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Dermatosis papulosa nigra (DPN) is a variant of seborrheic keratosis that typically presents as hyperpigmented pedunculated papules on the face, trunk, and/or back in those with skin of color. Although benign, the lesions can cause significant discomfort and distress. Management options are limited and often unaffordable, as treatment is elective in most cases. This study was undertaken to provide an updated summary of safe and efficacious treatments for DPN. Five databases were searched to identify full‐text publications reporting on treatments and outcomes in adults with DPN. Seventeen publications met inclusion criteria and were included: six cohort studies, one randomized controlled trial, five case report studies, one case series, and four pilot studies. Treatment options included simple excision, curettage, electrodesiccation, cryotherapy, topicals, and laser therapies. These modalities varied in their documented outcomes and associated potential adverse effect profiles. Postinflammatory hyperpigmentation is a common adverse effect that often leads to patient dissatisfaction. It can be mitigated with topical treatments aimed at reducing local inflammation. Study limitations included small sample size in individual studies, lack of consistent reporting of Fitzpatrick skin type, and lack of comparison to the standard treatment of electrodesiccation or curettage. Ultimately, treatment should consider the patient's Fitzpatrick type, treatment area, associated costs, and potential adverse effects. [ABSTRACT FROM AUTHOR]

This research work focuses on the design and implementation of a highly advanced field-programmable gate array (FPGA)-based system-on-chip (SoC) solution for real-time edge detection. By utilizing a Zynq processor and leveraging the powerful Vivado software, the aim is to overcome the significant computational challenges associated with achieving real-time edge detection. Edge detection in real-time scenarios presents several obstacles, including the possibility of missing edges due to noise and the substantial processing requirements of any edge detection technique. To address these challenges, the proposed SoC system synergistically combines the computational capabilities of an FPGA board and a Zynq processor, harnessing hardware acceleration to achieve high-performance edge detection. The OV7670 camera module serves as the primary input medium, capturing image frames for subsequent processing. These captured frames undergo initial processing before being seamlessly transferred to the FPGA fabric through customized intellectual property (IP) blocks. These IP blocks efficiently handle crucial tasks such as frame capturing, conversion to AXI Stream interface signals, and integration with the video direct memory access (VDMA) IP. The VDMA IP plays a pivotal role by facilitating high-speed data movement between the FPGA fabric and the Zynq processor IP, thereby enabling streamlined and efficient data transfer and processing. At the heart of this project lies the real-time edge detection algorithm, which is skillfully implemented on the Zynq processor. The resulting edge-detected frames are then visually presented and displayed on an output device utilizing the AXI4-Stream to Video Out IP. To ensure optimal utilization of available hardware resources, the comprehensive Vivado software suite provides a wide array of tools for designing, implementing, and programming the FPGA fabric. By leveraging FPGA-based systems, this project effectively addresses the critical need for real-time edge detection in time-sensitive scenarios. The result is a portable and manageable device that exhibits versatility, as it can be employed in various applications while reliably detecting edges in real-time situations. [ABSTRACT FROM AUTHOR]

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Proper timing synchronization is important when data from sensors are acquired by different devices. This paper proposes a simple but effective solution for System on Chip (SoC) architectures that integrates a general-purpose Field Programmable Gate Array (FPGA) with a CPU. The proposed approach relies on a network synchronization protocol implemented in software, such as Network Time Protocol (NTP) or Precision Time Protocol (PTP), and uses the FPGA to generate a clock reference that is maintained in step with the synchronized system clock. The clock generated by the FPGA is obtained from the FPGA oscillator via appropriate fractional clock division. Clock drift is avoided via a software program that periodically compares the FPGA and the system counters, respectively, and adjusts the fractional clock divider in order to slightly adjust the FPGA clock frequency using a Proportional Integral controller. A specific implementation is presented on the RedPitaya platform, generating a 1 MHz clock in step with the NTP synchronized system clock. The presented system has been used in a distributed data acquisition system for fast transient recording in the neutral beam test facility for the ITER nuclear fusion experiment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink. [ABSTRACT FROM AUTHOR]

The application of green manure is a traditional and valuable practice to improve the fertility of saline soil. However, the impact of environmental factors, green manure types and returning methods on the changes in soil fertility and soil salinity remain poorly quantified at a large scale. In the present study, we conducted a meta-analysis to generate a comprehensive evaluation of the effects of green manure on soil organic carbon (SOC), soil salt content, and soil nutrients compared to bare soil in China. The results showed that compared with bare soil, green manure planting could significantly increase the SOC content of saline soil, reduce salt content, and improve the soil total nitrogen (N), soil available phosphorus (P) and soil available potassium (K) contents. On average, green manure significantly enhanced SOC by 34.82% (percentage change), soil total N by 32.23%, soil available P by 34.34% and soil available K by 17.43%, while reducing soil salt content by 47.75%, compared to bare soil. In areas with a mean annual temperature (MAT) of <10 °C or a mean annual precipitation (MAP) of 200–400 mm, green manure had the largest increase in SOC, soil total N, soil available P, and soil available K. The smallest increases were observed in areas with an MAT above 15 °C and MAP greater than 800 mm. Green manure types influenced the improvement effect of green manure on saline soil. Green manure mixtures were more conducive to increases in SOC, while the increases in soil total N resulting from mixed green manure were lower in comparison to those from both legumes and non-legumes. In addition, the initial salt content, experimental years, and returning method influenced the improvement effect of green manure on saline soil. Therefore, this meta-analysis identified green manure as a promising practice for significantly improved saline soil in China. [ABSTRACT FROM AUTHOR]

Soft sensors offer an alternative approach for measuring process variables that cannot be directly obtained from hardware sensors, crucial for optimizing process outputs. This paper outlines a method for implementing an embedded soft sensor to estimate lactose concentration in a bioreactor using the MicroBlaze Soft Processor found in the Zynq SoC. The study focuses on employing the Extended Kalman Filter (EKF) algorithm for this purpose. The methodology involves designing a block using the MicroBlaze soft processor core in VIVADO, utilizing the Zybo board as the hardware platform. The embedded soft sensor is developed as a software application in C, configured to operate on the soft processor within the Zynq device. A comparative analysis is conducted among lactose output values obtained from simulation, hardware implementation, and true values derived from laboratory analysis. This approach underscoresthe efficacy of embedded soft sensors in enhancing process monitoring and optimization. [ABSTRACT FROM AUTHOR]

