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With the demand for energy sustainability and the development of the low-carbon power system, distributed energy, energy storage, demand response, and other diverse market participants will participate in the electricity market, which brings new changes to the transaction organization process of the electricity market. This paper first introduces the changes in the electricity market under the participation of diverse market participants from two aspects of market transactions and market power. Diverse participants can provide a variety of auxiliary services in the electricity market while they have potential market power in the process. Secondly, the current supervision measures used in the foreign electricity market in the order of ex-ante, interim, and ex-post are reviewed and four common supervision indexes are introduced. Finally, the market power and the supervision necessity of the electricity market under the participation of energy storage are analyzed by case studies. As shown by the simulation results, energy storage with excellent regulating performance has market power in providing frequency regulation services, but the conventional supervision indexes fail to detect the market power of energy storage effectively. The purpose of this paper is to illustrate the necessity of the market supervision method for adapting to the changes under the participation of diverse participants.

Abstract Background Peucedanum praeruptorum Dunn has typical stacked umbels and medicinal value; however, the lack of an effective tissue culture system for P. praeruptorum has limited the large-scale propagation of its seedlings. Results We systematically established an in vitro regeneration system for P. praeruptorum using young leaves and stems as explants. Tissue culture plantlets were successfully obtained within 123 and 90 d of somatic embryogenesis and organogenesis, respectively. Combined plant growth regulators (PGRs) were optimized to promote efficient plant regeneration at each stage of the culture process. Specifically, embryogenic callus induction was superior in Murashige and Skoog (MS) medium supplemented with 0.5 mg/L 6-benzyladenine (BA) and 2.0 mg/L 2,4-dichlorophenoxyacetic acid (2,4-D). For somatic embryonic development, the highest differentiation rates were achieved using BA, 2,4-D, and 6-furfuryl aminopurine (6-KT). Induction of organogenesis resulted in the highest differentiation rates and proliferation coefficients of buds in MS medium supplemented with BA and α-naphthaleneacetic acid (NAA). Moreover, regeneration of P. praeruptorum seedlings was achieved by adjusting the BA and indole-3-butyric acid (IBA) concentrations in 1/2 MS medium. Conclusion Our results provide a technical system for the rapid propagation of P. praeruptorum, which can facilitate germplasm improvement, resource conservation, and further genetic transformation of Peucedanum species.

Abstract Additive manufacturing (AM) shows promise as a method for producing soft-magnetic multicomponent alloys for use in electric motors and sustainable electromobility applications. However, the simultaneous achievement of a high saturation magnetic flux density (B s) and a low coercivity (H c) in AM soft-magnetic materials remains challenging. Herein, we present an approach that integrates an elemental powder mixture of Fe45Co30Ni25 with Fe2O3 nano-oxides, which is then subjected to laser powder bed fusion (LPBF) followed by high-temperature annealing to achieve an FCC-structured Fe45Co30Ni25 MEA/FeO composite. The FeO nanoparticles, a byproduct of the reaction between Fe powders and Fe2O3 nano-oxides, serve as nucleation sites for the formation of a single FCC phase in the MEA matrix. The resulting LPBF MEA/FeO composite has a B s of 2.05 T and an exceedingly low H c of 115 A m−1, compared to those of the BCC/FCC dual phase MEA and other state-of-the-art additively manufactured soft-magnetic alloys. In situ Lorentz transmission electron microscope (TEM) revealed that the low H c of the FCC-structured MEA/FeO composite originates from the reduced pinning effect of grain boundaries in the FCC phase on domain wall movement compared with those in the FCC/BCC dual phase.

Abstract Iron is an essential biomineral in the human body. Here, we describe a subset of iron-loaded cancer-associated fibroblasts, termed as FerroCAFs, that utilize iron to induce immunosuppression in prostate cancer and predict an unfavorable clinical outcome. FerroCAFs secrete myeloid cell-associated proteins, including CCL2, CSF1 and CXCL1, to recruit immunosuppressive myeloid cells. We report the presence of FerroCAFs in prostate cancer from both mice and human, as well as in human lung and ovarian cancers, and identify a conserved cell surface marker, the poliovirus receptor. Mechanistically, the accumulated iron in FerroCAFs is caused by Hmox1-mediated iron release from heme degradation. The intracellular iron activates the Kdm6b, an iron-dependent epigenetic enzyme, to induce an accessible chromatin state and transcription of myeloid cell-associated protein genes. Targeting the FerroCAFs by inhibiting the Hmox1/iron/Kdm6b signaling axis incurs anti-tumor immunity and tumor suppression. Collectively, we report an iron-loaded FerroCAF cluster that drives immunosuppression through an iron-dependent epigenetic reprogramming mechanism and reveal promising therapeutic targets to boost anti-tumor immunity.

Abstract Transformer-based methods effectively capture global dependencies in images, demonstrating outstanding performance in multiple visual tasks. However, existing Transformers cannot effectively denoise large noisy images captured under low-light conditions owing to (1) the global self-attention mechanism causing high computational complexity in the spatial dimension owing to a quadratic increase in computation with the number of tokens; (2) the channel-wise self-attention computation unable to optimise the spatial correlations in images. We propose a local–global interaction Transformer (LGIT) that employs an adaptive strategy to select relevant patches for global interaction, achieving low computational complexity in global self-attention computation. A top-N patch cross-attention model (TPCA) is designed based on superpixel segmentation guidance. TPCA selects top-N patches most similar to the target image patch and applies cross attention to aggregate information from them into the target patch, effectively enhancing the utilisation of the image’s nonlocal self-similarity. A mixed-scale dual-gated feedforward network (MDGFF) is introduced for the effective extraction of multiscale local correlations. TPCA and MDGFF were combined to construct a hierarchical encoder-decoder network, LGIT, to compute self-attention within and across patches at different scales. Extensive experiments using real-world image-denoising datasets demonstrated that LGIT outperformed state-of-the-art (SOTA) convolutional neural network (CNN) and Transformer-based methods in qualitative and quantitative results.

