UNASSIGNED: Numerous studies have developed or validated prediction models to estimate the likelihood of postoperative pneumonia (POP) in esophageal cancer (EC) patients. The quality of these models and the evaluation of their applicability to clinical practice and future research remains unknown. This study systematically evaluated the risk of bias and applicability of risk prediction models for developing POP in patients undergoing esophageal cancer surgery.
UNASSIGNED: PubMed, Embase, Web of Science, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature (CINAHL), China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), WanFang Database and Chinese Biomedical Literature Database were searched from inception to March 12, 2024. Two investigators independently screened the literature and extracted data. The Prediction Model Risk of Bias Assessment Tool (PROBAST) checklist was employed to evaluate both the risk of bias and applicability.
UNASSIGNED: A total of 14 studies involving 23 models were included. These studies were mainly published between 2014 and 2023. The applicability of all studies was good. However, all studies exhibited a high risk of bias, primarily attributed to inappropriate data sources, insufficient sample size, irrational treatment of variables and missing data, and lack of model validation. The incidence of POP in patients undergoing esophageal cancer surgery ranged from 14.60% to 39.26%. The most frequently used predictors were smoking, age, chronic obstructive pulmonary disease(COPD), diabetes mellitus, and methods of thoracotomy. Inter-model discrimination ranged from 0.627 to 0.850, sensitivity ranged between 60.7% and 84.0%, and specificity ranged from 59.1% to 83.9%.
UNASSIGNED: In all included studies, good discrimination was reported for risk prediction models for POP in patients undergoing esophageal cancer surgery, indicating stable model performance. However, according to the PROBAST checklist, all studies had a high risk of bias. Future studies should use the predictive model assessment tool to improve study design and develop new models with larger samples and multicenter external validation.
UNASSIGNED: https://www.crd.york.ac.uk/prospero, identifier CRD42024527085.

Mathematical modeling plays a crucial role in understanding and managing urban water systems (UWS), with mechanistic models often serving as the foundation for their design and operations. Despite the wide adoptions, mechanistic models are challenged by the complexity of dynamic processes and high computational demands. Data-driven models bring opportunities to capture system complexities and reduce computational cost, by leveraging the abundant data made available by recent advance in sensor technologies. However, the interpretability and data availability hinder their wider adoption. This paper advocates for a paradigm shift in the application of data-driven models within the context of UWS. Integrating existing mechanistic knowledge into data-driven modeling offers a unique solution that reduces data requirements and enhances model interpretability. The knowledge-informed approach balances model complexity with dataset size, enabling more efficient and interpretable modeling in UWS. Furthermore, the integration of mechanistic and data-driven models offers a more accurate representation of UWS dynamics, addressing lingering uncertainties and advancing modelling capabilities. This paper presents perspectives and conceptual framework on developing and implementing knowledge-informed data-driven modeling, highlighting their potential to improve UWS management in the digital era.

UNASSIGNED: Countries are recommended to immunise adolescent girls routinely with one or two doses of human papillomavirus (HPV) vaccines to eliminate cervical cancer as a public health problem. With most existing vaccine doses absorbed by countries (mostly high-income) with existing HPV vaccination programmes, limited supply has been left for new country introductions until 2022; many of those, low- and middle-income countries with higher mortality. Several vaccination strategies were considered by the Strategic Advisory Group of Experts on Immunization to allow more countries to introduce vaccination despite constrained supplies.
UNASSIGNED: We examined the impact of nine strategies for allocating limited vaccine doses to 100 pre-introduction countries from 2020 to 2030. Two algorithms were used to optimise the total number of cancer deaths that can be averted worldwide by a limited number of doses (knapsack and decreasing order of country-specific mortality rates), and an unoptimised algorithm (decreasing order of Human Development Index) were used.
UNASSIGNED: Routinely vaccinating 14-year-old girls with either one or two doses and switching to a routine 9-year-old programme when supply is no longer constrained could prevent the most cervical cancer deaths, regardless of allocation algorithm. The unoptimised allocation averts fewer deaths because it allocates first to higher-income countries, usually with lower cervical cancer mortality.
UNASSIGNED: To optimise the deaths averted through vaccination when supply is limited, it is important to prioritise high-burden countries and vaccinating older girls first.
UNASSIGNED: WHO, Bill & Melinda Gates Foundation.

