Available Potential Energy Research Articles

Variations of Pre-monsoon season related atmospheric parameters over Kakinada region

Pre-monsoon showers occur before the beginning of the rainy season. From the months of March to May, they take place, and can be anything from little drizzles to powerful thunderstorms. March, April and May are known as the pre-monsoon season (PRMS). The precipitation patterns recorded in PRMS are critical because they have an impact on a wide range of crop-related operations across the country. During PRMS, the maximum temperature (TMAX), minimum temperature (TMIN), soil moisture, relative humidity, latent heat, convective available potential energy (CAPE), and total precipitable water (TPW) were analysed at Kakinada region. These variables were collected using daily ERA5 reanalysis data for the PRMS from 1981 to 2021. Studying the convection-related characteristics over the Kakinada station during the PRMS was our goal. During the study period, the five years with the highest PRMS rainfall were 1990, 1995, 2008, 2010 and 2016 and the five years with the lowest rainfall were 1990, 1995, 2008, 2010 and 2016. In the months leading up to the monsoon, the Kakinada station experiences CAPE values between 1000 and 6000 J/kg, while TPW values are between 25 and 60 mm. The PRMS values for CAPE and TPW both show that the prerequisites for moderate to severe convection activity have been fulfilled. TPW, soil moisture, relative humidity, and CAPE parameters during PRMS were well estimated using the ARMA and ARIMA models.

The Belgian physiotherapy reimbursement criteria for fall prevention fails in screening appropriately fall-prone community-dwelling older adults

ABSTRACT Objectives The incidence of falling in older adults has remained unchanged over the past decades, despite evidence-based prevention initiatives. Therefore, it is appropriate to reflect on the current screening approach for preventive initiatives. The objective of this study was to determine whether the multifactorial algorithm proposed by Lusardi et al. (2017) exhibits superior predictive validity compared to the currently employed algorithm by the Belgian National Institute for Health and Disability Insurance (NIHDI). Methods The current study includes a secondary analysis of data collected from a falls-related study in the Department of Rehabilitation Sciences at Ghent University to compare the predictive validity of the two algorithms. Sensitivity, specificity, positive and negative predictive value and area under the curve (AUC) were calculated to ascertain which algorithm is more accurate. Results The database included a total of 94 community-dwelling older adults (mean age 76 years ±7.4, 35% male). Thirty-nine participants experienced at least one fall in the 8 month follow up. Lusardi's approach has a higher sensitivity score (89.7% compared to 10.3%) and negative predictive value (89.9% compared to 61.1%), but a lower specificity score (61.8% compared to 100%) and positive predictive value (62.2% compared to 100%) than the NIHDI approach. The AUC is 0.76 for Lusardi's approach and 0.55 for the NIHDI approach. Conclusion The use of the multifactorial algorithm proposed by Lusardi et al. may be significant and more accurate in identifying adults at risk to falls. Further research is needed particularly with a larger, more heterogenous group of older adults.

Diurnal variations in lightning over India and three lightning hotspots: A climatological study

Advancements have been made in the field of lightning research. A study on lightning frequency in India, a tropical hotspot, has not been conducted despite the existence of a global climatology on mean flash rate lightning. Lightning climatology in India is determined using high-resolution datasets from MicroLab-1 and Tropical Rainfall Measuring Mission (TRMM) satellites and Lightning Imaging Sensor (LIS) diurnal datasets spanning from 1996 to 2013. The study analyzed the difference in density of lightning flashes during two time periods, 1996–2001 and 2008–2013, on a daily, monthly, and seasonal level. Three regions with high lightning activity have been selected: East Khasi Hills in the northeast with 101.25 flashes/Km2/yr, Rajouri in Jammu & Kashmir with 121.40 flashes/Km2/yr, and Kottayam with 92.94 flashes/Km2/yr. The study focuses on the rainfall, convective available potential energy (CAPE), and lightning density. Due to localized climatological factors, lightning events vary in diurnal and seasonal patterns across the three hotspots.Lightning strikes are predominantly distributed in certain regions of India, including Jammu, Kashmir, the foothills of the Himalayan belt, and the North-Eastern region, encompassing Bangladesh, Bihar, Jharkhand, and Orissa. The frequency of lightning strikes ranges from 0.2 to 0.45 flashes/Km2/day. Lightning activity experienced a slight rise between 2008 and 2013. There was a rise in lightning activity (0.04 flashes/Km2/day) in India and its three hotspots in 2010. The study aims to investigate the daily changes in lightning activity. A significant portion of daily lightning activity occurs at approximately 1000 UTC and generally decreases in the afternoon across all seasons. The study examines the seasonal variation in lightning activity across India, specifically during pre-monsoon, monsoon, post-monsoon, and winter. The results are significant for developing lightning climatology in the region. Lightning density in India experiences a slight increase during both pre-monsoon and monsoon seasons, with a noticeable variation observed across India and three hotspot locations.

