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Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) emerged in March 2020 in Italy, leading to the pandemic of coronavirus disease 2019 (COVID-19) that continues to cause high global morbidity and mortality in human populations. Numerous studies have focused on the spread and persistence of the virus in the hospital setting. New scientific evidence shows that SARS-CoV-2 is present in different community environments. Although aerosol is one of the main routes of transmission for SARS-CoV-2, indirect contact through virus-contaminated surfaces could also play a key role. The survival and persistence of SARS-CoV-2 on surfaces appear to be influenced by the characteristics of the material, temperature, and humidity. In this study, we investigated the presence of SARS-CoV-2 RNA on surfaces in 20 supermarkets throughout the Apulia region during the lockdown period. We collected a total of 300 swab samples from various surfaces including supermarket scales, trolley handles, refrigerator and freezer handles, and keyboards. In total, 13 (4.3%) surfaces were positive for SARS-CoV-2 RNA contamination, with shopping trolley handles being the most frequently contaminated. This study showed that contamination in public spaces can occur, so we remark the importance to adopt adequate preventive measures, including environment ventilation, careful surfaces sanitation, hand hygiene, and correct usage of masks, to reduce the likelihood of virus transmission.

Given a handle decomposition of a 4-manifold with boundary and an open book decomposition of the boundary, we show how to produce a trisection diagram of a trisection of the 4-manifold inducing the given open book. We do this by making the original proof of the existence of relative trisections more explicit in terms of handles. Furthermore, we extend this existence result to the case of 4-manifolds with multiple boundary components and show how trisected 4-manifolds with multiple boundary components glue together.

\"Digital\" agriculture is rapidly affecting the value of agricultural output. Robotic picking of the ripe agricultural product enables accurate and rapid picking, making agricultural harvesting intelligent. How to increase product output has also become a challenge for digital agriculture. During the cherry growth process, realizing the rapid and accurate detection of cherry fruits is the key to the development of cherry fruits in digital agriculture. Due to the inaccurate detection of cherry fruits, environmental problems such as shading have become the biggest challenge for cherry fruit detection. This paper proposes an improved YOLO-V4 deep learning algorithm to detect cherry fruits. This model is suitable for cherry fruits with a small volume. It is proposed to increase the network based on the YOLO-V4 backbone network CSPDarknet53 network, combined with DenseNet The density between layers, the a priori box in the YOLO-V4 model, is changed to a circular marker box that fits the shape of the cherry fruit. Based on the improved YOLO-V4 model, the feature extraction is enhanced, the network structure is deepened, and the detection speed is improved. To verify the effectiveness of this method, different deep learning algorithms of YOLO-V3, YOLO-V3-dense and YOLO-V4 are compared. The results show that the mAP (average accuracy) value obtained by using the improved YOLO-V4 model (YOLO-V4-dense) network in this paper is 0.15 higher than that of yolov4. In actual orchard applications, cherries with different ripeness of cherries in the same area can be detected, and the fruits with larger ripeness differences can be artificially intervened, and finally, the yield of cherry fruits can be increased.

Tensor analytics lays the mathematical basis for the prosperous promotion of multiway signal processing. To increase computing throughput, mainstream processors transform tensor convolutions into matrix multiplications to enhance the parallelism of computing. However, such order-reducing transformation produces data duplicates and consumes additional memory. Here, we propose an integrated photonic tensor flow processor (PTFP) without digitally duplicating the input data. It outputs the convolved tensor as the input tensor ‘flows’ through the processor. The hybrid manipulation of optical wavelengths, space dimensions, and time delay steps, enables the direct representation and processing of high-order tensors in the optical domain. In the proof-of-concept experiment, an integrated processor manipulating wavelengths and delay steps is implemented for demonstrating the key functionalities of PTFP. The multi-channel images and videos are processed at the modulation rate of 20 Gbaud. A convolutional neural network for video action recognition is demonstrated on the processor, which achieves an accuracy of 97.9%. Convolutional operation is a very efficient way to handle tensor analytics, but it consumes a large quantity of additional memory. Here, the authors demonstrate an integrated photonic tensor processor which directly handles high-order tensors without tensor-matrix transformation.

