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A modified QuEChERS pretreatment method and LC-MS/MS technique were performed to simultaneously determine four pesticide (Hexachlorophene, Dinex, Dinosam, Dinoterb) residues in agricultural products. Through the optimization of LC-MS/MS detection parameters in SIM mode, the optimal instrument conditions are obtained. The modified QuEChERS method was used to pretreat the samples. Solid phase extractants PSA, C18 and GCB were used for sample purification. The research results showed that the correlation coefficients of the four pesticides were all greater than 0.991, which had a good linear relationship. The limits of quantitation (LOQ) of the four pesticides were 0.05–0.56 μg/kg. The recoveries were 70.51–113.20% with relative standard deviations (RSDS) of 1.6–11.2%. The developed method can provide reliable data support for the subsequent simultaneous detection of these four pesticides. [ABSTRACT FROM AUTHOR]

In this study, polyacrylic acid (PAA) films were deposited onto a quartz surface acoustic wave (SAW) resonator using a spin-coating technique for ammonia sensing operated at room temperature, and the sensing mechanisms and performance were systematically studied. The oxygen-containing functional groups on the surfaces of the PAA film make it sensitive and selective to ammonia molecules, even when tested at room temperature. The ammonia molecules adsorbed by the oxygen-containing functional groups of PAA (e.g., hydroxyl and epoxy groups) increase the membrane's stiffness, which was identified as the primary mechanism leading to the positive frequency shifts. However, mass loading due to adsorption of ammonia molecules is not a major reason as it will result in a negative frequency shifts. When the PAA coated SAW sensor was exposed to ammonia with a low concentration of 500 ppb, it showed a positive frequency shift of 225 Hz, with both good repeatability and stability, as well as a good selectivity to ammonia compared with those to C2H5OH, H2, HCl, H2S, CO, NO2, NO, and CH3COCH3. [ABSTRACT FROM AUTHOR]

The degradation modes and characteristics of different pesticides were introduced. In addition, this paper also describes the degradation mechanism of different pesticides, classifies, and summarizes the methods of degradation products identification. For the sake of human life health and better biological environment, we should have a familiar knowledge of the natural degradation of pesticides and understand the photo-hydrolysis and its influencing factors (temperature, pH, light, etc.). Through the degradation mechanism and influencing factors, the degradation time could be accelerated and it also provides a theoretical basis and basic support for the treatment of pesticide residues in the future. [ABSTRACT FROM AUTHOR]

Background: Cardiac kinases play a critical role in the development of heart failure, and represent potential tractable therapeutic targets. However, only a very small fraction of the cardiac kinome has been investigated. To identify novel cardiac kinases involved in heart failure, we used an integrated transcriptomics and bioinformatics analysis and identified Homeodomain-Interacting Protein Kinase 2 (HIPK2) as a novel candidate kinase. The role of HIPK2 in cardiac biology is unknown.Methods: We used the Expression2Kinase algorithm for the screening of kinase targets. To determine the role of HIPK2 in the heart, we generated cardiomyocyte (CM)-specific HIPK2 knockout and heterozygous mice. Heart function was examined by echocardiography, and related cellular and molecular mechanisms were examined. Adeno-associated virus serotype 9 carrying cardiac-specific constitutively active MEK1 (TnT-MEK1-CA) was administrated to rescue cardiac dysfunction in CM-HIPK2 knockout mice.Results: To our knowledge, this is the first study to define the role of HIPK2 in cardiac biology. Using multiple HIPK2 loss-of-function mouse models, we demonstrated that reduction of HIPK2 in CMs leads to cardiac dysfunction, suggesting a causal role in heart failure. It is important to note that cardiac dysfunction in HIPK2 knockout mice developed with advancing age, but not during development. In addition, CM-HIPK2 knockout mice and CM-HIPK2 heterozygous mice exhibited a gene dose-response relationship of CM-HIPK2 on heart function. HIPK2 expression in the heart was significantly reduced in human end-stage ischemic cardiomyopathy in comparison to nonfailing myocardium, suggesting a clinical relevance of HIPK2 in cardiac biology. In vitro studies with neonatal rat ventricular CMscorroborated the in vivo findings. Specifically, adenovirus-mediated overexpression of HIPK2 suppressed the expression of heart failure markers, NPPA and NPPB, at basal condition and abolished phenylephrine-induced pathological gene expression. An array of mechanistic studies revealed impaired extracellular signal-regulated kinase 1/2 signaling in HIPK2-deficient hearts. An in vivo rescue experiment with adeno-associated virus serotype 9 TnT-MEK1-CA nearly abolished the detrimental phenotype of knockout mice, suggesting that impaired extracellular signal-regulated kinase signaling mediated apoptosis as the key factor driving the detrimental phenotype in CM-HIPK2 knockout mice hearts.Conclusions: Taken together, these findings suggest that CM-HIPK2 is required to maintain normal cardiac function via extracellular signal-regulated kinase signaling. [ABSTRACT FROM AUTHOR]

MoSi 2 -Mo 5 Si 3 -Mo 5 SiB 2 composite is a kind of potential wear resistance material in high temperature environments. However, it is not yet understood how the dry sliding wear properties of the composite. In this work, a series of ball (Si 3 N 4)-on-disk (composite) sliding wear tests were conducted to evaluate the tribological properties of the composite at different temperatures and loads. The results show that the temperature and load have a slight influence on the wear rate of the composite, while the coefficient of friction (COF) is strongly dependent on the temperatures and loads. The COF and wear rate are (0.34–0.62) and (6.4–7.5) × 10−6 mm3N−1m−1 at 25–1000 °C, respectively. The wear mechanism is dominated by the brittle fracture wear below 400 °C, then transforms to the oxidative wear (600–1000 °C), and the plastic deformation (1000 °C). In additional, the increasing loads (2.5–10 N) lead to a severer plastic deformation tribolayer on the worn surface, which causes low COFs (0.30–0.56) as well as low wear rates (7.38–7.54 × 10−6 mm3N−1m−1) at 1000 °C, and the corresponding wear mechanisms exhibit the adhesive and oxidation wear. The composite shows a promising application as tribological components at elevated temperature in oxidizing environment owing to the development of a protective oxide layer or a plastic deformation tribolayer on the top of the wear surface. • MoSi 2 -Mo 5 Si 3 -Mo 5 SiB 2 composite with excellent tribological properties was prepared. • With increasing temperature, the wear mechanisms change from brittle fracture to oxidative wear and plastic deformation. • At 1000 °C, the wear mechanisms are adhesive and oxidation wear at 2.5–10 N, and accompanied plastic deformation over 5 N. [ABSTRACT FROM AUTHOR]

