Multiple Indicators Research Articles

Effect of transgene on salt tolerance of tobacco.

To explore the effects of salt-tolerance gene accumulation on salt tolerance in transgenic plant, we used four types of plant expression vector (N27, N28, N29, and N30) carrying mtlD, mtlD + gutD, mtlD + gutD + BADH, mtlD + gutD + BADH + sacB genes respectively, to transform tobacco through Agrobacterium-mediated method. Transgenic lines were identified through polymerase chain reaction (PCR) detection. Transgenic lines and non-transgenic plant (CK) were subjected to 6‰ sodium chloride solution stress; then, fluorescence quantitative PCR (FQ-PCR) and salt tolerance indexes were used to assess characteristics. PCR showed the exogenous genes had been integrated into the tobacco genome. FQ-PCR showed under clean water treatment the target genes were expressed in all transgenic plants at the transcriptional level. The transcript abundances of target genes changed with the number of genes increased, and improved following salt stress. Comparative analyses of salt tolerance indexes showed height growth, biomass (except for N29), chlorophyll content, net photosynthetic rate, Fv/Fm, and PI of all transgenic plants and CK were lower under salt stress than under clean water treatment, to varying degrees. However, the descent ratio was smaller in transgenic plants. A comprehensive evaluation of multiple salt-tolerance indicators performed using the membership function method showed the average salt tolerance of each vector transgenic line was higher than that of CK, and salt tolerance was greater in transgenic polyvalent gene lines than in transgenic monovalent gene lines. The average salt tolerance was N29 > N28 > N30 > N27 > CK. This study provides a theoretical and practical reference for salt tolerance breeding in other plants.

Uniform Differential Item Functioning Across Gender, Grade Level and Racial Groups: A MIMIC Investigation of the Non-clinical Parent Ratings of the Pediatric Symptom Checklist-17

The Pediatric Symptom Checklist-17 (PSC-17) is a screening instrument designed to measure children’s behavioral and emotional problems. This study investigated the factor structure of the PSC-17 and the uniform differential item functioning (DIF) of the PSC-17 item scores as a function of school children’s gender, grade level, and racial/ethnic groups. Parent ratings of 1,305 children from pre-K to Grade 1 were used. Confirmatory factor analysis (CFA) of the scale’s factor structure indicated that parent ratings of PSC-17 were composed of three subscales: Externalizing Problems, Internalizing Problems, and Attention Problems. A multiple indicators multiple causes (MIMIC) analysis results showed that four of the PSC-17 items exhibited statistically uniform DIF as a function of race/ethnicity but not as a function of gender or grade level. Uniform DIF had little impact on latent mean differences of Internalizing, Attention, and Externalizing Problems among gender, race/ethnicity, and grade-level groups. This study’s results implied that teachers or schools should be cautious with comparing racial/ethnic groups at the item level. However, they can compare children’s subscale scores across gender, race/ethnicity, and grade levels with parent ratings of the PSC-17.

Latent Variable Interactions with Categorical Indicators: Continuous and Categorical Latent Moderated Structural Equations Approaches

Social science phenomena are often predicted by interactions between variables. When these variables cannot be directly observed, one option is to model them as latent variables that are measured by multiple indicators. When indicators are continuous, latent interactions can be modeled and estimated using the latent moderated structural equations (LMS) approach. A categorical LMS (LMS-cat) approach with full information estimation was more recently developed. While previous research suggests that ordered categorical indicators can sometimes be treated as continuous, comparisons between LMS and LMS-cat are sparse. In this study, we evaluate continuous and categorical LMS for the estimation of latent interactions under ordinal indicators with 2, 3, 5, and 7 categories. Further, we compared the performance of frequentist and Bayesian estimation for both LMS models. Results suggest that categorical approaches are a safer choice, and that frequentist and non-informative Bayesian estimation approaches perform similarly.

