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It is a common knowledge that the efficiency of a PV solar device is very small chiefly as a result of the exhibition of non-linear characteristics of its output parameters. However, this paper presents an exploratory work on the comparison of a single diode model and double diode model based on InGaN that has better conversion efficiency than its silicon pairs. This work further illustrates how efficiency of a solar cell can be improved. MATLAB is employed as the simulation tool for modelling and analysing the two models, mathematical modelling is designed using a Simulink. The paper further explains in great details the various parameters of a PV module and under variable temperature and irradiance checks for the accurate performance of both double and single diode model using MSX60 solar module. Parameters like maximum voltage, maximum power and efficiency are used to evaluate the accuracy in order to justify better model. The results obtained portray that the models have the ability to extract PV parameters efficiently. It was also observed that both two models performed well at different conditions of temperature and irradiance, even though at higher temperature, double diode outperformed single diode. Hence, it can be concluded that double diode irrespective of computational complexity is better than single diode.

In order to explore the accompanying examination goals for facial expression recognition, a proper combination of classification and adequate feature extraction is necessary. If inadequate features are used, even the best classifier could fail to achieve accurate recognition. In this paper, a new fusion technique for human facial expression recognition is used to accurately recognize human facial expressions. A combination of Discrete Wavelet Features (DWT), Local Binary Pattern (LBP), and Histogram of Gradients (HoG) feature extraction techniques was used to investigate six human emotions. K-Nearest Neighbors (KNN), Decision Tree (DT), Multi-Layer Perceptron (MLP), and Random Forest (RF) were chosen for classification. These algorithms were implemented and tested on the Static Facial Expression in Wild (SWEW) dataset which consists of facial expressions of high accuracy. The proposed algorithm exhibited 87% accuracy which is higher than the accuracy of the individual algorithms.

In this paper, improved finite control set model predictive voltage control (FCS-MPVC) is proposed for the distributed energy resource (DER) in AC islanded microgrid (MG). Typically, AC MGs have two or more power electronic-based DERs, which have the ability to maintain a constant voltage at the point of common coupling (PCC) as well as perform power sharing among the DERs. Though linear controllers can achieve above-mentioned tasks, they have several restrictions such as slow transient response, poor disturbance rejection capability etc. The proposed control approach uses mathematical model of power converter to anticipate the voltage response for possible switching states in every sampling period. The proposed dual-objective cost function is designed to regulate the output voltage as well as load current under fault condition. Two-step horizon prediction technique reduces the switching frequency and computational burden of the designed algorithm. Performance of the proposed control technique is demonstrated through MATLAB/Simulink simulations for single distributed generator (DG) and AC MG under linear and non-linear loading conditions. The investigated work presents an excellent steady state performance, low computational overhead, better transient performance and robustness against parametric variations in contrast to classical controllers. Total harmonic distortion (THD) for linear and non-linear load is 0.89% and 1.4% respectively as illustrated in simulation results. Additionally, the three-phase symmetrical fault current has been successfully limited to the acceptable range.

High carbon to nitrogen ratio and complex lignocellulosic structure of wheat straw (WS) are the main hindrances in anaerobic digestion of this substrate. This research, conducted in two experiments, focused on the combined effect of co-digestion and thermal pretreatment of WS and sunflower meal (SM) on biogas production. In the first experiment, five co-digestion mixtures of WS and SM were made and assessed for biogas production. In the second experiment, the optimum co-digestion mixture selected from the first experiment was thermally pretreated at 120, 140, 160 and 180 °C and was evaluated for biogas production. Compared to control, the mixture M5 produced 61% higher biogas. It also showed better buffering capacity as compared to other mixtures and control. Combined effect of co-digestion and thermal pretreatment of the same mixture at 180 °C increased biogas yield by 94.3% as compared to control. Moreover, the experimental data obtained from both the experiments were validated using three different non-linear kinetic models.

Cyber-bullying has been on the rise especially after the explosive widespread of various cyber-attacks. Various types of techniques have been used to tackle cyber-bullying. These techniques focused primarily on data traffic for monitoring malicious activities. This research proposes a methodology where we can detect early Denial of service (DoS) and Distributed Denial of Service (DDoS) attacks. First, we formulate the problem in a practical scenario by comparing flow and non-flow-based datasets using Mann Whitney U statistical test. Flow and non-flow-based datasets and Artificial Neural Network (ANN) and Support Vector Machine (SVM) is used for classification. To keep original features, we use variance, correlation, ¾ quartile method to eliminate the unimportant features. The forward selection wrapper method for feature selection is used to find out the best features. To validate the proposed methodology, we take multiple DoS and DDoS single flow and validate it on 10%, 20%, 30%, 40%, and 50%. For validation, the experimental results show + 90% accuracy on the early 10% flow.

The SPEC CPU Benchmarks are used extensively for evaluating and comparing improvements to computer systems. This ubiquity makes characterization critical for researchers to understand the bottlenecks the benchmarks do and do not expose and where new designs should and should not be expected to show impact. However, in characterization there is a tradeoff between accuracy and reusability: The more precisely we characterize a benchmark’s performance on a given system, the less usable it is across different micro-architectures and varying memory configurations. For SPEC, most existing characterizations include system-specific effects (e.g., via performance counters) and/or only look at aggregate behavior (e.g., averages over the full application execution). While such approaches simplify characterization, they make it difficult to separate the applications’ intrinsic behavior from the system-specific effects and/or lose the diverse phase-based behaviors. In this work we focus on characterizing the applications’ intrinsic memory behaviour by isolating them from micro-architectural configuration specifics. We do this by providing a simplified generic system model that evaluates the applications’ memory behavior across multiple cache sizes, with and without prefetching, and over time. The resulting characterization can be reused across a range of systems to understand application behavior and allow us to see how frequently different behaviors occur. We use this approach to compare the SPEC 2006 and 2017 suites, providing insight into their memory system behaviour beyond previous system-specific and/or aggregate results. We demonstrate the ability to use this characterization in different contexts by showing a portion of the SPEC 2017 benchmark suite that could benefit from giga-scale caches, despite aggregate results indicating otherwise.

