Explore the vast range of titles available.

In the existing recycling system for end-of-life vehicles (ELVs), online recycling based on the Internet platform is a useful supplement. In this paper, a Stackelberg game pricing model, which is dominated by ELV part remanufacturers and composed of remanufacturers, recyclers, and consumers, is constructed considering consumer preferences for recycling channels. The influence of different subsidy strategies on the optimal pricing, profit, and recycling volume of the reverse supply chain (RSC) of ELVs is discussed, and the effects of factors such as subsidy amount and consumer preferences on the RSC of ELVs are analyzed using numerical simulation. The results show that the increase in consumers’ online recycling preferences has a positive effect on the recycling volume and profit of the RSC of ELVs. Considering the recycling volume of the RSC, when fewer subsidies are given, more recycling volume can be generated by subsidizing remanufacturers, and, on the contrary, recycling volume will be generated by subsidizing consumers. Considering the profit of the RSC, when subsidies are given at the lower-middle level, higher profits can be earned by subsidizing remanufacturers, and, on the contrary, higher profits can be earned by subsidizing consumers.

Social sensing, using humans as sensors to collect disaster data, has emerged as a timely, cost-effective, and reliable data source. However, research has focused on the textual data. With advances in information technology, multimodal data such as images and videos are now shared on media platforms, aiding in-depth analysis of social sensing systems. This study proposed an analytical framework to extract disaster-related spatiotemporal information from multimodal social media data. Using a pre-trained multimodal neural network and a location entity recognition model, the framework integrates disaster semantics with spatiotemporal information, enhancing situational awareness. A case study of the April 2024 heavy rain event in Guangdong, China, using Weibo data, demonstrates that multimodal content correlates more strongly with rainfall patterns than textual data alone, offering a dynamic perception of disasters. These findings confirm the utility of multimodal social media data and offer a foundation for future research. The proposed framework offers valuable applications for emergency response, disaster relief, risk assessment, and witness discovery, and presents a viable approach for safety risk monitoring and early warning systems.

Type 2 diabetes is a common metabolic disorder related to insulin resistance, or deficiency of insulin secretion, caused by decreased insulin sensitivity and destruction of islet structure and function. As the second human genome, the microbiota has been observed to have a growing relationship with diabetes in recent years. Microbiota imbalance has been hypothesized to be involved in the regulation of energy metabolism and the inflammatory immune response in diabetes. The present study aimed to investigate whether fecal microbiota transplantation (FMT) could alleviate the symptoms associated with type 2 diabetes. To this end, a type 2 diabetes mouse model was first established through the consumption of a high-fat diet combined with streptozotocin (100 mg/kg), and FMT was used to rebuild the gut microbiota of diabetic mice. Fasting blood glucose, oral glucose tolerance tests, and HbA1c levels were monitored, while the hypoglycemic effects of FMT were also observed. Insulin levels were tested by ELISA and related indexes such as HOMA-IR, HOMA-IS, and HOMA-β were calculated. We found that insulin resistance and pancreatic islet β-cells were improved after FMT treatment. Meanwhile, the markers of inflammation in the pancreatic tissue were detected by ELISA and immunohistochemistry, which indicated that inflammatory response decreased following FMT treatment. Furthermore, flow cytometry and western blot results revealed that FMT inhibited the β-cell apoptosis. Here, the effect of FMT on hypoglycemia in type 2 diabetes was addressed by improving insulin resistance and repairing impaired islets, thereby providing a potential treatment strategy for type 2 diabetes.

It is intuitive that semantic representations can be useful for machine translation, mainly because they can help in enforcing meaning preservation and handling data sparsity (many sentences correspond to one meaning) of machine translation models. On the other hand, little work has been done on leveraging semantics for neural machine translation (NMT). In this work, we study the usefulness of AMR (abstract meaning representation) on NMT. Experiments on a standard English-to-German dataset show that incorporating AMR as additional knowledge can significantly improve a strong attention-based sequence-to-sequence neural translation model.

The introduction of fluoroalkyl groups into pharmaceutical compounds has the potential to enhance their therapeutic properties. Nevertheless, the synthesis of enantiomerically pure C( sp³ )–CF₃ compounds poses a significant challenge. Biocatalysis offers precise stereochemical control, however, the scarcity of fluorine-containing natural products makes it difficult to find enzymes capable of incorporating fluoroalkyl groups. Herein, we develop a ground-state flavin-dependent enzyme-catalyzed strategy for the radical-mediated enantioselective trifluoromethylation. Two engineered flavin-dependent enzymes are successfully developed to catalyze stereoselective hydrotrifluoromethylation and trifluoromethyl-alkyl cross-electrophile coupling reactions using trifluoromethyl thianthrenium triflate as a radical donor. Experimental investigations and computational simulations demonstrate that the reaction is initiated through single-electron transfer from the ground state flavin hydroquinone (FMN hq ) and quenched through hydrogen atom transfer by flavin semiquinone (FMN sq ). This strategy provides an opportunity to bridge the gap between biocatalysis and organic fluorides but also introduces an alternative approach to address challenging stereoselective fluoroalkylation reactions in organic synthesis. The introduction of fluoroalkyl groups into pharmaceutical compounds has the potential to enhance their therapeutic properties, but synthesis of enantiomerically pure C(sp³)–CF₃ compounds is challenging. Here, the authors develop a ground-state flavin-dependent enzyme catalyzed strategy for the radical-mediated enantioselective trifluoromethylation.

