Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment

The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising solution.Here,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML models.The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and interpretability.Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4%and a coefficient of determination of 0.948.Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control methods.The potential application of these strategies in critical water science domains is discussed.To foster accessibility and promote widespread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

Effects of precipitation strengthening heat treatment for Al-Mg alloy

The corrosions resulting from defects in painting layers frequently occur in Al alloys, so the application of corrosion preventing systems is also very important. Optimum conditions in terms of electrochemistry in relation to solution treatment, quenching and artificial aging treatment were established in order to optimize precipitation strengthening conditions intended to enhance the strength of Al alloys. Slow strain rate tests （SSRT） at various applied potentials were conducted in potential range from -1.8 to 0.5 V. The results show that the maximum tensile strengths, elongations and time-to-fracture are shown to be high values. After precipitation strengthening heat treatment, a tendency appear that time-to-fracture increases as elongation increases. In the potential range from -1.3 V to -0.7 V, the specimens show excellent mechanical properties, and thus this range is considered to be a corrosion prevention range.

Enhanced strengthening by two-step progressive solution and aging treatment in AM50-4%(Zn,Y) magnesium alloy

AM50-4%(Zn,Y)alloy with a Zn/Y mole ratio of6:1was subjected to thermal analysis,and the results were used for designing a two-step progressive solution treatment process.The effects of solution and aging treatments on the microstructure and mechanical properties of the AM50-4%(Zn,Y)alloy were investigated using OM,XRD,SEM/EDS,TEM,tensile test and hardness test.The experimental results demonstrated that the two-step progressive solution treatment could make theΦandβphases sufficiently dissolve into the matrix which possessed higher supersaturated degree of the dissolved solute compared with the one-step solution treatment.This resulted in a certain enhancement of the precipitation strengthening effect during the subsequent aging process.The precipitation of theФphase had a greater impact on the comprehensive mechanical properties of the alloy thanβphase precipitation when the aging treatment was performed at180℃.The peak aging strength of the AM50-4%(Zn,Y)alloy which was subjected to the two-step progressive solution treatment process(345℃for16h and375℃for6h)was obtained after the aging treatment at180℃for12h.

Quantifying Solid Solution Strengthening in Nickel-Based Superalloys via High-Throughput Experiment and Machine Learning

Solid solution strengthening(SSS)is one of the main contributions to the desired tensile properties of nickel-based superalloys for turbine blades and disks.The value of SSS can be calculated by using Fleischer’s and Labusch’s theories,while the model parameters are incorporated without fitting to experimental data of complex alloys.In thiswork,four diffusionmultiples consisting of multicomponent alloys and pure Niare prepared and characterized.The composition and microhardness of singleγphase regions in samples are used to quantify the SSS.Then,Fleischer’s and Labusch’s theories are examined based on high-throughput experiments,respectively.The fitted solid solution coefficients are obtained based on Labusch’s theory and experimental data,indicating higher accuracy.Furthermore,six machine learning algorithms are established,providing a more accurate prediction compared with traditional physical models and fitted physical models.The results show that the coupling of highthroughput experiments and machine learning has great potential in the field of performance prediction and alloy design.

Luster Polish Strengthening Treatment for Raceway Surface of Aeroengine Bearings

A new surface strengthening technology, luster polish strengthening treatment, was proposed to treat the raceway surface of aeroengine bearings （9Cr18Mo） with the centrifugal strengthening machine exclusively designed for luster polish strengthening treatment. The experimental results showed that luster polish strengthening treatment produced a compressive residual stress layer with a depth of over 80 μm below the surface of the bearing raceway, and thus effectively removed the metamorphic layer in the raceway surface. After luster polish strengthening treatment, the average surface hardness of the aeroengine bearing raceway was increased from 61.02 HRC to 63.01 HRC, the surface roughness was reduced from 0.06 μm to 0.03 μm, and the contact fatigue life of the aeroengine bearings was improved by about 90%, with the dispersion of fatigue life being reduced remarkably. Theoretical calculation result agrees with that obtained by experiment.

