Morality as performance: Studying the rise of performance measurement in citizen governance through the case of Chinese Social Credit System

Acute kidney injury (AKI) is a common and serious condition associated with prolonged hospitalization, chronic kidney disease, and increased mortality. Early prediction of AKI offers an opportunity to mitigate these adverse outcomes, yet existing models often fail to generalize to the emergency department (ED) setting, particularly for patients discharged directly to the community. We sought to develop and validate machine learning models that predict new or progressive AKI within 72 hours of ED departure, addressing challenges related to missing outcome data for discharged patients. This retrospective, multicenter study of adult patients from 5 EDs within a large health-care system included adult patients with at least 1 serum creatinine measurement during their ED visit. AKI was defined using Kidney Disease Improving Global Outcomes serum creatinine-based criteria, and prediction models relied on demographic, clinical, and laboratory data routinely collected during ED care. Extreme gradient boosting algorithms were trained using 4 approaches to handle missing outcome data: incomplete case exclusion, negative outcome assumption, multiple imputation, and inverse probability weighting. Model performance was evaluated via 10-fold cross-validation and external temporal validation using area under the receiver operating characteristic curve, precision, recall, calibration curve analyses, and measurement of diagnostic performance across a range of risk thresholds. A total of 1,124,017 ED visits between 2017 and 2024 were included in the study; 5.7% (22,093) met AKI progression outcome criteria. The models demonstrated robust predictive performance for any new or progressive AKI (area under the receiver operating characteristics curve, 0.81-0.82) and severe AKI (area under the receiver operating characteristics curve, 0.87-0.88) across validation cohorts. Inverse probability weighting provided a reliable and consistent method for handling missing outcome data, ensuring accurate risk estimates for both hospitalized and discharged patients. Models performed similarly across diverse subgroups and ED sites. Machine learning models trained on routinely collected ED data can provide reliable early predictions of AKI progression, supporting actionable clinical decision making for a broad spectrum of patients. This study advances the real-world usability of such models by expanding their applicability to discharged patients and by enabling estimation of ongoing kidney risk, irrespective of AKI status on arrival.

Population-Based Training (PBT) has the drawback of using fixed, pre-programmed mutation and selection rules to optimize hyperparameters, which are not always flexible across reinforcement learning (RL) tasks. To address this, we introduce LLM-Guided Population-Based Reinforcement Learning (LPBRL), a scalable methodology in which the reasoning capability of Large Language Models (LLMs) is used to manage population evolution dynamically. LPBRL operates through a six-phase cycle in which the LLM analyzes real-time performance measurements from parallel workers and produces adaptive population-update recommendations as a substitute for static rules. In contrast to conventional PBT, and unlike prior LLM-assisted optimization frameworks that typically operate outside the recurrent population loop, LPBRL places language-model reasoning directly inside the selection-mutation stage of training. This enables task-aware hyperparameter adaptation that improves convergence speed and training stability. We evaluated LPBRL on CartPole-v1 with 8 parallel workers over 150 episodes and observed clear gains over conventional PBT, with best- and average-reward convergence improving by 62.5 percent and 68.2 percent, respectively. Although the approach requires access to LLM APIs and compatible RL tooling such as Stable-Baselines3, the results show strong potential for large-scale training workflows in which adaptive hyperparameter control is essential. Overall, the empirical findings support the claim that language-model reasoning can make effective optimization decisions in RL while preserving the practical strengths of population-based training.•Large Language Models are integrated as adaptive decision-makers inside the recurrent population-evolution loop, replacing static task-agnostic mutation and selection rules with context-aware reasoning.Real-time worker metrics, trajectory trends, and LLM-guided hyperparameter adaptation accelerate convergence and improve stability across discrete and continuous-control RL settings.•The methodology provides a reproducible implementation path with structured prompts, deterministic parsing, bounded updates, and compatibility with multiple RL algorithms (PPO, SAC, TD3), supporting large-scale applications.

