Changes in land-use have been observed in banana-based systems in the African Great Lakes region affected by Xanthomonas wilt disease (XW) of banana. Through participatory focus group discussions (FGDs) and the 4-cell method, changes in land-use were retrospectively assessed in 13 XW-affected landscapes/villages along a 230 km transect from Masisi (XW arrived in 2001) to Bukavu (XW arrived around 2014) in eastern Democratic Republic of Congo during 2015. The four-cell chart ranked land-use by mapping the area under production and the number of households involved in production. Farmers’ perceptions on the sustainability of new land-uses were also documented. Soil nutrient content and erosion levels were measured for five major land-use options/ trajectories on 147 fields across 55 farms in three landscapes along the transect. From being ranked the most important crop (92% of landscapes i.e. produced on large areas of land and by many households) before XW outbreaks, its importance had declined with most households in 36% of the landscapes growing it on smaller farms while in 64% of cases by few households on smaller pots. Farmers uprooted entire banana mats or fields, expanding land under other crops, mainly beans, taro, sweet potato, cassava, maize, coffee and eucalyptus. Species richness did not change at landscape level, though 21 crops were introduced at farm level. Land-use for banana is however still perceived to be more sustainable due to its multi-functional roles. Soils under banana plots were found in general to be better in their chemical attributes while high erosion levels (Mg ha-1 year-1) were observed under cassava (1.7-148.9) compared with banana (0.3-10.7) and trees (0.3-5.9). The current shift away from banana could thus have profound effects on supply of key services and sustainability of the production systems. This study offers a good basis/entry point for interventions in the XW-affected landscapes.

Accurate channel estimation is of utmost importance for massive MIMO systems that allow providing significant improvements in spectral and energy efficiency. In this work, we investigate the spectral efficiency performance and present a channel estimator for multi-cell massive MIMO systems subjected to pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without prior knowledge of the inter-cell large-scale channel coefficients and noise power. The estimator approximates the performance of a linear Minimum Mean Square Error (MMSE) as the number of antennas increases. Following, we derive a lower bound closed-form spectral efficiency of the Maximum Ratio Combining (MRC) detector in the proposed channel estimator. The simulation results highlight that the proposed estimator performance approaches the linear minimum mean square error (LMMSE) channel estimator asymptotically.

Accurate channel estimation is of utmost importance for massive MIMO systems to provide significant improvements in spectral and energy efficiency. In this work, we present a study on the distribution of a simple but yet effective and practical channel estimator for multi-cell massive MIMO systems suffering from pilot-contamination. The proposed channel estimator performs well under moderate to aggressive pilot contamination scenarios without previous knowledge of the inter-cell large-scale channel coefficients and noise power, asymptotically approximating the performance of the linear MMSE estimator as the number of antennas increases. We prove that the distribution of the proposed channel estimator can be accurately approximated by the circularly-symmetric complex normal distribution, when the number of antennas, M, deployed at the base station is greater than 10.

Existing methods for detecting magnetic leakage signals from damaged wire ropes require axial saturation magnetisation, and the accuracy of the detection equipment depends on the saturation degree of magnetisation. Moreover, the expected magnetisation effect is usually difficult to achieve owing to the special characteristics of the rope structure, diameter, and operating environment of the wire rope. Consequently, in addition to other issues, the detection accuracy and versatility of the equipment are low. In this study, a method based on spatial multidimensional orthogonal array loop magnetisation for detecting mine wire rope defects is proposed. Firstly, a new sensor excitation structure model is developed. Then a method of radiant magnetic flux of permanent magnet array is analysed, and the influence law of the number of permanent radiant magnetic blocks on the magnetisation effect is studied. In addition, a method of wire rope detection based on the principle of clustering is investigated, according to which the influence law of the shape, structure, and size of the polyurethane device on the effect of magnetisation is discussed. Finally, through laboratory experiments, the test model and proposed method are verified. The results show that the magnetisation effect is better and more cost-effective when the number of permanent magnet radiation magnetic block is n=8, and the proposed detection method can effectively distinguish damaged wire rope joints. Furthermore, the proposed equipment achieved signal-to-noise ratio of the signals, improving the reliability of damage detection.

Negative corona discharges occur widely in high voltage transmission lines and charged aircraft, which can cause strong electromagnetic interference. Negative corona discharge is typically performed simultaneously at multiple discharge points. In this study, the current and its EM radiation characteristics of single-needle and multi-needle negative corona discharge in different conditions were tested. The current and electromagnetic radiation characteristics of the two discharge structures were compared. The dipole radiation model was established to analyze the EM radiation characteristics of the negative corona discharge.The results show that, It is only when the voltage reaches a certain threshold that the current and electromagnetic radiation fields of the multi-needle discharge structure will be superimposed and their amplitudes will increase significantly. The frequency of electromagnetic radiation signal does not change with the number of needles, cathode geometry and applied voltage, but only depends on ambient pressure. It provides a basis for detecting corona discharge sources under different conditions.

In the oil palm industry, stands age is an important parameter to monitor the sustainability of cultivation, to develop the growth yield model, to identify the disease or stressed area, and to estimate the carbon storage capacity. This research is focused to estimate and distinguish oil palm stands age based on crown/ canopy density obtained using Forest Canopy Density (FCD) model derived from four indices as follows; Advanced Vegetation Index, Bare Soil Index, Shadow Index, and Thermal Index. FCD model employs multi temporal image analysis resulting four classes of oil palm stands age categorized as seed with FCD value of 29–56% (0 years), young with FCD value of 56–63% (1–9 years), teen with FCD value of 63–80% (10–15 years), and mature with FCD value of >80% (>15 years). Minimum canopy density value is 29% even in the zero years old indicates incomplete land clearance or the type of seed planted in the land.

With quick improvement in the field of network, everything becomes online. Remote user authentication is a mechanism in which remote server verify the correctness of user over common or public channel. Remote user authentication can be Single server authentication or Multi server authentication. The disadvantage of single server authentication is that the user needs to recall user id and password for each service, he/she need to utilize, however it can overcome by Multi-server authentication in which user needs to register himself with Registration Center (RC) for the first run through and after that onwards he can utilize each service (which are given by servers, associated with RC) by recollecting only one user id and password. In this paper, we analyze Chen's authentication scheme (based on multi server authentication) and show that this scheme is vulnerable to password guessing attack, replay attack, RC spoofing attack, session key verification attack and perfect forward secrecy attack. In this Paper, we propose a biometric based remote user authentication scheme in multi-server environment. Proposed scheme is more secure and efficient as compared to chen's scheme\cite{23}.

UAS-based multi-spectral imagery is becoming increasingly popular for the improved monitoring and managing of various horticultural crops. However, for UAS data to be used as an industry standard for assessing tree structure and condition as well as production parameters, it is imperative that the appropriate data collection and pre-processing protocols are established to enable multi-temporal comparison. There are several UAS-based radiometric correction methods commonly used for precision agricultural purposes. However, their relative accuracies have not been assessed for data acquired in complex horticultural environments. This study assessed the variations in estimated surface reflectance values of different radiometric corrections applied to multi-spectral UAS imagery acquired in both avocado and banana orchards. We found that inaccurate calibration panel measurements, inaccurate signal-to-reflectance conversion, and high variation in geometry between illumination, surface, and sensor viewing produced significant radiometric variations in at-surface reflectance estimates. Potential solutions to address these limitations included appropriate panel deployment, site-specific sensor calibration, and appropriate BRDF correction. Future UAS based horticultural crop monitoring can benefit from the proposed solutions to radiometric corrections to ensure they are using comparable image-based maps of multi-temporal biophysical properties.

There is an ongoing global pandemic of human respiratory syncytial virus (RSV) infection that results in substantial annual morbidity and mortality. In Australia, RSV is the major cause of acute lower respiratory tract infections (ALRI). Nevertheless, little is known about the extent and origins of genetic diversity of RSV in Australia, nor the factors that shape this diversity. We conducted a genome-scale analysis of RSV infections in New South Wales (NSW). RSV genomes were successfully sequenced for 144 specimens collected between 2010-2016. Of these, 64 belonged to the RSVA and 80 to the RSVB subtype. Phylogenetic analysis revealed a wide diversity of RSV lineages within NSW and that both subtypes evolved rapidly in a strongly clock-like manner, with mean rates of approximately 6-8 x 10^-4 nucleotide substitutions per site per year. There was only weak evidence for geographic clustering of sequences, indicative of fluid patterns of transmission within the infected population, and no evidence of any clustering by patient age such that viruses in the same lineages circulate through the entire host population. Importantly, we show that both subtypes circulated concurrently in NSW with multiple introductions into the Australian population in each year, and only limited evidence for multi-year persistence.

We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.

Abstract: Comparing outcrop data to laboratory results is important to verify and validate experiments of analogue and reservoir materials especially regarding conditions for deformation experiments. This is important better understand highly complex carbonate reservoir strata and their response to changes in subsurface conditions, reducing subsurface uncertainty. This study develops methods to allow for a more straightforward comparison of outcrop data (m-scale) with experimentally created fracture arrays developed in cylindrical samples (cm-scale). The main objective is to assess usefulness of experimentally-produced fracture networks as analogues for subsurface structures, typically at the meter and above scale by developing new techniques to use the lab deformation. It analyses key characteristics of laboratory-induced fracture networks by adapting scanline methods to use with x-ray tomography (XRT) images to allow for comparison with outcrop and field data. To test and verify these new methods two low permeability carbonate samples were used for deformation testing and analysis. Applying the different scanline methods we show that they can be used to analyse lab induced fractures (mm to cm-scale) identified in XRT images for comparison with outcrop data (m-scale). In addition, these methods also allow for quantification of fracture network attributes e.g. fracture spacing, fracture apertures, orientation. This new data bridges the gap between micro-scanlines using thin sections and outcrop scanlines.

The RPL protocol is a routing protocol for low power and lossy networks. In such a network, energy is a very scarce resource; so many studies are focused on minimizing global energy consumption. End-to-end latency is another important performance indicator of the network, but existing research tends to focus more on energy consumption and ignore the end-to-end delay of data transmission. In this paper, we propose a kind of energy equalization routing protocol to maximize the surviving time of the restricted nodes so that the energy consumed by each node is close to each other. At the same time, a multi-path forwarding route is proposed based on the cache utilization. The data is sent to the sink node through different parent nodes at a certain probability, not only by selecting the preferred parent node, thus avoiding buffer overflow and reducing end-to-end delay. Finally, the two algorithms are combined to accommodate different application scenarios. The experimental results show that the proposed three improved schemes improve the reliability of the routing, extend the lifetime of the network, reduce the end-to-end delay, and reduce the number of DAG reconfiguration.

Recent analyses of cosmic microwave background surveys have revealed hints that there may be a non-trivial running of the running of the spectral index. If future experiments were to confirm these hints, it would prove a powerful discriminator of inflationary models, ruling out simple single field models. We discuss how isocurvature perturbations in multi-field models can be invoked to generate large runnings in a non-standard hierarchy, and find that a minimal model capable of practically realising this would be a two-field model with a non-canonical kinetic structure. We also consider alternative scenarios such as variable speed of light models and canonical quantum gravity effects and their implications for runnings of the spectral index.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

We hypothesize that the first ancestral "ante-cell" molecular structures, i.e., first RNAs and peptides which gradually transformed into real cells once the Earth had cooled sufficiently for organic molecules to appear there, have leaved traces in RNAs and genes of present cells. We propose a circular RNA which could have been one of these ancestral structures whose vestigial pentameric subsequences would mark the evolution from this key moment when the ante-cells have begun to join the living organisms. In particular, we propose that in present RNAs (ribosomal or messenger) playing an important role in the metabolism of current cells, we look for traces of the proposed primitive structure in the form of pentamers (or longer fragments) belonging to their nucleotide sequence. The result obtained can be summarized in the existence of a gradient of occurrence of such pentamers, with a high frequency for the most vital functions (protein synthesis, nucleic synthesis, cell respiration, etc.). This gradient is also visible between organisms, from the oldest (Archaea) to the most recent (Eukaryotes) in evolution of species.

