DR Model Research Articles

A constrained price-based demand response framework employing utility functions in three-state Overlapping Generation and Gift and Bequest based model in distribution system

Demand response provides an opportunity for consumers to play a significant role in the operation of the electric grid by reducing or shifting their electricity usage during peak periods in response to time-based rates or other forms of financial incentives. These programs are important as they have the potential to help electricity providers save money through reductions in peak demand and the ability to defer construction of new power plants and power delivery systems specifically, those reserved for use during peak times. For the successful application of DR in day-to-day life, DR models are necessary to be implemented. Many of the existing DR models primarily focus on the formulation of after-DR demand based on price elasticity. Though these models are devoid of basic humans’ micro-economic behavior, which is an essential part of a DR stakeholder. Considering these shortcomings of the existing DR literature, this paper envisages formulating DR models based on the foundation of basic humans’ manifestations of demand flexibility, willingness, load recovery, and altruistic behavior. Hence, this paper proposes two price-based DR models known as the three-state Overlapping Generation (OLG) model and the Gift and Bequest (G&B) based DR model. These models are based on customers’ microeconomic behaviors and are suitable for representing load recovery with minimal parameters. Both three-state OLG and G&B-based DR models are examined on IEEE 33-bus and 118-bus distribution systems and are compared with the existing price-elasticity model (PEM) and two-state OLG-based DR model.

NSUN2 affects diabetic retinopathy progression by regulating MUC1 expression through RNA m5C methylation

BackgroundDiabetic retinopathy (DR) is the leading cause of blinding eye disease among working adults and is primarily attributed to the excessive proliferation of microvessels, which leads to vitreous hemorrhage and retinal traction, thereby significantly impairing patient vision. NSUN2-mediated RNA m5C methylation is implicated in various diseases, and in this investigation, we focused on elucidating the impact of NSUN2 on the regulation of the expression of the downstream gene MUC1, specifically through RNA m5C methylation, on the progression of DR.MethodUtilizing Microarray analysis, we examined patient vitreous fluid to pinpoint potential therapeutic targets for DR. Differential expression of NSUN2 was validated through qRT-PCR, Western blot, and immunofluorescence in human tissue, animal tissue, and cell model of DR. The relationship between NSUN2 and DR was explored in vitro and in vivo through gene knockdown and overexpression. Various techniques, such as MeRIP-qPCR and dot blot, were applied to reveal the downstream targets and mechanism of action of NSUN2.ResultsThe levels of both NSUN2 and RNA m5C methylation were significantly elevated in the DR model. Knockdown of NSUN2 mitigated DR lesion formation both in vitro and in vivo. Mechanistically, NSUN2 promoted MUC1 expression by binding to the RNA m5C reader ALYREF. Knockdown of ALYREF resulted in DR lesion alterations similar to those observed with NSUN2 knockdown. Moreover, MUC1 overexpression successfully reversed a series of DR alterations induced by NSUN2 silencing.ConclusionsNSUN2 regulates the expression of MUC1 through ALYREF-mediated RNA m5C methylation, thereby regulating the progression of DR and providing a new option for the treatment of DR in the future.

Modeling of multi-temperature Type I and II benzene/ammonia adsorption isotherms: Dual LF model and linearized DR model

Adsorption isotherm is basic data for evaluating the porous adsorbent adsorption capacity and explaining gas–solid adsorption behavior. Adsorption equilibrium modeling is a simple method to describe isotherm data in application. In this paper, taking C6H6 and NH3 as an example, multi-temperature Type I or Type II adsorption isotherms on AC, ASZMT, and 13X were investigated and fitted. Results show that C6H6 and NH3 adsorption behaviors on 13X both existed in chemisorption. At certain adsorbed capacity, the isosteric heats are higher than 50 kJ·mol−1. Type I and Type II C6H6 or NH3 adsorption isotherms could be successfully described using the extended Dual LF model·NH3 temperature-independent characteristic curves on AC and ASZMT were obtained using the Linearized DR model and successfully used to predict NH3 adsorption data at any pressure within the whole room temperature. Further, the Dual LF model was extended to describe other adsorption isotherms including IUPAC Type III, IV, and V. The R2 between the model and data were higher than 0.98, proving that it has universal applicability than other classical models such as Langmuir, Freundlich, Toth, and so on. It has the potential to be used as a general equilibrium model for adsorption process simulation in the future.