Spatiotemporal patterns and influencing factors of remotely sensed regional heat islands from 2001 to 2020 in Zhengzhou Metropolitan area

Developing urban agglomerations and establishing metropolitan areas have led to urban heat islands breaking through the original single urban scale to form regional heat islands (RHIs) with a large influencing range in recent decades. However, the spatial and temporal patterns of regional heat islands and their evolution are still poorly understood, which limit the decisionmakers to make more informed decisions to improve the regional environment. We selected Zhengzhou Metropolitan Area as the case study with MOD11A2 surface temperature products from 2001 to 2020 to describe the intensity and range of RHIs by calculating the relative surface temperature. Then multi-scale spatiotemporal patterns and characteristics of RHI were identified with the help of Sen + MK trend analysis, contribution index analysis and standard deviation ellipse analysis, and the main driving factors of RHI and their internal relations were discussed by correlation analysis and Random Forest (RF) regression analysis. We found that (1) RHIs in the Zhengzhou Metropolitan Area increased from 2970 km2 in 2001 to 5776 km2 in 2020, a growth rate of about 0.46% per year. The contributions of each county-level region to the RHI differ significantly. The positive contribution areas include Zhengzhou City, Kaifeng City, Zhongmu County, and Xinzheng City, while the negative areas include Lankao County, Fengqiu County, and Huixian City. (2) There are two aggregated RHIs within the metropolitan area. Xinxiang City and Jiaozuo City formed a complete, cross-city, large-area RHI from 2001 to 2005, accounting for 63% of the total area. The RHI in Zhengzhou, Kaifeng, and Xuchang Cities developed in an all-round, cross-regional, and rapid manner from 2010 to 2020, accounting for 78% of the total area. (3) Different land cover types have varying effects on RHIs. The cooling effect of forests and water is better than that of grasslands. Human and social factors positively affect the RHI, vegetation cover strongly inhibits the RHI, and meteorological factors have less influence. The methods and results of this study play a significant role in analyzing urban construction status, optimizing urban spatial layouts, and improving urban thermal environments of the Zhengzhou Metropolitan Area. Insights into thermal environment research and practice are also provided for other urban agglomerations.

Analysis of the Final Energy Use by Sectors in City of Vidin

According to the initiative "The Covenant of Mayors for Climate and Energy (CoM)" and within the framework of the EU-funded "Positive Energy Areas Project", the Municipality of Vidin should establish a "Sustainable Energy and Climate Action Plan". The city's sustainable energy development plans aim to reduce CO2 emissions by 40% up to 2030 and develop a comprehensive climate change adaptation strategy at the local level or integrate it into existing plans. The initiative envisages actions both for mitigating climate change and also for climate adaptation at the local level. This paper presents the results of the first part of the process for establishing the "Sustainable Energy and Action Plan of the Municipality of Vidin", including the assessment and calculation of the final energy use in the city by the different sectors. The research aims to present the main data sources necessary for СО2 emissions inventory at the city level and to process, validate, and analyze used statistical data for defining the final energy use by different sectors in one Bulgarian municipality. The purpose of this assessment is to determine final energy use by different sectors and by types of energy sources. The results from the analysis and evaluation of the final energy use in the sectors "Residential buildings", "Municipal buildings", "Industry", "Tertiary buildings", "Public lighting" and "Transport" will serve to conduct a baseline of CO2 emissions inventory, which is a part of the “Sustainable Energy and Climate Action Plan”. These results also could help the local authorities and key energy players to identify and implement the most suitable measures and actions for climate change adaptation and mitigation.