Voltage stacking is a technique in which multiple integrated circuits are stacked in series between the supply voltage instead of in parallel, thus improving the energy efficiency of the power distribution network. Unfortunately, voltage stacking presents stability challenges for integrated circuits within the stack. A first-order model to quantify variability, stability, and power metrics for an array of voltage-stacked asynchronous integrated circuits is presented. Voltage variability and power consumption are accounted for and discussed. Limitations of the model are identified outside of the nominal behavior. The number of columns in the architecture, chip leakage, and supply voltage are shown to be the key contributors to the stability, performance, and energy efficiency of a system of voltage-stacked asynchronous processors. A higher leakage to active power ratio, though usually avoided by chip designers, is shown to improve stability and be key in designing stacks without external balancing. Outputs of the model enable system and chip designers to evaluate first-order trade-offs in energy efficiency, performance, and system cost. These fundamental data allow designers to make informed design and optimization trade-offs between asynchronous voltage-stacked architectures and the integrated circuits used therein. Analysis of this model shows that various voltage-stacked configurations, such as one with a 48 V supply using 100 rows and 11 columns, can be designed with less than 10% voltage variation per chip, mitigating the need for external voltage balancing. [ABSTRACT FROM AUTHOR]

Electric vehicle systems and smart grid systems are setting stringent development targets to respond to global trends in energy saving, carbon reduction, and sustainable environmental development. In the field of batteries, there has been extensive discussion on the estimation of battery charge. In battery management systems (BMSs) and charging/discharging systems, the accuracy of the measurement of battery physical parameters is critical, as it directly affects the system, alongside the algorithm's estimation and error correction. Therefore, this paper proposes incorporating a low-power continuous-time delta-sigma analogue-to-digital converter into a battery measurement system to support deep learning algorithms for battery state estimation. This approach aims to maintain the accuracy of battery state estimation while reducing latency and overall system power consumption. Implemented using the UMC 0.18 μm CMOS 1P6M process, the proposed design achieves a measured signal-to-noise distortion ratio (SNDR) of 78.42 dB, an effective number of bits (ENOB) of 12.73 bits, and a power consumption of approximately 15.97 μW. The chip layout area is 0.67 mm × 0.56 mm. By applying delta-sigma modulators to energy management systems, this solution aims to increase the total number of battery monitoring units while reducing overall power consumption and construction costs. [ABSTRACT FROM AUTHOR]

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells' thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits. [ABSTRACT FROM AUTHOR]

Accurate estimation of the state of charge (SOC) of lithium-ion batteries is very important for the development of energy storage systems. However, batteries are subject to characteristic changes in complex environments, making it difficult to accurately estimate SOC online. In this paper, an adaptive feedback particle swarm with multi-innovation singular decomposition unscented Kalman filtering method is proposed. The idea of the real-time change of inertia weight and learning factor is used to balance the particle searchability, and the information feedback mechanism is established to make the local optimal position constantly updated, which solves the problem that the standard particle swarm optimization algorithm is easy to fall into the local optimal solution. Singular decomposition (SVD) is used to replace Cholesky decomposition in traditional UKF to avoid algorithm divergence. At the same time, a strategy of noise variance Q varying with multi-time errors is introduced to further improve the estimation accuracy. The results show that under different working conditions, the SOC estimation accuracy based on adaptive feedback particle swarm optimization and multi-information singular decomposition unscented Kalman filter is improved by 76.6% and 67.6% respectively, and the algorithm convergence speed is improved by 88.9% and 77.5%, respectively. [ABSTRACT FROM AUTHOR]

The technology of 3D-ICs has been widely used in designing core-based systems-on-chip (SoC), which comprises vertical stacking of multiple silicon and metal layers, interconnected using through-silicon vias (TSV). Testing of the embedded cores in such a device not only requires a specially designed test access mechanism (TAM) but proper scheduling of tests in order to minimize the overall test time. Because of the design constraints on the number of TSVs (TSV count), the accessibility to different layers is often compromised, leading to an increase in test time. Also, the energy consumed during the execution of tests mandates the deployment of thermal vias to facilitate heat dissipation from various layers. In this manuscript, a scheduling-based test time reduction scheme for post-bond testing of 3D SoCs is proposed, which aims to reduce the test time under the constraints on TAM width, TSV count, and power budget. We address this problem by proposing a new partitioning method for assigning the layers to cores satisfying these optimization criteria. The proposed method has been tested on several ITC-02 benchmarks, and results show a notable reduction in test time for most of the cases compared to previous work. [ABSTRACT FROM AUTHOR]

Aiming at the current lithium-ion battery storage power station model, which cannot effectively reflect the battery characteristics, a proposed electro-thermal coupling modeling method for storage power stations considers the characteristics of the battery body by combining the equivalent circuit model and accounting for the effect of temperature on the battery. Based on the modeling of a single lithium-ion battery, the equivalent circuit model and thermal model are integrated to create the battery's electro-thermal coupling model. The parameters of this coupling model are determined using the particle swarm algorithm. On this basis, the battery compartment model of the energy storage station is analyzed and verified by utilizing the circuit series-parallel connection characteristics. Subsequently, the electro-thermal coupling model of the energy storage station is established. The dual Kalman filter algorithm is utilized to simulate and validate the electric-thermal coupling model of the energy storage power station, considering ontological factors such as battery voltage, current, and temperature. The results demonstrate that the established coupling model can accurately determine the SOC and temperature of the power station. This ability allows for a more precise reflection of the battery characteristics of the energy storage station. It also validates the accuracy and effectiveness of the electric-thermal coupling model of the energy storage station. This finding is crucial for assessing the state and ensuring the safe operation of the battery system in the energy storage station. [ABSTRACT FROM AUTHOR]

Converting natural vegetation for agriculture has resulted in the loss of approximately 5% of the current global terrestrial soil organic carbon (SOC) stock to the atmosphere. Increasing the agricultural area under grassland may reverse some of these losses, but the effectiveness of such a strategy is limited by how quickly SOC recovers after conversion from cropland. Using soil data and extensive land‐use histories gathered during the national German agricultural soil inventory, this study aims to answer three questions regarding agricultural land‐use change (LUC): (i) how do SOC stocks change with depth following LUC; (ii) how long does it take to reach SOC equilibrium after LUC; and (iii) what is the legacy effect of historic LUC on present day SOC dynamics? By using a novel approach that substitutes space for time and accounts for differences in site properties using propensity score balancing, we determined that sites that were converted from cropland to grassland reached a SOC equilibrium level 47.3% (95% confidence interval (CI): 43.4% to 49.5%) above permanent cropland levels 83 years (95% CI: 79 to 90 years) after conversion. Meanwhile, sites converted from grassland to cropland reached a SOC equilibrium level −33.6% (95% CI: −34.1% to −33.5%) below permanent grassland levels after 180 years (95% CI: 151 to 223 years). We estimate that, over the past century, today's German agricultural soils (16.6 million ha) have gained about 40 million Mg C. Furthermore, croplands with historic LUC from grassland are losing SOC by −0.26 Mg ha−1 year−1 (10% of agricultural land) while grasslands historically converted from cropland are gaining SOC by 0.27 Mg ha−1 year−1 (18% of agricultural land). This study shows that even long‐standing temperate agricultural sites likely have ongoing SOC change as a result of historical LUC. [ABSTRACT FROM AUTHOR]

