Ultrafast Response and Threshold Adjustable Intelligent Thermoelectric Systems for Next‑Generation Self‑Powered Remote IoT Fire Warning

Fire warning is vital to human life,economy and ecology.However,the development of effective warning systems faces great challenges of fast response,adjustable threshold and remote detecting.Here,we propose an intelligent self-powered remote IoT fire warning system,by employing single-walled carbon nanotube/titanium carbide thermoelectric composite films.The flexible films,prepared by a convenient solution mixing,display p-type characteristic with excellent high-temperature stability,flame retardancy and TE(power factor of 239.7±15.8μW m^(-1) K^(-2))performances.The comprehensive morphology and structural analyses shed light on the underlying mechanisms.And the assembled TE devices(TEDs)exhibit fast fire warning with adjustable warning threshold voltages(1–10 mV).Excitingly,an ultrafast fire warning response time of~0.1 s at 1 mV threshold voltage is achieved,rivaling many state-of-the-art systems.Furthermore,TE fire warning systems reveal outstanding stability after 50 repeated cycles and desired durability even undergoing 180 days of air exposure.Finally,a TED-based wireless intelligent fire warning system has been developed by coupling an amplifier,analogto-digital converter and Bluetooth module.By combining TE characteristics,high-temperature stability and flame retardancy with wireless IoT signal transmission,TE-based hybrid system developed here is promising for next-generation self-powered remote IoT fire warning applications.

Reevaluating health metrics:Unraveling the limitations of disabilityadjusted life years as an indicator in disease burden assessment

In 1993,the World Bank released a global report on the efficacy of health promotion,introducing the disability-adjusted life years(DALY)as a novel indicator.The DALY,a composite metric incorporating temporal and qualitative data,is grounded in preferences regarding disability status.This review delineates the algorithm used to calculate the value of the proposed DALY synthetic indicator and elucidates key methodological challenges associated with its application.In contrast to the quality-adjusted life years approach,derived from multi-attribute utility theory,the DALY stands as an independent synthetic indicator that adopts the assumptions of the Time Trade Off utility technique to define Disability Weights.Claiming to rely on no mathematical or economic theory,DALY users appear to have exempted themselves from verifying whether this indicator meets the classical properties required of all indicators,notably content validity,reliability,specificity,and sensitivity.The DALY concept emerged primarily to facilitate comparisons of the health impacts of various diseases globally within the framework of the Global Burden of Disease initiative,leading to numerous publications in international literature.Despite widespread adoption,the DALY synthetic indicator has prompted significant methodological concerns since its inception,manifesting in inconsistent and non-reproducible results.Given the substantial diffusion of the DALY indicator and its critical role in health impact assessments,a reassessment is warranted.This reconsideration is imperative for enhancing the robustness and reliability of public health decisionmaking processes.

基于VMD-Self-attention-LSTM的水闸深基坑变形智能预测方法

针对水闸深基坑变形监测数据具有非稳定性的特点,提出了基于VMD-Self-attention-LSTM的水闸深基坑变形预测方法。该方法分为三个主要模块,第一模块采用VMD算法自适应调整变形分解模数,将原始变形数据分解为若干具有明显周期规律的分量,有效解决变形数据的非稳定性,为提升变形预测精度奠定初步基础;第二模块,在传统LSTM算法基础上进一步发展Self-attention-LSTM方法,提升模型对基坑变形样本的时序关系挖掘能力,进而提升基坑变形预测精度;第三模块,将各分量对应的变形预测结果重构得到最终预测值。实例分析可知,所提方法有效解决了非稳定特性给变形预测精度带来的不利影响,与VMD-LSTM、Self-attention-LSTM、LSTM等深度学习方法相比,VMD-Self-attention-LSTM的预测精度最大提升41.49%,与BP、ELM等传统机器学习算法相比,预测精度最大提升50.43%,为水闸深基坑安全监控模型的构建提供了新思路。

Self Adjusting Feedforward Compensation Tracking Control for Proportional Valve Controlled Motor

Aim Aiming at the position tracking control for valve controlled motor electrohydraulic proportional servo systems mainly driving the static load torque, the tracking performance was studied in the presence of the variable gain and deadzone. Methods On the basis of conventional composite control with the deadzone compensation method, a comprehensive control approach with the deadzone and self adjusting feedforward compensation was proposed. Results Experimental results showed that the good tracking performance was achieved for the sinusoidal and constant velocity position tracking under a wide variations of load torque. Conclusion The position tracking accuracy for valve controlled motor electrohydraulic proportional servo systems has been solved by using the comprehensive control approach with the deadzone and self adjusting feedforward compensation.

