Spectral Analysis Research Articles

Thermocapillary convection in a cuboid pool with a sidewall of different temperature sections

Thermocapillary convection in a liquid cuboid pool with a sidewall of different temperature sections is investigated using 3D numerical simulation. One half section of the sidewall is heated but the other cooled. Numerical simulation is performed for Marangoni number (Ma) from 100 to 4.0 × 106 with Prandtl number (Pr) of 28.6 and aspect ratios (Ah and Aw) of 0.43. A temperature field with a weak thermocapillary convection flow is dominated by conduction for small Ma. As Ma increases, the flow becomes stronger and reveals a complex transition route to a chaotic state with a series of bifurcations. It has been demonstrated that the symmetry of the temperature field breaks between Ma = 102 and 103. A Hopf bifurcation occurs between Ma = 8.0 × 105 and 9.0 × 105 in which the flow becomes periodic. As Ma increases further, a sequence of period-doubling bifurcations occurs between Ma = 2.78 × 106 and 2.95 × 106. Once Ma exceeds 3.0 × 106, the flow becomes chaotic. Typical thermocapillary convection flows in the pool are characterized with topological analyses, spectral analyses, attractors, Lyapunov exponents, and fractal dimensions. Furthermore, the heat and mass transfer in the cuboid pool have been investigated.

Blade imbalance fault identification in doubly fed induction generator through current signature analysis using wavelet transform

Using wind turbines (WTs) equipped with doubly fed induction generators (DFIG) is a popular technology for generating renewable energy. To ensure safe operation, prompt maintenance, and better operational reliability, the induction generator used in wind energy must be monitored. In this paper, an analysis is carried out on stator currents of the DFIG machine in a wind farm to identify any blade imbalances in the wind farm. A fault characteristics extraction analysis is carried out on the machine stator currents to detect the fault in the system. Firstly, the mathematical equation of the DFIG blade unbalanced stator current is generated using the DFIG model. Secondly, Park's Transformation is used to modify the stator's 3-phase current. Further, by evaluating the feature frequency amplitude variation in the squared signal by doing a spectral analysis on the stator current vector's squared signal. Lastly, a Simulink model for the DFIG is developed. The suggested approach analyses the fault signal of the imbalanced blade fault at various wind velocities. The outcomes show that the suggested method for diagnosing impeller imbalance faults can successfully locate the fault.

EXPERIMENTAL AND THEORETICAL INTERPRETATIONS OF STRUCTURAL, OPTICAL, MECHANICAL AND THERMAL PROPERTIES OF 2 AMINOPYRIDINIUM PHTHALATE SINGLE CRYSTALS

Nonlinear optical good quality single crystals of 2-aminopyridinium phthalate single crystals are synthesized by solution growth technique. Structural parameters are elucidated by Single Crystal X-ray diffraction method which reveals that the material crystallizes in non-centrosymmetric crystalline framework. Vibrational assignments are interpreted by Fourier Transform Infrared spectral analysis. The thermal, mechanical and laser stability are explored by TG/DTA, microhardness and Laser Damage threshold analysis. The material has good emission nature in the violet emission region. The linear optical properties are elucidated with the help of UV-visible spectral analysis. The intermolecular charge transfer interactions are analyzed by HOMO-LUMO and MESP maps by using Gaussian ’09 software program. The second harmonic generation efficiency of the synthesized material are predicted by Kurtz Perry powder technique.

Spectral analysis of seismic data based on a combined generalized S transform

The S transform is widely used to delineate seismic spectral anomalies associated with hydrocarbons during reservoir characterization. To overcome the relatively poor time-frequency resolution due to the fixed varying pattern of the window used in the S transform, we present a combined generalized S transform (CGST) for the spectral analysis of seismic data. First, the CGST decomposes the signal into a series of intrinsic mode functions (IMFs) using a complete ensemble empirical mode decomposition (CEEMD). An objective function is defined to quantitatively determine the spectral energy concentration criteria for optimal parameter selection of the improved Gaussian windows. Then, the time-frequency spectra for all the IMFs within narrower frequency bands are calculated using the respective Gaussian windows with normalized energy and individual optimal controlling parameters. We applied the CGST to spectral analyses of both synthetic signals and seismic field data. The results indicate that the CGST exhibits good performance with a higher time-frequency resolution. Field examples illustrate that the CGST can be used to characterize the spectral behavior of thin beds and indicate low-frequency shadows associated with hydrocarbon reservoirs.