The use of renewable energy presents a viable alternative to fossil fuels. However, their intermittent nature does not allow for an immediate response to energy demand. Thus, it is necessary and beneficial to harness various renewable sources and integrate a storage system as an auxiliary source to mitigate this intermittency. The hybridization of energy sources requires efficient management of power flows to ensure the proper functioning of the overall system, regardless of changing weather conditions. In this paper, we propose an intelligent power management control for hybrid wind-solar-battery systems connected to micro-grids based on fuzzy logic. The proposed control approach addresses several specific challenges compared to conventional methods in the intelligent energy management of renewable hybrid systems. It effectively manages the uncertainties and nonlinearities inherent in weather variations, optimizes performance by dynamically adjusting the operations of energy sources and storage systems, and ensures efficient realtime utilization of available energy resources, thus providing greater flexibility and adaptability. Additionally, it enhances the stability and reliability of micro-grids by integrating more flexible and adaptive decision-making mechanisms. The simulation results using MatLab/Simulink demonstrate the significant advantage of this intelligent management lies in its ability to precisely control the state of charge of the battery across five distinct levels, which is not achievable using traditional management practices that rely solely on the maximum and minimum levels of the state of charge. [ABSTRACT FROM AUTHOR]

This paper proposes a method that leads to a highly accurate state-of-charge dependent multi-stage constant current (MCC) charging algorithm for electric bicycle batteries to reduce the charging time without accelerating aging by avoiding Li-plating. First, the relation between the current rate, state-of-charge, and Li-plating is experimentally analyzed with the help of three-electrode measurements. Therefore, a SOC-dependent charging algorithm is proposed. Secondly, a SOC estimation algorithm based on an Extended Kalman Filter is developed in MATLAB/Simulink to conduct high accuracy SOC estimations and control precisely the charging algorithm. The results of the experiments showed that the Root Mean Square Error (RMSE) of SOC estimation is 1.08%, and the charging time from 0% to 80% SOC is reduced by 30%. [ABSTRACT FROM AUTHOR]

Biomass-derived sustainable aviation fuel holds significant potential for decarbonizing the aviation sector. Its long-term viability depends on crop choice, longevity of soil organic carbon (SOC) sequestration, and the biomass-to-biojet fuel conversion efficiency. We explored the impact of fuel price and SOC value on viable biojet fuel production scale by integrating an agroecosystem model with a field-to-biojet fuel production process model for 1,4-dimethylcyclooctane (DMCO), a representative high-performance biojet fuel molecule, from Miscanthus, sorghum, and switchgrass. Assigning monetary value to SOC sequestration results in substantially different outcomes than an increased fuel selling price. If SOC accumulation is valued at $185/ton CO2, planting Miscanthus for conversion to DMCO would be economically cost-competitive across 66% of croplands across the continental United States (US) by 2050 if conventional jet fuel remains at $0.74/L (in 2020 US dollars). Cutting the SOC sequestration value in half reduces the viable area to 54% of cropland, and eliminating any payment for SOC shrinks the viable area to 16%. If future biojet fuel prices increase to $1.24/L-Jet A-equivalent, 48 to 58% of the total cultivated land in the United States could support a more diverse set of feedstocks including Miscanthus, sorghum, or switchgrass. Among these options, only 8-14% of the area would be suitable exclusively for Miscanthus cultivation. These findings highlight the intersection of natural solutions for carbon removal and the use of deep-rooted feedstocks for biofuels and biomanufacturing. The results underscore the need to establish clear and consistent values for SOC sequestration to enable the future bioeconomy.

Abstract Microgrids offer an optimistic solution for delivering electricity to remote regions and incorporating renewable energy into existing power systems. However, the energy balance between generation and consumption remains a significant challenge in microgrid setups. This research presents an adaptive energy management approach for grid-interactive microgrids. The DC microgrid is established by combining solar PV with a battery-supercapacitor (SC) hybrid energy storage system (HESS). The proposed approach integrates the frequency separation strategy with a rule-based algorithm to ensure optimal power sharing among sources while maintaining the safe operation of storage units. Specifically, the battery meets steady-state energy demands, the SC addresses transient power requirements, and the grid support is tailored to system needs. The method employs the dq reference frame technique to control the grid inverter (VSC). The key merits include efficient power allocation, fast regulation of the DC link voltage irrespective of load or generation variations, seamless transition between scenarios, and introduction of a straightforward battery state of charge (SOC)-based coefficient for allocating power between the battery and the grid while enhancing the power quality within the grid. Moreover, safety measures prevent the SC from overcharging, the battery from high current, overcharging, and deep discharging, potentially extending their lifespan. Validation and implementation of the method are conducted using MATLAB/Simulink.

The key to a Battery Management System (BMS) is the accurate and real-time prediction of the State of Charge (SOC) of the power battery. Currently, there is relatively little research on the construction methods of battery models within a wide temperature range. A second-order RC equivalent circuit is selected to establish an Improved Equivalent Circuit Model (IECM) based on temperature compensation. The identification of IECM parameters is completed by using the multi strategy improvement of Artificial Bee Colony (MSIABC) algorithm combining with pulse discharge experimental data under different temperature conditions (-20 °C to 60 °C). Based on the experimental data of the UDDS condition and the hybrid dynamic condition under low temperature, high temperature, and time-varying temperature environments, the battery SOC is estimated by combining the IECM and Adaptive Extended Kalman Filtering (AEKF) algorithm. The experimental results show that compared with the conventional ECM-AEKF estimation method, IECM-AEKF has higher SOC estimation accuracy and environmental temperature adaptability.