Behavioural or outside influences? A triple bottom line review of supply chain performance measurement in SMEs within a developing economy

This study presents a systematic literature review aimed at identifying and analyzing Key Performance Indicator (KPI) determination methods in the context of sustainability performance measurement. A total of 56 peer-reviewed articles published between 2011 and 2025 were examined across various sectors including manufacturing, supply chains, organizations, and urban areas. This study employed a Systematic Literature Review approach. This review categorized the literature based on three main dimensions: first, the Triple Bottom Line (TBL) domains of economic, social, and environmental; second, the object level of individual, organization, process, and sector; and fourth, the tools or methods used for KPI determination, including the Balanced Scorecard, Analytical Hierarchy Process (AHP), Performance Prism, and literature-based approaches. The findings indicate that most studies focus on the organizational and supply chain levels, using various approaches for KPI selection and weighting. Although some studies integrate all TBL domains, there is still a gap in comprehensive measurement of social and environmental aspects. This review provides an overview of methodological trends in KPI determination for sustainability and identifies opportunities for developing more contextual and integrated models in future research.

PT Perkebunan Nusantara IV Regional II Kebun Perbaungan operates within a framework that demands the achievement of clearly defined performance targets. Effective implementation of responsibility accounting is expected to improve management performance in conducting company activities, while in turn, effective management performance supports the optimal application of responsibility accounting, enabling the achievement of organizational goals. This study aims to analyze the application of responsibility accounting at PT Perkebunan Nusantara IV Regional II Kebun and PKS Adolina Perbaungan, and to examine how responsibility accounting is utilized as a tool for measuring management performance. A qualitative descriptive methodology was employed, with data collected through observation, interviews, and documentation techniques. The findings reveal that the average realized production costs exceeded the established budget, generating unfavorable variances particularly in palm oil production activities. This indicates that cost control was not yet operating optimally and requires more intensive managerial attention. Nevertheless, the responsibility accounting system functioned effectively as a management performance measurement tool, with evaluations carried out through internal management reports containing production realization data, cost utilization information, and target achievement results, followed by corrective actions for each identified deviation

High quality reference limits for nerve conduction studies (NCS) are essential for diagnosis of neuromuscular disorders. Examining healthy controls to calculate reference limits directly is expensive and time consuming. Indirect methods, including extrapolated norms (E-norms), extrapolated reference values (E-Ref), and multivariate extrapolated reference values (MeRef), use historical hospital data instead. These methods sort the historical measurements in increasing order, creating so-called S-curves, and select normal measurements based on the shape of these S-curves. Current versions of these methods have several limitations, and we aimed to improve them by modifying how they select normal measurements. E-norms, E-Ref, and MeRef were modified with new S-curve selection algorithms. The modified versions of the methods were used to calculate reference limits for common NCS measurements from a large historical database. The results were compared to reference limits calculated from 680 healthy subjects using Youden's J statistic and z-score deviation. The modified methods provided reference limits with Youden's J > 0.8 and z-score deviation < 0.9 for most types of NCS measurements and similar or higher Youden's J than the unmodified methods. In most cases, the methods required at least 500 measurements and fewer than 20% abnormal measurements for good performance. Changing the S-curve selection algorithms improves E-norms, E-Ref, and MeRef. The modifications require a sufficient number and proportion of normal measurements in the historical database. When these prerequisites are met, a combination of indirect methods can be used when developing reference limits from historical patient data.

ABSTRACT This study examines the factors driving the use of performance measurement in homeless service networks, where service delivery is complex and multidimensional. While existing literature acknowledges the importance of performance measurement in public and nonprofit sectors, little is known about its application to collaboration in homeless services. Using data from a national survey of Continuum of Care (CoC) networks that serve people experiencing homelessness in the US, this study investigates how leadership style, shared accountability, and governance structure influence the use of performance measurement. Findings reveal that transformational leadership and shared accountability significantly enhance performance measurement use. This research advances our understanding of performance measurement use in services for individuals experiencing homelessness and offers insights for service leaders seeking to implement data-driven decision-making in collaborative homeless service networks.

