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Disease-gene association, an important problem in the biomedical area, can be used to early intervene the treat of deadly diseases. Recently, models based on graph convolutional networks (GCNs) have outperformed previous linear models on predicting the new disease-gene associations, due to its strong capability to capture the relevance of disease and gene in the new semantic embedding space. However, a single embedding vector cannot informatively represent a disease or gene and cannot characterize the uncertainty of their features. We propose to learn a distribution for a disease or gene under the variational autoencoder framework, which enables disease-gene associations to be modeled by the Kullback-Leibler divergence. The experiment results show that our model outperforms the state-of-the-art models significantly in various metrics.

Problem How to design an intelligent multimodal fusion hazard prediction system using AIoT (Artificial Intelligence & Internet of Things) technology to predict some potential dangers. This article will provide some ideas and methods. Introduction This article designs an intelligent multimodal fusion hazard prediction system based on AIoT technology. The system mainly consists of an IoT hardware device and an AI multimodal and multi-dimensional Hazard Prediction Algorithm. Method This article will take baby cribs as an example, using this system to empower traditional baby cribs and transform them into intelligent baby cribs. Results In this example, the system can detect the real-time status of the baby and predict upcoming dangers, including kicking the quilt, wetting the bed, fever, crying, climbing over the crib, and turning over. Hypothesis Assuming that intelligent systems can detect the status of infants in real-time and make predictions before potential dangers occur, to promptly alert parents. Perhaps it can prevent some irreversible dangers from occurring. Background This system solves the pain point problem of parents having to take care of their babies after working hard. Purpose In this scenario, the system provides a design scheme for an intelligent multimodal fusion hazard prediction system with a temporary care function. The application of this system to baby cribs not only reduces the burden on parents but also ensures the safety and comfort of the baby. Transferability At the same time, the system has transferability, and its design concept can be transferred to other application scenarios, such as hospital care for infants, young children, or elderly patients, as well as childcare stations for abandoned children in society. Significance This innovative system design scheme has a positive significance for family harmony and social development.

Abstract A technique known as domain adaptation is utilised to address classification challenges in an unlabelled target domain by leveraging labelled source domains. Previous domain adaptation approaches have predominantly focussed on global domain adaptation, neglecting class‐level information and resulting in suboptimal transfer performance. In recent years, a considerable number of researchers have explored class‐level domain adaptation, aiming to precisely align the distribution of diverse domains. Nevertheless, existing research on class‐level alignment tends to align domain features either on or in proximity to classification boundaries, which introduces ambiguous samples that can impact classification accuracy. In this study, the authors propose a novel strategy called class guided constraints (CGC) to tackle this issue. Specifically, CGC is employed to preserve the compactness within classes and separability between classes of domain features prior to class‐level alignment. Furthermore, the authors incorporate CGC in conjunction with similarity guided constraint. Comprehensive evaluations conducted on four public datasets demonstrate that our approach outperforms numerous state‐of‐the‐art domain adaptation methods significantly and achieves greater improvements compared to the baseline approach.

Walking is one of the most common daily movements of the human body. Therefore, quantitative evaluation of human walking has been commonly used to assist doctors in grasping the disease degree and rehabilitation process of patients in the clinic. Compared with the kinematic characteristics, the ground reaction force (GRF) during walking can directly reflect the dynamic characteristics of human walking. It can further help doctors understand the degree of muscle recovery and joint coordination of patients. This paper proposes a GRF estimation method based on the elastic elements and Newton-Euler equation hybrid driving GRF estimation method. Compared with the existing research, the innovations are as follows. 1) The hardware system consists of only two inertial measurement units (IMUs) placed on shanks. The acquisition of the overall motion characteristics of human walking is realized through the simplified four-link walking model and the thigh prediction method. 2) The method was validated not only on 10 healthy subjects but also on 11 Parkinson’s patients and 10 stroke patients with normalized mean absolute errors (NMAEs) of 5.95%±1.32%, 6.09%±2.00%, 5.87%±1.59%. 3) This paper proposes a dynamic balance assessment method based on the acquired motion data and the estimated GRF. It evaluates the overall balance ability and fall risk at four key time points for all subjects recruited. Because of the low-cost system, ease of use, low motion interference and environmental constraints, and high estimation accuracy, the proposed GRF estimation method and walking balance automatic assessment have broad clinical value.

Abstract The interrupted time series (ITS) design is widely used to examine the effects of large-scale public health interventions and has the highest level of evidence validity. However, there is a notable gap regarding methods that account for lag effects of interventions. To address this, we introduced activation functions (ReLU and Sigmoid) to into the classic segmented regression (CSR) of the ITS design during the lag period. This led to the proposal of proposed an optimized segmented regression (OSR), namely, OSR-ReLU and OSR-Sig. To compare the performance of the models, we simulated data under multiple scenarios, including positive or negative impacts of interventions, linear or nonlinear lag patterns, different lag lengths, and different fluctuation degrees of the outcome time series. Based on the simulated data, we examined the bias, mean relative error (MRE), mean square error (MSE), mean width of the 95% confidence interval (CI), and coverage rate of the 95% CI for the long-term impact estimates of interventions among different models. OSR-ReLU and OSR-Sig yielded approximately unbiased estimates of the long-term impacts across all scenarios, whereas CSR did not. In terms of accuracy, OSR-ReLU and OSR-Sig outperformed CSR, exhibiting lower values in MRE and MSE. With increasing lag length, the optimized models provided robust estimates of long-term impacts. Regarding precision, OSR-ReLU and OSR-Sig surpassed CSR, demonstrating narrower mean widths of 95% CI and higher coverage rates. Our optimized models are powerful tools, as they can model the lag effects of interventions and provide more accurate and precise estimates of the long-term impact of interventions. The introduction of an activation function provides new ideas for improving of the CSR model.

