Human behavior sensing has been receiving growing attention from both academia and industry in recent years. We report - to the best of our knowledge - the first AI-driven nationwide human behavior sensing system called SMILE in urban last-mile delivery. SMILE detects real-time human behaviors with self-supervised sensor data pretraining and uploads detection results to cloud servers with mobile networks. During its full deployment phase at JD Logistics, SMILE is deployed on over 500,000 mobile devices carried by over 300,000 delivery couriers who travel more than 10 million KM every day in 366 Chinese cities. SMILE serves the delivery of 7 billion E-commerce orders every year for more than 500 million customers. SMILE detects walking, upstairs, downstairs, still, and driving behaviors. SMILE has been fully deployed at JD Logistics to support two real-world applications that benefit both the delivery couriers and the logistics platform: (1) workload measurement to improve couriers' welfare; (2) large-scale delivery map data generation to improve the logistics delivery efficiency.

Recent advances in chromatin conformation capture technologies, such as SPRITE and Pore-C, have enabled the detection of simultaneous contacts among multiple chromatin loci. This has made it possible to investigate the cooperative transcriptional regulation involving multiple genes and regulatory elements at the resolution of a single molecule. However, these technologies are unavoidably subject to the random polymer looping effect and technical biases, making it challenging to distinguish genuine regulatory relationships directly from random polymer interactions. Here, we present HyperloopFinder, a method for identifying regulatory multi-way chromatin contacts (hyperloops) by jointly modeling the random polymer looping effect and technical biases to estimate the statistical significance of multi-way contacts. The results show that our model can accurately estimate the expected interaction frequency of multi-way contacts based on the distance distribution of pairwise contacts, revealing that most multi-way contacts can be formed by randomly linking the pairwise contacts adjacent to each other. Moreover, we observed the spatial colocalization of the interaction sites of hyperloops from image-based data. Our results also revealed that hyperloops can function as scaffolds for the cooperation among multiple genes and regulatory elements. In summary, our work contributes novel insights into higher-order chromatin structures and functions and has the potential to enhance our understanding of transcriptional regulation and other cellular processes.

MOTIVATION: CTCF-mediated chromatin loops underlie the formation of topological associating domains and serve as the structural basis for transcriptional regulation. However, the formation mechanism of these loops remains unclear, and the genome-wide mapping of these loops is costly and difficult. Motivated by the recent studies on the formation mechanism of CTCF-mediated loops, we studied the possibility of making use of transitivity-related information of interacting CTCF anchors to predict CTCF loops computationally. In this context, transitivity arises when two CTCF anchors interact with the same third anchor by the loop extrusion mechanism and bring themselves close to each other spatially to form an indirect loop. RESULTS: To determine whether transitivity is informative for predicting CTCF loops and to obtain an accurate and low-cost predicting method, we proposed a two-stage random-forest-based machine learning method, CTCF-mediated Chromatin Interaction Prediction (CCIP), to predict CTCF-mediated chromatin loops. Our two-stage learning approach makes it possible for us to train a prediction model by taking advantage of transitivity-related information as well as functional genome data and genomic data. Experimental studies showed that our method predicts CTCF-mediated loops more accurately than other methods and that transitivity, when used as a properly defined attribute, is informative for predicting CTCF loops. Furthermore, we found that transitivity explains the formation of tandem CTCF loops and facilitates enhancer-promoter interactions. Our work contributes to the understanding of the formation mechanism and function of CTCF-mediated chromatin loops. AVAILABILITY AND IMPLEMENTATION: The source code of CCIP can be accessed at: https://github.com/GaoLabXDU/CCIP. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Nowadays, MEMS testing has become a growing problem because it usually needs specific and sophisticated testing equipment and is very time-consuming. To solve this problem, this paper proposes a Built-In Self-Test (BIST) method for membrane MEMS piezoresistive sensor. With the proposed method, an on-chip electric signal can be used as the test stimuli, and process defects of piezoresistive sensor can be diagnosed by analyzing the output response of piezoresistive sensor on chip. The simulation shows that the proposed MEMS BIST scheme can effectively replace the physical testing stimuli with electric signal, thus reduce the dependence on external signal sources and the cost of manufacturing devices.

MEMS devices are expected to be used in a growing number of high-volume and low-cost applications. However because they usually require complex test stimuli rather than simple digital electronic signals as common VLSI systems to verify their specifications, testing and calibration costs have actually become a bottleneck to reduce the overall production cost of MEMS sensors. To address this issue, this paper presents an on-chip scheme to calibrate the responsivity of infrared thermopile temperature sensor with digital control signals. With the proposed method, the responsivity related to the ambient temperature can be calibrated before the target temperature being measured thus to achieve accurate temperature measurement. The proposed self-calibrating thermopile sensor design has been realized by CMOS-compatible process to prove the effectiveness of the self-calibration temperature measurement method.

The conversion of cropland to forest project as one of the nationwide forestry key programs was started in 1999, aiming at prevention of soil erosion, water resources conservation, combating land desertification in the key areas and mitigation of global climate change. Afforestation is the primary measure of the Project. Because of low land quality, ecological species planted and short-term growing, mid-coarse satellite data is serious limited to check planting effect in many areas. With the development of remote sensing, finer images provide better choice to exactly detect the distribution of afforestation parcels and forested area of the Project. In this study, an exact automatic recognition algorithm for the Project parcels is developed. It is based on finer RS data (less than 5m spatial resolution), and integrates object-oriented, BP artificial neural network, multi-segmentation and expert knowledge, considering the differential features between trees and other land vegetation in terms of image hue, grey value, textural features, RGB and NDVI. Kangping county is taken as an example to test the algorithm at the support of 5-times SPOT5 images since 2003. The study results show that: 1) most of planted parcels can be extracted more than two years old, and the accuracy can reach 85%, 2) closed arbor planted longer than forested year limit can be clear detected, the total accuracy for forest extraction of the Project parcels exceeds 85%.

Presented in this paper is our effort to create a multifunctional swimming robot, i.e., robotic fish, inspired by the well-integrated, configurable multiple control surfaces existing in real fish. By virtue of the hybrid propulsion capability in the tail plus the caudal fin and the maneuverability in accessory fins, a novel, synthesized propulsion scheme composed of multiple artificial control surfaces is proposed, involving the tail plus the caudal fin, pectoral fins, pelvic fin, and dorsal fin. Multimodal locomotion is then accomplished by manipulation of control surfaces, separately or cooperatively, allowing the robot to maneuver more diversely and agilely. In particular, bio inspired Central Pattern Generators (CPGs) based locomotion control is adopted for online swimming gait generation. Aquatic testing has been carried out to demonstrate the improved maneuverability and stability of the robotic fish underwater as well as the effectiveness of the conceived multi-fin mechatronic design.