Missing data is a challenge in clinical research, especially in real-world data (RWD), where complete case analysis can bias results and reduce power. Ensemble learning approaches like Super Learner (SL) show strong numerical performance for prediction problems, but their use for missing value imputation (MVI) in oncology datasets is unexplored. We sought to develop and evaluate a novel SL-based imputation function that can impute multiple variables and quantify observation-specific uncertainty. We analyzed two independent cohorts of acute myeloid leukemia patients (n = 1641), 546 patients from the University of Colorado and 1095 from an external real-world cohort. The SL-based MVI function includes data processing, predictor selection, binary and continuous variable pipelines, and automatic performance measurement. Ensembles for both binary and continuous variables integrate diverse base learners, such as generalized linear models, random forests, and neural networks, via a meta learner that optimizes predictive accuracy. The binary variable pipeline's SL ensemble was optimized using area under the curve (AUC), while the continuous variable pipeline's SL ensemble was optimized via non-negative least squares. Performance was compared to multiple imputation by chained equations (MICE) using balanced accuracy, F1-score, root mean square error (RMSE), and visualizations. Observation-specific uncertainty was quantified for all imputations of both binary and continuous variables, with both additionally having lower and upper resampling-based potential imputation values. The SL cross-validation loop, SL ensemble trained for imputation, and resampling all supported parallelization. Clinically significant features of the cohorts were selected a priori based on prior literature. In a numerical experiment with 9 clinically important binary features, the proposed MVI function imputed and achieved higher balanced accuracy than MICE for 7/9 variables (mean balanced accuracy 89.04% vs. 80.75%) with comparable performance for the other 2 variables. The continuous variable SL ensemble, across 4 variables, showed an average 24.45% lower RMSE than MICE. On average, the SL ensemble trained for prediction took 145.02s to process for binary targets. This study demonstrates that the SL-based imputation function has improved performance over MICE in high-dimensional RWD while providing novel, observation-level uncertainty quantification.

This research examined the performance of a machine learning algorithm when predicting the surface roughness of tempered steel AISI 1060. Different machine learning algorithms, such as decision tree (DT), random forest (RF), adaptive boosting (ADB), gradient boosting (GB), and extreme gradient boosting (XGB), were optimized by using 10-fold cross-validation and the grid search method. From these optimized models, the decision tree, adaptive boosting, gradient boosting, and extreme gradient boosting were used as base models to develop a more powerful machine learning model called super learner machine learning. The linear regression (LR) was used as a meta-model in developing super learner machine learning. The developed super learner model performance was then validated against all machine learning models used in this research. For performance measurement metrics such as mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R²) has been used. The developed super learner model achieved the highest R² of 99.2% and the lowest MAPE of 2.6% on the test data set when compared with other machine learning models. Further, the SHAP method shows that hardness has the highest effect, followed by feed rate and cutting speed, respectively. Most machine learning approaches are not used practically for user applications, but in this research, a graphic user interface framework called fast, accurate, and intelligent (FAI) frame was developed to predict the surface roughness of tempered steel AISI 1060. This research is used for practical application for any user in industry and for research purposes.

The study examines the inter-relationship between cost, profitability and market valuation with the help of the ARIMA time series model. The study aims to understand the relationship between the cost components and profitability and between profitability and market valuation of a firm. The research seeks to understand the cost, profitability and its effect on the market valuation. The secondary data is obtained from the financial reports from the public domain for the period (2016-2025). Return on Equity (ROE), Return on Assets (ROA) and Net Profit Margin (NPM) are the measures of profitability. The share performance measurements chosen are share price, Earnings Per Share (EPS) and Price-Earnings (P/E) ratio. Trend analysis, correlation analysis and ARIMA modelling are used to detect trends and inter-relationships. The findings reveal that the total salaries and wages, finance costs and depreciation negatively affect profitability, whereas some operating expenses positively affect revenue. Further, profitability has a significant positive impact on market valuation, indicating that profitability boosts investor confidence. The findings indicate that cost efficiency leads to better profitability and hence a better market valuation. Profitability is a link between financial efficiency and market valuation.

