Muroran Institute of Technology

Deep learning is a promising approach for extracting accurate information from raw sensor data from IoT devices deployed in complex environments. Because of its multilayer structure, deep learning is also appropriate for the edge computing environment. Therefore, in this article, we first introduce deep learning for IoTs into the edge computing environment. Since existing edge nodes have limited processing capability, we also design a novel offloading strategy to optimize the performance of IoT deep learning applications with edge computing. In the performance evaluation, we test the performance of executing multiple deep learning tasks in an edge computing environment with our strategy. The evaluation results show that our method outperforms other optimization solutions on deep learning for IoT.

The genome of Ectocarpus, a model organism for brown algae, has been sequenced. Brown algae are complex photosynthetic organisms that have adapted to life in rocky coastal environments. Genome analysis sheds light on this adaptation and reveals an extended set of light-harvesting and pigment biosynthesis genes and novel metabolic processes such as halide metabolism. Comparative genomic analyses highlight the likely importance of a family of receptor kinases and related molecules in the evolution of multicellularity in plants, animals and brown algae. The genome of Ectocarpus siliculosis, a model for the study of brown algae, has been sequenced. These seaweeds are complex photosynthetic organisms that have adapted to rocky coastal environments. Genome analysis sheds light on this adaptation, revealing an extended set of light-harvesting and pigment biosynthesis genes, and new metabolic processes such as halide metabolism. Comparative analyses are also significant with respect to the evolution of multicellularity in plants, animals and brown algae. Brown algae (Phaeophyceae) are complex photosynthetic organisms with a very different evolutionary history to green plants, to which they are only distantly related1. These seaweeds are the dominant species in rocky coastal ecosystems and they exhibit many interesting adaptations to these, often harsh, environments. Brown algae are also one of only a small number of eukaryotic lineages that have evolved complex multicellularity (Fig. 1). We report the 214 million base pair (Mbp) genome sequence of the filamentous seaweed Ectocarpus siliculosus (Dillwyn) Lyngbye, a model organism for brown algae2,3,4,5, closely related to the kelps6,7 (Fig. 1). Genome features such as the presence of an extended set of light-harvesting and pigment biosynthesis genes and new metabolic processes such as halide metabolism help explain the ability of this organism to cope with the highly variable tidal environment. The evolution of multicellularity in this lineage is correlated with the presence of a rich array of signal transduction genes. Of particular interest is the presence of a family of receptor kinases, as the independent evolution of related molecules has been linked with the emergence of multicellularity in both the animal and green plant lineages. The Ectocarpus genome sequence represents an important step towards developing this organism as a model species, providing the possibility to combine genomic and genetic2 approaches to explore these and other4,5 aspects of brown algal biology further.

BACKGROUND: Aberrant crypt foci of the colon are possible precursors of adenoma and cancer, but these lesions have been studied mainly in surgical specimens from patients who already had colon cancer. METHODS: Using magnifying endoscopy, we studied the prevalence, number, size, and dysplastic features of aberrant crypt foci and their distribution according to age in 171 normal subjects, 131 patients with adenoma, and 48 patients with colorectal cancer. We also prospectively examined the prevalence of aberrant crypt foci in 11 subjects (4 normal subjects, 6 with adenoma, and 1 with cancer) before and after the administration of 100 mg of sulindac three times a day for 8 to 12 months and compared the results with those in 9 untreated subjects (4 normal subjects and 5 with adenoma). All 20 subjects had aberrant crypt foci at base line. RESULTS: We identified 3155 aberrant crypt foci, 161 of which were dysplastic; the prevalence and number increased with age. There were significant (P<0.001) correlations between the number of aberrant crypt foci, the presence of dysplastic foci, the size of the foci, and the number of adenomas. After sulindac therapy, the number of foci decreased, disappearing in 7 of 11 subjects. In the untreated control group, the number of foci was unchanged in eight subjects and slightly increased in one (P<0.001 for the difference between the groups). CONCLUSIONS: Aberrant crypt foci, particularly those that are large and have dysplastic features, may be precursors of adenoma and cancer.

With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient.

We have established a plot of the anion height dependence of Tc for the typical Fe-based superconductors. The plot appeared a symmetric curve with a peak around 1.38 A. Both data at ambient pressure and under high pressure obeyed the unique curve. This plot will be one of the key strategies for both understanding the mechanism of Fe-based superconductivity and search for the new Fe-based superconductors with higher Tc.