Surface-energy anisotropy of metals is crucial for the stability and structure, however, its determining factors and structure-property relationship are still elusive. Herein, we identify three key factors for predicting surface-energy anisotropy of pure metals and alloys: the surface-atom density, coordination numbers and atomic radius. We find that the coupling rules of surface geometric determinants, which determining surface-energy anisotropy of face-centred-cubic (FCC), hexagonal-close-packed (HCP) and body-centred-cubic (BCC) metals, are essentially controlled by the crystal structures instead of chemical bonds, alloying or electronic structures. Furthermore, BCC metals exhibit material-dependent surface-energy anisotropy depending on the atomic radius, unlike FCC and HCP metals. The underlying mechanism can be understood from the bonding properties in the framework of the tight-binding model. Our scheme provides not only a new physical picture of surface stability but also a useful tool for material design.

In 2020, anthropogenic methane (CH4) emissions decreased due to COVID-19 containment policies, but there was a substantial increase in the concentration of atmospheric CH4. Previous research suggested that this abnormal increase was linked to higher wetland CH4 emissions and a decrease in the atmospheric CH4 sink. However, the impact of changes in the soil CH4 sink remained unknown. To address this, we utilized a process-based model to quantify alterations in the soil CH4 sink of terrestrial ecosystems between 2019 and 2020. By implementing the model with various datasets, we consistently observed an increase in the global soil CH4 sink, reaching up to 0.35 ± 0.06 Tg in 2020 compared to 2019. This increase was primarily attributed to warmer soil temperatures in northern high latitudes. Our results emphasize the importance of considering the CH4 sink in terrestrial ecosystems, as neglecting this component can lead to an underestimation of both emission increases and reductions in atmospheric CH4 sink capacity. Furthermore, these findings highlight the potential role of increased soil warmth in terrestrial ecosystems in slowing the growth of CH4 concentrations in the atmosphere.

Floating photovoltaics (FPV) are an emerging renewable energy technology. Although they have received extensive attention in recent years, understanding of their environmental impacts is limited. To address this knowledge gap, we measured water temperature and meteorological parameters for six months under FPV arrays and in the control open water site and constructed a numerical model reflecting the water energy balance. Our results showed that FPV arrays caused diurnal variation in water temperature and microclimate. Specifically, we found that FPV had a cooling effect on their host waterbody during the daytime and a heat preservation effect at night, reducing diurnal variation. The diel oscillation of water temperature below FPV panels lagged behind that of open waters by approximately two hours. The microclimate conditions below FPV panels also changed, with wind speed decreasing by 70%, air temperature increasing during the daytime (averaging +2.01°C) and decreasing at night (averaging -1.27°C). Notably, the trend in relative humidity was the opposite (-3.72%, +14.43%). Correlation analysis showed that the degree of water temperature affected by FPV was related to local climate conditions. The numerical model could capture the energy balance characteristics with a correlation coefficient of 0.80 between the simulated and actual data. The shortwave radiation and latent heat flux below FPV panels was significantly reduced, and the longwave radiation emitted by FPV panels became one of the heat sources during the daytime. The combined variations of these factors dominated the water energy balance below FPV panels. The measured data and simulation results serve as a foundation for evaluating the impact of FPV systems on water temperature, energy budget, and aquatic environment, which would also provide a more comprehensive understanding of FPV systems.

Moisture content testing of agricultural products is critical for quality control, processing efficiency and storage management. Testing foxtail millet moisture content ensures stable foxtail millet quality and helps farmers determine the best time to harvest. A differential capacitance moisture content detection device was designed based on STM32 and PCAP01 capacitance digital converter chip. The capacitance method combined with the back-propagation(BP) algorithm and the extreme learning machine(ELM) algorithm was chosen to construct an analytical model for foxtail millet moisture content, temperature, and volume duty cycle. This work performs capacitance measurements on foxtail millet with different moisture contents, temperatures, and proportions of the measured substance occupying the detection area (that is, the volumetric duty cycle). On this foundation, the sparrow search algorithm (SSA) is used to optimize the BP and ELM models. However, SSA may encounter problems such as falling into local optimization solutions due to the reduction of population diversity in the late iterations. As a consequence, Logistic algorithm is introduced to optimize SSA, making it more appropriate for solving specific problems. Upon comparative analysis, the model predicted using the Logistic-SSA-ELM algorithm was more accurate. The results indicate that the predicted values of prediction set coefficient of determination (RP), prediction set root mean square error (RMSEP) and prediction set ratio performance deviation (RPDP) were 0.7016, 3.7150 and 1.4035, respectively. This algorithm has excellent prediction performance and can be used as a model for detection of foxtail millet moisture content. In view of the important role of foxtail millet moisture content detection in acquisition and storage, it is particularly important to study a nondestructive and fast online real-time detection method. The designed capacitive sensor with differential structure has well stabilization and high accuracy, which can be further studied in depth and gradually move towards the general trend of agricultural development of smart agriculture and precision agriculture.

