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Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms. [ABSTRACT FROM AUTHOR]

Identifying protective factors that promote academic resilience is vital. Nevertheless, due to the variations in the operationalizations of academic resilience, timeframes, data sources, and employed research methods, it remains unclear whether the impact of protective factors identified across studies can be attributed to the factors themselves or to these variations. By addressing these uncertainties, this study aims to provide an overview of the protective factors that have been extensively investigated in academic resilience and their degree of influence. A literature search found 119 empirical studies on protective factors in education settings for children and adolescents. The review analyzed five protective factors groups (individual, family, school, peer, community), three operationalizations of academic resilience (simultaneous, progressive, instrumental), two timeframes (longitudinal, non-longitudinal), three data sources (self-collected, national/local assessments, international large-scale assessments), and commonly employed research methods. The studies analyzed in this review yielded mixed results regarding the impact of the examined protective factors, with measurement instruments and statistical power playing a significant role in explaining the variations. Individual and school-level characteristics emerged as the most well-studied protective factors; individual characteristics were often investigated through "instrumental" operationalization and structural equational models, whereas school-level characteristics were typically explored through "simultaneous" or "progressive" operationalizations and multilevel modeling. Approximately 31 and 16% of the studies utilized national assessments and international large-scale assessment data, respectively. Both data sources promoted the exploration of school-level factors, with the former facilitating the exploration of protective factors across time and the latter contributing to the investigation of teaching-related factors. [ABSTRACT FROM AUTHOR]

Automated above-water hyperspectral observations are often subject to inaccuracies caused by instrument malfunction and environmental conditions. This study evaluates the influence of atmospheric and water surface conditions on above-water hyperspectral measurements through statistical methods and Radiative Transfer (RT) modelling. Initially, we developed a general quality control method based on statistical assessment to detect the suspicious spectra. Subsequently, Radiative Transfer (RT) models were used to assess low light conditions, distortions in the spectral shape of above-water solar downwelling irradiance (E S (λ), mW m−2 nm−1) particularly those caused by intense atmospheric scattering and/or reddish hue of dusk or dawn radiation, the effect of atmospheric humidity and precipitation on the intensity and shape of spectra, and the influence of sun glint and surface perturbations on sky (L S (λ), mW m−2 nm−1 sr−1) and water surface (L T (λ), mW m−2 nm−1 sr−1) radiances. The proposed methods were applied to the entire archive of automated above-water hyperspectral measurements collected every ten minutes from 2020 to 2022 at the Royal Netherland Institute for Sea Research (NIOZ) at Jetty Station (NJS) located in the Marsdiep tidal inlet of the Duch Wadden Sea, the Netherlands. The findings demonstrate that low light conditions are characterized by E S (λ) max ≤ 25 mW m−2 nm−1. Red-shifted or distorted spectra are indicated by a ratio of E S (4 8 0)/ E S (6 8 0) ≤ 1.0 and E S (λ max)/ E S (8 6 5) ≤ 1.25. High humidity/precipitation conditions are identified by the ratio of E S (9 4 0)/ E S (8 6 5), which varies with the Solar Zenith Angle (SZA). Furthermore, significant sun glint and surface perturbations, such as whitecaps and foam, are indicated when the minimum ratio of L T (800 nm-950 nm)/ E S (800 nm-950 nm) > 0.025 sr−1, and the ratio of L T (850 nm)/ E S (850 nm) ≥ 0.025 sr−1. [ABSTRACT FROM AUTHOR]

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals. [ABSTRACT FROM AUTHOR]

The simulation of rainfall interception by vegetation is essential to water resource management, considering both changing land use and climate change effects. In the rainfall interception models, the evaporation rate is frequently estimated by means of the Penman-Monteith method, but the Priestley-Taylor equation appears as a promising approach with fewer input requirements. In this study these both formulations were evaluated with the sparse Gash model with variable parametrization for estimating rainfall interception by four tree species in a Brazilian dry tropical forest. The Penman-Monteith equation was used with the canopy resistance set to zero, and the momentum method was applied for estimating the aerodynamic resistance. The Priestley-Taylor formulation was tested with the proportional coefficients (α) of 1.26 and 1.34. The results of rainfall predictions were compared with the measurements by statistical indicators, which pointed slightly favorable to Penman-Monteith method. The Priestley-Taylor with α = 1.26 resulted in predictions better than with α = 1.34. Most of the simulations were classified as good (CMRE varying from 5.5 − 9.3%). The Priestley-Taylor method can be used for estimating the evaporation rate in simulations based on the sparse Gash model with variable parametrization in the studied dry tropical forest, under situations with restrictions of micrometeorological measurements or minimal processing time requirement. [ABSTRACT FROM AUTHOR]

Malaria is a deadly disease that can take a person's life if not predicted or cured correctly. Numerous factors like temperature, humidity, precipitation, etc., impact India's increasing cases of malaria diseases. This research presents an advanced machine learning regression technique recently developed to anticipate the prevalence of malaria in Tripura using a real-world data. The proposed structure uses nine different regression methods, such as multilayer perceptron (MLP), random forest, support vector regressor, gradient boosting regressor, Bayesian ridge, kernel ridge, extreme gradient boost regressor (XGB), light gradient boosting machine (LGBM regressor and linear regression, to predict malaria using the most affecting factors of malaria diseases, namely temperature, humidity, precipitation, month, and years as input. Furthermore, to opt out the best suited technology for malaria cases prediction, the range of value method (ROVM)–multi-criteria decision methods (MCDM) technique has been applied, considering various statistical measurements as criteria. Ultimately, a comparison of various MCDM techniques reveals that MLP, XGB, and RF emerge as the top three choices. MLP regression, with root-mean-square error (RMSE) value of 0.03357, yields the lowest RMSE value, and the coefficient of determination (R2) is 0.97616, yielding the maximum among other regressions. To effectively battle the illness in Tripura, it could be useful for continuing intervention tactics by governmental, profit and nonprofit organizations. [ABSTRACT FROM AUTHOR]

