This article investigatedthe acoustic vibration coupling response of a fully elastic cavity structure controlled by piezoelectric shunt technology under an internal sound source excitation.Based on the acoustic vibration equation, this article analyzed the dominant factors of the acoustic vibration response of the fully elastic cavity structure, revealed the coupling mechanism between the different panels and the acoustic cavity. In fully elastic cavity vibration, the coupling between the panel mode and the acoustic cavity mode can be calculated using orthogonal integration, and there exists a corresponding dominant panel for a specific resonance frequency. Piezoelectric shunt regulation based on dominant panel strategy can maximize control effectiveness. A vibration and noise control system for an elastic cavity based on a piezoelectric shunt circuit was established. By controlling the vibration in the dominant frequency range corresponding to the panels of the elastic cavity, multiple resonance peaks were suppressed in the overall response of the elastic cavity system. The vibration response of the wall panels at the first two resonant frequencies, 88 Hz and 144 Hz, decreased by 17 dB and 9 dB respectively, while the peak sound pressure inside the cavity reduced by 17 dB and 7 dB, and the peak radiated sound pressure outside the cavity decreased by 13 dB and 4 dB.

To systematically evaluate and optimize the development roadmap of noise−reduction technologies for civil aircraft, a comprehensive assessment framework integrating the analytic hierarchy process (AHP) and quality function deployment (QFD) is established. Starting from representative aircraft noise sources, a technology list consisting of 23 noise−reduction approaches is compiled. Seven key evaluation criteria, including airworthiness compliance, total noise−reduction potential, and technology readiness level, are defined, and the corresponding weights are determined through AHP with consistency verification. Based on these weights, three QFD−based scoring methods are applied to compare the influence of individual judgment matrices and an aggregated judgment matrix on the final technology ranking. The results indicate that, provided all expert judgment matrices satisfy the consistency requirement, both the overall scores and rankings of the technologies remain stable across the three scoring methods. According to the results of Scoring Method I, hierarchical development lists and priority development lists of noise−reduction technologies are formulated. Among these technologies, nacelle acoustic liners and low−noise aerodynamic design of high−lift devices are recommended as priority development directions. This study provides a quantitative decision−making basis for the strategic planning and prioritization of noise−reduction technologies for civil aircraft.

Technological talents are the core element for achieving self−reliance and self−improvement in China's science and technology, and innovation willingness is the key to stimulating the vitality of technological talents. This article takes 236 scientific and technological talents as the survey subjects, adopts the fuzzy set qualitative comparative analysis method, and explores the configuration path of multi factor synergy affecting the innovation willingness of scientific and technological talents under the TPB framework. Research has found that there are four driving types of high innovation willingness among technology talents, namely demonstrative norms innovation attitude driven, directive norms innovation attitude driven, endogenous attitudes subjective norm driven, and innovation attitude driven. Innovation attitude is an important condition for technology talents to generate innovation willingness; Directive norms and demonstrative norms are key elements in regulating the innovation willingness of scientific and technological talents, and there is a substitution relationship between the two; The perceived behavioral control of technology talents has a weaker effect on enhancing innovation willingness. This article reveals the complex causal mechanism of the synergistic effect of planned behavior theory elements on the innovation willingness of scientific and technological talents from a configuration perspective, providing important reference for organizations to cultivate the innovation willingness of scientific and technological talents.

Quantum computing threatens classical cryptographic system and brings new challenges to the security of blockchain network communications. Quantum secure direct communication can achieve end−to−end security and has the ability to detect and prevent eavesdropping. Applying quantum secure direct communication in blockchain networks based on classical confidential communication technology can help blockchain networks resist quantum computing attacks and ensure blockchain network security. This paper explores the technical solution of applying quantum secure direct communication in blockchain networks, and conducts feasibility analysis and experimental verification. The experiment successfully verifies the integration of quantum secure direct communication and blockchain networks, uses quantum secure direct communication technology to safeguard blockchain networks, enhances the security of information transmission between nodes in blockchain networks, and helps the financial industry build a more secure and reliable digital value network.

The (B₄C+Al₂O₃)/Al composite is a critical neutron absorbing material for dry storage systems of spent fuel, and its long−term high temperature service reliability is directly associated with the safety of nuclear waste management. As a structural−functional integrated material, it is susceptible to phase transformation of amorphous Al₂O₃ (am−Al₂O₃) into γ−Al₂O₃ at elevated temperatures, which may adversely affect its mechanical properties. However, the microstructural evolution and long−term properties stability of this composite under prolonged thermal exposure remain insufficiently understood. In this work, (B₄C+Al₂O₃)/Al composites were subjected to stabilization treatments at 400°C for 100 h and 550°C for 8 h, followed by long−term annealing at 400°C for up to 4000 h to evaluate their thermal stability. The results demonstrate that, after stabilization, the composites retained tensile strengths exceeding 220.0 MPa at room temperature and 100.0 MPa at 350°C, satisfying the requirements for engineering applications. Microstructural characterization revealed a partial transformation of am−Al₂O₃ to γ−Al₂O₃; however, both the microstructure and mechanical properties exhibited stable behavior during prolonged annealing. These findings indicate that both stabilization treatments effectively enhance the thermal stability of the (B₄C+Al₂O₃)/Al composite, thereby providing a materials science basis for the long−term safe service of dry storage containers for spent fuel.

