Recognizing trust as a pivotal element for success within Human-Robot Collaboration (HRC) environments, this article examines its nature, exploring the different dimensions of trust, analysing the factors affecting each of them, and proposing alternatives for trust measurement. To do so, we designed an experimental procedure involving 50 participants interacting with a modified 'Inspector game' while we monitor their brain, electrodermal, respiratory, and ocular activities. This procedure allowed us to map dispositional (static individual baseline) and learned (dynamic, based on prior interactions) dimensions of trust considering both demographic and psychophysiological aspects. Our findings challenge traditional assumptions regarding the dispositional dimension of trust and establish clear evidence that the first interactions are critical for the trust-building process and the temporal evolution of trust. By identifying more significant psychophysiological features for trust detection and underscoring the importance of individualized trust assessment, this research contributes to understanding the nature of trust in HRC. Such insights are crucial for enabling more seamless human-robot interaction in collaborative environments.

Pool boiling is extensively utilized in various fields, such as electronics cooling, battery cooling, nuclear power plants, and chemical processing. In the process of temperature control through boiling, the generation and transportation of bubbles play a crucial role in calculating the heat transfer capacity of the system. Therefore, it holds immense significance to gain a profound understanding of the mechanisms underlying bubble formation, growth, and detachment. Currently, numerous mechanistic explanations and empirical correlations have been proposed to elucidate various parameters of bubbles during the boiling process. These findings were considered valuable references in selecting appropriate boiling media and designing efficient heating surfaces. To comprehensively present the progress of bubble formation and heat transfer during the boiling process, the forces exerted on the bubbles was highlighted in this article. And a meticulous review on bubble force analysis and correlation formulas pertaining to various relevant parameters (e.g. nucleation sites density, bubble growth rate, detachment diameter, detachment frequency) were conducted. This review article also were expected to provide a novel foundation for further exploration of enhanced boiling heat transfer.

Developmental hazard evaluation is an important part of assessing chemical risks during pregnancy. Toxicological outcomes from prenatal testing in pregnant animals result from complex chemical-biological interactions, and while New Approach Methods (NAMs) based on in vitro bioactivity profiles of human cells offer promising alternatives to animal testing, most of these assays lack cellular positional information, physical constraints, and regional organization of the intact embryo. Here, we engineered a fully computable model of the embryonic disc in the compucell3d.org modeling environment to simulate epithelial-mesenchymal transition of epiblast cells and self-organization of mesodermal domains (chordamesoderm, paraxial, lateral plate, posterior/extraembryonic). Cell fate in the model is determined by an autonomous homeobox (HOX) clock driven by morphogenetic signals (e.g., FGF, WNT, ATRA, CDX). Executing the model renders a quantitative cell-level computation of mesodermal subpopulations and consequences of perturbation based on known embryogeny. For example, synthetic perturbation of the control network rendered altered phenotypes (cybermorphs) mirroring experimental mouse embryology, with 50% reductions in FGF4, FGF8 and BMP4 signaling resulting in 86%, 98% and 59% reductions, respectively in the posterior mesodermal population, while ATRA exposure also resulted in a 78% decrease in this population. This model enables integration of in vitro chemical bioactivity data for specific molecular targets with known embryology to test mechanistic veracity and quantitative prediction of altered development.

In recent years, applying deep learning to solve physics problems has attracted much attention. Data-driven deep learning methods produce operators that can learn solutions to the whole system of partial differential equations. However, the existing methods are only evaluated on simple flow equations (e.g., Burger's equation), and only consider the generalization ability on different initial conditions. In this paper, we construct CFDBench, a benchmark with four classic problems in computational fluid dynamics (CFD): lid-driven cavity flow, laminar boundary layer flow in circular tubes, dam flows through the steps, and periodic Karman vortex street. Each flow problem includes data with various boundary conditions, fluid physical properties, and domain geometries. Compared to existing datasets, the advantages of CFDBench are (1) comprehensive. It contains common physical parameters such as velocity, pressure, and cavity fraction. (2) realistic. It is very suitable for deep learning solutions of fluid mechanics equations. (3) challenging. It has a certain learning difficulty, prompting to find models with strong learning ability. (4) standardized. CFDBench facilitates a comprehensive and fair comparison of different deep learning methods for CFD. We make appropriate modifications to popular deep neural networks to apply them to CFDBench and enable the accommodation of more changing inputs. The evaluation on CFDBench reveals some new shortcomings of existing works and we propose possible directions for solving such problems.

This study focuses on the aerodynamic optimization of the re-entry capsule (REC) Orion Crew Exploration Vehicle (CEV) module, evaluating its performance using computational fluid dynamics (CFD). Three different models, namely Baseline, 2-Duct, and 4-Duct, are investigated under a wide range of flow speeds, from supersonic (Ma=1.6) to hypersonic (Ma=6) regimes. Thermofluids aspects, drag coefficient, surface heat flux, and the effectiveness of the modified geometry are assessed using aerodynamic and temperature-based models. The results show that the introduction of ducts in the 2-Duct and 4-Duct models leads to an average decrease in the drag coefficient of approximately 2.1% and 2.9%, respectively, compared to the baseline model, across all Mach numbers. The maximum reduction in drag coefficient is 3.55%, observed at Ma=1.4 using the 4-Duct capsule. However, it is found that the addition of extra ducts to the model increases the maximum heat transfer rate. Specifically, the heat flux is increased by up to 69% for the 4-Duct model at Ma=6, compared to the baseline model. On average, the heat flux is increased by approximately 28.6% for all conditions. The findings indicate that the introduction of ducts in the REC model improves its aerothermodynamic performance by reducing drag but also increases the heat transfer rate on the surface.

Computational Fluid Dynamics (CFD)-based simulation has been the traditional way to model complex industrial systems and processes. One very large and complex industrial system that has benefited from CFD-based simulations is the steel blast furnace. The problem with the CFD-based simulation approach is that it tends to be very slow to generate data. The CFD-only approach may not be fast enough for use in real-time decision-making. To address this issue, in this work, the authors propose the use of machine learning techniques to train and test models based on data generated via CFD simulation. Regression models based on neural networks are compared to tree boosting models. In particular, several areas (tuyere, raceway, and shaft) of the blast furnace are modeled using these approaches. The results of the model training and testing are presented and discussed. The obtained R2 metrics are, in general, very high. The results look promising and may help to improve the efficiency of operator and process engineer decision-making when running a blast furnace.

Backgroumd: The feeling of nasal obstruction is one of the most complaints of the Ear Nose and Throats (ENT) patients. The reason of this complaint is variable, such as Nasal Septal Deviation (NSD). NSD is a significant obstacle to normal respiration and the volume of air entering the lungs. Understanding how the deviation causes the obstruction is so important, because it leads to better management of this disease. Rhinomanometry and acoustic rhinometry are methods that are used for objective evaluation of upper airway, but they are semi invasive and the accuracy are not guaranteed. With development of Computed Tomography (CT scan) and Computational Fluid Dynamics (CFD), numerical studies on airflow in different parts of the nasal cavity have been performed. Objectives: The purpose of this study was to compare nasal airflow patterns between patient with NSD and healthy nasal cavity using a computational fluid dynamics (CFD) Methods: A patient with NSD and a healthy human were selected. Using CFD, the maximal airflow velocity, nasal resistance, maximal Wall Shear Stress (WSS), and pressure drop in bilateral nostrils of both cases were assessed. Results: Pressure and velocity profiles are evaluated at a mass flow rate of 17.4 Liters Per Minute (LPM). The velocity profile shows an abnormal flow profile in the septal deviated nasal cavity and the peak velocity is observed at the middle nasal region, whereas this point is at the nasal valve level in a healthy nasal cavity. Wall shear stress (WSS) caused by NSD is lower than in normal nasal cavities. The pressure drop in a nasal cavity with deviated septum is higher than in a healthy nasal cavity. Based on the results of this investigation nasal resistance in NSD was higher than in normal, healthy nasal cavity. Conclusions: NSD plays an important role in nasal airflow regime and it results in the feeling of obstruction comparing to normal nasal cavity. Also there is a promising future for CFD to become a useful tool in understanding airflow physiology and pathology.

Prediction markets are heralded as powerful forecasting tools, but models to describe them often fail to capture the full complexity of the underlying mechanisms that drive price dynamics. To address this issue, we propose a model in which agents belong in a social network, have an opinion about the probability of a particular event to occur, and bet on the prediction market accordingly. Agents update their opinions about the event by interacting with their neighbours in the network, following the Deffuant model of opinion dynamics. Our results suggest that a simple market model that takes into account opinion formation dynamics is capable to replicate the empirical properties of historical prediction market time series, including volatility clustering and fat-tailed distribution of returns. Interestingly, the best results are obtained when there is the right level of variance in the opinions of the agents. Moreover, this paper provides a new way to indirectly validate opinion dynamics models against real data by using historical data obtained from PredictIt, an exchange platform whose data has never been used before to validate models of opinion diffusion.

The agricultural sector's progress towards climate neutrality is of great importance not only in the climate but also in the economic and social context. The agricultural sector is the 3rd largest emission-generating sector both in Latvia and Europe, and it directly and/or indirectly affects all other sectors. Climate-neutral agriculture is highly dependent on innovations that ensure maximum efficient farming, which not only reduces emissions but also ensures competitiveness in the market, but all of this is fundamentally influenced by well considered policies. Although the goals of the Green Deal are united, each country's situation must be thoughtfully considered individually to evaluate the best action plan for integrating the EU's common agricultural policy without reducing harvest. In addition, due to the complicated structure of the sector, it tends to be very difficult or even impossible to determine the real obstacles and mistakes that delay the progress of sustainable farming. Therefore, this research aims to create a system dynamics model using Latvia as a case study, which would not only provide an insight into the system’s structure but also identify the system's weak links and allow for the development of recommendations. The results could help policymakers in any country to make rational, non-controversial decisions simultaneously in the context of economic and Green Deal objectives. The results of the study demonstrate that to increase economic competitiveness and reduce emissions in agriculture, the most important aspect is the ability to invest in innovations and new technologies that would achieve not only the lowest emissions, but also the highest productivity and competitiveness in the market. It was found that by investing in the level of manure management it is possible to reduce GHG emissions in dairy farming by 8%, while by improving feed quality, manure management and thermoregulation all together, resulted in 60% GHG emission reduction. In addition, in a comprehensive improvement of the new innovations, the case study company managed to increase milk yield by 69%.

Remote sensing plays a central role in the assessment of environmental phenomena and has increasingly become a powerful tool for monitoring shorelines, rivers morphology, flood waves delineation and floods assessment. Optical based monitoring and characterization of river evolution at long time scales is a key tool in fluvial geomorphology. However, the evolution occurring during extreme events is crucial for the understanding of the river dynamics under severe flow conditions and requires the processing of data from active sensors to overcome cloud obstructions. This work proposes a cloud-based unsupervised algorithm for the intra-event monitoring of river dynamics during extreme flow conditions based on time series of Sentinel-1 SAR data. The method allows the extraction of multi-temporal series of spatially explicit geometric parameters at high time and spatial resolutions, linking them to the hydrometric levels acquired by reference gauge stations. Intra-event reconstruction of inundation dynamics has led to the estimation of the relationship between hydrometric level and wet area extension and the assessment of bank erosion phenomena. Time series of SAR acquisitions, provided by Copernicus Sentinel-1 satellites, were analyzed to quantify changes in the wet area of a reach of the Tagliamento river under different flow conditions. The algorithm, developed within the Python-API of GEE, first empowers the Sentinel-1 images with the hydrometric level, then involves radiometric slope correction and speckle noise filtering. The Otsu method is then used for image segmentation leading to a water and dry land binary classification. Results support many types of analysis about river dynamics, including morphological changes, floods monitoring and relief efforts and bio-physical habitat dynamics. The results encourage future advancements and applications of the algorithm, specifically exploring SAR data from ICEYE and Capella Space constellations, which offer significantly higher spatial and temporal resolutions compared to Sentinel-1 data.

