This study aimed to optimize the rice-fried processing technology of Ganoderma lucidum, determine the optimal processing parameters, and evaluate the impact of fried Ganoderma lucidum rice on its antioxidant activity.The processing temperature, processing time, and ratio of medicinal material to auxiliary material were used as the influencing factors, and the contents of Ganoderma lucidum polysaccharides, triterpenoids, and extracts, as well as the appearance score of the processed product were used as evaluation indexes. The optimal rice-fried processing parameters were determined using the analytic hierarchy process (AHP)-entropy weight method combined with Box-Behnken design and response surface method.The antioxidant activity before and after processing was compared using ABTS⁺, DPPH, and hydroxyl radical scavenging assays. The optimized process conditions were as follows:processing temperature of 158.91 ℃, frying time of 10.18 minutes, and a material-to-auxiliary ratio of 1∶0.20. The average comprehensive score obtained from process validation was 91.24 with a relative standard deviation of 0.81%. In vitro antioxidant activity assay results showed that the radical scavenging ability of Ganoderma lucidum significantly increased after processing(P<0.01). The optimized rice-fried process is reliable, stable, and feasible. The rice-fried Ganoderma lucidum prepared using this process exhibited enhanced antioxidant activity, providing a reference for the processing technology and clinical application of Ganoderma lucidum.

Ginsenosides are important chemical components of Panax quinquefolius L. and are closely related to its pharmacological activities. In this study, ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS) was used for high-throughput analysis of ginsenosides in the roots, leaves, and seeds of Panax quinquefolius L., leading to the identification of 72 ginsenosides across these parts. Both univariate and multivariate analyses revealed significant differences in the content and types of ginsenosides among the different plant parts. Based on statistical significance (p<0.05) and biological significance (fold change>4), 23 differential saponins were identified in the roots vs. leaves comparison, 26 in the roots vs. seeds comparison, and 27 in the leaves vs. seeds comparison. Content analysis revealed that the total amounts of Rg₁, Re, and Rb₁ in the leaves and roots were roughly equivalent and higher than in the seeds, while most other ginsenosides were more abundant in the leaves and roots than in the seeds. The pseudo-ginsenoside F11, which is unique to Panax quinquefolius L., was most abundant in the leaves, whereas acetylated pseudo-ginsenoside F11 was most abundant in the roots. 20(S)-Ginsenoside Rh₁, acetylated ginsenoside Rg₁, and quinquenoside IV were all significantly different among the three parts and could serve as markers for differentiation. This study reveals distinct ginsenoside profiles in the roots, leaves, and seeds of Panax quinquefolius L., providing technical support for its efficient development and utilization.

With the increasing number of new energy vehicles globally, the density and spatial distribution of urban public charging infrastructure lag behind demand. Moreover, supply-demand imbalance has become an increasingly prominent issue, posing a key bottleneck in the development of green mobility. To address this challenge, this study considers Shunyi District, Beijing, as a case study to propose a comprehensive evaluation and location optimization method for public charging stations using multisource spatiotemporal data. By combining multisource spatiotemporal data such as vehicle trajectories and points of interest, we constructed a spatiotemporal distribution model of urban charging demand to accurately characterize the dynamic charging loads in different functional zones. Furthermore, through traffic accessibility analysis and charging behavior simulation, the effectiveness of the layout of the existing stations is quantitatively assessed and service blind spots are identified. The results reveal that the service capacity in some high-demand areas of Shunyi District is insufficient, with considerable coverage gaps. To overcome this issue, we used the K-means clustering algorithm to identify the cores of unmet demand and proposed a priority-based construction plan for new stations. This study provides a theoretical basis and a practical approach for mitigating regional supply-demand imbalances and enhancing the scientific layout and systemic adaptability of urban public charging facilities.

