Monte Carlo Test Research Articles

FinFET-Based Feedback Control Assisted Near-Threshold SRAM Cell

This paper presents a single-ended 12-transistor SRAM cell capable of operating at near-threshold voltage in FinFET technology. This cell employs Schmitt trigger-based inverters and feedback cut write assist technique to improve performance properties. The simulations have been done through HSPICE using 10 nm FinFET technology at a supply voltage of 0.45 V and a temperature of 25°C under 5000 Monte Carlo tests. The proposed cell improves the read static noise margin by at least 1.01 times. The write static noise margin is also increased by at least 1.07. The proposed single-ended cell leads to a reduction of α BL by 50% and offers low dynamic write power. The leakage power is also reduced by 1.03 times due to the use of single bitline in the cell structure, stack transistors in the write path and elimination of leakage in the read path.

Predefined time attitude control of aircraft based on predefined time sliding mode control

This paper proposes a sliding mode control strategy with predefined time convergence for addressing the attitude control problem of aircraft. Firstly, a predefined time extended state observer is developed to realize the estimation of modelling error, uncertainty and external disturbance of aircraft within a predefined time. Secondly, a predefined time non-singular terminal sliding mode and a predefined time sliding mode reaching law are proposed for attitude control of aircraft. Especially, to address potential singularities arising from negative power terms, a switching strategy is employed. Finally, the numerical simulations and Monte Carlo test are used to validate the effectiveness of the proposed controller.

Reconstructing daytime and nighttime MODIS land surface temperature in desert areas using multi-channel singular spectrum analysis

The availability of continuous spatiotemporal land surface temperature (LST) with high resolution is critical for many disciplines including hydrology, meteorology, ecology, and geology. Like other remote sensing data, satellite–based LST is also encountered with the cloud issue. In this research, over 5000 daytime and nighttime MODIS–LST images are utilized during 2014–2020 for Yazd–Ardakan plain in Yazd, Iran. The multi–channel singular spectrum analysis (MSSA) model is employed to reconstruct missing values due to dusts, clouds, and sensor defect. The selection of eigenvalues is based on the Monte Carlo test and the spectral analysis of eigenvalues. It is found that enlarging the window size has no effect on the number of significant components of the signal which account for the most variance of the data. However, data variance changes for all the three components. Employing two images per day, window sizes 60, 180, 360, and 720 are examined for reconstructing one year LST, where these selections are based on monthly, seasonal, semi-annual, and annual LST cycles, respectively. The results show that window size 60 had the least computational cost and the highest accuracy with RMSE (root mean square error) of 2.6 °C for the entire study region and 1.4 °C for a selected pixel. The gap–filling performance of MSSA is also compared with the one by the harmonic analysis of time series (HANTS) model, showing the superiority of MSSA with an improved RMSE of about 2.7 °C for the study region. In addition, daytime and nighttime LST series for different land covers are compared. Lastly, the maximum, minimum, and average LST for each day and night as well as average and standard deviation of LST images in the seven-year-long time series are also computed.

A Potential Link between Space Weather and Atmospheric Parameters Variations: A Case Study of November 2021 Geomagnetic Storm

On 4 November 2021, during the rising phase of solar cycle 25, an intense geomagnetic storm (Kp = 8−) occurred. The effects of this storm on the outer magnetospheric region up to the ionospheric heights have already been examined in previous investigations. This work is focused on the analysis of the solar wind conditions before and during the geomagnetic storm, the high-latitude electrodynamics conditions, estimated through empirical models, and the response of the atmosphere in both hemispheres, based on parameters from the ECMWF ERA5 atmospheric reanalysis dataset. Our investigations are also supported by counter-test analysis and Monte Carlo tests. We find, for both hemispheres, a significant correspondence, within 1–2 days, between high-latitude electrodynamics variations and changes in the temperature, specific humidity, and meridional and zonal winds, in both the troposphere and stratosphere. The results indicate that, in the complex solar wind–atmosphere relationship, a significant role might be played by the intensification of the polar cap potential. We also study the reciprocal relation between the ionospheric Joule heating, calculated from a model, and two adiabatic invariants used in the analysis of solar wind turbulence.

