Ranking Research Articles

Blood:A Matter of Life and Death

Blood: A Matter Of Life And Death is a unique initiative that simplifies blood donation by eliminating issues like donor shortages, long search times, and limited availability. The website uses location-based services to connect donors and recipients, reducing waiting times and risks. A sophisticated ranking system ensures an active donor pool, and direct communication channels facilitate matching blood types. Only individuals under 60 can register to donate, and a temporary donor-blocking mechanism encourages continued participation. Data management is securely stored within an SQLite database server, enhancing privacy and efficiency. This approach supports a safe and reliable blood donation process.

An Empirical Case Study GeoAI Usage for Sustainability Scoring of University Campuses

UI GreenMetric (GM) ranking system is a methodology to score and rank the university campuses in several categories. The campuses are measured according to their various sustainability indicators in this methodology. The base of this research to study on how we can use geoAI to calculate GM Setting & Infrastructure (SI) score. By this empirical case study to have ideas connected with the relation of score point and geospatial metrics by recognized of google map geotiff images of university campuses. To develop a geoIA methodology with KooMap cloud service in the purpose of sustainability scoring would provide a practical, accurate and pretty objective algorithm for a smart campus evaluation. In the way of transforming to smart city these applications and utilizations of geoAI are the digital trends of new era.

Construction and application of product optimisation design model driven by user requirements

User requirements serve as the primary reference content in product design. The effective capture of crucial user requirements, followed by the development of a product technical solution aligned with these requirements, stands as a pivotal approach to enhancing design efficiency. In order to explore the problem of generating and decision-making of product technical solutions in the case of complex user demands, this study constructs a user requirements driven product optimization design model, which is used to complete the generation and decision-making of product design solutions in a more reasonable way. The model unfolds across three key stages: Firstly, a user requirements importance ranking system is crafted leveraging Kano Model and Pairwise Analysis. Next, employing the Functional Analysis System Techniques (FAST) theory and the Quality Function Deployment (QFD) theory, user requirements undergo transformation into technical solutions. Finally, these technical solutions are amalgamated into diverse technical combinations, with decisions facilitated by a game theory model to yield the optimal overall design solution. The new optimal design model reduces the influence of subjectivity and ambiguity in the process of user requirements analysis, increases the reliability of the transformation of user requirements into technical solutions, and improves the efficiency of the generation and decision-making of product design solutions under multiobjective situations. The model proposed in this study is exemplified through the development of a medicated bath water heater. Results indicate that the technical solution derived from the model surpasses similar products in terms of user satisfaction, thereby validating its feasibility.

Assessing Spatial Audio: A Listener-Centric Case Study on Object-Based and Ambisonic Audio Processing

The research explores the production and critical evaluation of two distinct mixes of “Dancing Ends”, a musical composition by Łukasz Pieprzyk. These mixes were engineered using two cutting-edge spatial sound technologies: Dolby Atmos and Ambisonics. The recording process incorporated overdub and multitrack recording techniques. Once created, the mixes were evaluated using a method of direct rating, based on an average rank system from 1 to 5, adhering strictly to the (ITU-R, 2015) BS.1116-3 and (ITU-R, 2019) BS.1284-2 standards. Evaluation criteria included factors such as mix selectivity, depth, width, and height of the sound stage, sound envelopment, tonal brightness, and quality of source localization. Additionally, some criteria were specifically tailored to evaluate characteristics unique to the composition. The evaluations were performed on three different listening systems and environments: surround systems of 5.1 and 7.1.4, and binaural listening. Although Ambisonics’ mix received higher ratings in several categories, Dolby Atmos’ mix was preferred across all listening environments. The results underscore the potential benefits of employing spatial sound technologies in music production and evaluation, offering insight into the capabilities of Dolby Atmos and Ambisonics.

