ALBERT

Description: Around half of methadone maintenance treatment (MMT) participants choose the tapering phase, however, the guidelines on tapering differ between countries and only include the tapering rate. Physicians need more evidence to guide clinical practice. We aimed to explore a specific tapering strategy to improve heroin abstinence among MMT participants. We conducted a retrospective study from 2006 to 2017 at nine MMT clinics in Guangdong, China, involving 853 participants with 961 treatment episodes. We performed two-level hierarchical logistic regression models to identify tapering phase characteristics associated with heroin abstinence. Among all treatment episodes, 419 (43.6%) were heroin abstinent. Participants who started tapering after 52 weeks, had a taper start dose of less than 60 mg and a taper ratio of less than 5%/week, while a dose reduction in 75%–89% of the tapering weeks provided the highest odds of heroin abstinence. This study highlights the need for a more gradual taper than current guidelines recommend and strongly suggests the inclusion of other tapering phase characteristics. Those who start the tapering phase later, have a lower dose of methadone, with a more gradual rate of taper, and a dose reduction in 75%–89% of the tapering phase increased the odds of heroin abstinence.

Description: Establishing the spatial relationship between 2D images captured by real cameras and 3D models of the environment (2D and 3D space) is one way to achieve the virtual–real registration for Augmented Reality (AR) in outdoor environments. In this paper, we propose to match the 2D images captured by real cameras and the rendered images from the 3D image-based point cloud to indirectly establish the spatial relationship between 2D and 3D space. We call these two kinds of images as cross-domain images, because their imaging mechanisms and nature are quite different. However, unlike real camera images, the rendered images from the 3D image-based point cloud are inevitably contaminated with image distortion, blurred resolution, and obstructions, which makes image matching with the handcrafted descriptors or existing feature learning neural networks very challenging. Thus, we first propose a novel end-to-end network, AE-GAN-Net, consisting of two AutoEncoders (AEs) with Generative Adversarial Network (GAN) embedding, to learn invariant feature descriptors for cross-domain image matching. Second, a domain-consistent loss function, which balances image content and consistency of feature descriptors for cross-domain image pairs, is introduced to optimize AE-GAN-Net. AE-GAN-Net effectively captures domain-specific information, which is embedded into the learned feature descriptors, thus making the learned feature descriptors robust against image distortion, variations in viewpoints, spatial resolutions, rotation, and scaling. Experimental results show that AE-GAN-Net achieves state-of-the-art performance for image patch retrieval with the cross-domain image patch dataset, which is built from real camera images and the rendered images from 3D image-based point cloud. Finally, by evaluating virtual–real registration for AR on a campus by using the cross-domain image matching results, we demonstrate the feasibility of applying the proposed virtual–real registration to AR in outdoor environments.

Description: Although electric vehicles (EVs) have been regarded as promising to reduce tailpipe emissions and energy consumption, a mixed traffic flow of EVs and internal combustion engine vehicles (ICEVs) makes the energy/emissions reduction objective more difficult because EVs and ICEVs have various general characteristics. This paper proposes a low-emission-oriented speed guidance model to address the energy/emission reduction issue under a mixed traffic flow at an isolated signalized intersection to achieve the objective of reducing emissions and total energy consumption while reducing vehicle delay and travel time. The total energy/emissions under different market penetration rates of EVs with various traffic volumes are analyzed and compared. Numerical examples demonstrate that the proposed speed guidance model has better performance than those without considering the impact of queues. For a certain traffic volume, the energy/emission reduction effects under speed guidance will increase with an increasing share of EVs. This paper also explores the impact of the time interval for guidance renewal on vehicle emissions in practice.

Description: Accidents occur frequently in traffic-intensive waters, which restrict the safe and rapid development of the shipping industry. Due to the suddenness, randomness, and uncertainty of accidents in traffic-intensive waters, the probability of the risk factors causing traffic accidents is usually high. Thus, properly analyzing those key risk factors is of great significance to improve the safety of shipping. Based on the analysis of influencing factors of ship navigational risks in traffic-intensive waters, this paper proposes a cloud model to excavate the factors affecting navigational risk, which could accurately screen out the key risk factors. Furthermore, the risk causal model of ship navigation in traffic-intensive waters is constructed by using the infectious disease dynamics method in order to model the key risk causal transmission process. Moreover, an empirical study of the Yangtze River estuary is conducted to illustrate the feasibility of the proposed models. The research results show that the cloud model is useful in screening the key risk factors, and the constructed causal model of ship navigational risks in traffic-intensive waters is able to provide accurate analysis of the transmission process of key risk factors, which can be used to reduce the navigational risk of ships in traffic-intensive waters. This research provides both theoretical basis and practical reference for regulators in the risk management and control of ships in traffic-intensive waters.

Description: Water transportation accidents have occurred frequently in recent years. In order to improve the emergency response capability of water transportation systems under traffic-intensive conditions, this paper identifies and analyzes the vulnerability in traffic-intensive areas of water transportation systems. Firstly, the vulnerability identification model was constructed based on the analysis of characteristics and the vulnerability-influencing factors of water transportation systems. The newly proposed model is composed of three parts including the DEMATEL (Decision Making Trial and Evaluation Laboratory) method, ISM (interpretative structural modeling) model, and AHP (Analytic Hierarchy Process)–entropy weight method. Finally, a case study of the Yangtze River was conducted to test the logicality and feasibility of the proposed model. The research results reveal that traffic flow density, ship traffic, tides, fog, and bad weather are the key factors affecting the vulnerability of water transportation in traffic-intensive areas of the Yangtze River estuary. However, the influence of navigation aid configuration, berth, anchorage, and obstruction on the system vulnerability is relatively lower. The findings of this study can provide helpful references for maritime administration authorities on the management of water transportation safety.

