Local Parameters Research Articles

Influence of the Clamping Force and the Formation of Liquid Water Along the Channel on the Local Diffusion Processes in PEMFCs

A novel measurement setup, technique, and segmented model are introduced to spatially resolve mass transport losses along the channel in a PEMFC. The newly designed segmented cell enables not only the measurement of local current and voltage data but also the capture of local temperature and compression levels. By extending the commonly used limiting current method, the local mass transport resistance is determined as a function of local operating parameters. The local current and temperature are directly measured, while the operating conditions along the channel such as pressure, oxygen fraction, and relative humidity are obtained empirically from a 1-D differential model. Leveraging both measured and empirically-based simulation results, this comprehensive approach facilitates the characterization of mass transport resistance distribution along the gas channel. The learnings from this study provide valuable insight into the optimization of channel and materials design for enhancing large active area fuel cell performance and durability.

Influence of the Clamping Force and the Formation of Liquid Water Along the Channel on the Local Diffusion Processes in PEMFCs

A novel measurement setup, technique, and segmented model are introduced to spatially resolve mass transport losses along the channel in a PEMFC. The newly designed segmented cell enables not only the measurement of local current and voltage data but also the capture of local temperature and compression levels. By extending the commonly used limiting current method, the local mass transport resistance is determined as a function of local operating parameters. The local current and temperature are directly measured, while the operating conditions along the channel such as pressure, oxygen fraction, and relative humidity are obtained empirically from a 1-D differential model. Leveraging both measured and empirically-based simulation results, this comprehensive approach facilitates the characterization of mass transport resistance distribution along the gas channel. The learnings from this study provide valuable insight into the optimization of channel and materials design for enhancing large active area fuel cell performance and durability.

High-Resolution Sea Surface Target Detection Using Bi-Frequency High-Frequency Surface Wave Radar

The monitoring of the sea surface, whether it is the state of the sea or the position of targets (ships), is an up-to-date research topic. In order to determine localization parameters of ships, we propose a high-resolution algorithm for primary signal processing in high-frequency surface wave radar (HFSWR) which operates at two frequencies. The proposed algorithm is based on a high-resolution estimate of the range–Doppler (RD-HR) map formed at every antenna in the receive antenna array, which is an essential task, because the performance of the entire radar system depends on its estimation. We also propose a new focusing method allowing us to have only one RD-HR map in the detection process, which collects the information from both these carrier frequencies. The goal of the bi-frequency mode of operation is to improve the detectability of targets, because their signals are affected by different Bragg-line interference patterns at different frequencies, as seen on the RD-HR maps during the primary signal processing. Also, the effect of the sea (sea clutter) manifests itself in different ways at different frequencies. Some targets are masked (undetectable) at one frequency, but they become visible at another frequency. By exploiting this, we increase the probability of detection. The bi-frequency architecture (system model) for the localization of sea targets and the novel signal model are presented in this paper. The advantage of bi-frequency mode served as a motivation for testing the detectability of small boats, which is otherwise a very challenging task, primarily because such targets have a small radar reflective surface, they move quickly, and often change their direction. Based on experimentally obtained results, it can be observed that the probability of detection of small boats can also be significantly improved by using a bi-frequency architecture.

Incorporation of specific defects through ion bombardment for better ferroelectrics

AbstractIncorporating specific defects into complex oxides facilitates the exploration of exotic phenomena and novel functionalities based on the intricate coupling between the defects and lattice/charge. However, methods for maximizing the density of specific defects while enhancing the desired properties have been rarely explored. In this study, the effect of N+ ion bombardment‐driven specific defects on the properties of bismuth ferrite (BFO) thin films was investigated. Furthermore, atomic structure characterization and computational processing revealed the displacement and orientation of the Fe atoms, which are linearly related to the degree of polarization. The ion bombardment introduced deep‐level trap states within the lattice, leading to a significant reduction in the leakage current and improved insulation performance of the films. By precisely engineering the defect content through N+ ion bombardment, the pure BFO thin films with remarkable and stable ferroelectric properties (remnant polarization, Pr = ∼116.8 µC·cm−2; leakage current, J = ∼1.5 × 10−8 A·cm−2) were fabricated. This innovative defect engineering‐based approach enables the customization and optimization of local ferroelectric order parameters, thereby establishing a solid foundation for designing functionalities across various functional material systems.