The penetrability and directivity of ultrasound in different media is of interest in engineering and medical applications (imaging, nondestructive testing, sonochemistry, among others). The nonlinearity of a liquid can be used in the parametric antenna framework to generate low-frequency components with particular features from several ultrasonic signals at the source. Bubbly liquids are dispersive liquids in which a small amount of tiny gas bubbles leads to the increase of the nonlinear parameter of the media over certain frequency ranges. Parametric antenna applied to this huge nonlinear media gives rise to low-frequency components with relatively small intensity at the source. The evolution of a low-frequency component (difference-frequency component obtained from two primary signals) during its propagation in a bubbly liquid is somehow unknown. It is thus interesting to analyse its characteristics to establish whether this component can benefit from the quality of its own frequency and from the primary frequencies in terms of directivity and penetrability into the medium. It must be noted that no such study in bubbly liquids exists in the litterature, but only for homogeneous media. To this end, several numerical models developed previously are used here to analyse the difference-frequency component obtained from a parametric antenna emitting from two ultrasonic signals at the source in one and two-dimensional domains. These models allow us to observe the behavior of this frequency component. An angle that measures the directivity of a beam is also defined. Our results show a point hardly found in the literature: the high directivity and the huge penetrability of the secondary beam associated to the difference-frequency component into the bubbly liquid, compared to the same frequency signal excited directly from the source in the bubbly liquid and to the parametric acoustic array in homogeneous fluids.

An optical frequency comb (OFC) generator based on an electro-optic Talbot laser and cross-phase modulation (XPM) in a high nonlinear fiber (HNLF) is designed and demonstrated. The Talbot laser is an electro-optic frequency shifting loop to produce repetition rate multiplied pulses, and these pulses working as a pump signal induce XPM process in the HNLF to modulate the phase of a probe signal. At the output of the HNLF, OFCs with a multiplied line-spacing can be generated. The effects of the pump power and the HNLF length on the performance of the generated OFCs are theoretically analyzed. In the experiments, the line-spacing of the generated OFCs is respectively multiplied to be 10 GHz, 15 GHz and 20 GHz with a factor of 2, 3 and 4. The center of the OFCs is tuned in a 4-nm range by adjusting the wavelength of the probe signal.

A review on planar printed structures that are based on Matryoshka-like geometries is presented. These structures use the well-known principle of Matryoshka dolls that are successively nested inside each other. The well-known advantages of the planar printed technology and of the meandered nested Matryoshka geometries are combined to generate miniaturized, multi-resonance and/or wideband configurations. Both metal and complementary slot structures are considered. Closed and open configurations are analyzed. The working principles are explored do get physical insight of their behavior. Low cost and single layer applications as frequency selective surfaces, filters, antennas, and sensors, in the microwave frequency region, are reviewed. Potential future research perspectives and new applications are discussed.

Adjustable frequency converters are widely used in modern automated asynchronous drives due to their high efficiency and high responsiveness control strategies. Control algorithms for frequency converters are divided into two main groups: constant frequency commutation and random frequency commutation. Research results on various commutation frequencies, conducted using Matlab/Simulink software, show that the use of random frequency commutation provides better efficiency by significantly reducing the amplitude of harmonic oscillations at different frequencies, with a reduction in switching losses in transistors. Currently, a converter with random frequency commutation is being researched and applied in the direct torque control system. The traditional direct control system, based on a two-level inverter, is widely used due to its simplicity and ease of implementation. However, this method results in the output current of the two-level converter, supplied to the motor stator, having many harmonic distortions, leading to strong torque fluctuations regardless of the rotation speed and ultimately causing additional losses in the stator windings and reducing the motor's lifespan. To enhance the efficiency of automated asynchronous drives, this article presents an algorithm for a direct control system based on a three-level NPC inverter using a PI speed regulator. The proposed algorithm's research is validated in the Matlab/Simulink environment. The research results show that in the absence of harmonic filtering, the stator current harmonic distortion coefficient is approximately 11%, and torque fluctuations are less than 5% of the resistive torque. The fluctuations between the two capacitors remain almost unchanged throughout the entire process.

This paper presents the design and experimental results of a long cavity length Nd: YAG laser with large stable zone for water jet guided laser (WJGL) applications. The design is based on the light transmission matrix and resonator stability conditions, aiming to achieve a large stable zone and a short cut-off thermal focal length (CTFL). A folded concave resonator is researched to enhance the cavity length, and the influence of the tun-able cavity arm length on the oscillating beam in the resonator and in the YAG crystal is theoretically studied. Moreover, the effects of the output mirror curvature and the cavity arm length on the range of the stable area and the cut-off thermal focal length are also investigated. Experimental results show that a stable green laser output is obtained after second harmonic generation (SHG), with a pulse width ranging from 43ns to 143ns within the laser operating frequency range of 5-20kHz. At an operation frequency of 10kHz, the output power is 21.33W, and the instability of the output power within 400 minutes is 0.88%. The laser source achieves a maximum power of 25.7W at 20kHz, and the maximum single pulse energy reaches 2.7mJ at 6kHz. Finally, this is used as the laser source to couple with a water jet with a diameter of 100 microns, achieving a lossless water conductivity transmission over 60mm length with a peak power density of 0.742GW/cm2. These results demonstrate the suitability of the designed laser source for WJGL technology research.

This paper first introduces the main classification of brain diseases and the main causes of these diseases. It is followed by wavelet analysis, which is a mathematical function or wave-like pattern used to transform data, which can decompose the signal into different wavelet functions, each of which is related to a specific scale or frequency. Wavelet analysis has been applied in many fields such as image processing, data analysis and engineering. In addition, wavelet is also used in the analysis of biological signals. Wavelet analysis breaks down brain signals, such as EEG or fMRI data, into various frequency components at different scales. Wavelet analysis provides time-frequency localization. Pattern recognition can be enhanced by isolating salient features in the data. Wavelet denoising can effectively separate noise from underlying brain signals. Wavelet denoising usually adopts threshold method. Wavelet analysis can enhance pattern recognition by isolating salient features in the data. Researchers can train machine learning models based on these wavelet-derived features to recognize specific patterns associated with different neurological disorders. Wavelet analysis can track these changes by continuously evaluating the frequency content of the neuroimage data over time. Through continuous efforts, wavelet theory and technology have become valuable tools in the field of neuroscience and brain disease research.

Species richness is a widely used measure for assessing the diversity of a particular area. However, observed richness often underestimates the true richness due to resource limitations, particularly in the small-sized sample or highly heterogeneous assemblage. Estimating species richness in a large-scale region typically involves an integrated data set consisting of subsamples collected independently from different subregions. However, the pooled sample of integrated data is no longer a random sample from the entire region, and the use of different sampling schemes results in variations in data formats. Consequently, employing a single sampling distribution to model the pooled sample becomes impractical, rendering existing richness estimators inadequate. This study theoretically explains the applicability of Chao's lower bound estimators in estimating species richness for large-scale areas using the pooled sample. Additionally, a new nonparametric estimator is introduced, which adjusts the bias of Chao's lower bound estimator by leveraging the Good-Turing frequency formula. This proposed estimator only utilizes the pooled sample's singleton, doubleton, and tripleton richness. Simulated data sets across various models are employed to demonstrate the statistical performance of the estimator, showcasing its ability to reduce bias and provide accurate 95% confidence intervals. Real data sets are also utilized to illustrate the practical application of the proposed approach.

The gate resistance is a parasitic element in transistors for RF and millimeter-wave circuits that can negatively impact power gain and noise figure. To develop accurate device models, a reliable measurement methodology is crucial. This article reviews the standard measurement methodology used in the literature and proposes also an additional method, which is evaluated using suitable test structures in a 16nm FinFET process. The advantages and disadvantages of the two approaches are discussed along with their respective application scenarios

Two algorithms of data processing are proposed to shorten the unmeasurable dead-zone close to the zero-position of measurement, i.e., the minimum working distance of a dispersive interferometer using optical frequency comb, which is a critical issue in millimeter-order short-range absolute distance measurement. After demonstrating the limitation of the conventional data processing algorithm, the principles of the proposed algorithms, namely the spectral fringe algorithm, and the combined algorithm that combines the spectral fringe algorithm with the excess fraction method, are presented, together with simulation results for demonstrating the possibility of the proposed algorithms for shortening the dead-zone with a high accuracy. An experimental setup of dispersive interferometer is also constructed for implementing the proposed data processing algorithms over spectral interference signals. Experimental results demonstrate that the dead-zone by the proposed algorithms can be as small as half of that by the conventional algorithm while the measurement accuracy can be further improved with the combined algorithm.

Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

The term deviation frequency denotes the boundary between the variable part of the amplitude and phase scintillation spectrum and the part of uninformative noises. We suggested the concept of the “characteristic deviation frequency” during the observation period which is defined as the most probable value of the deviation frequency under current local conditions. This work is a case study of the characteristic deviation frequency (fd) registered for GPS, GLONASS, Galileo and SBAS signals under quiet and weakly disturbed geomagnetic conditions in April 2021 at the mid-latitude GNSS station. Our results demonstrated that the fd value for all signal components of GPS, GLONASS and GALILEO signals varied within 15-22 Hz. The characteristic deviation frequency was 20 Hz for the mentioned GNSS signals. In difference, the deviation frequency was limited within 13-20 Hz for SBAS with the lower characteristic deviation frequency at 18 Hz. We assume that the concept of the characteristic deviation frequency can be used to determine the optimal sampling rate of the GNSS carrier phase data for the ionospheric studies. The characteristic deviation frequency can also characterize the state of the regular trans-ionospheric radio channel.

Improving the acquisition and retention of a new motor skill is of great importance. The present study (i) investigated the effects of difficulty manipulation strategies (gradual difficulty), combined with different modalities of feedback (FB) frequency on performance accuracy and consistency when learning a novel fine motor coordination task, and (ii) examined relationships between novel fine motor task performance and executive function (EF), working memory (WM), and perceived difficulty (PD). Thirty-six, right-handed, novice physical education students volunteered to participate in this study. Participants were divided into three progressive difficulty groups (PDG), 100% visual FB (FB1), 50% FB (FB2), and 33% FB (FB3). Progressive difficulty was increased by the manipulation of the distance to the target; 2 m, 2.37 m, and 3.56 m. Three FB modalities were investigated (i.e.: 100% visual FB (100% FB), 50% reduced feedback condition (50% RFB), and 33% reduced feedback conditions (33% RFB)). Performance assessments were conducted following familiarization, acquisition, and retention learning phases. Two stress-conditions of dart throws were investigated (i.e.: free condition (FC) and time pressure condition (TPC)). After the learning intervention, data showed that, under the free condition, the 100% FB group had a significant improvement in accuracy during all learning phases. Under time pressure condition, for the 50% RFB and the 33% RFB group, the measured variable (accuracy and consistency) showed a significant linear improvement in performance. The association between the percentage of RFB frequencies and the task difficulty (50% group) may be a more appropriate and manageable cognitive load compared to the 33% RFB and the 100% FB group. The present findings could have practical implications for practitioners because, while strategies are clearly necessary for improving learning, the efficacy of the process appears to be essentially based on the characteristics of the learners.

This paper presents an improved droop control strategy for grid-connected inverter power stability and power quality under distorted with consideration of grid fluctuation and inter-harmonics. An instantaneous frequency without PLL and amplitude of capacitor voltage feed-forward control strategy is given to power stability control, meanwhile a grid current feedback control is given by an incomplete derivation with a high-pass filter, so that the harmonics and inter-harmonics current can be suppressed. These approaches can provide both good active and reactive power dynamic response under fluctuation of frequency, and rejection ability against harmonic and inter-harmonic voltage. Based on model of inverter, the proposed control strategies are designed in detail. Simulations and experiments are present to validate the effectiveness of proposed method.

Chaparral shrublands are the dominant wildland vegetation type in southern California and the most extensive ecosystem in the state. Disturbance by wildfire and climate change have created a dynamic landscape in which biomass mapping is key in tracking the ability of chaparral shrub-lands to sequester carbon. Despite this importance, most national and regional scale estimates do not account for shrubland biomass. Employing plot data from several sources, we built a random forest model to predict above ground live biomass in southern California using remote sensing data (Landsat NDVI) and a suite of geophysical variables. By substituting the NDVI and precipi-tation predictors for any given year we were able to apply the model to each year from 2000-2019. Using a total of 980 field plots, our model had a k-fold cross validation R2 of 0.51 and a RMSE of 3.9. Validation by vegetation type ranged from R2 = 0.17 (RMSE=9.7) for Sierran mixed conifer to R2 = 0.91 (RMSE = 2.3) for sagebrush. Our estimates showed an improvement in accuracy over a two other biomass estimates that included shrublands, with an R2 = 0.82 (RMSE = 4.7) compared to R2 = 0.068 (RMSE = 6.7) for a global biomass estimate and R2 = 0.29 (RMSE = 5.9) for a regional biomass estimate. Given the importance of accurate biomass estimates for resource managers we calculated the mean year 2010 shrubland biomass for the four national forests which ranged from 3.5 kg/m2 (Los Padres) to 2.3 kg/m2 (Angeles and Cleveland). Finally, we compared our estimates to field-measured biomass from the literature summarized by shrubland vegetation type and age class. Our model provides a transparent and repeatable method to generate biomass measure-ments in any year, thereby providing data to track biomass recovery after management actions or disturbances such as fire.

