Standardized Performance Metrics Research Articles

Best Practices for Experiments and Reports in Photocatalytic Methane Conversion.

Efficiently converting methane into valuable chemicals via photocatalysis under mild condition represents a sustainable route to energy storage and value-added manufacture. Despite continued interest in this area, the achievements have been overshadowed by the absence of standardized protocols for conducting photocatalytic methane oxidation experiments as well as evaluating the corresponding performance. In this review, we present a structured solution aimed at addressing these challenges. Firstly, we introduce the norms underlying reactor design and outline various configurations in the gas-solid and gas-solid-liquid reaction systems. This discussion helps choosing the suitable reactors for methane conversion experiments. Subsequently, we offer a comprehensive step-by-step protocol applicable to diverse methane-conversion reactions. Emphasizing meticulous verification and accurate quantification of the products, this protocol highlights the significance of mitigating contamination sources and selecting appropriate detection methods. Lastly, we propose the standardized performance metrics crucial for evaluating photocatalytic methane conversion. By defining these metrics, the community could obtain the consensus of assessing the performance across different studies. Moving forward, the future of photocatalytic methane conversion necessitates further refinement of stringent experimental standards and evaluation criteria. Moreover, development of scalable reactor is essential to facilitate the transition from laboratory proof-of-concept to potentially industrial production.

Best Practices for Experiments and Reports in Photocatalytic Methane Conversion

AbstractEfficiently converting methane into valuable chemicals via photocatalysis under mild condition represents a sustainable route to energy storage and value‐added manufacture. Despite continued interest in this area, the achievements have been overshadowed by the absence of standardized protocols for conducting photocatalytic methane oxidation experiments as well as evaluating the corresponding performance. In this review, we present a structured solution aimed at addressing these challenges. Firstly, we introduce the norms underlying reactor design and outline various configurations in the gas–solid and gas–solid–liquid reaction systems. This discussion helps choosing the suitable reactors for methane conversion experiments. Subsequently, we offer a comprehensive step‐by‐step protocol applicable to diverse methane‐conversion reactions. Emphasizing meticulous verification and accurate quantification of the products, this protocol highlights the significance of mitigating contamination sources and selecting appropriate detection methods. Lastly, we propose the standardized performance metrics crucial for evaluating photocatalytic methane conversion. By defining these metrics, the community could obtain the consensus of assessing the performance across different studies. Moving forward, the future of photocatalytic methane conversion necessitates further refinement of stringent experimental standards and evaluation criteria. Moreover, development of scalable reactor is essential to facilitate the transition from laboratory proof‐of‐concept to potentially industrial production.

Effect of COVID-19 Curriculum Changes on Medical Student Exam Performance: A Case Series.

The COVID-19 pandemic caused medical schools to convert to an online format, necessitating a swift change in medical education delivery. New teaching methods were adapted, with some schools having greater success than others. Kirk Kerkorian School of Medicine (KSOM) employed a small-group interactive learning style that consists of eight or fewer medical students and one faculty mentor engaging in group problem-based learning (PBL) twice weekly. This style had clear signs of struggle with a significant decrease in exam performance. Rocky Vista University College of Osteopathic Medicine (RVUCOM) employed a large-group didactic lecture style that consisted of one faculty mentor lecturing hundreds of medical students in a pre-recorded setting five times weekly.This style had greater success with its curriculum adaptation leading to minimal effect on their exam performance. This study aims to investigate whether the type of medical school curriculum (small-group interactive vs. large-group didactic) impacts student exam performance during online learning transitions forced by the COVID-19 pandemic. KSOM and RVUCOM students were grouped into above-expectationsand below-expectationscategories based on each institution's standardized exam performance metrics. Independently sampled t-tests were performed to compare groups. KSOM was classified as a small-group interactive curriculum through its heavy reliance on student-led PBL, whereas RVUCOM was classified as a large-group didactic curriculum through its extensive proctor-led slideshow lectures. KSOM's transition to online PBL resulted in fewer students scoring above the national average on the National Board of Medical Examiners (NBME) exams compared to previous cohorts (55% vs. 77%, respectively; N = 47 and 78; P< 0.01). RVUCOM's transition to online large-group lectures yielded no significant differences between students who performed above expectationsand students who performed below expectationsbetween their cohorts (63% vs. 65%, respectively; N= 305 and 300; P> 0.05). KSOM's COVID-19 cohort performed significantly worse than RVUCOM's COVID-19 cohort during their medical school organ-system exams. We believe that the small-group learning at KSOM is less resilient for online curricula compared to the large-group didactics seen at RVUCOM. Understanding which didactic methods can transition to online learning more effectively than others is vital in guiding effective curriculum adjustments as online delivery becomes more prominent.

