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Process uncertainties have been a big challenge to the stable operation and control of process industries. The current study is based on the use of an artificial intelligence model as a surrogate in the online optimization of process conditions of reactive units of a petroleum refinery under uncertainty. Initially, a steady-state Aspen model was used to perform exergy analysis for quantifying the exergy efficiency, irreversibility, and improvement potential of the plant. The process model was then transformed to a dynamic mode by inserting ±5 % uncertainty in process conditions, i.e., mass flow rate, pressure, and temperature, to generate a dataset of 216 samples. An artificial neural network (ANN) model was developed using the dataset to predict exergy efficiency. The ANN model was used as a surrogate in GA and PSO environments to achieve higher exergy efficiency under uncertainty. The optimized process condition derived through GA and PSO based approaches were fed to Aspen model for cross-validation. The Fourier Amplitude Sensitivity Test (FAST) was performed to identify the most influential process conditions in terms of their effect on process exergy efficiency. The overall plant had an exergy efficiency, irreversibility, and improvement potential of 50.57 %, 34,955.55 kW, and 17,276.98 kW, respectively. The correlation coefficient of ANN model was 0.97432. The use of ANN as a surrogate in the optimization frameworks outperformed the standalone (SA) Aspen model of the process in attaining the highest exergy efficiency. Overall the performance of both the GA and the PSO based approaches were comparable. Based on sensitivity analysis, inlet temperatures of reactors were the most sensitive variables affecting the process exergy efficiency. The current study helps in laying a foundation for the simulation of Refinery 4.0. [Display omitted] [ABSTRACT FROM AUTHOR]

The present research, a numerical approach to examine magnetohydrodynamics (MHD) Casson nanofluid flow in a porous medium along a stretchable surface with different slips using artificial neural networks (ANNs) by taking inverse multiquadric (IMQ) radial basis function (RBF) as an activation function. i.e. ANNs-IMQ-RBF. The hybridization of genetic algorithms (GAs) and sequential quadratic programming (SQP) is used for learning in ANNs-IMQ-RBF. The PDEs representing the fluid flow are converted into a nonlinear system of dimensionless form of ODEs through an appropriate transformation while effects of variation in the values of Casson parameter (β), Brownian motion parameter (Nb), Prandtl number (Pr), stretching parameter (n), porosity parameter (P), Lewis number (Le) along with temperature slip parameter (λ 2) on velocity, temperature and nanofluid concentration are depicted through graphs. The effectiveness, convergence and accuracy of the proposed solver are validated evidently through boxplot analysis, histograms and cumulative distribution function (CDF) plots. [Display omitted] • A new ANNs-IMQ-RBF solver is designed for MHD Casson nanofluid problem in the shape of a system of nonlinear ODEs. • Velocity, temperature and concentration profiles are calculated for sundry cases of physical parameters of the interest. • Results are compared with the reference solutions of Adams numerical technique to analyze the accuracy and convergence. • Statistical operators via box plot, CDF and histograms analyses are used to verify the effectiveness and stability. [ABSTRACT FROM AUTHOR]

• Substrates inoculated with bacterial and fungal strains had highest germination percentage and earliest mean germination. • Trichoderma spp. (108 CFU mL−1) inoculated substrate produced uniform healthy marigold seedlings. • Enterobacter MN-17 (107 CFU mL−1) inoculated substrate resulted in uniform seed germination of zinnia. • Physico-chemical attributes of PGPR inoculated substrates were within recommended levels. Nursery raising of ornamental flowering annuals using soilless substrates is becoming a lucrative industry worldwide. Pakistan, being an agricultural country, still doesn't have its own cheaper and sustainable soilless substrates for high quality nursery production. Therefore, a study was conducted at University of Agriculture, Faisalabad, Pakistan and Purdue University, West Lafayette, IN, USA, for development and evaluation of a cheaper, sustainable, and easily available soilless substrate using agricultural by-products along with its value addition by inoculating it with plant growth promoting rhizobacteria. For this purpose, coco-coir, rice hulls ash and composted sugarcane pressmud were combined in ratio of 1:1:1; v/v/v and were inoculated with Bacillus (MN-54), Enterobacter (MN-17) and Trichoderma species in 108 CFU mL−1 and 107 CFU mL−1 solutions. These inoculated treatments along with uninoculated treatment, a negative control (sand: silt; 1:1, v/v) were evaluated for their physico-chemical and nursery raising aspects for 'Inca' marigold and 'Giant Dahlia Flowered' zinnia. The study revealed that Enterobacter MN-17 (107 CFU mL−1) inoculated substrate had highest water holding capacity (72.9 %), total porosity (81.04 %) and organic matter contents (92.9 %). Whereas, Trichoderma spp. (108 CFU mL−1) inoculated substrate had better electrical conductivity (1.59 dS m−1) and pH (5.64) and resulted in highest final germination percentage (93.6 %), with greatest seedling vigor index (2195), seedling shoot length (10.2 cm), root length (12.5 cm) and leaf total chlorophyll contents (57.4 SPAD) in marigold. However, in zinnia, Enterobacter MN-17 (107 CFU mL−1) inoculated substrate resulted in highest germination percentage (98.3 %), greatest seedling vigor index (3302), seedling root length (15.8 cm) and leaf total chlorophyll contents (55.8 SPAD), whereas same strain with 108 CFU mL−1 took shortest time for mean germination (3.8 days). In conclusion, substrate enriched with Enterobacter MN-17 (107 CFU mL−1) or Trichoderma spp. (108 CFU mL−1) may be used to produce high quality ornamental flowering annuals nursery. [ABSTRACT FROM AUTHOR]

The aim of study is to investigate the mass and heat transfer phenomena in hybrid hydro-nanofluidic system involving Al 2 O 3 –Cu–H 2 O over the rotating disk in porous medium with viscous dissolution and Joule heating through the stochastic solver by way of Levenberg-Marquardt backpropagation neural networks. The mathematical model in system of PDEs describes the physical phenomena of the hybrid hydro-nanofluid flow problem are converted into set of ODEs by means of scaling group transformations. The datasets are constructed by utilizing the power of explicit Runge-Kutta numerical method that help to the develop a continuous neural networks mapping. The validation, training and testing processes are utilized to learn the neural network mapping to estimate the solution of various scenarios with cases that are constructed by varying different values of physical constraints such as porosity factor, inertia coefficient, Prandtl number, Brinkman number, radiation parameter, mgnetic parameter, concentration of nanoparticles on the velocities and temperature profiles. Determination, convergence, verification and stability of Levenberg-Marquardt backpropogation neural network mappings are validated on the assessment of achieved accuracy through regression based statistical analysis, mean squared error and error histograms for hybrid hydro-nanofluidic model. • Neuro-intelligent mappings computing for viscous dissolution and Joule heating. • A computing backpropagation networks was found working for hydro-nanofluidic system. • Designed technique is implemented to measure the dynamics of different scenarios. • Study the dynamics of velocities and temperature profiles. • Determination and stability of scheme are validated on rigorous analysis. [ABSTRACT FROM AUTHOR]

The porous media transport theories are thoroughly operative to analyse transferral phenomenon in reducing the bio-convective flow instabilities and biological tissues. The present study is designed to investigate the heat transfer phenomena in nanofluidic system involving Cu − H 2 O over the stretched porous media with the strength of stochastic solver via Levenberg-Marquardt backpropagation networks. The mathematical model of physical phenomena is described in PDEs that are reduced to system of ODEs through scaling group transformations. The datasets are determined through explicit Runge-Kutta numerical method and used as a target parameter for the development of continuous neural networks mapping. The training, testing and validation processes are utilized in learning of neural network models based on backpropagation of Levenberg-Marquardt technique to determines the solution of different scenarios constructed on the various values of porosity parameter along with six different cases based on the stretching ratio values. Validation and verification of neural network model to find the solution of nanfluidic problem is endorsed on the assessment of achieved accuracy through mean squared error, error histograms and regression studies. [ABSTRACT FROM AUTHOR]

Researchers working in the field of unmanned aerial vehicles are observing rapid developments in the field, such as the use of quadrotors to grasp, manipulate, and transport objects. These advancements are made possible through sophisticated control systems, new types of actuation and sensing technologies, and a greater understanding of the aerodynamic effects and gyroscopic moment generated by the rotating blades of the quadrotor involved. Therefore, this research work proposes four novel optimized control laws, i.e., conditioned adaptive barrier-based double integral super twisting sliding mode controller (CABDIST-SMC), BF double integral SMC (BF-DISMC), BF integral SMC, and barrier function-based SMC control laws, for attitude, heading, position, and altitude trajectory tracking of the quadcopter. The non-linear model of the quadcopter is developed using the Lagrange formalism in MATLAB ODE-45 environment, including the gyroscopic moments and aerodynamic effects, and a Lyapunov stability analysis is performed to ensure the asymptotic stability of the system. A 3D-helical complex trajectory is employed to analyze the performance and consistency of the proposed optimized controllers. For performance comparison, the proposed four variants of the SMC are compared with three traditional control strategies, i.e., backstepping SMC, optimized adaptive SMC via particle swarm optimization algorithm, and improved adaptive SMC. These traditional control strategies have also been developed and tested in MATLAB-Simulink. All the controllers are optimized using the improved grey wolf optimization algorithm, and to determine the best control law, six performance indexes, i.e., mean absolute percentage error (MAPE), root mean square error (RMSE), integral square error (ISE), integral absolute error (IAE), integral time absolute error (ITAE), and integral time square error (ITSE) are used. A hardware-in-loop (HIL) test is also performed to validate the performance of the designed novel control law. The C2000 Delfino MCU F28379D launchpad is used for performing the HIL validation test. The simulated results showed that the performance of optimized CABDIST-SMC surpassed all other control laws for altitude, heading, position, and attitude trajectory tracking. It has achieved MAPE of 0.02%, RMSE of 3.9912e−5, ISE of 3.1859e−8, IAE of 7.9823e−4, ITAE of 0.0080, and ITSE of 3.1849e−7 in case of yaw angle control. The proposed control laws have successfully passed the HIL validation test, and real-time implementation of the proposed control laws indicates no significant variations compared to the simulated results. [ABSTRACT FROM AUTHOR]

