A multispectral superconducting nanowire single photon detector sensitive to different incident photon wavelength bands, is proposed. The SNSPD consists of a Distributed Bragg Reflector, a gold mirror, and two regions employing four NbN nanowires. Using the DBR both as a filter and a reflector, two distinct detection bands are created. The first detection band has a peak absorbance of > 0.75 at a wavelength of 1143 nm, while the second band has an absorbance of > 0.70 in the wavelength range 1440 to 2000 nm. The design of the device can be tuned to provide sensitivity in different wavelength bands. While SNSPDs do not typically provide photon wavelength sensitivity, the band-selection design proposed in this work opens up potential applications for future quantum communication technology.

This paper deals with developing a new class of quaternions, called hyper-dual Horadam quaternions which are constructed from the quaternions whose components are hyper-dual Horadam numbers. We investigate the algebraic properties of these quaternions.

Frames are more stable as compared to bases under the action of a bounded linear operator. Sums of different frames under the action of a bounded linear operator are studied with the help of analysis, synthesis and frame operators. A simple construction of frames from the existing ones under the action of such an operator is presented here. It is shown that a frame can be added to its alternate dual frames, yielding a new frame. It is also shown that the sum of a pair of orthogonal frames is a frame. This provides an easy construction of a frame where the frame bounds can be computed easily. Moreover, for a pair of orthogonal frames, the necessary and sufficient condition is presented for their alternate dual frames to be orthogonal. This allows for an easy construction of a large number of new frames.

Recent years have seen a massive increase in research on mindfulness as both a therapeutic practice as well as a protective health factor. While many associations have been made between mindfulness and clinical outcomes, understanding of the underlying mechanistic processes is still in its infancy. In this review, associations between mindfulness and other established frameworks in cognitive control are explored—in particular the dual modes of control framework in cognitive control theory. Studies in mindfulness providing evidence towards a dual-mode mindfulness model are reviewed and compared with the proactive and reactive modes of cognitive control to identify common underlying principles. Studies involving cognitive testing, advanced neuroimaging, and neurotransmitter levels and their associations with proactive and reactive control are reviewed in the context of the dual modes of mindfulness. While evidence supporting relationships between proactive and reactive modes of mindfulness and cognitive control are preliminary, existing literature on mindfulness already supports the notion of two components of mindfulness and is in line with the hypothesis that cognitive control and mindfulness are closely related. Since preliminary evidence suggests that proactive forms of mindfulness are more associated with improvement in clinical outcomes, mindfulness programs could be modified to target proactive mindfulness practices to maximize outcomes. The implications of proactive and reactive mindfulness on therapy, limitations of the framework, and necessity of studies providing direct evidence are further discussed.

In this contribution, different lane-keeping control strategies for Autonomous Ground Vehicles (AGV) have been analyzed and compared. The AGV must be oriented and kept within a given reference path using the front wheel steering angle as the control action for a specific longitudinal velocity. While non-linear models can describe the lateral dynamics of the vehicle in an accurate manner, they might lead to difficulties when computing some real-time control laws such as Model Predictive Control (MPC). Linear Parameter Varying (LPV) models can provide a trade-off between computational complexity and model accuracy. Another way to reduce computational complexity is to explore other control strategies, for example, the one based on the Inverse Kinematic Bicycle model (IKIBI). Additionally, AGV sensors typically work at different measurement acquisition frequencies so that Kalman Filters (KF) are usually needed for sensor fusion. If these frequencies are slower than the actuation rate, a multi-rate KF may be needed. The two control strategies (MPC using a LPV model and IKIBI) have been compared in simulations over a circuit path in the presence of process and measurement Gaussian noise. The MPC controller has shown to provide a more accurate lane-keeping behavior than an IKIBI control strategy. Finally, it has been seen that Dual-Rate Extended Kalman Filters (DREKF) constitute an essential tool when only slow and noisy sensor feedback is available in an AGV lane-keeping application.

Continuous water resources monitoring is needed for sustainable urban water supply. Remote sensing techniques have proven useful for monitoring some water qualitative parameters with optical characteristics. The study area was the Marateca reservoir in central inland Portugal. The aims were the following: (1) to explore the water quality parameters at the monitoring points of the Marateca reservoir that may explain the event; (2) to validate optical water quality parameters with the monitoring points data; and (3) to model the reservoir water characteristics regarding its deepness, trophic state, and turbidity. The parameters total phosphorus, total nitrogen, and chlorophyll-a were used to compute a trophic level index. The Sentinel-2 imagery was used to compute spectral indices and bands image ratio; to obtain spectral signatures for the monitoring points, and to model water characteristics. The water parameters were above the recommended values at the reservoir entry point from the Ocreza River. The reservoir trophic level was Hypereutrophic and Eutrophic. The spectral signatures confirmed a Hypereutrophic pattern in the entry point. The Marateca reservoir’s water characteristics modeling forecasted problematic zones by contamination. The methodological approach developed can be easily applied to other reservoirs and is a key support tool for decision-makers.

With the development of hyperspectral technology, to establish an effective spectral data compressive reconstruction method that can improving data storage, transmission and maintaining spectral information is critical for quantitative remote sensing research and application in vegetation. By introducing the idea of compressive sensing in compressed reconstruction, the spectral adaptive grouping distributed compressive sensing algorithm is proposed, which enables a distributed compressed sensing reconstruction of plant hyperspectral data. The experimental results showed that comparing with orthogonal matching pursuit(OMP) and gradient projection reconstruction(GPSR), the proposed algorithm can significantly improve the visual effect of image reconstruction in the spatial domain. The PSNR in low sampling rate(sampling rate is lower than 0.2) increases by 13.72dB than OMP and 1.66dB than GPSR. In the spectral domain, the average normalized root mean square error、the mean absolute percentage error and the mean absolute error of the proposed algorithm is35.38%，31.83% and 33.33% lower than GPSR respectively.. Therefore, the proposed algorithm can achieve relatively high reconstructed efficiency.

The modern era of polarimetric radar begins with radiowave propagation research starting in the early 1970s with applications to measurement and modeling of wave attenuation in rain and depolarization due to ice particles along satellite-earth links. While there is a rich history of radar in meteorology after World War II, the impetus provided by radiowave propagation requirements lead to high quality antennas and feeds. Our journey starts by describing the key institutions and personnel responsible for development of weather radar polarimetry. The early period was dominated by circularly polarized radars for propagation research and at S-band for hail detection. By the mid-to late 70s, a paradigm shift occurred which led to the dominance of linear polarizations with applications to slant path attenuation prediction as well as estimation of rain rates and inferences of precipitation physics. The period from early 1980s to 1995 can be considered as the “golden” period of rapid research that brought in meteorologists, cloud physicists and hydrologists. This article describes the evolution of this technology from the vantage point of the authors. Their personal reflections and “behind the scenes” descriptions offer a glimpse into the inner workings at several key institutions which cannot be found elsewhere.

The goal of this paper is to propose a dual version of the direct cosine simplex algorithm (DDCA) for general linear problems. Unlike the two-phase and the big-M methods, our technique does not involve artificial variables. Our technique solves the dual Klee-Minty problem in two iterations and solves the dual Clausen’s problem in four iterations. The utility of the proposed method is evident from the extensive computational results on test problems adapted from NETLIB. Preliminary results indicate that this dual direct cosine simplex algorithm (DDCA) reduces the number of iterations of two-phase method.

The identification of bloodstain is one of the most important approaches in obtaining evidence in criminalistics. A threshold method based on spectral co-efficient and interclass variance is proposed in this paper, it is a non-contact, non-destructive method for quickly identifying bloodstains. The spectra of bloodstains and other suspected substances were all extracted from their hyper-spectral image. Then calculate the correlation coefficients of these spectral and interclass variances, analyze the differences between substances. The best blood recognition threshold was determined as 0.9. After preprocessing for eliminating systematic errors, experiments with the threshold 0.9 are carried out to identify bloodstains on the calico and red T-shirt. The method can remarkably identify the bloodstain from other non-blood substances both quickly and efficiently. The blood extraction rate can reach to 93.35% and 89.19%, respectively. It is an important step toward the implementation of bloodstain non-contact and non-destructive identification in forensic casework.

Perilla frutescens leaves are expected to exhibit antioxidant activity and amyloid-β (Aβ) aggregation inhibitory activity due to the presence of polyphenol-type compounds, etc. These activities fluctuate daily; however, measuring the contents of constituents and functionalities is cumbersome and unsuitable for instantaneous measurements in the field. In this study, we developed a facile method to analyze the activity and content of the components in leaves on-site based on the fluorescence spectra obtained by irradiating the perilla leaves with excitation light. The constituent contents (Chlorophyl (Chl), Total polyphenol content (TPC), Total flavonoid content (TFC), rosmarinic acid (RA) and functional properties DPPH radical scavenging activity, ferric reducing antioxidant power (FRAP), oxygen radical absorbance capacity (ORAC), Aβ aggregation inhibitory activity) were evaluated using established analytical techniques. The correlation between the fluorescence spectrum and the contents and activities was calculated using normalized difference spectral index (NDSI), ratio spectral index (RSI), and difference spectral index (DSI) analyses. We identified a predictive model with a high coefficient of determination R² ≥ 0.57 for content of the constituents, and R² ≥ 0.49 for functional properties. This allows convenient, simultaneous, and non-destructive monitoring of the constituent contents and the functionalities of perilla leave and a simple harvest-time diagnosis. The model established using this method is simple and suggesting that it can simultaneously infer the constituents and functionalities of perilla leaves.

Atmospheric correction is a necessary step in image processing data and spectra recorded by spaceborne sensors for pure cloudless atmosphere, primarily in the visible and near-IR spectral range. We have present a fast and sufficiently accurate method of atmospheric correction based on the proposed analytical solutions describing with high accuracy the spectrum of outgoing radiation at the top boundary of the cloudless atmosphere. This technique includes the model of the atmosphere and its optical parameters that are important in terms of radiation transfer. The solution of the inverse problem for finding unknown parameters of the model is carried out by the method of non-linear least squares (Levenberg-Marquardt algorithm) for an individual selected pixel of the image (its spectrum), taking into account the adjacency effects. Using the found parameters of the atmosphere and the average surface albedo, assuming homogeneity of the atmosphere within a certain area of the hyperspectral image (or the whole frame), the spectral albedo at the Earth's surface is calculated for all other pixels. It is essential that the procedure of the numerical simulation with non-linear least squares of the direct transfer problem is based on using analytical solutions, which provides a very short calculation time of the atmospheric parameters (seconds or less) and the ability to perform atmospheric correction "on-fly." Testing methods of atmospheric correction was performed using the synthetic outgoing radiation spectra at the top of the atmosphere (TOA), obtained by numerical simulation in the LibRadTran code, as well as spectra of real space images of the Hyperion hyperspectrometer. A comparison with the results of atmospheric correction in module FLAASH of ENVI package has been performed. Finally, to validate our data obtained by the SHARK method, a comparative analysis with ground-based measurements of Radiometric Calibration Network (RadCalNet) was carried out.