Description: In recent years, wearable devices have been popularly applied in the health care field. The electrocardiogram (ECG) is the most used signal. However, the ECG is measured under a body-motion condition, which is easily coupled with some noise, like as power line noise (PLn) and electromyogram (EMG). This paper presents a grey spectral noise cancellation (GSNC) scheme for electrocardiogram (ECG) signals where two-stage discrimination is employed with the empirical mode decomposition (EMD), the ensemble empirical mode decomposition (EEMD) and the grey spectral noise estimation (GSNE). In the first stage of the proposed GSNC scheme, the input ECG signal is decomposed by the EMD to obtain a set of intrinsic mode functions (IMFs). Then, the noise energies of IMFs are estimated by the GSNE. When an IMF is considered as noisy one, it is forwarded to the second stage for further check. In the second stage, the suspicious IMFs are reconstructed and decomposed by the EEMD. Then the IMFs are discriminated with a threshold. If the IMF is considered as noisy, it is discarded in the reconstruction process of the ECG signal. The proposed GSNC scheme is justified by forty-three ECG signal datasets from the MIT-BIH cardiac arrhythmia database where the PLn and EMG noise are under consideration. The results indicate that the proposed GSNC scheme outperforms the traditional EMD and EEMD based noise cancellation schemes in the given datasets.

Description: The influence of dredging season on sediment properties and nutrient fluxes across the sediment–water interface remains unknown. This study collected sediment cores from two sites with different pollution levels in Meiliang Bay, Taihu Lake (China). The samples were used in simulation experiments designed to elucidated the effects of dredging on internal loading in different seasons. The results showed that dredging the upper 30 cm of sediment could effectively reduce the contents of organic matter, total nitrogen, and total phosphorus in the sediments. Total biological activity in the dredged sediment was weaker (p 〈 0.05) than in the undredged sediment in all seasons for both the Inner Bay and Outer Bay, but the effect of 30-cm dredging on sediment oxygen demand was negligible. Dredging had a significant controlling effect on phosphorus release in both the Inner Bay and Outer Bay, and soluble reactive phosphorus (SRP) fluxes from the dredged cores were generally lower (p 〈 0.05) than from the undredged cores. In contrast, NH4+-N fluxes from the dredged cores were significantly higher (p 〈 0.05) than from the undredged cores in all seasons for both sites, this indicates short-term risk of NH4+-N release after dredging, and this risk is greatest in seasons with higher temperatures, especially for the Inner Bay. Dredging had a limited effect on NO2−-N and NO3−-N fluxes at both sites. These results suggest that dredging could be a useful approach for decreasing internal loading in Taihu Lake, and that the seasons with low temperature (non-growing season) are suitable for performing dredging projects.

Description: The aim of the study was to determine the predictive effects of sex, age, depression, and problematic behaviors on the incidence and remission of internet addiction (IA) in college students over a one-year follow-up. A total of 500 college students (262 women and 238 men) were recruited. The predictive effects of sex, age, severity of depression, self-harm/suicidal behaviors, eating problems, risk-taking behaviors, substance use, aggression, and uncontrollable sexual encounters on the incidence and remission of IA over a one-year follow-up were examined. The one-year incidence and remission rates for IA were 7.5% and 46.4%, respectively. Severity of depression, self-harm and suicidal behaviors, and uncontrollable sexual encounters at the initial investigation predicted the incidence of IA in a univariate analysis, whereas only severity of depression predicted the incidence of IA in a multivariable logistic regression (p = 0.015, odds ratio = 1.105, 95% confidence intervals: 1.021–1.196). A relatively young age predicted the remission of IA. Depression and young age predicted the incidence and remission, respectively, of IA in college students in the one-year follow-up.

Description: To elucidate the physicochemical properties of the artificial silicate composite material, K-feldspar and lime were reacted in mild hydrothermal conditions (different reaction temperatures and various K-feldspar/lime ratios). Formed phases were investigated using various techniques, such as X-ray powder diffraction, the Rietveld method, scanning electron microscopy (SEM), and inductively coupled plasma-optical emission spectrometry. The analysis revealed that tobermorite, grossular (hydrogarnet), alpha-dicalcium silicate hydrate (α-C2SH), amorphous calcium silicate hydrate, potassium carbonate, bütschliite, calcite, and calcium hydroxide formed with various conditions. Both the temperature and the Ca/Si molar ratio in the starting material greatly affected the formation of phases, especially the generation of tobermorite and α-C2SH. The substitution of H4O4 ↔ SiO4 proceeded with the increase of the Ca/Si molar ratio rather than the reaction temperature and the reaction time. More hydrogen was incorporated in hydrogarnet through the substitution of H4O4 ↔ SiO4 with the increase of the Ca/Si molar ratio in the starting material. Due to the properties of tobermorite as a cation exchanger and its potential applications in hazardous waste disposal, experimental parameters should be optimized to obtain better performance of the artificial silicate composite material from K-feldspar and lime hydrothermal reaction. The dissolution mechanism of K-feldspar was also discussed.

Description: Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily available in most cities, can record urban waterlogging processes in video form. These video data could be a valuable data source for waterlogging depth extraction. The present paper is aimed at demonstrating a new approach to extract urban waterlogging depths from video images based on transfer learning and lasso regression. First, a transfer learning model is used to extract feature vectors from a video image set of urban waterlogging. Second, a lasso regression model is trained with these feature vectors and employed to calculate the waterlogging depth. Two case studies in China were used to evaluate the proposed method, and the experimental results illustrate the effectiveness of the method. This method can be applied to video images from widespread cameras in cities, so that a powerful urban waterlogging monitoring network can be formed.