The Internet of Things (IoT) is an interface with the physical world that usually operates in random-sparse-event (RSE) scenarios. This article discusses main challenges of IoT chips: power consumption, power supply, artificial intelligence (AI), small-signal acquisition, and evaluation criteria. To overcome these challenges, many works recently aimed at IoT system design have emerged. This work reviews the architecture and circuit innovations that have contributed to IoT developments. This paper does not cover security of IoT. Event-driven architectures and nonuniform sampling ADCs significantly reduce the long-term average power. Besides, embedding AI engines in IoT nodes (AIoT) is one critical trend. The computing-in-memory technique improves the energy efficiency of the AI engine. Asynchronous spike neural networks (ASNNs) AI engines show low power potential. In addition to data processing, small-signal acquisition is also critical. The charge-domain analog-front-end (AFE) techniques such as floating inverter-based amplifiers improve energy efficiency. In addition to the above low power and high energy efficiency technologies, energy harvesting can also enhance the lifetime of AIoT devices. This article discusses recent ambient RF and natural energy harvesting approaches and high-efficiency DC-DC with a wide load range. Finally, novel evaluation criteria are introduced to establish benchmark standards for AIoT chips.
Widespread antibiotic resistance across Earth's habitats has become a critical health concern. However, large-scale investigation on the distribution of antibiotic resistance genes (ARGs) in the microbiomes from most types of ecosystem is still lacking. In this study, we provide a comprehensive characterization of ARGs for 52,515 microbial genomes covering various Earth's ecosystems, and conduct the risk assessment for ARG-carrying species based on further identification of mobile genetic elements (MGEs) and virulence factor genes (VFGs). We identify a total of 6159 ARG-carrying metagenome-assembled genomes (ACMs), and most of them are recovered from human gut and city subway. Our results show that efflux pump is the most common mechanism for bacteria to acquire multidrug resistance genes in Earth's microbiomes. Enterobacteriaceae species are the largest hosts of ARGs, accounting for 14% of total ACMs with 64% of the total ARG hits. Most of ARG-carrying species are unique in the different ecosystem categories, while 33 potential background ARGs are commonly shared by all ecosystem categories. We then detect 36 high-risk ARGs that likely threat public health in all ACMs. Based on ranking the importance of ARG-carrying species in the different ecosystem categories, several bacterial taxa such as Escherichia coli, Enterococcus faecalis, and Pseudomonas_A stutzeri are recognized as priority species for surveillance and control. Overall, our study gives a broad view of ARG-host associations in the environments.
Understanding and manipulating wettability alterations has tremendous implications in theoretical research and industrial applications. This study proposes a novel idea of applying ultrasonic for wettability alterations and also provides its quantitative characterizations and in-depth analyses. More specifically, with pretreatment of ultrasonic, mechanisms of wettability alteration were characterized from the contact angle measurements, as well as the in-depth analyses from atomic force microscopy (AFM), X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FTIR). After ultrasonic treatments, the wettability of mineral with low permeability is determined to altered from strong hydrophilic to intermediate wettability. The mechanism interpretations are conducted by means of the AFM, XRD, and FTIR. Basically, as the time of ultrasonic treatment increases, the AFM results indicate that the roughness of rock surface and oil/rock interface (contact area) with surroundings of brine is enhanced. Meanwhile, the XRD results show the diffusions of clays from the rock surface to the aqueous phase, and FTIR indicates that the number of functional groups of Si–O–Si, C–O–C, C–O, C═O, and OH decreases while the number of COOH and C═C═O groups increases. This study clearly reveals the surface chemistry of oil-rock wettability alteration in the subsurface conditions, which would provide technical support for subsurface usage of geo-energy productions and carbon sequestrations.
Hospitalized self-inflicted firearm injuries have not been extensively studied, particularly regarding clinical diagnoses at the index admission. The objective of this study was to discover the diagnostic phenotypes (DPs) or clusters of hospitalized self-inflicted firearm injuries. Using Nationwide Inpatient Sample data in the US from 1993 to 2014, we used International Classification of Diseases, Ninth Revision codes to identify self-inflicted firearm injuries among those ≥18 years of age. The 25 most frequent diagnostic codes were used to compute a dissimilarity matrix and the optimal number of clusters. We used hierarchical clustering to identify the main DPs. The overall cohort included 14072 hospitalizations, with self-inflicted firearm injuries occurring mainly in those between 16 to 45 years of age, black, with co-occurring tobacco and alcohol use, and mental illness. Out of the three identified DPs, DP1 was the largest (n=10,110), and included most common diagnoses similar to overall cohort, including major depressive disorders (27.7%), hypertension (16.8%), acute post hemorrhagic anemia (16.7%), tobacco (15.7%) and alcohol use (12.6%). DP2 (n=3,725) was not characterized by any of the top 25 ICD-9 diagnoses codes, and included children and peripartum women. DP3, the smallest phenotype (n=237), had high prevalence of depression similar to DP1, and defined by fewer fatal injuries of chest and abdomen. There were three distinct diagnostic phenotypes in hospitalizations due to self-inflicted firearm injuries. Further research is needed to determine how DPs can be used to tailor clinical care and prevention efforts.