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Abstract With the global consensus to achieve carbon neutral goals, power systems are experiencing a rapid increase in renewable energy sources and energy storage systems (ESS). Especially, recent development of hub substations (HS/S) equipped with ESS, applicable for resolving site constraints if implemented as mobile transformers, is expanding the development of ESS-equipped facilities. However, these units require centralized control strategies considering variability within integrated networks. While studies on electric vehicle charging considering the variability of renewable energy or load are widely studied, ESS management scheme for individual substations requires further optimization, especially considering the state of distributed sources at lower levels and transmission system operators. Thus, in this study, an optimal control approach for ESS located at the connection point of transmission and distribution systems, including further consideration of the loss in distribution lines and the constraints of renewable energy sources is presented. This study attempts to derive proactive control strategies for ESS in HS/S to operate with various distribution networks. By establishing control priorities for each source through optimal operation strategy, a suitable capacity of ESS and its economic benefits for distribution network management can be examined. Validation of the current analysis results is performed by utilizing MATPOWER. By adapting the operational range of design scenarios, diverse distribution systems can be tested against multiple configurations of connected devices.

With the growth of online networks, understanding the intricate structure of communities has become vital. Traditional community detection algorithms, while effective to an extent, often fall short in complex systems. This study introduced a meta-heuristic approach for community detection that leveraged a memetic algorithm, combining genetic algorithms (GA) with the stochastic hill climbing (SHC) algorithm as a local optimization method to enhance modularity scores, which was a measure of the strength of community structure within a network. We conducted comprehensive experiments on five social network datasets (Zachary's Karate Club, Dolphin Social Network, Books About U.S. Politics, American College Football, and the Jazz Club Dataset). Also, we executed an ablation study based on modularity and convergence speed to determine the efficiency of local search. Our method outperformed other GA-based community detection methods, delivering higher maximum and average modularity scores, indicative of a superior detection of community structures. The effectiveness of local search was notable in its ability to accelerate convergence toward the global optimum. Our results not only demonstrated the algorithm's robustness across different network complexities but also underscored the significance of local search in achieving consistent and reliable modularity scores in community detection.

Abstract In this manuscript, we develop a multi-party framework tailored for multiple data contributors seeking machine learning insights from combined data sources. Grounded in statistical learning principles, we introduce the Multi-Key Homomorphic Encryption Logistic Regression (MK-HELR) algorithm, designed to execute logistic regression on encrypted multi-party data. Given that models built on aggregated datasets often demonstrate superior generalization capabilities, our approach offers data contributors the collective strength of shared data while ensuring their original data remains private due to encryption. Apart from facilitating logistic regression on combined encrypted data from diverse sources, this algorithm creates a collaborative learning environment with dynamic membership. Notably, it can seamlessly incorporate new participants during the learning process, addressing the key limitation of prior methods that demanded a predetermined number of contributors to be set before the learning process begins. This flexibility is crucial in real-world scenarios, accommodating varying data contribution timelines and unanticipated fluctuations in participant numbers, due to additions and departures. Using the AI4I public predictive maintenance dataset, we demonstrate the MK-HELR algorithm, setting the stage for further research in secure, dynamic, and collaborative multi-party learning scenarios.

Abstract We propose an integrated methodology for the design and fabrication of 3D micromodels that are suitable for the pore-scale study of transport processes in macroporous materials. The micromodels, that bear the pore-scale characteristics of sandstone, such as porosity, mean pore size, etc, are designed following a stochastic reconstruction algorithm that allows for fine-tuning the porosity and the correlation length of the spatial distribution of the solid material. We then construct a series of 3D micromodels at very fine resolution (i.e. $$16\,\upmu $$ 16 μ m) using a state-of-the-art 3D printing infrastructure, specifically a ProJet MJP3600 3D printer, that utilizes the Material Jetting technology. Within the technical constraints of the 3D printer resolution, the fabricated micromodels represent scaled-up replicas of natural sandstones, that are suitable for the study of the scaling between the permeability, the porosity and the mean pore size. The REV- and pore-scale characteristics of the resulting physical micromodels are recovered using a combination of X-ray micro-CT and microfluidic studies. The experimental results are then compared with single-phase flow simulations at pore-scale and geostatistic models in order to determine the effects of the design parameters on the intrinsic permeability and the spatial correlation of the velocity profile. Our numerical and experimental measurements reveal an excellent match between the properties of the designed and fabricated 3D domains, thus demonstrating the robustness of the proposed methodology for the construction of 3D micromodels with fine-tuned and well-controlled pore-scale characteristics. Furthermore, a pore-scale numerical study over a wider range of 3D digital domain realizations reveals a very good match of the measured permeabilities with the predictions of the Kozeny–Carman formulation based on a single control parameter, $$k_0$$ k 0 , that is found to have a practically constant value for porosities $$\phi \ge 0.2$$ ϕ ≥ 0.2 . This, in turn, enables us to customize the sample size to meet REV constraints, including enlarging pore morphology while considering the Reynolds number. It is also found that at lower porosities there is a significant increase in the fraction of the non-percolating pores, thus leading to different $$k_0$$ k 0 , as the porosity approaches a numerically determined critical porosity value, $$\phi _c$$ ϕ c , where the domain is no longer percolating.