Characteristics and Dynamics of the Interannual Eddy Kinetic Energy Variation in the Central Pacific Sector of the Southern Ocean

AbstractMesoscale eddies play a crucial role in the dynamical balance of the Southern Ocean (SO) circulation. Yet, it remains unclear why the SO transient eddy activity has significant variations on interannual time scales, and to what extent these low‐frequency variations are attributed to wind forcing changes. Here we use a functional analysis tool, namely, the multiscale window transform (MWT), the MWT‐based theory of canonical transfer and a time‐dependent energetics framework to investigate these issues. Our focus is on the central Pacific sector of the SO in which there is a significant time‐mean and interannual variability of the eddy kinetic energy (EKE). It is found that wind stress does not directly contribute to the interannual EKE variability through wind power injection to the eddies; instead, the influence is fulfilled indirectly through two internal pathways. First, the baroclinic pathway in which the wind‐generated mean kinetic energy (MKE) is converted to the mean available potential energy (APE), and is further released to eddy APE and finally to EKE through baroclinic instability, is the dominant one. Second, the barotropic pathway, in which MKE directly fuels EKE through barotropic instability, is faster but secondary, and its influence is only concentrated along the axis of the Antarctic Circumpolar Current. Our results highlight the dynamical connections between the internal and external processes in the SO energy system, and provide insights into the predictability of the SO eddies on interannual time scales.

Optimizing and coordinating the location of raw material suitable for cement manufacturing in Wasit Governorate, Iraq

Abstract The cement industry is considered one of the strategic industries, because it is directly related to construction work and cement is used as a hydraulic binder. However, it is a simple industry compared to major industries and depends on the availability of the necessary raw materials. This study focuses on optimizing and coordinating the location of raw materials needed for the cement manufacturing in Wasit Governorate in Iraq. Field works include detailed reconnaissance, topographic work, and description and sampling of 24 lithological sections that represent the carbonate deposits, which crop out in the area. The investigated area has the following specifications: The weighted averages of chemical components in the industrial bed are as follows: CaO = 47.83%, MgO = 1.12%, SiO2 = 7.28%, SO3 = 0.34%, Fe2O3 = 1.85%, Al2O3 = 1.85%, L.O.I = 39.26%, Na2O = 0.29%, and K2O = 0.38%. The average thickness of the investigated raw materials is 15.68 m. The average bulk density of the investigated raw materials is 2.32 g/cm3. The compressive strength of the investigated raw materials ranges from 6.182 to 55.21 MN/m2. The positive area is 922,552 m2. The volume of the industrial bed is 14,466,242 m3. The economic reserve of the industrial bed is 33,561,682 tons.

Invasive physiologic assessment of coronary artery stenosis by resting full-cycle ratio and fractional flow reserve: a prospective observational study

Resting full-cycle ratio (RFR), an alternative to fractional flow reserve (FFR) for evaluating intermediate coronary artery stenosis, helps reduce patients’ time, cost, and discomfort. However, the validation data for RFR and FFR are lacking. We aimed to assess the diagnostic accuracy of RFR and FFR and evaluate effective decision-making for revascularization using their values. Patients subjected to an invasive physiological study for intermediate coronary artery stenosis in Yongin Severance hospital between October 2020 and April 2022 were prospectively and consecutively recruited. We evaluated the correlation between RFR and FFR measurements and the diagnostic performance of RFR (≤ 0.89) versus FFR (≤ 0.80). In all, 474 intermediate coronary stenosis lesions from 400 patients were evaluated using RFR and FFR values. There was a strong linear relationship between RFR and FFR (r = 0.75, 95% CI 0.70–0.78, p < 0.01). Comparing diagnostic performance between RFR and FFR, RFR demonstrated diagnostic accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 85.0%, 80.0%, 86.7%, 67.1%, and 92.7%, respectively. We analyzed the RFR value in the hyperemia zone (0.86–0.93) according to positive (RFR: 0.86–0.89) and negative (RFR: 0.90–0.93) areas. PPV in positive area is 47.8% (95% Confidence Interval [CI]: 33.8% to 62.0%) and NPV in negative area is 87.7% (95% CI: 80.3% to 93.1%). Excellent correlation exists between RFR and FFR and the diagnostic value of RFR without hyperemia compared with FFR in establishing the accurate functional significance of coronary artery stenosis was shown. RFR alone could evaluate the functional significance of coronary artery stenosis without unnecessary hyperemia, except in the positive area.Trial registration: URL: http://trialsearch.who.int; Unique identifier: KCT0005255.