Proposal of fully-coupled actuated disk model for wind turbine operation modeling in turbulent flow field due to complex topography

The wind speed and direction of flow fields in the time and space domains are complicated owing to topographic relief, and it is not yet known how time-varying incoming wind speed and automatic windward effect interact with the wake characteristics of wind turbines. To address this, semi-coupled ADM-SR model (SR model) and a fully coupled ADM-DR model (DR model) are proposed in this study. The wakes of wind turbines located at various locations on the slant ridge were then compared. The wake velocity deficit of the wind turbines using the SR and DR models were found to be up to 40% and 20% upstream of the ridge, respectively, with large variations. Downstream, the wind turbine wakes of both models were similar. The DR model with the automatic windward orientation caused the wind turbine actuator disk to rotate by 9.24° and 7.40° perpendicular to the tangential direction of the streamline, and the accuracy of the control procedure was verified. Furthermore, the lower the turbulence intensity, the greater the difference between the predictions of the two models, and the power prediction value of the DR model was approximately 80% higher than that of the SR model.

Demand‐side price‐responsive flexibility and baseline estimation through end‐to‐end learning

AbstractTime‐varying electricity tariffs provide consumers the flexibility to adjust their consumption patterns in response to price variations to reduce the cost of electricity while at the same time contributing to grid operation. As more homes and buildings utilize time‐varying tariffs, utilities and regulators must seek ways to model demand‐side flexibilities to predict future demands and design new incentives. This paper proposes a novel end‐to‐end deep learning framework that simultaneously identifies demand baselines and the price‐response model from the net demand measurements and price signals. A gradient‐descent approach is then proposed that backpropagates the net demand forecast errors to update the weights of the price‐response model and the weights of the baseline demand forecast jointly. The effectiveness of the approach is demonstrated through computation experiments with synthetic demand response traces and a large‐scale real‐world DR dataset. The results show that the approach accurately identifies the DR model, even without prior knowledge about the baseline demand.

Apigenin inhibits angiogenesis in retinal microvascular endothelial cells through regulating of the miR-140-5p/HDAC3-mediated PTEN/PI3K/AKT pathway

BackgroundDiabetic retinopathy (DR) is a common cause of visual impairment. Apigenin has been shown to have antiangiogenic effects in various diseases. Our study aimed to investigate the role of apigenin in DR and elucidate the underlying mechanism.MethodsHuman retinal microvascular endothelial cells (HRMECs) were exposed to high glucose (HG) to establish a DR model. HRMECs were treated with apigenin. Then we knocked down or overexpressed miR-140-5p and HDAC3, and added PI3K/AKT inhibitor LY294002. The expression levels of miR-140-5p, HDAC3, and PTEN were measured using qRT-PCR. Western blot analysis was performed to assess the expression of HDAC3, PTEN, and PI3K/AKT pathway-related proteins. Finally, cell proliferation and migration were evaluated using MTT, wound-healing assay, and transwell assay, while angiogenesis was examined using the tube formation assay.ResultsHG treatment resulted in reduced miR-140-5p expression and overexpression of miR-140-5p suppressed proliferation, migration, and angiogenesis of the HG-induced HRMECs. Apigenin treatment significantly restored the decreased level of miR-140-5p caused by HG treatment and inhibited proliferation, migration, and angiogenesis of the HG-induced HRMECs by upregulating miR-140-5p. Moreover, miR-140-5p targeted HDAC3, and overexpression of miR-140-5p reversed the HG-inducted upregulation of HDAC3 expression. HDAC3 was found to bind to the promoter region of PTEN, inhibiting its expression. Knockdown of HDAC3 suppressed the PI3K/AKT pathway by elevating PTEN expression. Furthermore, apigenin inhibited angiogenesis in DR cell models through the regulating of the miR-140-5p/HDAC3-mediated PTEN/PI3K/AKT pathway.ConclusionsApigenin effectively suppressed angiogenesis in HG-induced HRMECs by modulating the miR-140-5p/HDAC3-mediated PTEN/PI3K/AKT pathway. Our study may contribute to the development of novel therapeutic approaches and identification of potential targets for the treatment of DR.