Thrombosis can cause life-threatening disorders. Unfortunately, current therapeutic methods for thrombosis using injecting thrombolytic medicines systemically resulted in unexpected bleeding complications. Moreover, the absence of practical imaging tools for thrombi raised dangers of undertreatment and overtreatment. This study develops a theranostic drug carrier, Pkr(IR-Ca/Pda-uPA)-cRGD, that enables real-time monitoring of the targeted thrombolytic process of deep vein thrombosis (DVT). Pkr(IR-Ca/Pda-uPA)-cRGD, which is prepared from a Pickering-emulsion-like system, encapsulates both near-infrared-II (NIR-II) contrast agent (IR-1048 dye, loading capacity: 28%) and urokinase plasminogen activators (uPAs, encapsulation efficiency: 89%), pioneering the loading of multiple drugs with contrasting hydrophilicity into one single-drug carrier. Upon intravenous injection, Pkr(IR-Ca/Pda-uPA)-cRGD considerably targets to thrombi selectively (targeting rate: 91%) and disintegrates in response to acidic thrombi to release IR-1048 dye and uPA for imaging and thrombolysis, respectively. Investigations indicate that Pkr(IR-Ca/Pda-uPA)-cRGD enabled real-time visualization of targeted thrombolysis using NIR-II imaging in DVT models, in which thrombi were eliminated (120 min after drug injection) without bleeding complications. This may be the first study using convenient NIR-II imaging for real-time visualization of targeted thrombolysis. It represents the precision medicine that enables rapid response to acquire instantaneous medical images and make necessary real-time adjustments to diagnostic and therapeutic protocols during treatment.

Allergic asthma is a type 2 immune-response-mediated chronic respiratory disease. Mast cell activation influences the pathogenesis and exacerbation of allergic asthma. Therefore, the development of mast cell-targeting pharmacotherapy is important for managing allergic airway inflammation. We investigated the efficacy of hispidulin (HPD), natural flavone, in a mast-cell-mediated ovalbumin (OVA)-induced allergic airway inflammation model. HPD alleviated symptoms of allergic asthma and decreased the levels of immunoglobulin (Ig) E, type 2 inflammation, immune cell infiltration, and mast cell activation in the lung. Furthermore, in vivo analysis confirmed the efficacy of HPD through the evaluation of IgE-mediated allergic responses in a mast cell line. HPD treatment inhibited mast cell degranulation through inhibition of the FcεR1 signaling pathway and suppressed the expression of inflammatory cytokines (TNF-α, IL-4, IL-6, and IL-13) through suppression of the NF-κB signaling pathway. The antioxidant effects of HPD in activated mast cells were identified through modulation of antioxidant enzymes and the Nrf2/HO-1 signaling pathway. In conclusion, HPD may be a potential therapeutic candidate for allergic airway inflammation of asthma and acts by suppressing mast cell activation and oxidative stress.