The rise of virtual reality and haptic technologies has created exciting new applications in medical training and education. In a dental simulation, haptic technology can create the illusion of substances (teeth, gingiva, bone, etc.) by providing interaction forces within a simulated virtual world of the mouth. In this article, a haptic periodontal training simulation system, named Haptodont, is developed and evaluated for simulating periodontal probing. Thirty-two faculty members from New York University College of Dentistry were recruited and divided into three groups to evaluate three fundamental functionalities: Group 1 evaluated bi-manual 3 Degrees of Freedome (DoF) haptic interaction, Group 2 evaluated bi-manual 3 DoF haptic interaction with a finger support mechanism, and Group 3 evaluated bi-manual 3 DoF haptic interaction with finger support mechanism and vibrotactile feedback. The probe and mirror interactions were simulated with the Geomagic Touch haptic device whereas the finger support was implemented using the Novint Falcon device. The three groups conducted two probing tasks: healthy gingiva scenario with no pockets (2- to 3-mm depth) and periodontitis scenario with deep pockets (4- to 8-mm depth). Results demonstrated that experts performed comparably to clinical settings in terms of probing depth error (within 0.3 to 0.6 mm) and probing forces (less than 0.5 N). Furthermore, the finger support mechanism significantly improved the probing accuracy for periodontitis condition in the lingual region. The argument that probing the lingual region is more difficult than the buccal region is supported by quantitative evidence (significantly higher probing depth error and probing force). Further research is planned to improve the usability of the finger support, integrate the Haptodont system into the pre-clinical curriculum, and evaluate the Haptodont system with dental students as a learning tool.

The present work investigated the degradation of norfloxacin (NOR) antibiotics as a model pollutant in the bioelectro-Fenton (BEF) system. The BEF was catalyzed by three types of composite electrod...

In this project, the prime focus was to synthesize zinc oxide nanoplates (ZnO-Nps) by a reliable, simple, and low cost facile hydrothermal method. Zinc acetate was used as major precursors; the synthesis was standardized in a 100 mL autoclave, at 180 °C for 24 h which results in nanoplates with 150–300 nm width and thickness 10–60 nm size and mesoporous nature with a specific surface area 126 cm2 g−1. The prepared sample was characterized by X-ray powder diffraction (XRPD), scaning electron microscopy (SEM), transmission electron microscopy (TEM), energy dispersive microscopy (EDS), Fourior transfer infrared (FTIR) spectroscopy, X-ray photo electron microscopy (XPS), Brunauer–Emmett–Teller (BET), Photoluminance (PL), and Electrochemical impedance spectroscopy (EIS) techniques. For application purposes, photocatalytic and antimicrobial activities of ZnO-Nps were studied systematically. The material was made photo-active under visible light irradiation for the degradation of the Methyl orange dye in aqueous media. The antimicrobial activity of ZnO-Nps was checked by time kill method, presented the inhibition of growth against clinical isolates of Methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli (E. coli) after 24 h. However, 12.5 and 6.25 µg is minimal inhibitory concentration (MIC) and 25 and 12.5 µg minimal bactericidal concentration (MBC) of ZnO-Nps against MRSA and E. coli respectively.

Pullulan is an imperative microbial exo-polymer commercially produced by yeast like fungus Aureobasidium pullulans. Its structure contains maltosyl repeating units which comprises two α-(1 → 4) linked glucopyranose rings attached to one glucopyranose ring through α-(1 → 6) glycosidic bond. The co-existence of α-(1 → 6) and α-(1 → 4) glycosidic linkages endows distinctive physico-chemical properties to pullulan. It is highly biocompatible, non-toxic and non-carcinogenic in nature. It is extremely resistant to any mutagenicity or immunogenicity. The unique properties of pullulan make it a potent candidate for biomedical applications viz. drug delivery, gene delivery, tissue engineering, molecular chaperon, plasma expander, vaccination, etc. This review highlights the potential of pullulan in biomedical research and development.

A sensorimotor skill is a sequence of motions generated in response to external stimuli and aiming to accomplish a particular task. It can be communicated to reproduce the task in a distant environment with similar settings. In this work, we conceptualize a multi-modal sensorimotor skill communication system that incorporates modeling, simulation, and evaluation of the sensorimotor skill. The proposed sensorimotor skill communication system can be applied for learning a specific style of human sensorimotor skill and teaching the skill to distant learners, which can be implemented in a variety of applications such as Tele-consultation, Tele-diagnosis, Tele-treatment, Tele-monitoring, and Tele-support. To understand the processes behind the communication of sensorimotor skill we review the representation of a human sensorimotor system from the neurobiological perspective. Then we analyze the existing literature on sensorimotor skill communication systems and propose a taxonomy of currently available methods for sensorimotor skill modeling, simulation, and evaluation. Furthermore, we propose a benchmark for evaluating the quality of the sensorimotor skill communication system. We present a case study aiming to demonstrate modeling the dental sensorimotor skill of periodontal probing. Lastly, we discuss challenges and limitations and provide perspectives for future research in developing sensorimotor skill communication systems.

A layer-by-layer titanium (IV) chloride treatment was applied on different layers of TiO2 in dye-sensitized solar cells (DSSCs). The effects were analysed and compared with standard untreated devices. A significant increase in short-circuit current density (JSC) was observed by employing layer-by-layer TiCl4 treatment of TiO2 in DSSCs. This increase of JSC is attributed to the increased inter-particle connectivity and increase in TiO2 nanoparticle size, resulting in better electron transfer and a lower charge carrier recombination rate. The DSSC fabricated with layer-by-layer-treated TiO2 achieved power conversion efficiency of 8.3%, which is significantly higher than the 6.7% achieved for the DSSC fabricated without TiCl4 treatment. Electrochemical impedance spectroscopy (EIS) was performed to assess the better performance of the device fabricated with TiCl4 treatment. Atomic force microscopy and surface roughness were studied to visualize and statistically determine the function of TiCl4 treatment on different layers of TiO2. Transient photocurrent and transient photovoltage measurements were also performed to gain insight into interfacial charge carrier recombination.

The paper presents a 1 × 2 B-shaped antenna array for dual-band operation at 4 and 8 GHz. The antenna design consists of a rectangular patch with two annular-strip lines fabricated on the top layer and finite ground plane on the bottom layer. The array is formed by designing an optimum T-shaped microstrip line for impedance matching. The dimensions of the antenna array are 78 × 36 × 1.6 mm3. Full-wave simulations have been conducted and the measured results are in good consent with the simulated results. The measured impedance bandwidth (reference −10 dB) has been observed at 3.84–4.16 and 7.78–8.38 GHz. Measured peak gain and radiation efficiency at 4 and 8 GHz are 8.3, 9.4 dB and 82.5 and 81.2%, respectively.

