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This study aims to investigate the influence of cadmium (Cd) speciation transformation on P-wave velocity under different soil moisture conditions, providing critical insights into the subsurface characteristics of contaminated soils. Taking Cd-contaminated soil as the research subject, P-wave velocity and the speciation distribution of Cd in soils with different moisture contents and Cd adsorption levels were measured. The results reveal that when the soil is contaminated by Cd, the porosity is altered and it eventually lead to change P-wave velocity. By increasing the moisture content of soils, the redox potential (Eh) rises and the pH decreases, which lead to the speciation transformation of Cd from carbonate-bound state (CAB), Fe-Mn oxide-bound state (FMO), and organic and sulfide-bound state (ORB) to the exchangeable state (EX). These transformations of Cd to EX result in the increase in soil porosity, which lead to the decrease in P-wave velocity. In addition, linear regression analysis was conducted the P-wave velocity (∆V) and the EX (∆EX) at various Cd adsorption levels. The analysis shows that there is a strong linear relationship between exchangeable Cd content and P-wave velocity, and the determination coefficient is about 0.9, which provides a reliable basis for monitoring soil Cd contamination by using P-wave velocity. This study provides valuable insights into the relationship between the speciation distribution of heavy metals in soil and the properties of acoustic wave. [ABSTRACT FROM AUTHOR]

This paper introduces a novel retaining wall structure that integrates a traditional mechanically stabilized earth (MSE) retaining wall with foamed lightweight soil (FLS) as the fill material. To evaluate the performance of the structure, a numerical approach based on the finite difference method was employed. Firstly, numerical models were developed based on a centrifuge test model designed by previous researchers, and the results were compared with the measured data. The close agreement between the experimental values and simulations demonstrates the reliability and validity of the proposed numerical models. Subsequently, a series of parametric studies were conducted to reveal the effect of key parameters on the performance of the newly proposed retaining wall. Furthermore, this paper proposes a modified harmonic search algorithm (MHSA), which is based on the original harmonic search algorithm (OHSA), to optimize the design of the proposed retaining wall structure. The results indicate that the proposed retaining wall structure can effectively reduce the differential settlement between the existing road and the newly constructed road at a relatively lower cost. The MHSA can serve as a practical design guidebook for engineers and potential users, enabling rapid and efficient design. [ABSTRACT FROM AUTHOR]

A plant species can have diverse hydraulic strategies to adapt to different environments. However, the water transport divergence of co-occurring species in contrasting habitats remains poorly studied but is important for understanding their ecophysiology adaptation to their environments. Here, we investigated whole-branch, stem and leaf water transport strategies and associated morphology traits of 11 co-occurring plant species in Yuanjiang valley-type savanna (YJ) with dry–hot habitats and Xishuangbanna tropical seasonal rainforest (XSBN) with wet–hot habits and tested the hypothesis that plants in YJ have a lower water transport efficiency than co-occurring species in XSBN. We found high variation in whole-branch, stem and leaf hydraulic conductance (Kshoot; Kstem and Kleaf) between YJ and XSBN, and that Kstem was significantly higher than Kleaf in these two sites (Kstem/Kleaf: 16.77 in YJ and 6.72 in XSBN). These plants in YJ with significantly lower Kshoot and Kleaf but higher sapwood density (WD) and leaf mass per area (LMA) showed a lower water transport efficiency regarding less water loss and the adaptation to the dry–hot habitat compared to co-occurring species in XSBN. In contrast, these co-occurring plants in XSBN with higher Kshoot and Kleaf but lower WD and LMA tended to maximize water transport efficiency and thus growth potential in the wet–hot habitat. Our findings suggest that these co-occurring species employ divergent hydraulic efficiency across YJ and XSBN so that they can benefit from the contrasting hydraulic strategies in adaptation to their respective habitats. [ABSTRACT FROM AUTHOR]

Fluorinated carbon cathode materials have extremely high theoretical specific energy among known cathode materials of lithium primary batteries. Nevertheless, current fluorinated carbon cannot meet the performance demands of future applications due to the rate performance. This work innovatively applies hollow carbon spheres with a porous structure as carbon sources to prepare fluorinated hollow porous carbon spheres (FHPCS) with high energy density and power density. The porous structure provides more reaction sites for the fluorination process and also shortens the diffusion path of lithium ions during the discharge. Additionally, the hollow porous structure offers more interfacial contact areas and reduces volumetric expansion during discharge reactions. The Li/CFx primary battery has a maximum specific energy of 2007 Wh kg−1 and a maximum power density of 30,400 W kg−1 and can have a capacity retention rate of 80.8% at a current density of 16 A g−1. In addition, FHPCS also has the highest specific energy of 1999 Wh kg−1 and 1711 Wh kg−1 in Na/CFx and K/CFx primary batteries, respectively. The diffusion efficiency of an alkali metal ion is analyzed by the different discharge depths with electrochemical impedance spectroscopy and galvanostatic intermittent titration technique. This effort introduces a new high-performance fluorinated carbon featuring a hollow porous structure and puts forward an innovative approach to designing fluorinated carbon materials. [ABSTRACT FROM AUTHOR]

Beam bridges, as the primary structural form of medium and small-sized bridges, are extensively utilized for road and railway crossings over rivers and valleys. Ensuring their reliability during earthquakes is crucial not only for maintaining traffic flow but also for mitigating the seismic impact on the economy and society. Considering earthquake intensity and uncertain parameters, this paper proposes an innovative method for assessing the seismic reliability of simply-supported beam bridges under three different levels of seismic design: minor, moderate, and major earthquakes. The proposed method first estimates the probability of encountering three typical earthquake intensities during the design life of simply-supported beam bridges based on crowd intensity, benchmark intensity, and major earthquake intensity. It then introduces uncertain parameters and employs the point estimation method to calculate the probability of bridge passage under specific earthquake intensities. Finally, it combines these earthquake intensities to calculate the overall seismic reliability of simply-supported beam bridges. The effectiveness and efficiency of this method are demonstrated through calculations for a three-span, double-degree-of-freedom simply-supported beam bridge, and validated using Monte Carlo simulations. This research provides solid theoretical support for seismic assessment, design, and intensity-based reliability analysis of simply-supported beam bridges. [ABSTRACT FROM AUTHOR]