Abstract: The article proposes to consider the possibility of connecting the problems of engineering synthesis of frequency control systems for asynchronous electric drives with the basic provisions of the theory of identification of asynchronous electric motors based on the equations of a generalized AC electric machine.The article presents the results of experimental studies of load parrying processes in asynchronous electric drives with vector and scalar controls. These results indicate the absence of fundamental advantages as processes in a drive with vector control. This advantage should have manifested itself in a more efficient formation of the moment and fast transients. It is suggested that the reason for this is in too significant errors, assumptions and simplifications of the equations adopted in the derivation of vector control and which are its theoretical basis. A method is proposed for describing asynchronous electric motors by nonlinear transfer functions, which made it possible to formulate the principle of correction of electric drives and a method for assessing their efficiency.The article shows. that the correction based on the proposed nonlinear transfer functions of the induction motor is much more efficient than the traditional vector control, which was confirmed by detailed experiments and modeling. The results of the most important of which are given in the article. An assumption was made. that the advantage in efficiency is due to a more accurate identification of the dynamics of an asynchronous electric motor with a gear function instead of vector equations.

Research on Traffic Light Recognition (TLR) has grown in recent years, primarily driven by the growing interest in autonomous vehicles development. Machine Learning algorithms have been widely used to that purpose. Mainstream approaches, however, require large amount of data to properly work, and as a consequence, a lot of computational resources. In this paper we propose the use of Expert Instruction (IE) as a mechanism to reduce the amount of data required to provide accurate ML models for TLR. Given an image of the exterior scene taken from the inside of the vehicle, we stand the hypothesis that the picture of a traffic light is more likely to appear in the central and upper regions of the image. Frequency Maps of traffic light location were thus constructed to confirm this hypothesis. The frequency maps are the result of a manual effort of human experts in annotating each image with the coordinates of the region where the traffic light appears. Results show that EI increased the accuracy obtained by the classification algorithm in two different image datasets by at least 15%. Evaluation rates achieved by the inclusion of EI were also higher in further experiments, including traffic light detection followed by classification by the trained algorithm. The inclusion of EI in the PCANet achieved a precision of 83% and recall of 73% against 75.3% and 51.1%, respectively, of its counterpart. We finally presents a prototype of a TLR Device with that expert model embedded to assist drivers. The TLR uses a smartphone as a camera and processing unit. To show the feasibility of the apparatus, a dataset was obtained in real time usage and tested in an Adaptive Background Suppression Filter (AdaBSF) and Support Vector Machines (SVMs) algorithm to detect and recognize traffic lights. Results show precision of 100% and recall of 65%.

Radio Frequency Interference (RFI) detection and characterization play a critical role to in ensuring the security of all wireless communication networks. Advances in Machine Learning (ML) have led to the deployment of many robust techniques dealing with various types of RFI. To sidestep an unavoidable complicated feature extraction step in ML, this paper proposes an efficient end-to-end method using the latest advances in deep learning to extract the appropriate features of the RFI signal. Moreover, this study utilizes the benefits of transfer learning to determine both the type of received RFI signals and their modulation types. To this end, the scalogram of the received signals is used as the input of the pre-trained convolutional neural networks (CNN), followed by a fully-connected classifier. This study considers a digital video stream as the signal of interest (SoI), transmitted in a real-time satellite-to-ground communication using DVB-S2 standards. To create the RFI dataset, the SoI is combined with three well-known jammers namely, continuous-wave interference (CWI), multi- continuous-wave interference (MCWI), and chirp interference (CI). This study investigated four well-known pre-trained CNN architectures, namely, AlexNet, VGG-16, GoogleNet, and ResNet-18, for the feature extraction to recognize the visual RFI patterns directly from pixel images with minimal preprocessing. Moreover, the robustness of the proposed classifiers is evaluated by the data generated at different signal to noise ratios (SNR).

We report the negative effective mass metamaterials based on the electro-mechanical coupling exploiting plasma oscillations of a free electron gas. The negative mass appears as a result of vibration of a metallic particle with a frequency of ω which is close to the frequency of the plasma oscillations of the electron gas m_2 relatively to the ionic lattice m_1. The plasma oscillations are represented with the elastic spring k_2=ω_p^2 m_2, where ω_p is the plasma frequency. Thus, the metallic particle vibrated with the external frequency ω is described by the effective mass m_eff=m_1+(m_2 ω_p^2)/(ω_p^2-ω^2 ) , which is negative when the frequency ω approaches ω_p from above. The idea is exemplified with two conducting metals, namely Au and Li embedded into various matrices. The one-dimensional lattice built of the identical metallic micro-elements m_eff connected by ideal springs k_1 representing various media such as polydimethylsiloxane and soda-lime glass is treated. The optical and acoustical branches of longitudinal modes propagating through the lattice are elucidated for various ratios ω_1/ω_p . The 1D lattice built of the thin metallic wires giving rise to the low frequency plasmons is treated. The possibility of the anti-resonant propagation, strengthening the effect of the negative mass occurring under  = p = 1 is addressed.

Background: The dietary factors and nutrients contributing to the prevention of microalbuminuria in type 2 diabetic nephropathy is unclear, so we investigated dietary factors affecting urinary albumin excretion in patients with type 2 diabetes. Methods: 42 patients with type 2 diabetes were participated, the subjects were divided to a normal albuminuria group (urinary albumin / creatinine ratio of less than 30 mg / g Cr) and a microalbuminuria group of 30 mg / g to 299 mg / g Cr. We performed casual blood sampling and conducted a food frequency questionnaire survey. Results: There were no significant differences in age, BMI and other physiological and biochemical data, the average daily intake of energy and many of nutrients, while β-cryptoxanthin was significantly lower in the microalbuminuria group than in the normal group (506.4 ± 793.9 μg/day vs. 715.3 ± 500.3 μg/day, p <0.05). The intake of 17 food groups per day showed that the intakes of fruits were significantly lower in the microalbuminuria group than in the normal group (76.9 ± 134.1 g vs. 111.9 ± 84.5 g, p <0.05). Conclusion: These results suggest that fruits and foods rich in β-cryptoxanthin would make it possible to prevent diabetic nephropathy progression.

In this paper, a novel pendulum-type accelerometer based on hetero-core fiber optics has been proposed for structural health monitoring targeting large-scale civil infrastructures. Vibration measurement is a non-destructive method for diagnosing the failure of structures by assessing natural frequencies and other vibration patterns. The hetero-core fiber optic sensor utilized in the proposed accelerometer can serve as a displacement sensor with robustness to temperature changes in addition to immunity to electromagnetic interference and chemical corrosions. Thus the hetero-core sensor inside the accelerometer measures applied acceleration by detecting the rotation of an internal pendulum. A series of experiments showed that the hetero-core fiber sensor linearly responded to the rotation angle of the pendulum ranging within ±5°, and furthermore the proposed accelerometer could reproduce the waveform of input vibration in a frequency band of several Hz order.

This paper proposes a single-stage asymmetrical half-bridge fly-back (AHBF) converter with resonant mode using dual-mode control. The presented converter has an integrated boost converter and asymmetrical half-bridge fly-back converter and operates in resonant mode. The boost-cell always operates in discontinuous conduction mode (DCM) to achieve high power factor. The presented converter operates simultaneously using a variable-frequency-controller (VFC) and pulse-width-modulation (PWM) controller. Unlike the conventional single-stage design, the intermediate bus voltage of this controller can be regulated depending on the main power switch duty ratio. The asymmetrical half-bridge fly-back converter utilizes a variable switching frequency controller to achieve the output voltage regulation. The asymmetrical half-bridge fly-back converter can achieve zero-voltage-switching (ZVS) operation and significantly reduce the switching losses. Detailed analysis and design of this single-stage asymmetrical half-bridge fly-back converter with resonant mode is described. A wide AC input voltage ranging from 90 to 264 Vrms and output 19 V/ 120 W prototype converter was built to verify the theoretical analysis and performance of the presented converter.

The principal aim of a spectral observer is twofold: the reconstruction of a signal of time via state estimation and the decomposition of such a signal into the frequencies that make it up. A spectral observer can be catalogued as an online algorithm for time-frequency analysis because is a method that can compute on the fly the Fourier transform (FT) of a signal, without having the entire signal available from the start. In this regard, this paper presents a novel spectral observer with an adjustable constant gain for reconstructing a given signal by means of the recursive identification of the coefficients of a Fourier series. The reconstruction or estimation of a signal in the context of this work means to find the coefficients of a linear combination of sines a cosines that fits a signal such that it can be reproduced. The design procedure of the spectral observer is presented along with the following applications: (1) the reconstruction of a simple periodical signal, (2) the approximation of both a square and a triangular signal, (3) the edge detection in signals by using the Fourier coefficients, (4) the fitting of the historical Bitcoin market data from 2014-12-01 to 2018-01-08 and (5) the estimation of a input force acting upon a Duffing oscillator. To round out this paper, we present a detailed discussion about the results of the applications as well as a comparative analysis of the proposed spectral observer vis-à-vis the Short Time Fourier Transform (STFT), which is a well-known method for time-frequency analysis.

The airport ground handling service (AGHS) equipment vendor selection (AGHSEVS) problem is critical for ramp work safety management, because AGHS equipment malfunctions affect airport ramp work safety. Appropriate vendor selection can prevent aircraft damage and delays in airlines schedules, and ensure reliable and high-quality ground handling service. The AGHSEVS problem is a time-consuming and complex process that requires professional knowledge and experience to make judgments. Specifically, AGHSEVS is a multi-criteria decision-making (MCDM) problem. Previous research has seldom integrated MCDM methods with linear and goal programming to solve the AGHSEVS problem. The objective of this study was to develop a new system evaluation model for AGHSEVS by considering both qualitative and quantitative methods. We test the proposed approach on an AGHS company in Taiwan.

In this paper, we study the effect of parasitic channel on electrical characteristics and power consumptions of vertically stacked gate-all-around (GAA) silicon nanosheet (NS) MOSFETs of the bulk and silicon-on-insulator (SOI) substrates. Through deliberate control of the bottom parasitic channel coverage, we systematically assess its influence on DC, AC, and RF characteristics including power consumption. The observed findings highlight a significant differentiation among the bottom parasitic channels: GAA (i.e., control sample with 100% coverage), omega-shaped gate NS (70% and 80% coverages), and FinFET (60% coverage). Compared with the device with a 60% coverage channel, the device with a 100% coverage channel possesses 3520% and 8030% normalized leakage current improvement for both NFET and PFET under the bulk substrate, respectively. Compared with bulk-substrate devices, the SOI-substrate devices further significantly improve the leakage current. Due to enhanced off-state current leakages in the 100% covered bulk-substrate device, the static power demonstrates a notable improvement (about 98.1%), compared to 60% covered devices. Hence, the utilization of a 60% covered channel will result in a degradation of both device and circuit performance. The observation of GAA NS MOSFET indicates that an increase in the bottom parasitic channel coverage ratio leads to a positive impact on power consumption and leakage currents. The examination presented here is useful in the fabrication of stacked GAA NS MOSFETs.

