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The aim of this paper was to validate factors affecting the in-stand landscape quality and how important each factor was in determining scenic beauty of natural secondary forests. The study was limited to 23 stand-level cases of natural secondary forests in Shen Zhen city in southern China. Typical samples of photographs and public estimations were applied to evaluate scenic beauty inside the natural secondary forests. The major factors were then selected by multiple linear-regression analysis and a model between scenic beauty estimation (SBE) values and in-stand landscape features was established. Rise in crown density, fall in plant litter, glow in color of trunk, fall in arbor richness, and rise in visible distance increased scenic beauty values of in-stand landscape. These five factors significantly explained the differences in scenic beauty, and together accounted for 45% of total variance in SBEs. Personal factors (e.g. gender, age and education) did not significantly affect the ratings of landscape photos, although variations of landscape quality were affected by some personal factors. Results of this study will assist policymakers, silviculturists and planners in landscape design and management of natural secondary forests in Shenzhen city. People can improve the scenic beauty values by pruning branches and clearing plant litter, which subsequently improve the forest health and contribute to forest recreation.

Cartographic animation has been developed and widely used in geo-visualisation and many other areas in recent years.The usability of animated maps is a key characteristic affecting map users’effectiveness and efficiency in accomplishing tasks.In this paper,an eye tracking approach was proposed as a visual analytics method to evaluate the usability of animated maps by capturing participants’eye movement data and quantitatively analysing the accuracy（effectiveness）and response time（efficiency）of users’task completion.In the study,a set of animated traffic maps represented by three important visual variables（colour hue,size and frequency）was used for the usability evaluation.The experimental results showed that the usability of these three visual variables for cartographic animation affects the usability of animated maps.Red,yellow,and aqua were found to convey map information more effectively than other colour hues.Size was found to be more usable than colour hues for both animated maps and static maps.Usability was not found to be proportional to the playback rate of animated maps.Furthermore,the usability of the frequency,colour hue,and size was found to be related to the display’s size.We hope that the analysis approach presented in this paper and the results of this study will be of help in the design of cartographic animation displays with better usability.

Human activities along with climate change have unsustainably changed the land use in coastal zones. This has increased demands and challenges in mapping and change detection of coastal zone land use over long-term periods. Taking the Bohai rim coastal area of China as an example, in this study we proposed a method for the long time-series mapping and change detection of coastal zone land use based on Google Earth Engine (GEE) and multi-source data fusion. To fully consider the characteristics of the coastal zone, we established a land-use function classification system, consisting of cropland, coastal aquaculture ponds (saltern), urban land, rural settlement, other construction lands, forest, grassland, seawater, inland fresh-waters, tidal flats, and unused land. We then applied the random forest algorithm, the optimal classification method using spatial morphology and temporal change logic to map the long-term annual time series and detect changes in the Bohai rim coastal area from 1987 to 2020. Validation shows an overall acceptable average accuracy of 82.30% (76.70–85.60%). Results show that cropland in this region decreased sharply from 1987 (53.97%) to 2020 (37.41%). The lost cropland was mainly transformed into rural settlements, cities, and construction land (port infrastructure). We observed a continuous increase in the reclamation with a stable increase at the beginning followed by a rapid increase from 2003 and a stable intermediate level increase from 2013. We also observed a significant increase in coastal aquaculture ponds (saltern) starting from 1995. Through this case study, we demonstrated the strength of the proposed methods for long time-series mapping and change detection for coastal zones, and these methods support the sustainable monitoring and management of the coastal zone.

At present, there are still many old-fashioned water meters in the society, and the water department needs to send staff to read the water meter after arriving at the scene with a handheld all-in-one machine. However, there are many problems in this manual meter reading method. First, a large number of meter reading work leads to low efficiency of the entire water department, consuming a lot of time and energy, and high labor costs; second, the water meters in natural scenes have problems such as serious dial contamination and other environmental factors that interfere with the meter reading staff, and the results of the meter reader cannot be verified later. In response to these problems, this paper studies a deep learning method for automatic detection and recognition of water meter readings. This paper first introduces the existing in-depth learning models, such as Faster R-CNN, SSD, and YOLOv3. Then two datasets are sorted out, one is the original water table picture dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Then two plans are proposed, one is the original water table image dataset, and the other is a dataset cut out from the water meter image with the black bounding box showing the water meter readings. Finally, by comparing the three models from different angles, it is determined that YOLOv3 in the second solution has the best recognition effect, and the accuracy rate reaches 90.61%, which can greatly improve work efficiency, save labor costs, and assist auditors in reviewing the read water meter readings.

