Performance Indicators Research Articles

Social protection comparison across the world: a study of fiscal, structural and institutional performance discrepancies

PurposeSocial protection states the public-mandated (strategies and programmes) to address the vulnerability and risk among poor and near-poor households. Social protection must not only help people meet their basic needs but also contribute to the long-term well-being and broader societal goals of equity, social justice and empowerment. The role of social protection in achieving these goals has not been adequately documented. This paper examines the performance of the social protection mechanism from the perspective of the regional structural and institutional performance indicators. Social protection outlay dynamics are synthesized in the structural and institutional conducts.Design/methodology/approachA world regional comparability is made by considering the panel dataset for the time interval 1995–2020. In empirics, the unconditional and conditional ranks are constructed, and regression analyses are made subject to (1) the fiscal constraint of social protection, (2) structural performance indicators and (3) the institutional performance indicators of the targeted regions across the globe. The fully modified-ordinary least square (FM-OLS) method is used to construct the ranks.FindingsThe rank analysis demonstrates that the developed regions (i.e. Europe, Central Asia and North America) have relatively good welfare standards and fiscal capacity for social protection drives compared to the developing/underdeveloped regions (i.e. Sub-Saharan Africa and South Asia). The structural/institutional performance indicators have shown their significance in determining/utilizing their budget for social protection and maintaining welfare standards. Interestingly, most developed regions are relatively reluctant to maintain the structural/institutional performance for the determination/execution of the funds for social protection as compared to the developing and underdeveloped regions.Practical implicationsIn policy discourse, this research suggests that governments must make some market operations to enhance their regions’ structural and institutional performance to get better outcomes of social protection spending in the form of attractive welfare standards.Originality/valueMultiple studies have been done considering the social protection dynamics at nano, micro and macro levels, while this study considered the mega dataset across the globe to analyse the social protection dynamics in consideration of structural and institutional performances.

Does diversity matter? Explaining the effects of team diversity on team performance using data from a student’s business simulation game

Purpose The purpose of this study is to determine the impact of different diversity criteria (age and gender) on team performance using a business simulation game, where performance is operationalized by market valuation (Tobin’s Q) and profitability (cumulative net income). Design/methodology/approach The authors collected data of a business simulation game at a German public university (128 student groups and 645 individuals). Using multiple linear regression models, they investigated the impact of the different diversity criteria on team performance. Findings The authors found no significant effects of gender diversity on market valuation and profitability. However, the results of this study indicate a positive relationship between age diversity and both performance variables. Originality/value This paper contributes to the debate on the relationship between diversity and team performance by using data from a business simulation game. This study adds value by considering different diversity factors, objective performance indicators and the endogeneity of team formation (the seminar leaders randomly assign students to teams) which has not been applied in similar studies yet. Because of the setting of a simulated business game, the results could also be applied to the real economy where we observe working teams every day.

The Efficacy of Open Performance Review and Appraisal System on Teachers’ Work Performance in Tanzania. A Case of Selected schools of Vwawa, Iyula and Msangano Secondary Schools in Mbozi District

This study mainly sought to determine the effectiveness of the Open Performance Review and Appraisal System (OPRAS) on Teachers' work performance in the selected secondary schools of Vwawa, Iyula and Msangano in Mbozi district council. The study was also driven by the following main objective, which includes; how the practice of OPRAS has improved the work performance of secondary school teachers in Mbozi district council. A mixed research approach both qualitative and quantitative approach with 93 respondents was employed. Questionnaires and interviews were used to obtain data. Quantitative data was analyzed through descriptive statistics where inferences were presented in the form of frequencies and percentiles. Qualitative information was also analyzed through thematic and content analysis and presented in the form of concepts and narrations. Although Mbozi district has been effectively implementing OPRAS for the past 15 years, active data show that the district has a stable progression performance rate of 30.1 % since 2018 to date. The key variable that was tested was increased teacher commitment. Considerably, this has been established due to constant growth rates of indicators of performance against the performance of teachers from 2018 to 2023. The study recommended that the government should set and engage a realistic budget for training, and empowerment workshops and highly capacitate the Office of the public communications for the purpose of intensifying communication and feedback in public organizations including secondary schools

