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Chronic kidney disease (CKD) is a complication of diabetes that affects circulating drug concentrations and elimination of drugs from the body. Multiple drugs may be prescribed for treatment of diabetes and co-morbidities, and CKD complicates the pharmacotherapy selection and dosing regimen. Characterizing variations in renal drug clearance using models requires large clinical datasets that are costly and time-consuming to collect. We propose a flexible approach to incorporate impaired renal clearance in pharmacokinetic (PK) models using descriptive statistics and secondary data with mechanistic models and PK first principles. Probability density functions were generated for various drug clearance mechanisms based on the degree of renal impairment and used to estimate the total clearance starting from glomerular filtration for metformin (MET) and dapagliflozin (DAPA). These estimates were integrated with PK models of MET and DAPA for simulations. MET renal clearance decreased proportionally with a reduction in estimated glomerular filtration rate (eGFR) and estimated net tubular transport rates. DAPA total clearance varied little with renal impairment and decreased proportionally to reported non-renal clearance rates. Net tubular transport rates were negative to partially account for low renal clearance compared with eGFR. The estimated clearance values and trends were consistent with MET and DAPA PK characteristics in the literature. Dose adjustment based on reduced clearance levels estimated correspondingly lower doses for MET and DAPA while maintaining desired dose exposure. Estimation of drug clearance rates using descriptive statistics and secondary data with mechanistic models and PK first principles improves modeling of CKD in diabetes and can guide treatment selection.

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The ethanol oxidation process in fuel cells is most efficient when conducted by platinum based catalysts. Our research team endeavored to find affordable and efficient catalysts, synthesizing catalysts based on metal oxides of nickel and molybdenum in the form of NiO/MoO2and NiO/MoO2hybridized with activated carbon obtained from the wheat husk (ACWH) through a hydrothermal method. After precise physical characterization, the capability of these catalysts in the ethanol oxidation process was measured through electrochemical analyses in an alkaline environment. The presence of ACWH in the catalyst structure significantly improves the active surface and electrocatalytic activity. NiO/MoO2/ACWH with a current density of 16 mA cm-2at a peak potential of 0.55 V and 93% cyclic stability after 5000 alternate CV cycles, can be an appealing, relatively efficient, and stable option in ethanol oxidation.

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The potential of metal oxides in electrochemical energy storage encouraged our research team to synthesize molybdenum oxide/tungsten oxide nanocomposites (MoO3/WO3) and their hybrid with reduced graphene oxide (rGO), in the form of MoO3/WO3/rGO as a substrate with relatively good electrical conductivity and suitable electrochemical active surface. In this context, we presented the electrochemical behavior of these nanocomposites as an electrode for supercapacitors and as a catalyst in the oxidation process of methanol/ethanol. Our engineered samples were characterized by X-ray diffraction pattern and scanning electron microscopy. As a result, MoO3/WO3 and MoO3/WO3/rGO indicated specific capacitances of 452 and 583 F/g and stability of 88.9% and 92.6% after 2000 consecutive GCD cycles, respectively. Also, MoO3/WO3 and MoO3/WO3/rGO nanocatalysts showed oxidation current densities of 117 and 170 mA/cm2 at scan rate of 50 mV/s, and stability of 71 and 89%, respectively in chronoamperometry analysis, in the MOR process. Interestingly, in the ethanol oxidation process, corresponding oxidation current densities of 42 and 106 mA/cm2 and stability values of 70 and 82% were achieved. MoO3/WO3 and MoO3/WO3/rGO can be attractive options paving the way for prospective alcohol-based fuel cells.