De la Vallée Poussin filtered polynomial approximation on the half–line

On the half line, we introduce a new sequence of near-best uniform approximation polynomials, easily computable by the values of the approximated function at a truncated number of Laguerre zeros. Such approximation polynomials come from a discretization of filtered Fourier–Laguerre partial sums, which are filtered using a de la Vallée Poussin (VP) filter. They have the peculiarity of depending on two parameters: a truncation parameter that determines how many of the n Laguerre zeros are considered, and a localization parameter, which determines the range of action of the VP filter we will apply. As n→∞, under simple assumptions on such parameters and the Laguerre exponents of the involved weights, we prove that the new VP filtered approximation polynomials have uniformly bounded Lebesgue constants and uniformly convergence at a near–best approximation rate, for any locally continuous function on the semiaxis.The numerical experiments have validated the theoretical results. In particular, they show a better performance of the proposed VP filtered approximation versus the truncated Lagrange interpolation at the same nodes, especially for functions a.e. very smooth with isolated singularities. In such cases, we see a more localized approximation and a satisfactory reduction of the Gibbs phenomenon.

Numerical analysis of catalytic reaction kinetics in methanol steam reformer with variable cross-sectional channel

ABSTRACT Methanol steam reforming (MSR) is a hydrogen production method recognized for its low harmful emissions and high cost-effectiveness. In this study, a three-dimensional numerical model of MSR within channels of variable cross-section. A quantitative relationship between local equilibrium constants and channel structural parameters was established based on the partial pressures of the components. Additionally, the mechanisms by which variable cross-sectional channel structural parameters regulate MSR reaction performance were quantitatively analyzed, considering fluid dynamics, hydrogen production efficiency, and chemical kinetics rates. The results indicate that the kinetic rate of MSR and local equilibrium constants are significantly influenced by the modulation of variable cross-sectional channel structural parameters, with a maximum variation of 50.6%. A positive correlation between methanol conversion rate and the local equilibrium constant of MSR was observed. Utilizing channel structural parameters to enhance MSR led to an 8.75% increase in methanol conversion rate within the same reaction volume. These conclusions may provide theoretical support for regulating volumetric reactions through variable cross-sectional channel structural parameters.

Antenna optimization using machine learning with reduced-dimensionality surrogates

In modern times, antenna design has become more demanding than ever. The escalating requirements for performance and functionality drive the development of intricately structured antennas, where parameters must be meticulously adjusted to achieve peak performance. Often, global adjustments to geometry are necessary for optimal results. However, direct manipulation of antenna responses evaluated with full-wave electromagnetic (EM) simulation models using conventional nature-inspired methods entails significant computational costs. Alternatively, surrogate-based techniques show promise but are impeded by dimensionality-related challenges and nonlinearity of antenna outputs. This study introduces an innovative technique for swiftly optimizing antennas. It leverages a machine learning framework with an infill criterion employing predicted enhancement of the merit function, utilizing a particle swarm optimizer as the primary search engine, and employs kriging for constructing the underlying surrogate model. The surrogate model operates within a reduced-dimensionality domain, guided by directions corresponding to maximum antenna response variability identified through fast global sensitivity analysis, tailored explicitly for domain determination. Operating within this reduced domain enables building dependable metamodels at a significantly lower computational cost. To address accuracy loss resulting from dimensionality reduction, the global optimization phase is supplemented by local sensitivity-based parameter adjustment. Extensive comparative experiments involving various planar antennas demonstrate the competitive operation of the presented technique over machine learning algorithms operating in full-dimensionality space and direct EM-driven bio-inspired optimization techniques.