This study is based on gray-box modeling for the prediction of optimum mass flow rates of inlet streams of a Plate and Fin Heat Exchanger under uncertainty. A first principle model of the Plate and Fin Heat Exchanger was developed in Aspen Exchanger Design and Rating. Genetic algorithm was integrated with the first principle model to achieve the highest possible exit temperature of the cold process stream under uncertainty. A dataset of uncertain process conditions and their corresponding optimum inlet flow rates derived through the first principle-genetic algorithm integration was used to develop an artificial neural networks model. The artificial neural networks model was then integrated with the first principle model by replacing the genetic algorithm to form a novel gray-box framework. The proposed gray box model, i.e., artificial neural networks and first principle integration, achieved a higher effectiveness and higher outlet temperature than those derived through the straight run first principle model. The performance of the gray box framework was also comparable to the first principle integrated genetic algorithm approach and significantly minimized the computation time needed for estimating the optimum process conditions. First principle integrated genetic algorithm approach enhanced the effectiveness of the straight run model by 3.05%. Performance of the proposed gray-box model was comparable to the integrated framework of the genetic algorithm with the first principle model but was significantly faster. The developed artificial neural networks model was employed as a surrogate in Sobol and FAST sensitivity analysis framework to identify the impact of input variables on output variables. The proposed gray box based method enhanced the capability of the plate and fin heat exchanger to recover energy from the process stream and its robustness to cope with uncertainty. The proposed approach is suitable for real-time application and would contribute to laying a foundation for petroleum refinery 4.0. • A Gray-box (GB) modeling framework was developed for the optimization of process conditions of a plate and fin heat exchanger of a tile industry under uncertainty. • The GB framework was based on the integration of first principle (FP) model and Artificial Neural Networks (ANN) model. • The proposed GB approach attained the optimum conditions in a significantly less period of time than the conventional FP-GA framework. [ABSTRACT FROM AUTHOR]

• Citrus reticulata was under environmental stresses. • Micronutrients and melatonin present in high concentration in ecotypes under stress. • Antioxidants were checked. • Effects of antioxidants were checked on photosynthetic pigments. • Melatonin and other antioxidants were alleviated in stress environment. A major commercial fruit in Pakistan, citrus plays an important role in the economy of the country. The current study examined antioxidant responses in citrus plants exposed to various abiotic stresses like industrial pollution, dust pollution, water logging, drought, and salinity, varying in soil properties and agro-climatic conditions, citrus species and cultivars are most susceptible to abiotic stresses in most citrus-producing countries. The effects of abiotic stresses on citrus plants and fruit physiology were studied in six different sites like Thal desert and pollution stressed areas of Faisalabad. A study of the physiological characteristics of various plants has revealed that citrus plants contain more chlorophyll as the concentration of melatonin increases. Integrative elemental and metabolic techniques have been used to study the potential role of melatonin in citrus defense against abiotic stress and to understand the interactions between melatonin and stress-related phytohormones at the elemental and metabolic levels. Citrus plants sensed abiotic stresses based on morphological observations and physiological analysis. Morphological analysis shows that the abiotic stresses adversely affected the leaves, stems and roots. Physiological analysis shows that antioxidant and micronutrients concentrations (boron (B), zinc (Zn), manganese (Mn), iron (Fe), copper (Cu), molybdenum (Mo), chlorine (Cl)) were also adversely effected in citrus plants. Iron concentration was also found higher in healthy plants with a slight difference. Results show that the activity of antioxidants was increased in stressed plants but the activity of melatonin was slightly changed in healthy plants. [ABSTRACT FROM AUTHOR]

• Riparian vegetation and Phyla nodiflora was under industrial pollution stress. • Macronutrients and melatonin present in high concentration in ecotypes under stress. • Antioxidants were checked. • Effects of antioxidants were checked on photosynthetic pigments. • Melatonin and other antioxidants were alleviated in stress environment. This study explores the macro nutritional contents and endogenous melatonin in the structural differences of riparian flora and horticultural ecotype growing in industrially contaminated environments. Water pollution caused by anthropogenic activities poses uncompromising threats to rivers and water sources, resulting in the extinction of many species and the reduction of others to endangered status. Effluents from the industrial hub of Faisalabad are discharged into the Chenab River in Punjab province. As in other parts of the world, effluents from Faisalabad industries pose a great threat to native vegetation in the area. Therefore, this study was designed to verify the presence of industrial pollution. Moreover, the effects of industrial pollution on native vegetation and the survival strategies of adapted species were also assessed. Results showed that several abiotic stresses have been mitigated by endogenous melatonin in herbs and herb-based products. The survival of herbs depends on specific changes to biochemical compounds like melatonin content and vascular structures. Overall, industrial pollution harmed herbs' metabolism and antioxidant activity, although the reactions of specific species to industrial pollution varied. Changes in the concentration of endogenous melatonin metabolism have been identified as promising indicators for industrial pollution biomonitoring. Internal alterations of plants vegetating at various industrially polluted locations contributed significantly to the high level of tolerance of these plants. The typical reaction of all herbs to increased pollution was growth modification in terms of endogenous melatonin content, moisture content, carbohydrates, crude protein, and dry matter. Most of the proximate, melatonin and chlorophyll content were significantly high in indirectly affected vegetation and high in directly affected industrial pollution. Biochemical investigation specifies that in response to industrial pollution four species Calotropis procera, Eclipta alba, Phyla nodiflora (Ornamental plant) and Ranunculus sceleratus increased the primary metabolites like melatonin, carbohydrates, crude protein, crude fibers, and fats. [ABSTRACT FROM AUTHOR]

The present study aims to provide an innovative stochastic numerical solver's application by the use of neural networks with Levenberg-Marquardt backpropagation to examine the dynamics of hydrogen possessions and variable viscosity in the fluidic system of electrically conducting copper and silver nanoparticles with mixed convection. The system of PDEs obtained by mathematical modeling of the physical phenomena are reduced into non-linear ODEs by utilizing suitable transformations. The ODEs dataset is constructed through Adams numerical solver and target parameters for input and output parameter of neural networks. The testing, validation and training processes are exploited in neural network models with learning based on backpropagation of LM method to calculate the solution for different scenarios created on variation of physical parameters of the proposed flow of Reynolds and Vogel models. Validation and verification of neural network model to find the solution of fluid flow problem is endorsed on the assessment of achieved accuracy through mean squared error, error histograms and regression studies. • Novel application of stochastic computing for fluidic system as hydrogen-possessions. • Fluidic model analyzed by exploitation with Levenberg-Marquardt backpropagation. • Dynamics of both Reynold and Vogel models are presented sundry physical parameters. • Validation are endorsed on MSE through histograms and regression analysis. [ABSTRACT FROM AUTHOR]

• Corms soaked in 5% MLE exhibited early sprouting and tallest plants in shorter time. • Corm soaking produced more flowers and marketable stems with longest vase life. • Foliar application of 3% MLE produced tallest plants with shortest production time. • MLE application significantly improved growth, quality and yield of Freesia hybrida. Moringa leaf extract (MLE) is an organic bio-stimulant that can enhance crop growth and alleviate various stresses. The use of MLE might address a number of production issues on freesia, which is a popular bulbous cut flower grown and shipped worldwide. A field study was conducted to compare the effects of corm soaking with preharvest foliar applications of MLE on various morphological, physiological and yield attributes of freesia (Freesia hybrida L.). Two experiments were conducted; in experiment I, corms were soaked in 1%, 2%, 5% or 10% MLE solution for 24 h followed by air drying before planting, while in experiment II, untreated corms were planted and MLE at 1%, 2%, 3% or 5% was sprayed until runoff at 30 and 60 days after planting. Corms soaked in 5% MLE exhibited rapid 50% sprouting (3.2 d earlier compared to control), produced tallest plants (58.6 cm) with shortest production time (9 d earlier than control), highest number of leaves per plant (21.6), leaf area (109.2 cm2), leaf total chlorophyll contents (64.1 SPAD), stem diameter (4.3 mm), number of flowers per stem (6.6), number of marketable stems (3.1) and longest vase life (9 d), while flower diameter was highest (36.7 mm) when corms were treated with 2% MLE. In experiment II, foliar application of 3% MLE at 30 and 60 d after planting produced the tallest plants (16.3 cm taller than control) with shortest production time (9 d earlier than control), greatest leaf area (88.6 cm2), leaf total chlorophyll contents (57.02 SPAD), flower diameter (34.9 mm), number of leaves per plant (21.9), stem length (53.3 cm), stem diameter (3.9 mm), number of flowers per stem (6.5), and number of marketable stems (2.8) with longest vase life (3.3 d longer than control). Results demonstrated that MLE application significantly improved growth, quality and yield of Freesia hybrida by providing growth substances and minerals required for optimal growth and may be used by the growers as corm soak or foliar application for high quality cut freesia production. Among application methods, corm soaks were better than foliar applications. [ABSTRACT FROM AUTHOR]

