scholarly journals Smart E bicycle: an efficient and effective way to greener future

2018 ◽  
Vol 7 (4.5) ◽  
pp. 477
Author(s):  
Harvinder Singh ◽  
Pinky .

This paper presents and proposes a smart electric bicycle(SeB) leveraging the power of wireless technologies, artificial intelli- gence and cloud computing in order make its user’s experience smooth, safe and enjoyable hence encouraging the user to choose SeB over other modes of transportation. The proposed system introduces an Electric Bicycle connected with a smartphone in one variant or with “smartphone and cloud” in another variant for smart decision-making and efficiency and other related tips for the user. The range of bicycle is predicted based upon the user profile (weight, age etc.), route details (inclinations, distances of al- ternative routes), State of Charge(Soc) and State of Health(SoH) of the battery used. Multiple user profiles and minute details of the route (slope, speed breakers etc.) are captured using sensor like accelerometer and basis on these data smart decisions for pow- er saving and range extensions are made. Also, safety critical and predictive maintenance features are presented.  

2022 ◽  
Vol 9 (3) ◽  
pp. 0-0

This paper presents the work done on recommendations of healthcare related journal papers by understanding the semantics of terms from the papers referred by users in past. In other words, user profiles based on user interest within the healthcare domain are constructed from the kind of journal papers read by the users. Multiple user profiles are constructed for each user based on different categories of papers read by the users. The proposed approach goes to the granular level of extrinsic and intrinsic relationship between terms and clusters highly semantically related relevant domain terms where each cluster represents a user interest area. The semantic analysis of terms is done starting from co-occurrence analysis to extract the intra-couplings between terms and then the inter-couplings are extracted from the intra-couplings and then finally clusters of highly related terms are formed. The experiments showed improved precision for the proposed approach as compared to the state-of-the-art technique with a mean reciprocal rank of 0.76.


Life ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 310
Author(s):  
Shih-Chia Chang ◽  
Ming-Tsang Lu ◽  
Tzu-Hui Pan ◽  
Chiao-Shan Chen

Although the electronic health (e-health) cloud computing system is a promising innovation, its adoption in the healthcare industry has been slow. This study investigated the adoption of e-health cloud computing systems in the healthcare industry and considered security functions, management, cloud service delivery, and cloud software for e-health cloud computing systems. Although numerous studies have determined factors affecting e-health cloud computing systems, few comprehensive reviews of factors and their relations have been conducted. Therefore, this study investigated the relations between the factors affecting e-health cloud computing systems by using a multiple criteria decision-making technique, in which decision-making trial and evaluation laboratory (DEMATEL), DANP (DEMATEL-based Analytic Network Process), and modified VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) approaches were combined. The intended level of adoption of an e-health cloud computing system could be determined by using the proposed approach. The results of a case study performed on the Taiwanese healthcare industry indicated that the cloud management function must be primarily enhanced and that cost effectiveness is the most significant factor in the adoption of e-health cloud computing. This result is valuable for allocating resources to decrease performance gaps in the Taiwanese healthcare industry.


2012 ◽  
Vol 10 ◽  
pp. 1146-1151 ◽  
Author(s):  
Timothy Patterson ◽  
Sally McClean ◽  
Philip Morrow ◽  
Gerard Parr

2021 ◽  
Author(s):  
Shaunagh O'Sullivan ◽  
Lianne Schmaal ◽  
Simon D'Alfonso ◽  
Yara J Toenders ◽  
Lee Valentine ◽  
...  

BACKGROUND Multicomponent digital interventions offer the potential for tailored and flexible interventions that aim to address high attrition rates and increase engagement, an area of concern in digital mental health. However, increased flexibility in usage makes it difficult to determine which components lead to improved treatment outcomes. OBJECTIVE This study aimed to identify user profiles on Horyzons, an 18-month digital relapse prevention intervention that incorporates therapeutic content and social networking, along with clinical, vocational and peer support, and to examine the predictive value of these user profiles for treatment outcomes. A secondary objective was to compare each user profile with young people receiving treatment as usual (TAU). METHODS Participants comprised 82 young people (16-27 years of age) with access to Horyzons and 84 receiving TAU, recovering from first-episode psychosis. Six-month usage data from the therapy and social networking components of Horyzons were used as features for K-means clustering for joint trajectories to identify user profiles. Social functioning, psychotic symptoms, depression and anxiety were assessed at baseline and six-month follow-up. General linear mixed models were used to examine the predictive value of user profiles for treatment outcomes, and between each user profile with TAU. RESULTS Three user profiles were identified based on system usage metrics including: (a) low usage; (b) maintained usage of social components; and (c) maintained usage of both therapy and social components. The maintained therapy and social group showed improvements in social functioning (F (2, 51) = 3.58; P = .04), negative symptoms (F (2, 51) = 4.45; P = .02) and overall psychiatric symptom severity (F (2, 50) = 3.23; P = .048) compared to the other user profiles. This group also showed improvements in social functioning (F (1, 62) = 4.68; P = .03), negative symptoms (F (1, 62) = 14.61; P = <.001) and overall psychiatric symptom severity (F (1, 63) = 5.66; P = .02) compared to TAU. Conversely, the maintained social group showed increases in anxiety compared to TAU (F (1, 57) = 7.65; P = .01). No differences were found between the low usage group and TAU on treatment outcomes. CONCLUSIONS Continued engagement with both therapy and social components might be key in achieving long-term recovery. Maintained social usage and low usage outcomes were broadly comparable to TAU, emphasizing the importance of maintaining engagement for improved treatment outcomes. Although the social network may be a key ingredient to increase sustained engagement, as users engaged with this more consistently, it should be leveraged as a tool to engage young people with therapeutic content to bring about social and clinical benefits.


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