In this paper, we introduce the generalized dual hyperbolic Fibonacci numbers. As special cases, we deal with dual hyperbolic Fibonacci and dual hyperbolic Lucas numbers. We present Binet's formulas, generating functions and the summation formulas for these numbers. Moreover, we give Catalan's, Cassini's, d'Ocagne's, Gelin-Cesàro's, Melham's identities and present matrices related with these sequences.

Spectral calibration consists of the calibration of wavelengths and the measurement of the instrument's spectral response function (SRF). Unlike conventional slits, the absorbed aerosol sensors (AAS) are used as a slit homogenizer, in which the spectral response function (SRF) is not a conventional Gaussian curve. To be more precise, the SRF is the convolution of the slit function of the spectrometer, the line spread function of the optical system, and the detector response function. The SRF of the slit homogenizer is a flat-topped multi-Gaussian function. Considering the convenience of fitting, a super-Gaussian function, which looks like a similar distribution to the flat-topped multi-Gaussian function, is used to fit the measured data in a spectral calibration. According to the results, the SRF’s shapes, due to the Earth port, resemble a Gaussian curve with a flatted top could be concluded, which contains an FWHM of 1.78–1.82 nm for the AAS. The results show that the correlation coefficients are about 0.99, which proves that the fitting function could better characterize the SRF of the instrument.

In gravitational theory and astrophysical dynamics, singular initial value problems (IVPs) are frequently encountered. Finding the solutions to this class of IVPs can be challenging due to their complex nature. This study strives to circumvent the complexity by proposing a numerical method for solving such problems. The approach proposed in the current research seeks solutions to the IVP by partitioning the domain [0,L] of the problem into two intervals and solving the problem on each domain. The study seeks a closed-form solution to the IVP in the interval containing the singular point. A linearization technique and piecewise partitioning of the domain not containing the singularity are applied to the nonlinear IVP. The resulting linearized differential equation is solved using the Chebyshev spectral collocation method. Some examples are presented to illustrate the efficiency of the proposed method. Numerical analysis of the solution and residual errors are shown to ascertain convergence and accuracy. The results suggest that the technique gives accurate convergent solutions using a few collocation points.

Over the past 30 years, the use of field spectroscopy has risen in importance in remote sensing studies for the characterization of the surface reflectance of materials in situ within a broad range of applications. Potential uses range from measurements of individual targets of interest (e.g. vegetation, soils, validation targets etc.), to characterizing the contributions of different materials within larger spatially-mixed areas as would be representative of the spatial resolution captured by a sensor pixel (UAV to satellite scale). As such, it is essential that a complete and rigorous assessment of both the data-acquisition procedures, and the suitability of the derived data product be carried out. The measured energy from solar-reflected range spectroradiometers is influenced by the viewing and illumination geometries and the illumination conditions which vary due to changes in solar position and atmospheric conditions. By applying corrections, the estimated absolute reflectance (Rabs) of targets can be calculated. This property is independent of illumination intensity or conditions and is the metric commonly suggested to be used to compare spectra even when data are collected by different sensors or acquired under different conditions. By standardizing the process of estimated Rabs, as is provided in the described toolkit, consistency and repeatability in processing are ensured and the otherwise labor intensive and error-prone processing steps are streamlined. The resultant end data product (Rabs) represents our best current effort to generate consistent and comparable ground spectra which have been corrected for viewing and illumination geometries as well as other factors such as the individual characteristics of the reference panel used during acquisition.

The Geostationary Environment Monitoring Spectrometer (GEMS) onboard the Geostationary Korean Multi-Purpose Satellite 2B was successfully launched in February 2020. GEMS is a hyperspectral spectrometer measuring solar irradiance and Earth radiance in the range of 300 to 500 nm. This paper introduces the spectral calibration algorithm for GEMS, which uses a nonlinear least-squares approach. To assess the performance of the algorithm, sensitivity tests for a series of spectral parameters such as shift, spectral range for fitting, signal-to-noise ratio, spectral response function (SRF), and reference spectrum have been conducted. To improve the assessment, a synthetic GEMS spectrum using the prelaunch GEMS SRF is adopted here. The test results show that the required accuracy (0.002 nm) is achievable for the expected uncertainties of the parameters except for the SRF and the choice of high-resolution reference spectrum, which degrade the algorithm performance by an order magnitude. To mitigate the sensitivity to SRF, retrieval of in-orbit SRF using an analytic function is suggested. Finally, a few candidates for the high-resolution solar reference spectrum are prepared for testing by the instrument during in-orbit tests.

In this paper, we identify issues and possible solutions in the key area of large-scale antenna systems, also know as Massive Multiple Input Multiple Output (MIMO) systems. Additionally, we propose the use of Massive MIMO technology as a means to tackle the uplink mixed-service communication problem. Under the assumption of an available physical narrowband shared channel (PNSCH), the capacity of the MTC network and, in turn, that of the whole system, can be increased by grouping Machine-Type Communication (MTC) devices into clusters and letting each cluster share the same time-frequency physical resource blocks. We study the feasibility of applying sub-optimal linear detection to the problem of detecting a large number of MTC devices sharing the same time-frequency resources at the uplink of a base station (BS) equipped with a large number of antennas, M. In our study, we derive the achievable lower-bound rates for the studied sub-optimal linear detectors and show that the transmitted power of each MTC device can be reduced as M increases, which is a very important result for powerconstrained MTC devices running on batteries. Our simulation results suggest that, as M is made progressively larger, the performance of sub-optimal linear detection methods approach the matched filter bound, also known as perfect interference-cancellation bound.

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where for each matching pair there are always two possible non-matching patches; one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral albedo using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi - zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral albedo for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

This paper provides a comprehensive overview of the potential applications of dual energy CT (DECT) in improving image quality and diagnostic capabilities of CT arthrography (CTA) in clinical practice. The paper covers the use of virtual non contrast (VNC) images, in which the injected contrast medium is reduced from the articular cavity, in order to better analyze 2D and 3D images of the bone. Moreover , virtual monoenergetic imaging (VMI) applications and their potential use for the reduction of metal artifacts and improving image contrast are reviewed. The role of virtual non calcium (VNCa) in detecting bone marrow edema surrounding the imaged joint will be discussed. Furthermore , the role of iodine map to enhance the contrast between soft tissues, optimizing the visualization of contrast material, and to distinguish contrast material from calcifications is described. Finally, a case series including different joints is provided to underline the additional advantages of high spatial resolution dual energy CT reconstructed images.

Based on the principle of Pancharatnam-Berry phase, we propose an approach to construct dual-functional dielectric metasurface doublets. This type of doublet, which is composed of a substrate and two metasurface layers, performs two different functionalities at two different operating wavelengths. The simulated results of the examples show that the designed dual-functional doublet works well as expected. The proposed approach can be used to design dielectric meta-structures with more layers of meta-surface and thereby with more functionalities, and would be of interest for miniaturization and integration.

Forward-bias hole injection from 10 nm-thick p-type Nickel Oxide layers into 10 um-thick n-type Gallium Oxide in a vertical NiO/Ga2O3 p-n heterojunction leads to more than a factor of 2 enhancement of photoresponse measured from this junction. While it takes only 600 seconds to obtain such a pronounced increase in photoresponse, it persists for hours, indicating feasibility of photovoltaic device performance control. The effect is ascribed to charge injection-induced increase of minority carrier (hole) diffusion length (resulting in improved collection of photogenerated non-equilibrium carriers) in n-type beta-Ga2O3 epitaxial layers, due to trapping of injected charge (holes) on deep meta-stable levels in the material and subsequent blocking of non-equilibrium carrier recombination through these levels. Suppressed recombination leads to increased non-equilibrium carrier lifetime, in turn determining a longer diffusion length and being the root-cause for the effect of charge injection.

This study aims to assess the feasibility of delineating and identifying mineral ores from hyperspectral images of tin-tungsten mine excavation faces using machine-learning classification. We compiled a set of hand samples of minerals of interest from a tin-tungsten mine and analyzed two types of hyperspectral images: 1) images acquired with a laboratory set-up under close-to-optimal conditions; and 2) scan of a simulated mine face using a field set-up, under conditions closer to those in the gallery. We have analyzed the following minerals: cassiterite (tin ore), wolframite (tungsten ore), chalcopyrite, malachite, muscovite, and quartz. Classification (Linear Discriminant Analysis, Singular Vector Machines and Random Forest) of laboratory spectra had a very high overall accuracy rate (98%), slightly lower if the 450 – 950 nm and 950 – 1780 nm ranges are considered independently, and much lower (74.5%) for simulated conventional RGB imagery. Classification accuracy for the simulation was lower than in the laboratory but still high (85%), likely a consequence of the lower spatial resolution. All three classification methods performed similarly in this case, with Random Forest producing results of slightly higher accuracy. The user’s accuracy for wolframite was 85%, but cassiterite was often confused with wolframite (user’s accuracy: 70%). A lumped ore category achieved 94.9% user’s accuracy. Our study confirms the suitability of hyperspectral imaging to record the spatial distribution of ore mineralization in progressing tungsten-tin mine faces.

We review the state of dynamical downscaling with scale-constrained regional and global models. The methodology, in particular spectral nudging, has become a routine and well-researched tool for hindcasting climatologies of sub-synoptic atmospheric disturbances in coastal regions. At present, the spectrum of applications is expanding to other phenomena, but also to ocean dynamics and to extended forecasting. Also new diagnostic challenges are appearing such as spatial characteristics of small-scale phenomena such as Low Level Jets.

The superharmonic imaging of tissue has the potential for high spatial and contrast resolutions, compared to the fundamental and second harmonic imaging. For this technique, the spectral bandwidth of an ultrasound transducer is divided for transmission of ultrasound and reception of its superharmonics (i.e., higher than the second harmonic). Due to the spectral division for the transmission and reception, transmitted ultrasound energy is not sufficient to induce superharmonics in media without using contrast agents, and it is difficult that a transducer has a -6-dB fractional bandwidth of higher than 100%. For the superharmonic imaging of tissue, thus, multi-frequency array transducers are the best choice if available; transmit and receive elements are separate and have different center frequencies. However, the construction of a multi-frequency transducer for intravascular ultrasound (IVUS) imaging is particularly demanding because of its small size of less than 1 mm. Here, we report a recently developed dual-element focused IVUS transducer for the third harmonic imaging of tissue, which consists of a 35-MHz element for ultrasound transmission and a 105-MHz element for third harmonic reception. For high quality third harmonic imaging, both elements were fabricated to have the same focus at 2.5 mm. The results of tissue mimicking phantom tests demonstrated that the third harmonic images produced by the developed transducer had higher spatial resolution and deeper imaging depth than the fundamental images.

Previous research has shown that the effectiveness of selecting filter set from a large set of commercial broadband filters by vector analyzing method based on maximum linear independence (MLI). However, the traditional MLI is suboptimal due to predefining the first filter of the selected filter set being the maximum ℓ2 norm among all those of the filters. An exhaustive imaging simulation is conducted to investigate the features of the most competent filter set. In the simulation, every filter in a subset of all the filters is selected serving as the first filter in turn. From the results of the simulation, we found there are remarkable characteristics for the most competent filter set. Besides smaller condition number, the geometric features of the best-performed filter set comprise the distinct peak of the transmittance of the first filter, the generally uniform distributing of the peaks of the transmittance curve of the filters, the substantial overlapping of the transmittance curves with those of the adjacent filer sets. Therefore, the best-performed filter sets can be decided intuitively by simple vector analyzing and just a few experimental verifications. This work would be useful for optimizing spectral sensitivity of broadband multispectral imaging sensors or SFA sensors.

