This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Large-scale hydrological modeling in China is challenging given the sparse meteorological stations and large uncertainties associated with atmospheric forcing data.Here we introduce the development and use of the China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) in the Heihe River Basin(HRB) for improving hydrologic modeling, by leveraging the datasets from the China Meteorological Administration Land Data Assimilation System (CLDAS)(including climate data from nearly 40000 area encryption stations, 2700 national automatic weather stations, FengYun (FY) 2 satellite and radar stations). CMADS uses the Space Time Multiscale Analysis System (STMAS) to fuse data based on ECWMF ambient field and ensure data accuracy. In addition, compared with CLDAS, CMADS includes relative humidity and climate data of varied resolutions to drive hydrological models such as the Soil and Water Assessment Tool (SWAT) model. Here, we compared climate data from CMADS, Climate Forecast System Reanalysis (CFSR) and traditional weather station (TWS) climate forcing data and evaluatedtheir applicability for driving large scale hydrologic modeling with SWAT. In general, CMADS has higher accuracy than CFRS when evaluated against observations at TWS; CMADS also provides spatially continuous climate field to drive distributed hydrologic models, which is an important advantage over TWS climate data, particular in regions with sparse weather stations. Therefore, SWAT model simulations driven with CMADS and TWS achieved similar performances in terms of monthly and daily stream flow simulations, and both of them outperformed CFRS. For example, for the three hydrological stations (Ying Luoxia, Qilian Mountain, and ZhaMasheke) in the HRB at the monthly and daily Nash-Sutcliffe efficiency ranges of 0.75-0.95 and 0.58-0.78, respectively, which are much higher than corresponding efficiency statistics achieved with CFSR (monthly: 0.32-0.49 and daily: 0.26 – 0.45). The CMADS dataset is available free of charge and is expected to a valuable addition to the existing climate reanalysis datasets for deriving distributed hydrologic modeling in China and other countries in East Asia.

When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm (n) observations are generated for the parameters, and in the inner level (m) observations of the simulation model are generated with the value of parameters fixed at the value generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent, showing how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand; and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments using this forecast model.

Conditions under which compositions of component systems form a well-defined system-of-systems here are formulated at a fundamental level. Statement of what defines a well-defined composition and sufficient conditions guaranteeing such a result offers insight into exemplars that can be found in special cases such as differential equation and discrete event systems. For any given global state of a composition, two requirements can be stated informally as: 1) the system can leave this state, i.e., there is at least one trajectory defined that starts from the state, and 2) the trajectory evolves over time without getting stuck at a point in time. Considered for every global state, these conditions determine whether the resultant is a well-defined system and if so, whether it is non-deterministic or deterministic. We formulate these questions within the framework of iterative specifications for mathematical system models that are shown to be behaviorally equivalent to the Discrete Event System Specification (DEVS) formalism. This formalization supports definitions and proofs of the afore-mentioned conditions. Implications are drawn at the fundamental level of existence where the emergence of a system from an assemblage of components can be characterized. We focus on systems with feedback coupling where existence and uniqueness of solutions is problematic.

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e. professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

To conduct high-precision and high-resolution numerical simulation of complex flow structures in turbomachinery, a high-order finite volume weighted essentially non-oscillatory (WENO) scheme for the large eddy simulation (LES) is improved and embedded into the three-dimensional viscous unsteady CFD solver NUAA-Turbo. Firstly, the spectral characteristics and unsteady convergence of the improved WENO scheme are studied. Compared with the classical WENO scheme, the improved WENO scheme has better dissipation and dispersion characteristics and faster convergence speed. Then, the coefficient CW of the Wall-Adapting Local Eddy-viscosity (WALE) model is calibrated by decaying homogeneous isotropic turbulence (DHIT) test case. Finally, the scheme is applied to conduct high-precision LES calculations of turbulent boundary layer flow, supersonic compression corner flow, and low-pressure turbine cascade separation flow. The numerical simulation results show that this WENO scheme has excellent shock wave capture ability and turbulence resolution and can capture more flow field details with less mesh size, greatly reducing the computational cost and laying a foundation for large-scale engineering application of LES.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

FRAP technique have been used for decades to measure movements of molecules in 2D. Data obtained by FRAP experiments in cell plasma membranes are assumed to be described through means of two parameters, a diffusion coefficient D (as defined in a pure Brownian model) and a mobile fraction M. Nevertheless, it has also been shown that recoveries can be nicely fit using anomalous sub-diffusion. FRAP at variable radii has been developed using the Brownian diffusion model to access geometrical characteristics of the surrounding landscape of the molecule. Here we performed numerical simulations of continuous time random walk (CTRW) anomalous subdiffusion and interpreted them in the context of variable radii FRAP. These simulations were compared to experimental data obtained at variable radii on living cells using the PH domain of the membrane binding protein EFA6 (exchange factor for ARF6, a small G protein). This protein domain is an excellent candidate to explore the structure of the interface between cytosol and plasma membrane in cells. By direct comparison of our numerical simulations to the experiments, we show that this protein does not exhibit anomalous diffusion in BHK cells. The non Brownian PH-EFA6 dynamics observed here is more related to spatial heterogeneities such as cytoskeleton fences effects.

Estimating ground surface PM2.5 with fine spatiotemporal resolution is a critical technique for exposure assessments in epidemiological studies of its health risks. Previous studies have utilized monitoring, satellite remote sensing or air quality modeling data to evaluate the spatiotemporal variations of PM2.5 concentrations, but such studies rarely combined these data simultaneously. We develop a three-stage model to fuse PM2.5 monitoring data, satellite-derived aerosol optical depth (AOD) and community multi-scale air quality (CMAQ) simulations together and apply it to estimate daily PM2.5 at a spatial resolution of 0.1˚ over China. Performance of the three-stage model is evaluated using a cross-validation (CV) method step by step. CV results show that the finally fused estimator of PM2.5 is in good agreement with the observational data (RMSE = 23.00 μg/m^3 and R2 = 0.72) and outperforms either AOD-retrieved PM2.5 (R2 = 0.62) or CMAQ simulations (R2 = 0.51). According to step-specific CVs, in data fusion, AOD-retrieved PM2.5 plays a key role to reduce mean bias, whereas CMAQ provides all-spacetime-covered predictions, which avoids sampling bias caused by non-random incompleteness in satellite-derived AOD. Our fused products are more capable than either CMAQ simulations or AOD-based estimates in characterizing the polluting procedure during haze episodes and thus can support both chronic and acute exposure assessments of ambient PM2.5. Based on the products, averaged concentration of annual exposure to PM2.5 was 55.75 μg/m3, while averaged count of polluted days (PM2.5 > 75 μg/m3) was 81, across China during 2014. Fused estimates will be publicly available for future health-related studies.

In this paper we report a new development on the numerical model for aluminum-steam combustion. This model is based on diffusion flame of continuum regime and the thermal equilibrium between the particle and the flow field, which can be used to calculate the aluminum particle combustion model for two phase calculation conditions. The model prediction is in good agreement with the experimental data. A new type of vortex combustor was proposed for the combustion of aluminum and steam, and the mathematical model of the two phase reacting flow with in this combustor was established. The turbulence effects are modeled using the Reynolds Stress Model (RSM) with Linear Pressure-Strain approach, and the Eddy-Dissipation model is used to simulate the gas phase combustion. Aluminum particles are injected into the vortex combustor and form a swirling flow around the chamber and their trajectories are traced using the Discrete Phase Model (DPM). The simulation results show that the vortex combustor can achieve high efficient combustion of aluminum and steam. The influencing factors, such as the eccentric distance of the inlet of aluminum particles, particle size and steam inlet diameter, etc., are studied. The work described in this paper represents an attempt to the design of a vortex combustor in order to increase aluminum combustion efficiency.

Benveniste’s experiments – known in the lay press as the “water memory” phenomenon – are generally considered to be a closed case. However, the amount of data generated by twenty years of well-conducted experiments prevents closing the file so simply. An issue, which has been little highlighted so far, merits to be emphasized. Indeed, if Benveniste failed to persuade his peers of the value of his experiments, it was mainly because of a stumbling block, namely the difficulty of convincingly proving the causal relationship between the supposed cause (“informed water”) and the experimental outcomes in different biological models. To progress in the understanding of this phenomenon, we abandon the idea of any role of water in these experiments (“water memory” and its avatars). In other words, we assume that “control” and “test” conditions to be evaluated were physically identical and differed only by their respective designations. We show in this article how simple probabilistic considerations allow to build a model that accounts for all aspects of Benveniste’s experiments. In this model based on probability amplitudes, constructive and destructive interferences emerge – or not – according to the experimental context. The recording of a statistical regularity shapes the intertwined whole constituted by the probability amplitudes of the states of the experimenter’s cognitive structures and of the experimental system. This model provides an alternative explanation to Benveniste’s experiments where water plays no role and where the place of the experimenter is central.

Frontal Polymerization (FP) is recognized as a polymer synthesis method capable of generating a highly localized and self-propagating exothermic front, considered a more rapid and energy-efficient manufacturing scheme for polymer-based fiber-reinforced composite materials. This study conducts a numerical investigation based on the Finite Element Method into the initiation and propagation of frontal polymerization of acrylamide. Utilizing Differential Scanning Calorimetry (DSC) experiments, the study phenomenologically tests the curing kinetics reactions of acrylamide (AM) in deep eutectic solvent (DES)-based polymerizable systems, thereby establishing a curing kinetics model for the autocatalytic reaction of polyacrylamide. By coupling the heat conduction diffusion equation of the frontal polymerization reaction, the Finite Element Method is employed to solve the reaction-diffusion model, aiming to explore the evolution of temperature and degree of curing during the manufacturing process. The results demonstrate the reliability and accuracy of the nth-order autocatalytic model in the study of curing kinetics for DES-based synthesis, indicating that the front propagation speed of the AM-based FP reaction is influenced by the activation temperature, duration of activation temperature, and initial temperature of the solution. Experimental validation of the polyacrylamide manufacturing through frontal polymerization corroborates the accuracy and reliability of the model predictions, providing a reference for the numerical model study and temperature control in the synthesis of acrylamide composite hydrogels via FP.

The human mind is multimodal. Yet most behavioral studies rely on century-old measures of behavior—task accuracy and latency (response time). Multimodal and multisensory analysis of human behavior creates a better understanding of how the mind works. The problem is that designing and implementing these experiments is technically complex and costly. This paper introduces versatile and economical means of developing multimodal-multisensory human experiments. We provide an experimental design framework that automatically integrates and synchronizes measures including electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, virtual reality (VR), body movement, mouse/cursor motion and response time. Unlike proprietary systems (e.g., iMotions), our system is free and open-source; it integrates PsychoPy, Unity and Lab Streaming Layer (LSL). The system embeds LSL inside PsychoPy/Unity for the synchronization of multiple sensory signals—gaze motion, electroencephalogram (EEG), galvanic skin response (GSR), mouse/cursor movement, and body motion—with low-cost consumer-grade devices in a simple behavioral task designed by PsychoPy and a virtual reality environment designed by Unity. This tutorial shows a step-by-step process by which a complex multimodal-multisensory experiment can be designed and implemented in a few hours. When conducting the experiment, all of the data synchronization and recoding of the data to disk will be done automatically.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

The conventional explanation of delayed-choice experiments seems to violate our causal intuition. This apparent violation is caused by a misinterpretation of the conventional formulation of quantum mechanics. I reanalyze these experiments using advanced and time-symmetric formulations of quantum mechanics. All three formulations give the same experimental predictions, but the advanced and time-symmetric formulations violate our causal intuition that effects only happen after causes. I explore reasons why our causal intuition may be wrong at the quantum level. I also suggest how conventional causation might be recovered in the classical limit, and speculate on cosmological boundary conditions.

