Investigation into a modified deterministic rumour spread model using mathematical epidemiology approach as an extension of Daley and Kendall (D-K) model was carried out. It is proven that the spread of a rumour is fairly comparable to a contagious disease transmission, except that the spread depends solely on the fraction of individuals who are aware of the rumour at the early stage as well as the intrinsic rumour spread rate . Model simulation reveals a rumour diminishing strategy so far as is maintained.

Abstract: A perspective is provided which illustrates the central role of quantum mechanics in the structure and periodically varying properties of atoms and in the formation of molecules. Despite having only about one hundred basic building blocks for all substances, chemistry is a subtle discipline due to it’s fundamentally quantum mechanical origin. The atoms, and their behavior as systematized in the Periodic Table, intimately reflect mechanisms natural to quantum mechanics but foreign to classical mechanics. Given their very small mass, the electrons form atomic structures only predictable by the use of quantum mechanics. Thereby the foundation of chemistry is totally dominated by quantum effects which are generally known only empirically, e.g. as in the Periodic Table. Recently these quantum effects have increasingly become known computationally, but not necessarily understood physically and therefore still challenge students and users of chemistry. In order to provide physical insight we extract here, from known facts and relations of quantum mechanics, but without reliance on detailed mathematics or numerical analysis, a simplest, yet fully consistent, physical explanation of atomic structure and periodically variable reactivity. Atomic reactivity is found to arise due to dynamical constraints on electron motion which lead to degeneracy in the atomic energy spectrum. There is a strong correlation between energy degeneracy and reactivity with the inert gas atoms found to display no degeneracy. Chemical bonding follows as a means of atomic reactivity relaxation and reduction of energy degeneracy, achieved by permanent electron rearrangement (ionic bonding) or by oscillatory electron motion between atoms (covalent bonding). Ionic bonding is directly related to the variable stability of atomic structures which extends from neutral atoms to atomic ions. These stable ions then form molecular structures by electrostatic attraction in neutral ionic clusters. The covalent bonding mechanism is fundamentally dynamical and interatomic since it depends directly on the facility with which electrons can move between atomic centers in a molecule. Both the ionic and the covalent mechanism, interatomic displacement and oscillation of electrons, are active in any bond between atoms of different species.

Keywords: xylose reductase; cashew apple bagasse; bioprocesses; fluid dynamics; xylitol.

This paper delves into the concept of information within the context of conscious systems, emphasizing the pivotal role of entropy as a fundamental aspect in comprehending consciousness. Drawing on insights from Integrated Information Theory (IIT) and information theory, the argument posits that consciousness entails dynamic information processing, distinct from the static storage and retrieval model employed by modern computers. The intrinsic dynamics of neural systems are explored, illuminating how they integrate incoming stimuli into a unified state of subjective experience, thereby shaping conscious awareness. By considering entropy as a temporal and qualitative value, its significance in reducing uncertainty and facilitating adaptability in conscious systems is underscored. Through a series of principles, the intricate relationship between entropy, neural dynamics, and consciousness is elucidated. This analysis suggests the necessity of rethinking traditional notions of information when applied to conscious systems, highlighting the dynamic nature of consciousness and its interaction with the environment.

This paper addresses the parameter identification of a one-link flexible manipulator based on the experimental measurement of the inputs/outputs, the finite element model, and the application of evolutionary algorithms. A novel approach is proposed to find the values of inertia, stiffness, and damping parameters by minimizing the difference between the numerical model’s outputs and the testbed’s outputs, considering the joint position and acceleration of the link’s tip. The dynamic model is initially obtained using the finite element method and the Lagrange principle. A prototype of a single one-link flexible manipulator is used in the experimental application wherein the servomotor applies the input torque, and the outputs are the joint angle and the link’s tip acceleration. Then, an optimization problem minimizes the difference between numerical and experimental outputs to determine the set of parameters using evolutionary algorithms. A comparative analysis to obtain the identified parameters is established using genetic algorithms, particle swarm optimization, and differential evolution. The proposed identification approach permitted the determination of the dynamic parameters based on the complete dynamic model of the flexible-link manipulator, which is different from the approaches reported in the literature that identify a simplified model. This information is essential for the design of the motion and vibration control laws.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive sense single stranded RNA genome containing virus which has infected millions of people all over the world particularly China. The virus has been mutating rapidly enough resulting in the emergence of new variants and sub-variants which have reportedly been spread from Wuhan city of China, the epicenter of the virus, to the rest of China and all over the world. The occurrence of mutations in the viral genome especially in the viral spike protein region has resulted in the evolution of multiple variants and sub-variants which gives the virus benefit of host immune evasion and render the modern-day vaccines and therapeutics ineffective. Therefore, there is a continuous need to study the genetic characteristics and evolutionary dynamics of the SARS-CoV-2 variants. Hence, in this study, a total of 833 complete genomes of SARS-CoV-2 variants (including the wild-type or reference sequence) from Taiyuan and Wuhan cities of China were genetically characterized and studies their phylogenetic and evolutionary dynamics using phylogenetics, genetic similarity, and phylogenetic network analysis. Our results show a lot of useful information regarding the evolutionary dynamics of the viral variants, their genetic similarities/dissimilarities, and their phylogenetic relationships. It has been reported in this study that the SARS-CoV-2 variants have nine recombinant events which suggest very important information regarding the viral evolution. In addition, the phylogenetic network analysis shows the number of mutations resulting in emergence and clustering of the viral variants of both cities which reveal significant information regarding the phylodynamics of the virus. This study, to the best of our knowledge, is the first ever genetic comparative study of Taiyuan and Wuhan cities. This study will help better understand the virus, cope with the emergence, and spread of new variants at local level as well as at international level and inform the public health authorities to make better informed decisions in designing new viral vaccines and therapeutics.

The complex dynamics of the neocortex, the outermost layer of the brain, have been a subject of intense research in neuroscience. Recent studies have suggested that magnetic fields generated by neuronal activity may play a significant role in neocortical dynamics. This paper presents a simplified mathematical model that explores the hypothesized interactions between magnetic dynamos, nondissipative systems, Hamiltonian equations, and the Lorenz force in the context of neocortical dynamics. The proposed model is based on a set of coupled Hamiltonian equations inspired by the Lorenz system, a well-known chaotic dynamical system. The generalized coordinates in the model represent the magnetic field components, while the generalized momenta are related to the electric field components or other relevant quantities. The Hamiltonian equations describe the conservative dynamics of the system, and the coupling terms in the equations are interpreted as representing the interactions between the magnetic and electric field components, analogous to the Lorenz force in electromagnetism. The model is implemented using Python, and the Hamiltonian equations are numerically solved using the odeint function from the scipy.integrate module. The resulting dynamics of the magnetic field components are visualized using matplotlib. While the model is highly simplified and does not capture the full complexity of neocortical dynamics, it serves as a starting point for investigating the potential role of magnetic dynamos and Lorenz force interactions in the brain. The paper discusses the limitations of the current model and highlights the need for further refinements, such as incorporating more realistic biological constraints, considering energy dissipation and compensation mechanisms, and validating the model against experimental data. Future work should focus on developing a more comprehensive and biologically plausible model that integrates the latest findings from neuroscience, physics, and computational modeling. By presenting this simplified Hamiltonian model, the paper aims to stimulate further research and discussion on the potential significance of magnetic dynamos and Lorenz force interactions in neocortical dynamics. The model provides a framework for exploring these hypothesized mechanisms and may guide future experimental and theoretical investigations in this emerging field of neuroscience

This work is aimed at investigating the capabilities and limits of the mid-fidelity numerical solver DUST for the evaluation of wind turbines aerodynamic performance. In particular, this study was conducted by analysing the benchmarks NREL- 5MW and Phase VI wind turbines, widely investigated in literature by from experimental and numerical activities. The work started by simulating simpler configuration of the NREL- 5MW turbine to progressively integrate complexities such as shaft tilt, cone effects and yawed inflow conditions, offering a detailed portrayal of their collective impact on turbine performance. A particular focus was then given to the evaluation of aerodynamic responses from the tower and nacelle, as well as aerodynamic behavior in yawed inflow condition, crucial for optimizing farm layouts. In the second phase, the work was focused on NREL Phase VI turbine due to the availability of experimental data on this benchmark case. Comparison of DUST simulations results with both experimental data and high-fidelity CFD tools shows the robustness and adaptability of this mid-fidelity solver for this applications, thus opening a new scenario for the use of such mid-fidelity tools for the preliminary design of novel wind turbines configurations or complex environments as wind farms, characterised by robust interactional aerodynamics.

