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Traditional physical methods for measuring solubility were often affected by viscosity, density, and surface tension. In this study, we selected appropriate electrolytes to determine the solubility of impurities in the lead-bismuth circuit at reactor operating temperatures using electrochemical methods. Taking the measurement of the solubility of impurity Ce in liquid Bi as an example, we demonstrated that electrochemical methods can accurately determine its solubility in the LiCl-KCl molten salt. Furthermore, the successful measurement of other impurities such as La, Ce, U, and Mn in lead-bismuth eutectic (LBE) using the designed system has confirmed the universality of this method.

An evaluation of low damage guar gum fracturing fluid system based on deep coalbed methane reservoir stimulation was carried out, its on-site application research was conducted. The results indicate that the gel breaking time decreases with increasing temperature. Low damage guar gum fracturing fluid has a significant breaking expression in a short period of time at temperature above 40 ℃, which is not obvious at 20 ℃ and 30 ℃. The permeability of coal core is sensitive to temperature, which leads to an increase in core damage rate, but the amplitude of change is relatively small. For low damage guar gum fracturing fluid, the increase rate of temperature sensitivity of coal core to damage rate is much lower than the decrease rate of guar gum fracturing fluid damage rate to coal core with the increasing temperature from 20 ℃ to 40 ℃. Overall, the damage rate of guar gum fracturing fluid to coal core significantly decreases with the increase of temperature. For active water, the damage rate of active water to coal core increases when the temperature rises from 20 ℃ to 40 ℃. The backflow fluid viscosity reaches 5 mPa·s of guar gum fracturing in 2 hours at temperatures above 40 ℃, meeting the needs of on-site construction. By comparing the fracturing results of low damage guar gum fracturing and active water in the same well and horizontal well at the same layer, low damage guar gum fracturing fluid can achieve a fracturing process with less injection volume, larger sand volume, higher sand ratio, and smoother pressure.

The roofs of heritage buildings are subject to long-term degradation, resulting in poor heat insulation, heat regulation, and water leakage prevention. Researchers have predominantly employed feature-based traditional machine learning methods or individual deep learning techniques for the detection of natural deterioration and human-made damage on the surfaces of heritage building roofs for preservation. Despite their success, balancing accuracy, efficiency, timeliness, and cost remains a challenge, hindering practical application. The paper proposes an integrated method that employs a convolutional autoencoder, thresholding techniques, and a residual network to automatically detect anomalies on heritage roof surfaces. Firstly, unmanned aerial vehicles (UAVs) were employed to collect the image data of the heritage building roofs. Subsequently, an artificial intelligence (AI)-based system was developed to detect, extract, and classify anomalies on heritage roof surfaces by integrating a convolutional autoencoder, threshold techniques, and residual networks (ResNets). A heritage building project was selected as a case study. The experiments demonstrate that the proposed approach improved the detection accuracy and efficiency when compared with a single detection method. The proposed method addresses certain limitations of existing approaches, especially the reliance on extensive data labeling. It is anticipated that this approach will provide a basis for the formulation of repair schemes and timely maintenance for preventive conservation, enhancing the actual benefits of heritage building restoration.

This study examines the overall needs of the green construction scheme with ‘carbon neutrality’ as the centre in the Zhejiang provincial green development target area. By aggregating and organising the construction and development data of Zhejiang Province, the entropy weight TOPSIS model is formed according to the statistical modelling for quantitative examination of the data, and the scientific assessment scheme of ‘carbon neutrality’ in the regional construction industry of Zhejiang Province is developed. This study aids in completely exhibiting and dynamically understanding the advancement of the ‘carbon neutral’ capacity of the urban construction industry. The objective is to discover the weak link in the advancement of carbon neutrality in several regional construction industries, which is of great relevance for further examining and forecasting the strategic outlook of carbon neutrality and modifying the planning of carbon neutrality strategy in special regional construction industries.