In this paper, Homotopy Perturbation Method is applied to solve the nonlinear MHD Jeffery–Hamel arterial blood flow problem. Primarily, two-dimensional nonlinear Navier–Stokes equations have been converted into third order one-dimensional equation by means of transformation rule. Later the solution of governed equation is obtained by using Homotopy Perturbation Method. The proposed numerical results show a good agreement with reference solution for finite interval and emphasize to understand the human arterial blood flow rate. Further, accuracy and reliability of the proposed method is checked by increasing the iteration process up to third order. Finally, the results showed that product of angle between plates " α " and Reynolds number " Re " is directly proportional to the MHD Jeffery–Hamel flow. • A computing method is designed for nonlinear MHD Jeffery–Hamel arterial blood flow problem. • In modern studies related to flow rate of blood has been observed as a cross sectional dependent and time dependent. • Comparison is presented with reported results like RK-4 numerical results verify the correctness of the scheme. • Accuracy and convergence of the solver is verified from the statistical analysis. • Proposed scheme is an alternate and reliable method for such nonlinear systems. [ABSTRACT FROM AUTHOR]

Abstract This paper investigates the interfacial bonding, interfacial nanostructure and reinforcing ability of 4 vol% multi-walled carbon nanotubes (mw CNT) and 6 vol% silicon carbide nanoparticles (SiC NP) in inductively sintered alumina ceramic hybrid nanocomposites. Homogenously dispersed reinforcing inclusions led hybrid nanocomposites to three-fold finer microstructure and respective 81% and 25% higher fracture toughness and microhardness over the monolithic alumina. Better mechanical properties were correlated with distinctive toughening mechanisms of crack-deflection as well as crack-bridging induced by reinforcing phases upon establishing sturdy interfaces with the alumina matrix. Moreover, the bonding mechanisms and nanostructural development at the mw CNT/alumina and SiC NP /alumina interfacial regions were probed by means of both qualitative and quantitative approaches. The results show strong interfacial bonding of alumina with mw CNT and SiC NP through an intermediate interfacial layer in the former case and direct physical sticking in the latter one. The bonding chemistry and nanostructural features at the interfaces are meticulously discussed. We also discuss the high temperature stability of the hybrid nanocomposites under air and argon atmospheres. Highlights • Hybrid alumina nanocomposites containing SiC/CNT were inductively sintered. • Dense nanocomposite showed higher 81% toughness and 25% hardness over alumina. • Interfacial bonding was appraised by qualitative and quantitative methods. • Bonding chemistry and nanostructures at interface are scrupulously discussed. • Hybrid nanocomposite manifested high thermal stability under inert atmosphere. [ABSTRACT FROM AUTHOR]

Abstract Hybrid alumina (Al 2 O 3) ceramic nanocomposites containing 3 wt% silicon carbide nanoparticles (SiCnp) and 0.5 wt% multilayer graphene (MLG) were consolidated to near theoretical density (99%) by an inductive hot-pressing (IHP) technology. The enhanced fracture toughness and hardness by 97% and 15% respectively, for the hybrid nanocomposites over the reference Al 2 O 3 were attributed to the higher densification, uniformly dispersed reinforcing constituents within the base matrix, toughening mechanisms of crack-deflection by the 0D-SiCnp and grain-anchoring as well as crack-bridging induced by the 2D-MLG. Thermal investigations of the hybrid nanocomposite verified the occurrence of a chemical reaction at the MLG/Al 2 O 3 interface and thermal diffusion at SiCnp/Al 2 O 3 interface during inductive hot-pressing. The bonding characteristics and interfacial microstructure were appraised by infra-red spectroscopy and high-resolution TEM microscope. Furthermore, the thermal stability in different gaseous environments and the ballistic performance of the hybrid nanocomposites were characterized and discussed. The produced hybrid nanocomposites may be suitable for high-temperature aerospace structural parts and may have applications in high-velocity impact technology. Highlights • Dense hybrid nanocomposites were prepared by inductive hot pressing method. • The hybrid nanocomposites demonstrated 97% higher fracture toughness over Al 2 O 3. • Hybrid nanocomposites manifested high thermal stability and ballistic performance. • Reinforcing phases (SiC and MLG) formed sturdy interface with Al 2 O 3 matrix. [ABSTRACT FROM AUTHOR]

Abstract This paper addresses the attitude estimation problem for rigid bodies moving in 3D-space with not negligible and time varying acceleration. For this case, we propose a simple and efficient globally asymptotic attitude estimation by using measurements from low-cost IMU and GPS. It consists of an observer based on proportional-integral (PI) term with discontinuous switching function to estimate the linear acceleration in the inertial frame for which we obtain global exponential convergence, along with a complementary filter for raw vectors from IMU to remove noise and bias. For this one, we get global asymptotic convergence of the observer taking into account gyro bias. Thereafter the filtered data is processed with an algebraic algorithm (e.g. TRIAD) for the attitude estimation. Note that when no gyro bias is considered, we obtain global exponential convergence for the overall system. To show the importance of filtering these raw vectors and the convergence behaviour of the proposed observers, simulation results are also given. [ABSTRACT FROM AUTHOR]

Climate change is posing stresses on water resources, food security, population, environment and economy of the southern Punjab. Integrated climate change risk assessment is carried out using assessed likelihood approach for defined mean, hot & dry, central, warm & wet climate models over selected time slices and adaptation plans. Climate models are based on the 5 th , 50 th & 95 th percentiles of PRECIS RCM projections of temperature & precipitation under IPCC A2 & A1B scenarios. Four time slices 2015, 2035, 2065 and 2085 are selected to assess the temporal climate change risk and to evaluate the performance of selected adaptations to reduce climate threats over considered assets. Results are presented in terms of risk indices and risk reduction units (RRUs). In first half of the 21 st century, climate change risk will continue to increase from current level and is high (>10) in most of the selected time slices. Maximum ensembles of climate models, time slices and adaptation plans observe moderate (37–40 RRUs) and high (40–55 RRUs) risk class. Cumulative risk has been calculated through integration of sectoral sensitivity e.g. population density, land use, food security and multidimensional poverty to climate change risk class using AHP and overlaying in GIS environment. About 90% and 83% area of southern Punjab is falling in high cumulative risk. About 13% area, comprising Muzaffargarh and Rajanpur district is under very high cumulative risk. Water induced adaptations like development of water resources, dam & flood control protection, temporary flood barriers and water resource acquisition are the preferred and suitable adaptations as these observed >100 RRUs for most of the ensembles. Assessing baseline vulnerability and sectoral sensitivity to climate stimuli are the hot spots requiring priority attention and firm decision making by disaster management authorities and communities residing in southern Punjab. [ABSTRACT FROM AUTHOR]

This article presents the development of multi-mode vibration based electromagnetic type micro power generator (MPG). Along the stainless steel planar spring (SPS) and magnet assembly, a movable coil (MC) is a novel addition to the resonant frequencies of the device. The design with MCs on either side of the magnet provide higher power generation for the same footprint of the device at multiple modes. Computer numerical control (CNC) milling machine, wire-cut electric discharge machine (EDM) and 3D printer are used to fabricate the MCs, SPS and spacers respectively to yield a MPG with an overall volume of 29.03 cm 3 . The developed MPG is characterized to generate power at four different resonant frequencies below 100 Hz. At 3 rd mode (82 Hz) the developed MPG is able to produce a maximum 33 mV open circuit voltage, 14.5 mV load voltage and a power of 68.4 μW. Furthermore, the present development and experimentation of the MPG with a yielded power density of 2.35 μW/cm 3 at 2 g acceleration can be regarded as guidelines for multi-mode devices. [ABSTRACT FROM AUTHOR]