Facial expressions play a crucial role in modulating the emotional responses in the viewers. Touch is an important factor in shaping human emotions and social communication. The objective of this study is to investigate the effects of viewing and touching a virtual emotional face on the emotional responses of a viewer/toucher. In the case of touching the model, the effects of physical properties, namely stiffness and texture, are examined. Emotional facial expressions for neutrality, anger, fear, disgust, happiness, surprise, and sadness are developed and experimentally validated for the visual stimuli whereas four combinations of stiffness/texture properties are examined for the physical properties (low/high stiffness and smooth/rough texture). 25 participants viewed and touched the virtual emotional face and reported their respective emotional responses. The results showed that watching angry, happy, surprised, and sad faces significantly increased their anger, happiness, surprise, and sadness levels, respectively (p

Gait has emerged as an important biometric feature which is capable of identifying individuals at distance without requiring any interaction with the system. Various factors such as clothing, shoes, and walking surface can affect the performance of gait recognition. However, cross-view gait recognition is particularly challenging as the appearance of individual’s walk drastically changes with the change in the viewpoint. In this paper, we present a novel view-invariant gait representation for cross-view gait recognition using the spatiotemporal motion characteristics of human walk. The proposed technique trains a deep fully connected neural network to transform the gait descriptors from multiple viewpoints to a single canonical view. It learns a single model for all the videos captured from different viewpoints and finds a shared high-level virtual path to project them on a single canonical view. The proposed deep neural network is learned only once using the spatiotemporal gait representation and applied to testing gait sequences to construct their view-invariant gait descriptors which are used for cross-view gait recognition. The experimental evaluation is carried out on two large benchmark cross-view gait datasets, CASIA-B and OU-ISIR large population, and the results are compared with current state-of-the-art methods. The results show that the proposed algorithm outperforms the state-of-the-art methods in cross-view gait recognition.

Aviation plays a fundamental role in the growth of trade, tourism, economy as well as sustainable development in Pakistan. The two main influential factors Crew Training and Safety Management impacting operations of Engineering Management was found on the basis of literature and analyzed. Primarily, the numerous organizations training methodologies to get their crew staff training was collected from both primary sources that’s questionnaire instrument and secondary sources which is Literature. The reliability and validity of the instrument was tested with the help of Cronbach Alpha and the survey questionnaire was distributed to obtain primary data. The used Non-Probability Sampling technique analyzed thru Statistical Package for Social Sciences software. It was revealed that both the variables i-e Crew Training and Safety Management have a significant impact on improving aviation operational management. Therefore, the management should take remedial measures to work out on Crew Training and Safety Management factors that may augment aviation operational management according to latest National Aviation Policy 2019.

Sparse representation can be extended to high dimensions and can be used in many applications, including three-dimensional (3D) Inverse synthetic aperture radar (ISAR) imaging. In this study, the high-dimensional sparse representation problem and a recovery method called high-dimensional smoothed least zero-norm (HDSL0) are formulated. In this method, the theory and computation of tensors and approximating L0 norm using Gaussian functions are used for sparse recovery of high-dimensional data. To enhance the performance of HDSL0, modified regularised high-dimensional SL0 (MRe-HDSL0) algorithm, which benefits from the regularised form of SL0 and an additional hard thresholding step, is proposed. According to the numerical simulations, the recovery signal to noise ratio for MRe-HDSL0 compared to HDSL0, under the same experimental conditions, is 10, 9, and 8 dB higher in 1D, 2D, and 3D cases, respectively. The proposed algorithm also maintains the benefits of high speed and low computational cost of SL0. Besides, the formulation of compressed sensing-based 3D ISAR imaging is expressed. Finally, the proposed algorithm is applied to reconstruct 3D ISAR images of two synthetic targets, which are created based on the scattering point model. Based on the achieved results, the quality of reconstructed images using MRe-HDSL0 is better than other simulated methods.

Background: Silver nanoparticles synthesized by the bio-green method have been applied to various biomedical applications. These procedures are simple, eco-friendly and serve as an alternative to complex chemical methods for the preparation of nanomaterials. Objective: : In the present study, phytosynthesis of silver nanoparticles, to examine their antioxidant potential, toxic effects towards bacterial-, fungal-strains, brine shrimp nauplii and cancer cells was focused. Methods: Methanolic extract of Euphorbia wallichii roots was used for the synthesis of silver nanoparticles. The synthesis was monitored and confirmed by UV-visible spectroscopy, Fourier Transform Infra-Red (FTIR) spectrometric analysis, Field Emission Scanning Electron Microscope (FESEM), Energy Dispersive X-ray (EDX) and X-Ray Powder Diffraction (XRD). Results: The synthesized particles were average 63±8 nm in size. Involvement of phenolic (46.7±2.4 µg GAE/mg) and flavonoid (11.7±1.2 µg QE/mg) compounds as capping agents was also measured. Nanoparticles showed antioxidant properties in terms of free radical scavenging potential (59.63±1.0 %), reducing power (44.52±1.34 µg AAE/mg) and total antioxidant capacity (60.48±2.2 µg AAE/mg). The nanoparticles showed potent cytotoxic effects against brine shrimp nauplii (LD50 66.83 µg/ml), proliferation and cell death of HeLa cells as determined by MTT (LD50 0.3923 µg/ml) and TUNEL assays, respectively. Antimicrobial results revealed that silver nanoparticles were found to be more potent against pathogenic fungal (maximum active against A. fumigatus, MIC 15 µg/disc) and bacterial strains (maximum active against S. aureus, MIC 3.33 μg/disc) than the E. wallichii extract alone. Conclusion: These results support the advantages of using an eco-friendly and cost-effective method for synthesis of nanoparticles with antioxidant, cytotoxic and antimicrobial potential.