Impact speed and angle constrained guidance law for unpowered gliding vehicle

In this paper, a novel guidance law is proposed which can achieve the desired impact speed and angle simultaneously for unpowered gliding vehicles. A guidance law with only impact angle constraint is used to produce the guidance profile, and its convergence in the varying speed scenario is proved. A relationship between flight states, guidance input and impact speed is established. By applying the fixed-time convergence control theory of error dynamics, an impact speed corrector is built with the above guidance profile, which can implement impact speed correction without affecting the impact angle constraint. Numerical simulations with various impact speed and angle constraints are conducted to demonstrate the performance of the proposed guidance law, and the robustness is also verified by Monte Carlo tests.

Segmented parabolic adjustment of the FAST reflector utilizing spatial coordinate rotation transformation

Abstract Since the middle of the twentieth century, the advent of radio telescopes has brought a whole new way and approach to astronomical observation. For Arecibo-type radio telescopes, the tuning optimization of the active reflecting surface (working paraboloid) is the main factor affecting the reflectance calibration. In this study, leveraging the transformation of spatial coordinates through rotation, we introduce an innovative optimization model specifically for the segmented paraboloid of the Five-hundred-meter Aperture Spherical radio Telescope (Hereinafter referred to as FAST) designed by China astronomer and scientist Nan Rendong. This research constructs the equation for an ideal paraboloid and adjusts the working paraboloid to fit within specified constraints such as the orientation of the target star, the adjustment limit of the actuator, and the spatial coordinates. The study employs a combination of coarse and fine grid searches to identify and record the optimal adjustment scheme of the main cable nodes at different angles and the corresponding 2226 actuator coordinates and telescoping length, based on which we build a back propagation model to continuously modify the adjustment scheme. A combination of geometric simulation and Monte Carlo tests were also used for verification. Furthermore, we delve into the impact of variations between adjacent nodes of the modulating actuators, as well as potential longitudinal and radial changes. Compared to the conventional conditioning model, the segmented solution idealized paraboloid we created increases the original reflection efficiency from 77.92% to 95.56% in the working area of 300 m aperture, it will contributes to enhancing the overall performance of FAST.

Simulasi Monte Carlo Dalam Prediksi Penjualan Pempers Makuku

Predicting sales is an important aspect in sales development. Sales prediction simulation is an estimated calculation of the level of product sales in a certain period. Sales of pempers tend to fluctuate due to the choice of many brands available, resulting in sales having a little difficulty in estimating sales of MAKUKU diaper products at Greens Mart stores. This research aims to predict sales Pempers products. This research uses several stages of identifying problems, determining research objectives, collecting data, data collected or obtained from interviews with salespeople, managing data using Monte Carlo stages, implementing/testing data and testing results to see the accuracy of the method used. The analysis results show that Comfit M and Comfit L have almost the same level of accuracy, namely Comfit M is around 90.63% and sales of Comfit L are 90.48%. These values ​​provide an indication of the level of accuracy of the sales predictions made.

Nonparametric Testing of the Covariate Significance for Spatial Point Patterns under the Presence of Nuisance Covariates

Determining the relevant spatial covariates is one of the most important problems in the analysis of point patterns. Parametric methods may lead to incorrect conclusions, especially when the model of interactions between points is wrong. Therefore, we propose a fully nonparametric approach to testing significance of a covariate, taking into account the possible effects of nuisance covariates. Our tests match the nominal significance level, and their powers are comparable with the powers of parametric tests in cases where both the model for intensity function and the model for interactions are correct. When the parametric model for the intensity function is wrong, our tests achieve higher powers. The proposed methods rely on Monte Carlo testing and take advantage of the newly introduced concepts: the covariate-weighted residual measure and nonparametric residuals. We also define a correlation coefficient between a point process and a covariate and a partial correlation coefficient quantifying the dependence between a point process and a covariate of interest while removing the influence of nuisance covariates. Supplementary materials for this article are available online.