Driven by dual demands of intensive frequency utilization in military electronic warfare equipment and high−spectral−efficiency communication in civilian devices, the co−time co−frequency transmit−receive array technology has become a research hotspot. However, this technology faces serious self−interference problems, which restricts its performance improvement. This review comprehensively synthesizes recent advancements in self−interference suppression techniques, covering interference coupling channels, spatial, analog, and digital domain mitigation strategies, and experimental validations. Current state−of−the−art demonstrates a transmit–receive isolation of 137.3 dB for a 256×256 transmit–receive separated array at a 26.4 GHz center frequency in China, and 140.5 dB for a 4×4 transmit–receive separated array at 2.45 GHz in America, approaching engineering viability. Nonetheless, the widespread adoption of large−scale multi−antenna systems in complex environments exposes these arrays to intense near−field multi−dimensional cross−coupling interference. Future research priorities should include elucidating near−field interference mechanisms, optimizing spatial degrees of freedom, simplifying analog domain processing, and refining non−ideal factor compensation models, thereby enabling practical deployment of this transformative technology.

The permanent magnet synchronous motor (PMSM) drive with small direct− current link (DC−link) capacitors has significant advantages of high power density and high reliability, and has been widely used in the industrial applications such as transportation and home appliances. It also has broad application prospects in high−end equipment fields such as aerospace. However, due to the reduction of DC−link capacitance, there exists the harmonics coupling between dq−axis current, which increase the difficulty of extracting the beat amplitude directly and the performance reduction of traditional method, which only suppressing current harmonics in single axis. To realize the direct regulation of the beat phenomenon, this paper proposes an impedance reshaping strategy based on the reconstruction of the beat envelope, which can eliminate the coupling of beat amplitude from dq−axis harmonics. The relationship among the current harmonics, the voltage harmonics in dq−axes, and the beat envelope amplitude is analyzed through the impedance model in the field weakening region. On this basis, the motor voltage harmonics in dq−axes are applied to extract the beat amplitude with the mean values of the motor currents in dq−axes, whose accuracy can be maintained under the current coupling. The closed loop of the beat amplitude is built to improve he adaptability to the motor speed and torque, and the output is the adjusting angle for the voltage reference vector, which is used to reshape the impedance relationship between the beat amplitude and rectifier voltage. Experimental results show that the strategy can suppress the beat phenomenon of the motor current effectively and solve the scientific problem of motor oscillation.

Cross−seasonal heat storage technology can effectively coordinate the mismatch between energy supply and demand in time and space, and use solar energy, air energy and other energy clean energy or industrial waste heat and building air conditioning waste heat as heat sources to realize the summer storage and winter use of energy, and provide a new technical route for building winter heating, coal to clean energy, and regional energy supply. This paper summarizes the classification and system working principle of cross−seasonal heat storage technology, compares the main technologies, lists relevant policies at the national and local levels, reviews the research status of cross−seasonal heat storage technology at home and abroad, and the patent development of this technology, and looks forward to the future development of cross−seasonal heat storage technology.

Ergothioneine (EGT) is a potent natural sulfur−containing antioxidant with broad application potential in the pharmaceutical, cosmetic, and nutraceutical industries. However, its traditional production methods are inefficient and fail to meet market demand. Recent advancements in synthetic biology offer promising avenues for the efficient bio−manufacturing of EGT. This review systematically summarizes the research progress on using Schizosaccharomyces pombe as a promising chassis organism for EGT production. It focuses on its endogenous biosynthetic capabilities and metabolic engineering strategies, such as promoter engineering, nutrient stress regulation, and mutagenesis screening. We also discuss the major challenges hindering the industrial application of S. pombe, including gaps in fundamental knowledge, unclear physiological functions of EGT, and a lack of standardized analytical methods. Finally, future research directions are proposed, including elucidating the metabolic regulatory network, integrating green production processes, and establishing standardized evaluation systems. This review aims to provide a theoretical foundation for the further development and optimization of S. pombe as a robust platform for EGT synthesis.

Ecological stoichiometry characteristics of carbon (C), nitrogen (N), and phosphorus (P) in plant organs and sediments of the exotic mangrove Sonneratia apetala and the native Kandelia obovata were compared, and the relationship between the ecological stoichiometry parameters of mangrove plant organs and key physico−chemical properties of sediments was examined. The C, N, and P contents in leaves, branches, roots, and the 0~1 m sediment layer were analyzed for an unvegetated mudflat, 12−year−old (SA12) and 18−year−old (SA18) S. apetala plantations, and a 40−year−old native K. obovata forest in Qi'ao Island, Zhuhai. It was shown that no significant difference in C and N contents of the same plant organ existed between the different−aged S. apetala forests. Higher P content and a lower C∶P ratio were observed in the leaves of the 12−year−old S. apetala trees compared to those of the 18−year−old trees, indicating more rapid growth in the younger stand. The SOC and N contents in the 0~20 cm sediment layer of the S. apetala forests were found to be significantly higher than those in the unvegetated mudflat. The SOC content, N content, and C∶N ratio in the 0~20 cm sediment layer of the 12−year−old S. apetala forest were found to be significantly lower than those of the 18−year−old forest. In the 0~60 cm sediment layer, the SOC and N contents, as well as the C∶N ratio, were found to be significantly lower in the S. apetala forests than in the K. obovata forest. It was indicated by correlation analysis (RDA) that sediment bulk density was the major factor affecting the ecological stoichiometry of mangrove plant organs. These findings suggest that for long−term carbon sequestration in mangrove restoration, native species like K. obovata should be prioritized; management of S. apetala plantations should be age−specific (e.g., addressing N limitation in young stands and P limitation in older stands); and the improvement of sediment physical structure (e.g., bulk density) can enhance the overall ecosystem functionality.

