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With the development of the coal mine industry, the treatment of domestic sewage in coal mines has become an essential topic of environmental protection. Traditional sewage treatment methods have problems such as low efficiency and complicated management, and it is challenging to meet the increasingly strict environmental protection requirements. Therefore, the research of intelligent dosing and control systems becomes necessary. Based on the STM32F303 microcontroller, this study designs and implements an intelligent dosing and control analysis system for coal mine domestic sewage. The system monitors critical parameters such as pH value, turbidity, and dissolved oxygen in sewage in real time and adopts a fuzzy control algorithm and Proportional-Integral-Derivative Control Algorithm (PID) control strategy to realize intelligent adjustment of the dosing process. Through experimental verification, the system can effectively reduce Chemical Oxygen Demand (COD) and Biochemical Oxygen Demand (BOD) in sewage under different pollution load conditions, improve treatment efficiency, and ensure the sewage effluent quality meets the national discharge standard. The experimental data show that the control error of the system is less than ±2%, the dosing accuracy is more than 10% higher than that of the traditional system, the degree of automation is significantly improved, and the operation is convenient.

Objective: Suboptimal insulin dosing in type 1 diabetes (T1D) is frequently associated with time-varying insulin requirements driven by various psycho-behavioral and physiological factors influencing insulin sensitivity (IS). Among these, physical activity has been widely recognized as a trigger of altered IS both during and following the exercise effort, but limited indication is available for the management of structured and (even more) unstructured activity in T1D. In this work, we present two methods to inform insulin dosing with biosignals from wearable sensors to improve glycemic control in individuals with T1D. Research Design and Methods: Continuous glucose monitors (CGM) and activity trackers are leveraged by the methods. The first method uses CGM records to estimate IS in real time and adjust the insulin dose according to a person’s insulin needs; the second method uses step count data to inform the bolus calculation with the residual glucose-lowering effects of recently performed (structured or unstructured) physical activity. The methods were tested in silico within the University of Virginia/Padova T1D Simulator. A standard bolus calculator and the proposed “smart” systems were deployed in the control of one meal in presence of increased/decreased IS (Study 1) and following a 1-hour exercise bout (Study 2). Postprandial glycemic control was assessed in terms of time spent in different glycemic ranges and low/high blood glucose indices (LBGI/HBGI), and compared between the dosing strategies. Results: In Study 1, the CGM-informed system allowed to reduce exposure to hypoglycemia in presence of increased IS (percent time < 70 mg/dL: 6.1% versus 9.9%; LBGI: 1.9 versus 3.2) and exposure to hyperglycemia in presence of decreased IS (percent time > 180 mg/dL: 14.6% versus 18.3%; HBGI: 3.0 versus 3.9), tending toward optimal control. In Study 2, the step count-informed system allowed to reduce hypoglycemia (percent time < 70 mg/dL: 3.9% versus 13.4%; LBGI: 1.7 versus 3.2) at the cost of a minor increase in exposure to hyperglycemia (percent time > 180 mg/dL: 11.9% versus 7.5%; HBGI: 2.4 versus 1.5). Conclusions: We presented and validated in silico two methods for the smart dosing of prandial insulin in T1D. If seen within an ensemble, the two algorithms provide alternatives to individuals with T1D for improving insulin dosing accommodating a large variety of treatment options. Future work will be devoted to test the safety and efficacy of the methods in free-living conditions.

Intravenous (IV) infusion therapy allows the infusion fluid to be inserted directly into the patient’s vein. It is used to place medications directly into the bloodstream or for blood transfusions. The probability that a hospitalized patient will receive some kind of infusion therapy, intravenously, is 60–80%. The paper presents a smart IV infusion dosing system for detection, signaling, and monitoring of liquid in an IV bottle at a remote location. It consists of (i) the sensing and computation layer—a system for detection and signaling of fluid levels in the IV bottle and a system for regulation and closing of infusion flow, (ii) the communication layer—a wireless exchange of information between the hardware part of the system and the client, and (iii) the user layer—monitoring and visualization of IV therapy reception at a remote location in real time. All layers are modular, allowing upgrades of the entire system. The proposed system alerts medical staff to continuous and timely changes of IV bottles, which can have positive effects on increasing the success of IV therapy, especially in oncology patients. The prescribed drip time of IV chemotherapy for the full effect of cytostatics should be imperative.