ASSESSING THE POTENTIAL OF ENERGY STORAGE SOLUTIONS FOR GRID EFFICIENCY: A REVIEW

Energy storage solutions play a pivotal role in enhancing grid efficiency and reliability, offering a multitude of benefits for grid operators, utilities, and consumers alike. This comprehensive review examines the potential of energy storage technologies in optimizing grid performance. The review begins by outlining the significance of energy storage in addressing the challenges of grid modernization and the integration of renewable energy sources. It explores various types of energy storage technologies, including batteries, pumped hydro storage, compressed air energy storage, and thermal energy storage, assessing their capabilities, limitations, and suitability for grid applications. Furthermore, the review delves into the benefits that energy storage solutions bring to the grid, such as peak shaving, load leveling, frequency regulation, and grid stabilization. It analyzes real-world case studies and examples of energy storage deployments, highlighting their effectiveness in improving grid reliability and resilience while reducing operational costs. Despite the promising potential of energy storage, the review also addresses the challenges and barriers hindering widespread adoption. These include cost considerations, regulatory frameworks, technical constraints, and the need for standardized performance metrics and interoperability standards. Looking ahead, the review discusses emerging trends and opportunities in energy storage technologies, such as advancements in battery technology, grid-scale storage projects, and the integration of storage with renewable energy systems and smart grid infrastructure. It emphasizes the importance of continued research, innovation, and collaboration to unlock the full potential of energy storage solutions for grid efficiency. In conclusion, the review underscores the critical role of energy storage in optimizing grid efficiency, enhancing renewable energy integration, and ensuring grid stability and resilience. It calls for concerted efforts from policymakers, industry stakeholders, and researchers to overcome existing challenges and accelerate the deployment of energy storage technologies for a more sustainable and resilient energy future.&#x0D; Keywords: Efficiency, Grid, Solutions, Energy Storage, Potential.

Green Data Center : Reviews And Challenges.

Data is undoubtedly the currency of the 21st century. The data that we are providing to or retrieving from the users has a very important role to play in the evolution of services in both the computing and non-computing spheres. Various internet services, such as web hosting, e-commerce, and Social Networking, find their roots in the Data centers. With the ever-increasing concerns about carbon emissions, reliance on non-renewable energy resources, and energy consumption by the cooling systems in conventional Data centers, it is imperative to shift our focus to newer, greener, and cleaner technologies that will aim to reduce the overall carbon footprint, technologies that will reduce energy consumption, and the integration of renewable energy resources into the existing setup. There are several studies that have highlighted challenges regarding the adoption of technologies in the hardware and software domains and the standardization of performance metrics, which are generally used to study trends in energy consumption and net emissions and, therefore, assess the sustainability and efficiency of Data center operations. This shift to Green Data centers will certainly involve both standardized performance metrics, effective strategies to reduce energy consumption, and the integration of renewable energy resources to reduce the carbon footprint. Furthermore, we are going to discuss data center network architecture that will make our future Green Data centers scalable, fault-tolerant, agile, and energy efficient. This paper reviews various strategies for designing an efficient data center, ways to incorporate renewable energy resources, standardization and benchmarking of performance metrics, and optimizing the cooling systems to improve energy efficiency. Finally, we are going to outline the future directions and opportunities for further research and innovation.

