Applications in engineering science最新文献

{"title":"Influence of the supported part surface area on part properties in Laser Powder-Bed Fusion of 316L for gas measurement accessory","authors":"Oliver Maurer ,&nbsp;Michael Stopp ,&nbsp;Christian Bur ,&nbsp;Dirk Bähre","doi":"10.1016/j.apples.2026.100309","DOIUrl":"10.1016/j.apples.2026.100309","url":null,"abstract":"<div><div>Laser Powder-Bed Fusion (L-PBF) is one of the most used Additive Manufacturing technologies for metals. The high degree of freedom in part design and the possibility for functional integration make this process suitable for the production of e.g. medical devices. However, the utilization of additive manufacturing for metals has yet to be explored in analytical instruments or gas measurement systems that prioritize surface and chemical inertness. In the event that L-PBF is to be utilized in novel domains, it is imperative that components adhere strictly to all stipulated criteria. Chemically inert metals like stainless steel 316L are of particular interest for gas measuring applications including exhaled breath analysis, but especially warping and geometrical inaccuracies of additively manufactured 316L parts inhibit the adoption for accessory fabrication. Support structures are considered as an inefficient waste of material increasing post-processing efforts, but they are one design feature to achieve high part quality by e.g. warping reduction. This study analyzes properties of cube samples and gas-carrying parts to gain insights into the influence of the support structure wall thickness and resulting quality. The portion of supported downskin surfaces is introduced after cross-section area calculations to give a transferable measure that is compared to other support strategies of other materials in literature. Especially roughness and geometrical accuracy of gas-carrying parts formed a foundation to select the best support structure compromise between saving material and achieving high part quality. As a result, trends are documented of increasing supported area portions are transferred to gas-carrying parts. There, 49% of supported part surface or 0.4 mm thick walls of block support structures,respectively, mark the best compromise between material efficiency and part quality. This gas-carrying part has the highest degree of geometrical accuracy in a horizontal build orientation of 0°.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100309"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420838","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based prediction of geopolymer concrete compressive strength using boosting and SVR models","authors":"Mohammad Bahram,&nbsp;Hossein khosravi","doi":"10.1016/j.apples.2026.100307","DOIUrl":"10.1016/j.apples.2026.100307","url":null,"abstract":"<div><div>Accurate prediction of the compressive strength of geopolymer concrete is essential for designing sustainable and cost-effective construction materials. This study evaluates and compares the performance and accuracy of four machine learning models data, and <em>R</em> = 0.123 and RMSE = 16.37 for the testing data, indicating poor generalization capability. In contrast, Jafari et al. achieved better performance using more advanced algorithms, with R—XGBoost, LightGBM, CatBoost, and Support Vector Regression (SVR)—for predicting compressive strength based on 162 geopolymer concrete mix designs. Each sample includes 16 input features, such as curing temperature, chemical composition of sodium silicate and fly ash, superplasticizer content, water, fine and coarse aggregates, and oxides (Fe₂O₃, Al₂O₃, CaO, Na₂O, SiO₂), with compressive strength as the target variable. All data processing and analysis were performed using Python software.To assess the relative performance of the developed models, the results were compared with previous studies. Loukog et al. employed machine learning models to predict the compressive strength of geopolymer concrete and reported a correlation coefficient of <em>R</em> = 0.768 and RMSE = 8.764 for the training = 0.89 and RMSE = 4.83 for the training set, and <em>R</em> = 0.826 and RMSE = 5.96 for the testing set. Compared with these studies, the models developed in the present research—particularly the XGBoost model—demonstrated substantially higher accuracy. In the testing set, XGBoost achieved a correlation coefficient of 0.86 and an RMSE of 5.59, outperforming both previous works. Moreover, other Boosting-based models (CatBoost and LightGBM) also exhibited competitive results, performing similarly to or better than previous studies. This comparison highlights that the adoption of Boosting algorithms can significantly enhance the prediction accuracy and stability of compressive strength estimation in geopolymer concrete.In addition to these performance advantages, the application of these predictive models results in considerable savings in laboratory time and costs, while significantly reducing human errors that may occur during traditional experimental procedures. The use of these algorithms facilitates faster and more optimized mix design, minimizing the risk of errors associated with manual testing.