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Efficient control schemes for ill-conditioned systems, such as the high-purity distillation column, can be challenging and costly to design and implement. In this paper, we propose a distributed control scheme that utilizes well-designed excitation signals to identify the system. Unlike traditional systems, we found that a summation of correlated and uncorrelated signals can yield better excitation of the plant. Our proposed distributed model predictive control (MPC) scheme uses a shifted input sequence to address loop interactions and reduce the computational load. This approach deviates from traditional schemes that use iteration, which can increase complexity and computational load. We initially tested the proposed method on the linear model of a highly coupled 2 × 2 process and compared its performance with decentralized proportional-integral-derivative (PID) controllers and centralized MPC. Our results show improved performance over PID controllers and similar results to centralized MPC. Furthermore, we compared the performance of the proposed approach with a centralized MPC on a nonlinear model of a distillation column. The results for the second study also demonstrated comparable performance between the two controllers with the decentralised control slightly outperforming the centralised MPC in some cases. These findings are promising and may be of interest to practitioners that are more comfortable with tuning decentralised loops.

The ability to monitor intracellular calcium concentrations using fluorescent probes has led to important insights into biological signaling processes at the cellular level. An important challenge is to relate such measurements to broader patterns of signaling across fields of view that are inaccessible to optical techniques. To meet this need, we synthesized molecular probes that couple calcium-binding moieties to lanthanide texaphyrins, resulting in complexes endowed with a diverse complement of magnetic and photophysical properties. We show that the probes permit intracellular calcium levels to be assessed by fluorescence, photoacoustic, and magnetic resonance imaging modalities and that they are detectable by multimodal imaging in brain tissue. This work thus establishes a route for monitoring signaling processes over a range of spatial and temporal scales., National Institutes of Health (NIH)

Visual question answering (VQA) is a task that generates or predicts an answer to a question in human language about visual images. VQA is an active field combining two AI branches: NLP and computer vision. VQA in the medical field is still at an early stage, and it needs vast efforts and exploration to reach practical usage. This paper proposes two models that are utilized in the latest vision and NLP transformers that outperform the SOTA and have not yet been utilized in medical VQA. The ELECTRA-base transformer is used for textual feature extraction, whereas SWIN is used for visual feature extraction. In the SOTA medical VQA, selecting the model is based on the model that achieves the highest validation accuracy or the last model in training. The first proposed model, the best-value-based model, is selected based on the highest validation accuracy. The second model, the greedy-soup-based model, uses a greedy soup technique based on the fusion of multiple fine-tuned models to set the model parameters. The greedy soup selects the model parameters by fusing the model parameters that have significant performance on the validation accuracy in training. The greedy-soup-based model outperforms the best-value-based model, and both proposed models outperform the SOTA, which has an accuracy of 83.49%. The greedy-soup-based model is optimized with batch size and learning rate. During the optimization, seven extra models exceed the SOTA accuracy. The best model trained with a learning rate of 1.0×10−4 and batch size 16 achieves an accuracy of 87.41%.

In the last decade, robotic-mediated rehabilitation has emerged as a potential solution to improve repetitive task training. Each device in this field has a unique development history shaped by engineers’ expertise in specific programming languages or platforms. In this work we adopt an approach that tries to abstract from the final implementation with the aim to make control logic more shareable and understandable. The authors will present the outcomes of the application of a Rapid Control Prototyping strategy to an upper-limb robotic exoskeleton. A model-based design approach implemented on a real-time target machine is presented. This modern design approach was explored with several control strategies and was used to test the exoskeleton’s performances. The proposed method highlights how it is possible to develop the entire control architecture in a single programming environment.

This review intends to illustrate the fruitful collaboration between chemometrics and cultural heritage science. It showcases past achievements, aiming to inspire numerous cultural heritage researchers who have yet to incorporate multivariate techniques in analysing their research findings. The goal is to practically discuss applications with examples extracted from the literature. These range from the most basic early applications, influenced by the limited laboratory equipment available at those times, to the more contemporary research endeavours. Recently, numerous new analytical instrumentations have gained widespread adoption, some of which were previously inconceivable for field use or analysing micro-samples, characteristics needed to work with cultural heritage. Chemometrics serves as the binding element in this process. It handles the multivariate data generated by analytical instruments, even from multiple instruments used to characterize the same sample and yields easily interpretable graphs that encapsulate all the information considered simultaneously. This review will delve into the challenges of achieving commendable results and constructing effective descriptive or predictive models. Additionally, it will offer essential theoretical insights crucial for a comprehensive grasp of the fundamental algorithms employed. We have explored fundamental qualitative and quantitative models capable of addressing most issues encountered in studying a historical or artistic artifact. Additionally, focus was directed towards data pre-processing, at times essential for enhancing model outcomes.

Carbon dioxide (CO2) is a major greenhouse gas responsible for global warming and climate change. The development of sensitive CO2 sensors is crucial for environmental and industrial applications. This paper presents a novel CO2 sensor based on perovskite nanocrystals immobilized on graphene and functionalized with oxygen plasma treatment. The impact of this post-treatment method was thoroughly investigated using various characterization techniques, including Raman spectroscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The detection of CO2 at parts per million (ppm) levels demonstrated that the hybrids subjected to 5 min of oxygen plasma treatment exhibited a 3-fold improvement in sensing performance compared to untreated layers. Consequently, the CO2 sensing capability of the oxygen-treated samples showed a limit of detection and limit of quantification of 6.9 and 22.9 ppm, respectively. Furthermore, the influence of ambient moisture on the CO2 sensing performance was also evaluated, revealing a significant effect of oxygen plasma treatment.