Direct Output Research Articles

PHYSIOLOGY OF AFFERENT AND CENTRAL PARTS OF AUTONOMIC NERVOUS SYSTEM

The Autonomic Nervous System (ANS) and the endocrine system control the internal environment of the body. The organization of the ANS is on the basis of the reflex arc. Changes in chemical composition, blood pressure, osmotic pressure, stretch in viscera, and temperature are detected by autonomic receptors. The visceral afferent fibres are myelinated and they accompany the visceral motor fibres. The sensory autonomic neurons are pseudo-unipolar cells located in the dorsal root ganglia of somatic spinal nerves. Once the afferent fibres gain entrance to the spinal cord or the brainstem, they are thought to travel alongside the somatic afferent fibres to the autonomic centres in different parts of the brain. Information reaches the higher autonomic centres from viscera through an ascending system that involves the nucleus tractus solitarius, the parabrachial nucleus, the periaqueductal gray matter, and the hypothalamus. The hypothalamic paraventricular nucleus, pontine A5 cell group, rostral ventrolateral medulla, and medullary raphe nuclei send direct output to the preganglionic autonomic neurons. The amygdala, mesencephalic periaqueductal gray, caudal ventrolateral medulla, nucleus of the tractus solitarius, and medullary lateral tegmental field feed into these direct pathways. Pak J Physiol 2023;19(4):1-2

Mitigation and risk management of climate change in crop cultivation through the adoption of Agromet Advisory Bulletin (AAB) in NICRA adopted villages in Punjab

Crop production is a direct output of manageable (agronomic) and unmanageable (weather) inputs. A farmer can cut down losses in crop production due to aberrant weather conditions by following weather forecast. India Meteorological Department is providing weather forecast on eight weather parameters at district and block level. Under All India Coordinated Research Project on Agrometeorology-National Innovations in Climate Resilient Agriculture, an Agromet Advisory Bulletin (AAB) is prepared by using this forecast for coming five days and disseminated to farmers. To evaluate the impact of AAB in three selected villages Badoshe Kalan and Bauranga Zer (district Fatehgarh Sahib) and Rampur Fasse (district Rupnagar) a survey from 110 farmers was conducted. Amongst the 110 farmer, 70 were marginal/small farmers (landholding <2.0ha) and 40 were medium farmers (landholding 2-10ha) who adopted the information given in AAB in crop cultivation. The analysis revealed that by following AAB in rice and wheat crops 65-93% farmers benefitted by managing biotic stresses, 65-85% farmers by irrigation management, 75-78% farmers by adjusting sowing and 62-65% farmers by nutrient management. The farmers who scheduled irrigations to their crop by adopting AAB in rice-wheat cropping system reduced ~34.2 metric tonnes of CO2 emissions by preventing wasteful burning of diesel. The adopters of AAB in rice and wheat crop were able to harness an average yield increase of 2.25-3.75q/ha and 1.75-4.50 q/ha, respectively and save nearly Rs 4100 to 7000/ha and Rs 3200-9200/ha, respectively with lesser expenditure. Hence, AAB can help boost crop productivity as well as help reduce carbon footprints and make agriculture an eco-friendly and profitable venture.

Combination of Electro- and Radiochemical Processes for Hydrogen and Oxygen Obtaining

It is shown that increase in the hydrogen production process efficiency can be ensured by integrating radiochemical and electrochemical processes. In this case, the obtained effect depends not only on the direct radiolysis of water, but also on the involvement of the ionizing radiation energy in the electrolysis process for the excitation of water molecules that undergo electrolysis, which leads to a decrease in the consumption of electricity for the decomposition of its gaseous components. An analysis of the main factors influencing the reduction of electricity consumption during electrolysis is presented, and the affinity of the spectra of radical ions involved in the radiation and electrochemical processes of water decomposition is shown. As a result of radiation exposure, the most energy-intensive stage of water decomposition, associated with the breaking of intermolecular bonds and the formation of active particles involved in the electrochemical process, begins. It was established that the formation of hydrogen increases due to the addition of its direct output during radiolysis and indirect production during electrolysis, initiated by the activation effects caused by ionizing radiation. It is shown that in order to increase the direct radiolytic yield of hydrogen, elements containing nanosized zirconium dioxide powder should be placed in the interelectrode space of the electrolyzer. It has also been proven that the irradiation of zirconium dioxide placed in water leads to a 4-fold increase in the yield of hydrogen compared to the option of irradiating pure water. To increase the energy potential utilization coefficient of nuclear fuel at NPPs, it is expedient to use the energy of fuel elements located in spent nuclear fuel storage pools for the application of the proposed technology. This will ensure the utilization of the ionizing radiation energy, which in existing technologies is irretrievably lost, because it is discharged in the form of low-temperature thermal emissions into the environment, which leads to thermal pollution of the atmosphere.

