COMPUTATIONAL MODELING OF MULITSENSORY PROCESSING USING NETWORK OF SPIKING NEURONS

Multisensory processing in the brain underlies a wide variety of perceptual phenomena, but little is known about the underlying mechanisms of how multisensory neurons are generated and how the neurons integrate sensory information from environmental events. This lack of knowledge is due to the difficulty of biological experiments to manipulate and test the characteristics of multisensory processing. By using a computational model of multisensory processing this research seeks to provide insight into the mechanisms of multisensory processing. From a computational perspective, modeling of brain functions involves not only the computational model itself but also the conceptual definition of the brain functions, the analysis of correspondence between the model and the brain, and the generation of new biologically plausible insights and hypotheses. In this research, the multisensory processing is conceptually defined as the effect of multisensory convergence on the generation of multisensory neurons and their integrated response products, i.e., multisensory integration. Thus, the computational model is the implementation of the multisensory convergence and the simulation of the neural processing acting upon the convergence. Next, the most important step in the modeling is analysis of how well the model represents the target, i.e., brain function. It is also related to validation of the model. One of the intuitive and powerful ways of validating the model is to apply methods standard to neuroscience for analyzing the results obtained from the model. In addition, methods such as statistical and graph-theoretical analyses are used to confirm the similarity between the model and the brain. This research takes both approaches to provide analyses from many different perspectives. Finally, the model and its simulations provide insight into multisensory processing, generating plausible hypotheses, which will need to be confirmed by real experimentation

Customer process management A framework for using customer-related data to create customer value

Purpose The proliferation of customer-related data provides companies with numerous service opportunities to create customer value. The purpose of this study is to develop a framework to use this data to provide services. Design/methodology/approach This study conducted four action research projects on the use of customer-related data for service design with industry and government. Based on these projects, a practical framework was designed, applied, and validated, and was further refined by analyzing relevant service cases and incorporating the service and operations management literature. Findings The proposed customer process management (CPM) framework suggests steps a service provider can take when providing information to its customers to improve their processes and create more value-in-use by using data related to their processes. The applicability of this framework is illustrated using real examples from the action research projects and relevant literature. Originality/value "Using data to advance service" is a critical and timely research topic in the service literature. This study develops an original, specific framework for a company's use of customer-related data to advance its services and create customer value. Moreover, the four projects with industry and government are early CPM case studies with real data

Driving Risk Assessment Using Non-Negative Matrix Factorization With Driving Behavior Records

Aggressive driving behavior (ADB) is a major cause of traffic accidents. As ADB is controllable, ADB-based driving risk assessment is an effective method for drivers and transportation companies to ensure driving safety. Conventionally, the relationships between ADBs and accident-related records are analyzed when assessing driving risk. However, such records typically overlook driver responsibility for driving risks and depend considerably on the person producing the data (e.g., police officers or insurance managers). Foremost, conventional approaches do not consider non-accident situations that comprise most driving scenarios. Thus, we propose a novel driving risk assessment method that uses only ADB data. In this method, interpretable latent risk factors are extracted from ADB data via sparse non-negative matrix factorization (NMF), and then the driving risk score is computed on a scale of 0-100. The proposed method was validated by adopting a real-world application to assess the driving risk of bus drivers in South Korea and by conducting an evaluation performed by transportation experts in conjunction with the Korea Transportation Safety Authority. Results revealed that the proposed method can discriminate between high-and low-risk driving, thus providing clear guidelines to improve driving. Then, the proposed driving risk score assessment method using NMF was compared with existing machine learning-based risk assessment methods. The proposed method outperformed the conventional methods in terms of driving risk discrimination and interpretability. This study can provide risk assessment guidelines based on driving behavior records and contribute to the application of machine learning in transportation safety management

High-resolution analysis of condition-specific regulatory modules in Saccharomyces cerevisiae

A novel approach for identifying condition-specific regulatory modules in yeast reveals functionally distinct coregulated submodules

Possible link between Arctic Sea ice and January PM10 concentrations in South Korea