The World Health Organization (WHO) documents malaria as one of the leading causes of high morbidity and mortality worldwide. The disease affects millions and kills thousands of people annually. Efforts to reduce the global burden of malaria have prompted WHO to recommend prevention strategies like using anti-malarial drugs. However, these strategies have been ineffective because of anti-malarial drug resistance. The only efficacious malaria treatment is Artemisinin-based Combination Therapy (ACT). However, the extended ACTs clearance times, linked to the emergence of artemisinin monotherapy resistance recorded most recently in Africa and the Great Mekong region, pose a danger to its efficacy. Therefore, better efficacious malaria drugs are required. Since P. falciparum heat shock protein 90 (PfHsp90) is a well-characterized malaria drugs target, this study uses it to discover more efficacious malaria drugs. An in-silico approach was used to discover PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria using molecular dynamics simulation (MDS) and hierarchical virtual screening. Geldanamycin (GDM), a well-known anti-PfHsp90 compound, was used to identify PfHsp90 inhibitors with pharmacological properties against Plasmodium malaria by screening it against the ZINC database via the ZINCPHARMER web server. This virtual screening process resulted in 17 hits. These ZINCPHARMER hits were subjected to drug-likeness and pharmacokinetics properties analysis in the SwissADME web server, and 9 of them satisfied the requirements. The 9 ZINC compounds were docked with PfHsp90 using the PyRx software to understand their interactions. From the molecular docking results, ZINC09060002 (-8.2 kcal/mol), ZINC72133064 (-7.8 kcal/mol), ZINC72163401 (-7.7 kcal/mol), ZINC72358537 (-8.1 kcal/mol), and ZINC72358557 (-7.6 kcal/mol) had better binding affinities to PfHsp90 than GDM (-7.5 kcal/mol). The stability of these molecularly docked protein-inhibitor complexes was assessed through MDS using GROMACS 2022. ZINC72163401, ZINC72358537, and ZINC72358557 formed stable complexes with PfHsp90. The lead compounds were subjected to in vitro validation for their inhibitory capability. They showed promising inhibition of parasite growth with IC50 values ranging between 200 – 400 ng/ml. In this regard, the three PfHsp90 inhibitors can be used as anti-malarial drugs. However, further structural optimization studies and clinical (in vivo) tests are necessary to ascertain the antimalarial activity of these compounds in humans.

To study the coupling mechanism and dynamic response of the end-face movable gear trans-mission system under complex excitation, a specific configuration of end-meshing movable gear reduction mechanism was used to achieve predetermined rigid thrust transmission and mis-matched gear meshing functions, which solved the inherent defects of traditional harmonic gear mechanism thin-wall flexible wheel easy to be damaged by fatigue. Considering the phenomenon of elastic deformation of live teeth accompanied by significant changes in meshing characteristics in the transmission process of end-meshing harmonic reducer, the influence of dynamic meshing parameters, live tooth deformation, time-varying stiffness of tooth meshing, and time-varying backlash on nonlinear dynamic performance was explored, as well as the mechanism of mul-ti-parameter coupling on transmission performance. The nonlinear dynamics model of the end-meshing harmonic reducer is established to solve the chattering prediction problem. Finally, a comprehensive test bed for the transmission system of harmonic reducer with meshing type with adjustable characteristic end was built to verify the correctness of the theoretical model and provide the theoretical and technical basis for exploring the optimal parameter selection of pas-sive vibration suppression problem.

This work is an addition to the previously developed two-dimensional model of the shock-plasma interaction extending it into the third dimension. The model allows tracing the evolution of the state of the hypersonic flow and the shock front refracted at a thermal discontinuity. The advantages of using the spherical coordinate system for this type of the problems include more transparency in interpreting the solution and a shortened calculation procedure, because all the changes to the front become reduced to one distortion component only. Although the vorticity generation triggered at the interface is the consequence of the refraction and tied to the steep changes of the front, it is shown here that it is not because of an instant parameter jump at the interface due to refraction itself.

This comprehensive research investigates the multifaceted role of integration in modern education systems, aiming to advance educational equity and inclusivity.Introduction: The introduction sets the stage by delineating the historical evolution of integration in education, highlighting pivotal milestones, and contextualizing the current discourse on fostering inclusive learning environments.Materials and methods: This section details the research design, participant selection criteria, and data collection methods. By employing a mixed-methods approach, combining surveys, interviews, and observations, the study seeks to capture the complexities of integration. Results: Presenting the results entails a meticulous examination of the impact of integration on academic outcomes and social dynamics within educational settings. Statistical analyses and qualitative insights converge to reveal the nuanced relationships between integration practices and student experiences. Discussion: The discussion section critically analyzes the findings in the context of existing literature, offering insights into the broader implications of integration. By addressing challenges and barriers identified during the study, the discussion aims to guide educators, administrators, and policymakers in navigating the complexities of integrating diverse student populations effectively. Conclusions: The concluding section synthesizes the key findings, emphasizing their significance in advancing educational equity through integration. Recommendations are made for educators, policymakers, and researchers to leverage the transformative potential of integration. The section acknowledges study limitations, calls for future research directions, and concludes with a resounding commitment to fostering an inclusive educational system that reflects the principles of equity and social justice.

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. In this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example.

In our previous work, we demonstrated the potential of the amphiphilic cyclic peptide [W4R4] as a promising antimicrobial agent. The extensive data accumulated on the antimicrobial activity of this peptide and its homologs presented an opportunity to unravel the molecular mechanisms governing their action and establish crucial structure-activity relationships (SAR). To achieve this, we employed a comprehensive approach involving peptide synthesis, in vitro biological testing, and molecular modeling to systematically analyze the SAR of a series of cyclic peptides derived from the [W4R4] scaffold. Minimal modifications were introduced to the original peptide by incorporating either one aromatic hydrophobic residue (W) or one polar positively charged residue (R) while maintaining the sequential W/R arrangement. Additionally, we introduced an "intermediate" substitution by incorporating 2,5-diketopiperazine (DKP), possessing both aromatic and polar properties, into the macrocycle [W4R4] between the W and R residues. Notably, this study marked the first synthesis and evaluation of the [W4R4(DKP)] peptide. Structural, dynamic, hydrophobic, and membrane-binding properties of four cyclic peptides ([W4R4], [W5R4], [W4R5], [W4R4(DKP)]) were explored using molecular dynamics simulations within a DOPC/DOPG lipid bilayer that mimics the bacterial membrane. The results obtained revealed distinct SAR profiles for all peptides, enabling the establishment of correlations between their antimicrobial activity and structural/dynamic parameters. These parameters included the degree of conformational plasticity, the depth of immersion into the bi-layer, and the population of the membrane binding mode characterized by the embedding of the hydrophobic peptide pattern into the acyl chain region of lipids. In particular, the peptide [W4R5] exhibited an optimal "activity/binding to the bacterial membrane" pattern. This work highlights the significance of our multidisciplinary approach in efficiently deciphering finely regulated SAR profiles for a series of homologous peptides with diverse activities. Furthermore, this research provides a solid foundation for the rational design of novel antimicrobial peptides.

This research, which focuses on wave energy generating technology, suggests a brand-new oscillating float-type wave energy conversion system that is appropriate for the offshore wave environment. To accurately model the device, a three-dimensional viscous numerical pool is created based on STAR-CCM+. The array-type power generation device is numerically simulated using theoretical analysis and numerical simulation, and the wave energy power generation device is arranged as double floats in the front and rear rows, representing triangular array type and rectangular array type, respectively. The triangular array type is a superior layout and can make greater use of marine resources, according to this paper's analysis of the hydrodynamic and power generation impacts of the three array types. The findings are meant to serve as a guide for engineering practice.

This study investigates the stochastic dynamics of hepatitis B virus (HBV) infection using a newly proposed stochastic model. Contrary to deterministic models that fail to encapsulate the inherent randomness and fluctuations in biological processes, our stochastic model provides a more realistic representation of HBV infection dynamics. It incorporates random variability, thereby acknowledging the changes in viral and cellular populations and uncertainties in parameters such as infection rates and immune responses. We examine the solution's existence, uniqueness, and positivity for the proposed model, followed by a comprehensive stability analysis. We provide the necessary and sufficient conditions for local and global stability, offering deep insight into the infection dynamics. Furthermore, we utilize numerical simulations to corroborate our theoretical results. This research provides a robust tool for understanding the complex behavior of HBV dynamics, contributing significantly to the ongoing quest for more effective HBV control and prevention strategies.

In an era characterized by the ever-shifting landscapes of the digital realm, this paper intricately examines the interplay between digital culture, cultural dynamics, and beliefs in academia. Utilizing insights from cutting-edge language models, the study uncovers nuanced cultural expressions among Arts and Sciences students navigating the digital domain. Employing qualitative methods including interviews, observations, in-depth literature reviews, and thematic analysis, findings highlight digital interactions' transformative impact on cultural narratives and the potential of digital ethnography as a lens for understanding intricate cultural phenomena. This research enriches digital ethnography and cultural dynamics, offering fresh perspectives on the symbiotic relationship between digital culture and traditional beliefs, underscored by data from a maximum purposeful sampling of thirty (30) participants. Overall, this exploration emphasizes digital culture's dynamic role in shaping and reflecting cultural convictions, contributing to the understanding of cultural dynamics and evolving digital ethnography in the academic landscape.

An axial wind turbine's performance is analyzed numerically using geometrical and operational parameters. The performance of an axial wind turbine is simulated using three-dimensional computational fluid dynamics (CFD) and compared with experimental data, which confirms the validity of the assumptions and method used. A study of the effects of air in clean and sand particlesy conditions is then conducted. A study was also conducted on mechanical power and the impact of the blade tip on the extension of the vortex. It has been found that different sand particle sizes have a significant impact on wind performance numerically from 0.1 to 0.9, and the results indicate that mechanical power and thrust increase as wind velocity increases, but larger sand particle sizes reduce power, thrust, and torque as wind velocity increases. The turbulence intensity causes the pressure distribution on the walls to be irregular, thereby increasing mechanical power and propulsion force. Moreover, as the wind velocity increases, the tip speed ratio increases and the vortexes are subsequently extended.