Three monoruthenium complexes 1(PF6)23(PF6)2 bearing a N(CH3)-bridged ligand have been synthesized and characterized. These complexes have a general formula of [Ru(bpy)2(L)](PF6)2, where L is a 2,5-di(N-methyl-N-(pyrid-2-yl)amino)pyrazine (dapz) derivative with various substituents and bpy is 2,2′-bipyridine. The photophyscial and electrochemical properties of these compounds have been examined. The solid-state structure of complex 3(PF6)2 is studied by single crystal X-ray analysis. These complexes show two well-separated emission bands centered at 451 and 646 nm (max = 195 nm) for 1(PF6)2, 465 and 627 nm (max = 162 nm) for 2(PF6)2, and 455 and 608 nm (max = 153 nm) for 3(PF6)2 in dilute acetonitrile solution, respectively. The emission maxima of the higher-energy emission bands of these complexes are similar, while the lower-energy emission bands are dependent on the electronic nature of substituents. These complexes display two consecutive redox couples owing to the stepwise oxidation of the N(CH3)-bridged ligand and ruthenium component. In addition, density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations are carried out to elucidate these experimental findings.

Digital technology makes energy systems more dynamic, efficient, reliable and sustainable. Promoting the digital governance of renewable energy is not only a key part of energy transformation, but also an important initiative to cope with the global energy crisis, which provides a new impetus for realizing the goal of net-zero emissions. In order to achieve the "dual-carbon" goal, China has been actively promoting energy reform and enhancing the wide application of digital technologies in the development and utilization of renewable energy. The digital governance system and governance capacity of renewable energy is an important support for the modernization of the national governance system and governance capacity. Although the digital governance of renewable energy in China has better policy support and advantages, the dilemmas it faces are still relatively prominent, including unsound laws and policies, insufficient governance capacity, and security loopholes in digital technology, etc. Therefore, this paper proposes a problem-oriented approach to the digital governance of renewable energy. Therefore, this paper puts forward problem-oriented optimization suggestions to innovate the legal system of renewable energy digital governance, promote the digital transformation of China's renewable energy industry, provide development space for renewable energy digital governance in a diversified mode, and enhance the digital governance capacity by improving the renewable energy security system.

For developmental processes we know most of the gene networks controlling specific cell responses. We still have to determine how these networks cooperate and how signals become integrated. The JNK pathway is one of the key elements modulating cellular responses during development. Yet, we still know little on how the core components of the pathway interact with additional regulators or how this network modulates cellular responses in the whole organism in homeostasis or during tissue morphogenesis. We have performed a promoter analysis searching for potential regulatory sequences of puc and identified different specific enhancers directing gene expression in different tissues and at different developmental times. Remarkably, some of these domains respond to the JNK activity, but not all. Altogether, these analyses show that puc expression regulation is very complex and that JNK activities participate in non-previously known processes during the development of Drosophila.

Recombinant laboratory plasmids (RLPs) are common in biological research and freely shared among academic research laboratories (ARLs), a practice required by many research funding agencies. However, the generation of accurate, reproducible results in experiments utilizing RLPs can be hampered by a lack of accompanying sequence information and metadata. This culture of RLP sharing without knowledge of sequence or etiology is accepted by publishers, not regulated by governments, and outside the realm of bio-industry. In addition, no centralized infrastructure currently exists to collate such data, which at the moment is fragmented across companies, non-profits, and governments and thus is not easily accessed or enacted toward threat assessment. The ubiquity, free exchange, and dual-use risk of RLPs exemplifies a biosecurity threat and elevates the need to characterize their composition to facilitate improved biorisk management by the academic community. A number of common sense solutions are available to create a culture that addresses the biosecurity gap posed by RLP sharing. Culture shift in RLP management will require new norms, effective data management for collation of RLP sequences and metadata, and an incentive structure that encourages sequencing by stakeholders. The next generation of researchers must initiate and champion this shift with support from funding agencies and endorsement from governments and international organizations. Coordination of efforts and stakeholders will require international public/private collaboration, a structure that will be critical to ensure widespread utility as well as the ability of lower-resourced partners to participate, contribute, and benefit.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

With the changing climate, there is an increasing emphasis on drought-resistant varieties including the ability to maintain quality production. As there is also interest in ancient wheat varieties, the aim of this study was to evaluate the ancient Khorasan (Kamut ®) with the modern Kabot spring wheat variety using remote sensing data. Images from unmanned aerial vehicles during four growing seasons were processed. Based on vegetation indices, the growth of these varieties and their response to meteorological conditions were evaluated, as well as the ability to resist drought and higher temperatures with respect to specific soil conditions under conventional (CT), minimum (MTC) and minimization (MTD) tillage. It was found that the Khorasan had the lowest values of the vegetation indices on the CT variant in the dry years 2022 and 2023. On the contrary, in the previous wet years 2020 and 2021, both varieties showed similar results. Regarding to water stress, the CT variant was also the least suitable for ancient Khorasan (average CWSI = 0.38). On the contrary, this variant seems to be suitable for the modern Kabot variety (CWSI = 0.29), while no significant difference between tillage variants was found for this variety. In general, water stress was easily detectable by the observed parameters in the growth phase of stem elongation (R2 up to 0.88).

Wastewater (WW) analysis is a critical step in various operations such as control of a WW treatment facility, and speeding-up the analysis of WW quality can significantly improve such operations. This work demonstrates the capability of neural network (NN) regression models to estimate WW characteristic properties such as biochemical oxygen demand (BOD), chemical oxygen demand (COD), ammonia (NH3-N), total dissolved substances (TDS), total alkalinity (TA), and total hardness (TH) by training on WW spectral reflectance in the visible to near-infrared spectrum (400nm-2000nm). The dataset contains samples of spectral reflectance intensity, which were the inputs, and the WW parameter levels (BOD, COD, NH3-N, TDS, TA, and TH), which were the outputs. Various NN model configurations were evaluated in terms of regression model fitness. The mean-absolute-error (MAE) was used as the metric for training and testing the NN models, and the coefficient of determination (R2) between the model predictions and true values was also computed to measure how well the NN models predict the true values. With online spectral measurements, the trained neural network model can provide non-contact and real-time estimation of WW quality at minimum estimation error.

In this study, we define the concept of pseudo-prime subhypermodules of hypermodules as a generalization of the prime hyperideal of commutative hyperrings in [17]. Firstly we examine the spectrum of the Zariski topology, which we built on the element of the pseudo-prime subhypermodules of a class of hyper-modules, then we give some relevant properties of the hypermodule in this topological hyperspace.

We present the results of direct numerical simulations of power spectral densities for kinetic energy, convective entropy and heat flux for unsteady Rayleigh-Bénard magnetoconvection in the frequency space. For larger values of frequency, the power spectral densities for all the global quantities vary with frequency f as f⁻². The scaling exponent is independent of Rayleigh number, Chandrasekhar’s number and thermal Prandtl number.

ATLID (ATmospheric LIDar) is the atmospheric backscatter LIDAR (Light Detection and Ranging) on board of the EarthCARE (Earth Cloud, Aerosol and Radiation Explorer) mission, the sixth Earth Explorer Mission of the ESA (European Space Agency) Living Planet Programme [1-5]. ATLID’s purpose is to provide vertical profiles of optically thin cloud and aerosol layers, as well as the altitude of cloud boundaries [6-10]. In order to achieve this objective ATLID emits short duration laser pulses in the UV, at a repetition rate of 51 Hz, while pointing in a near nadir direction along track of the satellite trajectory. The atmospheric backscatter signal is then collected by its 620 mm aperture telescope, filtered through the optics of the instrument focal plane assembly, in order to separate and measure the atmospheric Mie and Rayleigh scattering signals. With the completion of the full instrument assembly in 2019, ATLID has been subjected to an ambient performance test campaign, followed by a successful environmental qualification test campaign, including performance calibration and characterization in thermal vacuum conditions. In this paper the design and operational principle of ATLID is recalled and the major performance test results are presented, addressing the main key receiver and emitter characteristics. Finally, the estimated instrument, in-orbit, flight predictions are presented; these indicate compliance of the ALTID instrument performance against its specification and that it will meet its mission science objectives for the EarthCARE mission, to be launched in 2023.

A long standing problem in paleontology is the presence of giant arthropods during the so-called carboniferous era. The same difficulty occurs for the size of plants at the same period. We devise a possible answer to this question entirely different from the theories developed until now, based on the hypothesis that the size of atoms is decaying for very large times and the dimensions of polymers such as chitin and cellulose, containing expansible holes, are decreasing more slowly than rigid materials such as metals, alloys, composite materials and stone. Incidentally, this hypothesis might also give an alternative statement to the expansion of space.

The emission of mercury (II) from coal combustion and other industrial processes continues to be a concern and have local impact on water resources. The detection of these ions in water with sensitive but rapid testing methods is desirable for environmental screening and fieldwork. Nanoparticles of various chemistries have shown promise for this purpose, as they can be used in simple colorimetric analyses. Silver nanoprisms were chemically synthesized resulting in a blue reagent solution, that transitioned towards yellow and colorless solutions when exposed to Hg²⁺ ions at various concentrations. A rapid galvanic reduction of Hg²⁺ onto the nanoprism surfaces is apparently responsible for a change in shape towards spherical nanoparticles, leading to the change in color. There were no interferences by other metal ions in solution, and pH had minimal effect in the range of 6.5 to 9.8. The silver nanoprism reagent provided a detection limit of approximately 0.5 µM (100 µg/L) for mercury (II), which compares favorably with other nanoparticle-based techniques. Further optimization may reduce this detection limit.

The assessment of biomass carbon stocks was conducted at plot scale as a sample to estimate for all vegetation areas by using destructive sampling and or allometric equation method. Remote sensing is one of the techniques can be used to estimate and mapping biomass carbon stock for the entire areas. The objectives of the study are the identification and determine the range of electromagnetic wave of Landsat 8 satellite data that most suitable for assessing and mapping biomass carbon stock distribution. This research analyses exponential regression equation between spectral radiance values (Lλi) for with biomass measurement results on the field to find the best correlation based on the coefficient of determination value (R2). It also analyses the relationship between field biomass and NDVI value (Normal Differences Vegetation Index) from satellite data. The study area consists of 54.9% bush (Bs), 24.5% scrub (Sc), 16.8% secondary forest (Sf), while the rest (3.8%) is a water body. The with average biomass carbon stock value 4.11 tons.ha-1, 64.43 tons.ha-1, and 85.36 tons.ha-1, for strata Sc, Bs, and Sf respectively. Spectral radiance of SWIR (Shortwave Infra-Red) band 6 is determined as a spectral characteristic that can be used to estimating carbon stock with following the equation Y= 12657(EXP(-0.642(Lλband6)) with r2 = 0.75. Correlation NDVI and field biomass showed the low r2 value (0.08), so in this study, NDVI cannot be used to estimate the biomass carbon stock.

