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To address the issue of reduced sowing depth detection accuracy caused by varying soil topography during the operation of wheat row drills, an indoor bench test device suitable for wheat row drills was developed. The device integrates a laser sensor and an array sensor for terrain and sowing depth detection. The laser sensor provides the detected sowing depth values, while the array sensor captures different terrain features. The actual sowing depth values are obtained through the indoor experimental setup. The experiment includes three types of terrain: convex, concave, and flat. The terrain slope matrix is obtained using the array sensor, and terrain feature values are extracted. The laser sensor is then used to obtain the detected sowing depth, and the actual sowing depth is manually measured. PCA analysis is conducted to correlate terrain feature values with sowing depth deviations. Results indicate that under different terrain conditions, the slope mean and slope standard deviation are the main components affecting sowing depth deviations. Compared to using a single sensor, this system enables more accurate sowing depth measurement by analyzing terrain features. The device provides valuable data support for controlling sowing depth under varying terrain conditions in subsequent operations.

A new meteorological station (DMS) was established at the Morning Glory summit in Zhejiang Province to provide regional background information on atmospheric composition in the Yangtze River Delta (YRD) region, China. This study investigated the first carbon monoxide (CO) records at DMS from September 2020 to January 2022. The annual average concentration of CO was 233.4 ± 3.8 ppb, which exceeded the measurements recorded at the other Asian background sites. The winter CO concentration remained elevated but peaked in March in the early spring due to the combined effect of regional emissions within the YRD and transportation impacts of North China and Southeast Asia sources. The diurnal cycle had a nocturnal peak and a morning valley but with a distinct afternoon climb, as the metropolis in the YRD contributed to a local concentration enhancement. The back trajectory analysis and the Weighted Potential Sources Contribution Function (WPSCF) maps highlighted emissions from Anhui, Jiangxi, Zhejiang, and Jiangsu provinces as significant sources. Due to well-mixed air conditions and fewer anthropogenic influences, DMS records closely aligned with the CO averages derived from the Copernicus Atmospheric Monitoring Service (CAMS) covering the YRD, confirming its representativeness for regional CO levels. This study underscored DMS as a valuable station for monitoring and understanding CO spatiotemporal characteristics in the YRD region.

Energy recovery turbines (ERTs) play an important role in reducing the energy consumption of seawater reverse osmosis (SWRO). The internal flow loss mechanisms and hydraulic vibration characteristics of a centrifugal ERT model used in SWRO were studied under multiple conditions. The results show that the high-efficiency range of the ERT tends toward high-flow conditions. The energy loss is mainly concentrated at the tongue in the volute, which is due to the generation of flow separation vortices. There is a flow separation phenomenon on the suction surface side of the blade, trailing under the 0.8Qd condition. The energy loss of the ERT is the lowest under the 1.0Qd condition, because the interaction between the fluid and the blades is the weakest. There are vortices formed by flow impact on the blade pressure surface under the 1.2Qd condition. There are vortices on the blade suction face, which are generated by fluid separation. The closer the relative liquid flow angle is to the blade settle angle, the smaller the energy loss is in the impeller. Therefore, the ERT should first reduce rated flow appropriately before hydraulic design, as this is beneficial for efficient operation under multiple conditions.

Sweet potato is essential for food security, but its transplanting efficiency is currently low. To improve this, we developed a belt-type transplanter that uses seedling belt technology for automated feeding, increasing efficiency by over 25%. The use of the seedling belt is key to significantly enhancing the transplanter’s efficiency. Building on seedling belt feeding, we designed a “trumpet-shaped” staggered detachable seedling belt using LDPE film. Seedlings are fixed at intervals between two film layers via thermal bonding points. Mechanical analysis showed the optimal peeling strength is 13.2 N, ensuring smooth seedling transport during transplanting. Peeling strength depends on the heat sealing temperature, time, and pressure. To optimize these parameters, we designed an experimental device and conducted single-factor experiments to determine their effects on the peeling strength and establish reasonable ranges. Results show that, at a peeling strength of 13.2 N, the thermal bonds separate smoothly without tearing the film. A three-factor, three-level orthogonal experiment further optimized the heat sealing parameters, concluding that, at 140 °C, 1.2 s, and 0.4 MPa, the average peel force of the seedling tapes obtained from the experiments was 14.31 N, at which point the peeling effect was optimal.

In view of issues affecting manual on-demand sowing of spinach, such as high working intensity, low seeding efficiency, and high cost, a mechanized precision seeding device for spinach seeds is proposed, based on a spoon-wheel including a combined cell with two intersecting surfaces. The key structure was designed, and motion analysis was conducted employing SolidWorks software (2022). Additionally, the seeding process was simulated using EDEM simulation software (2022). The number of spoons, the radius of the spoons, and the speed of the seed wheel were chosen as the influencing factors, while the qualified index, reseeding index, and missing index were utilized as the evaluation indexes. A response surface method test based on the Box–Behnken design was carried out, and the test results were analyzed to obtain the optimal parameter combination for the seed-metering device. The field test results showed that when the rotation speed was 18 rpm, the radius of each seed spoon was 2.5 mm, and the number of seed spoons was 50, the average qualified index was 90.89%, the reseeding index was 8.22%, and the missed seeding index was 0.89%. The test results satisfy the technical production requirements for spinach seed sowing.

The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter.

