Robust Approach Research Articles

Impact of removing the healthcare mask mandate on hospital-acquired COVID-19 rates

Mandatory mask-wearing policies were one of several measures employed to reduce hospital-acquired SARS-CoV-2 infection throughout the pandemic. Many nations have removed healthcare mask mandates, but there remains a risk of new SARS-CoV-2 variants or epidemics of other respiratory viruses. To demonstrate the impact of removing the healthcare mask mandate. SARS-CoV-2 infections were analysed in a large teaching hospital for 40 weeks in 2022 using a controlled interrupted time-series design. The intervention was the removal of a staff/visitor surgical mask-wearing policy for the most wards at week 26 (intervention group) with a subset of specific wards retaining the mask policy (control group). The hospital-acquired SARS-CoV-2 infection rate was adjusted by the underlying community infection rate. In the context of a surge in SARS-CoV-2 infection, removal of the mask mandate for staff/visitors was not associated with a statistically significant change in the rate of nosocomial SARS-CoV-2 infection in the intervention group (incidence rate ratio: 1.105; 95% confidence interval: 0.523-2.334; P= 0.79) and there was no post-intervention trend (1.013; 0.932-1.100; P= 0.76) to suggest a delayed effect. The control group also showed no immediate or delayed change in infection rate. No evidence was found that removal of a staff/visitor mask-wearing policy had a significant effect on the rate of hospital-acquired SARS-CoV-2 infection. This does not demonstrate that masks were ineffective through the pandemic, but provides some objective evidence to justify the removal of healthcare mask mandates once there was widespread immunity and reduced disease severity.

Neuroprotective and Anti-inflammatory Effects of Rubiscolin-6 Analogs with Proline Surrogates in Position 2

Naturally occurring peptides, such as rubiscolins derived from spinach leaves, have been shown to possess some interesting activities. They exerted central effects, such as antinociception, memory consolidation and anxiolytic-like activity. The fact that rubiscolins are potent even when given orally makes them very promising drug candidates. The present work tested whether rubiscolin-6 (R-6, Tyr-Pro-Leu-Asp-Leu-Phe) analogs have neuroprotective and anti-inflammatory effects. These hypotheses were tested in the 6-hydroxydopamine (6-OHDA) injury model of human neuroblastoma SH-SY5Y and lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages. The determination of reactive oxygen species (ROS), mitochondrial membrane potential (MMP), Caspase-3 activity, lipid peroxidation and nitric oxide (NO) production allowed us to determine the effects of peptides on hallmarks related to Parkinson’s Disease (PD) and inflammation. Additionally, we investigated the impact of R-6 analogs on serine-threonine kinase (also known as protein kinase B, AKT) and mammalian target of rapamycin (mTOR) activation. The treatment with analogs 3 (Tyr-Inp-Leu-Asp-Leu-Phe-OH), 5 (Dmt-Inp-Leu-Asp-Leu-Phe-OH) and 7 (Tyr-Inp-Leu-Asp-Leu-Phe-NH2) most effectively prevented neuronal death via attenuation of ROS, mitochondrial dysfunction and Caspase-3 activity. Peptides 5 and 7 significantly increased the protein expression of the phosphorylated-AKT (p-AKT) and phosphorylated-mTOR (p-mTOR). Additionally, selected analogs could also ameliorate LPS-mediated inflammation in macrophages via inhibition of intracellular generation of ROS and NO production. Our findings suggest that R-6 analogs exert protective effects, possibly related to an anti-oxidation mechanism in in vitro model of PD. The data shows that the most potent peptides can inhibit 6-OHDA injury by activating the PI3-K/AKT/mTOR pathway, thus playing a neuroprotective role and may provide a rational and robust approach in the design of new therapeutics or even functional foods.