Treatment response prediction of neoadjuvant chemotherapy for rectal cancer by deep learning of colonoscopy images.

In current clinical practice, several treatment methods, including neoadjuvant therapy, are being developed to improve overall survival or local recurrence rates for locally advanced rectal cancer. The response to neoadjuvant therapy is usually evaluated using imaging data collected before and after preoperative treatment or postsurgical pathological diagnosis. However, there is a need to accurately predict the response to preoperative treatment before treatment is administered. The present study used a deep learning network to examine colonoscopy images and construct a model to predict the response of rectal cancer to neoadjuvant chemotherapy. A total of 53 patients who underwent preoperative chemotherapy followed by radical resection for advanced rectal cancer at the Osaka University Hospital between January 2011 and August 2019 were retrospectively analyzed. A convolutional neural network model was constructed using 403 images from 43 patients as the learning set. The diagnostic accuracy of the deep learning model was evaluated using 84 images from 10 patients as the validation set. The model demonstrated a sensitivity, specificity, accuracy, positive predictive value and area under the curve of 77.6% (38/49), 62.9% (22/33), 71.4% (60/84), 74.5% (38/51) and 0.713, respectively, in predicting a poor response to neoadjuvant therapy. Overall, deep learning of colonoscopy images may contribute to an accurate prediction of the response of rectal cancer to neoadjuvant chemotherapy.

Optimizing noble gas pressure for enhanced self-compensation in spin-exchange relaxation-free comagnetometers.

The coupling of electron spin and nuclear spin through spin-exchange collisions compensates for external magnetic field interference in the spin-exchange relaxation-free (SERF) comagnetometer. However, the compensation ability for magnetic field interference along the detection axis is limited due to the presence of nuclear spin relaxation. This paper aims to enhance the self-compensation capability of the system by optimizing the pressure of the noble gas during cell filling. Models are established to describe the relationships between the nuclear spin polarization, the polarizing magnetic field of nuclei, the magnetic field suppression factors, and the pressure of the noble gas in the K-Rb-21Ne atomic ensemble. Experiments are conducted using five cells with different pressure. The results indicate that in the positive pressure area, the nuclear spin polarization decreases while the equivalent magnetic field experienced by the noble gas increases with increasing pressure. The magnetic field suppression factor for transverse fields increases as the pressure increases, leading to a decrease in the ability to suppress low-frequency magnetic field interference. Moreover, at the cell temperature of 180°C and a transverse residual field gradient of 4.012 nT/cm, the system exhibits its strongest capability to suppress transverse magnetic field interference when the pressure of 21Ne is around 0.7 atm.

Melatonin attenuates cellular senescence and apoptosis in diabetic nephropathy by regulating STAT3 phosphorylation

AimsMelatonin is an endogenous hormone related to the regulation of biorhythm. Previous researchers have found that melatonin can ameliorate diabetic nephropathy (DN), but the mechanism remains to be elucidated. To discover the possible mechanism by which melatonin prevents DN, we investigated the potential effects of melatonin on signal transducer and activator of transcription 3 (STAT3) on the progression of cellular senescence and apoptosis. Main methodsCellular senescence, apoptosis and the underlying mechanism of melatonin were investigated both in vivo and in vitro. C57BL/6 mice were intraperitoneally injected with streptozotocin (STZ) to establish DN. For an in vitro model of DN, human renal cortex proximal epithelial tubule (HK-2) cells were exposed to high glucose conditions. Key findingsMelatonin inhibited the phosphorylation of STAT3, decreased the expression of senescence proteins p53, p21 and p16INK4A. Melatonin also downregulated the expression of apoptotic proteins, including cleaved PARP1, cleaved caspase-9 and -3. Melatonin treatment decreased the positive area of senescence-associated galactosidase (SA-β-gal) staining and the number of TUNEL-positive cells in kidneys of DN mice. In vitro, melatonin inhibited STAT3 phosphorylation and lowered cellular senescence and apoptosis markers, in a manner similar to the STAT3 inhibitor S3I-201. In addition, the inhibition effect of melatonin on cellular senescence and apoptosis in HK-2 cells was reversed by the usage of recombinant IL-6 (rIL-6), which can induce STAT3 phosphorylation. SignificanceWe, for the first time, demonstrate that melatonin inhibits STAT3 phosphorylation, which is involved in alleviating the cellular senescence and apoptosis in DN.

