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Summary Leaf dark respiration (R_dark), an important yet rarely quantified component of carbon cycling in forest ecosystems, is often simulated from leaf traits such as the maximum carboxylation capacity (V_cmax), leaf mass per area (LMA), nitrogen (N) and phosphorus (P) concentrations, in terrestrial biosphere models. However, the validity of these relationships across forest types remains to be thoroughly assessed.Here, we analyzedR_darkvariability and its associations withV_cmaxand other leaf traits across three temperate, subtropical and tropical forests in China, evaluating the effectiveness of leaf spectroscopy as a superior monitoring alternative.We found that leaf magnesium and calcium concentrations were more significant in explaining cross‐siteR_darkthan commonly used traits like LMA, N and P concentrations, but univariate trait–R_darkrelationships were always weak (r² ≤ 0.15) and forest‐specific. Although multivariate relationships of leaf traits improved the model performance, leaf spectroscopy outperformed trait–R_darkrelationships, accurately predicted cross‐siteR_dark(r² = 0.65) and pinpointed the factors contributing toR_darkvariability.Our findings reveal a few novel traits with greater cross‐site scalability regardingR_dark, challenging the use of empirical trait–R_darkrelationships in process models and emphasize the potential of leaf spectroscopy as a promising alternative for estimatingR_dark, which could ultimately improve process modeling of terrestrial plant respiration. 

Home networks lack the powerful security tools and trained personnel available in enterprise networks. This compli- cates efforts to address security risks in residential settings. While prior efforts explore outsourcing network traffic to cloud or cloudlet services, such an approach exposes that network traffic to a third party, which introduces privacy risks, particularly where traffic is decrypted (e.g., using Transport Layer Security Inspection (TLSI)). To enable security screening locally, home networks could introduce new physical hardware, but the capital and deployment costs may impede deployment. In this work, we explore a system to leverage existing available devices, such as smartphones, tablets and laptops, already inside a home network to create a platform for traffic inspection. This software-based solution avoids new hardware deployment and allows decryption of traffic without risk of new third parties. Our investigation compares on-router inspection of traffic with an approach using that same router to direct traffic through smartphones in the local network. Our performance evaluation shows that smartphone middleboxes can substantially increase the throughput of communication from around 10 Mbps in the on-router case to around 90 Mbps when smartphones are used. This approach increases CPU usage at the router by around 15%, with a 20% CPU usage increase on a smartphone (with single core processing). The network packet latency increases by about 120 milliseconds. 

The security of Internet-of-Things (IoT) devices in the residential environment is important due to their widespread presence in homes and their sensing and actuation capabilities. However, securing IoT devices is challenging due to their varied designs, deployment longevity, multiple manufacturers, and potentially limited availability of long-term firmware updates. Attackers have exploited this complexity by specifically targeting IoT devices, with some recent high-profile cases affecting millions of devices. In this work, we explore access control mechanisms that tightly constrain access to devices at the residential router, with the goal of precluding access that is inconsistent with legitimate users' goals. Since many residential IoT devices are controlled via applications on smartphones, we combine application sensors on phones with sensors at residential routers to analyze workflows. We construct stateful filters at residential routers that can require user actions within a registered smartphone to enable network access to an IoT device. In doing so, we constrain network packets only to those that are consistent with the user's actions. In our experiments, we successfully identified 100% of malicious traffic while correctly allowing more than 98% of legitimate network traffic. The approach works across device types and manufacturers with straightforward API and state machine construction for each new device workflow. 

Given the complexity of modern systems, it can be difficult for device defenders to pinpoint the user action that precipitates a network connection. Mobile devices, such as smartphones, further complicate analysis since they may have diverse and ephemeral network connectivity and support users in both personal and professional capacities. There are multiple stakeholders associated with mobile devices, such as the end-user, device owner, and each organization whose assets are accessed via the device; however, none may be able to fully manage, troubleshoot, or defend the device on their own. In this work, we explore a set of techniques to determine the root cause of each new network flow, such the button press or gesture for user-initiated flows, associated with a mobile device. We fuse the User Interface (UI) context with network flow data to enhance network profiling on the Android operating system. In doing so, we find that we can improve network profiling by clearly linking user actions with network behavior. When exploring effectiveness, the system enables allow-lists to reach over 99% accuracy, even when user-specified destinations are used. 

Summary Allocation of leaf phosphorus (P) among different functional fractions represents a crucial adaptive strategy for optimizing P use. However, it remains challenging to monitor the variability in leaf P fractions and, ultimately, to understand P‐use strategies across diverse plant communities.We explored relationships between five leaf P fractions (orthophosphate P, P_i; lipid P, P_L; nucleic acid P, P_N; metabolite P, P_M; and residual P, P_R) and 11 leaf economic traits of 58 woody species from three biomes in China, including temperate, subtropical and tropical forests. Then, we developed trait‐based models and spectral models for leaf P fractions and compared their predictive abilities.We found that plants exhibiting conservative strategies increased the proportions of P_Nand P_M, but decreased the proportions of P_iand P_L, thus enhancing photosynthetic P‐use efficiency, especially under P limitation. Spectral models outperformed trait‐based models in predicting cross‐site leaf P fractions, regardless of concentrations (R² = 0.50–0.88 vs 0.34–0.74) or proportions (R² = 0.43–0.70 vs 0.06–0.45).These findings enhance our understanding of leaf P‐allocation strategies and highlight reflectance spectroscopy as a promising alternative for characterizing large‐scale leaf P fractions and plant P‐use strategies, which could ultimately improve the physiological representation of the plant P cycle in land surface models.