The quantitative evaluation and prediction of ecosystem service value (ESV) in the Lhasa River Basin can provide a basis for ecological environment assessment and land-use optimization and adjustment in the future. Previous studies on the ESV in the Lhasa River Basin have focused mainly on static assessment and evolution analysis based on historical data, and have not considered future development trends. Moreover, most of the current driving factors selected in land use and ESV prediction studies are homogeneous, and do not reflect the geographical and cultural characteristics of the study area well. With the Lhasa River Basin as the research focus, 20 driving factors were selected according to the characteristics of the plateau alpine area, and the land-use changes under three developmental orientations, namely, natural evolution, ecological protection, and agricultural development, were predicted for the year 2030 with the FLUS-Markov model. Based on these predictions, the values of ecosystem services were calculated, and their spatiotemporal dynamic characteristics were analyzed. The results show that the model has high accuracy in simulating land-use change in the Lhasa River Basin, with a kappa coefficient of 0.989 and an overall accuracy of 99.33%, indicating a high applicability. The types of land use in the Lhasa River basin are dominated by the existence of grassland, unused land, and forest, with a combined proportion of 94.3%. The change trends of each land-use type in the basin under the three scenarios differ significantly, with grassland, cropland, and building land showing the most significant changes. The area of cropland increased only in the agricultural development scenario; the areas of forest and grassland increased only in the ecological protection scenario; and the expansion of building land was most effectively controlled in the ecological protection scenario. The ESV increased in all three scenarios, and the spatial distribution of the ESV per unit area in the middle and lower reaches was greater than that in the upper reaches. The ESV was the greatest in the ecological protection scenario, with grasslands, forests, and water bodies contributing more to the ESV of the basin. This study provides decision-making references for the effective utilization of land resources, ecological environmental protection planning, and urban construction in the Lhasa River Basin in the future.

Combined SINS/GPS navigation systems have been widely used. However, when the traditional combined SINS/GPS navigation system travels between tall buildings, in the shade of trees, or through tunnels, the GPS encounters frequent signal blocking, which leads to the interruption of GPS signals, and as a result, the combined SINS/GPS-based navigation method degenerates into a pure inertial guidance system, which will lead to the accumulation of navigation errors. In this paper, an adaptive Kalman filtering algorithm based on polynomial fitting and a Taylor expansion is proposed. Through the navigation information output from the inertial guidance system, the polynomial interpolation method is used to construct the velocity equation and position equation of the carrier, and then the Taylor expansion is used to construct the virtual measurement at the moment of the GPS signal interruption, which can make up for the impact of the lack of measurement information on the combined SINS/GPS navigation system when the GPS signal is interrupted. The results of computer simulation experiments and road measurement tests based on the loosely combined SINS/GPS navigation system show that when the carrier faces a GPS signal interruption situation, compared with a combined SINS/GPS navigation algorithm that does not take any rescue measures, our proposed combined SINS/GPS navigation algorithm possesses a higher accuracy in the attitude angle estimation, a higher accuracy in the velocity estimation, and a higher accuracy in the positional localization, and the system possesses higher stability.

Because the conventional Temperature Drift Error (TDE) estimation model for Capacitive MEMS Gyros (CMGs) has inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification to improve their bias stability, its novel model based on thermal stress deformation analysis is presented. Firstly, the TDE of the CMG is traced precisely by analyzing its structural deformation under thermal stress, and more key decisive TCQs are explored, including ambient temperature variation ∆T and its square ∆T2, as well its square root ∆T1/2; then, a novel TDE estimation model is established. Secondly, a Radial Basis Function Neural Network (RBFNN) is applied to identify its parameter accurately, which eliminates local optimums of the conventional model based on a Back-Propagation Neural Network (BPNN) to improve bias stability. By analyzing heat conduction between CMGs and the thermal chamber with heat flux analysis, proper temperature control intervals and reasonable temperature control periods are obtained to form a TDE precise test method to avoid time-consuming and expensive experiments. The novel model is implemented with an adequate TCQ and RBFNN, and the Mean Square Deviation (MSD) is introduced to evaluate its performance. Finally, the conventional model and novel model are compared with bias stability. Compared with the conventional model, the novel one improves CMG’s bias stability by 15% evenly. It estimates TDE more precisely to decouple Si-based materials’ temperature dependence effectively, and CMG’s environmental adaptability is enhanced to widen its application under complex conditions.

The integration of Design for Manufacture and Assembly (DfMA) into the design process of industrialized construction has the potential to reduce errors and changes occurring after the design has been finalized, ultimately improving overall productivity. Based on DfMA, the designers would need to consider whether their designs meet the architectural and performance requirements, as well as the manufacturing and assembly requirements from assembly and manufacturing technicians. However, some limitations present challenges for DfMA-oriented prefabricated design, such as lack of information interoperability, lack of conflict detection and management, and inefficient data processing and requirement checking. Thus, this research presents a novel BIM and ontology-based framework for DfMA of prefabricated and modular components. Various types of algorithms, plugins, and programming are also integrated to support the operation of the framework. The primary functions of this framework include: (1) collection of various stakeholder requirements in a standardized data format; (2) conflict detection and resolution between the design, manufacturing, and assembly requirements; and (3) automated compliance checking of whether the designed BIM models meet DfMA requirements. This research applies the framework on a prefabricated hotel project as a case study to validate the feasibility of the framework. Based on the results of a user experience survey, the developed framework shows promise for improving the DfMA process and stakeholder communication. Although a few limitations were encountered, such as the low computer operating speed and the limited ontology, the framework has been validated and shows great potential in advancing prefabricated component design applications

In order to meet the sowing demand of the hybrid rice plug seedling, a horizontal rotary precision seed metering device was proposed. The key structure parameters were designed, the kinematics analysis of the seed-filling process was carried out, and the main influencing factors of the seeding performance evaluation index were determined. Taking motor speed, the eccentric distance of the disk, and seed layer thickness as test factors, and the taking qualification rate, repetition rate, and miss-seeding rate as seeding performance test indexes, the quadratic regression orthogonal rotation combination test was carried out, and the relationship between the test factors and evaluation indexes was approached by a quadratic polynomial. On the basis of discussing the influence of the test factors on the performance index, the regression surrogate mathematical model between the main performance index and the influencing factors was established. The value of the evaluation index of correlation degree p between the model variables was less than 0.01. Based on the regression surrogate mathematical model, the response surface of the qualified rate of the seeding performance evaluation index was established, and the test data were analyzed by multiple regression analysis. The rapid prediction of qualified rates under the influence of test factors was realized, and the optimum test parameters were obtained as follows: motor speed 165 r/min, an eccentric distance of disk 22 mm, seed layer thickness 28 mm, qualification rate 98.55%, miss-seeding rate 1.45%, and repetition rate 0%. According to the best combination of test parameters, the performance evaluation index test of the prototype was carried out, and the qualification rate, miss-seeding rate, and repetition rate were 98.34%, 1.66%, and 0%. The experimental results were in good agreement with the theoretical analysis, indicating that the horizontal rotary precision seed metering device can meet the agronomic requirements of hybrid rice. The research results could provide some reference for the design of a precision seeding device for industrialized plug seedlings.

