Multimodal Properties Research Articles

Mask communication: The development of the face covering as a semiotic resource through government public health posters in England and Wales

This paper will explore the multi-modal semiotic properties of a selection of key public health information posters issued by the UK Westminster government on the use of masks and face coverings during the first year of the COVID-19 pandemic. Using multi-modal critical discourse analysis, we show how the posters featuring masks sustained consistent government-led branding, while drawing upon what we describe as “synthetic personalisation” to manage the orientation of the crisis as the pandemic progressed. Through this analysis, the article will highlight the possible contribution of these posters to an environment characterised by political confusion and enabling of a relatively widespread rejection of mask-wearing as a public health responsibility. Examining this within a broader decline in trust in government, we suggest the various attempts to produce a positive message about mask-wearing contributed instead to the appropriation of masks as symbols of individual alignment within a contested political field.

Stochastic multiscale multimode interlaminar fracture toughness of buckypaper nanocomposites

Delamination is one of the primary damage mechanisms in laminated composites that affects the lifetime integrity of the composite systems. The present study examines the effects of Buckypaper (BP) nano-reinforcement on the multimodal interlaminar fracture properties of the carbon fiber reinforced polymer (CFRP) composites reinforced with BP membranes. The novelty and the focus of this study is the effects of multi-wall carbon nanotube (MWCNT) network size and interphase in the interlaminar fracture toughness of BP nanocomposites. Dry and pre-infused non-functionalized MWCNT BP were integrated at the mid layer of the composite before laminate curing. The Atomic Force Microscopy Peak Force Quantitative Nanomechanics Mapping (AFM PFQNM) technique and the Weibull model were applied to study the effects of random CNT network size and interphase on mode I, II, and mixed-mode I-II fracture properties. Weibull analysis of 150 dry and pre-infused BP data confirms a high scatter of CNT network size in dry BP compared to pre-infused BP. Weibull modulus of CNT network size (2.33 for large size and 5.18 for small size) and interphase thickness (3.46) show a robust, consistent entangled structure of CNTs in pre-infused BP. Dry BP improves the propagation energy release rate of the CFRP laminates up to 54%. However, it does not provide additional toughening in the initiation crack energy for modes I, II, and I-II. Mode I initiation and propagation, mode II initiation and mixed-mode I/II fracture energy of pre-infused BP nanocomposites are ∼33%, ∼52.5%, ∼14%, and ∼28% higher than the reference. The interlaminar crack either moves exclusively through the BP layer by circumventing the rigid CNT networks or moves forward through the interface of the carbon fiber monofilament above and below the reinforcing BP layer. This saw-like interlaminar crack path triggers numerous toughening mechanisms such as nano-toughened epoxy, nanoscale CNT bridging and pullout, and crack deflection, and it increases the total crack propagation path.

Multimodal ultra-small CoFe-WOx nanohybrids synthesized by a pilot microfluidic system

Among all the nanomaterials, ultra-small nanohybrids have gained considerable attention as drugs, contrast agents, molecular nanoprobes, and catalysts owing to their multi-functionality and size effect. However, their extension and commercialization are restricted due to low synthetic efficiency and uncontrollable structure. Herein, a pilot microfluidic system (PMS) was built to successfully synthesize the ultra-small (2.3 ± 0.7 nm) CoFe-WOx nanohybrids (NHs) with a tiny CoFe core and a mixed metal oxides shell (CoWO4-Fe2WO6-WO3). Furthermore, the CoFe-WOx NHs displayed excellent multi-modal imaging properties, which could be observed optically via a dark-field microscope, imaged through X-ray computed tomography and magnetic resonance imaging device. For the first time, hybridization between metal cores and metal-oxide shells produced two strong absorption peaks in the long band of near infrared radiation, one at 1450 nm and the other at 1980 nm. Overall, CoFe-WOx NHs emonstrated distinct magnetic and optical properties and multimode imaging capabilities due to their microstructure and composition. These findings may prove to be effective in finding alternatives for nanoprobes that can be used for visible/infrared/MRI/CT imaging, as well as pre-drugs for photothermal/photodynamic and magnetothermal/magneto-dynamic therapies.

Multi-modal optimization to identify personalized biomarkers for disease prediction of individual patients with cancer.

