With the growing popularity of smartphones, user identification has become an essential component of maintaining security and privacy. This study investigates how smartphone accelerometer data can be used to identify users, and it makes recommendations for the ideal application parts. Accelerometer data from the HMOG public dataset was used to train deep learning, conventional classifiers, and voting classifiers, which were then utilized to identify users. To enhance performance, feature selection and pre-processing techniques were researched. The results show that RFE feature selection outperforms other approaches and that LSTM followed by XGBoost has the best identification performance as indicated by a relatively large number of machine learning performance measures. The proposed identification system nevertheless performed well and outperformed existing methods, which were principally created and tested on the same HMOG public smartphone dataset, even with a larger number of users. Further work would be necessary for such an application to reach its full potential, though.

Misuse of measurement units and orientations leads to errors in scientific applications, Cyber Physical Systems (CPS), and IoT C/C++ programs. Standard type system are inadequate in preventing such errors. Although dimensional and orientational analysis in physics can manually detect these errors in equations, analyzing complex code with intricate physical computations is impractical. To overcome this challenge, we propose an advanced type system that incorporates units and orientations as integral components within a specialized type library. Our enhanced type system automatically detects potential errors during compile time by representing physical quantities as types and utilizing dimensional analysis, orientational analysis, and metaprogramming techniques. Our improved type system enables formal verification of C++ software, successfully verifying programs with extensive codebases. We also employ it for runtime verification of dynamic linking and pointer operations in C++ programs. The integration of compile-time verification, dimensional analysis, orientational analysis, and advanced type system enhances the robustness and accuracy of scientific applications, CPS, and IoT C/C++ programs. By leveraging these approaches, we ensure precise calculations and prevent errors related to measurement units and orientations, resulting in substantial improvements in reliability and accuracy.

We derive a quantum speed limit for unitary evolution for the case of mixed quantum states using the stronger uncertainty relation for mixed quantum states. This bound can be optimized over different choices of Hermitian operators for a better bound. We illustrate this with some examples and show its better performance with respect to three existing bounds for mixed quantum states.

This paper is concerned with the optimal linear quadratic Gaussian (LQG) control problem for discrete time-varying system with multiple input delays and multiplicative noises. The main contributions are two-fold. Firstly, when the state variables can be observed exactly, we obtain a necessary and suffcient condition for the multiple-delays system in terms of the non-homogeneous relationship between the state and costate, which is the solution to the coupled forward and backward stochastic difference equations. Secondly, when the state variables are partially observed, we derive a suboptimal linear output feedback controller for the discrete-time system based on the obtained results of the optimal LQG control. Numerical examples are shown to illustrate the proposed algorithm.

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

This paper applies a new artificial intelligence architecture, the Temporal Fusion Transformer (TFT), for the joint GDP forecasting of 25 OECD countries at different time horizons. This new attention-based architecture offers significant advantages over other deep learning methods. First, results are interpretable since the impact of each explanatory variable on each forecast can be calculated. Second, it allows to visualize persistent temporal patterns and to identify significant events and different regimes. Third, it provides quantile regressions and permits to train the model on multiple time series from different distributions. Results suggest that TFTs outperform regression models, especially in periods of turbulence such as the COVID-19 shock. Interesting economic interpretations are obtained depending on whether the country is domestic demand-led or export-led growth. In essence, TFT is revealed as a new tool that artificial intelligence provides to economists and policy makers, with enormous prospects for the future.

Respiratory rate is an important biomarker that indicates changes in the clinical condition of critically ill patients, so a surveillance tool that can accurately monitor the changing respiratory rate in real time is needed. Through investigating various pairs of machine learning models, we proposed new machine learning model for real-time respiratory rate estimation using photoplethysmogram. New photoplethysmogram-driven respiratory rate dataset(StMary) was collected from surgical intensive care unit of a tertiary referral hospital, using photoplethysmogram signal collector. For 50patients and 50healthy volunteers, 2-minute photoplethysmogram was collected for each subject twice. To evaluate the respiratory rate of subject, it was inputted into the deep neural network model we built, and dataset was splitted into training, validation, testing dataset, then 4-fold cross validation was exploited. Our deep neural network model trained with StMary and two public datasets(BIDMC and CapnoBase) individually, or selectively merged dataset had shown a low error rate in respiration rate measurements. Our model trained with StMary showed low mean absolute error score(1.0273±0.8965), and trained with 3 datasets(CapnoBase, BIDMC and StMary) showed a lower error rate(1.7359±1.6724) than the model trained with CapnoBase and BIDMC(1.9480±1.6751). We could verify the performance of model evaluating respiratory rate from photoplethysmogram, and our dataset could contribute as the clinical research data that supports artificial intelligence models evaluating respiratory rate and surveillance tools to test whether their monitoring function works properly.

Positive invariant set is an important concept of dynamic systems. The purpose of this paper is to study the sufficient and necessary conditions that the set of ellipsoids and the Lorenz cone are positive invariant sets of discrete time systems. By means of nonlinear programming and induced norm, the problem of positive invariance is formulated as an optimization problem, and the equivalent dual form optimization is also presented using the dual optimization method. Our results provide more alternative methods for determining the positive invariance of quadratic form convex sets from the point of view of optimization and dual optimization. The effectiveness of this method is demonstrated by numerical examples.

By shifting eating hours from daytime to nighttime, Ramadan intermittent fasting (RIF) illustrates how religious beliefs influence human dietary behavior and subsequent anthropometric and metabolic measures. This study aimed to investigate socioeconomic characteristics and examine how observing RIF affects dietary intake, anthropometric indices, and metabolic markers in healthy premenopausal (PRE-M) and post-menopausal (POST-M) Saudi women. Before starting the study, the authors hypothesized that the RIF improves some nutritional aspects and health in respondents, but not after breaking the fast.. The study includes 62 women (31 PRE-M, 21-43 years, and 31 POST-M, 44-68 years). A structured questionnaire was used to collect socioeconomic data. Physical activity, anthropometric, dietary, and biochemical assessments were undertaken before RIF and at the end of the third week of Ramadan fasting month. Socioeconomic data varied among respondents. Observing RIF was associated with significantly (at either p ≤ 0.01 or p ≤ 0.05) lower intake of calories, macronutrients, minerals (except for Na), and vitamins than before RIF for both groups. In terms of body mass index (BMI), and waist-to-height ratio (WHtR) for PRE-M, the percentage of overweight decreased significantly (p≤0.01) while obesity remained unchanged. In contrast, for POST-M, the percentage of overweight increased significantly (p≤0.05), but obesity decreased after RIF. Both groups' waist-to-hip ratio (WHR), body fat (BF), and fat mass (FM) decreased after RIF. High-density lipoprotein cholesterol (HDL-C), fasting blood glucose (FBG), triglycerides (TG), and blood pressure (SBP, DBP) were generally maintained at acceptable normal levels in most responders before and after RIF. However, low-density lipoprotein cholesterol (LDL-C) was significantly decreased during RIF than before, especially for POST-M. Age, occupation, and monthly income were the most important factors influencing women’s nutritional status and body fat. In conclusion, observing RIF by PRE- and POST-M Saudi women was associated with significant improvements in variable health indicators, with a few exceptions, particularly POST-M, and may help lower risk factors for chronic diseases.

Since the early 1990s, the European (ESA) and Italian (ASI) space agencies have managed and distributed a huge amount of satellite-recorded SAR data to the research community and private industries. Moreover, the availability of advanced cloud computing services implementing different multi-temporal SAR interferometry techniques allows the generation of deformation time series from massive SAR images. We exploit the information provided by a large PS dataset to determine the temporal trend of ground deformation and the relative deformation rate with millimetric accuracy to analyse the spatial and temporal distribution of land subsidence induced by water pumping from a deep confined aquifer in the Northern Valle Umbra Basin (Central Italy), exploiting 24 years of Permanent Scatterers – Interferometric SAR data archives. The SAR images were acquired between 1992 and 2016 by satellite ERS1/2, ENVISAT and Sentinel 1 ESA missions, and the COSMO-SkyMed ASI mission). We observe ground velocities and deformation geometries between 1992 and 2016, with displacements of more than 70 cm and velocities of up to 55 mm/yr and the results suggest that the shape and position of the surface ground displacement are controlled by the fault activity hidden under the valley deposits.

In the present study, a simple approximation expression is given for the relationship between the period and amplitude of a simple pendulum under magnetic action. The analytical solution presented for the given problem. Two numerical quadrature methods Simpson's and Boole's method were utilized to demonstrate a new approximation of the problem. The results of the numerical quadrature have been compared to the exact solution. Absolute and relative mistakes of the problem have been presented. The Matlab program 2013R has created a numerical method that is used to analyze the outcome, It has been determined that the comparison's outcomes attest to the method's suitability and correctness. Moreover, the results show that numerical solution is suitable for the problem.

The notion of oriented set is the basic elementary concept of the theory of changeable sets. The main motivation for the introduction of changeable sets was the sixth Hilbert problem, that is, the problem of mathematically rigorous formulation of the fundamentals of theoretical physics. In the present paper the necessary and sufficient condition of the existence of one-point time on an oriented set is established. From the intuitive point of view, one-point time is the time associated with the evolution of a system consisting of only one object (for example, from one material point). Namely, it is proven that the one-point time exists on the oriented set if and only if this oriented set is a quasi-chain. Also, using the obtained result, the problem of describing all possible images of linearly ordered sets is solved. This problem naturally arises in the theory of ordered sets.

This article presents the implementation of a model to estimate the future price of commodities in the Brazilian market from time series of short-term technical evaluation. For this, data from two databases were used, one referring to the foreign market (opening values, maximum, minimum, closing, closing adjustment and volume) and the other, from the Brazilian market (the price of the day), considering commodities, sugar, cotton, corn, soybean and wheat. Subsequently, the technical indicators were calculated from the TA-Lib technical analysis library. Pearson’s correlation coefficient was applied, records with low correlation were removed, and then the database was consolidated. From the pre-processed data, Long Short-Term Memory (LSTM) recurrent neural networks were used to perform data prediction at the one and three day interval. These models were evaluated using the mean square error (MSE), obtaining results between 0.00010 and 0.00037 on test data one day ahead, and from 0.00017 to 0.00042 three days ahead. However, based on the results obtained, it was observed that the developed model obtained a promising forecasting performance for all the commodities evaluated. As a main contribution, there is the consolidation of databases that can be used in future scientific research. Furthermore, based on its interpretation, it can assist in decision making regarding the buying and selling of commodities to increase financial gains.