This paper generalizes the positive−and−negative response processes of alpine forest ecosystems to avalanches and their underlying mechanisms, as well as the impacts of climate change on avalanche activities and their ecological effects. The current researches have indicated that: 1) Snow avalanches have positive effects, such as increasing biodiversity and improving soil conditions, but they also have negative effects such as vegetation destruction and the decline of biodiversity. 2) Under climate change, shifts in avalanche types, frequency, and magnitude significantly compromise stability of forest ecosystem structure and function. Notably, the increasing prevalence of wet snow avalanches—characterized by higher mobility and erosivity—exacerbates soil erosion and impedes vegetation recovery. 3) Under global warming, thermal stress drives upward forest migration, while frequent avalanche disturbances force treeline depression. This two−pronged pressure greatly increase the risk of forest loss in avalanche−prone areas. Such ecosystem degradation will severely undermine regional carbon sequestration capacity, further amplifying localized warming and triggering a positive feedback loop of enhanced avalanche activity. Consequently, vegetation autonomously adapts to dynamic avalanche environments through altered survival and reproductive strategies. Meanwhile, it is imperative to advance research on the feedback mechanisms between avalanches and ecosystems. This can be achieved by implementing human interventions such as identifying genes of dominant species and afforestation in avalanche−prone areas to enhance the resilience of these ecosystems under climate change and mitigate avalanche risks.

Functional near−infrared spectroscopy (fNIRS) neurofeedback technology is an emerging non−invasive brain function modulation technique. This article reviews the relevant studies on fNIRS neurofeedback in improving post−stroke dyskinesia, cognitive impairment, and emotional disorders, analyzes the feasibility and effectiveness of fNIRS neurofeedback in post−stroke rehabilitation training, then discusses the problems in the development of this field, and finally summarizes the future research trend of fNIRS neurofeedback in post−stroke rehabilitation training, to provide a reference for follow−up research.

While diffusion models have demonstrated remarkable capabilities in generative tasks, their application to visual perception tasks such as depth estimation and portrait segmentation remains underexplored. This paper proposes Diffusion Perception, a unified framework based on diffusion models for high−quality depth estimation and portrait segmentation. By reformulating traditional perception tasks as conditional generation problems, the framework leverages the denoising characteristics of latent diffusion models (LDMs) to optimize prediction results in latent space. The innovative design incorporates three core processing stages: multimodal feature encoding, noise input prediction, and text−controlled feature extraction and reconstruction, enabling the transition of diffusion models from generative paradigms to visual perception task paradigms. Experimental results demonstrate that on our custom depth estimation dataset, the proposed method achieves evaluation metrics of 93.98% Relative Accuracy (RR), 99.61% Plane Estimation Accuracy (Plane), and 93.61% Scene Consistency (Consistence), outperforming existing state−of−the−art depth estimation methods. Furthermore, in portrait segmentation tasks, the method achieves Intersection over Union (IoU) and mean IoU (mIoU) scores of 96.98% and 91.98% respectively, surpassing existing segmentation algorithms. This study provides novel insights into applying diffusion models in visual perception, where their generative paradigm naturally handles prediction uncertainty and is well−suited for robust perception in dynamic environments.

The collaborative development of Intelligent Construction and Building Industrialization is a complex system and an important way to promote the transformation and upgrading of the construction industry. In this paper, the collaboration degree of Intelligent Construction and Building Industrialization in various regions of China is measured by cloud model and composite system cooperation degree model. It is found that the development of Intelligent Construction and Building Industrialization is spatially related in China, and the development of Intelligent Construction and Building Industrialization in one region often drives the development of corresponding subsystems in other regions. The degree of cooperative development of Intelligent Construction and Building Industrialization has little relationship with the region. In the process of promoting the cooperative development of Intelligent Construction and Building Industrialization, each region should formulate corresponding policies according to local conditions.

Quantum key distribution (QKD) provides information−theoretic security to address security challenges faced by traditional encryption techniques and threats posed by quantum computers. This paper investigates the application of QKD in wireless public network power services, accounting for parameters such as electromagnetic interference and communication distance through modeling and simulation. Results demonstrate that the pattern matching protocol QKD exhibits excellent performance when the pairing interval exceeds 10⁴, surpassing the PLOB boundary while still achieving transmission distances exceeding 400 km. This ensures the security of communication links and provides theoretical support for the application of pattern matching protocol QKD in wireless public network power services.

Selective laser melting (SLM), as a prominent metal additive manufacturing technology, has achieved industrial applications in aerospace, biomedical, and energy sectors due to its near−net−shape forming capability and ability to fabricate complex structures. Superalloys, owing to their excellent high−temperature strength, oxidation resistance, and creep performance, are the preferred materials for SLM−fabricated heat−resistant components. However, the unmelted powder generated during SLM undergoes degradation (including thermal cycling, oxidation, and particle size distribution alterations), posing significant challenges to its efficient recycling and reconditioning, which constitutes a critical bottleneck for sustainable industrial development. This article is centered on the research progress concerning the recycling of superalloy powders during selective laser melting (SLM). It systematically analyzes the evolution of physical and chemical characteristics of powders throughout the recycling process, elucidates the impact of recycled powders on the formation of defects and mechanical properties of printed parts. As the number of recycling cycles increases, satellite particles and irregular granules appear on the powder surface, leading to an increase in surface roughness, along with a continuous rise in oxygen content. Additionally, the recycled powder results in an increased defect density in the fabricated parts, manifested as a higher prevalence of unmelted pores and micropores, while the mechanical properties exhibit complex variations. Technological breakthroughs and application cases in efficient recycling and reconditioning methods were summarized. Furthermore, it proposes future research priorities and development directions in this field.

