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Publications (10 of 114) Show all publications
Shahid, Z., Saguna, S. & Åhlund, C. (2024). Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes. In: The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.: . Paper presented at The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.. New York, NY, USA,
Open this publication in new window or tab >>Federated Learning for Unsupervised Anomaly Detection in ADLs of Elderly in Single-resident Smart Homes
2024 (English)In: The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain., New York, NY, USA,, 2024, p. -3Conference paper, Published paper (Refereed)
Abstract [en]

One of the main concerns regarding the facilitation of the elderly well-being monitoring system is to preserve the participants’ privacy, enable the older adults to live longer independently, and support caregivers. Human Activity Recognition (HAR) in smart homes allows us to foresee the residents’ needs by identifying changes in behaviour that might link to possible health conditions. We propose a Federated learning (FL) model within the health monitoring application to generalize for diverse participant populations and to achieve comparable performance without disclosing the raw data to the traditional centralized approach, which raises privacy issues. In this study, we evaluate an unsupervised variational autoencoder (VAE) in centralized, individualized, compared to federated learning settings to learn the features of normal patterns of daily activities and build an anomaly detector based on reconstructed error resulting as outcomes by the trained model. Further, we validated our proposed approach on real-world datasets collected over three years from six single-resident elderly households. The individual and centralized-based learning models were used as a baseline to compare with FL. Our results show that the personalized FedAvg models achieve RMSE of about 1%, while the Global FL models achieve RMSE of approximately. 4%. The centralized model achieves RMSE of about 0.5%, and the RMSE of individual models based on local training ranges between 1% to 6%. The FL models are relatively comparable to the centralized baseline model.

Place, publisher, year, edition, pages
New York, NY, USA,: , 2024
Series
The 39th ACM/SIGAPP Symposium On Applied Computing Avila, Spain, ISSN 979-8-4007-0243-3/24/04 ; 4
Keywords
Applied computing → Health informatics; • Computing methodologies → Neural networks; Anomaly detection
National Category
Engineering and Technology
Identifiers
urn:nbn:se:ltu:diva-103826 (URN)10.1145/3605098.3636163 (DOI)979-8-4007-0243-3 (ISBN)
Conference
The 39th ACM/SIGAPP Symposium on Applied Computing (SAC ’24), April 8–12, 2024, Avila, Spain.
Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-02-08
Mololoth, V. K., Åhlund, C. & Saguna, S. (2023). A Private Blockchain Based P2P Energy Trading Platform for Energy Users. In: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023: . Paper presented at 9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>A Private Blockchain Based P2P Energy Trading Platform for Energy Users
2023 (English)In: Proceedings of 2023 IEEE International Smart Cities Conference, ISC2 2023, Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Series
Proceedings of the IEEE International Smart Cities Conference, ISSN 2687-8852, E-ISSN 2687-8860
National Category
Computer Sciences Energy Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103305 (URN)10.1109/ISC257844.2023.10293651 (DOI)2-s2.0-85178342222 (Scopus ID)979-8-3503-9775-8 (ISBN)979-8-3503-9776-5 (ISBN)
Conference
9th IEEE International Smart Cities Conference, ISC2 2023, Bucharest, Romania, September 24-27, 2023
Available from: 2023-12-13 Created: 2023-12-13 Last updated: 2023-12-13Bibliographically approved
Kim, J. C., Saguna, S. & Åhlund, C. (2023). Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study. JMIR Human Factors, 10, Article ID e42145.
Open this publication in new window or tab >>Acceptability of a Health Care App With 3 User Interfaces for Older Adults and Their Caregivers: Design and Evaluation Study
2023 (English)In: JMIR Human Factors, E-ISSN 2292-9495, Vol. 10, article id e42145Article in journal (Refereed) Published
Abstract [en]

Background: The older population needs solutions for independent living and reducing the burden on caregivers while maintaining the quality and dignity of life.

Objective: The aim of this study was to design, develop, and evaluate an older adult health care app that supports trained caregivers (ie, formal caregivers) and relatives (ie, informal caregivers). We aimed to identify the factors that affect user acceptance of interfaces depending on the user’s role.