Soil organic matter and water content affect the community characteristics of arbuscular mycorrhizal fungi in Helan mountain, an arid desert grassland area in China.

Arbuscular mycorrhizal fungi (AMF) are vital in terrestrial ecosystems. However, the community structure characteristics and influencing factors of AMF in the forest ecosystems of arid desert grassland areas require further investigation. Therefore, we employed high-throughput sequencing technology to analyze the soil AMF community characteristics at different elevations in the Helan mountains. The results revealed that significant differences (P < 0.05) were observed in the soil physicochemical properties among different elevations, and these properties exhibited distinct trends with increasing elevation. Through high-throughput sequencing, we identified 986 operational taxonomic units (OTUs) belonging to 1 phylum, 4 classes, 6 orders, 12 families, 14 genera, and 114 species. The dominant genus was Glomus. Furthermore, significant differences (P < 0.05) were observed in the α-diversity of the soil AMF community across different elevations. Person correlation analysis, redundancy analysis (RDA), and Monte Carlo tests demonstrated significant correlations between the diversity and abundance of AMF communities with soil organic matter (OM) (P < 0.01) and soil water content (WC) (P < 0.05). This study provides insights into the structural characteristics of soil AMF communities at various altitudes on the eastern slope of Helan mountain and their relationships with soil physicochemical properties. The findings contribute to our understanding of the distribution pattern of soil AMF and its associations with environmental factors in the Helan mountains, as well as the stability of forest ecosystems in arid desert grassland areas.

Discovering the ecological structure of different macrophyte groups in rivers using non-parametric and parametric multivariate ordination techniques

This paper analyses various methods of ecological ordering that are often used in modelling the relationship between vegetation and habitat. The results of direct gradient ordination by Canonical correspondence analysis (CCA), which is based on correlation, were compared with Non-metric multidimensional scaling (NMDS), which is based on rank analyses. Both tools were also compared with Detrended correspondence analysis (DCA), which is a popular indirect gradient analysis method. The macrophyte assessment was conducted at 98 river locations in the lowland regions of Poland. Each of the surveyed locations falls within a consistent abiotic category: small to medium-sized lowland rivers with a sandy bottom. Habitat elements analysed included limnological variables and geographic parameters, and the botanical survey focused on submerged macrophytes, including vascular plants, as well as bryophytes and algae. Firstly, it was shown that various analytical tools for determining the importance of ecological factors (Monte Carlo test, BIOENV) identify slightly different significant factors responsible for the development of macrophytes in rivers. Secondly, considerable similarity was found in the structure of macrophyte communities generated on NMDS and DCA biplots, while macrophyte communities were presented very differently based on CCA. Thirdly, the ecological preferences of aquatic plants based on one-dimensional analyses primarily reflected the results of CCA, whereas they did not always follow the ecological pattern revealed by NMDS. Finally, by conducting separate studies for non-vascular plants and vascular macrophytes, it was confirmed that different ecological drivers are responsible for the development of particular groups of macrophytes

Retrospective analysis of an outbreak of scarlet fever in United Arab Emirates.

Scarlet fever is an infectious disease caused by Streptococcus pyogenes. However, there is limited data regarding the disease in the Arab World, including the United Arab Emirates. To analyse a scarlet fever outbreak in United Arab Emirates. This retrospective cross-sectional study included scarlet fever cases diagnosed at the Kanad Hospital, Al Ain, United Arab Emirates in 2022 and 2023. Data were retrieved from the hospital records and analysed using SPSS version 23.0. Chi-Square, Mann-Whitney, and Monte Carlo tests were applied. Two hundred and twenty-two cases (13.5% in 2022 and 86.5% in 2023) were confirmed (P<0.001). Majority (67.1%) of the patients were aged 3-6 years, with a mean age of 4.56 ± 1.99 years. Rash, fever and sore throat were observed in 100%, 99.1%, and 82.0% of cases, respectively. The majority (85.1%) were managed as outpatients and 77.0% responded to oral penicillin. Patients' age was not significantly associated with nonresponse to penicillin and in-hospital admission. The outbreak had winter and summer peaks, with the highest incidence occurring during January and February 2023. This study serves as a valuable reference for other studies, which should include antimicrobial susceptibility testing and the prevailing genetic variance of Streptococcus pyogenes.

