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The loss of rural kindergarten teachers has become a common social concern in China, which is of great importance to the development of preschool education. This study conducted a survey of 2944 kindergarten teachers in mainland China, to explore the relationship between work–family conflict and turnover intention, the mediating effect of emotional exhaustion, and the moderating effect of professional identity. The study used the work–family conflict questionnaire, the emotional exhaustion scale, the turnover intention questionnaire, and the professional identity questionnaire. The results showed that (1) work–family conflict significantly predicted turnover intention; (2) emotional exhaustion played a mediating role between work–family conflict and turnover intention; and (3) professional identity moderated the latter half path of the mediation model, that is, strong professional identity alleviated the indirect predicting effect of work–family conflict on turnover intention through emotional exhaustion. The results clarified the influencing mechanism of work–family conflict on turnover intention, which could help improve rural preschool teachers’ positive emotions and reducing turnover.

Candida albicans is an opportunistic pathogenic yeast that can survive in both normoxic and hypoxic environments. The involvement of C. albicans secretome on host biological processes has been demonstrated. However, the immunoregulatory function of C. albicans secretome released under hypoxic condition remains unclear. This study demonstrated the differences in cytokine responses and protein profiles between secretomes prepared under normoxic and hypoxic conditions. Furthermore, the immunoregulatory effects of heat shock protein SSA1(Ssa1), a protein candidate enriched in the hypoxic secretome, were investigated. Stimulation of mouse bone marrow-derived macrophages (BMMs) with Ssa1 resulted in the significant production of interleukin (IL)-10, IL-6, and tumor necrosis factor (TNF)-α as well as the significant expression of M2b macrophage markers (CD86, CD274 and tumor necrosis factor superfamily member 14), suggesting that C. albicans Ssa1 may promote macrophage polarization towards an M2b-like phenotype. Proteomic analysis of Ssa1-treated BMMs also revealed that Ssa1 reduced inflammation-related factors (IL-18-binding protein, IL-1 receptor antagonist protein, OX-2 membrane glycoprotein and cis-aconitate decarboxylase) and enhanced the proteins involved in anti-inflammatory response (CMRF35-like molecule 3 and macrophage colony-stimulating factor 1 receptor). Based on these results, we investigated the effect of Ssa1 on C. albicans infection and showed that Ssa1 inhibited the uptake of C. albicans by BMMs. Taken together, our results suggest that C. albicans alters its secretome, particularly by promoting the release of Ssa1, to modulate host immune response and survive under hypoxic conditions.

Background: Spontaneous bacterial peritonitis (SBP) is a severe complication in cirrhosis patients with ascites, leading to high mortality rates if not promptly treated. However, specific prediction models for SBP are lacking. Aims: This study aimed to compare commonly used cirrhotic prediction models (CTP score, MELD, MELD-Na, iMELD, and MELD 3.0) for short-term mortality prediction and develop a novel model to improve mortality prediction. Methods: Patients with the first episode of SBP were included. Prognostic values for mortality were assessed using AUROC analysis. A novel prediction model was developed and validated. Results: In total, 327 SBP patients were analyzed, with HBV infection as the main etiologies. MELD 3.0 demonstrated the highest AUROC among the traditional models. The novel model, incorporating HRS, exhibited superior predictive accuracy for in-hospital in all patients and 3-month mortality in HBV-cirrhosis, with AUROC values of 0.827 and 0.813 respectively, surpassing 0.8. Conclusions: MELD 3.0 score outperformed the CTP score and showed a non-significant improvement compared to other MELD-based scores, while the novel SBP model demonstrated impressive accuracy. Internal validation and an HBV-related cirrhosis subgroup sensitivity analysis supported these findings, highlighting the need for a specific prognostic model for SBP and the importance of preventing HRS development to improve SBP prognosis.

