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The need for high performance models in the queuing systems; availability in the fields of computing and communication systems and logistics management poses extensive challenges in design and development of the appropriate modeling. In this work, we propose a robust probabilistic model to mitigate these problems by appropriately choosing a multi-server queuing system with dynamic service facility. The arrival substances, such as packets or jobs or customers or consumers, follow a Poisson arrival process and these arrivals enter a queuing system according to FIFO discipline. The system designed in the system is armed with a fixed number of service stations, in which some servers are capable of allocating additional service capacity by adjusting dynamically. When a customer arrives at the system, if a free server is available, it is immediately served by one of the free processors; if no server is free, that is all are busy, the arrived job is accommodated in the queue and waits for service in the system. In this paper, we proposed a stochastic model to handle peak loads efficiently, boosting service capacity during burst arrivals. Numerical results presented in this paper are generated by the spectral expansion method to demonstrate the model’s performance, offering insights into its efficiency and accuracy. Furthermore, we derived some important special cases of speedup factor, which provide the mathematical estimation of system performance in terms of computation and communication times.

Well-differentiated papillary mesothelial tumor (WDPMT) is an uncommon tumor, formerly named well-differentiated papillary mesothelioma in the 2015 World Health Organization classification. It has a characteristic papillary architecture, bland cytologic features, a tendency toward superficial spread without invasion, and a good prognosis due to its clinically indolent behavior with prolonged survival. Rare cases with superficial invasion are termed WDPMT with invasive foci. WDPMT occurs primarily in the peritoneum of reproductive- age women, but also rarely in the pleura. We report a case of a 60-year-old woman who developed WDPMT with minimal invasion in the pleura with atypical radiological features and a family history of mesothelioma and indirect asbestos exposure.

Natural chemical compounds have been widely investigated for their programmed necrosis causing characteristics. One of the conventional methods for screening such compounds is the use of concentrated plant extracts without isolation of active moieties for understanding pharmacological activity. For the last two decades, modern medicine has relied mainly on the isolation and purification of one or two complicated active and isomeric compounds. The idea of multi-target drugs has advanced rapidly and impressively from an innovative model when first proposed in the early 2000s to one of the popular trends for drug development in 2021. Alternatively, fragment-based drug discovery is also explored in identifying target-based drug discovery for potent natural anticancer agents which is based on well-defined fragments opposite to use of naturally occurring mixtures. This review summarizes the current key advancements in natural anticancer compounds; computer-assisted/fragment-based structural elucidation and a multi-target approach for the exploration of natural compounds.

We conducted a preregistered multilaboratory project (k = 36; N = 3,531) to assess the size and robustness of ego-depletion effects using a novel replication method, termed the paradigmatic replication approach. Each laboratory implemented one of two procedures that was intended to manipulate self-control and tested performance on a subsequent measure of self-control. Confirmatory tests found a nonsignificant result (d = 0.06). Confirmatory Bayesian meta-analyses using an informed-prior hypothesis (δ = 0.30, SD = 0.15) found that the data were 4 times more likely under the null than the alternative hypothesis. Hence, preregistered analyses did not find evidence for a depletion effect. Exploratory analyses on the full sample (i.e., ignoring exclusion criteria) found a statistically significant effect (d = 0.08); Bayesian analyses showed that the data were about equally likely under the null and informed-prior hypotheses. Exploratory moderator tests suggested that the depletion effect was larger for participants who reported more fatigue but was not moderated by trait self-control, willpower beliefs, or action orientation.

Facial masks have become and may remain ubiquitous. Though important for preventing infection, they may also serve as a reminder of the risks of disease. Thus, they may either act as cues for threat, priming avoidance-related behavior, or as cues for a safe interaction, priming social approach. To distinguish between these possibilities, we assessed implicit and explicit evaluations of masked individuals as well as avoidance bias toward relatively unsafe interactions with unmasked individuals in an approach-avoidance task in an online study. We further assessed COVID-19 anxiety and specific attitudes toward mask-wearing, including mask effectiveness and desirability, hindrance of communication from masks, aesthetic appeal of masks, and mask-related worrying. Across one sample of younger (18-35 years, N = 147) and one of older adults (60+ years, N = 150), we found neither an average approach nor avoidance bias toward mask-wearing compared to unmasked individuals in the indirect behavior measurement task. However, across the combined sample, self-reported mask-related worrying correlated with reduced avoidance tendencies toward unmasked individuals when COVID-19 anxiety was low, but not when it was high. This relationship was specific to avoidance tendencies and was not observed in respect to explicit or implicit preference for mask-wearing individuals. We conclude that unsafe interaction styles may be reduced by targeting mask-related worrying with public interventions, in particular for populations that otherwise have low generalized COVID-19 anxiety.

Joint action theorizing implies that any coordinated behaviour that induces co‐representation with a partner should increase social identification, especially when the associated actions require a high degree of coordination and are experienced as being performed effectively. The current research provides a first test of this new theoretical prediction for complementary (rather than synchronous) joint actions. In each of two pre‐registered experiments establishing a novel paradigm, participants performed a digital joystick task with a joint performance goal with three different partners. The task varied in coordination requirements across partners. In Experiment 1, results showed that when task segments were discrete between partners, they identified less as a group than when they had to coordinate their behaviour. Surprisingly, although constant coordination increased co‐representation relative to intermittent coordination, it did not correspondingly increase social identification. However, performance correlated positively with identification; as performance was worse when participants had to coordinate, this may explain the results. Experiment 2 showed that performance is causally linked to identification when coordination is necessary. Taken together, our results suggest that experiencing effective coordination leads to greater social identification. In general, paradigms capable of examining the perceptual and motor aspects of collective behaviour may offer a new perspective on social identification in general and the performance‐identification link in particular. [ABSTRACT FROM AUTHOR]

Cancer remains one of the most challenging afflictions facing humanity, lacking a definitive cure. Among its prevalent forms, breast cancer stands as a significant health concern, disproportionately affecting women aged 20-59. Nonetheless, mortality rates can be mitigated through timely screening and treatment interventions. The intricacies of breast cancer detection and classification, particularly distinguishing between cancerous and healthy cells, pose formidable challenges. This study proposes an innovative approach harnessing artificial intelligence (AI) in conjunction with the Internet of Medical Things (IoMT) to address early-stage breast cancer detection and classification. The devised artificial intelligence-enabled heuristic framework (AI-HF) leverages machine intelligence techniques (MIT) to identify cancerous cells by analyzing shape and texture-based features. Subsequently, machine learning (ML) classification techniques, notably support vector machines (SVMs) are employed to discern cancerous cells from healthy ones. Experimental findings, derived from real datasets, underscore the efficacy of the proposed method, achieving a commendable accuracy rate of 98.0% in detecting and categorizing abnormal cells within breast cancer images. Importantly, the AI-HF classifier emerges as a swift and efficient model, outperforming traditional approaches by swiftly distinguishing between cancerous and healthy cells based on extracted cell image attributes. [ABSTRACT FROM AUTHOR]