The continuous nature of space and time is a fundamental tenet of many scientific endeavours. That digital representation imposes granularity is well recognized but whether it is possible to address space completely remains unanswered. Part 1 argues that Hales’ proof of Keppler’s conjecture on the packing of hard spheres suggests the answer to be ‘no’, providing examples of why this matters in GIS generally and spatio-temporal GIS in particular. Part 2 seeks to resolve the dichotomy between continuous and granular space, showing how a continuous space may be emergent over a random graph. However, its projection into 3D/4D imposes granularity. Perhaps surprisingly, representing space and time as locally conjugate may be key to addressing a ‘smooth’ spatial continuum. This insight leads to the suggestion of Face Centered Cubic Packing as a space-time topology but also raises further questions for spatio-temporal representation.

Dolomite is a common type of natural soluble rock. The strength of rock decreases under the action of corrosion, which has a significant impact on the self-stability and long-term safety of the tunnel surrounding the rock. To reveal the microscopic structure evolution and strength-damage law of carbonate rock caused by corrosion, chemical corrosion, rock uniaxial compression, and electron microscope scanning tests are conducted at different pH values on the dolomite of the Doushantuo Formation. The rock dissolution at different pH values exhibits four typical stages: the initial dissolution stage, secondary dissolution acceleration stage, stable dissolution rate stage, and dissolution attenuation stage. During the dissolution process, the initial dissolution rate is 25.91 times that of the stable stage, and the maximum strength attenuation is 76.2% after 21 days of dissolution. For macroscopic failure, the rock is developed from 1 to 2 external fractures to multiple internal and external fractures and penetrated, and the specimen transforms from brittle to flexible. For microstructure, the sample exhibits corrosion characteristics along the joint surface, intensified corrosion at the edge, etc. The porosity increase rate is 0.6%/d; however, the length–width ratio of the pores is maintained at 1.7–1.85, indicating that the development rate of pores in different directions is similar. The results of this study have enriched the study of the dolomite dissolution mechanism and have important reference value for the stability evaluation of tunnel surrounding rock in karst environments.

The efficacy of time-restricted feeding for weight loss has not been established as prior studies were limited by lack of controlled isocaloric designs. This study describes the design and implementation of a controlled feeding study evaluating time-restricted feeding. We designed a randomized, controlled, parallel-arm, feeding study comparing time restricted feeding (TRF) to a usual feeding pattern (UFP) for the primary outcome of weight change. Participants were aged 18-69 years with prediabetes and obesity. TRF consumed 80% of calories by 1300, and UFP consumed ≥50% of calories after 1700. Both arms consumed identical macro- and micro-nutrients, based on a healthy palatable diet. We calculated individual calorie requirements which were maintained throughout the intervention. We randomized 41 participants who all completed the study. The desired distribution of calories across feeding windows in both arms was achieved, as were weekly averages for macronutrients and micronutrients. All randomized participants completed the study. We actively monitored participants and adapted diets to facilitate adherence. We provide the first report, to our knowledge, on the design and implementation of a feeding study that isolated the effect of meal timing on weight, while maintaining constant caloric intake and identical diets during the study period.

To improve the monitoring of the electrical power grid it is necessary to evaluate the influence of contamination in relation to leakage current and its progression to a disruptive discharge. In this paper, insulators were tested in a saline chamber to simulate the increase of salt contamination on their surface, and to evaluate the supportability of these components. From the time series forecasting of the leakage current, it is possible to evaluate the development of the fault before a flashover occurs. Choosing which method to use is always a difficult task since some models may have higher computational effort. In this paper, for a complete evaluation, the long short-term memory (LSTM), group method of data handling (GMDH), adaptive neuro-fuzzy inference system (ANFIS), bootstrap aggregation (bagging), sequential learning (boosting), random subspace, and stacked generalization (stacking) ensemble learning models are analyzed. A review and comparison of these well-established methods for time series forecasting is performed. From the results of the best structure of the model, the hyperparameters are evaluated and the Wavelet transform is used to obtain an enhanced model.

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

In this study, we present a numerical method for solving two--dimensional space--time fractional partial differential equations (FPDEs), where the solutions of the FPDEs are expanded in terms of the shifted Chebyshev polynomials. The numerical approximations are evaluated at the Chebyshev-Gauss-Lobatto points. In the case when the FPDE is nonlinear, we employ a Newton-Raphson approach to linearize the equation. Both the linear and nonlinear cases lead to a consistent system of linear algebraic equations. The scheme is tested on selected FPDEs and the numerical results show that the proposed numerical scheme is accurate and computationally efficient in terms of CPU times. To establish the accuracy of the method, we also present an error analysis which shows the convergence of the numerical method. These positive attributes make the proposed method a good approach for solving two--dimensional fractional partial differential equations with both space and time fractional orders.

The temporal evolution of pandemics described by the susceptible-infectious-recovered (SIR)-compartment model is sensitively determined by the time dependence of the infection (a(t)) and recovery (u(t)) rates regulating the transitions from the susceptible to the infected and from the infected to the recovered compartment, respectively. Here approximated SIR-solutions for different time dependencies of the infection and recovery rates are derived which are based on the adiabatic approximation assuming time-dependent ratios k(t)=u(t)/a(t) varying slowly in comparison to the typical time characteristics of the pandemic wave. For such slow variations the available analytical approximations from the KSSIR-model, valid for a stationary value of the ratio k, are used to insert a-posteriori the adopted time-dependent ratio of the two rates. Instead of investigating endless different combinations of the time dependencies of the two rates a(t) and u(t) a suitably parameterized reduced time dependence of the ratio k(tau) is adopted. Together with the definition of the reduced time this parameterized ratio k(tau) allows us to cover a great variety of different time dependencies of the infection and recovery rates. The agreement between the solutions from the adiabatic approximation in its four different studied variants and the exact numerical solutions of the SIR-equations is remarkably good providing strong confidence in the accuracy of the proposed adiabatic approximation.

This paper describes the successful application of the Transformer model used in the natural language processing and vision tasks as a means of processing the time series of signals from gyroscope and accelerometer sensors for the classification of human activities. The Transformer model is based on deep neural networks with many layers which can generalize well on signals. All measured signals come from a smartphone placed in a waist bag. Activity prediction is sequence-to-sequence, each time step of the signal is assigned a designation of the performed activity. Emphasis is placed on attention mechanisms, which express individual dependencies between signal values within a time series. In comparison with another recent result, the recognition precision was improved from 89.67 percent to 99.2 percent. The transformer model should in the future be included among the top options in machine learning methods for human activity recognition.

This work focuses on analyzing acoustic emission (AE) signals as a means to predict failure in structures. Two main approaches are considered: (i) long-range correlation analysis using both the Hurst (H) and the Detrended Fluctuation Analysis (DFA) exponents, and (ii) natural time domain (NT) analysis. These methodologies are applied to the data collected from two application examples: a glass fiber reinforced polymeric plate and a spaghetti bridge model, where both structures were subjected to increasing loads until collapse. A traditional (AE) signal analysis is also performed to reference the study of the other methods. Results indicate that the proposed methods yield a reliable indication of failure in the studied structures.

The Einstein’s Boxes paradox was developed by Einstein, de Broglie, Heisenberg, and others to demonstrate the incompleteness of the Copenhagen Formulation of quantum mechanics. I explain the paradox using the Copenhagen Formulation. I then show how a Time-Symmetric Formulation of quantum mechanics resolves the paradox in the way envisioned by Einstein and de Broglie. Finally, I describe an experiment that can distinguish between these two formulations.

In the early 1980s it was well established that Leray solutions of the unforced Navier-Stokes equations in Rn decay in energy norm for large time. With the works of T. Miyakawa, M. Schonbek and others it is now known that the energy decay rate cannot in general be any faster than t^-(n+2)/4 and is typically much slower. In contrast, we show in this note that, given an arbitrary Leray solution u(.,t), the difference of any two Stokes approximations to the Navier-Stokes flow u(.,t) will always decay at least as fast as t^-(n+2)/4, no matter how slow the decay of || u(.,t) ||_L2 might happen to be.

The suspended test masses of gravitational-wave (GW) detectors require precise alignment to be able to operate the detector stably and with high sensitivity. This includes the continuous counter-acting of seismic disturbances, which, below a few Hertz, are not sufficiently reduced by the seismic isolation system. The residual angular motion of suspended test masses is further suppressed by the Angular Sensing and Control (ASC) system. However, in doing so, the angular motion can be enhanced by the ASC at higher frequencies where the seismic isolation system is very effective. This has led to sensitivity limitations between about 10 Hz and 25 Hz of the LIGO detectors in past observation runs. The observed ASC noise was larger than simple models predict, which means that more accurate detector models and new simulation tools are required. We present Lightsaber, a new time-domain simulator of the ASC in LIGO. The simulation is a nonlinear simulation of the optomechanical system consisting of the high-power cavity laser beam and the last two stages of suspension in LIGO including the ASC. The main noise inputs are power fluctuations of the laser beam at the input of the arm cavities, read-out noise of sensors used for the ASC, displacement noise from the suspension platforms, and noise introduced by the suspension damping loops. While the plant simulation uses local degrees of freedom of individual suspension systems, the control is applied on a global angular basis, which requires a conversion between the local and global bases for sensing and actuation. Some of the studies that can be done with this simulation concern mis-centering of the beam-spot (BS) position on the test masses, the role of laser power fluctuations for angular dynamics, and the role of the various nonlinear dynamics.

In this article, we assume that the beginning of the universe was before the Big Bang. In the beginning, all matter in the universe was combined in an infinitesimal spherical shape. This sphere was compressed to an incomprehensible value for a period, and then exploded and expanded time and space. We are referring to the negative time before the Big Bang. The evolution of the universe before the Big Bang, passing through the moment of the explosion to the end of the universe at the Big Rip, has been studied. In this article, we try to answer the questions; did the universe exist before the Big Bang? What is the origin of the universe and how did it arise? What are the stages of the evolution of the universe until the moment of the Big Rip? What is the length of time for the stages of this development?