PG16 is a broadly neutralizing antibody that binds to the gp120 subunit of the HIV-1 Env protein. The major interaction site is formed by the unusually long complementarity determining region (CDR) H3. The CDRH3 residue Tyr100H is known to represent a tyrosine sulfation site; however, this modification is not present in the experimental complex structure of PG16 with full-length HIV-1 Env. To investigate the role of sulfation for this complex, we modeled the sulfation of Tyr100H and compared the dynamics and energetics of the modified and unmodified complex by atomistic molecular dynamics simulations. Our results show that sulfation does not affect the overall conformation of CDRH3, but still enhances gp120 interactions both at the site of mutation and for the neighboring residues. This stabilization affects not only protein-protein contacts, but also the interactions between PG16 and the gp120 glycan shield. Further, we also investigated whether PG16-CDRH3 is a suitable template for the development of peptide mimetics. For a peptide spanning residues 93-105 of PG16 we obtained an experimental EC50 value of 3nM for the binding of gp120 to the peptide. This affinity can be enhanced by almost one order of magnitude by artificial disulfide bonding between residues 99 and 100F. In contrast, any truncation results in significantly lower affinity, suggesting that the entire peptide segment is involved in gp120 recognition. Their high affinity makes PG16-derived peptides useful building blocks for further optimization to obtain a potent inhibitor that efficiently blocks HIV-1 infection.

Wear usually occurs in the loaded part of hydrodynamic bearings and leads the bearing geometry imperfections. This paper investigates the effects of wear-induced geometric imperfections of tilting pad journal bearings (TPJBs) on the dynamic behavior of the system. Furthermore, the effect of wear on the journal-bearing rub-induced contact pressure severity is investigated. A novel tribo-dynamic model is proposed for a flexible rotor-worn TPJB which integrates a mixed elastohydrodynamic model with a rotor-worn TPJB thermal and dynamic model to assess the effects of the bearing wear progression on the rotor-TPJB behavior. Based on the results, wear changes the temperature distribution of the pads and oil film as well as the dynamic behavior of the system. Dynamic simulations reveal a higher vibration level and contact pressure for the worn TPJBs near the system's critical speed and service speed. Finally, thermal and dynamic condition indicators are suggested to detect TPJB wear severity at its early stages.

The human serotonin transporter (hSERT) terminates neurotransmission by removing serotonin from the synaptic cleft, which is an essential process plays an important role in depression. In addition to substrate serotonin, hSERT is also the target of drugs of abuse like cocaine and clinically used antidepressants such as escitalopram and paroxetine. To date, few studies attempt to investigate the unbinding mechanism underlying the orthosteric and allosteric modulation of hSERT. The high-resolution X-ray structure of hSERT resolved recently enables us to theoretically study the unbinding of the above four ligands against the S1 or S2 site of hSERT, by means of molecular docking, molecular dynamics (MD) and potential of mean force (PMF) simulations. We proved that for either the S1 or S2 site, the other three ligands (cocaine, escitalopram and paroxetine) are much more favorable than the original substrate serotonin, whether in kinetics along the unbinding pathways or in thermodynamics at the equilibrium states. Furthermore, the S1 site is much more favorable than the S2 site, for each ligand. Interestingly, inspection revealed that there are ~ 3Å lengths between the allosteric site of serotonin and cocaine, and an unseen un-binding pathway for escitalopram at the S1 site except for verification of the broadest trail.

Phytoseiidae inhabit a wide range of herbs, shrubs and trees. Aesculus hippocastanum is an important ornamental tree in Europe and is likely reservoir of these mites. We therefore assessed the species composition and the spatial and seasonal variability in the abundance of Phytoseiidae in city parks in South Bohemia, Czech Republic. Leaf samples were randomly collected from horse chestnut tree branches at eight sites, five times during the vegetation season in 2013. The mites were collected by washing technique and mounted on slides for identification. In total, 13,903 specimens of phytoseiid mites were found, and eight species were identified: Amblyseius andersoni, Euseius finlandicus, Kampimodromus aberrans, Neoseiulella tiliarum, Phytoseilus macropilis, Paraseiulus talbii, Paraseiulus triporus and Typhlodromus (Typhlodromus) pyri. Paraseiulus talbii and P. macropilis were recorded on the leaves of horse chestnut trees for the first time in the Czech Republic in this study. The predominant species was E. finlandicus (96.25%). The number of mites per compound leaf was, on average, 2.53, 10.40, 23.54, 11.59 and 9.27 on the sampling dates in each month between May and September, respectively. The mite density was significantly affected by the sampling site and date.

Aquaporins (AQPs) are water channels widely distributed in living organisms and involved in many pathophysiologies as well as in cell volume regulations (CVR). In the present study, based on the structural homology existing between mineralocorticoids receptors (MR), glucocorticoids receptors (GR), cholesterol consensus motif (CCM) and the extra-cellular vestibules of AQPs, we investigated the binding of corticosteroids on AQP family through in silico molecular dynamics simulations of AQP2 interactions with cortisol. We propose for the first time a putative AQPs corticosteroids binding site (ACBS) and discussed its conservation through structural alignment. Corticosteroids can mediate non-genomic effects, nonetheless, the transduction pathways involved are still misunderstood. Moreover, a growing body of evidence is pointing out toward the existence of a novel membrane receptor mediating part of these rapid corticosteroids effects. Our results suggest that the naturally produced glucocorticoid cortisol inhibits channel water permeability. Based on these results, we propose a detailed description of a putative underlying molecular mechanism. In this process, we also bring new insights on the regulatory function of AQPs extra-cellular loops and on the role of ions in tuning the water permeability. Altogether, this work brings new insights into corticosteroids non-genomic effects through the proposition of AQPs as membrane receptor of this family of regulatory molecules. This original result is the starting point for future investigations to define more in depth and in vivo the validity of this functional model.

Photoplethysmography is a widely used technique to noninvasively assess heart rate, blood pressure, and oxygen saturation. This technique has a large potential for further applications, for example in the field of physiological and mental health monitoring. However, advanced applications of photoplethysmography have been hampered by the lack of accurate and reliable methods to analyze the characteristics of the complex nonlinear dynamics of the photoplethysmogram. Methods of nonlinear time series analysis may be used to estimate the dynamical characteristics of the photoplethysmogram but they are highly influenced by the length of the time series, which is often limited in practical photoplethysmography applications. The aim of this study was to evaluate the error in the estimation of the dynamical characteristics of the photoplethysmogram associated to the limited length of the time series. The dynamical properties were evaluated using recurrence quantification analysis, and the estimation error was computed as a function of the length of the time series. Results demonstrated that properties such as de-terminism and entropy can be estimated with an error lower than 1% even for short photople-thysmogram recordings. Additionally, the lower limit for the time series length to estimate the average prediction time was computed.

The Failure Mode and Effect Analysis (FMEA) is often used to improve a system's reli-ability. This paper proposes a new approach that aims to overcome the most critical defects of the traditional FMEA. This new methodology combines the Entropy and Best Worst Method (BWM) methodology with the EDas and System Dynamics, FMECA: The EN-B-ED Dynamic FMECA. The main innovation’s point of the proposed work is the presence of an unknown factor (Cost), that allows to obtain an objective weighted factor, a risk index when a machine failure occurs. The criticality analysis has been carried out using software (Vensim PLE x64) to simulate System Dynamics models to identify corrective actions and evaluating the possible implementation of these ac-tions. The methodology proposed is applied to a case study in a relevant Italian com-pany in the agri-food sector.

Electrostatic interactions have a determining role in conformational and dynamic behavior of polyelectrolyte molecules [1]. In this study, anionic polyelectrolyte molecules, poly(glutamic acid) (PGA) and poly(aspartic acid) (PASA), in water solution with the most commonly used K+ or Na+ counterions were investigated using atomistic molecular dynamics (MD) simulations. Seven common force fields, AMBER99SB-ILDN, AMBER14SB, AMBER-FB15, CHARMM22*, CHARMM27, CHARMM36m and OPLS-AA/L, both with their native parameters and with the non-bonded fix (NBFIX) and electronic continuum corrections (ECC) to were studied. These corrections have bene introduced to correct for the problem of overbinding of ions to the charged groups of polyelectrolytes. Physical properties, such as molecular sizes, local structure and dynamics, were studied using two types of common counterions, potassium and sodium. The results show that in some cases, the macroion size and dynamics depend strongly on the models (parameters) for the counterions due to strong overbinding of ions and charged side chain groups. The local structures and dynamics are more sensitive on dihedral angle parameterization resulting in a preference for defined monomer conformations amd the type of correction used.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

The spectra of the plasma emitted from the studied samples consist of several dozens of narrow bands superimposed on each other. Tables of spectral lines were used to interpret the spectra. It turned out that the largest number of bands corresponds to the radiation of positively charged ions and atoms of elements that make up the crystal lattices of minerals that make up the studied rocks. Thus, the spectra of the plasma emitted from quartz corresponded to the radiation of atoms and positively charged silicon ions, the charge of which varied from 1 to 4, as well as atoms and positively charged oxygen ions, the charge of which varied from 1 to 3. Positively charged ions and atoms of Si, O, K, Ca, Al and Na, which are part of the crystal lattices of quartz and feldspar, flew out of granites. Positively charged ions and Ca, C and O atoms flew out of the calcite.

The dynamics of a low-energy charged particle in an axis-symmetric magnetic field is known to be a regular superposition of periodic–although possibly incommensurate–motions. The projection of the particle orbit along the two non-ignorable coordinates (x,y) may be expressed in terms of each other: y=y(x), yet–to our knowledge–such a functional relation has never been directly produced in literature, but only by way of a detour: first, equations of motion are solved, yielding x=x(t),y=y(t), and then one of the two relations is inverted, x(t)→t(x). In this paper we present a closed-form functional relation which allows to express coordinates of the particle’ orbit without the need to pass through the hourly law of motion.

Mechanical properties of tumor cytoskeleton are extremely vital for any phases of cancer, especially in tumor invasion and metastasis. However, in current category of anticancer drugs, the cytoskeleton-targeting drugs are limited and its role in tumor progression is unclear. Here, we present the mechanical characteristics of tumor stiffness are tightly regulated by cancer cytoskeleton including actin filaments and microtubule during tumor initiation, growth and metastasis, and review the natural drugs that target cancer cytoskeleton. We define cytoskeleton dynamics as target mechanisms for anticancer drug, and summary the plant, microbial and marine sources of natural products. Furthermore, the material approaches to active cancer mechanics are supplied in this review. We aim to promote the development of anticancer drugs that target tumor mechanics by using those material approaches in future and find its pharmacological application.

Melatonin is a hormone used for treating several disorders. However, its oral administration is problematic due to the variable absorption and extensive first-pass metabolism. The topical application of this drug does not present these disadvantages and can be used for therapeutic and skin protection purposes. The adsorption and release of melatonin in the montmorillonite clay was investigated experimentally and theoretically. The drug-clay interaction products were prepared and characterized, showing a modified and improved diffusion and release of melatonin. The crystal structure and spectroscopic properties of melatonin and melatonin-water-mineral materials were studied by molecular modeling, finding that the adsorption of this drug is energetically favourable, and the results were consistent with the experimental data. Ab initio molecular dynamics simulations with the metadynamics methodology showed the release of melatonin from the confined interlayer nanospace of montmorillonite to the liquid bulk water with a 20 kcal/mol free energy barrier.

Syncytium formation, i.e., cell-cell fusion resulting in the formation of multinucleated cells, is a hallmark of infection by paramyxoviruses and other important viruses. This natural mechanism has historically been a diagnostic marker for paramyxovirus infection in vivo and is now widely studied for virus-induced membrane fusion in vitro. However, the role of syncytium formation in within-host dissemination and pathogenicity of viruses remains poorly understood. The diversity of henipaviruses and their wide host range and tissue tropism make them particularly appropriate models to characterize the drivers of syncytium formation and its implications for virus fitness and pathogenicity. Based on the henipavirus literature, we summarized current knowledge on the mechanisms driving syncytium formation, mostly acquired from in vitro studies, and on the in vivo distribution of syncytia. While these data suggest that syncytium formation widely occurs across henipaviruses, hosts and tissues, we identified important data gaps that undermined our understanding of the role of syncytium formation in virus pathogenesis. Based on these observations, we propose solutions of varying complexity to fill these data gaps, from better practices in data archiving and publication for in vivo studies, to experimental approaches in vitro.

Determining the vibration environment is crucial to analyzing a design of any mechanical system, especially such dynamic systems as sounding rockets. Accuracy of measurement using accelerometers could be improved by application of mechanical vibration filtering and amplifying devices. This work presents a theoretical description of a tunable filter and amplifier. Principle of work is provided as well as results from application of the device on a sounding rocket are provided. It is shown that implementation of such devices allowed for enhancing the accuracy of acceleration measurements. Conclusions on future implementations are also provided.