A new process for the detection of red cell (RBC) antigen antibody reactions is described. It is applicable to most of the tests performed in blood group serology. The procedures are standardized and easy, and they provide clear and stable reactions that improve the interpretation of results. The process uses special microtubes filled with a mixture of gel, buffer, and reagent. Depending on the test to be carried out, the test uses a neutral gel containing no reagents (reagents are added to top of gel) or a specific gel containing reagents (e.g., antiglobulin serum or anti-A, -B, -D, etc.). A suspension of RBCs (for typing or the direct antiglobulin test) or a mixture of RBCs and serum (for reverse ABO typing or antibody characterization) is centrifuged through the gel under precise conditions. In negative reactions, the RBCs pass through the gel and pellet in the bottom of the tube, whereas, in positive reactions, they are trapped in the gel and the reaction may be read for hours afterwards. The test is easy to perform, sensitive, and reproducible. The antiglobulin tests can be performed without washing of the RBCs. There should be a reduction of risk from biohazardous materials.

Since the 1950s, the number of natural and man-made disasters has increased exponentially and the facility location problem has become the preferred approach for dealing with emergency humanitarian logistical problems. To deal with this challenge, an exact algorithm and a heuristic algorithm have been combined as the main approach to solving this problem. Owing to the importance that an exact algorithm holds with regard to enhancing emergency humanitarian logistical facility location problems, this paper aims to conduct a survey on the facility location problems that are related to emergency humanitarian logistics based on both data modeling types and problem types and to examine the pre- and post-disaster situations with respect to facility location, such as the location of distribution centers, warehouses, shelters, debris removal sites and medical centers. The survey will examine the four main problems highlighted in the literature review: deterministic facility location problems, dynamic facility location problems, stochastic facility location problems, and robust facility location problems. For each problem, facility location type, data modeling type, disaster type, decisions, objectives, constraints, and solution methods will be evaluated and real-world applications and case studies will then be presented. Finally, research gaps will be identified and be addressed in further research studies to develop more effective disaster relief operations.

The low temperature properties of ${\mathrm{PrRu}}_{4}{\mathrm{P}}_{12}$ and ${\mathrm{PrOs}}_{4}{\mathrm{P}}_{12}$ have been studied by means of electrical resistivity and magnetic susceptibility measurements. The resistivity of ${\mathrm{PrRu}}_{4}{\mathrm{P}}_{12}$ decreases with decreasing temperature from room temperature to about 60 K, but increases sharply with decreasing temperature below 60 K. A metal-insulator transition is found at around 60 K. The susceptibility of the phosphide shows no distinct anomaly at this temperature. No significant change in the powder x-ray diffraction pattern of ${\mathrm{PrRu}}_{4}{\mathrm{P}}_{12}$ is detected down to 10 K. The anomalous behavior may arise from a $4f$ instability of the Pr ion.

In this paper, we propose and evaluate six methods for the segmentation of skin lesions in dermoscopic images. This set includes some state of the art techniques which have been successfully used in many medical imaging problems (gradient vector flow (GVF) and the level set method of Chan et al.[(C-LS)]. It also includes a set of methods developed by the authors which were tailored to this particular application (adaptive thresholding (AT), adaptive snake (AS), EM level set (EM-LS), and fuzzy-based split-and-merge algorithm (FBSM)]. The segmentation methods were applied to 100 dermoscopic images and evaluated with four different metrics, using the segmentation result obtained by an experienced dermatologist as the ground truth. The best results were obtained by the AS and EM-LS methods, which are semi-supervised methods. The best fully automatic method was FBSM, with results only slightly worse than AS and EM-LS.

Wireless sensor networks (WSNs) are increasingly being deployed in security-critical applications. Because of their inherent resource-constrained characteristics, they are prone to various security attacks, and a black hole attack is a type of attack that seriously affects data collection. To conquer that challenge, an active detection-based security and trust routing scheme named ActiveTrust is proposed for WSNs. The most important innovation of ActiveTrust is that it avoids black holes through the active creation of a number of detection routes to quickly detect and obtain nodal trust and thus improve the data route security. More importantly, the generation and the distribution of detection routes are given in the ActiveTrust scheme, which can fully use the energy in non-hotspots to create as many detection routes as needed to achieve the desired security and energy efficiency. Both comprehensive theoretical analysis and experimental results indicate that the performance of the ActiveTrust scheme is better than that of the previous studies. ActiveTrust can significantly improve the data route success probability and ability against black hole attacks and can optimize network lifetime.

The moment‐rotation relationships of semirigid steel beam‐to‐column connections including single and double web‐angle connections and top‐ and seat‐angle connections with or without double web angle, are developed. The initial elastic stiffness and ultimate moment capacity of the connections are determined by a simple analytical procedure. The shape parameter is then determined by a least‐square curve fitting with experimental tests in Purdue University's data bank. Using the initial connection stiffness, ultimate moment capacity, and shape parameter so obtained, a three‐parameter power model adequate for representing the moment‐rotation relationships of these connections. The power model is simple to use and provides a realistical representation of the actual moment‐rotation behavior of each connection type with angles, and can be easily implemented in a second‐order frame analysis.

