Composite Index Research Articles

Composite anthropometric data quality index for children under the age of 5 on the Brazilian National Food and Nutrition Surveillance System, 2019–2021

BackgroundA composite evaluation that merges various data quality indicators separately enabled the researchers to score the overall data quality of the research. In this context, the objective of the present study is to develop composite anthropometric data quality indices for children under 5 registered on the Brazilian National Food and Nutrition Surveillance System (SISVAN) from 2019 to 2021.MethodsAnthropometric data quality indicators were generated for 5,210 Brazilian municipalities: coverage, completeness, the ratio between the sexes, age difference index, preference for height and weight digits, biologically implausible z-score values, and standard deviation. Principal component analysis [PCA] was used to generate a composite anthropometric data quality index for standardized height-for-age (HAZ) and weight-for-height z score (WHZ) indices. The municipalities were ranked in descending order, following their anthropometric quality index values: lowest [worst quality] and highest values [best quality].ResultsIn total, 29,367,435 records and 8,930,881 children with anthropometric measurement information were identified. The dispersion indicators, the percentage of biologically implausible values [BIV] and the digit preference had the highest factor loadings. We observed that the worst index values were found in municipalities in the country’s poorest and most vulnerable regions [North, Northeast, and Central-West]. The correlation between the HAZ and WHZ quality indices was 0.74.ConclusionThe proposed index provides a coherent measurement to discriminate municipal anthropometric data quality.

Managing social capital networks in digital international innovation ecosystems

This study operationalizes the concepts of social capital theory through social network analysis. By using this methodology, this research aims to deepen the understanding of how the dynamics of social capital networks are related to the consolidation and sustainability of innovation ecosystems, particularly in the context of digital entrepreneurship platforms. This case study focuses on the International Entrepreneurship Lab Smart Money (IELSM), a digital platform comprising more than 100 organizations from various sectors and countries, mostly in Latin America. Three network subsets around business models were constructed through in-depth interviews with key actors in the IELSM platform. Subsequently, five structural characteristics of the networks were studied via quantitative analysis: ecosystem connectivity, reciprocity, resistance to linkages across sectors, stakeholder diversity, and resource flow difficulty. Based on correlational analysis, a composite index that reflects the maturity of the social capital network is proposed. When applied in similar contexts, this index can provide a comprehensive understanding of social capital dynamics and guide the development of effective strategies for network management. Our findings revealed a strong correlation between reciprocity and ecosystem connectivity. This suggests that a network with greater social cohesion generates more links in which organizations share resources bidirectionally, representing significant potential for mutual value creation. Negative correlations between reciprocity and stakeholder diversity, as well as between ecosystem connectivity and stakeholder diversity, highlight intersectoral cooperation challenges. Social capital networks exhibit complex, nonlinear growth linked to variations in behavior and structure, highlighting the practical implications for managing innovation ecosystems.

Computing multidimensional composite indicators for small areas in presence of missing variables: a data integration approach

Abstract We evaluate data integration methods to estimate small area composite indicators, when some of the single indicators cannot be computed due to completely missing variables needed for their computation. The parameter is a multidimensional poverty index, where some of the required variables are not available in the population Census, which is used as the main source to compute the indicator. We propose two approaches to generate these missing variables, considering an auxiliary sample survey. Specifically, the performance of an approach based on a generalized linear mixed model is compared with a two-step imputation technique. The measurement of multidimensional poverty, also including nonmonetary dimensions is crucial and aligned with the Sustainable Development Goals defined by the United Nations. We consider Colombia as a case study, which has a recent population Census providing most of the information necessary to compute the indicator at small area level. Our methodologies can be greatly of interest of other Latin American countries having similar indices, and other countries computing poverty indicators with missing variables. The approaches are evaluated via simulations. We show an application based on the National Population Census, 2018 and the Great Integrated Household Survey 2018 of Colombia, focusing on the Antioquia region.

Financing options for infrastructure and life ladder in Africa

This article examines the impact of infrastructure progress on the well-being of people in Africa. It uses a composite index of infrastructure development and data on subjective well-being from 32 African countries for the period 2006–2022. Robust estimation methods are used, including generalized least squares and Driscol-Kraay fixed effects. The results show a positive and significant impact of infrastructure development on the well-being of the African population, which is related to the way infrastructure is financed. Based on the results obtained, we recommend strengthening public–private partnerships for infrastructure financing, considering each country's specific needs.