Electrical contacts are important circuit components with diverse industrial applications, and their failure can lead to multiple unwanted effects. Hence, the behavior of electrical contacts is a widely studied topic in scientific literature based on various approaches, tools, and techniques. The present study proposes a numerical modeling and simulation approach based on the Holm contact theory to study the dependence between the electric potential and the temperature within an electrical contact. Structured in 5 sections, the research was conducted using COMSOL Multiphysics software and its solid-state mechanics, electric current, and heat transfer modules in order to highlight contact behavior from mechanical, electrical and thermal points of view: the von Mises stress, contact force, the electric field amplitude, the variation of the electrical potential along the current path, the temperature gradient, and dependence of temperature along the contact elements edges were obtained by simulation and graphically represented. The results are in line with the expectations and with past research that was studied in the literature review.

Shear dynamics simulation under extreme pressure was used to optimise the composition of ester-based lubricant base oil and extreme pressure additives. As a result, the ester-based lubricant in the weak-gel drilling fluid achieves reduction and increases the efficiency in drilling construction, and the field operation efficiency improves. The results of base oil lubrication simulation show that three base oils, namely, methyl oleate, ethyl oleate and butyl oleate, can be stably adsorbed on the surface layer of the shear surface. Methyl oleate has the lowest system temperature after shear stabilisation and has a high load-bearing capacity. The shear stress of methyl oleate decreases by changing the initial shear temperature and speed. It increases by 6.36 MPa when the speed increases from 10 m/s to 30 m/s. At this time, the shear stress of butyl oleate increases by 10.11 MPa. The results of the four-ball friction experiments show that the radius of the abrasion spot of methyl oleate is 179.83 μm, which is smaller than that of the other two base oils. Moreover, the friction coefficient of methyl oleate is 0.0018, which is approximately 1/3 of that of butyl oleate. Methyl oleate has better lubrication efficiency. The shear simulation results are consistent with the experimental results. The simulation can be used for the molecular design of lubricant materials. The oleic acid amide–containing amine group and the glyceride oleic–containing hydroxyl group were selected as the extreme pressure functional components for shear simulation. Results show that the shear stress of oleic acid amide is much lower than that of oleic acid glyceride when the initial shear temperature and speed change. The extreme pressure lubrication component–containing amine group is better than the extreme pressure lubrication material–containing hydroxyl group. The effects of different oleamide contents in the two-component mixtures on the lubricating properties were investigated through shear simulation, with methyl oleate as the base oil and oleamide as the extreme pressure functional component. The simulation results show that the lubricant has the optimal shear stress when 10% oleamide is added to the lubricant. The base properties of the lubricant were tested using a mixture of 90% methyl oleate and 10% oleamide, formulated as a lubricant. Results show that the friction coefficient can be reduced to 0.03 when 0.5% of the lubricant is added to the base mud. Moreover, the lubricant film formed shows high strength. Then, 1% lubricant was added to the weak-gel drilling fluid to evaluate the effect of the lubricant on the gel structure of the drilling fluid. Results show that adding lubricant does not affect the rheological properties of the drilling fluid. The gel structure of the system also remains stable even after 7 days of continuous ageing. The results of the field experiments in Hua3-01 well show that adding 1% ester-based lubricant to the field drilling fluid reduces the drilling friction by 33%. Thus, the efficiency of directional side drilling can be effectively improved, complicated accidents, such as poor starting and stopping of drilling and stuck jamming, can be avoided, and the goals of reducing quantity and increasing efficiency can be completed.

Although, promotion of urbanization culture in recent decades has made inevitable development of cities in the world, however, the development can be guided in a direction that leave, to the extent possible, minimum socioeconomic and environmental impacts. For this, it is required to first forecast auto-spreading orientation of cities and suburbs in rural areas over time and then avoid shapeless growth of cities. This paper is an attempt to develop a dynamic hybrid model based on logistic regression (LR), Markov chain (MC), and cellular automata (CA) for prediction of future urban sprawl in fast-growing cities. The model was developed using 12 widely-used urban development criteria, whose significant coefficient was determined by logistic regression, and validated by relative operating characteristic (ROC) analysis. The validated model was run in Guilan, a tourist province in northern Iran with a very high rate of urban development. For this, changes in the area of urban land use were detected over the period of 1989 to 2013 and then, future sprawl of the province was forecasted by the years 2025 and 2037. The analysis results revealed that the area of urban land use was increased by more than 1.7 % from 36012.5 ha in 1989 to 59754.8 ha in 2013, and the area of Caspian Hyrcanian forestland was reduced by 31628 ha. The results also predicted an alarming increase in the rate of urban development in the province by the years 2025 and 2037, during which urban land use is predicted to develop 0.9 % and 1.38 %, respectively. The development pattern is expected to be uneven and scattered, without following any particular direction. The development will occur close to the existing or newly-formed urban basements as well as around major roads and commercial areas. This development, if not controlled, will lead to the loss of 13863 ha of Hyrcanian forests and if the trend continues, 21013 ha of Hyrcanian forests and 20208 ha of Barren/open lands are expected to be destroyed by the year 2037. In general, the proposed model is an efficient tool for the support of urban planning decisions and facilitates the process of sustainable development of cities by providing decision-makers with an overview on future development of cities where the growth rate is very fast.

A power factor correction (PFC) rectifier is compulsory for any electronic circuitry connected to the electrical grid. The majority of PFCs that are utilized are based on a step-up converter that supplies high DC voltage at its output terminal. Electric vehicle battery voltage varies from a few hundred to almost a thousand volts. Thus, an additional conversion step is obligatory when designing an all-brand universal charger. This paper presents a new type of front-end versatile step up / down three voltage level PFC. The rectifier can operate in continuous conduction mode or discontinuous conduction mode. First, the rectifier’s principle of operation is described, and then an innovative rectifier is analyzed for continuous and discontinuous conduction modes. Finally, the proposed theory is experimentally validated in a multiplier-less dual loop mode at discontinuous conduction modes. It was shown that although there is no multiplier use in the control circuitry, the power factor is near unity. It was revealed that the rectifier could swing the output voltage from 50 V to 900 V while the input voltage was 230 Vrms. The innovative topology suits any standard PFC rectifier, three-level voltage supplement, and dual-stage low-voltage power supply.

Two coupled climate models which participated the CMIP6 project (Coupled Model Intercomparison Project Phase 6), the Earth System Model of Chinese Academy of Sciences version 2 (CAS-ESM2-0) and the Nanjing University of Information Science and Technology Earth System Model version 3 (NESM3), were assessed in terms of the impact of four new sea ice parameterization schemes. These four new schemes are related to air-sea heat flux, radiation penetration and absorption, melt ponds, ice-ocean flux, respectively. To evaluate the effectiveness of these schemes, key sea ice variables with and without these new schemes, such as sea ice concentration (SIC) and sea ice thickness (SIT), were compared against observation and reanalysis product from 1980 to 2014. The simulations followed the design of historical experiments within the CMIP6 framework. The results revealed that both models demonstrated improvements in simulating Arctic SIC and SIT when the new parameterization schemes were implemented. The model bias of SIC in some marginal sea ice zones of Arctic was reduced, especially during March. The SIT was increased and the transpolar gradient of SIT was reproduced. The changes of spatial pattern of SIC and SIT after adding new schemes bear similarities between the two coupled models. This suggests that the new schemes have potential for broad application in climate models, especially for those with underestimated SIT.

This paper presents a quantitative approach to poetry, based on the use of several statistical measures (entropy, informational energy, N-gram, etc.) applied to a few characteristic English writings. We found that English language changes its entropy as time passes, and that entropy depends on the language used and on the author. In order to compare two similar texts, we were able to introduce a statistical method to asses the information entropy between two texts. We also introduced a method of computing the average information conveyed by a group of letters about the next letter in the text. We found a formula for computing the Shannon language entropy and we introduced the concept of N-gram informational energy of a poetry. We also constructed a neural network, which is able to generate Byron-type poetry and to analyze the information proximity to the genuine Byron poetry.

Tinnitus is a common phantom auditory percept, believed to be related to plastic changes in the brain due to hearing loss. However, tinnitus can also occur in absence of any clinical hearing loss. Then, since there is no hearing loss, the mechanisms that drive plastic changes remain largely enigmatic. Previous studies showed subtle differences in sound-evoked brain activity associated with tinnitus in subjects with tinnitus and otherwise, normal hearing, but the results are not consistent across studies. Here, we aim to investigate those differences using monaural rather than binaural stimuli. Sound-evoked responses were measured using functional magnetic resonance imaging (MRI) in participants with and without tinnitus. All participants had clinically normal audiograms. The stimuli were pure tones with frequencies between 353 and 8000 Hz, presented monaurally. A Principal Component Analysis (PCA) of the response in the auditory cortex revealed no difference in tonotopic organization, which confirmed earlier studies. A GLM analysis showed hyperactivity in the lateral areas of the bilateral auditory cortex. Consistent with the tonotopic map, this hyperactivity mainly occurred in response to low stimulus frequencies. This may be related to hyperacusis, which was associated with tinnitus found in the parahippocampus. This may reflect an interference between tinnitus and spatial orientation.

In this work, two plasmonic structures with two different periodic nanostructures (nanodisk and nanostrip arrays) separated from the gold layer by an ultrathin dielectric Al2O3 layer are proposed, and their optical properties are simulated as 3-dimensional models using COMSOL Multiphysics software tool. The optical responses, such as the amplification of the electric field and the scattering, absorption and extinction cross sections of the two structures, are investigated and compared. The results show that both structures show strong field enhancement in their gap layer, and the structure with the periodic array of metal nanodisks shows stronger field enhancement and larger absorption, extinction, and scattering cross sections in its dipole resonance wavelength, which is defined as a bright plasmonic mode. In addition, some weaker resonant modes are observed for both structures and the reason for their generation is dis-cussed. The analysis of different possible modes in such structures is crucial for their application in various fields.

Gas and power networks are tightly coupled and interact with each other due to physically interconnected facilities. In an integrated gas and power network, a contingency observed in one system may cause iterative cascading failures, resulting in network wide disruptions. Therefore, understanding the impacts of the interactions in both systems is crucial for governments, system operators, regulators and operational planners, particularly, to ensure security of supply for the overall energy system. Although simulation has been widely used in the assessment of gas systems as well as power systems, there is a significant gap in simulation models that are able to address the coupling of both systems. In this paper, a simulation framework that models and simulates the gas and power network in an integrated manner is proposed. The framework consist of a transient model for the gas system and a steady state model for the power system based on AC-Optimal Power Flow. The gas and power system model are coupled through an interface which uses the coupling equations to establish the data exchange and coordination between the individual models. The bidirectional interlink between both systems considered in this studies are the fuel gas offtake of gas fired power plants for power generation and the power supply to LNG terminals and electric drivers installed in gas compressor stations and underground gas storage facilities. The simulation framework is implemented into an innovative simulation tool named SAInt (Scenario Analysis Interface for Energy Systems) and the capabilities of the tool are demonstrated by performing a contingency analysis for a real world example. Results indicate how a disruption triggered in one system propagates to the other system and affects the operation of critical facilities. In addition, the studies show the importance of using transient gas models for security of supply studies instead of successions of steady state models, where the time evolution of the line pack is not captured correctly.

