Main Effective Parameters Research Articles

Colorimetric and fluorimtric microenvironment responsivity of oxazolidine in solvatochromic latex nanoparticles: Versatile intelligent polymeric materials with advanced applications

Oxazolidine, a new category of stimuli-chromic dyes, has displayed a wide range of responsivities, such as halochromism, solvatochromism, ionochromism, and hydrochromism. The optical properties of oxazolidine molecules can be influenced notably by the polarity of solvent, media, and polymer nanoparticles. In this study, multi-functionalized acrylic latex nanoparticles containing different polar functional groups such as amide, amine, acid, hydroxyl, epoxide, and long-chain ester were synthesized by one-pot emulsion copolymerization. Prepared latex nanoparticles have a mean particle size in the range of 45–170 nm and spherical or non-spherical (anisotropic) morphologies depending on the polarity of functional groups. To prepare multi-color photoluminescent latex nanoparticles and investigation of solvatochromism in both colloidal solution and solid phase, as prepared multi-functionalized latex nanoparticles were modified physically with an oxazolidine derivative (5 wt%). Investigation of the solvatochromism of the oxazolidine in colloidal solution and solid phase indicated that the media is the main effective parameter for solvatochromism in colloidal solution. In the case of solid-state solvatochromism, the optical properties of oxazolidine were influenced significantly by the polarity of functional groups, because the interactions of oxazolidine molecules with functional groups of latex nanoparticles is the main parameter that influenced the solvatochromism. The solvatochromic properties of oxazolidine in solid polymer powders are useful for developing multi-color photoluminescent materials with advanced applications in the dual-mode visualization of latent fingerprints (LFPs), anticounterfeiting solid inks, and organic light-emitting diodes (OLEDs). Results displayed potential applications of multi-color polymer powders for the detection of LFPs under visible light and UV-light irradiation, for printing of optical security tags, and construction of OLEDs. The presented study introduces a new category of multi-color solid photoluminescent nanomaterials with multiple applications using solvatochromic properties.

Solid-state solvatochromism of oxazolidine in multi-functionalized copolymer nanoparticles: Development of advanced materials with multi-color fluorescence

Investigating the solvatochromism of stimuli-chromic compounds such as oxazolidine in polymer matrices with various polarities or functionalities is a unique approach to developing advanced materials for anticounterfeiting, sensor, optoelectronic, and displayers. In a novel strategy, multi-functionalized copolymer nanoparticles were synthesized by copolymerization of methyl methacrylate (MMA) with various functional comonomers in emulsion media for post-polymerization modification with oxazolidine and investigation of its solvatochromism in both colloidal solution and solid phase. The particle size and morphology of nanoparticles were influenced significantly by the polarity of functional groups, and the morphology evolution from sphere to anisotropic shapes was observed by increasing the polarity of functional groups. Investigation of oxazolidine solvatochromism in colloidal solution and solid polymer powders indicated different mechanisms for solvatochromism, in which the polarity of media is the main effective parameter in colloidal solution and the polarity of functional groups in the polymer structure is the main effective parameter in solid polymer phase. The solvatochromism of oxazolidine was confirmed by observed red shift and blue shift in UV–Vis and fluorescence spectra, in which the intensity of absorbance and emission peaks was changed as a function of the polarity of functional groups. Solvatochromic photoluminescent polymer nanoparticles were used for several advanced applications such as solid anticounterfeiting inks to information encryption, dual-mode visualization of latent fingerprints, and also development of organic light-emitting diodes (OLEDs). For the first time, the results indicate a significant effect of polymer chain local polarity induced by functional groups on the tuning of optical properties of the oxazolidine by the solid-state solvatochromic phenomenon. Solvatochromism of oxazolidine in functionalized polymer nanoparticles is an interesting approach to developing novel intelligent materials that have advanced applications in different fields such as anticounterfeiting and information encryption, optoelectronic, polarity sensor, and visualization of latent fingerprints.

Can Architectural Identity Be Measured?

