SiO4 and AlO4 tetrahedron network structure, accompanied by zeolite like phase, carbonate, calcium hydroxide and other substances. These substances can make geopolymer paste compact and promote its strength growth. [ABSTRACT FROM AUTHOR]

In this paper, we study stochastic optimization of two-level composition of functions without Lipschitz continuous gradient. The smoothness property is generalized by the notion of relative smoothness which provokes the Bregman gradient method. We propose three stochastic composition Bregman gradient algorithms for the three possible relatively smooth compositional scenarios and provide their sample complexities to achieve an ϵ -approximate stationary point. For the smooth of relatively smooth composition, the first algorithm requires O (ϵ - 2) calls to the stochastic oracles of the inner function value and gradient as well as the outer function gradient. When both functions are relatively smooth, the second algorithm requires O (ϵ - 3) calls to the inner function value stochastic oracle and O (ϵ - 2) calls to the inner and outer functions gradients stochastic oracles. We further improve the second algorithm by variance reduction for the setting where just the inner function is smooth. The resulting algorithm requires O (ϵ - 5 / 2) calls to the inner function value stochastic oracle, O (ϵ - 3 / 2) calls to the inner function gradient, and O (ϵ - 2) calls to the outer function gradient stochastic oracles. Finally, we numerically evaluate the performance of these three algorithms over two different examples. [ABSTRACT FROM AUTHOR]

In order to efficiently utilize the waste bituminous pavement materials and waste bio-oil, the composition of bio-oil rejuvenator was optimized. The rejuvenation efficiency of the waste bio-oil rejuvenator was evaluated from rheological properties and chemical composition. In addition, a quantitatively evaluation index for the rejuvenation efficiency was proposed. The results show that the central composite design and response surface methodology is effective in optimizing composition of rejuvenators. The dosage of waste bio-oil in the rejuvenated bitumen reached 9%. Compared to another two commercial rejuvenators, waste bio-oil rejuvenator rejuvenated the aged bitumen more effectively. Its rejuvenation efficiency for rheological parameters of aged bitumen was close to 100%. The waste bio-oil rejuvenator restored the performance of aged bitumen by reducing the proportion of macromolecular substances. The rejuvenation efficiency of aromatics, colloidal instability index and carbonyl functional groups were 57.1%, 126.9% and 78.7% respectively. [ABSTRACT FROM AUTHOR]

In the aerospace sector, the development of lightweight aircraft heavily relies on the utilization of advanced aluminum–lithium alloys as primary structural materials. This study introduces an investigation aimed at optimizing the composition of an Al-2.32Li-1.44Cu-2.78Mg-0.3Ag-0.3Mn-0.1Zr alloy. The optimization process involves the selection of alloying elements through the application of machine learning techniques, with a focus on expected improvements in the specific modulus of these alloys. Expanding upon the optimization of the benchmark alloy’s components, a more generalized modulus prediction model for Al–Li alloys was formulated. This model was then employed to evaluate the anticipated specific modulus of alloys within a virtual search space, encompassing substitutional elements. The study proceeded to validate six Al–Li alloys with a notably high potential for achieving an improved specific modulus. The results revealed that an alloy incorporating 0.96 wt.% of Ga as a substitutional element exhibited the most favorable microstructure. This alloy demonstrated optimal tensile strength (523 MPa) and specific modulus (31.531 GPa/(g·cm−3)), closely resembling that of the benchmark alloy. This research offers valuable insights into the application of compositional optimization to enhance the mechanical properties of Al–Li alloys. It emphasizes the significance of selecting alloying elements based on considerations such as their solid solubility thresholds and the expected enhancement of the specific modulus in Al–Li alloys.

Sinter composition optimization is an important process of iron and steel companies. To increase companies’ profits, they often rely on innovative technology or the workers’ operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process.