Finding personalized biomarkers for disease prediction of patients with cancer remains a massive challenge in precision medicine. Most methods focus on one subnetwork or module as a network biomarker; however, this ignores the early warning capabilities of other modules with different configurations of biomarkers (i.e. multi-modal personalized biomarkers). Identifying such modules would not only predict disease but also provide effective therapeutic drug target information for individual patients. To solve this problem, we developed a novel model (denoted multi-modal personalized dynamic network biomarkers (MMPDNB)) based on a multi-modal optimization mechanism and personalized dynamic network biomarker (PDNB) theory, which can provide multiple modules of personalized biomarkers and unveil their multi-modal properties. Using the genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, we validated the effectiveness of the MMPDNB model. The experimental results showed that compared with other advanced methods, MMPDNB can more effectively predict the critical state with the highest early warning signal score during cancer development. Furthermore, MMPDNB more significantly identified PDNBs containing driver and biomarker genes specific to cancer tissues. More importantly, we validated the biological significance of multi-modal PDNBs, which could provide effective drug targets of individual patients as well as markers for predicting early warning signals of the critical disease state. In conclusion, multi-modal optimization is an effective method to identify PDNBs and offers a new perspective for understanding tumor heterogeneity in cancer precision medicine.

Large-scale Multimodal Recordings on a High-density Neurochip: Olfactory Bulb and Hippocampal Networks.

A striking example of the brain's complexity and continued plasticity is the addition of new neuronal components to a circuit in a process called neurogenesis. Two brain regions exhibit profound circuit remodeling through this process - the olfactory bulb and hippocampus. However, how local network changes in both regions influence global circuit rewiring and dynamic network features remain largely unexplored due to the lack of spatiotemporal resolution technology and large-scale electrophysiological activity recordings. Here, we demonstrate large-scale recordings using a high-density neurochip to reveal multimodal circuit-wide electrophysiological properties and layer-specific functional connectivity in the olfactory bulb and hippocampal networks. Our findings illustrate simultaneous recordings from the entire network, which allows us to quantify synchronous electrophysiological parameter differences and layer-specific waveform markers. Examining pairwise cross-covariance between active electrode pairs reveals individual neuronal ensemble contributions to synchronous activation between layers and hub microcircuits, demonstrating network-wide rewiring. Our study suggests a novel tool to address the computational implications of large-scale activity patterns in functional multimodal neurogenic circuits.

Sustainable synthesis and theoretical studies of polyhedral gold nanoparticles displaying high SERS activity, NIR absorption, and cellular uptake

The preparation of gold nanoparticles with multimodal properties such as near-infrared absorption, high surface-enhanced Raman scattering, cell internalization and low cytotoxicity is a challenging task. In this study, we developed a sustainable protocol to develop gold nanoparticles, using trisodium citrate as reducing, stabilizing, and shape-modulating agent at ambient conditions (25 °C). Reduction of gold salt at room temperature at a peculiar ratio R(Ccitrate/CHAuCl4) and concentration of reactants resulted in the formation of non-spherical, homogenous mixture of gold nanoparticles with polyhedral shapes. The protocol is extremely simple and does not even require stirring or mechanical shaking. The as-synthesized gold nanoparticles, even though multi-shaped, displayed a single monomodal peak in dynamic light scattering with polydispersity index of 0.098, representative of a fairly good monodisperse system. We investigated the optical properties of the nanoparticles both experimentally and by two-dimensional Finite-Difference-Time-Domain modeling. Due to the presence of shapes such as nanorods, nanotriangles, and prismatic, these nanoparticles exhibited a high surface-enhanced Raman scattering activity and a wide absorption range extending up to the near-infrared region, which makes them useful candidate for photothermal therapy too. We characterized the nanoparticles by electron microscopy, UV–vis–NIR spectroscopy, Raman spectroscopy, and X-ray photoelectron spectroscopy. We have also developed an enhanced numerical diffusion limited aggregation model to simulate the growth of the particles, including the particle interfacial energy as a parameter of the system. Numerical results matched with the experimental data, and the model revealed being effective in reproducing size, shape, and morphological characteristics of non-spherical nanoparticles obtained under real experimental conditions. Finally, in vitro studies and nanoparticles cellular uptake were performed on a model cell line of mouse brain endothelium (bEnd.3) to assess biocompatibility. The main advantage of the proposed method lies in its simplicity with no other requirement like refluxing at elevated temperature, mechanical stirring, electromagnetic radiations, ultrasound, toxic chemicals, or seed mediation.