Risk Management As A Determinant Of Indonesian Banking Financial Performance: A Systematic Literature Approach

This study aims to understand how risk management affects financial performance in the Indonesian banking sector. In a business environment full of uncertainty, especially in the banking sector, risk management is key to ensuring financial stability. Systematic Literature This review (SLR) analyzes the findings of articles published between 2016 and 2022, focusing on the main risks faced by banks: credit risk, market risk, liquidity risk, and operational risk. The selected articles were obtained from leading journal sources in the fields of economics and business. The literature selection process includes the stages of identification, screening, and thematic analysis to identify trends and correlations between risk management and financial performance indicators such as Return on Assets (ROA) and Return on Equity (ROE). The results of the study indicate that credit risk and operational risk have the most significant impact on financial performance, while market and liquidity risks show varying effects depending on the market situation and internal conditions of the bank. These findings underline the importance of a proactive approach to risk management, including the implementation of integrated risk mitigation policies and the use of effective internal controls. Thus, risk management not only serves as a safeguard against potential losses but also as a support for business sustainability. This study recommends further research to explore new risks such as cyber threats and regulatory changes, which are increasingly relevant in the context of the modern banking industry. These results are expected to provide insights for financial practitioners and banking leaders in improving the resilience and financial performance of their companies.

Exploring statistical and machine learning methods for modeling probability distribution parameters in downtime length analysis: a paper manufacturing machine case study

Manufacturing companies focus on improving productivity, reducing costs, and aligning performance metrics with strategic objectives. In industries like paper manufacturing, minimizing equipment downtime is essential for maintaining high throughput. Leveraging the extensive data generated by these facilities offers opportunities for gaining competitive advantages through data-driven insights, revealing trends, patterns, and predicting future performance indicators like unplanned downtime length, which is essential in optimizing maintenance and minimizing potential losses. This paper explores statistical and machine learning techniques for modeling downtime length probability distributions and correlation with machine vibration measurements. We proposed a novel framework, employing advanced data-driven techniques like artificial neural networks (ANNs) to estimate parameters of probability distributions governing downtime lengths. Our approach specifically focuses on modeling parameters of these distribution, rather than directly modeling probability density function (PDF) values, as is common in other approaches. Experimental results indicate a significant performance boost, with the proposed method achieving up to 30% superior performance in modeling the distribution of downtime lengths compared to alternative methods. Moreover, this method facilitates unsupervised training, making it suitable for big data repositories of unlabelled data. The framework allows for potential expansion by incorporating additional input variables. In this study, machine vibration velocity measurements are selected for further investigation. The study underscores the potential of advanced data-driven techniques to enables companies to make better-informed decisions regarding their current maintenance practices and to direct improvement programs in industrial settings.

Evaluation of barcode-assisted medication preparation technology for liquid medication doses

Abstract Disclaimer In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Purpose The purpose of this study was to evaluate the outcomes associated with barcode-assisted medication preparation (BCMP) technology and practice workflows for oral syringe dose preparation in a health-system pharmacy department. Methods This evaluative study was conducted at a flagship quaternary academic medical center. An electronic medical record (EMR)–integrated BCMP workflow was implemented in the central pharmacy operational area to enhance the safety of oral syringe dose preparation. The primary endpoints assessed compliance with BCMP implementation and the rate at which potential preparation errors were identified. The secondary endpoints evaluated operational markers of dose preparation batching, information technology enhancement needs, and medication waste avoidance. Results A 95% rate of compliance with the BCMP workflow was observed over 2 years. The composite near-miss detection rate improved from year 1 to year 2 of implementation (0.89% vs 0.94%). The composite rate was influenced by increased yearly compliance with BCMP (93.8% vs 95.3%). A total of 176,679 preparations were reviewed in the 2-year period, including 81,240 in year 1 and 89,638 in year 2. The rate at which orders were rejected by pharmacists decreased over time (0.26% in year 1 vs 0.24% in year 2). Of the 1,005 wrong ingredient warnings, only 4 were overridden; in all other instances, the order was rejected at pharmacist checking due to use of an incorrect product in the preparation history. Wrong ingredient warnings led to canceled preparations in 96.1% of alert instances. Conclusion EMR-integrated BCMP technology aligned with safety efforts in the oral syringe dose preparation process reduced potential waste of medications and allowed insight into operational performance and volume indicators.