Developing an agent-based microsimulation for predicting the Bus Rapid Transit (BRT) demand in developing countries: A case study of Dhaka, Bangladesh

Bus Rapid Transit (BRT) has been widely recognised as an affordable and effective mass transport system that can solve various mobility issues in countries that are unable to afford rail-based mass transit options. However, it is extremely challenging to predict the demand for the first BRT service in a city of a developing country with a weak public transport system using aggregate models, given the radical difference in the level of service between the BRT and the existing modes. Further, there can be substantial changes in the activity and travel patterns in a city after the introduction of the BRT which simpler disaggregate level analysis tools are unable to predict. Agent-based simulation tools, which are the state-of-the-art tools for simulating complex travel behaviour, are hence more appropriate for predicting the network conditions after the introduction of a new BRT system. But the application of such simulation tools has been primarily limited to developed countries where the transport landscape and the travel behaviour are very different from the developing countries. To address this gap, this paper presents a demand forecasting model for BRT and integrates it into an activity-based micro-simulation tool in the context of Dhaka, the capital of Bangladesh and one of the fastest growing megacities in the world. The model was developed based on an existing multi-agent, activity-based, travel demand simulator (MATSim). The MATSim implementation in the context of Dhaka focused on two aspects: (1) implementing behaviour models in MATSim to reflect the mode choice in the presence of the proposed BRT (2) integrating multiple data sources (including stated-preference data) for calibrating the mode choice and other components of MATSim to realistically mimic the travel behaviour in the city. Once calibrated, different access scenarios for BRT were simulated using MATSim, and the sensitivity of the outputs to different modelling assumptions is tested. Results from the simulation showed that the marginal utility of travel time, travel cost, and pricing structure of BRT significantly influenced BRT travel demands. Also, BRT demand was found to be the highest (25% of the total trips) in the scenario with multi-modal access/egress connections. While such direct model outputs presented in this paper will be useful for the planners to maximise the ridership of the proposed BRT, the calibrated simulator will be also useful for the evaluation of other innovative transport modes in the context of Dhaka in the future.

Heterogeneous presynaptic receptive fields contribute to directional tuning in starburst amacrine cells.

The processing of visual information by retinal starburst amacrine cells (SACs) involves transforming excitatory input from bipolar cells (BCs) into directional calcium output. While previous studies have suggested that an asymmetry in the kinetic properties of BCs along the soma-dendritic axes of the postsynaptic cell could enhance directional tuning at the level of individual branches, it remains unclear whether biologically relevant presynaptic kinetics contribute to direction selectivity (DS) when visual stimulation engages the entire dendritic tree. To address this question, we built multicompartmental models of the bipolar-SAC circuit and trained them to boost directional tuning. We report that despite significant dendritic crosstalk and dissimilar directional preferences along the dendrites that occur during whole-cell stimulation, the rules that guide BC kinetics leading to optimal DS are similar to the single-dendrite condition. To correlate model predictions to empirical findings, we utilized two-photon glutamate imaging to study the dynamics of bipolar release onto ON- and OFF-starburst dendrites in the murine retina. We reveal diverse presynaptic dynamics in response to motion in both BC populations; algorithms trained on the experimental data suggested that the differences in the temporal release kinetics are likely to correspond to heterogeneous receptive field properties among the different BC types, including the spatial extent of the center and surround components. In addition, we demonstrate that circuit architecture composed of presynaptic units with experimentally recorded dynamics could enhance directional drive but not to levels that replicate empirical findings, suggesting other DS mechanisms are required to explain SAC function. Our study provides new insights into the complex mechanisms underlying DS in retinal processing and highlights the potential contribution of presynaptic kinetics to the computation of visual information by SACs.