Biochar-derived dissolved organic matter enhanced the release of residual ciprofloxacin from the soil solid phase

Biochar has been utilized to reduce ciprofloxacin (CIP) residues in soil. However, little is known about the effect of biochar-derived dissolved organic matter (DOM) on residual CIP transformation. Thus, we analyzed the residual soil CIP as influenced by biochar generated from rice straw (RS3 and RS6), pig manure (PM3 and PM6), and cockroach shell (CS3 and CS6) at 300 °C and 600 °C. The three-dimensional excitation-emission matrix (3D-EEM), parallel factor analysis (PARAFAC) and two-dimensional correlation spectral analysis (2D-COS) were used to describe the potential variation in the DOM-CIP interaction. Compared with CK, biochar amendment increased the water-soluble CIP content by 160.7% (RS3), 55.2% (RS6), 534.1% (PM3), 277.5% (PM6), 1160.6% (CS3) and 703.9% (CS6), indicating that the biochar feedstock controlled the soil CIP release. The content of water-soluble CIP was positively correlated with the content of dissolved organic carbon (r = 0.922, p < 0.01) and dissolved organic nitrogen (r = 0.898, p < 0.01), suggesting that the major influence of the water-soluble CIP increase was DOM. The fluorescence quenching experiment showed that the interaction between DOM and CIP triggered static quenching and the creation of a DOM complex. The mean log K of protein-like material (4.977) was higher than that of terrestrial humus-like material (3.491), suggesting that the protein-like material complexed CIP was more stable than the humus-like material. Compared with pyrolysis at 300 °C, pyrolysis at 600 °C decreased the stability of the complex of protein-like material and CIP by 0.44 (RS), 1.689 (PM) and 0.548 (CS). This result suggested that the influence of temperature change was more profound on PM biochar-derived DOM than on RS and CS. These insights are essential for understanding CIP transportation in soil and controlling CIP contamination with biochar.

Spectrum estimation for voiced speech using average weighted linear prediction

In many different areas, such as neurophysics, geophysics, and communication, it is vital to effectively handle signals when there is unwanted disturbance. Linear prediction (LP) methods provide precise modeling capabilities for various parametric models and are used in a wide range of fields such as predicting future data, compressing speech, analyzing spectra, filling in missing signals, and decreasing noise. LP is especially beneficial in speech processing systems. Speech analysis and synthesis greatly relies on spectral envelopes, which are closely connected to models of speech production and perception. However, the performance of the LP method may decline when limited information is involved in pitch synchronous analysis. In order to overcome this constraint, we developed the implementation of the Average Weighted Linear Prediction (AWLP) technique, which does not require the use of a window. The AWLP utilizes a signal of weighted prediction error acquired from the autocorrelation process of LP. By performing this action, it provides a more precise estimation of the power spectrum and coefficients used for vocal tract parametric models. Importantly, the effectiveness of the AWLP can be seen in both pitch synchronous and asynchronous analyses, which means it is appropriate for situations where segments are chosen randomly. We confirm the effectiveness of the AWLP technique by employing it on both synthetic voice and real speech. Additionally, we assess its effectiveness by comparing it to the direct autocorrelation technique and a modified autocorrelation (MA) approach. To sum up, the AWLP method presents a hopeful solution for reliable spectral analysis that addresses the divide between pitch synchronous and asynchronous situations.

Cotton seed cultivar identification based on the fusion of spectral and textural features.