Programmable Photoluminescence via Intrinsic and DNA-Fluorophore Association in a Mixed Cluster Heterometallic MOF.

A rapid and facile design strategy to create a highly complex optical tag with programmable, multimodal photoluminescent properties is described. This was achieved via intrinsic and DNA-fluorophore hidden signatures. As a first covert feature of the tag, an intricate novel heterometallic near-infrared (NIR)-emitting mesoporous metal-organic framework (MOF) was designed and synthesized. The material is constructed from two chemically distinct, homometallic hexanuclear clusters based on Nd and Yb. Uniquely, the Nd-based cluster is observed here for the first time in a MOF and consists of two staggered Nd μ3-oxo trimers. To generate controlled, multimodal, and tailorable emission with difficult to counterfeit features, the NIR-emissive MOF was post-synthetically modified via a fluorescent DNA oligo labeling design strategy. The surface attachment of several distinct fluorophores, including the simultaneous attachment of up to three distinct fluorescently labeled oligos was achieved, with excitation and emission properties across the visible spectrum (480-800 nm). The DNA inclusion as a secondary covert element in the tag was demonstrated via the detection of SYBR Gold dye association. Importantly, the approach implemented here serves as a rapid and tailorable way to encrypt distinct information in a facile and modular fashion and provides an innovative technology in the quest toward complex optical tags.

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Adversarial Ensemble Modeling of Multi-modal Mechanical Properties for Iron-Based Alloys

Particle Size Analysis in Aerosol-Generating Dental Procedures Using Laser Diffraction Technique.

The global outbreak of coronavirus disease 2019 (COVID-19) has raised concerns about the risk of airborne infection during dental treatment. Aerosol-generating dental procedures (AGDP) produce droplets and aerosols, but the details of the risks of COVID-19 transmission in AGDP are not well-understood. By discriminating between droplets and aerosols, we devised a method to measure particle size using laser diffraction analysis and evaluated aerosols generated from dental devices for providing a basis for proper infection control procedures. The droplets and aerosols generated from dental devices were characterized by multimodal properties and a wide range of droplet sizes, with the majority of droplets larger than 50 μm. AGDP emitted few aerosols smaller than 5 μm, which are of concern for pulmonary infections due to airborne transmission. In addition, the use of extraoral suction was found to prevent the spread of aerosols from high-speed dental engines. This study suggests that the risk of aerosol infections is considerably limited in regular dental practice and that current standard precautions, such as mainly focusing on protection against droplet and contact infections, are sufficient. While several cases of airborne transmission of COVID-19 in general clinics and emergency hospitals have been reported, cluster outbreaks in dental clinics have not yet been reported, which may indicate that AGDP does not pose a significant threat in contributing to the spread of SARS-CoV-2.

Research on Probability Distribution of Short-Term Photovoltaic Output Forecast Error Based on Numerical Characteristic Clustering.

The forecast error characteristic analysis of short-term photovoltaic power generation can provide a reliable reference for power system optimal dispatching. In this paper, the total in-day error level was stratified by fuzzy C-means algorithm. Then the historical PV output data based on the numerical characteristics of point prediction output were classified. A General Gauss Mixed Model was proposed to fit the forecast error distribution of various photovoltaic output forecast error distribution. The impact of meteorological factors together with numerical characteristics on the forecast error was taken into full consideration in this analysis method. The predicted point output with high volatility can be accurately captured, and the reliable confidence interval is given. The proposed method is independent of the point prediction algorithm and has strong applicability. The General Gauss Mixed Model can meet the peak diversity, bias, and multimodal properties of the error distribution, and the fitting effect is superior to the normal distribution, the Laplace distribution, and the t Location-Scale distribution model. The error model has a flexible shape, a concise expression, and high practical value for engineering.

