Built-in Metrics Research Articles

AstroSA: An astronomical observation scheduler assessment framework in python

Time-domain astronomy, as a leading aspect of astronomical research, demands a significant increase in telescope hours. An efficient scheduler is crucial to handle the large number of observational requests effectively. However, the commonly used schedulers in observatories have not yet fully utilized the advancements in mathematics and computer science. In order to establish a connection between astronomy and the latest achievements in these fields, we propose the Astronomical Observing Scheduler Assessment Framework (AstroSA), implemented as a Python package. The AstroSA offers a rapid and user-friendly quantitative evaluator of the scheduler with five built-in metrics: expected quality of observed data, overhead ratio, scientific value, schedule rate, and ratio to the best airmass. Additionally, AstroSA includes a default virtual telescope and a night of cloud coverage, so that users can start to use it with minimal settings.

ИДЕНТИФИКАЦИЯ ПАРАМЕТРОВ СХЕМЫ ЗАМЕЩЕНИЯ АСИНХРОННЫХ ДВИГАТЕЛЕЙ ПРИ ПОМОЩИ НЕЙРОННЫХ СЕТЕЙ

Relevance AC electric drives holds a significant share in industry (80–90 %), as they are widely used in various devices and machines of various industrial capacities, ranging from conveyors, pumps and cranes, to drives for metallurgical production mechanisms. This is due to their ease of operation, reliability and high efficiency. The most common adjustable AC electric drive in industry is the «Frequency Converter — Asynchronous Motor» (FC–AM) electric drive. Knowing the exact parameters of the AM in such electric drives makes it possible to optimize their operation, improve efficiency and extend their service life. Also, this helps prevent overloads and increase the reliability of equipment, which in turn reduces operating costs and the accidents risks. With the development of technology, parameter identification is becoming increasingly important for adapting motors to new operating conditions and production requirements. Accurate parameters identification of also makes it possible to detect and predict the motors condition, which reduces the malfunctions risk and increases the equipment service life. Thus, knowledge of the asynchronous electric motors parameters plays a key role in increasing production efficiency and reducing operating costs. Aim of research The article purpose is to modernize the existing methodology for conducting asynchronous motors tests, regulated by the current State Standard 7217-87, through the introduction of neural networks for further identification of parameters based on them. The article provides a rationale for choosing the neural networks structure, its training algorithms, and also experimentally selected the hidden layer optimal configuration for each experiment. Neural networks were trained on motors with a power of 10-100 kW with a rated voltage of 380 V, a frequency of 50 Hz, and a synchronous rotation speed of 1500 rpm. Research methods The studies were carried out using Matlab Simulink software, data processing was carried out in Matlab and MS Excel software. The experiments, regulated by the current State Standard 7217-87, were carried out on computer models. Among the research methods used in the article, we can distinguish such as experiment (simulation in accrdance with State Standard 7217-87 measuring experiments), measurement (obtaining output parameters of the AM current strength in a computer model), comparison (comparing the results of training neural networks using built-in metrics tools). Results Neural networks have been developed to identify asynchronous motors parameters that process the experiments results in the current State Standard 7217-87. Also, the creating neural networks methodics describe in the article can be used to create similar neural networks for other types of AM.

Using a Community-Based Participatory Approach to Address Gender Equity in Academic Medicine: The Center for Women in Medicine and Science at the University of Minnesota

Many medical schools are instituting gender equity initiatives to address long-standing inequities (e.g., salary, leadership positions, resource distribution) between women and men in academic medicine. However, few theory-driven models exist with built-in metrics to assess the impact of gender equity initiatives. The authors describe the theory- and metric-driven process used to create the Center for Women in Medicine and Science (CWIMS) at the University of Minnesota (UMN) Medical School. An innovative theory-driven approach using community-based participatory research (CBPR) was used to create and organize CWIMS. CBPR acknowledges community members (e.g., faculty members, staff), academic organizational representatives (e.g., department heads, center directors), and administrative leaders (e.g., deans) as equal contributors in carrying out all aspects of gender equity work. CBPR values collaborative approaches that empower faculty, promote co-learning and co-creation of initiatives among all university partners, and build upon already existing community strengths and resources. Four CWIMS action groups were created using CBPR principles. The action groups are retention and recruitment; mentoring; salary, resource, and leadership equity; and strategic communications and collaborations. Faculty members across all medical school departments joined these 4 action groups to co-create and carry out all CWIMS gender equity initiatives. The process of developing the CWIMS center and action groups, the CBPR theoretical model guiding the approach, the initiatives developed by the action groups and metrics created, and the outcomes achieved to date are described. In addition, 4 lessons learned from the development of the CWIMS-use of theoretically driven and evidence-based models is key to building a sustainable organization; bottom-up and top-down engagement of partners is crucial for sustainability; passion and innovation are critical for long-term momentum; and not all faculty members and leaders will be enthusiastic about gender equity issues-are shared for the benefit of other medical schools wanting to develop similar centers.