Performance tracker: Real-time CPU Metrics and Gamified Ranking System

The study presents an innovative approach aimed at amplifying student engagement with the Selfmade Ninja labs, utilizing a reward-centric framework that prioritizes user efficiency. This approach involves the meticulous calculation of various CPU metrics, encompassing elements such as CPU usage, memory usage, download and upload statistics, process identifiers, as well as read and write statistics. These metrics collectively offer a comprehensive view of user interactions within the platform. The gathered data is thoughtfully curated and stored in a JSON file, facilitating efficient data management and analysis. To facilitate the realization of this approach, a sophisticated machine-learning model is deployed. This model serves the pivotal purpose of predicting user efficiency, a crucial factor in determining the efficacy of their engagement with the Selfmade Ninja labs. Building upon this predictive prowess, a system of credits is established, intricately tied to a leaderboard that reflects individual user performances. Through this dynamic reward distribution mechanism, users are incentivized to actively participate and continually enhance their proficiency, thereby fostering a vibrant learning ecosystem. The culmination of this endeavour is a finely tuned predictive model that seamlessly allocates rewards to users based on their demonstrated engagement and proficiency. This tailored approach not only magnifies user motivation but also significantly augments the overall educational impact of the Selfmade Ninja platform. The integration of insights derived from both exploratory data analysis (EDA) and the predictive model ensures a holistic understanding of user behaviors and preferences. Consequently, the proposed reward-based system is elevated to a new level of efficacy, nurturing a learning environment where students are empowered to engage more meaningfully with the Selfmade Ninja labs, fostering enhanced learning outcomes.

Evaluative Item-Contrastive Explanations in Rankings

AbstractThe remarkable success of Artificial Intelligence in advancing automated decision-making is evident both in academia and industry. Within the plethora of applications, ranking systems hold significant importance in various domains. This paper advocates for the application of a specific form of Explainable AI—namely, contrastive explanations—as particularly well-suited for addressing ranking problems. This approach is especially potent when combined with an Evaluative AI methodology, which conscientiously evaluates both positive and negative aspects influencing a potential ranking. Therefore, the present work introduces Evaluative Item-Contrastive Explanations tailored for ranking systems and illustrates its application and characteristics through an experiment conducted on publicly available data.

Comparative Efficacy of Various Exercise Therapies and Combined Treatments on Inflammatory Biomarkers and Morphological Measures of Skeletal Muscle among Older Adults with Knee Osteoarthritis: A Network Meta-Analysis.

Osteoarthritis is associated with high risks of sarcopenia in older populations. Exercise interventions are promising treatments for musculoskeletal impairments in knee osteoarthritis (KOA). The purpose of this study was to identify the comparative effects of exercise monotherapy and its adjunct treatments on muscle volume and serum inflammation for older individuals with KOA. A literature search in the electronic databases was comprehensively performed from this study's inception until April 2024 to identify relevant randomized controlled trials (RCTs) that reported muscle morphology and inflammation outcomes after exercise. The included RCTs were analyzed through a frequentist network meta-analysis (NMA). The standard mean difference (SMD) with a 95% confidence interval was estimated for treatment effects on muscle morphology and inflammation biomarkers. The relative effects on each main outcome among all treatment arms were compared using surface under the cumulative ranking (SUCRA) scores. The certainty of evidence (CoE) was assessed by the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) ranking system. Probable moderators of the treatment efficacy were investigated by network meta-regression analysis. This study included 52 RCTs (4255 patients) for NMA. Among the 27 identified treatment arms, isokinetic training plus physical modality as well as low-load resistance training plus blood-flow restriction yielded the most optimal treatment for inflammation reduction (-1.89; SUCRA = 0.97; CoE = high) and muscle hypertrophy (SMD = 1.28; SUCRA = 0.94; CoE = high). The patient's age (β = -0.73), the intervention time (β = -0.45), and the follow-up duration (β = -0.47) were identified as significant determinants of treatment efficacy on muscle hypertrophy. Exercise therapy in combination with noninvasive agents exert additional effects on inflammation reduction and muscle hypertrophy compared to its corresponding monotherapies for the KOA population. However, such treatment efficacy is likely moderated by the patient's age, the intervention time, and the follow-up duration.

P-194 Developing a classification system for euploid embryos: leveraging morphology grades and biopsy day to accurately rank them based on ongoing pregnancy potential