This paper proposed a three-layer distributed simulation network architecture: 1. Distributed virtual simulation network, 2. Perception and control sub-network, and 3. Collaborative communication service network. This simulation architecture runs on a distributed platform. The platform can provide a unique virtual scenario and several simulation services to verify the individual robot system's basic perception, control, and planning algorithms and the distributed collaborative algorithms of heterogeneous multi-robot systems. Furthermore, we designed a simulation experiment scenario for classical heterogeneous robot systems such as unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). At last, We gave a rough estimation model of the bandwidth.

In the recent years, tubular nanostructures have been related with immense advances in various fields of science and technology. Considerable research efforts have been centered on the theoretical prediction and manufacturing of non-carbon nanotubes (NTs), which meet modern requirements for the developing of novel devices and systems. In this context, diatomic inorganic nanotubes formed by atoms of elements from the 13th group of the periodic table (B, Al, Ga, In, Tl) and nitrogen (N), have received much research attention. In the present work, the elastic properties of single-walled boron nitride, aluminium nitride, gallium nitride, indium nitride and thallium nitride nanotubes were numerically evaluated using the nanoscale continuum modelling approach (also called molecular structural mechanics). The elastic properties (rigidities, surface Young´s and shear moduli, and Poisson´s ratio) of nitride nanotubes are discussed with respect to the bond length of the corresponding diatomic hexagonal lattice. The results obtained contribute to a better understanding of the mechanical response of nitride compound-based nanotubes, covering a broad range from the well-studied boron nitride NTs to the hypothetical thallium nitride NTs.

In this paper, the boiling flow inside a nanochannel with 700000 argon particle has been simulated by molecular dynamics (MD) simulation. This approach has been employed to analysis the superheated flow and its heat transfer pattern as well. For all simulations an external thrust force varying from 1 PN to 12 PN is exerted on inlet nanoparticles along the channel to have the forced annular boiling flow. Computations reveal that saturation condition and superheat degree have significant impacts on the liquid-vapor interface. Furthermore, because of the major influence of surface tension throughout a nanochannel, the x-velocity of liquid film and vapor core has not considerable fluctuations and stay smooth. All provided results show the behaviors completely similar to the available outcomes in the literature.

Cirina forda caterpillars are a significant protein source in Central Africa, yet their preservation and consumption pose challenges. This study investigates the drying of Cirina forda caterpillars to enhance preservation methods and product quality. Caterpillars were subjected to drying experiments at temperatures ranging from 40°C to 70°C, using tunnel and oven dryers. The drying kinetics was analyzed using mathematical models, revealing a water diffusion-limited mechanism in all the tested conditions. The Newton model provided adequate estimations of the drying behavior, facilitating further practical dryer design. Shrinkage analysis indicated a two-step process independent of the drying conditions. Achieving a moisture content suitable for prolonged storage resulted in substantial volume loss, highlighting the challenges of preserving dried caterpillars. These findings shed light on how to optimize drying processes for Cirina forda caterpillars, crucial for sustaining protein sources in Central Africa.

Scientists are gradually becoming aware of the challenges in the understanding of the very root mechanisms of massively parallel computations that are observed in literally all scientific disciplines ranging from cosmology, physics, across chemistry, biochemistry, and ending in biology. This leads us to the main motivation and simultaneously to the central thesis of this review: “Can we design artificial, massively-parallel, self-organized, emergent, error-resilient, computational environments?” A large number of simulations along with examples and counter-examples, finalized by a list of the future directions, are giving hints and partial answers to the main thesis. This all together is opening the crucial question whether there is existing a deeper, beyond the Turing machine theoretical description of massively-parallel computing. Important information dealing with this topic is reviewed along with highly expressive animations generated by the open-source, Python software GoL-N24. The perspective, future directions including applications in robotics and biology of this research are discussed in the light of known information.

This paper proposed three-dimensional numerical simulation method by coupling of electrostatic and fluid fields to evaluating the performance of electrical sensor in the concentration measurement of gas/solid two-phase flow. Compared with the static numerical simulation, this real-time dynamic 3-D simulation method can research on a designed capacitance sensor combining the dynamic characteristics of the two-phase flows for concentration measurement. Several fluid-electrostatic models of transmission pipes with different sensor structures are built. Under different test positions and different particle concentrations, the flow characteristics and the corresponding electric signals can be obtained, and the correlation coefficient between the concentration values and the capacitance values are used for performance evaluation of the sensors. The effects of flow regimes on concentration measurement are also been investigated in this paper. To validate the results of simulation, an experimental platform with horizontal straight pipe for phase volume concentration measurement of solid/air two-phase flow is built, and the experimental results agree well with simulation conclusions. The simulation and test results show that the coupling models can give constructive reference opinions for the sensor design and collection of installation position in different transmission pipelines, which are very important for the practical process of pneumatic conveying system.

Utilization of wasted heat instead of fuel combustion is effective to reduce primary energy consumption for mitigating global warming problem. Because wasted heat sources are not necessarily located close to areas of heat demand, one of the difficulties is that wasted heat has to be transferred from heat source side to heat demand side, which may require heat transportation over long distance. From this point we proposed and have examined new idea of heat transportation using ammonia-water as the working fluid which system is named Solution Transportation Absorption chiller, in short STA. Our previous studies of STA were mainly the experimental investigation with STA facility which cooling power was 25RT (90kW). As a result, the COP of STA was found almost same value 0.65 with the conventional absorption chiller without depending on the transportation distances. The simulation using AspenHYSYS also examined with same experimental condition. The experimental data showed good agreement with the simulation calculation. In this study, we examined the large-scale cooling power STA on simulation. The examination cooling powers were from 90 kW(25RT) to 3517 kW(1000RT). All cooling power achieved around COP 0.64 including pump power consumptions. In addition, we performed the dynamic simulation. As the results, there was no effect of pipeline size on the cooling capacities and mass flow rates. Furthermore, the stability time of the cooling capacities and mass flow rates were almost same regardless of the pipeline size and cooling capacity. In other words, STA may be achieved the same COP even though having various complex conditions compared with the conventional absorption chiller.

“Non-Locality is most naturally incorporated into a theory in which there is a special frame of reference. One possible candidate for this special frame of reference is the one in which the Cosmic Microwave Background (CMB) is isotropic. However, other than the fact that a realistic interpretation of quantum mechanics requires a preferred frame and the CMB provides us with one, there is no readily apparent reason why the two should be linked” (L. Hardy). Starting from this remark we first argue that, given the present view of the vacuum, the basic tenets of Quantum Field Theory cannot guarantee that Einstein Special Relativity, with no preferred frame, is the physically realized version of relativity. Then, to try to understand the nature of the hypothetical preferred Σ−frame, we consider the so called ether-drift experiments, those precise optical measurements that try to detect in laboratory a small angular dependence of the two-way velocity of light and then to correlate this angular dependence with the direct CMB observations with satellites in space. By considering all experiments performed so far, from Michelson-Morley to the present experiments with optical resonators, and analyzing the small observed residuals in a modern theoretical framework, the long sought Σ− frame tight to the CMB naturally emerges. Finally, if quantum non-locality reflects some effect propagating at vastly superluminal speed vQI→∞, its ultimate origin could be hidden somewhere in the infinite speed cs→∞ of vacuum density fluctuations.

Agricultural greenhouse is largely answered in the agricultural sphere, despite the shortcomings it has, including overheating during the day and night cooling which sometimes results in the thermal inversion mainly due to its low inertia. The glasshouse dressed chapel is relatively more efficient than the conventional tunnel greenhouse. Its proliferation on the ground is more or less timid because of its relatively high cost[14-22]. Agricultural greenhouse aims to create a favorable microclimate to the requirements of growth and development of culture, from the surrounding weather conditions, produce according to the cropping calendars fruits, vegetables and flower species out of season and widely available along the year. It is defined by its structural and functional architecture, the quality thermal, mechanical and optical of its wall, with its sealing level and the technical and technological accompanying[12-13]. The greenhouse is a very confined environment, where multiple components are exchanged between key stakeholders and them factors are light, temperature and relative humidity[8]. This state of thermal evolution is the level sealing of the cover of its physical characteristics to be transparent to solar, absorbent and reflective of infrared radiation emitted by the enclosure where the solar radiation trapping effect otherwise called "greenhouse effect" and its technical and technological means of air that accompany. The socio-economic analysis of populations in the world leaves appear especially the last two decades of rapid and profound transformations These changes are accompanied by changes in eating habits, mainly characterized by rising consumption spread along the year[14]. To effectively meet this demand, greenhouse-systems have evolved, particularly towards greater control of production conditions (climate, irrigation, ventilation techniques, CO2 supply, etc ...). Technological progress has allowed the development of greenhouses so that they become increasingly sophisticated and of an industrial nature (heating, air conditioning, control, computer, regulation, etc ...). New climate driving techniques have emerged, including the use of control devices from the classic to the use of artificial intelligence[10-11] such as neural networks and / or fuzzy logic, etc... As a result, the greenhouse growers prefer these new technologies while optimizing the investment in the field to effectively meet the supply and demand of these fresh products cheaply and widely available throughout the year.

In order to study the adsorption process and adsorption characteristics of shale oil at the macro scale, the isothermal adsorption experiments of illite and kerogen on heptadecane (oil phase) solution were carried out by infrared spectrophotometry and gas chromatography-mass spectrometry. The reaction mechanism of alkane solution concentration and temperature in the adsorption process was investigated. The adsorption characteristics of heptadecane on the surface and pores of illite and kerogen were elucidated by adsorption isotherm model and adsorption thermodynamic model. The results show that: The concentration of alkane solution contributes to the adsorption of illite and kerogen. As the concentration of the solution continues to increase, the adsorption rate gradually slows down. However, the temperature hardly affects the adsorption rate. At 25℃, 50℃ and 60℃, the adsorption process of illite on alkane solution is more consistent with Langmuir model and Freundlich model, respectively. The difference is that Langmuir model is suitable for describing the whole adsorption process of kerogen on alkane solution. 3) The adsorption process of illite and kerogen to heptadecane solution is an endothermic reaction. Heating helps to accelerate the migration rate of alkanes, which is conducive to the adsorption reaction. The research results clarify the adsorption characteristics of shale oil heavy components from the macro level, and fill the research gap in the application of solid-liquid isothermal adsorption physical experiments to the adsorption and occurrence of shale oil.

We report on the design and construction of a spin-flip Zeeman slower, quadrupole magnetic trap, and Feshbach fields for a new machine for ultra-cold Li-7. The small mass of the Li-7 atom, and the tight lattice spacing, will enable us to achieve a 100-fold increase in tuneling rates over comparable Rb-87 optical lattice emulator experiments. These improvements should enable to access new regimes in quantum magnetic phase transitions and spin dynamics.

When participants in a statistical learning paradigm are asked to learn from two incompatible or competing inputs, they often fail to learn from one or both inputs. This study presents the results of two experiments that were both completed by one group of typically developing four-year-old children. One experiment targeted word-medial consonant patterns (phonotactics), whereas the other targeted strong-weak and weak-strong stress patterns (prosody). The order of the experiments was critical for learning outcomes in the phonotactics experiment: When children learned phonotactics first, their production accuracy increased following exposure to a high frequency input. When children learned phonotactics second, however, their production accuracy dropped when they were exposed to the high frequency input. Results from the prosody experiment were inconclusive, with limited evidence of any learning effect. Overall, the results suggest that children may conflate learning experiences, and patterns learned from an initial experimental input compete with patterns in a subsequent experiment. When considering natural language acquisition, the results suggest that an isolated episode of learning may lead to generalizations that are incompatible with later input, and possibly, with larger patterns in the language.

