Water treatment public-private partnership (PPP) projects are pivotal for sustainable water management but are often challenged by complex risk factors. Efficient risk management in these projects is crucial, yet traditional methodologies often fall short of addressing the dynamic and intricate nature of these risks. Addressing this gap, this comprehensive study introduces an advanced risk classification prediction model tailored for water treatment PPP projects, aimed at enhancing risk management capabilities. The proposed model encompasses an intricate evaluation of crucial risk areas: the natural and ecological environments, socio-economic factors, and engineering entities. It delves into the complex relationships between these risk elements and the overall risk profile of projects. Grounded in a sophisticated ensemble learning framework employing stacking, our model is further refined through a weighted voting mechanism, significantly elevating its predictive accuracy. Rigorous validation using data from the Jiujiang City water environment system project Phase I confirms the model's superiority over standard machine learning models. The development of this model marks a significant stride in risk classification for water treatment PPP projects, offering a powerful tool for enhancing risk management practices. Beyond accurately predicting project risks, this model also aids in developing effective government risk management strategies.
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http://dx.doi.org/10.2166/wst.2024.052 | DOI Listing |
Medicine (Baltimore)
January 2025
Department of Thyroid Surgery Nursing, The First Hospital of Jilin University, Changchun, China.
Background: This study aimed to investigate the effect of incentive nursing care (ICN) as a supplementary therapy to routine nursing care (RNC) on depression and anxiety (DA) in patients undergoing thyroid cancer (TC) surgery during the perioperative period (PPP).
Methods: A thorough search was conducted across various electronic databases, including PubMed, EMBASE, Cochrane Library, Chinese Biomedical Literature Database, WANGFANG, and China National Knowledge Infrastructure, spanning from their inception to October 1, 2023. The inclusion criteria were limited to randomized controlled trials focusing on the effects of ICN and RNC on DA in TC surgery during PPP.
Alzheimers Dement (Amst)
January 2025
Alzheimer Center Amsterdam, Neurology Amsterdam University Medical Center Amsterdam the Netherlands.
Introduction: We examined semantic and phonemic fluency in individuals with subjective cognitive decline (SCD) in relation to amyloid status and clinical progression.
Methods: A total of 490 individuals with SCD (62 ± 8 years, 42% female, 28% amyloid-positive, 17% clinical progression) completed annual fluency assessments (mean ± SD follow-up 4.3 ± 2.
MDM Policy Pract
January 2025
Department of Biomedical Signals and Systems, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
Unlabelled: Many breast cancer survivors experience cancer-related fatigue (CRF), and several interventions to treat CRF are available. One way to tailor intervention advice is based on patient preferences. In this study, we explore preference heterogeneity regarding between-attribute and within-attribute preferences.
View Article and Find Full Text PDFESC Heart Fail
January 2025
Department of Cardiology, Beijing Anzhen Hospital, Capital Medical University, National Clinical Research Center for Cardiovascular Diseases, Beijing, China.
Aims: This study aimed to investigate potential biomarkers for predicting incident heart failure (HF) in patients with atrial fibrillation and flutter (AF and AFL), utilizing proteomic data from the UK Biobank Pharma Proteomics Project (UKB-PPP).
Methods: This study analysed data from AF and AFL patients, split into discovery (n = 1050) and replication (n = 305) cohorts. Plasma biomarkers were screened using a multivariable-adjusted Cox proportional hazards model.
Am J Hum Genet
January 2025
Division of Biostatistics and Health Data Science, University of Minnesota, Minneapolis, MN, USA. Electronic address:
Alzheimer disease (AD) is a complex and progressive neurodegenerative disorder that accounts for the majority of individuals with dementia. Here, we aim to identify causal plasma proteins for AD, shedding light on the etiology of AD. We utilized the latest large-scale plasma proteomic data from the UK Biobank Pharma Proteomics Project (UKB-PPP) and AD genome-wide association study (GWAS) summary data from the International Genomics of Alzheimer's Project (IGAP).
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