The United Nations Organization in its agenda for 2023 approved Health and Well-being as one of the objectives of sustainable development, in this sense, Higher Education Insti-tutions contribute with interventions to improve each human process that generates a healthy life. In Latin America as well as in European countries, studies have been con-ducted to observe psychological well-being at this stage of life. The present study analyzes in a longitudinal way the psychological well-being in three times in people who attend group dynamics programs. The sample is 668 participants, with an age range between 65 and 80 years. The sample has been distributed in two analysis groups controlling for gender and age: group 1 (with group dynamics) and group 2 (without group dynamics). The Spanish adaptation of Ryff's (2006) Psychological Well-being scale α= 0.87 was ap-plied to both groups. The scale is composed of six dimensions of psychological well-being (self-acceptance, positive relationships, autonomy, mastery of the environment, purpose in life and personal growth). Statistical analyses showed significant differences between the two groups: greater psychological well-being and differences in the dimensions of positive relationships and personal growth in group 1. In addition, logistic regression analysis was carried out to test the predictive capacity of psychological well-being by the existence or not of group dynamics, with significant results in several dimensions. With the results obtained, we can conclude that group dynamics have a relevant role in con-tributing to the development of better levels of psychological well-being of the elderly with longitudinal increase in time. Future studies should be aimed at evaluating the value of group dynamics in the way of successful aging.

Water scarcity is a worldwide concern, considering that water is a limited resource and essential for life. In Brazil, approximately 30% of its population lives in one of the areas that most suffer from lack of water, the semi-arid region covering about 20% of the country's territorial extension. The lack of water, mainly in the northeast of the country, has been a problem for years, where people who live in this territory have been suffering for months from the lack of water and the poor distribution of the resource, which increases the degree of inequality between the regions of the country. The research aims to show the effect of the hydrological cycle on the quality of vegetation and how such processing can end up affecting people's lives and the environment. The municipality of Floresta is within the scope of sub-basin 73, the territory is part of the municipalities benefited from the PISF- São Francisco River integration project works, and the study has raised discussion on the part of the scientific community regarding the effectiveness of the population project and the possible risks caused to the environment. This study carried out a temporal analysis, 1961-2021, to investigate water availability and how the hydrological system can affect society, raising the degree of its importance for a better quality of life and consequently reducing hydric inequalities. It was also evaluated how climate changes can interfere with these environmental changes and the quality of life at the local scale. The hydrological model system used to assess water availability was the Pernambuco Hydrological Response Units SUPer-System. In addition, this system helped to understand the changes that occurred in the studied sub-basin. Yet was used UAV-Unmanned Aerial Vehicles to view relationship between lives in house and environment conditions. The study can obtain accurate and necessary information to understand the dynamics of the hydrological system and the possible changes of social hydric inequalities. Thus, it is concluded that climate change will have different impacts on small scales and lives conditions due to different characteristic's environment. It is very important to carry out studies on a detailed scale to provide better public policies for mitigating the effects of climate change on people's lives.

Using LSMS-ISA panel data, we analyzed determinants of diversification into Rural Non-Farm Enterprises (RNFEs) and dynamics of the sector by studying the transition between farming and both low- and high-return RNFEs. We found that diversification into RNFE is determined by household characteristics mainly age and gender of the household head, whereas initial capital endowments such as assets, human capital, land and livestock significantly affect level of participation than entry into RNFE. Furthermore, analysing dynamics of RNFE separately for the youth and matured households, our study finds that continuous participation in RNFE is higher among the youth than among matured households. Similarly, initial capital endowments mainly educational level and access to credit are important factors for the youth to transit to high return RNFE, implying that entrepreneurial skills development and access to finance that enhance investments and gradual accumulation of capital are vital for the youth to benefit from RNFEs. Moreover, shocks that affect agricultural productivity are significant in increasing the probability of transition out of high-return RNFE signifying the need for social protection measures to ensure growth of RNFEs.

This study examines the geopolitical dynamics of water conflicts in Central Asia, with a specific focus on Uzbekistan and its neighbors. Drawing upon a mixed methods approach within the framework of political geography, the research investigates the interactions among regional nations and the implications of population growth, agricultural industrialization, and economic competition, which heavily rely on shared irrigation systems and watersheds. The study establishes clear research objectives aimed at evaluating the water crisis in the region, addressing challenges in water management, and analyzing contentious issues. By uncovering historical and contemporary factors influencing water conflicts, regional power dynamics, and the complexities of water governance, the study highlights the potential for conflict, regional instability, and disruptions to peace resulting from population growth and economic competition within shared watersheds. The study concludes by providing recommendations to enhance regional cooperation, promote sustainable water management, and foster peacebuilding efforts. These recommendations draw on “soft power theory” and advocate for collaborative approaches. The findings of this research offer valuable insights for policymakers, researchers, and stakeholders involved in water management and regional cooperation amid evolving geopolitical challenges. By contributing to informed decision-making and promoting strategies for sustainable development in the region, this study aims to support efforts toward effective water resource management and long-term stability in Central Asia. The implications of this research extend beyond the immediate region, serving as a reference for other areas facing similar water-related conflicts and providing a foundation for future studies in the field of geography.

Equine metabolic syndrome (EMS) is a major health threat in veterinary endocrinology medicine worldwide, and there is growing interest in utilizing molecular agents for modulation of hepatocyte function for potential clinical applications with recent studies showing promising results in the inhibition of PTP1B on adipose-derived stromal cells. In this study, we investigated the effects of MSI-1436 on equine hepatic progenitor cells (HPCs) in terms of their proliferative activity, glucose uptake, and mitochondrial morphogenesis under lipotoxic condition. Our study found that MSI-1436 promotes the entry of HPCs in the cell cycle and rescues them from palmitate-induced apoptosis by regulating mitochondrial dynamics and biogenesis. MSI-1436 also increases glucose uptake and protects HPCs from palmitate-induced stress by reorganizing the morphological architecture of the cells. Additionally, our findings suggest that MSI-1436 enhances the 2-NBDG internalization by increasing the expression of SIRT1, which is associated with liver insulin sensitivity, and promotes mitochondrial dynamics by modulating their number and morphotype through PINK1, MFN1, and MFN2 increased expression. Our study provides evidence that MSI-1436 has a positive impact on equine hepatic progenitor cells, indicating its potential therapeutic value in the treatment of EMS and insulin dysregulation.

This study explores the geopolitical dynamics of water conflicts in Central Asia, focusing on Uzbekistan and its neighboring countries. Using a mixed methods approach within the framework of political geography, the research analyzes interactions among regional countries and the implications of population growth, agricultural industrialization, and economic competition, which heavily rely on shared irrigation systems and watersheds. It establishes research objectives, evaluates the water crisis, addresses challenges in water management, and examines contentious issues like hydropower, resource hegemony, and ethnic conflict. Also, the study uncovers historical and contemporary factors influencing water conflicts, regional power dynamics, and the complexities of water governance. The findings highlight the potential for conflict, regional instability, and disruptions to peace resulting from population growth and economic competition within shared watersheds. The study concludes with recommendations to enhance regional cooperation, sustainable water management, and peacebuilding, drawing on soft power theory and advocating for collaborative approaches. This research provides valuable insights for policymakers, researchers, and stakeholders involved in water management and regional cooperation amid evolving geopolitical challenges, contributing to informed decision-making, and promoting strategies for sustainable development in the region.

Neuromorphic computing, reconfigurable optical metamaterials operational over a wide spectral range, holographic and nonvolatile displays of extremely high resolution, integrated smart photonics and many other applications need phase-change materials (PCMs) of the next generation with better energy efficiency, wider temperature and spectral range for reliable operation compared to current flagship PCMs as Ge2Sb2Te5 or doped Sb2Te. Gallium tellurides are promising candidates to achieve the necessary requirements because of higher melting and crystallization temperatures, combined with a low switching power and fast switching rate. At the same time, Ga2Te3 and non-stoichiometric alloys appear to be atypical PCMs, characterized by regular tetrahedral structure and the absence of metavalent bonding. The sp3 gallium hybridization in cubic and amorphous Ga2Te3 is also different from conventional p-bonding in flagship PCMs, raising a question of the phase-change mechanism. Besides, gallium tellurides exhibit a number of unexpected and highly unusual phenomena as nanotectonic compression or viscosity anomaly just above melting. Using high-energy X-ray diffraction, supported by first-principles simulations, we will unravel the atomic structure of amorphous Ga2Te5 PLD films, compare it with the crystal structure of tetragonal gallium pentatelluride, and investigate electrical, optical and thermal properties of these two materials to estimate their potential for memory applications as well as for other fields.

The β-amyloid cleaving enzyme 1 (BACE1) is regarded as an important target of drug design toward treatment of Alzheimer's disease (AD). In this study, three separate molecular dynamics (MD) simulations and calculations of binding free energies were carried out to comparatively probe identification mechanism of BACE1 on three inhibitors 60W, 954 and 60X. The analyses on MD trajectories indicate that the presence of three inhibitors influences structural stability, flexibility and internal dynamics of BACE1. Binding free energies calculated by using solvated interaction energy (SIE) and molecular mechanics generalized Born surface area (MM-GBSA) methods reveal that the hydrophobic interactions provide decisive forces for inhibitor-BACE1 binding. The calculations of residue-based free energy decomposition suggest that the sidechains of residues L91, D93, S96, V130, Q134, W137, F169 and I179 play key roles in inhibitor-BACE1 binding, which provides a direction for future drug design toward treatment of AD.