Sustainable agriculture is essential to provide food security for a growing world population without further sacrificing the integrity of the environment. To make progress towards agricultural sustainability we must consider ecological and socioeconomic processes within the agricultural socio-ecosystem and involve stakeholders in the research process. We propose an innovative experimental approach for examining how natural regulation of ecosystems may provide an alternative to increasing external inputs in agriculture while improving the socio-economic welfare of farmers. These “social-ecological experiments” go further to participatory action research by not only involving stakeholders in the research process but also by manipulating simultaneously socioeconomic and ecological processes under real field conditions to give a faster route to sustainability. Social-ecological experiments are undertaken in real field conditions, explicitly involving stakeholders, and help untangle the drivers of social-ecological dynamics under various land management and farming practices. Social-ecological experiments are distinct from adaptive management and scenario-planning approaches as they highlight the interactions between ecological and social processes, manipulate the social and ecological processes shaping the system and show causal links between patterns and processes. As an example, we describe a social-ecological experiment for reducing herbicide use. Social-ecological experiments offer great opportunities for increasing stakeholders’ acceptance of environmental policies implemented through adaptive management. These experiments may help to identify management practices that optimize multiple objectives, deliver a portfolio of ecosystem services and satisfy key stakeholders.

Decomposition of natural and synthetic polymeric materials (peat, humates, biochar, strongly swelling hydrogels and other soil conditioners) in a biologically- and chemically- active soil environment inevitably leads to their degradation and ability to improve the structure, water-retention, absorptive capacity and actual fertility of artificial soil constructions in urbanized ecosystems and agro landscapes (constructozems). Quantitative assessment of the biodegradation process using field and laboratory incubation experiments, as well as mathematical modeling, showed the possibility of significant (up to 30-50% per year) losses of organic matter of constructozems and a corresponding deterioration in their quality. Incubation experiments with the analysis of carbon dioxide emission for polymeric materials under given thermodynamic conditions allow to estimate the potential rates of their decomposition (half-life) and their dependence on the dose of inhibitors of microbial activity. Special nomographs provide an opportunity to determine the optimum depth of the arrangement of organic components in soil constructions to ensure their stable functioning during a fixed operating time in urban conditions. The results of the study are useful for geo-engineers and landscaping practitioners.

Purification of biologically-derived therapeutics is a major cost contributor to the production of this rapidly growing class of pharmaceuticals. Monoclonal antibodies comprise a large percentage of these products therefore new antibody purification tools are needed. Small peptides, as opposed to traditional antibody affinity ligands such as Protein A, may have advantages in stability and production costs. Multiple heptapeptides that demonstrate Fc binding behavior that have been identified from a combinatorial peptide library using M13 Phage Display are presented herein. Seven unique peptide sequences of diverse hydrophobicity and charge were identified. All seven peptides showed strong binding to the four major human IgG isotypes, human IgM, as well as binding to canine, rat, and mouse IgG. These seven peptides were also shown to bind human IgG4 from DMEM cell culture media with 5% FCS and 5 g/L ovalbumin present. These peptides may be useful as surface ligands for antibody detection and purification purposes. Molecular docking and classical molecular dynamics (MD) simulations were conducted to elucidate the mechanisms and energetics for the binding of these peptides to the Fc region. The binding site was found to be located between the two glycan chains inside the Fc fragment. Both hydrogen bonding and hydrophobic interactions were found to be crucial for the binding interactions. Excellent agreement for the binding strength was obtained between experimental results and simulations.

It was recently shown that urban networks might exhibit two different critical points, one for the percolation of congested links through the network, and another one for the flow of vehicles through the network. It was observed that the percolation critical point happens after the flow critical point, where network throughput is maximized. This result is important because if a causal relationship exists between these two processes, it can be leveraged for improving control methods to avoid network collapse. This paper presents the results of numerical experiments to understand the possible causal relationship between the percolation and flow processes by focusing on the relative timing between the two critical points. The goal is to understand the impacts of important factors such as: (i) urban network type: long- and short-block networks, (ii) signal timing control type: fixed, random, responsive, and (iii) route choice: turning proportions at intersections. We find that the threshold density $k^*_c$ to determine if a link is congested has the main impact on the timing of the percolation transition, where lower values trigger earlier transitions. This implies that $k^*_c$ can be set such that the percolation transition happens before the flow critical point, opening the door for improved congestion management strategies. If $k^*_c$ is set to the average network critical density, maximum network throughput happen simultaneously with the percolation transition. Surprisingly, it appears that these results, and more generally, the relationship between the percolation and flow processes is independent of (i) and (ii) above.

The bottleneck in creating dynamic models of biological networks and processes often lies in estimating unknown kinetic model parameters from experimental data. In this regard, experimental conditions have a strong influence on parameter identifiability and should therefore be optimized to give the maximum information for parameter estimation. Existing model-based design of experiment (MBDOE) methods commonly rely on the Fisher Information Matrix (FIM) for defining a metric of data informativeness. When the model behavior is highly nonlinear, FIM-based criteria may lead to suboptimal designs since the FIM only accounts for the linear variation of the model outputs with respect to the parameters. In this work, we developed a multi-objective optimization (MOO) MBDOE, where model nonlinearity was taken into consideration through the use of curvature. The proposed MOO MBDOE involved maximizing data informativeness using a FIM-based metric and at the same time minimizing the model curvature. We demonstrated the advantages of the MOO MBDOE over existing FIM-based and other curvature-based MBDOEs in an application to the kinetic modeling of fed-batch fermentation of Baker's yeast.

This study presents the B92 Quantum Key Distribution scheme and its optimization and implementation considerations. Simulation software was developed to model and analyze the B92 protocol under varying conditions. Another contribution of this work is investigating how this protocol runs on real quantum computing hardware. New eavesdropping schemes were proposed and modeled.

Dynamic virtual testing by Hardware in the Loop (HIL) real time simulation systems is common to minimize physical tests to validate and verify control algorithms, logics and software, under normal and emergency conditions. The accurate but costly HIL models setup activities could have an extended use to simulate faulty conditions that are seldom observable in the real system, yet they must be detected in advance and recognized. Data driven diagnostic methods have reached a mature development, in various sectors, proving effective in early detecting slowly varying deviations from nominal patterns of signals, even without a priori knowledge of their nature and behavior. These methods rely on vast amount of data logged from real systems that are ever and ever richer of monitoring telemetries that are worth being exploited. However, due to the usually reliable and safe systems being managed, it is often difficult to collect enough data on anomalous situations to validate fault detection and isolation and limit false positives. HIL model-based simulations allow generating and observing operational domains or transients rarely observed in real operations. This allows identifying the signals that are most correlated to faults and that could be selected to setup data-based diagnostic models of parts or subsystems, which are more suitable than the whole HIL model for online diagnostics, as well as to identify the features and trends that are most symptomatic of anomalous conditions.

Nowadays, unmanned vehicles are widely used in various fields. However, the safety of unmanned vehicles is directly determined by the autopilot systems, and sensor faults are a very critical factor that affects the normal operation of the autopilot systems. To efficiently and comprehensively test unmanned systems for sensor faults, this paper proposes a chip-level testing platform for unmanned vehicle autopilot systems based on FPGA hardware-in-the-loop simulation. Unlike existing testing platforms, the introduction of FPGA technology allows us to achieve nanosecond real-time simulation frequency, thus realizing chip-level simulation of sensors and black-box automated testing. Firstly, we propose a set of general platform and modeling methods, and the paper presents a detailed application of the four most common necessary sensors in autopilot systems as examples. Secondly, we propose for the first time a chip-level fault testing method for unmanned systems, and build a test platform with a quadrotor vehicle, the PX4 software platform, and the Pixhawk4 hardware platform as examples. Finally, the experimental results verify that the testing platform proposed in this paper can adapt to various autopilot systems and has high simulation test reliability.

In this study, a new structure is proposed based on the body-centered cubic (BCC) lattice structure: adding a cubic truss in the center of the BCC structure, which is denoted by TLC (Truss-lattice-cube). The different dimensions of the central cube can affect the mechanical properties of the lattice structure notably. With a fixed length (15mm) of a unit cell, the optimal size for the central cube is determined to be 5 mm. Quasi-static compressive tests are performed on specimens made of Polylactic Acid (PLA) using additive manufacturing technology. The deformation characteristics of the new structure is analyzed in detail by experiments and numerical simulations. Compared to the BCC structure, the mechanical properties of the TLC structure are significantly improved. The initial flow stress of the TLC has been increased by 122% at a strain of 0.1; the specific strength has been enhanced by 293% at a strain of 0.5; and the specific energy absorption has been improved by 312% at the strain of 0.6. Printing defects in the lattice structure could deteriorate the mechanical property remarkably. In this work, incorporation of microcracks into the finite element model makes the simulation capture the influence of printing defects and improve the predictive accuracy of simulation significantly.

The study of co-fusion was one of the essential topics in the history of metallurgy in China. Simulation experiments had been an essential concept in the study of the co-fusion steelmaking process. This paper mainly studied the simulation experiments of co-fusion from two aspects: the replication of co-fusion swords by three different methods, and the micro-analysis of the co-fusion samples. The experimental results indicated that several co-fusion swords could be made by different processes, but the carbon content and surface hardness were quite different. During repeated forging welded, the microstructure of the samples transformed from laminated to homogenized, finally the steel with a uniform carbon content was obtained. It was challenging to find the characteristics of co-fusion from the homogenized samples. The results prompted people to rethink the microstructure characteristics of ancient co-fusion artifacts.

This paper studies the impact of agricultural subsidies and product certification on the use rate of green pesticides based on experimental economics. We found that agricultural subsidies effectively increased the utilization rate of green pesticides. If the agricultural subsidies raised from 20% to 100%, the green pesticides’ using rate increased by 438.51%. We also found that product certification increased the utilization rate of green pesticides by 376.16%%.The increase of agricultural subsidies is more effective than the product certification. Under a higher proportion of agricultural subsidies, farmers’ behavior will maintain “status bias”. Therefore, there are three suggestions proposed. Firstly, because of high price of green pesticides and lower production, the subsidies for agricultural materials should raise greatly to effectively improve the utilization rate of green pesticides. It is recommended that green pesticide provided free of charge in some wealthy areas. Secondly, both subsidies and product certification can improve the use rate of green pesticides. However, since the effect of agricultural subsidies is better than product certification, and farmers may have status bias. Therefore, it is recommended to give priority to the substantial increase on the proportion of agricultural subsidies, and then to product certification.

Inspired by works of W. H. Zurek and others, a mathematical, physical theory, entirely within a quantum mechanical formalism, is proposed for cognitive processes in terms of an abstract Hilbert-space for the conscious state that is an exact replica of the Hilbert-space for the neuronic physical state. Thus, any actual state of consciousness arises by its formal alignment (identification) with some-one in the set of the neuronic states, with the latter undergoing perpetual changes in the wake of life-long experiences. It is posited that these changes become expressed by an increase in the number of ordered, coherent neuronic states at the expense of the pre-ordinal random neuronic states. Changes, transitions between states are induced by a Gorini-Kossakowski-Sudarshan-Lindblad formalism, which is also instrumental in the effect of the conscious state on bodily actions. The paradigmatic findings of R.N. Shepard (1958 - 2011) and of S. Sternberg (1966 - 2016) for long- and short-term recalls are interpreted within the model.