The mixing of cotton seeds of different cultivars and qualities can lead to differences in growth conditions and make field management difficult. In particular, except for yield loss, it can also lead to inconsistent cotton quality and poor textile product quality, causing huge economic losses to farmers and the cotton processing industry. However, traditional cultivar identification methods for cotton seeds are time-consuming, labor-intensive, and cumbersome, which cannot meet the needs of modern agriculture and modern cotton processing industry. Therefore, there is an urgent need for a fast, accurate, and non-destructive method for identifying cotton seed cultivars. In this study, hyperspectral images (397.32 nm-1003.58 nm) of five cotton cultivars, namely Jinke 20, Jinke 21, Xinluzao 64, Xinluzao 74, and Zhongmiansuo 5, were captured using a Specim IQ camera, and then the average spectral information of seeds of each cultivar was used for spectral analysis, aiming to estab-lish a cotton seed cultivar identification model. Due to the presence of many obvious noises in the < 400 nm and > 1000 nm regions of the collected spectral data, spectra from 400 nm to 1000 nm were selected as the representative spectra of the seed samples. Then, various denoising techniques, including Savitzky-Golay (SG), Standard Normal Variate (SNV), and First Derivative (FD), were applied individually and in combination to improve the quality of the spectra. Additionally, a successive projections algorithm (SPA) was employed for spectral feature selection. Based on the full-band spectra, a Partial Least Squares-Discriminant Analysis (PLS-DA) model was established. Furthermore, spectral features and textural features were fused to create Random Forest (RF), Convolutional Neural Network (CNN), and Extreme Learning Machine (ELM) identification models. The results showed that: (1) The SNV-FD preprocessing method showed the optimal denoising performance. (2) SPA highlighted the near-infrared region (800-1000 nm), red region (620-700 nm), and blue-green region (420-570 nm) for identifying cotton cultivar. (3) The fusion of spectral features and textural features did not consistently improve the accuracy of all modeling strategies, suggesting the need for further research on appropriate modeling strategies. (4) The ELM model had the highest cotton cultivar identification accuracy, with an accuracy of 100% for the training set and 98.89% for the test set. In conclusion, this study successfully developed a highly accurate cotton seed cultivar identification model (ELM model). This study provides a new method for the rapid and non-destructive identification of cotton seed cultivars, which will help ensure the cultivar consistency of seeds used in cotton planting, and improve the overall quality and yield of cotton.

Experimental Study on the Sloshing of a Rectangular Tank under Pitch Excitations

Fluid sloshing within containers subjected to external motion is a crucial yet intricate phenomenon with implications across various industries. This study investigates sloshing in a rectangular liquid tank through a series of experiments examining pitch excitations with diverse excitation frequencies and amplitudes across different liquid carrying rates. By analyzing pressure data and imagery of the free liquid surface, statistical trends in peak pressure at measurement points within the tank are identified, revealing the nonlinear behavior of the fluid. Spectral analysis generates power spectrum curves that delineate frequency components and energy distribution within the sloshing dynamics. Key findings include the identification of resonance-induced violent sloshing at a 20% liquid-carrying rate and a resonant frequency shift at a 70% liquid-carrying rate due to nonlinearity, displaying a “soft spring” characteristic in the frequency response. The free liquid surface exhibits four distinct waveforms depending on frequency. Notably, at a 70% liquid-carrying rate and resonant frequency excitation, three-dimensional vortex waves emerge, highlighting a complex three-dimensional effect within the tank. The power spectrum shows that the dominant response frequency aligns with the excitation frequency and its multiples. This investigation enhances our understanding of the intricate nature of sloshing in various liquid-carrying conditions, offering insights valuable for diverse industrial applications.

Chromite ore: X-ray fluorescence spectral analysis of Kβ and L-lines by WD-XRF

Chromite ore: X-ray fluorescence spectral analysis of Kβ and L-lines by WD-XRF

Spectroscopic survey of faint planetary-nebula nuclei

We present spectroscopy of three hydrogen-deficient central stars of faint planetary nebulae, with effective temperatures (Teff) in excess of 100 000 K. The nucleus of RaMul 2 is a Population II Wolf-Rayet star of spectral type [WC], and the central stars of Abell 25 and StDr 138 are two new members of the PG1159 class. Our spectral analyses reveal that their atmospheres have a similar chemical composition. They are dominated by helium and carbon, which was probably caused by a late helium-shell flash. Coincidentally, the three stars have similar masses of about M = 0.53 M⊙ and hence form a post-asymptotic giant branch (AGB) evolutionary sequence of an initially early-K-type main sequence star with M = 0.8 M⊙. The central stars cover the period during which the luminosity fades from about 3000 to 250 L⊙ and the radius shrinks from about 0.15 to 0.03 R⊙. The concurrent increase of the surface gravity during this interval from log g = 5.8 to 7.2 causes the shutdown of the stellar wind from an initial mass-loss rate of log Ṁ/(M⊙ yr−1) = −6.4, as measured for the [WC] star. Along the contraction phase, we observe an increase in Teff from 112 000 K, marked by the [WC] star, to the maximum value of 140 000 K, and a subsequent cooling to 130 000 K, marked by the two PG1159 stars.

