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Optimal Transmission Switching (OTS) problems minimize operational costs while treating both the transmission line energization statuses and generator setpoints as decision variables. The combination of nonlinearities from an AC power flow model and discrete variables associated with line statuses makes AC-OTS a computationally challenging Mixed-Integer Nonlinear Program (MINLP). To address these challenges, the DC power flow approximation is often used to obtain a DC-OTS formulation expressed as a Mixed-Integer Linear Program (MILP). However, this approximation often leads to suboptimal or infeasible switching decisions when evaluated with an AC power flow model. This paper proposes an enhanced DC-OTS formulation that leverages techniques for training machine learning models to optimize the DC power flow model's parameters. By optimally selecting parameter values that align flows in the DC power flow model with apparent power flows—incorporating both real and reactive components—from AC Optimal Power Flow (OPF) solutions, our method more accurately captures line congestion behavior. Integrating these optimized parameters into the DC-OTS formulation significantly improves the accuracy of switching decisions and reduces discrepancies between DC-OTS and AC-OTS solutions. We compare our optimized DC-OTS model against traditional OTS approaches, including DC-OTS, Linear Programming AC (LPAC)-OTS, and Quadratic Convex (QC)-OTS. Numeric results show that switching decisions from our model yield better performance when evaluated using an AC power flow model, with up to 44% cost reductions in some cases. 

The lipid-rich copepodNeocalanus flemingeriis abundant throughout the subarctic Pacific, with a biogeographic range that includes the open ocean, marginal seas and fjord systems. Two distinct genetic variants have been reported based on differences in size: the ‘small form’, with a 1 yr life cycle, is found throughout the region, and the ‘large form’, with a 2 yr life cycle, is found in the western Pacific, where it is most abundant in the Sea of Okhotsk. Using a molecular approach, this study examined the genetic composition ofN. flemingeripopulations in the Gulf of Alaska from multiple stations over a 9 yr period. This is the first report of the occurrence of the large form in the eastern Pacific, where it exhibits a significant presence in fjord systems. However, in this region, both the small- and large-formN. flemingerihave annual life cycles. Collections from nearshore to offshore locations over multiple years indicated both interannual and spatial differences in the relative proportion of the 2 variants. Our results show that the forms inhabit overlapping yet distinct habitats, potentially due to adaptation to contrasting environmental conditions. 

Reconfigurable microrobots promise advancements in microsurgical tools, self‐healing materials, and environmental remediation by enabling precise, adaptive functionalities at small scales. However, predicting their behaviors a priori remains a significant challenge, limiting the pace of design and discovery. To address this, a Monte Carlo simulation framework is presented for predicting the folding behavior of self‐assembled, sequence‐encoded microrobot chains composed of magnetic particles, enabling efficient exploration of their large design space. This computational framework employs metrics of radius of gyration, tortuosity, and symmetry score to map the design space, identify functional sequences, and predict likely folding behaviors before fabrication. The framework through experiments to demonstrate accuracy in capturing folding behaviors is validated. Statistical analysis reveals adherence to self‐avoiding walk principles from polymer theory, providing a foundation for understanding how input sequences drive folding capabilities. Moreover, the simulation surpasses current experimental capabilities, enabling exploration of novel microrobot designs, such as sequences incorporating mixtures of cubes and triangular prism subunits, which exhibit distinct compressive behaviors. Beyond the sequence‐encoded microrobots investigated in this study, this framework offers broad utility for the design of reconfigurable microscale systems by reducing reliance on experimental prototyping and accelerating discovery of new functional microrobots for use in biomedicine, materials engineering, and sustainability. 

Abstract The existence of highly productive coral reefs within oligotrophic gyres is in part due to intensive recycling of macronutrients and organic matter by microbes. Therefore, characterizing reef bacterioplankton communities is key for understanding reef metabolism and biogeochemical transformations. We performed a high‐resolution survey of waters surrounding Mo'orea (French Polynesia), coupling 16S metabarcoding with biogeochemical and physical measurements. Bacterioplankton communities differed markedly among reef ecosystems on three sides of the island, and within each system distinct communities emerged in forereef, backreef and reef pass habitats. The degree of habitat differentiation varied among the island sides according to current speeds inferred from wave power. Oceanic‐associated taxa were enriched in forereefs and throughout western reefs with highest wave power and lowest productivity. Reef‐associated taxa were enriched in backreef and pass habitats most strongly on northern reefs with lowest wave power and highest productivity. Our results offer insight into dynamics regulating reef microbial communities. 