Microclimate analysis of Ganga riverfront in Patna: spatial form perspective

ABSTRACT The investigation in this research scrutinizes the influence of spatial form factors on thermal comfort along the Ganga riverfront trail during the spring season. Understanding how spatial form factors impact local climate parameters and PET values is crucial for optimizing thermal comfort in urban waterfronts. The research addresses the need to improve thermal comfort in rapidly urbanizing areas through strategic urban design. Data were collected from eight measurement points along the riverfront, analyzing Air Temperature (Ta), Relative Humidity (RH), Wind Velocity (v), Surface Temperature (Ts), Global Radiation (G) and Mean Radiant Temperature (Tmrt). The study employs a combination of field measurements and statistical analysis to assess the correlation between spatial form factors and thermal comfort indicators. Findings reveal that lower building height (Hb) values are associated with more favorable thermal comfort conditions, while higher Hb negatively impacts thermal comfort. The study also highlights that building spacing (ADb) influences thermal comfort, with findings contrasting those from composite climates, suggesting a need for context-specific design strategies. The research underscores the significant role of Ta and v in shaping Physiological Equivalent Temperature (PET), aligning with previous studies in diverse urban contexts. The results emphasize the critical impact of spatial form on microclimate and thermal comfort, informing urban design and planning to enhance livability along the Ganga riverfront. This study contributes to the field by providing empirical evidence and practical insights for developing thermally comfortable walking trails.

DDPG-AdaptConfig: A deep reinforcement learning framework for adaptive device selection and training configuration in heterogeneity federated learning

Federated Learning (FL) is a distributed machine learning approach that protects user privacy by collaboratively training shared models across devices without sharing their raw personal data. Despite its advantages, FL faces issues of increased convergence time and decreased accuracy due to the heterogeneity of data and systems across devices. Existing methods for solving these issues using reinforcement learning often ignore the adaptive configuration of local training hyperparameters to suit varying data characteristics and system resources. Moreover, they frequently overlook the heterogeneous information contained within local model parameters. To address these problems, we propose the DDPG-AdaptConfig framework based on Deep Deterministic Policy Gradient (DDPG) for adaptive device selection and local training hyperparameters configuration in FL to speed up convergence and ensure high model accuracy. Additionally, we develop a new actor network that integrates the transformer mechanism to extract heterogeneous information from model parameters, which assists in device selection and hyperparameters configuration. Furthermore, we introduce a clustering-based aggregation strategy to accommodate heterogeneity and prevent performance declines. Experimental results show that our DDPG-AdaptConfig achieves significant improvements over existing baselines.

Improved Dipole Source Localization from Simultaneous MEG-EEG Data by Combining a Global Optimization Algorithm with a Local Parameter Search: A Brain Phantom Study.

Dipole localization, a fundamental challenge in electromagnetic source imaging, inherently constitutes an optimization problem aimed at solving the inverse problem of electric current source estimation within the human brain. The accuracy of dipole localization algorithms is contingent upon the complexity of the forward model, often referred to as the head model, and the signal-to-noise ratio (SNR) of measurements. In scenarios characterized by low SNR, often corresponding to deep-seated sources, existing optimization techniques struggle to converge to global minima, thereby leading to the localization of dipoles at erroneous positions, far from their true locations. This study presents a novel hybrid algorithm that combines simulated annealing with the traditional quasi-Newton optimization method, tailored to address the inherent limitations of dipole localization under low-SNR conditions. Using a realistic head model for both electroencephalography (EEG) and magnetoencephalography (MEG), it is demonstrated that this novel hybrid algorithm enables significant improvements of up to 45% in dipole localization accuracy compared to the often-used dipole scanning and gradient descent techniques. Localization improvements are not only found for single dipoles but also in two-dipole-source scenarios, where sources are proximal to each other. The novel methodology presented in this work could be useful in various applications of clinical neuroimaging, particularly in cases where recordings are noisy or sources are located deep within the brain.

