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.

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.

Research on Demand Response Potential of Adjustable Loads in Demand Response Scenarios

To address the issues of limited demand response data,low generalization of demand response potential evaluation,and poor demand response effect,the article proposes a demand response potential feature extraction and prediction model based on data mining and a demand response potential assessment model for adjustable loads in demand response scenarios based on subjective and objective weight analysis.Firstly,based on the demand response process and demand response behavior,obtain demand response characteristics that characterize the process and behavior.Secondly,establish a feature extraction and prediction model based on data mining,including similar day clustering,time series decomposition,redundancy processing,and data prediction.The predicted values of each demand response feature on the response day are obtained.Thirdly,the predicted data of various characteristics on the response day are used as demand response potential evaluation indicators to represent different demand response scenarios and adjustable loads,and a demand response potential evaluation model based on subjective and objective weight allocation is established to calculate the demand response potential of different adjustable loads in different demand response scenarios.Finally,the effectiveness of the method proposed in the article is verified through examples,providing a reference for load aggregators to formulate demand response schemes.

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。

Deep Learning-Based Control System Design for Emergency Vehicles through Intersections

This paper addresses the challenge of integrating priority passage for emergency vehicles with optimal intersection control in modern urban traffic. It proposes an innovative strategy based on deep learning to enable emergency vehicles to pass through intersections efficiently and safely. The research aims to develop a deep learning model that utilizes intersection violation monitoring cameras to identify emergency vehicles in real time. This system adjusts traffic signals to ensure the rapid passage of emergency vehicles while simultaneously optimizing the overall efficiency of the traffic system. In this study, OpenCV is used in combination with Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) to jointly complete complex image processing and analysis tasks, to realize the purpose of fast travel of emergency vehicles. At the end of this study, the principle of the You Only Look Once (YOLO) algorithm can be used to design a website and a mobile phone application (app) to enable private vehicles with emergency needs to realize emergency passage through the application, which is also of great significance to improve the overall level of urban traffic management, reduce traffic congestion and promote the development of related technologies.

Adjustable robust low-carbon dispatch for interconnected power systems in Northeast Asian countries

Interconnected power systems that link several countries and fully utilize their individual resources in a complementary manner are becoming increasingly important.As these systems enhanee accommodation of renewable energy,they also represent a move toward low-carbon and low-emissi on power systems.In this paper,a low-carb on dispatch model is proposed to coo rd i nate the gen erati on output betwee n several coun tries where the carb on emissi on constraint is a priority.An adjustable robust optimization approach is used to find the optimal solution under the worst-case scenario to address the uncertainties associated with renewable energy resources.A specific constraint is that the area control error for each country should be self-balanced.Furthermore,a reformation using participation factors is presented to simplify the proposed robust dispatch model.Simulation results for practical interconnected power systems in northeast Asian countries verify the effectiveness of the proposed model.

A Light Source System of Multi-star Simulator with Adjustable Background and Controllable Magnitude

A light source of multi-star simulator capable of background adjustment and magnitude control has been designed.Two integrating spheres are employed as the star-point light source and the background light source respectively.A beam splitter prism has been designed to serve as the beam combiner for the star-point and the background light sources,and a mathematical model has been constructed respectively to compute the light flux of the two integrating spheres.A magnitude testing system and a background testing system have been created using low-light illuminometer,luminance meter,and testing instruments to measure the star-point magnitude and the background luminance of the multi-star simulator.The test results suggest that the star-point magnitude is adjustable from0 to+5 m_v,with a simulation precision superior to ±0.026 m_v.The maximum background luminance is 3.8×10~5 cd·m^(-2),and the minimum background luminance is6.4×10^(-2)cd·m^(-2).The designed light source system can meet the requirements for simulating the stellar map with a sky background.

Adjustable quantum coherence effects in a hybrid optomechanical system

We propose a system for achieving some adjustable quantum coherence effects, including the normal-mode splitting（NMS）, the optomechanically induced transparency（OMIT）, and the optomechanically induced absorption（OMIA）. In this system, two tunnel-coupled optomechanical cavities are each driven by a coupling field and coupled to an atomic ensemble.Besides, one of the cavities is also injected with a probe field. When the system works under different conditions, we can obtain the NMS, the OMIT, and the OMIA, respectively. These effects can be flexibly adjusted by the tunnel coupling between the two cavities, the power of the coupling lasers, and the coupling strength between the atomic ensembles and the cavity fields. Furthermore, we can realize the OMIT even if the oscillating mirrors have relatively larger decay rates.

