Search Results (159)

Accurate prediction of lithium-ion battery capacity degradation is crucial for reliable state-of-health estimation and long-term performance assessment in battery management systems. This study presents an empirical modeling approach based on experimental data collected from four lithium iron phosphate (LFP) battery packs cycled over 75 to 100 charge–discharge cycles. Several mathematical models—including linear, quadratic, single-exponential, and double-exponential functions—were evaluated for their predictive accuracy. Among these, the linear and single-exponential models demonstrated strong performance in early-cycle predictions. It was found that using 30 to 40 cycles of data is sufficient for reliable forecasting within a 100-cycle range, reducing the mean absolute error by over 80% compared to using early-cycle data alone. Although these models provide reasonable short-term predictions, they fail to capture the nonlinear degradation behavior observed beyond 80 cycles. To address this, a modified linear model was proposed by introducing an exponentially decaying slope. The modified linear model offers improved long-term prediction accuracy and robustness, particularly when data availability is limited. Capacity forecasts based on only 40 cycles yielded results comparable to those using 100 cycles, demonstrating the model’s efficiency. End-of-life estimates based on the modified linear model align more closely with typical LFP specifications, whereas conventional models tend to underestimate the cycle life. The proposed model offers a practical balance between computational simplicity and predictive accuracy, making it well suited for battery health diagnostics. Full article

Radar echo extrapolation is a critical forecasting tool in the field of meteorology, playing an especially vital role in nowcasting and weather modification operations. In recent years, spatiotemporal sequence prediction models based on deep learning have garnered significant attention and achieved notable progress in radar echo extrapolation. However, most of these extrapolation network architectures are built upon convolutional neural networks, using radar echo images as input. Typically, radar echo intensity values ranging from −5 to 70 dBZ with a resolution of 5 dBZ are converted into 0–255 grayscale images from pseudo-color representations, which inevitably results in the loss of important echo details. Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. These variables are encoded jointly with high-resolution (0.5 dB) radar mosaic data to form multiple radar cells as input. A multi-channel radar echo extrapolation network architecture (MR-DCGAN) is then designed based on the DCGAN framework; (3) Since radar echo decay becomes more prominent over longer extrapolation horizons, this study departs from previous approaches that use a single model to extrapolate 120 min. Instead, it customizes time-specific loss functions for spatiotemporal attenuation correction and independently trains 20 separate models to achieve the full 120 min extrapolation. The dataset consists of radar composite reflectivity mosaics over North China within the range of 116.10–117.50°E and 37.77–38.77°N, collected from June to September during 2018–2022. A total of 39,000 data samples were matched with the initial zero-hour fields from RMAPS-NOW, with 80% (31,200 samples) used for training and 20% (7800 samples) for testing. Based on the ConvLSTM and the proposed MR-DCGAN architecture, 20 extrapolation models were trained using four different input encoding strategies. The models were evaluated using the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Compared to the baseline ConvLSTM-based extrapolation model without physical variables, the models trained with the MR-DCGAN architecture achieved, on average, 18.59%, 8.76%, and 11.28% higher CSI values, 19.46%, 19.21%, and 19.18% higher POD values, and 19.85%, 11.48%, and 9.88% lower FAR values under the 20 dBZ, 30 dBZ, and 35 dBZ reflectivity thresholds, respectively. Among all tested configurations, the model that incorporated three physical variables—relative humidity (rh), u-wind, and v-wind—demonstrated the best overall performance across various thresholds, with CSI and POD values improving by an average of 16.75% and 24.75%, respectively, and FAR reduced by 15.36%. Moreover, the SSIM of the MR-DCGAN models demonstrates a more gradual decline and maintains higher overall values, indicating superior capability in preserving echo structural features. Meanwhile, the comparative experiments demonstrate that the MR-DCGAN (u, v + rh) model outperforms the MR-ConvLSTM (u, v + rh) model in terms of evaluation metrics. In summary, the model trained with the MR-DCGAN architecture effectively enhances the accuracy of radar echo extrapolation. Full article