Background: Colorectal cancer is considered the third most fetal among all type of cancer. Spleen tyrosine kinase (SYK) is a non-receptor type tyrosine-protein that plays crucial role in signaling mediated via immune receptor. We adopted an onco-informatics analysis to evaluate the SYK expression and prognostic value of SYK in colorectal cancer, and identification of potential phytochemicals which may inhibit overexpression of SYK protein as well as minimized colorectal cancer. Materials & Methods: Differential expression of SYK gene was analyzed using the several transcriptomic databases including Oncomine, UALCAN, GENT2 and GEPIA2. The server, cBioPortal was used to analyze mutation and copy number alterations whereas GENT2, GEPIA, OncoLnc and PrognoScan were employed to examine the survival rate. A protein-protein interaction network of SYK and co-expressed genes of SYK was conducted via GeneMANIA. Considering SYK gene encoding protein as drug target, selected phytochemicals were assessed by molecular docking using PyRx 0.8 packages. YASARA molecular dynamics simulators were applied for the post validation of the molecular docking data. Results: We have observed significant overexpression of mRNA expression levels of SYK gene colorectal adenocarcinoma (COAD) samples compared with normal tissues. Significant methylation level and various genetic alterations are assembled in SYK gene which can lead to the development of colorectal cancer. As a result, lower level of SYK expression was related to the more chances of patients’ survival by which all the outcomes from the multiple bioinformatics platforms and web resources have demonstrated the significant evidences that the SYK kinsase can possess as a potential biomarker for the treatment of colorectal cancer. Here, aromatic phytochemicals namely, Kaempferol and Glabridin targeting SYK showed more stability compared to controls and may be useful for the treatment of colorectal cancer. Conclusion: Our study showed dysregulated expression of SYK in colorectal cancer and potentiality to act as a biomarker for the prognosis of CRC. Moreover, we have shown phytochemicals (Kaempferol and Glabridin) target SYK as potential treatment strategies and drug repositioning potentiality in colorectal cancer.

Complex systems can be described as the network of interactions between their components. In directed networks, information, energy and matter flow in dominant directions. Undirected networks, on the other hand, cannot easily capture these asymmetries. The local flow of information depends on the node spin, which is a function of the number of incoming and outgoing links attached to it. And yet, how the heterogeneous association between in-degree and out-degree constraints global dynamics remains poorly understood. Here, we develop a new theoretical model to study the relationship between node directionality and global flow dynamics. Our computational analysis of random and scale-free directed networks indicates that structural diversity constrains the dimensionality of reachable dynamical space. These results suggest how local directionality might be an universal driver of global dynamics in many systems, from social networks, to technological systems to the connectome.

New class of conserved quantities is constructed. These quantities find direct application in mechanics of dissipative (generally non-conservative) dynamical systems. Approach demands formulation in the language of geometric mechanics, providing theoretical framework for situations with energy flow in and out of the system. As a by product, we suggest possibility of existence of Hamiltonian form for every autonomous ODE system, evolution of which is governed by non-potential generator of motion. Various examples are provided, ranging from physics and mathematics, to chemical kinetics and population dynamics in biology. Applications of these ideas in geometric integration techniques (GNI) of numerical analysis are discussed, and as an example of such, new discrete gradient-based numerical method is introduced.

Thermal shape memory polymers (SMPs) find increasing applications in biology and medicine due to their promising potential for improved biocompatibility. In such applications, the inevitable penetration of small molecules from the ambient fluid into the polymer influences the shape recovery process and often leads to a reduction of the temperature, at which shape recovery takes place. We show here via molecular dynamics simulations that the size of additive molecules plays a key role for this process. While the effect of concentration on the recovery rate is monotonic in the investigated range, a non-monotonic dependence on the size of additive molecules emerges at temperatures close to the glass transition. This work thus identifies the additives’ size to be a qualitatively novel parameter for switching the recovery process in polymer-based shape memory materials.

The search for new antibacterial agents that could decrease bacterial resistance is a subject that is continuously developing. The Gram-negative and Gram-positive bacteria have a metalloproteins group belonging to the M4 family. That is the main virulence factor of these bacteria. In this work, we have used a computational protocol based on the comprehensive analysis of the results of docking, molecular dynamics simulation, MM-PBSA, ligand efficiency, and ADME-Tox properties of ligand designed in silico in the previous manuscript using the Thermolysin from Bacillus thermoproteolyticus, a metalloprotein of the M4 family as a target. The principal results obtained were the designed ligands were adequately oriented in the thermolysin active center. The Lig783, Lig2177, and Lig3444 compounds were those with better dynamic behavior, however, when analyzing the results extracted from the ADME-Tox properties, only Lig783 was the best antibacterial agent candidate.

Routing quantum information among different nodes in a network is a fundamental prerequisite for a quantum internet. While single-qubit routing has been largely addressed, many-qubit routing protocols have not been intensively investigated so far. Building on the many-excitation transfer protocol in Ref. , we apply the perturbative transfer scheme to a two-excitation routing protocol on a network where multiple two-receivers block are coupled to a linear chain. We address both the case of switchable and permanent couplings between the receivers and the chain. We find that the protocol allows for efficient two-excitation routing on a fermionic network, although for a spin-12 network only a limited region of the network is suitable for high-quality routing.

Ferric uptake regulation protein is a repressor protein which binds an AT rich region of DNA (the iron box). Fur binds as a dimer in a helix turn helix mode and it is activated by iron(II) and other divalent transition metal ions at elevated concentrations in a process to regulate the ion uptake. Each transition metal ion induces certain conformational changes to aid the Fur binding, both the N-terminal and C-terminal domains take part in binding to DNA in addition to His 88 and His 86 residues. The process is discussed in view of experimental reports. Fe(II), Mn(II) and Co(II) activate Fur to bind DNA experimentally but Zinc plays a structural role and does not activate Fur to bind DNA.

Air sampling using vortex air samplers combined with species specific amplification of pathogen DNA, was carried out over two years in four or five locations in the Salinas Valley of California. The resulting time series data for the abundance of pathogen DNA trapped per day displayed complex dynamics with features of both deterministic (chaotic) and stochastic dynamics. Methods of nonlinear time series analysis developed for the reconstruction of low dimensional attractors provided new insights into the complexity of the pathogen abundance data, but also indicated that practicality may limit the capacity for definitively classifying the dynamics of air borne plant pathogen inoculum. Over the two years of the study five location/year combinations were classified as having stochastic linear dynamics and four were not. Calculation of entropy values for either the number of pathogen DNA copies or for a binary string indicating the pathogen abundance data were increasing or not, revealed (1) some robust differences in the dynamics between seasons that were not obvious in the time series data themselves, and also (2) that the series were almost all at their theoretical maximum entropy value when considered from the simple perspective of whether instantaneous change along the sequence is positive or not.

We study a minimal model of the stress-driven p53 regulatory network that includes competition between active and mutant forms of the tumor-suppressor gene p53. Depending on the nature of the external stress signal, four distinct dynamical states are observed. These states can be distinguished by dierent dynamical properties and correspond to active, apoptotic, pre-malignant and cancer states. Transitions between any two of these states are found to be unidirectional and irreversible if the stress signal is either oscillatory or constant. When the signal decays exponentially, the apoptotic state vanishes, and for low stress the pre-malignant state is bounded by two critical points, allowing the system to transition reversibly from the active to the pre-malignant state. For signicantly large stress, the range of the pre-malignant state expands and the system moves to the cancerous state which is a stable attractor. This suggests that identification of the pre-malignant state may be important both for therapeutic intervention as well as for drug discovery.

Climate projections show that southern Indonesia such as West Nusa Tenggara is projected to experience a lower precipitation and higher temperatures. To date, research on climate change impact on Indonesian rice production yield is limited. As climate change is projected to decrease rainfall and to increase temperatures, this paper offers a qualitative analysis using system archetypes to understand the impacts of climate change on rice production. Two system archetypes are identified including Limits to Growth and Success to Successful. Both archetypes explain that rice production is hampered by high minimum temperature as photosynthesis output is decreased by increasing respiration. This paper shows that using a simple tool, system archetypes, we can describe the impacts of climate change on rice production. The outputs of this study such as a causal loop diagram and system archetypes can be a basis to develop a simulation model in understanding the impacts of climate change on main crops.

Salmonella, especially invasive non-typhoidal Salmonella (iNTS) are responsible for developing various invasive diseases, and possess higher mortality rate, due to their higher antibiotic resistance profile than the other bacteria. Therefore, the present study was concerned to develop epitope based peptide vaccine against iNTS species as a successive and substitute protective measures. The study considered comprehensive Immunoinformatic approaches, followed by molecular docking and molecular dynamics simulation to predict the efficient vaccine candidate T cell and B cell epitopes, based on the outer membrane proteins. The study identified two best epitopes YGIFAITAL and KVLYGIFAI from total iNTS outer membrane proteins, which showed higher immunity, non-allergenicity, non-toxicity and also showed higher conservancy and population coverage values. Both epitopes showed higher binding affinity and stability towards HLA-C* 03:03. The MM-PBSA binding free energy showed the YGIFAITAL epitope binds more tightly with both MHC-I and MHC-II molecules. The total contact, H-bond analysis and RMSF results also validate the efficiency of these epitopes as vaccine candidate. The projected B cell epitopes AAPVQVGEAAGS, TGGGDGSNT and TGGGDGSNTGTTTT showed higher antigenicity. Overall, the study concluded that these epitopes can be considered as the potential vaccine candidate to make a successive vaccine against iNTS species. However, this result further needs to be validate by wet lab research to make successive vaccine with these projected epitopes.

In this age of Facebook, Instagram, and Twitter, there is rapidly growing interest in understanding network-enabled opinion dynamics in large groups of autonomous agents. The phenomena of opinion polarization, the spread of propaganda and fake news, and the manipulation of sentiment is of interest to large numbers of organizations and people. Whether it is the more nefarious players such as foreign governments that are attempting to sway elections or it is more open and above board, such as researchers who want to make large groups of people aware of helpful innovations, what is at stake is often significant. In this paper, we review opinion dynamics including the extensions of many classical models as well as some new models that deepen understanding. For example, we look at models that track the evolution of an individual’s power, that include noise, and that feature sequentially dependent topics, to name a few. While the first papers studying opinion dynamics appeared over 60 years ago, there is still a great deal of room for innovation and exploration. We believe that the political climate and the extraordinary (even unprecedented) events in the sphere of politics in the last few years will inspire new interest and new ideas. It is our aim to help those interested researchers understand what has already been explored in a significant portion of the field of opinion dynamics. We believe that in doing this, it will become clear that there is still much to be done.

In this paper an integrated methodology for the coupling between 1D- and 3D-CFD simulation codes is presented, which has been developed to support the design and calibration of new diesel engines. The aim of the proposed methodology is to couple 1D engine models, which may be available in the early-stage engine development phases, with 3D predictive combustion simulations, in order to obtain reliable estimates of engine performance and emissions for newly designed automotive diesel engines. The coupling procedure features simulations performed in 1D-CFD by means of GT-SUITE and in 3D-CFD by means of Converge, executed within a specifically designed calculation methodology. An assessment of the coupling procedure has been performed by comparing its results with experimental data acquired on an automotive Diesel engine, considering different working points including both part load and full load conditions. Different multiple injection schedules have been evaluated for part-load operation, including pre and post injections. The proposed methodology, featuring detailed 3D chemistry modeling, was proven to be capable to properly assess pollutant formation, specifically to estimate NOx concentrations. Soot formation trend was also well-matched for most of the explored working points. The proposed procedure can therefore be considered as a suitable methodology to support the design and calibration of new Diesel engines, thanks to its ability to provide reliable engine performance and emissions estimations from the early-stage of a new engine development.

The molecular dynamics simulation has been used to investigate the structural and transport properties of (Ba_0.5-xSr_x)La_0.5InO_3-δ (x=0, 0.1, 0.2) oxygen-ion conductor. The previous studies reported that the ionic conductivity of Ba-doped LaInO3 decreases because Ba dopant forms narrow oxygen path in the lattice, which could hinder the diffusion of oxygen ion. In this study, we reveal the mechanism to improve the ionic conductivity by Ba and Sr co-doping on La site in LaInO3 perovskite oxide. The results show that the ionic conductivity of (Ba_0.5-xSr_x)La_0.5InO_3-δ increases with increasing numbers of Sr ions, which oxygen diffusion paths including Sr ion have larger critical radius than Ba ions. The RDF calculations showed the heights of peak in composition including Sr ions is lower and broaden, so oxygen ions moved easily into other oxygen sites.

