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This paper focuses on the bus route network optimization where two bus companies compete with each other to increase a convenience of passengers and profits of bus companies, and analyses how the optimized bus route networks are affected by their initial route networks. The intensive simulation of the competition of bus companies in the Mandl benchmark problem has revealed that (1) the similar initial route networks derive the highly competitive situation where the lines of either company dominate but the topology and characteristic (i.e. the number of edges) of the network of another company remain in the optimized network; (2) the different initial route networks derive the moderately competitive situation where the number of lines of the two companies become equal and the characteristic (i.e. the number of ring routes and edges) of the networks of two companies is averaged; and (3) the intermediately similar initial route networks do not only show the similar tendency of the similar initial route network but also derive a new feature of merging the topology and characteristic of the networks of two companies.

Background/Objective: Frailty is a growing public health challenge in Japan's rapidly aging population, where 28.8% are aged ≥ 65. While multicomponent interventions have shown potential in preventing frailty, traditional face-to-face programs face accessibility challenges. This study evaluated the feasibility and short-term changes of an online multicomponent intervention on frailty-related factors among community-dwelling older adults. Methods: In this single-arm feasibility study, 132 participants (mean age 75.7; standard deviation 4.8 years, 65.2% women) completed a six-week online intervention combining exercise, nutrition education, cognitive activities, and the Coimagination Method to foster social connections, meeting weekly for 75 min sessions in groups of up to 10 participants. Results: The intervention demonstrated feasibility with a 96.4% retention rate and a 94.0% average participation rate. While no significant changes were observed in physical activity levels, autonomic nervous system indicators, or cognitive function, carotenoid scores and hemoglobin concentration improved significantly, with more pronounced improvements among women than men. Conclusions: This study demonstrates the high feasibility of online multicomponent interventions for older adults and suggests potential benefits for nutritional status markers, particularly among women. These findings indicate a promising and accessible approach to frailty prevention, though randomized controlled trials with longer intervention periods and direct frailty assessments are required to establish effectiveness conclusively. Study Trial registration: UMIN Clinical Trials Registry (UMIN000053089).

Promoting subjective well-being is a crucial challenge in aging societies. In 2022, we launched a community-based intervention trial (the Chofu-Digital-Choju Movement). This initiative centered on fostering in-person and online social connections to enhance the subjective well-being of older adults. This paper describes the study design and baseline survey. This quasi-experimental study involved community-dwelling older adults aged 65-84 years in Chofu City, Tokyo, Japan. A self-administered questionnaire was distributed to 3742 residents (1681 men and 2061 women), and a baseline survey was conducted in January 2022. We assessed subjective well-being (primary outcome); psychosocial, physical, and dietary factors; and the use of information and communication technology variables (secondary outcomes) among the participants. After the intervention involving online classes, community hubs, and community events, a 2-year follow-up survey will be conducted to evaluate the effects of the intervention, comparing the intervention group (participants) with the control group (non-participants). We received 2503 questionnaires (66.9% response rate); of these, the analysis included 2343 questionnaires (62.6% valid response rate; mean age, 74.4 (standard deviation, 5.4) years; 43.7% male). The mean subjective well-being score was 7.2 (standard deviation, 1.9). This study will contribute to the development of a prototype subjective well-being strategy for older adults.

Towards early detection of Alzheimer dementia (AD), this paper focuses on time-series instability of heart rate of AD patient, and proposes the AD detection method based on heart rate acquired by an unconstrained mattress sensor for daily life use. Through the human subject experiments on 124 days of heart rate of one AD patient and 39 days of heart rate of 21 healthy subjects, the following implication have been revealed: (1) an accuracy of the proposed AD detection method based on the time-series features of heart rate is approximately 98%; and (2) the time-series instability of heart rate is found in the rapid oscillation of heart rate (i.e., an increase/decrease of heart rate over 2 seconds).

This paper focused on ultradian rhythms (a sleep cycle of approximately 60 to 120 minute) for personalizing sleep stage estimation, and proposed a personalized sleep stage estimation method that weights the results estimated by machine learning with the predicted ultradian rhythms. The ultradian rhythms are predicted by the body movement density which is correlated with ultradian rhythm. To investigate the effectiveness of the proposed method, this paper conducts human subjects experiment for eight subjects.Clinical relevance- The proposed method is compared with the results estimated by conventional ML, and the result of the proposed method is competitive with their conventional counterparts. This indicates that the ultradian rhythm has the potential for developing personalized sleep stage estimation.