Developing innovative financial products for sustainable economic growth

In today’s competitive landscape, technology firms increasingly turn to advanced data analytics as a strategic tool for boosting revenue growth and operational efficiency. This review explores how leveraging sophisticated analytical techniques can enable these organizations to extract actionable insights from vast datasets, thereby enhancing decision-making processes and driving business performance. Advanced data analytics encompasses a range of methodologies, including predictive analytics, machine learning, and big data analysis, which collectively empower firms to identify trends, optimize resources, and tailor products to meet evolving customer demands. One of the primary benefits of utilizing advanced data analytics is the ability to enhance revenue growth through improved customer targeting and segmentation. By analyzing customer behaviors and preferences, technology firms can develop personalized marketing strategies that resonate with their target audiences, resulting in increased conversion rates and customer loyalty. Moreover, data analytics facilitates the identification of new market opportunities, enabling firms to innovate and expand their product offerings effectively. Operational efficiency is another critical area where advanced data analytics proves invaluable. By monitoring key performance indicators (KPIs) and operational metrics, technology firms can streamline their processes, reduce costs, and minimize waste. Predictive analytics, in particular, allows organizations to anticipate potential disruptions in their operations, enabling proactive measures to mitigate risks and maintain smooth workflows. This capability not only enhances productivity but also fosters a culture of continuous improvement. Additionally, advanced data analytics supports informed strategic planning by providing insights into market dynamics and competitive positioning. Firms can leverage these insights to make data-driven decisions, allocate resources effectively, and align their business strategies with market trends. In conclusion, advanced data analytics serves as a catalyst for revenue growth and operational efficiency in technology firms. By embracing these analytical techniques, organizations can harness the power of data to drive innovation, improve customer engagement, and achieve sustainable competitive advantage. As technology continues to evolve, the strategic implementation of advanced data analytics will be essential for firms seeking to thrive in an increasingly data-driven world. Keywords: Advanced Data Analytics, Revenue Growth, Operational Efficiency, Technology Firms, Predictive Analytics, Customer Targeting, Strategic Planning.

Corporate Sustainability Reporting and Financial Performance: An Empirical Analysis of Indian Listed Companies

This empirical analysis examines the relationship between corporate sustainability reporting and financial performance among Indian listed companies. In recent years, sustainability has gained prominence in corporate strategy due to increasing regulatory requirements, investor interest, and stakeholder expectations. The study explores how sustainability reporting, as disclosed through corporate sustainability reports (CSR), affects the financial performance of companies. Using a sample of Indian companies listed on major stock exchanges, the study analyzes data on sustainability disclosures, financial performance indicators (such as return on assets, return on equity, and market valuation), and various control variables. The analysis employs statistical methods, including regression models, to identify the correlation and potential causal linkages between sustainability reporting and financial performance. The findings suggest a positive relationship between comprehensive sustainability reporting and financial performance, indicating that companies with better sustainability practices tend to experience higher financial returns and market valuation. The results also highlight the role of regulatory frameworks, industry-specific factors, and company size in shaping the strength of this relationship. This study contributes to the growing body of literature on corporate sustainability by providing insights specific to the Indian context, where sustainability reporting practices are evolving. It suggests that companies engaging in transparent and detailed sustainability reporting may achieve not only environmental and social benefits but also financial gains, making sustainability a strategic priority for long-term value creation.

