Input Values Research Articles

The effect of surface flow conditions on WEPP interrill erodibilities

The Water Erosion Prediction Project erosion model (WEPP) was developed as an event-based more process-based replacement for Universal Soil Loss Equation (USLE) based models in predicting soil erosion as a guide to conserving soil in the USA. However, WEPP has not been shown to predict event soil losses for bare fallow USLE plots better than USLE-based models when stipulated input values have been used. To some extent, this problem can be attributed to the availability of more refined and site-specific input parameters for the USLE-based models. This is true with respect to WEPP’s capacity to model raindrop-driven erosion on USLE plots under natural rainfall. Stipulated values for WEPP interrill erodibilities in the USA were determined only for ridge side slopes but USLE-based models focus on the prediction of long-term average annual soil loss caused by both sheet and rill erosion on planar surfaces. The interrill erodibilities determined for ridge side slopes did not correlate well with those on adjacent flat surfaces sloping along the land slope under the same rainfall conditions. Surface flow conditions, particularly flow depth, differ between the short high gradient side slopes associated with ridges, and the lower gradient slopes in interrill and sheet erosion areas on USLE plots. Failure of WEPP to account for the influence of different flow conditions between ridged and flat areas on raindrop-driven erosion affects the veracity of WEPP to predict average annual soil loss on USLE plots under natural rainfall and the use of WEPP to act as a replacement for USLE based models as an aid to make land management decisions to conserver soil in the USA.

Genetic Neuro-Fuzzy Approach towards Routing in Industrial IoT

The Internet of Things has rapidly grown in the past years as emerging technology. Moreover, 5G networks start to offer communication infrastructure for applications of the Industrial Internet of Things (IIoT). However, due to energy limitations of IIoT devices and heterogeneity of 5G networks, managing IIoT networks is a challenging task. One of the most critical issues in IIoT that requires consideration is traffic routing that has a significant impact on energy consumption, and thus, lifetime of the network. Artificial Intelligence (AI) has been widely employed to solve complex scientific and practical problems. Such AI techniques as neural networks, fuzzy systems, genetic algorithms are commonly employed in wireless networks to promote their optimization, prediction, and management. This study suggests using an Adaptive Neuro-fuzzy Inference System (ANFIS) in 5G networks of IIoT for improving the routing process. A flow-chat of routing protocol was suggested. For input and output values of the ANFIS linguistic variables, terms and membership functions were defined. A rules base was developed. To improve the rule base of the ANFIS, a genetic algorithm was proposed. The operation of ANFIS was simulated in Matlab software.

A CIGALE module tailored (not only) for low-luminosity active galactic nuclei

The spectral energy distribution (SED) of low-luminosity active galactic nuclei (LLAGN) presents unique challenges as the emission from these objects is comparable to the radiation from their host galaxy and the accretion physics involved is particularly complex. This study introduces a novel CIGALE module specifically designed to address these challenges. The module combines the empirical $L_ X $--$L_ m $ relationship with physically motivated accretion models, such as advection-dominated accretion flows (ADAFs) and truncated accretion disks, providing a more accurate depiction of LLAGN central engine emission. A mock analysis of the module revealed good recovery of true parameters, with only a slight bias toward higher input values, further validating its reliability. We tested the module on a sample of 50 X-ray-detected local galaxies, including low-ionization nuclear emission-line regions (LINERs) and Seyferts, and demonstrated its capacity to accurately estimate bolometric luminosities, even in the presence of significant galaxy contamination. Notably, the previous X-ray module failed to provide AGN solutions for this sample, stressing the need for a novel approach. Comparisons with mid-luminosity AGN datasets confirm the module’s robustness and applicability up to $L_ X $ erg/s. We also expanded the X-ray-to-bolometric correction formula, making it applicable to AGN spanning ten orders of magnitude in luminosity, and revealing lower $k_ X $ values for LLAGN than typically assumed. Additionally, our analysis of the $ ox $ index, which represents the slope between UV and X-ray emissions, uncovered trends that differ from those observed in high-luminosity AGN. Unlike quasars, where $ ox $ correlates with $ Edd $, LLAGN exhibit nearly constant or weakly correlated $ ox $ values, suggesting a shift in accretion physics and photon production mechanisms in low-luminosity regimes. These results underscore the importance of a multiwavelength approach in AGN studies and reveal distinct behaviors in LLAGN compared to quasars. Our findings significantly advance our understanding of LLAGN and offer a comprehensive framework for future research to complete the AGN population census.

