Performance Indicators Research Articles

AI-Augmented Kidney Stone Composition Analysis with Auto-Release Improves Quality, Efficiency, Cost–Effectiveness, and Staff Satisfaction

Abstract Background We sought to evaluate key performance indicators related to an internally developed and deployed artificial intelligence (AI)-augmented kidney stone composition test system for potential improvements in test quality, efficiency, cost-effectiveness, and staff satisfaction. Methods We compared quality, efficiency, staff satisfaction, and financial data from the 6 months after the AI-augmented laboratory test system was deployed (test period) with data from the same 6-month period in the previous year (control period) to determine if AI-augmentation improved key performance indicators of this laboratory test. Results In the 6 months following the deployment (test period) of the AI-augmented kidney stone composition test system, 44 830 kidney stones were analyzed. Of these, 92% of kidney stones were eligible for AI-assisted interpretation. Out of these AI-eligible stones, 45% were able to be auto-released by the AI-augmented test system without human secondary review. Furthermore, the new AI-augmented kidney stone test system resulted in an apparent 40% reduction in incorrect laboratory results. Additionally, the new AI-augmented test system improved laboratory efficiency by 20%, improved staff satisfaction, and reduced the average analysis cost per kidney stone by $0.23. Conclusions The AI-augmented test system improved test quality, efficiency, cost-effectiveness and staff satisfaction related to this kidney stone composition test.

Multi-dimensional Dataset of Stress Measurements based on Performance, Behavioural, and Perceptual Indicators

The role of humans in industry is changing due to the technological transformation of production processes, which represents a potential source of stress for workers. In this context, the NO-STRESS project aims to combine objective and subjective data collected during task execution to explore causes of work-related stress and propose solutions to mitigate them. This paper describes the within-subjects study design used in the project, where each participant was exposed to all experimental conditions, and outlines the workflow developed during the study. Moreover, it describes the behavioural, performance and perceptual indicators of participants’ stress collected during the execution of full-kitting, component quality control, assembly and product quality monitoring tasks. The data collection sessions were conducted in different manufacturing contexts. The collected data may be reused to compare stress intensity among different subjects considering individual factors and experimental conditions. In addition, the results of stress in manufacturing may be compared to the intensity of this phenomenon in other sectors. The consequent outcomes may have practical implications for work contexts and benefits for society as a whole.

The role of a structured community health worker network in achieving malaria elimination goals in the Dominican Republic: An impact evaluation amid COVID-19 disruptions.

Community health workers (CHWs) can play a key role in malaria elimination through expanding access to malaria testing and treatment. The Dominican Republic, a low endemic country committed to eliminate malaria by 2025, implemented a structured malaria CHW network in the three main urban foci of Greater Santo Domingo. This research quantifies the networks' contributions towards malaria elimination from its implementation in 2019 until 2022. The study highlights the impact of COVID-19 disruptions on CHWs' performance and explores the network's ability to bounce back from health systems' shocks. The performance of the malaria CHW network was evaluated using weekly data collected from CHWs and routinely collected surveillance data from the Ministry of Public Health (MoH). We assess performance of CHWs by analyzing key variables including (1) reporting compliance, (2) household visitation rates, (3) malaria rapid diagnostic tests performed, (4) malaria cases detected, and (5) time between symptom onset and malaria diagnosis. To evaluate the impact of COVID-19 on the network, CHW's performance indicators are evaluated across three time periods (prior to, during, and after COVID-19 interruptions). Over the evaluation period, reported malaria cases in study foci decreased from 1,243 cases in 2019 to 6 cases in 2022. CHWs diagnosed and treated over 43% of malaria cases in study foci before COVID-19 interruptions and 14% during interruptions. 83% of cases detected by CHWs were detected through active case detection, with 17% detected passively. CHWs detected malaria cases and initiated treatment 1.5 days earlier than health facilities and MoH personnel performing active case detection. This evaluation provides evidence that a structured CHW network with clearly defined responsibilities and management protocol can help curb local malaria transmission. It adds to a growing body of research on the feasibility and benefits of CHW-led proactive household visitation.