Reliability optimization through soft digital twins

This paper presents a framework for developing a “soft digital twin” (SDT) for reliability optimization through modeling and simulation. The focus is on demonstrating the effectiveness of the SDT in modeling maintenance task processes within mission-critical facilities, which is particularly essential for reliable operations in critical infrastructure such as nuclear facilities. The need for efficient completion of maintenance tasks (MTs) to ensure high reliability and minimal downtime underscores the challenge faced by managers, who desire a predictive tool akin to a “crystal ball” for optimal resource configuration in the face of uncertainties from equipment failures, staffing issues, and supply chain disruptions. The proposed SDT functions as this predictive tool, leveraging the simulation’s versatility to provide insights into resource configurations and staff planning. This work extends the prior research by incorporating additional model validation, sensitivity testing, and analysis of the impact of various resource changes on the system. It employs a combination of data-driven frameworks and stochastic modeling methods to construct an adaptive SDT capable of accommodating changes in system behavior. The work provides a framework for the construction of SDT, testing and validation of its results, and its application to a case study of a mission-critical facility focused on replication and then optimizing of two key performance indicators based on human resource mixtures. Initial findings suggest that the SDT yields reliable results comparable to retrospective test datasets, offering the potential to minimize MTs’ time in the system while maximizing throughput within specified time frames through scenario experimentation and optimization.

Sustainable Synchronization of Truck Arrival and Yard Crane Scheduling in Container Terminals: An Agent-Based Simulation of Centralized and Decentralized Approaches Considering Carbon Emissions

Background: Container terminal congestion is often measured by the average turnaround time for external trucks. Reducing the average turnaround time can be resolved by controlling the yard crane operation and the arrival times of external trucks (truck appointment system). Because the truck appointment system and yard crane scheduling problem are closely interconnected, this research investigates synchronization between the approaches used in truck appointment systems and yard crane scheduling strategies. Rubber-tired gantry (RTG) operators for yard crane scheduling operations strive to reduce RTG movement time as part of the container retrieval service. However, there is a conflict between individual agent goals. While seeking to minimize truck turnaround time, RTGs may travel long distances, ultimately slowing down the RTG service. Methods: We address a method that balances individual agent goals while also considering the collective objective, thereby minimizing turnaround time. An agent-based simulation is proposed to simulate scenarios for yard crane scheduling strategies and truck appointment system approaches, which are centralized and decentralized. This study explores the combined effects of different yard scheduling strategies and truck appointment procedures on performance indicators. Various configurations of the truck appointment system and yard scheduling strategies are modeled to investigate how those factors affect the average turnaround time, yard crane utilization, and CO2 emissions. Results: At all levels of truck arrival rates, the nearest-truck-first-served (NTFS) scenario tends to provide lower external truck turnaround times than the first-come-first-served (FCFS) and nearest-truck longest-waiting-time first-served (NLFS) scenario. Conclusions: The decentralized truck appointment system (DTAS) generally shows slightly higher efficiency in emission reduction compared with centralized truck appointment system (CTAS), especially at moderate to high truck arrival rates. The decentralized approach of the truck appointment system should be accompanied by the yard scheduling strategy to obtain better performance indicators.

Design of a Novel Macro-Micro Integrated Brain Surgery Robot Based on Modular Parallel Mechanisms

The advancement and development of medical surgical robots have provided new technological support for brain surgery and neurosurgical procedures, improving the reliability of highly complex and precise surgeries. In turn, this urges the design and development of novel surgical robots to possess higher precision, stability, and enhanced motion capabilities. In response to this practical demand, this paper introduces a macro-micro integrated medical brain surgery robot system based on the concept of modular PMs (parallel mechanisms), which have a total of 13 active DOFs (degrees of freedom). This system divides the motion process of brain surgery into a large-scale macro-motion space and a small-scale high-precision motion space for design and planning control. The introduction of the design concept that combines multiple modular parallel sub-mechanisms has brought a significant level of decoupling characteristics to the mechanism itself. A comprehensive introduction and analysis of the surgical robot are provided, covering aspects such as design, kinematics, motion planning, and performance indicators. To address the pose allocation and coordination of motion between the macro platform and the micro platform, a pose allocation algorithm based on the decoupling and non-decoupling characteristics in various dimensions of the macro-micro platform is proposed. The designed measurement experiments have demonstrated that the repeatability in positioning accuracy of the macro and micro platform reaches the level of micron and submicron respectively. Practical experiments of motion control and simulated brain electrode implantation validate the excellent performance and stability of the entire surgical robot system. This research contributes innovative insights to the development of medical surgical robot systems, particularly in the domain of mechanism design.

