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The rapid spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the resulting worldwide death toll have prompted worries regarding its transmission mechanisms. Direct, indirect, and droplet modes are the basic mechanisms of transmission. SARS-CoV-2 spreads by respiratory droplets (size range10 μm size ranges), aerosols (5 μm), airborne, and particulate matter. The rapid transmission of SARS-CoV-2 is due to the involvement of tiny indoor air particulate matter (PM

In large buildings, during the situation of fire or other hazards, a smart evacuation guidance system needs to fully consider manifold aspects of hazards to guide evacuees through exit gates as fast as possible by dynamic and safe routes. In this paper, we propose a smart evacuation guidance system with a dynamic evacuation routing approach by using the LCDT (Length-Capacity-Density-Trustiness) weighted graph model and partial view (PV) information which represents the hazard intensity and the crowd congestion information of a group of sections/floors in the building. The proposed system is designed as a distributed system with multiple layers of computing by using smart indicators. Given such a system, we develop an efficient distributed approach, so-called LCDT&PV, to find out effective evacuation routes dynamically. We then propose an estimating congestion strategy in order to improve the efficiency of dynamic evacuation routes. To validate the proposed system, we implement a simulator to compare the proposed evacuation routing approach with baseline approaches. Experimental results show that the proposed approach reduces up to 30% of the total evacuation time compared with others. Furthermore, through the results of initial smart indicator implementation, which can interact with the simulator, we show the viability of the proposed system.

Modern hybrid rockets are susceptible to the highly ablative environment in which they operate, as introducing an adequate cooling system has so far been a challenge. Ablative material has been widely adopted for use in hybrid rockets, which can only survive for the limited operation time. The use of ablative materials such as pyrolytic composite or graphite is advantageous for use in a simple system in an uncooled state. However, ablation causes severe enlarging in the nozzle throat area and a significant drop in the pressure during operation. Recently, Ultra High-Temperature Ceramics (UHTCs) have been noted as superior in terms of ablation and oxidation resistance. The most promising HfC-SiC composite ceramic has demonstrated an enhanced performance for thermal and mechanical properties. In this study, an HfC-SiC composite was embedded in the graphite casing as a nozzle insert throat to analyze the feasibility of its application in rockets. An experimental analysis of the ablation in the HfC-SiC was carried out with a 250 N scale hybrid thruster using High Test Peroxide (HTP) and High-Density Polyethylene. The hot-fire condition was set to above 30 bar for 25 s combustion, with the purpose of producing significant erosion on the nozzle materials. The graphite nozzle of the same shape was also tested under the same experimental conditions for comparison of the erosion. The hot-fire test with the HfC-SiC insert resulted in a stable rocket performance, with improvements to the chamber pressure and the thrust, whereas the combustion performance varied undesirably in the test using the graphite nozzle due to ablation. The rate of ablation in the throat was significantly reduced to 15.81 μm/s using HfC-SiC, which is 46.5% of the erosion rate found in the graphite. The adoption of the HfC-SiC composite effectively inhibited the ablation on the throat. However, substantial erosion occurred on the interfaces due to the different ablation resistances. The feasibility of adopting hafnium-based carbide on the rocket was also evaluated based on a material analysis of the surface oxidation.

Regular path queries (RPQs) are widely used on a graph whose answer is a set of tuples of nodes connected by paths corresponding to a given regular expression. Traditional automata-based approach for evaluating RPQs is restricted in the explosion of graph size, which makes graph searching take high cost (i.e. memory space and response time). Recently, a cost-based optimization technique using rare labels has been proved to be effective when it is applied to large graph. However, there is still a room for improvement, because the rare labels in the graph and/or the query are coarse information which could not guarantee the minimum searching cost all the time. This is our motivation to find a new approach using fine-grained information to estimate correctly the searching cost, which helps improving the performance of RPQs evaluation. For example, by using estimated searching cost, we can decompose an RPQ into small subqueries or separate multiple RPQs into small batch of queries in an efficient way for parallelism evaluation. In this paper, we present a novel approach for estimating the searching cost of RPQs on large graphs with cost functions based on the combinations of the searching cost of unit-subqueries (i.e. every smallest possible query). We extensively evaluated our method on real-world datasets including Alibaba, Yago, Freebase as well as synthetic datasets. Experimental results show that our estimation method obtains high accuracy which is approximately 87% on average. Moreover, two comparisons with automata-based and rare label based approaches demonstrate that our approach outperforms traditional ones.

