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Purpose: This systematic review aims to gain the studies regarding the effect of Nigella Sativa (N. sativa) on adipokines including leptin, adiponectin and resistin. Design/methodology/approach: Search was carried out using databases Scopus, Web of Science, PubMed and Google Scholar with no restriction on language or date until February 2023 and alert services were applied to identify any paper after the primary search. Findings: Eighteen animal and human studies were eligible for the current systematic review. Leptin and resistin levels showed a downward tendency after consuming N. sativa and its ingredients [e.g. oil, thymoquinone (TQ) and thymol] as well as its extracts (e.g. water extract). Furthermore, considering 4 of 8 animal research studies and 2 of 5 human studies that evaluated adiponectin levels, a significant increase was observed after using N. sativa and its ingredients (e.g. oil, TQ and thymol). Originality/value: The present paper collates evidence from animal and human studies regarding the effect of N. sativa on adipokines including leptin, adiponectin and resistin. [ABSTRACT FROM AUTHOR]

Background Objectives Methods Results Discussion Conclusion Stakeholders working on the COVID‐19 pandemic response needed access to evidence, requiring a systematic approach to identify and disseminate relevant research.Outline the stages of development of a COVID‐19 Literature Digest; demonstrate the impact the Digest had on decision‐making and knowledge gain; identify the lessons learned.A standardised process was developed to identify and select papers. The main sources for content were PubMed, bioRxiv and medRxiv. A shared EndNote library was used to deduplicate and organise papers. Three user surveys obtained feedback from subscribers to determine if the Digest remained valuable, and explore the benefits to individuals.40–60 papers were summarised each week. 211 Digests were produced from March 2020 to March 2022, with around 10,000 papers included altogether. Survey results suggest benefits of the Digest were gaining new knowledge, saving time and contributing to evidence‐based decision making.Digest procedures constantly evolved and were adapted in response to survey feedback. Lessons identified: learn from failure, communication is key, measure your impact, work collaboratively, reflect and be flexible.The Digest was successfully produced within the limits of available resource. The learning from this Digest will inform evidence monitoring, selection and dissemination for future health crises. [ABSTRACT FROM AUTHOR]

Falls pose a major threat for the elderly as they result in severe consequences for their physical and mental health or even death in the worst-case scenario. Nonetheless, the impact of falls can be alleviated with appropriate technological solutions. Fall detection is the task of recognising a fall, i.e. detecting when a person has fallen in a video. Such an algorithm can be implemented in lightweight devices which can then cater to the users' needs, e.g. alerting emergency services or caregivers. At the core of those systems, a model capable of promptly recognising falls is crucial for reducing the time until help comes. In this paper we propose a fall detection solution based on transformers, i.e. state-of-the-art neural networks for computer vision tasks. Our model takes a video clip and decides if a fall has occurred or not. In a video stream, it would be applied in a sliding-window fashion to trigger an alarm as soon as it detects a fall. We evaluate our fall detection backbone model on the large UP-Fall dataset, as well as on the UR fall dataset, and compare our results with existing literature using the former dataset. • Fall detection task tackled using only RGB data. • The network directly learns from RGB data, no extra features are computed. • UP-Fall and UR Fall dataset are used for the evaluation. [ABSTRACT FROM AUTHOR]

In a dynamic financial ecosystem, digitalisation is supporting banks in revamping their business processes to be more efficient, reducing costs, coping with customers' evolving demands, and keeping them abreast of market competition. This two-stage research aims to investigate three issues in Pakistan's banking industry from 2006 to 2020: (i) banking efficiency; (ii) the impact of digitalisation on banking efficiency; and (iii) banking efficiency's absolute and conditional convergence. In our first-stage analysis, bootstrap data envelopment analysis has been applied, which exhibits bias-corrected overall, pure, and scale efficiencies of 74, 77, and 96%, respectively. In the second-stage analysis, we executed Tobit and two-step dynamic panel data system generalised method of moments (DPDSYS-GMM) models, and the results uncover that digitalisation has a positive influence on banking efficiency. Findings confirm that return on assets, bank size, interest rate, and gross domestic product growth rate have a positive association with banking efficiency. Our research reveals that state-controlled banks outperform their private sector and special-purpose counterparts. Our DPDSYS-GMM findings validate β and σ-convergence, implying that initially, low-efficient banks caught up (converged) to the more efficient opponents, reducing cross-sectional efficiency dispersion and attaining common equilibrium. The findings of banking efficiency conditional β-convergence assert that the adoption of digital technology has played a critical role in the convergence process and that digitalisation has acted as a catalyst for less efficient banks to catch up significantly faster to their more efficient rivals. This study displays digitalisation's disruptive influence on the overall transformation of Pakistan's banking system in the modern era, resulting in significant efficiency gains. [ABSTRACT FROM AUTHOR]

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This study aims to shed light on the contribution of universal versus language specific factors on reading. We examined eye movements of Arabic readers and analyzed effects specific to Arabic such as perceptual complexity, diglossia and morphology, in addition to universal factors such as word length and frequency. Twenty native Arabic speakers read continuous texts in Modern Standard Arabic (MSA) while their eye movements were monitored. A corpus-based analyses was carried to test effects specific to Arabic and effects of the benchmark eye movement factors. We found that perceptually more complex words received longer fixation durations, moreover, differences in processing words unique in MSA versus words shared between MSA and spoken Arabic Vernacular were found. This is the first indication for these effects during an eye movement reading task. However, the effect of morphological length was not significant when included in the model with all predictors. Lastly, the benchmark factors were significant showing effects for word length, word frequency and part of speech. Short and frequent words are processed faster than longer and less frequent words. Function words are often skipped. We conclude that eye movement of Arabic readers reflect proficient reading, yet they also exhibit an on-going challenge in processing the written language. [ABSTRACT FROM AUTHOR]