Li-ion batteries can be charged with different techniques according to the charging time and required capacity usage. Most charging techniques face difficulties when implemented in PV systems due to the intermittent and unpredictable nature of the power supply. This paper addresses the issue of determining the appropriate charging technique for Li-ion batteries in a PV system. We have developed a mode-selective control approach that determines the optimal charging mode according to the given SOC and solar irradiation, aiming to maximize the utilization of the generated PV power. The developed control approach has been implemented using a dual-switched buck converter in the MATLAB/Simulink environment. The key control algorithm focused on regulating current, with different references being used based on the selected charging mode. Three references for charging current were set: the maximum current, the required current assigned based on the given SOC, and the pulsed current. The pulsed current reference was employed during a stage of the charging process to accelerate charging and prevent dissipation of PV power. Furthermore, a gain-scheduled controller with carefully picked control parameters was used to ensure stable operation across different modes. The results proved the effectiveness of the proposed control in reducing charging time and minimizing PV power dissipation without resorting to the use of harmful charging currents.

The design of an efficient thermal management system for a lithium-ion battery pack hinges on a deep understanding of the cells’ thermal behavior. This understanding can be gained through theoretical or experimental methods. While the theoretical study of the cells using electrochemical and numerical methods requires expensive computing facilities and time, the Equivalent Circuit Model (ECM) offers a more direct approach. However, upfront experimental cell characterization is needed to determine the ECM parameters. In this study, the behavior of a cell is characterized experimentally, and the results are used to build a second-order equivalent electrical circuit model of the cell. This model is then integrated with the cooling system of the battery pack for effective thermal management. The Equivalent Circuit Model estimates the internal heat generation inside the cell using instantaneous load current, terminal voltage, and temperature data. By extrapolating the heat generation data of a single cell, we can determine the heat generation of the cells in the pack. With the implementation of the ECM in the cooling system, the coolant flow rate can be adjusted to ensure the attainment of a safe operating cell temperature. Our study confirms that 14% of pumping power can be reduced when compared to the conventional constant flow rate cooling system, while still maintaining the temperature of the cells within safe limits.

Purposes This research aims to improve the stability of energy system power variation and fuel economy during flight of hydrogen-hybrid fixed-wing UAV. Methods A variable equivalent factor considering the influence of battery pack power variation amplitude on energy consumption is introduced on the basis of the traditional equivalent consumption minimization strategy theory. This factor is weighted with the original equivalent factor to form a compound equivalent factor. A UAV hybrid simulation platform suitable for complex flight conditions is established, and the influence of compound equivalent factors on the control effect of equivalent consumption minimization strategy is studied. Findings The results show that the equivalent consumption minimization strategy with compound equivalent factor can effectively reduce the power fluctuation amplitude of the energy system. The amplitude of power fluctuation of hydrogen fuel cell during cruise flight is reduced by 94.7% compared with that of the traditional equivalent consumption minimization strategy, and the accumulative SOC variation of battery pack during flight is reduced by 46%. In 3.5 h flight missions, 193 standard liters of hydrogen can be saved, improving fuel economy. When the SOC of the battery pack approaches equilibrium, the change rate of charging power can be greatly slowed down. The improved equivalent consumption minimization strategy is beneficial to prolonging the service life of energy systems and improving the safety of fuel cells and battery packs.

In high-penetration wind power generation systems， accurately assessing the support potential for virtual inertia of energy storage and fully releasing its energy reserves to render the systems active in grid frequency regulation are crucial for ensuring the systems’ frequency safety and stability. To achieve this， a dynamic frequency response model for wind-storage power generation systems to calculate the system’s time of safe frequency rise and fall and the extreme values of frequency deviation. Next， considering the constraints on the safe operating condition of the state of charge （SOC） during energy storage， the virtual inertia of energy storage during the time of safe frequency rise and fall is quantitatively evaluated. Moreover， an active frequency support technology for energy storage based on fixed-frequency control is introduced， aiming to fully exploit the frequency support capability of energy storage during the frequency drop and recovery periods. Finally， a simulation model of high-penetration wind power generation systems is constructed for validation. Under the proposed control strategy， energy storage can provide effective inertia support and offer sustained frequency response， thereby significantly enhancing the systems’ frequency stability.

The ever-growing deluge of astronomical data challenges traditional server-based processing, hindering real-time analysis and scientific discovery. This paper proposes a novel approach: edge computing directly on an sCMOS camera using a System-on-Chip (SoC) architecture currently developed at Creotech Instruments. We present a custom-designed camera equipped with an FPGA-based SoC, enabling on-board preprocessing and feature extraction of astronomical images. This significantly reduces data transmission, minimizes latency, and empowers real-time decision-making for critical observations. We showcase the camera's capabilities through real-world scenarios, demonstrating its usability in astronomy.

Accurately estimating the state of charge （SOC） of energy storage batteries is of paramount importance for achieving balanced charging and discharging， and mitigating capacity degradation caused by overcharging and overdischarging. In view of the complex chemical states and nonlinear time-varying characteristics of SOC in energy storage batteries， this paper proposes a joint SOC estimation method for lithium-ion batteries based on the variable forgetting factor recursive least squares （VFFRLS） and unscented Kalman filter （UKF） algorithms. The VFFRLS algorithm is employed for online identification of battery model parameters such as resistance and capacitance， and based on the identification results， the UKF algorithm is utilized for real-time SOC estimation. Experimental results demonstrate that the proposed joint method exhibits high accuracy and stability.

Abstract Background Soil organic carbon (SOC) is a critical component of the global carbon cycle, and an accurate estimate of regional SOC stock (SOCS) would significantly improve our understanding of SOC sequestration and cycles. Zoige Plateau, locating in the northeastern Qinghai-Tibet Plateau, has the largest alpine marsh wetland worldwide and exhibits a high sensitivity to climate fluctuations. Despite an increasing use of optical remote sensing in predicting regional SOCS, optical remote sensing has obvious limitations in the Zoige Plateau due to highly cloudy weather, and knowledge of on the spatial patterns of SOCS is limited. Therefore, in the current study, the spatial distributions of SOCS within 100 cm were predicted using an XGBoost model—a machine learning approach, by integrating Sentinel-1, Sentinel-2 and field observations in the Zoige Plateau. Results The results showed that SOC content exhibited vertical distribution patterns within 100 cm, with the highest SOC content in topsoil. The tenfold cross-validation approach showed that XGBoost model satisfactorily predicted the spatial patterns of SOCS with a model efficiency of 0.59 and a root mean standard error of 95.2 Mg ha−1. Predicted SOCS showed a distinct spatial heterogeneity in the Zoige Plateau, with an average of 355.7 ± 123.1 Mg ha−1 within 100 cm and totaled 0.27 × 109 Mg carbon. Conclusions High SOC content in topsoil highlights the high risks of significant carbon loss from topsoil due to human activities in the Zoige Plateau. Combining Sentinel-1 and Sentinel-2 satisfactorily predicted SOCS using the XGBoost model, which demonstrates the importance of selecting modeling approaches and satellite images to improve efficiency in predicting SOCS distribution at a fine spatial resolution of 10 m. Furthermore, the study emphasizes the potential of radar (Sentinel-1) in developing SOCS mapping, with the newly developed fine-resolution mapping having important applications in land management, ecological restoration, and protection efforts in the Zoige Plateau.