The Development and Validation of a Nomogram for Predicting Sepsis Risk in Diabetic Patients with Urinary Tract Infection

Background and Objectives: Urinary tract infection (UTI) is a common comorbidity in diabetic patients, making up one of the causes of sepsis. This study aims to develop a nomogram to predict the risk probability of sepsis in diabetic patients with UTI (DPUTIs). Materials and Methods: This is a retrospective observational study. Clinical data for DPUTIs were extracted from the Medical Information Mart for Intensive Care IV database. Eligible DPUTIs were randomly divided into training and validation cohorts in a 7:3 ratio. Independent prognostic factors for sepsis risk were determined using least absolute shrinkage and selection operator (LASSO) regression and multivariate logistic regression. A corresponding nomogram based on these factors was constructed to predict sepsis occurrence in DPUTIs. The discrimination of the nomogram was assessed by multiple indicators, including the area under the receiver operating characteristic curve (AUC), net reclassification improvement index (NRI), and integrated discrimination improvement (IDI). In addition, a calibration curve and decision curve analysis (DCA) were used to evaluate the performance of the nomogram. Results: A total of 1990 DPUTIs were included. Nine independent prognostic factors were identified as predictive factors for sepsis risk in DPUTIs. The prognostic factors included urine red blood cell classification (urine RBC cat), urine white blood cell classification (urine WBC cat), blood glucose, age, temperature, white blood cells (WBCs), sequential organ failure assessment (SOFA) score, lymphocytes, and hematocrit. The AUC, NRI, and IDI of the nomogram indicated robust discrimination. The calibration curve and Hosmer–Lemeshow test showed good calibration of the nomogram. The DCA curve demonstrated a better clinical utility of the nomogram. Conclusions: The nomogram established in this study helps clinicians predict the probability of sepsis in DPUTIs, providing evidence for optimizing the management of related risk factors.

Quality of antenatal care services in Afghanistan: findings from the national survey 2022–2023

BackgroundThe optimum use of antenatal care (ANC) services can reduce pregnancy-related complications and deaths. However, there is limited information on the quality of ANC services in Afghanistan. This study aimed to assess the quality of ANC services and examine the influence of sociodemographic factors on the quality of ANC services received by pregnant women in Afghanistan.MethodsThis study analyzed the data from the 2022–23 Afghanistan Multiple Indicators Cluster Survey (MICS), including 9,243 ever-married women aged 15–49 years who had given birth two years prior to the survey. ANC quality was classified as "good" if a woman had at least four ANC visits, initiated ANC during the first trimester, and received all four essential ANC services. Binary logistic regression was employed to examine the likelihood of receiving good-quality ANC across various sociodemographic variables.ResultsOnly 6.2% of women received good-quality ANC. Factors significantly associated with receiving good-quality ANC included woman’s secondary (AOR:1.87, 95%CI: 1.33–2.63) and higher education levels (AOR: 1.75, 95%CI: 1.01–3.03), household head’s higher education level (AOR:1.63, 95%CI: 1.11–2.40), and wealth status (1.71, 2.26, 1.92, and 1.97 higher odds of receiving good-quality ANC for 2nd, 3rd, 4th, and 5th as compared to 1st quintiles of wealth, respectively).ConclusionThe low utilization of good-quality ANC among women in Afghanistan, particularly among those at lower wealth status, with low education levels, underscores the need for targeted interventions to improve access and utilization of antenatal care services.

The global Multidimensional Poverty Index: Harmonised level estimates and their changes over time

This paper describes the database The global Multidimensional Poverty Index (MPI): Harmonised level estimates and their changes over time. The global MPI is an international poverty measure based on ten deprivation indicators in three dimensions: health, education, and living standards. The database contains estimates for the MPI itself (the adjusted headcount ratio); related partial indices such as headcount ratio, intensity, indicator-specific indices, and several auxiliary statistics; and changes over time for most quantities. For this database, all deprivation indicators have been harmonised over time. Our database covers estimates for 84 countries and 814 subnational regions for up to four points of observation. The estimates are based on 211 individual survey datasets, provided primarily by the Demographic Health Surveys (DHS) and the Multiple Indicator Cluster Surveys (MICS). Combining information about different dimensions of human wellbeing, the global MPI inherently invites interdisciplinary research.

