Multiple Complex Research Articles

Robust state-of-charge estimation for LiFePO4 batteries under wide varying temperature environments

During the driving process of electric vehicles, the ambient temperature exhibits diverse variations with regional characteristics. To achieve robust state of charge (SOC) estimation for lithium-ion batteries under various varying temperature environments, this paper proposes an enhanced model-based closed-loop SOC estimation approach. First, beginning with a mechanistic analysis of batteries, the traditional second-order equivalent circuit model is enhanced by incorporating critical solid-phase diffusion effects during battery operation. Furthermore, utilizing data collected from multiple constant temperature environments, the complete enhanced battery model that accounts for the influence of current rates across a wide temperature range is constructed. Subsequently, under environments of different varying temperature settings, we design a series of complex operation experiments to verify the accuracy and generalizability of the established battery model. Meanwhile, a high-performance adaptive diagonalization of matrix cubature Kalman filter is introduced to address the challenge of fluctuating sampling noises in battery operation. Finally, the robustness and generalization of the proposed SOC estimation method are verified in multiple complex operating experiments under varying temperatures with non-Gaussian noise interferences and with non-full charging schemes. Remarkably, the proposed approach consistently delivers high-precision SOC estimation results across all scenarios, maintaining root mean square error and mean absolute error below 1.5%.

Air Traffic Flow Management Delay Prediction Based on Feature Extraction and an Optimization Algorithm

Air Traffic Flow Management (ATFM) delay can quantitatively reflect the congestion caused by the imbalance between capacity and demand in an airspace network. Furthermore, it is an important parameter for the ex-post analysis of airspace congestion and the effectiveness of ATFM strategy implementation. If ATFM delays can be predicted in advance, the predictability and effectiveness of ATFM strategies can be improved. In this paper, a short-term ATFM delay regression prediction method is proposed for the characteristics of the multiple sources, high dimension, and complexity of ATFM delay prediction data. The method firstly constructs an ATFM delay prediction network model, specifies the prediction object, and proposes an ATFM delay prediction index system by integrating common flow control information. Secondly, an ATFM delay prediction method based on feature extraction modules (including CNN, TCN, and attention modules), a heuristic optimization algorithm (sparrow search algorithm (SSA)), and a prediction model (LSTM) are proposed. The method constructs a CNN-LSTM-ATT model based on SSA optimization and a TCN-LSTM-ATT model based on SSA optimization. Finally, four busy airports and their major waypoints in East China are selected as the ATFM delay prediction network nodes for example validation. The experimental results show that the MAEs of the two models proposed in this paper for ATFM delay regression prediction are 4.25 min and 4.38 min, respectively. Compared with the CNN-LSTM model, the errors are reduced by 2.71 min and 2.59 min, respectively. Compared with the TCN-LSTM model, the times are 3.68 min and 3.55 min, respectively. In this paper, two improved LSTM models are constructed to improve the prediction accuracy of ATFM delay duration so as to provide support for the establishment of an ATFM delay early warning mechanism, further improve ATFM delay management, and enhance resource allocation efficiency.

On the effective version of Serre's open image theorem

AbstractLet be an elliptic curve without complex multiplication. By Serre's open image theorem, the mod Galois representation of is surjective for each prime number that is sufficiently large. Under the generalized Riemann hypothesis, we give an explicit upper bound on the largest prime , linear in the logarithm of the conductor of , such that is nonsurjective.

Strategies for Constructing Macrocyclic Arene-Based Color-Tunable Supramolecular Luminescent Materials.

