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Finite size scaling for a first order phase transition, where a continuous symmetry is broken, is tested using an approximation of Gaussian probability distributions with a phenomenological "degeneracy" factor. Predictions are compared to the data from Monte Carlo simulations of the Lebwohl–Lasher model on L × L × L simple cubic lattices. The data show that the intersection of the fourth-order cumulant of the order parameter for different lattice sizes can be expressed in terms of the relative degeneracy q = 4π of the ordered and disordered phases. This result further supports the concept of universality at first order transitions developed recently. [ABSTRACT FROM AUTHOR]

Machine learned potentials (MLPs) have been widely employed in molecular dynamics simulations to study thermal transport. However, the literature results indicate that MLPs generally underestimate the lattice thermal conductivity (LTC) of typical solids. Here, we quantitatively analyze this underestimation in the context of the neuroevolution potential (NEP), which is a representative MLP that balances efficiency and accuracy. Taking crystalline silicon, gallium arsenide, graphene, and lead telluride as examples, we reveal that the fitting errors in the machine-learned forces against the reference ones are responsible for the underestimated LTC as they constitute external perturbations to the interatomic forces. Since the force errors of a NEP model and the random forces in the Langevin thermostat both follow a Gaussian distribution, we propose an approach to correcting the LTC by intentionally introducing different levels of force noises via the Langevin thermostat and then extrapolating to the limit of zero force error. Excellent agreement with experiments is obtained by using this correction for all the prototypical materials over a wide range of temperatures. Based on spectral analyses, we find that the LTC underestimation mainly arises from increased phonon scatterings in the low-frequency region caused by the random force errors. [ABSTRACT FROM AUTHOR]

The spectrum of localized hole states (traps) in amorphous silicon nitride, a-Si3N4, is experimentally studied using the method of thermally stimulated depolarization. The experiment is compared with theoretical calculations using three models of the energy spectrum of traps: discrete spectrum (monoenergetic trap), continuous spectrum, and Gaussian trap distribution. The experiment is quantitatively described by a model of a discrete spectrum of traps with an energy of 1.15 eV and a width of no more than 0.01 eV. In the case of a continuous and Gaussian spectrum of traps, the contribution to depolarization is made by the deepest traps. The blurring of the trap energy level in a-Si3N4 due to the absence of long-range order (fluctuations in the Si–N bond length and fluctuations in the N–Si–N and Si–N–Si angles) does not exceed 0.01 eV. [ABSTRACT FROM AUTHOR]

Magnetic and magnetocaloric properties of L a 0.85 S r 0.15 M n 0.99 F e 0.01 O 3 perovskite oxides are investigated in the framework of the mean-field theory with a goal to develop a comprehensive model with parameters that can be used to optimize the caloric performances for cooling applications. Using the experimental magnetic isotherms M(H,T), we estimate and compare the exchange parameter (λ) , the saturation magnetization (M 0) , the total angular momentum (J) , and the gyromagnetic factor (g) for two different samples annealed at 1170 and 1250 °C. These parameters are used, in turn, in the simulation of the magnetic and the magnetocaloric properties of these L a 0.85 S r 0.15 M n 0.99 F e 0.01 O 3 compounds assuming imperfect samples with compositional and/or magnetic inhomogeneities. For this purpose, a Gaussian distribution of the Curie temperature is assumed. The temperature dependence of the magnetic entropy change, − Δ S M (T) , resulting from an applied field variation is simulated for both samples. The selected distribution captures the rounding of the − Δ S M (T) peak at its maximum and its broadening with growth conditions, features that are constantly observed in many bulk polycrystalline compounds. [ABSTRACT FROM AUTHOR]

We present a theoretical model to study the effect of counterion size on the effective charge, size, and thermodynamic behavior of a single, isolated, and flexible polyelectrolyte (PE) chain. We analyze how altering counterion size modifies the energy and entropy contributions to the system, including the ion-pair free energy, excluded volume interactions, entropy of free and condensed ions, and dipolar attraction among monomer–counterion pairs, which result in competing effects challenging intuitive predictions. The PE self-energy is calculated using the Edwards–Muthukumar Hamiltonian, considering a Gaussian monomer distribution for the PE. The condensed ions are assumed to be confined within a cylindrical volume around the PE backbone. The dipolar and excluded volume interactions are described by the second and third virial coefficients. The assumption of freely rotating dipoles results in a first-order coil–globule transition of the PE chain. A more realistic, weaker dipolar attraction, parameterized in our theory, shifts it to a second-order continuous transition. We calculate the size scaling-exponent of the PE and find exponents according to the relative dominance of the electrostatic, excluded volume, or dipolar effects. We further identify the entropy- and energy-driven regimes of the effective charge and conformation of the PE, highlighting the interplay of free ion entropy and ion-pair energy with varying electrostatic strengths. The crossover strength, dependent on the counterion size, indicates that diminishing sizes favor counterion condensation at the expense of free ion entropy. The predictions of the model are consistent with trends in simulations and generalize the findings of the point-like counterion theories. [ABSTRACT FROM AUTHOR]

Planning decisions are generally subject to some level of uncertainty. In forestry, data describing the resources available have a major impact on operations performance and productivity. This paper aims to present a method to improve decision-making in the forest supply chain by taking supply uncertainty into account using the results of data quality assessments. The case study describes the operations planning process of a Canadian forest products company dealing with an uncertain volume of wood supply. Three approaches to constructing probability distributions based on data quality are tested. Each approach offers a different level of precision: (1) a frequency distribution of accuracy, (2) a normal distribution based on average accuracy, and (3) a normal distribution based on data quality classification. Using stochastic programming to plan transport and production shows that lower costs can be achieved with a general characterisation of the data accuracy. Not considering uncertainty when planning operations leads to a significant replanning transportation cost. Using classes of data quality to include uncertainty in operations planning contributes to reducing the transportation cost from $15.90/m3 down to $15.32/m3 representing 3.6%. [ABSTRACT FROM AUTHOR]

The role of curvature effects, in the thermal-field (TF) emission of electrons from nanotips, has been investigated. It is found that for field emitter tips having tip radius of curvature R a < 50 nm, the error on using the planar tunneling potential to evaluate the current density is significant compared to the curvature-corrected potential. Furthermore, the error is found to be strongly temperature dependent at low apex fields, whereas at high fields, it is only moderate. For emitter tips having tip radius R a ≥ 5 nm, a unified expression for current density applicable for field, thermal-field, and thermionic emission is obtained. In the analysis, a single linearization point corresponding to the peak of the normal energy distribution of electrons is chosen, about which the Gamow exponent is expanded. The temperature-dependent curvature-corrected current density equation is numerically validated against a benchmark that performs the current density integral with the Kemble form of the Wentzel–Kramers–Brillouin transmission coefficient up to the peak of the tunneling potential, while above the barrier, the Morse transmission coefficient is used. The agreement is found to be good. It is inferred that the curvature plays a significant role in field and thermal-field emission, whereas in thermionic emission, it is not as important. In the limit where the apex radius of curvature R a → ∞ , it is able to retrieve the Murphy–Good equation as well as the Richardson–Laue–Dushman equation in their respective limits. [ABSTRACT FROM AUTHOR]