Hydraulic measures such as hydraulic slotting and hydraulic fracturing are commonly used in coal seam pressure relief and permeability enhancement. Two-phase flow patterns of CH4–water in pore-sized coal seams after hydraulic measures are critical to improve gas extraction efficiency. The phase field module in COMSOL Multiphysics™ 5.4 and the classical ordered porous media model were used in this paper. The characteristics of CH4–water two-phase immiscible displacement in coal seams under different capillary numbers (Ca) and viscosity ratios (M) were simulated and quantitatively analyzed. By changing the contact angle of the porous media, the flow patterns of CH4–water two-phase in coal with different wettability were simulated. Results show that wettability significantly affects the displacement efficiency of CH4. Additionally, by constructing a dual-permeability model to simulate the varying local permeability of the coal, the flow patterns of different Ca and M in dual-permeability media were further investigated. It is found that CH4 preferentially invades high-permeability regions, and the displacement efficiency in low-permeability regions increases with higher Ca and M, providing a reference for gas extraction from coal seams after hydraulic measures.

Abstract Background The interrupted time series (ITS) design is a widely used approach to examine the effects of interventions. However, the classic segmented regression (CSR) method, the most popular statistical technique for analyzing ITS data, may not be adequate when there is a transitional period between the pre- and post-intervention phases. Methods To address this issue and better capture the distribution patterns of intervention effects during the transition period, we propose using different cumulative distribution functions in the CSR model and developing corresponding optimized segmented regression (OSR) models. This study illustrates the application of OSR models to estimate the long-term impact of a national free delivery service policy intervention in Ethiopia. Results Regardless of the choice of transition length ( $$L$$ L ) and distribution patterns of intervention effects, the OSR models outperformed the CSR model in terms of mean square error (MSE), indicating the existence of a transition period and the validity of our model’s assumptions. However, the estimates of long-term impacts using OSR models are sensitive to the selection of L, highlighting the importance of reasonable parameter specification. We propose a data-driven approach to select the transition period length to address this issue. Conclusions Overall, our OSR models provide a powerful tool for modeling intervention effects during the transition period, with a superior model fit and more accurate estimates of long-term impacts. Our study highlights the importance of appropriate statistical methods for analyzing ITS data and provides a useful framework for future research.

Artificial improvement of coal seam permeability is the key to solve the low gas drainage efficiency and prevent gas dynamic disaster. Plasma based on physical discharge is one of the effective means of coal seam antireflection. However, previous studies focus on the characterization of pore-fissure structure of single breakdown, ignore the polarization effect of plasma on coal, and lack of in-depth research on the electrical properties and the evolution characteristics of pore-fissure structure under the action of cyclical plasma of loaded coal. In this paper, the cyclical plasma breakdown experiments of coal samples were carried out. The voltage and current waveforms in the process of coal breakdown were monitored thorough combining with high-voltage attenuation rod and the Roche coil, and the variation of electrical parameters such as pre-breakdown period, plasma breakdown period, number of wave peaks, peak voltage, peak current and energy conversion efficiency were analyzed. The evolution of small pores, medium pores, large pores and micro cracks under the conditions of 1, 5, 10, 15 and 20 times of breakdown was tested by NMR. The fractal dimension of seepage pore was also discussed through geometric fractal theory. The results show that the pre-breakdown period is at the level of thousands of microseconds only for the first time, and then it will drop to tens of microseconds, while the plasma breakdown period shows a “ladder” growth with the increase of breakdown times. The energy conversion efficiency is maintained at 28.7%−55.9%, showing a trend of rapid increase at first and then stable, indicating that the polarization effect of plasma on the electrical properties of coal is limited. The growth rate of medium and large pores is the most obvious, and some micro fracture structures will achieve a “0” breakthrough, indicating that plasma will significantly improve the seepage of gas. The fractal dimension shows a decreasing trend after breakdown, which proves that the originally isolated pore structure will be connected by the fracture from the perspective of pore fracture space dimension. The similarity between the change trend of porosity and energy conversion efficiency shows that plasma has an impact on the electrical properties of coal, and the change of coal electrical properties will also have an impact on the distribution of plasma channels.

Northern elephant seals engage in large-scale foraging migrations traveling up to 15,000 Km over 8 months in the northeast Pacific. While traditionally considered solitary migrants, we demonstrate here that female seals migrate in a surprisingly coherent manner, for individual northern elephant seals traveling in over such a large region of the ocean. Animal movement remained coherent, in terms of the direction of individual swimming relative to group movement, throughout much of their migrations. Movement coherence remained well above the value expected if the movement was independent until the migrating seals were further than 1,000 Km from the colony, beyond which movement coherence declined. Migrating seals presented regional aggregations consisting of female seals traveling within the center of the aggregation, closely following the main migration pathway, with individuals isolated on the extremes of the aggregation. These formations were preserved in the out-and-return migration trips. Animals at the edges of the group show an absence of correlation in their movement with the rest of animals. The observed movements exhibited a lag in the group movement patterns that was greater for female animals > 1,000 Km apart. A model that reproduced movement based on the average individual movement properties failed to reproduce the observed movement patterns. In turn, when a parameter was introduced that reflected group behavior, the resulting modelled movement conformed to the observed patterns, thereby demonstrating the presence of coherent, or synchronized, movement. Whereas the duration of female migration is ultimately constrained by reproductive biology, the coherent movement may involve both endogenous and exogenous cues determining the timing of the initiation of return across 25 million Km2 in the northeast Pacific.

