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Abstract-Software logging is a conventional programming practice. While its efficacy is often important for users and developers to understand what have happened in the production run, yet software logging is often done in an arbitrary manner. So far, there have been little study for understanding logging practices in real world software. This paper makes the first attempt (to the best of our knowledge) to provide a quantitative characteristic study of the current log messages within four pieces of large open-source software. First, we quantitatively show that software logging is pervasive. By examining developers' own modifications to the logging code in the revision history, we find that they often do not make the log messages right in their first attempts, and thus need to spend a significant amount of efforts to modify the log messages as after-thoughts. Our study further provides several interesting findings on where developers spend most of their efforts in modifying the log messages, which can give insights for programmers, tool developers, and language and compiler designers to improve the current logging practice. To demonstrate the benefit of our study, we built a simple checker based on one of our findings and effectively detected 138 pieces of new problematic logging code from studied software (24 of them are already confirmed and fixed by developers).

A central concept in molecular bioscience is how structure formation at different length scales is achieved. Here we use spider silk protein as a model to design new recombinant proteins that assemble into fibers. We made proteins with a three-block architecture with folded globular domains at each terminus of a truncated repetitive silk sequence. Aqueous solutions of these engineered proteins undergo liquid–liquid phase separation as an essential pre-assembly step before fibers can form by drawing in air. We show that two different forms of phase separation occur depending on solution conditions, but only one form leads to fiber assembly. Structural variants with one-block or two-block architectures do not lead to fibers. Fibers show strong adhesion to surfaces and self-fusing properties when placed into contact with each other. Our results show a link between protein architecture and phase separation behavior suggesting a general approach for understanding protein assembly from dilute solutions into functional structures.

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With the emerging vehicular applications such as real-time situational awareness and cooperative lane change, there exist huge demands for sufficient computing resources at the edge to conduct time-critical and data-intensive tasks. This paper proposes Folo, a novel solution for latency and quality optimized task allocation in Vehicular Fog Computing (VFC). Folo is designed to support the mobility of vehicles, including vehicles that generate tasks and the others that serve as fog nodes. Considering constraints on service latency, quality loss, and fog capacity, the process of task allocation across stationary and mobile fog nodes is formulated into a joint optimization problem. This task allocation in VFC is known as a non-deterministic polynomial-time hard (NP-hard) problem. In this paper, we present the task allocation to fog nodes as a bi-objective minimization problem, where a trade-off is maintained between the service latency and quality loss. Specifically, we propose an event-triggered dynamic task allocation (DTA) framework using Linear Programming based Optimization (LBO) and Binary Particle Swarm Optimization (BPSO). To assess the effectiveness of Folo, we simulated the mobility of fog nodes at different times of a day based on real-world taxi traces and implemented two representative tasks, including video streaming and real-time object recognition. Simulation results show that the task allocation provided by Folo can be adjusted according to actual requirements of the service latency and quality, and achieves higher performance compared with naive and random fog node selection. To be more specific, Folo shortens the average service latency by up to 27% while reducing the quality loss by up to 56%.

The origin of the idea of moving objects by acoustic vibration can be traced back to 1787, when Ernst Chladni reported the first detailed studies on the aggregation of sand onto nodal lines of a vibrating plate. Since then and to this date, the prevailing view has been that the particle motion out of nodal lines is random, implying uncontrollability. But how random really is the out-of-nodal-lines motion on a Chladni plate? Here we show that the motion is sufficiently regular to be statistically modelled, predicted and controlled. By playing carefully selected musical notes, we can control the position of multiple objects simultaneously and independently using a single acoustic actuator. Our method allows independent trajectory following, pattern transformation and sorting of multiple miniature objects in a wide range of materials, including electronic components, water droplets loaded on solid carriers, plant seeds, candy balls and metal parts.

Droplets slip and bounce on superhydrophobic surfaces, enabling remarkable functions in biology and technology. These surfaces often contain microscopic irregularities in surface texture and chemical composition, which may affect or even govern macroscopic wetting phenomena. However, effective ways to quantify and map microscopic variations of wettability are still missing, because existing contact angle and force-based methods lack sensitivity and spatial resolution. Here, we introduce wetting maps that visualize local variations in wetting through droplet adhesion forces, which correlate with wettability. We develop scanning droplet adhesion microscopy, a technique to obtain wetting maps with spatial resolution down to 10 µm and three orders of magnitude better force sensitivity than current tensiometers. The microscope allows characterization of challenging non-flat surfaces, like the butterfly wing, previously difficult to characterize by contact angle method due to obscured view. Furthermore, the technique reveals wetting heterogeneity of micropillared model surfaces previously assumed to be uniform.

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