Constrained Parameterized Differential Dynamic Programming for Waypoint-Trajectory Optimization

Unmanned aerial vehicles (UAVs) are required to pass through multiple important waypoints as quickly as possible in courier delivery, enemy reconnaissance and other tasks to eventually reach the target position. There are two important problems to be solved in such tasks: constraining the trajectory to pass through intermediate waypoints and optimizing the flight time between these waypoints. A constrained parameterized differential dynamic programming (C-PDDP) algorithm is proposed for meeting multiple waypoint constraints and free-time constraints between waypoints to deal with these two issues. By considering the intermediate waypoint constraints as a kind of path state constraint, the penalty function method is adopted to constrain the trajectory to pass through the waypoints. For the free-time constraints, the flight times between waypoints are converted into time-invariant parameters and updated at the trajectory instants corresponding to the waypoints. The effectiveness of the proposed C-PDDP algorithm under waypoint constraints and free-time constraints is verified through numerical simulations of the UAV multi-point reconnaissance problem with five different waypoints. After comparing the proposed algorithm with fixed-time constrained DDP (C-DDP), it is found that C-PDDP can optimize the flight time of the trajectory with three segments to 7.35 s, 9.50 s and 6.71 s, respectively. In addition, the maximum error of the optimized trajectory waypoints of the C-PDDP algorithm is 1.06 m, which is much smaller than that (7 m) of the C-DDP algorithm used for comparison. A total of 500 Monte Carlo tests were simulated to demonstrate how the proposed algorithm remains robust to random initial guesses.

Is tourism conducive to green growth? An investigation of the role of tourism-based economic expansion in the nexus of leisure tourism and business tourism

PurposeThe gradual expansion of the tourism sector is raising concerns about whether tourism-based economies are conducive to supporting green growth. Hence, the current study aims to analyze the direct impact of tourism motives on green growth along with the indirect impact of tourism-based economic expansion while controlling for country risk and renewable energy.Design/methodology/approachAn unbalanced panel data for a sample of 21 countries comprising OECD and non-OECD economies are employed for the analysis.FindingsRegression results reveal that leisure tourism (LT) significantly and positively influences CO2 intensity compared to business tourism (BT). Propensity score matching results show that the most traveled tourist destinations contribute more to CO2 intensity than those less traveled. Mediation analysis by employing Baron and Kenny’s three-step regression, Sobel’s test and Monte Carlo test shows that tourism-based economic expansion significantly mediates between the nexus of LT and CO2 intensity.Practical implicationsResults of the study provide useful practical implications for sustainable economy and green growth. It recommends to mitigate the challenges of LT, reducing the negative impact and to harness the potential of BT, enhancing the positive influence, through various policies and practices.Originality/valueThis study is the first to examine the impact of LT and BT on green growth, to explore the role of destination popularity and the mediating role of tourism-based economic expansion in this relationship.

Physiological synchronization and innovative moments in psychotherapy: A single-case study of micro-process.

Interpersonal synchronization is increasingly studied as a biomarker of empathy, therapeutic alliance, and treatment outcome. However, most studies average data over sessions, leaving associations between synchrony and actual interactions largely unexplored. We aim to showcase a novel approach examining synchronization during specific micro-processes: Innovative Moments (IM) as markers of exceptions to clients' problematic patterns of meaning. Electrodermal activity was recorded over 15 sessions of a psychodynamic psychotherapy single case. Moment-to-moment patient-therapist synchrony was calculated using the Adaptive Matching Interpolated Correlations (AMICo) algorithm. The Innovative Moments Coding System was utilized to identify IMs within session transcripts with precise timing. Monte-Carlo permutation tests were conducted to examine the association between physiological synchrony and IM Levels of increasing complexity (Levels 1-3). Higher-than-random synchronization emerged during Level 3 IMs (p = 0.046; d = 0.21) but not in lower Levels. Post-hoc qualitative analyses linked high synchrony to sub-processes of Level 3 IMs, such as positive contrasts and attributions for change. Our findings show it is possible to link moment-by-moment physiological co-regulation to theoretically identified meaning-making processes. While generalization of these observations is undue, this work demonstrates a robust and promising application of a multimodal approach to investigating psychotherapy, providing insights into both the clinical case and the theoretical model adopted.

