Video-Based Deception Detection with Non-Contact Heart Rate Monitoring and Multi-Modal Feature Selection

Deception detection plays a crucial role in criminal investigation.Videos contain a wealth of information regarding apparent and physiological changes in individuals,and thus can serve as an effective means of deception detection.In this paper,we investigate video-based deception detection considering both apparent visual features such as eye gaze,head pose and facial action unit(AU),and non-contact heart rate detected by remote photoplethysmography(rPPG)technique.Multiple wrapper-based feature selection methods combined with the K-nearest neighbor(KNN)and support vector machine(SVM)classifiers are employed to screen the most effective features for deception detection.We evaluate the performance of the proposed method on both a self-collected physiological-assisted visual deception detection(PV3D)dataset and a public bag-oflies(BOL)dataset.Experimental results demonstrate that the SVM classifier with symbiotic organisms search(SOS)feature selection yields the best overall performance,with an area under the curve(AUC)of 83.27%and accuracy(ACC)of 83.33%for PV3D,and an AUC of 71.18%and ACC of 70.33%for BOL.This demonstrates the stability and effectiveness of the proposed method in video-based deception detection tasks.

Design of a Multi-Stage Ensemble Model for Thyroid Prediction Using Learning Approaches

This research concentrates to model an efficient thyroid prediction approach,which is considered a baseline for significant problems faced by the women community.The major research problem is the lack of automated model to attain earlier prediction.Some existing model fails to give better prediction accuracy.Here,a novel clinical decision support system is framed to make the proper decision during a time of complexity.Multiple stages are followed in the proposed framework,which plays a substantial role in thyroid prediction.These steps include i)data acquisition,ii)outlier prediction,and iii)multi-stage weight-based ensemble learning process(MS-WEL).The weighted analysis of the base classifier and other classifier models helps bridge the gap encountered in one single classifier model.Various classifiers aremerged to handle the issues identified in others and intend to enhance the prediction rate.The proposed model provides superior outcomes and gives good quality prediction rate.The simulation is done in the MATLAB 2020a environment and establishes a better trade-off than various existing approaches.The model gives a prediction accuracy of 97.28%accuracy compared to other models and shows a better trade than others.

Strategic Planning for Equitable RWIS Implementation:A Comprehensive Study Incorporating a Multi-variable Semivariogram Model

This paper extends the previously developed method of optimizing Road Weather Information Systems(RWIS)station placement by unveiling a sophisticated multi-variable semivariogram model that concurrently considers multiple vital road weather variables.Previous research primarily centered on single-variable analysis focusing on road surface temperature(RST).The study bridges this oversight by introducing a framework that integrates multiple critical weather variables into the RWIS location allocation framework.This novel approach ensures balanced and equitable RWIS distribution across zones and aligns the network with areas both prone to traffic accidents and areas of high uncertainty.To demonstrate the effectiveness of this refinement,the authors applied the framework to Maine’s existing RWIS network,conducted a gap analysis through varying planning scenarios and generated optimal solutions using a heuristic optimization algorithm.The analysis identified areas that would benefit most from additional RWIS stations and guided optimal resource utilization across different road types and priority locations.A sensitivity analysis was also performed to evaluate the effect of different weightings for weather and traffic factors on the selection of optimal locations.The location solutions generated have been adopted by MaineDOT for future implementations,attesting to the model’s practicality and signifying an important advancement for more effective management of road weather conditions.