Methods: We designed and developed an app with 3 user interfaces that enable remote sensing of an older adult’s daily activities and behaviors. We conducted user evaluations (N=25) with older adults and their formal and informal caregivers to obtain an overall impression of the health care monitoring app in terms of user experience and usability. In our design study, the participants had firsthand experience with our app, followed by a questionnaire and individual interview to express their opinions on the app. Through the interview, we also identified their views on each user interface and interaction modality to identify the relationship between the user’s role and their acceptance of a particular interface. The questionnaire answers were statistically analyzed, and we coded the interview answers based on keywords related to a participant’s experience, for example, ease of use and usefulness.

Results: We obtained overall positive results in the user evaluation of our app regarding key aspects such as efficiency, perspicuity, dependability, stimulation, and novelty, with an average between 1.74 (SD 1.02) and 2.18 (SD 0.93) on a scale of −3.0 to 3.0. The overall impression of our app was favorable, and we identified that “simple” and “intuitive” were the main factors affecting older adults’ and caregivers’ preference for the user interface and interaction modality. We also identified a positive user acceptance of the use of augmented reality by 91% (10/11) of the older adults to share information with their formal and informal caregivers.

Conclusions: To address the need for a study to evaluate the user experience and user acceptance by older adults as well as both formal and informal caregivers regarding the user interfaces with multimodal interaction in the context of health monitoring, we designed, developed, and conducted user evaluations with the target user groups. Our results through this design study show important implications for designing future health monitoring apps with multiple interaction modalities and intuitive user interfaces in the older adult health care domain.

Place, publisher, year, edition, pages
JMIR Publications, 2023
Keywords
Internet of Things, health monitoring, older adults, augmented reality, user experience, independent living, design study, mobile phone
National Category
Human Computer Interaction
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-95822 (URN)10.2196/42145 (DOI)001017203700025 ()36884275 (PubMedID)2-s2.0-85149873927 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-08-10 (joosat);

Funder: Swedish Governmental Agency for Innovation Systems (grant 2017-02807)

Licens fulltext: CC BY License

Available from: 2023-03-09 Created: 2023-03-09 Last updated: 2023-09-05Bibliographically approved
Souza Rossi, H., Mitra, K., Åhlund, C., Cotanis, I., Ögren, N. & Johansson, P. (2023). ALTRUIST: A Multi-platform Tool for Conducting QoE Subjective Tests. In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX): . Paper presented at 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), June 20-22, 2023, Ghent, Belgium (pp. 99-102). IEEE
Open this publication in new window or tab >>ALTRUIST: A Multi-platform Tool for Conducting QoE Subjective Tests
Show others...
2023 (English)In: 2023 15th International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2023, p. 99-102Conference paper, Published paper (Refereed)
Abstract [en]

Quality of Experience (QoE) subjective assessment often demands setting up expensive lab experiments that involve controlling several software programs and services. In addition, these experiments may pose significant challenges regarding man-agement of testbed software components as they may have to be synchronized for efficient data collection, leading to human errors or loss of time. Further, maintaining error-free repeatability between subsequent subjective tests and comprehensive data collection is essential. Therefore, this paper proposes, develops and validates ALTRUIST, a multi-platform tool that assists the experimenter in conducting subjective tests by controlling external applications, facilitates data collection and automates test execution for conducting repeatable subjective tests in broad application areas.

Place, publisher, year, edition, pages
IEEE, 2023
Series
International Workshop on Quality of Multimedia Experience, QoMEX, ISSN 2372-7179, E-ISSN 2472-7814
National Category
Computer Sciences Software Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-100658 (URN)10.1109/QoMEX58391.2023.10178508 (DOI)2-s2.0-85167362907 (Scopus ID)979-8-3503-1173-0 (ISBN)979-8-3503-1174-7 (ISBN)
Conference
2023 15th International Conference on Quality of Multimedia Experience (QoMEX), June 20-22, 2023, Ghent, Belgium
Available from: 2023-08-21 Created: 2023-08-21 Last updated: 2023-10-11Bibliographically approved
Mololoth, V. K., Åhlund, C. & Saguna, S. (2023). An energy trading framework using smart contracts. In: 2023 IEEE Green Technologies Conference, GreenTech: . Paper presented at 15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023 (pp. 214-218). IEEE
Open this publication in new window or tab >>An energy trading framework using smart contracts
2023 (English)In: 2023 IEEE Green Technologies Conference, GreenTech, IEEE , 2023, p. 214-218Conference paper, Published paper (Refereed)
Abstract [en]

