Search Results (27)

In the power system of a polysilicon reduction furnace, especially during the silicon rod growth process, the issue of insufficient temperature control accuracy arises due to the system’s nonlinear and time-varying characteristics. To address this challenge, a dual-loop control system is proposed, combining model-free adaptive control (MFAC) with an improved PID controller. The inner loop utilizes a hysteresis PID controller for dynamic current regulation, ensuring fast and accurate current adjustments. Meanwhile, the outer loop employs a hybrid MFAC-based improved PID algorithm to optimize the temperature tracking performance, achieving precise temperature control even in the presence of system uncertainties. The MFAC component is adaptive and does not require a system model, while the improved PID enhances stability and reduces the response time. Simulation results demonstrate that this hybrid control strategy significantly improves the system’s performance, achieving faster response times, smaller steady-state errors, and notable improvements in the uniformity of polysilicon deposition, which is critical for high-quality silicon rod growth. The proposed system enhances both efficiency and accuracy in industrial applications. Furthermore, applying the dual-loop model to actual industrial products further validated its effectiveness. The experimental results show that the dual-loop model closely approximates the polysilicon production model, confirming that dual-loop control can allow the system to rapidly and accurately reach the set values. Full article

Wind energy is a vital pillar of modern sustainable power generation, yet wind turbine generators remain vulnerable to incipient inter-turn short-circuit (ITSC) faults in their stator windings. These faults can cause fluctuations in the output voltage, frequency, and power of wind turbines, eventually leading to overheating, equipment damage, and rising maintenance costs if not detected early. Although significant progress has been made in condition monitoring, the current methods still fall short of the robustness required for early fault diagnosis in complex operational settings. To address this gap, this study presents a novel deep learning framework that involves traditional baseline machine-learning algorithms and advanced deep network architectures to diagnose seven distinct ITSC fault types using signals from current, vibration, and axial magnetic flux sensors. Our approach is rigorously evaluated using metrics such as confusion matrices, accuracy, recall, average precision (AP), mean average precision (mAP), hypothesis testing, and feature visualization. The experimental results demonstrate that deep learning models outperform machine learning algorithms in terms of precision and stability, achieving an mAP of 99.25% in fault identification, with three-phase current signals emerging as the most reliable indicator of generator faults compared to vibration and electromagnetic data. It is recommended to combine three-phase current sensors with deep learning frameworks for the precise identification of various types of incipient ITSC faults. This study offers a robust and efficient pipeline for condition monitoring and ITSC fault diagnosis, enabling the intelligent operation of wind turbines and maintenance of their operating states. Ultimately, it contributes to providing a practical way forward in enhancing turbine reliability and lifespan. Full article

Visual place recognition (VPR) is essential for robots and autonomous vehicles to understand their environment and navigate effectively. Inspired by face recognition, a recent trend for training a VPR model is to leverage classification objective, where the embeddings of images are trained to be similar to corresponding class centers. Ideally, the predicted similarities should be negative correlated to the geographic distances. However, previous studies typically used loss functions from face recognition due to the similarity between the two tasks, which cannot guarantee the rank consistency above as face recognition is unrelated to geographic distance. Current methods for distance-similarity or ordinal constraint are either for sample-to-sample training, only partially meet the constraint, or are incapable for the VPR task. To this end, we provide a mathematical definition geographic distance consistent defining the above consistency that the loss function for VPR should adhere to. Based on it, we derive the upper bound of cross-entropy softmax loss under the desired constraint to minimize, and propose a novel loss function for VPR that is geographic distance consistent, called GDCPlace. To the best of our knowledge, GDCPlace is the first classification loss function designed for VPR. To evaluate our loss, we collected 11 benchmarks that have high domain variability to test on. As our contribution is on the loss function and previous classification-based VPR methods mostly adopt face recognition loss function, we collect several additional loss functions to compare, e.g., loss for face recognition, image retrieval, ordinal classification, and general purpose. The results show that GDCPlace performs the best among different losses and former state-of-the-art (SOTA) for VPR. It is also evaluated for ordinal classification tasks to show the generalizability of GDCPlace. Full article

