Fracture behavior dependence on load-bearing capacity of filler in nano- and microcomposites of polypropylene containing calcium carbonate

The fracture toughness and deformation mechanism of PP/CaCO₃ (15 wt.%) composites were studied and related to load-bearing capacity of the particles. To alter the load-bearing capacity of the particles, different particle sizes (0.07–7 μm) with or without stearic acid coating were incorporated. The fracture toughness of the composites was determined using J-Integral method and the deformation mechanism was studied by transmission optical microscopy of the crack tip damage zone. It was observed that the load-bearing capacity of the particles decreased by reduction of particle size and application of coating. A linear relationship between normalized fracture toughness and inverse of load-bearing capacity of particles was found. The crack tip damage zone in composites, which consists in massive crazing, further grows by reduction in load-bearing capacity.     

Data Transformation in Cross-project Defect Prediction

Software metrics rarely follow a normal distribution. Therefore, software metrics are usually transformed prior to building a defect prediction model. To the best of our knowledge, the impact that the transformation has on cross-project defect prediction models has not been thoroughly explored. A cross-project model is built from one project and applied on another project. In this study, we investigate if cross-project defect prediction is affected by applying different transformations (i.e., log and rank transformations, as well as the Box-Cox transformation). The Box-Cox transformation subsumes log and other power transformations (e.g., square root), but has not been studied in the defect prediction literature. We propose an approach, namely Multiple Transformations (MT), to utilize multiple transformations for cross-project defect prediction. We further propose an enhanced approach MT+ to use the parameter of the Box-Cox transformation to determine the most appropriate training project for each target project. Our experiments are conducted upon three publicly available data sets (i.e., AEEEM, ReLink, and PROMISE). Comparing to the random forest model built solely using the log transformation, our MT+ approach improves the F-measure by 7, 59 and 43% for the three data sets, respectively. As a summary, our major contributions are three-fold: 1) conduct an empirical study on the impact that data transformation has on cross-project defect prediction models; 2) propose an approach to utilize the various information retained by applying different transformation methods; and 3) propose an unsupervised approach to select the most appropriate training project for each target project.     

Design and implementation of an adaptive fuzzy sliding mode controller for drug delivery in treatment of vascular cancer tumours and its optimisation using genetic algorithm tool

In this paper, the side effects of drug therapy in the process of cancer treatment are reduced by designing two optimal non‐linear controllers. The related gains of the designed controllers are optimised using genetic algorithm and simultaneously are adapted by employing the Fuzzy scheduling method. The cancer dynamic model is extracted with five differential equations, including normal cells, endothelial cells, cancer cells, and the amount of two chemotherapy and anti‐angiogenic drugs left in the body as the engaged state variables, while double drug injection is considered as the corresponding controlling signals of the mentioned state space. This treatment aims to reduce the tumour cells by providing a timely schedule for drug dosage. In chemotherapy, not only the cancer cells are killed but also other healthy cells will be destroyed, so the rate of drug injection is highly significant. It is shown that the simultaneous application of chemotherapy and anti‐angiogenic therapy is more efficient than single chemotherapy. Two different non‐linear controllers are employed and their performances are compared. Simulation results and comparison studies show that not only adding the anti‐angiogenic reduce the side effects of chemotherapy but also the proposed robust controller of sliding mode provides a faster and stronger treatment in the presence of patient parametric uncertainties in an optimal way. As a result of the proposed closed‐loop drug treatment, the tumour cells rapidly decrease to zero, while the normal cells remain healthy simultaneously. Also, the injection rate of the chemotherapy drug is very low after a short time and converges to zero.     

Toward an integrated dynamic defense system for strategic detecting attacks in cloud networks using stochastic game

Telecommunication Systems - In a complex network as a cloud computing environment, security is becoming increasingly based on deception techniques. To date, the static nature of cyber networks...     

Effect of number of layers on erosion,corrosion, and wear resistance of multilayer Cr–N/Cr–Al–N coatings on AISI 630 stainless steel

In the present study, multilayered Cr–N/Cr–Al–N coatings were prepared by cathodic arc physical vapor deposition (PVD) with different numbers of layers and the same total thickness on AISI 630 steel in an attempt to improve the wear and erosion–corrosion resistance. Structural analysis of the coatings was performed by field scanning electron microscopy, X-ray diffraction (XRD), and energy-dispersive spectroscopy. Depth profiles and roughness parameters of worn surfaces were calculated after erosion and wear tests. XRD indicated that nitride compounds were formed in multilayer coatings by PVD. The Cr–N/Cr–Al–N coating exhibited superior corrosion resistance compared with AISI 630 substrate. The erosion–corrosion results revealed that the smoothest wear track with the minimum erosion rate and wear depth was obtained for five- and seven-layered coatings. The failure mechanism of the bare substrate was influenced by plastic deformation via cutting and plowing, while the failure mechanism for coated samples was chipping and delamination. According to the wear results, the multilayer coatings showed a lower friction coefficient and better surface morphology that demonstrated their high ability for wear protection.     