Background: Transcranial direct current stimulation (tDCS) is a neuromodulatory technique that delivers low levels of a constant current via scalp electrodes to specifically targeted areas of the brain. The effects of tDCS on whole-body exercise performance has been of interest in recent literature. The purpose of the current investigation was to investigate if tDCS, administered via Halo Sport, influences time trial performance in trained cyclists, and if changes in exercise performance are associated with prefrontal cortex (PFC) activation and/or muscle oxygenation (SmO2). Methods: Twelve recreationally trained cyclists volunteered to participate in two 10-kilometer time trials following 20 minutes of tDCS or a sham condition. Results: T-tests showed there was no significant difference in performance (time to completion) or physiological measures (BLa-, HR, SmO2, PFC oxygenation) between the Halo and sham conditions. Conclusions: These results indicate that the application of tDCS via Halo Sport does not induce changes in exercise performance or related physiological parameters during a 10-kilometer cycling time trial.

We utilize how Weber in 1961 initiated the process of quantization of early universe fields to the problem of what may be emitted at the mouth of a wormhole. While the wormhole models are well developed, there is as of yet no consensus as to how, say GW or other signals from a wormhole mouth could be quantized, or made to be in adherence to a procedure Weber cribbed from Feynman, in 1961. In addition, we utilize an approximation for the Hubble parameter parameterized from Temperature using Sarkar’s H ~ Temperature relations, as given in the text . Finally after doing this we go to the Energy as E also ~ Temperature, and from there use E (energy) as ~ signal frequency. This gives us an idea of how to estimate frequency generated at the mouth of a wormhole.

Tomography based on cosmic muon absorption is a rising technique because of its versatility and its consolidation as geophysics tool over the past decade. It allows to address major societal issues such as long-term stability of natural and man-made large infrastructures or sustainable underwater management. Traditionally, muon trackers consist of hodoscopes or multilayer detectors. For applications with challenging available volumes or wide field of view required, a thin time projection chamber (TPC) associated with a Micromegas readout plane can provide a good tradeoff between compactness and performance. This paper details the design of such a TPC aiming at maximizing primary signal and minimizing track reconstruction artifacts. The results of the measurements performed during a case study addressing the aforementioned applications are discussed. The current works lines and perspectives of the project are also presented.

Purpose of the study was to explore the association of cognition with hazardous drinking, binge drinking and alcohol use disorder in schizophrenia and schizoaffective disorder. Cognitive deficits are common in schizophrenia. Alcohol might be associated with additional cognitive impairment in schizophrenia patients. The study population included 3362 schizophrenia and schizoaffective disorder patients in Finland. Hazardous drinking was screened with the AUDIT-C (Alcohol Use Disorders Identification Test for Consumption) screening tool. Binge drinking was obtained from the AUDIT-C. Alcohol use disorder (AUD) diagnoses were obtained from the national registrar data. Participants performed two computerized tasks from the Cambridge automated neuropsychological test battery (CANTAB) on tablet computer: the 5-choice serial reaction time task (5-CSRTT), or, reaction time (RT) test and the Paired Associative Learning (PAL) test. Association of alcohol use with RT test and PAL test was analyzed with log-linear regression and logistic regression, respectively. After adjustment for age, education and age at first psychotic episode, hazardous drinking in females was associated with lower median RT. Compared to never binge drinkers, male and female participants drinking 6 or more doses of alcohol monthly or less had lower median RT. In the PAL test both first trial memory score (FTMS) and total errors adjusted score (TEAS) were associated with better performance in males drinking 6 or more doses of alcohol weekly or more and in females drinking 6 or more doses monthly or less. Higher PAL TEAS was associated with AUD in females Some positive associations between alcohol and cognition were found in male and female schizophrenia and schizoaffective disorder patients with hazardous drinking and binge drinking.

Unique analytical challenges arise when drivers, who face a route choice between a toll lane and a set of free lanes, have different values of time. The most complex situation is one in which multiple sub-populations of drivers exist, each with their own unique mean and coefficient of variation of value of time. This situation, when imbedded within a larger network cannot be tackled using existing planning models, and consequently is usually only approximated. This paper examines these different approximations, the resulting numerical solutions and the implications of these approximations on the estimate of the number of expected toll lane users. The paper also shows how this problem can be solved using a combined traffic assignment/simulation model. The first part of this paper develops an analytical formulation for solving the toll lane scenario using the “value of time” representations range from the simplest to the most complex. It is shown that one of the most critical issues is a determination of who the marginal users are of the toll lane, at each level of usage, as the perceived disutility of the last marginal toll lane user depends dynamically upon that driver’s value of time. Analytical formulations based on these different approximations are then solved numerically in the second part of the paper. These numerical solutions show that significant different lane use estimates result, depending upon the representation of value of time. Consequently, it is clear that solving this problem with the fewest approximations is both of theoretical and practical importance. The third part of the paper illustrates the solution to the toll lane problem, with each level of approximation, using a combined traffic assignment/simulation model. The simulated resulting estimates of the toll lane usage for each case matches both the relative and absolute trends found in analytical solutions. However, the solution using the assignment/simulation model is not only much faster and simpler to obtain, but is also scalable both in size and complexity. The additional complexities, that are associated with a less approximate representation of value of time, should therefore be incorporated in all future assessments of toll lane facilities, be they analyzed analytically or through simulation.

We reduplicate the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, given zero-point energy calculation with an unexpected “length’ added to the ‘width’ of a graviton wave just prior to specifying the creation of ‘gravitons’, while using Karen Freeze’s criteria as to the breakup of primordial black holes to give radiation era contributions to GW generation. The GW generation will be when there is sufficient early universe density so as to break apart Relic Black holes of the order of Planck mass (10^15 grams) which isabout when the mass of relic black holes is created, 10^-27 or so seconds after expansion starts. Need to state a key result will be in the initial potential V calculated, in terms of other input variables

The efficiency of performance in various sports has the development of certain specific skills at its core. In ice hockey, both the technical aspects (techniques, stance) and the cognitive ones (keeping attention on the puck, game strategy, etc.) are highly important. This study is aimed at the identification of specific features that determine the performance efficiency of professional hockey players. We used virtual reality (VR) to study the differences between professional ice hockey players and novices in terms of motor responses to the puck’s presentation on different levels of difficulty. The study involved 22 participants, 13 of them being professional ice hockey players (M_age=20±2.9; mean age of training experience М=14.18±3.8) and 9 being not experienced participants (M_age=20±1.4). The study showed that the stick response time of professional hockey players is significantly higher (0.98 ms vs 1.5 ms, p≤0.05) in more difficult situations close to a real game. Moreover, professionals proved to have more stable movement patterns of the knee and hip joints. They also make fewer head movements as a response to stimuli during all runs (0.66 vs 1.25, p≤0.05). Therefore, the results indicate specific spatial-temporal, technical and tactical, and energetic determinants, that ensure higher performance efficiency in hockey players

Significant progress has been made in understanding the computational and neural architecture that mediates eye and hand movements made in isolation. However, less is known about the mechanisms that control these movements when they are coordinated. Here, we outline our computational approaches using accumulation-to-threshold and race-to-threshold models to elucidate the mechanisms that initiate and inhibit these movements. We suggest that, depending on the behavioral context, the initiation and inhibition of coordinated eye-hand movements can operate in two modes- coupled and decoupled. The coupled-mode operates when the task context requires a tight coupling between the effectors; a common command initiates both effectors, and a unitary inhibitory process is responsible for stopping them. Conversely, the decoupled mode operates when the task context demands weaker coupling between the effectors; separate commands initiate the eye and hand, and separate inhibitory processes are responsible for stopping them. We hypothesize that higher-order control processes assess the behavioral context and choose the most appropriate mode. This computational architecture can explain heterogeneous results observed across many studies that have investigated the control of coordinated eye-hand movements and may also serve as a general framework to understand the control of complex multi-effector movements.

Non-orthogonal multiple access (NOMA) has been investigated to support massive connectivity for Internet-of-things (IoT) networks. However, since most IoT devices suffer from limited power and decoding capabilities, it is not desirable to pair a large number of devices simultaneously, which encourages two-user NOMA grouping. Additionally, most existing techniques have not considered the diversity in the target QoS of IoT devices, which may lead to spectrum inefficiency. Few investigations have partially considered that issue by using an order-based power allocation (OPA) approach, where the power is allocated according to the order to the user's target throughput within a priority-based NOMA (PNOMA) group. However, this does not fully capture the effects of diversity in the values of the users' target throughputs. In this work, we handle both problems by considering a throughput-based power allocation (TPA) approach, that captures the QoS diversity, within a three-users PNOMA group as a compromise between spectral efficiency and complexity. Specifically, we investigate the performance of a time-division PNOMA (TD-PNOMA) scheme, where the transmission time is divided into two-time slots with two-users per PNOMA group. The performance of such TD-PNOMA is compared with a fully PNOMA (F-PNOMA) scheme, where the three users share the whole transmission time, in terms of the ergodic capacity under imperfect successive interference cancellation (SIC). The results reveal the superiority of TPA compared with OPA approach in both schemes, besides that the throughput of both schemes can outperform each other under imperfect SIC based on the transmit signal-to-noise ratio and the deployment scenarios.

We first of all define the arrow of time. Definition of the arrow of time will allow choosing different initial starting points. One of the issues we will also discuss is the interconnection be-tween the arrow of time, entropy and quantum information. Seth Lloyd in his 2001 work made a linkage between entropy, bits, and information via an axiomatic approach involving time in-tervals. Our take is a bit more general. We will discuss as well the t’Hooft’s statement as to in-itial conditions and times arrow, and how different cosmological models may influence initial conditions. Spoilers alert, if a nonsingular start to expansion existed, this would provide the most straightforward way to avoid a datum from classical relativity. That is, that in the actual equations of classical GR, there is no reason to have time asymmetry. Time asymmetry is built into initial conditions and we will detail several candidates. The first half of the paper brings up cosmology models and forming the arrow of time. The second is related to entropy itself and the problem of information. .

