Towards portable muography with small-area, gas-tight glass Resistive Plate Chambers

Imaging techniques that use atmospheric muons, collectively named under the neologism "muography", have seen a tremendous growth in recent times, mainly due to their diverse range of applications. The most well-known ones include but are not limited to: volcanology, archaeology, civil engineering, nuclear reactor monitoring, nuclear waste characterization, underground mapping, etc. These methods are based on the attenuation or deviation of muons to image large and/or dense objects where conventional techniques cannot work or their use becomes challenging. In this context, we have constructed a muography telescope based on "mini glass-RPC planes" following a design similar to the glass-RPC detectors developed by the CALICE Collaboration and used by the TOMUVOL experiment in the context of volcano radiography, but with smaller active area (16 $\times$ 16 cm$^{2}$). The compact size makes it an attractive choice with respect to other detectors previously employed for imaging on similar scales. An important innovation in this design is that the detectors are sealed. This makes the detector more portable and solves the usual safety and logistic issues for gas detectors operated underground and/or inside small rooms. This paper provides an overview on our guiding principles, the detector development and our operational experiences. Drawing on the lessons learnt from the first prototype, we also discuss our future direction for an improved second prototype, focusing primarily on a recently adopted serigraphy technique for the resistive coating of the glass plates.Comment: 8 pages, 7 figures, XV Workshop on Resistive Plate Chambers and Related Detectors (RPC2020

Line tension and structure of smectic liquid crystal multilayers at the air-water interface

At the air/water interface, 4,-8-alkyl[1,1,-biphenyl]-4-carbonitrile (8CB) domains with different thicknesses coexist in the same Langmuir film, as multiple bilayers on a monolayer. The edge dislocation at the domain boundary leads to line tension, which determines the domain shape and dynamics. By observing the domain relaxation process starting from small distortions, we find that the line tension is linearly dependent on the thickness difference between the coexisting phases in the film. Comparisons with theoretical treatments in the literature suggest that the edge dislocation at the boundary locates near the center of the film, which means that the 8CB multilayers are almost symmetric with respect to the air/water interface.Comment: 21 pages, 6 figure

Developing a scalable training model in global mental health: pilot study of a video-assisted training Program for Generalist Clinicians in Rural Nepal.

BackgroundIn low- and middle-income countries, mental health training often includes sending few generalist clinicians to specialist-led programs for several weeks. Our objective is to develop and test a video-assisted training model addressing the shortcomings of traditional programs that affect scalability: failing to train all clinicians, disrupting clinical services, and depending on specialists.MethodsWe implemented the program -video lectures and on-site skills training- for all clinicians at a rural Nepali hospital. We used Wilcoxon signed-rank tests to evaluate pre- and post-test change in knowledge (diagnostic criteria, differential diagnosis, and appropriate treatment). We used a series of 'Yes' or 'No' questions to assess attitudes about mental illness, and utilized exact McNemar's test to analyze the proportions of participants who held a specific belief before and after the training. We assessed acceptability and feasibility through key informant interviews and structured feedback.ResultsFor each topic except depression, there was a statistically significant increase (Δ) in median scores on knowledge questionnaires: Acute Stress Reaction (Δ = 20, p = 0.03), Depression (Δ = 11, p = 0.12), Grief (Δ = 40, p < 0.01), Psychosis (Δ = 22, p = 0.01), and post-traumatic stress disorder (Δ = 20, p = 0.01). The training received high ratings; key informants shared examples and views about the training's positive impact and complementary nature of the program's components.ConclusionVideo lectures and on-site skills training can address the limitations of a conventional training model while being acceptable, feasible, and impactful toward improving knowledge and attitudes of the participants

[Accepted Manuscript] Formative qualitative research to develop community-based interventions addressing low birth weight in the plains of Nepal.

To explore the factors affecting intra-household food allocation practices to inform the development of interventions to prevent low birth weight in rural plains of Nepal. Qualitative methodology using purposive sampling to explore the barriers and facilitating factors to improved maternal nutrition. Rural Dhanusha District, Nepal. We purposively sampled twenty-five young daughters-in-law from marginalised groups living in extended families and conducted semi-structured interviews with them. We also conducted one focus group discussion with men and one with female community health volunteers who were mothers-in-law. Gender and age hierarchies were important in household decision making. The mother-in-law was responsible for ensuring that a meal was provided to productive household members. The youngest daughter-in-law usually cooked last and ate less than other family members, and showed respect for other family members by cooking only when permitted and deferring to others' choice of food. There were limited opportunities for these women to snack between main meals. Daughters-in-law' movement outside the household was restricted and therefore family members perceived that their nutritional need was less. Poverty affected food choice and families considered cost before nutritional value. It is important to work with the whole household, particularly mothers-in-law, to improve maternal nutrition. We present five barriers to behaviour change: poverty; lack of knowledge about cheap nutritional food, the value of snacking, and cheap nutritional food that does not require cooking; sharing food; lack of self-confidence; and deference to household guardians. We discuss how we have targeted our interventions to develop knowledge, discuss strategies to overcome barriers, engage mothers-in-law, and build the confidence and social support networks of pregnant women