The adoption of blockchain in various industries is gaining more popularity, especially in the energy industry. With the increase of distributed energy resources (DER), energy users can generate, store, and trade their resources with others. Utility companies or energy users are influenced by blockchain-based peer-To-peer (P2P) energy trading markets. Blockchain adds transparency and immutability to the involved transactions. Smart contracts in blockchain automatically execute when the conditions are met without any third-party intervention. Motivated by these benefits, in this paper an energy trading framework is developed using Ethereum smart contracts. Energy users can trade their excess energy or buy energy using the smart contract functions. Smart contract written in solidity is compiled and deployed using remix with injected metamask provider. Ganache is used to create accounts and these accounts are imported to metamask for signing transactions. We also discuss alternative methods for smart contract deployment. Computational cost analysis is performed by evaluating the gas consumption analysis for the smart contract functions.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE Green Technologies Conference, ISSN 2166-546X, E-ISSN 2166-5478
Keywords
blockchain, distributed energy sources, Energy trading, smart contracts
National Category
Energy Systems Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-100640 (URN)10.1109/GreenTech56823.2023.10173814 (DOI)2-s2.0-85166216071 (Scopus ID)978-1-6654-9287-4 (ISBN)978-1-6654-9288-1 (ISBN)
Conference
15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-17Bibliographically approved
Shahid, Z. K., Saguna, S. & Åhlund, C. (2023). Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden. In: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023): . Paper presented at 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, June 6-9, 2023. Institute of Electrical and Electronics Engineers Inc.
Open this publication in new window or tab >>Autoencoders for Anomaly Detection in Electricity and District Heating Consumption: A Case Study in School Buildings in Sweden
2023 (English)In: Proceedings - 2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe, (EEEIC / I&CPS Europe 2023), Institute of Electrical and Electronics Engineers Inc. , 2023Conference paper, Published paper (Refereed)
Abstract [en]

Real-time anomaly detection in real-time energy consumption helps identify the technical and infrastructure issues and events that result in significant energy waste and provides the end users feedback to address the issues, which reduces drift operation costs and saves energy in the building. This study proposes deep learning reconstruction models to detect anomalies in daily energy consumption data for nine school buildings. We evaluated the performance of three proposed models, stacked RNN-LSTM autoencoder, CNN-LSTM autoencoder, and LSTM Variational Autoencoder (VAE), to learn the features of normal consumption in an unsupervised manner and detect anomalies based on reconstruction error. We used Exponential Moving Average (EMA) and static threshold to detect local and global anomalies. The experimental results demonstrate that the local CNN-LSTM autoencoder performs better than the local Stacked Autoencoder(AE), with RMSE values ranging between 8-13% for electricity and 11-19% for district heating compared to 12-17% and 15-34% resulting from AE model, respectively. Local LSTM-Variational Autoencoder (VAE) outperformed both methods, with RMSE 4-6% for electricity and 5-7% for district heating. LSTM-VAE trained model on grouped training datasets of schools with similar energy consumption and building profiles has improved the local model by lowering RMSE values to 2-3% for electricity and 3-4% in district heating.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc., 2023
Keywords
Anomaly Detection, Autoencoder, CNN-LSTM, electricity and district heating, Daily Consumption, RNN-LSTM, school buildings
National Category
Energy Systems Energy Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-101201 (URN)10.1109/EEEIC/ICPSEurope57605.2023.10194605 (DOI)2-s2.0-85168668252 (Scopus ID)979-8-3503-4744-9 (ISBN)979-8-3503-4743-2 (ISBN)
Conference
2023 IEEE International Conference on Environment and Electrical Engineering and 2023 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Madrid, Spain, June 6-9, 2023
Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-10-14Bibliographically approved
Mololoth, V. K., Saguna, S. & Åhlund, C. (2023). Blockchain and Machine Learning for Future Smart Grids: A Review. Energies, 16(1), Article ID 528.
Open this publication in new window or tab >>Blockchain and Machine Learning for Future Smart Grids: A Review
2023 (English)In: Energies, E-ISSN 1996-1073, Vol. 16, no 1, article id 528Article, review/survey (Refereed) Published
Abstract [en]