VLSI implementation of a modified min-max median filter using an area and power competent tritonic sorter for image denoising

The prominence of image processing in today’s cutting-edge technology is undeniable. Integrating software with hardware leverages both strengths, resulting in a real-time processing system that is efficient and streamlined. Raw images are usually affected by noise, which hinders the acquisition of good-quality and detailed images; hence, denoising becomes necessary. This paper proposes a modified min-max median (MMM) filter to remove impulse noise and a Tritonic sorter to localize corrupted pixels. The proposed denoising method focuses on localizing noisy pixels, unlike traditional denoising approaches, which focus only on noise detection and filtering. A min-max sheet provides the location of the corrupted pixels, and filtering is performed on them. The Tritonic Sorter, consisting of a max locator and a min locator, compares three input values and finds the minimum, maximum and median values among them. Compared to other state-of-the-art methods, the proposed method minimizes the number of comparators needed to carry out the sorting process. The proposed method was synthesized in the ZedBoard Zynq kit using the Vivado tool. The results show that the area improved by 27%, and the power improved by 16.23% compared with those of the existing method.

GPU Acceleration of the Boys Function Evaluation in Computational Quantum Chemistry

ABSTRACTThe Boys function, a mathematical integral function, plays a pivotal role and is frequently evaluated in ab initio molecular orbital computations. The main contribution of this paper is to accelerate the bulk evaluation of the Boys function through the effective utilization of GPUs. The proposed GPU implementation addresses GPU‐specific programming issues such as warp divergence and coalesced/stride access to global memory, and we employ the optimal numerical evaluation method from four methods based on input values to ensure efficient computation with sufficient accuracy. Moreover, to consider actual computation of molecular integrals, we have implemented and evaluated the proposed method in two scenarios: single evaluation, which computes a single value of the Boys function for a single input, and incremental evaluation, which computes multiple values of the Boys function incrementally. The execution time of the proposed GPU implementation was evaluated for both scenarios using an NVIDIA A100 Tensor Core GPU. As a result, the GPU‐accelerated bulk evaluation has achieved a throughput of computing the values of the Boys function times per second for the single evaluation and times per second for the incremental evaluation, respectively. Our parallelized CPU and GPU implementation is available at https://github.com/sstsuji/Boys‐function‐GPU‐library.

Climatic Specific Energy Rating Analysis of Outdoor PV Field Data

ABSTRACTThe IEC 61853 standard series define the Climatic Specific Energy Rating (CSER), which can be used within the photovoltaic (PV) community as a reliable and accurate tool to differentiate between PV modules performance for different reference climates. The CSER is assumed to represent the expected PV device operation under real‐world conditions. The rating model combines a set of measurements on modules with a numerical approach. So far, research has mainly focused on the implementation of algorithms to determine the input parameters and to calculate the CSER values based on indoor measurement performance data. There is lack of information and technical specification how to obtain these parameters from outdoor PV field data in natural sunlight. In the present study, we first estimated the CSER parameter (according to IEC 61853‐3) for a PV module tested indoor using input values measured or defined according to IEC 61853‐1&2 with associated measurement uncertainties. Then, we analysed long‐term outdoor field data of the same device for a period of 36 consecutive months in order to understand how the length and seasonality of the data acquisition period impacts the reliability of irradiance and temperature power matrix values, particularly in relation to CSER calculations. Uncertainties estimations for the outdoor procedure have been also defined for rating accuracy. The PV outdoor performance has been validated against its indoor counterpart. The results concluded that a period of 9 or 12 months provides a good compromise between accuracy, time and cost‐effectiveness. The time period may be reduced to 6 months if the meteorological conditions are sufficiently diverse in this time frame. The study might raise worthwhile discussion regarding the revision of the IEC 61853‐1 for determining the (G‐T) matrix of PV modules from outdoor measurements under natural sunlight. It could enhance important insights to the PV community since the CSER parameter may become a compulsory requirement for each PV module produced, imported or sold in Europe if the potential European Union (EU) Ecodesign Directive and the Energy Labelling Regulation will be implemented.