Nearly every form of the heart disease is associated with myocardial fibrosis, which is characterized by the accumulation of activated cardiac fibroblasts (CFs) and excess deposition of extracellular matrix (ECM). Although, CFs are the primary mediators of myocardial fibrosis in a diseased heart, in the traditional view, activated CFs (myofibroblasts) and resulting fibrosis were simply considered the secondary consequence of the disease, not the cause. Recent studies from our lab and others have challenged this concept by demonstrating that fibroblast activation and fibrosis are not simply the secondary consequence of a diseased heart, but are crucial for mediating various myocardial disease processes. In regards to the mechanism, the vast majority of literature is focused on the direct role of canonical SMAD-2/3-mediated TGF-β signaling to govern the fibrogenic process. Herein, we will discuss the emerging role of the GSK-3β, β-catenin and TGF-β1-SMAD-3 signaling network as a critical regulator of myocardial fibrosis in the diseased heart. The underlying molecular interactions and cross-talk among signaling pathways will be discussed. We will primarily focus on recent in vivo reports demonstrating that CF-specific genetic manipulation can lead to aberrant myocardial fibrosis and sturdy cardiac phenotype. This will allow for a better understanding of the driving role of CFs in the myocardial disease process. We will also review the specificity and limitations of the currently available genetic tools used to study myocardial fibrosis and its associated mechanisms. A better understanding of the GSK-3β, β-catenin and SMAD-3 signaling network may provide a novel therapeutic target for the management of myocardial fibrosis in the diseased heart. [ABSTRACT FROM AUTHOR]

A novel model-based principal component analysis (PCA) method is proposed in this paper for wide-area power system monitoring, aiming to tackle one of the critical drawbacks of the conventional PCA, i.e. the incapability to handle non-Gaussian distributed variables. It is a significant extension of the original PCA method which has already shown to outperform traditional methods like rate-of-change-of-frequency (ROCOF). The ROCOF method is quick for processing local information, but its threshold is difficult to determine and nuisance tripping may easily occur. The proposed model-based PCA method uses a radial basis function neural network (RBFNN) model to handle the nonlinearity in the data set to solve the no-Gaussian issue, before the PCA method is used for islanding detection. To build an effective RBFNN model, this paper first uses a fast input selection method to remove insignificant neural inputs. Next, a heuristic optimization technique namely Teaching-Learning-Based-Optimization (TLBO) is adopted to tune the nonlinear parameters in the RBF neurons to build the optimized model. The novel RBFNN based PCA monitoring scheme is then employed for wide-area monitoring using the residuals between the model outputs and the real PMU measurements. Experimental results confirm the efficiency and effectiveness of the proposed method in monitoring a suite of process variables with different distribution characteristics, showing that the proposed RBFNN PCA method is a reliable scheme as an effective extension to the linear PCA method. [ABSTRACT FROM AUTHOR]

Abstract: By sensitizing with 514nm green light, 488nm blue light and 390nm ultraviolet light, respectively, recording with 633nm red light, effect of wavelength of sensitizing light on holographic storage properties in LiNbO3:Fe:Ni crystal is investigated in detail. It is shown that by shortening the wavelength of sensitizing light gradually, nonvolatile holographic recording properties of oxidized LiNbO3:Fe:Ni crystal is optimized gradually, 390nm ultraviolet light is the best as the sensitizing light. Considering the absorption of sensitizing light, to obtain the best performance in two-center holographic recording we must choose a sensitizing wavelength that is long enough to prevent unwanted absorptions (band-to-band, etc.) and short enough to result in efficient sensitization from the deep traps. So in practice a trade-off is always needed. Explanation is presented theoretically. [Copyright &y& Elsevier]

Accurate estimations of battery state-of-charge (SOC) for energy storage systems are popular research topics in recent years. Numerous challenges remain in several aspects, especially in dealing with the conflict of high model accuracy and complex model structure with heavy computational cost. This paper proposes a compact and optimized SOC estimation model, integrating a fast input selection algorithm to choose important terms as input variables, followed by a simple and efficient JAYA optimization scheme to tune the key parameters of neural network functions. From the real-system experiment results, it can be seen that the estimation model errors are greatly reduced by applying optimization method, and the model performance is validated through statistical error values including root mean square error, mean absolute error, mean absolute percentage error and SOC error. The experimental results demonstrate that the SOC estimations can be greatly improved after optimization of neural network parameters under different charging and discharging process. • A novel compact and optimized radial basis function neural network is proposed. • The method is adopted for the SOC estimation of energy storage systems. • Simulation results validate the excellent performance of the proposed method. [ABSTRACT FROM AUTHOR]

Mucosal-like ZnS nanostructures were synthesized on surface of ST-cut quartz surface acoustic wave (SAW) device using a chemical bath deposition method for ammonia sensing applications. Results showed the SAW device with ZnS mucosal nanostructures achieved a good sensitivity, e.g., with a frequency shift of ~190 Hz upon exposure to 1 ppm ammonia gas. This is mainly attributed to the large specific surface areas and more active sites on the surfaces of the ZnS mucosal nanostructures. Selectivity of the SAW device with the ZnS mucosal nanostructure for ammonia sensing was excellent in comparisons with those for other types of gases such as hydrogen sulfide, hydrogen, nitrogen dioxide, carbon monoxide and ethanol. Unlabelled Image • Mucosal-like ZnS nanostructures were deposited on surface of SAW device for sensing ammonia. • The SAW device with ZnS mucosal nanostructures achieved a good sensitivity and selectivity to ammonia. • The negative frequency shift of SAW sensor is associated to the effects of mass loading and electrical loading. [ABSTRACT FROM AUTHOR]

Tb3+-activated LTA zeolite-derived boro-aluminosilicate glass samples with a composition of xTb2O3-68(Na2O-Al2O3-SiO2)–32B2O3 (x = 0.2, 1.0 and 10 extra wt%) were prepared using the melt-quenching method. The emission spectra recorded upon ultraviolet (UV) excitation with two different wavelengths of 193 and 378 nm showed blue light (5D3 to 7FJ=6,5,4 and 5D4 to 7F6 transitions of Tb3+) and green light (5D4 to 7F5 transition of Tb3+) emissions with comparable intensities up to a Tb3+ concentration of 10 extra wt%. Of note, the mean decay times of the green luminescence of the glass samples were relatively fast (<20 μs). The synthesized glass has potential in applications concerning UV imaging, UV detection, and plasma display panels. [ABSTRACT FROM AUTHOR]