Abstract Study question Can morphology grades and day of trophectoderm (TE) biopsy be utilized to accurately rank euploid blastocysts according to their potential for ongoing pregnancy (OP)? Summary answer The proposed ranking system categorized euploid blastocysts accurately in terms of their ongoing pregnancy potential. What is known already Implementing a ranking for embryo selection within a pool is a valuable optimization strategy. Many studies have documented the importance of inner cell mass (ICM) and trophectoderm (TE) grading, as well as the day (D) at which blastocysts are biopsied on clinical outcomes. However, literature sparks controversy on clinical findings in relation to blastocyst quality. This is primarily because it reports a diversity in blastocyst grade-ranking systems, and none of them accounting for the day of biopsy. Enhancing consistency and comparability across various research studies could be achieved by establishing a standardized approach for qualitatively classifying and ranking euploid blastocysts. Study design, size, duration This retrospective cohort study included 2209 single euploid frozen embryo transfers (SeFET) between 2017 and 2023. Blastocyst (BL) expansion, TE and ICM were assessed before biopsy according to Gardner’s criteria. Only blastocysts graded ≥BL3CC were biopsied on D5, D6 or D7. Classification models (in-house, logistic regression, gradient boost) were assessed for their calibration, i.e., correct prediction of ongoing pregnancy (OP) probability. Participants/materials, setting, methods In-house ranking system categorized euploid transferred blastocysts (BL3-BL6) as ‘Top-quality’ for blastocysts AA/AB/BA on D5; ‘Good-quality’ for blastocysts BB on D5 and AA/AB/BA/BB on D6; ‘Fair-quality’ for AC/BC/CA/CB on D5, BC on D6 and AA/AB/BA/BB on D7; and ‘Poor-quality’ for blastocysts CC on D5, AC/CA/CB/CC on D6 and CA/CB/AC/BC/CC on D7. The in-house classification ranking system was compared to other developed models to evaluate whether performance could be increased. Main results and the role of chance A total of 406 blastocysts were ranked as Top-quality (100% D5), 1323 as Good-quality (51.3% D5 and 48.7% D6), 190 Fair-quality (57.9% D5, 36.3% D6 and 5.8% D7) and 290 as Poor-quality (12.8% D5, 73.8% D6 and 13.4% D7). The ongoing pregnancy rate was significantly higher for blastocysts ranked as Top compared to Good, Fair and Poor-quality (58.9%, 52.2%, 35.8% and 26.6%, respectively). Miscarriage rates were significantly associated with ranking quality (20.9%, 24.3%, 30.6% and 39.4% for Top, Good, Fair and Poor-quality blastocysts, respectively). We further developed two new classification ranking systems using logistic regression model and XGBoost machine learning model. But the new models provided similar AUC and calibration slope (CS) values as the in-house ranking system (AUC [CS]: 0.604 ± 0.0167 [0.962 ± 0.213] and 0.601 ± 0.0177 [0.949 ± 0.219] vs.0.597 ± 0.015 [0.980 ± 0.230] for logistic regression, XGBoost versus in-house model, respectively). Limitations, reasons for caution The present embryo classification ranking system may not be extrapolated to non-PGT-A cycles. Wider implications of the findings Different grade-ranking systems of blastocysts keep being reported in the different publications. Reaching a consensus to rank euploid blastocysts qualitatively, as the hereby proposed classification, may improve consistency and comparability across different research studies. Crucially, the present ranking-system aids embryo selection for transfer from available pool. Trial registration number not applicable

Formulas of High MW Unknown Compounds from Accurate Mass Differences and Ranking of Best Candidates from First Principles.

The number of possible candidate formulas for high molecular weight unknown compounds (e.g., 7000-8000 Da for common 20-mer oligonucleotides) by high-resolution mass spectrometry is in the order of several hundred thousand even at the highest level of experimental accuracy. In demanding analytical applications involving new chemistries and synthetic routes where little is known about the chemical nature or mechanisms of formation of the unknown compounds (e.g., impurities), the generation of a short list of the most plausible formulas would be highly desirable. Such an approach has been developed in the current work. The concept of mass difference from a reference compound is introduced to simplify the approach and greatly reduce the number of possible formulas. The approach allows for the generation of candidate formulas by both the addition and subtraction of atoms to account for all possible molecular changes from the parent compound. A reduction of 3 orders of magnitude in the number of possible formulas has been achieved by the approach. Ranking of the formulas by the product of the sums of the absolute changes in the total number of all atoms and all heteroatoms in the proposed difference formula successfully ranked the correct formula within the top 10 from a list of 200-250 best candidate formulas. There is a tendency for the impurities to be formed involving the least change in the number of atoms and heteroatoms. ΔfHo and ΔfG'o values can be used as a complementary ranking system of the top candidates. The approach is applicable to unknowns in any other systems of high MW compounds.