The use of carbon nanotubes to improve the mechanical properties of polymers is one of the promising directions in materials science. The addition of single-walled carbon nanotubes (SWCNT) to a polymer results in significant improvement of mechanical, electrical, optical, and structural properties. However, the addition of SWCNTs does not always improve the polymer properties. Also, when a certain content of SWCNTs is exceeded, the mechanical properties of the nanocomposite are getting worse. This article reports the results of computer simulations for predicting the mechanical properties of polymer/single-walled carbon nanotube nanocomposites. The efficiency of reinforcing polymer composites is considered depending on the concentration of carbon nanotubes in the polymer matrix, their size and structure. The elastic moduli of the nanocomposites were predicted using computer simulations for unit cell tension (0.1%). General trends in the mechanical properties of composites with polypropylene, poly (ethyl methacrylate), polystyrene matrices and SWCNTs are shown.

We propose Reduced Collatz Conjecture that is equivalent to Collatz Conjecture, which states that every positive integer can return to an integer less than it, instead of 1. Reduced Collatz Conjecture should be easier because some properties are presented in reduced dynamics, rather than in original dynamics (e.g., ratio and period). Reduced dynamics is a computation sequence from starting integer to the first integer less than it, and original dynamics is a computation sequence from starting integer to 1. Reduced dynamics is a component of original dynamics. We denote dynamics of x as a sequence of either computations in terms of ``I'' that represents (3*x+1)/2 and ``O'' that represents x/2. Here 3*x+1 and x/2 are combined together, because 3*x+1 is always even and followed by x/2. We formally prove that all positive integers are partitioned into two halves and either presents ``I'' or ``O'' in next ongoing computation. More specifically, (1) if any positive integer x that is i module $2^t$ (i is an odd integer) is given, then the first t computations (each one is either ``I'' or ``O'' corresponding to whether current integer is odd or even) will be identical with that of i. (2) If current integer after t computations (in terms of ``I'' or ``O'') is less than x, then reduced dynamics of x is available. Otherwise, the residue class of x (namely, i module $2^t$) can be partitioned into two halves (namely, i module $2^{t+1}$ and $i+2^t$ module $2^{t+1}$), and either half presents ``I'' or ``O'' in intermediately forthcoming (t+1)-th computation.

The higher-order interactions in complex systems are gaining attention. Extending the classic bounded confidence model where an agent’s opinion update is the average opinion of its peers, this paper proposes a higher-order version of the bounded confidence model. Each agent organizes a group opinion discussion among its peers. Then, the discussion’s result influences all participants’ opinions. Since an agent is also the peer of its peers, the agent actually participates in multiple group discussions. We assume the agent’s opinion update is the average over multiple group discussions. The opinion dynamics rules can be arbitrary in each discussion. In this work, we experiment with two discussion rules: centralized and decentralized. We show that the centralized rule is equivalent to the classic bounded confidence model. The decentralized rule, however, can promote opinion consensus. In need of modeling specific real-life scenarios, the higher-order bounded confidence is convenient to combine with other higher-order dynamics, from the contagion process to evolutionary dynamics.

We study the deterministic dynamics of N point particles moving at constant speed in a 2D table made of two polygonal urns connected by an active rectangular channel, which applies a feedback-control on the particles, inverting the horizontal component of their velocities, when their number in the channel exceeds a fixed threshold. Such a bounce--back mechanism is non-dissipative: it preserves volumes in phase space. An additional passive channel closes the billiard table forming a circuit in which a stationary current may flow. Under specific constraints on the geometry and on the initial conditions, the large N limit allows nonequilibrium phase transitions between homogeneous and inhomogeneous phases. The role of ergodicity in making a probabilistic theory applicable is discussed both for rational and irrational urns. The theoretical predictions are compared with the numerical simulation results. Connections with the dynamics of feedback-controlled biological systems are highlighted.

The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of ​​native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.

Complex colloidal fluids, depending on particulates’ shapes and packing fractions, may have a wide range of shear thinning and thickening behaviors. A particular interesting way to transition between different types of such behavior is by infusing functional complex particles that can be manufactured using modern techicques such as 3D printing. In this paper, we display 2D molecular dynamics simulations of such fluids with infused star-shaped functional particles, with variable leg length and number of legs, as they are infused in a non-interacting, coarse-grained fluid. We vary the packing fraction (ϕ) of the system, and for each different system we apply shear with various strain rate that turns the fluid into a jammed state and rise the apparent viscosity of fluid. We demonstrate the dependence of viscosity with the particles’ packing fraction. We show the role of shape and design dependence of the functional particles towards the transition to a shear thickening fluid .

Overexpression of BRF2, a selective subunit of RNA polymerase III, has been shown to be crucial in the development of several types of cancers, including breast cancer and lung squamous cell carcinoma . Predominately, BRF2 acts as a central redox-sensing transcription factor (TF) and is involved in rescuing oxidative stress (OS) -induced apoptosis. Here, we showed a novel link between BRF2 and DNA damage response. Due to the lack of BRF2 specific inhibitors, through virtual and molecular dynamics screening, we identified potential drug candidates that interfere with BRF2-TATA-binding Protein (TBP)-DNA complex interactions based on binding energy, intermolecular, and torsional energy parameters. We experimentally tested Bexarotene as a potential BRF2 inhibitor. We found that Bexarotene (Bex) treatment resulted in a dramatic decline in oxidative stress (Tert-butylhydroquinone (tBHQ))-induced levels of BRF2 and consequently, lead to a decrease in cellular proliferation of cancer cells which may in part be due to drug pretreatment induced reduction of ROS generated by the oxidizing agent. Our data thus, provide the first experimental evidence that BRF2 is a novel player in DNA damage response pathway and Bexarotene can be used as a potential inhibitor to treat cancers with the specific elevation of oxidative stress.

Mitochondria are essential to cell homeostasis, and alterations in mitochondrial distribution, segregation or turnover have been linked to complex pathologies such as neurodegenerative diseases or cancer. Understanding how these functions are coordinated in specific cell types is a major challenge to discover how mitochondria globally shape cell functionality. In this review, we will first describe how mitochondrial transport and dynamics are regulated throughout the cell cycle in yeast and in mammals. Second, we will explore the functional consequences of mitochondrial transport and partitioning on cell proliferation, fate acquisition, stemness, and on the way cells adapt their metabolism. Last, we will focus on how mitochondrial clearance programs represent a further layer of complexity for cell differentiation, or in the maintenance of stemness. Defining how mitochondrial transport, dynamics and clearance are mutually orchestrated in specific cell types may help our understanding of how cells can transition from a physiological to a pathological state.

When dealing with galactic dynamics, or more specifically, with galactic rotation curves, one basic assumption is always taken: the frame of reference relative to which the rotational velocities are given is assumed to be inertial. In other words, fictitious forces are assumed to vanish relative to the observational frame of a given galaxy. It might be interesting, however, to explore the outcomes of dropping that assumption; that is, to search for signatures of non-inertial behavior in the observed data. In this work, we show that the very discrepancy in galaxy rotation curves could be attributed to non-inertial effects. We derive a model for spiral galaxies that takes into account the possible influence of fictitious forces and find that the additional terms in the new model, due to fictitious forces, closely resemble dark halo profiles. Following this result, we apply the new model to a wide sample of galaxies, spanning a large range of luminosities and radii. It turns out that the new model accurately reproduces the structures of the rotation curves and provides very good fittings to the data.

The PI3K/mTOR signalling pathway plays a central role in the governing of cell growth, survival and metabolism. As such, it must integrate and decode information from both external and internal sources to guide efficient decision-making by the cell. To facilitate this, the pathway have evolved an intricate web of complex regulatory mechanisms and elaborate crosstalk with neighbouring signalling pathways, making it a highly non-linear system. Here, we describe the mechanistic biological details that underpin these regulatory mechanisms, covering a multitude of negative and positive feedback loops, feed-forward loops, competing protein interactions, and crosstalk with major signalling pathways. Further, we highlight the non-linear and dynamic network behaviours that arise from these regulations, uncovered through both computational and experimental studies. Given the pivotal role of the PI3K/mTOR network in cellular homeostasis and its frequent dysregulation in pathologies including cancer and diabetes, a coherent and systems-level understanding of the complex regulation and consequential dynamic signalling behaviours within this network is imperative for advancing biology and development of new therapeutic approaches.

To sample from complex, high-dimensional distributions, one may choose algorithms based on the Hybrid Monte Carlo (HMC) method. HMC-based algorithms generate nonlocal moves alleviating diffusive behavior. Here, I build on an already defined HMC framework, Hybrid Monte Carlo on Hilbert spaces [A. Beskos, F.J. Pinski, J.-M. Sanz-Serna and A.M. Stuart, Stoch. Proc. Applic. 121, 2201 - 2230 (2011); doi:10.1016/j.spa.2011.06.003] that provides finite-dimensional approximations of measures π which have density with respect to a Gaussian measure on an infinite-dimensional Hilbert (path) space. In all HMC algorithms, one has some freedom to choose the mass operator. The novel feature of the algorithm described in this article lies in the choice of this operator. This new choice defines a Markov Chain Monte Carlo (MCMC) method which is well defined on the Hilbert space itself. As before, the algorithm described herein uses an enlarged phase space Π having the target π as a marginal, together with a Hamiltonian flow that preserves Π. In the previous method, the phase space π was augmented with Brownian bridges. With the new choice for the mass operator, π is augmented with Ornstein-Uhlenbeck (OU) bridges. The covariance of Brownian bridges grows with its length, which has negative effects on the Metropolis-Hasting acceptance rate. This contrasts with the covariance of OU bridges which is independent of the path length. The ingredients of the new algorithm include the definition of the mass operator, the equations for the Hamiltonian flow, the (approximate) numerical integration of the evolution equations, and finally the Metropolis-Hastings acceptance rule. Taken together, these constitute a robust method for sampling the target distribution in an almost dimension-free manner. The behavior of this novel algorithm is demonstrated by computer experiments for a particle moving in two dimensions, between two free-energy basins separated by an entropic barrier.