The sustainable breadth of architectural identity is a remarkable phenomenon with many dimensions. These dimensions are melded together to produce an architectural form. Form as the final architectural product is shaped by the visual cues that produce symbols as a powerful tool in identifying a specific architectural trend. This study aims to construct a theoretical framework for the permanence of local identities to answer the main assumption, which is the following: Can identity be measured? It endeavors to clarify the main effective parameters that affect the permanency of architectural identity. It assesses the measurement variables of architectural identity based on multiple architectural perspectives and different points of view. The methodology of this study contains two broad approaches: a checklist and a questionnaire. The results provide a new model that includes three significant poles of architectural identity (mental images, originality, and building regulation). The findings enhanced the sustainability concept of architectural identity, which forecasts the permanency of architectural identity.

A comparison of synthetic data generation and federated analysis for enabling international evaluations of cardiovascular health

Sharing health data for research purposes across international jurisdictions has been a challenge due to privacy concerns. Two privacy enhancing technologies that can enable such sharing are synthetic data generation (SDG) and federated analysis, but their relative strengths and weaknesses have not been evaluated thus far. In this study we compared SDG with federated analysis to enable such international comparative studies. The objective of the analysis was to assess country-level differences in the role of sex on cardiovascular health (CVH) using a pooled dataset of Canadian and Austrian individuals. The Canadian data was synthesized and sent to the Austrian team for analysis. The utility of the pooled (synthetic Canadian + real Austrian) dataset was evaluated by comparing the regression results from the two approaches. The privacy of the Canadian synthetic data was assessed using a membership disclosure test which showed an F1 score of 0.001, indicating low privacy risk. The outcome variable of interest was CVH, calculated through a modified CANHEART index. The main and interaction effect parameter estimates of the federated and pooled analyses were consistent and directionally the same. It took approximately one month to set up the synthetic data generation platform and generate the synthetic data, whereas it took over 1.5 years to set up the federated analysis system. Synthetic data generation can be an efficient and effective tool for enabling multi-jurisdictional studies while addressing privacy concerns.

Advances in Management of Ovarian Cancer through Natural Resources: An Update

Since times immemorial, mankind has been dependent on Mother Nature in terms of natural resources for alleviating its sufferings. As per W.H.O., developing countries should fully explore their flora and fauna to become economically self-reliant. Country India enjoys having diverse climatic conditions of heavy rainfall, sandy deserts & snow-clad mountains, which play a perfect condition and host for the growth of plenty of medicinal plants with desirable phytoconstituents. Presently mankind is suffering from many diseases that were not so common in the olden days. Does it is a signal from our mother Nature to once again rediscover its treasure of natural resources for exploring answers for various prophylaxis and therapeutic needs of modern-day disorders and diseases? Cancer is the most fatal disease worldwide caused due to the uncontrolled growth of abnormal cells in the body. Ovarian Cancer is the fifth leading cause of death in females. The incidence and mortality rate of Ovarian Cancer is increasing day by day across the world. Biomarkers such as CA125, HE4, OVA1, and Cancer stem cells have been used for the detection of Ovarian Cancer. The current treatment of Ovarian Cancer is surgery and some common treatments include chemotherapy, radiation therapy, adoptive cell therapy, use of medicinal plants and their derivatives. Some prominent medicinal plants like Quercus tinctoria, Curcuma longa, Taxus brevifolia, Allium sativum, Asparagus racemosa, Camptotheca acuminate, Symplocus racemosa, Ginkgo biloba, Genistein, Zingiber officinale, Azadirachta indica, Embilica officinalis, Podophyllum peltatum, Camellia sinensis, Saraca indica. A large number of phytochemicals which has anticancer compounds are flavonoids, alkaloids, terpenoids, carotenoids, phenolics, and organosulfur compounds. Ayurvedic drugs highly effective on Ovarian Cancer are Arka, Bhallataka, Ahipena, Bhanga, Vishamushti, and Jayapala. The diagnosis of Ovarian Cancer in the initial stage is not possible mainly due to a lack of awareness on patients’ behalf. The main effective parameter is imaging for checking the extent and location of the disease. Some other parameters also included physical examination and transvaginal ultrasonography. Thus, the present review is an attempt to compile published reports through authentic sources regarding the current management scenario of Ovarian Cancer with the help of natural drugs or medicinal plants that inhibits or hinders the growth of cancer cell, which may serve as a ready reference for future researchers.

Molybdenum Carbide/Ni Nanoparticles Embedded into Carbon Nanofibers as an Effective Non-Precious Catalyst for Green Hydrogen Production from Methanol Electrooxidation.