Lightweight high-temperature titanium alloys are a key material for aero-engines. With the increasing use of new titanium alloys in aero-engines, titanium fire has become a typical catastrophic fault that plagues material design and selection. A burn-resistant titanium alloy is a special material developed to deal with the problem of titanium fire. Its application in aero-engines has become one of the key technologies for the prevention and control of titanium fire. Therefore, explaining the influence of the alloying elements of burn-resistant titanium alloys on mechanical properties is important to provide an important theoretical basis for the design and application of these alloys. Based on the experimental data, the relationship model between the alloying elements and mechanical properties of a burn-resistant titanium alloy was established using a support vector machine algorithm, and the effect of the alloying elements on the mechanical properties was analyzed. The input parameters of the model were V, Al, Si, and C elements, and the output parameters were the room temperature tensile properties (tensile strength, yield strength, elongation, and the reduction of area). Results show that the linear correlation coefficient of each mechanical property of the SVM model is above 0.975, which signifies good prediction ability. The absolute percentage error between the predicted and experimental values of each mechanical property test sample is within 5%, indicating good generalization ability and an effective reflection of the quantitative relationship between the alloying elements and mechanical properties of the burn-resistant titanium alloy for optimizing the composition of the alloy. The mechanical properties of the Ti–35V–15Cr alloy can be improved by adding 0–0.1% Si element and 0.05%–0.125% C element and reducing 2%–5% V element. Meanwhile, the mechanical properties of the Ti–25V–15Cr alloy can be improved by adding 1.5%–1.8% Al element and 0.15%–0.2% C element.

5083-aluminum alloy with different Mg and Mn contents was designed in this study. The effects of Mg and Mn contents on the microstructure, mechanical properties and corrosion resistance of 5083 aluminum alloy were studied via room temperature tensile test, hardness test and exfoliation corrosion test, and the optimal composition was obtained. When Mg content is 4.3%-4.4%, 5083 has higher mechanical properties and corrosion resistance. When Mn content is 0.7%-0.8%, 5083 shows higher mechanical properties; When Mn content is 0.5%-0.6%, 5083 alloy shows lower exfoliation corrosion. The 5083-aluminum alloy with excellent mechanical properties and corrosion resistance obtained under laboratory conditions has a Mg content of 4.38% and a Mn content of 0.61%. The tensile strength of the alloy at the annealed state is 209.1 MPa, the hardness is 87 HV, the elongation after fracture is 16.04%, and the corrosion grade is PA. Mg can form Mg2Al3 phase in 5083 alloy, and the composite phases Al-Mg-Cr and Al-Mg-Mn-Fe are brittle hard phases, which will reduce the plasticity and toughness of 5083 alloy. Mn can form AlMn0.75 Fe2.25, Fe19Mn and other phases in 5083. The interface between Al-Mg-Mn-Fe, Al-Mn-Fe-Si, Al-Mn-Fe composite phase and Al matrix is the main source of exfoliation corrosion. [ABSTRACT FROM AUTHOR]

Scintillators in general and organic liquid scintillator specifically are widely used as a medium for the detection of charged particles for numerous applications in science, medicine, engineering, and other areas. The composition of the scintillator affects not only its direct performance characteristics, but also the overall cost. Optimization of this composition provides the ability to design particle detectors with an optimized light yield and emission spectra of the detection medium while optimizing the expenses at the same time. This article describes work on toluene-based liquid scintillator component optimization, where PPO is used as a fluor and POPOP as a shifter. The light yield vs. concentration and the changes in the output spectra will be presented. The empirical fit of the output spectrum using the measured contributions of the components is discussed. Further plans include the light attenuation measurements for different compositions. [ABSTRACT FROM AUTHOR]