Deep Learning-Driven Compiler Enhancements for Efficient Matrix Multiplication

Matrix multiplication is a fundamental operation in many computational fields, requiring optimization to handle increasing data sizes efficiently. In this paper, the implementation of Deep Learning in Matrix multiplication is reviewed, which is considered important nowadays due to the growing complexity of matrix multiplication for gaming and complex programs. The current standard matrix multiplication and the time taken by it on different matrix sizes are described. The Tiled Matrix multiplication, which trims the matrix into various pieces and calculates the product for each piece, and thereafter combines the result, is also described. The times taken by both methods for different matrix sizes were compared. The main idea was to use Deep Neural Networks (DNN) to compare and rank code variants that are obtained in pieces and determine their relative performance. A tournament-based ranking system is used for assigning ranks to the code versions. The effectiveness of these techniques was evaluated on various matrix multiplication operations commonly found in deep learning workloads. Up to 8.844x speedup over the naive implementation for a matrix size of 1024 is achieved by this approach. The results demonstrate the effectiveness of combining compiler optimization techniques and deep learning models in optimizing matrix multiplication.

백제의 한자 수용과 문서 행정 시기

Baekje showed mature Chinese literature, such as writing poetry and literature using Chinese characters, no different from China, in the Sabi era. And document administration was thoroughly implemented. However, The period of Baekje's acceptance of Chinese characters and administration of documents cannot be specified due to a lack of related historical and archaeological materials, or it tends to be considered too late as the period of King Geunchogo, so I wrote this article to look into it. Baekje's acceptance of Chinese characters was examined through whether and when the Baekje constituent group accepted Chinese characters. Baekje was composed of the ruling class of the Buyeo and Goguryeo migrant groups and the under-dominated class of the Han tribes and Ye tribes. The Baekje ruling group split from Goguryeo, which accepted Chinese characters from the time of its founding and started document administration. The Baekje ruling group also knew about Chinese characters and document administration as the ruling group of Goguryeo at one time. Moreover, the Baekje ruling group established a site in the Han River basin after passing through the Chinese county area, where thorough document administration was implemented. The Baekje ruling group had an understanding of Chinese characters and document administration. In addition, the Han and Ye tribes, which were the dominant class of Baekje, were at the center of the Han River basin, and the Jin and Han societies, which were first located in the Han River basin, also accepted Chinese characters through the influence of Chinese residents and exchanges with Hansa-gun during each period of chaos in Chinese history, and knew or participated in the document administration of Chinese county. Baekje accepted and used Chinese characters from the beginning of its foundation because both the ruling and under-dominated groups of Baekje accepted Chinese characters. Since there was no direct record of the beginning of the administration of documents in Baekje, the beginning of the administration of documents was inferred by reviewing historical facts that presuppose the beginning of the administration of documents in Baekje. The establishment of educational institutions and the compilation of history books took place during the reign of King Geunchogo. In addition, Archaeological materials related to inscriptions from the Hanseong period are generally considered to be from the 4th century or later. This can be said to have begun the administration of documents from the time of King Geuncho's reign, as previous understanding. However, looking at the floor stone of the 道(直) name, the period of implementation of Baekje's ritual system and official ranking system, and diplomatic relations with China and counties, there is room to place the beginning of Baekje's document administration in the reign of King Goi. If we limit the period, Baekje document administration began at least from 260, the 27th year of King Goi's reign, when the official rank system was implemented. If interpreted more positively, document administration may have been implemented from the 7th year of King Goi's reign (240), when the military commander was established and the military authority of the central and local regions was reorganized. The reorganization of central and local military authority was carried out by the military command and control of all Baekje armies. This means unification of military government, and this can only be achieved through clear document administration.

Optimisation and improvement of police officers’ special rank types

The article examines the process of reforming the law enforcement system in Ukraine, in particular the creation of the National Police in 2015, and its impact on the rank system of police officers. Special attention is paid to the main changes that occurred as a result of this reform. In particular, such positive aspects as bringing police standards closer to the European level, as well as creating a new police uniform and changing the rank system are highlighted. It is important to note that the reform is aimed at improving the efficiency of law enforcement agencies and their interaction with the public. The article also highlights the problem of the lack of additional ranks and limited career opportunities for junior police officers, which can lead to professional burnout and reduced staff motivation. An analysis of the positive experience of using the extended rank of junior and sergeant ranks in countries such as Canada, Germany, Greece, Spain, as well as the states of Virginia and Delaware in the USA is conducted. The proposed ways of solving the shortcomings in the rank system are aimed at improving the working conditions and motivation of the police personnel. In addition, it is proposed to introduce new ranks for junior and non-commissioned officers of the police in order to stimulate further self-development of professional skills and career growth prospects. In addition, each new rank will have separate duties and functions that will facilitate mutual assistance and cooperation with other police officers. It is also important to emphasise the need to update the design for cadets of higher education institutions with specific learning conditions and lyceums.