Abstract A high quality (Q) factor is essential for enhancing the performance of resonant nanoelectromechanical systems (NEMS). NEMS resonators based on two-dimensional (2D) materials such as molybdenum disulfide (MoS2) have high frequency tunability, large dynamic range, and high sensitivity, yet room-temperature Q factors are typically less than 1000. Here, we systematically investigate the effects of device size and surface nonidealities on Q factor by measuring 52 dry-transferred fully clamped circular MoS2 NEMS resonators with diameters ranging from 1 μm to 8 μm, and optimize the Q factor by combining these effects with the strain-modulated dissipation model. We find that Q factor first increases and then decreases with diameter, with an optimized room-temperature Q factor up to 3315 ± 115 for a 2-μm-diameter device. Through extensive characterization and analysis using Raman spectroscopy, atomic force microscopy, and scanning electron microscopy, we demonstrate that surface nonidealities such as wrinkles, residues, and bubbles are especially significant for decreasing Q factor, especially for larger suspended membranes, while resonators with flat and smooth surfaces typically have larger Q factors. To further optimize Q factors, we measure and model Q factor dependence on the gate voltage, showing that smaller DC and radio-frequency (RF) driving voltages always lead to a higher Q factor, consistent with the strain-modulated dissipation model. This optimization of the Q factor delineates a straightforward and promising pathway for designing high-Q 2D NEMS resonators for ultrasensitive transducers, efficient RF communications, and low-power memory and computing.

Background: Immunotherapy has emerged as a novel strategy for cancer treatment following surgery, radiotherapy, and chemotherapy. Immune checkpoint blockade and Chimeric antigen receptor (CAR)-T cell therapies have been successful in clinical trials. Cancer cells evade immune surveillance by hijacking inhibitory pathways via overexpression of checkpoint genes. The Cluster of Differentiation 47 (CD47) has emerged as a crucial checkpoint for cancer immunotherapy by working as a “don’t eat me” signal and suppressing innate immune signaling. Furthermore, CD47 is highly expressed in many cancer types to protect cancer cells from phagocytosis via binding to SIRPα on phagocytes. Targeting CD47 by either interrupting the CD47-SIRPα axis or combing with other therapies has been demonstrated as an encouraging therapeutic strategy in cancer immunotherapy. Antibodies and small molecules that target CD47 have been explored in pre- and clinical trials. However, formidable challenges such as the anemia and palate aggregation cannot be avoided because of the wide presentation of CD47 on erythrocytes. Aim of view: This review summarizes the current knowledge on the regulation and function of CD47, and provides a new perspective for immunotherapy targeting CD47. It also highlights the clinical progress of targeting CD47 and discusses challenges and potential strategies. Key scientific concepts of review: This review provides a comprehensive understanding of targeting CD47 in cancer immunotherapy, it also augments the concept of combination immunotherapy strategies by employing both innate and adaptive immune responses.

Abstract The East Asian summer monsoon (EASM) supplies vital rainfall for over one billion people. El Niño-Southern Oscillation (ENSO) markedly affects the EASM, but its impacts are more robust following El Niño than La Niña. Here, we show that this asymmetry arises from the asymmetry in ENSO evolution: though most El Niño events last for one year, La Niña events often persist for 2-3 years. In the summers between consecutive La Niña events, the concurrent La Niña opposes the delayed effect of the preceding winter La Niña on the EASM, causing a reduction in the magnitude and coherence of climate anomalies. Results from a large ensemble climate model experiment corroborate and strengthen the observational analysis with an order of magnitude increase in sample size. The apparent asymmetry in the impacts of the ENSO on the EASM can be reduced by considering the concurrent ENSO, in addition to the ENSO state in the preceding winter. This has important implications for seasonal climate forecasts.

Abstract BCL-2 inhibitors such as venetoclax offer therapeutic promise in acute myeloid leukemia (AML) and other cancers, but drug resistance poses a significant challenge. It is crucial to understand the mechanisms that regulate venetoclax response. While correlative studies have identified numerous genes linked to venetoclax sensitivity, their direct impact on the drug response remains unclear. In this study, we targeted around 1400 genes upregulated in venetoclax-sensitive primary AML samples and carried out a CRISPR knockout screen to evaluate their direct effects on venetoclax response. Our screen identified the transcription factor ZNF740 as a critical regulator, with its expression consistently predicting venetoclax sensitivity across subtypes of the FAB classification. ZNF740 depletion leads to increased resistance to ventoclax, while its overexpression enhances sensitivity to the drug. Mechanistically, our integrative transcriptomic and genomic analysis identifies NOXA as a direct target of ZNF740, which negatively regulates MCL-1 protein stability. Loss of ZNF740 downregulates NOXA and increases the steady state protein levels of MCL-1 in AML cells. Restoring NOXA expression in ZNF740-depleted cells re-sensitizes AML cells to venetoclax treatment. Furthermore, we demonstrated that dual targeting of MCL-1 and BCL-2 effectively treats ZNF740-deficient AML in vivo. Together, our work systematically elucidates the causal relationship between venetoclax response signature genes and establishes ZNF740 as a novel transcription factor regulating venetoclax sensitivity.

Abstract Doctors typically use non‐contrast‐enhanced computed tomography (NCECT) in the treatment of kidney cancer to map kidney and tumour structural information to functional imaging single‐photon emission computed tomography, which is then used to assess patient kidney function and predict postoperative recovery. However, the assessment of kidney function and formulation of surgical plans is constrained by the low contrast of tumours in NCECT, which hinders the acquisition of accurate tumour boundaries. Therefore, this study designed a radiomics feature‐enhanced level‐set evolution (RFLSE) to precisely segment small‐sample low‐contrast kidney tumours. Integration of high‐dimensional radiomics features into the level‐set energy function enhances the edge detection capability of low‐contrast kidney tumours. The use of sensitive radiomics features to control the regional term parameters achieves adaptive adjustment of the curve evolution amplitude, improving the level‐set segmentation process. The experimental data used low‐contrast, limited‐sample tumours provided by hospitals, as well as the public datasets BUSI18 and KiTS19. Comparative results with advanced energy functionals and deep learning models demonstrate the precision and robustness of RFLSE segmentation. Additionally, the application value of RFLSE in assisting doctors with accurately marking tumours and generating high‐quality pseudo‐labels for deep learning datasets is demonstrated.

