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We investigated how elevated CO 2 affects the responses of Gracilariopsis lemaneiformis and Ulva lactuca to NH 4 + enrichments. All algae were incubated under four nutritional conditions (zero addition, 100, 500, and 2500 μM NH 4 +), and two CO 2 levels (390 ppm and 1000 ppm). The growth, photosynthesis, and soluble protein contents of both species increased under the eutrophication condition (100 μM NH 4 +). However, the growth and carotenoid contents of the two species declined when NH 4 + concentration increased. Under the super eutrophication condition (2500 μM NH 4 +), all indexes measured in G. lemaneiformis were suppressed, while the growth and photosynthesis in U. lactuca changed indistinctively, both compared with the control. Moreover, under the super eutrophication condition, elevated CO 2 reduced the suppression in the growth of G. lemaneiformis , but decreased the growth of U. lactuca. Nonetheless, G. lemaneiformis displayed much lower growth rates than U. lactuca under the super eutrophication and elevated CO 2 condition. • Responses of two algal species to NH 4 + and CO 2 enrichments were investigated. • 100 μmol L−1 NH 4 + enhanced growth and photosynthesis in both algal species. • 2500 μmol L−1NH 4 + was toxic to Gracilariopsis lemaneiformis but not Ulva lactuca. • Elevated CO 2 reduced the toxicity of 2500 μmol L−1 NH 4 + to G. lemaneiformis. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) play a crucial role in the life processes of organisms and affect the occurrence of many complex human diseases. Discovering potential links between diseases and miRNAs can aid in the prevention, diagnosis, and therapy of complex human diseases. Identifying diseases and miRNA associations through existing biological experiments is expensive, while scientific computing approaches can quickly and efficiently predict the potential links between diseases and miRNAs. We propose SODNMF-DM, a SODNMF based Disease-MiRNA matrix factorisation algorithm. The SODNMF-DM model exhibited an AUC of 0.9051 and an AUPR of 0.4774, respectively. To directly verify the effectiveness of SODNMF-DM, we studied three common human cancers. The experimental results showed that 25 of the top 30 potential miRNAs predicted by our proposed SODNMF-DM model were confirmed by HMDDv3.0 database. Conclusion: SODNMF-DM outperformed four other approaches in AUC and AUPR. In addition, our proposed approach exhibited good performance in practical cases. The approach presented in this study is thus expected to complement the field of biological research. • We fully consider disease similarity and miRNA similarity from multiple perspectives. • We use the alternating gradient descent method to ensure the stability of the solution. • The SODNMF-DM model has low overall complexity, simple operation and easy replication. [ABSTRACT FROM AUTHOR]

App usage is now a ubiquitous phenomenon, but little is known about what types of psychological needs are met by which apps. We proposed a method to label automatically mobile apps in terms of whether and to what extent they can satisfy users’ particular psychological needs. First, using the grounded theory approach, we conducted semi-structured in-depth interviews to identify types of needs associated with app usage. Substantive and theoretical coding of the data from the interviews as well as data from samples of app reviews yielded eight types of psychological needs app users had: utilitarian, low-cost, security, health, hedonic, social, cognitive, and self-actualization needs. Second, using the needs corpus (words and phrases) generated above, a classifier was trained using latent Dirichlet allocation (LDA) and support vector machine (SVM) algorithms to filter reviews in terms of whether they included needs-related comments. The classifier showed good performance. Finally, Labeled-LDA was used to automatically provide each review with multiple labels of the types of needs mentioned and the apps were analyzed for the different types of needs they satisfied. [ABSTRACT FROM AUTHOR]

Automatic, accurate and online fault detection of heating ventilation air conditioning (HVAC) subsystems, such as chillers, is highly demanded in building management system (BMS) to prevent energy waste and high maintenance cost. However, most fault detection techniques require rich faulty training data which is usually unavailable. In this study, a novel hybrid method is proposed to detect faults for chiller subsystems without any faulty training data available, i.e. by training the normal data only. A hybrid feature selection algorithm is applied to the chiller dataset collected by ASHRAE project 1043-RP to select the most significant feature variables. An online classification framework is introduced by combining an Extended Kalman Filter (EKF) model and a recursive one-class support vector machine (ROSVM). Experiment results show that the proposing algorithm detects typical chiller faults with high accuracy rates and requires less feature variables compared to existing works. [ABSTRACT FROM AUTHOR]