Using Kohn–Sham density functional theory, we investigate the Diels–Alder reaction between cyclopentadiene and methyl vinyl ketone as a model system to elucidate the regulatory mechanism and microscopic origin of oriented external electric fields (OEEF) on cycloaddition reactivity. The results demonstrate that both the direction and magnitude (F_Z) of the electric field significantly affect the reaction rate and selectivity: When F_Z>0, the reaction rate accelerates and the endo product ratio increases, whereas F_Z<0 leads to a slower reaction but favors exo product selectivity. Mechanistic analysis reveals that OEEF induces electronic polarization and decreases the HOMO_CP−LUMO_MVK gap, thereby improving the electron transfer capability between molecular orbitals, strengthening the interactions between bonding atoms in the transition state, and facilitating interfragment charge transfer. These effects stabilize the transition state and lower the activation barrier. Furthermore, electronic structure descriptors, including electrostatic potential, electron density difference, orbital energy gaps, fragment charges, and dipole moment, are provided to rationalize and predict the influence of OEEF on Diels−Alder reactivity.

Iron is a crucial element for maintaining immune system function, and iron imbalance can lead to immune dysregulation. The liver, as the central organ for iron storage and regulation, maintains iron homeostasis by precisely sensing systemic iron levels and modulating hepcidin secretion. This review summarizes the impact of iron imbalance on liver immune function and its underlying mechanisms, with a focus on both the innate and adaptive immune systems. In innate immunity, iron deficiency suppresses macrophage polarization toward the M1 phenotype, impairs neutrophil differentiation, and enhances the cytotoxic activity of NK cells. In contrast, iron overload promotes M1 macrophage polarization, inhibits neutrophil extracellular trap (NET) formation and reactive oxygen species (ROS) production, while its effect on NK cells remains unclear.Regarding adaptive immunity, iron deficiency inhibits T cell activation and B cell antibody production, whereas iron overload induces mitochondrial dysfunction, promotes the differentiation of pathogenic T cells, and impairs regulatory T cell function. Current research still presents several gaps; for instance, the regulatory mechanisms of iron deficiency on M2 macrophage polarization and the effects of iron overload on NK cell function remain to be fully elucidated. Future studies should strengthen research on related signaling pathways and clinical translation, exploring immune intervention strategies targeting iron metabolism to provide new insights for the prevention and treatment of liver diseases associated with iron metabolism disorders.

In the last decade, the fatigue damage of machine-made sand concrete caused by cyclic loading has gradually started to attract attention, but the test results are diverse and discrete due to the influence of test equipment, test conditions, and environment, etc. No uniform standard has been reached. To further analyze the fatigue performance of machine-made sand concrete and improve the accuracy of fatigue life prediction, this paper starts from the influencing factors of its mechanical properties—such as the content of stone powder, stress level, and machine-made sand replacement ratio, summarizes the underlying mechanisms and patterns, and analyzes the relationship between the static properties and fatigue life of concrete with the Aas–Jakobsen equation. An empirical fatigue life prediction formula is proposed, which considers the transformation relationships among the compressive, flexural, and tensile strengths of machine-made sand concrete. In addition, for the first time, this study conducted a microscopic analysis of the composition and microstructure of the interfacial transition zone (ITZ) and hydration products in concrete at different machine-made sand replacement rates, using scanning electron microscopy (SEM) and microhardness. Afterward, fatigue tests were carried out on concrete specimens at different replacement rates (0, 30 %, 70 %, and 100 %) of machine-made sand and the optimal replacement rate of machine-made sand for C30 concrete mix ratio was around 50%. Based on the experimental data, a fatigue life prediction model incorporating the machine-made sand replacement rates was developed. The proposed model avoids the need for extensive additional experiments and demonstrates greater applicability in engineering scenarios with significant variations in material composition or limited on-site experiment conditions.

With the rapid development of high−speed transportation networks and the continuous advancement of sixth−generation (6G) mobile communication technologies, the demand for reliable wireless connectivity in high−mobility scenarios has grown significantly. However, the high Doppler shift induced by user mobility leads to rapidly time−varying channels, significantly degrading communication reliability and transmission quality. To address this challenge, this paper proposes an integrated sensing and communication (ISAC) approach, where the base station simultaneously communicates with high−speed users and receives echo signals to predict the Doppler shift. The predicted Doppler shift is then pre−compensated to reduce signal processing complexity at the receiver and improve communication quality. Specifically, this paper introduces an intelligent online Doppler shift prediction method based on long short−term memory (LSTM) networks for orthogonal frequency division multiplexing (OFDM) systems in high−speed mobility scenarios. In this method, the base station estimates the current Doppler shift based on received echo signals and e^*mploys an LSTM model to predict Doppler shifts in real−time for the subsequent moment. To effectively handle dynamic environments, the proposed model utilizes an online updating strategy, where LSTM model parameters are updated in real−time after receiving echoes and estimating Doppler shift. To evaluate the performance of the proposed model, we compare the LSTM−based prediction results with those obtained using an unscented Kalman filter (UKF). Prediction accuracy is analyzed under varying conditions of mobile speeds and signal−to−noise ratios. Simulation results demonstrate that the proposed online LSTM prediction model exhibits superior accuracy and robustness in nonlinear Doppler shift prediction compared to the UKF model, providing an efficient and reliable solution for online Doppler shift prediction in highly dynamic communication environments.

