Real-time urban traffic amount prediction models for dynamic route guidance systems

The route guidance system (RGS) has been considered an important technology to mitigate urban traffic congestion. However, existing RGSs provide only route guidance after congestion happens. This reactive strategy imposes a strong limitation on the potential contribution of current RGS to the performance improvement of a traffic network. Thus, a proactive RGS based on congestion prediction is considered essential to improve the effectiveness of RGS. The problem of congestion prediction is translated into traffic amount (i.e. the number of vehicles on the individual roads) prediction, as the latter is a straightforward indicator of the former. We thereby propose two urban traffic prediction models using different modeling approaches. Model-1 is based on the traffic flow propagation in the network, while Model-2 is based on the time-varied spare flow capacity on the concerned road links. These two models are then applied to construct a centralized proactive RGS. Evaluation results show that (1) both of the proposed models reduce the prediction error up to 52% and 30% in the best cases compared to the existing Shift Model, (2) providing proactive route guidance helps reduce average travel time by up to 70% compared to providing reactive one and (3) non-rerouted vehicles could benefit more from route guidance than rerouted vehicles do. Document type: Articl

走行ルートの動的な車両別変更によって都市道路交通網における車両走行時間の短縮を図るルート案内システム

学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 若原 恭, 東京大学教授 相田 仁, 東京大学教授 浅見 徹, 東京大学教授 森川 博之, 東京大学准教授 川原 圭博, 東京大学准教授 関谷 勇司, 東京大学准教授 中山 雅哉University of Tokyo(東京大学

Strong Photoluminescence Enhancement of MoS2 through Defect Engineering and Oxygen Bonding

We report on a strong photoluminescence (PL) enhancement of monolayer MoS2 through defect engineering and oxygen bonding. Micro- PL and Raman images clearly reveal that the PL enhancement occurs at cracks/defects formed during high temperature vacuum annealing. The PL enhancement at crack/defect sites could be as high as thousands of times after considering the laser spot size. The main reasons of such huge PL enhancement include: (1) the oxygen chemical adsorption induced heavy p doping and the conversion from trion to exciton; (2) the suppression of non-radiative recombination of excitons at defect sites as verified by low temperature PL measurements. First principle calculations reveal a strong binding energy of ~2.395 eV for oxygen molecule adsorbed on an S vacancy of MoS2. The chemical adsorbed oxygen also provides a much more effective charge transfer (0.997 electrons per O2) compared to physical adsorbed oxygen on ideal MoS2 surface. We also demonstrate that the defect engineering and oxygen bonding could be easily realized by oxygen plasma irradiation. X-ray photoelectron spectroscopy further confirms the formation of Mo-O bonding. Our results provide a new route for modulating the optical properties of two dimensional semiconductors. The strong and stable PL from defects sites of MoS2 may have promising applications in optoelectronic devices.Comment: 23 pages, 9 figures, to appear in ACS Nan

Co-design personal sleep health technology for and with university students

University students often experience sleep disturbances and disorders. Personal digital technologies present a great opportunity for sleep health promotion targeting this population. However, studies that engage university students in designing and implementing digital sleep health technologies are scarce. This study sought to understand how we could build digital sleep health technologies that meet the needs of university students through a co-design process. We conducted three co-design workshops with 51 university students to identify design opportunities and to generate features for sleep health apps through workshop activities. The generated ideas were organized using the stage-based model of self-tracking so that our findings could be well-situated within the context of personal health informatics. Our findings contribute new design opportunities for sleep health technologies targeting university students along the dimensions of sleep environment optimization, online community, gamification, generative AI, materializing sleep with learning, and personalization

Are Proselfs More Deceptive and Hypocritical? Social Image Concerns in Appearing Fair

Deception varies across individuals and social contexts. The present research explored how individual difference measured by social value orientations, and situations, affect deception in moral hypocrisy. In two experiments, participants made allocations between themselves and recipients with an opportunity to deceive recipients where recipients cannot reject their allocations. Experiment 1 demonstrated that proselfs were more deceptive and hypocritical than prosocials by lying to be apparently fair, especially when deception was unrevealed. Experiment 2 showed that proselfs were more concerned about social image in deception in moral hypocrisy than prosocials were. They decreased apparent fairness when deception was revealed and evaluated by a third-party reviewer and increased it when deception was evaluated but unrevealed. These results show that prosocials and proselfs differed in pursuing deception and moral hypocrisy social goals and provide implications for decreasing deception and moral hypocrisy

Prompt engineering for digital mental health: a short review

Prompt engineering, the process of arranging input or prompts given to a large language model to guide it in producing desired outputs, is an emerging field of research that shapes how these models understand tasks, process information, and generate responses in a wide range of natural language processing (NLP) applications. Digital mental health, on the other hand, is becoming increasingly important for several reasons including early detection and intervention, and to mitigate limited availability of highly skilled medical staff for clinical diagnosis. This short review outlines the latest advances in prompt engineering in the field of NLP for digital mental health. To our knowledge, this review is the first attempt to discuss the latest prompt engineering types, methods, and tasks that are used in digital mental health applications. We discuss three types of digital mental health tasks: classification, generation, and question answering. To conclude, we discuss the challenges, limitations, ethical considerations, and future directions in prompt engineering for digital mental health. We believe that this short review contributes a useful point of departure for future research in prompt engineering for digital mental health

Novel method combining multiscale attention entropy of overnight blood oxygen level and machine learning for easy sleep apnea screening