Ecological engineering (EE) has a profound impact on land-use dynamics, leading to alterations in ecosystem services (ESs). However, an appropriate EE implementation intensity that can balance the tradeoffs associated with altered ESs well has always been a concern for researchers and policymakers. In this study, we set the transition probability of farmland, bare land, and desertification land to forest and natural shrub, with 2010–2020 as the natural implementation scenario, as 10% for the low-intensity implementation scenario (LIS), 30% for the medium-intensity scenario, and 50% for the high-intensity scenario. The patch-generating land-use simulation (PLUS) model was used to project land-use patterns and the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model was used to simulate changes in the quality of ESs under four EE implementation intensities in 2030. We then performed a quantitative tradeoff analysis on the dominant ESs under four scenarios and used the production possibility frontier (PPF) curve to identify the optimal EE implementation intensity scenario. Our results indicated that an increase in EE implementation intensity would lead to an increase in soil retention, water purification, habitat quality, and carbon storage, but also to a decrease in water yield, aggravating the tradeoffs between water yield and other ESs. In all EE implementation intensity scenarios, the LIS had the lowest tradeoff intensity index and balanced ESs well, and thus was the optimal EE implementation scenario in Qinghai province. Our results provide knowledge to help decision makers select the appropriate EE intensity to maintain sustainable development. The integrated methodology can also be applied in other conservation regions to carry out practical land management. [ABSTRACT FROM AUTHOR]

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios. [ABSTRACT FROM AUTHOR]

As one of the main external factors affecting the fire extinguishing accuracy of sprinkler systems, it is necessary to analyze and study random wind. However, in practical applications, there is little research on the impact of random wind on sprinkler fire extinguishing points. To address this issue, a new random wind acquisition system was constructed in this paper, and a method for predicting jet trajectory falling points in Random Forest (RF) under the influence of random wind was proposed, and compared with the commonly used prediction model Support Vector Machine (SVM). The method in this article reduces the error in the x direction of the 50 m prediction result from 2.11 m to 1.53 m, the error in the y direction from 0.64 m to 0.6 m, and the total mean absolute error (MAE) from 31.3 to 23.5. Simultaneously, predict the falling points of jet trajectory at different distances under the influence of random wind, to demonstrate the feasibility of the proposed method in practical applications. The experimental results show that the system and method proposed in this article can effectively improve the influence of random wind on the falling points of a jet trajectory. In summary, the image acquisition system and error prediction method proposed in this article have many potential applications in fire extinguishing. [ABSTRACT FROM AUTHOR]

Carbon quantum dots (CQDs) have already demonstrated their utility as lubricant additives, and non-contact temperature sensing based on CQDs offers considerable potential for condition monitoring in mechanical, electrical, and other fields, as well as lubrication-temperature multifunctional applications in lubricants. In this paper, we have successfully synthesized and designed high-brightness carbon quantum dots/polyvinyl alcohol (PVA) temperature sensor thin film and dispersions of CQDs in a liquid paraffin lubrication system. Based on fluorescence intensity and the fluorescence intensity ratio, the carbon quantum dot/PVA film exhibited exponential temperature-dependent properties with a wide applicability range, a high goodness of fit (R2 > 0.99), and high relative thermal sensitivity (relative sensitivities of 1.74% K−1 and 1.39% K−1 for fluorescence intensity and fluorescence intensity ratio, respectively). In addition, based on the fluorescence intensity, the CQDs exhibited a wide temperature range (20–90 °C), a high goodness of fit (R2 > 0.99), and higher sensitivity (2.84% K−1) in a liquid paraffin lubrication system, which reflects the temperature responsive properties of carbon quantum dots as additives in lubrication systems. These findings provide convenient and effective possibilities for the sensing and monitoring of carbon quantum dots and their multifunctional applications under lubrication systems. [ABSTRACT FROM AUTHOR]

Formation leak-off pressure, which sets the upper limit of the safe drilling fluid density window, is crucial for preventing wellbore accidents and ensuring safe and efficient drilling operations. The paper thoroughly examines models of drilling physics alongside artificial intelligence techniques. The study introduces a dual-driven method for predicting reservoir pore pressure by integrating long short-term memory (LSTM) and backpropagation (BP) neural networks, where the core component is the LSTM-BP neural network model. The input data for the LSTM-BP model include wellbore diameter, formation density, sonic time, natural gamma, mud content, and pore pressure. The study demonstrates the practical application of the method using two vertical wells in Block M, employing the M-1 well for training and the M-2 well for validation. Two distinct input layer configurations are devised for the LSTM-BP model to evaluate the influence of formation density on prediction accuracy. Notably, Scheme 2 omits formation density as a variable in contrast to Scheme 1. The study's results indicate that, for input layer configurations corresponding to Scenario 1 and Scenario 2, the LSTM-BP model exhibits relative error ranges of (−2.467%, 2.510%) and (−6.141%, 5.201%) on the test set, respectively. In Scenario 1, the model achieves mean squared error (MSE), mean absolute error (MAE), and R-squared (R2) values of 0.000229935, 0.011198329, and 0.92178272, respectively, on the test set. Conversely, for Scenario 2, the model demonstrates a substantial escalation of 992.393% and 240.674% in MSE and MAE, respectively, compared to Scenario 1; however, R2 diminishes by 66.920%. Utilizing the trained LSTM-BP model, predictions for formation lost pressure in Well M-2 reveal linear correlation coefficients of 0.8173 and 0.6451 corresponding to Scenario 1 and Scenario 2, respectively. These findings imply that the predictions from the Scenario 1 model demonstrate stronger alignment with results derived from formulaic calculations. These observations remain consistent for both the BP neural network algorithm and the random forest algorithm. The aforementioned research results not only highlight the elevated predictive precision of the LSTM-BP model for intelligent prediction of formation lost pressure, a product of this study, thereby furnishing valuable data points to enhance the security of drilling operations in Block M, but also underscore the necessity of deliberating both physical relevance and data correlation during the selection of input layer variables. [ABSTRACT FROM AUTHOR]

The special spatial structure and the long-term harsh working environment make the transient response of wire rope complicated, so accurate assessment and prediction of its mechanical characteristics are of great significance to ensure the safe and stable operation of related equipment. In this paper, a method is proposed for the analysis of the mechanical characteristics of a 6 × K31WS + FC wire rope with or without wire breakage. Firstly, an accurate parametric geometric model of the wire rope is established based on the Frenet frame method, the material properties of the wire rope are acquired by experiments, and the finite element model of the wire rope is built. Then, a mechanical model of the wire rope is proposed to verify the validity of the finite element model. Finally, the influences of the number, distribution, and location of the broken wires on the mechanical characteristics of the wire rope are thoroughly analyzed. This work proposes a comprehensive framework that can quantitatively analyze the mechanical characteristics of the complex wire rope with or without wire breakage, which provides a practical method for its reliability and maintenance evaluation and has guiding significance for the shape design and structural optimization of the wire rope. [ABSTRACT FROM AUTHOR]