To analyze the research trends and hot frontiers in the field of fruit nondestructive testing and provide a reference for the research in this field, based on the bibliometric software CiteSpace, relevant literatures published in the core databases of CNKI and Web of Science from 2012 to 2022 are taken as the main body. Quantitative visualization research was conducted on the number of publications, authors, countries, institutions, hotspots, and frontiers of this research field. A total of 1322 literatures in Chinese and 3526 literatures in foreign languages were included. The number of literatures published at home and abroad showed an overall increasing trend. The cooperation groups of foreign researchers were larger, and China and the United States have become the most influential countries in this field. It can be seen from the analysis that the research focus is the apple, the quality detection of fruit is the research frontier, and the spectral analysis technology is the most commonly used research method in this field. Relevant researches at home and abroad were consistent in the hot fields, but the emphases and research progress were slightly different. Based on this, China should strengthen the academic cooperation between research groups in the future, and pay attention to the research frontier in this field. Combined with the updating of computer field, the new detection technology will be a new research direction.

Walnut shell breaking is an important aspect of post-harvest processing. However, shell-breaking results differ significantly from ideal uniaxial compression results. In this paper, the effect of multipoint loading on the walnut shell-breaking mechanism by combining FDEM with cohesive elements was investigated. The model for walnuts was created using micro-X-ray CT scans and experimental data. Simulation parameters describing mechanical properties were calibrated through modeling the uniaxial compression experiment. The loading point locations were quantitatively described in terms of top and azimuthal angles. The results show that the crack ex-tension rate increases with the number of loading points and the singularity value, and the maximum value of the four-point loading speed is about 450 m/s. The number of cracks is only related to the number of loading points and is not related to the location of the loading point. In addition, three loading methods are proposed, and the fastest crack propagation rate is achieved when the azimuthal angle of four-point loading is 0, 150, 180, and 330, respectively. These findings may serve as a theoretical foundation for studying shell crushing.

To understand the moisture transfer mechanism of walnuts during the combination of hot air (HA) and microwave–vacuum (MV) drying (HA-MVD) process, the drying characteristics and moisture diffusion characteristics of walnut during HA-MVD were investigated. The results indicated that the HA-MVD of walnuts occurred mainly in the falling-rate stage. The value of effective moisture diffusivity (Deff) dropped continuously with the decrease in moisture content (MC) during the HA drying, while switching to MV drying could truncate the decrease in Deff and still maintain a high value until the end of drying. The HA temperature, MC of the transition point, microwave power, and MV thermostatic temperature have significant effects on the moisture diffusion characteristics of walnuts. The values of Deff for walnuts ranged from 2.33 × 10−9 m2/s to 6.89 × 10−8 m2/s. The third-order polynomial prediction model of Deff related to the sample MC and drying conditions was established to describe the dynamic change in the Deff of walnuts during the HA-MVD process. The application of MVD in the final stage of drying could rapidly increase the internal vapor pressure of the walnuts, accelerate the diffusion speed of the internal moisture, and re-enhance the drying rate. The findings have practical value for the development of efficient and energy-saving drying methods in the walnut industry.

This study was conducted to investigate the drying kinetics and internal and external mass transfer characteristics of walnuts for an understanding of the drying mechanism. The drying characteristics, mass transfer characteristics, and color of walnut during hot air drying (HAD) were investigated under different initial moisture content (IMC) (0.35, 0.39, and 0.43 g water/g wet mass) and drying temperatures (50, 60, 70, and 80 °C). The results indicated that the IMC and drying temperature both have significant effects on the drying process of walnut, showing the higher the IMC, the longer the preheating time, the smaller the effective moisture diffusivity (Deff) and mass transfer coefficient (hm), and the longer the drying time, but reverse results for drying temperature. The values of Deff and hm for walnut ranged from 4.94 × 10−10 to 1.44 × 10−9 m2/s and 1.24 × 10−7 to 3.90 × 10−7 m/s, respectively. The values of activation energy for moisture diffusion and mass transfer ranged from 21.56 to 23.35 kJ/mol and 28.92 to 33.43 kJ/mol, respectively. Multivariate linear prediction models were also established for estimating the Deff and hm as a function of the HAD process parameters. The drying temperature has a greater effect on the walnut kernel lightness than the IMC. The Verma et al model could be used to describe the HAD process of the walnut. The findings contribute to the understanding of moisture transfer mechanisms in walnuts and have practical value for the evaluation and improvement of drying systems.

