Multisite Model Research Articles

Efficiency Limits of Energy Conversion by Light-Driven Redox Chains.

The conversion of absorbed sunlight to spatially separated electron-hole pairs is a crucial outcome of natural photosynthesis. Many organisms achieve near-unit quantum yields of charge separation (one electron-hole pair per incident photon) by dissipating as heat more than half of the light energy that is deposited in the primary donor. Might alternative choices have been made by Nature that would sacrifice quantum yield in favor of producing higher energy electron/hole pairs? Here, we use a multisite electron hopping model to address the kinetic and thermodynamic compromises that can be made in electron transfer chains, with the aim of understanding Nature's choices and opportunities in bioinspired energy-converting systems. We find that if the electron-transfer coordinates are even weakly coupled to a high-frequency vibrational mode, substantial energy dissipation is necessary to achieve the maximum possible energy storage in an electron-transfer chain. Since high-frequency vibronic coupling is common in physiological redox cofactors, we posit that biological reaction centers have recruited a strategy to convert light energy into redox potential with the near-optimum energy efficiency that is possible in an electron-transfer chain. Our simulations also find that charge separation in electron-transfer chains is subject to a minimum intercofactor separation distance, beneath which energy-dissipating charge recombination is unavoidable. We find that high quantum yield and low energy dissipation can thus be realized simultaneously for multistep electron transfer if recombination pathways are uncoupled from high-frequency vibrations and if the cofactors are held at small-to-intermediate distances apart (ca. 3 to 8 Å edge-to-edge). Our analysis informs the design of bioinspired light-harvesting structures that may exceed 60% energy efficiency, as opposed to the ∼30% efficiency achieved in natural photosynthesis.

The centrality of nursing in realizing high quality palliative care: Exploring Canada’s framework on palliative care priorities

BackgroundFollowing an earlier mixed-method survey in which we asked stakeholders to report on their perceptions of the progress made in relation to Canada’s Framework on Palliative Care and Action Plan, the purpose of this study was to conduct an in-depth qualitative exploration of the factors influencing that progress, or lack thereof.MethodsThis was a qualitative interview study conducted in Canada. Inclusion criteria included experience with palliative care in Canada in a professional or volunteer capacity. Interviews were conducted by telephone using an interview guide that asked specific questions in relation to the Framework on palliative care priorities (e.g., education, caregiver support, and equitable access). Data was analyzed using qualitative descriptive methods.ResultsThirty-five diverse stakeholders with extensive experience in palliative care were interviewed. In relation to palliative education, participants indicated that although there were excellent palliative care resources available across the country there was further need for embedding palliative care in undergraduate education and for mentored opportunities to engage in care across diverse contexts. The identification, development, and strategic positioning of champions was an important strategy for improving palliative care knowledge and capacity. The development of standard competencies was viewed as an important step forward; although, there was a need to include more members of the care-team and to create pathways for life-long learning. In relation to support for family caregivers, even as participants cited numerous community-based resources offered by not-for-profit organizations, they described significant barriers including a shortage of in-home support, lack of understanding of what caregivers do, and policy-based contractual and privacy issues. In relation to palliative care access, participants described a nurse-centered, consult-based, multi-site and multi-provider model of care that was facilitated by technology. Barriers to this model were systemic healthcare issues of siloed, fragmented, and for-profit care.ConclusionParticipants in this study had clear insights into the factors that would support or impede progress to the development of palliative care in Canada. Some of those factors were achievable within current health and educational systems. Other factors were going to require longer term and more comprehensive solutions.

A novel endoscopic artificial intelligence system to assist in the diagnosis of autoimmune gastritis: a multicenter study.