High-order harmonic techniques can be used to recreate neutron flux distributions in reactor cores using the neutron diffusion equation. However, traditional source iteration and source correction iteration techniques have sluggish convergence rates and protracted calculation periods. The correctness of the implicitly restarted Arnoldi method (IRAM) in resolving the eigenvalue problems of the one-dimensional and two-dimensional neutron diffusion equations was confirmed by computing the benchmark problems SLAB_1D_1G and two-dimensional steady-state TWIGL using IRAM. By integrating Galerkin projection with Proper Orthogonal Decomposition (POD) techniques, a POD-Galerkin reduced-order model was developed and the IRAM model was used as the full-order model. For 14 macroscopic cross-section values, the TWIGL benchmark problem was perturbed within a 20% range. We extracted 100 sample points using the Latin hypercube sampling method, and 70% of the samples were used as the testing set to assess the performance of the reduced-order model The remaining 30% were utilized as the training set to develop the reduced-order model, which was employed to rebuild the TWIGL benchmark problem. The reduced-order model demonstrates good flexibility and can efficiently and accurately forecast the effective multiplication factor and neutron flux distribution in the core. The reduced-order model predicts keff and neutron flux distribution with a high degree of agreement compared to the full-order model. Additionally, the reduced-order model's computation time is only 10.18% of that required by the full-order model.The neutron flux distribution of the steady-state TWIGL benchmark was recreated using the reduced-order model. The obtained results indicate that the reduced-order model can accurately predict the keff and neutron flux distribution of the steady-state TWIGL benchmark.Overall, the proposed technique not only has the potential to accurately project neutron flux distributions in transient settings, but is also relevant for reconstructing neutron flux distributions in steady-state conditions; thus, its applicability is bound to increase in the future.

Traditional text-based system engineering, which has been used in the design and application of passive residual heat removal system (PRHRS) for lead-cooled fast reactors, is prone to several problems such as low development efficiency, long iteration cycles, and model ambiguity. This study aims to effectively address the above-mentioned problems by adopting a model-based system engineering (MBSE) method, which has been preliminarily applied to meet the design requirements of a PRHRS. The design process has been implemented based on the preliminary design of the system architecture and consists of three stages: top-level requirement analysis, functional requirements analysis, and design requirements synthesis. The results of the top-level requirements analysis and the corresponding use case diagram can determine the requirements, top-level use cases, and scenario flow of the system. During the functional requirements analysis, the sequence, activity, and state machine diagrams are used to develop the system function model and provide early confirmation. By comparing these sequence diagrams, the requirements for omissions and inconsistencies can be effectively checked. In the design requirements synthesis stage, the Analytic Hierarchy Process is used to conduct preliminary trade-off calculations on the system architecture, after which a white box model is established during the system architecture design. Through these two steps, the analysis and design of the system architecture are ultimately achieved. The resulting system architecture ensures the consistency of the design requirements. Ultimately, a functional hazard analysis was conducted for a specific incident to validate case requirements and further refine the system architecture. Future research can further reduce the design risk, improve the design efficiency, and provide a practical reference for the design and optimization of PRHRS in digital lead-cooled fast reactors.

Current cytotoxic T lymphocyte (CTL) activating immunotherapy requires a major histocompatibility complex I (MHC-I)-mediated presentation of tumor-associated antigens, which malfunctions in around half of patients with triple-negative breast cancer (TNBC). Here, we create a LCL161-loaded macrophage membrane decorated nanoparticle (LMN) for immunotherapy of MHC-I-deficient TNBC. SIRPα on the macrophage membrane helps LMNs recognize CD47-expressing cancer cells for targeted delivery of LCL161, which induces the release of high mobility group protein 1 and proinflammatory cytokines from cancer cells. The released cytokines and high mobility group protein 1 activate antitumor immunity by increasing the intratumoral density of the phagocytic macrophage subtype by 15 times and elevating the intratumoral concentration of CTL lymphotoxin by 4.6 folds. LMNs also block CD47-mediated phagocytosis suppression. LMNs inhibit the growth of MHC-I-deficient TNBC tumors, as well as those resistant to combined therapy of anti-PDL1 antibody and albumin-bound paclitaxel, and prolong the survival of animals, during which process CTLs also play important roles. This macrophage membrane-decorated nanoparticle presents a generalizable platform for increasing macrophage-mediated antitumor immunity for effective immunotherapy of MHC-I-deficient cancers.

Photoacoustic spectroscopy (PAS) gas sensors based on optomechanical resonators (OMRs) have garnered significant attention for ultrasensitive trace-gas detection. However, a major challenge lies in balancing small size with high performance when developing ultrasensitive miniaturized optomechanical resonant PAS (OMR-PAS) gas sensors for space-constrained applications. Here, we present a miniature optical fiber PAS gas sensor based on a planar-spiral spring OMR (PSS-OMR) that is in situ 3D micro-printed on the end-face of a fiber-optic ferrule. Experimental results demonstrate that mechanical vibrational resonance can enhance the sensor's acoustic sensitivity by over two orders of magnitude. Together with a 1.4 μL non-resonant photoacoustic cell, it can detect C2H2 gas concentration at the 45-ppb level, and its response is very fast approximating 0.2 seconds. This optical fiber OMR-PAS gas sensor holds great promise for the detection or monitoring of rapidly varying trace gas in many applications ranging from production process control to industrial environmental surveillance.

Reducing agricultural carbon emissions is critical to achieving green agricultural development and the “dual carbon” goals. The present study conducts empirical analysis using provincial panel data from 29 provinces in China from 2011 to 2022 combined with econometric models based on the mechanism of the impact of digital rural construction on agricultural carbon emission intensity. The entropy method and carbon emission factor method are used to determine the level of digital rural construction and agricultural carbon emission intensity. The fixed effect and intermediary effect models are used to empirically analyze the impact of digital rural construction on agricultural carbon emission intensity. The results indicate that (1) digital rural construction significantly inhibits agricultural carbon emission intensity, and there are differences in different regions and dimensions of digital rural construction; (2) the construction of digital rural areas can indirectly reduce the intensity of agricultural carbon emissions by promoting the level of rural human capital; (3) financial support for agriculture played significant positive regulatory effect. The policy recommendations are proposed to provide a reference for promoting agricultural carbon reduction and digital rural construction in other countries.