Методика анализа исполнения бюджета энергосбытовой компании в комбинированных моделях аддитивно-мультипликативного типа

The article presents a methodology developed by the authors for analyzing the budget execution of the power supply company utilizing combined additive-multiplicative models. The relevance of this methodology is underscored by the need for organizations to enhance the effectiveness of management decisions through such analytical frameworks. The novelty of this work lies in the adaptation of classical deterministic analysis models to the specific context of power supply companies, taking into account the peculiarities of their financial and economic activities. A plan-fact analysis of the budget execution of the power supply company was conducted using the chain substitution method, which was implemented across all types of deterministic factor models: additive, multiplicative, multiple, and mixed (combined). This analytical approach enabled the determination of the impact of individual factors on the variation of the performance indicator by gradually substituting the baseline value of each factor indicator within the performance measure with the actual value for the reporting period. The authors conclude that the proposed methodology facilitates the analysis of changes in the performance indicator (marginal profit) attributable to factors of the I, II, III levels. It is applied in practice for the formulation of managerial reporting and the adjustment of the marketing policy of the power supply company operating at the territory of Saint Petersburg.

Preload Multi-Objective Optimization Method for Ultrasonic Motors Based on NSGA-II

The preload has a significant impact on the output performance of ultrasonic motors. To achieve optimal output from the ultrasonic motor, this paper proposes a novel multi-objective optimization method for preload, utilizing the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to solve the problem of determining the optimal preload. Firstly, a platform for preload adjustment and testing is established to conduct experimental tests on the motor’s characteristics. Based on the experimental data, the influence of preload on the motor’s output performance is analyzed. Secondly, linear regression models are established for three performance indicators: no-load speed, stall torque, and output efficiency with respect to preload, and the objective functions are fitted accordingly. Finally, the NSGA-II algorithm is used for multi-objective optimization with three objectives to obtain the Pareto front and determine the optimal preload for the ultrasonic motor. Simulation and experimental results show that after preload optimization, the no-load speed increased by 4.93%, stall torque increased by 26.10%, and output efficiency increased by 11.28%. Compared to existing optimization methods, this approach has lower computational complexity and better optimization performance, ensuring higher optimization accuracy and precision. The preload optimization method based on NSGA-II can improve the performance of ultrasonic motors in practical applications.

EDUCADORAS DA INFÂNCIA E O DESENVOLVIMENTO DA LINGUAGEM MATEMÁTICA NO TERRITÓRIO DAS CRIANÇAS DE ZERO A TRÊS ANOS

We analyzed written narratives (reports of experiences) from educators of babies and very young children (zero to three years old). The objective of the work, the result of the research we developed, lies in understanding the dynamics of pedagogical work with mathematical language based on the analysis of narratives from a collaborative group. To this end, with a qualitative approach, we focus our efforts on describing and analyzing the written reflection of teachers' reports when experiencing contexts of mathematical exploration. The theoretical framework discusses aspects of child care, post-LDB, conceptualizes the Early Childhood Education curriculum, as well as presenting the expected and possible mathematical notions when working in this area of knowledge, intentionally, in the space-time of the daycare center. The results indicate that: 1) mathematical language can and should be worked with babies and very young children in routine situations; 2) the narratives demonstrate the possibility of self-reflection regarding the proposed pedagogical experience; and 3) performance indicators were identified, in pedagogical practice, of how to explore senses of magnitudes and measurements, resolution of unconventional problems and spatial issues. Thus, the contribution of this article implies recognition of the need for teaching professionalization of educators who work directly in the field of Brazilian Early Childhood Education, with emphasis on processes in which care and education are integrated as binomials in the area.

