Search

Abstract Analyzing the dynamics of parking traffic can better represent the real dynamic states of road networks, thereby allowing for a deeper analysis of the parking system’s impact. This paper comparatively investigates the impact of parking policies on two traffic networks with different infrastructure, socio-economic, and policy characteristics. Parking space, average parking duration, and parking fee policies were analyzed as a function of cruising distances and cruising time with indirect effects on traffic emissions. Empirically, the system dynamics model application is tested and validated with the macroscopic data from two central business districts (CBDs) in Shanghai (Xujiahui area) and Zurich (Bahnhofstrasse area). Results showed Bahnhofstrasse CBD is more sensitive against the policy shifts with relatively higher elasticity and indicated greater responsiveness in aggregating traffic emissions when compared with Xujiahui CBD. The findings of this study may provide an overall framework to empirically assess the performance of different traffic conditions and strategies on urban parking systems.

Safety is the primary guarantee for achieving coal mine production. The degree of water in the overlying rock of the coal seam has a significant impact on the mechanical strength of the rock, which can lead to subsidence, roof collapse, and rock burst in the mining area. Therefore, it is important to study the law of water-rich damage and deterioration of coal seam overlying rock, as well as the characteristics of bursting liability to prevent and control mining disasters. This paper aims to investigate the change in compressive strength, elastic modulus, and bursting liability of coal measure strata under different water content and their relationship with lithology, particle size, pore characteristics, and mineral characteristics. We carried out uniaxial compression tests on drilled and prepared samples of coal measure strata in the east of Ordos City. We also monitored the acoustic emission characteristics of the rock failure process under different water content. The results demonstrate that, in the dry state, the compressive strength of siltstone >fine sandstone >medium sandstone >coarse sandstone. As the water content increases, the rock transitions from brittle to plastic, and the compressive strength, elastic modulus, and bursting liability gradually decrease. The stress-strain curve changes from a single peak to bimodal and multimodal, and the active degree of acoustic emission events decreases, especially the low-value ringing count. Calcareous cementation, clay minerals, and chlorite exhibit strong softening properties when subjected to water, while the grain skeleton (comprising quartz, feldspar, mica) and siliceous cementation displays weak softening properties in such conditions. The most significant is the mechanical strength deterioration of siltstone containing a clay matrix. The research findings can provide reference and theoretical support for safe mining practices in coal mines.

One of the challenges in a viral pandemic is the emergence of novel variants with different phenotypical characteristics. An ability to forecast future viral individuals at the sequence level enables advance preparation by characterizing the sequences and closing vulnerabilities in current preventative and therapeutic methods. In this article, we explore, in the context of a viral pandemic, the problem of generating complete instances of undiscovered viral protein sequences, which have a high likelihood of being discovered in the future using protein language models. Current approaches to training these models fit model parameters to a known sequence set, which does not suit pandemic forecasting as future sequences differ from known sequences in some respects. To address this, we develop a novel method, called PandoGen, to train protein language models towards the pandemic protein forecasting task. PandoGen combines techniques such as synthetic data generation, conditional sequence generation, and reward-based learning, enabling the model to forecast future sequences, with a high propensity to spread. Applying our method to modeling the SARS-CoV-2 Spike protein sequence, we find empirically that our model forecasts twice as many novel sequences with five times the case counts compared to a model that is 30× larger. Our method forecasts unseen lineages months in advance, whereas models 4× and 30× larger forecast almost no new lineages. When trained on data available up to a month before the onset of important Variants of Concern, our method consistently forecasts sequences belonging to those variants within tight sequence budgets.

Abstract Background Acute lung injury (ALI) is a common and serious complication of sepsis with high mortality. Ferroptosis, categorized as programmed cell death, contributes to the development of lung injury. Protectin conjugates in tissue regeneration 1 (PCTR1) is an endogenous lipid mediator that exerts protective effects against multiorgan injury. However, the role of PCTR1 in the ferroptosis of sepsis-related ALI remains unknown. Methods A pulmonary epithelial cell line and a mouse model of ALI stimulated with lipopolysaccharide (LPS) were established in vitro and in vivo. Ferroptosis biomarkers, including ferrous (Fe2+), glutathione (GSH), malondialdehyde (MDA) and 4-Hydroxynonenal (4-HNE), were assessed by relevant assay kits. Glutathione peroxidase 4 (GPX4) and prostaglandin-endoperoxide synthase 2 (PTGS2) protein levels were determined by western blotting. Lipid peroxides were examined by fluorescence microscopy and flow cytometry. Cell viability was determined by a CCK-8 assay kit. The ultrastructure of mitochondria was observed with transmission electron microscopy. Morphology and inflammatory cytokine levels predicted the severity of lung injury. Afterward, related inhibitors were used to explore the potential mechanism by which PCTR1 regulates ferroptosis. Results PCTR1 treatment protected mice from LPS-induced lung injury, which was consistent with the effect of the ferroptosis inhibitor ferrostatin-1. PCTR1 treatment decreased Fe2+, PTGS2 and lipid reactive oxygen species (ROS) contents, increased GSH and GPX4 levels and ameliorated mitochondrial ultrastructural injury. Administration of LPS or the ferroptosis agonist RSL3 resulted in reduced cell viability, which was rescued by PCTR1. Mechanistically, inhibition of the PCTR1 receptor lipoxin A4 (ALX), protein kinase A (PKA) and transcription factor cAMP-response element binding protein (CREB) partly decreased PCTR1 upregulated GPX4 expression and a CREB inhibitor blocked the effects ofPCTR1 on ferroptosis inhibition and lung protection. Conclusion This study suggests that PCTR1 suppresses LPS-induced ferroptosis via the ALX/PKA/CREB signaling pathway, which may offer promising therapeutic prospects in sepsis-related ALI.