Research on precision seeders operated in the dry field shows that an electric driving system used for a seed meter can significantly improve the planting uniformity. The measuring method for forward speed greatly affects the control accuracy of the electric driving system. However, the performance of an electric drive system for precision seeders operating in wet fields, such as paddy, is still unclear. The operating conditions of dry and wet fields are quite different, and it is necessary to study the effects of an electric driving system and corresponding speed measuring methods on a wet-field operating seeder. In this paper, field experiments were carried out by using a wet direct planter of rice under three driving methods for seed meter, including classic mechanical driving system (MDS), electric driving system with speed acquiring from an encoder (EDSE), and electric driving system with speed acquiring from the global positioning system (EDSG), at forward speeds of 4.62, 5.81, and 7.23 km·h−1. The seeding uniformity (seeding rates, coefficient of variations) and the slippage (slippage rate, broken strips) were investigated. Results showed that the EDSG exhibited more qualified operation performance compared with the MDS and the EDSE. For both the MDS and EDSE, the seeding rates significantly decreased and deviated from the desired value as the forward speed increased. The negative slippages of MDS and EDSE were 3.47–31.72% and 2.13–7.7%, respectively, and they significantly increased when the forward speeds increased from 4.62 to 7.23 km·h−1. Analysis of the seeding uniformity and slippage presented that the EDSG exhibited a more qualified operation and is recommended for the wet direct seeder of rice.

An industrial furnace, such as a blast furnace, molten salt furnace and a basic oxygen furnace, is a popular reactor, where the distribution of liquid, flow pattern of the fluid and the velocity of the fluid determine the energy distribution and chemical reaction in the reactor. Taking a furnace as the research object, this paper studies the effects of different inlet velocities, liquid densities and viscosity on bubble and velocity distribution. A three-dimensional mathematical model of the furnace is set up by a numerical simulation, and the volume-of-fluid (VOF) method is used to study the behavior of bubbles. The accuracy of the simulation parameters selected in the simulation calculation is verified by comparing the simulation with the experimental results. The findings show that an excessive or too small an inlet velocity will lead to an uneven distribution of chlorine in the furnace, therefore, an inlet velocity of about 30 m/s is more appropriate. In addition, changing the liquid density has little effect on the bubble and velocity distribution while choosing the appropriate liquid viscosity can ensure the proper gas holdup and fluidity of chlorine in the furnace.

Owing to the fact that the conventional Temperature Drift Error (TDE) precise estimation model for a MEMS accelerometer has incomplete Temperature-Correlated Quantities (TCQ) and inaccurate parameter identification to reduce its accuracy and real time, a novel TDE precise estimation model using microstructure thermal analysis is studied. First, TDE is traced precisely by analyzing the MEMS accelerometer’s structural thermal deformation to obtain complete TCQ, ambient temperature T and its square T2, ambient temperature variation ∆T and its square ∆T2, which builds a novel TDE precise estimation model. Second, a Back Propagation Neural Network (BPNN) based on Particle Swarm Optimization plus Genetic Algorithm (PSO-GA-BPNN) is introduced in its accurate parameter identification to avoid the local optimums of the conventional model based on BPNN and enhance its accuracy and real time. Then, the TDE test method is formed by analyzing heat conduction process between MEMS accelerometers and a thermal chamber, and a temperature experiment is designed. The novel model is implemented with TCQ and PSO-GA-BPNN, and its performance is evaluated by Mean Square Error (MSE). At last, the conventional and novel models are compared. Compared with the conventional model, the novel one’s accuracy is improved by 16.01% and its iterations are reduced by 99.86% at maximum. This illustrates that the novel model estimates the TDE of a MEMS accelerometer more precisely to decouple temperature dependence of Si-based material effectively, which enhances its environmental adaptability and expands its application in diverse complex conditions.

Due to the significant spatial variation of the performance of Moderate Resolution Imaging Spectrometer (MODIS) aerosol optical depth (AOD) retrievals, validation is very important for applications of MODIS AOD products at regional scales. This study presents a comparative analysis of Collection 6.1 MODIS AOD retrievals and ground measurements from five local sites and one Aerosol Robotic Network (AERONET) site in the Yangtze River Delta (YRD) region, which significantly complements the previous validation that utilized limited AERONET measurements. Generally, MODIS AOD retrievals showed a reasonable agreement with collocated ground measurements (R2 > 0.7), with 66% of Dark Target (DT) 10 km retrievals, 56% of Deep Blue (DB) 10 km retrievals, and 69% of DT 3 km retrievals falling within the expected error (EE = ±(0.05 + 0.2 × AOD)). Nevertheless, it was found that the DT AOD retrievals tended to be overestimated over urbanized and lakeside sites, while the DB AOD retrievals tended to be underestimated over all ground sites except for lakeside sites. Such patterns appeared to be linked with the systematic biases of the single-scattering albedo estimation in the AOD retrieval algorithms. Another significant finding of this study is that the uncertainties of the MODIS AOD retrievals were highly correlated with the land cover proportions of urbanized features and water (LCP_UW) in the surrounding region, especially for the DT products. An empirical correction method based on these correlations could substantially reduce the uncertainties of DT AOD products over high LCP_UW areas. The results not only highlight the significant impacts of both urban and water areas on the MODIS AOD retrieval algorithms but also create new possibilities to correct such impacts once the universal correlations between LCP_UW and the uncertainty measures are established.