Heterogeneous presynaptic receptive fields contribute to directional tuning in starburst amacrine cells

The processing of visual information by retinal starburst amacrine cells (SACs) involves transforming excitatory input from bipolar cells (BCs) into directional calcium output. While previous studies have suggested that an asymmetry in the kinetic properties of BCs along the soma-dendritic axes of the postsynaptic cell could enhance directional tuning at the level of individual branches, it remains unclear whether biologically relevant presynaptic kinetics contribute to direction selectivity (DS) when visual stimulation engages the entire dendritic tree. To address this question, we built multicompartmental models of the bipolar–SAC circuit and trained them to boost directional tuning. We report that despite significant dendritic crosstalk and dissimilar directional preferences along the dendrites that occur during whole-cell stimulation, the rules that guide BC kinetics leading to optimal DS are similar to the single-dendrite condition. To correlate model predictions to empirical findings, we utilized two-photon glutamate imaging to study the dynamics of bipolar release onto ON- and OFF-starburst dendrites in the murine retina. We reveal diverse presynaptic dynamics in response to motion in both BC populations; algorithms trained on the experimental data suggested that the differences in the temporal release kinetics are likely to correspond to heterogeneous receptive field properties among the different BC types, including the spatial extent of the center and surround components. In addition, we demonstrate that circuit architecture composed of presynaptic units with experimentally recorded dynamics could enhance directional drive but not to levels that replicate empirical findings, suggesting other DS mechanisms are required to explain SAC function. Our study provides new insights into the complex mechanisms underlying DS in retinal processing and highlights the potential contribution of presynaptic kinetics to the computation of visual information by SACs.

Compact polarization beam splitter based on metal-capped thin-film LiNbO3-on-insulator plasmonic waveguide

A compact polarization beam splitter based on a triple-waveguide plasmonic directional coupler is proposed and modeled. The directional coupler consists of two usual lithium-niobate-on-insulator (LNOI) ridge waveguides and a metal-capped plasmonic LNOI waveguide located between them. It utilizes stronger polarization dependence of modal birefringence of a plasmonic waveguide. By appropriately designing the plasmonic waveguide geometry, the TM component wave meets the resonant coupling condition and outputs at cross port. Instead, the TE component wave does not meet the resonant coupling condition, cannot be resonant in the triple-waveguide directional coupler and outputs at through port directly. Excellent performance is expected for an optimal device. First, the device shows an ultra-high polarization extinction ratio(PER) of 28.3 dB and an ultra-low insertion loss(IL) of 0.06 dB for the TE-polarized wave, and of 31.7 dB and of 0.31 dB in order for the TM-polarized wave. Second, the working bandwidth with PER > 25 dB and IL < 0.08 dB is 100 nm (1500–1600 nm) for the TE-polarized wave, and that with PER > 20 dB and IL < 0.4 dB is 30 nm (1534–1564 nm) for the TM-polarized wave. Third, the device has a compact structure of 33 μm in length and exhibits definite fabrication tolerance. Overall, the proposed device exhibits ultra-high PER and ultra-low IL, and has a compact size suitable for on-chip use. A comparison shows that present device exhibits advantage in IL and/or size over that based on other LNOI structures or the SOI platform.

Wind gust forecasting by post-processing the WRF model outputs using ANN

Strong and highly variable winds and gusts are major hazards to infrastructure, properties, and life. Consequently, accurate prediction and timely detection of wind gust intensity have always been a focus of interest for earth scientists and weather forecasters.In this study, The WRF (Weather Research and Forecasting) post-process diagnostic of wind gusts (WPD method) was utilized to predict non-convective wind gust speeds using the direct outputs of the WRF model. To improve the prediction accuracy of this method, the results were post-processed using an artificial neural network (ANN). Multiple different ANN algorithms were examined to achieve the most accurate predictions possible. The results were evaluated using observational data extracted from 32 synoptic stations across Iran during the time period from 2014 to 2018.The results indicate that employing a multilayer perceptron ANN with a hybrid structure, consisting of one input layer comprising five parameters (10 m wind speed, sea level pressure, temperature, relative humidity, and predicted wind gust speed obtained from the WPD method), one hidden layer with a sigmoid activation function and 12 neurons, one output layer with a linear activation function and using the BR (Bayesian Regularization) training algorithm, significantly improve the accuracy of the WPD wind gust speed prediction method. The RMSE for wind gust speed prediction has decreased from 3.68 m/s (WPD method) to 1.88 m/s for the validation dataset. Additionally, there were considerable improvements of 50 %, 74 %, and 17 % in the MAE, MSE, and R2, respectively.