Summary: Generative design of promoters has enhanced the efficiency of de novo creation of functional sequences. Though several deep generative models have been employed in biological sequence generation, including variational autoencoder (VAE) or Wasserstein generative adversarial network (WGAN), these models might struggle with mode collapse and low sample diversity. In this study, we introduce the multinomial diffusion model (MDM) for promoter sequence design and propose a structured set of criteria for effectively comparing the performance of generative models. In silico experiments demonstrate that MDM outperforms existing generative AI approaches. MDM demonstrates superior performance in various computational evaluations, remains robust during the training process, and exhibits a strong ability in capturing weak signals. In addition, we experimentally validated that the majority of our model designed promoters have expression activities in vivo, indicating the practicality and potential of MDM for bioengineering.

Abstract This study employs a fixed-effects model to investigate the holiday effect in the cryptocurrency market, using trading data for the top 100 cryptocurrencies by market capitalization on Coinmarketcap.com from January 1, 2017 to July 1, 2022. The results indicate that returns on cryptocurrencies increase significantly during Chinese holiday periods. Additionally, we use textual analysis to construct an investor sentiment indicator and find that positive investor sentiment boosts cryptocurrency market returns. However, when positive investor sentiment prevails in the cryptocurrency market, the holiday effect weakens, implying that positive investor sentiment attenuates the holiday effect. Robustness tests based on the Bitcoin market generate consistent results. Moreover, this study explores the mechanisms underlying the cryptocurrency holiday effect and examines the impact of epidemic transmission risk and heterogeneity characteristics on this phenomenon. These findings offer novel insights into the impact of Chinese statutory holidays on the cryptocurrency market and illuminate the role of investor sentiment in this market.

Accurately predicting the thermal hydraulic parameters of a transient reactor core under different working conditions is the first step toward reactor safety. Mass flow rate and temperature are important parameters of core thermal hydraulics, which have often been modeled as time series prediction problems. This study aims to achieve accurate and continuous prediction of core thermal hydraulic parameters under instantaneous conditions, as well as test the feasibility of a newly constructed gated recurrent unit (GRU) model based on the soft attention mechanism for core parameter predictions. Herein, the China Experimental Fast Reactor (CEFR) is used as the research object, and CEFR 1/2 core was taken as subject to carry out continuous predictive analysis of thermal parameters under transient conditions., while the subchannel analysis code named SUBCHANFLOW is used to generate the time series of core thermal-hydraulic parameters. The GRU model is used to predict the mass flow and temperature time series of the core. The results show that compared to the adaptive radial basis function neural network, the GRU network model produces better prediction results. The average relative error for temperature is less than 0.5 % when the step size is 3, and the prediction effect is better within 15 s. The average relative error of mass flow rate is less than 5 % when the step size is 10, and the prediction effect is better in the subsequent 12 s. The GRU model not only shows a higher prediction accuracy, but also captures the trends of the dynamic time series, which is useful for maintaining reactor safety and preventing nuclear power plant accidents. Furthermore, it can provide long-term continuous predictions under transient reactor conditions, which is useful for engineering applications and improving reactor safety.

Abstract Background To introduce the surgical technique and our team’s extensive experience with tunnel method in laparoscopic adrenalectomy. Methods From July 2019 to June 2022, we independently designed and conducted 83 cases of " Tunnel Method Laparoscopic Adrenalectomy,” a prospective study. There were 45 male and 38 female patients, ages ranged from 25 to 73 years(mean: 44.6 years).The cases included 59 adrenal cortical adenomas, 9 pheochromocytomas, 6 cysts, 4 myelolipomas, 1 ganglioneuroma, and 4 cases of adrenal cortical hyperplasia. In terms of anatomical location, there were 39 cases on the left side, 42 on the right side, and 2 bilateral cases. Tumor diameters ranged from 0.6 to 5.9 cm(mean: 2.9 cm). Utilizing ultrasound monitoring, percutaneous puncture was made either directly to the target organ or its vicinity, and the puncture path was manually marked. Then, under the direct view of a single-port single-channel laparoscope, the path to the target organ in the retroperitoneum or its vicinity was further delineated and separated. This approach allowed for the insertion of the laparoscope and surgical instruments through the affected adrenal gland, thereby separating the surface of the target organ to create sufficient operational space for the adrenalectomy. Results All 83 surgeries were successfully completed. A breakdown of the surgical approach reveals that 51 surgeries were done using one puncture hole, 25 with two puncture holes, and 7 with three puncture holes. The operation time ranged from 31 to 105 min (mean: 47 min), with a blood loss of 10 to 220mL (mean: 40 mL). Notably, there were no conversions to open surgery and no intraoperative complications. Postoperative follow-up ranged from 6 to 28 months, during which after re-examination using ultrasound, CT, and other imaging methods, there were no recurrences or other complications detected. Conclusions The completion of the tunnel method laparoscopic adrenalectomy represents a breakthrough, transitioning from the traditional step-by-step separation of retroperitoneal tissues to reach the target organ in conventional retroperitoneoscopic surgery. This method directly accesses the target organ, substantially reducing the damage and complications associated with tissue separation in retroperitoneoscopic surgery, As a result, it provides a new option for minimally invasive surgery of retroperitoneal organs and introduces innovative concepts to retroperitoneoscopic surgery.

Macroautophagy/autophagy is an intracellular degradation pathway that has an important effect on both healthy and diseased pancreases. It protects the structure and function of the pancreas by maintaining organelle homeostasis and removing damaged organelles. A variety of pancreas-related diseases, such as diabetes, pancreatitis, and pancreatic cancer, are closely associated with autophagy. Genetic studies that address autophagy confirm this view. Loss of autophagy homeostasis (lack or overactivation) can lead to a series of adverse reactions, such as oxidative accumulation, increased inflammation, and cell death. There is growing evidence that stimulating or inhibiting autophagy is a potential therapeutic strategy for various pancreatic diseases. In this review, we discuss the multiple roles of autophagy in physiological and pathological conditions of the pancreas, including its role as a protective or pathogenic factor.