Background: Insertion of an external ventricular drain (EVD) is a first-line treatment of acute hydrocephalus caused by aneurysmal subarachnoid haemorrhage (aSAH). Once the patient is clinically stable, the EVD is either removed or replaced by a permanent internal shunt. The optimal strategy for cessation of the EVD is unknown. Prompt closure carries a risk of acute hydrocephalus or redundant shunt implantations, whereas gradual weaning may increase the risk of EVD-related infections. Methods: DRAIN (Danish RAndomised Trial of External Ventricular Drainage Cessation IN Aneurysmal Subarachnoid Haemorrhage) is an international multicentre randomised clinical trial comparing prompt closure versus gradual weaning of the EVD after aSAH. The primary outcome is a composite of VP-shunt implantation, all-cause mortality, or EVD-related infection. Secondary outcomes are serious adverse events excluding mortality and health-related quality of life (EQ-5D-5L). Exploratory outcomes are modified Rankin Scale, Fatigue Severity Scale, Glasgow Outcome Scale Extended, and length of stay in the neurointensive care unit and hospital. Outcome assessment will be performed 6 months after ictus. Based on the sample size calculation (event proportion 80% in the gradual weaning group, relative risk reduction 20%, alpha 5%, power 80%), 122 participants are required in each intervention group. Outcome assessment for the primary outcome, statistical analyses, and conclusion drawing will be blinded. Two independent statistical analyses and reports will be tracked using a version control system, and both will be published. Based on the final statistical report, the blinded steering group will formulate two abstracts. Conclusion: We present a pre-defined statistical analysis plan for the randomised DRAIN trial, which limits bias, p-hacking, and data-driven interpretations. This statistical analysis plan is accompanied by tables with simulated data, which increases transparency and reproducibility. Trial registration: ClinicalTrials.gov identifier: NCT03948256. Registered on May 13, 2019. [ABSTRACT FROM AUTHOR]

The nonclassical behavior of the optical model has important applications in numerous quantum information fields. The suggested optical model in this study is based on a five-level atom (5LA) influenced by a two-mode quantized field (2MQF) inside a high-Q cavity that is filled with three types of nonlinear Kerr media. The nonlinear optical model has been solved analytically in which the components of this optical model are in perfect resonance. By considering the initial state of 5LA is in the upper state (excited state), whilst the initial state of 2MQF is in the coherent state. Some useful statistical measurements such as population inversion, Shannon entropy, second-order correlation, as well as Cauchy–Schwarz inequality that corresponds to P-functions are studied and discussed. The effects of the Kerr parameters on these statistical measurements are analyzed. All proposed statistical measurements confirm the existence of nonclassical behavior with time except for some small-time intervals. [ABSTRACT FROM AUTHOR]

Perform radiologic measurements and analysis of normal brain computed tomography (CT) scans; delineate a new ventricular entry point from cutaneous landmarks, highlighting the potential surgical implications of these findings. Six radiologic distances (AR; BR; AL; BL, C, and D) were measured in normal brain CT scans using Horos software. Statistical analysis of the measurements was performed with minitab18 software based on age, sex, and side. 132 brain CT scans were analyzed, yielding the following mean results: AR distance: 2.1 cm; BR distance: 7 cm; AL distance: 2.1 cm; BL distance: 7.1 cm; C distance: 12.4 cm; D distance: 7 cm; new ventricular entry point: 12.4 cm posterior to the nasion, and 2.1 cm lateral to the midline. The freehand technique for accessing the lateral ventricles is a common neurosurgical procedure but is often accompanied by complications. To address this, we suggest a novel entry point for ventricular access, determined by cutaneous reference points. This point is situated 12.4 cm posterior to the nasion along the midline and 2.1 cm lateral to the midline. Although our findings may play a role in presurgical planning for ventricular pathologies, future prospective studies are warranted. [ABSTRACT FROM AUTHOR]

In this work, we convert a common verification lab in the physics curriculum, measuring the charge-to-mass ratio of the electron (e/m), into an investigative lab on systematic uncertainty. The Bainbridge apparatus, commonly used to measure e/m, can have significant systematic uncertainties, leading to large discrepancies with the accepted value. Students were asked to quantify possible systematic uncertainties in the apparatus and correct them. Building upon each other's work from semester to semester, students characterized multiple sources of systematic uncertainty. Not only did the students learn about uncertainty analysis techniques that reveal systematic uncertainties, but they also dramatically improved the accuracy of the apparatus, reducing the discrepancy from 15% to 0.5%. This paper describes a pedagogical approach to exploring unknown systematic uncertainties in an intermediate laboratory setting and the student-learning benefits of such an approach. In addition, it provides detailed information about untangling and correcting the sources of systematic uncertainty in the Bainbridge apparatus. Editor's Note: This work will be very interesting to two audiences. Its most obvious audience is those who teach the e/m experiment. These readers may have observed that the statistical measurement uncertainties are seldom large enough to make the result consistent with the accepted value. This paper explains these deviations. The paper may be even more useful to the less obvious audience: those who teach upper-level laboratories or projects. This paper may inspire these instructors to involve their students in extended investigations into this and other classic experiments. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

Background: The amide proton transfer‐weighted (APTw) imaging for kidney diseases is important. However, the breathing patterns on APTw imaging remains unexplored. Purpose: This study aimed to investigate the effects of intermittent breath‐hold (IBH) and free breathing (FB) on renal 3D‐APTw imaging. Study Type: Healthy volunteers were enrolled prospectively, and renal clear cell carcinoma (RCCC) patients were included retrospectively. Population: 58 healthy volunteers and 10 RCCC patients. Field Strength/Sequence: 3‐T, turbo spin echo, and fast field echo. Assessment: 3D‐APTw imaging was scanned using the IBH and FB methods in volunteers and using the IBH method in RCCC patients. The image quality was evaluated by three observers according to the 5‐point Likert scale. Optimal images rated at three points or higher were used to measure the APT values. Statistical Analysis: The measurement repeatability was assessed using the intraclass correlation coefficient (ICC) and the Bland–Altman plot. The APT values were analyzed using McNemar's test, one‐way analysis of variance, and t test. Results: 50 healthy volunteers and 8 RCCC patients were enrolled. Renal 3D‐APTw imaging using the IBH method revealed a higher success rate (88% vs 78%). The ICCs were excellent in the IBH group (ICCs > 0.74) and were good in the FB group (ICCs < 0.74). No significant differences in the APT values among various zones using the IBH (P = 0.263) or FB method (P = 0.506). The mean APT value using the IBH method (2.091% ± 0.388%) was slightly lower than the FB method (2.176% ± 0.292%), but no significant difference (P = 0.233). The APT value of RCCC (4.832% ± 1.361%) was considerably higher than normal renal using the IBH method. Conclusions: The study demonstrated that the IBH method substantially increased the image quality of renal 3D‐APTw imaging. Furthermore, APT values may vary between normal and tumor tissues. Level of Evidence: 2 Technical Efficacy: Stage 2 [ABSTRACT FROM AUTHOR]

With our increasing reliance on technology, there is a growing demand for efficient and seamless access control systems. Smartphone-centric biometric methods offer a diverse range of potential solutions capable of verifying users and providing an additional layer of security to prevent unauthorized access. To ensure the security and accuracy of smartphone-centric biometric identification, it is crucial that the phone reliably identifies its legitimate owner. Once the legitimate holder has been successfully determined, the phone can effortlessly provide real-time identity verification for various applications. To achieve this, we introduce a novel smartphone-integrated detection and control system called Identification: Legitimate or Counterfeit (ILC), which utilizes gait cycle analysis. The ILC system employs the smartphone's accelerometer sensor, along with advanced statistical methods, to detect the user's gait pattern, enabling real-time identification of the smartphone owner. This approach relies on statistical analysis of measurements obtained from the accelerometer sensor, specifically, peaks extracted from the X-axis data. Subsequently, the derived feature's probability distribution function (PDF) is computed and compared to the known user's PDF. The calculated probability verifies the similarity between the distributions, and a decision is made with 92.18% accuracy based on a predetermined verification threshold. [ABSTRACT FROM AUTHOR]

Copyright of Psychological Science is the property of Psychological Science 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.)

