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In this paper, time series model of ARIMA is used to make short-term forecasting of property crime for one city of China. With the given data of property crime for 50 weeks, an ARIMA model is determined and the crime amount of 1 week ahead is predicted. The model's fitting and forecasting results are compared with the SES and HES. It is shown that the ARIMA model has higher fitting and forecasting accuracy than exponential smoothing. This work will be helpful for the local police stations and municipal governments in decision making and crime suppression.

Hybrid organic-inorganic perovskite, well-known as light-absorbing materials in solar cells, have recently attracted considerable interest for applications in resistive switching (RS) memory. A better understanding of the role of interface state in hybrid perovskite materials on RS behavior is essential for the development of practical devices. Here, we study the influence of interface state on the RS behavior of an Au/CHNHPbI/FTO memory device using a simple air exposure method. We observe a transition of RS hysteresis behavior with exposure time. Initially no hysteresis is apparent, but air exposure induces bipolar RS and a negative differential resistance (NDR) phenomenon. The reductions of I/Pb atomic ratio and work function on the film surface are examined using XPS spectra and Kelvin probe technique, verifying the produce of donor-type interface states (e.g., iodine vacancies) during CHNHPbI film degradation. Studies on complex impedance spectroscopy confirm the responsibility of interface states in NDR behavior. Eventually, the trapping/detrapping of electrons in bulk defects and at interface states accounts for the bipolar RS behavior accompanied with the NDR effect.

In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

This paper uses resilience as a lens through which to analyse disasters and other major threats to patterns of criminal behaviour. A set of indicators and mathematical models are introduced that aim to quantitatively describe changes in crime levels in comparison to what could otherwise be expected, and what might be expected by way of adaptation and subsequent resumption of those patterns. The validity of the proposed resilience assessment tool is demonstrated using commercial theft data from the COVID-19 pandemic period. A 64 per cent reduction in crime was found in the studied city (China) during an 83-day period, before daily crime levels bounced back to higher than expected values. The proposed resilience indicators are recommended as benchmarking instruments for evaluating and comparing the global impact of COVID-19 policies on crime and public safety.

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We present a new analog-to-digital converter (ADC)-based architecture of a phase-tracking receiver (PT-RX) optimized for ultra-low-power (ULP) and ultra-low-voltage (ULV) operations for the Internet of Things (IoT). The RX employs a type-II loop configuration that offers improved stability compared with the previous type-I PT-RX solutions. In addition, the type-II loop is also very tolerant of long run-lengths of consecutive "1" or "0" symbol sequences. Fabricated in 28-nm CMOS, the prototype PT-RX targets Bluetooth low energy (BLE) standard consuming only 1.5 mW at a supply of ≤0.7 V. It maintains an adjacent-channel rejection (ACR) of ≥−11/3.5/17/27 dB at 0/±1/±2/±3 MHz offset and can tolerate out-of-band (OOB) blockers of minimum −21 dBm across 1.0-3.5 GHz while also offering a best-in-class figure of merit (FoM) of 181 dB, with a 1-Mb/s BLE sensitivity of −93 dBm. Index Terms-Bluetooth low energy (BLE), digitally controlled oscillator (DCO)-based receivers (RXs), discrete-time (DT) filter, Internet-of-Things (IoT), phase-tracking RXs (PT-RXs), successive-approximation-register (SAR)-analog-to-digital converter (ADC), ultra-low power (ULP), ultra-low voltage (ULV). I. INTRODUCTION T HE massive deployment of Internet-of-Things (IoT) applications calls for ultra-low-power (ULP) and ultralow-voltage (ULV) design techniques for system-on-chip (SoC) devices realized in nanoscale CMOS [1]-[4]. The RF receiver (RX) is a key IoT subsystem that takes a significant portion of the IoT's total power budget. In the industry, commercial RXs using Cartesian [i.e., in-phase/quadrature (I/Q)] topology [5], [6] aimed at Bluetooth low energy (BLE), a dominant standard in IoT devices, consume 5-10 mW. A more recent industry work [1], a superheterodyne discrete-time (DT) Cartesian RX, achieves the lowest power of 2.75 mW with a sensitivity of −95 dBm. However, it becomes more and more challenging to further reduce the power allocation for the RX,

With the acceleration of urbanization, waterlogging has become an increasingly serious issue. Road waterlogging has a great influence on residents’ travel and traffic safety. Thus, evaluation of residents’ travel difficulties caused by rainstorm waterlogging disasters is of great significance for their travel safety and emergency shelter needs. This study investigated urban rainstorm waterlogging disasters, evaluating the impact of the evolution of such disasters’ evolution on residents’ evacuation, using Daoli District (Harbin, China) as the research demonstration area to perform empirical research using a combination of scenario simulations, questionnaires, GIS spatial technology analysis and a hydrodynamics method to establish an urban rainstorm waterlogging numerical simulation model. The results show that under the conditions of a 10-year frequency rainstorm, there are three street sections in the study area with a high difficulty index, five street sections with medium difficulty index and the index is low at other districts, while under the conditions of a 50-year frequency rainstorm, there are five street sections with a high difficulty index, nine street sections with a medium difficulty index and the other districts all have a low index. These research results can help set the foundation for further small-scale urban rainstorm waterlogging disaster scenario simulations and emergency shelter planning as well as forecasting and warning, and provide a brand-new thought and research method for research on residents’ safe travel.