The performances of construction supervisors are essential for the monitoring, control, and coordination of the construction process of a project in order to adhere to a predefined schedule, cost, quality and other factors. However, it is challenging to evaluate their performance due to limitations such as data deficiency, human error, etc. Thus, this paper proposes an approach to data-driven quantitative performance evaluation of construction supervisors by integrating an analytic hierarchy process (AHP) and activity tracking. The proposed approach contains three parts, namely, index extraction, weighting, data-driven index calculation, and then validation by case study. Firstly, performance indexes were developed based on a literature review as well as surveys and function analysis of the information system for construction supervision (CSI system). Then, the weights of and relationships among of the indexes are determined by AHP. After that, with daily workflow and inspection activities tracked in the CSI system, a method and a software module for automatic calculation of indexes were developed. Lastly, the proposed approach was validated by a real-world case. The result showed that the proposed approach can quantify the performance of a construction supervisor systematically and automatically, which shed lights on how to evaluate the performance of a worker based on the tracking of daily activities. The data-driven process enhanced our strong interpretation of member actions and evaluation indexes, and can boost the performance of every member in an organization. [ABSTRACT FROM AUTHOR]

Epithelial barrier dysfunction is a hallmark of gastroesophageal reflux disease (GERD) related to symptom origination, inflammatory remodeling and carcinogenesis. Alginate-based antireflux medications were previously shown to topically protect against peptic barrier disruption, yet the molecular mechanisms of injury and protection were unclear. Herein, Barrett's esophageal (BAR-T) cells were pretreated with buffered saline (HBSS; control), dilute alginate medications (Gaviscon Advance or Gaviscon Double Action, Reckitt Benckiser), a viscosity-matched placebo, or ADAM10 and matrix metalloproteinase (MMP) inhibitors before exposure to HBSS pH7.4 or pH4 ± 1 mg/mL pepsin for 10–60 min. Cell viability was assessed by ATP assay; mediators of epithelial integrity, E-cadherin, ADAM10, and MMPs were examined by Western blot and qPCR. Alginate rescued peptic reduction of cell viability (p < 0.0001). Pepsin-pH4 yielded E-cadherin fragments indicative of regulated intramembrane proteolysis (RIP) which was not rescued by inhibitors of known E-cadherin sheddases. Transcriptional targets of E-cadherin RIP fragments were elevated at 24 h (MMP-1,2,9,14; p < 0.01). Alginate rescued E-cadherin cleavage, ADAM10 maturation, and MMP induction (p < 0.01). Results support RIP as a novel mechanism of peptic injury during GERD. Alginate residue after wash-out to mimic physiologic esophageal clearance conferred lasting protection against pepsin-induced molecular mechanisms that may exacerbate GERD severity and promote carcinogenesis in the context of weakly acidic reflux. [ABSTRACT FROM AUTHOR]

Residential electricity consumption forecasting plays a crucial role in the rational allocation of resources reducing energy waste and enhancing the grid-connected operation of power systems. Probabilistic forecasting can provide more comprehensive information for the decision-making and dispatching process by quantifying the uncertainty of electricity load. In this study, we propose a method based on stationary wavelet transform (SWT), quantile regression (QR), Bidirectional nested long short-term memory (BiNLSTM), and Depthwise separable convolution (DSC) combined with attention mechanism for electricity consumption probability prediction methods. First, the data sequence is decomposed using SWT to reduce the complexity of the sequence; then, the combined neural network model with attention is used to obtain the prediction values under different quantile conditions. Finally, the probability density curve of electricity consumption is obtained by combining kernel density estimation (KDE). The model was tested using historical demand-side data from five UK households to achieve energy consumption predictions 5 min in advance. It is demonstrated that the model can achieve both reliable probabilistic prediction and accurate deterministic prediction. [ABSTRACT FROM AUTHOR]

Gastroesophageal reflux disease (GERD) significantly impacts patient quality of life and is a major risk factor for the development of Barrett's esophagus (BE) and esophageal adenocarcinoma (EAC). Proton pump inhibitors (PPIs) are the standard-of-care for GERD and are among the most prescribed drugs in the world, but do not protect against nonacid components of reflux such as pepsin, or prevent reflux-associated carcinogenesis. We recently identified an HIV protease inhibitor amprenavir that inhibits pepsin and demonstrated the antireflux therapeutic potential of its prodrug fosamprenavir in a mouse model of laryngopharyngeal reflux. In this study, we assessed the capacity of amprenavir to protect against esophageal epithelial barrier disruption in vitro and related molecular events, E-cadherin cleavage, and matrix metalloproteinase induction, which are associated with GERD severity and esophageal cancer. Herein, weakly acidified pepsin (though not acid alone) caused cell dissociation accompanied by regulated intramembrane proteolysis of E-cadherin. Soluble E-cadherin responsive matrix metalloproteinases (MMPs) were transcriptionally upregulated 24 h post-treatment. Amprenavir, at serum concentrations achievable given the manufacturer-recommended dose of fosamprenavir, protected against pepsin-induced cell dissociation, E-cadherin cleavage, and MMP induction. These results support a potential therapeutic role for amprenavir in GERD recalcitrant to PPI therapy and for preventing GERD-associated neoplastic changes. [ABSTRACT FROM AUTHOR]

The complex sliding behavior inside ball bearings seriously affects the mechanical system's performance. Current dynamic models for predicting this behavior suffer from poor generality and convergence. To address this issue, different interactions between the ball and raceway are proposed in this paper to simulate the dynamic behavior by analyzing the bearing assemblies' motion features under typical operating conditions. The number of variables and equations to be solved is determined adaptively according to the bearing load characteristics, thus improving the efficiency and convergence of the model solution. The good agreement between simulation results and experimental test results validates the reliability of the model. The sliding behavior at the ball/raceway interface under different conditions is further investigated. The results show that the heavy external loads can avoid severe sliding at the interface but shorten the bearing's fatigue. When the bearing is subjected to combined load conditions, the increased radial force inhibits bearing sliding while increasing the non-uniformity of the sliding velocity distribution. [ABSTRACT FROM AUTHOR]

With the rapid development of solar energy plants in recent years, the accurate prediction of solar power generation has become an important and challenging problem in modern intelligent grid systems. To improve the forecasting accuracy of solar energy generation, an effective and robust decomposition-integration method for two-channel solar irradiance forecasting is proposed in this study, which uses complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a Wasserstein generative adversarial network (WGAN), and a long short-term memory network (LSTM). The proposed method consists of three essential stages. First, the solar output signal is divided into several relatively simple subsequences using the CEEMDAN method, which has noticeable frequency differences. Second, high and low-frequency subsequences are predicted using the WGAN and LSTM models, respectively. Last, the predicted values of each component are integrated to obtain the final prediction results. The developed model uses data decomposition technology, together with advanced machine learning (ML) and deep learning (DL) models to identify the appropriate dependencies and network topology. The experiments show that compared with many traditional prediction methods and decomposition-integration models, the developed model can produce accurate solar output prediction results under different evaluation criteria. Compared to the suboptimal model, the MAEs, MAPEs, and RMSEs of the four seasons decreased by 3.51%, 6.11%, and 2.25%, respectively. [ABSTRACT FROM AUTHOR]

