Life cycle energy and carbon footprint analysis of photovoltaic battery microgrid system in India

Electricity supply in India is from a centralized grid. Many parts of the country experience grid interruptions. Life cycle energy and environmental analysis has been done for a 27 kWp photovoltaic system which acts as grid backup for 3 h outage in an Indian urban residential scenario. This paper discusses energy requirements and carbon emission for a PV storage system for five different battery technologies in Indian context. This can be used as a metric for comparative analysis for new batteries, with an undeveloped market. The energy requirements for the components are quantified and are compared in terms of energy payback time (EPBT) and Net Energy Ratio (NER). All the calculations are done for Indian context. EPBT is found to be in the range of 2–4.5 years for all the systems, while NER is in the range of 6.6–2.52. NaS has the highest emission factor of 0.67 kgCO₂/kWh and the least for NiCd (0.091 kgCO₂/kWh). These factors can be used to select a PV battery option and to target selection of materials and systems based on the reported values.     

Metallic nanoparticle synthesised by biological route: safer candidate for diverse applications

Biological synthesis of nanoparticles (NPs) involves greater prospect; however, a detailed review is required for ecofriendly, faster and stable NP formulation in large scale for different commercial applications. The present article highlighted recent updates on biological route of single and bimetallic NP synthesis wherein the chemical reducing agents are eliminated and biological entities are utilised to convert metal ions to NPs. Application of the biological reducing agents ranging from bacteria to fungi and even natural plant extracts have emerged as eco‐friendly and cost‐effective routes for the synthesis of metal nanomaterials. Potential applications of such NPs, a wide range of analytical techniques used for characterisation and factors influencing the synthesis of NPs are focused. Further, elucidation of the mechanisms associated with the NP formation using microorganisms, as well as plant‐based materials are analysed which would be helpful for wide range of readers in the field of NP research for future selection and commercial implementation.Inspec keywords: nanoparticles, nanobiotechnology, nanofabrication, reviews, microorganisms, botanyOther keywords: metallic nanoparticle, biological synthesis, chemical reducing agents, biological reducing agents, bacteria, fungi, natural plant extracts, eco‐friendly synthesis, cost‐effective synthesis, microorganisms, plant‐based materials, review     

Offspring of normal and diabetic rats fed saturated fat in pregnancy demonstrate vascular dysfunction

BACKGROUND: Disturbances of the in utero environment may "program" for disease in later life. In this study, we determined whether dietary fat supplementation and/or diabetes in pregnancy can adversely affect vascular function in the offspring. METHODS AND RESULTS: Female Sprague-Dawley rats were fed a breeding diet or a diet high in saturated fat (30% wt/wt) for 10 days before mating, throughout pregnancy, and postpartum. Endothelium-dependent relaxation to acetylcholine was blunted in isolated femoral arteries of 15-day-old weanling pups from dams fed the 30%-fat diet. Endothelial dysfunction and enhanced constrictor responses to norepinephrine were also observed in an additional study of 60-day-old offspring of dams fed 20% saturated fat. Rats with streptozotocin-induced diabetes were also fed saturated fat during pregnancy. Femoral arteries from their 15-day-old offspring showed impairment of endothelium-dependent dilation and enhanced constrictor responses to norepinephrine and the thromboxane mimetic U46619 compared with young offspring of high-fat-fed normal dams. The 30%-fat diet was also deleterious to vascular function in the maternal diabetic animals when assessed in mesenteric arteries 16 days postpartum. CONCLUSIONS: A high-fat diet in pregnancy led to vascular dysfunction in rat weanlings and young adult offspring. Vascular function further deteriorated in weanlings if the maternal rat was diabetic.     

The flavin-containing monooxygenase 2 gene (FMO2) of humans, but not of other primates, encodes a truncated, nonfunctional protein

Flavin-containing monooxygenases (FMOs) are NADPH-dependent flavoenzymes that catalyze the oxidation of heteroatom centers in numerous drugs and xenobiotics. FMO2, or "pulmonary" FMO, one of five forms of the enzyme identified in mammals, is expressed predominantly in lung and differs from other FMOs in that it can catalyze the N-oxidation of certain primary alkylamines. We describe here the isolation and characterization of cDNAs for human FMO2. Analysis of the sequence of the cDNAs and of a section of the corresponding gene revealed that the major FMO2 allele of humans encodes a polypeptide that, compared with the orthologous protein of other mammals, lacks 64 amino acid residues from its C terminus. Heterologous expression of the cDNA revealed that the truncated polypeptide was catalytically inactive. The nonsense mutation that gave rise to the truncated polypeptide, a C --> T transition in codon 472, is not present in the FMO2 gene of closely related primates, including gorilla and chimpanzee, and must therefore have arisen in the human lineage after the divergence of the Homo and Pan clades. Possible mechanisms for the fixation of the mutation in the human population and the potential significance of the loss of functional FMO2 in humans are discussed.     

Fault containment in weakly stabilizing systems

Research on fine tuning stabilization properties has received attention for more than a decade. This paper presents probabilistic algorithms for fault containment. We demonstrate two exercises in fault containment in a weakly stabilizing system, which expedite recovery from single failures, and confine the effect of any single fault to the constant-distance neighborhood of the faulty process. The most significant aspect of the algorithms is that the fault gap, defined as the smallest interval after which the system is ready to handle the next single fault with the same efficiency, is independent of the network size. We argue that a small fault gap increases the availability of the fault-free system.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

ILP-assisted de novo drug design

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intra-patient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB–MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.     

A revisit to non-maximally entangled mixed states: teleportation witness,noisy channel and discord

We constructed a class of non-maximally entangled mixed states (Adhikari et al. in Quantum Inf Comput 10:0398, 2010) and extensively studied their entanglement properties and also their usefulness as teleportation channels. In this article, we have revisited our constructed state and have studied it from three different perspectives. Since every entangled state is associated with a witness operator, we have found a suitable entanglement as well as teleportation witness operator for our non-maximally entangled mixed states. We considered the noisy channel’s effects on our constructed states to see how much it affects the states’ capacities as teleportation channels. For this purpose, we have mainly focussed on amplitude damping channel. A comparative study on concurrence and quantum discord of our constructed state of Adhikari et al. (2010) has also been carried out here.     

Surface modes of a sessile water drop: an optical tweezer based study

A high-precision method to study the dynamics of two-fluid interfaces using an optical tweezer and a phase-sensitive detection technique are described. The disturbances set up at the interface are studied by analyzing the motion of an optically trapped particle in the bulk of the fluid, i.e., away from the interface. The usefulness of the technique is demonstrated for the well-known problem of a horizontally vibrated sessile liquid drop. The vibrational modes of the liquid drop excited by sinusoidally vibrating the support in a horizontal plane appear as resonances in the motion of the trapped particle. The nature of the resonance is studied in detail by measuring the real part, the imaginary part, and the phase response of the motion of the particle as a function of the "effective" size of the liquid drop. Excellent quantitative agreement with the theoretically predicted values of the eigenfrequencies and damping of the surface modes is obtained.