The Composite Digital Therapeutic Index (cDTI): A Multidimensional Framework and Proof-of-Concept Application to FDA-Authorized Treatments.

Introduction Prescription digital therapeutics (PDTs) are an emerging class of regulated software-based interventions with increasing FDA authorizations. However, there is no standardized framework to evaluate and benchmark their overall therapeutic value. We aimed to develop and validate a Composite Digital Therapeutic Index (cDTI) framework. This proof-of-concept application integrates efficacy, engagement, quality of evidence, and safety profile from registrational trials to compare PDTs using publicly available regulatory summaries, registries, and study readouts. Methods We developed a scoring framework incorporating four domains (efficacy, engagement, evidence quality, and safety), each quantified and combined into a composite index. Efficacy was calculated using standardized mean difference (SMD; Hedges' g) adjusted for statistical significance. Engagement was based on the mean proportion of patients achievingtherapeutic module completion. Evidence quality was graded using an adapted American Academy of Neurology Class of Evidence framework. Safety was assessed based on treatment-emergent adverse events (TEAEs) using a hierarchical penalty system. In a proof-of-concept application of the framework, we applied cDTI to two FDA-cleared first- and second-generation PDTs, reSET-O for opioid use disorder (Pear Therapeutics, Boston, MA, USA) and CT-132 for episodic migraine (Click Therapeutics, New York, NY, USA), respectively. Results CT-132 achieved a cDTI score of 0.296, substantially (1287%) higher than reSET-O, which scored 0.023. CT-132's superior performance was driven by a statistically significant efficacy outcome (adjusted SMD 0.33), high engagement (89.7%), Class I evidence quality with sham and double-blind, and no treatment-related adverse events. In contrast, reSET-O showed a non-significant primary outcome (adjusted SMD 0.0895), moderate engagement (63.0%), Class III evidence quality without patient blind and usual-care control, and missing systematic safety reporting requiring a conservative penalty. Discussion The cDTI successfully differentiated the overall therapeutic profiles of two PDTs across multiple clinical and usability domains. This multidimensional approach highlights how digital therapeutics with similar regulatory status can differ meaningfully in trial quality, efficacy, engagement, and safety. The cDTI offers a transparent, reproducible method for comparing PDTs and may aid stakeholders including providers, payers, regulators, and patients in decision-making about digital therapeutic adoption and coverage. Conclusion The cDTI provides a reproducible framework to evaluate PDTs based on clinical, usability, and safety parameters. Future work aims to expand the framework across all FDA-cleared PDTs and explore incorporation of real-world effectiveness, equity, and cost-effectiveness domains.

Association between composite dietary antioxidant index and cataract in American adults aged ≥50 years: A cross-sectional study from NHANES 2003-2008.

Oxidative stress is one of the crucial pathogeneses of cataract. The composite dietary antioxidant index (CDAI) represents the antioxidant capacity of one's diet. The aim of this study was to explore the association between the CDAI and cataract. The data were obtained from the National Health and Nutrition Examination Survey 2003 to 2008. A weighted multiple logistic regression, generalized weighted models, and smoothed fitted curves were performed to investigate the association between the CDAI and cataract. A total of 5814 participants aged ≥50 years with complete data were included in the study. According to the fully adjusted model, the odds ratio (95% confidence interval) for the association between the CDAI and cataract was 0.95 (0.93, 0.98). After dividing continuous CDAI into tertiles, consistent negative associations between CDAI and cataract were observed in the highest tertile compared to the lowest tertile (odds ratio = 0.77; 95% confidence interval, 0.62-0.95). The CDAI components (zinc, magnesium, vitamin A, and vitamin E) were also negatively associated with cataract odds in the fully adjusted model. Subgroup analysis showed inconsistent associations among subgroups, but no statistically significant interaction effects were found. This cross-sectional study revealed that a higher CDAI was associated with lower odds of cataract. These findings may contribute to cataract prevention through antioxidant dietary patterns.

Sex differences in associations between body composition and cardiometabolic indicators in Chinese children: a cross-sectional study.

