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With the increase of urban gas pipeline operation time, more and more underground gas pipelines are facing various problems, and old gas pipelines urgently need to be updated and repaired. Realizing the comprehensive excavation, demolition, and replacement of old pipes with new ones poses pain points such as high construction difficulty, repair costs, and uncertainty of routing construction planning. Therefore, the implementation of trenchless repair and transformation of old gas pipelines has positive and practical significance. The CIPP flipped lining repair technology, as an emerging trenchless pipeline repair technology, provides an efficient and cost-effective solution for the repair of old gas pipelines. A case study on the application of CIPP flipped lining method was conducted for the rehabilitation project of Liangshen Road gas pipeline in Weituo Industrial Park, Chongqing. By comparing the characteristics of different trenchless pipeline repair techniques, the technical adaptability of CIPP flipped lining method was analyzed, and the construction parameter design, key process flow, and construction difficulties of using CIPP flipped lining to repair gas pipelines were explored and revealed, as well as the control of engineering quality impact. Practice has shown that CIPP flipped lining can provide a feasible technical choice for the preventive repair of urban gas pipelines. The repaired pipeline performance meets the operational requirements of urban gas pipelines, and has broad prospects and promotion value. It can provide reference for the non-excavation repair and utilization of old gas pipelines in the future.

Pepper ( Capsicum spp.) is one of the earliest cultivated crops and includes five domesticated species, C. annuum var. annuum , C. chinense , C. frutescens , C. baccatum var. pendulum and C. pubescens . Here, we report a pepper graph pan-genome and a genome variation map of 500 accessions from the five domesticated Capsicum species and close wild relatives. We identify highly differentiated genomic regions among the domesticated peppers that underlie their natural variations in flowering time, characteristic flavors, and unique resistances to biotic and abiotic stresses. Domestication sweeps detected in C. annuum var. annuum and C. baccatum var. pendulum are mostly different, and the common domestication traits, including fruit size, shape and pungency, are achieved mainly through the selection of distinct genomic regions between these two cultivated species. Introgressions from C. baccatum into C. chinense and C. frutescens are detected, including those providing genetic sources for various biotic and abiotic stress tolerances. Existing genetics and genomics studies of peppers mainly focus on single species. Here, the authors report a pepper graph pan-genome and a genome variation map of 500 accessions from five domesticated species and close wild relatives to reveal their domestication, introgression and population differentiation.

In response to the growing demand for sustainable aviation, a fuel consumption prediction model based on Radial Basis Function (RBF) Neural Networks was proposed. Using high-resolution onboard Quick Access Recorder (QAR) data, which contains richer flight parameters and higher accuracy, RBF models were constructed based on the extracted key influencing factors for different flight phases, including takeoff/climb, cruise, and descent/approach. The model provides a lightweight and computationally efficient solution for high-dimensional, nonlinear flight data, ensuring accuracy with lower computational burdens. It is suitable both for pre-flight ground-based fuel consumption prediction and deployment in resource-constrained onboard environments, enabling real-time prediction during flight operations. Experimental results showed that the RBF model’s prediction errors for the takeoff/climb, cruise, and descent/approach phases were 5.73%, 3.36%, and 14.04%, respectively, significantly outperforming the comparison models. The error variances from ten-fold cross-validation were 0.31%, 0.15%, and 0.29%, respectively, confirming the robustness of the model. Further analysis indicated that the model can be employed to evaluate the “fuel penalty for carrying additional fuel” patterns and enhance fuel efficiency. This study provided valuable insights and theoretical support for airlines in optimizing flight planning and minimizing fuel consumption, thereby contributing to the sustainable development of green aviation.

