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Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an 'Internet of vehicles' with the potential to significantly enhance citizens' mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.

Looking at current enterprise resource planning systems shows that material requirements planning (MRP) is one of the main production planning approaches implemented there. The MRP planning parameters lot size, safety stock, and planned lead time, have to be identified for each MRP planned material. With increasing production system complexity, more planning parameters have to be defined. Simulation-based optimization is known as a valuable tool for optimizing these MRP planning parameters for the underlying production system. In this article, a fast and easy-to-apply simheuristic was developed with the objective to minimize overall costs. The simheuristic sets the planning parameters lot size, safety stock, and planned lead time for the simulated stochastic production systems. The developed simheuristic applies aspects of simulation annealing (SA) for an efficient metaheuristic-based solution parameter sampling. Additionally, an intelligent simulation budget management (SBM) concept is introduced, which skips replications of not promising iterations. A comprehensive simulation study for a multi-item and multi-staged production system structure is conducted to evaluate its performance. Different simheuristic combinations and parameters are tested, with the result that the combination of SA and SBM led to the lowest overall costs. The contributions of this article are an easy implementable simheuristic for MRP parameter optimization and a promising concept to intelligently manage simulation budget., Competing Interests: Conflicts of InterestThe authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results., (© 2022 by the authors.)

The design of supply chain networks (SCNs) aims at determining the number, location, and capacity of production facilities, as well as the allocation of markets (customers) and suppliers to one or more of these facilities. This paper reviews the existing literature on the use of simulation-optimization methods in the design of resilient SCNs. From this review, we classify some of the many works in the topic according to factors such as their methodology, the approach they use to deal with uncertainty and risk, etc. The paper also identifies several research opportunities, such as the inclusion of multiple criteria (e.g., monetary, environmental, and social dimensions) during the design-optimization process and the convenience of considering hybrid approaches combining metaheuristic algorithms, simulation, and machine learning methods to account for uncertainty and dynamic conditions, respectively., (© 2020 Elsevier B.V. All rights reserved.)

Rivastigmine is a very important drug prescribed for the treatment of Alzheimer's disease (AD) symptoms. It is a dual inhibitor, in that it inhibits both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE). For our screening program on the discovery of new rivastigmine analogue hits for human butyrylcholinesterase (hBuChE) inhibition, we investigated the interaction of this inhibitor with BuChE using the complimentary approach of the biophysical method, saturation transfer difference (STD)-NMR and molecular docking. This allowed us to obtain essential information on the key binding interactions between the inhibitor and the enzyme to be used for screening of hit compounds. The main conclusions obtained from this integrated study was that the most dominant interactions were (a) H-bonding between the carbamate carbonyl of the inhibitor and the NH group of the imidazole unit of H434, (b) stacking of the aromatic unit of the inhibitor and the W82 aromatic unit in the choline binding pocket via π-π interactions and (c) possible CH/π interactions between the benzylic methyl group and the N-methyl groups of the inhibitor and W82 of the enzyme., (Copyright © 2016 Elsevier Inc. All rights reserved.)

A library of isoquinolinone and azepanone derivatives were screened for both acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) activity. The strategy adopted included (a) in vitro biological assays, against eel AChE (EeAChE) and equine serum BuChE (EqBuChE) in order to determine the compounds IC50 and their dose-response activity, consolidated by (b) molecular docking studies to evaluate the docking poses and interatomic interactions in the case of the hit compounds, validated by STD-NMR studies. Compound (1f) was identified as one of these hits with an IC50 of 89.5μM for EeAChE and 153.8μM for EqBuChE, (2a) was identified as a second hit with an IC50 of 108.4μM (EeAChE) and 277.8μM (EqBuChE). In order to gain insights into the binding mode and principle active site interactions of these molecules, (R)-(1f) along with 3 other analogues (also as the R-enantiomer) were docked into both RhAChE and hBuChE models. Galantamine was used as the benchmark. The docking study was validated by performing an STD-NMR study of (1f) with EeAChE using galantamine as the benchmark., (Copyright © 2016 Elsevier Inc. All rights reserved.)

As Wireless Sensor Networks are being adopted by industry and agriculture for large-scale and unattended deployments, the need for reliable and energy-conservative protocols become critical. Physical and Link layer efforts for energy conservation are not mostly considered by routing protocols that put their efforts on maintaining reliability and throughput. Gradient-based routing protocols route data through most reliable links aiming to ensure 99% packet delivery. However, they suffer from the so-called "hot spot" problem. Most reliable routes waste their energy fast, thus partitioning the network and reducing the area monitored. To cope with this "hot spot" problem we propose ZERO a combined approach at Network and Link layers to increase network lifespan while conserving reliability levels by means of probabilistic load balancing techniques.

Inhibition of the epidermal growth factor receptor (EGFR) tyrosine kinase is one among the pivotal targets for the treatment of cancer. The structural investigation directly halting the EGFR autophosphorylation is expected to give insights into alternatively blocking the aberrant activity of EGFR. The three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed from the systematic search conformer-based alignment method. Models derived from the training set of 95 compounds showed superior CoMFA as compared with CoMSIA (CoMFA: q(2)=0.50, r(2)=0.74, N=5, F=48.83, r(2)(pred)=0.56 while CoMSIA: q(2)=0.48, r(2)=0.62, N=2, F=72.70, r(2)(pred)=0.51). Validation of the models by test set prediction of 26 compounds was in good agreement with the experimental results. Further validation by molecular docking superimposition into the 3D-QSAR contour maps was found in agreement with each other. We identified that the structural modification of compound 19 by attachment of a bulky group on pyrrole ring along with an electronegative group on quinazoline ring and a hydrogen-bond donor on methyl formate opens a new avenue towards the optimization of novel chemical entities to develop potent inhibitors for EGFR autophosphorylation.

