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This paper examined the research landscape on the applications of machine learning in finance (MLF) research based on the published documents on the topic indexed in the Scopus database from 2007 to 2021. Consequently, the publication trends on the published documents data were examined to determine the most prolific authors, institutions, countries, and funding bodies on the topic. Next, bibliometric analysis (BA) was employed to analyse and map co-authorship networks, keywords occurrences, and citations. Lastly, a systematic literature review was carried out to examine the scientific and technological developments in the field. The results showed that the number of published documents on MLF research has soared tremendously from 5 to 398 between 2007 and 2021, which signifies an enormous increase (~7,900%) in the subject area. The high productivity is partly ascribed to the research activities of the most research-active academic stakeholders namely Chihfong Tsai (National Central University in Taiwan) and Stanford University (United States). However, the National Natural Science Foundation of China (NSFC) is the most active funder in the United States and has the largest number of published documents. BA analysis revealed high collaboration rates, published documents, and citations among the stakeholders. Keywords occurrence analysis revealed that MLF research is a highly inter- and multidisciplinary area with numerous hotspots and themes ranging from systems, algorithms and techniques to the security and crime prevention in Finance using ML. Citation analysis, the most prominent (and by extension the most prestigious) source titles on MLF are IEEE Access, Expert Systems with Applications and ACM International Conference Proceedings Series (ACM-ICPS). The systematic literature review revealed the various areas and applications of MLF research, particularly in the areas of predictive/forecasting analytics, credit assessment and management, as well as supply chain, carbon trading, neural networks, and artificial intelligence, among others. It is expected that MLF research activities and their impact on the wider global society will continue to increase in the coming years

In wind turbine engineering, stability and control rely on precision. A new approach for discrete-time systems is presented in this study, which makes use of constrained Gramians and frequency weights. Wind turbines with a double-fed induction generator and dynamic rotational speeds can have their model order reduced using the suggested method, which makes use of sophisticated state-space representations. A novel balanced realization method, along with frequency-weighted and limited Gramians, successfully lowers the dimensionality of large state models. Minimizing approximation errors and ensuring stability are both achieved by the resulting lower-order system. This paper makes a significant contribution by offering an a priori formula for error boundaries, which allows for more efficient and faster computations. A paradigm shift in improving the accuracy of modeling techniques is marked by this groundbreaking method, which applies frequency-weighted and limited Gramians to real-time systems like wind turbines.

he acoustic emission technique has been applied successfully for the identification, characterization, and localization of deformations in civil engineering structures. Numerous localization techniques, such as Modal Acoustic Emission, Neural Networks, Beamforming, and Triangulation methods with or without prior knowledge of wave velocity, have been presented. Several authors have conducted in-depth research in the localization of AE sources. However, existing review papers do not focus on the performance evaluation of existing source localization techniques. This paper discusses these techniques based on the number of sensors used and the geometry of the structures of interest. Furthermore, it evaluates them on the basis of their performance. At the end of this paper, a comparative analysis of existing methods has been presented based on their basic principles, key strengths, and limitations. A deep learning circular sensor cluster-based solution has the potential to provide a low-cost reliable localization solution for acoustic emission sources.

Cloud computing is a rapidly growing paradigm which has evolved from having a monolithic to microservices architecture. The importance of cloud data centers has expanded dramatically in the previous decade, and they are now regarded as the backbone of the modern economy. Cloud-based microservices architecture is incorporated by firms such as Netflix, Twitter, eBay, Amazon, Hailo, Groupon, and Zalando. Such cloud computing arrangements deal with the parallel deployment of data-intensive workloads in real time. Moreover, commonly utilized cloud services such as the web and email require continuous operation without interruption. For that purpose, cloud service providers must optimize resource management, efficient energy usage, and carbon footprint reduction. This study presents a conceptual framework to manage the high amount of microservice execution while reducing response time, energy consumption, and execution costs. The proposed framework suggests four key agent services: (1) intelligent partitioning: responsible for microservice classification; (2) dynamic allocation: used for pre-execution distribution of microservices among containers and then makes decisions for dynamic allocation of microservices at runtime; (3) resource optimization: in charge of shifting workloads and ensuring optimal resource use; (4) mutation actions: these are based on procedures that will mutate the microservices based on cloud data center workloads. The suggested framework was partially evaluated using a custom-built simulation environment, which demonstrated its efficiency and potential for implementation in a cloud computing context. The findings show that the engrossment of suggested services can lead to a reduced number of network calls, lower energy consumption, and relatively reduced carbon dioxide emissions.

Acoustic emission (AE) as a non-destructive monitoring method is used to identify small damage in various materials effectively. However, AE signals acquired during the monitoring of oil and gas steel pipelines are always contaminated with noise. A noisy signal can be a threat to the reliability and accuracy of the findings. To address these shortcomings, this study offers a technique based on discrete wavelet transform to eliminate noise in these signals. The denoising performance is affected by several factors, including wavelet basis function, decomposition level, thresholding method, and the threshold selection criteria. Traditional threshold selection rules rely on statistical and empirical variables, which influence their performance in noise reduction under various conditions. To obtain the global best solution, a threshold selection approach is proposed by integrating particle swarm optimization and the late acceptance hill-climbing heuristic algorithms. By comparing five common approaches, the superiority of the suggested technique was validated by simulation results. The enhanced thresholding solution based on particle swarm optimization algorithm outperformed others in terms of signal-to-noise ratio and root-mean-square error of denoised AE signals, implying that it is more effective for the detection of AE sources in oil and gas steel pipelines.

Objective: To determine the qualities of a good medical teacher from a trainee's perspective, to evaluate the best teaching method, and to explore the association of specialty and level of training with teaching methods and qualities of a medical teacher., Methods: The questionnaire-based cross-sectional survey was conducted in two tertiary care teaching hospitals in Lahore, Pakistan, from July 2019 to December 2020, and comprised all house officers and postgraduate residents. Data was collected using a self-administered questionnaire that had three sections: demographics, best method of teaching, and the characteristics of a good medical teacher. Data was analysed using SPSS 20., Results: Of the 135 subjects, 56(41.5%) were males; 79(58.5%) were females; 76(56%) were junior trainees; and 77(57%) belonged to surgical specialties. The overall mean age was 27.6±2.4 years. Best teaching method identified by the trainees was 'asking of problem-based questions' 46(33.6%). Good communication skills 61(44.5%), and calm and non-humiliating demeanour 61(44.5%) were the most frequently identified qualities. More surgical trainees considered the generation of competition among learners as the best teaching method than the trainees from other specialties (p=0.001). House officers reported 'unbiased' (p=0.001) and 'moral and ethical' (p=0.001) significantly more frequently as a good trait in their teachers compared to the residents., Conclusions: The most important qualities in a medial teacher, as identified by the learners, were good communication skills and calm and polite attitude.