To address the challenges of significant scale variations, numerous environmental interferences, high real-time requirements, and the difficulty in achieving a good balance between detection accuracy and computational cost faced by existing bicycle-type vehicle detection models, this study proposes YOLO-DBG, a lightweight and efficient bicycle-type vehicle detection model based on computer vision. First, a novel dual-branch pooling & depthwise separable convolution bottleneck module is designed, which synchronously extracts global contour and local detail features of bicycle-type vehicles through a differentiated feature aggregation strategy, thereby enhancing multiscale feature extraction capabilities and reducing model computational costs by integrating depthwise separable convolution. Second, a weighted bidirectional feature pyramid network architecture is introduced in the neck network, which enhances the fusion of key vehicle features through bidirectional cross-scale connections and a dynamic weighting mechanism, and effectively reduces model computational costs through node pruning. In addition, ghost convolution is used as a downsampling operator, which considerably compresses the model while maintaining the feature expression ability. These three modules work together to construct an effective lightweight network architecture. Experiments demonstrate that the proposed model achieves a 0.2% increase in mean average precision while reducing parameters, giga floating point operations, and model size by 55.8%, 37.0%, and 53.1%, respectively. The proposed method achieves ideal lightweighting without compromising detection accuracy, offering a novel solution for real-time detection of bicycle-type vehicles.

Soil microbial fuel cells (SMFCs) are low-cost devices capable of converting chemical energy of organic matter into electricity through anodic microorganisms while simultaneously degrading organic pollutants in the soil. Therefore, they have promising applications in soil remediation and sustainable agriculture. However, the existing SMFCs still face limitations in terms of power generation and pollutant degradation efficiency. Moreover, many gaps are observed in their application for soil remediation of organic pollutants. Therefore, this article systematically reviews the research progress of SMFCs for degrading various organic pollutants in the soil, aiming to provide references for enhancing the power generation and degradation efficiency of SMFCs and guiding future research directions. First, the article elucidates the principles of SMFCs in degrading soil organic pollutants, analyzes the application of top-, insertion-, and U-type SMFCs, and deeply discusses the impacts of electrode materials and soil media on the SMFCs performance as well as the criteria for their selection. Second, it analyzes the different microbial species roles in the anode and their distribution changes in SMFCs. Accordingly, this article summarizes various methods to improve the power generation and degradation efficiency of SMFCs, such as optimizing electrode structures, adding electron mediators, and regulating environmental conditions. Future directions for developing SMFCs should focus on enhancing their stability and scalability, exploring highly efficient electrode materials and microbial strains, and expanding their applicability across different soil types and pollutants.

Land-use change, as a key driving force of the terrestrial ecosystem carbon cycle, plays a critical role in assessing carbon balance and promoting sustainable development. Based on the land-use change patterns in the source region of the Yellow River in the mountainous areas of the Qinghai-Xizang Plateau from 2000 to 2020, this study developed an integrated CA-Markov-InVEST-OPGD model to reveal the spatiotemporal evolution of land cover and carbon storage, analyze the driving mechanism of carbon-storage changes, and predict the characteristics of carbon storage changes in a natural change scenario in the source region of the Yellow River in 2030. The results showed that from 2000 to 2020, grasslands in the source region of the Yellow River declined, while unused land and water areas expanded, leading to an overall reduction in carbon storage, with high-value areas shifting toward the northwest. NDVI, elevation, and temperature were the main factors affecting carbon storage in the region, with NDVI interacting most significantly with temperature, precipitation, and population density. In the natural change scenario in 2030, the grassland area will continue to shrink while the water area will increase significantly. Carbon storage in the east and south will decline significantly, while carbon storage in some areas in the west and north will increase significantly. The overall spatial concentration of carbon storage will tend to decrease, with notable local increases and decreases. Therefore, it is essential to undertake grassland ecological protection and restoration efforts to enhance carbon sink capacity and promote regional carbon balance.