To achieve rapid post-earthquake repair of self-centering ribbed floor flat beam frame structures, a ductile hybrid joint consisting of dog bone weakened energy dissipating steel bars connected to the upper and lower column sections through high-strength threads based on the damage control design concept is proposed. By moving the ductile energy dissipating zone out to the locally weakened section of the energy dissipating steel bars and the locally unbonded prestressed steel bars in the core area, the residual deformation was limited and the seismic performance was improved. Based on the working principle of hybrid joints, low cycle loading tests were conducted on two joint specimens to analyze the influence of lateral prestress on the seismic performance of the hybrid joints. Numerical modeling methods were used to compare the position of the energy-consuming steel bars in the composite layer and the friction performance of joints. The research results indicated that the hybrid joint had stable load-bearing, deformation, and energy dissipation capabilities, with damage primarily concentrated on the energy-consuming steel bars. Even at an inter-story displacement angle of 5.5%, the upper and lower column segments remained elastic. After unloading, the connection seam at the joint was closed, and the self-centering performance was good. When the inter-story displacement angle reached 5.5%, the lateral prestress increased from 150 kN to 250 kN, the ultimate bearing capacity of the joint increased by 16.3%, and the cumulative energy consumption increased by 30.0%. The influence of the friction coefficient of the joint surface on the structural performance was set at a threshold of 0.7. When it was less than the threshold, the ultimate bearing capacity and initial stiffness of the joint increased with an increase in the friction coefficient. After reaching the threshold, the increase in the ultimate bearing capacity of the joint slowed down, and the rate of stiffness degradation gradually accelerated. This joint had excellent seismic performance and can also achieve post-earthquake repair of structures.

A finite element model of 100m X-BOW polar exploration cruise is established. The entire ship frequency response analysis was carried out, comparing the simulated values with the test values, the maximum error is 22%, but only 0.24mm/s difference, the error can be ignored from the perspective of comfort, therefore, the simulation results meet the requirements of engineering accuracy and verify the effectiveness of the model. According to the solution method for global modal equivalent mass, combing the spatial distribution principle of local modal mass, the method for solving the equivalent mass of a single local mode in mixed mode is proposed, the equivalent mass of the local vibration region of 100m X-BOW polar exploration cruise ship is solved by this method, tuned mass dampers mass ratio parameters are calculated using the equivalent mass. According to the analysis, tuned mass dampers has 31dB vibration absorption effect at 13.4Hz when the mass ratio is 0.05, it also has certain control effect at 10Hz and 18.8Hz, the control effect is 3dB and 2dB respectively, the overall control frequency band is flat and robust.

Investigating transportation disasters in Western Asia over two decades (2003-2023), this study provides a meticulous examination of a comprehensive dataset, shedding light on the dynamics and nuances of these catastrophic events in terms of frequency, modality (air, rail, road, and water), and subsequent outcomes. The data reveals a concerning uptrend in mishaps from 2003 to 2010, which fortunately reverses into a notable decline in the subsequent years. Noteworthy is the predominance of road-related incidents, yet an alarming 73.8% of events are ambiguously categorized under "Unknown", hinting at potential gaps or inconsistencies in record-keeping. With Turkey being a significant epicenter, accounting for nearly 45% of all incidents, the regional distribution of these disasters becomes evident. Advanced ANOVA analyses discern marked variations in fatality rates over the years and between nations. However, the contrast in injury rates among different disaster categories did not achieve statistical significance. While the post-2010 era showcases a commendable reduction in calamities, the data punctuates the persistent necessity for robust safety measures, targeted public awareness campaigns, and infrastructural advancements. In synthesis, this study vehemently advocates for heightened regional collaboration and systematic knowledge dissemination as cornerstones for bolstering transportation safety across Western Asia.

Channel estimation is an important module to enhance the performance of orthogonal frequency division multiplexing (OFDM) systems. However, the presence of a large amount of noise in multipath fading time-varying channels significantly affects the channel estimation accuracy and thus the recovery quality of the received signals. Therefore, this paper proposes a double-threshold (DT) channel estimation method based on adaptive frame statistics (AFS). The method first adaptively determines the number of statistical frames based on the temporal correlation of the received signals, and preliminarily detects the channel structure by analyzing the distribution characteristics of multipath sampling points and noise sampling points during adjacent frames. Subsequently, a multi-frame averaging technique is used to expand the distinction between multipath and noise sampling points. Finally, the DT is designed to better recover the channel based on the preliminary detection results. Simulation results show that the proposed adaptive frame statistics-double-threshold (AFS-DT) channel estimation method is effective and has better performance compared with many existing channel estimation methods.

A low-resource emotional speech synthesis system for empathetic speech synthesis based on modelling prosody features is presented here. Secondary emotions, identified to be needed for empathetic speech, are modelled and synthesised in this paper. As secondary emotions are subtle in nature, they are difficult to model compared to primary emotions. They are also less explored, and this is one of the few studies that model secondary emotions in speech. Current speech synthesis research uses large databases and deep learning techniques to develop emotion models. There are many secondary emotions, and hence, developing large databases for each of the secondary emotions is expensive. This research presents a proof-of-concept using hand-crafted feature extraction and modelling of these features using a low resource-intensive machine learning approach, thus creating synthetic speech with secondary emotions. Here, a quantitative model-based transformation is used to shape the emotional speech fundamental frequency contour. Speech rate and mean intensity are modelled via rule-based approaches. Using these models, an emotional text-to-speech synthesis system to synthesise five secondary emotions - anxious, apologetic, confident, enthusiastic and worried is developed. A perception test to evaluate the synthesised emotional speech is also conducted.

The article analyzes existing materials and structures with quadratic-nonlinear optical properties that can be used to generate a difference frequency in the terahertz and sub-terahertz frequency ranges. The principle of constructing a nonlinear optical-radio converter, based on an optical focon (a focusing cone), is proposed. Based on the assumption that this focon can be implemented from the metal-organic framework (MOF), we propose a technique for modeling its parameters. The mathematical model of the process of propagation and nonlinear interaction of waves inside the focon is based on a simplification of the nonlinear wave equation. Within the framework of the developed model, the following parameters are approximately determined: the 3D gradient of the linear refractive index and the function determining the geometric profile of the focon, which provide a few-mode-based generation of the difference frequency.

The method of doublet frequency distance (DFD-) maps was applied, here, for visualization of DNA sequence structures of yeast chromosomes. The colour scale “rainbow” of the Octave programming tool is well suited for such visualization. The DFD-maps are generated by comparison of the differences in the number of all base doublets in a shifting frame with all other positions of this frame in a regarded DNA sequence section. This procedure can be applied from DNA-sections of between a few hundred bases (bps) up to a complete chromosome with a million or more bps. An orthogonal DFD-map pattern dominates all chromosomes and its parts. It can be interpreted by a large number of successive mutation events during evolution. In contrast, diagonal patterns indicate duplications of sequence sections and multiple tandem repeats. These periodic structures are found in several chromosomes and are more or less regular or noised. The larger tandem repeat in the subtelomeric region of chromosome 12 presents a characteristic example of a nested multiple-repeat structure. Its pattern in the DFD-map illustrates obviously a temporal order of duplication and mutation events leading to a hierarchical sequence architecture.

Ocean surface waves have been utilized as fundamental information in various fields of oceanic research. In this paper, we suggest a simulation and modelling technique for generating an ocean surface wave using an inverse Fast Fourier Transform (iFFT), and we subsequently verify the wave’s accuracy. The conventional method, linear superposition, requires recursive calculation because of the double summation and the time variable; to circumvent this issue, the new algorithm is presented. The Joint North Sea Wave Project (JONSWAP) spectrum is utilized for the ocean surface wave simulation example, and the parameters are the significant wave height (HS) and the zero-crossing wave period (TZ). A coordinate transform for the wavenumber domain was used to apply the inverse FFT algorithm. To verify the accuracy of the simulation result, the relative error between the input condition and the analysis result was calculated. The result for TZ is below 4% relative error, and the maximum relative error for HS is 7%. To avoid the Nyquist frequency for wave-field analysis and simulation, the minimum grid size was calculated by twice applying the maximum wavenumber.

A hemispheric asymmetry is known for the processing of global vs. local visual information. In this study, we investigated the existence of a hemispheric asymmetry for visual processing of low vs. high spatial frequency gratings. Event-related potentials were recorded in a group of healthy right-handed volunteers from 30 scalp sites. Six types of stimuli (1.5, 3 and 6 c/deg gratings) were randomly flashed 180 times in the left and right upper hemi-fields. Stimulus duration was 80 ms and ISI ranged between 850-1000 ms. Participants had to pay attention and respond to targets based on their spatial frequency and location, or to passively look at the stimuli. C1 and P1 visual responses, as well as a later Selection negativity and a P300 components of ERPs were quantified and subjected to repeated-measure ANOVAs. Overall, performance was faster for the RVF, thus suggesting a left hemispheric advantage for attentional selection of local elements. Similarly, the analysis of mean area amplitude of C1 (60-110 ms) sensory response showed a stronger attentional effect (F+L+ vs. F-L+) at left occipital areas, thus suggesting the sensory nature of this hemispheric asymmetry.

This work presents an alternative to harnessing wind energy with an Electromagnetic Frequency Regulator (EFR) coupled to a hydrostatic transmission and associated with a horizontal axis wind turbine, a bidirectional frequency inverter and a secondary energy source, in a hybridized system. The hydrostatic transmision is composed by a fixed displacement axial piston pump and a variable displacement, swash plate, axial piston motor. Feedback linearization was used as a technique to control the motor geometric displacement, and a prediction algorithm for the steady state rotations of the armature and the electromagnetic field has been developed. A 5\,kW project was simulated on a Scilab platform, with combinations of constant or variable load, and constant or variable wind speed. The results indicated that the system was able to supply the generator load, adapting to fluctuations in wind speed. The possibility of storing wind energy through the inverter has also been proven. The system can accumulate energy in batteries during the fastest wind regimes, to use it when the turbine power is lower than the load.

The article presents a new approach to the analysis of the stability of automatic systems with discrete links.In almost all modern automatic control systems, there are links that break signals in time. These are power controlled switches - transistors or thyristors operating in a pulsed mode and digital links in regulators.Time discretization significantly affects the stability of processes in the automatic control system. The theoretical analysis of such systems is rather complicated and requires a significant change in engineering approaches to analysis. In connection with the improvement of digital controllers and a significant increase in their performance, in recent years this problem has practically not been remembered. However, its mathematical "content" has not changed since the 80s of the 20th century, when discreteness began to play a major role among the problems hindering progress in automatic control systems, in terms of the transition to digital systems.In this paper, a new approach is proposed, which consists in interpreting the sampling operation by a link with the proposed frequency characteristic, which determines the suppression of input high-frequency signals. This link greatly simplifies engineering calculations and demonstrates the new capabilities of sampling systems. These possibilities include the rational distribution of digitalization resources - the number of bits and the sampling interval between the regulator channels, depending on the frequency range of the efficiency of these channels. Theoretical statements have been verified and confirmed by simulation. It is shown how this approach makes it possible to formulate new principles of construction of seemingly well-known controllers - PID controllers and variable structure systems (VSS).

Based on electromagnetic heat transfer and metal phase transformation co-simulations, we modeled an AISI 1045 specimen under high-frequency heat treatment. Hardening zone predictions were confirmed through cooling and metal phase transformation simulations after obtaining the results from electromagnetic heat transfer simulations. The cooling process was modeled by applying the cooling coefficient of the cooling water in the same way as the actual heat-treatment process. To obtain the current flowing through the coil during high-frequency induction heating, the voltage was measured and applied using the resistance–inductance–capacitance circuit calculation method. Experimental and simulated results of the heating temperature and curing depth of an AISI 1045 specimen with a carbon content of 0.45% were compared; the comparison indicated good agreement between the two. Using the simulation results, we established a method for obtaining the current flowing through the induction coil for predicting the extent and depth of the hardening zone during high-frequency induction heat treatment.

Diet influences, and is influenced by, a wide range of socioeconomic, cultural, geographic, and genetic variables. Here we survey a matrix of such interactions as well as their connection to a variety of health outcomes, in a cohort of 689 diverse adults employed at Emory University and enrolled in the Center for Health Discovery and Well-Being (CHDWB) study. Principal component analysis (PCA) of the Block Food Frequency Questionnaire revealed seven PC cumulatively explaining 25.8% and each individually at least 2% of the proportional consumption of 110 food items. PC1 is strongly correlated with the Healthy Eating Index-2015 measure, and accordingly healthier scores associate with multiple measures of physical and mental health. It, as well as PC2 (likely a measure of food expense) and PC3 (carbohydrate versus protein consumption) show significant geographic structure across the Atlanta metropolitan area, correlating with race and ethnicity, income level, age and sex. Notably, a polygenic score for body mass index (BMI) consisting of 281 SNPs explains 2.8% of the variance in PC5, which is as strong as its association with BMI itself. PC5 appears to differentiate participants with respect to conscious eating behavior related to the choice of diet or comfort foods. Our analysis adds to the growing literature on factor analysis of socio-demographic influences on nutrition and health.