Mapping and assessment of lake ecosystem services in Lithuania

Lakes provide essential Ecosystem Services (ES) that support human well-being. However, due to anthropogenic environmental degradation, lake ecosystem conditions and related ES supply are affected. To assess these impacts and ES supply alterations, mapping and assessing lake ES is key. Spatiotemporal lake ES studies are lacking, especially at the national scale. In this study, we mapped and assessed lake ES nationally in Lithuania. In total, 1032 lakes were considered. The supply of five ES were studied in 1990, 2000, 2006, 2012, and 2018: 1) fibres and other materials for construction (fibres ES); 2) water for non-drinking purposes (water ES); 3) the maintenance of nursery populations (nursery ES); 4) the maintenance of freshwater chemical conditions (nutrient regulation ES), and 5) direct and indirect cultural outputs (recreation ES). For fibres ES, an increase over time was observed, following an increase in reed areas around the lakes. The results showed that significant differences among periods were identified in all ES except for water ES supply. Generally, the supply of fibres, nutrient, nursery and recreation ES increased between 1990 and 2018. Spatially, the north-eastern, central and easternmost parts of Lithuania showed a high supply of multiple ES (e.g., fibres ES, nutrient ES), while the western parts of the country were seen as essential for nursery ES supply. Except for water ES supply, all ES showed a clustered pattern. The hot–cold spots identified followed the areas of high and low ES supply. Bundle analysis identified three clusters. Recreation ES supply was not associated with fibres, nursery, nutrient, and water ES supply. Fibres, nursery, and nutrient ES supply were not related to water ES supply. The results obtained in this work are critical for better management and help to identify lakes that supply high and low ES.

Indra-kaví

The paper aims at illustrating the possible interconnection between Ṛgvedic poetry and the bovine environment in relation to which the proto-Vedic clan-based society ensured its own subsistence. Given that the protection of livestock was one of the functions attributed to chieftainship, especially during the phase of clan mobility (yóga), and that the figure of the proto-Vedic kaví, the so-called ‗sage poet,‘ is correlated to the milieu of the lordship, it is most likely that the bovine imagery and rhetorical devices, particularly connected to sonority, stemmed precisely from that environment where the human and animal dimensions were symbiotically associated to ensure clan‘s prosperity. Therefore, Ṛgvedic poetical expressions are not only the artful means to mark the liturgical language, but also a direct output of the expertise of the warrior-cowherd, identified especially with the mythological figure of Indra, who could, by way of sonorous enchanting of both, the livestock and the enemies, yoke the former and keep away the latter, guaranteeing subsistence to his own clan.

Investigating the Performance of CMIP6 Seasonal Precipitation Predictions and a Grid Based Model Heterogeneity Oriented Deep Learning Bias Correction Framework

AbstractClimate change is expected to alter the magnitude and spatiotemporal patterns of hydro‐climate variables such as precipitation, which has significant impacts on the ecosystem, human societies and water security. Global Climate Models are the major tools to simulate historical as well as future precipitation. However, due to imperfect model structures, parameters and boundary conditions, direct model outputs are subject to large uncertainty, which needs serious evaluation and bias correction before usage. In this study, seasonal precipitation predictions from 30 Coupled Model Inter‐comparison Project Phase 6 (CMIP6) models and Climate Research Unit observations are used to evaluate historical precipitation climatology in global continents during 1901–2014. A grid based model heterogeneity oriented Convolutional Neural Network (CNN) is proposed to correct the ensemble mean precipitation bias ratio. Besides, regression based Linear Scaling (LS), distribution based Quantile Mapping (QM) and spatial correlation CNN bias correction approaches are employed for comparison. Results of model performance evaluation indicate that generally precipitation prediction is more reliable in JJA than DJF on the global scale. Most models tend to have larger bias ratio for extreme precipitation. In addition, current CMIP6 models still have certain issues in accurate simulation of precipitation in mountainous regions and the regions affected by complex climate systems. Moreover, the proposed grid based model heterogeneity oriented CNN has better performance in ensemble mean bias correction than LS, QM, and spatial correlation CNN, which could consider the relative model performance and capture the features similar to actual climate dynamics.