Swallowing disorders have a high incidence among patients with cognitive dysfunction and significantly impact their quality of life. The Swallowing Quality of Life questionnaire (SWAL−QOL), a widely used tool for assessing swallowing function and related quality of life, has previously excluded individuals with Mild to moderate cognitive dysfunction from its applicable population in prior studies. This study is the first to systematically validate the applicability of the Chinese version of SWAL−QOL in patients with cognitive dysfunction. The research included 122 patients with Mild to moderate cognitive dysfunction, and explored the inter−rater reliability, internal consistency reliability, content validity and construct validity of the results among the participants. The results demonstrated that the inter−rater reliability coefficients for each dimension (except for the food choice dimension) were all above 0.8, and the overall internal consistency Cronbach's α value reached 0.971. The construct validity analysis showed that the scale was significantly correlated with swallowing function assessment tools such as the Kubota Water Swallowing Test and the EAT−10 scale. Three common factors were extracted: psychosocial function, nutritional intake patterns, and physiological function regulation (cumulative variance contribution rate of 72.742%), revealing the multidimensional impact of dysphagia on patients' psychological, physiological, and social functions. Research indicates that the Chinese version of the SWAL−QOL scale can serve as a reliable assessment tool for swallowing−related quality of life in patients with Mild to moderate cognitive dysfunction, providing theoretical support for the development of individualized intervention strategies and the enhancement of patients' quality of life.

To address the issues of significant errors in minimum hop number selection and average hop distance estimation when applying the traditional DV-Hop localization algorithm in wireless sensor network nodes, an improved DV-Hop localization algorithm is proposed incorporating multi-communication radius hop optimization and hop distance weighted correction. Firstly, by dividing communication radius into hierarchical layers, the error in minimum hop number selection is reduced. Subsequently, the hop distance weighted method is implemented to further mitigate distance calculation errors between the anchor nodes and unknown nodes caused by irregular network topologies. Finally, the unknown nodes calculate their coordinates using the least squares method. MATLAB simulation results demonstrated that through these two-step improvements, the proposed DV-Hop algorithm achieves higher localization accuracy compared with the traditional DV-Hop algorithms and related methods across various simulated environments.

In order to improve the security of wireless public power systems, a solution based on high-dimensional twin-field quantum key distribution is proposed in this study. The scheme employs polarization and longitudinal momentum coupling for high-dimensional quantum coding of a single photon, which enables the photon to carry 2-bits of information and is subject to only one-sided channel attenuation. Simulation results show that compared with the traditional two-dimensional protocol, the security code rate of this protocol is improved by nearly an order of magnitude. Additionally, it has the capability to surpass the transmission distance limitations imposed by relay-free key rates, while the quantum bit error rate is only 0.04. This solution significantly improves the security of the wireless public power system, and provides a new option for reliable power transmission and data communication.

The sedimentary types of turbidite in the Binnan-Lijin area are diverse, making it difficult to predict effective reservoirs. Based on the development location, distance from the source and different flow states of turbidite, the types and sedimentary models of turbidite in the study area were summarized, and the favorable development areas of turbidite were predicted by 5D seismic data. The results indicate that there are four different sedimentary models of turbidite in study area: steep slope-near source-structural slope break type, steep slope-far source-turbidity current type, steep slope-far source-debris flow type and gentle slope-far source-turbidity current type, and there are differences in lithology, logging facies, seismic reflection characteristics and development location. Stacking seismic data along source direction can describe the distribution range of turbidite along the long axis, and stacking data vertically can describe the boundaries of turbidite along the short axis. The sum and product attributes of seismic data stacked in different azimuth can reflect the maximum range of turbidit, and the difference attribute can more clearly reflect the turbidite boundary.

Helium is a strategically key resource closely tied to national development and is one of China's extremely scarce mineral resources. Currently, the assessments of helium generation often rely on test results from core or field samples, making it difficult to conduct continuous, comprehensive evaluations of a region. To address this issue, considering that the helium generation is a function of the concentrations of radioactive elements uranium or thorium and time, a method for assessing helium resource based on natural gamma−ray spectrometry logging is proposed. By employing gamma detectors to measure the gamma spectra resulting from natural radioactivity in rock layers and analyzing the variations in gamma spectra caused by different radioactive nuclides, it becomes feasible to continuously determine the uranium and thorium content of each stratum. This result can then be utilized to analyse the helium release content of rock minerals, identify primary helium−bearing rocks, and evaluate the potential helium resources within the strata. Currently, this approach is being applied to forecast helium resource distribution and conduct strategic assessments in the Gucheng, Yakela, Tuofutai, Hetianhe area, as well as across the entire Tarim Basin. In the future, it is necessary to further refine the determination of the helium generation amount in crystalline basement, absolute ages of helium−releasing minerals, and the process of helium dissipation in gas reservoirs during the use of this method.