Research on noise-induced hearing loss based on functional and structural MRI using machine learning methods

Noise-induced hearing loss (NIHL) is a common occupational condition. The aim of this study was to develop a classification model for NIHL on the basis of both functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) by applying machine learning methods. fMRI indices such as the amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree of centrality (DC), and sMRI indices such as gray matter volume (GMV), white matter volume (WMV), and cortical thickness were extracted from each brain region. The least absolute shrinkage and selection operator was used to reduce and select the optimal features. The support vector machine (SVM), random forest (RF) and logistic regression (LR) algorithms, were used to establish the classification model for NIHL. Finally, the SVM model based on combined fMRI indices, achieved the best performance, with area under the receiver operating characteristic curve of 0.97 and an accuracy of 95%. The SVM classification model that integrates fMRI indicators has the greatest potential for identifying NIHL patients and healthy people, revealing the complementary role of fMRI indicators in classification and indicating that it is necessary to include multiple indicators of the brain when establishing a classification model.

Impact of Leadership Style on Employee Green Behavior: The Mediating Role of Employee Over-Qualification Perception

This study examines the impact of Leadership Style on Employee Green Behavior and the mediating role of Employee Over-Qualification Perception. The findings reveal that Transformational Leadership significantly enhances Employee Green Behavior, while leadership support reduces Employee Over-Qualification Perception, thereby promoting pro-environmental actions. It is recommended that organizations focus on developing leadership capabilities and creating supportive environments to encourage Employee Green Behavior. The effect of leadership style is more pronounced for employees with high self-efficacy, indicating that companies should design incentive programs tailored to individual employee characteristics. The study also identifies that the influence of leadership style on pro-environmental behavior varies by company size, with larger enterprises needing to adjust strategies to effectively promote green behavior. Continuous training and cultural development are essential for building sustainable green organizations. Companies should establish dynamic evaluation systems to regularly update strategies and ensure the sustainability of pro-environmental behavior. Leaders should act as role models for green behavior, enhancing employees’ sense of belonging and fostering a culture of environmental participation across the organization. Evaluation systems should include multiple indicators to ensure the effectiveness and specificity of strategies. Through diversified training and cultural initiatives, companies can strengthen environmental awareness, maintain the continuity and stability of green behavior, and ultimately achieve harmony between corporate development and environmental sustainability.

Multi-Year Multi-Indication Agreements for Supporting Patient Access to Oncology Medicines with Multiple Indications: An Experimental Approach or Here to Stay?

Over the past decade, an increasing number of oncology medicines with indications for multiple cancer types have been delivering benefits to patients. To ensure these products reach patients, pricing and reimbursement systems have had to adapt to address the value assessment, time-to-access, affordability, and budget uncertainty challenges this creates. Multi-year multi-indication (MYMI) agreements are made between payers and manufacturers and aim to ensure that patients have access to effective treatments for multiple conditions over time; this includes future indications of the treatment. MYMI agreements were first introduced as a solution in several European countries in 2017, offering a range of potential benefits. MYMI agreements have since demonstrated evidence of success in mitigating many of the challenges associated with assessing and reimbursing multi-indication products, time-to-patient access, and budget impact. The purpose of this article is to discuss the recent progress made with MYMI implementation across countries and provide a view on whether it is delivering for patients, healthcare systems, and innovators. We find that MYMI is not a one-size-fits-all solution but a model that needs to be adapted to the unique needs and characteristics of different healthcare systems. The intended benefits of MYMI to patients (speed and breadth of access to new indications) appear to have been realised in practice in some countries but not all. However, the administrative burden associated with MYMI implementation in some countries risks jeopardising the intended efficiency benefits. Payers and policymakers can also benefit from improved budget predictability and sustainability.