A Review of Research on the Impact Mechanisms of Green Development in the Transportation Industry

Green development in the transportation industry is a new type of development. As the huge energy consumption and carbon emissions generated by the transportation industry have caused many environmental problems, the healthy and environmentally friendly mode of industrial development has received more and more attention. However, the quantification of green development in the transportation industry varies in terms of boundaries, scope, and methods. Due to digital empowerment, the degree of influence and direction of the factors affecting the green development are not fixed. The prediction of future development prospects is relatively single-minded, lacking a comprehensive simulation scenario setting from multiple perspectives. This paper systematically reviews the research progress of green development of the transportation industry from three aspects: development performance assessment, influence mechanism analysis, and development path exploration. After a critical analysis, this study concludes that (1) a clear methodology is needed to assess the direct and indirect non-desired output results of the transportation industry; (2) considering the endogenization of the level of technology, the influence of the interaction between the influencing factors, etc., on the degree and direction of the role of the factors, a more scientific econometric model should be established for in-depth discussion; (3) resident travel options are an important factor affecting environmental issues in transportation. Carbon emission projections and analyses of emission reduction scenarios should integrate the multiple possibilities of residential preferences and policy incentives. The findings of this paper provide valuable references to the energy saving and emission reduction goals of the transportation industry, and the coordinated development of the industry and the economy.

Machine learning based post‐processing of model‐derived near‐surface air temperature – A multimodel approach

AbstractIn this article, a machine‐learning‐based tool for calibrating numerical forecasts of near‐surface air temperature is proposed. The study area covers Poland representing a temperate type of climate with transitional features and highly variable weather. The direct output of numerical weather prediction (NWP) models is often biased and needs to be adjusted to observed values. Forecasters have to reconcile forecasts from several NWP models during their operational work. As the proposed method is based on deterministic forecasts from three short‐range limited‐area models (ALARO, AROME and COSMO), it can support them in their decision‐making process. Predictors include forecasts of weather elements produced by the NWP models at synoptic weather stations across Poland and station‐embedded data on ambient orography. The Random Forests algorithm (RF) has been used to produce bias‐corrected forecasts on a test set spanning one year. Its performance was evaluated against the NWP models, a linear combination of all predictors (multiple linear regression, MLR) as well as a basic Artificial Neural Network (ANN). Detailed evaluation was done to identify potential strengths and weaknesses of the model at the temporal and spatial scale. The value of RMSE of a forecast obtained by the RF model was 11% and 27% lower compared to the MLR model and the best‐performing NWP model respectively. The ANN model turned out to be even superior, outperforming RF by around 2.5%. The greatest improvement occurred for warm bias during the nighttime from July to September. The largest difference in forecast accuracy between RF and ANN appeared for temperature drops inApril nights. Poor performance of RF for extreme temperature ranges may be suppressed by training the model on forecast error instead of observed values of the variable.

A 4-Channel Optogenetic Stimulation, 16-Channel Recording Neuromodulation System with Real-Time Micro-LED Detection Function

Neuromodulation techniques are essential for exploring brain science and supporting treatments for neurological disorders. Compared to electrical neuromodulation, optogenetic neuromodulation offers advantages in cell type specificity and spatial precision. However, existing optogenetic neuromodulation systems have limited functionality (unable to simultaneously possess functions including optogenetic stimulation, recording, and micro-LED (micro-Light-Emitting Diode) status monitoring) and will restrict normal biological activities due to their large size. To this end, this paper presents an optogenetic neuromodulation system, including a specified neuromodulation IC (Integrated Circuit) and a customized optrode. The ASIC (Application Specific Integrated Circuit) includes a 16-channel neural signal recording module, a 4-channel optogenetic neurostimulator module, and a 4-channel micro-LED detection module. The micro-LED detection module monitors the micro-LED’s long-term status in real time and provides the direct output of its working status for convenient user access. The neuromodulation ASIC was fabricated in the TSMC 65 nm process, and an in situ normal saline experiment was conducted to test the neuromodulation system’s function.

Self-Powered Potentiometric Sensor Based on a Passive Signal Amplifier with Electronic Paper Display.