In this paper, we consider the problem of asynchronous estimation in the presence of packet losses for the randomly sampling nonlinear system. Packet losses occur at the control input and at the measurement side. Firstly, the synchronization of the asynchronous sampling system is realized by weighting the state of the adjacent state update points. Secondly, the projection theorem is used to estimate the system state at the sampling time. Due to modeling errors and unmodeled dynamics, obtaining an accurate dynamic model is challenging. Therefore, observation inference based on interpolation techniques is proposed to solve the asynchronous estimation problem. Furthermore, the algorithm is extended to multi-sensor systems to obtain a distributed fusion estimator. Finally, simulation experiments are conducted to validate the effectiveness of the algorithm.

As urbanization accelerates, understanding and managing carbon emissions from urban sewer networks have become crucial for sustainable urban water cycles. This review examines the factors influencing greenhouse gas (GHG) emissions within urban sewage systems, analyzing the complex effects between water quality, hydrodynamics, and sewer infrastructure on GHG production and emission processes. It reveals significant spatiotemporal heterogeneity in GHG emissions, particularly under long-term scenarios where flow rates and temperatures exhibit strong impacts and correlations. Given the presence of fugitive and dissolved potential GHGs, standardized monitoring and accounting methods are deemed essential. Advanced modeling techniques emerge as crucial tools for large-scale carbon emission prediction and management. The review identifies that traditional definitions and computational frameworks for carbon emission boundaries fail to fully consider the inherent heterogeneity of sewers and the dynamic changes and impacts of multi-source pollution within the sewer system during the urban water cycle. This includes irregular fugitive emissions, the influence of stormwater systems, climate change, geographical features, sewer design, and the impacts of food waste and antibiotics. Key strategies for emission management are discussed, focusing on the need for careful consideration of approaches that might inadvertently increase global emissions, such as ventilation, chemical treatments, and water management practices. The review advocates for an overarching strategy that encompasses a holistic view of carbon emissions, stressing the importance of refined emission boundary definitions, novel accounting practices, and comprehensive management schemes in line with the water treatment sector\'s move towards carbon neutrality. It champions the adoption of interdisciplinary, technologically advanced solutions to mitigate pollution and reduce carbon emissions, emphasizing the importance of integrating cross-scale issues and other environmentally friendly measures in future research directions.

In late March 2020, SARS-CoV-2 arrived in Manaus, Brazil, and rapidly developed into a large-scale epidemic that collapsed the local health system and resulted in extreme death rates. Several key studies reported that ∼76% of residents of Manaus were infected (attack rate AR≃76%) by October 2020, suggesting protective herd immunity had been reached. Despite this, an unexpected second wave of COVID-19 struck again in November and proved to be larger than the first, creating a catastrophe for the unprepared population. It has been suggested that this could be possible if the second wave was driven by reinfections. However, it is widely reported that reinfections were at a low rate (before the emergence of Omicron), and reinfections tend to be mild. Here, we use novel methods to model the epidemic from mortality data without considering reinfection-caused deaths and evaluate the impact of interventions to explain why the second wave appeared. The method fits a \"flexible\" reproductive number R0(t) that changes over the epidemic, and it is demonstrated that the method can successfully reconstruct R0(t) from simulated data. For Manaus, the method finds AR≃34% by October 2020 for the first wave, which is far less than required for herd immunity yet in-line with seroprevalence estimates. The work is complemented by a two-strain model. Using genomic data, the model estimates transmissibility of the new P.1 virus lineage as 1.9 times higher than that of the non-P.1. Moreover, an age class model variant that considers the high mortality rates of older adults show very similar results. These models thus provide a reasonable explanation for the two-wave dynamics in Manaus without the need to rely on large reinfection rates, which until now have only been found in negligible to moderate numbers in recent surveillance efforts.