In order to understand the effect of sand type on the rheological properties of self-compacting mortar, four varieties of sand, namely, quartz sand (QS), river sand (RS), and two kinds of manufactured sand, marked as MS-A and MS-B, were studied. As part of this study, the sands’ particle shape parameters, such as their length:width ratio and roundness, were determined. Mortars containing the four varieties of sand were tested using the slump flow test and the V-funnel test in oven-dried (OD) and saturated surface-dried (SSD) conditions in order to identify the water absorption, shape-related differences, and specific gravity in their rheological performance. The changing trends of the slump flows and the V-funnel times of the different mortars in OD and SSD were similar. By eliminating the influence of water absorption on mortar rheology, shape–weight parameters, such as the ratio between the length:width ratio and specific gravity (LWS) and the product of roundness and specific gravity (ROS), were defined in order to quantify the compound effects of sand type on mortar rheology. The regression analysis showed an excellent linear correlation between slump flow and both LWS and ROS, and a very good linear correlation was also demonstrated between the V-funnel time and both LWS and ROS. Based on the particle shape–weight parameters, the rheological properties of mortars can be predicted. Based on the mortar rheological threshold theory, the self-compacting mortar (SCM) zone can be drawn. The predicted SCM zone overlaps considerably with the experimental SCM zone for MS-A.

Congestion control is one of the key research topics in relation to the routing algorithms of wireless sensor networks (WSNs). In this paper, we propose a congestion-aware routing algorithm (CARA) for unlimited-lifetime wireless sensor networks by integrating the geographic distance and traffic load of sensor nodes. The algorithm takes alleviating congestion as the primary purpose and considers the traffic of the node itself and local network traffic. According to the geographic distance between nodes, CARA defines four decision parameters (node load factor, forward rate, cache remaining rate, and forward average cache remaining rate), selecting the best node as the next-hop through the multi-attribute decision-making method. Compared with the two existing algorithms for congestion control, our simulation results suggest that the CARA algorithm alleviates network congestion and meets reasonable network delay and energy consumption requirements.

Based on the statistical stability of the inter-system bias (ISB), we propose a tightly coupled Differential Global Navigation Satellite System (DGNSS) positioning method by using ionosphere-free combination for the long baseline applications. The proposed method is compatible with the traditional Radio Beacon (RBN) base station implementation. The tightly coupled DGNSS positioning method is utilized at the long baseline rover by eliminating the effect of ionosphere delay with ionosphere-free (IF) based differential ISB calibration. The improved positioning model strength can be obtained with the proposed method when compared with the traditional loosely coupled method, particularly under the satellite-deprived environment. GNSS datasets of different baselines were collected to test the proposed method. The results of the ISB stability show that the ISB has long-term stability and needs to be calibrated when the receiver is rebooted. The positioning results show that when compared with the IF-based loosely coupled method, the IF-based tightly coupled DGNSS method based on ISB calibration can obtain better positioning performance of accuracy and continuity within 240 km baselines.

Anomalous ionospheric gradient is a critical risk to be monitored by ground-based augmentation systems (GBASs) utilized for safety-of-life navigation applications. A dual-frequency carrier phase-based ionospheric gradient monitoring method is proposed under the mixed Gaussian distribution. The minimum detection error of the proposed method can be greatly reduced by allowing acceptable ambiguity resolution failure modes, given the required averaging length. The real BeiDou navigation satellite system data were utilized to test the proposed method. The experimental results showed that the minimum detection error (MDE) of the proposed dual-frequency ionospheric gradient monitoring method can be reduced by at least 30% in comparison with the maximum acceptable anomalous ionospheric gradient of category III GBAS. This study demonstrated that the proposed method can be used to protect against the ionospheric gradient for a ground-based augmentation system.

To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noise into the decomposition process, CEEMDAN can effectively separate the original signal into different intrinsic mode functions (IMFs) with distinctive frequency scales. Afterward, the approximate entropy value of each IMF is calculated to eliminate noisy IMFs. Then, correlation coefficient analysis is employed to select useful IMFs that represent dominant PD features. Finally, real IMFs are extracted for PD signal reconstruction. On the basis of EEMD, CEEMDAN can further improve reconstruction accuracy and reduce iteration numbers to solve mode mixing problems. The results on both simulated and on-site PD signals show that the proposed method can be effectively employed for noise suppression and successfully extract PD pulses. The fusion algorithm combines the CEEMDAN algorithm and the ApEn algorithm with their respective advantages and has a better de-noising effect than EMD and EEMD.

Considering the increasing use of emerging technologies in industrialized construction in recent years, the primary objective of this article is to develop and validate predictive models to predict the emerging technology utilization level of industrialized construction industry practitioners. Our preliminary research results indicate that the company background and personal career profiles can significantly affect practitioners’ technology utilization level. Thus, our prediction model is based on four variables: company size, company type, working experience, and working position. The United States and China are selected as the case studies to validate the prediction model. First, a well-designed questionnaire survey is distributed to the industrialized construction industry practitioners from the two countries, which leads to 81 and 99 valid responses separately. Then, ordinal logistic regression is used to develop a set of models to predict the practitioners’ utilization level of the four main technology types. Finally, the external test dataset consisting of 16 cases indicates the prediction models have a high accuracy. The results also reflect some differences of the technology utilization status in the industrialized construction industry between the United States and China. The major contribution of this research is offering an efficient and accurate method to predict practitioners’ technology utilization level in industrialized construction. Significantly, the models are believed to have a wide application in promoting the emerging technologies in the actual industrialized construction.

The conventional temperature drift error (TDE) compensation model cannot decouple temperature dependence of Si-based materials because temperature correlated quantities (TCQ) have not been obtained comprehensively, and Micro-Electro-Mechanical System gyros’ (MEMS-gyros’) environmental adaptability is reduced in diverse, complicated conditions. The study presents modification of TDE compensation model of MEMS-gyros based on microstructure thermal effect analysis (MTEA). First, Si-based materials’ temperature dependence was studied in microstructure with thermal expansion effect and TCQ that determines the structural deformation were extracted to modify the conventional model, including temperature variation and its square. Second, a precise TDE test method was formed by analyzing heat conduction process between MEMS-gyros and thermal chamber, and temperature experiments were designed and conducted. Third, the modified model’s parameters were identified based on radical basis function artificial neural network (RBF ANN) and its performance was evaluated. Last, the conventional and modified models were compared in performance. The experimental results show MEMS-gyros’ bias stability was up to 10% of the conventional model, the temperature dependence of Si-based materials was decoupled better by the modified one and the environmental adaptability of MEMS-gyros was improved to expand their application in diverse complicated conditions.