Developments such as the increasing electrical energy demand, growth of renewable energy sources, cyber–physical security threats, increased penetration of electric vehicles (EVs), and unpredictable behavior of prosumers and EV users pose a range of challenges to the electric power system. To address these challenges, a decentralized system using blockchain technology and machine learning techniques for secure communication, distributed energy management and decentralized energy trading between prosumers is required. Blockchain enables secure distributed trust platforms, addresses optimization and reliability challenges, and allows P2P distributed energy exchange as well as flexibility services between customers. On the other hand, machine learning techniques enable intelligent smart grid operations by using prediction models and big data analysis. Motivated from these facts, in this review, we examine the potential of combining blockchain technology and machine learning techniques in the development of smart grid and investigate the benefits achieved by using both techniques for the future smart grid scenario. Further, we discuss research challenges and future research directions of applying blockchain and machine learning techniques for smart grids both individually as well as combining them together. The identified areas that require significant research are demand management in power grids, improving the security of grids with better consensus mechanisms, electric vehicle charging systems, scheduling of the entire grid system, designing secure microgrids, and the interconnection of different blockchain networks.

Place, publisher, year, edition, pages
MDPI, 2023
Keywords
blockchain, demand response management, electric vehicles, energy trading, machine learning, security, smart grids
National Category
Computer Sciences Energy Systems Computer Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-95274 (URN)10.3390/en16010528 (DOI)000909000800001 ()2-s2.0-85145775833 (Scopus ID)
Note

Validerad;2023;Nivå 2;2023-01-16 (hanlid);

Funder: Stiftelsen Rönnbäret

Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-08-28Bibliographically approved
Shahid, Z. K., Saguna, S. & Åhlund, C. (2023). Forecasting and Detecting Anomalies in ADLs in Single-Resident Elderly Smart Homes. In: RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems: . Paper presented at 2023 International Conference on Research in Adaptive and Convergent Systems, RACS 2023, Gdansk, Poland, August 6 - 10, 2023. New York: Association for Computing Machinery, Article ID 20.
Open this publication in new window or tab >>Forecasting and Detecting Anomalies in ADLs in Single-Resident Elderly Smart Homes
2023 (English)In: RACS '23: Proceedings of the 2023 International Conference on Research in Adaptive and Convergent Systems, New York: Association for Computing Machinery , 2023, article id 20Conference paper, Published paper (Refereed)
Abstract [en]

As the ageing population increases, predictive health applications for the elderly can provide opportunities for more independent living, increase cost efficiency and improve the quality of health services for senior citizens. Human activity recognition within IoT-based smart homes can enable the detection of early health risks related to mild cognitive impairment by providing proactive measurements and interventions to both the elderly and supporting healthcare givers. In this paper, we developed and evaluated a Multivariate long short-term memory (LSTM) to learn to forecast activities of daily living and detect anomalous behaviour using motion sensor data in 6 single-resident smart homes of the elderly. We use Mahalanobis distance to identify anomalies based on distance scores to build thresholds. The model's performance in terms of NMAE error values ranges between 2\% and 6\%. The experimental results show that the performance of LSTM for predicting the direct next activity versus the multiple forecasts is close. The method could identify participants' changing health conditions through the used predictive model and unsupervised anomaly detection method.

Place, publisher, year, edition, pages
New York: Association for Computing Machinery, 2023
Series
ACM Conferences
Keywords
Unsupervised learning, Predictive health analytics, Multivariate-LSTM, IoTs, Health/wellbeing applications, HAR, Forecasting, Anomaly Detection
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-103819 (URN)10.1145/3599957.3606216 (DOI)2-s2.0-85174221774 (Scopus ID)979-8-4007-0228-0 (ISBN)
Conference
2023 International Conference on Research in Adaptive and Convergent Systems, RACS 2023, Gdansk, Poland, August 6 - 10, 2023
Note

Full text: CC BY 4.0 License

Available from: 2024-01-18 Created: 2024-01-18 Last updated: 2024-02-08Bibliographically approved
Shahid, Z. K., Saguna, S. & Åhlund, C. (2023). Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden. In: 2023 IEEE Green Technologies Conference, GreenTech: . Paper presented at 15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023 (pp. 169-175). IEEE
Open this publication in new window or tab >>Forecasting Electricity and District Heating Consumption: A Case Study in Schools in Sweden
2023 (English)In: 2023 IEEE Green Technologies Conference, GreenTech, IEEE , 2023, p. 169-175Conference paper, Published paper (Refereed)
Abstract [en]