Network Long-Term Evolution Quality of Service Assessment Using a Weighted Fuzzy Inference System

The United Nations has pushed for improved mobile connectivity, ensuring that 97% of the world’s population lives within reach of a mobile cellular signal. This is within the framework of objective nine regarding industry, innovation, and infrastructure for sustainable development. The next challenge is for users to know the quality of this service. The Long-Term Evolution (LTE) network’s quality of service (QoS) is evaluated with key performance indicators (KPI) that only specialists can interpret. This work aims to assess the QoS and effectiveness of the fourth-generation (4G) LTE network using a weighted fuzzy inference system. Analytic Hierarchy Process (AHP) is integrated to rank the fuzzy rules. The KPIs that are considered for the evaluation are download speed, upload speed, latency, jitter, packet loss rate, reference received signal power (RSRP), and reference received signal quality (RSRQ). The evaluated data were collected collaboratively with end-user equipment (UEs). Different usage scenarios are contemplated to define the importance according to the positive impact of the QoS of the LTE mobile network. The advantage of the weighted fuzzy inference system concerning the fuzzy inference system is that each KPI is assigned a different weight, which implies having rules with hierarchies. In this way, the weighted fuzzy inference system provides two indices of quality and effectiveness. It can be a valuable tool for end users and regulatory bodies to identify the quality of the LTE mobile network.

A System Dynamics Stability Model for Discrete Production Ramp-Up

Manufacturing companies are increasingly challenged to deliver customizable products with shorter time to market and higher quality while adhering to sustainability requirements. To meet these challenges, the frequency and importance of production ramp-ups will increase in the future. However, most ramp-ups still fail to meet targets due to unpredictable equipment failures, operator errors, and system complexity. We propose a system dynamics model that captures the unique dynamics of ramp-up phases by integrating stability and disturbance factors that influence the key performance indicators overall equipment effectiveness, process capability, and production output. A systematic literature review informed the identification of stability factors, which were validated through expert interviews in the automotive industry. Our system dynamic simulation results indicate that control factors realistically influence production system behaviour during different ramp-up phases. Despite some limitations regarding the effects of maintenance personnel and engineering changes on key performance indicators, our model effectively simulates realistic ramp-up behaviour. The findings highlight the need for tailored models that consider specific ramp-up contexts and emphasize the importance of data acquisition for enhanced performance prognosis in future research.

An exploratory qualitative study of inter-agency health and social service partnerships focused on Aboriginal and Torres Strait Islander clients

BackgroundThe siloed nature of the health and social service system threatens access for clients engaging numerous organisations. Many Aboriginal and Torres Strait Islander people face adverse circumstances which contribute to multiple health and social needs. Effective relationships between health and social services are integral to coordinated service provision to meet the diverse needs of Aboriginal and Torres Strait Islander clients. Place-specific insights into inter-agency relationships are needed to inform targeted strategies that bolster service coordination to benefit Aboriginal and Torres Strait Islander people.MethodsThis study sought to understand experiences of inter-agency partnerships among health and social service providers on Kaurna Country in northern Adelaide using semi-structured interviews and yarning circles to explore partnership actions, outcomes, enablers, challenges, and identify strategies to strengthen partnerships. Fifty-nine service providers (78% female, 62% Aboriginal) participated including six from non-government organisations, 17 from Aboriginal community-controlled services and 36 from government organisations.ResultsA content analysis identified partnership actions such as client advocacy, referrals, sharing information, case management meetings and collaborative tender submissions which were seen to improve client access, navigation and outcomes and strengthen worker connectedness and job satisfaction. Motivated workers, listening to Aboriginal people, shared goals and values, and partnership agreements (e.g., memorandum of understanding, service contracts) were identified enablers of partnerships. Racism and ignorance, lack of networking events, communication breakdown, red tape and administrative barriers, competition between services, short-term funding, high turnover of staff and a focus on key performance indicators rather than community needs were among the challenges. Effective partnerships to benefit Aboriginal communities in northern Adelaide was reported to require aligned intersectoral strategic intentions, reforms to service commissioning processes, sustainable funding, regular network events for management and frontline workforce, Aboriginal practitioner-led service coordination approaches and a network of Aboriginal and Torres Strait Islander workers across organisations.ConclusionsThis study identified key leverage points for action on inter-agency partnerships to benefit Aboriginal and Torres Strait Islander communities on Kaurna Country. System drivers such as funded inter-agency networks and reforms to commissioning of services must support organisational- and practitioner-level enablers to strengthen partnerships between health and social services across northern Adelaide.

