An optimized design modelling of PV integrated SEPIC-based four-switch inverter for sensorless PMBLDC motor control

The design of PV-based high gain SEPIC converter integrated with four-switch strategy, which has been used to achieve sensorless speed control of Permanent magnet Brushless DC motor (PMBLDC) is analysed in this work. Hence SEPIC converter coupled with Fuzzy Logic, MPPT Algorithm is employed to retain voltage. SEPIC converter is chosen as it has a continuous current operation with high gain; Fuzzy MPPT algorithm is used as it provides accurate results faster while the classical MPPT techniques provide the results with fluctuations in attaining the maximum power. Regarding the sensorless control of PMBLDC motor, the conventional six-switch strategy is replaced by four-switch strategy and the sensors are replaced by back EMF method. Four-switch strategy has the capability of reducing the losses, size, cost and complexity of control. For achieving the nominal speed, a closed-loop control is implemented with PI controller, which is tuned by GWO technique. The proposed methodology is more efficient as the motor speed remains unchanged even under the full load condition. The end result of traditional PI algorithm and PI algorithm, which have been tuned by GWO algorithm, is compared and simulated through MATLAB. This is also implemented and validated in hardware by FPGA Spartan 6E controller

Clinical and Molecular Characterisation Of Hyperinsulinaemic Hypoglycaemia In Infants Born Small-For-Gestational Age

OBJECTIVE: To characterise the phenotype and genotype of neonates born small-for-gestational age (SGA; birth weight <10th centile) who developed hyperinsulinaemic hypoglycaemia (HH). METHODS: Clinical information was prospectively collected on 27 SGA neonates with HH, followed by sequencing of KCNJ11 and ABCC8. RESULTS: There was no correlation between the maximum glucose requirement and serum insulin levels. Serum insulin level was undetectable in five infants (19%) during hypoglycaemia. Six infants (22%) required diazoxide treatment >6 months. Normoglycaemia on diazoxide <5 mg/kg/day was a safe predictor of resolved HH. Sequencing of KCNJ11/ABCC8 did not identify any mutations. CONCLUSIONS: Serum insulin levels during hypoglycaemia taken in isolation can miss the diagnosis of HH. SGA infants may continue to have hypofattyacidaemic hypoketotic HH beyond the first few weeks of life. Recognition and treatment of this group of patients are important and may have important implications for neurodevelopmental outcome of these patients

Computational Notebooks as Co-Design Tools: Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people’s needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through codesign workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles

Computational Notebooks as Co-Design Tools:Engaging Young Adults Living with Diabetes, Family Carers, and Clinicians with Machine Learning Models

Engaging end user groups with machine learning (ML) models can help align the design of predictive systems with people's needs and expectations. We present a co-design study investigating the benefits and challenges of using computational notebooks to inform ML models with end user groups. We used a computational notebook to engage young adults, carers, and clinicians with an example ML model that predicted health risk in diabetes care. Through co-design workshops and retrospective interviews, we found that participants particularly valued using the interactive data visualisations of the computational notebook to scaffold multidisciplinary learning, anticipate benefits and harms of the example ML model, and create fictional feature importance plots to highlight care needs. Participants also reported challenges, from running code cells to managing information asymmetries and power imbalances. We discuss the potential of leveraging computational notebooks as interactive co-design tools to meet end user needs early in ML model lifecycles

Generalized space vector control for current source inverters and rectifiers

Current source inverters (CSI) is one of the widely used converter topology in medium voltage drive applications due to its simplicity, motor friendly waveforms and reliable short circuit protection. The current source inverters are usually fed by controlled current source rectifiers (CSR) with a large inductor to provide a constant supply current. A generalized control applicable for both CSI and CSR and their extension namely current source multilevel inverters (CSMLI) are dealt in this paper. As space vector pulse width modulation (SVPWM) features the advantages of flexible control, faster dynamic response, better DC utilization and easy digital implementation it is considered for this work. This paper generalizes SVPWM that could be applied for CSI, CSR and CSMLI. The intense computation involved in framing a generalized space vector control are discussed in detail. The algorithm includes determination of band, region, subregions and vectors. The algorithm is validated by simulation using MATLAB /SIMULINK for CSR 5, 7, 13 level CSMLI and for CSR fed CSI