The wear resistance of an Fe-1 2.2wto/oCr-0.84wto/oMo-0.43wto/oV-1 .44wto/oC al]oy tool steel after cold treatment at 223 K (subzero treatment) and after cryogenic treatment at 93 K (u]tra-subzero treatment) has been investigated. The wear resistance of steels after cryogenic treatment is superior to that after cold treatment. The effects of cryogenic treatment on the microstructure were also studied by means of X-ray diffraction and transmission electron microscopy methods. Unlike cold treatment, cryogenic treatment improves the preferential precipitation of fine n-carbides instead of 8-carbides. These fine carbide particles enhancethe strength and toughness of the martensite matrix and then increase the wear resistance. The formation mechanism of fine n-carbide is discussed.

We report on the photoluminescence (PL) mechanisms and the nature of the related electronic states of AgInS2 quantum dots (QDs) synthesized via a metathesis reaction of metal complexes. A broad PL band with a large Stokes shift is apparent in the PL spectra of AgInS2 QDs whose average diameter is 2.6 nm. The characteristic decay behavior of the PL spectra and the peak shift of the PL band depending on the excitation intensity indicate that the PL is attributed to the donor−acceptor (DA) pair recombination. The binding energies of the donor and acceptor are estimated to be 100 and 220 meV. These values are derived from the temperature dependence of the PL intensity and an analysis of the spectral profile of the PL spectrum considering the DA pair recombination processes. Furthermore, we show that the phonon sidebands constitute the dominant contribution to the PL spectra because of the strong electron−phonon interaction of carriers trapped by these donors or acceptors.

Smart grid has emerged as a successful application of cyber-physical systems in the energy sector. Among numerous key technologies of the smart grid, vehicle-to-grid (V2G) provides a promising solution to reduce the level of demand-supply mismatch by leveraging the bidirectional energy-trading capabilities of electric vehicles. In this paper, we propose a secure and efficient V2G energy trading framework by exploring blockchain, contract theory, and edge computing. First, we develop a consortium blockchain-based secure energy trading mechanism for V2G. Then, we consider the information asymmetry scenario, and propose an efficient incentive mechanism based on contract theory. The social welfare optimization problem falls into the category of difference of convex programming and is solved by using the iterative convex-concave procedure algorithm. Next, edge computing has been incorporated to improve the successful probability of block creation. The computational resource allocation problem is modeled as a two-stage: 1) Stackelberg leader-follower game and 2) the optimal strategies are obtained by using the backward induction approach. Finally, the performance of the proposed framework is validated via numerical results and theoretical analysis.

The autonomous vehicle, as an emerging and rapidly growing field, has received extensive attention for its futuristic driving experiences. Although the fast developing depth sensors and machine learning methods have given a huge boost to self-driving research, existing autonomous driving vehicles do meet with several avoidable accidents during their road testings. The major cause is the misunderstanding between self-driving systems and human drivers. To solve this problem, we propose a humanlike driving system in this paper to give autonomous vehicles the ability to make decisions like a human. In our method, a convolutional neural network model is used to detect, recognize, and abstract the information in the input road scene, which is captured by the on-board sensors. And then a decision-making system calculates the specific commands to control the vehicles based on the abstractions. The biggest advantage of our work is that we implement a decision-making system which can well adapt to real-life road conditions, in which a massive number of human drivers exist. In addition, we build our perception system with only the depth information, rather than the unstable RGB data. The experimental results give a good demonstration of the efficiency and robustness of the proposed method.

Device-to-device (D2D) communication is a key enabler to facilitate the realization of the Internet of Things (IoT). In this paper, we study the deployment of D2D communications as an underlay to long-term evolution-advanced (LTE-A) networks based on novel architectures such as cloud radio access network (C-RAN). The challenge is that both energy efficiency (EE) and quality of service (QoS) are severely degraded by the strong intracell and intercell interference due to dense deployment and spectrum reuse. To tackle this problem, we propose an energy-efficient resource allocation algorithm through joint channel selection and power allocation design. The proposed algorithm has a hybrid structure that exploits the hybrid architecture of C-RAN: distributed remote radio heads (RRHs) and centralized baseband unit (BBU) pool. The distributed resource allocation problem is modeled as a noncooperative game, and each player optimizes its EE individually with the aid of distributed RRHs. We transform the nonconvex optimization problem into a convex one by applying constraint relaxation and nonlinear fractional programming. We propose a centralized interference mitigation algorithm to improve the QoS performance. The centralized algorithm consists of an interference cancellation technique and a transmission power constraint optimization technique, both of which are carried out in the centralized BBU pool. The achievable performance of the proposed algorithm is analyzed through simulations, and the implementation issues and complexity analysis are discussed in detail.

