Set Of Inputs Research Articles

Influence of meteorological reanalysis field on air quality modeling in the Yangtze River Delta, China

The meteorological reanalysis field is one of the key inputs to drive mesoscale meteorology simulations. To investigate the impacts of different reanalysis data on predictions of the meteorology parameters, as well as predictions of air quality, this study utilized the Weather Research and Forecasting/Community Multiscale Air Quality modeling system (WRF/CMAQ) with two sets of meteorological reanalysis inputs, the NCEP Final Operational Global Analysis (FNL) and the ECMWF Reanalysis v5.0 (ERA5) to predict the concentrations of ozone (O3) and fine particulate matter (PM2.5) in the Yangtze River Delta (YRD) in 2018. The results showed that ERA5 outperformed FNL in simulating 2-m temperature and relative humidity, while FNL demonstrated better accuracy in predicting wind fields. Both FNL and ERA5 underestimated the PM2.5 in spring and summer while overestimating it in autumn and winter. Furthermore, the Normalized mean bias (NMB) value for ERA5 exceeded the standard in winter (0.34), and the Normalized mean error (NME) value exceeded the standard in autumn (0.54). Both FNL and ERA5 underestimated the PM2.5 concentration during the pollution episodes, and FNL's simulations of PM2.5 concentrations were closer to observations than ERA5's as pollution levels intensified. Regarding O3 concentrations, ERA5 exhibited better performance than FNL, with the NMB (0.30, 0.27) and NME (0.39, 0.37) of FNL and ERA5 both exceeding the standard only in wintertime.

Technical efficiency drivers of farmer-led restoration strategies, and how substantial is the unrealised potential for farm output?

CONTEXTIn Ethiopia, similar to other parts of Sub-Saharan Africa, the effects of farmland degradation on ecosystem services and well-being are especially prominent as 85% of the population lives in rural areas and depends on agriculture for their livelihoods. Existing farmer-led restoration activities involve integrating trees on farms (ToF) through traditional agroforestry or woodlots systems. Although ToF practices can potentially bring economic and ecological gains, their production systems and efficiency in terms of input use remain overlooked in the current literature. OBJECTIVEThe study analyses the technical efficiency levels in Ethiopia's home gardens and woodlot systems and examines the drivers contributing to technical efficiency. METHODSThe study estimates the technical efficiency of farmers who implement the ToF strategies, i.e. the extent to which the farmers can achieve the highest output level given their set of inputs. Deviation from the total technical efficiency is then referred to as unrealised potential. We also analyse the drivers of technical inefficiency by controlling for geographical, biophysical, and production system attributes of woodlots and agroforestry patches, as well as household characteristics and institutional settings. We use a one-step Latent Class Stochastic Frontier Modelling approach based on the cross-sectional household data of 543 home gardens and 426 woodlot systems patches in Ethiopia. RESULTS AND CONCLUSIONSAccording to the results, in woodlots, extensive farms lose 38% and intensive farms 22% of the potential output. In home-gardens, intensive farms lose 39%, and extensive farms lose 49% of the output due to technical inefficiency. Significant determinants of technical efficiency in both systems are ecological zones, access to markets, communal carbon payments, forest user groups, training, land title factors, rotation age and product diversification, among others. SIGNIFICANCEOur results help identify priority policy and practical action fields for improving farmer-led restoration strategies' technical efficiency and management in Ethiopia and the global south. Future national reforestation policies could reduce inefficiencies in farmer-led systems across Sub-Saharan Africa by targeting the factors exacerbating technical inefficiency per class in a given strategy. Thus, handling these drivers increases the success of farmer-led restoration strategies and contributes to achieving restoration pledges and AFR100 commitments in mosaic landscapes.