Harmonizing Wide Voltage Window and High Energy Density toward Asymmetric All-Solid-State Supercapacitor.

All-solid-state supercapacitors are known for their safety, stability, and excellent cycling performance. However, their limited voltage window results in lower energy density, restricting their widespread application in practical scenarios. Therefore, in this work, CC/MoO3@Ti3C2Tx negative electrode and Mo1Al1-MnO2/CC positive electrode materials are synthesized and prepared by electrochemical deposition co-coating and one-step hydrothermal methods, respectively, and assembled into an asymmetric supercapacitor (ASC) device based on the two electrode materials. The study reveals that the surface capacitances of the positive and negative electrodes are 1685.5 mF cm-2 and 1134.98mFcm-2 correspondingly, with potential windows of both as high as 1.1V. Surprisingly, the potential window of the all-solid-state supercapacitor assembled based on the two electrodes reaches 2.2V, and the energy density reaches 0.44m W h cm-2, which is much higher than the performance indicators based on similar electrodes. The resulting excellent performance parameters are mainly attributed to the efficient synergy between the pseudo-capacitance effect of the MoO3 film and the high electrical conductivity of the Ti3C2Tx sheets, as well as the great improvement of the intrinsic electron mobility and ion diffusion channel stability of MnO2 by Mo and Al bimetallic doping.

Effect of artificial intelligence on the financial performance of Indian banking sector

Purpose: The purpose of this paper is to explore the impact of Artificial Intelligence on the performance of Indian Banks in terms of financial metrics. The study focused specifically on the NIFTY Bank Index. The paper also advocates that a greater transparency in disclosing AI related information in a Bank’s annual report is required even if it is voluntary. Design/Methodology/Approach: The paper uses a mixed method approach where quantitative and qualitative analysis is combined. A dynamic panel data model is used to understand the impact of AI of Return on Equity (RoE) of 12 Indian Banks in the NIFTY Bank Index over a five-year period. In addition to that, Content analysis of annual reports of banks was conducted to examine AI related disclosure and transparency. Findings: The paper highlights that the integration of Artificial Intelligence (AI) significantly influences the financial performance of sample banks of India. Return on Equity the specific parameter positively influenced with adoption of AI. The profitability of banks is positively impacted by reduced errors and improved operational efficiency. The content analysis of annual reports of the banks indicates different approach for AI disclosure where some banks give detailed information and some are not transparent about AI initiatives. The findings suggest that a higher level of transparency could enhance confidence of all stakeholders. Theoretical Implications: The positive relation between adoption of AI and financial performance, specifically ROE, gives a foundation for academic research to explore the dynamics of emerging technology and financial systems. The study can be extended to explore the impact on other performance indicators in different sectors. Practical Implications: The findings of this study emphasize the importance of transparent AI related disclosures. A detailed reporting about integration of AI helps in enhanced stakeholders’ confidence in case of banking industry. The regulatory framework of banks may also consider making mandatory AI disclosure practices to ensure due accountability to maximize the benefits of AI in banking.

Recent Strategies of Oxygen Carrier Design in Chemical Looping Processes for Inherent CO2 Capture and Utilization

AbstractChemical looping processes are considered a promising pathway for the efficient production of various fuels and chemicals. Temporally or spatially separated reduction and oxidation reaction in chemical looping can offer various advantages such as enhancing energy efficiency, surpassing equilibrium limitations, and eliminating the need for separation. However, the efficiency of the chemical looping process highly depends on the performance of the oxygen carrier. Higher gas conversion can increase separation efficiency and higher solid conversion can reduce the amount of cycled oxygen carrier. The performance indicators are highly related to the thermodynamic properties of the oxygen carriers and their redox kinetics. This review introduces some key articles and recent achievements for the enhancement of such properties. The different research strategies are discussed for enhancing the performance of stoichiometric and non-stoichiometric oxygen carriers. Through the rational design of oxygen carrier material, an energy-efficient chemical looping process is possible.