Difluoromethylene moiety has gained widespread applications in pharmaceuticals, agrochemicals, and materials owing to its augmented lipophilicity and being bioisosteric to ethereal oxygen. Possessing two orthogonal reactivity modes for bridging an electrophile and a radical acceptor to give gem- difluorides (R 1 -CF 2 -R 2 ), the efficient difluoromethylene radical anion synthon (diFRAS) has been long sought after. In this work, we successfully utilize the readily available difluoromethyl phenyl sulfone (PhSO 2 CF 2 H) to couple with electrophiles and radical acceptors, thereby enabling PhSO 2 CF 2 H to serve as a novel diFRAS in organic synthesis. The generation of radicals (•CF 2 R) via visible light-promoted homolytic cleavage of C−S bonds in (phenylsulfonyl)difluoromethylated derivatives (PhSO 2 CF 2 R) is the linchpin in the diFRAS strategy to construct gem- difluorides (R 1 -CF 2 -R 2 ) with structural complexity. Difluoromethylene moiety is widely used in industry, though it is challenging to generate difluoromethylene radical anion synthons. Here the authors show a method to generate these radicals from difluoromethyl phenyl sulfone, triggered by the cleavages of C-S bonds.

Visual odometry is pivotal in robotics and autonomous driving, serving as a key component of visual simultaneous localization and mapping technology. In real-world scenarios, humans in local low-light conditions perceive less information, which can impact our judgments and actions. Similarly, visual odometry can become confused under these conditions, leading to compromised performance. To address the challenges posed by local low-light images on monocular visual odometry, we propose an unsupervised framework for monocular visual odometry. To the best of our knowledge, this is the first instance of unsupervised monocular visual odometry and local low-light image enhancement accomplished within a unified framework. Initially, we employ retinex theory and the discrete Fourier transform to decompose, filter, and synthesize the original image. For the filtering process, we propose a novel learnable global filtering network. Subsequently, we input the enhanced images into the depth and pose networks, generating the corresponding depth maps and inter-frame poses. Ultimately, we construct a photometric consistency loss, a depth loss, and a novel low-light smoothness loss to train the entire network. Through experimental validation, our method exhibits superior performance on the KITTI dataset. Furthermore, it demonstrates satisfactory generalization ability in unseen environments from the Oxford RobotCar dataset.

The mechanisms by which animals respond to rapid changes in temperature are largely unknown. Here, we found that polymodal ASH sensory neurons mediate rapid cooling-evoked avoidance behavior within the physiological temperature range in C. elegans . ASH employs multiple parallel circuits that consist of stimulatory circuits (AIZ, RIA, AVA) and disinhibitory circuits (AIB, RIM) to respond to rapid cooling. In the stimulatory circuit, AIZ, which is activated by ASH, releases glutamate to act on both GLR-3 and GLR-6 receptors in RIA neurons to promote reversal, and ASH also directly or indirectly stimulates AVA to promote reversal. In the disinhibitory circuit, AIB is stimulated by ASH through the GLR-1 receptor, releasing glutamate to act on AVR-14 to suppress RIM activity. RIM, an inter/motor neuron, inhibits rapid cooling-evoked reversal, and the loop activities thus equally stimulate reversal. Our findings elucidate the molecular and circuit mechanisms underlying the acute temperature stimuli-evoked avoidance behavior. The mechanisms by which animals respond to rapid cooling stimuli are largely unknown. Here, the authors dissect the molecular and circuit mechanisms underlying rapid cooling-evoked avoidance behavior in C. elegans .

Polar skyrmions are topologically nontrivial polarization textures that demonstrate exotic physical phenomena and novel memory applications. Thus far, these textures have primarily been reported in oxide-ferroelectric-based epitaxial heterostructures because their stabilization requires an elastic energy penalty from the epitaxial strains. Here, without the epitaxial-strain engineering, we discover polar skyrmion bubbles in stand-alone van der Waals ferroelectric CuInP 2 S 6 crystal through the combination of piezoelectric force microscopy, high-resolution transmission electron microscopy, and phase-field simulations. In a thick CuInP 2 S 6 flake of over −100 nm, skyrmion bubbles feature an elliptical hedgehog-like state with center-divergent or center-convergent configurations. Progressively thinning the flake thickness to −8 nm allows a topological transition from elliptical to circular skyrmionic patterns. Interestingly, the skyrmions can be switched with the change in helicity by probe-applied electrical and mechanical stimuli, which is distinct from the creation and annihilation of other reported skyrmions. Both theoretical and experimental data proves that the formation and thickness-dependence of skyrmion textures primarily stem from charge-related energy penalty. This work opens up a new material system (i.e., two-dimensional layered ferroionic materials) for exploring uncharted polar-topology physics and prospective neuromorphic devices. Polar skyrmions are a topological polarization texture. While they have now been detected in numerous material systems, near universally these have required careful strain engineering. Here, Xue, Zhang, Zheng, Tong, Wang and coauthors observe polar skyrmions in the van der Waals ferroelectric CuInP2S6, without strain-engineering.