Advanced stability analysis of a fractional delay differential system with stochastic phenomena using spectral collocation method.

In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

Data-driven identification and comparison of full multivariable models for propofol–remifentanil induced general anesthesia

In this paper, we present results with clinical data to enable a 2x2 input–output multivariable patient model for hypnosis and analgesia. Nonlinear multi-drug interaction models are identified from data recorded from 70 patients undergoing surgery during total intravenous anesthesia (TIVA) with several medical monitors for variables such as Bispectral Index, Nociception level (Medasense), skin conductance (Medstorm) and advanced spectral analysis conductance (AnspecPro). Bispectral index measures the depth of hypnosis (lack of consciousness), while nociception related indices from Medasense, Medstorm, and AnspecPro devices measure levels related to analgesia (lack of reaction to noxious stimuli). A comparison is given among three response surface model (RSM) structures – Minto, Greco, and Reduced Greco – for hypnotic and analgesic states during Propofol–Remifentanil interaction. The identified models capture the pharmacodynamic properties of dose–effect concentrations of Propofol/Remifentanil while the pharmacokinetic part of the patient model is calculated from patient’s biometric values using Schnider/Minto (SM), and Eleveld/Eleveld (EE) models. In presence of strict clinical protocols delivering data under poor identifiability conditions, we propose two methods of identification: (i) based on steady-state gains, and (ii) using all available data which includes part of the dynamic transient. The model parameters are optimized with Genetic Algorithm based on a goodness of fit performance measure complemented with root mean square error. The results suggest that the EE model combination is advantageous for Bispectral index pharmacokinetic modeling at the cost of numerical complexity, therefore reducing the uncertainty left to be identified in the pharmacodynamic part of the patient model. By contrast, the SM model combination is less computationally demanding and provides some improvement in the RSM accuracy for nociception level indicators. The comparison of three devices for nociception levels evaluation suggests that clinical data captured with the Medasense monitor provides best fitted RSMs with the Reduced Greco RSM structure, despite having fewer parameters.

Correction: Spectral analysis of bounded operators on semi-Hilbertian spaces

Correction: Spectral analysis of bounded operators on semi-Hilbertian spaces

Utilizing Multi-Source Data and Cloud Computing Platform to Map Short-Rotation Eucalyptus Plantations Distribution and Stand Age in Hainan Island

Short-rotation eucalyptus plantations play a key positive role in the forestry economy due to their fast-growing and high-yielding properties. However, some studies have suggested that eucalyptus plantations introductions may have negative impacts on biodiversity and ecosystems’ stability. In order to precisely and promptly determine the influence of eucalyptus plantations on soil characteristics and hydrological processes, based on the rotation change rules of eucalyptus plantations, this study combined the continuous change detection and classification and spectral mixture analysis (CCDC-SMA) algorithm and the random forest (RF) algorithm to map the distribution and stand age of short-rotation eucalyptus plantations in Hainan Island. First, the forest distribution map was used to mask out the rubber plantations, and forest disturbances were extracted through the CCDC-SMA algorithm to determine the potential short-rotation eucalyptus plantations distribution. Second, using CCDC-SMA algorithm fitting coefficients, field surveys, original spectral bands, vegetation indices, and digital elevation models (DEM) as inputs to the RF algorithm, short-rotation eucalyptus plantations distribution maps were created and evaluated based on Google Earth images. Finally, the stand age of the newly mapped short-rotation eucalyptus plantations was estimated based on the breakpoints of the CCDC-SMA algorithm. The results showed that the producer, user, and overall accuracies of the 2022 short-rotation eucalyptus plantations map were estimated at 0.95, 0.95, and 0.94, respectively, and the validation R2 of the estimated stand ages was at 0.97. The eucalyptus plantations in Hainan Island had a total area of roughly 9.93 × 104 ha in 2022. Danzhou City had the highest planting area of eucalyptus plantations, followed by Changjiang County, Chengmai County, and Lingao County. It was worth noting that the eucalyptus plantations were mostly located in places with low altitudes (&lt;200 m) and flat slopes (&lt;10°). Approximately 43.91% of eucalyptus plantations were located in the three major watersheds. In addition, the 1-year-old eucalyptus plantations accounted for the highest areal percentage of 30.58%. These datasets are valuable tools to aid sustainable production, ecological assessment, and conservation of eucalyptus plantations.

