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Surface Water Mapping (SWM) is essential for studying hydrological and ecological phenomena. SWM holds significant importance in water resource management, environmental conservation, and disaster preparation. Recently, rapid urbanization, overutilization, and environmental degradation have seriously impacted surface water bodies. Rapid advancement in remote sensing data and technologies has promoted the SWM to a new era. Timely and precise SWM is crucial for water resource preservation and planning. This paper critically reviews the extraction of surface water bodies from optical sensors using Spectral Indices (SI), Machine Learning (ML), Deep Learning (DL), and Spectral unmixing with a comprehensive overview of satellite data, study areas, methodologies, results, advantages, and disadvantages, especially over the last decade. The extensive review of SWM reveals that DL outperforms ML and SI. DL outperforms other methods because it incorporates crucial elements in network design, like skip connections, dilation convolution, attention mechanisms, and residual blocks. The spectral unmixing addresses the mixed pixel misclassification problem. Some SI, ML, and DL methods are implemented, and the results are discussed. Integrating the DL technique with spectral unmixing, fusing multisource data (SAR and optical) and integrating it with ancillary data (DEM) is the future direction for improved SWM. [ABSTRACT FROM AUTHOR]

Hybrid nanofluids are a promising class of fluids that combine the advantages of nanoparticles with a base fluid to enhance heat transfer performance. They have potential applications in various fields and are the subject of ongoing research and development. Understanding the properties and behaviour of hybrid nanofluids is essential to explore their potential and optimize their use in various applications. Heat exchangers represent the most widespread thermal devices, and increasing their efficiency is the key to significant energy savings. This review article mainly focuses on the overall heat transfer process analysing the influence of thermophysical properties of single-component and hybrid nanofluids (using different base fluids). Finally, a case study is performed with SiC nanoparticles suspended in a mixture of ethylene glycol (EG) and water (H2O) in a 20:80 volume ratio (20:80 EG–W), whose stability is confirmed by zeta potential determination and showed an increase in heat transfer. [ABSTRACT FROM AUTHOR]

Fresh water is vital for all living creatures and maintains the hydrological cycle. Surface water bodies conserve freshwater and exhibit dynamic changes yearly due to high/low rainfall and over/underutilization. Therefore, extracting water bodies and determining their extent is imperative for effective water resource management. Water body extraction using Water Indices (WI) and Machine Learning (ML) face threshold selection and feature optimization challenges, respectively. This paper proposes a Lightweight Attention-based Multiscale Neural Network (LWAMNNet) for surface water body extraction from Linear Imaging Self Scanning‒III (LISS-III) remote sensing images. The LWAMNNet is an encoder-decoder architecture designed using a modified residual block in both the encoder and decoder to extract high-level features. Feature Extraction Module (FEM) is sandwiched between encoder and decoder to extract global contextual features. The LWAMNNet replaces convolutions with depthwise separable convolutions to reduce computation complexity. In the decoder, the attention module is incorporated to provide attention to fused features (i.e., combined deep features with spatial encoder features) at different scales. The LWAMNNet effectively extracts different-sized water bodies with non-linear boundaries. The proposed LWAMNNet qualitatively and quantitatively outperforms other DL models in performance metrics (accuracy of 99.5%) and computation complexity (in terms of trainable parameters and time). Additionally, the water extent of five major reservoirs in south India was determined annually from 2016 to 2019. Also, the reason for water dynamics is analyzed with the help of rainfall and water availability data provided by the Indian Metrological Department (IMD). [ABSTRACT FROM AUTHOR]