It is well-known that the −70 °C ultra-low temperature freezers have been widely concerned for vaccine and biomedical products storage due to the outbreak of COVID-19. This paper carries out the experimental investigation on performance characteristics of a −70 °C ultra-low temperature medical freezer with single-stage Joule-Thomson refrigeration system by using binary hydrocarbon mixture R601a/R1150 and ternary mixture R601a/R290/R1150. The aim is to explore and verify hydrocarbon refrigerants that can achieve lower energy consumption, thereby providing a scientific basis for the optimization of ultra-low temperature medical freezers. The experimental results show that both R601a/R1150 and R601a/R290/R1150 with a charge of 250 g could achieve the refrigeration temperature below −70 °C in a 568 L large volume vertical freezer. Comparing binary with ternary refrigerants, it is found that the addition of medium-boiling component R290 is beneficial to reduce the daily energy consumption. When the mass fraction of R601a/R1150 is 0.70/0.30, the lowest daily energy consumption for −70 °C is 9.06 kW h·24 h−1. When the mass fraction of the R601a/R290/R1150 is 0.70/0.10/0.20, the daily energy consumption is 8.42 kW h·24 h−1, which is 7.06 % lower than that when using the R601a/R1150. This indicates that careful selection of refrigerant components and composition optimization can lead to substantial energy savings. Furthermore, it is also observed that among these three components, the low-boiling component R1150 has the most significant influence on the system pressures and operating power, followed by R601a and R290. • R601a/R1150 and R601a/R290/R1150 for J-T system could reach temperature below −70 °C. • The addition of R290 is beneficial to reduce system energy consumption. • The appropriate mass fraction range for R290 is 0.10–0.15. • Low-boiling component R1150 has the greatest impact on system pressure and power. [ABSTRACT FROM AUTHOR]

• Without any design change, drop-in sustainable aviation fuel (SAF100) can be tailored to promote energy savings of at most 0.33 %. Such fuel would have an energy density of 34.3 GJ/m3 which is just inside the ± 2σ experience interval of conventional jet fuel; 34.0–35.6 GL/m3. • By eliminating the minimum aromatics content imposed on SAF100, optimized fuel could afford at most 0.40% energy savings for a fuel with an energy density of 34.2 GJ/m3, or 0.33% energy savings for a fuel with an energy density of 35.7 GJ/m3. • By raising the minimum energy density requirement (or design point reference fuel) by 4% and increasing the lower heating value of the design point reference fuel by 1%, both of which are attainable for optimized, 100% SAF without aromatics (Jet-X), supersonic aircraft can be made at most 2.3% more energy efficient. • The composition and property characteristics of optimized SAF100 and Jet-X are discussed at length in this article. Whether the feedstock for sustainable aviation fuel (SAF) originates from agriculture or from waste streams, life cycle CO 2 emissions per unit enthalpy are lower for SAF than they are for petroleum distillates primarily because of differences on the front end such as fostered growth of crops or decreased demand for resources or acreage to manage wastes. This work, however, is concerned with what happens on the consumption side. Sustainable aviation fuel is required by ASTM D4054 / D7566 to meet a higher thermal stability standard than petroleum distillate fuels and this characteristic can be leveraged to improve energy efficiency in new engine or aircraft designs where a commitment has been made to burning fuel that meets a specification beyond that of conventional JetA. Beyond thermal stability, non-drop-in SAF (Jet-X) developers have the opportunity to further increase the value of their product by infusing higher-than-conventional-JetA energy density (enthalpy per unit volume, ED) into their SAF. Finally, fuel specific energy (enthalpy per unit mass, LHV) has a direct impact on aircraft efficiency which we have determined to be 0.43% per MJ/kg increase in LHV depending on the mission and aircraft model, and this is applicable to both drop-in and non-drop-in applications. While higher energy density fuels may be leveraged in a new aircraft design to decrease drag and weight, aircraft development potential with reduced tank volumes is typically constrained by other factors such as wing packaging, passenger volume requirements and overall center of gravity and flight control law restrictions. [ABSTRACT FROM AUTHOR]

We consider a distributionally robust formulation of stochastic optimization problems arising in statistical learning, where robustness is with respect to ambiguity in the underlying data distribution. Our formulation builds on risk-averse optimization techniques and the theory of coherent risk measures. It uses mean–semideviation risk for quantifying uncertainty, allowing us to compute solutions that are robust against perturbations in the population data distribution. We consider a broad class of generalized differentiable loss functions that can be non-convex and non-smooth, involving upward and downward cusps, and we develop an efficient stochastic subgradient method for distributionally robust problems with such functions. We prove that it converges to a point satisfying the optimality conditions. To our knowledge, this is the first method with rigorous convergence guarantees in the context of generalized differentiable non-convex and non-smooth distributionally robust stochastic optimization. Our method allows for the control of the desired level of robustness with little extra computational cost compared to population risk minimization with stochastic gradient methods. We also illustrate the performance of our algorithm on real datasets arising in convex and non-convex supervised learning problems. [ABSTRACT FROM AUTHOR]