一种实现Wi-SUN网络多速率自适应切换的方法

针对Wi-SUN(Wireless Smart Utility Network)FAN(Field Area Network)网络通信速率单一导致的数据吞吐量和通信可靠性不平衡问题,设计了一种Wi-SUN网络多速率自适应切换方法。该方法使用判定机制根据节点与邻节点通信情况判定节点间是否需要切换链路速率,自适应调整合适的通信速率方案,且使用基于跳频技术设计的速率匹配机制,使节点可以在网状网络中随时切换与其它邻节点通信的速率。通过将传统单一速率Wi-SUN网络与多速率Wi-SUN网络模拟对比,结果表明多速率网络比传统单一速率网络可以被应用在更多场景下,且证明多速率网络能够平衡数据吞吐量和通信可靠性。

let-7i-5p在肿瘤中的研究进展

let-7i-5p是非编码小分子RNA(miRNA)中的一员,它通过多靶点和信号通路来抑制肿瘤的生长、增殖和迁移,还具有调节肿瘤微环境延缓癌症进展和改善肿瘤耐药的作用。文章综述了let-7i-5p在肝癌、乳腺癌、结直肠癌、宫颈癌、鼻咽癌、肾癌和胶质母细胞瘤中的研究进展。有望发掘let-7i-5p成为新肿瘤标志物,为肿瘤的防治提供新策略。

The Probability Density Function Related to Shallow Cumulus Entrainment Rate and Its Influencing Factors in a Large-Eddy Simulation

The process of entrainment-mixing between cumulus clouds and the ambient air is important for the development of cumulus clouds.Accurately obtaining the entrainment rate(λ)is particularly important for its parameterization within the overall cumulus parameterization scheme.In this study,an improved bulk-plume method is proposed by solving the equations of two conserved variables simultaneously to calculateλof cumulus clouds in a large-eddy simulation.The results demonstrate that the improved bulk-plume method is more reliable than the traditional bulk-plume method,becauseλ,as calculated from the improved method,falls within the range ofλvalues obtained from the traditional method using different conserved variables.The probability density functions ofλfor all data,different times,and different heights can be well-fitted by a log-normal distribution,which supports the assumed stochastic entrainment process in previous studies.Further analysis demonstrate that the relationship betweenλand the vertical velocity is better than other thermodynamic/dynamical properties;thus,the vertical velocity is recommended as the primary influencing factor for the parameterization ofλin the future.The results of this study enhance the theoretical understanding ofλand its influencing factors and shed new light on the development ofλparameterization.

Image Tamper Detection and Multi-Scale Self-Recovery Using Reference Embedding with Multi-Rate Data Protection

This paper proposes a multi-scale self-recovery(MSSR)approach to protect images against content forgery.The main idea is to provide more resistance against image tampering while enabling the recovery process in a multi-scale quality manner.In the proposed approach,the reference data composed of several parts and each part is protected by a channel coding rate according to its importance.The first part,which is used to reconstruct a rough approximation of the original image,is highly protected in order to resist against higher tampering rates.Other parts are protected with lower rates according to their importance leading to lower tolerable tampering rate(TTR),but the higher quality of the recovered images.The proposed MSSR approach is an efficient solution for the main disadvantage of the current methods,which either recover a tampered image in low tampering rates or fails when tampering rate is above the TTR value.The simulation results on 10000 test images represent the efficiency of the multi-scale self-recovery feature of the proposed approach in comparison with the existing methods.

Exploring the thermal stability of lithium-ion cells via accelerating rate calorimetry:A review

Given the importance of lithium-ion cell safety,a comprehensive review on the thermal stability of lithium-ion cells investigated by accelerating rate calorimetry(ARC),is provided in the present work.The operating mechanism of ARC is discussed first,including the usage and the reaction kinetics.Besides that,the thermal stability of the cathode/anode materials at elevated temperatures is revealed by examining the impacts of some significant factors,i.e.,the lithium content,particle size,material density,lithium salt,solvent,additive,binder and initial heating temperature.A comparison of the common cathode materials indicates that the presence of Mn and polyanion could significantly enhance the thermal stability of cathode materials,while the doping of Al also helps to restrain the reactivity.Except for their high capacity,some alloy materials demonstrate more competitive safety than traditional carbon anode materials.Furthermore,the thermal behaviors of full cells under abusive conditions are reviewed here.Due to the sensitivity of ARC to the kinetic parameters,a reaction kinetic modeling can be built on the basis of ARC profiles,to predict the thermal behaviors of cell components and cells.Herein,a shortcircuit modeling is exampled.