Impact of AWiFS derived land use/land cover over the intensely urbanised domain of National Capital Region (NCR) - Delhi in simulating monsoon weather

Land-Use/Land-Cover (LULC) plays a crucial role in meteorological models because they determine the crustal properties that interfere with the exchange of energy, momentum, and moisture between the land surface and the atmosphere. The current study integrates a legitimate ground-truth LULC based on the Advanced Wide Field Sensor (AWiFS) in the Weather Research and Forecasting (WRF) model framework for simulating short-term monsoon weather over the National Capital Region (NCR Delhi), the biggest urban settlement in the Monsoon Zone. AWiFS provides LULC data at 56 m spatial resolution. The newly implemented AWiFS LULC precisely distinguishes the WRF model default Moderate Resolution Imaging Spectroradiometer (MODIS) classification and urban representation. Both simulations most well capture the urban meteorological influence over the urban core. However, the AWiFS more accurately depicts NCR urban Land Use (LU), highlighted in the spatial and Probability Density Function (PDF) distributions of various meteorological variables, and captures numerous small-scale suburban settlements outside the central core zone. The impact of the omission of the Noida suburban in MODIS LU is also notable in the planetary boundary layer (PBL). Convective parameters and precipitation correlate strongly, and the AWiFS simulation considerably enhanced Convective Available Potential Energy (CAPE), Convective Inhibition (CIN) and other associated variables. Further, it indicates that a higher magnitude (>200 J kg−1 in difference) of CAPE is simulated over Faridabad, likely an urban-induced downwind effect due to the accurate representation of the Noida suburban in AWiFS. The study highlights the importance of updated LULC in enhancing model realisation, and AWiFS-based simulation showed improved performance.

Ropivacaine promotes axon regeneration by regulating Nav1.8-mediated macrophage signaling after sciatic nerve injury in rats.

Continuous nerve block with ropivacaine is commonly performed after repair surgery for traumatic peripheral nerve injuries. After peripheral nerve injury, tetrodotoxin-resistant voltage-gated sodium channel Nav1.8 is upregulated and contributes to macrophage inflammation. We investigated whether ropivacaine promotes peripheral nerve regeneration through Nav1.8-mediated macrophage signaling. A sciatic nerve transection-repair (SNT) model was established in adult Sprague-Dawley rats of both sexes. The rats received 0.2% ropivacaine or 10 μM Nav1.8-selective inhibitor A-803467 around the injured site or near the sacrum for 3 days. Nerve regeneration was evaluated using behavioral, electrophysiological, and morphological examinations. Moreover, myelin debris removal, macrophage phenotype, Nav1.8 expression, and neuropeptide expression were assessed using immunostaining, enzyme-linked immunosorbent assay, and western blotting. Compared to the SNT-plus-vehicle group, the sensory, motor, and sensory-motor coordination functions of the two ropivacaine groups were significantly improved. Electrophysiological (mean ± SD: recovery index of amplitude, vehicle 0.43 ± 0.17 vs. ropivacaine 0.83 ± 0.25, n = 11, P ˂ 0.001) and histological analysis collectively indicated that ropivacaine significantly promoted axonal regrowth (percentage of NF-200 positive area: vehicle 19.88 ± 2.81 vs. ropivacaine 31.07 ± 2.62, n = 6, P ˂ 0.001). We also found that, compared to the SNT-plus-vehicle group, the SNT-plus-ropivacaine group showed faster clearance of myelin debris, accompanied by significantly increased macrophage infiltration and transition from the M1 to M2 phenotype. Moreover, ropivacaine significantly attenuated Nav1.8 upregulation at 9-days after sciatic nerve transection (vehicle 4.12 ± 0.30-fold vs. ropivacaine 2.75 ± 0.36-fold, n = 5, P ˂ 0.001), which coincided with the increased expression of chemokine ligand 2 and substance P. Similar changes were observed when using the selective Nav1.8 channel inhibitor A-803467. Continuous nerve block with ropivacaine promotes the structural and functional recovery of injured sciatic nerves, possibly by regulating Nav1.8-mediated macrophage signaling.