Patients with deaths from COVID-19 often have co-morbid cardiovascular disease. Real-time cardiovascular disease monitoring based on wearable medical devices may effectively reduce COVID-19 mortality rates. However, due to technical limitations, there are three main issues. First, the traditional wireless communication technology for wearable medical devices is difficult to satisfy the real-time requirements fully. Second, current monitoring platforms lack efficient streaming data processing mechanisms to cope with the large amount of cardiovascular data generated in real time. Third, the diagnosis of the monitoring platform is usually manual, which is challenging to ensure that enough doctors online to provide a timely, efficient, and accurate diagnosis. To address these issues, this paper proposes a 5G-enabled real-time cardiovascular monitoring system for COVID-19 patients using deep learning. Firstly, we employ 5G to send and receive data from wearable medical devices. Secondly, Flink streaming data processing framework is applied to access electrocardiogram data. Finally, we use convolutional neural networks and long short-term memory networks model to obtain automatically predict the COVID-19 patient’s cardiovascular health. Theoretical analysis and experimental results show that our proposal can well solve the above issues and improve the prediction accuracy of cardiovascular disease to 99.29%.

Using a demolition hammer among the power tools is one of the most challenging job tasks for the operators of these tools, which exposes users to high levels of hand-transmitted vibrations. The focus of this study was for finding the vibration levels transmitted to the handles, the frequency content transmitted by the tool, and the effect of using insert bits with different tip shapes and lengths using conventional demolition hammers under controlled operating conditions on typical concrete slabs in the field. Three demolition hammers (weighing between 14 and 27 kg) with two handles installed were subjected to simultaneous measurements based on ISO 5349 as the field measurement technique. The findings demonstrated that even while the vertical axis dominated, several samples also produced equivalent x-axis results. The impact energy of the tools employed and the transmitted vibration to the tool handles are not significantly correlated with each other. All mean values were higher in the slabs with a thickness of 15 than those with 10 cm. The mean values of the total results for the two forms of flat and point tip inserted bits were 16.85 and 15.86 m/s2, respectively, and in the samples with the same operating factors, this difference was more than 3 m/s2 (p < .03). The a wrms (frequency-weighted root mean square acceleration) produced at tools using 60 cm bits were more than those with a length of 40 cm, both for the average of the total results and the average of the results of each hammer. The difference between the average bit length groups over samples with the same factors reached 4.65 m/s2 (p = .02). Frequency analysis for the flat and point insert bits with 60 cm length in the concrete slabs with a thickness of 15 cm showed that the dominant frequencies detected at lower frequencies for point bits. This difference is almost two frequency parts of 1/3 octave band.

The boundary sum of the product of a circle with a 3-ball and the product of a disk with a 2-sphere is called a pochette. Pochette surgery, which was discovered by Iwase and Matsumoto, is a generalization of Gluck surgery and a special case of torus surgery. For a pochette P embedded in a 4-manifold X , a pochette surgery on X is the operation of removing the interior of P and gluing P by a diffeomorphism of the boundary of P . We present an explicit diffeomorphism of the boundary of P for constructing a 4-manifold after any pochette surgery. We also describe a necessary and sufficient condition for some pochette surgeries on any simply-connected closed 4-manifold create a 4-manifold with the same homotopy type of the original 4-manifold. In this paper we construct infinitely many embeddings of a pochette into the 4-sphere and prove that homotopy 4-spheres obtained from surgeries along these embedded pochettes are all diffeomorphic to the 4-sphere by some explicit handle calculus and relative handle calculus.

The role of mobile robots for cleaning and sanitation purposes is increasing worldwide. Disinfection and hygiene are two integral parts of any safe indoor environment, and these factors become more critical in COVID-19-like pandemic situations. Door handles are highly sensitive contact points that are prone to be contamination. Automation of the door-handle cleaning task is not only important for ensuring safety, but also to improve efficiency. This work proposes an AI-enabled framework for automating cleaning tasks through a Human Support Robot (HSR). The overall cleaning process involves mobile base motion, door-handle detection, and control of the HSR manipulator for the completion of the cleaning tasks. The detection part exploits a deep-learning technique to classify the image space, and provides a set of coordinates for the robot. The cooperative control between the spraying and wiping is developed in the Robotic Operating System. The control module uses the information obtained from the detection module to generate a task/operational space for the robot, along with evaluating the desired position to actuate the manipulators. The complete strategy is validated through numerical simulations, and experiments on a Toyota HSR platform.

We show that there are well-defined maps on sutured ECH induced by contact 2-handle attachments and that the sutured ECH contact class is functorial under such maps.

From the topological viewpoint, Morse shellings of finite simplicial complexes are pinched handle decompositions which extend the classical shellings of combinatorial topology. We prove that every discrete Morse function on a finite simplicial complex induces Morse shellings on its second barycentric subdivision whose critical tiles—or pinched handles—are in one-to-one correspondence with the critical faces of the function, preserving the index. The same holds true, given any smooth Morse function on a closed manifold, for any piecewise-linear triangulation on it after sufficiently many barycentric subdivisions.