LIVIVO - Search results -

Search results

Result 1 - 10 of total 41

Search options

Article ; Online: The Influence of Sidedness in Unilateral Cleft Lip and Palate on Mid Facial Growth at Five Years of Age.

Fell, Matthew / Chong, David / Parmar, Paras / Su, Ting-Li / Enocson, Lars / Richard, Bruce

The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association

2024 , Page(s) 10556656241233220

Abstract: Objective: To determine whether facial growth at five years is different for children with a left versus right sided cleft lip and palate.: Design: Retrospective cohort study.: Setting: Seven UK regional cleft centres.: Patients: Patients born ... ...

Abstract	Objective: To determine whether facial growth at five years is different for children with a left versus right sided cleft lip and palate. Design: Retrospective cohort study. Setting: Seven UK regional cleft centres. Patients: Patients born between 2000-2014 with a complete unilateral cleft lip and palate (UCLP). Main outcomes measure: 5-Year-Old's Index scores. Results: 378 children were included. 256 (68%) had a left sided UCLP and 122 (32%) had a right sided UCLP. 5-Year-Old's index scores ranged from 1 (good) to 5 (poor). There was a higher proportion of patients getting good scores (1 and 2) in left UCLP (43%) compared to right UCLP (37%) but there was weak evidence for a difference (Adjusted summary odds ratio 1.27, 95% CI 0.87 to 1.87; Conclusions: Whilst maxillary growth may be different for left versus right sided UCLP, definitive analysis requires older growth indices and arch forms.
Language	English
Publishing date	2024-02-12
Publishing country	United States
Document type	Journal Article
ZDB-ID	1069409-2
ISSN	1545-1569 ; 0009-8701 ; 1055-6656
ISSN (online)	1545-1569
ISSN	0009-8701 ; 1055-6656
DOI	10.1177/10556656241233220
Database	MEDical Literature Analysis and Retrieval System OnLINE

In stock of ZB MED Cologne/Königswinter

Zs.A 451: Show issues

Location:
Je nach Verfügbarkeit (siehe Angabe bei Bestand)
bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
Jg. 1995 - 2021: Lesesall (1.OG)
ab Jg. 2022: Lesesaal (EG)

Order via subito

This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

Details ▾
- See ZB MED holdings
- Order with fees

Article ; Online: Regional variation in longitudinal trajectories of primary care opioids prescribing across Health Boards in Scotland: a population-based study.

Chen, Teng-Chou / Kurdi, Amanj / Su, Ting-Li / Chen, Li-Chia

Expert review of clinical pharmacology

2022 Volume 15, Issue 7, Page(s) 897–905

Abstract: Background: This study aims to describe the longitudinal trajectory of opioid prescribing at the practice level and assess associated factors, including Health Boards and socioeconomic status.: Research design and methods: This drug utilization ... ...

Abstract	Background: This study aims to describe the longitudinal trajectory of opioid prescribing at the practice level and assess associated factors, including Health Boards and socioeconomic status. Research design and methods: This drug utilization research used practice-level dispensing data from 2016 to 2018. Practice-level prescription opioids dispensed were quantified by the defined daily doses (DDDs) per 1000 registrants. Group-based trajectory models were used to identify groups of practices with similar trajectories based on the difference in monthly opioid utilization. Characteristics of registrants were associated with the trajectory by a conditional logistic regression and the prescription opioids dispensed by a random-effect regression model. Results: Of the 798 practices, 29.5% increased opioid prescription by an additional 100 DDDs/1000 registrants/month during 2017 and 2018. Practice in southwest Scotland tended to be categorized into the group with increasing opioid utilization. Deprived socioeconomic status was associated with increasing opioid utilization (odds ratio: 2.2; 95% confidence interval: 1.5, 3.2) or higher annual opioid utilization (coefficient: 358.2; 95% confidence interval: 327.6, 388.8). Conclusions: Increasing opioid utilization over time was related to deprived socioeconomic status associated with chronic pain conditions and inequality in pain services. Further strategies to balance inequality are needed, which needs further investigation.
MeSH term(s)	Analgesics, Opioid/therapeutic use ; Chronic Pain/drug therapy ; Drug Prescriptions ; Humans ; Practice Patterns, Physicians' ; Primary Health Care ; Scotland
Chemical Substances	Analgesics, Opioid
Language	English
Publishing date	2022-07-19
Publishing country	England
Document type	Journal Article
ISSN	1751-2441
ISSN (online)	1751-2441
DOI	10.1080/17512433.2022.2102972
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article: Location estimation based on feature mode matching with deep network models.