Obesity is a growing global public health problem that increases the risk of cardiovascular disease. The aim of the present study was to assess the effects of body composition on cardiometabolic indicators in children. Cross-sectional analysis. China, the Beijing Children and Adolescents Health Cohort Study between 2022 and 2023. This cross-sectional study included 5555 children and adolescents aged 6 to 17 years from 11 kindergartens and schools. We measured body composition using multifrequency bioelectrical impedance analysis and assessed the cardiometabolic indicators, including blood pressure, plasma glucose and lipids. Linear regression and binary logistic regression were performed to assess the associations between body composition and cardiometabolic abnormalities. In boys, fat mass index (FMI) was positively correlated with total cholesterol (TC) (in normal fat-free mass (FFM) group, β=0.036, 95% CI 0.027 to 0.046; in high FFM group, β=0.034, 95% CI 0.016 to 0.051) and fasting plasma glucose (FPG) (in normal FFM group, β=0.019, 95% CI 0.012 to 0.026; in high FFM group, β=0.030, 95% CI 0.005 to 0.054). FFMI was negatively associated with TC only in the normal fat group (β=-0.047, 95% CI -0.069 to -0.034) in boys. However, in girls, FMI was not significantly associated with TC and was positively associated with FPG only in the normal FFM group (β=0.033, 95% CI 0.024 to 0.041), and FFMI was negatively correlated with TC (in normal fat group, β=-0.058, 95% CI -0.079 to -0.038; in high fat group, β=-0.049, 95% CI -0.084 to -0.015). Normal FFM-high fat (OR=2.065, 95% CI 1.379 to 3.091) and increased visceral fat region (OR=1.357, 95% CI 1.195 to 1.540) were risk factors for high TC in boys but not in girls. Body composition was significantly associated with cardiometabolic risk factors, and fat stored in different regions has differential influences on cardiometabolic indicators. There were sex differences in the relationships between body composition and cardiometabolic indicators. The findings suggest that body composition is more strongly correlated with cardiometabolic indicators in boys than in girls. Prevention of obesity and cardiometabolic abnormalities may be more important in boys.

Extra-Thyroidal Impacts of Serum Iodine Concentrations During Early Pregnancy on Metabolic Profiles and Pregnancy Outcomes: Prospective Study Based on Huizhou Mother–Infant Cohort

Objectives: This study aimed to test the extra-thyroidal impacts of maternal serum iodine concentrations (SICs) on metabolic factors and subsequent pregnancy outcomes. Methods: Single pregnant women aged 18–49 years were recruited during their first prenatal visits. SICs at first trimester (T1) were tested by ICP-MS. Metabolic factors [body mass index (BMI), fat %, glucose, lipids, uric acid, and blood pressure] were measured, and composite indices [the triglyceride–glucose (TyG) index, TyG-BMI, and the Framingham steatosis index (FSI)] were estimated. Obstetric and birth outcomes were retrieved from the hospital information system, including gestational diabetes (GDM), gestational hypertension (GH), fetal distress, postpartum hemorrhage, premature rupture of membrane, small and large for gestational age (SGA and LGA), preterm birth, and low birth weight. Multivariable linear and logistic regression models were applied to explore the associations between maternal SIC, metabolic factors, and pregnancy outcomes. Results: A total of 1456 mothers were included for analysis. Maternal LgSIC values at T1 were inversely associated with early gestational weight gain (β = −0.113, p < 0.001) and BMI at T1 (β = −0.070, p = 0.006), but they were positively associated with triglycerides (β = 0.142, p < 0.001), the TyG index (β = 0.137, p < 0.001), and uric acid (β = 0.060, p = 0.018). However, upon further adjustment for thyroid hormones, the associations were attenuated. The joint effects of high SIC and metabolic conditions (hyperlipidemia, high FSI, and GH) suggested increased adverse pregnancy outcomes (increased postpartum bleeding, reduced birth length, and reduced delivery weeks). Conclusions: Our prospective data in the iodine replete region indicated that high SICs at T1 were associated with increased risk of metabolic conditions and adverse birth outcomes, with the associations being independent of thyroid hormones.