On the use of artificial intelligence in predicting the compressive strength of various cardboard packaging

AbstractArtificial intelligence is increasingly used in various branches of engineering. In this article, artificial neural networks are used to predict the crush resistance of corrugated packaging. Among the analysed packages were boxes with ventilation openings, packages with perforations and typical flap boxes, which make the proposed estimation method very universal. Typical shallow feedforward networks were used, which are perfect for regression problems, mainly when the set of input and output parameters is small, so no complicated architecture or advanced learning techniques are required. The input parameters of the neural networks are selected so as to take into account not only the material used for the production of the packaging but also the dimensions of the box and the impact of ventilation holes and perforations on the load capacity of individual walls of the packaging. In order to maximize the effectiveness of neural network training process, the group of input parameters was changed so as to eliminate those to which the sensitivity of the model was the lowest. This allowed the selection of the optimal configuration of training pairs for which the estimation error was on the acceptable level. Finally, models of neural networks were selected, for which the training and testing error did not exceed 10%. The demonstrated effectiveness allows us to conclude that the proposed set of universal input parameters is suitable for efficient training of a single neural network model capable of predicting the compressive strength of various types of corrugated packaging.

A simple and efficient preprocessing step for convex hull problem

This paper is concerned with a recursive algorithm as a preprocessing step to find the convex hull of [Formula: see text] random points uniformly distributed in the plane. For such a set of points, it is shown that eliminating all but [Formula: see text] of points can derive the same convex hull as the input set. Finally it will be shown that the running time of the algorithm is [Formula: see text].

Identification of vortex in unstructured mesh with graph neural networks

Deep learning has been employed to identify flow characteristics from Computational Fluid Dynamics (CFD) databases to assist the researcher to better understand the flow field, to optimize the geometry design and to select the correct CFD configuration for corresponding flow characteristics. Convolutional Neural Network (CNN) is one of the most popular algorithms used to extract and identify flow features. However its use, without any additional flow field interpolation, is limited to the simple domain geometry and regular meshes which limits its application to real industrial cases where complex geometry and irregular meshes are usually used. Aiming at the aforementioned problems, we present a Graph Neural Network (GNN) based model with U-Net architecture to identify the vortex in CFD results on unstructured meshes. The graph generation and graph hierarchy construction using algebraic multigrid method from CFD meshes are introduced. A vortex auto-labeling method is proposed to label vortex regions in 2D CFD meshes. We precise our approach by firstly optimizing the input set on CNNs, then benchmarking current GNN kernels against CNN model and evaluating the performances of GNN kernels in terms of classification accuracy, training efficiency and identified vortex morphology. Finally, we demonstrate the adaptability of our approach to unstructured meshes and generality to unseen cases with different turbulence models at different Reynolds numbers.

Machine learning-based probabilistic fatigue assessment of turbine bladed disks under multisource uncertainties

PurposeThe multisource uncertainties, including material dispersion, load fluctuation and geometrical tolerance, have crucial effects on fatigue performance of turbine bladed disks. In view of the aim of this paper, it is essential to develop an advanced approach to efficiently quantify their influences and evaluate the fatigue life of turbine bladed disks.Design/methodology/approachIn this study, a novel combined machine learning strategy is performed to fatigue assessment of turbine bladed disks. Proposed model consists of two modeling phases in terms of response surface method (RSM) and support vector regression (SVR), namely RSM-SVR. Two different input sets obtained from basic variables were used as the inputs of RSM, then the predicted results by RSM in first phase is used as inputs of SVR model by using a group data-handling strategy. By this way, the nonlinear flexibility of SVR inputs is improved and RSM-SVR model presents the high-tendency and efficiency characteristics.FindingsThe accuracy and tendency of the RSM-SVR model, applied to the fatigue life estimation of turbine bladed disks, are validated. The results indicate that the proposed model is capable of accurately simulating the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction strategy compared to RSM and SVR for fatigue analysis of complex structures.Originality/valueThe results indicate that the proposed model is capable of accurately simulate the nonlinear response of turbine bladed disks under multisource uncertainties, and SVR-RSM model provides an accurate prediction compared to RSM and SVRE for fatigue analysis of turbine bladed disk.