We reduplicate the Book “Dark Energy” by M. Li, X-D. Li, and Y. Wang, zero-point energy calculation with an unexpected “length’ added to the ‘width’ of a graviton wavefunction just prior to the entrance of ‘gravitons’ to a small region of space-time prior to a nonsingular start to the universe. We compare this to a solution worked out using Klauder Enhanced quantization, for the same given problem. The solution of the first Cosmological Constant problem relies upon the geometry of the multiverse generalization of CCC cosmology which is explained in this paper. The second solution, used involves Klauder enhanced quantization. We look at energy given by our methods and compare and contrast it with the negative energy of the Rosen model for a mini sub universe and estimate GW frequencies

Long-term continuous time series of SO2 emissions are considered critical elements of both volcano monitoring and basic research into processes within magmatic systems. One highly successful framework for computing these fluxes involves reconstructing a representative time-averaged SO2 plume from which to estimate the SO2 source flux. Previous methods within this framework have used ancillary wind datasets from reanalysis or numerical weather prediction (NWP) to construct the mean plume and then again as a constrained parameter in the fitting. Additionally, traditional SO2 datasets from ultraviolet (UV) sensors lack altitude information which must be assumed to correctly calibrate the SO2 data and to capture the appropriate NWP wind level which can be a significant source of error. We have made novel modifications to this framework which do not rely on prior knowledge of the winds and therefore do not inherit errors associated with NWP winds. To perform the plume rotation, we modify a rudimentary computer vision algorithm designed for object detection in medical imaging to detect plume-like objects in gridded SO2 data. We then fit a solution to the general time-averaged dispersion of SO2 from a point source. We demonstrate these techniques using SO2 data generated by a newly developed probabilistic layer height and column loading algorithm designed for the Cross-track Infrared Sounder (CrIS), a hyperspectral infrared sensor aboard the Joint Polar Satellite System’s Suomi-NPP and NOAA-20 satellites. This SO2 data source is best suited to flux estimates at high-latitude volcanoes and at low-latitude, but high-altitude volcanoes. Of particular importance, IR SO2 data can fill an important data gap in the UV-based record: estimating SO2 emissions from high-latitude volcanoes through the polar winters when there is insufficient solar backscatter for UV sensors to be used.

Three different approaches show that, contrary to a longstanding conviction older than 160 years, the advance of Mercury perihelion can be achieved in Newtonian gravity with a very high precision by correctly analysing the situation without neglecting Mercury's mass. General relativity remains more precise than Newtonian physics, but Newtonian framework is more powerful than researchers and astronomers were thinking till now, at least for the case of Mercury. The Newtonian formula of the advance of planets' perihelion breaks down for the other planets. The predicted Newtonian result is indeed too large for Venus and Earth. Therefore, it is also shown that corrections due to gravitational and rotational time dilation, in an intermediate framework which analyzes gravity between Newton and Einstein, solve the problem. By adding such corrections, a result consistent with the one of general relativity is indeed obtained. Thus, the most important results of this paper are two: i) It is not correct that Newtonian theory cannot predict the anomalous rate of precession of the perihelion of planets' orbit. The real problem is instead that a pure Newtonian prediction is too large. ii) Perihelion's precession can be achieved with the same precision of general relativity by extending Newtonian gravity through the inclusion of gravitational and rotational time dilation effects. This second result is in agreement with a couple of recent and interesting papers of Hansen, Hartong and Obers. Differently from such papers, in the present work the importance of rotational time dilation is also highlighted.

This study investigates the impact of Foreign Direct Investment (FDI) on economic growth and examines the causality between FDI and economic growth in Bangladesh during 1972-2013. Gross Domestic Product (GDP), export performance (EXP), Foreign Direct Investment (FDI), and Gross Fixed Capital Formation (GFCF) are considered to capture the objective of the study. The study methodology includes some systematic steps. As the data used in the study is time-series in nature, the author employs unit root tests, and in this case, Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) tests are used. Then Johansen’s cointegration test, Granger causality test, regression with Newey-West Standard Error and Vector Error Correction Model (VECM) are applied. By using the ADF and PP test the study reveals that the variables of four-time series are integrated of I (1) i.e. they are stationary at first difference. Regression analysis result demonstrates that FDI has a positive effect on economic growth. The Granger Causality test discloses that there is a unidirectional relationship between FDI and economic growth. But the VECM estimation finds that in the long run FDI negatively affects economic growth.

Active galactic nuclei (AGNs) are throwing in the interstellar space huge jets of energy in the form of elementary particles. The calculation of the energy density of space in the centre of the black hole with the mass of the Sun shows that in the space-time singularity of such a black hole energy density of space there is so low that atoms become unstable and fall apart into elementary particles. In this sense, AGN is a rejuvenating system of the universe. It transforms its own old matter into fresh energy in the form of jets.

The novelty of 21st-century physics is the development of the “superfluid quantum vacuum” model, also named “superfluid quantum space” SQS. This SQS is replacing space-time as the fundamental arena of the universe. It also represents the model that has the potential of unifying four fundamental forces of the universe. SQS in this article is represented as the time-invariant fundamental field of the universe where time is merely the duration of material changes.

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

We discuss methods for calculation of critical indices and amplitudes from the perturbative expansions. Several important examples of the Stokes flow through 2D and 3D channels are brought up. Power series for the permeability derived for small values of amplitude are employed to calculation of various critical exponents in the regime of large amplitudes. Special nonlinear approximations valid for arbitrary values of the wave amplitude are derived from the expansions. The technique developed for critical phenomena is applied then for relaxation phenomena. The concept of time-translation invariance is discussed, its spontaneous violation and restoration considered. Emerging probabilistic patterns correspond to a local breakdown of time-translation invariance. Their evolution leads to the timetranslation symmetry complete (or partial) restoration. We estimate typical time extent, amplitude and direction for such restorative process. The new technique is based on explicit introduction of origin in time. After some transformations we come to the exponential and generalized, exponential-type solution with explicit finite time scale, which was only implicit in initial parametrization with polynomial approximation. The concept of crash as a relaxation phenomenon, consisting of time-translation invariance breaking and restoration, is put forward. COVID-19 related mini-crash in the time series for Shanghai Composite is discussed as an illustration.

Metasurfaces of gold (Au) nanoparticles on a SiO2-Si substrate were fabricated for the enhancement of second harmonic generation (SHG) using electron beam lithography and lift-off. Triangular Au nanoprisms which are non-centro-symmetric and support the second- order non-linearity were examined for SHG. The thickness of the SiO2 spacer is shown to be an efficient parameter to spectrally tune to maximise SHG. Electrical field enhancement at the fundamental wavelength was shown to define the intensity of the second harmonics. Numerical modeling of light enhancement was verified by experimental measurements of SHG and reflectivity spectra at the normal incidence. At the plasmonic resonance, SHG is enhanced up to ∼3.5×103 times for the optimised conditions.

In the present work, a novel conductive liquids method of study has been proposed. It is based on the phenomenon of radiofrequency anisotropy of electrolyte solution discovered by us. It arises in response to mechanical or acoustic excitation of the solution. We have observed the phenomenon during the development of an RF polarimetric contactless cardiograph. The electric field vector of the transmitted 433.82 MHz signal rotated after its transition through the pericardial region. That rotation depends on the change of blood acceleration when passing through the chambers of the heart and large vessels. It has also been revealed that rotation occurs after RF wave passage through the physiological saline (0.9% NaCl) subjected to any mechanical excitation inside it like a jet appearing or soundwave passing. No significant difference was detected experimentally between NaCl and KCl solutions behavior. It means that the mechanism of hydrodynamic separation of ions is apparently not suitable to explain the phenomenon. The response we have registered resembles the magnetization process of spin glasses. From the nature of the observed response, we have concluded that a fundamentally new physical effect is discovered. It may provide wide opportunities for remote measurement of the electrolyte solution parameters with polarized radio-frequency signals.

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99-1.22 cm for diameter at breast height (Δdbh), 44.14-55.49 cm2 for basal area (Δg), and 1.91-4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60-1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81-2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40-2.34 m2/ha for changes in mean basal area (ΔG), and 74-193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

Cloud computations are based on the computer networks such as Internet which presents a new pattern to provide, consume and deliver services such as infrastructure, software, ground and other resources using network. The inappropriate timing of assigning loads to the virtual machines in the computational space could lead to unbalance in the system. One of the challenging planning problems. In the cloud data centers is considering both assigning and migration (transfer) of the virtual machines with the ability of reconfiguration and the integrated features of the hosting physical machines. In this article, we introduce an integrated and dynamic timing algorithm based on the Genetic evolution algorithm. The suggested method was evaluated based on these factors and different inputs. Our suggested method is done using Java programming language and cloud-SME simulation. The results show that the execution time and the response time were improved by 12 and 1 percent respectively.

Modern energy systems collect high volumes of data that can provide valuable information about energy consumption. Electric companies can now use historical data to make informed decisions on energy production by forecasting the expected demand. Many deep learning models have been proposed to deal with these type of time series forecasting problems. Deep neural networks, such as recurrent or convolutional, can automatically capture complex patterns in time series data and provide accurate predictions. In particular, Temporal Convolutional Networks (TCN) are a specialised architecture that has advantages over recurrent networks for forecasting tasks. TCNs are able to extract long-term patterns using dilated causal convolutions and residual blocks, and can also be more efficient in terms of computation time. In this work, we propose a TCN-based deep learning model to improve the predictive performance in energy demand forecasting. Two energy-related time series with data from Spain have been studied: the national electric demand, and the power demand at charging stations for electric vehicles. An extensive experimental study has been conducted, involving more than 1900 models with different architectures and parametrisations. The TCN proposal outperforms the forecasting accuracy of Long Short-Term Memory (LSTM) recurrent networks, which are considered the state-of-the-art in the field.

This Letter revisits the instability of jet produced by shaped charge. Scaling laws for shaped charge jet are derived by dimensional analysis. It shows that the scaling laws are universal when cross-sectional shrinkage rate is not included in the formulation.

Inland lake variations are considered sensitive indicators of global climate change. However, human activity is playing as a more and more important role in inland lake area variations. Therefore, it is critical to identify whether anthropogenic activity or natural event is playing as the dominant factor in inland lake surface area change. In this study, we proposed a Douglas-Peucker simplification algorithm and bend simplification algorithm combined method to locate major lake surface area disturbances; these disturbances were then characterized to extract the time series change features according to documented records; and the disturbances were finally classified into anthropogenic or natural. We took the nine lakes in Yunnan Province as test sites, a 31 years long (from 1987 to 2017) time series Landsat TM/OLI images and HJ-1A/1B used as data sources, the official records was used as references to aid the feature extraction and disturbance identification accuracy. Results of our method for both disturbance location and the disturbance identification could be concluded as follows: 1) The method can accurately locate the main lake changing events based on the time series lake surface area curve. The accuracy of this model for segmenting the lake area time series curves in our study area was 95.24%. 2) Our proposed method achieved an overall accuracy of 91.67%, with F-score of 94.67 for anthropogenic disturbances and F-score of 85.71 for natural disturbances. 3) According to our results, lakes in Yunnan Provence, China, have undergone extensive disturbances, and the human-induced disturbances occurred almost twice as often as natural disturbances, indicating intensified disturbances caused by human activities. This inland lake area disturbance identification method is expected to uncover whether a disturbance to inland lake area is human activity-induced or natural event.