Implementation of Parallel Optimization Algorithms for NLP: Mini-batch SGD, SGD with Momentum, AdaGrad Adam

Abstract. With the rapid development of machine learning technology, optimization algorithms and optimizers have become key to the development of related technologies contemporarily. Models need the help of optimizers to meet other performance indicators while saving computing resources. This research focuses on comparisons between optimizers, in the context of text sentiment classification tasks. The optimizers mainly compared in this article are mini batch SGD, momentum SGD, Adagrad and Adam. Through comparative experiments, it was found that SGD and its variants have a high dependence on the initial learning rate setting, while the performance of Adagrad and Adam is relatively balanced. Although the training time of Adagrad is shorter than that of Adam, its principal formula has flaws, which are not reflected in this task. The conclusions drawn in this article through comparison can point out the advantages and disadvantages of each optimizer, and can help realize better optimizers in subsequent research.

An evaluation of the Hawthorne effect in a clinical trial of trauma care in Ghana

AbstractIntroductionHawthorne effect refers to changes in clinician behavior in response to being observed. It has been minimally evaluated in trauma and surgical research, especially in low‐ and middle‐income countries (LMICs).MethodsWe used data from the first (control) phase of a randomized clinical trial in Ghana (2020‐21). Trained observers were stationed in emergency units in eight non‐tertiary hospitals to observe the achievement of key performance indicators (KPI) (KPIs) of trauma care. We hypothesized that the observers' presence would create a Hawthorne effect, resulting in clinicians (doctors, nurses) performing above their usual level, with performance highest immediately after the observers were introduced, and with performance then declining over time toward the usual baseline.ResultsWe enrolled 2067 injured patients. Achievement of 13 out of 16 KPIs declined significantly over the 14 months after the introduction of observers. For example, airway assessment declined from 77% initially to 60% at the end of the phase (p < 0.001). Chest examination declined from 66% to 31% (p < 0.001). Distal pulses checked declined from 45% to 7% (p < 0.001). The slope of decline in KPI achievement ranged from 0.8%/month (mobility assessed) to 15.9%/month (distal pulses checked).ConclusionsThe decline in KPI achievement over time is consistent with the hypothesis that clinicians performed above their usual level just after the observers were introduced, with performance then declining toward the usual baseline. This study is among the first to provide quantitative data on the Hawthorne effect for trauma research in LMICs. These data are useful for the design of future observational studies and clinical trials.

Evaluating Software-Based Methods for Replacing Manual Collection of Midnight Totals: A Study from Sri Lanka

The manual collection of midnight totals in hospitals has long been used to calculate key performance indicators such as bed occupancy rates. Despite technological advancements, many hospitals still rely on labour-intensive manual methods prone to inaccuracies at multiple stages. This study introduces and evaluates the Smart Hospital Metrics Calculator, a tool developed to automate the calculation of midnight totals and same-day discharges using patient admission and discharge dates from the electronic Indoor Morbidity and Mortality Return (eIMMR). Both manual and software-based methods exhibited inaccuracies, but errors in software-based calculations were primarily due to issues with entering dates, which can be mitigated by improving workflows and refining the eIMMR system. In contrast, improving manual methods requires addressing challenges across multiple levels. Data from three hospitals were analysed, and while the results do not yet demonstrate these outcomes, it is postulated that with enhanced date entry processes, software-based methods, such as the Smart Hospital Metrics Calculator, could achieve near-zero inaccuracies. These findings suggest that transitioning to this automated approach has the potential to streamline data collection, enable real-time monitoring, and improve hospital data management—without requiring additional workload or significant investment. If successfully implemented, this change could represent a seamless transformation, realizing benefits without disrupting current operations.