The algal pyrenoid is a large plastid body, where the majority of the CO2-fixing enzyme, ribulose-1,5-bisphosphate carboxylase/oxygenase (RubisCO) resides, and it is proposed to be the hub of the algal CO2-concentrating mechanism (CCM) and CO2 fixation. The thylakoid membrane is often in close proximity to or penetrates the pyrenoid itself, implying there is a functional cooperation between the pyrenoid and thylakoid. Here, GFP tagging and immunolocalization analyses revealed that a previously unidentified protein, Pt43233, is targeted to the lumen of the pyrenoid-penetrating thylakoid in the marine diatom Phaeodactylum tricornutum The recombinant Pt43233 produced in Escherichia coli cells had both carbonic anhydrase (CA) and esterase activities. Furthermore, a Pt43233:GFP-fusion protein immunoprecipitated from P. tricornutum cells displayed a greater specific CA activity than detected for the purified recombinant protein. In an RNAi-generated Pt43233 knockdown mutant grown in atmospheric CO2 levels, photosynthetic dissolved inorganic carbon (DIC) affinity was decreased and growth was constantly retarded; in contrast, overexpression of Pt43233:GFP yielded a slightly greater photosynthetic DIC affinity. The discovery of a θ-type CA localized to the thylakoid lumen, with an essential role in photosynthetic efficiency and growth, strongly suggests the existence of a common role for the thylakoid-luminal CA with respect to the function of diverse algal pyrenoids.

A new mechanism is proposed for securing a blockchain applied to contracts management such as digital rights management. This mechanism includes a new consensus method using a credibility score and creates a hybrid blockchain by alternately using this new method and proof-of-stake. This makes it possible to prevent an attacker from monopolizing resources and to keep securing blockchains.

The Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2020 (J-SSCG 2020), a Japanese-specific set of clinical practice guidelines for sepsis and septic shock created as revised from J-SSCG 2016 jointly by the Japanese Society of Intensive Care Medicine and the Japanese Association for Acute Medicine, was first released in September 2020 and published in February 2021. An English-language version of these guidelines was created based on the contents of the original Japanese-language version. The purpose of this guideline is to assist medical staff in making appropriate decisions to improve the prognosis of patients undergoing treatment for sepsis and septic shock. We aimed to provide high-quality guidelines that are easy to use and understand for specialists, general clinicians, and multidisciplinary medical professionals. J-SSCG 2016 took up new subjects that were not present in SSCG 2016 (e.g., ICU-acquired weakness [ICU-AW], post-intensive care syndrome [PICS], and body temperature management). The J-SSCG 2020 covered a total of 22 areas with four additional new areas (patient- and family-centered care, sepsis treatment system, neuro-intensive treatment, and stress ulcers). A total of 118 important clinical issues (clinical questions, CQs) were extracted regardless of the presence or absence of evidence. These CQs also include those that have been given particular focus within Japan. This is a large-scale guideline covering multiple fields; thus, in addition to the 25 committee members, we had the participation and support of a total of 226 members who are professionals (physicians, nurses, physiotherapists, clinical engineers, and pharmacists) and medical workers with a history of sepsis or critical illness. The GRADE method was adopted for making recommendations, and the modified Delphi method was used to determine recommendations by voting from all committee members.As a result, 79 GRADE-based recommendations, 5 Good Practice Statements (GPS), 18 expert consensuses, 27 answers to background questions (BQs), and summaries of definitions and diagnosis of sepsis were created as responses to 118 CQs. We also incorporated visual information for each CQ according to the time course of treatment, and we will also distribute this as an app. The J-SSCG 2020 is expected to be widely used as a useful bedside guideline in the field of sepsis treatment both in Japan and overseas involving multiple disciplines.

Energy-efficiency (EE) is critical for device-to-device (D2D) enabled cellular networks due to limited battery capacity and severe cochannel interference. In this paper, we address the EE optimization problem by adopting a stable matching approach. The NP-hard joint resource allocation problem is formulated as a one-to-one matching problem under two-sided preferences, which vary dynamically with channel states and interference levels. A game-theoretic approach is employed to analyze the interactions and correlations among user equipments (UEs), and an iterative power allocation algorithm is developed to establish mutual preferences based on nonlinear fractional programing. We then employ the Gale-Shapley algorithm to match D2D pairs with cellular UEs, which is proved to be stable and weak Pareto optimal. We provide a theoretical analysis and description for implementation details and algorithmic complexity. We also extend the algorithm to address scalability issues in large-scale networks by developing tie-breaking and preference-deletion-based matching rules. Simulation results validate the theoretical analysis and demonstrate that significant performance gains of average EE and matching satisfactions can be achieved by the proposed algorithm.