The usefulness of the modified deep convolutional neural network model in improving the detection of COVID-19 on chest X-ray images

Background: The COVID-19 pandemic has rapidly spread worldwide, leading to a global health crisis. Although the real-time polymerase chain reaction (RT-PCR) test is highly specific and sensitive in detecting COVID-19, chest X-rays have emerged as an optional diagnostic tool for COVID-19-induced lung lesions. Artificial intelligence (AI), particularly deep learning, is a rapidly evolving field with significant potential in medical image analysis, including the quick detection of COVID-19 to improve accuracy. Objectives: This study aims to enhance the accuracy of COVID-19 image detection on chest X-ray images by modifying the deep convolutional neural network. Materials and methods: We conducted lung segmentation and COVID-19 image classification experiments using a dataset of chest X-rays. The U-net algorithm was utilized for lung segmentation of COVID-19 and non-COVID-19 images. We developed a Modified Deep Convolutional Neural Network (MD-CNN) to classify the two image classes. The MD-CNN model was compared with two other models, ResNet and AlexNet, and evaluated for accuracy, sensitivity (recall), specificity, positive predictive value (precision), F1-score, and area under the curve (AUC). Results: Our experimental results demonstrate that the MD-CNN model achieved an accuracy of 97.95%, outperforming ResNet and AlexNet, which achieved 90.25% and 78.95%, respectively. The MD-CNN model also exhibited better sensitivity, F1-score, and AUC than the other models, while its specificity and precision were comparable to those of the ResNet model. Conclusion: The proposed MD-CNN model demonstrates significant potential for high accuracy in COVID-19 image detection compared to ResNet and AlexNet. It can serve as a useful tool for radiologists in the COVID-19 screening process, potentially reducing the workload, and improving the efficiency of COVID-19 diagnosis.

Tenascin-C as a potential marker for immunohistopathology of doxorubicin-induced cardiomyopathy.

Doxorubicin is used in classical chemotherapy for several cancer types. Doxorubicin-induced cardiomyopathy (DOX-CM) is a critical issue among cancer patients. However, differentiating the diagnosis of DOX-CM from that of other cardiomyopathies is difficult. Therefore, in this study, we aimed to determine novel histopathological characteristics to diagnose DOX-CM. Twelve consecutive patients with DOX-CM who underwent cardiac histopathological examination in two medical centres were included. Twelve patients with dilated cardiomyopathy, who were matched with DOX-CM patients in terms of age, sex, and left ventricular ejection fraction, formed the control group. Another control group comprised five consecutive patients with cancer therapy-related cardiac dysfunction induced by tyrosine kinase inhibitors or vascular endothelial growth factor inhibitors were the controls. The positive area of tenascin-C, number of infiltrating macrophages, and presence of p62- and ubiquitin-positive cardiomyocytes were evaluated. Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were used for in vitro investigation. The myocardium exhibited significantly greater tenascin-C-positive area and macrophage number in the DOX-CM group than in the control groups (P < 0.01). The tenascin-C-positive area correlated with the number of both CD68- and CD163-positive cells (r = 0.748 and r = 0.656, respectively). Immunostaining for p62 was positive in 10 (83%) patients with DOX-CM. Furthermore, western blotting analysis revealed significant increase in tenascin-C levels in hiPSC-CMs upon doxorubicin treatment (P < 0.05). The combined histopathological assessment for tenascin-C, macrophages, and p62/ubiquitin may serve as a novel tool for the diagnosis of DOX-CM. Doxorubicin may directly affect the expression of tenascin-C in the myocardium.