Abstract Half Heusler materials exhibit excellent thermoelectric and mechanical properties, rendering them potential candidates for advanced thermoelectric devices. Currently, the developments on interrelated devices are impeded by their inherent brittleness and limited ductility. Nevertheless, it exists the potential ductility on half Heusler materials with face-centered cubic sub-lattices through the expectation of the occurrence of shear-induced ‘catching bonds’ which can result in excellent ductility on other face-centered cubic materials. This work focuses on half Heusler thermoelectric materials XFeSb (X = Nb, Ta) and SnNiY (Y = Ti, Zr, Hf), the shear deformation failure processes are deeply investigated through the first principle calculations. Shear-induced ‘catching bonds’ are found on XFeSb (X = Nb, Ta) along the (111)/ slip system, which releases the internal stress and exactly resulting in the potential ductility. According to the thermodynamic criterion based on generalized stacking fault energy, the essence of shear-induced ‘catching bonds’ are interpreted as the (111)/ slips formed by several 1/3(111)/ partial dislocations motions. During the (111)/ shear on SnNiY (Y = Ti, Zr, Hf), the structural integrity is maintained without inducing ‘catching bonds’. Different deformation processes occurring in the identical crystal structure are elucidated through the energy explanation, revealing that shear-induced ‘catching bonds’ originate from the crystal plane cleavage on the (111) plane. The present works offer significant advantages for the assessment and comprehension of shear-induced ‘catching bonds’ in other materials and facilitate the development of XFeSb (X = Nb, Ta)-based thermoelectric devices with excellent ductility.

Abstract Arising from the extreme/saddle point in electronic bands, Van Hove singularity (VHS) manifests divergent density of states (DOS) and induces various new states of matter such as unconventional superconductivity. VHS is believed to exist in one and two dimensions, but rarely found in three dimension (3D). Here, we report the discovery of 3D VHS in a topological magnet EuCd2As2 by magneto-infrared spectroscopy. External magnetic fields effectively control the exchange interaction in EuCd2As2, and shift 3D Weyl bands continuously, leading to the modification of Fermi velocity and energy dispersion. Above the critical field, the 3D VHS forms and is evidenced by the abrupt emergence of inter-band transitions, which can be quantitatively described by the minimal model of Weyl semimetals. Three additional optical transitions are further predicted theoretically and verified in magneto-near-infrared spectra. Our results pave the way to exploring VHS in 3D systems and uncovering the coordination between electronic correlation and the topological phase.

Abstract Background Studies on the factors affecting vault after posterior chamber phakic Implantable Collamer Lens (ICL) have been carried out, but most of them are single-centered and subjective selections of parameters. The present study aimed to systematically analyze the factors for vault. Methods A systematic review of case series, case-control, and cohort studies derived from the articles published in PubMed, the Cochrane Library, Embase, Web of Science, CNKI, CBM, Wanfang and VIP, as well as ClinicalTrials, which were conducted to search for studies on factors of vault using four core terms: phakic intraocular lenses, vault, risk factor and observational study, from January 01, 1997, to February 20, 2023. The included studies were meta-analyzed quantitatively and described qualitatively. Subsequently, meta-regression and subgroup analysis were used. Results We identified 13 studies (1,607 subjects), and 14 factors were considered. Meta-analysis showed that anterior chamber depth (ACD), horizontal corneal white-to-white (hWTW), ICL-size, and age are dual effects of the abnormal vaults; anterior chamber volume (ACV) and lens thickness (LT) are a one-way effect; while axial length (AL), ICL- spherical equivalent (ICL-SE) and Km are insignificant. In addition, descriptive analysis of anterior chamber angle (ACA), horizontal sulcus to sulcus (hSTS), ciliary processes height (T value), crystalline lens rise (CLR), and gender showed that all factors except gender tend to have significant effects on vault. Sensitivity analysis showed stable combined results. Country and design respectively affect the heterogeneity in ACD and ICL-size at low vault, while design affects the heterogeneity in ACD at high vault. No publication bias exists. Conclusions Vault after ICL is related to multiple factors, especially anterior segmental biologic parameters, and they are weighted differently. We hope to provide a reference for the selection and adjustment of ICL.

Given the escalating potential risk associated with X-ray radiation exposure to patients, scholars have been dedicated to investigating advanced algorithms for low-dose CT (LDCT) imaging. The utilization of Generative Adversarial Networks (GANs) has gained significant traction in the field of LDCT denoising research over the past few years, owing to its notable performance advantages. However, variations in the distribution intensity of noise and artifacts in the LDCT image can compromise the robustness and denoising performance of GAN. In this paper, we propose a scale-sensitive generative adversarial network to enhance the robustness of GAN used in LDCT denoising. Our proposed network incorporates an error feedback pyramid generator to improve the denoising network’s sensitivity towards noise and artifacts present in LDCT images by performing feature extraction and noise reduction at different scales. Additionally, we introduce a multi-scale residual discriminator with a wider receptive field and stronger feature extraction ability to enhance the discrimination between true and false samples. Furthermore, specific constraints are incorporated into the loss functions based on the network structure, thereby enhancing the effect of adversarial training between the error feedback pyramid generator and multi-scale residual discriminator while improving the performance of the denoising network. Experimental results demonstrate that our proposed network outperforms state-of-the-art methods in terms of artifact suppression, structural preservation, and model generalization capability for LDCT denoising.

Density Peak Clustering (DPC) is known for its rapid identification of cluster centers and successful clustering tasks. However, traditional DPC encounters several issues, which include simplifications in local density and distance metrics, a non-robust single-allocation strategy and limited fault tolerance. To address these challenges, this study introduces an innovative density peak clustering algorithm, named Graph Distance and Adaptive K-Nearest Neighbors Selection-Based Density Peak Clustering (GAK-DPC). Our goal with the approach is to enhance the algorithm’s adaptability to non-linear and complex data structures. We achieve this by replacing the traditional Euclidean distance with graph distance. Additionally, we redefine the method for computing local density based on information from K-nearest neighbor data points. By introducing the concept of natural neighbors, the neighborhood radius r is obtained when all instances in the dataset have at least one natural neighbor. Then for the current data point, the number of data points falling within a circle centered on it with radius r is counted as the K-value of that data point. Thus, we achieve the adaptive selection of the K-value. This adaptive K-value strategy takes into account the dataset’s characteristics and inter-point neighbor relationships, which enhances the algorithm’s adaptability and robustness. Finally, we optimize the secondary allocation strategy for sample points to improve the algorithm’s fault tolerance. By conducting comparisons with traditional clustering algorithms on UCI datasets and synthetic datasets, we demonstrate the effectiveness of GAK-DPC.