Modeling the power trading strategies of a retailer with multi-microgrids: a robust bi-level optimization approach

ABSTRACT The primary contribution of this paper lies in the development of a bi-level optimization framework designed to model a retailer’s power trading strategies within the context of multi-microgrids. Another noteworthy aspect of this research is managing the risk associated with the retailer’s decision-making process, achieved by addressing the unpredictability of market prices through a robust bi-level structure. In this model, the upper-level problem focuses on the retailer’s decision-making, while the lower-level problem addresses microgrid considerations. Furthermore, a load-shifting strategy is integrated into the microgrid problem to enhance the profitability-to-cost ratio for both the retailer and the microgrids. The results demonstrate a 3% increase in the retailer’s profits and a significant 30% reduction in the microgrids’ costs using the load-shifting strategy. In the most unfavorable scenario, the retailer experiences a profit reduction of 21.67%, while the maximum increase in the microgrids’ costs reaches 238.3%.

Unraveling The Influence of Factors on Micro Small and Medium-Sized Enterprises’ Adoption of Social Media Marketing and Its Business Impact

The continuously growing opportunities in the digital economy have prompted the Indonesian government to encourage Micro, Small, and Medium-sized Enterprises (MSMEs) to undergo digital transformation. While there are various approaches available, marketing products/services through social media is believed to be one of the most effective methods. To maximize the benefits of social media marketing adoption for MSMEs, it is indeed crucial to identify the influential factors related to its adoption. Therefore, this study aims to quantitatively identify the factors influencing social media adoption among MSMEs in Indonesia. In this study, attention is directed toward five factors: performance expectancy, social influence, effort expectancy, facilitating conditions, and operating costs. The study incorporated 136 actively participating MSMEs that employed social media marketing. We opted to employ Partial Least Squares Structural Equation Modeling (PLS-SEM) due to its suitability for our sample size and potential data characteristics. As we were unsure about the normality of our data and the presence of measurement issues, PLS-SEM provided a robust and flexible approach. It allows us to analyze complex models and handle potential non-normal data and measurement concerns, ensuring reliable results even if such issues arise. Data is processed using the SmartPLS 3.0 software. The research findings indicate that the usage of social media marketing is influenced by performance expectancy and social influence. However, factors such as effort expectancy, facilitating conditions, and costs do not have a significant impact on social media adoption among MSMEs. Furthermore, the results of the study highlight the positive impact of utilizing social media for marketing on the performance of MSMEs, particularly in terms of increasing sales, enhancing customer relationships, improving productivity, and fostering creativity. These findings suggest that MSMEs can leverage social media platforms to achieve tangible benefits and enhance various aspects of their business operations.

Unanchored Population-Adjusted Indirect Comparison Methods for Time-to-Event Outcomes Using Inverse Odds Weighting, Regression Adjustment, and Doubly Robust Methods With Either Individual Patient or Aggregate Data

ObjectivesSeveral methods for unanchored population-adjusted indirect comparisons (PAICs) are available. Exploring alternative adjustment methods, depending on the available individual patient data (IPD) and the aggregate data (AD) in the external study, may help minimize bias in unanchored indirect comparisons. However, methods for time-to-event outcomes are not well understood. This study provides an overview and comparison of methods using a case study to increase familiarity. A recent method is applied to marginalize conditional hazard ratios, which allows for the comparisons of methods, and a doubly robust method is proposed. MethodsThe following PAIC methods were compared through a case study in third-line small cell lung cancer, comparing nivolumab with standard of care based on a single-arm phase II trial (CheckMate 032) and real-world study (Flatiron) in terms of overall survival: IPD-IPD analyses using inverse odds weighting, regression adjustment, and a doubly robust method; IPD-AD analyses using matching-adjusted indirect comparison, simulated treatment comparison, and a doubly robust method. ResultsNivolumab extended survival versus standard of care with hazard ratios ranging from 0.63 (95% CI 0.44-0.90) in naive comparisons (identical estimates for IPD-IPD and IPD-AD analyses) to 0.69 (95% CI 0.44-0.98) in the IPD-IPD analyses using regression adjustment. Regression-based and doubly robust estimates yielded slightly wider confidence intervals versus the propensity score-based analyses. ConclusionsThe proposed doubly robust approach for time-to-event outcomes may help to minimize bias due to model misspecification. However, all methods for unanchored PAIC rely on the strong assumption that all prognostic covariates have been included.