The diffusion process of water molecules within a polyetherimide (PEI) glassy matrix has been analyzed by combining the experimental analysis of water sorption kinetics performed by FTIR spectroscopy with theoretical information gathered from Molecular Dynamics simulations and with the expression of water chemical potential provided by a non-equilibrium lattice fluid model able to describe the thermodynamics of glassy polymers. This approach allowed to construct a convincing description of the diffusion mechanism of water in PEI providing molecular details of the process related to the effects of the cross- and self-hydrogen bondings established in the system on the dynamics of water mass transport.

In the present study, we compare energy transition scenarios from a new set of Integrated Assessment Models, the suite of MEDEAS models, based on a systems dynamic modelling approach, with scenarios from two already well know structurally and conceptually different Integrated Assessment Models, the Integrated MARKAL-EFOM System (TIMES) and the Long-range Energy Alternatives Planning system (LEAP). The investigation was carried out to cross-compare and benchmark the response of MEDEAS models with TIMES and LEAP in depicting the energy transition in two different countries, Austria and Bulgaria. The preliminary results show a good agreement across all the models in representing scenarios projecting historical trends, while a major discrepancy is detectable when the rate of implementation of renewable energy is forced to increase to achieve energy system decarbonization. The discrepancy is mainly traceable to the differences in the models’ conception and structures rather than in a real mismatch in representing the same scenarios. The present study is put forward as a guideline for validating new modelling approaches that link energy policy decision tools to the global biophysical and socioeconomic constraints.

Adhesion systems are very important in robots for infrastructure inspection (especially in vertical walls). They present the challenge of optimizing the ratio vacuum/power consumption in battery-powered robots. In this paper a CFD (Computer Fluid Dynamics) modelling and optimization process of a robot adhesion system is carried out to determine the best performing configuration in terms of vacuum and power consumption. Analytical and numerical models were developed to predict the behaviour of the system for different configurations. The models were validated, using test rig measurements, by calibrating an arbitrary defined inlet height that simulates the leakage flow. Then, different geometric parameters were varied to determine the best performing configuration based on the vacuum/power consumption ratio value. The model presented in the paper was capable of predicting the behaviour of the system for different configurations, with a margin of error of 15% for the vacuum prediction and a 25% for the motor power calculation. Finally, the model was used to optimize parameters of the system, like the number of blades of the impeller. The adhesion system was conceived for the modular autonomous climbing legged robot ROMERIN.

The discovery of a growing number of exoplanets and even extrasolar systems supports the scientific consensus that it is possible to find other signs of life in the universe. The present work proposes for the first time, an explicit mechanism inspired by the dynamics of biological dispersion, widely used in ecology and epidemiology, to study the dispersion of biogenic units, interpreted as complex organic molecules, between rocky or water exoplanets (habitats) located inside star clusters. The results of the dynamic simulation suggest that for clusters with populations lower than 4 M+/ly³ it is not possible to obtain biogenic worlds after 5 Gyr. Above this population size, biogenic dispersion seems to follow a power law, the larger the density of worlds lesser will be the impact rate (β ) value to obtain at least one viable biogenic Carrier habitat after 5 Gyr. Finally, when we investigate scenarios by varying β, a well-defined set of density intervals can be defined in accordance to its characteristic β value, suggesting that biogenic dispersion has a behavior of “minimal infective dose” of “minimal biogenic effective” events by interval i.e. once this dose has been achieved, doesn’t matter if additional biogenic impact events occur on the habitat.

Finding the proper entropy functional associated with the inelastic Boltzmann equation for a granular gas is a yet unsolved challenge. The original H-theorem hypotheses do not fit here and the H-functional presents some additional measure problems that are solved by the Kullback–Leibler divergence (KLD) of a reference velocity distribution function from the actual distribution. The right choice of the reference distribution in the KLD is crucial for the latter to qualify or not as a Lyapunov functional, the “homogeneous cooling state” (HCS) distribution of the freely cooling system being a potential candidate. Due to the lack of a formal proof, the aim of this work is to support this conjecture aided by molecular dynamics simulations of inelastic hard disks and spheres in a wide range of values for the coefficient of restitution (α). Our results reject the Maxwellian distribution as a possible reference, whereas reinforce the HCS one. Moreover, the KLD is used to measure the amount of information lost on using the former rather than the latter, and reveals a nonmonotonic dependence with α. Additionally, a Maxwell-demon-like velocity-inversion experiment highlights the microscopic irreversibility of the granular gas dynamics.

This paper revisits a classic problem in physics - Hertz elastic dynamics of two colliding elastic spheres. This study obtains impact period in terms of hypergeometric function and successfully combines Deresiewicz's three segmental solutions into one single solution. Our numerical investigation confirms that Deresiewicz's inversion is a good approximation. As an essential part of this study, a general Maple code is provided.

Among the best-known capillarity phenomena is a capillary rise, the understanding of which is essential in fluidics. Some capillary flows rise monotonically whereas others oscillate, but until now no criteria have been formulated for this scenario. In this paper, the Levine's capillary rise modelling is computed numerically, then the critical radius of the capillary tube is formulated by using the dimensional method and data fitting for identification of exponent index. The phase space diagram of capillary velocity versus height is obtained for the first time and shows that the phase transition from oscillating to monotonic rising happens when the phase trajectory decreases exponentially to somewhere other than the "attractor." Two general Maple codes of the problem are provided as an essential part of this paper.

One of the biggest queries in cognitive sciences is the emergence of consciousness from matter. Modern neurobiological theories of consciousness propose that conscious experience is the result of interactions between large-scale neuronal networks in the brain, traditionally described within the realm of classical physics. Here, we propose a generalized connectionist framework in which the emergence of “conscious networks” is not exclusive of large brain areas, but can be identified in sub-cellular networks exhibiting non-trivial quantum phenomena. The essential feature of such networks is the existence of strong correlations in the system (classical or quantum coherence) and the presence of an optimal point at which the system’s complexity is maximized, expressed either by maximization of the information content in large scale functional networks or by achieving optimal efficiency through the quantum Goldilock effect.

This study developed a system dynamics model to understand the spread of coronavirus disease (COVID-19) in Japan. The model is built on the generic SEIR (Susceptible, Exposed, Infected, and Recovered) framework adopted by Ghaffarzadegan and Rahmandad (2020) to build a system dynamics model for the spread of COVID-19 in Iran. Japan seems to be successful in containing the spread compared with other countries, and its first peak has passed. However, because this also leaves a large population still susceptible to the virus, it could cause a second and potentially higher peak of infection after the state of emergency aimed at reducing contact rate is lifted. As the government has proposed the “New Lifestyle,” it is critical to behave cautiously so as not to be infected. While the model focuses on the SEIR structure, the reflection of other sub-structures such as economy and hospital capacity that have tradeoffs with reducing contact rate should be beneficial. However, before extending the model, it is also critical to conduct estimates using confidence intervals rather than point estimates to better reflect uncertainties.

Persistent, non-explosive passive degassing is a common characteristic of active volcanoes. Distinct periodic components in measurable parameters of gas release have been widely identified over timescales ranging from seconds to months. The development and implementation of high temporal resolution gas measurement techniques now enables the robust quantification of high frequency processes operating on timescales comparable to those detectable in geophysical datasets. This review presents an overview of the current state of understanding regarding periodic volcanic degassing, and evaluates the methods available for detecting periodicity, e.g., autocorrelation, variations of the Fast Fourier Transform (FFT), and the continuous wavelet transform (CWT). Periodicities in volcanic degassing from published studies are summarised and statistically analysed, together with analyses of literature-derived datasets where periodicity had not previously been investigated. Finally, an overview of current knowledge on drivers of periodicity is presented and discussed in the framework of four main generating categories, including: (1) non-volcanic (e.g., atmospheric or tidally generated); (2) gas-driven, shallow conduit processes; (3) magma movement, intermediate to shallow storage zone; and (4) deep magmatic processes.

The establishment and spread of exotic species is a contemporary major concern. Alien species may become invasive in their new habitat, leading to both/either environmental and/or economic impacts. I briefly reviewed the literature in the last decade about the relationship of exotic species and native communities. I identified that professionals usually approach the subject in two main points of view: (1) researchers tend to point out the impacts of alien species on entire communities, evaluating if the relationship is positive, negative or neutral; (2) they focus on the eco-evolutionary processes involved in the introductions, the dynamics of invasion, and individual study cases. When evaluating the response of introductions to entire communities, evidence seems to be ambiguous and may support positive, negative or neutral relationship, especially depending on the scale approached. The unique eco-evolutionary pathways of each introduction may be a great shortcoming in the searching for generalities. On the other hand, advances have been made in understanding the dynamics of invasion on different lineages through a more selective/individualized approach. I suggest that the dynamics of invasion might be studied through a perspective in which different eco-evolutionary processes, levels of organization (from gene to entire communities), the history of the organism(s) and time are taken into account. Individual cases might be compared in attempt to understand how the relationship exotic and native works and in the search for generalities.