Autonomous and intelligent unmanned surface vehicles (USVs) have a wide range of applications in science, commerce, and military fields. Due to the underactuated nature commonly possessed by USVs and the complexity of the maritime environment, the path planning for USVs remains an extremely challenging issue and is also a key technology for USVs. This paper reviews the existing algorithms and the latest research on traditional global and local path planning, intelligent path planning, swarm path planning, and path optimization for USVs. The various constraints considered in USV path planning are summarized and the innovative points, performance, limitations, and practical application scenarios of various path planning methods are analyzed comparatively. The importance of evaluating path under multi−constraint conditions is analyzed and emphasized. Based on existing problems and future trends, some suggestions for future research are proposed.

Magnetic induction (MI) communication has the characteristics of being less affected by the medium and having a stable channel, which can effectively solve the problem of excessive transmission loss of electromagnetic wave (EW) communication in extreme environments. However, as a near−field communication technology, the limitation of its near−field effective range leads to the restricted coverage of magnetic induction communication. Therefore, in−depth research on the magnetic induction boundary is of great significance for guiding the design and optimization of magnetic induction communication systems. This paper studies the spatial distribution of the electromagnetic field of a thin ring antenna in free space, comprehensively considers parameters such as antenna size, and proposes a decision parameter to give a stricter magnetic induction boundary. On this basis, the range of ring parameters under different frequencies and spatial angles is determined through error analysis, providing theoretical support for the parameter design of magnetic induction communication. By analyzing the variation laws of three verification parameters, namely the amplitude of wave impedance, the phase difference between the spatial electric field and magnetic field, and the spatial variation rate of the magnetic induction receiving intensity factor, with distance, the physical characteristics at the magnetic induction boundary are deeply revealed. The research finds that at the magnetic induction boundary, the magnetic field energy is dominant, the electromagnetic field distribution has not yet reached a stable state, and the physical property of rapid attenuation of the magnetic induction receiving energy is presented. Simulation results show that the proposed magnetic induction boundary formula can accurately describe the magnetic induction limit distance and ensure the effective performance of magnetic induction communication within the boundary range.

Major depressive disorder is a prevalent psychological disorder, and screening is currently based on depression diagnostic scales and physician interviews. Based on artificial intelligence technology, computer−aided depression detection is an emerging approach to depression screening. Aiming at the current status and shortcomings of traditional measurement tools, this paper reviews current computer−aided depression detection methods, discusses the current research status of depression detection datasets and depression detection methods based on multimodal data such as facial pictures, speech, and text, and summarizes and outlooks the advantages and challenges of computer−aided depression detection. Computer−aided depression detection can provide a relatively simple and standardized screening method with the potential to synergize with the widely used scale screening and physician diagnosis, but still faces the challenges of the insufficient interpretation of model parameters and features, the Chinese dataset to be expanded, and the small sample size of the existing dataset. In the future, researchers need to further improve the sample size and model accuracy of depression detection datasets, conduct theoretical and experimental analyses of feature extraction and model construction, and promote the clinical application of computer−aided depression detection.

The accurate and efficient extraction of building from remote sensing images is fundamental for applications such as fine urban management, high−precision mapping, and land resource investigation. It is essential to investigate how to leverage image features for intelligent interpretation. This study introduces a global self−attention network with edge−enhancement (E−GSANet) for remote sensing building extraction. The network integrate the edge enhancement module into the encoder backbone, providing the network with a priori knowledge about boundaries, and then establish long−distance dependency relationships between features using the global self−attention feature expression module, enabling the fusion of salient features with edge−enhanced features. A stepwise up−sampling decoding module is designed to fusing the shallow features with rich spatial detail information and the deep features with high−order semantic information to obtain accurate extraction results of buildings. The comparison experiments between E−GSANet and the current mainstream methods is conducted based on two open−source remote sensing building datasets. The quantitative analysis and qualitative demonstrations prove that E−GSANet achieves optimal results across all evaluation metrics, yielding more complete building extractions, precise edges, and higher accuracy. Additionally, network structure ablation experiments and analysis demonstrate the effectiveness of each module.

With the intensification of global population aging, smart walkers, as rehabilitative assistive devices that enhance the quality of life and independence for individuals with gait disorders, have garnered widespread attention regarding the development of their human−machine interaction (HMI) strategies. This paper surveys the research progress on HMI technologies for smart walkers: Firstly, the application background and market demand for smart walkers are introduced. Subsequently, HMI technologies based on motion signals, visual signals, and other interaction modalities (such as physiological electrical signals and virtual reality) are explored. Despite significant advances in related research on smart walker HMI, challenges persist in areas such as motion intention recognition, navigation and obstacle avoidance, and interaction safety. Future research should focus on optimizing motion intention recognition algorithms, developing navigation strategies for complex environments, strengthening research on interaction safety, and promoting the development of multi−modal HMI technologies. These endeavors aim to achieve more accurate intention recognition and a higher level of synergy within the human−machine−environment systems, ultimately assisting individuals with gait disorders in achieving their independent living aspirations.