INTERDISCIPLINARIDADE E BNCC: LIMITES E PERSPECTIVAS

The sectorization of thinking, work and performance indicators (generally focused on economic indicators) are obstacles to achieving more comprehensive goals, intensifying fractional work, specialization, the sectoral focus and individualism over the systemic approach. From the above, the need arises to implement a holistic, comprehensive and interdisciplinary vision to solve current problems, which can only be achieved through teamwork. The general objective of this article is to analyze how the BNCC contemplates interdisciplinary practices in its content. According to the BNCC, students must have their education guided by training itineraries capable of integrating curricular knowledge with their professional needs and knowledge necessary for functioning in society. Such itineraries are formed by the structuring axes scientific research, mediation and sociocultural intervention, creative processes and entrepreneurship. Finally, there are many more reflections and criteria that could be made about teaching and learning processes with an interdisciplinary approach, which will be the subject of future work. However, it is possible to conclude by placing special emphasis on the duty of all those who have taken on the responsibility of training new generations to implement scientific and quality teaching in pedagogical practice in order to raise the culture of their students and their reflective development. and critical thinking about the problems and realities of the environment in which they develop and in this aspiration the interdisciplinary conception of education must play a decisive role.

The Strategic Integration of ESG Metrics in Performance Evaluation: Insights from Management Accounting Practices

The integration of Environmental, Social, and Governance (ESG) metrics into performance evaluation has become increasingly vital in achieving corporate sustainability and competitive advantage. This study investigates the impact of ESG metrics on organizational performance within the framework of strategic management accounting (SMA). Using a quantitative approach, secondary data were collected from ESG reports of 100 publicly listed companies across various industries. The analysis employs multiple linear regression to examine the relationships between ESG dimensions—Environmental, Social, and Governance—and financial performance indicators, including Return on Assets (ROA), Return on Equity (ROE), and Tobin’s Q. The results reveal a significant positive correlation between ESG scores and financial performance, with the Environmental dimension exhibiting the strongest influence (β = 0.45, p < 0.01), followed by Social (β = 0.30, p < 0.05) and Governance (β = 0.25, p < 0.05). Firms with high ESG scores (≥75) reported 15% higher ROA and 20% higher ROE than those with lower scores. These findings underscore the importance of integrating ESG into SMA practices to enhance transparency, stakeholder trust, and sustainable decision-making processes. This study contributes to the literature by providing a comprehensive evaluation framework that aligns ESG metrics with corporate objectives. Practical recommendations include adopting global ESG standards, improving transparency in ESG reporting, and prioritizing strategic initiatives in sustainability. Future research could explore the long-term effects of ESG integration on non-financial outcomes, such as brand reputation and employee satisfaction.

Multi-objective optimization of truss structures using the enhanced Lichtenberg algorithm

Abstract The primary objective of numerous optimization problems is to enhance a single metric whose lowest or highest value accurately reflects the response quality of a system. However, in some instances, relying solely on one metric is not practical, leading to the consideration of multi-objective (MO) optimization problems that aim to improve multiple performance indicators simultaneously. This approach requires the use of a multi-objective optimization method adept at handling the intricacies of scenarios with various indices. Consequently, researchers have not explored multi-objective truss optimization as extensively as single-objective (SO) scenarios. The novel multi-objective Lichtenberg algorithm with two archives (MOLA-2arc) has been developed to address this. The efficacy of MOLA-2arc is evaluated against eight other MO algorithms, including the multi-objective bat algorithm (MOBA), multi-objective crystal structure algorithm (MOCRY), multi-objective cuckoo search (MOCS), multi-objective firefly algorithm (MOFA), multi-objective flower pollination algorithm (MOFPA), multi-objective harmony search (MOHS), multi-objective jellyfish search (MOJS) algorithm, and the original multi-objective Lichtenberg algorithm (MOLA). The challenge is to minimize structural mass and compliance while adhering to stress limitations. The outcomes demonstrate that MOLA-2arc shows notable improvements over its predecessor, MOLA, and surpasses all other competing algorithms in this study.