Evaluation of the Intensive Acute Flaccid Paralysis Surveillance System in Ghana: Post the Switch from tOPV to bOPV

The Global Polio Eradication Initiative was adopted by Ghana in 1996, and through robust AFP surveillance was able to interrupt the circulation of wild poliovirus in 2008. However, the country suffered vaccine-derived poliovirus type 2 outbreaks in 2019 and 2022. We conducted a retrospective analysis of all AFP surveillance data received by the polio program in Ghana from 2018 to 2022. An analysis of the WHO performance indicators for evaluating a surveillance system was conducted using Epi Info 3.5.4 and Microsoft Excel. Of the 4832 cases investigated, 56.3% were males, 71.1% comprised children aged 5 years and below, and more than half (65.2%) had received a maximum of three doses of OPV. Over 77% (3028) had a fever at the onset of paralysis, and 67.8% had paralysis progression within 3 days. The non-polio AFP rate of ≥2 and the stool adequacy rate exceeded the target of ≥80% in nearly every study year. The proportion of non-polio enteroviruses isolated surpassed the target of ≥10% in all years except 2018. The AFP surveillance system in Ghana is sensitive and representative. Though the surveillance became more intensive and proactive during the outbreak, the system needs to focus on improving the completeness of the data as well as the timeliness of the arrival of stool specimens within 3 days of collection.

The barriers and enablers to completing the RPS core advanced pharmacist credentialing programme via the CPPE/RPS pilot programme

Abstract Introduction The RPS and CPPE launched a pilot programme in March 2023 to support 300 pharmacists to evidence their advanced practice via an e-portfolio aligned to the RPS Core Advanced curriculum1 in a 12-week period. Successful pharmacists are credentialed as Advanced Pharmacists. The pilot involved CPPE education supervisors (ESs) support, an online learning platform and small group learning and support. Aim To ascertain barriers and enablers to completing an advanced e-portfolio and to identify how to improve the on-going programme. Methods The invitation to complete the anonymous survey in May 2024 was administered via www.onlinesurveys.ac.uk and developed linking to key performance indicators around education supervision support, to understand why candidates were deferring cohorts and to ascertain additional support accessed by candidates. Ethical approval was not required as study is service evaluation. Candidates who responded included those who successfully submitted their e-portfolio ‘submitted candidates’ n=10, candidates who are resubmitting their portfolio after not passing ‘resubmission candidates’ n=8, pharmacists currently completing their portfolio ‘current candidates’ n=44 and those who started but left without submitting ‘attrition candidates’ n=13. Results All submitted candidates scored ES support as very good/excellent; 60% agreed strongly they would not have submitted their portfolio without this support. Each cohort had two small-group tutorials where candidates accessed peer support and joined WhatsApp groups. Only 38% of resubmission candidates, compared to 80% of portfolio submitted candidates attended both group tutorials. Resubmission candidates were less likely to access additional peer support (38% for resubmission candidates vs 60% for submitted candidates) or to join the cohort WhatsApp group (75% for resubmission candidates vs 90% for submitted candidates). Resubmission candidates rated the remedial SMART action planning support highly. Submitted candidates would have found it easier to complete their portfolio with more comprehensive (60%) and timely responses (70%) from their collaborators. This compares to 55% and 59% of current candidates recognising the same issues. Submitted candidates wanted more allocated time during working hours to support completion of the portfolio (60% rising to 80% for current candidates). There is a notable increase for current candidates wanting a longer period to complete their portfolio (75% compared to 40% of submitted candidates). Half of submitted candidates (50%) reported spending more than 5 hours (average) each week on their portfolio compared to 18% of current candidates. Top reasons for attrition include too busy with work, family responsibilities, personal health challenges and no support from the workplace. Candidates across all groups suggested programme improvement ideas including more evidence examples, making the RPS platform less difficult to navigate and use, and more education supervisor support. Motivating factors for completion include recognition of advanced level working and preparation for the consultant portfolio. Conclusion Enablers include education supervision, CPPE resources/facilitated support events, peer support, WhatsApp groups, recognition and career progression. Key barriers include needing more time and lack of dedicated time at work to complete e-portfolio, lack of timely and comprehensive support from collaborators and finding the RPS e-portfolio platform difficult to navigate. Limitations – low numbers and response rate. Data collection is on-going. Reference 1. Royal Pharmaceutical Society, Core Advanced Pharmacist Curriculum, 2022 https://www.rpharms.com/Portals/0/Credentialing/RPS%20-%20Core%20Advanced%20curriculumFINAL.pdf?ver=8gc3s2aE9t1nZN37CSU7Lw%3d%3d (Accessed 28 May 2024)

