Supply-demand Mismatch Research Articles

Subchondral Bone Alignment in Osteochondral Allograft Transplants for Large Oval Defects of the Medial Femoral Condyle: Comparison of Lateral versus Medial Femoral Condyle Donors.

Supply-demand mismatch of medial femoral condyle (MFC) osteochondral allografts (OCAs) remains a rate-limiting factor in the treatment of osteochondral defects of the femoral condyle. Surface contour mapping was used to determine whether a contralateral lateral femoral condyle (LFC) versus ipsilateral MFC OCA differs in the alignment of donor:native subchondral bone for large osteochondral defects of the MFC. Thirty fresh-frozen human femoral condyles were matched by tibial width into 10 groups of 3 condyles (MFC recipient, MFC donor, and LFC donor) each for 3 cartilage surgeons (90 condyles). The recipient MFC was imaged using nano-computed tomography scan. Donor oval grafts were harvested from each matched condyle and transplanted into a 17 mm × 36 mm defect created in the recipient condyle. Following the first transplant, the recipient condyle was imaged and superimposed on the native condyle nano-CT scan. The donor plug was removed and the process repeated for the other donor. Surface height deviation and circumferential step-off height deviation were compared between native and donor subchondral bone surfaces for each transplant. There was no statistically significant difference in mean subchondral bone surface deviation (LFC = 0.87 mm, MFC = 0.76 mm, P = 0.07) nor circumferential step-off height (LFC = 0.93 mm, MFC = 0.85 mm, P = 0.09) between the LFC and MFC plugs. There were no significant differences in outcomes between surgeons. There were no significant differences in subchondral bone circumferential step-off or surface deviation between ipsilateral MFC and contralateral LFC oval-shaped OCAs for 17 mm × 36 mm defects of the MFC.

Identification of Urban Ecological Security Pattern Based on Ecosystem Services Supply–Demand

The construction of ecological security pattern (ESP) is an effective way to ensure regional ecological security. Although the method of constructing the regional ESP based on ecosystem services (ESs) has been widely recognized and applied, the spatial characteristics of ESs supply–demand mismatch has not been well included into ESP construction. We constructed a regional ESP framework connecting demand sources and ecological sources from the perspective of ESs supply–demand mismatch. Taking the Wuhan urban agglomeration (WUA), findings indicated distinct spatial aggregations of ecological sources and demand sources due to the supply–demand mismatch of ESs. Ecological sources (12,406.29 km 2 or 21.42%) were primarily located in the south and north of WUA, while demand sources (1,191.26 km 2 or 2.07%) were concentrated mainly in the central. Two types of corridors jointly ensured regional ecological security. A total of 86 supply–supply corridors in the north ensured the supply ability of ESs by connecting ecological sources, while 35 supply–demand corridors in the south alleviated the supply–demand mismatch of ESs. The targeted implementation of ecological governance based on the corridor types provides a new approach to coordinate the mismatch of ESs supply–demand and enhance ecological security. However, 63.38 km 2 of pinch points, recognized as high-flow areas within the corridors, primarily comprised fragmented landscapes, and barriers covering 99.67 km 2 obstructed corridor flow, notably surrounding the demand sources. These regions should be prioritized for ecological conservation. Overall, this research framework provides a reliable scientific basis for configuring spatial landscape patterns and developing ecological strategies in urban agglomerations.

Troponin Elevation in Patients Undergoing Percutaneous Hepatic Perfusion for Metastatic Uveal Melanoma.