Confidence interval of is an interval corresponding to a specified confidence and including the true value. It can be used to describe the precision of a statistical quantity and quantify its uncertainty. Although the principle of maximum entropy (POME) has been used for a variety of applications in hydrology, the confidence intervals of the POME quantile estimators have not been available. In this study, the calculation formulas of asymptotic variances and confidence intervals of quantiles based on POME for Gamma, Pearson type 3 (P3) and Extreme value type 1 (EV1) distributions were derived. Monte Carlo Simulation experiments were performed to evaluate the performance of derived formulas for finite samples. Using four data sets for annual precipitation at the Weihe River basin in China, the derived formulas were applied for calculating the variances and confidence intervals of precipitation quantiles for different return periods and the results were compared with those of the methods of moments (MOM) and of maximum likelihood (ML) method. It is shown that POME yields the smallest standard errors and the narrowest confidence intervals of quantile estimators among the three methods, and can reduce the uncertainty of quantile estimators

This paper propose an original approach of achieving a Cymatics based visual perception of isolated speech commands. The idea is to smartly combine the effective speech processing and analysis methods with the phenomena of Cymatics. In this context, an effective approach for automatic isolated speech based message recognition is proposed. The incoming speech segment is enhanced by applying the appropriate pre-emphasis filtering, noise thresholding and zero alignment operations. The Mel-Frequency Cepstral coefficients (MFCCs), Delta coefficients and Delta-Delta coefficients are extracted from the enhanced speech segment. Later on, the Dynamic Time Warping (DTW) technique is employed to compare these extracted features with the reference templates. The comparison outcomes are used to make the classification decision. The classification decision is transformed into a methodical excitation. Finally, this excitation is converted into the systematic visual perceptions via the phenomenon of Cymatics. The system functionality is tested with an experimental setup and results are presented. The approach is novel and can be employed in various applications like visual art, encryption, education, archeology, architecture, integration of impaired people, etc.

The open communication is an exigent need for future power system where the time delay is unavoidable. In order to secure the stability of the grid, the frequency must remain within its limited range which is achieved through the load frequency control. The load frequency control signals are transmitted through communication networks which induces time delay that could destabilize the power systems. So, in order to guarantee the stability the delay margin should be computed. In this paper, we present a new method for calculating the delay margin in load frequency control systems. The transcendental time delay characteristics equation is transformed to frequency dependant equation. The spectral radius is used to find the frequencies at which the roots crosses the imaginary axis. The crossing frequencies are determined through the sweeping test and the binary iteration algorithm. A one-area load frequency control system is chosen as case study. The effectiveness of the proposed method has been proved through comparing with the most recent published methods. The method shows its merit with less conservativeness and less computations. The PI controller gains are preferable to be chosen large to reduce the damping, however, the delay margin decreases with increasing the PI controller gains.

During the recent years, there has been a tremendous increase in use of mobile phones which resulted in an increase of the exposure to electromagnetic radiations in our life. Human saliva is considered as a potential source of biomarkers to monitor changes that occur under pathological conditions. The main objective of the current experiment was to determine the effect of mobile phone radiation on general health, electrolytes and salivary function among Islamic University students who use mobile phones. A questionnaire was designed and applied to 167 healthy and 36 deaf female students to select cases whose meeting the inclusion criteria. A total of 103 students who met the inclusion criteria were included to investigate the influence of mobile phone radiations on their general health. For assessment of salivary parameters, a total of 55 students were chosen and classified into three groups. Group I was the control group, which included 17 deaf students who did not use the mobile phone at all. Group II was healthy students who have mobile phone for less than 5 years. Group III was healthy students who have mobile phone for 5 years or more. Descriptive data that included mean, standard deviation, and percentages was calculated for each group. Multiple group comparisons were made by one-way analysis of variance (ANOVA) followed by Tukey test for pairwise comparisons. Categorical data were analyzed by Chi square (χ2) test. For all the tests, a P value of 0.05 or less was considered for statistical significance. The results showed that the participants who use mobile phone had several problems in their health including dry mouth, bad odor from mouth, drooling of saliva, as well as ear and eye pain. The majority of the participants who use mobile phone complained of headache, anxiety, insomnia and forgetfulness as compared to deaf participants. Also, the study showed that there was no significant difference between salivary pH in all tested groups. Regarding to salivary flow rate, the differences were no significant in all tested groups. In addition, this study has also shown that there was significant difference between the salivary Na+ and K+ levels of the three groups. Salivary level of Na+ and K+ were significantly lower in mobile phone users when compared to non users of mobile phone.

Objective: to evaluate the effects of excitatory repetitive transcranial magnetic stimulation (rTMS) of the international 10-20 system P3 point (intraparietal sulcus region) in chronic patients with a frontal lesion and parietal sparing due to stroke on the impaired upper (UL) and lower limb (LL) as measured by Fugl-Meyer Assessment (FMA). Methods: three patients (C1: 49.83/2.75, C2: 53.17/3.83, C3: 63.33/3.08 years-old at stroke/ years post-stroke, respectively) received two weeks (five days/ week) of rTMS at 10 Hz of P3. A patient was treated in similar conditions with a sham coil (S1: 56.58/4.33). No complimentary therapy was delivered during the study. Patients were evaluated before, after- and two months post-treatment (A1, A2 and A3, respectively). Results: we found increased scores for LL in motor function subsection for C1 and C3 and in sensory function for C2 by A2 that remained at A3. We also found an increased score for UL motor function for C2 and C3, but the score decreased by A3 for C2. C3 score for UL range of motion increased by A3 compared to A1 and A2. Conclusion: In a variable way, P3 excitatory rTMS increased FMA scores in different upper and lower limb subsections of our three treated patients.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

ABSTRACT Lubricants play a crucial role in reducing friction and wear between surfaces in relative motion. Engine oil, as a lubricant, is specifically designed to minimize friction and wear between the moving parts of different equipment and machinery. Engine oil blending serves the purpose of adjusting the variety of lubricants available to meet specific performance requirements. Through blending, it is possible to tailor the characteristics of the lubricating oil, such as viscosity, stability, and additive content, to optimize its effectiveness in reducing friction, protecting engine components, and enhancing overall equipment performance. By customizing lubricant formulations through blending, manufacturers can address the diverse lubrication needs of modern engines and machinery, ensuring smooth operation, longevity, and efficiency.To enhance the quality of the lubricant and achieve the desired high-performance multi-grade engine oil, a systematic approach was followed. The process began with testing the specific gravity of the oil using a hydrometer and thermometer to ensure accurate measurements. Subsequently, 5 liters of base oil were carefully measured and poured into a stainless steel reactor pot to serve as the blending vessel. Following this, 1 liter of paraffin oil (100N) was added to the base oil to introduce specific properties to the blend. To further optimize the formulation, 0.42 kg (7% of the total blend) of additives, including a selection of specific performance-enhancing compounds, was incorporated. These additives, such as Aspen, Tackifier, and other essential components like anti-wear, corrosion inhibitors, detergents, dispersants, and antioxidants, were meticulously measured and added to the mix. Through this precise blending process, the goal was to craft a high-quality multi-grade engine oil that meets the stringent performance requirements for various operating conditions and equipment types. After the melted Aspen and dissolved tackifier were added to the mixture, thorough stirring was carried out to ensure uniform distribution until a homogeneous blend was achieved. The mixture was then exposed to heat to facilitate the reaction between the base oil and additives, reaching a temperature of 70°C for optimal blending. Subsequently, 0.5 kg of viscosity index improver was introduced into the mixture and stirred continuously for a period of 5 minutes to ensure proper integration. Following this step, 5 grams of dye were carefully added to the mixture, and stirring was continued to evenly disperse the coloring agent throughout the blend. This meticulous process aimed to ensure that all components were effectively incorporated, facilitating the production of a high-quality, customized multi-grade engine oil with enhanced performance characteristics and optimal lubricating properties. After subjecting the mixture to a temperature of 70°C, the heat source was removed, allowing the product to gradually cool down to room temperature, which was estimated to be around 30°C. Following this cooling process, the product underwent a filtration step to remove any residual impurities present in the mixture. A sample of the purified product was then extracted for further analysis. Quality control tests were meticulously carried out on the sample to ensure that the product met the rigorous standards set by the Standard Organization of Nigeria (SON). The results obtained from the quality control tests were highly promising, indicating that the product indeed complied with the stringent specifications outlined by the regulatory body. This adherence to quality standards is essential in ensuring the product's reliability and performance in various applications. The successful outcome of the quality control test underscores the meticulous attention to detail and commitment to excellence in the production process. The rigorous quality control procedures implemented in the analysis of the product highlight the commitment to upholding high standards of quality and safety in the manufacturing process. By consistently meeting and exceeding industry regulations and standards, the product not only demonstrates its reliability but also reflects a dedication to customer satisfaction and product excellence. The adherence to the SON standards showcases a commitment to producing top-quality products that meet the needs and expectations of consumers and industry stakeholders alike.

The multi-objective optimization design of hydraulic climbing mechanism (the core mechanism of gantry crane) was realized by using the method of combining central composite design (CCD), Kriging and multi-objective genetic algorithm (MOGA). Firstly, the hydraulic climbing mechanism model was established and the appropriate design variables were selected. The mass, maximum deformation and maximum equivalent stress of the hydraulic climbing mechanism was taken as the optimization objectives. Through the comparative analysis of the parameters of wedge block, anchor block, anchor plate、baffle, the parameters that have the greatest impact on the optimization objectives are selected as geometric constraints. Then, according to the results of experimental design, Kriging model was used to establish the response surface optimization model of objective function. Finally, the optimal results were obtained by using MOGA method. The experimental results show that the optimization strategy is reliable, and the mass of the optimized model is reduced by 10.28%. While meeting the actual production requirements, the lightweight design of the hydraulic climbing mechanism is realized, which saves the design cost and improves the material utilization rate.

The satellite Earth observation (EO) sector is burgeoning with hundreds of commercial satellites being launched each year, delivering a rich source of data that could be exploited for societal benefit. Data streams from the growing constellation of commercial satellites are of variable quality, limiting the potential for their combined use in science applications that need long time-series data from multiple sources. The quality of calibration performed on optical sensors onboard many satellite systems is highly variable due to calibration methods, sensor design, mission objective, budget, or other operational constraints. A small number of currently operating well-characterised satellite systems with onboard calibration, such as Landsat-8/9, Sentinel-2, and planned future missions like the NASA Climate Absolute Radiance and Refractivity Observatory (CLARREO) Pathfinder, the European Space Agency (ESA) Traceable Radiometry Underpinning Terrestrial and Helio Studies (TRUTHS), and LIBRA from China, are considered benchmarks for optical data quality due to their traceability to international measurement standards. This paper describes the concept of a space-based transfer calibration radiometer called the Satellite Cross-Calibration Radiometer (SCR) that would enable the calibration parameters from satellites such as Landsat-8/9, Sentinel-2, or other benchmark systems to be transferred to a range of commercial optical EO satellite systems while in orbit. A description of the key characteristics of the SCR to successfully operate in orbit and transfer calibration from Reference systems to Client systems is presented. A system like the SCR in orbit could complement SI-Traceable satellites (SITSats) to improve data quality and consistency and facilitate the interoperable use of data from multiple optical sensor systems for delivering higher returns on the global investment in EO.

This paper focuses on improving the tracking accuracy for servo systems and increasing the contouring performance of precision machining. The dynamic friction during precision machining is analyzed using the LuGre model. The dynamic and static parameters in the friction model are efficiently and accurately identified using the improved Drosophila Swarm Algorithm based on cross-mutation. The friction tracking error can be deduced by the friction state space and an expression is derived. To compensate for the tracking error caused by friction, a feedforward compensation control is designed to avoid signal lag in traditional friction controllers. Furthermore, the factors of multi-axis parameter mismatching that impact the machining profile accuracy are analyzed for multi-axis control. An adaptive cross-coupled control-based pre-compensation strategy of contour error is designed to reduce both the tracking error and the contour error. The effectiveness of the proposed method is validated through several experiments, which demonstrate a remarkable improvement in tracking performance and contour accuracy.