Statistical Analysis of Levelized Round Trip Cost of Grid Scale Electrical Energy Storage in Batteries with Different Chemistries

Electrical energy storage is a crucial component of the clean energy transition for integrating high share of renewable electricity generators into the supply mix. In this study, the round-trip costs of grid scale electrochemical energy storage from 2 up to 24 hours for peak power ratings of 1 MW and 10 MW in lithium-ion LFP, lithium-ion NMC, Pb-acid and vanadium redox flow batteries are compared using their currently projected techno-economic characteristics for year 2030. A statistical approach is used for estimating mean costs and quantifying the uncertainties in these estimates, since many of the techno-commercial features are still evolving and are uncertain. Using Monte Carlo simulations to derive the input parameters, the levelized round trip cost of energy storage is found to vary from Rs 10.73±0.77/kWh(e) to Rs 15.96±1.12/kWh(e) for Li-LFP batteries, Rs 14±1/kWh(e) to Rs 20.3±1.46/kWh(e) for Li-NMC batteries, Rs 19.1±1.39/kWh(e) to Rs 58.3±4.28/kWh(e) for Pb-acid batteries and Rs 18.7±1.37/kWh(e) to Rs 44.2±3.19/kWh(e) for Vanadium redox flow batteries, for the range of input values considered in this study. Through sensitivity analysis and calculation of correlation coefficients, the battery installed capital cost and useful lifetime are found to be the two most influential parameters affecting life cycle electrical energy storage cost.

Long-short-term memory model for quantitative analysis of corrosion products in concrete

A quantitative analysis model is proposed to describe the content evolution of corrosion products in concrete structures subjected to sulfate or chloride attack. The model is inspired by the idea of the Long-Short-Term Memory (LSTM) algorithm in the machine learning field. For establishing the relationship between engineering data and theoretical computations, the training samples of the model are generated by solving the rate equations of the sulfate corrosion reactions in concrete with wide-range initial conditions and validated by the existing experimental data. The Pearson correlation coefficient is employed to determine the model features. The model performance is comprehensively evaluated and discussed. The results show that the proposed model has enough accuracy and feasibility. It could effectively predict the contents of the corrosion products during the sulfate attack by several input values rather than the initial contents of all chemical constituents. The proposed model builds a bridge between experimental methods and theoretical predictions, adequately inheriting their advantages.

BSO algorithm and artificial neural network aided emission optimisation for gas turbine engine

Abstract Aircraft play a major role in meeting the fast and efficient transportation needs of modern society, thanks to their advanced features. However, gas turbine engines used in aircraft have many negative effects on human health. One of the negative effects is the exhaust gases released by these engines to nature. In this study, it is discussed to present alternative models based on heuristic methods to reduce the emission values of the synthetic fuel mixture used in the combustion chamber of gas turbine engines. For this purpose, a model based on artificial neural networks (ANN) based on the back-tracking search optimisation (BSO) algorithm is proposed by using experimentally obtained emission values found in the literature. In the proposed model, the parameters of the optimum ANN structure are first determined by the BSO algorithm. Then, by using the optimum ANN structure, the most appropriate input values were found with the BSO algorithm, and the emission values were reduced. The simulation results have shown that the proposed method will be a fast and safe alternative method for reducing emission values.