Background: Transit-time flow measurement (TTFM) is frequently used to evaluate intraoperative quality control during coronary artery bypass grafting (CABG). Although TTFM has the ability to assess graft failure intraoperatively, the perioperative factors affecting TTFM during CABG surgery remain poorly understood. Methods: Patients who underwent CABG surgery at a single institution between July 2016 and May 2018 were prospectively evaluated. Patients’ demographic characteristics, previous medical history, Euroscore, the results of preoperative blood tests, and intraoperative data were recorded. TTFM and blood viscosity were measured hemodynamically, and mean flow (mL/min) and pulsatility index (PI) were recorded. Arterial blood gas was analyzed immediately after anastomosis of the left internal mammary artery (LIMA) to the left descending artery (LAD) and before sternal closure. Factors associated with TTFM were assessed by multiple linear regression analysis. Results: Of the 62 patients who underwent CABG surgery during the study period, 57 were evaluated, including 49 who underwent off-pump and eight who underwent on-pump surgery. Blood viscosity was not significantly associated with TTFM (p > 0.05). However, TTFM was significantly associated with body mass index (BMI), systolic blood pressure, and cardiac index (p < 0.05 each). Conclusions: Blood viscosity was not significantly associated with intraoperative graft flow. Blood flow of graft vessels, however, was significantly associated with BMI, systolic blood pressure, and cardiac index.

Purpose: To evaluate seasonal variation in intra-ocular pressure (IOP) with and without short tear break-up time (SBUT, BUT ≤ 5 s). Methods: This study enrolled 176 patients who visited one of six eye clinics in Japan for IOP measurement at every season. The mean patient age was 67.9 years, including 79 males. We compared the seasonal variation in IOP (mean ± SD) across spring (Mar-May), summer (Jun-Aug), fall (Sep-Nov), and winter (Dec-Feb). Results: The IOP (mmHg) in winter and summer, respectively, was 12.8 ± 3.7 and 12.8 ± 3.1 for non-glaucoma patients without SBUT (n = 47, P = 0.964), 14.8 ± 3.4 and 13.3 ± 3.4 for non-glaucoma patients with SBUT (n = 57, P < 0.001), 14.3 ± 3.2 and 14.1 ± 3.4 for glaucoma patients without SBUT (n = 36, P = 0.489), and 13.3 ± 3.0 and 11.6 ± 2.9 for glaucoma with SBUT (n = 36, P < 0.001). Seasonal variation was largest across the seasons in the glaucoma with SBUT group, and the magnitude of seasonal variation correlated with BUT (β = 0.228, P = 0.003). Conclusions: Seasonal variation tended to be larger in patients with SBUT than those without SBUT.

Advances in the availability of multitemporal and global built-/human-settlements datasets as derived from Remote Sensing (RS) can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. We present a flexible modelling framework for producing annual built-settlement extents in the near future past last observed extents as provided by RS-based data. Using a random forest and autoregressive temporal models with short time-series of built-settlement extents and subnational level data, we predict annual 100m resolution binary settlement extents five years beyond the last observations. We applied this framework within varying contexts and predicted annual extents from 2010 to 2015. We found that our model framework preformed consistently across all sample countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and validation extents. When comparing building footprints of small settlements to forecast extents, we saw that the modelling framework had a 12 percent increase in ground-truth accuracy. This framework shows promise for predicting near-future settlement extents, and provides a foundation for forecasts further into the future.

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

The future of the Colorado River water supply (WS) affects millions of people and the U.S. economy. A recent study suggested a cross-basin correlation between the Colorado River and its neighboring Great Salt Lake (GSL). Following that study, the feasibility of using the previously developed multi-year prediction of the GSL water level to forecast the Colorado River WS was tested. Time-series models were developed to predict the changes in WS out to 10 years. Regressive methods and the GSL water level data were used for the depiction of decadal variability of the Colorado River WS. Various time-series models suggest a decline in the 10-year-averaged WS since 2013 before starting to increase around 2020. Comparison between this WS prediction and the WS projection published in a 2012 government report (derived from climate models) reveals a widened imbalance between supply and demand by 2020. Further research to update similar multi-year prediction of the Colorado River WS is needed. Such information could aid in management decision making in the face of future water shortages.

In this paper, the physical station of space network was extended in time dimension by combining the train diagram information and station technical operation standard time. At the same time, the topology of railway space-time network which considered the secondary operation process of train was constructed and an improved A* algorithm based on car flow routing was proposed to generate feasible path sets. On this basis, a dynamic car flow organization optimization model was built to simulate the railway car flow organization process under abnormal conditions, and the results of solving the model could be used to obtain the real-time quantity of cars at each station.This paper can identify the dynamic bottleneck by comparing the real-time quantity of cars with the maximum quantity of cars at the station. Finally, the feasibility of this method was analyzed and verified by a case.

The vertebrate anterior-posterior (A-P) body axis arises due to time space translation (TST). BMP dependent Hox temporal collinearity in early embryonic mesoderm generates the initial vertebrate axial pattern because the Hox codes associated with sequential times are frozen sequentially by BMP inhibiting signals from the embryonic organiser or node. There are three reasons why it is now opportune to review TST. 1/ It has become clear that this mechanism is highly relevant for current and emergent directions in medicine. Making a particular tailored stem cell or culturing a specific organoid in vitro both depend on it. 2/ This unexpected and perhaps unlikely sounding mechanism has recently been thoroughly validated. 8 recent primary publications from 6 major groups confirm that TST is the mechanism for primary axial patterning in the 4 best investigated vertebrate embryos. 3/ Its mechanism is now becoming clear. Previous publications propose it involves Hox regulation of cell movement during gastrulation or sequential stabilisation of Hox codes by anti BMP as above. Neither of these processes works alone but together they amount to a very convincing mechanism.

Phenomenon of vacuum fluctuations of virtual particles in high energy physics has been mathematically modeled by a third order differential equation. Aim of present theory is to unravel underlying mathematical equation capable of explaining microscopic phenomenon of vacuum fluctuations. Solution of such a differential equation after applying appropriate boundary conditions gives an acceptable wave function i.e. . Operation of energy and time operator on this wave function proves that these operators do not hold commutative property. Time energy uncertainty principle has been derived by calculating variance of energy and time for the same wave function.

Three-phase interactions (metal-slag-argon) in ladle stirring operations have strong effects on metal-slag mass transfer processes. Specifically, the thickness of the slag controls the fluid turbulence to an extent that once trespassing a critical thickness, increases of stirring strength have not further effects on the flow. To analyze these conditions, a physical model considering the three phases was built to study liquid turbulence in the proximities of the metal-slag interface. A velocity probe placed close to the interface permitted the continuous monitoring and statistical analyses of turbulence. The slag-eye opening was found to be strongly dependent on the stirring conditions, and the mixing times decreased with thin slag thicknesses. Slag entrainment was enhanced with thick slag layers, and high flow rates of the gas phase. A multiphase model was developed to simulate these results finding a good agreement between experimental and numerical results.

Fire accidents are common accidents amongst residential house occupants in Libya. Some of the factors that can mitigate fire accidents in Libyan residential houses include fire safety awareness and knowledge. This study investigated the level of awareness and knowledge of fire safety amongst residential house occupants in Libya. The possibility and consequences of fire hazards and risk existing in residential houses in Libya were also studied. Experimental data obtained for 1.5 m/s and 3.5 m/s as walking and running evacuation time, respectively amongst residential house occupants in Libya were also compared. The sample consist of 90 respondents with instruments of fire safety awareness and knowledge, fire hazard and consequences, and the involvement of residential house occupants in fire emergency. Statistical analysis was performed at p < 0.05 to determine the levels of residential awareness and knowledge regarding fire safety and the hazard and consequences. The findings illustrated that the items were highly reliable (α > 0.7) and normally distributed. Also, the results illustrated that the majority of residential house occupants in Libya have fair level of fire safety awareness and knowledge with overall percentage of 80% and 90%, respectively. On the other hand, the results further showed that majority of residential house occupants have good level of fire emergency involvement with 90%. This paper proposes a fire safety assessment method which may form the basis of future fire safety practice in Libya.

This paper examines the effect of chronotype on the delinquent credit card payments and stock market participation through preference channels. Using an online survey of 455 individuals who have been working for 3 to 8 years in companies in mainland China, the results reveal that morningness is negatively associated with delinquent credit card payments. Morningness also indirectly predicts delinquent credit card payments through time preference, but this relationship only exists when individuals’ monthly income is at low and average level. On the other hand, financial risk preference accounts for the effect of morningness on stock market participation. Consequently, an additional finding is that morningness is positively associated with financial risk preference, which contradicts previous finding in the literature. Finally, based on the empirical evidence, we discuss the plausible mechanisms that may drive these relationships and the implications for theory and practice. The current study contributes to the literature by examining the links between circadian typology and particular financial behaviours of experienced workers.

Herein, we present preparation of solid polymer electrolyte (SPE) comprising of PEO, NaPF₆ and varying fraction of Succinonitrile (SN) by standard solution cast technique. The morphological features and structural properties were studied by the FESEM, XRD, respectively. FTIR was performed to study the interactions between polymer host, salt, and SN. Impedance spectroscopy, Transference number measurements, LSV and CV were used to examine the electrochemical properties. The complex permittivity/conductivity & modulus were studied to understand the dielectric properties by evaluating the dielectric strength, relaxation time, hopping frequency and dc conductivity. Based on the experimental results an interaction mechanism is presented.

From the theory of relativistic chemical kinetics [M. W. Baig, Int. J. Mod. Phys. B 31, 1750177 (2017)] relativistic thermodynamics and kinetics for electrode processes have been developed to explain time dilation for electrode processes. For a moving observer moving at fractions of the speed of light, cell potential is observed to decrease. This results in the slower oxidation and reduction of ions at the respective electrodes. The newly formulated Lorentz transformation of the electrode and cell potential is explained in terms of generation of spin 2-boson “gravitons” from fusion of spin-1 boson “virtual-photons” mediating electrostatic force of attraction between ions and electrodes. It is postulated that birth of spin 2-boson i.e. gravitons is followed by their eventual escape in any of higher 4+n dimensions. To demonstrate the effectiveness of the present theory, the Daniel cell is considered as a numerical example.

