Output Data Research Articles

Graphical Interface for Electrical Dimensioning of an On-Grid Photovoltaic System

Abstract Energy is an essential element in the development of mankind and is the engine that has made possible all the discoveries and innovations of recent decades. It is found behind the comfort we have in everyday life, whether at home, at the office, or when traveling: an optimal temperature in the room, light at anytime, appliances to ease us and make our lives more beautiful. An energy system based on renewable energy has the ability to provide electricity in an economical, sustainable, long-term, non-polluting way and can serve the increasing population. One such example is represented by photovoltaic (PV) panels, which produces electricity from captured solar energy and is one of the solutions considered for reducing energy. The dimensioning of a photovoltaic system, from a technical and economic efficiency point of view, involves the analysis of input data, output data and hence the characteristics of an efficient system. The analysis can be done analytically, but also with the help of dedicated software, the result being, in the end, a safe system in terms of preventive operation and maintenance. In the article was made the dimensioning of a system, analytical and automatic – using software application – to determine if the result obtained – the photovoltaic system – is the same regardless of the method used.

Design and application of data security privacy protection technology in dance action sequence arrangement and display system

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.

Adapting reservoir operations for optimal water management under varying climate and demand scenarios using metaheuristic algorithms

The operational rule curve is significant for proper reservoir operations and water resources management of the dam. The optimisation of the release policy is rather a complicated and stochastic problem. Various types of algorithms have been used to develop the release policy for the Klang Gate Dam (KGD), but alas, climate parameters associated with climate change, be it regional or globally, e.g., temperature factors, had not been considered then. In this study, the consideration of maximum and minimum temperature factors is given emphasis, while water demand is evaluated in the optimisation problem using simulation-optimisation approaches. The support vector regression simulation model (SVRS), and the multilayer perceptron simulation model (MLPS) were used to simulate the demand for the scenario cases. SVRS utilises the hyperplane theory to study the relationship between the input and output data, whereas MLPS employs the human neural system to weight and process the inputs via multiple layers of neurons into a targeted output. Four distinct types of simulation-optimisation combination model, including the SVRS-firefly algorithm (SVRS-FA), the SVRS-particle swarm optimisation (SVRS-PSO), the MLPS-firefly algorithm (MLPS-FA), and the MLPS-particle swarm optimisation (MLPS-PSO), were applied and investigated. The ultimate goal of this study was to minimise the water deficit. The PSO optimisation models overall have outperformed the FA optimisation models in all aspects. The periodic reliability of the MLPS-PSO was found to be the highest when the minimum temperature scenario was considered, and is 0.6491, with the least shortage period of 80 months. The MLPS-PSO is also the most resilient model in the same scenario, with a resilience value of 0.6750. Whereas the SVRS-PSO was the least vulnerable model in the minimum temperature scenario with the lowest shortage index of 0.0047 and a volumetric deficiency of about 2.44 MCM in total. The SVRS-PSO model showed excellent performance for high and low inflow conditions, while the MLPS-PSO model was better during medium inflows. In short, each of the models investigated has its particular field of advancement, but since the maximum temperatures sounded critical, additional research for the maximum temperature case could be pursued further in depth.

Placental tissues from nonhypertensive black patients demonstrate lower placental leptin expression compared to white patients independent of BMI

Hypertensive disorders of pregnancy increases risk of maternal and fetal morbidity and mortality. In the US, clinical data indicates that black women are more likely to develop hypertensive pregnancy than white women for reasons largely unknown. The goal of this study was to determine if expression of factors associated with hypertensive pregnancies, in the absence of hypertension milieu, differed between placental tissues from black versus white patient deliveries. We hypothesized that placental tissues from black patient deliveries demonstrate increases in gene expression of these factors compared to white patient deliveries. We collected placentas at time of delivery from healthy, nonhypertensive black (n=13) and white (n=14) patients from AU Medical Center. Snap frozen tissue from maternal side of placenta was processed to isolate RNA, reverse transcriptase for cDNA generation and then subjected to digital droplet PCR analysis of known markers of preeclampsia: leptin (Lep), preproendothelin-1 (PPET1), endothelin converting enzyme-1 (ECE-1) and placental growth factor (PLGF). Data output was recorded for each expression in copies/ul. Body mass index (BMI) at delivery did not differ between black (32.0±2.1 kg/m2) and white patients (31.3±1.4). In addition, correlation analysis (Pearson’s Correlation Coeffcient) was performed for BMI vs expression of Lep, PPET1, ECE-1 and PLGF in all samples from black and white patients and no significant association was found (P>0.05, R2<0.5 for all comparisons). Comparisons of gene expression between white and black patients was performed by 2-tailed t test. Lep expression significantly decreased in black compared to white patient placenta (74±61 vs 590±173 copies/ul, *p<0.05). However, placental expressions of PPET1 (844±185 black vs 626±164 white, copies/ul), ECE-1 (327±117 black vs 626±164 white, copies/ul), and PLGF (305±80 black vs 217±39 white, copies/ul) did not significantly increase or decrease in black compared to white patients. Collectively, these data surprisingly indicate that in nonhypertensive pregnancies, black patients demonstrate lower leptin expression compared to white patients. However, endothelin-1 associated ECE-1 and PPET1 expressions do not increase in nonhypertensive black patient placenta compared to white. As leptin expression is characteristically elevated in hypertensive pregnancy, future studies will determine if placentas collected from hypertensive black and white pregnancies demonstrate a discrepant gene expression in genes associated with increased risk of hypertensive pregnancy. NIH grants 1R01HL169576, 4R00HL146948. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