Automatic lip-reading classification using deep learning approaches and optimized quaternion meixner moments by GWO algorithm

Lip-reading classification has received a lot of interest in recent decades because it is widely used in a variety of fields. It plays an important role in interpreting spoken words in noisy situations and reconstructing communication processes for those with hearing impairments. Despite significant advancements in this field, there are still several drawbacks in existing work such as feature extraction and Model capability for visual speech recognition. For these reasons, the current paper suggests an Optimized Quaternion Meixner Moments Convolutional Neural Network (OQMMs-CNN) method that intends to develop a Visual Speech Recognition (VSR) system based only on video images. This unique method combines OQMMs optimized for the GWO algorithm and convolutional neural networks taken from deep learning techniques with the aim of recognizing digits, words, or letters displayed as input videos.The OQMMs are used here as descriptors with the purpose of identifying, holding, and extracting essential information from video images (lips images) and generating moments for CNN input. The latter uses Meixner polynomials, which are defined by local parameters α and β. Then, the Grey Wolf optimization method (GWO) is applied to enssure excellent classification accuracy by optimizing those local parameters. After being tested on three public datasets such as AVLetters, Grid, AVDigits, and LRW, and comparing to several ways using complicated models and deep architecture, the method emerges as an excellent solution for reducing the high dimensionality of video pictures and training time.

Synchronization processes in fNIRS visibility networks

We employ Kuramoto model to assess the presence of synchronization in individuals who fulfill a cooperation task. Our input data is a couple of signals obtained from functional Near-Infrared Spectroscopy Data Acquisition and Pre-processing technology that is used to capture the brain activity of an individual by measuring the oxyhemoglobin (HbO) level. We consider 1 min signal for individuals in three distinct states: (i) rest; (ii) before a disturb happens; (iii) after the disturbance. We estimate global and local order parameters synchronization with the purpose to compare the conditions of reaching a synchronous state in the networks corresponding to different states for distinct individuals and hemispheres of the prefrontal cortices of same individual. Experimental results confirmed once more that coherent state is reached not for same conditions in both individuals and hemispheres of the prefrontal cortices. Furthermore, condition changes even for different events. The computation of the effective frequencies for each degree class indicates clearly the network difference in rest, before and after disturb. Finally, we investigate the dynamic connectivity matrix and consider the similarity between distinct prefrontal cortices over time.

Oceanic and Sedimentary Microbial Sulfur Cycling Controlled by Local Organic Matter Flux During the Ediacaran Shuram Excursion in the Three Gorges Area, South China.

The increased difference in the sulfur isotopic compositions of sedimentary sulfate (carbonate-associated sulfate: CAS) and sulfide (chromium-reducible sulfur: CRS) during the Ediacaran Shuram excursion is attributed to increased oceanic sulfate concentration in association with the oxidation of the global ocean and atmosphere. However, recent studies on the isotopic composition of pyrites have revealed that CRS in sediments has diverse origins of pyrites. These pyrites are formed either in the water column/shallow sediments, where the system is open with respect to sulfate, or in deep sediments, where the system is closed with respect to sulfate. The δ34S value of sulfate in the open system is equal to that of seawater; on the contrary, the δ34S value of sulfate in the closed system is higher than that of seawater. Therefore, obtaining the isotopic composition of pyrites formed in an open system, which most likely retain microbial sulfur isotope fractionation, is essential to reconstruct the paleo-oceanic sulfur cycle. In this study, we carried out multiple sulfur isotope analyses of CRS and mechanically separated pyrite grains (>100 μm) using a fluorination method, in addition to secondary ion mass spectrometry (SIMS) analyses of insitu δ34S values of pyrite grains in drill core samples of Member 3 of the Ediacaran Doushantuo Formation in the Three Gorges area, South China. The isotope fractionation of microbial sulfate reduction (MSR) in the limestone layers of the upper part of Member 3 was calculated to be 34ε = 55.7‰ and 33λ = 0.5129 from the δ34S and Δ33S' values of medium-sized pyrite grains ranging from 100 to 300 μm and the average δ34S and Δ33S' values of CAS. Model calculations revealed that the influence of sulfur disproportionation on the δ34S values of these medium-sized pyrite grains was insignificant. In contrast, within the dolostone layers of the middle part of Member 3, isotope fractionation was determined to be 34ε = 47.5‰. The 34ε value in the middle part of Member 3 was calculated from the average δ34S values of the rim of medium-sized pyrite grains and the average δ34S values of CAS. This observation revealed an increase in microbial sulfur isotope fractionation during the Shuram excursion at the drill core site. Furthermore, our investigation revealed correlations between δ34SCRS values and CRS concentrations and between CRS and TOC concentrations, implying that organic matter load to sediments controlled the δ34SCRS values rather than oceanic sulfate concentrations. However, these CRS and TOC concentrations are local parameters that can change only at the kilometer scale with local redox conditions and the intensity of primary production. Therefore, the decreasing δ34SCRS values likely resulted from local redox conditions and not from a global increase in the oceanic sulfate concentration.