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A clear understanding of soil-machine interaction is utilised in many areas, such as rational design and performance optimisation of soil-engaging tools/implements. This research developed a new soil bin to investigate the interaction between soil-narrow tines and soil failure. The new soil bin consisted of a chassis, a bin, a variable speed carriage and a bolt and nut type power transmission system between the motor and carriage. The design criteria of the new soil bin were based on the drive system's immediate acceleration of the carriage unit. A curved chisel tine was tested to evaluate the system's capabilities. Three parameters, including lift height, failure side area and forward failure distance, were investigated at two forward speeds of 0.037 and 0.05 m/s and three rake angles of 5°, 10° and 20°. An analysis of variance (ANOVA) test revealed the effect of rake angle on the lift height, failure side area, and forward failure distance was significant (P < 0.01). However, the forward speed did not have any significant influence on parameters. Also, the lift height and failure side area increased significantly (P < 0.01) by increasing the rake angle, while the forward failure distance decreased. Regarding soil failure, the results were in harmony with Godwin and Spoor's model. The regression and ANFIS models were developed to predict the output parameters by considering input parameters. The R2 values and ANFIS models were 0.4895, 0.7264, 0.9856, and 0.9999, 1, 1, respectively, for lift height, side area, and forward distance. Therefore, ANFIS approach was more accurate for predicting soil failure parameters.

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BACKGROUND: Hybrid closed-loop control of glucose levels in people with type 1 diabetes mellitus (T1D) is limited by the requirements on users to manually announce physical activity (PA) and meals to the artificial pancreas system. Multivariable automated insulin delivery (mvAID) systems that can handle unannounced PAs and meals without any manual announcements by the user can improve glycemic control by modulating insulin dosing in response to the occurrence and intensity of spontaneous physical activities. METHODS: An mvAID system is developed to supplement the glucose measurements with additional physiological signals from a wristband device, with the signals analyzed using artificial intelligence algorithms to automatically detect the occurrence of PA and estimate its intensity. This additional information gained from the physiological signals enables more proactive insulin dosing adjustments in response to both planned exercise and spontaneous unanticipated physical activities. RESULTS: In silico studies of the mvAID illustrate the safety and efficacy of the system. The mvAID is translated to pilot clinical studies to assess its performance, and the clinical experiments demonstrate an increased time in range and reduced risk of hypoglycemia following unannounced PA and meals. CONCLUSIONS: The mvAID systems can increase the safety and efficacy of insulin delivery in the presence of unannounced physical activities and meals, leading to improved lives and less burden on people with T1D.

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Hemodynamic and intravascular volume monitoring has been utilized and significantly improved thanks to the technology revolution. Goal-Directed Therapy (GDT) derived from this advanced monitoring is beneficial for complex surgeries, and it shifted the medical approaches from static therapy to more personalized functional treatments. Conventional monitoring methods such as blood pressure, heart rate, urinary output, and central venous pressure are commonly used. However, studies have shown these routine parameters often cannot precisely estimate the quality of tissue perfusion. Tissue hypoperfusion and hypoxia play a crucial role in initiating a systemic inflammatory response after prolonged surgeries, resulting in unstable hemodynamic condition of the patients. Several studies reported the importance of GDT in non-cardiac surgeries and there are few reports on cardiac surgeries. However, tissue perfusion and fluid management are more critical in complex and prolonged cardiovascular surgeries to avoid complications such as low cardiac output syndrome and renal or pulmonary dysfunction. Different advanced hemodynamic monitorings have been utilized perioperatively in cardiac surgery to help decision-making on inotrope and fluid management. In this article we present 5 cases of usefulness hemodynamic monitoring in patients who underwent cardiovascular surgeries.

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Background: Believability and thoughts are considered as the pillars of behaviors over time, and anxious feelings are a risk factor for mental disorders, especially during the coronavirus disease (COVID-19) pandemic. This study aimed to explore the thought believability and anxious feelings of COVID-19 among infected and healthy families. Methods: In this hospital-based matched case-control study, health surveillance files of COVID-19 patients were collected from January to June 2021 and were analyzed statistically. In this study, two questionnaires of demographic characteristics and the Believability of Anxious Feelings and Thoughts (BAFT) were used. Data were analyzed using the One-way ANOVA. Results: Of 600 participants, 300 (50%) were PCR-confirmed and 300 (50%) were non-infected. Overall, 163 (54.33%) of infected people were male, 146 (48.67%) single, and 156 (52.00%) government employees. The results showed that the mean scores of physical anxiety (PA) and negative evaluation (NE) in the case group is significantly higher than the control group (P = 0.001); while emotional regulation (ER) in the control group was significantly higher than the case group (P = 0.001). Conclusion: Having high believability to the risks of COVID-19 may be a predictor of preventive behaviors in individuals. Worrying about COVID-19 can increase the perceived risk of a pandemic in societies and consequence, increase the general public health.