Aiming at the current power control problems of grid-side electrochemical energy storage power station in multiple scenarios, this paper proposes an optimal power model prediction control (MPC) strategy for electrochemical energy storage power station. This method is based on the power conversion system (PCS) grid-connected voltage and current to establish a power prediction model for energy storage power stations, achieving a one-step prediction of the power of the power station. The power prediction error is used as a power regulation feedback quantity to correct the reference power input. Considering the state of charge ( S O C ) constraint of the battery, partition the S O C into different states. Using S O C as the power regulation feedback, the power of the battery compartment can be adjusted according to the range of the battery S O C to prevent S O C from exceeding the limit value, simultaneously calculating the power loss of the energy storage power station to improve the energy efficiency. The objective function is to minimize the power deviation and power loss of the power station. By solving the objective function, the optimal switching voltage vector of the converter output is achieved to achieve optimal power control of the energy storage power station. The simulation results in various application scenarios of the energy storage power station show that the proposed control strategy enables the power of the storage station to quickly and accurately track the demand of grid scheduling, achieving the optimal power control of the electrochemical energy storage power station. [ABSTRACT FROM AUTHOR]

Li-ion batteries can be charged with different techniques according to the charging time and required capacity usage. Most charging techniques face difficulties when implemented in PV systems due to the intermittent and unpredictable nature of the power supply. This paper addresses the issue of determining the appropriate charging technique for Li-ion batteries in a PV system. We have developed a mode-selective control approach that determines the optimal charging mode according to the given SOC and solar irradiation, aiming to maximize the utilization of the generated PV power. The developed control approach has been implemented using a dual-switched buck converter in the MATLAB/Simulink environment. The key control algorithm focused on regulating current, with different references being used based on the selected charging mode. Three references for charging current were set: the maximum current, the required current assigned based on the given SOC, and the pulsed current. The pulsed current reference was employed during a stage of the charging process to accelerate charging and prevent dissipation of PV power. Furthermore, a gain-scheduled controller with carefully picked control parameters was used to ensure stable operation across different modes. The results proved the effectiveness of the proposed control in reducing charging time and minimizing PV power dissipation without resorting to the use of harmful charging currents. [ABSTRACT FROM AUTHOR]

In order to solve the problem of low efficiency of traditional balanced electric topology, a two-level balanced topology is proposed. This balancing topology divides the battery pack into two forms: Intra-group and inter-group. Buck-Boost equalization circuit is used in the group, and reconfigurable equalization circuit is used between the groups. The intra-group and inter-group can be equalized at the same time, which improves the equalization efficiency. Taking SOC(State of Charge) as the equalization variable, the intra-group equalization algorithm adopts the fuzzy logic control strategy based on SOC to reduce the equalization time and improve the equalization efficiency. MATLAB/Simulink software is used to model and simulate the circuit topology and the results are compared with the traditional Buck-Boost circuit. The simulation results show that compared with the traditional Buck-Boost circuit, the proposed algorithm and equalization topology reduce the equalization time by about 28% under the charge-discharge state, indicating that the equalization circuit and algorithm have good performance. [ABSTRACT FROM AUTHOR]

Heat released from soil organic carbon (SOC) decomposition (referred to as microbial heat hereafter) could alter the soil's thermal and hydrological conditions, subsequently modulate SOC decomposition and its feedback with climate. While understanding this feedback is crucial for shaping policy to achieve specific climate goal, it has not been comprehensively assessed. This study employs the ORCHIDEE‐MICT model to investigate the effects of microbial heat, referred to as heating effect, focusing on their impacts on SOC accumulation, soil temperature and net primary productivity (NPP), as well as implication on land‐climate feedback under two CO2 emissions scenarios (RCP2.6 and RCP8.5). The findings reveal that the microbial heat decreases soil carbon stock, predominantly in upper layers, and elevates soil temperatures, especially in deeper layers. This results in a marginal reduction in global SOC stocks due to accelerated SOC decomposition. Altered seasonal cycles of SOC decomposition and soil temperature are simulated, with the most significant temperature increase per unit of microbial heat (0.31 K J−1) occurring at around 273.15 K (median value of all grid cells where air temperature is around 273.15 K). The heating effect leads to the earlier loss of permafrost area under RCP8.5 and hinders its restoration under RCP2.6 after peak warming. Although elevated soil temperature under climate warming aligns with expectation, the anticipated accelerated SOC decomposition and large amplifying feedback on climate warming were not observed, mainly because of reduced modeled initial SOC stock and limited NPP with heating effect. These underscores the multifaceted impacts of microbial heat. Comprehensive understanding of these effects would be vital for devising effective climate change mitigation strategies in a warming world. [ABSTRACT FROM AUTHOR]

Native forests host important pools of soil organic carbon (SOC). This is a key element not only for ecosystem functioning but also for the global carbon cycle. Globally, and particularly in Argentina, native forests are being rapidly replaced by other land uses, raising questions about the impact of these transformations on SOC and its environmental controls. Based on the construction of the largest SOC database in Argentina to date, we investigated the patterns and controls of changes in SOC stocks associated with the replacement of native forests by other land uses. We constructed the database with a total of 818 sites with SOC data (0–30 cm depth), covering the main ecoregions, to which we added environmental information (e.g. satellite data, soil database and climate database), to study the environmental controls on SOC change after deforestation and on the original SOC content of native forests. Considering all ecoregions and all land use alternatives together, we found an average decrease in SOC stock of 18.2 Mg C ha−1, which represents a loss of more than a quarter of the original SOC stock of the native forest sites. A boosted regression tree explained 89% of the variation in SOC stock change and indicated that the initial forest SOC stock and the post‐deforestation land use were the most important variables explaining this variation (relative influence of 30.9% and 18.2%, respectively). The replacement of native forests by rainfed annual crops resulted in the largest decrease in SOC (−28 Mg C ha−1), which was twice as large as the decrease observed in rangelands (−14 Mg C ha−1). On the contrary, neither irrigated croplands nor tree plantations of fast‐growing species caused a decrease in SOC stocks (p >.10). Climate and soil texture had an indirect effect on SOC changes through a strong influence on the initial SOC stocks in native forests (p <.01). Our study highlighted the significant impact of land use change on SOC stocks, overshadowing other relevant environmental controls. Understanding how the SOC pool responds to land use change, environmental conditions and management practices is essential to increase the effectiveness of practices implemented to improve soil properties and mitigate climate change. [ABSTRACT FROM AUTHOR]