This study proposes an advanced thermodynamic energy equation to accurately simulate the stress–dilatancy relationship in granular soils for both uncrushed and crushed sands. Traditional energy formulations primarily consider dissipation energy and often neglect the role of free energy. Recent developments have introduced free energy components to account for plastic energy contributions from dilation and particle crushing. However, significant discrepancies between theoretical predictions and experimental observations remain, largely due to the omission of complex mechanisms such as contact network rearrangement, force-chain buckling, grain rolling, rotation without slip, and particle crushing. To address these gaps, the proposed model incorporates dual exponential decay functions into the free energy framework. Rather than explicitly modeling each mechanism, this formulation aims to phenomenologically capture the interplay between fundamentally opposing thermodynamic forces arising from complex mechanisms during granular microstructure evolution. The model’s applicability is validated using the experimental results from both uncrushed silica sand and crushed calcareous sand. Through extensive comparison with over 100 drained triaxial tests on various sands, the proposed model shows substantial improvement in reproducing stress–dilatancy behavior. The average discrepancy between predicted and measured $\eta –D$ relationships is reduced to below 15%, compared to over 60% using conventional models. This enhanced energy equation provides a robust and practical tool for predicting granular soil behavior, supporting a wide range of geotechnical engineering applications. Full article

We investigate an inverse problem involving source and damping term with variable-exponent nonlinearities. We establish adequate conditions on the initial data for the decay of solutions as the integral overdetermination approaches zero over time within an acceptable range of variable exponents. This class of inverse problems, where internal terms such as source and damping are to be determined from indirect measurements, has significant relevance in real-world applications—ranging from geophysical prospecting to biomedical engineering and materials science. The accurate identification of these internal mechanisms plays a crucial role in optimizing system performance, improving diagnostic accuracy, and constructing predictive models. Therefore, the results obtained in this study not only contribute to the theoretical understanding of nonlinear dynamic systems but also provide practical insights for reconstructive analysis and control in applied settings. The asymptotic behavior and decay conditions we derive are expected to be of particular interest to researchers dealing with stability, uniqueness, and identifiability in inverse problems governed by nonstandard growth conditions. Full article

The thermal management system (TMS) of electric vehicles (EVs) plays a pivotal role in vehicle performance, driving range, battery lifespan, and passenger comfort. Precise control of compressor speed, informed by real-time sensor data, is essential for improving TMS efficiency and extending EV range. This study proposes a control strategy based on the PID Search Algorithm (PSA), ensuring optimal thermal management for an integrated battery and cabin TMS. A co-simulation platform combining AMESim and Simulink is developed for validation, utilizing various sensors to monitor system performance. Simulations are conducted under target temperatures of 20 °C and 25 °C to replicate various operating conditions. The optimized strategy is compared with the most commonly used PID controllers, fuzzy controllers, and PID fuzzy control strategies. The results demonstrate that the PSA-Optimized control strategy significantly outperforms the other three strategies. For a target of 25 °C, the PSA-Optimized control strategy shows a minimal temperature overshoot of 0.012 °C, with COP improvements of 0.06, 0.04, and 0.03 compared to the other three control strategies, respectively. For a target of 20 °C, the overshoot is further reduced to 0.010 °C, while the coefficient of performance (COP) increases by 0.14, 0.01, and 0.07 relative to the same benchmarks. Overall, the results indicate that the PSA-Optimized control strategy effectively utilizes sensor data to reduce cabin temperature overshoot, stabilize compressor speed fluctuations, slow the decay of the battery’s state of charge (SOC), and enhance the system’s COP. Full article

Background/Objectives: Gliomas, including the most aggressive subtype—glioblastoma multiforme, are brain tumors with an unfavorable prognosis and high mortality. Early diagnosis is essential to improve treatment efficacy. Positron emission tomography PET with O-(2-[¹⁸F] fluoroethyl)-L-tyrosine ([¹⁸F]FET) has been supported by clinical studies for its role in diagnosis and monitoring the disease. However, the low availability of [¹⁸F]FET in Italy has limited its use in clinical praxis. This study describes the technological transfer of the radiopharmaceutical IASOglio^® (the commercial [¹⁸F]FET developed by Curium Pharma in Italy), with the aim of improving national access to this advanced diagnostic technology. Methods: Three consecutive batches were produced using the automated Trasis AllinOne module, and quality control was performed, including chemical and microbiological tests, to successfully validate the production process. Additionally, the stability of the radiopharmaceutical for its entire shelf life has been demonstrated with stability testing at 14 h after end of synthesis (EOS). Results: The production of [¹⁸F]FET achieved a non-corrected yield between 49% and 52%, with a corrected decay rate ranging from 73% to 79%. The process met the required quality specifications, including bio-burden control and filter integrity. The technological transfer was successfully completed, and production authorization was obtained from the Italian Medicines Agency (AIFA) for the Officina Farmaceutica of Institute of Clinical Physiology of the National Research Council (CNR-IFC) located in Pisa. Conclusions: Local production of [¹⁸F]FET in Italy marks a milestone in glioma diagnosis, thereby contributing to timely treatment and improved clinical outcomes. Full article