Reconfigurable source illusion device for airborne sound using an enclosed adjustable piezoelectric metasurface

Source illusion is an important issue in acoustic fields that has significant applications in various practical scenarios.Recent progress in acoustic metasurfaces has broken the limitation of manipulating large-scale waves at subwavelength scales and enables a better illusion capability,while there is still a problem that most previous studies are hampered by a lack of tuning capability.Here we propose a reconfigurable source illusion device capable of providing azimuthallydependent phase delay in real-time via changing the static voltage distribution.The resulting device is implemented by employing an adjustable piezoelectric metasurface with a subwavelength thickness that can achieve a full 2π-phase shift while maintaining efficient transmittance.The effectiveness of our mechanism is demonstrated via two distinctive source illusion phenomena of shifting and transforming a simple point source without changing the device geometry.We anticipate that our methodology,which does not require a large device size or a complicated phased array,will open up new avenues for the miniaturization and integration of source illusion devices and may promote their on-chip applications in a variety of fields,such as acoustic camouflage and manipulation precision.

Experimental Analysis of the Flow Characteristics of an Adjustable Critical-Flow Venturi Nozzle

The response of an adjustable critical-flow Venturi nozzle is investigated through a set indoor experiments aimed to determine the related critical flow rate,critical pressure ratio,and discharge coefficient.The effect of a variation in the cone displacement and liquid content on the critical flow characteristics is examined in detail and it is shown that the former can be used to effectively adjust the critical flow rate.The critical pressure ratio of the considered nozzle is above 0.85,and the critical flow control deviation of the gas flow is within±3%.Liquid flow can reduce the gas critical mass flow rate accordingly,especially for the cases with larger liquid volume and lower inlet pressure.The set of results and conclusions provided are intended to support the optimization of steam injection techniques in the context of heavy oil recovery processes.

Novel Radiation-Adjustable Heating Terminal Based on Flat Heat Pipe Combined with Air Source Heat Pump

The electrification of building heating is an effective way to meet the global carbon target. As a clean and sustainable electrified heating technology, air-source heat pumps (ASHPs) are widely used in areas lacking central heating. However, as a major component of space heating, heating terminals might not fit well with ASHP in order to achieve both intermittency and comfort. Therefore, this study proposes a novel radiation-adjustable heating terminal combined with an ASHP to achieve electrification, intermittency, and better thermal comfort. Radiant terminals currently suffer from three major problems: limited maximum heating capacity, inability to freely adapt, and difficulty with combining them with ASHPs. These problems were solved by improving the structural design of the novel terminal (Improvement A–E). Results showed that the maximum heating capacity increased by 23.6% and radiation heat transfer ratio from 10.1% to 30.9% was provided for users with the novel terminal. Further, new flat heat pipe (FHP) design improved stability (compressor oil return), intermittency (refrigerant thermal inertia), and safety (refrigerant leakage risk) by reducing the length of exposed refrigerant pipes. Furthermore, a new phased operation strategy was proposed for the novel terminal, and the adjustability of the terminal was improved. The results can be used as reference information for decarbonizing buildings by electrifying heating terminals.

Proposing Adjustments to the Dry Season Inter-Reservoir Operation Rules of the Red River System to Improve Water Use Efficiency of the Reservoirs

The Red-Thai Binh River system is an important water resource to the Northern Delta, serving the development of agriculture, people’s livelihood and other economic sectors through its upstream reservoirs and a system of water abstraction works along the rivers. However, due to the impact of climate change and pressure from socio-economic development, the operation of the reservoir system according to Decision No. 740/QD-TTg was issued on June 17, 2019 by the Prime Minister of Government promulgating the Red-Thai Binh River system inter-reservoir operation rules (Operation rules 740) has some shortcomings that need adjustments for higher water use efficiency, meeting downstream water demand and power generation benefits. Through the results of water balance calculation and analysis of economic benefits from water use scenarios, this research proposed adjustment to the inter-reservoir operation during dry season in the Red River system. The result showed that an average water level of 1.0 - 1.7 m should be maintained at Hanoi during the increased release period.

Accelerating Oxygen Electrocatalysis Kinetics on Metal-Organic Frameworks via Bond Length Optimization

Metal-organic frameworks(MOFs)have been developed as an ideal platform for exploration of the relationship between intrinsic structure and catalytic activity,but the limited catalytic activity and stability has hampered their practical use in water splitting.Herein,we develop a bond length adjustment strategy for optimizing naphthalene-based MOFs that synthesized by acid etching Co-naphthalenedicarboxylic acid-based MOFs(donated as AE-CoNDA)to serve as efficient catalyst for water splitting.AE-CoNDA exhibits a low overpotential of 260 mV to reach 10 mA cm^(−2)and a small Tafel slope of 62 mV dec^(−1)with excellent stability over 100 h.After integrated AE-CoNDA onto BiVO_(4),photocurrent density of 4.3 mA cm^(−2)is achieved at 1.23 V.Experimental investigations demonstrate that the stretched Co-O bond length was found to optimize the orbitals hybridization of Co 3d and O 2p,which accounts for the fast kinetics and high activity.Theoretical calculations reveal that the stretched Co-O bond length strengthens the adsorption of oxygen-contained intermediates at the Co active sites for highly efficient water splitting.