Narcissus ( Narcissus tazetta L.) and tuberose ( Polianthes tuberosa L.) are commercially important cut flowers being grown in various countries, but their postharvest handling procedures need to be optimized to preserve quality and extend vase life. Effect of storage method (wet vs. dry) and durations were compared to improve the postharvest performance of cut narcissus and tuberose stems. Increasing storage duration reduced vase life, but more rapidly for stems stored dry compared to wet storage for both tested species. Narcissus stems last 1.4 d longer when stored in buckets containing water, while tuberose spikes lasted 1.2 d longer than dry-stored stems. Moreover, as storage duration increased from 0 to 6 d for narcissus and to 12 d for tuberose vase life gradually decreased. Narcissus stems stored up to 2 days at 3 ± 1 °C had a similar vase life to unstored stems, while storage duration of 4 or 6 days reduced vase life by 1.2 or 1.5 d, respectively. For tuberose, vase life decreased by 0.7 days when stored for 3 days to 2.1 d when stems were stored for 12 d. Water uptake also gradually decreased with increase in storage duration. In summary, storage in water may be used for short durations for holding cut stems of narcissus and tuberose. [ABSTRACT FROM AUTHOR]

Vibration and acoustic energies are widely harvested for the power requirements of wireless sensor nodes (WSNs) but rarely we can see the simultaneous conversion of vibration and acoustic using a single energy harvester. To attain the self-sustained long-standing operation of WSNs, we present a novel hybrid vibro-acoustic energy harvester (HVA-EH) utilizing a spherical magnet to harvest acoustic and vibration energies at the same time. The development, characterization, and power management of the proposed HVA-EH are illustrated in this paper. The Prototype comprises of 3D printed structure, a cantilever beam with a cavity for the spherical magnet which is used to add more resonance to the system, a Helmholtz cavity (HC) and a flexible micro planar coil (FMPC). Due to base excitation, the cantilever beam vibrates and as a result, the magnet oscillates, while on the other side the acoustic waves enter the HC through its neck and oscillate the FMPC. Because of these oscillations, the FMPC experiences variation in magnetic flux and an electromotive force is generated. The device was tested under standalone vibration and acoustic environment as well as under a hybrid environment and the output parameters were noted. It was observed that when the device was exposed to a hybrid environment, the results showed enhanced output parameters and generates higher energy. By adding vibration to the standalone acoustic device, an increase of 44.72% was observed in the output power. The developed prototype was also connected to a power management circuit developed in this work, and an output DC power of 5056 μW was achieved. Additionally, a 1.5 V AAA battery was successfully charged by the HVA-EH in an hour's time duration. The output power produced by the novel HVA-EH is significant and is quite enough to operate the WSNs. This work paves a new way for developing hybrid environment-type energy harvesters for efficiency enhancement. [Display omitted] • A novel spherical magnet-based electromagnetic type of hybrid vibro-acoustic energy harvester is presented. • The developed prototype shows its effectiveness by harvesting vibration and acoustic energies simultaneously. • A 1.5 V battery was successfully charged by the developed prototype in an hour time duration. • A maximum output DC power of 5056 μW was achieved at an acceleration level of 0.3 g under sound level pressure of 130 dB. [ABSTRACT FROM AUTHOR]

The current study explores a new direction of research in which the magnetohydrodynamic (MHD) effects on two-dimensional nanofluid boundary layer flow under the influence of radiation are investigated in a porous medium using inverse multiquadric (IMQ) radial basis neural networks (RBNNs) i.e. IMQ-RBNNs. A quite new computational approach is used here in which artificial neural networks (ANNs) acquired IMQ radial basis function as an activation function in an amalgam of globally searching solver named genetic algorithms (GAs) together with an efficient local searching sequential quadratic programming (SQP) solver. The basic equations that represent the fluid flow are first obtained in the form of partial differential equations (PDEs) and then converted into dimensionless ordinary differential equations (ODEs) by applying similarity transformations. The dynamical properties of the nanofluid including velocity, temperature along mass concentration are analyzed by varying physical parameters including radiation parameter, magnetic parameter, constant moving parameter, heat source parameter, Brownian motion parameter and Eckert number. Moreover, the escalation in the value of the constant moving parameter diminishes the fluid velocity but this effect is reversed in the case of magnetic parameter. The increasing values of heat source parameter, Brownian motion parameter and Eckert number inflate the nanofluid temperature but a reversal effect is observed in the case of radiation parameter. It is also observed that larger values of heat Brownian motion parameter and radiation parameter amplify the nanofluid concentration but an opposite effect is obtained in the case of heat source parameter. The numerical results of the proposed model obtained through the IMQ-RBNNs based solver optimized with GASQP algorithm. The efficacy and tendency of rapid convergence of the proposed solver are endorsed through graphical illustrations and tables using well-known statistical operators with various fitness analyses on MATLAB are also part of this research. [ABSTRACT FROM AUTHOR]

Synergic role of 2 wt% MWCNTs and 5 wt% SiC nanoparticles on the structure and properties of the hybrid alumina nanocomposites fabricated through fast sintering route was studied. The hybrid nanocomposite demonstrated fourfold reduced grain size with substantial improvement in fracture toughness (110%) and hardness (30%) against bench-marked monolithic Al 2 O 3 . Fine-grained microstructure and the harder SiC nanoparticles led hybrid nanocomposite to superior hardness while better toughness was attributed to the synergic toughening mechanisms induced by the 0D SiC nanoparticles and 1D MWCNTs. The stability of MWCNTs and SiC nanoparticles and their interfaces with Al 2 O 3 matrix were appraised by combined electron microscopes. [ABSTRACT FROM AUTHOR]

Basement complex of the Aravalli craton (NW India) known as the Banded Gneissic Complex (BGC) is classified into two domains viz. Archean BGC-I and Proterozoic BGC-II. We present first comprehensive geochemical study of the Archean metasedimentary rocks occurring within the BGC-I. These rocks occur associated with intrusive amphibolites in a linear belt within the basement gneisses. The association is only concentrated on the western margin of the BGC-I. The samples are highly mature (MS m ) to very immature (MS i ), along with highly variable geochemistry. Their major (SiO 2 /Al 2 O 3 , Na 2 O/K 2 O and Al 2 O 3 /TiO 2 ) and trace (Th/Sc, Cr/Th, Th/Co, La/Sc, Zr/Sc) element ratios, and rare earth element (REE) patterns are consistent with derivation of detritus from the basement gneisses and its mafic enclaves, with major contribution from the former. Variable mixing between the two end members and closed system recycling (cannibalism) resulted in the compositional heterogeneity. Chemical index of alteration (CIA) of the samples indicate low to moderate weathering of the source terrain in a sub-tropical environment. In A-CN-K ternary diagram, some samples deceptively appear to have undergone post-depositional K-metasomatism. Nevertheless, their petrography and geochemistry (low K 2 O and Rb) preclude the post-depositional alteration. We propose non-preferential leaching of elements during cannibalism as the cause of the deceptive K-metasomatism as well as enigmatic low CIA values of some highly mature samples. The Archean metasedimentary rocks were deposited on stable basement gneisses, making the BGC-I a plausible participant in the Archean Ur supercontinent. [ABSTRACT FROM AUTHOR]

• Development of detailed thermal modeling of a furnace in Aspen HYSYS and Aspen exchanger design and rating (EDR) environment. • Implementation of genetic algorithm (GA) based optimization technique for the maximization of efficiency. • Artificial neural network (ANN) model training and validation on the data generated from interfacing of Aspen HYSYS and MATLAB software. • Exergy analysis and comparison of the exergy efficiency predicted by the straight run and ANN. • Development of a graphical user interface (GUI) based on the proposed ANN model. The current work developed an integrated framework of artificial intelligence and genetic algorithm (GA) for the furnace of a petroleum refinery to predict the optimum amount of excess air and mass flow rates of crude oil and fuel stream in the presence of uncertainty in process conditions. A furnace model was built in Aspen Exchanger Design and Rating (EDR) using industrial data. Then interface between the furnace model and MATLAB was established using the com server. Data points were generated by varying the crude oil composition as well as the inlet temperature and pressure of cold crude oil, fuel, and air stream through ±1%, ±2%, ±3%, ±4%, and ±5% change in their original values. Then GA was used to determine the optimal amount of excess air and mass flow rates of crude oil and fuel for each data point. In total, 360 data points were generated and 70 % of the data set was utilized for training an Artificial Neural Networks (ANN) model while the remaining data points were evenly distributed for its validation and testing. The proposed ANN model achieved high performance with a correlation coefficient of 0.99984. Furthermore, exergy analysis of the standalone furnace model, the GA based optimized model, and the ANN integrated model was performed. The GA based approach outperformed the standalone model in terms of exergetic efficiency. On the hand, the ANN based framework achieved comparable exergetic efficiency to the GA based approach within significantly lesser period of time. [ABSTRACT FROM AUTHOR]