By adopting a systematic literature and science mapping review, this paper aims to explore the impact of artificial intelligence (AI) on organizational justice and project performance. A total of 47 bibliographic records from the Scopus database were analyzed. The results revealed the annual publication trends of research articles and relevant peer-reviewed journals in the studied domain. It was found that while AI technology has made significant progress in several fields, its application areas in project management and organizational justice are still relatively low. Moreover, it objectively discussed the co-occurrence analysis of keywords, co-authors, countries/regions, and documents in the fields, revealing the current research topics. The main research topics include the (1) AI’s influence on organizational justice, decision analysis, and digital transformation, (2) fostering organizational justice and AI’s role in enhancing project performance, and (3) improving organizational performance approaches. Furthermore, this paper proposed research gaps and future research directions, including (1) advancing business intelligence strategies, (2) unlocking AI technology potential on organizational justice and project performance, (3) the adaption of cultural, diversity, environmental, and social factors, (4) the impact of AI on complex and challenging leadership styles, and (5) developing a comprehensive understanding of the agile framework. The findings of this paper could contribute to a better understanding of how AI shapes project/construction management and organizational justice, providing practical solutions for innovative development for researchers and policymakers.

The conditioning of moisture content is an effective way to improve walnut-crushing performance. In this study, firstly, walnuts with different moisture contents were used to conduct the crushing experiments. The distributions of fragment sizes of shells and kernels with different moisture contents were analyzed by an image processing and sieving method, respectively. The results show that moisture content significantly affects the fragmentation degree of the shell and kernel, as shown by the differences in the fractal dimensions of shell fragments and the average fragment size of kernel fragments. The critical moisture contents of the shells and kernels, corresponding to the specific states of broken shells and whole kernels, were determined. Then, taking the critical moisture content as the target moisture content, the combination of hot air and microwave vacuum drying applied to dry walnuts was presented and the process parameters were optimized. Optimized process parameters include a hot air temperature of 63.23 °C, a moisture content of the transition point of 24.88%, microwave power of 588.24 W and a microwave vacuum thermostatic temperature of 49.01 °C. At this point, the unit time drying rate, unit mass drying energy consumption, mean square error of moisture content and kernel color parameters were 0.215%/min, 3.03 kW·h/kg, 2.93% and 6.42, respectively. It was confirmed that drying the walnuts to a critical moisture content using an optimized process could both maximally improve the crushing performance and significantly reduce the drying time. The findings provide important information for conditioning the crushing state of walnuts and improving crushing performance and have practical value for the improvement of drying systems.

Clarifying the dissipated energy required for crack expansion is an effective way to control material crushing. Therefore, based on the material fracture probability model and fractal theory, the energy range required for crack extension was determined, and the morphology of the cracks was quantified. This study investigates the influence of walnut size on crack propagation characteristics; this includes its effects on the crack initiation threshold energy, representing resistance to crack initiation, and the crack bifurcation threshold energy, representing resistance to crack bifurcation. The results show that crack extension has a well-defined threshold energy below which cracks do not initiate or bifurcate. The size of walnuts significantly impacts crack propagation characteristics, showing that both crack initiation threshold energy and crack bifurcation threshold energy decrease with increasing walnut sizes. In addition, there is a positive correlation function between the crack initiation threshold energy and the crack bifurcation threshold energy. The experimental results can offer fresh insights into material fracture prediction and serve as a reference for numerical simulations.

The detection method for technological parameter is outdates as the traditional test cycle is long as well as the measurement error and the test amount are huge. Moreover, it is difficult to disclose the operation mechanism of devices as the operation is time-consuming and laborious. Therefore, numerical simulation was used in this study to reveal the mechanism of the walnut shell-kernel winnowing device. Moreover, the influence of baffle opening combinations, inlet wind velocity and inlet angle on cleaning rate and loss rate was predicted by the neural network model. The results demonstrated that inlet wind velocity was the primary influencing factor of cleaning rate, followed by baffle opening and inlet angle. Besides, inlet wind velocity was the primary influencing factor of loss rate, followed by inlet angle and baffle opening. The winnowing device performed best (79.91% cleaning rate, 14.37% loss rate) when the baffle opening, inlet wind velocity and inlet angle were 7.01 cm, 24.36 m/s, and 9.47°. In addition, 1/8 walnut shells and 1/4 walnut kernels were incorrectly classified due to the increase in inlet wind velocity. The inlet wind velocity was considered the major cause behind the deteriorating winnowing performance of the device. Finally, the bench test and simulation optimization results were compared. The cleaning rate and loss rate relative error during the simulation test was lower than 1.06%, which ascertained the feasibility and validity of the neural network as well as the combined numerical simulation method. This study could be useful for future research and development of shell-kernel winnowing devices for hard nuts.