Prediction of Convective Available Potential Energy and Equivalent Potential Temperature using a Coupled WRF and Deep Learning for Typhoon Identification

To predict typhoons in the western North Pacific Ocean, it is required to predict the determinants of typhoon activities. The formation of the typhoon can be controlled by Convective Available Potential Energy (CAPE) and Equivalent Potential Temperature (theta-e). To predict the variables, a mesoscale numerical model of Weather Research and Forecasting (WRF) can be used. However, the output of WRF needs to improve to obtain a more accurate CAPE and theta-e prediction. This study uses a coupled WRF model and Deep Learning (DL) Multilayer Perceptron Regressor approach to increase CAPE and theta-e prediction skills. Simulation with dataset scenarios with WRF outputs as predictors and sounding data as predictors are developed and tested to obtain the most appropriate package of deep learning simulation. The study found that coupled models provide increased mean accuracy of theta-e and CAPE, namely 16.6% and 32.0% higher than using original WRF, respectively. This study also shows the difference of skill scores in the spatial distribution of CAPE and theta-e of WRF result and its coupled model.

Polarimetric Radar Observations of a Long-Lived Supercell and Associated Tornadoes on 10–11 December 2021

Abstract We present environmental and polarimetric radar observations of a long-lived December supercell that tracked approximately 750 km from Arkansas to northern Kentucky. The storm was associated with two long-track EF4 tornadoes, one of which was among the longest-tracked tornadoes recorded in the United States. The supercell’s life cycle is documented from 2000 UTC 10 to 0700 UTC 11 December 2021, using data from five operational polarimetric weather radars. After convection initiation in central Arkansas, it took nearly 4 h for a supercell to develop. Afterward, the storm’s ZDR column and arc became anomalously large leading up to genesis of the first EF4 tornado. During this time, the storm’s environment had moderate convective available potential energy (CAPE) and strong deep-layer shear. A cell interaction at about 0200 UTC disrupted the supercell updraft, weakening the ZDR arc and column, and initiating the largest radar-implied hailfall event observed with this storm. The remnant circulation associated with the first EF4 tornado did not fully dissipate, and it appeared to merge with the low-level mesocyclone on the nose of a rear-flank downdraft surge likely initiated by the hailfall. It is hypothesized that this merger was important to the intensification of the storm’s second EF4 tornado, which lasted nearly 3 h and traveled approximately 267 km. During the second EF4 tornado the storm experienced decreasing CAPE and increasing storm relative helicity. Increasing interactions with other cells eventually weakened the storm, and its original updraft was obscured before the storm’s remnants dissipated in northern Kentucky. Significance Statement In December 2021, a long-lived supercell thunderstorm produced two long-track, violent tornadoes, including one that produced historic damage across western Kentucky. Radar observations indicate that, once the storm became a supercell, its updraft became anomalously large relative to similar storms studied prior. Simultaneously its storm-relative inflow strengthened markedly, supporting the first long-lived tornado. Interactions with a developing thunderstorm disrupted the supercell’s updraft, leading to hail fallout and updraft weakening. The remnant circulation associated with the first strong tornado merged with the supercell’s updraft and became a rotation source for the second long-track tornado, which persisted for nearly 3 h. Eventually interactions with other thunderstorms weakened the supercell.

Performance of Lightning Potential Index, Lightning Threat Index, and the Product of CAPE and Precipitation in the WRF Model

