Performance of corrugated actuator-tabs of aspect ratio 2.0 on supersonic jet mixing enhancement

Journal of Mechanical Science and Technology - The present experimental investigation explores the ability of the corrugated actuator-tabs (or simply, the corrugated actuators) of aspect ratio 2 in...     

Kinetics and performance evaluation of microbial fuel cell supplied with dairy wastewater with simultaneous power generation

Due to the growing demand for energy in the present-day world, it is obligatory to look for alternative sources of renewable energy. The derivation of power from microbial fuel cells (MFCs) has developed at the vanguard of the alternative source of renewable energy through the concomitant treatment of wastewater. Hence, the process development of MFC is obligatory for creating a sustainable source of renewable energy through the treatment of wastewater. To that end, an attempt was taken in the present study for sustainable power generation from single chamber microbial fuel cell (SCMFC) using Pseudomonas aeruginosa-MTCC-7814. The experiments were carried out in a batch process for 15 days with real dairy wastewater (RDW) having initial chemical oxygen demand (COD) of 8000 mg/L. The open-circuit voltage (OCV) found after 72 h of batch operation was 658 mV, which was maximum within the batch operation. The columbic efficiency (CE) of the batch process was found to be 46.59%. The maximum specific growth rate (μ_max) of Pseudomonas aeruginosa-MTCC-7814 was found to be 0.432 day^?1 during batch operation. However, saturation constant (Ks) and inhibition coefficient (K_i) were calculated as 608.74 mg/L, and 6582 mg/L, respectively. The maximum current density (I_max) and saturation constant (K_c) predicted from batch kinetics study were 132 mA/m² and 321 mg/L, respectively, which has resemblance with the data obtained from experiments. The maximum current density and power density from experiments were found to be 161 mA/m² and 34.82 mW/m², respectively. Results showed that a higher power density and current density values were obtained from the present study as compared to the earlier reports that utilized wastewater as the substrate for the MFC. Thus, the study suggests that Pseudomonas aeruginosa, (MTCC-7814) can be used as a promising biocatalyst in MFC for sustainable power generation through the utilization of wastewater treatment.     

Systemic Evaluation of Mechanism of Cytotoxicity in Human Colon Cancer HCT-116 Cells of Silver Nanoparticles Synthesized Using Marine Algae Ulva lactuca Extract

In the current study, biogenic silver nanoparticles (U-AgNPs) were synthesized using marine green macro-algal Ulva lactuca extract, and evaluated mechanism behind its anticancer activity against the Human colon cancer (HCT-116). The biogenic U-AgNPs were characterized using various physiochemical techniques. The TEM micrographs confirmed the spherical morphology of synthesized U-AgNPs, with a mean size of 8–14 nm. EDX spectrum as well as ICP-OES confirmed that AgNPs was nearly 90% purity for silver. FTIR Spectra analysis of U-AgNPs confirmed U. lactuca extract bioactive molecules presence over U-AgNPs surface as a stabilizing agent, thereby improving biocompatibility. The cytotoxicity study revealed the dose dependent cell death in colon cancer cells with no loss of viability in normal human colon epithelial cells. Furthermore, the fluorescence micrographs of nucleus staining assay revealed the DNA fragmentation and nucleus condensation of cancer cells treated with U-AgNPs, indicating an apoptosis-mediated cell death. The western bolt and RT-PCR analysis of U-AgNPs treated cancer cells showed the rise in proapoptotic markers (P53, Bax, and P21) and decline in anti-apoptotic markers (Bcl-2), thus confirming the p53-dependent apoptosis mediated cell death in HCT-116. Overall, our study concluded that novel biogenic U-AgNPs nanoparticles, synthesized using marine green macro-algal U. lactuca extract showed efficient anticancer activity against HCT-116 cell line and hence could work as potential therapeutic agent for targeted anti-cancer therapy.