Modeling the Failure Rate of a Standby Multi-Component System and Improving Reliability

Evaluating the reliability of a system requires knowledge of the failure modes to which it is subjected. Complex topology systems generally require a high level of availability, which is a function of the arrangement of elements (components) in the system. To avoid serious failures for such complex systems, recourse can be had to the redundancy techniques available in the literature. These techniques help to improve system reliability, without affecting the reliability of system components. This paper is interested in the proposal of a model for evaluating the failure rate of a standby multi-components system and in improving the reliability of mechanical systems, arranged in a topology (series, parallel, or mixed).

Synthesis and Performance of an Associative Anti-shear Drag Reducer Based on Hydrogen Bond Association of Dodecyl Methacrylate

Using K2S2O8-Na2SO3 as the redox initiation system,a hydrogen-bond-association-based dodecyl methacrylate system associative anti-shear drag reducer was synthesised by standard emulsion polymerisation.The reaction process was simple and gentle as well as safe and stable.Molecular design was carried out using molecular dynamics simulation methods.The results of infrared spectroscopy,thermogravimetric analysis,differential scanning calorimetry,gel chromatography,and laser light scattering showed that the reaction polymerisation was relatively complete,the product was uniform,the molecular weight distribution was controllable,and the synthesised polymer had good flexibility.The donor lauryl methacrylate-styrene-methacrylic acid(LMA-St-MAA)and acceptor lauryl methacrylate-styrene-dimethylaminoethyl methacrylate(LMA-St-DMA)polymers had an associative intermolecular interaction force,which increased the molecular cluster size of the associative system complex.The complex had good shear resistance,and the test results of the tube pump shear test showed that the synthesised associative oil-soluble polymer drag reduction system exhibited better drag reduction rate performance than poly-α-olefins over repeated cycles.The research results provide a reference plan for minimising the number of station-to-station inputs,thereby ensuring the stability of oil pipelines and reducing transportation costs.

Construction of a Computational Scheme for the Fuzzy HIV/AIDS Epidemic Model with a Nonlinear Saturated Incidence Rate

This work aimed to construct an epidemic model with fuzzy parameters.Since the classical epidemic model doesnot elaborate on the successful interaction of susceptible and infective people,the constructed fuzzy epidemicmodel discusses the more detailed versions of the interactions between infective and susceptible people.Thenext-generation matrix approach is employed to find the reproduction number of a deterministic model.Thesensitivity analysis and local stability analysis of the systemare also provided.For solving the fuzzy epidemic model,a numerical scheme is constructed which consists of three time levels.The numerical scheme has an advantage overthe existing forward Euler scheme for determining the conditions of getting the positive solution.The establishedscheme also has an advantage over existing non-standard finite difference methods in terms of order of accuracy.The stability of the scheme for the considered fuzzy model is also provided.From the plotted results,it can beobserved that susceptible people decay by rising interaction parameters.

Investigates of substrate mingling ratio and organic loading rate of KOH pretreated corn stover and pig manure in batch and semi-continuous system:Anaerobic digestion performance and microbial characteristics

The effects of substrate mingling ratio(SMR)(1:1,1:2,1:3,3:1,and 2:1)and organic loading rate(OLR)(50-90 g total solids per liter per day)on anaerobic co-digestion performance and microbial characteristics were investigated for pig manure(PM)and pretreated/untreated corn stover in batch and semicontinuous anaerobic digestion(AD)system.The results showed that SMR and pretreatment affected co-digestion performance.The maximum cumulative methane yield of 428.5 ml·g^(-1)(based on volatile solids(VS))was obtained for PCP13,which was 35.7%and 40.0%higher than that of CSU and PM.In the first 5 days,the maximum methane yield improvement rate was 378.1%for PCP13.The daily methane yield per gram VS of PCP13 was 11.4%-18.5%higher than that of PC_(U)13.Clostridium_sensu_stricto_1,DMER64,and Bacteroides and Methanosaeta,Methanobacterium,and Methanospirillum had higher relative abundance at the genus level.Therefore,SMR and OLR are important factor affecting the AD process,and OLR can affect methane production through volatile fatty acids.