Ciprofloxacin Removal Using Pillared Clays

In this work, Ti-pillared bentonites were evaluated to remove ciprofloxacin (CIP) from the aqueous solution. Pillared sodium bentonite (BSP) and pillared calcium bentonite (BCP) were prepared by means of the sol–gel method using titanium tetraisopropoxide with calcination for 3 h at 500 °C. They were characterized using the BET method for N2 adsorption, and subjected to X-ray diffraction and Fourier transform infrared spectroscopy. The results show that the surface area of the pillared bentonites increased after the process, more than the natural ones. Pillared sodium bentonite has a more porous structure, larger surface areas, and higher adsorption capacity than pillared calcium bentonite. The kinetic adsorption of ciprofloxacin (CIP) onto pillared bentonites is well described by the pseudo second-order kinetic model. The BSP isotherm well fitted the Freundlich model, while the BCP isotherm fits the BET model better, suggesting multilayer adsorption. DR model shows mostly physical adsorption for CIP on the two adsorbents. The pH influence study indicated that CIP is adsorbed at pH between 6 and 8, which facilitates the use of BCP and BSP in wastewater treatment, whose pH generally oscillates between these ranges.

Changes of adrenocorticotropic hormone rhythm and cortisol circadian rhythm in patients with depression complicated with anxiety and their effects on the psychological state of patients.

Objective: This work was to explore the rhythm of adrenocorticotropic hormone (ACTH) and cortisol in patients with depression and anxiety and their effects on mental state. In this work, with depression complicated with anxiety patients as the A-MDD group (n = 21), and depression without anxiety symptoms as the NA-MDD group (n = 21). Firstly, data features were extracted according to the electroencephalo-graph (EEG) data of different patients, and a DR model was constructed for diagnosis. The Hamilton Depression Scale 24 (HAMD-24) was employed to evaluate the severity, and the ACTH and cortisol levels were detected and compared for patients in the A-MDD group and NA-MDD group. In addition, the psychological status of the patients was assessed using the Toronto Alexithymia Scale (TAS). As a result, the AI-based DR model showed a high recognition accuracy for depression. The HAMD-24 score in the A-MDD group (31.81 ± 5.39 points) was statistically higher than the score in the NA-MDD group (25.25 ± 5.02 points) (P < 0.05). No visible difference was found in ACTH levels of patients in different groups (P > 0.05). The incidence of cortisol rhythm disorder (CRD) in the A-MDD group was much higher (P < 0.05). The differences in TAS scores between the two groups were significantly statistically significant (P < 0.01). In conclusion, the AI-based DR Model achieves a more accurate identification of depression; depression with or without anxiety has different effects on the mental state of patients. CRD may be one of the biological markers of depression combined with anxiety.

Blockade of CB1 or Activation of CB2 Cannabinoid Receptors Is Differentially Efficacious in the Treatment of the Early Pathological Events in Streptozotocin-Induced Diabetic Rats.

Oxidative stress, neurodegeneration, neuroinflammation, and vascular leakage are believed to play a key role in the early stage of diabetic retinopathy (ESDR). The aim of this study was to investigate the blockade of cannabinoid receptor 1 (CB1R) and activation of cannabinoid receptor 2 (CB2R) as putative therapeutics for the treatment of the early toxic events in DR. Diabetic rats [streptozotocin (STZ)-induced] were treated topically (20 μL, 10 mg/mL), once daily for fourteen days (early stage DR model), with SR141716 (CB1R antagonist), AM1710 (CB2R agonist), and the dual treatment SR141716/AM1710. Immunohistochemical-histological, ELISA, and Evans-Blue analyses were performed to assess the neuroprotective and vasculoprotective properties of the pharmacological treatments on diabetes-induced retinal toxicity. Activation of CB2R or blockade of CB1R, as well as the dual treatment, attenuated the nitrative stress induced by diabetes. Both single treatments protected neural elements (e.g., RGC axons) and reduced vascular leakage. AM1710 alone reversed all toxic insults. These findings provide new knowledge regarding the differential efficacies of the cannabinoids, when administered topically, in the treatment of ESDR. Cannabinoid neuroprotection of the diabetic retina in ESDR may prove therapeutic in delaying the development of the advanced stage of the disease.

