Search Results (336)

Poplars and aspens (Populus L. spp.) are undervalued options for use in managed landscapes. The genus comprises a multitude of taxa often negatively associated with disease susceptibility and short lifespans; however, it also hosts a diverse range of abiotic stress tolerances. The objective of this study was to generate a relative scale of the predicted drought tolerance of Populus spp. to inform site and taxon selection in managed settings. Utilizing vapor pressure osmometry, this study examined seasonal osmotic adjustment and predicted leaf water potential at the turgor loss point (Ψ_po) among several Populus taxa. All evaluated taxa demonstrated the ability to osmotically adjust (ΔΨ_π100) throughout the growing season. Bigtooth aspen (P. grandidentata Michx.) exhibited the most osmotic adjustment (−1.1 MPa), whereas black cottonwood (P. trichocarpa Torr. & A. Gray ex Hook.) exhibited the least (−0.44 MPa). Across the taxa, the estimated mean Ψ_po values in spring and summer were −1.8 MPa and −2.8 MPa, respectively. Chinese aspen (P. cathayana Rehder) exhibited the lowest Ψ_po (−3.32 MPa), whereas black cottonwood exhibited the highest (−2.47 MPa). The results indicate that drought tolerance varies widely among these ten Populus species and hybrids; bigtooth aspen and Chinese aspen are the best suited to tolerating drought in managed landscapes. Full article

Rotary drilling systems with PDC bits, commonly used for drilling deep wells in the production and exploration of oil and natural gas, frequently encounter severe vibrations. These vibrations can cause significant damage to the drilling system, particularly its downhole components, leading to drilling performance inefficiencies, notably reducing the rate of penetration and incurring high costs. This paper presents a parametric study on a proposed new axial semi-active tool designed to mitigate these unwanted vibrations. The tool, an axial absorber with tunable stiffness and damping coefficients over a wide range, composed of a hybrid magnetorheological elastomer-fluid (MRE-F), is installed above the PDC bit. In this study, the lumped parameter model considering axial and torsional vibrations is followed to assess the effectiveness of including the proposed absorber in the drill-string system’s behavior and to estimate the optimal coefficient values for achieving high-efficiency drilling. The drilling system response shown in this study indicates that, with optimal axial absorber coefficient values, the bit dynamically stabilizes, and unwanted vibrations are minimized, effectively eliminating the occurrence of bit-bounce and stick–slip, even when operating at critical frequencies. The proposed semi-active control tool has been proven to significantly reduce maintenance time, reduce the costs associated with severe vibrations, extend the lifespan of bottom-hole assembly components, and achieve smoother drilling with a simple addition to the drilling system. Full article

Background: CDK 4/6 inhibitors (CDK4/6is) are the first-line treatment for metastatic luminal-like breast cancer (BC). These drugs induce macrocytosis without anemia in most patients. The mechanism for the red blood cell (RBC) changes is unknown. In vitro and animal studies show that RBCs from CDK6-knockout mice have increased membrane fragility, but the clinical impact of CDK4/6is on human RBC lifespan is not known. We sought to determine the impact of CDK4/6is on RBC lifespan and detect changes in the regulation of hemoglobin production. Using the mean corpuscular volume (MCV) measurements at several time points, we can study the evolution of MCV, mean corpuscular hemoglobin concentration (MCHC), and RBC count over time. From this, one can estimate the RBC lifespan under CDK4/6is. Methods: We performed a unicentric retrospective study. Based on published models of RBC population dynamics, we have coded a biologically inspired model which allowed us to extract values for biological parameters, including the RBC lifespan. Results: A total of 122 patients were identified, and 1959 laboratory measurements were analyzed. After the pre-treatment RBCs were replaced, the mean MCV increased by 12.6 femtoliter (fL) (95% Bayesian credible interval [CdI] 13–14), the MCHC increased slightly by 0.69 g/dL (95% CdI 0.42–0.96), and the RBC count decreased by 0.77 × 10⁹/L (95% CdI 0.42 × 10⁹/L–0.96 × 10⁹/L). The net result was a 0.64 g/dL (95% CdI 0.48–0.80) rise in hemoglobin. The mean total RBC lifetime was 118 days (95% CdI 114–122), similar to the value measured in healthy persons. Discussion and Conclusions: These findings suggest that, despite changes in RBC volume, CDK4/6is do not predispose patients to RBC destruction and do not impair regulation of hemoglobin homeostasis. We show that CDK4/6is do not decrease the RBC lifespan in pre-treatment erythrocytes. Unfortunately, this method cannot determine the lifespan of post-treatment RBCs, but further research could help answer this question. Full article

