Miracle-dz/UniFAR-FTUs huggingface dataset:
Hollow Rutile Cuboid Arrays Grown on Carbon Fiber Cloth as a Flexible Electrode for Sodium‐Ion Batteries

Flexible electrodes with high charge storage capacity, low solid‐state diffusion resistance toward charges, and excellent mechanical properties are needed for fabricating flexible energy storage devices. In this paper, an approach is described to synthesize a composite material with hollow rutile TiO2 cuboid arrays supported on carbon fiber cloth (H‐TiO2@CFC). This composite material is used as a self‐supporting and binder‐free anode for sodium‐ion storage, delivering reversible charge capacities of 287.3 at 0.1 C and 103.3 mAh g−1 at 50 C, respectively. The observed excellent electrochemical performance of the H‐TiO2@CFC electrode is mainly attributed to its unique architecture, which facilitates ion transport, improves electron conductivity, and enables active surface reactivity toward sodium ions. In situ X‐ray diffraction characterization results reveal a nearly zero strain structure of the hollow rutile TiO2 in H‐TiO2@CFC against electrochemical cycling. An analysis of the charge storage mechanism reveals a significant pseudocapacitance contribution to the charge storage, accounting for the observed high‐rate performance of the electrode. A flexible full cell fabricated with the H‐TiO2@CFC as the anode and Na3V2(PO4)3 as the cathode exhibits both excellent electrochemical properties and flexibility against mechanical deformation, demonstrating the great promise of the H‐TiO2@CFC composite material for flexible battery cells.
Severity Analysis of Mitral Regurgitation Using Discrete Wavelet Transform

This paper exhibits a computer-aided diagnosis system for the severity analysis of mitral regurgitation (MR) and assesses the discriminatory capability of Daubechies wavelet-based texture modeling. The Daubechies wavelet family has been utilized for the image decomposition because of its approximate shift invariance property. Seven statistical texture features have been utilized after the decomposition of the image up to four levels and after that concatenated. A supervised classifier, support vector machine (SVM) has been utilized with 10-fold cross-validation approach. The highest classification accuracies are 99.12 ± 0.44 utilizing db2, 99.70 ± 0.29 utilizing db4, and 97.68 ± 1.04 utilizing db4 wavelet in A2C, A4C, and PLAX respectively. The exploratory outcomes show that the proposed algorithm is effective and db4 beat among the Daubechies wavelet family considered amid this audit for precise severity investigation of the MR images.
A Brief Overview of Nutrient Anti-Inflammatory Molecules and their In Vitro and In Vivo Activity

Anti-inflammatory nutrients are components of normal dietary intake with reported anti-inflammatory properties. In comparison to traditional pharmacological anti-inflammatory drugs, antiinflammatory nutrients may have lower side effects. However, one limitation of anti-inflammatory nutrients is their inefficiency compared to current pharmacological anti-inflammatory drugs, as higher nutrient concentrations may be required to provide similar pharmacological effect. A large range of anti-inflammatory nutrients are currently under investigation for their therapeutic use and efficacy: these include amino acids, fats, vitamins and flavonoids. This review provides a brief overall perspective of the existing literature on anti-inflammatory nutrients and their proposed mechanisms of action.
Normal internal coordinates, force fields, and vibrational study of species derived from antiviral adamantadine

Complete vibrational assignments have been performed for free base, cationic and hydrochloride species derived from antiviral adamantadine by combination of hybrid B3LYP with the 6-31G* and 6-311++G** basis sets and the SQMFF methodology. Normal internal coordinates and scaling factors were used to obtain the harmonic force fields and scaled force constants of three species in gas phase and in aqueous solution. Bond lengths and angles of cationic and hydrochloride species show very good concordances with the experimental of amantadinium azide. The cationic species reveals higher solvation energy value, as compared with antiviral agents, however, brincidofovir, the antiviral used to ebola disease presents a higher reactivity against to adamantadine. Positive value of Mulliken charge on N1 of hydrochloride species in solution could justify the ionic character of H29***Cl30 bond, as evidenced by bond order and AIM calculations. The hydrochloride species is the most reactive in both media while the cationic species the less reactive. High electrophilicity and nucleophilicity indexes of cationic species in both media justify its higher hydration. Good concordances were observed between experimental and predicted 1H and 13C NMR and electronic spectra. In solution, the three species are present as revealed by the experimental UV spectrum of hydrochloride amantadine
Subseasonal Forecast Skill for Weekly Mean Atmospheric Variability Over the Northern Hemisphere in Winter and Its Relationship to Midlatitude Teleconnections