ActivBench: Leveraging Human Activity Inference from Smartphone Sensors for Human Computer Interactions

Human activity recognition (HAR) from smartphone sensors has gained significant attention due to its potential to enhance user experience (UX) and human computer interaction (HCI) in various domains, HAR can enable personalized, context-aware, and adaptive interfaces that improve accessibility and promote health and wellness in various applications such as healthcare, smart homes, fitness tracking, and context-aware systems. However, evaluating the performance of different machine learning (ML) algorithms on activity recognition tasks remains challenging, primarily due to the lack of standardized benchmark datasets and evaluation protocols. In this paper, we presented ActivBench, an end-to-end computational intelligence benchmark designed to facilitate the evaluation and comparison of ML algorithms for human activity inference from smartphone sensors. We addressed the challenges in benchmarking activity recognition systems by providing a unified evaluation protocol and standardized performance metrics. Through extensive experiments using various state-of-the-art algorithms, we demonstrated the effectiveness of ActivBench in assessing the strengths and limitations of different approaches. The benchmark results provide valuable insights into the strengths and limitations of different algorithms, facilitating the development of robust and accurate activity recognition systems that can enhance human computer interaction in various applications. ActivBench is serving as a valuable resource for researchers and practitioners in human activity recognition and human-computer interaction, enabling fair comparisons and fostering advancements in the field. It also serves as a catalyst for advancements in the field, enabling the exploration of novel algorithms, feature engineering techniques, and sensor modalities.

A Generalizable Approach to Predicting Performance on USMLE Step 2 CK.

IntroductionThe elimination of the USMLE Step 1 three-digit score has created a deficit in standardized performance metrics for undergraduate medical educators and residency program directors. It is likely that there will be greater emphasis on USMLE Step 2 CK, an exam found to be associated with later clinical performance in residents and physicians. Because many previous models relied on Step 1 scores to predict student performance on Step 2 CK, we developed a model using other metrics.Materials and MethodsAssessment data for 228 students in three cohorts (classes of 2018, 2019, and 2020) were collected, including the Medical College Admission Test (MCAT), NBME Customized Assessment Service (CAS) exams and NBME Subject exams. A linear regression model was conducted to predict Step 2 CK scores at five time-points: at the end of years one and two and at three trimester intervals in year three. An additional cohort (class of 2021) was used to validate the model.ResultsSignificant models were found at 5 time-points in the curriculum and increased in predictability as students progressed: end of year 1 (adj R2 = 0.29), end of year 2 (adj R2 = 0.34), clerkship trimester 1 (adj R2 = 0.52), clerkship trimester 2 (adj R2 = 0.58), clerkship trimester 3 (adj R2 = 0.62). Including Step 1 scores did not significantly improve the final model. Using metrics from the class of 2021, the model predicted Step 2 CK performance within a mean square error (MSE) of 8.3 points (SD = 6.8) at the end of year 1 increasing predictability incrementally to within a mean of 5.4 points (SD = 4.1) by the end of year 3.ConclusionThis model is highly generalizable and enables medical educators to predict student performance on Step 2 CK in the absence of Step 1 quantitative data as early as the end of the first year of medical education with increasingly stronger predictions as students progressed through the clerkship year.

Community Development Financial Institution (CDFI) program evaluation: a luxury but not a necessity?

ABSTRACT Community development financial institutions (CDFIs) are an integral component of US community economic development policy. These organizations differ in structure and client base, but they all seek to promote sustainable and equitable growth at the local level. By injecting capital into under-served markets, CDFIs facilitate development that may not otherwise occur. Attempts to measure and evaluate CDFI performance have often yielded disparate results. A systematic review of the literature reveals that capacity constraints have resulted in CDFI evaluations with wide variance in scope and rigor. Making comparisons across these institutions is difficult because there are few standardized performance metrics that could inform evaluations. Major CDFI funders in the philanthropic and public sectors often have competing demands and do not consistently use evaluations to guide their decisions. To incrementally advance CDFI research and promote a more accurate understanding of their impact, we recommend a shift towards utilization-focused evaluations (UFEs).