With the rapid advancements in remote sensing technology, the spectral information from hyperspectral remote sensing images has become increasingly rich, facilitating detailed spectral analysis of the Earth's surface objects. However, this abundance of spectral data poses significant challenges in data processing, such as the curse of dimensionality leading to the “Hughes” phenomenon, "strong correlation" due to high resolution, and "non-linear characteristics" caused by varied surface reflectance rates. Therefore, dimensionality reduction of hyperspectral data has become a crucial task. This paper, grounded in manifold theory and manifold learning techniques, and considering the non-linear structures and features in hyperspectral remote sensing data, elucidates the principles and processes of dimensionality reduction in hyperspectral remote sensing images using manifold learning, with a formalized expression of the process. This article introduces spectral information divergence (SID) into the nearest neighbor graph computation of manifold learning algorithms. The principles and computational processes of nearest neighbor graph algorithms based on Euclidean distance (ED), spectral angle mapping (SAM), and SID are studied, and a comparative analysis of the dimensionality reduction effects under these three metrics in hyperspectral data is conducted. Experiments on feature extraction under different metrics were performed using the publicly available Indian Pines hyperspectral dataset. The intrinsic features obtained post-dimensionality reduction were used as inputs for classification algorithms in ground objects classification experiments, with algorithm runtime, overall accuracy, and Kappa coefficient as evaluation metrics for dimensionality reduction quality. The results demonstrate that nearest neighbor graph computation based on SAM and SID outperforms traditional ED methods; SAM-based computation has the lowest time complexity, while SID-based manifold learning yields the highest accuracy in ground objects classification. Thus, manifold learning based on SAM and SID metrics proves to be an effective method for feature extraction in hyperspectral remote sensing data, underscoring the potential of manifold learning techniques in the dimensionality reduction of hyperspectral remote sensing images.

The p53 tumor suppressor protein is an activator of transcription. Diverse stress factors lead to various sets of posttranslational modifications of p53 what results in different sets of upregulat-ed genes. We noticed that actinomycin D and nutlin-3a (A+N) synergize in inducing activating phosphorylations of p53 and upregulation of selected p53-target genes. Here we found that one of these genes is DUSP13, which codes for poorly-studied, dual-specificity phosphatase having at least two isoforms, one expressed in testis and the other in skeletal muscles. In cancer cells ex-posed to A+N, DUSP13 is expressed from an alternative promoter in intron, what results in ex-pression of isoform named TMDP-L1. The luciferase reporter tests demonstrated that this pro-moter is activated by both endogenous and ectopically expressed p53. We showed for the first time that mRNA expressed from this promoter actually produces the protein, which can be de-tected by Western blotting in all examined cancer cell lines with wild-type p53 exposed to A+N. In some cell lines it is also induced by clinically relevant camptothecin or by nutlin-3a acting alone. This isoform, fused with green fluorescent protein localizes in perinuclear region of cells. Thus, TMDP-L1 isoform may be an important element of p53-regulated stress response system.

Clinical screening tests for balance and mobility often fall short of predicting fall risk. Cognitive distractors and unpredictable external stimuli, common in busy natural environments, contrib-ute to this risk, especially in the elderly. Less is known about the effects of upper sensory-motor coordination such as coordinating one’s hand with an external stimulus. We combined move-ment sonification and affordable inertial motion sensors to develop a task for the precise meas-urement and manipulation of full-body interaction with stimuli in the environment. In a double-task design, we studied how a supra-postural activity affected quiet stance. The supra-postural task consisted of rhythmic synchronization with a repetitive auditory stimulus. The stimulus was attentionally demanding because it was being modulated continuously. The participant’s hand movement was sonified in real-time, and their goal was to synchronize their hand move-ment with the stimulus. In the unpredictable condition, the tempo changed at random points in the trial. A separate sensor recorded postural fluctuations. Young healthy adults were compared to elderly participants without known risk of falling. Results supported the hypothesis that su-pra-postural coordination would entrain postural control. The effect was stronger in the elderly, supporting the idea that diminished reserve capacities reduce the ability to isolate postural con-trol from sensory-motor and cognitive activity.

The limitations of conventional sensors have made phased array antennas increasingly crucial for gathering information and communication applications in intelligent transportation systems. Compact cylindrical arrays, in particular, are favored for their ability to achieve azimuth angle scan. However, the substantial mutual coupling effect between elements on curved surfaces and its implication in these arrays remains unclear, which is a crucial factor to consider when such arrays are used for multibeam applications. This study investigated the effect of mutual coupling in a dual-slant-polarized cylindrical array. The results showed that mutual coupling is essential for achieving an ultra-wide bandwidth. Mutual coupling is predominantly observed among elements that are closely positioned. The study also analyzes the impact of mutual coupling on scan impedance and radiation characteristics for multibeam applications and reveals that these arrays exhibit robust multibeam capability, hence having great potential for use in intelligent transportation systems.

Although hundreds of keV in energy gain have already been demonstrated in dielectric laser accelerators (DLAs), there remains challenge in creating structures that can confine electrons for multiple millimeters. We focus here on dual gratings with single sided drive, which have experimentally demonstrated energy modulation numerous times. Using a FTDT simulation to find the fields within various DLA structures and correlating these results with particle tracking simulation, we look at the impact of teeth height and width, as well as gap and offset, on the performance of these structures. We find a tradeoff between electron throughput and acceleration, but that for any given grating geometry there is a gap and offset that will allow some charge acceleration. For our 780 nm laser wavelength, this results in a 1200 nm optimal gap size for most gratings.

Objective: To analyze the clinical efficacy and safety of monotherapy and dual therapy in patients with early ischemic stroke. Methods: The study was conducted from August 2020 to August 2021, and 80 patients with early ischemic stroke who received treatment at our hospital during this period were selected as the study population. They were randomly divided into a control group and an observation group, with 40 patients in each group. The control group received conventional monotherapy, while the observation group received conventional dual therapy. The efficacy and safety indicators of the two groups were compared.Results: The treatment efficacy, functional recovery, blood indicators, and inflammation markers in the observation group were all superior to those in the control group (all, P<0.05); there was no significant difference in the drug safety profile between the two groups (P>0.05).Conclusion: The dual antiplatelet therapy is more ideal than monotherapy for treating patients with early ischemic stroke. It can further promote the recovery of neurological and cognitive function, improve hematological and inflammatory response indicators in patients, and the safety of this regimen is comparable to monotherapy.

To maintain a healthy lifestyle, adults rely on their ability to walk while simultaneously managing multiple tasks that challenge their coordination. This study investigates the impact of cognitive dual-task on lower limb muscle activities in 21 healthy young adults during both gait initiation and steady-state gait. We utilized wireless electromyography sensors to measure muscle activities, along with a 3D motion capture system and force plates to detect the phases of gait initiation and steady-state gait. Participants were asked to walk at their self-selected pace, and we compared single-task and dual-task conditions. We analyzed mean muscle activation and co-contraction in the biceps femoris, vastus lateralis, gastrocnemius, and tibialis anterior muscles. The findings revealed that during gait initiation with the dual-task condition, there was a decrease in mean muscle activation and an increase in mean muscle c-contraction between the swing and stance limb compared to the single-task condition. In steady-state gait, there was also a decrease in mean muscle activation in the dual-task condition compared to the single-task condition. When participants performed dual-task activities during gait initiation, early indicators of reduced balance capability were observed. Additionally, during dual-task steady-state gait, knee stabilizer muscles exhibited signs of altered activation, contributing to balance instability.

Vehicle re-identification research under surveillance cameras has yielded impressive results. However, the challenge of Unmanned Aerial Vehicle (UAV)-based vehicle re-identification (ReID) presents a high degree of flexibility, mainly due to complicated shooting angles, occlusions, low discrimination of top-down features, and significant changes in vehicle scales. To address this, we propose a novel Dual Mixing Attention Network (DMANet) to extract discriminative features robust to variations in viewpoint. Specifically, we first present a plug-and-play Dual Mixing Attention Module (DMAM) to capture pixel-level pairwise relationships and channel dependencies, where DMAM is composed of Spatial Mixing Attention (SMA) and Channel Mixing Attention (CMA). First, the original feature is divided according to the dimensions of spatial and channel to obtain multiple subspaces. Then, a learnable weight is applied to capture the dependencies between local features in the mixture space. Finally, the features extracted from all subspaces are aggregated to promote their comprehensive feature interaction. Moreover, the DMAM can be readily inserted into backbone networks at any depth to improve vehicle discrimination. The experiments show that the proposed structure gains a better performance compared with the representative methods in the UAV-based vehicle ReID task. Our code and models will be publicly released.

Background: Patients undergoing dual antiplatelet therapy (DAPT) may experience recurrent gastrointestinal bleeding (GIB). We investigated the clinical characteristics and risk factors for recurrent non-variceal upper gastrointestinal bleeding (NVUGIB) in patients who had experienced NVUGIB during receiving DAPT. Methods: We enrolled patients diagnosed with NVUGIB during receiving DAPT between 2006 and 2020. Definite bleeding was confirmed by esophagogastroduodenoscopy in all NVUGIB patients. Results: A total of 124 patients were diagnosed with NVUGIB during receiving DAPT. Male was predominant (n = 103, 83.1%), bleeding mostly from the stomach (n = 94, 75.8%) and peptic ulcers (n = 72, 58.1%). After successful hemostasis of NVUGIB, 36 patients (29.0%) experienced at least one episode of recurrent upper GIB, 19 patients (15.3%) died, and 7 (5.6%) patients had a bleeding-related death. Multivariate analysis showed that age was a significant factor for re-bleeding (odds ratio [OR],1.050; 95% confidence interval [CI]:1.001-1.102, P-value:0.047), all-cause mortality (OR,1.096; 95% CI:1.020-1.178, P=0.013), and re-bleeding related mortality (OR,1.187; 95% CI:1.032-1.364, P-value:0.016). In Kaplan-Meier analysis, the cumulative probabilities of re-bleeding, death, and bleeding-related death were significantly higher in patients aged 70 and older (P=0.008, &lt;0.001, and 0.009, respectively). Conclusion: Clinicians should be cautious about re-bleeding and mortality when elderly patients who experienced NVUGIB during receiving DAPT.Keywords: dual antiplatelet therapy; upper gastrointestinal tract; bleeding; mortality

Percutaneous coronary intervention(PCI) with stenting for the treatment of acute coronary syndrome(ACS) is the contemporary standard of care. Such treatment is followed by Dual anti-platelet therapy(DAPT) comprising of aspirin and a P2Y12 inhibitor. The efficacy of this therapy has been well established but the optimal duration of DAPT remains elusive, and has thus far attracted a prodigious deal of scientific attention. Decision regarding DAPT duration can be challenging clinically in the modern era with the evolution of newer stents, more potent antiplatelet agents and novel anticoagulant drugs in addition to an older patient population with multiple comorbidities. Major societal guidelines have emphasized comprehensive assessment of ischemic and bleeding risk, in turn recommending individualization of DAPT duration, thus encouraging "shared decision making". The following review is aimed at critically evaluating the available evidence to help make these crucial clinical decisions regarding duration of DAPT and triple therapy.

