Communication is a key method of expressing one's thoughts and opinions. Amongst many modalities, speech and writing are the most powerful and common forms of human communication. Analysing what and how people think has inherently been an interesting and progressive research domain. This includes bimodal sentiment analysis which is an emerging area in natural language processing (NLP) and has received a great deal of attention in recent years in a variety of areas including social opinion mining, health care, banking, and so on. At present, there are limited studies on bimodal conversational sentiment analysis as it proves to be a challenging area given the complex nature of the way humans express sentiment cues across various modalities. To address this gap, a comparison of the performance of multiple data modality models has been conducted on the MELD dataset, a widely-used dataset for benchmarking sentiment analysis within the research community. Our work then demonstrates the results of combining acoustic and linguistic representations. Lastly, our proposed neural network-based ensemble learning technique is employed over six transformer and deep learning-based models, achieving a State-Of-The-Art (SOTA) accuracy.

The asymmetric bimodal exponential power (ABEP) distribution is an extension of the generalized gamma distribution to the real line via adding two parameters which fit the shape of peakedness in bimodality on real line. The special values of peakedness parameters of the distribution are combination of half Laplace and half normal distributions on real line. The distribution has two parameters fitting the height of bimodality, so capacity of bimodality is enhanced by using these parameters. Adding a skewness parameter is considered to model asymmetry in data. The location-scale form of this distribution is proposed. The Fisher information matrix of these parameters in ABEP is obtained explicitly. Properties of ABEP are examined. Real data examples are given to illustrate the modelling capacity of ABEP. The replicated artificial data from maximum likelihood estimates of parameters of ABEP and distributions having an algorithm for artificial data generation procedure are provided to test the similarity with real data.

Understanding of reflection characteristics of coastal seawalls is crucial for design. Wave reflection can cause difficulties to small vessel manoeuvring at the harbour entrance and constitute damaging scouring at the toe of coastal structures. Previous studies have considered reflection characteristics of coastal seawalls under wind-generated random waves without paying attention to the effects of wave bimodality created by the presence of swell waves. The present study focuses on the influence of random wave bimodality on reflective characteristics of coastal seawalls. More than eight hundred experimental tests have been conducted to examine the reflection performance of impermeable sloping seawalls under bimodal waves. Reflection coefficients were computed from each test. Analysis of results suggests that both unimodal and bimodal waves give similar reflection characteristics. However, the reflection coefficient in bimodal sea states seems to be more prolonged than in the unimodal sea states. It was found that the reflection coefficient of coastal seawalls is strongly influenced by the seawall slope, the wave steepness, relative water depth, and the surf similarity parameters. A new empirical reflection equation to describe the influence of wave bimodality on the reflection characteristics of coastal seawalls has been formulated based on this study.

The bimodal grain-size distribution 0.9KNbO₃-0.1BaTiO₃ ceramics, with a typical perovskite structure in tetragonal phase at room temperature, were successfully prepared by an induced abnormal grain growth (IAGG) method at a relatively low sintering temperature. In this bimodal grain-sized distribution structure, the extra-large grains (about 10‒50 μm) were evolved from the micron-sized filler powders and the fine grains (about 0.05‒0.35 μm) were derived from the sol precursor matrix. The 0.9KNbO₃-0.1BaTiO₃ ceramics exhibit relaxor-like behavior with the diffused phase transition near room temperature, and confirmed by the existence of the polar nanodomain regions (PNRs) using the HRTEM images. The large room-temperature electrocaloric (EC) effect was found, characterized by an adiabatic temperature drop of 1.5 K, an isothermal entropy change of 2.48 J kg^-1 K^-1, and high EC strengths of ïDT/DEï = 1.50×10^-6 KmV^-1 and DS/DE = 2.48×10^-6 Jmkg^-1K^-1V^-1directly measured under E = 1.0 MV/m. These excellent ECEs demonstrate that this simple IAGG method is highly appreciated for synthesizing high-performance EC materials for efficient cooling devices.

Regression models are mostly used in all fields of sciences for modelling the relationship between a dependent variable and independent variable(s). The least square method is often used to estimate the parameters in a linear model because it is the best linear unbiased estimator. These estimates can only be reliable if the assumption of normality is satisfied. In some cases the dependent variable might be bimodal and shows a non-linear relationship with the independent variable(s). In this case, a non-linear model should be used. In non-linear model, the standard errors are often obtained by linearizing the nonlinear function around the parameter, assuming central limit theorem. After the linearization, the least square parameter estimates are obtained. It should be noted that the error of the non-linear model is different from that of the transformed linear model. Thus, there is a need to transform back to the original non-linear model. In this note, a novel non-linear function was developed into a non-linear regression model, called Normal-Power model. The least square method was used to estimate the parameter of the transformed model. Its usefulness in regression model was demonstrated using real data of Nigeria Economy-Tourism model.

Based on a very fast numerical procedure for the determination of the subsurface stress field beneath frictional contacts of axisymmetric elastic bodies under arbitrary 2D oblique loading, the contact mechanical influences of loading parameters and contact profile geometry on the Smith-Watson-Topper (SWT) fatigue crack initiation parameter in elastic fretting contacts with superimposed normal and tangential oscillations are studied in detail. The efficiency of the stress calculation allows for a comprehensive physical analysis of the multi-dimensional parameter space of influencing variables. It is found that a superimposed normal oscillation of the contact can significantly increase or decrease the SWT parameter, depending on the initial phase difference and frequency ratio between the normal and tangential oscillation. Written in proper non-dimensional variables, the rounded flat punch always exhibits smaller values of the SWT parameter, compared to a full paraboloid with the same curvature, while the truncated paraboloid exhibits larger values. A small superimposed profile waviness also significantly increased or decreased the SWT parameter, depending on the amplitude and wave length of the waviness. While both the load protocol and the profile geometry can significantly alter the SWT parameter, the coupling between both influencing factors is weak.

Surface water distribution extracted from remote sensing data has been used in water resource assessment, coastal management, and environmental change studies. Traditional manual methods for extracting water bodies cannot satisfy the requirements for mass processing of remote sensing data; therefore, accurate automated extraction of such water bodies has remained a challenge. The histogram bimodal method (HBM) is a frequently used objective tool for threshold selection in image segmentation. The threshold is determined by seeking twin peaks, and the valley values between them; however, automatically calculating the threshold is difficult because complex surfaces and image noise which lead to not perfect twin peaks (single or multiple peaks). We developed an operational automated water extraction method, the modified histogram bimodal method (MHBM). The MHBM defines the threshold range of water extraction through mass static data; therefore, it does not require the identification of twin histogram peaks. It then seeks the minimum values in the threshold range to achieve automated threshold. We calibrated the MHBM for many lakes in China using Landsat 8 Operational Land Imager (OLI) images, for which the relative error (RE) and squared correlation coefficient (R2) for threshold accuracy were found to be 2.1% and 0.96, respectively. The RE and root-mean-square error (RMSE) for the area accuracy of MHBM were 0.59% and 7.4 km2. The results show that the MHBM could easily be applied to mass time-series remote sensing data to calculate water thresholds within water index images and successfully extract the spatial distribution of large water bodies automatically.