Platform-based product development is a cost-efficient approach to satisfy wide range of customer preferences. One important problem is the product platform configuration, in which two types of platform configuration are widely used, either module selection or module integration. The platform configuration based on module selection provides a broader solution space of platform selection, while module integration facilitates product platform commonality to gain economic benefits. Up to date, most research focused on the platform configuration based on module selection with a given module set. In this paper, we propose a new model to determine the optimal platform configuration for an external product family, considering both module selection and integration. The demand for the external product family is uncertain and assumed to follow normal distribution. A hybrid methodology combining simulated annealing and variable neighbourhood search is proposed. A numerical examination is carried out for the purpose of evaluation. The results show that the proposed model can provide a practically good solution. Main contributions of this research are two folds, one is the balance point between module selection and module integration in the platform configuration and the other is that we take into account the operation cost of module acquisition to the platform configuration problem. [ABSTRACT FROM AUTHOR]

Summary: Information technology enables the process of spectral sensing and spectral efficiency (SE) with the help of different strategies attracted by researchers in cooperative cognitive radio networks (CCRN). Compared with other wireless technologies, spectral sharing in green metric CCRN (GMCCRN) is an effective strategy. Due to the collaboration between the unlicensed and licensed customers, the spectral sharing between the cooperative customers possesses various challenges. Here, the effectiveness of green CCRN is demonstrated through a variety of useful techniques. The proposed work designed a channel using Markov Gaussian wideband distribution (MGWD), and for communication, dynamic optimal relay‐based protocol (DORP) is used. Also, an effective optimization known as adaptive dynamic group‐based optimization algorithm (ADGCO) is used to examine the false alarm detection and finest spectral sensing. Finally, the effectiveness of GMCCRN is validated in terms of outage probability, spectral efficiency, energy efficiency, and throughput. Furthermore, the results revealed that the proposed method in CCRN reduces power consumption at both the secondary user (SU) and primary user (PU) sides. Also, the method maximized the throughput compared with existing schemes and achieved 0.3 as error prospect and 92.6% as accuracy. [ABSTRACT FROM AUTHOR]

The traditional factor analysis model assumes that the factors obey a normal distribution, which is not appropriate in fields whose data are nonnegative. For this kind of problem, we construct a more practical factor model, assuming that the factors obey a Gamma distribution. We develop a new factor analysis model and discuss its true loading matrix. Then we study its parameter estimation with the maximum likelihood estimation (MLE) method based on an Expectation-Maximization (EM) algorithm, where step E is realized by the Metropolis-Hastings (M-H) algorithm in the Markov Chain Monte Carlo (MCMC) method. We use the new model to empirically study real data, and evaluate its information extraction ability, using the defined true loading matrix to calculate the true loading of the factor. We compare the new model and traditional factor analysis models on simulated and real data, respectively, whose results show that the new model has better information extraction ability for nonnegative data when the number of factors is the same. [ABSTRACT FROM AUTHOR]

• Using a cut-off value for a short cervix introduces selection bias and affects the normal distribution and incidence of a short cervical length. • Irrespective of the chosen cut-off value, the deviation from a normal distribution is most pronounced around this threshold. • If essential decisions depend on a cut-off value, healthcare providers should be aware of potential bias and measure with caution. A prior study suggested that implementing a cut-off value of ≤30 mm for a short cervical length (CL) could potentially introduce selection bias and alter the distribution of CL measurements. As such, the objective of this study is to evaluate how CL distribution and incidence of short CL are affected when using different cut-off values for a short CL. This is a secondary analysis of the Quadruple P (QP) Screening study; a prospective cohort study that included low-risk patients with singleton pregnancies undergoing fetal anomaly scan at 18–22 weeks of gestation, including a CL measurement. Patients with a short cervix, defined as ≤35 mm, were subsequently counseled for the QP trial; a randomized controlled trial (RCT) comparing progesterone to cervical pessary for the prevention of preterm birth. If participation to the RCT was refused, patients with a CL ≤25 mm were advised to use progestogen. The primary objective of this current study was to assess the normal distribution of CL across the entire cohort and to assess the incidence of short CL when using the cut-off values of ≤35 and ≤25 mm. Normal distributions for CL were simulated based on mean and standard deviation(SD) of the original data. The Kolmogorov-Smirnov test was used to evaluate the distribution of the CL measurements. Moreover, to evaluate the motives behind ultrasound measurements around the cut-off value, sonographers were asked to fill out a qualitative questionnaire. The total cohort included 19.171 eligible participants who underwent CL measurement, with a mean CL of 43.9 mm (±8.1 SD). The distribution of all CL observed measurements deviated significantly from the normal distribution (p < 0.001). A total of 1.852 (9.7%) patients had short CL ≤35 mm, which was significantly lower than expected when compared to the simulated normal distribution (n = 2.661, 13.9%; p < 0.001). The incidence of short CL ≤25 mm in our cohort statistically differed from the simulated normal distribution (238, 1.2% vs 177, 0.9%; p=0.003). When comparing our data to the simulated normal distribution, the difference in distributions is most pronounced when examining the difference between 35 and 36 mm. Results of the questionnaire reveal sonographers claimed not to be influenced by a cut-off value for study participation or progesterone treatment. This study demonstrates that using any cut-off value for a short CL influences the incidence and distribution of CL. When using a cut-off value of ≤35 mm for study inclusion, the incidence of measurements of a short CL is lower than the anticipated incidence compared to a normal distribution. However, when using a cut-off value of ≤25 mm for progesterone treatment, the frequency of CL measurements is higher than expected below this threshold compared to a normal distribution. This study highlights the risk of introducing selection bias, most likely unintentionally, when cut-off values for short CL are used, regardless of the specific value chosen. Therefore healthcare providers should measure the CL with caution if essential decisions depend on a specific cut-off value. [ABSTRACT FROM AUTHOR]

Fabric anisotropy is a sought-after micro index to correlate macro mechanical responses of granular materials. In this work, the discrete-element method is utilised to simulate multi-directional bender element tests in granular soils to obtain the evolution of wave velocities during drained conventional triaxial (CT) and true triaxial (TT) tests; the contact normal-based fabric is simultaneously monitored for bridging the fabric anisotropy and wave velocity anisotropy. The results show that stress-normalised wave velocities and microscopic fabric, including contact normal distribution and coordination number, remain nearly constant until a stress ratio threshold is reached. After the threshold value is reached, stress-normalised wave velocities start to decrease, especially in the minor principal stress direction, accompanied by significant adjustment of coordination number and fabric anisotropy. The results also reveal that the normalised wave velocity depends on the contact normal densities in the wave propagation and particle oscillation directions. With the contact normal distribution represented by a density function, a good linear relationship between the microscopic fabric anisotropy and macroscopic wave velocity anisotropy is obtained for both CT and TT tests. [ABSTRACT FROM AUTHOR]