Image hub explorer: evaluating representations and metrics for content-based image retrieval and object recognition

We present a novel tool for image data visualization and analysis, Image Hub Explorer. It is aimed at developers and researchers alike and it allows the users to examine various aspects of content-based image retrieval and object recognition under different built-in metrics and models. Image Hub Explorer provides the tools for understanding the distribution of influence in the data, primarily by examining the emerging hub images. Hubness is an aspect of the well-known curse of dimensionality that hampers the effectiveness of many information systems. Its consequences were thoroughly examined in the context of music/audio search and recommendation, but not in case of image retrieval and object recognition. Image Hub Explorer was made with the goal of raising awareness of the hubness phenomenon and offering potential solutions by implementing state-of-the-art hubness-aware metric learning, ranking and classification methods. Various visualization components allow for a quick identification of critical issues and we hope that they will prove helpful in working with large image datasets. We demonstrate the effectiveness of the implemented methods in various object recognition tasks.

New opportunities for repositories in the age of altmetrics

AbstractEditor's SummaryFor institutional repositories, alternative metrics reflecting online activity present valuable indicators of interest in their holdings that can supplement traditional usage statistics. A variable mix of built‐in metrics is available through popular repository platforms: Digital Commons, DSpace and EPrints. These may include download counts at the collection and/or item level, search terms, total and unique visitors, page views and social media and bookmarking metrics; additional data may be available with special plug‐ins. Data provide different types of information valuable for repository managers, university administrators and authors. They can reflect both scholarly and popular impact, show readership, reflect an institution's output, justify tenure and promotion and indicate direction for collection management. Practical considerations for implementing altmetrics include service costs, technical support, platform integration and user interest. Altmetrics should not be used for author ranking or comparison, and altmetrics sources should be regularly reevaluated for relevance.

LapMentor Metrics Possess Limited Construct Validity

Many surgical training programs are introducing virtual-reality laparoscopic simulators into their curriculum. If a surgical simulator will be used to determine when a trainee has reached an "expert" level of performance, its evaluation metrics must accurately reflect varying levels of skill. The ability of a metric to differentiate novice from expert performance is referred to as construct validity. The present study was undertaken to determine whether the LapMentor's metrics demonstrate construct validity. Medical students, residents and faculty laparoscopic surgeons (n = 5-14 per group) performed 5 consecutive repetitions of 6 laparoscopic skills tasks: 30 degrees Camera Manipulation, Eye-Hand Coordination, Clipping/Grasping, Cutting, Electrocautery, and Translocation of Objects. The LapMentor measured performance in 4 to 12 parameters per task. Mean performance for each parameter was compared between subject groups for the first and fifth repetitions. Pairwise comparisons among the 3 groups were made by post hoc t-tests with Bonferroni technique. Significance was set at P < 0.05. Of the 6 tasks evaluated, only the Eye-Hand Coordination task (3/12 parameters) and the Clipping and Grasping (1/7 parameters) had expert-level discrimination when performance was compared after completion of 1 repetition. Comparison of the fifth repetition performance (representing the plateau of the learning curves), demonstrated that the parameters Time and Score had expert level discrimination on the Eye-Hand Coordination task, and Time on the Cutting task. The remaining LapMentor tasks evaluated did not exhibit the ability to differentiate level of expertise based on the built-in metrics on either repetition 1 or 5. The majority of the LapMentor tasks' metrics were unable to differentiate between laparoscopic experts and less skilled subjects. Therefore, performance on those tasks may not accurately reflect a subject's true level of ability. Feedback to the manufacturer about these findings may encourage the development of evaluation parameters with greater sensitivity.