Study of the effectiveness of the criteria used to evaluate the work of specialists of a pharmaceutical company in an unfavorable epidemiological situation (using the example of the sales department)

Introduction. During the coronavirus pandemic, due to the current situation in the pharmaceutical sector of the economy, specialists from a number of departments of pharmaceutical companies were forced to switch completely or partially to remote work. This transformation has necessitated the need to adapt the employee performance assessment systems that have developed in organizations, including revising the list of indicators and criteria. However, to date there are no unified scientifically based methods for assessing the labor efficiency of specialists, based on the selection and measurement of criteria that take into account the characteristics of both the external and internal environment of the organizational structures of pharmaceutical organizations, including those forced to work in unfavorable epidemiological conditions. The proposed methodological approach based on the use of Key Performance Indicators (KPI) will allow the management of a pharmaceutical company to create a more balanced and flexible assessment system that stimulates both individual achievements and teamwork, as well as to form an intelligent analytical basis for making management decisions that bring the company closer to its organizational goals. The purpose of the study is to analyze the effectiveness of the employee performance assessment system that has developed in a pharmaceutical company and to justify the effectiveness of using KPIs in an unfavorable epidemiological situation (using the example of the sales department). Material and methods. The evaluation study is based on primary data from reports of pharmaceutical companies and data from a survey of 20 sales employees in pharmaceutical companies using a developed questionnaire. Results. A system for evaluating the work of employees in the sales department of a pharmaceutical company based on KPIs is proposed using three table layouts that allow ratings to be translated into a 10-point scale out of 100 possible. The assessment is carried out according to two main criteria: 1) achievement of sales volume and 2) implementation of the plan for the target product, taking into account the quality of the employee’s work, confirmed by his immediate supervisor. This approach will create a more balanced and flexible assessment system in the organization, stimulating both individual achievements and teamwork. Conclusion. As part of the study, proposals were developed for the development of KPIs for the criteria for assessing the work of specialists of a pharmaceutical company in an unfavorable epidemiological situation.

Why do I (not) trust my employees? Predicting factors for leaders’ trust in employees

Despite the growth of research on interpersonal trust and the establishment of a widely accepted trust determinants model, studies have focused mostly on employees’ trust in leaders. This study innovates by investigating factors that determine leaders’ trust in employees. We focus on cognitive and affective trust and examine how leaders place trust in their employees based on perceived in-role and extra-role performance. Furthermore, we examine how performance leads to inferring trustworthiness traits that affect cognitive and affective trust. Survey data were collected from three Bhutanese organizations comprising 72 leaders who evaluated 302 employees over three different time points with a one-month interval between them. Results from the multi-level modeling revealed distinct predictors of the two trust bases: in-role performance for cognitive trust and extra-role performance for affective trust. These performance indicators are positively associated with trustworthiness traits and trust but have a multifaceted effect. While in-role performance is strongly linked to ability and cognitive trust, and extra-role performance is linked to benevolence and affective trust, both performance dimensions are associated with integrity. Our findings highlight the importance of perceived performance in inferring trustworthiness and subsequently building trust.