Accurate weather forecasting plays a crucial component in everyday human life, especially in the agricultural and industrial sectors around the world. The time series prediction of meteorological variables by organizations or government agencies may assist in the decision-making process to plan their daily activities, safeguarding agricultural and water resources. However, the models used by them are computationally expensive. With the rapid development of Artificial Intelligence (AI), a univariate time series model for forecasting average daily temperature and Relative Humidity prediction was developed using Deep Learning (DL) models. Feed-Forward Neural Networks (FFNN), Long Short-Term Memory (LSTM), Gated Recurrent units, Bidirectional LSTM, Convolutional Neural Networks combined with LSTM (CNN+LSTM) and Convolutional LSTM (ConvLSTM) are the DL models considered for this study. In addition, the proposed models are trained and tested with different optimizers (Stochastic Gradient Descent, Root Mean Square Propagation, and Adaptive Moment Estimation (ADAM)). The experimental results show that the CNN+LSTM with ADAM optimizer is the best forecasting model for average temperature and Relative Humidity prediction, followed by ConvLSTM with ADAM optimizer compared to the other DL models. Highlights: Accurate Temperature and Relative Humidity forecasting plays an important role in planning everyday human activities in the agricultural and industrial sectors. Univariate deep learning forecasting models are designed and implemented to predict the future trends of these climatic variables. Experimental results show the CNN+LSTM outperforms other deep learning forecasting models. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Thermophysical properties and heat transfer in mono and hybrid nanofluids with different base fluids: an overview" published in the Journal of Thermal Analysis & Calorimetry. The correction addresses an error in the author's name, where T. M. Yunus Khan was incorrectly written as T. M. Y. Khan. The original article has been corrected. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are T. Kanthimathi, P. Bhramara, Vinay Atgur, B. Nageswara Rao, Nagaraj R. Banapurmath, Ashok M. Sajjan, Irfan Anjum Badruddin, Sarfaraz Kamangar, T. M. Yunus Khan, Rahmath Ulla Baig, Chandramouli Vadlamudi, and Sanjay Krishnappa. [Extracted from the article]

Categorical neural responses underlie various forms of selection and decision-making. Such binary-like responses promote robust signaling of the winner in the presence of input ambiguity and neural noise. Here, we show that a 'donut-like' inhibitory mechanism in which each competing option suppresses all options except itself, is highly effective at generating categorical neural responses. It surpasses motifs of feedback inhibition, recurrent excitation, and divisive normalization invoked frequently in decision-making models. We demonstrate experimentally not only that this mechanism operates in the midbrain spatial selection network in barn owls, but also that it is necessary for categorical signaling by it. The functional pattern of neural inhibition in the midbrain forms an exquisitely structured 'multi-holed' donut consistent with this network's combinatorial inhibitory function for stimulus selection. Additionally, modeling reveals a generalizable neural implementation of the donut-like motif for categorical selection. Self-sparing inhibition may, therefore, be a powerful circuit module central to categorization. Decision making is facilitated by categorical neuronal responses, which robustly signal a winner despite input noise. In this study, the authors demonstrate that a donut-like inhibition motif effectively generates such categorical responses. [ABSTRACT FROM AUTHOR]

The main goal of the present work was to develop a value-added product of biodegradable material for sustainable packaging. The use of agriculture waste-derived carboxymethyl cellulose (CMC) mainly is to reduce the cost involved in the development of the film, at present commercially available CMS is costly. The main focus of the research is to translate the agricultural waste-derived CMC to useful biodegradable polymer suitable for packaging material. During this process CMC was extracted from the agricultural waste mainly sugar cane bagasse and the blends were prepared using CMC (waste derived), gelatin, agar and varied concentrations of glycerol; 1.5% (sample A), 2% (sample B), and 2.5% (sample C) was added. Thus, the film derived from the sample C (gelatin + CMC + agar) with 2.0% glycerol as a plasticizer exhibited excellent properties than other samples A and B. The physiochemical properties of each developed biodegradable plastics (sample A, B, C) were characterized using Fourier Transform Infra-Red (FTIR) spectroscopy and Differential Scanning Calorimetry (DSC), Thermogravimetric analysis (TGA). The swelling test, solubility in different solvents, oil permeability coefficient, water permeability (WP), mechanical strength of the produced material was claimed to be a good material for packaging and meanwhile its biodegradability (soil burial method) indicated their environmental compatibility nature and commercial properties. The reflected work is a novel approach, and which is vital in the conversion of organic waste to value-added product development. There is also another way to utilize commercial CMC in preparation of polymeric blends for the packaging material, which can save considerable time involved in the recovery of CMC from sugarcane bagasse. [ABSTRACT FROM AUTHOR]