Background: Selectively targeting dopamine receptors has been a persistent challenge in the last years for the development of new treatments to combat the large variety of diseases evolving these receptors. Although, several drugs have been successfully brought to market, the subtype-specific binding mode on a molecular basis has not been fully elucidated. Methods: Homology modeling and molecular dynamics were applied to construct robust conformational models of all dopamine receptor subtypes (D1-like and D2-like receptors). Fifteen structurally diverse ligands were docked to these models. Contacts at the binding pocket were fully described in order to reveal new structural findings responsible for DR sub-type specificity. Results: We showed that the number of conformations for a receptor:ligand complex was associated to unspecific interactions > 2.5 Å and hydrophobic contacts, while the decoys binding energy was influenced by specific electrostatic interactions. Known residues such as 3.32Asp, the serine microdomain and the aromatic microdomain were found interacting in a variety of modes (HB, SB, π-stacking). Purposed TM2-TM3-TM7 microdomain was found to form a hydrophobic network involving Orthosteric Binding Pocket (OBP) and Secondary Binding Pocket (SBP). T-stacking interactions revealed as especially relevant for some large ligands such as apomorphine, risperidone or aripiprazole. Conclusions: This in silico approach was successful in showing known receptor-ligand interactions as well as in determining unique combinations of interactions, key for the design of more specific ligands.

This paper aims to show that sustainable behavior by firms may be impaired by regulatory restrictions. We challenge the assumption that regulation aimed at curbing greenhouse gas emissions (GHG) on the form of a target to meet the Country’s GHG emissions commitments will promote sustainable corporations. We argue that, in fact, such regulation may impair sustainability practices because it creates unintended consequences. This paper tackles the efficiency of the institutional framework chosen through the lenses of the analytical themes of fit, scale and interplay, then we use a systems dynamic approach to represent how regulation in the arenas of energy efficiency and GHG emissions reduction may withhold competitive business outcomes and corporate sustainability schemes. We exemplify and simulate a single regulation scheme and found that as a result of the institutional scheme chosen, the system is dominated by negative feedback processes resulting in lesser outcomes that would be better tackled by firms not being subject to the restrictions imposed by the regulation.

This work aims to investigate the effect of Pt concentration on crystal growth mechanism of Platinum-Palladium (Pt-Pd) binary alloy system during the annealing process starting from amorphous phase until some definite temperatures. The calculations have been performed by using molecular dynamic (MD) simulations. Interatomic interactions are described by on Sutton-Chen type Embedded Atom Potential Energy function. In order to understand the main structural properties at the stable and unstable phases, changes in RDF curves versus time have been analysed for different annealing temperatures. Crystalline type bonded pairs have been determined using MD calculations which is required for the computation of Avrami coefficients and for understanding crystal growth mechanism. The results demonstrate that the increase in concentration of Pt during annealing leads to migration of atoms in the crystal lattice points, elimination of dislocations and formation of perfect crystal structure.

Remanufacturing is a viable option to extend the useful life of an end-of-use product or its parts, ensuring sustainable competitive advantages under the current global economic climate. Challenges typical to remanufacturing still persist, despite its many benefits. According to the European Remanufacturing Network a key challenge is lack of accurate, timely and consistent product knowledge as highlighted in a 2015 survey of 188 European remanufacturers. With more data being produced by electric and hybrid vehicles, this adds to the information complexity challenge already experienced in remanufacturing. Therefore, it is difficult to implement real-time and accurate remanufacturing for the shop floor; there are no papers that focus on this within an electric and hybrid vehicle environment. To address this problem this paper attempts to (1) identify the required parameters/ variables needed for fuel cell remanufacturing by means of interviews (2) rank the variables by Pareto analysis (3) develop a casual loop diagram for the identified parameters/ variables to visualise its impact on remanufacturing (4) model a simple stock and flow diagram to simulate and understand data and information-driven schemes in remanufacturing.

We consider a random dynamical system arising in a model of associative memory. This system can be seen as a small (stochastic and deterministic) perturbation of a determinstic system having two weak attractors which are destroyed after the perturbation. We show, with a computer aided proof, that the system has a kind of chaotic itineracy. Typical orbits are globally chaotic, while they spend relatively long time visiting attractor's ruins.

In this paper, the nanoscale dissipative mechanisms of a Cu pad in a Ball Grid Array (BGA) packaging structure during isothermal ageing and uniaxial tension were investigated by the molecular dynamics (MD) method and experiments. From the result of the isothermal ageing test, a nonuniform consumption of Cu and large amount of Kirkendall voids were observed at the interface of Cu and Cu₃Sn. To study the effect of pressure and orientation on this phenomenon, MD simulations were conducted on four types of Cu-Cu₃Sn interface structures with different orientations of Cu. By comparing the diffusion coefficients of atoms in those cases, it was found that the tensile stress would inhibit the diffusion of atoms, whereas compressive stress would accelerate it, and this would be more significant under a larger magnitude of stress and temperature. Note that, in the model with the (101) surface Cu at the interface, both Cu and Cu₃Sn have a higher diffusion coefficient compared with the model with (001) surface Cu. Thus, the orientation of Cu will also contribute to the uniform consumption of the pad. Uniaxial tension simulation combined with DXA and CSP analyses on those models also shows the model with (001) surface Cu has a greater mechanical reliability in our simulations and related experiments.

Due to poor ventilation conditions in the tunnel, if gas pipeline leaks, the consequence of the accident will be more serious. Therefore, before the emergency repair, gas in the tunnel needs to be excharged so as not to explode during the repair process. Therefore, it is necessary to study the ventilation of gas in the tunnel. Based on the computational fluid dynamics (CFD) theory and taking the Yanyingshan tunnel section of China-Myanmar pipeline as an example, this paper uses Fluent software to establish the leakage model of the gas pipeline and fan model in the tunnel and analyzes the influence of different fan locations and number of fans on gas concentration. It can be concluded that: (1) the use of press-in method makes it more efficient to discharge gas out of the tunnel. (2) In order to make ventilation efficient, the fan should be arranged in a higher position and needs to be at a distance from the top of the tunnel. (3) Parallel use of two fans has better ventilation effect than single fan.

Osteoarthritis (OA) is a common joint disorder found mostly in elderly people. The role of mechanical behavior in the progression of OA is complex and remains unclear. The stress-relaxation behavior of human articular cartilage in clinically defined osteoarthritic stages may have importance in diagnosis and prognosis of OA. In this study we investigated differences in the biomechanical responses among human cartilage of ICRS grades I, II and III using polymer dynamics theory. We collected 24 explants of human articular cartilage (eight each of ICRS grade I, II and III) and acquired stress-relaxation data applying a continuous load on the articular surface of each cartilage explant for 1180 s. We observed a significant decrease in Young’s modulus, stress-relaxation time, and stretching exponent in advanced stages of OA (ICRS grade III). The stretch exponential model indicated that significant loss in hyaluronic acid polymer might be the reason for the loss of proteoglycan in advanced OA. This work encourages further biomechanical modelling of osteoarthritic cartilage utilizing these data as input parameters to enhance the fidelity of computational models aimed at revealing how mechanical behaviors play a role in pathogenesis of OA.

Starting with a hypothetical geostrophic zonal current in an unbounded ocean, the investigation points out the response of this simple system to a thermal forcing, applied to the free surface and consistent with the maintenance of the geostrophic balance. The main result is the formation of a meridional component of the current, according to the Sverdrup relation, such that the full velocity vector rotates clockwise for heating and anticlockwise for cooling to adjust eventually in the initial zonal direction for large depths.

Copper demand for a country's copper industry has a greater pull effect. China's copper consumption in 2015 has accounted for 50% of the world. The scientific forecast of China's copper demands trend is also an important basis for analyzing its future environmental impact. This paper assumes that China's economy will be developing high, medium and low scenarios, and forecasts economic and social indicators such as total GDP, population and per capita GDP in China from 2016 to 2030. Then, predicted the demand of copper resources in China from 2016 to 2030 by the combination of system dynamics model, vector autoregressive moving average model and inverted U-type empirical model. The results show that: (1) in 2020, 2025 and 2030, China's refined copper demand will be 13 Mt, 15 Mt and 15.5 Mt. (2) China's copper demand growth slowed down significantly from 2016-2030. (3) 2025-2030, China's copper resource demand is stable, into the platform of demand growth, the highest peak value in 2027 will be 15.5 Mt. (4) 2030 years later, China's copper resource demand will enter a slow decline.

Identifying or authenticating a computer user are necessary steps to keep systems secure on the network and to prevent fraudulent users from accessing accounts. Keystroke dynamics authentication can be used as an additional authentication method. Keystroke dynamics involves in-depth analysis of how you type on the keyboard, analysis of how long a key is pressed or the time between two consecutive keys. This field has seen a continuous growth in scientific research. In the last five years alone, about 10,000 scientific researches in this field have been published. One of the main problems facing researchers is the small number of public data sets that include how users type on the keyboard. This paper aims to provide researchers with a data set that includes how to type free text on the keyboard by 80 users. The data were collected in a single session via a web platform. The dataset contains 410,633 key-events collected in a total time interval of almost 24 hours. In similar research, most datasets are with texts written by users in English. The language in which the users wrote for this research is Romanian. This paper also provides an extensive analysis of the data set collected and presents relevant information for the analysis of the data set in future research.