The recalcitrant structure of raw poplar limited the production of fermentable sugars when applied as the material in the pretreatment of biochemical conversions. Phosphoric acid pretreatment is an efficient method to destroy the compact lignocellulose matrix presence in the poplar. In this study, phosphoric acid pretreatment of poplar was optimised by an orthogonal experimental design [L9(33)] to improve enzymatic digestibility through investigating the effects of reaction temperature, time duration, and phosphoric acid concentration. The optimal conditions were selected based on the variance of chemical compositions, hemicellulose removal ratio, and delignification of the woody material after pretreatment. The optimum enzymatic hydrolysis yield of up to 73.44% was obtained when the phosphoric acid pretreatment performed at 190°C for 150 min under 1.5% of v/v phosphoric acid concentration.

Abstract Background Modern Next Generation- and Third Generation- Sequencing methods such as Illumina and PacBio Circular Consensus Sequencing platforms provide accurate sequencing data. Parallel developments in Deep Learning have enabled the application of Deep Neural Networks to variant calling, surpassing the accuracy of classical approaches in many settings. DeepVariant, arguably the most popular among such methods, transforms the problem of variant calling into one of image recognition where a Deep Neural Network analyzes sequencing data that is formatted as images, achieving high accuracy. In this paper, we explore an alternative approach to designing Deep Neural Networks for variant calling, where we use meticulously designed Deep Neural Network architectures and customized variant inference functions that account for the underlying nature of sequencing data instead of converting the problem to one of image recognition. Results Results from 27 whole-genome variant calling experiments spanning Illumina, PacBio and hybrid Illumina-PacBio settings suggest that our method allows vastly smaller Deep Neural Networks to outperform the Inception-v3 architecture used in DeepVariant for indel and substitution-type variant calls. For example, our method reduces the number of indel call errors by up to 18%, 55% and 65% for Illumina, PacBio and hybrid Illumina-PacBio variant calling respectively, compared to a similarly trained DeepVariant pipeline. In these cases, our models are between 7 and 14 times smaller. Conclusions We believe that the improved accuracy and problem-specific customization of our models will enable more accurate pipelines and further method development in the field. HELLO is available at https://github.com/anands-repo/hello

AbstractPre-trained Transformer-based models have achieved state-of-the-art performance for various Natural Language Processing (NLP) tasks. However, these models often have billions of parameters, and thus are too resource- hungry and computation-intensive to suit low- capability devices or applications with strict latency requirements. One potential remedy for this is model compression, which has attracted considerable research attention. Here, we summarize the research in compressing Transformers, focusing on the especially popular BERT model. In particular, we survey the state of the art in compression for BERT, we clarify the current best practices for compressing large-scale Transformer models, and we provide insights into the workings of various methods. Our categorization and analysis also shed light on promising future research directions for achieving lightweight, accurate, and generic NLP models.

One-step thermoforming represents an effective approach to preparing glue-free biocomposites. This study aimed to produce glue-free biocomposites with high-temperature resistance and mechanical properties using phosphoric acid pretreatments combined with thermoforming. Due to the hot-moulding process, the cell wall was destroyed, which allowed the fibres to adhere closely together. Most hemicelluloses were hydrolysed through pretreatment with phosphoric acid, and the contact area between the cellulose and lignin was significantly increased. The biocomposites prepared by ball milling demonstrated remarkable flexural strength (49.03 MPa) and tensile strength (148.23 MPa). Moreover, they had excellent thermal stability, with the maximum temperature for pyrolysis rate at 374 °C, which was much higher than that of poplar (337 °C). In addition, the material released no formaldehyde during the preparation process, which is in line with the concept of green production.