Breast cancer is one of the most prevalent malignant tumors in women worldwide. Nearly half of patients exhibit a human epidermal growth factor receptor 2 (HER2)-low expression profile, rendering them ineligible for traditional HER2-directed therapy as they fall under the category of HER2-negative breast cancer. The distinct clinicopathologic features of HER2-low breast cancer as compared with HER2-negative breast cancer have intrigued a growing number of researchers. This review provides a comprehensive overview of advancements in the definition of HER2-low breast cancer, its characteristics in epidemiology, clinicopathology and prognosis, its molecular genetic features, heterogeneity, and relevant agents. Emerging evidence suggests that HER2-low breast cancer may represent a distinct clinicopathological subtype. However, current data remain insufficient to establish it as an independent prognostic factor. Furthermore, the lack of standardized criteria for interpreting HER2 immunohistochemistry (IHC) 0 and 1+scores has led to inconsistencies in classification. The absence of refined subclassification within the HER2 IHC 0 population may further obscure potential benefits from targeted treatments for this group. This review emphasizes the need for future studies to refine and standardize the diagnostic procedures and treatment regimens for HER2-low breast cancer. Establishing consistent diagnostic protocols and treatment strategies may enhance clinical management for these patients.

In 5G/6G radio-over-fiber (RoF) networks, remote radio units (RUs) require multi-beamforming functionality to ensure reliable access for ubiquitous mobile terminals. To meet this requirement, multi-core fibers (MCFs) have emerged as a promising solution for RoF links due to their advantages of supporting multiple channels and maintaining excellent inter-channel delay consistency. Here, we proposes a remote optical true-time-delay multi-beamforming architecture based on MCFs, which is suitable for 5G RoF networks. The architecture utilizes MCFs as the link, while deploying chirped fiber Bragg gratings with equal dispersion spacing to provide equal-space time delays at the centralized unit. By independently tuning the wavelengths of each optical carriers, the corresponding beam direction can be continuously adjusted, enabling centralized multi-beam control. To validate the feasibility of this architecture, we use a 2-km 7-core fiber as the RoF link for experiment and build a 2×2 remote beamforming system. Experimental results demonstrate that by tuning the wavelength of each optical carrier, independent control of each beam direction can be achieved. Compared to single-mode fibers, MCF reduces inter-channel delay jitter by more than an order of magnitude, with a maximum delay jitter of 1.7 ps, ensuring long-term stability of the beam direction. Furthermore, the inter-core crosstalk of MCF has a negligible impact on both the preset delays and the signal-to-noise ratio of broadband wireless signals. This architecture provides a feasible and stable solution for realizing remote beamforming, offering significant application value for 5G/6G mobile access networks.

Searchable encryption is a key technology for enabling data encrypted search, and it has significant application value for cloud storage. However, existing schemes generally adopt a single-user model and are vulnerable to insider keyword guessing attacks, which exposes cloud data to the risk of privacy leakage. Therefore, there is an urgent need to design a searchable encryption scheme that support multi-user models and provide higher security to meet the privacy-preserving of cloud data. In response, this paper proposes an authenticated ciphertext retrieval scheme for cloud data access control. In terms of access control, the scheme embeds attributes into users' secret key to generate search trapdoor and incorporates access policies into the keyword ciphertext. The matching of attributes and access policies is achieved through threshold secret sharing techniques, thus establishing a fine-grained retrieval permission control mechanism. To enhance security, the secret key of the data owner is embedded into the keyword ciphertext to provide ciphertext authentication, effectively preventing insider keyword guessing attacks. Performance analysis shows that the trapdoor generation algorithm in our proposed scheme are computationally efficient, while the user secret key has relatively low storage overhead, making our scheme suitable for cloud storage applications.

Soil antibiotic pollution has become a significant issue in China's agricultural environment, impacting ecosystem health and food safety. This study, based on the Geographically Optimal Similarity (GOS) model, systematically predicts the spatial distribution of soil antibiotics in Chinese farmlands and explores the impact of agricultural management measures on soil antibiotic residues through scenario analysis. The results indicate that the average antibiotic concentration in China's farmland soil is approximately 16.9 ng/g, with distinct regional variations. The eastern and central regions generally show higher soil antibiotic concentrations, which are primarily associated with irrigation and fertilization management intensity. When the amount of organic fertilizer is reduced by more than 40%, the soil antibiotic concentration significantly decreases; conversely, when the irrigation area increases by more than 60%, the concentration of soil antibiotics increases rapidly. The study suggests that optimizing agricultural management measures, particularly reasonable fertilization and irrigation in high-risk areas, can effectively control soil antibiotic pollution and promote agricultural environmental protection and sustainable agricultural development.

近年来，通过材料创新、结构优化和管理系统升级，动力电池在能量密度、充电速度和使用寿命等方面取得了显著进展。然而，随着新技术的应用，动力电池的安全设计问题依然不容忽视，尤其在快充、底部磕碰和热事件等使用场景中仍存在较高的安全风险。系统梳理了动力电池技术革新的主要方向，包括结构设计创新及化学体系升级，并分析了由此引入的安全性能提升策略。从测试评价的角度，深入分析了电池快充、底部冲击及热扩散行为等前沿安全问题，并提出了针对电池全生命周期的安全评价技术。通过综述实验与仿真测试技术，构建了多层级的动力电池安全评价框架，为动力电池技术的进一步发展和测试评价的优化提供了理论支持与实践指导。

