Robust H∞ control of networked control systems with access constraints and packet dropouts

We consider a class of networked control systems (NCSs) where the plant has time-varying norm-bounded parameter uncertainties, the network only provides a limited number of simultaneous accesses for the sensors and actuators, and the packet dropouts occur randomly in the network. For this class of NCSs with uncertainties and access constraints as well as packet dropouts, we derive sufficient conditions in the form of linear matrix inequalities that guarantee robust stochastic stabilisation and synthesis of H∞ controller. An example is provided to illustrate our proposed method

Sensitivity Analysis for Quantiles of Hidden Biases in Matched Observational Studies

In matched observational studies, the inferred causal conclusions pretending that matching has taken into account all confounding can be sensitive to unmeasured confounding. In such cases, a sensitivity analysis is often conducted, which investigates whether the observed association between treatment and outcome is due to effects caused by the treatment or it is due to hidden confounding. In general, a sensitivity analysis tries to infer the minimum amount of hidden biases needed in order to explain away the observed association between treatment and outcome, assuming that the treatment has no effect. If the needed bias is large, then the treatment is likely to have significant effects. The Rosenbaum sensitivity analysis is a modern approach for conducting sensitivity analysis for matched observational studies. It investigates what magnitude the maximum of the hidden biases from all matched sets needs to be in order to explain away the observed association, assuming that the treatment has no effect. However, such a sensitivity analysis can be overly conservative and pessimistic, especially when the investigators believe that some matched sets may have exceptionally large hidden biases. In this paper, we generalize Rosenbaum's framework to conduct sensitivity analysis on quantiles of hidden biases from all matched sets, which are more robust than the maximum. Moreover, we demonstrate that the proposed sensitivity analysis on all quantiles of hidden biases is simultaneously valid and is thus a free lunch added to the conventional sensitivity analysis. The proposed approach works for general outcomes, general matched studies and general test statistics. Finally, we demonstrate that the proposed sensitivity analysis also works for bounded null hypotheses as long as the test statistic satisfies certain properties. An R package implementing the proposed method is also available online

Intelligent Hydrogen Fuel Cell Range Extender for Battery Electric Vehicles

Road transport is recognized as having a negative impact on the environment. Policy has focused on replacement of the internal combustion engine (ICE) with less polluting forms of technology, including battery electric and fuel cell electric powertrains. However, progress is slow and both battery and fuel cell based vehicles face considerable commercialization challenges. To understand these challenges, a review of current electric battery and fuel cell electric technologies is presented. Based on this review, this paper proposes a battery electric vehicle (BEV) where components are sized to take into account the majority of user requirements, with the remainder catered for by a trailer-based demountable intelligent fuel cell range extender. The proposed design can extend the range by more than 50% for small BEVs and 25% for large BEVs (the extended range of vehicles over 250 miles), reducing cost and increasing efficiency for the BEV. It enables BEV manufacturers to design their vehicle battery for the most common journeys, decreases charging time to provide convenience and flexibility to the drivers. Adopting a rent and drop business model reduces the demand on the raw materials, bridging the gap in the amount of charging (refueling) stations, and extending the lifespan for the battery pack

Stability of Networked Control Systems with Random Buffer Capacity

Stability of the discrete-time networked control systems is analysed, where the controller is updated with the buffered sensor information at stochastic intervals and the amount of the buffered data for transmission under the buffer capacity constraint is time-varying. The adopted controller switches between open-loop and closed-loop modes to estimate the plant behaviour. The sufficient condition for the Lyapunov stability with the generic arbitrary transmission is derived, and the sufficient conditions for the mean square stability with the Markovian transmission are also established. An example is given to demonstrate the effectiveness of our method

Intelligent Chemical Purification Technique Based on Machine Learning

We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise data acquisition and employing advanced machine learning algorithms, we constructed predictive models to forecast key separation parameters, thereby enhancing the efficiency and quality of chromatographic processes. The application of transfer learning allows the model to adapt across various column specifications, broadening its utility. A novel metric, separation probability ($S_p$), quantifies the likelihood of effective compound separation, validated through experimental verification. This study signifies a significant step forward int the application of AI in chemical research, offering a scalable solution to traditional chromatography challenges and providing a foundation for future technological advancements in chemical analysis and purification.Comment: 22 pages, 5 Figures, Submitted to Nature Machine Intelligenc

A Dynamic Credit Evaluation Approach Using Sensitivity-Optimized Weights for Supply Chain Finance

Supply chain financing provides important funding channels for micro and small enterprises (MSEs), but effectively evaluating their creditworthiness remains challenging. Past methods overly rely on static financial indicators and subjective judgment in determining credit evaluation weights. This study proposes a dynamic credit evaluation approach that uses sensitivity analysis to optimize the weighting scheme. An indicator system is constructed based on the unique characteristics of e-commerce MSEs. The weight optimization integrates subjective, objective, and sensitivity-based methods to reflect specific financing scenarios. A system dynamics model simulates the credit evaluation mechanism and identifies the sensitivity of each influencing factor. The resultant comprehensive weights are applied in a TOPSIS-GRA dynamic evaluation model to assess MSE credit levels over time. An empirical analysis of 20 online stores demonstrates the proposed model\u27s advantages in accurately revealing credit rankings relative to conventional static models. This research provides an effective data-driven weighting technique and dynamic evaluation framework for supply chain finance credit assessment