Metagenomics-Based Microbial Ecological Community Threshold and Indicators of Anthropogenic Disturbances in Estuarine Sediments.

Assessing the impacts of cumulative anthropogenic disturbances on estuarine ecosystem health is challenging. Using spatially distributed sediments from the Pearl River Estuary (PRE) in southern China, which are significantly influenced by anthropogenic activities, we demonstrated that metagenomics-based surveillance of benthic microbial communities is a robust approach to assess anthropogenic impacts on estuarine benthic ecosystems. Correlational and threshold analyses between microbial compositions and environmental conditions indicated that anthropogenic disturbances in the PRE sediments drove the taxonomic and functional variations in the benthic microbial communities. An ecological community threshold of anthropogenic disturbances was identified, which delineated the PRE sediments into two groups (H and L) with distinct taxa and functional traits. Group H, located nearshore and subjected to a higher level of anthropogenic disturbances, was enriched with pollutant degraders, putative human pathogens, fecal pollution indicators, and functional traits related to stress tolerance. In contrast, Group L, located offshore and subjected to a lower level of anthropogenic disturbances, was enriched with halotolerant and oligotrophic taxa and functional traits related to growth and resource acquisition. The machine learning random forest model identified a number of taxonomic and functional indicators that could differentiate PRE sediments between Groups H and L. The identified ecological community threshold and microbial indicators highlight the utility of metagenomics-based microbial surveillance in assessing the adverse impacts of anthropogenic disturbances in estuarine sediments, which can assist environmental management to better protect ecosystem health.

Highly Versatile Preparation of Imidazo[1,5‐a]quinolines and Characterization of Their Photoluminescent Properties

AbstractIn this work, we describe a simple and robust synthetic approach for the formation of 1,3‐substituted imidazo[1,5‐a]quinolines. This was achieved by developing a mild and selective bromination with N‐bromosuccinimide (NBS) in 3‐position of the imidazole ring. This was followed by a Negishi coupling, which we performed with various coupling partners, resulting in a wide range of different combinations, usually unattainable by other approaches and similar coupling reactions. Fluorescence measurements identified beneficial substitution patterns for the future use of imidazo[1,5‐a]quinolines in optical applications, such as organic light‐emitting diodes (OLEDs).

Optimization-based level-set re-initialization: A robust interface preserving approach in multiphase problems

In spite of its overall efficiency and robustness for capturing the interface in multiphase fluid dynamics simulations, the well-known shortcoming of the level-set method is associated with the lack of a systematic approach for preserving the regularity of the distance function. This is mainly due to the stretching (or compressing) effect of the strain rate especially in the vicinity of the liquid-gas interface. Level-set re-initialization is an effective treatment for this issue. However, the traditional approach based on the hyperbolic Hamilton–Jacobi equation is a computationally expensive procedure. Crucially, due to the hyperbolic nature of the formulation, the accuracy of the results hinges significantly on the specialized handling of blind spots near the liquid-gas interface intersecting the substrate. The present work proposes a two-step elliptic level-set re-initialization approach that strictly preserves the location of zero level-set via incorporation of an element splitting process. The primary initialization step helps remove any non-smoothness in the to-be regularized level-set function dramatically improving the efficiency of the secondary optimization step. Geometric representation of the boundary conditions is utilized in the initialization step, while the optimization step minimizes the reliance of the results on the treatment of the blind spots. The performance of the proposed method is examined for free and sessile three-dimensional droplets.

A robust approach to shear strength prediction of reinforced concrete deep beams using ensemble learning with SHAP interpretability

A robust approach to shear strength prediction of reinforced concrete deep beams using ensemble learning with SHAP interpretability

Using advanced mixed methods approaches: Combining PLS-SEM and qualitative studies

The PLS-SEM method is a robust approach to researching consumer behavior. However, understanding a phenomenon only through a quantitative approach may not be sufficient in some situations. This work explains how PLS-SEM results can be enhanced by complementing a quantitative approach with other methods proposing an advanced mixed methods design. We provide an actionable guideline, illustrating the approach through three studies in a retail context and using both an exploratory and intervention design. Our results show how exploratory studies help produce relevant contingent concepts for PLS-SEM testing. Building on PLS-SEM findings, a final qualitative study offers fine-grained intervention insights. We present a checklist for researchers on integrating advanced mixed methods and PLS-SEM.

