Performance Trends Research Articles

Study on the effect of thickness on sound absorption performance of foam aluminum

Abstract This paper investigates the effects of different thicknesses on the sound absorption performance of foam aluminum with different pore sizes through a combination of experiments and theoretical research. The results show that the average sound absorption coefficient of small-pore foam aluminum increases with increasing material thickness. As the thickness increases, there is a trend of the peak sound absorption performance shifting towards lower frequencies. In the mid-frequency range, although the sound absorption coefficient increases with the thickness of foam aluminum, the growth rate decreases. The average sound absorption coefficient of large-pore foam aluminum significantly increases with the material thickness and is reflected across various frequency ranges. With the increase in thickness, the sound absorption efficiency also improves, but the rate of improvement gradually slows down after reaching a certain thickness. Samples with the same thickness exhibit different performances in sound absorption coefficient, especially in frequencies above 2000Hz, where these differences are more pronounced. In the high-frequency range, the sound absorption coefficient of 100mm-thick large-pore foam aluminum material approaches 1, approximately 0.95.

Innovative Approaches to Assessing the Competitiveness of Business Structures Using the Method of Analysis of Hierarchies in Multi-Criteria Ranking

The primary objective of this paper is the practical testing of a multi-criteria rating methodology for assessing the competitiveness of business structures. This aims to evaluate critical functional areas of their operations and justify specific managerial decisions to ensure sustainable competitiveness. Maintaining the competitiveness of business structures becomes a primary task in the context of high uncertainty and competition, as well as the presence of crises and politico-economic problems. To achieve the set objective, the Analytic Hierarchy Process (AHP), developed by T. Saaty, was utilized. This practical method allows for multi-criteria comparisons using expert evaluations and calculations of eigenvectors and values, providing systematization and quantitative assessment of criteria and alternatives' priorities. AHP includes procedures for synthesizing multiple judgments and obtaining priorities, allowing for consideration of the 'human factor' in decision-making preparation. The study results demonstrate that the multi-criteria approach to rating the competitiveness of business structures effectively accounts for all areas of their activities and forms an adequate comprehensive measure of competitiveness. Within the study's framework, three business structures with different performance trends were analyzed. The comparative importance of criteria and sub-criteria for competitiveness assessment was determined using the Analytic Hierarchy Process, considering the specifics of the business structures and their business activities. The proposed methodology for multi-criteria rating of business structures' competitiveness allows for a comprehensive assessment of their activities and justification of specific managerial decisions to ensure sustainable competitiveness. The use of the Analytic Hierarchy Process helps to consider the influence of various factors and conditions, contributing to a more objective assessment and strategic decision-making. The application of the proposed methodology is universal and can be effectively used in various sectors of business structures' activities, showcasing its adaptability.

Comparative analysis of Physician Assistant/Associate National Certifying Examination (PANCE) performance trends: pre and during COVID-19 era

BackgroundCOVID-19 significantly impacted physician assistant/associate (PA) education programs. Most programs transitioned didactic and clinical education from in-person to remote, and clinical training opportunities diminished. Graduates of accredited PA programs take the Physician Assistant National Certifying Examination (PANCE), a five-hour exam with 300 multiple-choice questions, and must attain or exceed the scaled passing score of 350 (range: 200–800). We examined first-time examinees’ trends in PANCE scores and passing rates three years prior to the pandemic and three years during.MethodsWe analyzed data (N = 59,459) from the National Commission on Certification of Physician Assistants. The two primary outcomes were PANCE scores and pass rates. The main exposure was the timeframe: three years pre-pandemic (2017–2019) and three years during the pandemic (2020–2022). The 2017–2018 scores were equated to the new passing standard implemented in 2019. Covariates included age, gender, years the PA program has been accredited, program region, and rural-urban setting. Analyses consisted of descriptive, bivariate, and multivariate statistics.ResultsThe mean PANCE score and pass rate during the six-year study period were 463 and 93%, respectively. In unadjusted analyses comparing each year individually, mean PANCE score was highest in 2020 and lowest in 2022 than in all other years except for 2017. When comparing each pandemic year to the pooled three pre-pandemic years and adjusting for test-taker and PA program covariates, examinees scored significantly higher in 2020; there was no difference in 2021, and they scored lower in 2022. When controlling for covariates, examinees had 1.24 higher odds of failing in 2022 compared to the pooled pre-pandemic period.ConclusionFindings suggest that PANCE scores and pass rates were impacted during the third year of the pandemic. PANCE assesses if examinees have the essential clinical knowledge to enter the PA profession. It is crucial to determine whether the pandemic affected PANCE scores and pass rates to ensure PAs provide safe and high-quality patient care.