Anomalous road manhole covers pose a potential risk to road safety in cities. In the development of smart cities, computer vision techniques use deep learning to automatically detect anomalous manhole covers to avoid these risks. One important problem is that a large amount of data are required to train a road anomaly manhole cover detection model. The number of anomalous manhole covers is usually small, which makes it a challenge to create training datasets quickly. To expand the dataset and improve the generalization of the model, researchers usually copy and paste samples from the original data to other data in order to achieve data augmentation. In this paper, we propose a new data augmentation method, which uses data that do not exist in the original dataset as samples to automatically select the pasting position of manhole cover samples and predict the transformation parameters via visual prior experience and perspective transformations, making it more accurately capture the actual shape of manhole covers on a road. Without using other data enhancement processes, our method raises the mean average precision (mAP) by at least 6.8 compared with the baseline model. [ABSTRACT FROM AUTHOR]

To study the variation rules of nonlinear stiffness of double-row angular contact ball bearings (DR-ACBB), this paper proposed a general mathematic model for DR-ACBB under three different configurations based on the improved quasi-static model of ball bearings, an explicit expression stiffness matrix of DR-ACBB is analytically derived, and a double-layer nested iterative algorithm based on the Newton–Raphson method is designed to realize the efficient solution of the proposed model. Then, the effects of the preload, speeds, and loads on the nonlinear stiffness variations of DR-ACBB under different arrangements are comparatively analyzed. The results show that DR-ACBB under the DB and DF configurations have the same variation rule in axial and radial stiffness; that is, a nonlinear soft-spring stiffness characteristic (i.e., the stiffness decreases with the external load) within the low-speed range and light load condition, and a nonlinear hard spring stiffness characteristic (i.e., the stiffness increases with the external load) within the high-speed range or heavy load condition. [ABSTRACT FROM AUTHOR]

The traditional method of relying on human hearing to detect foreign object debris (FOD) events during rocket tank assembly processes has the limitation of strong reliance on humans and difficulty in establishing objective detection records. This can lead to undetected FOD entering the engine with the fuel and causing major launch accidents. In this study, we developed an automatic, intelligent FOD detection system for rocket tanks based on sound signals to overcome the drawbacks of manual detection, enabling us to take action to prevent accidents in advance. First, we used log-Mel transformation to reduce the high sampling rate of the sound signal. Furthermore, we proposed a multiscale convolution and temporal convolutional network (MS-CTCN) to overcome the challenges of multi-scale temporal feature extraction to detect suspicious FOD events. Finally, we used the proposed post-processing strategies of label smoothing and threshold discrimination to refine the results of FOD event detection and ultimately determine the presence of FOD. The proposed method was validated through FOD experiments. The results showed that the method had an accuracy rate of 99.16% in detecting FOD and had a better potential to prevent accidents compared to the baseline method. [ABSTRACT FROM AUTHOR]

Introduction: Providing quality healthcare for children includes assessing and responding to needs of their family caregivers. Three salient domains to consider are caregivers' early adverse childhood experiences (ACEs), their current levels of distress, and their resilience in coping with both prior and current stressors. Objective: Determine acceptability of assessing caregiver ACEs, current distress, and resilience in pediatric subspecialty care settings. Methods: Caregivers of patients in two pediatric specialty care clinics completed questionnaires about their ACEs, recent emotional distress, and resilience. Importantly, caregivers also rated the acceptability of being asked these questions. Participants included 100 caregivers of youth ages 3–17 across Sickle Cell Disease and Pain clinic settings. The majority of participants were mothers (91.0%) who identified as non-Hispanic (86.0%). Caregiver race was primarily African American/Black (53.0%) and White (41.0%). The Area Deprivation Index (ADI) was used to assess socioeconomic disadvantage. Results: High levels of caregiver acceptability or neutrality with assessing ACEs and distress, as well as high ACEs, distress, and resilience. Associations were found between caregiver ratings of acceptability with caregiver resilience and socioeconomic disadvantage. Caregivers reported openness to being asked about their experiences during childhood and recent emotional distress, although ratings of acceptability varied according to other contextual variables, such as level of socioeconomic disadvantage and caregiver resilience. In general, caregivers perceived themselves to be resilient in the face of adversity. Conclusions: Assessing caregiver ACEs and distress in a trauma-informed way may provide opportunities for better understanding the needs of caregivers and families in order to support them more effectively in the pediatric setting. [ABSTRACT FROM AUTHOR]

Apigenin is a natural flavonoid with significant biological activity, but poor solubility in water and low bioavailability limits its use in the food and pharmaceutical industries. In this paper, apigenin-7-O-β-(6″-O)-d-glucoside (AG) and apigenin-7-O-β-(6″-O-succinyl)-d-glucoside (SAG), rare apigenin glycosyl and succinyl derivatives formed by the organic solvent-tolerant bacteria Bacillus licheniformis WNJ02 were used in a 10.0% DMSO (v/v) system. The water solubility of SAG was 174 times that of apigenin, which solved the application problem. In the biotransformation reaction, the conversion rate of apigenin (1.0 g/L) was 100% at 24 h, and the yield of SAG was 94.2%. Molecular docking showed that the hypoglycemic activity of apigenin, apigenin-7-glucosides (AG), and SAG was mediated by binding with amino acids of α-glucosidase. The molecular docking results were verified by an in vitro anti-α-glucosidase assay and glucose consumption assay of active compounds. SAG had significant anti-α-glucosidase activity, with an IC50 of 0.485 mM and enhanced glucose consumption in HepG2 cells, which make it an excellent α-glucosidase inhibitor. [ABSTRACT FROM AUTHOR]