Recent years have seen a growing interest in the development of person‐fit statistics for tests with polytomous items. Some of the most popular person‐fit statistics for such tests belong to the class of standardized person‐fit statistics, T, that is assumed to have a standard normal null distribution. However, this distribution only holds when (a) the true ability parameter is known and (b) an infinite number of items are available. In practice, both conditions are violated, and the quality of person‐fit results is expected to deteriorate. In this paper, we propose three new corrections for T that simultaneously account for the use of an estimated ability parameter and the use of a finite number of items. The three new corrections are direct extensions of those that were developed by Gorney et al. (Psychometrika, 2024, https://doi.org/10.1007/s11336‐024‐09960‐x) for tests with only dichotomous items. Our simulation study reveals that the three new corrections tend to outperform not only the original statistic T but also an existing correction for T proposed by Sinharay (Psychometrika, 2016, 81, 992). Therefore, the new corrections appear to be promising tools for assessing person fit in tests with polytomous items. [ABSTRACT FROM AUTHOR]

Accurate intraday forecasts of the power output by photovoltaic (PV) systems are critical to improve the operation of energy distribution grids. We describe a neural autoregressive model that aims to perform such intraday forecasts. We build upon a physical, deterministic PV performance model, the output of which is used as covariates in the context of the neural model. In addition, our application data relate to a geographically distributed set of PV systems. We address all PV sites with a single neural model, which embeds the information about the PV site in specific covariates. We use a scale-free approach which relies on the explicit modeling of seasonal effects. Our proposal repurposes a model initially used in the retail sector and discloses a novel truncated Gaussian output distribution. An ablation study and a comparison to alternative architectures from the literature show that the components in the best performing proposed model variant work synergistically to reach a skill score of 15.72% with respect to the physical model, used as a baseline. [ABSTRACT FROM AUTHOR]

Nonlocal models have demonstrated their indispensability in numerical simulations across a spectrum of critical domains, ranging from analyzing crack and fracture behavior in structural engineering to modeling anomalous diffusion phenomena in materials science and simulating convection processes in heterogeneous environments. In this study, we present a novel framework for constructing nonlocal convection–diffusion models using Gaussian‐type kernels. Our framework uniquely formulates the diffusion term by correlating the constant diffusion coefficient with the variance of the Gaussian kernel. Simultaneously, the convection term is defined by integrating the variable velocity field into the kernel as the expectation of a multivariate Gaussian distribution, facilitating a comprehensive representation of convective transport phenomena. We rigorously establish the well‐posedness of the proposed nonlocal model and derive a maximum principle to ensure its stability and reliability in numerical simulations. Furthermore, we develop a meshfree discretization scheme tailored for numerically simulating our model, designed to uphold both the discrete maximum principle and asymptotic compatibility. Through extensive numerical experiments, we validate the efficacy and versatility of our framework, demonstrating its superior performance compared to existing approaches. [ABSTRACT FROM AUTHOR]

This study introduces the Multi-objective Generalized Normal Distribution Optimization (MOGNDO) algorithm, an advancement of the Generalized Normal Distribution Optimization (GNDO) algorithm, now adapted for multi-objective optimization tasks. The GNDO algorithm, previously known for its effectiveness in single-objective optimization, has been enhanced with two key features for multi-objective optimization. The first is the addition of an archival mechanism to store non-dominated Pareto optimal solutions, ensuring a detailed record of the best outcomes. The second enhancement is a new leader selection mechanism, designed to strategically identify and select the best solutions from the archive to guide the optimization process. This enhancement positions MOGNDO as a cutting-edge solution in multi-objective optimization, setting a new benchmark for evaluating its performance against leading algorithms in the field. The algorithm's effectiveness is rigorously tested across 35 varied case studies, encompassing both mathematical and engineering challenges, and benchmarked against prominent algorithms like MOPSO, MOGWO, MOHHO, MSSA, MOALO, MOMVO, and MOAOS. Utilizing metrics such as Generational Distance (GD), Inverted Generational Distance (IGD), and Maximum Spread (MS), the study underscores MOGNDO's ability to produce Pareto fronts of high quality, marked by exceptional precision and diversity. The results affirm MOGNDO's superior performance and versatility, not only in theoretical tests but also in addressing complex real-world engineering problems, showcasing its high convergence and coverage capabilities. The source codes of the MOGNDO algorithm are publicly available at https://nimakhodadadi.com/algorithms-%2B-codes. [ABSTRACT FROM AUTHOR]

Due to the typically high flow rate of nozzles on large sprinkler machines, direct use for pesticide application can lead to pesticide waste and non-point source pollution. To fulfill the multifunctional needs of large sprinkler machines for irrigation, fertilization, and pesticide application, this study installed atomizing micro-sprinklers on the machines to enable their pesticide application capabilities. Based on the spraying patterns of the atomizing micro-sprinklers, three types were selected for this study: hollow cone, solid cone, and fan-shaped micro-sprinklers. Three working pressure levels were set at 0.2 MPa, 0.3 MPa, and 0.5 MPa, with the height of the micro-sprinklers above the ground set at three levels: 0.8 m, 1.2 m, and 1.5 m. A completely randomized design was used for the experiment, which included hydraulic performance tests and software simulation. The results indicated that the spray volume of the hollow cone, solid cone, and fan-shaped atomizing micro-sprinklers approximately presented a hollow conical, solid conical, and normal distribution, respectively. The spray intensity (average and maximum values) of the atomizing micro-sprinklers increased with the increase in pressure, while the median showed an initial increase followed by a decrease as pressure increased. Under the same pressure conditions, the spray intensity of the different atomizing micro-sprinklers decreased with the increase in height above the ground, while the spray width increased with height. By comparing the spraying characteristics of micro-sprinklers with various patterns, large sprinkler machines can select atomizing micro-sprinklers in different spraying statuses according to actual conditions and determine the optimal height of the atomizing micro-sprinklers above the ground to achieve efficient and uniform pesticide application effects. This reduces the evaporation and drift loss of liquid medicine. The research findings provide a scientific basis for achieving the pesticide application function of large sprinkler machines and expanding their usage capabilities. [ABSTRACT FROM AUTHOR]

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Occasional outliers that might be a natural part of a process may distort the properties of a control chart. In this paper, we show that a recently proposed adaptive EWMA (AE) mean chart is highly sensitive to the outliers. The false alarm rate of the AE chart increases when the proportion and/or magnitude of the outliers increase and vice versa. In order to circumvent this demerit of the AE chart, we propose a truncated normal distribution-based AE (TAE) chart for monitoring the mean of a normal process in the presence of outliers. The zero-state and steady-state average run-length profiles of the proposed chart are estimated using Monte Carlo simulations. Based on detailed run-length comparisons, it is found that the TAE chart may outperform the existing EWMA chart (based on a truncated normal distribution) when detecting various mean shift sizes of an outlier-prone normal process. Illustrative examples are also included in this study to demonstrate the implementation of the existing and proposed charts. [ABSTRACT FROM AUTHOR]