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionisation. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foregrounds and possible variations in the instrument response. Understanding how the measurement errors propagate in a realistic instrument system and affect system calibration is the focus of this work. We simulate a 21 cm global spectrum observation based on the noise wave calibration scheme. We focus on how measurement errors in reflection coefficients affect the noise temperature, and how typical errors impact on the recovery of the 21 cm signal, especially in the frequency domain. Results show that for our example set up, a typical VNA measurement error in the magnitude of the reflection coefficients of the antenna, receiver, open cable which are 0.001, 0.001, 0.002 (linear) respectively would result in a 200 mK deviation on the detected signal, and a typical measurement error of 0.48∘, 0.78∘, 0.15∘ in the respective phases would cause a 40 mK deviation. The VNA measurement error can greatly affect the result of a 21 cm global spectrum experiment using this calibration technique, and such a feature could be mistaken for or be combined with the 21 cm signal.

A physical scrutiny of experimental results published in Physical Review Letters (December 2015, M. Giustina, et al., Phys. Rev. Lett. 115, 250401, and L. K. Shalm et al., Phys. Rev. Lett. 115, 250402) is undertaken. These articles reported that measured outcomes were fitted with quantum states possessing a dominant component of non-entangled photons, thereby contradicting their own claim of quantum nonlocality. With probabilities of photon detections lower than 0.1 %, the alleged quantum nonlocality cannot be classified as a resource for developing quantum computing devices, despite recent publicity. Experimental evidence of a feasible process for quantum-strong correlations has been identified (M. Iannuzzi, et al., Phys. Lett. A, 384 (9), 126200, 2020) in terms of correlations between independent and multi-photon states evaluated as Stokes vectors on the Poincaré sphere. As single-photon sources are not needed, the design and implementation of quantum computing operations will be significantly streamlined.

Quantum theory predicts a whole class of non-local phenomena, observable via coincident detection of EPR-type systems. An important feature of these observations is their non-signaling character. Technically, non-local phenomena should only be observable for post-selected sub-ensembles, rather than for complete projections. Otherwise, superluminal telegraphy becomes possible. Yet, a couple of recent Bell experiments reported the observation of quantum non-locality for 100% of the detected events. Does it follow that signaling non-locality is possible? If so, was quantum theory falsified? This puzzle is solved by revisiting the interpretation of the spin projection operator, with special focus on its dual nature (combining spectral decomposition with spectral transformation). “Component switching” is not a loophole, but rather a requirement of quantum mechanics in this context, because sharp spin projections are partial (as well as partially overlapping). Surprisingly, it is possible to pre-select incompatible statistical sub-ensembles with heralded detection and to reveal the same behavior as in post-selected observations. Therefore, Bell experiments confirm the predictions of quantum theory without violating the non-signaling principle.

Many scientific and technological problems are studied with the help of computer codes that simulate the phenomena of interest rather than via traditional laboratory experiments. Such models play an important role in neuroscience where they are used to mimic brain function from the sub-cellular to the macroscopic level. Exploration with computer models carries with it a number of statistical challenges: where to sample the input space for the simulator, how to make sense of the data that is generated, how to estimate unknown parameters in the model, how to validate a model. The simulator setting also has some unique problems and possibilities. This review paper describes statistical research on these issues and how that work might be applied to neural simulations.

We present a tutorial to realize a multidimensional fitting procedure capable of extracting all the relevant information contained in a sequence of charged particle images acquired as a function of time in femtosecond pump-probe experiments. The images are reproduced using a 3D fitting method, which provides the velocity (or center-of-mass kinetic energy) and angular distributions contained in the images and their time evolution. A detailed example of the method is shown through the analysis of the time-resolved predissociation dynamics of CH₃I on the B-band origin [Gitzinger et al., J. Chem. Phys. 133, 234313 (2010)]. We show that the multidimensional approach is essential for the analysis of complex images that contain several overlapping contributions where reduced dimensionality analyses cannot provide a reliable description of the features present in the image sequence. This methodology can be generalized to many types of multidimensional data analysis.

Background: Benveniste’s biology experiments suggested the existence of molecular-like effects without molecules (“memory of water”). In this article, it is proposed that these disputed experiments could have been the consequence of a previously unnoticed and non-conventional experimenter effect. Methods: A probabilistic modelling is built in order to describe an elementary laboratory experiment. A biological system is modelled with two possible states (“resting” and “activated”) and exposed to two experimental conditions labelled “control” and “test”, but both biologically inactive. The modelling takes into account not only the biological system, but also the experimenters. In addition, an outsider standpoint is adopted to describe the experimental situation. Results: A classical approach suggests that, after experiment completion, the “control” and “test” labels of biologically-inactive conditions should be both associated with “resting” state (i.e. no significant relationship between labels and system states). However, if the fluctuations of the biological system are also considered, a quantum-like relationship emerges and connects labels and system states (analogous to a biological “effect” without molecules). Conclusions: No hypotheses about water properties or other exotic explanations are needed to describe Benveniste’s experiments, including their unusual features. This modelling could be extended to other experimental situations in biology, medicine and psychology.

: Suicide is a serious public health problem, with a global mortality rate of 1.4% of all deaths worldwide, and the leading cause of unnatural death in Spain. The use of clinical simulation has proven to be a beneficial tool in the training of nursing students. Such experiences allow them to develop cognitive and affective skills that are fundamental for the detection of warning signs and use of interventions in cases of people who want to take their own lives. Working in a mental health environment can be difficult for nursing students; therefore, the purpose of this study was to explore the perceptions of nursing students on the approach, management and intervention of suicidal crisis through clinical mental health simulation. Methods: qualitative descriptive phenomenological study through focus groups and reflective narratives in a sample of 45 students. A thematic analysis was performed using ATLAS-ti. Results: After the analysis, three themes were obtained: (a) management and handling of emotions (b) identification of suicide motives (c) intervention in suicidal crisis. Discussion: Clinical simulation in mental health allows students to exercise clinical judgment reasoning, detect warning signs for a better treatment approach, and provide tools for effective intervention and management of patient care. The results of this study indicate that nursing students face challenges in approaching mental health clinical simulation due to lack of prior exposure.

After a long a glorious history, marked by the first direct proofs of neutrino existence and of the mixing between the first and third neutrino generations, the reactor antineutrino experiments are still well alive and will continue to give important contributions to the development of elementary particle physics and astrophysics. In parallel to the SBL experiments, that will be dedicated mainly to the search for sterile neutrinos, a new kind of experiments will start playing an important role: the medium baseline reactor experiments, aiming to study the neutrino mass ordering. The first example of this kind, the liquid scintillator JUNO experiment, characterized by a very high mass and unprecedented energy resolution, will soon start data taking in China. Its main aspects are discussed here, together with its potentialities for what concerns the mass ordering investigation and also the other issues that can be studied with this detector, spanning from the accurate oscillation parameter determination, to the study of solar neutrinos, geoneutrinos, atmospheric neutrinos and neutrinos emitted by supernovas and to the search for signals of potential Lorentz invariance violation.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

The principle of the constancy of the velocity of light, which stated that the light velocity is invariant to the motion of the emitter, was well established and directly proven by experiments. Interestingly, the further assumption that the light velocity is independent of the motion of the observer was, arguably, never directly proven by any experiment for a century. This paper proposed the design of two experiments to directly test this assumption, which tried to address some perceived technical difficulty in such experiments. One is to directly measure the light speed as to moving sensors. The experiment setup is designed in such a way that the concerns of time synchronization and dilation can be avoided. Another experiment is to test the isotropy of the light speed to a moving particle in the electromagnetic accelerator by measuring the momentum to acceleration ratio. The experiment result, if positive, will provide direct and solid proof of the assumption. Otherwise, it may imply a need for further investigation. Since the light speed invariance to moving observers is a key assumption of some fundamental physical theory, either way, the experiments will have significant meanings.

Drought is among the most important abiotic stressors influencing food-crop production worldwide. Currently, drought-tolerant maize materials are rarely used for actual breeding because corn production primarily focuses on heterosis to generate desired varieties. In this article, we reviewed current work on assessing maize drought tolerance. We suggested that the development of enhanced screening techniques must clearly consider the connection between theory and application. We strongly recommend that agricultural scientists focus on translating the results of laboratory experiments into practical methods for improving crop productivity.

This study presents a pre-processing approach adopted for the radar reflectivity data assimilation and results of simulations with the Harmonie numerical weather prediction model. The method shows an improvement of precipitation prediction within the radar location area in both the rain rates and spatial pattern presentation. With the assimilation of radar data, the model simulates larger water content in the middle troposphere within the layer from 1 to 6 km, with major variations at 2.5–3 km; it also reproduces better the mesoscale belt and cell patterns of precipitation fields.

Due to their strong immersion and real-time interactivity, helmet-mounted VR devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough and will continue to coexist with PC-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to solve the heterogeneous problems caused by various types of clients, and support implementation of virtual crowd evacuation experiments, with large numbers of concurrent participants. In this study, we developed an HDVGE framework and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture and proposed an abstract interaction layer, a protocol-based interaction algorithm and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system, PC and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency LAN environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE could serve as a new means of obtaining observational data about individual and group behavior in support of human geography research.

As part of valorisation of agricultural waste into bioactive compounds, a series of structurally novel oleanolic acid ((3β-hydroxyolean-12-en-28-oic acid, OA-1)-phtalimidines (isoindolinones) conjugates 18a-v bearing 1,2,3-triazole moieties were designed and synthesized by treating a previously azide 4 prepared from OA-1 isolated from olive pomace (Olea europaea L.) with a wide range of propargylated phtalimidines using the Cu(I)-catalyzed click chemistry approach. OA-1 and its newly prepared analogues 18a-v were screened in vitro for their antibacterial activity against two Gram-positive bacteria, Staphylococcus aureus and Listeria monocytogenes; and two Gram-negative bacteria, Salmonella thyphimurium and Pseudomonas aeruginosa. Attractive results were obtained notably against L. monocytogenes. Compounds 18d, 18g and 18h exhibited the highest antibacterial activity when compared with OA-1 and other compounds in the series against tested pathogenic bacterial strains. Molecular docking study was performed to explore the binding mode of the most active derivatives against the target protein from L. monocytogenes. Results showed the importance of both hydrogen bonding and hydrophobic interactions with the target protein and are in favor to the experimental data.

We start with the discussion on misapplication of classical probability theory by Feynman in his analysis of the two slit experiment (by following the critical argumentation of Koopman, Ballentine, and the author of this paper). The seed of Feynman's conclusion on the impossibility to apply the classical probabilistic description for the two slit experiment is treatment of conditional probabilities corresponding to different experimental contexts as unconditional ones. Then we move to the Bell type inequalities. Bell applied classical probability theory in the same manner as Feynman and, as can be expected, he also obtained the impossibility statement. In contrast to Feynman, he formulated his no-go statement not in the probabilistic terms, but by appealing to nonlocality. This note can be considered as a part of the author's attempts for getting rid off nonlocality from quantum physics.