In the paper, we study a class of novel stochastic composition optimization problems over Riemannian manifold, which have been raised by multiple emerging machine learning applications such as distributionally robust learning in Riemannian manifold setting. To solve these composition problems, we propose an effective Riemannian compositional gradient (RCG) algorithm, which has a sample complexity of O (ϵ − 4) for finding an ϵ -stationary point. To further reduce sample complexity, we propose an accelerated momentum-based Riemannian compositional gradient (M-RCG) algorithm. Moreover, we prove that the M-RCG obtains a lower sample complexity of O ̃ (ϵ − 3) without large batches, which achieves the best known sample complexity for its Euclidean counterparts. Extensive numerical experiments on training deep neural networks (DNNs) over Stiefel manifold and learning principal component analysis (PCA) over Grassmann manifold demonstrate effectiveness of our proposed algorithms. To the best of our knowledge, this is the first study of the composition optimization problems over Riemannian manifold. [ABSTRACT FROM AUTHOR]

Sinter composition optimization is an important process of iron and steel companies. To increase companies' profits, they often rely on innovative technology or the workers' operating experience to improve final productions. However, the former is costly because of patents, and the latter is error-prone. In addition, traditional linear programming optimization methods of sinter compositions are inefficient in the face of large-scale problems and complex nonlinear problems. In this paper, we are the first to propose a regressive convolutional neural network (RCNN) approach for the sinter composition optimization (SCORN). Our SCORN is a single input and multiple outputs regression model. Sinter plant production is used as the input of the SCORN model, and the outputs are the optimized sintering compositions. The SCORN model can predict the optimal sintering compositions to reduce the input of raw materials consumption to save costs and increase profits. By constructing a new neural network structure, the RCNN model is trained to increase its feature extraction capability for sintering production. The SCORN model has a better performance compared with several regressive approaches. The practical application of this predictive model can not only formulate corresponding production plans without feeding materials but also give better input parameters of sintered raw materials during the sintering process. [ABSTRACT FROM AUTHOR]

Abstract Magnetic carbon-based composites are the most attractive candidates for electromagnetic (EM) absorption because they can terminate the propagation of surplus EM waves in space by interacting with both electric and magnetic branches. Metal-organic frameworks (MOFs) have demonstrated their great potential as sacrificing precursors of magnetic metals/carbon composites, because they provide a good platform to achieve high dispersion of magnetic nanoparticles in carbon matrix. Nevertheless, the chemical composition and microstructure of these composites are always highly dependent on their precursors and cannot promise an optimal EM state favorable for EM absorption, which more or less discount the superiority of MOFs-derived strategy. It is hence of great importance to develop some accompanied methods that can regulate EM properties of MOFs-derived magnetic carbon-based composites effectively. This review comprehensively introduces recent advancements on EM absorption enhancement in MOFs-derived magnetic carbon-based composites and some available strategies therein. In addition, some challenges and prospects are also proposed to indicate the pending issues on performance breakthrough and mechanism exploration in the related field.

Ultra-high strength martensitic stainless steel plays an essential role in aerospace, nuclear and other fields. This paper addresses laser-based powder bed fusion of martensitic stainless steel, based on thermodynamic software and computer languages for high-throughput calculation in combination with the hot tear sensitivity index criterion, Optimization of the alloy composed of Fe11Cr8Ni5Co3Mo martensitic stainless steel for resistance to hot tearing. The martensitic stainless steel composition with the lowest crack sensitivity was selected from hundreds of millions of data sets, reducing the hot tearing sensitivity index from 6841 to 1861. The alloy with the lowest hot tearing sensitivity was Fe11.3Cr8.18Ni2.0Mo2.8Co0.24Mn0.02V0.02Si0.01C0.17Mg. The hot tearing sensitivity has been experimentally verified in conjunction with additive manufacturing, and a comparative study of the building state organization and mechanical properties has been made.