Development and implementation of standardized study performance metrics for a VA healthcare system clinical research consortium

The cost of conducting clinical trials is continuously increasing and is driven in large part by the time and resources required to activate trials and reach accrual targets. The impact of low enrollment in a clinical trial can negatively affect the validity of study results and delay its generalizability to the broader population. Quality is a multidimensional concept which could relate to the design, conduct, and analysis of a trial, its clinical relevance, protection/safety of study participants, or quality of reporting. Furthermore, the quality of controlled trials is of obvious relevance to systematic reviews and if the “raw material” or “data” is flawed then the conclusions of systematic reviews cannot be trusted. To date, the literature surrounding the establishment of standardized study enrollment and quality metrics to assess site performance in clinical trial consortiums is scarce. The lack of these metrics presents challenges to study site teams, sponsors, and other clinical research enterprise key stakeholders for adequately monitoring and evaluating study site performance as it relates to fulfilling trial enrollment and quality goals.The Department of Veterans Affairs (VA) Cooperative Studies Program (CSP) Network of Dedicated Enrollment Sites (NODES) undertook an effort to determine the feasibility of establishing and implementing standardized study enrollment and quality metrics for a clinical research consortium (NODES) as a tool to evaluate its performance. In this manuscript, we describe the development and implementation of standardized study enrollment and quality metrics to assess site performance across studies in our clinical research consortium.

A comprehensive overview and critical evaluation of gene regulatory network inference technologies.

Gene regulatory network (GRN) is the important mechanism of maintaining life process, controlling biochemical reaction and regulating compound level, which plays an important role in various organisms and systems. Reconstructing GRN can help us to understand the molecular mechanism of organisms and to reveal the essential rules of a large number of biological processes and reactions in organisms. Various outstanding network reconstruction algorithms use specific assumptions that affect prediction accuracy, in order to deal with the uncertainty of processing. In order to study why a certain method is more suitable for specific research problem or experimental data, we conduct research from model-based, information-based and machine learning-based method classifications. There are obviously different types of computational tools that can be generated to distinguish GRNs. Furthermore, we discuss several classical, representative and latest methods in each category to analyze core ideas, general steps, characteristics, etc. We compare the performance of state-of-the-art GRN reconstruction technologies on simulated networks and real networks under different scaling conditions. Through standardized performance metrics and common benchmarks, we quantitatively evaluate the stability of various methods and the sensitivity of the same algorithm applying to different scaling networks. The aim of this study is to explore the most appropriate method for a specific GRN, which helps biologists and medical scientists in discovering potential drug targets and identifying cancer biomarkers.

Experimental analysis of the energy performance of an ACTive, RESponsive and Solar (ACTRESS) façade module

Responsive Building Elements (RBEs) are technologies enabling the exploitation of the opportunities offered by the environment and of renewable energy sources at the building scale. Amongst RBE concepts, Advanced Integrated Façades (AIFs) is probably one of the most promising technologies. In fact important players in the field of the façade engineering have started to develop integrated modular façade systems (Multifunctional Façade Modules – MFMs), with a dynamic behaviour interacting with the other building services, to reduce the building energy use and maximize the indoor comfort conditions.In this framework, a research activity aimed at the development of solar and active building skins was carried out. A MFM has been conceived and a prototype realised for experimental evaluations. The work presented in this paper illustrates the outcomes of the experimental investigation of the MFM called ACTRESS (ACTive, RESponsive and Solar), which has been tested by means of an outdoor test cell apparatus.In the paper, the ACTRESS module is presented and its performance assessed by means of standardized and non-standardized building performance metrics. The results of the experimental campaign show an overall very good performance of the technology in terms of improved building energy efficiency, although some critical aspects are revealed too. Moreover the façade module shows a very high degree of adaptability, as different strategies and behaviours can be achieved according to the desired performance or requirements.