The key to evaluating the health status of cable-stayed bridges lies in the accuracy of cable force measurement. When measuring the cable force using the conventional frequency method, the clearance between the bracing cable and the protective tube is typically disregarded. Moreover, due to their large size, existing vibration sensors are difficult to install into protective tubes for steel strand-type bracing cables to measure the cable force. To address the above difficulties, a type of thin rod vibration sensor only 5 mm in diameter was designed based on the high sensitivity of Fiber Bragg grating (FBG), and high-throughput data processing software for engineering calculation (EC) was self-developed. Then, the recognition principle of the thin rod vibration sensor was theoretically analyzed and a step-by-step tension test was carried out. The results demonstrated that the relative error of the cable force measured by the thin rod vibration sensor within 12.865 Hz was less than 5% and the sensitivity reached 28.7 pm/Hz, indicating its high measurement precision. Upon subsequent application of the thin rod vibration sensor to a monitoring test in the field, the relative error of the fundamental frequency between artificial and natural excitations was less than 4%. In addition, the error relative to both the theoretical frequency and the third-party sampling frequency was less than 5%, further verifying the accuracy and applicability for monitoring the cable force of bridges under natural excitation. Compared with the traditional cantilever FBG sensor, the improved sensor with supporting data processing software has the advantages of small cross-section, high reliability, and good sensitivity. The research results can provide a reference for the subsequent accurate measurement of cable force and the development of a supporting sensor data processing system.

Tamias Sibiricus is the only member of the genus Tamias, a significant and vigorous seed distributor and vital food for their predators. No information is known about the strict diet, gut microbiota structure, and metabolism profile of chipmunks and how they diversify seasonally. The above factors, as well as flexibility toward seasonal shifts, are critical in defining its growth rates, health, survivorship, and population stability. This study explored the diet, gut microbiota composition, and chipmunk metabolism. Additionally, the influence of different seasons was also investigated by using next-generation sequencing. Results revealed that seasons strongly affected a diet: streptophyte accounted for 37% in spring, which was lower than in summer (34.3%) and autumn (31.4%). Further, Ascomycota was observed at 43.8% in spring, which reduced to 36.6% in summer and the lowest (31.3%) in autumn. Whereas, nematodes showed maximum abundance from spring (15.8%) to summer (20.6%) and autumn (24.1%). These results signify the insectivorous nature of the chipmunk in summer and autumn. While herbivorous and fungivorous nature in spring. The DNA analysis revealed that chipmunk mainly feeds on fungi, including Aspergillus and Penicillium genus. Similar to diet composition, the microbiome also exhibited highly significant dissimilarity (p < 0.001, R = 0.235) between spring/autumn and spring/summer seasons. Proteobacteria (35.45%), Firmicutes (26.7%), and Bacteroidetes (23.59%) were shown to be the better discriminators as they contributed the most to causing differences between seasons. Moreover, PICRUSt showed that the assimilation of nutrients were also varied seasonally. The abundance of carbohydrates, lipids, nucleotides, xenobiotics, energy, terpenoids, and polyketides metabolism was higher in spring than in other seasons. Our study illustrates that seasonal reconstruction in the chipmunk diet has a significant role in shaping temporal variations in gut microbial community structure and metabolism profile.

The technology of fault diagnosis is helpful to improve the reliability of wind turbines, and further reduce the operation and maintenance cost at wind farms. However, in reality, wind turbines are not allowed to operate with faults, so few fault samples could be obtained. With a small amount of training data, traditional fault diagnosis models that need huge samples under a deep learning framework are difficult to maintain with high accuracy and effectiveness. Few-shot learning can effectively solve the problem of overfitting caused by fewer fault samples in model training. In view of model-agnostic meta-learning (MAML), this paper proposes a model for few-shot fault diagnosis of the wind turbines drivetrain, which is named model-agnostic meta-baseline (MAMB). The training data is input to the base classification model for pre-training, then, some data is randomly selected from the training set to form multiple meta-learning tasks that are utilized to train the MAML to finally fine-tune the later layers of the model at a smaller learning rate. The proposed model was analyzed by the small samples of the bearing data from Case Western Reserve University (CWRU) data, the generator bearings, and gearboxes vibration data in wind turbines under randomly changing operating conditions. The results verified that the proposed method was superior in one-shot, five-shot, and ten-shot tasks of wind turbines.