The paper highlights the performance of the seismic isolation devices, installed on retrofitted buildings in reducing the seismic response when subjected to earthquakes. Two buildings from the beginning of the XX-th century in Bucharest are chosen from many more, monitored over the city area. We discuss the response of these base-seismic isolated structures, relying on good quality data acquired from the recent strong earthquake (03.11.2022, MW = 5.0). Elastic response spectra computed from recordings at two levels of each structure are used, placed under and right above the isolating layer. At one building the existence of previous recordings and the particularity of the sensors allow a comparison with other two relatively recent medium intensity earthquakes. The assessment is carried out in terms of maximum acceleration amax, measured at certain levels in each structure, spectral acceleration amplitude SAmax and spectral-peak corresponding period. We find that the base-isolation methodology is effective in reducing the response of the building right above the isolating layer, an observation valid for both structures, all components of the recordings, and spectral acceleration values. Moreover, the outcomes from the modal evaluation performed prior to rehabilitation and seismic isolation process are presented, by pointing out a higher newly acquired fundamental period of the isolated structures.

The spectrums of one type of object under different conditions have the same features (up, down, protruding, concave) at the same spectral positions, which can be used as primary parameters to evaluate the difference among remotely sensed pixels. Wavelet-feature correlation ratio Markov clustering algorithm (WFCRMCA) for the remotely sensed data is proposed based on an accurate description of abrupt spectral features and an optimized Markov clustering in the wavelet feather space. The peak points can be captured and identified by applying a wavelet transform to spectral data. The correlation ratio between two samples is a statistical calculation of the matched peak point positions on the wavelet feature within an adjustable spectrum domain or a range of wavelet scales. The evenly sampled data can be used to create class centers, depending on the correlation ratio threshold at each Markov step, accelerating the clustering speed by avoiding the computation of Euclidean distance for traditional clustering algorithms, such as K-means and ISODATA. Markov clustering applies several strategies, such as a simulated annealing method and gradually shrinking the clustering size, to control the clustering convergence. At each clustering tempera-ture, it can obtain the best class centers quickly. The experimental results of the Airborne Visible/ Infrared Imaging Spectrometer (AVIRIS) and Thermal Mapping (TM) data have verified its ac-ceptable clustering accuracy and high convergence velocity.

The paper deals with applying Artificial Intelligence techniques to examine CHIRP-recorded data in sand and sandstone sea-bottom sites. The provided analysis of the state of the art portrays that actual time series or spectrum backscattered data from a point on the sea bottom were rarely used as the features for machine learning models. The results of the examination indicate that types of sea bottom can be quantitatively characterized by applying logistic regression models to either the backscatter time series of a frequency-modulated signal or the spectrum of that backscatter. The examination accuracy reached 90% for the time series and 94% for the spectra. The application of spectral data as features for more advanced machine learning algorithms, and the advantages of its combination with other types of data have great potential for future research and the enhancement of remote marine soil classification.

In the context of the Atmospheric Observing System (AOS) international program, a new generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a Nature Run (NR) modeled by the MOCAGE Chemical Transport Model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel for measuring aerosol backscatter profiles with respect to simple backscatter measurements is shown. Observations independent from an a-priori lidar Ratio assumption, as done typically for simple backscattering instruments, allows probing the vertical structure of aerosol layers without divergence, even in case of intense episodes. Relative error in the backscatter coefficient profiles are observed to lay between +40% and -40% for low abudancies, with mean biases between +5% and -5%. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%.

We develop the theory of Heyting small spaces and prove a new version of Esakia duality for such spaces. To do this, we notice that spectral spaces may be seen as sober small spaces with all smops compact and introduce the method of the standard spectralification. This helps to understand open continuous definable mappings between definable spaces over o-minimal structures.

Extensive growth in the volume of irregular brain cells is known as brain tumor. Human brain is surrounded by stiff skull. There are various issues that occur due to the growth of any tumor inside this restricted space. The malignant and benign are two main categories of the brain tumor. The skull is pressurized to enlarge from inside in case of growth of any benign or malignant tumor. This tumor leads to harm in brain and it may be dangerous to life also. The brain tumor is divided into two kinds - primary or secondary. The brain tumor detection techniques have various phases. In this paper, comparative study of CNN with GLCM approach and superpixel based spectral clustering is done tumor. This work takes into account metrics like accuracy, sensitivity and specificity for drawing the comparison between both the techniques.

Bistable rhodopsins have two stable forms which can be interconverted by light. Due to their ability to act as photoswitches, these proteins are considered as ideal candidates for applica-tions such as optogenetics. In this work we analyze a recently crystalized bistable rhodopsin, namely the jumping spider rhodopsin-1 (JSR1). This rhodopsin exhibits identical absorption maxima for the parent and the photoproduct form, which impedes its broad application. We have performed hybrid QM/MM simulations to study three isomers of the retinal chromo-phore: the 9-cis, 11-cis and all-trans configurations. The main aim was to gain insight into the specific interactions of each isomer and their impact on the absorption maximum in JSR1. The absorption spectra were computed using sampled snapshots from a QM/MM molecular dy-namics trajectory and compared to experimental counterparts. The chromophore-protein in-teractions were analyzed by visualizing the electrostatic potential of the protein and projecting it onto chromophore. It was found that the distance between a nearby tyrosine (Y126) residue plays a larger role in the predicted absorption maximum than the primary counterion (E194). Geometric differences between the isomers were also noted including a structural change in the polyene chain of the chromophore as well as changes in the nearby hydrogen bond network.

In cotton cultivars, an insect that causes irreversible damage is the Spodoptera frugiperda, known as the fall armyworm. Since the visual detection of plants is a burdensome task for human inspection, the spectral information related to plant damage, registered on a spectral scale, can be useful. These measurements, associated with machine learning techniques, produce useful information for a rapid and non-invasive inspection method development. To contribute to this gap fulfillment, this paper proposes a machine learning framework to model the spectral response of cotton plants under the attack of the fall armyworm. Additionally, a theoretical model is presented, built from the results of the machine learning analysis, to infer this damage with up-to-date orbital sensors. The data was composed of the reflectance measurements collected at a cotton field with control plants and plants submitted to Spodoptera frugiperda damage. Their spectral response was recorded with a hand-held spectroradiometer ranging from 350 to 2,500 nm, for eight consecutive days. Different machine learning models were evaluated and the overall best model was defined by accuracies comparisons on a testing-set. A ranking approach was adopted based on the model accuracy, returning the most contributive wavelengths for the classification. Sequentially, an unsupervised neural network (Self-Organizing Map - SOM) was implemented to reduce data-dimensionality and assist in the definition of important spectral regions. The regions were associated with the spectral bands of the two sensors (OLI and MSI) and a theoretical model using a band simulation process with the overall best machine learning model was proposed. The results indicated that the Random Forest (RF) algorithm is the most suitable to predict cotton-plants damaged by insects and that the last day of analysis (8th day) was better to separate it, with F-measure equals 0.912. The ranking approach combined with the SOM method indicated the spectral regions at the red to near-infrared (650 to 1,350 nm) and shortwave infrared (1,570 to 1,640 nm) as the most important regions to the analysis. The proposed theoretical model simulated with the OLI and MSI sensor-bands returned an F-Measure of 0.865 and 0.886, respectively. In conclusion, this framework can be used to map cotton-plants under insect-attack. The theoretical model presents high accuracy to infer the insect-damaged on cotton plants based on multispectral bands from other sensors, being a useful tool for future research that intends to evaluate it in other areas and at different field scales.

With the dramatic increase in world population, continued advances in modern greenhouse agriculture and plant growth practices are expected to help overcome the global problem of future food shortages. Next generation greenhouse design practices will need to address a range of issues, ranging from energy and land use efficiency to providing plant-optimised growth techniques. In this paper, we focus on investigating the optimum irradiation spectra matched to the lettuce species (Lactuca sativa, L.), which is commonly grown in greenhouse environments, in order to develop low-emissivity glass panes that maximize the biomass productivity of glass greenhouses. This low-emissivity glass passes the solar spectral components needed for crop growth, while rejecting other unwanted radiations, leading to significant energy savings and other beneficial effects related to greenhouse climate control, in a range of climates. This is due to reducing both the solar heat gain and photosaturation, which can raise the temperature of the crops to harmful levels. Experimental results show that substantial biomass productivity improvements in lettuce (up to ~14.7%) can be attained using spectrally optimized illumination, compared with white light irradiation. We also report on the development of advanced metal-dielectric thin-film filters that produce the optimum illumination spectrum when exposed to sunlight.

We calculate the spectral function of a boson ladder in an artificial magnetic field by means of analytic approaches based on bosonization and Bogoliubov theory. We discuss the evolution of the spectral function at increasing effective magnetic flux, from the Meissner to the Vortex phase, focussing on the effects of incommensurations in momentum space. At low flux, in the Meissner phase, the spectral function displays both a gapless branch and a gapped one, while at higher flux, in the Vortex phase, the spectral function displays two gapless branches and the spectral weight is shifted at a wavevector associated to the underlying vortex spatial structure which can indicate a 8 supersolid-like behavior. While the Bogoliubov theory, valid at weak interactions, predicts sharp delta-like features in the spectral function, at stronger interactions we find power-law broadening of 10 the spectral functions due to quantum fluctuations as well as additional spectral weight at higher 11 momenta due to backscattering and incommensuration effects. These features could be accessed in 12 ultracold atom experiments using radio-frequency spectroscopy techniques.

In this study, we investigate spectral structure of conformable Sturm-Liouville problems and with this end, we obtain representation of solutions under different initial conditions and asymptotic formulas for eigenfunctions, eigenvalues, norming constants and normalized eigenfunctions. Consequently, we prove the existence of infinitely many eigenvalues. Also, we compare the solutions with graphics with different orders, different eigenvalues, different potentials and so, we observe the behaviors of eigenfunctions. We give an application to the α-orthogonality of eigenfunctions and reality of eigenvalues for conformable Sturm-Liouville problems defined by [15] in the last section.

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.

This algorithm is designed to perform Discrete Fourier Transforms (DFT) to convert temporal data into spectral data. This algorithm obtains the Fourier Transforms by studying the Coefficient of Determination of a series of artificial sinusoidal functions with the temporal data, and normalizing the variance data into a high-resolution spectral representation of the time-domain data with a finite sampling rate. What is especially beneficial about this DFT algorithm is that it can produce spectral data at any user-defined resolution.

In the present study, field observation wind data from the time of the wind turbine blade damage accident on Shiratakiyama Wind Farm were analyzed in detail. In parallel, high-resolution LES turbulence simulations were performed in order to examine the model’s ability to numerically reproduce terrain-induced turbulence. The comparison of the observed and simulated time series (1 second average values) from a 10 minute period from the time of the accident led to the conclusion that the settings of the horizontal grid resolution and time increment are important to numerically reproduce the terrain-induced turbulence that caused the wind turbine blade damage accident on Shiratakiyama Wind Farm. A spectral analysis of the same set of observed and simulated data revealed that the simulated data reproduced the energy cascade of the actual terrain-induced turbulence well.