Estimation of global GPP from GOME-2 and OCO-2 SIF by considering the dynamic variations of GPP-SIF relationship

Previous studies have indicated that gross primary production (GPP) and solar-induced chlorophyll fluorescence (SIF) have a strong linear relationship, and usually exhibit similar spatial and temporal patterns. However, the responses of GPP and SIF to the environment may be different, which will lead to a variant GPP-SIF relationship. To better investigate the impact of the dynamics in GPP-SIF relationship on GPP estimation, we established two GPP models. An inconstant GPP/SIF ratio model (Dynamic-Ratio model, DR model) was first established using meteorological variables and leaf area index (LAI) based on random forest regression algorithm. The model was then used to estimate GPP (referred to as GPP_DR) with different satellite SIF datasets i.e., downscaled fine resolution SIF from the Orbiting Carbon Observatory-2 (GOSIF) and Global Ozone Monitoring Experiment‐2 SIF (downscaled GOME-2 SIF). The second model (SIF-Climate-LAI model, SCL model) was also based on the random forest algorithm but was directly driven by meteorological variables, LAI and SIF data, and no GPP/SIF ratio was used in the model. As a comparison, the linear relationship between GPP and SIF was also established using eddy covariance tower GPP (GPP_EC) and SIF datasets based on linear regression without considering variations of GPP-SIF relationship (Fixed-Ratio model, FR model). Considering the spatio-temporal variations of GPP-SIF relationship can improve the GPP simulation to a certain extent by mitigating the underestimation of peak GPP values. This improvement was found for both DR and SCL models. Owing to the dynamic variations of GPP/SIF ratio and associated uncertainties, the performance of DR model was not as good as that of SCL model. GPP estimation derived from GOSIF matched better with GPP_EC than that from downscaled GOME-2 SIF for DR, SCL and FR models. Our findings suggested that GPP can be better derived from satellite SIF by considering the variations of GPP-SIF relationship.

Characterizing energy flexibility of buildings with electric vehicles and shiftable appliances on single building level and aggregated level

Residential energy flexibility is considered one of the efficient concepts to alleviate the ever-increasing concerns of better balancing supply and demand. A positive assumption that all buildings have the same energy flexibility potential, is not applicable in a realistic situation especially when direct load control is not applied for each. This paper proposes a novel approach to characterize the energy flexibility of shiftable appliances and EVs (as two main sources of energy flexibility) protecting consumers’ data privacy and considering the usage behavior. First, an xg-boost regression is utilized to non-intrusively extract the consumption of appliances. Then, the uncertainty of the power values and the operation time of each appliance is computed based on the extracted consumption patterns. Finally, a price-based DR model is used to determine their energy flexibility and prioritize them according to the change in their operating time before and after optimization. Case studies are conducted and results prove that the proposed method is computationally cost-effective and outperforms other methods in terms of accuracy, customer privacy and comfort. Moreover, the results show that the proposed model can significantly decrease the flattening signal up to 3% for each residential building and up to 25% in the residential aggregated level.

Luminescence age calculation through Bayesian convolution of equivalent dose and dose-rate distributions: the De_Dr model

Abstract. In nature, each mineral grain (quartz or feldspar) receives a dose rate (Dr) specific to its environment. The dose-rate distribution therefore reflects the micro-dosimetric context of grains of similar size. If all the grains were well bleached at deposition, this distribution is assumed to correspond, within uncertainties, with the distribution of equivalent doses (De). The combination of the De and Dr distributions in the De_Dr model proposed here would then allow calculation of the true depositional age. If grains whose De values are not representative of this age (hereafter called “outliers”) are present in the De distribution, this model allows them to be identified before the age is calculated, enabling their exclusion. As the De_Dr approach relies only on the Dr distribution to describe the De distribution, the model avoids any assumption about the shape of the De distribution, which can be difficult to justify. Herein, we outline the mathematical concepts of the De_Dr approach (more details are given in Galharret et al., 2021) and the exploitation of this Bayesian modelling based on an R code available in the R package “Luminescence”. We also present a series of tests using simulated Dr and De distributions with and without outliers and show that the De_Dr approach can be an alternative to available models for interpreting De distributions.