This paper proposes a method of forecasting US recessions beginning with data displays that contain the last 12 quarters of seven US expansions. These end‐of‐expansion displays allow observers to see for themselves what is different about the last year before recessions compared with the two earlier years. Using a statistical model that treats this historical data as draws from a 12‐dimensional multivariate normal distribution, the most recent data are probabilistically inserted into these images where the recent data are most like the historical data. This is a recession forecast based not on presumed patterns but on patterns revealed by the data. [ABSTRACT FROM AUTHOR]

Serotonin, a monoamine neurotransmitter, is important in both the central nervous system (CNS) and the peripheral nervous system. Malfunction of serotonin signaling leads to various disorders. We studied serotonin signaling from serotonergic neurons inside the ventral nerve cord of Drosophila melanogaster. Serotonergic neurons and stimulated release were visualized and achieved with mCherry and channelrhodopsin‐2 (an optogenetically transfected ion channel), respectively, and two electrochemical techniques quantified serotonin release and vesicular content. Mean vesicular serotonin content released during exocytosis from these neurons was 84 %, considerably higher than reported in previous studies regarding octopamine (4.5 %) and glutamate release (31 %). Serotonin content within all vesicles is uniformly changed when serotonin concentration is inhibited or enhanced. However, serotonin release exhibits two Gaussian distributions: higher frequency of small release events, and similar or slightly higher frequency of large events, resulting in differential release fractions ranging from partial (13–18 %) to full (100 %) release after treatment with agents to either enhance or diminish release. This is the first example of consistent full exocytotic release events we have observed in any system. We suggest one pool of vesicles can release significantly diverse fractions of transmitter load during exocytosis, a potentially novel pathway to regulate exocytosis and neuronal signaling. [ABSTRACT FROM AUTHOR]

For stationary time series with regularly varying marginal distributions, an important problem is to estimate the associated tail index which characterizes the power‐law behavior of the tail distribution. For this, various results have been developed for independent data and certain types of dependent data. In this article, we consider the problem of tail index estimation under a recently proposed notion of serial tail dependence called the tail adversarial stability. Using the technique of adversarial innovation coupling and a martingale approximation scheme, we establish the consistency and central limit theorem of the tail index estimator for a general class of tail dependent time series. Based on the asymptotic normal distribution from the obtained central limit theorem, we further consider an application to cluster a large number of regularly varying time series based on their tail indices by using a robust mixture algorithm. The results are illustrated using numerical examples including Monte Carlo simulations and a real data analysis. [ABSTRACT FROM AUTHOR]

Video anomaly detection is always formulated as frame prediction task which only learned on normal data and detects deviations as anomalies. However, previous methods lack sufficient spatiotemporal constraints on moving objects, making it difficult to learn compact normal distributions and anomalies near the boundary will be misclassified as normal. Besides, the inadequate exploration of diverse normal patterns results in mode missing and unlearned normal patterns will be misclassified as anomalies. To address these problems, we propose an object-level Diverse Motion-conditioned Adversarial Predictive Network for video anomaly detection which combines conditional variational generation with adversarial learning to mitigate false detection. We design a motion-guided generator that controls the generation process conditioned on optical flows to accurately memorize spatiotemporal correlations of normal data. We employ the diversity regularization strategy which explicitly preserves the recurrent structure of normal data in continuous latent space to ensure full utilization of diverse patterns. Additionally, we combine an input clip with the object it generates to synthesize an anomaly near the boundary, then employ a video discriminator to perceive subtle differences between normal and abnormal data, making them more distinguishable. Extensive experiments conducted on public datasets illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Objectives: The purpose of this multicenter retrospective study was to perform a two-dimensional analysis of upper airway changes in adolescent patients following molar distalization with the Pendulum appliance. Materials and methods: The study involved the cephalometric analysis of 88 patients, retrospectively categorized into two groups: skeletal Class II with a dental Class II molar relationship (36 patients, mean age 12.6 ± 1.1 years) and skeletal Class I with a dental Class II molar relationship (54 patients, mean age 12.3 ± 1.2 years). Changes were observed using lateral radiographs before (T0) and after Pendulum appliance removal (T1); treatment time averaged 7 months. Upper airways were subsequently analyzed by tracing lateral radiographs. The Shapiro-Wilk test showed a normal distribution of the data, therefore parametric tests were used for statistical analysis. Intragroup changes between T0 and T1 were evaluated using paired t-tests, and intergroup differences were assessed using independent student t-tests; statistical significance was set at 0.05. Results: Statistically significant differences were observed in the skeletal measurements that characterized both groups, particularly in ANB and Wits appraisal, at T0 (P < 0.001). After molar distalization, Class I and Class II groups reported no statistically significant differences with changes almost equal to zero between timepoints (P > 0.05). Additionally, intergroup comparisons of airway changes at T1 did not show statistically significant differences (P > 0.05). Conclusions: The Pendulum appliance does not significantly change the upper airway dimensions in Class I and Class II malocclusion patients, thereby minimizing potential respiratory risks. [ABSTRACT FROM AUTHOR]

Objective Methods Results Conclusions Based on the varying number and relative positions of cervical disc replacement (CDR) and anterior cervical discectomy and fusion (ACDF) procedures, three‐segment hybrid surgery (HS) presents a diverse structural approach. Currently, the potential differential effects of HS with different segment combinations and surgical procedures on overloaded vertebral body (OVB) occurrence remain unexplored. The purpose of this retrospective study is to compare the clinical and radiological outcomes of HS and ACDF in treating cervical degenerative disc disease (CDDD), aiming to provide further insights into OVB.This study included patients with three‐level CDDD who underwent ACDF or HS at our institution. Eligible patients were divided into three groups: Type I (one‐level CDR and two‐level ACDF), Type II (two‐level CDR and one‐level ACDF), and ACDF (three‐level ACDF). For radiographic analysis, patients were further divided into the Replacement Segment Group and the Nonreplacement Segment Group based on the presence of replacement segments above and below the OVB. Clinical outcomes were evaluated using visual analog scale (VAS) scores for neck and arm pain, Japanese Orthopedic Association (JOA) scores, and neck disability index (NDI) scores. The cervical radiological parameters assessed included (1) vertebral cross‐sectional area (CSA), (2) wedge angle (WA), (3) anterior vertebral height (AH), (4) posterior vertebral height (PH), and (5) Hounsfield unit (HU) values. Statistical methods included paired t‐test, ANOVA test, and chi‐square test. Independent samples t‐test, Mann–Whitney U test, and Wilcoxon signed‐rank test were used to compare the differences between two groups according to the results of normal distribution test.A total of 123 patients, evenly distributed among three groups, were included and were well matched in terms of demographic characteristics. The likelihood of vertebral body collapse (VBC) was notably higher in the ACDF group (41.5%) compared with the Type I (17.9%) and Type II (8.9%) groups (p < 0.01). Following surgery, both at 3 and 6 months, the ACDF group demonstrated higher VAS neck scores and NDI scores compared with the Type I and Type II groups (p < 0.01). Additionally, the WA and AH values of the upper and lower adjacent OVB were consistently lower in the ACDF group than in the Type I and Type II groups at 6 and 12 months and at the final follow‐up (p < 0.01). Notably, in the Nonreplacement Segment Group, WA significantly decreased at 12 months postoperatively and at the final follow‐up compared with the Replacement Segment Group (p < 0.01).Three levels of HS appear to reduce stress concentrations and alleviate morphological changes in OVB. The occurrence of more VBC patients with OVB was associated with the use of Zero‐P or Zero‐P VA implants. [ABSTRACT FROM AUTHOR]