Molybdenum carbide co-catalyst and carbon nanofiber matrix are suggested to improve the nickel activity toward methanol electrooxidation process. The proposed electrocatalyst has been synthesized by calcination electrospun nanofiber mats composed of molybdenum chloride, nickel acetate, and poly (vinyl alcohol) under vacuum at elevated temperatures. The fabricated catalyst has been characterized using XRD, SEM, and TEM analysis. The electrochemical measurements demonstrated that the fabricated composite acquired specific activity for methanol electrooxidation when molybdenum content and calcination temperature were tuned. In terms of the current density, the highest performance is attributed to the nanofibers obtained from electrospun solution having 5% molybdenum precursor compared to nickel acetate as a current density of 107 mA/cm2 was generated. The process operating parameters have been optimized and expressed mathematically using the Taguchi robust design method. Experimental design has been employed in investigating the key operating parameters of methanol electrooxidation reaction to obtain the highest oxidation current density peak. The main effective operating parameters of the methanol oxidation reaction are Mo content in the electrocatalyst, methanol concentration, and reaction temperature. Employing Taguchi's robust design helped to capture the optimum conditions yielding the maximum current density. The calculations revealed that the optimum parameters are as follows: Mo content, 5 wt.%; methanol concentration, 2.65 M; and reaction temperature, 50 °C. A mathematical model has been statistically derived to describe the experimental data adequately with an R2 value of 0. 979. The optimization process indicated that the maximum current density can be identified statistically at 5% Mo, 2.0 M methanol concentration, and 45 °C operating temperature.

Synthesis of chemically-crosslinked multi-arm star-shaped polyurethane with triple-shape memory effect

In this study, chemically-crosslinked multi-arm star-shaped polyurethanes (SPUs) were prepared using three, four, and six-arm polycaprolactone, hexamethylene diisocyanate, and 1, 4-butanediol. The hydrogen bonding indices of soft and hard segments were calculated using Fourier transform infrared spectra. The results indicated that the phase separation among hard and soft segments increased with the increment of PCL arm numbers. Moreover, the results of X-ray diffraction and differential scanning calorimetry showed that the crystallization ability of the three and four-arm SPUs were lower than that for six-arm SPU (6SPU), which is due to their higher crosslinking densities. In addition, the results of the mechanical studies showed that the crosslinking density and degree of crystallinity are the main effective parameters controlling the mechanical properties, by which 6SPU showed higher Young's modulus and lower elongation at break compared to other SPUs. Cyclic shape memory studies showed that 6SPU could fix approximately all the temporary shapes during three cycles and recover 100% of its original shape. Moreover, 6SPU could show triple-shape memory effect (TSME) by which it could fix two different temporary shapes. These results show that 6SPU has a high potential for practical applications due to its good mechanical properties, shape memory fatigue resistance, and TSME.

Modelling and prediction of groundwater level using wavelet transform and machine learning methods: A case study for the Sahneh Plain, Iran

AbstractDue to the complexity of numerical models and the need for much information and data in these models, one of the important solutions is to use artificial intelligence models with a small number of required inputs and a simpler structure. In this study, using wavelet transform analysis (cross wavelet transform and wavelet transform coherence), a model was developed to extract the relationship between climatic data (i.e., temperature and precipitation) and groundwater level changes. The developed model provides the best relationship between the main effective climatic parameters and groundwater level changes in the Sahneh Plain. The results showed that the climatic variables of average temperature and precipitation are the parameters with the highest correlation coefficient with groundwater level changes in the main wells of the study area. The results of verification and validation of the numerical prediction model of groundwater level based on the climatic parameters indicate that the average error is between 3% and 10% in different areas of the plain, which indicates the high accuracy of the developed model. In areas with a lack of data and information required for numerical simulation, the proposed model used in this research can be used as a comprehensive model for assessing and predicting groundwater levels based on climatic parameters.