Biomechanical trajectories are often routed through a chain of processing steps prior to statistical analysis. As changes in processing parameter values can affect these trajectories, care is required when choosing data processing specifics. The purpose of this Research Note was to demonstrate a simple way to propagate data processing parameter uncertainty to statistical inferences regarding biomechanical trajectories. As an example application. the correlation between foot contact duration and vertical ground reaction force during constant-speed treadmill walking was considered. Uncertainty was modeled using plausible-range uniform distributions in three data processing steps, and Monte Carlo simulation was used to construct probabilistic representations of both individual vertical ground reaction force measurements and the ultimate statistical results. Whereas an initial. plausible set of parameter values yielded a significant correlation between contact duration and late-stance vertical ground reaction force, Monte Carlo simulations revealed strong sensitivity, with 'significance" being reached in fewer than 40% of simulations. with relatively little net effect of parameter uncertainty magnitude. These results indicate that propagating processing parameter uncertainty to statistical results promotes a cautious, nuanced, and robust view of observed effects. By extension, Monte Carlo simulations may yield greater interpretive consistency across studies involving data processing uncertainties. [ABSTRACT FROM AUTHOR]

The growing use of hydrogen as an energy carrier in the context of a renewed energy landscape is showing very promising potential. Most notably, polymer electrolyte membrane fuel cells (PEMFCs) are at the heart of the energy revolution and provide an efficient alternative to fossil fuels. The need for accurate modeling to analyze and develop the performance of PEMFCs has prompted many researchers to make major efforts in this area. This paper proposes a new approach based on an enhanced version of the bio-inspired metaheuristic salp swarm algorithm (ESSA) to identify the unknown parameters of the PEMFC model. This innovative approach was implemented by the combination of the merits of four approaches, namely the opposition-based learning, the new category of salps, referred to as exploration salps, the crossover and mutation operators, and the survival of the fittest mechanism. The newly introduced mechanisms succeed to overcome the problem of low population diversity of the SSA algorithm, accelerate its speed of convergence, strengthen the salp exploration capabilities, and improve its convergence accuracy. The performance of this technique has been tested and validated experimentally on the voltage–current characteristic on two commercial PEMFC stacks, such as 250 W and BCS500 W. The found results are compared to those of the original SSA and six recently published competitive metaheuristic algorithms simulated in various conditions as well as nine other well-cited methods from the literature. The performance comparison involved the calculation of the sum of squared errors (SSE), Standard Deviation (STD), computational speed, and statistical measurements. In this context, the best SSE and STD values obtained for Case I range 2 were 0.60132 and 1.0597 E − 14 , while those of Case II were 0.01167 and 1.1127E−15, respectively. Based on the results, the ESSA-based approach demonstrates superior performance in determining PEMFC parameters from experimental measurements, offering enhanced stability, faster convergence speed, and greater accuracy. This method thus emerges as a highly promising avenue for PEMFC parameter estimation, providing a valuable tool for optimizing fuel cell performance and design. • Modeling and parameter extraction methods for PEMFC models are investigated. • A new ESSA is proposed to tackle the parameterization problem of PEMFC. • ESSA uses OBL, exploration salps, crossover and mutation operators, and survival of the fittest mechanism. • Experimental tests show ESSA's best accuracy, convergence speed, and stability for 250 W and BCS500 W PEMFC. • ESSA is useful to develop precise simulators, and master PEMFC design and control. [ABSTRACT FROM AUTHOR]

Background: Orbital floor fractures (OFFs) represent an interesting chapter in maxillofacial surgery, and one of the main challenges in orbit reconstruction is shaping and cutting the precise contour of the implants due to its complex anatomy. Objective: The aim of the retrospective study was to demonstrate, through pre- and postoperative volumetric measurements of the orbit, how the use of a preformed titanium mesh based on the stereolithographic model produced with 3D printers ("In-House" reconstruction) provides a better reconstruction volumetric compared to the intraoperatively shaped titanium mesh. Materials and Methods: The patients with OFF enrolled in this study were divided into two groups according to the inclusion criteria. In Group 1 (G1), patients surgically treated for OFF were divided into two subgroups: G1a, patients undergoing orbital floor reconstruction with an intraoperatively shaped mesh, and G1b, patients undergoing orbital floor reconstruction with a preoperative mesh shaped on a 3D-printed stereolithographic model. Group 2 (G2) consisted of patients treated for other traumatic pathologies (mandible fractures and middle face fractures not involving orbit). Pre- and postoperative orbital volumetric measurements were performed on both G1 and G2. The patients of both groups were subjected to the measurement of orbital volume using Osirix software (Pixmeo SARL, CH-1233 Bernex, Switzerland) on the new CT examination. Both descriptive (using central tendency indices such as mean and range) and regressive (using the Bravais–Pearson index, calculated using the GraphPad program) statistical analyses were performed on the recorded data. Results: From 1 January 2017 to 31 December 2021, of the 176 patients treated for OFF at the "Magna Graecia" University Hospital of Catanzaro 10 fulfilled the study's inclusion criteria: 5 were assigned to G1a and 5 to G1b, with a total of 30 volumetric measurements. In G2, we included 10 patients, with a total of 20 volumetric measurements. From the volumetric measurements and statistical analysis carried out, it emerged that the average of the volumetric differences of the healthy orbits was ±0.6351 cm3, the standard deviation of the volumetric differences was ±0.3383, and the relationship between the treated orbit and the healthy orbit was linear; therefore, the treated orbital volumes tend to approach the healthy ones after surgical treatment. Conclusion: This study demonstrates that if the volume is restored within the range of the standardized mean, the diplopia is completely recovered already after surgery or after one month. For orbital volumes that do not fall within this range, functional recovery could occur within 6 months or be lacking. The restoration of the orbital volume using pre-modeled networks on the patient's anatomical model, printed internally in 3D, allows for more accurate reconstructions of the orbital floor in less time, with clinical advantages also in terms of surgical timing. [ABSTRACT FROM AUTHOR]