Process capability indices (PCIs) have been widely used for process performance assessment. It provides a quantitative measure of performance of an industrial production process. Several PCIs have been proposed in the literature. Cpk is one of the most useful capability index. Statistical properties of the estimator of Cpk have been discussed by many researches under the assumption of identically and independently normal distribution. Some statistical properties of the estimator of Cpk for autocorrelated data in presence of gauge measurement errors have studied by Scagliarini (2010). However, we found some critical error in the expression of expectation and variance of the estimator when process is autocorrelated as well as measurement error is present. Through this article, we present a corrected version of expectation and variance of the estimator. [ABSTRACT FROM AUTHOR]

AbstractThe shallow straight-link-shaped structure of 2.5D woven composites (2.5D-SSLS-WC) is a novel material with a wide range of promising applications, but the randomness of its internal structure has brought challenges to performance prediction and engineering design. The gap of existing unit-cell models in capturing the internal randomness of the 2.5D-SSLS-WC material limits the accurate prediction of properties and the optimization of engineering design. In this work, the modeling method for random unit-cell model based on Gaussian distribution is proposed for 2.5D-SSLS-WC structure considering random distribution property of weft yarns and extrusion effects between structural surfaces and internal yarns during the molding process, which can more effectively reflect the practical mesoscopic structure of the material, reveal the micro mechanical behavior inside materials and provide theoretical basis for material design and performance optimization. The mechanical property and damage evolution of the proposed model is predicted and analyzed using finite element (FE) analysis and progressive damage method. The maximum errors of equivalent stiffness and strength between prediction and experimental values are 6.7% and 2.3%, respectively. The results show that the proposed model has high prediction accuracy and can reasonably reflect the damage extension and ultimate failure mode of the material during the loading process, which verifies the rationality of the proposed unit-cell modeling strategy. The modeling method proposed in this paper provides a new perspective for the in-depth understanding and description of the microstructure and mechanical behavior of 2.5D-SSLS-WC materials, which is of significant theoretical and engineering value and can be extended to the modeling of other woven composites. [ABSTRACT FROM AUTHOR]

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the "11th Five-Year Plan" and "12th Five-Year Plan" periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin's overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the "11th Five-Year Plan" and "12th Five-Year Plan" periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin. [ABSTRACT FROM AUTHOR]

Introduction: This study aimed to verify whether implicit and explicit informational constraints generate differences in tactical performance and behavior in U-20 national-level soccer players. Methods: Thirty-two under-20 male athletes from two clubs participated. Four 4-a-side small-sided games (SSG) protocols were used: R1 - explicit rule for highpress marking, R2 - implicit rule for high-press marking, R3 - game with both previous rules simultaneous and FR - free game, without additional rules. SSGs comprised 4 vs. 4+ goalkeepers games on a 42 m × 29 m field. Position data from 10 Hz Global Positioning System (GPS) devices were used to evaluate individual and collective tactical behavior (spatial occupation metrics) and performance (interpersonal coordination). MANOVA was used for normally distributed variables, and Friedman's with Dunn or Bonferroni post hoc was used for variables without normal distribution. For SEI, an ANOVA was used with Bonferroni post hoc. Results: The R1 protocol showed higher SEI, length, width, and LpWratio than the FR protocol (p = 0.009). There was an effect of the different protocols in SEI values (p < 0.001). Under the explicit rule, players also showed the highest in-phase interpersonal coordination values (p < 0.001). Discussion: Providing players with explicit tactical instructions improves tactical performance acutely for high-pressing defensive actions. [ABSTRACT FROM AUTHOR]

In this paper, we extend a Central Limit Theorem, recently established for the Thompson group F = F2 by Krishnan, to the Brown–Thompson groups Fp, where p is any integer greater than or equal to 2. The non-commutative probability space considered is the group algebra ℂ[Fp], equipped with the canonical trace. The random variables in question are an := (xn + xn−1)/2, where {xi}i≥0 represents the standard family of infinite generators. Analogously to the case of F = F2, it is established that the limit distribution of sn = (a0 + ⋯ + an−1)/n converges to the standard normal distribution. Furthermore, it is demonstrated that for a state corresponding to Jones’s oriented subgroup is denoted by F→, such a Central Limit Theorem does not hold. [ABSTRACT FROM AUTHOR]

In an analysis of 1,825 state or provincial election outcomes in five federal democracies the rate of decay of incumbency (K) serves to partition the distribution of the vote for the incumbent party, the party of the head of government, between those who win a subsequent term and those who do not. In conjunction with the mean and standard deviation of the distribution, the weighted mean of the vote in re-election and defeat is identified. The model’s predictions are generally within 2–3 percentage points of the actual outcome. [ABSTRACT FROM AUTHOR]

In the last years special “ovals” appear increasingly often in diagrams and applets for discussing crucial items of statistical inference (when dealing with confidence intervals for an unknown probability p; approximation of the binomial distribution by the normal distribution; especially in German literature, see e.g. [Meyer, Stochastik in der Schule 2011; 31:18–22] or [Riemer, Statistik unterrichten. Eine handlungsorientierte Didaktik der Stochastik; 2023, p. 69ff]). These applets concerning confidence intervals aim at illustrating vividly the crucial differences (as we know, often vague for students) and connections between confidence intervals and prediction intervals. In English literature such applets seem to be not so well known, thus at the beginning, we describe how they work and highlight their high potential for teaching and learning. The fact that the mentioned ovals are, actually, ellipses is not important for stochastic purposes which is probably the reason why one can hardly find related explanations in the literature. Another aim of this paper is to explain the ellipse shape in a preferably elementary manner involving geometry, even spatial geometry. [ABSTRACT FROM AUTHOR]

Mitochondrial functions can be regulated by membrane contact sites with the endoplasmic reticulum (ER). These mitochondria–ER contact sites (MERCs) are functionally heterogeneous and maintained by various tethers. Here, we found that REEP5, an ER tubule-shaping protein, interacts with Mitofusins 1/2 to mediate mitochondrial distribution throughout the cytosol by a new transport mechanism, mitochondrial “hitchhiking” with tubular ER on microtubules. REEP5 depletion led to reduced tethering and increased perinuclear localization of mitochondria. Conversely, increasing REEP5 expression facilitated mitochondrial distribution throughout the cytoplasm. Rapamycin-induced irreversible REEP5–MFN1/2 interaction led to mitochondrial hyperfusion, implying that the dynamic release of mitochondria from tethering is necessary for normal mitochondrial distribution and dynamics. Functionally, disruption of MFN2–REEP5 interaction dynamics by forced dimerization or silencing REEP5 modulated the production of mitochondrial reactive oxygen species (ROS). Overall, our results indicate that dynamic REEP5–MFN1/2 interaction mediates cytosolic distribution and connectivity of the mitochondrial network by “hitchhiking” and this process regulates mitochondrial ROS, which is vital for multiple physiological functions. [ABSTRACT FROM AUTHOR]

