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Aster varieties are widely used for medicinal purposes, landscaping, and ecological restoration, but their growth and reproduction are significantly threatened by the seed predator Tephritis angustipennis (Diptera: Tephritidae). The cultivation of pest-resistant varieties offers an effective, economical, and eco-friendly approach to managing T. angustipennis infestations. This study evaluates the impact of T. angustipennis on ten Aster varieties in the Three Rivers Source Region (TRSR), with a focus on population density, plant damage rate, and the activity of resistance enzymes and insect-resistant metabolites. The results classified the ten varieties into four resistance groups: one highly resistant variety [HR: Aster altaicus (MQAA)], four moderately resistant varieties [MR: Aster asteroides (DRAA), Aster flaccidus (QLAF), Aster tongolensis (BMAT), Aster poliothamnus (MQAP)], two moderately susceptible varieties [MS: Aster diplostephioides (QLAD), Aster souliei (DRAS)], and three highly susceptible varieties [HS: A. diplostephioides (MQAD), Aster yunnanensis var. labrangensis (MQAY), Aster farreri (MQAF)]. Notably, HR and MR varieties exhibited significantly higher activities of catalase (CAT), peroxidase (POD), superoxide dismutase (SOD), phenylalanine ammonia lyase (PAL), polyphenol oxidase (PPO), trypsin inhibitor (TI), and chymotrypsin inhibitor (CI), as well as higher contents of tannins (TN) and flavonoids (FN), compared to MS and HS varieties. Specifically, the HR variety (MQAA) showed the highest levels of CAT, POD, SOD, and TN, significantly enhancing its resistance to T. angustipennis . Statistical analyses further revealed that MDA, TN, FN, and antioxidant enzyme activities were found to be key factors influencing insect resistance across the different varieties and resistance levels. These findings enhance our understanding of the physiological and biochemical mechanisms underlying resistance in Aster spp. and offer valuable insights for developing integrated pest management strategies. By identifying and promoting resistant varieties, this study lays the groundwork for effective, sustainable control measures that protect Aster crops from T. angustipennis damage.

Hydrosilylation of unsaturated carbon-carbon bonds with hydrosilanes is a very important process to access organosilicon compounds and ranks as one of the most fundamental reactions in organic chemistry. However, catalytic asymmetric hydrosilylation of activated alkenes and internal alkenes has proven elusive, due to competing reduction of carbon-carbon double bond or isomerization processes. Herein, we report a highly enantioselective Si-C coupling by hydrosilylation of carbonyl-activated alkenes using a palladium catalyst with a chiral TADDOL-derived phosphoramidite ligand, which inhibits O-hydrosilylation/olefin reduction. The stereospecific Si-C coupling/hydrosilylation of maleimides affords a series of silyl succinimides with up to 99% yield, >99:1 diastereoselectivity and >99:1 enantioselectivity. The high degree of stereoselectivity exerts remote control of axial chirality, leading to functionalized, axially chiral succinimides which are versatile building blocks. The product utility is highlighted by the enantioselective construction of N-heterocycles bearing up to three stereocenters. Catalytic asymmetric hydrosilylation of internal alkenes has proven elusive due to more favourable double bond reduction or isomerization. Here, the authors show an enantioselective Si-C coupling by hydrosilylation of activated alkenes using a palladium/phosphoramidite catalyst affording axially chiral succinimides.

As COVID-19 sweeps through the whole world, human activities have been changed significantly. Under such circumstances, the electricity sector is deeply affected and faced with great challenges. This paper provides a comprehensive review of the impacts that the pandemic has caused on the electricity sector. Electricity demand has dropped sharply as governments around the world executed lockdown restrictions, while the load composition and daily load profile have also changed. The share of renewable generation has increased against the decline of the total electricity generation. Changed power balance situation and increased uncertainty of demand have posed higher pressure on system operators, along with voltage violation issue and challenges for system maintenance and management. The electricity market is also substantially influenced, while long-term investment in clean energy is expected to be stable. The externality such as emission reduction is also discussed.