Hydropower remains the largest source of renewable electricity while most hydropower plants, especially commercial hydropower plants, require accurate future energy or reservoir incoming flow ahead prediction for ensuring effective generation planning and revenue maximization as well as for flood control, and environmental protection purposes. This study develops effective daily and monthly prediction models using a Bi-Directional Long Short-Term Memory (Bi-LSTM) network-based meteorological and hydrological data, properly pre-processed by cross-correlation function (CCF), and normalization techniques for predicting the future energy portion corresponding to the natural incoming flow expected flowing into the reservoir (reservoir inflow) in the near future (days and months ahead) for Nam Theun 2 Hydropower Plant (NT2HPP) in Laos. The proposed models were tested with a separated dataset excluded from the training dataset and then evaluated using measurement or statistical indices including the root mean square error (RMSE), relative error (RE), and efficiency index (EI) in couple with Taylor diagrams. For comparison purposes, the two widely used prediction approaches namely, Feedforward Backpropagation Neural Network (FFBPNN), and Long Short-Term Memory (LSTM) based on the same datasets were also adopted and analyzed. The study results revealed that the proposed daily prediction model, with the statistical indices of EI = 74.872%, RMSE = 11.313, and RE = 4.560% for 2020; EI = 90.910%, RMSE = 7.237, and RE = 1.896% for 2021; and EI = 82.807%, RMSE = 4.918, and RE = 2.763% for 2022, was superior to the other models. Similarly, the proposed monthly prediction model, with the statistical indices of EI = 98.133%, RMSE = 55.213, and RE = 8.407% for 2020; EI = 99.819%, RMSE = 19.314, and RE = 0.611% for 2021; and EI = 97.316%, RMSE = 44.780, and RE = 6.878% for 2022, was also superior to the other models. These proved that the proposed models were more robust and efficient for future energy prediction. [ABSTRACT FROM AUTHOR]

Stitching methods allow one to measure a wider surface without the loss of resolution. The observation of small details with a better topographical representation is thus possible. However, it is not excluded that stitching methods generate some errors or aberrations on topography reconstruction. A device including confocal microscopy (CM), focus variation (FV), and coherence scanning interferometry (CSI) instrument modes was used to chronologically follow the drifts and the repositioning errors on stitching topographies. According to a complex measurement plan, a wide measurement campaign was performed on TA6V specimens that were ground with two neighboring SiC FEPA grit papers (P#80 and P#120). Thanks to four indicators (quality, drift, stability, and relevance indexes), no measurement drift in the system was found, indicating controlled stitching and repositioning processes for interferometry, confocal microscopy, and focus variation. Measurements show commendable stability, with interferometric microscopy being the most robust, followed by confocal microscopy, and then focus variation. Despite variations, robustness remains constant for each grinding grit, minimizing interpretation biases. A bootstrap analysis reveals time-dependent robustness for confocal microscopy, which is potentially linked to human presence. Despite Sa value discrepancies, all three metrologies consistently discriminate between grinding grits, highlighting the reliability of the proposed methodology. [ABSTRACT FROM AUTHOR]

Spectral characterization of noise environments that lead to the decoherence of qubits is critical to developing robust quantum technologies. While dynamical decoupling offers one of the most successful approaches to characterize noise spectra, it necessitates applying large sequences of π pulses that increase the complexity and cost of the method. Here, we introduce a noise spectroscopy method that utilizes only the Fourier transform of free induction decay or spin echo measurements, thus removing the need for the application many π pulses. We show that our method faithfully recovers the correct noise spectra for a variety of different environments (including 1/f-type noise) and outperforms previous dynamical decoupling schemes while significantly reducing their experimental overhead. We also discuss the experimental feasibility of our proposal and demonstrate its robustness in the presence of statistical measurement error. Our method is applicable to a wide range of quantum platforms and provides a simpler path toward a more accurate spectral characterization of quantum devices, thus offering possibilities for tailored decoherence mitigation. [ABSTRACT FROM AUTHOR]

To accurately simulate the hydration process of cementitious materials, understanding the growth rate of C‐S‐H layers around clinker grains is crucial. Nonetheless, the thickness of the hydrate layer shows substantial variation around individual grains, depending on their surrounding. Consequently, it is not feasible to measure hydrate layers manually in a reliable and reproducible manner. To address this challenge, a software has been developed to statistically determine the C‐S‐H thickness, requiring minimal manual interventions for thresholding and for setting limits like particle size or circularity. This study presents a tool, which automatically identifies suitable clinker grains and and perform statistical measurements of their hydrate layer up to a specimen age of 28 days. The findings reveal a significant increase in the C‐S‐H layer, starting from 0.45 μm$\umu {\rm {m}}$ after 1 day and reaching 3.04 μm$\umu {\rm {m}}$ after 28 days. However, for older specimens, the measurement of the C‐S‐H layer was not feasible due to limited pore space and clinker grains. [ABSTRACT FROM AUTHOR]

Congenital heart disease (CHD) is the most common type of birth defect, representing a significant cause of perinatal morbidity and mortality. Early diagnosis of such anomalies is crucial for improving outcomes. Current protocols recommend a qualitative assessment of cardiac structures using two-dimensional ultrasound (2DUS) and color Doppler imaging. In cases of suspected abnormalities, quantitative assessments through cardiac structure measurements and reference curves can aid in accurate diagnosis. Similar to centiles widely employed in obstetrics, Z-scores provide more precise quantification of various cardiac structures, particularly at the extremes of the curve. While the development of reference curves and Z-scores has progressed over the past two decades, a lack of standardization in measurements and statistical methodology for their determination is evident. Establishing reference curves requires adherence to specific recommendations to improve their accuracy. The purpose of this study is to provide a narrative review of the major studies that have generated reference values for cardiac structures using 2DUS and Z-scores, to evaluate their methodology, and to provide a summary of the results. [ABSTRACT FROM AUTHOR]

This paper aims to give statistical significance to the measurement of spatial concentration in the context of entropy-based approaches. We simulate confidence intervals based on a null hypothesis able to capture systematic spatial concentration of firms from random patterns, and dissimilarities between the distributions of firms and employees. We implement this two-step methodology to the European manufacturing economy, and we find a substantive spatial clustering of establishments whereby the spatial divergence between employees and firms is significant both for small-scale industries typically considered as localized because of industry-specific Marshallian external economies and for those industries characterized by considerable internal scale economies. We suggest that a high heterogeneity in firm size may have positive implications for aggregate competitiveness at the sectoral level. [ABSTRACT FROM AUTHOR]

Copyright of PODIUM- Revista de Ciencia y Tecnologia en la Cultura Física is the property of Universidad de Pinar del Rio 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.)

