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Characterizing the reaction energies and barriers of reaction networks is central to catalyst development. However, heterogeneous catalytic surfaces pose several unique challenges to automatic reaction network characterization, including large sizes and open-ended reactant sets, that make ad hoc network construction the current state-of-the-art. Here, we show how automated network exploration algorithms can be adapted to the constraints of heterogeneous systems using ethylene oligomerization on silica-supported single-site Ga 3+ as a model system. Using only graph-based rules for exploring the network and elementary constraints based on activation energy and size for identifying network terminations, a comprehensive reaction network is generated and validated against standard methods. The algorithm (re)discovers the Ga-alkyl-centered Cossee-Arlman mechanism that is hypothesized to drive major product formation while also predicting several new pathways for producing alkanes and coke precursors. These results demonstrate that automated reaction exploration algorithms are rapidly maturing towards general purpose capability for exploratory catalytic applications. This study demonstrates how reaction network characterization can be performed on heterogeneous catalytic surfaces predictively, rather than retrospectively, using automated exploration algorithms on an ethylene oligomerization exemplar reaction.

The Internet of Vehicles plays a crucial role in advancing intelligent transportation systems, with In-Vehicle Ethernet serving as the fundamental backbone network of the new generation of in-vehicle communication. However, In-Vehicle Ethernet faces various network security threats, including data theft, data tampering, and malicious attacks. This study focuses on network intrusion and security issues in In-Vehicle Ethernet, by analyzing the data characteristics of Audio Video Transport Protocol and potential network attack means. We innovatively propose a network intrusion detection method based on a weighted histogram algorithm. This method aims to enhance the security of In-Vehicle Ethernet. Experimental results show that the anomaly detection rate of the proposed weighted histogram algorithm in this study is 99.7%, which shows an improvement of 15.8% compared with the traditional Bayesian algorithm, and 6.9% higher than the decision tree algorithm. Thus, our approach enhances the stability and anti-attack ability of In-Vehicle Ethernet, providing a solid network security for In-Vehicle Networks.

With the rapid development of intelligent driving technology, in-vehicle bus networks face increasingly stringent requirements for real-time performance and data transmission. Traditional bus network technologies such as LIN, CAN, and FlexRay are showing significant limitations in terms of bandwidth and response speed. In-Vehicle Ethernet, with its advantages of high bandwidth, low latency, and high reliability, has become the core technology for next-generation in-vehicle communication networks. This study focuses on bandwidth waste caused by guard bands and the limitations of Frame Pre-Emption in fully utilizing available bandwidth in In-Vehicle Ethernet. It aims to optimize TSN scheduling mechanisms by enhancing scheduling flexibility and bandwidth utilization, rather than modeling system-level vehicle functions. Based on the Time-Sensitive Networking (TSN) protocol, this paper proposes an innovative Adaptive Frame Segmentation (AFS) algorithm. The AFS algorithm enhances the performance of In-Vehicle Ethernet message transmission through flexible frame segmentation and efficient message scheduling. Experimental results indicate that the AFS algorithm achieves an average local bandwidth utilization of 94.16%, improving by 4.35%, 5.65%, and 30.48% over Frame Pre-Emption, Packet-Size Aware Scheduling (PAS), and Improved Qbv algorithms, respectively. The AFS algorithm demonstrates stability and efficiency in complex network traffic scenarios, reducing bandwidth waste and improving In-Vehicle Ethernet’s real-time performance and responsiveness. This study provides critical technical support for efficient communication in intelligent connected vehicles, further advancing the development and application of In-Vehicle Ethernet technology.