Autoimmune gastritis (AIG), distinct from Helicobacter pylori-associated atrophic gastritis (HpAG), is underdiagnosed due to limited awareness. This multicenter study aims to develop a novel endoscopic artificial intelligence (AI) system assisting in AIG diagnosis. Patients diagnosed with AIG, as well as HpAG and non-atrophic gastritis (NAG), were retrospectively enrolled from six centers. Endoscopic images with relevant demographic and medical data, were collected for the development of AI-assisted system, SEER-SCOPE AI, based on multi-site feature fusion model. The diagnostic performance of SEER-SCOPE AI was evaluated in the internal and external datasets. Endoscopists' performance with and without AI support was tested and compared using Mann-Whitney U test. Heatmap analysis was performed to interpret SEER-SCOPE AI. 1 070 patients (294 AIG, 386 HpAG, 390 NAG) with 18 828 endoscopy images were collected. SEER-SCOPE AI achieved strong performance for identifying AIG, with 96.9% sensitivity, 92.2% specificity and an AUROC of 0.990 internally, and 90.3% sensitivity, 93.1% specificity and an AUROC of 0.973 externally. The performance of SEER-SCOPE AI (sensitivity 91.3%) was comparable to experts (87.3%) and significantly outperformed non-experts (70.0%). With AI support, the overall performance of endoscopists was improved (sensitivity: 90.3% [95% CI 86.0%-93.2%] vs. 78.7% [95% CI 73.6%-83.2%], p=0.008). Heatmap analysis revealed consistent focus of SEER-SCOPE AI on regions corresponding to atrophic areas. SEER-SCOPE AI demonstrated expert-level performance in identifying AIG, and enhanced the diagnostic ability of endoscopists. Its application holds promise as a potent endoscopy-assisted tool for guiding biopsy sampling and early detection of AIG.

‘Grand designs?’ Investigating the cultural and spatial logics of Channel 4’s media hub workspaces in Leeds, Bristol and Glasgow

This article explores the evolving spatiality of media production through an analysis of recent developments in UK broadcaster Channel 4’s move to a multi-site operating model. Historically, media buildings have served as landmarks of power and influence, embodying the aspirations and identities of media organisations and, as such, offer a fruitful lens for the specific exploration of the spaces of contemporary media production. Through a detailed analysis of Channel 4’s decision-making process, site selections and the significance of the chosen buildings within areas with an urban regeneration focus, the study investigates the complex interplay between media industry dynamics, regional development policies, and wider political and economic forces. Drawing on a range of academic literature and evidence from media, industry and policy reporting, the article highlights the strategic motivations behind Channel 4’s decentralisation efforts, positioned by the organisation as an effort to address diversity in terms of nations and regions representation but significantly shaped by political factors. However, it also raises critical questions about the long-term sustainability, economic viability and cultural impact of these relocation decisions. The article discusses how Channel 4’s regional media spaces and decentralisation reflect the impermanence and crisis facing Public Service Media, emphasising the shifting power dynamics within the media landscape and the need for adaptive strategies in an increasingly volatile industry environment. By examining the evolving investment in media buildings, this research contributes to a growing body of research seeking to understand the complex dynamics shaping contemporary media landscapes and urban environments.

On the Use of the Multi-Site Langmuir Model for Predicting Methane Adsorption on Shale

Shale gas, mainly consisting of adsorbed gas and free gas, has served a critical role of supplying the growing global natural gas demand in the past decades. Considering that the adsorbed methane has contributed up to 80% of the total gas in place (GIP), understanding the methane adsorption behaviors is imperative to an accurate estimation of total GIP. Historically, the single-site Langmuir model, with the assumption of a homogeneous surface, is commonly applied to estimate the adsorbed gas amount. However, this assumption cannot depict the methane adsorption characteristics due to various compositions and pore sizes of shales. In this work, a multi-site model integrating the energetic heterogeneity in adsorption is derived to predict methane adsorption on shale. Our results show that the multi-site model is capable of addressing the heterogeneity of shales by a wide range of adsorption energy distributions (owing to the complex compositions and different pore sizes), which is different from the single-site model only characterized by single adsorption energy. Consequently, the multi-site model results have better accuracy against the experimental data. Therefore, applying the multi-site Langmuir model for estimating GIP in shales can achieve more accurate results compared with using the traditionally single-site model.