Several ongoing research efforts aim to design potential Future Internet Architectures, among which Named-Data Networking (NDN) introduces a shift from the existing host-centric Internet Protocol-based Internet infrastructure towards a content-oriented one. However, researchers have identified some design limitations in NDN, among which some enable to build up a new type of Distributed Denial of Service attack, better known as Interest Flooding Attack (IFA). In IFA, an adversary issues not satisfiable requests in the network to saturate the Pending Interest Table (PIT) of NDN routers and prevent them from properly handling the legitimate traffic. Researchers have been trying to mitigate this problem by proposing several detection and reaction mechanisms, but all the mechanisms proposed so far are not highly effective and, on the contrary, heavily damage the legitimate traffic. In this paper, we propose a novel mechanism for IFA detection and mitigation, aimed at decreasing the memory consumption of the PIT by effectively reducing the malicious traffic that passes through each NDN router. In particular, our protocol exploits an effective management strategy on the PIT, through which the Malicious Interest (MIs) already stored in the PIT are removed and the new incoming MIs are dropped. In addition, the proposed countermeasure provides an additional security wall on the edges of the network to detect and mitigate the attack as early as possible and improve the network health, i.e., routers PIT occupancy during IFA. To evaluate the effectiveness of our work, we implemented the proposed countermeasure on the open-source ndnSIM simulator and compared its effectiveness with the state of the art. The results show that our proposed countermeasure effectively reduces the IFA damages both in terms of preserved legitimate traffic and availability of routers PIT. Considering the legitimate traffic, the amount of Benign Interests preserved by our approach increases from 5% to 40% with respect to the preservation guaranteed by the state-of-the-art solutions. Concerning the routers PIT availability, our approach guarantees that the 97% of the PIT size is left free for handling the legitimate traffic.

We studied charge carrier relaxation in ZnS:Mn nanostructures synthesized by vapor-liquid-solid (VLS) technique for increasing thickness (tc = 0.5, 1 and 3 nm) of thermal deposited Mn catalyst. The dominant phase of the synthesized ZnS:Mn nanostructures was hexagonal Wurtzite, and no trace of secondary phase was observed. The longitudinal optical (LO) and transverse optical (TO) Raman modes in these structures were found at 269 and 348 cm−1, respectively, which were typical of Wurtzite ZnS. Temperature-dependent photoluminescence (PL) spectroscopy showed violet band-to-band transition (~380 nm) along with defect related blue and green bands (~420 and 510 nm), while Mn:d→d band peak was observed at ~ 570 nm. In addition, red band (~670 nm) showed strong appearance at low temperature (10 K) and disappeared at high temperatures was attributed to the formation of possible MnS clusters present in the ZnS:Mn nanostructures. The PL integrated intensities of various bands demonstrated mixed anomalous Berthelot and Arrhenius behavior with increase in temperature. All the emission bands obtained from ZnS:Mn (tc = 3 nm) nanostructures showed a Berthelot-type behavior, where the carrier escape (activation) energies were found to be 29 ± 2 (2.3 ± 0.3), 21 ± 1 (2.6 ± 0.5), 11 ± 1 (1.8 ± 0.4) meV and 51 ± 3 meV (1.5 ± 0.1 meV) for the 390 nm, 470 nm, 520 nm and 670 nm bands, respectively. The green and red band of ZnS:Mn (tc = 0.5 nm) nanostructures showed a normal Arrhenius behavior whereas, the violet band exhibited the abnormal Berthelot behavior. Increasing Mn thickness also exhibited large modulation of color perception, which is critical to realize single active-layer white light-emitting diodes.

To illuminate the lignin evolution after hydrogen peroxide presoaking prior to ammonia fiber expansion pretreatment (H-AFEX) pretreatment and enzymatic hydrolysis, ball-milled wood lignins were sep...

Flow behaviors of particles and power-law fluids were numerically simulated by means of Euler-Euler model with kinetic theory of granular flow in liquid-solid fluidized beds, and effects of flow behavior index (n) and consistency coefficient (K) were studied. The simulation results were compared with the experimental measurements, and good agreements were achieved. The effective viscosity of the shear-thinning fluid decreases as it flows through the expanded bed due to the increase in fluid strain rate. Unlike the homogeneous distributions of particles in Newtonian liquid fluidized beds, four regions for particle flow were observed along the bed height when a non-Newtonian fluid acts as the fluidizing agent, which indicates the non-uniformity of particles distribution. With the decrease of n, the enhancement of shear-thinning characteristics of the fluidizing agent thickens the relative inlet region and enhances the non-uniformity of particles along the bed height. The increase of n accelerates the circulation of particles in the fluidized bed, which speeds up the renewal of the particulate materials throughout the reactor space. The increase in K increases the apparent viscosity of the fluidizing agent. The bed height increases and the average solid volume fraction decreases. However, the influence of K on the non-uniformity of particles in the bed is less significant than that of n.

Knee issues are very frequent among people of all ages, and osteoarthritis is one of the most common reasons behind them. The primary feature in observing extremity and advancement of osteoarthritis is joint space narrowing (cartilage loss) which is manually computed on knee x-rays by a radiologist. Such manual inspections require an expert radiologist to analyze the x-ray image; moreover, it is a tedious and time-consuming task. In this paper, we present a computer-vision-based system that can assist the radiologists by analyzing the radiological symptoms in knee x-rays for osteoarthritis. Different image processing techniques have been applied on knee radiographs to enhance their quality. The knee region is extracted automatically using template matching. The knee joint space width is calculated, and the radiographs are classified based on the comparison with the standard normal knee joint space width. The experimental evaluation performed on a large knee x-ray dataset shows that our method is able to efficiently detect osteoarthritis, achieving more than 97% detection accuracy.

In recent years, the current Internet has experienced an unexpected paradigm shift in the usage model, which has pushed researchers towards the design of the Information-Centric Networking (ICN) paradigm as a possible replacement of the existing architecture. Even though both Academia and Industry have investigated the feasibility and effectiveness of ICN, achieving the complete replacement of the Internet Protocol (IP) is a challenging task. Some research groups have already addressed the coexistence by designing their own architectures, but none of those is the final solution to move towards the future Internet considering the unaltered state of the networking. To design such architecture, the research community needs now a comprehensive overview of the existing solutions that have so far addressed the coexistence. The purpose of this paper is to reach this goal by providing the first comprehensive survey and classification of the coexistence architectures according to their features (i.e., deployment approach, deployment scenarios, addressed coexistence requirements and architecture or technology used) and evaluation parameters (i.e., challenges emerging during the deployment and the runtime behaviour of an architecture). We believe that this paper will finally fill the gap required for moving towards the design of the final coexistence architecture., Comment: 23 pages, 16 figures, 3 tables

This work aims to enhance and optimize the methane production by anaerobic co-digestion of soybean straw (SS) and farm wastewater. Thermo oxidation and alkali pretreatments were applied during the present study. The effects of chemical composition were investigated and the influence of thermo-chemical pretreatment on methane production by SS with wastewater was evaluated by Batch and semi-continuous experiments. All pretreatments were found significant (P