The LHCb VELO Upgrade II: design and development of the readout electronics

The LHCb collaboration proposes a Phase-II Upgrade of the detector, to be installed during the LHC Long Shutdown 4 (2032–2034). Operating in the HL-LHC environment poses significant challenges to the design of the upgraded detector, and in particular to its tracking system. The primary and secondary vertices reconstruction will become more difficult due to the increase, by a factor of 7.5, of the average number of interactions per bunch crossing. The performance of the VErtex LOcator (VELO), which is the tracking detector surrounding the interaction region, is essential to the success of this Phase-II Upgrade. Data rates are especially critical for the LHCb full software trigger, and with the expected higher particle flux, the VELO Upgrade-II detector will have to tolerate a dramatically increased data rate: assuming the same hybrid pixel design and detector geometry, the front-end electronics (ASICs) of the VELO Upgrade-II will have to cope with rates as high as 8 Ghits/s, with the hottest pixels reaching up to 500 khits/s. With this input rate, the data output from the VELO will exceed 30 Tbit/s, with potentially a further increase if more information is added to the read-out. This paper outlines the challenges being addressed and the solutions under investigation for reading out the VELO sub-detector.

Data- and simulation-based material behaviour prediction

AbstractIn research environments and laboratories e.g. for material sciences the in- and output of simulation data is manually managed. Therefore, physical experiments as well as simulations might be carried out several times, learnings are not systematically gathered, and experiments do not systematically build on learnings from data. This paper proposes to engage an ontology in conjunction with a simulation to use data from already carried out experiments and on that basis predict material behaviour under certain condition and plan further physical experiments.

CARDIAC OUTPUT ESTIMATION: PULSE WAVE ANALYSIS (ANTARES) HIGHLY CORRELATES TO ECHOCARDIOGRAPHY ASSESSMENT

Objective: Evaluation of cardiac output (CO) is crucial for understanding cardiac function and diagnosis of heart failure. While invasive thermodilution procedures serve as the gold standard for CO assessment, routine clinical practice has widely adopted transthoracic Doppler echocardiography (TTE) as the established non-invasive method. Nevertheless, there is a persistent demand for alternative non-invasive approaches, aiming to improve the efficiency of CO estimation, mitigate complications associated with invasive methods, and extend its application to non-intensive care settings, all of which potentially can be offered by oscillometric pulse wave analysis (PWA). This study aims to compare the non-invasive estimation of CO using TTE and the oscillometric PWA algorithm Antares. Design and method: We compared non-invasive cardiac output data obtained through two-dimensional transthoracic echocardiography with data obtained from an oscillometric blood pressure device (custo med GmbH, Ottobrunn, Germany) utilizing the integrated Antares algorithm (Redwave Medical GmbH, Jena, Germany). A total of 59 patients, undergoing elective cardiac catheterization for clinical reasons, were included in the study (mean age 71±10 years, 24% females), but only echocardiography-data with CO were used for analysis. Agreement between the two CO measurement methods (TTE versus Antares) was evaluated through Bland-Altman analysis, Student's t-test, and Pearson correlations. Results: The mean difference in CO was 0.04 ± 1.03 l/min. No statistically significant difference in means was observed between the two CO measurement methods. Significant correlations between transthoracic echocardiography (TTE) and Antares CO were evident in the overall population (r=0.71), as well as in both, female (r=0.80) and male patients (r=0.67; all p<0.001). Conclusions: The oscillometric PWA algorithm Antares and the established TTE exhibit strong correlations for non-invasive CO estimation in both male and female patients, with no statistically significant differences between the two approaches. This study opens the door to a valid and simple detection of heart failure that is seamlessly integrated into a quick and easy-to-perform upper arm blood pressure measurement.