An Effect of Screw Extrusion Parameters On a Pottery Model Forming by A Clay Printing Machine

This study presents the development of a clay printing machine based on the screw-based extrusion technique in which the clay is extruded through a circular nozzle to form clay model layer by layer. The developed machine consists of a material container for delivering system, a screw extruder for extruding clay through a circular nozzle and movement system.Traditionally, pottery models are formed by using paster mold or hand throwing, which require experience and proficient workers to form the complex pottery models. Therefore, the clay printing machine has been developed to improve pottery manually fabrication to automatic construction. To investigate a capability material deposition of the clay printing machine, the screw is the key component of an extruder that is the recommended choice for extrudability. The screw extrusion and machine parameters that effect on the appearance of clay filament were studies to find the optimal conditions of the machine. Analysis of variance (ANOVA) is used to analyze main effect parameters. The experimental results shown that the 6 mm nozzle diameter, 19 mm/s screw extruder velocity and 24 mm screw pitch were printing parameters for providing an appropriate appearance of clay filament. A mathematical model was formulated to propose the relationship between response and main effects with their interactions.

SimDFBA: A framework for bioprocess simulation and development

Many efforts have been made to model bio-processes and investigate the main effective parameters using conventional known softwares. The main shortcoming of such models is that the bioreactor is considered a black box. Such simplification causes uncertainty in following different metabolic pathways of microorganisms. This study presents a new method for simulating bio-processes by integrating available conventional simulation softwares such as ASPEN PLUS and MATLAB. The former is used to simulate common process equipment, and the latter is employed to model Bio-reactor. Besides, the Bio-reactor is modeled mathematically with Dynamic Flux Balance Analysis (DFBA) concept using the metabolic network reconstruction model. This framework will simultaneously provide a rigorous basic model of both upstream and downstream equipment. This approach can also be used as a toolbox to implement process optimization and modern control strategies on the bio-processes. A syngas fermentation bioprocess case study was chosen to implement the new framework.

Optimization and analysis of bioenergy production using machine learning modeling: Multi-layer perceptron, Gaussian processes regression, K-nearest neighbors, and Artificial neural network models

Since fossil fuels are slowly depleting, bio and renewable energies are now given more attention. The main purpose of this research is to investigate and optimize the influencing parameters of bioenergy production through transesterification process. The application of artificial intelligence (AI) in bioenergy production studies has become increasingly popular due to its capability of interpreting nonlinear relationships between inputs and outputs for complex systems. Here, after conducting library studies and carefully reviewing the existing methods, the multi-layer perceptron (MLP), K-nearest neighbors (KNN), Artificial neural network (ANN), and Gaussian processes regression (GPR) models were selected for simulation and prediction of the efficiency of fatty acid methyl ester (FAME) production. The main effective transesterification parameters on production of biodiesel including the temperature of reaction (°C), catalyst mass to oil mass ratio (wt.%), and the molar ratio of methanol to oil were set as the input variables in all studied models. For reaction between oil and short chain alcohols, wollastonite (a calcium metasilicate, CaSiO3) was utilized as a phase boundary catalyst. By carefully selecting the execution conditions of the algorithms in the model selection phase, all three models reached a result above 0.99 and close to 1 with the square R criterion. Also, the RMSE values for the studied models were 3.95 for MLP, 1.09 for KNN, 0.13 for ANN and 3.60 for GPR models. Therefore, it can be concluded that although the ANN model was to be a better model in process efficiency prediction in terms of error, but all three algorithms had high accuracy because of different generality types. The optimum yield of 97.8% for FAME production was observed at optimum methanol to oil molar ratio, reaction temperature, and catalyst mass to oil mass ratio 65°C, 15, and 9.21 wt%, respectively.

Assumption-Lean Cox Regression

Inference for the conditional association between an exposure and a time-to-event endpoint, given covariates, is routinely based on partial likelihood estimators for hazard ratios indexing Cox proportional hazards models. This approach is flexible and makes testing straightforward, but is nonetheless not entirely satisfactory. First, there is no good understanding of what it infers when the model is misspecified. Second, it is common to employ variable selection procedures when deciding which model to use. However, the bias and uncertainty that imperfect variable selection adds to the analysis is rarely acknowledged, rendering standard inferences biased and overly optimistic. To remedy this, we propose a nonparametric estimand which reduces to the main exposure effect parameter in a (partially linear) Cox model when that model is correct, but continues to capture the (conditional) association of interest in a well understood way, even when this model is misspecified in an arbitrary manner. We achieve an assumption-lean inference for this estimand based on its influence function under the nonparametric model. This has the further advantage that it makes the proposed approach amenable to the use of data-adaptive procedures (e.g., variable selection, machine learning), which we find to work well in simulation studies and a data analysis. Supplementary materials for this article are available online.