Previous studies generally reckon that the full converter-base wind power generation (FCWG) is a ’decoupled’ power source from the grid, which hardly participates in electromechanical oscillations. However, it is found recently that strong interaction could be induced which might incur severe resonance incidents in electromechanical dynamic timescale. In this paper, the participation of FCWG in electromechanical dynamics is extensively investigated, and particularly, an unusual transition of electromechanical oscillation mode (EOM) is uncovered for the first time. The detailed mathematical models of open-loop and closed-loop power systems are firstly established, and modal analysis is employed to quantify the FCWG participation in electromechanical dynamics, with two new mode identification criteria, i.e., FCWG dynamics correlation ratio (FDCR) and quasi-electromechanical loop correlation ratio (QELCR). On this basis, the impact of different wind penetration levels and controller parameter settings on the participation of FCWG is investigated. It is revealed that if an FOM has a similar oscillation frequency to the system EOMs, there is a high possibility to induce strong interactions between FCWG dynamics and system electromechanical dynamics of the external power systems. In this circumstance, an interesting phenomenon may occur that an EOM may be dominated by FCWG dynamics, and hence is transformed into a quasi-EOM, which actively involves the participation of FCWG quasi-electromechanical state variables.

This qualitative research explores negotiation dynamics within procurement, focusing on strategies, challenges, and outcomes in contemporary business environments. Negotiation in procurement plays a crucial role in shaping organizational strategies, supplier relationships, and operational efficiencies. In a globalized economy characterized by rapid technological advancements and market uncertainties, effective negotiation practices are essential for organizations seeking competitive advantage and sustainable growth. Through semi-structured interviews with procurement professionals, supply chain executives, and industry experts, this study examines the nuanced strategies employed in negotiation processes. Thematic analysis of the data reveals key themes including negotiation strategies (e.g., preparation, flexibility), challenges (e.g., price volatility, regulatory constraints), and outcomes (e.g., cost savings, innovation incentives). Relationship management emerges as pivotal, highlighting the importance of trust, transparency, and mutual respect in fostering collaborative supplier partnerships. The study also explores contextual factors (e.g., organizational culture, industry dynamics) and emotional dimensions (e.g., emotional intelligence, interpersonal dynamics) that influence negotiation effectiveness.

Drawing from literature and Geert Hofstede’s cultural dimensions, this study delves into the impact of cultural factors on family structures, roles, and relationships. Ghana and Latvia have different cultural and social orientations that influence family dynamics. Through a comparative lens, this paper will contribute to understanding how a country’s culture influences family dynamics, highlighting the influence of Power Dynamics of parenting style and Individualism vs. Collectivism on interaction levels within the family.

The Chikungunya virus (CHIKV) poses a significant global public health concern, especially in Africa. Since its first isolation in Tanzania in 1953, CHIKV has caused recurrent outbreaks, challenging healthcare systems in low-resource settings. Recent outbreaks in Africa highlight the dynamic nature of CHIKV transmission and the challenges of underreporting and underdiagnosis. Here, we review literature and analyze publicly available cases, outbreaks, and genomic data providing insights into the epidemiology, genetic diversity, and transmission dynamics of CHIKV in Africa. Our analyses reveal the circulation of geographically distinct CHIKV genotypes, with certain regions experiencing a disproportionate burden of disease. Phylogenetic analysis of sporadic outbreaks in West Africa suggests repeated emergence of the virus through enzootic spillover, which is markedly different from inferred transmission dynamics in East Africa, where the virus is often introduced from Asian outbreaks, including the recent reintroduction of the Indian Ocean lineage from the Indian subcontinent to east Africa. Furthermore, there is limited evidence of viral movement between these two regions. Understanding the history and transmission dynamics of outbreaks is crucial for public health planning. Despite advances in surveillance and research, diagnostic and surveillance challenges persist. This review and secondary analysis highlight the importance of ongoing surveillance, research, and collaboration to mitigate the burden of CHIKV in Africa and improve public health outcomes.

The study investigates the influence of power dynamics on supply chain decision-making processes through a qualitative lens, aiming to understand how various forms of power impact interactions, relationships, and strategic outcomes within supply chains. Using semi-structured interviews with supply chain managers and executives from diverse industries, the research explores the multifaceted nature of power, identifying key sources such as economic leverage, expertise, resource control, and network centrality. Findings reveal that power dynamics significantly shape decision-making by determining negotiation outcomes, governance structures, and operational efficiencies. Economic power, often exercised by larger firms, enables them to dominate negotiations and impose favorable terms, creating pressures on smaller partners and potentially leading to conflicts. Expertise and resource control allow firms with specialized knowledge or unique inputs to influence product development and process innovation, further dictating supply chain configurations. The study differentiates between coercive and non-coercive power strategies, showing that while coercive power enforces compliance, it can erode trust and collaboration. Non-coercive power, on the other hand, promotes positive relationships through incentives and collaborative approaches, fostering trust and alignment of supply chain objectives. Governance structures imposed by dominant firms often reflect their strategic priorities but can burden less powerful partners, highlighting the need for balanced oversight. Power also plays a crucial role in risk management, resilience, sustainability, and innovation within supply chains, with digitalization introducing new dimensions of influence. The research underscores the importance of balanced and equitable power dynamics to enhance cooperation, resilience, and innovation in supply chains. These insights provide valuable implications for practitioners and scholars in developing more effective and adaptive supply chain strategies.

A novel investigation into the structure-function relationship was undertaken to enhance understanding of the mechanism of action of the vasoactive peptide angiotensin II (AngII) using unconventional substitutions such as AIB (α-aminobutyric acid), CHA (cyclohexylalanine acid), IAP (isonipecotic acid), TOAC (2,2,6,6-tetramethylpiperidine-1-oxyl-4-amine-4-carboxylic acid) at position 3, and DMT (2,6-dimethyl-Tyrosine) at position 4. Biological potency assays were conducted on guinea pig ileum, and structural features were analyzed using circular dichroism (CD) and molecular dynamics (MD). CHA3-AngII and AIB3-AngII analogs retained some agonistic activity, whereas IAP3-AngII and TOAC3-AngII derivatives were inactive. CD results indicated low conformational flexibility for AIB3-AngII and CHA3-AngII, and greater conformational restrictions for IAP3-AngII and TOAC3-AngII, consistent with computational modeling analyses. DMT4-AngII exhibited significant agonistic activity compared to the inactive Phe4AngII. Molecular modeling suggested a potential interaction between the hydroxyl group of Tyr4 and the amide bond between positions 7 and 8. AngII showed greater conformational restriction compared to DMT4-AngII and Phe4-AngII, underscoring the importance of tyrosine at position 4.

Strategic intelligence covers the examination of what impacts and influences the business and the related SWOT analysis, (strengths, weaknesses, opportunities, and threats mapping). Through this research, leaders will be able to properly allocate and re-route resources, identify areas of improvement, and ensure that strategic initiatives are aligned with the organization's mission. Strategic intelligence is, therefore, effective through the use of critical thought and data-driven decisions and seeing the outcomes of different positioned alternatives. It must also form and support a culture of cooperation and open communication within the organizations, stimulating employees' inputs and learning curves. The article's subject is the necessity for strategic intelligence, a crucial competence for leaders, managers, and companies striving to remain competitive, to be utilized and best leveraged in today's volatile and dynamic business contexts. It helps the leadership turn proactive, foresee trends, and make correct, agile decisions that enforce companies' long-term growth and success. This study follows a research design that includes a thorough literature review and bibliometric research, executed via VoSViewer, illustrating the concept of strategic intelligence from a leadership point of view as a driver of competitive advantage. Further, the article points out that the limitations are acknowledged because the literature on the matter is limited, as strategic intelligence is relatively new to the business sector; moreover, more research is required to fully understand and use the potential of strategic intelligence in business growth. By overcoming these limitations and continuing to study this concept, businesses can gain a competitive edge and make informed decisions that fuel their success in an ever-evolving market. As a result, this study contributes to developing strategic intelligence in leadership and business and paves the road for further research and applications of strategic intelligence theories in organizational contexts.

Fibroin-chitosan composites, especially those containing nanohydroxyapatite, show potential for bone tissue regeneration. The physicochemical properties of these biocomposites depend on the compatibility between their components. In this study, the intermolecular interactions of fibroin and chitosan were analyzed using a molecular dynamics approach. Two types of systems were investigated: one containing acetic acid and the other containing calcium (Ca²⁺) and hydrogen phosphate (HPO₄²⁻) ions mimicking hydroxyapatite conditions. After obtaining the optimal equilibrium structures, the distributions of several types of interactions including hydrogen bonds, ionic contacts, and hydrophobic contacts along with the structural and energetical features were examined. The calculated binding energy values for the fibroin-chitosan complexes confirm their remarkable stability. The high affinity of fibroin for chitosan can be explained by the formation of a dense network of interactions between considered biopolymers. These interactions were found to be primarily hydrogen bonds and ionic contacts involving ALA, ARG, ASN, ASP, GLN, GLU, GLY, LEU, PRO, SER, THR, TYR, and VAL residues. As it was established, the complexation of fibroin with chitosan maintains the β-sheet conformation of the peptide. β-Sheet fragments in fibroin are involved in the formation of significant amount of hydrogen bonds and ionic contacts with chitosan.