Self-adjusting decision feedback equalizer for variational underwater acoustic channel environments

Aimed at the abominable influences to blind equaliza-tion algorithms caused by complex time-space variability existing in underwater acoustic channels, a new self-adjusting decision feedback equalization （DFE） algorithm adapting to different under-water acoustic channel environments is proposed by changing its central tap position. Besides, this new algorithm behaves faster convergence speed based on the analysis of equalizers’ working rules, which is more suitable to implement communications in dif-ferent unknown channels. Corresponding results and conclusions are validated by simulations and spot experiments.

Micro-dynamic Behavior and Self-adjusting Water Transmit Mechanism of Water-transferring Composite

Constructional and micro-dynamic process of the water-transferring composite was analyzed. This composite can transmit water to soil with a self-adjustable speed to ensure the survival of seedlings in arid and semi-arid regions when it is embedded in soil around the roots of the seedlings. It is obtained from natural plant fiber coated with a colloid made by mixing a certain proportion of polyacrylamide and montmorillonite. The rules of water being transmitted to soil by the coating under different condition were tested by M-30 quick moisture measure instrument. The process of water-desorption of the coating material was investigated by a Perkin Elmer Diamond S Ⅱ thermal multi-analyzer. Moreover, the micro-dynamic behavior was detected by a FEIQuanta 2000 environment scanning electron microscope. The results demonstrate that montmorillonite has lower water-desorption energy barrier than polyacrylamide and can lose water more easily. montmorillonite particles bridge up to be the main water-transmit material at low water potential （when the soil relatively dry or when the temperature is high）, and they break bridge at high water potential while the polyacrylamide acts as the main water-transmit material.

Traffic prediction using a self-adjusted evolutionary neural network

Short-term prediction of traffic flow is one of the most essential elements of all proactive traffic control systems.The aim of this paper is to provide a model based on neural networks(NNs)for multi-step-ahead traffic prediction.NNs'dependency on parameter setting is the major challenge in using them as a predictor.Given the fact that the best combination of NN parameters results in the minimum error of predicted output,the main problem is NN optimization.So,it is viable to set the best combination of the parameters according to a specific traffic behavior.On the other hand,an automatic method—which is applicable in general cases—is strongly desired to set appropriate parameters for neural networks.This paper defines a self-adjusted NN using the non-dominated sorting genetic algorithm II(NSGA-II)as a multi-objective optimizer for short-term prediction.NSGA-II is used to optimize the number of neurons in the first and second layers of the NN,learning ratio and slope of the activation function.This model addresses the challenge of optimizing a multi-output NN in a self-adjusted way.Performance of the developed network is evaluated by application to both univariate and multivariate traffic flow data from an urban highway.Results are analyzed based on the performance measures,showing that the genetic algorithm tunes the NN as well without any manually pre-adjustment.The achieved prediction accuracy is calculated with multiple measures such as the root mean square error(RMSE),and the RMSE value is 10 and 12 in the best configuration of the proposed model for single and multi-step-ahead traffic flow prediction,respectively.