Development and application of ex-ante performance evaluation framework for health projects in China

China National Health Commission issued an implementation plan for budget performance management in health projects in April 2021, which emphasizes all health projects must be evaluated in advance prior to implementation. The study aims to develop an ex-ante performance evaluation framework for health projects and formulate a self-assessment report template. The study also attempts to gain insights into the ex-ante performance of health projects in China, identify issues and explore the factors that influence the ex-ante performance of health projects and make suggestions about improvement strategies. The framework was established by using a framework development methodology. To develop the framework, the findings from existing research were pooled and organized to create indicators and key points. The preliminary ex-ante performance evaluation framework was refined, validated and tested by the experts. Weight analysis was conducted by using the Analytic Hierarchy Process. Then the indicator system has been transformed into a project ex-ante performance self-assessment report template. Samples of 698 municipal health projects in China from 2021 to 2022 were analyzed. We invited experts to complete the scoring task by using our ex-ante performance evaluation framework. The ex-ante performance evaluation framework primarily examines five aspects, the necessity of the project, economy of input, rationality of performance indicators, feasibility of implementation plans, and compliance of financing. The overall ex-ante performance evaluation score for the sample projects was (91.19 ± 8.02). The sub-scores for necessity of project were (14.74 ± 1.85), economy of input was (34.45 ± 2.43), reasonableness of performance indicators was (8.27 ± 2.28), feasibility of implementation plan was (23.99 ± 3.84), and compliance of financing was (9.75 ± 1.47). Factors such as whether it was an epidemic prevention and control project, the project budget, project content, project objective, and project implementation organization were all influencing the ex-ante performance of health projects (P < 0.05). We have developed a ex-ante performance evaluation framework for health projects and established a project ex-ante performance self-assessment report template. They can offer a sufficient reference for project process management. Project implementation organization should elevate the awareness and proficiency of project managers regarding budget performance management. Government should improve the ability of ex-ante performance evaluation, and eliminate ineffective projects through ex-ante performance evaluation.

Machine learning for epithelial ovarian cancer platinum resistance recurrence identification using routine clinical data

