Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating the Impacts of the New-Type Urbanization Policy on Rural Settlement Changes: A Case Study in Dingzhou, China

Abstract: To cope with the difficulties of integrating migrant workers into urban life and inefficient land use caused by conventional urbanization, China has proposed a new type of urbanization policy. This policy may have a significant impact on the spatial patterns of rural settlements in China. Exploring this potential impact is conducive to the proposal of scientific plans for the spatial patterns of rural settlements. Therefore, this paper chooses Dingzhou, one of the pilot cities of this new-type urbanization, as the research area to carry out a simulation study on the impact of the new-type urbanization policy. Dingzhou has invested heavily in the new-type urbanization construction in recent years, but the influence of the policy on rural settlements remains unclear. Based on the theoretical framework of previous studies, this paper set up three scenarios, namely “conventional urbanization”, “new-type urbanization”, and “counter-urbanization”. This paper used FLUS (Future Land Use Simulation) Model and various spatial data to simulate the spatial patterns of rural settlements in Dingzhou in 2030 under the different scenarios. By comparing the different scenarios, the impacts of the new-type urbanization policy on the spatial patterns of rural settlements in Dingzhou were evaluated. The results indicated that: (1) From 2000 to 2015, the area of rural settlements in Dingzhou increased by 11.12%. Spatially, the density of rural settlements around the cities and towns increased, and rural settlement areas were mainly converted from cultivated land. Rural settlements were mainly transformed into urban land and cultivated land. (2) The overall simulation accuracy of FLUS was 0.89, so it can be well applied to the simulation of rural settlements. (3) In all three scenarios, rural settlements expanded along their edges, and the closer they were to towns, the more obvious the expansion was. In the counter-urbanization scenario, the change of rural settlements was most dramatic. (4) The new-type urbanization policy makes the spatial patterns of rural settlements in Dingzhou more stable and more intensive.
The 30m-NDVI-Based Alpine Grassland Changes and Climate Impacts in the Three-River Headwaters Region on the Qinghai-Tibet Plateau from 1990 to 2018

Abstract: The response of long-term vegetation changes and climate change has been a hot topic in recent research. Previously, a Landsat-based fusion model was developed and used to produce a dataset of normalized vegetation index (NDVI) for the Three-River Headwater region on the Qinghai-Tibet Plateau with a spatial resolution of 30 m and the time spanning the nearly 30 years from 1990 to 2018. In this study, the NDVI was applied to an analysis of the spatial and temporal changes in the alpine grassland and the impacts from climate change using the Theil-Sen Median method and linear regression. The results showed that: (1) The regional mean NDVI was 0.39 and showed a spatial pattern of decreasing from the southeast to the northwest in the recent three decades. Among the three parks, the Lancang River Park had the highest NDVI (0.43), followed by the Yellow River Park (0.38) and Yangtze River Park (0.23). (2) An upward trending was found in the NDVI time series at a rate of 0.0031 yr–1 (R2 =0.62, P < 0.01) over the whole period of 1990–2018. The increasing rate (0.00649 yr–1, R2 =0.71, P < 0.01) in the latter period of 2005–2018 was nearly 2.3 times of that (0.00284 yr–1, R2 =0.31, P < 0.01) in the previous period of 1990–2005. In the latest periods, the three parks experienced rates that were 2.3 to 63 times the corresponding values in the early period. (3) The NDVI is correlated more positively with temperature than precipitation. The impacts of climate change decreased along with the coverage fraction from the higher, median and then lower levels. The climate change can explain 34% of the variability in the NDVI time series of the areas with a higher fraction of grassland coverage, while it was 31% for the median fraction and 20% for the lower fraction. This study is the first to use the 30 m NDVI dataset spanning nearly 30 years to analyze the spatial and temporal variability and climate impacts in the alpine grasslands of the Three-River Headwater region of the Qinghai-Tibet Plateau. The results provide a basis for assessments on the ecological management effects and ecological quality based on long-term baseline data with a higher spatial resolution.
Assessing the Ecological Carrying Capacity of Countries along the Belt and Road

