Background
Accurate prediction of traffic flow is an integral component in most of the Intelligent Transportation Systems (ITS) applications. The data driven approach using Box-Jenkins Autoregressive ...Integrated Moving Average (ARIMA) models reported in most studies demands sound database for model building. Hence, the applicability of these models remains a question in places where the data availability could be an issue. The present study tries to overcome the above issue by proposing a prediction scheme using Seasonal ARIMA (SARIMA) model for short term prediction of traffic flow using only limited input data.
Method
A 3-lane arterial roadway in Chennai, India was selected as the study stretch and limited flow data from only three consecutive days was used for the model development using SARIMA. After necessary differencing to make the input time series a stationary one, the autocorrelation function (ACF) and partial autocorrelation function (PACF) were plotted to identify the suitable order of the SARIMA model. The model parameters were found using maximum likelihood method in R. The developed model was validated by performing 24 hrs. ahead forecast and the predicted flows were compared with the actual flow values. A comparison of the proposed model with historic average and naive method was also attempted. The effect of increase in sample size of input data on prediction results was studied. Short term prediction of traffic flow during morning and evening peak periods was also attempted using both historic and real time data.
Concluding remarks
The mean absolute percentage error (MAPE) between actual and predicted flow was found to be in the range of 4–10, which is acceptable in most of the ITS applications. The prediction scheme proposed in this study for traffic flow prediction could be considered in situations where database is a major constraint during model development using ARIMA.
Microgrid is a combination of distributed generators, storage systems, and controllable loads connected to low-voltage network that can operate either in grid-connected or in island mode. High ...penetration of power at distribution level creates such multiple microgrids. This paper proposes a two-level architecture for distributed-energy-resource management for multiple microgrids using multiagent systems. In order to match the buyers and sellers in the energy market, symmetrical assignment problem based on naíve auction algorithm is used. The developed mechanism allows the pool members such as generation agents, load agents, auction agents, grid agents, and storage agents to participate in market. Three different scenarios are identified based on the supply-demand mismatch among the participating microgrids. At the end of this paper, two case studies are presented with two and four interconnected microgrids participating in the market. Simulation results clearly indicate that the agent-based management is effective in resource management among multiple microgrids economically and profitably.
The study aims to evaluate the potency of two hundred natural antiviral phytocompounds against the active site of the Severe Acquired Respiratory Syndrome - Coronavirus − 2 (SARS-CoV-2) Main-Protease ...(M
pro
) using AutoDock 4.2.6. The three- dimensional crystal structure of the M
pro
(PDB Id: 6LU7) was retrieved from the Protein Data Bank (PDB), the active site was predicted using MetaPocket 2.0. Food and Drug Administration (FDA) approved viral protease inhibitors were used as standards for comparison of results. The compounds theaflavin-3-3'-digallate, rutin, hypericin, robustaflavone, and (-)-solenolide A with respective binding energy of −12.41 (Ki = 794.96 pM); −11.33 (Ki = 4.98 nM); −11.17 (Ki = 6.54 nM); −10.92 (Ki = 9.85 nM); and −10.82 kcal/mol (Ki = 11.88 nM) were ranked top as Coronavirus Disease − 2019 (COVID-19) M
pro
inhibitors. The interacting amino acid residues were visualized using Discovery Studio 3.5 to elucidate the 2-dimensional and 3-dimensional interactions. The study was validated by i) re-docking the N3-peptide inhibitor-M
pro
and superimposing them onto co-crystallized complex and ii) docking decoy ligands to M
pro
. The ligands that showed low binding energy were further predicted for and pharmacokinetic properties and Lipinski's rule of 5 and the results are tabulated and discussed. Molecular dynamics simulations were performed for 50 ns for those compounds using the Desmond package, Schrödinger to assess the conformational stability and fluctuations of protein-ligand complexes during the simulation. Thus, the natural compounds could act as a lead for the COVID-19 regimen after in-vitro and in- vivo clinical trials.
Communicated by Ramaswamy H. Sarma
The performance limits of monolayer transition metal dichalcogenide ( MX 2 ) transistors are examined with a ballistic MOSFET model. Using an ab initio theory, we calculate the band structures of 2-D ...transition MX 2 . We find the lattice structures of monolayer MX 2 remain the same as the bulk MX 2 . Within the ballistic regime, the performances of monolayer MX 2 transistors are better compared with those of the silicon transistors if a thin high-κ gate insulator is used. This makes monolayer MX 2 promising 2-D materials for future nanoelectronic device applications.