Over the past decades, supramolecular luminescent materials (SLMs) have attracted considerable attention due to their dynamic noncovalent interactions, versatile functions, and intriguing applications in many research fields. From construction to application, great efforts and progress have been made in color-tunable SLMs in recent years. In order to realize multicolor luminescence, various design strategies have been proposed. Macrocyclic chemistry, one of the brightest jewels in the field of supramolecular chemistry, has played a crucial role in the construction of stimuli-responsive and emission-tunable SLMs. Moreover, the flexible and tunable conformation and multiple noncovalent complexation sites of the macrocyclic arenes (MAs) afford a new opportunity to create such dynamic smart luminescent materials. Inspired by our reported work on the color-tunable supramolecular crystalline assemblies modulated by the conformation of naphth[4]arene, this Concept provides a summary of the latest developments in the construction of color-tunable MA-based SLMs, accompanied by the various construction strategies. The aim is to provide researchers with a new perspective to construct color-tunable SLMs with fascinating functions.

CRISPR-COPIES: an in silico platform for discovery of neutral integration sites for CRISPR/Cas-facilitated gene integration.

The CRISPR/Cas system has emerged as a powerful tool for genome editing in metabolic engineering and human gene therapy. However, locating the optimal site on the chromosome to integrate heterologous genes using the CRISPR/Cas system remains an open question. Selecting a suitable site for gene integration involves considering multiple complex criteria, including factors related to CRISPR/Cas-mediated integration, genetic stability, and gene expression. Consequently, identifying such sites on specific or different chromosomal locations typically requires extensive characterization efforts. To address these challenges, we have developed CRISPR-COPIES, a COmputational Pipeline for the Identification of CRISPR/Cas-facilitated intEgration Sites. This tool leverages ScaNN, a state-of-the-art model on the embedding-based nearest neighbor search for fast and accurate off-target search, and can identify genome-wide intergenic sites for most bacterial and fungal genomes within minutes. As a proof of concept, we utilized CRISPR-COPIES to characterize neutral integration sites in three diverse species: Saccharomyces cerevisiae, Cupriavidus necator, and HEK293T cells. In addition, we developed a user-friendly web interface for CRISPR-COPIES (https://biofoundry.web.illinois.edu/copies/). We anticipate that CRISPR-COPIES will serve as a valuable tool for targeted DNA integration and aid in the characterization of synthetic biology toolkits, enable rapid strain construction to produce valuable biochemicals, and support human gene and cell therapy applications.

The association between sedentary behavior and obstructive sleep apnea: a cross-sectional study from the NHANES (2007–2008 to 2015–2020)

BackgroundSedentary behavior (SB) may contribute to obesity and lower extremity fluid retention, which may favor the development of obstructive sleep apnea (OSA). However, linking sedentary behavior to OSA is unclear. The purpose of this study was to determine if there is an association between SB and OSA.MethodsThree typical questions in the NHANES questionnaire(①The frequency of feeling excessively sleepy per month. ②The frequency of gasping, snorting or stopping breathing per week. ③The frequency of snoring per week.) have been used for the assessment of OSA. A physical activity questionnaire(On a typical day, the amount of time you spend sitting or reclining.) was used to assess SB. This secondary analysis included National Health and Nutrition Examination Survey (NHANES) participants (unweighted = 20,115). Weighted sample and multiple logistic regression complex sample analysis techniques were used in this study.ResultsAfter adjustment for confounders, participants with SB(> 8 h/d) had a higher risk of OSA compared to SB(< 4 h/d). Stratified analysis by gender showed that there was no significant association of SB and OSA in men. However, in women, with SB(< 4 h/d) as the reference, participants with(≥ 4 h/d) had an increased risk of OSA. By age-stratified analysis, the association of SB with OSA was stronger among older participants.ConclusionAnalysis in this study showed a positive association between SB and OSA, more pronounced in women and participants older than 60 years old.

