A New Approach for a Safe and Reproducible Seeds Positioning for Diffusing Alpha-Emitters Radiation Therapy of Squamous Cell Skin Cancer: A Feasibility Study

The purpose of this study is to discuss how to use an external radio-opaque template in the Diffusing Alpha-emitters Radiation Therapy (DaRT) technique’s pre-planning and treatment stages. This device would help to determine the proper number of sources for tumour coverage, accounting for subcutaneous invasion and augmenting DaRT safety. The procedure will be carried out in a first phase on a phantom and then applied to a clinical case. A typical DaRT procedure workflow comprises steps like tumour measurements and delineation, source number assessment, and therapy administration. As a first step, an adhesive fiberglass mesh (spaced by 2 mm) tape was applied on the skin of the patient and employed as frame of reference. A physician contoured the lesion and marked the entrance points for the needles with a radio opaque ink marker. According to the radio opaque marks and metabolic uptake the clinical target volume was defined, and with a commercial brachytherapy treatment planning system (TPS) it was possible to simulate and adjust the spatial seeds distribution. After the implant procedure a CT was again performed to check the agreement between simulations and seeds positions. With the procedure described above it was possible to simulate a DaRT procedure on a phantom in order to train physicians and subsequently apply the novel approach on patients, outlining the major issues involved in the technique. The present work innovates and supports DaRT technique for the treatment of cutaneous cancers, improving its efficacy and safety.

An Efficient DOA Estimation Algorithm Based on Diagonal-Symmetric Loading

In order to solve the problem that the subspace-like direction of arrival (DOA) estimation performs poor due to the error of sources number, this paper proposes a new super-resolution DOA estimation algorithm based on the diagonal-symmetric loading (DSL). Specifically, orthogonality principle of the minimum eigenvector of the specific covariance matrix and the source number estimation based on the improved K-means method were adopted to construct the spatial spectrum. Then, by considering the signal-to-interference-to-noise ratio (SINR), the theoretical basis for selecting parameters was given and verified by numerical experiment. To evaluate the effectiveness of the proposed algorithm, this paper compared it with the methods of minimum variance distortionless response (MVDR) and new signal subspace processing (NSSP). Experimental results prove that the proposed DSL has higher resolution and better estimation accuracy than the MVDR and NSSP.

A Hybrid Metaheuristic Based on Neurocomputing for Analysis of Unipolar Electrohydrodynamic Pump Flow

A unipolar electrohydrodynamic (UP-EHD) pump flow is studied with known electric potential at the emitter and zero electric potential at the collector. The model is designed for electric potential, charge density, and electric field. The dimensionless parameters, namely the electrical source number (Es), the electrical Reynolds number (ReE), and electrical slip number (Esl), are considered with wide ranges of variation to analyze the UP-EHD pump flow. To interpret the pump flow of the UP-EHD model, a hybrid metaheuristic solver is designed, consisting of the recently developed technique sine–cosine algorithm (SCA) and sequential quadratic programming (SQP) under the influence of an artificial neural network. The method is abbreviated as ANN-SCA-SQP. The superiority of the technique is shown by comparing the solution with reference solutions. For a large data set, the technique is executed for one hundred independent experiments. The performance is evaluated through performance operators and convergence plots.

227 Multiple Outbreak Of SARS-COVID-19 In Surgical Wards at The Tertiary Hospital

Background Since the second surge of SARS-COVID-19 on 18th of September, additional several measures were introduced, and pathways created in order to execute safe surgical practices and protect both patients and staff from SARS-COVID-19. Despite these measures, there have been reported cases of outbreaks in various parts of UK amongst patients and clinical staff. Method Three outbreaks in the past 6 weeks (10, September 2020 to 21, October 2020) were reported in our surgical wards and we compiled the timing, initial source, number of affected individuals and immediate management steps taken. Results Following the first outbreak on wards, 28-day surveillance helped us understand responsible variants. Several staff members were found walking out of hospital, in groups once they had removed their PPEs. Communal lunching with inadequate social distancing, attending work with symptoms (albeit atypical for COVID), sharing cars to/from work or not wearing PPE correctly were highlighted in the subsequent investigations. The reduction in number of affected individuals during the last two incidents reflected a degree of efficacy of the implemented preventative measures, which were reviewed again following the subsequent incidents. Conclusions In the present climate, a robust and prompt response to outbreaks is required. Continual iteration with regards to the need for PPE, adequate social distancing and avoiding over-crowding in communal areas is paramount to reduce the probability of ward outbreaks and inter-professional transmission. Asymptomatic staff testing, particularly in high-risk areas could also be considered but would require adequate laboratory capacity and rapid turnaround of test results.

Sound field separation of multiple coherent sound sources with single measurement surface

Sound field separation based on near-field acoustical holography has been developed worldwide, but with the increase in the number of sound sources, traditional measurement methods and calculation methods will generate more workload. To reduce the number of measuring points and save calculation time, the sound field separation of multiple coherent sources with a single measurement surface is proposed. On the basis of separating two coherent sources with this method, the separation formula of more sources based on an equivalent source method is given. Through numerical simulation, the effects of the number of holographic surface measuring points, measuring distance, array shape, and equivalent source number on the calculation accuracy of the sound field separation were compared at different frequencies. The correctness and effectiveness of the sound field separation method with a single surface are verified by actual experiments.

Crosscorrelation and DOA Estimation for L-Shaped Array via Decoupled Atomic Norm Minimization

A novel two-phase method for two-dimensional (2D) direction-of-arrival (DOA) estimation with L-shaped array based on decoupled atomic norm minimization (DANM) is proposed in this paper. In the first phase, given the sample crosscorrelation matrix, the gridless DANM technique considering the noise and finite snapshots effects is employed to exploit the structure and sparse properties of the crosscorrelation matrix. The resulting DANM-based algorithm not only enables the crosscorrelation matrix reconstruction (CCMR) but also reconstructs the covariance matrix of the L-shaped array. Hence, sequentially, in the second phase, the conventional 2D DOA estimators for the L-shaped array can be adopted for the angle estimation. With appropriate 2D DOA estimators, the resulting proposed algorithms can not only achieve better performance but also detect more source number, compared with conventional crosscorrelation-based DOA estimators. Moreover, the proposed method, termed CCMR-DANM, not only has blind characteristic that it does not require the prior information of source numbers but also is more efficient than the existing CCMR-based counterparts. Numerical simulations demonstrate the effectiveness and outperformance of the proposed method.

A Source Number Estimation Algorithm Based on Data Local Density and Fuzzy C-Means Clustering

An advanced source number estimation (SNE) algorithm based on both fuzzy C-means clustering (FCM) and data local density (DLD) is proposed in this paper. The DLD of an eigenvalue refers to the number of eigenvalues within a specific neighborhood of this eigenvalue belonging to the data covariance matrix. This local density essentially as the one-dimensional sample feature of the FCM is extracted into the SNE algorithm based on FCM and can enable to improve the probability of correct detection (PCD) of the SNE algorithm based on the FCM especially for low signal-to-noise ratio (SNR) environment. Comparison experiment results demonstrate that compared to the SNE algorithm based on the FCM and other similar algorithms, our proposed algorithm can achieve highest PCD of the incident source number in both cases of spatial white noise and spatial correlation noise.