The production of electric vehicle (EV) batteries is playing an increasingly significant role in the decarbonization of the mobility sector. In order for EV batteries to be competitive against internal combustion engines, it is crucial to maximize the primary and secondary life cycles of batteries. This necessitates a battery management system that can ensure performance, safety, and longevity. State of Charge (SoC) estimation is important for such a system, as it ensures efficiency of the battery’s performance, and it is necessary for the prediction of the battery’s health and lifespan. Existing SoC estimation methods heavily depend on laboratory tests, which are both costly and time consuming. Additionally, the simulated nature of laboratory settings cannot guarantee robustness when the same method is applied to field data collected from real-world scenarios. A suitable alternative to this problem is the use of data-driven approaches. The goal of this work is the estimation of SoC with a real-world dataset using neural networks. Furthermore, we demonstrate how explainable AI (xAI) and importance estimate can be applied to inform what signals and which parts of a signal are important for SoC estimation. This helps to reduce redundancy, and it provides more information regarding the relationships within battery cells that are otherwise obscured by the complexity of the battery. The methods that we used resulted in a mean squared error (MSE) of as low as 3 × ${10}^{-4}$, and the information provided by xAI suggested that it is possible to discard up to 25% of the input profile whilst retaining similar performance. Full article

The ageing of cellulose paper-based transformer insulation is a critical factor influencing the reliability and lifespan of power transformers, as insulating paper is not easily replaced or repaired. Therefore, this review explores the degradation mechanisms of insulating paper, emphasising the roles of dissolved gases, chemical markers, and macromolecular characterisation in assessing paper deterioration. Likewise, the impact of moisture and thermal stress on the breakdown of cellulose fibres are discussed, especially acid hydrolysis, which serves as the main degradation mechanism in cellulose insulating paper. Advanced diagnostic techniques for insulation condition monitoring, such as molecular simulations, glass transition temperature analysis, and DP estimation models, are highlighted. Furthermore, special attention is given to natural esters as alternative insulating liquids, demonstrating their ability to slow cellulose ageing through moisture absorption, hydrogen bonding stabilisation, and transesterification reactions. This paper also evaluates key chemical markers, including 2FAL and methanol, for estimating paper degradation. A comprehensive understanding of these mechanisms and diagnostic approaches can enhance predictive maintenance strategies and improve transformer longevity. Full article

Forests and scrubland comprise a large proportion of terrestrial ecosystems and, due to the long lifespan of trees and shrubs, their capacity to grow and store carbon as lasting woody tissues is particularly sensitive to warming-enhanced drought occurrence. Climate change may trigger a transition from forests to scrubland in many drylands during the coming decades due to the higher resilience of shrubs. However, we lack standardized frameworks to compare the response to drought of woody plants. We present a framework and develop an index to estimate the drought-induced vulnerability (DrVi) of trees and shrubs based on the radial growth trajectory and the response of growth variability to a drought index. We used tree-ring width series of three tree (Pinus halepensis Mill., Juniperus thurifera L., and Acer monspessulanum L.) and three shrub (Juniperus oxycedrus L., Pistacia lentiscus L., and Ephedra nebrodensis Tineo ex Guss.) species from semi-arid areas to test this framework. We compared the DrVi values between species and populations and explored their temporal changes. Across species, the strongest DrVi values were found in declining P. halepensis stands and J. oxycedrus from the same site, while the lowest DrVi values were found in A. monspessulanum, P. lentiscus, and E. nebrodensis. Across populations, J. oxycedrus presented higher vulnerability in one of the dry sites. The P. halepensis declining stand showed a steady increase in DrVi value after the 1980s as the climate shifted toward warmer and drier conditions. We conclude that the DrVi allows comparing species and populations using a standardized general framework. Full article

Italy ranks among the leading countries in photovoltaic (PV) adoption, having installed 6.80 GW of new PV capacity, bringing the total installed capacity to 37.09 GW in 2024. However, this widespread deployment also leads to a substantial amount of PV waste as systems reach the end of their lifespan. This study aims to estimate the volume of PV waste expected to be generated in Italy due to the decommissioning of end-of-life (EoL) PV panels and to explore landfill and recovery scenarios that could offer the most sustainable management strategies. The findings indicate that 4520 kilotonnes of PV waste will be produced in Italy between 2030 and 2050. Of this, a significant share consists of glass (2704.9 kilotonnes) and aluminum (762.1 kilotonnes). Additionally, Italy will produce 174.6 kt of landfill waste in 2036. In 2049 and 2050, the total composition recovery is predicted to reach 571 kt and 604.7 kt, respectively. To summarize, the main contributions of this work include (1) projections of the EoL of crystalline silicon PV waste by material quantity for 2050, (2) the economic value share of PV module materials based on waste estimates and recovery, and (3) the estimation of the EoL solar compositions generated by 2050. Full article