ABSTRACT We consider the problem of combining multiple biomarkers to improve the diagnostic accuracy of detecting a disease when only group‐tested data on the disease status are available. There are several challenges in addressing this problem, including unavailable individual disease statuses, differential misclassification depending on group size and number of diseased individuals in the group, and extensive computation due to a large number of possible combinations of multiple biomarkers. To tackle these issues, we propose a pairwise model fitting approach to estimating the distribution of the optimal linear combination of biomarkers and its diagnostic accuracy under the assumption of a multivariate normal distribution. The approach is evaluated in simulation studies and applied to data on chlamydia detection and COVID‐19 diagnosis. [ABSTRACT FROM AUTHOR]

Process capability indices (PCIs), are widely used to gauge how well a process performs within its requirements. While greater PCIs are indicative of improved process “quality,” they do not necessarily translate into lower rejection rates. Therefore, using a loss‐based PCI to gauge process capability makes more sense. In this paper, the PCI Spk′$ S^{\prime }_{pk}$ has been considered for normal random variable. The article attempts to study the classical and the Bayesian estimation of Spk′$ S^{\prime }_{pk}$ for type‐II progressive right censored sample under symmetric as well as asymmetric loss functions, namely squared error loss function, and LINEX loss function, respectively. The Markov chain Monte Carlo simulation technique has been efficiently used here to have the approximate solution for Spk′$S^{\prime }_{pk}$. Through an extensive Monte Carlo simulation study along with two real life examples related to electronic industry, we compare the performances of the classical and the Bayes estimates based on symmetric and asymmetric loss functions and compared among the asymptotic confidence interval and highest posterior density credible intervals, in terms of average width and corresponding coverage probabilities of Spk′$S^{\prime }_{pk}$, respectively. [ABSTRACT FROM AUTHOR]

Color correction is an important methodology where a digital image's colors undergo a transformation to more accurately represent their appearance using a predefined set of illumination conditions. Colorimetric measurements in diagnostics are sensitive to very small changes in colors and therefore require consistent, reproducible illumination conditions to produce accurate results, making color correction a necessity. This paper presents an image color correction pipeline developed by HueDx, Inc., using transfer algorithms that improve upon existing methodologies and demonstrates real-world applications of this pipeline in colorimetric clinical chemistry using a smartphone enabled, paper-based total protein diagnostic assay. Our pipeline is able to compensate for a variety of illumination conditions to provide consistent imaging for quantitative colorimetric measurements using white-balancing, multivariate gaussian distributions and histogram regression via dynamic, non-linear interpolating lookup tables. We empirically demonstrate that each point in the color correction pipeline provides a theoretical basis for achieving consistent and precise color correction. To show this, we measure color difference with deltaE (ΔE00), alongside quantifying performance of the HueDx color correction system, including the phone hardware, color sticker manufacturing quality and software correction capabilities. The results show that the HueDx color correction system is capable of restoring images to near-imperceptible levels of difference independent of their original illumination conditions including brightness and color temperature. Comparisons drawn from the paper-based total protein assay calibrated and quantified with and without using the HueDx color correction pipeline show that the coefficient of variation in precision testing is almost twice as high without color-correcting. Limits of blank, detection and quantitation were also higher without color-correction. Overall, we were able to demonstrate the HueDx platform improves reading and outcome of the total protein diagnostic assay and is useful for the development of smartphone-based quantitative colorimetric diagnostic assays for point-of-care testing. [ABSTRACT FROM AUTHOR]

Fragile X Syndrome (FXS) is an X-linked disorder leading to the loss of expression of FMR1-protein product, FMRP. The absence or deficiency of FMRP is thought to result in the characteristic FXS phenotypes, including intellectual disability. Identifying the relationship between FMRP levels and IQ may be critical to better understand underlying mechanisms and advance treatment development and planning. A sample of 143 individuals with FXS (69% male), aged 8–50 years, completed IQ testing and blood draw via venipuncture to determine the relationship between Deviation IQ scores and FMRP levels as well as the distribution of Deviation IQ scores. In both males and females with FXS, higher FMRP levels were associated with higher Deviation IQ. However, this relationship was no longer significant when only examining full mutation, fully-methylated males. Yet, both the full and restricted male samples showed a downward shifted but otherwise normal distribution of Deviation IQ scores. Our findings support and extend previous studies establishing molecular markers of disease severity in FXS as well as provide novel evidence of a "FXS IQ standard curve". This latter finding suggests inter-individual variation in Deviation IQ in FXS, especially among males, may be driven by similar factors known to impact cognitive outcomes in typically-developing individuals. Thus, future work aimed at understanding the mechanisms by which FMRP loss leads to intellectual disability should revisit the biological/genetic, socio-environmental, and epigenetic factors contributing to inter-individual variation in IQ in FXS. [ABSTRACT FROM AUTHOR]

This paper proposes novel methods for extracting a single Speech signal of Interest (SOI) from a multichannel observed signal in underdetermined situations, i.e., when the observed signal contains more speech signals than microphones. It focuses on extracting the SOI using prior knowledge of the SOI's Direction of Arrival (DOA). Conventional beamformers (BFs) and Blind Source Separation (BSS) with spatial regularization struggle to suppress interference speech signals in such situations. Although Switching Minimum Power Distortionless Response BF (Sw-MPDR) can handle underdetermined situations using a switching mechanism, its estimation accuracy significantly decreases when it relies on a steering vector determined by the SOI's DOA. Spatially-Regularized Independent Vector Extraction (SRIVE) can robustly enhance the SOI based solely on its DOA using spatial regularization, but its performance degrades in underdetermined situations. This paper extends these conventional methods to overcome their limitations. First, we introduce a time-varying Gaussian (TVG) source model to Sw-MPDR to effectively enhance the SOI based solely on the DOA. Second, we introduce the switching mechanism to SRIVE to improve its speech enhancement performance in underdetermined situations. These two proposed methods are called Switching weighted MPDR (Sw-wMPDR) and Switching SRIVE (Sw-SRIVE). We experimentally demonstrate that both surpass conventional methods in enhancing the SOI using the DOA in underdetermined situations. [ABSTRACT FROM AUTHOR]