This study investigates the spatiotemporal variation characteristics of negative air ion (NAI) concentration and the primary factors influencing it across different seasons using Jiyuan Nanshan Forest as the research site. Based on continuous observations conducted from June 2021 to May 2022 using RR-9411A monitoring equipment and data obtained from an automatic weather station, this study analyzes the diurnal and seasonal variations in NAI concentration and its correlation with environmental factors. Results show that the order of seasonal average NAI concentrations is autumn (889 ion·cm^-3) > summer (756 ion·cm^-3) > winter (601 ion·cm^-3) > spring (430 ion·cm^-3). The diurnal variation patterns reveal that (i) in autumn, peak NAI concentration in appears at 11:00 (1 030 ion·cm^-3); (ii) in summer, a “double-peak and double-valley” pattern is observed; (iii) in winter, the peak lags behind noon; and (iv) in spring, fluctuations are minimal. Correlation analysis reveals significant seasonal differences in the relationships between NAI concentration and environmental factors, namely, wind speed, temperature, relative humidity, and particulate matter (PM)—specifically, PM with a diameter of ≤10 μm and PM with a diameter of ≤2.5 μm. In spring, the NAI concentration is primarily influenced by wind speed and humidity. Moreover, in summer, it is positively correlated with temperature and wind speed, and in winter, it is significantly correlated with PM levels and relative humidity. Furthermore, vegetation photosynthesis intensity, meteorological conditions (e.g., precipitation and inversion layer), and human activities are found to be the key drivers of the seasonal differentiation in NAI. This study provides a scientific basis for optimizing forest ecological management and improving air quality assessments.

The Xiaoqing River serves as a multifunctional waterway in Shandong Province, supporting flood control, irrigation, and navigation activities. Accurate evaluation of its water ecological quality is crucial for informing ecological restoration and guiding the resumption of navigation. This study conducted field monitoring to assess correlations between water quality parameters and biodiversity indices, aiming to identify appropriate biological indicators at the species, population, and community levels for the Jinan section. A hierarchical evaluation framework was developed by integrating indicators across three levels: physicochemical characteristics, habitat quality, and ecological structure. This system was applied for the first time to assess the water ecological quality of the Jinan section. The results aligned with the observed spatial trend of water quality, characterized by a “high-low-high” pattern from upstream to downstream. The study provides a scientific foundation for ecological restoration and navigation planning in the Xiaoqing River basin and offers a methodological reference for evaluating urban river ecosystems in China.

In recent years, extreme precipitation events have become increasingly frequent in Shandong Province. To improve the monitoring, forecasting, and early warning of these short-duration events, this study analyzes minute-level precipitation data from 99 national meteorological stations in Shandong Province, covering the period from 1991 to 2020. Using the climate tendency rate, wavelet analysis, and empirical orthogonal function decomposition, we examine the spatiotemporal distribution and variability of annual maximum precipitation across different short durations. The results indicate that both the average and maximum values of annual maximum precipitation for all short durations increase progressively from central and eastern Shandong outward, peaking in southern Shandong. In most parts of southern Shandong, these annual maxima show a decreasing trend over time, whereas most other areas exhibit an increasing trend. For 5 min durations, the spatial variability trends are generally consistent, with intensity centers located in northwestern Shandong. For the 30 min and 60 min durations, eastern and southern Shandong, as well as the eastern Shandong Peninsula, show trends opposite to those in other regions, with negative intensity centers in central-western, northwestern, and southwestern Shandong. For the 90 min, 120 min, and 180 min durations, the eastern central region and the eastern Shandong Peninsula display opposite trends, with positive intensity centers in southwestern Shandong. Each duration shows significant 2 to 3 year periodicities. An abrupt change was detected in 2004 for the 5 min duration, leading to a marked decrease thereafter, whereas no abrupt changes were observed for other durations. Overall, most annual maximum short-duration precipitation events in Shandong exhibit an increasing trend. These findings underscore the importance of enhancing monitoring efforts and revising storm intensity formulas, particularly in southern and northwestern Shandong.