Abstract Many subsurface engineering technologies or natural processes cause porous medium properties, such as porosity or permeability, to evolve in time. Studying and understanding such processes on the pore scale is strongly aided by visualizing the details of geometric and morphological changes in the pores. For realistic 3D porous media, X-Ray Computed Tomography (XRCT) is the method of choice for visualization. However, the necessary high spatial resolution requires either access to limited high-energy synchrotron facilities or data acquisition times which are considerably longer (e.g. hours) than the time scales of the processes causing the pore geometry change (e.g. minutes). Thus, so far, conventional benchtop XRCT technologies are often too slow to allow for studying dynamic processes. Interrupting experiments for performing XRCT scans is also in many instances no viable approach. We propose a novel workflow for investigating dynamic precipitation processes in porous media systems in 3D using a conventional XRCT technology. Our workflow is based on limiting the data acquisition time by reducing the number of projections and enhancing the lower-quality reconstructed images using machine-learning algorithms trained on images reconstructed from high-quality initial- and final-stage scans. We apply the proposed workflow to induced carbonate precipitation within a porous-media sample of sintered glass-beads. So we were able to increase the temporal resolution sufficiently to study the temporal evolution of the precipitate accumulation using an available benchtop XRCT device.

Abstract Pediatric steroid-sensitive nephrotic syndrome (pSSNS) is the most common childhood glomerular disease. Previous genome-wide association studies (GWAS) identified a risk locus in the HLA Class II region and three additional independent risk loci. But the genetic architecture of pSSNS, and its genetically driven pathobiology, is largely unknown. Here, we conduct a multi-population GWAS meta-analysis in 38,463 participants (2440 cases). We then conduct conditional analyses and population specific GWAS. We discover twelve significant associations—eight from the multi-population meta-analysis (four novel), two from the multi-population conditional analysis (one novel), and two additional novel loci from the European meta-analysis. Fine-mapping implicates specific amino acid haplotypes in HLA-DQA1 and HLA-DQB1 driving the HLA Class II risk locus. Non-HLA loci colocalize with eQTLs of monocytes and numerous T-cell subsets in independent datasets. Colocalization with kidney eQTLs is lacking but overlap with kidney cell open chromatin suggests an uncharacterized disease mechanism in kidney cells. A polygenic risk score (PRS) associates with earlier disease onset. Altogether, these discoveries expand our knowledge of pSSNS genetic architecture across populations and provide cell-specific insights into its molecular drivers. Evaluating these associations in additional cohorts will refine our understanding of population specificity, heterogeneity, and clinical and molecular associations.

Throughput prediction is crucial for reducing latency in time-critical services. We study the attention-based LSTM model for predicting future throughput. First, we collected the TCP logs and throughputs in LTE networks and transformed them using CUBIC and BBR trace log data. Then, we use the sliding window method to create input data for the prediction model. Finally, we trained the LSTM model with an attention mechanism. In the experiment, the proposed method shows lower normalized RMSEs than the other method.

Abstract Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an “integrative prior” for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

Summary: Genome-wide association studies (GWASs) have identified numerous variants associated with polygenic traits and diseases. However, with few exceptions, a mechanistic understanding of which variants affect which genes in which tissues to modulate trait variation is lacking. Here, we present genomic analyses to explain trait heritability of blood pressure (BP) through the genetics of transcriptional regulation using GWASs, multiomics data from different tissues, and machine learning approaches. Approximately 500,000 predicted regulatory variants across four tissues explain 33.4% of variant heritability: 2.5%, 5.3%, 7.7%, and 11.8% for kidney-, adrenal-, heart-, and artery-specific variants, respectively. Variation in the enhancers involved shows greater tissue specificity than in the genes they regulate, suggesting that gene regulatory networks perturbed by enhancer variants in a tissue relevant to a phenotype are the major source of interindividual variation in BP. Thus, our study provides an approach to scan human tissue and cell types for their physiological contribution to any trait.

Abstract Background Airway remodeling is an important pathological feature of chronic airway diseases, which leads to a progressive decline in lung function. The present study examined the anti-remodeling and anti- inflammatory effect of BIBF1000, a triple-tyrosine kinase inhibitor that targets VEGF, PDGF, and FGF receptor signaling in a mouse model of repeated ovalbumin (OVA) challenges. Methods Female Balb-c mice were immunized intraperitoneally on days 0 and 12 with 50 µg ovalbumin plus 1 mg of Al(OH)3 in 200 μl saline. Intranasal OVA challenges (20 µg/50 µl in PBS) were administered on days 26, 29, and 31, and were repeated twice a week for 3 months. Animals received vehicle or BIBF1000 (25 mg/kg, b.i.d.) through gavage from day 26 to the end of fourth month. On day 120, bronchoalveolar lavage (BAL) and lung tissue were collected for biochemical and immunohistological analysis. Results Compared to vehicle controls, treatment with BIBF1000 reduced the numbers of BAL eosinophils, macrophages, neutrophils, and lymphocytes by 70.0%, 57.9%, 47.5%, and 63.0%, respectively, and reduced IL-5 and IL-13 in BAL. Treatment with BIBF1000 reduced airway mucus secretion, peribronchial fibrosis, small airway, and pulmonary arterial wall thickness, compared to vehicle controls. Furthermore, treatment with BIBF1000 also reduced the expression of inflammatory mediators (TNF-α, IL-1β, IL-5, IL-13, MMP-2, MMP-9, COX-2, and iNOS) and inhibited ERK and AKT phosphorylation. Conclusions The protective effect afforded by triple-tyrosine kinase inhibition with BIBF1000 in reducing allergen-induced airway and arterial remodeling was associated with down-regulation of inflammatory mediators, as well as inhibition of ERK and AKT signaling pathways.