Improving goal-concordant care via a uniquely collaborative and multi-site PFAC model.

265 Background: Studies reveal a mismatch between the goals of care (GOC) of cancer patients and actual care received. The 10 members of the Alliance of Dedicated Cancer Centers (ADCC) convened the Improving Goal Concordant Care (IGCC) initiative to address system gaps. IGCC was led by palliative care and oncology experts, with guidance from patient and family advisors. Uniquely, the advisors were identified from patient family advisory councils (PFAC) at each center, forming a collaborative Advisory Group (AG) with the lived experience of each center and the knowledge of advocating for the patient/family voice locally. Methods: PFAC members were recruited from each site to serve on the AG. The 19 member AG met quarterly, with minutes shared with the IGCC oversight group, the Implementation Workgroup (IWG). In September 2023, all AG members were surveyed about perceptions of PFAC engagement and local impact of IGCC. In May 2024, 20 IWG members were surveyed about implementation successes and barriers, including items related to PFAC engagement. Results: AG evaluation and recommendations altered IGCC planned core implementation and evaluation actions meaningfully; for example in 1) selection of structured fields in electronic health record (EHR) and universally accessible GOC documentation and in 2) centering patients in the GOC measure set, including incorporating “Heard & Understood” survey into IGCC evaluation. Other AG guidance around direct patient engagement readiness for GOC conversations and implementing patient GOC surveys was left to individual sites to consider with variable adoption. AG Survey: 63% response rate. 80% noted that the GOC program was discussed with their local PFAC and 92% experienced changed routine patient care after GOC implementation. IWG Survey: 100% response rate. 68% agreed that their center sought guidance from their PFAC during GOC implementation and that leadership presented to their PFAC about their GOC implementation. 26% indicated reporting data to their PFAC; of those that did, 80% agreed that this positively impacted their GOC implementation. Conclusions: While PFACs are occasionally included in quality interventions, this model centered the patient/family voice by supporting a PFAC stakeholder feedback loop working collaboratively and locally to support implementation and evaluation of IGCC objectives. While not all AG recommendations were formally integrated into IGCC, all were considered and many adopted at individual centers. Despite ongoing IGCC-level involvement, center-specific engagement of PFACs varied. Future efforts can track this during process evaluation as local PFAC engagement was highly valued. Consensus building for national best practice implementation and evaluation by recruiting local experts including PFAC members into national collaborative efforts is a successful model for local change.

A comprehensive study of the enhanced oil recovery potential of low-salinity water in heavy oil sandstone reservoir

To determine the enhanced oil recovery potential of low-salinity brine for a heavy oil sandstone reservoir, a holistic approach including both experimental and theoretical studies was employed in this study. Macro-scale forced and spontaneous imbibition tests were performed to quantify the oil recovery potential by our considered three brines. Furthermore, micro-scale experiments, including FTIR (Fourier-Transform Infrared Spectroscopy), TGA (Thermogravimetric Analysis) and zeta potential, were executed to explore the micro-scale oil-brine-rock interactions (i.e., polar component desorption from the rock surface) that has controlled the macro-scale oil recovery efficiency. In addition, to further investigate the underlying physical mechanism that leads to the discrepancy in polar component desorption when subjected to different brines, charge-distribution multi-site complexation model (CD-MUSIC) and DLVO-based disjoining pressure calculation were performed to describe the molecular-level electrokinetic interactions at the oil-brine and rock-brine interfaces, as well as the total interaction force between these two interfaces. Our results show that the oil recovery efficiency of the low-salinity aquifer water (LSAW) was markedly higher than produced water (PW) and seawater (SW) from the forced and spontaneous imbibition tests. Combined with the marginal difference on interfacial tension (IFT) results, it guided us to conclude that the micro-scale oil-brine-rock interaction controlled the macro-scale oil recovery performance. FTIR and TGA experimental results on pristine quartz, oil aged quartz, as well as different water treated aged quartz powders revealed that the polar component desorption efficiency in different brines followed the order of LSAW > PW > SW, which was consistent with the macro-scale oil recovery trend. The total disjoining pressure result gave rise to the conclusion that the most repulsive disjoining pressure in LSAW leads to the most efficient desorption of polar oil component from the rock surface. In addition, the electrokinetic properties of the oil/brine and rock/brine interfaces, as well as the consequent electrical double layer force play a crucial role in differentiating the oil-brine-rock interactions in different brines. This study has shed light on the further application of low-salinity aquifer water in our target heavy oil reservoir.