In the past decade, Deep Neural Networks (DNNs) have achieved breakthrough collaborations in developing smart intelligent systems within the field of computer vision, natural language processing, autonomous systems, etc. Recent research has revealed that stability of such smart systems is at greater risk when they come across to adversarial perturbations. Although, these perturbations may not be perceivable in nature when seen from naked eye, yet, they are capable enough to fool state-of-the-art DNN classifiers. Till now, much of the previous work related to fool such classifiers focuses on generating adversaries that directly change pixel values of an image in spatial-domain. In this paper, we propose a novel transform-domain imperceptible attack methodology “TDIAM” to generate adversaries based on image steganography-approach using a “single carefully selected targeted watermark”. We use three different frequency-domain approaches, i.e., Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Fast Fourier Transform (FFT) to craft perturbations in selective frequency component which makes it robust and it requires less computational time as it is a non-gradient approach. We present our case study on MNIST handwritten digits dataset. Our results demonstrate that the generated perturbation vector successfully fool simple Convolutional Neural Network (CNN), LeNet-5 and AlexNet architectures by increasing probability of adversarial examples for the targeted class (to which the targeted watermark belongs) in both “black-box” and “white-box” adversarial attacks. The results have shown that among these three perturbation approaches, DWT based perturbation shown promising results by effectively fooling DNNs while ensuring the high imperceptibility as well.

The draft fan is used to generate a controlled transportation of particles to enhance entrainment of gas and particles from the particles bed. Present investigations show the entrainment behavior of particles induced by an axial 4-blade draft fan hovering over the particles bed. The distributions of velocities and volume fractions of gas and particles are simulated using Euler-Euler two-fluid model (TFM) with kinetic theory of granular flow (KTGF) at different hovering heights and rotational speeds of the draft fan. The dense region with high solids volume fraction and low particles velocity and the dilute region with low solids volume fraction and high particles velocity exist beneath the draft fan along hovering heights. The entrainment of particles increases with the decrease of hovering height and increase of rotational speed of the draft fan. Present numerical simulations confirm that the gas-solid TFM with the kinetic theory of granular flow and multiple reference frame model can be effectively applied to analysis for entrainment of particles induced by draft fan.

Internet of Medical Things (IoMT) envisions a network of medical devices and people, which use wireless communication to enable the exchange of healthcare data. Healthcare costs and prices for services have been increasing with the growing population and the use of advanced technology. The combination of IoMT and healthcare can improve the quality of life, provide better care services and can create more cost-effective systems. This paper introduces the status of IoMT for healthcare industry, including research and development plans and applications. The implementation of the IoMT in healthcare has exponentially increased across the world, but still, it has many technical and design challenges. This paper depicts such challenges and shows a generic IoMT framework that consists of three main components, data acquisition, communication gateways, and servers/cloud, to meet the aforementioned challenges. Finally, this paper discusses the opportunities and prospects of IoMT in practice while emphasizing the corresponding open research issues.

Myeloid-derived suppressor cells (MDSCs) promote tumor immune escape through multiple mechanisms including suppressing antitumor activities of T lymphocytes. However, therapeutic abrogation of MDSCs often causes severe adverse effects, compensatory recruitment of alternative cell populations, and the multiplicity and complexity of relevant cytokines/receptors. Alternatively, suppressing the expansion and tumor trafficking of MDSCs may be a proficient and safe way for cancer treatment. Here we report that pseudoneutrophil cytokine sponges (pCSs) can disrupt expansion and tumor trafficking of MDSCs and reverse immune tolerance. Coated with plasma membranes of neutrophils phenotypically and morphologically similar to polymorphonuclear MDSCs (PMN-MDSCs), the nanosized pCSs inherited most membrane receptors from the "parental" neutrophils, enabling the neutralization of MDSC-related cytokines. Upon pCSs administration, the expansion of MDSCs and their enrichment in peripheral lymphoid organs and tumors were reduced without the compensatory influx of alternative myeloid subsets. In murine breast cancer and melanoma syngeneic models, pCSs treatment dramatically increased the number of tumor-infiltrating T lymphocytes and restored their antitumor functions. In addition, when pCSs were combined with the programmed cell death protein 1 (PD-1), the immune checkpoint blockade synergistically suppressed tumor progression and prolonged animal survival. Overall, the pseudocell nanoplatform opens up new paths toward effective cancer immunotherapy.

In this study, nano-TiO2 sulfonated with 1,3-propane sultone (STiO2) was incorporated into the chitosan (CS) matrix for the preparation of CS/STiO2 nanocomposite membranes for fuel cell applications. The grafting of sulfonic acid (–SO3H) groups was confirmed by Fourier transform infrared spectroscopy, thermogravimetric analysis and energy-dispersive X-ray spectroscopy. The physicochemical properties of these prepared membranes, such as water uptake, swelling ratio, thermal and mechanical stability, ion exchange capacity and proton conductivity, were determined. The proton conducting groups on the surface of nano-TiO2 can form continuous proton conducting pathways along the CS/STiO2 interface and thus improve the proton conductivity of CS/STiO2 nanocomposite membranes. The CS/STiO2 nanocomposite membrane with 5 wt% of sulfonated TiO2 showed a proton conductivity (0.035 S·cm−1) equal to that of commercial Nafion 117 membrane (0.033 S·cm−1). The thermal and mechanical stability of the nanocomposite membranes were improved because the interfacial interaction between the -SO3H group of TiO2 and the –NH2 group of CS can restrict the mobility of CS chains to enhance the thermal and mechanical stability of the nanocomposite membranes. These CS/STiO2 nanocomposite membranes have promising applications in proton exchange membrane fuel cells.

Biomass is considered a suitable coal alternative energy source because of its lower net emissions and acceptable heating values. The direct combustion of biomass in an industrial boiler can cause slagging and fouling of the boiler tubes because of the high content of alkali and alkaline earth metals (AAEMs) such as Na, Ca, Mg, and K present in the biomass. These minerals especially Na and K lower the melting point of ash and hinder complete combustion of the biomass. To mitigate such issues, biomass valorization methods are recommended out of which demineralization using acidic and basic reagents is notable. In this study, the effect of HCl, HNO3, and NaOH reagents on the physiochemical and thermal properties of corncob is investigated. Results show that the demineralization significantly affects the physiochemical and thermal characteristics of the biomass. Percent ash reduction is found highest (92.81%) when biomass is treated with 0.3 M HCl. However, the acidic treatment also dissolves cellulose and hemicellulose which degrades the thermal characteristics of the biomass. Treatment with NaOH is found less effective in removing AAEM; however, it increases the heating value of biomass by 11.02% and decreases the sulfur content by 68.89%.