Background and Aims: The Albumin-Bilirubin (ALBI) grade is a good index for liver function evaluation and is also associated with the outcomes of hepatocellular carcinoma patients receiving TACE. However, the correlation between the dynamic change to the ALBI score and clinical outcome is seldom discussed. Therefore, this study aimed to investigate the application of ALBI grade and dynamic change of ALBI grade (delta ALBI grade) after first TACE for prognosis prediction in HCC patients with chronic hepatitis C infection. Method: From January 2005 to December 2015, newly diagnosed naive chronic hepatitis C-hepatocellular carcinoma (CHC-HCC) patients who were treated with TACE as the initial treatment at the Chang Gung Memorial Hospital, Linkou Medical Center, were retrospectively recruited. The pre-treatment host factors, tumor status and noninvasive markers were collected. The Cox regression model was used to identify independent predictors of overall survival and tumor recurrence. Results: Among 613 treatment-naive CHC-HCC patients, 430 patients died after repeated TACE during a median follow-up of 26.9 months. Complete remission after repeated TACE occurred in 46.2% patients, and 208 patients (33.9%) had tumor recurrence, with a median recurrence-free interval of 8.5 months. In Cox regression analysis, ALBI grade II/III (aHR: 1.088, p = 0.035) and increased delta ALBI grade (aHR: 1.456, p = 0.029) were independent predictive factors for tumor recurrence. Furthermore, ALBI grade II/III (aHR: 1.451, p = 0.005) and increased delta ALBI grade during treatment (aHR: 1.436, p = 0.006) were predictive factors for mortality, while achieving complete response after repeated TACE (aHR: 0.373, p < 0.001) and anti-viral therapy (aHR: 0.580, p = 0.002) were protective factors for mortality. Conclusion: Both ALBI and delta ALBI grade are independent parameters to predict survival and tumor recurrence of CHC-HCC patients receiving TACE treatment.

Vibration analysis is an effective tool for the condition monitoring and fault diagnosis of wind turbine drivetrains. It enables the defect location of mechanical subassemblies and health indicator construction for remaining useful life prediction, which is beneficial to reducing the operation and maintenance costs of wind farms. This paper analyzes the structure features of different drivetrains of mainstream wind turbines and introduces a vibration data acquisition system. Almost all the research on the vibration-based diagnosis algorithm for wind turbines in the past decade is reviewed, with its effects being discussed. Several challenging tasks and their solutions in the vibration-based fault detection of wind turbine drivetrains are proposed from the perspective of practicality for wind turbines, including the fault detection of planetary subassemblies in multistage wind turbine gearboxes, fault feature extraction under nonstationary conditions, fault information enhancement techniques and health indicator construction. Numerous naturally damaged cases representing the real operational features of industrial wind turbines are given, with a discussion of the failure mechanism of defective parts in wind turbine drivetrains as well.

Hyperspectral band selection algorithms are crucial for processing high-dimensional data, which enables dimensionality reduction, improves data analysis, and enhances computational efficiency. Among these, attention-based algorithms have gained prominence by ranking bands based on their discriminative capability. However, they require a large number of model parameters, which increases the need for extensive training data. To address this challenge, we propose Band Selection through Discrete Relaxation (BSDR), a novel deep learning-based algorithm. BSDR reduces the number of learnable parameters by focusing solely on the target bands, which are typically far fewer than the original bands, thus resulting in a data-efficient configuration that minimizes training data requirements and reduces training time. The algorithm employs discrete relaxation, transforming the discrete problem of band selection into a continuous optimization task, which enables gradient-based search across the spectral dimension. Through extensive evaluations on three benchmark datasets with varying spectral dimensions and characteristics, BSDR demonstrates superior performance for both regression and classification tasks, achieving up to 25% and 34.6% improvements in overall accuracy, compared to the latest attention-based and traditional algorithms, respectively, while reducing execution time by 96.8% and 97.18%. These findings highlight BSDR’s effectiveness in addressing key challenges in hyperspectral band selection.

Predicting the remaining useful life (RUL) of critical subassemblies can provide an advanced maintenance strategy for wind turbines installed in remote regions. This paper proposes a novel prognostic approach to predict the RUL of bearings in a wind turbine gearbox. An artificial neural network (NN) is used to train data-driven models and to predict short-term tendencies of feature series. By combining the predicted and training features, a polynomial curve reflecting the long-term degradation process of bearings is fitted. Through solving the intersection between the fitted curve and the pre-defined threshold, the RUL can be deduced. The presented approach is validated by an operating wind turbine with a faulty bearing in the gearbox.