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Today, finding low-cost electro-catalysts for methanol and ethanol oxidation with high performance and stability is one of the new research topics. A nanocatalyst based on metal oxides in the form of MnMoO4 was synthesized by a hydrothermal method for methanol (MOR) and ethanol (EOR) oxidation reactions. Adding reduced graphene oxide (rGO) to the catalyst structure improved the electrocatalytic activity of MnMoO4 for the oxidation processes. The crystal structure and morphology of the MnMoO4 and MnMoO4-rGO nanocatalysts were investigated by physical analyses such as scanning electron microscopy and X-ray diffraction. Their abilities for MOR and EOR processes in an alkaline medium were evaluated by performing electrochemical tests such as cyclic voltammetry, chronoamperometry, and electrochemical impedance spectroscopy. MnMoO4-rGO showed oxidation current densities of 60.59 and 25.39 mA/cm2 and peak potentials of 0.62 and 0.67 V in MOR and EOR processes (at a scan rate of 40 mV/s), respectively. Moreover, stabilities of 91.7% in MOR and 88.6% in EOR processes were obtained from the chronoamperometry analysis within 6 h. All these features make MnMoO4-rGO a promising electrochemical catalyst for the oxidation of alcohols.

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Molecular p-dopants designed to undergo electron transfer with organic semiconductors are typically planar molecules with high electron affinity. However, their planarity can promote the formation of ground-state charge transfer complexes with the semiconductor host and results in fractional instead of integer charge transfer, which is highly detrimental to doping efficiency. Here, we show this process can be readily overcome by targeted dopant design exploiting steric hindrance. To this end, we synthesize and characterize the remarkably stable p-dopant 2,2',2''-(cyclopropane-1,2,3-triylidene)tris(2-(perfluorophenyl)acetonitrile) comprising pendant functional groups that sterically shield its central core while retaining high electron affinity. Finally, we demonstrate it outperforms a planar dopant of identical electron affinity and increases the thin film conductivity by up to an order of magnitude. We believe exploiting steric hindrance represents a promising design strategy towards molecular dopants of enhanced doping efficiency.

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The advent of new nanomaterials has resulted in dramatic developments in the field of energy production and storage. Due to their unique structure and properties, transition metal dichalcogenides (TMDs) are the most promising from the list of materials recently introduced in the field. The amazing progress in the use TMDs for energy storage and production inspired us to review the recent research on TMD-based catalysts and electrode materials. In this report, we examine TMDs in a variety of electrochemical batteries and solar cells with special focus on MoS2 as the most studied and used TMD material.

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BACKGROUND: Cold stress is an effective factor in reducing production and injuring fruit trees. Various materials, such as salicylic acid, ascorbic acid, and putrescine, are used to alleviate the damage of abiotic stress. RESULTS: The effect of different treatments of putrescine, salicylic acid, and ascorbic acid on alleviating the damage of frost stress (- 3 °C) to grapes 'Giziluzum' was investigated. Frost stress increased the amount of H2O2, MDA, proline, and MSI. On the other hand, it decreased the concentration of chlorophyll and carotenoids in the leaves. Putrescine, salicylic acid and ascorbic acid significantly increased the activities of catalase, guaiacol peroxidase, ascorbate peroxidase, and superoxide dismutase under frost stress. Following frost stress, the grapes treated with putrescine, salicylic acid, and ascorbic acid showed higher levels of DHA, AsA, and AsA/DHA than the untreated grapes. Our results showed that the treatment with ascorbic acid outperformed the other treatments in adjusting frost stress damages. CONCLUSION: The use of compounds, such as ascorbic ac id, salicylic acid, and putrescine, modulates the effects of frost stress, thereby increasing the antioxidant defense system of cells, reducing its damage, and stabilizing stable cell conditions, so it can be used to reduce frost damage to different grape cultivars.