LocCams

Unlawful wireless cameras are often hidden to secretly monitor private activities. However, existing methods to detect and localize these cameras are interactively complex or require expensive specialized hardware. In this paper, we present LocCams, an efficient and robust approach for hidden camera detection and localization using only a commodity device (e.g., a smartphone). By analyzing data packets in the wireless local area network, LocCams passively detects hidden cameras based on the packet transmission rate. Camera localization is achieved by identifying whether the physical channel between our detector and the hidden camera is a Line-of-Sight (LOS) propagation path based on the distribution of channel state information subcarriers, and utilizing a feature extraction approach based on a Convolutional Neural Network (CNN) model for reliable localization. Our extensive experiments, involving various subjects, cameras, distances, user positions, and room configurations, demonstrate LocCams' effectiveness. Additionally, to evaluate the performance of the method in real life, we use subjects, cameras, and rooms that do not appear in the training set to evaluate the transferability of the model. With an overall accuracy of 95.12% within 30 seconds of detection, LocCams provides robust detection and localization of hidden cameras.

Prediction of acute kidney injury incidence following acute type A aortic dissection surgery with novel biomarkers: a prospective observational study

BackgroundAcute kidney injury (AKI) is a prevalent complication following acute type A aortic dissection (ATAAD) surgery and is closely associated with unfavorable prognostic outcomes. Hence, the development of a robust and efficient diagnostic approach to identify high-risk patients is of paramount importance.MethodsWe conducted a prospective study involving 328 patients who underwent ATAAD surgery at our institution, comprising three distinct cohorts. In addition, 52 patients undergoing alternative cardiopulmonary surgeries and 37 healthy individuals were enrolled as control groups. Employing proteomic analysis, we initially identified plasma proteins potentially linked to AKI occurrence within the plasma proteomic cohort. Subsequent validation was performed in an independent cohort. Utilizing predictors derived from multivariate logistic regression analysis, a nomogram was meticulously formulated and its efficacy was validated in the model construction cohort.ResultsProteomics revealed significant elevation of plasma levels of S100A8/A9, pentraxin 3 (PTX3), and chitinase 3-like 1 (CHI3L1) immediately post-surgery in patients who developed ATAAD surgery-associated AKI (ASA-AKI). Receiver operating characteristic (ROC) curves demonstrated impressive predictive performance of S100A8/A9, PTX3, and CHI3L1 at 0 h post-surgery, yielding area under the curve (AUC) values of 0.823, 0.786, and 0.803, respectively, for ASA-AKI prediction. Furthermore, our findings exhibited positive correlations between plasma levels of S100A8/A9, PTX3, CHI3L1, and urinary neutrophil gelatinase-associated lipocalin (NGAL) at 0 h post-surgery, along with correlations between plasma S100A8/A9, CHI3L1 levels, and the Cleveland Clinic score. A logistic regression model incorporating plasma S100A8/A9, PTX3, CHI3L1 levels, urinary NGAL levels, and the Cleveland Clinic score facilitated the construction of a predictive nomogram for ASA-AKI. This nomogram demonstrated robust discriminative ability, achieving an AUC of 0.963 in the model construction cohort.ConclusionsOur study underscored the augmentation of plasma S100A8/A9, PTX3, and CHI3L1 levels immediately post-surgery in patients developing ASA-AKI. The incorporation of these three biomarkers, in conjunction with the Cleveland Clinic score and NGAL, into a nomogram demonstrated commendable predictive efficacy. This presents a practical tool for identifying patients at an elevated risk of AKI following ATAAD surgery.