Accurately estimating the State of Charge (SOC) of power batteries is crucial for the Battery Management Systems (BMS) in new energy intelligent connected vehicles. It directly influences vehicle range, energy management efficiency, and the safety and lifespan of the battery. However, SOC cannot be measured directly with instruments; it needs to be estimated using external parameters such as current, voltage, and internal resistance. Moreover, power batteries represent complex nonlinear time-varying systems, and various uncertainties—like battery aging, fluctuations in ambient temperature, and self-discharge effects—complicate the accuracy of these estimations. This significantly increases the complexity of the estimation process and limits industrial applications. To address these challenges, this study systematically classifies existing SOC estimation algorithms, performs comparative analyses of their computational complexity and accuracy, and identifies the inherent limitations within each category. Additionally, a comprehensive review of SOC estimation technologies utilized in BMS by automotive OEMs globally is conducted. The analysis concludes that advancing multi-fusion estimation frameworks, which offer enhanced universality, robustness, and hard real-time capabilities, represents the primary research trajectory in this field. Full article

Lithium-ion batteries (LIBs) are widely utilized in consumer electronics, electric vehicles, and large-scale energy storage systems due to their high energy density and long lifespan. Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of cells is crucial to ensuring their safety and preventing potential risks. Existing state estimation methodologies primarily rely on electrical signal measurements, which predominantly capture electrochemical reaction dynamics but lack sufficient integration of thermomechanical process data critical to holistic system characterization. In this study, relevant thermal and mechanical features collected during the formation process are extracted and incorporated as additional data sources for battery state estimation. By integrating diverse datasets with advanced algorithms and models, we perform correlation analyses of parameters such as capacity, voltage, temperature, pressure, and strain, enabling precise SOH estimation and RUL prediction. Reliable predictions are achieved by considering the interaction mechanisms involved in the formation process from a mechanistic perspective. Full lifecycle data of batteries, gathered under varying pressures during formation, are used to predict RUL using convolutional neural networks (CNN) and Gaussian process regression (GPR). Models that integrate all formation-related data yielded the lowest root mean square error (RMSE) of 2.928% for capacity estimation and 16 cycles for RUL prediction, highlighting the significant role of surface-level physical features in improving accuracy. This research underscores the importance of formation features in battery state estimation and demonstrates the effectiveness of deep learning in performing thorough analyses, thereby guiding the optimization of battery management systems. Full article

In the era of Industry 4.0, equipment maintenance is shifting toward data-driven strategies. Traditional methods rely on usage time or cycle counts to estimate component lifespan. This often causes early replacement of parts, leading to increased production costs. This study focuses on the cutter system of a roller laminator used in printed circuit board (PCB) manufacturing. An accelerometer is used to collect vibration signals under normal and abnormal states. Fast Fourier transform (FFT) is used to convert time-domain data into the frequency domain, then key statistical features from critical frequency bands are extracted as independent variables. The study applies logistic regression (LR), random forest (RF), and support vector machine (SVM) for predictive modeling of the cutting tool’s condition. The results show that the prediction accuracies of these models are 87.55%, 93.77%, and 94.94%, respectively, with SVM performing the best. Full article

The performance analysis of a 10 kW_p rooftop grid connected solar photovoltaic (PV) system located in Sagolband, Imphal, India has been studied for 5 years. The key technical parameters such as array yield ($Y_A$), reference yield ($Y_R$), final yield ($Y_F$), capacity utilization factor (CUF), PV system efficiency (${\eta }_{Sys}$), and performance ratio (PR) were used to investigate its performance. In this study, the experimentally measured results of the system’s performance for the five years (i.e., July 2018 to June 2023) were compared with the predicted results, which were obtained using PVsyst V7.3.0 software. The measured energy generation in 5 years (including 40 days OFF due to inverter failure on 17 June 2019 because of a surge, which was resolved on 27 July 2019) was 58,911.3 kWh as compared to the predicted 77,769 kWh. The measured daily average energy yield was 3.2 kWh/kW_p as compared to the predicted 4.2 kWh/kW_p. It can be seen that there was a large difference between the real and predicted values, which may be due to inverter downtime, local environmental variables (e.g., lower-than-expected solar irradiation and temperature impacts), and the possible degradation of photovoltaic modules over time. The measured daily average PR of the system was 70.71%, and the maximum occurred in the months of October, November, December, and January, which was almost similar to the predicted result. The measured daily average CUF of the system was 13.36%, and the maximum occurred in the months of March, April, and May. The measured daily average system efficiency was 11.31%. Moreover, the actual payback was 4 years and 10 months, indicating strong financial viability despite the system’s estimated lifespan of 25 years. This study highlights the importance of regular maintenance, fault detection, and better predictive modelling for more accurate energy projections, and also offers an understanding of real-world performance fluctuations. Full article