The precise regulation of hinge moment and pitch angle driven by the pitch regulated mechanism is crucial for modulating thrust requirements and ensuring stable attitude control in Martian coaxial rotorcraft. Nonetheless, the aerodynamic hinge moment in rotorcraft presents time-dependent dynamic properties, posing significant challenges for accurate measurement and assessment for such characteristics. In this study, we delve into the detailed aerodynamic hinge moment characteristics associated with the pitch regulated mechanism of Mars rotorcraft under a spectrum of control strategies. A robust computational fluid dynamics model has been developed to simulate rotor’s aerodynamic loads, accompanied by a quantitative hinge moment characterization that takes into account the effects of varying rotor speeds and pitch angles. Our investigation has yielded a thorough understanding of the interplay between aerodynamic load behavior and rotor surface pressure distributions, leading to the creation of an empirical mapping model for hinge moments. To validate our findings, we engineered a specialized test apparatus capable of measuring the hinge moments of the pitch regulated mechanism, facilitating empirical assessments under replicated atmospheric conditions of both Earth and Mars. The result indicates aerodynamic hinge moments depend non-linearly on rotational speed, peaking at a 0° pitch angle and showing minimal sensitivity to pitch under 0°. Above 0°, hinge moments decrease, reaching a minimum at 15° before rising again. Simulation and experimental comparisons demonstrate that under Earth conditions, the aerodynamic performance and hinge moment errors are within 8.54% and 24.90%, respectively. For Mars conditions, errors remain below 11.62%, proving the CFD model’s reliability. This supports its application in the design and optimization of Mars rotorcraft systems, enhancing their flight control through accurate prediction of aerodynamic hinge moments across various pitch angles and speeds.

Estimating the infection risks in indoor environments comprises the assessment of the behavior of virus-laden aerosols, i.e. spreading, mixing, removal by air purifiers etc. A promising experimental approach is based on using non-hazardous surrogate aerosols of similar size, e.g. salt particles, to mimic the virus aerosol behavior. This manuscript addresses the issue how a successful transfer of such experiments can be accomplished. Corresponding experiments in two very different environments, a large community hall and a seminar room, with optional use of air purifiers in various constellations were conducted. While high particle concentrations are advantageous in terms of avoiding influence of background aerosol concentrations, it is shown that appropriate consideration of aggregation and settling are vital to theoretically describe the experimentally determined course of particle concentrations. A corresponding model equation for a well-mixed situation is derived and the required parameters are thoroughly determined in separate experiments independently. It is demonstrated that clean air delivery rates (CADR) of air purifiers determined with this approach may differ substantially from common approaches not explicitly taking into account aggregation.

3D bioprinting, like 3D printing, is a process that builds structures by depositing material layer by layer. In the specific case of bioprinting, these layers are composed of a biocompatible material together with living cells, allowing the creation of three-dimensional structures that recreate functional tissues and organs. Bioprinting cell-laden structures is complicated by the high rate of cell damage and stress that occurs during the process, caused by the high pressures and stresses to which they are subjected. To minimise damage during the process, it is important to study and optimise certain bioprinting conditions beforehand and to analyse how they affect the cells by means of computational simulations. In this review, both quantitative and qualitative data are collected to improve the nozzle geometry through computational simulation studies. Optimal ranges for nozzle diameter (0.2-1mm) and length (8-10mm; 300-900μm) have been defined, and recommendations for improving nozzle performance during bioprinting, such as the provision of an inner angle of 20-30°, an inner EDTA coating and a shear stress of less than 10kPa, have been gathered. Finally, based on the data collected, a design of experiments is proposed to obtain an optimal bioprinting configuration for a particular bioink.

Traditional potential transformers have problems of large volume, difficulty in insulation, iron core saturation, ferroresonance overvoltage and poor transient response characteristics. The voltage sensor based on the principle of electric field coupling and differential input structure does not need to contact the measured object or ground, and can avoid the above problems. However, it requires a sufficiently high capacitance between the differential electrodes to obtain sufficient accuracy and a high voltage division ratio. The existing method of using mutual capacitance between the differential electrodes will cause many problems and fail to meet the practical needs. To solve the above problems, this paper innovatively uses multi-layer ceramic capacitor to replace the mutual capacitance and designs a new type of voltage sensor. In addition, by using single bypass small resistance grounding method to increase the input impedance of the differential signal processing circuit, error of the sensor is further reduced. The experimental results show that the sensor has excellent accuracy and great transient response characteristics. The ratio error under power frequency is within ±0.5% and the phase error is within 1. The ratio error in the range of 500 Hz∼30 kHz is within ±5% and the phase error is within 5. Moreover, it has the advantages of low cost, miniaturization, flexible shape and easy to adjust the voltage division ratio. These characteristics indicate that the sensor has good voltage measurement and sensor network potential.

Chironomus riparius is a well-established model organism in various fields such as ecotoxicology and ecology, and therefore environmental preferences, ecological interactions and metabolic traits are well-studied. With the recent publication of a high-quality draft genome, as well as different population genetic parameters such as mutation and recombination rate, the species can be used as an alternative to the Drosophila models in experimental population genomics or molecular ecology. To facilitate access to this promising experimental model species for a wider range of researchers, we describe experimental methods to first create and sustain long term cultures of C. riparius and then use them to perform repeatable and comparable experiments for various research questions.

Corrugated board is a composite material characterized by strong orthotropy in both the elastic and plastic phases. Additionally, due to its layered structure, which consists of alternately arranged flat and wavy layers of paper, its mechanics becomes complicated. Therefore, a precisely selected set of laboratory tests is needed to correctly identify all the necessary material parameters of cardboard. The work attempts to define a full set of laboratory tests in order to determine material parameters describing the linear-elastic, orthotropic behavior of cardboard and the basic parameters defining popular plasticity criteria, such as the asymmetric orthotropic Hoffman criterion. For this purpose, sensitivity analysis and numerical models of several selected basic laboratory tests were used. Since the aim of the work is to choose appropriate laboratory tests necessary to correctly determine the material parameters of corrugated board (not its individual layers), the numerical models do not contain detailed cardboard geometry, but only its homogenized representation in the form of the flat shell models. All numerical models are based on the nonlinear finite element method and constitutive modelling in the form of UMAT subroutines implemented in the ABAQUS software. Based on the analyses, appropriate measurements and laboratory tests were indicated that are most sensitive to the material parameters sought.

Recent theory of tachyon neutrinos, providing an explanation for Dark Matter, leads to two sets of experiments for testing that theory. One predicts an enhanced signal in detection of the Cosmic Neutrino Background. The other compares models of Dark Matter used to fit data on galaxy rotation curves and also gravitational lensing. In addition, an apparent contradiction within the new theory, related to Dark Energy, is identified and addressed.

We investigated rainfall patterns in India over during the 60-year period from 1951 to 2010 and predicted changes for the next century (2051-2100) with an assumed 4K warming from large ensemble experiments (190 members). We focused on rainfall patterns during two periods of present-day climate (1951-1980 and 1981-2010) and their projected changes for the near and far future (2051-2080 and 2081-2110). Our analysis revealed that the northeastern region of India and some southern regions received higher rainfall during the period of 1951-2010. This finding is consistent with daily observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE). In a warming climate, rainfall events in India are predicted to carry more precipitation, with the northeast and southern regions experiencing stronger rainfall events. The frequency and intensity of these events (with more than 20mm of rainfall per day, on average) are also expected to increase. Overall, our study suggests that water-related disasters such as flooding and landslides could be much worse in India in the future due to climate warming.

Background: The human mind is multimodal. Yet most behavioral studies rely on century-old measures such as task accuracy and latency. To create a better understanding of human behavior and brain functionality, we should introduce other measures and analyze behavior from various aspects. However, it is technically complex and costly to design and implement the experiments that record multiple measures. To address this issue, a platform that allows synchronizing multiple measures from human behavior is needed. Method: This paper introduces an opensource platform named OpenSync, which can be used to synchronize multiple measures in neuroscience experiments. This platform helps to automatically integrate, synchronize and record physiological measures (e.g., electroencephalogram (EEG), galvanic skin response (GSR), eye-tracking, body motion, etc.), user input response (e.g., from mouse, keyboard, joystick, etc.), and task-related information (stimulus markers). In this paper, we explain the structure and details of OpenSync, provide two case studies in PsychoPy and Unity. Comparison with existing tools: Unlike proprietary systems (e.g., iMotions), OpenSync is free and it can be used inside any opensource experiment design software (e.g., PsychoPy, OpenSesame, Unity, etc., https://pypi.org/project/OpenSync/ and https://github.com/moeinrazavi/OpenSync_Unity). Results: Our experimental results show that the OpenSync platform is able to synchronize multiple measures with microsecond resolution.

We summarize how to obtain protein crystals from which better diffraction images can be obtained. In particular, we describe in detail the quality evaluation of the protein sample, the crystallization methods and crystallization conditions, the flash-cooling protection of the crystal, and the crystallization under a microgravity environment.

In this work, we have studied the propagation of ultrasonic waves of lysozyme solutions characterized by different degrees of aggregation and networking. The experimental investigation has been performed by means of the Transient Grating (TG) spectroscopy as a function of temperature; this technique enables to measure the ultrasonic acoustic proprieties over a wide time window, ranging from nanoseconds to milliseconds. The fitting of the measured TG signal allows the extraction of several dynamic properties, here we focused on the speed and the damping rate of sound. The temperature variation induces in the lysozyme solutions a series of processes: protein folding-unfolding, aggregation and sol-gel transition. Our TG investigation shows how these self-assembling phenomena modulate the sound propagation, affecting both the velocity and the damping rate of the ultrasonic waves. In particular, the damping of ultrasonic acoustic waves proves to be a dynamic property very sensitive to the protein conformational rearrangements and aggregation processes.

The challenges to sustainability governance across multiple geographical/cultural contexts lead us to the “piecemeal engineering” idea advocated by the philosopher Karl Popper, which explicitly considers context. We argue for adopting the piecemeal engineering approach, augmented by adaptive policies and modern (online) collaboration platforms to maximize the prospects of sustainable practices worldwide. This recommended course is not intended to be a theory in itself. Rather, it is a well-grounded, practical and practicable stop-gap measure in times when complexity and change outpace theories and strategies. We present a philosophical foundation for this “Augmented Popperian Experimentation.” Focusing on The Water Network (the largest collaborative platform for water researchers and professionals), we show that sustainability-oriented organizations in the water realm and others are inching toward the practice we advocate. We discuss implications.

It has been an outstanding challenge for global climate models to simulate and predict East Asia (EA) summer monsoon (EASM) rainfall. This study evaluates the dynamical hindcast skills with the newly developed Nanjing University of Information Science and Technology Earth System Model version 3.0 (NESM3.0). To improve the poor prediction of an earlier version of NESM3.0, we have modified convective parameterization schemes to suppress excessive deep convection and enhance insufficient shallow and stratiform clouds. The new version of NESM3.0 with modified parameterizations (MOD hereafter) yields significantly improved rainfall prediction in the northern and southern China but not over the Yangtze River Valley. The improved prediction is primarily attributed to the improvements in the predicted climatological summer mean rainfall and circulations, seasonal march of the subtropical rain belt, Nino 3.4 SST anomaly, and the rainfall anomalies associated with the development and decay of El Nino events. However, the MOD still has notable biases in the predicted leading mode of interannual variability of precipitation. The leading mode captures the dry (wet) anomalies over the South China Sea (northern EA) but misplaced precipitation anomalies over the Yangtze River Valley. The model can capture the interannual variation of the circulation indices very well, but the bias in the circulation-rainfall connection caused predicted rainfall errors. The results here suggest that over EA land regions, the skillful rainfall prediction relies on not only model’s capability in predicting better summer mean and seasonal march of rainfall and ENSO teleconnection with EASM, but also accurate prediction of the leading modes of interannual variability.