Integrated analysis and separation of 5-lipoxygenase inhibitors from triterpenes of Poria cocos using bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking and target-based multiple complex networks

Poria cocos (Schw.) Wolf (P. cocos) has been widely used as medical plant in East Asia with remarkable anti-Alzheimer's disease (anti-AD) activity. However, the underlying mechanisms are still confused. In this study, based on the β-Amyloid deposition hypothesis of AD, an integrated analysis was conducted to screen and separation 5-lipoxygenase (5-LOX) inhibitors from triterpenoids of P. cocos and investigate the anti-AD mechanisms, containing bioaffinity ultrafiltration UPLC-Q-Exactive, molecular docking, and multiple complex networks. Five triterpenoids were identified as potential 5-LOX inhibitors, including Tumulosic acid, Polyporenic acid C, 3-Epi-dehydrotumulosic acid, Pachymic acid and Dehydrotrametenolic acid. Five potential 5-LOX inhibitors were screened by ultrafiltration affinity assay in P. cocos. The molecular docking simulation results are consistent with the ultrafiltration experimental results, which further verifies the accuracy of the experiment. The commercial 5-LOX inhibitor that Zileuton was used as a positive control to evaluate the inhibitory effect of active ingredients on 5-LOX. Subsequently, the established separation method allowed the five active ingredients (Pachymic acid, 3-Epi-dehydrotumulosic acid, Dehydrotrametenolic acid, Tumulosic acid and Polyporenic acid C) with high purity to be isolated. Targeting network pharmacology analysis showed that five active ingredients correspond to a total of 286 targets. Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis found that target cells were mainly enriched in Pathways in cancer, Lipid and atherosclerosis. Our results indicate that P. cocos extract has the potential to be used in the prevention and treatment of neurodegenerative diseases. This will help elucidate the mechanisms of action of various medicinal plants at the molecular level and provide more opportunities for the discovery and development of new potential treatments from health food resources.

Enhancing ASIC chip performance through integrated algorithm optimization

As a crucial arithmetic logic unit, the multiplier plays a significant role in digital signal processing. However, multiplication operations often require a large number of calculations and logic gates, leading to increased circuit complexity and power consumption. To enhance the performance and efficiency of multipliers, this paper presents an optimization analysis based on the Wallace Tree and Booth algorithms. The Wallace Tree algorithm decomposes multiplication operations into multiple stages and employs both separate operations and bit-level parallelism to accelerate multiplication, achieving efficient parallel multiplication computations and reducing both latency and area complexity of multiplication. On the other hand, the Booth algorithm is an optimization method for signed binary multiplication. By introducing the concept of Booth encoding, it transforms signed multiplication into unsigned multiplication, thereby reducing the number of multiplication operations. This paper analyses the application and research progress of the Wallace Tree and Booth algorithms in the field of multiplier optimization to improve computational speed and reduce power consumption of multipliers.

Study on the enrichment mechanism of low to medium rank coalbed gas (CBG) from multiple sources and complex components

Unlike high-rank coalbed gas (CBG), which is dominated by thermogenic gas with relatively single components, the low to medium rank CBG has complex components (N2 and CO2 rich CBG blocks are frequently found) and diverse sources (a mixture of thermogenic and biogenic gas, or even dominated by biogenic gas), and the enrichment model of CBG reservoirs is not clear yet. The present study was carried out in the southern Junggar Basin. The field desorption data were utilized to analyze the composition. CH4 was found to be the main component of CBG in the southern Junggar Basin, and high N2 and CO2 were found to exist in the Manasi-Hutubi and Miquan areas, respectively. Gas composition, gas and water stable isotopes were tested to clarify the source and fate of each gas (CH4, CO2 and N2). The results shows that biogenic CH4 is absolutely dominant in the low-medium rank CBG in the southern Junggar Basin, with microbial CH4 accounting for more than 90% of the total in the Liuhuanggou and Miquan areas in particular. Thermogenic, biogenic, and crustal CO2 are all detected in the southern Junggar Baisn. Manasi-Hutubi, Liuhuanggou, Fukang, and Jimushaer CO2 exhibit biogenicity. The intrusion of CO2 from middle to late Miocene crustal sources mainly triggered the anomalously high CO2 in the Miquan area. Excess N2 in the Manasi-Hutubi area originated from the atmosphere and was a consequence of hydrodynamic destruction of CBG reservoirs. In addition, the enrichment pattern of low to medium rank CBG reservoirs with biogenicity as the main source was revealed. Low to medium rank CBG reservoirs could be classified from shallow to deep: (1) Gas weathering zone; (2) Microbial gas zone; (3) Mixed gas zone; (4) Thermogenic gas zone. Microbial gas zone and the mixed gas zone are the enrichment zones of low to medium rank CBG (coal seams from 600 m to 1000 m in the southern Junggar Basin).