Word Sense Disambiguation (WSD) is a significant and challenging task for text understanding and processing. This paper presents an unsupervised approach based on Weighted Co-occurrence bio-Term Graph (WCOTG) for performing WSD in the biomedical domain. The graph is automatically created from biomedical terms that are extracted from a corpus of downloaded scientific abstracts. Two kinds of weights are introduced on the links of the built bio-term graph and are taken as important factors in the process of disambiguation. The modified Personalised PageRank (PPR) algorithm is used for performing WSD. When evaluated on the NLM-WSD and MSH-WSD test datasets, and an acronym test set, the method outperforms the widely used unsupervised ones addressing the same problem, and the average result is almost equal to that of the BlueBERT_LE-based method. In contrast, our method has no additional enhancement or training for BERT-based models. Comparative experiments validate the positive effect of links’ weight on disambiguation efficiency. Last, the statistical experiments on the relation among system accuracy, the numbers of medical abstracts in the corpus, and the corresponding extracted terms suggest an excellent minimum corpus scale, when resources are limited.

Machine learning has demonstrated effectiveness in optimizing complex physical structures. In this study, the authors employ a machine learning approach to inversely design non-Hermitian layered optical systems with gain and loss modulation, showing that the trained network can reveal the relation between asymmetric transmission and reflection spectra.

A generative deep learning approach for shape recognition of an arbitrary object from its acoustic scattering properties is proposed and demonstrated. The strategy exploits deep neural networks to learn the mapping between the latent space of a 2D acoustic object and the far‐field scattering amplitudes. A neural network is designed as an adversarial autoencoder and trained via unsupervised learning to determine the latent space of the acoustic object. Important structural features of the object are embedded in lower‐dimensional latent space which supports the modeling of a shape generator and accelerates the learning in the inverse design process. The proposed inverse design uses the variational inference approach with encoder‐ and decoder‐like architecture where the decoder is composed of two pretrained neural networks: the generator and the forward model. The data‐driven framework finds an accurate solution to the ill‐posed inverse scattering problem, where nonunique solution space is overcome by the multifrequency phaseless far‐field patterns. This inverse method is a powerful design tool that doesn't require complex analytical calculation and opens up new avenues for practical realization, automatic recognition of arbitrary‐shaped submarines or large fish, and other underwater applications.

Global health services are disrupted by the COVID-19 pandemic. We evaluated extent and duration of impacts of the pandemic on health services utilization in different economically developed regions of mainland China. Based on monthly health services utilization data in China, we used Seasonal Autoregressive Integrated Moving Average (S-ARIMA) models to predict outpatient and emergency department visits to hospitals (OEH visits) per capita without pandemic. The impacts were evaluated by three dimensions:1) absolute instant impacts were evaluated by difference between predicted and actual OEH visits per capita in February 2020 and relative instant impacts were the ratio of absolute impacts to baseline OEH visits per capita; 2) absolute and relative accumulative impacts from February 2020 to March 2021; 3) duration of impacts was estimated by time that actual OEH visits per capita returned to its predicted value. From February 2020 to March 2021, the COVID-19 pandemic reduced OEH visits by 0.4676 per capita, equivalent to 659,453,647 visits, corresponding to a decrease of 15.52% relative to the pre-pandemic average annual level in mainland China. The instant impacts in central, northeast, east and west China were 0.1279, 0.1265, 0.1215, and 0.0986 visits per capita, respectively; and corresponding relative impacts were 77.63%, 66.16%, 44.39%, and 50.57%, respectively. The accumulative impacts in northeast, east, west and central China were up to 0.5898, 0.4459, 0.3523, and 0.3324 visits per capita, respectively; and corresponding relative impacts were 23.72%, 12.53%, 13.91%, and 16.48%, respectively. The OEH visits per capita has returned back to predicted values within the first 2, 6, 9, 9 months for east, central, west and northeast China, respectively. Less economically developed areas were affected for a longer time. Safe and equitable access to health services, needs paying great attention especially for undeveloped areas.

Abstract Topological phases of matter are conventionally characterized by the bulk‐boundary correspondence in Hermitian systems. The topological invariant of the bulk in d dimensions corresponds to the number of (d − 1)‐dimensional boundary states. By extension, higher‐order topological insulators reveal a bulk‐edge‐corner correspondence, such that nth order topological phases feature (d − n)‐dimensional boundary states. The advent of non‐Hermitian topological systems sheds new light on the emergence of the non‐Hermitian skin effect (NHSE) with an extensive number of boundary modes under open boundary conditions. Still, the higher‐order NHSE remains largely unexplored, particularly in the experiment. An unsupervised approach—physics‐graph‐informed machine learning (PGIML)—to enhance the data mining ability of machine learning with limited domain knowledge is introduced. Through PGIML, the second‐order NHSE in a 2D non‐Hermitian topoelectrical circuit is experimentally demonstrated. The admittance spectra of the circuit exhibit an extensive number of corner skin modes and extreme sensitivity of the spectral flow to the boundary conditions. The violation of the conventional bulk‐boundary correspondence in the second‐order NHSE implies that modification of the topological band theory is inevitable in higher dimensional non‐Hermitian systems.