The accurate prediction of photovoltaic (PV) power is essential for planning power systems and constructing intelligent grids. However, this has become difficult due to the intermittency and instability of PV power data. This paper introduces a deep learning framework based on 7.5 min-ahead and 15 min-ahead approaches to predict short-term PV power. Specifically, we propose a hybrid model based on singular spectrum analysis (SSA) and bidirectional long short-term memory (BiLSTM) networks with the Bayesian optimization (BO) algorithm. To begin, the SSA decomposes the PV power series into several sub-signals. Then, the BO algorithm automatically adjusts hyperparameters for the deep neural network architecture. Following that, parallel BiLSTM networks predict the value of each component. Finally, the prediction of the sub-signals is summed to generate the final prediction results. The performance of the proposed model is investigated using two datasets collected from real-world rooftop stations in eastern China. The 7.5 min-ahead predictions generated by the proposed model can reduce up to 380.51% error, and the 15 min-ahead predictions decrease by up to 296.01% error. The experimental results demonstrate the superiority of the proposed model in comparison to other forecasting methods. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) online monitoring is a common technique for high-voltage equipment diagnosis. However, due to field interference, the monitored PD signal contains a lot of noise. Therefore, this paper proposes a novel method by integrating the flower pollination algorithm, variational mode decomposition, and Savitzky–Golay filter (FPA-VMD-SG) to effectively suppress white noise and narrowband noise in the PD signal. Firstly, based on the mean envelope entropy (MEE), the decomposition number and quadratic penalty term of the VMD were optimized by the FPA. The PD signal containing noise was broken down into intrinsic mode functions (IMFs) by optimized parameters. Secondly, the IMFs were classified as the signal component, the noise dominant component, and the noise component according to the kurtosis value. Thirdly, the noise dominant component was denoised using the SG filter, and the denoised signal was mixed with the signal component to reconstruct a new signal. Finally, threshold denoising was used to eliminate residual white noise. To verify the performance of the FPA-VMD-SG method, compared with empirical mode decomposition with wavelet transform (EMD-WT) and adaptive singular value decomposition (ASVD), the denoising results of simulated and real PD signals indicated that the FPA-VMD-SG method had excellent performance. [ABSTRACT FROM AUTHOR]

Smart health systems typically integrate sensor technology with the Internet of Things, enabling healthcare systems to monitor patients. These biomedical applications collect healthcare data through remote sensors and transfer the data to a centralized system for analysis. However, the communication between the edge node and the mobile user is susceptible to impersonation attacks in mobile edge computing (MEC) for the biomedical application. For this purpose, we propose a detection mechanism for medical and healthcare services, i.e., reinforcement learning for impersonation attacks. We construct a system model of MEC, a key generation model (KGM), and an impersonation attack model (IAM). In addition, we also design an impersonation attack detection algorithm based on the SARSA technique under the IAM. In our proposed work, the SARSA-based method outplays the detection of impersonation attacks in the dynamic environment compared to the traditional Q-learning technique. Finally, we evaluate the false alarm rate (FAR), miss detection rate (MDR), and average error rate (AER) in the hypothesis tests to compare the performance of our proposed method with the traditional Q-learning. In comparison to the classic Q-learning based technique, simulation experiments show that the suggested approach can avoid impersonation attacks in a dynamic environment for medical and healthcare services. The results also indicate that the SARSA technique has a high detection accuracy and low average error rate compared to the conventional Q-learning based approach. [ABSTRACT FROM AUTHOR]

To increase the low utilization rate of spodumene ore during lithium extraction, spodumene ore was subjected to carbothermic reduction to enrich lithium and prepare a manganese-silicon alloy. The experimental results showed that during thermal reduction, lithium was volatilized and collected in the condensation zone. The Li2O content in the lithium condensate was 41.72%, which was 10.85 times higher than that of the raw material. The effects of varying reduction temperatures and times on the lithium volatilization rate and direct yield of Mn5Si3 alloy were investigated. The best process conditions were 1873 K for 6 h. Under these conditions, the lithium volatilization rate was 97.65%, and the direct yield of Mn5Si3 was 86.47%. [ABSTRACT FROM AUTHOR]

The upper Yellow River Basin (UYRB) is an important water source and conservation area. As well as a warming climate, the region has experienced long-term human interventions, such as grazing, farming and reservoir construction, since the mid-1980s. The runoff dynamics and its drivers in the UYRB remain poorly constrained, especially the differences between pastoral and agricultural areas, due to a lack of detailed measurements. Here, the contributions of climate change, land-use change and anthropogenic water consumption to runoff changes observed at hydrological stations were evaluated by combining the observations with scenarios simulated by a process-based hydrological model. Changes in the pastoral and agricultural areas of the UYRB were then compared at 10-year intervals during the period 1986–2020. The results indicated lower runoff in the UYRB over the past 35 years, when compared with that in the baseline period of 1965–1985, due to the negative influences of both climate change and anthropogenic water consumption. Meanwhile, land-use change had only a weak influence, due to stable land-use patterns. However, spatial differences in dominant drivers of runoff reductions were evident between the pastoral area and agricultural area. Specifically, runoff decreases were caused by the negative influences of climate change in the pastoral area and anthropogenic water consumption in the agricultural area. A shift in the dominant drivers after the period 2006–2015 was attributed to the positive influence of climate change and a weakening of agricultural water consumption and reservoir inflow during the same period. These changes initially caused a decrease in runoff, and later an increase. These findings contribute to a crucial evidence base for optimizing water resource management, ecological protection and high-quality development in the Yellow River Basin. [ABSTRACT FROM AUTHOR]

Based on the mechanical model, the friction loss between the ball and the raceway along the major axis of the contact ellipse is analyzed. The result shows that this part of the loss accounts for about 13.67% of the overall loss, which is mainly determined by the ball sliding length and cannot be ignored. The effects of the radial force, torque, rotational speed and groove curvature ratio on the sliding are all studied. Compared with other factors, radial force has the greatest influence on the sliding loss. As bearing speed gradually grows, the sliding on the inner raceway gradually increases while it gradually decreases on the outer raceway. Compared to the outer raceway curvature ratio, the sliding length is less sensitive to changes in the curvature ratio of the inner raceway. The paper provides theoretical guidance for the design and application of low-friction bearings. [ABSTRACT FROM AUTHOR]

RTA (Rate Transient Analysis) is a valuable method for obtaining reservoir parameters and well performance, but current RTA models hardly consider the MLVF (Multi-Layer Vertical Fractured) well in a layered tight gas reservoir. To capture the production response caused by the fracture with non-uniform length and conductivity, a novel RTA model for an MLVF well in a layered tight reservoir was presented. In this paper, we present a novel tight gas reservoir RTA model, an extended MLVF well with non-uniform fracture length and conductivity to investigate the production decline feature by the combined RTA type curves. After that, the proposed RTA model is verified to ensure calculation accuracy. Sensitivity analysis is conducted on the crucial parameters, including the formation transmissibility, formation storability, fracture length, fracture conductivity, and fracture extension. Research results show that there are three rate decline stages caused by a multi fracture with non-uniform conductivity. The wellbore storage and formation skin can be ignored in the rate transient analysis work. The formation transmissibility affects the rate transient response more than the formation storability. The increase in fracture length, fracture conductivity, and the extension of a high conductivity fracture will improve the well's production rate in a tight gas reservoir's early production stage. Therefore, it is significant to incorporate how the effects of the MLVF well intercepting with non-uniform length fractures change conductivity. The RTA model proposed in this paper enables us to better evaluate well performance and capture the formation of complex fracture characteristics in a layered tight gas reservoir based on rate transient data. [ABSTRACT FROM AUTHOR]