Enhancing climate resilience of pastoral communities through informal institutions: evidence from Inner Mongolia

ABSTRACT Under global warming, the pastoral community’s resilience is being severely impacted by climate change. Institutional adaptations and transformations play a crucial role in enhancing the resilience of societies against the impacts of climate change. Collective reserved pastures, as an informal institution, are pastures retained by communities to mitigate the damages caused by natural disasters and alleviate the man-land conflict. This paper constructed a dimension-determinant-indicator table consisting of compositional and contextual indicators to understand the characteristics of both individuals and community. Subsequently, it utilized the Composite Indicator Approach to calculate the pastoral community resilience index based on 104 households in 6 pastoral communities in Inner Mongolia. The main results indicated that the average resilience indices of communities in meadow steppe, desert steppe and typical steppe was 0.50, 0.46 and 0.44, respectively. Analysis of variance within communities of the same grassland type and between communities of different grassland types demonstrated that collective reserved pasture significantly improved community resilience. Collective pastures can contribute to community resilience through both long-term and short-term adaptations. The findings can offer valuable insights for the management of other common-pool resources such as forests and fisheries to cope with climate impact from the perspective of informal institutions.

Mapping anthropogenic pressure in the Brahmani-Baitarani River Basin: a PCA-based approach.

This study presents a robust objective methodology for assessing and mapping anthropogenic pressure in the Brahmani-Baitarani River Basin (BBRB) through an objective principal component analysis (PCA)-based approach. Nine key stressors-Aerosol Trend (AERT), Degree of Urbanization Change (DUC), Land Use/Land Cover Change (LULCC), Normalized Difference Moisture Index Trend (NDMIT), Normalized Difference Vegetation Index Trend (NDVIT), Observed Minus Reanalysis Temperature (OMR), Nightlights Change (NLC), Population Count Change (PPC), and Water Balance Trend (WBT)-were used to construct a composite pressure index. These stressors were selected based on their direct link to human-induced environmental modifications. The datasets, spanning from 2000 to 2023, were preprocessed and normalized to ensure comparability. The suitability of the dataset for PCA was confirmed through the Kaiser-Meyer-Olkin (KMO = 0.61) and a significant Bartlett test (χ2 = 378037,p < 0.01). PCA was applied to determine variable weightings, reducing redundancy and highlighting dominant stressors, with OMR, DUC, and LULCC contributing the highest weights. The final composite anthropogenic pressure raster (CAPR) was generated with index ranging from 9 to 78%. Spatial analysis reveals significant variations in anthropogenic pressure, with towns such as Byasanagar and Lohardaga experiencing very high mean anthropogenic pressures (~ 50%). Conversely, regions like Deogarh, Gua, and Kiriburu exhibit relatively very low anthropogenic pressures (~ 20%). Further, the CAPR was grouped into 7 categories based upon natural breaks in the data and pseudo-F-statistics-based elbow test, providing a clear representation of the spatial distribution of anthropogenic impacts, serving as a critical tool for sustainable management and policy formulation.

How to Distinguish Income Indicators of Energy and Transport Vulnerability—A Case Study of Greece

This article examines Greek households’ energy and transport poverty and vulnerability, two concepts often intertwined in policy, making use of both single and composite indicators. The methodology employed aligns with the European Social Climate Fund regulation, using household income from economically active members and the share of expenditures on fossil-based energy use in buildings and transport, as well as the required investment costs in energy efficiency improvements for dwellings or transport decarbonization measures. Through these indicators, the proportion of energy-vulnerable households in Greece ranges from 19% to 40%, while transport vulnerability varies between 22% and 43%. Notably, the analysis reveals that households in higher income categories can still be highly vulnerable, depending on household size, composition and dependency, which impact energy and transport needs. As such, the research findings reveal that the current legislative frameworks may not fully capture the vulnerability of certain demographic groups in the event of additional costs of fossil fuels due to new climate policies. Therefore, it is strongly recommended that policymakers adjust criteria to better target vulnerable households based on their unique characteristics and needs, and use improved data collection systems to monitor energy and transport poverty and vulnerability.

Analysis of Forest Change Detection Induced by Hurricane Helene Using Remote Sensing Data

The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity.