Percutaneous Hepatic Perfusion (PHP) is a liver directed regional therapy recently FDA approved for metastatic uveal melanoma to the liver involving percutaneous isolation of liver, saturation of the entire liver with high-dose chemotherapy and filtration extracorporeally though in line filters and veno-venous bypass. The procedure is associated with hemodynamic shifts requiring hemodynamic support and blood product resuscitation due to coagulopathy. To assess the cardiac safety and subsequent clinically significant sequalae of this therapy. Consecutive PHP procedures done at our center between 2010-2022 were assessed retrospectively. Cardiac risk factors, post procedural cardiac enzymes, electrocardiograms, and transthoracic echocardiograms along with 90-day cardiac outcomes were reviewed. All data were reviewed by cardio-oncologists at our institution. Of 37 patients reviewed, mean age was 63years and 57% were women. 132 procedures were performed with an average of 3.57 procedures per patient. 68.6% of patients had elevated troponin during at least 1 procedure. No patients were found to have acute coronary syndrome, heart failure, unstable arrhythmias, or cardiac death. No patients had notable echocardiographic changes. 10.8% of patients with positive troponin had asymptomatic transient electrocardiographic changes not meeting criteria for myocardial infarction. One patient had non-sustained ventricular tachycardiac intra-operatively which did not recur subsequently. Three patients died from non-cardiac causes within 90-days. There was no oncology treatment interruption, even in those with troponin elevation. In multivariable analysis, a history of hyperlipidemia was a predictor of postoperative troponin elevation. (P = .042). Percutaneous Hepatic Perfusion is safe and associated with a transient, asymptomatic troponin elevation peri-operatively without major adverse cardiac events at 90days. The observed troponin elevation is likely secondary to coronary demand-supply mismatch related to procedural hemodynamic shifts, hypotension, and anemia.

Reinforcement Learning With Data Envelopment Analysis and Conditional Value-At-Risk for the Capacity Expansion Problem

The capacity expansion problem is solved by accurately measuring the existing demand-supply mismatch and controlling the emissions output, considering multiple objectives, specific constraints, resource diversity, and resource allocation. This article proposes a reinforcement learning (RL) framework embedded with data envelopment analysis (DEA) to generate the optimal policy and guide the productivity improvement. The proposed framework uses DEA to evaluate efficiency and effectiveness for reward estimation in RL, and also assesses conditional value-at-risk to characterize the risk-averse capacity decision. Instead of focusing on short-term fluctuations in demand, RL optimizes the expected future reward with sequential capacity decisions over time. An empirical study of U.S. power generation validates the proposed framework and provides the managerial implications to policy makers. The results show that the RL agent can successfully learn the optimal policy through observing the interactions between the agent and the environment, and suggest the capacity adjustment that can improve efficiency by 8.3% and effectiveness by 0.9%. We conclude that RL complements productivity analysis, and emphasizes ex-ante planning over ex-post evaluation.

Analyzing competing effects between heat transfer area and natural convection to enhance heat transfer in latent heat storage

Phase change materials (PCMs) are effective means of storing thermal energy and to balance temporal supply-demand mismatch. To enhance heat transfer within the PCM, internal fins are often employed to increase the heat exchange area, but they usually suppress simultaneously natural convection reducing the performance of the PCM storage. To better understand these two conflicting effects and to find a better trade-off between the different heat transfer means, a comprehensive numerical study of PCM storage with different fin shapes (straight and sinusoidal wavy fins) and surface areas, but with equivalent volume fraction, was accomplished. The results show that increasing the surface area of sinusoidal fins improves only marginally the heat transfer, mainly because the flow velocity of natural convection along the normal direction of the solid interface is inhibited. The velocity normal to the solid interface was found to be the most critical factors to the overall heat transfer efficiency of the system and to the time needed to complete the phase change in the PCM container. The study clearly shows that to enhance the heat transfer in PCM, the effects from natural convection (direction and strength) play a more important role than the heat exchange surface area. To improve the thermal performance and efficiency of PCM storage systems both factors need to be combined in an optimal way.

Capacity sharing between competing manufacturers: A collective good or a detrimental effect?

Capacity sharing has become a prevalent solution for addressing the supply-demand mismatch by optimizing capacity allocation among participating manufacturers. However, effective coordination and cooperation are required among involved parties, particularly if capacity sharing occurs between competing manufacturers. This paper investigates capacity sharing between competing manufacturers and examines the optimal sharing service fee policy, taking external market, internal operational, and relationship-specific factors into consideration. Our study incorporates product substitution rate, information symmetry (or asymmetry), and interfirm power relationship to go beyond the immediate direct cost-benefit analysis of capacity sharing and evaluates the trade-off between capacity sharing associated benefits and costs. Our analysis results illustrate how the market and relationship-specific factors interact with operational factors such as production cost difference and capacity constraints to influence firms' capacity sharing decisions. The broad set of decision outcomes derived from our comprehensive analyses is beneficial for companies of all sizes and shapes to make informed strategic and operational decisions according to their own circumstances.