     

Pt Dopant: Controlling the Ir Oxidation States toward Efficient and Durable Oxygen Evolution Reaction in Acidic Media

Dissolution of Ir oxides in Ir‐based catalysts, which is closely linked to the catalyst activity and stability toward the oxygen evolution reaction (OER) in acidic media, is a critical unresolved problem in the commercialization of water electrolysis. Doping foreign elements into the Ir oxides can accomplish an optimal combination of Ir oxidation states that is conducive to the leaching‐resistance of active catalytic sites. Here, it is reported that Pt doping into IrO_x‐based nanoframe is beneficial in both terms of activity and stability. The Pt‐doped IrO_x‐based nanoframe achieves the mass activity of 0.644 A mg^?1_Ir+Pt at 1.53 V_RHE, which is 15‐fold higher than that of the commercial IrO₂. During the accelerated durability test, the Ir^IV‐to‐Ir^III ratio of 5 is maintained in the presence of Pt dopant to effectively mitigate the degradation of Ir catalyst, leading to the superb catalyst durability in acidic media.     

Energy levels,radiative rates,and lifetimes for transitions in W XL

Energy levels and radiative rates are reported for transitions in Br-like tungsten, W XL, calculated with the general-purpose relativistic atomic structure package (grasp). Configuration interaction (CI) has been included among 46 configurations (generating 4215 levels) over a wide energy range up to 213 Ryd. However, for conciseness results are only listed for the lowest 360 levels (with energies up to ∼43 Ryd), which mainly belong to the 4s²4p⁵,4s²4p⁴4d,4s²4p⁴4f,4s4p⁶,4p⁶4d,4s4p⁵4d,4s²4p³4d², and 4s²4p³4d4f configurations, and provided for four types of transitions, E1, E2, M1, and M2. Comparisons are made with existing (but limited) results. However, to fully assess the accuracy of our data, analogous calculations have been performed with the flexible atomic code, including an even larger CI than in grasp. Our energy levels are estimated to be accurate to better than 0.02 Ryd, whereas results for radiative rates (and lifetimes) should be accurate to better than 20% for a majority of the strong transitions.     

A Novel Machine Learning–Based Hand Gesture Recognition Using HCI on IoT Assisted Cloud Platform

Machine learning is a technique for analyzing data that aids the construction of mathematical models. Because of the growth of the Internet of Things (IoT) and wearable sensor devices, gesture interfaces are becoming a more natural and expedient human-machine interaction method. This type of artificial intelligence that requires minimal or no direct human intervention in decision-making is predicated on the ability of intelligent systems to self-train and detect patterns. The rise of touch-free applications and the number of deaf people have increased the significance of hand gesture recognition. Potential applications of hand gesture recognition research span from online gaming to surgical robotics. The location of the hands, the alignment of the fingers, and the hand-to-body posture are the fundamental components of hierarchical emotions in gestures. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. Linguistic gestures may be difficult to distinguish from nonsensical motions in the field of gesture recognition. In this scenario, it may be difficult to overcome segmentation uncertainty caused by accidental hand motions or trembling. When a user performs the same dynamic gesture, the hand shapes and speeds of each user, as well as those often generated by the same user, vary. A machine-learning-based Gesture Recognition Framework (ML-GRF) for recognizing the beginning and end of a gesture sequence in a continuous stream of data is suggested to solve the problem of distinguishing between meaningful dynamic gestures and scattered generation. We have recommended using a similarity matching-based gesture classification approach to reduce the overall computing cost associated with identifying actions, and we have shown how an efficient feature extraction method can be used to reduce the thousands of single gesture information to four binary digit gesture codes. The findings from the simulation support the accuracy, precision, gesture recognition, sensitivity, and efficiency rates. The Machine Learning-based Gesture Recognition Framework (ML-GRF) had an accuracy rate of 98.97%, a precision rate of 97.65%, a gesture recognition rate of 98.04%, a sensitivity rate of 96.99%, and an efficiency rate of 95.12%.     

