Preparation of cross-linked nanoporous poly(ethylene glycol) diacrylate membrane in hexagonal lyotropic liquid crystal phases

Cross-linked poly(ethylene glycol) diacrylate (PEGDA) membranes were prepared by polymerization in periodic nanostructured lyotropic liquid crystals (LLC) hexagonal phases under UV light. A series of membranes were prepared under different purification treatment conditions. Polarized light microscope was employed to determine the LLC phase texture of LLC system before and after polymerization. It is found that the LLC hexagonal structure retained to some degree after polymerization. The interior structures of final membranes were investigated with scanning electron microscope (SEM). The results suggested that purification process affect the structure retention.<br /

The influence of geometry and wall character of pores on the permeation of ions and water through desalination membranes

The transport of water and ions across mimicked nanotube membranes with pseudo atoms is studied using molecular dynamics simulations under equilibrium conditions and hydrostatic pressure. Different pore surface properties are constructed by assigning partial charges on the sites of specified atoms to explore the influence of charges and polarity. The energetics of water and ion transports through the nanopores was calculated to evaluate their filterability to water. The simulation results show that the free energy barriers to water and ion conductions much depend on the charges at the pore entrance and the dipole within the pore. The membranes with hydrophobic pores and negatively charged entrances would be very efficient in the water transport and ion rejection. The charges and dipoles of the pore wall and the aligned dipoles of water molecules in the pore can create a significant force on ions.<br /

Characterization of membranes with X-ray ultramicroscopy

Non-invasive characterization and observation of synthetic membranes is an important practice to monitor the performance of membrane process. Primarily there are two techniques&mdash;optical and non-optical for this purpose. Among them, X-ray computed tomography, as a non-optical technique, has been extensively used for the measurement of fibre distribution and air pockets trapped in the modules. However, the micro resolution of most commercial systems has limited its application which can hardly be used for the sub-micro characterization of membrane processes. A novel micro and nano characterization method is introduced in the current work by exploring an innovative development of the X-ray ultramicroscope (XuM) and micro-tomographic techniques. The XuM, based on using a scanning electron microscope as host, provides a new approach to X-ray projection microscopy. It has demonstrated the ability to characterize very small features in objects, down to of order 100 nm, including the use for dry, wet and even liquid samples. It can also distinguish objects with very subtle difference in density.<br /

Detecting Energy Theft in Different Regions Based on Convolutional and Joint Distribution Adaptation

© 2023 IEEE. This is the accepted manuscript version of an article which has been published in final form at https://doi.org/10.1109/TIM.2023.3291769Electricity theft has been a major concern all over the world. There are great differences in electricity consumption among residents from different regions. However, existing supervised methods of machine learning are not in detecting electricity theft from different regions, while the development of transfer learning provides a new view for solving the problem. Hence, an electricity-theft detection method based on Convolutional and Joint Distribution Adaptation(CJDA) is proposed. In particular, the model consists of three components: convolutional component (Conv), Marginal Distribution Adaptation(MDA) and Conditional Distribution Adaptation(CDA). The convolutional component can efficiently extract the customer’s electricity characteristics. The Marginal Distribution Adaptation can match marginal probability distributions and solve the discrepancies of residents from different regions while Conditional Distribution Adaptation can reduce the difference of the conditional probability distributions and enhance the discrimination of features between energy thieves and normal residents. As a result, the model can find a matrix to adapt the electricity residents in different regions to achieve electricity theft detection. The experiments are conducted on electricity consumption data from the Irish Smart Energy Trial and State Grid Corporation of China and metrics including ACC, Recall, FPR, AUC and F1Score are used for evaluation. Compared with other methods including some machine learning methods such as DT, RF and XGBoost, some deep learning methods such as RNN, CNN and Wide & Deep CNN and some up-to-date methods such as BDA, WBDA, ROCKET and MiniROCKET, our proposed method has a better effect on identifying electricity theft from different regions.Peer reviewe

Improved 3D thinning algorithms for skeleton extraction

In this study, we focused on developing a novel 3D Thinning algorithm to extract one-voxel wide skeleton from various 3D objects aiming at preserving the topological information. The 3D Thinning algorithm was testified on computer-generated and real 3D reconstructed image sets acquired from TEMT and compared with other existing 3D Thinning algorithms. It is found that the algorithm has conserved medial axes and simultaneously topologies very well, demonstrating many advantages over the existing technologies. They are versatile, rigorous, efficient and rotation invariant.<br /