Development of high copper concentration, low operating temperature, and environmentally friendly electroless copper plating using a copper ‐ glycerin complex solution

Few researchers have studied high metal ions concentration electroless plating solutions since increasing metal ions concentration shortens the solution life considerably. Nevertheless, as the required thickness of the electroless plating film reduces gradually, the limited solution life will not be a large problem. There are no detailed studies on the influence of high copper concentration electroless copper plating solution and their composition on the plating rate and decomposition time. Therefore, this paper explores the plating characteristics of high copper concentration from a copper‐glycerin complex solution. Taguchi's approach was used to optimize the plating rate and decomposition time based on an L18 orthogonal array with six main effect parameters and three levels. Main effect response tables and plots were employed to investigate the parameter influence. The significant factors of plating rate are temperature and pH, and the concentration of copper sulfate has much influence on the decomposition time. Temperature and copper sulfate concentration are the key factors to optimize the formulation of the plating solution. The optimized results were obtained at 0.18 M copper sulfate, 0.133 M formaldehyde, 0.36 M glycerin, 3.2 × 10−5 M 2,2’-dipyridyl, 29 °C, pH = 12.8. The copper concentration was more than four times higher than the conventional electroless copper plating solution, and the critical factors of high copper concentration in the electroless copper plating solution were revealed. It was found that copper formed 1:2 complexes with glycerin at a pH value of 12.8. In addition, the surface morphology and the uniformity of the plating layer were included as well.

Tetracycline capture from aqueous solutions by nanocomposite of MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan.

The presence of pharmaceuticals as the emerging contaminates needs novel approaches and new materials to be remediated. This study aimed to develop and apply MWCNTs reinforced with glutaraldehyde cross-linked poly (vinyl alcohol)/chitosan nanocomposite (MWCNTs/CS-PVA/GA NC) for removal of tetracycline (TC) as a model of antibiotics from aqueous solutions. The successful synthesis of NC was supported by techniques of SEM, XRD, TGA, FTIR, and EDX. The prepared NC was then utilized for TC adsorption under the main effective parameters of TC concentration (25-125mg/L), sonication time (0-8min), NC dose (1-130mg), and tempearure (5-45°C). The process behavior was comparably explored with different methods of central composite design (CCD), artificial neural networks (ANN), and general regression neural network (GRNN). The results showed that under the optimum settings presented by desirability function (DA), in which the respective values for the factors were 125mg/L, 6.8min, 130mg, and 45°C, the efficiency and adsorption capacity of NC is supposed to be 99.07% and ∼525mg/g, respectively. From the models studied, although all were able to express the process with satisfactory accuracy, ANN provided the best accuracy and reliability owning to the highest R2 (0.999) and lowest RMSE, ADD, MAE. The kinetics, isotherms, and thermodynamic studies showed that the process is fast (over 4.5min), chemisorption, heterogeneous with multilayer nature, spontaneous, feasible, and endothermic. In addition, the as prepared NC could be recycled for five times without significant fail in its performance. All in all, the developed MWCNTs/CS-PVA/GA NC can be considered as a promising candidate in dealing with aqueous solutions' pollution with antibiotic.

Efficient removal of levofloxacin by a magnetic NiFe-LDH/N-MWCNTs nanocomposite: Characterization, response surface methodology, and mechanism

Antibiotic pollutants in water bodies, was studied to remove using an oxidized, nitrogen-doped, and Fe3O4 and NiFe-LDH decorated MWCNT (magnetic NiFe-LDH/N-MWCNTs) nanocomposite (NC). The novel, engineered NC was characterized by different techniques of SEM, XRD, TEM, EDX, and XPS and then examined under different main effective parameters of NC dose, levofloxacin (LVX) concentration, pH, time, and temprature. The experimentally obtained data then evaluated using the modeling approaches of RSM, GRNN, and ANFIS. The as prepared adsorbent showed an excellent adsorption performance (removal efficiency = 95.28% and adsorption capacity = 344.83–454.55 mg/g) under the respective values of the mentioned parameters of 0.152 g, 23.01 mg/L, 12.00 min, and 37.5 °C, respectively. The comparison of the models showed that although all of them accurately predicted the removal efficiency, ANFIS presented the best capability with R2, RMSE, MSE, MAE, as well as AAD of 0.9998, 0.0082, −0.0004, 0.0069, 0.1322, respectively. The adsorption by the NC followed Freundlich isotherm (R2 = 0.9993) and PSO kinetic (>0.998) models, confirming a heterogenous chemisorption process. The thermodynamic parameters showed an endothermic and spontaneous nature for LVX removal by magnetic NiFe-LDH/N-MWCNTs NC. A high-performance efficiency, appropriate reusability (five times without loss of efficiency), as well as easy separation due to magnetic properties, makes the NC to a promising option in removing LVX from water.