Background This study evaluated the minimal invasiveness, safety, and accuracy of robot-assisted pedicle screw placement procedure using a modified tracer fixation device. Methods Patients were randomly assigned to conventional fixation group (25 patients) and modified fixation group (27 patients). Results No baseline statistical difference was observed between the groups ( P  > 0.05). The length of unnecessary incision, amount of bleeding, and fixation duration for tracer fixation respectively were 6.08 ± 1.02 mm, 1.46 ± 0.84 ml, and 1.56 ± 0.32 min in the modified fixation group and 40.28 ± 8.52 mm, 12.02 ± 2.24 ml, and 5.08 ± 1.06 min in the conventional group. The difference between both groups was significant ( P  < 0.05). However, no significant difference between the two groups was observed in terms of the accuracy of pedicle screw placement ( P  > 0.05). Conclusions The modified minimally invasive procedure for tracer fixation results in minimal trauma and is simple, reliable, and highly safe. Additionally, the procedure does not compromise the accuracy of pedicle screw placement. Thus, it has great clinical applicable value. Trial registration Chinese Clinical Trial Registry: Registration number, ChiCTR1800016680 ; Registration Date, 15/06/2018.

Uncomfortable ear pressure during vehicle door closure is associated with enhanced vehicle airtightness and an unreasonable reduction in cabin noise. Current computational simulation methods for ear pressure, however, suffer from low accuracy and inefficiency. In this study, we propose a porous medium model to represent the dynamic airflow resistance characteristics of the pressure relief valve, which improves both the efficiency and accuracy of precise ear pressure simulation. Additionally, an optimized sound insulation cover design is shown to effectively improve ear comfort. Using the overset mesh technique in STAR-CCM+, a transient in-vehicle flow field model was established. Model parameters were calibrated experimentally, and the optimization scheme was validated through combined simulation and experimental data. Results demonstrate that removing the silencer cover of the pressure relief valve reduces the peak ear pressure at the third-row right seat by 20%, with all simulation errors at the human ear remaining below 8%. Compared to the traditional fixed-opening model, the porous medium approach significantly improves the accuracy of simulating the actual pressure relief process. The dynamic pressure relief model and the optimized sound insulation cover effectively enhance ear pressure comfort, offering theoretical guidance for balancing door closing sound quality and vehicle sealing performance in automotive engineering.

To properly repair and maintain implants, which are bone tissue implants that replace natural tooth roots, it is crucial to accurately identify their brand and specification. Deep learning has demonstrated outstanding capabilities in analysis, such as image identification and classification, by learning the inherent rules and degrees of representation of data models. The purpose of this study is to evaluate deep learning algorithms and their supporting application software for their ability to recognize and categorize three dimensional (3D) Cone Beam Computed Tomography (CBCT) images of dental implants. By using CBCT technology, the 3D imaging data of 27 implants of various sizes and brands were obtained. Following manual processing, the data were transformed into a data set that had 13,500 two-dimensional data. Nine deep learning algorithms including GoogleNet, InceptionResNetV2, InceptionV3, ResNet50, ResNet50V2, ResNet101, ResNet101V2, ResNet152 and ResNet152V2 were used to perform the data. Accuracy rates, confusion matrix, ROC curve, AUC, number of model parameters and training times were used to assess the efficacy of these algorithms. These 9 deep learning algorithms achieved training accuracy rates of 100%, 99.3%, 89.3%, 99.2%, 99.1%, 99.5%, 99.4%, 99.5%, 98.9%, test accuracy rates of 98.3%, 97.5%, 94.8%, 85.4%, 92.5%, 80.7%, 93.6%, 93.2%, 99.3%, area under the curve (AUC) values of 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00, 1.00. When used to identify implants, all nine algorithms perform satisfactorily, with ResNet152V2 achieving the highest test accuracy, classification accuracy, confusion matrix area under the curve, and receiver operating characteristic curve area under the curve area. The results showed that the ResNet152V2 has the best classification effect on identifying implants. The artificial intelligence identification system and application software based on this algorithm can efficiently and accurately identify the brands and specifications of 27 classified implants through processed 3D CBCT images in vitro, with high stability and low recognition cost.