Identification and Validation of Autophagy-Related Genes in Diabetic Retinopathy.

BackgroundDiabetic retinopathy (DR) is one of the most common microvascular complications of diabetes, which is associated with damage of blood-retinal barrier and ischemia of retinal vasculature. It devastates visual acuity due to leakage of retinal vessels and aberrant pathological angiogenesis in diabetic patients. The etiology of DR is complex, accumulated studies have shown that autophagy plays an important role in the pathogenesis of DR, but its specific mechanism needs to be further studied.MethodsThis study chose the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE146615 to carry on the research. Autophagy-related genes that were potentially differentially expressed in DR were screened by R software. Then, the differentially expressed autophagy-related genes were analyzed by correlation analysis, tissue-specific gene expression, gene-ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis. Finally, retinal pigment epithelial cell line (ARPE-19) incubated with high glucose (HG) was used to mimic the DR model, and the mRNA level of key genes was verified by quantitative real-time polymerase chain reaction (qRT-PCR) in vitro.ResultsA total of 23 differentially expressed autophagy-related genes (9 up-regulated genes and 14 down-regulated genes) were identified by differential expression analysis. The analysis of tissue-specific gene expression showed that these differentially expressed autophagy-related genes were enriched in the retina. GO and KEGG enrichment analysis showed that differentially expressed autophagy-related genes were significantly enriched in autophagy-related pathways such as regulation of autophagy and macroautophagy. Then 10 hub genes were identified by PPI network analysis and construction of key modules. Finally, qRT-PCR confirmed that the expression of MAPK3 in the DR model was consistent with the results of bioinformatics analysis of mRNA chip.ConclusionThrough bioinformatics analysis, we identified 23 potential DR autophagy-related genes, among which the down-regulated expression of MAPK3 may affect the occurrence and development of DR by regulating autophagy. It provides a novel insight into the pathogenesis of DR.

Surfactant-induced adsorption of Pb(II) on the cracked structure of microplastics

Surfactant molecules can change the hydrophobic nature of microplastic surfaces, thereby affecting the adsorption of heavy metals in the environment onto the microplastics. It is essential to explore the role of crack structure of microplastics in the adsorption of heavy metals, especially in the presence of surfactants. In this study, polyethylene (PE) and polypropylene (PP) were evaluated for Pb(II) adsorption and desorption mechanism in the presence of two surfactants: cetyltrimethylammonium bromide (CTAB) and sodium dodecylbenzenesulfonate (SDBS). The experimental results were analyzed using kinetics and the isothermal model fitting and spectrogram (FTIR, XPS). This study showed that the application of surfactants could greatly enhance the Pb(II) adsorption capacities of PE and PP by promoting Pb(II) into the fissures. The Pb(II), S, and N contents did not significantly decrease at different depths in the presence of surfactants, and the Pb(II) content without surfactants decreased with an increasing depth. The adsorption behavior was consistent with the Bangham channel diffusion model and the DR model, suggesting that the adsorption process was related to the pore structure of the microplastics. Furthermore, the release of Pb(II) from desorption using high concentration of surfactant solution was less than that of low concentration, it was difficult to release heavy metals primarily because of the crack structure of the microplastics, especially when more surfactant molecules entered the pores. This work contributes to a better understanding of the adsorption mechanism of heavy metals on microplastics in the presence of surfactants, which will better control the ecological risks of microplastics.