Bionanocomposite hydrogels are suitable candidates for the preparation of the ideal wound dressings due to their unique hydrophilic properties, biocompatibility and appropriate control in delivering therapeutic agents such as drugs, proteins and cells. In this work, bionanocomposite hydrogel wound dressings based on egg white, poly (vinyl alcohol) and clay nanoparticles loaded with honey, as a natural antibiotic, were prepared using the freezing–thawing cyclic process and their performances evaluated in vitro and in vivo as novel wound dressings. The results showed that the swelling and dehydration capabilities of the prepared bionanocomposite hydrogel wound dressings decreased by increasing the content of incorporated clay nanoparticles. Honey release test showed that the presence of clay nanoparticles acts as a key factor in the release of honey and causes control over its release from the wound dressings. The in vivo results exhibited the excellent ability of the honey-loaded bionanocomposite hydrogel wound dressings in the creation and keeping a moist region on the surface of the infected and non-infected wounds and their capability in accelerating the healing process of animal wounds.

This paper reports on the thin film characterization of a synthesized small molecular semiconductor N-butyl-N′-(6-hydroxyhexyl) perylene-3,4,9,10-tetracarboxylic acid diimide (N-BuHHPDI) and its potential use in Ag/N-BuHHPDI/PEDOT:PSS/p-Si heterojunction device. The device is fabricated using spin coating of a conducting polymer PEDOT:PSS on p-Si substrate followed by thermal deposition of a 100-nm-thin layer of N-BuHHPDI. To complete the fabrication of Ag/N-BuHHPDI/PEDOT:PSS/p-Si heterostructure, silver (Ag) is used as the top electrode. The device shows non-ohmic and asymmetrical current–voltage (I–V) characteristics in dark conditions at 25°C which confirm the successful formation of rectifying heterojunction. Various diode parameters such as ideality factor (n), barrier height (ϕb), series resistance (Rs) and charge carrier mobility across the interface of the heterojunction are measured from the I–V characteristics. The non-ideal behavior of the diode is correlated with the film morphology obtained by atomic force microscopy. Fourier transformed infrared spectroscopy is performed to confirm the successful preparation of N-BuHHPDI. The fluorescence lifetime (22 ns) of the N-BuHHPDI thin film is measured via fluorescence spectroscopy. Different charge conduction mechanisms including the dominant one are studied for the fabricated device.

In this work, nanocomposite hydrogel drug delivery systems based on polyvinyl alcohol and montmorillonite loaded with the capecitabine, as an anti-cancer drug, were developed for oral admin...

Gait refers to the walking style of a person and it has emerged as an important biometric feature for person identification. The gait recognition algorithms proposed in literature exploit various types of information from the gait video sequence, such as, the skeletal data, human body shape, and silhouettes; and use these features to recognize the individuals. This paper presents the proposal of using a generic codebook in gait recognition. The idea is built upon a novel gait representation which exploits the spatiotemporal motion characteristics of the individual for identification. In particular, we propose to use a set of sample gait sequences to construct a generic codebook and use it to build a gait signature for person identification. To this end, we chose synthetic gait sequences of CMU MoCap gait database due to its diversity in walking styles. A set of spatiotemporal features are extracted from these sequences to build a generic codebook. The motion descriptors of real gait sequences are encoded using this generic codebook and Fisher vector encoding; the classification is performed using support vector machine. An extensive evaluation of this novel proposal is carried out using five benchmark gait databases: NLPR, CMU MoBo, TUM GAID, CASIA-B, and CASISA-C. In all experiments, the generic codebook is used in feature encoding. The performance of the proposed algorithm is also compared with the state-of-the-art gait recognition techniques and the results show that the idea of using a generic codebook in gait recognition is practical and effective.

The process where solids circulate in an internally circulating fluidized bed (ICFB) with two or more chambers can be developed by applying unequal gas velocities in these chambers. In this study, a twin chamber ICFB with a binary mixture (two particles comprising G116 and P275, which differed in terms of their size and density) was simulated using the multi-fluid Eulerian model. The effects of variations in the gas velocities, mixing of solids, and mixture composition were investigated, and the results were quantified in terms of the solids circulation rate (Gs). An increase in the gas velocity in the reaction chamber (RC), UR, at a constant gas velocity in the heat exchange chamber (HEC), UH, resulted in an increase in the circulation rate for the binary mixture. Solid particles flowed upward in the RC and downward into the HEC. The ratio of the circulation rate for G116 relative to that for P275 was approximately equal to the ratio of their masses in the ICFB. An increase in UH at constant UR led to a decrease in the circulation rate of the binary mixture. Similar to UR and UH, the mixture composition played a critical role in controlling the circulation rate of the binary mixture in the ICFB. Furthermore, the ICFB exhibited a higher capacity for solids mixing compared with a conventional bubbling fluidized bed.

Environmental pollution and energy are interlinked; thereby an integrated approach is needed based on intra- and cross-disciplinary solutions. Microbial electro-Fenton, an innovative form of microbial electrochemical technologies, is an essential approach in this regard, which couples waste bioremediation and pollutant removal simultaneously from anode and cathode chambers with bioelectricity production. This article reviews the application of microbial electro-Fenton systems for environmental remediation and clean energy generation. Recent advances in fabrication of electrode materials to increase anodic biocompatibility and cathodic oxygen reduction reaction, cell design/configuration, membranes/separators and iron species as Fenton catalysts are discussed in detail. In addition, contemporary developments in the application of microbial electro-Fenton systems to deal with real wastewaters, wide variety of industrial pollutants and various classes of emerging contaminants are elucidated. Furthermore, the limitations and future perspectives are pointed out in detail. We believe that this in-depth review may aid as a reference guide for environmental scientists working on microbial electro-Fenton system.

Nitrogen removal from landfill leachate via anaerobic ammonium oxidation (Anammox) process has been considered as an innovative and sustainable approach to the traditional nitrification and denitrification process. However, the various technologies for rapid start-up of Anammox are still being explored. In this study, two strategies (inoculating anaerobic sludge and without inoculation) were applied to treat landfill leachate based on biological nitrogen removal processes. The start-up and mechanism of de-nitrogen process in landfill bioreactor was explored using 15N stable isotopic tracing, quantitative polymerase chain reaction (qPCR) and high-throughput sequencing methods. Results showed that inoculating anaerobic sludge was beneficial to enhance the nitrogen removal at the initial stage (from day 10 to day 25), but no significant increase was found during days 25–55 (p > 0.05). 15N stable isotopic tracing demonstrated that the inoculation of sludge accelerated by denitrification other than Anammox. Inoculation of sludge was conducive to increase of ammonia-oxidizing bacteria (AOB)- amoA and niK genes. Thauera was the dominant genus for nitrogen removal due to inoculation of sludge in landfill bioreactor, whereas the abundance of Candidatus Kuenenia did not increase by inoculating the sludge. Moreover, seeding anaerobic sludge could not provide Anammox's ecological niches. The results will provide a scientific basis for the selection of suitable operational condition for the rapid start-up in the landfill bioreactor.