Diffusion Monte Carlo method for barrier heights of multiple proton exchanges and complexation energies in small water, ammonia, and hydrogen fluoride clusters.

Proton exchange reactions are of key importance in many processes in water. However, it is nontrivial to achieve reliable barrier heights for multiple proton exchanges and complexation energies in hydrogen-bonded systems theoretically. Performance of the fixed-node diffusion quantum Monte Carlo (FN-DMC) with the single-Slater-Jastrow trial wavefunction on total energies, barrier heights of multiple proton exchanges, and complexation energies of small water, ammonia, and hydrogen fluoride clusters is investigated in this study. Effects of basis sets and those of locality approximation (LA), T-move approximation (T-move), and determinant localization approximation (DLA) schemes in dealing with the nonlocal part of pseudopotentials on FN-DMC results are evaluated. According to our results, diffuse basis functions are important in achieving reliable barrier heights and complexation energies with FN-DMC, although the cardinal number of the basis set is more important than diffuse basis functions on total energies of these systems. Our results also show that the time step bias with DLA and LA is smaller than T-move; however, the time step bias of DMC energies with respect to time steps using the T-move is roughly linear up to 0.06a.u., while this is not the case with LA and DLA. Barrier heights and complexation energies with FN-DMC using these three schemes are always within chemical accuracy. Taking into account the fact that T-move and DLA are typically more stable than LA, FN-DMC calculations with the T-move or DLA scheme and basis sets containing diffuse basis functions are suggested for barrier heights of multiple proton exchanges and complexation energies of hydrogen-bonded clusters.

Higher Rates Of Homelessness Are Associated With Increases In Mortality From Accidental Drug And Alcohol Poisonings.

Alcohol and drug overdoses have multiple complex causes. In this article we contribute to the literature that links homelessness, the most extreme form of housing disruption, to accidental SUD-related poisonings. Using plausibly exogenous variation from a state's landlord-tenant policies that influence evictions, we estimated the causal impact of homelessness on SUD-related mortality. We found large effects of homelessness on SUD-related poisonings (for example, a 10percent increase in homelessness led to a 3.2percent increase in opioid poisonings in metropolitan areas). Our findings indicate that reducing local homelessness rates from the seventy-fifth to the fiftieth percentile levels could have saved more than 1,900 lives from opioid overdoses across all metropolitan localities in the final year of our study data. We conclude that strengthening the social safety net in terms of housing security could help curb the ongoing SUD-related poisoning epidemic in the US.

Decreased PDLIM1 expression in endothelial cells contributes to the development of intracranial aneurysm.

Intracranial aneurysm (IA) is a common vascular enlargement that occurs in the wall of cerebral vessels and frequently leads to fatal subarachnoid hemorrhage. PDZ and LIM domain protein 1 (PDLIM1) is a cytoskeletal protein that functions as a platform for multiple protein complex formation. However, whether PDLIM is involved in the pathogenesis of IA remains poorly understood. Loss-of-function and gain-of-function strategies were employed to determine the in vitro roles of PDLIM1 in vascular endothelial cells (VECs). A rat model of IA was generated to study the role of PDLIM1 in vivo. Gene expression profiling, Western blotting, and dual luciferase reporter assays were performed to uncover the underlying cellular mechanism. Clinical IA samples were used to determine the expression of PDLIM1 and its downstream signaling molecules. PDLIM1 expression was reduced in the endothelial cells of IA and was regulated by Yes-associated protein 1 (YAP1). Genetic silencing of PDLIM1 inhibited the viability, migratory ability, and tube formation ability of VECs. Opposite results were obtained by ectopic expression of PDLIM1. Additionally, PDLIM1 overexpression mitigated IA in vivo. Mechanistic investigations revealed that PDLIM1 promoted the transcriptional activity of β-catenin and induced the expression of v-myc myelocytomatosis viral oncogene homolog (MYC) and cyclin D1 (CCND1). In clinical settings, reduced expression of PDLIM1 and β-catenin downstream target genes was observed in human IA samples. Our study indicates that YAP1-dependent expression of PDLIM1 can inhibit IA development by modulating the activity of the Wnt/β-catenin signaling pathway and that PDLIM1 deficiency in VECs may represent a potential marker of aggressive disease.