Cancers remain the lead cause of disease-related, pediatric death in North America. The emerging field of complex systems has redefined cancer networks as a computational system with intractable algorithmic complexity. Herein, a tumor and its heterogeneous phenotypes are discussed as dynamical systems having multiple, strange attractors. Machine learning, network science and algorithmic information dynamics are discussed as current tools for cancer network reconstruction. Deep Learning architectures and computational fluid models are proposed for better forecasting gene expression patterns in cancer ecosystems. Cancer cell decision-making is investigated within the framework of complex systems and complexity theory.

Entropic Dynamics (ED) is a framework in which Quantum Mechanics is derived as an application of entropic methods of inference. In ED the dynamics of the probability distribution is driven by entropy subject to constraints that are codified into a quantity later identified as the phase of the wave function. The central challenge is to specify how those constraints are themselves updated. In this paper we review and extend the ED framework in several directions. A new version of ED is introduced in which particles follow smooth differentiable Brownian trajectories (as opposed to non-differentiable Brownian paths). To construct the ED we make use of the fact that the space of probabilities and phases has a natural symplectic structure (i.e., it is a phase space with Hamiltonian flows and Poisson brackets). Then, using an argument based on information geometry, a metric structure is introduced. It is shown that the ED that preserves the symplectic and metric structures -- which is a Hamilton-Killing flow in phase space -- is the linear Schrödinger equation. These developments allow us to discuss why wave functions are complex and the connections between the superposition principle, the single-valuedness of wave functions, and the quantization of electric charges. Finally, it is observed that Hilbert spaces are not necessary ingredients in this construction. They are a clever but merely optional trick that turns out to be convenient for practical calculations.

InGaN/GaN samples grown on c-plane sapphire substrate with different In concentrations by metal organic chemical vapor deposition are demonstrated. The subsequent capping GaN layer growth opens a possibility for dislocation reduction due to the lateral strain relaxation in growth geometry. We present the further growth optimization and innovative characterization of InGaN layers overgrown on different structures with varying In concentrations. The photoelectrical and optical properties of the InGaN layers with or without capping GaN layer were investigated by time-resolved picosecond transient grating and temperature dependence photoluminescence. We note a 10-fold increase in carrier lifetime in the InGaN layers when the sample structure changes from PIN to single InGaN layer.

Cancers are complex, adaptive ecosystems. They remain the leading cause of disease-related death among children in North America. As we approach computational oncology and Deep Learning Healthcare, our mathematical models of cancer dynamics must be revised. Recent findings support the perspective that cancer-microenvironment interactions consist of turbulent flows. As such, cancer pattern formation, protein-folding and metastatic invasion are discussed herein as processes driven by chemical turbulence within the framework of complex systems theory. Current state-of-the-art quantitative approaches used in reconstructing cancer stem cell networks are reviewed. To conclude, cancer stem cells are presented as strange attractors of the Waddington landscape.

Statistical mechanics investigates how emergent properties of macroscopic systems (such as temperature and pressure) relate to microscopic state fluctuations. The underlying idea is that global statistical descriptors of order and variability can monitor the relevant dynamics of the whole system at hand. Here we test the possibility of extending such an approach to psychotherapy research investigating the possibility of predicting the outcome of psychotherapy on the sole basis of coarse-grained empirical macro-parameters. Four good-outcome and four poor-outcome brief psychotherapies were recorded, and their transcripts coded in terms of standard psychological categories (abstract, positive emotional and negative emotional language pertaining to patient and therapist). Each patient-therapist interaction is considered as a discrete multivariate time series made of subsequent word-blocks of 150-word length, defined in terms of the above categories. Static analysis (Principal Component Analysis) highlighted a substantial difference between good-outcome and poor-outcome cases in terms of mutual correlations among those descriptors. In the former, the patient’s use of abstract language correlated with therapist’s emotional negative language, while in the latter it co-varied with therapist’s emotional positive language, thus showing the different judgment of the therapists regarding the same variable (abstract language) in poor and good outcome cases. On the other hand, the dynamic analysis, based on five coarse-grained descriptors related to variability, degree of order and complexity of the series, demonstrated a relevant case-specific effect, pointing to the possibility of deriving a consistent picture of any single psychotherapeutic process. Overall, the results showed that the systemic approach to psychotherapy (an old tenet of psychology) is mature to shift from a metaphorical to a fully quantitative status.

Sustainable consumption became the community’s attention as the respond of worrying consumption growth direction that tends to excessively exploit nature without considering the continuity of the next generation. Take into account the growing coffee consumption in Indonesia, this article tries to prove whether the connoisseur consumers (CCs) are capable to mediate dynamics capabilities (DCs) of single origin coffee shops (SOCSs) and to encourage the consumption sustainability. In-depth interview to 30 SOCS managers, 60 baristas and 450 consumers, it is found that CCs are capable of supporting the business continuity of SOCSs because they are loyal consumers with the contribution of more than 20% from the total consumers. Correlation testing between connoisseurs’ attributes and the number of visits also shows positive value. It means there is a significant relationship. In addition, related to the sustainability attribute, the existence of CCs has encouraged the practice of consumption sustainability. At the same time, CCs are capable to moderate SOCSs to improve sensing, seizing, and transforming shop management to put up with the competition. Based on the above findings, CCs are moderating SOCSs in improving their DCs to be more potential in improving the sustainable consumption of coffee commodity in the future.

The active power-assist function greatly expands the potential applications of exoskeleton robots, yet the motion intention estimation (MIE) for active power-assist strategy is quite problematic. Through the analysis of the conduction path and the different stage manifestations of motion intention in human body, we confirmed that the joint torque of human body meets the basic requirements of MIE for the active power-assist that we suggest, namely: (i) it reflects the direction and intensity of the wearer’s efforts; (ii) it precedes the human limb motion; (iii) it generates real-time and continuous output. Thus, an online calculation method of human joint torque was proposed. The sensing system integrated in exoskeleton robots was designed to perceive motion data and foot contact force of a human body. A special inverse dynamics with a parameterized model of the human body was proposed. Contrast experiments were carried out with the motion capture system, which results’ accuracy and similarity were evaluated via the root mean square error and correlation coefficient. The comparative analysis of two synchronous results shows good accuracy of the proposed MIE method, which lays the foundation for the realization of active power-assist.

Nanoparticles -- regularly patterned or randomly dispersed -- are a key ingredient for emerging technologies in photonics. Of particular interest are scattering and field enhancement effects of metal nanoparticles for energy harvesting and converting systems. An often neglected aspect in the modeling of nanoparticles are light interaction effects beyond classical electrodynamics stemming from electron dynamics in confined and accelerated systems. We give a detailed account on free electron phenomena in metal nanoparticles and discuss analytic expressions, stemming from microscopic and semi-classical theories. These can improve standard computational schemes to produce more reliable results on the optical properties of metal nanoparticles. We combine these solutions into a single framework and study their joint impact on isolated Au, Ag, and Al nanoparticles as well as dimer structures.

Relationships among the population abundance of four predator groups for rice insect pests, namely: carabid beetles, staphylinid beetles, green mirid bugs, and spiders in three landscape categories were evaluated. Both rice plots and the associated bund margins of these rice plots found among three Bangladesh landscape categories were sampled by sweep net. The results revealed that the abundance significantly varied across landscapes. The rice landscape of one location harbored higher numbers of a specific predator than other location in other regions of Bangladesh. The results also showed a dependency on the width of the rice bund margins of the rice plots, where spiders populations increased with increased bund widths, but the population abundance of these predators did not depend on the diversity of the number of weed species found on the rice bund margins. The relative abundance of predator populations also significantly differed among the three landscapes, with the green mirid bug having the highest number among the four predators. This study indicates that predators of rice insect pests are highly landscape specific. In order to design integrated pest management systems for different Bangladeshi rice production locales, considerations unique to the characteristics of each locale are necessary. Preliminary efforts to apply variography analyses to the RED spectral band of LANDSAT 8 imagery from December 2016 are presented as first step toward learning a suite of methods which describe useful local characteristics affecting rice pest predators.

In this paper, the current technologies of the regenerative shock absorber systems have been categorized and evaluated. Three drive modes of the regenerative shock absorber systems, namely the direct drive mode, the indirect drive mode and hybrid drive mode are reviewed for their readiness to be implemented. The damping performances of the three different modes are listed and compared. Electrical circuit and control algorithms have also been evaluated to maximize the power output and to deliver the premium ride comfort and handling performance. Different types of parameterized road excitations have been applied to vehicle suspension systems to investigate the performance of the regenerative shock absorbers including that of the nonlinear regenerative shock absorber. The research gaps for comparison of the different drive modes and the nonlinearity analysis of the regenerative shock absorbers are identified and, the corresponding research questions have been proposed for future work.

Assessment of the trends of land cover and vegetation dynamics (VD) using remote sensing (RS) and indicators such as anthropogenic activities and the socio-demographic information is essential in order to make proper planning for sustainable management. This paper attempts to evaluate land cover change (LCC) and VD in Kebbi State, Nigeria using historical Landsat data from 1986-2016 by means of remote sensing. The Driver-Pressure-State-Impact-Response (DPSIR) framework was later employed using both primary and secondary data for a better understanding of the drivers, the state of the environmental condition, the causes as well as the impact of the change. The images were classified into five thematic land cover classes as Dense Vegetation, shrubs/built area, farmland, bare/grassland and water body by means of Maximum likelihood supervised classification technique in accordance with Anderson classification scheme level 1, with acceptable accuracy. Pre-classification and post-classification change detection (CD) methodologies were executed using Normalized difference vegetation index (NDVI) and Image differencing respectively. The study illustrates a steady decline in dense vegetation and shrubs/build areas while farmland and bare/grassland increases, however, water bodies remain unchanged. The DPSIR pin-point that the major drivers of change in the study area have been the pressing need for farming land as the population grows and socioeconomic demands including fuelwood consumption and endemic poverty. Expansion of Farming land, fuelwood consumption and the need for construction materials are identified as the main key elements exerting pressure for the change. The state of the condition indicates a steady decline in dense vegetation and shrubs areas while farmland and bare/grassland are increasing significantly. The impacts include land degradation, the decline in the provision ecosystem goods and services, biodiversity loss through loss of habitats. The study, however, noted that many international and national policies in response to land degradation are channelled toward land restoration and remediating of the environment, through afforestation programs and improving the livelihood of the rural people through providing alternative income sources since they depend heavily on land for sustenance. However, the state governments, communities and individual commonly organized annual tree planting campaign with the main purpose of environmental protection.

The concept of smart morphing blades, which can control themselves to reduce or eliminate the need for active control systems, is a highly attractive solution in blade technology. In this paper an innovative passive control system based on Shape Memory Alloys (SMAs) is proposed. On the basis of previous thermal and shape characterization of a single morphing blade for a heavy-duty automotive cooling axial fan, this study deals with the numerical analysis of the aerodynamic loads acting on the fan. By coupling CFD and FEM approaches it is possible to analyze the actual blade shape resulting from both the aerodynamic and centrifugal loads. The numerical results indicate that the polymeric blade structure ensures proper resistance and enables shape variation due to the action of the SMA strips.

This paper focuses on the problem of finite-time stabilization of homogeneous, non-Lipschitz systems with dilations. A key contribution of this paper is the design of a virtual recursive Hölder, non-Lipschitz state feedback which renders the non-Lipschitz systems in the special case dominated by a lower-triangular nonlinear system, finite-time stable. The proof is based on a recursive design algorithm developed recently, to construct the virtual Hölder continuous, finite-time stabilizer as well as a C¹ positive definite and proper Lyapunov function that guarantees finite-time stability of the non-Lipschitz non-linear systems.

