With the continuous progress of development, deep learning has made good progress in the analysis and recognition of images, which has also triggered some researchers to explore the area of combining deep learning with hyperspectral medical images and achieve some progress. This paper introduces the principles and techniques of hyperspectral imaging systems, summarizes the common medical hyperspectral imaging systems, and summarizes the progress of some emerging spectral imaging systems through analyzing the literature. In particular, this article introduces the more frequently used medical hyperspectral images and the pre-processing techniques of the spectra, and in other sections, it discusses the main developments of medical hyperspectral combined with deep learning for disease diagnosis. On the basis of the previous review, tne limited factors in the study on the application of deep learning to hyperspectral medical images are outlined, promising research directions are summarized, and the future research prospects are provided for subsequent scholars.

This study experimentally investigated the effects of hydrogen direct injection on combustion and the cycle-by-cycle variations in a spark ignition n-butanol engine under lean burn conditions. For this purpose, a spark ignition engine installed with a hydrogen and n-butanol dual fuel injection system was specially developed. Experiments were conducted at four excess air ratios, four hydrogen fractions(φ(𝐻2)) and pure n-butanol. Engine speed and intake manifold absolute pressure (MAP) were kept at 1500 r/min and 43 kPa, respectively. The results indicate that the θ0–10 and θ10–90 decreased gradually with the increase in hydrogen fraction. Additionally, the indicated mean effective pressure (IMEP), the peak cylinder pressure (Pmax) and the maximum rate of pressure rise ((dP/dφ)max) increased gradually, while their cycle-by-cycle variations decreased with the increase in hydrogen fraction. In addition, the correlation between the (dP/dφ)max and its corresponding crank angle became weak with the increase in the excess air coefficient (λ), which tends to be strongly correlated with the increase in hydrogen fraction. The coefficient of variation of the Pmax and the IMEP increased with the increase in λ, while they decreased obviously after blending in the hydrogen under lean burn conditions. Furthermore, when λ was 1.0, a 5% hydrogen fraction improved the cycle-by-cycle variations most significantly. While a larger hydrogen fraction is needed to achieve the excellent combustion characteristics under lean burn conditions, hydrogen direct injection can promote combustion process and is beneficial for enhancing stable combustion and reducing the cycle-by-cycle variations.

Using n-butanol as an alternative fuel can effectively alleviate the increasingly prominent problems of fossil resource depletion and environmental pollution. Combined injection technology can effectively improve engine combustion and emission characteristics while applying combined injection technology to n-butanol engines has not been studied yet. Therefore, this study adopted butanol port injection plus butanol direct injection mode. The engine test bench studied the combustion and emission performance under different direct injection ratios (NDIr) and excess air ratios (λ). Results show that with increasing NDIr, the engine torque (Ttq), peak in-cylinder pressure (Pmax), peak in-cylinder temperature (Tmax), and the maximum rate of heat release (dQmax), all rise first and then drop, reaching the maximum value at NDIr = 20%. The θ0-90 and COV_IMEP decrease first and then increase as NDIr increases. NDIr = 20% is considered the best injection ratio to obtain the optimal combustion performance. NDIr has little affected on CO emission, and the NDIr corresponding to the lowest HC emissions are concentrated at 40% to 60%, especially at lean burn conditions. NOx emissions increase with increasing NDIr, especially at N20DI, but not by much at NDIr of 40–80%. With the increase in NDIr, the number of nucleation mode particles, accumulation mode particles, and total particle decrease first and then increase. Therefore, the n-butanol combined injection mode with the appropriate NDIr can effectively optimize SI engines’ combustion and emission performance.