Gene co-expression network and differential expression analyses reveal key genes for weaning weight in Simmental-Holstein crossbred cattle

Weaning weight is a key indicator of the early growth performance of cattle. An understanding of the genetic mechanisms underlying weaning weight will help increase the accuracy of selection of breeding animals. In order to identify candidate genes associated with weaning weight in Simmental-Holstein crossbred cattle, this study generated RNA-Sequencing (RNA-seq) data from 86 crossbred calves (37 males and 49 famales) and measured their weaning weight and body size traits (wither height, body length, chest girth, rump width, and rump length). Differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) were performed. A total of 498 differentially expressed genes (DEGs) were identified between the low weaning weight (LWW) group and the high weaning weight (HWW) group. Weaning weight was transcriptionally correlated (FDR < 0.05) with four of the eleven co-expression gene modules. By intersecting DEGs and hub genes of the four modules, we identified a final set of 37 candidate genes enriched in growth, development, or immune-related processes. In addition, one co-expression module was significantly correlated with all the five body size traits (P < 0.05), from which MX1 was identified as a key candidate gene through protein-protein interaction (PPI) analysis of hub genes. Further evidence from cattle transcriptome-wide association study analysis (TWAS) and human phenome-wide association study (PheWAS) validated significant associations of CACNA1S, SEMA7A, VCAN, CD101, CD19, and CSF2RB with growth and development traits (P < 0.05). Notably, CACNA1S and CD19 were also associated with typical immune traits such as B cell proliferation, differentiation, and activation. In conclusion, this study reveals new candidate genes significantly associated with weaning weight in Simmental-Holstein crossbred cattle, providing a basis for further exploration of the genetic mechanisms behind growth traits of cattle.

THE IMPACT OF CAPITAL STRUCTURE ON BANK PERFORMANCE IN TURKIYE

The aim of this study is to examine the impact of capital structure on Turkish banking performance by using panel regression model for the period between 2012 and 2023. The ratio of return on assets (ROA) and return on equity (ROE) are used as a dependent variable which are one of the important indicators of the banking performance. There are 9 bank-spesific and 2 macroeconomic (inflation and economic growth) independent variables in the analysis. Panel data analysis results reveal that the capital structure [Debt ratio (DTA) and Debt to equity ratio (DTE)] negatively impacts business performance (ROA) of Turkish private deposit banks. The financial performance, which is measured by ROA, is significantly and negatively associated with variables such as Non-Performing loan ratio (NPL), credit risk (CIR) and cost management (OA), whereas effect of bank size (BS), economic growt (GDP) and inflation (INF) have positive impact. On the other hand, the effects of DTA, DTE, BS, NPL, CIR, OA and GDP on ROE are significant. Besides, the effects of DTA, BS and GDP are positive, whereas the effect of DTE, NPL, CIR and OA are negative. The study concludes that the capital structure proxies have impact on financial performance of Turkish private deposit banks measured both by return on assets and return on equity.

The silent barrier: exploring data availability in Small Island Developing States.

To quantify three aspects of data-related developmental progress across 57 Small Island Developing States (SIDS) recognized by the United Nations: statistical capacity measured using the Statistical Performance Indicators (SPI), data availability using the Sustainable Development Goal (SDG) indicators, and gender-stratified indicators. SIDS as a single country group were compared with other World Bank regions, using SPI, availability of SDG metrics, and availability of gender-stratified metrics. Using population size as a proxy for "smallness," its association with data capacity and availability was modeled. SPI among SIDS was lower than in any world region - broadly equivalent to Sub-Saharan Africa. Two-thirds of SDG indicators were available for SIDS in 2022; other world regions having between 71% and 87% availability. One-third of gender-stratified indicators were available for SIDS in 2022; other world regions having between 58% and 68% availability. Population size was strongly associated with each outcome, with SIDS having a statistical capacity 18.0 percentage points lower than other countries [95% CI (8.5, 27.4)], SDG data availability 17.4 percentage points lower [95% CI (13.1, 21.7)], and gender-stratified indicator availability 28.8 percentage points lower [95% CI (24.5, 33.0)]. As global demand and associated rewards for electronic data increase, our analysis highlights the challenges introduced by island "smallness" in the global push for digital transformation. Regional cooperation mechanisms, sustained international support, and systematic monitoring of data availability are urgently required to monitor capacity development.