Each application of neurostimulators requires unique stimulation parameter specifications to achieve effective stimulation. Balancing the current magnitude with stimulation resolution, waveform, size, and channel count is challenging, leading to a loss of generalizability across broad neural interfaces. To address this, this paper proposes a highly scalable, programmable neurostimulator with a System-on-Chip (SOC) capable of 32 channels of independent stimulation. The compliance voltage reaches up to ±22.5 V. A pair of 8-bit current-mode DACs support independent waveforms for source and sink operations and feature a user-selectable dual range for low-current intraparenchymal microstimulation with a resolution of 4.31 μ A/bit, as well as high current stimulation for spinal cord and DBS applications with a resolution of 48.00 μ A/bit, achieving a wide stimulation range of 12.24 mA while maintaining high-resolution biological stimulation. A dedicated communication protocol enables full programmable control of stimulation waveforms, effectively improving the range of stimulation parameters. In vivo electrophysiological experiments successfully validate the functionality of the proposed stimulator. This flexible stimulator architecture aims to enhance its generality across a wide range of neural interfaces and will provide more diverse and refined stimulation strategies. [ABSTRACT FROM AUTHOR]

Changes in vegetation and land use alter the biogeochemical cycles of soil C and N, as a result of agricultural practices that alter soil quality and function. Adopting sustainable agricultural practices improves soil health by increasing its capacity to store C in the long term. The objective of this study was to evaluate the content and stocks of C and N and the C:N ratio, under different land uses in an Andosol. Four composite soil samples were collected in a pine-oak forest, conventional avocado, organic avocado and organic macadamia soil uses at 0-20 and 20-40 cm soil depths (n=32). The total C content was determined by the Walkley and Black method (Woerner, 1989), the total N by the Kjeldahl method with the Velp Scientifica's UDK159 model equipment (Bremner and Mulvaney 1982). In order to detect significant differences, a two factor analysis of variance (ANOVA) was applied C (%), N (%), SOC (Mg ha-1 ), Nt (Mg ha-1 ) and C:N ratio variables. When applied, a Tukey comparison of means (p≤0.05) was performed. The results indicate that conventional avocado land use presented the highest C and N content at both soil depths (C: 9.22 and 7.32%, N: 0.70 and 0.40%), while the C:N ratio was higher in the forest land use in the first depth (14.5) and higher in the organic avocado land use in the second soil depth (25.8). The stock of SOC varied from 65.27 to 267.30 (Mg ha-1 ) and the Nitrogen from 4.61 to 14.61 (Mg ha-1 ). The C:N ratio showed significant variations between land uses, specifically in the first depth it ranged from 10.33 (O. Macadamia) to 14.51 (Forest), while in the second depth it varied from 13.09 (Conventional) to 25.83 (O avocado). The pine-oak forest, organic avocado and organic macadamia systems, with the smallest reseivors, were statistically similar only in C content, while the conventional system, with the largest stocks, was differente in C and N content. The change in land use from forest to agricultural caused a significant effect on the carbon and nitrogen content and the stocks in the Andosol soil, which may be attributes to the different agricultural applied practices such as pruning, pesticides and fertilization used in the organic and conventional regimes, respectively. The C:N ratio indicates that mineralization rates decrease as depth increases because the quantity and quality of soil organic matter also decreases. Specifically, the conventional avocado practice compared to organic avocado and macadamia practices maximized crop yields. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Thanks to advancements in silicon photomultiplier sensors (SiPMs) and system-on-chip (SoC) technology, our INFN Roma1 group developed ArduSiPM in 2012, the first all-in-one scintillator particle detector in the literature. It used a custom Arduino Due shield to process fast signals, utilizing the Microchip Sam3X8E SoC's internal peripherals to control and acquire SiPM signals. The availability of radiation-tolerant SoCs, combined with the goal of reducing system space and weight, led to the development of an innovative second-generation board, a better-performing device called Cosmo ArduSiPM, suitable for space missions. The architecture of the new detector is based on the Microchip SAMV71 300 MHz, 32-bit ARM® Cortex®-M7 (Microchip Technology Inc., Chandler, AZ, USA). While the analog front-end is essentially identical to the ArduSiPM, it utilizes components with the smallest possible package. The board fits in a CubeSat module. Thanks to the compact design, the board has two independent channels, with a total weight of only 40 grams within a CubeSat form factor. The ArduSiPM architecture is based on a single microcontroller and fast discrete analog electronics. It benefits from the continued development of SoCs related to the IoT (Internet of Things) market. Compared with a system with a custom ASIC, this architecture based on software and SoC capabilities offers considerable advantages in terms of cost and development time. The ability to incorporate new commercial SoCs, continuously emerging from advancements in the aerospace and automotive industries, provides the system with a robust foundation for sustained growth over the years. A detailed characterization of the hardware and the system's response to different photon fluxes is presented in this article. Additionally, coupling the device with a scintillator was tested at the end of this article as a preliminary trial for future measurements, showing potential for further enhancement of the detector's capabilities. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R2 of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies. [ABSTRACT FROM AUTHOR]

The temperature is a very important parameter for an operating vanadium redox flow battery (VRFB). During charging and discharging, the temperature of VRFB is constantly changing. In this paper, a self‐made 35 kW vanadium stack was charged & discharged at the current density of 100 and 120 mA cm−2 to investigate the change trend of real‐time operating temperature. The results show that the temperature decreases during charging and increases during discharging. And the average temperature of each cycle increases with continuous charge‐discharge cycles. The influences of real‐time temperature on performance of columbic efficiency (CE), voltage efficient (VE), over‐potential, capacity, energy, state of charge (SOC), and pressure loss were further investigated. The results show that the temperature fluctuation largely affects the electrochemical properties of VRFB. With the real‐time average temperature increased from 16.02 °C to 29.91 °C, the VE, capacity, energy and the SOC range of the battery increased, while the CE, the over‐potential and the average pressure loss decreased. This work not only gives data support to temperature management of VFB, but also provides direction to the engineering application of all flow batteries. [ABSTRACT FROM AUTHOR]