Hemodynamic relevance of differently located stenoses of the internal jugular veins remains undetermined. It particularly concerns nozzle-like strictures in the upper parts of these veins and stenotic jugular valves located at the end of this vein. This study was aimed at understanding flow disturbances caused by such stenoses. Computational fluid dynamics software, Flowsquare+, was used. We constructed 3-dimensional models of venous outflow, comprising two alternative routes: tube representing the internal jugular vein and irregular network representing the vertebral veins. At the beginning of the tube representing the internal jugular vein, differently-shaped and sized short strictures, representing nozzle-like strictures were built in. At the end of this tube, differently-shaped membranes, representing the jugular valve, were built in. With the use of computational fluid dynamics modeling, we studied how these two obstacles influenced the outflow. We found that the most relevant outflow disturbances were evoked by nozzle-like strictures in the upper part of the internal jugular vein that were either small, long or asymmetrically positioned. Very tight stenotic valves and septum-like malformed valve were equally hemodynamically relevant. These findings suggest that both upper and lower strictures of the internal jugular vein can be of clinical significance.

Motivated by experimental success in designing linear amphiphilic copolymers with selective demicellization capabilities through head-to-tail depolymerization triggered by specific stimuli, we advanced the design paradigm by increasing the number of triggering groups on the copolymer chain through architectural modifications. Utilizing molecular dynamics simulations, we explored the demicellization and cargo release dynamics of linear and miktoarm copolymers, featuring one, two and three hydrophobic block or branches, each capable for head-to-tail depolymerization. Our findings revealed that, under stoichiometric trigger molecule concentrations, miktoarms with three branches exhibited consistently faster depolymerization rates than those with two branches and linear copolymers. Conversely, at constant trigger molecule concentrations, the depolymerization rates of copolymers exhibited more complex behavior influenced by two opposing factors: the excess of trigger molecules, which increased with a decrease in the number of hydrophobic branches or blocks, and simultaneous head-to-tail depolymerization, which intensified with an increasing number of branches. Our study elucidates the intricate interplay between copolymer architecture, trigger molecule concentrations, and depolymerization dynamics, providing valuable insights for the rational design of amphiphilic copolymers with tunable demicellization and cargo release properties.

The emergence of electric vehicles has brought new issues such as the problem of rolling element bearings (REBs) operating at high speeds. Losses due to these components in mechanical transmissions are a key issue and must therefore be taken into account right from the design stage of these systems. Among these losses, the one induced by the motion of rolling elements , known as drag loss, becomes predominant in high-speed REBs. Although an experimental approach is still possible, it is difficult to isolate this loss in order to study it properly. A numerical approach based on CFD is therefore a possible way forward, even if other issues arise. The aim of this article is to study the ability of such an approach to correctly estimate the drag coefficient associated with the motion of rolling elements. The influence of the numerical domain extension, the mesh refinement, the simplification of the ring shape and the presence of the cage, on the values of the drag coefficient is presented. While it seems possible to compromise on the calculation domain and mesh size, it appears that the other parameters must be taken into account as much as possible to obtain realistic results.

In this study, modified red mud after phosphorus adsorption was used as the adsorbent, and hydrochloric acid and deionized water were used as desorbents to desorb phosphorus. The components in the adsorbent were optimized based on density functional theory, and adsorbent and desorbent models were established. Molecular dynamics simulation was performed to determine the phosphorus concentration before and after desorption, interaction energies, radial distribution function(RDF), mean-square displacement(MSD), and diffusion coefficient. The Monte Carlo method was used to simulate the desorption isotherm, desorption site, heat of desorption, and desorption energy. Simulation results showed that deionized water could only desorb phosphorus on the adsorbent surface, and the stability of the system deteriorated upon adding hydrochloric acid. Hydrochloric acid destroyed the ionic and hydrogen bonding between the O atoms in H2PO4− and reactive metal and oxygen atoms in the activated red mud particles. Moreover, the van der Waals force decreased considerably. The ionic and hydrogen bonds between H2PO4− and the surface of activated red mud particles were broken by hydrochloric acid, which accelerated the desorption of phosphorus from the adsorbent surface. The interaction between hydrochloric acid and phosphorus accelerated the diffusion, which decreased the adsorption capacity. Moreover, the desorption capacity increased with increasing temperature.

Shut-in after fracturing is generally adopted for wells in shale oil reservoirs, and imbibition occurring in matrix nanopores has been proven as an effective way to improve recovery. In this research, the molecular dynamics (MD) simulation was used to investigate the effects of wettability and pressure on nanopores imbibition during shut-in for a typical shale reservoir Jimsar. The results indicate that the microscopic advancement mechanism of the imbibition front is the competitive adsorption between “interfacial water molecules” at the imbibition front and “adsorbed oil molecules” on the pore wall. The essence of spontaneous imbibition involves the adsorption and aggregation of water molecules onto the hydroxyl groups on the pore wall. The flow characteristics of shale oil suggest that the overall push of the injected water to the oil phase is the main reason for the displacement of adsorbed oil molecules. Thus, shale oil especially the heavy hydrocarbon component in the adsorbed layer tends to slip on the walls. However, the weak slip ability of heavy components on the wall surface is an important reason that restricts the displacement efficiency of shale oil during spontaneous imbibition. The effectiveness of spontaneous imbibition is strongly dependent on the hydrophilicity of the matrix pore's wall. The better hydrophilicity of the matrix pore's wall facilitates higher levels of adsorption and accumulation of water molecules on the pore wall and requires less time for “interfacial water molecules” to compete with adsorbed oil molecules. During the forced imbibition process, the pressure difference acts on both the bulk oil and the boundary adsorption oil, but mainly on the bulk oil, which leads to the occurrence of wetting hysteresis. Meanwhile, shale oil still existing in the pore always maintains a good stratified adsorption structure. Because of the wetting hysteresis phenomenon, as the pressure difference increases, the imbibition effect gradually increases, but the actual capillary pressure gradually decreases and there is a loss in the imbibition velocity relative to the theoretical value. Simultaneously, the decline in hydrophilicity further weakens the synergistic effect of pressure difference because of the more pronounced wetting hysteresis. Thus, selecting an appropriate well pressure enables cost savings and maximizes the utilization of the formation's natural power for EOR.

The Hollow fiber membrane contactor (HFMC) is an intensification process that combines membrane and absorption technologies, being a technology in expansion, so the dynamics of this process is crucial for an increase in industrial scale-up. In this manuscript a rigorous dynamic model of an HFMC is developed and simulated in Python software for the absorption of post-combustion CO2 using a monoethanolamine (MEA) solution, which was validated using available data in literature in steady state. Dynamic simulations were performed to analyze the model's capability to predict the effect of disturbances on the responses of CO2 capture ratio and loading. Four distinct experiments were conducted by step changes and pulse disturbances at the following variables: gas and liquid volumetric flow rates, CO2 composition in flue gases, and CO2 lean load. The gas phase showed a faster response compared to the liquid. The CO2 capture ratio and rich loading presented opposite responses when disturbing both liquid flows, and for the composition and lean loading variations, the capture ratio presented an inverse response characteristic, while the loading showed a delayed response. As the model proved to be capable of returning to the reference state, it can potentially be used for control studies purposes.

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.

An extensive review of literature simulations of polymer melts is given, considering results that test aspects of the Rouse model in the melt. We focus on the mean-square amplitudes < (X_p(0))^{2} > and time correlation functions < X_p(0) X_p(t) > of the Rouse modes $X_p(t)$. Contrary to the Rouse model: (i) Mean-square Rouse mode amplitudes < (X_p(0))^2> do not scale as sin^{-2}(p \pi/2N), N being the number of beads in the polymer. For small p (say, p <= 3) < (X_p(0))^2> scales with p as p^{-2}$; for larger p it scales as p^{-3}. (ii) Rouse mode time correlation functions < X_p(t) X_p(0) > do not decay with time as exponentials; they instead decay as stretched exponentials exp(-a t^b)$. b depends on p, typically with a minimum near N/2 or N/4. (iii) Polymer bead displacements are not described by independent Gaussian random processes. (iv) For p not equal to q, < X_p(t) X_{q}(0) > is sometimes non-zero. (v) The response of a polymer coil to a shear flow is a rotation, not the affine deformation predicted by Rouse. Simulations thus conclusively demonstrate that the Rouse model is invalid in polymer melts. We also briefly consider the Kirkwood-Riseman polymer model.

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.

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.

In the present study we introduce a deterministic mathematical model in order to study the interaction between the human immune system and a virus, like COVID 19. The mathematical analysis is based on the tools of dynamical systems theory, by modeling the interactions between the immune system and the virus, using a predator-prey method and following the ideas of G. Moza, from \cite{TGV}. It will be obtained some conclusions with medical relevance and the main three of them are the followings: 1) A deficiency of a single type of immunity in the early stages of virus proliferation, may lead to a large virus multiplication and the human body can loses the fight against this virus; 2) If the level of all components of the immunity system are at the normal threshold from the first moment of the infection and the immune system kill the virus at higher rate than the rate of virus reproduction, then the human body has the ability to stop the multiplication of the virus and liquidate it, that means the virus will be destroyed; 3) If the level of at least one type of immunity can be increased beyond the normal threshold, by medical interventions (like vaccination) in the early stages of virus infection, then the immune system has a better chance to win the fight with the virus.

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.