A Closer Look at Classification Evaluation Metrics and a Critical Reflection of Common Evaluation Practice

Abstract Classification systems are evaluated in a countless number of papers. However, we find that evaluation practice is often nebulous. Frequently, metrics are selected without arguments, and blurry terminology invites misconceptions. For instance, many works use so-called ‘macro’ metrics to rank systems (e.g., ‘macro F1’) but do not clearly specify what they would expect from such a ‘macro’ metric. This is problematic, since picking a metric can affect research findings and thus any clarity in the process should be maximized. Starting from the intuitive concepts of bias and prevalence, we perform an analysis of common evaluation metrics. The analysis helps us understand the metrics’ underlying properties, and how they align with expectations as found expressed in papers. Then we reflect on the practical situation in the field, and survey evaluation practice in recent shared tasks. We find that metric selection is often not supported with convincing arguments, an issue that can make a system ranking seem arbitrary. Our work aims at providing overview and guidance for more informed and transparent metric selection, fostering meaningful evaluation.

Enhancing hydrological data completeness: A performance evaluation of various machine learning techniques using probabilistic fusion imputer with neural networks for streamflow data reconstruction

The present-day accessibility of streamflow data, particularly in the developing countries, is often marked by a multitude of data shortfalls or distortions. This study investigates the estimate of missing streamflow data using machine learning approaches, including K-nearestneighbour (KNN), Predictive Mean Matching (PMM), Random Forest (RF) and a novel technique of Probabilistic Fusion Imputer with Neural Networks (PROFINN). This study tackles the issue of data insufficiency in such time series considering the inclusion of numerous hydrological parameters by means of RF feature selector, to determine the most significant ones among them. The study explores the efficacy of selected models on various data gaps under different hydrological scenarios, including diverse flow characteristics (mean, high and low flows) and gap lengths (long and short gaps, continuous and discontinuous gaps) and presents a pattern of ranking system that assesses the level of suitability of each technique for various data gaps. This study underscore PROFINN’s remarkable performance across all scenarios, yielding an average Root Mean Square Error (RMSE) of 0.91 and an average Nash-Sutcliffe Efficiency (NSE) value of 0.93 when applied for an intermittent river system of Pamba in Southern Kerala, India. RF follows PROFINN in the imputation of extreme flows as well as long and short gap scenarios. KNN closely follows PROFINN for the imputation of continuous and discontinuous gap scenarios. This study augments the significance of tailored machine learning techniques in enhancing the integrity of hydrological datasets, offering valuable insights for effective decision-making in water resource management and related fields. Furthermore, the success of PROFINN in streamflow data imputation suggests its potential extension to other hydrological research domains, like historical data reconstruction assisting climate change studies and future water resources planning, emphasizing the broader relevance and applicability of this study’s findings.

Construction of a Scoring and Ranking System for Multi-Factor Quantitative Stock Selection Strategy with Daily Rebalancing: An Analysis of SSE A-shares Component Stocks

Construction of a Scoring and Ranking System for Multi-Factor Quantitative Stock Selection Strategy with Daily Rebalancing: An Analysis of SSE A-shares Component Stocks

SISTEM INFORMASI PERANGKINGAN KINERJA SALES PADA PT CAHAYA MOTOR SURABAYA MENGGUNAKAN METODE AHP

In today's modern era, giving awards to sales who have high performance is needed so that sales performance is always good and optimal. At PT Cahaya Motor Surabaya, the ranking system has obstacles, namely the assessment is carried out by the slim leader and the system cannot be seen by all users. For this reason, an information system is needed that can facilitate leaders in ranking sales. One of them is a ranking information system that uses the AHP or Analytical Hierarchy Process method and is website-based. By doing this research, it is hoped that a good ranking monitoring information system will be formed so that the Chairperson can give awards to sales who have the highest prospects easily.