Optimized ensemble deep random vector functional link with nature inspired algorithm and boruta feature selection: Multi-site intelligent model for air quality index forecasting

Air quality index (AQI) forecasting is complex due to its variability, instability, and inconsistent trends resulting from dynamic atmospheric conditions, various contaminants, and interactions between environmental factors. Advanced modeling techniques are needed to accurately forecast AQI values to capture subtle patterns and variations in air quality data. Thus, a new forecasting model is suggested in this study to improve the accuracy of AQI forecasting. The model integrates three-phase decomposition technique, a feature selection approach, and ensemble Deep Random Vector Functional Link (EDRVFL), optimized using adaptive teaching-learning-based optimization and differential evolution (ATLDE). The AQI series was first broken down into a group of intrinsic mode functions (IMFs) with different frequencies using multivariate variational mode decomposition (MVMD). Subsequently, a feature selection method based on the Boruta technique was applied to identify the most significant input variables. Finally, for daily AQI levels forecasting, ATLDE optimized the EDRVFL model (EDRVFL-ATLDE). Three daily AQI series gathered from Chengdu, Wuhan, and Taiyuan in China from January 1, 2018, to December 30, 2022, were used to test and confirm the proposed model via empirical research. Based on the results, the proposed model can yield the superior results for three cities (Chengdu: correlation coefficient (R = 0.987), root mean square error (RMSE = 5.583), Wuhan: (R = 0.987), (RMSE = 3.299), and Taiyuan: (R = 0.996), (RMSE = 4.521)) in China. The experimental findings demonstrated the feasibility of the three-phase hybrid methodology, outperforming all other models regarding forecast accuracy.

Graph neural network coarse-grain force field for the molecular crystal RDX

Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable approach to develop CG force fields for molecular crystals combining graph neural networks (GNN) and data from an all-atom simulations and apply it to the high-energy density material RDX. We address the challenge of expanding the training data with relevant configurations via an iterative procedure that performs CG molecular dynamics of processes of interest and reconstructs the atomistic configurations using a pre-trained neural network decoder. The multi-site CG model uses a GNN architecture constructed to satisfy translational invariance and rotational covariance for forces. The resulting model captures both crystalline and amorphous states for a wide range of temperatures and densities.

A rigorous model for hydrogen response in industrial propylene polymerization loop reactors: From micro-mechanism to macro-behavior

In spite of playing a chain transfer agent role, the hydrogen activation effect in propylene polymerization with Ti-based Ziegler-Natta catalysts, so called “hydrogen response”, is usually explained by the dormant-site theory in polymer chemistry. However, how the micro-mechanism at the catalyst site scale affects the macro-behavior of industrial processes is unrevealed. This work aims at developing a rigorous model that can accurately describe hydrogen response and predict polymer chain microstructure for an industrial propylene polymerization process consisting of loop reactors in series. The model is comprised of a kinetic model, a thermodynamic model, and a macroscopic non-ideal loop reactor model. To describe hydrogen response, an elaborated kinetic model is established based on a consistent dormant site reactivation mechanism assuming a multi-site model. The model predictions are in good agreement with plant data, after taking the dormant sites reactivated by hydrogen into account. The proposed model is capable of fully assessing the effects of various process conditions, during steady-state and grade transition processes, on the macro-behavior (i.e., production, slurry density) as well as polymer properties (i.e., melt flow index, molecular weight distribution) in the industrial loop reactors. The underlying correlations are also discussed.