Disaster management with cloud-based geographic information systems: site selection of landfill areas after Kahramanmaraş, Türkiye earthquake sequence

On February 6, 2023, the earthquakes centered on Kahramanmaraş-Pazarcık and Kahramanmaraş-Elbistan affected several provinces in the region and generated a vast amount of demolition waste. This study aims to develop a site selection model for the most suitable landfill areas after the earthquake using open-source Geographic Information Systems (GIS) software and the Best-Worst Method (BWM). First, the study identifies decision criteria with a Delphi expert panel. Next, it determines the weights using the BWM based on pairwise comparison. Spatial data of the determinants were obtained from open data portals, and proximity, overlay, and slope analyses were carried out for the disaster region. Combining the output data layer of each criterion, a suitability map of the landfills was produced. As a result, 36,2% (29,826 kms square) of the study area was classified as suitable and very suitable. In this study, a cloud GIS-based decision support platform was developed to act quickly and initiate recovery processes within the scope of disaster management of the destructive earthquake. The suitability map and potential sites were shared with the responsible teams in the disaster area so that the potential sites could be displayed on the map and the suitable areas could be navigated on appropriate routes. The findings of the study revealed that the use of open data and cloud GIS framework in the disaster management process enables quick, reliable, and cost-effective actions in the immediate aftermath of disasters.

3185: Retrospective analysis of output data for ten linear accelerators at Northern Ireland Cancer Centre

3185: Retrospective analysis of output data for ten linear accelerators at Northern Ireland Cancer Centre

Design of Human Posture Recognition System Based on Internet of Things Technology and Image Segmentation Algorithm

Abstract To address the problem of the traditional human posture recognition system being easily affected by environmental factors such as noise, a human posture recognition system based on Internet of Things technology and an image segmentation algorithm was designed. To do this, we selected the attitude sensor, designed the attitude sensor structure based on micro electro mechanical system technology, and had a variety of selectable data output modes. A 36 V power supply was selected as the circuit design index to provide sufficient power for the system. The main control chip was designed, and its state information description and external output were installed in a single chip or the same component. The denoising process based on image segmentation algorithm was designed, the error correction model was constructed, the measured values were normalized, and the image segmentation model was constructed. We extracted the node features of human body posture, used the image segmentation algorithm to construct the structure diagram of human body posture nodes, located the local recognition area of the node structure map, and designed the human body posture recognition system. The experimental results show that the designed human gesture recognition system has better effect and better recognition performance.

Applying High‐Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network

AbstractThe high‐speed video (HSV) images were taken as the input data of the nonlinear autoregressive network (NARX) to reconstruct the return‐stroke channel base current (CBC). The integrated luminosity (IL) of discharge channel is firstly obtained by summing the pixel values in HSV images, and the correlation between the IL and CBC is analyzed as well. Then, a NARX model is trained by taking the IL and CBC as the input and output data, respectively. With the input of the IL of another discharge channel, the trained NARX model is applied to inverse the corresponding CBC. The inversed result suggests that the proposed method can be used to infer the CBC that was not yet measured.

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

The research conducted focused on the capabilities of various non-linear and machine learning (ML) models in estimating the higher heating value (HHV) of biomass using proximate analysis data as inputs. The research was carried out to identify the most appropriate model for the estimation of HHV, which was determined by a statistical analysis of the modeling error. In this sense, artificial neural networks (ANNs), support vector machine (SVM), random forest regression (RFR), and higher-degree polynomial models were compared. After statistical analysis of the modeling error, the ANN model was found to be the most suitable for estimating the HHV biomass and showed the highest specific regression coefficient, with an R2 of 0.92. SVM (R2 = 0.81), RFR, and polynomial models (R2 = 0.84), on the other hand, also exhibit a high degree of estimation, albeit with somewhat larger modelling errors. The study conducted suggests that ANN models are best suited for the non-linear modeling of HHV of biomass, as they can generalize and search for links between input and output data that are more robust but also more complex in structure.