With the integration of an increasing number of outward-facing components in intelligent and connected vehicles, the open controller area network (CAN) bus environment faces increasingly severe security threats. However, existing security measures remain inadequate, and CAN bus messages lack effective security mechanisms and are vulnerable to malicious attacks. Although encryption algorithms can enhance system security, their high bandwidth consumption negatively impacts the real-time performance of intelligent and connected vehicles. Moreover, the message authentication mechanism of the CAN bus requires lengthy authentication codes, further exacerbating the bandwidth burden. To address these issues, we propose an improved dynamic compression algorithm that achieves higher compression rates and efficiency by optimizing header information processing during data reorganization. Additionally, we have proposed a novel dynamic key management approach, incorporating a dynamic key distribution mechanism, which effectively resolves the challenges associated with key management. Each Electronic Control Unit (ECU) node independently performs compression, encryption, and authentication while periodically updating its keys to enhance system security and strengthen defense capabilities. Experimental results show that the proposed dynamic compression algorithm improves the average compression rate by 2.24% and enhances compression time efficiency by 10% compared to existing solutions. The proposed security protocol effectively defends against four different types of attacks. In hardware tests, using an ECU operating at a frequency of 30 MHz, the computation time for the security algorithm on a single message was 0.85 ms, while at 400 MHz, the computation time was reduced to 0.064 ms. Additionally, for different vehicle models, the average CAN bus load rate was reduced by 8.28%. The proposed security mechanism ensures the security, real-time performance, and freshness of CAN bus messages while reducing bus load, providing a more efficient and reliable solution for the cybersecurity of intelligent and connected vehicles.

Despite the importance of memories in everyday life and the progress made in understanding how they are encoded and retrieved, the neural processes by which declarative memories are maintained or forgotten remain elusive. Part of the problem is that it is empirically difficult to measure the rate at which memories fade, even between repeated presentations of the source of the memory. Without such a ground-truth measure, it is hard to identify the corresponding neural correlates. This study addresses this problem by comparing individual patterns of functional connectivity against behavioral differences in forgetting speed derived from computational phenotyping. Specifically, the individual-specific values of the speed of forgetting in long-term memory (LTM) were estimated for 33 participants using a formal model fit to accuracy and response time data from an adaptive paired-associate learning task. Individual speeds of forgetting were then used to examine participant-specific patterns of resting-state fMRI connectivity, using machine learning techniques to identify the most predictive and generalizable features. Our results show that individual speeds of forgetting are associated with resting-state connectivity within the default mode network (DMN) as well as between the DMN and cortical sensory areas. Cross-validation showed that individual speeds of forgetting were predicted with high accuracy ( r = .77) from these connectivity patterns alone. These results support the view that DMN activity and the associated sensory regions are actively involved in maintaining memories and preventing their decline, a view that can be seen as evidence for the hypothesis that forgetting is a result of storage degradation, rather than of retrieval failure.

As the intensity of work increases, many of us sit for long hours while working in the office. It is not easy to sit properly at work all the time and sitting for a long time with wrong postures may cause a series of health problems as time goes by. In addition, monitoring the sitting posture of patients with spinal disease would be beneficial for their recovery. Accordingly, this paper designs and implements a sitting posture recognition system from a flexible array pressure sensor, which is used to acquire pressure distribution map of sitting hips in a real-time manner. Moreover, an improved self-organizing map-based classification algorithm for six kinds of sitting posture recognition is proposed to identify whether the current sitting posture is appropriate. The extensive experimental results verify that the performance of ISOM-based sitting posture recognition algorithm (ISOM-SPR) in short outperforms that of four kinds of traditional algorithms including decision tree-based (DT), K-means-based (KM), back propagation neural network-based (BP), self-organizing map-based (SOM) sitting posture recognition algorithms. Finally, it is proven that the proposed system based on ISOM-SPR algorithm has good robustness and high accuracy.

In heterogeneous catalysis, olefin oligomerization is typically performed on immobilized transition metal ions, such as Ni 2+ and Cr 3+ . Here we report that silica-supported, single site catalysts containing immobilized, main group Zn 2+ and Ga 3+ ion sites catalyze ethylene and propylene oligomerization to an equilibrium distribution of linear olefins with rates similar to that of Ni 2+ . The molecular weight distribution of products formed on Zn 2+ is similar to Ni 2+ , while Ga 3+ forms higher molecular weight olefins. In situ spectroscopic and computational studies suggest that oligomerization unexpectedly occurs by the Cossee-Arlman mechanism via metal hydride and metal alkyl intermediates formed during olefin insertion and β-hydride elimination elementary steps. Initiation of the catalytic cycle is proposed to occur by heterolytic C-H dissociation of ethylene, which occurs at about 250 °C where oligomerization is catalytically relevant. This work illuminates new chemistry for main group metal catalysts with potential for development of new oligomerization processes. Silica-supported, single site, main group Zn(II) and Ga(III) ions catalyze ethylene and propylene oligomerization. Here, experimental and theoretical evidence suggests a Cossee-Arlman reaction mechanism similar to that for transition metal catalysts.