A novel electrochemical sensor based on molecularly imprinted polymer nanocomposite platform for sensitive and ultra-selective determination of citalopram

• A novel sensing platform based on MIP/hNiNS/AMWCNT@GONRs composite was designed as the modifier for GCE. • For the first time the modified glassy carbon electrode (MIP/hNiNS/AMWCNT@ GONRs/GCE) was used to determine citalopram (CIT). • The sensor showed excellent selectivity and sensitivity towards determination of CIT. • The proposed sensor was applied successfully for determination of CIT in real samples. Citalopram (CIT) is an antidepressant of the selective serotonin reuptake inhibitor class widely used worldwide, so the development of analytical methods to determine it in real samples is essential for treatment purposes and drug development. This work presented an electrochemical-specific sensor for CIT based on the molecular imprinting technique. The sensor was fabricated by electropolymerizing molecularly imprinted polymer (MIP) film onto hollow nickel nanospheres (hNiNS)/activated multiwalled carbon nanotubes@graphene oxide nanoribbons (AMWCNTs@GONRs) composite modified glassy carbon electrode (GCE). The excellent synergistic effect of the combination of hNiNS and AMWCNTs@GONRs composite shows significantly enhanced electrocatalytic activity for CIT, which lead to high sensitivity of the sensor. The electropolymerized MIP provides specific imprinted sites, which can increase the selectivity for CIT. The preparation of the MIP/hNiNS/AMWCNT@GONRs/GCE was characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD) and electrochemical methods, including cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The electrochemical properties of the sensor were studied using differential pulse voltammetry (DPV) and CV. Main effective parameters on the performance of the imprinted electrodes such as electropolymerization cycles, pH buffer solution, incubation time and concentrations of template molecules were checked and optimized. The fabricated sensor displayed two linear dynamic ranges from 0.5 to 10 µM and 10 to 190 µM with a limit of detection (LOD, S/N = 3) of 0.042 µM. Examining the interfering effect of some structurally related compounds and some potential interferences existing in biological fluids on the analysis of citalopram showed that the designed MIP sensor could identify CIT selectively. Moreover, the constructed electrode exhibited good repeatability, reproducibility, stability and rapid response time for the electrochemical analysis of CIT. The presented sensor was successfully utilized for determining the CIT in real samples.

Designing the best ANN topology for predicting the dynamic viscosity and rheological behavior of MWCNT-CuO (30:70)/ SAE 50 nano-lubricant

Heat transfer optimization has a special priority in all industries. Nanoparticles dramatically develop the thermophysical properties of the base fluid when they are suspended uniformly in the base fluid. In this paper, the dynamic viscosity (µnf) of MWCNT-CuO (30:70)/ SAE 50 nano-lubricant is investigated. To model this parameter versus three main effective parameters, the volume fraction of nanoparticles (φ), temperature, and shear rate, an ANN is constructed and trained using the experimental data. The results show that by increasing φ from φ = 0.0625–1%, the µnf is increased from 180 to 220 cP. The temperature and shear rate have massive effects on the µnf. Increasing the temperature from 25° to 50°C, lowers the µnf from 480 to 120 cP. Moreover, the shear rate has a similar trend to temperature; an increase in shear rate from 50 to 600 rpm decreases the µnf from around 500–125 cP. The best ANN has attained a supreme performance with MSE= 0.003756 and R2 = 0.999 which are entirely appropriate for a fitting ANN.