Multi-site and long-term estimation of phosphorus dynamics for paddy surface water in China based on new MLEpaddy-P

Paddy surface water serves as the primary source of artificial drainage and rainfall runoff leading to phosphorus (P) loss from paddy fields. The quantification of P dynamics in paddy surface water on a large scale is challenging due to the spatiotemporal heterogeneity of influencing factors and the limitations of field measurements. Based on 1226 data sets from 33 field sites covering the three main rice-growing regions of China (the Southeast Coast, the Yangtze River Basin, and the Northeast Plain), we analyzed the spatiotemporal characteristics of P attenuation in paddy surface water and its influencing factors. A new multi-site and long-term phosphorus estimation model for paddy (MLEpaddy-P) was proposed to evaluate the total phosphorus (TP) dynamics at national scale by improving the initial concentration (C0) and attenuation coefficient (k) of the first-order kinetic model (Ct=C0∙e−k(t−1)). Our study showed that: (1) Fertilizer amounts, soil organic matter content, soil Olsen-P content, soil pH, and soil total phosphorus are the primary factors affecting the variation of C0 and k; (2) Yangtze River Basin possessed the highest C0 (6.87 ± 12.97 mg/L) and high k ≤ 7 (0.262 in 1–7 days after fertilization), followed by Southeast Coast (4.15 ± 5.33 mg/L; 0.263) and Northeast Plain (1.33 ± 1.50 mg/L; 0.239), respectively; (3) MLEpaddy-P performed well in daily TP dynamics estimation at national scale with R2 of 0.74–0.85; (4) Middle and lower reaches of the Yangtze River Basin were the critical regions with high TP concentration due to high fertilizer amount and soil Olsen-P content. The new universal model realizes the multi-site and long-term estimation of P dynamics while greatly saving multi-site monitoring costs. This study provides a basis for early warning and targeted control of P loss from paddies.

A novel multisite model to facilitate hepatitis C virus elimination in people experiencing homelessness

Background/aims: Only a handful of countries are on target to achieve hepatitis C virus (HCV) elimination by 2030. People experiencing homelessness (PEH) remain an important HCV reservoir. The END C study evaluated clinical, patient reported and health economic outcomes of a decentralised integrated model. MethodsThis prospective study assessed a decentralised regional service based at multiple homeless sites in southeast England. Novel linkage-care strategies were utilised. We assessed generic and liver specific health-related quality of life (HRQoL) (SF-12v2; EQ-5D-5L and SFLDQol) pre/post HCV treatment and cost per HCV case detected and cured. Primary outcome was intention-to-treat (ITT) sustained virological response (SVR12). ResultsWe recruited 418 individuals, mean age 44.45 ± 10.6, 78% were male, 74% being currently homeless, current injecting drug use/alcohol use being 25% and 65% respectively. Prevalence of cirrhosis (liver stiffness measurement >12kPa) was 12%. Twenty-eight percent (n=116) were HCV PCR positive, of whom n=105 received direct acting antiviral treatment. ITT SVR12 rates were 81% (95% CI 72%-88%), the only predictor of SVR12 being >80% treatment adherence (OR 20.69, 95% CI 6.227-68.772, p<0.001). HRQoL improved significantly after SVR12: SF-12v2 (General Health, Mental Health, Social Functioning, Mental Health Composite Score p<0.049); SFLDQoL (Symptoms/Effects of Liver Disease, Distress, Loneliness p<0.004) and EQ-5D-5L (Index Score, Visual Analog Scale p<0.001). Costs (British pound 2022) per HCV case detected and per case cured were £359 and £257 respectively. Reinfection rates were 6.82/100 person years. ConclusionThe END C study endorses a multisite decentralised service for PEH enabling excellent linkage to care, high SVR12 rates and significant improvements in generic and liver specific HRQoL, all being achieved at modest costs. Such services are paramount to help achieve HCV elimination. Impact and implicationsIn people experiencing homeless, we found a high prevalence of HCV, alcohol and substance misuse including overdoses and mental health issues. Despite this, an integrated and decentralised service resulted in excellent linkage to care with high SVR12 rates. Even in this complex cohort with multiple comorbidities, SVR12 was associated with significant improvements in both generic and liver specific health-related quality of life. This was all achieved at modest costs in a community setting. Such models of care are feasible, easy to replicate and essential if we are to achieve HCV elimination.