This paper focuses on the REM sleep estimation with bio-vibration data acquired from mattress sensor, and proposes its "correction" method based on Time-Series Confidence (TSC) of the REM sleep prediction calculated by Random Forest (RF) as one of the Machine Learnings (MLs). Unlike the conventional MLs that classify whether the REM sleep or not as its binary prediction, the proposed method determines whether the estimated REM sleep should be corrected or not from its continuous prediction. Concretely, the proposed method computes the REM sleep prediction as the percentage of trees that classify the REM sleep for each epoch (30 seconds), calculates TSC of the REM sleep prediction by windowing the REM sleep prediction of a certain number of epochs to smooth them, and the REM sleep estimated by other MLs is corrected when TSC is lower than a certain threshold. Through the human subject experiments, the following implications have been revealed: (1) the proposed method shows a small TSC in the sudden wrong REM sleep estimation, which contributes to correct it; and (2) because of this feature of the proposed method, the number of False-Positive of the REM sleep estimation is successfully reduced, which improves Precision from 51.4% (w/o TSC) to 59.4% (w/ TSC).

It is important to detect daily Alzheimer dementia (AD) possibility using unconstrained mattress sensors because dementia takes time before subjective symptoms appear and the main treatment is to slow the rate of progression. Forcusing on circadian rhythm disorder which tend to occur with AD, this paper analyzes the features of unstable circadian rhythms of heart rate associated with its weakening and misalignment in AD. It is found that the external feature of heart rate of AD that of up and down changes seen frequently compared to that of healthy subjects makes the multiple types of effects on the estimation process of the AD detection method based on the circadian instability represented by the trigonometric regression equation estimated from the heart rate. So, we designed two feature values from the effects and analyzed them in an analysis experiment. An analysis experiment was conducted on the heart rate of total 72 days data of one AD patient and total 30 days data of 21 healthy people, and the result confirmed significant differences between the AD and the healthy people at from 0.01% to 0.1 % level for the first feature and at from 0.1% to 5% level for the second feature. And the result shows the possibility that differences in the heart rate features is found between AD and healthy people.

This paper proposes the novel Sleep Apnea Syndrome (SAS) detection method based on the frequency analysis of the overnight bio-vibration data acquired from mattress sensor. Concretely, this paper designs the index called Degree of Convexity of the Logarithmic Spectrum (DCLS), which quantifies the degree of convexity by computing the difference between the waveform of the averaged logarithmic spectrum and the waveform of its approximation formula, and employs it to detect SAS. Through the human subject experiment on the SAS detection, the following implications have been revealed: (1) the SAS subjects tend to have the large density around 3Hz, and the average of DCLS in SAS subjects and healthy subjects are 98.6±10.1 and 48.2±6.8 respectively, which succeeds to correctly separate the nine SAS subjects and the nine healthy subjects; and (2) the characteristics of the WAKE stage are different between the SAS and healthy subjects.

This paper proposes the novel Alzheimer dementia (AD) detection method based on unstable circadian rhythm of heartrate acquired from mattress sensor. Concretely, the pro-posed method, UCRADD (Unstable Circadian Rhythm based Alzheimer Dementia Detection), estimates the circadian rhythm of heartrate by calculating the regression of the trigonometric functions with the maximum likelihood estimation, and judges instability of the circadian rhythm by the coefficients of the equation estimated trigonometric functions. Through the human subject experiment with one elderly AD subject in two months (i.e., August and December), three elderly (age from 60-70) non-AD subjects, the ten middle-aged non-AD subjects and eight young non-AD subjects, the following implications have been revealed: (1) UCRADD succeeds to detect the AD patients in the high rate and keeps it in two months (78.9% in August and 82.4% in December), while our previous method cannot keep the rate at the same level in two months (57.9% in August and 82.4% in December). (2) the instability of circadian rhythm of heartrate has the potential of being new symptom of AD.