In recent years, a growing amount of research has focused on improving the performance of industrialized construction using emerging technologies. It is still necessary to have an in-depth understanding of the industry practitioners’ perspectives on the application of emerging technologies. Thus, a well-designed survey was distributed to industry practitioners who have been involved in industrialized construction projects. Then, a set of data analysis methods were utilized on the collected data to address the proposed four specific research questions. Results indicate that 3D and nD models, sensing techniques, and business information models are the technologies with the highest current utilization level. Extended reality, additive manufacturing, and advanced data analytics are the technologies with the highest development potential. Project inputs (e.g., cost, time, and labor), as well as implementation cost and software constraints (e.g., capital costs, software upgrading, and compatibility), are the main factors that affect practitioners’ decisions to adopt emerging technologies in industrialized projects. Inter-group comparison results indicate that company background has little significant influence on practitioners’ perspectives, while personal career profiles can significantly affect practitioners’ perspectives. Significantly, by uncovering the suggestions and viewpoints of practitioners, this paper aligns academic research with industry needs, ultimately providing guidance on future research directions and applications.

This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements.

Fiber-optic sensing is a field that is developing at a fast pace. Novel fiber-optic sensor designs and sensing principles constantly open doors for new opportunities. In this paper, we review a fiber-optic sensing technique developed in our research group called frequency-shifted interferometry (FSI). This technique uses a continuous-wave light source, an optical frequency shifter, and a slow detector. We discuss the operation principles of several FSI implementations and show their applications in fiber length and dispersion measurement, locating weak reflections along a fiber link, fiber-optic sensor multiplexing, and high-sensitivity cavity ring-down measurement. Detailed analysis of FSI system parameters is also presented.

Power transformers are important equipment in power systems and their reliability directly concerns the safety of power networks. Dissolved gas analysis (DGA) has shown great potential for detecting the incipient fault of oil-filled power transformers. In order to solve the misdiagnosis problems of traditional fault diagnosis approaches, a novel fault diagnosis method based on hypersphere multiclass support vector machine (HMSVM) and Dempster−Shafer (D−S) Evidence Theory (DET) is proposed. Firstly, proper gas dissolved in oil is selected as the fault characteristic of power transformers. Secondly, HMSVM is employed to diagnose transformer fault with selected characteristics. Then, particle swarm optimization (PSO) is utilized for parameter optimization. Finally, DET is introduced to fuse three different fault diagnosis methods together, including HMSVM, hybrid immune algorithm (HIA), and kernel extreme learning machine (KELM). To avoid the high conflict between different evidences, in this paper, a weight coefficient is introduced for the correction of fusion results. Results indicate that the fault diagnosis based on HMSVM has the highest probability to identify transformer faults among three artificial intelligent approaches. In addition, the improved D−S evidence theory (IDET) combines the advantages of each diagnosis method and promotes fault diagnosis accuracy.

Non-intrusive load monitoring (NILM) is a cost-effective technique for extracting device-level energy consumption information by monitoring the aggregated signal at the entrance of the electric power. With the large-scale deployment of smart metering, NILM should ideally be designed to operate purely on the low-rate data from smart meters. In this paper, an approach based on Graph Shift Quadratic Form constrained Active Power Disaggregation (GSQF-APD) is proposed, which is built upon matrix factorization and introduces graph shift quadratic form constraint according to piecewise smoothness of the power signal. In addition, a two-step iterative optimization method is designed to solve this problem. The first step minimizes the regularization term to find the signal with minimum variation, and then the second step uses the simulated annealing (SA) algorithm to iteratively minimize the objective function and constraint based on the total graph variation minimizer. Using one open-access dataset, the strength of GSQF-APD is demonstrated through three sets of experiments. The numerical results show the superior performance of GSQF-APD, with Graph Laplacian Quadratic Form constrained Active Power Disaggregation (GLQF-APD) and the state-of-the-art NILM methods as benchmarks.

Optical sensors have been investigated and widely deployed in industrial andscientific measurement and control processes, mainly due to their accuracy, high sensitivityand immunity to electromagnetic interference and other unique characteristics. They areespecially suited for harsh environments applications, where no commercial electricalsensors are available for long-term stable operations. This paper reports a novel contactoptical high temperature sensor targeting at harsh environments. Utilizing birefringentsingle crystal sapphire as the sensing element and white light interferometric signalprocessing techniques, an optical birefringence based temperature sensor was developed.With a simple mechanically structured sensing probe, and an optical spectrum-codedinterferometric signal processor, it has been tested to measure temperature up to 1600 °Cwith high accuracy, high resolution, and long-term measurement stability.

A special period in Beijing from 6 August to 17 September 2015, during which the World Athletics Championships and Victory Day military parade took place, and which involved measures to restrict traffic and reduce factory emissions, was selected to analyze the aerosol optical properties and the impact of meteorological conditions on pollution levels. The study was based on AERONET observational and retrieval data, particulate matter measurements (TEOM 1405), meteorological data, and then the HYSPLIT model was used to analyze the pollution sources. The study period was divided into three sub-periods according to the different stages of implementation of the control measures, and the main conclusions can be summarized as follows. During the period in which the restrictive measures were applied, the air quality improved significantly, with the average value of the AOD being 0.34 ± 0.20, about 69% less than before. Meanwhile, the average Ångström exponent was about 9.5% higher than before, with an average value of 1.38 ± 0.25, indicating that the main pollutants were fine particles. Single scattering albedo decreased as wavelength increased, being higher than in the other two stages (mean value of 0.944 ± 0.045). This showed that the strong scattering capacity and absorption aerosol optical depth was at its lowest, at about 0.008 ± 0.009. The peaks of aerosol volume concentration in the fine and coarse mode were significantly reduced. Meteorological conditions also had a certain effect on the aerosol optical properties, with the blowing of clean and dry wind and the occurrence of precipitation contributing to the overall improvement in air quality.