Computer Aided Design of Heat Treatment for AISI P20+Ni Mold Steel with Good Machinability

Computer aided design of heat treatment for AISI P20 mold steel with good machinability is attempted to proceed by the commercial software package Thermo-Calc (TCP+DICTRA). Through experimental and theoretical analysis of phase transformation during heat treatment, further knowledge of designing proper heat treatment is obtained. Then the machinability of AISI P20+Ni steel under given heat treatment condition is studied and the influencing factors to their machinability are analyzed. It is shown that heat treatment designed by computer simulation of carbide transformation is applicable to AISI P20+Ni steel with good machinability; AISI P20+Ni steel with tempered sorbite treated by quenching & tempering has optimal machinability; normalizing at the temperature of 910°C & tempering can avoid cracking and result in acceptable machinability in small thickness module.

Formability and strengthening mechanism of AA6061 tubular components under solid granule medium internal high pressure forming

A new technological process of tube forming was developed, namely solution treatment → granule medium internal high pressure forming → artificial aging. During this process, the mechanical properties of AA6061 tube can be adjusted by heat treatment to satisfy the process requirements and the processing method can also be realized by granule medium internal high pressure forming technology with the features of convenient implementation, low requirement to equipment and flexible design in product. Results show that, at a solution temperature of 560 ℃ and time of 120 min, the elongation of AA6061 increases by 313%, but the strength and the hardness dramatically decrease. At an aging temperature of 180 ℃ and time of 360 min, the strength and hardness of AA6061 alloy are recovered to the values of the as-received alloy. The maximum expansion ratio（MER） of AA6061 tube increases by 25.5% and the material properties of formed tube reach the performances of raw material.

Screening on oil-decomposing microorganisms and application in organic waste treatment machine

As an oil-decomposable mixture of two bacteria strains(Bacillus sp. and Pseudomonas sp.), Y3 was isolated after 50 d domestication under the condition that oil was used as the limited carbon source. The decomposing rate by Y3 was higher than that by each separate individual strain, indicating a synergistic effect of the two bacteria. Under the conditions that T=25—40℃,pH=6—8, HRT(Hydraulic retention time)=36 h and the oil concentration at 0.1%, Y3 yielded the highest decomposing rate of 95.7 %. Y3 was also applied in an organic waste treatment machine and a certain rate of activated bacteria was put into the stuffing. A series of tests including humidity, pH, temperature, C/N rate and oil percentage of the stuffing were carried out to check the efficacy of oil-decomposition. Results showed that the oil content of the stuffing with inoculums was only half of that of the control. Furthermore, the bacteria were also beneficial to maintain the stability of the machine operating. Therefore, the bacteria mixture as well as the machines in this study could be very useful for waste treatment.

Effects of honing treatment on AIP-TiN and TiAlN coated end-mill for high speed machining

The objective of this work is to compare the tool performance of TiN and TiA1N coated carbides end-mills deposited by an arc ion plating （ALP） method, using honing treatment to polish the cutting edge surface sleekly. The curve of surface roughness versus honing time showed a rapid improvement initially and thereafter became steady, manifesting a saturation effect. The optimal honing time related to surface roughness was determined to be approximately 20 s. As the surface roughness increased, the critical loads reduced. At an average surface roughness （Ra） of 0.028 p.m, the highest critical loads of TiN and TiAlN coating layers were 98 and 114 N, respectively. Tool performances of uncoated and coated tools were conducted under high speed machining （HSM） of AISI D2 cold-worked die steel （62 HRC）. Consequently, the TiAlN coated end-mill using honing treatment showed excellent tool life under HSM conditions.