Prediction of the success of electrical cardioversion in patients with atrial fibrillation using parameters obtained by spectral and vectorcardiographic analysis of the ECG

Abstract Introduction Electrical cardioversion (ECV) still presents a treatment option for atrial fibrillation (AF). Despite the great success rate in the acute setting, its long-term success rate is low. The aim of the study was to find predictors of SR maintenance after ECV using spectral and vectorcardiographic (VCG) analysis of the ECG curve. Methods Consecutive patients with AF referred for elective ECV were prospectively enrolled. The digital ECG recording obtained before the ECV was analyzed using spectral and VCG analysis. All patients had Holter monitoring and clinical follow-up three months after cardioversion to assess SR maintenance. Atrial fibrillation activity was analyzed by spectral analysis, determining the dominant frequency (DF), RI (regularity index) and OI (organization index). QRS complexes were analyzed by vectorcardiography and dXmean, dYmean and dZmean (derivation of VCG signals) parameters were determined. Using logistic regression (LR) with backward parameter reduction, separate and combined predictive models of SR maintenance were calculated based on both the analysis from the VCG data and the spectral analysis data. Additional models with a forward selection of clinical variables were also calculated. The efficiency of the models was evaluated on a separate test subset of the data (N=32). Results A total of 80 patients were enrolled (age 70+10 years, 48 (64%) men, CHADSVASc 3.17+1.5, BMI 29.75kg/m2). At the 3-month follow-up, AF recurrence was present in 36 patients (45%). Using only vectorcardiographic parameters, dXmean (OR 3.07, 95 %CI 1.14- 8.63), p=0.001) and dYmean (OR 0.26, 95% CI 0.09-0.72), p &amp;lt;0.001) best predicted the SR maintenance (AUC of 0.72). Using only spectral analysis parameters, the best predictor was DF (OR 3.07, 95%CI 1.14-8.63), p=0.012) with AUC of 0.66. By combining the results of spectral analysis and vectorcardiography, an AUC of 0.78 was achieved. Among the clinical parameters, the most significant predictors were insufficiently controlled arterial hypertension and previous history of stroke. Conclusion Digital analysis of ECG curves using deeper mathematical models provides a better predictive model than classical clinical and echocardiographic models. The combination of spectral analysis of fibrillation atrial activity and vectorocardiographic analysis of QRS complexes revealed more accurate predictive information compared to both analyses alone.

Distinct intraseasonal periodicity of Siberian High as modulated by ENSO: dominance of Madden-Julian Oscillation

Abstract While previous studies have demonstrated that the El Niño-Southern Oscillation (ENSO) can impact global climate systems on the intraseasonal timescale, how ENSO affects the intraseasonal variability of the Siberian High (SH) still remains unclear. Based on spectral analysis, the SH exhibits evident intraseasonal periodicity (ISP) differences, with 25–50 days during El Niño winters and 50–90 days during La Niña winters. The northward propagation of the Rossby wave from the tropics during the phase transition of the SH leads to the Madden Julian Oscillation (MJO) dominating the difference in the ISP of the SH. The faster eastward propagation of the MJO in El Niño winters leads to a quicker evolution of the SH. Accompanied by the eastward propagation of the MJO, when the tropical westerly in the lower troposphere is located over the Indian Ocean, it can deflect to the north and involved the Eurasian cyclonic circulation, which contributes to the negative phase of the SH. When the tropical westerly moves eastward and the easterly is occupied the Indian Ocean, the middle-to-high latitude northerly completely dominates the East Asia region, penetrates southward to the South China Sea, deflects westward, and involved the tropical easterly, acting as the positive peak stage of the SH. The upper-level tropical zonal wind overrides the low-level tropical zonal wind, forming a coupled circulation and air temperature pattern together with the low level. The faster propagation of the MJO in El Niño winters leads to the intraseasonal evolution of the El Niño-related SH to change from lagging behind the La Niña-related SH to exceeding it, thereby contributing to the ISP of the SH being much shorter during El Niño winters.