Book Reviews

JOHN FLOWER, Accounting and Distributive Justice (New York, NY: Routledge, 2010, ISBN 978-0-415-87177-8, pp. 201)

Framework for Performance Evaluation of Face, Text, and Vehicle Detection and Tracking in Video: Data, Metrics, and Protocol

Common benchmark data sets, standardized performance metrics, and baseline algorithms have demonstrated considerable impact on research and development in a variety of application domains. These resources provide both consumers and developers of technology with a common framework to objectively compare the performance of different algorithms and algorithmic improvements. In this paper, we present such a framework for evaluating object detection and tracking in video: specifically for face, text, and vehicle objects. This framework includes the source video data, ground-truth annotations (along with guidelines for annotation), performance metrics, evaluation protocols, and tools including scoring software and baseline algorithms. For each detection and tracking task and supported domain, we developed a 50-clip training set and a 50-clip test set. Each data clip is approximately 2.5 minutes long and has been completely spatially/temporally annotated at the I-frame level. Each task/domain, therefore, has an associated annotated corpus of approximately 450,000 frames. The scope of such annotation is unprecedented and was designed to begin to support the necessary quantities of data for robust machine learning approaches, as well as a statistically significant comparison of the performance of algorithms. The goal of this work was to systematically address the challenges of object detection and tracking through a common evaluation framework that permits a meaningful objective comparison of techniques, provides the research community with sufficient data for the exploration of automatic modeling techniques, encourages the incorporation of objective evaluation into the development process, and contributes useful lasting resources of a scale and magnitude that will prove to be extremely useful to the computer vision research community for years to come.

Meaningful performance evaluation conditions for fire service thermal imaging cameras

Thermal imaging cameras (TIC) are rapidly becoming integral equipment for the fire service for use in structure fires and other emergencies. The Building and Fire Research Laboratory (BFRL) at the National Institute of Standards and Technology has conducted research to establish test conditions that best represent the environment in which TIC are used. Firefighters may use TIC for field operations ranging from fire attack, search/rescue, hot spot detection, overhaul activities, to detecting the location of hazardous materials. To develop standardized TIC performance metrics and test methods that capture the harsh environment in which TIC may be used, information was collected from users, the literature, and from fire tests conducted at BFRL. A workshop was held to facilitate knowledge transfer from the fire service and TIC manufacturers. Full-scale and bench-scale experimental work focused on temperature extremes and the presence of obscuring media such as smoke, dust and water. Consolidation of fire environment data with fire fighting operations and imaging needs resulted in a set of performance metrics and test methods that relate to the conditions and tasks encountered by firefighters in structural fire fighting applications. This work is included in a new draft standard on fire service TIC.

Input versus output‐based performance mesurement in the NHS – the current situation

The concept of service provision through output specifications rather than input‐based specifications is currently occupying directors and managers within the service. While the debate continues, little research has been undertaken to find out the current spread of the new output specification and how it operates in NHS Trusts compared to the longer established input‐based service specification. This paper presents a study of around one‐third of the Trusts in England and provides a comparative analysis of the different specifications in use, whether in‐house or outsourced providers deliver the services to these Trusts, and how these providers are assessed for satisfactory performance. In addition, through a series of structured discussion forums, users’ requirements for the development of standardized performance metrics are established for the future management of output specifications.

The onset of sound wave distortion and cavitation in water and sea water

The aim of this paper is to provide a review of micromirror array (MMA) technologies (2631 MMA research papers and patents were reviewed for this effort). The performance capabilities of 277 MMA designs from 49 companies and 23 academic research groups are categorized and compared. The designs are categorized according to (i) their array’s dimension (e.g., 2D arrays consisting of mirrors that cover a surface, 1D arrays consisting of mirrors in a row, and 0D arrays consisting of only a single mirror), (ii) the nature of the surface of their mirrors (e.g., continuous or discrete), (iii) what combination of tip, tilt, and/or piston degrees of freedom (DOFs) they achieve, and (iv) how they are actuated. Standardized performance metrics that can be systematically applied to every MMA design (e.g., mirror area, fill factor, pitch, range of motion, maximum acceleration, actuator energy density, and number of uncontrolled DOFs) are defined and plotted for existing designs to enable their fair comparison. Theoretical bounds on what is physically possible for MMAs to achieve are also derived and depicted in these plots to highlight the amount of performance improvement that remains to be achieved by future designs and guidelines are provided to aid in the development of these future designs.