Synthesis and flash sintering of (Hf1-xZrx)B2 solid solution powders

(Hf_1-xZr_x)B₂ solid solution powders were synthesized by two methods. First, solution-based processing of HfCl₄, ZrCl₄, sucrose, and H₃BO₃ was conducted followed by heat treatment in Argon to carry out the carbothermal reduction (CTR) reaction to form the diboride powders. Alternatively, in the so-called borohydride reduction (BHR) method, HfCl₄, ZrCl₄ and NaBH₄ were mixed in an Argon glove box followed by heat treatment in Argon at 700?1500 °C. The synthesized powders were characterized by XRD, SEM, TEM, EDS, and TGA, and the influence of different parameters such as starting composition, heat treatment temperature and time on products characteristics were revealed. Both CTR and BHR solid solution powders were then consolidated within ～5 min in a homemade flash sintering (FS) setup. The composition, microstructure, hardness, and thermal-oxidation properties of flash sintered ceramics were characterized, and the implication of this study and directions for future research were discussed.     

The effect of MIO pigments on corrosion resistance and adhesion of synthetic rubber-based primer

In this research, the corrosion resistance and adhesion property of a synthetic rubber-based primer reinforced with different ratios of micaceous iron oxide (MIO) pigments were studied. Coatings were applied on carbon steel panels and also on steel pipes of 219.1 mm outer diameter. Scanning electron microscopy (SEM) was used to investigate the dispersion of MIO particles in the rubbery matrix. The anticorrosion performance of the coatings was studied, using electrochemical impedance spectroscopy (EIS) and salt spray tests. In addition, the adhesion of primers to carbon steel substrates was evaluated by pull-off test. In order to investigate the effect of MIO particles on the flexibility of the pigmented primers, a cupping test was conducted. The adhesion of cold-applied tape to the formulated primers was assessed by peel adhesion test using hanging mass method. The results indicated that adding 5, 10, and 15 wt% of MIO pigments into the primer improved corrosion resistance of the coatings. An increase in the MIO loading up to 10 wt%, improved the adhesion of the primer to both steel substrate and cold-applied tape.     

Domain-Size-Dependent Residual Stress Governs the Phase-Transition and Photoluminescence Behavior of Methylammonium Lead Iodide

Methylammonium lead iodide (MAPbI₃) perovskite has garnered significant interest as a versatile material for optoelectronic applications. The temperature-dependent photoluminescence (TDPL) and phase-transition behaviors revealed in previous studies have become standard indicators of defects, stability, charge carrier dynamics, and device performance. However, published reports abound with examples of irregular photoluminescence and phase-transition phenomena that are difficult to reconcile, posing major challenges in the correlation of those properties with the actual material state or with the subsequent device performance. In this paper, a unifying explanation for the seemingly inconsistent TDPL and phase-transition (orthorhombic-to-tetragonal) characteristics observed for MAPbI₃ is presented. By investigating MAPbI₃ perovskites with varying crystalline states, ranging from polycrystal to highly oriented crystal as well as single-crystals, key features in the TDPL and phase-transition behaviors are identified that are related to the extent of crystal domain-size-dependent residual stress and stem from the considerable volume difference (ΔV ≈ 4.5%) between the primitive unit cells of the orthorhombic (at 80 K) and tetragonal phases (at 300 K) of MAPbI₃. This fundamental connection is essential for understanding the photophysics and material processing of soft perovskites.     

Nanofluids thermal conductivity prediction applying a novel hybrid data-driven model validated using Monte Carlo-based sensitivity analysis

Accurate estimation of the thermal conductivity of nanofluids plays a key role in industrial heat transfer applications. Currently available experimental and empirical relationships can be used to estimate thermal conductivity. However, since the environmental conditions and properties of the nanofluids constituents are not considered these models cannot provide the expected accuracy and reliability for researchers. In this research, a robust hybrid artificial intelligence model was developed to accurately predict wide variety of relative thermal conductivity of nanofluids. In the new approach, the improved simulated annealing (ISA) was used to optimize the parameters of the least-squares support vector machine (LSSVM-ISA). The predictive model was developed using a data bank, consist of 1800 experimental data points for nanofluids from 32 references. The volume fraction, average size and thermal conductivity of nanoparticles, temperature and thermal conductivity of base fluid were selected as influent parameters and relative thermal conductivity was chosen as the output variable. In addition, the obtained results from the LSSVM-ISA were compared with the results of the radial basis function neural network (RBF-NN), K-nearest neighbors (KNN), and various existing experimental correlations models. The statistical analysis shows that the performance of the proposed hybrid predictor model for testing stage (R = 0.993, RMSE = 0.0207) is more reliable and efficient than those of the RBF-NN (R = 0.970, RMSE = 0.0416 W/m K), KNN (R = 0.931, RMSE = 0.068 W/m K) and all of the existing empirical correlations for estimating thermal conductivity of wide variety types of nanofluids. Finally, robustness and convergence analysis were conducted to evaluate the model reliability. A comprehensive sensitivity analysis using Monte Carlo simulation was carried out to identify the most significant variables of the developed models affecting the thermal conductivity predictions of nanofluids.