Development of the Senseiver for efficient field reconstruction from sparse observations

AbstractThe reconstruction of complex time-evolving fields from sensor observations is a grand challenge. Frequently, sensors have extremely sparse coverage and low-resource computing capacity for measuring highly nonlinear phenomena. While numerical simulations can model some of these phenomena using partial differential equations, the reconstruction problem is ill-posed. Data-driven-strategies provide crucial disambiguation, but these suffer in cases with small amounts of data, and struggle to handle large domains. Here we present the Senseiver, an attention-based framework that excels in reconstructing complex spatial fields from few observations with low overhead. The Senseiver reconstructs n-dimensional fields by encoding arbitrarily sized sparse sets of inputs into a latent space using cross-attention, producing uniform-sized outputs regardless of the number of observations. This allows efficient inference by decoding only a sparse set of output observations, while a dense set of observations is needed to train. This framework enables training of data with complex boundary conditions and extremely large fine-scale simulations. We build on the Perceiver IO by enabling training models with fewer parameters, which facilitates field deployment, and a training framework that allows a flexible number of sensors as input, which is critical for real-world applications. We show that the Senseiver advances the state-of-the-art of field reconstruction in many applications.

Two-layer neural network on infinite-dimensional data: global optimization guarantee in the mean-field regime *

The analysis of neural network optimization in the mean-field regime is important as the setting allows for feature learning. The existing theory has been developed mainly for neural networks in finite dimensions, i.e. each neuron has a finite-dimensional parameter. However, the setting of infinite-dimensional input naturally arises in machine learning problems such as nonparametric functional data analysis and graph classification. In this paper, we develop a new mean-field analysis of a two-layer neural network in an infinite-dimensional parameter space. We first give a generalization error bound, which shows that the regularized empirical risk minimizer properly generalizes when the data size is sufficiently large, despite the neurons being infinite-dimensional. Next, we present two gradient-based optimization algorithms for infinite-dimensional mean-field networks, by extending the recently developed particle optimization framework to the infinite-dimensional setting. We show that the proposed algorithms converge to the (regularized) global optimal solution, and moreover, their rates of convergence are of polynomial order in the online setting and exponential order in the finite sample setting, respectively. To the best of our knowledge, this is the first quantitative global optimization guarantee of a neural network on infinite-dimensional input and in the presence of feature learning.

Evaluation of the effectiveness of the functioning of production and consumption waste management systems in the Arctic regions of Russia based on Data Envelopment Analysis

Subject of research: is the functioning of the production and consumption waste management systems of the regions (RSOO) of the Arctic zone of the Russian Federation for the period from 2016 to 2020.
 Purpose of research: is to quantify production efficiency production efficiency coefficients (СEP) of the functioning of these systems based on shell data analysis (Data Envelopment Analysis, DEA). This method is based on the repeated application of linear programming to a set of input and output indicators recorded by the current system of state statistics and characterizing the activities of sets of similar specialized enterprises in the regions for waste management (production facility, software).
 Methods and objects of research: to assess the СEP of the software functioning by the DEA method, the "time slice" and "time window" schemes were used. Based on the calculated СEP values, stable subgroups of software with low, medium and high СEP values were identified. For insufficiently effective software, target values of input indicators were calculated, the achievement of which allows them to maximize СEP values. At the same time, for these software, a recommendatory choice of a leader/landmark object was carried out, which can be considered as a carrier of best practices and successful experience in organizing work, which is relevant for the development of the RSOO.
 Main results of research: the formed classification of RSOO was compared with the normative indicators of waste processing given in the State Program of the Russian Federation "Environmental Protection" for 20122020.

Review on mechanical and micro-structure properties of dissimilar joining of IS 2062 mild steel

Most steel and aluminum alloys can be fused using the MIG and TIG welding methods, which provides higher mechanical properties and microstructure at optimized input settings. Several studies tested the effectiveness of MIG and TIG welding for connecting a wide range of alloys used in a variety of applications. MIG and TIG welding process parameters are critical aspects that determine weld quality. The goal of this review study is to synthesize the published literature on various MIG and TIG welding variants and to investigate the effect of various welding input process parameters on the microstructure, hardness, tensile strength, and impact strength of the weldment. The results showed that changing the value of any of the parameters had a considerable impact on the weld qualities. Welding current and welding speed are the two most essential welding input factors. Lowering the welding voltage and gas flow rate while increasing the welding speed and wire feed rate results in the highest ultimate tensile strength of the weldment. When compared to traditional weld methods, TIG produces better mechanical characteristics and lower residual stresses. To increase weld quality, there is a greater scope of research on the mechanism of residual stresses and their association with microstructure and mechanical properties.