Multiphoton imaging of melanoma 3D models with plasmonic nanocapsules

We report the synthesis of plasmonic nanocapsules and the cellular responses they induce in 3D melanoma models for their perspective use as a photothermal therapeutic agent. The wall of the nanocapsules is composed of polyelectrolytes. The inner part is functionalized with discrete gold nanoislands. The cavity of the nanocapsules contains a fluorescent payload to show their ability for loading a cargo. The nanocapsules exhibit simultaneous two-photon luminescent, fluorescent properties and X-ray contrasting ability. The average fluorescence lifetime (τ) of the nanocapsules measured with FLIM (0.3 ns) is maintained regardless of the intracellular environment, thus proving their abilities for bioimaging of models such as 3D spheroids with a complex architecture. Their multimodal imaging properties are exploited for the first time to study tumorspheres cellular responses exposed to the nanocapsules. Specifically, we studied cellular uptake, toxicity, intracellular fate, generation of reactive oxygen species, and effect on the levels of hypoxia by using multi-photon and confocal laser scanning microscopy. Because of the high X-ray attenuation and atomic number of the gold nanostructure, we imaged the nanocapsule-cell interactions without processing the sample. We confirmed maintenance of the nanocapsules’ geometry in the intracellular milieu with no impairment of the cellular ultrastructure. Furthermore, we observed the lack of cellular toxicity and no alteration in oxygen or reactive oxygen species levels. These results in 3D melanoma models contribute to the development of these nanocapsules for their exploitation in future applications as agents for imaging-guided photothermal therapy. Statement of SignificanceThe novelty of the work is that our plasmonic nanocapsules are multimodal. They are responsive to X-ray and to multiphoton and single-photon excitation. This allowed us to study their interaction with 2D and 3D cellular structures and specifically to obtain information on tumor cell parameters such as hypoxia, reactive oxygen species, and toxicity. These nanocapsules will be further validated as imaging-guided photothermal probes.

Поведінкові розлади у пацієнтів з деменцією: клініка, діагностика та лікування

В огляді літератури представлені сучасні дані щодо нейропсихіатричних (некогнітивних) симптомів при різних видах деменції, які є досить гетерогенними та в більшості випадків непередбачуваними, уражають емоційну сферу, сприйняття та рухову функцію пацієнтів. Наведено типові розлади поведінки, асоційовані з певними типами деменції. Зазначено, що для хворих з усіма типами та різними ступенями тяжкості деменції із супутніми некогнітивними симптомами рекомендовано поєднувати нефармакологічні втручання з фармакотерапією. Описані терапевтичні можливості різних груп препаратів: інгібіторів ацетилхолінестерази, антагоністів NMDA-рецепторів, антиконвульсантів, антидепресантів. Відповідно до сучасних настанов, рекомендовано уникати або у разі необхідності мінімізувати призначення атипових антипсихотиків. З позиції доказової медицини, ґрунтуючись на даних численних клінічних досліджень, показана ефективність антидепресанту з мультимодальними властивостями тразодону у лікуванні некогнітивних проявів деменції (дратівливості, ажитації, тривоги, депресивних розладів, порушення сну та харчової поведінки).

Discriminative Analysis of Schizophrenia Patients Using Topological Properties of Structural and Functional Brain Networks: A Multimodal Magnetic Resonance Imaging Study.

Interest in the application of machine learning (ML) techniques to multimodal magnetic resonance imaging (MRI) data for the diagnosis of schizophrenia (SZ) at the individual level is growing. However, a few studies have applied the features of structural and functional brain networks derived from multimodal MRI data to the discriminative analysis of SZ patients at different clinical stages. In this study, 205 normal controls (NCs), 61 first-episode drug-naive SZ (FESZ) patients, and 79 chronic SZ (CSZ) patients were recruited. We acquired their structural MRI, diffusion tensor imaging, and resting-state functional MRI data and constructed brain networks for each participant, including the gray matter network (GMN), white matter network (WMN), and functional brain network (FBN). We then calculated 3 nodal properties for each brain network, including degree centrality, nodal efficiency, and betweenness centrality. Two classifications (SZ vs. NC and FESZ vs. CSZ) were performed using five ML algorithms. We found that the SVM classifier with the input features of the combination of nodal properties of both the GMN and FBN achieved the best performance to discriminate SZ patients from NCs [accuracy, 81.2%; area under the receiver operating characteristic curve (AUC), 85.2%; p < 0.05]. Moreover, the SVM classifier with the input features of the combination of the nodal properties of both the GMN and WMN achieved the best performance to discriminate FESZ from CSZ patients (accuracy, 86.2%; AUC, 92.3%; p < 0.05). Furthermore, the brain areas in the subcortical/cerebellum network and the frontoparietal network showed significant importance in both classifications. Together, our findings provide new insights to understand the neuropathology of SZ and further highlight the potential advantages of multimodal network properties for identifying SZ patients at different clinical stages.