A resilience assessment framework for slopes considering multiple performance indicators

The performance assessment of slopes during various disturbances is important to ensure the safe service of slopes. Due to the four characteristics of slope engineering, it is difficult for existing resilience evaluation models to accurately reflect slope performance. To address this problem, a resilience evaluation model applicable to slopes was developed in this study. In this model, a typical slope performance curve is proposed according to the characteristics of slope engineering and the calculation formula of slope resilience metric is derived. In order to facilitate engineering application, this study proposes a slope resilience assessment framework with the evaluation model as the core. In the resilience assessment framework, a multi-indicator system for slope performance was established to cover the slope components, and the CRITIC theory was used to combine the subjective and objective weights for each indicator. Finally, the proposed slope resilience assessment framework was applied to a slope case under the influence of continuous excavation and rainfall. The results show that the proposed resilience assessment framework can more accurately reflect the performance variations of slopes during disturbance and recovery processes than the existing model, and the resilience metric is 23.4% higher than that of the existing model. This study was the first attempt to establish a resilience assessment framework applicable to slopes, which informs subsequent research.

Association of biochemical indicators with multimorbidity in 19,624 older adult individuals with chronic diseases: a study from Jindong District, Jinhua City, China

BackgroundChronic disease multimorbidity is influenced by multiple factors, but with little knowledge on the impact of biochemical indicators. This study aims to investigate the prevalence of multimorbidity of chronic diseases among older adult individuals in the community, as well as the factors related to biochemical indicators associated with chronic disease multimorbidity.MethodsThe study included 19,624 older adult individuals aged 60 and above in Jindong District, Jinhua City, Zhejiang Province, China. Participants completed a national standardized older adult health examination in the community. Chi-square tests and logistic regression were employed to evaluate the potential factors of biochemical indicators related to multimorbidity of chronic diseases.ResultsThe multimorbidity rate of chronic diseases in older adult patients is 70.3%. Each chronic disease coexists with one or more other chronic diseases in over 75% of cases. Among the biochemical indicators, hemoglobin (Hb) (OR = 1.46, 95%CI: 1.13–1.90), white blood cell count (WBC) (OR = 1.25, 95%CI: 1.02–1.54), red blood cell count (RBC) (OR = 1.36, 95%CI: 1.10–1.69), urinary protein (U-PRO) (OR = 1.10, 95%CI: 1.02–1.19), urinary glucose (U-GLU) (OR = 1.44, 95%CI: 1.23–1.67), alanine aminotransferase (ALT) (OR = 1.71, 95%CI: 1.39–2.10), aspartate aminotransferase (AST) (OR = 1.22, 95%CI: 1.05–1.41), creatinine (Cr) (OR = 1.28, 95%CI: 1.16–1.42), uric acid (UA) (OR = 1.36, 95%CI: 1.22–1.51), total cholesterol (TC) (OR = 1.76, 95%CI: 1.59–1.95), triglycerides (TG) (OR = 2.63, 95%CI: 2.46–2.82), low-density lipoprotein cholesterol (LDL-C) (OR = 1.84, 95%CI: 1.60–2.11), high-density lipoprotein cholesterol (HDL-C) (OR = 10.99, 95%CI: 8.12–14.90), and fasting blood glucose (FBG) (OR = 1.89, 95%CI: 1.74–2.05) are associated with the risk of multimorbidity of chronic diseases (p < 0.05). Among these, lipid parameters demonstrated the strongest associations with multimorbidity risk, with low HDL-C showing an 11-fold increase and elevated TG a 2.63-fold increase.ConclusionThis study found that the prevalence of multimorbidity among older adult individuals in this region reached 70.3%. Multiple biochemical indicators were significantly associated with multimorbidity, particularly lipid parameters (low HDL-C and elevated TG), glucose parameters (elevated FBG and positive U-GLU), liver function (elevated ALT), and hemoglobin levels. These findings provide important evidence for research on factors associated with multimorbidity in the older adult population.

Global trends in surgically based treatment of anal fistula in Crohn's disease: a bibliometric and visualization analysis.