As the fourth paradigm of materials research and development, the materials genome paradigm can significantly improve the efficiency of research and development for austenitic stainless steel. In this study, by collecting experimental data of austenitic stainless steel, the chemical composition of austenitic stainless steel is optimized by machine learning and a genetic algorithm, so that the production cost is reduced, and the research and development of new steel grades is accelerated without reducing the mechanical properties. Specifically, four machine learning prediction models were established for different mechanical properties, with the gradient boosting regression (gbr) algorithm demonstrating superior prediction accuracy compared to other commonly used machine learning algorithms. Bayesian optimization was then employed to optimize the hyperparameters in the gbr algorithm, resulting in the identification of the optimal combination of hyperparameters. The mechanical properties prediction model established at this stage had good prediction accuracy on the test set (yield strength: R2 = 0.88, MAE = 4.89 MPa; ultimate tensile strength: R2 = 0.99, MAE = 2.65 MPa; elongation: R2 = 0.84, MAE = 1.42%; reduction in area: R2 = 0.88, MAE = 1.39%). Moreover, feature importance and Shapley Additive Explanation (SHAP) values were utilized to analyze the interpretability of the performance prediction models and to assess how the features influence the overall performance. Finally, the NSGA-III algorithm was used to simultaneously maximize the mechanical property prediction models within the search space, thereby obtaining the corresponding non-dominated solution set of chemical composition and achieving the optimization of austenitic stainless-steel compositions.

In order to improve the efficiency of manufacturing service composition optimization in the cloud manufacturing environment, this paper proposed a composition optimization method based on the modified polar bear algorithm. This method encoded the manufacturing services in real numbers, took the service function and service quality as evaluation indicators, a nd used the modified polar bear algorithm to solve the optimization problem of manufacturing cloud service portfolio, a nd obtained the optimal service portfolio plan. At the same time, this article used the dynamic vision to adjust the local search of the algorithm, and combined it with the mutation strategy in the genetic algorithm. Through these improvements improved the efficiency of the algorithm for solving multi-objective problems and reduced the possibility of the algorithm falling into local optimum due to the influence of initial parameters. Example analysis shows that compared with the original polar bear algorithm, the standard genetic algorithm, the improved gray wolf optimizer and the improved particle swarm optimization algorithm, the modified polar bear algorithm is more efficient in solving the problem of manufacturing cloud service portfolio optimization. [ABSTRACT FROM AUTHOR]

High strength and large ductility of existing Mn-containing brass alloys need to be further improved when used as slippers of friction-pair materials, which could be achieved by tuning alloy composition and thermomechanical treatments appropriately. The present work optimized the amount of minor-alloying elements M (M = Mn, Fe, Si) in Cu-Zn alloy via a cluster formula approach and then investigated the microstructural evolution of the designed alloy with different thermomechanical treatments. As-cast alloy ingots were solid-solutioned at 1093 K (820 °C) for 3 h, hot-rolled at 923 ~ 1023 K (650 ~ 750 °C), and then aged at 673 ~ 723 K (400 ~ 450 °C) for 1 ~ 2 h, respectively. It is found that the alloy matrix consists of the main FCC-α phase plus a small amount of BCC-β and M5Si3 phases, among which the M5Si3 exhibits three types of primary, fine, and nano-scaled particles. The mechanical property varies with the thermomechanical treatments due to diverse microstructures (especially the morphology of M5Si3 particles), in which the high strength (σUTS > 580 MPa) and large ductility (δ = 16.3 ~ 29.4%) could be achieved simultaneously in 673 K (400 °C). The optimal matching of high strength and large ductility makes the current alloy more suitable as an alternative slipper material. The strengthening effect was further discussed in light of various strengthening mechanisms, and the calculated strength increment is well consistent with the experimentally measured yield strength. [ABSTRACT FROM AUTHOR]