Time-Dependent Prediction Models for Individual Prognosis of Chronic Postsurgical Pain following Knee Replacement Based on an Extensive Multivariable Data Set

(1) Background: Clinically useful prediction models for chronic postsurgical pain (CPSP) in knee replacement (TKA) are lacking. (2) Methods: In our prospective, multicenter study, a wide-ranging set of 91 variables was collected from 933 TKA patients at eight time points up to one year after surgery. Based on this extensive data pool, simple and complex prediction models were calculated for the preoperative time point and for 6 months after surgery, using least absolute shrinkage and selection operator (LASSO) 1se and LASSO min, respectively. (3) Results: Using preoperative data only, LASSO 1se selected age, the Revised Life Orientation Test on pessimism, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC)—subscore pain and the Timed “Up and Go” Test for prediction, resulting in an area under the curve (AUC) of 0.617 and a Brier score of 0.201, expressing low predictive power only. Using data up to 6 months after surgery, LASSO 1se included preoperative Patient Health Questionnaire-4, Knee Injury and Osteoarthritis Outcome Score (KOOS)—subscore pain (pain) 3 months after surgery (month), WOMAC pain 3 and 6 months, KOOS subscore symptoms 6 months, KOOS subscore sport 6 months and KOOS subscore Quality of Life 6 months. This improved the predictive power to an intermediate one (AUC 0.755, Brier score 0.168). More complex models computed using LASSO min did little to further improve the strength of prediction. (4) Conclusions: Even using multiple variables and complex calculation methods, the possibility of individual prediction of CPSP after TKA remains limited.

Soil properties shape the heterogeneity of denitrification and N2O emissions across large‐scale flooded paddy soils

AbstractFlooded paddy soils after rewetting dry soils accompanied by extensive nitrogen fertilizer input are important anthropogenic N2O emitters due to the denitrification process. Owing to multiple complex denitrifying N2O sources, however, the extent to which biotic (fungal or bacterial) and abiotic (chemical) denitrification contribute to total N2O emissions remains largely unquantified. Here we sampled across eight provinces where most of the flooded paddy soils were in China to explore microbial and abiotic denitrification potentials and decipher N2O dynamics. N2O isotopocules and site preference (δ15NSP) analyses found that in most of the sampled paddy soils, fungi‐mediated denitrification was the largest N2O contributor (51%–63%); while bacterial and chemical denitrifications contributed 12%–31% and 12%–28% of N2O emissions, respectively. Further, using 15N labeling, a significant spatial heterogeneity of denitrification performance was observed among these flooded paddy soils. As indicated by variance partitioning and regression analyses, this heterogeneity was mainly determined by soil properties (especially soil organic carbon and total nitrogen) rather than by denitrifying communities. Our findings provide insights into the establishment of predictive models of future N2O emission from global paddy soils considering both the biotic and abiotic contributions.