Crohn's disease (CD) is a chronic, recurrent gastrointestinal disorder characterized by a complex etiology. Among its perianal complications, anal fistulas represent a challenging comorbidity. With the increase of surgical options, a comprehensive bibliometric analysis was deemed necessary to consolidate the vast array of research in this field. We extracted 1608 articles spanning from 1 January 1994, to 1 May 2024, from the Web of Science Core Collection. Using VOSviewer, CiteSpace, and Scimago Graphica for visual analytics, we synthesized key trends across multiple bibliometric indicators, encompassing geographic and institutional contributions, individual authorship, journal prominence, citation metrics, and thematic prevalence. From the delineated corpus, we identified publications from 325 countries and 5110 research institutions, with the US and UK at the forefront of publication volume and academic impact. The data indicated a leading role for institutions like the Cleveland Clinic and Imperial College London. "Diseases of the Colon and Rectum" emerged as a central journal due to its high publication and citation frequency. Distinctly, the analysis uncovered trending keywords, signifying the field's prioritization on surgical intervention, biologic therapy, imaging modalities, and emerging biological treatments. Our findings elucidate a trajectory toward prominent advancements in CD fistula research. This analysis underscores the field's shift towards integrative treatment strategies, spotlighting the pressing need for comprehensive comparative studies of surgical approaches. It underscores the imperative for robust clinical trials to standardize treatments and extend care to a broader CD patient population.

Pricing for Multi-Indication Drugs in the Italian Regulatory Context.

The authorization of new therapeutic indications for drugs already reimbursed by the Italian National Health Service (NHS) represents a matter of importance. This study aims to estimate the additional discount attributed to the extension of indications (EoIs) to explore the potential correlation between spending and negotiated discounts and to find specific factors (determinants) that impact on discount. The study focused on drugs approved in Italy between 2003 and 2017 with at least four therapeutic indications, including the first approved and EoIs, with follow-up extended until 2021 to acquire all the information on the negotiation process that has been completed. Data were obtained from reimbursement and pricing dossiers, and negotiation assessments. Trends in the number of EoIs submitted and the additional discounts negotiated were analyzed, along with the relationship between the negotiated discount and subsequent drug expenditure. Determinants influencing the extent of the negotiated discount were assessed, including drug type, orphan drug designation, innovativeness status, number of EoIs, disease incidence and prevalence, estimated number of patients, revenue projections, availability of therapeutic alternatives, and efficacy outcomes. A Wilcoxon nonparametric test was used to evaluate the associations between determinants and the negotiated additional discount, with a significance level of 0.05. The study identified nine medicines: five of these were used in onco-hematologic therapeutic areas, while the remaining four were immunosuppressants for dermatologic and/or rheumatologic conditions. These nine active substances accounted for 65 approved therapeutic indications, of which 50 were reimbursed by the Italian NHS, including the first indication; the analysis focused only on 40 reimbursed EoIs. The additional discount obtained for EoIs averages approximately 12.5% (95% CI 9.4-16.6%), with a median value of approximately 11%. This latter value was used as the threshold in the analysis of the determinants potentially impacting the negotiated discount amount. Discounts greater than 11% were significantly associated with EoI beyond the fifth and oncology drugs. The additional discount seemed small when compared with the increased spending. AML seems to incur substantial direct economic expenses. Reducing the days of hospitalization can significantly decrease the economic burden of AML in the European Union. Moreover, there is a necessity for studies that comprehensively evaluate the economic implications, particularly concerning total and indirect costs. The study provides valuable insights into the negotiation outcomes for medicines with multiple therapeutic indications, particularly in onco-hematologic and immunosuppressive areas. The analysis revealed that additional discounts for EoIs averaged 12.5%, with a median of 11%, a value used to assess the impact of specific determinants. A discount higher than 11% was statistically correlated with drugs having more than five indications and oncology treatments, showing their influence in negotiations. However, the savings from discounts were modest relative to the increased drug spending as more indications were approved. This suggests an imbalance between the cost control achieved through discounts and the rising expenditure due to expanded drug use.