Entangled metallic wire material (EMWM) can be utilized as a novel elastic element in vibration isolation devices for mechanical actuators. This paper presents a vibration experiment aimed at investigating the degradation behavior of mechanical performance in EMWM under cyclic compressive environment. An electric vibration testing system, coupled with an isolation structure, is employed to apply compressive loads to the EMWM specimens. Through visual observations and quasi-static compression tests, the variations in geometric morphology and mechanical properties are studied, considering different relative densities and stress amplitudes. The results incidate a significant reduction in the compressed dimension of the specimens as the number of cycles increases, without any wire fractures or wear. Moreover, the mechanical properties exhibit an increasing secant modulus and a decreasing loss factor. These variations ultimately lead to a gradual deviation of the vibration characteristics of the isolation structure from its design state. To predict the mechanical property degradation of EMWM, prediction models are proposed, incorporating dimension, modulus and damping by fitting the obtained results. This research provides valuable experimental data and presents an effective method to determine the operational lifetime of vibration isolators utilizing EMWM.

Dysregulation of the arsenal of cells, growth factors, cytokines, proteases, and proteins involved in wound healing (WH), could delay the healing process. Plants and plant-derived molecules (PDMs) have been used as medicines for skin wounds. This study aimed to select and evaluate the pro-healing capacity of PMDs. Thirty-three PDMs and 10 therapeutic targets (TTs) were chosen for molecular docking and PDMs: aristolochic acid (AA), α-copaene, selinenes, β-caryophyllene (BC), and BCoxide, and TTs: metalloproteinase (MMP) 3, MMP13, and tumor necrosis factor (TNF α) for molecular dynamics. The bests inhibitor was AA, but because of its toxicity, BC and BCoxide were chosen for evaluation in HaCat cells and BALB/c mice excisional model. Both compounds were not toxic, and higher percentages of lesion area closure (LAC) were induced by BC treatment (p<0.05). All mice healed; however, an initial delay of LAC was induced by 0.05, 0.1% BCoxide, 0.1% BC, and allantoin. At the end of treatment, lower numbers of mast cells, level of cellular infiltrates (except 1% BCoxide and 0.05, 0.5%BC), epidermal thickness tendency (except 0.1, 0.5% BCoxide), and higher values of SC-thickness, and collagen fibers only by 0.5% BC treatment compared with untreated control (p<0.05) were observed. Our results show that BC is a promising multitarget candidate for wound healing treatment and optimization campaigns.

Understanding the chalk-fluid interactions and the associated mineralogical and mechanical alteration at sub-micron scale are major goals in Enhanced Oil Recovery. Mechanical strength, porosity, and permeability of chalk are linked to mineral dissolution that occurs during brine injections, and affect the reservoir potential. This paper presents a novel "single grain" methodology to recognize the varieties of carbonates in rocks and loose sediments: Raman spectroscopy is a non-destructive, quick, and user-friendly technique representing a powerful tool to identify minerals down to 1 µm. An innovative working technique for oil exploration is proposed, as the mineralogy of micron-sized crystals grown in two flooded chalk samples (Liége, Belgium) was successfully investigated by Raman spectroscopy. The drilled chalk cores were flooded with MgCl₂ for c. 1.5 (Long Term Test) and 3 years (Ultra Long Term Test) under North Sea reservoir conditions (Long Term Test: 130°C, 1 PV/day, 9.3 MPa effective stress; Ultra Long Term Test: 130°C, varying between 1-3 PV/day, 10.4 MPa effective stress). Raman spectroscopy was able to identify the presence of recrystallized magnesite along the core of the Long Term Test up to 4 cm from the injection surface, down to the crystal size of 1-2 µm. In the Ultra Long Term Test core the growth of MgCO₃ affected nearly the entire core (7 cm). In both samples, no dolomite or high-magnesium calcite secondary growth could be detected when analysing 557 and 90 Raman spectra on the Long and Ultra Long Term Test, respectively. This study can offer Raman spectroscopy as a breakthrough tool in petroleum exploration of unconventional reservoirs, due to its quickness, spatial resolution, and non-destructive acquisition of data. These characteristics would encourage its use coupled with electron microscopes and energy dispersive systems or even electron microprobe studies.

For order-of-addition experiments, the response is affected by the addition order of the experimental materials. Consequently, the main interest focuses on creating a predictive model and an optimal design for optimizing the response. Van Nostrand (1995) proposed the pairwise-order (PWO) model for detecting PWO effects. Under the PWO model, the full PWO design is optimal under various criteria but is often unaffordable because of the large run size. In this paper, we consider the D-, A- and M.S.-optimal fractional PWO designs. We first present some results on information matrices. Then, a flexible and efficient algorithm is given for generating these optimal PWO designs. Numerical simulation shows that the generated design has an appealing efficiency in comparison with the full PWO design, though with only a small fraction of runs. Several comparisons with existing designs illustrate that the generated designs achieve better efficiencies, and the best PWO designs and some selected 100% efficient PWO designs generated by the new algorithm are reported.

Adding steel fibers to concrete improves the capacity in tension-driven failure modes. An example is the shear capacity in steel fiber reinforced concrete (SFRC) beams with longitudinal reinforcement and without shear reinforcement. Since no mechanical models exist that can fully describe the behavior of SFRC beams without shear reinforcement failing in shear, a number of empirical equations have been suggested in the past. This paper compiles the existing empirical equations and code provisions for the prediction of the shear capacity of SFRC beams failing in shear as well as a database of 487 experiments reported in the literature. The experimental shear capacities from the database are then compared to the prediction equations. This comparison shows a large scatter on the ratio of experimental to predicted values. The practice of defining the tensile strength of SFRC based on different experiments internationally makes the comparison difficult. For design purposes, the code prediction methods based on the Eurocode shear expression provide reasonable results (with coefficients of variation on the ratio of tested to predicted results of 27% - 29%). None of the currently available methods properly describe the behavior of SFRC beams failing in shear. As such, this work shows the need for studies that address the different shear-carrying mechanisms in SFRC and its crack kinematics.

We examine the use of deep learning (neural networks) to predict the movement of the S&P 500 Index using past returns of all the stocks in the index. Our analysis finds that the future direction of the S&P 500 index can be weakly predicted by the prior movements of the underlying stocks in the index. Decomposition of the prediction error indicates that most of the lack of predictability comes from randomness and only a little from nonstationarity. We believe this is the first test of S&P500 market efficiency that uses a very large information set, and it extends the domain of weak-form market efficiency tests.

After a time away from the classrooms and laboratories due to the global pandemic, the return to the teaching activities during the semester represented a challenge to both teachers and students. Our particular situation in a Microbial Physiology course was the necessity of imparting in a shorter time, laboratory practices that usually take longer. This article describes a two-week long laboratory exercise that covers several concepts in an interrelated way: conjugation as a gene transfer mechanism, regulation of microbial physiology, production of secondary metabolites, degradation of macromolecules and biofilm formation. Utilizing a Quorum Quenching (QQ) strategy, the Quorum Sensing (QS) system of Pseudomonas aeruginosa is first attenuated. Then, phenotypes regulated by QS are evidenced. QS is a regulatory mechanism of the microbial physiology that relays on signal molecules. QS is related in P. aeruginosa to several virulence factors, some of which are exploited in the laboratory practices presented in this work. QQ is phenomenon by which QS is interrupted or attenuated. We utilized a QQ approach based on the enzymatic degradation of the P. aeruginosa QS signals in order to put in evidence QS-regulated traits that are relevant for our Microbial Physiology course.

Development and engineering of protein crystals regarding mechanical stability and crystallizability occurs on a small scale. Later in the process chain of industrial production however, filtration properties are important to separate the crystals from mother liquor. Many protein crystals are sensitive to mechanical stress which is why it is important to know the filtration behavior early on. In this study we analyze settling and filtration behavior of isometric, rod-like and needle shaped lysozyme and rod-like alcohol dehydrogenase (ADH) crystals on a small scale using an optical analytical centrifuge. Needle shaped lysozyme and rod-like ADH crystals show compressible material behavior. With the results from settling and filtration experiments the flux density function is calculated and modeled which can be used to describe the whole settling and permeation process in dependency of the solids volume fraction. This is also an issue for simulations of industrial processes.

In order to clarify the nature of the halogen bond (XB), we considered the prototype noble gas – dihalogen molecule (Ng-X2) systems, focusing on the nature, range and strength of the interaction. We exploited data gained from molecular beam scattering experiments with the measure of interference effects to obtain a suitable formulation of the interaction potential, with the support of high-level ab initio calculations, and charge displacement analysis. The essential interaction components involved in the Ng-X2 adducts have been characterized, pointing at their critical balance in the definition of the XB. Particular emphasis is devoted to the energy stability of the orientational Ng-X2 isomers, the barrier for the X2 hindered rotation, and the influence of the X2 electronic state. The present integrated study returns reliable force fields for molecular dynamics simulations in Ng-X2 complexes that can be extended to systems with increasing complexity and whose properties depend on the selective formation of XB.

Framed experiments and games are a useful medium to understand how context affects individual and group decision-making. They are particularly relevant for field research in agriculture, where alternative experimental designs can be costly and unfeasible. After a systematic review of the literature, we found that the volume of published studies employing coordination and cooperation games increased during the 2000-2020 period. In recent years, there has been greater attention given to natural resource management, conservation, and ecology areas, especially in strategic regions for agriculture sustainability. Other games, such as trust and risk games, have come to be regarded as standards of framed field experiments in agriculture. Regardless of sectoral focus, most games' results are subject to internal and external validity criticism. In particular, a significant portion of the games showed potential recruitment biases against women and no opportunities for a continued impact assessment. However, games' validity should be judged on a case-by-case basis. Specific cultural aspects of games might reflect the real context, and generalizing games' conclusions to different settings is often constrained by cost and utility. Overall, games in agriculture could benefit from more significant, frequent, and inclusive experiments and data – all possibilities offered by digital technology. Present-day physical distance restrictions may accelerate this shift. New technologies and engaging ways to approach farmers might represent a turning point for games in agriculture in the 21st century.

The objective of this paper is to investigate the debonding behavior of the interface between continuously and discontinuously fiber reinforced thermoplastics using the climbing drum peel test. The study emphasizes the importance of considering different climatic boundary conditions due to the properties of thermoplastics. Specimens with varying moisture contents are prepared and tested. It is observed that an increase in moisture content initially results in a higher fracture surface energy being required to separate the two materials, but a further increase results in a decrease of the required energy. The study presents an explanatory model of increasing plasticization of the polymer due to increased polymer chain mobility, which results in more deformation energy being required to propagate the crack. The experiment is also modeled numerically for the first time with cohesive surfaces, which successfully reproduces the force-displacement curve in the experiment.

Sources are classified into two depending upon the factor of reviving. These sources, which cannot be revived into their original shape once they are consumed, are considered as nonrenewable energy resources, i.e., (coal, fuel) Moreover, those energy resources which are revivable to the original condition even after being consumed are known as renewable energy resources, i.e., (wind, solar, hydel) Renewable energy is a cost-effective way to generate clean and green electrical energy Now a day’s majority of the countries are paying heed to energy generation from RES Pakistan is mostly relying on conventional energy resources which are mostly nonrenewable in nature coal, fuel is one of the major resources, and with the advent of time their prices are increasing on the other hand RES have great potential in the country with the deployment of RES greater reliability and an effective power system can be obtained In this thesis, a similar concept is being used and a hybrid power system is proposed which is composed of intermixing of renewable and nonrenewable sources The Source side is composed of solar, wind, fuel cells which will be used in an optimal manner to serve load The goal is to provide an economical, reliable, uninterruptable power supply. This is achieved by optimal controller (PI, PD, PID, FOPID) Optimization techniques are applied to the controllers to achieve the desired results. Advanced algorithms (Particle swarm optimization, Flower Pollination Algorithm) will be used to extract the desired output from the controller Detailed comparison in the form of tables and results will be provided, which will highlight the efficiency of the proposed system.

