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Networked mission-critical applications (e.g., avionics control and industrial automation) demand deterministic packet transmissions to meet stringent sensing and control timing requirements. While specialized infrastructures such as Time-Triggered Ethernet and Time-Sensitive Networking (TSN) ensure deterministic data delivery across switches, end devices still require specialized NICs (e.g., TSN NICs or NVIDIA Mellanox) to eliminate endpoint indeterminism. However, deploying such NICs at every endpoint is costly and hinders compatibility with legacy systems. To address this challenge, we propose KeepON, a novel software-based driver model that enables deterministic packet transmission on commodity NICs. The core idea is to continuously transmit fixed-size placeholder packets, establishing a predictable transmission pattern. Mission-critical packets are then precisely inserted into this stream by replacing placeholders at their scheduled transmission slots, ensuring timing accuracy. The placeholder packets are efficiently dropped at the first-hop switch, avoiding negative impacts on network performance. We prototype KeepON by modifying the standard NIC driver of a Raspberry Pi, and integrate it into a real-world TSN testbed. Experimental results show that KeepON achieves up to 130× improvement in scheduling accuracy compared to the default driver, and 2.1× improvement over a hardware-based solution. 

Thin-wall geometries produced by laser powder bed fusion combine high manufacturing efficiency, design flexibility, and cost-effectiveness for specialized applications. In such features, surface quality directly impacts dimensional accuracy and functional performance. This study investigates the effects of laser power, scan path, build orientation, and nominal gap distance on the top- and vertical-surface roughness, surface features, and dimensional error (DE) of 316L stainless steel thin walls. Optical microscopy was employed to characterize melt pool morphology and surface characteristics. Increasing laser power enlarges melt pools, promotes lateral migration, and induces dross formation on vertical surfaces, raising roughness and DE. Incorporating a contour scan with an inward offset reduces the scanned area, limits melt pool migration, and improves dimensional accuracy. Print orientation has a negligible influence on DE under the tested conditions, while small gaps may close entirely at high power due to large melt pools and migration. Compared to cubes fabricated with identical parameters, thin walls exhibit rougher top surfaces at high power, attributed to reduced track overlap, limited wetting from previous layers, and powder redistribution near vertical edges, whereas vertical-surface behavior remains similar. These findings provide practical guidelines for optimizing dimensional accuracy and surface quality in thin walls through coordinated control of process parameters and geometry. 

Time-sensitive networking (TSN) has been recognized as one of the key enabling technologies for Industry 4.0 and has been deployed in many mission- and safety-critical applications e.g., industry automation, automotive and aerospace systems. Given the stringent real-time requirements of these applications, the Time-Aware Shaper (TAS) draws special attention among TSN’s many traffic shapers due to its ability to achieve deterministic timing guarantees. Many scheduling methods for TAS shapers have been recently developed that claim to improve system schedulability. However, these scheduling methods have not yet been thoroughly evaluated, especially through experimental comparisons, to provide a systematical understanding of their performance using different evaluation metrics in diverse application scenarios. In this article, we fill this gap by presenting a systematic review and experimental study on existing TAS-based scheduling methods for TSN. We first review and categorize the system models employed in these works along with the specific problems they aim to solve, and outline the fundamental and additional considerations in the designs of TAS-based scheduling methods. We then perform an extensive evaluation on 17 representative solutions using both high-fidelity simulations and a real-life 16-node TSN testbed, and comparing their performance in terms of schedulability, scalability, and schedule quality. Through these experimental studies, we identify the limitations of individual scheduling methods and highlight important findings. We also summarize the open issues and future research directions in this area. We expect this work will provide foundational knowledge and performance benchmarks for future studies on real-time TSN scheduling and beyond. 

With the introduction of Cyber-Physical Systems (CPS) and Internet of Things (IoT) technologies, the automation industry is undergoing significant changes, particularly in improving production efficiency and reducing maintenance costs. Industrial automation applications often need to transmit time- and safety-critical data to closely monitor and control industrial processes. Several Ethernet-based fieldbus solutions, such as PROFINET IRT, EtherNet/IP, and EtherCAT, are widely used to ensure real-time communications in industrial automation systems. These solutions, however, commonly incorporate additional mechanisms to provide latency guarantees, making their interoperability a grand challenge. The IEEE 802.1 Time-Sensitive Networking (TSN) task group was formed to enhance and optimize IEEE 802.1 network standards, particularly for Ethernet-based networks. These solutions can be evolved and adapted for cross-industry scenarios, such as large-scale distributed industrial plants requiring multiple industrial entities to work collaboratively. This paper provides a comprehensive review of current advances in TSN standards for industrial automation. It presents the state-of-the-art IEEE TSN standards and discusses the opportunities and challenges of integrating TSN into the automation industry. Some promising research directions are also highlighted for applying TSN technologies to industrial automation applications. 

PurposeSurface quality and porosity significantly influence the structural and functional properties of the final product. This study aims to establish and explain the underlying relationships among processing parameters, top surface roughness and porosity level in additively manufactured 316L stainless steel. Design/methodology/approachA systematic variation of printing process parameters was conducted to print cubic samples based on laser power, speed and their combinations of energy density. Melt pool morphologies and dimensions, surface roughness quantified by arithmetic mean height (Sa) and porosity levels were characterized via optical confocal microscopy. FindingsThe study reveals that the laser power required to achieve optimal top surface quality increases with the volumetric energy density (VED) levels. A smooth top surface (Sa < 15 µm) or a rough surface with humps at high VEDs (VED > 133.3 J/mm³) can serve as indicators for fully dense bulk samples, while rough top surfaces resulting from melt pool discontinuity correlate with high porosity levels. Under insufficient VED, melt pool discontinuity dominates the top surface. At high VEDs, surface quality improves with increased power as mitigation of melt pool discontinuity, followed by the deterioration with hump formation. Originality/valueThis study reveals and summarizes the formation mechanism of dominant features on top surface features and offers a potential method to predict the porosity by observing the top surface features with consideration of processing conditions. 

Abstract People often choose suboptimal attentional control strategies during visual search. This has been at least partially attributed to the avoidance of the cognitive effort associated with the optimal strategy, but aspects of the task triggering such avoidance remain unclear. Here, we attempted to measure effort avoidance of an isolated task component to assess whether this component might drive suboptimal behavior. We adopted a modified version of the Adaptive Choice Visual Search (ACVS), a task designed to measure people’s visual search strategies. To perform optimally, participants must make a numerosity judgment—estimating and comparing two color sets—before they can advantageously search through the less numerous of the two. If participants skip the numerosity judgment step, they can still perform accurately, albeit substantially more slowly. To study whether effort associated with performing the optional numerosity judgment could be an obstacle to optimal performance, we created a variant of the demand selection task to quantify the avoidance of numerosity judgment effort. Results revealed a robust avoidance of the numerosity judgment, offering a potential explanation for why individuals choose suboptimal strategies in the ACVS task. Nevertheless, we did not find a significant relationship between individual numerosity judgment avoidance and ACVS optimality, and we discussed potential reasons for this lack of an observed relationship. Altogether, our results showed that the effort avoidance for specific subcomponents of a visual search task can be probed and linked to overall strategy choices.