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Empirical work on people’s perceptions of AI advisors has found evidence for both “algorithm aversion” and “algorithm appreciation.” We investigated whether these differing reactions stem from two different paths of processing: assessing the content of the advice and evaluating the source (AI vs. human advisor). In two survey studies, people were as strongly persuaded by the advice of an AI as that of a human advisor; nonetheless, people’s approval of and trust in the AI advisor was consistently lower. This pattern of dissociation suggests that algorithm aversion and algorithm appreciation can occur at the same time, but along different response paths.

Sub–Nyquist sampling is a technique which has various applications in signal processing and communication technologies. Memory relaxation, peripherals operating at relatively lower speeds, and consequently low–power consumption designs are prominent motivations in sub–Nyquist receiver studies. Although these motivations seem to be attractive, instability and information loss are major drawbacks of sub–Nyquist approaches in comparison with Nyquist–Shannon sampling applications. Nevertheless, sub-Nyquist receivers could still provide several ways of recovering information to some extent in case they are fortified with appropriate interpolation mechanisms. Hence, this study proposes a fractionally asymmetric sub–Nyquist baseband receiver design for phase modulation (PM) signal reception. The proposed method takes advantage of statistical behaviors of the received signal passing through physical propagation medium and adopts an autoregressive process to recover the phase information required. The proposed design is implemented on a software-defined radio (SDR) platform operating in near real–time and tested in a real–world communication environment. Results and discussions are provided along with the future directions.

In this paper, we investigate the general solutions to the Kolmogorov-Petrovskii-Piskunov equation using the generalized Kudryasov method. It was demonstrated that all produced answers are supplied by exponential function solutions using the symbolic computer program Maple. These solutions are helpful for fluid dynamics, optics, and other fields. Lastly, we have presented some graphs for exact solutions of these equations with special parameter values. For the development of this method, the versatility and dependability of programming offer eclectic applicability to highdimensional nonlinear evolution equations. The obtained results provided us with valuable insights on the suitability of the novel Kudryashov technique. [ABSTRACT FROM AUTHOR]

The attainment of postsecondary education is critical to leading a successful life. Substance abuse is one area that may negatively impact academic and, ultimately, employment outcomes in the student veteran population. We conducted a parallel mediation analysis to examine the relationship between substance use and academic outcomes in a sample of 129 college veterans with disabilities. We explored interpersonal problems, physical health problems, depression, and anxiety as possible mediators. Our results concluded that only interpersonal problems and anxiety mediated the relationship. Implications for practice and research are provided. [ABSTRACT FROM AUTHOR]

Complex biological processes in cells are embedded in the interactome, representing the complete set of protein–protein interactions. Mapping and analyzing the protein structures are essential to fully comprehending these processes' molecular details. Therefore, knowing the structural coverage of the interactome is important to show the current limitations. Structural modeling of protein–protein interactions requires accurate protein structures. In this study, we mapped all experimental structures to the reference human proteome. Later, we found the enrichment in structural coverage when complementary methods such as homology modeling and deep learning (AlphaFold) were included. We then collected the interactions from the literature and databases to form the reference human interactome, resulting in 117 897 non-redundant interactions. When we analyzed the structural coverage of the interactome, we found that the number of experimentally determined protein complex structures is scarce, corresponding to 3.95% of all binary interactions. We also analyzed known and modeled structures to potentially construct the structural interactome with a docking method. Our analysis showed that 12.97% of the interactions from HuRI and 73.62% and 32.94% from the filtered versions of STRING and HIPPIE could potentially be modeled with high structural coverage or accuracy, respectively. Overall, this paper provides an overview of the current state of structural coverage of the human proteome and interactome. [ABSTRACT FROM AUTHOR]

Considering the significance of transportation in everyday life, issues related to transportation are commonly encountered in developing and developed countries. The problems encountered in most countries are likewise evident in our country and manifest throughout several domains. Factors contributing to the issue include excessive passengers, inadequate trip availability to fulfill demand, ineffective line and route organization in addressing passenger requests, incomplete routes, and insufficient flight frequency. Insufficient service availability during peak hours and a preference for private automobiles are additional examples that can be mentioned. These negative factors reduce passenger happiness, lead to disturbances inside the company, and have a significant financial impact. Considering all these aspects, attempts were made to predict the future number of crossings using various forecasting algorithms based on the hourly public transportation dataset to achieve the lowest error rate. Based on the obtained results, an assessment was conducted on the areas with insufficient or missing flight frequencies, and recommendations were provided to enhance the number of flights in these regions, considering peak hours. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic has emerged as one of the most significant global health crises, affecting people worldwide and resulting in the loss of millions of lives. It was officially named "Coronavirus disease 2019" (COVID-19) when it first appeared at the end of 2019. Multisystem Inflammatory Syndrome in Children (MIS-C) is a relatively new disease entity that has arisen in the wake of the COVID-19 pandemic. While MIS-C is recognized to manifest with various symptoms, our understanding of it continues to evolve as more articles and case reports are published in the scientific literature. Although MIS-C affects multiple organ systems, there have also been reported cases of neurological involvement. According to the literature, cases of hemiparesis without imaging findings in MIS-C have rarely been reported. To the best of our knowledge, this is a rare reported case of hemiparesis without intracranial and spinal pathology in the context of MIS-C reported in this article. [ABSTRACT FROM AUTHOR]

Android is the dominant operating system in the smartphone market and there exists millions of applications in various application stores. The increase in the number of applications has necessitated the detection of malicious applications in a short time. As opposed to dynamic analysis, it is possible to obtain results in a shorter time in static analysis as there is no need to run the applications. However, obtaining various information from application packages using reverse engineering techniques still requires a substantial amount of processing power. Although some attempts have been made to solve this problem by analyzing binary files without decoding the source code, there is still more work to be done in this area. In this study, we analyzed the applications in bytecode level without decoding the binary source files. We proposed a model using Term Frequency - Inverse Document Frequency (TF-IDF) word representation for feature extraction and Extreme Gradient Boosting (XGBoost) method for classification. The experimental results show that our model classifies a given application package as a malware or benign in 2.75 s with 99.05% F1-score on a balanced dataset, and in 3.30 s with 99.35% F1-score on an imbalanced dataset containing obfuscated malwares. [ABSTRACT FROM AUTHOR]