High-precision, low-cost, and wide coverage indoor positioning technology is the key to indoor and outdoor integrated location-based services, and it has broad market prospects and social value. However, achieving sub-meter level positioning accuracy in indoor environments remains a real challenge due to the blockage of indoor Global Navigation Satellite System (GNSS) signals, the complexity of indoor environments, and the unpredictability of user behavior. In this paper, we introduce a multi-module BLE broadcaster (MMBB)-based indoor positioning solution in which a tightly coupled fusion architecture is implemented on a smartphone. The solution integrates ranging measurements from multiple MMBB and the measurements of the accelerometer built into a smartphone. It becomes an instant positioning solution without any training phase by adopting a calibrated linearly segmented path loss model for ranging. We apply the pedestrian walking speed derived by the smartphone accelerometer to constrain an unscented Kalman filter method that is used to estimate the location and speed. The accuracy of the proposed method is 50% at 0.79 m and 95% at 1.6 m at in terms of horizontal error distance. Position update frequency is 10 Hz and the time to first fix is 0.1 s. The system can easily adapt a global coordinator system so that it can seamlessly work together with the GNSS to form an indoor/outdoor positioning solution. [ABSTRACT FROM AUTHOR]

The fault quantification of a tapered roller bearing (TRB) can provide a reliable guide for predictive maintenance. Currently, damage size estimation based on vibration signals has been proposed and developed. However, most approaches are focused on the theory of ball bearings. Unlike the point contact of a ball bearing, the contact between the tapered roller and the raceway is a line contact. So, the current practices based on the micro-motion theory of ball bearings are limited when estimating the TRB's fault. To accurately estimate the TRB's damage size, a dynamic model of non-penetrating damaged TRB was established to research the vibration response mechanism and explain the influence. The model takes the deflection factor of a tapered roller into consideration and uses the elastohydrodynamic lubrication model to simulate the influence of the lubrication factor. Then, a revised formula for estimating the TRBs' fault size is proposed by uncovering the relationship between the impact time and the damage location. Simulation analysis and experimental analysis prove the correctness of the dynamic model and the effectiveness of the size estimation formula. [ABSTRACT FROM AUTHOR]

Retailers advertise on different sales channels. When consumers buy online, the effectiveness of advertising is discounted because they cannot feel the product. Observing this phenomenon, this paper studies the advertising strategy of a dual-channel retailer. In this paper, we build stylized game models for the retailer's price and advertising levels on online and offline channels, respectively. Our contribution is to provide prescriptions for how dual-channel retailers make price and advertise decisions and determine which channel is more profitable for retailers. We find that advertising discounts are not always harming the retailer's profits. The level of advertising discounts causes retailers to increase the selling price when consumers engage in online shopping. Also, we derive that retailers can choose the sales channels based on the level of consumers' channel preference of consumers. Interestingly, an increase in the level of advertising discounts will contribute to the growth of the retailer's profit. Finally, by numerical analysis, we demonstrate the robustness of the results. [ABSTRACT FROM AUTHOR]

The increasing number of residential buildings that are installing distributed energy resources enforces the need for schemes to facilitate a local energy balance. With the continuing evolution of Internet of Things (IoT) technology, Peer-to-Peer (P2P) energy trading is becoming a viable solution to incentivize prosumers and promote efficient energy sharing in a community. This paper develops a model to quantitatively analyze the potential benefits of P2P energy trading for residential buildings that have installed photovoltaic battery systems. The integration of the bidding strategy into a residential energy-management system is feasible to realize cost savings for prosumers. However, the coordination between the bidding strategy and the optimal scheduling of energy has received far too little attention. To better participate in the P2P market, we propose a novel separate bidding energy-management system (SBEMS) that can realize rolling optimal energy scheduling while determining energy bids. The model's effectiveness is verified via case studies of 75 participants in a community. The results indicate that the prosumers can reduce their costs by up to 24% by employing the proposed SBEMS in the P2P market. In addition, the proposed method is found to offer better performance in terms of economic and technical indices. [ABSTRACT FROM AUTHOR]

The spindle and grinding wheel flange (GWF) adopt double positioning of a tapered surface and end face. Due to the processing quality, the contact state of the spindle–GWF interface cannot be determined accurately. Based on the theory of finite element and the Yoshimura integral method, an analysis method for the contact stiffness of the spindle–GWF interface was established. In addition, the effects of the spindle–GWF interface's original clearance and tightening torque on the contact pressure, final contact state and contact stiffness of the spindle–GWF interface were investigated and experimentally verified. It was found that the final contact state of the spindle–GWF changed when the tightening torque increased, especially when the original contact state was tapered contact, and the final contact state changed significantly after assembly. The contact pressure and contact stiffness of the spindle–GWF interface are increased by increasing the tightening torque. The radial stiffness is more affected by the end clearance variation compared to the axial and angular stiffness. When the original contact state is tapered contact, the radial stiffness of the interface is at maximum in three contact states. This research provides theoretical guidance for GWF assembly on gear-grinding machines. [ABSTRACT FROM AUTHOR]

Following the application of Deep Learning to graphic data, Graph Neural Networks (GNNs) have become the dominant method for Node Classification on graphs in recent years. To assign nodes with preset labels, most GNNs inherit the end-to-end way of Deep Learning in which node features are input to models while labels of pre-classified nodes are used for supervised learning. However, while these methods can make full use of node features and their associations, they treat labels separately and ignore the structural information of those labels. To utilize information on label structures, this paper proposes a method called 3ference that infers from references with differences. Specifically, 3ference predicts what label a node has according to the features of that node in concatenation with both features and labels of its relevant nodes. With the additional information on labels of relevant nodes, 3ference captures the transition pattern of labels between nodes, as subsequent analysis and visualization revealed. Experiments on a synthetic graph and seven real-world graphs proved that this knowledge about label associations helps 3ference to predict accurately with fewer parameters, fewer pre-classified nodes, and varying label patterns compared with GNNs. [ABSTRACT FROM AUTHOR]

Cage stability directly affects the dynamic performance of rolling bearing, which, in turn, affects the operating state of rotating equipment. The random collision between the rolling elements and the cage pocket is the main reason for cage instability. In this paper, from the perspective of the relative sliding velocity between the rolling elements and the bearing raceway, the interactions of the rolling elements and the cage pockets were analyzed, and the four zones with different collision features were defined. On this basis, and on the basis of the bearing dynamics model, the interaction of two adjacent rolling elements and the cage pockets in the a'–b' area is discussed, and the peak impact force of the adjacent two balls and the cage pockets was investigated in terms of the rotation speed, radial load, acceleration/deceleration, and materials. When the ball runs close to the loaded zone, the probability of multiball random collision increases, which leads to an increase in the cage instability. At the entrance of the loaded zone, the peak impact force has the greatest impact on the cage stability during the acceleration process. Compared to the radial load applied to the bearing, the peak impact force is more sensitive to the bearing speed changes. The multiball collision analysis method provides a new idea for the research of cage stability. [ABSTRACT FROM AUTHOR]