Bis(6′-carb­oxy-2,2′-bipyridine-6-carboxyl­ato-κ3 N,N′,O 6)nickel(II) tetra­hydrate

In the title compound, [Ni(C12H7N2O4)2]·4H2O, the Ni atom is located at the centre of a distorted octa­hedron, formed by four N atoms and two O atoms from the same two tridentating chelated 6-carb­oxy-2,2′-bipyridine-6′-carboxyl­ate (L) ligands. Face-to-face π-stacking inter­actions between inversion-related pyridine rings with centroid–centroid distances of 3.548 (3) and 3.662 (3) Å (perpendicular distances between the respective rings are 3.314 and 3.438 Å) are found. Inter­molecular O—H⋯O hydrogen bonds between water mol­ecules and L ligands form R 5 3(10), R 6 5(14) and R 5 5(12) rings and also a centrosymmetric cage-like unit of water mol­ecules, which link eight adjacent NiII centers, forming a three-dimensional framework

Unraveling Trends in Schistosomiasis: Deep Learning insights into National Control Programs in China

OBJECTIVES: to achieve the ambitious goal of eliminating schistosome infections, the Chinese government has implemented diverse control strategies. This study explored the progress of the 2 most recent national schistosomiasis control programs in an endemic area along the Yangtze River in China. METHODS: We obtained village-level parasitological data from cross-sectional surveys combined with environmental data in Anhui Province, China from 1997 to 2015. A convolutional neural network (CNN) based on a hierarchical integro-difference equation (IDE) framework (i.e., CNN-IDE) was used to model spatio-temporal variations in schistosomiasis. Two traditional models were also constructed for comparison with 2 evaluation indicators: the mean-squared prediction error (MSPE) and continuous ranked probability score (CRPS). RESULTS: The CNN-IDE model was the optimal model, with the lowest overall average MSPE of 0.04 and the CRPS of 0.19. From 1997 to 2011, the prevalence exhibited a notable trend: it increased steadily until peaking at 1.6 per 1000 in 2005, then gradually declined, stabilizing at a lower rate of approximately 0.6 per 1000 in 2006, and approaching zero by 2011. During this period, noticeable geographic disparities in schistosomiasis prevalence were observed; high-risk areas were initially dispersed, followed by contraction. Predictions for the period 2012 to 2015 demonstrated a consistent and uniform decrease. CONCLUSION: The proposed CNN-IDE model captured the intricate and evolving dynamics of schistosomiasis prevalence, offering a promising alternative for future risk modeling of the disease. The comprehensive strategy is expected to help diminish schistosomiasis infection, emphasizing the necessity to continue implementing this strategy

Implications from assessing environmental effects on spatio-temporal pattern of schistosomiasis in the Yangtze Basin, China

Schistosomiasis remains a major public health problem in the South China, particularly in lake and marshland regions. Modelling the spatio-temporal pattern of schistosomiasis guides disease prevention and control programs and is a research area of growing interest. However, few attempts have been made to evaluate the changing (nonlinear) effects of environmental determinants on schistosomiasis. In this context, a hierarchical spatiotemporal model was applied to evaluate how environmental determinants affect the changing trend of schistosomiasis in Anhui Province, China, based on annual parasitological and environmental data for the period 1997-2010. Results showed that – compared to changing effect – environmental factors had a constant (linear) effect on schistosomiasis. The disease was also found to fluctuate over time, which was due to the two latest national schistosomiasis control programs. In addition to statistical benefits of this approach, our analysis implied that climate change might not contribute to variation of schistosomiasis; rather, prevention activities affect schistosomiasis when the disease prevalence remains at a low level. Finally, the analytical method proposed in our study provides a template for modelling the spatio-temporal pattern of a disease whose transmission is largely determined by environmental determinants

System-level biological effects of extremely low-frequency electromagnetic fields: an in vivo experimental review

During the past decades, the potential effects of extremely low-frequency electromagnetic fields (ELF-EMFs) on human health have gained great interest all around the world. Though the International Commission on Non-Ionizing Radiation Protection recommended a 100 μT, and then a 200 μT magnetic field limit, the long-term effects of ELF-EMFs on organisms and systems need to be further investigated. It was reported that both electrotherapy and possible effects on human health could be induced under ELF-EM radiation with varied EM frequencies and fields. This present article intends to systematically review the in vivo experimental outcome and the corresponding mechanisms to shed some light on the safety considerations of ELF-EMFs. This will further advance the subsequent application of electrotherapy in human health