Cardioneuroablation for vasovagal syncope. Results from an EHRA Survey

Abstract Background Cardioneuroablation (CNA) is a recently introduced catheter-based therapy for vasovagal syncope (VVS), involving the modulation of the cardiac autonomic nervous system. Over the past decade, the body of evidence supporting CNA has consistently grown, sparking increased interest, and leading to broader adoption of this novel therapy for VVS treatment. However, several knowledge gaps persist, particularly concerning clinical indications, methodology, and long-term outcomes. Purpose The aim of this survey is to provide a comprehensive overview of the daily utilization of CNA in Europe. Methods and Results Among 204 participants, half reported that they are already performing CNA in their centres. The survey indicated CNA as an emerging therapy, with most centres initiating this treatment within the last two years. A significant majority of those who responded (71%) adopted a systematic approach by targeting ganglionated plexuses (GPs) in both the right atrium (RA) and left atrium (LA) as their first-line approach. The second most common approach (16%) involved performing ablation LA GPs only in the cases where there is no response following RA ablation. Figure 1 shows the GPs usually targeted for ablation. The most frequent procedural endpoint was the increase in the heart rate. Ninety percent of respondents relied on an anatomical approach for GP localization, while 59% also guided the procedure using electrogram analysis. Less frequently used approaches included pre-procedural imaging (20%), high-frequency stimulation (17%), and spectral analysis (10%). Only 11% of physicians discharged patients who underwent CNA without anticoagulation or antiplatelet therapy. In most cases (58%), respondents discharged their patients with anticoagulation, while 28% on single antiplatelet therapy. Overall, CNA is considered an effective procedure (80% of responders rating its effectiveness at or above 70%) and a safe procedure (with an estimated 1% rate of procedure-related complications, with symptomatic sinus tachycardia being the most frequently reported). Conclusions This survey confirmed high expectations for the future of this therapy, as most responders anticipate that CNA will be indicated as a first-line therapy for patients with recurrent cardioinhibitory VVS. Future efforts are necessary to standardize the procedural flow and peri-procedural patient management.

Constructing and Evaluating Machine-Learned Interatomic Potentials for Li-Based Disordered Rocksalts.

Lithium-based disordered rocksalts (LDRs), which are an important class of positive electrode materials that can increase the energy density of current Li-ion batteries, represent a significantly complex chemical and configurational space for conventional density functional theory (DFT)-based high-throughput screening approaches. Notably, atom-centered machine-learned interatomic potentials (MLIPs) are a promising pathway to accurately model the potential energy surface of highly disordered chemical spaces, such as LDRs, where the performance of such MLIPs has not been rigorously explored yet. Here, we represent a comprehensive evaluation of the accuracy, transferability, and ease of training of five atom-centered MLIPs, including the artificial neural network potentials developed by the atomic energy network (AENET), the Gaussian approximation potential (GAP), the spectral neighbor analysis potential (SNAP) and its quadratic extension (qSNAP), and the moment tensor potential (MTP), in modeling a 11-component LDR chemical space. Specifically, we generate a DFT-calculated data set of 10,842 configurations of disordered LiTMO2 and TMO2 compositions, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni, and/or Cu. To provide a point-of-comparison on the performance of atom-centered MLIPs, we also trained the neural equivariant interatomic potential (NequIP) on a subset of our data. Importantly, we find AENET to be the best potential in terms of accuracy and transferability for energy predictions, while MTP is the best for atomic forces. While AENET is the fastest to train among the MLIPs considered at low number of epochs (300), the training time increases significantly as epochs increase (3300), with a corresponding reduction in training errors (∼60%). Note that AENET and GAP tend to overfit in small data sets, with the extent of overfitting reducing with larger data sets. Finally, we observe AENET to provide reasonable predictions of average Li-intercalation voltages in layered, single-TM LiTMO2 frameworks, compared to DFT (∼10% error on average). Our study should pave the way both for discovering novel disordered rocksalt electrodes and for modeling other configurationally complex systems, such as high-entropy ceramics and alloys.