The emergence of the smart grid has empowered the consumers to manage the home energy in an efficient and effective manner. In this regard, home energy management (HEM) is a challenging task that requires efficient scheduling of smart appliances to optimize energy consumption. In this paper, we proposed a meta-heuristic based HEM system (HEMS) by incorporating the enhanced differential evolution (EDE) and harmony search algorithm (HSA). Moreover, to optimize the energy consumption, a hybridization based on HSA and EDE operators is performed. Further, multiple knapsacks are used to ensure that the load demand for electricity consumers does not exceed a threshold during peak hours. To achieve multiple objectives at the same time, hybridization proved to be effective in terms of electricity cost and peak to average ratio (PAR) reduction. The performance of the proposed technique; harmony EDE (HEDE) is evaluated via extensive simulations in MATLAB. The simulations are performed for a residential complex of multiple homes with a variety of smart appliances. The simulation results show that EDE performs better in terms of cost reduction as compared to HSA. Whereas, in terms of PAR, HSA is proved to be more efficient as compared to EDE. However, the proposed scheme outperforms the existing meta-heuristic techniques (HSA and EDE) in terms of cost and PAR.

Flow behavior of topped gas-particles jet is simulated by means of gas–solids multi-fluid model in a bubbling fluidized bed. The finer particles from the topped gas-particles jet are ejected into a fluidizing coarse particles bed. The interactions of finer particles and coarse particles are simulated by means of the kinetic theory of binary particles with unequal component granular temperatures. The two-equation k-e model is used to model gas turbulence of the topped gas-particles jet. The effects of the top gas-particles jet velocity and fluidizing gas velocity on the distributions of volume fraction, velocity and component granular temperatures are analyzed along radial and axial directions of the bed. Simulated volume fractions and axial velocities of finer particles show that the fluidized bed is divided into three regions as (a) the diffusion region near the bottom, (b) the concentrating region and (c) the transport region along bed height. The circulation of particles is formed with the down-flow at the center and the up-flow between the center and the walls. Numerical simulations show that the coarse particles volume fractions decrease and the finer particles volume fractions increase with the increase of topped gas-particles jet velocity and fluidizing gas velocity in the bottom of the bed. The component granular temperatures are larger in the concentrating region than that in the diffusion and transport regions. The penetration length of the topped gas-particles jet with the change of topped gas-particles jet velocity and fluidizing gas velocity is discussed.

Tuning ice recrystallization (IR) has attracted tremendous interest in fundamental research and a variety of practical applications, including food and pharmaceutical engineering, fabrication of anti-icing coating and porous materials, and cryopreservation of biological cells and tissues. Although great efforts have been devoted to modulation of IR for better microstructure control of various materials, it still remains a challenge, especially in cryopreservation, where insufficient suppression of IR during warming is fatal to the cells. Herein, we report an all-in-one platform, combining the external physical fields and the functional materials for both active and passive suppression of IR, where the photo- and magnetothermal dual-modal heating of GO-Fe3O4 nanocomposites (NCs) can be used to suppress IR with both enhanced global warming and microscale thermal disturbance. Moreover, the materials alginate hydrogels and GO-Fe3O4 NCs can act as IR inhibitors for further suppression of the IR effect. As a typical application, we show that this GO-Fe3O4 nanocomposite-alginate hydrogel platform can successfully enable low-cryoprotectant, high-quality vitrification of stem cell-laden hydrogels. We believe that the versatile ice recrystallization inhibition platform will have a profound influence on cryopreservation and tremendously facilitate stem cell-based medicine to meet its ever-increasing demand in clinical settings.

Human beings follow a continuous learning paradigm, i.e., they learn to solve smaller and relatively easy problems, retain the learnt knowledge and apply that knowledge to learn and solve more complex and large-scale problems of the domain. Currently, most machine learning and evolutionary computing systems lack this ability to reuse the previous learnt knowledge. This paper presents a lifelong machine learning model for text classification that extracts the useful knowledge from simple problems of a domain and reuses the learnt knowledge to learn complex problems of the domain. The proposed approach adopts a rule-based learning classifier system, and a rich encoding scheme is used to extract and reuse building units of knowledge. The experimental results show that the continuous learning approach outperformed the baseline classifier system.

The integration of information and communication technologies in traditional grid brings about a smart grid. Energy management plays a vital role in maintaining the sustainability and reliability of a smart grid which in turn helps to prevent blackouts. Energy management at consumer's side is a complex task, it requires efficient scheduling of appliances with minimum delay to reduce peak-to-average ratio (PAR) and energy consumption cost. In this paper, the classification of appliances is introduced based on their energy consumption pattern. An energy management controller is developed for demand side management. We have used fuzzy logic and heuristic optimization techniques for cost, energy consumption and PAR reduction. Fuzzy logic is used to control the throttleable and interruptible appliances. On the other hand, the heuristic optimization algorithms, BAT inspired and flower pollination, are employed for scheduling of shiftable appliances. We have also proposed a hybrid optimization algorithm for the scheduling of home appliances, named as hybrid BAT pollination optimization algorithm. Simulation results show a significant reduction in energy consumption, cost and PAR.

Modern System-on-Chips (SoCs) are notoriously insecure. Hence, the fundamental security feature of IP isolation is heavily used, e. g., secured Memory Mapped IOs (MMIOs), or secured address ranges in case of memories, are marked as non-accessible. One way to provide strong assurance of security is to define isolation as information flow policy in hardware using the notion of non-interference. Since, an insecure hardware opens up the door for attacks across the entire system stack (from software down to hardware), the security validation process should start as early as possible in the SoC design cycle, i. e. at Electronic System Level (ESL). Hence, in this paper we propose the first dynamic information flow analysis at ESL. Our approach allows to validate the run-time behavior of a given SoC implemented using Virtual Prototypes (VPs) against security threat models, such as information leakage (confidentiality) and unauthorized access to data in a memory (integrity). Experiments show the applicability and efficacy of the proposed method on various VPs including a real-world system.