The capacity for climate governance is crucial for sustainable advancement, with data elements being a pivotal production factor in contemporary governance. This study examines the trajectory and strategy of digital transformation in climate governance, creating a three-dimensional dataset encapsulating 11 primary and 36 secondary indicators to facilitate the assessment of digital climate governance. Employing spatiotemporal analysis and coupling coordination models, this study evaluates the digitalization levels in climate governance across 30 regions in China, examining how to progress digital integration from governmental and market perspectives. Findings reveal a consistent improvement in China's regional digital climate governance, bolstering economic and social progress. Nonetheless, regional disparities and developmental lags persist, with convergence analysis indicating a divergence trend in provincial climate governance capabilities. Moreover, kernel density and Markov chain analyses suggest an ongoing evolution in regional digital climate governance efforts, aiming at achieving a higher development plateau. The study emphasizes the dual role of government and market dynamics in boosting digital governance levels, deducing from two-stage regression that effective government-market interplay is vital for elevating governance quality and fostering new productive forces, recommending an integrated governance mechanism for optimal synergy. [ABSTRACT FROM AUTHOR]

In this study, a deep learning algorithm namely long short-term memory (LSTM) has been developed for forecasting daily pan evaporation at Sydney airport, Australia. The accuracy of the developed LSTM model has been compared with a commonly used machine learning model, namely multilayer perceptron neural network (MLP-NN). The evaporation rate as a single parameter was used with one time-lag based on autocorrelation function (ACF). The utilized data duration was from January 2021 to February 2022 (during Covid-19 pandemic). Different statistical measurements have been applied in order to evaluate the performance of the proposed models. The results showed that the developed LSTM model outperformed MLP-NN. The LSTM performed well with RMSE = 1.074, MAE = 0.771, R2 = 0.97, while the MLP-NN had least performance with RMSE = 2.801, MAE = 1.994, and R2 = 0.57. The developed LSTM model could be utilized in other locations for forecasting daily pan evaporation. [ABSTRACT FROM AUTHOR]

Intelligence algorithm systems rely on a large dataset to effectively extract significant features that can recognize patterns for classification purposes and extensively utilized to assist the physicians in diagnosis of lung cancer. Extracting valuable features from the available dataset is crucial, especially in cases where additional real data may not be readily accessible. In this context, we propose a novel method called feature extraction based on centroid (FE_CXY) for lesion localization, utilizing a statistical approach. The approach begins with a segmentation process that employs image processing techniques to extract features of interest which is data centroid. This extracted data is then used to compute statistical measurements, revealing hidden patterns that contribute to distinguishing between lesion and non-lesion locations. The method's efficiency is reflected in the development of robust models with improved performance in localizing lung lesions. The study's statistical findings strongly indicate that FE_CXY plays a crucial role as an important feature for detecting lesion localization supported by a student's t-test, which identifies a statistically significant difference in the patterns between lesion and non-lesion localization (p<0.05). By incorporating this method into lung cancer detection systems, we anticipate improved accuracy and efficacy, thereby benefiting early diagnosis and treatment planning. [ABSTRACT FROM AUTHOR]

Nanotwinned face-centered-cubic (FCC) metals have gained significant attention recently due to their exceptional mechanical and electrical properties. Among all the FCC metals, Ag has the lowest stacking fault energy, which allows twin formation to easily occur. In this study, Ag films with a thickness of 4 µm were deposited on Si (100) substrates pre-coated with Ti adhesion layers by electron beam evaporation. With ion beam assistance during the evaporation process, highly (111)-textured and high-density nanotwinned Ag films with excellent properties were produced. Both x-ray diffraction (XRD) and electron backscatter diffraction (EBSD) analyses indicate that the Ag film possesses a strong (111) preferred orientation. In addition, statistical measurements from transmission electron microscopy (TEM) images show that the average twin spacing in nanotwinned Ag films with a deposition rate of 1.8 nm/s is only 6.1 nm. However, conventional models based on thermodynamics are insufficient to describe the formation of growth twins in an ion beam-assisted deposition system. Therefore, an ion bombardment stress model is introduced in this work. The results of the mechanism of deformation twinning are also discussed. [ABSTRACT FROM AUTHOR]

2D memristors have demonstrated attractive resistive switching characteristics recently but also suffer from the reliability issue, which limits practical applications. Previous efforts on 2D memristors have primarily focused on exploring new material systems, while damage from the metallization step remains a practical concern for the reliability of 2D memristors. Here, the impact of metallization conditions and the thickness of MoS2 films on the reliability and other device metrics of MoS2‐based memristors is carefully studied. The statistical electrical measurements show that the reliability can be improved to 92% for yield and improved by ≈16× for average DC cycling endurance in the devices by reducing the top electrode (TE) deposition rate and increasing the thickness of MoS2 films. Intriguing convergence of switching voltages and resistance ratio is revealed by the statistical analysis of experimental switching cycles. An "effective switching layer" model compatible with both monolayer and few‐layer MoS2, is proposed to understand the reliability improvement related to the optimization of fabrication configuration and the convergence of switching metrics. The Monte Carlo simulations help illustrate the underlying physics of endurance failure associated with cluster formation and provide additional insight into endurance improvement with device fabrication optimization. [ABSTRACT FROM AUTHOR]

This research paper delves into the effectiveness and impact of behavior change techniques fostered by information technologies, particularly wearables and Internet of Things (IoT) devices, within the realms of engineering and computer science. By conducting a comprehensive review of the relevant literature sourced from the Scopus database, this study aims to elucidate the mechanisms and strategies employed by these technologies to facilitate behavior change and their potential benefits to individuals and society. Through statistical measurements and related works, our work explores the trends over a span of two decades, from 2000 to 2023, to understand the evolving landscape of behavior change techniques in wearable and IoT technologies. A specific focus is placed on a case study examining the application of behavior change techniques (BCTs) for monitoring vital signs using wearables, underscoring the relevance and urgency of further investigation in this critical intersection of technology and human behavior. The findings shed light on the promising role of wearables and IoT devices for promoting positive behavior modifications and improving individuals' overall well-being and highlighting the need for continued research and development in this area to harness the full potential of technology for societal benefit. [ABSTRACT FROM AUTHOR]

