Despite an inadequate filling of prescriptions among diabetic patients has been a major public health problem, little is known about the interventions effect on it. 

The aim of this systematic review and meta-analysis (SRMA) was to investigate the effectiveness of the various public health interventions on primary and secondary medication adherence among T2DM patients. 
Methods: Searching was done from the major databases; Cochrane Library, Medline/PubMed, EBSCOhost, and SCOPUS. Hand search was made to find grey literature. Articles focused on interventions to enhance primary and secondary medication among Type 2 diabetes mellitus patients were included. After screening and checking eligibility, the methodological quality was assessed. Secondary medication adherence was synthesized descriptively due to measurement and definitions variations across the studies. Finally, meta-analysis was made using fixed effects model for primary medication adherence.

Effectiveness of Public Health Interventions on Improving Prescription Redemptions and Medication Adherence Among Type 2 Diabetes Mellitus Patients: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Introduction: Nowadays, with the rapid development of the computer science, numerical methods have been widely used during all the design stages of an electrical machine, such as the electromagnetic design, mechanical structure optimization, thermal estimation etc. We may select the main dimension of the motor, according to the existing electromagnetic design scheme of the induction motor with the same power level. This approach is suitable for the conventional induction motor design, because the main performance is highly determined by the main dimension [1]. However, different from traditional induction motors, the water friction losses of wet submersible motors are much greater than the wind mill losses of air-cooled induction motors. As shown in Fig 1, unlike traditional industrial motors, submersible motors have a larger ratio of diameter to shaft Length The water wear of the water-filled submersible motor accounts for a large proportion of the overall losses, so how to reduce the water wear of the submersible motor has become the key to improving the efficiency of the submersible motor. Many scholars have put forward the numerical calculation method of water wear of water-filled submersible motor, which are shown in Formula (1)-(4) in Figure 2 [2-3]. It can be seen that the main factors affecting the water wear of submersible motors are the surface roughness of the rotor, rotor speed, outer diameter of the rotor and motor shaft length. The literature [4] verified the influence of surface roughness on water wear. For a submersible motor with a given output power, the speed and surface roughness are constant. In this paper, the diameter and shaft length ratio of the motor are optimized to achieve the purpose of reducing the water wear in the design stage. Optimization Method: It can be known from the main relation formula of AC motor that the motor power with constant effective volume of rotor is also approximate. Therefore, keeping the effective volume of the rotor constant, reducing the outer diameter of the rotor and increasing the shaft length can ensure the output power and reduce the water wear of the motor. However, reducing the water wear through the above methods will also increase the electrical losses of the motor. In this paper, the electrical losses of a series of size motors are solved by electromagnetic simulation, and the curve of electrical losses as a function of the rotor outer diameter is fitted by the cubic spline interpolation. A new curve is obtained by adding the water wear and electrical losses of the motor, and the minimum of the curve is the optimal rotor outer diameter. Results: As shown at point M in Fig.2, when the rotor outer diameter is 0.183 m, the sum of electrical losses and water wear of the motor are the minimum. Compared with the loss when the outer diameter of the rotor is 0.225, the loss is reduced by 360 W. This method realizes the optimization of the diameter-axle-length ratio of the submersible motor and reduces the water wear of the submersible motor in the design stage. Detailed results and discussion will be given in the full text.

**References:**

[1] J. Pyrhonen, T. Jokinen, and V. Hrabovcova. Design of Rotating Electrical Machines, 2nd ed. Hoboken, NJ, USA; wiley, (2014). [2] S. Li and S. Liu, Pump Technol., vol. 33, pp. 21–25(1988). [3] H. Liu, M. Tan, and S. Yuan, Trans. Chin. Soc. Agricult. Eng., vol. 22, pp. 107–109(2006). [4] X. He , W. Jiao , C. Wang , et al. IEEE Access, vol. 7, pp. 105331-105341(2019).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/4e584412cd93d4f18a9970cc97483c8a.jpg)
Fig. 1 A submersible motor with large ratio of diameter to shaft length.

![](https://assets.underline.io/uploads/markdown_image/1/image/ec1237c7417ea9ffc06e457d88d0505b.jpg)
Fig. 2 Losses at different rotor outer diameters.