Drowsiness poses a serious challenge to road safety and various in-cabin sensing technologies have been experimented with to monitor driver alertness. Cameras offer a convenient means for contactless sensing, but they may violate user privacy and require complex algorithms to accommodate user (e.g., sunglasses) and environmental (e.g., lighting conditions) constraints. This paper presents a lightweight convolution neural network that measures eye closure based on eye images captured by a wearable glass prototype, which features a hot mirror-based design that allows the camera to be installed on the glass temples. The experimental results showed that the wearable glass prototype, with the neural network in its core, was highly effective in detecting eye blinks. The blink rate derived from the glass output was highly consistent with an industrial gold standard EyeLink eye-tracker. As eye blink characteristics are sensitive measures of driver drowsiness, the glass prototype and the lightweight neural network presented in this paper would provide a computationally efficient yet viable solution for real-world applications.

High-speed detection of abnormal frames in surveillance videos is essential for security. This paper proposes a new video anomaly–detection model, namely, feature trajectory–smoothed long short-term memory (FTS-LSTM). This model trains an LSTM autoencoder network to generate future frames on normal video streams, and uses the FTS detector and generation error (GE) detector to detect anomalies on testing video streams. FTS loss is a new indicator in the anomaly–detection area. In the training stage, the model applies a feature trajectory smoothness (FTS) loss to constrain the LSTM layer. This loss enables the LSTM layer to learn the temporal regularity of video streams more precisely. In the detection stage, the model utilizes the FTS loss and the GE loss as two detectors to detect anomalies. By cascading the FTS detector and the GE detector to detect anomalies, the model achieves a high speed and competitive anomaly-detection performance on multiple datasets.

Monolayer Molybdenum Disulfide (MoS 2 ) is a promising anode material for lithium ion batteries because of its high capacities. In this work, first principle calculations based on spin density functional theory were performed to investigate adsorption and diffusion of lithium on monolayer MoS 2 with defects, such as single- and few-atom vacancies, antisite and grain boundary. The values of adsorption energies on the monolayer MoS 2 with the defects were increased compared to those on the pristine MoS 2 . The presence of defects causes that the Li is strongly bound to the monolayer MoS 2 with adsorption energies in the range between 2.81 and 3.80 eV. The donation of Li 2 s electron to the defects causes an enhancement of adsorption of Li on the monolayer MoS 2 . At the same time, the presence of defects does not apparently affect the diffusion of Li and the energy barriers are in the range of 0.25–0.42 eV. The presence of the defects can enhance the energy storage capacity, suggesting that the monolayer MoS 2 with defects is a suitable anode material for the Li-ion batteries.

In the north of Songliao basin, some high CO2 gas reservoirs have been found while great breakthroughs have been made in hydrocarbon natural gas. Based on the analysis of the reservoir characteristics of CO2 gas reservoirs in Xudong area, this paper clarifies that the Yingcheng formation CO2 is inorganic gas, volcanic rock is the main reservoir, and establishes the differential accumulation model of CO2 gas reservoirs in the study area, which can also guide further research and exploration.

Contactless bubble manipulation with a high spatiotemporal resolution brings a qualitative leap forward in a variety of applications. Despite considerable advances, light-induced bubble maneuvering remains challenging in terms of robust transportation, splitting and detachment. Here, a photopyroelectric slippery surface (PESS) with a sandwich structure is constructed to achieve the versatile bubble manipulation. Due to the generated dielectric wetting and nonuniform electric field under the irradiation of near infrared (NIR) light, a bubble is subject to both the Laplace force and dielectrophoresis force, enabling a high-efficiency bubble steering. We demonstrate that the splitting, merging and detachment of underwater bubbles can be achieved with high flexibility and precision, high velocity and agile direction maneuverability. We further extend the capability of bubble control to microrobots for cargo transportation, micropart assembly and transmission of gear structures. We envision this robust bubble manipulation strategy on the PESS would provide a valuable platform for various bubble-involved processes, ranging from microfluidic devices to soft robotics. Light-induced bubble maneuvering remains challenging in terms of response and functional adaptability due to the single driving mechanism including the Marangoni effect or asymmetrical deformation. Using a photopyroelectric slippery surface (PESS), Liu et al. demonstrate the splitting, merging, and detachment of underwater bubbles with high flexibility and precision.