Walnut shell–kernel separation is an essential step in the deep processing of walnut. It is a crucial factor that prevents the increase in the added value and industrial development of walnuts. This study proposes a walnut shell–kernel detection method based on YOLOX deep learning using machine vision and deep-learning technology to address common issues, such as incomplete shell–kernel separation in the current airflow screening, high costs and the low efficiency of manually assisted screening. A dataset was produced using Labelme by acquiring walnut shell and kernel images following shellshock. This dataset was transformed into the COCO dataset format. Next, 110 epochs of training were performed on the network. When the intersection over the union threshold was 0.5, the average precision (AP), the average recall rate (AR), the model size, and floating point operations per second were 96.3%, 84.7%, 99 MB, and 351.9, respectively. Compared with YOLOv3, Faster Region-based Convolutional Neural Network (Faster R-CNN), and Single Shot MultiBox Detector algorithms (SSD), the AP value of the proposed algorithm was increased by 2.1%, 1.3%, and 3.4%, respectively. Similarly, the AR was increased by 10%, 2.3%, and 9%, respectively. Meanwhile, walnut shell–kernel detection was performed under different situations, such as distinct species, supplementary lighting, or shielding conditions. This model exhibits high recognition and positioning precision under different walnut species, supplementary lighting, and shielding conditions. It has high robustness. Moreover, the small size of this model is beneficial for migration applications. This study’s results can provide some technological references to develop faster walnut shell–kernel separation methods.

This study designed two levels of quantitative fertilizer distribution to investigate precision fertilization applications in orchards in South Xinjiang, China, which have vast rows and narrow plant spaces. The machine comprised a base frame, a ditching device, a fertilizing apparatus and an earth-covering device. The design parameters of the flow stabilization screw, conveyor screw and single-ring fertilizer quantity were summarised using theoretical analysis. The single-ring fertilizer quantity of the conveyor screw was verified via an experiment by combining EDEM software. Three-factor and three-level Box–Behnken tests were conducted using the spiral rotation speed of the conveyor, advancing of the speed and the opening degree of fertilizer outlet as the test factors and using the coefficient of variation (CV) of uniformity as the test index—thus obtaining the optimal working parameters. The simulation test results revealed that the single-ring fertilizer quantity of the fertilizing apparatus was 145.6 g, fulfilling the design requirements. The prototype testing results showed that the CV of uniformity was 6.521% when the spiral rotation speed of the conveyor, the opening degree of the fertilizer outlet and the advancing speed were 66 RPM, 42% and 2.7 km/h—thus meeting the needs of precision fertilization operations. The two designed levels of the quantitative fertilizer distributors were applied to fertilization processes in orchards with wide-row spaces and narrow plant spaces in South Xinjiang, China and were able to effectively carry out the precision fertilization applications. These data could also provide references for the optimization of spiral quantitative fertilizer distributors.

With the increasing application of lithium-ion batteries, accurate estimation of the state of health (SOH) of lithium-ion batteries is of great significance for the safe operation of lithium-ion battery systems and the reduction of operation and maintenance costs. The complex physical and chemical reactions inside the lithium battery and the complex external working conditions make it challenging to achieve an accurate health-state estimation and life prediction. Therefore, the accurate estimation of the SOH of lithium-ion batteries is an important issue. At present, electrochemical impedance spectroscopy (EIS) is widely used in the study of battery-power impedance characteristics and battery-state estimation due to its advantage of nondestructive measurement. For this reason, this paper summarizes the research progress of lithium-ion SOH estimation based on EIS in recent years and details it layer by layer, mainly from two aspects: first, the quantitative relationship model between the characteristic parameters and SOH is established by constructing a frequency domain-equivalent circuit model. Secondly, we construct a quantitative relationship model between EIS data and SOH using the data-driven method. Finally, the advantages and disadvantages of different methods and estimation accuracy are analyzed and compared, and the future estimation of SOH based on EIS is prospected.