The sustainable development and operation of mining enterprises as major sources of economic growth in many countries is determined by both a balance of economic, environmental, and social objectives and the resources required to achieve these objectives. Transportation is one of the main resources at open pits. It accounts for up to 70% of open pit operating expenses. Various parameters of transportation means should be coordinated both with the parameters of the open pit and meet the conditions of its operation. The problem of selecting a dump truck model in the paper is solved using a universal system of selection criteria developed using an original methodology. The two-level system of criteria includes all currently known selection criteria. The peculiarity of the methodology is the use of the multi-criteria FUCOM method for ranking the criteria. In addition, it is proposed to differentiate the criteria evaluations according to the experts' competence levels. Sets of criteria for evaluation by experts of different levels are proposed. The obtained criterion ranks are recommended to be used by managers of mining enterprises to choose a dump truck model. The presented methodology is suitable for the development of new systems of criteria, considering significant changes in operating conditions or the emergence of factors not considered in this study. Evaluation of all models of dump trucks on the market using the developed system of criteria is envisaged by the authors in a future study.

The dynamic factors in the environment violate the static environment assumption of the SLAM algorithm, leading to a decrease in the accuracy of camera positioning. In recent years, many studies have attempted to address dynamic objects by combining semantic constraints and geometric constraints, but issues such as poor real-time performance, the potential for mistakenly treating humans as rigid objects, and subpar performance in high-dynamic scenes still persist. This paper proposes a dynamic scene visual SLAM algorithm called Dynamic SLAM based on target tracking and multi-view geometry (TKG-SLAM), based on object detection, Kalman filters, and multi-view geometry. This algorithm takes into consideration both real-time performance and algorithm accuracy. It combines semantic constraints and multi-view geometry constraints, selectively running the algorithm in different scenarios, filtering and preserving static points for camera pose estimation. Experimental results demonstrate that, compared to current state-of-the-art dynamic SLAM methods, our approach achieves optimal performance in some scenarios and exhibits stronger real-time capabilities.

This article deals with the queueing-inventory system, composed of c junior servers, a senior server, two finite waiting halls, and an infinite orbit. On occasion, junior servers encounter challenges during customer service. In these instances, they approach the senior server for guidance in resolving the issue. Suppose the senior server is engaged with another junior server. The approaching junior servers await their turn in a finite waiting area with a capacity of c for consultation. Concerning this, we study the performance of junior servers approaching the senior server in the retrial queueing-inventory model with the two finite waiting halls dedicated to the primary customers and the junior servers for consultation. We formulate a level-dependent QBD process and solve its steady-state probability vector using Neuts and Rao’s truncation method. The stability condition of the system is derived, and the R matrix is computed. Optimum total cost has been obtained, and sensitivity analyses, which include expected total cost, waiting time of customers in waiting hall and orbit, number of busy servers, and fraction of successful retrial rate of the model, are done numerically.

Remote distributed generation control and demand response is nowadays a basic need, especially for the Distributed Network Operators (DNOs). The integration of Renewable Energy Sources (RES) with stochastic production characteristics into the grid in conjunction with the newly appeared stochastic demand consumers (i.e., electric vehicles) hardens the ef-forts of the DNOs to keep the grid’s operation within safe limits and prevent cascading black-outs while staying in compliance with the SAIDI and SAIFI indices during maintenance and re-pair operations. Taking also under consideration the aging of the existing grid infrastructure, making it year by year more prone to failure, it is yet of great significance for the DNOs to have access to real-time feedback from grid’s infrastructure, fast, with low-cost upgrade interventions, easily deployed on the field and with fast response potential, in order to be able to perform real-time grid management (RTGM). In this article, we describe the architecture, development and deployment of a hybrid real-time control system of distributed generation (DG) units, which has the potential to be applied easily to DNO’s substations, RES plants and consumers (i.e. electric vehicles’ charging stations), that combines a low-cost minimum intervention into the electricity grid and its components and eliminates all communication interoperability problems between the different vendors’ installed electrical equipment.

Multi-cancer early detection (MCED) tests are being developed, but little is known about patient receptivity to their use for cancer screening. The current study assessed patient interest in such testing. Our team conducted a prospective, observational study among primary care patients in a large, urban health system. They were asked to complete a telephone survey that briefly described a new blood test in development to identify multiple types of cancer, but was not currently recommended or covered by insurance. The survey included items to assess respondent background characteristics, perceptions about MCED testing, and interest in having such an MCED test. We also used multivariable analyses to identify factors associated with patient interest in test use. In 2023, we surveyed 159 (32%) of 500 identified patients. Among respondents, 125 (79%) reported a high level of interest in having an MCED test. Interest was not associated with personal background characteristics, but was positively associated with the following expectations: testing would be recommended for cancer screening, be convenient, and be effective in finding early-stage disease (OR=11.70, 95% CI: 4.02, 34.04, p < 0.001). Research is needed to assess patient interest and actual uptake when detailed information on testing is presented in routine care.

The benefits of multi-robot systems are substantial, bringing gains in efficiency, quality, and cost, and are useful in a wide range of environments from warehouse automation, to agriculture, and even extend in part to entertainment. In multi-robot system research, the main focus is on ensuring efficient coordination in the operation of the robots, both in task allocation and navigation. However, much of this research seldom strays from theoretical bounds; there are many reasons for this, with the most prominent and impactful being resource limitations. This is especially true for research in areas such as multi-robot path planning (MRPP) and navigation coordination. This is a large issue in practice as many approaches are not designed with meaningful real-world implications in mind and are not scalable to large multi-robot systems. This survey aims to look into the coordination and path-planning issues and challenges faced when working with multi-robot systems, especially those using a prioritised planning approach and identify key areas which are not well-explored and the scope of applying existing MRPP approaches to real-world settings.

Building facade design plays an essential role in enhancing energy efficiency and reducing environmental impact in high-performance building design. Balancing the conflicts among various building facade design parameters to satisfy different optimization objectives constitutes a highly complex optimization problem. The rapidly increasing number of studies demonstrates a significant interest in implementing multi-objective optimization methods to tackle building facade optimization problems. This study conducts a systematic review of optimization methods for building facade optimization (BFO). The optimization objectives and design variables are categorized based on their characteristics. The efficiency and effectiveness of optimization algorithms in addressing BFO problems are compared. Building optimization techniques and tools are showcased, along with their functions and limitations. Key findings highlight the robust feasibility and effectiveness of optimization algorithms, methods, and techniques in resolving a diverse range of BFO challenges. The limitations, challenges, and future potential of these methods are summarized and proposed.

Autophagy is an evolutionarily conserved mechanism for degrading and recycling different cellular components in both normal development and stress conditions. Our recent research demonstrated that autophagy-mediated compartmental cytoplasmic deletion is essential for pollen germination. However, how autophagy regulates pollen germination to ensure its fertility remains largely unknown. Here, we applied multi-omic analyses to investigate the downstream pathways of autophagy in the process of pollen germination. Although ATG2 and ATG5 play similar roles in regulating pollen germination, high-throughput transcriptomic analysis reveals that silencing ATG5 has greater impact on the transcriptome than silencing ATG2. Cross-comparisons of transcriptome and proteome analysis reveal that gene expression at mRNA level and protein level are differentially affected by autophagy. Furthermore, high-throughput metabolomics analysis demonstrates that pathways related amino acid metabolism and aminoacyl-tRNA biosynthesis can be affected by both ATG2 and ATG5 silencing. Collectively, our multi-omic analyses reveal a central role of autophagy in cellular metabolism, which is critical for the initiation of pollen germination and the guarantee of pollen fertility.

The protein extract of Ulva australis hydrolyzed with alcalase and flavourzyme investigated multi-functional properties, including total antioxidant capacity (TAC), collagenase inhibitory and antibacterial activities. The #5 fraction (SP5) and #7 fraction (SP7) of U. australis hydrolysate from cation-exchange chromatography showed significantly high TAC, collagenase inhibitory and antibacterial effects against Propionibacterium acnes, and only Q3 fraction from anion-exchange chromatography showed high multi-functional activities. The eight peptides among 42 peptides identified by MALDI-TOF/MS and Q-TOF/MS/MS were selected from the results to screen molecular docking on target proteins, and were synthesized. Thr-Gly-Thr-Trp (TGTW) showed ABTS [2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid)] radical scavenging activity. Effect TAC as Trolox equivalence was dependent on the concentration of TGTW. The Asn-Arg-Asp-Tyr (NRDY) and Arg-Asp-Arg-Phe (RDRF) possessing collagenase inhibitory activity increased according to the increase of concentration and the IC50 values of them were 0.95 mM and 0.84 mM, respectively. Peptides, RDRF and His-Ala-Val-Tyr (HAVY) showed anti-P. acnes effects with IC50 values of 8.57 mM and 13.23 mM, respectively. These results suggest that the U. australis hydrolysate could be a resource for the application of effective nutraceuticals and cosmetics.

As an important means of controlling the spread of cities, large-scale urban green belts have been widely used in the planning and construction of green space systems in various mega-size cities around the world. In previous studies, there has been more research on the overall isolation effect of green belts, while there has been less research on changes in their internal structure. There have been more perspectives on the land-use change (LUC) caused by urban expansion, and less systematic research on the dual driving factors of LUC. In this paper, we constructed a multi-dimensional index system to measure the "contraction-expansion" mechanism of land use in the overlapping vegetation areas of large green belts under the dual influence of urban expansion and urban greening, from the perspectives of quantity, space, and connectivity during different construction periods. Taking the boundary of two green belts in Beijing as an example, we hope to provide scientific support for the construction and ecological protection of Beijing's green belts.

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

Memristor has attracted a lot of interest due to its high processing speed, low power consumption and high integration ability, which is critical for electronic systems and memory-centric computing. However, the memristor programming circuit and strategy are still inflexible and complex, since the signal generator/collector and stimulate pulse must be carefully matched and designed based on memristor intrinsic characteristics without reconfigurable. Here, a simple and effective circuit only consists a parallel reference-resistor-and-NMOS is designed to program memristor with a more than 99% memristance precision. And the amplitude and width of stimulate pulse are fixed to ±4V and 5ms, respectively. In order to cope with the device variation, such as ±10% tolerance of transition voltage, an optimized programming strategy was proposed and demonstrated great robustness. Additionally, a set of reference resistors and NMOSs have been added to facilitate multi-level memristance operation without requiring any changes to the circuit structure. This program circuit was also employed to program memristor crossbar remains 99% precision. In the end, a memristor-based convolutional neural network which controlled by our optimized programming circuit was used for image recognition, and 89.36% accuracy can be achieved even under 15.8% memristance tolerance. This novel circuit demonstrates a simple and flexible strategy in memristor programming, providing a new way to control memristor crossbar for practical application.

In this work, an novel flow-electric field coupling configuration for enhanced Zn-Cd cementation was designed and utilized. A series of tests were conducted to explore the optimization of Zn-Cd cementation process and its mechanism.Firstly, the various characterisations of the cadmium sponge at different locations in the device were compared and it was concluded that the purity of the cadmium sponge was optimal from the anode surface. The generation and stripping of cadmium sponge was revealed for the first time by cross-sectional electron microscopy. The four stages of the apparent reaction in the system were analysed in relation to the pH, cadmium concentration and cadmium sponge flaking at each flow rate. It was proved that the separation of cadmium sponge mainly occurred in the third phase. Secondly, by comparing the morphology and specific surface area of cadmium sheep at different flow rates, the optimum flow field velocity was identified as 30 ml/s. Six flow field configurations were compared and preferred. The results demonstrate that the LCAH modulation results in a more sparse structure of the cadmium sponge, which is more easily exfoliated from the zinc anode surface by fluid impact. This was considered to be the most beneficial flow field configuration for improving the cadmium cementation rate and reducing the cost of the reaction. Further, the reaction steps of the system were analysed. The diffusion process under different flow field configurations was confirmed by calculations to follow first order kinetics. The diffusion coefficient of LACH proved to be the highest in the comparative tests and this result was also supported by the apparent experimental results.