Trends in enrolment and academic performance of scheduled tribe students at the undergraduate level in Assam: A decadal analysis

This research work examines the enrolment and performance pattern of the Scheduled Tribe (ST) students at the undergraduate level in Assam for the period of 2012-2022. Based on the enrolment data collected from various ST students, the study examines the gender pattern and course selection of students in different disciplines such as B.A., B.Com., and B.Sc. The study reveals major trends in enrolment increase, gender distribution, and course choices for the decade. Using chi-square tests and comparative performance measures, the study reveals factors that affect academic performance and enrolment. Furthermore, bar graphs and line charts are used to present these trends and to give further analysis. The conclusion of this study provides significant implications for the policymakers and educators who are interested in improving the educational access and opportunities for ST students in the higher education in Assam.

Empowering the circular economy practices: Lifecycle assessment and machine learning-driven residual value prediction in IoT-enabled microwave oven

In an era of resource scarcity and environmental concerns, integrating Internet of Things (IoT) technology into the circular economy (CE), particularly for household appliances like microwaves, is crucial. The lack of systematic assessment of their post-use residual values often reduces utilization and shortens lifespans. Inadequate disposal and management contribute to electronic waste and environmental pollution. Addressing these challenges is vital for efficient appliance management within resource constraints, ensuring meaningful contributions to sustainable resource management. Thus, this study addresses these concerns by integrating IoT technology into microwave ovens, enabling real-time monitoring of key parameters such as voltage, current, door closures, and motor/blade rotations. Data from integrated sensors enables performance analysis and trend tracking, offering potential for advancing CE practices and sustainable product management. Subsequently, utilizing the insights stored from IoT data analysis and tailored surveys, a predictive maintenance model is developed, aiming to predict the life cycles of microwave oven components and categorize them within the CE principles, including reuse, repair, remanufacturing, and cascade. Finally, to mitigate the challenges of lower effective utilization and shortened operating lifespans observed in household appliances, this research employs machine learning models such as Random Forest, Gradient Boosting, and Decision Tree to accurately predict the residual values of IoT-enabled microwaves. Notably, Random Forest demonstrates superior accuracy compared to the other models. Therefore, these technological advancements allow household appliances to be utilized more effectively, thereby enhancing resource utilization.

Analyzing the Performance Trends in Licensure Examination for Teachers (LET) Among Bachelor of Elementary Education (BEEd) Graduates: A Quantitative Approach

The study analyzes the performance trends in the Licensure Examination for Teachers (LET) among Bachelor of Elementary Education (BEEd) graduates from Sultan Kudarat State University – Kalamansig Campus from 2018 to 2023. A descriptive-comparative research design was employed to statistically measure the objectives of the study. Data were obtained from the Professional Regulation Commission (PRC). The study’s respondents are 73 first-timers and 112 repeaters. A total of 185 BEEd examinees were the respondents of the study. Also, the study utilized frequency and percentage counts, z-scores, and p-values to compare the performance and test for significant differences. The results revealed that the performance trends among the BEEd graduates was fluctuating. Performance of first-timers are higher than repeaters in most periods, with institutional passing rates below national passing rates. Notably, exceptional performances were recorded during the March 2019, June 2022, and October 2022 examination periods, where institutional passing rates surpassed national passing rates. The study identifies several factors influencing LET performance, including academic preparation, review sessions, and external disruptions such as the COVID-19 pandemic, which impacted the consistency and preparation of examinees. The data suggest that while first-timers showed relatively strong performance, repeaters faced significant challenges, underscoring the need for targeted support systems to enhance their preparedness and outcomes. The findings highlight the critical importance of continuous monitoring and strategic interventions to improve LET performance. Implementing effective support mechanisms for repeaters, improving academic and review processes, and addressing external factors are essential steps toward enhancing the quality of teacher education.