Cancer cells circumvent immune surveillance via diverse strategies. In accordance, a large number of complex studies of the immune system focusing on tumor cell recognition have revealed new insights and strategies developed, largely through major histocompatibility complexes (MHCs). As one of them, tumor-specific MHC-II expression (tsMHC-II) can facilitate immune surveillance to detect tumor antigens, and thereby has been used in immunotherapy, including superior cancer prognosis, clinical sensitivity to immune checkpoint inhibition (ICI) therapy and tumor-bearing rejection in mice. NK cells play a unique role in enhancing innate immune responses, accounting for part of the response including immunosurveillance and immunoregulation. NK cells are also capable of initiating the response of the adaptive immune system to cancer immunotherapy independent of cytotoxic T cells, clearly demonstrating a link between NK cell function and the efficacy of cancer immunotherapies. Eosinophils were shown to feature pleiotropic activities against a variety of solid tumor types, including direct interactions with tumor cells, and accessorily affect immunotherapeutic response through intricating cross-talk with lymphocytes. Additionally, microbial sequencing and reconstitution revealed that commensal microbiota might be involved in the modulation of cancer progression, including positive and negative regulatory bacteria. They may play functional roles in not only mucosal modulation, but also systemic immune responses. Here, we present a panorama of the cancer immune network mediated by MHCI/II molecules, immune cells and commensal microbiota and a discussion of prospective relevant intervening mechanisms involved in cancer immunotherapies.

Data-to-text generation plays an important role in natural language processing by processing structured data and helping people understand those data by generating user-friendly descriptive text. It can be applied to news generation, financial report generation, customer service, etc. However, in practice, it needs to adapt to different domains that may lack an annotated training corpus. To alleviate this dataset scarcity problem, distantly-supervised data-to-text generation has emerged, which constructs a training corpus automatically and is more practical to apply to new domains when well-aligned data is expensive to obtain. However, this distant supervision method of training induces an over-generation problem since the automatically aligned text includes hallucination. These expressions cannot be inferred from the data, misguiding the model to produce unfaithful text. To exploit the noisy dataset while maintaining faithfulness, we empower the neural data-to-text model by dynamically increasing the weights of those well-aligned training instances and reducing the weights of the low-quality ones via meta learning. To our best knowledge, we are the first to alleviate the noise in distantly-supervised data-to-text generation via meta learning. In addition, we rewrite those low-quality texts to provide better training instances. Finally, we construct a new distantly-supervised dataset, DIST-ToTTo (abbreviation for Distantly-supervised Table-To-Text), and conduct experiments on both the benchmark WITA (abbreviation for the data source Wikipedia and Wikidata) and DIST-ToTTo datasets. The evaluation results show that our model can improve the state-of-the-art DSG (abbreviation for Distant Supervision Generation) model across all automatic evaluation metrics, with an improvement of 3.72% on the WITA dataset and 3.82% on the DIST-ToTTo dataset in terms of the widely used metric BLEU (abbreviation for BiLingual Evaluation Understudy). Furthermore, based on human evaluation, our model can generate more grammatically correct and more faithful text compared to the state-of-the-art DSG model.

Additively manufactured (AM) composites based on short carbon fibers possess strength and stiffness far less than their continuous fiber counterparts due to the fiber’s small aspect ratio and inadequate interfaces with the epoxy matrix. This investigation presents a route for preparing hybrid reinforcements for AM that comprise short carbon fibers and nickel-based metal-organic frameworks (Ni-MOFs). The porous MOFs furnish the fibers with tremendous surface area. Additionally, the MOFs growth process is non-destructive to the fibers and easily scalable. This investigation also demonstrates the viability of using Ni-based MOFs as a catalyst for growing multi-walled carbon nanotubes (MWCNTs) on carbon fibers. The changes to the fiber were examined via electron microscopy, X-ray scattering techniques, and Fourier-transform infrared spectroscopy (FTIR). The thermal stabilities were probed by thermogravimetric analysis (TGA). Tensile and dynamic mechanical analysis (DMA) tests were utilized to explore the effect of MOFs on the mechanical properties of 3D-printed composites. Composites with MOFs exhibited improvements in stiffness and strength by 30.2% and 19.0%, respectively. The MOFs enhanced the damping parameter by 700%.

Running can promote public health. However, the association between running and the built environment, especially in terms of micro street-level factors, has rarely been studied. This study explored the influence of built environments at different scales on running in Inner London. The 5Ds framework (density, diversity, design, destination accessibility, and distance to transit) was used to classify the macro-scale features, and computer vision (CV) and deep learning (DL) were used to measure the micro-scale features. We extracted the accumulated GPS running data of 40,290 sample points from Strava. The spatial autoregressive combined (SAC) model revealed the spatial autocorrelation effect. The result showed that, for macro-scale features: (1) running occurs more frequently on trunk, primary, secondary, and tertiary roads, cycleways, and footways, but runners choose tracks, paths, pedestrian streets, and service streets relatively less; (2) safety, larger open space areas, and longer street lengths promote running; (3) streets with higher accessibility might attract runners (according to a spatial syntactic analysis); and (4) higher job density, POI entropy, canopy density, and high levels of PM 2.5 might impede running. For micro-scale features: (1) wider roads (especially sidewalks), more streetlights, trees, higher sky openness, and proximity to mountains and water facilitate running; and (2) more architectural interfaces, fences, and plants with low branching points might hinder running. The results revealed the linkages between built environments (on the macro- and micro-scale) and running in Inner London, which can provide practical suggestions for creating running-friendly cities.

In order to quantitatively evaluate the degree of protection in protective layer mining and provide guidance for the design of a secondary outburst elimination scheme, a variable weight-projection gray target dynamic evaluation model for the effectiveness of protective layer mining is established. The improved order relation analysis method was used to determine the subjective weight of each index toward the decision-making goal based on numerical diversity characteristics, and the initial fixed-weight calculation for mixed multi-attribute metrics was processed through the degree of index action. The variable weight function was used to dynamically adjust the fixed weight through the penalty and incentive index methods. Four indexes (gas content, gas pressure, coal seam permeability coefficient, and expansion deformation) were selected, the outburst elimination and anti-reflection were taken as the guide, and the critical value of each index for eliminating burst and the critical value of pressure relief were taken as the positive and negative bullseyes. Based on the variable weight-projection gray target decision model, the distance between the two target centers of each scheme was calculated; at the same time, the variable weight vector changed dynamically with the evaluation scheme to achieve the dynamic quantitative evaluation of the degree of protection. Additionally, compared with the calculation results of fixed weights, it was found that the variable weight-projection bullseye distance can more accurately reflect the dynamic control effect of differences in numerical combinations of multi-attribute indexes in different decision schemes based on the degree of protection of protective layer mining. Taking a mine in PingMei as the engineering background, Ding protected the Wu area, and the degree of protection in the Wu group coal seam reached 116.29%, eliminating the outburst risk of the coal seam. The Wu protected the Ji group coal seam, with the degree of outburst risk in the Ji group being reduced by 14.27, and the Ding + Wu group protected the Ji group coal seam, with the degree of outburst risk of the Ji group being reduced by 20.71%, but not eliminated. The evaluation model quantifies the degree of protection of protective layer mining, and provides a theoretical basis for further assessing whether the working face should strengthen the enhancing permeability or whether it needs to be used in tandem with high-strength outburst elimination methods.

