Proton inelastic scattering reveals deformation in 8He

A measurement of proton inelastic scattering of 8He at 8.25A MeV at TRIUMF shows a resonance at 3.54(6) MeV with a width of 0.89(11) MeV. The energy of the state is in good agreement with coupled cluster and no-core shell model with continuum calculations, with the latter successfully describing the measured resonance width as well. Its differential cross section analyzed with phenomenological collective excitation form factor and microscopic coupled reaction channels framework consistently reveals a large deformation parameter = 0.40(3), consistent with no-core shell model predictions of a large neutron deformation. This deformed double-closed shell at the neutron drip-line opens a new paradigm.Oficina de Física Nuclear, Departamento de Energía de los EE. UU. DE-FG02-96ER40963 y DE-SC0018223Ministerio de Ciencia, Innovación y Universidades de España y Fondos FEDER de la U. E. FIS2017-88410-PUnión Europea. Programa Horizonte 2020, subvención n.º 654002

Detecting somatisation disorder via speech: introducing the Shenzhen Somatisation Speech Corpus

Objective Speech recognition technology is widely used as a mature technical approach in many fields. In the study of depression recognition, speech signals are commonly used due to their convenience and ease of acquisition. Though speech recognition is popular in the research field of depression recognition, it has been little studied in somatisation disorder recognition. The reason for this is the lack of a publicly accessible database of relevant speech and benchmark studies. To this end, we introduce our somatisation disorder speech database and give benchmark results. Methods By collecting speech samples of somatisation disorder patients, in cooperation with the Shenzhen University General Hospital, we introduce our somatisation disorder speech database, the Shenzhen Somatisation Speech Corpus (SSSC). Moreover, a benchmark for SSSC using classic acoustic features and a machine learning model is proposed in our work. Results To obtain a more scientific benchmark, we have compared and analysed the performance of different acoustic features, i. e., the full ComParE feature set, or only MFCCs, fundamental frequency (F0), and frequency and bandwidth of the formants (F1-F3). By comparison. the best result of our benchmark is the 76.0 % unweighted average recall achieved by a support vector machine with formants F1–F3. Conclusion The proposal of SSSC bridges a research gap in somatisation disorder, providing researchers with a publicly accessible speech database. In addition, the results of the benchmark show the scientific validity and feasibility of computer audition for speech recognition in somatization disorders

Battling with the low-resource condition for snore sound recognition: introducing a meta-learning strategy

Snoring affects 57 % of men, 40 % of women, and 27 % of children in the USA. Besides, snoring is highly correlated with obstructive sleep apnoea (OSA), which is characterised by loud and frequent snoring. OSA is also closely associated with various life-threatening diseases such as sudden cardiac arrest and is regarded as a grave medical ailment. Preliminary studies have shown that in the USA, OSA affects over 34 % of men and 14 % of women. In recent years, polysomnography has increasingly been used to diagnose OSA. However, due to its drawbacks such as being time-consuming and costly, intelligent audio analysis of snoring has emerged as an alternative method. Considering the higher demand for identifying the excitation location of snoring in clinical practice, we utilised the Munich-Passau Snore Sound Corpus (MPSSC) snoring database which classifies the snoring excitation location into four categories. Nonetheless, the problem of small samples remains in the MPSSC database due to factors such as privacy concerns and difficulties in accurate labelling. In fact, accurately labelled medical data that can be used for machine learning is often scarce, especially for rare diseases. In view of this, Model-Agnostic Meta-Learning (MAML), a small sample method based on meta-learning, is used to classify snore signals with less resources in this work. The experimental results indicate that even when using only the ESC-50 dataset (non-snoring sound signals) as the data for meta-training, we are able to achieve an unweighted average recall of 60.2 % on the test dataset after fine-tuning on just 36 instances of snoring from the development part of the MPSSC dataset. While our results only exceed the baseline by 4.4 %, they still demonstrate that even with fine-tuning on a few instances of snoring, our model can outperform the baseline. This implies that the MAML algorithm can effectively tackle the low-resource problem even with limited data resources

A sustainable biochar catalyst synergized with copper heteroatoms and CO2 for singlet oxygenation and electron transfer routes

We have developed a wood waste-derived biochar as a sustainable graphitic carbon catalyst for environmental remediation through catalytic pyrolysis under the synergistic effects between Cu heteroatoms and CO2, which for the first time are found to significantly enhance the oxygen functionalities, defective sites, and highly ordered sp2-hybridized carbon matrix. The copper-doped graphitic biochars (Cu-GBCs) were further characterized by XRD, FTIR, Raman, XPS, etc., revealing that the modified specific surface area, pore structure, graphitization, and active sites (i.e., defective sites and ketonic group) on the Cu-GBCs corresponded to the synergistic Cu species loading and Cu-induced carbon-matrix reformation in CO2 environment during pyrolysis. The catalytic ability of Cu-GBCs was evaluated using the ubiquitous peroxydisulfate (PDS) activation system for the removal of various organic contaminants (i.e., rhodamine B, phenol, bisphenol A, and 4-chlorophenol), and gave the highest degradation rate of 0.0312 min-1 in comparison with those of pristine GBCs and N2-pyrolyzed Cu-GBCs ranging from 0.0056 to 0.0094 min-1. The synergistic effects were attributed to the encapsulated Cu heteroatoms, evolved ketonic groups, and abundant unconfined π electrons within the carbon lattice. According to scavenger experiments, ESR analysis, and two-chamber experiments, selective and sustainable non-radical pathways (i.e., singlet oxygenation and electron transfer) mediated by the Cu-induced metastable surface complex were achieved in the Cu-GBC/PDS system. This study offers the first insights into the efficacy, sustainability, and mechanistic roles of Cu-GBCs as an emerging carbon-based catalyst for green environmental remediation

Finding sands in the eyes: vulnerabilities discovery in IoT with EUFuzzer on human machine interface

In supervisory control and data acquisition (SCADA) systems or the Internet of Things (IoT), human machine interface (HMI) performs the function of data acquisition and control, providing the operators with a view of the whole plant and access to monitoring and interacting with the system. The compromise of HMI will result in lost of view (LoV), which means the state of the whole system is invisible to operators. The worst case is that adversaries can manipulate control commands through HMI to damage the physical plant. HMI often relies on poorly understood proprietary protocols, which are time-sensitive, and usually keeps a persistent connection for hours even days. All these factors together make the vulnerability mining of HMI a tough job. In this paper, we present EUFuzzer, a novel fuzzing tool to assist testers in HMI vulnerability discovery. EUFuzzer first identifies packet fields of the specific protocol and classifies all fields into four types, then using a relatively high efficiency fuzzing method to test HMI. The experimental results show that EUFuzzer is capable of identifying packet fields and revealing bugs. EUFuzzer also successfully triggers flaws of actual proprietary SCADA protocol implementation on HMI, which the SCADA software vendor has confirmed that four were zero-day vulnerabilities and has taken measures to patch up