Abstract: The Belt and Road Initiative (B&R Initiative) is a crucial strategy to promote regional sustainable development in the new era. However, the realization of the B&R Initiative faces huge challenges because of the dual characteristics of a fragile eco-environment and strong dependence on ecological resources for economic development in the Belt and Road (B&R) countries. The ecological carrying capacity (ECC) is a crucial indicator for evaluating regional sustainable development. From the perspective of the relationship between the supply and consumption of ecological resources, this study uses net primary productivity data to measure the supply capacity of ecological resources, and it uses the agricultural production and trade data provided by the United Nations Food and Agriculture Organization to measure the level of ecological resource consumption. These supply and consumption data are then used to assess the ECC and ecological carrying status (ECS) of the B&R countries in 2017. The results show that: (1) The ECC of the B&R is 11.097 billion people; the ecosystem can also support 6.433 billion people, and the ECC is in a state of rich and surplus. (2) The ECS is polarized among the regions and countries along the B&R. Of the 65 countries, the ECC of 40 countries is in a rich and surplus state, mainly in Mid-East Europe and Southeast Asia, while the ECC of 19 countries is in severe overload, mainly in West Asia/Middle East. (3) Although the ecosystems still have ample carrying space in countries along the B&R, ecological protection is still facing enormous challenges during the implementation of the B&R Initiative combined with the internationally recognized ecological protection standards as well as the forecasts of the population and economic development. As the core content of building a new international trade network, the B&R Initiative will help to solve the spatial mismatch between the supply and consumption of ecological resources, which provides a new opportunity to coordinate the contradiction between the ecological protection and social demands of the B&R countries.
Software pre-execution for irregular memory accesses in the HBM era

The introduction of High Bandwidth Memory (HBM) necessitates the use of intelligent software prefetching in irregular applications to utilize the surplus bandwidth. In this work, we propose Software Pre-execution (SPE), a technique that relies on pre-executing a minimal copy of the loop of concern (we call the pre-execution loop) for the purpose of prefetching irregular accesses. This is complemented by the compiler's enforcing a certain prefetch distance through apriori strip-mining of the original loop such that the execution of the pre-execution loop is interspersed with the main loop to ensure timeliness of prefetches. We find that this approach provides natural advantages over prior art such as preservation of loop vectorization, handling short loops, avoiding performance bottlenecks, amenability to threading and most importantly, effective coverage. We demonstrate these advantages using a variety of benchmarks on Fujitsu's A64FX processor with HBM2 memory - we outperform prior art by 1.3x and 1.2x when using small and huge pages, respectively. Simulations further show that our approach holds stronger promise on upcoming processors with HBM2e.
High-precision remote sensing mapping of aeolian sand landforms based on deep learning algorithms

Abstract It is significant to adopt deep learning algorithms and higher-resolution remote sensing images in mapping large-scale and high-precision of aeolian landform. In this study, the western part of Horqin Sandy Land was taken as the study area. Based on the data collected from 2,786 verification points located in sandy land and remote sensing images of high-spectral and spatial resolution Sentinel-1, Sentinel-2, and GDEM (V3), this article made a research on data of large-scale and high-precision mapping classification of this area between 2015 and 2020 by using convolutional neural network deep learning algorithm. The results showed that the types of aeolian sandy landform in the west of Horqin Sandy Land mainly include longitudinal dune, flat sandy land, mild undulating sand land, nest-shaped land, parabolic dune, barchan dune, and dune chain, with an area of 1735.62, 51.32, 251.38, 902.07, 49.57, and 101.63 km2. Among them, longitudinal dune, barchan dune, and dune chain have the largest area, while parabolic dunes and flat sand land are smaller. Between 2015 and 2020, the area of aeolian landforms was reduced by 89.27 km2 and transformed into an oasis from a desert. This study adopted remote sensing data by high-resolution Sentinel and GDEM (V3) and convolutional neural network deep learning algorithm to map the aeolian landforms effectively. The precision of aeolian landform classification and Kappa coefficient in the western part of Horqin Sandy Land is as high as 95.51% and 0.8961. Combined with Sentinel-1, Sentinel-2, and GDEM (V3), the deep learning algorithm based on the convolution neural network can timely and effectively monitor the changes of sand dunes, which can be used for large-scale aeolian landforms.
Intravenous infusion of the exosomes derived from human umbilical cord mesenchymal stem cells enhance neurological recovery after traumatic brain injury via suppressing the NF-κB pathway