The global issue of water scarcity is escalating due to urbanization and increased demand. This paper proposes a machine learning (ML) regression-based model for automatic coagulant dosing control in smart water purification plants (SWPPs).1 The model uses random forest (RF), light gradient boosting machine (LGBM), extreme gradient boosting (XGB), and k-nearest neighbors (KNN) algorithms. Performance metrics include MAE, MSE, RMSE, MAPE, and R2. The RF algorithm showed superior performance, with MAE of 0.005, MSE of 0.002, RMSE of 0.05, and MAPE of 0.000 for anion-poly aluminum chloride dosing, and MAE of 0.007, MSE of 0.00, RMSE of 0.02, and MAPE of 0.000 for Polymax dosing. The RF model's performance is due to its robust handling of large datasets and ensemble learning approach. Limitations include testing only two coagulants and reliance on historical data. These findings suggest integrating advanced water management, energy systems, and facility management to make SWPPs feasible and efficient, establishing a foundation for future applications of ML in chemical processes.

Mobile health (mHealth) is a rapidly emerging market, which has been implemented in a variety of different disease areas. Tuberculosis remains one of the most common causes of death from an infectious disease worldwide, and mHealth apps offer an important contribution to the improvement of tuberculosis treatment. In particular, apps facilitating dose individualization, adherence monitoring, or provision of information and education about the disease can be powerful tools to prevent the development of drug-resistant tuberculosis or disease relapse. The aim of this review was to identify, describe, and categorize mobile and Web-based apps related to tuberculosis that are currently available. PubMed, Google Play Store, Apple Store, Amazon, and Google were searched between February and July 2019 using a combination of 20 keywords. Apps were included in the analysis if they focused on tuberculosis, and were excluded if they were related to other disease areas or if they were games unrelated to tuberculosis. All apps matching the inclusion criteria were classified into the following five categories: adherence monitoring, individualized dosing, eLearning/information, diagnosis, and others. The included apps were then summarized and described based on publicly available information using 12 characteristics. Fifty-five mHealth apps met the inclusion criteria and were included in this analysis. Of the 55 apps, 8 (15%) were intended to monitor patients' adherence, 6 (11%) were designed for dosage adjustment, 29 (53%) were designed for eLearning/information, 3 (6%) were focused on tuberculosis diagnosis, and 9 (16%) were related to other purposes. The number of mHealth apps related to tuberculosis has increased during the past 3 years. Although some of the discovered apps seem promising, many were found to contain errors or provided harmful or wrong information. Moreover, the majority of mHealth apps currently on the market are focused on making information about tuberculosis available (29/55, 53%). Thus, this review highlights a need for new, high-quality mHealth apps supporting tuberculosis treatment, especially those supporting individualized optimized treatment through model-informed precision dosing and video observed treatment.

Purpose of this Review Colorectal cancer is the third most common cancer in the USA. Colonoscopy is considered the gold standard for colorectal cancer screening and can offer both diagnosis and therapy. The bowel preparation remains a significant barrier for patients who need to undergo colonoscopy and is often cited as the most dreaded aspect of the colonoscopy process. Inadequate bowel preparations still occur in 10–25% of colonoscopies, and this in turn can lead to increased procedural times, lower cecal intubation rates, and shorter interval between colonoscopies. From a quality standpoint, it is imperative that we do what we can to decrease the rate of inadequate bowel preparations. This review will focus on recent data regarding bowel preparation and offers a glimpse into what may be coming in the future. Recent Findings Recent advances in the field have been made to improve tolerability of bowel preparations and allow for more adequate colonoscopies. Newer, lower volume, flavored preparations, the use of adjuncts, and using split-dose preparations all can help with tolerability, compliance, and, in turn, preparation quality. Edible bowel preparations may become available in the near future. Early data on the use of artificial intelligence for assessment of preparation quality has been promising. Additionally, utilization of smartphone technology for education prior to the bowel preparation has also been shown to improve the adequacy of bowel preparations. Conclusions Ongoing efforts to improve the tolerability and palatability of colonoscopy bowel preparations are important from a quality improvement standpoint to ensure the adequacy of colonoscopy. Incorporating patient-specific factors and comorbidities is also an essential aspect of improving the quality of bowel preparation. Leveraging technology to better communicate with and educate patients on the bowel preparation process is likely to play a larger role in the coming years.