Semantic segmentation of airborne point clouds is crucial for 3D scene reconstruction and remote sensing in surveying applications. Current deep learning methods for point clouds primarily focus on effectively aggregating local neighborhood information. However, they often overlook the fusion of global context information and elevation features, which are vital for airborne point clouds. In this study, we propose Dense-LGEANet, a novel network with dense connected architecture and multiscale feature supervision based on our designed LGEA module. The key component of our LGEA module is the combination of the graph convolution block and the transformer block with elevation attention. It can effectively fuse local neighborhood information and global context information to improve the accuracy of semantic segmentation of airborne point cloud. Moreover, the designed dense connected network architecture can enhance the feature extraction capability for point cloud objects at different scales by facilitating interactions between multiple up-sampling and down-sampling layers. We have conducted multiple experiments on the public point cloud dataset. Experimental results show that our method can achieve an mIoU of 58.5% and an mF1 of 72.0% on the ISPRS Vaihingen 3D dataset, while an mIoU of 67.2% and an mF1 of 78.3% on the LASDU dataset. It demonstrates the superior performance of our network and the effectiveness of the proposed feature enhancement module and network architecture.

The interaction between cluster of differentiation 47 (CD47) and signal regulatory protein α (SIRPα) protects healthy cells from macrophage attack, which is crucial for maintaining immune homeostasis. Overexpression of CD47 occurs widely across various tumor cell types and transmits the “don't eat me” signal to macrophages to avoid phagocytosis through binding to SIRPα. Blockade of the CD47-SIRPα axis is therefore a promising approach for cancer treatment. Lymphoma is the most common hematological malignancy and is an area of unmet clinical need. This review mainly described the current strategies targeting the CD47-SIRPα axis, including antibodies, SIRPα Fc fusion proteins, small molecule inhibitors, and peptides both in preclinical studies and clinical trials with Hodgkin lymphoma and non-Hodgkin lymphoma.

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.

BackgroundIntestinal microbiota have been demonstrated to be involved in the development of NAFLD, while the relationship between the severity of NAFLD and intestinal microbiota is still not fully elucidated. Sheng-Jiang Powder (SJP) showed exact efficacy in treating SFL and great potential in regulating intestinal microbiota, but the effects need to be further addressed in NASH and liver fibrosis.ObjectivesTo investigate the differences in intestinal microbiota of NAFLD with different severity and the effect of SJP on liver damage and intestinal microbiota.DesignNAFLD mice models with different severity were induced by high-fat diet (HFD) or choline-deficient, L-amino acid-defined high-fat diet (CDAHFD) feeding and then treated with SJP/normal saline.MethodsBiochemical blood tests, H&E/Masson/Oil Red O/IHC staining, Western blot, and 16SrDNA sequencing were performed to explore intestinal microbiota alteration in different NAFLD models and the effect of SJP on liver damage and intestinal microbiota.ResultsIntestinal microbiota alteration was detected in all NAFLD mice. SJP induced increased expression of Pparγ and alleviated liver lipid deposition in all NAFLD mice. Microbiome analysis revealed obvious changes in intestinal microbiota composition, while SJP significantly elevated the relative abundance of Roseburia and Akkermansia, which were demonstrated to be beneficial for improving inflammation and intestinal barrier function.ConclusionOur results demonstrated that SJP was effective in improving lipid metabolism in NAFLD mice, especially in mice with SFL. The potential mechanism may be associated with the regulation of intestinal microbiota.

Background: Rheumatoid arthritis is a systemic inflammatory autoimmune disease that severely impacts physical and mental health. Autophagy is a cellular process involving the degradation of cellular components in lysosomes. However, from a bioinformatics perspective, autophagy-related genes have not been comprehensively elucidated in rheumatoid arthritis. Methods: In this study, we performed differential analysis of autophagy-related genes in rheumatoid arthritis patients using the GSE93272 dataset from the Gene Expression Omnibus database. Marker genes were screened by least absolute shrinkage and selection operator. Based on marker genes, we used unsupervised cluster analysis to elaborate different autophagy clusters, and further identified modules strongly associated with rheumatoid arthritis by weighted gene co-expression network analysis. In addition, we constructed four machine learning models, random forest model, support vector machine model, generalized linear model and extreme gradient boosting based on marker genes, and based on the optimal machine learning model, a nomogram model was constructed for distinguishing between normal individuals and rheumatoid arthritis patients. Finally, five external independent rheumatoid arthritis datasets were used for the validation of our results. Results: The results showed that autophagy-related genes had significant expression differences between normal individuals and osteoarthritis patients. Through least absolute shrinkage and selection operator screening, we identified 31 marker genes and found that they exhibited significant synergistic or antagonistic effects in rheumatoid arthritis, and immune cell infiltration analysis revealed significant changes in immune cell abundance. Subsequently, we elaborated different autophagy clusters (cluster 1 and cluster 2) using unsupervised cluster analysis. Next, further by weighted gene co-expression network analysis, we identified a brown module strongly associated with rheumatoid arthritis. In addition, we constructed a nomogram model for five marker genes (CDKN2A, TP53, ATG16L2, FKBP1A, and GABARAPL1) based on a generalized linear model (area under the curve = 1.000), and the predictive efficiency and accuracy of this nomogram model were demonstrated in the calibration curves, the decision curves and the five external independent datasets were validated. Conclusion: This study identified marker autophagy-related genes in rheumatoid arthritis and analyzed their impact on the disease, providing new perspectives for understanding the role of autophagy-related genes in rheumatoid arthritis and providing new directions for its individualized treatment.

The network topology discovery in OpenFlow-based software-defined networks is mainly achieved by utilizing the OpenFlow discovery protocol (OFDP).However, it has been observed in existing research that OFDP exhibits low updating efficiency and is susceptible to network topology pollution attacks.To address the efficiency and safety concerns of the network topology discovery protocol, an in-depth investigation was conducted on the mechanism and safety of OFDP network topology discovery.The characteristics of network topology establishment and updating in OFDP were analyzed, and an improved protocol named Im-OFDP (improved OpenFlow discovery protocol) based on the minimum vertex covering problem in graph theory was proposed.In Im-OFDP, the switch port table and network link table were initially established using prior information obtained from OFDP network topology discovery.Subsequently, a graph model of the network topology was constructed, and the minimum vertex covering algorithm in graph theory was employed to select specific switches for the reception and forwarding of topology discovery link layer discovery protocol (LLDP) packets.Multi-level flow tables were designed based on the network topology structure, and these flow entries were installed on the selected switches by the controller to process LLDP packets.To address security issues, dynamic check code verification in LLDP packets was employed to ensure the safety of network links.Additionally, a network equipment information maintenance mechanism was established based on known network topologies to ensure the safety of the network equipment.Experimental results demonstrate a significant reduction in the number of network topology discovery messages, bandwidth overhead, and CPU overhead through the deployment of Im-OFDP.Moreover, the response time for node failures and link recovery time after mode failure is substantially reduced.Im-OFDP also effectively mitigates various network topology pollution attacks, such as link fabrication and switch forgery attacks.Overall, Im-OFDP has the capability to enhance the efficiency and safety of SDN topology discovery.