Semi-open centrifugal pumps are widely used in various fields. However, the tip leakage vortex (TLV) has a malign effect on the impeller flow field. The structure and trajectory of a TLV under different discharge conditions were simulated and are discussed herein. Then, the characteristics of the TLV were analyzed using a new omega vortex identification method. The external characteristic and pressure fluctuation of the simulation and experiment were consistent. A secondary leakage vortex near the blade outlet was formed under the high-discharge condition. A leading-edge overflow phenomenon under the low-discharge condition and led to the formation of a reverse-flow vortex. The interface between the main flow and tip leakage flow moved toward the impeller upstream with decreased discharge. As a result, the peak of the entropy production curve moved upstream, and leading-edge overflow and reverse flow occurred. The tip leakage flow changed the blade pressure distribution, resulting in a decrease in the blade load.

The coronavirus disease (COVID-19) pandemic has resulted in an increasing population that is experiencing a wide range of long-lasting symptoms after recovery from the acute infection. Long COVID refers to this specific condition and is associated with diverse symptoms, such as fatigue, myalgias, dyspnea, headache, cognitive impairment, neurodegenerative symptoms, anxiety, depression, and a sense of despair. The potential of hyperbaric oxygen therapy (HBOT) to improve chronic fatigue, cognitive impairments, and neurological disorders has been established; therefore, the use of HBOT to treat long COVID has also been studied. We conducted a literature search between 1 January 2019 and 30 October 2023, focusing on the clinical efficacy and utility of HBOT for treating long COVID and found ten clinical studies that fit the review topic, including one case report, five one-group pretest-posttest design studies, one safety report from a randomized controlled trial (RCT), and three complete reports of RCTs. Most studies found that HBOT can improve quality of life, fatigue, cognition, neuropsychiatric symptoms, and cardiopulmonary function. Although HBOT has shown some benefits for long COVID symptoms, further rigorous large-scale RCTs are required to establish precise indications, protocols, and post-treatment evaluations.

The westerly circulation and the monsoon circulation are the two major atmospheric circulation systems affecting the middle latitudes of the Northern Hemisphere (NH), which have significant impacts on climate and environmental changes in the middle latitudes. However, until now, people’s understanding of the long-term paleoenvironmental changes in the westerly- and monsoon-controlled areas in China’s middle latitudes is not uniform, and the phase relationship between the two at different time scales is also controversial, especially the exception to the “dry gets drier, wet gets wetter” paradigm in global warming between the two. Based on the existing literature data published, integrated paleoenvironmental records, and comprehensive simulation results in recent years, this study systematically reviews the climate and environmental changes in the two major circulation regions in the mid-latitudes of China since the Middle Pleistocene, with a focus on exploring the phase relationship between the two systems at different time scales and its influencing mechanism. Through the reanalysis and comparative analysis of the existing data, we conclude that the interaction and relationship between the two circulation systems are relatively strong and close during the warm periods, but relatively weak during the cold periods. From the perspective of orbital, suborbital, and millennium time scales, the phase relationship between the westerly and Asian summer monsoon (ASM) circulations shows roughly in-phase, out-of-phase, and anti-phase transitions, respectively. There are significant differences between the impacts of the westerly and ASM circulations on the middle-latitude regions of northwest China, the Qinghai–Tibet Plateau, and eastern China. However, under the combined influence of varied environmental factors such as BHLSR (boreal high-latitude solar radiation), SST (sea surface temperature), AMOC (north Atlantic meridional overturning circulation), NHI (Northern Hemisphere ice volume), NAO (North Atlantic Oscillation), ITCZ (intertropical convergence zone), WPSH (western Pacific subtropical high), TIOA (tropical Indian Ocean anomaly), ENSO (El Niño/Southern Oscillation), CGT/SRP (global teleconnection/Silk Road pattern), etc., there is a complex and close coupling relationship between the two, and it is necessary to comprehensively consider their “multi-factor’s joint-action” mechanism and impact, while, in general, the dynamic mechanisms driving the changes of the westerly and ASM circulations are not the same at different time scales, such as orbital, suborbital, centennial to millennium, and decadal to interannual, which also leads to the formation of different types of phase relationships between the two at different time scales. Future studies need to focus on the impact of this “multi-factor linkage mechanism” and “multi-phase relationship” in distinguishing the interaction between the westerly and ASM circulation systems in terms of orbital, suborbital, millennium, and sub-millennium time scales.