Bai, Yu-Ting / Jia, Wei / Jin, Xue-Bo / Su, Ting-Li / Kong, Jian-Lei

Frontiers in neurorobotics

2023 Volume 17, Page(s) 1181864

Abstract: Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) ... ...

Abstract	Introduction: Global navigation satellite system (GNSS) signals can be lost in viaducts, urban canyons, and tunnel environments. It has been a significant challenge to achieve the accurate location of pedestrians during Global Positioning System (GPS) signal outages. This paper proposes a location estimation only with inertial measurements. Methods: A method is designed based on deep network models with feature mode matching. First, a framework is designed to extract the features of inertial measurements and match them with deep networks. Second, feature extraction and classification methods are investigated to achieve mode partitioning and to lay the foundation for checking different deep networks. Third, typical deep network models are analyzed to match various features. The selected models can be trained for different modes of inertial measurements to obtain localization information. The experiments are performed with the inertial mileage dataset from Oxford University. Results and discussion: The results demonstrate that the appropriate networks based on different feature modes have more accurate position estimation, which can improve the localization accuracy of pedestrians in GPS signal outages.
Language	English
Publishing date	2023-06-14
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2453002-5
ISSN	1662-5218
ISSN	1662-5218
DOI	10.3389/fnbot.2023.1181864
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: A Variational Bayesian Deep Network with Data Self-Screening Layer for Massive Time-Series Data Forecasting.

Jin, Xue-Bo / Gong, Wen-Tao / Kong, Jian-Lei / Bai, Yu-Ting / Su, Ting-Li

Entropy (Basel, Switzerland)

2022 Volume 24, Issue 3

Abstract: Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model ... ...

Abstract	Compared with mechanism-based modeling methods, data-driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time-series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understanding data to improve performance. Firstly, a data self-screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing's air quality and meteorological data are conducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.
Language	English
Publishing date	2022-02-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e24030335
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

Jin, Xue-Bo / Chen, Wei / Ma, Hui-Jun / Kong, Jian-Lei / Su, Ting-Li / Bai, Yu-Ting

Sensors (Basel, Switzerland)

2023 Volume 23, Issue 20

Abstract: GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise ... ...

Abstract	GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.
Language	English
Publishing date	2023-10-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s23208650
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Deep Spatio-Temporal Graph Network with Self-Optimization for Air Quality Prediction.

Jin, Xue-Bo / Wang, Zhong-Yao / Kong, Jian-Lei / Bai, Yu-Ting / Su, Ting-Li / Ma, Hui-Jun / Chakrabarti, Prasun

Entropy (Basel, Switzerland)

2023 Volume 25, Issue 2

Abstract: The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A ... ...

Abstract	The environment and development are major issues of general concern. After much suffering from the harm of environmental pollution, human beings began to pay attention to environmental protection and started to carry out pollutant prediction research. A large number of air pollutant predictions have tried to predict pollutants by revealing their evolution patterns, emphasizing the fitting analysis of time series but ignoring the spatial transmission effect of adjacent areas, leading to low prediction accuracy. To solve this problem, we propose a time series prediction network with the self-optimization ability of a spatio-temporal graph neural network (BGGRU) to mine the changing pattern of the time series and the spatial propagation effect. The proposed network includes spatial and temporal modules. The spatial module uses a graph sampling and aggregation network (GraphSAGE) in order to extract the spatial information of the data. The temporal module uses a Bayesian graph gated recurrent unit (BGraphGRU), which applies a graph network to the gated recurrent unit (GRU) so as to fit the data's temporal information. In addition, this study used Bayesian optimization to solve the problem of the model's inaccuracy caused by inappropriate hyperparameters of the model. The high accuracy of the proposed method was verified by the actual PM2.5 data of Beijing, China, which provided an effective method for predicting the PM2.5 concentration.
Language	English
Publishing date	2023-01-30
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2014734-X
ISSN	1099-4300 ; 1099-4300
ISSN (online)	1099-4300
ISSN	1099-4300
DOI	10.3390/e25020247
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Manchester Intermittent Diet in Gestational Diabetes Acceptability Study (MIDDAS-GDM): a two-arm randomised feasibility protocol trial of an intermittent low-energy diet (ILED) in women with gestational diabetes and obesity in Greater Manchester.