Self-powered potentiometric sensors are attractive because of their simple operation, low cost, fast response, and ability to be integrated with electronic components. Self-powered potentiometric sensors that give a direct colorimetric output are especially interesting, because no power supply is needed, which dramatically reduces waste. Recently reported work from our group using an electronic paper display, however, exhibits limitations, because the visualization of small pH changes is difficult. A self-powered ion-selective potentiometric sensor is introduced here that may amplify the e-paper pixel sensitivity by improving the self-powered circuit. The voltage is amplified by changing the circuit from incorporating parallel to incorporating serial capacitors. With three such capacitors, a greatly improved sensitivity is observed, amplifying the absorbance 3-fold. A portable device is realized that changes the position of the capacitors from parallel to serial through a simple mechanical sliding action. As a result, the pH information on the sample is more easily visualized with a pH uncertainty of about 0.1 when comparing the e-paper output to a color card.

Scientometric Analysis and Mapping of Scientific Articles on Multiple Sclerosis-Related Neurogenic Lower Urinary Tract Dysfunction.

Patients suffering from multiple sclerosis (MS) frequently experience lower urinary tract (LUT) dysfunction, which significantly impacts their quality of life. This study's objective was to conduct a scientometric analysis of the literature on MS-induced neurogenic LUT dysfunction. Using bibliometric methods, we examined the literature on neurogenic lower urinary tract dysfunction (NLUTD) in MS patients without restricting it to prevalence studies or specific management methods. We considered contributions from authors, organizations, nations/regions, as well as the evolution of theoretical frameworks, research subtopics, and influential papers. In January 2023, we searched the complete Scopus database, without imposing any language or date constraints, identifying relevant documents related to urology clinical investigations of MS-induced NLUTD. The original articles were categorized into 4 groups: narrative reviews, systematic reviews and meta-analyses, research of levels 1-4, and case reports/series. On January 1, 2023, our search yielded 72 sources published between 1977 and 2022, including journals and books. The average time before publication was 11.2 years. Each document received an average of 18.1 citations, totaling 1.299 citations per year. The author's analysis explored relationships, productivity, and coauthorship networks among authors and institutions based on bibliographic records. Chartier-Kastler E, Karmonik C, and Khavari R ranked highest with 8 publications each. The University of Catania claimed the top position, followed by Houston Methodist Hospital and Paris University, recognized as the leading institutions in this field. An analysis of diagnosis, therapy, and rehabilitation of MS-related NLUTD may be helpful for future bibliometric research in the field to better direct output.

Basal conditions of Denman Glacier from glacier hydrology and ice dynamics modeling

Abstract. Basal sliding in Antarctic glaciers is often modeled using a friction law that relates basal shear stresses to the effective pressure. As few ice sheet models are dynamically coupled to subglacial hydrology models, variability in subglacial hydrology associated with the effective pressure is often implicitly captured in the basal friction coefficient – an unknown parameter in the basal friction law. We investigate the impact of using effective pressures calculated from the Glacier Drainage System (GlaDS) model on basal friction coefficients calculated using inverse methods in the Ice-sheet and Sea-level System Model (ISSM) at Denman Glacier, East Antarctica, for the Schoof and Budd friction laws. For the Schoof friction law, a positive correlation emerges between the GlaDS effective pressure and basal friction coefficient in regions of fast ice flow. Using GlaDS effective pressures generally leads to smoother basal friction coefficients and basal shear stresses, and larger differences between the simulated and observed ice surface velocities compared with using an effective pressure equal to the ice overburden pressure plus the gravitational potential energy of the water. Compared with the Budd friction law, the Schoof friction law offers improved capabilities in capturing the spatial variations associated with known physics of the subglacial hydrology. Our results indicate that ice sheet model representation of basal sliding is more realistic when using direct outputs from a subglacial hydrology model, demonstrating the importance of coupling between ice sheet and subglacial hydrological systems. However, using our outputs we have also developed an empirical parameterization of effective pressure that improves the application of the Schoof friction law without requiring explicit hydrological modeling.