Centralized machine learning (ML) models often face challenges related to data privacy, messaging latency and limited internet access in rural areas. To address these issues, a federated learning (FL) based architecture for real-time detection of health and stress in cattle using sensors is deployed. This model enables edge devices such as smart collars, wearable sensors and cameras, to collaboratively learn a global model without sharing raw data. To create a FL-enabled architecture of real-time health and stress detection in cattle with distributed smart farming devices, and to deal with the issue of data privacy, latency and poor internet in rural conditions. In the proposed system, FL is used to allow edge devices, including smart collars, wearable sensors and cameras to jointly train a global model without exchange of raw data. There is the utilization of multimodal time-series data, such as temperature, heart rate, motion trajectories and environmental conditions. The architecture combines LSTM and CNNs to find anomaly and behaviour patterns. Measurement of performance is compared with centralized ML models with real or simulated livestock data. The results of an experiment prove that the FL-based model has better performance than centralized and baseline federated models and this has an accuracy of 93.1%. The model demonstrates better convergence properties and saves a lot in the transmission of the raw data. It is applicable in cattle, early stress detection, illness and abnormal behaviour. The possibility of the use of federated AI systems provides an accurate solution to secure, privacy-preserving and efficient livestock monitoring. The suggested solution can contribute to the improved economic performance and animal welfare in the future since it promotes sustainable smart agriculture and can be integrated with systems like managing irrigation in the future.

The study aimed to assess effects of supplemented co-fermented edible plants and probiotics (AEPP) on growth performance, disease resistance, plasma and rumen metabolites, and bacterial communities in the rumen and feces of pre-weaned calves. Twenty female Holstein calves (7±0.50 d, 41.65±6.20 kg) were randomly assigned to one of two treatments: the control group or the treatment group (30 g/head/day AEPP supplementation). Growth performance, blood, and fecal samples were measured on regular basis. On day 30 of the trial, rumen fluid and fecal samples were collected for multi-omics analysis. Dietary supplementation with AEPP enhanced calf growth and improved disease resistance, as evidenced by a reduced incidence of respiratory disease and diarrhea and a decreased frequency of antibiotic therapy (p<0.05). The treatment group exhibited enrichment of rumen microorganisms Prevotella, Ruminococcus, and Xylanibacter (linear discriminant analysis>2, p<0.05), along with increased activity in beneficial metabolites such as indoleacetic acid, which activated starch and sucrose metabolism and tryptophan metabolism pathway. This intervetion significantly improved average daily gain, feed efficiency, immunoglobulin G, total superoxide dismutase, and glutathione peroxidase activities, as well as significantly reduced levels of tumor necrosis factor-alpha and interleukin-6 (p<0.05), promoting calf growth and health. The elevated abundance of fecal microorganisms, Subdoligranulum and Bifidobacterium, in the treatment group altered fecal pH, short-chain fatty acids, and butyrate proportions (p<0.05). Feeding AEPP improved growth performance, disease resistance, and antioxidant function. It altered the bacterial communities and metabolic profiles in the rumen and feces of preweaning dairy calves, providing a data reference for the use of AEPP in young ruminant production.

In response to large-scale health crises, emergency healthcare infrastructure projects (EHIPs) have been implemented to provide healthcare services rapidly. However, few studies have systematically explored the success of these projects. This study aims to assess the success of EHIPs throughout the whole life cycle using a cloud matter-element model (CMEM). Considering multiple performance indicators that measure the success of EHIPs, a hierarchy model of the project success index is proposed through eight rounds of Delphi surveys. This model comprises 10 key performance indicators (KPIs) that are most appropriate for measuring the success of EHIPs and 20 quantitative metrics aligned with these KPIs across the life cycle. Based on the hierarchy model, the CMEM is applied to evaluate the success of EHIPs, which effectively addresses the fuzziness and randomness inherent in the Delphi process. Two cases were then utilized for verifying the practicality of the proposed project success assessment model. The findings demonstrate that the CMEM-based assessment offers not only significant flexibility but also high accuracy, robustness, and scalability. The CMEM-based assessment seamlessly integrates numerous incompatible indexes and their characteristic values, without being limited by the number of evaluation indexes. Further, sensitivity analyses are conducted to test the robustness of the assessment model and its results. These analyses confirmed that the CMEM is a robust, reliable, and adaptable tool for assessing the success of EHIPs. Theoretically, this study should set an exemplar of aggregating the performance measurement with emergency management and healthcare systems, facilitating an efficient response to major crises. It also provides a novel approach to evaluating multiple-criteria decision-making problems. Practically, this study should enable project management teams to identify and improve weak areas, ensuring continuous success in the EHIP-related domain.

