State Space Reduction on Wireless Sensor Network Verification Using Component-Based Petri Net Approach

With the recent advancement of Internet of Things, the applications of Wireless Sensor Networks (WSNs) are increasingly attracting attention from of both industry and research communities. However, since the deployment cost of a WSN is relatively large, one would want to make a logic model of a WSN and have the model verified beforehand to ensure that the WSN would work correctly and effectively once practically employed. Petri Net (PN) is very suitable to model a WSN, since PN strongly supports modeling concurrent and ad-hoc systems. However, verification of a PN-modeled system suffers from having to explore the huge state space of the system. In order to overcome it, in this paper we suggest a novel component-based approach to model and verify a PN-modeled WSN system. First of all, the original WSN system is divided into components, which can be further abstracted to reduce the model size. Moreover, when verifying the corresponding PN model produced from the abstracted WSN, we introduce a strategy of component-based firing, which can reduce the state space significantly. Compared to typical approach of PN-based verification, our method enjoys an impressive improvement of performance and resource consuming, as depicted in our experimental results

Generating Adversarial Examples with Task Oriented Multi-Objective Optimization

Deep learning models, even the-state-of-the-art ones, are highly vulnerable to adversarial examples. Adversarial training is one of the most efficient methods to improve the model's robustness. The key factor for the success of adversarial training is the capability to generate qualified and divergent adversarial examples which satisfy some objectives/goals (e.g., finding adversarial examples that maximize the model losses for simultaneously attacking multiple models). Therefore, multi-objective optimization (MOO) is a natural tool for adversarial example generation to achieve multiple objectives/goals simultaneously. However, we observe that a naive application of MOO tends to maximize all objectives/goals equally, without caring if an objective/goal has been achieved yet. This leads to useless effort to further improve the goal-achieved tasks, while putting less focus on the goal-unachieved tasks. In this paper, we propose \emph{Task Oriented MOO} to address this issue, in the context where we can explicitly define the goal achievement for a task. Our principle is to only maintain the goal-achieved tasks, while letting the optimizer spend more effort on improving the goal-unachieved tasks. We conduct comprehensive experiments for our Task Oriented MOO on various adversarial example generation schemes. The experimental results firmly demonstrate the merit of our proposed approach. Our code is available at \url{https://github.com/tuananhbui89/TAMOO}

Advanced Method for Motion Control of a 3 DOFs Lower Limb Rehabilitation Robot

This paper presents two motion control methods for a lower limb rehabilitation robot based on compensate gravity proportional-derivative and inverse dynamic proportional-derivative (PD) control algorithms. The Robot’s mechanism is comprised of three active joints: hip joint, knee joint and ankle joint, which are driven by DC motors. Firstly, based on Robot’s mechanism, a dynamic model of the Robot is built. Then, based on Robot’s model, motion control systems for Robot are designed. Simulation results show good performances and workability of these proposed controllers. Finally, the calculation of the joint angle errors and toque of each controller is performed. The comparison of simulation results between proposed controllers and the adaptive fuzzy controller allows to choice suitable motion control methods for Robot that can meet the requirements of a 3 DOFs lower limb rehabilitation robot for post-stroke patient

A long range, energy efficient internet of things based drought monitoring system

The climate change and global warning have been appeared as an emerging issue in recent decades. In which, the drought problem has been influenced on economics and life condition in Vietnam. In order to solve this problem, in this paper, we have designed and deployed a long range and energy efficient drought monitoring based on IoT (Internet of Things) for real time applications. After being tested in the real condition, the proposed system has proved its high dependability and effectiveness. The system is promising to become a potential candidate to solve the drought problem in Vietnam

Terpenoids from Dacrycarpus imbricatus.

The phytochemical investigation of the hexane extract from the twigs and leaves of Dacrycarpus imbricatus (Blume) de Laub led to the isolation of a rare sesquiterpene, spathulenol (1) along with three diterpenes named pimaric acid (2), trans-communic acid (3) and cis-communic acid (4). Their structures were determined by combination of spectral analysis and comparison with reported data. This is the first report on isolation of compound 1 from the Podocarpaceae family. Keywords. Dacrycarpus imbricatus, spathulenol, pimaric acid, communic acid

Diterpenoids from Fokienia hodginsii.

In continuous research on the chemical constituents of the twigs and leaves of Fokienia hodginsii (Dunn) A. Henry et Thomas growing in Highland, Lam Dong province 4 diterpenoids, including 3-oxo-totarol (totarolone, 1), 3β-hydroxytotarol (2), 15-nor-labda-8(17),12E-diene-14-carboxaldehyde-19-oic acid (3) and 13-oxo-15,16-dinorlabda-8(17),11E-diene-19-oic acid (4) were isolated. Their structures were elucidated by the spectroscopic methods and comparison with reported data. This is the first report on the isolation of compounds 1, 2 and 3 from this plant. Keywords. Fokienia hodginsii; totarane; nor-labdane diterpenoid

The acceptability of and willingness to pay for a herpes zoster vaccine: A systematic review

Patients, predominantly the elderly, with Herpes Zoster (HZ) not only suffer symptoms of the disease but also bear considerable expenses. This study systematically reviewed the acceptability of and willingness to pay for the HZ vaccine. This review was registered in PROSPERO 2023 (CRD42023403062). We used “acceptance”, “willing to pay”, and “HZ vaccine” (and variations thereof) as keywords in a systematic search for original English research articles published up to April 7, 2023. The search was conducted over Scopus, PubMed, ScienceDirect, Cochrane, and Google Scholar in accordance with PRISMA 2020 guidelines. The inclusion criteria were as follows: studies (1) that mentioned HZ vaccination, (2) related to acceptability or willingness to pay, and (3) with full texts available and peer-reviewed prior to final publication. Grey literature, letters to editors, commentaries, case reports or series, systematic reviews, meta-analyses, articles of poor quality, and articles with ambiguously defined and measured outcome variables were excluded. The Joanna Briggs Institute (JBI) critical appraisal checklist was used to evaluate the methodological quality of the studies. Finally, the search yielded 24 studies, of which 9 were conducted in Asia, 8 in Europe, and 7 in America. General adults or patients aged 50 or older were often the target populations, for whom treatments were accompanied by healthcare providers’ recommendations. The willingness to pay and willingness to accept the vaccine ranged from $8 to$150 and 16.6% to 85.8%, respectively. Compared to the US, Asia and Europe had higher acceptance rates for HZ immunization. The most frequent excuses given for not being vaccinated are side effects, cost, lack of recommendations, anti-vaccination views, ignorance about the HZ vaccine, and the belief that one is not at risk for the disease. National campaigns should be developed to increase public awareness of HZ, and more international research should be conducted to understand the WTA and WTP for HZ immunizations

A Framework for Fast Congestion Detection in Wireless Sensor Networks Using Clustering and Petri Net-based Verification

Abstract. Applications of Wireless Sensor Networks (WSN) in harsh conditions usually cover a vast area with sensors randomly deployed by an uncontrolled method, e.g. dropped by helicopters. Thus the actual topology is unpredictable and can suffer from possible congestion. We propose the FCD framework for congestion detection, based on clustering techniques combined with Petri nets modelling and verification