Abstract 14195: Right Ventricular Outflow Tract Diameter Increase With the Low-Moderate Workload: A Prospective Simultaneous Exercise Echocardiography and Invasive Cardiopulmonary Exercise Testing Study

Background: While cardiac output at rest can be estimated non-invasively with echocardiogram, change in left ventricular or right ventricular outflow tract diameter (RVOTd) with exercise makes the cardiac output estimation inaccurate. Hypothesis: RVOTd increases with exercise proportionally with an incremental workload and is determined by stroke volume augmentation. Aims: 1. Define a workload-based change in RVOTd in subjects undergoing invasive cardiopulmonary exercise test (iCPET). 2. Correlate RVOTd at exercise stages with iCPET metrics. Methods: 20 subjects undergoing simultaneous echocardiogram and iCPET were enrolled in a prospective study. Exercise datapoints were acquired at 25 W (n=20), 50W (n=16), 75W (n=14), 100W (n=6). Three hemodynamic groups included: HFpEF (n=12), PAH (n=5), controls (n=3). Utilizing iCPET-based direct Fick cardiac output and stroke volume, RVOTd was reverse engineered with RVOT velocity time integral, per ASE guidelines. Pearson correlation was used to assess correlation of RVOTd with hemodynamic metrics. p&lt;0.05* Results: In overall cohort, age was 64±12 years, BMI 31.2±4.9 Kg/m 2 , female 60%, peak VO 2 13.7±5.7 mL/Kg.min -1 and V E /VCO 2 37±8. Increase in RVOTd with exercise is represented in the Figure. In comparison to rest, RVOTd increased significantly even at low-25W (+4.1±13.6%*) and moderate-50W (+7.6±17.2%*) workloads. Overall, RVOTd increased by 4-5% every 25W. At high workload (100W), the diameter increased by 24% (n=6). There were no differences in RVOT diameter between subgroups. RVOTd at four exercise stages (25W, 50W, 75W, 100W) correlated significantly with stroke volume index (r=0.68*, 0.80*, 0.23, 0.64) and cardiac output (r=0.50*, 0.80*, 0.08, 0.68), but non-significantly with heart rate (r=-0.15, 0.19, -0.10, 0.68). Conclusion: RVOTd increases by 4-5% every 25W on an incremental workload protocol. This behavior is predominantly associated with an increase in stroke volume index with exercise.

CO2 emissions abatement costs and drivers for Indian thermal power industry

Indian power industry contributes nearly half of its overall CO2 emissions. In this study, we answer three related questions. First, what is the abatement cost of CO2 emissions for the Indian thermal power industry under three different policy scenarios - business as usual, emissions reduction only, and simultaneous emissions reduction and energy efficiency enhancement. Second, what are the determinants of these abatement costs of CO2 emissions. Third, which abatement policy scenario is more beneficial. For this, we employ a plant level dataset covering 93% of installed capacity and use a parametric quadratic directional output distance function to estimate marginal abatement costs by applying deterministic linear programming methods. Our findings are: First, the abatement costs range between US$ 46.43–71.11 per ton CO2, depending on the policy scenario chosen. Second, plant age, location, size, ownership, and CO2 intensity are all significant drivers of abatement costs. Third, while greater emissions reductions are possible under the emissions reduction only scenario vis-à-vis simultaneous emissions reduction and energy efficiency enhancement scenario, both scenarios are compatible with meeting the Paris agreement goals. This paper helps decision makers to allocate CO2 abatement targets to plants in line with their abatement costs and potential.

Predicting precipitation on the decadal timescale: A prototype climate service for the hydropower sector

Decadal predictions present an emerging opportunity for various socio-economic sectors affected by climate variability. However, the development of associated climate services is still in an incipient stage. This study focuses on developing a prototype climate service for an end-user in the hydropower sector. The service aimed at predicting precipitation in three drainage basins (Guadalquivir, Ebro and Po) for the next ten years, and was developed in close collaboration with the user. In this paper we do not provide the real-time forecasts, but we focus on describing and evaluating the methods and the models used. Using a European multi-model ensemble, the predictive skill for precipitation is found to vary with the calendar season, the forecast range and the drainage basin considered, though it is generally low for the purposes of supporting such a climate service. To overcome this deficiency, a hybrid approach was developed making combined use of the good skill in predicting the North Atlantic Oscillation (NAO) and the observed dominant influence of the latter on the decadal variability of precipitation in the areas of interest. Implementing this hybrid approach, which combines predictive information from the dynamical models with statistical information from observations, brings significant skill improvements in all basins during the extended cold season (November-March) for the first 10 forecast years. The hybrid model outperforms the direct multi-model ensemble output, exhibiting statistically significant skill for all basins. Our results suggest that utilising large-scale predictors can significantly improve regional predictions, and provide usable information for the hydropower sector.