The CIPM list ‘Recommended values of standard frequencies’: 2021 update

This paper gives a detailed account of the analysis underpinning the 2021 update to the list of standard reference frequency values recommended by the International Committee for Weights and Measures. This update focused on a subset of atomic transitions that are secondary representations of the second (SRS) or considered as potential SRS. As in previous updates in 2015 and 2017, methods for analysing over-determined data sets were applied to make optimum use of the worldwide body of published clock comparison data. To ensure that these methods were robust, three independent calculations were performed using two different algorithms. The 2021 update differed from previous updates in taking detailed account of correlations among the input data, a step shown to be important in deriving unbiased frequency values and avoiding underestimation of their uncertainties. It also differed in the procedures used to assess input data and to assign uncertainties to the recommended frequency values, with previous practice being adapted to produce a fully consistent output data set consisting of frequency ratio values as well as absolute frequencies. These changes are significant in the context of an anticipated redefinition of the second in terms of an optical transition or transitions, since optical frequency ratio measurements will be critical for verifying the international consistency of optical clocks prior to the redefinition. In the meantime, the reduced uncertainties for optical SRS resulting from this analysis significantly increases the weight that secondary frequency standards based on these transitions can have in the steering of International Atomic Time.

Virtual reality visualization of geophysical flows: A framework

This paper presents a comprehensive Virtual Reality (VR) based framework for visualizing numerical simulations of geophysical flows in a realistic and immersive manner. The framework allows integrating output data from various mesh-based Eulerian numerical models into a VR environment, enabling users to interact with and explore the terrain and geophysical flows through the VR experience. Three case studies, including a snow avalanche, quick clay landslide, and flash flood in Norway, demonstrate its versatility. The VR environment offers intuitive menus and user interactions, allowing users to read flow depth and velocity values, facilitating a direct link between numerical data and their visual representation. This framework can reshape geophysical flow hazard identification and disaster management by integrating physics-based numerical modeling results into VR Environments, thus enhancing knowledge dissemination among experts, the general public, non-expert stakeholders, and policymakers. The paper also highlights challenges and opportunities identified during the integration, guiding future developments.

Improved Real‐Time Post‐Processing for Quantum Random Number Generators

AbstractRandomness extraction is a key problem in cryptography and theoretical computer science. With the recent rapid development of quantum cryptography, quantum‐proof randomness extraction has also been widely studied, addressing the security issues in the presence of a quantum adversary. In contrast with conventional quantum‐proof randomness extractors characterizing the input raw data as min‐entropy sources, it is found that the input raw data generated by a large class of trusted‐device quantum random number generators can be characterized as the so‐called reverse block source. This fact enables us to design improved extractors. Two novel quantum‐proof randomness extractors for reverse block sources that realize real‐time block‐wise extraction are proposed specifically. In comparison with the general min‐entropy randomness extractors, the designs achieve a significantly higher extraction speed and a longer output data length with the same seed length. In addition, they enjoy the property of online algorithms, which process the raw data on the fly without waiting for the entire input raw data to be available. These features make the design an adequate choice for the real‐time post‐processing of practical quantum random number generators. Applying the extractors to the raw data generated by a widely used quantum random number generator, a simulated extraction speed as high as 300 Gbps is achieved.

Optimization design for the installation position of hold bracket in railway bogie system by uniform design method

ABSTRACT The sensor hold bracket design is an essential element for running safety in the railway vehicle bogie braking system. The installation position of the hold bracket in the bogie system affects the brake sensing performance. This paper aims to determine the optimal installation position of the hold bracket. Three installation angles and one geometry dimension of a holding bracket are selected for the control factors. The uniform design of the experiment method creates a set of experiments. The surrogate models from input and output data are obtained and compared by applying Kriging interpolation and radial basis functions. The final optimal design models of the holding bracket for two surrogate models are determined using the genetic algorithm. The results show that the optimized parameters could substantially improve the installation angle error of the pressure switch from 4° to 0.04°, which aligns with expectations. Finally, to confirm the effect of the control parameters and their reliability and validity, the impact of the pressure switch is expected when the optimal hold bracket model is installed in the railway vehicle bogie system. As a result, the optimal design procedure can improve and reduce the risk of sensor failure during train operation.