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Recently, transition metal oxides have been considered for various applications due to their unique properties. We present the synthesis of a three-component catalyst consisting of zirconium oxide (ZrO2), nickel oxide (NiO), and reduced graphene oxide (rGO) in the form of ZrO2/NiO/rGO by a simple one-step hydrothermal method. X-ray powder diffraction (XRD), scanning electron microscope (SEM), and bright-field transmission electron microscopy (BF-TEM) analyses were performed to accurately characterize the catalysts. Cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV) analyses were also carried out to investigate the methanol and ethanol alcohol electrooxidation ability of the synthesized nanocatalysts. Inspired by the good potential of metal oxides in the field of catalysts, especially in fuel-cell anodes, we investigated the capability of this catalyst in the methanol oxidation reaction (MOR) and ethanol oxidation reaction (EOR). After proving the successful synthesis and examining the surface morphology of these materials, detailed electrochemical tests were performed to show the outstanding capability of this new nanocatalyst for use in the anode of alcohol fuel cells. ZrO2/NiO/rGO indicated a current density of 26.6 mA/cm2 at a peak potential of 0.52 V and 99.5% cyclic stability in the MOR and a current density of 17.3 mA/cm2 at a peak potential of 0.52 V and 98.5% cyclic stability in the EOR (at optimal concentration/scan rate 20 mV/s), representing an attractive option for use in the anode of alcoholic fuel cells.

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This paper presents a linear quadratic Gaussian (LQG) controller for controlling the gait of a miniature, foldable quadruped robot with individually actuated and controlled legs (MinIAQ-III). The controller is implemented on a palm-size robot made by folding an acetate sheet. MinIAQ-III has four DC motors for actuation and four rotary sensors for feedback. It is one of the few untethered robots on a miniature scale capable of working with different gaits with the help of its individually-actuated legs and the developed controller. The presented LQG controller controls each leg's positions and rotational speeds by measuring the positions and estimating the rotational speeds, respectively. With the precise gait control on the robot, we demonstrate different gaits inspired by quadrupeds in nature and compare the simulation and experiment results for some of the gaits. An extensive simulation environment developed for robot dynamics helps us to predict the locomotion behavior of the robot in various environments. The match between the simulation and the experiment results shows that the proposed LQG controller can successfully control the miniature robot's gaits. We also conduct a case study that shows the potential to use the simulation to achieve different robot behavior. In a case study, we present our robot performing a prancing similar to horses. We use the simulation environment to find the required motor configuration phases and physical parameters, which can make our robot prance. After finding the parameters in simulation, we replicate the configuration in our robot and observe the robot making the same moves as the simulation.

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This work considers the problem of adaptive prior-informed model predictive control (MPC) formulations that explicitly incorporate prior knowledge in the model development and is robust to missing data in the output measurements. The proposed prediction model is based on a latent variables model to extract glycemic dynamics from highly-correlated data and incorporates prior knowledge of exponential stability to improve the prediction ability. Missing data structures are formulated to enable model predictions when output measurements are missing for short periods of time. Based on the latent variables model, the MPC strategy and adaptive rules are developed to automatically tune the aggressiveness of the MPC. The adaptive prior-knowledge-informed MPC is evaluated with computer simulations for the control of blood glucose concentrations in people with Type 1 diabetes (T1D) using simulated virtual patients. Due to the variability among people with T1D, the hyperparameters of the prior-knowledge-informed model are personalized to individual subjects. The percentage of time spent in the target range is 76.48% when there are no missing data and 76.52% when there are missing data episodes lasting up to 30 mins (6 samples). Incorporating the adaptive rules further improves the percentage of time in target range to 84.58% and 84.88% for cases with no missing data and missing data, respectively. The proposed adaptive prior-informed MPC formulation provides robust, effective, and safe regulation of glucose concentration in T1D despite disturbances and missing measurements.