The treatment of complex cerebrovascular diseases (CCVDs) at the skull base, such as complex intracranial aneurysms, carotid-cavernous sinus fistulas, and intracranial artery traumatic injuries, is a difficult clinical problem despite advances in endovascular and surgical therapies. Covered stents or stent graft insertion is a new concept for endovascular treatment that focuses on arterial wall defect reconstruction, differing from endovascular lesion embolization or flow diverter therapies. In recent years, covered stents specifically designed for cerebrovascular treatment have been applied in the clinical setting, allowing thousands of patients with CCVDs to undergo intraluminal reconstruction treatment and achieving positive results, even in the era of flow diverters. Since there is no unified reference standard for the application of covered stents for treating CCVDs, it is necessary to further standardize and guide the clinical application of this technique. Thus, we organized authoritative experts in the field of neurointervention in China to write an expert consensus, which aims to summarize the results of covered stent insertion in the treatment of CCVDs and propose suitable standards for its application in the clinical setting. Based on the contents of this consensus, clinicians can use individualized intraluminal reconstruction treatment techniques for patients with CCVDs.

The pursuit of electrochemical energy storage has led to a pressing need on materials with high capacities and energy densities; however, further progress is plagued by the restrictive capacity (372 mAh g−1) of conventional graphite materials. Tungsten trioxide (WO3)-based anodes feature high theoretical capacity (693 mAh g−1), suitable potential, and affordable cost, arousing ever-increasing attention and intense efforts. Nonetheless, developing high-performance WO3 electrodes that accommodate lithium ions remains a daunting challenge on account of sluggish kinetics characteristics and large volume strain. Herein, the well-designed hierarchical WO3 agglomerates assembled with straight and parallel aligned nanoribbons are fabricated and evaluated as an anode of lithium-ion batteries (LIBs), which exhibits an ultra-high capacity and excellent rate capability. At a current density of 1,000 mA g−1, a reversible capacity as high as 522.7 mAh g−1 can be maintained after 800 cycles, corresponding to a high capacity retention of ∼80%, demonstrating an exceptional long-durability cyclic performance. Furthermore, the mechanistic studies on the lithium storage processes of WO3 are probed, providing a foundation for further optimizations and rational designs. These results indicate that the well-designed hierarchical WO3 agglomerates display great potential for applications in the field of high-performance LIBs.

The fourth basic circuit component, the memristor, has been proposed for a long time, but it is not mentioned in the experiment teaching system of Electrical and Electronic Technology. In this paper, an RC bridge oscillation chaotic circuit based on memristor is designed to solve this problem. The dynamical behavior of the circuit system is analyzed using Lyapunov exponents spectrum, bifurcation diagram, phase portrait and Poincaré map. A series of complex dynamical behaviors such as symmetric single-scroll coexistence, asymmetrical single-scroll coexistence, symmetric double-scroll coexistence and asymmetrical limit–cycle coexistence exist in the circuit system. This research plays a critical role in enriching students’ knowledge and improving the experiment teaching system of Electrical and Electronic Technology.

We have experimentally studied the fabrication of ridge waveguides in zinc sulfide (ZnS) crystal by femtosecond laser ablation combined with Kr8+ ion irradiation. At the wavelength of 4 μm, the waveguide at TE mode shows better guiding properties than TM mode. The transmission performance of the waveguide is improved by using thermal annealing technology to reduce the color centers and point defects in the waveguide. The waveguide propagation loss at TE mode at 4 μm wavelength is reduced to as low as 0.6 dB/cm after annealing. Raman spectroscopy shows that Kr8+ ion irradiation does not cause large lattice damage to ZnS crystal.

Safety risk identification throughout deep excavation construction is an information-intensive task, involving construction information scattered in project planning documentation and dynamic information obtained from different field sensors. However, inefficient information integration and exchange have been an important obstacle to the development of automatic safety risk identification in actual applications. This research aims to achieve the requirements for information integration and exchange by developing a semantic industry foundation classes (IFC) data model based on a central database of Building Information Modeling (BIM) in dynamic deep excavation process. Construction information required for risk identification in dynamic deep excavation is analyzed. The relationships among construction information are identified based on the semantic IFC data model, involved relationships (i.e., logical relationships and constraints among risk events, risk factors, construction parameters, and construction phases), and BIM elements. Furthermore, an automatic safety risk identification approach is presented based on the semantic data model, and it is tested through a construction risk identification prototype established under the BIM environment. Results illustrate the effectiveness of the BIM-based central database in accelerating automatic safety risk identification by linking BIM elements and required construction information corresponding to the dynamic construction process.