 Efficiency Improvement for Submersible Motors by Optimizing the Ratio of Diameter to Shaft Length

Shape-anisotropy magnetic tunnel junction (MTJ) [1], having a cylindrical free layer, is promising for deeply scaled spin-transfer torque magnetoresistive random access memory (STT-MRAM). In shape-anisotropy MTJs, positively utilizing the shape anisotropy as well as the interfacial anisotropy allows one to reduce the MTJ diameter D below 10 nm while simultaneously keeping the thermal stability factor Δ high and capability of STT switching [1-4]. Of particular interest in the shape-anisotropy MTJs is the magnetization reversal mode, which is critical in quantifying Δ with high accuracy. In the conventional interfacial-anisotropy MTJs with such a small size (D &lt; 30 nm), magnetization dynamics of the free layer can be reasonably described by a single-domain (coherent reversal) model. On the other hand, in shape-anisotropy MTJs, micromagnetic simulation in previous studies suggests that magnetization reversal mode depends not only on D but also on the thickness t; the MTJs with too-thick a free layer (t &gt; ~30 nm) switch incoherently, imposing an upper limit of Δ and also causing a switching delay [2,5]. In this study, we experimentally study the magnetization reversal mode in shape-anisotropy MTJs with various free layer thicknesses and diameters to reveal the crossover of coherent/incoherent reversal. A stack structure of, from the substrate side, Ta (5)/Ru (10)/Ta (15)/synthetic ferrimagnetic reference layer/MgO/(Co0.25Fe0.75)75B25 (t = 15, 30, and 50)/MgO/Ru (5) is deposited by dc/rf magnetron sputtering on a thermally oxidized Si substrate. The numbers in parentheses are nominal thicknesses in nm and t represents the free-layer CoFeB layer thickness. For some samples with t = 50 nm, Ta (5) capping layer is used instead of MgO, which is confirmed to have little impact on the findings described below. The stacks are processed into circular MTJs with various D by electron beam lithography, reactive ion etching, and multistep Ar ion milling [1]. To study the magnetization reversal mode, we evaluate astroid curves by measuring the magnetic-field-angle dependence of switching fields [6,7]. We also investigate the thickness range in which the coherent reversal model is applicable by comparing Δ values experimentally determined under an assumption of the coherent reversal and those expected from the calculation. Figure 1 shows the magnetic-field-angle dependence of a switching field in a cartesian coordinate, the so-called astroid curve, for the shape-anisotropy MTJs with t of 15 nm and 50 nm. The solid curve is the best fit based on the coherent reversal model as used in [6]. As can be seen, the fitting degree is different for each t. For t = 15 nm, the experimental result is well explained by the fit, indicating a coherent reversal, which is consistent with the previous result on a FeB-based shape-anisotropy MTJ with the same free-layer thickness [5]. On the other hand, for the t = 50 nm case, the fit is poor; the switching field along the easy-axis (μ0Hx = 0 mT, where μ0 is the permeability in vacuum) is smaller compared with the other angles (|μ0Hx| &gt; 0). Such a distortion is also seen for t = 30 nm with D &gt; ~15 nm. Similar behavior was reported in a Py nanowire, and was explained by a buckling mode [9]. The similarity suggests that magnetic moments in the shape-anisotropy MTJs with a thicker and larger-diameter free layer is reversed incoherently via an oscillatory buckling along the thickness direction. Next, we measure the switching probability using a pulsed magnetic field and evaluate Δ with a coherent reversal model [10]. The obtained Δ is plotted as a function of D in Fig. 2. The solid curves represent the calculation based on the coherent reversal model, where the whole volume contributes to the energy barrier. For D &lt; ~15 nm, the measured Δ value agrees well with the calculated one, whereas D &gt; ~15 nm is much smaller, backing up interpretation of the difference observed in the astroid-curve measurement as crossover of the magnetization reversal mode. In summary, we investigate the magnetization reversal mode and thermal stability factor in shape-anisotropy MTJs with various free-layer thicknesses and diameters. We reveal that the magnetization reversal in the MTJs with thicker and larger-diameter free layer proceeds incoherently, affecting the retention property. This study provides a key understanding of the magnetization reversal in the shape-anisotropy MTJs. This work was supported in part by JST-OPERA, JSPS KAKENHI (JP19J12926 and JP19K04486), and DIARE. J.I. and K.W. acknowledge financial support from GP-Spin and JST-OPERA.