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BACKGROUND: Predicting carbohydrate intake and physical activity in people with diabetes is crucial for improving blood glucose concentration regulation. Patterns of individual behavior can be detected from historical free-living data to predict meal and exercise times. Data collected in free-living may have missing values and forgotten manual entries. While machine learning (ML) can capture meal and exercise times, missing values, noise, and errors in data can reduce the accuracy of ML algorithms. METHODS: Two recurrent neural networks (RNNs) are developed with original and imputed data sets to assess detection accuracy of meal and exercise events. Continuous glucose monitoring (CGM) data, insulin infused from pump data, and manual meal and exercise entries from free-living data are used to predict meals, exercise, and their concurrent occurrence. They contain missing values of various lengths in time, noise, and outliers. RESULTS: The accuracy of RNN models range from 89.9% to 95.7% for identifying the state of event (meal, exercise, both, or neither) for various users. "No meal or exercise" state is determined with 94.58% accuracy by using the best RNN (long short-term memory [LSTM] with 1D Convolution). Detection accuracy with this RNN is 98.05% for meals, 93.42% for exercise, and 55.56% for concurrent meal-exercise events. CONCLUSIONS: The meal and exercise times detected by the RNN models can be used to warn people for entering meal and exercise information to hybrid closed-loop automated insulin delivery systems. Reliable accuracy for event detection necessitates powerful ML and large data sets. The use of additional sensors and algorithms for detecting these events and their characteristics provides a more accurate alternative.

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The demands for alternative energy have led researchers to find effective electrocatalysts in fuel cells and increase the efficiency of existing materials. This study presents new nanocatalysts based on two binary transition metal oxides (BTMOs) and their hybrid with reduced graphene oxide for methanol oxidation. Characterization of the introduced three-component composite, including cobalt manganese oxide (MnCo2O4), nickel cobalt oxide (NiCo2O4), and reduced graphene oxide (rGO) in the form of MnCo2O4/NiCo2O4/rGO (MNR), was investigated by X-ray diffraction (XRD), scanning electron microscope (SEM), and energy-dispersive X-ray (EDX) analyses. The alcohol oxidation capability of MnCo2O4/NiCo2O4 (MN) and MNR was evaluated in the methanol oxidation reaction (MOR) process. The crucial role of rGO in improving the electrocatalytic properties of catalysts stems from its large active surface area and high electrical conductivity. The alcohol oxidation tests of MN and MNR showed an adequate ability to oxidize methanol. The better performance of MNR was due to the synergistic effect of MnCo2O4/NiCo2O4 and rGO. MN and MNR nanocatalysts, with a maximum current density of 14.58 and 24.76 mA/cm2 and overvoltage of 0.6 and 0.58 V, as well as cyclic stability of 98.3% and 99.7% (at optimal methanol concentration/scan rate of 20 mV/S), respectively, can be promising and inexpensive options in the field of efficient nanocatalysts for use in methanol fuel cell anodes.

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BACKGROUND AND OBJECTIVE: The glucose response to physical activity for a person with type 1 diabetes (T1D) depends upon the intensity and duration of the physical activity, plasma insulin concentrations, and the individual physical fitness level. To accurately model the glycemic response to physical activity, these factors must be considered. METHODS: Several physiological models describing the glycemic response to physical activity are proposed by incorporating model terms proportional to the physical activity intensity and duration describing endogenous glucose production (EGP), glucose utilization, and glucose transfer from the plasma to tissues. Leveraging clinical data of T1D during physical activity, each model fit is assessed. RESULTS: The proposed model with terms accommodating EGP, glucose transfer, and insulin-independent glucose utilization allow for an improved simulation of physical activity glycemic responses with the greatest reduction in model error (mean absolute percentage error: 16.11 ± 4.82 vs. 19.49 ± 5.87, p = 0.002). CONCLUSIONS: The development of a physiologically plausible model with model terms representing each major contributor to glucose metabolism during physical activity can outperform traditional models with physical activity described through glucose utilization alone. This model accurately describes the relation of plasma insulin and physical activity intensity on glucose production and glucose utilization to generate the appropriately increasing, decreasing or stable glucose response for each physical activity condition. The proposed model will enable the in silico evaluation of automated insulin dosing algorithms designed to mitigate the effects of physical activity with the appropriate relationship between the reduction in basal insulin and the corresponding glycemic excursion.