Comprehensive evaluation of community human settlement resilience and spatial characteristics based on the supply–demand mismatch between health activities and environment: a case study of downtown Shanghai, China

IntroductionUnder globalization, human settlement has become a major risk factor affecting life. The relationship between humans and the environment is crucial for improving community resilience and coping with globalization. This study focuses on the key contradictions of community development under globalization, exploring community resilience by analyzing the mismatch between residents' health activities and the environment.MethodsUsing data from Shanghai downtown, including land use, Sports app, geospatial and urban statistics, this paper constructs a comprehensive community resilience index (CRI) model based on the DPSIR model. This model enables quantitative analysis of the spatial and temporal distribution of Community Human Settlement Resilience (CR). Additionally, the paper uses geodetector and Origin software to analyze the coupling relationship between drivers and human settlement resilience.Resultsi) The scores of CR showed a "slide-shaped" fluctuation difference situation; ii) The spatial pattern of CR showed a "pole-core agglomeration and radiation" type and a "ring-like agglomeration and radiation" type. iii) Distance to bus stops, average annual temperature, CO2 emissions, building density and number of jogging trajectories are the dominant factors affecting the resilience level of community human settlement.ConclusionThis paper contributes to the compilation of human settlement evaluation systems globally, offering insights into healthy community and city assessments worldwide. The findings can guide the creation of similar evaluation systems and provide valuable references for building healthy communities worldwide.

A hub-and-spoke model for cardiac ablations: utilising satellite units to tackle the challenges from the pandemic and beyond

Abstract Background Since setting up the cardiac electrophysiology (EP) service at our tertiary centre (UH) in 2010, there has been a significant increase in referrals for EP procedures. We currently serve a large catchment area (>1.6 million people) and the increasing demand coupled with limited resources in a publicly funded health system has led to a huge supply-demand mismatch in our EP waiting list. This mismatch was compounded by the COVID-19 pandemic. Aim To share our experience of how we set up a hub-and-spoke model to mitigate the EP waiting list pressures before and during the pandemic by utilising satellite EP laboratory units. Methods Single shot cryoballoon pulmonary vein isolation (PVI) became the initial ablation strategy for all paroxysmal and persistent atrial fibrillation (AF) patients undergoing catheter ablation (due to shorter procedure times/standardised workflow). Almost all EP cases were performed under local anaesthesia. We transitioned from overnight-stay to same-day EP cases (all standard ablation and some complex ablation procedures) and demonstrated its safety and cost-effectiveness. We increased our EP laboratory capacity by developing a hub-and-spoke model in collaboration with 2 regional secondary care centres (WH & StX). During the pandemic, 3 national lockdowns were declared (Figure 1) and national healthcare services were restructured. Urgent inpatient cardiac EP cases were still performed at UH but elective EP procedures across all three sites (UH, WH & StX) were cancelled during the first lockdown. One of the steps taken to mitigate the impact of the pandemic on our EP waiting list was to collaborate with the private sector (MH) which functioned as a ‘clean’ site for elective EP procedures. Cryoballoon PVI and other EP cases resumed at our centre within 2 months of the first lockdown. Data from every EP case performed were routinely entered into an electronic database. Data was extracted from the 01/01/2018 (pre-pandemic) to 31/12/2022 (post-pandemic). UH was labelled as ‘tertiary unit’ while WH, StX and MH were pooled together (labelled as ‘satellite units’). Graphpad Prism was used for analysis (Chi-squared test for categorical variables and t-test for continuous variables). Results 3695 EP cases were performed over 5 years. Satellite units performed predominantly standard ablation and cryoballoon PVI cases (Table 1). The implementation of three national lockdowns led to a transient decrease in the number of EP procedures (Figure 1). However, the ‘clean’ patient pathway that we rapidly established at our satellite unit (MH) facilitated the sustained delivery of our cardiac EP service throughout the pandemic from 2020 to 2021. The higher complication rates in the tertiary unit likely reflected the higher proportion of complex ablation cases. Conclusion Our hub-and-spoke model is a safe and novel system in tackling the EP waiting list even throughout the pandemic.Figure 1Table 1

Abstract 16428: Impact of Low-Dose Rivaroxaban Plus Aspirin on Myocardial Infarction in Patients With Peripheral Artery Disease With and Without Concomitant Coronary Artery Disease: Insights From VOYAGER PAD