A New Remote Sensing Index for Forest Dryness Monitoring Using Multi-Spectral Satellite Imagery

Canopy water content is a fundamental indicator for assessing the level of plant water stress. The correlation between changes in water content and the spectral reflectance of plant leaves at near-infrared (NIR) and short-wave infrared (SWIR) wavelengths forms the foundation for developing a new remote sensing index, the Infrared Canopy Dryness Index (ICDI), to monitor forest dryness that can be used to predict the consequences of water stress. This study introduces the index, that uses spectral reflectance analysis at near-infrared wavelengths, encapsulated by the Normalized Difference Infrared Index (NDII), in conjunction with specific canopy conditions as depicted by the widely recognized Normalized Difference Vegetation Index (NDVI). Development of the ICDI commenced with the construction of an NDII/NDVI feature space, inspired by a conceptual trapezoid model. This feature space was then parameterized, and the spatial region indicative of water stress conditions, referred to as the dry edge, was identified based on the analysis of 10,000 random observations. The ICDI was produced from the combination of the vertical distance (i.e., under consistent NDVI conditions) from an examined observation to the dry edge. Comparisons between data from drought-affected and non-drought-affected control plots in the Australian Northern Jarrah Forest affirmed that the ICDI effectively depicted forest dryness. Moreover, the index was able to detect incipient water stress several months before damage from an extended drought and heatwave. Using freely available satellite data, the index has potential for broad application in monitoring the onset of forest dryness. This will require validation of the ICDI in diverse forest systems to quantify the efficacy of the index.

ECG screening and Sudden Cardiac Death prevention in endurance athletes

Abstract Introduction Sudden cardiac death (SCD) is the leading cause of mortality in athletes during sport activity. Identifying non-invasive and repeatable risk markers during annual athletes screening remains a crucial goal for the scientific community for the prevention of cardiovascular disease. The ECG, exercise test and spirometry are part of the annual screening program in the sport population over 40 years of age. The Heart Rate Variability (HRV) has been shown to be an independent predictor of mortality in populations with CV disease, but its relationship with SCD has been little explored in those at low risk, including athletes. This study aims to use a 5 minute ECG screening in a specific population of athletes practicing endurance sports and evaluate its clinical repercussions. Aims The primary aim of the study is to assess whether morphological and spectral ECG analysis in endurance athletes by the 5-minute 'mini Holter ECG' can add information to the annual screening for the prevention of CV diseases and SCD. Materials The study population were selected from Endurance Marathon athletes. A control group was also collected for comparison. The 5 minutes ECG was recorded at rest with the Kardia Mobile 6L™ device that use three electrodes, which allows 6 leads recording. HRV indices were obtained using the Kubios software. A morphological analysis was performed and the HRV parameters were analyzed in the time and frequency domains. Results A total of 554 subjects (220 athletes-334 controls, 325 male-229 women) were analyzed. A higher incidence of sinus arrhythmias, LAFB, 1stdegree AVB, J point/ST-segment abnormalities in athletes compared to controls was observed. Athletes had a significantly shorter QTc than the control group (394.87±30.55 ms vs. 407.01±29.09 ms), suggestive of greater protection from malignant ventricular arrhythmias. The HRV PNS (-0.73±1.25 vs -0.21±1.45) and SNS (1.71±1.91 vs 0.58 ± 1.43) indices were also higher and lower in the athlete group than in the control group, suggestive for higher vagal tone. Given the absence of a normality range of standard HRV parameters in the athlete population, values in the normal range of the 5th to 95th percentile were extrapolated in the whole population for the main HRV parameters. The project, therefore, is to evaluate patients with HRV values higher or lower than 5-95 percentile to identify those deserving further clinical investigations and closer follow-up for suspected increased risk of CV disease and SCD. Conclusions The 5-minute cardiac Holter, combining a more accurate analysis in terms of morphology and arrhythmias than the 12-lead ECG, allows a greater number of static and dynamic alterations to be detected; moreover, the analysis of HRV could provide even more information, such that it could be considered as a complementary tool during the examination and FUP of athletes. Further long-term prospective studies are needed to correlate alterations in HRV and clinical events.