Coaxial electrospinning (co-electrospinning) technique has greatly expanded the universality of fabricating core-shell polymer nanofibers. However, the effect of solution miscibility on the morphology of co-electrospun products remains unclear. Herein, different cellulose acetate (CA) solutions with high solution miscibility but distinctly different electrospinnability were used to survey the effect of solution miscibility on the co-electrospinning process. The structural characterizations show that co-electrospun products are composed of nanofibers with and without the core-shell structure. This indicates that partial solution mixing occurred during the co-electrospinning process instead of absolute no-mixing or complete mixing. Importantly, the solution miscibility also shows a significant influence on the product morphology. In particular, the transformation from nanofibers to microparticles was realized with the increase of core-to-shell flow ratio during the co-electrospinning of core electrosprayable CA/dimethylacetamide (DMAc) solution and shell electrospinnable CA/acetone-DMAc (2/1, v/v) solution. Results show that the solution miscibility exerts a significant effect on not only the formation of core-shell structure but also the product morphology. This work provides a new insight for the in-depth understanding of the co-electrospinning process. [ABSTRACT FROM AUTHOR]

The porous oil-containing cage achieves the storage, spillage, and suction of lubricating oil by its micro-pore structure, thus ensuring the self-lubricating performance of the bearing. Carrying out fast and accurate modeling of the cage microscopic pore structure is the key to the analysis of the self-lubricating mechanism of bearings. In response to the issues where current modeling methods of porous materials have a low similarity of pore distribution, morphology, structure, and size characteristics, and the transition of pore surfaces is sharp, this paper proposed a modeling method of a highly similar micro-pore structure based on the idea of median filtering, the quartet structure generation set (QSGS), and the slice method. By extracting and analyzing the pore characteristics of the porous model and comparing them with the experimental results of CT scanning, the advantages of the modeling method in terms of morphology and pore connectivity were verified. Finally, by carrying out simulation analysis of the centrifugal force of oil splashing and capillary oil absorption on the constructed model by combining the parameters of porous structures such as porosity and tortuosity, the advantages of the modeling method in the construction of the porous model and multi-physical field analysis were further verified. [ABSTRACT FROM AUTHOR]

The self-lubricating effect of the porous oil-containing cage is realized by storing and releasing lubricants through its internal micro-scale pore structure. The internal flow and heat transfer process in the micron-submicron pore structure is crucial to the self-lubricating mechanism of the porous oil-containing cage. To this end, a new modeling method of porous cage was proposed based on random seeds theory, and the local two-dimensional models of porous cage with different micro-scale pore structure were established. The multiphysics coupling simulation analysis of lubricating oil inside the porous cage with the effect of centrifugal force and thermal expansion was carried out based on the COMSOL Multiphysics platform. In order to characterize the micro-scale pore structure, new structural parameter indicators, such as relative surface perimeter, effective porosity, tortuosity and fluid properties related to the internal flow process, were all extracted from the above models. Combing with the Hagen–Poiseuille equation, a flow resistance model of oil flow inside the porous oil-containing cage was obtained. Finally, comparison of simulation results and analytical solutions of the micro-scale resistance model was carried out to verify the correctness of the micro-scale resistance model. The work provides a new direction for the study of the lubrication mechanism of the porous oil-containing cage. [ABSTRACT FROM AUTHOR]

Aiming at the problems of a complex structure or poor controllability of the existing bearing preload control devices, a method of self-regulation via a negative Poisson's ratio (NPR) spacer is proposed. Firstly, the principle of preload automatic adjustment at the bearing operation was introduced and the NPRs with three types of cell structures were analyzed. Furthermore, a thermo-mechanical coupling analysis model of the NPR spacer was established and the deformation and force output characteristics of the NPR spacer were studied and experimentally verified. It is found that the concave hexagonal cell structure has the optimal deformation characteristics for bearing preload adjustment. When the temperature is considered, the absolute value of Poisson's ratio of the NPR spacer decreases as the speed increases and the elongation of the NPR spacer and the output forces are much larger than those without temperature consideration. With the increase in temperature or rotating speed, the axial elongation and output forces of the NPR spacer increases while the effect of temperature is relatively larger. [ABSTRACT FROM AUTHOR]

In the era of big data, multi-task learning has become one of the crucial technologies for sentiment analysis and classification. Most of the existing multi-task learning models for sentiment analysis are developed based on the soft-sharing mechanism that has less interference between different tasks than the hard-sharing mechanism. However, there are also fewer essential features that the model can extract with the soft-sharing method, resulting in unsatisfactory classification performance. In this paper, we propose a multi-task learning framework based on a hard-sharing mechanism for sentiment analysis in various fields. The hard-sharing mechanism is achieved by a shared layer to build the interrelationship among multiple tasks. Then, we design a task recognition mechanism to reduce the interference of the hard-shared feature space and also to enhance the correlation between multiple tasks. Experiments on two real-world sentiment classification datasets show that our approach achieves the best results and improves the classification accuracy over the existing methods significantly. The task recognition training process enables a unique representation of the features of different tasks in the shared feature space, providing a new solution reducing interference in the shared feature space for sentiment analysis. [ABSTRACT FROM AUTHOR]

Green supply chain management has received increasing attention as consumers have become more environmentally conscious. Manufacturers are making green investments to meet consumers' demands, while retailers in different markets often engage in cooperative promotion to attract more consumers. This study develops game theoretic models for investigating cooperative promotion for two cross-market firms with different channel structures, i.e., decentralized and centralized. The manufacturer determines the wholesale price for the retailers and the green investment of a product, and the retailers determine the promotional effort and retail price. This study finds that whether the firms join in cooperative promotion mainly depends on the wholesale price, as well as the impacts of the price, green investment, and cooperative promotional activities on the demand. When the wholesale price is relatively low, the retail price of the decentralized green supply chain must be lower than that of the centralized green supply chain. On the contrary, the difference in the retail price between the two green supply chains varies with the impacts of green investment and cooperative promotional activities on demand. In addition, due to the influence of channel structure, the contribution to cooperative promotion of the centralized supply chain is more than that of the decentralized supply chain with the most given conditions. Moreover, as the impact of cooperative promotional activities on demand increases, the centralized green supply chain does not necessarily result in higher profits than the decentralized green supply chain. [ABSTRACT FROM AUTHOR]