• Process modeling and analysis of the oil shale retorting process. • Hybrid modeling by using MATLAB, Excel, and Aspen Plus software. • Comparison of Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques for the prediction model. • Using Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of oil shale in retorting process. • Sensitivity analysis of oil shale retorting process through Sobol and Fourier Amplitude Sensitivity Test (FAST) techniques. Shale oil reserves exploration is getting huge investments due to the depletion of conventional reserves. Computational methods have been used to realize optimum design and operation of shale oil and gas reserves exploration and processing. The uncertainty associated with the composition of shale reserves and operating conditions during processing put a challenge to the realization of high yield and mitigation of environmental impact. In the current work, machine learning (ML) based models are proposed for the estimation of the yield and environmental impact of the oil shale retorting process under uncertainty. An artificial uncertainty of 1% was inserted in feed composition and process conditions of an Aspen model of the process to generate data for the development of the ML models. Artificial Neural Network (ANN), Least Square Boosting (LSB), and Bagging techniques were compared to find the best ML model. ANN models, with the highest correlation coefficient of 0.995 and 0.999 for the oil yield and CO 2 content respectively, are used as surrogates in a Polynomial Chaos Expansion (PCE) framework for the uncertainty analysis of the process. For 1% uncertainty in feed composition and process conditions, a mean absolute deviation of 0.319 and 0.580 was obtained for the oil yield and carbon dioxide emissions respectively. To find the hierarchy in the process inputs in terms of their effect on the oil yield and carbon dioxide emissions, the ANN model is used as a surrogate in sensitivity analysis through Sobol and Fourier Amplitude Sensitivity Test (FAST) indices. The most sensitive input variables were feed temperature and air molar flow rate. The proposed modeling framework will provide a base for future real-time monitoring and analysis of the oil shale retorting processes. [ABSTRACT FROM AUTHOR]

In this study, an artificial intelligence-based framework was developed to monitor the efficiency of the carbon capture unit, i.e., the Sour Compression Unit (SCU) and a cryogenic unit, in a cement manufacturing plant. Initially, an Aspen Plus-based model of the SCU and the cryogenic unit was developed. The process model was then transformed into dynamic mode through the interfacing of MATLAB and Excel with Aspen Plus to capture real-time process behavior. Five hundred fifty data samples were generated by varying the process conditions, i.e., inlet water flow rate, temperature, pressure, etc. The dataset was then used to develop ensemble models. Output compositions of the models were CO 2 , SO 2 , and NO. The ensemble models were used as surrogates in Sobol and Fourier Amplitude Sensitivity Test (FAST) frameworks to find the most sensitive process conditions that affect the efficiency of the process. The correlation coefficients based on the ensemble models for estimation of the composition of CO 2 , SO 2 , and NO, were 0.9888, 0.9663, and 0.9970, respectively. Based on the sensitivity analysis, the inlet flue gas temperature and pressure were the dominant variables that affect the mole fraction of SO 2 and NO in the flue gas. Besides, the CO 2 recovery was found heavily dependent on temperature of the flash tanks. The proposed framework was highly accurate and will provide a base for real-time implementation of the concept of smart factory in cement manufacturing plants. [ABSTRACT FROM AUTHOR]

In this research-based study, the electro-magneto impacts are numerically observed during Darcy-Forchheimer (DF) viscous fluid flow having nonlinear form of thermal radiation. This two-dimensional fluid flow over a stretchable sheet is exposed to joule heating, thermal convection and viscous dissipation. The entire computational work is successfully performed using reliable neuro computing technique (NCT) through a viable combination of genetic algorithms (GAs) a well-known global solver and a famous local solver named sequential quadratic programming (SQP) i-e NCT-GA-SQP. Partial differential equations (PDEs) that express the DF viscous fluid flow are successfully converted into a third order nonlinear system of ordinary differential equations (ODEs) through manipulation of similarity variables. Numerical illustrations with strong accuracy and effective graphical representations are adopted to visualize the velocity profile, thermal distribution and concentration of fluid flow due to the variational effects of various physical parameters namely the electric field parameter (E), magnetic parameter (M), the porosity parameter (λ), the radiation parameter (Rd), the Prandtl number (Pr), the chemical reaction parameter (γ) and the Schmidt number (Sc). The reference solutions attained through well-known Adams method are used to validate the numerical results gathered through complexity analysis and different statistical operators and are closely matched which guarantees the reliability of the proposed solver. [ABSTRACT FROM AUTHOR]

Effects of paclobutrazol on postharvest ethylene sensitivity of potted cuphea ( Cuphea hyssopifolia Kunth) and petunia ( Petunia × hybrida Vilm.) plants, and uniconazole on portulaca ( Portulaca oleracea L.) plugs were evaluated. Paclobutrazol was applied as a drench at 0, 1, 2, or 4 mg L −1 per 12.5 cm pot for cuphea, or 0, 2, 4, or 8 mg L −1 per 15.2 cm pot for petunia, while uniconazole was applied at 0, 2.0, 4.0, or 8.0 mg L −1 with a volume of 0.21 L m −2 as a foliar spray for portulaca plugs. With an increase in paclobutrazol dose or uniconazole concentration, plant growth [plant height and diameter (potted plants only), shoot fresh weight, or dry weight] was controlled for all species tested. While use of paclobutrazol or uniconazole controlled excessive plant growth of all three species, the plant growth retardants had no effect on postharvest ethylene sensitivity as most flowers abscised from all plants exposed to exogenous ethylene. However, both paclobutrazol and uniconazole reduced postharvest flower abscission of plants not treated with exogenous ethylene. During simulated shipping and marketing, paclobutrazol and uniconazole maintained darker foliage and controlled height. Drench applications of 2–4 mg L −1 paclobutrazol for cuphea and 4–8 mg L −1 for petunia potted plants and spray applications of 4–8 mg L −1 uniconazole for portulaca plugs produced compact, high quality plants while also extending postharvest longevity and quality. [ABSTRACT FROM AUTHOR]

Sputter deposited thin film AlN:Er (1 at.%) emits at 554 nm and 561 nm as a result of 2 H 11/2 → 4 I 15/2 and 4 S 3/2 → 4 I 15/2 transitions under 532 nm NdYAG laser and 783.3 nm crystal laser excitation. An external magnetic field of 0.1 T enhances the green emission and splits the 4 S 3/2 energy level in two sub-levels with a difference of 0.013 eV. The splitting of energy level produces new emission from Er 3+ with a wavelength of 564.5 nm. Infrared emission is also observed at 1552 nm as a result of 4 I 13/2 → 4 I 15/2 transition. Enhanced luminescence shows the suitability of Er 3+ for high efficiency optical devices. [ABSTRACT FROM AUTHOR]

• Various homemade folk floral preservatives extended the vase life of cut lisianthus stems. • Stems pulsed with 2% sucrose + 150 mg L−1 citric acid for 24 h had longest vase life (10.3 days). • Continuous vase application resulted in longer vase life extension compared to pulsing. • Continuous use of sugar with lemon/lime juice produced a similar vase life as commercial preservatives. Lisianthus (Eustoma grandiflorum L.) is one of the most popular cut flowers worldwide due to its multi-flowered stems having single, semi-double and double rose-like flowers in a variety of colors. However, its postharvest life can be short if not handled properly. A study was conducted to evaluate the effectiveness of various homemade folk floral preservative solutions applied as pulses or vase solutions on cut lisianthus stems. Both experiments were laid out individually in completely randomized design, with five replications of two stems each and evaluated in a vase life evaluation room set at 20±2 °C temperature and 50±10% of relative humidity along with 12 h of light from white fluorescent tubes. Results revealed significant differences among different pulsing treatments as 2% sucrose + 150 mg L−1 citric acid had longest vase life (10.3 days), whereas, 2% sucrose + 3 mL L−1 lime juice, 2% sucrose + 5% aloe vera gel, 2% sucrose + 30 mL L−1 moringa leaf extract (MLE) and 2% sucrose + 15 mL L−1 bleach had best flower quality (8.3) and were statistically similar. Moreover, (50:50) lemon/lime soda and water had highest water uptake (98 mL) and ion leakage (326.8%), whereas, 2% sucrose had highest (735 µs cm−1) change in electric conductivity. Among vase treatments when compared to commercial solutions, Floralife Clear Professional Flower Food, Chrysal Clear Universal Flower Food and 2% sucrose + 4 mL L−1 lime juice had longest vase life (16.6, 15.9 and 15.7 days, respectively) and were statistically similar, whereas, highest microbial colonies (6.14 CFU mL−1) were observed in solution having 5% sucrose only and the lowest with the commercial solutions. In summary, lisianthus stems placed in vase treatments had longest vase life, while those pulsed ones had better visible quality and use of sugar with lemon/lime juice as a vase solution produced a similar vase life as commercial preservatives. [ABSTRACT FROM AUTHOR]

In this paper, a computational technique is introduced for the solution of the first order multi-pantograph differential equation (MPDE) through some well-known optimization algorithms like sequential quadratic programming (SQP) and Active Set Technique (AST). Furthermore, artificial neural network (ANN) is used for networking of the first order multi-pantograph differential equation in used to provide mathematical model based on unsupervised error for equation. Moreover, mathematical modeling has been performed perfectly through multi-runs for simulation to justify the better convergence of the solutions. Also, two examples are presented to exhibit the aptitude of the method SQP and AST. The comparative study will be made with reported techniques such as variational iteration technique (VIT) [6] and collocation based on Bernstein polynomial method (BCM) [6]. [ABSTRACT FROM AUTHOR]

