Confidence Regions for Parameters in Stationary Time Series Models With Gaussian Noise

This article develops two new empirical likelihood methods for long-memory time series models based on adjusted empirical likelihood and mean empirical likelihood. By application of Whittle likelihood, one obtains a score function that can be viewed as the estimating equation of the parameters of the long-memory time series model. An empirical likelihood ratio is obtained which is shown to be asymptotically chi-square distributed. It can be used to construct confidence regions. By adding pseudo samples, we simultaneously eliminate the non-definition of the original empirical likelihood and enhance the coverage probability. Finite sample properties of the empirical likelihood confidence regions are explored through Monte Carlo simulation, and some real data applications are carried out.

Post-arc current measurement and interruption performance of vacuum interrupter in DC interruption

In the DC grid, fault current rises very fast due to low impedance. Fast DC circuit breakers are needed to isolate faults and avoid a collapse of the common DC grid voltage. Based on the forced current zero (CZ) technology, the vacuum interrupter (VI) equipped with fast electromagnetic repulsion mechanism is a very promising solution of fast DC interruption. The experimental research on the DC interruption characteristics of a VI, namely by examining the post‑arc current (PAC) is presented in this paper. The dependence of the interruption capability on the PAC is analysed. What’s more, the failure modes of the VI under various experiment conditions are summarized. A former finding was that not all the arcing history which starts from the electrodes separating to the CZ has influence on the PAC but only a very short duration of several microseconds right before the CZ takes effects. New experiment results are added in this paper to support the former finding. Another former finding was that a longer electrode separation will bring about higher PAC. In this paper, both the influence of the arcing memory time and the electrodes separation are owed to a higher residual plasma density, which is verified by a model for calculating the residual plasma density and the continuous transient model (CTM) for calculating PAC.

Designing a Personalised Mobile Application to Improve Engagement With PFMT Therapy Amongst Women From Pregnancy To One Year After Delivery

<p>Women who are pregnant, or have given birth are at high risk of developing Pelvic Floor Disorder (PFD) due to the physical stress placed on the pelvic muscles during this time. When left untreated, PFD can cause symptoms such as incontinence, organ prolapse and pelvic pain in sufferers. Pelvic Floor Muscle Training (PFMT) is a highly effective means of treating and preventing symptoms of PFD. However, adherence rates to PFMT remain low. Amongst the biggest barriers to adherence are incorrect technique, lack of knowledge, memory, time, low motivation and stigma. As with the physical symptoms of PFD, these barriers impact sufferers in ways unique to each individual. Findings from the existing literature suggest that personalising the intervention to accommodate these varying factors may improve adherence. This study focuses on the development of a personalised mobile application to improve engagement with PFMT amongst women from pregnancy, up to one year after delivery. The goal of the application is to improve engagement with PFMT through addressing key barriers to adherence, and guiding correct performance of PFMT. An initial design criteria and five user personas were developed. The criteria and personas were used to develop prototypes, which were then user tested. The designs were then refined based on user feedback. Designs were also informed by feedback from interviews with clinicians and women. The results of this study indicate that a mobile application is an ineffective means of guiding PFMT technique. However the application proved effective in addressing the barrier of memory through the use of context based triggers. The integration of the Hooked model in the application design had a low to moderate effect on improving engagement with PFMT. Opportunities for a personalised design approach in the areas of instruction, facilitation of exercises and preferences for application features were identified.</p>

Designing a Personalised Mobile Application to Improve Engagement With PFMT Therapy Amongst Women From Pregnancy To One Year After Delivery

<p>Women who are pregnant, or have given birth are at high risk of developing Pelvic Floor Disorder (PFD) due to the physical stress placed on the pelvic muscles during this time. When left untreated, PFD can cause symptoms such as incontinence, organ prolapse and pelvic pain in sufferers. Pelvic Floor Muscle Training (PFMT) is a highly effective means of treating and preventing symptoms of PFD. However, adherence rates to PFMT remain low. Amongst the biggest barriers to adherence are incorrect technique, lack of knowledge, memory, time, low motivation and stigma. As with the physical symptoms of PFD, these barriers impact sufferers in ways unique to each individual. Findings from the existing literature suggest that personalising the intervention to accommodate these varying factors may improve adherence. This study focuses on the development of a personalised mobile application to improve engagement with PFMT amongst women from pregnancy, up to one year after delivery. The goal of the application is to improve engagement with PFMT through addressing key barriers to adherence, and guiding correct performance of PFMT. An initial design criteria and five user personas were developed. The criteria and personas were used to develop prototypes, which were then user tested. The designs were then refined based on user feedback. Designs were also informed by feedback from interviews with clinicians and women. The results of this study indicate that a mobile application is an ineffective means of guiding PFMT technique. However the application proved effective in addressing the barrier of memory through the use of context based triggers. The integration of the Hooked model in the application design had a low to moderate effect on improving engagement with PFMT. Opportunities for a personalised design approach in the areas of instruction, facilitation of exercises and preferences for application features were identified.</p>