With the growing demand of economically feasible, clean, and renewable energy, the use of solar photovoltaic (PV) systems is increasing. The PV panel performance to generate electrical energy depends on many factors among which tilt angle is also a crucial one. Among hundreds of research work performed pertinent to solar PV panels performance, this work critically reviews the role of tilt angles and particularly locating the optimum tilt angle using different methods. The past data collected for analysis can be categorized mainly into mathematical model based, experimental based, simulation based, or combination of any of these. Single-axis tracking, dual-axis tracking, simple glass cover, hydrophobic glass cover, soiled glass, clean glass, partial shadow, use of phase-change material, computational fluid dynamic analysis, etc., are the novel methods found in the literature for analysis and locating the optimum tilt angle. For illustration purpose, few figures are provided in which the optimum tilt angle obtained on monthly, seasonally, and annual basis is shown. Research works are growing in the field of computations and simulations using online software and codes. Pure mathematical-based calculations are also reported but the trend is to combine this method with the simulation method. As the PV panel performance is found to be affected by number of parameters, their consideration in any single study is not reported. In future, work is required to carry out the experiment or simulation considering the effect of soiling, glass material, temperature, and surrounding ambience on the location of optimum tilt angle. As a whole, the optimum tilt angles reported for locations exactly on the equator line, i.e., 0° latitude, ranges between − 2.5° and 2.5°, for locations just above the equator line, i.e., latitude 2.6°–30° N ranges between 5° and 28°, for 40°–70° N, it is 29°–40°, and for 71°–90° N, it is 41°–45°. For locations at 2.6°–30° S, optimum tilt angles range between − 4° and − 32°, 30°–46° S, it is − 33° to − 36°, 47°–65° S, it is − 34° to − 50°, and for 66°–90° S it is − 51° to − 62°. [ABSTRACT FROM AUTHOR]

A majority of small-scale renewable energy sources including the solar PV modules, fuel cells gives out output voltage in the range of around 15-40 V DC. This needs to be stepped up to suit load requirements using a high voltage gain converter. Since renewable sources inherently generate sudden variations in input voltage, a good output voltage profile even during such random variations in input conditions is essential. This paper presents modeling of a high step-up converter configuration with closed loop control. The converter topology is designed to operate with moderate duty ratios and the simple coupled inductor. The converter is capable of high step-up and can find application in solar PV systems. The controller response is good with low steady state error and required dynamics. The modeling and simulation is carried out using MATLAB/Simulink software package. [ABSTRACT FROM AUTHOR]

The subgranular zone (SGZ) of the hippocampal dentate gyrus harbors neural stem cells that provide continued adult neurogenesis. The signals from the microenvironment maintain the neural stem cell population in this region. Nonetheless, the identity of these signals is largely unknown. In our study, we analyzed the existence of activated Notch1 (NICD), Hes1, and NF-kB in the SGZ of the murine hippocampus to elucidate the involvement of the Notch pathway in the maintenance of neural stem cell population in this zone. We performed immunohistochemistry and immunoblotting for the above molecules on adult and postnatal developing murine hippocampal tissues. The immunoblot analysis showed a gradual increase in the expression of NICD, Hes1, and NF-kB, as the postnatal hippocampus develops. Immunohistochemical analysis showed a highly positive immunoreactive staining for NICD, Hes1, and NF-kB in cells of the SGZ. Altogether, our own data suggest that the Notch pathway is activated in the SGZ and can play an important role in the regulation of the neural stem cell population in the above. [ABSTRACT FROM AUTHOR]

Conceptual advances in the field of membrane transport have, in the main, utilized artificial membranes, both planar and vesicular. Systems of biological interest, viz., cells and organelles, resemble vesicles in size and geometry. Methods are, therefore, required to extend the results obtained with planar membranes to liposome systems. In this report we present an analysis of a fluorescence technique, using the divalent cation probe chlortetracycline, in small, unilamellar vesicles, for the study of divalent cation fluxes. An ion carrier (X537 A) and a pore former (alamethicin) have been studied. The rate of rise of fluorescence signal and the transmembrane ion gradient have been related to transmembrane current and potential, respectively. A second power dependence of ion conduction-including the electrically silent portion thereof - on X537 A concentration, has been observed. An exponential dependence of 'current' on 'transmembrane potential' in the case of alamethicin is also confirmed. Possible errors in the technique are discussed. [ABSTRACT FROM AUTHOR]

A synthetic peptide corresponding to the signal sequence of E. coli alkaline phosphatase has been synthesized by the solid phase method employing the transesterification method of cleavage from the resin. The protected peptide obtained after cleavage was purified to homogeneity by column chromatography on silica gel, followed by partition chromatography on Sephadex LH-20 using organic solvents like chloroform and methanol as eluants. [ABSTRACT FROM AUTHOR]

