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Virtual reality (VR) technology has gained popularity across various fields; however, its use often induces cybersickness, characterized by symptoms such as dizziness, nausea, and eye strain. This study investigated the differences in cybersickness levels and head movement patterns under three distinct VR viewing conditions: dynamic VR (DVR), static VR (SVR), and a control condition (CON) using a simulator. Thirty healthy adults participated, and their head movements were recorded using the Meta Quest 2 VR headset and analyzed using Python. The Virtual Reality Sickness Questionnaire (VRSQ) assessed subjective cybersickness levels. The results revealed that the SVR condition induced the highest VRSQ scores (M = 58.057), indicating the most severe cybersickness symptoms, while the DVR condition elicited significantly higher values in head movement variables, particularly in the coefficient of variation (CV) and integral values of head position along the vertical axis, and mean velocity (p < 0.05). These findings suggest that VR content characteristics directly influence users’ head movement patterns, closely related to cybersickness occurrence and severity. This study highlights the importance of analyzing head movement patterns in cybersickness research and provides insights for VR content design.

To compare the dynamic vault range (the difference in the central vault height from scotopic to photopic light condition) after implantation of the ICL/TICL (STAAR Surgical) and Eyecryl phakic/Eyecryl phakic toric intraocular lens (IOL) (Biotech Healthcare). This retrospective study included patients with myopia or myopic astigmatism eligible for phakic IOL implantation with either the ICL/TICL or Eyecryl phakic/Eyecryl phakic toric IOL. Vault changes in varying light conditions (scotopic, mesopic, and photopic) were assessed using an anterior segment optical coherence tomography-based tomographer and dynamic vault range (DVR) was compared between the two groups. A total of 60 eyes from 36 patients (30 eyes in each group) with a mean age of 28.63 ± 6.36 years were included. The mean postoperative follow-up at the time of assessment was 9.4 ± 5.3 and 8.9 ± 5.28 months ( = .75) in the ICL and Eyecryl groups, respectively. The mean values of scotopic, mesopic, and photopic vault were 490.56 ± 238.64, 453.56 ± 224.30, and 373.96 ± 200.24 μm in the ICL group and 515.46 ± 174.34, 490.26 ± 184.04, 450.43 ± 173.92 μm in the Eyecryl group ( .32, .24, and .05, respectively). The DVR was 116.6 ± 59.29 μm in the ICL group versus 65.03 ± 31.78 μm in the Eyecryl group ( < .001). The Eyecryl phakic IOL showed significantly fewer light-induced changes in the central vault height (DVR) compared to the ICL, which may be attributed to the difference in the material of the two phakic IOL models. This may be clinically significant in eyes with low postoperative vaults with respect to their follow-up and risk assessment of cataractogenesis in the long term. .

To overcome the problems of long production cycle and high cost in the product manufacturing process, a P2P (platform to platform) cloud manufacturing method based on a personalized custom business model has been proposed in this paper by integrating different technologies such as deep learning and additive manufacturing (AM). This paper focuses on the manufacturing process from a photo containing an entity to the production of that entity. Essentially, this is an object-to-object fabrication. Moreover, based on the YOLOv4 algorithm and DVR technology, an object detection extractor and a 3D data generator are constructed, and a case study is carried out for a 3D printing service scenario. The case study selects online sofa photos and real car photos. The recognition rates of sofa and car were 59% and 100%, respectively. Retrograde conversion from 2D data to 3D data takes approximately 60 s. We also carry out personalized transformation design on the generated sofa digital 3D model. The results show that the proposed method has been validated, and three unindividualized models and one individualized design model have been manufactured, and the original shape is basically maintained.

DC microgrids are highly compatible with photovoltaic (PV) generation because of their direct-current properties. However, with the increasing integration of PV sources into DC microgrids, traditional maximum power point tracking (MPPT) algorithms may cause problems such as overvoltage and power fluctuation, which makes it challenging to keep the stability of the DC-bus voltage due to the intermittent and stochastic nature of PVs. Consequently, in order to reduce the investment and maintenance costs of storage systems, innovative control methods are required for PVs to provide DC-bus voltage regulation services. In this paper, a novel active power control (APC) strategy, based on characteristic curve fitting, is proposed to flexibly regulate the PV output power. The transient process performance and robustness of the system are improved with the proposed APC strategy. Based on it, a V-P droop mechanism is designed to provide voltage regulating (DVR) service for the DC microgrid. The overall control strategy unifies the DVR function with the traditional MPPT function in the same control structure; thus, the PV source either works in the MPPT mode if the DC-bus is at its nominal value, or it works in the DVR mode if the DC-bus exceeds it. Switching between MPPT and DVR is autonomous, and it is fully decentralized, which improves the PV generation efficiency as well as ensures generation fairness among different parallel PV sources. Case studies including a real-world project analysis are carried out to validate the feasibility and effectiveness of the proposed strategy.