The Correlation Dynamics Between Sensex 30 Returns and INR/USD Exchange Rate Movements

The Sensex 30 is a benchmark index for India's largest companies, indicating the health of the stock market. Its movements are influenced by domestic economic conditions, corporate performance, and global trends. The INR/USD exchange rate, which represents the Indian rupee vs. the US dollar, is crucial for India's international trade and investment. A depreciating rupee benefits export-oriented companies but increases import costs, affecting market performance. Understanding this dynamic is essential for investors and policymakers. The fluctuating link between the INR/USD currency pair and SENSEX 30 stock index is examined in this study from 2014 to 2024, with an emphasis on how changes in the exchange rate affect stock market performance. As India continues to integrate into the global economy, the interaction between foreign investments and the Indian stock market has become increasingly relevant for understanding investor sentiment and economic stability. This study particularly examines how returns from the SENSEX 30, an index that monitors 30 of India's biggest and most traded firms, are impacted by changes in the INR/USD exchange rate. The research's main finding is that there is a strong positive correlation between the INR/USD exchange rate and the SENSEX 30 index, with a rise of 1 in the SENSEX 30 for every 0.90 increase in the exchange rate. This implies that the depreciation of the Indian currency (INR) in relation to the US Dollar (USD) may result in increased stock market returns, especially in industries that rely heavily on exports, as Indian goods become more competitive in international markets due to the weaker currency. On the other hand, persistent depreciation may also result in higher import prices and inflationary pressures, which might be detrimental to business profitability and the state of the economy as a whole. Given that currency changes may have an impact on their investment decisions, foreign institutional investors (FIIs) are essential to this dynamic.. Decreased international gains are eroded by a lower INR, which frequently leads to portfolio modifications and more market volatility. Exchange rate fluctuations and stock market performance are influenced by the participation of foreign institutional investors (FIIs) and the Reserve Bank of India's (RBI) monetary policy choices. For example, the RBI's changes to interest rates have an effect on investor mood and influence market movements. Global economic variables that affect the US stock market and exchange rate include geopolitical tensions, changes in the price of commodities, particularly oil, and US Federal Reserve policies. The study uses a variety of statistical techniques, including as trend analysis, a t-test, and correlation analysis, to assess the link between the performance of the SENSEX 30 and the INR/USD exchange rate during a ten-year period. The results of the t-test demonstrate that the difference between the mean values of the SENSEX 30 and the INR/USD exchange rate is statistically significant, with a t-statistic of 30.278 and very tiny p-values. The SENSEX 30 exhibits a significantly bigger variation than the INR/USD, suggesting that the stock market is more volatile than the currency rate. This result is in line with predictions because stock markets are often more erratic than exchange rates. As a result, the study emphasizes how much exchange rate fluctuations affect the Indian stock market. It is recommended that investors keep a careful eye on currency changes since they have the International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 09 | Sept - 2024 SJIF Rating: 8.448 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM37610 | Page 2 potential to impact business profitability and stock market performance, particularly in industries that depend on imports or exports. The results also emphasize how crucial it is to diversify portfolios and hedge against risk related to currencies in order to successfully manage the intricate relationships between monetary policy, market volatility, and global economic situations.

Protección sísmica de estructuras civiles mediante dispositivo INERTER en la provincia de Huancayo

This research work proposes the use of a Tuned-Mass-Damper-Inerter (TMDI), which combines a new configuration of passive vibration control using an “Inerter”. This two-terminal mechanical flywheel device, based on the well-known Tuned-Mass-Damper (TMD), generates resistance forces proportional to the relative acceleration of its terminals. TMDI takes advantage of the “mass amplification effect” of Inerter to improve performance compared to TMD. For harmonically excited primary linear systems, closed-form analytical software is modeled to obtain optimal design and tuning parameters of the TMDI, using established methods. It is observed that, with proper distribution of the dampers, the TMDI system is more effective in suppressing vibrations close to the natural frequency of the uncontrolled primary system and is more resistant to the effects of detuning and misdistribution. Furthermore, the results of the modeled structures show the effectiveness of the TMDI, modeled as a classical damper, in suppressing the fundamental mode of vibration for linear MDOF structures. The incorporation of the Inerter in the proposed TMDI configuration can replace part of the vibrating mass, achieving lightweight passive vibration control solutions or improving structural performance. The solution proposed and modeled with traditional dampers behaves linearly, in line with current performance trends for minimally damaged structures protected by passive control devices. Additionally, optimized TMDI is applied for vibration suppression and displacement control, offering optimal seismic protection.