Drought induces substitution of bacteria within taxonomic groups in the rhizosphere of native maize from arid and tropical regions

In Mexico, maize landraces are usually cultivated in milpa polyculture agroecosystems, which are maintained with ancestral practices, i.e. with very low external inputs such as chemicals and without irrigation systems. Several studies have characterized soil and maize-root microbiota from different milpas, however, robust experimental approaches are necessary to understand the relationship between a healthy microbial community structure and the health of milpa plants. We tested the hypothesis that different maize varieties not only differ in the microorganisms enriched in their rhizospheric environment, but also in the responses of these communities to drought. By suspending irrigation, we induced drought experimentally in three varieties of maize: one landrace from an arid milpa, one from a tropical milpa, and a commercial hybrid. The treatment elicited stress symptoms and growth declines in all three maize varieties. Nevertheless, the maize landrace from arid milpa displayed a better performance in biomass accumulation. The drought treatment did not significantly affect bulk soil bacterial communities; in contrast, all three varieties had shifts in their rhizospheric microbiomes, indicating that these responses were related to the presence of plant roots. The varieties differed in their changes of root-associated microbiota in response to drought, with only Paenarthrobacter genus increasing in all three. Responses mainly consisted of increases in the abundance of certain groups (e.g. Verrucomicrobiota) and substitution of strains within groups (e.g. Group 6 Acidobacteriota, family Streptomycetacea, and many others). The landrace from an arid region had the most bacteria substituted under drought, suggesting that a more stringent selection of microorganisms in roots could be part of the response of this landrace to drought. We propose that drought-tolerant maize landraces from arid milpas could be the source of bacterial strains or communities that aid drought-sensitive varieties in coping with reduced water availability expected due to climate change.

Expanding from unilateral to bilateral: A robust deep learning-based approach for predicting radiographic osteoarthritis progression

To develop and validate a deep learning (DL) model for predicting osteoarthritis (OA) progression based on bilateral knee joint views. In this retrospective study, knee joints from bilateral posteroanterior knee radiographs of participants in the Osteoarthritis Initiative were analyzed. At baseline, participants were divided into testing set 1 and development set according to the different enrolled sites. The development set was further divided into a training set and a validation set in an 8:2 ratio for model development. At 48-month follow-up, eligible patients were formed testing set 2. The Bilateral Knee Neural Network (BikNet) was developed using bilateral views, with the knee to be predicted as the main view and the contralateral knee as the auxiliary view. DenseNet and ResNext were also trained and compared as the unilateral model. Two reader tests were conducted to evaluate the model's value in predicting incident OA. Totally 3583 participants were evaluated. The BikNet we proposed outperformed ResNext and DenseNet (all area under the curve [AUC]<0.71, P<0.001) with AUC values of 0.761 and 0.745 in testing sets 1 and 2, respectively. With assistance of the BikNet increased clinicians' sensitivity (from 28.1-63.2% to 42.1-68.4%) and specificity (from 57.4-83.4% to 64.1-87.5%) of incident OA prediction and improved inter-observer reliability. The DL model, constructed based on bilateral knee views, holds promise for enhancing the assessment of OA and demonstrates greater robustness during subsequent follow-up evaluations as compared with unilateral models. BikNet represents a potential tool or imaging biomarker for predicting OA progression.

A reduction in global impacts through a waste-wastewater-energy nexus: A life cycle assessment

This work presents the results of a comparison of three different management scenarios (one current and two future) of waste, wastewater, and their related energy systems in Southern European regions. A Life Cycle Assessment (LCA) has been applied to all the different steps of the complex scenarios in order to provide a more holistic, robust, and scientific approach that is able to highlight the beneficial effects of the integration of different plants (Waste to Energy - WtE, Anaerobic digestion - AD, wastewater treatment plant - WWTP) included in a symbiotic nexus. The LCA results have shown that an important reduction in the impact of Municipal Solid Waste (MSW) management is obtained from the recovery of separately collected materials, but there is still a large gap that could be filled by the introduction of energy recovery (by means of AD and WtE) and through its symbiotic use to support the anaerobic digestion, wastewater reuse, and sludge recovery processes. The proposed approach, which is based on a waste-wastewater-energy nexus and industrial symbiosis, could be adopted to support these regions to significantly increase circularity and fill the gap, thus favoring a significant rapprochement in the path toward the New Green Deal promoted by the European Union.