Summary: Cell differentiation is associated with global changes in translational activity. Here, we characterize how mRNA poly(A) tail processing supports this dynamic. We observe that decreased translation during neuronal differentiation of P19 cells correlates with the downregulation of 5′-terminal oligopyrimidine (TOP) transcripts which encode the translational machinery. Despite their downregulation, TOP transcripts remain highly stable and show increased translation as cells differentiate. Changes in TOP mRNA metabolism are reflected by their accumulation with poly(A) tails ∼60-nucleotide (nt) long. The dynamic changes in poly(A) processing can be partially recapitulated by depleting LARP1 or activating the mTOR pathway in undifferentiated cells. Although mTOR-induced accumulation of TOP mRNAs with tails ∼60-nt long does not trigger differentiation, it is associated with reduced proliferation of neuronal progenitors. We propose that while TOP mRNAs are transcriptionally silenced, their post-transcriptional regulation mediated by a specific poly(A) processing ensures an adequate supply of ribosomes to complete differentiation.

Abstract Background and aims Kinin B1 receptors (B1Rs) are implicated in the pathogenesis of fibrosis. This study examined the anti-fibrotic effects of B1R blockade with BI 113823 in two established mouse models of hepatic fibrosis induced by intraperitoneal carbon tetrachloride (CCl4) injection or bile duct ligation (BDL). The mechanisms underlying the protection afforded by B1R inhibition were examined using human peripheral blood cells and LX2 human hepatic stellate cells (HSCs). Methods Fibrotic liver diseases were induced in mice by intraperitoneal carbon tetrachloride (CCl4) injection for 6 weeks, and by bile duct ligation (BDL) for 3 weeks, respectively. Mice received daily treatment of vehicle or BI 113823 (B1R antagonist) from onset of the experiment until the end of the study. Results B1Rs were strongly induced in fibrotic mouse liver. BI 113823 significantly attenuated liver fibrosis and portal hypertension (PH), and improved survival in both CCl4 and BDL mice. BI 113823 significantly reduced the expression of fibrotic proteins α-SMA, collagens 1, 3, 4, and profibrotic growth factors PDGF, TGFβ, CTGF, VEGF, proliferating cell nuclear antigen; and reduced hepatic Akt phosphorylation in CCl4- and BDL-induced liver fibrosis. BI 113823 also reduced expression of Cytokines IL-1, IL-6; chemokines MCP-1, MCP-3 and infiltration of inflammatory cells; and inhibited human monocyte and neutrophil activation, transmigration, TNF-α & MPO production in vitro. BI 113823 inhibited TGF-β and B1R agonist-stimulated human-HSC activation, contraction, proliferation, migration and fibrosis protein expression, and inhibited activation of PI3K/Akt signalling pathway. Conclusions B1Rs merits consideration as a novel therapeutic target for chronic liver fibrosis and PH.

Abstract Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback. However, unfairness could incur in personalized recommendation. In this paper, we study how to achieve user-side fairness in personalized recommendation. We formulate our fair personalized recommendation as a modified contextual bandit and focus on achieving fairness on the individual whom is being recommended an item as opposed to achieving fairness on the items that are being recommended. We introduce and define a metric that captures the fairness in terms of rewards received for both the privileged and protected groups. We develop a fair contextual bandit algorithm, Fair-LinUCB, that improves upon the traditional LinUCB algorithm to achieve group-level fairness of users. Our algorithm detects and monitors unfairness while it learns to recommend personalized videos to students to achieve high efficiency. We provide a theoretical regret analysis and show that our algorithm has a slightly higher regret bound than LinUCB. We conduct numerous experimental evaluations to compare the performances of our fair contextual bandit to that of LinUCB and show that our approach achieves group-level fairness while maintaining a high utility.