Electrochemical aspects of Li[Ni0.6Co0.2Mn0.2]O2 cathode materials doped by various ionic radius cations

The capacity of Li[Ni0.6Co0.2Mn0.2]O2 cathode materials decreases rapidly due to layer structure degradation, grain cracking, and electrolyte side reaction during cycling. In this paper, metal ions with different ionic radius (W6+= 0.62 Å, Mg2+= 0.72 Å, Y3+= 0.90 Å) were doped into single crystal Li[Ni0.6Co0.2Mn0.2]O2, which were synthesized by high-temperature solid-phase technology. X-ray diffraction (XRD), energy dispersive spectrometry (EDS) and scanning electron microscopy (SEM) were employed to characterize various samples doped with different ionic radius. The results reveal that the radius of the doped ions plays a crucial role in improving the stability of material structure. It is attributed to the incorporation of metal ions into the Li+ or Ni2+ site, thus maintaining the integrity of the layer and reducing cation mixing. However, it is also detrimental to the performance of single crystal Li[Ni0.6Co0.2Mn0.2]O2 because of lattice distortion caused by the ionic radius of the doped metal being much greater than that of the doped site. These results show superior capability and reveal the performance trend of Li[Ni0.6Co0.2Mn0.2]O2 with metal ions of different radius. The initial discharge capacity at 0.1 C for the undoped sample was 183.8 mAh/g, compared with 189.9, 192.3 and 199.8 mAh/g for NCM622-W, NCM622-Mg, and NCM622-Y, respectively. After 100 cycles, NCM, NCM-W, NCM-Mg, and NCM-Y delivered retentions of 90.88 %, 98.16 %, 97.13 %, 94.37 %, respectively. The defect model is mainly responsible for the improvement in electrochemical performance.

Influence of Cultural Factors on Organizational Performance of Multinational Corporations: A Bibliometric Review from 1983 to 2020

Cultural factors are critical in shaping the organizational performance of multinational corporations (MNCs) operating across diverse countries and cultures. This study conducts a comprehensive bibliometric analysis to explore scientific developments in this research area from 1983 to 2020, based on 856 documents published across 195 journals indexed in the Web of Science. The analysis provides insights into (i) productivity and performance trends within the field, (ii) the most influential countries, regions, journals, authors, and citations, and (iii) frequently occurring keywords that highlight future research directions. This study captures the historical evolution of research on cultural factors, providing a valuable baseline for understanding the pre-COVID-19 context and enabling MNCs to better navigate cross-cultural communication and collaboration in an increasingly complex global environment.

Gemini AI vs. ChatGPT: A comprehensive examination alongside ophthalmology residents in medical knowledge.

The rapid advancement of artificial intelligence (AI), particularly in large language models like ChatGPT and Google's Gemini AI, marks a transformative era in technological innovation. This study explores the potential of AI in ophthalmology, focusing on the capabilities of ChatGPT and Gemini AI. While these models hold promise for medical education and clinical support, their integration requires comprehensive evaluation. This research aims to bridge a gap in the literature by comparing Gemini AI and ChatGPT, assessing their performance against ophthalmology residents using a dataset derived from ophthalmology board exams. A dataset comprising 600 questions across 12 subspecialties was curated from Israeli ophthalmology residency exams, encompassing text and image-based formats. Four AI models - ChatGPT-3.5, ChatGPT-4, Gemini, and Gemini Advanced - underwent testing on this dataset. The study includes a comparative analysis with Israeli ophthalmology residents, employing specific metrics for performance assessment. Gemini Advanced demonstrated superior performance with a 66% accuracy rate. Notably, ChatGPT-4 exhibited improvement at 62%, Gemini at 58%, and ChatGPT-3.5 served as the reference at 46%. Comparative analysis with residents offered insights into AI models' performance relative to human-level medical knowledge. Further analysis delved into yearly performance trends, topic-specific variations, and the impact of images on chatbot accuracy. The study unveils nuanced AI model capabilities in ophthalmology, emphasizing domain-specific variations. The superior performance of Gemini Advanced superior performance indicates significant advancements, while ChatGPT-4's improvement is noteworthy. Both Gemini and ChatGPT-3.5 demonstrated commendable performance. The comparative analysis underscores AI's evolving role as a supplementary tool in medical education. This research contributes vital insights into AI effectiveness in ophthalmology, highlighting areas for refinement. As AI models evolve, targeted improvements can enhance adaptability across subspecialties, making them valuable tools for medical professionals and enriching patient care. What is known AI breakthroughs, like ChatGPT and Google's Gemini AI, are reshaping healthcare. In ophthalmology, AI integration has overhauled clinical workflows, particularly in analyzing images for diseases like diabetic retinopathy and glaucoma. What is new This study presents a pioneering comparison between Gemini AI and ChatGPT, evaluating their performance against ophthalmology residents using a meticulously curated dataset derived from real-world ophthalmology board exams. Notably, Gemini Advanced demonstrates superior performance, showcasing substantial advancements, while the evolution of ChatGPT-4 also merits attention. Both models exhibit commendable capabilities. These findings offer crucial insights into the efficacy of AI in ophthalmology, shedding light on areas ripe for further enhancement and optimization.

