Research on the protection and reuse of industrial heritage from the perspective of public participation—a case study of northern mining area of Pingdingshan, China

With the decline of the big industrial period, many industrial cities in China are facing the problem of urban transformation. Post-industrial economic activities and social life often replace the demand for land and population growth, and the particular type of cultural heritage of industrial heritage is often abandoned and decayed. Recent domestic and foreign research has responded to this problem and sought to provide solutions for the protection and reuse of industrial heritage. Despite some progress, the advice and feelings of ordinary citizens are often rarely considered, or how local urban characteristics become the core of urban reconstruction. To solve this problem, the focus of this study is the case study of Pingdingshan City. Pingdingshan is an industrial city with coal as its core industry. Shortly, the problem of industrial heritage will be a severe problem facing the city. The study included research designs and methods for collecting data from field observations, questionnaires, interviews, and literature studies. In the process, researchers have critically considered the importance and implications of public participation in exploring the way in which they are protected and reused through the protection and reuse of industrial heritage. It is particularly worth mentioning that in the reconstruction of the protection and reuse of industrial heritage in Pingdingshan, government officials and enterprises lack sensitivity to local conditions and the views of residents. The study concluded that the protection and reuse of industrial heritage require public participation and that the public’s demands can guide and determine the way industrial heritage is protected and reused

Transcriptome profiling reveals the role of ZBTB38 knock-down in human neuroblastoma

ZBTB38 belongs to the zinc finger protein family and contains the typical BTB domains. As a transcription factor, ZBTB38 is involved in cell regulation, proliferation and apoptosis, whereas, functional deficiency of ZBTB38 induces the human neuroblastoma (NB) cell death potentially. To have some insight into the role of ZBTB38 in NB development, high throughput RNA sequencing was performed using the human NB cell line SH-SY5Y with the deletion of ZBTB38. In the present study, 2,438 differentially expressed genes (DEGs) in ZBTB38−/− SH-SY5Y cells were obtained, 83.5% of which was down-regulated. Functional annotation of the DEGs in the Kyoto Encyclopedia of Genes and Genomes database revealed that most of the identified genes were enriched in the neurotrophin TRK receptor signaling pathway, including PI3K/Akt and MAPK signaling pathway. we also observed that ZBTB38 affects expression of CDK4/6, Cyclin E, MDM2, ATM, ATR, PTEN, Gadd45, and PIGs in the p53 signaling pathway. In addition, ZBTB38 knockdown significantly suppresses the expression of autophagy-related key genes including PIK3C2A and RB1CC1. The present meeting provides evidence to molecular mechanism of ZBTB38 modulating NB development and targeted anti-tumor therapies

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Performance and security enhancement solutions for positron emission tomography medical hardware

Positron Emission Tomography (PET) is an emerging medical imaging methodology for diagnosing cancer. The optimization and security solutions surrounding this technology are essential issues in biomedical engineering. Low-resolution and unauthorized modification of medical images will affect clinical analysis and medical diagnostics. To improve image quality and security while minimizing its impact on medical hardware, this paper analyzes a highly integrated data acquisition approach based on Time-over-Threshold (ToT) and proposes a lightweight security solution based on Physically Unclonable Function (PUF) for PET scan medical hardware. Compared to existing applications, the time-based sampling method can provide very good image quality, and the proposed watermarking and encryption method based on PUF enables enhanced privacy protection with fewer hardware costs for PET medical imaging technology

Effects of Freeze–Thaw Cycles on the Internal Voids Structure of Asphalt Mixtures

Freeze–thaw cycle is one of the main distresses of asphalt pavement, and the law of freeze–thaw damage has always been an important topic. In this paper, X-ray computed tomography (CT) of asphalt mixture before and after freezing and thawing was carried out, and its two-dimensional (2D) digital image was recognized. Firstly, the eigenvalues of internal voids of asphalt mixture are extracted. Then the distribution of internal voids was analyzed. Finally, the evolution law of internal voids was summarized. The research results show that the characteristic mean value of the 9th cycle is the irreversible limit of freeze–thaw damage, and the non-resilience after the large void area increases is the fundamental reason for the accumulation of freeze–thaw damage. The source of void damage shifts from large voids to small voids, and the middle-stage is a critical stage of freeze–thaw damage. This work quantitatively evaluates the internal freeze–thaw damage process of asphalt mixture, and a morphological theory of the evolution of void damage based on an equivalent ellipse is proposed, which is helpful for better understanding the freezing–thawing damage law of asphalt pavement