Learning-based spacecraft multi-constraint rapid trajectory planning for emergency collision avoidance

Aim at the emergency collision avoidance scenarios caused by the close-range space debris, a learning-based spacecraft rapid trajectory planning method, which can adapt to complex constraints and satisfy the requirements of the business service and real-time replanning, is proposed in this paper. First, the emergency collision avoidance scenarios are initialized and the optimal multi-constraint avoidance trajectories are generated based on the Gauss pseudo-spectral method with corresponding feasibility checks. Then, taking the generated collocation points as the initial guess, the new trajectories are regenerated by finetuning scenarios. When the trajectory data is collected to some extent, the scenarios will be reset. The “state-action” data set can be established and extended by the above “plan-check-finetune-reset” loops. On this basis, two types of “state-action” neural networks and the corresponding supervised training method are specially designed based on ideas of multi-layer and bidirectional long short-term memory networks by considering continuous-discrete hybrid control characteristics of spacecraft actuators and interdependent temporal logical relationships in the data generated by dynamic differential equations. The designed networks are trained based on the established data set. Finally, spacecraft attitude-orbit maneuvering instructions can be resolved in real-time by trained networks according to the perceptive information for space debris. Simulation results show that the runtime of the proposed method in each step can be maintained within 10.4 ms, and the overall avoidance success rate can reach 87.6 % in Monte Carlo test conditions.

Asociación entre sistemas venosos insuficientes y manifestaciones clínicas en enfermedad venosa crónica de miembros inferiores

Introduction: The clinical, etiological, anatomical, and pathophysiological classification (CEAP) standardizes the manifestations of lower limb venous disease. Objective: To investigate the association between insufficient venous systems and the clinical classification of CEAP. Methods: A quantitative, cross-sectional, analytical, correlational study was conducted. Sampling was non-probabilistic and convenience-based. The sample size was 136 lower limbs from 71 patients. Chi-square, Monte Carlo and Odd ratio (OR) statistical tests were used with 95% confidence intervals through bivariate logistic regression (p&lt;0.05). Results: 71.8% of the subjects were female, with an average age of 66.1 years. The most frequent insufficient venous system was the superficial system, present in 61.7% of cases. 100% of the dilated great saphenous veins (GSV) had insufficiency. The most common CEAP clinical class was C2 (44.9%); 35.1% of C1 (telangiectasia) cases had an insufficient venous system; 50% of C2 cases had GSV insufficiency (p=0.227). There was an association between the insufficiency of both superficial and deep venous systems and the CEAP clinical classification (p=&lt;0.001). The deep venous system was associated with severe chronic venous disease of the lower limbs, OR (6.04) with a 95% CI (1.02-35.73) and p=0.047. Conclusions: The study demonstrated an association between the insufficiency of both superficial and deep venous systems and the CEAP clinical classification. One third of the lower limbs with C1 (telangiectasias) had an insufficient venous system.

A high-order disturbance nutation/wobble control for unbalanced spinning satellites

This paper introduces a novel active nutation/wobble rejection control method for spinning satellites enduring thrusting disturbances and reaction wheel limitations. Our approach employs two tilted reaction wheels in reaction-momentum mode, actively controlling wobble/nutation while enhancing disturbance rejection for dynamically unbalanced satellites. This eliminates the need for balancing processes or additional ballast, reducing system weight. A new wheel arrangement and momentum analysis effectively dampens wobble generated by dynamic imbalances. We prove that increasing wheel angular speed about the spinning axis enhances immunity against transverse disturbances. Unlike existing controllers, our method minimizes average thrust/spin axis oscillation while eliminating undesired rates and wobble. Stability is rigorously established through linearized equations and Routh stability criteria. Validation via air-bearing hardware-in-the-loop (HIL) and Monte Carlo tests demonstrates close agreement between predictions and experimental data, highlighting the controller's robustness against high thrusting disturbances. This work represents a significant contribution by achieving active stabilization of unbalanced spin satellites using onboard reaction wheels, potentially paving the way for wider adoption in future spacecraft designs.