Optimization of the formulation of synbiotic yogurt ice cream low-lactose using lactose-free protein concentrate of buttermilk and yogurt with low lactose content is the goal of expanding the range of low-lactose dairy products and improving the functional and health properties of ice cream. Low-lactose ice cream formulation optimization was performed using a gradient numerical method, namely conjugated gradients (Conjugate Gradient). The optimization algorithm is implemented in Mathcad. An array of data with a set of indicators for the choice of a rational ratio of lactose-free protein concentrate of buttermilk and yogurt base and inulin content for ice cream mixtures is presented. The influence of the ratio of the main components of the mixtures on the foaming ability, which determines the quality of the finished product, has been studied. An important indicator is taken into account – the concentration factor of buttermilk, which is additionally purified from lactose by diafiltration. The graphic material presented in the work clearly demonstrates that the rational ratio of yogurt base and lactose-free protein concentrate of buttermilk, obtained by ultrafiltration with diafiltration purification at a concentration factor of FC = 5 is 40.6: 59.4. The content of additional components included in the recipe of a new type of ice cream is optimized in the work, the mass fractions of which were: inulin – 3.69 %; lactulose – 1 %; ginger – 0.3 %; citric acid – 0.15 %; stabilization system – 0.2 %. The chemical composition and quality indicators of the mixture for ice cream low-lactose synbiotic yogurt, consisting of raw materials in the optimal ratio, were determined. The lactose content in the test sample of the ice cream mixture was 0.99%, the antioxidant activity was 3.1 times higher than in the mixture for traditional yogurt ice cream. The most likely number of lactic acid microorganisms, CFU / cm3 is (2.8 ± 0.9) · 108, the number of bifidobacteria, CFU / cm3 is (2.5 ± 0.2) · 109. The results of the research will be implemented in dairy companies in the production of ice cream

Scintillators in general and organic liquid scintillator specifically are widely used as a medium for the detection of charged particles for numerous applications in science, medicine, engineering, and other areas. The composition of the scintillator affects not only its direct performance characteristics, but also the overall cost. Optimization of this composition provides the ability to design particle detectors with an optimized light yield and emission spectra of the detection medium while optimizing the expenses at the same time. This article describes work on toluene-based liquid scintillator component optimization, where PPO is used as a fluor and POPOP as a shifter. The light yield vs. concentration and the changes in the output spectra will be presented. The empirical fit of the output spectrum using the measured contributions of the components is discussed. Further plans include the light attenuation measurements for different compositions.

To alleviate the influence of extreme temperature on the service quality and life of asphalt pavement, the heat insulation effect of the pavement bonding layer was endowed, and the composition of the heat insulation bonding layer materials were optimized. The microstructure of heat insulation bonding layer material was revealed, and its optimal spraying amounts was determined. On this basis, the engineering application and service performance monitoring of heat insulation bonding layer materials were carried out. The results show that the curing rate of the heat insulation bonding layer is significantly higher than that of the ordinary emulsified asphalt (OEA) bonding layer. Hollow glass bead (HGB), floating bead (FB), and expanded perlite (EP) bonding layer materials exhibit excellent mechanical strength, and the interlayer bonding strength can reach 0.41 MPa, 0.49 MPa, and 0.49 MPa, respectively. The recommended dosage of HGB, FB, and EP is 15%, 20%, and 20%, respectively, and the spraying amounts of heat insulation bonding layer material is 1.2 kg/m2. The maximum temperature of the test section in July can be reduced by 2.2 °C–3.6 °C, the maximum annual and daily temperature differences can be reduced by 6.2 °C and 2.3°C–4.9 °C, and the rate of temperature changes can be reduced by 37.5%–63.64%. • The heat insulation effect of the pavement bonding layer was endowed. • Performance revelation and composition optimization of bonding layer materials. • The engineering application of heat insulation bonding layer materials. • On-site monitoring and analysis of heat insulation effect of bonding layer. [ABSTRACT FROM AUTHOR]