Adsorption characteristics and controlling factors of marine deep shale gas in southern Sichuan Basin, China

Deep shale gas (3500–4500 m) is the important replacement field of shale gas production growth in the future China. The research on key parameters of deep shale-gas reservoir is critical to determine its basic geological characteristics and establish a suitable development mode. In order to clarify the adsorption characteristics and controlling factors of deep shale gas in Longmaxi Formation, the main tests such as high-pressure methane adsorption, low-temperature nitrogen and carbon dioxide adsorption coupled with the adsorption fitting model and comparative analysis were conducted. The results show that the adsorption isotherms of deep shale gas also have a downward trend in spite of the higher pressure, and there is no obvious difference in adsorption characteristics, which is mainly due to the similar characteristics of microscopic pore-structure between deep shale and shallower shale. It is found that different adsorption models can well fit the experimental adsorption curve of deep shale gas, but the absolute adsorption capacity converted from excess adsorption capacity shows the same fitting result, i.e., DA-LF model > DR model > Langmuir model. Furthermore, DR model based on micropore filling theory is more suitable for characterizing the adsorption law of deep shale gas combined with the correlation analysis between pore structure and adsorbed-gas capacity. In addition, TOC is the key material factor controlling the adsorption capacity, and specific surface area of micropore is the key spatial factor. Compared to shallower shale, the deep shale shows higher siliceous content, lower calcite content, lower TOC content and lower adsorbed-gas content (the proportion of adsorbed-gas is about 30%).

Danhong injection represses diabetic retinopathy and nephropathy advancement in diabetic mice by upregulating microRNA-30d-5p and targeting JAK1

ABSTRACT Danhong injection (DHI) restrains diabetic retinopathy and nephropathy (DR and DN) advancement in diabetic mice. However, the downstream mechanism of its modulation is not fully studied. Diabetic model mice (db/db mice) were intravenously injected with DHI and corresponding virus particles. MiR-30d-5p and JAK1 were detected. The body weight and fasting blood glucose mice were measured every 4 weeks. The renal tissues and serum of mice were collected, and the contents of creatinine and blood urea nitrogen were biochemically analyzed. IL-6, IFN-γ and TNF-α were detected by ELISA, with the pathological conditions of renal tissues in mice by He staining, and the adjustment conditions by TUNEL. Human retinal pigment epithelium (ARPE-19) cells were selected to induce DR model in vitro by high glucose, and exposed to DHI for treatment. The corresponding plasmids were transfected, and miR-30d-5p and JAK1 were detected, with the proliferation ability by plate cloning, apoptosis by flow cytometry, and cell migration ability by Transwell. The angiogenesis ability of cells was assessed by tube formation assay. The targeting relationship between miR-30d-5p and JAK1 was detected. The results manifested that miR-30d-5p was declined in DR and DN, while JAK1 expression was elevated. DHI was able to improve DR and renal injury. DHI could regulate the miR-30d-5p-JAK1 axis in vivo, and miR-30d-5p targeted and regulated JAK1. Upregulation of miR-30d-5p or inhibition of JAK1 could improve DR and renal injury. The results implies that DHI can repress the development of DR and DN by elevating miR-30d-5p and targeting JAK1.

Trimetazidine mitigates high glucose-induced retinal endothelial dysfunction by inhibiting PI3K/Akt/mTOR pathway-mediated autophagy

ABSTRACT Trimetazidine (TMZ), as a metabolic regulator, has been widely testified to exhibit positive therapeutic effects on various disease models, but its role in diabetic retinopathy has not been reported. Therefore, this study was designed with the purpose of exploring the effects of TMZ on high-glucose (HG)-induced retinal endothelial dysfunction and its underlying mechanism. To establish DR model in vitro, 30 mM glucose was applied to induce human retinal endothelial cells (HRECs). Cell proliferation, invasion, and migration were examined by means of Cell Counting Kit-8, transwell, and wound healing assays, respectively. The tubule formation experiment was used to test the tubulogenesis ability and fluorescein isothiocyanate (FITC)-albumin was utilized to measure the permeability of monolayer HRECs. In addition, immunofluorescence and Western blot were employed to detect protein expression. Compared with the HG-induced group, TMZ concentration dependently inhibited the proliferation, migration, and angiogenesis of HG-induced HRECs, decreased the permeability of monolayer HRECs, and increased the protein expression levels of Claudin-5 and VE-cadherin. In addition, TMZ intervention increased the expression of p-PI3K, p-AKT, and p-mTOR but decreased the expression of LC3I, LC3II, and Beclin 1, which were then partially reversed by P13 K inhibitor (LY294002). Moreover, the autophagy agonist rapamycin (RAPA) was also testified to reverse the inhibitory effects of TMZ on the proliferation, migration, and angiogenesis of HG-induced HRECs. In summary, TMZ inhibited excessive autophagy by activating PI3K/Akt/mTOR pathway, thereby improving retinal endothelial dysfunction induced by HG.