The coffee roasting by-product, coffee silverskin, represents a serious problem in environmental pollution. Still, it is also an interesting source of chemical compounds that can be recovered and used in the food industry to improve the physical, chemical, and sensory properties of a wide range of food products. This study aimed to evaluate, for the first time, the effect of the coffee silverskin extract (CSE), applied as a dipping treatment, in preserving the storage and the qualitative decay of fresh-cut fennel slices during 14 days of storage at 4 °C. The experimental plan evaluated two dipping solutions (5% and 10%) with coffee silverskin extract and compared them with a conventional dipping in 2% ascorbic acid and a control (water). The use of CSE in the dipping of fresh-cut fennel permitted an increase in the phenolic (chlorogenic and caffeic acids) content for up to 14 days, with good sensory acceptability and physico-chemical and microbiological characteristics. To date, no applications of CSE in this form have been reported, nor has any food by-product extract been investigated for the preservation of fresh-cut fennel, which makes this study a novel contribution to the development of sustainable treatments for minimally processed vegetables. Full article

Photoacoustic tomography (PAT), also known as optoacoustic tomography, has been emerging as a biomedical imaging modality that can provide cross-sectional or three-dimensional (3D) visualization of biological tissues such as blood vessels and lymphatic vessels in vivo at high resolution. The principle behind the visualization involves the light being absorbed by the tissues which results in the generation of ultrasound. Depending on the strength of ultrasound and its decay rate, it could be used to visualize the absorber location. In general, pulsed lasers such as the Q-switched Nd-YAG and OPO lasers that provide high-energy widths in the range of a few nanoseconds operating at low repetition rates are commonly used as a light source in photoacoustic imaging. However, such lasers are expensive and occupy ample space. Therefore, PAT systems that use LED as the source instead of lasers, which have the advantage of being obtainable at low cost and portable, are gaining attention. However, LED light sources have significantly low energy, and the photoacoustic signals generated have a low signal-to-noise ratio (SNR). Therefore, in LED-based systems, one way to strengthen the signal and improve the SNR is to significantly increase the repetition rate of LED pulses and use signal processing, which can be achieved using a high-power LED along M-sequence signal decoding. M-sequence signal decoding is effective, especially under high repetition rates, thus improving the SNR. However, power supplies for high-power LEDs have a circuit jitter, resulting in random temporal fluctuations in the emitted light. Such jitters, in turn, would affect the M-sequence-based signal decoding. Therefore, we propose a new decoding algorithm which compensates for LED jitter in the M-sequence signal processing. We show that the proposed new signal processing method can significantly improve the SNR of the photoacoustic signals. Full article

In a plant factory, maintaining proper and uniform air/moisture movement above the crop canopy is crucial for aiding plant growth. This research has utilized a three-dimensional computation model to investigate airflow and heat transfer in a plant factory, where airflow, heat, and humidity distributions above plant crops were calculated concerning five categories of crop planting density (P_d) and air change rate (ACH) in the crop area. Spatial uniformities of airflow velocity, temperature, and relative humidity immediately above the crops are evaluated using the objective uniformity parameter (OU), relative standard deviation of temperature (RSD_T) and relative standard deviation of relative humidity (RSD_RH), respectively. Furthermore, a factor of effectiveness (θ) is defined, depending on the uniformity of velocity, temperature, and relative humidity distribution, to comprehensively evaluate the impact of various ACH with P_d on overall effectiveness. Full numerical results show that air velocity, temperature, and relative humidity above the crops are notably influenced by P_d and ACH. As ACH increases, the OU of the air above the indoor crop also expands. Moreover, higher OU values are observed for smaller crop P_d. However, excessively small crop area planting densities and excessively large ACH do not result in a higher OU for the air above the crop. As ACH increases, both RSD_T and RSD_RH decay for the whole range of crop P_d. Moreover, smaller P_d values could achieve the uniformity of thermal fields, while having minimal effects on the relative humidity distributions. Generally, increasing ACH and decreasing P_d could enhance overall value of θ. However, excessively increasing ACH and decreasing P_d does not have a significant effect on θ, which is jointly influenced by OU, RSD_T, and RSD_RH. Therefore, a more suitable combination of ACH and P_d is urgently required to improve the design of agricultural system to enhance crop microclimate uniformity for optimal plant growth and productivity. Full article