Land Surface Temperature (LST) is a basic parameter in energy exchange between the land and atmosphere and is frequently used in many sciences such as climatology, hydrology, agriculture, ecology, etc. LST time series data have usually deficient, missing and unacceptable data caused by the presence of clouds in images, presence of dust in atmosphere and sensor failure. In this study, Singular Spectrum Analysis (SSA) algorithm was used to resolve the problem of missing and outlier data caused by cloud cover. The region studied in the present research included an image frame of MODIS with horizontal number 22 and vertical number 05 (h22v05). This image involved a large part of Iran and Turkmenistan and Caspian Sea. In this study, MODIS LST sensor (MOD11A1) was used during 2015 with 1×1 Km spatial resolution and day/night LST data (daily temporal resolution). The results of the data quality showed that cloud cover caused 36.37% of missing data in the studied time series with 730 day/night LST images. Further, the results of SSA algorithm in reconstruction of LST images indicated the Root Mean Square Error (RMSE) of 2.95 K between the original and reconstructed data in LST time series in the study region. In general, the findings showed that SSA algorithm using spatio-temporal interpolation in LST time series can be effectively used to resolve the problem of missing data caused by cloud cover.

Using DCC-GARCH and EGARCH model, this paper finds that since 1990, the relationship between crude oil prices and the US dollar index is time-varying, demonstrating a process of “very weak correlation—negative correlation—enhanced negative correlation—weakening negative correlation”, but the existing research does not provide enough reasonable explanation. Therefore, this paper proposed a “key mediating factors” hypothesis which points out that whether there is a common “key mediating factor” is important source of the time-varying relationship between two assets. We argue that market trend and financial market sentiment undertook the role of “key mediating factor” during the period of “2002 to the financial crisis” and “financial crisis to 2013”, while other periods lack the “key mediating factors”.

The grid nudging technique is often used in regional climate dynamical downscaling to make the simulated large-scale fields consistent with the driving fields. In this study, we focused on two specific questions about grid nudging: (1) which nudged variable had a larger impact on the downscaling results and (2) what was the “optimal” grid nudging strategy for each nudged variable to achieve better downscaling result during summer over the Chinese mainland. To solve this queries, 41 3-month long simulations for the summer of 2009 and 2010 were performed using the Weather Research and Forecasting model (WRF) to downscale National Centers for Environmental Prediction (NCEP) Final Operational Global Analysis (FNL) data to a 30-km horizontal resolution. The results showed that nudging horizontal wind or temperature had significant influence on the simulation of almost all conventional meteorological elements; nudging water vapor mainly affected the precipitation, humidity, and 500 hPa temperature. Moreover, the optimum nudging scheme varied with simulated regions and layers. As a whole, the optimal nudging time was one hour or three hours for nudging wind, three hours for nudging temperature, and one hour for nudging water vapor. The optimal nudged level was above the planetary boundary layer for almost every nudged variable.

The purpose of the present study was to investigate the effects of psychological pressure on corticospinal excitability, the spinal reflex, lower limb muscular activity, and reaction times during a task involving dominant leg movements. Ten healthy participants performed a simple reaction time task by raising the heel of their dominant foot from a switch. After 20 practice trials, participants performed 20 non-pressure and 20 pressure trials in a counterbalanced order. Stress responses were successfully induced, as indexed by significant increases in state anxiety, mental effort, and heart rates under pressure. Significant increases in motor evoked potential (MEP) amplitude of the tibialis anterior muscle (TA) occurred under pressure. In terms of task-related EMG amplitude, the co-contraction rate between the soleus (SOL) and TA muscles significantly increased along with SOL and TA EMG amplitudes under pressure. Hoffmann reflexes for SOL and reaction times did not change under pressure. These results indicate that corticospinal excitability and leg muscle-related EMG activity increase homogeneously during lower limb movements that are performed under psychological pressure.

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

Using recent developments in econometrics and computational statistics we consider the estimation of the instantaneous rate of asset return process when the underlying Data Generating Mechanism (DGM) is an Ornstein-Uhlenbeck process, driven by fractional noise, and sampled at fixed intervals of length h. To address the problem we adopt throughout the paper an exact discretization approach. This enable us to exploit the fact that a flow sampling scheme arises naturally when observing the DGM. For, while the instantaneous rate of return process is unobservable at points in time, its time integral over successive observations is observable since it equals the increment of log-prices. Exact discretization delivers an ARIMA(1,1,1) model for log-prices with a fractional driving noise. Building on the resulting exact discretization formulae and covariance function, a new Markov Chain Monte Carlo (MCMC) scheme is proposed and we examine the properties of both the time and frequency domain likelihoods / posteriors through Monte Carlo. For the exact discrete model we adopt a general sampling interval of length h. This allow us to determine the optimal choice of h independent of the sample size. An empirical application using high frequency stock price data is presented showing the relevance of aggregation over time issues in modelling asset prices.

A model of space-time is presented, whose origin is set before the appearance of any universe. It uses concepts of previous work, a central one being that the information about the laws of nature is stored locally everywhere in space such that all physical processes obey them. Since information storage is bound to matter or energy, space is assumed to consist of dark energy (DE), which thus assumes an importance far beyond its function as a stopgap for an energy deficit in our universe ( U ). The primordial state (PS) is a timeless and relatively large space of 3 dimensions described by a Wheeler DeWitt (WDW) equation, whose solution is interpreted as an arrangement of very many space quanta and possesses properties that remind of the time-honored concept of the unmoved mover. The comparison of WDW solutions belonging to different mass densities of the DE favors one in which the space quanta are mini black holes. This allows the space quanta to be imagined as mass points which are kept at a distance compatible with the quantum volume by the Pauli principle or something equivalent. Triggered by a dramatic increase in probability, the PS is followed by a time-controlled sequence of quantized expansion states (ES i ). The concomitant emergence of time occurs in a lightning huge crash, consuming almost all DE of the PS. The subsequent quantized ES i emerge from a WDW equation, novelly derived from a solution of the modified cosmic momentum equation, which classically describes an exponential space expansion (CS). The huge volme of the ES i is also used as size of the PS. Information is assumed to be geometrically stored in arrangements of space quanta similar to how genetic information is stored in DNA molecules. During the CS information must be transferred from already existing "information quanta" to new ones. This requires time, slows down the expansion and is taken into account by a friction term in the cosmological momentum equation thus modified. Since the Friedmann-Lemaître (FL) equation remains fully valid, the model is widely based on recognized equations, so that some results may be understood as interpretations.

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.

Credit rationing, especially prevalent for smaller firms, impedes economic growth. A Central Bank aligned “not for profit” managed business to business “Stable-Coin” (“Synthetic Central Bank Digital Currency”) providing trade debt liquidity can lower small firm credit rationing. It raises growth by reducing monetary transmission imperfections consequent upon asymmetric information, commercial bank underwriting restrictions, market power dynamics and regulatory distortion. A simple framework is developed to contextualise small firm credit rationing and associated monetary transmission imperfections with broader credit flows into both the real and monetary sectors. Evidence is presented regarding US monetary transmission efficacy to firms, paving the way to proposing a trade debt business to business focused stable-coin to help close credit rationing gaps. Designed to minimise “moral hazard”, the proposed stable-coin provides an additional monetary transmission channel that supplies interest free trade debt funding to firms to support economic activity and raise growth. In addition to helping close the small firm financing gap, the stable-coin provides policy makers with an additional contra-cyclical monetary transmission instrument to minimise real economic disruption consequent upon financial system crises and liquidity events.

This study evaluates the incidence and characteristics of adverse events (AEs) following the second COVID-19 booster dose, leveraging Taiwan's distinctive approach of extending booster vaccinations to all citizens, unlike the targeted high-risk group strategies in other countries. Utilizing data from Taipei Veterans General Hospital's Vaccine Adverse Event Reporting System (VAERS) from October 27, 2022, to January 19, 2023, the research examines AEs in 441 out of 1711 second booster recipients, considering factors like age, vaccine brands, and booster combinations. The findings reveal incidence rates (IR) of 25.6% (95% CI: 21.1-30.8) after the first booster and 24.9% (95% CI: 20.5-30.0) after the second, mostly non-serious, with those having AEs post-first booster 5 times more likely to report them again (Incidence rat rato, 5.02, P&lt;0.001). Significantly, switching from the mRNA1273 vaccine to another brand reduced AE risk by 18%. The study underscores that AEs are more repetitive than cumulative with additional booster doses, advocating for personalized vaccination strategies based on individual medical histories and previous vaccine reactions. These insights are valuable for healthcare providers in discussing potential AEs with patients, thereby improving vaccine compliance and public trust, and for policymakers in planning future booster vaccination strategies.

This study delves into the essential characteristics of the Metaverse and its impact on the real world. The Metaverse, a parallel universe composed of countless 3D virtual worlds, has become a hot topic in technology. With the development of advanced technologies such as Virtual Reality (VR), Augmented Reality (AR), blockchain, and Artificial Intelligence, the Metaverse is gradually changing people's lifestyles and work methods, expanding the boundaries of the digital economy. This study profoundly investigates the attributes of the Metaverse, such as asset digitization, application scenario-based, scope economy, industry universality, integrated innovation, and the platform ecosystem. Additionally, we propose a theoretical framework to aid in understanding and shaping this emerging field. We conducted a thorough study and analysis of a specific case, explored the key features of the Metaverse, unveiled the profound impact of Metaverse characteristics on social and economic development, and verified our theoretical framework with this case. This research provides new perspectives and methods for academic studies and offers robust support and guidance for practical applications and decision-making.