Abstract Industrial processing of kelp generates large amounts of kelp-soaking wastewater (KSW), which contains a large amount of nutrient-containing substances. The plant growth-promoting effect might be further improved by combined application of growth-promoting bacteria and the nutrient-containing KSW. Here, a greenhouse experiment was conducted to determine the effect of the mixture of KSW and Bacillus methylotrophicus M4-1 (MS) vs. KSW alone (SE) on wheat seedlings, soil properties and the microbial community structure in wheat rhizosphere soil. The available potassium, available nitrogen, organic matter content and urease activity of MS soil as well as the available potassium of the SE soil were significantly different (p

Abstract Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

The production of green hydrogen by water splitting from renewable energy is of great significance for achieving sustainable energy development. Alkaline water electrolysis (AWE) is the most economical and feasible route at present due to its earth-abundant catalysts, but it is desirable and crucial to improve the efficiency and purity of hydrogen production. Hydrogen production purity and energy consumption affect the fluctuation range of input power and economy respectively. In this paper, an electrolyzer with independent fluid flow path (IFP) is designed, which improves the problem of shutting current, hydrogen purity and hydrogen production efficiency. It has the characteristics of bipolar plate, zero gap and IFP. A test platform was built to verify its performance. The results showed that the IFP structure significantly improved the purity of hydrogen production, and the energy efficiency value increased by more than 3.29%.

Allicin, as a natural antibacterial active substance from plants, has great medical and health care value. However, due to its poor stability, its application in the field of food and medicine is limited. So, in this paper, allicin–zein–sodium caseinate composite nanoparticles (zein–Ali–SC) were prepared by antisolvent precipitation and electrostatic deposition. Through the analysis of the particle size, ζ-potential, encapsulation efficiency (EE), loading rate (LC) and microstructure, the optimum preparation conditions for composite nanoparticles were obtained. The mechanism of its formation was studied by fluorescence spectrum, Fourier infrared spectrum (FTIR), X-ray diffraction (XRD) and thermogravimetric analysis (TGA). The stability study results showed that the particle size of composite nanoparticles was less than 200 nm and its PDI was less than 0.3 under different NaCl concentrations and heating conditions, showing good stability. When stored at 4 °C for 21 days, the retention rate of allicin reached 61.67%, which was 52.9% higher than that of free allicin. After freeze-drying and reheating, the nanoparticles showed good redispersibility; meanwhile, antioxidant experiments showed that, compared with free allicin, the nanoparticles had stronger scavenging ability of free radicals, which provided a new idea for improving the stability technology and bioavailability of bioactive compounds.

Background: Premature ventricular complex (PVC) induced cardiomyopathy (PVC-CMP) and exacerbated left ventricular systolic dysfunction (LVSD) are common in clinical scenarios. However, their precise risk factors are currently unclear. Methods: We performed a systematic review of PubMed, EMBASE, Web of Science, and Chinese-based literature database (CBM) to identify observational studies describing the factors associated with PVC-CMP and post-ablation LVSD reversibility. A total of 25 and 12 studies, involving 4863 and 884 subjects, respectively, were eligible. We calculated pooled multifactorial odds ratios (OR) and 95% confidence intervals (CI) for each parameter using random-effects and fixed-effects models. Results: The results showed that 3 independent risk factors were associated with PVC-CMP: being asymptomatic (OR and 95% CI: 3.04 [2.13, 4.34]), interpolation (OR and 95% CI: 2.47 [1.25, 4.92]), and epicardial origin (epi-origin) (OR and 95% CI: 3.04 [2.13, 4.34]). Additionally, 2 factors were significantly correlated with post-ablation LVSD reversibility: sinus QRS wave duration (QRSd) (OR and 95% CI: 0.95 [0.93, 0.97]) and PVC burden (OR and 95% CI: 1.09 [0.97, 1.23]). Conclusions: the relatively consistent independent risk factors for PVC-CMP and post-ablation LVSD reversibility are asymptomatic status, interpolation, epicardial origin, PVC burden, and sinus QRS duration, respectively.

Tomato fruit ripening is an elaborate genetic trait correlating with significant changes at physiological and biochemical levels. Sugar metabolism plays an important role in this highly orchestrated process and ultimately determines the quality and nutritional value of fruit. However, the mode of molecular regulation is not well understood. Galactinoal-sucrose galactosyltransferase (GSGT), a key enzyme in the biosynthesis of raffinose family oligosaccharides (RFOs), can transfer the galactose unit from 1-α-D-galactosyl-myo-inositol to sucrose and yield raffinose, or catalyze the reverse reaction. In the present study, the expression of SlGSGT2 was decreased by Potato Virus X (PVX)-mediated gene silencing, which led to an unripe phenotype in tomato fruit. The physiological and biochemical changes induced by SlGSGT2 silencing suggested that the process of fruit ripening was delayed as well. SlGSGT2 silencing also led to significant changes in gene expression levels associated with ethylene production, pigment accumulation, and ripening-associated transcription factors (TFs). In addition, the interaction between SlGSGT2 and SlSPL-CNR indicated a possible regulatory mechanism via ripening-related TFs. These findings would contribute to illustrating the biological functions of GSGT2 in tomato fruit ripening and quality forming.