Path Quality Aware Energy Efficient Technique to Increase Packet Delivery Ratio in the Internet of Things

Objectives: The key objectives are to enhance the performance of Internet of Things (IoT) networks to attain a high packet delivery ratio (PDR), efficient residual energy (RE) usage, minimum energy consumption, and decrease packet loss during data transmission. Performance metrics are used to discover and choose high, top-notch, and long-living paths, increasing network lifetime and optimizing resource management by preventing the participation of non-essential nodes and inefficient paths in the network routing process. Methods: To devise an energy-efficient technique and refine it with decision-making for selecting an optimal path, which employs a composite metric path quality index (PQI) to enhance path quality, comprising RE for priority of nodes with maximum remaining energy, link quality score (LQS), packet delivery ratio (PDR) and hop count (HC) to minimize the path length must be integrated into the routing process to make data transmission more efficient. Hence, intermediate nodes ensure path quality, determine if packet flow is good or bad, eliminate unnecessary nodes, and optimize the whole routing by deciding on a path. The proposed technique simulates IoT network scenarios in which nodes dynamically compute these metrics and select optimal paths for transmitting data to the sink node. Findings: The proposed technique achieves a much higher PDR and an energy-proficient routing mechanism by balancing the quality of the optimal path, the residual energy, and the number of hops, which can not only prolong the network lifetime but also ensures that resources are being used efficiently, in turn all leading to a better network performance than existing methods. Simulation results show that the proposed technique significantly improves the PDR over the existing methods and reduces overall energy consumption. In this work, the validated results show that metrics PQI, RE, LQS, and HC collectively improve data delivery and ensure higher survivability of the network by optimizing the path choosing and eliminating the unnecessary nodes from the source to the target. The proposed technique simulation showed a 32.15% increase in network RE and a 36.44% improvement in PDR. Novelty: The technique presented in this paper introduces a routing approach designed with the unique integration of RE, LQS, PDR, and HC, deriving PQI metric, which will improve the path selection in IoT networks. Unlike existing methods, this method considers energy efficiency, path quality instead of link quality, and path length together to avoid over-utilizing energy-draining nodes while favoring stable, efficient paths. The approach fills a gap in existing IoT environments by focusing on energy-wise efficiency and path reliability and reducing the number of nodes unnecessarily concerned with the dynamic optimal path selection. As IoT deployments increase, so does the need for energy-efficient data transmission, and the proposed technique supports the sustainability of IoT networks. Keywords: Energy Efficiency, Path Quality Indicator, Residual Energy, Hop Count, Quality of Service

Periodontal Inflammation and Serum Inflammatory Markers in Community-Dwelling Older Adults in Japan: The Otassha Study.

To investigate the associations between periodontal inflammation-as determined by the periodontal inflamed surface area (PISA)-and serum inflammatory markers in community-dwelling older adults in Japan. This cross-sectional study included 470 adults (mean age: 73.1 years). The composite inflammatory marker z-score (CIMZ) was calculated as the sum of the participants' individual z-scores for C-reactive protein (CRP), interleukin-1beta (IL-1β), IL-6 and tumour necrosis factor-α (TNF-α). The associations of PISA (quartiles) with individual biomarkers (continuous, log-transformed) and CIMZ (dichotomized, highest quartile or not) were assessed using linear or Poisson regression models. Compared with participants in the lowest PISA quartile (Q1), those in Q3 and Q4 had significantly (p < 0.05) higher CRP and IL-6 levels. Statistically significant linear trends (ptrend < 0.05) across the PISA quartiles were observed for CRP and IL-6. The multivariable adjusted prevalence ratios (95% confidence intervals) of high CIMZ (reference: Q1) were 1.20 (0.68-2.14), 1.66 (0.96-2.88) and 1.90 (1.08-3.34) (ptrend = 0.01) in individuals in PISA Q2-Q4. Older adults with high periodontal inflammation had high serum CRP and IL-6 concentrations and composite summary inflammatory indicator values. Periodontal inflammation is a potential modifiable factor of elevated inflammatory status among older adults in Japan.

The Effect of Exchange Rate Regimes on Price Stability in Sub-Saharan Africa (SSA): The Role of Institutions