The first part of this paper is devoted to study a model of one-dimensional random walk with memory to the maximum position described as follows. At each step the walker resets to the rightmost visited site with probability $$r \in (0,1)$$ and moves as the simple random walk with remaining probability. Using the approach of renewal theory, we prove the laws of large numbers and the central limit theorems for the random walk. These results reprove and significantly enhance the analysis of the mean value and variance of the process established in Majumdar et al. (Phys Rev E 92:052126, 2015). In the second part, we expand the analysis to the situation where the memory of the walker decreases over time by assuming that at the step n the resetting probability is $$r_n = \min \{rn^{-a}, \tfrac{1}{2}\}$$ with r, a positive parameters. For this model, we first establish the asymptotic behavior of the mean values of $$X_n$$ -the current position and $$M_n$$ -the maximum position of the random walk. As a consequence, we observe an interesting phase transition of the ratio $${{\mathbb {E}}}[X_n]/{{\mathbb {E}}}[M_n]$$ when a varies. Precisely, it converges to 1 in the subcritical phase $$a\in (0,1)$$ , to a constant $$c\in (0,1)$$ in the critical phase $$a=1$$ , and to 0 in the supercritical phase $$a>1$$ . Finally, when $$a>1$$ , we show that the model behaves closely to the simple random walk in the sense that $$\frac{X_n}{\sqrt{n}} \overset{(d)}{\longrightarrow } {\mathcal {N}}(0,1)$$ and $$\frac{M_n}{\sqrt{n}} \overset{(d)}{\longrightarrow } \max _{0 \le t \le 1} B_t$$ , where $${\mathcal {N}}(0,1)$$ is the standard normal distribution and $$(B_t)_{t\ge 0}$$ is the standard Brownian motion.

Propositional satisfiability solving (SAT) has been one of the most successful automated reasoning methods in the last decade in computer science by solving a wide range of both industrial and academic problems. One of the most widely used constraint during the process of translating a practical problem into a SAT instance is the at-most-one (AMO) constraint. Many studies on SAT encodings of the AMO constraint have been reported in the literature, however thorough comparisons of these encodings are largely missing. This paper not only provide such comparisons, but also discusses the similarity of the SAT encodings of the AMO constraint and SAT encodings of more general CSPs. More specifically, the paper empirically evaluates the most well-known AMO encodings on three state-of-the-art SAT solvers of a number of benchmarks and provides some guidelines for efficient SAT encodings of the AMO constraint leading.

Using Regular Path Queries (RPQs) is a common way to explore patterns in graph databases. Traditional automata-based approaches for evaluating RPQs on large graphs are restricted in the graph size and/or highly complex queries, which causes a high evaluation cost. Recently, the threshold rare label based approach applied on large graphs has been proved to be effective. Nevertheless, using rare labels in a graph provides only coarse information which could not always guarantee the minimum searching cost. Hence, the Unit-Subquery Cost Matrix (USCM) based approach has been proposed to reduce the parallel evaluation cost by estimating the searching cost of RPQs. However, the previous approach does not take the joining cost among subqueries into account. In this paper, the method of estimating joining cost of subqueries is proposed in order to accelerate the USCM based parallel evaluation of RPQs. Specifically, the proposed method is realized by estimating the result size of the subqueries. Through our experiments upon real-world datasets, it is depicted that estimating joining cost enhances USCM based approach up to around 20% in terms of response time.

In this paper, a loop control design for different DC-DC power converter operating in Boundary Conduction Mode is studied. The loop control circuit including a pulse frequency modulator allows the converter to adapt its switching frequency to load variations to maximize efficiency. Cadence simulation results using practical switch and circuit component models show that a buck converter employing the loop control circuit achieves a peak efficiency of 97.7% that is ~4 % higher than that of the conventional ones. The full loop control offers an increasing of efficiency at high load from 1 to 3% for a 4-to-l Dickson converter comparing to the same converter with only a PWM closed-loop with fixed dead-time.

빅데이터는 경제개발에서 흥미로운 기회와 도전을 보여왔다. 예를 들어, 농업 분야에서 날씨 데이터 및 토양데이터와 같은 복합데이터의 조합과 이들의 분석 결과는 농업종사자 및 농업경영체들에게 귀중하고 도움되는 정보를 제공한다. 그러나 농업 데이터는 센서들과 농업 웹 마켓 등의 다양한 형태의 장치 및 서비스들을 통해 매 분마다 대규모로 생성된다. 이는 데이터 수집, 저장, 분석과 같은 빅데이터 이슈들을 발생시킨다. 비록 몇몇 시스템들이 이 문제를 해결하기 위해 제안되었으나, 이들은 다루는 데이터 종류의 제약, 저장 방식의 제약, 데이터 크기의 제약 등의 문제를 여전히 가지고 있다. 이 논문에서는 농업데이터의 수집과 분석 플랫폼의 새로운 설계를 제안한다. 제안하는 플랫폼은 (1) Flume과 MapReduce를 이용한 다양한 데이터 소스들로부터의 데이터 수집 방법, (2) HDFS, HBase, 그리고 Hive를 이용한 다양한 데이터 저장 방법, (3) Spark와 Hadoop을 이용한 빅데이터 분석 모듈들을 제공한다.