Моделирование отклонений показателей качества объекта от нормы

The concept of a norm on the scale of an object's quality indicator in the context of determining the most preferable object or its state is explored. The notions of permissible and maximum permissible deviations from the norm are uti-lized, with maximum permissible deviations defining the boundaries of the indicator scale. The fundamental version of the norm is represented as a segment on the indicator scale that excludes these boundaries. Point and semi-interval norms (“no more”, “no less”) are considered special cases of the interval norm, where the semi-interval norm reflects the alignment of the original interval norm's boundary with the indicator scale's boundary. For certain indicators, devia-tion in one direction from the norm is not only acceptable but also beneficial. An indicator satisfying this condition is termed optimized, while an indicator with undesirable deviations from both ends of the norm is termed neutral. In the model of belonging to the norm, three classes are distinguished: “norm”, “no more” and “no less” than the norm. Piecewise linear and nonlinear membership functions for these classes are proposed. In the nonlinear model, the boundaries of the interval norm are expanded to acceptable limits, leading to the intersection of membership functions of adjacent classes. Classification into these three classes is conducted separately for neutral and optimized indicators. Interpreting deviations from the norm, both unacceptable and acceptable, necessitates introducing two fictitious clas-ses: “worse than the norm” and “better than the norm.” These classes are formed from the “not less than” and “not more than” classes. To calculate the membership function for each class across all indicators, a weighted average func-tion is employed. Aggregated indices of belonging to the classes “norm,” “better than norm,” and “worse than norm” across all indicators are referred to as indices of stability, development, and deterioration of an object, respectively. These indices are used for a comprehensive assessment of the object, termed an indicator of its condition. An example of analyzing deviations from the norm, implemented in the modified SVIR-M selection and ranking system, is provid-ed.

Automated Faculty Evaluation and Ranking System: Utilizing OCR, NER, and Decision Tree for a Web-Based Evaluation System

Automated Faculty Evaluation and Ranking System: Utilizing OCR, NER, and Decision Tree for a Web-Based Evaluation System

Revisiting the Global Methane Cycle Through Expert Opinion

AbstractAn accurate quantification of global methane sources and sinks is imperative for assessing realistic pathways to mitigate climate change. A key challenge of quantifying the Global Methane Budget (Saunois et al., 2020, https://doi.org/10.5194/essd‐12‐1561‐2020) is the lack of consistency in uncertainties between sectors. Here we provide a new perspective on bottom‐up (BU) and top‐down (TD) methane uncertainties by using an expert opinion analysis based on a questionnaire conducted in 2021. Expectedly, experts rank highest uncertainty and lowest confidence levels in the Global Methane Budget related to natural sources in BU budgets. Here, we further reveal specific uncertainty types and introduce a ranking system for uncertainties in each sector. We find that natural source uncertainty is related particularly to driver data uncertainty in freshwater, vegetation, and coastal/ocean sources, as well as parameter uncertainty in wetland models. Reducing uncertainties, most notably in aquatic and wetland sources will help balance future BU and TD global methane budgets. We suggest a new methane source partitioning over gradients of human disturbance and demonstrate that 76.3% (75.8%–79.4%) or 561 (443–700) Tg CH4 yr−1 of global emissions can be attributed to moderately impacted, man‐made, artificial, or fully anthropogenic sources and 23.7% (20.6%–24.2%) or 174 (115–223) Tg CH4 yr−1 to natural and low impacted methane sources. Finally, we identify current research gaps and provide a plan of action to reduce current uncertainties in the Global Methane Budget.

Chinese Academy of Science Journal Ranking System (2015–2023)

This work examines China's mainstream journal ranking system from the Chinese Academy of Science (CAS), including its history, recent reforms, and current operation, with a particular focus on Physics of Fluids. Using official statistics, this paper analyzes the Physics of Fluids standings in the CAS system from 2015 to 2023. During these years, Physics of Fluids experienced a significant increase in both popularity and impact. Meanwhile, the CAS system underwent substantial reforms, including the newly proposed Advanced edition, which universally boosted fluids journal academic weights in China. Most notably, the brand-new Field Normalized Citation Success Index (期刊超越指数 or FNCSI) offered a new lens through which to assess academic journal impact. Statistics reveal that Physics of Fluids is in a promising position to become the fourth Tier 1 fluids journal to enter any of CAS's Major Categories (after the Annual Review of Fluid Mechanics, Nuclear Fusion, and Plasma Sources Sciences and Technology) and the first-ever non-review-only Tier 1 fluids journal to enter the Engineering and Technology (工程技术 E&T) Major Category. Finally, this paper suggests several directions for Physics of Fluids to direct future endeavors as it enjoys heightening popularity in China.