Risk assessment study on the impact of sitting posture on lumbar vibration injury based on finite element method and neural network

Different driving postures may alter the impact of vibrations on the lumbar spine. This study explores the influence of posture on the lumbar spine by analyzing the risk of intervertebral disc vibration injury. Initially, Python language was utilized to develop code to obtain 48 different postural three-dimensional finite element models of the human-chair system. Subsequently, these models underwent harmonic response analyses at frequencies of 5, 7, and 9 Hz, with the maximum Von Mises stress on the L4–L5 intervertebral disc used to calculate the risk factor. Finally, a neural network model was trained using the frequency and posture angles as input values and the risk factor as the output values, enabling prediction of intervertebral disc risk factors in more precise sitting postures. The findings reveal that the posture of the upper body plays a primary role in influencing the lumbar spine, with the thigh posture having a greater impact when the upper body is closer to a vertical position, while the influence of the lower leg posture is minimal. Across all three frequencies, the optimal angle for the upper body is 109°, 109°, 115°, and for the thighs, it is 90°, 90°, 100°. A higher risk factor is observed when the upper body is angled between 90° and 100°. Conversely, an upper body angle between 110° and 120° is deemed acceptable. Vibration at 9 Hz had the greatest impact on the risk of lumbar injury when the upper body is in or close to a vertical sitting position.

Comparative Study of Seismic Hazard Analysis Using Classical PSHA and PSHA Method in Java Island, Indonesia

In general, seismic hazard analysis is conducted using a probabilistic approach (PSHA), whilst seismic risk analysis is computed using a stochastic approach. To figur out more differences between the two methods, a comparative study of those two approaches needs to be conducted. The study was conducted in Java Island which is the most populated island in Indonesia, and prone to earthquakes, particularly in the southern part due to the subduction of the Australian Plate and many active shallow faults along the island. To find out whether the hazards occurred in the risk analysis were closer to the results of Classical PSHA, it is necessary to examine the comparison of the two methods. The difference between the Event-Based and Classical methods is the use of a synthetic catalog that depends on the input value of SES (Stochastic Event Set). The research began with hazard computation with both methods, where the Event-Based PSHA method was given varying SES values. Determining the SES value in the Event-Based method is an important stage to conduct as a significant input parameter in a risk analysis, particularly the loss analysis. The comparison results conclude that the Event-Based PSHA with SES= 200,000 produced a wellconfirmed hazard map compared to the Classical PSHA result with the smallest standard deviation and variation, i.e. 0.0172 and 0.0003 respectively.

Predicting insurance charges using linear regression models

Abstract. Linear regression method can be performed to predict the outcome from one or many input values. Its versatility allows it to be applied on many datasets that contain correlated values. However, researches on the application of linear regression on medical insurance costs, a highly important part of peoples life, are few. This paper studies an insurance dataset from Kaggle by applying linear regression on it. The author validates the dataset at first and explores the correlation between each individual factor and their corresponding charges to better show how insurance costs differ from person to person with different background. Many figures are also included to help visualize the correlation between factors. In the end, the author creates a multilinear regression model to predict the insurance charges. The R-Squared score of the model and a result table including regression coeffects are also provided to show the accuracy and details of the model.

SAVE: Encoding spatial interactions for vision transformers

Transformers have achieved impressive performance in visual tasks. Position encoding, which equips vectors (elements of input tokens, queries, keys, or values) with sequence specificity, effectively alleviates the lack of permutation relation in transformers. In this work, we first clarify that both position encoding and additional position-specific operations will introduce positional information when participating in self-attention. On this basis, most existing position encoding methods are equivalent to special affine transformations. However, this encoding method lacks the correlation of vector content interaction. We further propose Spatial Aggregation Vector Encoding (SAVE) that employs transition matrices to recombine vectors. We design two simple yet effective modes to merge other vectors, with each one serving as an anchor. The aggregated vectors control spatial contextual connections by establishing two-dimensional relationships. Our SAVE can be plug-and-play in vision transformers, even with other position encoding methods. Comparative results on three image classification datasets show that the proposed SAVE performs comparably to current position encoding methods. Experiments on detection tasks show that the SAVE improves the downstream performance of transformer-based methods. Code is available at https://github.com/maxiao0234/SAVE.