Development and validation of a parsimonious prediction model for positive urine cultures in outpatient visits

Urine culture is often considered the gold standard for detecting the presence of bacteria in the urine. Since culture is expensive and often requires 24-48 hours, clinicians often rely on urine dipstick test, which is considerably cheaper than culture and provides instant results. Despite its ease of use, urine dipstick test may lack sensitivity and specificity. In this paper, we use a real-world dataset consisting of 17,572 outpatient encounters who underwent urine cultures, collected between 2015 and 2021 at a large multi-specialty hospital in Abu Dhabi, United Arab Emirates. We develop and evaluate a simple parsimonious prediction model for positive urine cultures based on a minimal input set of ten features selected from the patient’s presenting vital signs, history, and dipstick results. In a test set of 5,339 encounters, the parsimonious model achieves an area under the receiver operating characteristic curve (AUROC) of 0.828 (95% CI: 0.810-0.844) for predicting a bacterial count ≥ 105 CFU/ml, outperforming a model that uses dipstick features only that achieves an AUROC of 0.786 (95% CI: 0.769-0.806). Our proposed model can be easily deployed at point-of-care, highlighting its value in improving the efficiency of clinical workflows, especially in low-resource settings.

Estimation of reference evapotranspiration based on machine learning models and timeseries analysis: a case study in an arid climate

Water scarcity is a major challenge for irrigated agriculture, particularly in developing countries where access to meteorological data for calculating reference evapotranspiration (ETo) is limited. Thus, this study explores the potential of two machine learning models (random forest (RF) and long short-term memory (LSTM)) and autoregressive integrated moving average (ARIMA) to forecast ETo. The investigation was conducted for four weather stations in Egypt, from 1982 to 2020. The machine learning models were evaluated using four combinations of inputs: maximum and minimum temperature, relative humidity, and wind speed. The best results for both RF and LSTM models were achieved with the first set of inputs that included all four variables at both regional and local scales. For the regional scale, RF and LSTM models achieved R2 values of 0.85 and 0.86, respectively, with RMSE values of 0.69 and 0.68 mm/day. At the local scale, RF and LSTM models exhibited R2 values ranging from 0.92 to 0.95 and 0.93 to 0.95, respectively, while RMSE ranged between 0.38 and 0.46 mm/day and 0.37–0.43 mm/day, respectively. Additionally, ARIMA models were employed for tim series analysis of the same ETo data. ARIMA (2,1,4) and ARIMA (2,1,3) were found to be the most suitable models for the local-scale analysis while ARIMA (2,1,4) was identified as the optimal model for the regional-scale analysis. For the local-scale analysis, R2 values ranged from 0.86 to 0.91 and RMSE values ranged from 0.26 to 0.38. The regional scale analysis yielded an R2 value of 0.89 and an RMSE value of 0.58 mm/day. The developed models can be used in places where meteorological data for forecasting ETo are limited.

An evaluation of GPU filters for accelerating the 2D convex hull

The Convex Hull is one of the most relevant structures in computational geometry, with many applications such as in computer graphics, robotics, and data mining. Despite the advances in the new algorithms in this area, it is often needed to improve the performance to solve more significant problems quickly or in real-time processing. This work presents an experimental evaluation of GPU filters to reduce the cost of computing the 2D convex hull. The techniques first perform a preprocessing of the input set, filtering all points within an eight-vertex polygon to obtain a reduced set of candidate points. We use parallel computation and the use of the Manhattan distance as a metric to find the vertices of the polygon and perform the point filtering. For the filtering stage we study different approaches; from custom CUDA kernels to libraries such as Thrust and Cub. Four types of point distributions are tested: a normal distribution (favorable case), uniform (favorable case), circumference (the worst case), and a case where points are shifted randomly from the circumference (intermediate case). The experimental evaluation shows that the GPU filtering algorithm can be up to 17.5× faster than a sequential CPU implementation, and the whole convex hull computation can be up to 160× faster than the fastest implementation provided by the CGAL library for a uniform distribution and 23× for a normal distribution.