The Yanchang Formation of the Triassic in the Ordos Basin comprises various stratigraphic intervals. The Chang 8 reservoir represents a significant oil-producing section of the Yanchang Formation, and its hydrocarbon accumulation mechanism is complex. In this study, we analyzed the diagenetic evolution and reservoir-forming stages of the Chang 8 member of the Yanchang Formation in the Late Triassic in the Fuxian area, the southern Ordos Basin, via thin-section casting, scanning electron microscopy (SEM), X-ray diffraction, and fluid inclusion petrology and homogenization temperature analyses. The relationship between the petrogenesis and hydrocarbon charging history was analyzed, which provided guidance for identifying and predicting the hydrocarbon reservoir distribution. The results show that the main diagenesis types of the Chang 8 reservoir are compaction, cementation, dissolution, and metasomatism. The comprehensive analysis of the reservoir mineral types, diagenesis, diagenetic sequence, and thermal evolution degree of organic matter shows that the Chang 8 reservoir of the Yanchang Formation is in the A stage of the middle diagenesis stage. Under the overpressure of hydrocarbon generation, oil and gas migrated into the Chang 8 reservoir along fractures and connected pores. The earlier-stage hydrocarbon charging occurred after compaction and later than the early clay film formation and early calcite precipitation, and it also occurred earlier than or simultaneously with the quartz overgrowth. The later hydrocarbon charging occurred after the significant quartz overgrowth and late calcite pore filling. Depending on the homogenization temperature and salinity, the fluid inclusions can be divided into two types: low-temperature, low-salt (90–105 °C, 1.4%–11.2%) fluid inclusions and high-temperature, high-salt (115–120 °C, 2.2%–12.5%) fluid inclusions. According to the analysis of the evolution of the burial history, hydrocarbon charging in the Chang 8 reservoir of the Yanchang Formation in the Fuxian area occurred in two consecutive periods: 133~126 Ma and 122~119 Ma, demonstrating one-scene, two-stage reservoir formation, characterized by simultaneous reservoir densification and hydrocarbon charging. In this research, we precisely ascertained the regional diagenetic characteristics and patterns and periods of hydrocarbon charging, thereby furnishing crucial evidence that deepens the comprehension of sedimentary basin evolution. Full article

Palladium (Pd) catalysts play a crucial role in facilitating Suzuki cross-coupling reactions for the synthesis of valuable organic compounds. However, conventional heterogeneous Pd catalysts often encounter challenges such as leaching and deactivation during reactions, leading to reduced catalytic efficiency. In this study, we employed an innovative intercalation templating strategy to prepare two-dimensional carbon nanosheets with high nitrogen doping derived from petroleum asphalt, which were utilized as a versatile support for immobilizing Pd nanoparticles (Pd/N-CNS) in efficient Suzuki cross-coupling reactions. The results indicate that the anchoring effect of high-pyridinic N species on the two-dimensional carbon nanosheets enhances interactions between Pd and the support, effectively improving both the dispersibility and stability of the Pd nanoparticles. Notably, the Pd/N-CNS catalyst achieved an overall turnover frequency (TOF) of 2390 h⁻¹ for the Suzuki cross-coupling reaction under mild conditions, representing approximately a nine-fold increase in activity compared to commercial Pd/C catalysts. Furthermore, this catalyst maintained an overall TOF of 2294 h⁻¹ even after five reaction cycles, demonstrating excellent stability. Theoretical calculations corroborate these observed enhancements in catalytic performance by attributing them to improved electron transfer from Pd to the support facilitated by abundant pyridinic N species. This work provides valuable insights into feasible strategies for developing efficient catalysts aimed at sustainable production of biaromatic compounds. Full article