This work focused on Diplectanum aequans, a gill parasite of European seabass Dicentrarchus labrax. The analyses allowed us to detect factors regulating parasite distributions on Corsican fish farms and to highlight the ecological structure of D. aequans communities on gills of fish. The study of parasite distribution showed that bigger fish appear more parasitized and that the infection dynamics of D. aequans can be explained by several factors such as biotic factors or farm environment conditions. The study of gill repartition of D. aequans showed that parasites tend to have a homogeneous distribution with no statistically significant difference in infection between the two sides of fish. However, the distribution of the number of parasites on gill arches varied according to the total number of parasites. The results differ depending on infection degree and host weight. When parasites are numerous, the individuals are distributed on the gill arches according to an anterior-posterior gradient, while with low rates of infection, the parasites are randomly distributed on the four arches. The spatial distribution of D. aequans appears to be determined by the differential action of water flow through the gill arches and the sizes of anterior arches. We also proposed a tool to reduce sampling effort and allow optimal exploitation for fish farmers.

The study delves into the Zaalklapspruit valley-bottom wetland in South Africa, a site designed through ecological engineering and currently impacted by acid mine drainage due to an upstream defunct coal mine. The research conducted in 2022, aimed to uncover the dynamics of contaminant dispersal within such a wetland setting, focusing on the origins, routes, and ultimate recipients of various metals and sulfur compounds. Analysis revealed that the bottom sediment is rich in organic material, with pH levels observed between 6.05 and 6.59, and exhibited notably low oxidation-reduction potentials, reaching down to −219.67 mV at sample point S3. Notably, Manganese displayed the highest adsorption rates, whereas Iron was predominantly absorbed by the roots of Typha capensis and the algae Klebsormidium acidophilum. The wetland's macrophyte rhizospheres hosted a rich array of microbiota, including families like Helicobacteraceae and Hydrogenophilaceae, which are crucial in metal and sulfur processing. This investigation sheds light on the intricate biogeochemical processes taking place involving the wetland's sediment, macrophyte root systems, periphyton, and microbial populations, underscoring the potential of ecologically engineered wetlands in the remediation of waters affected by acid mine drainage.

Assessing carbon stocks in tropical forests is crucial for understanding their role in mitigating climate change. (1) Background: However, existing assessments have underestimated certain factors that can substantially influence carbon dynamics. (2) Methods: This study focuses on one such factor - buttress roots from a tropical forest. (3) Results: Our findings reveal that a significant proportion of trees (69.57%) had 3 to 5 buttress roots per tree. The total biomass of the buttress roots and the aboveground portion of the trees with buttress roots were calculated to be 8.5 tonnes/ha and 10.7 tonnes/ha respectively. The buttress root biomass accounted for 16.18% of the total tree biomass. It was observed that the presence of buttress roots during both the rainy and dry seasons increased the soil organic carbon content by an average of 20.8% in the upslope areas with buttress roots. During the rainy season, the presence of buttress roots did not affect the heavy fraction organic carbon in the soil layers in a statistically significant way. Regardless of the season, the soil respiration rate in the areas without buttress roots was higher than that in the areas with buttress roots. The presence of buttress roots had a positive effect on soil nutrient concentration in all seasons, creating a healthier environment for trees. Tree species with buttress roots had on average 20% higher organic carbon content during both the wet and dry seasons. (4) Conclusions: These findings highlight the importance of considering buttress roots in carbon stock assessments and forest management strategies. By integrating buttress roots into carbon accounting models, we can obtain more accurate estimates of carbon stock potential and develop more effective conservation and restoration strategies for tropical forests.

The planned closure of potash mines, achieved through the injection of highly mineralized brines into the worked-out area, is a complex and not fully studied process. A critical concern arises when brines obstruct the aerodynamic connections between the flooded mine's airspace and the atmosphere, potentially leading to the formation of closed cavities where explosive gases can accumulate. To address this hazard, it is imperative to develop systems capable of extracting the gas-air mixture from the unflooded domed portions of the worked-out area. This is the focus of this study, in which we analyze gas-dynamic processes within the potassium mine worked-out area during planned flooding. Two distinct scenarios are examined: the first involves controlled flooding with saturated brines, while the second contemplates flooding resulting from a hypothetical breakthrough of supra-salt strata, leading to the ingress of groundwater into the worked-out area. A novel mathematical model is introduced to predict the evolution of gas-air mixture parameters in the unflooded dome segment of the worked-out area. Utilizing this model, we assess the effectiveness of proposed measures designed to eliminate explosive gases from the worked-out area. Specifically, a pipeline system is proposed for the removal of gases, followed by their dilution below the maximum permissible concentrations in the effluent jet. The findings from this study contribute valuable insights into ensuring the safe and efficient closure of potash mines, shedding light on potential risks and effective mitigation strategies for gas-related hazards during planned flooding.

The growing interest in Kv7.2/7.3 agonists originates from the involvement of these channels in several brain hyperexcitability disorders. In particular, Kv7.2/7.3 mutants have been clearly associated with epileptic encephalopathies (DEE) as well as with a spectrum of focal epilepsy disorders, often associated with developmental plateauing or regression. Nevertheless, there is a lack of available therapeutic options, considering that retigabine, the only molecule used in clinic as broad-spectrum Kv7 agonist, has been withdrawn from the market in the late 2016. This is why several efforts have been made both by both academia and industry in the search for suitable chemotypes acting as Kv7.2/7.3 agonists. In this context, in silico methods have played a major role, since the precise structures of different Kv7 homotetramers have been only recently disclosed. In the present review the computational methods used for the design of Kv.7.2/7.3 small molecule agonists and the underlying medicinal chemistry are discussed in the context of their biological and structure-function properties.

This study presents a detailed computational investigation aimed at identifying and evaluating potential small organic molecular scaffolds as inhibitors targeting the Pks13 protein, a critical enzyme in tuberculosis (TB) drug discovery. Our approach integrates multiple computational techniques, including quantitative structure-activity relationship (QSAR) modelling, compound library generation, molecular docking, biological activity prediction, re-evaluation of screened compounds, molecular dynamics (MD) analysis, and binding free energy estimation using the MM-GBSA approach. We developed a robust QSAR model using multiple linear regression (MLR) techniques, specifically tailored to predict the minimum inhibitory concentration (MIC) values of potential anti-TB agents. This model demonstrated excellent predictive performance, validated through rigorous internal and external validation procedures. Through molecular docking simulations, we identified five promising inhibitors Ethyl (R,Z)-2-(2-((2-chlorobenzyl) oxy)benzylidene)-5-(4-chlorophenyl)-7-methyl-3-oxo-2,3-dihydro-5H-thiazolo [3,2-a] pyrimidine-6-carboxylate (PKD1), (7R,9aR)-7-(2-hydroxyphenyl)-4-(2-methoxyphenyl)-3-methyl-7,8,9,9a-tetrahydroisoxazolo[5,4-b]quinolin-5(6H)-one (PKD2), (7-(4-methoxy phenyl)-2-methyl-3-phenylpyrazolo[1,5-a]pyrimidin-5-yl)(4-(3-(trifluoromethyl)phenyl) piperazin-1-yl)methanone (PKD3), (8S,10S)-10-(((2R,4S,5S,6S)-4-amino-5-hydroxy-6-methyl tetrahydro-2H-pyran-2-yl)oxy)-6,8,11-trihydroxy-8-(2-hydroxyacetyl)-1-methoxy-7,8,9,10-tetra-hydrotetracene-5,12-dione (PKD4), 3-(3,4-dimethylphenyl)-5-((2-(4-fluorophenyl)-2-oxoethyl) thio)-6-phenyl-2-thioxo-2,3,5,7a-tetrahydrothiazolo[4,5-d]pyrimidin-7(6H)-one (PKD5)with high binding affinities, prioritizing them based on their binding energies. These selected compounds were further assessed for their biological activity predictions, guiding the selection of candidates for experimental validation. Subsequent re-evaluation using absolute binding free energy estimation and MD simulations provided insights into the stability and dynamics of the protein-ligand complexes over time. Our findings highlighted the potential of identified compounds as stable binders within the Pks13-TE domain binding pocket, underscoring their viability as drug candidates. Additionally, binding free energy analysis using MM-GBSA reaffirmed the strong affinity of the proposed compounds towards Pks13. Our integrated computational approach offers a systematic and detailed framework for the identification and characterization of potential Pks13 inhibitors with specific relevance to TB drug discovery.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

In this essay, I revisit Neural Networks (NNs) through the lens of evolutionary dynamics. From the two most important features of NNs, I recover the two most general equations of evolutionary dynamics. These two equations may thus serve as a connection between NNs and a body of knowledge that has been built over multiple centuries and has found application across a vast range of disciplines. This connection may therefore also help explain why NNs with the two features in question are applicable across a much broader range of domains than initially envisioned.

Coarse-grained molecular dynamics simulations are employed to investigate the spatiotemporal evolution of vesicles (polymersomes) by the self-assembly of randomly distributed amphiphilic BAB triblock copolymers with hydrophilic A and hydrophobic B blocks in an aqueous solution. The vesiculation pathway consists of several intermediate structures, such as an interconnected network of copolymer aggregates, a cage of cylindrical micelles and a lamellar cage. The cage-to-vesicle transition occurs at a constant aggregation number and practically eliminates the hydrophobic interfacial area between the B block and solvent. Molecular reorganization under-lying the sequence of morphology transitions from a cage-like aggregate to a vesicle is nearly isentropic. The end-to-end distances of isolated copolymer chains in solution and those within a vesicular assembly follow log normal probability distributions. This is attributed to the pre-ponderance of folded chain configurations in which the two hydrophobic end groups of a given chain stay close to each other. However, the probability distribution of end-to-end distances is broader for chains within the vesicle as compared to that of a single chain. This is due to the swelling of the folded configurations within the hydrophobic bilayer. Increasing the hydropho-bicity of the B block reduces the vesiculation time without qualitatively altering the self-assembly pathway.

The vagina is an essential component of the female reproductive system and is responsible for providing female sexual satisfaction. Vaginal smooth muscle contraction plays a crucial role in various physiological processes, including sexual arousal, childbirth, and urinary continence. In pathophysiological conditions such as pelvic floor disorders, aberrations in vaginal smooth muscle function can lead to urinary incontinence and pelvic organ prolapse. A set of cellular and sub-cellular physiological mechanisms regulates the contractile properties of the vaginal smooth muscle cells. Calcium influx is a crucial determinant of smooth muscle contraction, facilitated through voltage-dependent calcium channels and calcium release from intracellular stores. Comprehensive reviews on smooth muscle biophysics are relatively scarce within the scientific literature, likely due to the complexity and specialized nature of the topic. The objective of this review is to provide a comprehensive description of alterations in the cellular physiology of vaginal smooth muscle contraction. The benefit associated with this particular approach is that conducting a comprehensive examination of the cellular mechanisms underlying contractile activation will enable the creation of more targeted therapeutic agents to control vaginal contraction disorders.

The main inhibitory neurotransmitter in the mammalian brain is 4-aminobutyric acid (GABA). Phthalimide acts as a sodium channel antagonist, one of the finest targets for treating epilepsy is the sodium channel and JNK kinase activity is inhibited to treat epilepsy. a novel compound with multiple targets of action for the treatment of epilepsy also anti-oxidant activity has been designed. The compounds are designed in two series aldehydes and aniline. Docking of all the designed compounds has been done with the suitable protein GABA-AT, sodium channel, JNK protein, and anti-oxidant from protein bank. QSAR was done for constructing many linear regression models. In combination with the developed Docking and QSAR outcomes, ADMET has been performed for the active compounds. The result shows that 11 of the compounds have excellent binding energy in docking with all the receptors and also have good QSAR results and follow the ADMET. The structural understanding of human GABA-AT inhibition was obtained from molecular dynamics simulation studies for compound no. 11 at 5 nanoseconds. Synthesis and pharmacological activity also suggest that the designed compound no.11 be considered the best Novel Compound.

In an effort to enhance the efficiency and safety of open-pit mines, this study explores the optimization of end slope road parameters and slope structures, specifically focusing on unmanned driving lanes.A significant aspect of the study is the development of a truck trajectory offset model, which considers the different reaction times between automated sensors and human drivers in adapting to environmental changes. To test these concepts, the study uses numerical simulations to confirm the stability of the proposed end slope designs. Using Victory West Mine No. 1 as a case study, the research determines the optimized width for unmanned driving lanes and the maximum angle for safe steepening of end slopes. The findings indicate that the optimized unmanned lane width for NTE240 mining dump trucks is 1743 mm, allowing for a 2-degree increase in the slope angle at the south end slope. This optimization leads to a steep mining stripping volume of 3.2735 million m³ and a coal output of 2.49628 million tons, maintaining a stripping ratio of 1.31m³/t. These results demonstrate that the unmanned driving road width optimization not only ensures slope safety but also significantly boosts the economic benefits of steep mining in open-pit mines.