Abstract Viruses infecting aquatic microbes vary immensely in size, but the ecological consequences of virus size are poorly understood. Here we used a unique suite of diverse phytoplankton strains and their viruses, all isolated from waters around Hawai'i, to assess whether virus size affects the suppression of host populations. We found that small viruses of diverse genome type (3–24 kb genome size, 23–70 nm capsid diameter) have very similar effects on host populations, suppressing hosts less strongly and for a shorter period of time compared to large double‐stranded DNA viruses (214–1380 kb, 112–386 nm). Suppressive effects of larger viruses were more heterogeneous, but most isolates reduced host populations by many orders of magnitude, without recovery over the ~ 25‐d experiments. Our results suggest that disparate lineages of viruses may have ecological consequences that are predictable in part based on size, and that ecosystem impacts of viral infection may vary with the size structure of the viral community. 

ABSTRACT Benthic–pelagic coupling, the reciprocal exchange of materials between benthic and pelagic habitats, has traditionally emphasised pelagic influences on benthic systems. Yet, the role of benthic biological processes in shaping pelagic microbial dynamics remains underexplored. We investigated how surfgrass and mussels regulate nitrogen and dissolved organic matter (DOM) cycling and their cascading effects on heterotrophic bacteria in Oregon tide pools. We quantified biogeochemical fluxes and bacterial responses before and after foundation species removal during contrasting upwelling regimes. Mussel‐dominated pools released high concentrations of ammonium and nitrate, while surfgrass pools transformed DOM that fueled bacterial growth; upwelling intensified these benthic–pelagic linkages. Removing foundation species dampened nutrient release in mussel pools and reduced DOM‐fueled bacterial growth in surfgrass pools, ultimately decoupling benthic productivity from pelagic microbial growth. Our results demonstrate the critical role of foundation species to pelagic microbial processes and underscore the vulnerability of coastal microbial dynamics to their global decline. 

The thermal and thermomechanical behavior of multi-component organic solids is tuned at the molecular levelviapreparation of mixed cocrystals. 

The inherent nonlinearity of the power flow equations poses significant challenges in accurately modeling power systems, particularly when employing linearized approximations. Although power flow linearizations provide computational efficiency, they can fail to fully capture nonlinear behavior across diverse operating conditions. To improve approximation accuracy, we propose conservative piecewise linear approximations (CPLA) of the power flow equations, which are designed to consistently over- or under-estimate the quantity of interest, ensuring conservative behavior in optimization. The flexibility provided by piecewise linear functions can yield improved accuracy relative to standard linear approximations. However, applying CPLA across all dimensions of the power flow equations could introduce significant computational complexity, especially for large-scale optimization problems. In this paper, we propose a strategy that selectively targets dimensions exhibiting significant nonlinearities. Using a second-order sensitivity analysis, we identify the directions where the power flow equations exhibit the most significant curvature and tailor the CPLAs to improve accuracy in these specific directions. This approach reduces the computational burden while maintaining high accuracy, making it particularly well-suited for mixed-integer programming problems involving the power flow equations. 

Abstract Targeted protein degradation (TPD) is a powerful strategy for targeting and eliminating disease-causing proteins. While heterobifunctional Proteolysis-Targeting Chimeras (PROTACs) are more modular, the rational design of monovalent or molecular glue degraders remains challenging. In this study, we generated a small library of BET-domain inhibitor JQ1 analogs bearing elaborated electrophilic handles to identify permissive covalent degradative handles and E3 ligase pairs. We identified an elaborated fumaramide handle that, when appended onto JQ1, led to the proteasome-dependent degradation of BRD4. Further characterization revealed that the E3 ubiquitin ligase CUL4^DCAF16—a common E3 ligase target of electrophilic degraders—was responsible for BRD4 loss by covalently targeting C173 on DCAF16. While this original fumaramide handle, when appended onto other protein-targeting ligands, did not accommodate the degradation of other neo-substrates, a truncated version of this handle attached to JQ1 was still capable of degrading BRD4, now through targeting both C173 and C178. This truncated fumaramide handle, when appended on various protein targeting ligands, and was also more permissive in degrading other neo-substrates, including CDK4/6, SMARCA2/4, and the androgen receptor (AR). We further demonstrated that this optimized truncated fumaramide handle, when transplanted onto an AR DNA binding domain-targeting ligand, could degrade both AR and the undruggable truncation variant of AR, AR-V7, in androgen-independent prostate cancer cells in a DCAF16-dependent manner. Overall, we have identified a unique DCAF16-targeting covalent degradative handle that can be transplanted across several protein-targeting ligands to induce the degradation of their respective targets for the modular design of monovalent or bifunctional degraders.