This article presents a wide-band antenna designed for the Cube Satellite (CubeSat). Based on a quadrifilar structure, the antenna provides circular polarization radiation, which is suitable for satellite communication. Moreover, the antenna is designed and fabricated using two 1.6 mm thickness FR4-Epoxy boards that connect together by the metal pins. In order to improve the robustness, a ceramic spacer is placed in the centerboard, and four screws at the corners are added to fix the antenna with the CubeSat structure. These additional parts reduce antenna damage caused by vibrations in the launch vehicle lift-off stage. The proposal has a dimension of 77mm x 77mm x 10mm and covers the LoRa frequency bands of 868 MHz, 915 MHz, and 923 MHz. According to the measurements in an anechoic chamber, antenna gains are obtained with the values of 2.3 dBiC and 1.1 dBiC for the 870 MHz and 920 MHz, respectively. Finally, the antenna is integrated into a 3U CubeSat that was launched by a Soyuz launch vehicle. The terrestrial-to-space communication link was measured, and the antenna performance was confirmed in the real-life scenario.

The link of fracture analysis of large ships is an essential part of the structural safety of ships. However, the finite element analysis of large structure according to the traditional method not only has high requirements on the computer, but also the influence of the mesh encryption at special locations makes further requirements on the accuracy of the ship. In this paper, based on the extended finite element method (XFEM) and the multi-point constraint method (MPC), the dynamic crack expansion process of the rectangular plate containing cracks under the multi-scale model is comparatively studied by using ABAQUS simulation software Finally, the applicability of the method is verified by applying the multi-scale model of ship nodes. The results show that the computational effort of the multi-scale model is significantly reduced with the same accuracy and allowable error, which proves the accuracy and efficiency of the method.

According to the Global Report on Internal Displacement (GRID, 2022), the number of displaced people worldwide at the end of 2021 was 59.1 million, with 53.2 million of them being the result of conflicts and violence, and 5.9 million caused by climate-related disasters. Furthermore, the In-ternational Organization for Migration (IOM) predicts that the climate crisis will be solely re-sponsible for one billion migrants by 2050. In this context, the research examines the issue of coastal multi-risk arising from a combination of heavy rainfall, river's overflow, and sea-level rise phenomena, analysing it through the disciplinary perspective of urban planning. Specifically, the study focuses on the need to upgrade the Local Urban Plan from a climate-proof perspective by defining a method aimed at updating the territory's cognitive framework through the preparation of a multi-risk map (which considers different time horizons), as well as the prescriptive apparatus of the Plan through the definition of a toolkit of site-specific design actions oriented towards urban adaptation. The proposed methodology is verified on the case study of Isola Sacra, Fiumicino (Italy), and it is intended to be applicable to other territorial contexts. The research aims to outline guidelines for updating urban planning tools from a climate-proof perspective to promote long-term resilient urban development in risk-prone areas.

Purpose: Emotional voice conveys important social cues that demand listeners’ attention and timely processing. This event-related potential study investigated the feasibility of a multi-feature oddball paradigm to examine adult listeners’ neural responses to detecting emotional prosody changes in non-repeating naturally spoken words. Method: Thirty-three adult listeners completed the experiment by passively listening to the words in neutral and three alternating emotions while watching a silent movie. Previous research documented pre-attentive change-detection electrophysiological responses (e.g., MMN, P3a) to emotions carried by fixed syllables or words. Given that the MMN and P3a have also been shown to reflect extraction of abstract regularities over repetitive acoustic patterns, the current study employed a multi-feature oddball paradigm to compare listeners’ MMN and P3a to emotional prosody change from neutral to angry, happy, and sad emotions delivered with hundreds of non-repeating words in a single recording session. Results: Both MMN and P3a were successfully elicited by the emotional prosodic change over the varying linguistic context. Angry prosody elicited the strongest MMN compared to happy and sad prosodies. Happy prosody elicited the strongest P3a in the centro-frontal electrodes, and angry prosody elicited the smallest P3a. Conclusions: The results demonstrated that listeners were able to extract the acoustic patterns for each emotional prosody category over constantly changing spoken words. The findings confirm the feasibility of the multi-feature oddball paradigm in investigating emotional speech processing beyond simple acoustic change detection, which may potentially be applied to pediatric and clinical populations.

Background: The first few minutes after birth are the most dangerous for the survival of an infant. Babies in neonatal intensive care units are either under heated or overheated, and hypothermic infants remain hypothermic or develop a fever. As a result, special attention must be paid to monitoring and maintaining the time of recovery from hypothermia states. Despite numerous studies, only a few have examined the transition from neonatal hypothermia and associated risk factors in depth. Method: A retrospective observational study was conducted to track axillary temperatures taken at the time of neonatal intensive care unit admission, which were then tracked every 30 minutes until the newborn's temperature stabilized. All hypothermic neonates admitted to the neonatal intensive care unit between January 2018 and December 2020 was included in the study. Temperature data were available at birth and within the first three hours of admission for 391 eligible hypothermic neonates. The effect of factors on the transition rate in different states of hypothermia was estimated using a multi-state Markov model. Result: The likelihood of progressing from mild to severe hypothermia was 5%, while the likelihood of progressing to normal was 34%. The average time spent in a severe hypothermia state was 48, 35, and 24 minutes for three different levels of birth weight, and 53, 41, and 31 minutes for low, moderate, and normal Apgar scores, respectively. Furthermore, the mean sojourn time in a severe hypothermia state was 48, 39, and 31 minutes for three different levels of high, normal, and low pulse rate, respectively. Conclusion: For hypothermic survivors within the first three hours of life, very low birth weight, low Apgar, and high pulse rate had the strongest association with hypothermia and took the longest time to improve/recover. As a result, there is an urgent need to train all levels of staff dealing with maintaining the time of recovery from neonatal hypothermia.

In this note we describe a new approach to the option pricing problem by introducing the notion of the safe (and acceptable) for the writer price of an option, in contrast to the fair price used in the Black-Scholes model. Our starting point is that the option pricing problem is closely related with the hedging problem by practical techniques. Recalling that the Black - Scholes model does not give us the price of the option but the initial value of a replicating portfolio we observe easily that has a serious disadvantage because assumes the building of this replicating portfolio continuously in time and this is a disadvantage of any model that assumes such a construction. Here we study the problem from the practical point of view concerning mainly the over the counter market. This approach is not affected by the number of the underlying assets and is particularly useful for incomplete markets. In the usual Black-Scholes or binomial or some other approaches one assumes that one can invest or borrow at the same risk free rate $r>0$ which is not true in general. Even if this is the case one can immediately observes that this risk free rate is not a universal constant but is different among different people or institutions. So, the fair price of an option is not so much fair! Moreover, the two sides are not, in general, equivalent against the risk therefore the notion of a fair price has no meaning at all. We also define a variant of the usual binomial model, by estimating safe upward and downward rates $u,d$, trying to give a cheaper safe or acceptable price for the option.

Short-term changes in shallow bathymetry affect the coastal zone and therefore their monitoring is an essential task in coastal planning projects. This study provides a novel approach for monitoring shallow bathymetry change based on drone multispectral imagery. Particularly we apply a shallow water inversion algorithm on two composite multispectral datasets being acquired five months apart in a small Mediterranean sandy embayment (Chania, Greece). Initially, we perform radiometric corrections using proprietary software and following we combine the bands from standard and multispectral cameras resulting in a six-band composite image suitable for applying the shallow water inversion algorithm. Bathymetry inversion results showed good correlation and low errors (< 0.3m) with sonar measurements collected with an uncrewed surface vehicle (USV). Bathymetry maps and true-color orthomosaics assist in identifying morphobathymetric features representing crescentic bars with rip channel systems. The temporal bathymetry and true-color data reveal important erosional and depositional patterns, which were developed under the impact of winter storms. Furthermore, bathymetric profiles show that the crescentic bar appears to migrate across and along-shore over the 5-months period. Drone-based multispectral imagery proves to be an important and cost-effective tool for shallow seafloor mapping and monitoring when it is combined with shallow water analytical models.

Marketing is the manner of how to make our sales the best in the market, our prices the most accessible, our clients satisfactory, and thus our brand is the largest distributed. This needs sophisticated and advanced understanding of the total network related. Indeed, marketing data may be seen in different forms such as qualitative and quantitative. However, in the literature, it is easily noticed that large bibliography may be collected about qualitative studies, against few studies on the quantitative point of view. This is a major drawback that makes the marketing science still focusing on the design, although the market is strongly depending on quantities such as money and time. Indeed, marketing data may be a time series such as brand sales per specified periods, brand related-prices over specified periods, market shares, ..., etc. The purpose of the present work is to investigate some marketing models based on time series due to brands. We will precisely study the effect of the time scale on the persistence of brands sales in the market and on the forecasting of such a persistence according to the characteristics of the brand and the related market competition or competitors. Our study is acted on a sample of Saudi brands during the period November 22, 2017 to December 30, 2021.

The incidence and prevalence of food allergy (FA) are increasing. While several studies have established the safety and efficacy of early introduction of single allergens in infants for the prevention of FA, the exact dose, frequency, and number of allergens that can be safely introduced to infants particularly in those at high or low risk of atopy are still unclear. This 1-year study evaluated the safety of early introduction of single foods (milk, egg, or peanut), vs. two foods (milk/egg, egg/peanut, milk/peanut), vs. multiple foods (milk/egg/peanut/cashew/almond/shrimp/walnut/wheat/fish/soy at low, medium, or high doses) vs no early introduction in infants between 4-6 months of age. At the end of the study, they were evaluated for plasma biomarkers associated with food reactivity with standardized blood tests. Two to four years after the start of the study, participants were evaluated by standardized food challenges. The serving sizes for the single, double, and low dose mixtures were 300 mg total protein per day. The serving sizes for the medium and high dose mixture was 900 mg and 3000 mg total protein, respectively. Equal parts of each protein were used for double or mixture foods. All infants were breastfed until at least 6 months of age. Results demonstrate that infants at either high or low risk for atopy were able to tolerate early introduction of multiple allergenic foods with no increases in any safety issues, including eczema, FA, or food protein induced enterocolitis. The mixtures of foods at either low, medium, or high doses demonstrated trends for improvement in food challenge reactivity and plasma biomarkers compared to single and double food introductions. The results of this study suggest that early introduction of foods, particularly simultaneous mixtures of many allergenic foods is efficacious for preventing FA and can occur safely.

Often, engineers with machining experience often judge machining state and tool life according to chips’ features. Engineers' experience is digitized in this study. During the cutting process, the cutting tool coming in contact with the workpiece produces a shear zone, which causes plastic deformation and shear slip. The chips closest to the shear zone can directly show the state of the tool and workpiece when the material is SKD61. This study used chip color, vibration, and current signal integration for prediction of machining state and cutting tool life. When the cutting tool wears increased, the chip surface color changed in the following way: purpleè purple blueè blue ècyan, or even green and yellow. When the cutting tool was in the accelerating wear phase, the color change was particularly obvious. The Back-Propagation Levenberg–Marquardt (BP-LM) predictive methodology was used to compare the predictive ability of voltage, vibration signal, and chip color. The Mean Absolute Percentage Error (MAPE) for the voltage signal was 12.28%, for the vibration signal it was 11.38%, and for the chip color combined with multi-sensor characteristics it was 7.85%. The MAPE of the chip color was the smallest. Using the General Regression Neural Network (GRNN) methodology, the MAPE for the voltage signal was 10.74%, for the vibration signal 7.96%, and for the chip color combined with multi-sensor characteristics was 6.59%. The MAPE of the chip color was the smallest. Obviously, the chip color combined with multi-sensor signals provided better predictive results than the vibration signal or voltage signal alone. There is currently no research on the usefulness of monitoring chip color for tool life prediction.

In the mold machining process, the cutting tool is worn with machining time, thereby affecting the surface accuracy, leading to poor workpiece dimensions, even fracture. At present, many studies have used multiple sensors to detect the machining conditions of cutting tool and workpiece, including indirect measurement method and direct measurement method. The indirect measurement method, which has been studied widely, mainly uses sensors to capture signals for subsequent data analysis; the direct measurement method mainly analyzes the state of cutting shear zone. Due to the cut-in of cutting tool in the machining process, the workpiece is dislocated rapidly, generating considerable amount of heat, which is transferred to the chips, inducing color change on the surface of chips. Many engineers with machining experience often judge the machining state and tool life according to the chips. The engineers' experience is digitized in this study, and indirect measuring sensors are used to predict the tool life, so as to attain the objective for smart manufacturing, the average percentage error of MAPE using single vibration and voltage eigenvalues as input features is 10%, the voltage signal characteristic values and vibration signal characteristic values are combined. Finally, the chip surface chromaticity eigenvalue is combined with signal characteristic value. The average prediction error of BP-LM method is 7.85%, the average prediction error of GRNN method is 6.59%. Therefore, when the eigenvalue of chip surface chromaticity is added to the prediction result, it can enhance the accuracy of cutting tool wear value prediction more effectively than single sensor signal characteristic value.