Diabetic Retinopathy (DR) is one of the most common microvascular complications of diabetes. Diabetic Macular Edema (DME) is a concomitant symptom of DR. As the grade of lesion of DR and DME increase, the possibility of blindness can also increase significantly. In order to take the early interventions as soon as possible to reduce the likelihood of blindness, it is necessary to perform both DR and DME grading. We design a joint grading model based on multi-task learning and multi-branch networks (MaMNet) for DR and DME grading. The model mainly includes a multi-branch network (MbN), a feature fusion module, and a disease classification module. The MbN is formed by four branch structures, which can extract the low-level feature information of DME and DR in a targeted way; the feature fusion module is composed of a self-feature extraction module (SFEN), cross-feature extraction module (CFEN) and atrous spatial pyramid pooling module (ASPP). By combining various features collected from the aforementioned modules, the feature fusion module can provide more thorough discriminative features, which benefits the joint grading accuracy. The ISBI-2018-IDRiD challenge dataset is used to evaluate the performance of the proposed model. The experimental results show that based on the multi-task strategy the two grading tasks of DR and DME can provide each other with additional useful information. The joint accuracy of the model, the accuracy of DR and the accuracy of DME are 61.2%, 64.1% and 79.4% respectively.

Hydrogen injection timing (HIT) plays a crucial role in the combustion and emission characteristics of a hydrogen/n-butanol dual-fuel engine with hydrogen direct injection. This study employed an integrated approach combining three-dimensional simulation modeling and engine test bench experiments to investigate the effects of HIT on engine performance. In order to have a more intuitive understanding of the physical and chemical change processes, such as the stratification state and combustion status of hydrogen in the cylinder, and to essentially explore the internal mechanism and fundamental reasons for the improvement in performance of n-butanol engines by hydrogen addition, a numerical study was conducted to examine the effects of HIT on hydrogen stratification and combustion behavior. The simulation results demonstrated that within the investigated range, at 100 °CA BTDC hydrogen injection time, hydrogen forms an ideal hydrogen stratification state in the cylinder; that is, a locally enriched hydrogen zone near the spark plug, while there is a certain distribution of hydrogen in the cylinder. Meanwhile, the combustion state also reaches the optimal level at this hydrogen injection moment. Consequently, 100 °CA BTDC is identified as the optimal HIT for a hydrogen/n-butanol dual-fuel engine. At the same time, an experimental study was performed to capture the actual complex processes and comprehensively evaluate combustion and emission characteristics. The experimental results indicate that both dynamic performance (torque) and combustion characteristics (cylinder pressure, flame development period, etc.) achieve optimal values at the HIT of 100 °CA BTDC. Notably, under lean-burn conditions, the combustion parameters exhibit greater sensitivity to HIT. Regarding emissions, the CO and HC emissions initially decreased slightly, then gradually increased with advanced injection timing. The 100 °CA BTDC timing effectively reduced the CO emissions at λ = 0.9 and 1.0. CO emissions at λ = 1.2, and showed minimal sensitivity to the injection timing variations. Therefore, optimized HIT facilitates enhanced combustion efficiency and emission performance in hydrogen-direct-injection n-butanol engines.

Hyperspectral image denoising is crucial for restoring effective information from noisy data and plays a significant role in various downstream applications. Deep learning based methods have become the mainstream research direction due to their ability to handle complex noise. However, the spatial feature extraction of most existing methods is not comprehensive enough, and the adoption of a fixed spectral reconstruction mode does not fully utilize the spectral information of the original image. To address these issues, we propose a Spatial-Spectral Modulated Network for hyperspectral image denoising (SMDe). It consists of a spatial feature extraction network and a spectral modulation module. For the spatial feature extraction, we construct a hybrid network that combines Mamba and Transformer layers, which can effectively capture global and local spatial information. For the spectral modulation module, we design a discrimination strategy that adaptively preserves or reconstructs spectral information from the original image spectra. This information is then used to modulate spatial features, enhancing the spectral fidelity of the denoised image. Experiments on synthetic datasets show that SMDe outperforms other advanced methods in most noisy scenarios, not only restoring image details but also maintaining excellent spectral consistency. In cross-dataset and real-data evaluations, the denoising results of SMDe also demonstrated strong competitiveness.