An Intelligent Classification Framework for Complex PQDs Using Optimized KS-Transform and Multiple Fusion CNN

Intelligent classification of multiple power quality disturbances (PQDs) is a top priority in pollution control of the power grid. However, the large-scale application of renewable energy introduces lots of nonlinear and impact loads, which makes the PQDs more complex and challenges the effectiveness of conventional detection frameworks. In this paper, a novel framework based on optimized Kaiser window-based S-transform (OKST) and multiple fusion convolutional neural network (MFCNN) is proposed to identify multiple complex PQDs. First, the OKST is used for the time-frequency positioning of PQDs, where an improved control function is proposed to meet different detection requirements of time-frequency. Additionally, the parameters of the control function are adjusted automatically using maximum energy concentration. Then, the MFCNN based on residual networks (ResNets) is further proposed to extract and classify these time-frequency features automatically. In MFCNN, feature information is fused using different convolution kernels at a 2-dimensional level, which can effectively reduce information loss and improve classification performance. The network model is set up using the Pytorch platform, and the dataset containing 28 types of PQDs and 2 types of nonlinearly mixed PQDs is built to test our framework. The result shows that the proposed OKST-MFCNN obtains an average accuracy of 99.38% under the 20 dB noise level, which is more accurate and robust than some advanced PQDs detection frameworks. Moreover, the accuracy of 97.94% is achieved with satisfactory real-time performance in hardware platform experiments, proving its superior identification performance for complex PQDs.

A Multipopulation Evolutionary Algorithm Using New Cooperative Mechanism for Solving Multiobjective Problems With Multiconstraint

In science and engineering, multi-objective optimization problems usually contain multiple complex constraints, which poses a significant challenge in obtaining the optimal solution. This paper aims to solve the challenges brought by multiple complex constraints. First, this paper analyzes the relationship between single constrained Pareto Front (SCPF) and their common Pareto Front sub-constrained Pareto Front (SubCPF). Next, we discussed the SCPF, SubCPF, and Unconstrainti Pareto Front (UPF)’s help to solve constraining Pareto Front (CPF). Then further discusses what kind of cooperation should be used between multiple populations constrained multi-objective optimization algorithm (CMOEA) to better deal with multi-constrained multi-objective optimization problems (mCMOPs). At the same time, based on the discussion in this paper, we propose a new multi-population CMOEA called MCCMO, which uses a new cooperation mechanism. MCCMO uses C+2 (C is the number of constraints) populations to find the UPF, SCPF, and SubCPF at an appropriate time. Furthermore, MCCMO uses the newly proposed Activation Dormancy Detection (ADD) to accelerate the optimization process and uses the proposed Combine Occasion Detection (COD) to find the appropriate time to find the SubCPF. The performance on 32 mCMOPs and real-world mCMOPs shows that our algorithm can obtain competitive solutions on MOPs with multiple constraints.