Acute pancreatitis is an inflammatory disorder of the pancreas. Medical imaging, such as computed tomography (CT), has been widely used to detect volume changes in the pancreas for acute pancreatitis diagnosis. Many pancreas segmentation methods have been proposed but no methods for pancreas segmentation from acute pancreatitis patients. The segmentation of an inflamed pancreas is more challenging than the normal pancreas due to the following two reasons. 1) The inflamed pancreas invades surrounding organs and causes blurry boundaries. 2) The inflamed pancreas has higher shape, size, and location variability than the normal pancreas. To overcome these challenges, we propose an automated CT pancreas segmentation approach for acute pancreatitis patients by combining a novel object detection approach and U-Net. Our approach includes a detector and a segmenter. Specifically, we develop an FCN-guided region proposal network (RPN) detector to localize the pancreatitis regions. The detector first uses a fully convolutional network (FCN) to reduce the background interference of medical images and generates a fixed feature map containing the acute pancreatitis regions. Then the RPN is employed on the feature map to precisely localize the acute pancreatitis regions. After obtaining the location of pancreatitis, the U-Net segmenter is used on the cropped image according to the bounding box. The proposed approach is validated using a collected clinical dataset with 89 abdominal contrast-enhanced 3D CT scans from acute pancreatitis patients. Compared with other start-of-the-art approaches for normal pancreas segmentation, our method achieves better performance on both localization and segmentation in acute pancreatitis patients. [ABSTRACT FROM AUTHOR]

• Effectively detect anomalies in multivariate time series (MTS) is challenging. • In practical applications, few algorithms are widely used. • A space-embedding strategy for anomaly detection (SES-AD) in MTS was developed. • SES-AD model works efficiently in benchmark datasets and real datasets. • SES-AD outperformed other classical approaches. Anomaly detection of time series has always been a hot topic in academia and industry. However, many existing multivariant time series methods suffer from common challenges due to increased dimensionality. In this study, we developed a space-embedding strategy for anomaly detection in multivariant time series (SES-AD). As a hybrid model, SES-AD did not directly search the discords from the original time series, but projected the raw sequence to a lower dimensional space so that the significant abrupt change points in the new space can be easily captured from the dissimilarity vector. Finally, the potential abnormities were determined by a statistical strategy. To verify the performance of our method, SES-AD was applied to an extensive number of multivariate time series. The experimental results suggest that SES-AD is more efficient than five existing approaches. Overall, the SES-AD model is suitable for solving anomaly detection for high-dimensional datasets and guarantees computational effectiveness and accuracy. [ABSTRACT FROM AUTHOR]

• Inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated. • 1% (w/w) of κ-carrageenan caused a decreased formation of PhIP by around 90 %. • κ-Carrageenan inhibited PhIP formation via trapping both creatinine and phenylacetaldehyde. 2-amino-1-methyl-6-phenylimidazo[4,5- b ]pyridine (PhIP) is a mutagen and a rodent carcinogen mainly formed in thermally processed muscle foods. Hydrocolloids are widely used as thickeners, gelling agents and stabilizers to improve food quality in the food industry. In this study, the inhibitory effects of eight hydrocolloids on the formation of PhIP were investigated in both chemical models and beef patties. 1% (w/w) of carboxymethylcellulose V, κ-carrageenan, alginic acid, and pectin significantly reduced PhIP formation by 53 %, 54 %, 48 %, and 47 %, respectively in chemical models. In fried beef patties, κ-carrageenan appeared to be most capable of inhibiting PhIP formation among the eight tested hydrocolloids. 1% (w/w) of κ-carrageenan caused a decreased formation of PhIP by 90 %. 1% (w/w) of κ-carrageenan also significantly reduced the formation of other heterocyclic aromatic amines including MeIQx and 4,8-DiMeIQx by 64 % and 48 %, respectively in fried beef patties. Further mechanism study showed that κ-carrageenan addition decreased the PhIP precursor creatinine residue and reduced the content of Maillard reaction intermediates including phenylacetaldehyde and aldol condensation product in the chemical model. κ-Carrageenan may inhibit PhIP formation via trapping both creatinine and phenylacetaldehyde. The structures of adducts formed between κ-carrageenan and creatinine and κ-carrageenan and phenylacetaldehyde merits further study. [ABSTRACT FROM AUTHOR]