Towards detailed combustor wall kinetics: An experimental and kinetic modeling study of hydrogen oxidation on Inconel

Gas-phase hydrogen combustion is ubiquitous in industrial processes, and the associated surface kinetics on heat-resistant alloys plays a crucial role in designing efficient low-carbon technologies. We conducted new temperature programmed reduction experiments to determine the reducibility of materials following an oxidation cycle. These experiments were modeled using a thermoconsistent multi-site microkinetic model for H2 heterogeneous oxidation on Inconels, which was validated against literature experiments. This competitive adsorption model considers iron bulk content, hydrogen spillover and subsurface oxygen migration on hydrogen surface oxidation kinetics. New X-ray diffraction experiments confirmed the postulated crystallographic structures in the Inconel samples, suggesting their presence on the surface scale. The phenomenological model was coupled with several state-of-the-art gas-phase oxidation mechanisms to assess gas/surface reactions interaction as a function of material and temperature. The results reveal a complex interaction in which surface removes H radicals from the gas-phase, while the Bradford (H2+OH) reaction converts OH into H2O, promoting water adsorption on Inconel. This interaction was found to give rise to gas-phase thermokinetic oscillations. The model predicts a non-monotonous effect of reactor area-to-volume ratio on reactivity and emphasizes the impact of Inconel composition on product selectivity. Overall, the multi-site model provides new insight into the contrasting reactivities among active sites, bridging the gap between material science and heterogeneous combustion modeling.

Enhancing Air Quality Prediction with an Adaptive PSO-Optimized CNN-Bi-LSTM Model

Effective air quality prediction models are crucial for the timely prevention and control of air pollution. However, previous models often fail to fully consider air quality’s temporal and spatial distribution characteristics. In this study, Xi’an City is used as the study area. Data from 1 January 2019 to 31 October 2020 are used as the training set, while data from 1 November 2020 to 31 December 2020 are used as the test set. This paper proposes a multi-time and multi-site air quality prediction model for Xi’an, leveraging a deep learning network model based on APSO-CNN-Bi-LSTM. The CNN model extracts the spatial features of the input data, the Bi-LSTM model extracts the time series features, and the PSO algorithm with adaptive inertia weight (APSO) optimizes the model’s hyperparameters. The results show that the model achieves the best results in terms of MAE and RMSE. Compared to the PSO-SVR, BPTT, CNN-LSTM, and GA-ACO-BP models, the MAE improved by 9.375%, 6.667%, 2.276%, and 4.975%, while the RMSE improved by 8.371%, 8.217%, 6.327%, and 5.293%. These significant improvements highlight the model’s accuracy and its promising application prospects.

Legal Consequences of Establishing the Civil Rights of Illegitimate Children from the Progressive Fiqh Perspective

This study was motivated by a phenomenon that contrasts with justice for children, especially adulterous or born-out-of-wedlock children who cannot receive their basic rights, which should become both parents' responsibilities to fulfill. The responsibilities include the right to get recognition as children, to have their daily life needs fulfilled, to be protected, and to be legally borne. In addition, there is a phenomenon that the children do not get guardianship and inheritance rights. This problem needs to be revealed through a study using a qualitative method with a multi-site model. The research location is at the cities’ religious courts of Mojokerto, Malang, and Surabaya. Through a comprehensive study, it was found that 1) juridical implications of determining the civil rights of born-out-of-wedlock children at the Religious Courts of Mojokerto, Malang, and Surabaya showed that they have not been able to accommodate the principle of justice and the best interests of the child and have not fully implemented the principle of legal progress and 2) from the progressive fiqh (Islamic jurisprudence) perspective, legislative decisions, and juridical implications of determining born-out-of-wedlock children at the related Courts require renewal to formulate a proof mechanism of istilḥāq (children origin determination) and strengthen the Courts to carry out istilḥāq based on justice.