Magnesia tuned alumina ceramic nanocomposites, reinforced with multi-walled carbon nanotubes, were condensed using pressureless and hot-press sintering processes. Densification, microstructure and mechanical properties of the produced nanocomposites were meticulously investigated. Electron microscopy studies revealed the homogenous carbon nanotube dispersion within the alumina matrix and confirmed the retention of carbon nanotubes' distinctive tubular morphology and nanoscale features during the extreme mixing/sintering processes. Pressureless sintered nanocomposites showed meagre mechanical responses due to the poorly-integrated microstructures with a slight improvement upon magnesia addition. Conversely, both the magnesia addition and application of hot-press sintering technique resulted in the nanocomposite formation with near-theoretical densities (~ 99%), well-integrated microstructures and superior mechanical properties. Hot-press sintered nanocomposites incorporating 300 and 600 ppm magnesia exhibited an increase in hardness (10 and 11%), flexural strength (5 and 10%) and fracture toughness (15 and 20%) with respect to similar magnesia-free samples. Compared to monolithic alumina, a decent rise in fracture toughness (37%), flexural strength (22%) and hardness (20%) was observed in the hot-press sintered nanocomposites tuned with merely 600 ppm magnesia. Mechanically superior hot-press sintered magnesia tailored nanocomposites are attractive for several load-bearing structural applications. [ABSTRACT FROM AUTHOR]

Eremurus, commonly known as Foxtail lily, is a novel, commercially important specialty cut flower, but postharvest handling procedures need to be optimized for longest vase life. Cut inflorescences of ‘Line Dance’ and ‘Tap Dance’ eremurus were evaluated for optimal harvest stage, harvest procedures, effects of anti-ethylene agents, and storage methods and duration. Also, the effects of commercial hydrators, hydrators along with holding preservatives, or continuous use of floral foam saturated with or without commercial preservatives were investigated. Inflorescences of both cultivars harvested with 0 florets open had longer vase life than stems harvested with 3–5 rows of florets opened. Handling of ‘Line Dance’ stems in water after harvest extended the vase life, but reduced the vase life of ‘Tap Dance’ compared to inflorescences handled dry. Application of anti-ethylene agents had no significant effect on eremurus longevity. Inflorescences of both cultivars stored for 1 week had similar vase life as of unstored stems irrespective of dry or wet storage. Storage longer than 1 week greatly reduced the vase life of both cultivars. Use of either or both hydrator and holding preservatives did not result in significant differences in vase life for both cultivars, while pulsing with 10% sucrose supplemented with an antimicrobial agent, isothiazolinone at 0.007 mL L −1 , for 24 h extended vase life by 2.2 or 1.9 d for ‘Line Dance’ and ‘Tap Dance’ inflorescences, respectively, compared with tap water. Inflorescences of ‘Line Dance’ kept continuously in commercial preservatives had longest vase life compared with tap water and use of floral foam reduced vase life. However, inflorescences of ‘Tap Dance’ kept continuously in floral foam saturated with tap water, or just Floralife Clear Professional Flower food had longest vase life. [ABSTRACT FROM AUTHOR]

Abstract: Nanocomposites of Y2O3 doped Al2O3 reinforced with 2wt% MWCNTs were fabricated by hot-pressing. The influence of a small Y2O3 addition (300ppm) on the microstructure and mechanical properties of CNT-reinforced Al2O3 was investigated. The resulting Y2O3 doped Al2O3-CNT nanocomposites showed near theoretical densities (>99%) with an astounding 5 times finer grain size and substantial improvements in the fracture toughness (40%), flexural strength (20%) and hardness (18%) than pure Al2O3, prepared under identical experimental conditions. The Y2O3 addition improved the density of the matrix, eliminated the residual flaws and formed YAG (yttrium–aluminium–garnet) particles at the grain boundaries in the nanocomposites. The submicron YAG particles, together with the CNTs, promoted the Al2O3 matrix to form fine-grained microstructures and altered the fracture mode, thus leading to higher toughness and other mechanical properties. The improved mechanical properties allows for the Y2O3 doped Al2O3-CNT nanocomposites to be used for applications in load bearing structures. [Copyright &y& Elsevier]

• A novel hybrid vibro-acoustic energy harvester (HVA-EH) using electromagnetic transduction was developed. • The developed prototype can harvest low-frequency vibration and acoustic energy simultaneously. • The flexible micro planar coil in the harvester contributes to multiple natural frequencies. • A maximum power output of 656 μW was achieved at an acceleration level of 0.3 g under sound level pressure of 130 dB. • Demonstrates the possibility of new solutions to the problems of WSNs utilizing both sound and vibration energy resources. Condition monitoring systems (CMS) play a significant role in enhancing performance and lowering failure losses in any industrial setup. Appropriate wireless sensor nodes (WSNs) used in CMS will permit the user to check the live status of machines, however, these WSNs are facing the periodic replacement and recharging problems of batteries. Energy harvesters, used to scavenge ambient energy, are viable alternatives to batteries for achieving the self-sustained long-term operation of WSNs. Ambient vibration and acoustic energies are abundantly available in the industrial environment. The currently available energy harvesters are utilizing only vibration or acoustic energy, however, research on harvesters using both vibration and acoustic sources is missing. We present a new hybrid vibro-acoustic energy harvester (HVA-EH) using electromagnetic transduction, where the power to the WSNs can be harvested from both vibration and acoustic sources simultaneously. The architecture, working principle, fabrication, and experimentation of the HVA-EH are discussed. The HVA-EH was operated at a low vibration and acoustic frequencies and low acceleration and sound pressure levels (SPL). At matching impedance, the maximum power generated by the HVA-EH under vibration energy at zero SPL is 372 μW at acceleration level of 0.3 g and under acoustic energy at zero acceleration level is 271 μW under SPL of 130 dB. While, under a hybrid environment, the HVA-EH generates maximum power of 153 μW, 361 μW, and 656 μW at vibration levels of 0.1 g, 0.2 g, and 0.3 g respectively, under SPL of 130 dB at 2nd acoustic mode (495 Hz). The results are promising and the ability of harvesting low-frequency sound and vibration simultaneously makes the HVA-EH an energy efficient system. Furthermore, this study demonstrates the possibility of new solutions to the problems of WSNs utilizing both sound and vibration energy resources. [ABSTRACT FROM AUTHOR]

Conventional fuels for power systems are now plaguing the environment more than ever before. Efforts to curb the environmental impacts of fossil fuels are now driving modern microgrids (MGs) to utilize green energy sources. In this regard, DC microgrids are well known for their versatility. Considering the rising significance of DC-MGs in future, this paper presents an aggregated DC microgrid model comprising of wind resource, photovoltaics, fuel cell, battery, and a supercapacitor. The model also includes DC–DC power converters and a two-level control structure. Firstly, the high-level of the proposed control structure constitutes an artificial neural network for maximum power point tracking and an intelligent energy management powered by a fuzzy logic controller. At this control level, the unified operation of the fuel cell and storage components under various dynamic conditions is ensured. Furthermore, the low-level control structure is based on an adaptive terminal sliding mode non-linear controller that derives the control signal for the converters. Finally, results showed that the energy management system along with the adaptive terminal sliding mode control successfully regulated the DC bus voltage by keeping the sources within their operational constraints. Moreover, the proposed non-linear controller demonstrated model's robustness, successfully converged the sliding surface to zero in finite time, ensured global asymptotic stability and effectively dealt with the model uncertainties. All the simulation leading to these results were performed in MATLAB/Simulink and further evaluation was carried out by hardware in loop experiments using C2000 Delfino microcontroller F28397D launchpad. • An optimum design of hybrid DC microgrid integrated with two renewable energy resources, an auxiliary source and hybrid energy storage system. • Multi-level control based on ANN, fuzzy Logic and adaptive terminal sliding mode non-linear controller for power balance and control. • HIL verification of controller performance using Delfino Microcontroller F28397D launchpad. [ABSTRACT FROM AUTHOR]

Machine condition monitoring (MCM) plays an important role in reducing failure losses in the industry. Appropriate wireless sensor nodes (WSNs) used in MCM will allow the user to check the live status of machines. WSNs have numerous features, however, there are a lot of challenges in deploying the WSNs. One of the main challenges is the periodic replacement of batteries. Recently promising energy harvesting technologies have attracted the attention of researchers because of their potential to make the WSNs autonomous. This paper outlines the scopes of energy harvesters, potential ambient energy sources, and different energy harvesting techniques along with their advantages and disadvantages. In addition, various energy harvesting approaches established for WSNs have been reviewed and compared to enable easy selection of the best possible approach for MCM system. To provide intuition into future research prospects, the challenges faced by energy harvesting systems are summarized. [ABSTRACT FROM AUTHOR]