The marine carbonates in the Ordovician Majiagou Formation in the Ordos Basin have significant exploration potential. Research has focused on their thermal history and hydrocarbon accumulation stages, as these are essential for guiding the exploration and development of hydrocarbons. In this paper, we study the thermal evolution history of the carbonate reservoirs of the Ordovician Majiagou Formation in the east-central Ordos Basin. Furthermore, petrographic and homogenization temperature studies of fluid inclusions were carried out to further reveal the hydrocarbon accumulation stages. The results demonstrate that the degree of thermal evolution of the Ordovician carbonate reservoirs is predominantly influenced by the deep thermal structure, exhibiting a trend of high to low values from south to north in the central region of the basin. The Fuxian area is located in the center of the Early Cretaceous thermal anomalies, with the maturity degree of the organic matter ranging from 1 to 3.2%, with a maximum value of 3.2%. The present geothermal gradient of the Ordovician Formation exhibits the characteristics of east–high and west–low, with an average of 28.6 °C/km. The average paleo-geotemperature gradient is 54.2 °C/km, the paleoheat flux is 55 mW/m², and the maximum paleo-geotemperature reaches up to 270 °C. The thermal history recovery indicates that the Ordovician in the central part of the basin underwent three thermal evolution stages: (i) a slow warming stage before the Late Permian; (ii) a rapid warming stage from the end of the Late Permian to the end of the Early Cretaceous; (iii) a cooling stage after the Early Cretaceous, with the hydrocarbon production of hydrocarbon source rocks weakening. In the central part of the basin, the carbonate rock strata of the Majiagou Formation mainly developed asphalt inclusions, natural gas inclusions, and aqueous inclusions. The fluid inclusions can be classified into two stages. The early-stage fluid inclusions are mainly present in dissolution holes. The homogenization temperature is 110–130 °C; this coincides with the hydrocarbon charging period of 210–165 Ma, which corresponds to the end of the Triassic to the end of the Middle Jurassic. The late-stage fluid inclusions are in the dolomite vein or late calcite that filled the gypsum-model pores. The homogenization temperature is 160–170 °C; this coincides with the hydrocarbon charging period of 123–97 Ma, which corresponds to the late Early Cretaceous. Both hydrocarbon charging periods are in the rapid stratigraphic warming stage. Full article

Plants communicate underground by secreting multiple amino acids (AAs) through their roots, triggering defense mechanisms against cadmium (Cd) stress. However, the specific roles of the individual AAs in Cd translocation and detoxification remain unclear. This study investigated how exogenous AAs influence Cd movement from the roots to the shoots in Cd-resistant and Cd-sensitive Chinese cabbage cultivars (Jingcui 60 and 16-7 cultivars). The results showed that methionine (Met) and cysteine (Cys) reduced Cd concentrations in the shoots of Jingcui 60 by approximately 44% and 52%, and in 16-7 by approximately 43% and 32%, respectively, compared to plants treated with Cd alone. However, threonine (Thr) and aspartic acid (Asp) did not show similar effects. Subcellular Cd distribution analysis revealed that AA supplementation increased Cd uptake in the roots, with Jingcui 60 preferentially storing more Cd in the cell wall, whereas the 16-7 cultivar exhibited higher Cd concentrations in the organelles. Moreover, Met and Cys promoted the formation of Cd-phosphate in the roots of Jingcui 60 and Cd-oxalate in the 16-7 cultivar, respectively. Further analysis showed that exogenous Cys inhibited Cd transport to the xylem by downregulating the expression of HMA2 in the roots of both cultivars, and HMA4 in the 16-7 cultivar. These findings provide insights into the influence of exogenous AAs on Cd partitioning and detoxification in Chinese cabbage plants. Full article

Hyperspectral remote sensing technology plays a vital role in advancing modern precision agriculture due to its non-destructive and efficient nature. To achieve accurate monitoring of winter wheat chlorophyll content, this study utilized 68 sets of chlorophyll content data and hyperspectral measurements collected during the jointing stage of winter wheat over two consecutive years (2019–2020), under various fertilization types and nitrogen application levels. Continuous wavelet transform was applied to transform the original reflectance, ranging from 2¹ to 2¹⁰, and the correlation matrix method was utilized to identify the spectral index at each scale, with the highest correlation to winter wheat chlorophyll content as the optimal spectral index combination input. Subsequently, winter wheat chlorophyll content prediction models were developed using three machine learning methods: random forest (RF), support vector machine (SVM), and a genetic algorithm-optimized backpropagation neural network (GA-BP). The results indicate that the spectral data processed through continuous wavelet transform at seven scales, from 2¹ to 2⁷, show the highest correlation with winter wheat chlorophyll content at a scale of 2⁶, with a correlation coefficient of 0.738, compared with the correlation of 0.611 of the original reflectance, and the accuracy is improved by 20.7%. The average highest correlation value between the spectral index at scale 2⁶ and winter wheat chlorophyll content is 0.752. As the scale of wavelet transform increases, the correlation between the spectral index and winter wheat chlorophyll content and the accuracy of the predictive model show a trend of first increasing and then decreasing. The optimal input variables for predicting winter wheat chlorophyll content and the best machine learning method are the spectral data at a scale of 2⁶ processing combined with the GA-BP model. The optimal predictive model has a validation set coefficient of determination (R²) of 0.859, root mean square error (RMSE) of 1.366, and mean relative error (MRE) of 2.920%. The results show that the prediction model can provide a technical basis for improving the hyperspectral inversion accuracy of winter wheat chlorophyll and modern precision agriculture. Full article