To find ternary Cu-based catalysts (Cu/Cu 2 O/CuO) compositions with the highest M2 selectivity(S M2) in Rochow-Müller reaction, a machine learning (ML) framework including prediction and optimization models was created. The Stacking integration approach was used to describe the relationship between the catalyst's structures features and S M2 for prediction model. Further, the composition space was effectively searched for compositions with the highest S M2 by optimization model with the genetic algorithm (GA) in the base of prediction model. Finally, the greatest S M2 catalyst compositions were effectively suggested by the ML framework, and the constructed catalyst had an S M2 of 82.35%, with a relative error of only 0.93% from the predicted value. ML model combined with experimental characterization was used to infer the synergistic mechanism of the ternary Cu-based catalysts. Cu/Cu 2 O and Cu 2 O/CuO contact surfaces can create heterojunction and Schottky junction structures, respectively, to facilitate the transport of electron-hole pairs and the formation of active spices. ML framework achieved compositional optimization of catalysts and were successfully integrated with materials science theory. [Display omitted] • ML models predict precisely the M2 selectivity of ternary Cu-based catalyst. • Properties calculation optimized by GA have good agreement with experiments. • Analysis of synergistic effects by ML models combined with experiments characterization. • Analysis of the synergistic mechanism of ternary compositions of Cu/Cu 2 O/CuO. [ABSTRACT FROM AUTHOR]

With the flourishing development of advanced wireless technology, the situation of electromagnetic pollution and interference is becoming increasingly severe. The research and development of efficient electromagnetic wave absorption materials is extraordinary beneficial for solving these disgusting issues. Nitrogen (N)-doped carbons have shown great advantages as absorbents, such as lightweight, high surface area, excellent hydrophilicity, unique electronic properties, etc. Herein, we provide a perspective on state-of-art advances and challenges of N-doped carbons based absorbents. The effect of N-doping on the electromagnetic wave (EWs) response are dissected from the N-configurations and concentrations, which are of significant importance in understanding of electromagnetic response. And then we center the regulation of the N-doped carbon in the EWs responses by combing the eye-catching advances in N-doped carbon based EWs absorbing nano-materials from constituents and nanostructures. Finally, the outlook and challenges of N-doped carbons as absorbents for future research are proposed. This perspective combs the electronic structures of N configurations in N-doped carbons, and their electromagnetic responses are related to the N configurations. [Display omitted] • Effect of N-doping on the electrical and dielectric properties of carbons are dissected. • Regulation of N-doped carbon in the EWs responses is combed from constituents and nanostructures. • Bottlenecks and outlook of N-doped carbons as absorbents are proposed. [ABSTRACT FROM AUTHOR]

A two-scale calculation method is described that makes it possible to optimize the material composition of machine components and structural elements on the basis of the requirements for their deformation-strength and tribotechnical characteristics. The proposed method provides the combined use of analytical micromechanical modelling of structurally inhomogeneous structural materials in the form of disperse-reinforced composites and numerical (finite element) analysis of the stress-strain state of a particular product. The advantage of the method is ensuring of maximum strength, stiffness and wear resistance of the product with simultaneous correction of its geometric shape and the possibility of using the resulting refined CAD model for 3D printing of components of complex shape by extruded composites of optimized composition.

Deterioration of ancient masonry is a contemporary problem. The initial properties of the masonry material that determine its durability, deterioration and degree of preservation have led to the appearance of different approaches towards choosing the technology for restoring masonry. The success of any restoration is largely determined by its compatibility with the original materials used, which requires, as a rule, a complex and long process of selecting their composition. One of the main technological approaches in the science of restoration materials is to search for the optimal composition of the material within a given time limit. This paper presents an approach which uses a large number of variables within strict boundary conditions. The solution to this problem can be found in the field of mathematical modelling using experiment planning methods. The paper presents a method developed for mortar optimization which makes it possible to obtain the desired result within a relatively short period of time.