A nonlinear coupled dynamic model of a rod fastening rotor under rub-impact and initial permanent deflection was developed in this paper. The governing motion equation was derived by the D’Alembert principle considering the contact characteristic between disks, nonlinear oil-film force, rub-impact force, unbalance mass, etc. The contact effects between disks was modeled as a flexural spring with cubical nonlinear stiffness. The coupled nonlinear dynamic phenomena of the rub-impact rod fastening rotor bearing system with initial permanent deflection were investigated by the fourth-order Runge-Kutta method. Bifurcation diagram, vibration waveform, frequency spectrum, shaft orbit and Poincaré map are used to illustrate the rich diversity of the system response with complicated dynamics. The studies indicate that the coupled dynamic responses of the rod fastening rotor bearing system under rub-impact and initial permanent deflection exhibit a rich nonlinear dynamic diversity, synchronous periodic-1 motion, multiple periodic motion, quasi-periodic motion and chaotic motion can be observed under certain conditions. Larger radial stiffness of the stator will simplify the system motion and make the oil whirl weaker or even disappear at a certain rotating speed. With the increase of initial permanent deflection length, the instability speed of the system gradually rises, and the chaotic motion region gets smaller and smaller. The corresponding results can provide guidance for the fault diagnosis of a rub-impact rod fastening rotor with initial permanent deflection and contribute to the further understanding of the nonlinear dynamic characteristics of the rod fastening rotor bearing system.

The greater yam (Dioscorea alata), a widely cultivated and nutritious food crop, suffers from widespread yield reduction due to anthracnose caused by Colletotrichum gloeosporioides. Latent infection often occurs before anthracnose phenotypes can be detected, making early prevention difficult and causing significant harm to agricultural production. Through comparative genomic analysis of 60 genomes of 38 species from the Colletotrichum genus, this study identified 17 orthologous gene groups (orthogroups) that were shared by all investigated C. gloeosporioides strains but absent from all other Colletotrichum species. Four of the 17 C. gloeosporioides-specific orthogroups were used as molecular markers for PCR primer designation and C. gloeosporioides detection. All of them can specifically detect C. gloeosporioides out of microbes within and beyond the Colletotrichum genus with different sensitivities. To establish a rapid, portable, and operable anthracnose diagnostic method suitable for field use, specific recombinase polymerase amplification (RPA) primer probe combinations were designed, and a lateral flow (LF)-RPA detection kit for C. gloeosporioides was developed, with the sensitivity reaching the picogram (pg) level. In conclusion, this study identified C. gloeosporioides-specific molecular markers and developed an efficient method for C. gloeosporioides detection, which can be applied to the prevention and control of yam anthracnose as well as anthracnose caused by C. gloeosporioides in other crops. The strategy adopted by this study also serves as a reference for the identification of molecular markers and diagnosis of other plant pathogens.

Insect pests like Tribolium and Sitophilus siblings are major threats to grain storage and processing, causing quality and quantity losses that endanger food security. These closely related species, having very similar morphological and biological characteristics, often exhibit variations in biology and pesticide resistance, complicating control efforts. Accurate pest species identification is essential for effective control, but workplace safety in the grain bin associated with grain deterioration, clumping, fumigator hazards, and air quality create challenges. Therefore, there is a pressing need for an online automated detection system. In this work, we enriched the stored-grain pest sibling image dataset, which includes 25,032 annotated Tribolium samples of two species and five geographical strains from real warehouse and another 1774 from the lab. As previously demonstrated on the Sitophilus family, Convolutional Neural Networks demonstrate distinct advantages over other model architectures in detecting Tribolium. Our CNN model, MCSNet+, integrates Soft-NMS for better recall in dense object detection, a Position-Sensitive Prediction Model to handle translation issues, and anchor parameter fine-tuning for improved matching and speed. This approach significantly enhances mean Average Precision (mAP) for Sitophilus and Tribolium, reaching a minimum of 92.67 ± 1.74% and 94.27 ± 1.02%, respectively. Moreover, MCSNet+ exhibits significant improvements in prediction speed, advancing from 0.055 s/img to 0.133 s/img, and elevates the recognition rates of moving insect sibling species in real wheat storage and visible light, rising from 2.32% to 2.53%. The detection performance of the model on laboratory-captured images surpasses that of real storage facilities, with better results for Tribolium compared to Sitophilus. Although inter-strain variances are less pronounced, the model achieves acceptable detection results across different Tribolium geographical strains, with a minimum recognition rate of 82.64 ± 1.27%. In real-time monitoring videos of grain storage facilities with wheat backgrounds, the enhanced deep learning model based on Convolutional Neural Networks successfully detects and identifies closely related stored-grain pest images. This achievement provides a viable solution for establishing an online pest management system in real storage facilities.