Bulk photovoltaic (BPV) effect primarily stems from shift currents in symmetry-breaking materials, providing the potential to smash the Shockley-Queisser limit that constrains the performance of conventional p-n junctions-based solar cells. However, limited open circuit voltages (V oc ) or short circuit current densities (J sc ) from BPV devices still cause a low photoelectric conversion efficiency. Here, combining theoretical analysis and experimental evidence, we identify a range of BPV materials where both V oc and J sc can be co-optimized, and greatly boost the efficiency through ferroelectric engineered shift current. We select ferroelectric NbOBr 2 as an example and construct a two-dimensional in-plane device with a giant shift current-dominated BPV effect. In spontaneous polarization state, the devices demonstrate a record-high J sc among all ferroelectric materials. Moreover, the electrically aligned NbOBr 2 polarization enables the significant co-enhancement of both V oc and J sc , leading to a colossal improvement of photoelectric conversion efficiency up to four orders of magnitude (1.25%), which is approximately four times greater than that of state-of-the-art BPV devices. Our work provides a promising solution for screening and creating higher efficient BPV cells. The photoelectric conversion efficiency of bulk photovoltaic devices has been limited by open circuit voltages or short circuit current densities. Here, authors construct a 2D in-plane device based on ferroelectric NbOBr 2 to improve photovoltaic performance and achieve a device efficiency of 1.25%.

Predicting lithium-ion batteries’ state of charge (SOC) is essential to electric vehicle battery management systems. Traditional lithium-ion battery models mainly include equivalent circuit models (ECMs) and electrochemical models (EMs). ECMs are based on integer-order component modeling, which cannot characterize the internal electrochemical reaction mechanism of the battery, resulting in lower SOC prediction accuracy. In contrast, due to their complex structure, EMs are limited in their application. This study takes lithium batteries as the research object and proposes a fractional-order impedance model (FOIM) that characterizes the dynamic properties of the internal behavior of lithium-ion batteries using fractional-order elements. Considering the highly nonlinear characteristics of lithium-ion batteries, this study introduces the theory of fractional-order calculus into the extended Kalman filter (EKF) algorithm, and proposes the fractional-order extended Kalman filter (FEKF) algorithm applied to the prediction of battery charge state. Comparative analysis of simulation and experimental results shows that the accuracy of the FOIM, compared to ECMs, is significantly improved. The FEKF algorithm has good robustness in estimating the SOC, and the SOC prediction accuracy achieved with the algorithm is also improved compared with that obtained using the EKF algorithm of the integer-order model.

Frequent outbreaks of cyanobacterial blooms have become one of the most challenging water ecosystem issues and a critical concern in environmental protection. To overcome the poor stability of traditional detection algorithms, this paper proposes a method for detecting cyanobacterial blooms based on a deep-learning algorithm. An improved vegetation-index method based on a multispectral image taken by an Unmanned Aerial Vehicle (UAV) was adopted to extract inconspicuous spectral features of cyanobacterial blooms. To enhance the recognition accuracy of cyanobacterial blooms in complex scenes with noise such as reflections and shadows, an improved transformer model based on a feature-enhancement module and pixel-correction fusion was employed. The algorithm proposed in this paper was implemented in several rivers in China, achieving a detection accuracy of cyanobacterial blooms of more than 85%. The estimate of the proportion of the algae bloom contamination area and the severity of pollution were basically accurate. This paper can lay a foundation for ecological and environmental departments for the effective prevention and control of cyanobacterial blooms.