Influence of annealing treatment on properties and microstructures of alumina dispersion strengthened copper alloy

Alumina dispersion strengthened copper（ADSC） alloy was produced by internal oxidation. The hardness, ultimate tensile strength and electrical conductivity measurements and microstructure observation on the produced 0.12%ADSC （0.24% Al2O3, mass fraction） and 0.25%ADSC （0.50% Al2O3） subjected to different annealing treatments were conducted. The results show that the microstructure of the produced ADSC is characterized by an uniform distribution of nano-Al2O3 particles in Cu-matrix; the particles range in size from 20 to 50 nm with an interparticle spacing of 30100 nm. The produced 0.12%ADSC can maintain more than 87% hardness retention after 900 ℃, 1 h annealing treatment; the recrystallization can be largely retarded and is not fully completed even after annealing at 1 000 ℃ for 1 h, followed by cold deformation of 84%; local grain growth can be observed after 1 050 ℃, 1 h annealing treatment. The results also show that increasing either the alumina content or cold deformation degree increases the hardness of the produced ADSC.

Strengthening technology and mechanism for semi-solid die casting of aluminum alloy

Combined with theoretical evaluation, an optimized strengthening process for the semi-solid die castings of A356 aluminum alloy was obtained by studying the mechanical properties of castings solution treated and aged under different conditions in detail, then, the semi-solid die castings and liquid die castings were heat treated with the optimized process. The results show that the mechanical properties of semi-solid die castings of aluminum alloy are superior to those of the liquid die castings, especially the strengthening degree of heat treated semi-solid die castings is much greater than that of liquid die castings with the tensile strength more than 330 MPa and the elongation more than 10%, and this is mainly contributed to the non-dendritic and more compact microstructure of semi-solid die castings. The strengthening mechanism of heat treatment for the semi-solid die castings of A356 aluminum alloy is due to the dispersive precipitation of the second phase（Mg2Si） and formation of GP Zone.

A Novel Treatment Optimization System and Top Gene Identification via Machine Learning with Application on Breast Cancer

Traditional treatment selection of cancers mainly relies on clinical observations and doctor’s judgment, but most outcomes can hardly be predicted. Through Genomics Topology, we use 272 breast cancer patients’ clinical and gene information as an example to propose a treatment optimization and top gene identification system. This study faces certain challenges such as collinearity and the Curse of Dimensionality within data, so by the idea of Analysis of Variance (ANOVA), Principal Component Analysis (PCA) is implemented to resolve this issue. Several genes, for example, SLC40A1 and ACADSB, are found to be both statistically significant and biological-studies supported;the model developed can precisely predict breast cancer mortality, recurrence time, and survival time, with an average MSE of 3.697, accuracy rate of 88.97%, and F1 score of 0.911. The result and methodology used in this study provide a channel for people to further look into the more precise prediction of other cancer outcomes through machine learning and assist in the discovery of targetable pathways for next-generation cancer treatment methods.

Machinability of Polymeric Composites and Future Reinforcements—A Review

This paper reviews the machinability and mechanical properties of natural fiber-reinforced composites. Coupling agents, operating parameters, as well as chemical treatment effects on natural fiber-reinforced composites’ machinability are also reviewed. Moreover, the impacts of fibers’ physical properties on the machinability of the composite are mentioned. Fiber volume fraction (Vf), fiber orientation as well as chemical treatment effects on mechanical properties are also defined. Conclusively, the effect of fibers’ physical properties as well as mechanical properties is described. It was discovered that chemical treatment of natural fibers improved their compatibility with the matrix by removing their surface tissues, increasing the roughness average (Ra), and reducing moisture absorption. Also, the Orientation of the fiber plays an important role in controlling the mechanical properties of the composite. Moreover, some physical properties of the fibers, including quality of fiber distributed in the matrix;fiber size, length, and diameter;moisture absorption;porosity and the way fibers break during compounding with the matrix, were found to affect the mechanical properties of the composites formed.

Drug-Treatment Generation Combinatorial Algorithm Based on Machine Learning and Statistical Methodologies

Finding out the desired drug combinations is a challenging task because of the number of different combinations that exist and the adversarial effects that may arise. In this work, we generate drug combinations over multiple stages using distance calculation metrics from supervised learning, clustering, and a statistical similarity calculation metric for deriving the optimal treatment sequences. The combination generation happens for each patient based on the characteristics (features) observed during each stage of treatment. Our approach considers not the drug-to-drug (one-to-one) effect, but rather the effect of group of drugs with another group of drugs. We evaluate the combinations using an FNN model and identify future improvement directions.