Abstract Social media (e.g., Twitter) has been an extremely popular tool for public health surveillance. The novel coronavirus disease 2019 (COVID-19) is the first pandemic experienced by a world connected through the internet. We analyzed 105+ million tweets collected between March 1 and May 15, 2020, and Weibo messages compiled between January 20 and May 15, 2020, covering six languages (English, Spanish, Arabic, French, Italian, and Chinese) and represented an estimated 2.4 billion citizens worldwide. To examine fine-grained emotions during a pandemic, we built machine learning classification models based on deep learning language models to identify emotions in social media conversations about COVID-19, including positive expressions (optimistic, thankful, and empathetic), negative expressions (pessimistic, anxious, sad, annoyed, and denial), and a complicated expression, joking, which has not been explored before. Our analysis indicates a rapid increase and a slow decline in the volume of social media conversations regarding the pandemic in all six languages. The upsurge was triggered by a combination of economic collapse and confinement measures across the regions to which all the six languages belonged except for Chinese, where only the latter drove conversations. Tweets in all analyzed languages conveyed remarkably similar emotional states as the epidemic was elevated to pandemic status, including feelings dominated by a mixture of joking with anxious/pessimistic/annoyed as the volume of conversation surged and shifted to a general increase in positive states (optimistic, thankful, and empathetic), the strongest being expressed in Arabic tweets, as the pandemic came under control.

Objective: To compare the efficacy and safety of hyperthermic intravesical chemotherapy (HIVEC) and intravesical chemotherapy (IVEC) in patients with intermediate and high risk nonmuscle-invasive bladder cancer (NMIBC) after transurethral resection. Methods: We included 560 patients diagnosed with primary or recurrent NMIBC between April 2009 and December 2015 at 1 of 6 tertiary centers. We matched 364 intermediate or high risk cases and divided them into 2 groups: the HIVEC+IVEC group [chemohyperthermia (CHT) composed of 3 consecutive sessions followed by intravesical instillation without hyperthermia] and the IVEC group (intravesical instillation without hyperthermia). The data were recorded in the database. The primary endpoint was 2-year recurrence-free survival (RFS) in all NMIBC patients (n = 364), whereas the secondary endpoints were the assessment of radical cystectomy (RC) and 5-year overall survival (OS). Results: There was a significant difference in the 2-year RFS between the two groups in all patients (n = 364; HIVEC+IVEC: 82.42% vs. IVEC: 74.18%, P = 0.038). Compared with the IVEC group, the HIVEC+IVEC group had a lower incidence of RC (P = 0.0274). However, the 5-year OS was the same between the 2 groups (P = 0.1434). Adverse events (AEs) occurred in 32.7% of all patients, but none of the events was serious (grades 3–4). No difference in the incidence or severity of AEs between each treatment modality was observed. Conclusions: This retrospective study showed that HIVEC+IVEC had a higher 2-year RFS and a lower incidence of RC than IVEC therapy in intermediate and high risk NMIBC patients. Both treatments were well-tolerated in a similar manner.

Abstract Background Maize (Zea mays ssp. mays L.) is the most widely grown and yield crop in the world, as well as an important model organism for fundamental research of the function of genes. The functions of Maize proteins are annotated using the Gene Ontology (GO), which has more than 40000 terms and organizes GO terms in a direct acyclic graph (DAG). It is a huge challenge to accurately annotate relevant GO terms to a Maize protein from such a large number of candidate GO terms. Some deep learning models have been proposed to predict the protein function, but the effectiveness of these approaches is unsatisfactory. One major reason is that they inadequately utilize the GO hierarchy. Results To use the knowledge encoded in the GO hierarchy, we propose a deep Graph Convolutional Network (GCN) based model (DeepGOA) to predict GO annotations of proteins. DeepGOA firstly quantifies the correlations (or edges) between GO terms and updates the edge weights of the DAG by leveraging GO annotations and hierarchy, then learns the semantic representation and latent inter-relations of GO terms in the way by applying GCN on the updated DAG. Meanwhile, Convolutional Neural Network (CNN) is used to learn the feature representation of amino acid sequences with respect to the semantic representations. After that, DeepGOA computes the dot product of the two representations, which enable to train the whole network end-to-end coherently. Extensive experiments show that DeepGOA can effectively integrate GO structural information and amino acid information, and then annotates proteins accurately. Conclusions Experiments on Maize PH207 inbred line and Human protein sequence dataset show that DeepGOA outperforms the state-of-the-art deep learning based methods. The ablation study proves that GCN can employ the knowledge of GO and boost the performance. Codes and datasets are available at http://mlda.swu.edu.cn/codes.php?name=DeepGOA .

Pancreatic cancer (PC) remains a major health concern, with conventional cancer treatments exerting little influence on the disease course. MicroRNA-520b (miR-520b) functions as a tumor suppressor in several types of human cancers, whereas its anti-tumor property in the context of PC is still fundamental. The aim of this study is to identify the potential therapeutic role of miR-520b, transferred by exosomes, derived from normal fibroblasts (NFs) in PC progression. A gain-of-function study was performed to examine the roles of miR-520b in PC cell line SW1990, which suggested that miR-520b served as a tumor suppressor in PC. In order to confirm the role of exosomal miR-520b, exosomes were isolated from NF culture medium and cocultured with SW1990 cells. During the coculture experiments, we disrupted exosome secretion and upregulated exosomal miR-520b. The in vitro coculture studies revealed that miR-520b was transferred from NF-derived exosomes to PC cells and thereby suppressed PC cell proliferation, invasion, migration, and stimulated apoptosis. Furthermore, inhibited tumor growth and live metastasis upon elevated miR-520b in exosomes were observed in vivo. Conjointly, our study demonstrates that NF-derived exosomal miR-520b impedes the progression of PC, which contributes to a novel, therapeutic role of exosomal miR-520b for treating PC.