Pressure-induced collapsed tetragonal transition in optimally Na-doped CaFe2As2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{CaFe}}_{2} {\ ext{As}}_{2}$$\\end{document}

A comprehensive first-principles study on electronic structure of optimally doped Ca0.33Na0.67Fe2As2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hbox {Ca}_{0.33}\\hbox {Na}_{0.67}\\hbox {Fe}_2\\hbox {As}_2}$$\\end{document} under hydrostatic pressures in the presence of magnetic configurations is presented. A magneto-structural transition from a tetragonal to a collapsed tetragonal phase at 3 GPa hydrostatic pressure is predicted in double-stripe antiferromagnetic configuration that corroborates experimental observations. As the system enters the non-superconducting collapsed phase, significant deviations occur in the local structural parameters compared to those at optimal Tc\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\it{T}}_{c}$$\\end{document} values. This transition coincides with a sharp decrease in As density of states (DOS), accompanied by Fe magnetic moment collapse and substantial Fe square plane charge density modification. The structural transition induces a comprehensive reconstruction of the electronic structure, marked by distorted FeAs4\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {FeAs}_{4}$$\\end{document} tetrahedra, potentially leading to unfavourable nesting conditions, and complete suppression of magnetism, correlating with the observed disappearance of superconductivity. Increasing pressure leads to a rise in crystal field splitting, influencing the spin-state transition in Ca0.33Na0.67Fe2As2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\hbox {Ca}_{0.33}\\hbox {Na}_{0.67}\\hbox {Fe}_2\\hbox {As}_2}$$\\end{document}, ultimately resulting in a shift from tetragonal to non-magnetic collapsed tetragonal phase as the Fe2+\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {Fe}^{2+}$$\\end{document} spin state transitions to a low spin state. This demonstrates the intricate interplay between crystal field splitting, external pressure, and spin dynamics, highlighting the significant impact of magneto-volume effects on the structural phase of material under pressure.

A performance-based ballistic design framework for RC panels and a probabilistic model for crater quantification

Ballistic design is crucial for widely utilised concrete structures in today’s unpredictable world. In the past, extensive experimental and numerical studies have investigated concrete panels, resulting in design guidelines and empirical formulations for local damage parameters. However, with the advent of performance-based design, notably in seismic design, the ballistic design of concrete structures lacks a comprehensive design philosophy. Additionally, deterministic empirical formulations for local damage parameters, particularly in the case of reinforced concrete (RC) panels’ crater formation, necessitate a probabilistic treatment. Consequently, this paper addresses the need for a well-defined ballistic design philosophy for concrete structures and introduces a probabilistic approach for crater quantification. Firstly, a novel multi-level and multi-parameter performance-based design framework for RC panels is proposed, centred on damage states, namely depth of penetration and crater area. This framework establishes an innovative design philosophy featuring four damage levels for each state. Secondly, using dimensionless explanatory functions, a Bayesian inference model is developed to estimate the crater diameter in RC panels under hard projectile impact, accounting for the associated uncertainties. LS-Dyna is used for numerical modelling of RC panels and rigid projectiles. The numerical models are validated with the experimental data and are utilised to develop the probabilistic model. This study reveals the profound influence of projectile and concrete properties in addition to the incidence angle of the projectile on crater formation. The developed probabilistic model is validated with experimental results demonstrating its reliability and accuracy. Consequently, a reliable design formula for estimating crater diameter for RC panels under hard projectile impact is proposed in addition to the novel ballistic design framework.