This study assesses the subseasonal predictability of the weekly mean geopotential height anomaly at 500 hPa and its relationship to teleconnections over the Northern Hemisphere in winter. The skill over the North Pacific, Canada, and Greenland is higher than over other areas for weeks 3 and 4 forecasts. These peaks correspond to the centers of action for the Pacific–North American (PNA) pattern and the North Atlantic Oscillation (NAO). PNA (NAO phase) predictions are better for El Niño years at lead times of 3–4 weeks (2–4 weeks). The effects of La Niña forcing on PNA and NAO forecasts are small compared with the El Niño forcing. Numerical models have a negative PNA bias at these lead times in La Niña years. Results suggest that the improvement in the midlatitude upper‐level jet rather than in the response to El Niño–Southern Oscillation forcing in the tropics could lead to better S2S predictions.
Influence of the Coating System on the Acoustic, Thermal and Luminous Performance of Brazilian Buildings

This work presents an extensive numerical simulation to analyze the influence of the coating layers on the performance of construction systems, in order to make the constructions projects feasible, not only economically but also technically. Through numerical simulations based on a defined reference model, the present work studied the influence of different layers of floor, roof and internal and external wall systems, on the acoustic, thermal, and luminous performance of buildings in Brazil. The results showed the materials and elements with the greatest influence on: lighting performance are the internal finishes of the environment and the type of glass used in the external windows. On thermal performance, all elements of the roofing system and façades, especially an absence of external cladding and the use of thermal blankets on the roof, have greater influence. The acoustic performance of the façade function on the external windows and acoustic performance of the floor system are mainly influenced by the thickness of the structural element and the use of a ceiling and acoustic blanket; acoustic performance of internal walls is affected by typology of the structured element of the wall and thickness.
Online probabilistic model class selection and joint estimation of structures for post-disaster monitoring

Online selection of the appropriate model and identifying its parameters based on measured vibrational data are among the challenging issues in dynamic system identification. After a severe earthquake, quick monitoring and assessment of structural health status play a crucial role in effective critical risk management for the building owners and decision-makers. The Bayesian multiple modeling approach is a suitable tool for optimal model class selection, which is used in this article mainly for improving data fitting precision, decreasing dimensions of structural unknown vector through removing unnecessary parameters, detecting the occurrence and type of predominant phenomenon related to degradation and pinching, and finally increasing stability and convergence rate of the used identification algorithm. In this study, a reliable and effective time-domain method is proposed for online probabilistic model class selection and joint estimation of structures using the central difference Kalman filter and Bayesian multiple modeling approach. Also, in contrast to existing studies, the performance and efficiency of the proposed algorithm are investigated in nonlinear system identification with a large number of unknowns. The proposed algorithm is implemented on three moment-frame buildings with 8, 54, and 60 unknowns. In addition, a numerical example is provided to analyze the case in which the exact model is not among the plausible models. To verify the performance of the hybrid identification method, robust simulations with synthetic measurement noises and modeling errors were generated using the Monte Carlo random simulation method. The result shows the method can be used to simultaneously select the optimal model class and identify its unknown states and parameters in an online manner.
Gain Scheduling Output Feedback Control for Vehicle Path Tracking Considering Input Saturation