A large proportion of construction accidents are caused by workers' unsafe actions. Due to the complexity of the work environment and excessive demands of safety supervision on construction sites, many modern information technologies, such as computer vision (CV) technologies, have been increasingly applied and gradually replaced the traditional manual supervision to automatically identify unsafe behaviors from surveillance video data. Current models focus on high resolution (HR) video containing high quality features. However, challenges remain for more specific real-world construction scenarios, including but not limited to lack of high-quality data, far-field video recognition, privacy protection, and limited computing resources. To address the challenges, in this paper, a simple but effective method relying on the Teacher–Student Learning Architecture is proposed, where the teacher network assists the student network in effectively capturing useful features for downstream action recognition from extreme low resolution (eLR) video which can be easily transferred to other tasks. In addition, a Knowledge Learning Model and a similarity loss function are proposed to better guide the training of student network. In numerical experiments and case studies, our proposed training strategy achieves an top-1 accuracy of 28.53% and 71.81% on eLR HMDB dataset and eLR CMA dataset, 5.96% and 3.89% higher than the baseline model. The proposed strategy can make the automatic safety monitoring of construction works more accurate and reliable, thereby effectively reducing the accident rate and management cost. In addition, the performance of proposed strategy depends on efficient knowledge distillation methods, which may inspire future applications of CV-based deep learning models in construction management to achieve higher recognition results while maintaining low computational cost. [ABSTRACT FROM AUTHOR]

A surface acoustic wave (SAW) H 2 S gas sensor based on CuO-TiO 2 p-n heterojunction film was designed and fabricated. The sensor exhibits high sensitivity to H 2 S due to the significant current response of CuO-TiO 2 film to H 2 S gas and the appropriate work point of SAW sensor based on CuO-TiO 2 composite film. The linearity change for sensor frequency shift is strongly associated with the location of work point when the H 2 S concentration increases from 1 to 300 ppm. In addition, the porosity of CuO-TiO 2 film is beneficial to the efficient adsorption for H 2 S, leading to the higher frequency shift of the sensor. The SAW sensor also shows good selectivity and stability to H 2 S with stable frequency shift under different humidity levels. • A H 2 S gas sensor is designed and fabricated using CuO-TiO 2 composite film. • The establishment of CuO-TiO 2 p-n junction significantly enhances the sensitivity of SAW sensor to H 2 S. • The linearity variation of frequency shift is closely related to the position of work point at different H 2 S concentrations. • The key sensing mechanism of SAW sensor based on CuO-TiO 2 film is electric loading. • The SAW sensor shows fast response in high humidity level, and good gas selectivity and repeatability. [ABSTRACT FROM AUTHOR]

As the urgency to adopt renewable energy sources escalates, so does the need for accurate forecasting of power output, particularly for wind and solar power. Existing models often struggle with noise and temporal intricacies, necessitating more robust solutions. In response, our study presents the SL-Transformer, a novel method rooted in the deep learning paradigm tailored for green energy power forecasting. To ensure a reliable basis for further analysis and modeling, free from noise and outliers, we employed the SG filter and LOF algorithm for data cleansing. Moreover, we incorporated a self-attention mechanism, enhancing the model's ability to discern and dynamically fine-tune input data weights. When benchmarked against other premier deep learning models, the SL-Transformer distinctly outperforms them. Notably, it achieves a near-perfect R 2 value of 0.9989 and a significantly low SMAPE of 5.8507% in wind power predictions. For solar energy forecasting, the SL-Transformer has achieved a SMAPE of 4.2156%, signifying a commendable improvement of 15% over competing models. The experimental results demonstrate the efficacy of the SL-Transformer in wind and solar energy forecasting. [ABSTRACT FROM AUTHOR]

Abstract Conventional unit commitment is a mixed integer optimization problem and has long been a key issue for power system operators. The complexity of this problem has increased in recent years given the emergence of new participants such as large penetration of plug-in electric vehicles. In this paper, a new model is established for simultaneously considering the day-ahead hourly based power system scheduling and a significant number of plug-in electric vehicles charging and discharging behaviours. For solving the problem, a novel hybrid mixed coding meta-heuristic algorithm is proposed, where V-shape symmetric transfer functions based binary particle swarm optimization are employed. The impact of transfer functions utilised in binary optimization on solving unit commitment and plug-in electric vehicle integration are investigated in a 10 unit power system with 50,000 plug-in electric vehicles. In addition, two unidirectional modes including grid to vehicle and vehicle to grid, as well as a bi-directional mode combining plug-in electric vehicle charging and discharging are comparatively examined. The numerical results show that the novel symmetric transfer function based optimization algorithm demonstrates competitive performance in reducing the fossil fuel cost and increasing the scheduling flexibility of plug-in electric vehicles in three intelligent scheduling modes. Highlights • A new unit commitment model considering electric vehicles is established. • A binary symmetric based hybrid meta-heuristic method is proposed. • The impact of transfer function on unit commitment problem is evaluated. • Three flexible scheduling modes are comparatively studied. [ABSTRACT FROM AUTHOR]

With an estimated 38 million current patients, heart failure (HF) is a leading cause of morbidity and mortality worldwide. Although the aetiology differs, HF is largely a disease of cardiomyocyte (CM) death or dysfunction. Due to the famously limited amount of regenerative capacity of the myocardium, the only viable option for advanced HF patients is cardiac transplantation; however, donor's hearts are in very short supply. Thus, novel regenerative strategies are urgently needed to reconstitute the injured hearts. Emerging data from our lab and others have elucidated that CM-specific deletion of glycogen synthase kinase (GSK)-3 family of kinases induces CM proliferation, and the degree of proliferation is amplified in the setting of cardiac stress. If this proliferation is sufficiently robust, one could induce meaningful regeneration without the need for delivering exogenous cells to the injured myocardium (i.e. cardiac regeneration in situ). Herein, we will discuss the emerging role of the GSK-3s in CM proliferation and differentiation, including their potential implications in cardiac regeneration. The underlying molecular interactions and cross-talk among signalling pathways will be discussed. We will also review the specificity and limitations of the available small molecule inhibitors targeting GSK-3 and their potential applications to stimulate the endogenous cardiac regenerative responses to repair the injured heart. [ABSTRACT FROM AUTHOR]

Polyurethane coating materials with different compositions of low surface energy polydimethylsiloxane and degradable poly(L-lactic acid) were synthesized by three major steps. Initially, the hydroxylation-terminated poly(L-lactide)-functionalized graphene (G-g-PLLA) was prepared by ring-opening polymerization of L-lactic acid using the phenol-functionalized graphene (G-f-OH), which was prepared by 1,3-dipolar cycloaddition reaction of graphene and 3,4-dihydroxybenzaldehyde when N-methylglycine and tin octoate were used as initiator and catalyst, respectively. Subsequently, isocyanate-terminated polyurethane prepolymer with polydimethylsiloxane was obtained by condensation polymerization of polydimethylsiloxane and isocyanate-terminated polyurethane prepolymer that was obtained by the condensation polymerization of 4,4′-diphenylmethane diisocyanate and 1,4-butane diol. Finally, the novel polyurethane coating materials were prepared by the condensation polymerization of G-g-PLLA and isocyanate-terminated polyurethane prepolymer with polydimethylsiloxane. These synthesized materials were carefully analyzed with Fourier transform infrared spectroscopy (FTIR), proton nuclear magnetic resonance spectra (1H NMR), field-emission scanning electron microscopy (SEM), and high-resolution transmission (TEM). In addition, the water contact angles were measured. It was found that the surface free energy of the polyurethane coating materials decreased from 52.19 to 11.74 N/m2 with the increase of polydimethylsiloxane content from 0 to 20% and the water contact angle of the polyurethane coating materials increased from 71° to 108°. Moreover, the mechanical property was investigated. The studies also demonstrated that functionalized polyurethane was able to hydrolyze in seawater and the hydrolysis rate decreased as the PDMS content increased. At the same time, simulative ocean hanging plate experiment confirmed that the novel polyurethane coating materials exhibited a good antifouling performance, which indicated that the functionalized polyurethane has a potentiality in marine antifouling coating application. [ABSTRACT FROM AUTHOR]

