MultiFuseYOLO: Redefining Wine Grape Variety Recognition through Multisource Information Fusion

Based on the current research on the wine grape variety recognition task, it has been found that traditional deep learning models relying only on a single feature (e.g., fruit or leaf) for classification can face great challenges, especially when there is a high degree of similarity between varieties. In order to effectively distinguish these similar varieties, this study proposes a multisource information fusion method, which is centered on the SynthDiscrim algorithm, aiming to achieve a more comprehensive and accurate wine grape variety recognition. First, this study optimizes and improves the YOLOV7 model and proposes a novel target detection and recognition model called WineYOLO-RAFusion, which significantly improves the fruit localization precision and recognition compared with YOLOV5, YOLOX, and YOLOV7, which are traditional deep learning models. Secondly, building upon the WineYOLO-RAFusion model, this study incorporated the method of multisource information fusion into the model, ultimately forming the MultiFuseYOLO model. Experiments demonstrated that MultiFuseYOLO significantly outperformed other commonly used models in terms of precision, recall, and F1 score, reaching 0.854, 0.815, and 0.833, respectively. Moreover, the method improved the precision of the hard to distinguish Chardonnay and Sauvignon Blanc varieties, which increased the precision from 0.512 to 0.813 for Chardonnay and from 0.533 to 0.775 for Sauvignon Blanc. In conclusion, the MultiFuseYOLO model offers a reliable and comprehensive solution to the task of wine grape variety identification, especially in terms of distinguishing visually similar varieties and realizing high-precision identifications.

A carbon nanotube x-ray source array designed for a new multisource cone beam computed tomography scanner

Cone beam computed tomography (CBCT) is known to suffer from strong scatter and cone beam artifacts. The purpose of this study is to develop and characterize a rapidly scanning carbon nanotube (CNT) field emission x-ray source array to enable a multisource CBCT (ms-CBCT) image acquisition scheme which has been demonstrated to overcome these limitations. A CNT x-ray source array with eight evenly spaced focal spots was designed and fabricated for a medium field of view ms-CBCT for maxillofacial imaging. An external multisource collimator was used to confine the radiation from each focal spot to a narrow cone angle. For ms-CBCT imaging, the array was placed in the axial direction and rapidly scanned while rotating continuously around the object with a flat panel detector. The x-ray beam profile, temporal and spatial resolutions, energy and dose rate were characterized and evaluated for maxillofacial imaging. The CNT x-ray source array achieved a consistent focal spot size of 1.10 ± 0.04 mm × 0.84 ± 0.03 mm and individual beam cone angle of 2.4°±0.08 after collimation. The x-ray beams were rapidly switched with a rising and damping times of 0.21 ms and 0.19 ms, respectively. Under the designed operating condition of 110 kVp and 15 mA, a dose rate of 8245μGy s-1was obtained at the detector surface with the inherent Al filtration and 2312μGy s-1with an additional 0.3 mm Cu filter. There was negligible change of the x-ray dose rate over many operating cycles. A ms-CBCT scan of an adult head phantom was completed in 14.4 s total exposure time for the imaging dose in the range of that of a clinical CBCT scanner. A spatially distributed CNT x-ray source array was designed and fabricated. It has enabled a new multisource CBCT to overcome some of the main inherent limitations of the conventional CBCT.

Identification of Paddy Croplands and Its Stages Using Remote Sensors: A Systematic Review

Rice is a staple food that feeds nearly half of the world's population. With the population of our planet expected to keep growing, it is crucial to carry out accurate mapping, monitoring, and assessments since these could significantly impact food security, climate change, spatial planning, and land management. Using the PRISMA systematic review protocol, this article identified and selected 122 scientific articles (journals papers and conference proceedings) addressing different remote sensing-based methodologies to map paddy croplands, published between 2010 and October 2022. This analysis includes full coverage of the mapping of rice paddies and their various stages of crop maturity. This review paper classifies the methods based on the data source: (a) multispectral (62%), (b) multisource (20%), and (c) radar (18%). Furthermore, it analyses the impact of machine learning on those methodologies and the most common algorithms used. We found that MODIS (28%), Sentinel-2 (18%), Sentinel-1 (15%), and Landsat-8 (11%) were the most used sensors. The impact of Sentinel-1 on multisource solutions is also increasing due to the potential of backscatter information to determine textures in different stages and decrease cloud cover constraints. The preferred solutions include phenology algorithms via the use of vegetation indices, setting thresholds, or applying machine learning algorithms to classify images. In terms of machine learning algorithms, random forest is the most used (17 times), followed by support vector machine (12 times) and isodata (7 times). With the continuous development of technology and computing, it is expected that solutions such as multisource solutions will emerge more frequently and cover larger areas in different locations and at a higher resolution. In addition, the continuous improvement of cloud detection algorithms will positively impact multispectral solutions.