The advancement of two-dimensional (2D) photocatalysts is significantly hampered by the inability to perform quantitative, single-entity performance measurements, a capability already established for electrocatalytic counterparts. Here, using chemically synthesized, single-crystalline 2D BiOCl as a model catalyst, we report, for the first time, the accurate measurement of the photocatalytic hydrogen evolution rate at the single-catalyst level. More importantly, by integrating liquid-phase atomic force microscopy and total internal reflection fluorescence microscopy, we reveal that active sites on 2D BiOCl flakes are edge-enriched and spatially localized. Guided by this spatial distribution, we activated the basal plane of the 2D catalysts through plasma etching and elucidated the mechanism of this performance enhancement using density functional theory calculations. This work demonstrates that, by resolving the structure-activity relationship at the single-entity level, we can unlock the full catalytic potential of 2D materials, paving the way for the rational engineering of highly efficient photocatalysts.

Despite the growing importance of Environmental, Social, and Governance (ESG) performance in shaping brand perception and consumer trust, limited empirical evidence exists on how ESG indicators translate into measurable digital consumer engagement outcomes, particularly in ethically driven markets such as the vegan food sector. This study addresses this gap by examining how ESG performance translates into digitally observable consumer engagement and frames this relationship as a strategic performance measurement and decision-support problem. Building on the sector’s reliance on ethical positioning, trust, and online visibility, we integrate ESG indicators with digital marketing and web analytics metrics (e.g., traffic and engagement proxies) for a panel of five leading vegan food firms [Nestlé SA (Vevey, Switzerland), Kellanova (Chicago, IL, USA), Beyond Meat Inc. (El Segundo, CA, USA), Danone SA (Paris, France), and Conagra Brands Inc. (Chicago, IL, USA)], using data from the Semrush web analytics platform and the Eikon Refinitiv ESG database for the period January–December 2024. We employ a mixed-method design combining descriptive analytics with correlation analysis and simple linear regression to estimate the direction and strength of ESG–digital performance links, and we extend inference through Fuzzy Cognitive Mapping (FCM) using the MentalModeler platform to simulate “what-if” scenarios that support managerial foresight under digital uncertainty. Results indicate that stronger ESG profiles are associated with more favorable digital outcomes, with specific ESG mechanisms (e.g., human-capital and environmental initiatives) aligning with deeper engagement signals. The FCM scenarios further suggest that coordinated ESG improvements can amplify digital traction and reinforce sustainable brand growth. The proposed framework contributes to strategic management by operationalizing an ESG-enabled digital performance measurement system and a lightweight Decision Support System (DSS) that can guide resource allocation, KPI monitoring, and risk-aware positioning in sustainability-oriented markets.

This study systematically investigated the combined effects of minor additions of Sr, Fe, and Mn, together with T6 heat treatment, on intermetallic phase evolution and fatigue performance of a secondary Al‐Si‐Cu‐Mag cast alloy. The alloy was obtained by melting end‐of‐life motor vehicle engine cylinder heads. Novelty of this work is based on the proposed recycling approach and the establishment of a direct correlation between intermetallic morphology modification and fatigue life in recycled cylinder head alloys. The base alloy exhibited coarse, needle‐like eutectic silicon particles and Fe‐rich intermetallic compounds such as Al 2 Cu and AlCuNi. Addition of 0.02 wt.% Sr transformed silicon morphology into refined fibrous structure, while Fe and Mn additions modified the formation of Fe‐rich phases into less harmful α‐intermetallic phases. After T6 heat treatment, silicon particles became rounded and fragmented, and Al 2 Cu phases fully dissolved. Among as‐cast alloys, Sr‐modified variant demonstrated highest fatigue life (610,399 cycles), confirming the beneficial role of microstructural refinement in delaying crack initiation. The findings demonstrated that controlled alloy modification combined with heat treatment enabled recycled Al–Si–Cu–Mg alloys achieve enhanced fatigue resistance. These results provide practical guidelines for producing reliable automotive powertrain components and contribute to sustainable materials engineering.