Pancreatic ductal adenocarcinoma (PDAC) is an extremely aggressive pancreatic cancer with poor survival rate. Circular RNAs (circRNAs) signatures have been identified in some human cancers, but there are little data concerning their presence in PDAC. We investigated the role of hsa_circ_001653, a newly identified circRNA, in the development of PDAC. hsa-circ-001653 expression was measured in 83 paired normal and tumor tissues surgically resected from PDAC patients. Phenotypic changes of PDAC cells were evaluated by assays for cell viability, cell cycle, invasion, and apoptosis. Tube-like structure formation of human umbilical vein endothelial cells (HUVECs) was examined in the presence of PDAC cells. Cross-talk between hsa_circ_001653 and microRNA-377 (miR-377)/human homeobox C6 (HOXC6) was assessed using dual-luciferase reporter assay, Ago2 immunoprecipitation, and northern blot analysis. Nude mice were inoculated with human PDAC cells for in vivo analysis. hsa_circ_001653 was an upregulated circRNA in PDAC. Silencing of hsa_circ_001653 in PDAC cells via RNA interference inhibited cell viability, cell-cycle progression, in vitro angiogenesis, and invasive properties, showing a pro-apoptotic effect. hsa_circ_001653 was found to bind to miR-377, which in turn repressed HOXC6 expression. Inhibition of miR-377 by its specific inhibitor restored cell viability, cell-cycle progression, in vitro angiogenesis, and invasive properties in PDAC cells lacking endogenous hsa_circ_001653. When nude mice were inoculated with human PDAC cells, inhibition of hsa_circ_001653 had a therapeutic effect. Collectively, the present study provides an enhanced understanding of hsa_circ_001653 as a therapeutic target for PDAC.

The target in an augmented reality (AR) interface is often located in the distance and outside the initial AR viewport, requiring the user to rotate the AR device, instead of translating it, to complete the search and selection of the target. In order to quantitatively analyze rotating the AR device in the AR interface in the selection of a target located outside the initial visual window, we propose and experimentally verify a quantitative model based on angle measurement under two selection techniques: with and without cursor. The results show that the model proposed in this paper can accurately describe the interactive process when a rotating AR device completes the target selection task. With cursor angle measurement achieves a model fitting value of R2 = 0.937, and without cursor angle measurement achieves a model fitting value of R2 = 0.915; hence, good modeling of user performance in AR selection tasks is achieved.

Abstract High‐voltage electrical pulse (HVEP) technology, a potential method for degassing coal seam in the future, has made great progress in field application. However, the previous studies mainly concentrated on the coal‐crushing effect of the action of single pulse, ignoring the influence of cyclic single pulse on the fracture structure of coal. In this paper, Huaibei anthracite coal and Inner Mongolia bituminous coal were taken as the experimental objects to study the effect of cyclic single pulse on the fracture structure of coal. The results show that the uneven distribution of electric field in the coal causes the crack to break along the interface between the mineral and the coal. Besides, the characteristics of cracks and functional groups of coal before and after electrical breakdown were investigated through scanning electron microscope and Fourier transform infrared spectroscopy. The results suggest that the relative contents of oxygen‐containing functional groups are reduced after electrical breakdown, which promotes the gas desorption from the surfaces of coal samples. Moreover, changes in CH4 adsorption capacity of HVEP‐treated coal were studied through a high‐pressure adsorption instrument, with the adsorption temperature set from 40 to 120°C. The results show that the adsorption amount of electrically broken coal is smaller than that of raw coal, which proves that temperature and current exert similar influences on the adsorption capacity of coal sample. Additionally, current waveforms indicate that the peak current increases with the number of cycle, whereas the breakdown time decreases with it. However, the peak current and breakdown time will eventually become stable as the number of cycles grows, demonstrating that electrical properties of coal are changed in the process of breakdown, which thus affects the next discharge.

The voice-based technology is a promising alternative input modality to the conventional computer input devices, such as keyboards and mice. For mobility-impaired people who cannot use the traditional computer input devices, a voice-based interactive solution is highly desirable. Although some grid-based cursor control systems that use speech recognition show potentials, they come with limitations in processing time and difficulties in real-time applications. Since they are not natural enough to use, it is very slow to drill down the grid, and the point cursor selection range is prone to errors, and people need a more efficient and natural choice. To address these limitations, this paper proposes two novel speech-based target selection techniques: grid-based region selection (hereinafter referred to as “Region Selection”) and grid-based target search (hereinafter referred to as “Target Search”). Region Selection allows users to select targets directly using the selected area. Target Search permits users to move the cursor to a target in a specified direction. Experiments are designed and conducted to analyze the impacts of target size and target density on performance and processing time. The experimental results and comparison study with traditional solution show that both Region Selection and Target Search improve the participants’ accuracy of target selection and reduce the processing time, especially for small targets.

In this study we propose a “hand gesture + face expression” human machine interaction technique, and apply this technique to bedridden rehabilitation robot. “Hand gesture + Facial expression” interactive technology combines the input mode of gesture and facial expression perception. It involves seven basic facial expressions that can be used to determine a target selecting task, while hand gestures are used to control a cursor’s location. A controlled experiment was designed and conducted to evaluate the effectiveness of the proposed hybrid technology. A series of target selecting tasks with different target widths and layouts were designed to examine the recognition accuracy of hybrid control gestures. An interactive experiment applied to a rehabilitation robot is designed to verify the feasibility of this interactive technology applied to rehabilitation robots. The experimental results show that the “hand + facial expression” interactive gesture has strong robustness, which can provide a novel guideline for designing applications in VR interfaces, and it can be applied to the rehabilitation robots.