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021–2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R²) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth. Full article

Nitrogen is a fundamental component for building amino acids and proteins, playing a crucial role in the growth and development of plants. Leaf nitrogen concentration (LNC) serves as a key indicator for assessing plant growth and development. Monitoring LNC provides insights into the absorption and utilization of nitrogen from the soil, offering valuable information for rational nutrient management. This, in turn, contributes to optimizing nutrient supply, enhancing crop yields, and minimizing adverse environmental impacts. Efficient and non-destructive estimation of crop LNC is of paramount importance for on-field crop management. Spectral technology, with its advantages of repeatability and high-throughput observations, provides a feasible method for obtaining LNC data. This study explores the responsiveness of spectral parameters to soybean LNC at different vertical scales, aiming to refine nitrogen management in soybeans. This research collected hyperspectral reflectance data and LNC data from different leaf layers of soybeans. Three types of spectral parameters, nitrogen-sensitive empirical spectral indices, randomly combined dual-band spectral indices, and “three-edge” parameters, were calculated. Four optimal spectral index selection strategies were constructed based on the correlation coefficients between the spectral parameters and LNC for each leaf layer. These strategies included empirical spectral index combinations (Combination 1), randomly combined dual-band spectral index combinations (Combination 2), “three-edge” parameter combinations (Combination 3), and a mixed combination (Combination 4). Subsequently, these four combinations were used as input variables to build LNC estimation models for soybeans at different vertical scales using partial least squares regression (PLSR), random forest (RF), and a backpropagation neural network (BPNN). The results demonstrated that the correlation coefficients between the LNC and spectral parameters reached the highest values in the upper soybean leaves, with most parameters showing significant correlations with the LNC (p < 0.05). Notably, the reciprocal difference index (VI6) exhibited the highest correlation with the upper-layer LNC at 0.732, with a wavelength combination of 841 nm and 842 nm. In constructing the LNC estimation models for soybeans at different leaf layers, the accuracy of the models gradually improved with the increasing height of the soybean plants. The upper layer exhibited the best estimation performance, with a validation set coefficient of determination (R²) that was higher by 9.9% to 16.0% compared to other layers. RF demonstrated the highest accuracy in estimating the upper-layer LNC, with a validation set R2 higher by 6.2% to 8.8% compared to other models. The RMSE was lower by 2.1% to 7.0%, and the MRE was lower by 4.7% to 5.6% compared to other models. Among different input combinations, Combination 4 achieved the highest accuracy, with a validation set R² higher by 2.3% to 13.7%. In conclusion, by employing Combination 4 as the input, the RF model achieved the optimal estimation results for the upper-layer LNC, with a validation set R² of 0.856, RMSE of 0.551, and MRE of 10.405%. The findings of this study provide technical support for remote sensing monitoring of soybean LNCs at different spatial scales. Full article

The purchasing decisions of consumers increasingly incorporate considerations of freshness and the carbon footprint of agri-foods. This study aims to investigate the impact of consumer preferences on decision-making behavior within dual-channel supply chains. Specifically, it classifies the structure of the supply chain channels into two types: producer-led and seller-led online channels, and examines two distinct decision-making scenarios: centralized and decentralized decision-making. The study applies the game theory modeling method to analyze the differences in the selling prices, freshness, low carbon levels, and profits of agri-foods in these scenarios. The findings indicate that as consumer preference for the online channel grows, it becomes more challenging to sell homogeneous agri-foods at higher prices through physical (entity) channels. Moreover, the introduction of online channels by sellers leads to higher selling prices for agri-foods in the supply chain under decentralized decision-making compared to centralized decision-making, and the freshness and low carbon level of agri-foods primarily depend on the cost structure of the supply chain. From the perspective of enhancing produce quality, promoting low carbon development, and attaining high-quality products at a reasonable price, centralized decision-making within the supply chain and seller-led online channels are more advantageous. However, it is important to note that pursuing these benefits may result in a certain amount of sacrifice in terms of supply chain profit. Full article