The objective of this paper is to analyze the effect of exchange rate regimes on price stability through the role of institutions in order to draw lessons for SSA countries where this exchange rate policy has multiple financial, economic and social impacts. For this purpose, a panel data estimation using the Generalized Method of Moments in system was adopted and carried out on 35 countries for the period 1990-2020. Overall, the results show that, using the de facto classification of Ilzetzki, Reinhart and Rogoff (2019), under the direct effect on the one hand, membership of fixed exchange rate regimes reinforces the implementation of price stability policies, while flexible and intermediate exchange rate regimes rather disadvantage the extent of this stability. On the other hand, depending on the different combinations of exchange rate regimes with the composite index of political-economic institutions used, fixed and flexible exchange rate regimes reduce price volatility, while the intermediate exchange rate regime always disadvantages. We obtain a positive and significant effect of the composite index of institutions on inflation and also significant and expected effects of the different macroeconomic variables on inflation. For this reason, we recommend that these countries improve their framework of institutional configurations by providing favorable conditions for a monetary arrangement with capital mobility and a well-defined monetary regime, in order to maintain lower inflation in the long run, as well as political and economic stability in Sub-Saharan African countries. Then, as for the SSA countries that have opted for flexible and intermediate exchange rate regimes, we suggest that they opt for these regimes in order to further improve their room for manoeuvre offered by the natural autonomy of their monetary policy and take advantage of the competitiveness of their real equilibrium exchange rate regimes, which give better results in terms of financial equilibrium towards the path of sustained growth.

Integrated economic and fuzzy multi-criteria analysis for evaluating performance of fishing systems

Fishing has progressed from a subsistence activity to a multi-billion-dollar industry, but overfishing has put significant pressure on fishery resources, causing management and financial challenges. Evaluation of the comparative economics and performance of different fishing systems to identify the best fishing system will help to promote sustainable fishing envisioned by UN SDG Agenda 2030. The study was conceptualized to analyze the performance of fishing systems integrating economic and multi-criteria analysis. Total of 150 fishers from five different fishing systems viz. multiday trawler, multiday gillnetter, purse and ring seiner, motorized and non-motorized (traditional) systems has been considered for the study with eleven performance indicators. Methodologies like fuzzy VIKOR (FVIKOR), a multi-criteria decision-making approach that ranks alternatives by measuring their distance from the ideal solution, cost–benefit analysis and descriptive statistics were adopted to analyze the data. The FVIKOR analysis revealed that the motorized crafts followed by multiday gillnetter, purse and ring seiner, multiday trawler and non-motorized craft exhibited the best performance respectively. And the benefit-cost (BC) analysis revealed the maximum profitability for multiday gill netters with B-C ratio of 1.52 followed by motorized (1.49), purse & ring seiners (1.45), multiday trawler (1.20) and non-motorized (1.07) fishing systems. In contrast to the traditional deterministic and mono-criterial approach, integrating economic analysis with fuzzy multicriteria framework has helped to improve the knowledge of various fishing systems. FVIKOR methodology allows analysis of comprehensive performance of fishing systems more efficiently and conveniently. It is very helpful to aggregate the different indicators into a single composite index, making it easier to grasp the results, conveying relevant information for decision making in fisheries management.

Serum Lp(a) and hs-CRP correlate with somatic parameters including MLPA subgroups in Children with Prader-Willi Syndrome

Abstract Context Prader-Willi Syndrome is one of the commonest mono-chromosomal (15q11-13), polygenic syndromic childhood obesity. Objectives We primarily compares and correlate serum Lp(a), hs-CRP,and baseline clinical characteristics of genetically confirmed children with PWS at their growth-hormone treatment-naïve stage to their control groups. Secondary objectives were to correlate serum Lp(a) and hs-CRP concentration to MLPA subgroups,body composition indices,sleep apnea parameters,and hepatic shear-stress by 2D-Shear-Wave-Elastography in children with PWS. Methods Total 32 genetically confirmed PWS children (age 5-to-18-years),20simple obesity-&amp; 20healthy children as age-matched control groups were studied for the primary and secondary study objectives. Results Lipoprotein-(a) was in higher levels in study group compared to study control groups(p&amp;lt;.0001), but there was no difference found in-between the control groups(p=.9680).In addition,there was no correlation were detected in Lp(a) level in the study population in respect to their body weight,BMI and waist circumference.hs-CRP level was also higher in study population compared to both control groups(p=.0962;p&amp;lt;.0001); in contrast to Lp(a), in-between the control groups,it differs significantly(p=.002). Lower Fat-Free-Mass-Index(FFMI) correlated with higher levels of serum Lp(a)[r=-0.5525;p=.001]. Whereas,FFMI wasn’t correlated with hs-CRP level in PWS children(p=.657). Based on genomic subtypes,patients with PWS were divided into deletion and nondeletion genetic sub-group.We had significantly altered levels of Lp(a), hs-CRP,fat-free-mass,and sleep apnea parameters,particularly in the deletion subgroup. Conclusion Serum Lp(a) as-well-as hs-CRP stand out to be the core independent risk factors along with their strong correlation with the other study parameters, which necessitates the role of future targeted therapeutics in Prader-Willi Syndrome especially in Deletion pathology. Thus, Genetic subtyping during diagnostic confirmation endorse further prognostic elaboration.