Highlights • A new compact teaching-learning based optimization method is proposed. • 32 well-known benchmark tests are conducted for algorithm evaluation. • The algorithm maintains high performance and reduce memory requirement. • The algorithm is used to train two typical function neural networks. Abstract The majority of embedded systems are designed for specific applications, often associated with limited hardware resources in order to meet various and sometime conflicting requirements such as cost, speed, size and performance. Advanced intelligent heuristic optimization algorithms have been widely used in solving engineering problems. However, they might not be applicable to embedded systems, which often have extremely limited memory size. In this paper, a new compact teaching-learning based optimization method for solving global continuous problems is proposed, particularly aiming for neural network training in portable artificial intelligent (AI) devices. Comprehensive numerical experiments on benchmark problems and the training of two popular neural network systems verify that the new compact algorithm is capable of maintaining the high performance while the memory requirement is significantly reduced. It offers a promising tool for continuous optimization problems including the training of neural networks for intelligent embedded systems with limited memory resources. [ABSTRACT FROM AUTHOR]

A novel medium-length fluorinated diols and poly(L-lactide) (PLLA) were synthesized via Michael addition reaction and ring-opening polymerization, respectively. Subsequently, Synthetic medium-length fluorinated diols and PLLA were combined to prepare new polyurethane composites with degradability and low surface energy. The compositional analysis and structural characterization of synthetic materials were characterized by using fourier transform infrared spectroscopy (FT-IR) and proton nuclear magnetic resonance spectra (1HNMR). Thermogravimetric analysis(TGA) indicated that the introduction of medium-length fluorinated diols improved the thermal stability of the polyurethane. The biodegradation and low surface energy of the polyurethane were investigated by static hydrolysis experiment and water contact angle test. It was found that the degradation rate of the polyurethane increased as measurement time went on when the PLLA content was under 40%, and the water contact angle increased from 71.12° to 108.24° with the increase of fluorine content, which indicated that the degradable and low surface energy polyurethane has a potential as a coating material for a marine antifouling coating application. [ABSTRACT FROM AUTHOR]

Background:There is lack of further observations on the microstructure and material property of callus during bone defect healing and the relationships between callus properties and the mechanical strength.Methods:Femur bone defect model was created in rabbits and harvested CT data to reconstruct finite element models at 1 and 2 months. Three types of assumed finite element models were compared to study the callus properties, which assumed the material elastic property as heterogeneous (R-model), homogenous (H-model) or did not change from 1 to 2 months (U-model).Results: The apparent elastic moduli increased at 2 months (from 355.58 ± 132.67 to 1139.30 ± 967.43 MPa) in R-models. But there was no significant difference in apparent elastic moduli between R-models (355.58 ± 132.67 and 1139.30 ± 967.43 MPa) and H-models (344.79 ± 138.73 and 1001.52 ± 692.12 MPa) in 1 and 2 months. A significant difference of apparent elastic moduli was found between the R-model (1139.30 ± 967.43 MPa) and U-model group (207.15 ± 64.60 MPa) in 2 months.Conclusions:This study showed that the callus structure stability remodeled overtime to achieve a more effective structure, while the material quality of callus tissue is a very important factor for callus strength. At the meantime, this study showed an evidence that the material heterogeneity maybe not as important as it is in bone fracture model. [ABSTRACT FROM PUBLISHER]

A large proportion of construction accidents are caused by unintentional and unsafe actions and behaviors. It is of significant difficulties and ineffectiveness to monitor unsafe behaviors using conventional manual supervision due to the complex and dynamic working conditions on construction sites. Recently, surveillance videos and computer vision (CV) techniques have been increasingly adopted to automatically identify risky behaviors. However, the challenge remains that spatial and temporal features in video clips cannot be effectively captured and fused by current CV models. To address this challenge, this paper describes a deep learning model named Spatial Temporal Relation Transformer (STR-Transformer), where spatial and temporal features of work behaviors are simultaneously extracted in paralleling video streams and then fused by a specially designed module. To verify the effectiveness of the STR-Transformer, a customized dataset is developed, including seven categories of construction worker behaviors and 1595 video clips. In numerical experiments and case studies, the STR-Transformer achieves an average precision of 88.7%, 4.0% higher than the baseline model. The STR-Transformer enables more accurate and reliable automatic safety surveillance on construction projects, and is expected to reduce accident rates and management costs. Moreover, the performance of STR-Transformer relies on efficient feature integration, which may inspire future studies to identify, extract, and fuse richer features when applying CV-based deep learning models in construction management. • A deep learning model is proposed for action recognition. • The model consists of two feature encoders and a feature fusion model. • The model identify unsafe actions of construction workers. • A novel dataset of construction worker actions is proposed. [ABSTRACT FROM AUTHOR]

Construction projects face various constraints in terms of materials, labor, equipment, and documents, which can interrupt the scheduled work. Package‐based constraint management (PCM) is a state‐of‐the‐art graph‐based approach that follows the lean theory to effectively model, monitor, and remove constraints before the commencement of work, ensuring smooth construction and minimizing delay and waste. PCM relies on exploring and investigating project knowledge bases (KBs), formed by entity‐relation‐entity triples of constraints. However, most PCM KBs are incomplete and suffer from poor semantics, which hinders the PCM functions. Although many KB completion (KBC) methods exist in the field of artificial intelligence, they primarily focus on general knowledge and exclude the features of specific domains. Therefore, they cannot be directly applied to complete PCM KBs. To address the issue, this study proposes a novel deep learning model, referred to as the domain information enhanced graph neural network (D‐GNN). The features of the developed D‐GNN include (1) building a domain ontology to enrich semantics with rule reasoning, (2) applying the GNN to learn and encode embeddings of constraint entities and relations, and (3) employing a convolution neural network (CNN) for decoding and identifying missing triples. D‐GNN improves the existing KBC methods by integrating two types of domain information, namely, the ontological classes and working contexts into GNN and CNN, respectively. The experimental results verified that the D‐GNN reached an accuracy of 0.848–0.951, and the domain information integration increased the performance by up to 0.263. In practical testing, the D‐GNN significantly reduced the KBC time to 1/6–1/35 of the manual approach and reached higher accuracy. Therefore, the proposed D‐GNN can facilitate PCM by providing complete KBs and supporting downstream constraint monitoring and removal. [ABSTRACT FROM AUTHOR]