A method for swift selection of appropriate approximate multipliers for CNN hardware accelerators

As convolutional neural networks (CNNs) gain traction for embedded device implementation, there's a burgeoning interest in approximate computing technologies for increasing hardware efficiency. Most of the works in this field focus on proposing novel approximate hardware units and structures, but structured guidance for selecting optimal approximate calculation techniques for CNN accelerators remains scant. This paper introduces a novel error injection technique, leveraging the error rate matrix of approximate multipliers (AxMs), called Error Matrix Based Error Injected (EMEI). This facilitates the swift selection of appropriate AxMs for each PE in the CNN hardware accelerator. In addition, this approach is applied to a MobileNetV2-based CNN model on the CIFAR-10 dataset to demonstrate the performance. Experimental results show that our method adeptly optimises hardware resources by combining AxMs with different accuracy levels while ensuring accuracy. This innovation paves the way for streamlined CNN accelerator designs in embedded systems

Heating Control Strategy Based on Dynamic Programming for Building Energy Saving and Emission Reduction

Finding the optimal balance between end-user’s comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable ‘thermal energy storage in building’ is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set ‘baseline-20 °C’, which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation

Tamper resistant design of convolutional neural network hardware accelerator

The globalisation of supply chains and manufacturing processes can lead to loss of control over the manufacturing process and exposure to potentially malicious third parties, thus making the security of Convolutional Neural Network hardware accelerators compromised by emerging attacks (e.g., hardware Trojan(HT) insertion attacks and backdoor attacks from third-party dataset providers). In this paper, a new defence mechanism, called Shuffle and Substitution-Based Defence Mechanism(SSDM), is proposed to effectively defend against attacks launched by attackers from the third-party dataset providers and the Fabrication phase. The new countermeasure proposed in this paper can not only effectively suppress the activation of most existing HTs, but also greatly increase the difficulty for adversaries from third-party dataset providers to successfully execute backdoor attacks. The experimental results show that the new defensive countermeasures are effective in preventing HTs from being activated and significantly increasing the difficulty of backdoor attacks

Machine learning-based hybrid thermal modeling and diagnostic for lithium-ion battery enabled by embedded sensing

Accurate monitoring of internal temperature distribution is critical to the safety of lithium-ion batteries (LIBs). However, both the radial and the axial thermal inhomogeneities are remarkable in practical LIB utilizations, which challenges the control-oriented thermal modeling. Motivated by this, a novel smart battery implanting internally the distributed fibre optical sensor is designed to perceive the inhomogeneity of temperature distribution of LIB. Enabled by this, a hybrid lumped-thermal-neural-network (LTNN) model is proposed, for the first time, by combining the mechanism-driven distributed lumped thermal model and the machine learning-based axial thermal gradient compensation. A hybrid LTNN-based close-loop observer is further proposed to estimate the internal multi-point temperature of LIB in a real-time fashion. Experimental results suggest that the proposed hybrid LTNN model captures the complicated thermal distribution of LIB with remarkably elevated accuracy, compared with the traditional lumped thermal model. Moreover, the hybrid LTNN model is highly compatible with commonly-used state observation methods to realize accurate and space resolved internal thermal diagnostic for the LIB

Reduction of Moving Target Time-of-Flight Measurement Uncertainty in Femtosecond Laser Ranging by Singular Spectrum Analysis Based Filtering

Femtosecond laser ranging has drawn great interest in recent years, particularly based on an asynchronous optical sampling implementation where a pair of femtosecond lasers are used. High precision absolute ranging either relies on tightly-phase-locked optical frequency combs (a dual-comb setup) or multiple averaging of the measurements from two free-running femtosecond lasers. The former technique is too complicated for practical applications, while the latter technique does not apply to moving targets. In this report, we propose a new route to utilizing a powerful singular spectrum analysis (SSA) filtering method to improve femtosecond laser ranging precision for moving targets with acceleration. The SSA method is capable of separating complex patterns in signals without a priori knowledge of the dynamical model. Here, we utilize the basic SSA filter to extract the target trajectory in the presence of measurement noise both in numerical simulation and in the absolute ranging experiment based on a pair of free-running femtosecond lasers. The experimentally-achieved absolute ranging uncertainty of a moving target is well below 110 nm at a 200-Hz update rate by applying the basic SSA filter. This method paves the way to the practical applications of femtosecond absolute ranging for dynamic objects