This paper considers the problem of dynamic evacuation routing in large smart buildings. We investigate a scalable routing approach which not only generates effective routes for evacuees but also quickly updates routes as the disaster status and building conditions could change during the evacuation time. We first design a flexible and scalable evacuation system for large smart buildings with multiple levels of computational support. Given such a system, we develop a novel distributed algorithm for finding effective evacuation routes dynamically by using an LCDT (Length-Capacity-Density-Trustiness) weighted graph model, which is built upon the current disaster information and building conditions. Finally, we propose a caching strategy which expedites dynamic route generation with the current effective route part(s) in order to improve the performance of dynamic evacuation in large buildings. To validate our approach, we test the proposed algorithm with our implementation of an evacuation simulator and compare the results with other approaches. Experimental results show that our approach outperforms other ones in the aspect of the evacuation time reduction and the maximum number of people being evacuated in each time span.

In order to detect Denial of Service (DoS) attacks, victim-side detection methods are used popularly such as static threshold-based method and machine learning-based method. However, as DoS attacking methods become more sophisticated, these methods reveal some natural disadvantages such as the late detection and the difficulty of tracing back attackers. Recently, in order to mitigate these drawbacks, source-side DoS detection methods have been researched. But, the source-side DoS detection methods have limitations if the volume of attack traffic is relatively very small and it is blended into legitimate traffic. Especially, with the subtle attack traffic, DoS detection methods may suffer from high false positive, considering legitimate traffic as attack traffic. In this paper, we propose an effective source-side DoS detection method with traffic seasonality aware adaptive threshold. The threshold of detecting DoS attack is adjusted adaptively to the fluctuated legitimate traffic in order to detect subtle attack traffic. Moreover, by understanding the seasonality of legitimate traffic, the threshold can be updated more carefully even though subtle attack happens and it helps to achieve low false positive. The extensive evaluation with the real traffic logs presents that the proposed method achieves very high detection rate over 90% with low false positive rate down to 5%.

This paper considers the problem of partitioning large graphs. We propose a genetic algorithm to distribute large-scale graphs for parallel computations including individual representation, fitness function, and genetic operators. We first propose individual representation with the ability to change the number of clusters of the graph as well as the ability to flexibly change the cluster of each node. We then increase the diversity of the population by a random hybrid algorithm. Finally, we optimize the solution with the heuristic hybrid and the heuristic mutation algorithms. To validate our approach, we test the proposed algorithm and compare the results with other algorithms such as Greedy algorithm and Bulk Swap algorithm on both synthetic and real-world datasets. Experimental results show that our algorithm outperforms other ones in the aspect of the number of cross edges reduction among graph partitions.

Evaluating Regular Path Queries (RPQs) have been of interest since they were used as a powerful way to explore paths and patterns in graph databases. Traditional automata-based approaches are restricted in the graph size and/or highly complex queries, which causes a high evaluation cost (e.g., memory space and response time) on large graphs. Recently, although using the approach based on the threshold rare label for large graphs has been achieving some success, they could not often guarantee the minimum searching cost. Alternatively, the Unit-Subquery Cost Matrix (USCM) has been studied and obtained the viability of the usage of subqueries. Nevertheless, this method has an issue, which is, it does not cumulate the cost among subqueries that causes the long response time on a large graph. In order to overcome this issue, this paper proposes a method for estimating joining cost of subqueries to accelerate the USCM based parallel evaluation of RPQs on a large graph, namely USCM-Join. Through real-world datasets, we experimentally show that the USCM-Join outperforms others and estimating the joining cost enhances the USCM based approach up to around 20% in terms of response time.

This paper presents a 40V-to-8V hybrid DC-DC converter based on 5-to-1 Dickson converter. The converter can efficiently support wide and flexible output voltage ranges up to the input voltage, suitable for Smart Home LED driver applications. The GaN-based hybrid DC-DC converter prototype is experimentally verified with commercial LED lamp, achieving peak efficiency of 94% while providing ∼1,2A at wide output range from 4V to 28V. A modified bootstrap driver circuit is proposed to significantly reduce the number of gate drivers by ∼57%, leading to a substantial decrease of circuit size and cost. Measurement results show that 94% peak efficiency is achieved for the converter using the proposed bootstrap circuit.

Nowadays, network is one of the essential parts of life, and lots of primary activities are performed by using the network. Also, network security plays an important role in the administrator and monitors the operation of the system. The intrusion detection system (IDS) is a crucial module to detect and defend against the malicious traffics before the system is affected. This system can extract the information from the network system and quickly indicate the reaction which provides real-time protection for the protected system. However, detecting malicious traffics is very complicating because of their large quantity and variants. Also, the accuracy of detection and execution time are the challenges of some detection methods. In this paper, we propose an IDS platform based on convolutional neural network (CNN) called IDS-CNN to detect DoS attack. Experimental results show that our CNN based DoS detection obtains high accuracy at most 99.87%. Moreover, comparisons with other machine learning techniques including KNN, SVM, and Naive Bayes demonstrate that our proposed method outperforms traditional ones.