Clinically relevant Lys103Asn (K103N) mutant frequently observed in HIV-1 reverse transcriptase (RT) confers drug resistance. To obtain useful structural information necessary for targeted-inhibitor design, molecular docking combined with 3D-QSAR CoMFA and CoMSIA was applied to a set of 53 structurally diverse HIV-RT inhibitors. Two strategies were applied to generate 3D-QSAR models. The first strategy is the flexibility-based molecular alignment (FMA), similar to receptor-based alignment, which samples the biological space of K103N mutant HIV-RT. FMA was conducted by docking the compounds to four structural data of mutant HIV-RT with PDB codes: 1SV5, 2IC3, 1FKP and 1FKO, which are co-crystallized according to NNRTI inhibitors such as etravirine, HBY-097, nevirapine, and efavirenz. The best superposition of the compounds to the active site of 1FKP structure suggests specific inhibition of nevirapine-resistance. The second strategy is the dataset division which employs the principal component analysis (PCA) to classify the dataset into training and test sets that yields statistically significant and robust models. The PCA design selection tool by the most descriptive compounds (MDC) outperforms the largest minimum distance (LMD) for the present dataset. Overall, the results demonstrated the feasibility of the two strategies to the present case and hold a promise for its general applicability to future QSAR studies. The generated models are predictive based on reproducible values of the predicted compared with experimental activities. Further, the complementary analysis of contour maps to the mutant HIV-RT binding site suggested the anchor points for binding affinity. The present study introduced the concept 'clamp-flex' for the rational design of targeted-inhibitor to overcome the K103N pan-class resistance mutation. The predictive models offer new insights into binding modes involving the hydrophobicity and flexibility of the active site.

Molecular modeling by 3D-QSAR comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were employed on a series of phenylaminopyrimidine-based (PAP) Bcr-Abl inhibitors. The chemical structures of 63 PAP analogues were aligned using a template extracted from the crystal structure of STI571 bound to Abl kinase. Subsequently, the structures built were divided into training and test sets that include 53 and 10 compounds, respectively. Statistical results showed that the 3D-QSAR models generated from CoMSIA were superior to CoMFA (CoMSIA; q2=0.66, r2=0.94, N=3, F=139.09, r2pred=0.64 while CoMFA; q2=0.53, r2=0.73, N=3, F=43.53, r2pred=0.61). Based on the contour interpretation, the attachment of hydrophobic and bulky groups to the phenyl and pyrrolidine (D- and E-ring of NS-187, respectively) along with highly electronegative groups around the D-ring are important structural features for the design of second-generation Bcr-Abl inhibitors. The generated models are predictive based on reproducible values of the predicted compared with experimental activities in the test set. Further, the complementary analysis of contour maps to the Bcr-Abl binding site suggested the anchor points for binding affinity.

Microsomal prostaglandin E(2) synthase (mPGES-1) has been identified recently as a novel target for treating pain and inflammation. The aim of this study is to understand the binding affinities of reported inhibitors for mPGES-1 and further to design potential new mPGES-1 inhibitors. 3D-QSAR-CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis) - techniques were employed on a series of indole derivatives that act as selective mPGES-1 inhibitors. The lowest energy conformer of the most active compound obtained from systematic conformational search was used as a template for the alignment of 32 compounds. The models obtained were used to predict the activities of the test set of eight compounds, and the predicted values were in good agreement with the experimental results. The 3D-QSAR models derived from the training set of 24 compounds were all statistically significant (CoMFA; q (2) = 0.89, r (2) = 0.95, [Formula: see text], [Formula: see text] and CoMSIA; q (2) = 0.84, r (2) = 0.93, [Formula: see text], [Formula: see text]). Contour plots generated for the CoMFA and CoMSIA models reveal useful clues for improving the activity of mPGES-1 inhibitors. In particular, substitutions of an electronegative fluorine atom or a bulky hydrophilic phenoxy group at the meta or para positions of the biphenyl rings might improve inhibitory activity. A plausible binding mode between the ligands and mPGES-1 is also proposed.

The enzyme FabH catalyzes the initial step of fatty acid biosynthesis via a type II fatty acid synthase. The pivotal role of this essential enzyme combined with its unique structural features and ubiquitous occurrence in bacteria has made it an attractive new target for the development of antibacterial and antiparasitic compounds. Predictive hologram quantitative structure activity relationship (HQSAR) model was developed for a series of benzoylamino benzoic acid derivatives acting as FabH inhibitor. The best HQSAR model was generated using atoms and bond types as fragment distinction and 4-7 as fragment size showing cross-validated q2 value of 0.678 and conventional r2 value of 0.920. The predictive ability of the model was validated by an external test set of 6 compounds giving satisfactory predictive r2 value of 0.82. The contribution maps obtained from this model were used to explain the individual atomic contributions to the overall activity. It was confirmed from the contribution map that both ring A and ring C play a vital role for activity. Moreover hydroxyl substitution in the ortho position of ring A is favorable for better inhibitory activity. Therefore the information derived from the contribution map can be used to design potent FabH inhibitors.