Inter-Turn Short Circuit (ITSC) faults do not necessarily produce high temperatures but exhibit distinct heat distribution and characteristics. This paper proposes a novel fault diagnosis and identification scheme utilizing an improved You Look Only Once Vision 8 (YOLOv8) algorithm, enhanced with an infrared image slicing-aided hyper-inference (SAHI) technique, to automatically detect ITSC fault trajectories in dry-type transformers. The infrared image acquisition system gathers data on ITSC fault trajectories and captures images with varying contrast to enhance the robustness of the recognition model. Given that the fault trajectory constitutes a small portion of the overall infrared image and is subject to significant background interference, traditional recognition algorithms often misjudge or omit faults. To address this, a YOLOv8-based visual detection method incorporating Dynamic Snake Convolution (DSConv) and the Slicing-Aided Hyper-Inference algorithm is proposed. This method aims to improve recognition precision and accuracy for small targets in complex backgrounds, facilitating accurate detection of ITSC faults in dry-type transformers. Comparative tests with the YOLOv8 model, Fast Region-based Convolutional Neural Networks (Fast-RCNNs), and Residual Neural Networks (Retina-Nets) demonstrate that the enhancements significantly improve model convergence speed and fault trajectory detection accuracy. The approach offers valuable insights for advancing infrared image diagnostic technology in electrical power equipment.

RGB-D image mapping is an important tool in applications such as robotics, 3D reconstruction, autonomous navigation, and augmented reality (AR). Efficient and reliable mapping methods can improve the accuracy, real-time performance, and flexibility of sensors in various fields. However, the currently widely used Truncated Signed Distance Field (TSDF) still suffers from the problem of inefficient memory management, making it difficult to directly use it for large-scale 3D reconstruction. In order to address this problem, this paper proposes a highly efficient and accurate TSDF voxel fusion method, RGBTSDF. First, based on the sparse characteristics of the volume, an improved grid octree is used to manage the whole scene, and a hard coding method is proposed for indexing. Second, during the depth map fusion process, the depth map is interpolated to achieve a more accurate voxel fusion effect. Finally, a mesh extraction method with texture constraints is proposed to overcome the effects of noise and holes and improve the smoothness and refinement of the extracted surface. We comprehensively evaluate RGBTSDF and similar methods through experiments on public datasets and the datasets collected by commercial scanning devices. Experimental results show that RGBTSDF requires less memory and can achieve real-time performance experience using only the CPU. It also improves fusion accuracy and achieves finer grid details.

Wireless power transfer (WPT) technology is a novel power transmission technology using electromagnetic field as the transmission medium. It uses the advantages of electromagnetic coupling in the near field to realize contactless transmission of electric energy on the basis of resonance theory. This mode has the advantages of stability, flexibility, efficiency and safety. WPT is mainly used for non-contact charging of electrical equipment without electrical connection. It can safely, flexibly and quickly charge the electrical equipment, which makes up for the shortcomings of the traditional charging mode. Due to the change of the load or the transmission coil position in WPT system, the transmission efficiency will drop sharply. It is of great significance to maintain the maximum efficiency transmission. This paper proposes a novel maximum efficiency transmission method. This method can achieve the maximum efficiency transmission with constant voltage output by controlling the primary-side current without communication feedback between the primary-side and the-secondary side. The Buck-Boost converter with constant output voltage is introduced at the load-side to equivalent the actual load. By adjusting the duty ratio of the Buck converter on the primary side and detecting the minimum input current, the maximum transmission efficiency could be tracked. The theoretical conditions of the method are analyzed and experimental verification is performed. The experimental results of variable relative position load show that the transmission efficiency of the proposed method can still reach 83%.

After an emergency shutdown of a lead-bismuth fast reactor, thermal stratification occurs in the upper Plenum, which negatively impacts the integrity of the reactor structure and the residual heat removal capacity of natural circulation flow. The research on thermal stratification of reactors has mainly been conducted using an experimental method, a system program, and computational fluid dynamics (CFD). However, the equipment required for the experimental method is expensive, accuracy of the system program is unpredictable, and resources and time required for the CFD approach are extensive. To overcome the defects of thermal stratification analysis, a high-precision full-order thermal stratification model based on CFD technology is prepared in this study. Furthermore, a reduced-order model has been developed by combining proper orthogonal decomposition (POD) with Galerkin projection. A comparative analysis of thermal stratification with the proposed full-order model reveals that the reduced-order thermal stratification model can well simulate the temperature distribution in the upper plenum and rapidly elucidate the thermal stratification interface characteristics during the lead-bismuth fast reactor accident. Overall, this study provides an analytical tool for determining the thermal stratification mechanism and reducing thermal stratification.

Double-layer heat tubes have been designed to effectively reduce the occurrence of heat pipe rupture accidents. However, inter-tube thermal contact resistance can decrease heat transfer efficiency, thus hampering the heat dissipation in the primary loop system of lead-bismuth reactors. Therefore, optimizing the design of double-layer heat tubes is necessary. This work focuses on the double-layer heat exchanger of a lead-bismuth reactor and utilizes gallium-based graphene nanofluids as a thermal interface material to fill the gap between the heat tubes. Furthermore, the impact of the length, wall thickness, outer diameter, and spacing of heat tubes on the heat transfer performance of the double-layer heat exchanger with and without the nanofluids has been analyzed. The study aims to optimize the JF factor and cost-effectiveness ratio (CER). Genetic algorithms are employed to optimize and evaluate the heat transfer performance of the main heat exchanger based on the four aforementioned parameters. Consequently, a new design scheme is obtained for the double-layer heat exchanger, which increases the optimized overall heat transfer coefficient of the main heat exchanger by 5.79%, pressure drop in the primary loop by 2.32%, JF factor by 5%, and CER by 24.62%. These results demonstrate that the gallium-based graphene nanofluids can effectively enhance the heat transfer performance of the double-layer heat exchanger while reducing the likelihood of steam generator tube rupture accidents.

The semantic segmentation task is an essential issue in various fields, including land cover classification and cultural heritage investigation. The CNN and Transformer have been widely utilized in semantic segmentation tasks due to notable advancements in deep learning technologies. However, these methodologies may not fully account for remote sensing images' distinctive attributes, including the large intra-class variation and the small inter-class variation. Driven by it, we propose a category attention guided network (CAGNet). Initially, a local feature extraction module is devised to cater to striped objects and features at different scales. Then, we propose a novel concept of category attention for remote sensing images as a feature representation of category differences between pixels. Meanwhile, we designed the Transformer-based and CNN-based category attention guided modules to integrate the proposed category attention into the global scoring functions and local category feature weights, respectively. The network is designed to give more attention to the category features by updating these weights during the training process. Finally, a feature fusion module is developed to integrate global, local, and category multi-scale features and contextual information. A series of extensive experiments along with ablation studies on the UAVid, Vaihingen, and Potsdam datasets indicate that our network outperforms existing methods, including those based on CNN and Transformer.