Abstract Traumatic brain injury (TBI) is a predominant cause of death and permanent disability globally. In recent years, much emphasis has been laid on treatments for TBI. Increasing evidence suggests that human umbilical cord mesenchymal stem cells (HUCMSCs) can improve neurological repair after TBI. However, the clinical use of HUCMSCs transplantation in TBI has been limited by immunological rejection, ethical issues, and the risk of tumorigenicity. Many studies have shown that HUCMSCs-derived exosomes may be an alternative approach for HUCMSCs transplantation. We hypothesized that exosomes derived from HUCMSCs could inhibit apoptosis after TBI, reduce neuroinflammation, and promote neurogenesis. A rat model of TBI was established to investigate the efficiency of neurological recovery with exosome therapy. We found that exosomes derived from HUCMSCs significantly ameliorated sensorimotor function and spatial learning in rats after TBI. Moreover, HUCMSCs-derived exosomes significantly reduced proinflammatory cytokine expression by suppressing the NF-κB signaling pathway. Furthermore, we found that HUCMSC-derived exosomes inhibited neuronal apoptosis, reduced inflammation, and promoted neuron regeneration in the injured cortex of rats after TBI. These results indicate that HUCMSCs-derived exosomes may be a promising therapeutic strategy for TBI.
Assessment of ship structure under fatigue loading: FE benchmarking and extended performance analysis

Abstract This paper presents a numerical procedure based on the finite element (FE) method using ANSYS Workbench software to analyse fatigue phenomena in ship structures. Fatigue failure prediction is used as a stress–life approach, when the stress is still in a linear area. This condition is frequently referred as high-cycle fatigue. Five geometric shapes taken from midship points on the structure of a ship are sampled. There are four types of materials: HSLA SAE 950X, medium-carbon steel, SAE 316L, and SAE 304L. The types of loading imposed on each sample include three conditions: zero-based, zero mean, and ratio. Mesh convergence analysis is conducted to determine the most effective mesh shape and size for analysing the structure. The results showed that the configuration of the geometric shapes, materials used, loading schemes, and mean stress theory affect the fatigue characteristics of the structure.
Connection Admission Control in UMTS with respect to Network Capacity and Quality of Service

The increasing number of subscribers in third generation networks, such as UMTS, will push these networks to their capacity limits within the next years. Especially UMTS tends to become instable when operated close to the capacity limit. Therefore, to meet the subscribers? QoS requirements andmaximize the operators? profit, a thoroughly parameterized Connection Admission Control (CAC) is indispensable in order to keep the system in a stable state. In this paper, we analyze existing CAC strategies. Furthermore, we offer new strategies to enhance the system? s capacity without endangering network stability. This is of great interest for each UMTS network operator since a better CAC algorithm allows a higher network capacity without any additional hardware installation costs. For this investigation a new simulation platform to evaluate the performance of mobile radio networks at a very detailed level has been designed and implemented. A UMTS Radio Interface Simulator (URIS), which emulates the standardized UMTS protocol stack and the TCP/IP protocol suite, is used together with a Radio Interference Simulation Engine (RISE), which is in charge of calculating the inter- and intra-cell interference, to evaluate the performance of the CAC.
Accessibility Analysis of Growth Patterns in Buenos Aires, Argentina