In this work, we propose a novel family of procedures for deriving a discrete counterpart to a continuous probability distribution. They are based on a class of distances between cumulative distribution functions, including the Cramér, the Cramér-von Mises, and the Anderson-Darling distances as particular cases. The discrete counterpart is defined and derived as the random variable which minimizes its distance to the assigned continuous probability distribution among all the discrete random variables supported on the set of integers (or positive integers). Applications are provided with reference to the exponential and the normal distributions, among others; the discrete counterparts are derived, and their main properties are discussed, also in comparison with the one obtained through an existing discretization technique based on the preservation of the cumulative distribution function at integer values. Parameter estimation for these discrete analogs is discussed, along with an analysis of two real datasets, where they are compared in terms of goodness-of-fit with some popular discrete distributions. Furthermore, in order to highlight the effectiveness and the benefits derived from the proposed discretization procedures, we illustrate two practical applications in actuarial science and in reliability engineering. In the former case, the problem of determining the distribution of the total claims amount for a non-life insurance portfolio is considered, where the claim sizes can be modelled as iid random variables, and the number of claims is random as well. Actuaries use a recursive calculation method based on Panjer's formula, which requires an appropriate discretization of the individual claim distribution, and therefore the proposed procedures can be used. Since we consider two simple cases where the distribution of the total claims amount is analytically acquirable, the efficacy of the discretization procedures in the final approximation can be easily assessed and turns out to be satisfactory, especially when compared to the existing discretization. The latter case considers the determination of the reliability parameter for a complex stress-strength model. Here, the approximation by discretization is compared to Monte Carlo simulation and shown to be relevant: with a comparable if not smaller computational effort, discretization leads to similar results as simulation. Such discretizations can also naturally be applied to more complex problems such as scenario generation in stochastic programming. R code for this article is provided as supplementary material. [ABSTRACT FROM AUTHOR]

In order to clarify the bioactivity of fluazinam to Fusarium pseudograminearum, the mycelial growth of 86 strains of F. pseudograminearum isolated from various cities in Henan Province from April to May 2022 was determined. The sensitivity baseline was established, and the field control efficacy of fluazinam on Fusarium crown rot (FCR) was determined. The results showed that the minimum inhibitory concentration of fluazinam on mycelial growth was 20 µg/mL, the effective concentration (EC50) of fluazinam on the mycelial growth of 86 strains was 0.013-0.492 µg/mL, and the average EC50 value was 0.194 µg/mL. The sensitivity of 57 strains (66.3%) was approximately normal distribution, and the average EC50 value of (0.201±0.057) µg/mL was used as the sensitivity baseline of F. pseudograminearum to fluazinam. The sensitivity of strains in the same city was quite different, and the ratio of the maximum and minimum EC50 value was 2.8-36.7. The difference in Luohe was the largest, and the difference in Xuchang was the smallest. There was no significant difference in the sensitivity of strains to fluazinam in different cities. There was no significant correlation between the sensitivity of F. pseudogramine to fluazinam and the geographical origin of the strains in Henan Province. There was no significant correlation between the sensitivity to fluazinam and its sensitivity to carbendazim and tebuconazole. Seed soaking with 500 g/L fluazinam suspension concentrate (SC), the incidence of FCR in Yichuan County and Xin'an County decreased by 45.46% and 56.88%, respectively, at the double ridge stage, and the white spike rate decreased by 8.73% and 13.67%, respectively, at the milky stage. The control efficacy of seed soaking with 500 g/L fluazinam SC against FCR of wheat at the double ridge stage was better, reaching more than 51% in both places. This study laid a theoretical basis for the prevention and control of fluazinam in FCR. [ABSTRACT FROM AUTHOR]

To explore the precursory information and instability fracture of rocks, we conducted a series of uniaxial compression tests on flawed sandstone. An integrated warning modeling is developed to predict the fracture in flawed sandstone. The results show that both the peak strength and elastic modulus of flawed sandstone demonstrate an "inverted" Gaussian distribution relative to the ligament angle, reaching the minimum values at 60°. The elastic strain energy proportion shows a sharp drop‐off, whereas the proportion of dissipative energy increases in steps. The coalescence modes of flawed sandstone change from a mixed tensile‐shear failure approximately aligned with the axis to an oblique shear failure. An integrated warning model is developed by integrating a Multi‐Output Classifier (MOC) and Grid optimization (GO). The integrated warning model has an accuracy of 97.95%. Additionally, the sensitivity of the model is recorded at 99.26%, confirming its effectiveness in predicting the likelihood of fracturing. Highlights: The cracking process of flawed sandstone is classified based on energy characteristics.The elastic energy ratio and dissipative energy ratio of flawed sandstone are revealed.The precursory indicators are identified using b‐value, AE variance, and AE energy.An integrated warning model is developed in combination with RFA and MOC. [ABSTRACT FROM AUTHOR]

Background: Liver and splenic stiffness measurements (LSM and SSM) are useful to predict varices and clinical decompensation in cirrhosis. SSM values are highly variable and overlapping and no guidelines exists on what constitutes normal SSM, that might limit interpretation of results. Methods: Consecutive subjects with LSM < 6 kPa and reliable SSM (FibroScan630 Expert device with spleen-dedicated module) and no vascular liver disease were analysed for significant correlations of SSM values with age, sex, BMI, portal and splenic vein diameter, splenic diameter, liver fat and diabetes. Based on timeline of SSM, subjects were randomly assigned in 70:30 ratio into derivation [n = 502] and validation subset [n = 214]. Results: Of 7200 subjects with simultaneous reliable LSM and SSM, 715 fulfilled the selection criteria (mean age: 43.8 ± 12.8 years, 67.2% male, mean BMI-26.4 ± 4.5 kg/m2). The mean SSM was 22.6 ± 5.8 kPa (c10–c90 percentile range: 15.2–31.3 kPa) and followed the normal distribution curve. In the derivation subset, mean SSM for males was higher than female (23.06 ± 6.2 vs. 21.78 ± 5.93 kPa; p = 0.028). SSM value correlated with LSM (r = 0.454, p = 0.001). Mean SSM in subjects with LSM 3–4, 4.1–5 and 5.1–6 kPa were 21.7 ± 5.8, 22.27 ± 5.67 and 23.76 ± 5.88 kPa (p value = 0.001). There was no difference in SSM based on age, BMI, diabetes and liver fat on ultrasound. Above results hold true for subjects in validation subset. Conclusion: SSM range in subjects with normal LSM and no vascular liver disease using spleen-dedicated module varies from 15.2 to 31.3 kPa, values being higher in male and not affected by Age, BMI, spleen size, liver fat and diabetes. Our results may serve as reference point in evaluation of SSM in compensated advanced liver disease patients. [ABSTRACT FROM AUTHOR]