The hyper-arid environment is characterized by high inter-annual climatic fluctuations. The yearly average rainfall can change substantially for several years, forming wet or dry sub-periods. Observing rainfall trends over a sub-period can lead to a false perception of a change in the trend but may in fact represent a periodic cycle when examined on a larger time scale. We aimed to better characterize the rainfall regime prevailing in the hyper-arid Arava Valley (Israel/Jordan), and to examine the response of vegetation to annual rainfall. We hypothesized that annual and perennial vegetation would respond differently to wet and dry sub-periods, and that grazing activities will impact vegetation growth. We used a time series of monthly rainfall, from which we calculated Standard Precipitation Index (SPI), and calculated proxies of perennial and annual vegetation over the last four decades using Landsat-derived Normalized Difference Vegetation Index (NDVI). We found no clear trend in rainfall amounts during this period, however we did identify wet and dry sub-periods which were statistically distinct in rain and in vegetation patterns from each other. The highest levels of correlation between rainfall and the NDVI derived proxies were found when examining average rainfall over a period of two- three years for the annual vegetation and over four years for perennial vegetation. Using the Mann-Kendall test, we identified a time lag of two to four years, with the proxies of annual vegetation responding faster than the proxies of perennial vegetation, to shifts between wet and dry sub-period. In addition, we found a consistent difference between natural vegetation cover in Jordan (grazed) and Israel (non-grazed), favoring the development of natural vegetation on the Israeli side. We conclude that integrating between long-term remote sensing satellite imagery and climatic records revealed the greater resilience of perennial vegetation in the hyper-arid region to climatic fluctuation, and enabled us to identify the vegetation’s sensitivity to anthropogenic impact.

The purpose of the study is to establish compliance of the design of the modernized car with the requirements for safe operation. An option has been proposed for upgrading a passenger wagon to a Mobile Diagnostic Complex for transporting a combined vehicle equipped with appropriate flaw detection equipment intended for rail diagnostics. The authors have established the most optimal option for locating the diagnostic vehicle on board the Mobile Diagnostic Complex (MDC) wagon, taking into account the additional load from the vehicle. To achieve this goal, the article developed a three-dimensional model of the supporting structure of the wagon body, proposed a methodology and presented the results of a study of the stress-strain state of the wagon body, taking into account the additional weight of the wagon. The article develops a methodology for studying the natural frequencies of vibrations of the wagon body and develops a three-dimensional model of the wagon for calculating dynamic indicators in the Universal Mechanism software package. Taking into account the previously carried out strength calculations, as well as in order to maintain the factory life cycle indicators of the supporting structure of the wagon body, it is recommended to use the model in the first version.

Abdominal aortic aneurysm (AAA) is a chronic disease in which the abdominal aorta expands until it ruptures. AAA incidence: 10 million/year (India), deaths: 40%-90% (13th leading cause of death in the USA). Current treatment methods make diagnosis difficult due to the lack of noticeable symptoms and inaccurately planned operations. Rupture Scope is a mathematical model to dramatically reduce AAA deaths and improve treatment efficiency. Using a case-specific replica/simulation of real-time abdominal aortic blood flow for monitoring without frequent imaging scans and a novel derived mathematical linear regression model to predict rupture date and expansion rate with an overall accuracy of 95.64%.

This research paper presents a comprehensive study on the gasification of wheat straw pellets in a 10-kW fixed bed gasifier through a combination of Computational Fluid Dynamics (CFD) simulation and experimental validation. The developed 2D CFD model in ANSYS meshing simulates the gasification process in ANSYS Fluent software. The investigation evaluates key parameters such as equivalence ratio, higher heating value, and temperature distribution within the gasifier to enhance syngas production efficiency. The simulated results exhibit a noteworthy agreement with experimental data obtained from gasification. The impact of the equivalence ratio on gas production and lower heating value (LHV) is systematically explored, revealing that an equivalence ratio of 0.35 is optimal for syngas production. The resulting producer gas composition at this optimum condition includes CO (~27.67%), CH4 (~3.29%), CO2 (~11.09%), H2 (~11.09%), and N2 (~51%). The study further investigates the positive influence of reactor temperature on increasing syngas quantity while considering the uneven shape of the reactor and various thermal reactions affecting temperature, static pressure, velocity, and density. The findings contribute valuable insights to improve the efficiency of fixed-bed gasifiers, offering essential information on performance parameters for sustainable and optimized syngas production.

Explosive boiling is a fast phase transition from an ultra-thin liquid film to vapor under an extremely high heat flux, which typically has been studied using molecular dynamics simulation (MDS) method. The present MDS study investigated the explosive boiling of a liquid argon nanofilm over different solid copper surfaces with different nanowall patterns including parallel and cross nanowalls. For each surface, atomic motion trajectories, number of liquid and vapor argon atoms, heat flux, and, mainly, onset time of explosive boiling were investigated. The simulation results indicated that explosive boiling occurs earlier on parallel and cross nanowall surfaces than on an ideally smooth surface, regardless of the topology and configuration of nanowalls. Moreover, the results revealed that by using the cross nanowall surfaces, the onset time of explosive boiling decreased by 0.7–4% compared to the parallel nanowall surfaces. In addition, it was found that the onset time of explosive boiling strongly depends on the potential energy barrier and the movement space between nanowalls for both parallel and cross nanowall surfaces. Furthermore, simulation findings showed that even though increasing the height of cross nanowalls increases heat flux and temperature of the fluid argon domain, it does not necessarily result in a shorter onset time of explosive boiling. These findings demonstrate the capability of cross nanowall surfaces for explosive boiling, thereby could be utilized in future surface design for thermal management applications.

Stratified Taylor Couette Flow (STCF) undergoes transient growth. Recent studies have shown that there exists transient amplification in the linear regime of counter-rotating STCF. It is reported that as Gr increases, the maximum amplification factor ($G_0$) initially decays before it eventually starts growing after reaching a certain critical value Gr$_{c}$. Assuming other parameters of the model are kept constant, the value Gr$_{c}$ changes with respect to different speed ratios of the cylinders. The observed decay/growth pattern of $G_0$ is attributed to the interplay between the induced shear and buoyancy. In this study, the mechanism that triggers this observed pattern is investigated. The kinetic budget of the optimal transient perturbation is analysed numerically to simulate the interaction of the shear production (SP), buoyancy production (BP) and other energy components that contributes to the total optimal transient kinetic energy. These contributions affect the total energy by influencing the perturbation to extract kinetic energy (KE) from the mean flow. The decay of $G_0$ resulted from the positive amplification of both BP and SP, while the growth is attributed to the negative and positive amplification of BP and SP, respectively. The optimal SP is positively amplified, implying that there is the possibility of constant linear growth. These findings agree with the linear growth rate for increasing values of Gr.

Molecular dynamics (MD) simulations present a sophisticated nano-scale computational approach that can play a critical role in material design for next generation batteries. One critical piece of information needed for MD simulations is the non-bonded potential parameters, which can be obtained through quantum mechanics (QM) calculations or experimental methods. However, experimental data is not available for exploratory and novel materials to derive the potential parameters. Also, the QM approach needs significant computational power and is too time consuming for large systems. On the other hand, current Artificial Intelligence techniques are faster, but they cannot be generalized across molecules and properties. As a result, there is a significant barrier to discovering and exploring new materials. Overcoming this barrier, in this work, we propose a Machine Learning (ML) based technique that can learn inter-atomic potential parameters for various particle-particle interactions employing QM calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to MD simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach.

The population parameters of the ecologically important mud shrimp Upogebia major were analyzed from 2012 to 2015 in tidal flats in Seonjaedo and Jugyo on the West coast of Korea. The shrimp density averaged 265/m2 in Seonjaedo and 118/m2 in Jugyo. The sex ratio varied monthly, often male-biased, but by size, males were significantly dominant, with a carapace length (CL) over 30 mm. Although the shrimp settled almost annually, only a single size-group was observed from 2012 to 2014. The recruitment in Jugyo in 2014 was notably successful, resulting in two cohorts persisting until 2015. Many shrimp were parasitized at varying local rates: 6.0% in Seonjaedo and 37.1% in Jugyo. The growth curves revealed that the shrimp grew annually after settlement, reaching CLs of 11.90, 18.24, and 23.02 mm in Seonjaedo and 13.73, 20.86, and 25.82 mm in Jugyo. Annual mortality was 77.2% in Seonjaedo and 67.4% in Jugyo. The estimated lifespan (tmax) was 10 years in Seonjaedo and 8 years in Jugyo, whereas the 2010 cohort in Jugyo largely disappeared in 5 years. Ovigerous females appeared from November to June and benthic juveniles between June and September. The shrimp’s growth was slower relative to other East Asian populations, likely due to colder sea water temperatures and altered food conditions from a higher shrimp density.

The current study presents a computational fluid dynamics model designed to simulate the isolation of 2D graphene components from their dispersion using a microfiltration hollow fiber membrane unit. The model utilizes Navier-Stokes and mass conservation equations to predict the flow and concentration behaviors in the system, and it employs the resistance-in-series approach to couple the fouling of the non-exfoliated graphites in the dispersion to the model. Specifically, the model accounts for the effects of the concentration polarization and cake formation of the fouling component, resulting in the increment of the transmembrane pressure (TMP) of the system. Additionally, we assumed the wettability of polymeric membrane’s inner wall enhances upon fouling by the flat layered structure material, leading to the reduction of the transmemrabe pressure. This approach demosntrates accurate reproduction of the exceptional experimental TMP behavior corresponding to the fouling of the graphite.

The construction of 22 dams on the Euphrates has reduced the flow of this river, changed the hydrology and destroyed the ecosystem. In order to understand the effect of extreme management on water, 4 coastal points from the Euphrates catchment area were selected to investigate the dynamics of the water level. Using the Cadmium method, the radius of each point was calculated to be 140 meters to study the NDWI index and satellite monitoring. 546 Landsat 8 OLI/TIRS satellite images from 2013 to 2022 were used. The analysis showed that the water level of the Euphrates in the fourth coordinate is directly related to the data of the first and third coordinates and the dynamics of the water level in the fourth coordinate is 3.6 to 4.2 percent of the first coordinate and another 5.2 to 5.9 percent is affected by the third coordinate. Is. Also, the data from August 2013 to 2022 were analyzed. The results showed that the correlation coefficients of the data related to the first coordinates are related to the third and fourth coordinates. It was proved that fluctuations in the first coordinate led to 27% change in the third coordinate in the months of August. Also, the changes in the first point caused 18-19.8% change in the dynamic level of water in the fourth point, and 22.5% of the changes in the water level in the fourth coordinate were affected by the third point.

This study delves into the dynamic intersection of artificial intelligence (AI) and English Language Teaching (ELT) within the post-pandemic educational landscape. Grounded in Constructivism, Connectivism, and Technological Pedagogical Content Knowledge (TPACK), this research explores how AI integration impacts language instruction efficiency, benefits and challenges perceived by St. Michael's College students, differences in learning outcomes, and its potential to address the digital divide. Employing a qualitative approach, the study employs a rigorous method that includes intensive in-depth reviews of literature, maximum purposeful sampling, informal interviews, observation, and thematic analysis. Through these methods, data was collected from 30 participants, leading to the identification of five key themes. Findings suggest that AI enhances instructional efficiency, offers personalized learning experiences, and bridges gaps in remote settings. However, challenges and the importance of pedagogical balance emerged. The study underscores the need for adaptive pedagogical strategies to fully harness AI's potential. Future directions and limitations are discussed, emphasizing the study's implications for shaping the future of ELT.

The buildup dynamics of diode-pumped passively mode-locked solid-state laser is thoroughly explored by the real-time measurement with temporal sampling rate up to 40 GHz. A concise cavity is developed to ensure the transient dynamics purely arising from the gain medium and saturable absorber. Experimental results reveal that the laser output in the buildup process exhibits numerous passively Q-switched pulses followed with a damped relaxation oscillation prior to the stable mode locking. Furthermore, it is confirmed that the laser output has already displayed single clean mode-locked pulses inside the first several Q-switched envelopes before stepping into the stage of relaxation oscillation. The present real-time exploration is expected to provide important information for practical applications with temporal modulation of the pump intensity.