We report a novel dual-band barrier infrared detector (DBIRD) design using type-II superlattices (T2SLs). The DBIRD structure consists of back-to-back barrier diodes: a "blue channel" (BC) diode which has an nBp architecture, an n-type layer of larger bandgap for absorbing blue band infrared/barrier/p-type layer, and a "red channel" (RC) diode which has an pBn architecture, a p-type layer of smaller bandgap for absorbing red band infrared/barrier/n-type layer. Each has a unipolar barrier using a T2SL lattice matched to a GaSb substrate to impede the flow of majority carriers from the absorbing layer. Each channel in the DBIRD can be independently accessed with a low bias voltage, as is preferable for high-speed thermal imaging. Device modeling of DBIRDs and simulation results of the current-voltage characteristics under dark and illuminated conditions are also presented. They predict that dual-band operation of the DBIRD will produce low dark currents and 27-33% quantum efficiencies for the in-band photons in the BC with λc=5.58 μm, and a nearly-constant 33% in the RC with λc=8.05 μm. The spectral quantum efficiency of the BC for 500 K blackbody radiation is approximately 20% over the range of λ=3 − 4.2 μm, while that of the RC has a peak of 42% at 5.7 μm. The DBIRD may provide improved high-speed dual-band imaging in comparison with NBn dual-band detectors.

Background/Objectives: Population aging is rapidly increasing, and the importance of preventive medicine has been stressed. Health checkup, diet, and exercise are of paramount importance. This study aimed to evaluate the effectiveness of a personalized dual-task intervention that combined exercise with cognitive tasks in improving physical and cognitive functions among independently living elderly individuals. Methods: Participants were aged &gt;65 years who were mostly independent in their activities of daily living, divided into 2 groups. The group receiving the 20-min robot-assisted session was compared to the group receiving traditional functional restoration training. This randomized trial assessed the impact of this intervention on the 30-s chair stand test score and Montreal Cognitive Assessment-Japanese version score of the participants. Results: Both scores significantly improved in the intervention group, indicating enhanced lower-limb function and cognitive capabilities. Conclusions: These findings suggest that integrating cognitive tasks with physical exercise can be a potent strategy to improve overall well-being in the elderly, offering valuable insights for designing comprehensive preventive health programs tailored to this demographic.

In this paper, we establish the necessary and sufficient conditions for solving a dual split quaternion matrix equation AXB=C, and present the general solution expression when solvability is achieved. As an application, we delve into the necessary and sufficient condition for the existence of Hermitian solution to this equation by using a newly defined real representation method. Furthermore, we obtain the solutions for the dual split quaternion matrix equations AX=C and XB=C. Finally, we provide a numerical example to demonstrate the findings of this paper.

Hydrogen is considered as an ideal substitute to the traditional fossil fuels because of its widespread sources, high calorific value of combustion, and zero carbon emissions. The electrocatalytic water-splitting to produce hydrogen is also deemed to be the best approach, however, it is a challenge to make high-efficient and low-cost electrocatalysts. Single-atom catalysts (SACs) are considered the most promising candidate to replace the traditional noble-metal catalysts. Compared with SACs, the dual-atom catalysts (DACs) are of greater attraction including higher metal loading, more versatile active sites, and excellent catalytic activity. In the review, several general synthetic strategies and structural characterization methods of DACs are introduced, and the mechanisms of hydrogen evolution reaction (HER) and oxygen evolution reactions (OER) are discussed. The authors hope that the review has given some insights and inspiration to the researchers about DACs in electrocatalytic water-splitting.

Endoscopic medical images can suffer from uneven illumination, low contrast, and lack of texture information due to the use of point directional light sources and the presence of narrow tissue structures, posing diagnostic difficulties for physicians. In this paper, a deep learning-based su-pervised illumination enhancement network is designed for low-light endoscopic images, aiming to improve both global illumination and local details. Initially, a global illumination enhancement module is formulated utilizing a higher-order curve function to improve global illumination. Sec-ondly, a local feature extraction module incorporating dual attention is designed to capture local detailed features. Considering the significance of color fidelity in biomedical scenarios, the designed loss function prioritizes introducing color loss to alleviate image color distortion. Compared with seven state-of-the-art enhancement algorithms on Endo4IE endoscopic datasets, experimental re-sults show that the proposed method can better enhance low-light endoscopic images and avoid image color distortion. It provides an efficient method to enhance images captured by endoscopes which can effectively assist clinical diagnosis and treatment.

The groundbreaking Advanced Forest-Based Tagging Framework (AFTF) represents a paradigm shift in the domain of information extraction from medical texts. AFTF introduces a novel dual-binary tree structure that redefines how entity-relation triples are extracted. This innovative approach directly tackles the limitations of traditional linear and graph-based methods, effectively addressing challenges related to overlapping triples and computational efficiency. The AFTF model stands as a beacon of excellence, surpassing established baselines by significant margins in comprehensive evaluations on two pivotal medical datasets. Notably, AFTF achieves remarkable improvements in F1 scores, showcasing its prowess in accurate information extraction from complex medical narratives. Beyond its exceptional performance in the medical domain, AFTF exhibits remarkable versatility. This adaptability is vividly demonstrated through its robust performance across three diverse public datasets, further affirming its position as a versatile and reliable solution for information extraction tasks. This paper provides a comprehensive exposition of the AFTF architecture, shedding light on its innovative design principles and its efficient handling of intricate medical texts. AFTF represents a groundbreaking step forward in the realm of information extraction, promising enhanced accuracy, efficiency, and adaptability for a wide range of applications.

Micro-gyroscopes based on the Coriolis principle are widely employed in inertial navigation, motion control, and vibration analysis applications. This paper presents our main contributions which include a novel dual-frame optomechanical gyroscope, a unique photonic crystal cavity design, and advanced numerical simulation and optimization methods. The proposed design utilizes an optical cavity formed between dual oscillating frames, whereby input rotation induces a measurable phase shift via optomechanical coupling. Actuation of the frames is achieved electrostatically via an interdigitated comb-drive design. Through theoretical modeling based on cavity optomechanics and finite element simulation, the operating principle and performance parameters are evaluated in detail. Results indicate an expected angular rate sensitivity of 22.8 mV/°/s and angle random walk of 7.1×10-5 °/h1/2, representing superior precision than to existing micro-electromechanical systems gyroscopes of comparable scale. Detailed analysis of the optomechanical transduction mechanism suggests this dual-frame approach could enable angular vibration detection with resolution exceeding state-of-the-art solutions.

Bovine lactoferrin (bLf) is a milk-derived protein that exhibits multiple biological activities and has been explored towards different therapeutic applications. Since this molecule is susceptible to degradation and some of its properties depend on its tertiary structure, the encapsulation of bLf in stimuli-responsive therapeutic formulations provides an added value to potentiate its biological activities when administered. Plasmonic magnetoliposomes emerge as promising nanosystems for dual hyperthermia (magneto-photothermia) and local therapy, since the combination of magnetic and gold nanoparticles (NPs) in a single nanosystem (multifunctional liposomes) enables the targeting and controlled release of encapsulated drugs. In this work, plasmonic magnetoliposomes (PMLs) containing manganese ferrite nanoparticles (28 nm size) and gold nanoparticles (~ 5 nm size) functionalized with 11-mercaptoundecanoic acid or octadecanethiol, were synthesized and loaded with bLf. The structural, magnetic and optical properties of the nanoparticles were measured by TEM, SQUID and UV/vis/NIR absorption spectroscopy. Specific Absorption Rate was determined to assess the capabilities for magnetic and photothermal hyperthermia. Finally, Saccharomyces cerevisiae was used as an eukaryotic cellular model to assess the biological activity and the mechanism of entry of bLf-loaded PMLs, through counting of colony forming units and fluorescence microscopy, respectively. The results demonstrate that PMLs are mainly internalized through an energy- and temperature-dependent endocytic process, though the contribution of a diffusion component cannot be discarded. Most notably, only bLf-loaded PMLs exhibit cytotoxicity with an efficiency similar to free bLf, attesting their promising potential for bLf delivery in the context of therapeutic interventions.

Fractals offer the ability to generate fascinating geometric shapes with all sorts of unique characteristics (for instance, fractal geometry provides a basis for modelling infinite detail found in nature). While fractals are non-euclidean mathematical objects which possess an assortment of properties (e.g., attractivity and symmetry), they are also able to be scaled down, rotated, skewed and replicated in embedded contexts. Hence, many different types of fractals have come into limelight since their origin discovery. One particularly popular method for generating fractal geometry is using Julia sets. Julia sets provide a straightforward and innovative method for generating fractal geometry using an iterative computational modelling algorithm. In this paper, we present a method that combines Julia sets with dual-quaternion algebra. Dual-quaternions are an alluring principal with a whole range interesting mathematical possibilities. Extending fractal Julia sets to encompass dual-quaternions algebra provides us with a novel visualize solution. We explain the method of fractals using the dual-quaternions in combination with Julia sets. Our prototype implementation demonstrate an efficient methods for rendering fractal geometry using dual-quaternion Julia sets based upon an uncomplicated ray tracing algorithm. We show a number of different experimental isosurface examples to demonstrate the viability of our approach.

Despite the popularity of Zumba in several countries, research is scarce about its impact on older adults. Meanwhile, the integration of cognitive tasks with physical exercises, known as dual-tasking, is an evolving strategy to facilitate activities for older people. This study investigated the perceptions of older adults regarding Zumba and the potential of implementing it in a dual-task program. We conducted a qualitative-descriptive research involving 44 Filipino older adults from August to November 2020. Content analysis was employed to analyze the data. Four themes were identified: moving towards match or mismatch; balancing benefits with burdens; dual-tasking as innovative yet potentially challenging; and overcoming barriers with enablers. While Zumba is an inclusive and beneficial activity, individual and contextual limitations could hinder its suitability for older people. Dual-tasking in Zumba was also recognized as an innovative approach, although challenges should be addressed to promote its utility. Several strategies could support the design of these programs in communities. This is the first study to explore older adults’ perceptions towards Zumba and its potential utilization as a dual-tasking program. Findings could guide the implementation of appropriate Zumba and dual-tasking activities that seek to integrate cognitive and physical training for older adults.

Current therapeutic protocols for the treatment of HIV infection consist of the combination of diverse anti-retroviral drugs in order to reduce the selection of resistant mutants and to allow for the use of lower doses of each single agent to reduce toxicity. However, avoiding drugs interactions and patient compliance are issues not fully accomplished so far. In this respect the identification of single agents with multitarget potential might represent the ideal solution. Accordingly, a small library of biphenylhydrazo 4-arylthiazoles derivatives has been synthesised and evaluated to investigate the ability of the new derivatives to simultaneously inhibit both associated functions of HIV reverse transcriptase. All compounds were active towards the two functions, although at different concentrations. The substitution pattern on the biphenyl moiety appears relevant to determine the activity.

In this paper, we study singularities of the spherical indicatrix and evolute of spacelike ruled surface with spacelike ruling. Finally, we give an example to illustrate our results.