Early deficits in dentate circuit and behavioral pattern separation after concussive brain injury

Traumatic brain injury leads to cellular and circuit changes in the dentate gyrus, a gateway to hippocampal information processing. Intrinsic granule cell firing properties and strong feedback inhibition in the dentate are proposed as critical to its ability to generate unique representation of similar inputs by a process known as pattern separation. Here we evaluate the impact of brain injury on cellular decorrelation of temporally patterned inputs in slices and behavioral discrimination of spatial locations in vivo one week after concussive lateral fluid percussion injury (FPI) in mice. Despite posttraumatic increases in perforant path evoked excitatory drive to granule cells and enhanced ΔFosB labeling, indicating sustained increase in excitability, the reliability of granule cell spiking was not compromised after FPI. Although granule cells continued to effectively decorrelate output spike trains recorded in response to similar temporally patterned input sets after FPI, their ability to decorrelate highly similar input patterns was reduced. In parallel, encoding of similar spatial locations in a novel object location task that involves the dentate inhibitory circuits was impaired one week after FPI. Injury induced changes in pattern separation were accompanied by loss of somatostatin expressing inhibitory neurons in the hilus. Together, these data suggest that the early posttraumatic changes in the dentate circuit undermine dentate circuit decorrelation of temporal input patterns as well as behavioral discrimination of similar spatial locations, both of which could contribute to deficits in episodic memory.

An information-theoretically secure quantum multiparty private set intersection

In the current age of increasing data, Private Set Intersection (PSI) is one of the most prominent and important cryptographic building block. It enables two organizations to determine the intersection of their input sets in such a way that no other information is leaked. In real life, there are situations where more than two parties desire to compute the intersection of their private sets. Herein, we propose a generalized version of PSI, called Multi-party private set intersection (MPSI), where number of involved parties is not restricted to two. Nearly all of the existing MPSI protocols are relying on the hardness problems like integer factorization and discrete logarithm. However, these hard problems are unable to withstand the threat possessed by large scale quantum computers. Thus, there is a need of MPSI protocols which are secure from the quantum attacks and also provide long-term security. Quantum cryptography (QC) is one such tool that addresses this issue. In this work, we develop the first MPSI based on QC. The scheme put-forwarded by us attains long-term security and remains secure against quantum attacks.

Adaptive HVAC System Based on Fuzzy Controller Approach

Heating, ventilation, and air conditioning (HVAC) system performance research has received much attention in recent years. Many researchers suggest a set of appropriate fuzzy inputs that can be used to design fuzzy rules-based smart thermostats or controllers that can respond to demand-controlled ventilation, which in turn optimizes HVAC energy usage and provides satisfactory indoor temperatures. Previous research has focused on limited input parameters, such as indoor occupancy status, ambient temperature, and humidity constraints, which cannot efficiently and precisely manage thermal comfort. Hence, this study proposes a novel fuzzy controller with additional input parameters to keep indoor thermal comfort consistent with the corresponding number of occupants. The process employs an automatic fuzzy rule generation method to simplify the task of generating rules in the fuzzy inference system (FIS) using Mamdani FIS. A design-builder is used for modeling the HVAC systems. Local weather data were used to conduct simulations via EnergyPlus. The thermal comfort analysis using the Fanger model for three different scenarios shows that the proposed FIS controller can successfully respond to the indoor comfort variation in all possible scenarios and ensure a satisfactory comfort level. The proposed method demonstrates up to 50% energy savings if occupants do not worry about comfort.

INFORMATION TECHNOLOGY OF FORMING THE EDUCATIONAL NETWORK OF THE TERRITORIAL COMMUNITY

Context. Local government organizations have significant decision-making power in the field of education. This requires the development of understandable tools that help form a network of educational institutions that provide high-quality educational services. The object of the study was the process of forming an educational network (ENW) of the community in the implementation of territorial development projects as part of the reform of local self-government.
 Objective. The goal of the work is to increase objectivity of decisions made in the formation of the ENW territorial community, summarizing demographic, infrastructural, personnel, economic factors when choosing the location of ENW facilities.
 Method. The study of the classical placement problem and its further adaptation to real problems arising from the implementation of the education reform made it possible to represent ENW territorial communities as a set of independent complete bipartite graphs. In this case, to solve the problem of choosing the location of an educational institution on the network, an information technology (IT) was developed to form an ENW territorial community. Based on the adapted p-median model and methods of geospatial analysis, generalizing the requirements of the current legislation, a set of input and output parameters of IT and a set of its operations are formed. The representation of the IT structure in the form of an IDEF0-model clearly explains how a combination of various factors is processed and generalized when making decisions while creating ENW and looking for ways to improve it.
 Results. The developed IT was investigated in solving the problem of forming a network of lyceums in the Kharkiv region using geospatial information, open statistical data and data on lyceums. The proposed accommodation options make it possible to achieve a general level of accessibility of specialized secondary education of almost 94%. IT has additional tools for solving the problems of transporting students to the place of study and home.
 Conclusions. The experiments carried out confirmed the operability of the proposed IT. The generalization of the results obtained makes it possible to recommend it for practical use in solving the problems of analyzing the current state of ENW, finding ways to improve it and possible directions for development, as well as evaluating solutions related to the spatial planning of ENW.