• Magnetic chamber was used to align the steel fibers in fresh cementitious composites. • SEM, WFM and DKFC were used to compare the fracture parameters of SFRC and ASFRC. • The influence of specimen size and V f on fracture parameters were studied. • The fracture properties were improved significantly by alignment of steel fibers. • The fracture properties of ASFRC specimens with V f 0.8% are more or equal to that of SFRC with V f 1.2%. Experimental research was carried out to evaluate the comparative fracture performance of random steel fiber-reinforced composite (SFRC) and aligned steel fiber-reinforced composite (ASFRC). The desired direction of steel fiber in the fresh cementitious mortar was achieved by the electromagnetic field. Three-point bending tests were conducted on geometrically similar notched beam specimens. Various dimensions and steel fiber contents by volume V f were considered to study fracture performance and investigate the size effect. The fracture parameters were analyzed by nonlinear fracture models, namely the size effect model (SEM), work of fracture method (WFM), and double- K fracture criterion (DKFC). Initial fracture energy, critical fracture toughness K IC , length of fracture process zone, and critical tip opening displacement from SEM, total fracture energy, and characteristic length from WFM was studied for SFRC and ASFRC. The initial cracking load was detected with strain gages to evaluate initial fracture toughness K IC ini. Unstable fracture toughness K IC un from DKFC with special emphasis on size effect was obtained. The results show that the rise of specimen depth has no considerable effect on the K IC un while the size effect of K IC ini is not significant. As the increment of V f , both the K IC ini and K IC un show a linear ascending trend and both of them are greater for ASFRC than those of SFRC specimens. It is found that there exists a strong correlation between the fracture toughness measured by DKFC and SEM (K IC from SEM ≅ K IC un from DKFC). Generally, the fracture performance of ASFRC relative to that of random SFRC is improved by more than 80%. [ABSTRACT FROM AUTHOR]

The aim of this study is to analysis the mass and heat transfer in radiative three dimensional flow of hybrid nanofluid over the stretchable sheet by exploiting the strength of integrated computational intelligent algorithm by utilization of Gaussian wavelet neural networks (GWNNs) trained with the genetic algorithms (GAs) based global search supported with sequential quadratic programming (SQP) based local refinements i.e., GWNN-GA-SQP. The mean squared error based cost function is developed for the fluidic problem by applying Gaussian WaveNet GWNNs optimize with GAs and SQP. The numerical outcomes of the fluidic model are obtained by the proposed GWNN-GA-SQP solver to examine the thermal and velocities profile effect for three physical quantities based on magnetic parameter, nanomaterial concentration and transformated angular velocity. Moreover, a exhaustive analysis of the numerical solutions of GWNN-GA-SQP solver with reference Adams method endorse the stability, accuracy and consistency on multiple autonomous runs through different statistical performance operators and complexity analysis. [Display omitted] [ABSTRACT FROM AUTHOR]

Mueller matrix polarimetry (MMP) is a fast and non-invasive optical technique for characterization of turbid media such as biological tissues, where the experimental Mueller matrix M contains the complete information of polarization properties of the sample. The individual optical properties in M are present in a complex interwoven way and need to be extracted; Mueller matrix polar decomposition (MMPD) and Mueller matrix transformation (MMT) are two widely used methods for this purpose. Here, we compared these two methods (i.e. , MMPD and MMT) and assessed the possible correlation between their corresponding parameters in a comprehensive sample cohort containing different types of tissues and associated pathologies (n = 47), tissue phantoms (n = 21) and standard optical components (e.g. , air, polarizer, quarter wave plate, etc.) (n = 09). Specifically, we calculated two sets of optical variables (Δ T , b) and (δ , t) representing depolarization and retardance for the MMPD and MMT methods, respectively. Qualitative correlation via graphical tools (i.e. , violin, parallel coordinate, scatter, Bland and Altman plots) in tandem with correlation coefficients was investigated. The parameters of both methods are indicative of the structural features of the turbid samples; however, the MMT parameters (b , t) give higher values than the MMPD parameters (Δ T , δ) with moderate statistical correlation. This study assessed the link between the two methods and would provide a useful reference for such comparative analyses. [ABSTRACT FROM AUTHOR]

We report herein four new bioactive triorganotin (IV) amide based carboxylates. • Synthesis and characterization of amide based triorganotin(IV) carboxylates. • Complexes 1–4 have shown 1D polymeric structures in solid state. • Complexes 2, 3 and 4 have shown good anticancer activities while 2 was found the most active in antileshmanial activity. Four new triorganotin(IV) amide based carboxylates of general formula R 3 SnL1and R 3 SnL2, where R = Me(1 , 3) and n -butyl (2 , 4), and L1= (Z)-4-(p -methoxyphenylamino)-4-oxo-2-butenoic acid (HL1) L2 = (Z)-4-(3,5-bis(trifluoromethyl)phenylamino)-4-oxo-2-butenoic acid (HL2) have been synthesized by refluxing methanolic solution of organtin(IV) chloride and ligand (1:1 M ratio). The synthesized compounds were characterized by FT-IR, elemental analysis, NMR (1H, 13C, 119Sn &19F) and single crystal X-ray crystallography. The ligands coordinate to tin atom through oxygens (carboxylate and amide) showing distorted trigonal bipyramidal geometry with polymeric bridging behavior in solid state. However, the geometry is switched over from trigonal bipyramidal to tetrahedral upon dissolution as confirmed by multinuclear (1H, 13C, 19F and 119Sn) NMR. The prepared ligands and compounds 1 – 4 were screened for antimicrobial, antioxidant, cytotoxicity, hemolysis, antileishmanial and anticancer and noncancerous activities. The results showed significant antimicrobial activities, antioxidant, good cytotoxic LD 50 values, percent hemolytic values, antileishmanial and anti-cancer activities. Compound 2 and 4 were found the most active antileishmanial and anticancer agent, respectively. [ABSTRACT FROM AUTHOR]

• Novel computing method is designed for nonlinear Thomas-Fermi system. • Neural networks, active set algorithm, interior-point and sequential quadratic programming are exploited for the system. • Comparison from exact and numerical results verify the correctness of the scheme. • Accuracy and convergence of the solver is validated from the statistical analysis. • Proposed scheme is an alternate, accurate, and reliable methods for such systems. We propose a computational intelligence technique to solve the nonlinear Thomas-Fermi equation for a finite interval. This technique uses an optimized approximation to reduce the error between the reference solution and optimized numerical solution with trigonometric and hyperbolic functions. Computational intelligence technique is based on optimization tools like Active Set Technique (AST), Interior Point Technique (IPT) with Sequential Quadratic Programming (SQP) through artificial neural networks (ANN). Furthermore, the proposed model results can be applied in atomic models phenomena. Numerical results showed that the optimizers provided a high level of accuracy, as well as the efficiency with marginal computational requirements infinite domain. Statistical analysis is presented, particularly fitting of normal distribution based on absolute errors (AEs) of proposed solvers and Chi-square distribution data curve fitting. It provides evidence that the present solution is highly accurate in the context of confidence bands. [ABSTRACT FROM AUTHOR]

• P-doped fullerene selectively removed CO 2 from N 2 /CO 2 mixture. • CO 2 physisorbed in the absence of electric field. • Applied electric field transforms physisorption to chemisorption. • CO 2 is easily recovered by removing electric field. In this work adsorption of CO 2 on P-doped fullerene is explored through density functional theory in the presence and absence of an electric field. The calculated adsorption energy reveals physisorption in the absence of electric field, which changes to chemisorption by the application of field. Hence, adsorbent can be recovered easily for reuse by removing the electric field. It is further revealed that P-doped fullerene is an ideal adsorbent for the removal of CO 2 from the N 2 /CO 2 mixture. [ABSTRACT FROM AUTHOR]

• Both species demonstrated shorter crop time with delayed sowings, but delayed sowing reduced stem quality. • All Matthiola cultivars produced flowers at similar times for each sowing date. • Planting at 30 × 30 cm spacing produced more florets than 15 cm × 15 cm. • Antirrhinum cultivars may be sown at same time, but Matthiola cultivars should be grown using staggered sowing times. • Antirrhinum and Matthiola cultivars may be sown on 1st October and transplanted at 22.5 × 22.5 cm for best cut stem quality. A field study was conducted on selected cultivars of Antirrhinum majus L. and Matthiola incana L. at University of Agriculture, Faisalabad, Pakistan, to determine the best planting time and density to develop optimal planting protocols to grow these species as cut flowers in the sub-tropics. Antirrhinum and Matthiola seeds were sown five times at 15 or 16 d intervals on 15 Sept., 01 Oct., 15 Oct., 01 Nov. or 15 Nov. In a separate study, the optimum planting density was determined by transplanting seedlings at 15 × 15, 15 × 22.5, 22.5 × 22.5, 22.5 × 30 or 30 × 30 cm apart. For planting time, both species demonstrated shorter crop time with delayed sowings, but delayed sowing reduced stem quality. Cultivars of both species sown on 15 Sept. had the tallest plant height, 104.2 and 58.6 cm for Antirrhinum and Matthiola , respectively, along with the greatest number of leaves (176.7 and 28.9), leaf area (13.7 and 35.0 cm2), number of florets (32.8 and 14.8), floret diameter (41.3 and 47.3 mm), raceme length (27.2 and 14.6 cm) and stem diameter 14.8 and 9.9 mm for Antirrhinum and Matthiola , respectively, compared to those sown on 15 Nov. Moreover, vase life of stems of both species decreased (4.0 d for Antirrhinum and 5.5 d for Matthiola) with delayed sowing time. Among all tested cultivars of Antirrhinum, 'Admiral White' flowered the earliest, 'Potomac Orange' and 'Potomac Yellow' flowered mid season, and 'Potomac Rose' and 'Potomac White' took longest to produce flowers, while all Matthiola cultivars produced flowers at similar times for each sowing date. Antirrhinum and Matthiola planted at 30 × 30 cm spacing had greatest number of leaves (198.1 and 31.4 for Antirrhinum and Matthiola , respectively), leaf area (14.3 and 40.1 cm2), number of florets (32.4 and 15.4), floret diameter (36.1 and 47.7 mm), raceme length (26.2 cm of Antirrhinum only) and stem diameter (14.1 and 14.7 mm for Antirrhinum and Matthiola , respectively) compared to those planted at 15 cm × 15 cm. In summary, for extending harvest season, Antirrhinum cultivars may be sown at same time, but Matthiola cultivars should be grown using staggered sowing times. Antirrhinum and Matthiola cultivars may be sown on 15th September to 1st October and transplanted at 22.5 × 22.5 cm spacing for best cut stem quality. [ABSTRACT FROM AUTHOR]