The etiology of ‘dual harm’ (the co-occurrence of self-harm and externalized violence in the same individual) is under-researched. Risk factors have mostly been investigated for each behavior separately. We aimed to examine adversities experienced between birth and age 15 years among adolescents and young adults with histories of self-harm and violent criminality, with a specific focus on dual harm. Three nested case-control studies were delineated using national interlinked Danish registers; 58,409 cases in total aged 15-35 were identified: 28,956 with a history of violent criminality (but not self-harm), 25,826 with a history of self-harm (but not violent criminality), and 3987 with dual harm history. Each case was matched by date of birth and gender to 20 controls who had not engaged in either behavior. We estimated exposure prevalence for cases vs. controls for each of the three behavior groups, and incidence rate ratios (IRRs). Experiencing 5 or more childhood adversities was more prevalent among individuals with dual harm history (19.3%; 95% CI 18.0, 20.8%) versus self-harm (10.9%; 10.5, 11.3%) and violence (11.4%; 11.0%, 11.8%) histories. The highest IRRs for dual harm were linked with parental unemployment (5.15; 95% CI 4.71, 5.64), parental hospitalization following self-harm (4.91; 4.40, 5.48) or assault (5.90; 5.07, 6.86), and parental violent criminality (6.11; 5.57, 6.70). Growing up in environments that are characterized by poverty, violence and substance misuse, and experiencing multiple adversities in childhood, appear to be especially strongly linked with elevated dual harm risk. These novel findings indicate potential etiologic pathways to dual harm.

In this paper, the design process of BLDC adopting the dual rotor method that can reduce the overall size of the motor while generating the same torque as the conventional Permanent Magnet BLDC is analyzed. A simple size is selected by obtaining the torque per rotor volume (TRV), and a method of matching the counter electromotive force by selecting the pole arc of the magnet through a magnetic equivalent circuit is analyzed. Since the efficiency is low because the 120-degree commutation method is selected, the middle stator is optimized through detailed design through the experimental design method. Afterwards, it has the advantage of being able to shift without stopping due to the characteristic of a dual rotor. For this, an analysis of the driving characteristics for each mode is performed.

Single nanowires (NWs) are of great importance for optoelectronic applications, especially solar cells serving as powering nanoscale devices. However, weak off-resonant absorption can limit its light-harvesting capability. Here, we propose a single NW coated with the graded-index dual shells (DSNW). We demonstrate that with appropriate thickness and refractive index of the inner shell, the DSNW exhibits significantly enhanced light trapping compared with the bare NW (BNW), and the NW only coated with the outer shell (OSNW) and the inner shell (ISNW), which can be attributed to the optimal off-resonant absorption mode profiles due to the improved coupling between the reemitted light of the transition modes of the leak mode resonances of the Si core and the nanofocusing light from the dual shells with the graded refractive index. We found that the light absorption can be engineered via tuning the thickness and the refractive index of the inner shell, the photocurrent density is significantly enhanced by 134% (56%, 12%) in comparison with that of the BNW (OSNW, ISNW). This work advances our understanding of how to improve off-resonant absorption by applying graded dual-shell design and provides a new choice for designing high-efficiency single NW photovoltaic devices.

This paper presents a configuration of dual output single phase current source inverter with 6 switches for microgrid applications. The inverter is capable of delivering power to two independent set of loads of equal voltages or different voltages at the load end. The control strategy is based on Integral Sliding Mode Control (ISMC). The remote monitoring of the inverter is performed with cyber infrastructure. The cyber physical test bench is developed based on Reconfigurable I/O processor (NI MyRIO-1900) for control and monitoring of the inverter. The inverter prototype is tested in cyber physical test bench in laboratory conditions. The performance of the inverter is analyzed and monitored through the remote system. Also, the inverter is analyzed with different voltage conditions.

Continuous object tracking in WSNs, such as monitoring of mud-rock flows, forest fires etc., is a challenging task due to characteristic nature of continuous objects. They can appear randomly in the network field, move continuously, and can change in size and shape. Monitoring such objects in real-time generally require tremendous amount of messaging between sensor nodes to synergistically estimate object’s movement and track its location. In this paper, we propose a novel twofold-sink mechanism, comprising of a mobile and a static sink node. Both sink nodes gather information about boundary sensor nodes, which is then used to uniformly distribute energy consumption across all network nodes, thus helping in saving residual energy of network nodes. Numerous object tracking schemes, using mobile sink, have been proposed in the literature. However, existing schemes employing mobile sink cannot be applied to track continuous objects, because of momentous variation of network topology. Therefore, we present in this paper a mechanism, transformed from K-means algorithm, to find the best sensing location of the mobile sink node. It helps to reduce transmission load on the intermediate network nodes situated between static sink node and the ordinary network sensing nodes. The simulation results show that the proposed scheme can distinctly improve life time of the network, compared to one-sink protocol employed in continuous object tracking.

Although mammography has been the gold standard for early detection of breast cancer, if a woman has dense breast tissue, a false negative diagnosis may occur. Breast ultrasound, whether hand-held or automated, is a useful adjunct to mammography but adds extra time and cost. The primary aim was to demonstrate that our second-generation Aceso system, which combines full-field digital mammography (FFDM) and automated breast ultrasound (ABUS) in a single platform, is able to produce improved quality images that provide clinically meaningful results. Aceso was first tested using two industry standards: a CDMAM phantom to assess the FFDM images; and the CIRS 054GS phantom to evaluate the ABUS images. In addition, 25 women participated in a clinical trial: 14 were healthy volunteers while 11 were patients referred by the breast clinic at Groote Schuur Hospital. The CDMAM phantom results showed the FFDM results were better than EUREF’s standard of “acceptable” and were approaching “achievable”. The ABUS results showed lateral and axial spatial resolution of 0.5 mm and adequate depth penetration of 80 mm. Our second-generation Aceso system, with its improved quality of clinical FFDM and ABUS images, has demonstrated its potential for early detection of breast cancer in a busy clinic.

This is an exercise to explore the concentration of Lithium, Lithium-7 isotope and the possible presence of black dirty ice in the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice in the lunar surface are closely related to future human settlement missions in the Moon. We investigate the distribution of Lithium and 7 Li isotope on the Lunar surface, employing spectral data from the Clementine images. We utilized Visible (VIS-NIR) imagery at wavelengths of 450, 750, 900, 950, and 1000 nm, along with Near-Infrared (NIR-SWIR) at 1100, 1250, 1500, 2000, 2600, and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 meters, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of Lithium, ground truth sites were used to calibrate the Clementine images. Samples (12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS-NIR bands and five spectral ratios. The 5 spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their Lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts and High-T mare basalts and Orange soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the 8 Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios for each of the 8 lunar soils we performed linear regression models to estimate the concentration of Lithium and 7 Li. Also, we calculated Digital Terrain Models (DTMs; Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon extracted from Lunar Orbiter Laser Altimeter (LOLA) and located around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of find ice mixed with regolithe (black ice) we used a spectral library (350-2500nm) form ices dirty with different concentration of volcanic particles.

Currently, the chemistry of conjugated nitrodienes is becoming more and more popular. These molecules are successfully applied in cycloaddition to synthesize six-membered rings in Diels-Alder reactions. Nitrodienes can be also applied to obtain bis-compounds in [3+2] cycloaddition. Moreover, the presence of a nitro group in the structure provides a possibility of further modification of the products. The simplest symmetrical representative of conjugated nitrodienes is (1E,3E)-1,4-dinitro-1,3-butadiene. Although the first mentions of the compound date back to the early 1950s, the compound has not yet been examined thoroughly enough. Therefore, in this article a comprehensive study of (1E,3E)-1,4-dinitro-1,3-butadiene has been described. For this purpose, an experimental study including synthesis process as well as spectral characteristic has been conducted. So as to better understand the properties of this compound a computational study of reactivity indices based on MEDT and also assessment of pharmacokinetics and biological activity according to ADME and PASS methodologies have been made. On this basis, some future application trends of (1E,3E)-1,4-dinitro-1,3-butadiene have been proposed.

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, in south-central Chile by analyzing time series of Landsat-8 satellite images, specifically using the multispectral OLI sensor. The bio-optical parameters, i.e., chlorophyll-a (mg·m^-3) and turbidity (NTU) were also measured in-situ synchronized with the satellite passes to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in-situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process. Subsequently, we harnessed the reflectance data derived from the ACOLITE model to establish correlations between various spectral indices and the in-situ data. The empirical retrieval models (based on band combinations) that showed superior performance, as indicated by higher R² values, were subjected to rigorous statistical validation and optimization by applying a bootstrapping approach. Our analysis covered a spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014-2022) characterized by a decline in chlorophyll-a concentration in the lake possibly attributable to governmental measures in the region for the protection and conservation of the lake. The results of this initial study serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

The escalating frequency and severity of global wildfires necessitate an in-depth understanding and monitoring of wildfire smoke impacts, specifically its contribution to fine particulate matter (PM2.5). We propose a data-fusion method to study wildfire contribution to PM2.5 using satellite-derived smoke plume indicators and PM2.5 monitoring data. Our study incorporates two types of monitoring data, the high-quality but sparse Air Quality System (AQS) stations and the abundant but less accurate PurpleAir (PA) sensors that are gaining popularity among citizen scientists. We propose a multi-resolution spatiotemporal model specified in the spectral domain to calibrate the PA sensors against accurate AQS measurements, and leverage the two networks to estimate wildfire contribution to PM2.5 in California in 2020 and 2021. A Bayesian approach is taken to incorporate all uncertainties and our prior intuition that the dependence between networks, as well as the accuracy of PA network, vary by frequency. We find that 1% to 3% increase in PM2.5 concentration due to wildfire smoke, and that leveraging PA sensors improves accuracy.

This study investigates the applications of reconfigurable intelligent surfaces (RIS) in heterogeneous downlink networks (HetNet). Due to the network densification, the small cell base station (SBS) interferes with the macrocell users (MUE). In this paper, we utilize RIS to mitigate cross-tier interference in an HetNet via directional beamforming by adjusting the phase shift of the RIS. We consider an RIS-assisted HetNet consisting of multiple SBS nodes and MUEs that utilize both direct paths and reflected paths. Therefore, the aim of this study is to maximize the sum rate of all MUEs by jointly optimizing the transmit beamforming of the macrocell base station (MBS) and the phase shift of the RIS. An efficient RIS reflecting coefficients-based optimization (RCO) is proposed based on a successive convex approximation approach. Simulation results are provided to show the effectiveness of the proposed scheme in terms of its sum rate in comparison with the scheme HetNet without RIS and the scheme HetNet with RIS but with random phase shifts.