一种基于CatBoost优化的光伏阵列故障诊断模型

大部分光伏电站地处偏僻、地形复杂的区域,受到外界环境的影响,易发生各种故障.而传统的光伏阵列故障诊断方法存在精度不高以及光伏数据利用率低等问题.针对以上问题,本文先是通过引入Levy飞行策略和步长因子动态调整策略改进麻雀搜索算法(Sparrow Search Algorithm,SSA),降低SSA算法陷入局部最优的风险,提升SSA算法的寻优能力.然后采用改进的Levy步长调整麻雀搜索算法(Levy Adjustment Sparrow Search Algorithm,LASSA)对CatBoost模型关键超参数进行寻优,提出了一种基于CatBoost并以LASSA为优化策略的光伏阵列故障诊断模型LASSA-CatBoost,以实现光伏阵列的短路、开路、老化和阴影遮挡故障的精确诊断.实验结果表明,LASSA-CatBoost模型的故障诊断准确率为99.7%,相较于优化前的CatBoost模型,准确率提高了3.6%.与现有的光伏阵列故障诊断模型相比,LASSA-CatBoost模型的准确性和稳定性更高.

Effect of projectile head style on high gacceleration waveform of Hopkinson bar calibration system

The freestyle Hopkinson bar is a kind of main high g loading equipment utilized widely in calibration of high g accelerometer and other high shock conditions. The calibration experiment of accelerometer was conducted. With one-dimension stress wave theory, ANSYS/LS-DYNA software and experiment, the effect rules of the projectile＇s front-head style and the accelerometer＇s mounted base＇s length on acceleration waveform were analyzed. The results show that the acceleration duration inspired from Hopkinson bar is almost equal to the rising edge time of perfect half sine stress wave, and it is independent to the mounted base＇s length. Moreover, the projectile＇s fronthead style is a main affecting factor, and the projectiles with less Conical degrees will produce the lower amplitude and longer acceleration duration.

Research on Biometric Identification Method of Nuclear Cold Source Disaster Based on Deep Learning

In this paper, an improved Fast-R-CNN nuclear power cold source disaster biological image recognition algorithm is proposed to improve the safety operation of nuclear power plants. Firstly, the image data sets of the disaster-causing creatures hairy shrimp and jellyfish were established. Then, in order to solve the problems of low recognition accuracy and unrecognizable small entities in disaster biometrics, Gamma correction algorithm was used to optimize the image of the data set, improve the image quality and reduce the noise interference. Transposed convolution is introduced into the convolution layer to increase the recognition accuracy of small targets. The experimental results show that the recognition rate of this algorithm is 6.75%, 7.5%, 9.8% and 9.03% higher than that of ResNet-50, MobileNetv1, GoogleNet and VGG16, respectively. The actual test results show that the accuracy of this algorithm is obviously better than other algorithms, and the recognition efficiency is higher, which basically meets the preset requirements of this paper.

Multimodal Data-Driven Reinforcement Learning for Operational Decision-Making in Industrial Processes

Dear Editor, This letter proposes a multimodal data-driven reinforcement learning-based method for operational decision-making in industrial processes. Due to the frequent fluctuations of feedstock properties and operating conditions in the industrial processes, existing data-driven methods cannot effectively adjust the operational variables. In addition, multimodal data such as images, audio.

A real-time performance improvement method for composite time scale

The composite time scale(CTS)provides a stable,accurate,and reliable time scale for modern society.The improvement of CTS’s real-time performance will improve its stability,which strengths related applications’performance.Aiming at this goal,a method achieved by determining the optimal calculation interval and accelerating adjustment stage is proposed in this paper.The determinants of the CTS’s calculation interval(characteristics of the clock ensemble,the measurement noise,the time and frequency synchronization system’s noise and the auxiliary output generator noise floor)are studied and the optimal calculation interval is obtained.We also investigate the effect of ensemble algorithm’s initial parameters on the CTS’s adjustment stage.A strategy to get the reasonable initial parameters of ensemble algorithm is designed.The results show that the adjustment stage can be finished rapidly or even can be shorten to zero with reasonable initial parameters.On this basis,we experimentally generate a distributed CTS with a calculation interval of 500 s and its stability outperforms those of the member clocks when the averaging time is longer than1700 s.The experimental result proves that the CTS’s real-time performance is significantly improved.

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.

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%.