The Application of Calculus in Physics

The derivative is a fundamental tool that quantifies the sensitivity of change for a function’s output with respect to its input. The derivative of a function for a single variable at a chosen input value is the slope of the tangent line to the graph of the function at that point. The application of calculus, including derivative and integral, is widely used in solving problems in the subject of Physics Kinematics. This paper uses derivative to calculate problems involving position, velocity, and acceleration, as well as using integral to solve problems about continuous forces and forces in spring. In calculus, the main idea of derivative is the segmentation process which will be carried on indefinitely and the local range is infinitely smaller. The main idea of integral is the sum of the results of all the infinite differential elements. These ideas can be used to simplify the kinematics question and accurate the final answers.

Characterizing southeast Greenland fjord surface ice and freshwater flux to support biological applications

Abstract. Southeast Greenland (SEG) is characterized by complex morphology and environmental processes that create dynamic habitats for top marine predators. Active glaciers producing solid-ice discharge, freshwater flux, offshore sea ice transport, and seasonal landfast-ice formation all contribute to a variable, transient environment within SEG fjord systems. Here, we investigate a selection of physical processes in SEG to provide a regional characterization that reveals physical system processes and supports biological research. SEG fjords exhibit high fjord-to-fjord variability regarding bathymetry, size, shape, and glacial setting, influencing some processes more than others. For example, during fall, the timing of offshore sea ice formation near SEG fjords progresses temporally when moving southward across latitudes, while the timing of offshore sea ice disappearance is less dependent on latitude. The rates of annual freshwater flux into fjords, however, are highly variable across SEG, with annual average input values ranging from ∼ 1 × 108 to ∼ 1.25 × 1010 m3 (∼ 0.1–12.5 Gt) for individual fjords. Similarly, the rates of solid-ice discharge in SEG fjords vary widely – partly due to the irregular distribution of active glaciers across the study area (60–70° N). Landfast sea ice, assessed for eight focus fjords, is seasonal and has a spatial distribution highly dependent on individual fjord topography. Conversely, glacial ice is deposited into fjord systems year-round, with the spatial distribution of glacier-derived ice depending on the location of glacier termini. As climate change continues to affect SEG, the evolution of these metrics will vary individually in their response, and next steps should include moving from characterization to system projection. Due to the projected regional ice sheet persistence that will continue to feed glacial ice into fjords, it is possible that SEG could remain a long-term refugium for polar bears and other ice-dependent species on a centennial to millennial scale, demonstrating a need for continued research into the SEG physical environment.

Inverse uncertainty quantification for stochastic systems by resampling. Applications to modeling of alcohol consumption and infection by HIV

A random differential equation, or stochastic differential equation with parametric uncertainty, is a classical differential equation whose input values (coefficients, initial conditions, etc.) are random variables. Given data, the probability distributions of the input random parameters must be appropriately inferred, before proceeding to simulate the model’s output. This task is called inverse uncertainty quantification. In this paper, the goal is to study the applicability of the Bayesian bootstrap to draw inferences on the posterior distributions of the parameters, by resampling the residuals of the deterministic least-squares optimization with Dirichlet weights. The method is based on repeated deterministic calibrations. Thus, to alleviate the curse of dimensionality, the technique may be combined with the principle of maximum entropy for densities, when there are some parameters that are not optimized deterministically. For illustration of the methodology, two case studies on important health topics are conducted, with stochastic fitting to data. The first one, on past alcohol consumption in Spain, taking social contagion into account. The second one, on HIV evolution considering CD4+ T cells and viral load, with a patient in clinical follow-up. All these applied models are built from a compartmental viewpoint, with a randomized basic reproduction number that controls the long-term behavior of the system.

Modelling the cost-effectiveness of a chloramine gel in treating infected, non-healing diabetic foot ulcers.