This study used a lens of accessibility to examine trends in spatial development in the greater Buenos Aires, Argentina, area over the past two decades. The analysis utilized remote sensing data collected over three time periods in Buenos Aires and automobile and transit employment accessibility measures that were calculated with the use of an open-source accessibility tool to analyze the area's spatial growth patterns. The employment accessibility of different modes of travel in Buenos Aires was calculated. It was noted that, although the transit system provided high employment accessibility to the center city, as well as to the southwest and south of the city, few developments were built in these areas. Most of the developments were built on the periphery of the city, which offered poor transit employment accessibility. The research focused on the development of different development types, such as gated communities, social housing, and informal settlements, and the employment accessibility of the development locations. This information was used to assess the accessibility efficiency of growth in greater Buenos Aires, as well as the effective size and distribution of the area's labor market. How spatial growth affected accessibility for different segments of the population was examined. Policy implications are drawn and possible elements of an integrated regional-use transport strategy are described.
Facile fabrication of stretchable photonic Ag nanostructures by soft-contact patterning of ionic Ag solution coatings

Abstract We describe a rapid and simple method to create Ag nanostructures by using direct mechanical patterning of ionic Ag ink coating under gentle pressure, then thermal annealing to reduce the ionic Ag ink to a metallic Ag layer. The ionic liquid-phase Ag coating is easily obtained by spin-coating ionic Ag ink that has appropriate Ag concentration and can be either printed or imprinted on the desired substrate by using a soft elastomer patterning mold, then reduced to the Ag nanostructure by subsequent thermal annealing. More specifically, we present two methods: transfer printing and soft nanoimprinting. In transfer printing, the ionic Ag ink is first inked onto the elastomer mold which then contacts the target substrate to transfer the Ag nanopattern. In soft nanoimprinting, the elastomer mold conducts soft imprinting to engineer the ionic Ag ink coating to the Ag nanostructure. We systematically investigate the optimal patterning conditions by controlling the initial Ag ink concentration and the coating, printing, imprinting, and annealing conditions, to derive Ag architecture that has tunable photonic functionality. As an example, we demonstrate polarization-sensitive reflective color filters that exploit shape-tunable Ag nanostructures fabricated by soft nanoimprinting using a controllably-stretched elastomer mold.
Impacts of Sales Expense and Administrative Cost Stickiness on Earnings Management – Empirical Evidence from Vietnam

Abstract This study aims to examine effects of sales cost stickiness and administrative cost stickiness on earnings management level. Next, the authors explore whether financial leverage, company size audit firm type and operating cash flow affect the earnings management level of Vietnamese stock exchanges listed companies. The research sample consists of 249 observations (83 companies excluding companies in the banking, finance and insurance sectors) taken from two Vietnamese stock exchanges, HNX and HOSE. For data analysis we use feasible generalized least square regression method. The research results find that sales expense stickiness and administrative cost stickiness, included in the model, have an impact on earnings management level. Sales cost stickiness has a positive impact, while administrative cost stickiness has an impact (negative) on earnings management level. Besides, operating cash flow has an impact on earnings management level, while company size, financial leverage and audit firm type found out with no effect on earnings management level. The results of the study contribute to helping investors, creditors or others have a more comprehensive view of profits and business results of companies so that they will be able to make rational decisions.
A 0.0375mm2 203.5µW 108.8dB DR DT Single-Loop DSM Audio ADC Using a Single-Ended Ring-Amplifier-Based Integrator in 180nm CMOS

Demands for battery-powered consumer electronics have driven the evolution of power-efficient high-resolution low-bandwidth ADCs. Small area and low power are both critical for these applications due to increasing battery life and shrinking form-factors. Flicker noise poses an issue for such systems although the use of well-known techniques in state-of-the-art designs such as chopper stabilization [1]–[2] are often sufficient for its mitigation. Alternatively, the pseudo-pseudo-differential (PPD) architecture [3] has demonstrated flicker cancellation through the use of single-ended circuits although area and power savings promised by this technique remain undemonstrated. This paper presents a DT single-loop DSM audio ADC utilizing a single-ended ring-amplifier-based integrator to achieve 108.8dB DR with 203. 5µW power consumption within a compact area of 0.0375mm2. The use of the PPD architecture with a merged adder contribute to this work's state-of-the-art power and area efficiency.
A 65nm Systolic Neural CPU Processor for Combined Deep Learning and General-Purpose Computing with 95% PE Utilization, High Data Locality and Enhanced End-to-End Performance