Rebranding often becomes a strategic imperative, not merely an option, especially when a brand faces massive internal crises and market challenges. This qualitative study examines the rebranding strategy employed by Babe Bambang Pranoto, founder of PT Kutus-Kutus Herbal, in transforming the brand from "Kutus Kutus" to "Sanga Sanga". Through an in-depth analysis of autobiographical narratives, social media content, and public interviews, this research reveals how a defensive-progressive rebranding approach was implemented. The findings indicate that this strategy was constructed and communicated in social media as a means of restoring consumer trust, particularly through authentic storytelling and the strategic use of digital platforms, rather than through empirically measured consumer responses. The brand's repositioning into the premium segment, underpinned by locally resonant cultural symbolism articulated as a branding narrative, and integrate cultural symbolism in support of premium market repositioning. This study provides a valuable conceptual and analytical insights for understanding crisis-driven rebranding, especially for heritage brands navigating legitimacy, authenticity, and identity reconstruction in the digital era. These conclusions are derived from interpretive narrative and communication analysis rather than direct measurement of consumer attitudes or market performance.

This study investigated the factors influencing the implementation of Results-Based Monitoring and Evaluation (RBM&amp;E) systems in public secondary schools of Rulindo District, Rwanda. Specifically, the study examined the influence of management support, organisational capacity, and baseline data on the effectiveness of RBM&amp;E systems. The research is significant as it provides insights into how monitoring and evaluation practices can be strengthened to improve decision-making, accountability, and school performance. A mixed-methods approach was adopted, combining both quantitative and qualitative data collection techniques. Data were gathered through surveys, interviews, and observations involving school administrators, teachers, and students. The target population consisted of 185 respondents, from whom a stratified random sampling technique was used to ensure fair representation across administrative staff, teachers, and students. The reliability, validity, credibility, and dependability of research instruments were tested through a pilot study conducted at Kiyanza Secondary School. Secondary data from institutional reports and government policy documents complemented the primary data. The findings revealed that limited technical expertise, inadequate funding, and resistance to change are key barriers to the effective implementation of RBM&amp;E systems. Additionally, insufficient training and unclear performance indicators were found to hinder the proper use of monitoring and evaluation processes. However, the study also established that when effectively implemented, RBM&amp;E enhances transparency, supports evidence-based decision-making, and improves institutional performance. The study concludes that strengthening organisational capacity, improving management support, and ensuring the availability of reliable baseline data are critical for effective RBM&amp;E implementation. It recommends capacity-building initiatives, increased funding, and the development of clear performance measurement frameworks to enhance RBM&amp;E practices in public schools.

ABSTRACT The study evaluated KPI-based operational performance of industrial robotics at Arrowtek Robotic Pvt Ltd, Chennai. Data were collected from 120 employees using a structured questionnaire. The results showed that 95% of respondents agreed that KPIs are clearly defined, and maintenance frequency was the most tracked metric. The study found that industrial robotics improved productivity and accuracy. The Chi-Square test showed no significant relationship between demographic factors and operational performance. This means that the benefits of automation are equally recognized by all employees. The study concludes that clear KPI standards, real-time monitoring, and data-driven decisions can improve efficiency and help the company maintain a competitive advantage. Keywords: Industrial Robotics, Key Performance Indicators (KPI), Operational Performance, Arrowtek Robotic Pvt Ltd, Performance Measurement, Automation Efficiency.