The NiAuPd/G nanocatalyst was prepared by one-pot hydrothermal synthesis. The compositional analysis, structural characterization, morphology and performance of the resulting synthetic catalyst were done by Energy Dispersive X-ray Spectroscopy (EDX), X-ray diffraction (XRD) and Transmission electron microscope (TEM). In addition, catalytic activities of the NiAuPd/G catalyst was investigated. It is found that the NiAuPd/graphene exhibits a perfect crystal structure. The NiAuPd/graphene exhibits a high catalytic activity for the dehydrogenation of formic acid at room temperature and without additives. [ABSTRACT FROM AUTHOR]

Effective and timely fault detection and status monitoring of the industrial production process is essential to fully guarantee the operational safety. However, massive multi-source heterogeneous data analysis is facing many challenges. This paper proposes a process monitoring model combined with a parallel deep learning algorithm and Principal Component Analysis (PCA) method. Firstly, PCA is applied to realize fault diagnosis and extract fault characteristic variables, thus abnormal conditions can be revealed. In order to reduce the complexity of processing massive data, a parallel deep learning framework consisting of multi-models of convolutional neural network (CNN) and Long-Short Term Memory (LSTM) neural network is proposed to effectively predict the target variable status. Comprehensive experiments are taken under two real-system scenarios, and comparisons are made against four traditional neural network models to demonstrate its practicability and effectiveness. The generated results clearly show that the CNN-LSTM parallel model combined with PCA outperform other popular models due to its merged advantages of accurate time series prediction and effective fault feature extraction. [Display omitted] • A parallel deep learning approach with feature extraction and long time series prediction. • Multi-source unsupervised faulty variable extraction and detection can be realized. • Successfully solve the distribution power grid fault diagnosis in multiple locations. [ABSTRACT FROM AUTHOR]

Unstructured texts dominate data in construction projects. With the achievements of natural language processing (NLP) techniques, mining unstructured text data for smart construction has become increasingly significant. To understand state-of-the-art NLP for smart construction, uncover related issues, and propose potential improvements, this paper presents a comprehensive review of bottom-level techniques and mainstream applications of NLP in the industry. In total, 124 journal articles published in the last two decades are reviewed. NLP involves five core steps supported by various techniques, e.g., syntactic parsing, heuristic rules, machine learning, and deep learning. NLP has been applied for information extraction and exchanging and many downstream applications to facilitate management and decision-making. The role of NLP in smart construction and current challenges for fully reaping its benefits are discussed, and four research directions are identified, i.e., improving relation extraction, realising knowledge base auto-development, integrating multi-modal information, and achieving an accuracy-efficiency trade-off by developing an NLP application framework. It is envisioned that outcomes of this paper can assist both researchers and industrial practitioners with appreciating the research and practice frontier of NLP for smart construction and soliciting the latest NLP techniques. • An in-depth review of natural language processing (NLP) for smart construction is conducted. • 124 journal articles published in 2000–2021 are manually reviewed. • Bottom-level NLP techniques are thoroughly introduced. • NLP is used to extract and exchange information and to support downstream applications. • The role of NLP in smart construction, current challenges, and future directions are provided. [ABSTRACT FROM AUTHOR]

The optimization of maintenance scheduling and routing in offshore wind farms is crucial for the intelligent operation and maintenance of offshore wind energy. It involves determining the optimal timing, assigning vessels, planning routes, and efficiently completing maintenance tasks with maximum efficiency and minimal cost. Traditional branch-and-bound search methods, often used in multi-vessel cooperative maintenance scheduling models, face limitations in finding optimal solutions efficiently, while classic heuristic methods may compromise on global optimality. This paper introduces DivideMerge, a high-performance hybrid algorithm that synergizes heuristic and exact algorithms to address large-scale multi-vessel cooperative maintenance scheduling challenges in offshore wind farms. Initially, a maintenance task constraint decomposition heuristic method is utilized to break down the collaborative optimization scheduling problem into individual vessel scheduling sub-problems, ensuring adherence to the constraints of the original problem. Subsequently, the CP-SAT solver is employed to sequentially solve these sub-problems rapidly. The solutions for individual vessel scheduling are then merged to form a comprehensive solution for the multi-vessel cooperative maintenance scheduling problem. Computational results validate the effectiveness and robustness of DivideMerge, demonstrating a solution speed nearly 1000 times faster than the commercial Gurobi solver, thus offering a significant advancement in the field of offshore wind farm maintenance optimization. [ABSTRACT FROM AUTHOR]

Surface acoustic wave (SAW) technology has been widely used in the field of harmful gas detection, but there remain a lot of challenges, such as long response/recovery time as well as poor selectivity and repeatability. Herein, we activate graphene oxide (GO) using MXene with excellent adsorption capacity (MXene/GO) to realize efficient NO 2 gas detection. The SAW sensor assembled with the multilayered MXene/GO composite showcases an enhanced gas sensing performance to the NO 2 gas (1–200 ppm) enabling high sensitivity (Δf = 260 Hz/ppm), fast response/recovery time, excellent selectivity, and good repeatability at room temperature. The enhanced high sensitivity and selectivity detection of NO 2 gas is attributed to the large pore size, hydrophilicity, and abundant surface functional groups of the multilayered MXene/GO composite. This elaborate design is expected to propel the development of next-generation acoustic gas phase sensors. [Display omitted] • The MXene is investigated in surface acoustic wave sensors for the first time and filles the material gap. • The facile synthetic method for the MXene/GO composite is expected to realize rapid and large-scale synthesis of sensitive layers for surface acoustic wave sensors. • The working mechanism of MXene-based sensitive layers is proposed and the factors to influence sensing performance are also considered. • The excellent sensing performance of MXene/GO-based surface acoustic wave sensors is obtained under different conditions. • A novel MXene/GO composite is proposed for the first time to evaluate its working mechanism and sensing performance in SAW gas sensors. [ABSTRACT FROM AUTHOR]

This paper explores the wake of a wind turbine yaw and its influence on the output power of the downstream wind turbine. A wind turbine model (hub height and diameter of 1.2 m and 1.0 m, respectively) was used in the experiments, and the yaw angle of the upstream wind turbine was successively varied through values of 0°, 15°, and 30°. The wind speed and the intensity of turbulence in the wake region were measured using a hot‐wire anemometer. The measuring positions were changed to 3, 5, and 7 rotor diameters behind the upstream wind turbine. In addition, the output power of the downstream wind turbine model was measured using the rotational speed torque meter. The results show that, in the case of large‐angle yaw of the upstream wind turbine, the recovery of the wind speed in the wake area was greatly improved, and up to 30% recovery was achieved. The power output of the downstream wind turbine was also significantly increased. Under the yaw conditions of 15° and 30° for the upstream wind turbine, the average output power of the downstream wind turbine increased by 17.6% and 21.6%, respectively. [ABSTRACT FROM AUTHOR]