Although tendon-driven continuum manipulators have been extensively researched, how to realize tip contact force sensing in a more general and efficient way without increasing the diameter is still a challenge. Rather than use a complex modeling approach, this paper proposes a general tip contact force-sensing method based on a recurrent neural network that takes the tendons’ position and tension as the input of a recurrent neural network and the tip contact force of the continuum manipulator as the output and fits this static model by means of machine learning so that it may be used as a real-time contact force estimator. We also designed and built a corresponding three-degree-of-freedom contact force data acquisition platform based on the structure of a continuum manipulator designed in our previous studies. After obtaining training data, we built and compared the performances of a multi-layer perceptron-based contact force estimator as a baseline and three typical recurrent neural network-based contact force estimators through TensorFlow framework to verify the feasibility of this method. We also proposed a manually decoupled sub-estimators algorithm and evaluated the advantages and disadvantages of those two methods.

Background In China, respiratory syncytial virus (RSV) infections traditionally occur during the spring and winter seasons. However, a shift in the seasonal trend was noted in 2020–2022, during the coronavirus disease 2019 (COVID-19) pandemic. Methods This study investigated the seasonal characteristics of RSV infection in children hospitalized with acute lower respiratory tract infections (ALRTIs). The RSV epidemic season was defined as RSV positivity in > 10% of the hospitalized ALRTI cases each week. Nine RSV seasons were identified between 2013 and 2022, and nonlinear ordinary least squares regression models were used to assess the differences in year-to-year epidemic seasonality trends. Results We enrolled 49,658 hospitalized children diagnosed with ALRTIs over a 9-year period, and the RSV antigen-positive rate was 15.2% ( n  = 7,566/49,658). Between 2013 and 2022, the average onset and end of the RSV season occurred in week 44 (late October) and week 17 of the following year, respectively, with a typical duration of 27 weeks. However, at the onset of the COVID-19 pandemic, the usual spring RSV peak did not occur. Instead, the 2020 epidemic started in week 32, and RSV seasonality persisted into 2021, lasting for an unprecedented 87 weeks before concluding in March 2022. Conclusions RSV seasonality was disrupted during the COVID-19 pandemic, and the season exhibited an unusually prolonged duration. These findings may provide valuable insights for clinical practice and public health considerations.

This paper proposes a privacy-preserving energy management of a shared energy storage system (SESS) for multiple smart buildings using federated reinforcement learning (FRL). To preserve the privacy of energy scheduling of buildings connected to the SESS, we present a distributed deep reinforcement learning (DRL) framework using the FRL method, which consists of a global server (GS) and local building energy management systems (LBEMSs). In the framework, the LBEMS DRL agents share only a randomly selected part of their trained neural network for energy consumption models with the GS without consumer’s energy consumption data. Using the shared models, the GS executes two processes: (i) construction and broadcast of a global model of energy consumption to the LBEMS agents for retraining their local models and (ii) training of the SESS DRL agent’s energy charging and discharging from and to the utility and buildings. Simulation studies are conducted using one SESS and three smart buildings with solar photovoltaic systems. The results demonstrate that the proposed approach can schedule the charging and discharging of the SESS and an optimal energy consumption of heating, ventilation, and air conditioning systems in smart buildings under heterogeneous building environments while preserving the privacy of buildings’ energy consumption.