Chaotic discrete breathers in bcc lattice: Effect of the first- and second-neighbor interactions

Numerical simulations are performed to investigate the formation of chaotic discrete breathers (DBs) in a bcc lattice using four zone-boundary modes with frequencies exceeding the crystal’s phonon spectrum. The study analyzes the impact of the stiffness of elastic bonds between first and second neighbors and identifies a specific range where the formation of chaotic DBs due to modulational instability of four vibrational modes is possible. The time evolution of the energy localization parameter and the maximum energy of all particles were monitored to control the formation of chaotic DBs. Their spontaneous nucleation was observed in a wide range of stiffness of elastic bonds and depends on the mode’s symmetry. Considering DBs in bcc metals, the paper focuses on the scenario where first-neighbor bonds are stiffer than second-neighbor bonds, as bond stiffness typically decreases with interatomic distance. In all four zone-boundary modes, formation of chaotic DB is observed under this condition.

Workflow scheduling based on asynchronous advantage actor–critic algorithm in multi-cloud environment

Recently, the multi-cloud environment (MCE) has increasingly become the preferred choice of users. As with the cloud environment, efficient workflow scheduling in a MCE remains crucial for identifying the cost efficiency and overall performance of the MCE. In MCE, the resources exhibit heterogeneity, complexity, and dynamism. Simultaneously, the intricate inter-task dependencies among workflow tasks, diverse Quality of Service (QoS) metrics for users, and multiple cloud service providers’ (CSPs) billing mechanisms significantly amplify the workflow scheduling challenge. Motivated by the application of reinforcement learning (RL) in workflow scheduling in a cloud environment, this paper proposes a scheduling algorithm that takes advantage of the asynchronous advantage actor–critic algorithm (A3C) to balance cost, makespan and resource utilization in workflow scheduling in a MCE. By analyzing the elements in the MCE, we design and define multiple agents in the MCE, and each cloud service provider will have an agent to record the state and update the local parameters. For the workflow task submitted by the user, the action is selected according to the initialization policy and submitted to the scheduling action to allocate the task to a designated virtual machine in the MCE so that each agent can more clearly perceive the environment change and adapt to the MCE. In contrast to the traditional A3C algorithm, we design a new critic network according to the data characteristics of real-world scientific workflows so that each agent is more suitable for real-world scientific workflow data. Through multiple sets of simulation experiments, the workflow scheduling algorithm based on the A3C algorithm in the MCE (MCWS-A3C) was compared with three benchmark methods. The experimental results show that the proposed method has better advantages than other methods in terms of cost, makespan, and resource utilization. Specifically, on the Montage_100 dataset, the average cost was reduced by 55.12% compared to other methods. The pioneering introduction of the A3C algorithm that adapts to the dynamic environment into the MCE brings more possibilities to address the issue of workflow scheduling in the MCE.