基于Self-adjust网络模型的人脸图像情感分析方法

人脸表情具有丰富的情感内涵,是情感分析的一个重要研究方向。模糊的面部表情及标注者的主观性所带来的不确定性,给情感分析研究带来了挑战。鉴于此,提出了一种基于Self-adjust网络模型的人脸图像情感分析方法。首先用人脸对齐方法进行图像预处理,然后利用注意力机制来处理Focal损失加权,再对其进行秩正则化排序,最后通过重新分类对有误标签进行矫正,并用实验验证了该方法的有效性与优越性。该方法在准确率这个评价指标上有所提高,能够有效抑制人脸图像情感分析的不确定性,防止深层网络对不确定的人脸图像进行过拟合。

Self-adjustment of Carrying Capacity of Concrete Embedded with CFRC

By heating up the embedded carbon fiber reinforced cement based material （CFRC）, the carrying capacity and deformation of concrete member could be adjusted. The relationship between temperature difference and expansion strain of CFRC was demonstrated, and the temperature-deformation-load effect of concrete embedded with CFRC was studied. Heating the CFRC up to different temperatures resulted in different degree of inner pre-stress in concrete. Thus, the load capacity of concrete could be regulated owing to counteracting the pre-stress.

Self-adjusting dynamic characteristics of pulsed MIG welding for aluminum alloys

Pulsed MIG welding is suitable for aluminum alloys welding, because spray transfer and excellent profile can be arrived during whole welding current range, and the energy of droplet can be controlled to overcome losing of alloy elements with lower melting and steam point by controlling pulse current and pulse time. Because of the special physic properties of aluminum alloys, there are different requirements for pulsed MIG welding between starting arc short circuit and drop transfer short circuit, pulse period and base period. In order to satisfy the need of aluminum alloys MIG welding, self adjusting dynamic characteristics are designed to output different dynamic characteristics in different welding startes. The self adjusting dynamic characteristics of pulsed MIG welding are achieved through a short circuit controller and a dynamic electronic inductor. The welding machine(AL MIG 350) with self adjusting dynamic characteristics has a high rate of successfully starting arc up to 96%, and the short circuit time during transfer is less than 1 ms, in the mean time, the arc is stiffness, spatter is low and weld appearance is good.

A Self-calibration Bundle Adjustment Algorithm Based on Block Matrix Cholesky Decomposition Technology

In this study,the problem of bundle adjustment was revisited,and a novel algorithm based on block matrix Cholesky decomposition was proposed to solve the thorny problem of self-calibration bundle adjustment.The innovation points are reflected in the following aspects:①The proposed algorithm is not dependent on the Schur complement,and the calculation process is simple and clear;②The complexities of time and space tend to O(n)in the context of world point number is far greater than that of images and cameras,so the calculation magnitude and memory consumption can be reduced significantly;③The proposed algorithm can carry out self-calibration bundle adjustment in single-camera,multi-camera,and variable-camera modes;④Some measures are employed to improve the optimization effects.Experimental tests showed that the proposed algorithm has the ability to achieve state-of-the-art performance in accuracy and robustness,and it has a strong adaptability as well,because the optimized results are accurate and robust even if the initial values have large deviations from the truth.This study could provide theoretical guidance and technical support for the image-based positioning and 3D reconstruction in the fields of photogrammetry,computer vision and robotics.

基于Self-Attention-BiLSTM网络的西瓜种苗叶片氮磷钾含量高光谱检测方法

元素含量无损检测技术可以为植物生长发育的环境精准调控提供关键实时数据。以西瓜苗为例,提出了一种基于图谱特征融合的氮磷钾含量深度学习检测方法。首先,使用高光谱仪拍摄西瓜苗叶片的高光谱图像,使用连续流动化学分析仪测定叶片的3种元素含量。然后,采用基线偏移校正(BOC)叠加高斯平滑滤波(GF)的光谱预处理方法和随机森林算法(RF)建立预测模型,基于竞争性自适应重加权采样(CARS)和连续投影算法(SPA)2种算法初步筛选出特征波长,再综合考虑波长数和建模精度设计了一种最优波长评价方法,将波长数进一步减少到3~4个。最后,提取使用U-Net网络分割的彩色图像颜色和纹理特征,和光谱反射率特征一起作为输入,基于自注意力机制-双向长短时记忆(Self-Attention-BiLSTM)网络构建了3种元素含量的预测模型。实验结果表明,氮磷钾含量预测的R2分别为0.961、0.954、0.958,RMSE分别为0.294%、0.262%、0.196%,实现了很好的建模效果。使用该模型对另2个品种西瓜进行测试,R2超过0.899、RMSE小于0.498%,表明该模型具有很好的泛化性。该高光谱建模方法使用少量波长光谱即实现了高精度检测,在精度和效率上达成了很好的平衡,为后续便携式高光谱检测装备开发奠定了理论基础。