Organic chloramines have been developed as a topical wound bed preparation gel. This study aimed to estimate whether the addition of chloramine gel (Chlorasolv, RLS Global AB, Sweden) to standard care compared with standard care alone would afford a cost‑effective technology to the UK's health services for treating infected, non-healing diabetic foot ulcers (DFUs). A Markov model was developed to simulate the management of infected, non-healing DFUs. The model utilised data from a randomised controlled trial and was used to estimate the cost-effectiveness of chloramine plus standard care compared with standard care alone over a period of 24 weeks, expressed as the incremental cost per quality-adjusted life year (QALY) gained at 2021/22 prices. Using adjunctive chloramine to treat infected, non-healing DFUs was found to shorten the time to healing by 36% (from a mean of 17.1 weeks per ulcer to a mean of 11.0 weeks per ulcer). This translated into a 6% improvement in the probability of being healed by 24 weeks and a corresponding 3% improvement in health-related quality of life (HRQoL). Also, use of adjunctive chloramine was found to reduce the total cost of ulcer management by 13%. Sensitivity analysis found that adjunctive chloramine remained a cost-effective treatment, even when the value of the model inputs was varied by ±20%. Within the limitations of the study, treatment with adjunctive chloramine instead of standard care alone could potentially afford the UK's health services a cost-effective debridement strategy for infected, non-healing DFUs, due to its ability to accelerate the time to healing.

Developing a robust indicator to evaluate circular economy through reuse strategy: A case study of using water treatment sludge as a coagulant for dewatering of iron ore tailings slurry

Due to the lack of tools, which are simple enough to assess the reuse strategy in the concept of circular economy (CE), in the present paper, reuse efficiency index (REI) was introduced as a new indicator, inspired by previously developed indexes, namely circular economy index (CEI) and value-based resource efficiency (VRE). The REI, as a measure of economic efficiency of reuse strategy, is the ratio of the net value obtained from the reuse of end of life (EOL) product instead of using a new product over the value of materials and energy inputs required to reproduce the EOL product. To calculate REI for any specific EOL product, information regarding mass, physicochemical properties and operational data on the consumption of energy, which are mainly available in the financial archives of companies, is crucial and the market price of EOL product must also be predicted. In another part of the study, the reuse of ferric chloride-based water treatment sludge (FCS) as a coagulant, instead of using high cost polymeric coagulant, for iron ore tailings slurry dewatering was evaluated through REI. The results showed that depending on the market prices of energy and materials as well as the iron (FeCl3) content in the FCS, the REI could vary between 0.51 and 1.04, corresponding to 51–104% added value. Overall, REI is based on accessible information and its simplicity and comprehensibility makes it applicable for managers and policymakers to invest and create technologies for reaching closed-loop cycles in the concept of CE through reuse action.

Penerapan Jaringan Syaraf Tiruan untuk Pengenalan Pola Huruf Menggunakan Metode Bidirectional Associative Memory (BAM)

The process in artificial intelligence is similar to the process that occurs in the human brain. The pattern recognition process is to determine whether the input and output values ​​are the same so that the system can detect the pattern. A reciprocal relationship between the input and output levels is made possible by the two interconnected layers of the Bidirectional Associative Memory (BAM) technique. As a result, this technique can serve as an associative memory, meaning that some of the data stored in it can be called upon.. The load signal is transmitted from the input layer X to the output layer Y in this procedure. The weight value will be modified by computing the outcomes between the ranks [1,0]. In this work, the active sigmoid function is employed. Three input characters—the letters S, O, and X—are used in this study's 5x5 order matrix. A pattern [27 4] that matches the target is produced by the target letter S [1,-1], a pattern [27 -3] that does not match the target is produced by the target letter O [1.1], and a pattern [-37 21] that matches the target is produced by the target letter X [-1,1]. Consequently, not all patterns are able to meet the established objectives.

On-line evaluation of moisture transfer properties for a feed-forward control of a wood drying kiln with a mechanistic model

Kiln operators are forced to rely on their own experience and expertise when adapting standard drying schedules to tackle changing situations or to better suit their needs. The online tuning of a mechanistic model and its use as forward control is therefore proposed to address the current lack of tools. An inverse analysis of the drying kinetics of wooden boards dried in an industrial kiln is carried out to determine the moisture transfer properties of the boards. The values obtained by the inverse method are found to be relatively close to those obtained by direct measurement. The mechanistic model is then used with these input values to simulate the drying of a stack of boards to find the best drying strategy to meet the objectives. This work focuses on achieving a compromise between quality and time. Several difficulties were encountered in developing this approach for a semi-industrial dryer, and some recommendations are made for developing this innovative strategy further.