Laser doping of silicon with pulsed and scanned laser beams is now well-established to obtain defect-free, doping profile tailored, and locally selectively doped regions with a high spatial resolution. Picking the correct laser parameters (pulse power, pulse shape, and scanning speed) impacts the depth and uniformity of the melted region geometry. This work performs laser doping on the surface of single crystalline silicon, using a pulsed and scanned laser profile with a Gaussian intensity distribution. A deposited boron oxide precursor layer serves as a doping source. Increasing the local inter-pulse distance x irr between subsequent pulses causes a quadratic decrease of the sheet conductance G sh of the doped surface layer. Here, we present a simple geometric model that explains all experimental findings. The quadratic dependence stems from the approximately parabolic shape of the individual melted regions directly after the laser beam has hit the Si surface. The sheet resistance depends critically on the intersection depth d ch and the distance x irr of overlap between two subsequent, neighboring pulses. The intersection depth d ch quadratically depends on the pulse distance x irr and therefore also on the scanning speed v scan of the laser. Finally, we present a simple model that reduces the complicated three dimensional, laterally inhomogeneous doping profile to an effective two-dimensional, homogeneously doped layer which varies its thickness with the scanning speed. [ABSTRACT FROM AUTHOR]

The intelligent customer service conversations between customer and customer service agent exhibit characteristics such as serious colloquialism, a high diversity of words, and short text length, making traditional sentiment analysis algorithms less effective. Besides, relying solely on coarse-grained sentiment analysis could not fully display the text information of users in the dialogue, resulting in poor performance of sentiment classification prediction. To address these issues, this paper introduces a hybrid word embedding method (HWE) based on Gaussian distribution to leverage the emotional syntactic and semantic richness of the two distributed word representations (Word2Vec, and GloVe). Additionally, a stacked ensemble method is employed, combining outputs from three deep learning models—Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). This ensemble approach enables simultaneous prediction of both coarse-grained and fine-grained sentiment analysis for intelligent customer service conversation text. The results show that HWE method enhances the comprehension of word representations in the context of conversations, leading to more effective sentiment analysis. Furthermore, the proposed stacked ensemble model outperforms current state-of-the-art single-task models, showcasing a significant improvement in sentiment classification accuracy. [ABSTRACT FROM AUTHOR]

This paper proposes a novel method capable of both detecting OOD data and generating in-distribution data samples. To achieve this, a VAE model is adopted and augmented with a memory module, providing capacities for identifying OOD data and synthesising new in-distribution samples. The proposed VAE is trained on normal data and the memory stores prototypical patterns of the normal data distribution. At test time, the input is encoded by the VAE encoder; this encoding is used as a query to retrieve related memory items, which are then integrated with the input encoding and passed to the decoder for reconstruction. Normal samples reconstruct well and yield low reconstruction errors, while OOD inputs produce high reconstruction errors as their encodings get replaced by retrieved normal patterns. Prior works use memory modules for OOD detection with autoencoders, but this method leverages a VAE architecture to enable generation abilities. Experiments conducted with CIFAR-10 and MNIST datasets show that the memory-augmented VAE consistently outperforms the baseline, particularly where OOD data resembles normal patterns. This notable improvement is due to the enhanced latent space representation provided by the VAE. Overall, the memory-equipped VAE framework excels in identifying OOD and generating creative examples effectively. [ABSTRACT FROM AUTHOR]

The multiple regression model statistical technique is employed to analyze the relationship between the dependent variable and several independent variables. The multicollinearity problem is one of the issues affecting the multiple regression model, occurring in regard to the relationship among independent variables. The ordinal least square is the standard method to evaluate parameters in the regression model, but the multicollinearity problem affects the unstable estimator. Liu regression is proposed to approximate the Liu estimators based on the Liu parameter, to overcome multicollinearity. In this paper, we propose a modified Liu parameter to estimate the biasing parameter in scaling options, comparing the ordinal least square estimator with two modified Liu parameters and six standard Liu parameters. The performance of the modified Liu parameter is considered, generating independent variables from the multivariate normal distribution in the Toeplitz correlation pattern as the multicollinearity data, where the dependent variable is obtained from the independent variable multiplied by a coefficient of regression and the error from the normal distribution. The mean absolute percentage error is computed as an evaluation criterion of the estimation. For application, a real Hepatitis C patients dataset was used, in order to investigate the benefit of the modified Liu parameter. Through simulation and real dataset analysis, the results indicate that the modified Liu parameter outperformed the other Liu parameters and the ordinal least square estimator. It can be recommended to the user for estimating parameters via the modified Liu parameter when the independent variable exhibits the multicollinearity problem. [ABSTRACT FROM AUTHOR]

Recently, reservoir computing (RC) has emerged as one of the most effective algorithms to model and forecast volatile and chaotic time series. In this paper, we aim to contribute to the understanding of the uncertainty associated with the predictions made by RC models and to propose a methodology to generate RC prediction intervals. As an illustration, we analyze the error distribution for the RC model when predicting the price time series of several agri-commodities. Results show that the error distributions are best modeled using a Normal Inverse Gaussian (NIG). In fact, NIG outperforms the Gaussian distribution, as the latter tends to overestimate the width of the confidence intervals. Hence, we propose a methodology where, in the first step, the RC generates a forecast for the time series and, in the second step, the confidence intervals are generated by combining the prediction and the fitted NIG distribution of the RC forecasting errors. Thus, by providing confidence intervals rather than single-point estimates, our approach offers a more comprehensive understanding of forecast uncertainty, enabling better risk assessment and more informed decision-making in business planning based on forecasted prices. [ABSTRACT FROM AUTHOR]

In this study, in order to determine the effect of matrices' shape on the percolation threshold values, computer simulations were performed using the Monte Carlo method for a 200 × 200 square-shaped matrix and rectangular matrices containing the same number of nodes as the square matrix. Based on the simulations, the average values of the percolation thresholds and standard deviations for the current flow along and across the matrices were determined. It was determined that for a square-shaped matrix, the average values of the percolation thresholds in both directions of the current flow were the same. Extending the rectangular matrix while reducing its height causes the average value of the percolation threshold in the direction of the current flow along the matrix to increase from 0.592740 to 0.759847, while in the transverse direction, it decreases from 0.592664 to 0.403614. The values of the classical asymmetry coefficients of the probability distributions of the percolation thresholds for both directions of the current flow were determined. Histograms of the probability distributions of the percolation threshold values for a square-shaped matrix and rectangular matrices were made and compared with the normal distributions. It was found that the occurrence of two percolation thresholds in rectangular layers should be considered when analyzing the electrical conductivity measurements of nanocomposite thin films. [ABSTRACT FROM AUTHOR]

We derive explicit expressions for the directed likelihood statistic and its modified version for testing several hypotheses on the concentration parameter in the von Mises regression model. We verify that the standard normal distribution gives a poor approximation to the true distribution of the usual directed likelihood statistic to test the concentration parameter, while its modified version leads to very accurate inference even for very small samples. An empirical application is considered for illustrative purposes. [ABSTRACT FROM AUTHOR]

This paper delves into the Bayesian statistics applications of three preeminent models, Poisson distribution, Gaussian distribution, and Binomial distribution, in the continuous surveillance of artificial radionuclides. It introduces a slide-window method to accelerate the updating of the prior distribution of model parameters and compares the performances of three models before and after utilizing this method. Comparisons among the three models are made before and after using the slide-window. Experimental results demonstrate a marked enhancement in the performances of all models. [ABSTRACT FROM AUTHOR]