The Schrödinger equation is solved numerically for charmonium using the discrete variable representation (DVR) method. The Hamiltonian matrix is constructed and diagonalized to obtain the eigenvalues and eigenfunctions. Using these eigenvalues and eigenfunctions, spectra and various decay widths are calculated. The obtained results are in good agreement with other numerical methods and with experiments.

Many ecological restoration projects have been carried out in Southwest China; however, changes in vegetation cover in the dry valleys of Southwest China have rarely been reported. With their unique characteristics of high temperatures and low humidity, these dry valleys have considerably lower vegetation cover than their neighboring areas, making them the main sediment sources of rivers in Southwest China. Thus, it is imperative to understand changes in vegetation cover in China’s dry valleys, as well as the effects of changes in precipitation, since water deficit is the dominant cause of obstructed plant growth. In this study, changes in fractional vegetation cover (FVC) in dry valleys in the period 2000 to 2020 were analyzed based on MODIS-NDVI data, and the effects of precipitation were also analyzed. Our results indicated that: (1) the long-term mean annual FVC values in the dry–hot valleys (DHVs), dry–warm valleys (DWVs), and dry–temperate valleys (DTVs) were 0.426, 0.504, and 0.446, respectively; (2) significant decreasing trends in FVC were mainly found in DHVs and DWVs that were distributed in the southwestern part of the dry valley region (DVR), which was mainly due to the decrease in precipitation; and (3) significant increasing trends were reported in DTVs of the Min River and the Baishui River, which was probably due to the increase in precipitation. By analyzing the temporal trends of FVC in dry valleys, this study highlighted the effects of precipitation on the dynamics of FVC and demonstrated that anthropogenic activities such as urbanization, land use changes, and hydro-power project construction may also have considerable effects on FVC in dry valleys. Overall, this study not only provides insights that might inform further detailed studies on the dynamics and mechanisms of vegetation cover, but could also provide valuable guidance for ecological restoration management in the dry valley region.

In recent years, convolutional neural network (CNN)-based and transformer-based approaches have made strides in improving the performance of hyperspectral image (HSI) classification tasks. However, misclassifications are unavoidable in the aforementioned methods, with a considerable number of these issues stemming from the overlapping embedding spaces among different classes. This overlap results in samples being allocated to adjacent categories, thus leading to inaccurate classifications. To mitigate these misclassification issues, we propose a novel discrete vector representation (DVR) strategy for enhancing the performance of HSI classifiers. DVR establishes a discrete vector quantification mechanism to capture and store distinct category representations in the codebook between the encoder and classification head. Specifically, DVR comprises three components: the Adaptive Module (AM), Discrete Vector Constraints Module (DVCM), and auxiliary classifier (AC). The AM aligns features derived from the backbone to the embedding space of the codebook. The DVCM employs category representations from the codebook to constrain encoded features for a rational feature distribution of distinct categories. To further enhance accuracy, the AC correlates discrete vectors with category information obtained from labels by penalizing these vectors and propagating gradients to the encoder. It is worth noting that DVR can be seamlessly integrated into HSI classifiers with diverse architectures to enhance their performance. Numerous experiments on four HSI benchmarks demonstrate that our DVR scheme improves the classifiers’ performance in terms of both quantitative metrics and visual quality of classification maps. We believe DVR can be applied to more models in the future to enhance their performance and provide inspiration for tasks such as sea ice detection and algal bloom prediction in the marine domain.