Background: Patients with peripheral artery disease (PAD) are at heightened risk of major adverse cardiovascular events (MACE). In VOYAGER PAD, rivaroxaban 2.5 mg BID reduced ischemic risk in PAD patients after lower extremity revascularization (LER). Whether the benefits for MACE and in particular for Myocardial Infarction (MI) differ in those with and without clinically known coronary artery disease (CAD) has not been described. Hypothesis and Methods: VOYAGER PAD randomized patients with symptomatic PAD undergoing LER to rivaroxaban 2.5 mg BID plus aspirin versus aspirin alone. MACE was defined as composite of MI, ischemic stroke, or cardiovascular death. Subtypes of MI were adjudicated according to the Universal Definition. Comorbid CAD was a prespecified subgroup. Results: Of 6,564 symptomatic PAD randomized, 2,083 (32%) had documented CAD at baseline. The benefit of rivaroxaban plus aspirin was greater for MACE in those with vs without CAD (HR 0.79, 95% CI 0.62 - 1.00 vs HR 1.12, 95% CI 0.92 - 1.36; p-interaction 0.03). Similarly, MI reduction with rivaroxaban plus aspirin was greater for those with vs without CAD (HR 0.78, 95% CI 0.56 - 1.10 vs HR 0.96, 95% CI 0.69 - 1.34; p-interaction 0.39). Considering MI subtypes, the effect of rivaroxaban plus aspirin on type 1 MI was consistent in patients with CAD (HR 0.91, 95% CI 0.58 - 1.42) and without CAD (HR 0.74, 95% CI 0.74 - 1.11, p-interaction 0.50). However, a trend towards benefit of rivaroxaban plus aspirin was seen for Type 2 MI in those with CAD (HR 0.71, 95% CI 0.41 - 1.24) but not in patients without CAD (HR 1.66, 95% CI 0.89 - 3.09, p-interaction 0.0475, figure). Type 4 MI was infrequent regardless of CAD. Conclusions: Rivaroxaban plus aspirin had consistent effects in MACE for PAD and CAD patients undergoing LER. Our analysis of MI subtype suggests that events adjudicated as MI due to supply demand mismatch (Type 2) may have a thrombotic component and are lowered with rivaroxaban and aspirin in PAD and CAD patients undergoing LER.

A deep learning and clustering‐based topic consistency modeling framework for matching health information supply and demand

AbstractImproving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination.

Thermal performance research on a novel coupled heating system combined solar air heater with ventilation PCM wall

Solar heating technology is increasingly recognized as a prominent option for clean building heating. However, challenges such as intermittent thermal energy supply and supply–demand mismatch hinder its widespread adoption. To address this issue, this paper introduces a novel coupled heating system that combines a solar air heater with a ventilation Phase Change Material (PCM) wall. A rural residential house in Tianjin was chosen as an experimental platform and comparative experiments was conducted to compare the advantages of the ventilation PCM wall with the conventional wall. Additionally, numerical simulations were utilized to address the non-uniform heat transfer in the direction of the Heat Transfer Fluid (HTF) flow by employing cascaded PCM. The experimental results demonstrated that the coupled heating system exhibited high thermal storage and release efficiency, significantly improving indoor thermal comfort. Under optimal operating conditions (air supply temperature: 35-45℃, air supply speed: 3–4 m/s), the system achieved rapid thermal storage within 4 h, reaching a maximum thermal storage efficiency of up to 87.6 %. The test room maintained a nighttime temperature of 15.7 °C, exhibiting temperatures 6.3–10 °C higher than the control room. Numerical results revealed that the cascaded PCM enhanced the thermal storage rate, particularly at the first 2.8 h of the thermal storage period, contributing to the overall efficiency and flexibility of the system. This research provides theoretical guidance for the optimal design and practical application of the ventilation PCM wall.