A genetic algorithm-based rule extraction system

Individual classifiers predict unknown objects. Although, these are usually domain specific, and lack the property of scaling up prediction while handling data sets with huge size and high-dimensionality or imbalance class distribution. This article introduces an accuracy-based learning system called DTGA (decision tree and genetic algorithm) that aims to improve prediction accuracy over any classification problem irrespective to domain, size, dimensionality and class distribution. More specifically, the proposed system consists of two rule inducing phases. In the first phase, a base classifier, C4.5 (a decision tree based rule inducer) is used to produce rules from training data set, whereas GA (genetic algorithm) in the next phase refines them with the aim to provide more accurate and high-performance rules for prediction. The system has been compared with competent non-GA based systems: neural network, Naïve Bayes, rule-based classifier using rough set theory and C4.5 (i.e., the base classifier of DTGA), on a number of benchmark datasets collected from UCI (University of California at Irvine) machine learning repository. Empirical results demonstrate that the proposed hybrid approach provides marked improvement in a number of cases.     

Investigation of porous polydimethylsiloxane structures with tunable properties induced by the phase separation technique

This article reports the fabrication and characterization of porous polydimethylsiloxane (PDMS) structures developed by the solvent evaporation-induced phase separation technique. Ternary systems containing water/tetrahydrofuran (THF)/PDMS with various concentrations are produced to form a stable solution. The porous PDMS structures are formed by removing the solvent (THF) and nonsolvent (water) phases during the stepping heat treatment procedure. The analytical ternary phase diagram is constructed based on the thermodynamic equilibrium state in the polymer solution to explain the stable/unstable formulations and the possible composition change path. The results show that the isolated pores with the adjustable pore size ranging from 330 to 1900 μm are obtained by tuning the water to the THF ratio. The mechanical properties of the porous PDMS structures are determined by conducting the tensile tests on the prepared dog bone-shaped specimens. A wide range of elastic modulus ranging between 0.49 and 1.05 MPa was achieved without affecting the density of the porous sample by adjusting the solvent and non-solvent content in the solution. It is shown that the flexibility of the porous structures can be improved by reducing the ratio of water to THF and decreasing the PDMS content. The porosity measurements reveal that the PDMS concentration is the major phase controlling the porosity of the structure, while the effect of water/THF is negligible.     

Plant Secondary Metabolites as Defense Tools against Herbivores for Sustainable Crop Protection

Plants have evolved several adaptive strategies through physiological changes in response to herbivore attacks. Plant secondary metabolites (PSMs) are synthesized to provide defensive functions and regulate defense signaling pathways to safeguard plants against herbivores. Herbivore injury initiates complex reactions which ultimately lead to synthesis and accumulation of PSMs. The biosynthesis of these metabolites is regulated by the interplay of signaling molecules comprising phytohormones. Plant volatile metabolites are released upon herbivore attack and are capable of directly inducing or priming hormonal defense signaling pathways. Secondary metabolites enable plants to quickly detect herbivore attacks and respond in a timely way in a rapidly changing scenario of pest and environment. Several studies have suggested that the potential for adaptation and/or resistance by insect herbivores to secondary metabolites is limited. These metabolites cause direct toxicity to insect pests, stimulate antixenosis mechanisms in plants to insect herbivores, and, by recruiting herbivore natural enemies, indirectly protect the plants. Herbivores adapt to secondary metabolites by the up/down regulation of sensory genes, and sequestration or detoxification of toxic metabolites. PSMs modulate multi-trophic interactions involving host plants, herbivores, natural enemies and pollinators. Although the role of secondary metabolites in plant-pollinator interplay has been little explored, several reports suggest that both plants and pollinators are mutually benefited. Molecular insights into the regulatory proteins and genes involved in the biosynthesis of secondary metabolites will pave the way for the metabolic engineering of biosynthetic pathway intermediates for improving plant tolerance to herbivores. This review throws light on the role of PSMs in modulating multi-trophic interactions, contributing to the knowledge of plant-herbivore interactions to enable their management in an eco-friendly and sustainable manner.