Multi-moduli NTTs for Saber on Cortex-M3 and Cortex-M4

The U.S. National Institute of Standards and Technology (NIST) has designated ARM microcontrollers as an important benchmarking platform for its Post-Quantum Cryptography standardization process (NISTPQC). In view of this, we explore the design space of the NISTPQC finalist Saber on the Cortex-M4 and its close relation, the Cortex-M3. In the process, we investigate various optimization strategies and memory-time tradeoffs for number-theoretic transforms (NTTs).Recent work by [Chung et al., TCHES 2021 (2)] has shown that NTT multiplication is superior compared to Toom–Cook multiplication for unprotected Saber implementations on the Cortex-M4 in terms of speed. However, it remains unclear if NTT multiplication can outperform Toom–Cook in masked implementations of Saber. Additionally, it is an open question if Saber with NTTs can outperform Toom–Cook in terms of stack usage. We answer both questions in the affirmative. Additionally, we present a Cortex-M3 implementation of Saber using NTTs outperforming an existing Toom–Cook implementation. Our stack-optimized unprotected M4 implementation uses around the same amount of stack as the most stack-optimized Toom–Cook implementation while being 33%-41% faster. Our speed-optimized masked M4 implementation is 16% faster than the fastest masked implementation using Toom–Cook. For the Cortex-M3, we outperform existing implementations by 29%-35% in speed. We conclude that for both stack- and speed-optimization purposes, one should base polynomial multiplications in Saber on the NTT rather than Toom–Cook for the Cortex-M4 and Cortex-M3. In particular, in many cases, multi-moduli NTTs perform best.

Protection against SARS-CoV-2 Beta Variant in mRNA-1273 Boosted Nonhuman Primates

Neutralizing antibody responses gradually wane after vaccination with mRNA-1273 against several variants of concern (VOC), and additional boost vaccinations may be required to sustain immunity and protection. Here, we evaluated the immune responses in nonhuman primates that received 100 μg of mRNA-1273 vaccine at 0 and 4 weeks and were boosted at week 29 with mRNA-1273 (homologous) or mRNA-1273.β (heterologous), which encompasses the spike sequence of the B.1.351 (beta or β) variant. Reciprocal ID50 pseudovirus neutralizing antibody geometric mean titers (GMT) against live SARS-CoV-2 D614G and the β variant, were 4700 and 765, respectively, at week 6, the peak of primary response, and 644 and 553, respectively, at a 5-month post-vaccination memory time point. Two weeks following homologous or heterologous boost β-specific reciprocal ID50 GMT were 5000 and 3000, respectively. At week 38, animals were challenged in the upper and lower airway with the β variant. Two days post-challenge, viral replication was low to undetectable in both BAL and nasal swabs in most of the boosted animals. These data show that boosting with the homologous mRNA-1273 vaccine six months after primary immunization provides up to a 20-fold increase in neutralizing antibody responses across all VOC, which may be required to sustain high-level protection against severe disease, especially for at-risk populations.

Empirical Evaluation of Privacy Efficiency in Blockchain Networks: Review and Open Challenges

With the widespread of blockchain technology, preserving the anonymity and confidentiality of transactions have become crucial. An enormous portion of blockchain research is dedicated to the design and development of privacy protocols but not much has been achieved for proper assessment of these solutions. To mitigate the gap, we have first comprehensively classified the existing solutions based on blockchain fundamental building blocks (i.e., smart contracts, cryptography, and hashing). Next, we investigated the evaluation criteria used for validating these techniques. The findings depict that the majority of privacy solutions are validated based on computing resources i.e., memory, time, storage, throughput, etc., only, which is not sufficient. Hence, we have additionally identified and presented various other factors that strengthen or weaken blockchain privacy. Based on those factors, we have formulated an evaluation framework to analyze the efficiency of blockchain privacy solutions. Further, we have introduced a concept of privacy precision that is a quantifiable measure to empirically assess privacy efficiency in blockchains. The calculation of privacy precision will be based on the effectiveness and strength of various privacy protecting attributes of a solution and the associated risks. Finally, we conclude the paper with some open research challenges and future directions. Our study can serve as a benchmark for empirical assessment of blockchain privacy.