Design and Optimization of Steel Car Body Structures via Local Laser-Strengthening

Continuously rising demands of legislators require a significant reduction of CO2-emission and thus fuel consumption across all vehicle classes. In this context, lightweight construction materials and designs become a single most important factor. The main engineering challenge is to precisely adapt the material and component properties to the specific load situation. However, metallic car body structures using “Tailored blanks” or “Patchwork structures” meet these requirements only insufficiently, especially for complex load situations (like crash). An innovative approach has been developed to use laser beams to locally strengthen steel crash structures used in vehicle bodies. The method tailors the workpiece hardness and thus strength at selected locations to adjust the material properties for the expected load distribution. As a result, free designable 3D-strengthening-patterns surrounded by softer base metal zones can be realized by high power laser beams at high processing speed. The paper gives an overview of the realizable process window for different laser treatment modes using current high brilliant laser types. Furthermore, an efficient calculation model for determining the laser track properties (depth/width and flow curve) is shown. Based on that information, simultaneous FE modelling can be efficiently performed. Chassis components are both statically and cyclically loaded. Especially for these components, a modulation of the fatigue behavior by laser-treated structures has been investigated. Simulation and experimental results of optimized crash and deep drawing components with up to 55% improved level of performance are also illustrated.

Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algor

Metallic alloys for a given application are usually designed to achieve the desired properties by devising experimentsbased on experience, thermodynamic and kinetic principles, and various modeling and simulation exercises.However, the influence of process parameters and material properties is often non-linear and non-colligative. Inrecent years, machine learning (ML) has emerged as a promising tool to dealwith the complex interrelation betweencomposition, properties, and process parameters to facilitate accelerated discovery and development of new alloysand functionalities. In this study, we adopt an ML-based approach, coupled with genetic algorithm (GA) principles,to design novel copper alloys for achieving seemingly contradictory targets of high strength and high electricalconductivity. Initially, we establish a correlation between the alloy composition (binary to multi-component) andthe target properties, namely, electrical conductivity and mechanical strength. Catboost, an ML model coupledwith GA, was used for this task. The accuracy of the model was above 93.5%. Next, for obtaining the optimizedcompositions the outputs fromthe initial model were refined by combining the concepts of data augmentation andPareto front. Finally, the ultimate objective of predicting the target composition that would deliver the desired rangeof properties was achieved by developing an advancedMLmodel through data segregation and data augmentation.To examine the reliability of this model, results were rigorously compared and verified using several independentdata reported in the literature. This comparison substantiates that the results predicted by our model regarding thevariation of conductivity and evolution ofmicrostructure and mechanical properties with composition are in goodagreement with the reports published in the literature.

Evading strength−ductility trade-off of GH605 alloy using magnetic field-assisted undercooling treatment

Undercooling solidification under a magnetic field(UMF)is an effective way to tailor the microstructure and properties of Co-based alloys.In this study,by attributing to the UMF treatment,the strength−ductility trade-off dilemma in GH605 superalloy is successfully overcome.The UMF treatment can effectively refine the grains and increase the solid solubility,leading to the high yield strength.The main deformation mechanism in the as-forged alloy is dislocation slipping.By contrast,multiple deformation mechanisms,including stacking faults,twining,dislocation slipping,and their strong interactions are activated in the UMF-treated sample during compression deformation,which enhances the strength and ductility simultaneously.In addition,the precipitation of hard Laves phases along the grain boundaries can be obtained after UMF treatment,hindering crack propagation during compression deformation.