Healthcare is increasingly fragmented, resulting in escalating costs, patient dissatisfaction, and sometimes adverse clinical outcomes. Strategies to decrease healthcare fragmentation are therefore attractive from payer and patient perspectives. In this commentary, a patient-centered smart phone application called Virtual Twin for Healthcare Management (VTHM) is proposed, including its organizational layout, basic functionality, and potential clinical applications. The platform features a virtual twin hub that displays the body and its health data. This is a physiologically based human model that is “virtualized” for the patient based on their unique genetic, molecular, physiological, and disease characteristics. The spokes of the system are a full service and interoperable electronic-health record, accessible to healthcare providers with permission on any device with internet access. Theoretical case studies based on real scenarios are presented to show how VTHM could potentially improve patient care and clinical efficiency. Challenges that must be overcome to turn VTHM into reality are also briefly outlined. Notably, the VTHM platform is designed to operationalize current and future precision medicine initiatives, such as access to molecular diagnostic results, pharmacogenomics-guided prescribing, and model-informed precision dosing.

The aim of this study was to determine whether somatotype influences the risk of hydroxychloroquine (HC) retinopathy (HCR) and whether dosing by real body weight (RBW), ideal body weight (IBW), or the lesser of these better predicts the risk of HCR. A total of 565 patients taking HC for whom height and weight were recorded and a sensitive ancillary testing modality was used including 10-2 visual fields, spectral domain optical coherence tomography, fundus autofluorescence imaging, and multifocal electroretinography were enrolled. Body mass index (BMI) was compared for patients without and with HCR. Logistic regression models of age, cumulative dose, and daily dosing based on RBW, IBW, or lesser of these were compared. Area under the curve (AUC) of receiver operating characteristic plots was used to assess the diagnostic accuracy of RBW, IBW, and lesser of these guidelines for safe dosing. Probability plots for the risk of retinopathy versus BMI were compared for the different recommended guidelines on safe dosing. A total of 41 patients had HCR. The median BMI was 27.6 (interquartile range [IQR] 24.3, 32.6) and 24.0 (IQR 21.0, 31.6) for patients without and with HCR ( =0.0102), respectively. AUC for univariate receiver operating characteristic plots of retinopathy versus dosing by RBW, IBW, and lesser of these was 0.71, 0.72, and 0.76, respectively. AUC for multivariate receiver operating characteristic plots of retinopathy versus models incorporating gender, age, cumulative dose, and BMI and differing by including dosing by RBW, IBW, and lesser of these was 0.82, 0.82, and 0.83, respectively. For all of the multivariate logistic models, the risk of retinopathy was higher for lower BMIs. Short, asthenic women are at higher risk for HCR. The 2011 American Academy of Ophthalmology (AAO) guidelines are safer for short, obese women. The 2016 AAO guidelines are safer for short, asthenic patients. Choosing daily dosing based on the lesser of the RBW and IBW guidelines is safer for all patients.

In this paper, the development of a smart scalable system for liquid supply based on high-precision peristaltic pumps is described. The architecture of software and hardware for the proposed system is considered. This liquid supply system can be used for mixed and layered cocktail preparation in public catering establishments, such as bars, as well as for home use. Due to the flexibility and scalability of the system, it is possible to apply it in various branches of human activity, where fine dosing of liquids is required, e.g., for beverage mixing, cooking, health and medical applications. By using open architecture and software, this system can be built in a smart home environment. The cross-platform control software and an embedded Bluetooth module allow using the developed setup in various use case scenarios. The result of the project is a DIY-kit, capable of mixing 6 to 32 different liquids in specified proportions and the programmable sequence.