Understanding recreational ecosystem service supply-demand mismatch and social groups’ preferences: Implications for urban–rural planning

Recreational ecosystem service (RES) supply and demand are fundamentally influenced by urbanization and are closely related to residents’ well-being. Nevertheless, how socio-economic attributes affect spatial RES demand and preferences and can be integrated into urban–rural planning is still unclear. Taking the Beijing-Tianjin-Hebei urban agglomeration region of China as an example, based on survey and spatial data, we applied the Recreation Opportunity Spectrum and a new transferable Quasi-Poisson regression model to examine spatial RES supply and demand. Then, a scaling approach was adopted to interpret the urban–rural spatial patterns of RES. The results showed that the RES supply values exhibited either quadratic relationships or no particular patterns in most cities along urban–rural gradients, while the RES demand values monotonically decreased with an exponential decay or power law. The RES imbalance with higher demand (43%) in urban areas, the supply–demand balance (37%) in urban–rural fringes, and the supply sufficiency (64%) in rural areas were dominant. Divergent RES demand and preferences were identified across genders, ages, income, city scales, and household statuses. Females showed higher preferences for water, grassland, and agricultural landscapes than males; older groups showed higher preferences for natural landscapes but traveled less distance for RES; and high-income groups showed a lower frequency of visits but pursued longer travel distances. We suggest that at both the regional-city and local-community levels, more adaptive and elaborate landscape planning and design should be implemented by integrating urban–rural heterogeneity and the specific population’s preferences into RES management.

A novel approach to hybrid dynamic environmental-economic dispatch of multi-energy complementary virtual power plant considering renewable energy generation uncertainty and demand response

Renewable energy generation significantly reduces harmful emissions and promotes sustainable development. Still, its volatility leads to extensive grid connections, affecting electricity system security. Diversifying forms of energy consumption further expands the peak valley difference in electricity demand. This study aims to investigate the hybrid dynamic environmental-economic dispatch problem of multi-energy complementary virtual power plant, considering the renewable energy generation uncertainty and demand response to promote renewable energy integration and mitigate the electricity system supply-demand mismatch. It proposes a novel approach based on the sine cosine and multi-objective particle swarm optimization algorithm to reduce the economic and environmental costs of the multi-energy complementary virtual power plant. First, a hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant is established, considering climbing power, equality, and inequality constraints. Second, targeting the multi-objective, nonlinear, and high-dimension characteristics of the hybrid dynamic environmental-economic dispatch model of the multi-energy complementary virtual power plant, a sine cosine and multi-objective particle swarm optimization algorithm is proposed to optimize the particle position update method. Finally, simulation cases are constructed based on the development trend of the virtual power plant, setting various dispatching situations to validate the robustness of the proposed approach and determining a compromise solution by membership functions. The simulation results show that the lowest economic and environmental costs obtained by the sine cosine and multi-objective particle swarm optimization algorithm are at least 11.37 % and 2.79 % lower than those obtained by the NSGA-II algorithm and multi-objective particle swarm optimization algorithm when considering the renewable energy generation uncertainty and demand response. Therefore, the work contributes to decreasing the economic and environmental costs of multi-energy complementary virtual power plant and better enhancing the consumption ratio of renewable energy.

Modifying Candidacy and Management to Improve Extracorporeal Support During Supply-Demand Mismatch

BackgroundCandidacy for venovenous extracorporeal membrane oxygenation is dictated by ECMO to Rescue Lung Injury in Severe ARDS (EOLIA) criteria. We evaluated the effect of modifying candidacy on the basis of escalating demand and limited resources. MethodsWe retrospectively reviewed adult patients diagnosed with COVID-19–related severe acute respiratory distress syndrome who failed to respond to conventional ventilation and required extracorporeal support at our institution. Candidacy was restricted with a published probability model because of supply-demand mismatch and high mortality observed after the first surge. Age <55 years, mechanical ventilation days <3, and minimal comorbidities were prioritized. Primary outcomes included time to decannulation, extubation, tracheostomy, discharge, and death. Hospital and intensive care unit length of stay and hospitalization costs were evaluated. Predictors included cannulation strategy, before and after criteria implementation, use of cytoreductive techniques, timing of tracheostomy, and body mass index. Propensity score matching, multistate Cox proportional hazards models, and generalized linear models were used. ResultsOur sample comprised 105 patients, 26 from before criteria implementation (“before” phase) and 79 after (“after” phase). Propensity score results indicated no significant differences in death (P = .152) and costs (P = .063) between the groups. Patients who received cytoreductive therapy had lower total costs (P = .033). Those who underwent single-site cannulation had higher probability of decannulation (P = .009), discharge (P < .001), tracheostomy (P < .001), and extubation alive (P < .001) and lower risk of death (P = .017). ConclusionsModifying candidacy by objective criteria with the use of adjunctive therapies may improve outcomes and lower costs during periods of supply-demand mismatch.