Dapre, Elizabeth / Issa, Basil G / Harvie, Michelle / Su, Ting-Li / McMillan, Brian / Pilkington, Andrea / Hanna, Fahmy / Vyas, Avni / Mackie, Sarah / Yates, James / Evans, Benjamin / Mubita, Womba / Lombardelli, Cheryl

BMJ open

2024 Volume 14, Issue 2, Page(s) e078264

Abstract: Introduction: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy ... ...

Abstract	Introduction: The prevalence of gestational diabetes mellitus (GDM) is rising in the UK and is associated with maternal and neonatal complications. National Institute for Health and Care Excellence guidance advises first-line management with healthy eating and physical activity which is only moderately effective for achieving glycaemic targets. Approximately 30% of women require medication with metformin and/or insulin. There is currently no strong evidence base for any particular dietary regimen to improve outcomes in GDM. Intermittent low-energy diets (ILEDs) are associated with improved glycaemic control and reduced insulin resistance in type 2 diabetes and could be a viable option in the management of GDM. This study aims to test the safety, feasibility and acceptability of an ILED intervention among women with GDM compared with best National Health Service (NHS) care. Method and analysis: We aim to recruit 48 women with GDM diagnosed between 24 and 30 weeks gestation from antenatal clinics at Wythenshawe and St Mary's hospitals, Manchester Foundation Trust, over 13 months starting in November 2022. Participants will be randomised (1:1) to ILED (2 low-energy diet days/week of 1000 kcal and 5 days/week of the best NHS care healthy diet and physical activity advice) or best NHS care 7 days/week until delivery of their baby. Primary outcomes include uptake and retention of participants to the trial and adherence to both dietary interventions. Safety outcomes will include birth weight, gestational age at delivery, neonatal hypoglycaemic episodes requiring intervention, neonatal hyperbilirubinaemia, admission to special care baby unit or neonatal intensive care unit, stillbirths, the percentage of women with hypoglycaemic episodes requiring third-party assistance, and significant maternal ketonaemia (defined as ≥1.0 mmol/L). Secondary outcomes will assess the fidelity of delivery of the interventions, and qualitative analysis of participant and healthcare professionals' experiences of the diet. Exploratory outcomes include the number of women requiring metformin and/or insulin. Ethics and dissemination: Ethical approval has been granted by the Cambridge East Research Ethics Committee (22/EE/0119). Findings will be disseminated via publication in peer-reviewed journals, conference presentations and shared with diabetes charitable bodies and organisations in the UK, such as Diabetes UK and the Association of British Clinical Diabetologists. Trial registration number: NCT05344066.
MeSH term(s)	Female ; Humans ; Infant, Newborn ; Pregnancy ; Diabetes Mellitus, Type 2/drug therapy ; Diabetes, Gestational/diagnosis ; Diet ; Feasibility Studies ; Hypoglycemic Agents/therapeutic use ; Insulin/therapeutic use ; Metformin/therapeutic use ; Obesity/drug therapy ; State Medicine ; Randomized Controlled Trials as Topic
Chemical Substances	Hypoglycemic Agents ; Insulin ; Metformin (9100L32L2N)
Language	English
Publishing date	2024-02-10
Publishing country	England
Document type	Clinical Trial Protocol ; Journal Article
ZDB-ID	2599832-8
ISSN	2044-6055 ; 2044-6055
ISSN (online)	2044-6055
ISSN	2044-6055
DOI	10.1136/bmjopen-2023-078264
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

Article ; Online: Research on electromagnetic radiation based side-channel analysis method for hardware Trojan detection

TANG Yongkang, HU Xing, SU Ting, LI Shaoqing

网络与信息安全学报, Vol 7, Iss 2, Pp 43-

2021 Volume 56

Abstract: With the globalization of the integrated circuit industry, hardware Trojan is becoming the main threat to integrated circuits. At present, the side-channel analysis which can make a good trade-off between detection ability and cost, has attracted more ... ...