Using serum carbohydrate antigen 19-9 (CA 19-9) in discriminating between benign and malignant pancreatic disease remains controversial. We aim to evaluate the diagnostic value of serum CA 19-9 in predicting malignant pancreatic cystic lesions. Eligible studies were identified through searching MEDLINE and EMBASE prior to March 2016. Studies were assessed for quality using the Quality Assessment for Studies of Diagnostic Accuracy, 2nd version (QUADAS-2). Pooled sensitivity and specificity with 95% confidence interval (CI) were calculated using random-effects models. Summary receiver operator characteristic (SROC) curves and the area under curve (AUC) were performed. A total of thirteen studies including 1437 patients were enrolled in this meta-analysis. The pooled sensitivity and specificity were 0.47(95% CI: 0.35-0.59), and 0.88(95% CI: 0.86-0.91), respectively, and the AUC was 0.87(95% CI, 0.84-0.90). Meta-regression analysis showed that sample size, region and reference standards were not the main sources of heterogeneity. Serum CA 19-9 has satisfying pooled specificity while poor pooled sensitivity for discriminating benign from malignant PCNs. It deserves to be widely used as complementary to other clinical diagnostic methods.

A new energy industry represented by photovoltaic and wind power has been developing rapidly in recent years, and its randomness and volatility will impact the stable operation of the power system. At present, it is proposed to enrich the regulation of the power grid by tapping the regulation potential of load-side resources. This paper evaluates the overall voltage regulation capability of substations under the premise of considering the impact on network voltage security and providing a theoretical basis for the participation of load-side resources of distribution networks in the regulation of the power grid. This paper proposes a Zbus linear power flow model based on Fixed-Point Power Iteration (FFPI) to enhance power flow analysis efficiency and resolve voltage sensitivity expression. Establishing the linear relationship between the voltages of PQ nodes, the voltage of the reference node, and the load power, this paper clarifies the impact of reactive power compensation devices and OLTC (on-load tap changer) tap changes on the voltages of various nodes along the feeder. It provides theoretical support for evaluating the voltage regulation range for substations. The day-ahead focus is on minimizing network losses by pre-optimizing OLTC tap positions, calculating the substation voltage regulation boundaries within the day, and simultaneously optimizing the total reactive power compensation across the entire network. By analyzing the calculated examples, it was found that a pre-scheduled OLTC (on-load tap changer) can effectively reduce network losses in the distribution grid. Compared with traditional methods, the voltage regulation range assessment method proposed in this paper can optimize the adjustment of reactive power compensation devices while ensuring the voltage safety of all nodes in the network.

It has been demonstrated that circ_0001874 and circ_0001971 are potential biomarkers for the diagnosis of oral squamous carcinoma (OSCC). MiR-186 was reported to serve as a tumor suppressor in OSCC, and the down-regulation of miR-186 was reported to lead to higher expression of oncogenic factor SHP2 and the activation of growth promoting signaling. In this study, we aimed to explore the possible molecular role of circ_0001874 and circ_0001971 signaling in the pathogenesis of OSCC. RT-qPCR, Western blot, online bioinformatics tools and luciferase assay were utilized to study the molecular signaling pathways of circ_0001874 and circ_0001971. MTT assay and FCM assay were performed to investigate the synergistic effect of circ_0001971 and circ_0001874 on cell proliferation and apoptosis. By observing the effect of different miRNAs on the levels of circ_0001847 and circ_0001971, it was identified that circ_0001847 and circ_0001971 respectively sponged the expression of miR-296 and miR-186 via binding to these miRNAs. Also, SHP2 mRNA and PLK1 mRNA were respectively targeted by miR-186 and miR-296-5p. We also established two signaling pathways, i.e., circ_0001971/miR-186/SHP2 and circ_0001874/miR-296-5p/PLK1, and validated the synergistic effect of circ_0001971 and circ_0001874 via observing their positive effect on cell proliferation and negative effect on cell apoptosis. The expression of miR-186 and miR-296-5p was generally lower in saliva of OSCC patients compared with that in OLK patients, while the expression of miR-186 and miR-296-5p was specifically up-regulated in saliva of OSCC patients. In conclusion, the finding of this study demonstrated that the relative level of hsa_circ_0001971 and hsa_circ_0001874 were different in the saliva of OSCC patients and could be used as predictive biomarkers for the development of OSCC. Furthermore, oncogenic effects of hsa_circ_0001971 and hsa_circ_0001874 in the development of OSCC might be, at least partially, mediated by its downstream signaling pathways including hsa_circ_0001971/microRNA-186/SHP2 and hsa_circ_0001874/microRNA-297/PLK1.