Hierarchical framework for demand prediction and iterative optimization of EV charging network infrastructure under uncertainty with cost and quality-of-service consideration

As electric vehicles (EVs) continue to grow in popularity, the demand for efficient and effective EV charging infrastructure is rapidly increasing. However, the design and deployment of such infrastructure faces numerous challenges, some of which include demand uncertainty, EV network layout, maintenance cost, and quality of service (QoS), and have proven to be particularly challenging in cases where all objectives need to be addressed simultaneously. To address these challenges, this study proposes a hierarchical iterative optimization approach that aims to minimize the demand–supply mismatch at a higher level and considers both cost and QoS under uncertainty at a lower level in a dynamic EV charging network. Specifically, the proposed approach uses a time-series linear regression ensemble to accurately predict future demand at demand points, which is then used to match demand at demand points with supply at supply points. We then model the infrastructure allocation problem as a knapsack problem and use an allocation algorithm to determine the optimal number of charging stations at each supply point. The infrastructure is allocated to minimize total cost and maximize QoS. A framework for EV infrastructure optimization is designed and can be applied to any type of network. The effectiveness of our proposed approach is demonstrated through extensive experimentation on various datasets by comparing with existing state-of-the-art approaches. Simulation results show that the proposed approach is robust to different EV networks, handles uncertainty well, can effectively address the challenges associated with EV infrastructure optimization, and can provide policy makers and infrastructure planners with a practical and efficient tool for designing and deploying charging infrastructure. This study contributes to the field of EV infrastructure network optimization by modeling QoS and proposing a hierarchical optimization framework that effectively addresses the challenges associated with the design and deployment of charging infrastructure.

A supply-demand model of vegetation cooling for urban heatwave mitigation

The tradeoff between heatwave-induced human health risks and vegetation heat mitigation benefits is critical for mitigating heatwave hazards in urban regions. Currently, the heat risk study often focuses on the severity of heat risk to human health while the vegetation cooling is often ignored. Here, we proposed a new supply-demand matching model combining the vegetation cooling supply with the cooling demand induced by the high temperature. The cooling demand was assessed based on a heat exposure, population exposure, and heat vulnerability. The vegetation cooling supply was calculated by using a remote sensing-driven evapotranspiration (ET) model. Results showed: (1) The supply-demand matching model can identify cooling supply-demand mismatch zones in urban areas. (2) The vegetation ET model based on remote sensing can better represent the cooling benefits of vegetation compared to the vegetation abundance such as NDVI; (3) There was a special cluster of heat risk and cooling supply-demand mismatch in blocks of Beijing. The cooling supply and demand comparison model can inform vegetation-based intervention measures and urban planning guidelines for heat risk management.

Supply-demand relationship and spatial flow of urban cultural ecosystem services: The case of Shenzhen, China

Cultural ecosystem services (CES) have garnered significant attention due to their capacity to comprehensively evaluate the influence of ecosystem degradation on human well-being. It is very important to evaluate the spatial coordination relationship between the supply and demand of urban CES and explore its spatial flow mechanism for guiding the sustainable development of urban regions. However, research on the relationship between supply and demand of urban CES and their spatial flows is still in the conceptual exploration stage, and its quantitative assessment methods are not yet mature. Taking Shenzhen as an example, this study uses the Social Values for Ecosystem Services model to evaluate the supply of CES based on environmental background and survey statistics; Uses the topic model of natural language processing, quantify the demand of CES based on social media data. The Bivariate Local Indicators of Spatial Association index is utilized to examine the correlation between supply and demand, while a crowd activity extraction method based on mobile phone signaling data is developed to simulate the spatial flow pattern of CES. The results show that: (1) The supply of urban CES shows obvious spatial differentiation; There are differences in the perception of the needs of various CES. Among them, the beneficiaries have higher needs for Aesthetic and Recreation. (2) The relationship between supply and demand of urban CES shows different degrees of spatial mismatch, with the proportion of the supply-demand matching area to the total area being less than 20%, and the main type of supply-demand mismatch being low supply-high demand. (3) All kinds of CES flow from the center of the study area to the east, among which the flow of recreation and Aesthetic is more active, with the highest flow of 189.7 and 135.3, respectively. Overall, the coordination between various types of CES in the region is still insufficient. We should encourage the establishment and improvement of a diversified transportation system connecting the central urban area and other regions while enhancing the condition of the local ecological conditions, to improve the capacity of urban ring roads and rail transit, to promote the sustainable development of the regional ecological environment and social economy, and to enhance human well-being.