This paper presents a gain scheduling output feedback control method to reduce driver workload and improve driving performance by considering input saturation. The driver–vehicle system model is developed by considering tire cornering stiffness uncertainties and different driver parameter uncertainties. Meanwhile, the input saturation is also considered in the driver-vehicle system. A quadratic Lyapunov function is designed to solve the optimization problem with uncertainties and input saturation. The results, which are based on the MATLAB-CarSim co-simulation platform, indicate that the robust controller not only improves the convergence rate of the state but also reduces the steering workload of the driver.
Morpho-physiological and biochemical responses of bladder cherry (Physalis alkekengi L.) induced by multienzymatic biostimulant, IBA, and citric acid

Abstract Plant enzymes, growth regulators and organic acids are the main groups of plant biostimulants (PBs), and their combined use in the final formulation may be important for increasing the quantitative and qualitative composition of plant products. This study aimed to determine the effects of a multienzymatic biostimulant (MB), indole-3-butyric acid (IBA), and citric acid (CA) on the morphological and phytochemical traits of bladder cherry (Physalis alkekengi L.). The treatments included different concentrations of MB (0, 0.5 and 1.0%), IBA (0, 25, and 50 ppm), and CA (0, and 500 mg dm−3), which were sprayed four times during the vegetative stage, at 12-day intervals, 35 days after planting. The results showed that the treatments had a significant effect on plant height, stem number, diameter and weight, leaf number and weight, fruit number, diameter and weight, the amounts of total phenols, alkaloids and flavonoids, and on the radical scavenging activity. The most effective formulation for improving the fruit yield of bladder cherry was 1% MB with 50 ppm IBA and 500 ppm CA. However, the best treatment for increasing the total phenolic and alkaloid contents, and radical scavenging activity was 0.5% MB. In general, the maximum values of most traits were obtained by spraying the plants with 0.5 and 1% MB combined with IBA and CA. The concentration of alkaloids, the main pharmaceutical metabolites of bladder cherry, increased as a result of the application of the multienzymatic biostimulant.
Taxonomic survey compared to ecological sampling: are the results consistent for woodland epiphytes?

Abstract Field survey by a taxonomist or specialist biologist (‘taxonomic survey’) provides a comprehensive inventory of species in a habitat. Common and conspicuous species are rapidly recorded and search effort can be targeted to inconspicuous or rare species. However, the subjective nature of taxonomic survey limits its usefulness in ecological monitoring and analysis. In contrast, ‘ecological sampling’, focused on the standardized use of repeated sub-units such as quadrats, is designed to quantify the observational error of results, allowing for more robust statistical treatment. Nevertheless, the spatial extent of recording will be lower during ecological sampling, and rarities might be missed. Despite their differences, these two approaches are often assumed to be congruent for decision making. Taxonomic survey is commonly used to identify priority sites for conservation (including species-rich sites, or those with many rare/threatened species) while ecological sampling is used to design conservation strategy by relating species richness or composition to habitat dynamics. If these contrasting approaches are indeed congruent, then trends in species richness and community composition, detected by ecological sampling, will mirror the results of taxonomic survey so that management confidently protects the attributes for which a site was prioritized. This study performed both taxonomic survey and ecological sampling for lichen epiphytes in 13 woodland study sites in Scotland. To understand the procedure of taxonomic survey, fieldwork by a professional taxonomist was structured by effort into 15-minute time intervals. As expected, taxonomic survey discovered more species per site, while ecological sampling (allowing a measure of species frequency) resolved greater variation in community composition. However, the patterns of richness and species composition obtained from the different methods were correlated, suggesting an overall high degree of congruence in identifying and then managing priority sites. Furthermore, when exploring the taxonomic survey in detail, we found that a minimum effort of 45 minutes was required to accurately determine species richness differences among contrasting woodland sites.
Tolerance for Uncertainty and Patterns of Decision-Making in Complex Problem-Solving Strategies