**References:**

[1] K. Watanabe et al., Nature Commun. 9, 663 (2018). [2] N. Perrissin et al., Nanoscale 10 12187 (2018). [3] J. Igarashi et al., Appl. Phys. Lett., to be published (2021). [4] B. Jinnai et al., IEEE IEDM, 24.6.1 (2020). [5] K. Watanabe et al., International Conference on Magnetism 2018, F6-01 (2018). [6] N. Kikuchi et al., IEEE Trans. Magn. 41, 3613 (2005). [7] R. Dittrich et al., J. Appl. Phys. 97, 10J705 (2005). [8] J. Igarashi et al., Appl. Phys. Lett. 111, 132407 (2017). [9] A. B. Oliveria et al., Phys. Rev. B 78, 024423 (2008). [10] Z. Li et al., Phys. Rev. B 69, 134416 (2004).

**Images:**

![](https://assets.underline.io/uploads/markdown_image/1/image/09cef5af19c31a24332dd558ca93eedc.jpg)
Figure 1 Experimental results of magnetic-field-angle dependence of switching field in the shape-anisotropy MTJs for the thickness of (a) 15 nm and (b) 50 nm with MgO capping layer. The lines correspond to the best fitting based on the coherent reversal model.

![](https://assets.underline.io/uploads/markdown_image/1/image/7af8e6f3b37af4ec0b1579568cbc20eb.jpg)
Figure 2. Thermal stability factor Δ as a function of D in the shape-anisotropy MTJs for the thickness of 15 [4], 30, and 50 nm. For the samples with t = 50 nm, Ta (5) capping layer is used. The curves correspond to the calculation based on the coherent reversal model.

 Crossover of magnetization reversal mode with thickness and diameter in shape-anisotropy magnetic tunnel junctions

The disproportionate increase in cases of SARS-CoV-2 virus causing COVID-19 in communities of color across the U.S has sparked conversations on the effect that the deadly virus has on communities of color and the lingering question of why. COVID-19 was first reported in Wuhan, China in December 2019 and has subsequently spread worldwide thus, called a pandemic by the World Health Organization. Current data shows that communities of color have been impacted at a much higher rate than other ethnicities. This cohort study discusses why higher rates of diabetes among communities of color cause disproportionately higher rates of transmission and mortality as a result of COVID-19 and examines the mechanisms of diabetes that cause disproportionately higher rates of transmission and mortality as a result of COVID-19. By evaluating scientific evidence and physical data, our research findings show that the transmission and mortality as a result of COVID-19 among communities of color occur at a higher rate compared to other demographic groups due to underlying health conditions. In 2018, it was reported by the CDC that 11.7 percent of African Americans and 12.5 percent of Latinx people were diagnosed with diabetes. This research also indicates that people of color, specifically Latinx and African Americans, are disproportionately affected at higher rates by COVID-19 due to higher rates of diabetes compounded by medication that is commonly taken by diabetes patients, such as ACE inhibitors and ARBs. Both of these types of medications can potentially upregulate the ACE-2 receptor, which is the entryway for SARS-CoV-2 into the body. As a result, patients with diabetes have a higher susceptibility to severe SARS-CoV-2 infection, resulting in worse outcomes in communities of color. This is, however, contradicting speculation that these types of medications can be a potential treatment for COVID-19. Further, this research verifies the impact that socioeconomic factors have on minoritized people and how this may predispose them to diabetes and other underlying health conditions. In Dekalb County, Georgia, African Americans’ median household income was $50,901 in 2018; while their white peers' income was $95,450. The research study reflects the importance of good overall health and wellness management amongst communities of color, while also highlighting the importance of knowledge of and understanding about medical prescriptions, medications, and the effects that it has on the body.