Rationally designed Ru(II) metallacycles with tunable imidazole ligands for synergistical chemo-phototherapy of cancer.

Herein, we construct a series of Ru(II) metallacycles with multimodal chemo-phototherapeutic properties, which exhibited much higher anticancer activity and better cancer-cell selectivity than cisplatin. The antitumor mechanism could be ascribed to the activation of caspase 3/7 and the resulting apoptosis. These results open new possibilities for Ru(II) metallacycles in biomedicine.

Multimodal Emotion Classification Method and Analysis of Brain Functional Connectivity Networks

Since multimodal emotion classification in different human states has rarely been studied, this paper explores the emotional mechanisms of the brain functional connectivity networks after emotional stimulation. We devise a multimodal emotion classification method fusing a brain functional connectivity network based on electroencephalography (EEG) and eye gaze (ECFCEG) to study emotional mechanisms. First, the nonlinear phase lag index (PLI) and phase-locked value (PLV) are calculated to construct the multiband brain functional connectivity networks, which are then converted into binary brain networks, and the seven features of the binary brain networks are extracted. At the same time, the features of the eye gaze signals are extracted. Then, a fusion algorithm called kernel canonical correlation analysis, based on feature level and randomization (FRKCCA), is executed for feature-level fusion (FLF) of brain functional connectivity networks and eye gaze. Finally, support vector machines (SVMs) are utilized to classify positive and negative emotions in multiple frequency bands with single modal features and multimodal features. The experimental results demonstrate that multimodal complementary representation properties can effectively improve the accuracy of emotion classification, achieving a classification accuracy of 91.32±1.81%. The classification accuracy of pupil diameter in the valence dimension is higher than that of additional features. In addition, the average emotion classification effect of the valence dimension is preferable to that of arousal. Our findings demonstrate that the brain functional connectivity networks of the right brain exhibit a deficiency. In particular, the information processing ability of the right temporal (RT) and right posterior (RP) regions is weak in the low frequency after emotional stimulation; Conversely, phase synchronization of the brain functional connectivity networks based on PLI is stronger than that of PLV.

Nanomaterial‐Based Prosthetic Limbs for Disability Mobility Assistance: A Review of Recent Advances

The emergence of new hybrid nanomaterial has enabled prosthetic devices to have more performance and significantly improved the quality of life of the disabled. Due to the biosensing properties of prosthetic limbs made of nanomaterials, a large number of nanocomposites have been designed, developed, and evaluated for various prosthetic limbs, such as e‐skin, e‐skin’s neurotactility sensing, human prosthetic interface tissue engineering, bones, and biosensors. Nano‐based materials are also considered to be the new generation of scientific and technological materials for the preparation of various prosthetic devices for the disabled, which can effectively improve the sense of use of the disabled and achieve functional diversity. The study described various nanomaterials for prosthetic devices, and introduced some basic components of nanocomposites; their applications are in three areas, such as bone, skin, and nerve, and evaluated and summarized the advantages of these applications. The results show that (1) carbon‐based nanomaterials as neural materials have been studied most deeply. Due to that strong stability of the carbon‐based material and the simple transmission mechanism, the cost can be controlled in manufacturing the artificial limb. Materials with human‐computer interaction function are the research focus in the future. (2) Skin nanomaterials are mainly composite materials, generally containing metal‐ and carbon‐based materials. Ionic gels, ionic liquids, hydrogels, and elastomers have become the focus of attention due to the sensitivity, multimodal, and memory properties of their materials. (3) Outstanding nanomaterials for bone are fibrous materials, metallic synthetic materials, and composite materials, with extremely high hardness, weight, and toughness. Of the skeletal materials, the choice of prosthetic socket material is the most important and is typically based on fiber laminate composites. Some of these materials make sensors for durability and performance that can be used for large‐scale clinical testing.

Anticancer properties of melatonin and its role as an adjuvant in cancer treatment.

