The optical Hall effect manifests itself as angular momentum separation induced by the photonic spin-orbit interaction. Such a celebrated Hall effect, at the mercy of the angular momentum conservation law, has attracted tremendous interest owing to its science and potential applications in precision measurements, material characterizations, and photonic devices, as well as quantum optics. However, to date, the Hall effect only expresses angular momentum separation of the spin term (spin-spin separation) or the orbital term (orbit-orbit separation), whereas the spin-orbit angular momentum separation, named as the spin-orbit Hall effect, remains unexplored. Here we demonstrate for the first time that this spin-orbit effect could appear when the polarization state of the light beam evolves adiabatically from the equator toward the poles of the higher-order Poincaré sphere, rather than the conventional Poincaré sphere. In this scenario, the intrinsic spin and orbital components of the light beam become separated, leading to equal nonzero spin and orbital angular momenta in magnitude but with the opposite sign. We further show that the spin-orbit Hall effect can be controlled via crystal birefringence and hence holds promise for applications; e.g., it is shown that the separated orbital angular momentum could be utilized in particle manipulations.

We demonstrate an 806 nm laser diode end-pumped continuous-wave (CW) and actively Q-switched (AQS) orthogonally-polarized dual-wavelength Nd:YLF laser operating at 1314 and 1321 nm. Benefitting from the small difference of the stimulated emission cross sections at 1314 and 1321 nm in Nd:YLF crystal, the power equalized emissions at both wavelengths were achieved by simply titling the output coupler. A maximum CW output power of 9.2 W was obtained with the incident pump power of 32.5 W, giving an optical-to-optical conversion efficiency of approximately 28% and a slope efficiency of approximately 33%. Furthermore, active Q-switching was realized by inserting a Brewster-cut acousto optic modulator. For an incident pump power of 30 W, this oscillator delivered an average power of 6.5 W at a pulse repetition frequency (PRF) of 20 kHz, and a pulse energy of 2.6 mJ with a peak power of approximately 72 kW at a PRF of 1 kHz.

We construct a microscopic hyperspectral imaging system to distinguish between normal and cancerous gastric cells. We study common transmission-spectra features that only emerge when the samples are dyed with hematoxylin and eosin (H&E) stain. Subsequently, we classify the obtained visible-range transmission spectra of the samples into three zones. Distinct features are observed in the spectral responses between the normal and cancerous cell nuclei in each zone, which depend on the pH level of the cell nucleus. Cancerous gastric cells are precisely identified according to these features. The average cancer-cell identification accuracy obtained with a backpropagation algorithm program trained with these features is 95%.

The development of an objective and rapid method that can be used for the early diagnosis of gastric cancer has important clinical application value. In this study, the fluorescence hyperspectral imaging technique was used to acquire fluorescence spectral images. Deep learning combined with spectral-spatial classification methods based on 120 fresh tissues samples that had a confirmed diagnosis by histopathological examinations was used to automatically identify and extract the “spectral + spatial” features to construct an early diagnosis model of gastric cancer. The model results showed that the overall accuracy for the nonprecancerous lesion, precancerous lesion, and gastric cancer groups was 96.5% with specificities of 96.0%, 97.3%, and 96.7% and sensitivities of 97.0%, 96.3%, and 96.6%, respectively. Therefore, the proposed method can increase the diagnostic accuracy and is expected to be a new method for the early diagnosis of gastric cancer.

Early diagnosis is important to reduce the incidence and mortality rate of diabetes. The feasibility of early diagnosis of diabetes was studied via near-infrared spectra (NIRS) combined with a support vector machine (SVM) and aquaphotomics. Firstly, the NIRS of entire blood samples from the population of healthy, pre-diabetic, and diabetic patients were obtained. The spectral data of the entire spectra in the visible and near-infrared region (400–2,500 nm) were used as the research object of the qualitative analysis. Secondly, several preprocessing steps including multiple scattering correction, variable standardization, and first derivative and second derivative steps were performed and the best pretreatment method was selected. Finally, for the early diagnosis of diabetes, models were established using SVM. The first overtone of water (1,300–1,600 nm) was used as the research object for an aquaphotomics model, and the aquagram of the healthy group, pre-diabetes, and diabetes groups were drawn using 12 water absorption patterns for the early diagnosis of diabetes. The results of SVM showed that the highest accuracy was 97.22% and the specificity and sensitivity were 95.65 and 100%, respectively when the pretreatment method of the first derivative was used, and the best model parameters were c = 18.76 and g = 0.008583.The results of the aquaphotomics model showed clear differences in the 1,400–1,500 nm region, and the number of hydrogen bonds in water species (1,408, 1,416, 1,462, and 1,522 nm) was evidently correlated with the occurrence and development of diabetes. The number of hydrogen bonds was the smallest in the healthy group and the largest in the diabetes group. The suggested reason is that the water matrix of blood changes with the worsening of blood glucose metabolic dysfunction. The number of hydrogen bonds could be used as biomarkers for the early diagnosis of diabetes. The result show that it is effective and feasible to establish an accurate and rapid early diagnosis model of diabetes via NIRS combined with SVM and aquaphotomics.