Increased blood-brain barrier permeability (BBBp) has been hypothesized as a feature of aging that may lead to the development of Alzheimer's disease (AD). We sought to identify the brain regions most vulnerable to greater BBBp during aging and examine their regional relationship with neuroimaging biomarkers of AD. We studied 31 cognitively normal older adults (OA) and 10 young adults (YA) from the Berkeley Aging Cohort Study (BACS). Both OA and YA received dynamic contrast-enhanced MRI (DCE-MRI) to quantify Ktrans values, as a measure of BBBp, in 37 brain regions across the cortex. The OA also received Pittsburgh compound B (PiB)-PET to create distribution volume ratios (DVR) images and flortaucipir (FTP)- PET to create partial volume corrected standardized uptake volume ratios (SUVR) images. Repeated measures ANOVA assessed the brain regions where OA showed greater BBBp than YA. In OA, Ktrans values were compared based on sex, Aβ positivity status, and APOE4 carrier status within a composite region across the areas susceptible to aging. We used linear models and sparse canonical correlation analysis (SCCA) to examine the relationship between Ktrans and AD biomarkers. OA showed greater BBBp than YA predominately in the temporal lobe, with some involvement of parietal, occipital and frontal lobes. Within an averaged ROI of affected regions, there was no difference in Ktrans values based on sex or Aβ positivity, but OA who were APOE4 carriers had significantly higher Ktrans values. There was no direct relationship between averaged Ktrans and global Aβ pathology, but there was a trend for an Ab status by tau interaction on Ktrans in this region. SCCA showed increased Ktrans was associated with increased PiB DVR, mainly in temporal and parietal brain regions. There was not a significant relationship between Ktrans and FTP SUVR. Our findings indicate that the BBB shows regional vulnerability during normal aging that overlaps considerably with the pattern of AD pathology. Greater BBBp in brain regions affected in aging is related to APOE genotype and may also be related to the pathological accumulation of Aβ.

Treatments targeting TIGIT have gained a lot of attention due to strong preclinical and early clinical results, particularly with anti-PD-(L)1 therapeutics. However, this combination has failed to meet progression-free survival endpoints in phase III trials. Most of our understanding of TIGIT comes from studies of T cell function. Yet, this inhibitory receptor is often upregulated to the same, or higher, extent on NK cells in cancers. Studies in murine models have demonstrated that TIGIT inhibits NK cells and promotes exhaustion, with its effects on tumor control also being dependent on NK cells. However, there are limited studies assessing the role of TIGIT on the function of human NK cells (hNK), particularly in lung cancer. Most studies used NK cell lines or tested TIGIT blockade to reactivate exhausted cells obtained from cancer patients. For therapeutic advancement, a better understanding of TIGIT in the context of activated hNK cells is crucial, which is different than exhausted NK cells, and critical in the context of adoptive NK cell therapeutics that may be combined with TIGIT blockade. In this study, the effect of TIGIT blockade on the anti-tumor activities of human ex vivo-expanded NK cells was evaluated in vitro in the context of lung cancer. TIGIT expression was higher on activated and/or expanded NK cells compared to resting NK cells. More TIGIT+ NK cells expressed major activating receptors and exerted anti-tumor response as compared to TIGIT− cells, indicating that NK cells with greater anti-tumor function express more TIGIT. However, long-term TIGIT engagement upon exposure to PVR+ tumors downregulated the cytotoxic function of expanded NK cells while the inclusion of TIGIT blockade increased cytotoxicity, restored the effector functions against PVR-positive targets, and upregulated immune inflammation-related gene sets. These combined results indicate that TIGIT blockade can preserve the activation state of NK cells during exposure to PVR+ tumors. These results support the notion that a functional NK cell compartment is critical for anti-tumor response and anti-TIGIT/adoptive NK cell combinations have the potential to improve outcomes.

Purpose The difference between mean systemic filling (Pmsf) and central venous pressure (CVP) is the venous return gradient (dVR). The aim of this study is to assess the significance of the Pmsf analogue (Pmsa) and the dVR during a fluid challenge. Methods We performed a prospective observational study in postsurgical patients. Patients were monitored with a central venous catheter, a LiDCO™plus and the Navigator™. A 250-ml intravenous fluid challenge was given over 5 min. A positive response to the fluid challenge was defined as either a stroke volume (SV) or cardiac output increase of greater than 10 %. Results A total of 101 fluid challenges were observed in 39 patients. In 43 events (42.6 %) the SV and CO increased by more than 10 %. Pmsa increased similarly during a fluid challenge in responders and non-responders (3.1 ± 1.9 vs. 3.1 ± 1.8, p  = 0.9), whereas the dVR increased in responders (1.16 ± 0.8 vs. 0.2 ± 1, p  < 0.001) as among non-responders CVP increased along with Pmsa (2.9 ± 1.7 vs. 3.1 ± 1.8, p  = 0.15). Resistance to venous return did not change immediately after a fluid challenge. Heart performance (Eh) decreased significantly among non-responders (0.41 ± 0.15 vs. 0.34 ± 0.13, p  < 0.001) whereas among responders it did not change when compared with baseline value (0.35 ± 0.15 vs. 0.34 ± 0.12, p  = 0.15). Conclusions The changes in Pmsa and dVR measured at the bedside during a fluid challenge are consistent with the cardiovascular model described by Guyton.