The growing population and demand for new public buildings contribute to increased energy consumption and greenhouse emissions. In Sweden, the largest amount of energy is consumed in school buildings, i.e., where schools form the highest number of public properties (30 million m2). In total, schools consumed 4 222 GWh of district heating and about 3 GWh of electricity for heating and other purposes in 2020. These figures lead to the realization of the need to apply effective measures to meet the European Green Deal target for 2030. Accurately forecasting energy usage is important for all stakeholders to conduct economic analysis and optimize decision-making. It is equally important in maintenance operations to allocate resources and enable the staff and students to adjust their behaviours and address the issues in buildings where peak forecasts occur. This paper develops and evaluates a power and district heating consumption for a single day and multiple days forecasting using Multivariate Recurrent Neural Network (RNN)-Long-Short term memory (LSTM) and convolutional neural networks (CNNs) and Autoencoders (AE), using daily real consumption data of six public schools provided by Skelefteå municipality in Sweden. The experimental results demonstrate that the hybrid model CNN-LSTM has achieved good accuracy compared to others, with RMSE and nRMSE error between 18%-25% and 5%-6% for electricity, respectively, and between 20%-30% RMSE and 5% nRMSE for district heating.

Place, publisher, year, edition, pages
IEEE, 2023
Series
IEEE Green Technologies Conference, ISSN 2166-546X, E-ISSN 2166-5478
Keywords
AE, CNN-LSTM, district heating, electricity, Forecasting consumption, LSTM, school buildings, time series analysis
National Category
Energy Engineering Energy Systems
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-100641 (URN)10.1109/GreenTech56823.2023.10173792 (DOI)2-s2.0-85166255803 (Scopus ID)978-1-6654-9287-4 (ISBN)978-1-6654-9288-1 (ISBN)
Conference
15th Annual IEEE Green Technologies Conference, GreenTech 2023, Denver, United States, April 19-21, 2023
Available from: 2023-08-17 Created: 2023-08-17 Last updated: 2023-08-17Bibliographically approved
Shahid, Z. K., Saguna, S. & Åhlund, C. (2023). Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes. IEEE Internet of Things Journal
Open this publication in new window or tab >>Multi-Armed Bandits for Sleep Recognition of Elderly Living in Single-Resident Smart Homes
2023 (English)In: IEEE Internet of Things Journal, ISSN 2327-4662Article in journal (Refereed) Epub ahead of print
Abstract [en]

Sleep is an essential activity that affects an individual’s health and ability to perform Activities of Daily Living (ADL). Inadequate sleep reduces cognitive capacity and leads to health-related issues such as cardiovascular diseases. Sleep disorders are more prevalent in older adults. Therefore, it is essential to recognize sleep patterns and support older adults and their caregivers. In our study, we collect data in real-world unconstrained and non-intrusive environments. This paper presents a novel sleep activity recognition method using motion sensors for recognizing nighttime and daytime sleep, which can further enable the development of insightful healthcare applications. The research objectives are to evaluate the application of using Multi-Armed Bandit methods to (i) learn normal sleep patterns, (ii) evaluate sleep quality, and (iii) detect anomalies in sleep activity for 11 elderly participants living in single-resident smart homes. We evaluate the performance of Thompson Sampling, Random Selection, and Upper Confidence Bound MAB methods. Thompson Sampling outperformed the other two methods. Our findings show most elderly participants slept between 6 and 8 hours with 85% sleep efficiency and up to 3 awakenings per night.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023
Keywords
Anomaly detection, Elderly healthcare, Internet of Things, Medical services, Motion detection, Multi-Armed bandits, Older adults, Reinforcement learning, Sensors, Sleep, Sleep patterns, Smart homes
National Category
Computer Sciences
Research subject
Pervasive Mobile Computing
Identifiers
urn:nbn:se:ltu:diva-99666 (URN)10.1109/JIOT.2023.3300015 (DOI)2-s2.0-85166780685 (Scopus ID)
Available from: 2023-08-15 Created: 2023-08-15 Last updated: 2024-02-08
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Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0002-8681-9572

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