Chronic Mild Sleep Restriction Does Not Lead to Marked Neuronal Alterations Compared With Maintained Adequate Sleep in Adults

BackgroundSleep restriction (SR) has been shown to upregulate neuronal reward networks in response to food stimuli, but prior studies were short-term and employed severe SR paradigms. ObjectiveOur goal was to determine whether mild SR, achieved by delaying bedtimes by 1.5 h, influences neuronal networks responsive to food stimuli compared with maintained adequate sleep (AS) >7 h/night. MethodsA randomized controlled crossover study with 2 6-wk phases, AS (≥7 h sleep/night) and SR (−1.5 h/night relative to screening), was conducted. Adults with AS duration, measured using wrist actigraphy over a 2-wk screening period, and self-reported good sleep quality were enrolled. Resting-state and food-stimulated functional neuroimaging (fMRI) was performed at the endpoint of each phase. Resting-state fMRI data analyses included a priori region-of-interest seed-based functional connectivity, whole-brain voxel-wise analyses, and network analyses. Food task-fMRI analyses compared brain activity patterns in response to food cues between conditions. Paired-sample t tests tested differences between conditions. ResultsTwenty-six participants (16 males; age 29.6 ± 5.3 y, body mass index 26.9 ± 4.0 kg/m2) contributed complete data. Total sleep time was 7 h 30 ± 28 min/night during AS compared with 6 h 12 ± 26 min/night during SR. We employed different statistical approaches to replicate prior studies in the field and to apply more robust approaches that are currently advocated in the field. Using uncorrected P value of <0.01, cluster ≥10-voxel thresholds, we replicated prior findings of increased activation in response to foods in reward networks after SR compared with AS (right insula, right inferior frontal gyrus, and right supramarginal gyrus). These findings did not survive more rigorous analytical approaches (Gaussian Random Field theory correction at 2-tailed voxel P < 0.001, cluster P < 0.05). ConclusionsThe results suggest that mild SR leads to increased reward responsivity to foods but with low confidence given the failure to meet significance from rigorous statistical analyses. Further research is necessary to inform the mechanisms underlying the role of sleep on food intake regulation.This trial was registered at clinicaltrials.gov as NCT02960776.

Morphofunctional characterisation of axonal damage in different rat models of chemotherapy-induced peripheral neurotoxicity: The role of nerve excitability testing.

Chemotherapy-induced peripheral neurotoxicity (CIPN) is a common and long-lasting adverse event of several anticancer compounds, for which treatment has not yet been developed. To fill this gap, preclinical studies are warranted, exploiting highly translational outcome measure(s) to transfer data from bench to bedside. Nerve excitability testing (NET) enables to test in vivo axonal properties and can be used to monitor early changes leading to axonal damage. We tested NET use in two different CIPN rat models: oxaliplatin (OHP) and paclitaxel (PTX). Animals (female) were chronically treated with either PTX or OHP and compared to respective control animals. NET was performed as soon as the first injection was administered. At the end of the treatment, CIPN onset was verified via a multimodal and robust approach: nerve conduction studies, nerve morphometry, behavioural tests and intraepidermal nerve fibre density. NET showed the typical pattern of axonal hyperexcitability in the 72 h following the first OHP administration, whereas it showed precocious signs of axonal damage in PTX animals. At the end of the month of treatment, OHP animals showed a pattern compatible with a mild axonal sensory polyneuropathy. Instead, PTX cohort was characterised by a rather severe sensory axonal polyneuropathy with minor signs of motor involvement. NET after the first administration demonstrated the ongoing OHP-related channelopathy, whereas in PTX cohort it showed precocious signs of axonal damage. Therefore, NET could be suggested as an early surrogate marker in clinical trials, to detect precocious changes leading to axonal damage.