Surface ozone (O3) pollution has become one of the top environmental issues in recent years around the world and can be influenced by meteorological processes on multiple scales. Understanding the meteorological mechanism and contributions of O3 pollution is of great importance for O3 mitigation. In this study, we explored the impacts of meteorological conditions on O3 concentrations in a coastal city in Southeast China, with a particular focus on O3 pollution episodes inspringtime. A significant increase in the O3 pollution ratefrom 2015 to 2020 was observed (41.7% year−1) and the seasonal characteristics of O3 concentrations showed a two-peak pattern. We selected 12 pollution episodes during the springtime of 2015 to 2020 and identified four dominant synoptic weather patterns (SWPs) that could cause O3 pollution. The local meteorological conditions and vertical dynamic structures under different SWPs were analyzed. The results showed that high O3 levels tend to be associated with high temperature, weak wind, low relative humidity, and deep vertical sinking motion. We also established a quantitative linkage between the O3 values and meteorological factors. Based on meteorological conditions, 60.8~80.8% of the variation in O3 can be explained.

Data prediction and imputation are important parts of marine animal movement trajectory analysis as they can help researchers understand animal movement patterns and address missing data issues. Compared with traditional methods, deep learning methods can usually provide enhanced pattern extraction capabilities, but their applications in marine data analysis are still limited. In this research, we propose a composite deep learning model to improve the accuracy of marine animal trajectory prediction and imputation. The model extracts patterns from the trajectories with an encoder network and reconstructs the trajectories using these patterns with a decoder network. We use attention mechanisms to highlight certain extracted patterns as well for the decoder. We also feed these patterns into a second decoder for prediction and imputation. Therefore, our approach is a coupling of unsupervised learning with the encoder and the first decoder and supervised learning with the encoder and the second decoder. Experimental results demonstrate that our approach can reduce errors by at least 10% on average comparing with other methods.

Anthropogenic litter density and composition data were obtained by conducting aerial surveys on 44 beaches along the Saudi Arabian Coast of the Red Sea [1]. The aerial surveys were completed with commercial drones of the DJI Phantom suite flown at a 10 m altitude. The stills have a resolution of less than 0.5 cm pixels−1, hence, litter objects of few centimetres like bottle caps are easily detectable in the drone images. We here provide a subsample of the drone images acquired. To spare the time needed to visually count the litter objects in the thousands of drone images acquired, these were automatically screened using an object detection algorithm, specifically a Faster R-CNN, able to perform a binary classification in litter and non-litter and to categorize the objects in classes. The multi-class classification, however, is a challenging problem and, hence, it was conducted only on the 15 beaches that showed the highest performance after the binary classification. The performance of the algorithm was calculated by visually screening a subsample of images and it was used to correct the output of the Faster R-CNN. The described steps allowed to obtain an estimate of the litter density in 44 beaches and the litter composition in 15 beaches. By multiplying the relative abundance of each litter class and the median weight of objects belonging to each class, we obtained an estimate of the total mass of plastic beached on 15 beaches. Possible predictors of litter density and mass are the population and marine traffic densities at the site, the exposure of the beach to the prevailing wind and the wind speed, the fetch length and the presence of vegetation where litter could get trapped. Making such raw data (i.e. litter density and composition and their predictors) available can help building the base for a robust global estimate of anthropogenic litter in coastal environments and it is particularly important if data regards an understudied region like the Arabian Peninsula. Moreover, we share a subsample of the original drone images to allow usage from stakeholders.

Augmented reality (AR) capabilities on handheld mobile devices are increasing; therefore, interaction with a virtual scene is a key feature to explore the full potential of mobile AR. One of the primary interaction tasks is 3D target selection. Because touch screens provide a direct means of interaction with AR content by directly touching and manipulating AR elements, touch-based interaction is a natural and appealing style of input for AR applications. However, developing AR selection techniques for handheld devices using touch screens is not a straightforward task. One issue is that while interacting with AR targets, users occlude the targets with their fingers. Furthermore, because the user's finger covers a large area of the screen, the size of the target users can touch is limited. In addition, in a 3D AR environment, it is difficult to select targets by touch when they are occluded from each other due to differences in spatial depth. In this paper, we present four novels AR selection techniques for handheld mobile devices that can precisely select targets with small sizes even when they are occluded. Then, we present the results of an experimental study in which we evaluated the usability, performance, and error rate of our proposed and existing AR selection techniques.

Data mining has actively contributed to solving many real-world problems with a variety of techniques. Traditional approaches in this field are classification, clustering and regression. During the last few years a number of chal-lenges have emerged, such as imbalanced data, multi-label and multi-instance problems, low quality and/or noisy data or semi-supervised learning, among others [item 1) in the Appendix]. When these non-standard scenarios are encountered in the realm of big data, it remains an uncharted research territory, although a growing effort has been made to break the limits. The current trend is to address the classical and newly emerging data mining problems in big data and knowledge processing. Granular computing provides a powerful tool for multiple granularity and multiple-view data analysis at differ-ent granularity levels, which has demonstrated strong capabil-ities and advantages in intelligent data analysis, pattern recog-nition, machine learning and uncertain reasoning [item 2) inthe Appendix]. Big data often contains a significant amount of unstructured, uncertain and imprecise data. There are new challenges regarding the scalability of granular computing when addressing very big data sets [item 3) in the Appendix]. Big data mining relies on distributed computational strate-gies; it is often impossible to store and process data on one single computing node. The exploration of data mining and granular computing in big data and knowledge processing is an emerging field which crosses multiple research disciplines and industry domains, including transportation, communications, social network, medical health, and so on.