Wetland vegetation and ecology of Lake Abaya in the southern Ethiopia was studied to determine floristic composition, plant community type and vegetation ecology. A total of 102 plots were laid along transects that were set up preferentially across areas where there were rapid changes in vegetation or marked environmental gradients to collect data on estimate of percentage aerial cover of plant species and environmental variables. Vegetation data was analyzed by agglomerative hierarchical cluster analysis using similarity ratio as a resemblance index and Ward's linkage method. Multivariate data analysis was performed using appropriate packages in R version 2.14.0. Canonical Correspondence Analysis (CCA) was used to explore the relationship between the species composition and environmental variables. The environmental data included in the CCA were determined using stepwise backward and forward selection of variables by ANOVA test. Statistical measurement regarding species diversity, richness and evenness of the plant community types was carried out by using Shannon-Wiener diversity indices. A total of 92 plant species belonging to 66 genera and 34 families were identified. Families Poaceae, Asteraceae, Fabaceae, Cyperaceae, Solanaceae, Euphorbiaceae and Amaranthaceae account for about 56.99% of the total proportion. Based on the cluster analysis, five plant community types were identified. The most important factors influencing the plant species composition and pattern of wetland plant communities were water drainage, water depth, land use, slope, altitude, and hydrogeomorphology. Therefore, these factors should be considered in future management and protection under the circumstance of climate change and human activities. [ABSTRACT FROM AUTHOR]

Congestive heart failure (CHF) is one of the primary sources of mortality and morbidity among the global population. Over 26 million individuals globally are affected by heart disease, and its prevalence is rising by 2% yearly. With advances in healthcare technologies, if we predict CHF in the early stages, one of the leading global mortality factors can be reduced. Therefore, the main objective of this study is to use machine learning applications to enhance the diagnosis of CHF and to reduce the cost of diagnosis by employing minimum features to forecast the possibility of a CHF occurring. We employ a deep neural network (DNN) classifier for CHF classification and compare the performance of DNN with various machine learning classifiers. In this research, we use a very challenging dataset, called the Cardiovascular Health Study (CHS) dataset, and a unique pre-processing technique by integrating C4.5 and K-nearest neighbor (KNN). While the C4.5 technique is used to find significant features and remove the outlier data from the dataset, the KNN algorithm is employed for missing data imputation. For classification, we compare six state-of-the-art machine learning (ML) algorithms (KNN, logistic regression (LR), naive Bayes (NB), random forest (RF), support vector machine (SVM), and decision tree (DT)) with DNN. To evaluate the performance, we use seven statistical measurements (i.e., accuracy, specificity, sensitivity, F1-score, precision, Matthew's correlation coefficient, and false positive rate). Overall, our results reflect our proposed integrated approach, which outperformed other machine learning algorithms in terms of CHF prediction, reducing patient expenses by reducing the number of medical tests. The proposed model obtained 97.03% F1-score, 95.30% accuracy, 96.49% sensitivity, and 97.58% precision. [ABSTRACT FROM AUTHOR]

With inflation on the rise in recent years, there has been an important debate on price increases and their impact on different areas of the economy, politics, and society. This article analyzes different methods of statistical measurement of price increases at different stages of production and consumption, as well as inflation. It exemplifies, in particular, with cases from the United States and the Netherlands. It also points out some shortcomings in the methods applied, especially in neoclassical macroeconomic models. [ABSTRACT FROM AUTHOR]

Recently, the electron mobility in wedge-shaped c-GaN nanowall networks has been estimated to cross the theoretical mobility limit for bulk GaN. Significant blue-shift of the bandgap has also been observed. Both the findings are explained in terms of two-dimensional electron gas (2DEG) formed at the central vertical plane of the walls due to the polarization charges at the two inclined faces. Carrier concentration and mobility have earlier been determined from thermoelectric power and conductivity measurements with the help of a statistical model. Due to the network nature of the system, direct measurements of these quantities from Hall experiments are not possible. Search for a better way to estimate mobility in this system thus becomes important. Since, strain can also lead to the blue-shift of the bandgap, it is also imperative to evaluate carefully the role of strain. Here, using Raman spectroscopy, we have estimated carrier concentration and mobility in these nanowall networks with varied average tip-widths. Depth distribution of strain and luminescence characteristics are also studied. The study reveals that strain has no role in the bandgap enhancement. Moreover, the electron mobility, which is determined from the lineshape analysis of the A1(LO)-plasmon coupled mode in Raman spectra, has been found to be significantly higher than the theoretical limit of mobility for bulk GaN for the same electron concentration. These results thus corroborate the picture of polarization induced vertical 2DEG formation in these walls as predicted theoretically. [ABSTRACT FROM AUTHOR]

This article shows a method for the statistical analysis of signals. Firstly, this method was applied to analyze the processing of signs generated by an acquisition card for pulse measurement using the synchronous demodulation method. The application of the method allowed the study of each signal consisting of a descriptive statistical analysis, followed by the analysis of the trend and dynamics of the movement using the augmented Dickey–Fuller test and Hurst exponent, respectively. Secondarily, the method presented here supported the comparison between the pulse signals obtained by synchronous demodulation and plethysmography methods. In addition, the residuals from the pulse comparison of both methods were analyzed. To quantify the differences between the signals, these were compared using the mean-squared error, the root-mean-square error, the mean absolute error, the mean error, the mean absolute percentage error, and the mean percentage error. After this research, it was possible to analyze the signals knowing characteristics such as the following: the presence of normal, exponential, lognormal, and uniform distributions, stationary trend, and dynamic movement anti-persistent. The novelty that this article proposes is the use of concepts traditionally used in the study of time series and models of demand administration, now focused on supporting improvements over the different stages of design and conceptualization of signal processing devices. [ABSTRACT FROM AUTHOR]

In the realm of quality assurance, the significance of statistical measurement studies cannot be overstated, particularly when it comes to quantifying the diverse sources of variation in measurement processes. However, the complexity intensifies when addressing 3D topography data. This research introduces an intuitive similarity-based framework tailored for conducting measurement studies on 3D topography data, aiming to precisely quantify distinct sources of variation through the astute application of similarity evaluation techniques. In the proposed framework, we investigate the mean and variance of the similarity between 3D surface topography measurements to reveal the uniformity of the surface topography measurements and statistical reproducibility of the similarity evaluation procedure, respectively. The efficacy of our framework is vividly demonstrated through its application to measurements derived from additive-fabricated specimens. We considered four metal specimens with 20 segmented windows in total. The topography measurements were obtained by three operators using two scanning systems. We find that the repeatability variation of the topography measurements and the reproducibility variation in the measurements induced by operators are relatively smaller compared with the variation in the measurements induced by optical scanners. We also notice that the variation in the surface geometry of different surfaces is much larger in magnitude compared with the repeatability variation in the topography measurements. Our findings are consistent with the physical intuition and previous research. The ensuing experimental studies yield compelling evidence, affirming that our devised methods are adept at providing profound insights into the multifaceted sources of variation inherent in processes utilizing 3D surface topography data. This innovative framework not only showcases its applicability but also underlines its potential to significantly contribute to the field of quality assurance. By offering a systematic approach to measuring and comprehending variation in 3D topography data, it stands poised to become an indispensable tool in diverse quality assurance contexts. [ABSTRACT FROM AUTHOR]