Understanding and predicting “droughts” in wind and solar power availability can help the electric grid operator planning and operation toward deep renewable penetration. We assess climate models' ability to simulate these droughts at different horizontal resolutions, ∼100 and ∼25 km, over Western North America and Texas. We find that these power droughts are associated with the high/low pressure systems. The simulated wind and solar power variabilities and their corresponding droughts during historical periods are more sensitive to the model bias than to the model resolution. Future climate simulations reveal varied future change of these droughts across different regions. Although model resolution does not affect the simulation of historical droughts, it does impact the simulated future changes. This suggests that regional response to future warming can vary considerably in high‐ and low‐resolution models. These insights have important implications for adapting power system planning and operations to the changing climate. Plain Language Summary A carbon neutral future depends upon deep decarbonization of the electricity sector. As we move toward a future where we use cleaner energy sources, like wind and solar, it is important to ensure that the electric grid stays reliable. Scientists have been studying how climate change affects the weather, but we know less about how climate change affects periods when there is not enough wind or sunlight to generate renewable energy—known as “power droughts.” And to plan for the future, we need to study power droughts in global climate models. We analyze the climate models to assess how good they are at predicting when these energy shortages might happen in two regions of the United States. We found that adjusting the models to better match real‐world conditions improved how well they predicted the frequency of these power droughts. Refining model horizontal resolution from 100 to 25 km, did not impact the simulation of historical power droughts. Our results also show that in the future, the frequency of these power droughts will vary in a region‐dependent manner. Such information can help us plan for a future that is increasingly dependent on more sustainable energy sources. Key Points Wind and solar power droughts are linked to high/low pressure systems The simulated power droughts during historical periods are more sensitive to model bias than to model resolution Future simulations of these power droughts vary between low‐ and high‐resolution simulations, and across various regions

Multi-segment continuum robots (MSCRs) with switchable stiffness offer significant potential for advancing minimally invasive surgeries. Compliance is advantageous during navigation to prevent rigid collisions, while rigidity is required to ensure stability during surgical manipulation. However, most variable stiffness techniques involve additional structures, increasing the MSCRs’ size beyond clinical applicability. This study proposes to leverage the inherent friction between the joints of discrete-jointed MSCRs to achieve stiffness switching, eliminating extra friction mechanisms. A ball-and-socket joint-based MSCR with a compact diameter of 6 mm is introduced. During operations, the proximal segment of the MSCR is rigidly locked, providing stable support for the distal segment and resisting force coupling between the segments. To control the MSCR, a friction model for the ball-and-socket joints is developed and integrated into the Cosserat rod framework to characterize its kineto-static behavior. A multicore fiber with included fiber Bragg gratings (MCF-FBGs) is integrated for shape sensing. Based on the shape feedback from MCF-FBGs, a general workflow for the proposed prototype is established, detailing the actuation strategies. Experiments are conducted to validate the robot’s stiffness switching capability, quantify the modeling accuracy, and comprehensively evaluate the performance of the proposed prototype within the defined workflow.

Abstract Tephritis angustipennis (Diptera: Tephritidae) and Campiglossa loewiana (Diptera: Tephritidae) are phytophagous pests in China. Their damage has significantly impacted the collection and cultivation of germplasm resources of native Asteraceae plants. However, the genetic characteristics and structure of their population are unclear. This study focused on the highly damaging species of T. angustipennis and C. loewiana collected from the three-river source region (TRSR). We amplified the mitochondrial cytochrome C oxidase subunit I (mtCOI) gene sequences of these pests collected from this area and compared them with COI sequences from GenBank. We also analyzed their genetic diversity and structure. In T. angustipennis, 5 haplotypes were identified from 5 geographic locations; the genetic differentiation between France population FRPY (from Nylandia, Uusimaa) and China populations GLJZ (from Dehe Longwa Village, Maqin County), GLDR (from Zhique Village, Dari County), and GLMQ (from Rijin Village, Maqin County) was the strongest. GLJZ exhibited strong genetic differentiation from GLDR and GLMQ, with relatively low gene flow. For C. loewiana, 11 haplotypes were identified from 5 geographic locations; the genetic differentiation between the Chinese population GLMQ-YY (from Yangyu Forest Farm, Maqin County) and Finnish population FDNL (from Nylandia, Uusimaa) was the strongest, with relatively low gene flow, possibly due to geographical barriers in the Qinghai–Tibet plateau. Only 1 haplotype was identified across GLDR, GLMQ, and GLBM. High gene flow between distant locations indicates that human activities or wind dispersal may facilitate the dispersal of fruit flies and across different geographic. Geostatistical analysis suggested a recent population expansion of these 2 species in TRSR. Our findings provide technical references for identifying pests in the TRSR region and theoretical support for managing resistance, monitoring pest occurrences, analyzing environmental adaptability, and formulating biological control strategies for Tephritidae pests on Asteraceae plants.