Data-driven Automatic fault detection and diagnosis (AFDD) for air handling units (AHUs) is crucial for ensuring the stable operation and energy consumption of the heating ventilation air-conditioning (HVAC) system. However, traditional machine learning methods often underperform when confronted with insufficient training sample data, especially when lacking samples from the fault types. Based on the issues of insufficient samples from the fault types and imbalanced training dataset, this study proposes a novel AFDD approach using transformer integrated conditional Wasserstein generative adversarial network and deep reinforcement learning (TCWGAN-DRL) to synthesize the fault data and select high quality synthetic data samples. Firstly, we utilize the proposed TransCWGAN to synthesize fault samples. Then, reinforcement learning is utilized to select high quality synthetic samples. Finally, the filtered samples and the real fault samples are merged to form the training dataset for conventional supervised learning classifiers. Experimental results demonstrate that the enriched training dataset can effectively improve the AFDD results and outperforms recently published existing methods, for instance, compared to the suboptimal model, our method exhibits an increase in fault recognition accuracy of 4.9%, 3.66%, and 4.02% when the number of real fault samples is 15, 20, and 30, respectively. [ABSTRACT FROM AUTHOR]

Fault detection and diagnosis (FDD) plays an essential role of maintaining large-scale heating ventilation air conditioning (HVAC) systems for industrial usage. The process of selecting important features is a crucial step for FDD methods of HVAC components, such as chillers. A suitable feature selection method selects the minimum number of features to save the number of installing sensors, and simultaneously maximizes the FDD accuracy. According to the surveyed related works, it is found that most existing works only focus on maximizing the classification accuracy, and miss two important points. First, the misclassification costs of false positive and false negative are different for chiller FDD. Second, the selected feature subsets must be sequential for real-world applications. In this study, a cost-sensitive and sequential feature selection algorithm for chiller FDD is proposed to select the most important features using a back-tracing sequential forward feature selection (BT-SFS) algorithm. The ASHRAE dataset collected by project number 1043-RP is utilized. Support vector machine (SVM), which is proved to be one of the most effective FDD classification method for chillers in existing works, is employed for accuracy measurement. This work fills in the gap between theoretical HVAC FDD methods and real-world HVAC FDD applications. [ABSTRACT FROM AUTHOR]

• This work applies an unsupervised learning technique, generative adversarial network (GAN), to deal with the imbalanced training dataset problem in data-driven AHU FDD. • Detailed steps of applying GAN to generate artificial faulty training samples are demonstrated and deeply analyzed. • A complete AHU FDD framework integrating extensions of GAN is proposed. • The existing GAN-based AHU FDD framework has been optimized. • Comprehensive experimental results are shown before and after applying GAN extensions to the traditional supervised FDD framework to illustrate the importance of GAN. Supervised learning techniques have witnessed significant successes in fault detection and diagnosis (FDD) for heating ventilation and air-conditioning (HVAC) systems. Despite the good performance, these techniques heavily rely on balanced datasets that contain a large amount of both faulty and normal data points. In real-world scenarios, however, it is often very challenging to collect a sufficient amount of faulty training samples that are necessary for building a balanced training dataset. In this paper, we introduce a framework that utilizes the generative adversarial network (GAN) to address the imbalanced data problem in FDD for air handling units (AHUs). To this end, we first show the necessary procedures of applying GAN to increase the number of faulty training samples in the training pool and re-balance the training dataset. The proposed framework then uses supervised classifiers to train the re-balanced datasets. Finally, we present a comparative study that illustrates the advantages of the proposed method for FDD of AHU with various evaluation metrics. Our work demonstrates the promising prospects of performing robust FDD of AHU with a limited number of faulty training samples. [ABSTRACT FROM AUTHOR]