In this study, we investigated the possible teleconnection between PM10 concentrations in South Korea and Arctic Sea ice concentrations at inter-annual time scales using observed PM10 data from South Korea, NCEP R2 data, and NOAA Sea Ice Concentration (SIC) data from 2001 to 2018. From the empirical orthogonal function (EOF) analysis, we found that the first mode (TC1) was a large-scale mode for PM10 in South Korea and explained about 27.4% of the total variability. Interestingly, the TC1 is more dominantly influenced by the horizontal ventilation effect than the vertical atmospheric stability effect. The pollution potential index (PPI), which is defined by the weighted average of the two ventilation effects, is highly correlated with the TC1 of PM10 at a correlation coefficient of 0.75, indicating that the PPI is a good measure for PM10 in South Korea at inter-annual time scales. Regression maps show that the decrease of SIC over the Barents Sea is significantly correlated with weakening of high pressure over the Ural mountain range region, the anomalous high pressure at 500 hPa over the Korean peninsula, and the weakening of the Siberian High and Aleutian low. Moreover, these patterns are similar to the correlation pattern with the PPI, suggesting that the variability of SIC over the Barents Sea may play an important role in modulating the variability of PM10 in South Korea through teleconnection from the Barents Sea to the Korean peninsula via Eurasia

Ubiquitous Containerized Cargo Monitoring System Development based on Wireless Sensor Network Technology

Due to globalization, global trade is strongly growing nowadays. The use of containers has significantly increased and bringing the change on the shape of the world economy. Thus, monitoring every single container is a big challenge for port industries. Furthermore, rapid development in embedded computing systems has led to the emergence of Wireless Sensor Network (WSN) technology which enabled us to envision the intelligent containers. This represents the next evolutionary development in logistics industry to increase the efficiency, productivity, security of containerized cargo shipping. In this paper, we present a comprehensive containerized cargo monitoring system based on WSNs. We incorporated tilt/motion sensor to improve the network convergence time of container networks. Moreover, we periodically switch the nodes into sleeping mode to save energy and extend the lifetime of the network. Based on the technical implementation on a real container vessel, we strongly believed that our design which employed WSN technology is viable to be implemented in container logistics to improve port services and provide safe transport of containerized cargo

Multi-factor service design: identification and consideration of multiple factors of the service in its design process

Service design is a multidisciplinary area that helps innovate services by bringing new ideas to customers through a design-thinking approach. Services are affected by multiple factors, which should be considered in designing services. In this paper, we propose the multi-factor service design (MFSD) method, which helps consider the multi-factor nature of service in the service design process. The MFSD method has been developed through and used in five service design studies with industry and government. The method addresses the multi-factor nature of service for systematic service design by providing the following guidelines: (1) identify key factors that affect the customer value creation of the service in question (in short, value creation factors), (2) define the design space of the service based on the value creation factors, and (3) design services and represent them based on the factors. We provide real stories and examples from the five service design studies to illustrate the MFSD method and demonstrate its utility. This study will contribute to the design of modern complex services that are affected by varied factors

Fibroblast growth factor receptor isotype expression and its association with overall survival in patients with hepatocellular carcinoma

Background/AimsFibroblast growth factor signaling is involved in hepatocarcinogenesis. The aim of this study was to determine the fibroblast growth factor receptor (FGFR) isotype expression in hepatocellular carcinoma (HCC) and neighboring nonneoplastic liver tissue, and elucidate its prognostic implications.MethodsImmunohistochemical staining of FGFR1, -2, -3, and -4 was performed in the HCCs and paired neighboring nonneoplastic liver tissue of 870 HCC patients who underwent hepatic resection. Of these, clinical data for 153 patients who underwent curative resection as a primary therapy were reviewed, and the relationship between FGFR isotype expression and overall survival was evaluated (development set). This association was also validated in 73 independent samples (validation set) by Western blot analysis.ResultsFGFR1, -2, -3, and -4 were expressed in 5.3%, 11.1%, 3.8%, and 52.7% of HCCs, respectively. Among the development set of 153 patients, FGFR2 positivity in HCC was associated with a significantly shorter overall survival (5-year survival rate, 35.3% vs. 61.8%; P=0.02). FGFR2 expression in HCC was an independent predictor of a poor postsurgical prognosis (hazard ratio, 2.10; P=0.02) in the development set. However, the corresponding findings were not statistically significant in the validation set.ConclusionsFGFR2 expression in HCC could be a prognostic indicator of postsurgical survival