An Experimental Study on Flow Patterns and Width Adjustment in Self-formed Channels

The distribution of velocity is one of the basic issues in river dynamics.Based on the experimental data measured by ADV in the flume of State Key Hydraulics Laboratory (SKHL),this paper analyzed the ver- tical distribution of point velocity and the varying law of turbulence intensity in straight mobile compound chan- nel with an asymmetric floodplain.Above certain relative height,the streamwise point velocity follows the loga- rithmic distribution.Below the location,the velocity varies linearly approxim...

Self-Healable and Stretchable PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7) Hybrid Hydrogel Thermoelectric Materials

Thermoelectric power generators have attracted increasing interest in recent years owing to their great potential in wearable electronics power supply.It is noted that thermoelectric power generators are easy to damage in the dynamic service process,resulting in the formation of microcracks and performance degradation.Herein,we prepare a new hybrid hydrogel thermoelectric material PAAc/XG/Bi_(2)Se_(0.3)Te_(2.7)by an in situ polymerization method,which shows a high stretchable and self-healable performance,as well as a good thermoelectric performance.For the sample with Bi_(2)Se_(0.3)Te_(2.7)content of 1.5 wt%(i.e.,PAAc/XG/Bi2Se0.3Te27(1.5 wt%)),which has a room temperature Seebeck coefficient of-0.45 mV K^(-1),and exhibits an open-circuit voltage of-17.91 mV and output power of 38.1 nW at a temperature difference of 40 K.After being completely cut off,the hybrid thermoelectric hydrogel automatically recovers its electrical characteristics within a response time of 2.0 s,and the healed hydrogel remains more than 99%of its initial power output.Such stretchable and self-healable hybrid hydrogel thermoelectric materials show promising potential for application in dynamic service conditions,such as wearable electronics.

RepBoTNet-CESA:An Alzheimer’s Disease Computer Aided Diagnosis Method Using Structural Reparameterization BoTNet and Cubic Embedding Self Attention

Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.

Cameroon Climate Predictions Using the SARIMA-LSTM Machine Learning Model: Adjustment of a Climate Model for the Sudano-Sahelian Zone of Cameroon

It is acknowledged today within the scientific community that two types of actions must be considered to limit global warming: mitigation actions by reducing GHG emissions, to contain the rate of global warming, and adaptation actions to adapt societies to Climate Change, to limit losses and damages [1] [2]. As far as adaptation actions are concerned, numerical simulation, due to its results, its costs which require less investment than tests carried out on complex mechanical structures, and its implementation facilities, appears to be a major step in the design and prediction of complex mechanical systems. However, despite the quality of the results obtained, biases and inaccuracies related to the structure of the models do exist. Therefore, there is a need to validate the results of this SARIMA-LSTM-digital learning model adjusted by a matching approach, “calculating-test”, in order to assess the quality of the results and the performance of the model. The methodology consists of exploiting two climatic databases (temperature and precipitation), one of which is in-situ and the other spatial, all derived from grid points. Data from the dot grids are processed and stored in specific formats and, through machine learning approaches, complex mathematical equations are worked out and interconnections within the climate system established. Through this mathematical approach, it is possible to predict the future climate of the Sudano-Sahelian zone of Cameroon and to propose adaptation strategies.