From peak shedding to low-carbon transitions: Customer psychological factors in demand response

Incentive-based demand response (IBDR) programs have played an essential role in energy efficiency delivery, especially peak shedding. Recently, utilities have been challenged to consider the implications of low-carbon transitions and the wider benefits of IBDR. What's more, to fully activate demand-side resources, the IBDR scheme's design requires broadening the analysis beyond the traditional disciplines of economic entities and incorporating new psychological cues of customers. In this regard, this paper studies system operator (SO)'s carbon emissions abatement and incentive strategies in peaking shedding events when facing pressure from both emissions tax and customer non-economic response. We develop a trilayer economic-environmental-behavioral IBDR model for incentive price setting and investigate how carbon tax and customer psychological factors (CPFs) affect the scheme design. Initially, the interaction among hierarchical market participators is captured by a trilayer Stackelberg game. Then the SO's problem is formulated as multi-objective to minimize the procurement cost and emission. Moreover, CPFs are incorporated into the model by parameterized assumptions following behavioral economics. Results show that without consideration of CPFs will result in deviation in the DR model. With reasonable carbon prices, IBDR can be an effective tool for both energy efficiency improvement and decarbonization.

An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

PurposeA major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).Design/methodology/approachHere, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.FindingsImplicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.Research limitations/implicationsA future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.Practical implicationsThis paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.Originality/valueIn most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

The relationship between plasma serglycin levels and the diagnosis of diabetic retinopathy.

BackgroundDiabetic retinopathy (DR), a microvascular complication which is closely related to diabetes, remains the leading cause of vision loss around the world among older adults. Serglycin (SRGN) was known as a hematopoietic cell granule proteoglycan, exerting its function in the formation of mast cell secretory granules and mediates the storage of various compounds in secretory vesicles. The present study illustrates the potential clinical value and experimental mechanisms of SRGN in the DR.MethodsFirstly, the mRNA expression and protein expression of SRGN in plasma samples from NPDR, PDR patients, type 2 diabetes mellitus (T2Dm) cases, and healthy controls were measured by qPCR and Western blotting assays, respectively. Then, the potentials of SRGN functioning as a diagnostic indicator in DR were verified by the receiver operating characteristic (ROC) analysis. We established in vitro DR model of human retinal endothelial cells through high‐glucose treatment. The expression of SRGN and its mechanisms of regulating cellular processes were illustrated subsequently.ResultsOur data revealed that SRGN was dramatically upregulated in NPDR and PDR cases compared with healthy controls and T2DM patients; meanwhile, the expression of SRGN was further increased in the PDR group with regard to the NPDR group. Furthermore, the ROC analysis demonstrated that SRGN could distinguish the DR cases from type 2 diabetes mellitus (T2DM) patients and healthy controls with the area under the curve (AUC) of 0.7680 (95% CI = 0.6780 ~ 0.8576, sensitivity = 47.27%, specificity = 100%, cutoff value = 1.4727) and 0.8753 (95% CI = 0.8261 ~ 0.9244, sensitivity = 69.23%, specificity = 100%, cutoff value = 1.6923), respectively. In vitro high‐glucose treatment showed that the SRGN expressions were dramatically increased. The loss of SRGN could partially counteract the inhibition of HREC proliferation caused by high‐glucose stimulation. Meanwhile, SRGN knockdown could reverse the promotion of HREC apoptosis induced by high glucose as well.ConclusionsConsequently, our study implied that SRGN might serve as a promising biomarker with high specificity and sensitivity in the DR diagnosis and progression.