Abstract: Microarc oxidation coatings were fabricated on AZ31 magnesium alloy in the electrolyte of sodium phosphate. Potentiodynamic polarization and electrochemical impedance spectroscopy tests were employed to investigate the electrochemical corrosion behavior. The corroded surface was characterized by an optical microscope and X-ray diffraction. The influence of chloride ion concentration on the corrosion resistance of microarc oxidation coated AZ31 alloy is discussed. The corrosion current density enlarged ratio and the charge transfer resistance reduced ratio indicated that the extent of the corrosion damage of microarc oxidation coated AZ31 alloy is much higher when chloride ion concentration is greater than 5%. A corrosion mechanism related to the effect of chloride ion concentrations on the corrosion behavior is proposed. [Copyright &y& Elsevier]

Dense oxidation coatings have been successfully developed on biocompatible AZ31 magnesium alloy, using microarc oxidation technique, to improve the corrosion resistance. Three different deposition voltages of 250, 300, and 350 V have been employed. The effect of voltage on the coating corrosion resistance has been evaluated through electrochemical experiments in a simulated body fluid (SBF) up to 7 days. Potentiodynamic polarization and electrochemical impedance spectroscopy scans were performed in the SBF solution, followed by optical microscopy surface inspection. The results indicate that the corrosion rates of the coatings are in the order of 250 < 300 < 350 V after immersion for 7 days, and the charge transfer resistance ( R) of the three samples is in the order of 250 > 300 > 350 V. Both the electrochemical tests and the surface inspection suggest that the 250 V coating has the highest corrosion resistance, with lowest corrosion current density, highest R, and the best surface quality. [ABSTRACT FROM AUTHOR]

• A new unit commitment framework with different levels of DSM for PEVs is proposed. • A novel binary competitive swarm optimization method is proposed and utilized. • The uncoordinated and coordinated charging is measured by a weighting factor. • Numerical studies on various unit scales and levels of load are investigated. Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed-integer variables and multiple constraints – typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit. [ABSTRACT FROM AUTHOR]

• The sensor can detect H 2 S gas with a concentration as low as 0.5 ppm. • The frequency shift of sensor is derived from the change of mass loading on films. • The responses became much faster and stronger with the increase of RH. Surface acoustic wave (SAW) sensors based on sol–gel processed porous and nanoparticulate CuO films were explored for H 2 S detection operated at room temperature. The SAW sensor showed a negative frequency shift when exposed to H 2 S gas, due to the mass loading effect on the sensitive CuO film. It has a high sensitivity of H 2 S, a detection limit of 500 ppb and a good selectivity to H 2 S gas compared with H 2 , C 2 H 5 OH, acetone, NH 3 , CO, NO, NO 2 gases. Relative humidity (RH) was identified to have a critical influence on the sensing performance of the sensor. The responses became much faster and stronger with the increase of RH. The significant enhancement in the sensing performance is attributed to that the chemical reaction between H 2 S and CuO is hindered and the physical adsorption of CuO to H 2 S is enhanced. [ABSTRACT FROM AUTHOR]

The accurate determination of work package size is crucial for cost reduction and achieving efficient mass production in modular construction (MC) projects. However, effectively balancing sizing and cost poses a significant challenge due to the absence of a precise trade-off method. This paper describes an automatic work package sizing (AWPS) method in MC production to optimize work package sizing while minimizing costs. First, the multi-stakeholders-oriented MC production process is modeled to identify the tasks in MC production. Second, critical factors affecting the size of work packages are identified and analyzed. Then, a dynamic programming algorithm is developed to merge the tasks to work packages at a minimal cost. The AWPS method is validated in a case study, and the results show that the AWPS can effectively package production tasks while reducing costs by 1.28%–16.25% compared with traditional task-organizing methods. • Automatic work package sizing for cost reduction of modular construction (MC). • A framework for the multi-takeholders-oriented MC production processes. • A work package sizing model considering the size factors and cost performance. • A dynamic programming method for tasks merging with minimum cost. [ABSTRACT FROM AUTHOR]

Recent advancements in computer vision and augmented reality (AR) technology have created a pathway for enhanced human–computer interaction. Despite the potential benefits, the integration of these technologies is not yet common in the industry. Object detection is the foundation of AR systems for inspection, and an efficient object detection model will enable accurate object localization in AR devices. However, the limited computing capabilities and memory usage of wearable AR devices pose a significant challenge to the deployment of state-of-the-art detectors on these devices in real work scenarios. Moreover, as the person wearing the AR device keeps moving, the constantly changing scale of the captured image often leads to significant information loss when feature maps of different scales are fused. To address these challenges, this paper proposes a new compatible detector YOLO-Master based on improved YOLOv5 through deep learning and AR. The paper introduces several improvements, such as using MobileNetv3 as the backbone to reduce memory overhead and computational costs and designing an attention module MCA based on coordinate to reduce information loss. Besides, 20 hyperparameters of YOLOv5 are optimized by the genetic algorithm(GA) before model training, which can help reduces the loss value generated. YOLO-Master was trained and tested on hydropower device images collected from a power plant in the China Southern Power Grid, and the experiments on a self-made dataset demonstrated that the mAP of the proposed method can reach 70.1%, which is 2.9% higher than the baseline of 67.2%. Additionally, the number of parameters of the model was reduced by 66.7%, enabling the model to ensure accuracy while being compatible with real-time performance, meeting the urgent needs of engineering deployment. • A Compatible detector is proposed for device detection in AR inspection system. • Computation cost is improved by a lightweight backbone network. • A coordinate-based attention module is designed for effective feature fusion. • Optimizing hyperparameters of the new architecture to make the detector robust. [ABSTRACT FROM AUTHOR]

With the application of smart meters, more information is available from residential buildings for support heat load forecast. Yet, there is still a lack of an effective method to exploit the value of the high spatial granularity information, particularly for residential communities with high randomness in human behaviors. To fill this gap, this paper proposes a data-driven heat load forecast method based on the field measurements of smart meters A white-box machine learning algorithm, namely Dendritic Network, is employed to aggregate and analyze data obtained from different locations of district heating systems. An online correction mechanism is then proposed to regulate the forecast horizon and ensure the adaptiveness of the machine learning model under different scenarios. The results demonstrate that the dendritic network employed in the proposed machine learning method shows higher forecast accuracy, the root-mean-square error is 0.0029, in some research cases the coefficient of determination can reach 0.99–1, and wide scope of application in the area of heat load forecast. • Heat load prediction model is used for residential communities in central heating. • We used and compared 5machine learning prediction methods. • An online correction mechanism is proposed to ensure the accuracy. • The determination coefficient of modeling can reach more than 0.99 in some cases. [ABSTRACT FROM AUTHOR]