Capacity planning with limited information

Limited information about the demand for some of the resources needed to produce goods and services (e.g., incomplete and imperfect bills of materials) forces firms to use heuristics when planning resource capacity. We examine the performance of five heuristics: two drawn from practice, two that modify observed approaches, and one motivated by theory. We measure performance as the ratio of the expected cost of supply–demand mismatch from using a heuristic to the value in the full‐information solution. Numerical analysis shows that a simple heuristic that is common in practice—plan rigorously for a few “driver” resources with high‐quality information and use ratios (e.g., 0.25 indirect labor hours per machine hour) to project the capacities for the remaining “non‐driver” resources—is robust and efficient. Using more than one driver resource to plan for the same non‐driver resource delivers significant gains. Reducing measurement error with respect to the consumption of driver resources dominates the gain from reducing errors in other aspects. Indeed, with high measurement error, collecting information that reduces other sources of error could decrease overall performance. Finally, a greedy algorithm of choosing the most expensive resources as drivers is optimal.

A Data-Driven Analysis of Ward Capacity Strain Metrics That Predict Clinical Outcomes Among Survivors of Acute Respiratory Failure.

Supply-demand mismatch of ward resources ("ward capacity strain") alters care and outcomes. Narrow strain definitions and heterogeneous populations limit strain literature. Evaluate the predictive utility of a large set of candidate strain variables for in-hospital mortality and discharge destination among acute respiratory failure (ARF) survivors. In a retrospective cohort of ARF survivors transferred from intensive care units (ICUs) to wards in five hospitals from 4/2017-12/2019, we applied 11 machine learning (ML) models to identify ward strain measures during the first 24hours after transfer most predictive of outcomes. Measures spanned patient volume (census, admissions, discharges), staff workload (medications administered, off-ward transports, transfusions, isolation precautions, patients per respiratory therapist and nurse), and average patient acuity (Laboratory Acute Physiology Score version 2, ICU transfers) domains. The cohort included 5,052 visits in 43 wards. Median age was 65years (IQR 56-73); 2,865 (57%) were male; and 2,865 (57%) were white. 770 (15%) patients died in the hospital or had hospice discharges, and 2,628 (61%) were discharged home and 964 (23%) to skilled nursing facilities (SNFs). Ward admissions, isolation precautions, and hospital admissions most consistently predicted in-hospital mortality across ML models. Patients per nurse most consistently predicted discharge to home and SNF, and medications administered predicted SNF discharge. In this hypothesis-generating analysis of candidate ward strain variables' prediction of outcomes among ARF survivors, several variables emerged as consistently predictive of key outcomes across ML models. These findings suggest targets for future inferential studies to elucidate mechanisms of ward strain's adverse effects.

Dynamic modeling and response characteristics of a solar-driven polygeneration system coupled with hydrogen and thermal energy storage

In this paper, a solar-driven polygeneration system integrated with a solid oxide fuel cell, an absorption chiller, hydrogen storage, and thermal energy storage is proposed for maximally utilizing solar energy and addressing the supply–demand mismatch issue on the time scale. The dynamic model and energy management strategy of the proposed system are constructed, and the short- and mid-term dynamic response characteristics against step perturbations are investigated. Based on eco-office load demands during the typical summer and winter solstices, the long-term dynamic response behaviors of the system load-tracking are also studied. The results demonstrate that: the output dynamic response times are less than 1 s for the photovoltaic, 3–22 s for the proton exchange membrane electrolysis cell, and about 25 s for the solid-oxide fuel cell, all demonstrating short-term transient response characteristics in response to step perturbations of external parameters. In contrast, the dynamic response behaviors of the parabolic trough collector and the absorption chiller elapse 10 to 30 min to reach a stable value in response to the step changes, exhibiting typical mid-term dynamic response characteristics. Furthermore, the outputs of the system could fully satisfy the eco-office load demands in summer, while grid electricity is required to supply parts of power or heat for about 11 h in the morning and evening due to the low solar irradiation and short daylight duration in winter.