Hoechst 33342 (H33342) is a fluorescent probe that is commonly used to stain DNA of living cells. To do so, it needs to interact with and permeate through cell membranes. The pH of biological media may vary quite significantly and this influences H33342 ionization state, and may change its rate of permeation through biomembranes. In this work, we address the effect of pH in the association of H33342 with lipid bilayers using a combined experimental and computational approach. The partition of H33342 to 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) lipid membranes was experimentally quantified using fluorescence spectroscopy and isothermal titration calorimetry (ITC) measurements. Quantum chemical calculations were performed to select the most stable isomer of H33342 for the overall charges 0, +1, and +2, expected to predominate across the 5 < pH < 10 range. The interaction of these isomers with POPC bilayers was then studied by both unrestrained and umbrella sampling molecular dynamics simulations. While probe insertion occurs for all ionization states, with similar overall equilibrium location, orientation was found to depend on the probe charge, with the neutral form lying mostly parallel and the doubly charged displaying a significant tilt relative to the membrane plane. Both experimental results and computational free energy profiles indicate that the partition coefficient of H33342 displays a small variation over a wide pH range, not exceeding one order of magnitude. The enthalpy of interaction is negative and is larger at lower pH values. This suggests efficient hydrogen bonding between the probe and the lipid, namely for the protonated +2 form. The relatively high lipophilicity obtained for the charged species contrasts with the decrease in their general hydrophobicity as estimated from water and octanol partition. This highlights the distinction between lipophilicity and hydrophobicity, and the importance of considering association with lipid bilayers when predicting the affinity for biomembranes.

Background: Understanding the factors that influence the market entry, exit, and stability of community pharmacies (i.e., market dynamics) is important for stakeholders ranging from patients to health policymakers and small business owners to large corporate institutions. The purpose of this article is to provide an update to a 15-year ecological comparison for the market dynamics of Minnesota community pharmacies from 2002 to 2017 [1]. Objectives: The first objective was to update the 15-year study [1] by describing the market dynamics of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022). The second objective was to update the 15-year study by describing the number and proportion of community pharmacies for each county in the state of Minnesota every five years over a 20-year period (i.e., 2002-2022) by (1) ‘Business Organization Structure’ and (2) ‘Pharmacy Type.’ The third and fourth objectives were new for the 2022 study. In response to evolutionary trends in the pharmacy market, objective three described the number of Minnesota licensed pharmacies located in-state and out-of-state between 2002 and 2022. Objective four described the primary type of service provided by licensed pharmacies categorized as out-of-state in 2002 and in 2022. Methods: Data were obtained from the Minnesota Board of Pharmacy, US Census Bureau, and Minnesota State Demographic Center for 2002, 2007, 2012, 2017, and 2022. Descriptive statistics were used to tabulate and summarize the findings and test associations between study variables. Results: Over the 20-year study period, the total number of community pharmacies in Minnesota remained steady. Findings for 2022 were similar to those for 2017 regarding the effects of population density and metropolitan designation on the market dynamics of community pharmacies and regarding the organizational structures and types of pharmacies. The findings revealed significant growth in the number of proportion of pharmacies licensed in Minnesota that were located out-of-state. In 2002, there were 283 of these pharmacies (19% of the total). By 2022, this grew to 960 (46% of the total). Over that 20-year period, the number of mail order and medical supply service pharmacies rose from 196 to 413. The number of specialty pharmacy distributors rose from 37 to 230 and the number of compounding pharmacies rose from 33 to 202. Discussion: The findings confirmed the shift over the past 20 years from traditional retail convenience models to healthcare access models based on population health metrics. National scalability was another detected shift in order to have profitable margins for some services and was feasible due to advances in technologies. Conclusion: The findings showed that community pharmacy distribution in Minnesota’s 87 counties shifted between 2002 and 2022 from traditional retail convenience models to healthcare access models based on population health metrics and to nationally scalable models based on economic pressures. This signals the need for not only new approaches for tracking community pharmacy market dynamics but also adjustments by community pharmacies to remain relevant in a new environment of patient care services.

This article provides a comprehensive analysis of the global gas and liquefied natural gas (LNG) markets. The article begins by discussing the increasing demand for gas and LNG, particularly in Asia, as countries aim to transition to cleaner sources of energy. It explores how market volatility has led to energy security interventions and lasting economic and emissions impacts. It then explores the global supply and demand dynamics, highlighting the structural change expected in the market and the competition between Europe and Asia for limited new LNG supply. The article also focuses on Europe's increased flexibility and dependence on LNG imports, which have risen by 60% to 121 million tonnes, offsetting lower Russian pipeline imports. Furthermore, the article delves into global supply and demand dynamics, how the market is expected to remain tight, and how record gas and LNG prices have led to demand reductions. Additionally, it analyzes the future outlook and investment needs, highlighting the continued uptake of gas in heavy-duty transport and the need for further investment to avoid a supply-demand gap. The article concludes with an analysis of the implications for the future of the global gas and LNG markets.

The adsorption of proteins on polymer is widely used in biosensors. Here, molecular dynamics (MD) simulation was used to study the immobilization of angiotensin converting enzyme II (ACE2) with six initial orientations proposed on polystyrene (PS) at the ambient conditions of pH (4.5, 6, 7, 8, 9.5) and NaCl (0.01, 0.05, 0.1, 0.15, 0.2, 0.25 M). ACE2 immobilization under favorable ambient conditions was characterized by minimum distance (short), settlement time (fast), interaction energy (substantial) and protein configuration (stable). ACE2 orientations proposed in 0.15M NaCl were respectively preferable to (90, 0, 0), (0, 0, 0) and (0, 270, 0), (180, 0, 0) and (0, 90, 0), (90, 0, 0) at pH 4.5, 6, 7, 9.5. ACE2 immobilization was further evaluated at pH 7 by optimizing NaCl concentration. Its proposed orientations of (i) (0, 270, 0), (ii) (0, 0, 0) and (90, 0, 0), and (iii) (0, 90, 0) and (90, 0, 0) were preferable in 0.05, 0.1 and 0.2 M NaCl, respectively. The great significance of cubic-orientation settlement mode provides tangible improvement for the microfabrication of biochips for rapid diagnosis of severe acute respiratory syndrome coronavirus II (SARS-CoV-2).

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

The main objective of this work is to study the effects of carbon nanotubes (CNT) on the strength and electrical properties of cement mortar. Molecular dynamic simulations (MDS) were carried out for the mechanical and electrical properties of the cementitious composite and the associated mechanisms. To model the atomic structure of the Calcium-Silicate-Hydrates (C-S-H) gel, tobermorite 11Å was chosen. Single-walled carbon nanotubes (SWCNTs) embedded in tobermorite structure were tested numerically. In particular, the effect of the concentration of CNT in cement paste was studied. An innovative experiment of electrical characterization of a cementitious composite of carbon nanotubes has been developed.

In the context of market relations in agriculture in the Kyzylorda region, in order to meet the needs of animal husbandry in complete feed, it is necessary to create a solid forage base. The successful solution to the tasks of increasing the number of livestock and increasing the productivity of animal husbandry in the ecological conditions of the Aral Sea region largely depends on the timely procurement of feed and the transfer of feed production to an industrial basis. It is possible to increase the production of high-quality feeds through the introduction of high-yielding varieties, the use of new resource-saving cultivation and harvesting technologies, the optimal organization of cultivation production processes, and increasing the efficiency of the use of technical means. Of all the harvested types of feeds, alfalfa has the best effect on increasing milks yield and meat gains. The green mass of alfalfa is well-eaten by all farm animals. For example, when feeding cattle, its utilization rate is 92.6%. In terms of protein richness, alfalfa hay surpasses many other feeds, so 100 kg of alfalfa hay contains 11.6 kg of digestible protein, 1.77 kg of calcium, 0.22 kg of phosphorus, and 4.5 g of carotene. In addition, in the conditions of rice crop rotation, according to its biological characteristics, alfalfa (Medicago sativa L.) is considered indispensable in agrotechnical and reclamation relations, especially in saline soils. The economic value of alfalfa lies in its wide ecological plasticity, the ability to grow back quickly after mowing, and high productivity.

Unsmooth reversing is one of the most common faults in the four-way reversing valve of air conditioning system, and the airflow in the valve chamber and the shape of the slider of the reversing valve are the main factors in unsmooth reversing. In order to study the influence of the airflow in the chamber and the slider shape on the reversing process, the fluid flow in the valve chamber of a four-way reversing valve is obtained using computational fluid dynamics (CFD) simulations, and the influence of slider structure on slider thrust and resistance of the four-way reversing valve is analyzed. Four-way reversing valves with no-cutting, straight-cutting and arc-cutting sliders are employed to evaluate the reversing characteristics. A pressure measuring device for a four-way reversing valve chamber is designed, which is performed to test the chamber pressure of three different types of sliders of four-way reversing valves. The maximum error of experimental tests and simulations is within 5% of engineering tolerance, so as to verify the reliability of numerical simulation. The results indicate that the chamber pressure and resistance-thrust ratio with the cutting slider are reduced, which is beneficial to the reversing of the four-way reversing valve. Compared with the straight cutting structure, the arc cutting slider four-way reversing valve has a more stable reversing process and better comprehensive reversing performance. The results can provide a theoretical basis for engineers to improve the performance of four-way reversing valves.

In the course of SARS-CoV-2 infection, the 3CL or nsp5 protease plays a pivotal role as the most important viral protease required for the maturation of viral proteins during host infection. Herein, we simulated for 500 ns 3CLproWT, 3CLproH41A, , 3CLproBeta, and 3CLproOmicron, in complex with the substrates nsp 4|5 and nsp 5|6. Our results show that mutations in the 3CLpro present in the SARS-CoV-2 variant of concerns (VOCs) did not lead to significant conformational changes or changes in substrate binding affinities. However, significantly high cleavage rates for the boundary between nsp4 and nsp5 were obtained for 3CLproBeta and 3CLproOmicron and may play a key role in the viral replication and virus fitness gain. Our molecular dynamics data suggest that the cleavage rate of nsp4|5 may be related to the increased amount of viral load observed for these VOCs, releasing more nsp4 than other non-structural proteins. Based on our hydrogen bonding analyses, we also discovered that Gly143 and Glu166 are key residues in substrate recognition, suggesting that these residues may be incorporated as pharmacophoric centers for Beta and Omicron variants in drug design. Our results suggest that Gly143 and Glu166 are essential residues to interact with Gln6 of the different substrates and, therefore, are potential broad-spectrum pharmacophoric centers of SARS-CoV-2 3CLpro.

The evolution in the post shock nonequilibrium relaxation in a hypersonic plasma flow was investigated during a shock’s reflection off a thermal discontinuity. Within a transitional period, the relaxation zone parameters past both, the reflected and transmitted waves, evolve differently compared to that in the incident wave. In a numerical example for the non-dissociating N₂ gas heated to 5000 K/10,000 K across the interface and M = 3.5, the relaxation time for the transmitted wave is up to 50% shorter and the relaxation depth for both waves is significantly reduced, thus resulting in a weakened wave structure. The results of extension into larger values of heating strength and the shock Mach numbers are discussed. The findings can be useful in the areas of research involving strong shocks interacting with optical discharges or other heated media on the scale where the shock structure becomes important.

Electron can tunnel between cofactor molecules positioned along biological electron transport chains up to the distance of ≃20 Å on the millisecond time scale of enzymatic turnover. This tunneling range mostly determines the design of biological energy chains facilitating cross-membrane transport of electrons. Tunneling distance and cofactors’ redox potentials become main physical parameters of this design. The protein identity, flexibility, or dynamics are missing from this picture assigning universal charge-transport properties to all proteins. This paradigm is challenged by dynamical models of electron transfer showing that the hopping rate is constant within the crossover distance R*≃12 Å, followed with an exponential tunneling falloff at longer distances. In this view, energy chains for electron transport are best designed by placing redox cofactors near the crossover distance R*. Protein flexibility and dynamics affect the magnitude of the maximum hopping rate within the crossover radius. Protein charge transport is not driven by universal parameters anymore and protein identity matters.