Global fast terminal sliding mode control for automatic carrier landing with environmental disturbances

Purpose The purpose of this paper is to solve the challenging problem of automatic carrier landing with the presence of environmental disturbances. Therefore, a global fast terminal sliding mode control (GFTSMC) method is proposed for automatic carrier landing system (ACLS) to achieve safe carrier landing control. Design/methodology/approach First, the framework of ACLS is established, which includes flight glide path model, guidance model, approach power compensation system and flight controller model. Subsequently, the carrier deck motion model and carrier air-wake model are presented to simulate the environmental disturbances. Then, the detailed design steps of GFTSMC are provided. The stability analysis of the controller is proved by Lyapunov theorems and LaSalle’s invariance principle. Furthermore, the arrival time analysis is carried out, which proves the controller has fixed time convergence ability. Findings The numerical simulations are conducted. The simulation results reveal that the proposed method can guarantee a finite convergence time and safe carrier landing under various conditions. And the superiority of the proposed method is further demonstrated by comparative simulations and Monte Carlo tests. Originality/value The GFTSMC method proposed in this paper can achieve precise and safe carrier landing with environmental disturbances, which has important referential significance to the improvement of ACLS controller designs.

Impact and adoption of feed technologies at Nharira-Lancashire Dairy Scheme

Abstract The objective of this study was to investigate the impact and adoption of nutrition technologies and their effect on milk production at the Nharira-Lancashire dairy scheme. Sixty households (30 from each scheme) were randomly selected and interviewed by use of a semi-structured questionnaire. Fisher’s exact and Monte Carlo tests were carried out to determine the association between farmer experience and feed technology. Logistic regression was used to rank farmers’ awareness and adoption of feed technologies. The results indicated that farmers have been exposed to at least five new feed technologies and adoption is influenced by education status, sex, age and gender of household head (P &lt; 0.05). Feed technology adoption was in the order: silage 70.83% &gt; sunflower cake 41.66% &gt; legume reinforcement 27.1% &gt; legume trees 16.66% &gt; napier fodder 8.33% &gt; urea treatment 4.17%. Milk yield was affected by month and year (P &lt; 0.05). The impact of adoption was below anticipation, as indicated by lack of economic surpluses. A cost-benefit analysis needs to be carried out for all feed technologies within the study area to streamline viable options.

Weather types for the seasonal transitions in Central America

Unsupervised learning techniques are employed to study the relationship between atmospheric circulation and precipitation over Central America and its surrounding areas. Specifically, the clustering algorithm k-means++ is applied to three coarse-grained datasets from ERA-interim reanalysis that are the candidates for representing the atmospheric state vector, each candidate contains its full temporal variability. Datasets are composed of: a) wind fields at 925, 800 and 200 hPa, b) same as “a)” plus convective available potential energy and c) same as “a)” plus total column water vapor. Clustering metrics, namely the variance ratio criterion, the silhouette criterion and the mean squared error, are computed to quantify clustering quality. Clusters are interpreted as weather types, recurrent configurations of the atmospheric state vector associated with observable weather states. The correct number of clusters for each dataset is determined with a Monte Carlo test of normality, to assure cluster existence. The main objective is to obtain a set of weather types containing elements that characterize the transition from and to the rainy season over the Pacific side of Central America as well as other elements of the seasonal cycle of regional precipitation, such as the Mid-Summer Drought. Besides the statistical metrics, in order to select between candidate datasets and plausible number of clusters, focus is given to the temporal characteristics of the clusters. Existing literature does not provide a set of weather types suitable to analyze seasonal transitions and the differences in the mechanisms associated with rainfall maxima.