Supercapacitors (SCs) are increasingly recognized as a reliable energy storage solution in various industrial applications due to their high power density and exceptionally long lifespan. SC-powered systems demand precise parameter identification to enable effective energy management. Although various approaches exist for the offline identification of SCs, some parameters depend on factors such as state of health (SoH), aging, temperature, and their combination. Consequently, the variation in parameter values under different conditions highlights the importance of online identification based on a dynamic model structure. Among various SC models proposed in the literature, fractional-order models offer greater accuracy, making them a superior choice for SC modeling. However, the conventional formulation in these models requires a very long window of samples and coefficients for filter implementation. Additionally, due to the several orders of magnitude difference in the elements of matrices, numerical instability can arise, leading to errors and drift in the final calculations. In this paper, a novel online identification approach is introduced for differential order estimation in fractional-order SC models. The proposed method significantly shortens the long window while maintaining accuracy, making it feasible for implementation in low-cost microcontrollers and a viable solution for real-world applications. In addition, the proposed method addresses the drift error by applying online least squares error estimation that aligns it with its offline estimated value. Full article

One of the most critical items from the reliability and the State-of-Health (SOH) point of view of wireless sensor networks is represented by lithium batteries. Predicting the SOH of batteries in sensor-equipped smart grids is crucial for optimizing energy management, preventing failures, and extending battery lifespan. Accurate SOH estimation enhances grid reliability, reduces maintenance costs, and facilitates the efficient integration of renewable energy sources. In this article, a solution for SOH prediction and the estimation of the Remaining Useful Life (RUL) of lithium batteries is presented. The approach was implemented and tested using two training datasets: the first consists of raw data provided by the Prognostics Center of Excellence at NASA, comprising 168 records, while the second is based on the curve fitting of the measured data using a single exponential degradation model. Long Short-Term Memory networks (LSTMs) were trained using data from three different scenarios, where battery cycle consumption reached 30%, 50%, and 65% correspondingly. Various architectures and hyperparameters were explored to optimize the models’ performance. The key finding is that training one of the models with only 50 records (equivalent to 30% of battery usage) enables accurate SOH prediction, achieving a Mean Squared Error (MSE) of $1.68\times {10}^{-4}$ and Root Mean Squared Error (RMSE) of $1.30\times {10}^{-2}$. The best model trained with 110 records achieved an MSE of $2.51\times {10}^{-5}$ and an RMSE of $5.01\times {10}^{-3}$. Full article

A fixed ratio amount, i.e., L-arginine (LA) and trisodium phosphate dodecahydrate (TSP) at 2:0.25, is considered as a hybrid inhibitor. This research aims to extensively investigate the impact of utilizing the hybrid corrosion inhibitor on the corrosion resistance properties in accelerated condition, mechanical characteristics, and predictive estimation of the lifespan of reinforced concrete (RC) structures. Various experiments, such as setting time, slump, air content, porosity, compressive strength, and chloride diffusion coefficient, were conducted to elucidate the influence of the hybrid corrosion inhibitor on the mechanical properties of the concrete matrix. Meanwhile, linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) in 10 wt. % NaCl under wet–dry cycles are utilized to assess the corrosion resistance property, corrosion initiation time, and kinetics of the passive film formation on the steel rebar. Alternatively, both deterministic and probabilistic-based predictions of service life by Life 365 software are utilized to demonstrate the efficacy of the hybrid corrosion inhibitor in protecting the steel rebar in RC structures. All the results confirm that the HI-4 mix (LA:TSP = 3.56:0.44) exhibits excellence in preventing the corrosion and extending the service life of RC structures, due to the adsorption of inhibitor molecules and formation of P-Zwitterions-(Cl)-Fe, Zwitterions-(Cl)-Fe, and FePO₄ complexes onto the steel rebar surface. However, HI-3 shows the optimal mechanical and electrochemical properties for RC structures. Full article

The optimization of the manufacturing conditions in milling processes composes a crucial task for enhancing machining efficiency and extending the tool’s lifespan. This study presents an investigation of the cutting tool’s performance under varying machining parameters via the generation of an experimental dataset that was obtained through laboratory-controlled milling operations. Based on this dataset, artificial intelligence (AI) models, including artificial neural network (ANN), k-nearest neighbors (KNN), and support vector regression (SVR), were developed in order to predict the tool’s life as a function of the milling conditions. Additionally, finite element method (FEM) simulations were conducted to estimate tool wear and analyze the manufacturing process at a numerical level. In particular, FE models were utilized to compute the milling forces and the corresponding developed stress fields, as well as to assess the cutting tool’s performance based on certain machining variables. Furthermore, a comparative analysis between AI-driven forecasts and FEM simulations was performed to evaluate their effectiveness and reliability. The findings provide insights into the advantages and limitations of both methodologies, guiding the optimization of coated carbide tool performance. The outcomes of this study contribute to the advancement of predictive modeling in machining processes, offering a data-driven approach for improved tool wear assessment. Full article