Current studies of complex problem-solving do not commonly evaluate the regulatory role of such personality-based variables as tolerance for uncertainty, risk-readiness, and patterns for coping with decisional conflict. This research aims to establish the contribution of those traits into individual parameters of complex problem-solving strategies. The study was conducted on 53 healthy individuals 17 to 29 years old (M = 20.42; SD = 2.34). Our own computerized complex problem task “The Anthill” was developed for this research. We identified five measurable parameters of the participants’ problem-solving strategies: preferred orientational level (POL); orientational level variability (OLV); class quotas‘ range (R); mean and median quotas shift (MS and MeS); and abrupt changes of strategy (AC). Psychodiagnostic methods included: new questionnaire of tolerance/intolerance for uncertainty; personal decision-making factors questionnaire; Melbourne Decision Making Questionnaire; Subjective Risk Intelligence Scale; Eysencks’ Impulsiveness Scale. The study showed the role of tolerance for uncertainty, risk-readiness, negative attitude toward uncertainty, and decision-making styles in the regulation of complex problem-solving strategies. Specifically, procrastination, tolerance for uncertainty, and risk-readiness were significant predictors of individual strategy indicators, such as POL, OLV, and MeS. Thus, personality traits were shown to regulate resource allocation strategies and the required level of orientation in a complex problem.
Sliding Mode Observer-Based Fault-Tolerant Secondary Control of Microgrids

This paper proposes two sliding mode observer (SMO)-based fault-tolerant secondary control schemes for microgrids. The first scheme consists of a central SMO-based fault tolerant controller that uses outputs from the microgrid and estimates all states in the microgrid as well as the fault. The estimated fault is then used to reject the effect of faults on the microgrid. The second scheme is decentralised, where each distributed generator (DG) has its own SMO-based fault tolerant controller which would estimate faults affecting that DG alone, and compensate for faults in only that DG. By rejecting the effect of faults in each DG, the effect of faults on the entire microgrid can be negated. Finally, we simulate an example using both schemes, and its results verify the efficacy of the schemes for fault-tolerant secondary control of microgrids.
A comparative study of energy harvesting performance of polymer‐piezoceramic composites fabricated with different piezoceramic constituents

Polymer‐piezoceramic composites harness the flexibility of polymer and piezoelectric properties of ceramics to offer flexible piezoelectric materials for use in energy harvesting applications. Although, there is a general temptation to use piezoceramics which exhibits high piezoelectric coefficient (d33) in the fabrication of such composites, there are limitations posed by the processing conditions of the composite which eventually determines their overall performance. In the present work we have explored this aspect in detail. We fabricated composites with polyvinylidene fluoride (PVDF) as the polymer and three different piezoceramic powders namely BaTiO3 (BT), Ba(Ti0.97Sn0.03)O3 (BTS) and Sm‐doped Pb (Mg1/3Nb2/3)O3‐PbTiO3(Sm‐PMN‐PT). Our focus here is to understand the role of grain size of the ceramic powders in determining the dielectric, piezoelectric, and energy harvesting performance of the composites. To broaden the perspective, the results are compared with another analogous composite 0.59PbTiO3‐0.41Bi(Zr0.5Ni0.5)O3 (PT‐BNZ)‐PVDF reported before. We found that, although, the dense ceramic specimen of Sm‐PMN‐PT exhibits exceptionally large d33, the composite with PT‐BNZ exhibited the best piezoelectric and energy harvesting performance. BT, BTS and Sm‐PMN‐PT composites showed maximum surface power density of 6.24, 12.81 and 23.18 μW/cm2, respectively, and volume power density of 416.18, 854.1 and 1104 μW/cm3, respectively. Whereas surface power density and volume power density of PT‐BNZ was 101.80 μW/cm2 and 5088.80 μW/cm3 respectively. We have also attempted to establish a correspondence between the piezoelectric response, energy harvesting performance and the poling induced domain reorientation/structural changes in the ceramic grains of the composites.
Responses to a poached conspecific in wild forest elephants (Loxodonta africana cyclotis)