Study on Real-Time Heart Rate Detection Based on Multi-People

Heart rate is an important vital characteristic which indicates physical and mental health status.Typically heart rate measurement instruments require direct contact with the skin which is time-consuming and costly.Therefore,the study of non-contact heart rate measurement methods is of great importance.Based on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking algorithm.Then after the regional segmentation of the facial image,the signal acquisition of the region of interest is further resolved.Finally,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletfilter.The experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

In-plane Tensile Behaviors of Bi-axial Warp-Knitted Composites under Quasi-static and High Strain Rate Loading

The in-plane tensile behaviors of bi-axial warp-knitted(BWK) composites under quasi-static and high strain rates loading were experimentally analyzed in this article. The tensile tests were conducted along warp direction( 0°) and weft direction( 90°) at quasi-static rate of 0. 001 s^(-1) and high strain rates ranging from 1 450 to 2 540 s^(-1),respectively. It is found that the significant strain rate sensitivity can be observed in the stress-strain curves of BWK composites. The fracture morphologies of BWK composites demonstrate that the tensile failure modes are shear failure and fiber breakage under the quasi-static testing condition while interface failure and fibers pullout are at high strain rates.

Influence of quenching medium on the dendrite morphology,hardness,and tribological behaviour of cast Cu-Ni-Sn spinodal alloy for defence application

Cu-Ni-Sn spinodal alloys(Spinodal bronze)are potential materials with robust applications in components associated with defence applications like bearings,propellers,bushes,and shafts of heavily loaded aircraft,off-road vehicles,and warships.This paper presents a comparative study using water,Brine solution,and SAE 40 oil as the quenching media in regular bronze(Cu-6Sn)and spinodal bronze(Cu-9Ni-6Sn)alloys.Morphological analysis was conducted by optical microscopy,transmission electron microscopy(TEM),and X-ray diffraction technique(XRD)on bronze and spinodal bronze samples immersed in the three different quenching media to understand the grain size and hardness values better.Tribological analysis was performed to analyze the effect of quenching media on the wear aspects of bronze and spinodal bronze samples.The hardness value of the brine-aged spinodal bronze samples was as high as 320 Hv,and the grain size was very low in the range of 60μm.A quantitative comparison between brine-aged regular bronze and brine-aged spinodal bronze showed that the hardness(Hv)was almost 80%higher for brine-aged spinodal bronze.Further,the grain size was approximately 30%finer for spinodal bronze when compared with regular bronze.When the load was increased in spinodal bronze samples,there was an initial dip in wear rate followed by a marginal increase.There was a steady increase in friction coefficient with a rise in load for brine-aged regular bronze and spinodal bronze samples.These results indicate that brine solution is the most effective quenching medium for cast Cu-Ni-Sn spinodal alloys.

Study on Hydrogen Storage Properties,Anti-Oxide Ability and Rate Limiting Step of ZrCr-Based Alloy after Introducing Cobalt as Additive

In this work,the microstructure,hydrogen storage properties,anti-oxide ability and rate limiting step of Zr(Cr_(1−x)Co_(x))_(2)(x=0,0.2,0.4 and 0.6)alloys have been investigated.After studying the crystal structure,we found that all alloy samples could show C14-type phase but the alloy sample x=0 could also show a small amount of Cr phase.Rietveld fitting showed that lattice parameter and unit cell volume of C14-type phase decreased with increasing x.After further research,it was clear that the first hydrogen absorption capacity decreased with increasing x.But introducing more Co content had a positive influence on the effective hydrogen storage capacity and cyclic hydrogen absorption and desorption properties of the alloy sample.We also found that adding Co to ZrCr_(2)alloy could improve its anti-oxide ability.In addition to this,the rate limiting step model was also studied.