Metal‐catalyzed cross‐coupling reactions belong to the most important transformations in organic synthesis. Copper catalysis has received great attention owing to the low toxicity and low cost of ...copper. However, traditional Ullmann‐type couplings suffer from limited substrate scopes and harsh reaction conditions. The introduction of several bidentate ligands, such as amino acids, diamines, 1,3‐diketones, and oxalic diamides, over the past two decades has totally changed this situation as these ligands enable the copper‐catalyzed coupling of aryl halides and nucleophiles at both low reaction temperatures and catalyst loadings. The reaction scope has also been greatly expanded, rendering this copper‐based cross‐coupling attractive for both academia and industry. In this Review, we have summarized the latest progress in the development of useful reaction conditions for the coupling of (hetero)aryl halides with different nucleophiles. Additionally, recent advances in copper‐catalyzed coupling reactions with aryl boronates and the copper‐based trifluoromethylation of aromatic electrophiles will be discussed.
Recent advances in copper/ligand‐catalyzed cross‐couplings of (hetero)aryl halides and nucleophiles, copper‐catalyzed coupling reactions with aryl boronates, and copper‐enabled trifluoromethylations of aromatic electrophiles are discussed in this Review.
The electrocatalytic reduction of carbon dioxide at Cu based metal organic framework film surface was studied in N,N-dimethylformamide containing tetrabutylammonium tetrafluoroborate with saturated ...CO2. Cyclic voltammetric studies of the MOF film immobilized onto GC in 0.1MKCl clearly showed the well defined Cu(II)/Cu(I) and Cu(I)/Cu(0) reversible redox responses. In the presence of saturated CO2/TBATFB/DMF solution, the cyclic voltammetric studies revealed that the electrochemically generated Cu(I) formed adduct with carbon dioxide in-situ and on further formed oxalic acid. The formation of oxalic acid was confirmed by GCMS in bulk electrolysis experiment. A detailed mechanism for the formation of oxalic acid was also discussed in this communication.
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Over 380 host plant species have been known to develop leaf spots as a result of the fungus Alternaria alternata. It is an aspiring pathogen that affects a variety of hosts and causes rots, blights, ...and leaf spots on different plant sections. In this investigation, the lipopeptides from the B. subtilis strains T3, T4, T5, and T6 were evaluated for their antifungal activities. In the genomic DNA, iturin, surfactin, and fengycin genes were found recovered from B. subtilis bacterium by PCR amplification. From different B. subtilis strains, antifungal Lipopeptides were extracted, identified by HPLC, and quantified with values for T3 (24 g/ml), T4 (32 g/ml), T5 (28 g/ml), and T6 (18 g/ml). To test the antifungal activity, the isolated lipopeptides from the B. subtilis T3, T4, T5, and T6 strains were applied to Alternaria alternata at a concentration of 10 g/ml. Lipopeptides were found to suppress Alternaria alternata at rates of T3 (75.14%), T4 (75.93%), T5 (80.40%), and T6 (85.88%). The T6 strain outperformed the other three by having the highest antifungal activity against Alternaria alternata (85.88%).
We identified a novel plasmid-mediated colistin-resistance gene in porcine and bovine colistin-resistant Escherichia coli that did not contain mcr-1. The gene, termed mcr-2, a 1,617 bp ...phosphoethanolamine transferase harboured on an IncX4 plasmid, has 76.7% nucleotide identity to mcr-1. Prevalence of mcr-2 in porcine colistin-resistant E. coli (11/53) in Belgium was higher than that of mcr-1 (7/53). These data call for an immediate introduction of mcr-2 screening in ongoing molecular epidemiological surveillance of colistin-resistant Gram-negative pathogens.
Segmentation of the liver from computed tomography images is an essential and critical task in medical image analysis, with significant implications for liver disease diagnosis and treatment. Deep ...learning techniques have emerged as a powerful tool in this domain, offering unprecedented accuracy and robustness. This literature survey paper provides a comprehensive overview of deep learning techniques for segmentation of liver from CT images, aiming to synthesize recent advancements, identify key contributions, and address challenges in this rapidly evolving field. The survey covers various deep learning architectures, including convolution neural networks, U-Net, attention mechanisms, generative adversarial neural networks and transformer models, highlighting their strengths and weaknesses. Evaluation metrics and benchmark datasets commonly used for performance assessment are discussed in this survey. Furthermore, the survey delves into the challenges and limitations of deep learning methods, including interpretability, model robustness, and ethical considerations. The survey concludes by summarizing key findings, highlighting advancements, and outlining future research directions, such as interpretable models, ethical considerations, and bridging the gap between research and clinical implementation. This literature survey serves as a valuable reference for researchers, healthcare professionals, and developers in their pursuit of accurate liver segmentation and advancing medical image analysis.
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