Rapid live-cell hyperspectral imaging at large fields of view (FOVs) and high cell confluency remains challenging for conventional vibrational spectroscopy-based microscopy technologies. At the same time, imaging at high cell confluency and large FOVs is important for proper cell function and statistical significance of measurements, respectively. Here, we introduce phase-shifting mid-infrared optothermal microscopy (PSOM), which interprets molecular-vibrational information as the optical path difference induced by mid-infrared absorption and can take snapshot vibrational images over broad excitation areas at high live-cell confluency. By means of phase-shifting, PSOM suppresses noise to a quarter of current optothermal microscopy modalities to allow capturing live-cell vibrational images at FOVs up to 50 times larger than state of the art. PSOM also reduces illumination power flux density (PFD) down to four orders of magnitude lower than other conventional vibrational microscopy methods, such as coherent anti-Stokes Raman scattering (CARS), thus considerably decreasing the risk of cell photodamage. [ABSTRACT FROM AUTHOR]

This paper presents a novel and powerful population-based metaheuristic algorithm called Nizar Optimization Algorithm (NOA). This algorithm is based on two techniques. The first technique is to use effective mappings, which are divided into two types: mixing and transformation mappings. The mixing mappings return a mixed vector by replacing or shuffling the elements of two selected vectors, and the transformation mappings return the translation, dilation, or transfer of a selected vector. The second technique is to determine the effective points by using the effective mappings and individuals of the population, and then, these points are used in the learning process of NOA. To validate and demonstrate the performance of the proposed algorithm and its ability to balance exploration and exploitation, NOA is tested on 60 unconstrained benchmark functions and four classic constrained real-world engineering problems. The experimental results are verified by a comparative study with over 20 well-known and recently developed optimization algorithms. These results show that the proposed algorithm outperforms all other algorithms in terms of solution accuracy, convergence curve speed, statistical measurements, and computational expenses. [ABSTRACT FROM AUTHOR]

This document provides a summary of the achievements and career of Prof. RNDr. Gejza Wimmer, DrSc. in the fields of statistics and metrology. Prof. Wimmer has made significant contributions to statistical modeling, measurement science, and metrology. He has held various academic positions and has received multiple academic degrees, including a Ph.D. and a Doctor of Sciences. Prof. Wimmer's research has focused on areas such as linear and non-linear regression models, discrete distributions, and the application of mathematical statistics in medicine and phonetics. He has also been involved in the calibration of measurement devices and the evaluation of uncertainties in measurement. Additionally, Prof. Wimmer has a passion for music and the violin and has performed internationally with a folk musical group. He is recognized as one of the most prominent statisticians in Slovakia and has published numerous scientific papers, monographs, and book chapters. Professor Wimmer has supervised 9 Ph.D. candidates and has collaborated with experts in various fields such as metrology, medicine, music, geodesy, and phonetics. He has also served on the editorial boards of reputable journals and has organized statistical meetings and conferences. Professor Wimmer has received prestigious awards for his achievements in mathematical sciences, including the Medal of Jur Hronec and the Gold Medal of Matej Bel University. He is highly regarded both in Slovakia and internationally for his scholarly contributions. [Extracted from the article]

The paper offers a brief overview of the modern procedures for determining the chemical composition of copper alloys along with the applied methods of analytical control. It has been shown that to obtain reliable and comparable results of measuring the mass fractions of copper alloys elements within wide ranges of values, the most suitable procedures are based on the X‑ray fluorescence method, implemented using portable X‑ray fluorescence spectrometers. In order to select the optimal values of the influencing quantities when determining the chemical composition of copper alloys, a robust parameter design of a quantitative chemical analysis procedure was carried out using X‑50 Mobile X‑ray fluorescence spectrometer. The use of robust parameter design as opposed to conducting a full factorial experiment when developing a measurement procedure makes it possible to optimize the measurement conditions and obtain results of the required accuracy with a limited number of experiments and a maximum number of controlled factors. An example of drawing up an experimental plan for the parameter design of such procedure is provided, and a statistical analysis of the measurement results is carried out. The authors have analyzed and selected the optimal measurement conditions (influencing quantities), which ensure the minimum error of the results of measuring the mass fraction of elements. By selecting the optimal values of the influencing quantities based on the results of robust parameter design of the quantitative chemical analysis procedure, it became possible to increase the accuracy of determining the chemical composition of copper alloys and, hence, improve the reliability of the product quality control results at a relatively low cost. Such approach can be recommended for use by analytical laboratories developing procedures of quantitative chemical analysis for metallurgical enterprises. [ABSTRACT FROM AUTHOR]

In order to investigate the potential Hubble tension, we compile a catalogue of 216 measurements of the Hubble–Lemaître constant H 0 between 2012 and 2022, which includes 109 model-independent measurements and 107 ΛCDM model-based measurements. Statistical analyses of these measurements show that the deviations of the results with respect to the average H 0 are far larger than expected from their error bars if they follow a Gaussian distribution. We find that x σ deviation is indeed equivalent in a Gaussian distribution to x eqσ deviation in the frequency of values, where x eq = 0.72 x 0.88. Hence, a tension of 5σ, estimated between the Cepheid-calibrated type Ia supernovae and cosmic microwave background (CMB) data, is indeed a 3σ tension in equivalent terms of a Gaussian distribution of frequencies. However, this recalibration should be independent of the data whose tension we want to test. If we adopt the previous analysis of data of 1976–2019, the equivalent tension is reduced to 2.25σ. Covariance terms due to correlations of measurements do not significantly change the results. None the less, the separation of the data into two blocks with H 0 < 71 and H 0 ≥ 71 km s−1 Mpc−1 finds compatibility with a Gaussian distribution for each of them without removing any outlier. These statistical results indicate that the underestimation of error bars for H 0 remain prevalent over the past decade, dominated by systematic errors in the methodologies of CMB and local distance ladder analyses. [ABSTRACT FROM AUTHOR]