To overcome the issue of requiring additional heat sources during the expansion stage of advanced adiabatic compressed air energy storage (AA-CAES) systems, a combined cooling and power system with compressed air energy storage and auxiliary heating via parabolic trough solar collectors is proposed. The turbine inlet temperature of the CAES system is increased using the solar trough energy, thereby increasing its storage capacity and reducing the consumption of high-temperature heat transfer oil. The heat transfer oil saved via the first coupled parabolic trough solar system is used to drive the [mmim]DMP/CH₃OH compression-absorption refrigeration system. A dynamic mathematical model of the Combined Cooling with Solar Auxiliary (CCSA) system was established based on the conservation laws of mass and energy of each subsystem. The operating conditions of the CCSA system during the energy release phase under design conditions were simulated, and energy and exergy analyses were conducted. The impacts of months, latitude, high-pressure generator temperature, and auxiliary compressor pressure ratio of the refrigeration system on the thermodynamic performance of the CCSA system were investigated. The effective solar utilization efficiency of the CCSA system was compared with that of a conventional solar-driven ammonia power system. Moreover, its energy and exergy efficiencies were compared with those of the AA-CAES and Solar Auxiliary Reheating Compressed Air Energy Storage (SAR-CAES) systems. The results revealed that the effective solar utilization efficiency of the CCSA system was 8.44% to 13.87% higher than that of the solar-driven ammonia power system and its energy and exergy efficiencies were higher than those of the AA-CAES and SAR-CAES systems.

Mass concrete structures are extensively used in large-scale infrastructure projects; however, temperature cracks after concrete pouring severely affect structural durability and pose safety risks. This study focuses on a 5 MW wind turbine foundation at the Changqing Oilfield, China, to investigate the temperature evolution and spatial distribution of mass concrete after pouring. Based on the dynamic balance between hydration heat release and heat dissipation, the temperature evolution of mass concrete can be divided into three phases: rapid heating, peak maintenance, and slow cooling. The internal temperature of the concrete exhibits a distinct spatial heterogeneity, with higher temperatures at the center and lower temperatures at the edges. The maximum central temperature reaches 74.3 ℃, whereas the edge regions are considerably affected by ambient temperature(5 ℃ to 23 ℃). Theoretical calculations show that the temperature difference between the interior and exterior of the mass concrete after 3 days of pouring is 30.15 ℃. However, when plastic films and burlap are used as insulators, the maximum temperature difference within 10 days after pouring is only 25.2 ℃, thus indicating an effective reduction in the risk of temperature cracks. These findings offer theoretical and practical guidance for temperature control of mass concrete in similar environments, and the proposed insulation measures after pouring are critical for enhancing the durability of large-scale infrastructure projects, such as wind power foundations.

To address the issues of reduced available capacity and shortened lifespan in lithium battery packs caused by inconsistent capacities of individual cells, this paper proposes a lithium battery management system based on hybrid passive-active balancing. The system employs passive balancing for “peak clipping” during the charging phase by dissipating excess energy from the highest-voltage battery cell through a load resistor. During the discharging or idle phase, active balancing is used for “valley filling” by charging the battery cell with the lowest voltage using the energy from the entire pack, thereby enhancing the overall performance. Compared with traditional flyback transformer-based solutions, the proposed design features a simpler circuit and can meet the real-time balancing requirements during charging and discharging processes. The experimental results show that the system significantly improves the voltage consistency of the battery cells and facilitates capacity recovery, thus providing reliable support for the efficient and stable operation of the battery pack.

Science and technology (S&T) powerhouse provinces and China as an S&T powerhouse together constitute a coordinated transmission system of “provincial-level practice-national strategy”, serving as an important support for advancing China’s science and technology modernization. This study employs the visualization tool CiteSpace (version 6.1.R6) to conduct a knowledge graph analysis of 795 relevant articles indexed in core journals within the China National Knowledge Infrastructure(CNKI) database from 2006 to 2025. Using keyword co-occurrence analysis, cluster analysis, and burst-term detection, the study systematically examines research hotspots and evolutionary trajectories in this field. The findings indicate that domestic studies mainly focus on four themes: strategic coordination, technological breakthroughs, innovation ecosystems, and digital-intelligent transformation. The research paradigm demonstrates a full-chain, integrated evolution: “national strategy-regional implementation-technology application-ecosystem optimization”. Future research should place greater emphasis on differentiated provincial pathways, interdisciplinary integration, and international comparative studies to further deepen the development of this field.