In the TANGO study, the long-term noninferior efficacy of switching to dolutegravir/lamivudine (DTG/3TC) compared to continuing tenofovir alafenamide–based regimens was demonstrated in treatment-experienced adults with HIV. However, there is limited real-world data comparing the effectiveness and safety of bictegravir/emtricitabine/tenofovir alafenamide fumarate (BIC/FTC/TAF) and DTG/3TC in Asia. This retrospective observational study, conducted at a designated HIV-care hospital from March 2019 to January 2023, aimed to address this gap. Demographic, clinical, and laboratory data, including HIV viral loads and lipid profiles, were collected and analyzed. The primary outcome was the proportion of patients with HIV RNA <50 copies/mL at week 48 in both groups, utilizing a snapshot, per-protocol–exposed population. A total of 1086 patients were included, with 511 in the DTG/3TC group and 575 in the BIC/FTC/TAF group. At week 48, 98% of patients in the DTG/3TC group and 94% in the BIC/FTC/TAF group achieved HIV RNA <50 copies/mL (p <0.01). Logistic regression analysis identified risk factors for detectable viral loads at week 48, including HIV RNA >100,000 copies/mL (AOR 5.13, 95% CI 1.42–18.53), low-level viremia (AOR 3.91, 95% CI 1.07–14.25), and record of virologic failure ≥2 times (AOR 4.21, 95% CI 1.64–10.85) before the switch. Furthermore, no significant difference was found between the DTG/3TC and BIC/FTC/TAF regimens (AOR: 1.51, 95% CI 0.70–3.24). In conclusion, the study demonstrates that the effectiveness and safety profiles of DTG/3TC and BIC/FTC/TAF were comparable in treatment-experienced adults with HIV, particularly among those with undetectable viral loads at baseline.

Overcoming the issue of land value and cost in urban areas will not provide a miraculous solution to the problems there. Appropriate land use cost, especially for residential and commercial land, is just one of the issues to be settled in the debate. Therefore, this study aims to build a new urban land price determination model by investigating the urban syntactical analysis, street width, and their economic effects on land value. The study attempts to determine the impact of syntactic analysis of streets and street width on land prices; it also seeks to identify the factor most affected by the land cost. Ultimately, the study built a model for urban land price prediction. The case selected is evaluated and compared in three aspects of the analysis, including; the urban axial assessments and urban street width, to find out their impacts on the real estate’s land price in the context of the land use distributions, which are predominantly residential and commercial types of uses. Depth map X8, SPSS, and QGIS 3.16 were used for the study evaluations and assessments. The result showed a significant impact of the urban street network on the price of land; this result can be used to enhance future urban design regarding urban economy improvements.

In this study, we explore the impact of stochastic resetting on the dynamics of random walks on a T-fractal network. By employing the generating function technique, we establish a recursive relation between the generating function of the first passage time (FPT) and derive a relationship between the mean first passage time (MFPT) with resetting and the generating function of the FPT without resetting. Our analysis covers various scenarios for a random walker reaching a target site from the starting position, and for each case, we determine the optimal resetting probability γ* that minimizes the MFPT. We compare the results with the MFPT without resetting and find that the inclusion of resetting significantly enhances search efficiency, particularly as the size of the network increases. Our findings highlight the potential of stochastic resetting as an effective strategy for optimizing search processes in complex networks, offering valuable insights for applications in various fields where efficient search strategies are crucial.

Land cover refers to the combination of various material types and their natural characteristics on the Earth. Accurately mapping the spatial distribution and temporal changes of the earth's land cover is of great significance for studying the energy balance and carbon cycle of the earth system. However, there is still a high degree of human participation in the production of multi-temporal land cover products. Developing an automated method for multi-epoch land cover mapping has become a key research focus. To this end, an automatic training sample extraction method was first employed using multi-source prior land cover products. Then, based on the generated training dataset and the Random Forest classifier, local adaptive land cover classification models of the reference year were developed. Finally, by migrating the classification model to the target epoch, the multi-epoch land cover products were generated. Yuli County in Xinjiang and Linxi County in Inner Mongolia were used as test cases. The classification models were first generated in 2020 and then transferred to 2010 to enable automatic classification of multi-temporal land cover. The mapping results showed high accuracy in both regions, with Yuli County achieving 92.52% in 2020 and 88.33% in 2010, and Linxi County achieving 90.28% in 2020 and 85.28% in 2010. Additionally, uncertainty analysis of the model migration method revealed that land types such as water bodies, wetlands, and impervious surfaces, which exhibit significant spectral changes over time, are less suitable for the model migration. Our research can offer valuable insights for fine-resolution land cover mapping. Furthermore, the approach provides a scalable solution for multi-period land cover monitoring, which could facilitate more efficient and accurate environmental assessments.

The precise localisation of epileptic foci with the help of EEG or ECoG signals is still a clinical challenge that is rarely successful, except in the case of foci that are very close to the surface and allow a clear assignment to a single electrode. On the research side, dipole reconstruction of instantaneous excitation fields via the lead-field matrix or the evaluation of time-dependent phase shifts in the measurements seem to be still possible and are under development. For the latter, we have investigated the necessary correlation of signals in the time range up to a few hundred milliseconds in an EEG of a healthy subject (self-experiment). We find periods of up to 190 msec in length in which a correlation over short latencies succeeds very well, alternating with periods in which long-range correlations occur while the short latencies come to a standstill. An automatic propagation time focus localisation to be developed therefore appears possible if an underlying state monitoring system independently recognises the suitable time periods and then successfully performs the comparatively shortest-range triangulation calculations over these.

Acoustic emissions (AE) are produced by elastic waves generated by damage in solid materials. AE sensors have been widely used in several fields as a promising tool to analyze damage mechanisms such as cracking, dislocation movement, etc. However, accurately determining the location of damage in solids in a non-destructive manner is still challenging. In this paper, we propose a crack wave arrival time determination algorithm that can identify crack waves with low SNR(signal-to-noise ratios) generated in rocks. The basic idea comes from that the variances of the crack wave and noise have different characteristics depending on the size of the moving window. The results can be used to accurately determine the crack source location. The source location is determined by observing where the variance in the crack wave velocities of the true and imaginary crack location reach a minimum. By performing a pencil lead break test using rock samples, it was confirmed that the proposed method could successfully find wave arrival time and crack localization. The results using the proposed algorithm to detect crack wave source localization suggest use for evaluating and real-time monitoring of damage in tunnels or other underground facilities.

Accordingly, a lot of dry direct seeding rice would be dense planted, that the initial seedling quantity could approach or even exceed the targeted effective panicle number. While limit information is available to evaluate the effects of involved novel nitrogen application methods, that total nitrogen once applied at a determined leaf age stage, on the yield formation traits of such dense planted dry direct seeding rice. The two-year experiments with total 180 kg ha-1 nitrogen once applied time treatments at sixth (L6), seventh (L7), eighth (L8), ninth (L9) and tenth (L10) leaf age stages were carried out. Results showed that the above-ground dry matter weight at maturity and grain yield of L8 was 2.1% ~ 11.8% and 4.0% ~ 9.9% higher than those of other treatments. Compared to other treatments, the higher above-ground dry matter weight after heading stage, bigger mean leaf area index with higher leaf area index at heading stage, increased leaf area duration, crop growth rate and net assimilation rate from jointing to heading period were important traits of higher grain yield formation of L8. In addition, the L8 had relative higher grain-leaf ratio traits than other treatments. Overall, a total 180 kg ha-1 nitrogen with mixed urea and compound fertilizer once applied at eighth leaf age stage would be suggest a useful high yield cultivation method for dense planted dry direct seeding rice in rice-wheat rotation system.

In this paper, the P versus NP problem is studied based on a six-dimensional space-time perspective. The gap between the P-class and NP-class arises from the relationship between time and the solution, as well as the answer that is independent of time. The complexity of solving a problem depends on the time dependence of the answer. Solving problems whose answers do not depend on the passage of time is very difficult and impossible. Like the description of the gravitational factor in Newtonian physics, NP class problems require different mathematical tools. Based on the generalization of physical facts in mathematics, based on six-dimensional space-time, a method to solve problem P versus NP problem has been stated. The results of this study offer a fresh insight into information processing that occurs independent of the passage of time.

This research aims to explore the use of sodium benzoate C6H5-COOH as corrosion inhibitor added to the mixing water of concrete, and test the mechanical performances of five types of concretes over several time periods, 7, 28, 56 and 90 days, in the absence and in the presence of limestone fillers. Hence the results confirm the increase in this resistance over time. Through this, tests were carried out cement paste after addition of sodium benzoate and limestone fillers, we see clearly that the start and the and setting time of cement shift out considerably which indicates that sodium benzoate aims to delay start and end setting time of cement. In addition, Abrams cone slump tests on concrete in the fresh state were carried out in order to see the influence of the addition of sodium benzoate and tests on the cement consistencies in the absence and presence of superplasticizing adjuvant “ORAFLOW THM 17200” which has the main role of improving the workability and quality of the concrete while comparing with ordinary concrete.

With an aging population, the prevalence of geriatric diseases is increasing, especially among older individuals who live alone. This study investigates the potential of using TV viewing patterns to detect these diseases by analyzing variables such as daily average viewing time, peak viewing hours, and channel-switching behaviors. Significant correlations between TV viewing patterns and early indicators of geriatric diseases (depression, activities of daily living (ADL) performance, and cognitive function) were found. The correlations between the Korean form of the Geriatric Depression Scale (KGDS) and daily average viewing time and excessive channel switching were 0.320 and 0.308, respectively. The Korean version of the Modified Barthel Index (KMBI) showed correlations of -0.313 with daily average viewing time and -0.352 with excessive channel switching. Participants were divided into groups based on scores, and significant differences in peak viewing hours were observed among the different groups. The Korean version of the Mini-Mental State Examination (MMSE-K) groups showed significant differences at 3, 4, and 5 PM. The KGDS and KMBI groups showed significant differences at 8 PM and midnight and 1 AM and 2 AM, respectively. These findings suggest that TV viewing patterns can serve as effective early diagnostic indicators for geriatric diseases.

The study of Internet of Vehicle (IoV) based on Fog Computing (FC) and Artificial Intelligent (AI) has attracted more and more attention. However, there are still many problems require to investigate such as time-critical, scalability, load-balancing, energy consumption, and so on. Focusing on these problems, we proposed an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for IoVs network, which ensures reliable low-latency communication efficient tasks offloading in IoV network by using Software Defined Vehicular based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in IoV layer to implement task allocation of each vehicle. By applying AI technologies of Reinforcement Learning (RL), Markov decision process, and Deep Learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer, and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay.

The digital revolution has significantly impacted many Chinese Time-Honored businesses, particularly those slow to adapt to environmental changes, resulting in challenges related to long-term marketing capabilities and declining performance. This study explores the relationship between ambidextrous marketing capabilities and corporate performance. Utilizing a survey involving 91.8% of middle and upper-level managers across 47 Chinese time-honored firms, we construct a research model that demonstrates how the application of ambidextrous marketing capabilities can foster the development of Sustainable marketing capability, which in turn positively influence corporate performance. Sustainable marketing capability mediate the effect of ambidextrous marketing capabilities on corporate performance. Additionally, enhancements in market conditions or legislation may further amplify the impact of Sustainable marketing capability on business performance. In the context of Chinese time-honored firms, these findings offer managerial insights into building Sustainable marketing capability, enhancing performance, and achieving competitive advantages.