Highlights: This review introduces recent advances to optimize electromagnetic properties of metal-organic frameworks (MOFs)-derived magnetic carbon-based composites through rational microstructure design and composition optimization in detail. The challenges and outlooks in MOFs-derived magnetic carbon-based microwave absorbers are also proposed and analyzed, including low-frequency absorption, diversified MOFs precursors, structure-activity relationships, environmental tolerance.Magnetic carbon-based composites are the most attractive candidates for electromagnetic (EM) absorption because they can terminate the propagation of surplus EM waves in space by interacting with both electric and magnetic branches. Metal-organic frameworks (MOFs) have demonstrated their great potential as sacrificing precursors of magnetic metals/carbon composites, because they provide a good platform to achieve high dispersion of magnetic nanoparticles in carbon matrix. Nevertheless, the chemical composition and microstructure of these composites are always highly dependent on their precursors and cannot promise an optimal EM state favorable for EM absorption, which more or less discount the superiority of MOFs-derived strategy. It is hence of great importance to develop some accompanied methods that can regulate EM properties of MOFs-derived magnetic carbon-based composites effectively. This review comprehensively introduces recent advancements on EM absorption enhancement in MOFs-derived magnetic carbon-based composites and some available strategies therein. In addition, some challenges and prospects are also proposed to indicate the pending issues on performance breakthrough and mechanism exploration in the related field. [ABSTRACT FROM AUTHOR]

Epoxy resin is a general term for a class of thermosetting polymers containing two or more epoxy groups in the molecule and has an excellent comprehensive performance. The properties of the resin system vary greatly due to the different compositions of the base resin, curing agent, and toughening agent. In this study, an optimization method for the multi-component epoxy resin system was put forward by using molecular dynamics simulations and machine learning methods. An optimized high- performance epoxy resin system considered Young's modulus (E), Ultimate Tensile Strength (UTS), Elongation (δ), and the glass transition temperature (Tg) together was designed by using the proposed method. The influence of each component proportion on mechanical properties can also be obtained automatically. It was found that 4,4′-Diaminodiphenyl Sulfone (DDS) was a better curing agent to improve Tg, E, and δ, compared with Dicyandiamide (DICY). Tetraglycidyl Diamino Diphenylmethane (TGDDM) could ensure high Tg, E and UTS, but the system still needed some Diglycidyl Ether of Bisphenol A (DGEBA) to improve toughness. The toughening agent Polyether Sulfone (PES) improved the toughness of the epoxy resin system significantly. The presented method could be extended to other resin system composition optimization.

This paper reviewed the recent development of primary carbides in H13 steel from the aspects of solidification segregation theory, solidification method, production process, and alloy design. The relationship between the production process of H13 steel and the characteristics of primary carbides was clarified. During the solidification of H13 steel, primary carbides can be easily generated by dendritic segregation. The primary carbides in H13 steel can be divided into polygonal, stripy, blocky, and eutectic structures according to the different shapes and can be divided into MC, M2C, M7C3, and M23C6 according to the different structures. The primary carbides can also be classified as Mo-rich, V-rich, and Ti/Nb-rich carbides according to the different compositions. Primary carbides are detrimental to the performance of H13 steel because cracks can easily form around primary carbides during service of the materials. The widely used methods of controlling the primary carbides in H13 steel under industrial production conditions, including solidification control, modification treatment, high-temperature diffusion of the ingot, and alloy composition optimization, were introduced. Modification treatment and solidification control are able to control the size and quantity of primary carbides but are unable to avoid the precipitation of primary carbides entirely. The stability of primary carbides can be relieved by composition optimization. High-temperature homogenization treatment of ingot is the most important means of controlling primary carbides in H13 steel. However, the heating temperature and holding time need further investigation.

A multicomponent lubricant for increasing wear resistance in the wheel–rail system has been developed and studied. The lubricant composition effect on the wear intensity level of the wheel–rail system, the lubricant consumption and the friction coefficient in the wheel–rail system has been assessed based on simplex-lattice composition-property experimental designs. A reasonable composition of a three-component plastic lubricant consisting of 11% foam glass, 32% stearic acid, and 57% bitumen has been obtained. The industrial tests for the developed lubricant efficiency under the conditions of the North Caucasian Railway have been carried out. According to test results, the wear intensity level of the wheel flanges has exhibited a 28.4% decrease, whereas the lubricant consumption has shown a 29.5% decrease. [ABSTRACT FROM AUTHOR]