DeltaPhish: Detecting Phishing Webpages in Compromised Websites

The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceability, detection and blacklisting. To the best of our knowledge, this is the first work that specifically leverages this adversarial behavior for detection purposes. We show that phishing webpages can be accurately detected by highlighting HTML code and visual differences with respect to other (legitimate) pages hosted within a compromised website. Our system, named DeltaPhish, can be installed as part of a web application firewall, to detect the presence of anomalous content on a website after compromise, and eventually prevent access to it. DeltaPhish is also robust against adversarial attempts in which the HTML code of the phishing page is carefully manipulated to evade detection. We empirically evaluate it on more than 5,500 webpages collected in the wild from compromised websites, showing that it is capable of detecting more than 99% of phishing webpages, while only misclassifying less than 1% of legitimate pages. We further show that the detection rate remains higher than 70% even under very sophisticated attacks carefully designed to evade our system.Comment: Preprint version of the work accepted at ESORICS 201

Relating Satellite Imagery with Grain Protein Content

Satellite images, captured during the growing seasons of barley, sorghum and wheat were analysed to establish a relationship between the spectral response and the harvested grain protein content. This study was conducted near Jimbour (approx. 151 degrees 10'E and 27 degrees 05'S) in southern Queensland. Grain protein contents of the geo-referenced samples, collected manually during the harvest, were determined using a laboratory-based near-infrared spectrophotometer. Grain protein contents in grain varied between 7.4 - 15.2% in barley, 6.2 - 10.6% in sorghum and 13.1 - 15.6% in wheat. The Landsat images of 18 September 1999 (a week after barley flowering), 5 March 2000 (three weeks before sorghum harvest), and 15 August 2001 (two weeks before wheat flowering) were analysed. Additionally, an ASTER image of 24 September 2001 (three weeks after wheat flowering) was also examined. Digital numbers, extracted from raw image bands and derived indices, were correlated with grain protein contents. The grain protein content in barley was correlated strongly (r>0.80) with bands 2, 4 and 5 of the Landsat scene, first principal component, and the tasselled cap brightness and greenness indices. Similarly, wheat protein content was well correlated (r>0.75) with the near infrared band (band 4) of the Landsat scene, first principal component, and the tasselled cap brightness, greenness and wetness indices. The band 3 (near infrared band) of the ASTER image, captured well after flowering, was moderately correlated (

Multi-Agent Deep Reinforcement Learning-Driven Mitigation of Adverse Effects of Cyber-Attacks on Electric Vehicle Charging Station

An electric vehicle charging station (EVCS) infrastructure is the backbone of transportation electrification. However, the EVCS has myriads of exploitable vulnerabilities in software, hardware, supply chain, and incumbent legacy technologies such as network, communication, and control. These standalone or networked EVCS open up large attack surfaces for the local or state-funded adversaries. The state-of-the-art approaches are not agile and intelligent enough to defend against and mitigate advanced persistent threats (APT). We propose the data-driven model-free distributed intelligence based on multiagent Deep Reinforcement Learning (MADRL)-- Twin Delayed Deep Deterministic Policy Gradient (TD3) -- that efficiently learns the control policy to mitigate the cyberattacks on the controllers of EVCS. Also, we have proposed two additional mitigation methods: the manual/Bruteforce mitigation and the controller clone-based mitigation. The attack model considers the APT designed to malfunction the duty cycles of the EVCS controllers with Type-I low-frequency attack and Type-II constant attack. The proposed model restores the EVCS operation under threat incidence in any/all controllers by correcting the control signals generated by the legacy controllers. Also, the TD3 algorithm provides higher granularity by learning nonlinear control policies as compared to the other two mitigation methods. Index Terms: Cyberattack, Deep Reinforcement Learning(DRL), Electric Vehicle Charging Station, Mitigation.Comment: Submitted to IEEE Transactions on Smart Grid