A Plate–fin heat exchanger (PFHE) is a compact and efficient thermal device, whose performance strongly depends on its structural design. However, the design optimization of a PFHE is a mixed-integer optimization problem with a strong nonlinear characteristic, which presents significant challenges for existing optimization algorithms. Meta-heuristic algorithms (MAs) are competitive for solving complex non-linear optimization problems. In this paper, an improved dynamic-opposite learning Jaya (DOLJaya) method, the goal is to make the algorithm adaptable to each problem. The results of eighteen unimodal and multi-modal benchmarks and nine hybrid benchmarks demonstrate that the proposed DOLJaya has competitive robustness, efficiency and effectiveness for solving complex nonlinear problems compared to its popular counterparts. At the same time, we selected the optimization of the plate–fin heat exchanger as the industrial test benchmark for optimization, and the results of DOLJaya algorithm have been improved by a maximum average of 108.29% and 7.60% compared with the original Jaya, which are also satisfactory. [ABSTRACT FROM AUTHOR]

Adaptive work packaging is paramount in helping reduce dynamic gaps between design and manufacturing in modular construction (MC), particularly in mass customization. However, current work packaging methods fail to automatically extract complex semantic relations among work package elements (e.g., products, tasks, and their dependencies) and dynamically reason the implicit semantic knowledge (e.g., the different granularity of semantics) as the project progresses. To address these issues, this study proposes a knowledge graph-enabled adaptive work packaging (K-GAWP) approach to dynamically form semantic-enriched work packages with different granularities. Thus far, this study first models the data of tasks, products, and their spatial relationships for MC production as graphs. Second, a novel multi-granularity knowledge reasoning method (product2task) is developed to map products to tasks in an adaptive manner. Third, a dedicated hierarchical clustering method (task2package) involving multiple features from the dependency structure matrix is proposed for work-package generation (i.e., task knowledge fusion). Finally, the K-GAWP's performance is evaluated through controlled experiments in a real MC project. The results indicate that the K-GAWP approach performs work packaging in an adaptive, accurate, and efficient manner, thereby improving the distributed planning and control of MC projects. [ABSTRACT FROM AUTHOR]

The development of the building energy management systems (BEMS) enable users to intelligently control Heating, Ventilation, Air-conditioning and Cooling (HVAC) systems based on digital information. In order to reduce the power consumption cost of the HVAC system while ensuring user satisfaction, a novel HVAC control system for building system based on a multi-step predictive deep reinforcement learning (MSP-DRL) algorithm is proposed in this paper. In the proposed method, the outdoor ambient temperature is predicted first by a featured deep learning method named GC-LSTM, where the Long Short-term Memory (LSTM) is enhanced by the generalized correntropy (GC) loss function to deal with the non-Gaussian characteristics of the collected outdoor temperature. In addition, the proposed temperature prediction model is combined with a reinforcement learning algorithm named Deep Deterministic Policy Gradient (DDPG) aiming to flexibly adjust the output power of the HVAC system under the dynamic changing of electricity prices. Finally, comprehensive simulation based on real world data is delivered. Numerical results show that the GC-LSTM algorithm is more accurate than other counterparts prediction algorithms, and the proposed HVAC control system based on the multi-step prediction deep reinforcement learning algorithm is effective and could save over 12% cost compared to other approaches, where the user comfort is maintained simultaneously. • A multi-step predictive deep reinforcement learning algorithm is proposed. • A GC-LSTM algorithm is adopted in multi-step outdoor temperature prediction. • A model-free DDPG algorithm is adopted to manage HVAC system for building system. • The proposed method performs well in the HVAC system. [ABSTRACT FROM AUTHOR]

• The ambient temperature damping can be obtained in Ni-Fe-Ga strain glass alloys. • The maximum damping value is achieved in Ni-Fe-Ga alloys within the phase boundary. • The boundaries of martensitic nano-domains are very mobile in the phase boundary. [Display omitted] Strain glass alloys are promising candidates of high performance damping materials for their broad damping peaks. To realize the practical applications, the large damping capacity and broad ambient temperature range are highly desirable. However, the high damping behavior covering ambient temperature has not yet been achieved in strain glass systems, and how to obtain a maximum damping capacity in the same strain glass system remains unclear. Here, we reported the broad ambient temperature damping of Ni 73−x Fe x Ga 27 (x = 23.5–24.5) strain glass alloys. The damping capacity of Ni 73−x Fe x Ga 27 strain glass alloys increases as the compositions move towards the martensite/strain-glass phase boundary, and the maximum damping value (~0.12) is achieved in Ni 49.5 Fe 23.5 Ga 27 strain glass alloy within the phase boundary. The boundaries of martensitic nano-domain are very mobile in phase boundary region, which leads to broad and high damping peak around ambient temperature. Our results provide an effective way to design high performance damping materials. [ABSTRACT FROM AUTHOR]

Abstract: This paper addresses the long-term corrosion behavior of microarc oxidation coated Mg alloys immersed in simulated body fluid for 28days. The coatings on AZ31 Mg alloys were produced in the electrolyte of sodium phosphate (Na3PO4) at the concentration of 20g/L, 30g/L and 40g/L, respectively. Scanning electron microscope (SEM) and optical micrograph were used to observe the microstructure of the samples before and after corrosion. The composition of the MAO coating and corrosion products were determined by X-Ray Diffraction (XRD). Corrosion product identification showed that hydroxyapatite (HA) was formed on the surface of the corroded samples. The ratio of Ca/P in HA determined by the X-ray Fluorescence (XRF) technique showed that HA is an acceptable biocompatible implant material. The potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were employed to characterize the corrosion rate and the electrochemical impedance. The corrosion resistance of the coated Mg alloys can be enhanced by optimizing the electrolyte concentrations for fabricating samples, and is enhanced after immersing the coated samples in simulated body fluid for more than 14days. The enhanced corrosion resistance after long-term immersion is attributed to a corrosion product layer formed on the sample surface. The inhibition mechanism of the corrosion process is discussed and presented with an animation. [Copyright &y& Elsevier]

Abstract: The electrochemical corrosion behavior of microarc oxidation (MAO) coatings produced at various oxidation times on AZ31 Mg alloys was studied in a simulated body fluid (SBF). The potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) were used to characterize the corrosion behavior. The influences of the MAO time on the microstructure and corrosion properties are discussed. The initial porosity of the MAO coating was evaluated by the potentiodynamic polarization method. Post-corrosion phase identification showed that hydroxyapatite (HA) was formed on the surface of the samples. The ratio of Ca/P in HA was determined by the X-ray Fluorescence (XRF) technique. A physical model of the corrosion process with equivalent circuits at different corrosion stages is proposed. [Copyright &y& Elsevier]