The physical and chemical characteristics of soil and water sources affect desert plants' growth, which is essential for the ecological protection in arid areas. The typical patch patterns of Haloxylon ammodendron in the oasis-desert ecotone in the southern margin of the Manas River Basin consists of bare patches (BP) and vegetation patches (VP). The water sources of H. ammodendron were studied using stable isotope technology, and the soil physical and chemical properties were monitored and analyzed. The results showed that the soil moisture presented a reversed "S" type curve, and the total salt content of the soil presented an "S" type curve. A "wet island" and "cold island" were formed in the low salt area with H. ammodendron at the center. NaCl was most abundant in the BP soil, and the milligram equivalent of Cl− was 80–90%, while CaSO4 was most abundant in the VP soil, in which the milligram equivalent of SO42− was 80–100%. Before the rain, H. ammodendron mainly relied on the soil water from a deeper layer (≥60 cm) to maintain its growth. However, after the rain, H. ammodendron mainly relied on shallow soil water (<60 cm) to maintain its growth. [ABSTRACT FROM AUTHOR]

Conventional sparse spike deconvolution algorithms that are based on the iterative shrinkage-thresholding algorithm (ISTA) are widely used. The aim of this type of algorithm is to obtain accurate seismic wavelets. When this is not fulfilled, the processing stops being optimum. Using a recurrent neural network (RNN) as deep learning method and applying backpropagation to ISTA, we have developed an RNN-like ISTA as an alternative sparse spike deconvolution algorithm. The algorithm is tested with both synthetic and real seismic data. The algorithm first builds a training dataset from existing well-logs seismic data and then extracts wavelets from those seismic data for further processing. Based on the extracted wavelets, the new method uses ISTA to calculate the reflection coefficients. Next, inspired by the backpropagation through time (BPTT) algorithm, backward error correction is performed on the wavelets while using the errors between the calculated reflection coefficients and the reflection coefficients corresponding to the training dataset. Finally, after performing backward correction over multiple iterations, a set of acceptable seismic wavelets is obtained, which is then used to deduce the sequence of reflection coefficients of the real data. The new algorithm improves the accuracy of the deconvolution results by reducing the effect of wrong seismic wavelets that are given by conventional ISTA. In this study, we account for the mechanism and the derivation of the proposed algorithm, and verify its effectiveness through experimentation using theoretical and real data. [ABSTRACT FROM AUTHOR]

Accurate prediction of solar irradiance is beneficial in reducing energy waste associated with photovoltaic power plants, preventing system damage caused by the severe fluctuation of solar irradiance, and stationarizing the power output integration between different power grids. Considering the randomness and multiple dimension of weather data, a hybrid deep learning model that combines a gated recurrent unit (GRU) neural network and an attention mechanism is proposed forecasting the solar irradiance changes in four different seasons. In the first step, the Inception neural network and ResNet are designed to extract features from the original dataset. Secondly, the extracted features are inputted into the recurrent neural network (RNN) network for model training. Experimental results show that the proposed hybrid deep learning model accurately predicts solar irradiance changes in a short-term manner. In addition, the forecasting performance of the model is better than traditional deep learning models (such as long short term memory and GRU). [ABSTRACT FROM AUTHOR]

Ground surface settlement forecasting in the process of tunnel construction is one of the most important techniques towards sustainable city development and preventing serious damages, such as landscape collapse. It is evident that modern artificial intelligence (AI) models, such as artificial neural network, extreme learning machine, and support vector regression, are capable of providing reliable forecasting results for tunnel surface settlement. However, two limitations exist for the current forecasting techniques. First, the data provided by the construction company are usually univariate (i.e., containing only the settlement data). Second, the demand of tunnel surface settlement is immediate after the construction process begins. The number of training data samples is limited. Targeting at the above two limitations, in this study, a novel ensemble machine learning model is proposed to forecast tunnel surface settlement using univariate short period of real-world tunnel settlement data. The proposed Adaboost.RT framework fully utilizes existing data points with three base machine learning models and iteratively updates hyperparameters using current surface point locations. Experimental results show that compared with existing machine learning techniques and algorithms, the proposed ensemble learning method provides a higher prediction accuracy with acceptable computational efficiency. [ABSTRACT FROM AUTHOR]

A Ca(H2PO4)2/RM composite powder suppressant with core–shell structure was prepared with modified red mud (RM) as the carrier and Ca(H2PO4)2 as the loaded particles, using a solvent–antisolvent process, in an attempt to suppress aluminum dust explosion more effectively. The suppression effects of the Ca(H2PO4)2/RM composite powder suppressant for aluminum dust flame propagation and for explosion overpressure were tested in a vertical glass tube test apparatus and a 20 L explosion vessel. The results show that the Ca(H2PO4)2/RM composite powder suppressant was more effective in suppressing aluminum dust flame propagation and explosion overpressure than either Ca(H2PO4)2 or RM powder alone. Finally, the suppression mechanism of the Ca(H2PO4)2/RM composite powder suppressant was analyzed. On the one hand, a large amount of burning heat was absorbed through the decomposition of Ca(H2PO4)2 and the melting phase transformation of the decomposition product; on the other hand, the strong isolation provided by the RM helped limit flame propagation. The strong adsorptivity of RM allowed this material to adsorb the radicals from the explosion reaction perfectly. [ABSTRACT FROM AUTHOR]

Automated fault diagnosis (AFD) for various energy consumption components is one of the main topics for energy efficiency solutions. However, the lack of faulty samples in the training process remains as a difficulty for data-driven AFD of heating, ventilation and air conditioning (HVAC) subsystems, such as air handling units (AHU). Existing works show that semi-supervised learning theories can effectively alleviate the issue by iteratively inserting newly tested faulty data samples into the training pool when the same fault happens again. However, a research gap exists between theoretical AFD algorithms and real-world applications. First, for real-world AFD applications, it is hard to predict the time when the same fault happens again. Second, the training set is required to be pre-defined and fixed before being packed into the building management system (BMS) for automatic HVAC fault diagnosis. The semi-supervised learning process of iteratively absorbing testing data into the training pool can be irrelevant for industrial usage of the AFD methods. Generative adversarial network (GAN) is well-known as an unsupervised learning technique to enrich the training pool with fake samples that are close to real faulty samples. In this study, a hybrid generative adversarial network (GAN) is proposed combining Wasserstein GAN with traditional classifiers to perform fault diagnosis mimicking the real-world scenarios with limited faulty training samples in the training process. Experimental results on real-world datasets demonstrate the effectiveness of the proposed approach for fault diagnosis problems of AHU subsystem. [ABSTRACT FROM AUTHOR]