Over the past few years, ARM has been the dominant player in embedded systems and System-on-Chips (SoCs). With the emergence of hardware platforms based on the RISC-V architecture, a practical comparison focusing on their energy efficiency and performance is needed. In this study, our goal is to comprehensively evaluate the energy efficiency and performance of ARM and RISC-V SoCs in three different systems. We will conduct benchmark tests to measure power consumption and overall system performance. The results of our study are valuable to developers and researchers looking for the most appropriate hardware platform for energy-efficient computing applications. Our observations suggest that RISC-V Instruction Set Architecture (ISA) implementations may demonstrate lower average power consumption than ARM, but this does not automatically imply a superior performance per watt ratio for RISC-V. The primary focus of the study is to evaluate and compare these ISA implementations, aiming to identify potential areas for enhancing their energy efficiency. Furthermore, to ensure the practical applicability of our findings, we will use the Computational Fluid Dynamics software OpenFOAM. This step serves to validate the relevance of our results in real-world scenarios. It allows us to fine-tune execution parameters based on the insights gained from our initial study. By doing so, we aim not only to provide meaningful conclusions but also to investigate the transferability of our results to practical applications. Our analysis will also scrutinize the capabilities of these SoCs when handling nonsynthetic software workloads, thereby broadening the scope of our evaluation. [ABSTRACT FROM AUTHOR]

In order to reduce the negative influence of wind speed randomness and prediction error on frequency modulation, the reliability of the wind storage system was assessed effectively. In the wind storage frequency modulation system, a state of charge (SOC) adaptive adjustment method for wind speed randomness is proposed. Firstly, through the correlation analysis between the standby capacity of frequency modulation and the output power of wind turbine, the uncertainty of its frequency modulation capacity is revealed. Secondly, in view of the uncertainty of wind turbine frequency modulation, the output power of energy storage frequency modulation is optimized with the goal of minimizing the frequency modulation power deviation of the wind storage front under the framework of model predictive control, and the improved whale optimization algorithm (WOA) is used to solve the problem. Finally, the simulation results show that, under the given 5 min continuous disturbance, the root mean square of frequency regulation of the proposed restoration method is reduced by 56.65% compared to the SOC recovery base point set to 0.5. Under continuous large perturbations, the maximum frequency deviation is reduced by 0.0455 Hz. This effectively shows that this method can not only improve the frequency modulation reliability of wind power system but also improve the continuous frequency modulation capability of energy storage system. [ABSTRACT FROM AUTHOR]

This study explores the feasibility of analyzing soil organic carbon (SOC) in carbonate-rich soils using visible near-infrared spectroscopy (VIS-NIR). Employing a combination of datasets, feature groups, variable selection methods, and regression models, 22 modeling pipelines were developed. Spectral data and spectral data combined with carbonate contents were used as datasets, while raw reflectance, first-derivative (FD) reflectance, and second-derivative (SD) reflectance constituted the feature groups. The variable selection methods included Spearman correlation, Variable Importance in Projection (VIP), and Random Frog (Rfrog), while Partial Least Squares Regression (PLSR), Random Forest Regression (RFR), and Support Vector Regression (SVR) were the regression models. The obtained results indicated that the FD preprocessing method combined with RF, results in the model that is sufficiently robust and stable to be applied to soils rich in calcium carbonate. [ABSTRACT FROM AUTHOR]

Contemporary industry and science expectations towards technological solutions set the bar high. Current approaches to increasing the computing power of standard systems are reaching the limits of physics known to humankind. Fast, programmable systems with relatively low power consumption are a different concept for performing complex calculations. Highly parallel processing opens up a number of possibilities in the context of accelerating calculations. Application of SoC (System On Chip) with FPGA (Field-Programmable Gate Array) enables the delegating of a part of computations to the gates matrix, thereby expediting processing by using parallelization of hardware operations. This paper presents the general concept of using SoC FPGA systems to support the CPU (Central Processing Unit) in many modern tasks. While some tasks might be really hard to implement on an FPGA in a reasonable time, the SoC FPGA platform allows for easy low-level interconnections, and with such virtualized access to the hardware computing resources, it is seen as making FPGAs, or hardware in general, more accessible to engineers accustomed to high-level solutions. The concept presented in the article takes into account the limited resources of cheaper educational platforms, which, however, still provide an interesting and alternative hybrid solution to the problem of parallelization and acceleration of data processing. This allows encountered limitations to be overcome and the flexibility known from high-level solutions and high performance achieved with low-level programming to be maintained without the need for a high financial background. [ABSTRACT FROM AUTHOR]

Recent progress has shown promising advancements in enhancing the Reverse intersystem crossing (RISC) process between high‐energy triplet states (Tn, n ≥ 1) and radiative singlet states (Sm) through the utilization of the classic D‐A‐D' strategy. In this study, 12 molecules employing phenyl carbazole and a peripheral phenanthroimidazole as the donors and fused cores as acceptors are theoretically designed. Additionally, electron‐withdrawing groups such as fluorine and cyano, as well as electron‐donating group like methoxy, are incorporated into anthracene, napthaoxadiazole, and napthothiadiazole derivatives, acting as fused core components. Theoretical analyses reveal that among the molecules examined, eight adhere to the three golden principle rules, demonstrating a large energy gap between S1‐T1, the minimal ΔES1‐T2, and large T2‐T1. Additionally, these molecules exhibit significant Spin‐orbit coupling (SOC) and maintain a strong Hybridized local and charge transfer (HLCT) character, facilitating the fast hRISC from T2 to S1. The auxiliary inclusion of heteroatoms, such as oxygen and sulfur, on the anthracene core, aligns with the prerequisite strategy, ultimately reinforcing the SOC. Furthermore, replacing the methoxy group on the Nz bridge significantly enhances the SOC, resulting in a substantial increase in the hRISC rate (10 +08 s−1). Overall, the findings substantially elevate the photophysical attributes of the designed molecules through the utilization of the hRISC channel. [ABSTRACT FROM AUTHOR]

Aiming at the traditional CUK equalizer can only perform energy equalization between adjacent batteries, if the two single batteries that need to be equalized are far away from each other, there will be the problem of longer energy transmission path and lower equalization efficiency, this paper optimizes the CUK equalizer and optimizes its peripheral selection circuit, which can support the equalization of single batteries at any two positions. The control strategy adopts the open-circuit voltage (OVC) of the battery and the state of charge (SOC) of the battery as the equalization variables, and selects the corresponding equalization variables according to the energy conditions of the two batteries that need to be equalized, and generates the adaptive equalization current with an adaptive PID controller in order to improve the equalization efficiency. Simulation modeling is performed in Matlab/Simulink 2021b, and the experimental results show that the optimized CUK equalizer in this paper improves the equalization time by 25.58% compared with the traditional CUK equalizer. In addition, compared with the mean value difference (MVD) method, the adaptive PID method reduces the equalization time by about 30% in the static and charging and discharging experimental environments, which verifies the superiority of this equalization scheme. [ABSTRACT FROM AUTHOR]