Generalized space vector control for current source inverters and rectifiers

Current source inverters (CSI) is one of the widely used converter topology in medium voltage drive applications due to its simplicity, motor friendly waveforms and reliable short circuit protection. The current source inverters are usually fed by controlled current source rectifiers (CSR) with a large inductor to provide a constant supply current. A generalized control applicable for both CSI and CSR and their extension namely current source multilevel inverters (CSMLI) are dealt in this paper. As space vector pulse width modulation (SVPWM) features the advantages of flexible control, faster dynamic response, better DC utilization and easy digital implementation it is considered for this work. This paper generalizes SVPWM that could be applied for CSI, CSR and CSMLI. The intense computation involved in framing a generalized space vector control are discussed in detail. The algorithm includes determination of band, region, subregions and vectors. The algorithm is validated by simulation using MATLAB /SIMULINK for CSR 5, 7, 13 level CSMLI and for CSR fed CSI

Explainable Machine Learning for Real-Time Hypoglycemia and Hyperglycemia Prediction and Personalized Control Recommendations

Background: The occurrences of acute complications arising from hypoglycemia and hyperglycemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time glucose readings enabling users to manage their control proactively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer term control. Methods: We introduce explainable machine learning to make predictions of hypoglycemia (&lt;70 mg/dL) and hyperglycemia (&gt;270 mg/dL) up to 60 minutes ahead of time. We train our models using CGM data from 153 people living with T1D in the CITY (CGM Intervention in Teens and Young Adults With Type 1 Diabetes)survey totaling more than 28 000 days of usage, which we summarize into (short-term, medium-term, and long-term) glucose control features along with demographic information. We use machine learning explanations (SHAP [SHapley Additive exPlanations]) to identify which features have been most important in predicting risk per user. Results: Machine learning models (XGBoost) show excellent performance at predicting hypoglycemia (area under the receiver operating curve [AUROC]: 0.998, average precision: 0.953) and hyperglycemia (AUROC: 0.989, average precision: 0.931) in comparison with a baseline heuristic and logistic regression model. Conclusions: Maximizing model performance for glucose risk prediction and management is crucial to reduce the burden of alarm fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison with baseline models. SHAP helps identify what about a CGM user’s glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.</p

Explainable machine learning for real-time hypoglycaemia and hyperglycaemia prediction and personalised control recommendations

Background: the occurrences of acute complications arising from hypoglycaemia and hyperglycaemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time blood glucose readings enabling users to manage their control pro-actively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer-term control.Methods: we introduce explainable machine learning to make predictions of hypoglycaemia (&lt;70mg/dL) and hyperglycaemia (&gt;270mg/dL) 60 minutes ahead-of-time. We train our models using CGM data from 153 people living with T1D in the CITY survey totalling over 28000 days of usage, which we summarise into (short-term, medium-term, and long-term) blood glucose features along with demographic information. We use machine learning explanations (SHAP) to identify which features have been most important in predicting risk per user.Results: machine learning models (XGBoost) show excellent performance at predicting hypoglycaemia (AUROC: 0.998) and hyperglycaemia (AUROC: 0.989) in comparison to a baseline heuristic and logistic regression model.Conclusions: maximising model performance for blood glucose risk prediction and management is crucial to reduce the burden of alarm-fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison to baseline models. SHAP helps identify what about a CGM user’s blood glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications

Syndrome of inappropriate secretion of anti-diuretic hormone due to hypothalamic hamartoma: use of tolvaptan

Objectives: hypothalamic hamartoma (HH) typically presents with gonadotrophin-dependent precocious puberty and/or seizures. Other endocrine disturbances are rare. We describe an infant with syndrome of inappropriate secretion of anti-diuretic hormone (SIADH) and a HH.Case presentation: a 6-week-old infant presented with seizures and life-threatening hyponatremia. A HH was identified on magnetic resonance imaging. Clinical examination and biochemistry were consistent with SIADH, and serum copeptin was high during hyponatremia, further supporting this diagnosis. Tolvaptan was effective in normalizing plasma sodium and enabling liberalization of fluids to ensure sufficient nutritional intake and weight gain and manage hunger.Conclusions: hyponatremia due to SIADH is novel at presentation of a HH, and can be challenging to diagnose and manage. Successful management of hyponatremia in this case was achieved using tolvaptan.</p