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100307"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric optimization for better heat recovery from the lower convective zone of salt-gradient solar pond","authors":"Mariem Elakrout ,&nbsp;Walid Ben Amara ,&nbsp;Abdallah Bouabidi ,&nbsp;Ali M. Ashour ,&nbsp;Saif Ali Kadhim ,&nbsp;Issa Omle","doi":"10.1016/j.apples.2026.100312","DOIUrl":"10.1016/j.apples.2026.100312","url":null,"abstract":"<div><div>This study investigates the thermal performance of the Lower Convective Zone (LCZ) in a Salt-Gradient Solar Pond (SGSP) using numerical simulations. The model is developed from an experimental prototype reported in the literature, with a computational domain of 20 m × 20 m × 1.5 m. A heat exchanger tube of 0.10 m diameter is placed inside the LCZ to absorb the stored heat. The inlet mass flow rate is set to 0.078 kg/s, and the boundary conditions are based on climatic data from August 2016, where the ambient temperature was 29.5 °C and the solar radiation was 792 W/m². The model is validated against experimental results, showing an average deviation of 4 % in outlet fluid temperature. The effect of varying mass flow rates from 0.039 kg/s to 0.312 kg/s on heat transfer is evaluated. To enhance thermal extraction, four heat exchanger geometries are tested: standard straight tube, inverted-U tube, zigzag coil with three bends, and zigzag coil with four bends. Results indicate that higher mass flow rates reduce LCZ temperature, while modified geometries significantly improve thermal performance. The zigzag coil with four bends achieves the highest outlet temperature of 75.55 °C, compared to 51.38 °C for the standard configuration, confirming the advantage of spiral geometry in maximizing heat extraction.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100312"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147421336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Vibration analysis of a functionally Graded cracked shaft system and AI-based design optimization","authors":"Ioannis Tselios,&nbsp;Pantelis G. Nikolakopoulos","doi":"10.1016/j.apples.2025.100282","DOIUrl":"10.1016/j.apples.2025.100282","url":null,"abstract":"<div><div>This paper presents a novel AI-based framework for the design optimization of shafts made of Functionally Graded Materials (FGMs), along with a detailed vibration analysis for multiple conditions. Functionally Graded Material (FGM) shafts combine the high-temperature resistance of ceramics with the toughness of metals, making them valuable in high-performance rotating machinery. However, their dynamic behavior becomes significantly more complex in the presence of cracks, thermal gradients, and material gradation. In this work, a comprehensive numerical study of the vibration response of unbalanced FGM shafts with a transverse breathing crack is conducted across different material gradations, thermal gradients, and rotational speeds. To reduce the computational cost of the design optimization process, an integrated Artificial Intelligence framework combining Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) is introduced. The ANN serves as an accurate surrogate model for predicting key performance indicators, including critical speed, static deflection, weight, and effective fracture toughness, while the GA efficiently explores the design space for optimal shaft configurations. The results highlight the influence of FGM gradation and thermal loading on the vibrational characteristics of cracked rotors and demonstrate that the proposed ANN-GA framework delivers excellent multi-objective optimization performance with high predictive accuracy. This work provides both deeper insight into the dynamics of cracked FGM shafts and a computationally efficient tool for their design optimization, supporting more reliable rotor-bearing systems.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100282"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145797791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predictive assessment of delamination characteristics in E-glass/epoxy composites using sequential back-propagation neural networks","authors":"Saman Jajemizadeh,&nbsp;Mazaher Salamat-Talab,&nbsp;Amir Hossein Rabiee","doi":"10.1016/j.apples.2026.100292","DOIUrl":"10.1016/j.apples.2026.100292","url":null,"abstract":"<div><div>This study focuses on the detection and quantification of delaminations in E-glass/epoxy composite samples. A comprehensive investigation involving 603 distinct numerical tests was conducted, each varying in terms of the intensity, number, and spatial arrangement of damage traversing the sample's length and thickness. The primary objective was to extract the first five natural frequencies from these samples. To enable damage prediction, we devised a sequential back-propagation artificial neural network. This network was trained utilizing the initial five natural frequencies as input data. Importantly, the output of each network in the sequence was fed as input to the subsequent network. The results underscored the network's efficacy and robustness in predicting damage severity, count, and precise locations along both the sample's length and thickness. Furthermore, an exploration of the influence of delaminations on the natural frequency values revealed a coherent and meaningful correlation. Notably, variations in the natural frequency values demonstrated a consistent relationship with damage attributes, encompassing both intensity and spatial distribution (across the length and thickness of the sample). This study thus establishes a sound foundation for employing sequential neural networks in the accurate assessment of delamination characteristics within composite structures, while also shedding light on the interconnectedness of damage features with alterations in natural frequency behavior.