Understanding membrane-intensified catalytic CO2 reduction reactions to methanol by structure-based multisite micro-kinetic model

Membrane reactor processes can be used to overcome the constraints of the chemical rate equilibrium of methanol (MeOH) synthesis products. In this thermodynamics-limited work, three different selective sulfonated poly(ether ketones) (SPEEK) membranes were applied in an engineered unit operation with a commercial Cu/ZnO/Al2O3/MgO surface catalyst for several CO2/CO-involving chemistries. A detailed mathematical model with micro-kinetics was developed, optimised and utilised to assess the vessel with barrier by using CERRES (Chemical Reaction and Reactor Engineering Simulations). Scaled separation tests were described by the integrated reference values of permeance. The permeability for all compound molecules (H2, H2O, CO, CO2, MeOH) was determined by adjusting parameters to account for the experimental gas composition on the permeate, interface and retention segment side after reduction. The specific kinetic characteristics of the mechanism of elementary step reactions were analysed in fixed bed design. A comparison of the estimated data prediction for the packed system with related definite numbers showed excellent statistical agreement. Similarly, a very good reliability was obtained between the results for 3 SPEEK membrane cases. Thus, the defined particular evaluations of derived theoretical expressions were benchmarked accurately. Although (validated) performance, i.e. the yield of MeOH, was overestimated, discrepancy was not so large so as to simulate behaviour verily. The (3-aminopropyl)triethoxysilane (polyamide) over a SPEEK layer performed best for intensification. Herein, the pressurised (>50 bar) CO2 hydrogenation pathway was not only shifted by in situ removal as a proof of concept, but also modelled intrinsically, considering transport phenomena resistances, adsorption and desorption as well. The storage of hydrogen can benefit from MeOH production reengineering.

A Single-resident Experience of Overnight Call Activity at a Multi-site Academic Health System

BackgroundAfter-hours clinical coverage models vary significantly by specialty and institution. Coverage decisions must balance quality clinical care with a safe provider workload. Here, we characterize urology resident on-call activities overnight at a multi-site academic medical center and model the expected volume of clinical activity using inpatient beds, emergency room visits and attendings covered. Materials and MethodsOn-call activities for 70 thirteen-hour overnight shifts spanning five nonconsecutive months between May 2022 and February 2023 were recorded. Clinical coverage included five academic hospitals encompassing 1,761 staffed inpatient beds and an expected nightly volume of 255 Emergency Department visits. The time, source, and clinical features of every call were documented. ResultsAn average of 15 unique calls were received during each shift. Of these, 35% required an in-person evaluation and 12% required a bedside or operative procedure. Approximately a third of calls (36%) were received after midnight. An in-person evaluation occurred within the first hour of 53% of shifts and every shift required at least one evaluation. When normalized for inpatient bed volume, an average of seven unique patient communications occurred per 1,000 beds, leading to two in-person evaluations. When normalized for an expected number of overnight ED visits, an average of one new ED consultation occurred per 100 ED visits. ConclusionsPatient needs were appropriately addressed by a single overnight on-call resident, providing a robust clinical experience. The volume of patient care activities in this experience supports the practice of a “night-float” resident with the clear expectation on-site care is required.

Synchrony in adult survival is remarkably strong among common temperate songbirds across France.