MSSTNet:Multi-scale facial videos pulse extraction network based on separable spatiotemporal convolution and dimension separable attention

Background The use of remote photoplethysmography(rPPG)to estimate blood volume pulse in a noncontact manner has been an active research topic in recent years.Existing methods are primarily based on a singlescale region of interest(ROI).However,some noise signals that are not easily separated in a single-scale space can be easily separated in a multi-scale space.Also,existing spatiotemporal networks mainly focus on local spatiotemporal information and do not emphasize temporal information,which is crucial in pulse extraction problems,resulting in insufficient spatiotemporal feature modelling.Methods Here,we propose a multi-scale facial video pulse extraction network based on separable spatiotemporal convolution(SSTC)and dimension separable attention(DSAT).First,to solve the problem of a single-scale ROI,we constructed a multi-scale feature space for initial signal separation.Second,SSTC and DSAT were designed for efficient spatiotemporal correlation modeling,which increased the information interaction between the long-span time and space dimensions;this placed more emphasis on temporal features.Results The signal-to-noise ratio(SNR)of the proposed network reached 9.58dB on the PURE dataset and 6.77dB on the UBFC-rPPG dataset,outperforming state-of-the-art algorithms.Conclusions The results showed that fusing multi-scale signals yielded better results than methods based on only single-scale signals.The proposed SSTC and dimension-separable attention mechanism will contribute to more accurate pulse signal extraction.

Pulse rate estimation based on facial videos:an evaluation and optimization of the classical methods using both self-constructed and public datasets

Pulse rate is one of the important characteristics of traditional Chinese medicine pulse diagnosis,and it is of great significance for determining the nature of cold and heat in diseases.The prediction of pulse rate based on facial video is an exciting research field for getting palpation information by observation diagnosis.However,most studies focus on optimizing the algorithm based on a small sample of participants without systematically investigating multiple influencing factors.A total of 209 participants and 2,435 facial videos,based on our self-constructed Multi-Scene Sign Dataset and the public datasets,were used to perform a multi-level and multi-factor comprehensive comparison.The effects of different datasets,blood volume pulse signal extraction algorithms,region of interests,time windows,color spaces,pulse rate calculation methods,and video recording scenes were analyzed.Furthermore,we proposed a blood volume pulse signal quality optimization strategy based on the inverse Fourier transform and an improvement strategy for pulse rate estimation based on signal-to-noise ratio threshold sliding.We found that the effects of video estimation of pulse rate in the Multi-Scene Sign Dataset and Pulse Rate Detection Dataset were better than in other datasets.Compared with Fast independent component analysis and Single Channel algorithms,chrominance-based method and plane-orthogonal-to-skin algorithms have a more vital anti-interference ability and higher robustness.The performances of the five-organs fusion area and the full-face area were better than that of single sub-regions,and the fewer motion artifacts and better lighting can improve the precision of pulse rate estimation.

INTERFACE BEHAVIOR AND DECAY RATES OF COMPRESSIBLE NAVIER-STOKES SYSTEM WITH DENSITY-DEPENDENT VISCOSITY AND A VACUUM

In this paper,we study the one-dimensional motion of viscous gas near a vacuum,with the gas connecting to a vacuum state with a jump in density.The interface behavior,the pointwise decay rates of the density function and the expanding rates of the interface are obtained with the viscosity coefficientμ(ρ)=ρ^(α)for any 0<α<1;this includes the timeweighted boundedness from below and above.The smoothness of the solution is discussed.Moreover,we construct a class of self-similar classical solutions which exhibit some interesting properties,such as optimal estimates.The present paper extends the results in[Luo T,Xin Z P,Yang T.SIAM J Math Anal,2000,31(6):1175-1191]to the jump boundary conditions case with density-dependent viscosity.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.