Assumption-lean Inference for Generalised Linear Model Parameters

AbstractInference for the parameters indexing generalised linear models is routinely based on the assumption that the model is correct and a priori specified. This is unsatisfactory because the chosen model is usually the result of a data-adaptive model selection process, which may induce excess uncertainty that is not usually acknowledged. Moreover, the assumptions encoded in the chosen model rarely represent some a priori known, ground truth, making standard inferences prone to bias, but also failing to give a pure reflection of the information that is contained in the data. Inspired by developments on assumption-free inference for so-called projection parameters, we here propose novel nonparametric definitions of main effect estimands and effect modification estimands. These reduce to standard main effect and effect modification parameters in generalised linear models when these models are correctly specified, but have the advantage that they continue to capture respectively the (conditional) association between two variables, or the degree to which two variables interact in their association with outcome, even when these models are misspecified. We achieve an assumption-lean inference for these estimands on the basis of their efficient influence function under the nonparametric model while invoking flexible data-adaptive (e.g. machine learning) procedures.

Mechanical response of dot-by-dot wire-and-arc additively manufactured 304L stainless steel bars under tensile loading

With the advent of a new arc-based additive manufacturing (AM) process, referred to as Wire-and-Arc Additive Manufacturing (WAAM), the scale of the metal printed parts increased up to several meters, thus becoming suitable for large-scale applications in marine, aerospace and construction sectors. However, specific considerations in terms of geometrical and mechanical properties ought to be made in order to effectively use the printed outcomes for structural engineering purposes. The introduction of the novel printing strategy referred to as “dot-by-dot”, consisting in successive drops of molten metal, enabled the use of WAAM for complex lattice structures, made by continuous grids of WAAM bars. Nevertheless, their proper design requires an accurate evaluation of the influence of the non-negligible inherent geometrical irregularities on the mechanical response of the bars. Hence, extensive experimental work is needed in order to evaluate the mechanical response of WAAM bars with geometrical imperfections.The present study is thus focused on the assessment of the mechanical response in tension of WAAM-produced 304L stainless steel small bars in terms of key effective mechanical parameters. As such, the mechanical characterization through tensile tests is supported by microstructural investigations and detailed studies on the geometrical features. Three batches of bars are studied, each one printed at different build angles representative of limit cases for practical applications. The microstructural analysis confirms the preferential grain orientation typical of WAAM process for all three build angles. The results of the geometrical and mechanical characterization clearly evidence the non-negligible influence of the inherent geometrical imperfections on the mechanical response in tension of the printed bars, with a detrimental effect of the build angle on the main key effective mechanical parameters. Overall, the results highlight the need of specific investigations on both geometrical and mechanical properties of WAAM bars for structural design purposes.

Thermal conductivity prediction of nano enhanced phase change materials: A comparative machine learning approach

Thermal conductivity is one of the crucial properties of nano enhanced phase change materials (NEPCM). Then, in this study three different machine learning methods namely MARS (Multivariate Adaptive Regression Spline), CART (Classification and Regression Tree) and ANN (Artificial Neural Network) is applied to estimate the thermal conductivity of NEPCMs. To develop these models, the information of the different types of NEPCM were collected from 25 studies. The nano particle includes CNF, h-BN, CBNP, GNP, MWCNT, TiO2, SiC, GO, CuO, ZrO2, EG and the PCMs were Paraffin, Polyethylene glycol, Dimethylformamide, Myristic acid, High Density Polyethylene, Phenol, Stearic acid, Erythritol, Eicosane, Palmitic acid and n-octadecane. The total number of samples were more than 911 data to train, test and validate the models. The input parameters for the model were thermal conductivity of nano particle and PCM (W/m.K), phase of NEPCM (solid or liquid), temperature of NEPCM ( °C) and concentration of nano material (wt%) and the output of the models was the thermal conductivity of the NEPCM (W/m.K). The results of the study showed the thermal conductivity of PCM is main effective parameter on the thermal conductivity prediction of NEPCM in all three models. Moreover, the accuracy of the predicted values by ANN model has shown the ability of the ANN to find relationship between dependent and independent variables complex problem and R2 for MARS, CART and ANN model were 0.93, 0.93 and 0.96, respectively. Furthermore, the phase of the NEPCM, which has been used first time uniquely as a predictor in this study, has been second important variable in the developed models. Then, the developed ANN model in this study, as the first general ANN model for prediction of the thermal conductivity of NEPCM for carbon, metal and metal oxide nano particles combined with different types of PCM, can be used to estimate thermal conductivity of various type of NEPCM.