BackgroundMost epithelial ovarian cancer (EOC) eventually develops recurrence. Identification of high-risk patients can prompt earlier intervention and improve long-term outcomes. We used laboratory and clinical data to create models based on machine learning for EOC platinum resistance recurrence identification.MethodsThis study was designed as a retrospective cohort analysis. Initially, we identified 1,392 patients diagnosed with epithelial ovarian cancer who underwent platinum-based chemotherapy at Yunnan Cancer Hospital between January 1, 2012, and June 30, 2022. We collected data on the patients’ clinicopathologic characteristics, routine laboratory results, surgical information, details of chemotherapy regimens, and survival outcomes. Subsequently, to identify relevant variables influencing the recurrence of platinum resistance, we screened thirty potential factors using two distinct variable selection methods: Lasso regression and multiple logistic regression analysis. Following this screening process, five machine learning algorithms were employed to develop predictive models based on the selected variables. These included decision tree analysis (DTA), K-Nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and eXtreme gradient boosting (XGBoost). The performance of these models was compared against that of traditional logistic regression. To ensure robust internal validation and facilitate comparison among model performance metrics, a five-fold cross-validation method was implemented. Key performance indicators for the models included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and average accuracy. Finally, we will visualize these models through nomograms, decision tree diagrams, variable importance plots, etc., to assist clinicians in their practice.ResultsMultiple logistic regression analysis identified eight variables associated with platinum resistance recurrence. In the lasso regression, seven variables were selected. Based on the findings from both Lasso regression and multiple logistic regression analysis, models were developed using these 7 and 8 factors. Among these, the XGBoost model derived from multiple logistic regression exhibited superior performance and demonstrated good discrimination during internal validation, achieving an AUC of 0.784, a sensitivity of 0.735, a specificity of 0.713, an average accuracy of 80.4%, with a cut-off value set at 0.240. Conversely, the LR model based on lasso regression yielded commendable results as well; it achieved an AUC of 0.738, a sensitivity of 0.541, a specificity of 0.836, with a cut-off value established at 0.154 and an accuracy rate of 79.6%. Finally, we visualized both models through nomograms to illustrate the significance of each variable involved in their development.ConclusionsWe have successfully developed predictive models for platinum-resistant recurrence of epithelial ovarian cancer, utilizing routine clinical and laboratory data. Among these models, the XGBoost model—derived from variables selected through multiple logistic regression—demonstrated the best performance. It exhibited high AUC values and average accuracy during internal validation, making it a recommended tool for clinical use. However, due to variations in time and context, influencing factors may change over time; thus, continuous evolution of the model is necessary. We propose a framework for this ongoing model adaptation.

Leveraging Tropical Algebra to Assess Trustworthy AI

Given the complexity of the application domain, the qualitative and quantifiable nature of the concepts involved, the wide heterogeneity and granularity of trustworthy attributes, and in some cases the non-comparability of the latter, assessing the trustworthiness of AI-based systems is a challenging process. In order to overcome these challenges, the Confiance.ai program proposes an innovative solution based on a Multi-Criteria Decision Aiding (MCDA) methodology. This approach involves several stages: framing trustworthiness as a set of well-defined attributes, exploring attributes to determine related Key Performance Indicators (KPI) or metrics, selecting evaluation protocols, and defining a method to aggregate multiple criteria to estimate an overall assessment of trust. This approach is illustrated by applying the RUM methodology (Robustness, Uncertainty, Monitoring) to ML context, while the focus on aggregation methods are based on Tropical Algebra.

ANALYSIS OF THE POTENTIAL FOR WATERMELON PRODUCTION AND MARKETING IN THE STATE OF GOIÁS FROM 2019 TO 2023

The state of Goiás stands out in agribusiness, especially in fruit production, due to its growing domestic and foreign markets. Among fruits, watermelon (Citrullus lanatus) stands out, being relevant both economically and socially, generating jobs in several sectors. However, this emerging market lacks policies and techniques as developed as those present in sectors such as soybeans and corn, which highlights the underutilization of the potential of watermelon production in the state. This study aims to evaluate innovations, whether public policies or private initiatives, that can improve the production and marketing of watermelon in Goiás. The research seeks to analyze performance indicators and identify actions that promote efficiency and gains for the local community and producers, considering that watermelon is widely consumed in several sectors, standing out for its nutritional and cultural value. The study methodology follows two stages: a review of the literature on fruit production chains and the agricultural market in Goiás, followed by an analysis of data on competitiveness, consumption trends and opportunities in the watermelon production chain. Based on these results, strategies will be proposed to optimize production, increasing the competitiveness of producers and promoting the sustainable development of the sector. The study will also highlight the main weaknesses and strengths of marketing and production, using variables such as production size, demand, and available technologies.