Facing the future of respiratory virus surveillance: “The mosaic surveillance framework”

It is impossible to address the many complex needs of respiratory virus surveillance with a single system. Therefore, multiple surveillance systems and complementary studies must fit together as tiles in a “mosaic” to provide a complete picture of the risk, transmission, severity, and impact of respiratory viruses of epidemic and pandemic potential. Below we present a framework (WHO Mosaic Respiratory Surveillance Framework) to assist national authorities to identify priority respiratory virus surveillance objectives and the best approaches to meet them; to develop implementation plans according to national context and resources; and to prioritize and target technical assistance and financial investments to meet most pressing needs.

Refining epidemiological forecasts with simple scoring rules

Estimates from infectious disease models have constituted a significant part of the scientific evidence used to inform the response to the COVID-19 pandemic in the UK. These estimates can vary strikingly in their bias and variability. Epidemiological forecasts should be consistent with the observations that eventually materialize. We use simple scoring rules to refine the forecasts of a novel statistical model for multisource COVID-19 surveillance data by tuning its smoothness hyperparameter. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

Geometry calibration and image reconstruction for carbon-nanotube-based multisource and multidetector CT

Conventional intraoperative computed tomography (CT) has a long scan time, degrading the image quality. Its large size limits the position of a surgeon during surgery. Therefore, this study proposes a CT system comprising of eight carbon-nanotube (CNT)-based x-ray sources and 16 detector modules to solve these limitations. Gantry only requires 45° of rotation to acquire the whole projection, reducing the scan time to 1/8 compared to the full rotation. Moreover, the volume and scan time of the system can be significantly reduced using CNT sources with a small volume and short pulse width and placing a heavy and large high-voltage generator outside the gantry. We divided the proposed system into eight subsystems and sequentially devised a geometry calibration method for each subsystem. Accordingly, a calibration phantom consisting of four polytetrafluoroethylene beads, each with 15 mm diameter, was designed. The geometry calibration parameters were estimated by minimizing the difference between the measured bead projection and the forward projection of the formulated subsystem. By reflecting the estimated geometry calibration parameters, the projection data were obtained via rebinning to be used in the filtered-backprojection reconstruction. The proposed calibration and reconstruction methods were validated by computer simulations and real experiments. Additionally, the accuracy of the geometry calibration method was examined by computer simulation. Furthermore, we validated the improved quality of the reconstructed image through the mean-squared error (MSE), structure similarity (SSIM), and visual inspections for both the simulated and experimental data. The results show that the calibrated images, reconstructed by reflecting the calibration results, have smaller MSE and higher SSIM values than the uncalibrated images. The calibrated images were observed to have fewer artifacts than the uncalibrated images in visual inspection, demonstrating that the proposed calibration and reconstruction methods effectively reduce artifacts caused by geometry misalignments.