Reconstituted high-density lipoprotein nanoparticles (NPs), which mimic the structure and function of endogenous human plasma HDL, hold promise as a robust drug delivery system. These nanoparticles, when loaded with appropriate agents, serve as powerful tools for targeted drug delivery. The fundamental challenge lies in controlling and estimating the actual drug load and the efficiency of drug release at the target. In this report, we present a novel approach based on enhanced Förster Resonance Energy Transfer (FRET) to assess particle load and monitor payload release. The NPs are labeled with donor molecules embedded in the lipid phase, while the spherical core volume is filled with acceptor molecules. Highly enhanced FRET efficiency to multiple acceptors in the NP core has been observed at distances significantly larger than the characteristic Förster distance (R₀). To confirm that the observed changes in donor and acceptor emissions are a result of FRET, we developed a theoretical model for nonradiative energy transfer from a single donor to multiple acceptors enclosed in a spherical core volume. The load-dependent shortening of the fluorescence lifetime of the donor correlated with the presence of a negative component in the intensity decay of the acceptor clearly demonstrates that FRET can occur at a large distance comparable to the nanoparticle size (over 100 Å). Comparison of theoretical simulations with the measured intensity decays of the donor and acceptor fluorophores constitute a new method for evaluating particle load. The observed FRET efficiency depends on the number of acceptors in the core, providing a simple way to estimate the nanoparticle load efficiency. Particle disintegration and load release result in a distinct change in donor and acceptor emissions. This approach constitutes a novel strategy for assessing NP core load, monitoring NP integrity, and evaluating payload release efficiency to target cells. Significants: In the last decade, nanoparticles have emerged as a promising strategy for targeted drug delivery, with applications ranging from cancer therapy to ocular neurodegenerative disease treatments. Despite their potential, a significant issue has been the real-time monitoring of these drug delivery vehicles within biological systems. Effective strategies for monitoring NP payload loading, NP integrity, and payload release are needed to assess the quality of new drug delivery systems. In our study, we have found that FRET-enabled NPs function as an improved method for monitoring these aspects currently missing from current drug delivery efforts. Full article

This paper introduces an alternative forecasting approach that leverages the application of visibility graphs in the context of multivariate energy forecasting for a regional airport, which incorporates energy demand of diverse types of buildings and wind power generation. The motivation for this research stems from the urgent need to enhance the accuracy and reliability of load forecasting in microgrids, which is crucial for optimizing energy management, integrating renewable sources, and reducing operational costs, thereby contributing to more sustainable and efficient energy systems. The proposed methodology employs visibility graph transformations, the superposed random walk method, and temporal decay adjustments, where more recent observations are weighted more significantly to predict the next time step in the data set. The results indicate that the proposed method exhibits satisfactory performance relative to comparison models such as Exponential smoothing, ARIMA, Light Gradient Boosting Machine and CNN-LSTM. The proposed method shows improved performance in forecasting energy consumption for both stationary and highly variable time series, with SMAPE and NMRSE values typically in the range of 4–10% and 5–20%, respectively, and an $R^2$ reaching 0.96. The proposed method affords notable benefits to the forecasting of energy demand, offering a versatile tool for various kinds of structures and types of energy production in a microgrid. This study lays the groundwork for further research and real-world applications within this field by enhancing both the theoretical and practical aspects of time series forecasting, including load forecasting. Full article

