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Over the past few years, more attention has been paid to jingle and jangle fallacies in psychological science. Jingle fallacies arise when two or more distinct psychological phenomena are erroneously labeled with the same term, while jangle fallacies occur when different terms are used to describe the same phenomenon. Jingle and jangle fallacies emerge due to the vague linkage between psychological theories and their practical implementation in empirical studies, compounded by variations in study designs, methodologies, and applying different statistical procedures' algorithms. Despite progress in organizing scientific findings via systematic reviews and meta-analyses, effective strategies to prevent these fallacies are still lacking. This paper explores the integration of several approaches with the potential to identify and mitigate jingle and jangle fallacies within psychological science. Essentially, organizing studies according to their specifications, which include theoretical background, methods, study designs, and results, alongside a combinatorial algorithm and flexible inclusion criteria, may indeed represent a feasible approach. A jinglefallacy detector arises when identical specifications lead to disparate outcomes, whereas jangle-fallacy indicators could operate on the premise that varying specifications consistently yield overrandomly similar results. We discuss the role of advanced computational technologies, such as Natural Language Processing (NLP), in identifying these fallacies. In conclusion, addressing jingle and jangle fallacies requires a comprehensive approach that considers all levels and phases of psychological science. [ABSTRACT FROM AUTHOR]

The continuously updated database of failures and censored data of numerous products has become large, and on some covariates, information regarding the failure times is missing in the database. As the dataset is large and has missing information, the analysis tasks become complicated and a long time is required to execute the programming codes. In such situations, the divide and recombine (D&R) approach, which has a practical computational performance for big data analysis, can be applied. In this study, the D&R approach was applied to analyze the real field data of an automobile component with incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for analyzing such datasets. Further, the percentiles and reliability functions of the distribution under different covariate conditions were estimated to evaluate the component performance of these covariates. The findings of this study have managerial implications regarding design decisions, safety, and reliability of automobile components. [ABSTRACT FROM AUTHOR]

Orthogonal frequency division multiplexing (OFDM) inherits multi-carrier systems' inevitable high peak-to-average power ratio (PAPR) problem. In this paper, a novel alternating companding technique is proposed to combat the harassment of high PAPR. The sequential μ -law companding (SULC) and a tone with a lower PAPR result in only partial tones needing companding. The SULC scheme's PAPR and bit error rate (BER) performance has been balanced and improved. However, the computational complexity is still too high to be implemented. Therefore, this study sorted the transmission signals according to their amplitudes. Then, all the tones are divided into two groups by estimating the rough companding amount (around 54% of the subcarriers), using traditional parallel companding for the first group and the other group only by partial μ -law companding. This alternating μ -law companding (AULC) is proposed to improve the PAPR performance and simultaneously reduce complexity. Simulation results show that the proposed AULC method appreciably reduces the PAPR by about 5 dB (around 45%) compared with the original μ -law at complementary cumulative distribution function (CCDF) equal to 10 − 4 . Moreover, it only requires a moderate complexity to outperform the other companding schemes without sacrificing the BER performance in the OFDM systems. [ABSTRACT FROM AUTHOR]

With the ongoing integration of bionic technology and mechatronics, exoskeleton devices are finding applications in industry, healthcare, and logistics. This study is centered on enhancing the performance of heterogeneous sensor exoskeleton devices by presenting a six-degree-offreedom upper limb exoskeleton robot model based on the Denavit--Hartenberg (MDH) approach. The model's accuracy is verified using MATLAB. We construct a multi-objective optimization model that prioritizes workspace expansion. To realize this model, we propose an improved Harris Hawks algorithm (SCA-HHO) based on the sine cosine algorithm. The algorithm's effectiveness is compared with popular swarm intelligence methods (PSO, AOA, WOA) through cross-sectional simulations. SCA-HHO achieves average improvements of 6.30, 1.48, and 0.88% in objective function values compared with the swarm intelligence algorithms, respectively. This indicates SCA-HHO's superior suitability for solving the model proposed in this paper. [ABSTRACT FROM AUTHOR]