Background: This study aims to investigate the effects of visual neurofeedback stimulation on the brain activity in overweight cases. The neuroscience studies indicated the personal decision about eating under the impact of environmental factors such as (visually, smelling, tasting) is related to neural activity of the prefrontal lobe of the brain. Therefore, there were many attempts to modify the food intake behavior in overweight cases through the stimulation of the prefrontal cortex. However, the empirical viewing of EEG-neurofeedback experiments has not explicated the details about the effect of the EEG-NF, the specificity of positive treatment effects remains in a challenging scope.Methods: This study is a cue-exposure EEG-NF experiment to verify the hypothesis of effecting the EEG-NF on the electrical activity of PFC and modifying the general symptoms of food intake behavior in overweight cases. Twenty-four individuals were recruited as participants for this study. These participants were assigned randomly into two groups; the EX-Group (N=12) enrolled in 8 sessions of the EEG-NF experiment, and the C-Group (N=12) was listed in no EEG-NF sessions. The participants provided researchers with a self-report questionnaire relating to their observation of general symptoms of food intake behavior, and EEG signals recordings into the pre and posts stimulation phase. The power spectral density (PSD) method was applied for EEG parameters extraction.Results: The results of a two-way analysis of variance (ANOVA) explained that a significant variation in variables between the two groups after the EEG-NF experiment. The analysis of the quantitative variables indicated that the effect of EEG-NF experiment was a significant decrement in EEG power bands which significantly influenced changing the median of self-report questionnaire responses that is related to general symptoms of food intake behavior.Conclusions: This study provides preliminary support for the therapeutic potential of EEG-NF experiment that targets the prefrontal cortex, to influence neural processes underlying food intake behavior in overweight cases.

Hyperspectral image (HSI) classification is a mechanism of analyzing differentiated land cover in remotely sensed hyperspectral images. In the last two decades, a number of different types of classification algorithms have been proposed for classifying hyperspectral data. These algorithms include supervised as well as unsupervised methods. Each of these algorithms has its own limitations. In this research, three different types of unsupervised classification methods are used to classify different datasets i-e Pavia Center, Pavia University, Cuprite, Moffett Field. The main objective is to assess the performance of all three classifiers K-Means, Spectral Matching, and Abundance Mapping, and observing their applicability on different datasets. This research also includes spectral feature extraction for hyperspectral datasets.

The AtomDB project provides models of X-ray and EUV emitting astrophysical spectra for optically thin, hot plasma. We present the new software package, PyAtomDB, which now underpins the entire project, providing access to the underlying database, collisional radiative model calculations, and spectrum generation for a range of models. PyAtomDB is easily extensible allowing users to build new tools and models for use in analysis packages such as XSPEC. We present two of these, the kappa and ACX models for non-Maxwellian and Charge-Exchange plasmas respectively. In addition, PyAtomDB allows full open access to the apec code which underlies all of the AtomDB spectra and has enabled development of a module for estimating the sensitivity of emission lines and diagnostic line ratios to uncertainties in the underlying atomic data. We present these publicly available tools and results for several X-ray diagnostics of Fe L-shell ions and He-like ions as examples.

We specify basic definitions of the Chern-Simons current in cohomology theory, then we calculate its value by using a model of quantum machine learning, the so-called supersymmetric support Dirac machine. The supercurrent is generated from the coupling between three states of a quantum flux of modified Wilson loop of the Cooper pairs. We use Holo-Hilbert spectrum in frequency modulation to visualize the network as a coupling behavior of convolutional neuron network in superstatistical theory with the application to the theory of a superconductor. We also calculate the number of carbon atoms in the stable support spinor network structure of a graphene wormhole to produce a supercurrent of Cooper pairs as graviphoton states by using the Holo-Hilbert spectral analysis.

The advances mobile communications has seen in recent years has rendered the radio spectrum a limited and, hence, an expensive resource. Therefore, technologies that support unlicensed access to spectrum are needed. Therefore, the adoption of novel modulation schemes becomes of utmost importance to obtain better spectral-localization and reduce the OOBE (\textit{Out of Band Emission}) inherent to OFDM (\textit{Orthogonal Frequency Division Multiplexing}) and, consequently, mitigating the interference between secondary (\textit{unlicensed}) and primary users. In this scenario, we assess the gain in the bit error probability using f-OFDM (\textit{filtered-OFDM}) in MIMO systems, both used in the 5G RANGE project.

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.

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the second Joint Polar Satellite System (JPSS) completed its sensor level testing in February 2018. The JPSS-2 (J2) mission is scheduled to launch in 2022, and will be very similar to its two predecessor missions, the Suomi National Polar-orbiting Partnership (SNPP) mission, launched on 28 October 2011, and JPSS-1 (renamed NOAA-20) launched on 18 November 2017. VIIRS instrument has 22 spectral bands covering the spectrum between 0.4 and 12.6 mircron: 14 reflective solar bands (RSB), 7 thermal emissive bands (TEB), and one day-night band (DNB). It is a cross-track scanning radiometer capable of providing global measurements through observations at two spatial resolutions, 375 m and 750 m at nadir for the imaging bands and moderate bands, respectively. This paper will provide an overview of J2 VIIRS characterization methodologies and calibration performance during the pre-launch testing phases performed by the National Aeronautics and Space Administration (NASA) VIIRS Characterization Support Team (VCST) to evaluate the at-launch baseline radiometric performance and generate the parameters needed to populate the sensor data record (SDR) Look-Up-Tables (LUTs). Key sensor performance metrics include the signal to noise ratio (SNR), radiance dynamic range, reflective and emissive bands calibration performance, polarization sensitivity, spectral performance, response versus scan-angle (RVS), and scattered light response. A set of performance metrics generated during the pre-launch testing program will be compared to both the SNPP and JPSS-1 VIIRS sensors.

The early-season area estimation of the winter wheat crop as a strategic product is important for decision makers. Classification of multi-temporal images is an approach which is affected by many factors like appropriate training sample size, proper frequency and acquisition times, vegetation indices (VIs) type, temporal gradient of spectral bands and VIs, appropriate classifier and missed values because of cloudy conditions. This paper addresses the impact of appropriate frequency and acquisition times and VIs type along with the spectral and VI gradient on random forest (RF) classifier when missed values exist in multi-temporal images. To investigate the appropriate temporal resolution for image acquisition, the study area was selected on an overlapping area between two LDCM paths. In our developed method, the miss values of cloudy bands for each pixel are retrieved by the mean of k-nearest ordinary pixels. Then the multi-temporal image analysis is performed by considering different scenarios provided by decision makers in terms of desired crop types that should be extracted at early-season in the study areas. The classification results obtained by the RF decrease by 1.6% when temporally missed values retrieved by the proposed method, which is an acceptable result. Moreover, the experimental results demonstrated that if temporal resolution of Landsat 8 increased to one week the classification task can be conducted earlier with almost better results in terms of OA and kappa. The impact of incorporating VIs along with the temporal gradients of spectral bands and VIs as new features in RF demonstrated that the OA and Kappa are improved 3.1% and 6.6%, respectively. Furthermore, the obtained result showed that if only one image from seasonal changes of crops is available, the temporal gradient of VIs and spectral bands play the main role to discriminate remarkably wheat from barley. The experiments also demonstrated that if both wheat and barley merge to a single class the crop area can be estimated two months earlier with 97.1 and 93.5 in terms of OA and kappa, respectively.

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics and others. Due to its application in control theory, the matrix equation AXB=C has been extensively studied. However, there is currently limited information on matrix equation AXB=C over the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of the dual quaternion matrix equation AXB=C, and present the expression for the general solution when it is solvable. As an application, we derive the ϕ-Hermitian solutions for the dual quaternion matrix equation AXAϕ=C, where the ϕ-Hermitian extends the concepts of Hermiticity and η-Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper.

The co-occurrence of substance use disorders (SUDs) and psychiatric conditions, often referred to as comorbidity or concurrent disorders, presents intricate challenges in both diagnosis and treatment. This comprehensive narrative review aims to synthesize and critically evaluate the existing evi-dence surrounding the management of individuals with comorbid SUDs and psychiatric disorders. Comorbidity in these domains carries profound implications for clinical practice, research, and policymaking, emphasizing the need for a holistic understanding of the intricate dynamics that arise when these conditions coexist. This review explores recent research findings, evidence-based guidelines, and emerging trends within the field, offering valuable insights for clinicians, re-searchers, and policymakers seeking to navigate the complex terrain of comorbidity in substance use and psychiatric disorders.

FV (finger vein) identification is a biometric identification technology that extracts the features of FV images for identity authentication. To address the limitations of CNN-based FV identification, particularly the challenge of small receptive fields and difficulty in capturing long-range dependencies, an FV identification method Let-Net(Large kernel and attention mechanism Network) was introduced, which combines local and global information. Firstly, Let-Net employs large kernels to broaden the receptive field and incorporates depthwise convolution with residual connections to reduce parameter count. Secondly, Let-Net integrates an attention mechanism to enhance the information flows in channels and spaces for more comprehensive and distinctive FV feature extraction. The experimental results on nine public datasets show that Let-Net has excellent identification performance, and the EER and accuracy rate on the FV_USM dataset can reach 0.04% and 99.77%. The parameter number and FLOPs of Let-Net are only 0.89M and 0.25G, which means that the time cost of training and reasoning of the model is low, and it is easier to deploy and integrate into various applications.

Intrauterine adhesions (IUA) has become one of the main causes of female infertility, how to effectively prevent postoperative re-adhesion has been a clinical challenge. In this study, a mussel-inspired dual-network hydrogel was proposed for postoperative anti-adhesion of IUA. First, calcium alginate/polyacrylamide (CA-PAM) hydrogel was prepared by covalent and Ca2+ cross-linking. Benefiting from abundant phenolic hydroxyl groups, polydopamine (PDA) was introduced to further enhance the adhesion ability and biocompatibility. This CA-PAM hydrogel immersed in 10 mg/mL dopamine solution possessed remarkable mechanical strength (elastic modulus > 5 kPa) and super stretchability (with a breaking elongation of 720%). At the same time, it shown excellent adhesion more than 6 kPa. Surprisingly, the coagulation index of the hydrogel was 27.27 ± 4.91, demonstrating attractive coagulation performance in vitro and the potential for rapid hemostasis after surgery.

The utilization of dual-potential mode of integrated array (IDA) electrodes and other two-electrode systems has revolutionized electrochemical detection by enabling the simultaneous and independent detection of two species, accompanied by the exhibition of unique characteristics. In contrast to conventional dual-potential electrodes, the rotating ring disk electrodes (RRDE), IDA electrodes demonstrate analogous yet vastly improved performance metrics, characterized by remarkable collection efficiency and sensitivity. Notably, due to the distinctive microscale structure of IDA electrode, the special “feedback” effect makes IDA a unique signal amplifier. In recent decades, the research surrounding IDA electrodes has garnered escalating interest due to their attractive attributes. This review centers its focus on the fabrication and applications of IDA electrodes. In the realm of fabrication, two critical breakthroughs are poised for realization: the achievement of reduced dimensions and the diversification of materials. Established fabrication methods for IDA electrodes encompass photolithography, inkjet printing, and direct laser writing, each affording distinct advantages in terms of size and precision in IDA construction. Predominantly employed materials for IDA electrodes include gold, platinum, and carbon, with the selection of fabrication methods guided by considerations such as material properties, desired dimensions, cost-efficiency, and specific application requisites. Within the domain of applications, IDA electrodes, capitalizing on their distinctive attributes, have found utility in diverse fields. This review summarizes the applications of IDA electrodes based on their fundamental working principles, encompassing redox cycling, resistance modulation, capacitance variations, and more. The potential for further development of this specialized tool to exhibit enhanced properties holds substantial promise.