Despite recent progress on building highly efficient deep neural network (DNN) accelerators, few works have targeted improving the end-to-end performance of deep-learning tasks, where inter-layer pre/post-processing, data alignment and data movement across memory and processing units often dominate the execution time. An improvement to the end-to-end computation requires cohesive cooperation between the accelerator and the CPU with highly efficient data flow management. Figure 15.2. 1 shows the most commonly used heterogeneous architecture, containing a CPU core and an accelerator with data communication managed by a DMA engine. However, there remain the challenges of low utilization of PE cores and large latency due to the CPU workload and data movement across processing cores [1]–[4]. As shown in Fig. 15.2. 1, in an end-to-end deep learning task, the accelerator is often utilized at only 30-50% with the rest of time waiting for CPU processing and data movement between the CPU and accelerator cores. Some prior works have considered data compression, reduction of data movement or improvement of memory bandwidth. For instance, an accelerator coherency port (ACP) was designed to request data directly from the last level cache of the CPU instead of using the DMA engine to improve the efficiency of data transfer [3], [5]. In this work, we propose a new architecture, a systolic neural CPU (SNCPU), which fuses the operation of a conventional CPU and a systolic CNN accelerator in a single core. The contributions of this work include: 1) The proposed SNCPU architecture can be flexibly reconfigured into a multi-core RISC-V CPU or a systolic CNN accelerator, leading to PE utilization of over 95% for end-to-end operation; 2) with an overhead of less than 10%, the CNN accelerator can be reconfigured into a 10-core RISC-V CPU to improve throughput significantly compared with a conventional heterogeneous architecture having a CPU and an accelerator; 3) with a special bi-directional dataflow, expensive data movement for inter-layer pre/post-processing across cores can be avoided; 4) we demonstrate the SNCPU through a 65nm test chip with 39-to-64% latency improvement and 0.65-to-1.8TOPS/W energy efficiency on end-to-end image-classification tasks.
Machine learning applications to Covid-19: a state-of-the-art survey

There exists a large and rapidly growing body of literature related to applications of machine learning to Covid-19. Given the substantial volume of research, there is a need to organize and categorize the literature. In this paper, we provide the most up-to-date review as of the beginning of 2022. We propose an application-based taxonomy to group the existing literature and provide an analysis of the research in each category. We discuss the progress as well as the pitfalls of the existing research, and propose keys for improvement.
A 108nW 0.8mm2 Analog Voice Activity Detector (VAD) Featuring a Time-Domain CNN as a Programmable Feature Extractor and a Sparsity-Aware Computational Scheme in 28nm CMOS

An ultra-low-power always-on voice activity detector (VAD) is the key enabler of acoustic sensing in wearables. The VAD listens to the environment and wakes up the main system only when there is a right activity detected. Since most human-centric applications have infrequent activities, the VAD dominates the system power. The traditional VAD using the digital feature extractor and classifier [1] requires full-bandwidth and high-resolution data conversion before digital-signal processing, drawing a substantial power $(> 20\mu\mathrm{W})$. Recently, the analog feature extractor shows more promises in power reduction. In [2], the analog-filter bank brings the feature-extraction power down to $1\mu \mathrm{W}$ (Fig. 22.5.1, upper). Yet, the analog-filter bank does not support reprogramming and has a large area (∼0.1 mm2/channel) that limits the number of input channels of the following deep neural network (DNN). The mixer-based analog filter in [4] succeeds in squeezing the feature-extraction power (142nW), but the time-interleaved operation prolongs the decision latency (512ms), and limits the extractable features (only the diagonal information on a spectrogram). In [5], the SNR-based VAD avoids the analog-filter bank, but the involved active circuitry raises the power budget and limits the performance in term of decision latency and classification rate.