Decline in global biodiversity intactness over the past two decades.

Knowledge of the dynamics of biodiversity intactness and its spatial differentiation over different geographic regions of the world is crucial for the improved design of effective biodiversity conservation strategies. However, comprehensive investigations of the multiple indicators of biodiversity intactness across several spatial scales are lacking. The current study used an annual time series (2000-2020) of the biodiversity intactness index (BII) to investigate the mean magnitude, temporal trajectory, and relative changes in biodiversity intactness at the national, regional, and global levels. Global mean magnitude of BII was estimated to be 76±16% between 2000 and 2020, accompanied by the highly diverse BII variations across geographic regions. There has been a gradual decrease in biodiversity intactness over the past 20years, with the global mean BII trend of -0.3±1.9%/decade. Africa and Europe have the largest decrease and increase in BII of -1.4±2.2%/decade and 1.0±1.7%/decade, respectively. The countries with the top ten BII parameters are mainly located in Africa and Asia, whereas the opposite is true in Europe. The top 10 countries with positive BII trends were mainly in Europe (70%), followed by Africa (80%) and Asia (20%). There was a negative difference between the global mean BII for 2011-2020 and 2000-2010, as evidenced by the relative change in BII of 4.1%. This study provides an elaborate interpretation of the current status and possible future paths of abundance-based biodiversity intactness at multiple spatial scales, which is beneficial in elucidating biodiversity intactness dynamics and potentially supports biodiversity conservation.

Comprehensive evaluation of health community construction based on biomechanical perspective

The comprehensive evaluation of healthy community construction is an important means for managers and decision-makers to understand the development of community health within their jurisdiction, and also a scientific basis for formulating practical and feasible health policies and health development plans. Scientific evaluation of the overall level and differences in the construction of healthy communities has important theoretical significance and reference value. Based on the principal component clustering analysis method as well as the theoretical foundation, the multiple evaluation indicators in the evaluation index system of health community construction are standardized and dimensionally reduced. At the same time, the principal component factor load matrix clustering analysis is used, combined with the practical significance and evaluation direction of the indicator categories, to propose a design idea for the classification index system. The comprehensive principal component evaluation and clustering analysis are used to quantitatively examine the level of health community construction in specific regions and make horizontal comparisons, provide theoretical support and decision-making reference for the construction of healthy communities. The correctness and effectiveness of the proposed method system were verified by numerical examples.

The proper application of logistic regression model in complex survey data: a systematic review

BackgroundLogistic regression is a useful statistical technique commonly used in many fields like healthcare, marketing, or finance to generate insights from binary outcomes (e.g., sick vs. not sick). However, when applying logistic regression to complex survey data, which includes complex sampling designs, specific methodological issues are often overlooked.MethodsThe systematic review extensively searched the PubMed and ScienceDirect databases from January 2015 to December 2021, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focusing primarily on the Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS). 810 articles met the inclusion criteria and were included in the analysis. When discussing logistic regression, the review considered multiple methodological problems such as the model adequacy assessment, handling dependence of observations, utilization of complex survey design, dealing with missing values, outliers, and more.ResultsAmong the selected articles, the DHS database was used the most (96%), with MICS accounting for only 3%, and both DHS and MICS accounting for 1%. Of these, it was found that only 19.7% of the studies employed multilevel mixed-effects logistic regression to account for data dependencies. Model validation techniques were not reported in 94.8% of the studies with limited uses of the bootstrap, jackknife, and other resampling methods. Moreover, sample weights, PSUs, and strata variables were used together in 40.4% of the articles, and 41.7% of the studies did not use any of these variables, which could have produced biased results. Goodness-of-fit assessments were not mentioned in 75.3% of the articles, and the Hosmer–Lemeshow and likelihood ratio test were the most common among those reported. Furthermore, 95.8% of studies did not mention outliers, and only 41.0% of studies corrected for missing information, while only 2.7% applied imputation techniques.ConclusionsThis systematic review highlights important gaps in the use of logistic regression with complex survey data, such as overlooking data dependencies, survey design, and proper validation techniques, along with neglecting outliers, missing data, and goodness-of-fit assessments, all of which point to the need for clearer methodological standards and more thorough reporting to improve the reliability of results. Future research should focus on consistently following these standards to ensure stronger and more dependable findings.