Ultrasound contrast agents are clinically used for diagnosis of internal organs, but ultrasound contrast agents are rarely applied clinically in musculoskeletal disorders. Our study aims to comparatively analyze the differences between ultrasonographic images through peri-injury injection of the clinically used microbubble and researched nanoparticle contrast agents in various muscular injury models. To compare contrast-enhanced images in different muscle injury models, we prepared groups of rats with sham, laceration, punch, contusion, and toxin injection injuries. We measured H2O2 levels using the Amplex Red assay by extracting tissue from the damaged area. As comparative contrast agents, SonoVue®, a commercially available microbubble contrast agent, and poly(vanillinoxalte) (PVO) nanoparticles, which are H2O2-responsive nanoparticles, were used. The difference in contrast between the two contrast agents was recorded as an ultrasound movie, and J-image software 1.53p was used to quantify and analyze the maximum and minimum echogenicity values of the images after contrast enhancement. In the Amplex red assay for the highest H2O2 level in each muscle injury model, the maximum level showed 24 h after the modeling. In the sham rats, PVO injection showed no increased echogenicity except at the needle insertion site, but SonoVue® injection showed increased echo signal throughout the injected muscle immediately after injection. One day after the preparation of the lesion, PVO and SonoVue® were injected into the lesion site and ultrasound was performed on the lesion site. After the injection of PVO nanoparticles, contrast enhancement was observed at the lesion site immediately. SonoVue® injections, on the other hand, showed a widespread pattern of echo signals and an increase in echo retention only at the lesion site over time, but this was not clear. There were statistically significant differences between the highest and lowest echogenicity in PVO and SonoVue® contrast-enhanced images in all models. Contrast enhancement lasted more than 3 h in the PVO injection, but disappeared within 3 h in the SonoVue® injection. PVO nanoparticles showed the possibility of physiologic contrast by CO2 generated by conjugation with H2O2 generated by muscle injuries, and SonoVue® injection observed the possibility of microbubble contrast as a contrast agent with a pooling effect that lasts longer on the lesion. Further research is needed to investigate the use of various ultrasound contrast agents, including nanoparticles, in musculoskeletal disorders, as well as the potential for further utilities of microbubble contrast agents.

Nb-Ti binary alloys are widely employed as high value-added materials in the manufacture of super heat-resistant alloys, biomaterials, and superconductors. Therefore, there is significant interest to produce Nb-Ti master alloys in a cost-effective manner. In this study, we investigated the magnesiothermic reduction of Nb2O5 and Ti2Nb10O29 over the temperature range of 1073 to 1223 K and comparatively evaluated the reaction outcomes. The reduction product was composed of metal (Nb or Nb-Ti) particles and MgO, which covered the surface of the reduced metal particles. After the reduction reaction, the surface MgO phase was removed by pickling with hydrochloric acid (HCl) to finally recover the Nb metal or Nb-Ti alloy as a pure product. Scanning electron microscopy and X-ray diffraction analyses of the pure Nb metal and Nb-Ti alloy powders revealed that the reduction of both raw materials was successful at temperatures exceeding 1173 K. Reaction kinetics analysis revealed that the activation energy for the reduction of the mixed metal oxide (Ti2Nb10O29) is lower than that of Nb2O5 reduction. This is because of the different reaction mechanism behaviors during reduction and the different thermodynamic stabilities of the precursors.

UBE3A encodes ubiquitin protein ligase E3A, and in neurons its expression from the paternal allele is repressed by the UBE3A antisense transcript (UBE3A-ATS). This leaves neurons susceptible to loss-of-function of maternal UBE3A. Indeed, Angelman syndrome, a severe neurodevelopmental disorder, is caused by maternal UBE3A deficiency. A promising therapeutic approach to treating Angelman syndrome is to reactivate the intact paternal UBE3A by suppressing UBE3A-ATS. Prior studies show that many neurological phenotypes of maternal Ube3a knockout mice can only be rescued by reinstating Ube3a expression in early development, indicating a restricted therapeutic window for Angelman syndrome. Here, we report that reducing Ube3a-ATS by antisense oligonucleotides in juvenile or adult maternal Ube3a knockout mice rescues the abnormal electroencephalogram (EEG) rhythms and sleep disturbance, two prominent clinical features of Angelman syndrome. Importantly, the degree of phenotypic improvement correlates with the increase of Ube3a protein levels. These results indicate that the therapeutic window of genetic therapies for Angelman syndrome is broader than previously thought, and EEG power spectrum and sleep architecture should be used to evaluate the clinical efficacy of therapies.