选取人胃癌BGC-823细胞系为模型，通过多组学研究方法，探讨丁酸盐和B族维生素联用对癌细胞生长的影响及可能的作用机制。通过cck-8方法观察维生素B2与丁酸盐联合作用对胃癌细胞BGC-823的抑制作用，Chou-Talay中效分析法评价两组分的联合效应，并以光学显微镜观察细胞形态变化；通过流式细胞术观察维生素B2和丁酸盐联用对BGC-823细胞凋亡及细胞周期的影响；以Western blot方法测定抗癌基因蛋白PTEN及其下游PI3K蛋白表达情况；通过对荷瘤裸鼠模型的作用，观察二者联合体内抑瘤效果；通过胃癌组和健康人群对照组血清中多代谢物的代谢组学分析建立多元判别模型，初步筛选差异代谢物，并在这些代谢物基础上进行通路聚集分析和代谢途径分析。结果表明，cck-8细胞活力和Chou-Talay中效分析结果证明维生素B2可协同增强丁酸盐对BGC-823细胞的杀伤作用，且作用过程中影响了细胞分化，导致细胞形态变大，分裂受阻；流式细胞术显示维生素B2与丁酸盐联用可显著增加胃癌细胞的凋亡比例，将细胞周期阻滞在G2/M期；Western blot结果显示维生素B2和丁酸盐联用可使PTEN蛋白水平显著上升，其下游PI3K表达水平则显著下降；体内荷瘤裸鼠模型显示维生素B2和丁酸盐联用可显著抑制肿瘤的生长；人群代谢组学分析建立多元OPLS-DA模型，并提示丁酸和维生素B2均为胃癌患者体内显著差异代谢物；通路富集分析和途径分析显示脂肪酸和B族维生素的代谢通路在胃癌患者体内具有重要的作用。维生素B2和丁酸盐联用可协同增强对胃癌的抑制作用。

入侵检测作为软件定义网络(software defined networks，SDN)架构的关键安全防护手段，能有效保障SDN安全稳定运行。通过汇总基于机器学习、基于深度学习、基于强化学习和基于信息熵的入侵检测方法，总结并分析SDN环境中仍存在的问题总结并分析了SDN环境中仍存在的问题：单控制器易受网络威胁、缺乏可扩展性、缺乏缓解和预防的方法、缺乏低速率DDoS的攻击检测、缺乏用于训练的SDN特定数据集、应用层的防御方法较少，并指出了未来的研究方向。

基于静止轨道海洋彩色成像仪（GOCI）卫星遥感数据对黄河口及其毗邻海域表层水体叶绿素a（Chl-a）浓度进行反演，并根据2019年的实测黄河口表层水体Chl-a浓度对GOCI数据进行了验证，结果表明，反演得到的Chl-a浓度准确。同时，通过对比分析调水调沙年份和未调水调沙年份的黄河口及其毗邻海域表层水体Chl-a浓度的时空变化特征后发现：（1）在调水调沙年份，从调水调沙前到调水期结束Chl-a浓度呈持续降低的状态；从调沙期开始，由于短时间内携带大量营养物质的泥沙大量汇入研究海域，导致该海域内Chl-a浓度大幅增加；（2）Chl-a在调水调沙年份的平均浓度整体低于未调水调沙年份的平均浓度，从侧面表明调水调沙使得大量泥沙入海导致水体浊度增加，降低了光合作用效率，这在一定程度上抑制了浮游生物的爆发，进而防止了赤潮等生态问题的发生。研究结果表明，实施调水调沙工程在缓解黄河口上游水库及河床泥沙淤积的同时，并未对黄河口湿地及其毗邻海域的生态环境产生破坏；构建的监测模型对未来评估及动态监测调水调沙工程对黄河口湿地及其毗邻海域生态环境影响具有重要意义。

交叉学科是新科学的生长点，是科学发展的必然趋势。基于位置服务的重要应用——兴趣点推荐，作为计算机学科与地理信息学科相交叉的研究课题，对于推动2个学科在时空数据分析等相关领域的交叉融合研究具有重要作用。分析了兴趣点推荐的影响因素即地理位置、时间因素、社交关系和流行度，重点阐述了基于图神经网络的兴趣点推荐方法，包括基于图注意力网络、图卷积网络、图自编码器的推荐，并对其特点进行对比；讨论了在兴趣点推荐中存在的一些关键挑战，如数据稀疏性、冷启动问题和用户动态偏好问题，并针对各项挑战提出相应的解决思路，提出了结合多种影响因素的推荐，跨领域推荐以及动态偏好推荐的发展趋势。

智能建造数字化平台作为建筑行业数字化转型的关键支撑，在建筑信息模型（BIM）、云计算和大数据等核心技术的驱动下实现了突破性发展。从相关研究总体特征、相关研究现状和平台案例分析3个维度对2010—2023年的相关研究进行分析。研究发现，BIM技术通过三维可视化建模和全生命周期数据管理构建了平台的基础框架；云计算技术凭借其弹性计算资源和分布式处理能力为平台提供了强大的技术支撑；大数据技术则通过实时数据分析和智能决策优化了工程管理流程。以北京城市副中心项目为例，该平台创新性地采用“环状”数字集成交付模式，整合区块链数据确权技术，实现了政府投资项目设计-施工-运维全过程的数字化协同管理，有效解决了传统工程管理中的“三超”问题。然而当前研究仍存在明显局限，比如应用场景主要集中于单体项目层面，缺乏行业级系统整合；技术标准体系尚未统一，制约了数据互联互通；人工智能等新兴技术的融合应用研究相对不足。未来发展方向应着重于构建产业级集成平台，推动规模化应用；建立统一的技术标准和数据接口规范；深化人工智能、数字孪生等创新技术与传统建造技术的融合应用；探索数据资产交易机制，促进数据要素市场化。