The Effects of COVID-19 on Communities of Color Due to Diabetes Mellitus and Other Underlying Health Issues

Adipose Tissue – Therapeutic target for Diabetes'

Identifying patient information needs is an important issue for health care services and implementation of patient-centered care. A relevant number of people with diabetes mellitus experience a need for information during the course of the disease. Health-related online forums are a promising option for researching relevant information needs closely related to everyday life. In this paper, we present a novel data corpus comprising 4,664 contributions from an online diabetes forum in German language. Two annotation tasks were implemented. First, the contributions were categorised according to whether they contain a diabetes-specific information need or not, which might either be a non diabetes-specific information need or no information need at all, resulting in an agreement of 0.89 (Krippendorff’s α). Moreover, the textual content of diabetes-specific information needs was segmented and labeled using a well-founded definition of health-related information needs, which achieved a promising agreement of 0.82 (Krippendorff’s αu ). We further report a baseline for two sub-tasks of the information extraction system planned for the long term: contribution categorization and segment classification.

Annotating Patient Information Needs in Online Diabetes Forums

Maintaining blood glucose in a target range is essential for people living with Type 1 diabetes in order to avoid excessive periods in hypoglycemia and hyperglycemia which can result in severe complications. Accurate blood glucose prediction can reduce this risk and enhance early interventions to improve diabetes management. However, due to the complex nature of glucose metabolism and the various lifestyle related factors which can disrupt this, diabetes management still remains challenging. In this work we propose a novel deep learning model to predict future BG levels based on the historical continuous glucose monitoring measurements, meal ingestion, and insulin delivery. We adopt a modified architecture of the generative adversarial network that comprises of a generator and a discriminator. The generator computes the BG predictions by a recurrent neural network with gated recurrent units, and the auxiliary discriminator employs a one-dimensional convolutional neural network to distinguish between the predictive and real BG values. Two modules are trained in an adversarial process with a combination of loss. The experiments were conducted using the OhioT1DM dataset that contains the data of six T1D contributors over 40 days. The proposed algorithm achieves an average root mean square error (RMSE) of 18.34 ± 0.17 mg/dL with a mean absolute error (MAE) of 13.37 ± 0.18 mg/dL for the 30-minute prediction horizon (PH) and an average RMSE of 32.31 ± 0.46 mg/dL with a MAE of 24.20 ± 0.42 for the 60-minute PH. The results are compared for clinical relevance using the Clarke error grid which confirms the promising performance of the proposed model.

Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks

"An essential part of this work is to provide a data-driven
model for predicting blood glucose levels that will help to warn the
patient about a potential hypo- or hyper-glycemic event in an easyto-
manage and discreet way."

A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes

Control of blood glucose (BG) levels is essential for diabetes management, especially for long term health improvement. Predicting both hypoglycemic events (BG < 70 mg/dl) and hyperglycemic events (BG > 180 mg/dl) is essential in helping diabetics control their long term health. In this paper, we attempt to forecast future blood glucose levels, as well as analyze the efficiency of detecting both hypoglycemic events and hyperglycemic events. We do so by comparing Auto-Regressive Integrated Moving-Average, Vector Auto-Regression, Kalman Filter, Unscented Kalman Filter, Ordinary Least Squares, Support Vector Machines, Random Forests, Gradient Boosted Trees, XGBoosted Trees, Adaptive Neuro-Fuzzy Inference System (ANFIS), and Multi-Layer Perceptron in terms of Root Mean Squared Error, Mean Absolute Error, Coefficient of Determination, Matthews Correlation Coefficient, and Clarke Error Grid to compare their effectiveness in predicting future blood glucose levels, as well as predicting both hypoglycemic and hyperglycemic events.

Comparison of Forecasting Algorithms for Type 1 Diabetic Glucose Prediction on 30 and 60-Minute Prediction Horizons

People with Type 1 Diabetes must take great care to monitor their blood glucose levels and take actions to keep them from getting either too high or too low. Having a meal will raise blood glucose levels; however, the number of carbohydrates that should be consumed to reach a desired blood glucose level depends on the recent history of several factors, such as boluses and other meals.  We demonstrated the feasibility of a generic neural architecture for making recommendations for the number of carbs a meal should consist of in order to reach a specified target blood glucose level.