The increasing penetration of photovoltaic (PV) generators has led to a shift of the operational policy of the distribution system operator (DSO) from passive to active intervention in distribution networks (DNs), where a centralized controller governs the operation of voltage regulation devices. However, the PV output uncertainty hinders the verification of the impact of PVs on DNs. Therefore, the hosting capacity (HC) analysis framework for PV penetration should reflect both the operational benefit of the DSO and the PV output uncertainty. Thus, in this study, a two-stage optimization-based framework is proposed to analyze the probabilistic HC for PV under active network management (ANM) of DNs. In the first stage, the optimal PV base capacity (PVBC) to maximize the sum of the HC for PVs is determined based on a heuristic optimization method; in the second stage, using the predefined load and PV output profile, the maximum available power generation curve for PVBC is calculated, and the maximum HC for PV is derived by the calibration of PVBC through a comparison with the actual PV generation profile curve. The proposed method considers the time-series-based load flow results, reflecting the time-scheduling strategy by the DSO. Moreover, the uncertain characteristics of PV output are stochastically considered using a Monte Carlo simulation-based repetitive calculation approach. Case studies were implemented using the modified IEEE-123 test system, and the simulation results provided a quantitative comparison of the effect of the probabilistic HC improvement on the utilization of controllable resources and the centralized ANM by the DSO.

Air pollutants adversely affect human health, and thus a global improvement in air quality is urgent. A Geostationary Environment Monitoring Spectrometer (GEMS) was mounted on the geostationary Chollian 2B satellite in 2020 to observe the spatial distribution of air pollution, and sequential observations have been released since July 2022. The reliability of GEMS must be analyzed because it is the first payload on the geostationary Earth orbit satellite to observe trace gases. This study analyzed the initial results of GEMS observations such as the aerosol optical depth and vertical column densities (VCD) of ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO), and compared them with previous studies. The correlation coefficient of O3 ranged from 0.90 (Ozone Monitoring Instrument, OMI) to 0.97 (TROPOspheric Monitoring Instrument, TROPOMI), whereas that of NO2 ranged from 0.47 (winter, OMI and OMPS) to 0.83 (summer, TROPOMI). GEMS yielded a higher VCD of NO2 than that of OMI and TROPOMI. Based on the sources of O3 and NO2, GEMS observed the maximum VCD at a different time (3–4 h) to that of the ground observations. Overall, GEMS can make observations several times a day and is a potential tool for atmospheric environmental analysis.

Abstract Background This study examined whether BI113823, a novel selective kinin B1 receptor antagonist can reverse established pulmonary arterial hypertension (PAH), prevent right heart failure and death, which is critical for clinical translation. Methods Left pneumonectomized male Wistar rats were injected with monocrotaline to induce PAH. Three weeks later, when PAH was well established, the rats received daily treatment of BI113823 or vehicle for 3 weeks. Results Treatment with BI113823 from day 21 to day 42 after monocrotaline injection reversed established PAH as shown by normalized values of mean pulmonary arterial pressure (mPAP). BI113823 therapy reversed pulmonary vascular remodeling, pulmonary arterial neointimal formation, and heart and lung fibrosis, reduced right ventricular pressure, right heart hypertrophy, improved cardiac output, and prevented right heart failure and death. Treatment with BI113823 reduced TNF-α and IL-1β, and macrophages recruitment in bronchoalveolar lavage, reduced CD-68 positive macrophages and expression of proliferating cell nuclear antigen (PCNA) in the perivascular areas, and reduced expression of iNOS, B1 receptors, matrix metalloproteinase (MMP)-2 and MMP-9 proteins, and the phosphorylation of ERK1/2 and AKT in lung. Treatment with BI113823 reduced mRNA expression of ANP, BNP, βMHC, CGTF, collange-I and IV in right heart, compared to vehicle treated controls. In human monocytes cultures, BI113823 reduced LPS-induced TNF-α production, MMP-2 and MMP-9 expression, and reduced TNF-α-induced monocyte migration. Conclusions We conclude that BI113823 reverses preexisting severe experimental pulmonary hypertension via inhibition of macrophage infiltration, cytokine production, as well as down regulation of matrix metalloproteinase proteins.