Historical multi‐environmental trials reveal trends in hybrid grain sorghum performance from 1970 to 2021 in Texas

Historical multi‐environmental trials reveal trends in hybrid grain sorghum performance from 1970 to 2021 in Texas

347 Quantifying phenotypic and genetic variation for cow fertility phenotypes in American Simmentals using total herd reporting data

Abstract Inefficiencies in reproduction are the largest drivers of loss in United States beef production. Nearly all reproductive traits are at least modestly heritable, making genetic selection and improvement possible over the long term. Cow-centric reproductive traits present unique challenges, as they are not expressed in male animals and will not be observed until later in the productive life of a female, if at all. This makes cow-centric fertility traits ideal candidates for selection with Expected Progeny Differences (EPDs). We used a commercially generated dataset from the American Simmental Association (ASA) to explore the phenotypic and genetic variation in these traits. The dataset comprised Total Herd Enrollment (THE) records, which required unbiased reporting of enrollment, calving, and disposal for 303,158 females (132,403 cows and 170,755 heifers). Using the THE data, we calculated three continuous and two discrete traits that are indicators of heifer and cow fertility and rebreeding ability. These traits were: 1) calving date (calving date of the cow relative to the start of the calving season), 2) calving interval (days between calves), 3) first calving interval (calving interval observation between the first and second calving record for a female), 4) heifer pregnancy (did the animal calve as 2-yr-old?), and 5) discrete early calving (did animal calve in the first 30 d of the calving season?). We observed a significant difference in calving date in mature cows that calved in the first 30 d of the calving season of their contemporary group as heifers compared with those that calved later (Table 1). Early calving heifers averaged a nearly 10 d sooner calving date throughout their productive lives compared with those that calved after 30 d as heifers (29.9 d vs. 40.2 d, P-value < 0.01). We used a combined pedigree and genomic approach, implemented in GIBBSF90+, to estimate genetic, environmental, and permanent environmental variance components. We estimated heritabilities of 0.24 (± 0.037215) for heifer pregnancy, 0.06 (± 0.009265) for calving date, 0.07 (± 0.015451) for discrete early calving, 0.04 (± 0.003954) for calving interval, and 0.04 (± 0.009604) for first calving interval. We observed positive phenotypic and genetic correlations between these traits, ranging from 0.01 to 0.96 (Table 2). This work provides an extensive population survey of phenotypic trends in reproductive performance and management in a large seedstock population. The heritable nature of the inspected traits motivates further work in understanding their genetic architectures and how they are affected by heterosis in crossbreeding programs.

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

Governance matters: Exploring the impact of governance on routine immunization performance in 54 African countries: A 10-year (2012−2021) analysis using linear mixed models