Seismic Reprocessing Overcomes Uncertainty in Offshore Egyptian Field

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 214066, “Seismic Reprocessing Leads to New Breakthroughs—A Successful Case in the ASH Field, AG Basin in Egypt,” by Mohamed Elokr and Ahmed Lotfy, Kuwait Energy Egypt, and Wei Xing, United Energy Group, et al. The paper has not been peer reviewed. _ The ASH oil field is in the eastern portion of the AG Basin in Egypt. The Lower Cretaceous Alam El Bueib is the main oil-producing formation. Because of the high heterogeneity of the Abu Roash succession, in addition to the influence of thick limestone of the Upper Cretaceous and the influence of multiple complex faults, the quality of seismic data is very poor, requiring seismic reprocessing. Two key techniques were used to achieve the goal: New vertical seismic profile (VSP) well data were acquired to adjust the velocity model, and common reflection angle migration (CRAM) prestack depth migration (PSDM) was used for reprocessing. Introduction The ASH field produces oil from the Aptian Lower Cretaceous reservoir. The ASH structure is interpreted as an elongated east-northeast/west-southwest anticline with intensive breaching on the downthrown side of northwest/southeast to west-northwest/east-southeast-oriented normal faults with obvious signature of synsedimentary activities. The entire area was subject to a series of tectonic events affecting the Western Desert, which led to a complex fault regime clearly recognized in the ASH structure by a series of horizontal displacement across the northwest/southeast faults. Strike/slip to oblique-slip faults also are observed from thicknesses of the cretaceous formations, creating significant difficulty in fault definition. The 3D seismic data acquired in 2007 suffered from losing amplitude of the Lower Cretaceous and deep Jurassic reflectors because of the complex structure. The strong carbonate markers disappear north of the field, where high, dense fault intersection is present closing from the main bounding fault. Loss of seismic amplitude could have severe effects on of the future economics of the field. The seismic survey was reprocessed two times without significant enhancement of the fault imaging. PSDM was run without velocity control, resulting in an uncertain velocity model with inaccurate fault imaging and a mismatching between actual and prognosed depths of the drilled wells of greater than 50 m. This contradiction in reservoir mapping meant that the northern portion of the field remained unexplored. Acquiring VSP in the recently drilled well encouraged reprocessing of the 3D seismic survey using well velocity as a control point in adjusting the seismic velocity model, and application of CRAM technology to improve fault imaging and appraise the uncertain area of the ASH field. CRAM Methodology CRAM was developed as a type of beam migration. Asymptotic ray tracing is performed, assuming that one-way diffraction rays from the subsurface image point to the surface sources and receivers. The takeoff angles from the image point are measured around a given local normal to a background reflection surface for each source ray and receiver ray. A system of source and receiver ray pairs is formed, and the recorded seismic data can be mapped into a local angle domain based on the reflection angles at the subsurface image points.​​​​

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics.

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity-driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Finally, we construct the co-evolution framework of the PAD model and higher-order contagion dynamics and derive the critical conditions for phase transition and bistable phenomenon using theoretical and numerical methods. Results show that tendency of participating in higher-order interactions can promote the emergence of bistability but delay the outbreak under heterogeneous activity rates. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields.

FPGA Implementation of DFT Processor using Vedic Multiplier

Many a times Mathematical computations are easier in frequency domain than time domain. Discrete Fourier transform (DFT) is a tool to convert signals from time domain to frequency domain which is widely used mainly in Digital Signal and Image Processing applications. Fast Fourier Transform (FFT) is a method to find DFT in time constraint applications which is affected by number of complex multiplier. In this paper 16-bit DFT using Vedic Multiplier, a high- speed multiplier is proposed with an objective to replace conventional complex multiplier and to decrease computation time. The proposed system is implemented in Virtex-4 FPGA and the results show that FFT using Urdhva Tiryakbhyam algorithm of Vedic multiplier is faster than other methods of DFT

Understanding the relationship between idea contributions and idea enactments in student design teams: A social network analysis approach

AbstractBackgroundExisting research has demonstrated that student characteristics, such as race/ethnicity, sex, and personal beliefs about engineering knowledge, shape students' experiences in ill‐structured problem‐solving, such as engineering design, where ideas must be communicated, negotiated, and selected in complex social processes.PurposeThe purpose of this research was to examine the how student characteristics, such as race/ethnicity, sex, and epistemological beliefs, are associated with patterns of idea contributions and ideas enactments in collaborative project teams.MethodIn this article, I use the multilayer exponential random graph model (ERGM) for examining multiple complex social relationships simultaneously. Drawing on survey data from a study of engineering student teamwork, this research examines the relationship perceptions of idea contributions (layer 1) and idea enactments (layer 2) in collaborative project teams.ResultsResults indicated no sex differences in the perceptions of idea contributions and enactments in student design teams. However, underrepresented minority students and Asian America/Pacific Islander students were reported as less frequently having their ideas enacted. Further, epistemological beliefs similarity effects were a significant predictor on the idea contribution layer, and epistemological beliefs sociality effects were significant on the idea enactments layer.ConclusionAchieving equity in teamwork pedagogies requires understanding the dynamic social processes that shapes patterns of participation in student teams. This research demonstrates the power of social networks methodologies for modeling teamwork processes, pointing specifically to the ways that student characteristics are associated with perceptions shape idea contributions and enactments in student teams.