The synthesis of peptides by manual solid-phase methods requiring very small volume of solvents at different steps is described. The syntheses have been carried out on polystyrene-based resins using fluorenylmethoxy carboxyl chemistry. All reactions were carried out in vials, of capacity 1.2 or 6 ml, with gentle stirring. The volumes of solvents used were 1-2 ml for each operation. The crude peptides after cleavage from the resin were reasonably pure. The total volume of solvents required for the entire synthesis is substantially less as compared to volumes required in semi-automated and fully automated peptide synthesizers which renders the described method cost-effective. [ABSTRACT FROM AUTHOR]

Studies in our laboratory have been directed towards understanding the mechanism of action of two hydrophobic toxins, pardaxin comprising 33 residues and δ-toxin comprising 26 residues. Since isolation of these peptides in large amounts from natural sources is not convenient, we have explored synthetic approaches to get these peptides as well as their analogs. We have used chemistry specific to fluorenylmethoxycarbonyl (Fmoc) and t-butyloxycarbonyl (Boc) amino acids. Synthesis specific for Fmoc amino acids was carried out manually as well as on a semi-automated continuous flow peptide synthesizer. Synthesis specific for Boc amino acids was carried out manually. The protocols used by us have yielded 15-33 residue peptides which are of high purity. Even in peptides where heterogeneity was present, pure peptide could be obtained in good yields using simple gradients in fast performance liquid chromatography. The synthesis of pardaxin, δ-toxin and several analogs should help in identifying the molecular determinants of biological activity. [ABSTRACT FROM AUTHOR]

Proteins destined for regions other than the cytoplasm in cells have to cross at least one membrane barrier before reaching their proper destination. Almost all such proteins are initially biosynthesized as precursors with signal sequences at the amino terminus. Signal sequences are essential and also sufficient for proteins to be targeted to membranes and also for translocation across membranes. One striking feature that is clearly evident amongst signal sequences of secretory proteins is a positively charged amino terminus followed by a region comprising 10-12 very hydrophobic amino acids. The structural and physico-chemical properties of signal sequences have been analysed. On the basis of the analyses it is proposed that the structural feature of a positively charged amino terminal region followed by a hydrophobic stretch of amino acids, rather than a conformational one, is recognised by components of the cells export machinery. It is also postulated that signal sequences insert in the lipid bilayer of the translocation competent membrane after targeting. The presence of the signal sequence results in the formation of local 'defects' in the bilayer which have a role in translocation of proteins across membranes. [ABSTRACT FROM AUTHOR]

Background: There is considerable literature about revision anterior cruciate ligament (ACL) reconstruction in athletes vut there is little published evidence about the same in the nonathletes. The injury itself may remain underdiagnosed and untreated in nonsports persons. This study highlights the high incidence of ACL injury in the nonathletic patient cohort, revision rates, and the outcomes of revision ACL reconstruction., Materials and Methods: 856 nonathletic patients who underwent primary ACL reconstruction were included in this retrospective study. Patients were asked on phone whether they had undergone revision surgery and whether they had symptoms severe enough to seek reintervention. Clinical assessment and preoperative and postoperative International Knee Documentation Committee (IKDC) and Lysholm scoring were used to followup patients who underwent revision intervention., Results: Clinically, symptomatic revision rate was 5.9% (51 out of 856 patients), and 33 out of these 856 patients (3.9%) underwent revision ACL reconstruction. The reasons for revision were rupture of the previous graft in 21 and laxity (incompetence) of the graft in 12 patients. The mean preoperative and postoperative IKDC scores were 44.1 and 69.8, respectively, and the improvement was statistically significant ( P < 0.001). The IKDC score following revision ACL reconstruction was significantly better in those patients who underwent revision <1 year following the onset of recurrent symptoms ( P = 0.015). Meniscal tears were present in 47.6%, and chondral injuries were seen in 33.3% of patients. The tibial tunnel positioning was abnormal in 70% of patients. Femoral tunnel positioning was aberrant in all the patients., Conclusions: The revision rate of primary ACL reconstruction of 5.9% in nonathletes and revision ACL reconstruction rate of 3.9% are similar to the reported revision rates of 2.9%-5.8% in athletic patients. Similar to athletes, suboptimal tunnel placement is the major contributor to failure in nonathletes also., Competing Interests: There are no conflicts of interest.