BackgroundImmunization coverage across numerous African nations has, unfortunately, shown little improvement and, in some cases, has even decreased over the past decade, leaving millions of children vulnerable to vaccine-preventable diseases. While efforts to improve immunization performance have primarily focused on the health system, effective delivery of immunization services is intricately linked to a country's governance, which, in this context, reflects a government's ability to provide comprehensive services to its citizens. This study investigated the relationship between governance, measured using the Mo Ibrahim Index for African Governance, and the trajectory of immunization coverage for three vaccines in 54 African countries from 2012 to 2021. MethodsWe conducted an ecological study utilizing publicly available datasets, the WHO/UNICEF estimates of National Immunization Coverage and the Ibrahim Index of African Governance score (IIAG). We described the trends in routine immunization performance, evaluated and assessed the impact of governance on immunization coverage across 54 African countries for the period 2012 to 2021, using linear mixed models and focusing on three vaccines provided through the Expanded Program on Immunization (DTPCV1, DTPCV3, and MCV1). ResultsAmong the 54 African countries studied, 32 (59.3 %) witnessed an overall decrease (slope of change in immunization coverage over time < 0) in immunization coverage, with 16 (29.6 %) experiencing a significant decline (slope of change significantly different from zero (P < 0.05)) in coverage. For DTPCV3, 31 countries (57.4 %) demonstrated a decline in coverage, with 12 (22.2 %) being significant declines. Thirty-two countries (59.2 %) reported a decrease in MCV1 coverage over the analysis period, with 17 (31.5 %) significant. Across all three antigens, the IIAG overall score was positively associated with immunization coverage over time. One unit increase in the IIAG score correlated with an average annual increase of 0.64 (95 % CI: 0.35–0.93) percentage points in DTPCV1 coverage, 0.74 percentage points (95 % CI: 0.42–1.07) in DTPCV3 coverage, and 0.60 (95 % CI: 0.30–0.91) percentage points in MCV1 coverage. These findings suggest that an African country with an average IIAG score just one unit higher than their observed average value over the study period, would have achieved a 6.4 %, 7.4 %, and 6.0 % coverage for DTPCV1, DTPCV3, and MCV1, respectively, above its 2021 coverage levels. ConclusionThe Expanded Program on Immunization aspires to reach all eligible populations with life-saving vaccines, regardless of the context. We found that country governance may be an important determinant of immunization performance, potentially explaining the observed stagnation or decline in immunization performance and the heightened vulnerability of immunization programs to external shocks. Understanding the nexus between governance and service delivery suggests that immunization actors, funders, and other stakeholders may need to adjust their expectations of countries' immunization performance accordingly.

Series Multiblood Pump Design With Dual Activation for Pediatric Patients With Heart Failure.

The translational development of pediatric ventricular assist devices (VADs) lags years behind adult device options, negatively impacting pediatric patient outcomes. To address this need, we are developing a novel, series-flow, double-blood pump VAD that integrates an axial and centrifugal pump into a single device. The axial pump is used for initial circulatory assistance in younger patients; then, an internal activation mechanism triggers the centrifugal pump to activate in line with the axial pump, providing additional pressure and flow to match pediatric patient growth cycles. Here, we focused on the design and improvement of the device flow paths through computational analysis and in vitro hydraulic testing of a prototype. We estimated pressure-flow generation, fluid scalar stresses, and blood damage levels. In vitro hydraulic tests correlated well with shear stress transport (SST) predictions, with an average deviation of 4.5% for the complex, combined flow path. All data followed expected pump performance trends. The device exceeded target levels for blood damage in the blade tip clearances, and this must be both investigated and addressed in the next design phase. These study findings establish a strong foundation for the future development of the Drexel Double-Dragon VAD.

Data-Driven Radial Compressor Design Space Mapping

Abstract Estimates of turbomachinery performance trends inform system-level compromises during preliminary design. Existing empirical correlations for efficiency use limited experimental data, while analytical loss models require calibration to yield predictive results. From a set of 3708 radial compressor computations, this paper maps efficiency as a function of mean-line aerodynamics, and determines the governing loss mechanisms. An open-source turbomachinery design code creates annulus and blade geometry, then runs a Reynolds-averaged Navier–Stokes simulation for compressors sampled from the mean-line design space. Polynomial surface fits yield a continuous eight-dimensional representation of the design space for analysis, predicting efficiency with a root-mean-square error of 1.2% points. The results show a balance between surface dissipation in boundary layers and mixing loss due to casing separations sets optimum values for inlet Mach number, hub-to-tip ratio, de Haller number, and backsweep angle. Surface dissipation drives the effect of flow coefficient, with high surface areas at low values, and high velocities at high values. Compact compressor designs are achieved by increasing inlet Mach number, reducing hub-to-tip ratio, and minimizing the radial loading coefficient—all of which reduce efficiency approaching design space boundaries. An interactive web-based tool makes the results available to practising engineers, demonstrating large ensembles of automated designs and simulations as a higher-fidelity replacement for legacy empirical correlations in preliminary design.