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100292"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heat transfer enhancement in helical tubes using iron oxide nanofluid and spring turbulators with different pitches: Experimental and neural network study","authors":"Omid Lotfizadeh ,&nbsp;Seyyedabbas Arhamnamazi ,&nbsp;Mohammad Rakibul Hasan Chowdhury ,&nbsp;Melika kohan ,&nbsp;Reza Aghayari ,&nbsp;Davood Toghraie ,&nbsp;Soheil Salahshour","doi":"10.1016/j.apples.2026.100303","DOIUrl":"10.1016/j.apples.2026.100303","url":null,"abstract":"<div><div>The effect of simultaneously using nanofluid (NF) and spring turbulators on heat transfer in straight tubes has been repeatedly studied, whereas this effect in helical tubes has received little attention. Since the quality and quantity of the impact of these turbulators in spiral tubes and straight tubes are naturally different due to the presence of centrifugal forces and vortices, this study experimentally investigates the thermal performance of iron oxide nanofluid in a helical coil tube equipped with spring-wire inserts under a constant heat flux of 1000 W. Experiments were conducted with nanoparticle volume fractions ranging from 0.1 to 0.5% and wire pitches of 0.003, 0.006 and 0.009 m. Results indicated that increasing the nanoparticle concentration and decreasing the wire pitch significantly enhanced the Nusselt number. A maximum heat transfer improvement of approximately 40% was observed compared to water. Two artificial neural network (ANN) models, namely Multi-Layer Perceptron (MLP) and Self-Organizing Map (SOM), were employed to predict thermal behavior. The MLP model outperformed SOM, achieving an R² greater than 0.99 and lower error rates. To predict the Nu number with a self-organizing map (SOM) ANN with the number of 21 winning neurons with a 3–21–1 topology (including three inputs of Reynolds number, volume fraction, turbulator pitch, and one Nu number output), the obtained data were evaluated. According to the findings, at a pitch of 0.003 m and with a Reynolds number of 10,594, the convection heat transfer coefficient and the Nusselt number are 8200 (W/m²/K) and 110, respectively. This results in the optimal mode of increase for the helical tube. It should be noted that the circular motion of the fluid around the tube axis in spiral tubes is the result of centrifugal force, which causes the flow to transform from laminar to transient and then into turbulent. The experimental results showed that increasing the nanoparticle volume fraction from 0.1% to 0.5% and reducing the turbulator pitch from 0.009 m to 0.003 m significantly enhanced the Nu number by up to 38%, although the pressure drop also increased. The thermal performance evaluation criterion (PEC) reached a maximum value of 1.38 under optimal conditions. Furthermore, an ANN model with a 3–21–1 architecture and a sigmoid activation function was trained, achieving high predictive accuracy with an R-squared value (R²) of 0.989 and a mean square error (MSE) of 7.2434. Using the known enhancement techniques, this study's contribution lies in its systematic <strong>integration</strong> and <strong>multi-objective optimization</strong> within a helical coil system. The development of a high-precision ANN model provides a practical framework for designing compact heat exchangers.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100303"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147419630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Statics of nonlocal stress driven graded material beam - cauchy distribution approach","authors":"Indronil Devnath,&nbsp;Mohammad Nazmul Islam","doi":"10.1016/j.apples.2026.100306","DOIUrl":"10.1016/j.apples.2026.100306","url":null,"abstract":"<div><div>Nonlocal elasticity theories are essential for modeling size-dependent mechanical behavior in micro- and nano-structured components, yet existing stress-driven formulations rely almost exclusively on rapidly decaying kernels such as the Helmholtz or Gaussian forms. These kernels cannot represent algebraically decaying, scale-free interactions known to arise in materials with long-range microstructural coupling. This work addresses this limitation by developing the first stress-driven nonlocal Euler–Bernoulli beam model employing the Cauchy kernel. The governing equations are derived by coupling classical beam kinematics with a stress-driven integral constitutive law based on a normalized Cauchy attenuation kernel. Closed-form analytical solutions are obtained for simply supported, clamped, and cantilever beams where material gradations follow a power-law variation along the beam thickness. Parametric studies reveal that the Cauchy kernel induces stronger long-range nonlocal stiffening than exponential kernels, reduces maximum deflection more markedly, and maintains smooth convergence to the classical local model as the nonlocal parameter vanishes. These findings demonstrate that algebraic kernels fundamentally alter static response predictions and provide a physically motivated alternative for modeling nanoscale beams. The proposed formulation establishes a foundation for extending power-law-kernel SDM models to dynamics, instability, thermal fields, and experimental calibration.