Key leaf functional traits, such as chlorophyll and carotenoids content (Cab and Cxc), equivalent water thickness (EWT), and leaf mass per area (LMA), are essential to the characterization and monitoring of ecosystem function. Spectroscopy provides access to these four leaf traits by relying on their specific spectral absorptions over the 0.4–2.5 µm domain. In this study, we compare the performance of three categories of estimation methods to retrieve these four leaf traits from laboratory directional-hemispherical leaf reflectance and transmittance measurements: statistical, physical, and hybrid methods. To this aim, a dataset pooling samples from 114 deciduous and evergreen oak trees was collected on four sites in California (woodland savannas and mixed forests) over three seasons (spring, summer and fall) and was used to assess the performance of each method. Physical and hybrid methods were based on the PROSPECT leaf radiative transfer model. Physical methods included inversion of PROSPECT from iterative algorithms and look-up table (LUT)-based inversion. For LUT-based methods, two distance functions and two sampling schemes were tested. For statistical and hybrid methods, four distinct machine learning regression algorithms were compared: ridge, partial least squares regression (PLSR), Gaussian process regression (GPR), and random forest regression (RFR). In addition, we evaluated the transferability of statistical methods using an independent dataset (ANGERS Leaf optical properties database) to train the regression algorithms. Thus, a total of 17 estimations were compared. Firstly, we studied the PROSPECT leaf structural parameter N retrieved by iterative inversions and its distribution over our oak-specific dataset. N showed a more pronounced seasonal dependency for the deciduous species than for the evergreen species. For the four traits, the statistical methods trained on our dataset outperformed the PROSPECT-based methods. More particularly, statistical methods using GPR yielded the most accurate estimates (RMSE = 5.0 µg·cm−2; 1.3 µg·cm−2; 0.0009 cm; and 0.0009 g·cm−2 for Cab, Cxc, EWT, and LMA, respectively). Among the PROSPECT-based methods, the iterative inversion of this model led to the most accurate results for Cab, Cxc, and EWT (RMSE = 7.8 µg·cm−2; 2.0 µg·cm−2; and 0.0035 cm, respectively), while for LMA, a hybrid method with RFR (RMSE = 0.0030 g·cm−2) was the most accurate. These results showed that estimation accuracy is independent of the season. Considering the transferability of statistical methods, for the four leaf traits, estimation performance was inferior for estimators built on the ANGERS database compared to estimators built exclusively on our dataset. However, for EWT and LMA, we demonstrated that these types of statistical methods lead to better estimation accuracy than PROSPECT-based methods (RMSE = 0.0016 cm and 0.0013 g·cm−2 respectively). Finally, our results showed that more differences were observed between plant functional types than between species or seasons. [ABSTRACT FROM AUTHOR]

For many years, chaotic maps have been widely used in the design of various algorithms in cryptographic systems. In this paper, a new model (compound chaotic system) of quantum random walks controlled by a hyper-chaotic map is constructed and a novel scheme for constructing a dynamic S-Box based on the new model is proposed. Through aperiodic evaluation and statistical complexity measurement, it is shown that the compound chaotic system has features such as complex structure and stronger randomness than classical chaotic systems. Based on the chaotic sequence generated by the composite system, we design a dynamic S-Box generation mechanism. The mechanism can quickly generate high-security S-Boxes. Then, an example of randomly generating S-Boxes is given alongside an analytical evaluation of S-Box standard performance criteria such as bijection, boomerang uniformity, bit independence, nonlinearity, linear approximate probability, strict avalanche effect, differential uniformity, the and generalized majority logic criterion. The evaluation results confirm that the performance of the S-Box is excellent. Thus, the proposed dynamic S-Box construction technique can be used to generate cryptographically strong substitution-boxes in practical information security systems. [ABSTRACT FROM AUTHOR]

The results of airborne measurements and statistical characteristics of mesoscale fluctuations of wind velocity, temperature, and concentrations of gas constituents at different heights of a stably stratified troposphere are presented. The measurements were carried out in September 2022 in the Arctic region of Russia with the aircraft laboratory Tu-134 "Optik." The obtained spectra and structure functions of the fluctuations are interpreted with the theoretical model of formation of the spectrum of mesoscale wind velocity and temperature fluctuations described in the paper. The presence at high wavenumbers of a steep section in the obtained horizontal wavenumber spectra of the fluctuations of wind velocity and greenhouse gas concentration with a slope close to −3 is discussed. The fluctuation spectra along different slanted tracks of the aircraft crossing the tropospheric layer between altitudes of 1 and 9 km are also obtained and analyzed with the theoretical model. [ABSTRACT FROM AUTHOR]

In laboratory, a reliable method is very important to report precise and accurate results which are very important for clinical diagnosis and management decisions. In case the existing method needs to be replaced by new method due to poor results or financial issues, method comparison of a new measurement system/method with an established/referral method is required to see whether they agree sufficiently for the new to replace the old method or use the two interchangeably. Most commonly Pearson's product-moment correlation is used to ascertain whether two methods agree sufficiently so that the old method can be replaced by a new method. However, it is the most inappropriate method with number of limitations. In this paper, other methods are discussed with their merits and demerits with examples. [ABSTRACT FROM AUTHOR]

The present work aims to study morphometric profile, calculate body indices, and determine mathematical models that will be used for the estimation of adult live weights based on body measurements using multiple regression models in an endangered heritage breed, the Algerian Sloughi dog, raised in three different geographical regions of Algeria. The animal material of the study consisted of 105 Sloughi dogs, which included 30 individuals belonging to the eastern, 35 from the central, and 40 from the western regions of Algeria. Least squares means for height at withers and rump; head length and width; muzzle, ear, neck, body, tail lengths; muzzle, head, chest, waist, thigh, forearm, and wrist circumferences were used as body measurements. Least squares means for indices of massiveness, format, cephalic, body-tail, bone, muzzle-head, withers-rump, head-neck, ear-head length, and ear-head width were calculated using body measurements. Of the multiple regression models, the highest coefficients of determination (R2) were obtained from the models formed from height at withers, height at withers and thigh circumference or height at withers, thigh circumference and chest circumference together (R2 = 0.70, R2 = 0.79 and R2 = 0.86 respectively). This study concluded that live weight of Algerian Sloughi dog breed could be estimated with a high accuracy using body measurements and statistical methods. [ABSTRACT FROM AUTHOR]

The ability to predict the characteristics of engine exhaust is important to determining heat signatures on various components of the aircraft body. This is often obtained through numerical means such as RANS, which however relies closely on the choice of the turbulence model for accurate predictions. In this study, predictions of exhaust plumes from four turbulence models are compared against results from particle image velocimetry of a scaled helicopter engine exhaust. These models include the standard k − ϵ, realizable k − ϵ, shear-stress transport (SST) k − ω, and Durbin's turbulence models. All four turbulence models managed to capture the general shape of the exhaust when analyzed through the velocity contours at two measurement windows. However, the comparisons of velocity contours fail to describe the shift of the predicted plume from the experiments, which is important for fuselage/tail impingement. To obtain further insights to the shifts, a visual correlation in the form of a confidence ellipse through principal component analysis (PCA) is introduced and plotted for the predicted plumes. All the models' plume predictions showed quantifiable shifts in their mean-centers when compared to the measurements. In terms of matching the measurements' statistical maximum variance of the plume distribution at the furthermost plane, it was found that the realizable k − ϵ performed the best among the models. On the other hand, the SST k − ω and Durbin's model performed the best in predicting bivariate (x and y coordinates) distribution of the plume at the furthermost plane. [ABSTRACT FROM AUTHOR]