Abstract	With the globalization of the integrated circuit industry, hardware Trojan is becoming the main threat to integrated circuits. At present, the side-channel analysis which can make a good trade-off between detection ability and cost, has attracted more attention from the academia. The side-channel analysis method based on electromagnetic radiation is one of the hotspots in hardware security field. The ability evaluation of electromagnetic radiation analysis method to detect hardware Trojan was focused, and the factor that limited their detection performance was explored. The experimental results on FPGA show that the electromagnetic radiation analysis method can effectively detect hardware Trojan whose electromagnetic radiation distribution is significantly different from surrounding circuits, but it cannot be applied to hardware Trojan with complex frequency distribution of electromagnetic radiation.
Keywords	detection ability evaluation ; electromagnetic radiation ; hardware trojan detection ; side-channel analysis ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	535
Language	English
Publishing date	2021-04-01T00:00:00Z
Publisher	POSTS&TELECOM PRESS Co., LTD
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

Full text online

Full text

Inter-library loan at ZB MED

Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

Article ; Online: BMAE-Net: A Data-Driven Weather Prediction Network for Smart Agriculture

Kong, Jian-Lei / Fan, Xiao-Meng / Jin, Xue-Bo / Su, Ting-Li / Bai, Yu-Ting / Ma, Hui-Jun / Zuo, Min

Agronomy. 2023 Feb. 22, v. 13, no. 3

2023

Abstract: Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term ... ...

Abstract	Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term predictions of temperature and humidity are vital for guiding agricultural activities and improving crop yield and quality. However, existing intelligent models still have difficulties dealing with big weather data in predicting applications, such as striking a balance between prediction accuracy and learning efficiency. Therefore, a multi-head attention encoder-decoder neural network optimized via Bayesian inference strategy (BMAE-Net) is proposed herein to predict weather time series changes accurately. Firstly, we incorporate Bayesian inference into the gated recurrent unit to construct a Bayesian-gated recurrent units (Bayesian-GRU) module. Then, a multi-head attention mechanism is introduced to design the network structure of each Bayesian layer, improving the prediction applicability to time-length changes. Subsequently, an encoder-decoder framework with Bayesian hyperparameter optimization is designed to infer intrinsic relationships among big time-series data for high prediction accuracy. For example, the R-evaluation metrics for temperature prediction in the three locations are 0.9, 0.804, and 0.892, respectively, while the RMSE is reduced to 2.899, 3.011, and 1.476, as seen in Case 1 of the temperature data. Extensive experiments subsequently demonstrated that the proposed BMAE-Net has overperformed on three location weather datasets, which provides an effective solution for prediction applications in the smart agriculture system.
Keywords	Bayesian theory ; agronomy ; crop quality ; crop yield ; data collection ; humidity ; meteorological data ; prediction ; temperature ; time series analysis ; weather forecasting
Language	English
Dates of publication	2023-0222
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article ; Online
ZDB-ID	2607043-1
ISSN	2073-4395
ISSN	2073-4395
DOI	10.3390/agronomy13030625
Database	NAL-Catalogue (AGRICOLA)

Order via subito

Article ; Online: The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods.

Jin, Xue-Bo / Robert Jeremiah, Ruben Jonhson / Su, Ting-Li / Bai, Yu-Ting / Kong, Jian-Lei

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 6

Abstract: State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors ... ...

Abstract	State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems' development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.
Language	English
Publishing date	2021-03-16
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21062085
Database	MEDical Literature Analysis and Retrieval System OnLINE

Order via subito

To top

More links

Kategorien

In stock of ZB MED Cologne/Königswinter

Order via subito

More links

Kategorien

Order via subito

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito

Full text online

More links

Kategorien

Inter-library loan at ZB MED

More links

Kategorien

Order via subito

More links

Kategorien

Order via subito