Objective Sleep apnea is a common sleep disorder affecting a significant portion of the population, but many apnea patients remain undiagnosed because existing clinical tests are invasive and expensive. This study aimed to develop a method for easy sleep apnea screening. Methods Three supervised machine learning algorithms, including logistic regression, support vector machine, and light gradient boosting machine, were applied to develop apnea screening models at two apnea–hypopnea index cutoff thresholds: ≥ 5 and ≥ 30 events/hours. The SpO2 recordings of the Sleep Heart Health Study database ( N = 5786) were used for model training, validation, and test. Multiscale entropy analysis was performed to derive a set of multiscale attention entropy features from the SpO2 recordings. Demographic features including age, sex, body mass index, and blood pressure were also used. The dependency among the multiscale attention entropy features were handled with the independent component analysis. Results For cutoff ≥ 5/hours, logistic regression model achieved the highest Matthew’s correlation coefficient (0.402) and area under the curve (0.747), and reasonably good sensitivity (75.38%), specificity (74.02%), and positive predictive value (92.94%). For cutoff ≥ 30/hours, support vector machine model achieved the highest Matthew’s correlation coefficient (0.545) and area under the curve (0.823), and good sensitivity (82.00%), specificity (82.69%), and negative predictive value (95.53%). Conclusions Our models achieved better performance than existing methods and have the potential to be integrated with home-use pulse oximeters

Pro-Reactive Route Recovery and Automatic Route Shortening in Wireless Ad Hoc Networks

This thesis mainly addresses two problems in wireless ad hoc routing: route failure and route redundancy. Route failures occur frequently in ad hoc networks due to their highly dynamic topology as well as their unstable wireless communication medium. Existing route recovery methods lead to long latency and large control overhead. We proposed a novel relay recovery route maintenance protocol RELREC for ad hoc networks to combine the benefits of both proactive and reactive routing protocols and to minimize their drawbacks. In our proposal, one or more substitute routes usually become ready for the recovery of every link in a route before its break, while the route recovery process actually starts only when the upstream node of a link confirms that the link really breaks. Since this scheme does not broadcast any control packet, it can effectively recover a broken link without heavy control overhead traffic. Also, it helps reduce the time delay due to the recovery, since substitute routes are already available when the upstream node initiates the route recovery process. However, the route recovered by RELREC is usually longer than the original one. In fact, route redundancy occurs frequently in ad hoc networks due to highly dynamic topologies. In order to improve the integrated performance of an ad hoc network, we proposed two automatic route shortening strategies to optimize the route length in the network: relay route shortening and active route shortening. While relay shortening can only be initiated by the Relay Nodes in pro-reactive relay recovery processes, active shortening can be launched by any node whenever it overhears a shorter route from its neighbors. Note that the information in Relay Tables is only utilized in route recovery process in RELREC. In order to utilize the Relay Table information to the possible extent to further reduce the control overhead, we extended the usage of Relay Table to route discovery process by incorporating Relay Table information into gossip algorithm, and proposed Relay Gossip Routing (RGR). In RGR, only Relay Nodes are allowed to rebroadcast under a gossiping probability, which differs from existing gossip methods. The simulation study in ns-2 simulator has demonstrated clearly that our proposed RELREC scheme effectively reduces the average end-to-end time delay by up to 49% and 31%, and the normalized control overhead by up to 17% and 28% compared with the previous algorithms AODV and DRRS respectively in the best cases. According to the simulation results, the data delivery ratio by our proposed scheme is close to DRRS and AODV. The simulation results confirm that our proposal can provide quick and low-cost route recovery without degrading the packet delivery ratio, while it is adaptive to node mobility and traffic load. When it comes to the automatic route shortening algorithms, active shortening works more effectively than relay shortening does, leading to by far the shortest average route length and the smallest end-to-end time delay among all the algorithms in the simulation. However, the slightly larger control overhead traffic and relatively lower packet delivery ratio harms the performance of active route shortening. In the evaluation of RGR, in order to find the appropriate gossiping value for the Relay Nodes, we firstly studied the relation between packet delivery ratio and gossiping probability p. It is shown that the packet delivery ratio equals or is even higher than that of p = 1 when the value of p is larger than a certain point lying between 0.35 and 0.45; at the same time the total control overhead is smaller than that of p = 1. Accordingly, the gossiping probability is set to 0.5 in the performance evaluation of RGR. Simulation results confirm that RGR successfully reduces the normalized control overhead by up to 17% in the best case compared to RELREC, and ensures a similar performance to RELREC in terms of the packet delivery ratio. Although the basic gossip routing yields the lowest normalized control overhead, it yields the worst performance in terms of the packet delivery ratio. It is conspicuous that RGR strikes a better balance between control overhead and packet delivery ratio than basic gossip routing does. Besides, RGR also outperforms basic gossip routing in terms of average time-to-time end delay and average route length.報告番号: ; 学位授与年月日: 2011-09-27 ; 学位の種別: 修士 ; 学位の種類: 修士(工学) ; 学位記番号: ; 研究科・専攻: 工学系研究科電気系工学専

Context-Aware Sleep Health Recommender Systems (CASHRS): A Narrative Review

The practice of quantified-self sleep tracking has become increasingly common among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization and are incapable of providing actionable recommendations that are tailored to users’ physical, behavioral, and environmental context. A promising solution to address this gap is the context-aware sleep health recommender system (CASHRS), an emerging research field that bridges ubiquitous sleep computing and context-aware recommender systems. This paper presents a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges identified in peer-reviewed publications that meet the characteristics of CASHRS. The analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS