Charge Level Research Articles

Predicting and Monitoring Symptoms in Patients Diagnosed With Depression Using Smartphone Data: Observational Study.

Clinical diagnostic assessments and the outcome monitoring of patients with depression rely predominantly on interviews by professionals and the use of self-report questionnaires. The ubiquity of smartphones and other personal consumer devices has prompted research into the potential of data collected via these devices to serve as digital behavioral markers for indicating the presence and monitoring of the outcome of depression. This paper explores the potential of using behavioral data collected with smartphones to detect and monitor depression symptoms in patients diagnosed with depression. Specifically, it investigates whether this data can accurately classify the presence of depression, as well as monitor the changes in depressive states over time. In a prospective cohort study, we collected smartphone behavioral data for up to 1 year. The study consists of observations from 164 participants, including healthy controls (n=31) and patients diagnosed with various depressive disorders: major depressive disorder (MDD; n=85), MDD with comorbid borderline personality disorder (n=27), and major depressive episodes with bipolar disorder (n=21). Data were labeled based on depression severity using 9-item Patient Health Questionnaire (PHQ-9) scores. We performed statistical analysis and used supervised machine learning on the data to classify the severity of depression and observe changes in the depression state over time. Our correlation analysis revealed 32 behavioral markers associated with the changes in depressive state. Our analysis classified patients who are depressed with an accuracy of 82% (95% CI 80%-84%) and change in the presence of depression with an accuracy of 75% (95% CI 72%-76%). Notably, the most important smartphone features for classifying depression states were screen-off events, battery charge levels, communication patterns, app usage, and location data. Similarly, for predicting changes in depression state, the most important features were related to location, battery level, screen, and accelerometer data patterns. The use of smartphone digital behavioral markers to supplement clinical evaluations may aid in detecting the presence and changes in severity of symptoms of depression, particularly if combined with intermittent use of self-report of symptoms.

Hydrogen fuel cell energy regulators based on Boost DC-DC converters with switched capacitors

A new principle for constructing small-sized capacitor DC-DC regulators that provide energy-efficient conversion and multi-zone energy regulation of hydrogen fuel cells is proposed. It consists in converting the energy of their current into the energy of a constant voltage source with further alternating changes in its low-voltage regulation subranges. It is carried out by discretely changing the number of resonant chains of a step-up capacitor DC-DC converter in combination with a smooth change in the charge level of its first chain using a low-power classic PWM constant voltage regulator. Improving the energy properties of the regulator is achieved by multi-zone regulation and soft switching of transistors in the power circuit of the capacitor DC-DC converter. The energy properties of the proposed regulator have been studied. It is shown that with an increase in the number of resonant chains in the capacitor DC-DC converter, there is a tendency for the controller efficiency to increase. For the various regulator circuits considered in the work, the regulating characteristics were obtained and a comparative assessment of their efficiency was carried out. A comparative analysis of energy and design properties proves the significant advantage of a circuit using a Boost PWM constant voltage regulator.

Discrete control model Q-learning for an energy storage system with a hydrogen unit of an autonomous hybrid power plant of a railway substation

The paper considers the prospects for creating autonomous hybrid power plants using renewable energy sources and hydrogen as energy storage systems, as well as storage batteries, for the railway power supply system. A comparative analysis of various energy storage systems is carried out. A simulation model has been created that takes into account the characteristics of electric rolling stock trains, a model of the contact network of the inter-station zone of the railway stage, and a model of the energy storage system. Managing the flow of electricity in an autonomous hybrid power plant requires considering forecasts of electricity consumption and generation and the level of charge of the energy storage device. To solve the problem of synthesizing a control algorithm, the use of matrix Q-learning is proposed. The novelty of the study lies in the proposed approach of applying Q-learning to the problem under consideration based on discretization of input and output parameters of the model and verification of the approach on a simulation model built for a real railway section between the Yaya and Izhmorskaya stations (Kemerovo region of the Russian Federation). It is shown that the use of the proposed system makes it possible to equalize the voltage in the network between traction substations and provide a significant increase in the capacity of the railway section, and the proposed method of matrix Q-learning makes it possible to synthesize an effective algorithm for controlling the energy storage system as part of an autonomous hybrid power plant.

The Role of Excess Charge Mitigation in Electromagnetic Hygiene: An Integrative review.

The electromagnetic characteristics of many environments have changed significantly in recent decades. This is in large part due to the increased presence of equipment that emits electromagnetic radiation and materials that may often readily gain excess charge. The presence of excess charge can often increase risk of infection from pathogens, and likelihood of individuals experiencing compromised performance, respiratory problems and other adverse health issues from increased uptake of particulate matter. It is proposed that adopting improved electromagnetic hygiene measures, including optimized humidity levels, to reduce the presence of inappropriate levels of electric charge can help reduce the likelihood of ill health, infection and poor performance arising from contaminant inhalation and deposition, plus reduce the likelihood of medical devices and other electronic devices getting damaged and/or having their data compromised. It is suggested that such measures should be more widely adopted within clinical practice guidelines and water, sanitation and hygiene programs.

Optimization of PV benefits for electric vehicles charging station using a deterministic method and fuzzy inference system method

Utilizing renewable energy, specifically Photovoltaic (PV), for Electric Vehicle (EV) charging presents diverse technical and economic opportunities, reflecting a recent trend in innovation and research to address global transportation challenges while highlighting the value of renewable sources. In this context, this paper aims to devise an optimal power management strategy for an EV charging station powered by a PV-based microgrid (MG). The main purpose is to improve the benefits of PV production in recharging EVs to increase self-consumption, decrease dependency on the power grid, and reduce energy costs. Production data from a real-scale MG at the Catholic University in France were used as a case study. This MG comprises a heterogeneous fleet of EVs, PV sources, stationary storage, EV charging stations, and a connection to the power grid. The optimization of PV benefits relies on leveraging the knowledge of EV characteristics to modulate their charge profile. In this context, two methods are used and compared for the modulation of the EVs' charging profile: a deterministic method and a Fuzzy Inference System (FIS)-based method. For each method, different scenarios emerge based on the knowledge or lack thereof of the EVs' characteristics. This methodology permits the optimal distribution of energy among multiple EVs and regulates the necessary power to achieve the desired charge level upon departure. It encourages charging through PV production, resulting in reduced energy costs. The comparison of total costs reveals notable differences between the deterministic and FIS approaches for both slow and fast charging modes. In the case of slow charging, the deterministic approach achieves savings of −863.4c€/day, whereas the FIS method delivers greater savings of −898.7c€/day. For fast charging, the deterministic approach yields savings of −524.6c€/day, while the FIS method results in even higher savings of −540.7c€/day. These results highlight that the FIS approach enables better cost management, primarily due to more efficient utilization of PV energy. Additionally, simulation results from MATLAB/Simulink confirm the effectiveness of the FIS-based method, which improves PV energy utilization to 98.20 %, compared to 81.60 % with the deterministic approach. This highlights the superior efficiency of the proposed FIS.

Quantitative detection of refrigerant charge faults in multi-unit air conditioning systems based on machine learning algorithms

Refrigerant charging discrepancies constitute the predominant malfunctions in air conditioning systems. Achieving the optimal charging level is crucial for system performance, underscoring the importance of precise refrigerant level prediction. This study introduces an algorithm designed for the quantitative detection of refrigerant charging errors by integrating the Markov Transition Field (MTF), Convolutional Neural Networks (CNN), and Multi-head Self-Attention (MSA) mechanisms. A high-precision enthalpy difference chamber was employed to establish a Variable Refrigerant Flow (VRF) refrigerant charging test bench. This setup facilitated the analysis of system parameter sensitivity to charging faults and aided in the creation of a training dataset for the algorithm. Comparative analysis was conducted against Support Vector Machines (SVM), Random Forests (RF), CNN with Self-Attention (AT), and MTF-CNN-MSA. The findings reveal that our method adeptly captures temporal dependencies and dynamic shifts in time series as visual representations, offering novel insights for discerning fault patterns within such data. Notably, the maximum pressure variations at high-pressure and low-pressure points were 0.25 MPa and 0.07 MPa, respectively, with temperature shifts of 12 °C and 3.5 °C at the high and low-temperature points. The high-pressure and high-temperature points are particularly sensitive to changes in refrigerant charging, and parameters from these sections were utilized to construct the dataset. The CNN-MSA algorithm demonstrates consistent performance across various fault types, effectively delineating fault characteristics. The accuracies achieved by SVM, RF, CNN-AT, and MTF-CNN-MSA were 84.38 %, 73.75 %, 88.13 %, and 93.75 %, respectively. In comparison, the CNN-MSA algorithm was able to more accurately detect refrigerant charge faults at different levels.

Melatonin treatment maintains the quality properties and storability of carambola fruit by modulating energy metabolism

The influences of 150 μmol/L melatonin treatment on the quality properties, storability, and energy metabolism in carambola fruit were explored. The results showed that, compared to the control, melatonin treatment significantly retained higher rate of commercially acceptable fruit, and retarded the development of fruit browning and yellowing. Additionally, melatonin treatment displayed higher levels of chromaticity L⁎ and h° values, titratable acid, total soluble solids, total soluble sugars, sucrose, and vitamin C, but lower respiration rate, chromaticity a⁎ and b⁎ values, and reducing sugar content. Moreover, melatonin treatment presented higher levels of ATP, ADP, and energy charge, as well as higher activities of H+-ATPase, Mg2+-ATPase, and Ca2+-ATPase in the membranes of vacuole, mitochondria, and plasma, thereby reducing the damage to cell membranes. These results will provide a scientific basis and practical guidance for melatonin to maintain the quality properties and storability, and to prolong the shelf life of postharvest carambola fruit.

Aging Retardation of Lead-Acid Batteries by Adjusting Charge Controller Threshold Values in Off-grid Photovoltaic System

Hybrid systems or coupled power plants with energy storage are becoming more and more popular due to the rising need for energy. Researchers have been conducting several experiments to assure longer-lasting battery use due to the increasing widespread use of energy storage and the depletion of resources such as silicon and precious metals. In this study, a simulation study is carried out in PVSyst software on lead-acid batteries, which have a low cycle and a very traditional electrochemical structure. The simulation is realized by scaling Spain’s consumption curve in 2023, taken from the European Network of Transmission System Operators for Electricity (ENTSO-E), together with lead acid batteries and off-grid photovoltaic (PV) modules. After the off-grid system design is created and consumption data is imported, it is aimed to extend battery life by changing the charge-discharge threshold values of the charge controllers. Threshold values are in two categories, high and low, and seventeen different cases are simulated. According to the results of the simulation, the state of charge (SOC) levels of the batteries decreases significantly due to radiation and cloudiness in the winter months and remain high in the spring and summer months. When charge controllers are set to high thresholds levels, they cause gassing currents that rise to an average of 20 amps, while battery life extends up to 5.3 years. When low threshold values are used, negligible levels of oxygen and hydrogen gasses are formed in the battery, but battery life decreases to 4.1 years.

Chitosan-Based Charge-Controllable Supramolecular Carrier for Universal Immobilization of Enzymes with Different Isoelectric Points.

Electrostatic adsorption is an enzyme immobilization method that effectively maintains enzyme activity and exhibits considerable binding efficiency. However, enzymes carry different charges at their respective reaction pH levels, which prevents the use of the same carrier to immobilize enzymes with different charges. In this study, we employed a template-mediated polysaccharide-enzyme coupling self-assembly strategy to develop a charge-controllable supramolecular immobilization carrier by regulating the charge properties of carboxymethyl chitosan, enabling the universal immobilization of enzymes with different charge levels across a range of reaction pH values. By using silica nanoparticles of certain sizes as templates, the size of the carrier can be precisely controlled and the hollow network structure formed after removing the template can effectively reduce mass transfer resistance. Trypsin and papain are used as model enzymes, and the experimental results show that the supramolecular self-assembly immobilization strategy does not disrupt the secondary structure of the enzyme molecules. After 2 h of reaction, the enzyme activities of immobilized papain and immobilized trypsin are 13.2% and 7.7% higher than those of the free enzymes, respectively. After 10 consecutive reactions, the enzyme activities of immobilized papain and immobilized trypsin retained 56.3% and 64.3% of their initial values, respectively.

Distributed Secondary Control of DC Microgrid with Power Management Based on Time-of-Use Pricing and Internal Price Rate

This paper presents a novel approach to manage distributed DC microgrids (DCMG) by integrating a time-of-use (ToU) electricity pricing scheme and an internal price rate calculation mechanism. The proposed power-management system is designed to effectively handle uncertainties such as utility grid (UG) availability, fluctuating electricity prices, battery state of charge (SOC) levels, and frequent plug-ins and plug-outs of electric vehicles (EVs). Uncertainties in DCMG systems often lead to inefficiencies, power imbalances, and inexact voltage regulation issues within DCMGs. In addition, to maintain the power balance and constant voltage regulation under various operational states, the proposed scheme also incorporates secondary control into the DCMG power-management system. Unlike the existing approaches that often fail to adapt dynamically to changing conditions, the proposed method is the first approach to consider the concept of internal price rate in designing the DCMG power management. To address this challenge, this approach proposes a more resilient power-management strategy to enhance the efficiency and adaptability of DCMG systems. Extensive simulations and experimental validations demonstrate the practicality and adaptability of the proposed control strategy under diverse test conditions, including operation transitions between grid-connected mode (GCM) and islanded mode (IM), low battery SOC condition, operation transition from the current control mode (CCM) to distributed secondary control mode (DSCM), and EV plug-in scenarios. The test results confirm that the proposed method enhances the reliability, efficiency, and economic viability of DCMG systems, making it a promising solution for future smart grid and renewable energy integrations.

Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications

With the advancement of CMOS technology, the susceptibility of SRAM to single node upset (SNU), double node upset (DNU), and multiple node upset (MNU) induced by radiation has increased. To address this issue, various cutting-edge solutions, such as radiation hardened sextuple cross coupled (RHSCC)-16T and DNU-completely-tolerant memory (DNUCTM) cells, have been proposed. While the RHSCC-16T cell is robust against SNU, it may be vulnerable to DNU. The DNUCTM cell is resistant to both SNU and DNU, but it remains susceptible to MNU. In this paper, we propose a radiation hardened read-stability and speed enhanced (RHRSE)-20T SRAM, which is immune to all potential cases of SNU, DNU, and MNU. Additionally, the proposed design demonstrates improvements in read and write delays compared to conventional SRAM designs. Experimental results confirm that the RHRSE-20T SRAM maintains stability under various charge levels for SEU, DNU, and MNU. The proposed integrated circuit is implemented in a 90-nm CMOS process and operates on a 1 V supply voltage, offering significant advantages for next-generation radiation-hardened memory applications.

Experimental study of the performance of a water-to-water heat pump equipped with a liquid receiver with different charge levels

This paper presents an experimental investigation of the effects of a decreasing refrigerant charge on the performance and operating conditions of an R513A water-to-water heat pump equipped with a large liquid receiver, an electronic expansion valve, and a vapor accumulator at the compressor inlet. The mass of the refrigerant inside the machine is progressively extracted to simulate refrigerant leakages occurring during the normal operation or in case of failures. The results show that the subcooling is the parameter mostly affected by a refrigerant charge variation since it rapidly collapses to 0 K with the charge reduction. Furthermore, it is possible to identify three different zones in which almost all the properties analyzed (COP, heating capacity, operating pressures and expansion valve opening) exhibit peculiar trends with the charge decrease: subcooling sensitivity zone, constant parameters zone, and compressor failure risk zone. The extension of these zones is determined by the size of the liquid receiver which is installed in the system and, for the heat pump under consideration, is between 100% and 95% of the initial charge for the first zone, between 95% and 40% for the second zone, and below 40% for the third zone.

Deployment of Public Charging Stations for BEVs Using an Agent-Based Modeling Approach

A low utilization rate of public chargers and unmatched deployment of public charging stations (CSs) are partly attributed to inappropriate modeling of charging behavior and biased charging demand estimation. This study proposes an optimization methodology for public CS deployment, considering real charging behavior and interactions between battery electric vehicle (BEV) users and CSs. Realistic charging choice behavior is modeled based on surveys, and a dynamic charging decision chain is simulated, allowing interactions between BEV users and CSs through an agent-based modeling (ABM) approach. The charging-related activities are triggered by state of charge (SOC) levels randomly generated from distributions derived from real BEV operating data, including the random SOC levels at the start of a trip, the SOC level that prompts the user to charge the BEV, and the SOC level at which the user stops charging the BEV. A bi-level programming model is proposed to optimize the deployment schemes for building new CSs considering the existing CSs, to determine the location and the capacity of new CSs. The objective is to minimize the total time cost per BEV user, including travel time, charging time and waiting time in the queue. An application is conducted, for the deployment of fast CSs in Washington State, USA. The results show that our method could provide effective guidance for allocating new CSs that are good supplements to the existing heavy-load CSs to share their charging load and relieve their serious queuing problems. The optimized deployment scheme can efficiently alleviate long waiting times at existing CSs and leads to a more balanced utilization among CSs. The proposed approach is expected to contribute to better planning and deployment of public CSs, satisfaction of the booming charging demand and increased utilization of public CSs.

REFRIGERANT CHARGING UNIT FOR THE RESIDENTIAL AIR CONDITIONERS: AN EXPERIMENT

In the present work, an automatic R410A refrigerant charger for residential air conditioners is fabricated and tested. The charger operates on the throttling principle and uses the suction pressure of the compressor to estimate the refrigerant charge level. This helps to reduce the risk of compressor damage and ensures the correct composition ratio of R410A refrigerant when charged into the machine. The charging process is controlled by the LabVIEW platform, which provides adequate control and visualization of the charging process. The developed charger meets expectations in solving the technical problems encountered when charging R410A refrigerant for residential air conditioners. It is compact, portable and can be directly controlled through the LabVIEW interface, allowing real-time visualization of the charging process. The present work is expected to make a significant practical contribution, serving as a useful reference for the future manufacturing of compact portable equipment in the residential air conditioning field.

Evaluation of electric vehicle power routing and charging analysis using artificial neural network synchronized with 5G-network

To motivate the advanced National Clean Air Program (NCAP) strategy announced by the Indian Government in 2017, automizing the routing control on electric vehicle (EV) is a challenging area. According to the public authority guideline, the air contamination issues over the nation focus on accomplishing a 20% to 30% decrease in vehicular outflows in 2024 by enhancing EV mobility. However electric vehicle's limitations on battery capacity and route strategy to charging stations to preserve an adequate level of battery charge still need automized communication strategy. Fixed route solutions might not always be executable because battery consumption varies on several external factors. To better fit the real situation, this study proposes the notion of breaking down the self-adaptive online routing by encountering automatic path detection concerning battery efficiency with adaptive charging decisions. An inventory control-style charging technique is suggested to account for stochastic battery use. In the proposed work, by using an artificial neural network, the power demand and energy consumption of the EV world will be monitored and controlled by using 5G coverage on the smart city evolution. The obtained simulation analysis confirms that in comparison to current conventional and fixed tariff schemes, 5G accompanied EV accomplish an examined rate of SoC charge around 19.95 MW holding a peak range of 108.96 MW. Also, an evaluated typical power loss rate of 2.873 kWH which is less than 11.95% through frequency band analysis with signal-to-noise ratio and downlink power utilization.

Electrostatic charging of material webs in gravure printing presses

This paper deals with the electrostatic charging of material webs in production machines. Measurements were made with a special measurement setup inside a gravure printing press and the results were analyzed and interpreted. In order to gather as much information as possible, the electric field strength of a free-spanning web was monitored at different positions inside the machine and under different production conditions. The measurement results confirmed theoretical principles and practical experience, such as the fact that the type of material, printing inks, coatings, machine speed, web tension, printing process and corona pretreatment have a significant effect on the level and polarity of the electrostatic charging. During the series of experiments, peak field strengths of up to 300 kV/m resulting from the charge on the web were measured and changes in the sign were detected within a very short period of time.

Testing Algorithms for Controlling the Distributed Power Supply System of a Railway Signal Box

Trends in the use of renewable energy sources to power buildings do not bypass objects for which maintaining a power supply is critical. This also applies to railway signal boxes. The aim of the research work was to test the multisource power supply system for a railway signal box with power electronic converter systems and a DC bus, built as part of the research project. The assumption for powering the railway signal box building was to use renewable sources, energy storage devices, and a 3 kV DC traction network as the second required power supply grid. Both power grids were connected by power electronic converters, and the power values of the converters were set based on the calculated power balance values using the values measured at the system nodes and the set constraints. The tests primarily tested the response of the power supply system to changes in load power and power generated by the photovoltaic source, as well as the charge level of the energy storage devices. The correctness of the control algorithm’s operation was assessed based on the recorded power values in the power supply system nodes. The tests were carried out for 60 scenarios that covered all normal and emergency operating conditions. During the tests, delays in response to changes in the power supplied to the converters and the values of circular power flow between the power grid connections were recorded. The recorded delays ranged from 2 to about 50 s and the circular power flows did not exceed 1500 W. Based on the results of the tests, it was found necessary to improve the power measurement system in the power supply system nodes and to improve the quality of communication and the transmission time of measurement data transmission time.

Optimal recharge sequencing in multi-AGV systems: A mixed ILP approach

Battery level is a critical issue in AGV transportation systems, as AGVs cannot run out of power while operating. Hence, at some point they will be forced to stop and recharge at any of the charging stations available in the warehouse. The question that immediately arises is where, and when, to recharge in an optimal way. Assuming that an AGV has already been assigned a task sequence consisting of visiting a number of working stations, stops at charging stations should be optimally inserted. Here, we do so by minimizing the total tour time, including recharges. Recently, this problem was cast as a mixed integer nonlinear program that also included linear and nonlinear constraints. We recast this problem into a mixed integer linear program under linear constraints, which allows to find an exact solution. Moreover, we derive a procedure to reduce the number of variables of the problem. We also show that, when a charging station is visited, bringing the level of charge to the maximum allowed is optimal except for the case where the tour can be completed without other stops. Finally, we present a stochastic formulation of the problem to consider the presence of multiple AGVs in the layout, which may affect the time spent by AGVs at charging stations due to queuing and, consequently, the optimal solution. Interestingly, the linear formulation of the problem allows to fully characterize the optimal solution as a function of the expected occupancies of the charging stations.

Theory of time constant correlation of a porous bed thermal energy storage tank - Experimental and numerical proof of concept

Analytical modeling of energy systems is used to estimate the potential of the system, but several simplifications used lead to progressive deviations from the actual potential. Thermal Energy Storage tanks are most often treated as black boxes, by limiting their characteristics to their energy efficiency and basic capacity only. This paper introduces the theory of a time constant that correlates the basic parameters of a porous bed heat storage tank and allows the charge level of the tank to be determined during the charging stage. The correlation for the time constant has a coefficient that has been fully validated both experimentally and numerically for a wide range of parameters. Coefficient values for different precision test runs have been indicated, and the prediction deviation using the time constant does not exceed a value of 5 % relative to the actual results. The proposed methodology for the analytical model of the tank accurately represents the cyclic operation of the heat storage tank. It has also been shown that the cyclic operation of the heat storage tank fixes the charge level in the range of 0.16–0.79. The methodology presented can be used to modelling and designing energy systems with heat storage.

Three phase bidirectional DC-DC converters based neural network controller for renewable energy sources

In this study, we performed a detailed simulation of the PIDRN controller associated with a three-phase converter, taking into account different initial battery charging conditions. After introducing the concept of PIDRN and explaining the operation of the three-phase converter, we proceeded to model the system, defining the necessary parameters. We then configured several simulations, varying the initial charging conditions of the battery, and analyzed the numerical results obtained. This comparative analysis revealed variable system performance depending on the initial battery charge level, highlighting advantages and disadvantages in each case. In particular, we found that the PIDRN controller proves to be an optimal choice for this type of converter, thanks to its ability to effectively regulate voltage and current under varying battery charging conditions. We discussed the implications of these findings. In conclusion, this study provides an in-depth overview of the performance of the PIDRN controller in a three-phase converter context and highlights the importance of taking into account the initial battery conditions in the design and optimization of energy control.