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques.

Antimicrobial resistance among uropathogens is a particularly pressing problem in the Asia-Pacific region. The objectives of this study were to determine the incidence and susceptibility of uropathogens upon hospital admission and to develop a risk-scoring model to predict the presence of ceftriaxone-resistance uropathogens (CrP). This was a retrospective observational cohort study of patients with a positive urine culture within 48 h of presentation at National University Hospital, Singapore between June 2015 and August 2015. Escherichia coli was the most common uropathogen isolated (51.7%), followed by Klebsiella pneumonia (15.1%) and Pseudomonas aeruginosa (8.2%). Overall, 372 out of 869 isolates (42.8%) were resistant to ceftriaxone. Hospitalization for ≥2 days within past 30 days, antibiotic use within the past 3 months and male gender were associated with the presence of CrP. A risk score based on these parameters successfully predicted CrP with an area under the curve of 0.68. The risk score will help clinicians to accurately predict antibiotic resistance at the individual patient level and allow physicians to safely prescribe empiric ceftriaxone in patients at low risk of CrP, thus reducing the antibiotic selection pressure that is driving carbapenem resistance in hospitals throughout Asia.

Electroencephalogram-based emotion recognition (EEG-ER) has received increasing attention in the fields of health care, affective computing, and brain-computer interface (BCI). However, satisfactory ER performance within a bi-dimensional and non-discrete emotional space using single-trial EEG data remains a challenging task. To address this issue, we propose a three-layer scheme for single-trial EEG-ER. In the first layer, a set of spectral powers of different EEG frequency bands are extracted from multi-channel single-trial EEG signals. In the second layer, the kernel Fisher’s discriminant analysis method is applied to further extract features with better discrimination ability from the EEG spectral powers. The feature vector produced by layer 2 is called a kernel Fisher’s emotion pattern (KFEP), and is sent into layer 3 for further classification where the proposed imbalanced quasiconformal kernel support vector machine (IQK-SVM) serves as the emotion classifier. The outputs of the three layer EEG-ER system include labels of emotional valence and arousal. Furthermore, to collect effective training and testing datasets for the current EEG-ER system, we also use an emotion-induction paradigm in which a set of pictures selected from the International Affective Picture System (IAPS) are employed as emotion induction stimuli. The performance of the proposed three-layer solution is compared with that of other EEG spectral power-based features and emotion classifiers. Results on 10 healthy participants indicate that the proposed KFEP feature performs better than other spectral power features, and IQK-SVM outperforms traditional SVM in terms of the EEG-ER accuracy. Our findings also show that the proposed EEG-ER scheme achieves the highest classification accuracies of valence (82.68%) and arousal (84.79%) among all testing methods.

Major depressive disorder (MDD) has become a leading contributor to the global burden of disease; however, there are currently no reliable biological markers or physiological measurements for efficiently and effectively dissecting the heterogeneity of MDD. Here we propose a novel method based on scalp electroencephalography (EEG) signals and a robust spectral-spatial EEG feature extractor called kernel eigen-filter-bank common spatial pattern (KEFB-CSP). The KEFB-CSP first filters the multi-channel raw EEG signals into a set of frequency sub-bands covering the range from theta to gamma bands, then spatially transforms the EEG signals of each sub-band from the original sensor space to a new space where the new signals (i.e., CSPs) are optimal for the classification between MDD and healthy controls, and finally applies the kernel principal component analysis (kernel PCA) to transform the vector containing the CSPs from all frequency sub-bands to a lower-dimensional feature vector called KEFB-CSP. Twelve patients with MDD and twelve healthy controls participated in this study, and from each participant we collected 54 resting-state EEGs of 6 s length (5 min and 24 s in total). Our results show that the proposed KEFB-CSP outperforms other EEG features including the powers of EEG frequency bands, and fractal dimension, which had been widely applied in previous EEG-based depression detection studies. The results also reveal that the 8 electrodes from the temporal areas gave higher accuracies than other scalp areas. The KEFB-CSP was able to achieve an average EEG classification accuracy of 81.23% in single-trial analysis when only the 8-electrode EEGs of the temporal area and a support vector machine (SVM) classifier were used. We also designed a voting-based leave-one-participant-out procedure to test the participant-independent individual classification accuracy. The voting-based results show that the mean classification accuracy of about 80% can be achieved by the KEFP-CSP feature and the SVM classifier with only several trials, and this level of accuracy seems to become stable as more trials (i.e.,