Abstract: This paper addresses the effect of pulse frequency on the corrosion behavior of microarc oxidation (MAO) coatings on AZ31 Mg alloys in simulated body fluid (SBF). The MAO coatings were deposited by a pulsed DC mode at four different pulse frequencies of 300Hz, 500Hz, 1000Hz and 3000Hz with a constant pulse ratio. Potentiodynamic polarization and electrochemical impedance spectroscopy (EIS) tests were used for corrosion rate and electrochemical impedance evaluation. The corroded surfaces were examined by X-ray diffraction (XRD), X-ray fluorescence (XRF) and optical microscopy. All the results exhibited that the corrosion resistance of MAO coating produced at 3000Hz is superior among the four frequencies used. The XRD spectra showed that the corrosion products contain hydroxyapatite, brucite and quintinite. A model for corrosion mechanism and corrosion process of the MAO coating on AZ31 Mg alloy in the SBF is proposed. [Copyright &y& Elsevier]

Background: To introduce a model of community health service organization (as implemented in urban areas) to less developed rural areas in China and evaluate the impact of this model on health care utilization. Methods: The intervention involved developing leadership at county level, training rural health practitioners, providing clinical management guidelines and standards, encouraging clinic improvements and providing access to subsidies for public health work. We chose 7 townships and 49 administrative villages in Chongyi County as the intervention sites; 3 townships and 9 administrative villages in Luxi County as the comparison sites. Officers from county health bureaus and postgraduates from School of Public Health, Nanchang University visited each township hospital and village clinic in field together and made observations and interviewed clinic staff. Results: There was little change in health facilities or workforce in the two areas. However, there was an increase in the use of public health services at township and village level in the intervention sites in Chongyi. In these, the proportion of clinics which had developed a child health (under the age of 3) management system, maternal postpartum visit and chronic disease management increased from 53%, 51% and 47% to 78%, 73%, and 71% respectively. There was no significant change in the comparison sites. Conclusions: The trial demonstrated that it was feasible to implement a model of community health service delivery that was adapted to depressed rural areas because it required little organizational change, additional funding or personnel. The model had a positive impact on the provision of public health programs, a finding which has implications for efforts to improve access to primary health care in rural China. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we have investigated the grating erasure of a reduced LiNbO3:Fe crystal with different erasing wavelengths. The overall hologram evolution in the process of grating erasure is nonexponential due to strong absorption which is contrary to the mono-exponential law. The hologram in the rear part of the crystal can persist for a long time in the grating erasure due to weak erasing light intensity by strong absorption, which can enlarge the erasure time constant. From the erasure experiments, the global absorption can be taken as the optimum absorption to acquire a good trade-off between the sensitivity and hologram strength in the crystal. [Copyright &y& Elsevier]

Abstract: We have investigated ultraviolet (UV) photorefractive effect of lithium niobate doubly doped with Ce and Cu. It is found the diffraction efficiency shows oscillating behavior under UV-light-recording. A model in which electrons and holes can be excited from impurity centers in the UV region is proposed to study the oscillatory behavior of the diffraction efficiency. On the basis of the material equations and the coupled-wave equations, we found that the oscillatory behavior is due to the oscillation of the relative spatial phase shift Φ. And the electron–hole competition may cause the oscillation of the relative spatial phase shift. A switch point from electron grating to hole grating is chosen to realize nonvolatile readout by a red light with high sensitivity (0.4cm/J). [Copyright &y& Elsevier]

• CuO in mesoporous film can selectively adsorb and react with H 2 S gas molecules. • The adsorbed H 2 S changes both the film's mass and conductivity. • The SAW sensor has better performance because of the mass sensitive mechanism. • The SAW sensor can precisely detect H 2 S gas ranging from 5 ppb to100 ppm. Surface acoustic wave and chemiresistor based gas sensors integrated with a sensing layer of sol-gel CuO-Al 2 O 3 composite film were fabricated and their performance and mechanisms for H 2 S sensing were characterized and compared. In the composite film, CuO nanoparticles provide active sites for adsorption and reaction of H 2 S molecules while Al 2 O 3 nanoparticles help to form a uniform and mesoporous film structure, both of which enhance the sensitivity of the sensors by providing numerous active CuO surfaces. Through the comparative studies, the SAW based H 2 S sensor operated at room temperature showed a lower detection limit, higher sensitivity, better linearity and good selectivity to H 2 S gas with its concentration ranging from 5 ppb to 100 ppm, compared with those of the chemiresistor sensor, which are mainly attributed to the effective mass sensing properties of the SAW sensor, because a minor change in the mass of the film caused by adsorbed H 2 S molecules would lead to a significant and monotonous change of the resonant frequency of the SAW devices. [ABSTRACT FROM AUTHOR]

Gas sensors detecting low HCl gas concentrations used for early detection and continuous monitoring of leakage is of great concern because HCl gas is extremely hazardous. In order to tackle the potential hazardous threats, surface acoustic wave HCl gas sensors based on hydroxyl-rich sol-gel AlO x OH y films are reported. The fabricated SAW sensors show negative frequency shifts with exposure to HCl gas. The negative frequency shift is found to be caused by the increasing weight of sensitive films induced by the adsorption of HCl assisted by the hydroxyl groups and H 2 O captured on the films. The sensing performance of sensors is highly dependent on the heat-treatment temperature and thickness of films, as well as the ambient humidity because these parameters are highly influential on the amount of hydroxyl groups, H 2 O on films and the pore volume of films. For a sensor with a 198-nm-thick film annealed at 300 °C, it shows a response of ~ -1.4 KHz to HCl at a low concentration level (0.05 ppm) in an environment with a temperature of 25 °C and a RH value of 40%. Image 10822 • The sensor can detect HCl gas with a concentration as low as 0.05 ppm. • The hydroxyl groups and H 2 O on the sol-gel AlO x (OH) y films assist the response. • The frequency shift of sensor is derived from the change of weight of the films. [ABSTRACT FROM AUTHOR]

• The sensor can detect HCl gas with a concentration as low as 1 ppm. • H 2 O on CNCs layers assists the response. • Changes of mass of the CNCs layers cause the response. We report that cellulose nano-crystals (CNCs) can be used as a sensitive and selective layer in surface acoustic wave (SAW) sensors for in-situ HCl gas detection. CNCs were prepared through directly hydrolysis of cotton fiber and were spin-coated on quartz SAW resonators to form the sensitive layer. The CNCs have been identified to have abundant hydroxyl groups on their surfaces, which can act as the perfect adsorption sites for H 2 O, which can further act as the active sites for HCl gas adsorption. The absorption of HCl on the CNCs layer, thus leads to an increase of its mass, causing negative responses of the SAW sensors. Ambient humidity and thickness of CNCs layer are found to have significant influences on the responses of the SAW sensor. With an 80-nm-thick CNCs layer, the sensor shows a response of −2 kHz to 1 ppm HCl at 25 °C and relative humidity of 50 % with an excellent selectivity and recovery characteristics. [ABSTRACT FROM AUTHOR]

Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model. [ABSTRACT FROM AUTHOR]