Photovoltaic wastewater contains a large amount of thiourea that cannot be directly treated by biological methods because of its biotoxicity. In this study, a novel O3/H2O2 + BiPO4/UV synergy technique was used as a pre-treatment process to degrade thiourea. The effects of H2O2 and catalyst loading were investigated, and the transformation pathway of thiourea was predicted based on the intermediates detected by UPLC-Vion-IMS-QToF. The synergy technique degraded 89.14% thiourea within only 30 min, and complete degradation occurred after 150 min. The TOC removal of O3/H2O2 + BiPO4/UV was 1.8, 1.5, and 1.9 times that of O3/H2O2 and BiPO4/UV/H2O2 single processes and O3/H2O2 + UV process, respectively, which was due to the synergy between H2O2 residues and BiPO4. In addition, thiourea was mainly degraded by ·OH into thiourea dioxide and melamine (polymerized by other intermediates) and then further degraded into biuret and methyl carbamate by the holes of BiPO4, followed by complete mineralization into H2O and CO2. These results confirm that the O3/H2O2 + BiPO4/UV synergy technique is a promising option for the degradation of thiourea.

Flow behavior of gas and particles with a horizontal oppose-pulsed gas jets are simulated by means of a three dimensional Computational Fluid Dynamics (CFD) model with the kinetic theory of granular flow in a gas-particles bubbling fluidized bed. The effects of amplitudes and frequencies on the hydrodynamics of gas and particles are analyzed. The simulation results are presented in terms of phase velocity vector plot, volume fraction of phases, granular temperature, power spectrum and Reynolds stresses in the bed. Results show that the impingement caused by the oppose-pulsed gas jets oscillates with the variation of pulsed gas velocity. The impingement zone with the high solid volume fraction reciprocates from the left side to the right side through the bed center with the variation of pulsed jet gas velocities. The lateral velocity and gas turbulent kinetic energy, granular temperature and Reynolds stresses of gas and particles are larger near the pulsed gas jets than that at the center of the bed. The large dispersion coefficients of particles using the horizontal oppose-pulsed gas jets enhance the mixing of particles in gas-solid fluidized bed.

The water environment from river to reservoir has been considered as a hybrid river-reservoir system due to pronounced environmental properties. This study investigated the distribution and potential environmental behavior of pesticides in river-reservoir system, examining 31 target pesticides in water phase from a key drinking water source (Dongjiang River). The concentrations of ∑8OCPs, ∑16OPPs and ∑7SPs with the corresponding occurrence were in the range of 107.57–340.35 ng/L (moderate level), 232.65–1197.95 ng/L (moderate level) and 125.23–245.09 ng/L (low level), respectively. Ecological risk assessments indicated that most of the pesticides posed a high level of risk to the aquatic organisms. Moreover, seasonal agricultural application, rainfall and temperature could influence the levels and compositions of ∑8OCPs, ∑16OPPs and ∑7SPs in the river system, while seasonal hydrological processes could only influence their compositions in the reservoir system. In the wet season, the levels of ∑8OCPs and ∑7SPs decreased from the river system to reservoir system probably via biogeochemical processes, while the level of ∑16OPPs decreased along the environmental gradient probably via dilution process. Additionally, longer hydraulic retention time of the reservoir system could contribute to the biogeochemical processes for OPPs and SPs exported from the river system, but it might fail to facilitate this process for OCPs. Taken together, this study highlighted that the distributions and environmental behavior of pesticides in river-reservoir system varied seasonally from river system to reservoir system.

This report deals with the simple development of a method for the uptake of cadmium ions by polymer adsorbent from the main sources of cadmium like water. The monomer is synthesized by reacting 2,4,6-trichloro-1,3,5-triazine with N(4-aminophenyl)acetamide. The adsorbents synthesis is completed by reacting a synthesized monomer with 1,3,5-tribenzoylchloride, terephthaloyl chloride, and isophthaloyl chloride. The as-synthesized polymer exhibits high molecular mass with narrow molecular weight distribution and good thermal stability. The metal ion adsorption study is established by using atomic absorption spectroscopy at optimized parameters, showing excellent adsorption capacity of polymer adsorbent. Further, the isothermal equilibrium adsorption study reveals the monolayer adoption of the metal ion by the Langmuir model with experimental and theoretical parameters, optimized by the linear regression analysis using the curve fitting tool in MATLAB software.

Automation and modernization of the grid with the availability of micro-grids including non-conventional sources of energy are the main constituent of smart grid technology. Most energy demand is fulfilled by fossil fuel-based power plants. Inadequacy of fuel resources, higher operating costs, and ever-increasing carbon emissions are the primary constraints of fossil fuels-operated power plants. Sustainable energy resource utilization in meeting energy demand is thought to be a preferred solution for reducing carbon emissions and is also a sustainable economic solution. This research effort discusses an accurate mathematical modeling and simulation implementation of a sustainable energy resource model powered by solar, grid, and proton exchange membrane fuel cell (PEMFC) stack and focuses on the energy management of the model. In the proposed model, despite energy resources being sustainable, consumer side sustainability is achieved by using electrical charging vehicles (ECVs) to be integrated with sustainable resources. The proposed energy resource management (ERM) strategy is evaluated by simulating different operating conditions with and without distributed energy resources exhibiting the effectiveness of the proposed model. PEMFC is incorporated in the model to control fluctuations that have been synchronized with other energy resources for the distribution feeder line. In this proposed model, PEMFC is synchronized with grid and solar energy sources for both DC and AC load with ERM of all sources, making the system effective and reliable for consumer-based load and ECVs utilization.

A multi-layer feed-forward ANN-based approach is discussed in this paper to achieve maximum power point tracking (MPPT). 70% of the data-set is trained with ANN algorithm, 15% is used for validation and 15% is utilized in testing of neural network. The photo generated output power after MPPT is interfaced with photovoltaic (PV) converters and subsequently to the solar-driven lead-acid battery charge controllers. The research effort aims to have a comparative performance analysis of the ANN approach against the well-known Perturb and Observe algorithm (P&O) and incremental inductance considering MPPT effectiveness for the PV modules and the charging and discharging response of the lead-acid based battery. The simulation results reveal that the neural network algorithm-based approach is more effective than the conventional algorithms for solar MPPT to mitigate the mismatch losses due to partial shading condition as well as for achieving the optimal efficiency of PV battery charge controllers and the overall PV system.