This paper focuses on the detection of non-recurrent traffic anomaly caused by unexpected or transient incidents, such as traffic accidents, celebrations, and disasters. Comparing to existing approaches, it considers the spatial and temporal propagation of traffic anomalies from one road to other neighbor roads by proposing an STLP-OD framework. The experimental results on a real data set show that the proposed approach can improve the accuracy of traffic outlier detection baselines significantly.

The number of applications (apps) available for smart devices or Android based IoT (Internet of Things) has surged dramatically over the past few years. Meanwhile, the volume of ill-designed or malicious apps (malapps) has been growing explosively. To ensure the quality and security of the apps in the markets, many approaches have been proposed in recent years to discriminate malapps from benign ones. Machine learning is usually utilized in classification process. Accurately characterizing apps' behaviors, or so-called features, directly affects the detection results with machine learning algorithms. Android apps evolve very fast. The size of current apps has become increasingly large and the behaviors of apps have become increasingly complicated. The extracting effective and representative features from apps is thus an ongoing challenge. Although many types of features have been extracted in existing work, to the best of our knowledge, no work has systematically surveyed the features constructed for detecting Android malapps. In this paper, we are motivated to provide a clear and comprehensive survey of the state-of-the-art work that detects malapps by characterizing behaviors of apps with various types of features. Through the designed criteria, we collect a total of 1947 papers in which 236 papers are used for the survey with four dimensions: the features extracted, the feature selection methods employed if any, the detection methods used, and the scale of evaluation performed. Based on our in-depth survey, we highlight the issues of exploring effective features from apps, provide the taxonomy of these features and indicate the future directions.

Colon cancer is one of the most prevalent malignancies that lead to high occurrence of cancer-related deaths. Currently, chemotherapies and radiotherapies remain the primary treatments for advanced colon cancer. Despite the initial effectiveness, a fraction of colon cancer patients developed cisplatin resistance, resulting in therapeutic failure. The long non-coding RNA differentiation antagonizing non-coding RNA (DANCR) has been shown to be upregulated in multiple cancers, indicating an oncogenic role of DANCR. This study aims to elucidate the roles of DANCR in regulating cisplatin (CDDP) resistance of colon cancer. We found DANCR was significantly upregulated in colon cancer tissues and cells compared with normal colon tissues and cells. DANCR was upregulated in cisplatin-resistant colon cancer cells. Moreover, overexpression of DANCR significantly desensitized colon cancer cells to cisplatin. On the other way, silencing DANCR dramatically overrode CDDP resistance of colon cancer cells. Bioinformatics prediction revealed DANCR could bind to seeding region of miR-125b-5p as a competitive endogenous RNA. This interference was further validated by luciferase assay. Moreover, we detected a negative correlation between DANCR and miR-125b-5p in colon cancer patient tissues: miR-125b-5p was clearly downregulated in colon cancer tissues and cells. Overexpression of miR-125b-5p significantly sensitized cisplatin-resistant cells. Interestingly, we observed the cisplatin-resistant cells were associated with a significantly increased glycolysis rate. We further identified glycolysis enzyme, hexokinase 2 (HK2), as a direct target of miR-125b-5p in colon cancer cells. Rescue experiments showed overexpression of miR-125b-5p suppressed cellular glycolysis rate and increased cisplatin sensitivity through direct targeting the 3′ UTR of HK2. Importantly, silencing endogenous DANCR significantly induced the miR-125b-5p/HK2 axis, resulting in suppression of the glycolysis rate and increase in cisplatin sensitivity of colon cancer cell. Expectedly, these processes could be further rescued by inhibiting miR-125b-5p in the DANCR-silenced cells. Finally, we validated the DANCR-promoted cisplatin resistance via the miR-125b-5p/HK2 axis from an in vivo xenograft mice model. In summary, our study reveals a new mechanism of the DANCR-promoted cisplatin resistance, presenting the lncRNA-DANCR–miR-125b-5p/HK2 axis as a potential target for treating chemoresistant colon cancer.

Annotating the functional properties of gene products, i.e., RNAs and proteins, is a fundamental task in biology. The Gene Ontology database (GO) was developed to systematically describe the functional properties of gene products across species, and to facilitate the computational prediction of gene function. As GO is routinely updated, it serves as the gold standard and main knowledge source in functional genomics. Many gene function prediction methods making use of GO have been proposed. But no literature review has summarized these methods and the possibilities for future efforts from the perspective of GO. To bridge this gap, we review the existing methods with an emphasis on recent solutions. First, we introduce the conventions of GO and the widely adopted evaluation metrics for gene function prediction. Next, we summarize current methods of gene function prediction that apply GO in different ways, such as using hierarchical or flat inter-relationships between GO terms, compressing massive GO terms and quantifying semantic similarities. Although many efforts have improved performance by harnessing GO, we conclude that there remain many largely overlooked but important topics for future research.

Point-of-interest (POI) recommendation has attracted many interests recently because of its significant potential for helping users to explore new places and helping location-based service (LBS) providers to carry out precision marketing. Compared with the user-item rating matrix in conventional recommender systems, the user-location check-in matrix in POI recommendation is usually much more sparse, which makes the notorious cold start problem more prominent in POI recommendation. Trustoriented recommendation is an effective way to deal with this problem but it requires that the recommender has access to user check-in and trust data. In practice, however, these data are usually owned by different businesses who are not willing to share their data with the recommender mainly due to privacy and legal concerns. In this paper, we propose a privacy-preserving framework to boost data owners willingness to share their data with untrustworthy businesses. More specifically, we utilize partially homomorphic encryption to design two protocols for privacy-preserving trust-oriented POI recommendation. By offline encryption and parallel computing, these protocols can efficiently protect the private data of every party involved in the recommendation. We prove that the proposed protocols are secure against semi-honest adversaries. Experiments on both synthetic data and real data show that our protocols can achieve privacy-preserving with acceptable computation and communication cost.