A Previously Unknown Building Structure in Ancient Olympia (Western Peloponnese, Greece) Revealed by Geoarchaeological Investigations and Its Interpretation as a Possible Harbor

The ancient site of Olympia is located on the northern fringe of the Basin of Makrisia at the confluence of the Kladeos and Alpheios rivers (western Peloponnese, Greece) and was used as a venue for the Panhellenic Games from Archaic times until the 4th century AD. Geophysical prospection (frequency domain electromagnetic induction and electrical resistivity tomography) was carried out as a basis for detailed geoarchaeological investigations. In doing so, we identified a previously unknown building structure adjacent to the Altis, the inner part of the sanctuary at Olympia. Situated south of the Southwest Thermae, this structure measures at least 100 m (WSW-ENE) by 80 m (NNW-SSE). Its external orientation is in line with the orientation of the Southwest Thermae and the Leonidaion. We retrieved sediment cores from 17 different locations in combination with high-resolution direct push sensing from inside the newly found structure. All cores revealed distinct units of organic-rich limnic sediments dominated by clay and fine silt. Geochemical and micropaleontological analyses of selected sediment samples indicate highly eutrophic conditions, as evidenced by elevated phosphorous concentrations and the dominance of the ostracod species Cyprideis torosa, which is able to live under low-oxygen conditions. Moreover, molecular biomarker analyses show a significant input of lipid fecal markers, implying strong anthropogenic pollution. Further, the limnic sediments include numerous charcoal remains and abundant diagnostic artifacts such as ceramic fragments and building material. Radiocarbon dating documents that these limnic conditions persisted within the building structure from at least the 5th century BC to the 6th century AD. The identified building structure lies in the immediate proximity to the Lake of Olympia, which was recently found to have existed from the mid-Holocene to the Medieval period. Its characteristic filling with fine-grained sediments and multiple indications for a strongly polluted and heavily used standing water environment let us hypothesize that it was possibly used as a harbor installation. A harbor at ancient Olympia could have been used to reach the sanctuary by boat and to transport goods of all kinds.

Developing surrogate models for the early-stage design of residential blocks using graph neural networks

Abstract Building simulation based on physical modeling is commonly adopted for performance prediction, but the high time costs hinder its application in the early design stage of buildings. Data-driven surrogate models have been proposed as a means to replicate computationally expensive simulation models. However, existing surrogate models for sustainable residential block design are limited in scope, focusing either on individual buildings or on specific cases within multi-block projects. This study leverages graph neural networks to develop optimal surrogate models incorporating inter-building effects to predict multiple indicators of sustainable performance for residential blocks at a region level. A graph schema is proposed to represent the general geometric features and relations among buildings in residential block design. A regional dataset is generated for model training and testing, using real residential zones in Hong Kong. The surrogate models are developed and evaluated, using two kinds of architectures (individual architectures for specific indicators and an integrative architecture) and three different neural networks (graph attention network (GAT), graph convolutional network, and artificial neural network). The results showed that the surrogate models using the individual architectures and GAT outperform the models using other architectures and neural networks. These surrogate models achieve a high prediction accuracy with CV(RMSE)s of 11.79%, 7.63%, and 8.00% in terms of energy consumption, indoor thermal comfort, and daylighting, respectively, on the regional test set. Moreover, they enable a significant acceleration of the performance evaluation, reducing the calculation time from 6.346 min to 1.565 ms (243,297 times) per case compared to physics-based simulations.

Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators

Jointed SOH Estimation of Electric Bus Batteries Based on Operating Conditions and Multiple Indicators

Development and validation of machine learning models for MASLD: based on multiple potential screening indicators

Development and validation of machine learning models for MASLD: based on multiple potential screening indicators