Fermentation ability and alcohol production ability are important quality indicators of Chinese liquor Daqu, reflecting microbial growth and metabolic capacity and ethanol production capacity of Daqu microbiota, respectively. However, information on the microbial community related to the fermentation ability and alcohol production ability is unclear. In this study, fermentation functional microbiota (FFM) and alcohol functional microbiota (AFM) were obtained by correlating fermentation ability and alcohol production ability with Daqu microbiota. FFM and AFM consisted of 50 and 49 genera, respectively, which were basically the same at the phylum level but differed at the genus level. Correlation analysis showed that FFM and AFM were mainly affected by moisture, acidity, and humidity in the early stage of Daqu fermentation, and oxygen content was a critical factor for microbial succession in the middle stage of fermentation. FFM and AFM had commensal or synergistic interactions with multiple microbes. Function predictions indicated that fermentation functional bacterial microbiota was active in product synthesis and transport-related metabolic functions, and alcohol functional bacterial microbiota was very active in raw material utilization and its own metabolic synthesis. This study reveals the structural characteristics and formation mechanism of FFM and AFM, which is important for control of Daqu quality.

The rise of the Internet of Things has led to an explosion of sensor computing platforms. The complexity and applications of IoT devices range from simple devices in vending machines to complex, interactive artificial intelligence in smart vehicles and drones. Developers target more aggressive objectives and protect market share through feature differentiation; they just choose between low-cost, and low-performance CPU-based systems, and high-performance custom platforms with hardware accelerators including GPUs and FPGAs. Both CPU-based and custom designs introduce a variety of design challenges: extreme pressure on time-to-market, design cost, and development risk drive a voracious demand for new CAD technologies to enable rapid, low cost design of effective IoT platforms with smaller design teams and lower risk. In this article, we present a generic IoT device design flow and discuss platform choices for IoT devices to efficiently tradeoff cost, power, performance and volume constraints: CPU-based systems and custom platforms that contain hardware accelerators including embedded GPUs and FPGAs. We demonstrate this design process through a driving application in computer vision. We also present current critical design automation needs for IoT development and demonstrate how our prior work in CAD for FPGAs and SoCs begin to address these needs.

FPGAs are an attractive platform for applications with high computation demand and low energy consumption requirements. However, design effort for FPGA implementations remains high—often an order of magnitude larger than design effort using high-level languages. Instead of this time-consuming process, high-level synthesis (HLS) tools generate hardware implementations from algorithm descriptions in languages such as C/C++ and SystemC. Such tools reduce design effort: high-level descriptions are more compact and less error prone. HLS tools promise hardware development abstracted from software designer knowledge of the implementation platform. In this paper, we present an unbiased study of the performance, usability and productivity of HLS using AutoPilot (a state-of-the-art HLS tool). In particular, we first evaluate AutoPilot using the popular embedded benchmark kernels. Then, to evaluate the suitability of HLS on real-world applications, we perform a case study of stereo matching, an active area of computer vision research that uses techniques also common for image denoising, image retrieval, feature matching, and face recognition. Based on our study, we provide insights on current limitations of mapping general-purpose software to hardware using HLS and some future directions for HLS tool development. We also offer several guidelines for hardware-friendly software design. For popular embedded benchmark kernels, the designs produced by HLS achieve 4X to 126X speedup over the software version. The stereo matching algorithms achieve between 3.5X and 67.9X speedup over software (but still less than manual RTL design) with a fivefold reduction in design effort versus manual RTL design.

We propose a fast data relay (FDR) mechanism to enhance existing CGRA (coarse-grained reconfigurable architecture). FDR can not only provide multicycle data transmission in concurrent with computations but also convert resource-demanding inter-processing-element global data accesses into local data accesses to avoid communication congestion. We also propose the supporting compiler techniques that can efficiently utilize the FDR feature to achieve higher performance for a variety of applications. Our results on FDR-based CGRA are compared with two other works in this field: ADRES and RCP. Experimental results for various multimedia applications show that FDR combined with the new compiler deliver up to 29% and 21% higher performance than ADRES and RCP, respectively.

Interconnection networks for multicore processors are traditionally designed to serve a diversity of workloads. However, different workloads or even different execution phases of the same workload may benefit from different interconnect configurations. In this paper, we first motivate the need for workload-adaptive interconnection networks. Subsequently, we describe an interconnection network framework based on reconfigurable switches for use in medium-scale (up to 32 cores) shared memory multicore processors. Our cost-effective reconfigurable interconnection network is implemented on a traditional shared bus interconnect with snoopy-based coherence, and it enables improved multicore performance. The proposed interconnect architecture distributes the cores of the processor into clusters with reconfigurable logic between clusters to support workload-adaptive policies for inter-cluster communication. Our interconnection scheme is complemented by interconnect-aware scheduling and additional interconnect optimizations which help boost the performance of multiprogramming and multithreaded workloads. We provide experimental results that show that the overall throughput of multiprogramming workloads (consisting of two and four programs) can be improved by up to 60% with our configurable bus architecture. Similar gains can be achieved also for multithreaded applications as shown by further experiments. Finally, we present the performance sensitivity of the proposed interconnect architecture on shared memory bandwidth availability.