Gestational weight gain (GWG) may be affected by the timing of dietary intake. Previous studies have reported contradictory findings, possibly due to inconsistent characterizations of meal timing. We conducted a birth cohort study in Tianjin to determine the effect of daily energy and macronutrient distribution in mid and late pregnancy on GWG. Dietary intake information in the second and third trimesters used three 24-h dietary recalls, and meal timing was defined in relation to sleep/wake timing. The adequacy of GWG was assessed using recommendations from the Institute of Medicine guidelines. Pregnant women who had a relatively high average energy and macronutrient distribution in the late afternoon–early evening time window exhibited a greater GWG rate and a greater total GWG than that in morning time window during the third trimester (β = 0.707; β = 0.316). Carbohydrate intake in the morning of the second and third trimesters (β = 0.005; β = 0.008) was positively associated with GWG rates. Morning carbohydrate intake in the second trimester was also positively associated with total GWG (β = 0.004). Fat intake in the morning of the third trimester (β = 0.051; β = 0.020) was positively associated with the GWG rates and total GWG. Excessive GWG of Chinese pregnant women was related closely to eating behavior focused on the late afternoon–early evening and carbohydrate and fat intake in the morning during the second and third trimesters. Full article

Alkali-activated slag concrete (ASC) is regarded as one of the most promising sustainable construction materials for replacing ordinary Portland cement concrete (OPC) due to its comparable strength and outstanding durability in challenging environments. In this study, the corrosion of steel bars embedded in ASC and OPC was studied by means of an electrically accelerated corrosion test of steel bars in concrete. Meanwhile, the bond performance of the corroded steel bars embedded in ASC was tested and compared with corresponding OPC groups. The results showed that ASC and OPC behaved differently in terms of bond deterioration. The high chemical resistance of ASC decreased the corrosion of steel bars and, thus, increased the residue bond strength and the bond stiffness. Full article

The traditional rammed earth houses sharing similar patterns in the Lingnan region, south China, and distributed in rectangular arrays, are gradually losing their vitality and becoming uninhabited under modern living conditions. This research examined a typical pattern called the “Four-point gold” house and analyzed the suitability of different retrofitting technologies by field measurements and building simulation. To optimize energy consumption, indoor thermal comfort, and the corresponding economic performance of the retrofitting measures for the prototypical house, five measures, including wall insulation, reflective roof coating, carpet, sunshade, and natural ventilation, are proposed after considering the status quo of the building envelope. It is found that the best performance in energy-saving, dynamic investment payback period, and annual indoor thermal comfort are 2192.27 kWh/a, 9.17 years, and 1766 h, respectively. Different parameters are included to be clustered by K means clustering technique, and the comprehensively optimized scheme consists of a regime of 30 mm XPS 30 mm, ZS-221 white coating, carpet, 0.5 m sunshade width, and turning off windows (doors). The proposed retrofitting strategy can be promoted to a wide range of traditional rammed earth houses in the Lingnan region in China and holds a conspicuous energy-saving potential for the suburban and rural residential sectors in the region. Full article

This study explores the incidence and trend of zoonoses in China and its relationship with environmental health and proposes suggestions for promoting the long-term sustainable development of human, animal, and environmental systems. The incidence of malaria was selected as the dependent variable, and the consumption of agricultural diesel oil and pesticides and investment in lavatory sanitation improvement in rural areas were selected as independent variables according to the characteristics of nonpoint source pollution and domestic pollution in China’s rural areas. By employing a fixed effects regression model, the results indicated that the use of pesticides was negatively associated with the incidence of malaria, continuous investment in rural toilet improvement, and an increase in economic income can play a positive role in the prevention and control of malaria incidence. Guided by the theory of One Health, this study verifies human, animal, and environmental health as a combination of mutual restriction and influence, discusses the complex causal relationship among the three, and provides evidence for sustainable development and integrated governance. Full article