The increased attention towards deceased conspecifics in various social animal species is one of the most intriguing conundrums in animal behaviour. The factors that might explain the observed behavioural variation amongst individuals remain nebulous. Here we analyse forest elephants’ (Loxodonta africana cyclotis) responses to a poached adult male conspecific, using remote camera trapping during a period of eight months. After completely avoiding the carcass site for over a week, females and males substantially differed in behavioural responses. Males consistently stayed longer around the remains, showed signs of increased arousal, interacted with the dead body, and twisted trunks with each other. Females, in contrast, were more passively explorative and preferred to visit the site without their dependent offspring. Findings show a previously unknown sexual-dimorphism in forest elephant behaviour towards a poached conspecific and raise the possibility that individuals might be able to infer further context-specific information about the event.
Stability analysis in machining process by using adaptive closed-loop feedback control system in turning process

The study presents model-based mechanism of nonlinear cutting tool vibration in turning process and the strategy of improving cutting process stability by suppressing machine tool vibration. The approach used is based on the closed-loop feedback control system with the help of electro–magneto–rheological damper. A machine tool vibration signal generated by an accelerometer is fed back to the coil of a damper after suitable amplification. The damper, attached under the tool holder, generates counter forces to suppress the vibration after being excited by the signal in terms of current. The study also discusses the use of transfer function approach for the development of a mathematical model and adaptively controlling the process dynamics of the turning process. The purpose of developing such mechanism is to stabilize the machining process with respect to the dynamic uncut chip thickness responsible for the type-II regenerative effect. The state-space model used in this study successfully checked the adequacy of the model through controllability and observability matrices. The eigenvalue and eigenvector have confirmed the stability of the system more accurately. The characteristic of the stability lobe chart is discussed for the present model-based mechanism.
Site-Scale Disturbance Best Predicts Moss, Vascular Plant, and Amphibian Indices in Ohio Wetlands

Loss of wetland habitats and their associated biological communities is a major environmental concern. Quality assessment indices (QAIs) and indices of biological integrity (IBIs) are useful for assessing the responses of taxa to wetland habitat quality and land use in the surrounding landscape. We synthesized the results of our previous predictive modeling studies of five IBIs and QAIs for communities of mosses, vascular plants, and amphibians in forested and emergent wetlands in Ohio (USA). Overall, the single best predictor of these indices was a metric that estimated site-scale (i.e., within the wetland boundaries) substrate and habitat development, alteration, and disturbance. The second most important predictor was a metric that assessed site-scale wetland plant community types and quality, degree of interspersion, and microtopography. Landscape-scale variables better predicted moss and amphibian indices than either vascular plant index. Our results indicate that applying management practices that reduce the effects of site-scale anthropogenic disturbances and increase habitat complexity, such as creating forested buffers surrounding wetlands, increasing wetland contiguity, and creating hummocks and tussocks may simultaneously enhance amphibian, vascular plant, and moss communities in forested and emergent wetlands. Such a focused strategy may enable management agencies to more effectively apportion resources for wetland restoration and construction projects.
Neural Embeddings of Financial Time-Series Data

The dominant approaches for financial portfolio construction rely on estimating sample covariance and correlations matrixes, which serve as an input into a number of classical portfolio construction techniques. These classical approaches are not forward looking, are constrained by the ability to estimate covariance and correlation matrixes, and are inflexible to incorporating additional information. The authors propose a new approach of using learned representations from deep learning networks to augment such classical techniques. This approach can incorporate learned estimates of future performance and can be customized to create tailored representations best suited to meeting varying financial objectives. This article showcases one example of such an embedding, compares and contrasts it with classical approaches to portfolio construction, and discusses additional possibilities for applying representation learning in quantitative finance. TOPICS: Big data/machine learning, performance measurement, portfolio construction, simulations, statistical methods Key Findings • The authors propose a general methodology for creating tailored embeddings of financial time-series data. • The authors demonstrate one approach for creating such embeddings, analyze the induced similarity measure, and compare and contrast it with the traditional approach of using covariance and correlation as a similarity measure for securities. • The authors discuss other applications and advantages of this approach, including accounting for probabilistic estimates of future behavior and flexibility in incorporating varying business objectives.