Hybrid organohalide perovskites have received considerable attention due to their exceptional photovoltaic (PV) conversion efficiencies in optoelectronic devices. In this study, we report the development of a highly sensitive, self-powered perovskite-based photovoltaic photodiode (PVPD) fabricated by incorporating a poly(amic acid)-polyimide (PAA-PI) copolymer as an interfacial layer between a methylammonium lead iodide (CH₃NH₃PbI₃, MAPbI₃) perovskite light-absorbing layer and a poly(3,4-ethylenedioxythiophene)-poly(styrene sulfonate) (PEDOT: PSS) hole injection layer. The PAA-PI interfacial layer effectively suppresses carrier recombination at the interfaces, resulting in a high power conversion efficiency (PCE) of 11.8% compared to 10.4% in reference devices without an interfacial layer. Moreover, applying the PAA-PI interfacial layer to the MAPbI₃ PVPD significantly improves the photodiode performance, increasing the specific detectivity by 49 times to 7.82 × 10¹⁰ Jones compared to the corresponding results of reference devices without an interfacial layer. The PAA-PI-passivated MAPbI₃ PVPD also exhibits a wide linear dynamic range of ~103 dB and fast response times, with rise and decay times of 61 and 18 µs, respectively. The improved dynamic response of the PAA-PI-passivated MAPbI₃ PVPD enables effective weak-light detection, highlighting the potential of advanced interfacial engineering with PAA-PI interfacial layers in the development of high-performance, self-powered perovskite photovoltaic photodetectors for a wide range of optoelectronic applications. Full article

The notion of RNA-based therapeutics has gained wide attractions in both academic and commercial institutions. RNA is a polymer of nucleic acids that has been proven to be impressively versatile, dating to its hypothesized RNA World origins, evidenced by its enzymatic roles in facilitating DNA replication, mRNA decay, and protein synthesis. This is underscored through the activities of riboswitches, spliceosomes, ribosomes, and telomerases. Given its broad range of interactions within the cell, RNA can be targeted by a therapeutic or modified as a pharmacologic scaffold for diseases such as nucleotide repeat disorders, infectious diseases, and cancer. RNA therapeutic techniques that have been researched include, but are not limited to, CRISPR/Cas gene editing, anti-sense oligonucleotides (ASOs), siRNA, small molecule treatments, and RNA aptamers. The knowledge gleaned from studying RNA-centric mechanisms will inevitably improve the design of RNA-based therapeutics. Building on this understanding, we explore the physiological diversity of RNA functions, examine specific dysfunctions, such as splicing errors and viral interactions, and discuss their therapeutic implications. Full article

In this paper, the thermal insulation performance of a wooden house was evaluated with infrared thermographies which were captured by a non-contact and non-destructive method. Heat transmissions were determined by the difference between surface temperature of outdoor and indoor sides of the walls, which were measured with an IR ray signal, and indoor and outdoor air temperatures. The heat transmission coefficient, which was determined by IR thermography, was compared to the coefficient calculated with thermal conductivities of wall component materials. The heat transmission coefficient calculated through wall components was 0.24 W/m²·K, while the coefficients determined with IR thermography ranged from 0.27 to 4.61 W/m²·K. The invisible thermal insulation defects in the wall, such as heat losses from the premature deterioration of thermal insulation material and air leakage through windows, were observed by IR thermography. It is expected that the results of this study could be used effectively not only for improving thermal insulation performance but also for suppressing decay occurrence in wooden building materials. Full article

To address the demand for high-precision insulation testing in modern complex cable networks, this study proposes and implements an intelligent insulation testing system based on FPGA technology. The system integrates decision tree (DT) models to enable efficient anomaly detection and process optimization. Utilizing the voltage-divider principle for insulation testing, the system extracts features such as maximum values, minimum values, and entropy to construct an explainable classification model capable of accurately monitoring different types of currents (leakage, absorption, and capacitive currents) during their decay processes. An adaptive post-processing filtering method is introduced to enhance classification accuracy and optimize testing efficiency by minimizing redundant range switching. Experimental results demonstrate that the proposed system achieves exceptional recognition accuracy and process stability across a wide range of resistances, significantly advancing the intelligence of insulation testing while improving measurement efficiency by 54.71%. This innovative solution provides a robust approach for high-demand electrical performance assessments. Full article