Android platform has dominated the operating system of mobile devices. However, the dramatic increase of Android malicious applications (malapps) has caused serious software failures to Android system and posed a great threat to users. The effective detection of Android malapps has thus become an emerging yet crucial issue. Characterizing the behaviors of Android applications (apps) is essential to detecting malapps. Most existing works on detecting Android malapps were mainly based on string static features, such as permissions and API usage extracted from apps. There also exists work on the detection of Android malapps with structural features, such as control flow graph and data flow graph. As Android malapps have become increasingly polymorphic and sophisticated, using only one type of static features may result in false negatives. In this paper, we propose DroidEnsemble that takes advantages of both string features and structural features to systematically and comprehensively characterize the static behaviors of Android apps and thus build a more accurate detection model for the detection of Android malapps. We extract each app’s string features, including permissions, hardware features, filter intents, restricted API calls, used permissions, code patterns, as well as structural features like function call graph. We then use three machine learning algorithms, namely, support vector machine, k-nearest neighbor, and random forest, to evaluate the performance of these two types of features and of their ensemble. In the experiments, we evaluate our methods and models with 1386 benign apps and 1296 malapps. Extensive experimental results demonstrate the effectiveness of DroidEnsemble. It achieves the detection accuracy as 95.8% with only string features and as 90.68% with only structural features. DroidEnsemble reaches the detection accuracy as 98.4% with the ensemble of both types of features, reducing 9 false positives and 12 false negatives compared to the results with only string features.

The tunnel junction (TJ) is a crucial structure for numerous III-nitride devices. A fundamental challenge for TJ design is to minimize the TJ resistance at high current densities. In this work, we propose the asymmetric p-AlGaN/i-InGaN/n-AlGaN TJ structure for the first time. P-AlGaN/i-InGaN/n-AlGaN TJs were simulated with different Al or In compositions and different InGaN layer thicknesses using TCAD (Technology Computer-Aided Design) software. Trained by these data, we constructed a highly efficient model for TJ resistance prediction using machine learning. The model constructs a tool for real-time prediction of the TJ resistance, and the resistances for 22,254 different TJ structures were predicted. Based on our TJ predictions, the asymmetric TJ structure (p-Al0.7Ga0.3N/i-In0.2Ga0.8N/n-Al0.3Ga0.7N) with higher Al composition in p-layer has seven times lower TJ resistance compared to the prevailing symmetric p-Al0.3Ga0.7N/i-In0.2Ga0.8N/n-Al0.3Ga0.7N TJ. This study paves a new way in III-nitride TJ design for optical and electronic devices.

High-voltage thermal breakdown has great potential application in permeability enhancement of coal seam. The characteristics of the breakdown channel, coal element, porosity and microscopic coal petrography of coal under high-voltage electric load were experimentally studied. The coal interior left apparent tracks due to electric current burning with high temperature. The percentage of C, O, Al, Fe, and Si had slightly decreased, while the content of element N increased obviously. Low-pressure nitrogen gas adsorption (LP-N2GA) and mercury intrusion analysis showed that coal porosity increased. The increases of micropores and mesopores are beneficial to promotion of the ability of gas storage, and the increase of macropores could enhance the gas seepage and migration. The results of scanning electron microscope (SEM) show that there are many exogenous fractures in coal, which is also beneficial to gas seepage and migration. The results lay a theoretical foundation for application of high-voltage thermal breakdown in coal mines. Keywords: High-voltage thermal breakdown, Permeability enhancement, Porosity, Fracture, Gas adsorption

Abstract Background OPCML belongs to the IgLON family of Ig domain–containing GPI-anchored cell adhesion molecules and was recently found to be involved in carcinogenesis, while its role in gastric cancer remains unclear. Methods We assessed expression and biological behavior of OPCML in gastric cancer. Results OPCML expression was markedly reduced in tumor tissues and cancer cell lines. Decreased OPCML expression had a significant association with unfavorable tumor stage (p = 0.007) and grading (p

Concealing an object from incoming waves (light and/or sound) remained science fiction for a long time due to the absence of wave-shielding materials in nature. Yet, the invention of artificial materials and new physical principles for optical and sound wave manipulation translated this abstract concept into reality by making an object optically and acoustically “invisible.” Here, we present the notion of a machine learning driven acoustic cloak and demonstrate an example of such a cloak with a multilayered core-shell configuration. We develop deterministic and probabilistic deep learning models based on autoencoderlike neural network structure to retrieve the structural and material properties of the cloaking shell surrounding the object that suppresses scattering of sound in a broad spectral range, as if it was not there. The probabilistic model enhances the generalization ability of design procedure and uncovers the sensitivity of the cloak's parameters on the spectral response for practical implementation. This proposal opens up avenues to expedite the design of intelligent cloaking devices for tailored spectral response and offers a feasible solution for inverse scattering problems.

The analysis of animal movement from telemetry data provides insights into how and why animals move. While traditional approaches to such analysis mostly focus on predicting animal states during movement, we describe an approach that allows us to identify representative movement patterns of different animal groups. To do this, we propose a carefully designed recurrent neural network and combine it with telemetry data for automatic feature extraction and identification of non-predefined representative patterns. In the experiment, we consider a particular marine predator species, the southern elephant seal, as an example. With our approach, we identify that the male seals in our data set share similar movement patterns when they are close to land. We identify this pattern recurring in a number of distant locations, consistent with alternative approaches from previous research.