The aim of this work is to introduce a mathematical model representing the evolution of the temperature in a vegetation cover and the ground underneath it. Vegetation, and its interaction with soil, plays a very important role in the protection of soil surface from the action of sun and precipitations. A reduction in the vegetated mass increase the risk of desertification, soil erosion or surface runoffs which which can give rise to soil loss and sediment retention. These processes can favour climate change and global warming, which are major concerns nowadays. The mathematical model presented takes into account the main processes involved in vegetation cover and the interaction with the soil, among which, we can mention the Leaf Area Index, which is a dimensionless quantity defined as the one-sided green leaf area per unit ground surface area, or albedo and co-albedo which are clearly influenced by the vegetation. It is also considered a nonlinear heat capacity in the soil which incorporates the latent heat of fusion, when the phase change takes place. The numerical technique used to solve the mathematical model is based on a finite volume scheme with Weighted Essentially Non Oscillatory technique for spatial reconstruction and the third order Runge-Kutta Total Variation Diminishing numerical scheme is used for time integration. Some numerical examples are solved to obtain the distribution of temperature both in the vegetation cover and the soil.

AbstractTo overcome the negative impacts on the environment and other problems associated with fossil fuels have forced many countries to inquire into and change to environmentally friendly alternatives that are renewable to sustain the increasing energy demand. Solar energy is one of the best renewable energy sources with the least negative impacts on the environment. Different countries have formulated solar energy policies to reduce dependence on fossil fuel and increasing domestic energy production by solar energy. According to the 2010 BP Statistical Energy Survey, the world cumulative installed solar energy capacity was 22928.9 MW in 2009, a change of 46.9% compared to 2008. In this study, a PV generation system has been modeled and installed considering uncertain whether based on the hourly wind speed data of New York City (NYC) of year 2014. Regression models has been used to forecast the hourly, weekly, and monthly wind speed of NYC year 2014. Design of experiment (DOE) has been used to determine the optimal panel size (area), the battery capacity size, and other levels of factors.

A multitude of factors influence our well-being and health in everyday life. Some of which we are totally aware like a delayed train, car exhausts, or continuous work stress. Other factors influence us on a subconscious level, making them, however, not less important to address. One of these factors is the built environment surrounding us almost every second of our lives. The idea of biophilic design states the importance of natural elements implemented in architectural structures to improve the occupants’ health and well-being. Adding to this field of research, this article targets the impact of biophilic design on positive emotions and productivity by using novel approaches like Facial Emotion Recognition (FER) with Residual Masking Networks, and sentiment detection using Large Language Models. Two separate but related studies were conducted and their results reported here. The first study measured emotions of people when confronted with images of different kinds of architecture, via FER (Facial Emotion Recognition) and also via a user survey. We distinguish between self-stated emotions entered through a survey by study participants, and emotions detected from studying the participants’ faces through AI from our FER (Facial Emotion Recognition) system. We found clear trends for most detected emotions, and significant evidence for self-stated emotions, that architecture implementing biophilic design evokes more positive emotions than one that does not. The second study measures the influence of natural elements on productivity and team-engagement. We found that natural elements do influence productivity and sentiment positively; however, only the change in sentiment was shown to be significant. As our sample size, especially for the second study, was relatively small, future research will apply these ideas in a larger setup to acquire further evidence for the importance of biophilic design for human well-being and health.

We study the possibility of determining the octant of the neutrino mixing angle 23, that is, whether 23 > 45 or 23 < 45, in long baseline neutrino experiments. Here we numerically derived the sensitivity limits within which these experiments can determine, by measuring the probability of the  ! e transitions, the octant of 23 with a 5 certainty. The interference of the CP violation angle  with these limits, as well as the effects of the baseline length and the run-time ratio of neutrino and antineutrino modes of the beam have been analyzed.

Oleanolic acid (OA) being the ubiquitous compound in the plant kingdom is studied for both the neuroprotective and neurotoxic properties. The mechanism of acetylcholinesterase (AChE) inhibitory potential of OA is investigated using the molecular dynamic simulations (MD) and docking as well as membrane-like and in vitro tests. There are also applied SH-SY5Y human neuroblastoma cells as well as in vivo on the zebrafish tests. The inhibitory potential towards the AChE enzyme is examined using the TLC-bioautography assay (the IC50 value is 9.22 μM). The CH- interactions between the central fragment of the ligand molecule and the aromatic cluster created by the His440, Phe288, Phe290, Phe330, Phe331, Tyr121, Tyr334, Trp84 and Trp279 side chains are observed. The in vitro tests carried out on the SH-SY5Y cells indicates that the viability rate is reduced to 71.5%, 61%, and 43% at the concentrations of 100 µg/mL, 300 µg/mL, and 1000 µg/mL, respectively after 48 hours of incubation whereas cytotoxicity against the tested cell line with the IC50 value is 714.32 ± 32.40 µg/mL. The in vivo tests on the zebrafish prove that there is no difference between the control and experimental groups as regards the mortality rate and morphology (P>0.05).

Krill oil (KO) shows promise as a natural marine-derived ingredient for improving skin health. This study investigated its antioxidant, anti-inflammatory, anti-wrinkle, and moisturizing effects on skin cells and UVB-induced skin photoaging in hairless mice. In vitro assays on HDF, HaCaT, and B16/F10 cells, and in vivo experiments on 66 hairless mice, were conducted. Mice received oral KO administration (100, 200, or 400 mg/kg) once a day for 15 weeks and UVB radiation three times a week. In vitro, KO significantly countered UVB-induced oxidative stress, reduced wrinkles, and prevented skin water loss by enhancing collagen and hyaluronic synthesis. In vivo, all KO dosages showed dose-dependent inhibition of oxidative stress-induced inflammatory photoaging-related skin changes. Skin mRNA expressions for hyaluronan synthesis and collagen synthesis genes also increased dose-dependently after KO treatment. Histopathological analysis confirmed that Krill Oil (KO) ameliorated the damage caused by UVB-irradiated skin tissues. These findings suggest that KO may be a valuable intervention to mitigate UVB-induced skin photoaging and address various skin concerns.

Modern computational biology makes widespread use of mathematical models of biological systems, in particular systems of ordinary differential equations, as well as models of dynamic systems described in other formalisms, such as agent-based models. Parameters are numerical values of quantities reflecting certain properties of a modeled system and affecting model solu-tions. At the same time, depending on parameter values, different dynamic regimes - stationary, oscillatory, established as a result of transient modes of various types - can be implemented in the system. Predicting changes in the solution dynamics type depending on changes of model parameters is an important scientific task. Nevertheless, this problem does not have an analytical solution for all formalisms in a general case. The routinely used method of performing a series of computa-tional experiments, i.e. solving a series of direct problems with various sets of parameters fol-lowed by expert analysis of solution plots is labor-intensive with a large number of parameters and a decreasing step of the parametric grid. In this regard, the development of methods allow-ing to obtain and analyze information on a set of computational experiments in an aggregate form is relevant. This work is devoted to developing a method for visualization and classification of various dy-namic regimes of a model using a composition of the dynamic time warping (DTW-algorithm) and principal coordinates analysis (PCoA) methods. This method allows to obtain a qualitative visualisation of the results of the set of solutions of a mathematical model and to carry out the correspondence between the values of the model parameters and the type of dynamic regimes of its solutions. This method has been tested on the Lotka-Volterra model and artificial sets of var-ious dynamics

Laboratory experiments were performed to measure the supercritical CO2 (scCO2) storage ratio (%) of the conglomerate and sandstone in Janggi Basin, which are classified as rock in Korea available for CO2 storage. The scCO2 storage capacity was evaluated by direct measurement of the scCO2 amount replacing pore water in a reservoir rock core. The scCO2 sealing capacity of the cap rock (i.e., tuff and mudstone), was also compared by measuring the initial scCO2 seepage pressure (Δp) into the rock core. The measured average scCO2 storage ratio of the conglomerate and the sandstone in Janggi Basin was 30.7 % and 13.1 %, respectively, suggesting that the scCO2 storage capacity is greater than 360,000 metric tons in the Janggi Basin. The initial scCO2 seepage pressure of the tuff in the Janggi Basin was 15 bar and continuous scCO2 injection into the tuff core occurred at Δp higher than 20 bar. For the mudstone, the initial scCO2 seepage pressure was higher than 150 bar (10 times higher than that of the tuff), demonstrating that the mudstone is more suitable than the tuff to shield scCO2 leakage from the reservoir rock in the Janggi Basin.

We previously reported new superconductivity produced by an electrostatic field and a diffusion current in a semiconductor without refrigeration. In particular, the superconductivity was investigated theoretically and confirmed experimentally. Here, we determine that the derived superconducting quantum state can be reproduced in a capacitor. When circuits are formed with this new-type capacitor and diodes, a magnetic field is applied to the diodes’ depletion layer. The depletion layer is biased because of the conversion from the magnetic-field energy to electric-field energy, resulting in the diodes’ spontaneously emitting a current. Thus, the new-type capacitor is charged using no other energy source. This new phenomenon is described theoretically with assistance of initial experiments.

While SARS-CoV-2 uses angiotensin converting enzyme 2 (ACE2) as the receptor for cell entry, it is important to examine for other potential interactions between the virus and other cell receptors. Based on the clinical observation of low smoking prevalence among hospitalized COVID-19 patients, we recently identified a “toxin-like” amino acid (aa) sequence on the receptor binding domain of the spike glycoprotein of SARS-CoV-2 (aa 375-390) with homology to a sequence of a snake venom toxin, which could interact with nicotinic acetylcholine receptors (nAChRs). We now present computational molecular modelling and docking experiments using 3D structures of the SARS-CoV-2 spike glycoprotein and the extracellular domain of the nAChR alpha9 subunit. We identified an interaction between the aa381-386 of the SARS-CoV-2 spike glycoprotein and the aa189-192 of the extracellular domain of the nAChR alpha9 subunit, a region which forms the core of the “toxin-binding site” of the nAChRs. The mode of interaction is very similar to the interaction between the alpha9 nAChR and alpha-bungarotoxin. A similar interaction was observed between the pentameric alpha7 AChR and the SARS-CoV-2 spike glycoprotein. Our findings support the hypothesis that severe COVID-19 may be associated with disruption of the nicotinic cholinergic system which could be caused by an interaction between SARS-CoV-2 and nAChRs.

The rapid integration of Information and Communication Technology (ICT) is transforming the traditional electrical grid into a {\em Smart Grid}. Smart grids enable two-way communication and improved monitoring and control between utilities and customers. However, due to its heterogeneous nature, public exposure, and weak security at low-powered devices, the Smart Grid has vulnerabilities to various malicious threats, adversaries, and cyber attacks, which may affect cost and service availability. Additionally, when the systems' confidentiality, integrity, or availability are compromised, the resulting fallout can threaten national security and have cascading effects on human lives. Given the extreme consequences of an attack, smart-grid technology must be thoroughly tested for correct operation and security {\em before} it is deployed. As a result, vulnerability testing of smart grids, not only for correctness but for security purposes, has been the subject of numerous studies by academics, government agencies, and private companies. This paper reviews the vulnerabilities associated with the smart grid and spotlights simulation as the vulnerability testing methodology conducted in recent pertinent research works. It also presents various security aspects of the smart grid, including grid applications, system and network infrastructure and components, cyber threats and attacks, simulation, and different mitigation techniques. Finally, we analyze the gaps in the current research works, focusing on simulation. We briefly present a real-time simulation testbed that mimics customer behaviour and integrates hardware in the loop to apply attack methods, analyze vulnerabilities and risk mitigation associated with the smart grid system, and propose future work to improve the current framework.