Driving forces of CO2 emissions from the transport, storage and postal sectors: A pathway to achieving carbon neutrality

In tandem with the urbanisation process, China's transport sector is currently experiencing rapid development and was ranked third out of all the industrial sectors in terms of generating CO2 emissions in 2020, which poses a huge challenge to achieving carbon neutrality. Primarily using the energy consumption data from China's transport, storage and postal sectors (TSPS) and input and output data between 2007 and 2020, this study first uses the Tapio decoupling model to evaluate the decoupling effect in the TSPS. Structural decomposition analysis is then applied to explore sectoral linkages and decompose the forces driving CO2 emissions. Additionally, we explore the main determinants of the energy structure effect and final demand in terms of energy consumption and industrial sector demand. Our results show that the target sector experienced a weak decoupling, which implies that the low-carbon transformation of this sector became increasingly apparent. Factor decomposition shows that improvements in energy intensity, energy structure and the production input and output structure have contributed significantly to reducing CO2 emissions, but these gains have been largely offset by final demand, resulting in a net reduction of 27.97 million tons from 2007 to 2020. The increased usage of low carbon forms of energy, such as natural gas, is the key driver behind the emissions reduction effect in terms of the energy structure. However, the higher final demand from the construction sector and the wholesale and retail trades are the main factors that have increased CO2 emissions. By adopting a sectoral and energy structure decomposition perspective, our study can be used to provide guidance to governments seeking to pursue carbon-reduction policies to achieve carbon peak and carbon neutrality, in the TSPS in particular.

Pengaruh Celebrity Endorsement Dan Social Media Marketing Terhadap Purchase Intention

The focus of this research is to evaluate and determine how Celebrity Endorsements and Social Media Marketing influence the desire to purchase 3MONGGIS products. This local fashion product is quite successful in selling its various products. This research falls into the quantitative paradigm category. Sources of data collection include secondary data from the internet as well as journals and primary data collected through searching for main data output by looking for respondents who know the 3MONGGIS brand and distributing general news to predetermined respondents. Taking a sample of 140 people from respondents who were familiar with the 3MONGKIS brand, the purposive sampling method was used for sampling. The research results were evaluated and tested using classical assumptions and hypothesis testing with multiple linear regression analysis using the SPSS program. This study shows that Celebrity Endorsement and Social Media Marketing have a significant influence on Purchase Intention for 3MONGGIS products. Keyword: Celebrity Endorsment, Social Media Marketing, Purchase Intention

An Approach to Evaluate the Costs and Outputs of Academic Biobanks.

Academic biobanks commonly report sustainability challenges, which may be exacerbated by a lack of information on biobank value. To better understand the costs and supported outputs that contribute to biobank value, we developed a systematic, generalizable methodology to determine biobank inputs and publications arising from biobank-supported research. We then tested this in a small cohort (n = 12) of academic cancer biobanks in New South Wales, Australia. A proforma was developed to capture monetary and in-kind biobank costing data from biobank managers and publicly available sources. Participating biobanks were grouped and compared according to the following two classifications: open- versus restricted-access and high versus low total annual costs. Our methodology provides a feasible approach for capturing comprehensive costing data for a defined period. Characterization of biobanks using this approach showed that median total costs, as well as median staffing and in-kind costs, were comparable for open- and restricted-access biobanks, as were the quantity and journal impact metrics of supported publications. High- and low-cost biobanks supported similar median numbers of publications; however, high-cost biobanks supported publications with higher median journal impact factor and Altmetric scores. Overall, 9 of 10 biobanks had higher Field-Weighted Citation Impact scores than the global average for similar publications. This is the first tested, generalizable approach to analyze the costs and publications arising from biobank-supported research. By determining explicit cost and output data, academic biobanks, funders, and policymakers can engage in or support informed redirection of resourcing and/or benchmark setting with the aim of improving biobank support of research.

Symmetric U-Net Model Tuned by FOX Metaheuristic Algorithm for Global Prediction of High Aerosol Concentrations

In this study, the idea of using a fully symmetric U-Net deep learning model for forecasting a segmented image of high global aerosol concentrations is implemented. As the forecast relies on historical data, the model used a sequence of the last eight segmented images to make the prediction. For this, the classic U-Net model was modified to use ConvLSTM2D layers with MaxPooling3D and UpSampling3D layers. In order to achieve complete symmetry, the output data are given in the form of a series of eight segmented images shifted by one image in the time sequence so that the last image actually represents the forecast of the next image of high aerosol concentrations. The proposed model structure was tuned by the new FOX metaheuristic algorithm. Based on our analysis, we found that this algorithm is suitable for tuning deep learning models considering their stochastic nature. It was also found that this algorithm spends the most time in areas close to the optimal value where there is a weaker linear correlation with the required metric and vice versa. Taking into account the characteristics of the used database, we concluded that the model is capable of generating adequate data and finding patterns in the time domain based on the ddc and dtc criteria. By comparing the achieved results of this model using the AUC-PR metric with the previous results of the ResNet3D-101 model with transfer learning, we concluded that the proposed symmetric U-Net model generates data better and is more capable of finding patterns in the time domain.