Health-Oriented Leadership and Mental Health From Supervisor and Employee Perspectives: A Multilevel and Multisource Approach

The link between leadership and mental health at the workplace is well established by prior research. However, most of the studies have addressed this relationship from a single-source perspective. The aim of this study was to examine how supervisor and employee ratings of health-oriented leadership correspond to each other and which sources are predictive for employee mental health. We assessed data within 99 teams (headed by 99 supervisors) containing 713 employees in 11 different companies in Southern Germany. Supervisors and their staff completed questionnaires on the supervisors' health-oriented staff-care dimensions awareness, value of health and health behavior (Health-Oriented Leadership Scale, HoL) and current mental distress (Hospital Anxiety and Depression Scale, HADS). Hierarchical linear models revealed that supervisors' self-ratings were significantly related to their employees' ratings (at the team level) only on the health behavior dimension, but not on the health awareness and value of health dimensions. Also, supervisors rated themselves significantly higher on HoL compared to their employees. Employee ratings of HoL significantly predicted their own level of mental distress (direct within-level effect), whereas supervisor ratings of HoL did not predict employees' mental distress at the team level (direct cross-level effect). Supervisors' self-ratings of HoL did not influence the relationship between employee ratings of HoL and their mental distress on an individual level (cross-level interaction). These results highlight the complex relationship between multisource assessments of HoL and employee mental health, emphasizing the importance of subjective perception for mental health. Future studies should investigate under which conditions supervisor and employee ratings correspond to each other and are predictive for mental health at the workplace.

Non-insulated Fractional Microneedle Radiofrequency Treatment with Smooth Motor Insertion for Reduction of Depressed Acne Scars, Pore Size, and Skin Texture Improvement: A Preliminary Study

Background: Severe scarring caused by acne is associated with substantial physical and psychological distress, both in adolescents and adults. There are two basic types of scars: atrophic (depressed) and raised (hypertrophic). Microneedle fractional radiofrequency (MFRF) is a new technology that uses extra sharp microneedles to heat the depths of the dermis, which promotes dermal collagen remodeling. In this study, we used electronically controlled non-insulated microneedle radiofrequency treatment to treat atrophic acne scars, improve skin texture, and reduce pore size. Methods: Nineteen patients were enrolled in the study. The goal of the treatment was to improve acne scars and skin texture and to reduce pore size. Each patient received 2 to 6 treatments with one month intervals between treatments. Treated areas were visually assessed for skin response, including edema, erythema, and burns. We used the standard pain scale of 0 to 10 to assess pain during and after treatment. Results: All the patients completed a series of 2 to 6 treatments. No adverse events as burns, scarring, or hyper/hypopigmentation were reported. Subjective pain assessment was the average point of 1.5, as reported by the patients. The subjective and objective questionnaire revealed high satisfaction from the treatment. Conclusions: The presented study results show that the MFRF treatment provides a highly effective minimally invasive treatment for acne scars, skin texture improvement, and pore size reduction, with a short downtime and high subjective satisfaction rates.

Statistical tests and identifiability conditions for pooling and analyzing multisite datasets

When sample sizes are small, the ability to identify weak (but scientifically interesting) associations between a set of predictors and a response may be enhanced by pooling existing datasets. However, variations in acquisition methods and the distribution of participants or observations between datasets, especially due to the distributional shifts in some predictors, may obfuscate real effects when datasets are combined. We present a rigorous statistical treatment of this problem and identify conditions where we can correct the distributional shift. We also provide an algorithm for the situation where the correction is identifiable. We analyze various properties of the framework for testing model fit, constructing confidence intervals, and evaluating consistency characteristics. Our technical development is motivated by Alzheimer's disease (AD) studies, and we present empirical results showing that our framework enables harmonizing of protein biomarkers, even when the assays across sites differ. Our contribution may, in part, mitigate a bottleneck that researchers face in clinical research when pooling smaller sized datasets and may offer benefits when the subjects of interest are difficult to recruit or when resources prohibit large single-site studies.

Painless, safe, and efficacious noninvasive skin tightening, body contouring, and cellulite reduction using multisource 3DEEP radiofrequency

In the last decade, Radiofrequency (RF) energy has proven to be safe and highly efficacious for face and neck skin tightening, body contouring, and cellulite reduction. In contrast to first-generation Monopolar/Bipolar and "X -Polar" RF systems which use one RF generator connected to one or more skin electrodes, multisource radiofrequency devices use six independent RF generators allowing efficient dermal heating to 52-55°C, with no pain or risk of other side effects. In this review, the basic science and clinical results of body contouring and cellulite treatment using multisource radiofrequency system (Endymed PRO, Endymed, Cesarea, Israel) will be discussed and analyzed.