I present an experimental paradigm to explore the interpersonal dynamics generating a collective mind. I hypothesized that collective organization is based on dual interpersonal modes: (1) symmetrical and (2) anti‑symmetrical. I specified the geometric topology of these modes by detecting the spatiotemporal patterns that embed cooperative agents in a three‑dimensional matrix. I found that the symmetrical mode is executed automatically and without guidance. Conversely, the anti‑symmetrical mode required explicit direction and recruited attention for execution. I demonstrate that self‑other mirror‑symmetry stabilized group dynamics, enabled fast and efficient symmetrical imitation that optimized information transmission, whereas anti‑symmetrical imitation was comparatively slow, inefficient, and unstable. I determined that the anti‑symmetrical mode spontaneously transitioned to the symmetrical mode under perturbations. Crucially, this renormalization mechanism never transitioned from symmetrical to anti‑symmetrical. These self-organizing dynamics speak to interpersonal symmetry‑breaking. In the present work, spontaneous group choice mandated that agents synchronize cooperative cycles in symmetrical space under internal or external perturbations. I provide examples to illustrate that this self-regulating pullback attractor manifests in invertebrates and vertebrates alike. I conclude by suggesting that inter‑agent symmetry provides the social stability manifold through which attention-driven interactions enable intrapersonal and interpersonal change.

Turbulence is a fluid dynamic problem refractory to mathematical treatment. Examining a theoretical model of liquid water flowing in a cylinder at different Raleigh numbers, we propose a novel approach to elucidate the first stages of turbulent flows. The weakly bonded molecular assemblies of liquid water form a fluctuating branched polymer in which every micro-cluster displays different density. Against the common view of liquid water as an incompressible and continuous fluid, we suggest that the occurrence of transient local aggregates could be able to generate the vortices and eddies that are the hallmarks of turbulence. We quantify the local changes in velocity, diameter and density required to engender “obstacles” to the average flow. Then, we show how these microstructures, equipped with different Raleigh numbers and characterized by high percolation index, could generate boundary layers that contribute to micro-vortices production. We conclude that the genesis of turbulence cannot be assessed in terms of collective phenomena, rather is sustained, among many other factors, by the underrated microscopic inhomogeneities of fluids like liquid water.

The present study shows a methodology for analyzing and designing a cylindrical packed-bed reactor considering stationary and dynamic models. The design comprises the reactor's stationary and dynamic governing differential equations for mass and heat transfer under multi-dimensional approaches. The results included simulation of concentration, temperature, and reaction rate profiles via the 1-D and 2-D differential equations solution with FlexPDE software. The analysis was complemented with a scaled 3-D dynamic model implemented in COMSOL Multiphysics. Both FlexPDE and COMSOL Multiphysics relied on the finite element technique to solve the governing differential equations. The simulated concentration and temperature profiles from both FlexPDE and COMSOL models were compared to experimental data gathered from literature (specifically from a Fischer-Tropsch process to produce low-molecular-weight hydrocarbons in a configuration of cylindrical packed-bed reactors). Simulated concentration and temperature profiles from the 2-dimensional dynamic model and the COMSOL model were in good agreement with the trend observed in experimental data. Finally, the predicted reaction rate profiles from the COMSOL model and the 2-dimensional dynamic model followed the temperature trend, thus reflecting the temperature dependence of the reaction.

This study quantified effluents generated during processing in three industry types, estimated the energy potential from the quantified effluents in form of biogas generation, and determined the economic viability of the biogas recovered. Data were procured from relevant scientific publications to quantify the effluents generated from the production processes in the industry types examined using industrial process calculations. The effluent data generated was used on the 2-module biogas energy recovery model to estimate the bioenergy recovery potential within it. Economic and financial analysis was based on cash flow comparison of all costs and benefits resulting from its activities. The effluents generated average daily biogas of 2559 Nm³/gVS, having a daily potential combined heat and power of 0.52 GWh and 0.11 GWh respectively. The Life Cycle Analysis and cost-benefit analysis show the quantity of avoided emissions from using the effluents to generate heat and power for processes, and also the profitability of the approach. Conclusively, the study shows the use of biomass effluents to generate biogas for CHP is a viable one based on the technologies of a reciprocating engine, gas turbine, microturbine, and fuel cell. However, it is recommended that the theoretical estimation be validated using a field-scale project.

Deep eutectic solvents (DESs) are one of the most rapidly evolving types of solvents, appearing in a broad range of applications such as nanotechnology, electrochemistry, biomass transformation, pharmaceuticals, membrane technology, biocomposite development, modern 3D-printing, and many others. The range of their applicability continues to expand, which demands the development of new DESs with improved properties. To do so requires an understanding of the fundamental relationship between the structure and properties of DESs. Computer simulation and machine learning techniques provide a fruitful approach as they can provide predictions, reveal physical mechanisms and readily be linked to experiments. This review is devoted to the computational research of DESs and describes technical features of DES simulations and the corresponding perspectives on various DES applications. The aim is to demonstrate the current frontiers of computational research of DESs and discuss future perspectives.

Experimental test facilities are generally characterised using linear transfer functions to relate the wavemaker forcing amplitude to wave elevation at a probe located in the wavetank. Second and third order correction methods are becoming available but are limited to certain ranges of waves in their applicability. Artificial intelligence has been shown to be a suitable tool to find even highly nonlinear functional relationships. This paper reports on a numerical wavetank implemented using the OpenFOAM software package which is characterised using artificial intelligence. The aim of the research is to train neural networks to represent non-linear transfer functions mapping a desired surface-elevation time-trace at a probe to the wavemaker input required to create it. These first results already demonstrate the viability of the approach and the suitability of a single setup to find solutions over a wide range of sea states and wave characteristics.

Complex biological systems consist of large numbers of interconnected units, characterized by emergent properties such as collective computation. In spite of all the progress in the last decade, we still lack a deep understanding of how these properties arise from the coupling between the structure and dynamics. Here, we introduce the multiscale emergent functional state, which can be represented as a network where links encode the flow exchange between the nodes, calculated using diffusion processes on top of the network. We analyze the emergent functional state to study the distribution of the flow among components of 92 fungal networks, identifying their functional modules at different scales and, more importantly, demonstrating the importance of functional modules for information content of networks, quantified in terms of network spectral entropy. Our results suggest that the topological complexity of fungal networks guarantees the existence of functional modules at different scales keeping the information entropy, and functional diversity, high.

Land degradation a serious and nationwide environmental concern in Ethiopia. The problem is its iterative relationship between land degradation, climate change, and agriculture, exacerbating one another via negative and positive feedback loops. Due to the need for an efficient response to land degradation in the country, different sustainable land management practices have been implemented since the late 1980s. The objective of this study was to analyze land degradation neutrality status using remote sensing data in the study area. We have studied the land degradation neutrality conditions of the North Wello Zone by using indicators data, namely land cover change, land productivity dynamics, and soil organic carbon stock. The result shows that the settlement areas consistently expanded at the fifth speed (2010-2018) from 1995 to 2010. Between 1995 and 2010, forestland declined by 18 percent, while an increasing trend of 26.8 percent from 2010 to 2018. The assessment results also indicate that 52.8 percent of the total area is stable and characterized by less stressed land productivity. The soil organic carbon is comparatively abundant in the high and midland vegetation areas but very thin in lowland areas. Most of the highland and midland parts of the study areas are in the conditions of stable and increasing land productivity with high biomass and soil organic content. However, most lowland areas showed a decline in land productivity conditions.

In quantum many-body systems, a Hamiltonian is called an ``extensive entropy generator'' if starting from a random product state the entanglement entropy obeys a volume law at long times with overwhelming probability. We prove that (i) any Hamiltonian whose spectrum has non-degenerate gaps is an extensive entropy generator; (ii) in the space of (geometrically) local Hamiltonians, the non-degenerate gap condition is satisfied almost everywhere. Specializing to many-body localized systems, these results imply the observation stated in the title of Bardarson et al. [PRL 109, 017202 (2012)].

We have recently proposed a pre-quantum, pre-space-time theory as a matrix-valued Lagrangian dynamics on an octonionic space-time. This pre-theory offers the prospect of unifying the internal symmetries of the standard model with gravity. It can also predict the values of free parameters of the standard model, because these parameters arising in the Lagrangian are related to the algebra of the octonions which define the underlying non-commutative space-time on which the dynamical degrees of freedom evolve. These free parameters are related to the algebra $J_3(\mathbb O)$ [exceptional Jordan algebra] which in turn is related to the three fermion generations. The exceptional Jordan algebra [also known as the Albert algebra] is the finite dimensional algebra of 3x3 Hermitean matrices with octonionic entries. Its automorphism group is the exceptional Lie group $F_4$. These matrices admit a cubic characteristic equation whose eigenvalues are real and depend on the invariant trace, determinant, and an inner product made from the Jordan matrix. Also, there is some evidence in the literature that the groups $F_4$ and $E_6$ could play a role in the unification of the standard model symmetries, including the Lorentz symmetry. The octonion algebra is known to correctly yield the electric charge values (0, 1/3, 2/3, 1) for standard model fermions, via the eigenvalues of a $U(1)$ number operator, identified with $U(1)_{em}$. In the present article, we use the same octonionic representation of the fermions to compute the eigenvalues of the characteristic equation of the Albert algebra, and compare the resulting eigenvalues with the known mass ratios for quarks and leptons. We find that the ratios of the eigenvalues correctly reproduce the [square root of the] known mass ratios for quarks and charged leptons. We also propose a diagrammatic representation of the standard model bosons, Higgs and three fermion generations, in terms of the octonions, exhibiting an $F_4$ and $E_6$ symmetry. In conjunction with the trace dynamics Lagrangian, the Jordan eigenvalues also provide a first principles theoretical derivation of the low energy value of the fine structure constant, yielding the value $1/137.04006$. The Karolyhazy correction to this value gives an exact match with the measured value of the constant, after assuming a specific value for the electro-weak symmetry breaking energy scale.

The present work highlights some of the dynamic couplings observed in a series of tests performed in a wave basin with a scaled-model of a FOWT with semi-submersible substructure. The model was moored by means of a conventional chain catenary system and an actively controlled fan was used for emulating the thrust loads during the tests. A set of wave tests comprising regular and irregular waves was done for different wave angles and wind velocities. The experimental records illustrate the main coupling effects involved and how they affect the FOWT motions in waves, especially when the floater presents a non-negligible tilt angle.. In addition, an analysis of the frequency-domain dynamic model was made in order to evaluate its ability to capture these effects properly. The influence of different modes of fan response, floater trim angles (changeable with ballast compensation) and variations of the mooring stiffness with the offsets were investigated in the analysis. Results attest that significant changes in the FOWT responses may indeed arise from coupling effects, thus indicating that caution must be taken when simplifying the hydrodynamic frequency-domain models often used as a basis for the simulation of FOWTs in waves and in in optimization procedures for the design of the floater and mooring lines.

Dynamics of human populations can be affected by various socio-economic factors through their influence on the natality and mortality rates, and on the migration intensity and directions. In this work we study an economic-demographic model which takes into account the dependence of the wealth production rate on the available resources. In the case of nonlocal consumption of resources the homogeneous in space wealth-population distribution is replaced by a periodic in space distribution for which the total wealth increases. For the global consumption of resources, if the wealth redistribution is small enough, then the homogeneous distribution is replaced by a heterogeneous one with a single wealth accumulation center. Thus, economic and demographic characteristics of nonlocal and global economies can be quite different in comparison with the local economy.

Zinc finger proteins (ZFP) play important roles in cellular processes. The DNA binding region of ZFP consists of 3 zinc finger DNA binding domains connected by amino acid linkers, the sequence TGQKP connects ZF1 and ZF2, and TGEKP connects ZF2 with ZF3. Linkers act to tune the zinc finger protein in the right position to bind its DNA target, the type of amino acid residues and length of linkers reflect on ZF1-ZF2-ZF3 interactions and contribute to the search and recognition process of ZF protein to its DNA target. Linker mutations and the affinity of the resulting mutants to specific and nonspecific DNA targets were studied by MD simulations and MM_GB(PB)SA. The affinity of mutants to DNA varied with type and position of amino acid residue. Mutation of K in TGQKP resulted in loss in affinity due to the loss of positive K interaction with phosphates, mutation of G showed loss in affinity to DNA, WT protein and all linker mutants showed loss in affinity to a nonspecific DNA target, this finding confirms previous reports which interpreted this loss in affinity as due to ZF1 having an anchoring role, and ZF3 playing an explorer role in the binding mechanism. The change in ZFP-DNA affinity with linker mutations is discussed in view of protein structure and role of linker residues in binding.