Adjustment mechanism of blasting dynamic-static action in the water decoupling charge

Water decoupling charge blasting excels in rock breaking,relying on its uniform pressure transmission and low energy dissipation.The water decoupling coefficients can adjust the contributions of the stress wave and quasi-static pressure.However,the quantitative relationship between the two contributions is unclear,and it is difficult to provide reasonable theoretical support for the design of water decoupling blasting.In this study,a theoretical model of blasting fracturing partitioning is established.The mechanical mechanism and determination method of the optimal decoupling coefficient are obtained.The reliability is verified through model experiments and a field test.The results show that with the increasing of decoupling coefficient,the rock breaking ability of blasting dynamic action decreases,while quasi-static action increases and then decreases.The ability of quasi-static action to wedge into cracks changes due to the spatial adjustment of the blast hole and crushed zone.The quasi-static action plays a leading role in the fracturing range,determining an optimal decoupling coefficient.The optimal water decoupling coefficient is not a fixed value,which can be obtained by the proposed theoretical model.Compared with the theoretical results,the maximum error in the model experiment results is 8.03%,and the error in the field test result is 3.04%.

On calculating glacial isostatic adjustment

Modeling the earth's fluid and elastic response to the melting of the glaciers of the last ice age is the most direct way to infer the earth's radial viscosity profile.Here,we compare two methods for calculating the viscoelastic response to surface loading.In one,the elastic equation of motion is converted to a viscoelastic equation using the Correspondence Principle.In the other,elastic deformation is added to the viscous flow as isostatic adjustment proceeds.The two modeling methods predict adjustment histories that are different enough to potentially impact the interpretation of the observed glacial isostatic adjustment(GIA).The differences arise from buoyancy and whether fluid displacements are subjected to hydrostatic pre-stress.The methods agree if they use the same equations and boundary conditions.The origin of the differences is determined by varying the boundary conditions and pre-stress application.

Rigidloop Adjustable可调袢钢板治疗踝关节骨折并下胫腓联合损伤

目的比较Rigidloop Adjustable(RLA)可调袢钢板与螺钉治疗踝关节骨折合并下胫腓联合损伤的临床效果。方法回顾性分析2019年9月至2022年5月武汉市第三医院收治的44例踝关节骨折合并下胫腓联合损伤的患者。踝关节骨折行切开复位内固定后,下胫腓联合采用RLA可调袢钢板固定22例,2枚皮质骨螺钉三皮质骨固定22例。应用AOFAS踝与后足功能评分、术侧与健侧踝关节活动度平均差值及术前、术后随访时正位X线片下胫腓间隙(tibiofibular bottom clear space,TBCS)、下胫腓重叠距离(tibiofibular bottom overlap,TBOL)比较两组临床效果。结果患者均获得随访,随访时间12~18个月,平均(16.41±1.66)个月。患者踝关节功能满意,无伤口感染、皮肤激惹等并发症。术后3、6、12个月时,RLA可调袢钢板组双侧踝关节活动度差值均显著小于螺钉组,差异有统计学意义(P<0.05);术后3、6、12个月时,两组的AOFAS评分比较,差异无统计学意义(P>0.05);TBCS、TBOL术前、术后即刻、术后3个月及末次随访时,差异无统计学意义(P>0.05)。结论RLA可调袢钢板和螺钉均可有效固定下胫腓联合,与螺钉固定相比,RLA可调袢钢板组可早期功能锻炼,获得更好的踝关节活动度。

An extended state observer with adjustable bandwidth for measurement noise

In this paper,a bandwidth-adjustable extended state observer(ABESO)is proposed for the systems with measurement noise.It is known that increasing the bandwidth of the observer improves the tracking speed but tolerates noise,which conflicts with observation accuracy.Therefore,we introduce a bandwidth scaling factor such that ABESO is formulated to a 2-degree-of-freedom system.The observer gain is determined and the bandwidth scaling factor adjusts the bandwidth according to the tracking error.When the tracking error decreases,the bandwidth decreases to suppress the noise,otherwise the bandwidth does not change.It is proven that the error dynamics are bounded and converge in finite time.The relationship between the upper bound of the estimation error and the scaling factor is given.When the scaling factor is less than 1,the ABESO has higher estimation accuracy than the linear extended state observer(LESO).Simulations of an uncertain nonlinear system with compound disturbances show that the proposed ABESO can successfully estimate the total disturbance in noisy environments.The mean error of total disturbance of ABESO is 15.28% lower than that of LESO.