Autofocus is an essential part of the SAR imaging process. Multi-subaperture autofocus algorithm is a commonly used autofocus algorithm for processing SAR stripmap mode data. The multi-subaperture autofocus algorithm has two main steps, the first is to estimate the phase error gradient within the subaperture, the second is to splice the phase error gradient, that is, to remove the shift amount between the estimated adjacent subapertures’ error gradients. Previous gradient-splicing algorithms assume that the estimation of subaperture error is accurate, but when the estimation of subaperture phase error gradients is not accurate enough, these algorithm performance will be degraded. A new phase error gradient splicing algorithm is proposed in this paper. It roughly estimates the shift amount first, and then finely estimates the shift amount based on the minimum-entropy criterion, which can improve the robustness of splicing especially when the estimation of the phase error gradients of the subaperture is not accurate enough. To speed up the algorithm, a variable-step-size search method is used. Simulation and experimental results show that the algorithm has enough accuracy and still has good performance when other splicing algorithms doesn’t perform well.

Evolutionary convergence provides natural opportunities to investigate how, when, and why novel traits evolve. Many convergent traits are complex, highlighting the importance of explicitly considering convergence at different levels of biological organization, or ‘multi‐level convergent evolution’. To investigate multi‐level convergent evolution, we propose a holistic and hierarchical framework that emphasizes breaking down traits into several functional modules. We begin by identifying long‐standing questions on the origins of complexity and the diverse evolutionary processes underlying phenotypic convergence to discuss how they can be addressed by examining convergent systems. We argue that bioluminescence, a complex trait that evolved dozens of times through either novel mechanisms or conserved toolkits, is particularly well suited for these studies. We present an updated estimate of at least 94 independent origins of bioluminescence across the tree of life, which we calculated by reviewing and summarizing all estimates of independent origins. Then, we use our framework to review the biology, chemistry, and evolution of bioluminescence, and for each biological level identify questions that arise from our systematic review. We focus on luminous organisms that use the shared luciferin substrates coelenterazine or vargulin to produce light because these organisms convergently evolved bioluminescent proteins that use the same luciferins to produce bioluminescence. Evolutionary convergence does not necessarily extend across biological levels, as exemplified by cases of conservation and disparity in biological functions, organs, cells, and molecules associated with bioluminescence systems. Investigating differences across bioluminescent organisms will address fundamental questions on predictability and contingency in convergent evolution. Lastly, we highlight unexplored areas of bioluminescence research and advances in sequencing and chemical techniques useful for developing bioluminescence as a model system for studying multi‐level convergent evolution.

This investigation explores a novel path-planning and optimization strategy for multiple cooperative robotic agents, applied in a fully observable and dynamically changing obstacle field. Current dynamic path planning strategies employ static algorithms operating over incremental time-steps. We propose a cooperative multi-agent (CMA) based algorithm, based on natural flocking of animals, using vector operations. It is preferred over more common graph search algorithms like A* as it can be easily applied for dynamic environments. CMA algorithm executes obstacle avoidance using static potential fields around obstacles, that scale based on relative motion. Optimization strategies including interpolation and Bezier curves are applied to the algorithm. To validate effectiveness, CMA algorithm is compared with A* using static obstacles due to lack of equivalent algorithms for dynamic environments. CMA performed comparably to A* with difference ranging from -0.2% to 1.3%. CMA algorithm is applied experimentally to achieve comparable performance, with an error range of -0.5% to 5.2%. These errors are attributed to the limitations of the Kinect V1 sensor used for obstacle detection. The algorithm was finally implemented in a 3D simulated space, indicating that it is possible to apply with drones. This algorithm shows promise for application in warehouse and inventory automation, especially when the workspace is observable.

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework is implemented in R, providing a flexible and easy‐to‐use GUI interface. Since this allows appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety.

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, we propose a general formulation of the multi-objective Volt-VAr optimization problem. We consider the applicability of various multi-optimization techniques and discuss the operational interpretation of these solutions. We demonstrate the methods using simulation on a test feeder.

Biological gels (bio-gels) are hydrated polymer networks that serve diverse biological functions, which often lead to intentional or unintentional exposure to particulate matter. In this work, we derive a microscopically motivated framework that enables the investigation of penetration mechanisms into bio-gels. We distinguish between two types of mechanisms: spontaneous (unforced) penetration and forced penetration. Using experimental data available in literature, we exploit the proposed model to characterize and compare between the micro-structures of respiratory, intestinal, and cervicovaginal mucus and two types of biofilms. Next, we investigate the forced penetration process of spherical and ellipsoidal particles into a locally quadrilateral network. The proposed framework can be used to improve and complement the analysis of experimental findings in vitro, ex vivo, and in vivo. Additionally, the insights from this work pave the way towards enhanced designs of nano-medicines and allow to assess risk factors related to the nano-pollutants exposure.

A method for classification is introduced in this article, and it is tested on ADNI database to diagnose alzheimer’s disease (AD). It is obvious that tunning the performance of a classification to get better results is a complicated problem, and when we want model’s accuracy or other peformance measurments higher than 90%, the problem will be more complicated. In this study, we tried and succeeded to discover a method to solve this problem. The final feature set can be used clustering too, because outgrowth feature set of the proposed method is invigorated. In the recent years, a lot of activities is done to develop computer aided systems (CAD) for alzheimer’s disease diagnosis. Most of these recently developed systems concenterated on extracting and combining features from MRI, PET, CSF, and …; in this article, we made attempt to do so and utilized one more technique to increase classification performance. Finding and producing the best features to solve three binary classification problems of AD vs. Normal Control (NC), Mild Cognitive Impairment (MCI) vs. NC, and MCI vs. AD are the purposes of this article. Experiments indicate performance and effectiveness rates of the proposed method, which are accuracies of 98.81%, 81.61%, and 81.40% for AD vs. NC, MCI vs. NC, and AD vs. MCI classification problems, respectively. As can be seen, using this method increased the performance of the three binary problems incredibly.

The thermal lubrication of an entangled polymeric liquid in wall-driven shear flows between parallel plates is investigated by using a multiscale hybrid method coupling molecular dynamics and the hydrodynamics (i.e., the synchronized molecular dynamics method). The temperature of the polymeric liquid rapidly increases due to viscous heating once the drive force exceeds a certain threshold value. The rheological properties of the polymeric liquid drastically change at around the critical drive force. In the weak viscous-heating regime, the conformation of polymer chains is dominated by the local shear flow so that the anisotropy of the bond orientation tensor grows as the drive force increases. However, in the large viscous-heating regime, the conformation dynamics is dominated by the thermal agitation of polymer chains so that the bond orientation tensor recovers more uniform and random structures as the drive force increases, even though the local shear flows are further enhanced. Remarkably, these counter-intuitive transitional behaviors give an interesting re-entrant transition in the stress--optical relation, where a linear formalism in the stress--optical relation approximately holds even though each of the macroscopic quantities behaves nonlinearly. The robustness of the linear stress--optical relation is also confirmed in the spatiotemporal evolution at the hydrodynamic level.

Digital Elevation Models (DEMs) are widely used in geographic and environmental studies. In the current work, the fusion of multi-source DEMs is investigated to improve the overall accuracy of public domain DEMs. Multi-scale decomposition is an important analytical method in data fusion. Three multi-scale decomposition methods – the wavelet transform (WT), bidimensional empirical mode decomposition (BEMD) and nonlinear adaptive multi-scale decomposition (N-AMD) - are applied to the 1-arc-second Shuttle Radar Topography Mission Global digital elevation model (SRTM-1 DEM) and the Advanced Land Observing Satellite World 3D – 30 m digital surface model (AW3D30 DSM) in China. Of these, the WT and BEMD are popular image fusion methods. A new approach for DEM fusion is developed using N-AMD (which is originally invented to remove the cycle from sunspots). Subsequently, a window-based rule is proposed for the fusion of corresponding frequency components obtained by these methods. Quantitative results show that N-AMD is more suitable for multi-scale fusion of multi-source DEMs, taking the ice cloud and land elevation satellite (ICESat) global land surface altimetry data as a reference. The vertical accuracy of the fused DEM shows significant improvements of 29.6% and 19.3% in a mountainous region and 27.4% and 15.5% in a low-relief region, compared to the SRTM-1 and AW3D30 respectively. Furthermore, a slope position-based linear regression method is developed to calibrate the fused DEM for different slope position classes, by investigating the distribution of the fused DEM error with topography. The results indicate that the accuracy of the DEM calibrated by this method is improved by 16% and 13.6%, compared to the fused DEM in the mountainous region and low-relief region respectively, proving that it is a practical and simple means of further increasing the accuracy of the fused DEM.

Multi-task Gaussian processes (MTGPs) are a powerful approach for modeling structured dependencies among multiple tasks. Researchers on MTGPs have contributed to enhance this approach in various ways. Current MTGP methods, however, cannot model nonlinear task correlations in a general way. In this paper we address this problem. We focus on spectral mixture (SM) based kernels and propose an enhancement of this type of kernels, called multi-task generalized convolution spectral mixture (MT-GCSM) kernel. The MT-GCSM kernel can model nonlinear task correlations and mixtures dependency, including time and phase delay, not only between different tasks but also within a task at the spectral mixture level. Each task in MT-GCSM has its own generalized convolution spectral mixture kernel (GCSM) with a different number of convolution structures and all spectral mixtures from different tasks are dependent. Furthermore, the proposed kernel uses inner and outer full cross convolution between base spectral mixtures, so that the base spectral mixtures in the tasks are not necessarily aligned. Extensive experiments on synthetic and real-life datasets illustrate the difference between MT-GCSM and other kernels as well as the practical effectiveness of MT-GCSM.

An adaptive operation strategy for on-demand control of DMFC system is proposed as an alternative method to enhance the voltage stability. Based on a single-cell DMFC stack, a newly simplified semi-empirical model is developed from the uniform-designed experimental results to describe the I-V relationship. Integrated with this model, the multi-objective optimization method is utilized to develop an adaptive operation strategy. Although the voltage instability is frequently encountered in unoptimized operations, the voltage deviation is successfully decreased to a required level by adaptive operations with operational adjustments. Moreover, the adaptive operations are also found to be able to extend the range of operating current density or to decrease the voltage deviation according to ones requirements. Numerical simulations are implemented to investigate the underlying mechanisms of the proposed adaptive operation strategy, and experimental adaptive operations are also performed on another DMFC system to validate the adaptive operation strategy. Preliminary experimental study shows a rapid response of DMFC system to the operational adjustment, which further validates the effectiveness and feasibility of the adaptive operation strategy in practical applications. The proposed strategy contributes to a guideline for the better control of output voltage from operating DMFC systems.

This research proposes a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 zones in the UK. The framework includes optimisation techniques, decision-making methods and experts’ input in order to help stakeholders with investment decisions. Techno-economic, Life Cycle Costs (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision making process. A process on the criteria selections is also presented and seven conflicting criteria are being considered in TOPSIS methods in order to suggest the optimum location that was produced by the NSGAII algorithm. Seagreen Alpha was the most probable solution, followed by Moray Firth Eastern Development Area 1, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make more informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK.

The development of printing technologies has enabled the realization of electric circuit fabrication on flexible substrate. However, the current technique remains restricted to single-layer patterning. In this paper, we demonstrate a fully solution-processable patterning approach for multi-layer circuits using a combined method of laser sintering and ablation. Selective laser sintering of silver (Ag) nanoparticle-based ink is applied to make conductive patterns on a heat-sensitive substrate and insulating layer. The laser beam path and irradiation fluence are controlled to create circuit patterns for flexible electronics. Microvia drilling using femtosecond laser through the polyvinylphenol-film insulating layer by laser ablation, as well as sequential coating of Ag ink and laser sintering, achieves an interlayer interconnection between multi-layer circuits. The dimension of microvia is determined by a sophisticated adjustment of laser focal position and intensity. Based on these methods, the flexible electronic circuit with chip-size-package light-emitting diodes was successfully fabricated and demonstrated with functional operations.