As a superior solution to the developing demand for energy storage, lithium-ion batteries play an important role in our daily lives. To ensure their safe and efficient usage, battery management systems (BMSs) are often integrated into the battery systems. Among other critical functionalities, BMSs provide information about the key states of the batteries under usage, including state of charge (SOC) and state of health (SOH). This paper proposes a data-driven approach for the joint online estimation of SOC and SOH utilizing multi-task learning (MTL) approaches, particularly highlighting cross-stitch units and cross-stitch networks. The proposed model is able to achieve an accurate estimation of SOC and SOH in online applications with optimized information sharing and multi-scale implementation. Comprehensive results on training and testing of the model are presented. Possible improvements for future work are also discussed in the paper. [ABSTRACT FROM AUTHOR]

In the context of the carbon peak and carbon-neutral era, it is crucial to effectively utilize maize straw as a resource for achieving carbon (C) sequestration and emission reduction in rural agriculture. Maize straw carbon undergoes two processes after being added to the soil: mineralization (decomposition) and humification (synthesis) by soil animals and microorganisms. These processes contribute to the reintegration of carbon into the agroecosystem's carbon cycle. However, understanding of the transformation and stabilization of straw carbon, as well as the differences in C fixation capacity in soils with various fertilization treatments in black soils, remains limited. This study aims to quantify the relationship between straw carbon input and organic carbon sequestration in various fertilization treatments of black soil. Based on a long-term positional fertilization trial (45 years) in black soil, 13C-labeled maize straw (1.5 g in 120 g of dry soil) was applied and combined with an in situ incubation method using carborundum tubes. Throughout the 360-day trial, we observed the influence of fertilization on soil total organic C levels, organic carbon δ13C values, maize straw addition rate, and straw C fixation capacity. The decomposition of straw was most prominent during the initial 60 days of the incubation period, followed by a gradual decrease in the rate of decomposition. Compared with day 0, the SOC δ13C value and straw C residue rate were highest in the no-fertilization treatment (CK) after 360 days of incubation. The amount of organic carbon transformed and fixed in the soil was significantly higher in the organic fertilizer treatment (M) compared to other treatments, highlighting the stronger decomposition, transformation, and carbon fixation capacity of straw carbon in the M treatment. Moreover, the highest carbon storage of 43.23 Mg·ha−1 was observed in the M fertilization treatment after 360 days, which was significantly different from other treatments (p < 0.05). The study demonstrates that soil with low fertility exhibits increased sequestration potential for straw carbon. Additionally, organic fertilizer input would increase soil organic carbon storage and facilitate straw carbon conversion. [ABSTRACT FROM AUTHOR]

Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. Here, we employed a microbial ecological model (MEND), driven by downscaled output data from six Earth system models under two Shared Socio‐economic Pathways (SSP1‐2.6 and SSP5‐8.5) scenarios, to simulate long‐term soil biogeochemical processes. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). For the entire soil layer in both forests, we found that, compared to the baseline period of 2009–2020, the mean SOC during 2081–2100 increased by 40.9%–90.6% under the L or T change scenarios, versus 5.2%–31.0% under the W change scenario. However, soil moisture emerged as a key regulator of SOC, MBC and inorganic N dynamics in the topsoil of BF and PF. For example, W change led to SOC gain of 5.5%–37.2%, compared to the SOC loss of 15.5%–18.0% under L or T scenario. Additionally, a further reduction in soil pH by 0.2 units in the BF, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the LTW (simultaneous changes in the three factors) scenario. These results indicate that the results derived solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Plain Language Summary: Accurately predicting carbon‐climate feedbacks relies on understanding the environmental factors regulating soil organic carbon (SOC) storage and dynamics. We aim to analyze the responses of soil microbial and carbon‐nitrogen (C‐N) processes to changes in environmental factors, including litter input (L), soil moisture (W) and temperature (T), and soil pH, in a broadleaf forest (BF) and a pine forest (PF). We found that soil moisture change would be beneficial for SOC accumulation and serves as a key regulator of MBC and inorganic N in topsoil, whereas the change in litterfall or soil temperature are favorable for SOC accumulation in the entire soil profile. Additionally, a further reduction in soil pH by 0.2 units, representing the acid rain effect, significantly resulted in an additional SOC gain by 14.2%–21.3%, compared to the scenario with simultaneous changes in L, W, and T. These results indicate that findings solely from topsoil may not be extrapolated to the entire soil profile. Overall, this study significantly advances our comprehension of how different environmental factors impact the dynamics of SOC and the implications they have for climate change. Key Points: Soil C responses to climate change are depth dependent, therefore, results from just the topsoil may not apply to the entire soil profileSoil moisture change benefits topsoil SOC accumulation, whereas litterfall and soil temperature changes favor SOC accumulation in the entire soil profileWe need to pay more attention to the effects of soil moisture and pH rather than temperature and litter‐input on soil biogeochemical processes [ABSTRACT FROM AUTHOR]

Accurately obtaining the state of charge (SOC) and health (SOH) of all individual batteries in a battery pack can provide support for data acquisition, state estimation, and fault diagnosis. To verify the real-time performance and accuracy of the joint estimation algorithm for high-voltage battery packs composed of 96 individual cells in series, this article applies Simulink to develop a joint estimation algorithm for SOC and SOH based on the first-order RC equivalent circuit model (1RC ECM) and implements the algorithm's cyclic calling for series nodes, enhancing the algorithm's scalability. In the algorithm, the recursive least square method with fitting factor (FFRLS) is applied to calculate OCV, R0, and R1 in the time domain, and dual adaptive extended Kalman filter (DAEKF) is applied to joint estimation of SOC and SOH at multiple time scales. Finally, with the help of dSPACE and FASECU controllers, hardware in the loop (HIL) testing was completed in multiple scenarios. The results showed that the algorithm can accurately calculate the state of individual cells in real time, and even under various initial value deviations, it still has good regression performance, laying the foundation for future applications of electric vehicles. [ABSTRACT FROM AUTHOR]

Copyright of Bulletin of Soil & Water Conservation is the property of Bulletin of Soil & Water Conservation Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)