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100306"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147420835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal performance and entropy minimization of magnetohydrodynamic flow in a triangular domain with a rotating solid cylinder","authors":"Md. Tusher Mahmud,&nbsp;Fayes Us Shoaib,&nbsp;Sumon Saha","doi":"10.1016/j.apples.2026.100299","DOIUrl":"10.1016/j.apples.2026.100299","url":null,"abstract":"<div><div>Conjugate magnetohydrodynamic (MHD) mixed convection heat transfer in an isosceles triangular fluid domain with a heat-conducting spinning internal cylinder has been numerically examined in this study. The enclosure is filled with air, and the solid cylinder is made of low-carbon steel. A steady magnetic field is imposed in the domain, resulting in a magnetohydrodynamic effect on the system. The cylinder is placed at the center of the domain and spins in a clockwise or counterclockwise direction, inducing aiding or opposing forced flow within the system. The top surface of the domain is at an elevated temperature, while the right-tilted sidewall is at a reduced temperature, thereby enforcing a natural convection current. This conjugate thermal problem is mathematically modeled using the Navier-Stokes and energy equations, along with appropriate boundary and solid-fluid interface conditions. The numerical solutions are obtained by implementing the Galerkin finite element method. The present model is also validated before carrying out the parametric simulation for this study. The results are enumerated for the broad range of governing parameters, such as Reynolds number (31.62 ≤ <em>Re</em> ≤ 316.23), Richardson number (0.1 ≤ <em>Ri</em> ≤ 10), Grashof number (10³ ≤ <em>Gr</em> ≤ 10⁵), and Hartmann number (0 ≤ <em>Ha</em> ≤ 20) in terms of qualitative and quantitative evaluation of flow and thermal characteristics. The analysis reveals that introducing the MHD effect reduces heat transfer by approximately 5.3 % for both clockwise and counterclockwise rotations of the cylinder. It increases the average fluid temperature for clockwise rotation by up to 4.9 % and decreases it for counterclockwise rotation by approximately 2 %. However, the MHD effect reduces entropy generation as flow intensity increases, thereby reducing the irreversibility caused by fluid friction. Additionally, the clockwise rotation of the cylinder exhibits better heat transfer.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100299"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Research on thermo-mechanical coupled damage of high-temperature concrete based on close-packed model","authors":"Yi-Da Zhao,&nbsp;Xiao-Hui Liu,&nbsp;Zheng-Lin Lan,&nbsp;Zhong-Wei Yao","doi":"10.1016/j.apples.2026.100296","DOIUrl":"10.1016/j.apples.2026.100296","url":null,"abstract":"<div><div>This study employs ABAQUS numerical simulations to compare a model incorporating aggregates with a homogeneous model without aggregates. This comparison reveals the mechanical properties and micro-damage characteristics of concrete after exposure to high temperatures. The study systematically elucidates the intrinsic degradation mechanisms of concrete under thermo-mechanical coupling, spanning from macro to micro scales. It also highlights the advantages of the proposed close-packed model. The findings indicate that both peak stress and elastic modulus exhibit nonlinear reductions as temperature increases in high-temperature concrete. Stress-strain variation patterns demonstrate similarities across both models. The close-packed model effectively represents the mesoscale damage characteristics of high-temperature concrete. High temperatures significantly lower the stress threshold for damage initiation, and the damage evolution gradually slows down. The damage transitions from localized expansion at the aggregate-matrix interface to a globally diffuse expansion. Furthermore, the close-packed model effectively captures the settlement and packing characteristics of aggregates during the actual pouring process, addressing homogeneous models and random aggregate models that overlook physical processes.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100296"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Linearized instability of Couette flow in stress-power law fluids","authors":"Krishna Kaushik Yanamundra ,&nbsp;Lorenzo Fusi","doi":"10.1016/j.apples.2026.100304","DOIUrl":"10.1016/j.apples.2026.100304","url":null,"abstract":"<div><div>This paper examines the linearized stability of plane Couette flow for stress-power law fluids, which exhibit non-monotonic stress–strain rate behavior. The constitutive model is derived from a thermodynamic framework using a non-convex rate of dissipation potential. Under velocity boundary conditions, the system may admit three steady-state solutions. Linearized stability analysis reveals that the two solutions on ascending constitutive branches are unconditionally stable, while the solution on the descending branch is unconditionally unstable. For mixed traction-velocity boundary conditions, the base state is unique. Stability depends solely on whether the prescribed traction lies on an ascending (stable) or descending (unstable) branch of the constitutive curve. The results demonstrate that flow stability in these complex fluids is fundamentally governed by both boundary conditions and constitutive non-monotonicity.</div></div>","PeriodicalId":72251,"journal":{"name":"Applications in engineering science","volume":"25 ","pages":"Article 100304"},"PeriodicalIF":2.1,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146187966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}