The characteristics and sources of the hydrochemical composition of natural water are mainly influenced and limited at the basin scale by what factors, this has become a focal issue of environmental change in the middle latitude areas and even globally. In this study, three large drainage systems (Junggar, Yili, and Erlqis) in the north of the Tianshan Mountains were selected to study the hydrochemistry of different river basins and understand the relevant causes and attributions of different water bodies in arid environments in the Central Asian Orogenic Belt (CAOB). Natural water samples from the three drainage systems and their hydrochemical data, combined with literature data of the north and south Tianshan Mountains and surrounding areas, were systematically collected and comprehensively compared with other higher, middle, and low latitude watersheds with different climate conditions. The results show that the total dissolved solid concentrations in the CAOB drainage systems are generally higher than those of rivers in Chinese monsoon and humid regions, and also higher than the world average level. The relative concentrations of different ions are similar to those of rivers in the monsoon region of eastern China and most rivers in the world dominated by carbonate weathering. The ionic compositions of surface water bodies from different sub-basins in the study area are distributed near the Ca apex in the piper diagram, while those of phreatic and confined groundwater samples tend to the Na apex. The compositional differences in the anions are not like those in the cations, rarely distinguishable for these different water types. In a sub-drainage basin scale from I to VIII sub-basins in the study area, major ion concentrations and distributions in these basins are evidently heterogeneous. Almost all the cation and anion concentrations span > 1 order of magnitude, especially sodium and chlorine; however, the calcium and alkalinity concentrations and distributions are relatively homogeneous for some basins in the Junggar and Yili drainage systems. The relative homogeneity between anions and the relative heterogeneity between cations can be recognized in the ion chemistry of natural water samples between different types, indicating that the composition and distribution of cations in water is more conducive than those of anions to identifying the differences and commonalities between different regions. Atmospheric precipitation plays a key role on the hydrogeochemical process in the north of the Tianshan Mountains, and the elevation differentiation of chemical weathering and ion concentrations are closely related to it. The positive correlation between total solute flux and runoff cannot be explained by the dilution effect, indicating that hydroclimatic factors such as runoff and aridity have a strong effect in limiting the hydrochemistry of natural water bodies in the study area.

The Jimbo-Miwa equation (JME) that describes certain interesting (3+1)-dimensional waves in plasma physics is studied in this work. The Hirota bilinear equation is developed via the Cole-Hopf transform. Then, the symbolic computation, together with the ansatz function schemes, are utilized to seek exact solutions. Some new solutions, such as the multi-wave complexiton solution (MWCS), multi-wave solution (MWS) and periodic lump solution (PLS), are successfully constructed. Additionally, different types of travelling wave solutions (TWS), including the dark, bright-dark and singular periodic wave solutions, are disclosed by employing the sub-equation method. Finally, the physical characteristics and interaction behaviors of the extracted solutions are depicted graphically by assigning appropriate parameters. The obtained outcomes in this paper are more general and newer. Additionally, they reveal that the used methods are concise, direct, and can be employed to study other partial differential equations (PDEs) in physics.

It is a difficult and hot issue in the hydrological studies of arid areas to choose suitable methods to evaluate the recharge of atmospheric precipitation to groundwater and its response to climate change in desert areas. This study reviews the theories and problems of vadose (unsaturated)-zone tracing methods selected by predecessors in hydrological studies and takes the deserts in middle latitudes of northern China as an example to extract decadal, centennial, and millennian information of atmospheric precipitation to groundwater recharge on a regional scale since the late Holocene. The fluctuations of atmospheric precipitation and chronological sequences of desert unsaturated zone were estimated by using the chlorine mass balance (CMB) theory. It indicates that the Badain Jaran Desert in the central Alashan Plateau and the surrounding Gobi deserts have experienced fluctuations of groundwater recharge on a centennial scale during the late Holocene period from about 700 to 2000 years ago. Multiple CMB profile records can identify four periods of relative wetness (1330–1430, 1500–1620, 1700–1780, and 1950–1990) and three periods of relative drought (1430–1500, 1620–1700, and 1900–1950) over the past millennium. These records are consistent with other paleoclimatic records in the northern margin of the Qinghai-Tibet Plateau, and relatively correspond to those in the eastern part of China. This indicates that groundwater recharge in the Alashan Plateau broadly reflects the degree of climatic variability in northwest China over the centennial scale and may be affected by the changes in the intensity of the East Asian summer monsoon. The estimated average recharge rate of precipitation in the Alashan Plateau in the last millennium is about 1.3~2.6 mm/a, which brings new geological evidence for understanding the source of groundwater recharge in the region but is quite different from other environmental records. It should be noted that there are uncertainties in the CMB records of the vadose zone profiles, mainly due to the assumption of atmospheric Cl input in the CMB estimation and the selection of the homogeneous vadose profile (piston flow). This study suggests that this uncertainty and its error should be extensively tested in the future by comparing deterministic data (such as regional reference stations) with large-scale random atmospheric Cl input backgrounds.

Background: Melasma is a challenging, acquired hyperpigmentary disorder. The gold standard treatment is Kligman's formulation, which contains hydroquinone, tretinoin, and dexamethasone, but its long-term use is limited by the risk of exogenous ochronosis. Cysteamine, a tyrosinase inhibitor, reduces melanocyte activity and melanin production, showing strong depigmenting effects in patients resistant to Kligman's formulation. Nonetheless, clinical studies have yielded inconsistent efficacy results. This meta-analysis aimed to assess the efficacy of cysteamine in treating melasma and to identify potential factors that may impact its therapeutic outcomes. Methods: A systematic search of PubMed, Embase, Web of Science, and CENTRAL, from the earliest record until August 2024, was conducted. Randomized controlled trials and quasi-randomized design studies related to topical cysteamine on melasma patients were included. The primary outcome was MASI or mMASI assessment after treatments. The current meta-analysis was conducted with a random-effects model. Subgroup analyses and meta-regressions were performed based on baseline MASI, disease duration of melasma, patient age, and sample size of the included studies. Funnel plots and Duval and Tweedie's trim and fill method were adopted to assess the publication bias. Results: Eight studies were included for quantitative analysis. The analysis of MASI after topical cysteamine demonstrated a significant decrease compared to the placebo ( p = 0.002). Compared to other melasma treatments, cysteamine did not show superior efficacy in mMASI ( p = 0.277). The treatment efficacy of hydroquinone, modified Kligman's formula, and tranexamic acid mesotherapy for melasma was not statistically different when compared to cysteamine ( p = 0.434). Further analyses showed no benefit when allowing extended cysteamine application time ( p < 0.0001). The meta-regression revealed the efficacy of cysteamine decreased as the duration of melasma increased (coefficient = 0.38, p = 0.0001, R 2 = 0.99). The funnel plot displayed some asymmetry. The trim and fill method suggested the adjusted effect size was 0.607 (95% CI = -0.720 to 1.935). Conclusions: Cysteamine exhibited efficacy in treating melasma patients; however, its depigmentation effect was comparable to hydroquinone-based regimens, tranexamic acid mesotherapy, and modified Kligman's formula. Using cysteamine in patients with a short duration of melasma may result in better efficacy.