In this study, we investigate the reinforcing capability of multiwall carbon nanotubes (mwCNT) in alumina (Al 2 O 3) ceramic hybrid nanocomposites containing zirconium oxide nanoparticles (ZrO 2 np). For this purpose, highly dense hybrid nanocomposites containing well-dispersed ZrO 2 np (8 vol%) and mwCNT (4 vol%) were fabricated by the hot-pressing method. The resulting hybrid nanocomposite exhibited a ten-fold finer microstructure and 116% enhanced fracture toughness as well as 12% greater hardness over the benchmarked monolithic Al 2 O 3. The superior mechanical performance of the hybrid nanocomposite was attributed to the synergistic role of ZrO 2 np and mwCNT in refining the matrix microstructure and inducing unique toughening mechanisms of micro-cracking by ZrO 2 np and pull-out as well as crack-bridging by mwCNT. Qualitative and quantitative approaches were utilized to assess the individual and collective role of the reinforcing constituents in enhancing the performance of hybrid nanocomposite. The qualitative analysis by electron microscopes demonstrated strong interfacial adhesion of both reinforcing constituents with the based Al 2 O 3 matrix. Furthermore, the quantitative analysis verified that the enhanced mwCNT/Al 2 O 3 interfacial shear strength is caused by the intricate physical arrangement of the mwCNT within the matrix grains besides their chemical bonding at the interface. The role of fine-grained microstructure in establishing idiosyncratic mwCNT interlocking with the Al 2 O 3 matrix grains was meticulously investigated. Moreover, the influence of mwCNT/matrix interlocking on the mwCNT reinforcing ability and toughening mechanisms efficiency in the hybrid nanocomposite is discussed. • Al 2 O 3 /CNT/ZrO 2 hybrid nanocomposites were prepared by hot-pressing technique. • A 10 fold fine-grained microstructure was obtained for hybrid nanocomposites. • The hybrid nanocomposites showed 116% better fracture toughness over Al 2 O 3. • Reinforcing capability of the CNT in hybrid nanocomposite was gauged. [ABSTRACT FROM AUTHOR]

The inherent brittleness of monolithic alumina (Al 2 O 3) has consistently limited its potential for several high-performance structural applications such as engine turbine parts and high temperature space materials. The fracture toughness and wear properties of alumina can be improved by the uniform dispersion of sub-micron and nano-phase particles in the alumina parent matrix. In the current study, alumina-based nanocomposites were fabricated by homogeneous dispersion of zirconia (4 wt% and 10 wt%) and graphene (0.5 wt%) using colloidal mixing followed by hot-pressing at 1600 °C for 1 h. The microstructure of the processed composite was extensively observed under high-resolution electron microscopes. The effects of zirconia and graphene on the mechanical and tribological properties were also investigated. The final microstructures depict extensive grain-refinement by the uniform dispersion of 0.5 wt%Gn and 10 wt%ZrO 2 in the parent alumina matrix. A coarse-grain microstructure was observed with the addition of the 4 wt%ZrO 2 due to insufficient inhibition of alumina grain growth during sintering. The coefficient of friction results from the wear test using a UMT Tribolab equipment with constant normal load of 25 N, 100 mm/s sliding velocity for 1000s, under ASTM G133-05 illustrates a steep increase for the 0.4 wt% ZrO 2 addition but a significant decrease for the 10 wt%ZrO 2 addition. Consequently, the addition of 10 wt% ZrO 2 resulted in ∼98% decrease in the wear rate which was attributed to its refined microstructure and effective load-bearing activities of the uniformly dispersed ZrO 2 precipitates. Meanwhile, the addition of 0.5 wt%Gn revealed a fine-grained microstructure and consequent lower wear rate (∼90.5%), but there was a relative increase in the coefficient of friction, which suggested that there is a direct relationship between the microstructure and wear behaviour than the coefficient of friction. • Alumina-based nanocomposites were fabricated via hot-pressing of dispersed ZrO 2 (4 wt% and 10 wt%) and graphene(0.5 wt%). • Final microstructures depict grain-refinement by the uniform dispersion of 10 wt%ZrO 2 and 0.5 wt%Gn within Al 2 O 3 matrix. • However, a coarse-grained microstructure was observed with the addition of the 4 wt%ZrO 2 to the matrix. • The addition of 10 wt% ZrO 2 resulted in ∼98% decrease in the wear rate which was attributed to its refined microstructure. • The addition of 0.5 wt%Gn also revealed a fine-grained microstructure and consequently lower wear rate (∼90.5%). [ABSTRACT FROM AUTHOR]

Hybrid electric vehicles are getting popular due to the depletion of fossil fuel and natural gas reserves. It is important to utilize renewable energy sources to avoid the horrific effects of global warming and greenhouse gas emissions. This study presents an optimized nonlinear controller for a hybrid energy storage system (HESS) of fuel cell, battery, supercapacitor, and hybrid photoelectrochemical and photovoltaic cells (HPEV) based hybrid electric vehicles. All these energy sources are connected to a DC-DC power converter followed by a DC-AC inverter and a motor. Lyapunov based nonlinear controller is proposed to achieve tight DC bus regulation, good tracking of sources current, and global asymptotic stability of the closed loop system. The gain parameters of the proposed nonlinear controller are optimized using the grey wolf optimization (GWO) algorithm for performance improvement. Maximum power point tracking of HPEV is performed using an artificial neural network. Experimental data from the extra-urban driving cycle is used to demonstrate the performance of the proposed optimized HESS using Matlab/Simulink. To validate the performance of the proposed system the simulation results are compared with hardware in the loop experimental results. It can be observed from the simulation results that the proposed GWO optimized nonlinear controller decreases errors, and enhances the performance of the dynamical system. • Mathematical modeling of the proposed HPEV, FC, battery, and SC based HESS for HEVs. • MPPT of HPEV using ANN under varying temperature and irradiance. • Nonlinear controller is designed for DC bus voltage regulation of the proposed HESS. • Gain optimization of nonlinear controller using a grey wolf optimization algorithm. • Hardware in the loop experiment is performed to validate the proposed topology. [ABSTRACT FROM AUTHOR]

The onset of hybrid alumina-based composites, which combines two or more nano-particles within the alumina matrix has already shown promising improvements in the matrix material. However, variations in mechanical properties including the optimum compositions that give improved properties faced with the development of alumina-based composites require further studies to understand the underlying mechanisms and synergistic effects of the nano-particle additions on the alumina matrix. In the current study, the structure and properties of Al₂O₃-graphene (0.5 wt%) and Al₂O₃–ZrO₂ (4 wt% and 10 wt%) composites fabricated via hot-pressing was studied as a baseline for multiple combinations. Even though the addition of 10 wt%ZrO₂ resulted in a 23% reduction in the grain size of the alumina matrix, the 4 wt%ZrO₂ addition resulted in a 14% increase in grain size as compared to the parent alumina matrix. X-ray diffraction analysis revealed that there was approximately 85% monoclinic (m -ZrO 2) vs. 15% tetragonal (t -ZrO 2) crystal structures in the A4ZrO₂ sample whilst the A 10 ZrO₂ had approximately 93% m -ZrO 2 vs. 7% t -ZrO 2. The high-volume fraction of the monoclinic crystal structures in the A 10 ZrO₂ accounts for the induced microcracks in the sample since the transition from the ductile-tetragonal to brittle-monoclinic is associated with the exertion of compressive stresses on the alumina matrix by the associated elastic volume expansion of m -ZrO 2. Also, the addition of 0.5 wt%graphene resulted in about 37% reduction in the grain size of the alumina matrix, and approximately 10% increase in hardness as a result of the distribution of graphene along the grain boundaries of the parent alumina matrix, which restricts grain coalescence and growth during processing. Furthermore, an increase up to 115% and 164% were observed in the fracture toughness (K IC) with the inclusion of 0.5 wt%graphene and 10 wt%ZrO₂ respectively, which was primarily ascribed to the fine-grained microstructures and toughening mechanisms of the intergranular graphene and ZrO₂ particles. [ABSTRACT FROM AUTHOR]