Melatonin is an important hormone secreted from the pineal gland that mediates several biological functions in humans through circadian rhythm. The multimodal properties of melatonin when administered systemically have generated a lot of interest among researchers. The anticancer properties of melatonin per se and its importance in cancer patients when used as an adjunct to ongoing chemotherapy and radiotherapy have led to tremendous research in animals and humans with encouraging results. The present write-up discusses the current evidence of using melatonin as an adjunct in hormone-dependent and hormone-independent cancers.

Optical Waveguide Sensors for Measuring Human Temperature and Humidity with Gel Polymer Electrolytes.

In this work, multimodal responsive optical waveguide sensors using a stable cross-linking gel polymer electrolyte are successfully designed and fabricated by bottom metal-printing technology. Temperature and humidity sensing characterization based on the polymer electrolyte is simulated and analyzed. The multimodal responsive properties of the photonic chip are defined based on the analysis of ion relaxation dynamics: optical phase variable to monitor temperature and optical attenuation variable to detect humidity. In the supervising temperature (36.0-38.0 °C) and relative humidity (45-65%) range, the temperature and humidity sensitivities of the device are measured as 0.5π rad/°C and 1.14 dB/% RH, respectively. The fast-response time for both temperature and humidity of the multifunctional sensor can be obtained as 4.21 ms and 1.32 s, respectively. These findings provide a feasible scheme for the design and application of temperature and humidity sensors in potential medical treatment. From gel polymer electrolytes to multimode monitoring applications, the application exploration of high stability and ultrafast response multimode waveguide sensors is gradually being carried out. This study has great significance for the comprehensive monitoring of sophisticated human physical signs by multimodal responsive waveguide sensors.

Synthetic and Naturally Occurring Heterocyclic Anticancer Compounds with Multiple Biological Targets.

Cancer is a complex group of diseases initiated by abnormal cell division with the potential of spreading to other parts of the body. The advancement in the discoveries of omics and bio- and cheminformatics has led to the identification of drugs inhibiting putative targets including vascular endothelial growth factor (VEGF) family receptors, fibroblast growth factors (FGF), platelet derived growth factors (PDGF), epidermal growth factor (EGF), thymidine phosphorylase (TP), and neuropeptide Y4 (NY4), amongst others. Drug resistance, systemic toxicity, and drug ineffectiveness for various cancer chemo-treatments are widespread. Due to this, efficient therapeutic agents targeting two or more of the putative targets in different cancer cells are proposed as cutting edge treatments. Heterocyclic compounds, both synthetic and natural products, have, however, contributed immensely to chemotherapeutics for treatments of various diseases, but little is known about such compounds and their multimodal anticancer properties. A compendium of heterocyclic synthetic and natural product multitarget anticancer compounds, their IC50, and biological targets of inhibition are therefore presented in this review.

Closed‐form hydraulic conductivity equations for multimodal unsaturated soil hydraulic properties

AbstractClosed‐form expressions of the hydraulic conductivity function for linearly superposed subretention (multimodal) functions were derived for arbitrary sets of the Brooks and Corey (BC), van Genuchten (VG), and Kosugi (KO) water retention models. The generalized Mualem hydraulic conductivity model was evaluated using the mathematical approach of Priesack and Durner. Three types of modification to the multimodel were also proposed. Firstly, the derived conductivity equations can be simplified when the submodel parameters, for the BC model, for the VG model, and for the KO model have the same (common) value (denoted as CH). Secondly, as in the case of the modified single VG and KO models, a hypothetical air‐entry head near saturation can be introduced for the multimodal VG and KO models to prevent unrealistic reductions in the hydraulic conductivity near saturation when the VG n parameter approaches its lower limit of n = 1. Furthermore, the multimodal hydraulic conductivity functions become a simple sum of conductivity subfunctions when the exponent r is unity (such as for Burdine's model), which leads to independent tortuosity effects for each submodel. The models are illustrated for two soils: a highly aggregated Kumamoto Andisol and a relatively unimodal dune sand. The dual‐(BC, VG, KO) and the VG1BC2 models equally represented the water retention data of the Andisol, with similar hydraulic conductivity curves. The dual‐BC‐CH, dual‐VG‐CH, and VG1BC2‐CH models fitted the water retention data of the dune sand well, with the hydraulic conductivity curves of the dual‐porosity model being similar to those of the Fayer and Simmons (FS) model.