A General Neural Architecture for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Managment

For people with type 1 diabetes (T1D), the beta cells in pancreas are destroyed and can not produce insulin which is the only hormone that response to high glucose concentrations. So, people with T1D must administer exogenous insulin to maintain their BGC within an euglycemic range. Hyperglycemia (high BGC) and hypoglycemia (low BGC) can occur because of poor BGC management. Using recursively identified models to predict the future BGC values opens novel possibilities for improving the BGC regulation performance by adjusting the dose of insulin infusion, taking rescue carbohydrates, or both. In this paper, a latent variable (LV) based multivariable statistical modeling approach is applied to model BGC dynamics and forecast future BGCs. The model is evaluated with the Ohio T1DM dataset that contains BGCs from continuous glucose monitoring (CGM) sensors, basal and bolus insulin information from insulin pump, and additional information from wristbands or reported by the subject. The average RMSE of 6 subjects is 19.37 mg/dL for 30-min-ahead prediction and 32.59 mg/dL for 60-min-ahead prediction. The results indicate that the LV-based model can predict future BGC values with high prediction accuracy.

Prediction of Glucose Levels for People with Type 1 Diabetes using Latent Variable based Model

"The management of type 1 diabetes mellitus (T1DM) is a burdensome life-long task. In fact, T1DM individuals are request to perform every day tens of actions to adapt the insulin therapy, aimed at maintaining the blood glucose (BG) concentration as much as possible into a safe range coping with the day-to-day variability of their life style. The recent availability of continuous glucose monitoring (CGM) devices and other low-cost wearable sensors to track important vital and activity signals, is stimulating the development of decision support systems to lower this burden. Modern deep learning models, trained using rich amount of information, are a suitable and effective instrument for such purpose, especially if used to predict future BG values. However, the high accuracy of deep learning approaches is often obtained at the expense of less interpretability.

To surpass this limit, in this work we propose a new deep learning method for BG prediction based on a personalized bidirectional long short-term memory (LSTM) equipped with a tool that enables its interpretability. The OhioT1DM Dataset was used to develop a model targeting future BG at 30 and 60 minute prediction horizons (PH). The accuracy of model predictions was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the time gained (TG) to anticipate the actual glucose concentration.

The obtained results show fairly good prediction accuracy (for PH = 30/60 min): RMSE = 20.20/34.19 mg/dl, MAE = 14.74/25.98 mg/dl, and TG = 9.17/18.33 min.
Moreover, we showed, in a representative case, that our algorithm is able to preserve the physiological meaning of the considered inputs. 

In conclusion, we built a model able to provide reliable glucose performance ensuring the interpretability of its output. Future work will assess model performance against other competitive strategies. "

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Effectiveness of Public Health Interventions on Improving Prescription Redemptions and Medication Adherence Among Type 2 Diabetes Mellitus Patients: Systematic Review and Meta-Analysis of Randomized Controlled Trials

Efficiency Improvement for Submersible Motors by Optimizing the Ratio of Diameter to Shaft Length

Crossover of magnetization reversal mode with thickness and diameter in shape-anisotropy magnetic tunnel junctions

The Effects of COVID-19 on Communities of Color Due to Diabetes Mellitus and Other Underlying Health Issues

Adipose Tissue – Therapeutic target for Diabetes'

Annotating Patient Information Needs in Online Diabetes Forums

Blood Glucose Prediction for Type 1 Diabetes Using Generative Adversarial Networks

A Deep Learning Approach for Blood Glucose Prediction of Type 1 Diabetes

Comparison of Forecasting Algorithms for Type 1 Diabetic Glucose Prediction on 30 and 60-Minute Prediction Horizons

A General Neural Architecture for Carbohydrate and Bolus Recommendations in Type 1 Diabetes Managment

Prediction of Glucose Levels for People with Type 1 Diabetes using Latent Variable based Model

A Personalized and Interpretable Deep Learning Based Approach to Predict Blood Glucose Concentration inType 1 Diabetes

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