The domain of edge displays with 2.5D or 3D curved designs has been expanded to improve user convenience. The currently available 3D cover glass offers a limited curvature radius of at least 5 mm and a curvature less than 88°, due to limitations in the undercuts and formability of parts. The development of a full 3D cover, applicable to next-generation displays, requires cover glass molding technology with a curvature exceeding 90°. Here, a mold design and molding process, which addresses the current limitations by dividing the existing glass molding press (GMP) process into two stages, is proposed. The bending geometry of the glass prepared on the basis of the proposed mold design plan during single-step compression forming and two-step compression forming was predicted using commercial analysis software. A molding product with a curvature radius of 2.5 mm and an angle of curvature of 138.9° was produced when process conditions with bending by up to 180° with no damage were applied during actual forming experiments. Further research on annealing and cooling processes of GMP is expected to enable the design and process implementation to manufacture curved glass with a single curvature of at least 90° and multiple curvatures.

This study investigates the impact of industrial complexes on the air quality in the Ulsan Metropolitan City, Korea, by analyzing the concentration of trace substances. Importantly, this study performs segmentation and analysis of the components of particulate matter for tracking emission sources. Concentrations of particulate matter with aerodynamic diameters ≤10 and ≤2.5 µm (PM10 and PM2.5, respectively) and 19 substances comprising PM2.5 (such as ions, carbon, and nine elements) were measured hourly during the year 2017 in the southeastern intensive air quality monitoring station of the National Institute of Environmental Research, Korea. This study identified and investigated the time periods during which the vanadium content in PM2.5 was higher than the annual mean (1.026 ng/µg) through selection cases (SCs). The annual mean concentrations of PM2.5 and PM10 were 18.50 and 32.35 µg/m3, respectively, and were higher (i.e., 26.54 and 45.84 µg/m3, respectively) in SCs. Notably, the concentrations were high even when the main wind direction of SCs was southeasterly, which was mainly the case in summer. Furthermore, the emission sources contributing to PM2.5 were estimated using the correlations of organic carbon, elemental carbon, zinc, iron, manganese, and titanium concentrations in the SCs. This study demonstrated that a detailed tracking of the emission sources at a local scale is possible by analyzing the composition of the components of PM2.5.

Modern distribution networks face an increasing number of challenges in maintaining balanced grid voltages because of the rapid increase in single-phase distributed generators. Because of the proliferation of inverter-based resources, such as photovoltaic (PV) resources, in distribution networks, a novel method is proposed for mitigating voltage unbalance at the point of common coupling by tuning the volt–var curve of each PV inverter through a day-ahead deep reinforcement learning training platform with forecast data in a digital twin grid. The proposed strategy uses proximal policy optimization, which can effectively search for a global optimal solution. Deep reinforcement learning has a major advantage in that the calculation time required to derive an optimal action in the smart inverter can be significantly reduced. In the proposed framework, multiple agents with multiple inverters require information on the load consumption and active power output of each PV inverter. The results demonstrate the effectiveness of the proposed control strategy on the modified IEEE 13 standard bus systems with time-varying load and PV profiles. A comparison of the effect on voltage unbalance mitigation shows that the proposed inverter can address voltage unbalance issues more efficiently than a fixed droop inverter.

This study investigated the science competencies that scientists and students perceived as important and their science-related experiences and analyzed them comprehensively using activity system framework in order to explore which competencies need to be emphasized more and what experiences and environments should be provided in school science education. To accomplish this, we collected data from scientists and students from a variety of backgrounds through a descriptive questionnaire and used it in our analysis. We found significant insights arising from differences in the purposes and contexts in which they do science, although we also found similarities in the competencies that scientists and students perceived as important and the experiences that influenced their perceptions of science. These findings offer implications for the need to increase the frequency of science-related activities and to enhance the quality of inquiry based learning. Specifically, it is necessary to include the communication and collaboration skills that scientists value, and to incorporate the trial-and-error processes they experiences. In addition, it is also essential to develop valid assessment tools for evaluating students' inquiry skills.