Fluorescent Molecular Photoswitches for the Generation of All‐Optical Encryption Keys

AbstractHerein, we report a tri‐component photochromic molecular cocktail that can be used to encrypt and decrypt information. The time‐dependent fluorescent response of this cocktail is highly non‐linear with respect to the set of inputs used (concentrations of the three photochromic components, excitation‐ and emission wavelengths), a property required for the generation of so‐called encryption keys. The all‐optical system can generate more than 80 million unique fluorescence responses by applying different input combinations and is operated using a conventional fluorimeter.

Sequential Deep Operator Networks (S-DeepONet) for predicting full-field solutions under time-dependent loads

Deep Operator Network (DeepONet), a recently introduced deep learning operator network, approximates linear and nonlinear solution operators by taking parametric functions (infinite-dimensional objects) as inputs and mapping them to solution functions in contrast to classical neural networks that need re-training for every new set of parametric inputs. In this work, we have extended the classical formulation of DeepONets by introducing sequential learning models like the gated recurrent unit (GRU) and long short-term memory (LSTM) in the branch network to allow for accurate predictions of the solution contour plots under parametric and time-dependent loading histories, known as Sequential DeepONets (S-DeepONets). Two example problems, one on transient heat transfer and the other on path-dependent plastic loading, were shown to demonstrate the capabilities of the new architectures compared to the benchmark DeepONet model with a feed-forward neural network (FNN) in the branch. Despite being more computationally expensive, the GRU- and LSTM-DeepONets lowered the prediction error by half (0.06% vs. 0.12%) compared to FNN-DeepONet in the heat transfer problem, and by 2.5 times (0.85% vs. 3%) in the plasticity problem. In all cases, the proposed DeepONets achieved a prediction R2 value of above 0.995, indicating superior accuracy. Results show that once trained, the proposed S-DeepONets can accurately predict the final full-field solution over the entire domain and are at least two orders of magnitude faster than direct finite element simulations, rendering it an accurate and robust surrogate model for rapid preliminary evaluations.

SYNTHESIS OF MINIMAL SCHEMES OF SYSTEMS CONTROL OF HYDRAULIC AND PNEUMATIC DRIVES

The existing schemes of command apparatus are presented. Their analysis is carried out and the shortcomings are indicated. The scheme of afundamentally new command apparatus is given, which can significantly reduce the number of its elements. The synthesis of the pneumatic controlsystem of the foundry machine was carried out on the basis of the design approach proposed by M. Cherkashenko, which as a result led to a reductionin the devices by 3.5 times. A graph of operations of the pneumatic control system of the molding machine is constructed. A matrix ofcorrespondences is obtained, which determines the correspondence between the signals that cause transitions and the complete input sets acting in thetransitions. Analysis of the matrix of correspondences made it possible to identify and eliminate contradictory transitions. On the basis of theelongations, a system of equations describing the scheme of the control system is synthesized. Further, minimization is performed due to factorizationand decomposition of equations, and obtaining equations in their final form. A diagram is presented pneumatic control system of the molding machine,consisting of a command apparatus containing three cells, five cylinders, two vibrators, six limit switches, a time relay and other devices. The circuitsimultaneously uses the functional and logical capabilities of limit switches, the possibility of implementing the functions of three variables bydistributors. Thus, the use of the proposed command apparatus, in contrast to the existing schemes of command apparatus, can significantly reduce thenumber of distributors in the synthesis of the command apparatus itself and significantly reduce the number of elements when using the chosenapproach to the design of circuits of pneumohydraulic control systems.