A number of new rural management models have emerged to solve the problems of economic backwardness, insufficient resource utilization, and technical shortages in rural areas in the context of poverty alleviation to the rural revitalization strategy in China. However, the influence of new rural management model under all countermeasures for rural sustainable development with a comprehensive perspective is lacking. Therefore, exploring whether the new rural management model meets the requirements of sustainable development is an urgent issue. From the theory of system metabolism and emergy accounting method, this study classified the government funds for poverty alleviation measures as import resources, and analyzed the metabolic structure, efficiency, and the rural development factors of Chehe Village before and after poverty alleviation measures are carried out (the year of 2012 and 2019) to verify whether the new model was sustainable. According to the results of this study, the new management model of Chehe Village declined the rural system sustainability with the emergy sustainability index decreasing from 1.96 in 2012 to 0.32 in 2019. With the development of economy, the system metabolic efficiency of Chehe Village promoted and the metabolic structure became more reasonable manifesting in the decline of emergy use per unit GDP and the increase of emergy exchange rate. Moreover, production and livelihood had been highly valued in Chehe Village. In conclusion, it is feasible to add countermeasures of poverty alleviation and rural revitalization into the village system metabolism. The new management model of Chehe Village needs to change exogenous force into endogenous force to meet the requirements of rural sustainable development.

The traditional magnetorheological (MR) damper subject to the limited space has shortcomings such as small damping force, narrow dynamic range and low adaptability. In this study, a new MR damper with folded resistance gaps and bending magnetic circuit was proposed for improving the damping performance. The length of the resistance gap was increased by configuring the multi-stage folded annular gap structure, and the magnetic circuit was established to activate the non-flux region. The mathematical model was established for the MR damper to analyze the damper force, magnetic circuit and dynamic performance. Subsequently, the finite element analysis (FEA) methodology was utilized to investigate the changes of magnetic flux densities in the folded resistance gaps. The test rig was setup to explore and verify the dynamic performance of the proposed MR damper under different excitation conditions. The results indicate the maximum damping force is approximately 4346 N at the current of 1.5 A, frequency of 0.25 Hz and amplitude of 7.5 mm. The damping force and dynamic range of the proposed MR damper are enhanced by 55.82% and 62.21% compared to that of the traditional MR damper at the applied current of 1.5 A, respectively, thus highlighting its high vibration control ability.

The development and progression of gastrointestinal (GI) cancers not only depend on the malignancy of the tumor cells, but is also defined by the complex and adaptive nature of the tumor microenvironment (TME). The TME in GI cancers exhibits a complex internal structure, typically comprising cancer cells, cancer stem cells, cancer-associated fibroblasts, immune cells, and endothelial cells, all embedded within a dynamic extracellular matrix. This intricate ecosystem fuels tumor initiation, progression, metastasis, recurrence and therapy response through the heterogeneity and plasticity. Recent advances in single-cell sequencing have provided unprecedented resolution in profiling the cellular diversity and interactions within the TME. These technologies have uncovered previously unknown cell subtypes and intricate communication networks that drive therapy resistance and tumor relapse. In this review, we summarize and discuss the latest findings from single-cell sequencing of key cellular players and their interactions within the TME of GI cancers. We highlight single cell insights that are reshaping our understanding of tumor biology, with particular focus on their implications for overcoming therapy resistance and improving clinical outcomes. We believe that a deeper understanding of TME heterogeneity and plasticity at the single-cell level promises to transform the landscape of precision treatment in GI cancers.