Effect of thermo-mechanical treatment on microstructure and mechanical properties of wire-arc additively manufactured Al-Cu alloy

Wire-arc additive manufacture(WAAM)has great potential for manufacturing of Al-Cu components.However,inferior mechanical properties of WAAM deposited material restrict its industrial application.Inter-layer cold rolling and thermo-mechanical heat treatment(T8)with pre-stretching deformation between solution and aging treatment were adopted in this study.Their effects on hardness,mechanical properties and microstructure were analyzed and compared to the conventional heat treatment(T6).The results show that cold rolling increases the hardness and strengths,which further increase with T8 treatment.The ultimate tensile strength(UTS)of 513 MPa and yield stress(YS)of 413 MPa can be obtained in the inter-layer cold-rolled sample with T8 treatment,which is much higher than that in the as-deposited samples.The cold-rolled samples show higher elongation than that of as-deposited ones due to significant elimination of porosity in cold rolling;while both the T6 and T8 treatments decrease the elongation.The cold rolling and pre-stretching deformation both contribute to the formation of dense and dispersive precipitatedθ′phases,which inhibits the dislocation movement and enhances the strengths;as a result,T8 treatment shows better strengthening effect than the T6 treatment.The strengthening mechanism was analyzed and it was mainly related to work hardening and precipitation strengthening.

Extensive prediction of drug response in mutation-subtype-specific LUAD with machine learning approach

Lung cancer is the most prevalent cancer diagnosis and the leading cause of cancer death worldwide.Therapeutic failure in lung cancer(LUAD)is heavily influenced by drug resistance.This challenge stems from the diverse cell populations within the tumor,each having unique genetic,epigenetic,and phenotypic profiles.Such variations lead to varied therapeutic responses,thereby contributing to tumor relapse and disease progression.Methods:The Genomics of Drug Sensitivity in Cancer(GDSC)database was used in this investigation to obtain the mRNA expression dataset,genomic mutation profile,and drug sensitivity information of NSCLS.Machine Learning(ML)methods,including Random Forest(RF),Artificial Neurol Network(ANN),and Support Vector Machine(SVM),were used to predict the response status of each compound based on the mRNA and mutation characteristics determined using statistical methods.The most suitable method for each drug was proposed by comparing the prediction accuracy of different ML methods,and the selected mRNA and mutation characteristics were identified as molecular features for the drug-responsive cancer subtype.Finally,the prognostic influence of molecular features on the mutational subtype of LUAD in publicly available datasets.Results:Our analyses yielded 1,564 gene features and 45 mutational features for 46 drugs.Applying the ML approach to predict the drug response for each medication revealed an upstanding performance for SVM in predicting Afuresertib drug response(area under the curve[AUC]0.875)using CIT,GAS2L3,STAG3L3,ATP2B4-mut,and IL15RA-mut as molecular features.Furthermore,the ANN algorithm using 9 mRNA characteristics demonstrated the highest prediction performance(AUC 0.780)in Gefitinib with CCL23-mut.Conclusion:This work extensively investigated the mRNA and mutation signatures associated with drug response in LUAD using a machine-learning approach and proposed a priority algorithm to predict drug response for different drugs.

Intensive care unit-acquired weakness:Unveiling significant risk factors and preemptive strategies through machine learning

This editorial discusses an article recently published in the World Journal of Clinical Cases,focusing on risk factors associated with intensive care unit-acquired weak-ness(ICU-AW).ICU-AW is a serious neuromuscular complication seen in criti-cally ill patients,characterized by muscle dysfunction,weakness,and sensory impairments.Post-discharge,patients may encounter various obstacles impacting their quality of life.The pathogenesis involves intricate changes in muscle and nerve function,potentially leading to significant disabilities.Given its global significance,ICU-AW has become a key research area.The study identified critical risk factors using a multilayer perceptron neural network model,highlighting the impact of intensive care unit stay duration and mechanical ventilation duration on ICU-AW.Recommendations were provided for preventing ICU-AW,empha-sizing comprehensive interventions and risk factor mitigation.This editorial stresses the importance of external validation,cross-validation,and model tran-sparency to enhance model reliability.Moreover,the application of machine learning in clinical medicine has demonstrated clear benefits in improving disease understanding and treatment decisions.While machine learning presents oppor-tunities,challenges such as model reliability and data management necessitate thorough validation and ethical considerations.In conclusion,integrating ma-chine learning into healthcare offers significant potential and challenges.Enhan-cing data management,validating models,and upholding ethical standards are crucial for maximizing the benefits of machine learning in clinical practice.