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Recently, the use of metal oxides as inexpensive and efficient catalysts has been considered by researchers. In this work, we introduce a new nanocatalyst including a mixed metal oxide, consisting of manganese oxide, cerium oxide, and reduced graphene oxide (Mn3O4-CeO2-rGO) by the hydrothermal method. The synthesized nanocatalyst was evaluated for the methanol oxidation reaction. The synergetic effect of metal oxides on the surface of rGO was investigated. Mn3O4-CeO2-rGO showed an oxidation current density of 17.7 mA/cm2 in overpotential of 0.51 V and 91% stability after 500 consecutive rounds of cyclic voltammetry. According to these results, the synthesized nanocatalyst can be an attractive and efficient option in the methanol oxidation reaction process.

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We have developed a highly sensitive sensor of ZnFe2O4/reduced graphene oxide (ZnFe2O4/RGO) nanocomposite for electrochemical detection of hydrazine, fabricated by a simple hydrothermal protocol. Subsequently, a screen-printed electrode (SPE) surface was modified with the proposed nanocomposite (ZnFe2O4/RGO/SPE), and revealed an admirable electrocatalytic capacity for hydrazine oxidation. The ZnFe2O4/RGO/SPE sensor could selectively determine micromolar hydrazine concentrations. The as-produced sensor demonstrated excellent ability to detect hydrazine due to the synergistic impacts of the unique electrocatalytic capacity of ZnFe2O4 plus the potent physicochemical features of RGO such as manifold catalytic sites, great area-normalized edge-plane structures, high conductivity, and large surface area. The hydrazine detection using differential pulse voltammetry exhibited a broad linear dynamic range (0.03-610.0 µM) with a low limit of detection (0.01 µM).

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Metal organic frameworks (MOF) are a class of hybrid networks of supramolecular solid materials comprising a large number of inorganic and organic linkers, all bound to metal ions in a well-organized fashion. Zeolitic imidazolate frameworks (ZIFs) are a sub-group of MOFs with imidazole as an organic linker to metals; it is rich in carbon, nitrogen, and transition metals. ZIFs combine the classical zeolite characteristics of thermal and chemical stability with pore-size tunability and the rich topological diversity of MOFs. Due to the energy crisis and the existence of organic solvents that lead to environmental hazards, considerable research efforts have been devoted to devising clean and sustainable synthesis routes for ZIFs to reduce the environmental impact of their preparation. Green chemistry is the key to sustainable development, as it will lead to new solutions to existing problems. Moreover, it will present opportunities for new processes and products and, at its heart, is scientific and technological innovation. The green chemistry approach seeks to redesign the materials that make up the basis of our society and our economy, including the materials that generate, store, and transport our energy, in ways that are benign for humans and the environment and that possess intrinsic sustainability. This study covers the principles of green chemistry as used in designing strategies for synthesizing greener, less toxic ZIFs the consume less energy to produce. First, the necessity of green methods in today's society, their replacement of the usual non-green methods and their benefits are discussed; then, various methods for the green synthesis of ZIF compounds, such as hydrothermally, ionothermally, and by the electrospray technique, are considered. These methods use the least harmful and toxic substances, especially concerning organic solvents, and are also more economical. When a compound is synthesized by a green method, a question arises as to whether these compounds can replace the same compounds as synthesized by non-green methods. For example, is the thermal stability of these compounds (which is one of the most important features of ZIFs) preserved? Therefore, after studying the methods of identifying these compounds, in the last part, there is an in-depth discussion on the various applications of these green-synthesized compounds.