Perovskite LaFeO3 nanofillers (0.1%) are incorporated into a quaternized poly(vinyl alcohol) (QPVA) matrix for use as hydroxide-conducting membranes in direct alkaline methanol fuel cells (DAMFCs). The as-synthesized LaFeO3 nanofillers are amorphous and functionalized with cetyltrimethylammonium bromide (CTAB) surfactant. The annealed LaFeO3 nanofillers are crystalline without CTAB. The QPVA/CTAB-coated LaFeO3 composite membrane shows a defect-free structure while the QPVA/annealed LaFeO3 film has voids at the interfaces between the soft polymer and rigid nanofillers. The QPVA/CTAB-coated LaFeO3 composite has lower methanol permeability and higher ionic conductivity than the pure QPVA and QPVA/annealed LaFeO3 films. We suggest that the CTAB-coated LaFeO3 provides three functions to the polymeric composite: increasing polymer free volume, ammonium group contributor, and plasticizer to enhance the interfacial compatibility. The composite containing CTAB-coated LaFeO3 results in superior cell performance. A maximum power density of 272 mW cm−2 is achieved, which is among the highest power outputs reported for DAMFCs in the literature.

This work aims to clarify the effect of magnetic graphene oxide (GO) reorientation in a polymer matrix on the ionic conduction and methanol barrier properties of nanocomposite membrane electrolytes. Magnetic iron oxide (Fe3O4) nanoparticles were prepared and dispersed on GO nanosheets (GO-Fe3O4). The magnetic GO-Fe3O4 was imbedded into a quaternized polyvinyl alcohol (QPVA) matrix and crosslinked (CL-) with glutaraldehyde (GA) to obtain a polymeric nanocomposite. A magnetic field was applied in the through-plane direction during the drying and film formation steps. The CL-QPVA/GO-Fe3O4 nanocomposite membranes were doped with an alkali to obtain hydroxide-conducting electrolytes for direct methanol alkaline fuel cell (DMAFC) applications. The magnetic field-reoriented CL-QPVA/GO-Fe3O4 electrolyte demonstrated higher conductivity and lower methanol permeability than the unoriented CL-QPVA/GO-Fe3O4 membrane or the CL-QPVA film. The reoriented CL-QPVA/GO-Fe3O4 nanocomposite was used as the electrolyte in a DMAFC and resulted in a maximum power density of 55.4 mW·cm−2 at 60 °C, which is 73.7% higher than that of the composite without the magnetic field treatment (31.9 mW·cm−2). In contrast, the DMAFC using the CL-QPVA electrolyte generated only 22.4 mW·cm−2. This research proved the surprising benefits of magnetic-field-assisted orientation of GO-Fe3O4 in facilitating the ion conduction of a polymeric electrolyte.

Existing automatic building extraction methods are not effective in extracting buildings which are small in size and have transparent roofs. The application of large area threshold prohibits detection of small buildings and the use of ground points in generating the building mask prevents detection of transparent buildings. In addition, the existing methods use numerous parameters to extract buildings in complex environments, e.g., hilly area and high vegetation. However, the empirical tuning of large number of parameters reduces the robustness of building extraction methods. This paper proposes a novel Gradient-based Building Extraction (GBE) method to address these limitations. The proposed method transforms the Light Detection And Ranging (LiDAR) height information into intensity image without interpolation of point heights and then analyses the gradient information in the image. Generally, building roof planes have a constant height change along the slope of a roof plane whereas trees have a random height change. With such an analysis, buildings of a greater range of sizes with a transparent or opaque roof can be extracted. In addition, a local colour matching approach is introduced as a post-processing stage to eliminate trees. This stage of our proposed method does not require any manual setting and all parameters are set automatically from the data. The other post processing stages including variance, point density and shadow elimination are also applied to verify the extracted buildings, where comparatively fewer empirically set parameters are used. The performance of the proposed GBE method is evaluated on two benchmark data sets by using the object and pixel based metrics (completeness, correctness and quality). Our experimental results show the effectiveness of the proposed method in eliminating trees, extracting buildings of all sizes, and extracting buildings with and without transparent roof. When compared with current state-of-the-art building extraction methods, the proposed method outperforms the existing methods in various evaluation metrics.