Rotationally invariant local bond order parameters for accurate determination of hydrate structures

Averaged local bond order parameters based on spherical harmonics, also known as Lechner and Dellago order parameters, are routinely used to determine crystal structures in molecular simulations. Among different options, the combination of the q ¯ 4 and q ¯ 6 parameters is one of the best choices in the literature since distinguishes between solid- and liquid-like particles and between different crystallographic phases, including cubic and hexagonal phases. Recently, Algaba et al. [J. Colloid Interface Sci. 623, 354, (2022)] have used the Lechner and Dellago order parameters to distinguish hydrate- and liquid-like water molecules in the context of determining the carbon dioxide hydrate-water interfacial free energy. According to the results, the preferred combination previously mentioned is not the best option to differentiate between hydrate- and liquid-like water molecules. In this work, we revisit and extend the use of these parameters to deal with systems in which clathrate hydrates phases coexist with liquid phases of water. We consider carbon dioxide, methane, tetrahydrofuran, nitrogen, and hydrogen hydrates that exhibit sI and sII crystallographic structures. We find that the q ¯ 3 – q ¯ 12 combination is the best option possible between a large number of possible different pairs to distinguish between hydrate- and liquid-like water molecules in all cases.

Habitat selection interacts with social processes and spatial scale: Conservation implications for a lekking bird

Habitat selection is influenced by landscape composition, resource distribution and social behaviour. Habitat selection is scale-dependent, and the spatial extent and the scale at which processes are measured are therefore of paramount importance. This study focused on the habitat selection of the little bustard (Tetrax tetrax). We developed habitat selection models with the aim of disentangling the relative effects of local habitat parameters (crop), landscape features and social factors (to take into account the exploded lek mating system of this species). We used the SILand package in R, which allows the estimation of simple and interactive effects, using spatially accurate counts of male little bustards over a 1074-km2 study suite (over 11 consecutive years). Local and landscape-scale habitats were taken into account, including agri-environment schemes, allowing a quantitative analysis of these conservation measures. The predictive power of the models that only included local habitat was poor, but including landscape habitats – interacting with local habitat or not – statistically increased the model's predictive power (by 16 %). Adding the social factor further improved model quality (by 14 %). From a conservation perspective, the presence probability of a male increases by about 25 % when the field is managed with an agri-environment scheme. If the habitat around the field is of high quality (50 % of grasslands within a 500-m radius), this probability rises to 0.8, and if a male is already present within 250 m, it reaches 1. This confirms that conservation measures should preferentially target hotspots of male presence (i.e., larger leks).

Theoretical interpretation of electron delocalization potential of the substituent in o-hydroxynaphthalidene-p-substituted anilines for prospective molecular electronics applications

The delocalization phenomenon plays a vital role in the electron transport properties of a molecule, which is essential for developing molecular-based electronics. The current work examined the delocalization potential of five theoretically designed low molecular weight anils, o-hyroxynaphthalidene-p-substitutedanilines(NP-X) with the substituent X(X = -CH3, -NO2, -OCH3, and –Cl) being placed in phenyl ring at the p- position to its functional moiety, azomethine linkage and compared with the compound without substitution(X = H). The geometric optimization through DFT with B3LYP/6-311G (d, p) level basis set indicated the anils to be coplanar. Theoretical FTIR could discriminate the aromatic and aliphatic C-H vibrational frequencies and confirm the vectorial flow of the electrons when the o-OH group and p-NO2 group were placed at the two extreme ends of anil, further supported by Natural Bond Orbital analysis. Atomic charge analysis indicated the significant effect on the charge of azomethine carbon atom. It followed the order: OCH3 < CH3 < H < Cl < NO2 parallel with the σp values of the corresponding substituent. The molecular electrostatic potential mapping identified azomethine nitrogen as an electron-rich centre. The global and local reactivity parameters indicated the highest tendency of NO2 substituted compound to accept electrons among the other compounds. The HOMO–LUMO energy gap of the NP-X predicted from Natural Localized Molecular Orbital analysis was found to be NP-H: 4.02 eV, NP-CH3: 3.94 eV, NP-NO2: 3.69 eV, NP-OCH3: 4.18 eV, NP-Cl: 4.10 eV. The overall studies corroborated the optimized delocalization in NP-NO2 with the lowest HUMO-LUMO gap, which would be better suited for developing molecular-based electronic devices than others. The importance of the electron delocalization phenomenon in searching for a molecular probe to develop molecular electronics device applications has been discussed.Graphical