The relationship between Cho/NAA and glioma metabolism: implementation for margin delineation of cerebral gliomas

BACKGROUND: The marginal delineation of gliomas cannot be defined by conventional imaging due to their infiltrative growth pattern. Here we investigate the relationship between changes in glioma metabolism by proton magnetic resonance spectroscopic imaging ((1)H-MRSI) and histopathological findings in order to determine an optimal threshold value of choline/N-acetyl-aspartate (Cho/NAA) that can be used to define the extent of glioma spread. METHOD: Eighteen patients with different grades of glioma were examined using (1)H-MRSI. Needle biopsies were performed under the guidance of neuronavigation prior to craniotomy. Intraoperative magnetic resonance imaging (MRI) was performed to evaluate the accuracy of sampling. Haematoxylin and eosin, and immunohistochemical staining with IDH1, MIB-1, p53, CD34 and glial fibrillary acidic protein (GFAP) antibodies were performed on all samples. Logistic regression analysis was used to determine the relationship between Cho/NAA and MIB-1, p53, CD34, and the degree of tumour infiltration. The clinical threshold ratio distinguishing tumour tissue in high-grade (grades III and IV) glioma (HGG) and low-grade (grade II) glioma (LGG) was calculated. RESULTS: In HGG, higher Cho/NAA ratios were associated with a greater probability of higher MIB-1 counts, stronger CD34 expression, and tumour infiltration. Ratio threshold values of 0.5, 1.0, 1.5 and 2.0 appeared to predict the specimens containing the tumour with respective probabilities of 0.38, 0.60, 0.79, 0.90 in HGG and 0.16, 0.39, 0.67, 0.87 in LGG. CONCLUSIONS: HGG and LGG exhibit different spectroscopic patterns. Using (1)H-MRSI to guide the extent of resection has the potential to improve the clinical outcome of glioma surgery

A Message Passing Detection based Affine Frequency Division Multiplexing Communication System

The next generation of wireless communication technology is anticipated to address the communication reliability challenges encountered in high-speed mobile communication scenarios. An Orthogonal Time Frequency Space (OTFS) system has been introduced as a solution that effectively mitigates these issues. However, OTFS is associated with relatively high pilot overhead and multiuser multiplexing overhead. In response to these concerns within the OTFS framework, a novel modulation technology known as Affine Frequency Division Multiplexing (AFDM) which is based on the discrete affine Fourier transform has emerged. AFDM effectively resolves the challenges by achieving full diversity through parameter adjustments aligned with the channel's delay-Doppler profile. Consequently, AFDM is capable of achieving performance levels comparable to OTFS. As the research on AFDM detection is currently limited, we present a low-complexity yet efficient message passing (MP) algorithm. This algorithm handles joint interference cancellation and detection while capitalizing on the inherent sparsity of the channel. Based on simulation results, the MP detection algorithm outperforms Minimum Mean Square Error (MMSE) and Maximal Ratio Combining (MRC) detection techniques.Comment: 8 pages, 7 figure

Thermal properties of carbon black aqueous nanofluids for solar absorption

In this article, carbon black nanofluids were prepared by dispersing the pretreated carbon black powder into distilled water. The size and morphology of the nanoparticles were explored. The photothermal properties, optical properties, rheological behaviors, and thermal conductivities of the nanofluids were also investigated. The results showed that the nanofluids of high-volume fraction had better photothermal properties. Both carbon black powder and nanofluids had good absorption in the whole wavelength ranging from 200 to 2,500 nm. The nanofluids exhibited a shear thinning behavior. The shear viscosity increased with the increasing volume fraction and decreased with the increasing temperature at the same shear rate. The thermal conductivity of carbon black nanofluids increased with the increase of volume fraction and temperature. Carbon black nanofluids had good absorption ability of solar energy and can effectively enhance the solar absorption efficiency

AIMDiT: Modality Augmentation and Interaction via Multimodal Dimension Transformation for Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) is a popular task in natural language processing, which aims to recognize the emotional state of the speaker in conversations. While current research primarily emphasizes contextual modeling, there exists a dearth of investigation into effective multimodal fusion methods. We propose a novel framework called AIMDiT to solve the problem of multimodal fusion of deep features. Specifically, we design a Modality Augmentation Network which performs rich representation learning through dimension transformation of different modalities and parameter-efficient inception block. On the other hand, the Modality Interaction Network performs interaction fusion of extracted inter-modal features and intra-modal features. Experiments conducted using our AIMDiT framework on the public benchmark dataset MELD reveal 2.34% and 2.87% improvements in terms of the Acc-7 and w-F1 metrics compared to the state-of-the-art (SOTA) models