A decade in review: bibliometric analysis of sustainable performance trends in higher education institutes

The sustainable performance of higher education institutes (HEIs) is key to efficiently manage educational institutes in the long run, to get financial aid from the government, and to keep the interest of stakeholders in the institutions. Researchers have focused on examining the impact of a sustainable curriculum, sustainable teaching methodologies, sustainable campuses, and tools for assessing sustainability in HEIs in past literature. The present bibliometric analysis, combined with network analysis, aims to explore the literature to identify research gaps in the field of sustainable performance or sustainability performance of HEIs. We have employed a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) flow diagram to analyze the results obtained from Scopus, Web of Science (WoS), and EBSCOhost databases. A bibliometric analysis using Biblioshiny web application and VOSviewer software was performed on the 263 articles included in the study after screening. The results revealed that the annual growth rate of publications is increasing, indicating that the sustainability performance of HEIs is gaining momentum after the introduction of the Sustainable Development Goals (SDGs) in 2017. The production of articles in the field is highest from advanced European countries and the United States, but only a few articles are published from Asian countries. The authors with the highest number of publications and the most cited research article in the field are Leal Filho and Wals, respectively. Authors keywords in HEIs (or institutions) not only have a strong link with sustainability, sustainable education, green campus, and sustainability assessment but also have a small network with sustainable performance, green knowledge sharing, green knowledge management, green human resource management practices, green teams, change management, and organizational commitment. These findings can be used by future researchers to identify unexplored research areas in different contexts.

Experimental investigation for the influence mechanism of air intake method and humidity level on performance of proton exchange membrane fuel cells

As pivotal component of hydrogen fuel cell vehicles (HFCVs), proton exchange membrane fuel cells (PEMFCs) play a crucial role in determining performance and energy efficiency of HFCVs. In this study, comprehensive bench tests of fuel cell stack and electrochemical impedance spectroscopy (EIS) tests of single cells were conducted, and influence mechanisms of air intake methods and humidity levels on performance of stack and single cells at different locations were analyzed in depth. The results indicate that counter-flow inlet method exhibits superior voltage output performance and stability across various relative humidity (RH) and current densities (CDs). A moderate increase in RH can mitigate performance degradation, but excessive humidity can lead to flooding. When RH is increased to 60%, the voltage difference between co-flow and counter-flow of fuel cell stack does not exceed 0.05 V at 1.6 A/cm2. Meanwhile, the performance of cells near inlet end plate is consistently worse than those farther away under different inlet conditions. In addition, the performance trend of single cells in serpentine channels aligns with that of fuel cell stack, underscoring strong adaptability of mechanisms influencing performance under different conditions. These findings provide theoretical guidance and data support for optimizing performance and preventing failures in PEMFCs.

Supercapacitors and rechargeable batteries, a tale of two technologies: Past, present and beyond

Supercapacitors and rechargeable batteries are energy storage devices where the performance strengths of one are traditionally the weaknesses of the other. Batteries benefit from superior energy storage capacity while supercapacitors possess higher power rates and longer cycle life. The rapid adoption of these devices in electric vehicles and grid energy storage applications is driving their further development and production. Accumulating and comprehending the current knowledge of both device technologies shall serve as a foundation for the progress in future research and development within these two distinct fields that share common goals. Therefore, in this review, we aggregate the supercapacitor and battery energy-power performance trends from over the last 18 years, to construct a projection of where the technologies could be heading in the coming decade. We specifically discuss the influence of each of these technologies in the energy storage landscape and their effect on hybridization research. Projections of the trends suggest that by 2040 the best-performing asymmetric and hybrid supercapacitors could be comparable to commercial battery technologies that are currently under development, in terms of energy density (ED). In terms of power density (PD), battery technology can achieve performance comparable to certain electrical double layer (EDL)-based supercapacitors. For some applications, we foresee that the two devices will continue to hybridize to fill the energy-power gap in a way that the penalty to PD for enhanced ED becomes insignificant. This expected improvement may eventually reach a saturation point, suggesting that once a certain level of ED is achieved, any further enhancements of the metric only lead to severe trade-offs with PD, and vice versa. The saturation observed in these technologies has also prompted an exploration of new pathways, with a notable emphasis on sustainability, to achieve high performance using renewable materials and methods.