Synchronous variation in demographic parameters across species increases the risk of simultaneous local extinction, which lowers the probability of subsequent recolonization. Synchrony therefore tends to destabilize meta-populations and meta-communities. Quantifying interspecific synchrony in demographic parameters, like abundance, survival, or reproduction, is thus a way to indirectly assess the stability of meta-populations and meta-communities. Moreover, it is particularly informative to identify environmental drivers of interspecific synchrony because those drivers are important across species. Using a Bayesian hierarchical multisite multispecies mark-recapture model, we investigated temporal interspecific synchrony in annual adult apparent survival for 16 common songbird species across France for the period 2001-2016. Annual adult survival was largely synchronous among species (73%, 95% credible interval [47%-94%] of the variation among years was common to all species), despite species differing in ecological niche and life history. This result was robust to different model formulations, uneven species sample sizes, and removing the long-term trend in survival. Synchrony was also shared across migratory strategies, which suggests that environmental forcing during the 4-month temperate breeding season has a large-scale, interspecific impact on songbird survival. However, the strong interspecific synchrony was not easily explained by a set of candidate weather variables we defined a priori. Spring weather variables explained only 1.4% [0.01%-5.5%] of synchrony, while the contribution of large-scale winter weather indices may have been stronger but uncertain, accounting for 12% [0.3%-37%] of synchrony. Future research could jointly model interspecific variation and covariation in breeding success, age-dependent survival, and age-dependent dispersal to understand when interspecific synchrony in abundance emerges and destabilizes meta-communities.

Federated Offline Reinforcement Learning

Evidence-based or data-driven dynamic treatment regimes are essential for personalized medicine, which can benefit from offline reinforcement learning (RL). Although massive healthcare data are available across medical institutions, they are prohibited from sharing due to privacy constraints. Besides, heterogeneity exists in different sites. As a result, federated offline RL algorithms are necessary and promising to deal with the problems. In this article, we propose a multi-site Markov decision process model that allows for both homogeneous and heterogeneous effects across sites. The proposed model makes the analysis of the site-level features possible. We design the first federated policy optimization algorithm for offline RL with sample complexity. The proposed algorithm is communication-efficient, which requires only a single round of communication interaction by exchanging summary statistics. We give a theoretical guarantee for the proposed algorithm, where the suboptimality for the learned policies is comparable to the rate as if data is not distributed. Extensive simulations demonstrate the effectiveness of the proposed algorithm. The method is applied to a sepsis dataset in multiple sites to illustrate its use in clinical settings. Supplementary materials for this article are available online including a standardized description of the materials available for reproducing the work.

W-WaveNet: A multi-site water quality prediction model incorporating adaptive graph convolution and CNN-LSTM.

Water quality prediction is of great significance in pollution control, prevention, and management. Deep learning models have been applied to water quality prediction in many recent studies. However, most existing deep learning models for water quality prediction are used for single-site data, only considering the time dependency of water quality data and ignoring the spatial correlation among multi-sites. This research defines and analyzes the non-aligned spatial correlations that exist in multi-site water quality data. Then deploy spatial-temporal graph convolution to process water quality data, which takes into account both the temporal and spatial correlation of multi-site water quality data. A multi-site water pollution prediction method called W-WaveNet is proposed that integrates adaptive graph convolution and Convolutional Neural Network, Long Short-Term Memory (CNN-LSTM). It integrates temporal and spatial models by interleaved stacking. Theoretical analysis shows that the method can deal with non-aligned spatial correlations in different time spans, which is suitable for water quality data processing. The model validates water quality data generated on two real river sections that have multiple sites. The experimental results were compared with the results of Support Vector Regression, CNN-LSTM, and Spatial-Temporal Graph Convolutional Networks (STGCN). It shows that when W-WaveNet predicts water quality over two river sections, the average Mean Absolute Error is 0.264, which is 45.2% lower than the commonly used CNN-LSTM model and 23.8% lower than the STGCN. The comparison experiments also demonstrate that W-WaveNet has a more stable performance in predicting longer sequences.