Correlating the Effects of UV Aging on the Macro-Micro Behaviors of Asphalt with Its Molecular Mechanisms

UV radiation can change the internal molecular composition, macroscopic rheological properties, and microscopic chemical composition of asphalt. To study the effect of ultraviolet aging on asphalt and its structure–activity relationship, its rheological properties were measured by dynamic shear rheology and multiple stress recovery creep tests, its chemical compositions were measured by component composition, elemental composition, and infrared spectrum tests, and its molecular weight, distribution, and molecular structure were determined by gel permeation chromatography and nuclear magnetic resonance tests. Then, the molecular weight and molecular structure, rheological properties, and microchemical aging behavior of asphalt after UV aging were characterized by correlation analysis, and the structure–activity relationship was analyzed. The results show that the deformation resistance and elastic recovery ability of asphalt after UV aging are enhanced, and the flow performance is decreased. The ultraviolet radiation caused the aromatic hydrocarbons containing naphthenes and long alkyl chains in the asphalt to break and connect with asphaltenes with a ring structure. The asphaltene content in each bitumen sample exceeded 46%, and that in KL reached 55%, indicating that the bitumen changed into a gel structure. UV aging causes the aggregation of asphalt molecules, and the aggregation of molecules narrows the molecular distribution boundary and moves in the direction of macromolecules, resulting in the reduction of the dispersion coefficient by 2–10%. Hydrogen atoms will undergo condensation and substitution reactions due to long-chain breaking, cyclization, or aromatization under UV action, and the breaking of C=C bonds in carbon atoms will increase the stable aromatic ring, strengthen the stiffness of the molecular backbone, and make it difficult for the backbone to spin. Through correlation analysis, it was found that the molecular composition index could characterize the aging behavior index of asphalt, and that the aromatic structure was the most critical molecular change. Further, it was found that the sulfoxide group and carbonyl group could be used as evaluation criteria for the UV aging of asphalt because the correlation between them was above 0.7. This study provides an essential index reference for evaluating the performance change of asphalt under ultraviolet aging to save testing time. Moreover, the molecular structure characterization revealed the changes in internal molecular composition that were behind the observed aging properties, providing a theoretical basis for research on asphalt anti-aging technology.

Analysis of The Influence of Corporate Governance and Financial Performance on Firm Value

The energy sector plays a crucial role in increasing the productivity of companies while driving a country's economic growth. Throughout 2024, the sector recorded a positive performance with an increase in the energy stock index (IDX Energy) of 28.01%, despite the decline in the Jakarta Composite Index (JCI). This study aims to examine how corporate governance and financial performance contribute to increasing firm value. Using a literature review approach, the analysis was based on previous studies to understand the relationship between governance, profitability and firm value. The study results show that the implementation of good governance, such as the principles of transparency and accountability, can increase investor confidence, reduce agency conflicts, and optimize company performance. On the other hand, profitability as an indicator of financial performance is proven to have a positive impact on firm value, reflecting market confidence and long-term growth potential. The implications of this study emphasize the importance of implementing effective governance and increasing profitability to strengthen the competitiveness of companies at the national and international levels.

Determinants to Stay and Digital Nomad Satisfaction Index in Bali as Digital Nomad Agglomeration Area

Digital nomads choose destinations that provide tourism facilities, as well as support for remote workers. Bali, especially Canggu and Ubud, are among the world's five main destinations for digital nomads. This study aimed to analyse the determinants of the willingness of digital nomads to stay and their satisfaction with staying in Bali. Data collection in this study was carried out using a questionnaire instrument. The analysis was carried out using SEM-PLS regression analysis techniques, composite index analysis to determine the Digital Nomad Satisfaction Index (DNSI) and the Importance-Performance Analysis (IPA). Results analysis of determinants of willingness to stay: It is known that remote working environment, tourism environment, public facilities and services, community attachment, and cost and benefit have a significant positive effect on the willingness of digital nomads in Bali. Digital nomads are generally satisfied living in Bali. Future policies focus on improving performance in terms of visa processing, healthcare services, network expansion opportunities in events, and housing affordability.