The aim of this study was to analyze the relationship between healthy vascular aging (HVA) and the Mediterranean diet alongside other lifestyles in a Spanish population aged 35 to 75 years without previous cardiovascular diseases. Methods: Cross-sectional descriptive study with 501 individuals aged 35 to 75 years recruited from five health centers by random sampling stratified by age and sex (55.90±14.24 years, 49.70% men). HVA was determined in two steps. Step 1: Subjects with vascular damage to the carotid arteries or peripheral arterial disease were classified as non-HVA. Step 2: The study population was classified by age and sex using the percentiles of the vascular aging index (VAI), with VAI ≤p25 considered HVA and >p25 considered non-HVA. The VAI was estimated using the following formula (VAI = (log (1.09) × 10 cIMT + log (1.14) cfPWV) × 39.1 + 4.76. Carotid-femoral pulse wave velocity (cfPWV) was measured with the SphygmoCor^® device and carotid intima-media thickness using Sonosite Micromax^® ultrasound. Mediterranean diet (MD) adherence, alcohol and tobacco use were recorded through validated questionnaires. Physical activity was assessed with the ActiGraph-GT3X^® accelerometer. Results: The mean VAI value was 61.23±12.86; (Men 63.47±13.75 and women 59.04±11.54; p<0.001). HVA was found in 18.9% (men 19.9% and women 17.8%). In the multiple regression analysis after adjusting for possible confounding factors, the mean VAI value showed a positive association with alcohol use (β=0.020) and sedentary hours per week (β=0.109), and a negative association with hours of activity per week (β=-0.102) and with the number of healthy lifestyles (β=-0.640). In the logistic regression analysis after adjusting for possible confounding factors, and compared to those classified as non-HVA, subjects classified as HVA were more likely to show adherence to MD (OR = 0.571), perform more than 26 hours per week of physical activity (OR = 1.735), spend less than 142 hours per week being sedentary (OR = 1.696), and have more than two healthy lifestyles (OR = 1.877). Conclusion: The results of this study suggest that the more time spent doing physical activity and the less time spent sitting, the lower the vascular aging index and the greater likelihood of being classified in the group of subjects showing HVA.

(1) Background: São Paulo, Brazil, exhibits high mortality rates from pediatric solid tumors. This study analyzed the survival trends of patients aged 0-19 diagnosed with the state's five most prevalent solid tumors between 2000-2022. (2) Methods: An epidemiological, descriptive study utilized data from the Oncocentro Foundation of São Paulo, classified according to the International Classification of Childhood Cancer. The time between the first consultation and diagnosis, between diagnosis and treatment initiation, and patient survival were assessed using the Pe-to-Peto test. (3) Results: Analysis involved 11,067 cases: 53.5% male, with 89.6% diagnosed via microscopic confirmation. Tumor distribution comprised 34.3% central nervous system, 21.1% bone, 18.6% soft tissue, 14.2% germ cell, and 11.9% retinoblastomas. Main treatments included surgery with chemotherapy (26.5%), surgery alone (20.5%), and chemotherapy alone (15.6%). The average time between consultation and diagnosis was 22.94 ± 69.93 days, significant for treatments and recurrences, except for germ cell tumors (p=0.0178). The time between diagnosis and treatment was 25.46 ± 39.71 days, not significant for germ cell tumor treatments (p=0.0793). (4) Conclusions: Survival curves varied among neoplasm groups, with patients experiencing delays beyond recommended times, despite advanced healthcare services in the state.

This article presents an innovative approach to the design of a safe adaptive backstepping control system. Tailored specifically for underactuated marine robots, the system utilizes simple sensors for enhanced practicality and efficiency. Given their operation in diverse oceanic environments fraught with various sources of uncertainties, ensuring the system's safe and robust behavior holds paramount importance in the control literature. To address this concern, this paper introduces a control strategy designed to ensure robustness at both the kinematic and dynamic levels. By emphasizing the compensation for the system uncertainties, the design integrates a straightforward fuzzy system structure. To further ensure the system's safety, a funnel surface is defined, followed by the design of a suitable nonlinear sliding surface as a function of the funnel and tracking error. Specifically leveraging the Lyapunov function, the study formally establishes the Semi-globally Practically Finite-time Stability of the closed-loop system, validated through simulations conducted on underactuated marine robots.

Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1-measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data is expensive), and the complex relationships between anomalies not easily captured by traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model is deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.

In the last decade Electromagnetic Induction (EMI) measurements have been increasingly used for investigating the soil salinization caused by the use of brackish or saline water as irrigation source. EMI measurements proved to be a powerful tool for providing spatial information of the investigated soil because of the strict correlation between the output geophysical parameter, i.e. the electrical conductivity, to soil moisture and salinity. In addition, their non-invasive nature and their capability to collect a high number of data over broad areas and in relatively short time, makes these measurements attractive for monitoring flow and transport dynamics, otherwise undetectable with conventional measurements. In an experimental field, EMI measurements were collected during the growth season of the tomato, irrigated with three different irrigation strategies. The data were collected over three months in a time-lapse mode in order to visualize changes in electrical conductivity to be associated with soil salinity. A rigorous time-lapse inversion procedure has been set for modeling the soil salinization induced by brackish irrigation water.

In this work, a novel approach is presented for the multivariate prediction of hydrological variables, employing a specialized type of recurrent neural network (RNN) known as the long-term memory network (LSTM).The central objective is to develop a flood prediction model using data collected from two hydrological stations situated along the Atrato River in the Choc´o Department, Colombia. The interconnected variables of water level, water flow, and water precipitation are analyzed in this model to enhance flood prediction accuracy. The LSTM model, integrated into an RNN structure, is designed to capture the complex dynamics and cross-correlations inherent in these hydrological variables. Validation involves a comparison with linear and nonlinear Nonlinear AutoRegressive with eXogenous input (NARX) models, considering factors such as estimation error and computational time. Additionally, the study conducts further analysis for water level prediction under two scenarios: utilizing only outflow and inflow measurements or predicting outflow alongside measuring inflow, with the objective of utilizing the proposed approach as a flood early warning system. † This article is a revised and expanded version of a paper entitled Multivariable NARX based Neural Networks Models for Short-term Water Level Forecasting, which was presented at International Conference on Time Series and Forecasting (ITISE-2023), Gran Canaria, Spain, 12th-14th July, 2023.

Electrospun ultrathin fibers based on binary compositions of polylactide (PLA) and poly(ε-caprolactone) (PCL) with different ratios of polymer components from 0/100 to 100/0 wt. % were studied. Using a combination of thermal (DSC) and relaxation (EPR) measurements, the effect of biopolymers content on the characteristics of the crystal structure of PLA and PCL and the diffusive rotational mobility of the stable TEMPO radical in the amorphous regions of PLA/PCL compositions was shown. It was revealed that after PLA and PCL blending, significant changes in the degree of crystallinity of PLA, PCL segmental mobility, sorption capacity of the radical as well as activation energy of rotational diffusion in the amorphous regions of PLA/PCL composition are observed. The characteristic region of biopolymers’ composition from 50/50 to 30/70 % PLA/PCL blends ratio was found, where the inversion transition of PLA from dispersive medium to dispersive phase where an inversion transition is assumed when the continuous medium of the PLA transforms into a discrete phase. The performed studies made it possible for the first time to carry out a comprehensive analysis of the effect of the system component ratio on the structural and dynamic characteristics of the PLA/PCL film material at the molecular level.

To reduce the redundant counting of TDC in high-speed applications and thus reduce power consumption, a TDC architecture is proposed to quantify the difference between two adjacent rows of pixel signals. This structure can remove the identical part between two rows of pixel signals by adjusting the start and stop signal of the TDC, which will reduce the number of flipping of D flip-flops in the TDC. In the 110nm CMOS process, simulation results show that this design achieves an effective number of bits (ENOB) of 4.72 bits and a Figure of Merit (FoM) of 104.7 fJ/step - 162.3 fJ/step with a power consumption ranging from 60 µW to 93 µW. Compared with traditional counting methods, the proposed TDC can reduce counting power consumption by 30%.

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

The aim of the research was to determine the most productive tree cutting methods and the factors influencing them in flat and sloping terrain in tree plantations in Brazil. The study utilized drone-captured video material from eucalyptus and pine plantations harvesting operations. In both terrains, two cutting methods were compared, differing in the felling to the side method used: either at the edge of the harvesting front or inside. In addition, on flat terrain, the efficiency of forward felling was studied in relation to the aforementioned methods. In slope terrain, the machines were also equipped with a winch assistance system. Statistical tests were used to determine the most productive cutting methods by analyzing differences in productivity. With an average tree size of 0.3 m3, cutting productivity was 45 m3/E0h in pine cuttings and 55 m3/E0h in eucalyptus cuttings. The average cutting productivity on flat terrain was about 11 m3/E0h higher than on sloping terrain, mainly due to the time spent attaching the winch assistance system, which was a necessary phase of the work in sloping terrain. The research results suggest that it would be the most productive to use sideways felling inside the harvesting front method. However, the need for further research is evident if we want to precisely identify the harvesting conditions and work phases in the tree cutting cycle affecting the differences in the productivity of the harvesting chain in tree plantations.

When dealing with limited sample sizes or missing values in time series data, achieving high accuracy in forecasting becomes challenging. This challenge is particularly common in wind energy forecasting, where many wind farms lack complete data. In this paper, we employ the widely-used ARIMA (Autoregressive Integrated Moving Average) model for time series forecasting. To address the difficulties posed by limited sample sizes and missing values, we adopt a unique approach by utilizing multiple ARIMA models for different datasets. A key distinction from other multiple models methods for time series forecasting lies in each model within our approach using its own dataset, making the fusion process more intricate. To overcome this, we incorporate neural networks and two techniques for model fusion: direct fusion with a learning mechanism and compensation for missing data through training. For each ARIMA model, we introduce a noise estimation method to circumvent the white noise assumptions inherent in ARIMA models. Finally, we apply these methods to wind energy prediction, and our experimental results showcase a significant enhancement in forecasting accuracy.

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