AbstractLightning is a naturally occurring phenomenon with significant socioeconomic and environmental impacts, underlining the need for the application of advanced methods to predict it. This study aims to evaluate the performance of the Weather Research and Forecasting (WRF) model in the prediction of the lightning potential index (LPI), the lightning threat index (LTI), and the product of convective available potential energy (CAPE) and precipitation (CAPE × P). Based on the intensity of convection and the number of lightning flashes, we simulated seven thundercloud events that occurred in Tehran between 2008 and 2013. The simulated LPI, LTI, and CAPE × P values are compared both qualitatively and quantitatively against ground‐based lighting data obtained from the world wide lightning location network (WWLLN). LPI, LTI, and CAPE × P predict the location of lightning with relatively good accuracy, although LPI outperforms LTI and CAPE × P. We also compared the simulated area‐averaged LPI, LTI, and CAPE × P against the number of lightning flashes from the WWLLN data, based on which LPI shows a better performance. Overall, LPI can be used as an effective index for the prediction of lightning. As LPI is based on cloud microphysics and LTI and CAPE × P are thermodynamic‐based methods, a better performance of LPI implies that a method based on the microphysical approach can better predict lightning. The performance of LPI, LTI, and CAPE × P is also sensitive to the horizontal resolution of model simulations, with a considerable improvement in simulations with higher horizontal resolutions.

Influence of resampling techniques on Bayesian network performance in predicting increased algal activity

Early warning of increased algal activity is important to mitigate potential impacts on aquatic life and human health. While many methods have been developed to predict increased algal activity, an ongoing issue is that severe algal blooms often occur with low frequency in water bodies. This results in imbalanced data sets available for model specification, leading to poor predictions of the frequency of increased algal activity. One approach to address this is to resample data sets of increased algal activity to increase the prevalence of higher than normal algal activity in calibration data and ultimately improve model predictions. This study aims to investigate the use of resampling techniques to address the imbalanced dataset and determine if such methods can improve the prediction of increased algal activity. Three techniques were investigated, Kmeans under-sampling (US_Kmeans), synthetic minority over-sampling technique (SMOTE), and ‘SMOTE and cluster-based under-sampling technique’ (SCUT). The resampling methods were applied to a Bayesian network (BN) model of Lake Burragorang in New South Wales, Australia. The model was developed to predict chlorophyll-a (chl-a) using a range of water quality parameters as predictors. The original data and each of the balanced datasets were used for BN structures and parameter learning. The results showed that the best graphical structure was obtained by adding synthetic data from SMOTE with the highest true positive rate (TPR) and area under the curve (AUC). When compared using a fixed graphical structure for the BN, all resampling techniques increased the ability of the BN to detect events with higher probability of increased algal activity. The resampling model results can also be used to better understand the most important influences on high chl-a concentrations and suggest future data collection and model development priorities.

Novel treatment from a botanical formulation Si-Miao-Yong-an decoction inhibits vasa vasorum angiogenesis and stabilizes atherosclerosis plaques via the Wnt1/β-catenin signalling pathway

Context Si-Miao-Yong-An (SMYA) has been widely used for the clinical treatment of atherosclerosis (AS). Yet, its complete mechanism of action is not fully understood. Objective To investigate the mechanism by which SMYA stabilizes AS plaques from the perspective of inhibiting vasa vasorum (VV) angiogenesis. Materials and methods We used male ApoE-/- mice to establish an AS model. The mice were divided into model, SMYA (11.7 mg/kg/d), and simvastatin (SVTT) (2.6 mg/kg/d) groups. Mice were given SMYA or SVTT by daily gavage for 8 weeks. HE staining, immunofluorescence double-labelling staining, and immunohistochemical staining were used to observe the pathological changes in the plaques. Finally, the protein and mRNA expression levels of the Wnt1/β-catenin signalling pathway were detected by Western blot and qRT-PCR, respectively. Results SMYA significantly attenuated cholesterol crystallization, and lipid accumulation in AS plaques, resulting in smaller plaque size (0.25 mm2 vs. 0.46 mm2), and lowering ratio of plaque to lumen area (20.04% vs. 38.33%) and VV density (50.64/mm2 vs. 98.02/mm2). Meanwhile, SMYA suppressed both the positive area percentage of Wnt1 (2.53 vs. 3.56), β-catenin (3.33 vs. 5.65) and Cyclin D1 (2.10 vs. 3.27) proteins in the aortic root plaques, and mRNA expression of Wnt1 (1.38 vs. 2.09), β-catenin (2.05 vs. 3.25) and Cyclin D1 (1.39 vs. 2.57). Discussion and conclusions SMYA has a protective effect against AS, which may be related to its anti-VV angiogenesis in plaques, suggesting that SMYA has the potential as a novel botanical formulation in the treatment of AS.