A highly robust gas network simulation approach through an inherently solvable problem formulation for network states far from intended design points

Pipeline networks are an efficient and widespread transportation system for supplying natural gas as well as increasingly green gas to Europe. However, they are exposed to risks arising from environmental factors, accidents, crime and political issues. This work contributes to the assessment of gas transmission networks and enables a targeted improvement of their resilience. Therefore, the objective of this work is to develop a simulation tool that enables gas transmission system operators (TSOs) to identify weak spots, e.g. potential bottle necks, in their pipeline networks. To this end, a steady-state gas network simulation approach is developed that provides fast results for gas supply deficiencies for complete sets of multi-event disruption scenarios, enabling the identification of the most important network elements. This paper describes how to implement a computationally fast, numerically robust, and physically accurate gas network simulation and assessment tool. By solving mass and momentum balance equations, it accounts for key network elements such as flow control valves, pressure control valves, compressor stations, gas sources, and gas consumers. The central feature that distinguishes this approach from classical steady state solvers is the robustness of the gas flow calculation through the choice of a problem boundary condition formulation, which results in an inherently solvable system of equations. This feature is key in ensuring mathematical convergence of predictions for gas network states that strongly deviate from their original design point, where boundary conditions must not become mathematically invalid but need to reflect physically reasonable behavior. The capabilities of the system are demonstrated using a fictitious gas network and a representative national gas transmission network. Finally, the limits of applicability are outlined based on the size of the network under consideration and the types of analyses.

A distributionally robust optimization approach for optimal load dispatch of energy hub considering multiple energy storage units and demand response programs

The coupling relationship among various energy sources has been consistently reinforced by the advancement of the energy Internet. In the context of modeling multi-energy systems, the energy hub (EH) represents a significant and valuable approach. To attain optimal operation of the EH, this study proposes an optimal load dispatch model for a system that incorporates a combined heat and power (CHP) unit, gas boiler (GB), electric chiller (EC), absorption chiller (AC), heat energy storage (HES) unit, and electrical energy storage (EES) unit. A two-stage distributionally robust optimization (DRO) method that is driven by data is employed to address the uncertainties of electricity prices. Additionally, the proposed two-stage DRO model is effectively solved through the utilization of the column and constraint generation (C&CG) algorithm. Three cases are discussed in the paper with various energy storage units and demand response (DR) settings. The simulation results show that by deploying energy storage units and participating in DR projects, the EH system can reduce the total costs by 2.56 % and 10 %, respectively. Meanwhile, simulation results reveal that the proposed data-driven DRO model can achieve a compromise between the economy and robustness of the scheduling model compared with stochastic optimization (SO) and robust optimization (RO) methods. The simulation results illustrate the effectiveness of the proposed model for ensuring the economical and efficient operation of the system in the face of electricity price uncertainties.

The impact of COVID-19 on pulmonary, neurological, and cardiac outcomes: evidence from a Mendelian randomization study.

Long COVID is a clinical entity characterized by persistent health problems or development of new diseases, without an alternative diagnosis, following SARS-CoV-2 infection that affects a significant proportion of individuals globally. It can manifest with a wide range of symptoms due to dysfunction of multiple organ systems including but not limited to cardiovascular, hematologic, neurological, gastrointestinal, and renal organs, revealed by observational studies. However, a causal association between the genetic predisposition to COVID-19 and many post-infective abnormalities in long COVID remain unclear. Here we employed Mendelian randomization (MR), a robust genetic epidemiological approach, to investigate the potential causal associations between genetic predisposition to COVID-19 and long COVID symptoms, namely pulmonary (pneumonia and airway infections including bronchitis, emphysema, asthma, and rhinitis), neurological (headache, depression, and Parkinson's disease), cardiac (heart failure and chest pain) diseases, and chronic fatigue. Using two-sample MR, we leveraged genetic data from a large COVID-19 genome-wide association study and various disorder-specific datasets. This analysis revealed that a genetic predisposition to COVID-19 was significantly causally linked to an increased risk of developing pneumonia, airway infections, headache, and heart failure. It also showed a strong positive correlation with chronic fatigue, a frequently observed symptom in long COVID patients. However, our findings on Parkinson's disease, depression, and chest pain were inconclusive. Overall, these findings provide valuable insights into the genetic underpinnings of long COVID and its diverse range of symptoms. Understanding these causal associations may aid in better management and treatment of long COVID patients, thereby alleviating the substantial burden it poses on global health and socioeconomic systems.